Search Results (39,340)

Accurate identification of fire smoke is a key link in realizing early fire prevention and control. Traditional intelligent video and image processing technologies are significantly restricted by environmental factors, with weak anti-interference capabilities and limitations in distinguishing fire smoke, leading to a high false alarm rate of fires. To address this problem, this paper proposes an unconventional visual field smoke detection method based on image enhancement. The method innovatively improves the Retinex algorithm by integrating improved guided filtering, adaptive brightness correction, and CLAHE-WWGIF joint processing, which realizes targeted optimization for the unique interference factors of under-construction nuclear power plants such as water mist, low illumination, and equipment occlusion. First, an improved Retinex algorithm is used to process the image to improve the image brightness and contrast, retain edge details while avoiding halo artifacts, reduce the impact of noise, and optimize visual features. Then, the sample data set is integrated, and the YOLOv11 target detection algorithm is used to achieve accurate identification and positioning of smoke targets. Experimental data shows that the fire identification method achieves an accuracy rate of 93.6% and 92.3% for fire smoke identification in interference-prone scenarios such as dark nights and water mist, respectively, and the response time to fire smoke is only 1.8 s and 2.1 s. In practical on-site applications at nuclear power plant construction sites, the method is integrated into an “edge computing + distributed deployment” hardware system, which realizes real-time smoke detection in core areas such as nuclear islands and conventional islands with a false alarm rate of less than 5% and a detection delay of ≤300 ms, meeting the ultra-strict safety monitoring requirements of nuclear power projects. Experiments show that this method can be effectively applied to smoke detection scenarios under unconventional visual fields, accurately identify smoke, provide reliable technical support for fire smoke identification under unconventional visual fields, significantly reduce the false alarm rate of fire detection, and provide technical support for the safety of under-construction nuclear power plants. Full article

Underground gas storage (UGS) in salt caverns is increasingly important for a flexible and secure energy supply and for stabilizing the gas market. However, cavern operations can induce surface ground movements that must be monitored to safeguard infrastructure integrity and environmental compatibility. This research analyzes horizontal (W–E) and vertical ground movements above the cavern field Gronau-Epe in northwestern Germany, using radar interferometry (InSAR), specifically the SBAS (Small Baseline Subset) approach, combined with clustering and multi-criteria analysis. The study was conducted in cooperation between Uniper Energy Storage GmbH, the Research Center for Post Mining at THGA Bochum, and the company EFTAS. Freely available Copernicus Sentinel 1 data were integrated with public soil maps and operational storage information. A multistage workflow quantified deformation patterns, classified coherent deformation zones via clustering, and evaluated geological and technical drivers using multi-criteria analysis to better distinguish operational (primary) from overburden (secondary) influences. Results reveal long term deformation trends closely linked in time and space to injection/withdrawal cycles. Locally confined vertical and horizontal movements near caverns are attributed to salt convergence triggered by cyclic pressure changes, but they are linked to (hydro)geological and pedological factors. The developed approach shows strong monitoring potential in addition to classic mine surveying. Full article

The urban ecosystem constitutes a complex adaptive system comprising interdependent subsystems—environment, population, infrastructure, public services, environmental governance, and socio-economic factors. Conventional system dynamics (SD) modeling relies on expert-derived causal assumptions, which have limitations in objectivity, transferability, and adaptability. To solve these, this study develops a data-driven SD modeling framework that infers causal structures from time-series data of 38 sustainability indicators. The framework integrates multiple causal inference techniques to identify causal relationships among variables, then systematically identifies stock variables and constructs an SD simulation model. Applying it to panel data from 41 cities in China’s Yangtze River Delta (2013–2022), the study characterizes the causal network topology, interaction patterns between subsystems, dominant feedback loops, and temporal evolution trajectories of key stock variables. Results show: (1) There is significant cross-city variation in causal network structure due to differences in urban development and institutional configurations; (2) Environmental conditions are the most frequently affected terminal node with an average normalized causal strength of 0.277, higher than other subsystems; (3) Several cross-subsystem positive and negative feedback loops are identified, highlighting potential path dependencies and intervention-sensitive nodes for sustainable urban transitions. This study provides a replicable, comparable, and scalable framework for urban sustainable development analysis, offering data-driven support for smart city management and policy formulation. Full article

