Search Results (363)

Spatial accessibility is a key principle in urban studies, shaping how people reach amenities and services across cities. While most research concentrates on large metropolitan areas and central urban services, small and medium-sized cities and their main amenities remain less studied. To bridge this gap, this study explores spatial accessibility to public amenities in relation to population density in Odense, a medium-sized city known for its compact layout and robust infrastructure supporting walking, cycling, and public transport. Despite Odense’s proactive planning and multimodal transport network, marked accessibility inequalities still exist, especially in peripheral neighbourhoods. This research uses a data-driven approach combining network-based travel time analysis with grid-cell-based spatial visualisation. Additionally, a multi-criteria accessibility scoring framework is introduced, including indicators such as amenity density, diversity of services, temporal thresholds for walking and cycling, and population distribution. The results show an uneven accessibility landscape, with significant gaps in outer districts, highlighting the limitations of uniform planning thresholds. By applying spatial analytical principles, the study uncovers embedded socio-spatial inequalities in everyday urban access. These insights offer practical guidance for planners and policymakers, underscoring the importance of context-sensitive multimodal infrastructure and decentralised service provision to support sustainable urban growth. Full article

Bus stops in Riobamba had significant deficiencies in safety, accessibility, and comfort, which limited the effective use of public transport and affected the urban mobility of the population. Improving these conditions was crucial to promote sustainable, inclusive and safe mobility in the city. This study was quantitative and descriptive, based on 420 user surveys and the direct observation of 140 stops, complemented with georeferencing and comparative review of specialized literature. The findings showed that most of the stops lacked adequate lighting, shelter, signage and universal access, with 68% of users perceiving low safety. The most in-demand technologies included real-time information systems (72%) and video surveillance (65%). The proposed model of smart stops will improve accessibility, safety and comfort for users, encouraging greater use of public transport. By addressing the main infrastructure and technology gaps, the intervention contributed to inclusive and safe urban mobility, directly contributing to Sustainable Development Goal 11 and offering a replicable framework for other medium-sized cities seeking to optimize their public transport systems. Full article

Public events serve as a foundational mechanism for shaping the social and spatial dynamics of urban environments. Despite widespread recognition of their physical, psychological, and social impacts at the city scale, a significant gap persists in research addressing the social and spatial suitability of public spaces at the neighborhood level, particularly within the Arab urban context. This study investigates residential neighborhoods in Riyadh, Saudi Arabia, to assess how public events foster community engagement, cultural diversity, and social cohesion. Drawing on survey data from 510 residents, statistical analysis reveals that demographic variables such as age, gender, and professional sector influence participation, with youth and women demonstrating notably higher levels of engagement. Moreover, population density emerges as a critical factor in determining the appropriateness of event settings, with medium-sized gatherings in open spaces especially parks proving most effective. The findings emphasize the importance of designing inclusive and culturally responsive events, offering actionable insights for urban planning in rapidly growing cities. The study further highlights the need to reimagine neighborhood parks and open spaces as adaptable venues, equipped with essential infrastructure and governed by streamlined regulatory frameworks. Participants expressed a clear preference for accessible, medium-scale cultural events that prioritize safety, environmental sustainability, and enhanced public amenities, including transportation and sanitation services. Full article

Corporate Social Responsibility (CSR) is increasingly shaping the pathways of Small Medium-sized Enterprises (SMEs). This study presents an entity-relationship diagram (ERD) approach to 184 determinants of SME internationalization success, in order to provide structured inputs for Deep Learning (DL) Recommenders that can support CSR-aligned internationalization strategies. Employing Visual Paradigm 17.2 Professional software for modeling, the research synthesizes state-of-the-art findings on foreign market entry, and export performance, into ERDs. Then the market adoption drivers for such a DL tool are explored through semi-structured interviews with twelve stakeholders. The results reveal a propensity to adopt the DL recommender, with experts highlighting essential features for engagement, pricing, and implementation. The discussion contextualizes these findings, while the conclusion addresses gaps and future directions. The study’s focus in Portugal/Germany may limit worldwide extrapolation, yet it advances knowledge by consolidating success determinants, validating platform requirements, exposing gaps, and suggesting research in both CSR, AI and SME internationalization. Full article

