Search Results (1,409)

In intensive aquaculture, non-invasive real-time monitoring of morphological traits and body weight of largemouth bass (Micropterus salmoides) is essential for precision feeding and yield estimation. Manual measurement is laborious and stressful, whereas vision-based methods are challenged by insufficient dual-view feature fusion, poor robustness to underwater noise, and over-parameterized models unsuitable for edge deployment. To address these issues, a lightweight framework, MECT-MobileViT, is proposed based on MobileViT-xxs. A Morphometric-Guided Multi-Scale Fusion module is designed to couple physical priors with dual-branch visual features, strengthening shape–weight association. An ECA-NL attention block employing instance normalization, GLU gating, and threshold filtering is embedded to enhance feature robustness against visual disturbances typical in aquaculture and to accentuate critical morphological features. A three-stage synergistic pruning strategy—attention head pruning, structured channel pruning, and depthwise separable attention substitution—is applied to achieve substantial compression while preserving representational capacity. Experiments on a self-built lateral–dorsal dual-view dataset show that the proposed model significantly outperforms mainstream benchmarks. The pruned version attains an R² of 0.8266 and an RMSE of 16.4201, with less than 2% accuracy degradation relative to the best unpruned model, and contains only 7.34 M parameters. This study demonstrates a promising prototype for contactless, stress-free weight estimation in largemouth bass and offers new technical insights into feature fusion, noise suppression, and collaborative model compression for aquaculture visual perception. Full article

Thunderstorm disasters are one of the major meteorological disasters in China, causing significant human casualties and economic losses each year. Traditional loss compensation insurance is confronted with difficulties such as inspection and assessing, causing low claim processing efficiency, while index insurance can effectively overcome these deficiencies by triggering payment through objective indices. This paper is based on satellite remote sensing monitoring data, using a combination of principal component analysis, random forests, and fuzzy mathematical theory to construct a lightning risk index and design a complete index insurance product. Experimental validation based on historical satellite monitoring data has shown that the risk indices constructed in this paper can effectively capture the temporal and spatial variability of lightning activity. Random forest models have a relatively low fitting error of training labels, and the SHAP values reveal a characteristic weight of importance consistent with physical perception. The insurance product has a reasonable distribution of amount and compensation, and premium pricing balances actuarial fairness with market acceptability. The present methodology provides a transportable design path to monitor and transfer the lightning risk using multi-source remote sensing data, with some outreach value in the field of lightning and other natural disasters. Full article

Deep learning perception in autonomous vehicles (AVs) has created a critical attack surface in which adversarial patches and sensor-spoofing perturbations cascade from perception errors into unsafe driving decisions. Existing defenses face three limitations: most require retraining the perception network, making them impractical for already-deployed fleets; they operate almost exclusively at the perception layer, without verifying whether a compromised detection actually altered the driving action; and they leave temporal consistency across frames largely unexploited. This paper presents SENTINEL, a zero-modification, plug-and-play defense that wraps any deployed AV perception-and-planning stack without updating its weights, calibrating only the detection thresholds, score combination weights, and reference exemplars once on a small held-out calibration set. SENTINEL integrates a frozen foundation model verification ensemble (CLIP, DINOv2, SAM-2), a temporal consistency scorer that flags patches through anomalous frame-to-frame stability under ego-motion, a counterfactual policy verifier that replans under reconstructed perception and measures action-space divergence, and a risk-adaptive safety shield that modulates driving aggressiveness by verification confidence. Across CARLA, nuScenes, KITTI, and BDD100K, against five adversarial attacks and an adaptive adversary, SENTINEL reduces the attack success rate by up to 92%, keeps the clean accuracy loss to approximately 1.8 percentage points, reduces the collision rate under attack by approximately 87%, and adds under 45 ms latency on an RTX 4090 GPU. SENTINEL reframes adversarial robustness as a runtime property of the complete autonomous decision pipeline. Full article

