Search Results (771)

Continental high water-cut reservoirs commonly exhibit strong heterogeneity, high viscosity, and insufficient reservoir drive, which has motivated the deployment of polymer-based composite chemical flooding, such as surfactant–polymer (SP) and alkali–surfactant–polymer (ASP) processes. However, conventional experimental techniques have limited ability to resolve intermolecular forces, and the coupled mechanism linking “formulation composition” to “microstructural evolution” remains insufficiently defined, constraining improvements in field performance. Here, scanning electron microscopy (SEM), backscattered electron (BSE) imaging, and molecular dynamics (MD) simulations are integrated to systematically investigate microstructural features of polymer composite systems and the governing mechanisms, including hydrogen bonding and electrostatic interactions. The results show that increasing the concentration of partially hydrolyzed polyacrylamide (HPAM) promotes hydrogen bond formation and the development of network structures; a moderate amount of surfactant strengthens interactions with polymer chains, whereas overdosing loosens the structure via electrostatic repulsion; the introduction of alkali reduces polymer connectivity, shifting the system toward an ion-dominated dispersed morphology. These insights provide a mechanistic basis for elucidating the behavior of polymer composite formulations, support enhanced chemical flooding performance, and ultimately advance the economic and efficient development of oil and gas resources. Full article

The global secondhand apparel industry, valued at USD 256B in 2025, is expanding rapidly. The growing acceptance of secondhand fashion and advancements in retail technology have driven millions of individuals to resell, yet little research has analyzed the motivations behind these decisions. Guided by Consumption Values Theory and Goal-Framing Theory, this qualitative study uses ten in-depth interviews with experienced resellers to examine why individuals participate in apparel reselling. Analysis of the participants’ narratives indicates that financial gain is the dominant driver of participation, followed by the convenience provided by reselling platforms and channels, emotional satisfaction, and contributing to sustainability. Conceptually, the study integrates value-based and goal-based lenses to offer an extensive explanation of reseller motivations, shifting focus from the buyer perspective that has dominated prior research. Practically, the findings suggest that resale platforms can encourage participation by reducing visible fees, enabling faster payout, and simplifying the reselling process, while also making community and environmental benefits more visible. In all, these insights help retailers and sustainability advocates better design approaches that support individual resellers and sustain growth in apparel resale. Full article

The purpose of this study was to determine the motivation to buy eco-friendly products via machine learning techniques. With this in mind, a dataset was collected between November and December 2024 from 245 organic consumers in Maramureș County, Romania, via a questionnaire. Consumers’ main motivations to buy eco-friendly products were considered according to three categories: Health Care, Environmental Protection, and Superior Quality. In the analysis of the dataset, among the four feature selection techniques used, Random Forest was determined to be the best with the highest accuracy value. At the beginning of the study, 16 variables were thought to be important categorical factors for consumers’ eco-friendly product-buying motivations, with 5 of these being found to be the most effective with the Random Forest technique. Then, the SHAP method was applied to identify the contribution of driving factors to the buying motivation for eco-friendly products. All analyses were conducted with Python software. The results of the SHAP method indicated that while all factors perform well, consumers considering themselves as eco-friendly is the most important factor for the Environmental Protection category when buying eco-friendly products, while the most important criterion of the original certification category was found to be the Health Care category. The most effective factor for Superior Quality was determined as the high-price category, which is the main barrier to purchasing eco-friendly products. Full article