Vegetation coverage is a critical indicator for assessing urban ecosystems and is essential for sustainable development. However, the evolution patterns and driving mechanisms of vegetation change at the urban agglomeration scale remain underexplored. This study used the Google Earth Engine (GEE) to compute the kernel Normalized Difference Vegetation Index (kNDVI) for the Beibu Gulf Urban Agglomeration (BGUA), an important emerging coastal urban cluster in southern China, from 2000 to 2022. Trend analysis was employed to examine spatiotemporal changes in kNDVI, and an interpretable machine learning framework was applied to quantify the nonlinear, spatially heterogeneous effects of environmental and anthropogenic drivers. The results show that (1) kNDVI showed a general increasing trend, with medium-to-high kNDVI predominating. Approximately 91.91% of the region maintained an improving trend, whereas vegetation degradation concentrated in the core urban areas. (2) The Categorical Boosting model demonstrated superior performance in predicting kNDVI compared to other machine learning models. (3) The SHAP analysis identified land cover, elevation, and nighttime lights as the primary determinants of kNDVI change. These factors exhibited significant spatial heterogeneity in their nonlinear effects. These findings provide theoretical insights and practical guidance for ecological planning and environmental management in urban agglomerations. Full article

The Manila clam Ruditapes philippinarum is a commercially important bivalve worldwide, with China being the leading producer. While the reproductive biology of this species has been extensively studied in its native or long-established ranges, knowledge of how populations cultured from non-native seed sources adapt their reproductive cycles to new environmental conditions remains limited. In this observational study, we investigated the annual reproductive cycle of a Manila clam population originating from subtropical waters (Zhejiang Province, Southern China) that was cultured in temperate aquaculture grounds in Zhuanghe Bay, Northern China. Monthly histological examination of 50 clams demonstrated that the gametogenic cycle synchronized between male and female clams. Gametogenesis started in March when seawater temperature exceeded 5.7 °C, and most gametes matured by May. A distinct bimodal spawning pattern was observed: a minor spawning event occurred from May to July, followed by a major spawning phase from September to November after a one-month gonadal recovery period in August. The condition index (CI), analyzed monthly in 30 clams, effectively reflected reproductive stages, increasing during gametogenesis and declining sharply during spawning, with its amplitude indicating spawning intensity. Seawater temperature was identified as the primary regulatory factor driving reproductive development from gametogenesis to spawning, while food availability (indicated by chlorophyll a concentration) played a crucial role in supporting gonadal recovery during summer. These results align with observations in other temperate populations, demonstrating that subtropical-origin clams can successfully acclimate their reproductive cycles to temperate environmental conditions. This study provides the first comprehensive description of the reproductive biology of transplanted Manila clams in Northern China, offering critical benchmarks for optimizing hatchery production schedules and informing sustainable fishery management practices in the region. Full article

Additive manufacturing (AM) has rapidly evolved from a prototyping tool into an effective method for producing end-use components, thanks to its ability to produce complex, lightweight and customised parts. However, this technique requires a thorough understanding of the long-term behaviour and degradation mechanisms of components, especially when polymers are involved in the printing process. Unlike polymer components manufactured using traditional methods, polymers produced through AM exhibit unique microstructures, anisotropies, and interfacial characteristics due to the layer-by-layer fabrication process. These features can affect how these materials respond to thermal, mechanical and environmental stresses over time. Furthermore, technology-specific processing parameters directly govern porosity distribution, crystallinity evolution, interlayer bonding quality, and residual stress development, all of which are key factors for ensuring long-term performance. This review aims to support researchers in the development of durable additively manufactured polymer components by systematically analysing polymer degradation mechanisms, accelerated ageing and lifetime prediction methodologies. Following a PRISMA-based screening process, approximately 160 international standards relevant to polymer durability in additive manufacturing were selected from an initial corpus of about 620 documents for in-depth analysis. Processing–structure–property relationships specific to the AM processing of polymers, including the commonly used FFF (fused filament fabrication), SLA (stereolithography) and SLS (selective laser sintering), are examined in relation to crucial aspects for long-term structural integrity and degradation behaviour. Finally, limitations within the current normative framework are identified, emphasising the absence of process-aware durability assessment protocols and the need for dedicated standards tailored to additively manufactured polymer components. Full article

