Search Results (11,378)

Background: Healthcare systems contribute significantly to environmental pollution, energy consumption, and resource depletion, making sustainability an increasingly important environmental and public health priority. Nurses, as frontline healthcare professionals, play a critical role in promoting environmentally responsible practices and advocating for sustainable healthcare within clinical settings. Objective: The study aimed to examine the associations between nurses’ sustainability consciousness, green advocacy, and work grit in hospital settings. Methods: A descriptive cross-sectional correlational study was conducted among 377 nurses working in two university-affiliated hospitals in Egypt. Data were collected using validated instruments assessing sustainability consciousness, green advocacy, and work grit. Descriptive statistics were calculated to summarize participant characteristics and study variables. Associations among sustainability consciousness, green advocacy, and work grit were examined using Pearson correlation analysis. Multiple linear regression analysis was conducted to identify significant predictors of green advocacy, while noting that the study design allows for identification of associations rather than causal relationships. Results: The findings indicated generally high levels of sustainability consciousness among nurses. Significant positive associations were observed between sustainability consciousness, green advocacy, and work grit (p < 0.01). Multiple linear regression analysis identified sustainability consciousness and work grit as significant predictors of green advocacy, explaining 34.2% of its variance. Conclusions: These findings highlight the interconnected roles of sustainability awareness, advocacy behaviors, and psychological resilience in promoting environmentally sustainable healthcare practices. Strengthening nurses’ sustainability consciousness and work grit may enhance green advocacy and contribute to the development of sustainable healthcare systems, supporting global environmental and public health goals aligned with the United Nations Sustainable Development Goals. Full article

Firewall policy misconfigurations remain a major source of security vulnerabilities in modern networks, particularly as firewall rule sets grow in size and complexity. Such misconfigurations, commonly referred to as firewall anomalies, can lead to unintended access control behavior and undermine network security. In this paper, we propose a formal logic rule-based framework for the systematic detection and investigation of firewall anomalies, supported by knowledge graph-based visualization. First-order logic (FOL) is employed to precisely model firewall rules and to define major anomaly types, including shadowing, redundancy, correlation, generalization, and irrelevance, in both single and distributed firewall environments. The proposed framework introduces explicit and comprehensive logical definitions for each anomaly type, enabling deterministic, interpretable, and complete detection of rule conflicts and overlaps. Complex anomalies, particularly correlation and generalization, are systematically decomposed into well-defined logical cases to facilitate the accurate identification of subtle, order-dependent interactions among firewall rules. To enhance usability and analysis, firewall rules and detected anomalies are represented using Neo4j knowledge graphs, providing intuitive visual insights into rule relationships and anomaly causes. The effectiveness of the proposed approach is validated using a real operational backbone network dataset collected from Stanford University’s campus network. Experimental results demonstrate the framework’s ability to accurately detect both simple and complex firewall anomalies under realistic network conditions. To further validate the proposed logic rules, a machine learning-based evaluation was conducted. The findings confirm their effectiveness in accurately characterizing firewall anomalies. Unlike machine learning or heuristic-based methods, the proposed approach does not require training data and guarantees formal correctness and explainability. These features make it a robust and practical solution for firewall policy verification and network security management. Full article

Arsenic (As) contamination in groundwater represents a critical global environmental health issue. The reductive dissolution of arsenic-bearing iron oxides by dissimilatory metal-reducing bacteria (DMRB) is a key biogeochemical process driving arsenic mobilization and release in groundwater. However, the mechanism of exogenous electron shuttles in this process remains poorly understood. This study investigated the impact of the quinone-based electron shuttle anthraquinone-2,6-disulfonate (AQDS) on the reductive dissolution of arsenic-loaded goethite by the model DMRB Shewanella putrefaciens CN32 (S.P CN32). The mobilization and transformation behaviors of arsenic and iron were compared under different pH conditions and using different arsenic-loading methods (coprecipitation vs. adsorption). Results demonstrated that AQDS acted as an electron transfer mediator. It significantly enhanced the reductive dissolution of Fe(III). It also significantly enhanced the reduction of As(V). These actions collectively accelerated arsenic release and mobilization. The study also revealed a competitive preferential order in microbial reduction, where the thermodynamically more favorable Fe(III) reduction preceded As(V) reduction. Environmental pH co-regulated this process. Its influence worked through microbial activity and mineral surface properties. A neutral pH was most conducive to the AQDS-mediated bioreduction of arsenic and iron. This study elucidates the critical role of electron shuttles in the biogeochemical cycling of arsenic in contaminated sites, providing a scientific basis for a deeper understanding of the formation mechanisms and risk assessment of high-arsenic groundwater. Full article

