Search Results (8,628)

Monocular 3D object detection (Mono3D) is essential for autonomous driving and augmented reality, yet its performance degrades significantly at night due to the scarcity of annotated nighttime data. In this paper, we investigate the use of style transfer for nighttime data augmentation and evaluate its effect on individual components of 3D detection. Using CycleGAN, we generated synthetic night images from daytime scenes in the nuScenes dataset and trained a modular Mono3D detector under different configurations. Our results show that training solely on style-transferred images improves certain metrics, such as AP@0.95 (from 0.0299 to 0.0778, a 160% increase) and depth error (11% reduction), compared to daytime-only baselines. However, performance on orientation and dimension estimation deteriorates. When real nighttime data is included, style transfer provides complementary benefits: for cars, depth error decreases from 0.0414 to 0.021, and AP@0.95 remains stable at 0.66; for pedestrians, AP@0.95 improves by 13% (0.297 to 0.336) with a 35% reduction in depth error. Cyclist detection remains unreliable due to limited samples. We conclude that style transfer cannot replace authentic nighttime data, but when combined with it, it reduces false positives and improves depth estimation, leading to more robust detection under low-light conditions. This study highlights both the potential and the limitations of style transfer for augmenting Mono3D training, and it points to future research on more advanced generative models and broader object categories. Full article

Background: Only a limited number of studies have examined the effects of short-term, strength–speed-oriented velocity-based training (VBT) on lower-body power in female junior volleyball players and elite female artistic gymnasts. The present study aimed to investigate the impact of a four-week VBT intervention on jump performance and force–velocity characteristics in these athletes. Methods: Seven junior female volleyball players (age: 17.4 ± 0.9 years; height: 179.4 ± 6.5 cm; weight: 74.01 ± 3.5 kg) (top-league team members), and seven elite female artistic gymnasts (age: 17.6 ± 2.9 years; height: 159.6 ± 7.2 cm; weight: 59.3 ± 6.3 kg) (National Team members) completed two weekly training sessions for four weeks, each consisting of four sets of six repetitions of parallel back squats (PBSs) and hip thrusts (HTs). Training loads were regulated using barbell velocity targets (PBSs: 0.46–0.72 m/s; HTs: 0.36–0.60 m/s). Pre- and post-intervention assessments included loaded (15–60% body mass) and unloaded squat jumps (SJs) and countermovement jumps (CMJs) to determine peak power output, jump height, and force–velocity profiles. Results: Volleyball players showed significant improvements in peak power predominantly during loaded SJs (SJ45%: +5.5%, p < 0.01; SJ60%: +5.7%, p < 0.05), whereas gymnasts exhibited greater gains in loaded CMJs (CMJ60%: +7.7%, p < 0.01). In contrast, unloaded SJ and CMJ performances remained largely unchanged for all athletes. Both groups demonstrated a significantly steeper post-intervention force–velocity profile (p < 0.001), indicating an enhanced capacity to produce force at lower movement velocities under external loading. Conclusions: Strength–speed-oriented VBT was effective in improving power production under loaded conditions but had limited transfer to unloaded jump performance. These findings highlight the necessity of subsequent training blocks emphasizing high-velocity, sport-specific movements to optimize explosive performance. Future studies should further investigate low-velocity-loss training protocols as a potential means of enhancing unloaded jump outcomes. Full article

