Search Results (12,590)

Background/Objectives: Due to the numerical dominance of environmental and commensal strains, understanding antimicrobial resistance (AMR) transmission in Escherichia coli requires consideration of non-clinical as well as pathogenic isolates. In this cross-sectional study, associations between the genetic context of non-clinical E. coli and AMR carriage are examined in isolates sampled from different niches within a One Health continuum. Methods: Two hundred eighty-eight E. coli isolates collected in Alberta, Canada (2018–2019) from wastewater, well water, feces of broiler chickens and feedlot cattle, and retail beef and chicken meat were selected from existing surveillance collections using a stratified random sampling structure. Using short-read whole genome assemblies, phylogenetic relationships were inferred from pan-genome multiple sequence alignments. Principal coordinate analysis and permutational analysis of variance (PERMANOVA) of a Jaccard dissimilarity matrix derived from gene presence/absence data were used to investigate contributions of source and AMR strata to observe genetic dissimilarity. Population clustering and gene under- or over-representation by source and cluster were also explored. Results: Minimal phylogenetic segregation of isolates was noted based on source or AMR strata, and both contributed significant but small proportions of observed genetic dissimilarity, with the largest proportion attributed to phylogroup. There was notable diversity of E. coli within and between sources; however, in some larger clusters, differential gene presence/absence was potentially linked to ecological niche rather than source of isolation. Conclusions: This study highlights the ecological complexity of AMR in E. coli in non-clinical contexts, offering a novel lens on how niche-specific factors can influence population structure and AMR carriage. It also provides insight into apparent discrepancies in the literature regarding clustering of E. coli by source. These findings support a more integrative One Health approach to AMR surveillance, emphasizing the need to account for microbial diversity and niche-specific adaptation across interconnected systems. Full article

This study explores the role of sustainability-oriented education in supporting post-disaster recovery and resilience in Derna, Libya, following the catastrophic floods of September 2023. Using a qualitative descriptive design, twenty semi-structured interviews were conducted with academic experts, public health professionals, policymakers, and community leaders. The findings reveal that Education for Sustainable Development (ESD) is perceived as both a critical resilience tool and a moral imperative in fragile, disaster-affected contexts. However, institutional fragility, limited resources, and weak policy integration hinder its implementation. The study highlights the need to embed ESD within both formal education systems and informal community networks, aligning recovery strategies with local environmental realities. It offers practical recommendations for leveraging schools, faith-based institutions, and grassroots initiatives to foster adaptive capacity. These insights contribute to global debates on localising sustainable development in post-conflict settings and underscore the potential of ESD to bridge immediate recovery and long-term sustainability. The study explicitly aligns with the objectives of Sustainable Development Goal 4 (Quality Education) and Sustainable Development Goal 11 (Sustainable Cities and Communities). It demonstrates how sustainability-oriented learning can strengthen community resilience by connecting education with local recovery systems, environmental adaptation, and social rebuilding. Through this alignment, the research underscores the role of education as a mechanism for both immediate recovery and long-term sustainability within fragile and disaster-affected societies. Full article

Mountain regions are highly susceptible to severe hydro-meteorological events. These events induce substantial morphological changes that are preserved in the environment and cause significant economic losses, representing a major challenge for water resource management. Due to their abrupt nature, mitigating the impacts of such events requires preventive measures. The goal of the study was to comprehensively evaluate the impact of severe hydro-meteorological events on the mountain environment of the Ochotnica catchment, considering both environmental and economic aspects, over several years. This multi-year perspective also provided the opportunity to formulate some recommendations for the development of adaptation strategies for extreme hydro-meteorological events in mountain areas. The study demonstrates that delineation of the Maximum Probable Flood (MPF) hazard zone is a key element in building resilience to such events in mountain areas. Information related to the extent and depth of this zone, together with flow velocity, are critical components which may support actions aimed at reducing flood exposure and vulnerability, limiting the negative consequences of extreme hydro-meteorological events in mountain catchments prone to flash floods. Full article

