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Search Results (834)

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34 pages, 3431 KiB  
Article
Evaluation of Hierarchical Clustering Methodologies for Identifying Patterns in Timeout Requests in EuroLeague Basketball
by José Miguel Contreras, Elena Molina Portillo and Juan Manuel Fernández Luna
Mathematics 2025, 13(15), 2414; https://doi.org/10.3390/math13152414 - 27 Jul 2025
Viewed by 134
Abstract
This study evaluates hierarchical clustering methodologies to identify patterns associated with timeout requests for EuroLeague basketball games. Using play-by-play data from 3743 games spanning the 2008–2023 seasons (over 1.9 million instances), we applied Principal Component Analysis to reduce dimensionality and tested multiple agglomerative [...] Read more.
This study evaluates hierarchical clustering methodologies to identify patterns associated with timeout requests for EuroLeague basketball games. Using play-by-play data from 3743 games spanning the 2008–2023 seasons (over 1.9 million instances), we applied Principal Component Analysis to reduce dimensionality and tested multiple agglomerative and divisive clustering techniques (e.g., Ward and DIANA) with different distance metrics (Euclidean, Manhattan, and Minkowski). Clustering quality was assessed using internal validation indices such as Silhouette, Dunn, Calinski–Harabasz, Davies–Bouldin, and Gap statistics. The results show that Ward.D and Ward.D2 methods using Euclidean distance generate well-balanced and clearly defined clusters. Two clusters offer the best overall quality, while four clusters allow for meaningful segmentation of game situations. The analysis revealed that teams that did not request timeouts often exhibited better scoring efficiency, particularly in the advanced game phases. These findings offer data-driven insights into timeout dynamics and contribute to strategic decision-making in professional basketball. Full article
(This article belongs to the Section E: Applied Mathematics)
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27 pages, 4682 KiB  
Article
DERIENet: A Deep Ensemble Learning Approach for High-Performance Detection of Jute Leaf Diseases
by Mst. Tanbin Yasmin Tanny, Tangina Sultana, Md. Emran Biswas, Chanchol Kumar Modok, Arjina Akter, Mohammad Shorif Uddin and Md. Delowar Hossain
Information 2025, 16(8), 638; https://doi.org/10.3390/info16080638 - 27 Jul 2025
Viewed by 139
Abstract
Jute, a vital lignocellulosic fiber crop with substantial industrial and ecological relevance, continues to suffer considerable yield and quality degradation due to pervasive foliar pathologies. Traditional diagnostic modalities reliant on manual field inspections are inherently constrained by subjectivity, diagnostic latency, and inadequate scalability [...] Read more.
Jute, a vital lignocellulosic fiber crop with substantial industrial and ecological relevance, continues to suffer considerable yield and quality degradation due to pervasive foliar pathologies. Traditional diagnostic modalities reliant on manual field inspections are inherently constrained by subjectivity, diagnostic latency, and inadequate scalability across geographically distributed agrarian systems. To transcend these limitations, we propose DERIENet, a robust and scalable classification approach within a deep ensemble learning framework. It is meticulously engineered by integrating three high-performing convolutional neural networks—ResNet50, InceptionV3, and EfficientNetB0—along with regularization, batch normalization, and dropout strategies, to accurately classify jute leaf diseases such as Cercospora Leaf Spot, Golden Mosaic Virus, and healthy leaves. A key methodological contribution is the design of a novel augmentation pipeline, termed Geometric Localized Occlusion and Adaptive Rescaling (GLOAR), which dynamically modulates photometric and geometric distortions based on image entropy and luminance to synthetically upscale a limited dataset (920 images) into a significantly enriched and diverse dataset of 7800 samples, thereby mitigating overfitting and enhancing domain generalizability. Empirical evaluation, utilizing a comprehensive set of performance metrics—accuracy, precision, recall, F1-score, confusion matrices, and ROC curves—demonstrates that DERIENet achieves a state-of-the-art classification accuracy of 99.89%, with macro-averaged and weighted average precision, recall, and F1-score uniformly at 99.89%, and an AUC of 1.0 across all disease categories. The reliability of the model is validated by the confusion matrix, which shows that 899 out of 900 test images were correctly identified and that there was only one misclassification. Comparative evaluations of the various ensemble baselines, such as DenseNet201, MobileNetV2, and VGG16, and individual base learners demonstrate that DERIENet performs noticeably superior to all baseline models. It provides a highly interpretable, deployment-ready, and computationally efficient architecture that is ideal for integrating into edge or mobile platforms to facilitate in situ, real-time disease diagnostics in precision agriculture. Full article
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19 pages, 6150 KiB  
Article
Evaluation of Eutrophication in Small Reservoirs in Northern Agricultural Areas of China
by Qianyu Jing, Yang Shao, Xiyuan Bian, Minfang Sun, Zengfei Chen, Jiamin Han, Song Zhang, Shusheng Han and Haiming Qin
Diversity 2025, 17(8), 520; https://doi.org/10.3390/d17080520 - 26 Jul 2025
Viewed by 142
Abstract
Small reservoirs have important functions, such as water resource guarantee, flood control and drought resistance, biological habitat and maintaining regional economic development. In order to better clarify the impact of agricultural activities on the nutritional status of water bodies in small reservoirs, zooplankton [...] Read more.