Climatic heat stress in Hong Kong’s construction industry has been exacerbated by global climate change in recent times and the city has been taking proactive measures in protecting its workforce. Heat stress management systems refer to integrated frameworks, including policies, technologies, and practices, designed to monitor, mitigate, and prevent heat-related risks to workers’ health and productivity in hot environments. This scoping review investigates the existing heat stress management systems within Hong Kong’s construction industry, analyzing policies and academic research, and highlighting challenges and proposing solutions. A systematic scoping method was used to review and synthesize findings from 50 peer-reviewed articles (updated to 2025) and nine policy documents. This study highlights the interplay between research innovations like AI-driven models and wearable cooling technologies and policy frameworks. The results indicate substantial progress in Hong Kong’s drive to manage heat strain and accidents among construction workers over the years, with advancements in real-time advisory systems and protective equipment, improving worker safety and productivity. However, limited scalability, costs, socio-cultural compliance issues, gaps in addressing equity concerns among vulnerable workers, policy implementation, and other challenges persist. This review underscores the importance of building resilient systems against the escalating heat stress risks by proposing the integration of research-based technological innovation with policies and socio-organizational considerations. It contributes to providing the first updated scoping review post-2020, identifying implementation gaps (e.g., 40% non-compliance rate) and proposing a concrete action framework for future interventions. Recommendations for future research include cross-regional adaptations, cost-effective solutions for medium-sized construction enterprises, and the continuous re-evaluation and improvement of current interventions. Full article

This study examines the influence of institutional pressures and personal attributes on the perceived importance of financial reporting among micro-entrepreneurs in Malaysia. Survey data from 194 micro-entrepreneurs were analyzed using ordinary least squares (OLS) regression to test the proposed hypotheses. The results indicate that institutional pressures from Malaysian regulatory bodies, particularly the Inland Revenue Board, and the financial literacy of micro-entrepreneurs are significantly associated with stronger perceptions of the importance of financial reporting. These findings offer practical insights for policymakers and stakeholders seeking to enhance reporting practices and promote financial literacy within the microenterprise sector. While prior research has largely concentrated on small and medium-sized enterprises (SMEs), the financial reporting practices of micro-enterprises remain underexplored, despite their distinctive characteristics and critical role in the economy. By addressing this gap, this study enriches the financial reporting literature and advances a broader understanding of micro, small, and medium-sized enterprises (MSMEs). Full article

Artificial intelligence adoption in financial services presents uncertain implications for competitive dynamics, particularly for smaller institutions. The literature on AI in finance is growing, but there remains a notable absence regarding the impacts on small- and medium-sized financial services firms. We conduct a meta-analysis combining a systematic literature review, sentiment bibliometrics, and network analysis to examine how AI is transforming competition across different firm sizes in the financial sector. Our analysis of 160 publications reveals predominantly positive academic sentiment toward AI in finance (mean positive sentiment 0.725 versus negative 0.586, Cohen’s d = 0.790, p < 0.0001), with anticipatory sentiment increasing significantly over time ($\beta =2.10\times {10}^{-2},p=0.007$). However, network analysis reveals substantial conceptual fragmentation in the research discourse, with a low connectivity coefficient ($\varphi =0.125$) indicating that the field lacks unified terminology. These findings expose a critical knowledge gap: while scholars increasingly view AI as competitively advantageous, research has not coalesced around coherent models for understanding differential impacts across firm sizes. The absence of size-specific research leaves practitioners and policymakers without clear guidance on how AI adoption affects competitive positioning, particularly for smaller institutions that may face resource constraints or technological barriers. The research fragmentation identified here has direct implications for strategic planning, regulatory approaches, and employment dynamics in financial services. Full article

Ground-penetrating radar (GPR) is an important geophysical technique in subsurface detection. However, traditional numerical simulation methods such as finite-difference time-domain (FDTD) face challenges in accurately simulating complex heterogeneous mediums in real-world scenarios due to the difficulty of obtaining precise medium distribution information and high computational costs. Meanwhile, deep learning methods require excessive prior information, which limits their application. To address these issues, this paper proposes a novel two-step forward modeling strategy for GPR data of metal pipes. The first step employs the proposed Polarization Self-Attention Image Translation network (PSA-ITnet) for image translation, which is inspired by the process where a neural network model “understands” image content and “rewrites” it according to specified rules. It converts scene layout images (cross-sectional schematics depicting geometric details such as the size and spatial distribution of underground buried metal pipes and their surrounding medium) into simulated clutter-free GPR B-scan images. By integrating the polarized self-attention (PSA) mechanism into the Unet generator, PSA-ITnet can capture long-range dependencies, enhancing its understanding of the longitudinal time-delay property in GPR B-scan images. which is crucial for accurately generating hyperbolic signatures of metal pipes in simulated data. The second step uses the Polarization Self-Attention Style Transfer network (PSA-STnet) for style transfer, which transforms the simulated clutter-free images into data matching the distribution and characteristics of a real-world underground heterogeneous medium under unsupervised conditions while retaining target information. This step bridges the gap between ideal simulations and actual GPR data. Simulation experiments confirm that PSA-ITnet outperforms traditional methods in image translation, and PSA-STnet shows superiority in style transfer. Real-world experiments in a complex bridge support structure scenario further verify the method’s practicability and robustness. Compared to FDTD, the proposed strategy is capable of generating GPR data matching real-world subsurface heterogeneous medium distributions from scene layout models, significantly reducing time costs and providing an efficient solution for GPR data simulation and analysis. Full article

