Search Results (2,389)

Accurate, real-time, and cost-effective detection and counting of pre-weaning piglets are critical for improving piglet survival rates. However, achieving this remains technically challenging due to high computational demands, frequent occlusion, social behaviors, and cluttered backgrounds in commercial farming environments. To address these challenges, this study proposes a lightweight and attention-enhanced piglet detection and counting network based on an improved YOLOv8n architecture. The design includes three key innovations: (i) the standard C2f modules in the backbone were replaced with an efficient novel Multi-Scale Spatial Pyramid Attention (MSPA) module to enhance the multi-scale feature representation while a maintaining low computational cost; (ii) an improved Gather-and-Distribute (GD) mechanism was incorporated into the neck to facilitate feature fusion and accelerate inference; and (iii) the detection head and the sample assignment strategy were optimized to align the classification and localization tasks better, thereby improving the overall performance. Experiments on the custom dataset demonstrated the model’s superiority over state-of-the-art counterparts, achieving 88.5% precision and a 93.8% $mA{P}_{0.5}$. Furthermore, ablation studies showed that the model reduced the parameters, floating point operations (FLOPs), and model size by 58.45%, 46.91% and 56.45% compared to those of the baseline YOLOv8n, respectively, while achieving a 2.6% improvement in the detection precision and a 4.41% reduction in the counting MAE. The trained model was deployed on a Raspberry Pi 4B with $ncnn$ to verify the effectiveness of the lightweight design, reaching an average inference speed of <87 ms per image. These findings confirm that the proposed method offers a practical, scalable solution for intelligent pig farming, combining a high accuracy, efficiency, and real-time performance in resource-limited environments. Full article

Floods represent one of the most significant climate-related hazards, particularly in regions with complex topographies and variable precipitation patterns. This study assesses flood-prone areas within the Mantaro watershed, Peru, using Geographic Information Systems (GISs) and morphometric analysis. The methodology integrates digital elevation models (DEMs) with hydrological parameters, applying weighted sum analysis to classify 18 sub-watersheds into different flood priority levels. Morphometric parameters, including basin relief, drainage density, and slope, were analyzed to establish correlations between watershed morphology and flood susceptibility. The results indicate that approximately 74.38% of the watershed exhibits high to very high flood risk, with the most vulnerable sub-watersheds characterized by steep slopes, high drainage densities, and compact morphometric configurations. The correlation matrix confirms that watershed topography significantly influences surface runoff behavior, underscoring the necessity of incorporating geospatial analysis into flood risk assessment frameworks. The classification of sub-watersheds into priority levels provides a scientific basis for optimizing resource allocation in flood mitigation strategies. This study highlights the importance of integrating advanced geospatial technologies, such as GISs and remote sensing, into hydrological risk assessments. The findings emphasize the need for proactive watershed management, including the use of real-time monitoring and digital tools for climate adaptation. Future research should explore the influence of land-use changes and climate variability on flood dynamics to enhance predictive modeling. These insights contribute to evidence-based decision-making for disaster risk reduction, reinforcing resilience in climate-sensitive regions. Full article

The Directed Energy Deposition (DED) process has demonstrated high efficiency in manufacturing steel parts with complex geometries and superior capabilities. Understanding the complex interplays of alloy compositions, cooling rates, grain sizes, thermal histories, and mechanical properties remains a significant challenge during DED processing. Interpretable and data-driven modeling has proven effective in tackling this challenge, as machine learning (ML) algorithms continue to advance in capturing complex property structural relationships. However, accurately predicting the prime mechanical properties, including ultimate tensile strength (UTS), yield strength (YS), and hardness value (HV), remains a challenging task due to the complex and non-linear relationships among process parameters, material constituents, grain size, cooling rates, and thermal history. This study introduces an ML model capable of accurately predicting the UTS, YS, and HV of a material dataset comprising 4900 simulation analyses generated using the “JMatPro” software, with input parameters including material compositions, grain size, cooling rates, and temperature, all of which are relevant to DED-processed low-alloy steels. Subsequently, an ML model is developed using the generated dataset. The proposed framework incorporates a physics-based DED-specific feature that leverages “JMatPro” simulations to extract key input parameters such as material composition, grain size, cooling rate, and thermal properties relevant to mechanical behavior. This approach integrates a suite of flexible ML algorithms along with customized evaluation metrics to form a robust foundation to predict mechanical properties. In parallel, explicit data-driven models are constructed using Multivariable Linear Regression (MVLR), Polynomial Regression (PR), Multi-Layer Perceptron Regressor (MLPR), XGBoost, and classification models to provide transparent and analytical insight into the mechanical property predictions of DED-processed low-alloy steels. Full article

