Search Results (10,756)

Emotional Footprint Identification refers to the process of recognizing or understanding the emotional impact that a person, experience, or interaction leaves on others. Emotion Recognition plays an important role in human–computer interaction for identifying emotions such as fear, sadness, anger, happiness, and surprise on the human face during the conversation. However, accurate emotional footprint identification plays a crucial role due to the dynamic changes. Conventional deep learning techniques integrate advanced technologies for emotional footprint identification, but challenges in accurately detecting emotions in minimal time. To address these challenges, a novel Divergence Shepherd Feature Optimization-based Stochastic-Tuned Deep Multilayer Perceptron (DSFO-STDMP) is proposed. The proposed DSFO-STDMP model consists of three distinct processes namely data acquisition, feature selection or reduction, and classification. First, the data acquisition phase collects a number of conversation data samples from a dataset to train the model. These conversation samples are given to the Sokal–Sneath Divergence shuffling shepherd optimization to select more important features and remove the others. This optimization process accurately performs the feature reduction process to minimize the emotional footprint identification time. Once the features are selected, classification is carried out using the Rosenthal correlative stochastic-tuned deep multilayer perceptron classifier, which analyzes the correlation score between data samples. Based on this analysis, the system successfully classifies different emotions footprints during the conversations. In the fine-tuning phase, the stochastic gradient method is applied to adjust the weights between layers of deep learning architecture for minimizing errors and improving the model’s accuracy. Experimental evaluations are conducted using various performance metrics, including accuracy, precision, recall, F1 score, and emotional footprint identification time. The quantitative results reveal enhancement in the 95% accuracy, 93% precision, 97% recall and 97% F1 score. Additionally, the DSFO-STDMP minimized the in training time by 35% when compared to traditional techniques. Full article

Existing methods for identifying Tactics, Techniques, and Procedures (TTPs) from complex cyber-attack descriptions face three core challenges: (1) severe semantic asymmetry between unstructured attack narratives and standardized TTP definitions; (2) continuously distributed semantic relations that cannot be fully captured by hard-label supervision; and (3) an open, long-tailed TTP taxonomy that impairs model generalization. To address these limitations, we introduce DTGBI-TM, a lightweight dual-tower semantic matching framework that integrates soft-label supervision, hierarchical hard-negative sampling, and gated binary interaction modeling. The model separately encodes attack descriptions and TTP definitions and employs a gated interaction module to adaptively fuse shared and divergent semantics, enabling fine-grained asymmetric alignment. A confidence-guided soft–hard collaborative supervision mechanism unifies weighted classification, semantic regression, and contrastive consistency into a multi-objective loss, dynamically rebalancing gradients to mitigate long-tail effects. Leveraging ATT & CK hierarchical priors, the model further performs in-tactic and cross-tactic hard-negative sampling to enhance semantic discrimination. Experiments on a real-world corpus demonstrate that DTGBI-TM achieves 98.53% F1 in semantic modeling and 79.77% Top-1 accuracy in open-set TTP prediction, while maintaining high inference efficiency and scalability in deployment. Full article