Small reservoirs have important functions, such as water resource guarantee, flood control and drought resistance, biological habitat and maintaining regional economic development. In order to better clarify the impact of agricultural activities on the nutritional status of water bodies in small reservoirs, zooplankton were quantitatively collected from four small reservoirs in the Jiuxianshan agricultural area of Qufu, Shandong Province, in March and October 2023, respectively. The physical and chemical parameters in sampling points were determined simultaneously. Meanwhile, water samples were collected for nutrient salt analysis, and the eutrophication of water bodies in four reservoirs was evaluated using the comprehensive nutrient status index method. The research found that the species richness of zooplankton after farming (100 species) was significantly higher than that before farming (81 species) (p < 0.05). On the contrary, the dominant species of zooplankton after farming (7 species) were significantly fewer than those before farming (11 species). The estimation results of the standing stock of zooplankton indicated that the abundance and biomass of zooplankton after farming (92.72 ind./L, 0.13 mg/L) were significantly higher than those before farming (32.51 ind./L, 0.40 mg/L) (p < 0.05). Community similarity analysis based on zooplankton abundance (ANOSIM) indicated that there were significant differences in zooplankton communities before and after farming (R = 0.329, p = 0.001). The results of multi-dimensional non-metric sorting (NMDS) showed that the communities of zooplankton could be clearly divided into two: pre-farming communities and after farming communities. The Monte Carlo test results are as follows (p < 0.05). Transparency (Trans), pH, permanganate index (CODMn), electrical conductivity (Cond) and chlorophyll a (Chl-a) had significant effects on the community structure of zooplankton before farming. Total nitrogen (TN), total phosphorus (TP) and electrical conductivity (Cond) had significant effects on the community structure of zooplankton after farming. The co-linearity network analysis based on zooplankton abundance showed that the zooplankton community before farming was more stable than that after farming. The water evaluation results based on the comprehensive nutritional status index method indicated that the water conditions of the reservoirs before farming were mostly in a mild eutrophic state, while the water conditions of the reservoirs after farming were all in a moderate eutrophic state. The results show that the nutritional status of small reservoirs in agricultural areas is significantly affected by agricultural activities. The zooplankton communities in small reservoirs underwent significant changes driven by alterations in the reservoir water environment and nutritional status. Based on the main results of this study, we suggested that the use of fertilizers and pesticides should be appropriately reduced in future agricultural activities. In order to better protect the water quality and aquatic ecology of the water reservoirs in the agricultural area. Full article
(This article belongs to the Special Issue Diversity and Ecology of Freshwater Plankton)
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21 pages, 1622 KiB  
Article
Enhancing Wearable Fall Detection System via Synthetic Data
by Minakshi Debnath, Sana Alamgeer, Md Shahriar Kabir and Anne H. Ngu
Sensors 2025, 25(15), 4639; https://doi.org/10.3390/s25154639 - 26 Jul 2025
Viewed by 297
Abstract
Deep learning models rely heavily on extensive training data, but obtaining sufficient real-world data remains a major challenge in clinical fields. To address this, we explore methods for generating realistic synthetic multivariate fall data to supplement limited real-world samples collected from three fall-related [...] Read more.
