Search Results (1,984)

Background/Objectives: Global warming and rising CO₂ concentrations pose significant challenges to plant systems. Amid these pressures, this study contributes to understanding how tropical species respond by simultaneously evaluating reproductive and genetic traits. It specifically investigates the effects of maternal exposure to warming and elevated CO₂ on progeny physiology, genetic diversity, and population structure in Stylosanthes capitata, a resilient forage legume native to Brazil. Methods: Maternal plants were cultivated under controlled treatments, including ambient conditions (control), elevated CO₂ at 600 ppm (eCO₂), elevated temperature at +2 °C (eTE), and their combined exposure (eTEeCO₂), within a Trop-T-FACE field facility (Temperature Free-Air Controlled Enhancement and Free-Air Carbon Dioxide Enrichment). Seed traits (seeds per inflorescence, hundred-seed mass, abortion, non-viable seeds, coat color, germination at 32, 40, 71 weeks) and abnormal seedling rates were quantified. Genetic diversity metrics included the average (A) and effective (Ae) number of alleles, observed (Ho) and expected (He) heterozygosity, and inbreeding coefficient (Fis). Population structure was assessed using Principal Coordinates Analysis (PCoA), Analysis of Molecular Variance (AMOVA), number of migrants per generation (Nm), and genetic differentiation index (Fst). Two- and three-way Analysis of Variance (ANOVA) were used to evaluate factor effects. Results: Compared to control conditions, warming increased seeds per inflorescence (+46%), reduced abortion (−42.9%), non-viable seeds (−57%), and altered coat color. The germination speed index (GSI +23.5%) and germination rate (Gr +11%) improved with warming; combined treatments decreased germination time (GT −9.6%). Storage preserved germination traits, with warming enhancing performance over time and reducing abnormal seedlings (−54.5%). Conversely, elevated CO₂ shortened GSI in late stages, impairing germination efficiency. Warming reduced Ae (−35%), He (−20%), and raised Fis (maternal 0.50, progeny 0.58), consistent with the species’ mixed mating system; A and Ho were unaffected. Allele frequency shifts suggested selective pressure under eTE. Warming induced slight structure in PCoA, and AMOVA detected 1% (maternal) and 9% (progeny) variation. Fst = 0.06 and Nm = 3.8 imply environmental influence without isolation. Conclusions: Warming significantly shapes seed quality, reproductive success, and genetic diversity in S. capitata. Improved reproduction and germination suggest adaptive advantages, but higher inbreeding and reduced diversity may constrain long-term resilience. The findings underscore the need for genetic monitoring and broader genetic bases in cultivars confronting environmental stressors. Full article

The significant advances in the surgical and medical treatment of gynecological cancer have led to improved survival outcomes of several subgroups of patients that were until recently opted out of treatment plans. Surgical cytoreduction has evolved through advanced surgical complexity procedures and the need for critical care of gynecological cancer patients has increased. Despite that, however, articles focusing on the need of perioperative monitoring of these patients completely lack from the international literature; hence, recommendations are still lacking. Critical care may be offered in different types of facilities with specific indications. These include the post-anesthesia care unit (PACU), the high dependency unit (HDU) and the intensive care unit (ICU) which have discrete roles and should be used judiciously in order to avoid unnecessary increases in the hospitalization costs. In the present review we focus on the pathophysiological alterations that are expected in gynecological cancer patients undergoing surgical treatment, provide current evidence and discuss indications of hospitalization as well as discharge criteria from intensive care facilities. Full article

