Applied Sciences doi: 10.3390/app16136420

Authors: Sangwook Han Seonggyu Lee Jong Won Shin

Target speaker extraction (TSE) aims to isolate speech spoken by a target speaker out of a mixture using speaker information in an enrollment utterance. Recently, several methods have been proposed that exploit the relationship between the enrollment utterance and the input mixture using cross-attention, without extracting speaker embeddings from the enrollment. Previous approaches applied the cross-attention to the encoded representations or to the real and imaginary parts of the compressed spectrograms separately, which may not have a physical meaning. In this paper, we propose a two-stage TSE method with a physically interpretable modified cross-attention block and a dual post-refinement structure. In the first stage, the attention weights to fuse the enrollment and mixture are derived from the cross-correlation between the complex spectra for the two signals in a form analogous to the phase-sensitive mask. The fused features along with the mixture features were subsequently fed into a speech extraction network to obtain a coarsely extracted target speech. The second stage consists of two parallel branches, where one branch refines the first-stage output using the enrollment in a similar way to the first stage, and the other utilizes the mixture to complement possibly attenuated target speech. In addition, the low-dimensional speaker embeddings extracted from the enrollment and the first-stage output are incorporated into the second stage to exploit the speaker discriminability. Experimental results show that the proposed method consistently outperformed existing TSE methods on the Libri2Mix dataset under both clean and noisy conditions, in terms of speech quality, speech intelligibility, and signal distortion measures.

Applied Sciences doi: 10.3390/app16136418

Authors: Bodhi Thümmler Esther Schulz Maximilian Kellershoff Alexandra Kunst Nina Ulbrich Diemo Daum Sascha Rohn Claudia Keil Hajo Haase

Iron deficiency is a major global health concern, primarily attributable to inadequate dietary intake and limited absorption of iron. Foliar fertilization of vegetables like bell pepper (Capsicum annuum) may increase their iron content. When especially rich in ascorbic acid, intestinal iron absorption might be improved even further. However, iron content in foods and/or bioaccessibility after in vitro digestion alone would be unreliable predictors of iron bioavailability. Consequently, it seems to be necessary to reconsider digestion models and bioavailability evaluation. The objective of this study was to establish a mucin-enhanced in vitro digestion model to assess the bioavailability of non-heme iron from food matrices in combination with the widely utilized Caco-2 model of intestinal iron absorption. Compared with Fe(III), incubation with 20 µM and 200 µM Fe(II) sulfate increased ferritin formation (normalized to total protein (TP)) in differentiated Caco-2 cells by 80% and 130%, respectively. Furthermore, no loss of cellular viability was observed across the tested Fe(II) and Fe(III) concentrations (20–2000 µM). Three in vitro digestion models (DIN, DIN-lite, and DIN+G), differing mainly in digestive enzyme content, were evaluated for iron bioaccessibility and bioavailability. Only DIN-lite and DIN+G were compatible with Caco-2 cells. Although DIN-lite yielded 25% higher bioaccessible iron than DIN+G, both models resulted in comparable ferritin formation in Caco-2 cells. The DIN+G/Caco-2 model was applied to bell pepper cultivars (‘Ferrari’, ‘Morbidelli’, and ‘Jack Miller’), treated with foliar Fe(II) sprays during cultivation, achieving up to 3.9-fold increased iron content. However, this increase did not translate into enhanced in vitro iron bioavailability in the bell pepper pericarp. Consistent with previous studies, these findings indicate that iron content and bioaccessibility alone are insufficient predictors of iron bioavailability in plant-based foods. At the same time, the mucin-enhanced DIN+G/Caco-2 model proved to be a suitable approach for investigating iron bioavailability in plants.

Applied Sciences doi: 10.3390/app16136419

Authors: Rakesh Bharati Jyoti Bharti Vasudev Dehalwar

There is an enormous rise in the amount of user-generated content on social media. That makes it easier for hateful and fake messages to spread, and threatens both societal stability and public trust in institutions. Most of current solutions have fundamental limitations due to modal limitations (i.e., each solution only uses one type of data at a time), lack of user context integration, poor synchronization across different types of data, and poor resilience to manipulation by adversaries. As a result, most solutions are subject to compound loss in terms of their ability to generalize well, classify correctly, or remain reliable when deployed in real-world environments. To address all of the above challenges, we propose a comprehensive and modular analytical framework consisting of five interconnected components that integrate contextual representation learning, multimodal semantic alignment, graph-based propagation modeling, adaptive inference, and consistency validation for hate speech and fake post detection. First is our Context-Driven Social Vector Extraction methodology, which provides enriched contextual embeddings by extracting and combining text-based metadata, image-based metadata, temporal metadata, and behavioral metadata. We use those embeddings in our second module, Multimodal Label Fusion via Mutual Co-Attention (CMF-MCA). Our CMF-MCA module incorporates two transformers with co-attention mechanisms that can mutually annotate text and images. In our third methodology, Semantic Propagation Graph for Hate and Fake Correlation (SPG-HFC), we implement a relational graph attention mechanism that captures both the influence of semantics and how communities propagate information about hate and fake posts. The fourth module, Adaptive Modality Routing via Reinforcement (AMR-R), routes based on the modality of the input and whether the input is simple enough to be classified using machine learning or complex enough to require deep learning. Finally, our Counterfactual Consistency Validation Engine (CCVE) is used after prediction to validate that the model’s predictions are consistent with the output data by creating counterfactuals and validating them. Therefore, in addition to improving the overall accuracy of hate speech and fake post detections, our proposed framework also improves its scalability and inference reliability. Additionally, because our framework allows multimodal classifications that include both context and behavior, it enables the scalable and trustworthy development of content moderation systems.

Applied Sciences doi: 10.3390/app16136416

Authors: Jiho Shin Byoung Hun Moon

Databases serve as critical repositories of digital evidence in criminal investigations, and the recoverability of deleted data is a key determinant of forensic success. Microsoft SQL Server, one of the most widely deployed relational database management systems, has been the subject of multiple forensic studies examining how deleted records persist in physical database files across different acquisition methods. A previous study established a reference baseline using SQL Server 2008 and 2017, demonstrating that the Database Shrink operation causes version-specific and method-specific behavior: under logical collection with Shrink applied in SQL Server 2017, unallocated deleted data becomes fully initialized, rendering recovery impossible—a pattern not observed in SQL Server 2008 or under physical collection in either version. With the release of SQL Server 2025, the most significant architectural update to the platform in a decade, it remained unknown whether these forensic behaviors persist in the latest version. This study replicates the experimental design of in a controlled SQL Server 2025 environment, applying the same deletion scenario (DELETE command without conditions), the same two acquisition methods (logical and physical collection), and the same Shrink condition. The results demonstrate that SQL Server 2025 does not reproduce the version-specific initialization behavior observed in SQL Server 2017: across all four experimental conditions, deleted data residue in unallocated page space remains recoverable, indicating a fundamental change in the interaction between the Shrink operation and the logical collection mechanism. This recoverability is a double-edged property: while it benefits forensic investigators by preserving deleted evidence, it simultaneously represents a data-sanitization risk from a security and privacy standpoint, as deleted records are not reliably erased. These findings provide updated forensic guidance for digital investigators operating in contemporary SQL Server environments. Specifically, the results inform acquisition-method selection in real-world investigations where a suspect may have deleted records and where only a logical backup (.bak) is available to investigators.

Applied Sciences doi: 10.3390/app16136417

Authors: Guangdong Song Zunting Wang Jiulong Cheng Feng Zhu Jiqiang Wang Moyu Hou

Microseismic monitoring is essential for the early warning of mine dynamic disasters; however, weak signal characteristics and strong environmental noise often lead to missed detections and false alarms. To address these challenges, this study proposes an optimized U-Net model for multi-class microseismic signal recognition under low-signal-to-noise-ratio conditions. The method combines Short-Time Fourier Transform, a U-Net encoder–decoder architecture, residual learning, and squeeze-and-excitation attention modules to enhance weak feature extraction and noise suppression. A multi-source dataset containing microseismic, knocking, blasting, noise, and earthquake signals was constructed using both field-measured data and public seismic datasets. Experimental results show that the proposed model achieved an overall validation accuracy of 99.25% and excellent recall performance for microseismic events. Under extreme noise conditions with a signal-to-noise ratio of −5 dB, the model still maintained a microseismic recognition accuracy of 98.25%. Comparative experiments further demonstrate that the integration of Short-Time Fourier Transform and residual attention modules significantly improves robustness and weak-signal discrimination capability. The proposed method provides an effective approach for intelligent microseismic monitoring and mine dynamic disaster early warning.

Applied Sciences doi: 10.3390/app16136415

Authors: Feifan Cai Qi Zhang Chang’an Xu Youdong Ding

Old films suffer from scratches, dust, and brightness flicker caused by aging film stock and unstable analog exposure. Recurrent restoration frameworks suppress these artifacts under the guidance of degradation masks, yet pixel-domain frame differencing provides weak evidence for thin structural defects and confuses global brightness variation with content change. We present WaveletMask, a wavelet-domain degradation sensing framework that disentangles these two failure modes by construction: a high-frequency branch localizes transient structural defects from Haar detail-band differences between adjacent frames, a low-frequency branch isolates frame-level brightness deviations from coarse approximation responses, and a parameter-free maximum fusion rule passes the dominant cue to the recurrent gate. On the Synthetic and Real-World Old Video (SRWOV) benchmark, WaveletMask attains the best PSNR among ten re-trained methods (26.60 dB, +0.61 dB over the strongest competitor), and a paired comparison against the Recurrent Transformer Network (RTN) confirms a +0.45 dB gain while adding only 898 detector parameters. On real archival footage, WaveletMask removes scratches and flicker more cleanly while better preserving film texture and temporal stability. These results indicate that explicit wavelet-domain separation of structural and photometric cues offers a reliable, nearly cost-free upgrade for mask-guided recurrent restoration.

Applied Sciences doi: 10.3390/app16136413

Authors: Sui Peng Mengshu Zhu Yanfeng Wang Huiying Cao Junzhou Wang Junjie Tang

Traditional reserve planning methods often suffer when balancing system economics and reliability due to the uncertainty and transmission constraints of renewable energy sources (RESs) being neglected, affecting the availability of reserves. To overcome these challenges, in this study, we propose a new reserve planning method tailored to a renewable-energy-dominated power system, with particular consideration of reserve availability. First, the method assesses reserve availability using a unified reliability economics model at the composite system assessment (CSA) level; it also employs a conditional cost–benefit analysis (CCBA) to optimize the reliability–cost trade-off. To solve the planning problem, a particle swarm optimization (PSO)-based hierarchical optimization method is employed to co-optimize the allocation of reserve capacity, thus maximizing the comprehensive benefit. Furthermore, the analysis quantifies the impact of the RES penetration level and risk preference on the reserve capacity and its allocation. The results of tests conducted on IEEE RTS-79 demonstrate that the proposed method can effectively assess reserve availability, as well as achieve an increase of up to 19.5% in comprehensive benefit and a 73.8% reduction in EENS. Furthermore, increasing RES penetration and ensuring stronger risk preference can lead to reserve capacity increases of 42.4% and 76.0%, respectively.

Applied Sciences doi: 10.3390/app16136414

Authors: Diogo Alves Cardinot Bernardo Sotto-Maior Peralva Gustavo Barbosa Libotte Luciano Manhães de Andrade Filho

Particle accelerators are complex facilities that collide particles at extreme high speed, aiming to discover new physics. In this context, high-energy calorimeter systems play a crucial role, as they provide the particle energy quantity, which is important information for the potential new discoveries. Therefore, this work evaluates the performance of the commonly used Optimal Filter (OF) method and several Convolutional Neural Network (CNN) architectures in reconstructing the amplitude and phase of simulated signals that represent the response pulses produced by high-energy calorimeters. The comparison is conducted using quantitative metrics—including RMS, MAE, MedAE, and Coefficient of Determination. The results show that different CNN architectures exhibit varying performances depending on the calorimeter cell occupancy rate but generally outperform the typical linear OF method, providing more accurate signal reconstructions. Considering all evaluated occupancy levels (10%, 50%, 80%, and 100%), the CNN-based approaches achieved an average improvement of approximately 79% in amplitude RMS and 62% in amplitude standard deviation when compared to the OF method. For phase estimation, the CNNs achieved improvements of approximately 26% for both RMS and standard deviation metrics. Although the proposed strategy requires a large execution time due to the training process across multiple folds, these findings indicate that CNNs are promising alternatives for calorimeter energy reconstruction, particularly in high-occupancy conditions such as those expected for high-luminosity experiments.

Applied Sciences doi: 10.3390/app16136412

Authors: Ewa Jakubczyk Anna Kamińska-Dwórznicka Zuzanna Domżalska Małgorzata Nowacka Dorota Witrowa-Rajchert

This study aimed to determine the effect of enzymatic pre-treatment on the selected physical properties and freeze-drying kinetics of blueberries. Fresh fruits were treated with commercial pectinolytic and cellulolytic enzyme preparations for different durations (20–60 min). The influence of enzyme type and treatment time on water content, water activity, colour, mechanical properties, and drying kinetics was evaluated. The enzymatic treatment decreased the mechanical resistance of the fruits, but the colour changes were generally minor. The freeze-drying time for fresh fruits was significantly reduced from 2855 min to 845–1190 min with enzymatic pre-treatment. The extent of this reduction depended on the enzyme type used and the duration of the treatment. Pectinolytic enzymes were found to be more effective than cellulolytic enzymes in reducing drying time. It can be concluded that using pectinolytic enzymes for 30 min significantly reduced the drying time by a factor of three. Additionally, this process improved the fruit’s physical properties compared to fresh-dried blueberries.

Applied Sciences doi: 10.3390/app16136410

Authors: Luís Pereira Luís Godinho Fernando G. Branco Paulo da Venda Oliveira Pedro Alves Costa Aires Colaço

This research proposes a physics-based deep learning framework, developed as a proof-of-concept based on synthetic data, for estimating the mechanical properties of layered media—namely density (ρ), Young’s modulus (E), and top layer thickness (h1)—using synthetic seismogram images generated via Finite Element Method (FEM) simulations. The dataset, comprising 5000 simulations, incorporates physical constraints and empirical density–modulus correlations. While a ResNet-style Convolutional Neural Network (CNN) extracts density and stiffness parameters from composite time–frequency images, the estimation of h1 utilizes a direct time-domain raw-signal approach to preserve spatial resolution. A 5-fold nested cross-validation scheme with internal Bayesian Optimization ensures rigorous model evaluation, further validated by normality assessments and bootstrap confidence intervals. Performance was tested against synthetic Gaussian noise (0% to 50%) and benchmarked against classical Full Waveform Inversion (FWI). The results demonstrate high predictive accuracy for shallow properties, with R2 values reaching 0.96 for Young’s modulus and 0.83 for raw-signal thickness. The neural network model requires 0.035 s per inference compared to 180 s for the FWI approach, avoiding the local minima convergence issues typical of iterative inversion. The framework exhibits resilience under moderate noise levels (up to 30%), establishing a reliable baseline for future experimental validation.

Applied Sciences doi: 10.3390/app16136411

Authors: Xibo Chen Ziqi Zhang Haize Hu Jie Jin Fei Yu Lv Zhao

Knowledge Tracing (KT) plays a pivotal role in Intelligent Tutoring Systems (ITS) by dynamically assessing learners’ evolving knowledge states. However, tracking the acquisition of English presents unique challenges. Existing KT models typically employ homogeneous, predefined forgetting mechanisms that fail to capture the highly individualized nature of linguistic memory retention. Furthermore, language assessment data is notoriously noisy, which leads models to overfit superficial performance rather than capturing true underlying linguistic competence. To address these issues, we propose a novel framework to robustly trace English language competence. First, we introduce a Learning-Profile-Driven Adaptive Forgetting mechanism. Unlike methods with shared forgetting rates, our approach constructs a dynamic and strictly causal profile from historical interactions to generate personalized cognitive parameters (e.g., individualized forgetting rates). These parameters synchronously modulate the decay of multi-level knowledge states, enabling the model to accurately capture the heterogeneous memory retention patterns of different learners. Second, we design a Masked Consistency Regularization training paradigm. By applying stochastic masking to historical responses and enforcing predictive consistency, we prevent the model from exploiting localized noise and “shortcut” learning, compelling it to mine robust and invariant language representations. Extensive experiments on real-world educational datasets demonstrate that our proposed framework significantly outperforms state-of-the-art baselines in both prediction accuracy and noise resistance, offering a robust and interpretable solution for personalized language learning.

Applied Sciences doi: 10.3390/app16136409

Authors: Gerardo Antonio Castañón Ávila Ana Maria Sarmiento-Moncada Alejandro Aragón-Zavala Ivan Aldaya Garde

In this work, we investigate deterministic chaos in external-cavity semiconductor lasers with delayed optical self-feedback. A noise-free quadrature-based delay differential model is used to isolate the intrinsic nonlinear dynamics produced by phase-sensitive delayed reinjection and carrier–photon interactions. Sensitivity to initial conditions is quantified by computing the leading Lyapunov exponents through a variational approach that integrates the base delay differential equations together with their delayed variational equations using a fourth-order Runge–Kutta method of steps and periodic QR orthonormalization. High-resolution Lyapunov maps are constructed in the (log10C,ϕf) parameter space for different pump ratios and selected short-feedback delays. The delay values are interpreted through the reference-normalized ratio τf/TR,ref, where TR,ref≈131.9ps is a fixed reference timescale derived from a reference solitary-laser operating point. The results show that both the spatial organization of positive-λ1 regions and the mean positive Lyapunov exponent are strongly affected by feedback delay, feedback phase, feedback strength, and pump ratio. Within the selected short-delay set, delayed self-feedback produces broader, more connected, and more strongly unstable chaotic regions as the external-cavity memory time increases toward the fixed reference timescale, particularly at larger pump ratios. These findings show that short external-cavity self-feedback can support robust deterministic chaotic regimes relevant to compact and integrated photonic implementations. The proposed framework provides a reproducible deterministic reference for identifying and interpreting feedback-induced chaos in short-delay external-cavity semiconductor lasers, while stochastic effects such as spontaneous-emission and Langevin noise are left for future robustness studies.

