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22 pages, 7879 KB  
Review
Effectiveness of Small Hydropower Plants Dismantling in the Chishui River Watershed and Recommendations for Follow-Up Studies
by Wenzhuo Gao, Zhigang Wang, Ke Wang, Xianxun Wang, Xiao Li and Qunli Jiang
Water 2025, 17(19), 2909; https://doi.org/10.3390/w17192909 - 9 Oct 2025
Viewed by 217
Abstract
With the characteristic of “decentralized distribution and local power supply”, small hydropower (SHP) in China has become a core means of solving the problem of insufficient power supply in rural and remote mountainous areas, effectively promoting the improvement of local livelihoods. However, for [...] Read more.
With the characteristic of “decentralized distribution and local power supply”, small hydropower (SHP) in China has become a core means of solving the problem of insufficient power supply in rural and remote mountainous areas, effectively promoting the improvement of local livelihoods. However, for a long time, SHP has had many problems, such as irrational development, old equipment, and poor economic efficiency, resulting in some rivers with connectivity loss and reduced biodiversity, etc. The Chishui River Watershed is an ecologically valuable river in the upper reaches of the Yangtze River. As an important habitat for rare fish in the upper reaches of the Yangtze River and the only large-scale tributary that maintains a natural flow pattern, the SHP plants’ dismantling and ecological restoration practices in the Chishui River Watershed can set a model for regional sustainable development. This paper adopts the methods of literature review, field research, and case study analysis, combined with the comparison of ecological conditions before and after the dismantling, to systematically analyze the effectiveness and challenges of SHP rectification in the Chishui River Watershed. The study found that after dismantling 88.2% of SHP plants in ecologically sensitive areas, the number of fish species upstream and downstream of the original dam site increased by about 6.67% and 70%, respectively; the natural hydrological connectivity has been restored to the downstream of the Tongzi River, the Gulin River and other rivers, but there are short-term problems such as sediment underflow, increased economic pressure, and the gap of alternative energy sources; the retained power stations have achieved the success and challenges of power generation and ecological management ecological flow control and comprehensive utilization, achieving a balance between power generation and ecological protection. Based on the above findings, the author proposes dynamic monitoring and interdisciplinary tracking research to fill the gap of systematic data support and long-term effect research in the SHP exit mechanism, and the results can provide a reference for the green transition of SHP. Full article
(This article belongs to the Section Water Resources Management, Policy and Governance)
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18 pages, 7892 KB  
Article
Validation of an Eddy-Viscosity-Based Roughness Model Using High-Fidelity Simulations
by Hendrik Seehausen, Kenan Cengiz and Lars Wein
Int. J. Turbomach. Propuls. Power 2025, 10(4), 34; https://doi.org/10.3390/ijtpp10040034 - 2 Oct 2025
Viewed by 167
Abstract
In this study, the modeling of rough surfaces by eddy-viscosity-based roughness models is investigated, specifically focusing on surfaces representative of deterioration in aero-engines. In order to test these models, experimental measurements from a rough T106C blade section at a Reynolds number of 400 [...] Read more.
In this study, the modeling of rough surfaces by eddy-viscosity-based roughness models is investigated, specifically focusing on surfaces representative of deterioration in aero-engines. In order to test these models, experimental measurements from a rough T106C blade section at a Reynolds number of 400 K are adopted. The modeling framework is based on the k-ω-SST with Dassler’s roughness transition model. The roughness model is recalibrated for the k-ω-SST model. As a complement to the available experimental data, a high-fidelity test rig designed for scale-resolving simulations is built. This allows us to examine the local flow phenomenon in detail, enabling the identification and rectification of shortcomings in the current RANS models. The scale-resolving simulations feature a high-order flux-reconstruction scheme, which enables the use of curved element faces to match the roughness geometry. The wake-loss predictions, as well as blade pressure profiles, show good agreement, especially between LES and the model-based RANS. The slight deviation from the experimental measurements can be attributed to the inherent uncertainties in the experiment, such as the end-wall effects. The outcomes of this study lend credibility to the roughness models proposed. In fact, these models have the potential to quantify the influence of roughness on the aerodynamics and the aero-acoustics of aero-engines, an area that remains an open question in the maintenance, repair, and overhaul (MRO) of aero-engines. Full article
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18 pages, 7743 KB  
Article
Improved Daytime Cloud Detection Algorithm in FY-4A’s Advanced Geostationary Radiation Imager
by Xiao Zhang, Song-Ying Zhao and Rui-Xuan Tang
Atmosphere 2025, 16(9), 1105; https://doi.org/10.3390/atmos16091105 - 20 Sep 2025
Viewed by 333
Abstract
Cloud detection is an indispensable step in satellite remote sensing of cloud properties and objects under the influence of cloud occlusion. Nevertheless, interfering targets such as snow and haze pollution are easily misjudged as clouds for most of the current algorithms. Hence, a [...] Read more.
