Search Results (2,832)

As one of the major greenhouse gas emission contributors, the food service industry, particularly buffet-style restaurants, is responsible for reducing food waste. This study explores the factors that shape consumer behavior toward food waste reduction in buffet-style restaurants based on the Theory of Reasoned Action (TRA) and Social Exchange theory (SET), as well as analyzing the gender differences in these determinants, offering practical insights for the restaurant industry. This study also uses structural equation modeling and group analysis to examine a total of 547 valid responses gathered through an online survey, including 286 male (52.3%) and 258 female (47.2%) respondents. The findings underscore the attitudes, subjective norms, and establishment policies that emerge as critical drivers of consumer behavior in buffet-style dining settings. Notably, significant gender differences are observed in attitudes and establishment policies. In light of these results, we recommend strategies that include enhancing consumer attitudes and implementing penalty policies within restaurant operations. Restaurants could display visual signs and images related to reducing food waste, provide detailed portion size information, and apply monetary fines for excess waste to reduce consumers’ food waste intentions. These strategies are particularly effective for male consumers, who are more influenced by these factors compared to female consumers. This research contributes valuable guidance for the industry’s efforts to address food waste concerns, emphasizing gender differences and promoting environmentally responsible behavior among consumers. Full article

This study investigates the relationship between diet quality and body image disturbance among young women aged 18–24, a crucial period for establishing lifelong health behaviors. Given the increasing exposure to social media, which often promotes unrealistic beauty standards, this research aims to explore associations between eating behaviors, diet quality, and body image disturbance. A mixed-methods approach was employed, combining qualitative focus group discussions with quantitative analysis. Focus groups (n = 19) explored themes of body image dissatisfaction. The Body Image Disturbance Questionnaire (BIDQ) was administered to 50 participants (young women aged 18–24) to quantitatively assess body image disturbance, while diet quality was evaluated using the Australian Recommended Food Scores (ARFS). The Three-Factor Eating Questionnaire (TFEQ-R18) was also used to assess eating behaviors, including cognitive restraint, uncontrolled eating, and emotional eating. A social influence questionnaire (SIQ) was administered to measure the effect of social influence. Pearson’s correlation coefficient was used to determine the relationship between ARFS, BIDQ, and TFEQ-R18 scores. Qualitative findings revealed persistent dissatisfaction with body shape, largely influenced by social media. Quantitatively, 65% of participants scored above the clinical threshold for body image disturbance (mean BIDQ score = 4.2, SD = 0.8). The correlation between ARFS and BIDQ scores was weak and not statistically significant (r = 0.057, p = 0.711). However, a significant positive correlation was observed between time spent on social media and body image disturbance (r = 0.58, p < 0.01). Additionally, TFEQ-R18 results indicated that 45% of participants displayed moderate levels of uncontrolled eating, and 36.5% demonstrated moderate levels of emotional eating. While social media is associated with body image concerns, its effect on eating behaviors and diet quality shows weak correlations, suggesting that other factors may mediate these outcomes. These results suggest the complexity of the associations between body image, eating behaviors, and diet quality, indicating that interventions should consider psychological drivers behind these concerns alongside social media usage. Full article

Grasping how scale influences the interactions among ecosystem services (ESs) is vital for the sustainable management of multiple ESs at the regional level. However, it is currently unclear whether the actual ES interactions and their driving mechanisms are consistent across different spatial and temporal scales. Therefore, using the Lishui River Basin of China as a case study, we analyzed the spatial and temporal distribution of five key ESs across three scales (grid, sub-watershed, and county) from 2010 to 2020. We also innovatively used Pearson correlation analysis, Self-organizing Mapping (SOM), and random forest analysis to assess the dynamic trends of trade-offs/synergies among ESs, ecosystem service bundles (ESBs), and their main socio-ecological drivers across different spatiotemporal scales. The findings showed that (1) the spatial distribution of ESs varied with land use types, with high-value areas mainly in the western and northern mountainous regions and lower values in the eastern part. Temporally, significant improvements were observed in soil conservation (SC, 3028.23–5023.75 t/hm²) and water yield (WY, 558.79–969.56 mm), while carbon sequestration (CS) and habitat quality (HQ) declined from 2010 to 2020. (2) The trade-offs and synergies among ESs exhibited enhanced at larger scales, with synergies being the predominant relationship. These relationships remained relatively stable over time, with trade-offs mainly observed in ES pairs related to nitrogen export (NE). (3) ESBs and their socio-ecological drivers varied with scales. At the grid scale, frequent ESB flows and transformations were observed, with land use/land cover (LULC) being the main drivers. At other scales, climate (especially temperature) and topography were dominant. Ecosystem management focused on city bundles or downstream city bundles in the east of the basin, aligning with urban expansion trends. These insights will offer valuable guidance for decision-making regarding hierarchical management strategies and resource allocation for regional ESs. Full article