This study examines the evolution of willingness to accept the monkeypox (Mpox) vaccine in Romania between 2022 and 2025. It explores key sociodemographic and behavioral predictors of vaccine acceptance and investigates how public perceptions—particularly concerning disease severity and conspiracy beliefs—have shifted across two independent cross-sectional samples. Two nationally distributed surveys were conducted in July 2022 (n = 820) and January–February 2025 (n = 1029), targeting Romanian residents aged 18 and above. Data were analyzed using descriptive statistics, Chi-square tests, Kolmogorov–Smirnov tests, and a Random Forest classification model to assess the relative importance of predictors of vaccine acceptance. Between 2022 and 2025, vaccine acceptance increased modestly, particularly among individuals aged 36–65 and those with prior experience of voluntary or COVID-19 vaccination. Random Forest analysis identified behavioral factors as the strongest predictors of acceptance in both years, while the influence of education and gender varied over time. Belief in conspiracy theories slightly declined and lost predictive relevance by 2025. Perceptions of pandemic potential and fear of infection also decreased, suggesting reduced risk salience and possible pandemic fatigue. Despite a slight upward trend, overall Mpox vaccine acceptance in Romania remains among the lowest in Europe. These findings highlight the need for targeted public health communication, particularly toward skeptical or demographically vulnerable groups. Prior vaccination behavior emerged as a key driver of acceptance, indicating that trust-building strategies should capitalize on existing pro-vaccination habits. Future research should adopt qualitative and longitudinal approaches to better capture the evolving psychosocial dynamics of vaccine hesitancy. Full article

Background/Objectives: Alzheimer’s disease (AD) is a devastating neurodegenerative disorder that progressively impairs cognitive, neurological, and behavioral functions, severely affecting quality of life. The current diagnostic process relies on expert interpretation of extensive clinical assessments, often leading to delays that reduce the effectiveness of early interventions. Given the lack of a definitive cure, accelerating and improving diagnosis is critical to slowing disease progression. Electroencephalography (EEG), a widely used non-invasive technique, captures AD-related brain activity alterations, yet extracting meaningful features from EEG signals remains a significant challenge. This study introduces a machine learning (ML)-driven approach to enhance AD diagnosis using EEG data. Methods: EEG recordings from 36 AD patients, 23 Frontotemporal Dementia (FTD) patients, and 29 healthy individuals (HC) were analyzed. EEG signals were processed within the 0.5–45 Hz frequency range using the Welch method to compute the Power Spectral Density (PSD). From both the time-domain signals and the corresponding PSD, a total of 342 statistical and spectral features were extracted. The resulting feature set was then partitioned into training and test datasets while preserving the distribution of class labels. Feature selection was performed on the training set using Spearman and Pearson correlation analyses to identify the most informative features. To enhance classification performance, hyperparameter tuning was conducted using Bayesian optimization. Subsequently, classification was carried out using Support Vector Machines (SVMs) and k-Nearest Neighbors (k-NN) the optimized hyperparameters. Results: The SVM classifier achieved a notable accuracy of 96.01%, outperforming previously reported methods. Conclusions: These results demonstrate the potential of machine learning-based EEG analysis as an effective approach for the early diagnosis of Alzheimer’s Disease, enabling timely clinical intervention and ultimately contributing to improved patient outcomes. Full article

