Search Results (803)

Efficient object detection is crucial to real-time monitoring applications such as autonomous driving or security systems. Modern RGB cameras can produce high-resolution images for accurate object detection. However, increased resolution results in increased network latency and power consumption. To minimise this latency, Convolutional Neural Networks (CNNs) often have a resolution limitation, requiring images to be down-sampled before inference, causing significant information loss. Event-based cameras are neuromorphic vision sensors with high temporal resolution, low power consumption, and high dynamic range, making them preferable to regular RGB cameras in many situations. This project proposes the fusion of an event-based camera with an RGB camera to mitigate the trade-off between temporal resolution and accuracy, while minimising power consumption. The cameras are calibrated to create a multi-modal stereo vision system where pixel coordinates can be projected between the event and RGB camera image planes. This calibration is used to project bounding boxes detected by clustering of events into the RGB image plane, thereby cropping each RGB frame instead of down-sampling to meet the requirements of the CNN. Using the Common Objects in Context (COCO) dataset evaluator, the average precision (AP) for the bicycle class in RGB scenes improved from 21.08 to 57.38. Additionally, AP increased across all classes from 37.93 to 46.89. To reduce system latency, a novel object detection approach is proposed where the event camera acts as a region proposal network, and a classification algorithm is run on the proposed regions. This achieved a 78% improvement over baseline. Full article

This study investigates economic decision-making behaviors among welfare recipients in Miami, Florida, by leveraging well-established experimental protocols: the Guessing Game, the Prudence Measurement Task, the Risk Aversion Task, and the Stag Hunt Game. For this purpose, our study defines financial decisions as the underlying individual preferences that serve as validated proxies for savings behavior, debt management, job-search intensity, and participation in cooperative finance. A central objective is to compare the behavior of welfare recipients to that of undergraduate students, a cohort typically used in experimental economics research. The analysis reveals significant differences between the two groups in strategic thinking and coordination, particularly across ethnic and gender lines. Non-Hispanic/Latino participants in Miami displayed significantly higher average guesses in the Guessing Game compared to their counterparts in Tucson, indicating potential discrepancies in the depth of strategic reasoning. Additionally, female participants in Tucson exhibited higher levels of coordination in the Stag Hunt Game compared to females in Miami, suggesting variance in cooperative behavior between these groups. Despite these findings, regression models demonstrate that location, gender, and ethnicity collectively account for only a small fraction of the observed variance, as evidenced by low R² values and substantial mean squared errors across all games. These results suggest that individual heterogeneity, rather than broad demographic variables, may be more influential in shaping economic decisions. This study underscores the complexity of generalizing findings from traditional student samples to more diverse populations, highlighting the need for further investigation into the socioeconomic factors that drive financial decision-making. Full article

Organic agriculture is a complex, knowledge-intensive system, deeply aligned with sustainability goals. While the field has seen promising growth and innovation, it still grapples with significant challenges, particularly in how knowledge is shared, applied, and supported structurally within sustainability-oriented frameworks. To fill this gap, a systematic review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, screening publications from the Web of Science and Scopus databases. A total of 39 scientific studies were analysed using content analysis and a bibliometric methodological approach. Findings reveal a balanced geographical distribution of studies and a dominance of qualitative methodologies. While farmers, advisors, and researchers are frequently involved in data collection, broader stakeholder engagement is limited. Key actors—research institutions, advisory services, and sectoral organisations—emerge as central to driving innovation and enhancing farmers’ access to actionable knowledge. However, the analysis identifies three core challenges: tailoring knowledge and innovation to diverse farming contexts; strengthening the intermediary role of advisors to bridge science and practice; and integrating organic agriculture more explicitly within the frameworks of sustainability and agroecology. Future research should focus on improving participatory dissemination strategies and strengthening intermediary roles to advance sustainability-driven innovation in organic agriculture. Full article

