Search Results (3,094)

Quantitative source apportionment of groundwater contamination is essential for sustainable water resource management, yet the performance of receptor models in complex hydrogeological settings remains debated. This study employed Positive Matrix Factorization (PMF) and PCA-APCS-MLR (Principal Component Analysis–Absolute Principal Component Score–Multiple Linear Regression) models to analyze 16 hydrochemical parameters from 460 groundwater samples (collected at 339 sites), delineating pollution sources and characterizing the groundwater chemistry in the southern recharge zone of Poyang Lake, China’s largest freshwater lake. Both models consistently identified five primary pollution sources: mixed anthropogenic activities (contributing 13.6% and 8.6%, respectively), natural geological processes (28.9% and 45.6%), sewage discharge (23.5% and 24.4%), industrial effluents (13.3% and 12.1%), and agricultural practices (20.6% and 9.3%). Notably, heightened contamination was observed near industrial parks and urban centers through two models. The integrated analysis revealed that anthropogenic activities—particularly sewage discharge, agricultural practices, and industrial effluents—are the dominant drivers of groundwater quality deterioration. These human-induced inputs account for the vast majority of the pollution load (reaching up to ~71%), fundamentally altering the natural hydrochemical regime. Notably, elevated Mn²⁺ and NH₄⁺-N concentrations are intricately linked to a combination of industrial effluents and legacy domestic sewage, which exacerbate the mobilization of natural background elements within the aquifer. These findings provide critical mechanistic insights into the complex interplay between human activities and groundwater hydrochemistry, demonstrating how dual-receptor modeling can unravel overlapping natural and anthropogenic inputs. Ultimately, this study offers a scientific basis for targeted pollution control and the sustainable management of global freshwater lake recharge zones. Full article

The rapid transformation of rural societies and the progressive decline of traditional livelihoods have placed numerous expressions of intangible cultural heritage at risk of disappearance. In the Saraguro territory of southern Ecuador, muleteering (arriería) historically functioned as a means of transportation, trade, and cultural exchange, facilitating the transmission of knowledge, values, and practices among generations and diverse social groups. This study examines muleteering as a form of living heritage and explores its revitalization through intercultural dialogue and the recovery of ancestral knowledge. A qualitative ethnographic approach was employed, integrating documentary analysis, participant observation, and semi-structured interviews with former muleteers, community elders, cultural leaders, and local residents. The findings indicate that muleteering contributed significantly to territorial connectivity, economic exchange, collective memory, and the preservation of traditional ecological knowledge related to mobility, animal management, and community cooperation. Participants recognized muleteering as a central element of Saraguro’s cultural identity and emphasized its role in fostering intercultural interaction and inter-generational learning. The study concludes that the revitalization of muleteering through dialogue of knowledge can contribute to safeguarding intangible cultural heritage, strengthening cultural continuity, and supporting culturally sustainable development in indigenous territories. Full article

Background: Wastewater systems provide an integrated One Health perspective on antimicrobial resistance but remain uneven globally, with limited data from rapidly urbanizing and highly connected regions such as the Arabian Gulf. Methods: An eight-month prospective study was conducted in Dubai, United Arab Emirates, with monthly sampling from nine community and two hospital nodes and two wastewater treatment plants (WWTP). Samples were analysed using high-throughput quantitative PCR (HT-qPCR; Resistomap, Finland) with a 72-target One Health gene panel. Results: Across the 120 samples analyzed, the number of detected gene targets ranged from 26 to 68 genes, with the highest diversity in hospital wastewater and the lowest in WWTP effluent. Pathogen-associated markers were detected in all sources, with enterococci, Escherichia coli, and Klebsiella pneumoniae predominant. Hospital wastewater showed broader pathogen-associated gene markers, including those linked to Acinetobacter baumannii and Pseudomonas aeruginosa. Antibiotic resistance genes (ARGs) associated with macrolide–lincosamide–streptogramin B, tetracycline, and aminoglycoside resistance were widespread. Community and influent samples were dominated by msrE, tet(M), and aminoglycoside resistance genes, whereas hospital wastewater showed the highest ARG burden, including enrichment of aac(6′)-Ib, qnrS2, bla_GES, bla_TEM, bla_KPC-2, and bla_IMP-1. Several ARGs, including mcr-1, persisted in WWTP effluent. Mobile genetic elements (MGEs) were ubiquitous, with integron-associated markers prominent in WWTP effluent. ARG–MGE network analysis demonstrated extensive co-occurrence, with MGEs as central hubs linking multiple ARGs. Conclusions: Wastewater captures distinct resistome profiles across urban compartments, supporting its role for AMR surveillance. The persistence of ARGs and MGEs in WWTP effluent highlights the potential for environmental dissemination, through reuse of treated wastewater. Full article

