Search Results (18,194)

In SAE Level 3 automated driving, drivers are required to supervise vehicle operation and respond to overtaking requests. Owing to task monotony and insufficient workload, drivers are prone to passive fatigue, which may impair vigilance and safety. This study investigated passive fatigue development during automated driving and proposed a multimodal detection method. Thirty licensed participants completed one automated driving task and one manual driving task in a driving simulator. Eye-movement and ECG/heart rate variability indicators were synchronously collected, and fatigue states were assessed using the Karolinska Sleepiness Scale. Results show that passive fatigue during automated driving developed differently from active fatigue during manual driving. Based on PERCLOS, pupil diameter, pupil diameter variation, SDNN, and LF/HF, an SVM-based passive fatigue detection model was developed to classify alert and passive fatigue states. Across repeated subject-wise validation, the model achieved an accuracy of 89.19%, sensitivity of 91.83%, specificity of 86.54%, balanced accuracy of 89.19%, F1 score of 89.48%, and precision of 87.28%, outperforming the model trained on manual driving active fatigue data. These findings demonstrate the need for scenario-specific driver-state monitoring models in automated driving systems and provide an applied physiological sensing approach for passive fatigue detection and warning design. Full article

Pneumatic artificial muscles, including McKibben-type actuators, offer high power-to-weight ratio, compliance, and inherent safety, but their nonlinear pressure–contraction behavior, hysteresis, saturation, and load-dependent dynamics make accurate position control challenging. This study develops a practical data-driven workflow that derives a branchwise feedforward compensator and an LQI or PID controller from one open-loop characterization experiment. Quasi-static characterization first identifies a conservative control-ready voltage window. A bounded random excitation within this window is replayed with $4\mathrm{s}$ holds to expose terminal and transient behavior. The same experiment supplies branchwise discrete plant models and a feedforward lookup. Two open-loop-derived transient layers, voltage creep compensation and dynamic pressure referencing, are applied to the raw lookup before simulation. Four controller variants are compared on a common simulated closed-loop benchmark built from the identified plant: a feedforward-only baseline, a branchwise proportional–integral–derivative (PID) baseline, a base linear quadratic integral (LQI) controller with displacement and pressure feedback, and a velocity-state LQI extension with a filtered velocity estimate. A multi-metric optimization score balances tracking RMS, settled oscillation, command activity, saturation, and gain magnitude. The score selects the base LQI within the LQI family. The selected gains and transient layers are deployed in a real-time implementation with manually reduced position gains. The controllers are then evaluated on a common reference stream against the physical actuator. Although simulation metrics cannot be transferred directly to the real system, the combined-metric ranking of the controllers remains unchanged. Full article

This study examined communication practices in an upper primary school, focusing on interactions among students, teachers, and parents, as well as the role of digital communication in shaping these relationships. The study used a mixed-methods design, integrating quantitative questionnaire data with qualitative, semi-structured teacher interviews. The sample comprised 50 students, 50 parents, and 25 teachers. The questionnaires explored the frequency, forms, and perceived quality of communication, while the interviews provided deeper insights into the relational and emotional dimensions of communication. The findings indicate that communication is generally perceived as functional and supportive, although its quality and reciprocity vary across stakeholder groups. Students emphasized the importance of teacher support but noted a need for more frequent, meaningful personal interactions. Parents regarded communication as essential but reported only moderate satisfaction with its quality. Teachers identified limited time and the increasing demands of digital communication as the primary barriers to effective interaction. Based on these findings, despite well-established communication structures, schools should strengthen opportunities for reciprocal engagement and interpersonal interaction while maintaining an appropriate balance between digital and face-to-face communication. The findings contribute to a better understanding of school communication dynamics and offer practical implications for developing more effective, relationship-oriented communication practices in upper primary education. Full article