Small and medium-sized enterprises (SMEs) are critical contributors to economic growth, innovation, and employment. However, they often struggle in securing external financing. This financial gap mainly arises from perceived risks and information asymmetries creating barriers between SMEs and potential investors. To address this issue, our study proposes a machine learning (ML) framework for predicting the investment readiness (IR) of SMEs. All the models involved in this study are trained using data provided by the European Central Bank’s Survey on Access to Finance of Enterprises (SAFE). We train, evaluate, and compare the predictive performance of nine (9) machine learning algorithms and various ensemble methods. The results provide evidence on the ability of ML algorithms in identifying investment-ready SMEs in a heavily imbalanced and noisy dataset. In particular, the Gradient Boosting algorithm achieves a balanced accuracy of 75.4% and the highest ROC AUC score at 0.815. Employing a relevant cost function economically enhances these results. The approach can offer specific inference to policymakers seeking to design targeted interventions and can provide investors with data-driven methods for identifying promising SMEs. Full article

The global automotive industry’ s rapid transformation has led to electric vehicles (EVs) capturing a significant market share as a sustainable transportation option. To sustain this growth, it is crucial to not only attract new users but also retain existing ones through repurchases. This decision is shaped by both vehicle attributes and users’ prior experiences. This study examines the impact of five dimensions of expected utility and experienced utility gap (including cost utility, functional utility, emotional utility, environmental utility, and social utility) on the repurchase intentions of 863 Chinese EV users. Discrete choice experiments were used to analyze these factors, considering both vehicle and personal attributes. The results show that when emotional utility exceeds expectations, users are more likely to repurchase pure electric and plug-in hybrid electric vehicles. However, if environmental and social utilities fall short of expectations, users may be discouraged from choosing these two vehicle types. In contrast, decisions regarding gasoline vehicles are primarily driven by economic and habitual factors, with minimal influence from emotional, environmental, or social utilities. Additionally, EV users show a preference for medium-sized models that offer shorter charging times and longer driving ranges. These findings offer insights for enhancing consumer acceptance, accelerating EV market penetration, and supporting the automotive industry’s sustainable development, thereby contributing to the achievement of environmental sustainability goals. Full article

This study evaluates the degree to which small and medium sized enterprises (SMEs) are prepared to adopt smart manufacturing in contrast to large enterprises, a transition that depends on the effective use of the Internet of Things, artificial intelligence (AI), and advanced analytics. While many large multinational companies have already integrated such technologies, smaller firms still struggle because of tight budgets, limited technical expertise, and difficulties in scaling new systems. To capture these realities, the investigation refines the Initiative Mittelstand-Digital für Produktionsunternehmen und Logistik-Systeme (IMPULS) Industry 4.0 readiness model, which was initially developed to help German SMEs, so that it aligns with the circumstances faced by smaller manufacturers. A thorough review of published work first surveys existing readiness and maturity frameworks, highlights their limitations, and guides the selection of new, SME-specific indicators. The framework gauges readiness across six dimensions: strategic planning and organizational design, smart factory infrastructure, lean operations, digital products, data-driven services, and workforce capability. Each dimension is operationalized through a questionnaire that offers clear benchmarks and actionable targets suited to the current resources of each enterprise. Weaving strategic vision, skill growth, and cooperative support, the approach offers managers a direct path to sharper competitiveness and lasting innovation within a changing industrial landscape. Additionally, a separate Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis is provided for each dimension based on survey data offering decision-makers concise guidance for future investment. The proposed adaptation of the IMPULS framework, validated through empirical data from 31 SMEs, introduces a novel readiness index, diagnostic gap metrics, and actionable cluster profiles tailored to developing-country industrial ecosystems. Full article

Identifying the most reactive conformation of a molecule is a central challenge in computational chemistry, particularly when reactivity depends on subtle conformational effects. While most conformation search tools aim to find the lowest-energy structure, they often overlook the electronic descriptors that govern chemical reactivity. In this work, we present GAELLE, a cheminformatics tool that combines conformer generation with quantum reactivity descriptors to identify the most reactive structure of a molecule in solution. GAELLE integrates an evolutionary algorithm with fast semiempirical quantum chemical calculations (xTB), enabling the automated ranking of conformers based on HOMO–LUMO gap minimization (Pearson’s principle of maximum hardness) and electrophilicity index (Parr’s electrophilicity scale). Solvent effects are accounted for via implicit solvation models (GBSA/ALPB) to ensure realistic evaluation of reactivity in solution. The method is fully SMILES-driven, open-source, and scalable to medium-sized drug-like molecules. Applications to reactive intermediates, bioactive conformations, and pre-reactive complexes demonstrate the method’s relevance for mechanism elucidation, molecular design, and in silico screening. GAELLE is publicly available and offers a reactivity-focused alternative to traditional energy-minimization tools in conformational analysis. Full article