In an increasingly centralized digital society, the widespread reliance on third-party cloud services has heightened user concerns about data privacy and control, driving a significant movement toward self-hosted solutions. This study investigates factors influencing continued use of self-hosting, proposing an extended research model that combines classic TAM constructs with domain-specific factors like perceived autonomy, privacy concerns, perceived trust, personal innovativeness and perceived enjoyment. A quantitative survey was conducted with $n=2158$ active self-hosting users, and the data was analyzed using PLS-SEM. The findings reveal that perceived enjoyment, perceived autonomy and perceived usefulness are the most significant positive drivers of continuance intention, confirming that intrinsic satisfaction and the desire for control are powerful motivators for sustained engagement. The study also found that the relationship between intention and usage is significantly moderated by perceived competence, highlighting that a user’s technical skill strengthens the link between their intent and actual use. The research offers key insights for developers and policymakers and contributes to academic discourse on sustained technology use by providing a validated measurement scale for self-hosted software usage and underscoring the importance of user empowerment and an enjoyable experience to foster the sustained engagement of decentralized digital solutions essential for the socio-technical sustainability of the digital society. Full article

Haze significantly reduces visibility in critical applications such as autonomous driving, surveillance, and firefighting, making its removal essential for safety and reliability. Motivated by the limited robustness of the existing methods under non-uniform haze conditions, this study introduces a novel regression-based dehazing model that simultaneously incorporates the atmospheric light constant, transmission map, and scattering coefficient for improved restoration. Instead of relying on complex deep networks, the model leverages brightness–saturation cues and regression-driven scattering estimation with localized haze detection to reconstruct clearer images efficiently. Evaluated on the RESIDE dataset, the approach consistently surpasses state-of-the-art techniques including Dark Channel Prior, AOD-Net, FFA-Net, and Single U-Net, achieving SSIM = 0.99, PSNR = 22.25 dB, VIF = 1.08, and the lowest processing time of 0.038 s, demonstrating both accuracy and practicality for real-world deployment. Full article

Automated vehicle (AV) technology is expected to alter travel behavior and residential location choices, yet the psychological motivations behind relocation decisions under current partial automation (Level 2) remain underexplored, as most studies focus on fully autonomous scenarios. This study explores why individuals might relocate in response to AV availability in both short-term and long-term contexts and predicts how willingness to relocate changes as automation levels advance. In a survey of Kentucky residents, data were collected on demographic and economic characteristics, travel needs, built environment attributes, AV familiarity, comfort with different automation levels, and willingness to relocate if AVs were available. Multiple machine learning models with Shapley Additive Explanations (SHAP) were used to predict and interpret changes in relocation willingness. Results indicate that greater comfort with high-level automation and higher AV familiarity increase relocation intentions, particularly among men, older adults with higher incomes, and urban residents. SHAP analysis reveals that built environment, age, and comfort with fully autonomous driving are the most influential predictors of changes in relocation willingness. Findings inform land use and housing policy by identifying where perception-driven relocation pressures are likely to emerge and by outlining adaptive tools to guide spatial growth as AV technology advances. Full article

A major challenge that will confront our society in the coming years is the socioecological transition. This involves a profound, systemic shift in how human societies interact with ecological systems. Beyond merely becoming “greener” or adding new technologies, it is about reorganising economies, lifestyles, institutions and cultural values to align with the planet’s ecological limits. The change also requires transforming the fundamental structure of societies to ensure their deep interconnection and compatibility with natural flows and ecological systems. To this end, it is valuable to explore the small, scattered practices which are currently leading to new organisational solutions or socioecological improvements. These initiatives are often regarded as forms of community resistance, adopting various approaches and strategies, which result in a disparate array of configurations. A comprehensive approach is thus needed to identify common patterns of development. A set of meaningful practices was analysed. The sample actions all took place in the urban context of Seville, a city located in Southwestern Europe and spanned various arenas driven by the transition to sustainability. Following the principles of qualitative research and a case study design, we adopted a qualitative method based on open-ended interviews, emphasising situated knowledge and collective construction of meaning. Moreover, a methodological approach based on interviews and further categorisation was followed to describe and organise ideas, motivations, risks, outcomes, as well as how the experiences evolved. The findings revealed that the core motivation driving the initiative in its initial phases is key. Outcomes nevertheless vary significantly depending on the initiative objectives. Generally, actions focused on specific elements—such as defending precise locations or activities—tend to be more successful and abundant. But the ones based on professional developments end up being somewhat stifled since they depend on the market to succeed. However, most rely somehow on public subsidies or support from public institutions, and their activities tend to diminish when such resources are reduced or withdrawn. The question is therefore how to make these initiatives more resilient in the future. The socioecological transition offers a path to strengthen social cohesion, empower collective action, and generate locally rooted and ecologically sustainable alternatives. Building community resilience—the capacity of local communities to adapt, recover and thrive amid these challenges—is, therefore, essential. Full article