The Indoor Positioning System (IPS) is used to locate devices and people in smart environments. In recent years, position determination methods have evolved from simple Received Signal Strength Indicator (RSSI) measurements to more advanced approaches such as Channel State Information (CSI), Round Trip Time (RTT), and Angle of Arrival (AoA), increasingly combined with Machine Learning (ML). This article presents a systematic review of the literature on ML-based IPS using IEEE 802.11 Wireless LAN (Wi-Fi) and Bluetooth Low Energy (BLE), including studies published between 2020 and 2024 under the Preferred Reporting Items for Systematic Reviews and Meta-Analyse (PRISMA) methodology. This study examines the techniques used to collect measurements and the ML models used, and discusses the growing use of Deep Learning (DL) approaches. This review identifies some challenges that remain for the implementation of these systems, such as environmental variability, device heterogeneity, and the need for calibration. Future research should expand ML applications to RTT and AoA, explore hybrid multimetric systems, and design lightweight, adaptive DL models. Advances in wireless standards and emerging technologies are also expected to further enhance accuracy and scalability in next-generation IPS. Full article

This article presents a systemic framework for integrating Industry 4.0 technologies with sustainability practices, structured around three strategic pillars: technological selection, technological integration, and sustainability assessment. To support its development, a systematic literature review was conducted, applying the PICO methodology (Population, Intervention, Comparison, Outcome) to ensure structured and reproducible research, and following PRISMA guidelines to guarantee methodological transparency and rigor. Relevant studies focusing on Industry 4.0 and sustainability integration were identified, analyzed, and synthesized. The proposed framework comprises five iterative stages—diagnosis, selection and prioritization, integration, assessment, and continuous improvement—complemented by practical guidelines to facilitate implementation across diverse organizational contexts, including administrative, financial, and human resources departments. It enables organizations to select appropriate technologies, evaluate multidimensional sustainability impacts, and align innovation with environmental, economic, and social objectives, providing a structured roadmap for decision-making. Comparative analysis with selected literature highlights that the framework fills existing gaps in systemic integration, multidimensional assessment, and iterative adaptation. Although conceptual, it integrates literature review insights and three illustrative case studies, offering a practical pathway for sustainable technological adoption. Future research should focus on empirical validation and metric development to consolidate its applicability across industrial sectors. Full article

The accurate recognition of cattle behaviours is crucial for improving animal welfare and production efficiency in precision livestock farming. However, existing methods pay limited attention to recognising behaviours under occlusion or those involving subtle interactions between cattle and environmental objects in group farming scenarios. To address this limitation, we propose a novel spatio-temporal feature extraction network that explicitly models the associative relationships between key body parts of cattle and environmental factors, thereby enabling precise behaviour recognition. Specifically, the proposed approach first employs a spatio-temporal perception network to extract discriminative motion features of key body parts. Subsequently, a spatio-temporal relation integration module with metric learning is introduced to adaptively quantify the association strength between cattle features and environmental elements. Finally, a spatio-temporal enhancement network is utilised to further optimise the learned interaction representations. Experimental results on a public cattle behaviour dataset demonstrate that our method achieves a state-of-the-art mean average precision (mAP) of 87.19%, outperforming the advanced SlowFast model by 6.01 percentage points. Ablation studies further confirm the synergistic effectiveness of each module, particularly in recognising behaviours that rely on environmental interactions, such as drinking and grooming. This study provides a practical and reliable solution for intelligent cattle behaviour monitoring and highlights the significance of relational reasoning in understanding animal behaviours within complex environments. Full article

