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Search Results (12,334)

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20 pages, 2227 KB  
Article
Tuberculosis Detection from Cough Recordings Using Bag-of-Words Classifiers
by Irina Pavel and Iulian B. Ciocoiu
Sensors 2025, 25(19), 6133; https://doi.org/10.3390/s25196133 - 3 Oct 2025
Abstract
The paper proposes the use of Bag-of-Words classifiers for the reliable detection of tuberculosis infection from cough recordings. The effect of using both independent and combined distinct feature extraction procedures and encoding strategies is evaluated in terms of standard performance metrics such as [...] Read more.
The paper proposes the use of Bag-of-Words classifiers for the reliable detection of tuberculosis infection from cough recordings. The effect of using both independent and combined distinct feature extraction procedures and encoding strategies is evaluated in terms of standard performance metrics such as the Area Under Curve (AUC), accuracy, sensitivity, and F1-score. Experiments were conducted on two distinct large datasets, using both the original recordings and extended versions obtained by augmentation techniques. Performances were assessed by repeated k-fold cross-validation and by employing external datasets. An extensive ablation study revealed that the proposed approach yields up to 0.77 accuracy and 0.84 AUC values, comparing favorably against existing solutions and exhibiting robustness against various combinations of the setup parameters. Full article
(This article belongs to the Section Biomedical Sensors)
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22 pages, 2445 KB  
Article
The Construction of a Design Method Knowledge Graph Driven by Multi-Source Heterogeneous Data
by Jixing Shi, Kaiyi Wang, Zhongqing Wang, Zhonghang Bai and Fei Hu
Appl. Sci. 2025, 15(19), 10702; https://doi.org/10.3390/app151910702 - 3 Oct 2025
Abstract
To address the fragmentation and weak correlation of knowledge in the design method domain, this paper proposes a framework for constructing a knowledge graph driven by multi-source heterogeneous data. The process involves collecting multi-source heterogeneous data and subsequently utilizing text mining and natural [...] Read more.
To address the fragmentation and weak correlation of knowledge in the design method domain, this paper proposes a framework for constructing a knowledge graph driven by multi-source heterogeneous data. The process involves collecting multi-source heterogeneous data and subsequently utilizing text mining and natural language processing techniques to extract design themes and method elements. A “theme–stage–attribute” three-dimensional mapping model is established to achieve semantic coupling of knowledge. The BERT-BiLSTM-CRF (Bidirectional Encoder Representations from Transformers-Bidirectional Long Short-Term Memory-Conditional Random Field) model is employed for entity recognition and relation extraction, while the Sentence-BERT (Sentence Bidirectional Encoder Representations from Transformers) model is used to perform multi-source knowledge fusion. The Neo4j graph database facilitates knowledge storage, visualization, and querying, forming the basis for developing a prototype of a design method recommendation system. The framework’s effectiveness was validated through experiments on extraction performance and knowledge graph quality. The results demonstrate that the framework achieves an F1 score of 91.2% for knowledge extraction, and an 8.44% improvement over the baseline. The resulting graph’s node and relation coverage reached 94.1% and 91.2%, respectively. In complex semantic query tasks, the framework shows a significant advantage over traditional classification systems, achieving a maximum F1 score of 0.97. It can effectively integrate dispersed knowledge in the field of design methods and support method matching throughout the entire design process. This research is of significant value for advancing knowledge management and application in innovative product design. Full article
15 pages, 1965 KB  
Article
Description of the Distinctive Changes in the Colonic Microbiome Associated with Irritable Bowel Syndrome, Uncomplicated Diverticulitis, and Tubular Adenoma
by Ramón Saavedra-Bravo, Alfonso Méndez-Tenorio, Mario Angel López-Luis, Eduardo Alejandro Dávila-Martínez, Marco Antonio Vázquez-Ávila, Lenin García-Gutierrez, Gloria León-Avila, Cindy Bandala, Mónica Alethia Cureño-Díaz, Verónica Fernández-Sánchez, José Antonio Morales-González, Eleazar Lara-Padilla, Javier Mancilla-Ramírez and Gabriela Ibáñez-Cervantes
Biomedicines 2025, 13(10), 2424; https://doi.org/10.3390/biomedicines13102424 - 3 Oct 2025
Abstract
Background: The pathogenesis of various colon-related pathologies, including irritable bowel syndrome, uncomplicated diverticulitis, and tubular adenoma, remains unknown, primarily due to their multifactorial nature. These gastrointestinal diseases are increasing in prevalence in Western countries and are common conditions worldwide. Objective: To [...] Read more.