Oral pathobionts are essential in instigating local inflammation within the oral cavity and contribute to the pathogenesis of diseases in the gastrointestinal tract and other distant organs. Among the Gram-negative pathobionts, Porphyromonas gingivalis and Fusobacterium nucleatum emerge as critical drivers of periodontitis, exerting their influence not only locally but also as inducers of gut dysbiosis, intestinal disturbances, and systemic ailments. This dual impact is facilitated by their ectopic colonization of the intestinal mucosa and the subsequent mediation of distal systemic effects by releasing outer membrane vesicles (OMVs) into circulation. This review elucidates the principal components of oral pathobiont-derived OMVs implicated in disease pathogenesis within the oral–gut axis, detailing virulence factors that OMVs carry and their interactions with host epithelial and immune cells, both in vitro and in vivo. Additionally, we shed light on the less acknowledged interplay between oral pathobionts and the gut commensal Akkermansia muciniphila, which can directly impede oral pathobionts’ growth and modulate bacterial gene expression. Notably, OMVs derived from A. muciniphila emerge as promoters of anti-inflammatory effects within the gastrointestinal and distant tissues. Consequently, we explore the potential of A. muciniphila-derived OMVs to interact with oral pathobionts and prevent disease in the oral–gut axis. Full article

Wildfires are a critical driver of landscape transformation on Earth, representing a dynamic and ephemeral process that poses challenges for accurate early detection. To address this challenge, researchers have increasingly turned to deep learning techniques, which have demonstrated remarkable potential in enhancing the performance of wildfire detection systems. This paper provides a comprehensive review of fire detection using deep learning, spanning from 1990 to 2023. This study employed a comprehensive approach, combining bibliometric analysis, qualitative and quantitative methods, and systematic review techniques to examine the advancements in fire detection using deep learning in remote sensing. It unveils key trends in publication patterns, author collaborations, and thematic focuses, emphasizing the remarkable growth in fire detection using deep learning in remote sensing (FDDL) research, especially from the 2010s onward, fueled by advancements in computational power and remote sensing technologies. The review identifies “Remote Sensing” as the primary platform for FDDL research dissemination and highlights the field’s collaborative nature, with an average of 5.02 authors per paper. The co-occurrence network analysis reveals diverse research themes, spanning technical approaches and practical applications, with significant contributions from China, the United States, South Korea, Brazil, and Australia. Highly cited papers are explored, revealing their substantial influence on the field’s research focus. The analysis underscores the practical implications of integrating high-quality input data and advanced deep-learning techniques with remote sensing for effective fire detection. It provides actionable recommendations for future research, emphasizing interdisciplinary and international collaboration to propel FDDL technologies and applications. The study’s conclusions highlight the growing significance of FDDL technologies and the necessity for ongoing advancements in computational and remote sensing methodologies. The practical takeaway is clear: future research should prioritize enhancing the synergy between deep learning techniques and remote sensing technologies to develop more efficient and accurate fire detection systems, ultimately fostering groundbreaking innovations. Full article

Background: Patient satisfaction and loyalty are essential indicators of healthcare quality, directly impacting patient outcomes and the long-term success of healthcare facilities. Despite the growing importance of patient-centered care in Saudi Arabia, there is limited research exploring the factors that influence patient satisfaction and loyalty, particularly in the Riyadh region. Aim: This study aims to identify the key factors influencing patient satisfaction and loyalty among Saudi patients attending public and private healthcare facilities in the Riyadh region. The study focuses on how healthcare service quality, communication, and demographic factors contribute to patient satisfaction and loyalty. Methods: A cross-sectional study was conducted with a sample of 350 Saudi patients from 10 healthcare facilities in Riyadh. Data were collected using the Patient Satisfaction Questionnaire (PSQ-18) and the Patient Loyalty Questionnaire (PLQ). Descriptive statistics, Pearson correlation, and multiple linear regression were employed to identify predictors of patient satisfaction and loyalty. Results: Significant predictors of patient satisfaction included general satisfaction (β = 0.48, p < 0.001), communication (β = 0.35, p < 0.001), and the frequency of healthcare visits (β = 0.13, p = 0.011). Private healthcare facilities had higher satisfaction (p < 0.001) and loyalty scores (p < 0.001) compared to public facilities. Patient loyalty was strongly predicted by general satisfaction (β = 0.55, p < 0.001) and communication (β = 0.42, p < 0.001). Conclusions: Communication quality and patient satisfaction are key drivers of patient loyalty in Saudi healthcare facilities. Private facilities outperform public ones in patient satisfaction and loyalty. These findings emphasize the need for healthcare providers to enhance communication and service quality to foster patient loyalty. Tailored approaches to meet the diverse needs of patients, particularly in terms of education and visit frequency, are crucial for improving healthcare outcomes in Saudi Arabia. Full article

