Technologies doi: 10.3390/technologies14090519

Authors: Muhannad Maher Abdin Michael Connolly Alden Stockam Oleg Karaduta

Robotic-assisted cholecystectomy (RAC) is increasingly presented as the next step in minimally invasive biliary surgery, but its added value over laparoscopic cholecystectomy (LC) remains uncertain. Comparative evidence suggests that RAC may reduce conversion to open surgery in some settings, yet it has not demonstrated consistent improvement in postoperative outcomes and is generally associated with longer operative time, higher costs, and unresolved safety concerns during dissemination and learning. RAC may offer value in selected complex cases and within structured robotic training programs, but these roles require subgroup-specific evaluation. For an established, safe, and efficient procedure such as LC, noninferiority is not enough; routine adoption should depend on demonstrable clinical, educational, or operational added value.

Technologies doi: 10.3390/technologies14080518

Authors: Sweeta Agrawal Abayomi O. Agbeyangi

The rapid evolution of AI-driven language technologies has inadvertently widened the gap between high-resource and marginalised languages. Despite significant progress in AI-driven translation for high-resource languages, low-resource languages remain underrepresented due to limited data, a lack of benchmarks, and evaluation challenges. This study presents a comprehensive systematic review of machine translation for low-resource languages, focusing on advances in neural machine translation (NMT) and large language models (LLMs) between 2017 and 2025. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 63 studies were selected from the 1696 articles in the Scopus, Web of Science, and Google Scholar databases. The review identifies five dominant methodological approaches: data augmentation, back-translation, transfer learning, pre-training, and parameter-efficient fine-tuning. The findings reveal that model performance is highly dependent on resource availability: transformer-based NMT excels in moderate data settings, while LLMs demonstrate promising zero-shot and few-shot capabilities in extremely low-resource scenarios. Hybrid NMT–LLM approaches emerge as a particularly effective paradigm. The study also highlights critical challenges, including the absence of standardised benchmarks, over-reliance on inadequate evaluation metrics such as Bilingual Evaluation Understudy (BLEU), limited human evaluation, and significant geographic and linguistic underrepresentation. Additionally, ethical concerns related to bias, cultural representation, and community engagement are increasingly relevant. The findings contribute to advancing inclusive and equitable AI-driven language technologies.

Technologies doi: 10.3390/technologies14080517

Authors: Cesar H. Valencia-Niño Rafael A. Nuñez-Rodriguez Marley M. B. R. Vellasco Jeison Marin

We present ESNformer, a hybrid architecture that couples an Echo State Network (ESN) reservoir with a Transformer encoder for classification of structured, multi-indicator assessment data: a fixed-order vector of complementary indicators per assessment instance rather than a repeated-measures time series. The reservoir acts as a fixed nonlinear feature map over the indicator vector, while self-attention, made position-aware over the fixed column order, learns how each indicator’s evidence contributes to the final decision, so the two components, together, capture local, indicator-level detail and global, cross-indicator interactions within a single, end-to-end trainable model. Interpretability is treated as a first-class design requirement rather than an afterthought: the architecture is paired with an explainability layer combining SHAP feature attribution (reported both globally and per class), the model’s own attention weights, a deletion/insertion faithfulness test that quantitatively verifies which inputs the model actually relies on, and counterfactual maps that translate a prediction into an actionable, inspectable recommendation. We evaluate the architecture on a concrete case study, classifying Braille-literacy instructional recommendations from 15 pedagogical indicators grouped into three categories (Mangold’s, ABKL, and Progresar), using a benchmark of 900 real assessment instances (630 used, together with a class-conditional augmentation procedure, to build a 2100-instance training set) with validation and test partitions (135 instances each) kept exclusively real. On this benchmark, the tuned model reached 85.33% accuracy, 85.90% macro-precision, 85.33% macro-recall, an F1 score of 85.25%, and an AUC of 0.95 on the real test set. SHAP attribution, attention weights, and the faithfulness test converge on the same two dominant indicators (response time and error count): removing them alone collapses accuracy to chance, while retaining only them recovers most of the model’s accuracy. We report this transparently alongside a comparison against ESN-only, Transformer-only, and tabular baselines (logistic regression, decision tree, random forest, XGBoost, and an MLP) on the same data and discuss what the hybrid architecture and its explainability pipeline add beyond what the two dominant indicators already explain and how the approach generalizes to other tabular and mixed-granularity assessment settings that require both predictive accuracy and a verifiable account of what drove each decision.

Technologies doi: 10.3390/technologies14080516

Authors: Yinping Li Qing Cheng Wenquan Huang

This paper focuses on the binary task of parking-slot occupancy detection (vacant vs. occupied) from UAV aerial imagery. Accurate parking-slot detection from UAV imagery is challenged by small target sizes, highly regular rectangular shapes, large-scale variations, complex backgrounds, and perspective distortions. Compared with fixed surveillance cameras, UAV-based detection offers flexible deployment, wide-area coverage, and no requirement for pre-installed infrastructure, making it especially suitable for large open-air parking lots and temporary parking scenarios. To address this, this study proposes a task-specific framework for UAV-based parking-slot detection, with improvements in backbone design, attention modeling, and bounding-box regression. A lightweight MnasNet-inspired backbone is used to improve multi-scale feature extraction at low computational cost. An enhanced EMA module with adaptive grouping, FFT-based frequency enhancement, and gated fusion is introduced to better model the structured patterns of parking lot scenes. In addition, a UIoU+ loss tailored to rectangular geometry is proposed to improve localization quality. Sensitivity analysis and repeated experiments show that the method is stable and statistically reliable. All main metrics are evaluated on an independent held-out test set to ensure generalization. Extensive experiments demonstrate that each component brings consistent performance gains. The proposed model achieves 99.44 ± 0.12% mAP@0.5, 90.31 ± 0.27% mAP@0.5:0.95, 99.27 ± 0.15% precision, and 99.00 ± 0.18% recall on the self-built UAV Parking Lot dataset. Its mAP@0.5:0.95 is 26.01 percentage points higher than the YOLOv11n baseline. Consistent performance improvements are also validated on two additional public benchmarks (CARPK and PKLot), confirming the generalization of the proposed method beyond the self-built dataset. Most importantly, the method supports real-time inference on embedded UAV platforms and achieves state-of-the-art performance among lightweight detectors, making it an ideal solution for practical intelligent parking management. Ablation studies further confirm the complementary synergy between the proposed backbone, attention module, and loss function. Full implementation code, pre-trained weights, and detailed reproduction guidelines are publicly available to ensure research reproducibility.

Technologies doi: 10.3390/technologies14080515

Authors: Sasirot Khamkure Chidentree Treesatayapun Audberto Reyes-Rosas Alejandro Zermeño-González Javier de Jesús Cortés-Bracho Jose-Alexander Gil-Marin Etelberto Cortez-Quevedo Nakorn Tippayawong Patiroop Pholchan

This study converted pecan shell waste into magnetic Fe3O4@biocarbon for arsenic (V) removal from aqueous medium. A preliminary test was conducted on a binary system of arsenic (V) and lead. A dual-optimization approach was applied using a fuzzy decision network for material synthesis and response surface methodology (RSM) for adsorption performance. The fuzzy model predicted FS2 as an optimal design (particles between 0.38–0.7 mm in size, Fe ratio of 1:1) with high accuracy (R2 > 0.95). In the RSM, removal efficiency and adsorption capacity were estimated to find out the influential parameters, which turned out to be adsorbent dose and As(V) concentration. It was predicted that removal capacity would remove 90.99% As(V) at the dose of 0.95 mg L−1 As(V), pH 3.4 and 1.8 g L−1 dose. However, it was also revealed that the qe model provided a higher confidence (final conditions: 9.95 mg L−1 As(V), pH 3.0 and 0.5 g L−1 dose; qe = 3.96 mg g−1). Evaluation of Fe3O4@biocarbon was conducted at 0.217 mg L−1 As and 34.3 mg L−1 Pb. This suggests removal of lead in addition to arsenic, indicating that the method can be used in multicomponent metal removal. FTIR and XPS analysis showed that removal of As(V) took place through surface complexation with Fe-O and oxygen-containing functional groups.

Technologies doi: 10.3390/technologies14080514

Authors: Kirill A. Bashmur Alexander V. Zagulyaev Ivan S. Nekrasov

This study develops a theoretical and computational mechanics framework for regular dimples produced by rotary ball burnishing on an external cylindrical surface. The model combines the local quadratic ball–cylinder gap, an effective mean indentation pressure Heff, an unloading factor λ, residual dimple geometry, a Greenwood–Williamson pressure–approach law for the load-bearing lands, and an elastic spectral reference calculation. The dimple area fraction Fn is obtained from the periodic union of loaded-imprint footprints for the nominal stiffness maps, whereas the residual profile determines the specific oil capacity, defined as retained cavity volume per unit nominal area. Numerical checks include algebraic consistency, mesh-converged periodic FFT-BEM calculations, and a separate sinusoidal benchmark. For a representative 3mm ball and 25mm cylinder with Heff=2GPa, the model maps burnishing force and texture pitch to idle- and working-pressure secant stiffness. Within the stated range Fn≤0.20, the low-fidelity index preserves the ordering of all evaluated non-tied design points; the mean and maximum differences from the spectral reference are 6.6% and 15.2%. A discrete force–pitch grid yields three feasible points under illustrative stiffness windows and two candidates after non-dominated sorting and secondary selection by specific oil capacity. The selected textures reduce idle-pressure stiffness slightly more than working-pressure stiffness, increasing the nonlinearity ratio from 5.70 to 5.91. The framework provides a reproducible model-based tool for preliminary force–pitch selection; application to a specific material–process pair requires identification of Heff, λ, and the load-bearing-land response and validation against process-specific measurements.

Technologies doi: 10.3390/technologies14080513

Authors: Wenjing Zhu Jinhai Li Chaosan Yang Luqing Luo Wenxue Liu Xin Qiu

Limited snapshots and element-wise gain–phase mismatch jointly impair covariance estimation and array manifold accuracy in uniform planar arrays. This paper proposes a deep robust adaptive beamforming framework that combines statistical base-weight generation, element-wise array manifold calibration, and regularized response projection. The base-weight network extracts finite-snapshot covariance information, whereas the calibration network estimates a physically bounded element-wise complex-gain vector from covariance features and nominal direction context. Phase-aligned auxiliary supervision makes the calibration loss invariant to the unidentifiable common phase and is required only during training. The calibrated steering vectors define a closed-form minimum-distance projection that preserves the normalized base weight’s desired direction response while suppressing the calibrated interference responses. Across three training seeds, the method achieves 23.43 ± 0.06 dB output SINR and a −52.48 ± 0.09 dB average null level, improving the former by 5.64 dB and deepening the latter by 5.11 dB relative to the best-performing baseline under the main test distribution. Experiments on mismatch severity, input SNR, snapshot number, direction-of-arrival errors, controlled ablations, and computational cost characterize the performance and limitations of the method under the stated synthetic-array model.

Technologies doi: 10.3390/technologies14080512

Authors: Saya Sapakova Askar Sapakov Omirlan Auyelbekov Lyailya Tukenova Sakhybay Tynymbayev Zhomart Ualiyev Aigul Skakova Assem Kabdoldina

Urban air pollution driven by road traffic poses a significant public health challenge in cities with high vehicle density and frequent congestion, particularly in topographically constrained environments such as Almaty, Kazakhstan. This study presents a web-based digital twin prototype for the integrated monitoring and analysis of traffic flow and air quality in Almaty, Kazakhstan. The system autonomously collects data from the TomTom Traffic, OpenWeather Air Pollution, and WAQI APIs and official population statistics for five fixed monitoring stations, computing traffic density, vehicles per hour, road congestion, estimated CO2 emissions, an air pollution index, and a population exposure index, and providing real-time dashboard visualization alongside longitudinal data accumulation. Over a 50-day deployment (26 May–16 July 2026), 4961 real co-located observations across 18 active days were analyzed; records generated by the prototype’s fallback mechanism during API outages were excluded from the scientific analysis. During this summer period, PM2.5 was low (mean ≈ 6 µg/m3) and spatially uniform, and showed no statistically significant association with traffic intensity (r ≈ −0.03). Traffic indicators were instead weakly but significantly correlated with the vehicle-emitted gases NO2 (r ≈ 0.16) and CO (r ≈ 0.10), which they preceded by up to about one hour. A short-horizon PM2.5 nowcasting task, evaluated across temporal resolutions with time-series cross-validation, was dominated by temporal persistence, with traffic-derived features contributing negligibly. The absence of a summer traffic–PM2.5 association does not preclude such a relationship during the heating season, when particulate levels are higher. The results indicate that the traffic–air-quality relationship in Almaty is season- and pollutant-dependent, and demonstrate a lightweight, reproducible platform suitable for longitudinal monitoring and future heating-season assessment.

Technologies doi: 10.3390/technologies14080511

Authors: Lucía Osoro Elena Arbelo Deirdre A. Lane Davide Antonio Mei Nikola Kozhuharov Maura Zylla Brendan Collins Panos Vardas Giuseppe Boriani Joseph Figueras José Luis Merino Helmut Pürerfellner Haran Burri Rubén Casado-Arroyo

Artificial intelligence (AI) is transforming cardiovascular medicine through applications in disease detection, diagnosis, risk prediction, and clinical decision support. However, the clinical implementation of these technologies remains poorly characterised. This systematic review evaluated the current landscape of AI applications in cardiovascular medicine, focusing on implementation maturity and clinical translation. The review followed PRISMA guidelines and a prospectively registered PROSPERO protocol. Data extraction included study characteristics, cardiovascular domain, AI methodology, clinical application, validation strategy and implementation maturity, assessed using a predefined five-level framework. AI methodologies were classified as conventional machine learning, deep learning, hybrid ML/deep learning, multimodal AI, or large language models/generative AI. Seventy-four studies met the eligibility criteria. Conventional machine learning was the most frequently used methodology (47.3%), followed by deep learning (41.9%), whereas multimodal AI (5.4%), hybrid ML/deep learning (2.7%), and large language models/generative AI (2.7%) were uncommon. Applications focused mainly on screening and early detection (31.1%), risk stratification and prognosis (25.7%), treatment planning (16.2%), diagnosis (13.5%), and monitoring (12.2%). Most studies reached implementation maturity Level 3 (clinical validation, 47.3%) or Level 2 (technical validation, 35.1%), while only 13.5% achieved routine clinical implementation (Level 5) and 4.1% reached clinical deployment (Level 4). Although AI demonstrated promising diagnostic and prognostic performance across multiple cardiovascular conditions, most applications remain at the validation stage. Future research should prioritise implementation science, pragmatic evaluation, and real-world evidence to facilitate routine adoption and maximise patient benefit and healthcare value.

Technologies doi: 10.3390/technologies14080510

Authors: Jun Zhao Yuxiang Li Xueting Cheng Juan Wei Weiru Wang Lu Liu Yu Yang

In response to the severe challenges posed by extreme sandstorm weather to the operational safety of WTs and grid stability, this paper proposes an MPC-based power optimization control strategy for WFs. Simulation results indicate that, compared with the traditional PD strategy, the proposed MPC strategy significantly reduces the active power fluctuations of individual WTs, smoothly tracks grid dispatch orders with an overall power tracking accuracy improvement, and effectively lowers the operational risk index of turbines across the farm (ranging from 6.90% to 57.14% for the ten evaluated turbines). Furthermore, the proposed strategy substantially mitigates the angular acceleration fluctuation amplitude of the drive train components (e.g., reducing peak angular accelerations of drive-train masses by up to 35%) and reduces the fore-aft and lateral displacement oscillations of the tower top (reducing peak displacement variations by approximately 25% and 40%, respectively), providing comprehensive structural load mitigation while ensuring WF power output stability and grid safety. This study provides a theoretical basis and technical approach for the intelligent operation and risk prevention and control of WFs under extreme meteorological conditions.

Technologies doi: 10.3390/technologies14080509

Authors: Michaela Halmanová Ivona Černičková Patrícia Danišovičová Patrik Šulhánek Marián Drienovský Xabier Zubizarreta Cuerda Róbert Havlík Libor Ďuriška Marián Palcut

Sn-3Ag-0.5Cu-xBi alloys (SAC305-xBi) represent promising lead-free alternatives for low-temperature soldering. Low Bi concentrations can strengthen SAC-based solders through solid-solution strengthening, refining β–Sn grains and transforming needle-like Ag3Sn phases into equiaxed morphologies. However, excessive Bi alloying may induce precipitation of brittle Bi particles, cause microstructural instability and interfacial degradation, thereby weakening the solder joint performance. As such, the concentration of Bi in the SAC305 alloys should be carefully controlled. In this work, the microstructure and corrosion behavior of Sn-3Ag-0.5Cu-xBi solder alloys (SAC305-xBi, where x = 0, 1, 2 and 4 wt. %) were investigated. Attention has been paid to the influence of low Bi concentration on the microstructure, morphology, and chemical composition of the phases present in the solder alloys before and after corrosion exposure. The alloys were prepared by induction melting of Sn, Ag, Cu and Bi lumps under Ar gas. The microstructure of the SAC305 and SAC305-1Bi alloys represented a hypoeutectic microstructure with dendritic (Sn) grains and the ternary eutectic, consisting of (Sn), Cu6Sn5 and Ag3Sn, located in inter-dendritic regions. In the SAC305-2Bi and SAC305-4Bi alloys, a segregation of (Bi) particles was observed in addition to dendritic (Sn) and ternary eutectic. The (Bi) particles were located at the (Sn)Ag3Sn interface in the inter-dendritic spaces of the (Sn) solid solution. The corrosion resistance of the as-cast alloys was studied in aqueous NaCl electrolyte (3.5 wt. %) using electrochemical methods. Open circuit potentials of the alloys were found to increase with increasing concentration of Bi. The highest corrosion current was found for the SAC305-1Bi alloy. It was observed that micro-galvanic cells at the Sn-Ag3Sn interface were the initiating factors of corrosion in the SAC305-1Bi alloy. The corrosion activity of the SAC305-1Bi alloy is related to the high density of fine Ag3Sn particles. The higher fraction of Ag3Sn particles provided a dense network of local galvanic interaction sites, leading to the acceleration of the corrosion rate. The presence of discrete Bi precipitates in the SAC305-2Bi and SAC305-4Bi alloys, on the other hand, partially reduced the risk of galvanic corrosion. Since Bi has a higher standard electrode potential compared to Sn, the Bi/Ag3Sn and Bi/Cu6Sn5 couples were less prone to corrosion. The corrosion mechanism of the SAC305-xBi alloys is discussed, and results are compared to previously studied SAC-Bi alloys.