Background: The pathogenesis of various colon-related pathologies, including irritable bowel syndrome, uncomplicated diverticulitis, and tubular adenoma, remains unknown, primarily due to their multifactorial nature. These gastrointestinal diseases are increasing in prevalence in Western countries and are common conditions worldwide. Objective: To identify intestinal microbiota signs and their associations with the development of colonic pathologies, such as irritable bowel syndrome, uncomplicated diverticulitis, and tubular adenoma. Materials and Methods: An observational, prospective, cross-sectional study was conducted to compare the microbiome among three conditions via 16S rRNA sequencing of biopsy samples obtained via colonoscopy. Results: The microbiome of individuals with tubular adenoma was less diverse than that of patients with diverticulitis and irritable bowel syndrome, with a lower abundance of commensal bacterial genera, such as Catenibacterium, Bifidobacterium, and Faecalibacterium, and an increase in several genera with known pathogenic roles, including Escherichia–Shigella, Fusobacteria, Prevotella, and Haemophilus. No significant association was found between the type of pathology and the total pathogenic or commensal disease score; however, a ratio of 2.54 to pathogenic/commensal was observed in the IBS patient group. In contrast, in the diverticulitis and adenoma patient groups, this ratio was 8. Conclusions: These results provide evidence supporting the proposal that alterations in the colonic microbiome could be involved in various colonic pathogeneses and that an imbalance between commensal and pathogenic populations could be directly related to pathogenesis in the microsystem. It is important to highlight the need for future studies. Full article
(This article belongs to the Section Molecular and Translational Medicine)
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37 pages, 10380 KB  
Article
FEWheat-YOLO: A Lightweight Improved Algorithm for Wheat Spike Detection
by Hongxin Wu, Weimo Wu, Yufen Huang, Shaohua Liu, Yanlong Liu, Nannan Zhang, Xiao Zhang and Jie Chen
Plants 2025, 14(19), 3058; https://doi.org/10.3390/plants14193058 - 3 Oct 2025
Abstract
Accurate detection and counting of wheat spikes are crucial for yield estimation and variety selection in precision agriculture. However, challenges such as complex field environments, morphological variations, and small target sizes hinder the performance of existing models in real-world applications. This study proposes [...] Read more.
Accurate detection and counting of wheat spikes are crucial for yield estimation and variety selection in precision agriculture. However, challenges such as complex field environments, morphological variations, and small target sizes hinder the performance of existing models in real-world applications. This study proposes FEWheat-YOLO, a lightweight and efficient detection framework optimized for deployment on agricultural edge devices. The architecture integrates four key modules: (1) FEMANet, a mixed aggregation feature enhancement network with Efficient Multi-scale Attention (EMA) for improved small-target representation; (2) BiAFA-FPN, a bidirectional asymmetric feature pyramid network for efficient multi-scale feature fusion; (3) ADown, an adaptive downsampling module that preserves structural details during resolution reduction; and (4) GSCDHead, a grouped shared convolution detection head for reduced parameters and computational cost. Evaluated on a hybrid dataset combining GWHD2021 and a self-collected field dataset, FEWheat-YOLO achieved a COCO-style AP of 51.11%, AP@50 of 89.8%, and AP scores of 18.1%, 50.5%, and 61.2% for small, medium, and large targets, respectively, with an average recall (AR) of 58.1%. In wheat spike counting