Search Results (20,204)

Industrial site selection holds strategic importance in the layout of industrial facilities. Scientific decision-making in site selection not only enhances the economic and technical feasibility of a project but also lays the foundation for sustainable development. However, industrial site selection is considered an NP-hard problem. The criteria used to evaluate site suitability, the methods proven effective under different conditions, big data sources introduced, and the key data gaps, methodological limitations, and research priorities to improve decision quality are important for researchers and engineers. Based on the Web of Science (WOS) core collection as the data source, this paper retrieved the literature related to the themes of “industrial site selection” and “facility location decision making,” and selected 149 highly relevant papers. It systematically categorizes three mainstream site selection methods: operations research-based methods; the application of geographic information systems in site selection; and the application of artificial intelligence in site selection. On this basis, this paper provides a systematic review of the overall industrial site selection process and methodologies, aiming to offer references for subsequent site selection analysis research and practical site selection work. An “MCDM–GIS–AI” technology convergence roadmap is also proposed for industrial site selection to identify remaining research gaps and offer a set of “good-practice guidelines” to inform both practical applications and future analytical studies. Full article

Archaeological excavations today generate extensive datasets across survey, excavation, and analysis activities, especially when they are conducted in collaborative structures such as field schools. Working across such activities, data archivists contribute to the goals and research outcomes of the dig by establishing data practices that are participatory and educational (two pillars of data literacy) as they permanently record information about the archaeological results. At the Venus Pompeiana Project (VPP), a collaborative archaeological investigation of the Sanctuary of Venus in Pompeii, both provenance and provenience data are recorded into a database at the trenches’ edge, which optimises the accuracy of the data by allowing direct input and review by the data creators and archaeological site experts. When legacy data about work conducted decades or even centuries earlier are brought into the data picture, scholars stand to gain a deeper understanding of the geographic locations of key interest over time. Yet, the integration of analogue legacy and digital archival datasets is collaborative and longitudinal work. In this paper, we bring together experiential reflections on data archiving conducted at both the excavation site and in the physical archives of the Pompeii Archaeological Park. We then provide an integrative analysis of the outcomes of such data curation, highlighting what each data archiving contributor “discovered” about the site as a whole or a specific artefact, feature, or data category. Our findings contribute deeper insights into what data archiving and format-specific curation activities are most effective for learning experiences, archaeological scholarship, and professional practices. Full article

In a cellular maritime communication system, ocean buoys are essential to enable environmental monitoring, offshore platform management, and disaster response. Therefore, energy-efficient transmission from the buoys is a key requirement to prolong their operational time. A fixed uplink beamforming can be considered to save energy by leveraging its beam gain while managing the target link reliability. However, the dynamic condition of ocean waves causes buoys’ random orientation, leading to frequent misalignment of their predefined beam direction aimed at the base station, which degrades both the link reliability and energy efficiency. To address this challenge, we propose a wave-adaptive beamforming framework to satisfy data-rate demands within limited power budgets. This strategy targets scenarios where sea state information is unavailable, such as in network-assisted systems. We propose a Two-Dimensional Thompson Sampling (2DTS) scheme that jointly selects beamwidth and transmit power to satisfy the target-rate constraint with minimal power consumption and thus achieve maximal energy efficiency. This adaptive learning approach effectively balances exploration and exploitation, enabling efficient operation in uncertain and changing sea conditions. In simulation, under a moderate sea state, 2DTS achieves an energy efficiency of $1.26 \times {10}^4$ bps/Hz/J at round 600, which is $73.7\%$ of the ideal ($1.71 \times {10}^4$), and yield gains of $96.9\%$ and $447.8\%$ over exploration-based TS and conventional TS, respectively. Under a harsh sea state, 2DTS attains $3.09 \times {10}^4$ bps/Hz/J ($85.6\%$ of the ideal $3.61 \times {10}^4$), outperforming the exploration-based and conventional TS by $83.9\%$ and $113.1\%$, respectively. The simulation results demonstrate that the strategy enhances energy efficiency, confirming its practicality for maritime communication systems constrained by limited power budgets. Full article