For decades, visual perception was treated as a linear, feature-extracting mechanism driven almost exclusively by bottom-up sensory inputs. Emerging insights from affective neuroscience and cognitive psychology have systematically dismantled this view, revealing that vision operates within a continuous, bidirectional dialog with emotional systems. This review synthesizes the multi-layered neurobiological architectures underpinning this relationship. The pathways through which top–down emotional states recalibrate sensory processing are analyzed. Mechanisms including amygdalocortical feedback, frontoparietal attentional networks, and insular interoceptive monitoring are examined. These systems prioritize survival-driven motivational salience over objective accuracy. In the opposite direction, the text charts how ambient environmental features, such as lighting dynamics, spatial geometry, and structural ambiguity, immediately register along rapid subcortical and detailed cortical streams to instantiate emotional states. By situating these reciprocal dynamics within predictive coding and active inference frameworks, this paper illustrates how affective states function as precision weights that dynamically adjust internal perceptual priors. Finally, the clinical utility of these interconnected systems is evaluated, demonstrating how subtle visual aberrations like disrupted contrast suppression serve as diagnostic signatures for mood disorders, while structural retinal decay offers an accessible window into neurodegenerative pathology. Ultimately, the evidence indicates that conscious vision is fundamentally an affective construction, carrying transformative implications for early biomathematical and ocular screening in psychopathology. Full article

Inbound tourism has become an important indicator of destination openness, service capacity, cultural communication, and sustainable governance. However, existing evaluations often separate visitor experience, destination competitiveness, and sustainability, making it difficult to diagnose how service quality supports long-term competitiveness. This study develops a sustainability-oriented framework for evaluating inbound tourism service quality in 10 representative Chinese cities. Nineteen indicators are organized into four dimensions: basic service provision, cultural and experiential perception, safety and emergency response, and sustainable and resilient development. A TIFN-AHP-TOPSIS model is used to integrate official statistics, public tourism information, online-review evidence, and expert judgments while retaining uncertainty and hesitation in qualitative assessments. The results show that Shanghai, Beijing, and Hangzhou form the leading tier; Shenzhen, Chengdu, Guangzhou, Sanya, and Xiamen form the balanced tier; and Xi’an and Chongqing form the potential tier. Robustness checks based on risk-preference adjustment, entropy-weighted TOPSIS, grey relational TOPSIS, and perception-indicator perturbation confirm the stability of the tier classification. The findings suggest that inbound tourism competitiveness depends not only on transport access and reception capacity but also on cultural interpretation, digital convenience, safety governance, ecological quality, and resilience. The framework provides a diagnostic tool for improving sustainable destination competitiveness. Full article

This study presents the development and validation of a context-sensitive walkability assessment tool for Gulf Cooperation Council (GCC) commercial areas. Existing international audit tools often fail to assess the unique cultural and climatic environments of these areas. To address this gap, the Commercial-Central Street Walkability Audit Tool for GCC cities (CCSWAT-GCC) was developed using a mixed-method approach, including a literature review, expert opinions, public perception weighting, and field-based reliability testing, which includes 49 indicators. Reliability testing showed moderate to substantial agreement (81.21%, total kappa of 0.581) among raters, ensuring practical application. The CCSWAT-GCC contributes to walkability research by integrating local cultural and climatic considerations with micro-level environmental auditing practices. The findings provide urban planners and policymakers with a reliable framework for evaluating and improving commercial street environments in GCC cities, supporting evidence-based urban interventions in hot-climate contexts. Full article

Point cloud segmentation constitutes a core task in 3D computer vision. However, prevailing models suffer from inherent limitations, including the absence of polarity correlation (i.e., spatial attribute-containing features derived from the separation and calculation of positive/negative correlations within point cloud query–key pairs), inefficient feature fusion, loss of fine-grained geometric details, and excessive computational complexity in self-attention mechanisms. These deficiencies constrain both the performance and practical deployment of such models. To address these challenges, the Polarity-Aware Attention and Feature Dynamic Fusion Network (PA-DFNet) is proposed in this paper. Built upon the PointNet++ framework, PA-DFNet replaces the original Multilayer Perceptron (MLP) with a Polarity-Aware Network (PAN). The PAN enhances key semantic interactions by explicitly separating positive and negative correlations from point cloud query–key pairs, generates adaptive neighborhood weights via integration with a linear attention mechanism, and introduces a learnable power function to perform nonlinear scaling of attention, thereby improving the model’s structural perception capability. Additionally, a Point Cloud Feature Dynamic Fusion (PFF) module is proposed to enable adaptive fusion of encoder–decoder features, preserving rich geometric details. Experimental results demonstrate that, on the ModelNet40 classification task, the overall accuracy (OA) and mean accuracy (mAcc) of PA-DFNet are improved by 2.4% and 2.2%, respectively, compared with PointNet++. On the S3DIS semantic segmentation task, PA-DFNet achieves an mAcc of 72.8% and a mean Intersection over Union (mIoU) of 66.2%, while exhibiting a shorter training time than Point Transformer. In summary, PA-DFNet achieves an optimal balance between segmentation performance and efficiency by effectively controlling the number of model parameters and computational complexity. Full article