Planned capitals across the Global South frequently experience unplanned land use transitions that contradict their founding visions. Despite six decades of planning and academic inquiry, Islamabad’s research remains fragmented. Environmental studies have documented land use and land cover changes through remote sensing, while governance-oriented analyses have highlighted institutional weaknesses and policy failures. However, these domains rarely intersect, and few studies systematically link spatial transformations with the underlying governance structures and political–economic processes that drive them. Consequently, the existing literature provides valuable but partial explanations for why Islamabad’s planned order unraveled. This study examines Islamabad, conceived in 1960 as a model of order and green balance, where the built-up area expanded by 377 km² (from 88 to 465 km²; +426%) and forest cover declined by 83 km² (−40%) between 1979 and 2019. Using a PRISMA-guided systematic review integrating spatial, governance, and policy data, we synthesized 39 peer-reviewed and gray literature sources to explain why Islamabad’s planned order unraveled. The findings reveal that governance fragmentation between the Capital Development Authority (CDA) and Metropolitan Corporation Islamabad (MCI), combined with elite capture and weak enforcement of the 2020–2040 Master Plan, has produced enduring contradictions between policy intent and urban reality. These conditions mirror those of other planned capitals, such as Brasília and Abuja. Grounded in Pakistan’s institutional context, the study proposes four actionable reforms: (1) regularization frameworks for informal settlements, (2) cross-agency spatial and fiscal coordination, (3) ecological thresholds within zoning by-laws, and (4) participatory master-plan reviews. Islamabad’s experience illustrates how planned capitals can evolve toward inclusive and ecologically resilient futures through governance reform and adaptive planning. Full article

Toxin-antitoxin (TA) systems, ubiquitous in bacterial and archaeal genomes, play pivotal roles in responding to environmental stresses, forming biofilms, defending against phages, and influencing pathogen virulence. The marine environment harbors Earth’s most diverse and abundant microbial communities, where microorganisms have evolved unique genetic adaptations and specialized metabolic processes to thrive amid distinct environmental challenges. Research on the presence and function of TA systems in marine bacteria lags significantly behind that in model bacteria and pathogens. Here, we explored the diversity of the TA system in marine bacteria, including species from the Global Ocean Microbiome Catalogue (GOMC) and the Mariana Trench Environment and Ecology Research (MEER) databases. Our findings revealed that types I to VII (featuring protein toxins) of eight types of TA systems are prevalent in these microorganisms, with unidentified TA combinations diverging from previously characterized systems. Interestingly, some toxins or antitoxins lack canonical counterparts, indicating evolutionary divergence. Additionally, previously uncharacterized potential TA systems have been identified in extremophilic bacteria from the deep-sea Mariana Trench. These results highlight the adaptive importance of marine TA systems, which are likely operating through unconventional mechanisms. Full article

This study addressed the persistent limitation of discontinuous and labor-intensive compost monitoring procedures by developing and field-validating a low-cost sensor system for monitoring oxygen (O₂), carbon dioxide (CO₂), and methane (CH₄) under tropical windrow conditions. In contrast to laboratory-restricted studies, this framework integrated rigorous calibration, multi-layer statistical validation, and process optimization into a unified, real-time adaptive design. Experimental validation was performed across three independent composting replicates to ensure reproducibility and account for environmental variability. Calibration using ISO-traceable gas standards generated linear correction models, confirming sensor accuracy within ±1.5% for O₂, ±304 ppm for CO₂, and ±1.3 ppm for CH₄. Expanded uncertainties (U₉₅) remained within acceptable limits for composting applications, reinforcing the precision and reproducibility of the calibration framework. Sensor reliability and agreement with reference instruments were statistically validated using analysis of variance (ANOVA), intraclass correlation coefficient (ICC), and Bland–Altman analysis. Validation against a reference multi-gas analyzer demonstrated laboratory-grade accuracy, with ICC values exceeding 0.97, ANOVA showing no significant phase-wise differences (p > 0.95), and Bland–Altman plots confirming near-zero bias and narrow agreement limits. Ecological interdependencies were also captured, with O₂ strongly anticorrelated to CO₂ (r = −0.967) and CH₄ moderately correlated with pH (r = 0.756), consistent with microbial respiration and methanogenic activities. Nutrient analyses indicated compost maturity, marked by increases in nitrogen (+31.7%), phosphorus (+87.7%), and potassium (+92.3%). Regression analysis revealed that ambient temperature explained 25.8% of CO₂ variability (slope = 520 ppm °C⁻¹, p = 0.021), whereas O₂ and CH₄ remained unaffected. Overall, these findings validate the developed sensors as accurate and resilient tools, enabling real-time adaptive intervention, advancing sustainable waste valorization, and aligning with the United Nations Sustainable Development Goals (SDGs) 12 and 13. Full article