Wide-bandgap (WBG) perovskite solar cells (PSCs) are critical for high-efficiency tandem photovoltaic devices, but their practical application is severely limited by phase separation and poor film quality. To address these challenges, this study proposes a dual-additive passivation strategy using potassium thiocyanate (KSCN) and potassium chloride (KCl) to synergistically optimize the crystallinity and defect state of WBG perovskite films. The selection of KSCN/KCl is based on their complementary functionalities: K⁺ ions occupy lattice vacancies to suppress ion migration, Cl⁻ ions promote oriented crystal growth, and SCN⁻ ions passivate surface defects via Lewis acid-base interactions. A series of KSCN/KCl concentrations (relative to Pb) were tested, and the effects of dual additives on film properties and device performance were systematically characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), photoluminescence (PL), space-charge-limited current (SCLC), current-voltage (J-V), and external quantum efficiency (EQE) measurements. Results show that the dual additives significantly enhance film crystallinity (average grain size increased by 27.0% vs. control), reduce surface roughness (from 86.50 nm to 24.06 nm), and passivate defects-suppressing non-radiative recombination and increasing electrical conductivity. For WBG PSCs, the champion device with KSCN (0.5 mol%) + KCl (1 mol%) exhibits a power conversion efficiency (PCE) of 16.85%, representing a 19.4% improvement over the control (14.11%), along with enhanced open-circuit voltage (Voc: +2.8%), short-circuit current density (Jsc: +6.7%), and fill factor (FF: +8.9%). Maximum power point (MPP) tracking confirms superior operational stability under illumination. This dual-inorganic-additive strategy provides a generalizable approach for the rational design of stable, high-efficiency WBG perovskite films. Full article

This study presents SAIL-Y (Sailing Artificial Intelligence for Learning in Youth), a novel gender-focused recommender system designed to promote female participation in STEM careers through data-driven guidance. Drawing inspiration from the metaphor of an academic journey as a voyage, SAIL-Y functions as a digital compass—leveraging socioeconomic profiles and standardised test results (Saber 11, Colombia) to help students navigate career decisions in high-impact academic fields. SAIL-Y integrates multiple machine learning strategies, including collaborative filtering, bootstrapped data augmentation to rebalance gender representation, and socioeconomic-aware conditioning, to generate personalised and bias-controlled career recommendations. The system is explicitly designed to skew recommendations toward STEM disciplines for female students, countering systemic underrepresentation in these fields. Using a dataset of 332,933 Colombian students (2010–2021), we evaluate the performance of different recommendation architectures under the SAIL-Y framework. The results show that a gender-oriented recommender design increases the proportion of STEM career recommendations for female students by up to 25% compared to reference models. Beyond technical contributions, this work proposes an ethically aligned paradigm for educational recommender systems—one that empowers rather than merely predicts. SAIL-Y is thus envisioned as both a methodological tool and a socio-educational intervention, supporting more equitable academic journeys for future generations. Full article

Previous studies on building energy performance have focused on single buildings or theoretical scenarios, remaining largely at the building scale and emphasizing envelope parameters. This study addresses this gap by systematically examining morphological parameters at the urban block scale through a five-step framework derived from the historical zoning evolution of Gaziantep (Turkiye), a city in a hot–dry climate. Four representative neighborhoods, reflecting different planning periods, were modeled in DesignBuilder v6.1 under a standardized envelope defined by national regulations. The analysis considered building orientation (15° vs. 45°), number of storeys (5–15), inter-building distance, and number of apartments per floor. Simulation results indicate that cooling energy demand is significantly higher than heating, with potential savings of up to 22% in total energy consumption depending on urban fabric parameters. The Alleben neighborhood, characterized by the oldest planned fabric, consumed 30% less cooling energy compared to the other regions. Orientation alone increased cooling demand by up to 12%. At the same time, compact urban forms reduced loads through mutual shading, while higher apartments per floor increased energy use due to the larger façade area and internal gains. By linking historical zoning evolution with block-scale simulations, this study provides a transferable framework that highlights the decisive role of planning parameters and offers practical guidance for climate-sensitive urban development. Full article