To evaluate the potential biological effects of electromagnetic fields from offshore wind farms on Sebastes schlegelii, a laboratory-controlled chronic exposure experiment was conducted using a magnet-based static magnetic field system. Each group contained 60 fish distributed across four replicate tanks, with 15 fish per tank, and the fish were continuously exposed for 20 d under controlled water-quality conditions. Daily video monitoring of collective shoaling behavior was combined with multi-tissue physiological and biochemical analyses. Electromagnetic field exposure increased the swimming speed, burst frequency, activity ratio, spatial coverage, occupancy entropy, and polarization, while reducing the nearest neighbor distance, group radius, and group area. At the physiological level, cortisol increased mainly in the liver and brain, ACTH showed tissue-dependent modulation, SOD remained relatively stable, and glutathione increased in multiple tissues, especially in the liver, gut, and brain. Correlation analysis indicated a close coupling between behavioral reorganization and endocrine–redox regulation, suggesting that chronic EMF exposure shifted Sebastes schlegelii into a stress-associated but functionally coordinated collective state. Full article

Background: Accurate estimation of water and energy requirements is fundamental for establishing dietary reference values in young adults. However, evidence integrating objectively measured energy expenditure with detailed water turnover components remains limited in Chinese populations. Objectives: To quantify water intake, water loss, and energy expenditure in healthy young college students, and to examine how energy metabolism is associated with specific components of water turnover under free-living conditions. Methods: Twenty-one healthy adults aged 18–25 years participated in a 14-day observational study conducted in Beijing, China. Total energy expenditure (TEE) was measured over 14 days using the doubly labeled water (DLW) method. Physical activity was monitored over 7 consecutive days using a triaxial accelerometer. Water intake was assessed using multiple methods: water from beverages (including plain drinking water and other beverages) was recorded over 7 days using 24 h fluid intake records, while water from food was measured during days 5–7 using weighed food records combined with duplicate portion and direct drying methods. Urinary and fecal water loss were quantified using 24 h collections conducted during days 5–7. Metabolic water production and insensible water losses were estimated using established physiological equations. Multivariable linear regression analyses were conducted to examine associations between energy-related variables and components of water turnover. Results: Mean total daily water intake was 3023 mL, with water from beverages accounting for 54.1%, water from food for 36.7%, and metabolic water for 9.1%. Mean total daily water loss was 1931 mL, predominantly from urinary excretion (81.0%). DLW-measured TEE averaged 2018.6 kcal/day and was higher in males than in females. Most regression models examining total water intake and beverage-derived water were not statistically significant, and no consistent associations were observed between these variables and total energy intake, TEE, or PAEE. In contrast, TEE was positively associated with metabolic water production and respiratory water loss (both p < 0.001). Significant associations with total energy intake were observed for water from food and fecal water loss (both p < 0.01), whereas other water intake components showed no significant associations. Conclusions: In young adults, energy metabolism appears to be more closely associated with physiologically regulated components of water turnover than with voluntary water intake. These findings suggest a divergence between endogenous and behaviorally regulated pathways of water turnover and highlight the importance of considering component-specific water dynamics when examining hydration and energy balance, although confirmation in larger studies is warranted. Full article

Entrepreneurship education has been promoted as a pathway for strengthening entrepreneurial orientation among university students; however, uncertainty remains regarding the mechanisms through which it influences entrepreneurial intention, particularly in emerging economies. This study examines whether entrepreneurship education influences the entrepreneurial intention of university students in Honduras indirectly through entrepreneurial self-efficacy and entrepreneurial motivation. A quantitative, cross-sectional, and explanatory study was conducted with a sample of 431 university students. Data were collected using a structured 56-item questionnaire with a seven-point Likert scale. The analysis was carried out through confirmatory factor analysis and structural equation modeling using the WLSMV estimator. The results show that entrepreneurship education positively influences all dimensions of entrepreneurial self-efficacy and entrepreneurial motivation included in the model. Personal attitude emerged as the strongest predictor of entrepreneurial intention, followed by subjective norms and specific dimensions of self-efficacy related to business planning and management and entrepreneurial leadership. In contrast, the innovation and problem-solving dimension did not show a significant direct effect on entrepreneurial intention, while perceived behavioral control showed a marginal effect. The parsimonious model explained 75.9% of the variance in entrepreneurial intention. Overall, the findings suggest that entrepreneurship education exerts its influence primarily through indirect pathways by strengthening capability beliefs and motivational appraisals that are proximal to action. Full article