With the rapid expansion of AR/VR-based digital platforms, there is an increasing demand for the automated generation of size-varied 3D shoe assets. However, conventional CAD-based linear scaling and PCA-based global statistical shape models are limited in their ability to capture the non-uniform and locally non-linear deformations observed in insole contours. This study proposes a size-adaptive insole contour generation framework that integrates image-based contour extraction, B-spline parametric representation, and type-specific SVR-based local displacement regression. By decomposing control-point displacements into tangent–normal components, the proposed method directly models non-linear curvature variations associated with size progression without relying on dimensionality reduction. Quantitative evaluations under an eight-fold leave-one-insole-out (LOIO) protocol show that the proposed method achieves a mean Hausdorff distance of 4.73 mm, a Chamfer distance of 1.76 mm, and an IoU of 0.929. It significantly outperforms the no-clustering ablation in both the Hausdorff distance (6.13 mm; $p=0.032$) and IoU ($p=0.033$), as well as the PCA-based kernel ridge regression baseline across all three metrics ($p<0.05$). No statistically significant differences were observed between the proposed method and the ratio scaling, Gaussian process, or thin plate spline baselines ($p>0.05$). PCA-Linear showed a small numerical advantage in the Hausdorff distance (4.16 mm), but the difference was not statistically significant ($p=0.187$). A sensitivity analysis further reveals the existence of a practical control-point density region that balances geometric fidelity and model complexity. This work provides a data-driven parametric approach for standard size-based digital grading automation and establishes a technical foundation for future extension to full 3D shoe mesh generation. Full article

Autistic speech is highly heterogeneous, yet group-level comparisons may obscure meaningful individual acoustic patterns. This study used unsupervised machine learning to identify data-driven acoustic profiles in native speakers of Cypriot Greek, including autistic and neurotypical adults. Participants produced disyllabic pseudowords across controlled phonetic and stress conditions. Sixteen acoustic measures, including fundamental frequency, formants, duration, cepstral peak prominence, Mel-frequency cepstral coefficients, jitter, shimmer, harmonics-to-noise ratio, and intensity, were summarized at the participant level and normalized appropriately. Principal component analysis retained eight components explaining 81.4% of total variance, followed by k-means clustering. A three-cluster solution provided the best silhouette coefficient among tested solutions and showed good bootstrap stability. Cluster membership was significantly associated with diagnostic group: one profile was exclusively autistic, one was relatively balanced, and one was predominantly neurotypical. The dominant acoustic dimension was driven primarily by voice-quality and spectral measures, particularly cepstral peak prominence, intensity, shimmer, harmonics-to-noise ratio, and jitter, whereas pitch and formant measures contributed comparatively little. These findings demonstrate that unsupervised acoustic profiling can reveal stable, diagnostically relevant speech phenotypes that are not captured by conventional binary group comparisons, highlighting substantial within-group heterogeneity in autistic speech and supporting more individualized approaches to characterizing vocal variation. Full article

Hyperspectral image (HSI) classification is critical for remote sensing but faces challenges in balancing accuracy and inference efficiency. Existing graph-based knowledge distillation (KD) methods are confined to single geometric spaces, ignoring the hierarchical semantics of land-cover categories. Cross-geometry distillation compresses multiple geometries into a single Euclidean student, forcing one geometry to collapse into the other. In this paper, we propose Dual-Space Knowledge Distillation (DSKD), a novel dual-student dual-space KD framework integrating Euclidean (GCN) and Hyperbolic (HGCN) teachers to jointly train a native MLP student and a native HNN student. With cross-geometric feature bridging (CGFB) and output distribution cohesion (ODC), the two students mutually learn each other’s complementary geometry, so DSKD captures complementary spatial-spectral and hierarchical features while enabling graph-free inference without message passing. Extensive experiments on four HSI datasets and four general graph benchmarks demonstrate that DSKD outperforms single-space distillation and single-student cross-geometry baselines in most settings, confirming its effectiveness and generalization capability across diverse graph-structured data. Full article

This study investigated the sorption of three widely detected non-steroidal anti-inflammatory drugs, naproxen, diclofenac, and ibuprofen, onto biochars and activated carbons derived from coconut shell and orange peel. Activated carbons were prepared under chemical activation with ZnCl₂ and H₃PO₄. The sorbent materials were characterized in terms of surface functional groups, acid–base properties, and pzc, while the pH was monitored along the sorption experiments. Results suggest that the activating agent played an important role in controlling surface chemistry and acid–base properties; ZnCl₂-activated carbons exhibited near-neutral pzc values and a balanced distribution of functional groups, while H₃PO₄-activated materials showed highly acidic surfaces with lower pzc. The solution pH evolved during sorption instead of remaining at its initial value. The sorption performance showed a clear dependance on surface chemistry, ZnCl₂-activated carbons exhibited a superior sorption capacity as well as faster kinetics under neutral conditions, this behavior may be associated to reduced electrostatic repulsion toward anionic model molecules. Under acidic conditions, some H₃PO₄-activated materials showed comparatively favorable sorption behavior, consistent with possible contribution from hydrogen bonding and hydrophobic interactions. Kinetic analysis showed time-dependent uptake profiles consistent with more than one mass-transfer contribution; however, no single rate-controlling mechanism could be established from the available data. The results indicate that NSAID sorption under the investigated conditions was associated with the surface acid–base properties of the carbon materials, the initial-to-final pH shifts, and the pH-dependent speciation of the pharmaceuticals. These findings provide an empirical basis for evaluating the performance of agroindustrial waste-derived carbon materials toward ionizable pharmaceuticals and highlight the importance of considering both surface chemistry and the measured solution pH when interpreting sorption behavior. Full article