Before the onset of global warming concerns, the idea of manufacturing electric vehicles on a large scale was not widely considered. However, electric vehicles offer several advantages that have garnered attention. They are environmentally friendly, with simpler drive systems compared to traditional fossil fuel vehicles. Additionally, electric vehicles are highly efficient, with an efficiency of around 90%, in contrast to fossil fuel vehicles, which have an efficiency of about 30% to 35%. The higher energy efficiency of electric vehicles contributes to lower operational costs, which, alongside regulatory incentives and shifting consumer preferences, has increased their strategic importance for many vehicle manufacturers. In this paper, we present a thematic literature review on electric vehicles charging station location planning and scheduling. A systematic literature review across various data sources in the area yielded ninety five research papers for the final review. The research results were analyzed thematically, and three key directions were identified, namely charging station deployment and placement, optimal allocation and scheduling of EV parking lots, and V2G and smart charging systems as the top three themes. Each theme was further investigated to identify key topics, ongoing works, and future trends. It has been found that optimization methods followed by simulation and multi-criteria decision-making are most commonly used for EV infrastructure planning. A multistakeholder perspective is often adopted in these decisions to minimize costs and address the range anxiety of users. The future trend is towards the integration of renewable energy in smart grids, uncertainty modeling of user demand, and use of artificial intelligence for service quality improvement. Full article

The gradual recovery of the economy has positioned the digital economy as a vital force driving global economic growth. However, the sustainability of this emerging economic sector is being tested by unexpected systemic shocks. There is a scarcity of research on the factors influencing the sustainable development of the digital economy. Therefore, developing a framework to assess the sustainability of the digital economy is significant. Building on previous research, this study established an evaluation system that extracts key indicators across four dimensions: society, the economy, the environment, and technology. Data were then collected through questionnaires and in-depth interviews with experts. Subsequently, this study employed the fuzzy Decision-Making Trial and Evaluation Laboratory–Analytical Network Process (fuzzy DANP) method to determine the weight of each indicator and used the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method to evaluate the sustainability of the digital economy in three cities. Sensitivity analysis was conducted to validate this comprehensive evaluation method. The results indicate that society and the economy are the two most crucial dimensions, while the regional economic development level, enterprise innovation culture, and digital divide are the top three indicators affecting the sustainable development of the digital economy industry. This work suggests that the digital economy industry should enhance regional economic levels, strengthen technological and innovative corporate cultures, and narrow the digital divide to achieve the goal of sustainable development in the digital economy sector. Full article

The rapid development of generative artificial intelligence (AI) systems such as ChatGPT (GPT-4) could transform teaching and learning. Yet, integrating these tools requires insight into what drives students to adopt them. Research on ChatGPT acceptance has so far focused on university settings, leaving school contexts underexplored. This study addresses the gap by surveying 506 upper secondary students in Baden-Württemberg, Germany, using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Performance expectancy, habit and hedonic motivation emerged as strong predictors of behavioral intention to use ChatGPT for school purposes. Adding personality traits and personal values such as conscientiousness or preference for challenge raised the model’s explanatory power only marginally. The findings suggest that students’ readiness to employ ChatGPT reflects the anticipated learning benefits and enjoyment rather than the avoidance of effort. The original UTAUT2 is therefore sufficient to explain students’ acceptance of ChatGPT in school contexts. The results could inform educators and policy makers aiming to foster the reflective and effective use of generative AI in instruction. Full article

Remanufacturing in reverse logistics can not only support sustainable development but also provide a tractable way to achieve carbon neutrality. This study evaluates whether an original equipment manufacturer (OEM) should remanufacture outsource or authorize this reverse channel activity in the presence of government subsidies. Additionally, the model considers the equilibrium acquisition quantities, collection rates, prices, and effects of government subsidy under three reverse channel options: centralizing remanufacturing, outsourcing remanufacturing, and authorization remanufacturing. The analysis indicates that (i) a centralized approach with manufacturing and remanufacturing operations under a fixed government subsidy is always in the interest of the supply chain; (ii) that for the profit-maximizing third-party remanufacturer (3PR), the differentials in variable collection costs drive the strategy choice, and that a higher fixed scaling parameter of the collection cost favors outsourcing; and (iii) when the government aspires to reduce environmental effects and subsidy payments, the OEM and government have different reverse channel choice preferences. Surprisingly, profitability and environmental goals align under a high consumer acceptance of the remanufactured product. This paper extends the understanding of the remanufacturing strategy of an OEM and provides new insights on which reverse channel is optimal. Full article

