Search Results (26,313)

Shelter dogs may experience long-term environmental and social stressors that can affect their behaviour and welfare. Some individuals show reduced activity, low responsiveness to environmental stimuli, and limited interaction with their surroundings. This pilot study investigated behavioural patterns and allopregnanolone concentrations in shelter dogs exhibiting these behavioural characteristics. Ten shelter dogs were enrolled and divided into two groups: five dogs showing depression-like patterns and five matched control dogs. Each dog wore a three-axis accelerometer for 30 days to quantify activity levels. Behavioural observations were conducted using video recordings, and hair samples were collected at baseline and after 30 days to assess allopregnanolone concentrations as a potential stress marker. Dogs in the case group showed significantly longer resting time than controls (p ≤ 0.05), indicating reduced activity levels. Trends toward lower levels of exploratory and social behaviours were also observed, although the differences were not statistically significant. Allopregnanolone concentrations ranged from 0.6 to 4.6 pg/mg and showed considerable inter-individual variability, with no significant differences detected between groups. These findings provide preliminary evidence of behavioural and physiological alterations in shelter dogs displaying depression-like patterns. However, further studies with larger populations are needed to validate these findings and improve welfare assessment in shelter environments. Full article

Audio deepfake detection systems often achieve excellent internal validation performance but fail to generalize under real-world inference conditions involving synthetic speech generated with previously unseen AI tools. To address this limitation, this work proposes the Spectral Multi-Representation Fusion (SMRF) framework, which integrates multiple spectral representations and decision-level fusion strategies to improve robustness under cross-domain conditions. Additionally, a Stability-Aware Multi-Metric Selection (SAMMS) strategy is introduced to select architectures by jointly considering predictive performance and cross-representation stability. The proposed framework was evaluated using four spectral representations (log-magnitude spectrogram (LOG), Mel spectrogram (MEL), Discrete Wavelet Transform (DWT), and Constant-Q Transform (CQT)) combined with multiple convolutional architectures and complementary voting strategies. The experiments revealed that isolated models exhibiting validation metrics above 95% may still produce very poor synthetic-audio detection rates during external inference (even lower than 10%). In contrast, fusion-based strategies substantially improved robustness by exploiting complementary synthetic evidence across spectral domains. The results also demonstrated that both the voting strategy and the SAMMS stability parameter $\lambda$ strongly affect the final behavior of the system. In particular, hybrid fusion using One-Hard Voting with two architectures selected using $\lambda \ge 0.25$ achieved the best balance between synthetic-audio detection and real-audio preservation, outperforming individual models under cross-domain inference conditions, with detection rates close to 75% for both synthetic and real audio. These findings suggest that stability-aware fusion strategies constitute a promising direction for improving robustness in realistic audio deepfake detection scenarios. Full article

To clarify the nonlinear degradation of volumetric performance caused by piston–cylinder wear in axial piston pumps under multiple operating conditions, this study develops an integrated framework linking local wear-induced leakage to whole-pump output characteristics. A mathematical model incorporating piston kinematics, eccentric-clearance leakage, chamber-pressure dynamics, and whole-pump flow was established and implemented in Amesim. A four-factor mixed-level orthogonal design and analysis of variance were then employed to quantify the effects of wear clearance, eccentricity, load pressure, and shaft rotational speed. The results show that their contributions to volumetric efficiency follow the order: motor rotational speed > load pressure > wear clearance > eccentricity, whereas load pressure is the dominant factor affecting pressure ripple. A wear clearance of approximately 0.1 mm marks the onset of pronounced leakage-induced performance degradation. At this clearance and a load pressure of 20 MPa, increasing the rotational speed from 500 to 3000 r/min improves the volumetric efficiency from 67.48% to 94.44%, with an average increase of 5.39 percentage points per 500 r/min. Comparative experiments on normal and artificially worn pumps were conducted to validate the model. The measured motor-speed slip under high-load conditions was incorporated to correct the theoretical displacement and distinguish speed-induced flow reduction from internal leakage. After correction, the maximum relative error between the simulated and experimental results was below 3.7%. The proposed framework integrates mechanism-based leakage modeling, multi-factor contribution analysis, and speed-corrected experimental validation, providing a theoretical basis for the wear assessment, piston–cylinder clearance design, and flow compensation of axial piston pumps under variable operating conditions. Full article