Deep learning models rely heavily on extensive training data, but obtaining sufficient real-world data remains a major challenge in clinical fields. To address this, we explore methods for generating realistic synthetic multivariate fall data to supplement limited real-world samples collected from three fall-related datasets: SmartFallMM, UniMib, and K-Fall. We apply three conventional time-series augmentation techniques, a Diffusion-based generative AI method, and a novel approach that extracts fall segments from public video footage of older adults. A key innovation of our work is the exploration of two distinct approaches: video-based pose estimation to extract fall segments from public footage, and Diffusion models to generate synthetic fall signals. Both methods independently enable the creation of highly realistic and diverse synthetic data tailored to specific sensor placements. To our knowledge, these approaches and especially their application in fall detection represent rarely explored directions in this research area. To assess the quality of the synthetic data, we use quantitative metrics, including the Fréchet Inception Distance (FID), Discriminative Score, Predictive Score, Jensen–Shannon Divergence (JSD), and Kolmogorov–Smirnov (KS) test, and visually inspect temporal patterns for structural realism. We observe that Diffusion-based synthesis produces the most realistic and distributionally aligned fall data. To further evaluate the impact of synthetic data, we train a long short-term memory (LSTM) model offline and test it in real time using the SmartFall App. Incorporating Diffusion-based synthetic data improves the offline F1-score by 7–10% and boosts real-time fall detection performance by 24%, confirming its value in enhancing model robustness and applicability in real-world settings. Full article
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18 pages, 3675 KiB  
Article
Mechanical Property Prediction of Wood Using a Backpropagation Neural Network Optimized by Adaptive Fractional-Order Particle Swarm Algorithm
by Jiahui Huang and Zhufang Kuang
Forests 2025, 16(8), 1223; https://doi.org/10.3390/f16081223 - 25 Jul 2025
Viewed by 192
Abstract
This study proposes a novel LK-BP-AFPSO model for the nondestructive evaluation of wood mechanical properties, combining a backpropagation neural network (BP) with adaptive fractional-order particle swarm optimization (AFPSO) and Liang–Kleeman (LK) information flow theory. The model accurately predicts four key mechanical properties—longitudinal tensile [...] Read more.
This study proposes a novel LK-BP-AFPSO model for the nondestructive evaluation of wood mechanical properties, combining a backpropagation neural network (BP) with adaptive fractional-order particle swarm optimization (AFPSO) and Liang–Kleeman (LK) information flow theory. The model accurately predicts four key mechanical properties—longitudinal tensile strength (SPG), modulus of elasticity (MOE), bending strength (MOR), and longitudinal compressive strength (CSP)—using only nondestructive physical features. Tested across diverse wood types (fast-growing YKS, red-heart CSH/XXH, and iron-heart XXT), the framework demonstrates strong generalizability, achieving an average prediction accuracy (R2) of 0.986 and reducing mean absolute error (MAE) by 23.7% compared to conventional methods. A critical innovation is the integration of LK causal analysis, which quantifies feature–target relationships via information flow metrics, effectively eliminating 29.5% of spurious correlations inherent in traditional feature selection (e.g., PCA). Experimental results confirm the model’s robustness, particularly for heartwood variants, while its adaptive fractional-order optimization accelerates convergence by 2.1× relative to standard PSO. This work provides a reliable, interpretable tool for wood quality assessment, with direct implications for grading systems and processing optimization in the forestry industry. Full article
(This article belongs to the Section Forest Operations and Engineering)
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31 pages, 11068 KiB  
Article
Airport-FOD3S: A Three-Stage Detection-Driven Framework for Realistic Foreign Object Debris Synthesis
by Hanglin Cheng, Yihao Li, Ruiheng Zhang and Weiguang Zhang
Sensors 2025, 25(15), 4565; https://doi.org/10.3390/s25154565 - 23 Jul 2025
Viewed by 211
Abstract
Traditional Foreign Object Debris (FOD) detection methods face challenges such as difficulties in large-size data acquisition and the ineffective application of detection algorithms with high accuracy. In this paper, image data augmentation was performed using generative adversarial networks and diffusion models, generating images [...] Read more.
Traditional Foreign Object Debris (FOD) detection methods face challenges such as difficulties in large-size data acquisition and the ineffective application of detection algorithms with high accuracy. In this paper, image data augmentation was performed using generative adversarial networks and diffusion models, generating images of monitoring areas under different environmental conditions and FOD images of varied types. Additionally, a three-stage image blending method considering size transformation, a seamless process, and style transfer was proposed. The image quality of different blending methods was quantitatively evaluated using metrics such as structural similarity index and peak signal-to-noise ratio, as well as Depthanything. Finally, object detection models with a similarity distance strategy (SimD), including Faster R-CNN, YOLOv8, and YOLOv11, were tested on the dataset. The experimental results demonstrated that realistic FOD data were effectively generated. The Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR) of the synthesized image by the proposed three-stage image blending method outperformed the other methods, reaching 0.99 and 45 dB. YOLOv11 with SimD trained on the augmented dataset achieved the mAP of 86.95%. Based on the results, it could be concluded that both data augmentation and SimD significantly improved the accuracy of FOD detection. Full article
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20 pages, 2737 KiB  
Technical Note
Obtaining the Highest Quality from a Low-Cost Mobile Scanner: A Comparison of Several Pipelines with a New Scanning Device
by Marek Hrdina, Juan Alberto Molina-Valero, Karel Kuželka, Shinichi Tatsumi, Keiji Yamaguchi, Zlatica Melichová, Martin Mokroš and Peter Surový
Remote Sens. 2025, 17(15), 2564; https://doi.org/10.3390/rs17152564 - 23 Jul 2025
Viewed by 199
Abstract
The accurate measurement of the tree diameter is vital for forest inventories, urban tree quality assessments, the management of roadside and railway vegetation, and various other applications. It also plays a crucial role in evaluating tree growth dynamics, which are closely linked to [...] Read more.
