Search Results (2,536)

Immersive audio and vibroacoustic stimulation have gained increasing attention as non-invasive approaches for modulating human emotional and physiological states. The SonikB3D platform was previously introduced as a multisensory system combining immersive 3D audio, vibroacoustic stimulation, and physiological monitoring. Building upon this prior work, the present study advances the platform through a refined experimental protocol and a data-driven framework for the automatic assessment of relaxation using multimodal biosignals. A controlled pilot study was conducted with 20 participants exposed to 3D sound and vibroacoustic stimulation delivered through a massage table equipped with integrated transducers. Although the SonikB3D platform supports multiple stimulation scenarios, the present study focuses on a single controlled condition combining binaural 3D audio (binaural beats plus music) and vibroacoustic stimulation in order to ensure methodological consistency for multimodal modelling. Physiological responses were continuously recorded using a synchronized setup including electroencephalography (EEG), photoplethysmography (PPG), and electrodermal activity (EDA). Subjective emotional self-assessment questionnaires were collected before and after exposure to provide a multidimensional characterization of participant responses. Results show a statistically significant increase in self-reported relaxation (paired t-test = 3.05, p = 0.01), corresponding to an average 8% improvement in normalized relaxation scores. To support objective assessment, multimodal physiological features associated with autonomic and emotional regulation were extracted and used to develop a two-stage machine learning pipeline. The proposed model, combining a window-level Random Forest classifier with session-level aggregation, achieved an accuracy of 80% and an F1-score of 0.857 in classifying relaxation-related states. These findings provide preliminary evidence that combined 3D audio and vibroacoustic stimulation can produce measurable changes in subjective and physiological indicators of relaxation, while demonstrating the feasibility of automatic relaxation state inference from multimodal biosignals. Although exploratory due to the limited sample size and the absence of unimodal control conditions, this work contributes a data-driven methodology for studying human responses to multisensory sound and vibration metrics. Full article

Reliable wearable activity recognition requires not only a class label but also an auditable indication of whether that label is supported by historical sensor evidence. We present CC-NSIEA, a label-preserving neural-plus-rule-based class-contrast evidence audit for cross-subject wearable activity recognition. A Temporal Residual Perception Network supplies the sole activity label, posterior probabilities, and a normalized temporal embedding. A read-only Training-Subject Evidence Memory retrieves global, predicted-class, and competing-class records. A rule-based Evidence Consistency Audit combines data validity, dynamic/static motion coherence, retrieval support, and class separation. When first-round evidence is insufficient, Class-Contrast Evidence Refinement performs one deterministic contrast between the predicted class and the strongest posterior competitor; the audit cannot change the neural label. The term neuro-symbolic is used only in this restricted architectural sense: a neural predictor is coupled to explicitly represent deterministic predicates and a finite rule-based controller; the method does not perform symbolic inference, theorem proving, or knowledge-graph reasoning. On five subject-disjoint outer folds of the UCI HAR official training partition, the shared perception model achieved 90.13% accuracy and 90.55% macro-F1 across 7352 out-of-fold windows from 21 subjects. Relative to a matched dynamic deterministic controller, CC-NSIEA increased Error AUPRC from 0.423802 to 0.433057 and reduced AURC from 0.035941 to 0.035913. The 10,000-resample subject-cluster bootstrap interval for the AUPRC difference was [0.001595, 0.019547]. CC-NSIEA provides an evidence-centered complement to confidence-based reliability estimation. Full article

