Search Results (2,576)

To mitigate driving risks from brake failure on long and steep downhill sections, this study designs three deployment schemes for radar–video fusion devices: a baseline scenario with no coverage, a scenario with partial coverage in high-risk areas, and a scenario with full coverage. Corresponding information service strategies are delivered via Human–Machine Interfaces (HMIs), forming an integrated active prevention and control framework from risk perception to preventive action. Driving simulation experiments focusing on the car-following process were conducted to collect vehicle operational data and extract characteristic indicators based on the Wiedemann model. A Generalized Linear Mixed Model was employed to comprehensively examine the effects of HMIs on car-following behavior to identify the optimal active prevention strategy. Results show that drivers exhibit greater caution under the partial coverage scheme, with time headway increasing by 47.63% compared to the scheme with no radar–video fusion devices to ensure safety. Under full coverage conditions, drivers can obtain real-time information about the leading vehicle’s status and the distance between the two vehicles in key risk sections. Drivers choose to follow the leading vehicle, balancing both safety in car-following and efficiency on long and steep downhill sections. As the level of accompanying services improves, drivers engage in self-regulation to avoid rear-end collisions. Particularly under the scheme with full coverage of radar–video fusion devices, the standing distance significantly increases by 219.37% compared to the partial coverage condition. Drivers demonstrate optimal vehicle control capabilities. Furthermore, there is an interaction effect between the accompanying service strategy and drivers’ attributes on car-following behaviors. Under different schemes, more experienced drivers exhibit a certain degree of aggressiveness, providing a basis for the targeted design of information services for different types of drivers. The findings support the deployment and application of risk perception and prevention devices on long and steep downhill sections, which can effectively enhance the comprehensive safety of such special roads in the connected vehicle environment. Full article

This study examines how the 4Es framework—Experience, Exchange, Everyplace, and Evangelism—influences online cosmetics purchase behavior in Thailand, addressing the growing importance of digital consumer engagement in emerging markets. A mixed-methods approach was employed, combining quantitative data from a structured survey with qualitative insights from semi-structured interviews. Binary logistic regression analysis was used to assess the effects and predictive power of the 4Es and selected demographic and behavioral variables. The results indicate that all four dimensions significantly influence purchase behavior, with Evangelism emerging as the strongest predictor, followed by Experience, Everyplace, and Exchange. The model demonstrates strong predictive capability, highlighting the importance of behavioral factors such as platform usage, purchase frequency, and social media engagement, while several demographic variables show limited influence. Qualitative findings further support these results, revealing that consumers place strong emphasis on social influence, emotional engagement, and convenience in their online shopping experiences. The study concludes that online cosmetics purchase behavior is shaped by a combination of experiential, relational, and socially driven factors, with social influence playing a dominant role. These findings demonstrate that the 4Es framework remains highly relevant in digitally mediated consumer environments, where purchase decisions are increasingly influenced by interactive experiences, omnichannel accessibility, value co-creation, and consumer advocacy. By integrating quantitative and qualitative evidence, the study extends the application of the 4Es framework beyond traditional marketing contexts and demonstrates its value as a comprehensive model for understanding consumer engagement and online purchasing behavior in contemporary digital marketplaces. The mixed-methods approach provides both generalizable and contextually grounded insights, offering theoretical contributions to digital marketing literature and practical guidance for marketers seeking to strengthen consumer engagement and brand advocacy in increasingly competitive online markets. Full article

As augmented reality head-up display (AR-HUD) becomes increasingly integrated into intelligent vehicles, inappropriate interface designs may increase drivers’ cognitive workload and delay hazard responses. This study investigates how cognitive style, driving task type, and AR-HUD navigation design jointly influence drivers’ behavioral performance and visual attention. A total of 55 participants were recruited and screened using the Group Embedded Figures Test, with 38 drivers finally selected for a 2 × 4 × 2 driving-simulation experiment comparing world-fixed (WF) and screen-fixed (SF) interfaces across goal-directed and stimulus-driven tasks. Reaction times and eye-tracking indicators were analyzed using generalized linear models. Results show that stimulus-driven tasks significantly increased reaction times, with rear-vehicle scenarios producing the longest responses (mean = 1.420). During lane-change tasks, WF displays significantly reduced fixation duration (p < 0.001) and fixation counts (p < 0.001), whereas SF displays improved attentional efficiency during pedestrian-warning tasks. In addition, field-dependent drivers exhibited significantly larger pupil diameters, indicating higher cognitive workload. These findings provide sensor-based evidence for AR-HUD systems that dynamically optimize interface presentation according to task context and workload conditions. Full article