The accurate measurement of the tree diameter is vital for forest inventories, urban tree quality assessments, the management of roadside and railway vegetation, and various other applications. It also plays a crucial role in evaluating tree growth dynamics, which are closely linked to tree health, structural stability, and vulnerability. Although a range of devices and methodologies are currently under investigation, the widespread adoption of laser scanners remains constrained by their high cost. This study therefore aimed to compare high-end laser scanners (Trimble TX8 and GeoSLAM ZEB Horizon) with cost-effective alternatives, represented by the Apple iPhone 14 Pro and the LA03 scanner developed by mapry Co., Ltd. (Tamba, Japan). It further sought to evaluate the feasibility of employing these more affordable devices, even for small-scale forest owners or managers. Given the growing availability of 3D-based forest inventory algorithms, a selection of such processing pipelines was used to assess the practical potential of the scanning devices. The tested low-cost device produced moderate results, achieving a tree detection rate of up to 78% and a relative root mean square error (rRMSE) of 19.7% in diameter at breast height (DBH) estimation. However, performance varied depending on the algorithms applied. In contrast, the high-end mobile laser scanning (MLS) and terrestrial laser scanning (TLS) systems outperformed the low-cost alternative across all metrics, with tree detection rates reaching up to 99% and DBH estimation rRMSEs as low as 5%. Nevertheless, the low-cost device may still be suitable for scanning small sample plots at a reduced cost and could potentially be deployed in larger quantities to support broader forest inventory initiatives. Full article
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11 pages, 1205 KiB  
Article
Impact of Catheter Ablation on Functional Capacity and Cardiac Stress Markers in Patients with Premature Ventricular Contractions
by Vasileios Cheilas, Athanasios Dritsas, Antonios Martinos, Evangelia Gkirgkinoudi, Giorgos Filandrianos, Anastasios Chatziantoniou, Ourania Kariki, Panagiotis Mililis, Athanasios Saplaouras, Anna Kostopoulou, Konstantinos Letsas and Michalis Efremidis
Med. Sci. 2025, 13(3), 95; https://doi.org/10.3390/medsci13030095 - 23 Jul 2025
Viewed by 241
Abstract
Background: Premature ventricular contractions (PVCs) are common arrhythmias associated with symptoms such as fatigue and, in severe cases, PVC-induced cardiomyopathy. Catheter ablation (CA) is a primary treatment for symptomatic PVCs, particularly when pharmacological therapies fail or are undesired. While improvements in: quality-of-life following [...] Read more.