The management of waste has become increasingly complex due to the growing volume and diversity of waste generated by modern societies. Effective collection systems are essential for mitigating environmental impacts and promoting sustainability. However, the increasing complexity of waste management requires a comprehensive approach that considers multiple criteria in order to evaluate the performance of these systems. This study evaluates the environmental performance of waste collection systems by comparing various methods using the Analytic Hierarchy Process (AHP). The research involves identifying key performance indicators (KPIs) that could be relevant for all the stakeholders involved and important for environmental sustainability. These KPIs are then used as criteria for the AHP model, allowing for a detailed comparison of each collection method. Data is collected from a case study in the Friuli-Venezia Giulia region in Italy. The preliminary results indicate significant variations in environmental performance and user fruitfulness across different collection methods. Door-to-door collection was found to be the preferred methodology with an absolute weight of 0.527. The AHP framework proves to be a robust tool for integrating diverse criteria and stakeholder preferences, facilitating informed decision-making in waste management. Moreover, it underscores the importance of adopting a holistic approach to evaluate and improve recycling systems. By leveraging AHP, policymakers and waste management professionals can identify optimal strategies that align with environmental sustainability goals. Full article

Microplastics (MPs) are emerging airborne pollutants that can migrate through various environmental pathways, with air representing one of the most critical exposure routes. Their occurrence within suspended particulate matter (PM)—a major atmospheric pollutant associated with respiratory, cardiovascular, and neurological diseases—further amplifies the risks posed by air pollution. The main sources of airborne MPs include tire and road wear, degradation of larger plastic debris, and wind-driven resuspension from soil and landfills. This review provides a comprehensive synthesis of current knowledge on airborne MPs, integrating methodological and toxicological perspectives. It summarizes sampling and separation procedures (filtration, chemical digestion, density separation) and analytical techniques for qualitative and quantitative identification. Particular emphasis is placed on the toxicological implications of MPs, including oxidative stress, inflammatory responses, and potential carcinogenicity, as revealed by in vitro and mechanistic studies. In light of the absence of standardized methodologies, this work highlights the urgent need for harmonized protocols linking environmental monitoring with biological toxicity assessment. By combining information on analytical workflows and cellular responses, this review serves as a key reference for developing environmentally relevant experimental designs and evaluating health risks associated with airborne microplastics. It therefore bridges the gap between environmental analysis and toxicological research, outlining future priorities for methodological standardization and risk assessment. Full article

This study develops machine learning models to assess Media and Information Literacy (MIL) skills specifically in the context of disinformation among students, particularly future educators and communicators. While the digital revolution has expanded access to information, it has also amplified the spread of false and misleading content, making MIL essential for fostering critical thinking and responsible media engagement. Despite its relevance, predictive modeling of MIL in relation to disinformation remains underexplored. To address this gap, a quantitative study was conducted with 723 students in education and communication programs using a validated survey. Classification and regression algorithms were applied to predict MIL competencies and identify key influencing factors. Results show that complex models outperform simpler approaches, with variables such as academic year and prior training significantly improving prediction accuracy. These findings can inform the design of targeted educational interventions and personalized strategies to enhance students’ ability to critically navigate and respond to disinformation in digital environments. Full article

HIV remains a significant public health challenge in sub-Saharan Africa (SSA), with vulnerable communities disproportionately affected and further marginalised by the COVID-19 pandemic. HIV self-testing (HIVST) has emerged as a transformative, empowering tool to bridge testing gaps and promote health equity. This study examined post-COVID-19 strategies for leveraging HIVST to empower vulnerable populations and advance health promotion in SSA. Analysis was performed using secondary Demographic and Health Survey (DHS) data (2015–2022) collected across 24 SSA countries. In addition, qualitative interviews were conducted with female sex workers in Port Harcourt, Nigeria (18–31 May 2023). The study adopted an explanatory sequential mixed-methods design. Quantitative analysis using complex sample logistic regression revealed low awareness (16.3%) and uptake (2.5%) of HIVST among the 594,639 respondents. Key predictors of uptake included higher education (aOR, 7.36; 95% CI, 6.62–8.18), wealth (richest quintile aOR, 3.28; 95% CI, 2.95–3.65), and knowledge of HIV transmission (aOR, 33.43; 95% CI, 11.03–101.24). Thematic analysis highlighted privacy, autonomy, and convenience as key benefits, while cost, stigma, and fear of testing alone were major barriers. The participants emphasised peer-led outreach and integration of HIVST into public health systems as effective strategies. The findings were integrated interpretively, linking macro-level testing disparities with community-level experiences to inform post-pandemic policy and programme design. The study concludes that HIVST holds strong potential to empower marginalised groups and strengthen community-driven HIV prevention post-COVID-19, but success will depend on equity-driven policies and sustainable implementation frameworks, guided by affordability and community participation. Full article

