Search Results (217)

The Yangtze River fishing ban policy is a central measure in China’s watershed governance, and the adaptability of its policy tools and collaborative mechanisms directly influences the sustainability and effectiveness of basin management. This study systematically examines the evolution of policy themes, the characteristics of policy tool combinations, and their alignment with intergovernmental collaborative governance needs, drawing on 120 central government policy texts issued between 2002 and 2024. Using frequency analysis and policy tool coding, the findings reveal that (1) policy themes have shifted from fishery resource control to comprehensive ecological protection and, more recently, to integrated watershed management, thereby driving progressively higher demands for intergovernmental collaboration. (2) The policy tool structure has long been dominated by environmental tools, supplemented by supply-side tools, while demand-side tools remain underdeveloped. Imbalances persist, such as excessive emphasis on resource inputs over capacity building in supply-side tools, rigid constraints with limited flexibility in environmental tools, and a reliance on publicity while underutilizing market incentives in demand-side tools. (3) Tool combinations have adapted to changing collaboration needs, evolving from rigid constraints and fiscal subsidies to institutional frameworks and cross-regional cooperation, ultimately forming a governance model characterized by systemic guarantees and diversified collaboration. Based on these findings, this study recommends strengthening long-term governance mechanisms, improving cross-regional collaborative structures, authorizing local governments to design context-specific implementation details, enhancing fishermen’s livelihood security and social development, expanding public participation and oversight, and exploring market mechanisms for realizing ecological product value. These measures aim to advance collaborative governance in the Yangtze River Basin and foster a balanced integration of ecological protection and social development. Full article

Adverse road conditions, particularly foggy weather, significantly impair drivers’ abilities to gather information and make judgments in response to unexpected events. To investigate the impact of different Augmented Reality-Head-Up Display (AR-HUD) interfaces (words-only, symbols-only, and words + symbols) on driving behavior, this study simulated driving scenarios under varying visibility and risk levels in foggy conditions, measuring reaction time (RT), time-to-collision (TTC), the maximum lateral acceleration, the maximum longitudinal acceleration, and subjective data. The results indicated that risk levels significantly affected drivers’ RT, TTC, and maximum longitudinal and lateral accelerations. The three interfaces significantly differed in RT and TTC across different risk levels in heavy fog. In light fog, words-only and redundant interfaces significantly affected RT across different risk levels; words-only and symbols-only interfaces significantly affected TTC across different risk levels. In addition, participants responded faster when using text-related interfaces in the subject’s native language. After analyzing data on perceived usability across the three interfaces, the results indicated that under high-risk conditions, both in light fog and heavy fog, participants rated the redundant interface as having higher usability and preferred the redundant interfaces. Based on these findings, this paper proposes the following design strategies for AR-HUD visual interfaces: (1) Under low-risk foggy driving conditions, all three interface types are effective and applicable. (2) Under high-risk foggy driving conditions, redundant interface design is recommended. Although it may not significantly improve driving performance, this interface type was subjectively perceived as more useful and preferred by the subjects. The findings of this study provide support for design of AR-HUD interfaces, contributing to enhanced driving safety and human–machine interaction experience under complex meteorological conditions. This offers practical implications for the development and optimization of intelligent vehicle systems. Full article

Single-object vision–language tracking has become an important research topic due to its potential in applications such as intelligent surveillance and autonomous driving. However, existing cross-modal alignment methods typically rely on contrastive learning and struggle to effectively address semantic ambiguity or the presence of multiple similar objects. This study aims to explore how to achieve more robust vision–language alignment under these challenging conditions, thereby achieving accurate object localization. To this end, we propose a text heatmap mapping (THM) module that enhances the spatial guidance of textual cues in tracking. The THM module integrates visual and language features and generates semantically aware heatmaps, enabling the tracker to focus on the most relevant regions while suppressing distractors. This framework, developed based on UVLTrack, combines a visual transformer with a pre-trained language encoder. The proposed method is evaluated on benchmark datasets such as OTB99, LaSOT, and TNL2K. The main contribution of this paper is the introduction of a novel spatial alignment mechanism for multimodal tracking and its effectiveness on various tracking benchmarks. Results demonstrate that the THM-based tracker improves robustness to semantic ambiguity and multi-instance interference, outperforming baseline frameworks. Full article