Applied Sciences doi: 10.3390/app16136408

Authors: Adam Lubojański Katarzyna Szyszka Adam Watras Bartosz Mielan Maciej Dobrzyński Rafal J. Wiglusz

Background: Compomer materials combine the advantages of composite resins and glass ionomer cements, including fluoride release, durability, and aesthetics. This study evaluated the effects of silver nanoparticles (nAg0) and copper oxide (CuO) particles on fluoride ions release and the structural properties of a commercially available compomer. Methods: Compomer discs modified with 0.125 wt.%, 0.25 wt.%, and 0.5 wt.% nAg0 or CuO were prepared and analyzed in demineralized water and artificial saliva at various pH levels for 168 h. Fluoride release was measured using a fluoride-selective electrode, while structural and morphological properties were examined using X-ray diffraction (XRD) and scanning electron microscopy (SEM). Results: Under most of the tested conditions, the modified materials exhibited higher fluoride release than the unmodified compomer, with the greatest increase typically observed at higher additive concentrations. XRD analysis confirmed the presence of crystalline phases of Ag0 and CuO while maintaining the amorphous nature of the compomer matrix. SEM observations revealed better particle dispersion at lower additive concentrations and increased agglomeration at a 0.5% content. Conclusions: These results indicate that the incorporation of nAg0 and CuO particles may enhance the fluoride-releasing potential of compomer materials; however, further studies are necessary to evaluate their mechanical, antibacterial, cytotoxic, and aesthetic properties prior to clinical application.

Applied Sciences doi: 10.3390/app16136407

Authors: Mehtap Ülker

Accurate and up-to-date mapping of built-up areas is of great importance for sustainable urban planning, disaster management, and the monitoring of environmental changes. In this study, a residual U-Net-based deep learning architecture named FiveBandTTA is proposed for built-up area segmentation from Sentinel-2 multispectral satellite imagery. The proposed model aims to simultaneously learn spatial and spectral features by jointly processing RGB, NIR (B8), and SWIR (B11) bands within the same encoder–decoder structure. The model incorporates standard residual blocks following the conventional residual learning principle, multi-level skip connection mechanisms, and TTA-based inference strategies. Within the scope of the study, a multi-temporal built-up area dataset was constructed from Sentinel-2 imagery acquired over Kocaeli Province. The performance of the proposed model was comparatively evaluated against RGB Baseline, FiveBand Single, DeepLabV3+, and SegFormer models. Experimental results demonstrated that the proposed model achieved the highest segmentation performance among all compared approaches, obtaining 0.8447 IoU, 0.9124 Dice, and 0.9249 Precision scores. It was observed that the use of multispectral bands together with the residual encoder–decoder structure may contribute to improved representation of small-scale built-up regions and complex boundary structures. Furthermore, the comparative experiments indicated that the NIR and SWIR bands provide complementary spectral information for distinguishing built-up areas, while the TTA-based inference strategy may contribute to improved segmentation stability and prediction consistency. Overall, the obtained results demonstrate that the proposed approach is an effective and robust method for built-up area segmentation from medium-resolution Sentinel-2 imagery.

Applied Sciences doi: 10.3390/app16136406

Authors: Iana Fominska Gerardo Iovane Marta Chinnici

In an era where artificial intelligence is rapidly expanding into creative domains, the challenge of modeling human-like cognition and emotion in generative processes becomes increasingly central. The present study was made in connection with the exhibition of Munch’s works held in Rome from February to June 2025. Indeed, the paper introduces the concept of a Cognitive Digital Twin grounded in the Super Time-Cognitive Neural Network (STCNN) framework and applies it to the case of Edvard Munch, the iconic Norwegian expressionist. The proposed system—Munch Digital Twin—goes beyond static generative models by integrating temporal, emotional, and cognitive dimensions through a complex-valued time representation t = a + i·b, where a denotes chronological time and b encodes imagination, memory, and creativity. We define Edge Knowledge as an output-stage re-ranking criterion that admits a generated response only where corpus evidence, knowledge-graph constraints and the LLM surface jointly agree (the boundary, or ‘edge’, between documented identity and machine inference). STCNN allows this twin to process real inputs (text, visual prompts, emotional cues) and generate outputs that reflect both the rational and expressive styles of Munch. The imaginary components of the network enable speculative and affective expansions of known artworks—such as reinterpreting The Scream under new emotional or social contexts. This paper presents the theoretical underpinnings of cognitive digital twins, the architecture of the STCNN-based model, and a prototype implementation trained on Munch’s paintings, letters, and critical essays. The system—comprising a GPT-4-Turbo cloud profile and a 4-bit LLaMA-2-13B edge profile for language, Stable Diffusion 1.5 + LoRA for image generation, a Neo4j knowledge graph, and FAISS retrieval—is trained on approximately 600 letters, 100 artworks, and Munch’s diaries and criticism, and evaluated across 100 interactive sessions with 14 students and expert raters. Headline results against an unconditioned baseline include CLIPScore +13.8%, FID −25.5% (small-sample, indicative), and emotion-cosine similarity +44.9%. Ethical implications surrounding posthumous digital emulation, authorship, and emotional manipulation are also discussed. The Munch Digital Twin represents a new paradigm in AI-driven art, where machines do not merely replicate, but collaborate across time with human legacies, enabling an anticipatory and emotionally intelligent form of computational creativity. This work is primarily a conceptual and architectural contribution, supported by a proof-of-concept prototype and a preliminary, non-controlled user study; the quantitative results are indicative and not yet confirmatory.

Applied Sciences doi: 10.3390/app16136400

Authors: Rui Xiang Jie Yang Ke Wang Tianxiang He Jinsong Xia Junlin Zhou Yan Fu Duanbing Chen

In Underwater Acoustic Target Recognition (UATR), accurately extracting spectral lines from time–frequency spectra in complex ocean environments faces three critical challenges: low-frequency spectral confusion, line spectrum and noise mixture, and a computational efficiency vs. performance trade-off. To address these, we propose a deep learning-integrated framework based on application-oriented integration and adaptation of established techniques tailored to the underwater acoustic domain. The framework consists of the following: (1) the Line Spectrum Separation Network (LSS-Net), which integrates a Time–Frequency Joint LSTM and a Temporal Gated Cross-Attention (TGCA) module within an encoder–decoder architecture adapted for high-resolution underwater acoustic time–frequency spectra; (2) a physics-informed signal simulation approach that realistically models Doppler frequency drift and intensity fluctuations; and (3) a Peak-Tracking Line Extractor (PTLE) algorithm that leverages underwater acoustic-specific temporal constraints. The proposed framework achieves an MOTA of 0.89 on simulated data and 0.52 on real sea trial data, outperforming existing methods by 0.06-2.14 in MOTA and significantly suppressing high-resolution background noise.

Applied Sciences doi: 10.3390/app16136405

Authors: Xinlei Wu Ziqi Tian Yapeng Wu Jiewen Yang Xin Lu Zhong Tang

Sorghum is an important multi-purpose crop used for food, feed, brewing, and bioenergy. However, mechanised harvesting is hindered by its tall stature, complex panicle morphology, small and fragile grains, high-moisture stems and leaves, and susceptibility to lodging at maturity. This review provides a PRISMA-guided systematic literature search and narrative synthesis of mechanised sorghum harvesting from crop adaptability to intelligent equipment. The main literature search covered publications from January 1990 to May 2026 and included Web of Science Core Collection, Scopus, ScienceDirect, SpringerLink, Google Scholar, and other relevant sources. A total of 1928 records were identified, and 190 studies were finally included in the qualitative synthesis after duplicate removal, title-and-abstract screening, and full-text assessment. The review analyses how plant morphology, panicle exsertion, physical and mechanical properties, maturity stage, moisture content, varietal differences, and lodging affect harvesting suitability. The applicable conditions for segmented harvesting, panicle harvesting, direct grain harvesting, and multi-purpose coordinated harvesting are compared under different crop, regional, and machinery conditions. Key technological advances are synthesised in relation to header feeding, threshing and cleaning, straw management, and operating-parameter optimisation. Recent developments in machine vision, condition monitoring, adaptive control, DEM and CFD-DEM simulation, and digital twins are also assessed. The analysis shows that high-quality mechanised sorghum harvesting requires the coordinated optimisation of crop traits, harvesting methods, machine structures, operating parameters, and digital sensing–control feedback. The main contribution of this review is to establish an integrated crop–machine–operation framework for identifying technical constraints, comparing harvesting routes, and guiding the development of specialised, low-loss, low-breakage, and intelligent sorghum harvesting systems.

Applied Sciences doi: 10.3390/app16136404

Authors: Caroline C. B. Souza Franciele Parolini Márcio Fagundes Goethel Johan Robalino Gisela Rocha de Siqueira Alysson L. P. C. Silva Marcus Vinícius B. Rodrigues João Paulo Vilas-Boas Miguel Velhote Correia Marco Aurélio Benedetti Rodrigues Ana Paula de Lima Ferreira

Hydrotherapy is widely used in rehabilitation because it reduces mechanical loading while preserving neuromuscular and cardiovascular stimulation. However, the biomechanical characterization of deep-water running remains limited, particularly when using accessible wearable systems for cycle-based movement analysis. This study aimed to evaluate the concurrent validity and agreement of a low-cost accelerometry device for cycle-based analysis of deep-water running, using a commercial accelerometry system as the reference measurement system. Twenty-one healthy participants performed a 25 m deep-water running task with simultaneous data acquisition from mechanically coupled sensors to ensure alignment. A total of 75 synchronized cycles were processed using a standardized pipeline that included filtering, synchronization, cycle detection, and parameter extraction. Statistical analysis was conducted using the Wilcoxon signed-rank test, intraclass correlation coefficient, Spearman’s correlation, Bland–Altman analysis, and error metrics. The results showed good agreement for temporal and volumetric variables, including cycle duration (ICC = 0.84), cumulative acceleration (ICC = 0.82), and area under the curve (ICC = 0.68). However, lower agreement and systematic bias were observed for intensity-related variables, particularly RMS and peak acceleration, despite more than 92% of cycles falling within the 95% limits of agreement (LoA). These findings suggest that the proposed device provides acceptable agreement for temporal and volumetric variables during deep-water running and may represent a low-cost alternative for movement monitoring in aquatic environments. However, intensity-related variables should be interpreted with caution due to the systematic differences observed between systems.

Applied Sciences doi: 10.3390/app16136403

Authors: İbrahim Can Karslı Youssef A. S. A. Hassan Artur İsmatullaev Simge Taşın

Additively manufactured definitive restorative materials have gained popularity recently. The current in vitro study evaluated the effects of surface treatment and thermocycling on the shear bond strength (SBS) between four 3D-printed definitive restorative materials and a self-adhesive resin cement (SARC). Disc-shaped specimens were fabricated from four printable materials: two composite materials, Crowntec (Crowntec) and CRS Composite (Custom Resin Solutions), and two ceramic-filled composites, Alias Dental Crown (Alias) and Permanent Crown (PC) (n = 12 per subgroup). Specimens were divided into four surface treatment subgroups: control, 9% hydrofluoric acid etching (HF), 50 µm aluminum oxide (Al2O3) airborne-particle abrasion (S50), and 110 µm Al2O3 airborne-particle abrasion (S110). SARC was applied using Teflon molds (∅ 3 × 3 mm). After all specimens were stored in water at 37 °C for 24 h, half of the specimens were subjected to 10,000 thermocycles. Subsequently, SBS testing was performed for all specimens. Data were analyzed using Kruskal–Wallis and Mann–Whitney U tests (α = 0.05), and failure modes were classified microscopically. Before thermocycling, compared with control groups, HF significantly decreased SBS in Crowntec, whereas S110 significantly increased SBS in Alias (p < 0.05). After thermocycling, surface-treated CRS and Alias groups showed significantly higher SBS than controls (p < 0.05); Crowntec showed increased SBS after HF and S50, and Permanent Crown only after S50. No significant differences were found among control groups (p > 0.05). Surface-treated groups exhibited mainly mixed and cohesive failures. Surface treatments generally improved the SBS after thermocycling.

Applied Sciences doi: 10.3390/app16136402

Authors: Nikolaos Maniotis Ioanna Kranioti Nikolaos Vordos Michael Maragakis

Magnetic hyperthermia relies on the ability of magnetic nanoparticles (MNPs) to dissipate heat under alternating magnetic fields, with the heating efficiency commonly quantified through the specific loss power (SLP). Accurate estimation of SLP requires realistic modeling of the dynamic magnetic response of nanoparticle ensembles, particularly in the ferromagnetic single-domain regime where hysteresis losses dominate. In the present work, we developed a computational framework in Mathematica based on the thermally activated Stoner–Wohlfarth model to simulate dynamic hysteresis loops and estimate SLP in ensembles of non-interacting magnetic nanoparticles. The model incorporates experimentally relevant distributions of particle diameter, magnetic anisotropy, and easy-axis orientation, enabling realistic representation of nanoparticle polydispersity and orientation disorder. Thermal activation was introduced through Arrhenius-type Néel switching probabilities, while the dynamic magnetization evolution was obtained numerically through solution of the corresponding rate equations. The framework was tested for magnetite nanoparticles, one of the most widely used materials in magnetic hyperthermia, considering typical single-domain ferromagnetic particle sizes in the range of 15–30 nm and effective anisotropy Keff values representative, 3 kJ/m3 < Keff < 20 kJ/m3 of experimentally reported systems. Simulations were performed under clinically relevant alternating magnetic fields with amplitudes up to 24 kA/m and frequencies ranging from 100 to 765 kHz. The model successfully reproduced dynamic hysteresis loop evolution and enabled systematic investigation of the influence of nanoparticle size, anisotropy, and orientation distributions on loop shape, symmetry, and SLP. The developed code provides a computationally accessible tool for researchers working in magnetic hyperthermia, allowing direct connection between microscopic nanoparticle properties and macroscopic heating performance. By enabling parametric mapping of dynamic hysteresis behavior and SLP dependence, the framework may support the rational optimization of magnetic nanoparticle systems for biomedical hyperthermia applications.

Applied Sciences doi: 10.3390/app16136401

Authors: Minghao Ma Chen Liu Yulin Mu Jingqiu Chen Li Zhu

Wearable electrocardiogram (ECG) monitoring enables continuous cardiovascular assessment, yet signals acquired in ambulatory environments are inevitably corrupted by baseline wander, electrode motion artifacts, and muscle interference, which obscure diagnostically critical waveform features. Existing deep learning denoisers rely on heuristic attention mechanisms and time-domain-only losses, lacking principled control over what information the network retains or discards. To address this limitation, we propose EFIB-Net, an information bottleneck-guided multi-resolution network for robust ECG denoising. The framework introduces two complementary components: an efficient frequency-guided attention module that derives temporal attention weights directly from the energy distribution of parallel multi-resolution convolutional branches, requiring only four learnable parameters while providing physically interpretable feature selection that naturally highlights QRS complexes, and a variational information bottleneck constraint at the encoder–decoder bottleneck that forces the latent representation to retain only reconstruction-relevant information and discard noise, guided by a spectral–temporal composite loss. To the best of our knowledge, we are among the first to explicitly introduce the information bottleneck principle into deep-learning-based ECG signal denoising. Experiments on the MIT-BIH Arrhythmia Database show that EFIB-Net outperforms ten traditional and deep learning baselines across four standard metrics—signal-to-noise ratio (SNR), root mean square error, percentage root-mean-square difference, and correlation coefficient; at an input SNR of −5 dB it reaches 8.12 dB output SNR, surpassing the strongest attention-based competitor by 1.77 dB (p<0.01) while using only 0.45 M parameters and 10.8 ms inference latency per segment; downstream evaluation further demonstrates that the denoised signals achieve 99.18% R-peak detection sensitivity and 91.26% heartbeat classification F1-score, both within approximately one percentage point of the clean-signal upper bound, making it practical for real-time cardiac monitoring on resource-constrained wearable devices. Zero-shot cross-database evaluation on the QT Database further confirms generalizability, with only 0.54 dB degradation without retraining.

Applied Sciences doi: 10.3390/app16136399

Authors: Dimos Triantis Ilias Stavrakas Ermioni D. Pasiou Stavros K. Kourkoulis

The rate of change of the normalized Cumulative Counts of the acoustic hits, recorded while marble specimens are compressed uniaxially, and is analyzed in the Natural Time Domain. The analysis reveals the systematic presence of a characteristic plateau that could potentially serve as a precursor to fracture. Starting initially well below unity, the specific parameter increases towards a limit equal to one and is stabilized around this value with minor fluctuations. The starting “instant” of this plateau is linearly related to the loading rate applied. This “instant” and the respective load level are in very good agreement with the abrupt change of the average rate of generation of acoustic signals. These findings are juxtaposed to the respective ones drawn by analyzing data from previously published experimental protocols involving marble specimens of varying geometries subjected to various loading schemes and are found highly consistent with each other. The same holds true for the agreement between the data of the present study and recently published ones dealing with both plain and fiber-reinforced concrete beams. This consistency suggests that the conclusions drawn exhibit a kind of universality, highlighting the potential of this plateau’s onset to serve as a reliable index for Structural Health Monitoring purposes.