Deep learning (DL)-based percussion methods in concrete-filled steel-tube (CFST) void detection have gained much attention. However, the detection signal contains a large amount of noise, which affects the accuracy of qualitative and quantitative analyses of the subsequent detection results. To improve the signal-to-noise ratio (SNR) during percussion detection, this study proposes a CFST void detection method using the good point set and vibrational snow ablation optimizer (GVSAO) algorithm and dual-channel parallel convolutional neural networks (CNNs). The proposed method employs the gram angle field (GAF) to transform percussive sound signals into images. It then constructs a dual-channel parallel CNN structure, where the GAF is decomposed into the following two maps: the gram angle sum field (GASF) and the gram angle difference field (GADF). These maps are simultaneously fed into the CNN for training. The outputs from the two channels are concatenated and fused. Finally, the GVSAO algorithm was used for model optimization to improve convergence speed and recognition accuracy. Both the temporal and spatial characteristics of the knocking sound signal are fully preserved, while the interference of different construction noises is effectively avoided. Validation experiments were conducted on CFST specimens with different heights of voids (0, 50, 100, and 150 mm) under different pressure loads. The original sample dataset and the signal-enhanced dataset were obtained by adding background noise with different SNRs. The test results show that the prediction accuracies on the original signal dataset are consistently above 98.74%. Among them, the accuracy achieves 100% at pressure loads of 0 and 50 tons. Additionally, the prediction accuracies on the signal-enhanced dataset are all above 97.2%, indicating that the model maintains a high level of classification performance. This suggests that the model can effectively suppress noise and exhibits excellent robustness. Full article

Marine low clouds (MLCs) strongly affect Earth’s radiation budget due to their extensive coverage and strong reflection of incoming solar radiation. Despite their important role in the Earth system, the extent and mechanisms of MLC response to climate oscillations are not well understood. In this study, the effect of the El Niño–Southern Oscillation (ENSO) on cloud and meteorological properties across the Pacific Ocean is investigated by integrating various satellite observations and reanalysis datasets. The results reveal a pronounced hemispheric asymmetry in the response of subtropical MLCs to ENSO. During El Niño events, the Northeast Pacific exhibits reduced cloud cover and weaker shortwave radiative cooling, while an opposite response is observed over the Southeast Pacific, where cloudiness and radiative cooling are enhanced. These contrasting responses are linked to distinct ENSO-driven meteorological changes between the two hemispheres. Over the Northeast Pacific, El Niño conditions weaken inversion strength and the subtropical high, suppressing MLCs. In contrast, the Southeast Pacific experiences enhanced inversion strength and lower-tropospheric geopotential height during El Niño, which favor MLC development. It is suggested that hemispheric asymmetries in the climatological positions and ENSO-induced responses of the Pacific subtropical highs contribute to the opposite MLC responses between the two hemispheres. These findings highlight the importance of large-scale controls in shaping regional cloud responses to climate variability and provide insights for improving cloud representation in global climate models. Full article

Subsea tunnels constructed by the drill-and-blast method are increasingly required in modern infrastructure and are often exposed to high groundwater pressure and fractured rock conditions. In such environments, external water pressure acting on initial support strongly affects tunnel stability, durability, and construction safety. Because the initial support is temporary, discontinuous, and prone to cracking, evaluation of its water pressure response remains challenging. Current design practice relies on simplified assumptions and empirical approaches, inadequate for fractured rock masses under high water pressure. This review synthesizes research on external water pressure in tunnels, with emphasis on drill-and-blast subsea tunnels. Empirical reduction coefficient methods, theoretical analytical solutions, numerical techniques, and physical model testing are critically examined in terms of their theoretical basis, applicability, and limitations. Special attention is given to seepage behavior in fractured rock masses, including single-fracture seepage laws, equivalent continuum models, and discrete fracture network approaches, and their ability to represent fracture-controlled flow and water pressure redistribution. The review shows that conventional seepage or seepage–stress coupled methods are insufficient to capture stress redistribution, fracture evolution, and damage-induced permeability changes governing water pressure behavior. By contrast, advanced coupled stress–seepage–damage and stress–seepage–fracturing models provide more physically consistent frameworks for analyzing external water pressure acting on initial support. In addition, hydro-mechanical discrete lattice models are reviewed as a promising meso-scale framework for capturing crack initiation, crack coalescence, and crack-controlled seepage paths that may govern localized external water pressure redistribution behind initial support. However, their application to subsea tunnels remains limited, and current design codes still lack unified calculation methods. Major challenges remain, including the lack of consistent definitions of external water pressure, inadequate consideration of the interaction between tunnel support and surrounding rock, and insufficient validation through laboratory experiments and field observations. Future research should develop mechanism-based methods supported by monitoring and validation to improve subsea tunnel safety. Full article

