Search Results (35,858)

Mathematical modeling plays a key role in understanding and optimizing transport system operations under uncertain and dynamic conditions. This study proposes a data-driven predictive framework for estimating passenger-accepted vehicle occupancy, addressing a critical gap in transport system planning under public health-related constraints. Using data from a structured survey conducted across seven Southeast European countries (N = 476), the study integrates statistical analysis and machine learning approaches to model acceptable occupancy levels across multiple transport modes, including passenger cars, taxis, tourist buses, and public buses. The problem is formulated as a predictive mapping between multidimensional input variables and occupancy acceptance levels, modeled using both probabilistic and nonlinear function approximation methods. The results highlight that age, gender, and area of residence are the most significant determinants of occupancy acceptance, while education level has limited predictive relevance. Furthermore, a multi-layer feedforward artificial neural network is developed to capture nonlinear relationships between variables, achieving strong predictive performance (minimum MSE = 0.0089). The main contribution of this research lies in linking behavioral data with predictive modeling to quantify acceptable occupancy thresholds and support realistic simulation of passenger responses in crisis conditions. The proposed modeling framework contributes to transport system planning, enabling data-driven capacity management, enhanced safety strategies, and improved resilience of passenger transport operations. Full article

Background/Objective: The parotid gland is the largest salivary gland, and tumors arising from it exhibit wide histopathological diversity. Management approaches vary according to tumor characteristics and carry a risk of postoperative complications, particularly facial nerve injury. However, local data remain limited. This study aimed to describe the clinicopathological characteristics, surgical approaches, and postoperative outcomes of patients undergoing parotidectomy. Method: A retrospective cohort study was conducted at a high-volume tertiary center in Saudi Arabia. All consecutive patients who underwent parotidectomy between June 2015 and January 2025 were included. Demographic data, histopathological diagnoses, surgical procedures and postoperative complications were extracted from electronic medical records. Statistical analyses were performed using SPSS version 26, with A p-value of <0.05 considered statistically significant. Results: A total of 154 patients were included, with a mean age of 45.2 ± 12.6 years; 61% were male. Benign lesions constituted 87% of cases, with pleomorphic adenoma being the most common histopathological diagnosis. Malignancies accounted for 13% of cases, most frequently mucoepidermoid carcinoma. The most common postoperative complications were facial nerve palsy, followed by sensory numbness. Conclusions: The majority of parotid gland tumors in this cohort were benign, with pleomorphic adenoma as the most common histological subtype. Facial nerve palsy and sensory disturbances were the most common postoperative complications. These findings provide valuable local data on parotid gland lesions in Saudi Arabia and support current surgical management practices. Full article

Background: Small- and medium-sized enterprises (SMEs) constitute a large portion of the industrial energy demand in the emerging economies, but their shift to renewable energy is not well comprehended at the firm level. Bangladesh is a special case, since the country has adopted national commitments to Sustainable Development Goal 7 on clean energy, but the uptake of renewable energy by SMEs remains minimal due to complex socio-economic factors. Most of the literature has concentrated on household access to energy or national policy models, leaving a gap in empirically validated models of firm-level adoption in the manufacturing sector. Method: Based on the diffusion of innovation theory, institutional theory, and the resource-based view, this research paper formulates and empirically verifies a combined socio-economic model of renewable energy adoption. Partial least squares structural equation modeling (PLS-SEM) was used to analyze a cross-sectional survey of 426 owners and managers of manufacturing SMEs in Bangladesh’s textile and food processing sub-sectors. Findings: Four out of five hypothesized direct relationships were supported. The most important drivers were environmental orientation (β = 0.467, p < 0.001, f² = 0.413), market competitiveness (β = 0.287, p < 0.001, f² = 0.413), policy and institutional factors (β = 0.211, p < 0.001, f² = 0.413), and access to finance (β = 0.096, p = 0.004). Perceptions of cost did not become significant (β= −0.036, p = 0.279). Top management support significantly and negatively moderated the relationship between environmental orientation and adoption (β = −0.093, p = 0.003), possibly because it moderates the substitution mechanism in SME decision-making, which is highly centralized. The model accounted for 64.5% of the variation in renewable energy adoption (R² = 0.645). Conclusion: The results show that attitudinal and institutional factors tend to be more important than financial barriers in determining SMEs’ energy transitions. Environmental consciousness, market incentives, and streamlined institutional access should be the focus of policy interventions to hasten inclusive low-carbon transitions in emerging manufacturing economies. Full article