Applied Sciences doi: 10.3390/app16136398

Authors: Jiheng Yuan Jian-Yu Li

With the rapid growth of Chinese social media data, many language-driven analytical tasks, such as sentiment analysis and malicious account detection, are increasingly formulated as computationally expensive optimization problems, particularly in the context of hyperparameter tuning for deep learning models. Due to the intrinsic characteristics of Chinese text, including implicit word boundaries, strong context dependency, and high linguistic variability, the resulting feature representations are often high-dimensional, sparse, and heterogeneously distributed. From an optimization perspective, these properties induce highly irregular, non-smooth, and multimodal objective landscapes, posing significant challenges to conventional surrogate-assisted data-driven evolutionary algorithms (DDEAs). To address this problem, this paper proposes a Normal Selection-based data-driven evolutionary algorithm (NSEA) for improving surrogate-assisted optimization under complex conditions. Specifically, a Normal distribution-based selection strategy (NSS) is developed to enable probabilistic selection of surrogate models, balancing exploitation of high-performing models and exploration of alternative candidates, thereby alleviating premature convergence in multimodal search spaces. In addition, an exponential weighting ensemble (EWE) method is introduced to aggregate surrogate models based on their relative ranking performance, which enhances the stability and generalization capability of fitness approximation across different regions of the search space. Extensive experiments on benchmark functions demonstrate that the proposed NSEA consistently outperforms several state-of-the-art DDEAs in terms of optimization accuracy and robustness. Furthermore, a real-world application of cheating official account (COA) detection on Chinese social media is conducted, in which the hyperparameter optimization of a heterogeneous graph transformer (HGT) model is formulated as an EOP. The results further prove the effectiveness and practical applicability of the NSEA in complex data-driven scenarios. Overall, this study provides an effective optimization framework for handling EOPs with complex and multimodal characteristics and offers a feasible computational approach for tasks associated with large-scale Chinese textual data.

Applied Sciences doi: 10.3390/app16136396

Authors: Yiduan Hu Bipin Indurkhya Kaori Fujinami

Behavior change technologies are increasingly deployed in everyday contexts where perception errors are difficult to avoid. Such errors can undermine user trust and long-term engagement, while purely technical approaches to error elimination are often impractical in open-world environments. This study proposes a fault-tolerant design that translates algorithmic uncertainty into anthropomorphic expressions of vulnerability. By decoupling task-outcome feedback from internal confidence states, an embodied agent communicates uncertainty through a five-level nonverbal framework comprising posture, facial expression, and motion intensity. The approach was implemented in an interactive waste-sorting system and examined through a three-week field study in a semi-public university corridor. Three feedback strategies were compared: an outcome-only baseline, a persistently confident agent, and an adaptive agent whose vulnerability expression varied according to a transformed confidence signal. The findings suggest differences in user behavior across conditions. Under the adaptive condition, user sorting accuracy exhibited a fluctuation–recovery pattern during the final deployment phase, whereas accuracy under the confident-agent condition showed a declining trend. Correct-trial stay durations were shorter under the adaptive condition, consistent with the formation of a more streamlined interaction routine. In contrast, observations from error cases were limited by the small number of misclassification events. Due to the exploratory nature of the study, the small sample size of the questionnaire and the sequential deployment structure, the results should be considered preliminary evidence. Nevertheless, the findings suggest that expressing vulnerability in an anthropomorphic way may be a promising approach for communicating uncertainty in behavior change systems.

Applied Sciences doi: 10.3390/app16136397

Authors: Shengzhong Wang Hongjuan Wu Xiangyu Zhang Xiaohui Niu Rui Wang Wei Zhang Chengqin Chen Mohan Zhao

In northwestern China, loess is abundant whereas suitable aggregates for rural pavement bases are scarce, yet the transfer from laboratory stiffness of stabilized loess to field support and cement concrete slab response remains unclear. This study aims to establish an Ei–Et–pavement-response framework for cement–curing agent composite-stabilized loess and loess–sand mixtures. Compaction, unconfined compressive strength and uniaxial compression modulus tests were conducted; Et was calculated using a specification-based method, checked by falling weight deflectometer (FWD) back-calculation, and introduced into a three-dimensional finite element model. Composite stabilization markedly improved base stiffness because cementation, curing agent-assisted bonding, gradation optimization and skeleton–filling effects produced a denser load-bearing structure. After 90 days, the compressive modulus increased from 225 MPa for 6% cement-treated loess to 570 MPa for the 45% loess–sand mixture. The calculated Et agreed well with FWD back-calculated field values (R2 = 0.91). Increasing Et from 98.8 to 155.7 MPa reduced slab-bottom stress from 1.7449 to 1.1095 MPa and vertical displacement from 1.7102 to 0.2654 mm. Field engineers can use Et to select local loess-based base materials and coordinate base and slab thickness, provided that compaction quality, water stability and long-term durability are verified.

Applied Sciences doi: 10.3390/app16136395

Authors: Thiago Vinícius Barros Franciele Pereira Camacho Gabriel Omar Soto Huarca Marcelino Luiz Gimenes José Augusto de Oliveira Ana Caroline Raimundini Aranha Abhijit Data Biplob Pramanik Linhua Fan Veeriah Jegatheesan Lucio Cardozo-Filho

The recycling of spent lithium-ion batteries and selected complex electronic waste fractions is commonly evaluated using isolated metrics such as leaching yield, metal removal efficiency, and reagent consumption. However, this approach fails to address the central challenge of sustainable valorization: integrating upstream conversion with downstream selective recovery without shifting environmental and separation burdens. This review focuses specifically on spent LIBs as the primary model system, while also drawing insights from related e-waste streams (e.g., printed circuit boards and polymer-containing residues) where the interface-driven framework applies. It examines how key interfaces—solid–fluid, polymer–metal–fluid, membrane–solution, electrode–electrolyte, and crystal–solution—govern metal mobilization, selectivity, effluent quality, product purity, and scalability. Emphasis is placed on hydrothermal and supercritical water processing, PVC/CPVC (Polyvinyl Chloride/Chlorinated Polyvinyl Chloride)-assisted metal mobilization and membrane-based recovery techniques, including nanofiltration, membrane distillation, membrane distillation crystallization, ion exchange, and electrochemical methods. Supercritical water and membrane processes are complementary only when upstream chemistry is designed to facilitate downstream separation. PVC-rich waste is reconsidered as a reactive chlorine source, provided that corrosion, HCl formation, and salt precipitation are controlled. Critical gaps include incomplete mass balances, limited multicomponent studies, weak integration between process stages, and scarce techno-economic and life-cycle analyses. A roadmap is proposed for scalable, integrated hydrothermal–membrane systems enabling efficient resource recovery and water reuse.

Applied Sciences doi: 10.3390/app16136394

Authors: Bing Li Feiyang Zhang Linjian Shangguan Deshuo Zhang Huijun Jin

Isolated bridges may suffer serious damage under rare earthquakes, including excessive relative displacement and girder falling. To evaluate the seismic isolation performance of a simply supported curved girder bridge with a friction pendulum bearing (FPB) under rare earthquakes, the mechanical model for the collision function and the pile–soil interaction model are established based on the Kelvin collision model and the M-method, respectively. A nonlinear dynamic numerical simulation model of a seven-span simply supported bridge with FPB was established using SAP2000 v14. Parametric studies were then carried out to investigate the effects of curvature radius and pier height on the dynamic responses of the bridge system. The results indicate that the seismic capacity of a simply supported bridge with FPB will be seriously overestimated if only a unidirectional seismic wave is adopted, which leads to an insufficient seismic design safety margin for the bridge. The smaller curvature radius of the bridge has a more obvious effect on the dynamic response of the bridge, and the influence of curvature radius on the curved segment is significantly greater than that on the straight segment. The pier height has little effect on the dynamic response of the main beam due to the excellent isolation effect of FPB.

Applied Sciences doi: 10.3390/app16136392

Authors: Jakub Konwerski Jarosław Ziółkowski

This article presents a multi-indicator approach to forecasting the monthly workload of a military road freight transport system in support of operational planning. The empirical basis of the study consisted of real-world operational data from 2020 to 2025, aggregated into regular monthly time series. Four complementary workload indicators were analysed: the number of transport tasks, the number of vehicles assigned to task execution, the mass of transported cargo, and transport work expressed in tonne-kilometres. The research procedure comprised data preprocessing, indicator construction, seasonality analysis, time-series decomposition, comparison of classical forecasting models, and assessment of forecast uncertainty using prediction intervals. The forecasting models considered included the naive model, the moving-average model, Brown’s and Holt’s exponential smoothing models, ETS, and ARIMA. Model performance was evaluated using a rolling-origin validation procedure with an expanding training window, based on MAE, RMSE, MAPE, MASE, and Bias metrics. The results showed that the recommended model depends on the forecasted workload dimension: Brown’s model performed best for the number of transport tasks, ETS for the number of vehicles and transport work, whereas the 12-month moving-average model was most effective for transported cargo mass. All recommended models achieved MASE values below 1, indicating improved predictive performance compared with the naive benchmark. The study demonstrated that point forecasts supplemented with 80% and 95% prediction intervals can support monthly planning of fleet resources, transport capacity reserves, and future workload levels. Although the empirical analysis concerns a military transport system operating under peacetime conditions, the proposed framework may be adapted to support monthly workload forecasting and operational planning in other freight transport systems.

Applied Sciences doi: 10.3390/app16136393

Authors: Slawomir Winiarski Dorota Molek-Winiarska

Prolonged sitting is a common occupational exposure among administrative and office workers and is associated with increased postural muscle stiffness, fatigue, and musculoskeletal discomfort. This study aimed to evaluate whether a dedicated physiotherapeutic training programme can reduce biomechanical indicators of muscle overload in sedentary administrative staff. Forty-five female administrative employees were allocated to an intervention group (n = 22) or a control group (n = 23). The intervention group completed a four-week supervised physiotherapeutic programme comprising three 45 min sessions per week, including stretching, strengthening, and sensorimotor exercises targeting postural muscles. Muscle stiffness was assessed using myotonometry, while muscle fatigue was evaluated with surface electromyography based on median frequency slope analysis. The intervention effect was assessed using ANCOVA, with post-intervention values adjusted for corresponding baseline values. The intervention group showed significant reductions in muscle stiffness and fatigue, particularly in the upper trapezius and thoracic erector spinae, with moderate-to-large effect sizes. These findings indicate that targeted physiotherapeutic training can improve neuromuscular function and fatigue resistance in sedentary workers. Incorporating structured physiotherapeutic exercise into workplace health programmes may support musculoskeletal resilience and reduce the biomechanical consequences of prolonged sitting.

Applied Sciences doi: 10.3390/app16136391

Authors: Radosław Winiczenko

Welding technology is crucial to modern manufacturing, construction, and structural engineering [...]

Applied Sciences doi: 10.3390/app16136390

Authors: Manuel Lucio Fernández-Pintado Sergio Martinez Jorge Fabregat

The ceramic industry relies on several energy-intensive processes that currently use natural gas as their primary energy source. In line with the European Union’s objective of achieving carbon neutrality by 2050, alternative energy sources are being explored to replace natural gas. This study evaluates several potential pathways for decarbonizing ceramic production. The alternatives considered include the use of green hydrogen for combustion, the electrification of processes, the combustion of biomethane (produced from biogas), and the deployment of a small modular reactor (SMR), capable of supplying either thermal or electrical energy from nuclear power. A fifth option involves a hybrid approach combining hydrogen and electrification, with each technology applied according to the requirements of the specific process being decarbonized. The results of this study indicate that electrification is currently the most suitable option for immediate implementation. In contrast, SMRs appear to offer the most economically attractive long-term solution, although the technology is still under development, and political, environmental and societal concerns need to be accounted for.

Applied Sciences doi: 10.3390/app16136389

Authors: Ramona Iseppi Carla Sabia

Antimicrobial resistance is increasingly recognized as a major threat to the prevention and treatment of a growing number of infections [...]

Applied Sciences doi: 10.3390/app16136388

Authors: Guangzhao Zhang Ding Wang Mingtao Ji Xuchun Wang Zhengke Wang Jinhua Zhang

Existing component-based models have difficulty interpreting the field response of earth pressure balance (EPB) shield tunneling parameters when the excavation face gradually changes from hard rock to sandy strata. To address this problem, this study proposes a mechanism-informed semi-empirical analysis based on a continuous face sand fraction function, η(z), which represents the evolution of the sand-bearing area fraction at the excavation face along the shield advance direction. The function is constructed from the geological profile and is used as a continuous reformulation of existing face-composition descriptors such as rock/sand ratio and composite ratio. Based on η(z), engineering-equivalent models for total thrust and cutterhead torque are developed and evaluated using field tunneling data from Qingdao Metro Line 15 and Line 5. The Dandan right-line data are used for parameter calibration, while the Basi right-line data from Rings 590–650 are used for independent validation without further parameter tuning. The results show that the η-related correction term improves the independent validation performance of the total thrust model, reducing the MAPE from 22.56% to 14.25%. In contrast, cutterhead torque exhibits stronger operational variability and interval-specific baseline offset. After applying a baseline correction determined from the stable hard-rock section of the Basi interval, the ring-scale torque MAPE decreases from 44.07% to 17.25%, but the additional η-related torque contribution remains limited. These results indicate that total thrust is more sensitive to the gradual increase in face sand fraction, whereas cutterhead torque is more strongly influenced by machine condition, cutter wear, and operational control. The proposed approach provides a field-based semi-empirical reference for interpreting tunneling parameter responses in similar hard-rock-to-sandy-strata transition zones.

Applied Sciences doi: 10.3390/app16136387

Authors: Daniela Pricop Mihaela Racuciu Catalina Radu Gabriel Ababei Dumitru Daniel Herea Simona Dunca Lacramioara Oprica Mirela Nistor Daniel Timpu Silvestru-Bogdanel Munteanu Dorina Creanga

The experiments aimed at the green synthesis of silver nanoparticles (AgNPs) with various reducers like plant extracts and glucose and the evaluation of their antimicrobial efficiency versus their nanotoxicity. Precursor silver ions were reduced with extracts of Coffea arabica leaves, Thuja orientalis cones, and Cirsium arvense roots as well as with glucose. The AgNP microstructural properties were analyzed with transmission electron microscopy and dynamic light scattering that highlighted fine granulation (23 to 28 nm) and electrical stability (Zeta potential of −15 to −25 mV) while optical and spectral investigations like dark-field microscopy, UV-Vis spectroscopy and FTIR proved specific surface properties. Since cytotoxicity is related to the fate of AgNPs in the environment after their uses, we highlighted the presence of chromosomal alterations in the meristematic tissues of maize roots, such as delayed and expelled chromosomes, chromosome bridges, multi-polar anaphases, C-metaphases and others. Silver nanoparticle use in biomedical applications and antimicrobial activity against Gram-positive and Gram-negative pathogens was evidenced by the agar diffusion test, which suggested their usefulness in the case of possible antibiotic-resistant microbial strains with available natural ingredients and at low cost.

Applied Sciences doi: 10.3390/app16136386

Authors: Ştefan Stan Cora Crăciun Vasile Chiș

Accurate ionization energies are essential for understanding the electronic structures of atoms and molecules and benchmarking quantum-chemical methods. We report the calculated ionization energies of the H, C, N, O, F, P, and S atoms using several quantum-chemical approaches, aiming at reproducing the experimental values within chemical accuracy. The methods include the electron propagator approximations OVGF and P3+, the coupled-cluster methods CCSD(T), CCSDT, and IP-EOM-CCSD, and the composite methods G3 and CBS-QB3. The CCSD(T), CCSDT, G3, and CBS-QB3 methods, together with the DFT method with B2PLYP density functional and several post-Hartree–Fock methods, were used in conjunction with the energy-difference approach. The coupled-cluster calculations were combined with the aug-cc-pVXZ-DK, aug-cc-pVXZ, and ANO-RCC basis sets, all-electron correlation, DKH2 scalar relativistic corrections, atomic spin–orbit corrections, and extrapolation to the complete basis set (CBS) limit. The OVGF and P3+ methods do not achieve chemical accuracy on average, while CCSD(T) and CCSDT combined with the aug-cc-pVXZ-DK basis set and CBS extrapolation reach mean absolute errors of 0.0030 and 0.0026 eV, respectively, an order of magnitude below the chemical accuracy threshold. CCSD(T)/aug-cc-pVXZ-DK with CBS extrapolation provides the best compromise between accuracy and computational cost and can be used as a reference atomic benchmark for these ionization energies.

Applied Sciences doi: 10.3390/app16136385

Authors: Ioannis Apostolakis Stelios Xinogalos

Quantum computers are a new generation of computers, whose principles for designing algorithms have already been defined, despite their hardware implementation still being under development. Attracting more scientists to the field is crucial to developing new algorithms in order to make the most of quantum computers once they are available. Serious games can contribute a lot to this effort. This article focuses on the pilot evaluation of the Quantum Escape serious game, which is based on a 3D format for motivating students and a conceptual simplification strategy for making abstract concepts accessible. Specifically, the game design is grounded in the established Four-Dimensional framework, and it aims to transform abstract concepts like superposition and entanglement, as well as common quantum gates, into intuitive puzzles presented through probabilities. The design of Quantum Escape was evaluated by 27 Computer Science students and graduates, in terms of perceived player experience and perceived short-term learning using a questionnaire based on the established MEEGA+ evaluation framework. The design choices of Quantum Escape seem to be validated, since most novice participants found the game understandable and requiring no prior knowledge, while participants with relevant background in quantum computing also largely endorsed the content’s accuracy. Limitations in the extent of quantum computing concepts and the number of participants dictate further development and evaluation of the game.