The increasing adoption of digital technologies, robotic systems, and IoT applications in sectors such as medicine, agriculture, and industry drives a surge in data generation and necessitates secure and efficient encryption. For resource-constrained systems, lightweight yet robust cryptographic algorithms are critical. This study addresses the security demands of IoRT systems by proposing an enhanced chaos-based encryption method. The approach integrates the lightweight structure of NIST-standardized Ascon-AEAD128 with the randomness of the Zaslavsky map. Ascon-AEAD128 is widely used on many hardware platforms; therefore, it must robustly resist both passive and active attacks. To overcome these challenges and enhance Ascon’s security, we integrate into Ascon the keys and nonces generated by the Zaslavsky chaotic map, which is deterministic, nonperiodic, and highly sensitive to initial conditions and parameter variations.This integration yields a chaos-based Ascon variant with a higher encryption security relative to the standard Ascon. In addition, we introduce exploratory variants that inject non-repeating chaotic values into the initialization vectors (IVs), the round constants (RCs), and the linear diffusion constants (LCs), while preserving the core permutation. Real-time tests are conducted using Raspberry Pi 3B devices and ROS 2–based IoRT robots. The algorithm’s performance is evaluated over 100 encryption runs on 12 grayscale/color images and variable-length text transmitted via MQTT. Statistical and differential analyses—including histogram, entropy, correlation, chi-square, NPCR, UACI, MSE, MAE, PSNR, and NIST SP 800-22 randomness tests—assess the encryption strength. The results indicate that the proposed method delivers consistent improvements in randomness and uniformity over standard Ascon-AEAD128, while remaining comparable to state-of-the-art chaotic encryption schemes across standard security metrics. These findings suggest that the algorithm is a promising option for resource-constrained IoRT applications. Full article

Virtual reality driving simulators have been increasingly used for training purposes, but they are still lacking effective driver assistance features, and poor use of user interface (UI) and guidance systems leads to users’ performance being affected. In this paper, we investigate image–arrow aids in a virtual reality driving simulator (VRDS) that enables trainees (new drivers) to interpret instructions according to the correct course of action while performing their driving task. Image–arrow aids consist of arrows, texts, and images that are separately rendered during driving in the VRDS. A total of 45 participants were divided into three groups: G1 (image–arrow aids), G2 (audio and textual aids), and G3 (arrows and textual aids). The results showed that G1 (image–arrow guidance) achieved the best performance, with a mean error rate of 8.1 (SD = 1.23) and a mean completion time of 3.26 min (SD = 0.56). In comparison, G2 (audio and textual aids) had a mean error rate of 10.8 (SD = 1.31) and completion time of 4.49 min (SD = 0.67), while G3 (arrows and textual aids) had the highest error rate (18.4, SD = 1.43) and longest completion time (6.51 min, SD = 0.68). An evaluation revealed that the performance of G1 is significantly better than that of G2 and G3 in terms of performance measures (errors + time) and subjective analysis such as usability, easiness, understanding, and assistance. Full article

Ease of cognitive processing is an important predictor of aesthetic liking. However, many acclaimed artworks are fairly complex and require substantial cognitive effort. Are they aesthetically liked despite or because of this increased cognitive challenge? The present study pursued this question experimentally. The high syntactic complexity of Heinrich von Kleist’s narratives provided the test case. According to literary scholars, this high syntactic complexity should support increased levels of how “suspenseful,” “intense,” “interesting,” and evocative of a sense of “urgency” the texts are perceived, and it should thereby also support higher overall aesthetic liking. This expectation is in line with recent models in empirical aesthetics according to which higher ease of processing and higher cognitive challenge are not mutually exclusive, but can conjointly drive aesthetic liking to higher levels. The standard hypothesis of cognitive fluency instead predicts a disfluency-driven negative effect on aesthetic liking. We tested these two predictions in two studies by presenting excerpts from Kleist’s narratives in their original vs. syntactically simplified versions to participants. Results differ substantially depending on how the target variables are statistically modeled. If ease of processing and cognitive challenge are modeled separately as predictors of the aesthetically evaluative ratings, higher ease of processing is a strong positive and higher cognitive challenge a largely negative predictor. However, when the two complementary cognitive variables are modeled conjointly, they are both positive predictors of the aesthetically evaluative ratings. Their predictive power differs, however, significantly. Only the positive effect of ease of processing is pervasive across all readers. That of cognitive challenge is substantially modified by individual differences. Specifically, it was observed for readers who (1) are of higher age, (2) like to read narratives in general, and (3) reported prior positive experiences with Kleist. Supporting the ecological validity of our findings, readers meeting these criteria are more likely than others to actually read Kleist outside the laboratory. Full article

