Search Results (16,132)

Public passenger transport systems increasingly face the challenge of balancing economic efficiency with ecological sustainability, reflecting both policy objectives and passenger expectations. This study examines passenger perceptions of the economic and environmental aspects of public transport services and the factors influencing these perceptions, primarily based on survey data collected in Slovakia. The Slovak dataset was analysed using contingency analysis, namely Chi-square tests of independence, contingency coefficients, and sign scheme, and C5.0 decision tree classification models to identify key determinant of behavioural and attitudinal outcomes. In addition, descriptive comparisons with a complementary Polish sample are provided to illustrate potential differences in preference patterns across national contexts, without formal statistical inference. The results identify key socio-demographic and behavioural factors influencing passenger perceptions and usage patterns in Slovakia, while the complementary Polish sample is used to provide contextual descriptive comparison without formal testing. The study enhances scientific understanding of public transport by exploring the interaction between economic efficiency and ecological sustainability of transport services and provides practical recommendations for the strategic management of transport companies, especially in service modernisation, marketing communication, and support for sustainable mobility. The findings are relevant not only to Slovakia but also to broader European discussions on integrating economic and environmental dimensions into public transport development. Full article

Thermodynamic entropy generation quantifies irreversibility in energy conversion processes, providing rigorous thermodynamic foundations for optimizing efficiency and sustainability in thermal and energy systems. This critical review synthesizes advances in computational entropy modeling across numerical methods, optimization strategies, and sustainable energy applications. Computational fluid dynamics, finite element methods, and lattice Boltzmann methods enable spatially resolved entropy analysis in convective, conjugate, and microscale systems, but exhibit varying maturity levels and accuracy–cost trade-offs. The minimization of entropy generation and the integration of artificial intelligence demonstrate quantifiable performance improvements in heat exchangers, renewable energy systems, and smart grids, with reported efficiency gains of 15 to 39% in specific applications under controlled conditions. While overall performance depends critically on system scale, operating regime, and baseline configuration, persistent limitations still constrain practical deployment. Systematic conflation between thermodynamic entropy (quantifying physical irreversibility) and information entropy (measuring statistical uncertainty) leads to inappropriate method selection; validation challenges arise from entropy’s status as a non-directly-measurable state function; high-order maximum entropy models achieve superior uncertainty quantification but require prohibitive computational resources; and standardized benchmarking protocols remain absent. Research fragmentation across thermodynamics, information theory, and machine learning communities limits integrated frameworks capable of addressing multi-scale, transient, multiphysics systems. This review provides structured, cross-method, application-aware synthesis identifying where computational entropy modeling achieves industrial readiness versus research-stage development, offering forward-looking insights on physics-informed machine learning, unified theoretical frameworks, and real-time entropy-aware control as critical directions for advancing sustainable energy system design. Full article

Nanofertilizers have attracted increasing attention as an approach to improve the low nutrient use efficiency of conventional fertilizers, in which only a limited fraction of applied nitrogen, phosphorus, and potassium is ultimately taken up by crops. Beyond their capacity to minimize nutrient losses, nanofertilizers have attracted increasing attention for their possible role in addressing environmental issues, including soil eutrophication and the contamination of groundwater systems. Owing to their nanoscale characteristics, including large specific surface area and enhanced adsorption capacity, these materials enable more precise nutrient delivery to the rhizosphere and sustained release over extended periods, while also influencing soil–plant–microbe interactions. In this review, nanofertilizers are classified into six major categories—macronutrient-based, micronutrient-based, organic, controlled-release, composite, and nano-enhanced formulations—and representative examples and preparation routes are summarized, including green synthesis approaches and conventional chemical methods. The agronomic mechanisms associated with nanofertilizer application are discussed, with emphasis on enhanced nutrient uptake, modification of soil physicochemical properties, and shifts in microbial community composition. Reported studies indicate that nanofertilizers can increase crop yield across different crop species and formulations, while also contributing to improved nutrient cycling. Despite these advantages, several limitations continue to restrict their broader adoption. These include uncertainties regarding long-term environmental behavior, relatively high production costs compared with conventional fertilizers, and the absence of well-defined regulatory and safety assessment frameworks in many regions. Overall, this review highlights both the opportunities and challenges associated with nanofertilizer application and points to the need for further development of cost-effective formulations and standardized evaluation systems that account for their distinct environmental interactions. Full article