Amid the increasing complexity of urban risks, urban parks not only serve ecological and recreational functions but are increasingly becoming a critical spatial foundation supporting public psychological resilience and social recovery. This study aims to systematically evaluate the daily adaptability of urban parks in the context of micro-risks. The research integrates the theories of “restorative environments,” environmental safety perception, urban resilience, and social ecology to construct a five-dimensional framework for perceived resilience, encompassing resilience, safety, sociability, controllability, and adaptability. Additionally, a dynamic feedback mechanism of perception–behavior–reperception is introduced. Methodologically, the study utilizes the Fuzzy Delphi Method (FDM) to identify 17 core indicators, constructs a causal structure and weighting system using DEMATEL-based ANP (DANP), and further employs the VIKOR model to simulate public preferences in a multi-criteria decision-making process. Taking three representative urban parks in Guangzhou as empirical case studies, the research identifies resilience and adaptability as key driving dimensions of the system. Factors such as environmental psychological resilience, functional diversity, and visual permeability show a significant path influence and priority intervention value. The empirical results further reveal significant spatial heterogeneity and group differences in the perceived resilience across ecological, neighborhood, and central park types, highlighting the importance of context-specific and user-adaptive strategies. The study finally proposes four optimization pathways, emphasizing the role of feedback mechanisms in enhancing urban park resilience and shaping “cognitive-friendly” spaces, providing a systematic modeling foundation and strategic reference for perception-driven urban public space optimization. Full article

Meat quality is a critical determinant of consumer preference and economic value in the livestock industry. However, the relationship between carcass performance and meat quality remains poorly understood. In our study, we conducted an integrative analysis of transcriptomics and metabolomics to investigate the molecular mechanisms underlying meat quality differences in Hu sheep with high (HHS, n = 10) and low (LHS, n = 10) carcass performance. Phenotypic analysis revealed that the HHS group exhibited superior meat quality traits, including higher intramuscular fat (IMF) content (reflected in elevated marbling scores), along with lower shear force, drip loss, and cooking loss, compared to the LHS group. Transcriptomic analysis identified 376 differentially expressed genes (DEGs) enriched in pathways linked to lipid metabolism, such as the PPAR signaling pathway and long-chain fatty acid metabolic process. Weighted gene co-expression network analysis (WGCNA) revealed important modules and key genes (e.g., ELOVL6, PLIN1, and ARHGEF2) associated with meat quality traits. Metabolomic profiling identified 132 differentially accumulated metabolites (DAMs), with significant enrichment in amino acid metabolism pathways, including D-amino acid metabolism, arginine biosynthesis, and glycine, serine, and threonine metabolism. Integrative analysis of transcriptomic and metabolomic data highlighted six co-enriched pathways, such as the mTOR signaling pathway and amino acid metabolism, underscoring their role in regulating meat quality. These findings provide valuable insights into the genetic and metabolic networks driving meat quality variation and offer potential biomarkers for genetic selection and nutritional strategies to enhance both carcass yield and eating quality in Hu sheep. This research enhances knowledge of the molecular basis of meat quality and supports precision breeding in livestock production. Full article

This study explores the application of a Long Short-Term Memory (LSTM) model for river water level forecasting, emphasizing the critical role of hyper-parameters optimization. Similar to physical and numerical rainfall-runoff models, LSTM relies on parameters to drive its data-driven modeling process. The performance of such models is highly sensitive to the chosen hyper-parameters, making their optimization essential. To address this, three algorithms—Grid Search, Random Search, and Bayesian Search—were applied to identify the most effective hyper-parameter combinations. Cross-correlation analysis revealed that average rainfall had a stronger influence on river water levels than upstream point rainfall, leading to its selection as the model input. The optimization focused on five key hyper-parameters: neuron units, learning rate, dropout rate, number of epochs, and batch size. Results showed that, while Grid Search required the most computational time, both Random and Bayesian Search were more efficient. Notably, Bayesian Search yielded the best predictive performance with minimal time cost, making it the preferred optimization method. Additionally, reproducible LSTM simulations were conducted to ensure the consistency and practical applicability of the forecasting in real-world scenarios. Overall, Bayesian Search is recommended for optimizing LSTM models due to its balance of accuracy and computational efficiency in hydrological forecasting. Full article