Automated guided vehicles (AGVs) operating in uneven environments are typically designed with an elevated chassis to enhance obstacle-crossing. In inductive power transfer (IPT) systems for such AGVs, a long transmission distance along with limited installation space for coils leads to a medium distance-to-diameter ratio (DDR) (1 < DDR ≤ 2), which reduces coupling efficiency and degrades misalignment tolerance. To address this issue, this paper proposes a compact dual-receiver IPT system for medium DDR conditions. The system adopts a flat U-shaped solenoid (FUS) coil as both the transmitter and the primary receiver, and a square solenoid (SS) coil as the secondary receiver, forming the FUSS dual-receiver structure. The FUS coil is optimized through finite element analysis to improve coupling, while the SS coil captures vertical flux to compensate for misalignment losses, thereby enhancing misalignment tolerance. A hybrid rectifier integrating a full-bridge and voltage doubler topology is used to suppress output voltage fluctuation, reduce the number of receiver coil turns, and minimize system volume. A 300 W/100 kHz prototype with a coupler size of 183 × 126 × 838 mm³ achieves 83.51% efficiency under medium DDR and a 185 mm air gap. Voltage fluctuation remains within 5% under ±51.4% X-axis and ±51.7% Y-axis misalignment, confirming the stable power delivery and improved misalignment tolerance of the system. Full article

Virtual Reality (VR) simulations provide immersive, realistic educational experiences that are increasingly used to enhance teaching and learning in nursing and medicine; however, use in dietetics lags. To fill this gap, four Nutrition Counselor VR simulations were developed collaboratively with the goal of building confidence in dietetic students’ nutrition counseling skills. After formative testing, pilot testing, and refinements, simulations were field tested with 34 dietetic students (91% women; age 25.67 ± 3.79 SD years; 68% White) from four supervised practice programs using a standard protocol administered by trained researchers (N = 5). Students completed a pre-survey, one VR simulation (≥2 times w/varying outcomes), and a post-survey. Online pre- and post-surveys examined changes in nutrition counseling skills, knowledge and self-efficacy, and comfort in using nutrition counseling skills. Paired t-tests revealed significant (p < 0.05) mean differences in nutrition counseling skill self-efficacy (medium effect size, d = 0.46) and comfort in using nutrition counseling skills (large effect size, d = 0.96) between the pre- and post-survey. At post-survey, >75% agreed the simulations helped build their nutrition assessment skills (79%) and counseling skills (88%) and prepared them to work with real patients (97%). Findings suggest the Nutrition Counselor VR simulations provided a realistic and safe learning environment that may be a valuable learning tool for dietetic students. Full article

As critical infrastructure for flood control and disaster mitigation, the completeness of a dam spatial database directly impacts regional emergency disaster response. However, existing dam data in some developing countries suffer from severe gaps and outdated information, particularly concerning small- and medium-sized dams, hindering rapid response during disasters. There is an urgent need to improve the physical dam database and implement dynamic monitoring. Yet, current remote sensing identification methods face limitations, including a lack of diverse dam samples, limited analysis of geographical factors, and low efficiency in full-image processing, making it difficult to efficiently enhance dam databases. To address these issues, this study proposes a dam extraction framework integrating comprehensive geographical factor analysis with deep learning detection, validated in Sindh Province, Pakistan. Firstly, multiple geographical factors were fused using the Random Forest algorithm to generate a dam existence probability map. High-probability candidate areas were delineated using dynamic threshold segmentation (precision: 0.90, recall: 0.76, AUC: 0.86). Subsequently, OpenStreetMap (OSM) water body data excluded non-dam potential areas, further narrowing the candidate areas. Finally, a dam image dataset was constructed to train a dam identification model based on YOLOv11, achieving an mAP50 of 0.85. This trained model was then applied to high-resolution remote sensing imagery of the candidate areas for precise identification. Ultimately, 16 previously unrecorded small and medium-sized dams were identified in Sindh Province, enhancing its dam location database. Experiments demonstrate that this method, through the synergistic optimization of geographical constraints and deep learning, significantly improves the efficiency and reliability of dam identification. It provides high-precision data support for dam disaster emergency response and water resource management, exhibiting strong practical utility and regional scalability. Full article