Applied Sciences doi: 10.3390/app16136384

Authors: Menglong Li Xiangyu Wang Qingwei Wang Jianbiao Bai Guanghui Wang Jiaxin Zhao Shiqi Sun Feiteng Zhang

To address severe stress concentration, excessive convergence, and instability of the roadside backfill body (RBB) in gob-side entry retaining (GER) under thick and hard roof conditions, this study investigates the control mechanism of main roof fracture position on surrounding rock stability, using the 3−101 working face of Huoluowan Coal Mine as a case study. A combined approach integrating theoretical analysis, numerical simulation, and field investigation is adopted. A statically indeterminate mechanical model based on masonry beam theory is established to characterize the lateral roof fracture behavior. The deflection and bending moment distributions are derived, and a criterion for fracture position determination is developed based on the maximum bending moment condition. The theoretical results indicate that the natural fracture position is located approximately 9.4–11.2 m inside the gob boundary. Numerical simulations using UDEC Trigon under different fracture positions (−2 m, 1 m, 5 m, and 9 m) show that fracture location significantly affects the mechanical response of GER. Fractures occurring above the roadway or RBB induce large deformation levels and more extensive plastic zones, while gob-side fracture conditions correspond to relatively lower disturbance levels and improved structural stability. The RBB exhibits shear-dominated failure characteristics, and the displacement distribution is non-uniform along height, with larger deformation in the middle-to-upper region. To improve stability, a coordinated control strategy combining anchor cable reinforcement and directional long-distance hydraulic fracturing (HF) is proposed to regulate the main roof fracture position through the formation of artificial weak planes. Field monitoring results show that the maximum displacements of the roof, floor, and ribs are 558 mm, 233.5 mm, and 71.3 mm, respectively, with a convergence ratio of 19.8%. Borehole imaging confirms the development of hydraulic fractures within the designed roof stratum, supporting the effectiveness of the proposed control approach. These results demonstrate that the fracture position of the main roof plays a key role in controlling GER stability, and its regulation provides an effective means for improving roadway performance under complex geological conditions.

Applied Sciences doi: 10.3390/app16136381

Authors: Mohamed Arayedh Mahmoud Khlifi Ines Benaoun Riadh Ahmadi Noureddine Hamdi

Wellbore instability in clay-rich intervals remains a major drilling challenge, even when the selected fluid density satisfies the conventional pressure window. This study evaluates delayed instability during exposure to a low-salinity water-based drilling fluid using outcrop-derived Aleg and El Haria materials as analogs for clay-rich Tunisian drilling intervals. Mineralogical, chemical, geotechnical, and shear strength data were integrated with a coupled stability analysis to link fluid exposure, pore pressure redistribution, effective stress modification, and hydration-induced strength degradation. The two materials exhibited contrasting hydro-mechanical behavior. El Haria is clay-rich, with 80% total clay mineral content, including 41% smectite and 47% illite/smectite mixed layers, and has a swelling pressure of 2112 kPa. Aleg is more carbonate-influenced, with 66% total clay mineral content, 28% calcite, and a lower swelling pressure of 576 kPa. Freshwater hydration strongly reduced the shear strength envelope; between approximately 15% and 45% water content, cohesion decreased by approximately 91% in Aleg and 70% in El Haria. The stability profiles show that El Haria reached rc/a = 1.10 after 0.3 h and the critical threshold of rc/a = 1.30 after 21.7 h, whereas Aleg remained close to rc/a = 1.03. This defines a practical temporal stability window for planning open-hole exposure during logging, casing, and cementing operations.

Applied Sciences doi: 10.3390/app16136383

Authors: May Youssef Mohamed Saber Islam Shyha Dehong Huo Shaza Elmenshawy

Mandibular angle fractures pose a significant clinical challenge in maxillofacial surgery, and conventional fixation systems often show merely adequate biomechanical performance. This study presents a new mini-plate geometric configuration and outlines its rigorous verification and a preliminary validation procedure. The proposed ‘U’-shaped grade 4 titanium mini-plate with self-tapping screws was developed specifically for the stable fixation of mandibular angle fractures. A three-dimensional mandible model incorporating an angular fracture gap exceeding 1 mm was constructed and analyzed using SolidWorks. Finite Element Analysis (FEA) was employed as a verification tool to evaluate stress, strain, and displacement distributions in the mandibular ramus, plate, and screws under bilateral masticatory muscle loading, with material integrity assessed against yield-strength thresholds using von Mises’ stress theory. Rapid and functional prototypes were subsequently fabricated to physically validate the proposed mini-plate. The maximum stress across the entire model was 446.8 MPa, localized at the middle lower screw, while the maximum stress at the designed plate was 110 MPa, which remains well within the safe limits and is approximately 60.7% lower than the reported maximum stress values for conventional fixation systems. The new mini-plate exhibited robust biomechanical performance, offering a more favorable mechanical environment conducive to bone healing.

Applied Sciences doi: 10.3390/app16136382

Authors: Dovilė Gimžauskaitė Justas Eimontas

Rapid urbanization, the pursuit of a higher quality of life, increasing energy and fuel demands, and remaining dependence on traditional fossil fuels, along with an increasingly pronounced negative impact on the environment, encourage society to seek new, lower-emission solutions. Among them, the biomass-to-biofuels process shows a promising pathway. Biomass, a renewable energy source, can be converted into a variety of biofuels in gas, liquid, or solid forms (e.g., syngas, bio-oil, biochar). There are two primary methods for converting biomass into biofuels: biochemical and thermochemical conversion. The latter is considered more flexible and efficient than biochemical conversion. Thus, this review aims to provide a summary of the most recent results from experimental research on biomass conversion to biofuels using various thermochemical conversion methods. Additionally, this review covers fundamentals and highlights the main evaluation parameters of thermochemical conversion systems, helping readers better understand the results reported by the researchers discussed in this article. This review also briefly addresses prospects and projections for future biofuel production, as well as measures to promote biofuel production.

Applied Sciences doi: 10.3390/app16136380

Authors: Chu Qing Zhao Fang Ling Sun

Plant disease classification in natural scenes remains challenging because disease symptoms are often localized and imaging conditions are complex, including cluttered backgrounds, illumination variations, scale changes, and fine-grained inter-class similarities. To address these challenges, this study proposes FAF-Net, a frequency-aware fusion network with auxiliary supervision for plant disease classification in natural scenes. The proposed framework is built on EfficientNet-B3 and integrates three complementary strategies: CutMix augmentation, an FFT-based frequency branch, and a healthy/diseased auxiliary supervision branch. The RGB branch extracts spatial semantic features from natural-scene images, whereas the frequency branch converts the input image into a log-normalized Fourier magnitude spectrum and learns complementary texture representations. The auxiliary branch provides coarse-grained health-status supervision during training, encouraging the shared representation to capture disease-relevant features. Experiments were conducted on the PlantDoc dataset, which contains 2598 images from 27 healthy and diseased categories. Compared with the EfficientNet-B3 baseline, FAF-Net improved the classification accuracy from 69.49% to 74.58%, corresponding to a gain of 5.09 percentage points. Ablation results further indicate that CutMix, frequency-domain features, and auxiliary supervision provide complementary improvements. These results suggest that frequency-aware feature fusion and coarse-grained auxiliary supervision can enhance plant disease classification under natural-scene conditions.

Applied Sciences doi: 10.3390/app16136379

Authors: Hüseyin Nuri Gülmez Yunus Koç Agah Oktay Ertay Bora Döken Mesut Kartal

We present FilterForge, a chat-driven VS Code environment that pulls the synthesis, analysis, simulation, and optimization stages of microwave bandpass filter design, normally coordinated by hand across tools written in different languages, into one workflow. A deployed Model Context Protocol (MCP) server exposes deterministic Python implementations of coupling-matrix synthesis, uniform predistortion, topology reconfiguration, a genetic-algorithm transmission-zero selector, a mode-matching engine for H-plane iris-coupled rectangular waveguide geometries, and a skill that generates PyAEDT/HFSS notebooks for various dimensioning design-curves. A language-model orchestrator turns natural-language requests into typed tool calls, while every reported quantity stays inside the deterministic kernels, so the numerics remain reproducible and model-agnostic. We evaluate the call layer on a 45-task benchmark across the five tool categories: gemini-3-flash reaches 96.3% tool-selection and 94.8% full-call accuracy with an 88.9% pass3 rate, which an ablation traces to the curated tool-selection prompt rather than to raw model capability. The mode-matching engine is validated against full-wave HFSS on a six-pole 4 GHz Chebyshev filter tuned from the chat panel, and on an 8 GHz WR-112 counterpart taken end-to-end with no engineer in the loop, where a deterministic critique gates each round until a manufacturable geometry is reached. We then exercise the full workflow on two folded six-pole WR-90 cross-coupled filters at 10GHz, a high-selectivity design synthesized against a stop-band mask and a group-delay-equalized variant whose positive cross-coupling uses a pair of side-wall irises, the latter settling to a peak-to-peak in-band group-delay ripple below 1.5ns while recovering the synthesized return loss.

Applied Sciences doi: 10.3390/app16136378

Authors: Nawal Ferroudj Abderrezzaq Benalia Ouiem Baatache Amira Trodi Aya Mokhati Kerroum Derbal Amel Khalfaoui Antonio Pizzi Gennaro Trancone Antonio Panico Antonios N. Papadopoulos

Coagulation–flocculation is widely recognized as a fundamental step in wastewater treatment, as it promotes the aggregation and removal of suspended particles and organic contaminants following the addition of a coagulant. In this study, a bio-based coagulant was prepared from prickly pear (Opuntia ficus-indica) seed residues obtained after essential oil extraction. The extraction process for bioactive agents was successfully modeled using Central Composite Design (CCD)-based Response Surface Methodology (RSM). Optimal extraction was reached at pH 13, PPSM of 7.5 g, 0.75 M NaCl, and 40 min of stirring, providing maximum yields of 69.63 g proteins, 217.075 g total sugars, and 81.416 g polyphenols. The optimized extract was subsequently used as a bio-coagulant for the treatment of wastewater collected from the Chalghoum El Aid–Oued El Athmania wastewater treatment plant (Mila, Algeria). The effects of three operating parameters, initial turbidity, solution pH, and bio-coagulant dosage, on the coagulation–flocculation performance were investigated using a Box–Behnken design (BBD). Process efficiency was evaluated in terms of turbidity, chemical oxygen demand (COD), and organic matter (OM) removal. The raw wastewater exhibited initial values of 200 NTU for turbidity, 640 mg/L for COD, and 25 for organic matter. Statistical analysis revealed that the developed quadratic models were highly significant (p ≤ 0.05) and showed excellent predictive performance, with coefficients of determination (R2 ≥ 0.97). Optimal treatment conditions were identified at pH 7, a bio-coagulant dosage of 1 mL/L, and an initial turbidity of 200 NTU. Under these conditions, removal efficiencies exceeded 98% for turbidity and COD and reached 88.08% for organic matter. Furthermore, Fourier-Transform Infrared (FTIR) Spectroscopy analysis confirmed the presence of functional groups responsible for the coagulation activity of the bio-coagulant. These findings highlight the potential of prickly pear seed residues as an effective, sustainable, and low-cost alternative to conventional chemical coagulants in wastewater treatment.

Applied Sciences doi: 10.3390/app16136377

Authors: Yibo Zhou Chunlei Li Manying Ren

This study compared the effects of variable-resistance compound training versus constant-resistance compound training on lower-limb explosive power in judo athletes, aiming to identify an effective and safe training method. Methods: Sixteen judo athletes were randomized into VRT (n = 8) or RT (n = 8) groups for a 6-week, twice-weekly intervention. Outcomes included rate of force development (RFD), counter movement jump (CMJ), squat jump (SJ), eccentric utilization ratio (EUR), reactive strength index (RSI), squat 1RM, muscle architecture, and Special Judo Fitness Test (SJFT). After 6 weeks of training intervention, the time × group interaction effects were significant between the variable-resistance compound training group and the constant-resistance compound training group in the following parameters: CMJ (p < 0.01, η2 = 0.605), SJ (p < 0.01, η2 = 0.391), EUR (p < 0.01, η2 = 0.308), RSI (p < 0.01, η2 = 0.306), RFD (p < 0.01, η2 = 0.401), squat 1RM (p < 0.01, η2 = 0.328), SJFT index (p < 0.01, η2 = 0.537), femoral rectus feather angle (p < 0.05, η2 = 0.380), femoral rectus thickness (p < 0.05, η2 = 0.288), femoral rectus cross-sectional area (p < 0.01, η2 = 0.868), gastrocnemius feather angle (p < 0.05, η2 = 0.274), and gastrocnemius thickness (p < 0.05, η2 = 0.390). No significant group effects were observed for any of the parameters (p > 0.05). Conclusion: Both variable-resistance training (VRT) and constant-resistance training (CRT) are effective in enhancing lower-body power in judo athletes; both training methods can be regarded as effective options for developing lower-body power in judo athletes, although VRT may offer a slight advantage in specific performance domains.

Applied Sciences doi: 10.3390/app16136374

Authors: Domenico La Torre Attilio Della Torre Prospero Longo Ilaria Rania Emilio De Bartolo Giada Garufi Valeria Garufi Salvatore Massimiliano Cardali

Aim: This study aims to identify the primary differential MRI features between gliosarcoma (GS) and glioblastoma (GBM) based on a comprehensive review of existing literature. Given their close relationship, identifying preoperative neuroradiological markers is essential for distinguishing these two entities. Methods and Analysis: We reviewed studies involving patients with histologically confirmed GS and primary GBM. Our analysis, aligned with the 2021 WHO Classification of Tumors of the Central Nervous System, evaluated specific radiological characteristics in order to determine their diagnostic value in differential diagnosis. Results: While GS is categorized as a variant of GBM, distinct radiological patterns emerged from our literature review. Gliosarcomas are typically larger due to their sarcomatous component and exhibit more intense contrast enhancement and frequent cortical involvement. Notably, hemorrhagic patterns are more prevalent in GS, whereas GS displays less necrosis and a lower incidence of midline-crossing edema compared to GBM. Conversely, glioblastomas more frequently present with cystic components and pial involvements as well as a higher risk of ependymal invasion. Conclusions: Although specific radiological features—such as hemorrhage frequency and tumor size—can strongly suggest GS over GBM diagnosis, no single feature was considered pathognomonic. Among radiological tools, MRI remains the most important for guiding a diagnostic suspicion, but histopathological confirmation remains the gold standard for definitive diagnosis.

Applied Sciences doi: 10.3390/app16136375

Authors: Ruijiang Ran Jun Fang Long Yuan

The Engineering-Procurement-Construction (EPC) general contracting model has emerged as the dominant delivery method for large-scale infrastructure and industrial projects in China. However, contemporary EPC project cost control remains plagued by critical industry challenges, including fragmented cross-stage coordination, pervasive data silos, and the shallow integration of digital technologies into core management processes. This study considers three key stakeholders—government regulators, project owners, and EPC general contractors—and develops a tripartite evolutionary game model to analyze the strategic interactions underlying intelligent cost control in EPC projects. We examine the evolutionary stability of each stakeholder’s strategy selection, explore how various factors influence tripartite strategic choices, and further investigate the stability of equilibrium points in the game system. The key findings are summarized as follows: (1) Strengthening government incentives and penalties simultaneously promotes owners’ investment in intelligent cost control systems and general contractors’ active collaborative cost management. However, excessive incentive intensity undermines the government’s regulatory effectiveness. (2) Establishing a revenue-sharing mechanism for excess cost savings fully stimulates the spontaneous cooperation willingness of owners and general contractors, serving as the cornerstone for market-oriented operation of intelligent cost control. (3) Reducing owners’ intelligent construction investment costs and general contractors’ collaborative control costs effectively addresses practical implementation barriers and accelerates the digital upgrading of engineering cost management. Finally, numerical simulations are performed using MATLAB R2020b to validate theoretical findings.

Applied Sciences doi: 10.3390/app16136376

Authors: Jixing Sun Qianbing Wang Zhao Dong Yide Liu Yanhui Zhang Yuming Huo

Railway transportation networks increasingly share constrained corridors with transmission lines, buried pipelines, and other linear infrastructure. Electromagnetic interference in these corridors is important for safe railway planning and operation, particularly when nearby high-voltage lines cross oil and gas pipelines. This paper investigates transmission-line-induced pipeline potential under crossing conditions in the Zhangbei region. The CDEGS moment-method framework is applied with locally refined segmentation in the crossing regions, and an electromagnetic coupling model for multiple-crossing transmission line-oil and gas pipeline systems is established. The qualitative effects of crossing angle and parallel length on pipeline potential were obtained under both normal operating conditions and single-phase ground fault transient conditions. The results show that induced voltage decreases nonlinearly as the crossing angle increases and rises markedly with crossing length. The contribution of ground potential rise during transient processes to pipeline potential is significantly greater than that during steady-state processes. Installing zinc ribbons as a drainage measure can reduce the pipeline-to-ground voltage. However, supplementary mitigation measures may still be required under severe interference conditions. These findings are relevant to railway transportation because railway corridors often coexist with transmission lines and buried pipelines, making coordinated electromagnetic compatibility assessment essential for infrastructure safety and operational reliability. The proposed framework supports corridor planning, risk assessment, and protective design for railway-related infrastructure in complex shared corridors.

Applied Sciences doi: 10.3390/app16136373

Authors: Zhilin Wang Donghai Su Zhengwen Li

For the trajectory tracking control problem of a 7-DOF redundant hydraulic manipulator, an adaptive fuzzy sliding mode control method based on a novel fast reaching law is proposed. Based on the kinematic analysis of the manipulator, its dynamic equation is constructed using the Lagrange dynamic equation. A trajectory planning method for the manipulator, integrating seventh-order polynomial interpolation and genetic algorithm optimization, is proposed. Taking the planned trajectory as the expected trajectory, a sliding mode controller is designed to achieve trajectory tracking control of the manipulator. A sliding mode disturbance observer is used to observe the system uncertainties, and an adaptive fuzzy logic system is designed to estimate the observation error of the disturbance observer. The sliding mode control law is deduced based on the fast reaching law, which can achieve global fast convergence of the sliding mode function while reducing the chattering of the controller. The simulation results show that the proposed control method has good tracking performance and strong robustness.