Triboelectric materials can convert irregular mechanical stimuli from human motion or environmental sources into high surface charge densities and instantaneous electrical outputs. Their intrinsic properties, such as flexibility, stretchability, chemical tunability, and compatibility with diverse substrates, play a critical role in determining the efficiency and reliability of triboelectric devices. In the context of active health, triboelectric materials not only serve as the core functional layers for self-powered sensing but also enable real-time physiological monitoring, motion tracking, and human–machine interaction by directly transducing biomechanical signals into electrical information. Soft triboelectric sensors exhibit high sensitivity, wide operational ranges, excellent biocompatibility, and wearability, making them highly promising for active health monitoring applications. Despite these advantages, challenges remain in enhancing surface charge density, achieving effective signal multiplexing, and ensuring long-term stability. This review provides a comprehensive overview of triboelectric mechanisms, working modes, influencing factors, performance enhancement strategies, and wearable health applications. Finally, it systematically summarizes the key improvement approaches and future development directions of triboelectric materials for active health, offering valuable guidance for advancing wearable self-powered biosensors. Full article

Acinetobacter baumannii is a multidrug-resistant opportunistic pathogen that poses critical challenges in hospital settings due to its environmental resilience and high resistance to antibiotics. Genomic surveillance has become essential for identifying transmission patterns, guiding antimicrobial stewardship, and informing infection control policies. We conducted whole-genome sequencing on 44 A. baumannii isolates collected between 2022 and 2023 from diverse wards in an Italian hospital. Illumina-based sequencing was followed by a comprehensive bioinformatics pipeline, including genome assembly, taxonomic validation, MLST, SNP-based phylogeny, pan-genome analysis, antimicrobial resistance (AMR) gene profiling, and virulence factor prediction. Most isolates were classified as ST2; SAMPLE-34 was ST1 and genetically distinct. Phylogenetic analysis revealed four clonal clusters with cluster-specific AMR and accessory gene content. The pan-genome included 5050 genes, with notable variation linked to hospital ward origin. ICU and internal medicine strains carried higher loads of AMR genes, especially against aminoglycosides, β-lactams, and quinolones. Virulence profiling highlighted widespread immune evasion mechanisms; “Acenovactin” was predominant, while some isolates lacked key adhesion or toxin factors. Our findings underscore the clinical relevance of integrating genomic epidemiology into routine hospital surveillance. Identifying clonal clusters and resistance signatures supports real-time outbreak detection, risk stratification, and targeted infection prevention strategies. Full article

Multimodal contrastive learning has achieved significant performance advantages in self-supervised skeleton-based action recognition. Previous methods are limited by modality imbalance, which reduces alignment accuracy and makes it difficult to combine important spatial–temporal frequency patterns, leading to confusion between modalities and weaker feature representations. To overcome these problems, we explore intra-modality feature-wise self-similarity and inter-modality instance-wise cross-consistency, and discover two inherent correlations that benefit recognition: (i) Global Perspective expresses how action semantics carry a broad and high-level understanding, which supports the use of globally discriminative feature representations. (ii) Focus Adaptation refers to the role of the frequency spectrum in guiding attention toward key joints by emphasizing compact and salient signal patterns. Building upon these insights, we propose a novel language–skeleton contrastive learning framework comprising two key components: (a) Feature Modulation, which constructs a skeleton–language action conceptual domain to minimize the expected information gain between vision and language modalities. (b) Frequency Feature Learning, which introduces a Frequency-domain Spatial–Temporal block (FreST) that focuses on sparse key human joints in the frequency domain with compact signal energy. Extensive experiments demonstrate the effectiveness of our method achieves remarkable action recognition performance on widely used benchmark datasets, including NTU RGB+D 60 and NTU RGB+D 120. Especially on the challenging PKU-MMD dataset, MICA has achieved at least a 4.6% improvement over classical methods such as CrosSCLR and AimCLR, effectively demonstrating its ability to capture internal and contextual attention information. Full article