Technologies doi: 10.3390/technologies14080508

Authors: Lauren Genith Isaza Dominguez Nestor Suat-Rojas Alfonso A. Portacio

Mixed Reality (MR) serious games combine immersive technologies with game-based learning to support training, skill development, and decision-making across diverse disciplines in university education. Although previous research has demonstrated improvements in engagement, usability, and learning outcomes, less attention has been devoted to developing design recommendations and methodological approaches for studying MR serious games. A theory-derived Meta Quest 3-based MR serious game for diagnostic classification was developed by integrating learning theories, gameplay mechanics, and gamification features while functioning as both an educational intervention and a research instrument. The system was evaluated through two complementary studies involving undergraduate students: the first compared learning outcomes across instructional approaches, whereas the second examined learner performance and transfer using a mixed-method approach incorporating objective metrics, questionnaires, and interviews. The proposed system achieved learning outcomes comparable to expert-guided field training while significantly outperforming classroom instruction and self-directed study. Significant transfer to real-world diagnostic tasks was demonstrated, and complementary evidence was triangulated to derive preliminary design recommendations and a multi-dimensional methodological framework. These contributions provide an initial foundation for the systematic design and study of MR serious games in higher education.

Technologies doi: 10.3390/technologies14080507

Authors: Jacek Polechoński Jakub Ryśnik Anna Witkowska Małgorzata Dębska-Janus

Engaging adolescents in regular physical activity remains a major public health challenge; consequently, increasing attention is being directed toward technologies that make exercise more attractive, task-oriented, and engaging. Active video games and non-immersive virtual reality systems are particularly promising because they combine physical exertion with feedback, gamification, and movement-based interaction. This study examined whether a biathlon exergame performed on an unstable ICAROS Cloud platform using the ICAROS App can elicit favorable physiological and psychological responses in adolescents, and whether these responses depend on body position. Eighty secondary school students, including 41 girls and 39 boys, completed two 10 min Biathlon trials: one in a standing position and one in a quadruped kneeling position, with the trial order counterbalanced. The percentages of maximum heart rate (%HRmax), perceived exertion, enjoyment of physical activity, and flow state were assessed. The standing condition elicited higher exercise intensity than quadrupled kneeling (71.42 ± 9.61 vs. 62.09 ± 9.04%HRmax; p < 0.001), and was also associated with higher perceived exertion, enjoyment, and flow. Similar response patterns were observed in girls and boys. These findings highlight the potential of unstable-platform exergaming as a practical and engaging approach to technology-supported physical activity promotion in developmental ages.

Technologies doi: 10.3390/technologies14080506

Authors: Kenan Weber Tobias Hoinka Christine Preisach

Reliable wind turbine component diagnosis requires cross-machine knowledge transfer since verified fault cases are rare. However, coarse fault interval annotations and turbine-specific operating behavior make this transfer difficult. This paper introduces a benchmark framework for cross-machine wind turbine component diagnosis on a new industrial dataset derived from Supervisory Control and Data Acquisition (SCADA) and vibration-derived kinematics data. The dataset is publicly available upon request and contains anomaly score-based embeddings. These embeddings are feature vectors whose entries quantify how strongly signals or component-related features deviate from learned normal behavior. The benchmark compares classical machine learning models, deep learning models, adversarial domain adaptation variants, and a simple baseline. Hyperparameter selection is performed without target labels using source classification loss, target prediction entropy, or Soft Neighborhood Density (SND). Our experiments show that, in the SCADA validation stage, source classification loss achieves the highest mean case diagnosis score among the evaluated objectives. In our evaluation, simple baselines remain highly competitive. The SCADA component score baseline achieves the highest test case diagnosis accuracy, while an ensemble of one-class support vector machines achieves the highest kinematics test case diagnosis accuracy. These findings indicate that anomaly score-based embeddings provide a useful representation for real-world component diagnosis, but that the small number of verified cases, coarse fault interval annotations, and partial label space mismatch remain major obstacles for reliable cross-machine adaptation.

Technologies doi: 10.3390/technologies14080505

Authors: Denis A. Stadnik Nino M. Stadnik Alexey G. Zhilin Ruslan G. Kisnichian Eduard E. Permyakov

The development of potash deposits is generally accompanied by large losses of minerals in the subsurface. The main reason for these losses is the use of a room-and-pillar mining system, where left pillars hold the overlying rock strata and aquifers located above the productive seams. Over time, the bearing elements of the mining system begin to deteriorate, leading to a loss of continuity of the water-protective stratum, the formation of water-conducting fractures, salt dissolution, and consequently, the flooding of the potash mine. The most effective method for solving production problems in the field of increasing mineral recovery and mine safety is the introduction of backfilling technology. The aim of the study is to identify the effect of backfilling on the stress–strain state of the rock mass in the vicinity of stopping and backfilling operations, to develop a technology for potash ore extraction with increased recovery, and also to solve the fundamental issue of the proposed technology, namely, the transport and property considerations of the backfill mixture. Numerical modeling methods and analytical derivations of calculation formulas for backfill mixture transport are used in the work. A comparison of dry, hydraulic, and hardening backfill mixtures is carried out. The study established that hardening backfill ensures a faster transition to the stage of mining the remaining reserves. A technology for pillar extraction with the leaving of technologically necessary narrow pillars is proposed, allowing for the safety of mining operations. Formulas are derived for calculating the required strength of the backfill based on the loading degree of the technological pillar. Transportability criteria are formulated, and a calculation procedure for pipeline transport parameters under gravity and gravity-pneumatic modes is developed. The proposed technology for potash ore extraction with hardening backfill allows for increased mineral recovery while maintaining safe conditions for undermining the water-protective stratum.

Technologies doi: 10.3390/technologies14080504

Authors: Héctor Felipe Mateo-Romero Luis Hernández-Callejo Miguel Ángel González Rebollo Valentín Cardeñoso-Payo Victor Alonso Gómez Leonardo Cardinale-Villalobos Jose Ignacio Morales Aragonés Sara Gallardo Saavedra Abel Méndez Porras Mario Carbonó dela Rosa

Accurate prediction of photovoltaic (PV) cell power from electroluminescence (EL) images is a key enabler for automated quality assessment and performance estimation in PV manufacturing and diagnostics. However, most existing image-based deep learning models are developed and evaluated for a single PV cell technology, limiting their ability to generalize across the wide variety of cell types used in practice. This work investigates the impact of PV cell technology on power prediction accuracy and proposes a novel Convolutional Hierarchical Mixture of Experts (CHME) architecture to overcome these generalization limitations. First, convolutional neural networks and feature-based machine learning models are evaluated on multiple PV cell technologies. While technology-specific convolutional models achieve low mean absolute errors (MAEs) of 0.02–0.04 when tested on the same cell type, their performance deteriorates substantially (MAEs of 0.08–0.23) when applied to different technologies. Feature-based models exhibit greater robustness across technologies but at the cost of lower prediction accuracy. To address these limitations, the proposed CHME model combines multiple pretrained technology-specific convolutional experts with a discriminator network that automatically identifies the PV cell technology and selects the most appropriate expert for power prediction. Experimental results demonstrate that CHME achieves the best overall performance, reducing the MAE to 0.0262 compared with 0.0298 for the best standalone convolutional model, while preserving adaptability to heterogeneous datasets. These results demonstrate that explicitly accounting for PV cell technology significantly improves image-based power prediction and that the proposed hierarchical mixture-of-experts framework provides an accurate, scalable, and easily retrainable solution for real-world PV diagnostic systems.

Technologies doi: 10.3390/technologies14080503

Authors: Uvi Desi Fatmawati Iwa Garniwa Faiz Husnayain Sunarta Pranda Mulya Putra Garniwa

Tracking capacity fade and predicting the lifespan of Lithium Iron Phosphate (LiFePO4) batteries under calendar aging are crucial for the reliability of Battery Energy Storage Systems (BESSs) in tropical regions. Conventional empirical models often rely on static environmental averages and neglect coupled thermal–hygroscopic dynamics. To address these limitations, this paper introduces a multi-physics coupled state-space-based continuous integral model for battery degradation under dynamic tropical boundary conditions. The primary novelty of this research lies in the development of a continuous-time multi-physics state-space degradation model that explicitly captures the interconnected interactions between temperature, humidity, and State of Charge (SoC) under dynamically varying tropical microclimates. Calendar aging tests were conducted for 180 days inside an environmental test chamber under tropical microclimate conditions (average of 29.91 °C, RH of 77.26%), with reference performance tests executed at a low C-rate of C/20 to extract static electrochemical capacity. Parameter identification using an Ordinary Least Squares (OLS) solver demonstrates high model fitting, with R-squared values ranging from 0.8229 to 0.9429. Extrapolation results provide realistic end-of-life projections between 8.9 and 59.8 years and successfully identify the critical physical transition points P1 and P2 at the Solid Electrolyte Interphase (SEI) layer. Overall, this research provides a prognostic instrument for optimizing the operational management of utility-scale BESS in tropical climates.

Technologies doi: 10.3390/technologies14080502

Authors: Bangqiang Han Qing Cheng Shengbin Wang Wenquan Huang

Mechanical component detection in industrial scenes is challenged by cluttered backgrounds, large-scale variation, specular reflection, high inter-class similarity, and class imbalance. To address the above problems, this paper proposes a lightweight YOLO-style detector that integrates a PP-HGNetV2 tiny backbone, an enhanced normalization-based attention module (ImNAM), and an improved complete intersection-over-union loss (ImCIoU). The HGNetV2 backbone enhances hierarchical multi-scale feature extraction and keeps the deployable computational complexity low. ImNAM has been modified to enhance discriminative representation by introducing dual-statistics channel weighting, orthogonal edge-aware spatial modeling and bipolar adaptive residual gating. ImCIoU enhances the accuracy of localization by combining quality-aware box scaling, scale-sensitive modulation and dynamic IoU-guided weighting. A class-balancing augmentation pipeline was applied to the four-category industrial dataset of Bearing, Bolt, Gear and Nut. All experimental results are reported as the mean ± standard deviation of five independent two-tailed training runs with different random seeds, and statistical significance is verified by paired t-tests (p < 0.05) with Bonferroni correction for multiple comparisons. Experimental results show that the proposed method achieves 90.82 ± 0.35% mean average precision (mAP@0.5), 91.95 ± 0.42% precision, and 82.98 ± 0.51% recall, outperforming nine mainstream lightweight detectors, including the latest YOLOv12n (2025) and RT-DETR-tiny. Extended evaluation on mAP@0.5:0.95, per-class AP and F1 score further confirms the advantages in localization accuracy and classification performance. Ablation studies confirm that the HGNetV2 family backbone provides the largest performance gain, while the improved attention mechanism and regression loss further enhance localization accuracy and robustness. With only 4.44 M parameters and 9.96GFLOPs, the proposed detector has achieved a good accuracy–efficiency trade-off and shows strong potential for intelligent industrial inspection on resource-constrained platforms, subject to further hardware-level deployment verification.

Technologies doi: 10.3390/technologies14080501

Authors: Salman Ahmadi-Asl Naeim Rezaeian Cesar F. Caiafa André L. F. de Almeida

This paper introduces efficient randomized fixed-precision and single-pass algorithms for low-tubal-rank approximation of third-order tensors. The proposed fixed-precision algorithms are faster and more efficient than the existing algorithms for approximating the truncated tensor SVD (T-SVD). Furthermore, unlike existing single-pass methods, which directly extend early, unstable matrix algorithms, the proposed approach adapts enhanced and stabilized matrix techniques to the tensor setting. Through extensive numerical experiments, we identify a critical flaw in current single-pass algorithms: using sketching parameters of equal size often produces ill-conditioned tensor least-squares problems, leading to inaccurate approximations. The proposed algorithms are demonstrably robust to this issue, achieving superior performance under identical conditions. We also evaluate the robustness of existing single-pass methods on real-world data tensors, including images and videos, a topic that has not been thoroughly examined before. Numerical results confirm the effectiveness of the proposed methods. Three applications are presented: image compression, video super-resolution, and deep learning.

Technologies doi: 10.3390/technologies14080500

Authors: Carlos Jesús Sánchez-Morales Julia Claudia Mirza-Rosca

This paper presents a data-driven educational framework for teaching psychrometry and air quality control in hospitals, developed within an international pilot project involving universities and hospitals in Spain, Romania, and Turkey. The objective of this pilot study is to examine a multidisciplinary framework that equips engineering students with essential technical skills for managing hospital infrastructure, particularly in critical areas like operating rooms and intensive care units. The methodology integrates theoretical instruction, analogue instruments, and digital technologies, including Arduino-based sensing and AI tools, to facilitate data interpretation and critical thinking. By bridging manual measurements with digital monitoring, the framework aims to equalize proficiency among students from diverse engineering backgrounds. Quantitative results from 23 participants provide preliminary evidence of academic growth, consistent with the hypothesis that this integrated approach may facilitate conceptual mastery. This work offers preliminary insights into the advancement of data-driven modelling in engineering education, emphasizing the significance of multidisciplinary training and international collaboration in preparing future professionals for the oversight, operational management, and maintenance of modern healthcare facilities.

Technologies doi: 10.3390/technologies14080499

Authors: Mohcine Karroum Noureddine En-nahnahi

Multispectral remote sensing classification benefits from the complementary information carried by visible (VIS), near-infrared (NIR), and short-wave infrared (SWIR) Sentinel-2 bands, yet most deep models process them as a single stacked tensor without explicitly preserving their inter-group relationships. We propose Relation-Consistency Group Contrastive Learning (Group-CL-RC), a robustness-oriented framework combining group-level contrastive alignment with a relation-consistency regularizer defined over a compact VIS–NIR–SWIR similarity descriptor. The method is evaluated on EuroSAT All Bands using four backbones under radiometric drift, spatial masking, K-drop band removal, and compound spectral–spatial corruption (CS2C), and externally validated on Sentinel-2-only SEN12MS under standard and seasonal-shift protocols. Group-CL-RC preserves strong clean performance and yields statistically supported robustness gains over the multispectral-only baseline, with the largest improvements under K-drop and CS2C. SEN12MS supports the transfer of these robustness trends beyond EuroSAT, while showing that gains over standard Group-CL remain perturbation-dependent. Ablation studies further indicate that relation consistency is an effective robustness mechanism, particularly when spectral-group availability is degraded. Relation-deformation diagnostics show that Group-CL-RC primarily reduces decision-level sensitivity to relational distortions rather than uniformly minimizing raw deformation. Overall, inter-group relational geometry provides an interpretable and effective robustness target under controlled structured spectral and spectral–spatial degradation.

Technologies doi: 10.3390/technologies14080498

Authors: Harald Rietdijk Daniëlle Talen Patricia Conde-Cespedes Talko Dijkhuis Hilbrand Oldenhuis Maria Trocan

Wearable technology and the Internet of Things have increased access to personal data, enabling applications that deliver individualized treatment and therapy within clinical pathways. To optimize coaching and interventions within such pathways, it is essential to identify all relevant factors in the available data. Feature selection can be a useful tool for achieving this, but with small, high-dimensional datasets, common in healthcare, it can be challenging. The goal of this study is to develop a method for identifying the most relevant features in small, high-dimensional datasets and to introduce a new ordering index that measures the quality of the orderings produced by feature selection methods. This novel index is sensitive to the quality of feature ordering and to the prediction model’s performance metrics when combined with a feature selection method. The index reaches its maximum when the number-of-features-versus-accuracy graph has an ideal concave-downward shape, reflecting increasing accuracy with each informative feature added and decreasing accuracy with each confounding feature added. Using this index, we define six feature orderings derived from the results of four standard feature selection methods. Using the performance metrics and our new ordering index, we show that the resulting orderings can identify more relevant features and improve the overall performance of the classification models, and that the ordering index is a useful contribution to feature selection techniques.

Technologies doi: 10.3390/technologies14080497

Authors: Boyang Cui Ying Huang Yudong Wang Hong Li Haoran Wang

A cascaded dual-parameter fiber-optic sensor is presented, in which a single-mode–no-core–single-mode (SNS) multimode-interference (MMI) interferometer is integrated with a fiber Bragg grating (FBG) to achieve simultaneous refractive index (RI) and temperature sensing. The SNS segment, fabricated by fusion splicing a no-core fiber (NCF) between two single-mode fibers, exploits MMI to generate spectral features that are highly responsive to ambient RI changes. Meanwhile, the FBG serves as an independent temperature reference, owing to its negligible RI sensitivity. Based on the beam propagation method, the MMI characteristics in the NCF are analyzed. By combining the simulation results with the experimental spectra, the NCF length is optimized by comprehensively considering the interference-fringe visibility, free spectral range, and spectral separation from the FBG wavelength. Experimental results show that the maximum RI sensitivity of the SNS interferometric structure reaches 136.29 nm/RIU, with a corresponding temperature sensitivity of 9.14 pm/°C. The FBG exhibits a temperature sensitivity of 9.83 pm/°C while remaining virtually unresponsive to surrounding RI variations. By establishing a dual-parameter sensitivity matrix, RI and temperature variations can be simultaneously demodulated, enabling effective temperature compensation for RI sensing. The sensor requires no tapering, etching, or surface modification and can be fabricated using only conventional fiber-cleaving and fusion-splicing processes. With its simple fabrication, compact structure, low cost, and good mechanical stability, the sensor shows promising potential for temperature-compensated RI sensing, biochemical detection, liquid-concentration monitoring, and environmental sensing.

Technologies doi: 10.3390/technologies14080496

Authors: Wafa Baccouch Narjes Benameur Abdulrahman Abdullah Alsayyari Zeyad Alawaji Amani Kallel Abderrazak Jemai Salam Labidi

Heart failure (HF) remains a major global cause of morbidity and mortality, where early diagnosis is critical for improving patient outcomes. Conventional single-modality approaches often fail to capture the complex and multifactorial nature of HF. This study investigates the feasibility of a late-fusion framework that integrates modality-specific predictions derived independently from cine-MRI, electrocardiographic signals, biomarkers and demographic data for HF prediction. Independent cine-MRI data from 281 patients, ECG recordings from the PTB-XL PhysioNet database and biomarker profiles from 157 patients were retrospectively analyzed as separate modality-specific cohorts. Twenty-five features were extracted and processed. Modality-specific models (Attention U-Net, MLP, XGBoost) were trained separately on pre-extracted features to preserve predictive accuracy while minimizing computational cost. Their outputs were combined through ensemble meta-learning (XGBoost, LightGBM, Random Forest) with sample weighting to handle missing data. The final HF prediction probability was obtained by averaging the outputs across the three meta-learners. The proposed framework achieved competitive diagnostic performance, with 98.00% (95% CI: 94.96–99.45%) accuracy, 97.80% (95% CI: 92.28–99.73%) sensitivity, 98.17% (95% CI: 93.53–99.78%) specificity, an F1-score of 97.80% (95% CI: 93.6–99.8%) and an AUC of 0.978 (95% CI: 0.945–0.996) when evaluated against state-of-the-art methods. The results highlight the potential of late-fusion strategies for integrating independently trained modality-specific predictions, offering a feasible approach for HF risk assessment under heterogeneous data availability.