Background: Premature ventricular contractions (PVCs) are common arrhythmias associated with symptoms such as fatigue and, in severe cases, PVC-induced cardiomyopathy. Catheter ablation (CA) is a primary treatment for symptomatic PVCs, particularly when pharmacological therapies fail or are undesired. While improvements in: quality-of-life following ablation are documented, its impact on functional capacity remains underexplored. Objectives: This study evaluated the impact of CA on functional capacity and cardiac stress markers in patients with symptomatic PVCs using cardiopulmonary exercise testing (CPET) and NT-proBNP levels. Methods: A total of 30 patients underwent successful PVC ablation and completed baseline and follow-up CPET evaluations under the Bruce protocol. PVC burden, left ventricular ejection fraction (LVEF), NT-proBNP levels, and CPET parameters, including VO2 max, METS, ventilatory efficiency, and anaerobic threshold (AT), were analyzed pre- and post-ablation. Results: PVC burden significantly decreased post-ablation (23,509.3 ± 10,700.47 to 1759 ± 1659.15, p < 0.001). CPET revealed improved functional capacity, with VO2 max increasing from 24.97 ± 4.16 mL/kg/min to 26.02 ± 4.34 mL/kg/min (p = 0.0096) and METS from 7.16 ± 1.17 to 7.48 ± 1.24 (p = 0.0103). NT-proBNP significantly decreased (240.93 ± 156.54 pg/mL to 138.47 ± 152.91 pg/mL, p = 0.0065). LVEF and ventilatory efficiency metrics (VE/VO2 and VE/VCO2) remained stable. Conclusions: Catheter ablation improves functional capacity, reduces cardiac stress, and minimizes medication dependency in patients with symptomatic PVCs. These findings support the utility of ablation in enhancing aerobic capacity and overall exercise performance. Full article
(This article belongs to the Section Cardiovascular Disease)
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23 pages, 3725 KiB  
Systematic Review
The Value of MRI-Based Radiomics in Predicting the Pathological Nodal Status of Rectal Cancer: A Systematic Review and Meta-Analysis
by David Luengo Gómez, Marta García Cerezo, David López Cornejo, Ángela Salmerón Ruiz, Encarnación González-Flores, Consolación Melguizo Alonso, Antonio Jesús Láinez Ramos-Bossini, José Prados and Francisco Gabriel Ortega Sánchez
Bioengineering 2025, 12(7), 786; https://doi.org/10.3390/bioengineering12070786 - 21 Jul 2025
Viewed by 276
Abstract
Background: MRI-based radiomics has emerged as a promising approach to enhance the non-invasive, presurgical assessment of lymph node staging in rectal cancer (RC). However, its clinical implementation remains limited due to methodological variability in published studies. We conducted a systematic review and meta-analysis [...] Read more.
Background: MRI-based radiomics has emerged as a promising approach to enhance the non-invasive, presurgical assessment of lymph node staging in rectal cancer (RC). However, its clinical implementation remains limited due to methodological variability in published studies. We conducted a systematic review and meta-analysis to synthesize the diagnostic performance of MRI-based radiomics models for predicting pathological nodal status (pN) in RC. Methods: A systematic literature search was conducted in PubMed, Web of Science, and Scopus for studies published until 31 December 2024. Eligible studies applied MRI-based radiomics for pN prediction in RC patients. We excluded other imaging sources and models combining radiomics and other data (e.g., clinical). All models with available outcome metrics were included in data analysis. Data extraction and quality assessment (QUADAS-2) were performed independently by two reviewers. Random-effects meta-analyses including hierarchical summary receiver operating characteristic (HSROC) and restricted maximum likelihood estimator (REML) analyses were conducted to pool sensitivity, specificity, area under the curve (AUC), and diagnostic odds ratios (DORs). Sensitivity analyses and publication bias evaluation were also performed. Results: Sixteen studies (n = 3157 patients) were included. The HSROC showed pooled sensitivity, specificity, and AUC values of 0.68 (95% CI, 0.63–0.72), 0.73 (95% CI, 0.68–0.78), and 0.70 (95% CI, 0.65–0.75), respectively. The mean pooled AUC and DOR obtained by REML were 0.78 (95% CI, 0.75–0.80) and 6.03 (95% CI, 4.65–7.82). Funnel plot asymmetry and Egger’s test (p = 0.025) indicated potential publication bias. Conclusions: Overall, MRI-based radiomics models demonstrated moderate accuracy in predicting pN status in RC, with some studies reporting outstanding results. However, heterogeneity in relevant methodological approaches such as the source of MRI sequences or machine learning methods applied along with possible publication bias call for further standardization and preclude their translation to clinical practice. Full article
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11 pages, 386 KiB  
Article
Benchmarking AI Chatbots for Maternal Lactation Support: A Cross-Platform Evaluation of Quality, Readability, and Clinical Accuracy
by İlke Özer Aslan and Mustafa Törehan Aslan
Healthcare 2025, 13(14), 1756; https://doi.org/10.3390/healthcare13141756 - 20 Jul 2025
Viewed by 335
Abstract
Background and Objective: Large language model (LLM)–based chatbots are increasingly utilized by postpartum individuals seeking guidance on breastfeeding. However, the chatbots’ content quality, readability, and alignment with clinical guidelines remain uncertain. This study was conducted to evaluate and compare the quality, readability, and [...] Read more.