Plastic waste presents a significant environmental and public health concern in Malawi, where rapid urban growth, limited waste collection services, and informal disposal practices contribute to persistent plastic waste hotspots. In Lilongwe City, the waste collection rate has been reported ranges from 10% to 30%. This means that out of the 500 to 600 tons of municipal solid waste produced each day, only about 50 to 150 tons are collected daily. These hotspots occur in settings such as drains, markets, settlement edges, riverbanks, and lakeshore environments. They intensify health-relevant exposure pathways by encouraging stagnant water, increasing flood risk, facilitating open burning, and supporting the formation of plastisphere biofilms that can contain pathogenic and antimicrobial resistant organisms. This research synthesises evidence on the main sources of plastic waste in Malawi, the mechanisms of leakage across different environments, and the associated health implications. It uses a scoping approach aligned with PRISMA-ScR guidance and is informed by the UK Research and Innovation (UKRI) funded Sustainable Plastic Attitudes to benefit Communities and their Environments (SPACES project), which highlights the influence of behavioural, governance, and environmental factors on plastic pollution. A two phase, risk-based decision framework to support targeted management of plastic waste hotspots is described. Phase 1 focuses on rapid harm reduction through the identification and ranking of hotspots according to risk severity, spatial extent, and feasibility, guiding timely interventions such as drain clearance, waste capture, and temporary stabilisation. Phase 2 addresses longer term prevention by tackling upstream drivers through policy measures, improved services, reuse and reduction schemes, and community engagement. The framework has been developed using evidence from Malawi; however, its methodology could be applied to other low- and middle-income countries that experience similar constraints and exposure pathways. The framework offers a transparent and practical tool for decision makers seeking to allocate limited resources effectively while reducing environmental and health risks associated with plastic waste. Full article

Network latency remains a fundamental bottleneck in vehicle teleoperation, inducing instability and performance degradation in conventional control methods, while predictive techniques like the Smith Predictor offer a theoretical solution, their efficacy is often compromised by unmodelled dynamics and real-world disturbances. This paper presents the first experimental validation of the Successive Reference-Pose Tracking (SRPT) architecture. By streaming future reference poses rather than direct steering commands, SRPT leverages an onboard Nonlinear Model Predictive Controller to compute optimal vehicle actions while inherently accounting for dynamic constraints and network delays. Real-world human-in-the-loop experiments were conducted with four drivers on a test track featuring cornering, double lane-change, and slalom manoeuvres. Quantitative comparisons at 10 km/h across four modes—manual driving, direct teleoperation, a Smith Predictor, and SRPT—demonstrate that SRPT significantly outperforms other teleoperation methods, reducing cross-track error by up to 66% and yielding smoother, more stable control inputs. Furthermore, SRPT uniquely maintained stability during a proof-of-concept trial at 13 km/h, where it proactively moderated vehicle speed to respect actuator limits—a critical safety behavior absent in other modes. This work provides the first tangible evidence that SRPT is a robust and superior framework for delay-resilient vehicle teleoperation in real-world conditions. Full article

This study examines the structural relationships among vendor competency, perceived sanitation and safety, high-value gastronomy experiences, and behavioral intentions in Bangkok’s street food tourism. Using survey data and PLS-SEM, the results indicate that vendor competency significantly enhances both perceived sanitation and safety and high-value gastronomy experiences. Perceived sanitation and safety further strengthens experiential value and partially mediates this relationship. However, contrary to conventional expectations, high-value gastronomy experiences exhibit a significant negative effect on behavioral intentions, suggesting a novelty-seeking paradox in exploratory tourism contexts. This study contributes in three ways: (1) by positioning vendor competency as a foundational driver of experiential value in informal food settings, (2) by integrating sanitation and safety into experiential value formation, and (3) by challenging the linear satisfaction–loyalty assumption through evidence of paradoxical tourist behavior. The findings offer theoretical and managerial implications for gastronomy tourism and destination management. Full article