Background/Objectives: Cardiovascular disease is the leading cause of death in Mexico; this is due to the high prevalence of chronic non-communicable diseases (NCDs), including obesity, type 2 diabetes mellitus (T2DM), systemic arterial hypertension (SAH), cardiovascular disease, chronic kidney disease (CKD), and dyslipidemia. Primary care physicians require a classification tool that enables them to gain a broader understanding of their patients’ risks, thereby allowing them to make more informed clinical decisions. This study compared risk stratification for atherosclerotic cardiovascular disease (ASCVD) and Cardiovascular–Kidney–Metabolic (CKM) syndrome in a primary care setting in Mexico. Methods: An observational, descriptive, cross-sectional study analyzed 500 patients with T2DM, SAH, dyslipidemia, and/or CKD. Two ordinal logistic regression models were developed using a Chi-square test, Kruskal–Wallis test, and tetrachoric, polychoric, polyserial, and Pearson correlations. Results: Associations were found between ASCVD risk and factors like sex, age, and T2DM; for CKM syndrome, the associations were with age, T2DM, and dyslipidemia. Interestingly, 22% of advanced CKM patients had a low ASCVD risk. Alcohol consumption showed a strong positive relationship (42%) with CKM stages, while there was a negative relationship (33%) with the glomerular filtration rate. Conclusions: The ASCVD risk classification effectively identifies cardiac conditions, but the CKM syndrome score provides a broader assessment of comorbidities at earlier stages. Key factors like age, hypertension, T2DM, and smoking are crucial for cardiovascular risk but less so for CKM syndrome, highlighting the need for a broader stratification of risk. Full article

In many industrial facilities, online monitoring systems have improved the reliability of key equipment, reducing the cost of operation and maintenance over recent decades. However, it often requires additional on-site inspection of target facilities due to limited information from installed sensors. To systematically automate such processes, an adaptive online fault diagnosis framework is required, which consecutively selects variables to measure and updates its inference with additional information at each measurement step. In this paper, adaptive online fault detection models—grafted composite decision trees—are proposed for such a framework. While conventional decision trees themselves can serve two required objectives of the framework, information from monitored variables can be less utilized because decision trees do not consider if required input variables are always monitored when the models are trained. On the other hand, the proposed grafted composite decision tree models are designed to fully utilize both monitored and robot-measured variables at any stage in a given measurement sequence by grafting two types of trees together: a prior-tree trained only with observed variables and sub-trees trained with robot-measurable variables. The proposed method was validated on a cooling water system in a nuclear power plant with multiple leak scenarios, in which improved measurement selection and increase in inference confidence in each measurement step are demonstrated. The performance comparison between the proposed models and the conventional decision tree model clearly illustrates how the acquired information is fully utilized for the best inference while providing the best choice of the next variable to measure, maximizing information gain at the same time. Full article