Background and Objective: Large language model (LLM)–based chatbots are increasingly utilized by postpartum individuals seeking guidance on breastfeeding. However, the chatbots’ content quality, readability, and alignment with clinical guidelines remain uncertain. This study was conducted to evaluate and compare the quality, readability, and factual accuracy of responses generated by three publicly accessible AI chatbots—ChatGPT-4o Pro, Gemini 2.5 Pro, and Copilot Pro—when prompted with common maternal questions related to breast-milk supply. Methods: Twenty frequently asked breastfeeding-related questions were submitted to each chatbot in separate sessions. The responses were paraphrased to enable standardized scoring and were then evaluated using three validated tools: ensuring quality information for patients (EQIP), the simple measure of gobbledygook (SMOG), and the global quality scale (GQS). Factual accuracy was benchmarked against WHO, ACOG, CDC, and NICE guidelines using a three-point rubric. Additional user experience metrics included response time, character count, content density, and structural formatting. Statistical comparisons were performed using the Kruskal–Wallis and Wilcoxon rank-sum tests with Bonferroni correction. Results: ChatGPT-4o Pro achieved the highest overall performance across all primary outcomes: EQIP score (85.7 ± 2.4%), SMOG score (9.78 ± 0.22), and GQS rating (4.55 ± 0.50), followed by Gemini 2.5 Pro and Copilot Pro (p < 0.001 for all comparisons). ChatGPT-4o Pro also demonstrated the highest factual alignment with clinical guidelines (95%), while Copilot showed more frequent omissions or simplifications. Differences in response time and formatting quality were statistically significant, although not always clinically meaningful. Conclusions: ChatGPT-4o Pro outperforms other chatbots in delivering structured, readable, and guideline-concordant breastfeeding information. However, substantial variability persists across the platforms, and none should be considered a substitute for professional guidance. Importantly, the phenomenon of AI hallucinations—where chatbots may generate factually incorrect or fabricated information—remains a critical risk that must be addressed to ensure safe integration into maternal health communication. Future efforts should focus on improving the transparency, accuracy, and multilingual reliability of AI chatbots to ensure their safe integration into maternal health communications. Full article
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17 pages, 5116 KiB  
Article
Impact of Real-Time Boundary Conditions from the CAMS Database on CHIMERE Model Predictions
by Anita Tóth and Zita Ferenczi
Air 2025, 3(3), 19; https://doi.org/10.3390/air3030019 - 18 Jul 2025
Viewed by 181
Abstract
Air quality forecasts play a crucial role in informing the public about atmospheric pollutant levels that pose risks to human health and the environment. The accuracy of these forecasts strongly depends on the quality and resolution of the input data used in the [...] Read more.
Air quality forecasts play a crucial role in informing the public about atmospheric pollutant levels that pose risks to human health and the environment. The accuracy of these forecasts strongly depends on the quality and resolution of the input data used in the modelling process. At HungaroMet, the Hungarian Meteorological Service, the CHIMERE chemical transport model is used to provide two-day air quality forecasts for the territory of Hungary. This study compares two configurations of the CHIMERE model: the current operational setup, which uses climatological averages from the LMDz-INCA database for boundary conditions, and a test configuration that incorporates real-time boundary conditions from the CAMS global forecast. The primary objective of this work was to assess how the use of real-time versus climatological boundary conditions affects modelled concentrations of key pollutants, including NO2, O3, PM10, and PM2.5. The model results were evaluated against observational data from the Hungarian Air Quality Monitoring Network using a range of statistical metrics. The results indicate that the use of real-time boundary conditions, particularly for aerosol-type pollutants, improves the accuracy of PM10 forecasts. This improvement is most significant under meteorological conditions that favour the long-range transport of particulate matter, such as during Saharan dust or wildfire episodes. These findings highlight the importance of incorporating dynamic, up-to-date boundary data, especially for particulate matter forecasting—given the increasing frequency of transboundary dust events. Full article
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23 pages, 2536 KiB  
Article
AI-Enhanced Nonlinear Predictive Control for Smart Greenhouses: A Performance Comparison of Forecast and Warm-Start Strategies
by Hung Linh Le and Van-Tung Bui
Appl. Sci. 2025, 15(14), 7988; https://doi.org/10.3390/app15147988 - 17 Jul 2025
Viewed by 270
Abstract
Accurate, energy-efficient climate regulation is crucial for scaling smart greenhouse production. While nonlinear model predictive control (NMPC) can co-optimize yield and resource use, its efficacy hinges on short-range weather information and real-time solver feasibility. This paper investigates the performance of advanced NMPC strategies [...] Read more.