The accurate and rapid prediction of hazard gas dispersion fields in urban environments is essential for guiding emergency sensor deployment and enabling real-time risk assessment. However, the computational cost associated with Computational Fluid Dynamics (CFD) simulations hinders their use as real-time forward models, while simplified Gaussian plume models lack the fidelity to resolve building obstruction effects. This study proposes a morphology-guided conditional Generative Adversarial Network (cGAN) framework designed to achieve real-time gas dispersion field modeling in urban environments with complex building configurations. The urban area is discretized into 50 × 50 m grid cells, each characterized by six morphological parameters describing building geometry. K-means clustering categorizes these cells into distinct morphological types. High-fidelity dispersion datasets are then generated for each type using Lattice Boltzmann Method (LBM) simulations. Each sample encodes building geometry, release location, wind speed, and time as multi-channel input images, with the corresponding gas dispersion concentration field is recorded as the output. Two cGAN architectures, Image-to-Image Translation (Pix2Pix) and its high-resolution variant (Pix2PixHD), are employed to learn the mapping from input features to dispersion fields. Model performance is evaluated using four complementary metrics: Fraction within a Factor of Two (FAC2) for prediction accuracy, Normalized Root Mean Square Error (NRMSE) for precision, Fractional Bias (FB) for systematic error, and Structural Similarity Index (SSIM) for spatial pattern fidelity. A case study is conducted across a 1176 km² urban district in China. The results demonstrate that under varying wind speeds (0.5–1.5 m/s) and temporal scales (5–60 s), and across five morphological categories, the Pix2PixHD-based model achieves 92.5% prediction accuracy and reproduces 97.6% of the spatial patterns. The proposed framework accelerates computation by approximately 18,000 times compared to traditional CFD, reducing inference time to under 0.1 s per scenario. This sub-second capability enables real-time concentration field estimation for emergency management, and provides a physically informed, computationally feasible forward model that can potentially support sensor-based gas source localization and detection network planning in complex urban environments. Full article

Accurate sex identification in reptiles with genotypic sex determination is essential for breeding management, veterinary care and evolutionary research, yet commonly used methods are often invasive, stressful or unreliable. This study aimed to evaluate a dosage-based quantitative PCR approach for molecular sex determination in caenophidian snakes, using naturally shed epidermal skin as a non-invasive DNA source. Genomic DNA extracted from shed skin was analysed by qPCR targeting conserved Z-linked genes (ADARB2, ARMC4 and TANC2), together with autosomal and reference genes, to assess sex-specific differences in gene copy number. Sixteen caenophidian snake species were examined, including taxa for which molecular sexing data are currently scarce or unavailable. The autosomal control gene showed dosage ratios close to parity between sexes, supporting DNA quality and reference gene reliability; meanwhile, Z-linked markers generally exhibited reduced dosage in females relative to males, consistent with a ZZ/ZW sex determination system. These results demonstrate that dosage-based qPCR applied to shed epidermal skin provides a promising and non-invasive framework for molecular sex determination in caenophidian snakes, without compromising animal welfare. Full article

Objective: This retrospective observational four-year review (2019–2022) evaluated neonatal admissions to Malta’s NICU and their outcomes. Study Design: Data from neonates up to 28 days meeting NICU admission criteria with available EMRs were analyzed, focusing on demographic data such as gestation and birth weight, need for resuscitation at birth, admission reasons, and outcomes related to nutrition, respiratory support, congenital anomalies, prematurity-related complications, phototherapy, and infection. Results: Total admissions numbered 1303 (7.3% of total births), out of which 1234 had available electronic medical records and were included in the final analysis. The main reasons for admission were respiratory distress syndrome (27.7%), transient tachypnoea (16.3%), and sepsis (13.5%). Among preterm infants, conditions related to prematurity were observed at expected frequencies and are reported descriptively. Feeding practice resulted in delayed attainment of full enteral nutrition compared to international standards, with an exclusive breastfeeding rate below the EU average. Sepsis and CLABSI rates were low, indicative of robust infection prevention and control measures. Conclusions: This study provides a descriptive overview of NICU admissions and outcomes stratified by gestational age at a single tertiary center in Malta, and highlights areas for improvement. The findings highlight expected patterns of prematurity-related morbidity and differences in clinical management, particularly in nutritional and respiratory support. Future prospective studies incorporating standardized data collection and detailed maternal and demographic variables are needed to better inform neonatal care and service planning. Full article