Understanding rock behavior under cutting tools is critical for enhancing cutting processes and forecasting rock behavior in engineering contexts. This study examines the link between mechanical properties and indentation crater morphology of six rocks using a conical indenter until initial fracture. Through indentation testing, mechanical properties (indentation stiffness index k and hardness index HI) were assessed, and crater morphology was analyzed using a 3D laser profilometer. The rocks were categorized into three groups based on specific energy: Class I (slate, shale), Class II (sandstone, marble), and Class III (granite, gneiss). The morphological features of their indentation craters were analyzed both quantitatively and qualitatively. The linear model was used to establish the relationship between crater morphology indices and mechanical properties, with model parameters determined by linear regression. Key findings include: (1) Fracture depth, cross-sectional area, and contour roundness are independent morphological indicators, serving as characteristic parameters for crater morphology, with qualitative and quantitative analyses showing consistency; (2) Post-classification linear fitting revealed statistically significant morphological prediction models, though patterns varied across rock categories due to inherent properties like structure and grain homogeneity; (3) Classification by specific energy revealed distinct mechanical and morphological differences, with significant linear relationships established for all three indicators in Classes II and III, but only roundness showing significance in Class I (non-significant for cross-sectional area and depth). However, all significant models exhibited limited explanatory power (R² = 0.220–0.635), likely due to constrained sample sizes. Future studies should expand sample sizes to refine these findings. Full article

Infants born to mothers who self-identify as Hispanic account for a substantial and growing share of births in the United States, yet limited research has examined disparities in birth outcomes across Hispanic origin subgroups. This study aims to document trends and identify important factors associated with Cesarean section (C-section), low birthweight, and prematurity within the Hispanic population. We use data from the National Vital Statistics System (2011–2021), covering nearly all U.S. births. We compare outcomes across Hispanic, non-Hispanic White, and non-Hispanic Black mothers and further disaggregate by Hispanic origin (Mexican, Puerto Rican, Cuban, Central/South American, and Other/Unknown). We use logistic regression and classification tree models to assess associations between maternal, infant, and clinical factors and birth outcomes. We find that Hispanic mothers have birth outcomes similar to non-Hispanic Whites and better than non-Hispanic Blacks. However, prematurity rates among Hispanics have slightly increased over time. Mexican mothers exhibit the most favorable outcomes, while Cuban mothers show higher rates of C-section, and Puerto Rican mothers show higher rates of low birthweight and prematurity. Logistic regression results highlight multiple births, breech presentation, and hypertensive conditions as important factors associated with adverse birth outcomes. Our biomedical approach emphasizes physiological and clinical risk factors such as multiple births, breech presentation, hypertensive conditions, and obesity. In parallel, our biosocial analysis incorporates demographic, socioeconomic, and behavioral variables to contextualize how social determinants interact with biology to influence outcomes. Complementing these findings, our classification tree analysis identifies inadequate gestational weight gain (less than 15 pounds) as a prominent risk factor for both low birthweight and prematurity. Additionally, obesity emerges as a significant factor linked to an increased likelihood of C-section. While birth outcomes among Hispanic mothers are generally favorable, subgroup differences and emerging disparities highlight the need for disaggregated research and culturally tailored public health interventions. Full article

The environmental challenges posed by global warming have significantly increased the global pursuit of renewable and clean energy sources. Among these, solar energy stands out due to its abundance, renewability, low environmental impact, and favorable long-term economic viability. However, its intermittent nature and dependence on weather conditions hinder consistent and efficient utilization. To address these limitations, nanoparticle-enhanced phase change materials (NPCMs) have emerged as a promising solution for enhancing thermal energy storage in solar thermal systems. NPCMs incorporate superior-performance nanoparticles within traditional phase change material matrices, resulting in improved thermal conductivity, energy storage density, and phase change efficiency. This review systematically examines the recent advances in NPCMs for solar energy applications, covering their classification, structural characteristics, advantages, and limitations. It also explores in-depth analytical approaches, including mechanism-oriented analysis, simulation-based modelling, and algorithm-driven optimization, that explain the behavior of NPCMs at micro and macro scales. Furthermore, the techno-economic implications of NPCM integration are evaluated, with particular attention to cost-benefit analysis, policy incentives, and market growth potential, which collectively support broader adoption. Overall, the findings highlight NPCMs as a frontier in materials innovation and enabling technology for achieving low-carbon, environmentally responsible energy solutions, contributing significantly to global sustainable development goals. Full article