Previous research suggests that experiencing wellbeing in nature fosters environmental stewardship and sustainable behaviors. That makes it important to understand what mediates the relationship between an experience in nature and wellbeing. In the past, researchers have found that a sense of awe predicts feeling wellbeing in nature. However, research also suggests that awe does not account for the full relationship between experiencing nature and enjoying nature, suggesting additional mediators are necessary to fully understand how experiencing nature affects sustainability. In the current research, we examined collective effervescence—a feeling of connection combined with a feeling of sacredness—as a pathway between nature and feelings of enjoyment. To do so, we utilized an experimental memory-recall paradigm in which participants were prompted to re-experience either a nature memory or a neutral everyday memory, and then completed measures of collective effervescence, awe, and wellbeing. We found that collective effervescence mediated the relationship between prime and all measures of enjoying nature. In other words, when people experience nature, they sometimes feel collective effervescence. Collective effervescence then predicts an enjoyable experience. Full article

We study a retrial queueing system with N ranked heterogeneous service devices where processing times at each device follow a phase-type ($PH$) distribution with device-dependent parameters. Requests arrive according to a Batch Markovian Arrival Process ($BMAP$). The system uses a preemptive priority rule: idle devices with smaller serial numbers are preferred, and when a lower-numbered device completes service, the request being processed at the highest-numbered busy device is moved there and its service restarts. Requests that cannot be served immediately join an orbit of infinite capacity and retry after random time intervals. We describe the system dynamics by a multidimensional continuous-time Markov chain with a block upper-Hessenberg generator. A sufficient ergodicity condition for this Markov chain is derived. We present formulas for the key performance measures, including the mean orbit length, device utilizations, and the probability of immediate service. Numerical experiments show how the arrival rate and the coefficient of variation of processing times affect system performance. In particular, higher processing-time variability (hyperexponential case) in the considered example widens the stability region, while lower variability (Erlang case) narrows it, compared with exponential service. A supplementary study shows that higher arrival correlation under the chosen set of the system parameters amplifies orbit congestion and shifts utilization from the fastest server to the slower ones. Full article

Dental fluorosis (DF) is a common endemic disease that damages dental enamel. Traditionally, DF has been attributed to environmental fluoride overload. Accumulating evidence has demonstrated that genetic factors also modulate individual susceptibility. No dedicated fluoride ion channels have been identified in mammalian cells; fluoride uptake is believed to occur mainly through passive diffusion of HF and nonspecific anion pathways, including chloride channels. Different types of chloride channels are expressed in dental tissues, such as CFTR and voltage-gated chloride channels (ClCs), but it remains unknown whether these channels transport fluoride and whether their variants influence DF risk. This study combined human population-based investigations, mouse and zebrafish models, and in vitro experiments to confirm the significant genetic association of CFTR and CLCN3 variants with DF. A total of 889 DF cases and 834 matched controls were recruited from the same fluoride-contaminated region. Tag SNP screening of CFTR and eight ClC chloride channel genes (CLCNs) revealed that rs213950 in CFTR and three SNPs in CLCN3 were significantly associated with DF. CFTR and ClC-3 showed different fluoride tolerances. rs213950 in CFTR affected the efficiency of fluoride ion transport in Xenopus oocytes. ClC-3 enabled yeast cells to resist fluoride toxicity, whereas clcn3 deficiency disrupted tooth and craniofacial development in zebrafish. Fluoride exposure altered nucleoprotein binding to the rs10520161 region and changed the mRNA levels of various ClC-3 transcripts. These transcripts displayed different subcellular locations and fluoride conductances and acted synergistically to confer fluoride resistance. Together, these findings raise the possibility that variants in CFTR and CLCN3 may act synergistically to influence DF susceptibility. This potential interplay highlights DF as a complex trait involving dysregulated fluoride handling and underscores the multifactorial, gene-directed regulation of fluoride transport. Full article