Accurate, energy-efficient climate regulation is crucial for scaling smart greenhouse production. While nonlinear model predictive control (NMPC) can co-optimize yield and resource use, its efficacy hinges on short-range weather information and real-time solver feasibility. This paper investigates the performance of advanced NMPC strategies for smart greenhouse climate control, with particular emphasis on the roles of AI-driven disturbance prediction and warm-start initialization for real-time optimization. Six controller configurations, including feedback-only, LSTM-based forecast, and ideal disturbance models, each with and without warm-start, were tested in a 40-day simulation of a lettuce smart greenhouse. Performance metrics included final biomass, constraint violations, resource costs, profit, and solver time. Results show that feedback-only controllers maximize yield and profit, incurring higher CO2 costs but lower heating costs, alongside greater constraint violations compared to the predictive strategies. Predictive and ideal disturbance-aware controllers effectively reduce resource consumption and improve constraint compliance at the expense of lower yields. Importantly, warm-start initialization significantly accelerates computation without affecting control quality. The study also demonstrates that penalty parameters, rather than economic weight settings, predominantly determine aggregate constraint violation. The findings provide actionable insights for designing and deploying NMPC-based greenhouse controllers, highlighting the importance of warm-start techniques and the trade-offs between productivity, resource efficiency, and environmental compliance. Full article
(This article belongs to the Special Issue Future of Smart Greenhouses: Automation, IoT, and AI Applications)
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22 pages, 1837 KiB  
Article
Anthropometric Measurements for Predicting Low Appendicular Lean Mass Index for the Diagnosis of Sarcopenia: A Machine Learning Model
by Ana M. González-Martin, Edgar Samid Limón-Villegas, Zyanya Reyes-Castillo, Francisco Esparza-Ros, Luis Alexis Hernández-Palma, Minerva Saraí Santillán-Rivera, Carlos Abraham Herrera-Amante, César Octavio Ramos-García and Nicoletta Righini
J. Funct. Morphol. Kinesiol. 2025, 10(3), 276; https://doi.org/10.3390/jfmk10030276 - 17 Jul 2025
Viewed by 500
Abstract
Background: Sarcopenia is a progressive muscle disease that compromises mobility and quality of life in older adults. Although dual-energy X-ray absorptiometry (DXA) is the standard for assessing Appendicular Lean Mass Index (ALMI), it is costly and often inaccessible. This study aims to [...] Read more.
Background: Sarcopenia is a progressive muscle disease that compromises mobility and quality of life in older adults. Although dual-energy X-ray absorptiometry (DXA) is the standard for assessing Appendicular Lean Mass Index (ALMI), it is costly and often inaccessible. This study aims to develop machine learning models using anthropometric measurements to predict low ALMI for the diagnosis of sarcopenia. Methods: A cross-sectional study was conducted on 183 Mexican adults (67.2% women and 32.8% men, ≥60 years old). ALMI was measured using DXA, and anthropometric data were collected following the International Society for the Advancement of Kinanthropometry (ISAK) protocols. Predictive models were developed using Logistic Regression (LR), Decision Trees (DTs), Random Forests (RFs), Artificial Neural Networks (ANNs), and LASSO regression. The dataset was split into training (70%) and testing (30%) sets. Model performance was evaluated using classification performance metrics and the area under the ROC curve (AUC). Results: ALMI indicated strong correlations with BMI, corrected calf girth, and arm relaxed girth. Among models, DT achieved the best performance in females (AUC = 0.84), and ANN indicated the highest AUC in males (0.92). Regarding the prediction of low ALMI, specificity values were highest in DT for females (100%), while RF performed best in males (92%). The key predictive variables varied depending on sex, with BMI and calf girth being the most relevant for females and arm girth for males. Conclusions: Anthropometry combined with machine learning provides an accurate, low-cost approach for identifying low ALMI in older adults. This method could facilitate sarcopenia screening in clinical settings with limited access to advanced diagnostic tools. Full article
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41 pages, 4123 KiB  
Article
Optimal D-STATCOM Operation in Power Distribution Systems to Minimize Energy Losses and CO2 Emissions: A Master–Slave Methodology Based on Metaheuristic Techniques
by Rubén Iván Bolaños, Cristopher Enrique Torres-Mancilla, Luis Fernando Grisales-Noreña, Oscar Danilo Montoya and Jesús C. Hernández
Sci 2025, 7(3), 98; https://doi.org/10.3390/sci7030098 - 11 Jul 2025
Viewed by 343
Abstract
In this paper, we address the problem of intelligent operation of Distribution Static Synchronous Compensators (D-STATCOMs) in power distribution systems to reduce energy losses and CO2 emissions while improving system operating conditions. In addition, we consider the entire set of constraints inherent [...] Read more.