Cloud detection is an indispensable step in satellite remote sensing of cloud properties and objects under the influence of cloud occlusion. Nevertheless, interfering targets such as snow and haze pollution are easily misjudged as clouds for most of the current algorithms. Hence, a robust cloud detection algorithm is urgently needed, especially for regions with high latitudes or severe air pollution. This paper demonstrated that the passive satellite detector Advanced Geosynchronous Radiation Imager (AGRI) onboard the FY-4A satellite has a great possibility to misjudge the dense aerosols in haze pollution as clouds during the daytime, and constructed an algorithm based on the spectral information of the AGRI’s 14 bands with a concise and high-speed calculation. This study adjusted the previously proposed cloud mask rectification algorithm of Moderate-Resolution Imaging Spectroradiometer (MODIS), rectified the MODIS cloud detection result, and used it as the accurate cloud mask data. The algorithm was constructed based on adjusted Fisher discrimination analysis (AFDA) and spectral spatial variability (SSV) methods over four different underlying surfaces (land, desert, snow, and water) and two seasons (summer and winter). This algorithm divides the identification into two steps to screen the confident cloud clusters and broken clouds, which are not easy to recognize, respectively. In the first step, channels with obvious differences in cloudy and cloud-free areas were selected, and AFDA was utilized to build a weighted sum formula across the normalized spectral data of the selected bands. This step transforms the traditional dynamic-threshold test on multiple bands into a simple test of the calculated summation value. In the second step, SSV was used to capture the broken clouds by calculating the standard deviation (STD) of spectra in every 3 × 3-pixel window to quantify the spectral homogeneity within a small scale. To assess the algorithm’s spatial and temporal generalizability, two evaluations were conducted: one examining four key regions and another assessing three different moments on a certain day in East China. The results showed that the algorithm has an excellent accuracy across four different underlying surfaces, insusceptible to the main interferences such as haze and snow, and shows a strong detection capability for broken clouds. This algorithm enables widespread application to different regions and times of day, with a low calculation complexity, indicating that a new method satisfying the requirements of fast and robust cloud detection can be achieved. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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22 pages, 28581 KB  
Article
Remote Sensing Interpretation of Geological Elements via a Synergistic Neural Framework with Multi-Source Data and Prior Knowledge
by Kang He, Ruyi Feng, Zhijun Zhang and Yusen Dong
Remote Sens. 2025, 17(16), 2772; https://doi.org/10.3390/rs17162772 - 10 Aug 2025
Viewed by 729
Abstract
Geological elements are fundamental components of the Earth’s ecosystem, and accurately identifying their spatial distribution is essential for analyzing environmental processes, guiding land-use planning, and promoting sustainable development. Remote sensing technologies, combined with artificial intelligence algorithms, offer new opportunities for the efficient interpretation [...] Read more.