tasks, the model achieved an R2 of 0.941, MAE of 3.46, and RMSE of 6.25, demonstrating high counting accuracy and robustness. The proposed model requires only 0.67 M parameters, 5.3 GFLOPs, and 1.6 MB of storage, while achieving an inference speed of 54 FPS. Compared to YOLOv11n, FEWheat-YOLO improved AP@50, AP_s, AP_m, AP_l, and AR by 0.53%, 0.7%, 0.7%, 0.4%, and 0.3%, respectively, while reducing parameters by 74%, computation by 15.9%, and model size by 69.2%. These results indicate that FEWheat-YOLO provides an effective balance between detection accuracy, counting performance, and model efficiency, offering strong potential for real-time agricultural applications on resource-limited platforms. Full article
(This article belongs to the Special Issue Advances in Artificial Intelligence for Plant Research)
32 pages, 6548 KB  
Article
Smart City Ontology Framework for Urban Data Integration and Application
by Xiaolong He, Xi Kuai, Xinyue Li, Zihao Qiu, Biao He and Renzhong Guo
Smart Cities 2025, 8(5), 165; https://doi.org/10.3390/smartcities8050165 - 3 Oct 2025
Abstract
Rapid urbanization and the proliferation of heterogeneous urban data have intensified the challenges of semantic interoperability and integrated urban governance. To address this, we propose the Smart City Ontology Framework (SMOF), a standards-driven ontology that unifies Building Information Modeling (BIM), Geographic Information Systems [...] Read more.
Rapid urbanization and the proliferation of heterogeneous urban data have intensified the challenges of semantic interoperability and integrated urban governance. To address this, we propose the Smart City Ontology Framework (SMOF), a standards-driven ontology that unifies Building Information Modeling (BIM), Geographic Information Systems (GIS), Internet of Things (IoT), and relational data. SMOF organizes five core modules and eleven major entity categories, with universal and extensible attributes and relations to support cross-domain data integration. SMOF was developed through competency questions, authoritative knowledge sources, and explicit design principles, ensuring methodological rigor and alignment with real governance needs. Its evaluation combined three complementary approaches against baseline models: quantitative metrics demonstrated higher attribute richness and balanced hierarchy; LLM as judge assessments confirmed conceptual completeness, consistency, and scalability; and expert scoring highlighted superior scenario fitness and clarity. Together, these results indicate that SMOF achieves both structural soundness and practical adaptability. Beyond structural evaluation, SMOF was validated in two representative urban service scenarios, demonstrating its capacity to integrate heterogeneous data, support graph-based querying and enable ontology-driven reasoning. In sum, SMOF offers a robust and scalable solution for semantic data integration, advancing smart city governance and decision-making efficiency. Full article
(This article belongs to the Special Issue Breaking Down Silos in Urban Services)
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42 pages, 3952 KB  
Article
An Explainable Markov Chain–Machine Learning Sequential-Aware Anomaly Detection Framework for Industrial IoT Systems Based on OPC UA
by Youness Ghazi, Mohamed Tabaa, Mohamed Ennaji and Ghita Zaz
Sensors 2025, 25(19), 6122; https://doi.org/10.3390/s25196122 - 3 Oct 2025
Abstract
Stealth attacks targeting industrial control systems (ICS) exploit subtle sequences of malicious actions, making them difficult to detect with conventional methods. The OPC Unified Architecture (OPC UA) protocol—now widely adopted in SCADA/ICS environments—enhances OT–IT integration but simultaneously increases the exposure of critical infrastructures [...] Read more.