This study examines the influence of misinformation on vaccination decision-making and the perception of social security in Romania in the context of potential future pandemics. Using a survey-based design, data were collected through an online questionnaire administered to a sample of 1005 respondents. The analysis employed descriptive and inferential statistical methods, including chi-square tests, ANOVA, Kruskal–Wallis tests, principal component analysis (PCA), K-means clustering, random forest models, and Spearman correlations. The results indicate statistically significant associations between belief in misinformation and vaccination attitudes (p < 0.001), with moderate effect sizes. Effect size estimates indicated small-to-moderate associations (e.g., Cramér’s V up to 0.371 for key demographic differences, and Kendall’s W = 0.273 for the increase in willingness across the three severity scenarios). Individuals with higher levels of education, urban residence, and younger age were more likely to report higher willingness to vaccinate, whereas respondents from rural areas and those with lower educational levels showed greater susceptibility to misinformation. In addition, risk perception was significantly associated with vaccination intention, which increased as the severity of hypothetical pandemic scenarios intensified. Predictive modeling identified specific misinformation beliefs—particularly those related to vaccine safety and natural immunity—as key factors associated with vaccination decisions. These findings suggest that misinformation is strongly associated with both individual vaccination behavior and broader perceptions of social security. Full article

Public deployment of generative AI in cultural institutions is constrained by small, rights-restricted datasets, strict latency and runtime-stability requirements, and limits on visitor-data collection. This study presents a deployment-oriented framework for adapting a pre-trained text-to-image diffusion foundation model to a heritage-specific visual domain using Low-Rank Adaptation (LoRA). A Stable Diffusion v1.5 backbone is specialized through data-centric curation and LoRA fine-tuning, then served through an asynchronous edge architecture that links a Unity client and a local Python (version 3.10) inference server for public-facing operation on a native 400 × 1080 vertical canvas. To support deployment decisions without collecting personally identifiable information, the system records only anonymous operational logs and evaluates sustained-load behavior under repeated inference. In a 1000-iteration profiling test, the proposed configuration maintained stable runtime behavior without observable upward memory drift, with a peak allocated VRAM of 3.04 GB and an average end-to-end latency of 3.12 s. An 8 h field deployment further indicated service continuity under public interaction, while a CLIP-based proxy analysis under matched prompts and seeds suggested improved relative style controllability after adaptation (0.848 vs. 0.799). Rather than claiming cultural authenticity or visitor-level effects, this study offers a data-centric, deployment-oriented methodology for operating public-facing generative AI under small-data, latency, and privacy constraints. Full article

Providing dietary feedback is important for promoting healthy behaviors in weight management, but the rapid development of obesity and the shortage of medical nutrition human resources have limited this health service. The rise of large language models (LLMs) offers the possibility of using artificial intelligence (AI) to simulate the behavior of human dietitians. However, existing studies have only explored LLM performance when generating answers to common nutrition-related questions; the use of LLMs to generate situation-adapted dietary feedback in practical weight management scenarios still needs further research. In this study, we collected dietary records and dietary feedback from primary dietitians through an mHealth weight management application. We conducted topic modeling to generalize how dietitians deliver nutrition guidance in real-world dietary feedback scenarios. Combining the in-context learning capability of LLMs with real-world data, we proposed a synthetic data generation approach (HDI-SDG) and trained an LLM for dietary feedback with the synthetic data (LLMDF-EXP). Experiments on automatic and manual evaluation of LLMDF-EXP and an LLM trained directly with the real-world data as well as generalized LLMs illustrated that LLMDF-EXP performed most similarly to human experts. Notably, there were no significant differences from human experts in terms of professionalism (p-value = 0.510) and usefulness (p-value = 0.498). The study highlights that integrating LLMs with real-world data in health management processes can enhance the situational adaptability of LLMs in practical health management environment applications. Full article