Understanding spatial relationships between objects in images is crucial for robotic navigation, augmented reality systems, and autonomous driving applications, among others. However, existing vision-language benchmarks often overlook explicit spatial reasoning, limiting progress in this area. We attribute this limitation in part to existing open datasets and evaluation metrics, which tend to overlook spatial details. To address this gap, we make three contributions: First, we greatly extend the COCO dataset with annotations of spatial relations, providing a resource for spatially aware image captioning and visual question answering. Second, we propose a new evaluation framework encompassing metrics that assess image captions’ spatial accuracy at both the sentence and dataset levels. And third, we conduct a benchmark study of various vision encoder–text decoder transformer architectures for image captioning using the introduced dataset and metrics. Results reveal that current models capture spatial information only partially, underscoring the challenges of spatially grounded caption generation. Full article

The current agent-based evolutionary models for animal communication rely on simplified signal representations that differ significantly from natural vocalizations. We propose a novel agent-based evolutionary model based on text-to-audio (TTA) models to generate realistic animal vocalizations, advancing from VAE-based real-valued genotypes to TTA-based textual genotypes that generate bird songs using a fine-tuned Stable Audio Open 1.0 model. In our sexual selection framework, males vocalize songs encoded by their genotypes while females probabilistically select mates based on the similarity between males’ songs and their preference patterns, with mutations and crossovers applied to textual genotypes using a large language model (Gemma-3). As a proof of concept, we compared TTA-based and VAE-based sexual selection models for the Blue-and-white Flycatcher (Cyanoptila cyanomelana)’s songs and preferences. While the VAE-based model produces population clustering but constrains the evolution to a narrow region near the latent space’s origin where reconstructed songs remain clear, the TTA-based model enhances the genotypic and phenotypic diversity, drives song diversification, and fosters the creation of novel bird songs. Generated songs were validated by a virtual expert using the BirdNET classifier, confirming their acoustic realism through classification into related taxa. These findings highlight the potential of combining large language models and TTA models in agent-based evolutionary models for animal communication. Full article

Background: Human immunodeficiency virus and hypertension increasingly co-occur in South Africa. Despite publicly funded care, patients with multimorbidity face high out-of-pocket costs, yet limited evidence exists from the patient perspective. Purpose: To quantify the economic burden of comorbid HIV and hypertension, assess predictors of monthly out-of-pocket costs, and explore coping mechanisms. Methods: We conducted a cross-sectional analysis using patient-level data from the Mobile Phone Text Messages to Improve Hypertension Medication Adherence in Adults with HIV (MOPHADHIV trial) [Trial number: PACTR201811878799717], a randomized controlled trial evaluating short messages services adherence support for hypertension care in people with HIV. We calculated the monthly direct non-medical, indirect, and coping costs from a patient perspective, valuing indirect costs using both actual income and minimum wage assumptions. Generalized linear models with a gamma distribution and log link were used to identify cost determinants. Catastrophic expenditure thresholds (10–40% of monthly income) were assessed. Results: Among 683 participants, mean monthly total costs were ZAR 105.81 (USD 5.72) using actual income and ZAR 182.3 (USD 9.9) when valuing indirect costs by minimum wage. These time-related productivity losses constituted the largest share of overall expenses. Regression models revealed a strong income gradient: participants in the richest quintile incurred ZAR 131.9 (95% CI: 63.6–200.1) more per month than the poorest. However, this gradient diminished or reversed under standardized wage assumptions, suggesting a heavier proportional burden on middle-income groups. Other socio-demographic factors (gender, employment, education) not significantly associated with total costs, likely reflecting the broad reach of South Africa’s primary health system. Nearly half of the participants also reported resorting to coping mechanisms such as borrowing or asset sales. Conclusions: Comorbid HIV and hypertension impose substantial patient costs, predominantly indirect. Income disparities drive variation, raising equity concerns. Strengthening integrated human immunodeficiency virus—non-communicable diseases care and targeting financial support are key to advancing South Africa’s Universal Health Coverage reforms. Full article