Biometric authentication using human physiological and behavioral characteristics has been widely adopted, with speaker verification attracting attention due to its convenience and noncontact nature. Conventional speaker verification systems remain vulnerable to spoofing attacks, however, often requiring integration with separate spoofed speech detection models. In this work, the authors propose an emotion-dependent speaker verification system that integrates speaker characteristics with emotional speech characteristics, enhancing robustness against spoofed speech without relying on additional classification models. By comparing acoustic characteristics of emotions between registered and verification speech using pretrained models, the proposed method reduces the equal error rate compared to conventional speaker verification systems, achieving an average equal error rate of 1.13% for speaker verification and 17.7% for the anti-spoofing task. Researchers additionally conducted a user evaluation experiment to assess the usability of emotion-dependent speaker verification. The results indicate that although emotion-dependent authentication was initially cognitively stressful, participants adapted over time, and the burden was significantly reduced after three sessions. Among the tested emotions (anger, joy, sadness, and neutral), sadness proved most effective, with stable scores, a low error rate, and minimal user strain. These findings suggest that neutral speech is not always the optimal choice for speaker verification and that well-designed emotion-dependent authentication can offer a practical and robust security solution. Full article

The exploitation and development of photovoltaic (PV) modules faces several technical challenges, including those related to variability in electrical performance under real conditions, such as temperature fluctuations, irradiance variability, and dust accumulation. One solution for evaluating and controlling these performances is to conduct electrical characterization under natural conditions. Many characterization techniques have been developed and proposed in the literature, with the aim of verifying manufacturer performance guarantees and better understanding the behavior of PV modules in their installation environment, where the climatic parameters, such as solar irradiation and temperature, fluctuate constantly. These techniques are based on recognized standards, including those established by the International Electrotechnical Commission (IEC) and American Society for Testing and Materials (ASTM). They are also based on methods of transposing basic electrical parameters, allowing the prediction of the performance of modules under various environmental conditions. In this work, a classification and a critical analysis of the main methods of electrical characterization were undertaken, highlighting their respective advantages and disadvantages. The experimental protocols used to evaluate the impact of environmental parameters on the performance of PV modules were examined in detail. Full article

Background/Objectives: Thyroid cancer encompasses distinct histological subtypes with varying biological behavior and treatment implications. Accurate preoperative subtype differentiation remains challenging. Although ultrasound (US) is widely used for thyroid nodule evaluation, qualitative assessment alone is often insufficient to distinguish between papillary (PTC), follicular (FTC), and medullary thyroid carcinoma (MTC). Methods: A retrospective analysis was performed on patients with histologically confirmed PTC, FTC, or MTC. A total of 224 standardized B-mode ultrasound images were analyzed. A set of fully quantitative features was extracted, including morphological characteristics (aspect ratio and perimeter-to-area ratio), internal echotexture (echogenicity and local entropy), boundary sharpness (gradient measures and KL divergence), and structural components (calcifications and cystic areas). Feature extraction was conducted using semi-automatic algorithms implemented in MATLAB. Statistical differences were assessed using the Kruskal–Wallis and Dunn–Šidák tests. A Random Forest classifier was trained and evaluated to determine the discriminatory performance of individual and combined features. Results: Significant differences (p < 0.05) were found among subtypes for key features such as perimeter-to-area ratio, normalized echogenicity, and calcification pattern. The full-feature Random Forest model achieved an overall classification accuracy of 89.3%, with F1-scores of 93.4% for PTC, 85.7% for MTC, and 69.1% for FTC. A reduced model using the top 10 features yielded an even higher accuracy of 91.8%, confirming the robustness and clinical relevance of the selected parameters. Conclusions: Subtype classification of thyroid cancer was effectively performed using quantitative ultrasound features and machine learning. The results suggest that biologically interpretable image-derived metrics may assist in preoperative decision-making and potentially reduce the reliance on invasive diagnostic procedures. Full article

Background: Diabesity, the coexistence of obesity and type 2 diabetes, is a major public health concern. Shift work and unhealthy lifestyle behaviors may exacerbate its prevalence, particularly in working populations. Objective: This study aims to evaluate the association between sociodemographic characteristics, health behaviors, and shift work and the prevalence of diabesity, using both BMI and the CUN-BAE estimator, in a large cohort of Spanish workers. Methods: This cross-sectional study included 53,053 workers (59.8% men) aged 18–69 years who underwent occupational health examinations. Diabesity was defined as obesity (BMI ≥ 30 kg/m² or high CUN-BAE) plus fasting glucose ≥ 100 mg/dL or prior diagnosis of diabetes. Adherence to the Mediterranean diet was assessed by the MEDAS questionnaire, physical activity by the IPAQ, alcohol intake by standard drink units (UBEs), and socioeconomic class by the CNAE-11 classification. Shift work was defined according to ILO criteria. Logistic regression was used to assess associations, adjusting for potential confounders. Results: Shift work was independently associated with increased odds of diabesity both in men and women. Diabesity prevalence was higher when assessed by CUN-BAE compared with BMI. Age, male sex, lower socioeconomic class, physical inactivity, smoking, poor diet adherence, and alcohol intake were all significantly associated with higher risk. The CUN-BAE index showed superior sensitivity in identifying individuals at risk. Conclusions: Shift work and unhealthy behaviors are key determinants of diabesity among Spanish workers. The use of adiposity estimators beyond BMI, such as CUN-BAE, should be encouraged in occupational health surveillance. Workplace-targeted interventions are urgently needed to address this growing metabolic burden. Full article