This review will explore the role of executive functions and the impact they have in facilitating the skills of vehicle operation. Executive functions are critical for the decision-making process, problem-solving, and multitasking. They are considered the primary factors in driving cases that demand drivers to react quickly and adapt to certain situations. Based on the PRISMA 2020 guidelines, this study aims to investigate, analyze, and categorize higher mental skills and their qualities directly related to driving. The literature review was performed in the following databases: PubMed, Web of Science, Scopus, and Google Scholar, using the article collections’ snowball search technique. The results suggest that key executive functions like working memory and inhibitory control are closely related to risky behavior and driving errors that lead to accidents. This review adds valuable insight by highlighting the significance of their contribution to future research, driver educational programs, and technology for improving driver safety. Consequently, collecting recent data will contribute to understanding new parameters that influence driving behavior, creating the possibility for appropriate proposals for future research. Full article

In the transition to fully automated driving, the interaction between drivers and vehicles is crucial as drivers’ emotions directly influence their behavior, thereby impacting traffic safety. Currently, relying solely on a backbone based on a convolutional neural network (CNN) to extract single RGB modal facial features makes it difficult to capture enough semantic information. To address this issue, this paper proposes a Dual-branch Multimodal Fusion Network (DMFNet). DMFNet extracts semantic features from visible–infrared (RGB-IR) image pairs effectively capturing complementary information between two modalities and achieving a more accurate understanding of the drivers’ emotional state at a global level. However, the accuracy of facial recognition is significantly affected by variations in the drivers’ head posture and light environment. Thus, we further propose a U-Shape Reconstruction Network (URNet) to focus on enhancing and reconstructing the detailed features of RGB modes. Additionally, we design a Detail Enhancement Block (DEB) embedded in a U-shaped reconstruction network for high-frequency filtering. Compared with the original driver emotion recognition model, our method improved the accuracy by 18.77% on the DEFE++ dataset, proving the superiority of the proposed method. Full article

Climate warming, agricultural intensity, and urban growth are main forces triggering land degradation in advanced economies. Being active over different spatial and temporal scales, they usually reflect—at least indirectly—the impact of additional factors, such as wellbeing, demographic dynamics, and social development, on land quality. Using descriptive statistics and a multiple regression analysis, we analyzed the impact of these three processes comparatively over a decadal scale from 1960 to 2020 at the provincial level (Nuts-3 sensu Eurostat) in Italy. We enriched the investigation with a short-term forecast for 2030, based on four simplified assumptions grounded on a purely deterministic approach. Land degradation was estimated adopting the Environmental Sensitive Area Index (ESAI) measured at the spatio-temporal scale mentioned above. Computing on multiple observations at nearly 300,000 locations all over Italy, provinces were regarded as representative spatial units of the territorial pattern of land degradation. Between 1960 and 1990, the three predictors (climate, agriculture, and urbanization) explained a relatively high proportion of variance, suggesting a modest role for any other (unobserved) factor. All of these factors were found to be highly significant predictors of land degradation intensity across provinces, the most impactful being farming intensity. The highest adjusted-R² coefficient was observed in both 1990 and 2000, and suggests that the three predictors still reflect the most powerful drivers of land degradation in Italy at those times, with a marginal role for additional (unobserved) factors. The impact of farming intensity remained high, with the role of urbanization increasing moderately, and the role of climate aridity declining weakly between 2000 and 2010. In more recent times (2010 and 2020), and in future (2030) scenarios, the adjusted R² diminished moderately, suggesting a non-negligible importance of external (unobserved) factors and the rising role of spatial heterogeneity. The climate factor became progressively insignificant over time, while increasing the role of urbanization systematically. The impact of farming intensity remained high and significant. These results underlie a latent shift in the spatial distribution of the level of land vulnerability in Italy toward a spatially polarized model, influenced primarily by human pressure and socioeconomic drivers and less intensively shaped by biophysical factors. Climate aridity was revealed to be more effective in the explanation of land degradation patterns in the 1960s rather than in recent observation times. Full article