Technologies doi: 10.3390/technologies14080495

Authors: Nikolay Hinov

This paper proposes a testable three-layer retained-state framework for intelligent energy systems grounded in mem-element theory. The framework distinguishes constitutive physical memory (Layer I), distributed circuit/converter memory (Layer II), and functional operational memory (Layer III) while preventing the indiscriminate classification of any history-dependent model as a mem-system. A retained variable is admissible only when it satisfies persistence, trajectory dependence, observable engineering consequence, and positive relevance beyond an instantaneous reference. Quantitative trajectory-separation, retained-state relevance, and engineering-gain indices, together with observability and falsification conditions, convert the framework from a taxonomy into a testable methodology. Layer I was partially validated using 636 experimental discharge cycles from four cells in the public NASA Ames PCoE Li-ion Battery Aging Dataset. Across 120 cycle–disjoint within-cell pairs matched at closely similar voltages, currents, temperatures, and local slopes, the median future-trajectory separation (MTS) was 0.0688, the noise-normalized separation (MNS) was 20.13, and the remaining-discharge duration differed by 150.7 s. Leave-one-battery-out prediction yielded positive retained-state relevance (MRI = 0.159 with a random-forest model; 95% bootstrap interval: 0.121–0.194). Two reduced-order cross-scale applications were then used for Layers II and III. In resonant wireless EV charging, retained-state augmentation reduced the efficiency RMSE by 29.9–32.2% and the current MAE by 27.5–27.9% in disturbed scenarios. In EV charging/V2G scheduling, history-aware operation reduced the charging cost by 3.3%, the degradation proxy by 12.0%, thermal-limit violations by 27.3%, and aggressive cycling by 17.2% while accepting lower peak reduction and V2G revenue. Same-information controls produced identical numerical outputs to the structured models by construction, showing that the framework’s novelty lies in admissibility, falsifiability, and cross-scale interpretation rather than privileged input information. The NASA study provides bounded public-experimental-data validation of Layer I; Layers II and III remain proof-of-concept demonstrations.

Technologies doi: 10.3390/technologies14080494

Authors: Tao Wang Yanqiang Di Shaochong Feng Qiongyao Liu Haohao Cui Qing Liu

Fault diagnosis for complex industrial equipment plays a crucial role in safeguarding production safety and advancing the capabilities of intelligent operation and maintenance. Current deep learning approaches have demonstrated promising accuracy in fault classification tasks; however, their signal representations alone cannot provide a transparent interface for embedded large language models. To tackle the aforementioned challenges, we propose PGA-LLM, a novel fault diagnosis framework for industrial equipment that leverages large language models via probability-guided alignment. First, a variational autoencoder (VAE)-based signal encoder embedded with reconstruction constraints is established. Joint reconstruction and classification objectives balance discriminative representation learning and signal reconstruction. Second, the probability-guided alignment (PGA) module combines fault-class probability guidance with a residual feature path; a learned gate fuses both paths before continuous soft-prompt projection. Furthermore, a progressive three-stage training scheme is adopted, encompassing encoder pre-training, interface optimization, and low-rank adaptation (LoRA) of Qwen2.5-1.5B. Extensive experiments are carried out on four standard datasets, CWRU, Gear, Mixed, and MBHM, and the Stage 2 signal-side output achieves classification accuracies of 97.1%, 99.0%, 93.4%, and 96.3%, respectively. The report-generation branch provides a schema-constrained signal-to-language interface for maintenance-oriented reporting.

Technologies doi: 10.3390/technologies14080493

Authors: Shouki A. Ebad Aws I. Abueid Abdulbasit A. Darem

Background: The mining sector’s digital transformation increasingly relies on AI-driven digital twins (AI-DTs) that integrate real-time data with intelligent analytics. A recent systematic literature review (SLR) of 68 studies identified a critical gap: which technical choices guarantee industrial success? Objective: This study extends that SLR by validating a Deployment Readiness Score (DRS) to identify which combinations of technical and methodological choices are associated with high readiness AI-DT studies in the literature. Methods: Each study was coded across eight dimensions and assigned to a DRS based on data source, validation method, and operational metric reporting. A random forest classifier was used as a consistency check for the DRS framework. Results: The model achieved 100% test accuracy as an internal consistency check within the coded dataset, confirming that the DRS scoring rules produce a coherent classification across the reviewed studies. The data source was the strongest association (41.2%), followed by publication year (25.6%) and validation method (22.7%). AI technique showed minimal association (0.9%). Studies using sensory data achieved 100% high readiness within the DRS framework; mixed data achieved 95.2%; and experimental validation achieved 95.7%. The proportion of high-readiness studies increased from 35.7% in 2024 to 93.5% in 2025. Conclusions: Within the reviewed literature, high-fidelity data and rigorous validation show stronger associations with high readiness than algorithmic complexity. We provide a Deployment Readiness Scorecard and propose minimal reporting standards, shifting focus from theoretical algorithms to practical data acquisition and validation.

Technologies doi: 10.3390/technologies14080492

Authors: Miruna Ciolca Constantin Stochioiu Mihai Costea Alexandru Paraschiv Florin Baciu Daniel Vlăsceanu

Additive manufacturing enables the production of geometrically complex nickel-based superalloy components, but the high-temperature tensile response of selective laser-melted Inconel 718 remains strongly dependent on manufacturing route and build orientation. In this study, the tensile behavior of Inconel 718 specimens machined from wrought bar stock and fabricated by selective laser melting in horizontal and vertical build orientations was investigated at 23 °C, 450 °C, 550 °C and 750 °C. The experimental values were used in a finite element simulation to create a model that can accurately predict the mechanical behavior of IN718. The results show that the X-oriented SLM specimens exhibit tensile properties comparable to those of the wrought material, whereas the Z-oriented specimens display reduced strength and increased scatter, highlighting the effect of build orientation on mechanical performance. The numerical simulations reproduced the experimental stress–strain response with good agreement within the elastic and plastic deformation regimes, demonstrating the suitability of the proposed modeling approach for high-temperature structural assessment of Inconel 718 components manufactured by conventional and additive technologies.

Technologies doi: 10.3390/technologies14080491

Authors: Yahya Alsuwayyeh Thamer Almoneef Hamaskutty Vettikalladi

This study develops and experimentally evaluates a low-profile four-port circularly polarized metasurface MIMO antenna for fixed or sectorized wireless links in the 6 GHz band. The design begins with an aperture-coupled single element incorporating a 4×4 slotted metasurface and is subsequently extended to a 2×2 MIMO configuration. Mutual coupling between the closely spaced elements is controlled by a hybrid decoupling arrangement comprising metamaterial unit cells and slots etched in the common ground plane. The closely spaced metasurface resonances broaden the circular-polarization response, while the hybrid MTM and DGS decoupling structure suppresses complementary coupling paths between adjacent elements. A fabricated prototype was characterized to verify the simulated performance. The measured −10 dB impedance band extends from 5.56 to 7.85 GHz, corresponding to 34.15%, while the measured 3 dB axial-ratio band covers 5.60–7.13 GHz, corresponding to 24.03%. Across the operating region, the isolation exceeds 23 dB, and the maximum measured gain reaches 8.4 dBic. The MIMO characteristics include an envelope correlation coefficient below 0.0015, diversity gain above 9.98 dB, mean effective gain below −3 dB, and total active reflection coefficient below −10 dB. The measured results demonstrate that the proposed configuration combines wide circular-polarization bandwidth, low interelement correlation, high gain, and effective port isolation for fixed or sectorized IoT and Wi-Fi 6E applications.

Technologies doi: 10.3390/technologies14080490

Authors: Samaa Ahmed Doaa Saad Ahmed Yakoub

Effective fetal health prediction is crucial for early diagnosis of fetal distress and avoiding negative perinatal effects. Cardiotocography (CTG), which measures fetal heart rate and uterine contractions, is a popular method for prenatal examination; however, conventional interpretation is arbitrary and unreliable. Fetal health prediction continues to face challenges due to scarce and imbalanced CTG datasets and data leakage during model evaluation, which can lead to poor generalization and inaccurate performance estimates. Moreover, the absence of explainable AI reduces model clarity and restricts clinical utilization. This study offers a hybrid deep learning model that uses TabNet, Residual Multi-Layer Perceptron (Residual MLP), and Focal Loss to classify normal, suspect, and pathological fetal states. TabNet allows for attention feature learning from CTG data, Residual MLP increases predictive robustness, and Focal Loss aids minority abnormal case diagnosis. To achieve a reliable evaluation, data splitting is used to create a pipeline designed to eliminate conventional train–test data leakage by performing data partitioning before model training and evaluation. SHAP and LIME enhance interpretability by offering clear global and local explanations. The suggested model obtains 99.30% accuracy, 99.10% balanced accuracy, and 98.65% pathological recall on the augmented dataset. A comparative evaluation shows that fixing data leakage drops overinflated baseline accuracy from 96.80% to 93.14%, emphasizing the necessity of a robust experimental design. The results show that the proposed framework outperforms cutting-edge fetal health prediction approaches, offering a robust, understandable, and clinically reliable alternative for CTG-based fetal health evaluation.

Technologies doi: 10.3390/technologies14080489

Authors: Masateru Kawakubo Tamás Haidegger

Artificial Intelligence (AI) is a transformative technology producing fundamental changes in many fields [...]

Technologies doi: 10.3390/technologies14080488

Authors: Nathaniel Morales-Centla Richard Torrealba-Meléndez Edna Iliana Tamariz-Flores César Augusto Arriaga-Arriaga Mario López-López

This paper presents a quantitative evaluation of an automatic translation system between industrial robot programming languages based on a sequence-to-sequence (Seq2Seq) neural architecture using Long Short-Term Memory (LSTM) networks. The study addresses the interoperability problem between proprietary robot programming languages by proposing a data-driven approach capable of learning correspondences between structured code instructions. A parallel dataset of 28,000 aligned instruction pairs was constructed and preprocessed through tokenization and normalization to enable structured sequence learning. The model was trained under four configurations (50,100, 150 and 200 epochs) to analyze the impact of training duration on performance and generalization capability. The system was evaluated using multiple quantitative metrics, including accuracy, loss, BLEU, and Exact Match (EM), allowing assessment of both structural similarity and exact sequence correctness. Experimental results demonstrate that the 200-epoch configuration improves the performance across all metrics, achieving an accuracy of 0.9943, a BLEU score of 0.682, and an Exact Match of 0.970 on the test set. These results indicate that the model is capable of generating both structurally consistent and syntactically correct translations. The analysis shows that while BLEU captures structural similarity, EM provides a stricter evaluation of exact sequence correctness, which is critical in structured code translation tasks where minor variations may affect execution. The proposed approach demonstrates the feasibility of applying neural machine translation techniques to industrial robot programming, contributing to improved interoperability and reduced manual effort in multi-platform robotic environments.

Technologies doi: 10.3390/technologies14080487

Authors: Penio Lebamovski Evgeniya Gospodinova

Virtual reality (VR) provides an effective experimental environment for the controlled investigation of autonomic physiological responses under different levels of virtual immersion. This study investigated heart rate variability (HRV) responses to mental stress induced by an interactive VR game. Two stereoscopic conditions were compared: low immersion using polarising glasses and high immersion using a VR headset. Additionally, the applicability of visual, mathematical, and machine learning approaches for analysing VR-related changes in the autonomic nervous system is evaluated. The experiment involved 42 healthy volunteers (37 men and 5 women; mean age 26 ± 7 years). The HRV analysis is based on RR-intervals and includes time, frequency, and nonlinear indicators. Within the nonlinear framework, visual-mathematical methods, including Poincaré diagrams, recurrence plots, and histogram analysis, were applied. For automated classification of physiological states, a Random Forest model using 17 selected HRV parameters was applied. The results show exposure to the VR game elicited significant changes in autonomic cardiac regulation and heart rate variability, with different stereoscopic technologies producing distinct physiological responses. Nonlinear methods and applied visual-mathematical analyses reveal complex dynamic characteristics of heart rate that classical HRV indicators cannot fully capture. ROC and confusion matrix analyses show that the Random Forest model has good discriminative ability in distinguishing between a resting state and different levels of virtual immersion. The results confirm the potential of the proposed integrated approach, combining virtual reality, linear and nonlinear HRV analyses, and machine learning, as a tool for the objective assessment and modelling of autonomic physiological responses during VR exposure. The study has several limitations, including the relatively small and homogeneous sample size and the absence of stress and psychological state assessment through validated questionnaires, which should be addressed in future research.

Technologies doi: 10.3390/technologies14080486

Authors: Ulzhalgas Seidaliyeva Perizat Akylzhan Lyazzat Ilipbayeva Kyrmyzy Taissariyeva Aruzhan Nazarova Alima Mambetaliyeva Nurzhigit Smailov Maigul Zhekambayeva Gulbahar Yussupova Dina Bauyrzhankyzy

Respiratory sound analysis is becoming increasingly popular as a non-invasive method for detecting adventitious sounds and aiding in the diagnosis of respiratory diseases such as chronic obstructive pulmonary disease (COPD), asthma, and pneumonia. However, studies in this field differ significantly in datasets, recording devices, annotation techniques, preprocessing pipelines, model architectures, validation strategies, and evaluation metrics. This scoping review examines current research on artificial intelligence-based respiratory sound analysis, with a focus on datasets, acquisition and annotation practices, signal processing, feature representations, machine learning (ML) and deep learning (DL) methods, and evaluation protocols. The search identified 1056 database records, and 89 reports were included for the final evidence mapping. The reviewed datasets support event-, cycle-, recording-, and patient-level tasks and differ considerably in population, scale, acquisition hardware, annotation granularity, and label structure. The findings also show that acquisition and annotation are closely linked, creating potential device-, recording-site-, and label-related confounding. Methodologically, the literature can be summarized in four broad stages: handcrafted feature-based ML; deep spectrogram learning; representation learning and multimodality; and deployment-oriented, robustness-focused systems. Despite recent achievements, the field remains limited by small and imbalanced datasets, annotation uncertainty, device and population variability, inconsistent data splitting, and limited external validation. More reliable clinical use will require standardized acquisition and annotation, quality-controlled preprocessing, patient-independent evaluation, task-appropriate metrics, transparent reporting, and validation across independent devices, datasets, and clinical populations.

Technologies doi: 10.3390/technologies14080485

Authors: Shuang Wang Yuxin Xue Jingxian Zhou Huan Zhao Lei Ding

This article proposes a prediction model that combines network physical characteristics and deep learning methods to solve the problem of insufficient round-trip time (RTT) prediction accuracy caused by complex dynamic characteristics in civil aviation business data networks. This model is based on a physical information neural network framework, which embeds domain knowledge such as delay load relationships, burst traffic attenuation patterns, and inverse RTT window constraints into a long short-term memory (LSTM) network. In addition, the multi constraint loss function enhances the adaptability of the model to complex network activities. Moreover, in order to overcome the limitations of LSTM networks in modeling long-range dependencies, a multi head attention (MA) mechanism was implemented to capture long-term temporal dependencies in parallel, thereby improving the model’s ability to capture long-range time step correlations. Benchmarking and extension tests were conducted on four civil aviation business data network datasets. The experimental results show that compared with the baseline model, the proposed model significantly improves the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) for bidirectional network communication RTT prediction tasks. The study has verified that combining network physical attribute constraints with attention mechanisms can effectively improve the accuracy of transmission delay prediction, providing an effective method for effective traffic prediction in highly dynamic civil aviation network environments.

Technologies doi: 10.3390/technologies14080484

Authors: Anna A. Kamenskikh Dmitry O. Pustovalov Veronika I. Strukova

This current work considers six specialized castable photopolymers from different manufacturers that are promising for the formation of a process map for casting using burnt models: Harz Labs Dental Cast Cherry; Gorky Liquid Castable; Gorky Liquid Dental Castable; JAMG HE Ultra Cast UV Resin (90% WAX); JAMG HE Jewelry Casting Resin (25% WAX); Siraya Tech Cast Royal Blue. The additive growth parameters of the structures were selected to provide quality indicators of photopolymer models. Thermogravimetric analysis and dynamic mechanical analysis were chosen as the main methods of experimental analysis. Thermogravimetric analysis was performed at temperatures up to 700 °C. Thermal decomposition of castable photopolymers has several stages of decomposition with different rates of destruction. Gorky Liquid Castable has the fastest thermal decomposition. The material has less than 1% of the residual weight at 530 °C. The minimum ash content is in Siraya Tech Cast Royal Blue (less than 0.1%). Dynamic mechanical analysis was performed at temperatures up to 225 °C for samples grown in different printing directions. The printing direction affects the storage modulus, loss modulus, and tangent of the angle of mechanical losses of photopolymer materials, on average by no more than 35%. Glass transition temperatures of materials and zones of relaxation transitions are established. The glass transition temperatures depend little on the direction of printing of the samples. JAMG HE Ultra Cast UV Resin (90% WAX), a photopolymer with a maximum wax content, has the minimum glass transition temperature of 30–32 °C. JAMG HE Ultra Cast UV Resin (90% WAX) does not have a pronounced relaxation transition zone.

Technologies doi: 10.3390/technologies14080483

Authors: Feige Zhang Guo Li Wenjuan Zhang Kexue Liu Zhaohui Gao Chengfei Guo Shesheng Gao

In this paper, we propose a correntropy weighted extended Kalman filter (CWEKF) method to address the challenges of low estimation accuracy and poor robustness in sensorless rotor speed estimation for doubly-fed induction generators (DFIGs). Firstly, based on Faraday’s law of electromagnetic induction and the mechanical motion equation, we derive a DFIG nonlinear state-space model. This model quantifies the sources of nonlinearity arising from cross-coupling terms and product terms, providing a precise model foundation for rotor speed estimation. Secondly, we introduce correntropy theory to design a residual dynamic weighting scheme. By quantifying the local similarity between current and historical residuals, the scheme adaptively adjusts the noise covariance estimation weights, suppressing the interference of outdated data. Combined with the Chi-squared test, we derive an adaptive kernel bandwidth mechanism, balancing the response speed to noise variations and the estimation accuracy in steady-state. Additionally, we further integrate Huber robust weighting and regularization techniques for constructing a hybrid weighting mechanism and optimizing the covariance positive-definiteness correction to address the numerical stability deficiencies of the original algorithm. Using the Lipschitz condition and Lyapunov theory, we prove the mean-square exponential boundedness of the CWEKF estimation error. Finally, we build a DFIG vector control model using MATLAB R2021a and conduct comprehensive experiments, including simulation comparative experiments, open-loop speed identification experiments, and closed-loop sensorless control experiments. Comparative simulation experiments are conducted with EKF, AEKF, and RWEKF under three operating conditions; open-loop experiments verify that the constructed platform meets variable-speed constant-frequency (VSCF) power generation requirements, and closed-loop experiments compare CWEKF with MRAS under different speeds and parameter variations. The results show that the CWEKF has a maximum rotor speed estimation error <5 r/min, the response time has been reduced by over 65% compared to the traditional EKF, and it outperforms EKF, AEKF, RWEKF, and MRAS in estimation accuracy and stability, exhibiting significantly improved robustness under parameter variations and strong noise conditions.