Modern distribution networks operate under increasingly demanding conditions, characterized by the integration of distributed energy resources, unbalanced three-phase operation, low measurement redundancy, variable topologies, and data uncertainty. In this context, distribution system state estimation (DSSE) is a key tool for operational monitoring; however, its practical deployment is often hindered by topological inconsistencies and gross measurement errors. This paper proposes an integrated and modular deep learning-based methodological framework that combines active topology identification (ATI), gross error detection (GED), error-type identification (ETI), error-location identification (ELI), measurement reconstruction and correction (MRC), and DSSE. The ATI module is formulated as a global multiclass classifier, whereas the subsequent modules are trained as topology-specific models. Compromised measurements are handled through an iterative GED–ETI–ELI–MRC loop that detects, identifies, locates, and corrects one anomalous measurement per iteration before re-evaluating the input vector. The proposed methodology was validated by using simulation-based scenarios generated in OpenDSS for a real unbalanced three-phase 240-node distribution feeder. The results show that no single architecture is dominant across all subproblems: WaveNet1D achieved the best relative performance in ATI, GED, and ETI; EncDec-CNN in ELI; NBEATS1D in MRC; and EncDec-GRU in DSSE. Additionally, WLS estimators based on both nodal voltages and branch currents failed to achieve numerical convergence on the 240-node test system under the evaluated conditions, a finding consistent with recent literature reporting analogous convergence failures in distribution networks of similar or smaller scale. Furthermore, the integrated evaluation shows that omitting ATI increases the voltage-magnitude MAE by a factor of 12.3 and the voltage-angle MAE by a factor of 8.1 with respect to the complete framework, whereas omitting only the compromised-measurement treatment increases these errors by factors of 1.8 and 1.9, respectively. The total offline computational cost was approximately 1587.9 h (66.2 GPU-days), while the online inference latency was approximately 0.45 ms per sample, making the framework compatible with AMI- and SCADA-based monitoring cycles. These findings confirm that topological consistency is the dominant factor in DSSE accuracy and that iterative measurement correction meaningfully improves estimator robustness under anomalous measurement conditions. Full article

Field-Programmable Gate Array System-on-Chip (FPGA-SoC) platforms are increasingly adopted in modern industrial Cyber-Physical Systems (CPSs), enabling real-time control, monitoring, and automation of critical industrial processes. The increasing integration density of modern FPGA-SoC architectures introduces new thermal security challenges, where heat evolves from a reliability concern into a potential source of information leakage. Thermal Side-Channel Attacks (TSCAs) exploit runtime thermal variations to infer sensitive operational, architectural, or cryptographic information from the underlying hardware. While this study is centered on FPGA-SoC platforms, comparable thermal security challenges are increasingly reported across other densely integrated computing architectures, including Multiprocessor System-on-Chip (MPSoC), System-in-Package (SiP), and emerging Three-Dimensional Integrated Circuit (3D-IC) technologies. Consequently, the detection of thermal side-channel intrusions has become a critical hardware security challenge for next generation industrial CPS infrastructures. To address this challenge, an AI-enabled Digital Twin (DT) framework is introduced for TSCA detection in densely integrated FPGA-SoC microarchitectures. By combining thermal behavioral modeling, feature engineering, and machine learning-based anomaly detection, the proposed framework extends conventional Thermal Digital Twin (TDT) approaches beyond monitoring and mitigation toward autonomous thermal threat detection. The proposed framework is experimentally validated using an NI myRIO-1900 platform integrating a Xilinx Zynq-7010 FPGA-SoC representative of modern industrial embedded control architectures. Experimental results demonstrate the feasibility of the proposed framework, achieving an accuracy of approximately 75% with an Area Under the ROC Curve (AUC) of 0.76 using a lightweight Isolation Forest model. These results validate the capability of the proposed AI-enabled Digital Twin framework to learn normal thermal behavioral patterns and autonomously detect anomalous thermal activities potentially related to TSCAs. Full article

