Search Results (28,688)

Emerging from millions of years of evolutionary optimization, Natural products (NPs) remain unique, unparalleled sources of bioactive scaffolds. Unlike synthetic molecules engineered around single therapeutic targets, NPs often exhibit multi-target, system-level bioactivity, aligned with the principles of network pharmacology, which modulates pathways in a coordinated, non-disruptive manner. This approach reduces resistance, buffers compensatory feedback loops, and enhances therapeutic resilience. Fungal endophytes represent one of the most chemically diverse and biologically sophisticated NP reservoirs known, producing polyketides, alkaloids, terpenoids, and peptides with intricate three-dimensional architectures and emergent bioactivity patterns that remain exceptionally difficult to design de novo. Advances in artificial intelligence (AI), machine learning, deep learning, and multi-omics integration have redefined the discovery landscape, transforming previously intractable fungal metabolomes and cryptic biosynthetic gene clusters (BGCs) into tractable, predictable, and engineerable systems. AI accelerates genome mining, metabolomic annotation, BGC-metabolite linking, structure prediction, and activation of silent pathways. Generative AI and diffusion models now enable de novo design of NP-inspired scaffolds while preserving biosynthetic feasibility, opening new opportunities for direct evolution, pathway refactoring, and precision biomanufacturing. This review synthesizes the chemical and biosynthetic diversity of major NP classes from fungal endophytes and maps them onto the rapidly expanding ecosystem of AI-driven tools. We outline how AI transforms NP discovery from empirical screening into a predictive, hypothesis-driven discipline with direct industrial implications for drug discovery and synthetic biology. By coupling evolutionarily refined chemistry with modern computational intelligence, the field is poised for a new era in which natural-product leads are not only rediscovered but systematically expanded, engineered, and industrialized to address urgent biomedical and sustainability challenges. Full article

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

Achieving sustainable agricultural intensification in inland valleys while limiting the adverse environmental impacts and uncertainties related to water availability requires an analysis of the long-term hydrological behavior of the catchment. Such a task is particularly challenging in West Africa and Benin due to the limited availability of climate and hydrological data. This study evaluates the applicability of the lumped GR4J model for simulating streamflow in three inland valleys of the Sudanian zone of Benin (Lower-Sowé, Bahounkpo and Nalohou). Additionally, we test the reliability of satellite-based rainfall data (GPM-IMERG, CHIRPS or GSMAP) in modeling hydrological dynamics in these small catchments. The results demonstrate that the GR4J model is effective in simulating daily discharge in the three inland valleys (KGE > 0.5 during both calibration and validation periods), with particularly interesting performance in mean-flow conditions. The modeling using GPM-IMERG and GSMAP rainfall data shows mitigated results with acceptable performance at Nalohou and less accurate results at Bahounkpo and Lower-Sowé. CHIRPS emerged as the most consistent among the evaluated products, providing a sound basis for reconstructing general trends and seasonal variations in historical streamflow time series. The approach of combining historical CHIRPS data and the GR4J model provides insights and can support decision-making related to water resource management in terms of resource capacity and volume in the study area. Except for Nalohou (KGE = 0.19 with GPM-IMERG data), we observe limitations in predicting high flows with satellite-based climatic data at Bahounkpo (KGE = 0.02 with GPM-IR) and Lower-Sowé (KGE = −0.01 with CHIRPS), where the near-zero KGE scores indicate marginal improvement over a mean-flow benchmark. Future work should explore how hybrid or flexible modeling approaches can improve the accuracy of runoff simulations in inland valleys, particularly for extreme (low- and high-) flow conditions. Additionally, the analysis of the trends of indicators of hydrological alteration (IHA) must be deepened in these important ecosystems, especially under climate and land-use change scenarios. 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

Bacterial canker, caused by Pseudomonas syringae pv. actinidiae (Psa) is a major threat to global kiwifruit production. Copper-based bactericides remain widely used, but increasing resistance highlights the urgency of developing sustainable alternatives. Understanding the functional capabilities of phyllosphere bacteria under copper pressure is critical for designing microbiome-informed management strategies. This study provides a culture-based functional inventory of bacteria associated with Actinidia chinensis var. deliciosa leaves from Portuguese orchards under long-term copper management, aiming to identify native taxa with traits relevant to plant health and resilience. A total of 1058 isolates were recovered and grouped into 261 Random Amplification of Polymorphic DNA (RAPD) clusters, representing 58 species across 29 genera. Representative strains were screened for Plant Growth-Promoting (PGP) traits (Indole-3-acetic acid (IAA), siderophore production, phosphate solubilization, ammonia production), copper tolerance, and in vitro antagonism against Psa. Copper resistance was widespread (53.3% of isolates with MIC ≥ 0.8 mM), including the first evidence of a highly copper-resistant PSA strain in Portuguese kiwifruit orchards and an exceptionally resistant non-pathogenic strain closely related to Erwinia iniecta (MIC 2.8 mM). A subset of 25 isolates combined all four PGP traits, and several also exhibited antagonism against Psa in vitro, among them Bacillus pumilus consistently supressed pathogen growth. Notably, antagonistic and multifunctional traits co-occurred in some isolates, highlighting promising candidates for integrated biocontrol strategies. Overall, the findings reveal a functionally diverse and copper-resilient collection of cultured bacteria, offering both challenges and opportunities for microbiome-based disease management. This work establishes a robust functional basis for subsequent in planta validation and the development of sustainable, microbiome-informed approaches for Psa control. Full article