Technologies doi: 10.3390/technologies14080482

Authors: Nikola Lopac Karlo Severinski Neven Grubišić Jonatan Lerga

Vessel detection and multi-object tracking (MOT) in marinas remain challenging because of occlusions, small targets, cluttered backgrounds, and changing illumination. This paper presents a controlled, feasibility-focused comparative evaluation of YOLO-driven vessel detection and tracking in a fixed-camera marina environment, comparing YOLOv11 with the more attention-oriented YOLOv12 detector family. A dataset of 3546 annotated images was collected using a static ground-level camera at a marina in the northern Adriatic, Croatia. YOLOv11s, YOLOv11m, YOLOv12s, and YOLOv12m were fine-tuned using transfer learning, evaluated across five random seeds, and integrated into a common tracking-by-detection pipeline with Kalman filter (KF) and extended Kalman filter (EKF) motion models. Detection was assessed using COCO-style mAP metrics, while tracking was evaluated using MOT and HOTA-based metrics. YOLOv11m achieved the highest mean test-set mAP@50–95 (0.7458 ± 0.0027) and significantly outperformed YOLOv11s and YOLOv12s. Although YOLOv12m did not achieve the highest frame-level mAP, it obtained the highest mean AssA and IDF1 when averaged across the two motion models. KF achieved higher mean HOTA, DetA, AssA, MOTA, and IDF1 than EKF, whereas EKF achieved only marginally higher MOTP; the KF advantage was statistically significant for IDF1. Overall, the results show that frame-level detection accuracy does not necessarily determine downstream tracking performance, thereby supporting the joint evaluation of detector and motion-model choices.

Technologies doi: 10.3390/technologies14080481

Authors: Neylan Leal Dias Alexandre de Queiroz Ana Claudia de Jesus Golzio Ricardo Fonseca Buzo Simone de Almeida Delphin Leal Gabriel Henrique Doi Alfredo Bonini Neto

The estimation of nodal voltage angles is essential for the efficient and secure operation of electrical power systems. Traditionally, this estimation depends on the prior solution of the power flow problem, a procedure that may become computationally expensive in studies involving multiple operating scenarios. The present research demonstrates relevant potential impact by employing artificial neural networks for the direct estimation of nodal voltage angles. Although the power flow is used during the database generation stage, it is no longer required during the model application phase, resulting in a significant reduction in computational effort of approximately 80%. The proposed model was trained using the error backpropagation algorithm, achieving a mean squared error (MSE) on the order of 10−3 in only four iterations, with a training time of approximately 3 s and a correlation coefficient (R) of 0.99. Validation using unseen samples (10% of the samples) also demonstrated high accuracy, with an error on the order of 10−3 between the estimated and expected values. Furthermore, the P–θ curves were successfully obtained for the IEEE 14-, 30-, and 57-bus systems, enabling the identification of the maximum loading point with low estimation error. These results confirm the neural network’s ability to generalize and operate as a reliable estimator under different system loading conditions. Another relevant aspect of this work was the sensitivity analysis of the input variables, which made it possible to identify the variables that exerted the greatest influence on the neural network response. In addition, a normalization step was proposed to overcome distortions associated with the magnitudes of the input data, demonstrating the efficiency and robustness of the proposed method.

Technologies doi: 10.3390/technologies14080480

Authors: Fayha Almutairy

The Internet of Medical Things (IoMT) is growing quickly, which has greatly increased the cybersecurity attacks on the healthcare systems. Security techniques used for preventing attacks can improve patient safety, which is most important. As most of the datasets generated by the IoMT have high dimensions, feature selection is needed for accurate identification of data, along with deployment in real-time with limited resources. Moreover, the most influential features need to be identified for intrusion detection. Thus, this paper proposes a novel explainable hybrid framework, DeepMedShield-XAI, using the particle swarm optimization (PSO) method for feature selection and classifying the selected features using deep learning algorithms. The highest performing model among the three deep learning models is the deep neural network (DNN) with 99.68% and 99.87% test accuracy on the CICIoMT2024 and IoMT_TrafficData datasets. The findings of explainable artificial intelligence (XAI) methods reveal that the CICIoMT2024 dataset relies on connection-level features like length, protocol type, and TCP flags, while the IoMT_TrafficData dataset uses flow-based attributes like flow length, byte counts, and packet speeds without a single feature dominating across attack types. The proposed DeepMedShield-XAI framework: the results indicate that cyberattacks and unauthorized access attempts can be detected early by DeepMedShield-XAI, which can substantially improve the security of IoMT devices. This drives research on lightweight, explainable, and real-time security frameworks to protect patient data and ensure healthcare system reliability.

Technologies doi: 10.3390/technologies14080479

Authors: Hamed Azimi Rahim Shoghi Hodjat Shiri

Digital Twin (DT) technology has emerged as a transformative approach in pipeline engineering, enabling real-time monitoring, predictive analytics, and enhanced decision-making across the asset lifecycle. This review critically examines recent advancements in the application of digital twins for pipeline systems, with a particular focus on condition monitoring, leak detection, corrosion assessment, and predictive maintenance. The study synthesizes findings from a wide range of literature to identify key enabling technologies, including Internet of Things (IoT) sensors, data-driven modeling, computational fluid dynamics (CFD), and machine learning algorithms. Special attention is given to the integration of physics-based and data-driven models for improving the accuracy and reliability of digital twin frameworks. In addition, this paper proposes a unified reference architecture for pipeline digital twins, supported by a mathematical formulation of synchronization and a comparative synthesis of existing approaches. The review highlights how digital twins facilitate early fault detection and operational optimization by continuously synchronizing physical assets with their virtual counterparts. The review also emphasizes the importance of uncertainty-aware and reliability-informed digital twin frameworks for robust decision-making in safety-critical pipeline applications. Applications in subsea, oil and gas, and water distribution pipelines are explored, demonstrating the versatility of DT systems under different environmental and operational conditions. Despite significant progress, challenges remain in data integration, model validation, scalability, and cybersecurity. Furthermore, the lack of standardized architectures and interoperability frameworks limits widespread adoption. This paper concludes by outlining future research directions, including the development of hybrid modeling techniques, edge computing integration, and AI-driven autonomous decision systems. Overall, digital twin technology represents a paradigm shift in pipeline engineering, offering substantial potential to enhance safety, efficiency, and sustainability in complex infrastructure systems.

Technologies doi: 10.3390/technologies14080478

Authors: Santiago Laín Marcos Larrosa

This study investigates the potential for reducing or eliminating conventional pre-cooling requirements in hydrogen refueling stations (HRS) through the integration of turboexpanders, using a combined thermodynamic modeling and Computational Fluid Dynamics (CFD) approach. A key novelty of the present work is the explicit comparison between constant and variable isentropic efficiency formulations, enabling a more realistic representation of turboexpander performance under the highly transient operating conditions characteristic of hydrogen refueling processes. A simplified thermodynamic model is first used to estimate the transient inlet conditions associated with different pressure-reduction strategies, which are subsequently imposed as boundary conditions for three-dimensional CFD simulations of the fast filling of a 70 MPa Type III hydrogen cylinder. The CFD methodology is validated against experimental data for conventional refueling under prescribed inlet-temperature conditions, while the sensitivity of the predictions to turbulence modeling is also assessed. The validated framework is then applied to compare conventional throttling, a single turboexpander with constant isentropic efficiency, and a parallel-expander configuration accounting for variable off-design efficiency. The CFD predictions indicate that turboexpander-assisted refueling can reduce hydrogen heating, with the parallel-expander configuration yielding a final average hydrogen temperature approximately 15 K lower than that predicted for conventional throttling.

Technologies doi: 10.3390/technologies14080477

Authors: Víctor Mario Vélez-Marín Oscar Danilo Montoya Jesús C. Hernández

This paper presents a fully native optimization framework, implemented within DIgSILENT PowerFactory, which is aimed at solving the optimal conductor sizing problem (OCSP) in electrical distribution systems under realistic operating conditions. Our methodology integrates a tabu search algorithm (TSA) directly with the three-phase power flow routines and database objects available in the DigSILENT programming language (DPL), thereby eliminating the need for external data exchange and synchronization between independent optimization and network simulation environments. Our framework considers balanced and unbalanced operating conditions while incorporating peak demand, multilevel demand, and hourly demand load profiles. The optimization process minimizes annual investment and operating costs while satisfying voltage regulation and conductor ampacity constraints. The methodology was validated using a 27-bus benchmark system and the IEEE 33- and 123-bus distribution systems under different operating scenarios. The numerical results indicate that chronological demand scenarios significantly influence conductor allocation decisions and annual operating costs. Compared to the conventional peak demand load profile, the multilevel and hourly load profiles produced lower annual costs by distributing conductor sizing decisions across multiple operating states instead of considering worst-case loading conditions. Additionally, the unbalanced scenarios increased the operating losses and modified the conductor selection patterns due to unequal phase loading and current asymmetries. The proposed TSA-DPL implementation maintained stable convergence behavior and low statistical dispersion under all the evaluated benchmark systems and operating conditions. Even for the IEEE 123-bus feeder under unbalanced hourly operating conditions, the standard deviation remained below 0.70% of the average annual cost, confirming the robustness and repeatability of the methodology. Although the detailed three-phase chronological simulations increased the computational requirements, the proposed implementation demonstrated computational applicability to the evaluated benchmark systems. Overall, the proposed TSA-DPL framework constitutes a robust native implementation for realistic conductor sizing studies in modern three-phase distribution systems.

Technologies doi: 10.3390/technologies14080476

Authors: Fatema A. Albalooshi M. R. Qader

The rapid growth of Internet of Things (IoT) devices generates high-dimensional, high-velocity data streams that demand real-time machine learning (ML) inference under strict hardware constraints. We propose the Adaptivecoreset Selection Engine (ACS-Engine), a unified framework for adaptive, differentiable, and resource-aware coreset selection on streaming IoT data. ACS-Engine introduces three tightly integrated innovations: (i) Differentiable Greedy Sampling (DGS), which relaxes discrete subset selection via Gumbel-Softmax reparameterization to enable end-to-end gradient-based optimization; (ii) Entropy-Aware Regularization (EAR), which promotes coreset diversity and provides implicit concept drift detection through a self-calibrating entropy threshold; and (iii) Resource-Aware Memory Management (RAMM), which dynamically adjusts the target coreset size based on real-time hardware telemetry—available memory, CPU utilization, remaining energy, and sampling frequency. Evaluated on eight real-world IoT datasets spanning three heterogeneous edge platforms, ACS-Engine achieves 15× memory reduction and a 20% energy efficiency improvement while retaining 98% of full-dataset accuracy, with a per-sample latency of 2 ms that satisfies real-time edge deployment requirements.

Technologies doi: 10.3390/technologies14080475

Authors: Everardo Inzunza-Gonzalez Fabrizio Stasolla

Artificial intelligence (AI) and deep learning (DL) have rapidly moved from experimental research to the core of modern medical and assistive technologies [...]

Technologies doi: 10.3390/technologies14080474

Authors: Nenad Kojić Bojan Milošević

Reliable prediction of the compressive strength of self-compacting concrete with recycled coarse aggregate (SCRCAC) from mixture composition supports more rational mix design and fewer experimental tests. Using the benchmark dataset of the reference study (603 mixtures, eight input variables), this work re-examines machine-learning prediction of this property with an emphasis on honest evaluation rather than on a new model. A stacking ensemble of three gradient-boosting models (XGBoost, LightGBM, CatBoost) and an extremely randomized trees model, combined through a ridge meta-learner, is used as a representative model and compared with the four machine-learning models of the reference study (Random Forest, Extra Trees, XGBoost, LightGBM), the recent single-booster model of Abood et al., and the reference artificial neural network. Reported as the mean over 25 repeated 70/30 splits, the ensemble reaches R2 = 0.793 ± 0.038 and RMSE = 6.21 ± 0.48 MPa, above all four reference models (R2 = 0.7249–0.7635) and significantly, though only marginally, above a tuned single XGBoost. The central contribution is the evaluation itself. Because the dataset contains repeated identical compositions, a leakage-free protocol lowers the R2 of every model to between 0.60 and 0.71, showing that the values of about 0.81–0.87 usually reported are inflated by duplicate-composition leakage, and leave-one-source-out evaluation lowers it further to about 0.14. Mutual-information and partial-dependence analyses identify cement as the dominant predictor, with water acting mainly through a nonlinear dependence. Robust, leakage-aware evaluation, rather than model architecture, emerges as the key to credible strength prediction on this benchmark.

Technologies doi: 10.3390/technologies14080473

Authors: Burak Aggul

Touchless interfaces for embedded systems require models that are both compact and evaluated under protocols that reflect realistic generalization. Public low-resolution gesture datasets can obscure this requirement because repeated samples, temporal neighbors, or subject identity may leak across train and test partitions. This study presents a leakage-aware multi-corpus benchmark in which four public corpora are evaluated within their own dataset-specific partitions after conversion to a common 8 × 8 depth representation when needed: a public IEEE DataPort ToF CSV with 8400 frames from four gestures, DS.GestureRecognition_TOF.1.0 with 3109 frames extracted from 40 low-resolution ToF sequences and three gestures, the depth component of the public Kinect+Leap dataset with 1400 samples from 14 subjects and 10 gesture classes, and the public Senz3D depth archive with 1320 samples from 4 subjects and 11 gesture classes. No blurred RGB image dataset, deblurring target, or synthetic blur augmentation is produced in the experiments; the only spatial reduction applied to external depth corpora is deterministic block averaging for a shared low-resolution depth input. The term multi-corpus is used here to clarify scope: the experiments do not train on one dataset and test on another, but instead compare optimistic random splits against structure-aware alternatives matched to each corpus: chronological block-aware, sequence-aware, and subject-wise evaluation. On the original IEEE ToF benchmark, the best macro F1-score decreases from 1.0000 under random splitting to 0.8494 under block-aware evaluation. On DS.GestureRecognition_TOF.1.0, random splitting again reaches 1.0000, whereas the best sequence-aware result falls to 0.6697. On the downsampled Kinect+Leap depth benchmark, random frame classification reaches 0.7639 macro F1, while subject-wise evaluation drops to 0.0566. On Senz3D reduced to 8 × 8, random frame classification reaches 0.9068 macro F1, while subject-wise evaluation drops to 0.0717. Device-side validation on an NVIDIA Jetson Nano Developer Kit preserved a macro F1-score of 0.8667 for the selected GRU on the primary IEEE ToF dataset with 14.48 ms/window steady-state CPU latency. The results show that low-resolution gesture recognition conclusions are strongly protocol- and corpus-dependent: compact temporal models remain viable for genuine ToF streams, but naive random splitting can substantially overstate generalization, and subject-wise recognition after aggressive 8 × 8 conversion remains difficult for the evaluated datasets and architectures.

Technologies doi: 10.3390/technologies14080472

Authors: Leiyu Zhang Ziye Li Yihuan Wang Xiangying Guo

To address the inheritance dilemma of Danzhou Diaosheng, a national intangible cultural heritage, a dedicated dancing robot was developed, and in-depth research on its mechanical structure and motion analysis was conducted for accurate reproduction of the folk art’s typical dance movements. Based on the decomposition and measurement of dance movements, a coupled mechanism system integrating crank-slider and crank-rocker mechanisms was constructed using the concise design concept of mechanism coupling, single drive and trajectory constraint. A practical prototype was developed and exhibited, which has attracted nearly 10 million visits to date. An optical motion capture system was applied to verify the robot’s motion accuracy, and the motion errors of its trunk, hand and joints were maintained at a low level (RMSE ≤ 1.47 mm). The mechanical technology and intangible cultural heritage protection were organically integrated in this research, which provided a replicable technical solution for traditional folk-art inheritance and a feasible design reference for cultural and artistic robots.

Technologies doi: 10.3390/technologies14080471

Authors: Yousef Farhaoui Hamed Taherdoost

Sustainable development has become a global priority as societies face increasingly complex challenges related to climate change, resource scarcity, urbanization, food security, healthcare accessibility, industrial efficiency, and digital transformation [...]

Technologies doi: 10.3390/technologies14080470

Authors: Jose Cornejo

Autonomous subterranean mobility remains one of the least unified domains in robotics because locomotion emerges from coupled interactions among deformable geomaterials, structural mechanics, energy dissipation, and environment-dependent sensing constraints. This foundational pioneer technical review synthesizes 35 years of research on burrowing and underground robotic systems through a terradynamic and multiphysics perspective. Following PRISMA guidelines, 143 peer-reviewed studies were analyzed across granular soils, cohesive sediments, saturated media, fractured geomaterials, and extraterrestrial regolith analogs. The review evaluates six dominant locomotion classes, including peristaltic, undulatory, fluidization-assisted, excavation-based, tip-extension, and hybrid architectures. Results demonstrate that locomotion performance is governed primarily by regulation of substrate response rather than propulsion generation alone. Across all architectures, mobility depends on the coupled evolution of confinement-dependent stress redistribution, yielding mechanics, pore-pressure dynamics, fracture propagation, structural stability, thermomechanical loading, and energy partitioning. The analysis further reveals a convergence toward stress-regulated locomotion, where successful systems minimize drag accumulation, control force-chain evolution, and adapt to changing terradynamic conditions. Major unresolved challenges include the absence of transferable scaling laws, standardized benchmarking methodologies, predictive terradynamic models, and multi-medium autonomy. The review concludes by proposing the foundations of a unified multiphysics terradynamic robotics paradigm capable of linking robot design, substrate mechanics, control, and deployment across terrestrial and planetary subsurface environments.