In this paper, we address the problem of intelligent operation of Distribution Static Synchronous Compensators (D-STATCOMs) in power distribution systems to reduce energy losses and CO2 emissions while improving system operating conditions. In addition, we consider the entire set of constraints inherent in the operation of such networks in an environment with D-STATCOMs. To solve such a problem, we used three master–slave methodologies based on sequential programming methods. In the proposed methodologies, the master stage solves the problem of intelligent D-STATCOM operation using the continuous versions of the Monte Carlo (MC) method, the population-based genetic algorithm (PGA), and the Particle Swarm Optimizer (PSO). The slave stage, for its part, evaluates the solutions proposed by the algorithms to determine their impact on the objective functions and constraints representing the problem. This is accomplished by running an Hourly Power Flow (HPF) based on the method of successive approximations. As test scenarios, we employed the 33- and 69-node radial test systems, considering data on power demand and CO2 emissions reported for the city of Medellín in Colombia (as documented in the literature). Furthermore, a test system was adapted in this work to the demand characteristics of a feeder located in the city of Talca in Chile. This adaptation involved adjusting the conductors and voltage limits to include a test system with variations in power demand due to seasonal changes throughout the year (spring, winter, autumn, and summer). Demand curves were obtained by analyzing data reported by the local network operator, i.e., Compañía General de Electricidad. To assess the robustness and performance of the proposed optimization approach, each scenario was simulated 100 times. The evaluation metrics included average solution quality, standard deviation, and repeatability. Across all scenarios, the PGA consistently outperformed the other methods tested. Specifically, in the 33-node system, the PGA achieved a 24.646% reduction in energy losses and a 0.9109% reduction in CO2 emissions compared to the base case. In the 69-node system, reductions reached 26.0823% in energy losses and 0.9784% in CO2 emissions compared to the base case. Notably, in the case of the Talca feeder—particularly during summer, the most demanding season—the PGA yielded the most significant improvements, reducing energy losses by 33.4902% and CO2 emissions by 1.2805%. Additionally, an uncertainty analysis was conducted to validate the effectiveness and robustness of the proposed optimization methodology under realistic operating variability. A total of 100 randomized demand profiles for both active and reactive power were evaluated. The results demonstrated the scalability and consistent performance of the proposed strategy, confirming its effectiveness under diverse and practical operating conditions. Full article
(This article belongs to the Section Computer Sciences, Mathematics and AI)
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35 pages, 3495 KiB  
Article
Demographic Capital and the Conditional Validity of SERVPERF: Rethinking Tourist Satisfaction Models in an Emerging Market Destination
by Reyner Pérez-Campdesuñer, Alexander Sánchez-Rodríguez, Gelmar García-Vidal, Rodobaldo Martínez-Vivar, Marcos Eduardo Valdés-Alarcón and Margarita De Miguel-Guzmán
Adm. Sci. 2025, 15(7), 272; https://doi.org/10.3390/admsci15070272 - 11 Jul 2025
Viewed by 464
Abstract
Tourist satisfaction models typically assume that service performance dimensions carry the same weight for all travelers. Drawing on Bourdieu, we reconceptualize age, gender, and region of origin as demographic capital, durable resources that mediate how visitors decode service cues. Using a SERVPERF-based survey [...] Read more.
Tourist satisfaction models typically assume that service performance dimensions carry the same weight for all travelers. Drawing on Bourdieu, we reconceptualize age, gender, and region of origin as demographic capital, durable resources that mediate how visitors decode service cues. Using a SERVPERF-based survey of 407 international travelers departing Quito (Ecuador), we test measurement invariance across six sociodemographic strata with multi-group confirmatory factor analysis. The four-factor SERVPERF core (Access, Lodging, Extra-hotel Services, Attractions) holds, yet partial metric invariance emerges: specific loadings flex with demographic capital. Gen-Z travelers penalize transport reliability and safety; female visitors reward cleanliness and empathy; and Latin American guests are the most critical of basic organization. These patterns expose a boundary condition for universalistic satisfaction models and elevate demographic capital from a descriptive tag to a structuring construct. Managerially, we translate the findings into segment-sensitive levers, visible security for youth and regional markets, gender-responsive facility upgrades, and dual eco-luxury versus digital-detox bundles for long-haul segments. By demonstrating when and how SERVPERF fractures across sociodemographic lines, this study intervenes in three theoretical conversations: (1) capital-based readings of consumption, (2) the search for boundary conditions in service-quality measurement, and (3) the shift from segmentation to capital-sensitive interpretation in emerging markets. The results position Ecuador as a critical case and provide a template for destinations facing similar performance–perception mismatches in the Global South. Full article
(This article belongs to the Special Issue Tourism and Hospitality Marketing: Trends and Best Practices)
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