Background/Objectives: Diabetes mellitus type 2 (T2DM) is often associated with hypertension, necessitating treatment with combinations of medications that address both glycemic control and blood pressure. Whether commonly co-prescribed antihypertensives modify the glycemic efficacy of a sulfonylurea remains insufficiently characterized in controlled preclinical models. Methods: One hundred adult male Wistar rats were allocated to ten parallel groups (n = 10): healthy and diabetic untreated controls; glimepiride, bisoprolol or amlodipine monotherapy (in healthy and diabetic animals); and the diabetic combinations glimepiride+bisoprolol and glimepiride+amlodipine. T2DM was induced with a high-fat diet plus low-dose streptozotocin (35 mg/kg, i.p.) and confirmed by fasting blood glucose ≥ 200 mg/dL. Glycated hemoglobin (HbA1c) was measured weekly for 11 weeks. Non-parametric inference (Kruskal–Wallis, Dunn’s with Bonferroni correction, Mann–Whitney U, Wilcoxon signed-rank) was complemented by Random Forest regression and PCA/K-means clustering. Results: Week-11 HbA1c differed markedly across groups (Kruskal–Wallis H = 94.3, p < 0.001). Glimepiride + bisoprolol achieved near-normal control (4.37% ± 0.15), statistically indistinguishable from healthy groups (p ≥ 0.33), and was the only diabetic regimen with a declining trajectory (−0.66 percentage points; Wilcoxon p = 0.004). Adding either antihypertensive to glimepiride did not worsen glycemic control. Amlodipine monotherapy did not attenuate hyperglycemia (8.47% ± 0.20), approaching that of untreated diabetic controls (9.31% ± 0.18), consistent with the absence of intrinsic glucose-lowering activity. All agents showed pronounced disease-state dependence (healthy–diabetic divergence 2.33–3.13 points). Random Forest prediction was accurate (R² = 0.985), and unsupervised clustering separated effective from ineffective regimens, corroborating the statistical findings. Conclusions: In this model, bisoprolol co-administration enhanced and amlodipine co-administration preserved glimepiride-mediated glycemic control. Glimepiride+bisoprolol emerged as the most effective regimen, supporting cardioselective β-blockade as a metabolically favorable antihypertensive partner for sulfonylurea therapy and warranting clinical confirmation. More broadly, these results provide a preclinical, evidence-based rationale for selecting metabolically favorable antihypertensives in patients with coexisting T2DM and hypertension, with the potential to improve glycemic outcomes and reduce the risk of adverse drug–disease interactions during combination therapy. Full article

Green transformational leadership is a sustainability-oriented approach with a focus on implementing green strategies among employees and the organization to improve environmental performance. The current study examines the effects of green transformational leadership on perceived ESG performance among employees of Turkish SMEs. In this respect, the indirect effects of green innovation capability as a mediating element and strategic renewal as a moderating factor are also investigated. A total of 256 SME employees from Istanbul, Ankara, and Antalya were gathered through purposive and convenience sampling methods and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results suggest that green transformational leadership positively influences employees’ perceptions of ESG. This is because such a leadership style fosters the innovation capabilities of the enterprise, which in turn contributes to the formation of positive perceptions among employees. The results also show that the positive leadership–perceived ESG link is stronger as strategic renewal increases. Using the premises of Dynamic Capabilities Theory and the Resource-Based View model, the findings offer insight into how leadership activates and directs organizational capabilities, which enable green transformation and, thus, improve ESG performance. The current findings are relevant to academic and leadership practices within a green context. However, the findings are subject to the limitations inherent in self-reported survey designs, which restrict causal inference and generalizability. However, in terms of the theories used, this study contributes by showing how green leadership yields ESG outcomes through capability-based and renewal-based mechanisms rather than managerial influence alone. Full article

Electroencephalography (EEG) is closely related to neural activity underlying emotional states. It has become an important input modality for emotion recognition in affective computing. However, many existing studies rely on complex experimental paradigms and full-channel EEG signals. They often construct high-dimensional features for discrete emotion classification and pay less attention to continuous emotional dynamics. To address these issues, this study uses six frontal EEG channels, including Fp1, Fp2, AF3, AF4, F7, and F8. These channels are relatively easy to acquire and are closely associated with emotional activity. A frontal hemispheric relation-aware temporal convolutional network (FHR-TCN) is proposed for continuous emotion regression and discrete emotion classification. Experiments on MAHNOB-HCI and DEAP evaluated FHR-TCN for continuous emotion regression and discrete emotion classification, respectively. Under the reported protocols, FHR-TCN achieved higher average scores than the evaluated baselines. It also showed lower parameter counts, MACs, and GPU inference latency than GRU. These findings support further deployment-oriented evaluation under limited-channel conditions. Full article

