Technologies

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Open AccessArticle

In Pursuit of the Emergence Point: Extracting Phase Transitions in Multi-Agent Communication

by Alexander Chernyavskiy, Ivan Tomilov, Natalia Gusarova and Aleksandra Vatian

Technologies 2026, 14(7), 432; https://doi.org/10.3390/technologies14070432 - 14 Jul 2026

Modern multi-agent deep reinforcement learning algorithms have demonstrated empirical success in communication games, yet their black box nature precludes the analytical identification of the transition from random babbling to coordinated signalling. This study introduces an explicitly parameterised, interpretable surrogate model of belief evolution [...] Read more.

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

(This article belongs to the Section Information and Communication Technologies)

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Open AccessCommunication

Static Verification of the FA125 Hydraulic Drilling Rig Mast Under a Code-Based Load Combination: A Beam–Shell Finite Element Study

by Andrei Dimitrescu, Claudiu Babiș, Iulian Sorin Munteanu and Sorin Alexandru Fica

Technologies 2026, 14(7), 431; https://doi.org/10.3390/technologies14070431 - 14 Jul 2026

Abstract

This paper presents a code-based static verification of the FA125 hydraulic drilling rig mast under its governing design load combination. Unlike the previously published dynamic investigation of the same platform, the present work establishes the baseline static load path, identifies the governing structural [...] Read more.

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

(This article belongs to the Special Issue Technological Advances in Science, Medicine, and Engineering 2025)

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1880 KB

Open AccessSystematic Review

Gesture-Based Navigation of Smart Wheelchairs: A Review of Current Trends and Future Directions

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

Technologies 2026, 14(7), 430; https://doi.org/10.3390/technologies14070430 - 14 Jul 2026

Abstract

Gesture recognition systems powered by artificial intelligence provide a promising solution for mobility and independence for individuals with physical disabilities. However, the deployment of such systems remains limited due to some challenges related to robustness, different user requirements, affordability for lower income people, [...] Read more.

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

(This article belongs to the Special Issue Assistive Technologies in Care and Rehabilitation: Research, Developments, and International Initiatives—Second Edition)

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Open AccessArticle

An Improved A* Path Planning Method for Unmanned Vehicles in Off-Road Environments Based on Geometric and Support Passability Analysis

by Pengfei Zhang, Jinshuai Liu, Rong Hou, Yawen Li, Yuhan Wang, Zhengxuan Li and Huiyan Han

Technologies 2026, 14(7), 429; https://doi.org/10.3390/technologies14070429 - 14 Jul 2026

Abstract

To address the insufficient representation of terrain constraints and surface resistance in traditional path planning for off-road environments, this study proposes an improved A* path planning method for unmanned ground vehicles. First, an off-road environment model is constructed using Digital Elevation Model (DEM) [...] Read more.

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

(This article belongs to the Special Issue Emerging Paradigms in AI, Autonomous Systems, and Intelligent Technologies—2nd Edition)

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Graphical abstract

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Open AccessArticle

Planning and Design of a Photovoltaic Solar-Energy-Generation System in the Southeastern Amazon Region of Ecuador

by Carlos Brito-Brito, Luis Córdova-Cajamarca and Daniel Icaza-Alvarez

Technologies 2026, 14(7), 428; https://doi.org/10.3390/technologies14070428 - 14 Jul 2026

Abstract

This research evaluates the feasibility of implementing photovoltaic solar systems in the Ecuadorian Amazon to harness solar energy and increase energy security in the region. It is based on the need to reduce direct dependence on fossil fuels and existing hydroelectric systems. The [...] Read more.

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

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16 pages, 5157 KB

Open AccessArticle

A Robust Tunable Simulator of Atmospheric Turbulence for Performance Analysis of Wireless Optical Links

by Ilya Galaktionov

Technologies 2026, 14(7), 427; https://doi.org/10.3390/technologies14070427 - 14 Jul 2026

Abstract

Atmospheric turbulence distorts the wavefront of propagating optical radiation, degrading image resolution in astronomical telescopes and reducing power density at the target in focusing applications. These effects can be studied under controlled laboratory conditions using turbulence-generating devices—such as fan heaters (rough control), phase [...] Read more.