Applied Sciences doi: 10.3390/app16136372

Authors: Zhu Yang Germán Mazza Maarten Vanierschot Renaud Ansart Yimin Deng

Gas–solid bubbling fluidized beds involving Geldart Group B particles are fundamental to numerous industrial thermochemical processes, where bubble dynamics dictate the efficiency of heat and mass transfer. However, accurately predicting these complex hydrodynamic behaviors remains a challenge due to the non-linear coupling of phase interactions. This study presents a comprehensive validation of 2D and 3D Eulerian–Eulerian Two-Fluid Models (TFM) against an extensive experimental dataset. A ‘core-flow’ consistency principle is adopted, demonstrating that the 3D cylindrical simulation provides a physically equivalent representation of the central bubbling dynamics in the rectangular experimental bed. A key innovation of this work is a novel post-processing framework that bridges raw CFD datasets and quantitative bubbling metrics. Unlike traditional threshold-based segmentation or localized probe measurements, which are often limited by spatial resolution and noise sensitivity, the integrated use of Autodesk 3DS Max for volumetric reconstruction and customized MATLAB (R2024a) algorithms allows for the seamless processing of heterogeneous 2D and 3D data. This methodology significantly enhances the capability to track complex bubble coalescence and breakup events while improving batch-processing efficiency, providing a high-fidelity alternative for analyzing gas–-solid flow patterns in complex geometries. The results show that both experimental data and 2D simulations align with Werther’s correlation, yielding Mean Relative Errors (MRE) of 8.2% and 10.5%, respectively. In contrast, the 3D simulation tracks Darton’s prediction closely with a lower MRE of 7.4%, demonstrating superior concordance in volumetric bubble growth. The core innovation lies in the definition of a clear dimensional choice framework: 2D simulations are computationally sufficient and accurate for predicting macro-scale bubble heights and frequencies under pseudo-2D or narrow-bed constraints. However, 3D simulations are strictly necessary when evaluating unconstrained radial expansion, core-flow dynamics, and precise volumetric bubble diameters (dv) where full multi-directional degrees of freedom dictate hydrodynamics.

Applied Sciences doi: 10.3390/app16136371

Authors: Dong Xia Yu Ding Jie Xu

With the rapid growth of urban motorization, personalized travel modes, including taxis and private cars, have expanded considerably. However, conventional public transportation systems, constrained by fixed routes and limited service flexibility, often struggle to satisfy residents’ increasingly diversified and high-quality commuting needs. To address this issue, this study proposes an integrated planning framework for customized bus services using taxi trajectory data. First, passenger origin–destination (OD) information is extracted by detecting changes in the taxi passenger-status field. The extracted OD records are then used to identify potential commuting demand by jointly considering peak-hour travel characteristics and regional OD stability. Second, the identified potential commuting demand is used to generate candidate boarding and alighting stops through an improved DBSCAN-based clustering method, namely IDK-SG. For route planning among the candidate stops, a bi-objective optimization model is developed to simultaneously account for passenger travel-time costs and bus operating costs, and the model is solved using a genetic algorithm. Finally, timetable optimization is formulated as a Markov decision process and solved using a Deep Q-Network (DQN) algorithm. Case studies using taxi GPS trajectory data from Chongqing demonstrate that the proposed framework can effectively identify stable commuting demand, optimize stop layouts and route schemes, and improve vehicle occupancy and service quality. These findings provide practical decision-making support for the operation and dynamic scheduling of customized bus services in urban peak-hour commuting corridors.

Applied Sciences doi: 10.3390/app16136370

Authors: Renata Retkute Christopher A. Gilligan

Cassava is a critical staple crop for food security and rural livelihoods in Sub-Saharan Africa, yet high-resolution maps of its distribution remain scarce, particularly for smallholder systems. In this study, we generated a 10 m resolution cassava presence map for Uganda (CM24) by fine-tuning a Random Forest classifier on TESSERA foundation model embeddings derived from Sentinel-1 and Sentinel-2 time series. Using field survey data from the Copernicus4GEOGLAM campaign for training and validation, the model achieved excellent discriminative ability (validation AUC = 0.9532, test AUC = 0.9524). Visual validation against high-resolution satellite imagery confirmed good spatial agreement, capturing both large contiguous fields and small fragmented plots. Comparison with two existing global products (CassavaMap and SPAM2020) and two seasons of national survey data conducted by the Uganda Bureau of Statistics showed that CM24 produced national harvested area estimates that fell between the two survey totals, whereas CassavaMap and SPAM2020 systematically overestimated harvested area by factors of two to three. Our results demonstrate that foundation-model embeddings offer a robust and scalable approach for mapping cassava in heterogeneous smallholder landscapes. The resulting CM24 map provides a spatially explicit tool to support disease surveillance, agricultural monitoring, and food security planning in Uganda and beyond.

Applied Sciences doi: 10.3390/app16136368

Authors: Vikas Kumar Rathore Michael Evzelman Mor Mordechai Peretz

A coupling coefficient optimization procedure for a boost-extender topology converter with coupled inductors is presented. The method focuses on minimizing inductor current ripple to reduce losses and improve magnetic core utilization, enabling a potential increase in converter power density. The major innovation of this study is the theoretical formulation of the coupling coefficient and leakage inductance in the UU/UI cores. The study includes a theoretical loss analysis covering two dominant loss mechanisms of the topology. A magnetic core structure enabling current ripple cancelation is presented and analyzed using a reluctance model. To address the analytically intractable field distribution at the core center tap, an empirical leakage model is introduced. The model is validated through ANSYS Maxwell simulations 2023.R1 and corroborated by experimental measurements. The results demonstrate that appropriate coupling coefficient selection significantly suppresses current ripple and enables a more efficient and compact converter design. Simulation and experimental results on experimental prototype of 100 W running at 100 kHz and attaining voltage gain of 20 V to 200 V confirm the accuracy of the models and the effectiveness of the optimization procedures.

Applied Sciences doi: 10.3390/app16136369

Authors: Enze Zhou Lei Wang Xincheng Quan Daochun Huang Shiyan Lin Chao Chen Tianhao Peng Haiwen Xu

In wildfire environments, high temperatures generated by wildfires may cause thermal aging, deformation, and even burning damage to the silicone rubber sheds of composite insulators, thereby deteriorating their surface hydrophobicity and insulation characteristics. Meanwhile, ash and carbonaceous particles produced by vegetation combustion tend to accumulate on insulator surfaces, forming conductive contamination layers that reduce surface resistance, intensify leakage current activity, and increase the risk of flashover. To investigate these effects, FXBW4-35/70 composite insulators were selected as the research object. A simulated burning test platform was established to evaluate variations in the mechanical properties of insulator sheds under wildfire conditions. In addition, the feasibility of using simulated ash was assessed. AC flashover tests were conducted on contaminated insulators to quantify the influence of ash deposition on flashover performance. Beyond confirming the thermal aging behavior of silicone rubber under wildfire exposure, this study establishes a quantitative relationship between wildfire ash deposition, equivalent contamination severity, and flashover performance. A correction model for post-fire pollution withstand voltage is further proposed, providing a practical basis for condition assessment and maintenance of transmission line insulators after wildfire events.

Applied Sciences doi: 10.3390/app16136367

Authors: Leilei Chen Zirui Wu Zhijian Li Qingsong Hu Tianli Ma Jun Li

To clarify the spreading law of river crab pellet feed in a centrifugal spreading mechanism and provide a physical basis for the path planning of automatic feeding boats, this study took 4.0 mm sinking extruded river crab feed as the research object. A systematic research method combining physical experiments and Discrete Element Method (DEM) simulation was established. Physical experiments were conducted to calibrate the intrinsic parameters (density, Poisson’s ratio, elastic modulus) and contact parameters (friction coefficients and restitution coefficients between feed and 304 stainless steel/ABS plastic, as well as between feed particles) of the pellet feed. On this basis, a DEM simulation model of a vibration blanking-dual disc centrifugal spreading mechanism was constructed using the multi-sphere aggregation method and the Hertz-Mindlin (no-slip) contact model. A Central Composite Design (CCD) response surface experiment was employed to investigate the spreading law, with boat speed (0.5–1.5 m/s) and spreading disc rotation speed (800–1000 rpm) as independent variables, and unilateral spreading width (W), track superposition uniformity (ω), and transverse coefficient of variation (Cv) as response indicators to characterize spreading range and particle distribution. The results showed that the spreading disc rotation speed had an extremely significant effect (p < 0.0001) on all three response indicators, while boat speed had no significant effect. The feed exhibited a characteristic double fan-shaped superposition distribution pattern. Through multi-objective optimization, the optimal operational parameters were determined as a boat speed of 1.0 m/s and a spreading disc rotation speed of 879 rpm, yielding a unilateral spreading width of 2.9 m, a track superposition uniformity of 88.31%, and a transverse coefficient of variation of 8.33%. This study establishes a quantitative method for analyzing feed spreading characteristics and clarifies the spreading range and particle distribution law, providing a reliable physical basis for full-coverage path planning of crab pond feeding boats.

Applied Sciences doi: 10.3390/app16136366

Authors: Marija Krstić Dragan Soleša Lazar Krstić

Visual programming using blocks and diagrams facilitates understanding of fundamental programming concepts, which is particularly important for children with special educational needs because it reduces their cognitive load and encourages interactive learning. This study aimed to develop and apply a hybrid multi-criteria framework to evaluate, rank, and select visual programming software solutions intended for children with special educational needs. Based on an analysis of the educational context and the target population’s needs, a set of criteria was defined to evaluate and select the most suitable software solution. Data for the analysis were collected using a structured questionnaire, from which a decision matrix was developed. Within the proposed hybrid multi-criteria decision-making (MCDM) framework, criterion weights were determined using the Rank Order Centroid (ROC) method, and the ranking of alternatives was performed using the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE II). Additionally, a sensitivity analysis was conducted to assess the stability and robustness of the obtained rankings in relation to changes in the criterion weights. The results indicate a stable ranking of alternatives and the identification of the most favorable solution in the majority of scenarios. The projection quality of 91.1% in the Geometrical Analysis for Interactive Aid (GAIA) plane confirmed the reliability of the visual interpretation of the results. The proposed framework improves the decision-making process and provides a foundation for further research in educational software evaluation.

Applied Sciences doi: 10.3390/app16136365

Authors: Aleksandra Radziejowska Anna Sobotka

The article presents selected problems related to the modernization of large-panel buildings in the context of the requirements arising from the EU EPBD (Energy Performance of Buildings Directive). The study has an exploratory character and is based on qualitative case study analyses of selected large-panel residential buildings representing different prefabrication systems and modernization conditions. The characteristic features of prefabricated buildings are outlined, and the main modernization barriers are identified, including structural limitations, insufficient thermal performance of building envelopes, outdated technical systems, and organizational and legal challenges resulting from ownership structures. Particular attention is given to the EPBD requirements concerning energy efficiency improvement, CO2 emission reduction, and the implementation of the zero-emission building (ZEB) standard. The analysis indicates that the modernization of large-panel buildings requires a systemic approach integrating technical, economic, and organizational measures. The importance of comprehensive thermal retrofitting and the integration of renewable energy sources is emphasized. The findings also suggest that digital tools such as BIM (Building Information Modeling) may support modernization planning and building information management. The conclusions of the article indicate that the effective implementation of the EPBD provisions for large-panel buildings will only be possible with simultaneous systemic support, including financial and regulatory instruments, as well as the development of technical and organizational competencies within the construction sector.

Applied Sciences doi: 10.3390/app16136364

Authors: Ben Li Hui Li

Particle-laden flow through rough confined fractures is controlled by the coupled evolution of particle packing, load-bearing contacts, and rough-wall flow channels. In this study, conductivity experiments were performed on rough split-core fractures prepared from downhole tight-sandstone cores from the Tarim Basin, China, to examine how proppant size mixing and placement sequence regulate flow capacity under closure. Single-size 40/70 and 70/140 proppants and mixed-size systems with different size ratios were tested under staged and uniformly mixed placement schemes. Two equivalent placement levels, denoted as 1 mm and 2 mm, were considered. Three-dimensional laser scanning before and after conductivity testing was used to quantify rough-wall morphology using Ra, Rq, and Rz. The results show that fracture conductivity decreased with increasing closure pressure for all particle systems, indicating progressive narrowing and rearrangement of preferential flow channels. Coarse-particle-dominated systems consistently retained higher conductivity, with an overall ranking of 40/70 > 3:1 > 1:1 > 1:3 > 70/140 at both placement levels. Increasing the placement level from 1 mm to 2 mm markedly enhanced conductivity, especially for systems rich in 40/70 proppant. Staged placement yielded higher conductivity than uniformly mixed placement for the 3:1 and 1:1 systems, but this effect was negligible for the fine-particle-dominated 1:3 system. Post-test roughness changes indicate that sparse placement induced competing smoothing and roughening, whereas sufficient placement caused systematic roughening after closure. The proposed morphology-aware experimental workflow provides a laboratory-scale basis for interpreting the coupled evolution of fracture conductivity and rough-wall morphology in propped rough fractures. Although the workflow can be extended to other lithologies and fracture systems, quantitative field-scale prediction requires further calibration with larger datasets and reservoir-specific conditions.

Applied Sciences doi: 10.3390/app16136363

Authors: Kai Kwong Lai Man Lok Chong Pak Wai Chan

The performance of FastEddy, a GPU-based large eddy simulation model, in simulating building-induced turbulent flow in an operating airport is studied for the first time through four examples, including a super typhoon case at Hong Kong International Airport (HKIA) and a real case of low-level wind effect. The simulation results are quantitatively compared with wind observations from Light Detection and Ranging (LIDAR) systems for selected cases, and with aircraft data and pilot reports in one example of low-level wind effect. The FastEddy model is found to perform reasonably well through these case studies, even for the radial component of the winds exceeding 20 m/s in a highly turbulent airflow simulation of a typhoon, as well as turbulent airflow features in a building complex at and around HKIA. The building-induced turbulent flow as observed by the LIDARs and the aircraft are largely reproduced. The scatter plots of the model-simulated and the observed Doppler velocities have good correlation in terms of the slope of the best-fit linear equation, correlation coefficient and root-mean-square difference. Moreover, for the case of low-level wind effect, FastEddy simulation is found to provide useful insight into the turbulent flow arising from the new terminal building over the northeastern part of HKIA (near 22.325° N 113.918° E) under construction. Further research directions for studying the performance of FastEddy are also discussed, such as considering more complex urban environments, comparison with in situ measurements of anemometers, and direct output of the eddy dissipation rate (EDR) from the model for comparing with LIDAR and anemometer-based measurements.

Applied Sciences doi: 10.3390/app16136362

Authors: Jiarong Shen Pengxiang Qin Yunke Wang

The interfacial transition zone (ITZ) in the aggregate periphery is often regarded as the weakest area in concrete. In this study, the results of extensive image analysis provide a new insight. First, fluorescence microscopy (FM) was adopted to obtain the microstructure of “complete ITZ”, which overcomes several limitations of the scanning electron microscope method. Then, an ITZ recognition interactive algorithm was proposed, which quantitatively characterizes the pore distributions in the ITZ and cement paste in both lateral and longitudinal directions. Finally, based on the experimental and statistical results, the pore distributions around aggregates, coarse sand and fine sand were characterized. Along the lateral direction, a high non-uniformity was observed in the porosity between units, taken at the same distance from the aggregate/sand surface. On the contrary, along the longitudinal direction, statistical results show minimal increases in the porosity within the ITZ. This is also applicable even in the innermost ITZs.

Applied Sciences doi: 10.3390/app16136361

Authors: Ying Huang Huaixin Chen Zhixi Wang

As a next-generation automotive display technology, Augmented Reality Head-Up Display (AR-HUD) has demonstrated immense potential in reshaping driving safety and enhancing the human–computer interaction experience. To address the challenges of barrel distortion and perspective distortion inherent in HUD systems, we propose a joint-learning-based dual-path pre-correction method. This approach employs a shared encoder to extract image features, which are then decoupled into two parallel branches: a classification branch and a distortion flow prediction branch. Building upon this architecture, a model-fitting method is introduced to estimate the distortion model parameters in the parameter space using the predicted distortion types and flows, thereby reconstructing a refined distortion flow. Finally, image rectification is achieved through a resampling method. On the ARHDD dataset, the proposed method achieves a PSNR of 24.617 dB (barrel) and 25.062 dB (perspective), an SSIM of 0.845 and 0.873, and an NRMSE of 0.163 and 0.157, respectively. On the Places 365 dataset, it achieves a PSNR of 23.914 dB (barrel) and 21.870 dB (perspective), an SSIM of 0.812 and 0.748, and an NRMSE of 0.174 and 0.211, respectively. Both quantitative and qualitative comparative experiments against other state-of-the-art methods demonstrate that the proposed approach achieves superior correction performance for both types of distortion. Finally, the simulation verification of the HUD system proved that this correction method demonstrated excellent potential, but further verification is still needed in a real or semi-real environment.

Applied Sciences doi: 10.3390/app16136358

Authors: Sabina Saccomanno Lorenzo Ederli Silenzi Claudia Ciocia Francesca Calò Francesco Inchingolo Angelo Michele Inchingolo Grazia Marinelli Jorida Jubani Francesca Russo Alessio Danilo Inchingolo Gianna Dipalma

Background: Rapid palatal expansion (RPE) is the gold standard for maxillary deficiency in growing patients. Conventional soldered expanders often present challenges in adaptation, chairside procedures and bond stability. This study compares traditional RPEs with customized CAD (Computer Aided Design)/CAM (Computer Aided Manufacturing) expanders regarding clinical efficiency and patient experience. Methods: Thirty growing patients (mean age: 9.2 ± 1.1 years) were allocated to two groups: traditional RPEs (n = 15) and customized CAD/CAM RPEs (n = 15). Outcomes included bond failures, chairside time (from initial try into cementation) and short-term patient-reported discomfort via a 10-point Visual Analogue Scale (VAS) 24 h after appliance placement. Significance was set at p < 0.05. Results: The CAD/CAM group showed significantly fewer bond failures (0.2 ± 0.4 vs. 1.1 ± 0.8; p < 0.05) and shorter chairside time (12.4 ± 2.1 min vs. 24.6 ± 3.8 min; p < 0.001). Patient discomfort was also significantly lower in the CAD/CAM group (VAS: 4.1 ± 1.0 vs. 6.3 ± 1.2; p < 0.05). Conclusions: Within the limitations of this pilot exploratory non-randomized study, customized CAD/CAM RPEs were associated with fewer bond failures, shorter chairside application time, and lower short-term discomfort at 24 h compared with traditional appliances. These preliminary findings should be interpreted with caution and confirmed by larger randomized controlled studies.