Methane partial oxidation (POM) offers a promising pathway for syngas production, but achieving optimal catalyst performance requires precise control of promoter loading. We systematically investigated cerium (Ce) promotion on nickel-based catalysts supported on aluminum oxide (Ni/Al₂O₃) catalysts across 1–3 wt.% loadings and identified a critical discovery: catalyst performance exhibits a pronounced non-monotonic response to Ce concentration. The 1 wt.% Ce-promoted catalyst (Ni+1Ce/Al) achieved the superior performance with 65% methane conversion and 60% hydrogen yield at 650 °C, maintaining stable output over 275 min time-on-stream. This smaller Ce amount tunes NiO reducibility, oxygen mobility, and metal–support interactions, resulting in improved activity performance of Ni+1Ce/Al. Notably, Ce promotion shifts the H₂/CO ratio from 2.5 to 2.9, with the increased hydrogen yield arising from enhanced water–gas shift chemistry and indirect oxidation pathways. Excess cerium (2–3 wt.%) causes performance deterioration, Ni particle agglomeration, and thus loss of Ni active sites, demonstrating that Ce operates as a structural promoter with a well-defined appropriate concentration window. Moreover, the best performing catalyst (Ni+1Ce/Al) remained stable during 20-h long-term POM. An artificial neural network model achieved exceptional predictive accuracy (R = 0.9758 overall), validating the experimental findings. These results indicate that the best Ce loading for industrial application is 1 wt.% and the traditional alumina supports can be competitive in performance with the advantage of thermal stability and cost-effectiveness when doped with rare-earth elements. Full article

The widespread use of antimicrobial biocides and metals has led to the continuous accumulation of biocide and metal resistance genes (BMRGs) in the environment. The issue is of growing concern, as it reduces the efficacy of these agents and poses a potential threat to coastal ecological security. However, the extent of coastal BMRG pollution, its transmission mechanisms, and the influence of seasonal variations on its assembly remain poorly understood. In this study, metagenomic sequencing was employed to investigate BMRGs, microbiomes, and mobile genetic elements (MGEs) within the subtropical nearshore ecosystem of the Beibu Gulf during the autumn and winter seasons. A total of 33 BMRG types and 457 subtypes were detected, with higher subtype diversity in winter than in autumn (440 vs. 326 subtypes). Notably, genes resistant to multi-biocides exhibited the highest diversity, whereas those resistant to both biocides and metals were the most abundant. Co-occurrence network analysis showed that 22 of the 23 detected BMRGs in the winter network were associated with MGEs, especially transposase-related elements such as tnpA. Path modeling indicated that BMRG abundance was more strongly associated with bacterial community composition in autumn, whereas MGE-related variables showed stronger associations in winter. These findings suggest a pronounced seasonal shift in the underlying mechanisms shaping BMRG dynamics, with bacterial communities playing a dominant role in autumn and MGEs playing a more critical role in winter. This seasonal shift highlights the need for season-specific monitoring of BMRGs, coastal pollution control, and resistance-risk management in subtropical coastal ecosystems. Full article

The paper gives an overview of new ideas related to the characterization of the structural features of minerals and their synthetic analogs with high ion mobility. The main conditions for fast ionic transport are related to the disorder in the positions occupied by the mobile ions and the presence of conduction channels running inside the structure. Special attention is paid to the principle distinction between solid electrolytes and cathode materials. It is noted that the growing industry of modern electronic devices implies an increase in lithium production. The main mineral sources of this technologically important element are described. Full article

This study examines the processes of technology adoption and appropriation among older adults participating in two community-based digital inclusion workshops (LAB65+) in Madrid, exploring how digital technologies are appropriated within community learning environments and identifying the social, relational, and pedagogical factors that shape this process, with particular attention to the role of mutual support, warm experts, and community learning dynamics. Drawing on a series of workshops and group interaction recordings conducted with regular attendees, the research identifies a set of factors that consistently shape participants’ engagement with digital tools. Particular attention is given to socio-educational background, previous work experience, and prior exposure to technology, as well as to the everyday motivations associated with the use of mobile phones for communication through WhatsApp, online purchasing, access to health services, and routine banking procedures. Across both labs, the findings reveal that successful and sustained engagement with technology among older adults depends less on technical training per se than on elements related to motivation, self-efficacy, meaningful instruction, and the creation or reinforcement of social ties in familiar environments. Although minor differences emerge between the two settings, the evidence consistently underscores the centrality of these relational and contextual factors over purely operational or skill-based considerations. The study highlights the need for community-oriented approaches that recognize and build upon the social dimensions of learning and using technology in later life. Full article