Geological elements are fundamental components of the Earth’s ecosystem, and accurately identifying their spatial distribution is essential for analyzing environmental processes, guiding land-use planning, and promoting sustainable development. Remote sensing technologies, combined with artificial intelligence algorithms, offer new opportunities for the efficient interpretation of geological features. However, in areas with dense vegetation coverage, the information directly extracted from single-source optical imagery is limited, thereby constraining interpretation accuracy. Supplementary inputs such as synthetic aperture radar (SAR), topographic features, and texture information—collectively referred to as sensitive features and prior knowledge—can improve interpretation, but their effectiveness varies significantly across time and space. This variability often leads to inconsistent performance in general-purpose models, thus limiting their practical applicability. To address these challenges, we construct a geological element interpretation dataset for Northwest China by incorporating multi-source data, including Sentinel-1 SAR imagery, Sentinel-2 multispectral imagery, sensitive features (such as the digital elevation model (DEM), texture features based on the gray-level co-occurrence matrix (GLCM), geological maps (GMs), and the normalized difference vegetation index (NDVI)), as well as prior knowledge (such as base geological maps). Using five mainstream deep learning models, we systematically evaluate the performance improvement brought by various sensitive features and prior knowledge in remote sensing-based geological interpretation. To handle disparities in spatial resolution, temporal acquisition, and noise characteristics across sensors, we further develop a multi-source complement-driven network (MCDNet) that integrates an improved feature rectification module (IFRM) and an attention-enhanced fusion module (AFM) to achieve effective cross-modal alignment and noise suppression. Experimental results demonstrate that the integration of multi-source sensitive features and prior knowledge leads to a 2.32–6.69% improvement in mIoU for geological elements interpretation, with base geological maps and topographic features contributing most significantly to accuracy gains. Full article
(This article belongs to the Special Issue Multimodal Remote Sensing Data Fusion, Analysis and Application)
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16 pages, 1625 KB  
Article
Flow Characteristics by Blood Speckle Imaging in Non-Stenotic Congenital Aortic Root Disease Surrounding Valve-Preserving Operations
by Shihao Liu, Justin T. Tretter, Lama Dakik, Hani K. Najm, Debkalpa Goswami, Jennifer K. Ryan and Elias Sundström
Bioengineering 2025, 12(7), 776; https://doi.org/10.3390/bioengineering12070776 - 17 Jul 2025
Viewed by 738
Abstract
Contemporary evaluation and surgical approaches in congenital aortic valve disease have yielded limited success. The ability to evaluate and understand detailed flow characteristics surrounding surgical repair may be beneficial. This study explores the feasibility and utility of echocardiographic-based blood speckle imaging (BSI) in [...] Read more.
Contemporary evaluation and surgical approaches in congenital aortic valve disease have yielded limited success. The ability to evaluate and understand detailed flow characteristics surrounding surgical repair may be beneficial. This study explores the feasibility and utility of echocardiographic-based blood speckle imaging (BSI) in assessing pre- and post-operative flow characteristics in those with non-stenotic congenital aortic root disease undergoing aortic valve repair or valve-sparing root replacement (VSRR) surgery. Transesophageal echocardiogram was performed during the pre-operative and post-operative assessment surrounding aortic surgery for ten patients with non-stenotic congenital aortic root disease. BSI, utilizing block-matching algorithms, enabled detailed visualization and quantification of flow parameters from the echocardiographic data. Post-operative BSI unveiled enhanced hemodynamic patterns, characterized by quantified changes suggestive of the absence of stenosis and no more than trivial regurgitation. Rectification of an asymmetric jet and the reversal of flow on the posterior aspect of the ascending aorta resulted in a reduced oscillatory shear index (OSI) of 0.0543±0.0207 (pre-op) vs. 0.0275±0.0159 (post-op) and p=0.0044, increased peak wall shear stress of 1.9423±0.6974 (pre-op) vs. 3.6956±1.4934 (post-op) and p=0.0035, and increased time-averaged wall shear stress of 0.6885±0.8004 (pre-op) vs. 0.8312±0.303 (post-op) and p=0.23. This correction potentially attenuates cellular alterations within the endothelium. This study demonstrates that children and young adults with non-stenotic congenital aortic root disease undergoing valve-preserving operations experience significant improvements in flow dynamics within the left ventricular outflow tract and aortic root, accompanied by a reduction in OSI. These hemodynamic enhancements extend beyond the conventional echocardiographic assessments, offering immediate and valuable insights into the efficacy of surgical interventions. Full article
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21 pages, 2469 KB  
Article
Robust Low-Overlap Point Cloud Registration via Displacement-Corrected Geometric Consistency for Enhanced 3D Sensing
by Xin Wang and Qingguang Li
Sensors 2025, 25(14), 4332; https://doi.org/10.3390/s25144332 - 11 Jul 2025
Viewed by 895
Abstract
Accurate alignment of 3D point clouds, achieved by ubiquitous sensors such as LiDAR and depth cameras, is critical for enhancing perception capabilities in robotics, autonomous navigation, and environmental reconstruction. However, low-overlap scenarios—common due to limited sensor field-of-view or occlusions—severely degrade registration robustness and [...] Read more.