3D Multi-Object Tracking (3D MOT) is a critical task in autonomous systems, where accurate and robust tracking of multiple objects in dynamic environments is essential. Traditional approaches primarily rely on visual or geometric features, often neglecting the rich semantic information available in textual modalities. In this paper, we propose Text-Guided 3D Multi-Object Tracking (TG3MOT), a novel framework that incorporates Vision-Language Models (VLMs) into the YONTD architecture to improve 3D MOT performance. Our framework leverages RegionCLIP, a multimodal open-vocabulary detector, to achieve fine-grained alignment between image regions and textual concepts, enabling the incorporation of semantic information into the tracking process. To address challenges such as occlusion, blurring, and ambiguous object appearances, we introduce the Target Semantic Matching Module (TSM), which quantifies the uncertainty of semantic alignment and filters out unreliable regions. Additionally, we propose the 3D Feature Exponential Moving Average Module (3D F-EMA) to incorporate temporal information, improving robustness in noisy or occluded scenarios. Furthermore, the Gaussian Confidence Fusion Module (GCF) is introduced to weight historical trajectory confidences based on temporal proximity, enhancing the accuracy of trajectory management. We evaluate our framework on the KITTI dataset and compare it with the YONTD baseline. Extensive experiments demonstrate that although the overall HOTA gain of TG3MOT is modest (+0.64%), our method achieves substantial improvements in association accuracy (+0.83%) and significantly reduces ID switches (−16.7%). These improvements are particularly valuable in real-world autonomous driving scenarios, where maintaining consistent trajectories under occlusion and ambiguous appearances is crucial for downstream tasks such as trajectory prediction and motion planning. The code will be made publicly available. Full article

This study aims to bridge the research gap emerging from the relationships between Visual electronic Word-of-Mouth (VeWOM) and brand factors, and their impact on consumers’ behavior by exploring the causal effects of eWOM attributes on hotel brand factor spreading through Brand Awareness (BA) and Brand Perceived Value (BV) and its consequences on Purchase Decisions (PD) in the hospitality context. Attribution Theory was extended to incorporate brand-mediated effects and crisis-specific factors. The study investigates the impact of VeWOM on consumer Purchase Decisions (PD) in terms of hotel room bookings in the British hospitality market, emphasizing the mediating role of brand-related constructs. Drawing on Attribution Theory, the research proposes a structural model to assess both direct and indirect pathways through which VeWOM influences behavioral outcomes. A stratified, non-probability sampling approach yielded 443 valid responses from hotel bookers who engaged with user-generated visual content prior to booking. The Partial Least Squares Structural Equation Model (PLS-SEM) was employed to test the hypothesized relationships. The findings reveal that VeWOM significantly influences Brand Value (BV), eWOM Credibility, and Information Quality, which in turn shape consumer purchase behavior. Crucially, Brand Value emerges as a key mediating variable, bridging VeWOM and Purchase Decisions, while VeWOM alone does not directly affect booking behavior. Moreover, Brand Awareness showed no significant mediating effect. The study underscores the indirect attribution process in visual review contexts, demonstrating that the influence of VeWOM is channeled primarily through brand perception mechanisms rather than direct persuasion. These insights extend Attribution Theory by highlighting the distinct cognitive pathways activated by visual content compared to text-based reviews. Practically, the research suggests that hoteliers should focus on enhancing Brand Value via bundled offerings and relationship-based marketing rather than relying solely on visual appeal or awareness to drive bookings. The study contributes to the growing body of VeWOM literature by clarifying its nuanced effects on decision-making in digital hospitality environments. Full article

Bacterial diseases are a major constraint to aquaculture productivity, driving extensive antibiotic use and raising concerns over antimicrobial resistance, environmental contamination, and food safety. Curcumin, a polyphenolic compound from Curcuma longa, exhibits broad-spectrum antimicrobial and immunomodulatory activities but is limited by poor water solubility, instability, and low bioavailability. This review was conducted through a literature search of Scopus, PubMed, Web of Science, and Google Scholar using targeted keywords, including curcumin nanoparticles, antibacterial, aquatic pathogens, nanotechnology, synthesis, and disease control. Titles and abstracts were screened for relevance, followed by full-text evaluation of selected studies. Key findings were critically analyzed and incorporated into the review. Findings from the literature indicate that curcumin nanoparticles, synthesized via milling, anti-solvent precipitation, ionic gelation, emulsification, spray drying, and metal/polymer nanocomposite formation, exhibit enhanced antibacterial activity against aquatic pathogens, including Aeromonas hydrophila, Vibrio parahaemolyticus, Escherichia coli, and Staphylococcus aureus. Optimally engineered curcumin nanoparticles (<100 nm, being mostly spherical, highly negatively charged) can penetrate bacterial membranes, disrupt biofilms, lower minimum inhibitory concentrations, and improve in vivo fish survival. Practical applications include dietary supplementation to boost fish immunity and growth, water disinfection to reduce pathogen loads, immersion therapy for external infections, and antimicrobial coatings for aquaculture equipment and surfaces, resulting in reduced infections and outbreaks, reduced mortality, improved water quality, and decreased antibiotic dependence. In conclusion, curcumin nanoparticles and curcumin-based nanocomposites present a versatile, eco-friendly approach to sustainable aquaculture disease management. However, further field-scale validation, safety assessment, and cost-effective production methods are necessary to enable commercial adoption. Full article