To address the power and energy balancing challenges faced by high-penetration renewable energy systems under long-term intermittent output conditions, this study proposes a multi-source, multi-timescale collaborative dispatch strategy (2MT-S) integrating wind, solar, hydro, thermal, and hydrogen energy resources. First, a long-term-to-day-ahead coupled scheduling framework is established based on intermittent output duration forecasts (3-day/10-day). By integrating seasonal hydrogen storage and pumped-storage hydroelectric plants, this framework achieves comprehensive coordination among electrochemical storage, thermal power, and other flexible resources. Second, a multi-time-horizon optimization model is developed to simultaneously minimize system operating costs and load curtailment costs. This model dynamically adjusts day-ahead scheduling boundary conditions based on long-term and short-term scheduling results, enabling cross-period resource complementarity during wind and photovoltaic generation troughs. Finally, comparative analysis on an enhanced IEEE 30-bus system demonstrates that compared to traditional day-ahead scheduling, this strategy significantly reduces renewable energy curtailment rates and load curtailment volumes during sustained low-generation periods, fully validating its significant advantages in enhancing power supply reliability and economic benefits. 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

The global construction industry faces persistent challenges of low productivity and labor shortages, positioning Off-Site Construction (OSC) as a critical solution. However, standardized industrialization indices for objectively evaluating OSC adoption remain underdeveloped, particularly in emerging markets. This study aims to identify a benchmark policy model and derive design principles for future indices. Specifically, this study focuses on ‘policy-driven markets’ where strong government intervention is essential for initial ecosystem formation, excluding mature market-driven economies where the ecosystem is already established (e.g., USA, Sweden, Japan). To identify an optimal benchmark, a comparative assessment was conducted on five institutional frameworks across four countries (UK, Malaysia, Singapore, and China). Notably, within China, Hong Kong SAR was analyzed as a distinct regulatory jurisdiction separate from Mainland China due to its unique construction governance system. This assessment was based on five key policy dimensions: Legal Mandate, Scope, Indicator Composition, Enforcement Mechanism, and Sustainability. The analysis identified Singapore’s ‘Buildability Score’ as the most comprehensive model in terms of systemic completeness and practical efficacy. A virtual project simulation demonstrated that the scoring system functions as a powerful regulatory mechanism, effectively driving the adoption of standardized, dry-process, and modularized high-productivity methods from the earliest design stages. While Singapore’s system serves as an effective policy tool for OSC proliferation, it exhibits clear limitations regarding reduced architectural design flexibility and insufficient sustainability integration. Consequently, future industrialization indices must evolve to balance productivity with architectural design diversity and integrate sustainability criteria while reflecting specific regional construction ecosystems. Full article

The purpose of this paper is to analyze recent publications in scientific journals on the circular economy and energy through a systematic review using the PRISMA method and to propose a framework. In recent years, the circular economy has been widely recognized as a viable solution to address environmental and economic challenges. The transition to renewable sources, such as solar, wind, and biomass, is essential for a clean and balanced energy market. The methodology adopted was a systematic review of the scientific literature using the PRISMA method, which aims to categorize published research, evaluating it in terms of its objectives, methodologies, results, and conclusions. To this end, full articles published in scientific journals between 2021 and 2025 on the subject were identified. The analysis of the selected studies reveals an intrinsic relationship between the circular economy and sustainable energy, particularly in the context of Sustainable Development Goals (SDGs) 7 and 12. The results highlight that circular economy practices, such as waste recovery, bioenergy generation, and gasification, not only demonstrate their ability to create sustainable value chains but also contribute to reducing environmental impacts, promoting energy efficiency, and present a proposed framework for analysis and proposition. 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