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

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16 pages, 5625 KB

Open AccessArticle

A Comprehensive Evaluation of 3D-Printed Breast Phantoms: Impacts of Printing Technology and STL Processing on Multimodal Fidelity

by Nikolay Dukov, Vencislav Nastev, Viktoria Petkova, Ivan Buliev, Zhivko Bliznakov, Valentina Dobreva and Kristina Bliznakova

Technologies 2026, 14(7), 426; https://doi.org/10.3390/technologies14070426 - 12 Jul 2026

Abstract

Anthropomorphic breast phantoms are increasingly used for the development and evaluation of breast imaging technologies. The aim of this study is to evaluate the impacts of the material combination and stereolithography or Standard Tessellation Language (STL) export methodology on the multimodal imaging performance [...] Read more.

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

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22 pages, 19724 KB

Open AccessArticle

KNA-SG: Keyframe–Node-Associated Open-Vocabulary 3D Scene Graphs from RGB Sequences

by Yangbin Xu, Wenhui Shi, Jing Xing, Jiangang Yang and Jian Liu

Technologies 2026, 14(7), 425; https://doi.org/10.3390/technologies14070425 - 12 Jul 2026

Abstract

3D scene graphs organize objects and their relationships in a scene into structured representations, providing an interpretable and queryable foundation for relational reasoning and object grounding. Existing open-vocabulary 3D scene graph construction methods primarily focus on object-level feature representation and open-ended edge reasoning. [...] Read more.

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

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16 pages, 884 KB

Open AccessArticle

An Improved Deep Learning Framework for In Situ Detection of Geometric Keypoints of Heliostats in Concentrated Solar Power Plants

by Fen Xu and Hongyu Miao

Technologies 2026, 14(7), 424; https://doi.org/10.3390/technologies14070424 - 11 Jul 2026

Abstract

In situ detection of the tracking poses of heliostats can help improve the tracking accuracies of heliostats and reduce the task loads of heliostat calibration in a large-scale concentrated solar power (CSP) plant, as the traditional methods normally require the heliostats to be [...] Read more.

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

(This article belongs to the Special Issue Solar Thermal Power Generation Technology)

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29 pages, 10432 KB

Open AccessArticle

A Physical-Layer Threat Detection Framework for Secure IoT and Smart Grid Networks Using HHT-Based Multimodal Deep Learning

by Jie Ren, Chunhai Zhou, Chuyang Tan and Yan Wang

Technologies 2026, 14(7), 423; https://doi.org/10.3390/technologies14070423 - 11 Jul 2026

Abstract

Secure IoT and smart grid networks depend on reliable hardware operation to maintain continuous service and system availability. Physical-layer abnormalities such as partial discharge (PD) can weaken infrastructure components and disrupt connected systems before conventional monitoring methods detect the problem. PD is one [...] Read more.

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

(This article belongs to the Special Issue Research on Security and Privacy of Data and Networks)

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20 pages, 15131 KB

Open AccessArticle

Oil Capture Mechanism and Structural Optimization of Under-Race Lubrication Oil Scoop for High-Speed Bearings

by Xiaozhou Hu, Kang Duan and Bingkun Han

Technologies 2026, 14(7), 422; https://doi.org/10.3390/technologies14070422 - 10 Jul 2026

Abstract

The lubrication of high-speed bearings in variable-speed transmission systems is typically achieved through radial under-race lubrication devices, where the performance of the oil capture section significantly affects the overall lubrication performance. To improve the oil capture performance of radial under-race lubrication devices, this [...] Read more.

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

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12 pages, 1129 KB

Open AccessArticle

Combining Content-Based Filtering Methods for Building a Powerful Hybrid Recommender System to Improve Tourism in Drâa-Tafilalet Area

by Khalid al Fararni, Loukmane Maada, Badraddine Aghoutane, Abdelouahed Sabri, Ali Yahyaouy and Jamal Riffi

Technologies 2026, 14(7), 421; https://doi.org/10.3390/technologies14070421 - 9 Jul 2026

Abstract

This paper proposes a hybrid content-based recommender system aimed at enhancing personalized tourism experiences and supporting the promotion of tourism in Morocco, with particular emphasis on the Drâa-Tafilalet region. The proposed approach integrates three machine learning models—Decision Tree, k-nearest neighbors, and Support Vector [...] Read more.