The expansion of genomes is a major evolutionary force, yet its role in facilitating adaptation to extreme environments remains enigmatic. Here, we investigate alpine Parnassius butterflies, a rare genus characterized by exceptionally large genomes, to unravel the interplay between genome architecture and high-altitude colonization. We present a new, 1.46 Gb draft genome assembly for Parnassius epaphus and perform a comparative analysis across six species. Our findings reveal a massive 3- to 5-fold genome expansion driven predominantly by Long Interspersed Nuclear Elements (LINEs). Counterintuitively, we discover that larger genomes possess a proportionally smaller fraction of young, active transposable elements (TEs), challenging the prevailing paradigm that recent TE proliferation is the primary driver of genome size. Instead, our temporal analysis demonstrates that this expansion is a legacy of two ancient TE waves (~8 and ~14 Mya), which remarkably coincide with major uplift phases of the Tibetan Plateau. We propose a model where the selective retention of these ancient TEs, mechanistically linked to major geological upheavals, provided the crucial genomic plasticity for colonizing Earth’s most extreme terrestrial habitats. This study re-frames TEs not merely as genomic parasites but as pivotal architects of adaptive genome evolution in response to profound environmental change. Full article

The integration of modern artificial intelligence into software systems presents transformative opportunities and novel challenges for software quality assurance (SQA). While AI enables powerful enhancements in testing, monitoring, and defect prediction, it also introduces non-determinism, continuous learning, and opaque behavior that challenge traditional quality and reliability paradigms. This paper proposes a framework for addressing these issues, drawing on concepts from systems theory. We argue that AI-enabled software systems should be understood as dynamical systems, i.e., stateful adaptive systems whose behavior depends on prior inputs, feedback, and environmental interaction, as well as components embedded within broader socio-technical ecosystems. From this perspective, quality assurance becomes a matter of maintaining stability by enforcing constraints as well as designing robust feedback and control mechanisms that account for interactions across the full ecosystem of stakeholders, infrastructure, and operational environments. This paper outlines how the systems-theoretic perspective can inform the development of modern SQA processes. This ecosystem-aware approach repositions software quality as an ongoing, systemic responsibility, especially important in mission-critical AI applications. Full article

Sustainability assessment has emerged as a critical research area given the pressing challenges of balancing economic growth, environmental protection, and social equity. This study aims to develop an objective and reproducible framework to evaluate sustainability efficiency across countries by integrating multiple development dimensions into a unified decision model. Despite substantial prior research, inconsistencies often arise due to data heterogeneity and conflicting criteria. To address this gap, a hybrid multi-criteria decision-making (MCDM) framework was developed by combining the Double Normalization Method based on Removal Effects of Criteria (DNMEREC) for objective weighting and the Double Normalization Measurement of Alternatives and Ranking according to Compromise Solution (DNMARCOS) method for ranking alternatives. This integration ensures balanced consideration of beneficial and non-beneficial criteria while minimizing subjectivity. The model was empirically validated through a comparative assessment of G7 and BRICS countries using twelve sustainability indicators covering economic, environmental, and social dimensions. Results show significant variations in sustainability efficiency, with G7 countries generally demonstrating higher overall performance, while BRICS nations exhibit strong growth potential but face environmental and structural constraints. These findings confirm the robustness of the DNMEREC-DNMARCOS framework and highlight its adaptability to complex, multidimensional datasets. The study contributes a transparent methodological tool for researchers and policymakers seeking evidence-based strategies to enhance global sustainability performance and bridge development gaps. Full article