Speech perception relies on distributed neuronal populations, yet traditional decoding often utilizes static strategies that overlook inherent temporal dependencies and dynamic regulation. Therefore, we introduce the concept of system identification into multivariate decoding. By modeling brain response characteristics through time-lagged regression between speech stimuli and neural responses, we propose a temporal response function-based representational similarity analysis method (TRF-RSA). This method models the dynamic time-lag mapping from continuous stimulus features to neural responses, effectively separating stimulus-driven coherent activity from high-dimensional noise. More importantly, it elevates the analytical perspective from static comparisons of raw signals to dynamic trajectories in weight space. We conducted an auditory experiment and incorporated high spatiotemporal resolution optically pumped magnetometer magnetoencephalography magnetoencephalography (OPM-MEG) with electroencephalography (EEG). The results showed that TRF-RSA significantly enhanced the pattern similarity between speech sounds and the ability to discriminate between pattern differences. Furthermore, it revealed stronger similarities elicited by biological vocalizations, indicating a preference in the brain for these species-specific sounds. Source localization results not only confirmed the classical speech perception network but also revealed activation in limbic and deep brain regions. By modeling the relationship between stimulus features and neural responses, TRF-RSA dynamically quantified the spatiotemporal patterns of stimulus-driven neural activity, improving the sensitivity of representational pattern decoding during the encoding process. These findings suggest that this method is a sensitive neuroimaging tool that not only advances our understanding of the spatiotemporal dynamics of speech processing but also provides a new reference for population dynamics research. Full article

Objective Image Quality Assessment (IQA) is a fundamental pillar of computer vision, essential for optimizing tasks ranging from supervised machine learning to real-time video streaming. While IQA aims to quantify image degradation caused by noise and artifacts, a persistent gap remains between technical objective measurements and subjective human perception. Objective IQA has advanced significantly through full-reference (FR) metrics designed to approximate human judgment. Standard measures such as the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and root mean square error (RMSE) provide established benchmarks; however, they frequently fail to capture nuanced human visual preferences, often penalizing perceptually insignificant shifts or favoring overly smoothed images. Conversely, modern deep-learning metrics like LPIPS offer better perceptual alignment but remain computationally prohibitive for real-time, resource-constrained environments. This paper addresses these challenges through a dual-purpose approach. First, it provides a comprehensive survey and tutorial of the IQA landscape, offering self-contained mathematical derivations of classical error sensitivity measures, including MSE, RMSE, MAE, Euclidean distance, RMSLE, and Huber loss, as well as artificial neural network (ANN) approaches. This foundational review ensures a rigorous understanding of the field’s mathematical evolution. We introduce the Adaptive Contrast-Weighted Structural Similarity (ACSSIM) framework. ACSSIM is a lightweight hybrid metric that enhances classical FR-IQA by incorporating local weighting derived from human visual system (HVS) properties. Specifically, it targets Weber’s Law-based contrast and entropy, which are key elements of our hybrid quality assessment logic and key components of non-reference image quality metrics. Extensive numerical experiments on the TID2013 and KADID-10k benchmark show that ACSSIM improves correlation with human subjective judgments compared with the baseline PSNR and SSIM. Our results confirm that ACSSIM maintains low computational overhead, bridging the gap between efficiency and accuracy for practical deployment. We made our code publicly available to facilitate future research in efficient perceptual modeling. Full article

This study evaluated the effects of partial replacement of fishmeal with 11% defatted Hermetia illucens meal (corresponding to approximately 35% replacement of the fishmeal-derived animal protein fraction) on growth performance, fillet proximate composition, and instrumental sensory responses of gilthead seabream (Sparus aurata) reared under commercial offshore farming conditions. A total of 60,000 fish were distributed into four sea cages and fed either a control diet (FM) or an insect-based diet (HIM) for 181 days. No significant differences were observed between dietary treatments in final body weight, weight gain, specific growth rate, feed conversion ratio, protein efficiency ratio, or somatic indices, indicating that insect meal inclusion did not impair productive performance under farm-scale conditions. Fillet proximate composition was largely preserved. Fillet sensory characteristics were assessed using an integrated artificial sensing platform including an electronic eye (E-eye), electronic nose (E-nose), and electronic tongue (E-tongue) coupled with multivariate analysis. E-eye and E-nose analyses showed no clear discrimination between dietary groups, indicating that dietary insect meal inclusion had limited effects on fillet visual appearance and volatile compound profiles. In contrast, E-tongue analysis revealed a clear separation between treatments, suggesting selective modulation of taste-related attributes associated with dietary inclusion of insect meal. Overall, the results demonstrate that defatted H. illucens meal can be incorporated into practical seabream diets under commercial farming conditions without compromising productive performance or major fillet quality traits. Furthermore, this study provides farm-scale evidence that artificial sensing technologies can effectively detect subtle diet-related changes in sensory characteristics, particularly those associated with taste perception. Full article