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

(This article belongs to the Special Issue Emerging Technologies and Intelligent Systems for Sustainable Development)

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22 pages, 3499 KB

Open AccessArticle

Partitioned Calculation of Node-Level Carbon Emission Factors for Large-Scale Power Systems Based on Centralized Data Distribution Pattern and BiCGSTAB Algorithm

by Yushi Chen, Rouyi Chen, Hui Jiang, Yanlu Huang and Fan Zhang

Technologies 2026, 14(7), 420; https://doi.org/10.3390/technologies14070420 - 9 Jul 2026

Abstract

With the advancement of the new-type power system construction, the accurate and efficient calculation of node-level carbon emission factors (CEFs) has become a key basis for indirect carbon emission accounting in power systems. Existing centralized methods face two major challenges in large-scale power [...] Read more.

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

(This article belongs to the Special Issue AI for Smart Grid Optimization—Technological Advances and Future Perspectives)

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36 pages, 6711 KB

Open AccessArticle

Digital Shadowing-Enabled Deep Learning for Carbon-Aware Day-Ahead Scheduling of Integrated Energy Systems Under Forecast Uncertainty

by Yinuo Yang, Minglei You, Marco Rivera, Yupeng Wu and Alex Dario Navas-Fonseca

Technologies 2026, 14(7), 419; https://doi.org/10.3390/technologies14070419 - 8 Jul 2026

Abstract

Sustainable power systems increasingly require scheduling methods that can coordinate renewable generation, distributed flexibility, and conventional energy-conversion units under forecast uncertainty while directly supporting carbon-emission reduction. However, many data-driven scheduling models still enforce operational constraints through soft penalties or post-processing corrections, which may [...] Read more.

Sustainable power systems increasingly require scheduling methods that can coordinate renewable generation, distributed flexibility, and conventional energy-conversion units under forecast uncertainty while directly supporting carbon-emission reduction. However, many data-driven scheduling models still enforce operational constraints through soft penalties or post-processing corrections, which may lead to infeasible schedules during deployment and weaken their reliability in digital-shadow-assisted operation. In addition, conventional cost-oriented scheduling objectives do not explicitly account for the carbon impact of real-time imbalances caused by forecast errors. To address these challenges, this paper proposes a digital-shadowing-enabled deep learning framework for carbon-aware day-ahead scheduling of integrated energy systems. The main methodological contribution is a feasibility-by-design neural decoder that embeds hard physical constraints directly into the network forward pass. By classifying devices into non-memory fast units, non-memory ramp-limited units, and memory-type storage devices, the decoder applies tailored transformations to enforce capacity limits, ramp-rate restrictions, state-of-charge dynamics, and terminal energy consistency by construction. Therefore, the generated schedules are physically feasible without relying on post-hoc repair. In parallel, a carbon-first objective is developed to minimize both scheduled emissions and imbalance-driven emissions, allowing the scheduler to reduce not only planned carbon output but also the carbon impact of real-time corrective actions. Forecast uncertainty is represented through a digital shadow that stores historical forecast-error patterns and generates augmented training scenarios. Case studies based on U.K. data show that the proposed framework produces fully feasible schedules and reduces annual CO₂ emissions by approximately 4.0% compared with a forecast-driven baseline, with larger benefits during high-demand periods. These results demonstrate that combining digital shadowing, constraint-embedded neural decoding, and carbon-aware optimization provides a practical and reliable pathway for low-carbon smart-grid scheduling under uncertainty. Full article

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34 pages, 8284 KB

Open AccessArticle

A Reproducible Hybrid AI Framework for Early Soil Nutrient Screening from Sentinel-2 Remote Sensing Data

by Olzhas Nuridinov, Gulzira Abdikerimova, Dinara Kaibassova, Amir Orazbay, Zeinigul Sattybayeva, Akbota Yerzhanova, Ainur Orynbayeva, Gulkiz Zhidekulova and Aigul Kubegenova

Technologies 2026, 14(7), 418; https://doi.org/10.3390/technologies14070418 - 8 Jul 2026

Abstract

This paper proposes a hybrid, interpretable machine learning framework for the preliminary screening of soil macronutrients using Sentinel-2 and AgroLens data. This study aims not to replace laboratory analysis, but to test the feasibility of obtaining a useful proxy signal for estimating nitrogen [...] Read more.