Rapid industrialization and urbanization have progressed in Korea, yet public attention to hazardous pollutants emitted from industrial complexes remains limited. With the increasing coexistence of industrial and residential areas, there is a growing need for real-time monitoring and management plans that account for the rapid dispersion of hazardous air pollutants (HAPs). In this study, we conducted spatiotemporal data collection and analysis for the first time in Korea using real-time measurements obtained through mobile extractive differential optical absorption spectroscopy (Me-DOAS) mounted on a solar occultation flux (SOF) vehicle. The measurements were conducted in the Saha Sinpyeong–Janglim Industrial Complex in Busan, which comprises the Sasang Industrial Complex and the Sinpyeong–Janglim Industrial Complex. BTEX compounds were selected as target volatile organic compounds (VOCs), and real-time measurements of both BTEX and fine particulate matter (PM) were conducted simultaneously. Correlation analysis revealed a strong relationship between PM₁₀ and PM_2.5 (r = 0.848–0.894), indicating shared sources. In Sasang, BTEX levels were associated with traffic and localized facilities, while in Saha Sinpyeong–Janglim, the concentrations were more influenced by industrial zoning and wind patterns. Notably, inter-compound correlations such as benzene–m-xylene and p-xylene–toluene suggested possible co-emission sources. This study proposes a GIS-based, three-dimensional air quality management approach that integrates variables such as traffic volume, wind direction, and speed through real-time measurements. The findings are expected to inform effective pollution control strategies and future environmental management plans for industrial complexes. Full article

Altitude Test Facility (ATF) compressor systems are widely used in aero-engine tests. These systems achieve the control of gas pressure and transport through complex operation processes. With advancements in the aviation industry, there is a growing demand for higher performance, greater safety, and more energy efficiency in digital ATF systems. Hybrid modeling is a technology that combines many methods and can meet these requirements. The Modular Dynamic System Greitzer (MDSG) compressor model, including mechanistic and data-driven modeling approaches, is combined with a neural network to obtain a BPNN-MDSG hybrid modeling method for the digital turbine system. The digital simulation is linked with the physical sensors of the ATF system to realize real-time simulation and monitoring. The steady and dynamic conditions of the actual system are simulated in virtual space. Compared with the actual results, the average error of steady mass flow is less than 3%, and the error of pressure is less than 1%. The average error of dynamic mass flow is less than 5%, and the error of pressure is less than 3%. The simulation and characteristic predictions are carried out in BPNN-MDSG virtual space. The anti-surge characteristics of the ATF system under start-up conditions are obtained. The full-condition anti-surge operation map of the system is obtained, which provides guidance for the actual operation of the ATF system. Full article

This article presents the development, integration, and experimental validation of a modular microgrid for sustainable hydrogen production, addressing global electricity demand and environmental challenges. The system was designed for initial validation in a thermoelectric power plant environment, with scalability to other applications. Centered on a six-compartment skid, it integrates photovoltaic generation, battery storage, and a liquefied petroleum gas generator to emulate typical cogeneration conditions, together with a high-purity proton exchange membrane electrolyzer. A supervisory control module ensures real-time monitoring and energy flow management, following international safety standards. The study also explores the incorporation of blockchain technology to certify the renewable origin of hydrogen, enhancing traceability and transparency in the green hydrogen market. The experimental results confirm the system’s technical feasibility, demonstrating stable hydrogen production, efficient energy management, and islanded-mode operation with preserved grid stability. These findings highlight the strategic role of hydrogen as an energy vector in the transition to a cleaner energy matrix and support the proposed architecture as a replicable model for industrial facilities seeking to combine hydrogen production with advanced microgrid technologies. Future work will address large-scale validation and performance optimization, including advanced energy management algorithms to ensure economic viability and sustainability in diverse industrial contexts. Full article

The continuous variability in the microbiological quality of surface waters presents significant challenges for ensuring the production of safe drinking water in compliance with public health regulations. Inadequate treatment of surface waters can lead to the presence of pathogenic microorganisms in the drinking water supply, posing serious risks to public health. This research presents an in-depth data analysis using machine learning tools for the induction of models to describe and predict microbiological water quality for the sustainable management of the Butoniga drinking water treatment facility in Istria (Croatia). Specifically, descriptive and predictive models for total coliforms and E. coli bacteria (i.e., classes), which are recognized as key sanitary indicators of microbiological contamination under both EU and Croatian water quality legislation, were developed. The descriptive models provided useful information about the main environmental factors that influence the microbiological water quality. The most significant influential factors were found to be pH, water temperature, and water turbidity. On the other hand, the predictive models were developed to estimate the concentrations of total coliforms and E. coli bacteria seven days in advance using several machine learning methods, including model trees, random forests, multi-layer perceptron, bagging, and XGBoost. Among these, model trees were selected for their interpretability and potential integration into decision support systems. The predictive models demonstrated satisfactory performance, with a correlation coefficient of 0.72 for total coliforms, and moderate predictive accuracy for E. coli bacteria, with a correlation coefficient of 0.48. The resulting models offer actionable insights for optimizing operational responses in water treatment processes based on real-time and predicted microbiological conditions in the Butoniga reservoir. Moreover, this research contributes to the development of predictive frameworks for microbiological water quality management and highlights the importance of further research and monitoring of this key aspect of the preservation of the environment and public health. Full article