Workplace accidents and injuries continue to be a challenge in high-risk industries such as healthcare, where safety is a daily critical concern. Although organizational factors such as safety climate have been well-established as predictors of safety-related outcomes, less is known about the role of individual differences in workplace safety. This research investigates safety locus of control, which captures an employee’s tendency to believe that their safety-oriented behaviors actually play a role in preventing safety incidents. Individuals with a highly internal safety locus of control tend to recognize the importance of their own and others’ safety actions for promoting workplace safety and preventing safety-related incidents from occurring in their workplace, whereas employees with low internal safety locus of control tend to believe that adverse safety outcomes have less to do with employee behavior and are more the result of luck or chance (i.e., have a more external orientation). Across three studies (with a total of 792 participants), we developed a measure for assessing safety locus of control (Study 1), evaluated its construct validity (Study 2), and measured its incremental validity on workplace safety beyond other important constructs like safety climate (Study 3). Results suggest that safety locus of control helps to explain critical workplace safety outcomes (such as safety performance) beyond environmental factors such as safety climate alone and plays an influential role on well-established safety processes within the workplace. This research highlights the importance of considering individual differences alongside environmental factors in workplace safety models. Full article

Ultra-high-performance concrete (UHPC) exhibits exceptional mechanical properties and durability but faces challenges such as high heat of hydration and limited stiffness. Incorporation of coarse aggregates offers a potential solution; however, it alters the dispersion of steel fibers, thereby affecting the mechanical performance of UHPC under different loading conditions. This study systematically investigates the influence of coarse aggregates on UHPC performance under different loading conditions, including four-point bending, uniaxial compression, and triaxial compression tests. The spatial distribution of steel fibers was quantitatively analyzed via image analysis to elucidate changes induced by CA incorporation. Results reveal that with 20 vol% coarse aggregate (10 mm), UHPC’s flexural strength is essentially unchanged (≈23 MPa), whereas flexural toughness decreases by about one-third. This toughness loss is linked to a slight increase in the fiber orientation angle (from 48.77° to 48.90°) and reduced continuity, which together weaken crack-bridging. Moreover, both flexural strength and toughness are governed primarily by the local steel-fiber content within the tensile zone. Under triaxial compression, confinement dominates: as confining pressure rises from 0 to 30 MPa, compressive strength increases by approximately 32.6%, 52.6%, and 71.3%. Due to crack-suppression by confinement overlapping with fiber bridging, the contribution of fibers to strength gains decreases with increasing confinement, and the competing and complementary interaction between coarse aggregate and steel fibers correspondingly weakens. These findings clarify the coupled effects of coarse aggregate and fibers in UHPC-CA, guide mix-design optimization for improved mechanical performance, and support broader practical adoption. Full article

The concept of accountability is central to understanding how sustainable corporate governance (SCG) structures shape organizational behavior, legitimacy, and firm performance in the pursuit of sustainability goals. While widely invoked, accountability is often treated inconsistently across governance contexts—oscillating between technical compliance and ethical legitimacy. This paper provides a structured conceptual review of how accountability is framed and operationalized within sustainability governance, with a specific focus on its implications for sustainable performance, corporate sustainability strategies, and governance effectiveness. Based on a qualitative analysis of thirteen peer-reviewed articles published between 2006 and 2025, the study identifies three dominant conceptual clusters: compliance-oriented, legitimacy-oriented, and hybrid approaches. Each cluster reflects different accountability logics and governance mechanisms—ranging from ESG metrics and sustainability reporting frameworks to participatory forums and stakeholder engagement processes that support sustainable development. The article synthesizes theoretical contributions from institutional theory, stakeholder theory, and deliberative democracy to explore how accountability serves as a bridge between formal governance mechanisms and legitimacy claims. A conceptual framework is proposed to illustrate the tensions and complementarities between compliance-driven and legitimacy-driven governance models in sustainability contexts. By deepening the theoretical understanding of accountability in corporate sustainability, this review contributes to the literature on ESG governance, social and environmental reporting, and the legitimacy–performance nexus in corporate settings. The findings offer a foundation for advancing more inclusive, transparent, and sustainability-oriented corporate governance practices in response to global sustainability challenges and the Sustainable Development Goals (SDGs). Full article