Background: Current cosmological fits typically assume a direct relation between cosmic time (t) and the scale factor ($a\left(t\right)$), yet this ansatz remains largely untested across diverse observations. Objectives: We (i) test whether a single power-law scaling ($a\left(t\right)\propto t^{\alpha }$) can reproduce late- and early-time cosmological data and (ii) explore whether a dynamically evolving ($\alpha \left(t\right)$), modeled as a scalar–tensor field, naturally induces directional asymmetry in cosmic evolution. Methods: We fit a constant-$\alpha$ model to four independent datasets: 1701 Pantheon+SH0ES supernovae, 162 gamma-ray bursts, 32 cosmic chronometers, and the Planck 2018 TT spectrum (2507 points). The CMB angular spectrum is mapped onto a logarithmic distance-like scale ($\mu ={log}_{10}{D}_{\ell }$), allowing for unified likelihood analysis. Each dataset yields slightly different preferred values for $H_0$ and $\alpha$; therefore, we also perform a global combined fit. For scalar–tensor dynamics, we integrate $\alpha \left(t\right)$ under three potentials—quadratic, cosine, and parity breaking (${\alpha }^{3}sin\alpha$)—and quantify directionality via forward/backward evolution and Lyapunov exponents. Results: (1) The constant-$\alpha$ model achieves good fits across all datasets. In combined analysis, it yields $H_0\simeq 70\mathrm{km}{\mathrm{s}}^{-1}{\mathrm{Mpc}}^{-1}$ and $\alpha \simeq 1.06$, outperforming $\Lambda$CDM globally ($\Delta \mathrm{AIC}\simeq 401254$), though $\Lambda$CDM remains favored for some low-redshift chronometer data. High-redshift GRB and CMB data drive the improved fit. Numerical likelihood evaluations are approximately three times faster than for $\Lambda$CDM. (2) Dynamical $\alpha \left(t\right)$ models exhibit time-directional behavior: under asymmetric potentials, forward evolution displays finite Lyapunov exponents (${\lambda }_L\sim {10}^{-3}$), while backward trajectories remain confined (${\lambda }_L<0$), realizing classical arrow-of-time emergence without entropy or quantum input. Limitations: This study addresses only homogeneous background evolution; perturbations and physical derivations of potentials remain open questions. Conclusions: The time-scaling approach offers a computationally efficient control scenario in cosmological model testing. Scalar–tensor extensions naturally introduce classical time asymmetry that is numerically accessible and observationally testable within current datasets. Code and full data are available. Full article

Global warming is driving significant changes in aquatic ecosystems, where temperature fluctuations influence biological processes across multiple levels of organisation. As ectothermic organisms, fish are particularly susceptible, with even minor thermal shifts affecting their metabolism, behaviour, and overall fitness. Understanding these responses is essential for evaluating the ecological and evolutionary consequences of climate change. This study investigates the effects of acute (4-day) and chronic (21-day) exposure to three temperature regimes—18 °C (low), 26 °C (control), and 34 °C (high)—on the spatio-temporal shoaling behaviour of adult zebrafish (Danio rerio). Groups of four fish were tested for six minutes in water maintained at the same temperature as their prior acclimation. Shoaling behaviour was assessed by analysing shoal structure—encompassing shoal dimensions and cohesion—as well as spatial positioning. Parameters measured included inter-fish distance, shoal volume, shoal area, homogeneity index, distance to the centroid, and the shoal’s vertical and horizontal distribution. Results revealed complex behavioural changes influenced by both temperature and duration of exposure. At 18 °C, zebrafish showed a marked preference for the bottom zone and exhibited no significant temporal modulation in exploratory behaviour—patterns indicative of heightened anxiety-like responses. In contrast, exposure to 34 °C resulted in increased shoal cohesion, particularly under chronic conditions, and a progressive increase in environmental exploration over the six-minute test period. This enhancement in exploratory activity was especially evident when compared to the first minute of the test and was characterised by greater vertical movement—reflected in the increased use of the upper zone—and broader horizontal exploration, including more frequent occupation of peripheral areas. These findings align with previous research linking thermal variation to neurobiological and proteomic alterations in zebrafish. By elucidating how temperature modulates social behaviour in ectotherms, this study offers valuable insights into the potential behavioural impacts of climate change on aquatic ecosystems. Full article