Driving behavior recognition plays a crucial role in intelligent driving systems and road traffic safety. Due to insufficient driving experience and limited ability to allocate visual attention, novice drivers are considered a high-risk group for traffic accidents. Existing approaches primarily focus on experienced drivers and rely on single-modal eye-tracking data, making it difficult to model spatial attention distributions and long-term temporal dependencies simultaneously. Moreover, these methods are often affected by modality asynchrony during multimodal fusion, further limiting performance gains. To address these challenges, this study proposes a novice driver behavior recognition method based on bimodal eye-tracking features and a gated cross-modal attention fusion (GCMAF) mechanism. The model adopts a spatial–temporal dual-branch architecture. The spatial branch employs ResNet34 to extract eye-tracking heatmap features to represent the visual attention distribution. In contrast, the temporal branch integrates a 1D-CNN with the Mamba model to capture local dynamic patterns and long-range temporal dependencies. In the fusion stage, the GCMAF module is introduced to enhance cross-modal interactions, and a gating mechanism is further used to adaptively adjust modality weights, thereby mitigating the adverse effects of modality asynchrony. To validate the effectiveness and generalization ability of the proposed method, repeated experiments and five-fold cross-validation are conducted. The results demonstrate that the model achieves an average classification accuracy of 93.86% across four driving behavior categories, with standard deviations below 0.3%. Compared with baseline methods, paired t-test results show that the performance improvement is statistically significant (p < 0.01). Ablation studies further confirm the independent contribution of each component. Overall, the proposed method outperforms existing approaches in terms of accuracy and stability, providing effective support for driving behavior assessment and proactive safety warning systems. Full article

Precise control of sinter reducibility is essential for stable blast furnace operation. Each mineral phase present in sinter, such as hematite, magnetite and calcium ferrite exhibits different reducibility. In XRD analysis, the requirement for sample pulverization leads to the loss of mineralogical texture information. This makes it difficult to quantitatively correlate the complex mineral phases present in the sinter with reducibility. This study introduces a novel quantitative approach using hyperspectral imaging to distinguish specific mineral morphologies. Reduction experiments simulating blast furnace thermal and gas conditions were conducted on several sinters. Multiple regression analysis was applied to correlate mineral fractions and macroporosity with reduction rates across three distinct reduction stages. In the low-temperature stage, hematite, macroporosity and acicular calcium ferrites were identified as the primary drivers of reduction. In the intermediate stage, acicular calcium ferrites continued to enhance reactivity, whereas coarse calcium ferrite showed a significant negative influence. In the high-temperature stage, macroporosity strongly promoted reduction, while coarse calcium ferrite and magnetite hindered it due to the formation of shell-like metallic iron structures which impede gas diffusion. These findings demonstrate that hyperspectral imaging combined with multi-stage regression analysis offers a useful tool for designing optimal sinter mineralogy for blast furnace performance. Full article

The transition toward sustainable power systems requires load forecasting methods that can support renewable integration under increasing uncertainty. However, many deep learning models mix historical load, temporal priors, and external drivers in black-box structures, and often assume that true future driver values are available. To address these issues, this study proposes ADDF (Automatic Driver Discovery and Fusion), a semi-explicit self-driven framework for multi-step load interval forecasting. ADDF organizes historical load, calendar priors, and external drivers into three functional branches to distinguish load inertia, temporal regularity, and external forcing. The Driver Branch estimates future driver states under practical information constraints and uses dynamic gating to screen useful driving information. The three branch representations are adaptively integrated through Three-Way Fusion, followed by bounded residual correction to generate multi-step quantile forecasts. Experiments on the Panama electricity load dataset and ETTh1 dataset under one-step and 24-step settings show that ADDF achieves competitive point accuracy and interval prediction performance. Mechanism analyses indicate that the proposed branch-level structure provides clearer interpretability than post-hoc black-box explanations. The framework offers uncertainty-aware forecasting support for sustainable power system operation, including day-ahead scheduling, reserve planning, and energy management. Full article

Introduction: Freshwater ecosystems are among the most threatened worldwide, with river fragmentation, primarily caused by dams and weirs, identified as a major driver of biodiversity loss. This issue is particularly acute in Europe, where more than one million instream barriers disrupt longitudinal connectivity and compromise the movement of migratory fish. Fishways are widely implemented to mitigate these impacts, yet attraction efficiency at fishway entrances remains poorly understood, especially for Iberian potamodromous cyprinids, a group facing severe conservation pressures. Objective: This study aims to investigate how hydraulic conditions and social context influence the attraction and passage behavior of Luciobarbus bocagei, a rheophilic potamodromous cyprinid endemic to the Iberian Peninsula, in an experimental Vertical slot fishway (VSF) entrance. Methodology: Experiments were conducted in a controlled flume equipped with a VSF entrance design. Two hydraulic scenarios were tested, a Low Head Drop (LD) and a High Head Drop (HD), under a constant discharge of 34 L/s. A computational fluid dynamics (CFD) model was used to characterize and compare the flow field hydrodynamics. Fish were tested individually and in groups of three to assess the role of social dynamics. The metrics collected included time to first approach, first attempt, time to first successful passage, attraction efficiency, and passage efficiency. Cox proportional hazards models were applied to evaluate treatment effects. Results: Preliminary results showed that social context influenced fish attraction behavior. In the two hydraulic scenarios, individuals tested alone tend to exhibit lower likelihoods of approaching, attempting, and successfully negotiating the fishway compared to fish in schools. Delays were also evident for attempts and successful passages, with LD_Ind performing the worst. Conclusions: These findings highlight the importance of hydraulic conditions and social behavior in shaping attraction efficiency. They underscore the need to integrate species-specific behavioral ecology into fishway design, operation, and attraction assessment, acknowledging that fish attractivity is influenced by environmental and ecological factors beyond fishway structure, particularly in Mediterranean river systems where fragmentation pressures are high and potamodromous cyprinids are at risk. Full article