Technologies doi: 10.3390/technologies14080469

Authors: Andrey S. Svistunov Anna V. Shifrina Dmitry A. Rymov Alexander V. Kozlov Pavel A. Cheremkhin Rostislav S. Starikov Nikolay N. Evtikhiev

Digital holography is a powerful imaging technique for quantitative analysis in various fields of science and technology, including biomedical applications. However, the development and validation of neural network-based methods in this field are often limited by the unavailability of large, well-annotated experimental data. For example, in biological studies, the typical size of an experimental dataset is around 1000 images. In this work, we present a procedural framework for generating synthetic datasets of biological cell phase images and digital holograms. The generated datasets were tested in several key tasks of neural network applications, including cell classification, detection, and phase reconstruction from in-line holograms. High classification accuracy is achieved for both phase images and holograms, while phase reconstruction reaches high structural similarity indices. The applicability of the generated datasets is further demonstrated in complex scenarios involving multiple moving cells and multiple object planes. The proposed approach provides a flexible and scalable platform for method development, performance evaluation, and future integration with experimental holographic imaging systems.

Technologies doi: 10.3390/technologies14080468

Authors: Abzal E. Kyzyrkanov Yedil S. Nurakhov Zhenis Otarbay Danil V. Lebedev

Future sixth-generation (6G)-oriented networks require programmable control that can adapt routing to latency and congestion without unsafe online exploration. This study evaluates offline multi-agent deep deterministic policy gradient (MADDPG) with behavior-adjusted training rewards for latency-aware path control in software-defined networking (SDN). Each traffic pair is modeled as an agent selecting one of three retained candidate paths, while centralized critics learn coordinated decisions from topology-specific Ryu–Mininet transition datasets. Nine policies are compared using ten paired seeds on fat-tree, mesh-grid, and WAN-corridors topologies under a deployed utilization–latency weighting of 0.60/0.40, together with flow-completion, latency, congestion, architectural-comparison, sensitivity, robustness, statistical, and controller-overhead analyses. The utilization-aware path heuristic achieves the strongest overall reward ranking. MADDPG is the strongest learned policy on fat-tree, is not significantly outperformed by any evaluated policy on mesh-grid, and remains statistically tied with completion-matched policies on WAN-corridors. Behavior adjustment is topology-dependent rather than uniformly beneficial. The exported policy requires approximately 52μs per joint decision, whereas complete control-loop timing is dominated by network-statistics polling. These results support offline multi-agent SDN control as a competitive, low-overhead option when interpreted jointly with topology structure, flow completion, and strong heuristic baselines.

Technologies doi: 10.3390/technologies14080466

Authors: Semaan Amine Ossama Mokhiamar Eddie Gazo-Hanna

Electric-vehicle gearboxes remain key design elements because they determine how motor speed and torque are converted into wheel speed and tractive effort over a driving cycle. This design-oriented scoping review synthesizes EV gearbox architectures, gear ratio selection, efficiency losses, NVH, planetary and compound planetary systems, lubrication, thermal behavior, reliability, manufacturability, and cost within one evidence-informed architecture-selection perspective. A structured search of Scopus, IEEE Xplore, ScienceDirect, SpringerLink, and MDPI identified 312 records; after removal of 71 duplicates, screening of 241 titles and abstracts, and full-text assessment of 61 articles, 40 sources formed the reproducible structured-search core. A gap-directed supplementary search then added 12 sources in underrepresented areas, producing a 52-source synthesis set. The revised analysis reports publication trends, evidence-level distributions, technical-focus frequencies, and a dimension-separated evidence-count table for ratio count, gear train topology, and integration level. The evidence indicates that single-speed reduction gearboxes remain the mature baseline for many passenger EVs, whereas two-speed, multi-speed, planetary, compound planetary, and integrated e-axle solutions require application-specific justification based on system-level benefits and risks. An illustrative screening calculation demonstrates the framework logic without being presented as production-level validation. The principal gaps are experimentally validated loss and NVH maps, coupled efficiency–thermal–lubrication–durability analysis, reliability-aware mission-profile validation, standardized benchmarks, and transparent comparison of compound planetary and integrated e-axle systems. Across heterogeneous study conditions, reported energy benefits range from 2.4% for fixed-ratio optimization to 15% for selected multi-speed comparisons; these results are not pooled because the vehicles, motor maps, drive cycles, loss models, and validation methods differ.

Technologies doi: 10.3390/technologies14080467

Authors: Meiyu He Yufeng Li Erming Tian Fuhe Yang Huiyan Han

Infrared (IR) video target detection is important for long-range target perception in anti-UAV systems under complex lighting and background conditions. However, long-range tiny unmanned aerial vehicles (UAVs) usually occupy only a few pixels, exhibit weak thermal contrast, and are easily disturbed by cluttered backgrounds such as clouds, buildings, vegetation, feature edges, and thermal noise. Although YOLO-style detectors provide high real-time performance, their feature flow is prone to weak target response attenuation during downsampling, contextual ambiguity in deep feature representation, and background clutter propagation during cross-scale fusion. To address these degradation problems, this paper proposes FDR-YOLO, a feature-degradation-aware feature-flow reconstruction network based on YOLOv26. Specifically, LAE-based response-preserving downsampling (LAE-RPD) preserves weak but discriminative target responses during spatial compression; lightweight U-shaped dilated context aggregation (UCDC-Lite) enhances deep contextual discrimination between tiny UAV targets and cluttered backgrounds; and high-frequency prior-guided semantic injection fusion (HPG-SIF) uses shallow high-frequency priors to constrain the injection of deep semantic features. Experiments on multiple datasets show that FDR-YOLO improves detection accuracy while retaining lightweight and low-latency characteristics. On the Anti-UAV dataset, FDR-YOLO improves mAP50 and mAP50–95 by 3.0 and 3.3 percentage points, respectively, over YOLOv26s. Additional experiments on InfraredUAV and the RGB-based UAVSwarm dataset demonstrate the applicability of the proposed design to another infrared benchmark and to visible-light UAV detection under dataset-specific training.

Technologies doi: 10.3390/technologies14080465

Authors: Yuhan Yin Xiaoyi Liu Kunxiao Wu Jianyong Zheng

To address the sampling misalignment and detail loss caused by fixed-grid downsampling for small-scale defects, as well as the insufficient differentiated modeling and interaction of defect details and structural context in UAV-acquired power-line images, an enhanced lightweight YOLOv8n-based framework for power-line defect detection is developed. First, a content-adaptive downsampling (CAD) module is developed to predict input-dependent sampling offsets and normalized aggregation weights and to perform differentiable resampling. Combined with local-global interactive depthwise separable convolution, CAD improves the preservation of small-object details while maintaining relatively low computational complexity. Second, a dynamic subspace gated exchange (DSGE) module is proposed to adaptively partition features into a high-frequency detail subspace and a low-frequency structural subspace according to the input content. Heterogeneous branches and bidirectional gated exchange are then employed to jointly model fine-grained details and structural context. In addition, the lightweight mixed local channel attention (MLCA) mechanism is incorporated in the detection head as an auxiliary feature-enhancement component. Experimental results show that the proposed model achieves mAP@0.50 and mAP@0.50:0.95 values of 92.3% and 62.9%, respectively, outperforming the compared models under the current evaluation protocol. With 1.90 M parameters and 5.6 G FLOPs, the model reaches an inference speed of 134.7 FPS on the desktop GPU platform, demonstrating that content-adaptive sampling and dynamic detail–structure interaction can improve small-defect detection and complex-background suppression while maintaining relatively low model complexity.

Technologies doi: 10.3390/technologies14080464

Authors: Saqib Qamar Goram Mufarah M. Alshmrani

Biomedical vision–language models increasingly support image-grounded clinical dialogue, yet most deployable systems still depend on autoregressive language generation. Such systems tend to truncate answers, react poorly to length instructions, and offer no principled way to signal uncertainty when image evidence is weak. We present MedDiffVL, a biomedical vision-language model that pairs a masked language diffusion backbone with a SigLIP-2 visual encoder and a multimodal alignment pipeline that injects modality and question-type cues. Three inference-time mechanisms target the failure modes of diffusion-based generators in the clinical setting. An adaptive confidence-guided remasking rule uses a time-aware threshold and a short-window stability check to remove repetitive low-quality candidates. A clinically aware length controller selects a target length from question type, modality, and an internal uncertainty estimate. A reliability gate combines visual-evidence and answer-confidence scores to emit, hedge, or escalate a response. On VQA-RAD, SLAKE, and PathVQA, the model reaches 85.42, 92.78, and 94.91% closed-form accuracy and an overall conversation score of 53.42 against a fixed reference. Token repetition falls from 0.18 to 0.06. An ECE falls from 0.137 to 0.034, but this reflects an ECE-surrogate training loss and is not independently validated. These gains are not uniform. The closed-form gains over the prior diffusion model lie within run-to-run variance, and latency stays higher than autoregressive baselines. The main contribution is controllability and reliability-aware decoding, not higher closed-form accuracy. The results indicate that confidence-guided masked diffusion with reliability-aware decoding is a useful direction for controllable and reliability-aware clinical assistants.

Technologies doi: 10.3390/technologies14080463

Authors: Khaled Abdalgader Karam Altaouachi Abd Al Qader Ahmed Aqsa Malik

Learning analytics increasingly relies on large-scale educational activity logs to understand student behavior and support intelligent decision making. However, many existing approaches primarily exploit either semantic information or sequential dependencies independently while overlooking the joint influence of temporal context, behavioral transitions, and probabilistic reliability. This paper addresses the problem of next-event prediction in student activity streams by proposing the Time-Aware Student Event Model (TSEM), a unified framework that integrates semantic representation learning, temporal contextual encoding, and sequential transition modeling within a hybrid neural–probabilistic architecture. The proposed framework is evaluated using a large-scale institutional dataset containing approximately 2.49 million student activity events collected over four academic years, from which nearly 900,000 temporally ordered next-event prediction pairs are constructed. Extensive experiments compare the proposed approach against strong statistical and deep learning baselines, including Markov models, TF-IDF + Logistic Regression, LSTM, GRU, and Transformer architectures, under both random and temporal evaluation protocols. Furthermore, cross-domain validation is performed on the public EdNet benchmark to assess generalization across heterogeneous educational environments. Experimental results demonstrate that jointly modeling semantic, temporal, and sequential information consistently improves predictive performance, robustness to temporal distribution shifts, and probability calibration while maintaining competitive generalization across independent datasets. Ablation studies, statistical significance analysis, and calibration evaluation further confirm the contribution of each modeling component and the reliability of the proposed framework. These findings demonstrate that TSEM provides a robust and practically deployable solution for intelligent educational analytics, supporting applications such as adaptive learning recommendation, early student intervention, learning analytics, and confidence-aware educational decision support.

Technologies doi: 10.3390/technologies14080462

Authors: Victor A. Kovtunenko

Electrokinetic mechanisms of platinum catalyst degradation in polymer electrolyte membrane fuel cells are represented by platinum ion dissolution and platinum oxide formation. The other primary mechanism of degradation is the diffusion of platinum particles. The governing one-dimensional Holby–Morgan model across the catalyst thickness is described by nonlinear reaction–diffusion equations with Butler–Volmer reaction rates. To approximate properly spatial diffusion, the Chebyshev–Gauss–Lobatto pseudo-spectral collocation method is introduced and tested numerically within implicit–explicit time discretization. The accurate approximation makes it possible to simulate the nonlinear degradation of a platinum catalyst over a long cyclic voltammetry test until it loses its operational capacity.

Technologies doi: 10.3390/technologies14080461

Authors: Saadia Drissi Faiq Gmira Meriyem Chergui

This paper presents MI-DCC (Multi-Image Dynamic Cipher Composition), a content-aware framework for secure multi-image encryption. Unlike conventional schemes applying uniform cryptographic processes, MI-DCC combines structured cross-image region permutation with adaptive dynamic cipher selection. The framework establishes deterministic inter-image dependencies through key-driven region exchange, then analyzes regional statistical complexity using entropy, variance, intensity, and gradient descriptors. An adaptive Dynamic Cipher Composition mechanism assigns appropriate primitives from a heterogeneous cryptographic library according to computed complexity scores, improving diversity while maintaining competitive performance. A global diffusion stage propagates local modifications across the entire image batch, strengthening resistance against differential and statistical attacks. Experimental results on benchmark grayscale and color images demonstrate near-ideal entropy (7.9993 bits/pixel), high NPCR (99.612%), UACI (33.465%), and negligible pixel correlation. These results indicate that MI-DCC provides effective adaptive behavior, strong statistical diffusion, and exact reversibility under the evaluated experimental configuration.

Technologies doi: 10.3390/technologies14080460

Authors: Rodrigo Arturo Zarate-Torres C. Fabiola Rey-Sarmiento Julio Cesar Acosta-Prado Alvaro Moncada-Niño

Artificial Intelligence (AI) has evolved from a tool for automation into a strategic component of organizational decision-making. However, the extent to which the dimensions of Human–AI Interaction are associated with leadership remains underexplored, particularly in emerging economies. This study examines how interaction quality, productivity enhancement, user experience, organizational impact, ethical governance, and innovation are associated with Leadership Effectiveness and Organizational Sustainability in Colombian organizations. We applied Structural Equation Modeling (SEM) to data collected from 170 participants using a purpose-built 30-item instrument designed to measure eight dimensions of the human–AI relationship through a five-point Likert scale. Six dimensions assessed Human–AI Interaction (Interaction Quality, Productivity and Efficiency, User Experience and Acceptance, Organizational Impact, Ethical Governance, and Innovation and Transformation), while two dimensions assessed leadership (Leadership Effectiveness and Organizational Sustainability). Estimation used maximum likelihood (ML/FIML) as the primary method, with robust ML (MLR) and an item-level WLSMV estimator as sensitivity checks. Correlation analysis (Pearson, with Spearman as a robustness check) revealed consistent positive and significant associations among all construct indicators. Confirmatory Factor Analysis (CFA) confirmed convergent validity, with innovation (λ=0.88) emerging as the highest-loading dimension. The structural model demonstrated a significant association between Human–AI Interaction and leadership (two-parcel model: β=0.95, R2=0.91). Under a more conservative six-item leadership specification, the association attenuates to β=0.87 (R2=0.75), which we treat as the substantive estimate. Common-method-variance diagnostics (Harman’s first factor =48.8%; a common latent factor accounting for ≈41% of variance) and an elevated RMSEA (= 0.13) signal the need for expanded measurement models and longitudinal designs to address causality. The findings suggest that ethical governance and innovation-oriented AI interaction are primary correlates of leadership effectiveness in digital organizations operating in emerging economies.

Technologies doi: 10.3390/technologies14080459

Authors: Mohamed M. Elmeligy Ahmed El-Rabbany Saad Mesbah Abdelrahman Mohamed Mohasseb Mahmoud A. Hassaan Hamed Majidiyan

Reliable assessment of shorelines extracted from medium-resolution satellite imagery requires independent high-resolution reference data and statistical methods that account for spatial dependence. This study compared three conventional analyst-assisted shoreline-extraction workflows—histogram thresholding, band ratio, and the Normalised Difference Water Index (NDWI)—at Coronation Park, Lake Ontario, Canada, using Sentinel-2 Level-2A imagery. A manually digitised shoreline derived from a UAV-based orthomosaic acquired approximately 27 h before the Sentinel-2 scene served as the independent reference. The UAV-based reference and each Sentinel-2-derived shoreline were divided into 31 ordered segments. For each Sentinel-2-derived segment midpoint, the shortest planar Euclidean distance to the nearest UAV-based reference midpoint was calculated and used to derive mean absolute error (MAE) and root mean square error (RMSE). Residual spatial autocorrelation was assessed using Moran’s I with 9999 permutations. Because the paired differences departed from normality, the Friedman test was treated as the primary overall comparison, while contiguous spatial-block permutation tests across block sizes of two to eight shoreline locations assessed robustness to local spatial dependence. NDWI achieved the highest positional agreement (MAE = 5.645 m; RMSE = 6.429 m), followed by band ratio (MAE = 14.303 m; RMSE = 14.797 m) and histogram thresholding (MAE = 26.167 m; RMSE = 26.910 m). Significant positive residual spatial autocorrelation was identified for all three methods (Moran’s I = 0.587–0.832, all p < 0.001). The Friedman test confirmed a significant extraction-method effect, χ2(2) = 49.226, p < 0.001, Kendall’s W = 0.794, and the effect remained significant across all tested spatial-block sizes, with empirical p-values ranging from 0.000007 to 0.004630. Among the three conventional methods tested at this large-lake site, NDWI provided the highest positional agreement and therefore offers a defensible baseline for evaluating future Sentinel-2 image-enhancement approaches.

Technologies doi: 10.3390/technologies14080458

Authors: Esteban Inga Vladimir Robles Daniel Lopez Daniel Pinargo

This study presents GAGER, a governance-aware ontology-based framework for scholarly knowledge graph analytics evaluated as a single-university case study. The framework integrates bibliometric normalization, semantic organization, thematic clustering, provenance control, governance validation, and budgeted semantic coverage optimization. It transforms Scopus-based institutional bibliographic records into an operational knowledge infrastructure in which documents, authors, institutions, countries, publication venues, document types, knowledge areas, thematic clusters, and Sustainable Development Goal tags are jointly represented through a traceable semantic graph. GAGER uses the ontology to define candidate semantic anchors and applies a maximum-coverage optimization model to select the most informative governance-valid anchors under explicit budget constraints. Experiments using a real single-university corpus show that the proposed governance-validated ontology-plus-optimization strategy achieves higher semantic coverage and aggregate decision quality than ontology-only selection, random selection, and frequency-based selection baselines evaluated under the same budget constraints. The governance layer also has a measurable operational effect, reducing the initial candidate-anchor space from 27,542 anchors to 8320 validated anchors before optimization. The contribution is therefore a methodologically reproducible single-institution analytical framework that connects semantic governance, provenance, graph-based bibliometric representation, and budgeted optimization to support institutional research analytics, thematic prioritization, and evidence-based decision-making within the analyzed university context.