The interaction between increased dissolved carbon dioxide, rising temperatures, and oxygen loss—the so-called “deadly trio”—is expected to strongly affect marine biota over the coming years, undermining ocean services and uses. Nonetheless, no study has so far scrutinized the cumulative impact of these three stressors on fish embryos and larvae. To fill this knowledge gap, we conducted a fully multi-factorial experiment to investigate the effects of warming (+4 °C: 22 °C), acidification (Δ − 0.4 pH units: 7.7 pH, pCO₂ ~1000 μatm), and deoxygenation (Δ − 60% O₂ saturation: 3 mg O₂ L⁻¹) on physiological and behavioral responses of the commercially important species Sparus aurata. Deoxygenation was the primary factor reducing hatching rates (64.25%), survival (46.71%), and heart rates (31.99%) of recently hatched larvae, being generally further exacerbated when combined with warming and acidification. No larvae exposed to the interaction of the three treatments reacted to the phototactic behavior test. However, acidification alone caused a 50% reduction in phototactic behavior. Our findings demonstrate that the deadly trio is detrimental to early fish development, impacting several key features at this critical life stage, and the need to assess the impacts of stressors’ interaction on marine taxa to better predict future ecosystem responses to ocean changes. Full article

Brand personality is a strategic lever for higher education institutions facing intense competition. Although institutions commonly use social media video content to reach students, evidence on how content design affects brand personality perceptions is scarce. This study examines whether informational and entertaining Instagram videos differentially influence enrolled students’ brand personality perceptions and whether attitude toward the post mediates these effects. A between-subjects online experiment was conducted with enrolled students (N = 184) in Germany, a mature higher education system. Participants were randomly assigned to view a professionally produced video for a fictitious institution, either informational or entertaining. They then evaluated the post and the institution’s brand personality, measured with the University Brand Personality Scale and analyzed as vertical (prestige, sincerity, conscientiousness) and horizontal (appeal, liveliness, cosmopolitanism) facets. Effects were tested using simple mediation analyses. Informational content produced more favorable post attitudes than entertaining content. It had a large positive direct effect on vertical brand personality, complemented by a positive indirect effect via post attitude. For the horizontal facet, only the indirect effect was significant. The study provides the first experimental evidence on content-type effects on higher education brand personality in a mature system, guiding institutions’ social media communication. Full article

As artificial intelligence becomes increasingly embedded in healthcare operations, governance can no longer be treated solely as a static set of principles or regulatory requirements. This study proposes a Human-Centered Experience Engineering (HCEE)-based healthcare AI governance architecture designed as a closed-loop operational control system for stabilizing and adaptively managing AI-enabled healthcare systems. Rather than treating patient and employee experience as downstream outcomes, the framework repositions them as governance input signals, operationalized as the Patient Experience Index (PXI) and Employee Experience Index (EXI). The architecture integrates Policy, Governance, Control, and Experience through recurrent feedback, trigger-control rules, and adaptive learning mechanisms that translate experiential signals into ongoing operational adjustment. To examine how this architecture affects system behavior, agent-based simulation compares four scenarios: S1 (no governance), S2 (sense-only), S3 (full HCEE closed loop), and S4 (full HCEE under exogenous stress). Results show a consistent pattern of S1 < S2 < S3 in both PXI and EXI, indicating that sensing alone yields limited improvement, whereas feedback-coupled sensing, control, and learning produce stronger stabilization and performance gains. In S4, the same architecture demonstrates recovery, re-stabilization, and adaptive reinforcement under shock. These findings suggest that effective healthcare AI governance depends on whether feedback loops are architected to function operationally. These results are based on synthetic experience indices and are interpreted as conceptual, mechanism-level evidence. Full article