Background/Objectives: Acute respiratory distress syndrome (ARDS) represents a critical complication in polytrauma patients, characterized by diffuse lung inflammation and bilateral pulmonary infiltrates with mortality rates reaching 45% in intensive care units (ICU). The heterogeneous nature of ARDS and complex clinical presentation in severely injured patients poses substantial diagnostic challenges, necessitating early prediction tools to guide timely interventions. Machine learning (ML) algorithms have emerged as promising approaches for clinical decision support, demonstrating superior performance compared to traditional scoring systems in capturing complex patterns within high-dimensional medical data. Based on the identified research gaps in early ARDS prediction for polytrauma populations, our study aimed to: (i) develop a balanced random forest (BRF) ML model for early ARDS prediction in critically ill polytrauma patients, (ii) identify the most predictive clinical features using ANOVA-based feature selection, and (iii) evaluate model performance using comprehensive metrics addressing class imbalance challenges. Methods: This retrospective cohort study analyzed 407 polytrauma patients admitted to the ICU of the Center of Traumatology and Major Burns of Ben Arous, Tunisia, between 2017 and 2021. We implemented a comprehensive ML pipeline that incorporates Tomek Links undersampling, ANOVA F-test feature selection for the top 10 predictive variables, and SMOTE oversampling with a conservative sampling rate of 0.3. The BRF classifier was trained with class weighting and evaluated using stratified 5-fold cross-validation. Performance metrics included AUROC, PR-AUC, sensitivity, specificity, F1-score, and Matthews correlation coefficient. Results: Among 407 patients, 43 developed ARDS according to the Berlin definition, representing a 10.57% incidence. The BRF model demonstrated exceptional predictive performance with an AUROC of 0.98, a sensitivity of 0.91, a specificity of 0.80, an F1-score of 0.84, and an MCC of 0.70. Precision–recall AUC reached 0.86, demonstrating robust performance despite class imbalance. During stratified cross-validation, AUROC values ranged from 0.93 to 0.99 across folds, indicating consistent model stability. The top 10 selected features included procalcitonin, PaO₂ at ICU admission, 24-h pH, massive transfusion, total fluid resuscitation, presence of pneumothorax, alveolar hemorrhage, pulmonary contusion, hemothorax, and flail chest injury. Conclusions: Our BRF model provides a robust, clinically applicable tool for early prediction of ARDS in polytrauma patients using readily available clinical parameters. The comprehensive two-step resampling approach, combined with ANOVA-based feature selection, successfully addressed class imbalance while maintaining high predictive accuracy. These findings support integrating ML approaches into critical care decision-making to improve patient outcomes and resource allocation. External validation in diverse populations remains essential for confirming generalizability and clinical implementation. Full article

Objective: We aimed to develop multidisciplinary recommendations for the management of cardiovascular–kidney–metabolic (CKM) syndrome in Spain. Methods: The Delphi method was used. The final questionnaire comprised 61 statements that were assessed using a 9-point Likert scale of agreement, from 1 = fully disagree to 9 = fully agree. A consensus was reached when 80% of answers in all specialties were in the range of 7–9. The overall median was used as a measure of the strength of agreement. Results: A total of 70 (97%) panelists met the selection criteria and completed two rounds, including cardiology (13), endocrinology (12), internal medicine (12), nephrology (14), and primary care (19). Among the 61 statements, a consensus was reached in 54 (89%). The consensus to be highlighted included the following: an excess and/or dysfunction of adipose tissue as the initial driver of CKM syndrome (median 8), CKM syndrome that includes both patients at risk (median 8) and those with existing CVD (median 8), coordination of patient management by the family medicine physician (median 9), the essential role of primary prevention in maintaining CKM health (median 9), the administration of drugs with demonstrated CKM benefit in both early-stage patients (median 9) and those in the advanced stages of the syndrome (median 9), and the importance of lifestyle measures (median 9), with a focus on intensive weight loss (median 9). Conclusions: This Delphi consensus offers multidisciplinary recommendations highlighting the importance of early recognition, integrated management, and the implementation of preventive and therapeutic strategies with established cardiorenal and metabolic benefits. Full article