With the advancement of the new-type power system, the exponentially growing high-frequency distribution and consumption data imposes heavy transmission and processing pressure on resource-constrained edge devices. Existing compression methods face two core limitations: static algorithm configurations that fail to adapt to dynamic time-varying power load characteristics, and complex computations that are difficult to deploy on resource-constrained edge terminals. To address these issues, this paper proposes a cloud-edge collaborative adaptive compression method based on CNN-LSTM load forecasting and Q-learning decision-making. A three-layer “cloud-edge-terminal” architecture is built to decouple compression decision-making from edge execution. The cloud employs a hybrid one-dimensional CNN and single-layer LSTM (1D-CNN-LSTM) for high-precision short-term load forecasting, and establishes an adaptive Q-learning decision mechanism to issue differentiated compression instructions according to varying load characteristics. The edge terminals receive these instructions and perform lightweight lossless compression accordingly. Simulation results show that the CNN-LSTM model achieves a MAPE of 7.59%. The Q-learning agent converges to an average reward of 65.35% during training and achieves a 66.73% overall compression ratio on the unseen test set, outperforming the fixed LZW baseline by approximately 6 percentage points. Furthermore, the proposed method improves the edge processing throughput by approximately 6.5 to 10.4 times compared to the comparative baselines. These results suggest that the cloud-edge collaborative approach offers a promising direction for alleviating edge pressure and balancing compression efficiency with computational overhead in massive power data transmission scenarios. Full article

The preparation of reliable input data for groundwater flow modeling remains a persistent bottleneck in data-scarce regions, where parameter estimation relies heavily on subjective expert judgment. This study introduces ALAES, a novel object-oriented expert system that codifies formal and heuristic knowledge to guide hydrogeologists through the entire pre-modeling workflow. The knowledge base was developed through structured interviews with 20 international experts and formalized using the KOD methodology within the Kappa-PC shell. The system comprises 258 production rules, 114 classes, and 1136 instances. Validation on the data-scarce Rhis-Nekor aquifer in Morocco showed that ALAES recommended MODFLOW and diagnosed modeling feasibility as challenging. A comparative assessment revealed substantial improvements over a baseline model developed without ALAES guidance: spatial resolution increased by a factor of four in critical zones, steady-state water balance consistency improved from 87% to 94%, and mean absolute errors were reduced by over 50% under ±20% perturbations. The system guided parameter estimation, reducing porosity uncertainty by over 40%, and achieved strong transient calibration (R² = 0.99). Three future management scenarios were evaluated, enabling formulation of a recommended exploitation strategy. By bridging the gap between data availability and modeling requirements, ALAES provides an explicit, reproducible decision-support tool for sustainable groundwater management in data-limited environments worldwide. Full article

Estimating joint torque in athletes provides important information for understanding neuromuscular performance and optimizing high-intensity training. While surface electromyography (sEMG) offers a non-invasive measure of muscle activation, existing methods often struggle to balance prediction accuracy, computational efficiency, and generalizability across different movement conditions. This study proposes a Hierarchical Three-Stage Feature Selection (H3FS) strategy for efficient and stable sEMG-based joint torque estimation. H3FS integrates domain knowledge, statistical and model-driven screening to generate compact, physiologically meaningful feature sets tailored to various application contexts. Using isokinetic movement data from the shoulder, elbow, and knee joints of 39 young athletes, H3FS identified the optimal configuration, ELATE-3, comprising logarithmic mean absolute value (LMAV), waveform length (WL), and Teager–Kaiser energy operator (TKEO). The study also introduces an Integrated Performance Index (IPI) to evaluate prediction accuracy and computational cost. Results indicate that XGBoost with ELATE-3 achieves the best balance between accuracy and real-time performance (average R² = 0.85, RMSE = 16.2, IPI = 0.013). It outperforms both traditional and data-driven feature sets, demonstrating strong generalization and low latency across joints, making it highly suitable for low-latency estimation on the adopted computational platform. The proposed strategy offers a systematic, scalable solution for joint torque estimation, supporting athletic training optimization and neuromuscular assessment, with potential future applications in rehabilitation monitoring. Full article