Background: Dialogic Feminist Gatherings (DFGs) fostered gender violence prevention among adolescents and young women in diverse educational settings. However, little was known about their impact on adult and older women without higher education, particularly regarding their contributions to broader social change through family and community relationships. This study addressed that gap by analyzing a DFG held in an adult education school in Barcelona with women from diverse backgrounds, as part of the R + D + i ALL WOMEN research project, aligned with Sustainable Development Goal 5. Methods: Using a qualitative case study with communicative methodology, the research drew on communicative observations, life stories, and a focus group. Results: Findings revealed that DFGs empowered participants individually and had a ripple effect in their communities. Through intergenerational dialogues with children, grandchildren, nieces, and nephews, participants began to challenge and transform socialization patterns linked to gender violence risk factors. Conclusions: The study highlights the transformative potential of DFGs beyond formal education. It underscores the value of integrating dialogic and community-based approaches into adult education to promote gender equality and prevent violence across generations. Full article

This study investigates the challenge of affordable housing in Riyadh, a city undergoing rapid transformation aligned with Saudi Arabia’s Vision 2030. It aims to bridge the structural gap in the housing market by developing a comprehensive analytical framework that measures housing suitability for emerging middle-income families, linking it to economic, spatial, and behavioral dimensions. The research employs a sequential mixed-methods design. The first phase involved a Multi-Criteria Decision Analysis (MCDA) of 106 residential neighborhoods, constructing a Housing Suitability Index (HSI) based on financing cost (≤SAR 880,000), quality of urban life, and geographical accessibility. The second phase utilized focus groups with 16 participants from real estate developers and new families to explore behavioral drivers and subjective trade-offs. Quantitative results identified “convenience clusters” primarily in the city’s southeastern and southwestern sectors, offering an optimal balance between price and accessibility. Qualitative analysis revealed a significant trust gap and a misalignment of priorities: new families are increasingly willing to sacrifice unit size for central location and construction quality, a preference that conflicts with developers’ strategies focused on luxury units or peripheral projects for higher margins. The study concludes that achieving the 70% homeownership target requires a hybrid policy model, combining supply-side stimuli (e.g., subsidized land) with demand-side management (e.g., progressive mortgages). It recommends integrating the HSI into urban planning to direct investment towards logistically connected areas, fostering sustainable communities. Full article

To investigate the effects of mixed forests on soil bacterial characteristics in the Yellow River Delta, pure forests of Ailanthus altissima, Ulmus pumila, Robinia pseudoacacia, and Fraxinus velutina (hereafter Aa, Up, Rp, and Fv, respectively) and mixed forests of Aa-Rp, Up-Rp, Fv-Rp, and Ma (Melia azedarach)-Fv were selected as the research objects. High-throughput sequencing technology was employed to analyze the structure, diversity, and function of bacterial communities in this region. The results showed that Acidobacteria, Proteobacteria, and Actinobacteria were the dominant microbial groups. The relative abundance of Acidobacteria was reduced by mixed patterns of Up-Rp, Fv-Rp and Ma-Fv, and the abundance of Proteobacteria was increased in Ma-Fv. The Chao1, ACE and Shannon indices of Aa-Rp and Fv-Rp were increased, while the Chao1 and ACE indices of Up-Rp and Ma-Fv were decreased. Functional prediction revealed that chemoheterotrophic and aerobic chemoheterotrophic bacteria had the highest abundance, with mixed forests exerting variable effects on different functional bacterial groups. In conclusion, mixed forest management exerts a significant influence on shaping bacterial community structure, regulating its diversity, and facilitating the directional enrichment of functional bacteria, which is conducive to enhancing the stability and sustainability of plantations. Full article

Transdisciplinarity and the co-production of knowledge have become fundamental approaches to addressing complex social problems. However, the sustainability of collaborative partnerships remains underexplored from an empirical perspective. This article examines the mechanisms that shape the continuity of collaborative networks in social innovation projects in the field of social services, particularly those linked to community-based welfare systems in Andalusia (Spain). Drawing on a thematic qualitative analysis of 15 social innovation projects and 14 semi-structured interviews with project coordinators, the study explores how diverse actors (universities, public administrations, third-sector organisations, and citizens) mobilise different types of social capital within local social services. The findings reveal that collaboration success depends on a balance between relational enablers (trust and shared experiences) and structural barriers (bureaucracy, work overload, and lack of time). The analysis also shows that participatory methodologies and connections with pre-existing networks are essential for sustaining collaboration after project completion. The article concludes that the sustainability of transdisciplinary social innovation in social services requires moving beyond project management logics and investing in the care of invisible relational structures, with implications for public policies aimed at consolidating trust ecosystems and long-term collective learning. Full article