This paper focuses on the challenges in food safety governance in megacities, taking Shanghai as the research object. Aiming at the pain points in food sampling inspections, it proposes a risk prediction and regulatory optimization scheme combining text mining and machine learning. First, the paper uses the LDA method to conduct in-depth mining on over 78,000 pieces of food sampling data across 34 categories in Shanghai, so as to identify core risk themes. Second, it applies SMOTE oversampling to the sampling data with an extremely low unqualified rate (0.5%). Finally, a machine learning prediction model for food safety risks is constructed, and predictions are made based on this model. The research findings are as follows: ① Food risks in Shanghai show significant characteristics in terms of time, category, and pollution causes. ② Supply chain links, regulatory intensity, and consumption scenarios are among the core influencing factors. ③ The traditional “full coverage” model is inefficient, and resources need to be tilted toward high-risk categories. ④ Public attention (e.g., the “You Order, We Inspect” initiative) can drive regulatory responses to improve the qualified rate. Based on these findings, this paper suggests that relevant authorities should ① classify three levels of risks for categories, increase inspection frequency for high-risk products in summer, adjust sampling intensity for different business entities, and establish a dynamic hierarchical regulatory mechanism; ② tackle source governance, reduce environmental pollution, upgrade process supervision, and strengthen whole-chain risk prevention and control; and ③ promote public participation, strengthen the enterprise responsibility system, and deepen the social co-governance pattern. This study effectively addresses the risk early warning problems in food safety supervision of megacities, providing a scientific basis and practical path for optimizing the allocation of regulatory resources and improving governance efficiency. Full article

Positron emission tomography (PET) has become a powerful tool in Alzheimer’s disease (AD) research by enabling in vivo visualization of pathological biomarkers. Recent efforts have aimed to integrate PET-derived imaging phenotypes with genome-wide association studies (GWASs) to better elucidate the genetic architecture underlying AD. This systematic review examines studies that leverage PET imaging in the context of GWASs (PET-GWASs) to identify genetic variants associated with disease risk, progression, and brain region-specific pathology. A comprehensive search of PubMed and Embase databases was performed on 18 February 2025, yielding 210 articles, of which 10 met pre-defined inclusion criteria and were included in the final synthesis. Studies were eligible if they included AD populations, employed PET imaging alongside GWASs, and reported original full-text findings in English. No formal protocol was registered, and the risk of bias was not independently assessed. The included studies consistently identified APOE as the strongest genetic determinant of amyloid burden, while revealing additional significant loci including ABCA7 (involved in lipid metabolism and amyloid clearance), FERMT2 (cell adhesion), CR1 (immune response), TOMM40 (mitochondrial function), and FGL2 (protective against amyloid deposition in Korean populations). The included studies suggest that PET-GWAS approaches can uncover genetic loci involved in processes such as lipid metabolism, immune response, and synaptic regulation. Despite limitations including modest cohort sizes and methodological variability, this integrated approach offers valuable insight into the biological pathways driving AD pathology. Expanding PET-genomic datasets, improving study power, and applying advanced computational tools may further clarify genetic mechanisms and contribute to precision medicine efforts in AD. Full article

Language-guided multimodal fusion, which integrates information from both visible and infrared images, has shown strong performance in image fusion tasks. In low-light or complex environments, a single modality often fails to fully capture scene features, whereas fused images enable robots to obtain multidimensional scene understanding for navigation, localization, and environmental perception. This capability is particularly important in applications such as autonomous driving, intelligent surveillance, and search-and-rescue operations, where accurate recognition and efficient decision-making are critical. To enhance the effectiveness of multimodal fusion, we propose a text-guided infrared and visible image fusion network. The framework consists of two key components: an image fusion branch, which employs a cross-domain attention mechanism to merge multimodal features, and a text-guided module, which leverages the CLIP model to extract semantic cues from image descriptions containing visible content. These semantic parameters are then used to guide the feature modulation process during fusion. By integrating visual and linguistic information, our framework is capable of generating high-quality color-fused images that not only enhance visual detail but also enrich semantic understanding. On benchmark datasets, our method achieves strong quantitative performance: SF = 2.1381, Qab/f = 0.6329, MI = 14.2305, SD = 0.8527, VIF = 45.1842 on LLVIP, and SF = 1.3149, Qab/f = 0.5863, MI = 13.9676, SD = 94.7203, VIF = 0.7746 on TNO. These results highlight the robustness and scalability of our model, making it a promising solution for real-world multimodal perception applications. Full article