Dysregulated lipid metabolism is a core pathogenic driver of type 2 diabetes. Honokiol (HKL), the major bioactive constituent of Magnolia officinalis, possesses anti-diabetic and lipid-regulatory properties. However, the underlying molecular mechanism remains elusive. This study investigates how HKL ameliorates high-glucose/high-fat (HGHF)-induced hepatic lipid accumulation, with a focus on the role of SIRT3-mediated deacetylation of peroxisome proliferator-activated receptor γ (PPARG). The core targets of HKL were identified through network pharmacology and molecular docking. Human hepatic MIHA cells were treated with glucose (Glu, 40 mM) and palmitic acid (0.2~0.3 mM PA) to establish a lipid accumulation model, followed by treatment with HKL (5–10 μM) with or without a confirmed selective SIRT3 inhibitor 3-(1H-1,2,3-triazol-4-yl) pyridine (3-TYP). Lipid accumulation was assessed by Oil Red O staining and by measuring triglyceride (TG) and total cholesterol (TC) levels. Protein expression and the SIRT3-PPARG interaction were analyzed by Western blot and co-immunoprecipitation (Co-IP). SIRT3 and PPARG were identified as core targets of HKL, exhibiting strong binding with calculated energies of −6.834 and −6.579 kcal/mol, respectively. In MIHA cells, HGHF (40 mM Glu + 0.2–0.3 mM PA) induced lipid accumulation, including increased lipid droplets, and elevated TG (2.5–3.2-fold) and TC (2.2–2.8-fold) contents in a dose-dependent manner, accompanied by downregulated SIRT3/PPARG expression and heightened global protein acetylation. The non-cytotoxic HGHF-M condition (40 mM Glu + 0.2 mM PA) was selected for further experiments. HKL (5–10 μM) dose-dependently reduced lipid accumulation by ~38–60%, decreased TG and TC levels by up to ~13% and ~30%, and restored SIRT3/PPARG expression. The protective effects of HKL were reversed by inhibition of SIRT3 with 3-TYP. Co-IP confirmed the interaction between SIRT3 and PPARG, and SIRT3 overexpression significantly decreased the acetylation level of PPARG. This study suggests that HKL ameliorates hepatic lipid accumulation via SIRT3-mediated deacetylation of PPARG, providing an experimental basis for considering HKL as a potential therapeutic agent against metabolic disorders. Full article

Mitigating nitrous oxide (N₂O) emissions from cropland soils is a pressing challenge for climate change mitigation. This study evaluated rockwool-based fertigation (RF) in reducing N₂O emissions from tea plantations. A 17-month field experiment was conducted comparing RF with conventional surface fertilization (CK), measuring tea plant biomass, new tea shoots yield, new tea shoots quality indices, soil N₂O fluxes, physicochemical properties, and nitrogen (N)-cycling functional genes across different soil layers. Results showed that RF treatment significantly increased the aboveground pruning biomass of tea plants, suggesting that RF promotes tea plant growth. The RF treatment showed lower N₂O fluxes and cumulative N₂O emissions within 90 days post-fertilization across the tea-growing season compared with CK, demonstrating that RF effectively mitigates N₂O emissions from tea plantation soils. Random forest analysis further revealed that the RF-induced vertical redistribution of nutrients and N-cycling functional genes was the primary driver of N₂O mitigation. Our findings demonstrate that RF is an effective dual-benefit strategy that simultaneously enhances tea plant productivity and mitigates N₂O emissions by reshaping soil biogeochemical processes and their spatial distribution. Full article