Built heritage is increasingly affected by climate-driven processes, yet its capacity to inform broader understandings of urban environmental change remains insufficiently explored. Here, we synthesize the recent literature (2020–2024) on the application of the Historic Urban Landscape (HUL) approach to the integrated management of cultural heritage under climate risk, reframing the historic built environment as a multi-scale diagnostic medium for climate–urban interactions. We analyze the steps and tools employed to support decision-making across territorial planning, risk assessment, and heritage governance in the papers selected from Web of Science, Science Direct, and Scopus databases. Results show that the approach is a flexible analytical framework that allows the integration of heterogeneous data, multi-criteria evaluations, and diverse stakeholder perspectives across spatial and temporal scales. Information modeling tools are shown to play a central role in structuring territorial knowledge, identifying patterns of vulnerability, and supporting comparative analyses across urban contexts. Nonetheless, significant challenges persist, including limited quantification of climate-induced degradation mechanisms, uncertainties in linking vulnerability assessments to predictive models, structural constraints on participatory implementation, and a tendency to apply the approach as a checklist due to inadequate understanding of its holistic dimensions. Overall, the HUL approach emerges as a scalable and transferable framework for embedding cultural heritage within climate research, advancing the conceptual integration of built heritage into resilience science and sustainability-oriented urban systems. Full article

This paper reviews contemporary developments in systems analysis applied to water resources and agricultural management, highlighting the growing influence of artificial intelligence (AI) and machine learning (ML). The literature in this field encompasses a wide range of approaches, methods, and applications, including hydrological simulation models, decision-support systems, and participatory governance frameworks. In recent years, increasing attention has been devoted to systematically reviewing and categorizing these approaches, particularly in light of rapid advances in AI- and ML-based technologies. The present study focuses on the contributions and impacts of AI and ML on systems analysis methodologies compared with the state of the field approximately a decade ago. By revisiting and classifying key groups of approaches, methods, and software tools, the paper provides an updated overview of the current status of systems analysis in water resources and irrigation management. This overview also serves as a reference framework for assessing future methodological and technological developments. Adopting a systems-thinking perspective, the review spans multiple spatial and management scales, from plot-level irrigation practices to river-basin water allocation. The paper aims to support a more holistic understanding and improved design and evaluation of water–agriculture systems, while also strengthening policy support for sustainable resource management. Finally, it highlights the need for continued interdisciplinary integration, enhanced stakeholder participation, and the development of operational tools capable of translating complex systems insights into actionable water management strategies in the emerging context shaped by AI and ML. Full article

Background: Benign paroxysmal positional vertigo (BPPV) is the most common cause of peripheral vertigo and is diagnosed clinically, yet many patients initially present in primary care. Early identification may optimize referral and management. Objective: To perform a pilot Phase 1 validation of the Benign Paroxysmal Positional Vertigo Screening Questionnaire (BPPV-SQ), a brief screening questionnaire designed for future use in general practice (primary care settings where patients are initially evaluated by general practitioners), assessing its ability to identify BPPV, suggest canal involvement, and support progression to Phase 2 validation. Methods: In this prospective observational study, 108 patients with positional vertigo and no neurological signs were evaluated in a specialist setting. The 7-item dichotomous questionnaire (score 0–3 for diagnostic core) was administered prior to bedside examination, which served as the reference standard. Results: Higher questionnaire scores were associated with an increased probability of confirmed BPPV. Among patients with the maximum score of 3, BPPV was confirmed in 73.5% of cases, with a lateralization concordance of 69.4% between questionnaire responses and specialist diagnosis. In contrast, lower scores (0–1) were associated with a markedly lower rate of confirmed BPPV (14.3%). Conclusions: In this pilot Phase 1 validation, the BPPV-SQ demonstrated score-dependent diagnostic reliability and acceptable lateralization agreement in high-score patients, supporting progression to Phase 2 validation in primary care. Full article

This study explores how residents of Vrnjačka Banja (Serbia) perceive the impacts of tourism and how these attitudes influence their support for future tourism development. Specifically, it examines positive and negative economic, socio-cultural, and environmental impacts, as well as the types of tourism residents favor. Data were collected from 420 local residents using a structured survey, and the reliability of the scales was confirmed using Cronbach’s alpha. Descriptive statistics provided an overview of participant characteristics, while MANOVA and follow-up ANOVA tests were used to examine differences in perceived tourism impacts across tourism types. Multiple linear regression was used to assess how attitudes toward positive and negative impacts predict residents’ support for future tourism development. The results indicate that attitudes toward positive impacts are relatively consistent across residents, whereas negative socio-cultural and environmental impacts differ depending on the type of tourism they support. Regression analysis shows that positive socio-cultural and environmental impacts are the strongest drivers of residents’ support, while negative socio-cultural and economic impacts reduce support. These findings highlight the importance of social and environmental considerations in shaping community attitudes and suggest that sustainable tourism planning should prioritize local well-being and responsible environmental management alongside economic objectives. This study contributes to the literature by addressing the heterogeneity in residents’ attitudes through tourism type preferences, while also highlighting the limited research on this topic in spa destinations. It further provides practical guidance for destination managers and policymakers in developing more targeted and sustainable tourism strategies. Full article