A novel framework is proposed to support the prediction of the plastic waste (PW) collection demand, route optimization, and overall management of PW from individual facilities. Based on electronic manifests from a local recycling company in Fukuoka, Japan, we developed a two-step artificial intelligence (AI)-based approach for predicting the demand for industrial PW (IPW) collection from a hospital. The daily hospital visitor numbers were introduced as a new independent variable in the IPW collection demand prediction. The stability (robustness) of each model was measured by its variance through experiments for two variable groups in four validation months. We found that introducing the visitor variables into IPW collection demand predictions was effective. A high monthly mean accuracy (85.06%) was achieved in predicting the daily IPW collection demand, which exceeded the accuracy of predictions using models without visitor records (84.44%). The stability of the Fine tree model with the highest prediction accuracy for March 2020 was 0.0466 $\mp$ 0.0174. Based on the findings of this study, we propose several strategies for waste management: enhancing prediction models, controlling visitor flows, and analyzing working patterns. This study successfully links AI techniques with a human mobility monitoring system (location data) for waste management using MATLAB. Full article

An Application Programming Interface (API) is a formally defined interface that enables controlled interaction between software components, and is a key pillar of modern microservice-based architectures. However, asynchronous API changes often lead to breaking compatibility and introduce systemic instability across dependent services. Prior research has explored various strategies to manage such evolution, including contract-based testing, semantic versioning, and continuous deployment safeguards. Nevertheless, a comprehensive orchestration mechanism that formalizes dependency propagation and automates compatibility enforcement remains lacking. In this study, we propose a Compatibility-Driven Version Orchestrator, integrating semantic versioning, contract testing, and CI triggers into a unified framework. We empirically validate the approach on a Kubernetes-based environment, demonstrating the improved resilience of microservice systems to breaking changes. This contribution advances the theoretical modeling of cascading failures in microservices, while providing developers and DevOps teams with a practical toolset to improve service stability in dynamic, distributed environments. Full article

Accurate counting of cucumber flowers using intelligent algorithms to monitor their sex ratio is essential for intelligent facility agriculture management. However, complex greenhouse environments impose higher demands on the precision and efficiency of counting algorithms. This study proposes a dual-area counting algorithm based on an improved YOLOv8n-Track (YOLOv8n-T) and ByteTrack cascaded framework. This method accomplishes the cucumber flower counting task by detecting flower targets, tracking them frame-by-frame, and validating the count through dual-area counting. The YOLOv8n-T incorporates a Coordinate Attention (CA) mechanism and lightweight modules while optimizing the loss function, thereby improving floral feature extraction capabilities and reducing computational complexity. By integrating the ByteTrack tracking algorithm with a dual-area counting strategy, the robustness of flower counting in dynamic environments is strengthened. Experimental results show that the improved YOLOv8n-T achieves mAP and F1 scores of 86.9% and 82.1%, surpassing YOLOv8n by 3% and 2.6%, respectively, with a 0.3 G reduction in model parameters. The integrated framework achieves a detection accuracy of 82.4% for cucumber flower counting. This research provides a new method for monitoring cucumber flower sex ratios in facility agriculture, promoting the development of intelligent agricultural management. Full article