Stealth attacks targeting industrial control systems (ICS) exploit subtle sequences of malicious actions, making them difficult to detect with conventional methods. The OPC Unified Architecture (OPC UA) protocol—now widely adopted in SCADA/ICS environments—enhances OT–IT integration but simultaneously increases the exposure of critical infrastructures to sophisticated cyberattacks. Traditional detection approaches, which rely on instantaneous traffic features and static models, neglect the sequential dimension that is essential for uncovering such gradual intrusions. To address this limitation, we propose a hybrid sequential anomaly detection pipeline that combines Markov chain modeling to capture temporal dependencies with machine learning algorithms for anomaly detection. The pipeline is further augmented by explainability through SHapley Additive exPlanations (SHAP) and causal inference using the PC algorithm. Experimental evaluation on an OPC UA dataset simulating Man-In-The-Middle (MITM) and denial-of-service (DoS) attacks demonstrates that incorporating a second-order sequential memory significantly improves detection: F1-score increases by +2.27%, precision by +2.33%, and recall by +3.02%. SHAP analysis identifies the most influential features and transitions, while the causal graph highlights deviations from the system’s normal structure under attack, thereby providing interpretable insights into the root causes of anomalies. Full article
38 pages, 5753 KB  
Article
EfficientNet-B3-Based Automated Deep Learning Framework for Multiclass Endoscopic Bladder Tissue Classification
by A. A. Abd El-Aziz, Mahmood A. Mahmood and Sameh Abd El-Ghany
Diagnostics 2025, 15(19), 2515; https://doi.org/10.3390/diagnostics15192515 - 3 Oct 2025
Abstract
Background: Bladder cancer (BLCA) is a malignant growth that originates from the urothelial lining of the urinary bladder. Diagnosing BLCA is complex due to the variety of tumor features and its heterogeneous nature, which leads to significant morbidity and mortality. Understanding tumor [...] Read more.
Background: Bladder cancer (BLCA) is a malignant growth that originates from the urothelial lining of the urinary bladder. Diagnosing BLCA is complex due to the variety of tumor features and its heterogeneous nature, which leads to significant morbidity and mortality. Understanding tumor histopathology is crucial for developing tailored therapies and improving patient outcomes. Objectives: Early diagnosis and treatment are essential to lower the mortality rate associated with bladder cancer. Manual classification of muscular tissues by pathologists is labor-intensive and relies heavily on experience, which can result in interobserver variability due to the similarities in cancerous cell morphology. Traditional methods for analyzing endoscopic images are often time-consuming and resource-intensive, making it difficult to efficiently identify tissue types. Therefore, there is a strong demand for a fully automated and reliable system for classifying smooth muscle images. Methods: This paper proposes a deep learning (DL) technique utilizing the EfficientNet-B3 model and a five-fold cross-validation method to assist in the early detection of BLCA. This model enables timely intervention and improved patient outcomes while streamlining the diagnostic process, ultimately reducing both time and costs for patients. We conducted experiments using the Endoscopic Bladder Tissue Classification (EBTC) dataset for multiclass classification tasks. The dataset was preprocessed using resizing and normalization methods to ensure consistent input. In-depth experiments were carried out utilizing the EBTC dataset, along with ablation studies to evaluate the best hyperparameters. A thorough statistical analysis and comparisons with five leading DL models—ConvNeXtBase, DenseNet-169, MobileNet, ResNet-101, and VGG-16—showed that the proposed model outperformed the others. Conclusions: The EfficientNet-B3 model achieved impressive results: accuracy of 99.03%, specificity of 99.30%, precision of 97.95%, recall of 96.85%, and an F1-score of 97.36%. These findings indicate that the EfficientNet-B3 model demonstrates significant potential in accurately and efficiently diagnosing BLCA. Its high performance and ability to reduce diagnostic time and cost make it a valuable tool for clinicians in the field of oncology and urology. Full article
(This article belongs to the Special Issue AI and Big Data in Medical Diagnostics)
25 pages, 3675 KB  
Article
Gesture-Based Physical Stability Classification and Rehabilitation System
by Sherif Tolba, Hazem Raafat and A. S. Tolba
Sensors 2025, 25(19), 6098; https://doi.org/10.3390/s25196098 - 3 Oct 2025
Abstract
This paper introduces the Gesture-Based Physical Stability Classification and Rehabilitation System (GPSCRS), a low-cost, non-invasive solution for evaluating physical stability using an Arduino microcontroller and the DFRobot Gesture and Touch sensor. The system quantifies movement smoothness, consistency, and speed by analyzing “up” and [...] Read more.