This exploratory research investigates the perceived educational potential of visual, spatial, and auditory immersions as integral components of innovative healthcare simulation technologies. The study examines user experiences in learning health assessment concepts through purposefully designed 360° immersive virtual reality video (360° IVRV). Utilizing a case-study approach, insights were gathered from four subject-matter experts and four doctoral students regarding the perceived effectiveness of 360° IVRV for instructional activities focused on patient health assessment, commonly known as the Onset, Location, Duration, Characteristics, Aggravating/Alleviating factors, Related symptoms, Treatment, and Severity method (OLD CARTS). The research aimed to enhance the accessibility of learning materials by optimizing 360° IVRV content for personal phones and mobile devices, accommodating both online and traditional instructional formats. Interviews were transcribed and analyzed using qualitative data analysis software, with results categorized into subthemes, themes, and perspectives. The findings highlight the distinct perceived advantages of immersive technologies in advancing teaching methods for nursing practitioners. The discussion addresses concerns related to integrating 360° IVRV simulation technology in nursing education and the limitations of current instructional interventions. Practical implications for future research, design, and development of immersive learning materials and their integration with instructional design elements are emphasized. Full article

Modern military decision-making demands real-time integration of structured knowledge, causal reasoning, and dynamic operational context. We introduce ARES-KG (Actionable & Reasoned Edge Support over Knowledge Graphs), a hybrid decision-support framework whose central contribution is the integration of explicit causal graph semantics—relations such as BLOCKS_ROUTE, LIMITS_MOBILITY_OF, and DELAYS, elevated to first-class queryable edges—into an LLM–KG pipeline. This design lets commanders trace multi-order operational effects (e.g., bridge destruction → mobility degradation → resupply delay → mission slippage) and answer counterfactual queries through deterministic graph traversal rather than free-form LLM speculation. Three supporting elements operationalize the central claim: a closed-loop NL→Cypher interaction layer that makes the causal layer accessible at staff tempo with full auditability; a lightweight ontology-driven CSV→Neo4j architecture suitable for hybrid edge-cloud deployment; and an eight-dimensional human–AI evaluation rubric in which one dimension, Causal Foresight, directly tests the central claim. In a synthetic brigade-level case study, ARES-KG generated transparent, explainable reasoning chains and exposed hidden multi-hop dependencies across sustainment, maneuver, fires, ISR, and C2. In a small-scale study with 10 active-duty officers spanning ranks from second Lieutenant to Colonel, an ARES-KG–enabled LLM achieved a Decision Support Score in the upper band of the sample—at the field-grade rank-group mean and above every junior officer—while producing answers in seconds rather than minutes. ARES-KG thus represents a concrete step toward next-generation human–AI collaborative command systems that augment, rather than replace, expert judgment under operational time pressure. Full article

Shared bikes are frequently parked in disorder, resulting in low efficiency of manual collection and transfer and heavy workload for maintenance staff. Random parking across various areas forces shared bikes to occupy sidewalks and fire exits, damaging urban landscapes and disrupting traffic order. To tackle these industrial pain points, this paper develops an integrated intelligent robot system equipped with functions of multi-pose grasping, automatic transfer and fixed-point delivery of shared bikes, which can effectively address the drawbacks of low efficiency and high labor costs in traditional manual maintenance. This paper focuses on the completion of the robot’s overall mechanical structure design, stiffness–precision collaborative optimization model construction, finite-element static simulation verification, 1:7 scaled prototype development and performance testing. Firstly, the overall layout design of the multi-posture adaptive floating clamping mechanism, transfer-bearing frame, and Mecanum wheel omnidirectional mobile chassis is completed, and the structural parameters and assembly benchmarks of the core components are clarified. Secondly, a stiffness–precision coupling optimization model is established, and the static analysis under extreme load conditions is carried out through Abaqus finite-element software, which verifies the rationality of 45# carbon steel material selection and the safety of structural strength. Subsequently, a 1:7 scaled principle prototype is developed, and repetitive grabbing and transfer tests are carried out to verify the system operation feasibility, stability and grabbing accuracy. Finally, the statistical analysis of the test data and the horizontal comparison of similar schemes are completed. The test and simulation results show that the maximum stress of the system under extreme working conditions is 131.21 MPa, which is far lower than the allowable stress of 355 MPa of 45# steel, and the safety factor reaches 2.71. The maximum total deformation is 4.0552 mm, which is concentrated at the end of the front-end clamping mechanism, and is within the allowable stiffness deviation range of the transfer system. The average value of the single clamping positioning error of the scaled prototype is 0.476 mm, with a 95% confidence interval of 0.457–0.495 mm, which is converted to a positioning error of ≤3.4 mm for the full-scale prototype, which is far better than similar industry solutions. The average time of a single complete grabbing and transfer operation is 12.38 s, which is more than 45% higher than the traditional manual mode. The structural design, grabbing accuracy and operation stability of the robot designed in this paper all meet the requirements of actual working conditions of urban sidewalks, which can effectively reduce the intensity of manual labor and improve the operation and maintenance efficiency of shared bicycles. It has strong engineering application value and can provide reference for the design and manufacturing of intelligent collection and transfer systems for shared two-wheelers. Full article