Reading comprehension is a critical cognitive competency in higher education, although learners demonstrate substantial variability in responsiveness to metacognitive instructional interventions. The study focused on individual cognitive-response processes within the framework of the adaptive metacognitive reading system, which was realized through precision-learning architecture, which integrates latent learner-response phenotyping, explainable machine learning, Markov transition analysis, Bayesian adaptive inference, and reinforcement-learning optimization. The study employed a quasi-experimental longitudinal design involving an eight-week structured metacognitive reading intervention delivered through planning, monitoring, evaluation, strategic flexibility, and reading self-regulation activities. The psychometric analyses demonstrated satisfactory reliability of the adapted Metacognitive Awareness Inventory (MAI), with Cronbach’s α ranging from 0.83 to 0.89. A latent-profile model revealed significant heterogeneity of learner-response patterns among learners, with four learner-response phenotypes: High Responders, Strategic Improvers, Monitoring-Dependent Learners, and Low Responders. Explainable machine-learning models performed well in predicting individualized comprehension gains, with the model with the highest predictive accuracy being XGBoost (R² = 0.61). Markov transition modeling identified exploratory learner-state redistribution patterns following the intervention. Bayesian adaptive inference and reinforcement-learning optimization were subsequently conducted as post hoc simulation procedures to estimate hypothetical adaptive instructional calibration scenarios rather than as real-time instructional decision systems. Overall, the proposed Adaptive Cognitive Intervention Architecture (ACIA) should be interpreted as an exploratory computational framework for modeling learner heterogeneity, predicting comprehension gains, and simulating post hoc computational optimization in higher-education learning environments. Full article

Spatially resolved single-cell technologies can provide deep insights into cellular heterogeneity and tissue structural characteristics. However, the data obtained are purely observational and cannot reveal the specific mechanisms by which tissues respond to particular perturbations. Most computational models of single-cell perturbations either operate in a non-spatial latent space or fix tissue geometry within a static spatial structure, thereby limiting their ability to integrate molecular profiles with tissue topological remodeling. We propose SPAD-CFR (Spatial Point-cloud Attention-based Diffusion for CounterFactual Reprogramming). Each tissue is treated as a spatial point cloud containing cellular molecular profiles and physical coordinates. We implement Pearl’s three-step workflow for causal inference through deterministic diffusion inversion and sampling. This model can apply interventions to individual cells and generate counterfactual-style tissues in which molecular profiles and spatial coordinates change together. In validation across three datasets, SPAD-CFR reproduces the hierarchical structure of the mouse cerebral cortex, simulates phenotypic distribution differences across different histological grades of breast cancer, and reconstructs hypoxia-associated mesenchymal phenotypes at the invasion margins of triple-negative tumors. In melanoma, activation interventions targeting PD-1+ CD8+ T cells produce spatially confined, distance-dependent bystander cytotoxic effects. Based on these findings, we propose SPAD-CFR, a biologically informed generative framework for conducting counterfactual-style spatial simulations to validate hypotheses regarding microenvironment reprogramming. Full article