The renovation of dilapidated housing has become a focal point of social concern. However, traditional approaches—such as repair and reinforcement or unified demolition and relocation—face bottlenecks that hinder sustainability. There is an urgent need to explore new models for addressing the risks posed by dilapidated residential buildings. In recent years, multiple regions have explored the “original demolition and original reconstruction” approach for dilapidated housing. For instance, Zhejiang Province introduced the “Resident-led Renewal” model, sparking widespread attention and discussion. This model is characterized by residents serving as the primary investors. However, the manner in which stakeholders—particularly residents—collaborate in governance and interact during the renovation process under this model remains unclear. Using the Zhegong New Village original demolition and reconstruction project as a case study, this paper employs social network analysis to construct relational networks encompassing information, trust, consultation, and support. It quantitatively reveals the characteristics of social networks among stakeholders and their interactive practices within the Resident-led Renewal model. Findings reveal that in this case, “Resident-led Renewal” primarily manifested through residents serving as principal investors and establishing a Self-Driven Renewal Committee to submit the original demolition and reconstruction application on behalf of residents to local authorities. In stakeholder interactions, the government and community neighborhood committees play a coordinating role in the renovation process. However, resident organizations and residents themselves ranked lower in metrics such as reciprocity and degree centrality, indicating their limited influence during the renovation process. To alleviate the pressure of the government’s excessive involvement and enhance resident participation in the “original demolition and original reconstruction” process, efforts should focus on: raising residents’ awareness and capacity for participation; ensuring accessible channels for resident involvement; clarifying the rights and responsibilities of all stakeholders; and establishing a standardized approval process for “original demolition and original reconstruction” projects. This approach would realize a “Resident-led Renewal” model characterized by government guidance and resident participation. Full article

Organic food consumption exemplifies the broader shift toward sustainable lifestyles and environmentally responsible choices in the current market generation. Despite the extensive use of the Theory of Planned Behavior (TPB) to explain consumer purchase intentions, its predictive power for decision making has come into question. This paper aims to enhance the relevance of using TPB in an emerging country to forecast consumer behavior in contemporary conditions. This study set out to investigate the determinants of organic food purchase intention and behavior among South African Generation Y consumers by applying the TPB model. By combining structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA), the research could capture both the symmetrical (linear) and asymmetrical (configurational) causal mechanisms underlying purchase intention. While symmetrical results highlight that attitude, subjective norms, and purchase intention predict purchase behavior, asymmetrical findings indicate that no single antecedent is necessary for high purchase intention; rather, two sufficient causal configurations emerge, in which either attitude or perceived behavioral control acts as a core condition, depending on its combination with other antecedents. Attitude and perceived behavioral control thus serve as core conditions leading to high purchase intention among South African Generation Y consumers. In sum, the findings suggest that strengthening pro-environmental attitudes through targeted communication strategies and improving the accessibility and perceived ease of purchasing organic food can serve as practical and implementable pathways to foster more sustainable consumption in emerging markets. Full article