The transition to electric vehicles (EVs) will require significant changes in the automotive industry, particularly concerning its labour force. This study evaluates the impact of EVs on employment within South Africa’s automotive component manufacturing sector. A system dynamics model was developed to assess the effect of EV market penetration on component manufacturing employment over time. Key drivers of employment in the conventional and the EV component industries were identified and incorporated into the model. The results indicate a negative impact of EV penetration on employment of 18.3% when considering 20.0% EV sales (EV20) in 2040. Scenario analyses highlighted the influence of individual components, battery localisation, and load shedding on labour. Tyre and wheel manufacturing was found to be the most labour impactful component in the conventional industry against electrical engines in the EV counterpart. Localising 25.0% of battery production could increase employment by 6.9% and 2.7% in the EV40 and EV20 Scenarios. Load shedding has a detrimental effect on the country’s economy, assumed to reduce employment by 30.0%. However, strategic industry and policy interventions can mitigate the adverse effects of this transition. Full article

This study examines the influence of façade typologies on Life Cycle Embodied Carbon (LCEC) in retrofitting university buildings in South Korea. By analyzing 28 cases across seven retrofit scenarios, four main façade types—PW-1, PW-2 (Punched Walls), WW (Window Walls), and CW (Curtain Walls)—were identified as key drivers in retrofit outcomes. PW-1 and PW-2 often require over-cladding due to demolition complexities, whereas WW and CW, despite undergoing full demolition and re-cladding, do not necessarily result in higher carbon emissions. The use of Exterior Insulation and Finish Systems (EIFS) can achieve up to a 35% reduction in LCEC compared to traditional materials like stone, particularly in cases requiring minimal structural reinforcement. By balancing sustainability with architectural integrity, this study offers valuable guidance for similar projects globally, providing insights into optimizing retrofit strategies for more sustainable building practices. Full article

Land use and land cover change (LUCC) significantly influences the dynamics of carbon storage in thin terrestrial ecosystems. Investigating the interplay between land use alterations and carbon sequestration is crucial for refining regional land use configurations, sustaining the regional carbon balance, and augmenting regional carbon storage. Using land use data from the Pearl River Delta Urban Agglomeration (PRDUA) from 2010 to 2020, this study employed PLUS-InVEST models to analyze the spatiotemporal dynamics of land use and carbon storage. Projections for the years 2030, 2040, and 2050 were performed under three distinct developmental scenarios, namely, natural development (ND), city priority development (CPD), and ecological protection development (EPD), to forecast changes in land use and carbon storage. The geographic detector model was leveraged to dissect the determinants of the spatial and temporal variability of carbon storage, offering pertinent recommendations. The results showed that (1) during 2010–2020, the carbon storage in the PRDUA showed a decreasing trend, with a total decrease of 9.52 × 10⁶ Mg, and the spatial distribution of carbon density in the urban agglomeration was imbalanced and showed an overall trend in increasing from the center to the periphery. (2) Clear differences in carbon storage were observed among the three development scenarios of the PRDUA between 2030 and 2050. Only the EPD scenario achieved an increase in carbon storage of 1.10 × 10⁶ Mg, and it was the scenario with the greatest potential for carbon sequestration. (3) Among the drivers of the evolution of spatial land use patterns, population, the normalized difference vegetation index (NDVI), and distance to the railway had the greatest influence on LUCC. (4) The annual average temperature, annual average rainfall, and GDP exerted a significant influence on the spatiotemporal dynamics of carbon storage in the PRDUA, and the interactions between the 15 drivers and changes in carbon storage predominantly manifested as nonlinear and double-factor enhancements. The results provide a theoretical basis for future spatial planning and achieving carbon neutrality in the PRDUA. Full article