Background/Objectives: We analyzed the whole-genome sequences of ciprofloxacin-resistant (CIP-R) enteropathogenic Escherichia coli (EPEC) ST752 isolates in South Korea to characterize their molecular epidemiology. This lineage has emerged as the predominant CIP-R EPEC clone in South Korea, accounting for 28.8% of human clinical isolates and circulating within the One Health interface. Methods: We performed whole-genome sequencing (WGS) and reference-based core-genome single-nucleotide polymorphism (SNP) analysis on 26 CIP-R EPEC ST752 isolates (19 human clinical and 7 poultry-derived isolates). To elucidate their evolutionary history and transmission dynamics, Bayesian phylodynamic and phylogeographic reconstructions were implemented by integrating domestic isolates with a global genome dataset (n = 508). Results: Isolates from human and poultry sources clustered together with an identical virulence profile and minimal genetic distance. The Bayesian molecular clock analysis estimated that the time to the most recent common ancestor of the South Korean clade was 2000.65. Moreover, the phylogeographic analysis supported statistical evidence (Bayes factor 32.16) for the introduction of this lineage into South Korea from Denmark and revealed a strongly supported host transition from humans to poultry (Bayes factor > 10,000), although this requires cautious interpretation due to limited temporal sampling of poultry isolates. Conclusions: Continued integrated One Health surveillance across human, animal, and environmental reservoirs is needed to monitor and prevent the spread of high-risk antimicrobial-resistant clones. Full article

The growth of human activity in cities is a key factor in the degradation of air quality. Numerous studies have demonstrated the link between air quality and the existence of dangerous and chronic diseases that are extremely costly for individuals and society. This study presents an analytical framework that compares fixed and mobile air-quality monitoring approaches in cities with limited resources, using Mohammedia city, Morocco, as an example. The framework centers on mobile monitoring units mounted on vehicles and equipped with affordable sensors, GPS technology, and wireless communication systems to track important pollutants, including fine particulate matter (PM_2.5 and PM₁₀) and harmful gaseous compounds (NO₂, SO₂, CO, O₃). The evaluation relies on scenario-based modeling, performance data from existing literature, and calculations of costs throughout the system’s lifetime. To enhance measurement reliability, the researchers developed a correction system that addresses measurement errors caused by temperature, humidity, vehicle speed, vibrations, traffic-related interference, operational interruptions, and communication limitations. The findings indicate that fixed monitoring stations deliver superior measurement precision, with estimated uncertainty ranging from ±1.2–2.5%, though their coverage area is restricted to 0.534 km² (representing 1.6% of Mohammedia). In comparison, the suggested mobile setup could potentially monitor 9.8 km², covering approximately 30% of the city, while decreasing infrastructure needs and setup time (2–4 h compared to 2–4 weeks). Over 10 years, the total cost is EUR 252,000 for mobile monitoring, compared with EUR 3.6 million for a network of 20 fixed stations. These results demonstrate that corrected mobile monitoring systems offer significant promise as an economical and sustainable approach for managing urban environmental conditions. Full article

Engineering education often emphasizes technical competencies while underemphasizing and devaluing the social, ethical, and political contexts of engineering systems. This gap is particularly pronounced in middle-year courses, where students develop technical fluency but rarely confront the sociotechnical complexity of real-world problems. We propose sociotechnical judgment as a framework to help students see the intimately intertwining nature of technical knowledge and social, ethical, and contextual reasoning, using energy systems—particularly offshore wind—as an illustrative domain. We designed three course-integrated case studies in thermodynamics, circuits, and statics/dynamics to embed sociotechnical judgment in middle-year engineering courses. These cases include pedagogical strategies, such as project-based learning, problem-based learning, and role-play exercises connecting technical analysis with social, environmental, and policy considerations. The design of these case studies is rooted in real-world problems surrounding U.S. offshore wind, engineering science learning outcomes, and ABET student outcomes. In these pedagogies, we have created opportunities for students to analyze technical systems while engaging with social, ecological, and political factors. Offshore wind projects, including turbine siting, transmission system design, and efficiency trade-offs, provide opportunities to operationalize sociotechnical reasoning in authentic, regionally relevant contexts. Sociotechnical judgment provides a practical framework for bridging technical competency and contextual reasoning in engineering education. Integrating sociotechnical cases into core courses will prepare students to navigate complex, real-world systems through engagement with ethical, social, and environmental considerations inherent in engineering practice. Full article