This paper proposes a hybrid, interpretable machine learning framework for the preliminary screening of soil macronutrients using Sentinel-2 and AgroLens data. This study aims not to replace laboratory analysis, but to test the feasibility of obtaining a useful proxy signal for estimating nitrogen (N), phosphorus (P), and potassium (K) content using a limited set of remote sensing and agricultural features. The developed pipeline includes data auditing, leakage control, feature engineering, train-only normalization, group-aware partitioning, baseline/SOTA model comparison, hybrid regression modeling, SHAP interpretation, and uncertainty assessment. The experiment used 4471 AgroLens observations and 126 features derived from Sentinel-2 spectral aggregates, vegetation indices, temporal characteristics, and crop-related parameters. The evaluation indicated that the proposed approach consistently improves forecasting quality relative to baseline models under reduced-input conditions. Linear relationships between target variables ranged from 0.14 to 0.17, while nonlinear relationships reached 0.23. SHAP analysis revealed significant contributions from vegetation indices, crop-specific interactions, and Sentinel-2 spectral channels. The findings support the applicability of the proposed framework for preliminary monitoring, prioritizing field surveys, and decision support in digital agriculture. Although an additional AgroLens control segment was used to assess the robustness of the study, the study did not include independent external validation of the data collected across different geographic or agro-climatic conditions. Full article

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15 pages, 1023 KB

Open AccessArticle

A Quality-Driven Adaptive Coding and Modulation Framework for Enhanced Digital Video Broadcasting over Satellite Networks

by Ubong Ukommi, Mfonobong Uko, Sunday Ekpo and Ikpaya Ikpaya

Technologies 2026, 14(7), 417; https://doi.org/10.3390/technologies14070417 - 8 Jul 2026

Abstract

The exponential growth of digital video traffic over satellite networks demands innovative approaches to optimize spectral efficiency while ensuring high quality of experience (QoE). Conventional Adaptive Coding and Modulation (ACM) schemes respond solely to Channel State Information (CSI), neglecting the perceptual importance of [...] Read more.

The exponential growth of digital video traffic over satellite networks demands innovative approaches to optimize spectral efficiency while ensuring high quality of experience (QoE). Conventional Adaptive Coding and Modulation (ACM) schemes respond solely to Channel State Information (CSI), neglecting the perceptual importance of video content. This paper proposes a comprehensive Quality-Driven Adaptive Coding and Modulation (QACM) framework that dynamically allocates physical-layer resources based on joint channel conditions and content-aware quality metrics. The framework introduces a Quality Significance Factor (QSF) that quantifies video complexity and priority, enabling intelligent trade-offs between spectral efficiency and quality robustness. We implement a complete simulation testbed incorporating DVB-S2X-compliant ModCods with multiple code rates (1/2, 3/4, and 5/6) and higher-order constellations (up to 64QAM) over Additive White Gaussian Noise (AWGN) channels. Extensive experimental results using H.264/AVC sequences demonstrate that, while standard ACM achieves 30.14 dB PSNR for high-motion football sequences at 16 dB SNR with 64QAM-1/2, QACM improves this to 41.40 dB by switching to QPSK-1/2, representing an 11.26 dB gain. We provide comprehensive BER analyses across the 0–20 dB SNR range, statistical significance validation (p < 0.01 for quality improvements), computational complexity analysis showing 15.2% overhead, and detailed comparisons with prior arts. The framework demonstrates scalability to higher-order modulations while maintaining 23% weighted QoE improvement over conventional ACM. This work provides a validated, implementable cross-layer solution for next-generation satellite broadcasting systems. Full article

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5 pages, 147 KB

Open AccessEditorial

Sustainable Water and Environmental Technologies of Global Relevance

by Miklas Scholz

Technologies 2026, 14(7), 416; https://doi.org/10.3390/technologies14070416 - 8 Jul 2026

Abstract

These are uncertain political and socio-economic times worldwide [...] Full article

(This article belongs to the Special Issue Sustainable Water and Environmental Technologies of Global Relevance)

28 pages, 5022 KB

Open AccessReview

A Conceptual Study for Cognitive Bias Amplification in Agentic AI-Driven Business Processes, Management, and Intelligence

by Subhra Mondal, Subhankar Das and Vasiliki G. Vrana

Technologies 2026, 14(7), 415; https://doi.org/10.3390/technologies14070415 - 7 Jul 2026

Abstract

Agentic artificial intelligence (AAI) and retrieval-augmented generation (RAG) are increasingly embedded in organisational business intelligence (BI) and business process management (BPM). Unlike conventional AI, these systems set sub-goals, plan multi-step workflows, and retrieve and store information under limited human supervision. This article argues [...] Read more.