Accurate alignment of 3D point clouds, achieved by ubiquitous sensors such as LiDAR and depth cameras, is critical for enhancing perception capabilities in robotics, autonomous navigation, and environmental reconstruction. However, low-overlap scenarios—common due to limited sensor field-of-view or occlusions—severely degrade registration robustness and sensing reliability. To address this challenge, this paper proposes a novel geometric consistency optimization and rectification deep learning network named GeoCORNet. By synergistically designing a geometric consistency enhancement module, a bidirectional cross-attention mechanism, a predictive displacement rectification strategy, and joint optimization of overlap loss with displacement loss, GeoCORNet significantly improves registration accuracy and robustness in complex scenarios. The Attentive Cross-Consistency module of GeoCORNet integrates distance and angular consistency constraints with bidirectional cross-attention to significantly suppress noise from non-overlapping regions while reinforcing geometric coherence in overlapping areas. The predictive displacement rectification strategy dynamically rectifies erroneous correspondences through predicted 3D displacements instead of discarding them, maximizing the utility of sparse sensor data. Furthermore, a novel displacement loss function was developed to effectively constrain the geometric distribution of corrected point-pairs. Experimental results demonstrate that our method outperformed existing approaches in the aspects of registration recall, rotation error, and algorithm robustness under low-overlap conditions. These advances establish a new paradigm for robust 3D sensing in real-world applications where partial sensor data is prevalent. Full article
(This article belongs to the Section Sensing and Imaging)
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22 pages, 4290 KB  
Article
KCNH3 Loss-of-Function Variant Associated with Epilepsy and Neurodevelopmental Delay Enhances Kv12.2 Channel Inactivation
by Christiane K. Bauer, Arne Bilet, Frederike L. Harms and Robert Bähring
Int. J. Mol. Sci. 2025, 26(10), 4631; https://doi.org/10.3390/ijms26104631 - 13 May 2025
Viewed by 621
Abstract
A de novo missense variant in KCNH3 has been identified in a patient with neurological symptoms including seizures. Here, we confirm the previously reported loss-of-function features for the associated Kv12.2 mutant A371V and investigate the underlying mechanism. Loss of function was not rescued [...] Read more.
A de novo missense variant in KCNH3 has been identified in a patient with neurological symptoms including seizures. Here, we confirm the previously reported loss-of-function features for the associated Kv12.2 mutant A371V and investigate the underlying mechanism. Loss of function was not rescued by low temperature during channel biogenesis. Elevated external K+ reduced the rectification of Kv12.2 conductance as predicted by the GHK current equation, allowing the detection of currents mediated by homomeric A371V Kv12.2 channels and a detailed biophysical analysis of the mutant. Compared to wild-type, the voltage dependences of activation and deactivation of A371V Kv12.2 channels were shifted in the positive direction by 15 to 20 mV. Moreover, A371V Kv12.2 channels exhibited accelerated inactivation kinetics combined with a dramatic negative shift in the voltage dependence of inactivation by more than 100 mV. Even in heteromeric wild-type + A371V Kv12.2 channels, inactivation was enhanced, leading to a significant current reduction at physiological potentials. Our Kv12.2 data show similarities to Kv11 channels regarding C-type inactivation and differences regarding the sensitivity to external K+ and pharmacological inhibition of inactivation. The gating modification caused by the A371V amino acid substitution in Kv12.2 renders loss of function voltage-dependent, with a possible impact on neuronal excitability and firing behavior. Full article
(This article belongs to the Special Issue Voltage-Gated Ion Channels and Human Diseases)
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11 pages, 2591 KB  
Article
Accuracy Analysis of Slurry Characterization in a Rectifying Liquid Concentration Detection System
by Chao Wang, Pengfei Song, Zhiyang Li and Dong Yang
Processes 2025, 13(5), 1421; https://doi.org/10.3390/pr13051421 - 7 May 2025
Viewed by 537
Abstract
Accurately detecting coal slime water concentration during coal washing is crucial for optimizing dosing systems and improving separation efficiency. Traditional concentration detection methods are often affected by flow field disturbances. To address these limitations, this paper proposes a pressure differential concentration detection system [...] Read more.