Background: Functional impairments associated with bipolar disorder have a significant impact on daily life, including work, social relationships, and independent living. Bipolar disorder is treated with many approaches, with pharmacotherapy being the first choice; however, cases of relapse and side effects have been reported. The literature suggests that psychosocial interventions are effective in improving treatment adherence, recognizing early warning signs, enhancing self-management skills, and fostering open communication. The effects of interpersonal and social rhythm therapy (IPSRT) on circadian rhythm stability and social functioning in people with bipolar disorder remain uncertain. Therefore, this study is needed. Methods: This quasi-experimental study was conducted in the psychiatric outpatient clinic of a university hospital. Participants were recruited using convenience sampling from the psychiatric outpatient clinic. Eligible participants were then randomly allocated to either the intervention or control group using a coin-flip method. The dose of the intervention averaged 75 min per session with a weekly frequency over 12 weeks. Outcome measures included the Interpersonal Problem Areas Rating Scale, the Social Rhythm Metric Scale-II-5, and the Multnomah Community Ability Scale. Data were collected at baseline (week 0), post-intervention (week 12), and at follow-up (12 weeks post-intervention), and analyzed using repeated-measures ANOVA. Results: Participants in the IPSRT group demonstrated significant improvements in social rhythm regularity (SRM-II-5: 2.9 ± 1.3 at baseline, 3.7 ± 1.2 post-intervention, and 4.0 ± 1.5 at three-month follow-up; F = 18.5, p < 0.05, η² = 0.37). A significant between-group difference favoring IPSRT emerged at three months (t = 3.01, p < 0.05, d = 0.76). Social functioning also improved significantly in the intervention group (MCAS: 55.5 ± 7.4 at baseline, 63.7 ± 7.1 post-intervention, and 62.3 ± 6.9 at follow-up; F = 29.4, p < 0.05, η² = 0.49). Between-group differences were significant immediately post-intervention (t = 4.10, p < 0.001, d = 1.05) and at three-month follow-up (t = 2.73, p = 0.008, d = 0.72). Conclusions: IPSRT produced sustained improvements in social rhythm stability and social functioning, demonstrating its clinical value in the management of bipolar disorder. Full article

Crassulacean acid metabolism (CAM) is a specialized photosynthetic pathway that enhances water-use efficiency by temporally separating nocturnal CO₂ uptake from daytime decarboxylation and carbon fixation. To uncover the regulatory mechanisms coordinating these temporal dynamics, we generated high-resolution, 48 h time-course transcriptomes for the CAM model Kalanchoe fedtschenkoi under both 12 h/12 h light/dark (LD) cycles and continuous light (LL). A rhythmicity analysis revealed that diel light cues are the dominant driver of transcript oscillations: 16,810 genes (54.3% of annotated genes) exhibited rhythmic expression only under LD, whereas just 399 genes (1.3%) remained rhythmic under LL. A smaller set of 3009 genes (9.7%) oscillated in both conditions, indicating that the intrinsic circadian clock sustains rhythmicity for a limited subset of the transcriptome. A gene co-expression network analysis revealed extensive integration between circadian clock components, core CAM pathway enzymes, and stomatal regulators, defining regulatory modules that coordinate metabolic and physiological timing. Notably, key hub genes associated with post-translational and post-transcriptional regulation, including the E3 ubiquitin ligase HUB2 and several pentatricopeptide repeat (PPR) proteins, act as central nodes in CAM-associated networks. This discovery implicates epigenetic and organellar regulation as previously unrecognized critical tiers of control in CAM. Together, our results support a regulatory model in which CAM rhythmicity is governed by both external light/dark cues and the endogenous circadian clock through multi-level control spanning transcriptional and protein-level regulation. To support community exploration, we also provide an interactive eFP (electronic Fluorescent Pictograph) browser for visualizing time-resolved gene expression profiles. Full article

Sustainable and resilient communities increasingly rely on interdependent, data-driven building systems where material choices, energy performance, and lifecycle impacts must be optimized jointly. This study presents a digital-twin-ready, system-of-systems (SoS) decision-support framework that integrates BIM-enabled building energy simulation with an AI-enhanced lifecycle assessment (AI-LCA) pipeline to optimize fiber-reinforced concrete (FRC) façade systems for smart buildings. Conventional LCA is often inventory-driven and static, limiting its usefulness for SoS decision making under operational variability. To address this gap, we develop machine learning surrogate models (Random Forests, Gradient Boosting, and Artificial Neural Networks) to perform a dual prediction of façade mechanical performance and lifecycle indicators (CO₂ emissions, embodied energy, and water use), enabling a rapid exploration of design alternatives. We fuse experimental FRC measurements, open environmental inventories, and BIM-linked energy simulations into a unified dataset that captures coupled material–building behavior. The models achieve high predictive performance (up to 99.2% accuracy), and feature attribution identifies the fiber type, volume fraction, and curing regime as key drivers of lifecycle outcomes. Scenario analyses show that optimized configurations reduce embodied carbon while improving energy-efficiency trajectories when propagated through BIM workflows, supporting carbon-aware and resilient façade selection. Overall, the framework enables scalable SoS optimization by providing fast, coupled predictions for façade design decisions in smart built environments. Full article