Understanding the driving mechanisms of diversity across multiple dimensions is a fundamental task in biodiversity conservation. Here, we examined the alpha and beta diversity of macroinvertebrates in the taxonomic, functional, and phylogenetic dimensions in mountain streams of northwestern Hubei Province, China. We also assessed how much local environmental, land use, climatic, and spatial variables affected these diversities. We found that (1) there were generally weak or no correlations of alpha and beta diversity between taxonomic, functional, and phylogenetic dimensions; (2) compared to both functional and phylogenetic beta diversity, which was mainly determined by nestedness, taxonomic beta diversity was mostly molded by turnover and was much higher; and (3) local environmental variables predominantly influenced taxonomic and functional dimensions of alpha and beta diversity, while spatial factors primarily drove phylogenetic dimension. These results suggest that regulating local habitats is crucial for lotic biodiversity conservation efforts, though spatial processes cannot be overlooked. Furthermore, our findings verify the supplemental role of functional and phylogenetic data in enriching insights provided by taxonomic data alone. This underscores the importance of a multidimensional approach for a more nuanced understanding of community assembly mechanisms, which is crucial for efficient ecosystem management and biodiversity conservation. Full article

In traffic engineering, vehicle speed is a critical determinant of both the risk and severity of road crashes, a fact that holds particularly important for signalized intersections. Accurately selecting vehicle speeds is crucial not only for minimizing accident risks but also for ensuring the proper calculation of intergreen times, which directly influences the efficiency and safety of traffic flow. Traditionally, the design of signal programs relies on fixed speed parameters, such as the posted speed limit or the operational speed, typically represented by the 85th percentile speed from speed distribution data. Furthermore, many design guidelines allow for the selection of these critical speed values based on the designer’s own experience. However, such practices may lead to discrepancies in intergreen time calculations, potentially compromising safety and efficiency at intersections. Our research underscores the substantial variability in the speeds of passenger vehicles traveling intersections under free-flow conditions. This study encompassed numerous intersections with the highest number of accidents, using unmanned aerial vehicles to conduct surveys in three Polish cities: Toruń, Bydgoszcz, and Warsaw. The captured video footage of vehicle movements at predetermined measurement sections was analyzed to find appropriate speeds for various travel maneuvers through these sections, encompassing straight-through, left-turn, and right-turn relations. Our analysis focused on how specific infrastructure-related factors influence driver behavior. The following were evaluated: intersection type, traffic organization, approach lane width, number of lanes, longitudinal road gradient, trams or pedestrian or bicycle crossing presence, and even roadside obstacles such as buildings, barriers or trees, and others. The results reveal that these factors significantly affect drivers’ speed choices, particularly in turning maneuvers. Furthermore, it was observed that the average speeds chosen by drivers at signalized intersections did not reach the permissible speed limit of 50 km/h as established in typical Polish urban areas. A key outcome of our analysis is the recommendation for a more precise speed model that contributes to the design of signal programs, enhancing road safety, and aligning with sustainable transport development policies. Based on our statistical analyses, we propose adopting a more sophisticated model to determine actual vehicle speeds more accurately. It was proved that, using the developed model, the results of calculating the intergreen times are statistically significantly higher. This recommendation is particularly pertinent to the design of signal programs. Furthermore, by improving speed accuracy values in intergreen calculation models with a clear impact on increasing road safety, we anticipate reductions in operational costs for the transportation system, which will contribute to both economic and environmental goals. Full article

Previous studies have focused on the impact of individual factors on lane departure warning (LDW) utility during driving. However, comprehensive analysis has not been considered based on multiple variables, such as driver characteristics. This paper aims to propose a methodology in exploring the utility of LDW under varied warning timing situations, focusing on changes in driving style and distraction level to obtain the optimal warning timing matching relationship. A driving simulator experiment with a mixed 4 × 3 factor design was conducted. The design matrix includes four level of secondary task (ST) conditions and three warning timings situations for drivers with various driving styles. To estimate the utility of the LDW system, lane departure duration (LDD) was selected as a time-based measure of utility. Both the Kaplan-Meier method and COX model were applied and compared. Combined with questionnaire results, the results indicate that both driving style and distraction state are significant influence factors. Generally, the results suggest that the more aggressive drivers lead to the more severe lane departure behavior and they preferred late warning. In terms of distraction state, the LDD increases with the level of ST remarkably. This implies that the earlier warning timing should be given for the higher-level distraction state condition. It was also observed that adaptive warning timing is needed based on the analysis of the interactive effect among multiple variables. The results provide empirical data for the optimization of LDW system design. Full article