Accurately detecting coal slime water concentration during coal washing is crucial for optimizing dosing systems and improving separation efficiency. Traditional concentration detection methods are often affected by flow field disturbances. To address these limitations, this paper proposes a pressure differential concentration detection system utilizing interference rectification for a stabilized flow field and improved measurement accuracy. The experimental system comprises a circulating slurry tank, a defoamer, and a turbulence removal measuring tank. Numerical simulations and experimental studies investigated the effects of slurry concentration and inflow velocity on detection accuracy. Through dynamic measurement of pressure difference data under different concentrations and flow rates, the characteristics of a solid–liquid two-phase flow field are simulated using Fluent software. The results demonstrate that for low-concentration (C = 10%) and high-concentration (C = 30%) slurries, a flow velocity of ≥0.7 m/s significantly improves flow uniformity and achieves a stable particle suspension state, maintaining a measurement error within 1% for a flow rate of 0.7 m/s. However, flow rates exceeding 0.7 m/s decrease flow stability, increasing errors. Notably, the combination of sensors at positions No. 2 and No. 4 yields the lowest measurement errors, which verifies the influence of sensor layout on detection accuracy. A 0.7 m/s velocity is identified as the key threshold for flow field stability, and the nonlinear influence of the synergistic effect of flow rate and concentration on the detection stability is revealed. These findings provide valuable insights for optimizing pulp concentration detection systems and enhancing industrial dosing precision. Full article
(This article belongs to the Section Chemical Processes and Systems)
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15 pages, 19341 KB  
Article
SMILE: Segmentation-Based Centroid Matching for Image Rectification via Aligning Epipolar Lines
by Junewoo Choi and Deokwoo Lee
Appl. Sci. 2025, 15(9), 4962; https://doi.org/10.3390/app15094962 - 30 Apr 2025
Viewed by 691
Abstract
Stereo images, which consist of left and right image pairs, are often unaligned when initially captured, as they represent raw data. Stereo images are typically used in scenarios requiring disparity between the left and right views, such as depth estimation. In such cases, [...] Read more.
Stereo images, which consist of left and right image pairs, are often unaligned when initially captured, as they represent raw data. Stereo images are typically used in scenarios requiring disparity between the left and right views, such as depth estimation. In such cases, image calibration is performed to obtain the necessary parameters, and, based on these parameters, image rectification is applied to align the epipolar lines of the stereo images. This preprocessing step is crucial for effectively utilizing stereo images. The conventional method for performing image calibration usually involves using a reference object, such as a checkerboard, to obtain these parameters. In this paper, we propose a novel approach that does not require any special reference points like a checkerboard. Instead, we employ object detection to segment object pairs and calculate the centroids of the segmented objects. By aligning the y-coordinates of these centroids in the left and right image pairs, we induce the epipolar lines to be parallel, achieving an effect similar to image rectification. Full article
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15 pages, 6282 KB  
Article
Pulsed Laser Deposition Method Used to Grow SiC Nanostructure on Porous Silicon Substrate: Synthesis and Optical Investigation for UV-Vis Photodetector Fabrication
by Reem Alzubaidi, Makram A. Fakhri and László Pohl
Thermo 2025, 5(2), 13; https://doi.org/10.3390/thermo5020013 - 11 Apr 2025
Cited by 3 | Viewed by 1463
Abstract
In this study, a thin film of silicon carbide (SiC) was deposited on a porous silicon (P-Si) substrate using pulsed laser deposition (PLD). The photo–electrochemical etching method with an Nd: YAG laser at 1064 nm wavelength and 900 mJ pulse energy and at [...] Read more.