Applied Sciences doi: 10.3390/app16136360

Authors: Roberto Romaniello

The global agro-food sector is navigating a pivotal transition characterized by the integration of Agro-Food 4 [...]

Applied Sciences doi: 10.3390/app16136359

Authors: Yicheng Gao Guofu Feng

Diagnosing tilapia diseases in complex aquaculture environments is severely hindered by noisy backgrounds and limited high-quality pathological data. To overcome these bottlenecks, this study presents a two-stage diagnostic framework integrating an enhanced Segment Anything Model (SAM) with an Adaptive Neuro-Fuzzy Inference System (ANFIS). In the first stage, SAM is augmented with a Convolutional Block Attention Module (CBAM) feature adapter and a Region Proposal Network (RPN)-based prompt encoder. This design enables the automated and precise extraction of irregular disease lesions by self-generating spatial prompts, thereby isolating water background noise. In the second stage, clinical color features extracted from the lesion masks are classified using ANFIS. To optimize performance on small-scale datasets, ANFIS parameters are trained via Particle Swarm Optimization (PSO) under a numerically stable One-vs-Rest (OvR) binary cross-entropy loss. Validated on the public dataset “Enhancing Disease Detection in Nile Tilapia”, our method delivers an average segmentation Dice coefficient of 86.2% and a classification accuracy of 93.5%. This hybrid approach demonstrates strong potential as a foundational baseline for the automated monitoring of aquaculture diseases.

Applied Sciences doi: 10.3390/app16136357

Authors: Tianshu Chen Alexander Herzog Talita Schlichting Tran Quoc Khanh

The stroboscopic effect and phantom array effect caused by temporal light modulation (TLM) in light-emitting diode (LED) lighting are important temporal light artifacts (TLAs) that can influence visual perception, task performance, and visual comfort. This review systematically analyzes 40 studies published between 1998 and 2024 to provide a comprehensive overview of the current understanding of both effects. The reviewed literature covers visibility thresholds, influencing parameters, experimental methodologies, and assessment metrics. The analysis shows that reported visibility thresholds for the stroboscopic effect typically range from 550 to 1000 Hz, whereas thresholds for the phantom array effect may extend to 10–15 kHz, suggesting substantial differences in the underlying perceptual mechanisms. In addition to modulation frequency, modulation depth, waveform, duty cycle, luminance, retinal image motion, and observer factors have been identified as important determinants of visibility. The review further highlights significant methodological differences among studies, including variations in experimental design, stimulus generation, participant characteristics, and psychophysical procedures. Although the stroboscopic visibility measure (SVM) provides a standardized framework for evaluating the stroboscopic effect, no comparably validated metric is currently available for the phantom array effect. The review identifies major knowledge gaps regarding the interaction of influencing parameters and the lack of standardized assessment methods. Future research should focus on establishing unified experimental protocols and developing robust metrics for the phantom array effect to support comprehensive lighting standards that protect visual comfort, well-being, and consumer health.

Applied Sciences doi: 10.3390/app16136356

Authors: Jianchun Xu Yanxu Liu Baodi Wang Xuanjie Zhang Yanan Zhang Xin Wang

The Jiaduoling area is located in the northern segment of the Southwest Sanjiang Metallogenic Belt, a region characterized by complex geological structures and abundant mineral resources. This study systematically identifies the spatial correlation between subsurface magnetic bodies and tectonic structures by utilizing 1:50,000 high-precision aeromagnetic data. Advanced processing techniques—including upward continuation, vertical derivatives, total gradient modulus, and Euler deconvolution—were integrated to refine the structural framework and clarify the mechanisms of fault-controlled mineralization. The results indicate that the aeromagnetic anomaly pattern is predominantly governed by NW-trending faults. Specifically, the deep-seated major fault F1 (with a calculated depth exceeding 3 km) served as the primary migration channel for ore-forming fluids, while secondary faults created localized ore-hosting spaces. Physical property analysis reveals a significant magnetic contrast, where Mesozoic intermediate-acid magmatic rocks act as the essential source for mineralization, providing both material and thermal energy for the formation of porphyrite-type iron deposits. Based on these findings, a three-dimensional “aeromagnetic anomaly-structural framework-mineralization” correlation model was established. Finally, two high-potential metallogenic prospective zones (P1 and P2) were delineated, providing precise geophysical evidence and strategic guidance for regional mineral exploration and the targeting of concealed ore bodies.

Applied Sciences doi: 10.3390/app16136355

Authors: Samuel Durán-Agüero João P. M. Lima

Food systems are currently at the center of some of the most pressing global challenges, including climate change, food insecurity, malnutrition, biodiversity loss, and the growing burden of non-communicable diseases [...]

Applied Sciences doi: 10.3390/app16136354

Authors: Shengtao Wang Han Du Jiafeng Zhang

Efficient task–channel allocation in satellite communication networks becomes particularly challenging when tasks are subject to both time and frequency constraints, and when resource failures or environmental changes invalidate an initially feasible allocation. Existing studies often treat static allocation and dynamic adaptation separately, lacking a unified framework that ensures both a low resource fragmentation rate and low reconfiguration cost. This paper proposes a two-stage approach that integrates static task–channel allocation with dynamic reconfiguration. In the static stage, a greedy algorithm is developed to assign tasks to channels under time-window, bandwidth, and conflict-free constraints, aiming to achieve as low a resource fragmentation rate as possible within the heuristic search. When channel failures occur, a heuristic search-based reconfiguration algorithm is proposed to generate a sequence of reconfiguration events that transitions the initial static allocation strategy step by step to a feasible target static allocation strategy, while maintaining constraint satisfaction and an acceptable resource fragmentation rate throughout the process. Comparative experiments on both small-scale and large-scale datasets demonstrate that the unified framework effectively balances allocation quality, low-cost and compact dynamic reconfiguration, and adaptability in dynamic network environments.

Applied Sciences doi: 10.3390/app16136351

Authors: Zhenao Sun Weidong Wang Jiawei Ma Chuang Huang Guanfei Li Junchi Ma

Renewable energy is being increasingly integrated into power grids. As a result, the three-phase grid-connected inverter (GCI) faces power transfer limitations caused by small-signal stability issues. To improve energy utilization and enhance stability, this paper employs an impedance-based method to analyze the small-signal stability power boundary of the GCI. This boundary is then quantified using the generalized Nyquist criterion (GNC). Our analysis reveals that the power boundary decreases as the grid short-circuit ratio (SCR) decreases or the phase-locked loop (PLL) bandwidth increases. To address this problem, we propose an impedance reshaping method that cancels the negative resistance effect introduced by PLL feedforward. This approach raises the small-signal stability power limit to the rated power and ensures stable operation under grid impedance variations and high PLL bandwidth. Finally, impedance analysis and experimental verification confirm both the theoretical correctness and the practical effectiveness of the proposed method in extending the stability power boundary.

Applied Sciences doi: 10.3390/app16136353

Authors: Fatima L. Parada-Vargas Mercedes Salazar-Hernández Alfonso Talavera-López Oscar Joaquin Solis-Marcial Alba N. Ardila Arias Rosa Hernández-Soto Jose A. Hernández

The discharge of metal-containing effluents into aquatic systems remains a major environmental concern because metal ions can persist in water bodies and accumulate in biological systems, potentially affecting ecosystem and human health. Among these contaminants, Cr(III) is frequently encountered in waste streams generated by industrial activities, making its removal an important objective in water quality management. This study investigated the adsorption behavior of Cr(III) using lignocellulosic biosorbents obtained from tamarind shell (Tamarindus indica) after water, H2O2, and HCl pretreatments, with particular emphasis on equilibrium behavior, thermodynamic characteristics, and pretreatment-induced physicochemical modifications. Batch adsorption experiments were conducted to evaluate equilibrium behavior. The highest adsorption capacity (41.6 mg g−1) was obtained with the water-treated biosorbent at 60 °C. The equilibrium data were best represented by the Sips model, suggesting that Cr(III) adsorption occurred on surfaces containing adsorption sites with different energetic characteristics. Thermodynamic analysis revealed that the adsorption process was spontaneous, while the enthalpy changes indicated predominantly endothermic behavior for the pretreated biosorbents. ATR-FTIR, SEM, EDS, and XRD analyses were performed to characterize the biosorbents before and after adsorption. The characterization results indicated that oxygen-containing functional groups, particularly hydroxyl and carbonyl functionalities, were associated with the adsorption process. SEM images showed morphological changes associated with pore occupation, while EDS confirmed chromium adsorption and suggested possible ion-exchange mechanisms. XRD patterns indicated a mainly amorphous structure. The results demonstrated that pretreatment-induced modifications strongly influenced the adsorption performance of tamarind shell. Water pretreatment produced the most favorable adsorption behavior, yielding the highest adsorption capacity among the evaluated biosorbents. The combined interpretation of equilibrium, thermodynamic, and characterization results revealed a close relationship between surface properties and Cr(III) uptake.

Applied Sciences doi: 10.3390/app16136352

Authors: Junhong Lian Baolin Li Zhonghui Li Xiong Cao Xiayan Zhang Yiping Liu Nan Liang Meng Zhang Xuelong Li

Fractures are widely developed in deep coal-mine surrounding rocks. They weaken the load-bearing capacity and energy-storage capacity of rock specimens, which may induce surrounding-rock deformation, roof collapse, and other hazards. Current studies on fractured rock masses mainly focus on a single parameter, such as fracture number or fracture dip angle. However, their coupled effects remain unclear. Integrated analyses of mechanical behavior, crack propagation, and energy evolution are also limited. In this study, uniaxial compression simulations of intact sandstone, single-fracture sandstone, and double-fracture sandstone were conducted using PFC2D. The effects of fracture number and fracture dip angle on mechanical properties and failure characteristics were investigated. The results show that fractures reduced the peak stress and modulus of elasticity. A stronger weakening effect was observed with increasing fracture number. With increasing fracture dip angle, both peak stress and modulus of elasticity showed a V-shaped trend. The minimum peak stress occurred at 15°, while the minimum modulus of elasticity occurred at 45°. Sandstone failure was mainly dominated by tensile cracks. At 15°, the total crack number was the lowest, with 932 and 818 cracks for single-fracture and double-fracture specimens, respectively. Energy analysis showed that increasing fracture number reduced elastic strain energy and promoted dissipated energy. The weakest energy-storage capacity was observed at 30°. Overall, fracture number and fracture dip angle jointly controlled strength degradation, crack propagation, and energy evolution. This study provides a reference for fracture–damage assessment and disaster prevention in deep coal-bearing sandstone.

Applied Sciences doi: 10.3390/app16136350

Authors: Chunming Liu Shengqi Tian Hangting Du Jingdao Xu Weijian Zhou

Artificial-source frequency-domain electromagnetic methods are important tools for deep mineral exploration and concealed geological structure detection. Apparent resistivity is a key parameter linking measured electromagnetic fields to the interpretation of subsurface electrical structures, and its calculation method directly affects geological interpretation and engineering applicability. Although substantial efforts have been devoted to the theoretical development, data processing, and practical application of different apparent resistivity formulations, most previous studies have focused on the analysis and improvement of a single method. Systematic comparisons of the main apparent resistivity formulations under unified conditions remain limited, particularly in terms of deep basement characterization, anti-interference performance, and engineering applicability. To fill this gap, this study systematically compares the E−Ex wide-field apparent resistivity, the E−Ex far-zone apparent resistivity, and the E−Zxy Cagniard apparent resistivity. Through theoretical derivation, forward modeling of typical one-dimensional models, and field verification, the differences among these three formulations in geological characterization, anti-interference capability, and engineering applicability are analyzed, with the aim of clarifying their applicable boundaries and selection principles for artificial-source frequency-domain electromagnetic exploration.

Applied Sciences doi: 10.3390/app16136349

Authors: Agnieszka Lewińska Marta Domżał-Kędzia Katarzyna Wiercigroch-Walkosz Błażej Poźniak Krzysztof Bojanowski

The growing demand for effective delivery of active ingredients in cosmetic formulations has stimulated the development of advanced carrier systems. This study evaluates the potential of the dicephalic di-N-oxide surfactant N,N-bis [3,3-(dimethylamino)-propyl]dodecylamide (C12-(DAPANO)2) as a stabilizer for aqueous dispersions of solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs). Lipid nanoparticles were prepared using three classes of solid lipids—cetyl palmitate, glyceryl behenate, and stearic acid—through high-speed homogenization followed by ultrasonication. Their physicochemical properties were characterized using DLS, TEM, AFM, DSC, and TGA. All formulations exhibited particle sizes below 300 nm and a low polydispersity index (<0.30), indicating good uniformity. High absolute zeta potential values and stability studies confirmed excellent physical stability, with all dispersions remaining stable for at least 90 days at room temperature. Compared with bulk lipids, nanoparticles showed lower melting temperatures and reduced crystallinity. NLCs exhibited lower crystallization and melting temperatures than SLNs and displayed a more spherical morphology. Cytotoxicity assessment using J774.E macrophages revealed no adverse effects. These findings highlight the surfactant’s potential as a stabilizing agent for lipid-based cosmetic nanocarriers, supporting the development of stable systems with improved active ingredient loading and controlled release properties.

Applied Sciences doi: 10.3390/app16136348

Authors: Qinyi Xiao Xingbao Lyu Yiqun Ma Guijiang Liu Chengxun Yuan Jingfeng Yao Zhongxiang Zhou

Grain moisture content is a key variable for safe storage, drying control, and quality management. Microwave sensing is attractive because water strongly modulates the complex relative permittivity (ε* = ε′ – jε″) of granular agricultural products, thereby shaping broadband scattering-parameter spectra. This study presents a meter-referenced feasibility evaluation of an interpretable S-parameter–permittivity–moisture chain using a vector network analyzer over 2–18 GHz. Wheat, maize, and mung bean were prepared at six moisture levels, and the moisture values were referenced to two commercial grain moisture meters (MC_ref) to represent rapid on-site benchmarking rather than absolute gravimetric moisture determination. Therefore, the reported errors should be interpreted as commercial-meter-referenced calibration indicators rather than absolute gravimetric moisture prediction accuracy. Two free-space configurations were compared on the same platform: a two-horn transmission setup under controlled packing and a metal-backed double-pass reflection setup intended to represent single-sided access under loose bulk packing. After SOLT calibration and empty-holder background normalization, ε′ and ε″ were retrieved via complex-domain nonlinear least-squares fitting of physics-based slab models to measured S21 spectra. The results show that moisture-dependent dielectric responses were grain- and configuration-dependent. In particular, ε″ generally provided a more robust moisture-sensitive feature in the free-space transmission configuration, whereas the optimal single-parameter predictor in the metal-backed configuration differed among grains. A mid-band frequency window of approximately 8–16 GHz provided more stable inversion by avoiding low-frequency coupling artefacts and high-frequency signal-to-noise degradation. The metal-backed configuration preserved moisture trends but yielded lower effective ε′ values, likely due to increased air fraction under loose packing. These results indicate that packing state, grain type, and frequency-window selection are critical factors for transferring microwave moisture calibration from laboratory measurements to practical grain-handling scenarios.

Applied Sciences doi: 10.3390/app16136347

Authors: Soliman El kabir Rostand Moutou Pitti Naman Recho

This paper presents a three-dimensional generalization of the M-integral, formulated as an interaction integral based on a bilinear strain energy density, for the mixed-mode decoupling of crack front energies in finite structural components. The proposed Mθ3D integral combines real and virtual mechanical fields within a local spherical reference frame, enabling the separate evaluation of mode I (opening), mode II (in-plane shear) and mode III (out-of-plane shear) energy release rates along arbitrary crack front lines. The theoretical framework, derived from Noether’s theorem and the virtual work principle, is implemented in the Cast3M finite element code using a toroidal integration domain with a local theta weighting function. Numerical validations are conducted on the Mixed-Mode Crack Growth (MMCG) specimen, a geometry representative of structural components subjected to combined tension and shear. Three key findings are demonstrated: (i) practical domain independence is achieved for all three fracture modes; (ii) the three-dimensional approach converges to the plane-stress solution for thin specimens and reveals significant deviations from plane-strain assumptions; (iii) even under nominally mode I + II loading, a non-negligible mode III component emerges due to Poisson-induced out-of-plane effects, with magnitude increasing at free surfaces and for thicker geometries. These results indicate that finite-thickness and out-of-plane effects can significantly affect the partition of fracture energy between modes. For the MMCG configuration investigated here, the three-dimensional formulation shows the limitations of two-dimensional assumptions and provides an energetic basis for the analysis of mixed-mode fracture in finite-thickness components.

Applied Sciences doi: 10.3390/app16136346

Authors: Nawel Huenchuleo Samuel Sepúlveda Alejandro Fernández

Context: Modern quantum-computing experimentation generates heterogeneous, context-dependent execution data whose scientific value depends on preserving calibration state, compilation decisions, and run outcomes in a traceable and repository-ready form. In the NISQ era, probabilistic outputs, time-varying hardware conditions, and opaque transpilation pipelines create a data-management problem that directly affects reproducibility, traceability, and long-term reuse of experimental records. Goal: This paper aims to address this gap by proposing a specialized metadata and schema model for managing quantum-circuit execution data as governed, machine-interpretable, and evolvable repository artifacts. Proposal: We propose QC-MM, a platform-agnostic metadata model for capturing, validating, and relating contextual evidence of quantum-circuit experiments. The model integrates time-indexed calibration binding, transpilation traceability, lightweight provenance links, validation rules, and controlled schema evolution through a JSON Schema specification. Results: The evaluation follows a multi-scenario protocol and shows that QC-MM captures dynamic calibration context in IBM Quantum Cloud, remains interoperable through a local SpinQ NMR device, and makes transpilation effects traceable through structured records. It also supports repeated-run statistical reporting and links compilation decisions to execution outcomes, including circuit-depth reductions and changes in an estimated fidelity proxy under different optimization settings. Conclusions: QC-MM provides a specialized data-modeling and schema-governance foundation for traceable quantum-experiment repositories. Beyond improving reproducibility-oriented reporting, the proposal contributes to metadata validation, controlled schema evolution, and repository-oriented management of contextual experimental data.