The colonization of Legionella spp. in engineered water systems constitutes a major public health threat. In this study, a six-year environmental surveillance (2020–2025) of Legionella colonization in five different types of facilities in Crete, Greece is presented, including hotels, passenger ships, primary healthcare facilities, public hospitals, and private clinics. A total of 1081 water samples were collected and analyzed, and the overall positivity was calculated using culture-based methods. Only 16.46% of the samples exceeded the regulatory limit (>10³ CFU/L) in the total sample, with 44.59% overall Legionella positivity. Colonization by facility category showed the highest rates in primary healthcare facilities with 85.96%, followed by public hospitals (46.36%), passenger ships with 36.93%, hotels with 38.08%, and finally private clinics (21.42%). The association of environmental risk factors with Legionella positivity revealed a strong effect at hot water temperatures < 50 °C (RR = 2.05) and free chlorine residuals < 0.2 mg/L (RR = 2.22) (p < 0.0001). Serotyping analysis revealed the overall dominance of Serogroups 2–15 of L. pneumophila; nevertheless, Serogroup 1 was particularly prevalent in hospitals, passenger ships, and hotels. Based on these findings, the requirement for continuous environmental monitoring and risk management plans with preventive thermochemical controls tailored to each facility is highlighted. Finally, operational disruptions, such as those experienced during the COVID-19 pandemic, especially in primary care facilities and marine systems, require special attention. Full article

Leptospirosis is a (re-)emerging and global zoonotic disease. Given the complex host-pathogen interaction and the numerous environmental risk factors related to the transmission, a One Health approach to both disease prevention and control is needed. Occurring at the human–cattle–environment interfaces, bovine leptospirosis represents a zoonotic risk for the professionals in the field, besides being a potential cause of significant economic losses due to the bovine reproductive disorders. Although climatic change is a potential factor in exacerbating the risk of leptospirosis in Europe, this disease remains largely neglected, with several knowledge gaps in research, investigations, and diagnosis of bovine genital leptospirosis syndrome across the continent. The present report describes the results of the diagnostic investigations on a case of chronic bovine leptospirosis in a breeding bull. Following the seroconversion to Leptospira Sejroe var Hardjo after the arrival of the animal in a quarantine facility, a monitoring plan including both serological/molecular analyses and a therapeutic protocol was undertaken. The bull exhibited a persistent seroconversion and a repeated positivity for Leptospira to real-time PCR in urine samples, indicative of a chronic shedder pattern. This report emphasizes the diagnostic and management challenges in the context of such a complex but frequently overlooked disease. Full article

Pseudomonas aeruginosa is a highly adaptable opportunistic pathogen with significant clinical relevance in both human and veterinary medicine. Despite its well-documented role in hospital-acquired infections in human healthcare settings, its persistence and transmission within veterinary clinics remain underexplored. This review highlights the overlooked status of veterinary facilities as environmental reservoirs and amplification points for multidrug-resistant (MDR) P. aeruginosa, emphasizing their relevance to One Health surveillance. We examine the bacterium’s environmental survival strategies, including biofilm formation, resistance to disinfectants, and tolerance to nutrient-poor conditions that facilitate the long-term colonization of moist surfaces, drains, medical equipment, and plumbing systems. Common transmission vectors are identified, including asymptomatic animal carriers, contaminated instruments, and the hands of veterinary staff. The review synthesizes current data on antimicrobial resistance in environmental isolates, revealing frequent expression of efflux pumps and mobile resistance genes, and documents the potential for zoonotic transmission to staff and pet owners. Key gaps in environmental monitoring, infection control protocols, and genomic surveillance are identified, with a call for standardized approaches tailored to the veterinary context. Control strategies, including mechanical biofilm disruption, disinfectant cycling, effluent monitoring, and staff hygiene training, are evaluated for feasibility and impact. The article concludes with a One Health framework outlining cross-species and environmental transmission pathways. It advocates for harmonized surveillance, infrastructure improvements, and intersectoral collaboration to reduce the risk posed by MDR P. aeruginosa within veterinary clinical environments and beyond. By addressing these blind spots, veterinary facilities can become proactive partners in antimicrobial stewardship and global resistance mitigation. Full article