The demand for computing power has increased at a rate never seen before due to the quick development of artificial intelligence (AI) technologies and applications. Consequently, AI data centers, referring to computing facilities specifically designed for large-scale artificial intelligence workloads, have become one of the fastest-growing electricity consumers globally. Therefore, it is essential to understand the load characteristics of AI data centers and their impact on the grid. This paper provides a comprehensive review of the evolving energy landscape of AI data centers. Specifically, this paper (i) presents the energy consumption structure in AI data centers and analyzes the key workload features and patterns in four stages, emphasizing how high power density, temporal variability, and cooling requirements shape total energy use, (ii) examines the impacts of AI data centers for power systems, including impacts on grid stability, reliability and power quality, electricity markets and pricing, economic dispatch and reserve scheduling, and infrastructure planning and coordination, (iii) presents key technological, operational and sustainability challenges for AI data centers, including renewable energy integration, waste heat utilization, carbon-neutral operation, and water–energy nexus constraints, (iv) evaluates emerging solutions and opportunities, spanning grid-side measures, data-center-side strategies, and user-side demand-flexibility mechanisms, (v) identifies future research priorities and policy directions to enable the sustainable co-evolution of AI infrastructure and electric power systems. The review aims to support utilities, system operators, and researchers in maintaining reliable, resilient, and sustainable grid operation in the context of the rapid development of AI data centers. Full article

Dry machining of AISI 1045 steel is attractive for sustainable manufacturing but makes it more challenging to control surface roughness R_a. This work investigates dry turning of AISI 1045 using a 2³ factorial design with three center points (11 runs) and compares a traditional power-law correlation with a quadratic response surface model (RSM). The power-law fit on log-log data explains only about 20% of the variance, whereas the quadratic RSM achieves R² ≈ 0.98 with a root-mean-square error (RMSE) of 0.62–0.77 µm based on leave-one-out cross-validation and bootstrap resampling. Feed rate S is identified as the dominant factor, while cutting speed V and depth of cut t have secondary but non-negligible interactive effects. Sobol global sensitivity indices confirm that S and S² account for more than half of the output variance. The optimized setting within the tested domain (V ≈ 83 m/min, S = 0.60 mm/rev, t = 0.10 mm) yields a predicted R_a ≈ 5.3 µm, appropriate for semi-roughing prior to grinding. The proposed framework combines small-sample RSM, Lasso regularization, uncertainty quantification and Sobol analysis to provide an uncertainty-aware model for optimizing dry-turning parameters of AISI 1045 steel. Full article

The farmland protection compensation system plays a pivotal role in addressing the dual global crises of food insecurity and ecological degradation, as well as in overcoming persistent challenges in China’s agricultural governance. By internalizing the opportunity costs borne by stakeholders fulfilling statutory obligations for farmland protection, this mechanism offers effective incentives for their active engagement, thereby establishing a societal-level interest-balancing framework conducive to sustainable land management. Existing research in China has mainly concentrated on empirical analyses of implementation models, regional disparities, and policy effectiveness evaluations of farmland protection compensation schemes. Nevertheless, systematic exploration of the normative construction and improvement pathways of the compensation rules themselves remains relatively underdeveloped. Based on the practical requirements and institutional constraints of China’s current farmland protection compensation regime, this study adopts an integrated approach that combines comparative legal analysis, textual review of regulatory documents, and empirical research to critically examine feasible paths for institutional improvement. The research findings emphasize that the optimization of China’s farmland protection compensation rules should be guided by three core principles: market orientation, ecological sustainability, and precision-based targeting. Specifically, the establishment of scientifically sound methods for calculating compensation amounts is crucial for reconciling the interests of conservation actors with inter-regional development disparities. Meanwhile, the compensation mechanism should be strategically utilized to strengthen positive incentives for ecosystem conservation. Ultimately, such institutional improvement aims to ensure the sustainable utilization of farmland resources while safeguarding global food security and maintaining the Earth’s ecological balance. 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

Background: Truck drivers constitute a high-risk occupational group due to irregular schedules, prolonged sedentary work, fatigue, and limited access to healthcare, contributing to adverse physical and mental health outcomes. Although mobile health (mHealth) tools offer potential to support driver health, sustained engagement remains a persistent challenge. Objectives: This systematic review aimed to identify behavioural, technological, and contextual determinants influencing truck drivers’ compliance, retention, and long-term engagement with digital health interventions. Methods: Following the PRISMA 2020 guidelines, six eligible studies were identified and thematically synthesised across technology acceptance, behaviour change, and persuasive system design perspectives. Results: Across studies, sustained engagement was facilitated by self-monitoring, real-time feedback, goal-setting, coaching support, and simple, flexible system design. In contrast, technological complexity, high interaction demands, limited digital literacy, privacy concerns, misalignment with irregular schedules, and fatigue consistently undermined engagement and retention. Autonomy, trust, and voluntary participation emerged as cross-cutting determinants supporting continued use. Based on the synthesis, an integrative framework was developed to explain how behavioural, technological, and contextual factors interact to shape truck drivers’ compliance, engagement, and retention with mHealth. Despite generally moderate to high study quality, the evidence base remains fragmented and dominated by short-term evaluations. Conclusions: The findings highlight the importance of context-sensitive, user-centred design to support effective digital health interventions in the trucking sector. Full article