Background: Dry Eye Disease (DED) is a multifactorial disorder characterized by tear film instability and ocular surface inflammation. Murine models based on environmental stress are widely used to mimic evaporative DED, although many focus on limited disease features. This study aimed to provide an integrated characterization of ocular surface alterations induced by chronic desiccating stress. Methods: Adult mice were housed in a Controlled-Environmental Chamber (CEC) with low humidity and increased airflow for up to 21 days and sacrificed after 14 or 21 days. Corneal damage was assessed by fluorescein staining. Conjunctival histology was evaluated for epithelial morphology, goblet cell (GC) size, and mucin composition. Complement fractions C3 and C5a were assessed by immunohistochemistry. Expression of inflammatory markers (Major Histocompatibility Complex, Class II, DR, HLA-DR; interleukin-1β, IL-1β; tumor necrosis factor-α, TNF-α) was quantified by Real-Time PCR (RT-PCR) in corneal and conjunctival epithelium. Results: Fluorescein staining revealed progressive corneal epithelial damage over time. Histological analysis demonstrated conjunctival epithelial alterations characterized by a significant reduction in GC size and in neutral mucin-positive GCs, consistent with mucin remodeling of the ocular surface epithelium. Increased epithelial deposition of complement fractions C3 and C5a was observed, while molecular analysis confirmed upregulation of inflammatory markers, including HLA-DR, IL-1β, and TNF-α. Collectively, these findings indicate that the model captures key pathophysiological components of DED. Conclusions: The CEC model reproduces major features of evaporative DED, including epithelial damage, GC remodeling, immune activation, and inflammation. As a non-invasive desiccating stress model, it represents a relevant experimental platform for studying ocular surface inflammation and for preclinical evaluation of therapeutic strategies. Full article

This paper examines the connection between Environmental, Social, and Governance (ESG) factors and the risk of geopolitics, as defined by the Geopolitical Risk (GPR) index. The concept of geopolitical risk is conventionally defined as the direct result of political incidents, war, and international tensions. The current study argues that the concept should be understood in a more structural and sustainable manner, relating to the underlying forces driving geopolitical risk. The main research question is whether and how the three pillars of ESG factors contribute to explaining and understanding cross-country and over-time variations in geopolitical risk. In an effort to avoid information loss associated with the ESG index’s aggregate nature, the three factors are considered separately and the three pillars are analyzed individually. The empirical context is a balanced cross-country panel dataset including 42 countries over the 2000–2023 time period. Data for the three factors are obtained from the World Bank dataset to standardize and compare data across countries and over time. The GPR index measures the level of geopolitical risk and is defined by Dario Caldara and Matteo Iacoviello. The GPR index captures the level of geopolitical tensions by analyzing media signals. The combination of the three sources enables direct connections and correlations among the three factors and the internationally recognized GPR index. The paper uses an integrated methodological approach that combines results from three distinct methods. The first method uses panel data analysis to estimate average marginal effects while controlling for unobserved heterogeneity. The second method uses clustering to identify structural patterns and divide countries into groups based on their unique characteristics and risk profiles. The third method uses machine learning regressions and nonparametric analysis to capture the complex relationships and interactions in the data. The three-step method is used for each pillar to ensure consistency and comparability. The results suggest that the three factors contribute to the GPR index in a unique manner. The environment and energy structure contribute to the GPR index as a risk multiplier; the social factor relates to exposure to instability; and the governance factor is a central stabilizing factor. The paper makes a unique contribution to the literature by defining the three factors and their relationship to the GPR index in a clear, sustainable manner. Full article

The proposed study investigates the key factors influencing UAV adoption and proposes an integrated educational–operational framework to enhance implementation in agricultural practice. A case study in Sibiu County, Romania, combined survey-based empirical analysis (n = 80), strategic environmental assessment and the deployment of a demonstration aerial crop monitoring system for educational purposes (ACMS-E). We integrated the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB) to examine adoption intentions, revealing perceived usefulness (β = 0.355, p = 0.021) and positive attitudes (β = 0.382, p = 0.005) as the strongest predictors, explaining 44.1% of variance. Based on these findings, a modular training curriculum was designed, combining theoretical instruction, flight operation exercises, remote sensing techniques, data analytics and farm-management integration. ACMS-E provides hands-on training and promotes capacity-building, bridging the gap between technological availability and real-world adoption. By linking technological capabilities with structured training, ACMS-E bridges the gap between UAV availability and effective implementation, offering a scalable model for precision agriculture. This framework provides a pathway to accelerate UAV adoption, optimize field-level monitoring and support evidence-based, resource-efficient farm management in emerging and developed agricultural contexts. Full article