Tourism performance often depends on the joint provision of built (“hard”) and environmental (“green”) infrastructure, yet their combined effects are not well established. Using official data for Saudi Arabia’s 13 regions (2023–2024), this study constructs composite hard and green indices, estimates ordinary least squares models with heteroskedasticity-consistent inference, and probes spatial heterogeneity using geographically weighted regression (exploratory) alongside k-means/hierarchical clustering. Hard infrastructure is the strongest and most consistent correlate of overnight visitors and spending, whereas green infrastructure exhibits non-positive marginal effects over the observed range of hard capacity; a negative, statistically significant Hard × Green interaction indicates diminishing returns to greening as built capacity increases. Clustering differentiates metropolitan hubs from nature-oriented regions, underscoring place-specific policy needs. Practically, results support sequencing prioritizing foundational access and basic accommodation in under-served regions, quality upgrades and public-realm enhancement in mature centers, and targeted green interventions where marginal gains are greatest. Key limitations (cross-sectional design; coarse green metrics) motivate richer environmental indicators and longitudinal data to clarify dynamics and thresholds over time. Full article

The deployment of autonomous robots is critical for advancing sustainable agriculture, but their effectiveness hinges on visual perception systems that can reliably operate in natural, real-world environments. Selecting an appropriate vision model for these robots requires a practical evaluation that extends beyond standard accuracy metrics to include critical deployment factors such as computational efficiency, energy consumption, and robustness to environmental disturbances. To address this need, this study presents a deployment-oriented benchmark of state-of-the-art You Look Only Once (YOLO)-based models for orange detection and segmentation. Following a systematic process, the selected models were evaluated on a unified public dataset, annotated to rigorously assess real-world challenges. Performance was compared across five key dimensions: (i) identification accurac, (ii) robustness, (iii) model complexity, (iv) execution time, and (v) energy consump-tion. The results show that the YOLOv5 variants achieved the most accurate detection and segmentation. Notably, YOLO11-based models demonstrated strong and consistent results under all disturbance levels, highlighting their robustness. Lightweight architectures proved well-suited for resource-constrained operations. Interestingly, custom models did not consistently outperform their baselines, while nanoscale models showed demonstra-ble potential for meeting real-time and energy-efficient requirements. These findings offer valuable, evidence-based guidelines for the vision systems of precision agriculture robots. Full article

Drawing on prior research, this study analyzes panel data from listed companies (2013–2023) to examine how chairpersons’ local social connections affect corporate green innovation. Specifically, it explores how such networks provide access to resources and policy advantages through social capital, thereby shaping firms’ green innovation. The findings reveal that local chairpersons negatively affect firms’ green innovation. Firms led by local chairpersons undertake significantly fewer green innovation initiatives than those with non-local leaders. Moreover, ESG performance and a strong legal environment can mitigate the negative impact of local chairpersons on green innovation. In contrast, stable executive teams may alleviate the adverse effect of local chairpersons on corporate green innovation by curbing myopic managerial behavior and reinforcing a long-term strategic orientation. Full article

Traditional teaching methods are often far from aligning with professional practice demands. Team-Based Learning (TBL), a variant of Problem-Based Learning, may foster motivation, autonomy, and deeper knowledge acquisition, especially in those educative contexts linked to practical knowledge. The objective of the present research was to explore the impact of a TBL program with digital support on Sport Sciences students’ psychological and learning outcomes. A quasi-experimental design with pre- and post-tests was applied to 68 fourth-year students (mean age = 21.45 ± 1.57 years). The intervention spanned 12 weeks, where the students had to solve specific case studies linked to the theoretical content of the subject and its applicability. Variables measured included motivational climate, satisfaction of basic psychological needs, intrinsic motivation, transversal competences, and academic performance. Significant improvements were observed in task- and ego-oriented climate, autonomy, competence, relatedness, knowledge scores, and competence in scientific searches and academic dissemination (p < 0.05). No significant changes were found in intrinsic motivation or audiovisual material competence. Sex influenced several outcomes, while project marks and prior transversal skills did not. TBL combined with digital tools enhanced learning outcomes and key psychological needs, though intrinsic motivation remained unchanged. Findings highlight the value of active methodologies in higher education, while underscoring the need for long-term, broader studies. Full article