Technologies doi: 10.3390/technologies14080457

Authors: Juan Gabriel Noa Águila Yosbany Llody García Reinier Jiménez Borges Yoisdel Castillo Alvarez Ramón Quiza Sardiñas

The perforated backup plate of KARL KROYER MP 2C120 diaphragm pumps, used in the starch processing food industry, exhibits recurrent in-service fractures whose quantitative explanation and redesign solution have not been previously addressed in the literature. To solve this problem, an integrated analytical model is developed that combines, in a calibrable expression, small-magnitude spherical curvature, regular square-pattern perforation, and stepped peripheral clamping, calibrated against finite element numerical simulation and applied to the screening of eighteen geometrically feasible configurations. The chemical composition of the installed material was verified by portable optical emission spectrometry (PMI), identifying it as austenitic stainless steel AISI 301/1.4310 X10CrNi18-8, with nominal annealed yield strength σy=195 MPa according to EN 10088-2. The calibration quantifies an overestimation of 22.6% for the simply supported edge model and an underestimation of 51.7% for the clamped edge model; the dimensionless parameter ηBC=0.695 describes the effective degradation of clamping due to the reduced stiffness of the perforated belt adjacent to the edge and supports the use of the simply supported model as a conservative screening bound. The currently installed configuration operates with a safety factor FSop=0.89 with respect to yield strength, quantitatively explaining the observed fractures. Within the standard manufacturable space, the configuration d=5 mm, p=7.5 mm (η=0.333) minimizes the maximum equivalent stress predicted by the conservative screening model; this selection corresponds to the configuration with the maximal ligament efficiency and is preserved under the single-point calibration, which acts as a single global positive scale factor. The screening is verified by a direct finite element campaign performed on the complete d=5 mm family (configurations 1, 2 and 3) with the experimentally verified material properties. The verification confirms the predicted ranking and the peripheral location of the critical region, but shows that the smooth cross-center field is essentially invariant across configurations (187.3, 195.0 and 186.0 MPa), so the linear-scaling estimate previously used to anchor absolute margins is not supported; the benefit of the redesign accrues at the governing hole-edge concentration, which decreases from 338.2 to 261.5 MPa (22.7% reduction) yet remains above the yield strength of the annealed material. Geometric modification alone is therefore insufficient, and industrial adoption requires combining configuration 3 with material substitution to a cold-worked hardened state (C700: FS=1.91 on the governing peak) or a duplex alloy (FS=1.76), together with periodic non-destructive inspection. The central contribution of this work is the parameter ηBC as a quantitative descriptor of effective clamping degradation and the proposal of an industrially feasible redesign, integrated with complementary mitigation strategies, for KARL KROYER MP 2C120 pumps and geometrically equivalent equipment.

Technologies doi: 10.3390/technologies14080456

Authors: Asamaporn Sitthi Uday Pimple Pattamaporn Wongwiriya Can Trong Nguyen

This study aimed to investigate visitors’ perceptions of ecotourism landscapes in Thailand’s protected areas by integrating spatial, semantic, and affective information derived from social-sensing textual data. To this end, it examined the influence of ecosystem characteristics on thematic expressions and emotional tones across five national parks. Methodologically, a spatio-semantic, natural language processing (NLP) social-sensing framework was developed using geotagged Flickr tags and YouTube comments from international (English) and domestic (Thai) tourists. Textual data were preprocessed (cleaned, normalized, and tokenized) and analyzed using latent Dirichlet allocation (LDA), sentiment analysis, and diversity metrics. Concurrently, YouTube comments were similarly processed using LDA and rule-based sentiment analysis. Subsequently, a Diversity × Topic × Season matrix integrated Flickr-derived indicators with YouTube-derived sentiment and topic dominance. Binary logistic regression was applied to examine cross-platform relationships. The analysis identified three dominant themes, namely Nature & Landscapes, Travel & Activities, and Feelings & Experiences. Forest-mountain parks showed high semantic diversity and strong positive sentiment, whereas marine parks exhibited narrower but predominantly positive activity-driven discourses. Moreover, seasonal variation was evident, with summer and winter yielding the highest diversity and positivity. Building on these results, this study devised a spatio-semantic framework for analyzing variation in visitor expressions across parks and seasons. It captures eco-awareness, perceptions, and preferred activities in protected landscapes. From a practical perspective, semantic diversity and sentiment indicators can support ecotourism management through improved visitor monitoring and communication strategies.

Technologies doi: 10.3390/technologies14080455

Authors: Raluca Andreea Roșu Emilia Georgiana Prisăcariu Oana Dumitrescu

Unmanned aerial vehicles (UAVs) increasingly demand higher flight speeds, longer endurance, improved payload capacity, and greater operational flexibility across a wide range of applications. While battery-electric propulsion systems dominate small UAV platforms, their limited energy density significantly constrains range and mission duration. Consequently, turbine-based propulsion systems, including micro turbojets, turboprops, turboshafts, and hybrid-electric gas turbine architectures, are attracting growing attention as alternatives for advanced UAV operations. Unlike previous reviews that primarily focus on propulsion technologies or individual subsystem performance, this review provides an integrated assessment of the multidisciplinary challenges associated with turbine-powered UAVs, encompassing thermal, structural, aerodynamic, acoustic, operational, and stealth considerations within a unified framework. The presented synthesis identifies current knowledge gaps and emerging research directions, providing a comprehensive reference for the design and development of next-generation turbine-powered unmanned aerial platforms.

Technologies doi: 10.3390/technologies14080454

Authors: Yuhan Wang Pengfei Zhang Yawen Li Jinshuai Liu Zhengxuan Li Dian Rong Huiyan Han

Low-altitude urban environments pose significant challenges to multi-UAV trajectory planning because of dense buildings, constrained airspace, dynamic obstacles, inter-UAV conflicts, and terminal-area congestion. This study proposes a hierarchical three-dimensional cooperative trajectory-planning framework integrating dynamic-risk-aware Ant Colony Optimization (ACO) with cooperative Model Predictive Control–Gray Wolf Optimizer (MPC-GWO). Environmental costs and predicted dynamic-obstacle risks are incorporated into the ACO global search to generate risk-aware reference trajectories, while a sliding-window GWO improves trajectory smoothness and execution feasibility. During online execution, cooperative MPC-GWO combines dynamic-obstacle prediction, inter-UAV separation constraints, reconfigurable formation switching, and goal-neighborhood safety control to achieve adaptive obstacle avoidance, cooperative replanning, and orderly terminal arrival. Thirty-run Monte Carlo simulations show that the proposed method achieves a success rate of 93.3% ± 25.4% and the highest composite score of 96.20 ± 5.30, with zero dynamic-obstacle and inter-UAV collisions. Ablation experiments verify the effectiveness of the dynamic prediction, formation reconfiguration, and terminal safety-control mechanisms. The average online replanning time remains below 0.5 s, demonstrating satisfactory safety, coordination, adaptability, and real-time performance in small- to medium-scale simulated urban scenarios.

Technologies doi: 10.3390/technologies14070453

Authors: Suchada Sitjongsataporn Pipat Sakarin Theerayod Wiangtong

Addressing the critical challenge of signal degradation in biosensor cardiac monitoring, this paper introduces an efficient hybrid dilated–long short-term memory (LSTM) with residual learning (HDLR), which is a novel architecture engineered for a high-fidelity electrocardiogram (ECG) denoising signal. The proposed HDLR model synergistically integrates with dilated convolutions to expand the receptive field for multi-scale feature extraction. This is an LSTM-based backbone used to resolve long term temporal dependencies with residual learning paths to stabilize gradient flow and accelerate convergence. The proposed HDLR architecture integrates three core functional components with dilated convolutional layers utilized for local temporal feature extraction, where varying dilation rates expand the receptive field to capture both local waveform patterns and broader morphological structures without increasing computational complexity. Experimental results demonstrate a significant leap in performance, with the HDLR model achieving a mean squared error (MSE) of 0.002176, a signal-to-noise ratio (SNR) of 14.4420 dB, and a Matthews correlation coefficient (MCC) of 0.9822. Beyond quantitative metrics, the proposed HDLR architecture exhibits exceptional robustness in preserving cardiac morphology, specifically the P-wave and QRS complex of the ECG signal under stochastic noise conditions. These findings underscore the HDLR model’s potential as a backbone for next generation, real time diagnostic systems in intelligent healthcare.

Technologies doi: 10.3390/technologies14070452

Authors: Ines Frajtag Bojan Šekoranja Marko Švaco Filip Šuligoj

Facial attribute segmentation is challenging because facial regions differ in size, shape, texture, and visibility, especially under non-frontal viewing conditions. This study evaluates the segmentation of four facial attribute classes: hair, eyebrows, mustache, and beard. A dataset of 5000 manually annotated FFHQ facial images was used to train and compare approaches within the same evaluation framework: YOLO segmentation, YOLO detection combined with SAM2 segmentation, and U-Net. The methods were evaluated on annotated test set, per facial attribute, and on an additional controlled phantom-based dataset acquired at five viewing angles. The hybrid YOLO detection and SAM2 segmentation pipeline achieved the best overall performance, with micro IoU of 0.820 and a micro Dice score of 0.893 on the annotated test set. Hair and beard are segmented more reliably than eyebrows and mustache, while segmentation accuracy decreased as the viewing angle increased. These results show that facial attribute segmentation performance depends on the selected method, target class, and acquisition viewpoint. The findings provide a basis for selecting and further improving segmentation methods for future registration and medical robotic applications.

Technologies doi: 10.3390/technologies14070451

Authors: Madi Gali Aray Kassenkhan Yersain Chinibayev Aigerim Abshukirova Vassiliy Serbin

Static, one-time authentication mechanisms such as passwords and PINs are increasingly inadequate for protecting mobile devices throughout an active session. Behavioral biometric continuous authentication (BBCA) addresses this gap by passively monitoring user-specific interaction patterns—keystroke dynamics, touch and swipe gestures, gait, and motion—to verify identity on an ongoing basis. This systematic review synthesizes 80 studies selected via a PRISMA-compliant protocol from IEEE Xplore, ACM Digital Library, Scopus, ScienceDirect, Web of Science, and SpringerLink (2017–2025). We examine behavioral and multimodal biometric modalities, machine learning approaches ranging from classical classifiers to deep sequence and transformer architectures, and their integration with intelligent personal agents, wearable devices, and IoT/edge infrastructures. Security analyses cover spoofing, adversarial and generative attacks, mimicry, and model-level threats including membership inference and reconstruction. Privacy-preserving mechanisms—cancelable biometrics, Bloom filter encodings, zero-knowledge proof protocols, federated learning, and blockchain-based identity management—are evaluated against practical trade-offs in energy consumption and latency on resource-constrained devices. Key research gaps are identified: the absence of standardized adversarial benchmarks, lack of end-to-end pipeline evaluations under simultaneous adversarial and privacy threat models, and limited user-centered studies on consent and acceptance of privacy-preserving mechanisms under frameworks such as GDPR. Recommended future directions combine adaptive multimodal fusion, privacy-preserving cryptography, energy-aware modality selection, and interdisciplinary human-centered evaluation to advance practical, resilient continuous authentication for mobile and assistant-enriched environments.

Technologies doi: 10.3390/technologies14070450

Authors: Zhaoliang Zhu Xin Huang Shunzuo Qiu

Buried steel frame-reinforced polyethylene (SRPE) pipelines are vulnerable to bending-compression buckling, sectional distortion, and tensile rupture when crossing reverse faults. Nevertheless, their multi-mode failure mechanisms and strain evaluation frameworks have not been systematically clarified. Given this challenge, a three-dimensional nonlinear finite element model considering pipe–soil contact, material elasto-plasticity, and large deformation is established in this study to investigate the deformation characteristics, strain evolution, and buckling performance of SRPE pipelines under reverse fault inclination angles from 0° to 150°. The effects of internal pressure, steel frame diameter, and soil properties on pipeline mechanical behavior are analyzed, with the results being compared with the strain limits specified in the GB 50470-2017, CSA Z662-2023, and EN 13476-3 standards. The pipeline is subjected to a three-stage failure process involving local compressive buckling, overall bending, and tensile necking. Notably, coupled bending-compression failure is typical at low inclination angles, while high angles are dominated by shear-tension coupling. Excessive internal pressure increases the local buckling and concentration of compressive strains. An increase in the steel frame diameter contributes to an effective improvement in the buckling resistance. When the soil has higher cohesion and stiffness, the critical buckling displacement becomes larger. The standard constant strain limits currently adopted do not account for the transition in failure mode arising from inclination control and are therefore overly conservative for SRPE pipelines. This study provides a theoretical foundation and quantitative reference for the seismic design and safety assessment of SRPE pipelines crossing reverse fault zones.

Technologies doi: 10.3390/technologies14070449

Authors: Hafiz Abdul Wajid Muhammad Abid

Saudi Arabia is a water-stressed nation and meets much of its daily demand through desalination, an energy-intensive process with a significant carbon footprint. As a Muslim-majority country, residents perform ablution before five daily prayers, making this activity a substantial yet under-quantified component of residential water use. This study focuses on household-level ablution water savings across 13 regions for both Saudi and non-Saudi households by replacing standard taps with a flow rate of 5.7 L/min with a proposed Saudi Standards, Metrology and Quality Organization (SASO)-compliant ultra-low-flow tap (1.9 L/min). Moreover, this study evaluates this ultra-low-flow tap as an environmental technology capable of reducing ablution water consumption and found that per capita savings are identical for both demographic segments, but the total household savings differ because Saudi households are larger, supporting sustainable water management. Results show that under the stated assumptions, full national adoption of the proposed tap would reduce monthly ablution water use from 27 million m3 to 9 million m3, conserving 212.14 million m3 annually with 67% efficiency and offsetting 702,198 tonnes of desalination-related carbon emissions. This highlights the effectiveness of deploying a simple water-saving technology in a water-stressed environment. Conservation potential is concentrated in Riyadh, Makkah, and the Eastern Province due to their high household counts. A four-year phased implementation roadmap is proposed, beginning with 25% adoption in year one (53.01 million m3 annual savings), expanding to moderate-impact regions in year two, and reaching 75–100% adoption nationwide by years three and four. The findings demonstrate how simple and commercially available water-efficient technology can contribute to sustainable resource management by simultaneously reducing water demand, energy consumption associated with desalination, and related greenhouse gas emissions. This study supports Saudi Arabia’s Vision 2030 water strategy and can potentially support UN-SDGs 6, 7, and 13 by demonstrating the substantial water, carbon, and economic benefits of a simple, commercially available tap of 400 SAR. In addition, the study develops a regionally prioritized technology deployment framework that can support decision makers in planning large-scale implementation. The analysis assumes that household members perform ablution five times daily for approximately one minute, based on field measurements, and they require validation of projected gains through actual implementation.

Technologies doi: 10.3390/technologies14070448

Authors: Simona Filipova-Petrakieva

Under conditions of increasing traffic and limited parking options, mobile apps for real-time monitoring and booking of available spots in public parking lots are becoming an indispensable tool for making our daily lives easier. These apps offer convenience, save time, and reduce the hassles associated with finding a parking spot, while also contributing to a more efficient and organized urban space. Additionally, they help reduce traffic congestion and lower pollution levels by encouraging users to make more rational use of available parking resources, which benefits all city residents. This article describes the development of a mobile application for real-time booking of parking spots. The proposed application integrates useful features from existing mobile apps, adds its own new features, and is adapted to the conditions of life in Bulgaria. Its main advantage is that it is available completely free of charge. It offers the following functionalities: booking or recommending parking spots; checking parking spot availability; and providing feedback on current and past parking spot bookings, including ratings and comments. The application’s interface is intuitive, which makes it easy to use. The following technologies were used in its development: Java, Android Studio, XML, Gradle, Android SDK, and Firebase. The application was compared to similar ones, incorporating their best features and adding new ones that improve upon them. The current work aims to help Bulgarian users save time and money spent looking for parking spots.

Technologies doi: 10.3390/technologies14070447

Authors: Gulnara Kaziyeva Shynar Turmaganbetova Sandugash Bekenova Gulzira Abdikerimova Rysgul Baynazarova Aliya Abdukarimova Gulnaz Zhilkishbayeva Zhanar Azhibekova Bekezhan Zhumazhan

This study presents a spatially aware machine learning model for probabilistic groundwater potential assessment using multimodal geospatial data derived from topography, hydrotopography, climate, soil, land use, Sentinel-1 SAR, and water balance variables. The model incorporates spatially consistent data partitioning, leakage-controlled model development, and probabilistic forecasting to improve the robustness and transferability of groundwater potential assessment. A total of 2402 spatial observations, including 601 groundwater-related locations and 1801 spatially filtered pseudo-absence samples, were used to evaluate eleven machine learning and deep learning models. The proposed hybrid framework achieved the best overall validation results with ROC-AUC of 0.9319, PR-AUC of 0.8363, F1-measure of 0.8226, balanced accuracy of 0.8847, and MCC of 0.7613. Independent spatial block testing further demonstrated strong generalization ability, showing ROC-AUC of 0.9535, PR-AUC of 0.9002, F1-measure of 0.7983, balanced accuracy of 0.8594, and MCC of 0.7336. Comparative experiments demonstrated that the proposed framework remains competitive with state-of-the-art machine learning and deep learning approaches while providing robust probabilistic estimates in spatially separated validation. The resulting groundwater potential maps identify areas with environmental conditions similar to known groundwater observations and provide a reliable basis for prioritizing hydrogeological studies, groundwater exploration, and regional water resources planning in data-poor settings.

Technologies doi: 10.3390/technologies14070446

Authors: Omar Flor-Unda David Puga Hugo Alomoto Gabriela Eguez Xavier Chango David Fabara Freddy Villao Carlos Toapanta

Security infrastructure has evolved from the use of reactive models toward the adoption of predictive technologies, transforming and enhancing threat anticipation, improving monitoring capabilities, and strengthening strategic decision-making driven by the integration of emerging technologies and artificial intelligence. This review study synthesizes the changes occurring across spatial, maritime, aerial, border, and cybersecurity infrastructure, evidencing the impact of the transition toward predictive technologies; it addresses the challenges and limitations these technologies introduce as a consequence of their operational deployment, and concludes by examining future lines of development in this domain. The review was conducted following the PRISMA® methodology, analyzing the scientific literature retrieved from seven indexed databases—SCOPUS, ScienceDirect, Web of Science, IEEE Xplore, Taylor & Francis, ProQuest, and PubMed—consistent with the full search scope. Articles were independently assessed by two reviewers, yielding an initial Cohen’s Kappa coefficient of 0.458; following structured discussion and consensus resolution, the final inter-rater reliability reached κ = 0.71, meeting the accepted threshold for scoping review methodology. The findings demonstrate a global transition toward intelligent security infrastructures, with measurable improvements in performance, accuracy, and response times in the generation of actionable intelligence to support strategic decision-making.