In this study, a thin film of silicon carbide (SiC) was deposited on a porous silicon (P-Si) substrate using pulsed laser deposition (PLD). The photo–electrochemical etching method with an Nd: YAG laser at 1064 nm wavelength and 900 mJ pulse energy and at a vacuum of 10−2 mbar P-Si was utilized to create a sufficiently high amount of surface area for SiC film deposition to achieve efficient SiC film growth on the P-Si substrate. X-ray diffraction (XRD) analysis was performed on the crystalline structure of SiC and showed high-intensity peaks at the (111) and (220) planes, indicating that the substrate–film interaction is substantial. Surface roughness particle topography was examined via atomic force microscopy (AFM), and a mean diameter equal to 72.83 nm was found. Field emission scanning electron microscopy (FESEM) was used to analyze surface morphology, and the pictures show spherical nanoparticles and a mud-sponge-like shape demonstrating significant nanoscale features. Photoluminescence and UV-Vis spectroscopy were utilized to investigate the optical properties, and two emission peaks were observed for the SiC and P-Si substrates, at 590 nm and 780 nm. The SiC/P-Si heterojunction photodetector exhibited rectification behavior in its dark I–V characteristics, indicating high junction quality. The spectral responsivity of the SiC/P-Si observed a peak responsivity of 0.0096 A/W at 365 nm with detectivity of 24.5 A/W Jones, and external quantum efficiency reached 340%. The response time indicates a rise time of 0.48 s and a fall time of 0.26 s. Repeatability was assured by the tight clustering of the data points, indicating the good reproducibility and stability of the SiC/P-Si deposition process. Linearity at low light levels verifies efficient photocarrier generation and separation, whereas a reverse saturation current at high intensities points to the maximum carrier generation capability of the device. Moreover, Raman spectroscopy and energy dispersive spectroscopy (EDS) analysis confirmed the structural quality and elemental composition of the SiC/P-Si film, further attesting to the uniformity and quality of the material produced. This hybrid material’s improved optoelectronic properties, achieved by combining the stability of SiC with the quantum confinement effects of P-Si, make it useful in advanced optoelectronic applications such as UV-Vis photodetectors. Full article
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17 pages, 11684 KB  
Article
Wi-FiAG: Fine-Grained Abnormal Gait Recognition via CNN-BiGRU with Attention Mechanism from Wi-Fi CSI
by Anming Dong, Jiahao Zhang, Wendong Xu, Jia Jia, Shanshan Yun and Jiguo Yu
Mathematics 2025, 13(8), 1227; https://doi.org/10.3390/math13081227 - 9 Apr 2025
Viewed by 777
Abstract
Abnormal gait recognition, which aims to detect and identify deviations from normal walking patterns indicative of various health conditions or impairments, holds promising applications in healthcare and many other related fields. Currently, Wi-Fi-based abnormal gait recognition methods in the literature mainly distinguish the [...] Read more.
Abnormal gait recognition, which aims to detect and identify deviations from normal walking patterns indicative of various health conditions or impairments, holds promising applications in healthcare and many other related fields. Currently, Wi-Fi-based abnormal gait recognition methods in the literature mainly distinguish the normal and abnormal gaits, which belongs to coarse-grained classification. In this work, we explore fine-grained gait rectification methods for distinguishing multiple classes of abnormal gaits. Specifically, we propose a deep learning-based framework for multi-class abnormal gait recognition, comprising three key modules: data collection, data preprocessing, and gait classification. For the gait classification module, we design a hybrid deep learning architecture that integrates convolutional neural networks (CNNs), bidirectional gated recurrent units (BiGRUs), and an attention mechanism to enhance performance. Compared to traditional CNNs, which rely solely on spatial features, or recurrent neural networks like long short-term memory (LSTM) and gated recurrent units (GRUs), which primarily capture temporal dependencies, the proposed CNN-BiGRU network integrates both spatial and temporal features concurrently. This dual-feature extraction capability positions the proposed CNN-BiGRU architecture as a promising approach for enhancing classification accuracy in scenarios involving multiple gaits with subtle differences in their characteristics. Moreover, the attention mechanism is employed to selectively focus on critical spatiotemporal features for fine-grained abnormal gait detection, enhancing the model’s sensitivity to subtle anomalies. We construct an abnormal gait dataset comprising seven distinct gait classes to train and evaluate the proposed network. Experimental results demonstrate that the proposed method achieves an average recognition accuracy of 95%, surpassing classical baseline models by at least 2%. Full article
(This article belongs to the Special Issue Data-Driven Decentralized Learning for Future Communication Networks)
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25 pages, 34678 KB  
Article
Historical Coast Snaps: Using Centennial Imagery to Track Shoreline Change
by Fátima Valverde, Rui Taborda, Amy E. East and Cristina Ponte Lira
Remote Sens. 2025, 17(8), 1326; https://doi.org/10.3390/rs17081326 - 8 Apr 2025
Viewed by 1421
Abstract
Understanding long-term coastal evolution requires historical data, yet accessing reliable information becomes increasingly challenging for extended periods. While vertical aerial imagery has been extensively used in coastal studies since the mid-20th century, and satellite-derived shoreline measurements are now revolutionizing shoreline change studies, ground-based [...] Read more.