To address the lack of direct experimental characterization of multi-kernel group threshing during maize ear threshing, an experimental study on maize ear group threshing was conducted based on kernel arrangement unit characteristics. By constructing a torque testing system for maize ear detachment, we analyzed the temporal variation in torque during detachment and its response to different experimental conditions. Statistical evaluation of torque variability and stability was performed using analysis of variance (ANOVA) and error bars. Furthermore, high-speed photography was employed to capture continuous images and analyze the trajectories of kernel motion during critical detachment stages, revealing the movement characteristics and shedding behavior of kernel clusters. The results indicate that the maize ear threshing process does not involve individual kernel detachment but primarily manifests as group threshing behavior with the arrangement unit as the fundamental unit. Furthermore, the characteristics of the variation in threshing torque correspond to the collective detachment process of the kernel group. This study provides direct experimental evidence for the group threshing mechanism of maize ears through both torque statistical analysis and high-speed visualization. These findings offer valuable insights for threshing mechanism research and the optimization of threshing components. Full article

Attention-Deficit/Hyperactivity Disorder (ADHD), affecting 5–10% of children globally, faces treatment limitations due to adverse effects and uncertain long-term risks of current pharmacotherapies. This study investigated the therapeutic potential of sepia ink (SI), a marine-derived natural complex from cuttlefish, in a scopolamine-induced ADHD-like mouse model. The chemical constituents of SI were characterized via Ultra-Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS). The behavioral assessments, histopathological examinations, flow cytometry, and complete blood counts were utilized to evaluate its effects on ADHD-like phenotypes, neuroinflammation, and immune function. Integrated transcriptomic, plasma metabolomic, and 16S rRNA sequencing were used to explore the underlying mechanisms. SI significantly alleviated hyperactivity and improved spatial learning and memory deficits. It reduced hippocampal neuronal damage, attenuated neuroinflammation, and reversed scopolamine-induced immunosuppression in spleen and thymus. SI also restored the balance of immune cell subsets in both mesenteric lymph nodes and spleen, and the peripheral blood cell counts. Multi-omics analyses suggested that the beneficial effects of SI were associated with reduced neuroinflammation, rebalanced systemic immune responses, partial correction of lipid metabolic disturbances, and restoration of gut microbiota homeostasis. Collectively, our findings indicate that SI effectively mitigates the in vivo ADHD-like impairments by coordinating immune, metabolic, and gut microbiota-related processes, thereby supporting its potential as a marine-derived therapeutic candidate for further ADHD treatment. Full article

Organizations are increasingly required to promote a culture of sustainability among their employees. Accordingly, a growing number of organizations have implemented work practices centered on pro-environmental behaviors. However, the psychological mechanisms underlying these behaviors in the workplace remain insufficiently explored. Grounded in Social Cognitive Theory and normative frameworks, the present study proposes a moderated mediation model examining the relationship between collective efficacy and employees’ pro-environmental behaviors, the mediating role of personal norm, and the moderating role of injunctive norm. Data were collected from 906 Italian employees who completed an online questionnaire, and the hypothesized model was tested using moderated mediation analyses. The results showed that collective efficacy was positively associated with personal norm, which in turn was positively related to pro-environmental behaviors, indicating an indirect association pattern consistent with the hypothesized mediating role of personal norm. Moreover, injunctive norm strengthened the relationship between collective efficacy and pro-environmental behaviors. These findings highlight the central role of moral obligation in translating collective beliefs into sustainable action and underscore the importance of normative organizational climates. This study contributes to the organizational sustainability literature by integrating collective efficacy and normative processes as key drivers of everyday pro-environmental behavior at work. Full article