This research work presents a techno-economic assessment of biodiesel production with non-standard waste cooking oil (WCO) (brown grease of small restaurants, yellow grease of households) and semi-open Chlorella sp. microalgal cultivation, which covers the problematic areas of scale and cost-efficiency in sustainable biodiesel production. Cost-effective biodiesel feedstock research has been motivated by the urgency of finding sustainable sources of energy. With base-catalyzed transesterification optimized by ANOVA and response surface methodology (RSM), the present study recorded biodiesel yields of up to 99.08% in household WCO (at optimum conditions; 55 °C, 3.3 mg/g NaOH, ethanol) and 96.61% in restaurant WCO (at optimum conditions; 54 °C, 1.5 mg/g NaOH, methanol) compared to 28.6% in Chlorella sp. (semi-open photobioreactors). Concerning the two types of WCO feedstocks, the obtained equations are able to compute the biodiesel viscosity and yield, in good correlation with the experimental values, in relation to the temperature and ratio of catalyst to oil/alcohol solution. The assessed household WCO has better yield and quality as it contains fewer impurities, whereas the restaurant WCO needed to be further purified, driving up the prices. Although Chlorella biodiesel is carbon neutral, its production and extraction costs are higher, making it less economically feasible for biodiesel production. Economic analysis showed that the capital costs of household WCO, restaurant WCO, and Chlorella sp. are USD 190,000, USD 220,000, and USD 720,000, respectively, based on 1,000,000 L/year as biodiesel production rate. Low capital costs as well as byproduct glycerol income of the two investigated types of WCO play a role in their low payback periods (0.23–0.91 years) and high ROI (110–444.4%). The analysis highlights the economic and environmental benefits of WCO, especially household WCO, as a scalable biodiesel feedstock, which provides new insights into process optimization and sustainable biodiesel strategies. To enhance its sustainability and cost-effectiveness and contribute to the transition to renewable biofuels globally, future studies need to emphasize energy reduction in microalgae production and purification of restaurant WCO. Full article

With the rapid development of autonomous vehicles and intelligent transportation systems, vehicle detection and distance estimation have become critical technologies for ensuring driving safety. However, real-world in-vehicle environments impose strict constraints on computational resources, making it impractical to deploy high-end GPUs. This implies that even highly accurate algorithms, if unable to run in real time on embedded platforms, cannot fully meet practical application demands. Although existing deep learning-based detection and stereo vision methods achieve state-of-the-art accuracy on public datasets, they often rely heavily on massive computational power and large-scale annotated data. Their high computational requirements and limited cross-scenario generalization capabilities restrict their feasibility in real-time vehicle-mounted applications. On the other hand, traditional algorithms such as Semi-Global Block Matching (SGBM) are advantageous in terms of computational efficiency and cross-scenario adaptability, but when used alone, their accuracy and robustness remain insufficient for safety-critical applications. Therefore, the motivation of this study is to develop a stereo vision-based collision warning system that achieves robustness, real-time performance, and computational efficiency. Our method is specifically designed for resource-constrained in-vehicle platforms, integrating a lightweight YOLOv8n detector with SGBM-based depth estimation. This approach enables real-time performance under limited resources, providing a more practical solution compared to conventional deep learning models and offering strong potential for real-world engineering applications. Full article