Technologies doi: 10.3390/technologies14070445

Authors: Yernar Seksenbayev Saule Kudubayeva Abdykarim Baimankulov Aigerim Yerimbetova Elmira Daiyrbayeva Ulmeken Berzhanova Bakzhan Sakenov

We introduce the Probabilistic Multimodal Consistency Index (PMCI), a prototype measure of probabilistic semantic agreement used to quantify how much two modalities share in terms of mutually compatible affective evidence. PMCI does not model feature fusion or emotion classification directly; instead, each modality is mapped to a different probability distribution over learnable latent affective-agreement anchors, and the agreement of those distributions is quantified via the Jensen–Shannon divergence. Consequently, we propose PMCI not as a substitute for a discriminative model of pair matching, but rather as an auxiliary diagnostic index of cross-modal affective agreement. Experiments were conducted on pose-based facial and body keypoint sequences obtained from the RAVDESS and MELD datasets. In the updated RAVDESS setup, we extended the cache of preprocessed keypoint files to include all 24 actors, and we sampled four distinct pose-derived 12-frame crops per source video. This resulted in a total of 11,520 pose-derived windows, with the actor label determining the train/validation/test splits for the RAVDESS dataset. MELD contained 402 filtered pose-derived windows and served as an auxiliary in-the-wild validation setting with additional noise. When applying the updated RAVDESS standard pair-matching, DirectCosine_K0 achieved ROC–AUC = 0.943 and PR–AUC = 0.917, demonstrating that it is indeed the best exact pair-matching baseline and that PMCI-based configurations are not as accurate as DirectCosine_K0 when performing common exact face–body pair discrimination. Under this protocol, PMCI_K8, PMCI_K16, and PMCI_K32 produced ROC–AUC scores of 0.851, 0.805, and 0.887, respectively. Running a one-window-per-source-video experiment with 100 repetitions yielded similar results in the following order: DirectCosine_K0 = 0.942 plus-minus 0.009, PMCI_K16 = 0.807 plus-minus 0.016, and PMCI_K32 = 0.893 plus-minus 0.012, which shows that the results obtained were not the result of only repeated temporal crops. In the DirectCosine-mined hard negative setting, PMCI produced modest diagnostic separation above chance, where Direct+PMCI_K32 yielded ROC–AUC = 0.582. In a suite of shuffle control, permutation control, model initialization control, temperature control, anchor usage control, control latency, and pose perturbation control experiments, the diagnostic stability of PMCI was tested, confirming that PMCI is sensitive to both pose-estimation accuracy and domain shift. Finally, PMCI must be interpreted as a probabilistic diagnostic index of cross-modal affective agreement that is based on pose, not as a general-purpose emotion-recognition system.

Technologies doi: 10.3390/technologies14070444

Authors: Paweł Krowicki Fryderyk Gajdzik Roman Olejniczak Zofia Tomala Grzegorz Żurek

Background. Eye-controlled interfaces can support communication and selection tasks when physical interaction is difficult, but conventional multi-point calibration may be burdensome for users who fatigue quickly or have severe motor limitations. This study operationally evaluates a low-burden single-point adaptive gaze-to-cursor correction pipeline for a dwell-based selection interface. Methods. The pipeline combines central single-point offset estimation, robust median/median absolute deviation (MAD) sample handling, recorded-stream jump rejection, exponential moving average filtering, dwell-based selection, and post-selection offset adaptation. It was evaluated using application-level gaze-to-cursor coordinates from 43 participants across 129 sessions. The primary endpoint was target-proximity Root Mean Square (RMS) during automatically logged dwell-active selection periods. Because these periods were defined by the pipeline’s online state, the endpoint was algorithm-conditioned. Whole-quiz RMS was analyzed as a secondary full-trajectory descriptor rather than as independent validation. Results. Averaged across three repeated measurements, target-proximity RMS during dwell-active periods decreased from 193.40 pixels (px) (approximately 3.15°) for the recorded uncorrected coordinates to 56.02 px (0.91°) for the complete pipeline output. The mean reduction was 137.38 px (95% confidence interval (CI), 116.59–158.17 px; Cohen’s dz = 2.03), and all 43 participants showed a reduction. Conclusions. The results support the practical feasibility of the complete correction pipeline for target localization in the evaluated dwell-based interface. They do not independently validate native eye-tracker accuracy or establish superiority over conventional multi-point calibration.

Technologies doi: 10.3390/technologies14070443

Authors: Ilya V. Baimler Dmitriy A. Serov Valeriy A. Kozlov Eugeny M. Konchekov Ismail R. Seriev Sofia N. Bokova-Sirosh Maxim E. Astashev Ekaterina E. Karmanova Egor A. Turovsky Konstantin V. Sergienko Mikhail A. Sevostyanov Serazhutdin A. Abdullaev Pavel A. Ivliev Alexander V. Simakin

Nanotechnology currently offers two approaches to tumor therapy. The first involves coating the surface of nanoparticles with high-affinity molecules for targeted delivery. The second involves directing the nanoparticles to the desired area of the body using an external magnetic field. Such nanoparticles are often made of magnetic metals (iron, nickel, cobalt, etc.), but in living systems, the main problem with such nanoparticles is their toxicity. To address the toxicity issue, various barriers and coatings are primarily used. In this work, a laser technology for producing multifunctional nanocomposites based on graphene-coated iron nanoparticles decorated with gold nanoparticles was developed. Graphene-coated iron nanoparticles (200 nm) were synthesized using laser ablation in isopropanol. The presence of a graphene coating on the surface of the iron nanoparticles was confirmed by TEM, Raman spectroscopy, and luminescence analysis. A technology for depositing gold nanoparticles approximately 10 nm in size onto the graphene shell of the resulting iron nanoparticles was invented. The essence of the technology lies in creating critical conditions in a nanoparticle colloid, leading to intense aggregation with each other. Multifunctional nanocomposites based on graphene-coated iron nanoparticles decorated with gold nanoparticles did not exhibit acute toxicity to cell cultures under normal conditions. Moreover, under the combined influence of an alternating magnetic field and laser radiation, nanocomposites damaged 96% of neuroblastoma cells in culture.

Technologies doi: 10.3390/technologies14070442

Authors: Yagnik M. Bhavsar Mazad S. Zaveri Mehul S. Raval Pancham Shukla Shaheriar B. Zaveri

Adherence to right-of-way (RoW) rules at uncontrolled T-intersections helps avoid accidents and alleviate congestion. In non-uniform traffic, right-turning behaviour can be characterised by distinct driving traits, such as non-compliance (failure to yield), a nonchalant attitude, and competitive behaviour. This paper presents a cost-effective computer vision framework using UAV videos to analyse right-turning behaviour and assess safety and operational performance (congestion) at uncontrolled T-intersections. A conflict cone of a vehicle is defined to automatically detect a right-of-way violation (RoWV) and yield. The impact of driving-related parameters and external traffic on non-compliant behaviour is analysed using the Tweedie generalised linear model. This paper proposes an aggregated surrogate safety measure, condPET, and a novel parameter, congValue, to identify critical conflicts and congestion due to non-compliant behaviour. Lateral evasive action is used to detect a constrained path because of nonchalant and competitive behaviours. The results indicate that only 7.50% of vehicles yielded, 6.25% of conflicts were critical (compared to 38.94% using PET alone and 11.05% using CS), and localised congestion occurred for 44.00% of the total video time. Overall, 45.34% of vehicles created a constrained path, and 26.00% committed RoW violations, causing congestion and increasing the average travel time on major roads by 2.0 and 3.5 times, respectively. Our methodology enables computer vision-based automated assessments of both road traffic safety and operational performance at uncontrolled T-intersections under non-uniform traffic conditions, providing a valuable tool for road-traffic-monitoring systems.

Technologies doi: 10.3390/technologies14070441

Authors: Lin Guo Yuhang Yin Chen Wang Hongyu Chen Qinghua Cui Xiangkui Wan

Sleep staging based on PSG is largely confined to clinical settings, while home-based sleep monitoring often faces the challenges of insufficient unimodal information and missing modalities. Aiming to overcome these challenges, this paper proposes a unified multimodal model for sleep staging based on cardiopulmonary signals. First, a heterogeneous multi-scale feature encoder with long and short branches is adopted to adapt to the cross-modal heterogeneity of ECG and THX. It combines a Transformer encoder and a Dilated CNN to complete feature fusion and temporal modeling. Subsequently, the unified model adaptively handles flexible modality combinations by introducing global context via a modal feature alignment strategy, which is built upon a framework consisting of a bimodal global branch and unimodal dedicated branches. On the SHHS dataset, the proposed model achieved Cohen’s kappa coefficients of 0.7547, 0.7121, and 0.7305 for four-stage sleep classification under ECG+THX, ECG-only, and THX-only inputs, respectively, demonstrating consistent improvements over three separately trained individual models. Furthermore, the model exhibits robust generalization performance on the P2018 external dataset and across samples with different severity levels of SDB. This work establishes a reliable algorithmic baseline for unobtrusive, long-term home sleep monitoring with missing modalities.

Technologies doi: 10.3390/technologies14070440

Authors: Calin Vaida Daniel Horvath Ionut Zima Marius Miclaus Bogdan Gherman Corina Radu Paul Tucan Stefan Vegh Dragos Sebeni Adrian Pisla Damien Chablat Nadim Al Hajjar Doina Pisla

Remote center-of-motion mechanisms are essential in minimally invasive surgery because they allow surgical instruments or an endoscopic camera to pivot around a trocar entry point while eliminating lateral motion at the incision. This paper presents the design, kinematic modeling, prototype implementation and preliminary evaluation under laboratory conditions of a compact, spherical, remote center-of-motion robot for minimally invasive surgical orientation tasks. The proposed mechanism uses a spherical kinematic architecture actuated by a contra-rotating differential gearbox. This gearbox generates two coaxial output rotations of equal magnitude and opposite direction from a single input, mechanically synchronizing the opposed motion of the two base links and eliminating the need for cable-pulley transmission or dual electronically synchronized motors. A second actuator chain rotates the gearbox assembly around the base axis, thereby decoupling the extension–retraction motion from base-axis rotation. Forward and inverse kinematic formulations were derived for teleoperation of the robot using a 7 degrees of freedom haptic device and for remote center-of-motion orientation control using a 3-axis joystick. A proof-of-concept prototype was developed and integrated with a custom embedded controller, closed-loop motor control, a master-console interface and video feedback loop. The system was evaluated in a phantom-torso setup using a custom endoscopic camera, internal visual markers and an OptiTrack-based measurement of the remote center-of-motion accuracy. The qualitative experiment confirmed functional integration of the mechanical, electronic and software subsystems, while the optical-tracking measurement showed that the pivot constraint was maintained with a mean deviation of 1.69 mm and a root-mean-square deviation of 2.13 mm over the analyzed orientation sweep. The main limitations remain the 1:1 gearbox ratio, limited actuator torque, additively manufactured gearing and the absence of repeated-trial repeatability and full workspace characterization.

Technologies doi: 10.3390/technologies14070439

Authors: Brandon Cortés-Caicedo Oscar Danilo Montoya Santiago Bustamante-Mesa

The optimal expansion of unbalanced three-phase distribution networks in non-interconnected zones requires the simultaneous resolution of two highly complex planning decisions: the selection of feeder routes and the sizing of conductors. This problem, formulated as a non-convex mixed-integer nonlinear program (MINLP), poses significant computational challenges due to the combinatorial explosion of radial topologies, discrete conductor choices, and the nonlinearity of three-phase power-flow equations. While metaheuristics offer flexible exploration, they lack optimality guarantees and repeatability, whereas exact MINLP solvers provide rigorous solutions but become computationally intractable for systems of realistic size. To overcome these limitations, this paper introduces a novel hybrid exact–metaheuristic framework that synergistically combines the global exploration capabilities of the Equilibrium Optimizer (EO) with the rigorous evaluation power of an exact MINLP model. In this cascade architecture, EO efficiently navigates the discrete space of radial topologies, while the exact MINLP stage, solved using BONMIN with an interior-point branch-and-bound scheme, optimizes conductor selection and evaluates the full annualized cost, rigorously enforcing voltage, ampacity, and physical constraints. The proposed methodology was validated on 10-, 30-, 50-, and 110-node test systems derived from real Colombian non-interconnected zones (Nuquí, Leticia, San Andrés, and a large-scale urban case). Comparative analysis against pure metaheuristics (SSA, GWO, VSA) and standalone MINLP demonstrates that EO-MINLP consistently yields the lowest total annualized costs, achieving savings of up to 0.42%, 0.71%, and 1.36% over the best pure metaheuristic for the 10-, 30-, and 50-node systems, respectively. Crucially, the hybrid strategy dramatically enhances scalability, reducing the standalone MINLP computational time by 15.79%, 78.68%, and 88.95% for these cases, while preserving solution quality and improving repeatability (standard deviation reduced from over 1.2% to as low as 0.11%). For the challenging 110-node system, where the standalone MINLP proved computationally infeasible, the proposed method successfully delivered a feasible, high-quality solution with a standard deviation of just 0.43%, confirming its practical applicability to large-scale planning. These results demonstrate that the EO-MINLP framework provides a robust, scalable, and economically superior tool for the cost-effective design of unbalanced distribution networks, effectively bridging the gap between the flexibility of stochastic search and the rigor of mathematical programming.

Technologies doi: 10.3390/technologies14070438

Authors: Artem Sveshnikov Yury Nikitnikov Maxim Shiltsyn Denis Dedov Artem Obukhov

Choosing among closely related higher-education programmes requires interpretation of heterogeneous applicant data while preserving data confidentiality. This study compares ten locally executable large language model (LLM) configurations as semantic rankers, contextualises their performance against a hybrid term frequency–inverse document frequency (TF–IDF) cosine baseline, and evaluates robustness when relevant competencies are expressed indirectly. The main benchmark comprised 50 anonymised applicant profiles and 10 degree programmes; an additional processed-profile robustness set comprising 10 profiles was used to reduce direct lexical overlap with the programme catalogue. Rankings were evaluated using Accuracy@1, Accuracy@3, normalised discounted cumulative gain at 5 (NDCG@5) and mean reciprocal rank at 5 (MRR@5), while structured-output validity and inference time were assessed for the LLMs. Among the local LLMs, gpt-oss-20b-MXFP4 achieved the highest Accuracy@1 (0.76), whereas gemma-4-E4B-it-Q8_0 achieved the highest Accuracy@3 (0.92), and Ministral-3-14B-Reasoning-2512-Q4_K_M provided a favourable quality–latency balance. On the main benchmark, TF–IDF achieved Accuracy@1 = 0.88 and NDCG@5 = 0.9557, exceeding all LLM configurations and demonstrating a strong lexical signal. On the processed-profile robustness set, the Accuracy@1 of the TF–IDF baseline decreased to 0.60, whereas gemma achieved 0.90. The results provide initial evidence that several local LLM configurations can reproduce observed programme-selection patterns in a limited pilot benchmark. However, the findings should be interpreted as task-specific model-comparison results rather than as full validation of an autonomous career guidance system.

Technologies doi: 10.3390/technologies14070437

Authors: Abimael Rodríguez Andree Aranda-Cen Romeli Barbosa Jaime Ortegón-Aguilar Edith Osorio-de-la-Rosa Carlos Couder-Castañeda

Off-grid residential microgrids in tropical regions require storage architectures capable of maintaining renewable electricity supply under variable solar resources, evening demand peaks, and diverse household consumption levels. In PV–battery–hydrogen systems, however, economic indicators can be difficult to interpret when software-reported costs are compared directly with externally calculated LCOE values based on different accounting conventions. This study presents a metric-reconciled techno-economic reconstruction approach for retained PV–battery–hydrogen microgrid configurations serving off-grid residential demand in Chetumal, Mexico. The objective is not to introduce a new global optimization or to claim the universal superiority of a specific architecture, but to separate archived HOMER Pro benchmark outputs from an external techno-economic model (TEM). The TEM reconstructs net present cost, scheduled replacements, salvage treatment, discounted delivered electricity, HOMER-derived LCOE, TEM-derived LCOE, sensitivity indicators, and storage role metrics using declared accounting assumptions. The approach is applied to two representative residential demand scenarios of 16.67 and 53.42 kWh/day. Both retained configurations achieved a 100% renewable fraction with negligible unmet load. Battery discharge increased from 827.12 kWh/year in the low-demand case to 6125.52 kWh/year in the high-demand case, highlighting the increasing role of the battery in short-duration balancing. In contrast, the hydrogen pathway acted as a delayed-backup layer by converting surplus PV electricity into hydrogen and later recovering it through PEM fuel cell generation. The TEM closely matched the HOMER-derived LCOE benchmark, with deviations below 4%, yielding TEM-derived LCOE values of 0.3320 and 0.3571 USD/kWh for the low- and high-demand cases, respectively. Sensitivity analysis showed that delivered electricity, discount rate, PV cost, and battery cost were the main LCOE drivers, while deterministic multi-parameter scenarios confirmed the combined influence of financing, component costs, O&M, PV degradation, and electricity delivered. Overall, the proposed approach provides an auditable basis for metric reconciliation, early-stage technology assessment, and storage role interpretation in tropical off-grid microgrids. Future extensions should include architecture-level re-optimization, flexible loads, degradation-aware modeling, and part-load component behavior.

Technologies doi: 10.3390/technologies14070436

Authors: Ionuț Dumitriu Costel Ungureanu Ion V. Ion

Commercial refrigeration systems represent some of the largest energy consumers in supermarkets, and therefore particular attention needs to be paid to increasing energy efficiency to reduce overall energy consumption and meet climate goals by 2030. This study investigates the performance of a CO2 (R744) commercial refrigeration system with evaporators operating alternately in dry and flooded modes. This operation is possible due to a particular adjustment using liquid sensors installed in the middle of both low-temperature (LT) and medium-temperature (MT) liquid separators, which transmit information to the controllers that regulate the compressor rack and evaporators, to switch from dry to flooded operation when the liquid level rises and vice versa when the level drops. The results show that the correct regulation of the system of 50% with 6K superheat operation and 50% with 3K superheat operation on MT evaporators, respectively, and 50% with 6K superheat operation and 50% with 4K superheat operation on LT evaporators leads to a reduction of energy consumption compared to 100% operation of all evaporators with 6K superheat by 6.9% per year for the compressor rack.

Technologies doi: 10.3390/technologies14070435

Authors: Shinichiro Ishikawa Hiyori Sakemi Koki Hirose Tahsina Nabiha Khan Kenshin Mizoe Ikki Osaka Osamu Fukuda Nobuhiko Yamaguchi Masateru Kawakubo Hiroshi Okumura

Glaucoma is a leading cause of blindness, and early detection is critical. Convolutional neural networks (CNNs) have shown impressive performance in glaucoma diagnosis, but their black-box nature remains a barrier to clinical use. Existing explainable AI (XAI) methods such as Grad-CAM have limitations in identifying and quantifying subtle regional features. In this study, we propose a method to clarify what CNNs focus on by analyzing how model performance changes under localized adversarial noise. Using VGG16 for glaucoma classification, we applied noise generated by the Fast Gradient Sign Method (FGSM) to the whole fundus image and to specific subregions, then compared the impact on classification performance. Results showed that perturbations to the optic disc, especially its outer margin, had the greatest effect on model performance. This suggests that the CNN captures fine anatomical features such as optic disc cupping and neuroretinal rim thinning, which aligns with what ophthalmologists typically look for. At the same time, perturbations in the macula and perivascular regions also affected performance, indicating gaps between current clinical diagnostic criteria and the CNN’s decision-making process. This approach can help establish the clinical reliability of CNNs and may also reveal features that have not been recognized in conventional clinical practice.