This paper introduces the Gesture-Based Physical Stability Classification and Rehabilitation System (GPSCRS), a low-cost, non-invasive solution for evaluating physical stability using an Arduino microcontroller and the DFRobot Gesture and Touch sensor. The system quantifies movement smoothness, consistency, and speed by analyzing “up” and “down” hand gestures over a fixed period, generating a Physical Stability Index (PSI) as a single metric to represent an individual’s stability. The system focuses on a temporal analysis of gesture patterns while incorporating placeholders for speed scores to demonstrate its potential for a comprehensive stability assessment. The performance of various machine learning and deep learning models for gesture-based classification is evaluated, with neural network architectures such as Transformer, CNN, and KAN achieving perfect scores in recall, accuracy, precision, and F1-score. Traditional machine learning models such as XGBoost show strong results, offering a balance between computational efficiency and accuracy. The choice of model depends on specific application requirements, including real-time constraints and available resources. The preliminary experimental results indicate that the proposed GPSCRS can effectively detect changes in stability under real-time conditions, highlighting its potential for use in remote health monitoring, fall prevention, and rehabilitation scenarios. By providing a quantitative measure of stability, the system enables early risk identification and supports tailored interventions for improved mobility and quality of life. Full article
(This article belongs to the Section Biomedical Sensors)
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17 pages, 1835 KB  
Article
Management of Post-Colonoscopy Syndrome with a Nutraceutical Intervention Based on Hericium erinaceus: A Retrospective Two-Arm Multicentre Analysis
by Antonio Tursi, Alessandro D’Avino, Giovanni Brandimarte, Giammarco Mocci, Raffaele Pellegrino, Alessandro Federico, Edoardo Vincenzo Savarino, Antonietta Gerarda Gravina and the HERICIUM-COLON Study Group
Nutrients 2025, 17(19), 3152; https://doi.org/10.3390/nu17193152 - 2 Oct 2025
Abstract
Background: Post-colonoscopy syndrome is an emerging clinical entity characterised by the onset of gastrointestinal symptoms following a colonoscopy. The current management of this syndrome has not yet been established, although probiotics have been proposed. The therapeutic potential of a combination nutraceutical compound [...] Read more.
Background: Post-colonoscopy syndrome is an emerging clinical entity characterised by the onset of gastrointestinal symptoms following a colonoscopy. The current management of this syndrome has not yet been established, although probiotics have been proposed. The therapeutic potential of a combination nutraceutical compound based on HBQ-Complex®, butyrate, and probiotics (Lactobacillus acidophilus, Bifidobacterium animalis, and Lactiplantibacillus plantarum) in this setting remains unknown. Methods: A retrospective, multicentre, observational study was conducted in adult patients undergoing colonoscopy in the absence of known gastrointestinal diseases, assessing the onset of upper and lower gastrointestinal symptoms post-colonoscopy immediately after the procedure (T0), at 2 weeks (T1), and 4 weeks (T2) thereafter, using a VAS (0–10). Two groups were analysed, one undergoing nutraceutical supplementation and a control group. Results: A total of 599 patients were included (64.9% receiving nutraceutical supplementation and 35% in the control group). Several variations were observed involving the treated group compared to the control for abdominal pain (59.9% vs. 33.3%), meteorism (64.9% vs. 35.1%), diarrhoea (46.9% vs. 19.5%), and bloating (59.3% vs. 26.7%) (p < 0.001 for all). Logistic regression analysis showed a reduction in constipation (OR: 3.344) and bloating (OR: 3.791) scores. Conclusions: Nutraceutical supplementation based on this combinational compound was associated with a reduction in gastrointestinal symptoms arising after colonoscopy, suggesting potential benefit in this setting. These findings pose a rationale for controlled prospective studies to confirm such evidence in broader clinical settings. Full article
(This article belongs to the Topic News and Updates on Probiotics)
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27 pages, 3948 KB  
Article
Fully Automated Segmentation of Cervical Spinal Cord in Sagittal MR Images Using Swin-Unet Architectures
by Rukiye Polattimur, Emre Dandıl, Mehmet Süleyman Yıldırım and Utku Şenol
J. Clin. Med. 2025, 14(19), 6994; https://doi.org/10.3390/jcm14196994 - 2 Oct 2025
Abstract
Background/Objectives: The spinal cord is a critical component of the central nervous system that transmits neural signals between the brain and the body’s peripheral regions through its nerve roots. Despite being partially protected by the vertebral column, the spinal cord remains highly [...] Read more.