Background: In recent decades, the emigration of Bulgarian health professionals has become a persistent challenge driven by multiple interplaying factors. A comprehensive understanding of these key determinants is essential for the development of targeted retention strategies. This study examines the primary factors influencing the migration and retention of physicians and nurses in Bulgaria. Methods: A cross-sectional study was conducted in 2022 involving 338 health professionals without professional experience abroad. Data were collected using an online questionnaire administered by a polling agency. To identify the underlying structure of the driving and retention factors, we conducted Exploratory Factor Analysis (EFA). Subsequently, the Mann–Whitney U test was employed to examine the differences in retention and motivational factors between physicians and nurses. Results: EFA of the migration drivers identified a three-factor structure, explaining 70.34% of the total variance: professional advancement and work environment; financial incentives; and family benefits. The analysis of retention determinants revealed three distinct constructs, explaining 54.46% of the variance: socioeconomic and healthcare framework; employment and career development; and personal considerations and social environment. The Mann–Whitney U test indicated that the impact of financial incentives on migration decisions exerted a weaker influence on physicians’ intentions (r = −0.23). Personal considerations and the social environment emerged as more significant retention determinants for nursing professionals (r = −0.15). Conclusions: This study extends current understanding by identifying the underlying constructs of driving and retention factors in health professional migration. The findings underscore the need for evidence-based interventions to mitigate brain drain and retain skilled professionals. Full article

Self Determination Theory posits that individuals may be more likely to initiate and maintain behaviors tied to intrinsic (vs. extrinsic) motivations and may provide a useful framework for understanding youth participation in novel sports. Using the Intrinsic Motivation Inventory (IMI) and Patient-Centered Assessment and Counseling for Exercise Plus Nutrition (PACE+) surveys, motivation and physical activity habits were explored in 27 children/adolescents (ages 7–16) enrolled in Parkour, an individual, non-competitive youth sport. Fifteen Parkour participants were also interviewed to gain an understanding of their motivations for participating. Study participants had high median IMI subscale scores related to interest/enjoyment (6.71/7) and perceived choice (6.40/7) compared to the whole scale. Similarly median sub-scale Pros and Self-Efficacy scores for physical activity from the PACE+ were high (4.25/5 and 3.91/5, respectively). The themes of autonomy and enjoyment were consistently reported in the qualitative interviews, expanding on the quantitative results. Other themes included appreciation for camaraderie, creativity, and a drive for improvement. These results provide early evidence that Parkour, and similar lifestyle sports, may be tied more to intrinsic than extrinsic motivations and could have potential for adoption and maintenance by youth with low motivation to engage in physical activity to promote healthy behaviors. Full article

A sustainable economy and the drive to reduce agro-industrial waste worldwide motivate the increased interest in alternative uses of traditionally cultivated plants such as tobacco. Tobacco seeds are an underutilized resource with enormous potential for application in various areas of human life. The present study aims to characterize the phytochemical composition and nutritional potential of Oriental tobacco seeds grown in Bulgaria, in order to support their possible application in areas outside the tobacco industry. Two Oriental tobacco varieties (“Krumovgrad 90” and “Krumovgrad 58”) from three production regions were explored and comparatively evaluated in terms of their physical and chemical indicators, determined by standardized methods. The results showed high protein (22.57–23.84%) and energy content (482–531 kcal/100 g), combined with relatively low carbohydrate levels (3.79–4.03%) and the presence of bioactive compounds, such as polyphenols (288–357 mg GAE/100 g). The seeds contained significant amount of oil (36.31–39.24%), of which the fatty acid profile included 16 identified components, with linoleic (72.0–74.4%), oleic (11.2–13.5%), palmitic (9.6–10.2%), and stearic (1.8–2.5%) acids taking the greatest share. The sterol fraction was dominated by β-sitosterol (43.5–46.8%), followed by sitostanol, campesterol and stigmasterol, with a stable distribution between the samples. The main tocol was γ-tocotrienol (56.5–61.4%), with α-tocotrienol being detected only in one of the varieties (“Krumovgrad 58”, 13.3%). The phospholipid fraction showed variations between the samples, with a dominant presence of phosphatidylinositol (18.0–20.4%). The results from the study confirmed the tangible potential of tobacco seeds as a source of biologically active substances in the development of functional foods and dietary supplements. Full article