Machine learning (ML) is increasingly applied to geochemical baseline estimation and anomaly detection in soils and sediments, yet the methodological conditions under which machine learning outperforms traditional approaches—and which preprocessing and validation decisions most consequentially determine that advantage—remain incompletely characterized across environmental and mineral exploration domains. A structured systematic scoping review of 146 records from the Web of Science Core Collection applied sequential filtering to yield 78 thematically eligible studies, from which 20 were prioritized through a composite index integrating age-adjusted citation impact, platform usage, and semantic relevance. Four cross-cutting findings emerge. First, performance gains in environmental applications were driven primarily by spatial model structure rather than algorithm selection: incorporating a spatial covariate derived from geographically weighted regression raised test-set explained variance from $R^2=0.80$ to $R^2=0.96$ for cadmium mobility prediction in a geochemically heterogeneous karst setting, a gain the source study supported with a held-out test set and a Monte Carlo analysis of sensitivity to data size. Second, isometric or centered log-ratio preprocessing was applied in the majority of mineral exploration studies (three of five classical and hybrid studies and four of five deep-learning studies) but in none of the seven environmental studies, representing a systematic methodological gap with direct consequences for covariate importance estimates under compositional closure. Third, Shapley additive explanations and accumulated local effects functioned as instruments of operational value, enabling element-specific anomaly threshold derivation, training sample diagnosis, and grid-cell anomaly type classification; this evidence demonstrates that the accuracy–interpretability trade-off commonly assumed in the machine learning literature is not fundamental in geochemical applications but contingent on algorithm selection. Fourth, 90% of the 20 synthesized studies (18 of 20 by study-area location—13 in China and five in Iran) were evaluated under within-domain validation designs, and the consistently high performance metrics reported should be interpreted as interpolation estimates rather than evidence of transferable predictive capability. Geographic diversification of training datasets and spatially explicit cross-regional validation are identified as structural prerequisites for regulatory-grade applicability. Full article

The gut microbiome of rainbow trout (Oncorhynchus mykiss) and Atlantic salmon (Salmo salar) is increasingly recognized as a functional interface linking dietary inputs, epithelial barrier integrity, mucosal immunity, environmental stress, disease susceptibility, and antimicrobial-resistance risk in intensive aquaculture. Based on available salmonid studies and relevant evidence from broader fish and aquaculture systems, this review synthesizes current knowledge on salmonid gut microbial composition, nutritional modulation, microbiome–mucosal immune interactions, aquaculture stressors, antibiotic exposure, antibiotic resistance genes (ARGs), mobile genetic elements (MGEs), metagenomics, multi-omics, and emerging microbiome-informed decision-support tools. Current evidence does not support a universally stable single-core microbiota in these species. Instead, community structure is shaped by developmental stage, freshwater–seawater transition, intestinal segment, digesta versus mucosa sampling, diet, temperature, stress, health status, and methodological workflow. Feed substitution and functional additives can remodel the gut microbiota, but these shifts should be interpreted alongside histology, barrier function, metabolic profiles, immune indicators, and disease-resistance phenotypes. Antibiotic exposure may reduce acute bacterial disease pressure while disturbing community structure and potentially enriching ARGs or ARG–MGE associations. Risk assessment should therefore move beyond ARG abundance toward host–ARG–MGE linkage using shotgun metagenomics, metagenome-assembled genomes, long-read sequencing, Hi-C, and externally validated multi-omics models. Machine learning and artificial intelligence approaches may support feature screening, risk stratification, and decision support, but their application in salmonid gut-health management remains at an early stage and requires external validation across sites, production stages, diets, and seasons. Full article