Agentic artificial intelligence (AAI) and retrieval-augmented generation (RAG) are increasingly embedded in organisational business intelligence (BI) and business process management (BPM). Unlike conventional AI, these systems set sub-goals, plan multi-step workflows, and retrieve and store information under limited human supervision. This article argues that such autonomy not only transmits human cognitive bias into organisational decisions but also amplifies that bias under identifiable conditions. We develop the Bias Amplification Model (BAM), a three-layer account of how bias enters and escalates. In the injection layer, human biases enter through goal framing, prompt design, and data scoping. In the propagation layer, the agent compounds these biases across autonomous execution steps. In the crystallisation layer, RAG memory encodes biassed outputs as retrievable organisational knowledge that later cycles treat as evidence. A feedback loop links the three layers, converting episodic human bias into structural organisational bias. We state seven propositions, specify amplifying and mitigating conditions, and ground the model in a PRISMA-guided synthesis of 47 studies. A controlled, replicated experiment with an autonomous agent in a supplier-selection pipeline provides initial empirical support for injection, propagation, the mitigating effect of a single governance checkpoint, and bias transmission across a task boundary via retrieved memory. The framework reframes AI-driven process optimisation as a possible source of silent decline in decision quality. Full article

(This article belongs to the Special Issue Agentic AI-Driven Optimization in Advanced Manufacturing Systems)

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31 pages, 12215 KB

Open AccessArticle

NLOS-Aware LiDAR–UWB Fusion Localization for UAV Inspection in Converter Valve Halls

by Xiaoyi Liu, Yuhan Yin, Yetong Zhang, Kunxiao Wu, Jianyong Zheng and Fei Mei

Technologies 2026, 14(7), 414; https://doi.org/10.3390/technologies14070414 - 7 Jul 2026

Abstract

To address unavailable global navigation satellite system (GNSS) signals, dense metallic equipment, valve-tower occlusion, and the insufficient robustness of single-sensor localization in unmanned aerial vehicle (UAV) inspection of converter valve halls, this paper proposes a non-line-of-sight (NLOS)-aware LiDAR-ultra-wideband (UWB) fusion localization method. The [...] Read more.

To address unavailable global navigation satellite system (GNSS) signals, dense metallic equipment, valve-tower occlusion, and the insufficient robustness of single-sensor localization in unmanned aerial vehicle (UAV) inspection of converter valve halls, this paper proposes a non-line-of-sight (NLOS)-aware LiDAR-ultra-wideband (UWB) fusion localization method. The method uses LiDAR odometry to provide continuous local motion constraints and UWB ranging to provide global distance constraints. The geometric relationship among the UAV, UWB anchors, and valve-hall obstacles is used to evaluate the NLOS risk of each UWB link, and the equivalent ranging variance is adaptively adjusted before tight fusion optimization. To avoid overextending simulation conclusions, this study focuses on localization-layer modeling and simulation-based validation rather than full energized valve-hall flight deployment. In the grouped-bushing valve-hall scenario, the proposed method achieves an RMSE of 0.30 m, a mean error of 0.29 m, a P95 error of 0.43 m, and a maximum error of 0.48 m, reducing the RMSE by 50.0% compared with ordinary tight LiDAR-UWB fusion. Additional Monte Carlo tests under different trajectories, anchor layouts, anchor installation errors, and obstacle densities further verify the robustness of the proposed weighting mechanism. The results indicate that the method can suppress LiDAR accumulated drift and reduce the influence of UWB NLOS ranging in GNSS-denied metallic indoor environments, while real converter-valve-hall flight tests under energized electromagnetic conditions remain necessary before engineering deployment. Full article

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