The paper explores the nexus between indicators of development (the Human Development Index, Social Progress Index, happiness, employment in agriculture, industry, and services), indicators of habitat and species protection (Terrestrial Biome Protection based on national weights, TBN; Species Protection Index, SPI; Protected Area Representativeness Index, PAR; and Species Habitat Index, SHI) and indicators of governance (World Governance Indicators, WGI) and population density. The question at the heart of the research was whether various approaches to assessing development could be related to habitat and species protection, but this necessitated the inclusion of the quality of governance, as this has been established as being an important element for both. The analysis was based on data collected from 180 countries over the years 2018, 2020, 2022 and 2024, and was analyzed using OLS regression. The results suggest that the relationships between these factors are complex. Indicators of development having an element of perception (happiness and the Social Progress Index) had a statistically significant (p < 0.001) yet negative association with the SHI, but had no apparent association (p > 0.05) with the indicators based on protected areas (TBN, SPI and PAR). While the adjusted R² was low (<22%), results suggested that population density had a significant and negative association with the SHI indicator. The paper provides recommendations for future research to explore this important nexus between governance, development, population density and habitat and species protection. Full article

Background: Operating rooms generate substantial visual data that is rarely captured systematically. Image-based AI (IBAI) systems using computer vision offer a new approach to real-time perioperative workflow monitoring, but evidence of their impact on surgical case volume remains limited. The aim of this study was to evaluate the association between deployment of an IBAI system and monthly surgical case volume in a high-acuity cardiothoracic operating suite, using synthetic control with difference-in-differences estimation. Methods: We deployed an IBAI system with wall-mounted cameras and a YOLO-based (You Only Look Once) object detection model coupled with a transformer-based event detector in a 15-room cardiothoracic suite at Houston Methodist Hospital (HMH), the tertiary academic hospital of Houston Methodist health system. The deployment was conducted under an IRB-determined quality improvement framework with patient consent for ambient video capture, defined retention limits, and restricted access to recordings. Over a 16-month period spanning 6 months pre-deployment and 10 months post-deployment, the system monitored 5417 surgical cases and automatically detected additional perioperative events including patient entry, draping, and room turnover. Using a synthetic control methodology, we compared post-deployment outcomes at the intervention site against a weighted combination drawn from a pool of 11 Houston Methodist sites that did not yet implement IBAI (116,098 cases across the comparison sites; 121,515 cases in the full analytic dataset). Results: The synthetic control analysis with difference-in-differences estimation showed a statistically significant increase of approximately 25 cases per month (95% CI 8.3 to 41.0; p < 0.01; Bonferroni-adjusted p < 0.05), corresponding to a 7% increase in monthly case volume relative to baseline. Conclusions: Our findings suggest that IBAI can meaningfully improve OR efficiency and support data-driven perioperative management. Future work should evaluate whether case volume gains generalize across other surgical specialties, assess changes in operational outcomes such as turnover time and first-case on-time starts, and examine clinicians’ perceptions of IBAI. Full article

Driving simulation systems play a critical role in the “human-vehicle-road-environment” ecosystem of road traffic, where their effectiveness is fundamental for advancing scientific research. This study proposes a comprehensive evaluation framework for such systems, employing a Mul-Bayes-LSTM model to analyze multidimensional data encompassing drivers’ biopsychological and behavioral characteristics. The evaluation process integrates Bayesian hyperparameter optimization to enhance model performance, with rank correlation and R² as key indicators of model fit. The gray correlation analysis, integrated entropy method, and CRITIC analysis are utilized for weighting these indicators, ensuring robust assessment. The overall evaluation index is derived using entropy and CRITIC methods to provide a comprehensive measure of simulation effectiveness. The results from experimental validation indicate that driver-specific parameters obtained from the test simulator closely align with behavioral variables in risk scenarios, confirming the system’s applicability for research in traffic perception. The research results can evaluate the effectiveness of driving simulators based on the driver’s perception level, which has certain significance for promoting the development and application of driving simulation systems. Full article