Technologies doi: 10.3390/technologies14070434

Authors: Liviu Marian Ungureanu Iulian Sorin Munteanu

The first edition of the Special Issue entitled “Emerging Paradigms in AI, Autonomous Systems, and Intelligent Technologies”, published in Technologies within the Information and Communication Technologies section, was conceived around a practical observation that is now widely shared in engineering research: automation remains useful, but contemporary technological systems increasingly require autonomy, real-time decision making, transparent reasoning, robust communication, and reliable interaction with the physical world [...]

Technologies doi: 10.3390/technologies14070433

Authors: Umut Özkan Ibraheem Shayea Leila Rzayeva Alisher Batkuldin Nursultan Nyssanov

In the Argoverse 2 experiments reported here, the simplest Constant Turn Rate and Acceleration (CTRA) decoder was stable but missed many interaction-driven turns and merges, while residual decoders without enough control improved early displacement but increased final-horizon error. This paper therefore studies a compact decoder in which each of the six futures is represented as a CTRA anchor plus an autoregressive position residual. The residual gated recurrent unit (GRU) is initialized from fused target-history, top-k neighbor, and lane-polyline context, and its contribution is scaled by a mode-specific gate and learned exponential decay. On the 10k/2k sanity ablations, CTRA-only decoding reached minFDE6=7.189 m, while autoregressive residuals with a larger correction GRU reduced it to 4.157 m; removing the gate increased it again to 4.946 m. On the full Argoverse 2 validation split, the final configuration achieves a minimum average displacement error of minADE6=1.21 m and a minimum final displacement error of minFDE6=2.78 m. The reported diagnostics show that the compact model generates a useful six-mode set, but still needs better probability ranking for top-1 selection.

Technologies doi: 10.3390/technologies14070432

Authors: Alexander Chernyavskiy Ivan Tomilov Natalia Gusarova Aleksandra Vatian

Modern multi-agent deep reinforcement learning algorithms have demonstrated empirical success in communication games, yet their black box nature precludes the analytical identification of the transition from random babbling to coordinated signalling. This study introduces an explicitly parameterised, interpretable surrogate model of belief evolution in Lewis signalling games. The proposed ordinary differential equation retains the strategic structure of cheap talk while permitting the closed-form computation of the Jacobian spectrum at the uniform babbling equilibrium. It was proven that the onset of communication corresponded to a supercritical pitchfork bifurcation with a critical threshold determined by the dissipation and sensitivity parameters. Consequently, the leading eigenvalue of the dynamics serves as a detector of the emergence point. The analytical predictions were validated through iterative simulations of Lewis signalling games, illustrating how the critical threshold dictates the consistent and stable transition from stochastic babbling to separating equilibrium. Moreover, a phenomenological experiment demonstrates a possible path toward extending spectral diagnostics to policies parameterised by neural networks in a low-dimensional setting, serving as a bridge towards potential method adaptation for general deep reinforcement learning policies, without fully validating the theoretical framework.

Technologies doi: 10.3390/technologies14070431

Authors: Andrei Dimitrescu Claudiu Babiș Iulian Sorin Munteanu Sorin Alexandru Fica

This paper presents a code-based static verification of the FA125 hydraulic drilling rig mast under its governing design load combination. Unlike the previously published dynamic investigation of the same platform, the present work establishes the baseline static load path, identifies the governing structural members, evaluates the local stress state in the mast-to-support connection plates, and computes the effective safety coefficients. The mixed finite element model integrates the lattice mast, the support frame, and the base assembly, utilizing beam elements for the slender load-bearing members and shell elements for the localized plate-type connection regions. The governing load combination encompasses structural self-weight, maximum hook load (14.90 kN), and the reactive torque transmitted by the drilling head (0.50 kNm). The maximum mast-top displacement was limited to 4.75 mm. The critical beam elements were located within the lateral base-support region, developing peak compressive and tensile stresses of 70.08 MPa and 69.21 MPa, respectively. The highest localized shell-level von Mises stress (23.62 MPa) was concentrated within the mast-to-support interface connection plates. The results mathematically confirm that the existing FA125 steel structure satisfies the active design criteria, providing a distinct static reference map required for subsequent structural optimization, lightweighting, and selective material substitution.

Technologies doi: 10.3390/technologies14070430

Authors: Rakib Ahammed Diptho Safiul Haque Chowdhury Md Abdullah Al Mamun Md. Shakhawat Hosen Md. Shamsur Rahman Sarnali Basak Md Abul Kalam Azad

Gesture recognition systems powered by artificial intelligence provide a promising solution for mobility and independence for individuals with physical disabilities. However, the deployment of such systems remains limited due to some challenges related to robustness, different user requirements, affordability for lower income people, and adaptation to low-resource environments. This study presents a systematic review of gesture-controlled intelligent wheelchair systems published recently. After searching academic databases, 600 studies were found. After removing duplicate and irrelevant studies and applying the inclusion and exclusion criteria, 72 of the most relevant studies were selected for detailed analysis. The review identifies three major approaches: vision-based methods, sensor-based techniques, and signal-based techniques utilizing electromyography (EMG) and inertial measurement units (IMU), and hybrid multimodal frameworks. A comparative study is conducted to analyze performance metrics, computational requirements, datasets, and validation strategies among these approaches. The findings identify several critical research gaps, including limited real-world testing, insufficient handling of pathological tremors, weak environmental robustness, and the lack of culturally aligned gesture vocabularies. The findings identify important design considerations and research directions for developing robust, affordable, and accessible intelligent wheelchair systems suitable for underserved people in low-resource environments.

Technologies doi: 10.3390/technologies14070429

Authors: Pengfei Zhang Jinshuai Liu Rong Hou Yawen Li Yuhan Wang Zhengxuan Li Huiyan Han

To address the insufficient representation of terrain constraints and surface resistance in traditional path planning for off-road environments, this study proposes an improved A* path planning method for unmanned ground vehicles. First, an off-road environment model is constructed using Digital Elevation Model (DEM) and land cover data, and environment–vehicle traversability is evaluated by integrating geometric and support-based traversability analyses. Geometric constraints are determined using slope thresholds, minimum ground clearance, and approach/departure angles, while support-based traversability is quantified through a surface velocity influence coefficient to reflect traversal-efficiency differences under various surface conditions. These terrain and surface constraints are incorporated into the actual cost function of the A* algorithm, and a direction-corrected heuristic function is designed to enhance goal-directed search. Experiments conducted in Jiancaoping District, Taiyuan, show that, compared with the traditional A* algorithm, the proposed method reduces cumulative travel time, maximum path slope, and expanded nodes by 15.3%, 22.9%, and 47.8%, respectively, with only a 2.4% increase in path length. The results demonstrate that the proposed method effectively avoids steep and high-resistance areas while achieving coordinated optimization of path length, traversal efficiency, and terrain safety.

Technologies doi: 10.3390/technologies14070428

Authors: Carlos Brito-Brito Luis Córdova-Cajamarca Daniel Icaza-Alvarez

This research evaluates the feasibility of implementing photovoltaic solar systems in the Ecuadorian Amazon to harness solar energy and increase energy security in the region. It is based on the need to reduce direct dependence on fossil fuels and existing hydroelectric systems. The overall framework is to transform the energy matrix to utilize incident solar energy, integrating it with current hydroelectric and thermal generation. The fundamental goal is to evaluate the energy resource using specialized software such as Homer Pro and develop designs for the proper operation of photovoltaic solar technology, which will contribute its surplus energy to the National Interconnected System (SNI) and, therefore, reduce the country’s high dependence on the hydrological cycle. The results obtained demonstrate that solar power plants can be of great benefit to the country, especially when combined with wind and existing hydroelectric power. This will contribute to the diversification of energy sources and, consequently, to energy security through the increase in renewable energy. In the worst-case scenario, the cost of energy can be 7 cents per kWh, and in the best-case scenario, in a combined dispatch, 3 cents per kWh.

Technologies doi: 10.3390/technologies14070427

Authors: Ilya Galaktionov

Atmospheric turbulence distorts the wavefront of propagating optical radiation, degrading image resolution in astronomical telescopes and reducing power density at the target in focusing applications. These effects can be studied under controlled laboratory conditions using turbulence-generating devices—such as fan heaters (rough control), phase plates, or active mirrors (fine control)—in combination with a wavefront sensor for measurements. To support this research, we developed a software simulator for reconstructing atmospheric phase fluctuations. The integrated software–hardware system can generate phase screens following Kolmogorov turbulence statistics, incorporating parameters for wind velocity and the D/r0 ratio. Phase screens were produced with an average approximation error of 0.01 µm (less than 5%). The average reconstruction error was 0.017 µm, corresponding to approximately 8%. The newly developed phase screen simulator outperforms the fastest existing version in several key aspects. Its aperture size is doubled, increasing from 400 mm to 800 mm, while the phase screen generation resolution expands by half, from 700 × 700 pixels to 1024 × 1024 pixels. The operating wavelength range also broadens significantly—from a maximum of 2.2 µm in the existing tool to 10 µm in the new one. Additionally, the wind velocity range becomes 1.5 times wider, extending from 30 m/s to 50 m/s. The developed tool might be useful for the performance analysis of wireless links, particularly in the estimation of bit error rate and quantum efficiency using the wavefront root mean square error.

Technologies doi: 10.3390/technologies14070426

Authors: Nikolay Dukov Vencislav Nastev Viktoria Petkova Ivan Buliev Zhivko Bliznakov Valentina Dobreva Kristina Bliznakova

Anthropomorphic breast phantoms are increasingly used for the development and evaluation of breast imaging technologies. The aim of this study is to evaluate the impacts of the material combination and stereolithography or Standard Tessellation Language (STL) export methodology on the multimodal imaging performance of patient-derived breast phantoms. A breast model derived from segmented magnetic resonance imaging (MRI) data was used to fabricate four multi-material phantom sections representing adipose- and glandular-equivalent regions. Two material combinations—acrylic styrene acrylonitrile and high-impact polystyrene (ASA-HIPS) and acrylic styrene acrylonitrile and acrylonitrile butadiene styrene (ASA-ABS)—and two STL export procedures were evaluated, including a conventional mesh-based workflow and an in-house voxel-preserving approach designed to eliminate interface gaps. The phantoms were imaged using clinical computed tomography (CT), mammography, and digital breast tomosynthesis systems. Quantitative evaluation included contrast-based image characteristics and region of interest-based intensity distribution analysis. The results showed that ASA-HIPS phantoms demonstrated higher inter-material contrast and greater attenuation separation than ASA-ABS across all imaging modalities. The voxel-preserving STL export procedure improved physical interface integrity and eliminated visible inter-material gaps, while producing only minor differences in global radiological metrics compared with the conventional workflow. CT imaging of the breast samples acquired at 70 kVp showed attenuation characteristics consistent with clinically reported Hounsfield unit ranges for low-density breast tissues observed in clinical CT examinations.

Technologies doi: 10.3390/technologies14070425

Authors: Yangbin Xu Wenhui Shi Jing Xing Jiangang Yang Jian Liu

3D scene graphs organize objects and their relationships in a scene into structured representations, providing an interpretable and queryable foundation for relational reasoning and object grounding. Existing open-vocabulary 3D scene graph construction methods primarily focus on object-level feature representation and open-ended edge reasoning. However, they often lack explicit and retrievable associations between object nodes and keyframes, making it difficult to recall relevant visual evidence for target disambiguation and relationship verification in complex queries. Moreover, pre-constructed edges are inherently limited in their ability to cover the diverse linguistic expressions encountered in downstream tasks. To address these limitations, we propose KNA-SG, a framework for constructing open-vocabulary 3D scene graphs from RGB sequences with explicit keyframe–node associations. Built upon instance-grounded 3D reconstruction, KNA-SG represents each object instance as a graph node and uses a unique instance identifier to associate the node with the keyframes in which the instance is observed. The ID-annotated keyframes guide MLLM-based open-vocabulary semantic parsing, enabling semantic attributes to be assigned to these graph nodes. This design transforms keyframes into retrievable visual evidence for target disambiguation and relationship verification during query reasoning. Verified relationships are further written back into the scene graph as reusable relational memory to support subsequent queries. To ensure the effectiveness of selected keyframes, we design a two-stage keyframe selection strategy that combines visual quality assessment with semantic redundancy removal, preserving a set of clear keyframes that provide comprehensive scene coverage. Experimental results show that KNA-SG outperforms existing methods on open-vocabulary 3D semantic segmentation and 3D object grounding tasks.

Technologies doi: 10.3390/technologies14070424

Authors: Fen Xu Hongyu Miao

In situ detection of the tracking poses of heliostats can help improve the tracking accuracies of heliostats and reduce the task loads of heliostat calibration in a large-scale concentrated solar power (CSP) plant, as the traditional methods normally require the heliostats to be off from sun-tracking during the calibration process. This paper presents a deep learning-based framework for in situ detection of geometric keypoints of the heliostat surface. The proposed framework is built upon YOLOv8-Pose but integrates a high-resolution P2 feature branch to recover fine-grained spatial details that are otherwise lost in deep semantic layers. Further, a geometry-consistency loss is introduced to regularize the predicted quadrilateral, enforcing strict structural integrity under dynamically changing illumination. An experimental study on a real-world heliostat image dataset shows that the proposed framework achieves an end-to-end inference speed of 25.14 FPS. The mean end-point error (EPE) of detected keypoints is around 1.22 pixels, while the stringent mAP@0.5:0.95 metric reaches 0.9823. The keypoint detection framework could be integrated with an in-field heliostat control system for further improvement of the working efficiency of heliostats in a large-scale CSP plant in future.

Technologies doi: 10.3390/technologies14070423

Authors: Jie Ren Chunhai Zhou Chuyang Tan Yan Wang

Secure IoT and smart grid networks depend on reliable hardware operation to maintain continuous service and system availability. Physical-layer abnormalities such as partial discharge (PD) can weaken infrastructure components and disrupt connected systems before conventional monitoring methods detect the problem. PD is one of the earliest indicators of abnormal hardware activity in electrical infrastructure. If it is not detected in time, it can damage equipment, reduce system reliability, and increase the risk of service interruption in intelligent network environments. Existing detection methods often struggle with PD signals because these signals are non-stationary, vary over time, and frequently contain noise. This limits reliable physical-layer threat detection in secure IoT and smart grid networks. This study presents an integrated physical-layer threat-detection framework for secure IoT and smart grid networks that combines adaptive HHT-based signal decomposition with multimodal deep learning for early hardware threat identification. The framework first applies the Hilbert–Huang Transform (HHT) to decompose PD signals and extract time–frequency features that describe discharge behavior. A convolutional neural network with an attention-based fusion mechanism then learns patterns from electrical and acoustic signals. The model classifies hardware condition into normal operation, early abnormal activity, and critical discharge states associated with potential hardware threats. The framework is evaluated using two public datasets: the Dataset of Partial Discharge and Noise Signals and the Partial Discharge Localization (PD-Loc) dataset available through the IEEE DataPort. Experimental evaluation shows that the proposed framework achieves 97.8% detection accuracy, a 97.0% F1-score, and an average AUC of 0.98. The framework maintains 94.6% accuracy under severe noise conditions (10 dB SNR) and performs inference in approximately 12 ms per sample. Furthermore, component-wise analysis further shows that HHT-based feature extraction improves detection accuracy from 91.8% to 95.6%, while multimodal learning increases the final accuracy to 97.8%.

Technologies doi: 10.3390/technologies14070422

Authors: Xiaozhou Hu Kang Duan Bingkun Han

The lubrication of high-speed bearings in variable-speed transmission systems is typically achieved through radial under-race lubrication devices, where the performance of the oil capture section significantly affects the overall lubrication performance. To improve the oil capture performance of radial under-race lubrication devices, this study analyzed the flow mechanism of lubricating oil in the under-race oil capture section to investigate the main factors influencing the oil capture performance. Based on the bearing cavity structure of a specific high-speed bearing, a preliminary design of the oil scoop was conducted. A numerical model of the flow field in the radial under-race lubrication oil capture section was established using CFD methods. Simulations were performed to study the influence patterns of these factors on the oil capture efficiency of the scoop, followed by preliminary optimization of the oil scoop. Through visualization analysis, the main causes of lubricating oil loss were identified, and further optimization of the scoop structure was carried out, significantly improving the oil capture efficiency of the scoop. An oil scoop performance test bench was designed and constructed, and experiments were conducted. The experimental results showed good agreement with the simulation results.

Technologies doi: 10.3390/technologies14070421

Authors: Khalid al Fararni Loukmane Maada Badraddine Aghoutane Abdelouahed Sabri Ali Yahyaouy Jamal Riffi

This paper proposes a hybrid content-based recommender system aimed at enhancing personalized tourism experiences and supporting the promotion of tourism in Morocco, with particular emphasis on the Drâa-Tafilalet region. The proposed approach integrates three machine learning models—Decision Tree, k-nearest neighbors, and Support Vector Machine—to predict user ratings for historical tourism sites. Tourist attraction metadata and user-generated reviews are represented using TF-IDF vectorization, while the predictions produced by the individual models are combined through an inverse-error weighting strategy. The system is evaluated using two datasets: a subset of the Yelp dataset comprising reviews of historical buildings in the United States, and a regional Drâa-Tafilalet dataset. Experimental results, assessed using MAE and RMSE, indicate that the weighted hybrid model outperforms the individual recommendation models by achieving lower prediction errors. These findings demonstrate the potential of hybrid content-based recommendation approaches to improve the accuracy of personalized tourism recommendations, support tourist decision-making, and promote underrepresented regional destinations.

Technologies doi: 10.3390/technologies14070420

Authors: Yushi Chen Rouyi Chen Hui Jiang Yanlu Huang Fan Zhang

With the advancement of the new-type power system construction, the accurate and efficient calculation of node-level carbon emission factors (CEFs) has become a key basis for indirect carbon emission accounting in power systems. Existing centralized methods face two major challenges in large-scale power grids: a heavy computational burden caused by the expanding scale of carbon emission flow equations, and potential privacy leakage caused by the centralized aggregation of regional operational data. To address these issues, this paper proposes a partitioned iterative CEF calculation framework based on the centralized data distribution pattern (CDDP) and the biconjugate gradient stabilized (BiCGSTAB) algorithm. The power grid is naturally divided into multiple subregions according to the power supply jurisdiction of each node. Each subregion independently solves its local CEF model, while a centralized broker coordinates boundary information exchange among regions. During this process, each region only discloses the required boundary-node CEFs, which is consistent with the mechanism of unified dispatch and hierarchical management. Tests on the 2000-node, 10,000-node, and 25,000-node systems show that the maximum relative errors are 0.00019%, 0.0030%, and 0.0900%, respectively. These results verify the effectiveness and scalability of the proposed framework and provide a feasible engineering solution for efficient, privacy-preserving node-level CEF calculation in large-scale power grids.