Background/Objectives: The spinal cord is a critical component of the central nervous system that transmits neural signals between the brain and the body’s peripheral regions through its nerve roots. Despite being partially protected by the vertebral column, the spinal cord remains highly vulnerable to trauma, tumors, infections, and degenerative or inflammatory disorders. These conditions can disrupt neural conduction, resulting in severe functional impairments, such as paralysis, motor deficits, and sensory loss. Therefore, accurate and comprehensive spinal cord segmentation is essential for characterizing its structural features and evaluating neural integrity. Methods: In this study, we propose a fully automated method for segmentation of the cervical spinal cord in sagittal magnetic resonance (MR) images. This method facilitates rapid clinical evaluation and supports early diagnosis. Our approach uses a Swin-Unet architecture, which integrates vision transformer blocks into the U-Net framework. This enables the model to capture both local anatomical details and global contextual information. This design improves the delineation of the thin, curved, low-contrast cervical cord, resulting in more precise and robust segmentation. Results: In experimental studies, the proposed Swin-Unet model (SWU1), which uses transformer blocks in the encoder layer, achieved Dice Similarity Coefficient (DSC) and Hausdorff Distance 95 (HD95) scores of 0.9526 and 1.0707 mm, respectively, for cervical spinal cord segmentation. These results confirm that the model can consistently deliver precise, pixel-level delineations that are structurally accurate, which supports its reliability for clinical assessment. Conclusions: The attention-enhanced Swin-Unet architecture demonstrated high accuracy in segmenting thin and complex anatomical structures, such as the cervical spinal cord. Its ability to generalize with limited data highlights its potential for integration into clinical workflows to support diagnosis, monitoring, and treatment planning. Full article
(This article belongs to the Special Issue Artificial Intelligence and Deep Learning in Medical Imaging)
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28 pages, 17257 KB  
Article
A Box-Based Method for Regularizing the Prediction of Semantic Segmentation of Building Facades
by Shuyu Liu, Zhihui Wang, Yuexia Hu, Xiaoyu Zhao and Si Zhang
Buildings 2025, 15(19), 3562; https://doi.org/10.3390/buildings15193562 - 2 Oct 2025
Abstract
Semantic segmentation of building facade images has enabled a lot of intelligent support for architectural research and practice in the last decade. However, the classifiers for semantic segmentation usually predict facade elements (e.g., windows) as graphics in irregular shapes. The non-smooth edges and [...] Read more.
Semantic segmentation of building facade images has enabled a lot of intelligent support for architectural research and practice in the last decade. However, the classifiers for semantic segmentation usually predict facade elements (e.g., windows) as graphics in irregular shapes. The non-smooth edges and hard-to-define shapes impede the further use of the predicted graphics. This study proposes a method to regularize the predicted graphics following the prior knowledge of composition principles of building facades. Specifically, we define four types of boxes for each predicted graphic, namely minimum circumscribed box (MCB), maximum inscribed box (MIB), candidate box (CB), and best overlapping box (BOB). Based on these boxes, a three-stage process, consisting of denoising, BOB finding, and BOB stacking, was established to regularize the predicted graphics of facade elements into basic rectilinear polygons. To compare the proposed and existing methods of graphic regularization, an experiment was conducted based on the predicted graphics of facade elements obtained from four pixel-wise annotated building facade datasets, Irregular Facades (IRFs), CMP Facade Database, ECP Paris, and ICG Graz50. The results demonstrate that the graphics regularized by our method align more closely with real facade elements in shape and edge. Moreover, our method avoids the prevalent issue of correctness degradation observed in existing methods. Compared with the predicted graphics, the average IoU and F1-score of our method-regularized graphics respectively increase by 0.001–0.017 and 0.000–0.012 across the datasets, while those of previous method-regularized graphics decrease by 0.002–0.021 and 0.002–0.015. The regularized graphics contribute to improving the precision and depth of semantic segmentation-based applications of building facades. They are also expected to be useful for the exploration of data mining on urban images in the future. Full article
(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)
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19 pages, 14588 KB  
Article
Research on Evaporation Duct Height Prediction Modeling in the Yellow and Bohai Seas Using BLA-EDH
by Xiaoyu Wu, Lei Li, Zheyan Zhang, Can Chen and Haozhi Liu
Atmosphere 2025, 16(10), 1156; https://doi.org/10.3390/atmos16101156 - 2 Oct 2025
Abstract
Evaporation Duct Height (EDH) is a crucial parameter in evaporation duct modeling, as it directly influences the strength of the waveguide trapping effect and significantly impacts the over-the-horizon detection performance of maritime radars. To address the limitations of low prediction accuracy and limited [...] Read more.