The exploration of the ecosystem service value of rural tourism destinations and their driving factors is regarded as an important approach for promoting the high-quality development of rural tourism and achieving rural revitalization. At present, both industry and academia lack quantitative analyses and integrated optimization approaches for small-scale landscape elements within the context of rural tourism destinations. An examination of rural ecosystem service values from a small-scale perspective can assist rural areas in precisely enhancing ecological functions, advancing rural landscape construction, and fostering high-quality rural development. Six sample sites within rural tourism areas in the Yangtze River Delta region were selected, and the functional value method, willingness-to-pay method, and binary logistic regression model were comprehensively applied to assess ecosystem service values and their influencing factors. The results indicate that: (1) Significant differences exist in ecosystem service values across small-scale rural landscapes, primarily attributable to variations in cultural service values, which constitute the dominant component of the overall value. (2) Cultural service values are manifested in tourists’ willingness to pay, which is closely associated with the configuration types of rural small-scale landscapes. (3) Tourists’ willingness to pay is primarily influenced by their cognition of cultural services and by psychological perception factors. According to the regression results, perceived importance of cultural service protection, concerns regarding damage, levels of tourist satisfaction, and willingness to revisit exert significant negative effects on willingness to pay. (4) High-demand tourism motives exhibit only a weak correlation with willingness to pay. (5) Low-demand motivations significantly suppress willingness to pay. The conclusions serve as a reference for the quantification of small-scale rural landscape values and the examination of mechanisms underlying their multiple influencing factors, as well as for the optimization of rural landscape planning, design, and sustainable management practices. Full article

Classifying games according to their strategic properties provides meaningful insights into the motivations driving the interacting parties, suggests possible future trajectories, and in some cases also points to potential interventions aiming to influence the interactions’ outcomes. Here, we present a new classification that merges two perspectives: (i) a revised version of Rapoport and Guyer’s taxonomy, which extends beyond the original 78 games they describe by classifying all two-by-two games according to fundamental strategic properties, and (ii) a novel classification grounded in the theory of subjective expected relative similarity, which addresses not only the games’ payoffs but also the players’ strategic perceptions of their opponents. While Rapoport and Guyer’s original taxonomy classifies only strictly-ordinal games, the revised classification addresses all two-by-two games. It comprises eleven categories that are further grouped into five super-categories that focus on the game’s expected outcome and its strategic stability. The second, similarity-based, classification comprises four main categories, specifying whether players’ perceptions of their opponents have the potential to influence strategic decision-making. The merged classification comprises 14 game types, offering a holistic account of the strategic interaction, the players’ underlying motivations, and the expected outcome. It combines the fixed strategic properties with the variable social aspects of the interaction. Moreover, the novel classification points to the potential of social interventions that may influence the game’s outcome by altering strategic similarity perceptions. Therefore, the present work is relevant for both theoretical and experimental research, providing insights into actual choices expected inside and outside of the laboratory. Full article

This study explores how sustainability leadership and Green Human Resource Management (Green HRM) practices interplay to cultivate an environmentally responsible culture in organizations based in Jeddah. Through thematic analysis of participant interviews, the research identifies key leadership behaviors, such as visionary communication, role modeling, and operational integration, that align culturally grounded ethical values to drive sustainability. Green HRM practices, including green recruitment, targeted training, eco-friendly performance appraisals, and recognition systems, further reinforce these leadership efforts. The study highlights the importance of authentic alignment between leadership values and HRM policies to avoid perceptions of greenwashing and to institutionalize sustainable practices effectively. Findings emphasize that embedding sustainability within organizational culture requires a synergistic approach integrating leadership vision, HRM systems, and cultural context, fostering employee motivation and long-term environmental commitment. The implications provide valuable insights for organizations seeking to implement meaningful sustainability strategies aligned with both global goals and local values. Full article