Salinity is a long-standing global environmental stressor of terrestrial agroecosystems, with important implications for viticulture sustainability, especially in arid and semi-arid environments. Salt-induced physiological and biochemical disruptions to grapevines undermine yield and long-term vineyard sustainability. This review aims to integrate physiological, molecular, and omics-based insights to elucidate how grapevine rootstocks confer salinity tolerance and to identify future breeding directions for sustainable viticulture. This review critically assesses the ecological and molecular processes underlying salt stress adaptation in grapevine (Vitis spp.) rootstocks, with an emphasis on their contribution to modulating scion performance under saline conditions. Core adaptive mechanisms include morphological plasticity, ion compartmentalization, hormonal regulation, antioxidant defense, and activation of responsive genes to stress. Particular emphasis is given to recent integrative biotechnological developments—including transcriptomics, proteomics, metabolomics, and genomics—that reveal the intricate signaling and regulatory networks enabling rootstock-mediated tolerance. By integrating advances across eco-physiological, agronomic, and molecular realms, this review identifies rootstock selection as a promising strategy for bolstering resilience in grapevine production systems confronted by salinization, a phenomenon increasingly exacerbated by anthropogenic land use and climate change. The research highlights the value of stress ecology and adaptive root system strategies for alleviating the environmental consequences of soil salinity for perennial crop systems. Full article

In immersive digital devices, high environmental complexity can lead to rendering delays and loss of interactive details, resulting in a fragmented experience. This paper proposes a lightweight NeRF (Neural Radiance Fields) modeling and multimodal perception fusion method. First, a sparse hash code is constructed based on Instant-NGP (Instant Neural Graphics Primitives) to accelerate scene radiance field generation. Second, parameter distillation and channel pruning are used to reduce the model’s size and reduce computational overheads. Next, multimodal data from a depth camera and an IMU (Inertial Measurement Unit) is fused, and Kalman filtering is used to improve pose tracking accuracy. Finally, the optimized NeRF model is integrated into the Unity engine, utilizing custom shaders and asynchronous rendering to achieve low-latency viewpoint responsiveness. Experiments show that the file size of this method in high-complexity scenes is only 79.5 MB ± 5.3 MB, and the first loading time is only 2.9 s ± 0.4 s, effectively reducing rendering latency. The SSIM is 0.951 ± 0.016 at 1.5 m/s, and the GME is 7.68 ± 0.15 at 1.5 m/s. It can stably restore texture details and edge sharpness under dynamic viewing angles. In scenarios that support 3–5 people interacting simultaneously, the average interaction response delay is only 16.3 ms, and the average jitter error is controlled at 0.12°, significantly improving spatial interaction performance. In conclusion, this study provides effective technical solutions for high-quality immersive interaction in complex public scenarios. Future work will explore the framework’s adaptability in larger-scale dynamic environments and further optimize the network synchronization mechanism for multi-user concurrency. Full article

This study presents an adaptive conservation framework for the Fujia Residence, a vernacular house located in the tropical region of Hainan, China. The primary aim of this study is to develop a symbiotic design approach that integrates GIS spatial analysis, modular design, and community participation to ensure the long-term sustainability, cultural preservation, and resilience of vernacular housing in tropical regions. The framework leverages GIS data, including elevation, temperature distribution, ecological features, and water systems, to inform the design, ensuring it is both disaster-resilient and environmentally adaptive. The modular design components, such as prefabricated structures and flexible spaces, offer a sustainable and adaptable solution to meet residents’ needs while preserving cultural heritage. The community participation model, incorporating a revenue-sharing mechanism and government subsidies, encourages the long-term involvement of local residents in the maintenance and protection of the residence. The outcome of this study demonstrates that the proposed framework provides a replicable model for cultural heritage preservation in tropical and economically underdeveloped regions, offering a scalable and adaptable solution to address the challenges of vernacular housing conservation in similar contexts. Full article