Evaporation Duct Height (EDH) is a crucial parameter in evaporation duct modeling, as it directly influences the strength of the waveguide trapping effect and significantly impacts the over-the-horizon detection performance of maritime radars. To address the limitations of low prediction accuracy and limited interpretability in existing deep learning models under complex marine meteorological conditions, this study proposes a surrogate model, BLA-EDH, designed to emulate the output of the Naval Postgraduate School (NPS) model for real-time EDH estimation. Experimental results demonstrate that BLA-EDH can effectively replace the traditional NPS model for real-time EDH prediction, achieving higher accuracy than Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) models. Random Forest analysis identifies relative humidity (0.2966), wind speed (0.2786), and 2-m air temperature (0.2409) as the most influential environmental variables, with importance scores exceeding those of other factors. Validation using the parabolic equation shows that BLA-EDH attains excellent fitting performance, with coefficients of determination reaching 0.9999 and 0.9997 in the vertical and horizontal dimensions, respectively. This research provides a robust foundation for modeling radio wave propagation in the Yellow Sea and Bohai Sea regions and offers valuable insights for the development of marine communication and radar detection systems. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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20 pages, 27829 KB  
Article
Deep Learning Strategies for Semantic Segmentation in Robot-Assisted Radical Prostatectomy
by Elena Sibilano, Claudia Delprete, Pietro Maria Marvulli, Antonio Brunetti, Francescomaria Marino, Giuseppe Lucarelli, Michele Battaglia and Vitoantonio Bevilacqua
Appl. Sci. 2025, 15(19), 10665; https://doi.org/10.3390/app151910665 - 2 Oct 2025
Abstract
Robot-assisted radical prostatectomy (RARP) has become the most prevalent treatment for patients with organ-confined prostate cancer. Despite superior outcomes, suboptimal vesicourethral anastomosis (VUA) may lead to serious complications, including urinary leakage, prolonged catheterization, and extended hospitalization. A precise localization of both the surgical [...] Read more.
Robot-assisted radical prostatectomy (RARP) has become the most prevalent treatment for patients with organ-confined prostate cancer. Despite superior outcomes, suboptimal vesicourethral anastomosis (VUA) may lead to serious complications, including urinary leakage, prolonged catheterization, and extended hospitalization. A precise localization of both the surgical needle and the surrounding vesical and urethral tissues to coadapt is needed for fine-grained assessment of this task. Nonetheless, the identification of anatomical structures from endoscopic videos is difficult due to tissue distortions, changes in brightness, and instrument interferences. In this paper, we propose and compare two Deep Learning (DL) pipelines for the automatic segmentation of the mucosal layers and the suturing needle in real RARP videos by exploiting different architectures and training strategies. To train the models, we introduce a novel, annotated dataset collected from four VUA procedures. Experimental results show that the nnU-Net 2D model achieved the highest class-specific metrics, with a Dice Score of 0.663 for the mucosa class and 0.866 for the needle class, outperforming both transformer-based and baseline convolutional approaches on external validation video sequences. This work paves the way for computer-assisted tools that can objectively evaluate surgical performance during the critical phase of suturing tasks. Full article
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22 pages, 782 KB  
Article
Hybrid CNN-Swin Transformer Model to Advance the Diagnosis of Maxillary Sinus Abnormalities on CT Images Using Explainable AI
by Mohammad Alhumaid and Ayman G. Fayoumi
Computers 2025, 14(10), 419; https://doi.org/10.3390/computers14100419 - 2 Oct 2025
Abstract
Accurate diagnosis of sinusitis is essential due to its widespread prevalence and its considerable impact on patient quality of life. While multiple imaging techniques are available for detecting maxillary sinus, computed tomography (CT) remains the preferred modality because of its high sensitivity and [...] Read more.