Understanding long-term coastal evolution requires historical data, yet accessing reliable information becomes increasingly challenging for extended periods. While vertical aerial imagery has been extensively used in coastal studies since the mid-20th century, and satellite-derived shoreline measurements are now revolutionizing shoreline change studies, ground-based images, such as historical photographs and picture postcards, provide an alternative source of shoreline data for earlier periods when other datasets are scarce. Despite their frequent use for documenting qualitative morphological changes, these valuable historical data sources have rarely supported quantitative assessments of coastal evolution. This study demonstrates the potential of historical ground-oblique images for quantitatively assessing shoreline position and long-term change. Using Conceição-Duquesa Beach (Cascais, Portugal) as a case study, we analyze shoreline evolution over 92 years by applying a novel methodology to historical photographs and postcards. The approach combines image registration, shoreline detection, coordinate transformation, and rectification while accounting for positional uncertainty. Results reveal a significant counterclockwise rotation of the shoreline between the 20th and 21st centuries, exceeding estimated uncertainty thresholds. This study highlights the feasibility of using historical ground-based imagery to reconstruct shoreline positions and quantify long-term coastal change. The methodology is straightforward, adaptable, and offers a promising avenue for extending the temporal range of shoreline datasets, advancing our understanding of coastal evolution. Full article
(This article belongs to the Special Issue Advances in Remote Sensing of the Inland and Coastal Water Zones II)
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26 pages, 587 KB  
Article
GDPR and Large Language Models: Technical and Legal Obstacles
by Georgios Feretzakis, Evangelia Vagena, Konstantinos Kalodanis, Paraskevi Peristera, Dimitris Kalles and Athanasios Anastasiou
Future Internet 2025, 17(4), 151; https://doi.org/10.3390/fi17040151 - 28 Mar 2025
Cited by 3 | Viewed by 4745
Abstract
Large Language Models (LLMs) have revolutionized natural language processing but present significant technical and legal challenges when confronted with the General Data Protection Regulation (GDPR). This paper examines the complexities involved in reconciling the design and operation of LLMs with GDPR requirements. In [...] Read more.
Large Language Models (LLMs) have revolutionized natural language processing but present significant technical and legal challenges when confronted with the General Data Protection Regulation (GDPR). This paper examines the complexities involved in reconciling the design and operation of LLMs with GDPR requirements. In particular, we analyze how key GDPR provisions—including the Right to Erasure, Right of Access, Right to Rectification, and restrictions on Automated Decision-Making—are challenged by the opaque and distributed nature of LLMs. We discuss issues such as the transformation of personal data into non-interpretable model parameters, difficulties in ensuring transparency and accountability, and the risks of bias and data over-collection. Moreover, the paper explores potential technical solutions such as machine unlearning, explainable AI (XAI), differential privacy, and federated learning, alongside strategies for embedding privacy-by-design principles and automated compliance tools into LLM development. The analysis is further enriched by considering the implications of emerging regulations like the EU’s Artificial Intelligence Act. In addition, we propose a four-layer governance framework that addresses data governance, technical privacy enhancements, continuous compliance monitoring, and explainability and oversight, thereby offering a practical roadmap for GDPR alignment in LLM systems. Through this comprehensive examination, we aim to bridge the gap between the technical capabilities of LLMs and the stringent data protection standards mandated by GDPR, ultimately contributing to more responsible and ethical AI practices. Full article
(This article belongs to the Special Issue Generative Artificial Intelligence (AI) for Cybersecurity)
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14 pages, 503 KB  
Article
A Risk-Based Approach to Assess the Effectiveness of Sprinklers in Buildings with Combustible Cladding
by Kjetil Pedersen, Kate Nguyen and Ashley Hunt
Fire 2025, 8(4), 119; https://doi.org/10.3390/fire8040119 - 21 Mar 2025
Viewed by 1266
Abstract
This study investigates a risk-based approach to evaluate the effectiveness of sprinklers in residential buildings to offset the risk premium imposed by combustible cladding (expanded polystyrene and aluminium composite panels) installed on such buildings in Victoria, Australia. This approach builds upon the Initial [...] Read more.