Particle swarm optimization (PSO) is a bio-inspired stochastic optimization algorithm that simulates the foraging behavior of birds. Despite its simplicity and efficiency, PSO often suffers from premature convergence and a poor balance between exploration and exploitation. These drawbacks mainly arise from its limited learning sources and rigid position update scheme. To address these issues, this paper proposes an enhanced PSO framework, termed Exemplar Learning and Memory Retrieval-Based Particle Swarm Optimization (EMPSO). The design of EMPSO is inspired by the learning, memory, and adaptation mechanisms observed in biological collectives. It integrates three complementary strategies to improve swarm intelligence. First, an elite exemplar learning mechanism aggregates the positional information of top-performing particles to construct a more reliable guidance vector. Second, a memory recall strategy retains exemplars that have recently contributed to global improvements and reuses them probabilistically with a recency bias, thus enabling effective knowledge inheritance. Third, an adaptive position update scheme assigns exploration- or exploitation-oriented behaviors to particles based on fitness ranking, promoting dynamic role differentiation within the swarm. Comprehensive experiments on the CEC2017 and CEC2022 benchmark suites demonstrate that EMPSO consistently outperforms six representative algorithms. Furthermore, applications to three engineering design problems and the optimal PMU placement task verify its robustness and practical effectiveness. Full article

In the context of the deep restructuring of the global industrial chain and the concurrent pursuit of green and sustainable development, enterprises need to secure long-term, reliable supply chain competitiveness. The burgeoning wave of digitalization is simultaneously reshaping industry landscapes. Based on a sample of the Chinese manufacturing sector, this study explores how supply chain digital transformation enhances commercial credit financing performance by improving corporate adjustment capability. The research finds that supply chain digital transformation strengthens a firm’s commercial credit financing capacity through a dual-core mediating mechanism of corporate adjustment capability: (1) enhancing the adjustment capability of operational management, which mitigates the negative impact of cost stickiness on financing; (2) enhancing the adjustment capability of organizational management, which amplifies the positive effect of organizational resilience on financing. The study further reveals key moderating effects: (1) External Governance: Strong ESG performance strengthens the financing effect of digitalization by building reputational capital. High industry competition strengthens the financing effect by prompting firms to optimize operational efficiency. (2) Internal Endowments: Environmental risk aversion stemming from a firm’s polluting nature significantly weakens the credit supply effect of digitalization. The market-oriented foundation underpinning private ownership effectively activates the credit supply effect of digitalization. This study constructs an integrated pathway model of “Digital Transformation–Corporate Adjustment Capability–Supply Chain Credit Access.” It provides a research perspective for understanding how digitalization reshapes the logic of supply chain finance and offers empirical evidence for pathways empowering enterprises through digital transformation. Full article

The banking sector faces increasing pressure to balance financial performance with sustainability goals amid ongoing digital transformation, regulatory reform, and societal expectations for ethical responsibility. Entrepreneurial leadership has emerged as a pivotal approach for addressing these challenges; however, the behavioral and contextual mechanisms through which it shapes sustainability remain insufficiently understood. Drawing on Social Learning Theory (SLT), this study investigates how and when entrepreneurial leadership enhances sustainable bank performance through the mediating role of employee creativity and the moderating influence of an innovation-oriented climate. A two-wave multi-source survey was conducted among 459 employees and managers from Turkish banks, and the hypothesized model was tested using structural equation modeling to ensure robust empirical validation. The results indicate that entrepreneurial leadership significantly fosters employee creativity, which serves as a critical behavioral mechanism linking leadership behaviors to sustainability-oriented outcomes. Moreover, an innovation-oriented climate strengthens both the direct effect of entrepreneurial leadership on creativity and its indirect effect on sustainable bank performance, emphasizing the contextual importance of supportive organizational environments. Theoretically, this study extends the leadership and sustainability literature by illustrating how learning and behavioral modeling processes translate leadership vision into sustainable performance. Practically, it offers actionable guidance for bank executives to develop innovation-oriented climates, empower employees’ creative engagement, and design incentive systems that align leadership behavior with sustainability imperatives, thereby enhancing resilience and long-term competitiveness. Full article