Open workforce datasets can help to extend research on artificial intelligence (AI) only if they are sufficiently provenance-traceable, have good measurement quality, and have a relational structure suitable for behavioral inference. This study examines a benchmark dataset comprising 12,000 linked records across 15 industry categories and 47,206 AI tool-use records. Sampling, recruitment, questionnaire wording, respondent authentication, ethics procedures, and whether the records are real or synthetic are not documented, so the dataset is treated as a tabular source rather than verified workforce evidence. Analyses are limited to indicators that have been observed directly, including job satisfaction, work–life balance, career outlook, trust in AI, weekly learning hours, AI-use intensity, and employer-provided AI training. Assessment of behavioral interpretation of the data was conducted using Spearman correlations, HC3-robust regressions, false discovery rate adjustment, secondary industry interaction checks, and a relational-realism diagnostic. Concerns about AI replacing jobs were negligible and non-significant on a five-point scale, with an average of 2.745. Training also showed no robust association when evaluated against outcomes that did not contain training. The median absolute Spearman correlation across conceptually related observed variables was 0.0064, with the 95th percentile at 0.0180, which is very low. These estimates reflect the characteristics of the supplied documents rather than employee actions, and do not assess conservation of resources processes or tourism worker outcomes. Results from the analysis demonstrate the importance of checking open workforce data for construct validity, relational validity, sector fit and provenance before testing behavioral theories. Research for regenerative tourism requires reliable sector-specific samples, reliable multi-item measures, longitudinal design, and direct measures of social and destination outcomes. Full article

Bitcoin mining is a highly energy-intensive process whose economic sustainability depends on the complex interplay of electricity prices, Bitcoin prices, operating mode and technological efficiency. This paper analyses these relationships through simulation models built on an eleven-year dataset (2015–2025) of hourly electricity price data for France and fixed technological parameters. The analysis proceeds in five steps, comparing flexible and continuous mining, quantifying the effect of the electricity price threshold, examining the interaction between the BTC price and the sales strategy, identifying the energy profitability threshold, and finally determining the electricity price threshold that best balances profit and risk. The results show that, although flexible mining generates lower absolute profits than continuous mining, it substantially reduces the risk of losses and improves cost efficiency per BTC. The electricity threshold price proved to be a key parameter: raising it increases total profit, but also increases volatility and unit costs. The energy analysis identified a sustainability threshold of approximately 600,000 kWh/BTC, above which mining becomes unprofitable under the tested conditions. Finally, the risk-adjusted analysis shows that the threshold price that best balances long-term stability and profit is lower than the threshold that maximises absolute profit. The paper thus contributes new insights into the economic sustainability of Bitcoin mining and establishes a methodological framework for evaluating price-responsive flexible loads under volatile market conditions, with implications for miners, investors and policymakers concerned with the efficient use of energy resources. Full article

This study aimed to evaluate whether adding curcumin to prepartum cows’ diets positively affects metabolism, immune response, milk and colostrum production and quality. Forty Holstein cows were divided into two homogeneous groups, with 20 animals per treatment, balanced according to milk production (kg/day) during the previous lactation and parity. The experiment was conducted in a continuous trial, with each groups of animals randomly assigned to one of the treatments: (a) addition of curcumin to the diet (152 mg/day) in the prepartum period; (b) without additive, used as control. The study was carried out on a commercial farm in a compost barn confinement system. Cows were fed with curcumin only during the 21-day prepartum period. Postpartum monitoring, data collection, and sampling were conducted for 28 days without curcumin. After calving, the animals had access to free-flow robotic milking. The variables colostrum production and quality were evaluated, followed by milk analysis. Blood parameters for biochemical, proteinogram and antioxidant analyses were collected at strategic periods of the experiment. We found that the prepartum treatment had no effect on the milk production, composition and physicochemical properties (p > 0.05). The serum concentrations of beta-hydroxybutyrate (BHBA), albumin, AST, ALT and GGT of the cows were not affected by the treatments, while the globulin levels were higher in the curcumin group on the seventh day postpartum (p = 0.05). The addition of curcumin caused immunomodulation on globulins, where higher serum concentrations of IgA, Ig heavy chain, haptoglobin and transferrin (p ≤ 0.001) were observed, as well as lower serum levels of C-reactive protein and ceruloplasmin (p ≤ 0.0002). The serum and colostrum oxidant/antioxidant profile did not show any difference when curcumin was used prepartum, nor did the colostrum brix concentration. It was concluded that curcumin supplementation increased serum globulin levels, improving the immune response. Although the curcumin diet was offered only in the prepartum period, there was a positive impact on the immune response of cows for up to seven days postpartum, the most critical moment in dairy cattle farming. Full article