This study reviews heat transfer fluids (HTFs) and their velocity effects on the thermal behavior of ground heat exchangers (GHEs) within geothermal heat pump (GHP) applications. It examines the classification, thermophysical properties, and operational behavior of standard working fluids, including water–glycol mixtures, as well as emerging nanofluids. Fundamental heat exchange mechanisms are discussed, with emphasis on how conductivity, viscosity, and heat capacity interact with fluid velocity to influence energy transfer performance, hydraulic resistance, and system reliability. Special attention is given to nanofluids, whose enhanced thermal behavior depends on nanoparticle type, concentration, dispersion stability, and flow conditions. The review analyzes stabilization strategies, including surfactants, functionalization, and pH control, for maintaining long-term performance. It also highlights the role of velocity optimization in balancing convective benefits with pumping energy demands, providing velocity ranges suited to different GHE configurations. Drawing from recent experimental and numerical studies, the review offers practical guidelines for integrating nanofluid formulation with engineered operating conditions to maximize energy efficiency and extend system lifespan. Full article

This study examines the impact of Community Notes on users’ stance and posting behavior regarding the human papillomavirus (HPV) vaccine on X. Unlike previous research focusing on affected posts and authors, this study analyzes users who viewed Community Notes and their posting behavior before and after exposure. We analyzed posts related to the HPV vaccine using X’s official Community Notes dataset (January 2021–July 2024). Posts were classified as “Support,” “Oppose,” or “Neutral” using a large language model (GPT-4o, OpenAI), and changes in stance and posting frequency were evaluated. Findings show that 73% of users maintained their stance after viewing Community Notes. However, posting frequency increased sharply immediately after the note was added, especially among opposing users. This suggests that, since most Community Notes support vaccination, opposing users may have actively responded by posting critical responses. This study contributes by examining viewer behavior, not just post authors. However, limitations include GPT-4o’s classification accuracy and the restricted scope of topics and users covered in this study. Future research should improve the evaluation of Community Notes by verifying users who viewed Community Notes and enhancing stance classification through better prompts and model comparisons. Additionally, expanding the analysis beyond the HPV vaccine will help assess the broader applicability of the findings. Full article

Accurate and timely detection of islanding events is essential for ensuring the stability and safety of hybrid power systems with high penetration of distributed energy resources. Traditional islanding detection methods often face challenges related to detection speed, false alarms, and robustness under dynamic operating conditions. This paper proposes a Robust Random Vector Functional Link Network (RVFLN)-based detection framework that leverages engineered features extracted from voltage, current, and power signals in a hybrid microgrid. The proposed method integrates statistical, spectral, and spatiotemporal features—including the Dynamic Harmonic Profile (DHP), which tracks rapid harmonic distortions during disconnection, the Sub-band Energy Ratio (SBER), which quantifies the redistribution of signal energy across frequency bands, and the Islanding Anomaly Index (IAI), which measures multivariate deviations in system behavior—capturing both transient and steady-state characteristics. A real-time digital simulator (RTDS) is used to model diverse scenarios including grid-connected operation, islanding at the Point of Common Coupling (PCC), synchronous converter islanding, and fault events. The RVFLN is trained and validated using this high-fidelity data, enabling robust classification of operational states. Results demonstrate that the RVFLN achieves high accuracy (up to 98.5%), low detection latency (average 0.05 s), and superior performance across precision, recall, and F1 score compared to conventional classifiers such as Random Forest, SVM, and k-NN. The proposed approach ensures reliable real-time islanding detection, making it a strong candidate for deployment in intelligent protection and monitoring systems in modern power networks. Full article