Applied Sciences doi: 10.3390/app16136345

Authors: Luciano Galone Sebastiano D’Amico Emanuele Colica Chiara Torre Malik Adam Lluís Rivero

This study presents a compact controlled urban geophysics test site developed at the University of Malta to evaluate the response of complementary near-surface sensing methods under known shallow subsurface conditions. The experimental setup is designed to investigate buried target detection and the response to a simulated shallow leak, used here as a controlled water-release experiment in a shallow carbonate setting characterized by thin, laterally variable soil cover and anthropogenic disturbance. A preliminary passive seismic survey based on the horizontal-to-vertical spectral ratio (HVSR) method was used to compare candidate sectors and select the most suitable area for installation. The test site includes a buried iron plate and a perforated PVC pipe, the latter used to release water under controlled shallow conditions. Ground-penetrating radar (GPR), smartphone magnetometry, electrical resistivity tomography (ERT), and UAV-based thermal imaging were applied to assess target detectability and leak-related surface–subsurface responses. Results show that GPR provides the clearest response for static target detection, while smartphone magnetometry identifies the buried ferrous target under favourable conditions. For the simulated leak experiment, ERT provides the most robust subsurface evidence of moisture redistribution after water injection. UAV thermal imaging captures a complementary surface thermal response influenced by both moisture dynamics and local surface disturbance. The results show that a compact controlled test site can support the comparison of professional and low-cost sensing methods for shallow target detection and simulated leak assessment. In this configuration, the controlled water-release experiment provides a practical basis for evaluating leak-related surface–subsurface responses under known shallow conditions. The proposed setup has implications for methodological assessment, training, and near-surface environmental monitoring in heterogeneous urban settings.

Applied Sciences doi: 10.3390/app16136343

Authors: J. J. Hellín-Rodríguez I. Valverde-Palacios A. García-Jerez P. Martínez-Pagán M. Martínez-Segura

The MASW (Multichannel Analysis of Surface Waves) method oriented toward seismic microzoning has been evolving consistently and steadily for several decades, providing increasingly reliable solutions that are consistent with field and laboratory data typical of classical geotechnics. This study evaluates the improvement achieved when using a sequence of inversion algorithms on MASW test results: first with a global algorithm—specifically Differential Evolution (DE)—and subsequently, using the best model obtained from the global search, a second local algorithm—Trust Region Reflective (TRF). This second stage refines the previous model, further adjusting it to the borehole model used as the starting point of the sequence. The procedure has been automated using a Python script that incorporates two innovations compared to traditional inversion approaches. These consist of parameterising two variables: (i) an adaptive expansion factor for the Vs limits establisheda priori in the borehole model, and (ii) a subdivision into thinner layers for borehole models with excessively thick strata. This provides the algorithms with greater flexibility, particularly in scenarios with complex stratification. Additionally, to better define the deeper layers, the passive ESAC method in an “L-shape” configuration was also employed. The parameterised sequential hybrid inversion process was validated using synthetic data from two curves (Curve #1 and Curve #2), obtained by adding 5% Gaussian noise to the forward modelling results of the same initial synthetic model. The TRF refinement stage in the sequential hybrid inversion succeeded in reducing the error obtained by the global algorithm by percentages ranging from 59.7% to 5.8% across all conducted tests, confirming the stability of the methodology used.

Applied Sciences doi: 10.3390/app16136344

Authors: Domenico De Falco Nicol Macripò Margot Ringold Francesca Sodero Mario Kohlstetter Massimo Petruzzi

Background: Panoramic radiography is one of the most widely used imaging examinations in dental practice, providing a broad view of the jaws and adjacent head and neck structures. Although primarily prescribed for odontogenic assessment, its field of view may reveal non-odontogenic findings with potential clinical significance. Methods: A structured narrative review was conducted according to SANRA criteria. A literature search was performed in PubMed/MEDLINE, Embase, Scopus, and Web of Science for English-language publications from January 2010 to May 2026. Backward and forward citation tracking of relevant articles, key reviews, and reference textbooks was also performed. Eligible studies and authoritative sources addressing non-odontogenic findings detectable on panoramic radiographs were qualitatively synthesized. Results: The review focuses on carotid artery calcifications, maxillary sinus abnormalities, mandibular radiomorphometric signs related to low skeletal bone mineral density, elongation or calcification of the stylohyoid complex, sialoliths, tonsilloliths, calcified lymph nodes, phleboliths, and laryngeal cartilage calcifications. These findings range from benign anatomical variants to radiographic indicators that may require medical or specialist evaluation. Conclusions: Panoramic radiography should be regarded as a tool for recognition and clinical suspicion rather than definitive diagnosis of extraoral or systemic disease. Dentists play a central role in systematically assessing the entire image, documenting relevant abnormalities, correlating them with patient history and risk factors, and initiating appropriate referral when indicated.

Applied Sciences doi: 10.3390/app16136342

Authors: Xingrui Wu Jinting Zou Haiwei Wu

Real-time fruit maturity detection in unstructured orchards remains challenging because of variable illumination, fruit occlusion, complex backgrounds, and the limited computing capacity of edge devices. To address these challenges, this study proposes WSS-YOLO, a lightweight detection framework based on YOLOv11n for quince maturity detection. The model introduces WaveletPool to reduce texture loss during downsampling, adopts a GSConv-based Slim-neck to improve feature fusion with lower computational cost, and integrates SimAM to enhance discriminative fruit-region responses without adding trainable parameters. Experiments on a multi-scenario quince maturity dataset show that WSS-YOLO achieves 86.4% precision, 87.5% recall, and 93.4% mAP@0.5, improving the YOLOv11n baseline by 2.3, 1.7, and 2.5 percentage points, respectively. The model contains only 2.23 M parameters and requires 4.1 G FLOPs. Deployment on the NVIDIA Jetson Orin Nano achieved a real-time speed of 23.0 FPS, suggesting a favorable trade-off between detection accuracy and computational efficiency under the tested conditions.

Applied Sciences doi: 10.3390/app16136341

Authors: Lorenzo Brocchini Chenxi Wang Antonio Pratelli

Intelligent Transportation Systems (ITS) play a central role in the development of more efficient, adaptive, and resilient road networks. Traffic control strategies have progressively evolved from traditional approaches toward more intelligent and adaptive frameworks. This paper presents a taxonomy-based perspective on intelligent traffic control strategies for road networks, organizing existing approaches according to three complementary dimensions: control scope, decision-making mechanism, and control architecture. Based on this framework, the paper discusses representative methodologies, including rule-based control, model-based methods, simulation-based optimization, data-driven and artificial intelligence-based methods, and emerging cooperative strategies enabled by connected and automated vehicles (CAVs). The analysis also examines key application domains, such as traffic signal control, ramp metering, CAV-based traffic management, and simulation platforms, highlighting their operational principles, advantages, limitations, and implementation challenges. Particular attention is given to the transition from local and reactive control toward coordinated, predictive, and learning-based traffic management systems. The paper identifies major challenges related to scalability, robustness, interpretability, safety, real-world deployment, and the gap between simulation performance and practical implementation. The proposed taxonomy also supports practical comparison and preliminary selection of context-specific strategies. Future directions point toward integrated and hybrid frameworks combining data-driven adaptability, vehicle–infrastructure cooperation, and digital twin technologies.

Applied Sciences doi: 10.3390/app16136340

Authors: Jungwook Seo Changsu Shim Jongchil Park

This study proposes a practical framework for estimating instantaneous stay-cable tension in cable-stayed bridges based on the first-order frequency moment (FFM). The proposed framework combines cepstrum-guided modal decomposition, FFM-based instantaneous frequency estimation, windowed cepstrum-based consistency assessment, and energy-weighted multi-modal averaging to estimate instantaneous cable tension from measured vibration responses. Unlike conventional time–frequency analysis methods that rely on local peak extraction in the time–frequency domain, the proposed approach directly estimates instantaneous frequency from the local time–frequency energy distribution, thereby improving tracking robustness while maintaining computational efficiency under operational conditions. Numerical validation demonstrates reliable instantaneous frequency tracking under noisy and non-stationary vibration conditions while maintaining low computational cost. Field validation using acceleration- and displacement-based measurements from an in-service bridge further confirms the capability of the proposed framework to capture vehicle-induced transient tension variations. The results indicate that the framework provides reliable and physically consistent cable tension information under real operational conditions. These characteristics, together with computational efficiency and compatibility with existing monitoring systems, indicate strong potential for near-real-time structural health monitoring applications.

Applied Sciences doi: 10.3390/app16136339

Authors: Musa Mbedzi Thulane Paepae

The rapid expansion of online real estate (RE) platforms has intensified information overload, making property search and decision-making increasingly complex. Real estate recommendation systems (RERSs) have emerged as essential decision-support tools; however, their development has not kept pace with advances in explainable artificial intelligence (XAI), transfer learning (TL), and fairness-aware machine learning. This PRISMA-compliant systematic review synthesizes 59 peer-reviewed studies published between 2005 and 2025 to critically examine algorithmic approaches, data modalities, evaluation practices, and ethical considerations in RERS research. Our analysis reveals a substantial lag in the adoption of state-of-the-art AI techniques: While deep learning is employed in 15% of studies, no reviewed work implements state-of-the-art post hoc XAI or TL frameworks, despite their relevance for addressing interpretability and data scarcity challenges. Furthermore, we identify systemic research biases, including reliance on proprietary datasets (80%), geographic concentration in Asia (56%), the dominance of residential property studies (91%), and limited fairness auditing despite documented discrimination risks in housing markets. To address these gaps, we propose a trust-based evaluation (T-EVAL) framework that integrates predictive accuracy, user trust, fairness, and market efficiency, and introduces a comprehensive nine-layer conceptual architecture for transparent, ethical, and data-efficient next-generation RERS. This review establishes an empirical benchmark for technology adoption gaps and outlines a research agenda for advancing responsible AI in RE decision-support systems.

Applied Sciences doi: 10.3390/app16136331

Authors: Oscar Paredes Julio Pacher Alfredo Renault Jorge Rodas Leonardo Comparatore Carlos Paredes Paola Maidana Christian Medina Hugo Lezcano Marcos Gómez Marco Rivera Patrick Wheeler

This paper presents a comparative analysis of three-level (3L-NPC) and five-level (5L-NPC) Neutral-Point-Clamped converters using Finite Control Set Model Predictive Control (FCS-MPC) for reactive power compensation. The research addresses a critical gap by providing a direct performance comparison under identical operating conditions, supported by simulation and experimental validation of a 3L-NPC prototype. The study evaluates harmonic performance, dynamic response, and DC-link balance. Results demonstrate that the 5L-NPC topology significantly outperforms the 3L-NPC, achieving a simulated grid current Total Harmonic Distortion (THD) of 3.36% compared to 7.84% for the 3L-NPC. This 57.1% reduction in THD allows the 5L-NPC to comply with the IEEE Std. 519-2022 limit (<5%), whereas the 3L-NPC experimental results (9.9% THD) highlight the impact of practical non-idealities such as dead time and sensor noise. While the 5L-NPC offers superior power quality, it entails higher hardware complexity, evaluating 125 switching states compared to 27 in the 3L-NPC. These findings provide quantitative guidelines for selecting NPC topologies in high-performance grid compensation systems.

Applied Sciences doi: 10.3390/app16136337

Authors: Henda Adgaeg Muesser Nat

The prediction of student engagement characteristics involves forecasting and analyzing student interaction with educational materials using engagement prediction models. This process encompasses the prediction of cognitive, behavioral, and emotional dimensions of engagement. The existing student engagement prediction models have some limitations, including poor convergence, less generalizability, complexity issues, overfitting, false errors, and limited resources. Hence, the research proposes the Multi-Instance Localization-based Gradient Boosted Long Short-Term Memory (MIL-GBLTM) model to tackle the challenge of predicting student engagement characteristics in online classes. The integration of effective MIL with a Triplet Attention mechanism focuses on the significant features that help with engagement prediction; LSTM layers capture intricate sequential patterns, and fractional gradient boosting is used for fine-tuning for accurate prediction, alongside ensemble-based learning. The LSTM layers with the Triplet Attention module refine temporal attention, and Fractional Gradient Boosting ensures the model’s adaptability and robustness. By combining these components, the proposed model is able to predict accurate student engagement with high convergence. This integrated approach enhances the capabilities of engagement prediction models in educational contexts, facilitating more effective interventions and personalized student support in online learning environments. Experimental results demonstrate that the proposed MIL-GBLTM model outperforms other existing models by achieving the highest accuracy of 96.55% with a k-fold of 10, utilizing the wacv2016 dataset.

Applied Sciences doi: 10.3390/app16136338

Authors: Krishna Kumar Akila Venkatesan

Misinformation propagates more rapidly than factual content on social media, presenting significant challenges for automated misinformation detection. Existing approaches often focus solely on textual features without incorporating temporal information, treat timing and propagation as separate factors, or apply quantum-inspired methods primarily to multimodal data rather than text-centric misinformation. This study introduces QuST-TF (Quantum-inspired Semantic encoding and Temporal Transformer Fusion), a unified model designed to detect misinformation in tweets and news articles. QuST-TF integrates quantum-inspired (classical approximation) amplitude encoding, time-aware Transformer fusion, and propagation graph attention based on engagement data, without reliance on images, audio, or quantum hardware. Performance gains are achieved through quantum-inspired (classical approximation) nonlinear angular modulation (cosine and sine rotations) implemented via classical computation, rather than genuine quantum computing. All computations utilize classical Dense layers, Rectified Linear Unit (ReLU) activations, and cosine/sine functions on CPUs or GPUs; quantum hardware is not required. The quantum-inspired (classical approximation) layer applies classical rotation-based transformations to enrich the semantic representation of BERT (Bidirectional Encoder Representations and Transformer) embeddings. Temporal information is captured by a dual-attention Transformer encoder, while propagation graph attention monitors the spread of claims. Evaluation on FakeNewsNet and PHEME datasets demonstrates 91.4% and 95.5% accuracy, respectively, with 34% fewer trainable parameters compared to standard Transformers. Ablation studies indicate that quantum encoding is the most influential component (+3.0% versus without quantum encoding), surpassing the contributions of graph attention (+2.6%) and temporal attention (+2.2%). The integration of all three components yields a 1.3% synergistic improvement, confirming effective inter-module collaboration. Attention visualization enhances interpretability, supporting the utility of QuST-TF for fact-checking applications.

Applied Sciences doi: 10.3390/app16136336

Authors: Anja Batina Andrija Krtalić

Wildfires increasingly contribute to urban particulate matter (PM) exposure, particularly fine particles (PM2.5), through atmospheric transport processes influenced by meteorological conditions and terrain complexity. This study investigated wildfire impacts on PM10 and PM2.5 concentrations in Split, Solin, and Kaštela (Croatia) using a terrain-aware wildfire transport framework combined with statistical and machine learning (ML) approaches. Daily PM observations (2016–2024) from three air quality monitoring stations were integrated with meteorological data from six stations, wildfire polygons, and a digital elevation model (DEM). A wildfire influence index accounting for fire size, transport distance, wind conditions, and terrain-modified airflow was evaluated using Ordinary Least Squares (OLSs) regression, Random Forest (RF) modelling, and SHAP (SHapley Additive exPlanations) analysis. Results showed stronger wildfire-related effects for PM2.5 than for PM10, while meteorological variables remained the dominant predictors of PM variability. RF models improved predictive performance relative to OLS, achieving R2 = 0.474 for PM2.5 and R2 = 0.416 for PM10. SHAP analysis identified precipitation, temperature, and lagged wildfire transport variables as important predictors. A total of 84 wildfire events were classified as effective wildfires, with most measurable impacts occurring within approximately 30–70 km of monitoring stations, indicating that wildfire impacts on urban air quality in Mediterranean coastal environments are strongly mediated by atmospheric transport and meteorological conditions. The proposed framework demonstrates the potential of explainable and geospatially informed ML for environmental monitoring and wildfire-related urban air quality risk assessment.

Applied Sciences doi: 10.3390/app16136335

Authors: Jinsong Liu Huawei Li Wei Chai Shu Liu Ningning Ma

When photovoltaic (PV) power plants are connected to weak alternating current (AC) grids, the interaction between the plant and grid may induce wideband oscillation, posing a serious threat to the stability of grid-connected PV systems. To address this problem, this paper proposes an oscillation suppression method based on adaptive supplementary damping control of a Static Var Generator (SVG). First, a sequence impedance model of a PV power plant integrated with an SVG is established, and the Nyquist criterion is employed to analyze the mechanism underlying wideband oscillations. Then, a supplementary damping controller implemented in the SVG is designed to reshape the impedance characteristics of the PV power plant and enhance system damping. Furthermore, a Variational Mode Decomposition–Prony modal identification algorithm is introduced to extract oscillation mode information in real time. Based on the identified oscillation frequency, the parameters of the damping controller are adaptively adjusted, thereby improving the suppression capability for wideband oscillations with varying frequencies. Finally, a grid-connected PV power plant model with an SVG is developed, and the performance of the proposed adaptive suppression strategy is compared with that of conventional supplementary damping control. The results demonstrate that the proposed strategy provides stronger robustness and adaptability, effectively suppresses wideband oscillations under different operating conditions, and improves the stability of grid-connected PV systems.