The intensive care unit (ICU) plays a crucial role in modern hospitals. ICU patients endure severe physical and mental conditions, making it essential to create a healing environment that reduces stress and promotes recovery. Among common environmental parameters, lighting conditions are particularly critical, as patients often face challenges with mobility and body positioning. Kinetic facades with adjustable external shading elements have gained attention for their ability to regulate sunlight effectively. However, their complexity poses challenges for design and implementation. This study proposes a reinforcement learning-based method, using Q-learning to handle discrete facade configurations and adaptive control under varying solar conditions for optimizing facade configurations in ICU rooms. The method aims to: (1) reduce direct sunlight glare and heat; and (2) maximize landscape views. A case study at Providence Alaska Medical Center demonstrates the method’s effectiveness, showing reduced glare and heat gain and improved landscape view availability through simulation. The results highlight the potential of reinforcement learning to address ICU-specific environmental challenges. Full article

Pseudomonas aeruginosa is a notable opportunistic pathogen in the ESKAPE group due to its multidrug resistance (MDR) and its ability to cause severe healthcare-associated infections. Horizontal gene transfer (HGT) facilitates the dissemination of antibiotic resistance genes (ARGs) through mobile genetic elements (MGEs). A comprehensive genomic analysis of ARGs associated with these elements is essential to understand multidrug resistance in P. aeruginosa. Here, we analyzed 10,412 publicly available P. aeruginosa genome assemblies defined by the Genome Taxonomy Database (GTDB, release 226) species cluster, which provides standardized prokaryotic genome taxonomy. We identified plasmids, prophages, integrative and conjugative elements (ICEs), and integrative and mobilizable elements (IMEs) carrying ARGs. A group of highly prevalent ARG families was identified in P. aeruginosa, comprising mexD, fosA, catB7, blaPAO, and aph(3′)-IIb, each of which was detected in over 96% of the genome assemblies. In contrast, 313 ARG families were found in fewer than 20% of the genomes. Many ARGs were located on plasmids, with certain pairs co-occurring frequently, such as aph(3″)-Ib and aph(6)-Id, CmlA9 and aadA6, or aac(6′)-Ib3 and aph(3′)-XV, which were associated with specific plasmids. Some of these plasmids closely resembled plasmids from E. coli and K. pneumoniae. Moreover, other MGEs displayed distinct ARG cargo enrichment: mexD on IMEs, aph(3′)-IIb on prophages, and sul1, fosA, and catB7 on ICEs. Our study provides a high-resolution map of the P. aeruginosa MGE resistome and highlights the potential roles of MGEs in disseminating different resistance genes. Our results emphasize the significance of ICE- and plasmid-associated ARG dissemination, particularly sul1, which may be linked to class 1 integrons. They also suggest that interspecies plasmid exchange may contribute to the evolution of MDR in P. aeruginosa. Full article

Road dust is an important urban reservoir of potentially toxic elements (PTEs) and a relevant source of human exposure through resuspension and inhalation, particularly in large megacities. This study provides an integrated assessment of the seasonal variability, contamination levels, source identification, particle characteristics, lung bioaccessibility, and health risk of road dust in Mexico City, one of the world’s largest urban centers. A total of 74 road dust samples were collected during the dry and wet seasons, and V, Cr, Mn, Co, Ni, Cu, As, Cd, Sb, and Pb were analyzed by ICP–MS in the <20 µm fraction. Geochemical indices, spatial analysis, Pearson correlation, principal component analysis, SEM–EDS particle characterization, in vitro lung bioaccessibility (ALF), and human health risk models were applied. Sb, Cu, and Pb were identified as the most enriched elements, exceeded local background concentrations at all sampling sites. Spatial patterns revealed recurrent hotspots in the northern, northeastern, and central sectors of the city. SEM–EDS analyses showed that most particles belonged to the 2.5–5 µm equivalent-size class and included Fe-rich spherules, Pb-rich aggregates, silicate grains, and C-rich particles. Health risk assessment indicated acceptable risks for adults, whereas children exceeded the non-carcinogenic threshold (HI = 3.85–4.60) and slightly surpassed the upper acceptable carcinogenic risk level. Lung bioaccessibility results revealed low Pb solubility but high mobility of Ni and Cu, with some samples reaching complete dissolution under ALF conditions. These findings demonstrate that traffic-derived road dust represents a persistent urban exposure pathway in Mexico City and highlight the importance of integrating total concentrations, particle characteristics, and bioaccessibility data to improve environmental and health-risk assessments in urban environments. Full article