Large Language Models (LLMs) are driving a revolution in the way we access information, yet there remains a lack of exploration to capture people’s information interaction preferences in LLM environments. In this study, we designed a comprehensive analysis framework to evaluate students’ prompt texts during a professional academic writing task. The framework includes a dimensionality reduction and classification method, three topic modeling approaches, namely BERTopic, BoW-LDA, and TF-IDF-NMF, and a set of evaluation criteria. These criteria assess both the semantic quality of topic content and the structural quality of clustering. Using this framework, we analyzed 288 prompt texts to identify key topics that reflect students’ information interaction behaviors. The results showed that students with low academic performance tend to focus on structural clarity and task execution, including task inquiry, format specifications, and methodological search, indicating that their interaction mode is instruction-oriented. In contrast, students with high academic performance interact with LLM not only in basic task completion but also in knowledge integration and the pursuit of novel ideas. This is reflected in more complex topic levels and diverse, innovative keywords. It shows that they have stronger self-planning and self-regulation abilities. This study provides a new approach to studying the interaction between students and LLM in engineering education by using natural language processing to process prompts, contributing to the exploration of the performance of students with different performance levels in professional academic writing using LLM. Full article

In informal economic contexts, migrant entrepreneurs have been extolled as highly resilient and adaptable. This study critically investigates the adverse psychological foundations inherent in such enterprises, focusing on how dark triad personality traits emerge in the leadership orientations of marginal migrant entrepreneurs. Following a qualitative ethnographic approach, this research engaged 10–15 migrant employees through participant observation, field notes, and semi-structured interviews in an informal economic context. Thematic analysis revealed five dominant patterns: narcissistic leadership with entitlement and emotional disrespect; Machiavellian behavior of manipulation and deception; psychopathic detachment in emotional callousness; absence of light triad actions such as empathy, humility, and selflessness; and moral disengagement through rationalizations such as “everyone does it” or system blame. Migrant business owners prefer to rationalize their exploitative acts as being necessary for economic survival, thus legitimizing immoral conduct and suppressing moral self-regulation. The findings indicate that marginality not only drives entrepreneurial innovation, but also has the potential to create exploitative inclinations that are institutionally and morally unchecked. Solving this issue requires not only mere psychological awareness, but also systematic reforms that foster ethical robustness and emotional sensitivity. This study ultimately asserts the need to reframe migrant entrepreneurship discourse, including both ethical and psychological accountability. Full article

Rural areas are experiencing significant changes in socio-economic and spatial patterns, and research on the characteristics of rural restructuring is conducive to the planning of rural revitalization. However, few studies have focused on the changes in regional development imbalances in the process of rural restructuring. This study aims to explore whether rural restructuring mitigates or exacerbates existing regional disparities, and to assess the degree of coordination among economic, social, and spatial restructuring dimensions. In this study, the evolution of spatio-temporal patterns and divergence characteristics of unevenly developed regions in the process of rural restructuring from 2010 to 2020 were investigated by using the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) model and the coupled coordination model. We found the following: (1) The level of rural development has increased significantly and the overall pattern has not changed. Meanwhile, the degree of regional imbalance has deepened, evolving from a low level of disequilibrium to a pattern of high levels but more pronounced spatial polarization. (2) The impacts of different dimensions of rural restructuring on regional imbalance are not consistent, and the social and spatial dimensions are significantly more unbalanced than the economic dimension. (3) The analysis of the driving mechanism shows that there are significant spatial and temporal differences between a variety of driving factors, the strength of their role, positive and negative have evolved in stages, and the transition from a government-led to a market-driven trend is gradually obvious. In the future, rural planning should pay more attention to resource inputs in the social and spatial dimensions, and improve the equilibrium of the social and spatial dimensions, which is more conducive to mitigating the trend of regional polarization. Full article