This study investigates a risk-based approach to evaluate the effectiveness of sprinklers in residential buildings to offset the risk premium imposed by combustible cladding (expanded polystyrene and aluminium composite panels) installed on such buildings in Victoria, Australia. This approach builds upon the Initial Fire Spread in Cladding Assessment Number (IF-SCAN), a concept pioneered by Cladding Safety Victoria as a triage tool in their rectification program. The analysis uses published data from real fires in buildings with and without sprinkler systems installed. It considers three criteria: death rates, injury rates, and construction cost. The construction cost was determined using an existing costing model currently employed in Victoria. The results of this study suggest a higher risk tolerance can be accepted for combustible cladding on buildings equipped with sprinkler systems over that set out in government policy. More specifically, it was found that a building fully protected by sprinklers can generally counterbalance the fire risk posed by combustible cladding spanning up to seven apartments, while a span of up to ten apartments could be considered for buildings without balconies or a private courtyard. Full article
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27 pages, 11172 KB  
Article
ResGRU: A Novel Hybrid Deep Learning Model for Compound Fault Diagnosis in Photovoltaic Arrays Considering Dust Impact
by Xi Liu, Hui Hwang Goh, Haonan Xie, Tingting He, Weng Kean Yew, Dongdong Zhang, Wei Dai and Tonni Agustiono Kurniawan
Sensors 2025, 25(4), 1035; https://doi.org/10.3390/s25041035 - 9 Feb 2025
Cited by 1 | Viewed by 1394
Abstract
With the widespread deployment of photovoltaic (PV) power stations, timely identification and rectification of module defects are crucial for extending service life and preserving efficiency. PV arrays, subjected to severe outside circumstances, are prone to defects exacerbated by dust accumulation, potentially leading to [...] Read more.
With the widespread deployment of photovoltaic (PV) power stations, timely identification and rectification of module defects are crucial for extending service life and preserving efficiency. PV arrays, subjected to severe outside circumstances, are prone to defects exacerbated by dust accumulation, potentially leading to complex compound faults. The resemblance between individual and compound faults sometimes leads to misclassification. To address this challenge, this paper presents a novel hybrid deep learning model, ResGRU, which integrates a residual network (ResNet) with bidirectional gated recurrent units (BiGRU) to improve fault diagnostic accuracy. Additionally, a Squeeze-and-Excitation (SE) module is incorporated to enhance relevant features while suppressing irrelevant ones, hence improving performance. To further optimize inter-class separability and intra-class compactness, a center loss function is employed as an auxiliary loss to enhance the model’s discriminative capacity. This proposed method facilitates the automated extraction of fault features from I-V curves and accurate diagnosis of individual faults, partial shading scenarios, and compound faults under varying levels of dust accumulation, hence aiding in the formulation of efficient cleaning schedules. Experimental findings indicate that the suggested model achieves 99.94% accuracy on pristine data and 98.21% accuracy on noisy data, markedly surpassing established techniques such as artificial neural networks (ANN), ResNet, random forests (RF), multi-scale SE-ResNet, and other ResNet-based approaches. Thus, the model offers a reliable solution for accurate PV array fault diagnosis. Full article
(This article belongs to the Special Issue Fault Diagnosis for Photovoltaic Systems Based on Sensors)
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