The circular economy is built on minimizing waste and maximizing the use of resources. The goal is to protect the environment and ensure the supply of limited raw materials in accordance with sustainability. The circular economy enhances natural capital and thus helps to increase the competitiveness of the country. The main objective of the work is to determine whether higher support for research and development affects the development of the circular economy and the associated waste generation. Despite persistent geographical differences in innovation between EU 27 countries in R&D spending, this study demonstrates that high investment does not always equal sectoral efficiency. Using a comparative analysis including R&D expenditure, circular economy investment, and waste generation indicators, the research highlights that specific priorities often outweigh general economic strength. Furthermore, the findings revealed no direct link between agricultural R&D funding and waste generation, suggesting that waste levels are influenced by industry intensity and local legislation rather than the volume of research. Economic sustainability ultimately depends on the efficient conversion of resources into value through policy management and eco-innovation, not just the volume of spending itself. Full article

Background: Intensive care units (ICUs) are high-acuity environments that require respiratory therapists (RTs) to maintain vigilance, manage emotions, and make rapid clinical decisions. In such settings, performance stability is critical for patient safety. Although emotional intelligence (EI) and work–life balance (WLB) have been linked to professional outcomes in health care, their independent and direction-specific associations with job performance among ICU respiratory therapists remain underexamined. Methods: A national cross-sectional survey was conducted among respiratory therapists working in ICUs across Saudi Arabia (June 2025–January 2026). EI was measured using the Wong and Law Emotional Intelligence Scale. WLB was assessed using the work interference with personal life (WIPL), personal life interference with work (PLIW), and work–personal life enhancement (WPLE) scales. Job performance was evaluated using the Individual Work Performance Questionnaire. Correlation and multivariable linear regression analyses were performed to estimate independent associations. Results: A total of 392 RTs were included in the final analysis. Higher EI was independently associated with greater task performance (B = 0.21, p < 0.01) and contextual performance (B = 0.30, p < 0.001), and with lower counterproductive work behaviours (B = −0.24, p < 0.001). Among WLB dimensions, PLIW showed the strongest adverse association, predicting lower task performance (B = −0.20, p < 0.05) and higher counterproductive behaviours (B = 0.39, p < 0.001), but was not significantly associated with contextual performance in the fully adjusted model. WPLE demonstrated modest positive associations with performance, whereas WIPL was not significant in adjusted models. Conclusions: Job performance among ICU respiratory therapists is shaped by both emotional regulatory capacity and cross-domain strain. Personal life interference with work emerged as the most influential adverse predictor, whereas EI was associated with constructive performance patterns. Findings should be interpreted in light of the cross-sectional design and self-reported data. Sustaining performance in high-acuity settings requires attention to emotional competencies and structural sources of role conflict alongside clinical expertise. These findings inform workforce strategies to support performance and sustainability in critical care settings. Full article

Metro station emergencies are increasingly characterized by compound hazards, where multiple disruptions interact across emergency management stages. Existing approaches typically focus on technical failures or isolated stages, overlooking how management processes themselves can generate and amplify system-level risks. This study introduces the concept of stage-coupled failure, which explains how failures propagate across preparedness, response, and recovery through interdependent management mechanisms. A theory-building approach is adopted, combining systematic literature synthesis with scenario-based analysis. Four coupling mechanisms—resource, information, organizational, and temporal—are identified to structure cross-stage interactions. A semi-quantitative representation is further proposed to capture feedback loops and nonlinear dynamics. The findings show that failure escalation is driven by cross-stage interactions rather than isolated breakdowns. The proposed framework provides a new perspective for understanding compound hazard dynamics and supports applications in safety assessment and emergency management planning. Full article

The growing demand for clean and efficient fuels, along with the need to reduce environmental impacts and operational risks, has driven the development of sustainability strategies in refining processes such as gas oil hydrocracking. This paper evaluates the sustainability of an industrial gas oil hydrocracking process with mass and energy integration, using the Safety and Sustainability Weighted Return on Investment (SWROIM) metric. This metric integrates economic, energy, environmental, technical, and safety criteria into a single quantitative indicator. The process was modeled and simulated considering heat exchange networks and direct water recycle to improve the overall system efficiency. The main objective was to calculate the SWROIM of the integrated process and analyze the relative influence of each sustainability indicator through a sensitivity study based on varying weighting factors. The results show that the process achieves an SWROIM value of 127.39%, significantly higher than the return on investment (ROI), demonstrating favorable sustainable performance. This behavior is attributed to high exergy efficiency, a reduction in potential environmental impact, improvements in water management, and a decrease in the inherent risk of the process. Sensitivity analysis confirmed that the energy indicator has the greatest influence on SWROIM, while the technical criterion has a relatively minor impact. Overall, the results demonstrate that mass and energy integration, evaluated using advanced metrics such as SWROIM, is a robust tool to support decision-making in the sustainable design and optimization of hydrocracking processes, opening opportunities for future applications in other complex systems within the refining industry. Full article