How do microorganisms in litter and soil affect litter decomposition in a temperate forest? Here, we conducted an 18-month laboratory experiment to assess the decomposition of pure Robinia pseudoacacia, pure Platycladus orientalis, and mixed R. pseudoacacia–P. orientalis litters under four treatments, namely “no microbe” (NM), “litter microbes” (LM), “soil microbes” (SM), and “litter and soil microbes” (LM + SM). Results demonstrated that, compared with SM, LM significantly enhanced the litter weight-loss rate and elevated the potential activities of lignocellulolytic enzymes at 180 days, and this was accompanied by lower cellulose and hemicellulose contents. Structural equation modeling indicated that microorganisms may directly or indirectly influence weight mass loss, partly by regulating these potential enzyme activities that are associated with changes in the litter organic matter composition. Across three forest stands, microbial treatments significantly affected litter decomposition. The standardized direct path coefficients linking microorganisms to the litter-mass-loss rate from highest to lowest were LM + SM, LM, and SM, indicating a synergistic effect between LM and SM that promotes decomposition through coordination. Taxonomically, most bacterial genera differed significantly among microbial treatments, whereas most fungal genera did not. Notably, the standardized direct path coefficient linking bacteria to litter mass loss was larger than that for fungi in both the SM and LM + SM groups. Additionally, field decomposition was faster than in the laboratory, with distinct microbial communities, verifying the environmental modulation of decomposers and the home-field advantage. This study clarifies microbial mechanisms underlying litter decomposition and provides a theoretical basis for forest ecosystem stability and sustainable management. Full article

AI literacy has become a pressing concern across disciplines, calling for comprehensive measurement tools applicable to diverse age groups. Building on existing research, we propose a six-facet model encompassing affective experiences, usage skills, cognitive evaluation, ethical norms, responsible use, and self-development. The present study aimed to develop and validate the Six-Facet Artificial Intelligence Literacy Questionnaire (SFAILQ) among 2443 Chinese participants aged 12 to 60 years, spanning adolescence to middle adulthood, with a disproportionately larger proportion falling within the 18-to-40 age range. An item reduction analysis was conducted using the first split-half sample (N₁ = 1217), and reliability and validity analyses were performed with the second split-half sample (N₂ = 1226). The final SFAILQ consists of 32 items assessing six dimensions: affective experiences (5 items), usage skills (5 items), cognitive evaluation (6 items), ethical norms (6 items), responsible use (4 items), and self-development (6 items). All six dimensions and the total score correlated significantly and positively with academic self-efficacy (usage skills showing the strongest correlation) and with academic engagement (responsible use demonstrating the highest correlation). The SFAILQ demonstrated high internal consistency, construct validity, convergent validity, discriminant validity, and criterion-related validity. It may serve as an effective tool for evaluating AI literacy among adolescents, young adults, and midlife adults. Full article

Accurate single-step prediction of stack NO_x concentration is essential for emission monitoring and ammonia-injection control in cement kiln SNCR–SCR hybrid denitrification systems. However, this task is challenging because industrial kiln data are affected by nonstationary emission fluctuations, nonlinear multivariable coupling, process-dependent time delays, and online deployment constraints. To address these process-specific challenges, this study develops a leakage-free dynamic soft-sensing framework for stack NO_x concentration prediction. In the proposed framework, variational mode decomposition (VMD) is used to characterize the multi-scale nonstationarity of the stack NO_x sequence under a sliding-window protocol. Trend-guided maximal information coefficient (MIC) analysis is then applied for nonlinear feature selection and delay compensation using only the training data, and the identified feature subset and delay parameters are fixed for validation and testing. A TCN-SE-LSTM model is constructed to extract temporal dependencies, recalibrate informative feature channels, and capture long-lag dynamic behavior. In addition, the Dual Adaptive Constrained Ivy Algorithm (DAC-IVY) is used only for offline hyperparameter optimization, so that the online stage requires only the trained prediction model. Experiments using 21,600 raw samples collected from an actual cement kiln Distributed Control System (DCS) show that the proposed framework achieves an RMSE of 0.2084 mg/Nm³ and an $R^2$ of 0.9844 on the test set, outperforming conventional baseline models. These results indicate that the proposed framework can provide an effective soft-sensing basis for subsequent denitrification control and operational optimization. Full article