Background/Objectives: Reformulation of bakery products with functional ingredients and reduced-sugar alternatives is an important strategy for improving nutritional quality and lowering postprandial glycaemic response. This study evaluated the effects of Urtica dioica L. powder incorporation and sucrose replacement with erythritol on the sensory properties, glycaemic index (GI) and glycaemic load (GL) of shortbread cookies. Methods: Cookies were prepared by replacing wheat flour with 0–50% nettle powder and using either sucrose or erythritol as a sweetener. Sensory evaluation was performed by 120 consumers using a 9-point hedonic scale. Based on sensory acceptance, cookies containing 0%, 10% and 20% nettle powder were selected for in vivo determination of GI and GL in 22 healthy adults according to ISO 26642:2010. Data were analysed using one- and two-way ANOVA. Results: Increasing nettle incorporation significantly reduced sensory acceptance, particularly for colour, taste, odor and overall acceptability, whereas cookies containing 10–20% nettle powder remained well accepted. Nettle addition significantly reduced GI in both sucrose- and erythritol-sweetened cookies, from 73.77 ± 11.05 to 52.10 ± 11.02 and from 68.69 ± 12.10 to 48.10 ± 9.03, respectively. The lowest GI (48.10 ± 9.03) was observed in cookies containing 20% nettle powder and erythritol. Erythritol did not significantly affect GI (p = 0.797), but significantly reduced GL compared with sucrose (p < 0.001). The lowest GL was observed for the 20% nettle-erythritol formulation (4.16 ± 0.80). No significant interaction between nettle powder addition and sweetener type was observed for either GI or GL. Conclusions: Incorporation of 10–20% nettle powder provided the most favourable balance between sensory acceptance and reduced glycaemic impact, while higher substitution levels substantially impaired sensory quality. Erythritol further reduced GL by lowering the amount of available carbohydrates in a standard serving, although it did not significantly affect GI under the carbohydrate-standardised conditions used for GI determination. The combination of moderate nettle enrichment and erythritol may therefore provide a lower-GI, lower-GL food option with acceptable sensory properties. However, these findings are based on a small cohort of healthy young adults and should not be interpreted as evidence of a therapeutic effect or directly extrapolated to populations with impaired glucose metabolism. Full article

Background/Objectives: Guizhi Fuling Capsule (GZFL), a Traditional Chinese Medicine (TCM) formula, is widely used for primary dysmenorrhea and other blood-stasis gynecological disorders. This study aimed to characterize its material basis, elucidate its multi-component, multi-target mechanism against acute primary dysmenorrhea, and establish an integrated pharmacokinetic-pharmacometabolomic-pharmacodynamic (PK-PM-PD) framework for TCM efficacy evaluation. Methods: GZFL constituents and serum metabolites in an oxytocin-/estradiol-induced rat dysmenorrhea model were characterized by UPLC/Q-TOF-MS. Uterine effects of GZFL-containing serum were assessed ex vivo. The active components of GZFL were screened by Chinmedomics, with candidate targets investigated through network pharmacology, transcriptomics, and molecular docking. In total, 23 pharmacodynamic indicators were integrated by principal component analysis into an Efficacy Index (EI). Correlation analysis between pharmacometabolomic and pharmacodynamic data yielded a Metabolite-Efficacy Index (MEI), evaluated across a 21-day time course. Results: Among 197 constituents characterized in GZFL extract, 136 serum-exposed components were detected, with several key metabolites enriched via biotransformation. GZFL-containing serum bidirectionally regulated uterine contractility toward the control level. Integrated analyses revealed 68 candidate therapeutic targets. GZFL suppressed NF-κB/IKKβ signaling, down-regulated COX-2/iNOS, restored the PGF2α/PGE2 balance, and normalized inflammatory cytokines. Eleven efficacy-associated metabolites correlated with pharmacodynamic recovery were revealed and integrated, with MEI achieving the highest predictive performance among five integration strategies (AUC = 0.9) and robustly tracking the full 21-day disease-recovery trajectory. Conclusions: GZFL attenuates dysmenorrhea through coordinated regulation of inflammation, prostaglandin metabolism, and uterine functional homeostasis, rather than through inhibition of a single target. The PK-PM-PD framework, with EI and MEI, offers a reproducible paradigm for evaluating complex TCM therapies. Full article