Collapsible soils present significant geotechnical challenges due to their abrupt volume reduction and strength degradation upon wetting, which can lead to severe structural damage. This study evaluates the effectiveness of sustainable and eco-friendly additives—including rice husk ash, lime, eggshell powder, turmeric, polypropylene fibers, nanosilica, and Sarooj mortar—in stabilizing a naturally collapsible clay soil from Gorgan, Iran. A comprehensive experimental program comprising collapse potential, unconfined compressive strength (UCS), and unconsolidated undrained (UU) triaxial tests was conducted. The untreated soil exhibited a high collapse potential of approximately 11.1%, classifying it as severely collapsible. Upon stabilization, the collapse potential was significantly reduced to 1.35–4.63%, representing a reduction of up to ~88%, and reclassifying the soil into slight to moderate collapsibility. In terms of strength improvement, the UCS increased from 0.71 kg/cm² (untreated soil) to values exceeding 3.5–4.3 kg/cm² after 28 days of curing, corresponding to an increase of more than 4–5 times depending on the mixture composition. Additionally, triaxial test results indicated improvements of over 20% in shear strength parameters, including cohesion and friction angle, particularly after 28 days of curing. The observed improvements are attributed to the combined effects of pozzolanic reactions (lime, rice husk ash, nanosilica), cementitious bonding (Sarooj mortar), and mechanical reinforcement (polypropylene fibers), which collectively enhance soil structure, reduce the void ratio, and increase interparticle bonding. Among the tested mixtures, samples containing higher nanosilica and fiber content demonstrated superior performance in both strength and collapse resistance. Overall, the integration of traditional Sarooj mortar with modern eco-friendly additives provides a sustainable and efficient solution for mitigating collapse potential and enhancing the mechanical behavior of clayey soils. The proposed approach offers a low-carbon alternative to conventional stabilization methods, with significant implications for foundation engineering and infrastructure development in regions with problematic soils. Full article

Endometriosis is characterized by enhanced cellular proliferation, migration, and resistance to apoptosis, contributing to lesion persistence and progression. Targeting cellular plasticity and mesenchymal-associated functions may therefore represent a promising therapeutic strategy. Here, we investigated the effects of Pelargonium graveolens essential oil (PGEO) on proliferation, apoptosis, migration, cytoskeletal organization, transcriptional regulation, and metabolic alterations in human endometriotic 12Z cells. PGEO treatment suppressed proliferative capacity in a concentration-dependent manner and significantly impaired cell migration, accompanied by reduced β-tubulin expression and decreased levels of mesenchymal-associated markers CD73 and CD105. Increased GRP78 expression together with ultrastructural alterations, including cytoplasmic vacuolization and mitochondrial and endoplasmic reticulum changes, indicated activation of cellular stress responses. Although transcriptional analysis revealed increased CCND1 and PIK3CA mRNA levels, these changes did not parallel the observed suppression of proliferation, suggesting compensatory regulatory responses. Untargeted metabolomic profiling revealed alterations in energy metabolism characterized by increased levels of glycolysis-related metabolites, reduced levels of several amino acids including glutamine and histidine, and changes in lipid-associated metabolites. Collectively, these findings demonstrate that PGEO suppresses proliferative and migratory behavior in endometriotic cells while modulating cytoskeletal, transcriptional, and metabolic pathways, highlighting its potential as a candidate for further investigation in endometriosis-targeted therapeutic strategies. Full article

Background/Objectives: Circadian rhythm disruption is linked to cognitive decline, yet it remains unclear how behavioral and physiological rhythm markers are differently associated with cognition in amnestic mild cognitive impairment (aMCI). The primary aim of this study was to compare sleep–wake timing, rest–activity rhythm (RAR), and dim light melatonin onset (DLMO) between patients with aMCI and cognitively normal controls. Exploratory analyses further examined their associations with domain-specific cognitive performance. Methods: Eighteen aMCI patients and 21 cognitively normal controls (NC) enrolled. Cognitive function was assessed using the Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease Neuropsychological Battery (CERAD-K). Participants underwent 5-day actigraphy to assess sleep–wake timing and non-parametric RAR variables, including interdaily stability (IS), intradaily variability (IV), and relative amplitude (RA). DLMO was determined from hourly salivary melatonin samples collected over five hours before sleep onset under dim-light conditions. Group comparisons of circadian markers were conducted as the primary analyses, and generalized linear models were used for exploratory analyses of associations between circadian markers and cognitive outcomes. Results: Groups did not significantly differ in sleep–wake timing, RAR parameters and DLMO. Sleep–wake timing variables and DLMO were not significantly associated with cognitive performance. Higher IS was associated with better visuospatial memory and executive function, whereas higher RA was associated with poorer verbal memory among aMCI patients. Conclusions: Although sleep–wake timing and melatonin phase did not differ between groups nor predict cognitive performance, higher daily rhythm stability was linked to better non-verbal memory and executive functioning. In contrast, high RA may relate to poorer verbal memory in aMCI, suggesting that elevated RA may not reflect true circadian robustness required for optimal cognition. Full article