Applied Sciences doi: 10.3390/app16136333

Authors: Bin Zhang Ping Ji Zhiqiang Wen Ruixue Zhang

Film posters serve as front-end visual communication media that shape viewers’ initial judgments of film genre, emotional tone, and viewing appeal. However, whether the optimal overall color configuration follows a universal rule or varies across poster types remains insufficiently examined. This study investigated how overall lightness and chroma influence the communication effects of film posters and identified type-specific optimal color intervals. Based on a cross-type poster sample library, film posters were classified into four visual grammar types: affable-entertaining, relational-emotional, spectacle-dynamic, and threat-suspenseful. Type-specific quantile thresholds for lightness and chroma were established within each category. Eye-tracking data, subjective ratings, mixed-effects response surface modeling, and constrained desirability optimization were combined to identify optimal regions of overall color configuration. The results show that no single optimal lightness–chroma interval applies across all poster types. The dominant optimal interval was low lightness–high chroma for affable-entertaining and relational-emotional posters, high lightness–low chroma for spectacle-dynamic posters, and medium lightness–high chroma for threat-suspenseful posters. These findings indicate that overall color optimization varies across poster types within the present experimental context and provide practical support for evidence-based, type-specific poster color design.

Applied Sciences doi: 10.3390/app16136334

Authors: Mahmoud M. M. Nosser Artur İsmatullaev Çise Özal

Accurate transfer of implant position is essential for implant-supported prosthodontic workflows. This in vitro study compared the trueness and precision of five intraoral scanners in single crown, three-unit fixed partial denture, and full-arch implant-supported scanning scenarios using root mean square (RMS) deviation analysis. Two maxillary resin models, representing partially dentulous and fully edentulous conditions, were fabricated through a CAD/CAM and 3D-printing workflow with implant analogs and scan bodies. Reference datasets were obtained with an InEos X5 desktop scanner, and each intraoral scanner was used to perform 10 scans per scenario. After standardized scenario-specific trimming, datasets were analyzed in Geomagic Control X. Statistical analysis included two-way analysis of variance and follow-up one-way analysis of variance with Tukey post hoc comparisons using Bonferroni-adjusted thresholds. Trueness was affected by scanner type (p < 0.001) and scenario (p < 0.001), without interaction (p = 0.096). Precision was affected by scanner type (p = 0.012), scenario (p = 0.004), and their interaction (p < 0.001). iTero Lumina and Helios 600 showed lower trueness deviations, whereas Trios 5 showed greater deviations, especially in full-arch scans. Scanner selection and scan extent should therefore be considered when interpreting surface-based RMS accuracy in implant-supported digital scans.

Applied Sciences doi: 10.3390/app16136332

Authors: Erliang Zhao Ziyuan Zhu

During startup, ransomware is associated with abnormal fluctuations in underlying hardware resources. Hardware Performance Counters (HPC) can characterize this ultra-early behavior without interference from software-based countermeasures. However, existing studies lack a cross-platform hardware-layer analysis paradigm and typically neglect the first 10 s post-execution. This study selects two platforms—Windows 7 (homogeneous x86) and Windows 10 (Intel performance hybrid architecture with P-core (performance core) and E-core (efficiency core))—and constructs a large-scale dataset (1721 ransomware and 1039 benign samples on Windows 7; 1562 ransomware and 718 benign on Windows 10). On Windows 7, 25 HPC events are monitored. On Windows 10, each event yields two instance-level metrics (P-core and E-core), resulting in 42 instance-level metrics. Using statistical analysis (Pearson correlation, fold change) and feature selection (Random Forest + clustering), four core metrics are independently selected per platform. Windows 7 favors LLC and branch events (increasing trends, fold change ≥ 1.5, e.g., LLC-store_std), while Windows 10 favors P/E-core branch and cache events (decreasing trends, fold change ≤ 0.667, e.g., cpu_atom_branch-load-misses_max). The 10 s window is divided into startup (0–2 s), key generation (2–5 s), and encryption (5–10 s) phases. Results indicate opposite correlation patterns: resource-enhanced disturbance (positive correlation, fold change ≥ 1.5) on Windows 7 versus resource-suppressed disturbance (negative correlation, fold change ≤ 0.667) on Windows 10. Critically, startup-phase HPC events exhibit substantially stronger correlation on Windows 10 (S-level, >85%) compared to Windows 7 (A-level, 70–84%). This difference may be associated with the fine-grained P/E-core separation, which preserves core-type behavioral information that is aggregated and lost on homogeneous platforms. This study contributes a cross-platform correlation framework, observes an architecture-dependent inversion pattern of HPC responses, and suggests that core-type granularity—rather than event quantity—is associated with stronger feature–behavior correlations on heterogeneous architectures, providing preliminary empirical insights for future lightweight detection system design.

Applied Sciences doi: 10.3390/app16136330

Authors: Salvatore Pezzino Davide Tumino Caterina Crescimanno Tonia Luca Stefano Puleo Sergio Castorina

Organ failure remains one of the foremost medical and socioeconomic challenges of the twenty-first century, with global transplant waiting lists far exceeding the supply of donor organs. Chronic supportive therapies sustain life but do not restore organ function, underscoring an urgent need for curative alternatives. Bioartificial organs represent a major frontier in organ replacement, driven by converging advances in cell biology, biomaterials science, and bioengineering. By integrating living cells or biologically derived matrices with engineered devices or scaffolds, these systems aim to restore functions that purely mechanical supports cannot reproduce. This review examines the principal technological platforms underpinning the field, including cell encapsulation, decellularization and recellularization, three-dimensional bioprinting, organoids, organ-on-chip systems, and xenotransplantation, and discusses their application to kidney, liver, heart, pancreas, and lung replacement. Across organ systems, progress is advancing from experimental proof-of-concept toward modular and increasingly translational platforms, although whole-organ bioengineering remains largely preclinical for the most structurally complex targets. The major unresolved barriers include vascularization, immune compatibility, scalable cell manufacturing, durable function, and stable integration between biological and engineered components. Overall, bioartificial organ engineering is evolving toward clinically relevant therapeutic strategies capable of complementing, bridging, or eventually reducing dependence on donor-organ transplantation.

Applied Sciences doi: 10.3390/app16136329

Authors: Claudia Pașca Daniel Severus Dezmirean

Bee products—including honey, pollen, propolis, beeswax, royal jelly, bee bread, bee venom, and apilarnil (a Romanian bee-derived product)—have been utilized since antiquity for their nutritional value and diverse therapeutic applications [...]

Applied Sciences doi: 10.3390/app16136328

Authors: Yue Zheng Xianfeng Li Ying Zhan

Diffusion models have achieved remarkable success in image generation, but their iterative denoising process requires repeated evaluations of large neural networks, resulting in high inference latency. Recent training-free acceleration methods, such as DeepCache, exploit temporal redundancy in U-Net features by caching and reusing high-level representations across adjacent denoising steps. However, existing caching strategies usually adopt a static skip-branch selection throughout the sampling trajectory, ignoring the stage-dependent frequency evolution of diffusion sampling. In this paper, we propose an interval-guided adaptive branch routing strategy to improve training-free feature reuse. Motivated by the observation that low-frequency global structures change rapidly in early denoising stages while high-frequency details dominate later refinement, our method dynamically adjusts the skip branch according to the timestep. It preserves deeper computation in early stages for semantic reconstruction and progressively shifts to shallower branches in later stages to reduce redundant computation while maintaining fine-grained details. The proposed method requires no retraining and can be directly applied to pretrained U-Net-based diffusion models. Experiments show that FreqCache achieves up to 1.93× speedup on CIFAR-10, 1.50× speedup on LSUN-Bedroom and LSUN-Churches, and 10.60× speedup on ImageNet 256 × 256 compared with the baseline, while maintaining an Fréchet Inception Distance (FID) score comparable to or slightly better than DeepCache at the same cache interval.

Applied Sciences doi: 10.3390/app16136327

Authors: Vitaliy Kulanov Artem Perepelitsyn

Field-Programmable Gate Array (FPGA) technology allows for the creation of unique hardware implementations based on mass-produced chips. The process of project prototyping for such systems using Hardware Description Languages (HDLs) remains complex, even with modern tools. The comparison of HDL coding styles, for example, a behavioral case statement against a structural binary-tree decomposition, shows that the choice is capable of affecting post-implementation timing and area. The performed study, using the open-source yosys/nextpnr toolchain, shows that the validity of such a comparison is decided by the fabric domain. Logic that falls through to generic Look-Up Table (LUT) mapping is governed by the mapper’s heuristic fixed point rather than by source intent: on the crossbar, the behavioral and structural netlists become identical in cell composition; on the priority encoder, the verdict reverses; and on the barrel shifter, the LUT area collapses, so the comparison does not isolate the coding-style variable. It was measured that the keep_hierarchy attribute restores a meaningful comparison at ~17% LUT cost (N = 8) and provides a structural invariant to the ABC mapper variant, but the behavioral result is mapper-sensitive and the N = 4 verdict reverses under the legacy -noabc9 mapper (Cohen’s d from +2.4 to −1.6). By contrast, logic that involves a dedicated primitive before LUT mapping—an adder bound to the carry chain or a multiplier bound to a DSP block—yields source-meaningful verdicts that do not reverse with a mapper. Replication on a second fabric (Lattice iCE40) confirms that this behavior is fabric- rather than vendor-specific. The main contribution of this work is the proposed first fabric-domain characterization of synthesis canonicalization as a methodological hazard for cross-HDL FPGA studies on open-source toolchains, which identifies the two-phase synthesis mechanism that delimits it and supplies a decision rule (inspect post-synthesis composition) to identify whether a given comparison is susceptible.

Applied Sciences doi: 10.3390/app16136326

Authors: Desmond E. Ighravwe Charles Kokofi Olumide Ojo Moses Olubayo Babatunde Oludolapo A. Olanrewaju

The growth of advanced cyber threats has inspired organisations to start using powerful cybersecurity platforms, but the process of selection is analytically challenging due to the multidimensional, uncertain, and conflicting character of the evaluation criteria. The prevailing culture of decision-support frameworks is based on unyielding numerical evaluations that cannot reflect the underlying vagueness of expert judgment and the dynamic interplay of macro-environmental factors. This paper presents a combined Fuzzy Multi-Criteria Decision-Making (FMCDM) system, which uses polygonal fuzzy numbers, in particular pentagonal fuzzy representation, and four other complementary methods of MCDM (Fuzzy AHP, Fuzzy TOPSIS, Fuzzy VIKOR, and Fuzzy COPRAS), integrated by a Borda Count consensus system. Sixteen assessment sub-criteria are logically obtained through an analysis of PESTEL (Political, Economic, Social, Technological, Environmental, and Legal) and weighted using the Fuzzy Analytic Hierarchy Process. The model is used to compare six cybersecurity platforms, including Microsoft Security Framework, CrowdStrike Falcon, Cisco Cybersecurity Portfolio, Palo Alto Networks Cortex, Fortinet Security Fabric, and Sophos Central. In this study, Fuzzy AHP demonstrates that the aggregate weight of political factors is the highest (0.4181), followed by cross-border data management, regulatory compliance, and government incentives as the most popular sub-criteria. According to the results from the Fuzzy TOPSIS, Fuzzy VIKOR, and Fuzzy COPRAS methods, Microsoft Security Framework ranks consistently in the first place, and CrowdStrike Falcon and Cisco Cybersecurity Portfolio were ranked second and third, respectively. The framework presented in the study provides decision-makers with a reproducible, uncertainty-conscious basis for cybersecurity platform selection.

Applied Sciences doi: 10.3390/app16136325

Authors: Jie Peng Hengyu Liu Yun Lin Yanyan He

Uniaxial compressive strength (UCS) is a fundamental parameter for rock engineering design and stability assessment, but direct laboratory testing is costly, time-consuming, and often difficult for weak or fractured rocks. To improve predictive accuracy while preserving mechanical interpretability, this study proposes a Gated Empirical-Law LightGBM model (GEL-LightGBM). The framework embeds three representative rock-strength priors, including point-load strength, multi-index strength, and porosity-degradation relationships, as empirical-law experts. A sample-adaptive gating mechanism dynamically assigns its contributions for different rock states, while a controlled residual corrector captures nonlinear deviations between empirical estimates and measured UCS. Using 344 published rock-mechanics samples, porosity, Schmidt rebound hardness, P-wave velocity, and point-load strength index were used as predictors. GEL-LightGBM outperformed LightGBM, XGBoost, random forest, MLP, CNN, SVR, and BPNN, achieving a testing R2 of 0.9790 and an RMSE of 7.5623 MPa. SHAP analysis identified porosity as the dominant factor, contributing 49.0%, followed by rebound hardness (32.2%) and P-wave velocity (17.2%). The strongest interaction occurred between porosity and rebound hardness (2.31 MPa). These findings indicate that GEL-LightGBM provides accurate, stable, and physically interpretable UCS prediction for heterogeneous rock datasets.

Applied Sciences doi: 10.3390/app16136324

Authors: Mahmoud Alhalabi Temel Sonmezocak Fady El-Nahal

This paper presents a simulation-based 16-quadrature amplitude modulation (16-QAM) direct current-biased optical orthogonal frequency-division multiplexing (DCO-OFDM) visible-light optical wireless system using a 520 nm InGaN directly modulated laser (DML) and direct detection over 500 m. A 1024-point transform with 511 data subcarriers provides approximately 15 Gb/s gross and 14.82 Gb/s payload rates without external optical modulators or amplifiers. Under the adopted static line-of-sight model, the simulated bit-error rate (BER) falls below 10−3 at a receiver-side equivalent optical signal-to-noise ratio (OSNR) of about 17 dB and remains below this threshold for beam divergence up to 9 mrad. Gamma–Gamma simulations show that a 5 cm aperture maintains BER<10−3 at 20 dB OSNR up to Cn2≈5×10−14m−2/3. Pointing-error analysis gives per-axis angular-jitter standard deviations of 0.425, 0.515, and 0.564 mrad at 1% outage for 5, 10, and 15 cm apertures. The clear-air margin is exhausted at V2%≈0.66km, corresponding to V5%≈0.50km, or near 107 mm/h rain. For a 1.5 GHz bandwidth-limited DML, adaptive bit loading reaches 16.5 Gb/s at 28 dB OSNR. The results support a low-complexity medium-range architecture but remain numerical estimates requiring experimental validation under practical device, alignment, and weather conditions.

Applied Sciences doi: 10.3390/app16136323

Authors: Yu-Min Hsin Bo-Hao Zhou Chun-Yu Lin Cheng-Chien Kuo

With global demand for renewable energy increasing, the penetration of photovoltaic (PV) systems in power networks has risen significantly, introducing new challenges to microgrid stability. This study focuses on solar inverters using grid-forming (GFM) control, investigating their performance in black-start scenarios and in stabilizing microgrids with battery energy storage systems (BESSs). A MATLAB Simulink microgrid model integrating PV, BESS, and GFM inverters was developed to simulate scenarios including black start, load variation, grid synchronization, and power adjustment. Control techniques such as droop control, proportional–integral (PI) control, Clarke and Park transformations, and phase-locked loops (PLLs) were applied for precise regulation of voltage, frequency, and power. Results show that GFM inverters effectively stabilize voltage and frequency during load changes and PV grid connection, maintaining voltage between 0.96–1.003 p.u. and frequency within 59.87–60.07 Hz. The findings confirm the feasibility of GFM control for coordinated PV–BESS operation and support stable microgrid operation under high renewable penetration.

Applied Sciences doi: 10.3390/app16136322

Authors: Yiteng Lin Qingtian Su Fawas. O. Matanmi Xingfei Yan Shang Gao

This study focuses on a novel three-span hybrid continuous beam bridge, analyzing the force performance and key design parameters of the non-cellular post-support plate joint. A finite element model and parametric analysis were used to reveal the stress distribution patterns, the load-bearing characteristics of the connectors, and the load transfer path under negative bending moments. The study shows that the axial force within the joint is equitably shared among three load paths: the top slab concrete (20.7%), the bearing plate (40.1%), and the shear connectors (39.2%). Although interfacial friction contributes approximately 27.1% to the total shear resistance, it is conservatively recommended to neglect this effect in design due to inherent uncertainties. Parametric analysis reveals distinct marginal effects and efficiency thresholds: increasing the bearing plate thickness from 20 mm to 100 mm results in a mere 1.0 MPa reduction in the peak concrete stress, while extending the joint length beyond 1.0 times the beam height renders the central connectors ineffective. Furthermore, reducing the connector stiffness effectively lowers the non-uniformity coefficient from 2.3 to below 2.0. Notably, the first row of web PBLs carries 34.8% to 47.2% of the total shear force, with a stable non-uniformity coefficient of 1.05–1.06, establishing it as the critical control section for simplified design. These findings provide a theoretical basis and practical guidance for the design of similar joints in hybrid girder bridges.

Applied Sciences doi: 10.3390/app16136320

Authors: Qing Li Junfu Fan

PM2.5 is a major air pollutant that threatens urban air quality and public health. Its concentration is influenced by both meteorological conditions and air pollutants, exhibiting complex nonlinear and temporal characteristics. Traditional statistical methods are limited in their ability to model complex relationships among environmental variables, while machine learning models still require improvements in hyperparameter optimization and interpretability. Therefore, developing an accurate and interpretable PM2.5 estimation model remains an important research objective. This study used daily air-quality and meteorological data collected in Wuhan from 2016 to 2025 to develop six machine learning models: Decision Tree (DT), Random Forest (RF), XGBoost, LightGBM, Support Vector Machine (SVM), and Multilayer Perceptron (MLP). The Particle Swarm Optimization (PSO) algorithm was employed to optimize the hyperparameters of these models. By comparing the root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE) of each model on both the training and test sets, the PSO-MLP model was identified as the best-performing model. Furthermore, the Shapley Additive Explanations (SHAP) method was applied to perform both global and local interpretation analyses of the best-performing model. The results indicate that the PSO-MLP model achieved the highest estimation performance among all evaluated models, with an R2 value of 0.746 on the test set. SHAP analysis revealed that CO, Temperature (Temp), and NO2 were the most influential predictors, while all variables exhibited distinct nonlinear relationships with PM2.5 concentration. These findings may contribute to PM2.5 concentration estimation, air-quality management, and environmental decision-making.