Seasonal hydrological fluctuations strongly influence riparian habitats in the middle Yangtze River, yet the relationships of inundation duration with soil properties and riparian vegetation remain insufficiently understood in representative riparian sections. Here, field surveys and laboratory analyses were conducted to examine (1) inundation–soil associations and (2) soil–vegetation relationships. Soil moisture (W), pH, particle-size composition, soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) were measured, and vegetation parameters were compared among inundation-duration zones. Partial redundancy analysis (pRDA) was used to evaluate the relationships between environmental factors and vegetation parameters after controlling for elevation and shoreline distance. Vegetation occurrence, coverage, and diversity decreased with increasing inundation duration, and no vascular plants were recorded in the severe-inundation zone. After accounting for topographic factors, TN and gravel were the main soil variables associated with vegetation variation. Overall, inundation duration was closely associated with soil variation, whereas vegetation variation was mainly associated with selected soil environmental factors. These findings provide site-based evidence for riparian ecological restoration in representative riparian sections of the middle Yangtze River. Full article

Background/Objectives: Patients after cardiac surgery admitted to the intensive care unit (ICU) are exposed to environmental, procedural, and psychological stressors that may affect comfort and recovery. This study aimed to assess perceived ICU stressors in postoperative cardiac surgery patients, identify the most and least distressing factors, and examine associations between stressor intensity and selected clinical and organizational variables. Methods: A single-center cross-sectional survey was conducted in an ICU in Poland (January 2024–February 2024). Adult patients after cardiac surgery who provided informed consent and had no cognitive impairment were included; cognitive status was screened using the Montreal Cognitive Assessment (MoCA). Perceived stressors were measured using the Intensive Care Unit Environmental Stressor Scale (ICUESS; 40 items; 4-point Likert scale). Results: The highest-rated stressors were sleep problems (M = 2.30; SD = 0.86) and hearing heart monitor alarms (M = 2.16; SD = 0.82). The lowest-rated stressors were not knowing what day it was (M = 1.46; SD = 0.54) and nurses not introducing themselves (M = 1.50; SD = 0.54). Longer respiratory support and higher pain intensity were associated with higher stressor ratings for multiple ICUESS items, whereas age showed no significant association. Higher room occupancy was linked to higher perceived stress related to environmental disturbances. ICU length of stay showed only limited item-level associations. Conclusions: Postoperative cardiac surgery patients experience a multifactorial burden of ICU stressors, with sleep disruption and alarm-related noise among the most distressing. Prioritizing modifiable environmental factors, symptom control (particularly pain), and patient-centered communication may help reduce perceived stress, especially in shared-room settings and among patients requiring longer respiratory support. Full article

Background: Precision nutrition (PN) aims to personalize dietary guidance by accounting for inter-individual variability across biological, metabolic, lifestyle, and environmental factors influencing nutritional needs and health outcomes. While traditional Artificial Intelligence (AI) has advanced nutritional research through systems like automated dietary assessment, these models often operate rigidly. Generative AI (GenAI) introduces the capacity for adaptive interventions for enhanced PN. However, the scope and maturity of its applications remain insufficiently characterized. Objective: This review examined original works applying GenAI in PN, focusing on application, methodology, and limitations. Methods: A systematic search was conducted in PubMed, ACM Digital Library, and Scopus. Inclusion criteria focused on original works deploying GenAI models in PN contexts. Included works were further formally assessed based on data used, validation, transparency, bias, and security and privacy. Results: 21 eligible studies were identified, all published after 2024. The literature indicated a surge in large language model-based systems for personalized dietary recommendations, followed by applications in data foundation building and food effect understanding. A recurrent limitation was questionable evaluation on synthetic data and hallucinations, necessitating a human-expert-in-the-loop, especially in high-stakes clinical settings. Additionally, only 4 of 21 reviewed studies incorporated biological content or biological inputs, and fewer approached biologically grounded PN within implemented personalization workflows using metabolic and/or genomic variables. Conclusions: Although GenAI research in PN is expanding rapidly, most applications remain personalized at a user-preference level rather than including biological determinants. The need for standardized reporting, stronger genome-informed modeling, and consistent human-in-the-loop validation protocols is further highlighted to advance towards holistic PN. Full article