Large turbine generators rely on shaft earthing brushes to safely divert harmful shaft currents to ground, protecting bearings from electrical damage. This paper presents a novel deep learning-based diagnostic framework to detect and classify faults in shaft earthing brushes of large turbine generators. A key innovation lies in the use of FFT-derived spectrograms from both voltage and current waveforms as dual-channel inputs to the CNN, enabling automatic feature extraction of time–frequency patterns associated with different SEB fault types. The proposed framework combines advanced signal processing and convolutional neural networks (CNNs) to automatically recognize fault-related patterns in shaft grounding current and voltage signals. In the approach, raw time-domain signals are converted into informative time–frequency representations, which serve as input to a CNN model trained to distinguish normal and faulty conditions. The framework was evaluated using data from a fleet of large-scale generators under various brush fault scenarios (e.g., increased brush contact resistance, loss of brush contact, worn out brushes, and brush contamination). Experimental results demonstrate high fault detection accuracy (exceeding 98%) and the reliable identification of different fault types, outperforming conventional threshold-based monitoring techniques. The proposed deep learning framework offers a novel intelligent monitoring solution for predictive maintenance of turbine generators. The contributions include the following: (1) the development of a specialized deep learning model for shaft earthing brush fault diagnosis, (2) a systematic methodology for feature extraction from shaft current signals, and (3) the validation of the framework on real-world fault data. This work enables the early detection of brush degradation, thereby reducing unplanned downtime and maintenance costs in power generation facilities. Full article

Natural gas storage is an effective solution to address the energy supply–demand imbalance, and underground gas storage (UGS) is a primary method for storing natural gas. The overarching goal of this study is to monitor and analyze surface deformation at the Hutubi underground gas storage facility in Xinjiang, China, which is the largest gas storage facility in the country. This research aims to ensure the stable and efficient operation of the facility through long-term monitoring, using remote sensing data and advanced modeling techniques. The study employs the SBAS-InSAR method, leveraging Synthetic Aperture Radar (SAR) data from the TerraSAR and Sentinel-1 sensors to observe displacement time series from 2013 to 2024. The data is processed through wavelet transformation for denoising, followed by the application of a Gray Wolf Optimization (GWO) algorithm combined with Variational Mode Decomposition (VMD) to decompose both surface deformation and gas pressure data. The key focus is the development of a high-precision predictive model using a Gated Recurrent Unit (GRU) network, referred to as GWO-VMD-GRU, to accurately predict surface deformation. The results show periodic surface uplift and subsidence at the facility, with a notable net uplift. During the period from August 2013 to March 2015, the maximum uplift rate was 6 mm/year, while from January 2015 to December 2024, it increased to 12 mm/year. The surface deformation correlates with gas injection and extraction periods, indicating periodic variations. The accuracy of the InSAR-derived displacement data is validated through high-precision GNSS data. The GWO-VMD-GRU model demonstrates strong predictive performance with a coefficient of determination (R²) greater than 0.98 for the gas well test points. This study provides a valuable reference for the future safe operation and management of underground gas storage facilities, demonstrating significant contributions to both scientific understanding and practical applications in underground gas storage management. Full article

Agriculture and animal feeding operations are responsible for 87% of ammonia emissions in the UK. Controlling NH₃ concentrations below 20 ppm is crucial to preserve workers’ and livestock’s well-being. Therefore, ammonia control systems are required for maintaining adequate air quality in livestock facilities. This study assessed the ammonia reduction efficiency of a novel air pollution abatement (APA) system used in a pig farm building. The monitoring duration was 11 weeks. The results were compared with the baseline from a previous pig cycle during the same time of year in 2023. A ventilation-controlled room was monitored during a two-phase campaign, and the actual ammonia concentrations were measured at different locations within the site and at the inlet/outlet of the APA system. A 98% ammonia reduction was achieved at the APA outlet through NH₃ absorption in tap water. Ion chromatography analyses of farm water samples revealed NH₃ concentrations of up to 530 ppm within 83 days of APA operation. Further scanning electron microscopy and energy-dispersive X-ray inspections revealed the presence of salts and organic/inorganic matter in the solid residues. This research can contribute to meeting current ammonia regulations (NECRs), also by reusing the process water as a potential nitrogen fertiliser in agriculture. Full article