Introduction: Biological invasions are a major driver of biodiversity loss, particularly in freshwater ecosystems. The Iberian Peninsula, a hotspot of endemic diversity, is increasingly threatened by invasive predatory fish, which may exert higher predatory rates under warmer environmental conditions, disrupting/endangering native fish communities. One such species is the European catfish (Silurus glanis), a large and voracious apex predator. Despite growing research, most telemetry studies have focused on lentic systems, limiting our understanding of its behaviour in lotic environments. Moreover, high-resolution biologging approaches remain largely unexplored. Objective: This study aims to characterize the habitat use and activity patterns of European catfish in a non-native lotic section of the lower Tagus River, and to identify key environmental drivers shaping its predatory behaviour. Methodology: Adult individuals were tagged with radio telemetry transmitters equipped with temperature, pressure (depth), and 3D-accelerometer archival sensors. A preliminary controlled experiment established activity thresholds to classify behaviours. Ten adult fish were then actively tracked over one year, combining spatial data with high-resolution biologging. Habitat use and activity patterns were analyzed across seasonal and circadian scales. Generalized Additive Models (GAMs) were used to assess the effects of environmental variables on activity levels and depth use, while Hurdle models were applied to identify the environmental drivers influencing the occurrence and frequency of burst activity events (predatory behaviour proxies). Results: Fish displayed strong site fidelity, frequently using structured habitats near riverbanks. European catfish also showed clear seasonal and circadian patterns in habitat use and activity, occupying deeper habitats in winter and shallower areas in warmer seasons. Activity occurred year-round, increasing in spring and summer and peaking at dusk, being influenced by temperature, river flow, season, and time of day. Burst activity occurred more often in spring and at dusk. Conclusions: This study unveils insights on European catfish behaviour in invaded lotic systems, highlighting consistent patterns linked to environmental conditions. These findings can support more targeted and effective management strategies for controlling this invasive species. Full article

Optimizing speed breeding protocols for biennial crops requires matching the vernalization regime with the genetic background. In this study, nine sugar beet genotypes were exposed to 12, 13, 14 or 15 weeks of vernalization and subsequently grown under controlled speed breeding conditions. Survival analysis revealed a threshold-like acceleration of bolting and flowering: 12 and 13 weeks were largely equivalent, whereas 14–15 weeks sharply increased the bolting and flowering hazard rates. Genotypic variation strongly influenced reproductive success and seed yield traits; genotype MARGARITA KWS combined early flowering with the highest seed number (361 seeds per plant) and total seed weight (5.26 g), while genotype 1K073 did not flower under any vernalization duration. A separate mini-steckling root architecture experiment with 11 genotypes showed that slow-release Osmocote fertilizer significantly increased mini-steckling fresh weight, length and width, with the strongest responses in genotypes 1K073, 1K139 and SMART LIENNA KWS. The interaction between genotype and nutrition was significant for mini-steckling fresh weight and width, indicating that optimal nutrition can modulate the expression of genotypic differences. Multivariate analyses (PCA, CVA, Mahalanobis distances) confirmed that vernalization duration had a threshold-type effect and that genotype was the dominant factor for seed traits, whereas nutrition was the main driver of mini-steckling architecture. Overall, these findings suggest that tailoring vernalization duration and nutrition to the genetic background may substantially improve the efficiency of sugar beet speed breeding. Full article

Complex networks have become a fundamental paradigm for modeling real-world systems. Synchronization of such networks, particularly under hyperchaotic dynamics, presents a significant control challenge due to the high-dimensional state space and multiple positive Lyapunov exponents. This paper addresses the driver node selection problem in a 4D Hyperchaotic Lorenz complex network, formulating it as a constrained binary optimization task. We evaluate a pool of advanced metaheuristics, including the quantum genetic algorithm (QGA), seahorse optimizer (SHO), and artificial bee colony (ABC), across multiple network experiments conducted over 30 independent runs to guarantee statistical validity. The performance of these solvers is rigorously benchmarked against traditional topological heuristics, a random selection baseline comprising 600 feasible configurations, and verified through Wilcoxon statistical testing. Furthermore, addressing computational sustainability, we introduce a “Green-Artificial Intelligence” architecture based on dual-tier structured query language memoization (SQL-memoization) and provide a detailed runtime comparison evaluating its efficiency. The empirical results indicate that swarm-intelligence methods such as ABC and SHO exhibit robust competitive performance in minimizing synchronization errors while the Green-AI framework consistently and drastically reduces the computation of the repetitive simulations. Full article