Olive orchard soils are a source of microorganisms capable of inhibiting major olive pathogens. In this study, rhizobacteria were isolated and characterized based on plant growth-promoting traits, and soil 16 rRNA gene sequencing analysis was performed to analyze microbial communities at two key olive phenological stages (flowering and fruit formation). Using a culture-dependent approach, a total of 90 bacterial isolates representing distinct colony morphotypes were recovered from olive soils, with 35 during the flowering stage and 55 during the fruit formation stage, indicating a higher cultivable diversity during the latter period. We identified some bacterial strains with antagonistic activity and observed phenology-related shifts in the soil microbiome. Using differential abundance analysis, we identified bacterial taxa that were significantly enriched or depleted during olive fruit formation. Overall, this study demonstrates that olive-associated bacteria harbor antagonistic potential against olive pathogens. The use of bacteria adapted to olive agroecosystems represents a promising strategy for sustainable disease management. Full article

Background: Digital transformation in healthcare has advanced rapidly in hospitals and primary care, while long-term care facilities have often lagged behind. In nursing homes, nurses play a central role in coordinating care and accessing medical expertise, yet digital tools to support these tasks remain inconsistently implemented. The CareConnect study, funded under the German Model Program for Telecare (§ 125a SGB XI), aimed to develop and implement a multiprofessional telecare system tailored to nursing home care. Objective: This implementation study examined the feasibility, acceptability, and early adoption of a multiprofessional telecare system in nursing homes, focusing on implementation processes, contextual influences, and facilitators and barriers to integration into routine nursing workflows. Methods: A participatory implementation design was employed over 15 months (June 2024–August 2025), involving a university hospital, two nursing homes (NHs), and four medical practices in an urban region in Germany. The telecare intervention consisted of scheduled video-based teleconsultations and interdisciplinary case discussions supported by diagnostic devices (e.g., otoscopes, dermatoscopes, ECGs). The implementation strategy followed the Standards for Reporting Implementation Studies (StaRI) and was informed by the Consolidated Framework for Implementation Research (CFIR). Data sources included telecare documentation, nurse surveys, researcher observations, and structured feedback discussions. Quantitative and qualitative data were analyzed descriptively and triangulated to assess implementation outcomes and mechanisms. Results: A total of 152 documented telecare contacts were conducted with 69 participating residents. Most interactions occurred with general practitioners (48.7%) and dermatologists (23%). Across all contacts, in 79% of cases, there was no need for an in-person visit or transportation. Physicians rated most cases as suitable for digital management, as indicated by a mean of 4.09 (SD = 1.00) on a 5-point Likert scale. Nurses reported improved communication, time savings, and enhanced technical and diagnostic skills. Key challenges included delayed technical integration, interoperability issues, and varying interpretations of data protection requirements across facilities. Conclusions: This pilot study suggests that telecare can be feasibly introduced and accepted in nursing home settings when implemented through context-sensitive, participatory strategies. Implementation science approaches are essential for understanding how telecare can be sustainably embedded into routine nursing home practice. Full article

The growing demand for electricity in residential sectors and the global need to decarbonize power systems are accelerating the transformation toward smart and sustainable energy networks. Smart homes and smart grids, integrating renewable generation, energy storage, and intelligent control systems, represent a crucial step toward achieving energy efficiency and carbon neutrality. However, ensuring real-time optimization, interoperability, and sustainability across these distributed energy resources (DERs) remains a key challenge. This paper presents a comprehensive review of artificial intelligence (AI) applications for sustainable energy management and low-carbon technology integration in smart grids and smart homes. The review explores how AI-driven techniques include machine learning, deep learning, and bio-inspired optimization algorithms such as particle swarm optimization (PSO), whale optimization algorithm (WOA), and cuckoo optimization algorithm (COA) enhance forecasting, adaptive scheduling, and real-time energy optimization. These techniques have shown significant potential in improving demand-side management, dynamic load balancing, and renewable energy utilization efficiency. Moreover, AI-based home energy management systems (HEMSs) enable predictive control and seamless coordination between grid operations and distributed generation. This review also discusses current barriers, including data heterogeneity, computational overhead, and the lack of standardized integration frameworks. Future directions highlight the need for lightweight, scalable, and explainable AI models that support decentralized decision-making in cyber-physical energy systems. Overall, this paper emphasizes the transformative role of AI in enabling sustainable, flexible, and intelligent power management across smart residential and grid-level systems, supporting global energy transition goals and contributing to the realization of carbon-neutral communities. Full article