System time delays and nonlinear unmodeled dynamics severely constrain the control performance of the Active Suspension Electro-Hydraulic Servo Actuator (ASEHSA). To tackle these challenges, this paper presents a Dynamic Error Differentiation-based Model-Free Adaptive Control (DE-MFAC) strategy integrated with an Improved Particle Swarm Optimization (IPSO) algorithm. Established under the Model-Free Adaptive Control (MFAC) framework, the DE-MFAC integrates a dynamic error differentiation mechanism and an implicit expression of time delays, thus removing the dependence on a precise system model. The traditional PSO algorithm is improved by incorporating an inertia weight adjustment strategy and a boundary reflection wall strategy, which effectively mitigates the issues of local optima and boundary stagnation. In AMESim 2021, a 1/4 vehicle active suspension electro-hydraulic actuation system model is constructed. To ensure an impartial evaluation of controller performance, the IPSO algorithm is employed to optimize the parameters of the PID, MFAC, and DE-MFAC controllers, respectively. Co-simulations with Simulink 2023b are conducted under two time delay scenarios using a composite square-sine wave signal as the reference. The results indicate that all three IPSO-optimized controllers realize effective position tracking. Among them, the DE-MFAC controller exhibits the optimal performance, demonstrating remarkable advantages in reducing tracking errors and balancing settling time with overshoot. These findings verify the effectiveness of the proposed control strategy, time delay compensation mechanism, and optimization algorithm. Future research will involve validation on a physical ASEHSA platform, further exploration of the method’s applicability and robustness under diverse operating conditions, and extension to other industrial systems with similar nonlinear time delay features. Full article

Modern manufacturing robots must dynamically balance multiple conflicting objectives amid rapidly evolving production demands. Traditional control approaches lack the adaptability required for real-time decision-making in Industry 4.0 environments. This study presents an adaptive multi-objective reinforcement learning (MORL) framework integrating dynamic preference weighting with Pareto-optimal policy discovery for real-time adaptation without manual reconfiguration. Experimental validation employed a UR5 manipulator with RG2 gripper performing quality-aware object sorting in CoppeliaSim with realistic physics (friction μ = 0.4, Bullet engine), manipulating 12 objects across four geometric types on a dynamic conveyor. Thirty independent runs per algorithm (seven baselines, 30,000+ manipulation cycles) demonstrated +24.59% to +34.75% improvements (p < 0.001, d = 0.89–1.52), achieving hypervolume 0.076 ± 0.015 (19.7% coefficient of variation—lowest among all methods) and 95% optimal performance within 180 episodes—five times faster than evolutionary baselines. Four independent verification methods (WFG, PyMOO, Monte Carlo, HSO) confirmed measurement reliability (<0.26% variance). The framework maintains edge computing compatibility (<2 GB RAM, <50 ms latency) and seamless integration with Manufacturing Execution Systems and digital twins. This research establishes new benchmarks for adaptive robotic control in sustainable Industry 4.0/5.0 manufacturing. Full article

Cancer remains a leading global cause of morbidity and mortality. Modifiable lifestyle factors, including avoidance of tobacco use and excessive ultraviolet radiation, healthy dietary patterns, regular physical activity, and weight management, play key roles in prevention and care. This narrative review synthesizes evidence on lifestyle-based interventions influencing cancer risk, treatment tolerance, and survivorship. A literature search was conducted in PubMed and Scopus, supplemented by manual screening via Google Scholar. The time frame (2001–2025) was selected to reflect evidence produced within the modern era of molecular oncology and contemporary lifestyle medicine research. Eligible publications addressed carcinogen exposure (tobacco, alcohol, ultraviolet radiation), diet and nutritional strategies, physical activity, sedentary behavior, obesity, metabolic health, complementary therapies, and cancer outcomes. Evidence indicates that reducing exposure to tobacco and ultraviolet radiation remains central to cancer prevention. Adherence to predominantly plant-based diets, regular physical activity, and maintenance of healthy body weight are consistently associated with lower incidence of several cancers, including breast, colorectal, and liver cancer. Nutritional strategies such as caloric restriction, ketogenic diets, and fasting-mimicking diets show promise in improving treatment efficacy and quality of life. Complementary and mind–body therapies may alleviate treatment-related symptoms, although high-quality evidence on long-term safety and effectiveness is limited. Integrating lifestyle medicine into oncology offers a cost-effective, sustainable strategy to reduce cancer burden and enhance survivorship. Comprehensive programs combining carcinogen avoidance, dietary regulation, structured exercise, and effective radiation risk mitigation may extend healthspan, improve treatment tolerance, and help prevent recurrence. Full article