Public transit subsidization often suffers from a double (or bilateral) moral hazard problem, where both regulators and operators may reduce their efforts due to information asymmetry, thereby compromising service quality despite significant public investment. This paper develops a continuous-time principal-agent model to investigate optimal subsidy contract design under such conditions, where both parties exert costly, unobservable efforts that jointly determine stochastic service outcomes. Using stochastic dynamic programming and exponential utility functions, we derive closed-form solutions for the optimal contracts. Our analysis yields three key findings. First, under standard technical assumptions, the optimal subsidy contract takes a simple linear form based on final service quality, facilitating practical implementation. Second, the contract’s incentive intensity decreases with environmental uncertainty, highlighting a fundamental trade-off between risk-sharing and effort inducement. Third, a unique and mutually agreeable contract emerges as the parties’ risk preferences and productivity levels converge. This study extends the classic principal-agent framework by incorporating bilateral moral hazard in a dynamic setting, offering new theoretical insights into public-sector contract design. For policymakers, the results suggest that performance-based subsidies should be calibrated to account for operational uncertainty, and that regulators are active co-producers of service quality whose own unobservable efforts—distinct from the subsidy itself—are critical to outcomes.The proposed framework provides actionable guidance for designing effective, incentive-compatible subsidies to enhance public transit service delivery. Full article

Drought is one of the main climate-induced risks threatening agricultural sustainability in semi-arid regions. Northern Kazakhstan, a key grain-producing region in Central Asia, exhibits increasing vulnerability to droughts due to climatic variability and reliance on rainfed agriculture. This study evaluates the informativeness of drought indices based on the response of agricultural vegetation to dry conditions using remote sensing-based vegetation indices across Northern Kazakhstan from 1990 to 2024. Ground-based meteorological indices—the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), the Hydrothermal Coefficient (HTC), and the Modified China-Z Index (MCZI)—and vegetation indices—the Vegetation Condition Index (VCI), the Temperature Condition Index (TCI), and the Vegetation Health Index (VHI)—were analyzed using data from 11 representative meteorological stations. For the first time in Kazakhstan, the MCZI was calculated, demonstrating high sensitivity to local climate variability and strong agreement with the VHI. The SPI, MCZI, and HTC showed strong seasonal correlations with vegetation indices, whereas the SPEI had a weak correlation, limiting its applicability. The highest correlations (r ≥ 0.82) between meteorological and vegetation indices were recorded in summer, while spring and autumn were influenced by phenological and temperature factors. Persistent drying trends in the southern and southwestern areas contrasted with moderate wetting in the north. The combined use of the SPI, MCZI, HTC, and VHI proved effective for monitoring droughts. The results provide a reproducible foundation for local drought assessment and early warning systems, supporting climate-resilient agricultural planning and sustainable land and water resource management. The results also offer actionable insights to enhance adaptation strategies and support long-term agricultural and environmental sustainability in Central Asia and similar continental agroecosystems. Full article

Sustainable soil management requires striking a balance between productivity and soil health. While agroforestry practices are known to improve soil health and ecosystem functions, the contribution of microbial diversity to maintaining multifunctional soil processes in pecan (Carya illinoinensis) cultivation has yet to be fully elucidated. This study examined microbial diversity, soil functions, and multifunctionality across different pecan intercropping setups. We compared a monoculture pecan plantation with three agroforestry models: pecan–Paeonia suffruticosa–Hemerocallis citrina (CPH), pecan–P. suffruticosa (CPS), and pecan–P. lactiflora (CPL). We employed high-throughput sequencing (16S and ITS) to determine the soil bacterial and fungal communities and analyzed the species diversity, extracellular enzyme activities, and physicochemical properties. Soil multifunctionality (SMF) was evaluated using 20 indicators for nutrient supply, storage, cycling, and environmental regulation. Agroforestry increased soil fungal diversity and improved multifunctionality when compared to monoculture. The CPS and CPH models were the most beneficial, increasing multifunctionality by 0.74 and 0.55 units, respectively. Structural equation modeling revealed two key pathways: bacterial diversity significantly enhanced nutrient cycling and environmental regulation, whereas fungal diversity primarily promoted nutrient cycling. These pathways together delivered clear gains in multifunctionality. Random forest analysis identified key predictors (total nitrogen, total carbon, available potassium, β-1,4-N-acetylglucosaminidase, and alkaline phosphatase), highlighting the joint importance of nutrients and microbial enzymes. Our results demonstrate that selecting species in pecan agroforestry alters microbial communities and activates key functions that support soil health and long-term resilience. Hence, pecan agroforestry maintains SMF through microbial processes, with CPS showing the strongest effect. These results can inform species selection and encourage broader testing for resilient, biodiversity-based farming practices. Full article