Educational environments, particularly those with limited resources, require affordable mobile robots capable of combining human–robot interaction, autonomous assistance, and academic support without continuous dependence on cloud services. This work presents a low-cost ROS2-based mobile robot implemented on a Raspberry Pi 4B to provide educational assistance in Spanish within controlled classroom environments. The system integrates voice interaction, text-to-speech synthesis, YOLOv8n-based object perception, a specialized door detection model, ultrasonic and inertial sensing, differential-drive control, and a hybrid natural language processing architecture based on semantic caching, local inference, and optional cloud connectivity. Two task-dependent operating modes, education and navigation, selectively activate ROS2 nodes to reduce computational load and energy consumption. Experimental tests conducted in a university classroom evaluated speech recognition, vision models, natural language processing alternatives, sensor behavior, and battery life. The speech recognition module achieved 98% accuracy under both quiet and noisy conditions. YOLOv8n achieved an F1-score of 0.975 for common classroom objects, while the specialized door detector achieved 100% recall with 58.7% precision. The semantic cache correctly resolved recurrent academic queries in the exact-match evaluation, with an average latency of 3.8 s, reducing the need for external language models in known-question scenarios. The robot operated for 96 min in education mode and 75.6 min in navigation mode. These results demonstrate that Spanish voice interaction, reactive navigation, academic question answering, and resource-aware operation can be integrated into a single low-cost edge robotic platform for educational environments. Full article

With the rapid development of intelligent connected vehicles and autonomous driving, real-time and accurate road condition perception has become increasingly critical. Aiming at the limitations of traditional direct and indirect detection methods, this paper proposes an ultrasonic-based real-time detection system for road roughness. Most urban roads today feature asphalt pavements; therefore, this system focuses its research on asphalt pavements. Under the same pavement type (asphalt roads), there is a strong correlation between pavement roughness and the friction coefficient. By measuring the roughness of different pavements, the friction coefficient is estimated using the fuzzy processing method. Then the system through measuring ultrasonic echo amplitude and sensor–road distance, combined with software digital filtering, dual-parameter compensation (distance and temperature–humidity), probabilistic statistical analysis, and fuzzy inference, the mapping relationship among echo signals, road roughness and friction coefficient is established. The system mainly includes an ultrasonic transceiver module, a hardware signal conditioning module, and an MCU-based data processing, display and transmission module. Both simulated experiments and real asphalt pavement tests are carried out for verification. The results show that the system can effectively suppress noise, compensate distance attenuation and environmental interference, and achieve accurate real-time detection of road roughness, with a relative error less than 10% compared with the reference value. The proposed system can provide reliable data support for vehicle active safety systems and autonomous driving applications. Full article

Bovine Mycoplasma bovis pneumonia (MBP) is an important component of bovine respiratory disease, and its management is complicated by persistent infection and antimicrobial stewardship concerns. Houttuynia cordata Thunb. has reported anti-inflammatory and immunomodulatory activities, but its potential host-directed mechanisms in MBP remain unclear. This in silico study used network pharmacology and molecular docking to identify candidate compounds, common drug–disease targets, enriched biological functions, and predicted ligand–target interactions. A total of 145 putative targets of H. cordata and 474 MBP-associated disease targets were obtained from TCMSP, GeneCards, OMIM, and CTD, yielding 43 common drug–disease targets. Dual-confidence STRING analysis, cytoHubba ranking, and MCODE module analysis prioritized TNF, IL6, IL1B, PTGS2, PPARG, IFNG, CASP3, and MMP9 as candidate core targets. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment indicated convergence on cytokine-mediated signaling, inflammatory response, immune regulation, oxidative stress, IL-17 signaling, and TNF signaling. Molecular docking suggested favorable predicted interactions for quercitrin–PTGS2, quercetin–TNF, quercetin–IL6, and quercitrin–CASP3. These computational findings suggest that H. cordata may be associated with host inflammatory and immune-response modulation in MBP, mainly through flavonoid-related interactions with inflammation- and apoptosis-related targets. Further bovine-specific experimental validation is required before biological activity or practical application can be inferred. Full article