Flavor quality fundamentally influences the market value of bivalves, yet the molecular basis of interspecific umami variation remains poorly understood, hindering flavor-directed breeding. This study compared umami compounds and related gene expression across six economically important bivalve species from Southeast China: Crassostrea sikamea, Meretrix meretrix, M. mercenaria, Cyclina sinensis, Ruditapes philippinarum, and Sinonovacula constricta. Using quantitative chemical analysis and qPCR, key taste components and gene expression levels were assessed during the peak flavor season. Results identified glutamic acid, aspartic acid, guanosine monophosphate, and adenosine monophosphate as major umami contributors. Crassostrea sikamea showed the highest umami intensity (Equivalent umami concentration = 449.35 g Monosodium Glutamate/100 g dry weight), followed by Meretrix meretrix (EUC = 329.56 g MSG/100 g dry weight). Expression of glutamate dehydrogenase 1 strongly correlated with glutamic acid content (r = 0.90, p < 0.01), indicating its regulatory role. glutamic-oxaloacetic transaminase 1 and adenylosuccinate synthase also associated positively with aspartic and glutamic acids, respectively, while hypoxanthine phosphoribosyltransferase 1 correlated negatively. Bioinformatics revealed species-specific variations in key enzyme active sites. This study integrates flavor phenotyping with genetic analysis, offering novel insights into umami regulation and providing candidate genes for molecular breeding aimed at flavor enhancement, but subject to further functional validation and heritability analysis. Full article

The rise in social media has transformed Online Travel Agencies (OTAs) into platforms where users actively share their experiences and opinions. However, conventional opinion mining approaches often fail to capture nuanced emotional expressions or connect them to user influence. To address this gap, this study introduces an ontology-driven opinion mining framework that integrates multi-class emotion classification, aspect-based analysis, and influence modeling using Indonesian-language discussions from the social media platform X. The framework combines an OTA-specific ontology that formally represents service aspects such as booking support, financial, platform experience, and event with fine-tuned IndoBERT for emotion recognition and sentiment polarity detection, and Social Network Analysis (SNA) enhanced by entropy weighting and TOPSIS to quantify and rank user influence. The results show that the fine-tuned IndoBERT performs strongly with respect to identification and sentiment polarity detection, with moderate results for multi-class emotion classification. Emotion labels enrich the ontology by linking user opinions to their affective context, enabling the deeper interpretation of customer experiences and service-related issues. The influence analysis further reveals that structural network properties, particularly betweenness, closeness, and eigenvector centrality, serve as the primary determinants of user influence, while engagement indicators act as discriminative amplifiers that highlight users whose content attains high visibility. Overall, the proposed framework offers a comprehensive and interpretable approach to understanding public perception in Indonesian-language OTA discussions. It advances opinion mining for low-resource languages by bridging semantic ontology modeling, emotional understanding, and influence analysis, while providing practical insights for OTAs to enhance service responsiveness, manage emotional engagement, and strengthen digital communication strategies. Full article

China’s higher education system is the largest globally but faces significant spatial imbalance issues. While studies have examined the spatial distribution of universities, long-term dynamic analysis, quantitative exploration of influencing factors, and investigation of spatial heterogeneity are lacking. This study investigates the spatiotemporal evolution of China’s regular higher education institutions (HEIs) from 1952 to 2023 by using ArcGIS spatial analysis and integrating the Geographical Detector and Multi-scale Geographically Weighted Regression (MGWR) models. Findings reveal that (1) the spatial distribution of China’s HEIs has become increasingly clustered, transitioning from a “point-like” to a “network-like” and finally to a “surface-like” pattern, with its center shifting southwestward—this evolution reflects the gradual formation of a spatially sustainable layout that adapts to regional development needs. (2) Multiple interacting factors influence distribution—including the number of full-time faculty, regional GDP, national universities’ presence during the Republic of China era, and fiscal expenditure on education—with significant variations in their explanatory power. Regional population size also exerts a notable influence. (3) The impact of these factors exhibits significant spatial heterogeneity, with pronounced local imbalances. Thus, multi-scale processes operating at different geographical levels have shaped HEIs’ spatial pattern and addressing this heterogeneity is a key prerequisite for achieving sustainable and equitable development of higher education. These findings provide critical insights for optimizing higher education resource allocation, promoting balanced regional development, and advancing the construction of a high-quality education system in China. Full article