Accurate diagnosis of sinusitis is essential due to its widespread prevalence and its considerable impact on patient quality of life. While multiple imaging techniques are available for detecting maxillary sinus, computed tomography (CT) remains the preferred modality because of its high sensitivity and spatial resolution. Although recent advances in deep learning have led to the development of automated methods for sinusitis classification, many existing models perform poorly in the presence of complex pathological features and offer limited interpretability, which hinders their integration into clinical workflows. In this study, we propose a hybrid deep learning framework that combines EfficientNetB0, a convolutional neural network, with the Swin Transformer, a vision transformer, to improve feature representation. An attention-based fusion module is used to integrate both local and global information, thereby enhancing diagnostic accuracy. To improve transparency and support clinical adoption, the model incorporates explainable artificial intelligence (XAI) techniques using Gradient-weighted Class Activation Mapping (Grad-CAM). This allows for visualization of the regions influencing the model’s predictions, helping radiologists assess the clinical relevance of the results. We evaluate the proposed method on a curated maxillary sinus CT dataset covering four diagnostic categories: Normal, Opacified, Polyposis, and Retention Cysts. The model achieves a classification accuracy of 95.83%, with precision, recall, and F1 score all at 95%. Grad-CAM visualizations indicate that the model consistently focuses on clinically significant regions of the sinus anatomy, supporting its potential utility as a reliable diagnostic aid in medical practice. Full article
38 pages, 6435 KB  
Article
FedResilience: A Federated Classification System to Ensure Critical LTE Communications During Natural Disasters
by Alvaro Acuña-Avila, Christian Fernández-Campusano, Héctor Kaschel and Raúl Carrasco
Systems 2025, 13(10), 866; https://doi.org/10.3390/systems13100866 - 2 Oct 2025
Abstract
Natural disasters can disrupt communication services, leading to severe consequences in emergencies. Maintaining connectivity and communication quality during crises is crucial for coordinating rescues, providing critical information, and ensuring reliable and secure service. This study proposes FedResilience, a Federated Learning (FL) system for [...] Read more.
Natural disasters can disrupt communication services, leading to severe consequences in emergencies. Maintaining connectivity and communication quality during crises is crucial for coordinating rescues, providing critical information, and ensuring reliable and secure service. This study proposes FedResilience, a Federated Learning (FL) system for classifying Long-Term Evolution (LTE) network coverage in both normal operation and natural disaster scenarios. A three-tier architecture is implemented: (i) edge nodes, (ii) a central aggregation server, and (iii) a batch processing interface. Five FL aggregation methods (FedAvg, FedProx, FedAdam, FedYogi, and FedAdagrad) were evaluated under normal conditions and disaster simulations. The results show that FedAdam outperforms the other methods under normal conditions, achieving an F1 score of 0.7271 and a Global System Adherence (SAglobal) of 91.51%. In disaster scenarios, FedProx was superior, with an F1 score of 0.7946 and SAglobal of 61.73%. The innovation in this study is the introduction of the System Adherence (SA) metric to evaluate the predictive fidelity of the model. The system demonstrated robustness against Non-Independent and Identically Distributed (non-IID) data distributions and the ability to handle significant class imbalances. FedResilience serves as a tool for companies to implement automated corrective actions, contributing to the predictive maintenance of LTE networks through FL while preserving data privacy. Full article
(This article belongs to the Special Issue Data-Driven Decision Making for Complex Systems)
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