Fall-from-height (FFH) detection is a critical component in wearable safety systems, particularly in environments where high-intensity movements can lead to frequent false positives. Conventional approaches based on simple thresholding of acceleration signals often fail to reliably distinguish FFH events from non-fall activities due to overlapping signal characteristics. This paper proposes a data-driven FFH detection method that integrates multiple complementary features into a unified score-based model. The proposed approach first performs structured peak detection to extract candidate impact events while significantly reducing the number of samples requiring further processing. Each candidate is then evaluated using pre-peak structure, post-impact stability, and pressure variation, which respectively capture structural, temporal, and physical characteristics of FFH events. Based on statistical analysis, feature-wise score contributions are designed to reflect their discriminative strength, and the final FFH decision is performed using an additive scoring mechanism. This formulation enables flexible handling of ambiguous cases while preserving strong FFH characteristics. Experimental results demonstrate that the proposed method maintains 100% recall at the selected decision threshold while significantly reducing false positives from non-FFH activities. In addition, the peak detection stage reduces more than 99% of raw samples, enabling efficient on-device processing suitable for wearable systems. The proposed method also includes quantitative analysis of latency characteristics. Although FFH inference latency is influenced by asynchronous pressure sensing, the delay remains bounded and predictable, and most detections are completed within a practical time range for real-time wearable safety applications. Overall, the proposed method achieves a practical balance between detection sensitivity, false-positive suppression, computational efficiency, and real-time feasibility, demonstrating its applicability to wearable safety systems. Full article

This paper introduces an approach for inferring the gene regulatory networks in vortioxetine-induced glioblastoma cells to investigate vortioxetine’s systemic effects. The approach uses an ordinary differential equation (ODE)-based inverse problem to evaluate the drug-induced gene interactions within the GLIOMA and ERBB pathways, which are deeply intertwined in cancers, by using time-series datasets. Time-series datasets were generated in triplicate at 0, 3, 6, 9, 12, and 24 h. The results of the ERBB pathway confirmed that PIK3R5 was commonly activated, while JUN, as a proto-oncogene in glioblastoma, was inhibited by genes across all three datasets. In particular, PIK3R5 was commonly activated by PAK6 in all three datasets. The results of the GLIOMA pathway confirmed that CALML6 was commonly activated, while CDK4 and CCND1, which are mostly overexpressed in human cancers, were inhibited across all three datasets. Additionally, an analysis of the independent datasets generated at 6 and 22 h after the vortioxetine injection identified the most distinct variable genes between the two time points: CRK (1.96) and JUN (−3.02) for the ERBB signaling pathway, and BRAF (1.30) and MAP2K2 (−1.92) for the GLIOMA pathway. We conclude that vortioxetine, an antidepressant, decreases JUN, a proto-oncogene involved in the ERBB signaling pathway, and CCND1, another proto-oncogene involved in the GLIOMA pathway, over time in glioblastoma cells. Full article

Background: Gestational diabetes mellitus (GDM) is one of the most common medical complications of pregnancy and has a high prevalence in Saudi Arabia. GDM increases the risk of adverse maternal and fetal outcomes and is associated with cardiovascular risk factors. Glycemic severity, represented by the continuous 1 h plasma glucose value from the diagnostic two-hour 75 g oral glucose tolerance test (OGTT), may provide additional insight into glycemic status among women with GDM. Physical activity (PA) plays a vital role in maternal health. Therefore, the aim of this study was to identify the associations between PA and glycemic severity among pregnant women with GDM in Saudi Arabia and to identify factors associated with glycemic severity. Methods: This cross-sectional study enrolled 96 pregnant women during routine second-trimester visits at a maternity clinic in Riyadh. PA was assessed using the Pregnancy Physical Activity Questionnaire (PPAQ). Glycemic severity was assessed using the continuous 1 h plasma glucose value obtained from the diagnostic two-hour 75 g OGTT. Results: One-way ANOVA demonstrated a significant association between PA levels and glycemic severity (F = 2.78; p < 0.04). Multiple linear regression identified low-intensity PA, non-employment, and smoking during pregnancy were significantly associated with higher glycemic severity (p < 0.05). Conclusions: The study identifies a significant association between PA and glycemic severity. Furthermore, employment status and smoking were also significantly associated with glycemic severity. These findings suggest that PA and other modifiable lifestyle factors play role in glucose regulation during pregnancy. However, the cross-sectional design precludes any inference of causality. Full article