A promising approach to advancing sustainable urban mobility is the increased use of light electric vehicles, such as e-cycles and their cargo-carrying variants: e-cargo cycles. These micromobility vehicles fall between e-cycles and conventional vehicles in terms of transport capacity, range, and cost. A key advantage of e-cargo cycles over their non-electrified counterparts is the electric powertrain, which enables them to carry heavier payloads, travel longer distances, and reduce driver fatigue. Since the primary use of e-cargo cycles is urban parchment deliveries, trip efficiency plays a critical role in their effectiveness within urban logistics. This efficiency is influenced by factors such as travel distance, traffic density, and the weight and volume of the delivery payload. While higher delivery capacity generally enhances efficiency, studies have shown that as the drop size increases, the efficiency of e-cargo cycle delivery trips tends to decline. A practical way to address this limitation is the use of cargo attachments, such as trailers. These micromobility solutions are already widely implemented globally and significantly enhance transport capacity. This paper reports the process of designing and testing the control algorithm of an electrical system for an experimental attachment demonstrator that can be used to convert most cycle vehicles into cargo variants. The system integrates two 250 W BLDC hub motors, two 576 Wh lithium-ion batteries, dual load-cell sensing in the coupling element, and an STM32-based controller to provide independent propulsion and synchronization with the leading cycle. The force-based control strategy enables automatic adaptation to varying payloads typically encountered in urban logistics, which is supported by the variable storage volume capable of transporting payloads of up to 200 kg. Full article

Bacterial Chondronecrosis with Osteomyelitis (BCO) lameness is an infection of opportunistic bacteria in the structural skeletal bones impacting multiple animal species, particularly poultry species. BCO lameness results in significant financial losses to industrial poultry production and increases the risk of foodborne illnesses, posing a major threat to consumers’ food safety. As BCO lameness is an inherent risk of fast body weight gain in poultry species, especially broiler chickens, abundant studies have been conducted in broilers and turkeys. Nevertheless, BCO lameness incidence in ducks remains elusive. Thus, this is the first survey investigating the prevalence of BCO lameness cases in ducks. The survey was conducted in commercial duck farms in Indonesia, the fourth biggest duck-producing country globally. Two hundred birds from four commercial duck farms in Mojokerto, East Java, Indonesia, were necropsied to examine their lameness lesions in the femoral head and proximal tibia. Of the 44% birds showing BCO lameness lesions, 3% were evidently clinically lame birds, particularly exhibiting limping gait. Femoral head separation (FHS) and tibial head necrosis (THN) are the most frequently observed lesions in ducks, representing a mild-to-moderate BCO lameness state. Based on the results of this study, intervention measures to boost the immune system and skeletal bone integrity of ducks are urgently required. Full article

Counting small, densely clustered objects from low-altitude aerial views is challenging due to large scale variations, complex backgrounds, and severe occlusion, which often degrade the performance of fully supervised or density-regression methods. To address these issues, we propose a weakly supervised crowd counting framework that leverages point-level supervision and a feature-adaptive fusion strategy to enhance perception under low-altitude aerial views. The network comprises a front-end feature extractor and a back-end fusion module. The front-end adopts the first 13 convolutional layers of VGG16-BN to capture multi-scale semantic features while preserving crucial spatial details. The back-end integrates a Feature-Adaptive Fusion module and a Multi-Scale Feature Aggregation module: the former dynamically adjusts fusion weights across scales to improve robustness to scale variation, and the latter aggregates multi-scale representations to better capture targets in dense, complex scenes. Point-level annotations serve as weak supervision to substantially reduce labeling cost while enabling accurate localization of small individual instances. Experiments on several public datasets, including ShanghaiTech Part A, ShanghaiTech Part B, and UCF_CC_50, demonstrate that our method surpasses existing mainstream approaches, effectively mitigating scale variation, background clutter, and occlusion, and providing an efficient and scalable weakly supervised solution for small-object counting. Full article

Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and newer incretin-based co-agonists have transformed obesity and type 2 diabetes (T2D) management, achieving unprecedented weight loss and cardiometabolic benefits. However, their effects on body composition, particularly lean and skeletal muscle mass, remain incompletely defined. In this current review, we examined the influence of GLP-1 RAs and incretin hormone agonists on lean tissue, integrating physiological, clinical, and mechanistic perspectives. We first outlined the physiology of incretin hormones, with emphasis on their metabolic roles and potential relevance to muscle health. We then discussed sarcopenia and sarcopenic obesity as conditions of rising clinical concern, given their overlap with obesity and metabolic disease. Evidence from preclinical studies and randomized clinical trials indicates that while GLP-1-based therapies predominantly reduce adipose tissue, including visceral and ectopic depots, but they also produce absolute reductions in lean mass, generally representing 20–30% of total weight loss. The extent to which these changes translate into impaired muscle function or increased vulnerability to frailty remains unclear. Preservation of lean and skeletal muscle mass is a critical yet underexplored aspect of incretin-based weight loss. Current studies are constrained by methodological heterogeneity, small sample sizes, and limited assessment of functional outcomes. Data on dual and triple agonists are emerging but remain limited. Future research should integrate standardized body-composition measures, mechanistic exploration, and adjunctive interventions such as resistance training or protein optimization. Full article

Background: Overweight and obesity frequently occur as comorbid conditions in people with asthma, particularly among those with poor disease control or more severe clinical profiles. However, the extent to which exposure to grey spaces may influence the link between overweight/obesity and asthma remains insufficiently explored. Aim: To assess the association between overweight/obesity and asthma in an Italian general population sample and the influence of residential grey space on such relationship. Methods: A total of 2841 individuals (54.7% women; age range 8–97 years) residing in Pisa, Italy, were surveyed in 1991–1993 using a standardised questionnaire on health conditions and relevant risk factors. The proportion of grey space within a 1000 m buffer around each participant’s home was quantified using the CORINE Land Cover database. Multinomial logistic regression models were applied to assess the association between asthma status (1. asthma symptoms without doctor diagnosis, 2. diagnosis ± symptoms, 3. no diagnosis/symptoms − reference category) and overweight/obesity, adjusting for sex, age, educational level, smoking, physical activity and grey space exposure. Analyses were further stratified according to high vs. low grey space exposure (above vs. below 63%, corresponding to the second tertile). Mediation and interaction analyses were also performed. Results: The prevalence of asthma diagnosis ± symptoms, overweight and obesity was 18.7%, 35.8% and 12.8%, respectively. In the full sample, asthma symptoms without medical diagnosis were positively associated with overweight (Odds Ratio—OR 1.43; 95% Confidence Interval—CI 1.08–1.88), obesity (OR 1.99; 95% CI 1.38–2.88) and residential grey space (OR 1.06; 95% CI 1.01–1.13). Stratified models showed that, among participants with high exposure to grey areas, asthma symptoms were linked to both overweight (OR 2.03; 95% CI 1.29–3.19) and obesity (OR 2.57; 95% CI 1.36–4.86). In individuals with low grey space exposure, an association was observed only with obesity (OR 1.80; 95% CI 1.15–2.82). Mediation analysis did not reveal any weight-related effect modification. Measures of additive interaction indicated that 32% of asthma symptoms were attributable to the interaction between excess body weight and high grey space exposure. Conclusions: This study showed that overweight/obesity and grey space exposure are factors associated with asthma symptoms. These findings advocate for an early identification of overweight/obese-asthma symptom phenotype since it may help prevent the onset or worsening of asthma, particularly in urban environments. These insights highlight the need for integrated public health and urban planning strategies to promote more sustainable, health-supportive environments. Full article