Background: Implant supply strategy in spine surgery affects operative workflow, resource utilization, and packaging-related material use, yet has received limited systematic investigation. This study evaluates single-sterile implants versus multipack implants using a hospital-based Health Technology Assessment (HB-HTA) framework. Methods: A non-randomized, mixed-methods comparative study was conducted at a tertiary academic spine center. Time measurements were recorded during eight posterior fusion procedures (four per supply type; n = 18 single-pack screws, n = 20 multipack screws) across three process steps: implant retrieval, sterile transfer, and instrument preparation. Time measurements were recorded per packaging unit; per-implant comparisons were additionally derived for operational interpretation. Packaging volume, weight, and packaging-related CO₂-equivalent estimates were calculated per implant. Standardized questionnaires were distributed to operating-room (OR) nurses (n = 14/21; 66.7%) and institutional surgeons (n = 11/11; 100%). Manufacturer-provided descriptive process and cost data were analyzed. Results: Multipack implants were associated with consistently shorter handling times across all measured process steps. Mean retrieval time per packaging unit was 25.4 s (multipack) versus 58.7 s (single-pack); retrieval time was significantly shorter for multipack units on the Mann–Whitney U test (p = 0.004), a result that was robust to supply-related outlier events (p = 0.001 after their post hoc exclusion). Packaging-normalized sterile-transfer burden per implant was reduced by a factor of 4.76. Instrument preparation was faster with multipack systems (15.6 s vs. 25.2 s). Packaging volume per implant was reduced by a factor of 5.6, and packaging weight by a factor of 2. Packaging-related CO₂-equivalent estimates were lower for multipack implants (0.017 kg vs. 0.026 kg per implant). Survey responses indicated predominantly positive evaluations of workflow and handling efficiency. A trade-off was identified regarding the potential disposal of unused implants (noted by 73% of institutional surgeons). Manufacturer-provided descriptive data suggested scale effects in packaging and sterilization processes. Conclusions: Under high-volume academic conditions, multipack implants were associated with shorter implant-handling process times, favorable staff perceptions, and reduced packaging-related material burden while introducing trade-offs that require local evaluation. These exploratory findings suggest that the implant supply strategy is an underexplored but potentially relevant dimension of surgical process optimization in spine surgery. Full article

A participatory school climate refers to the involvement of school administrators, teachers, and other school members in decision-making processes, their sharing of responsibility, and their collaborative work for school improvement. Since this climate can be related to individual, organizational, and contextual factors such as leadership, job satisfaction, diversity beliefs, workload, well-being, and national context, identifying the key variables that support a participatory school environment is important. This study used TALIS 2024 school administrator data to identify the main predictors of participatory school climate and examined the mediating role of school administrator job satisfaction in the relationship between school administrator leadership, used here in line with school principal leadership, and participatory school climate. The research is based on a two-stage analytical framework. In the first stage, explanatory machine learning analysis was conducted by comparing Elastic Net, Random Forest, and XGBoost models; the relative significance levels of the variables were evaluated using permutation importance and SHAP methods. In the second stage, mediation analysis was performed using multi-level linear mixed models, considering clustering at the national level; the indirect association was evaluated using bootstrap confidence intervals. The analyses were conducted using data from 16,335 school administrators. The findings showed that the highest prediction performance was produced by the XGBoost model and that model performance improved with the inclusion of the country variable. Explainability analyses indicated that school administrator leadership was the strongest predictor of participatory school climate, followed by job satisfaction and diversity beliefs. Multilevel models suggested that the association between school administrator leadership and participatory school climate was consistent, with an indirect pathway through school administrator job satisfaction; bootstrap findings also supported the statistical stability of this indirect association. These findings suggest that a participatory school climate is associated not only with individual perceptions but also with multifaceted conditions such as leadership, job satisfaction, inclusivity, and country context. By combining explanatory machine learning with multilevel statistical modeling, this study identifies variables associated with participatory school climate and examines an indirect association among leadership, job satisfaction, and participatory climate. Because TALIS survey weights and the full complex sampling design were not incorporated, the findings should be interpreted as associations observed in the pooled analytical sample rather than as population-representative estimates for participating education systems. Full article