Search Results (2,295)

Constructed wetlands (CWs) are sustainable and cost-effective systems that utilise plant–microbe interactions and natural processes for wastewater treatment. Microbial communities play a pivotal role in pollutant removal by crucial processes like nitrogen transformations, phosphorus cycling, organic matter degradation and the breakdown of emerging contaminants. Dominant phyla, such as Proteobacteria, Bacteroidetes, Actinobacteria and Firmicutes, collectively orchestrate these biogeochemical functions. Advances in molecular tools, including high-throughput sequencing and metagenomics, have revealed the diversity and functional potential of wetland microbiomes, while environmental factors, i.e., temperature, pH and hydraulic retention time, strongly influence their performance. Phosphorus removal efficiency is often lower than nitrogen, and large land requirements and long start-up times restrict broader application. Microplastic accumulation, the spread of antibiotic resistance genes and greenhouse gas emissions (methane, nitrous oxide) present additional challenges. The possible persistence of pathogenic microbes further complicates system safety. Future research should integrate engineered substrates, biochar amendments, optimised plant–microbe interactions and hybrid CW designs to enhance treatment performance and resilience in the era of climate change. By acknowledging the potential and constraints, CWs can be further developed as next-generation, nature-based solutions for sustainable water management in the years to come. Full article

Background and Objectives: Endoscopic sleeve gastroplasty (ESG) is a minimally invasive endoscopic procedure that has demonstrated both safety and effectiveness in the treatment of obesity. By reducing the stomach’s volume without the need for surgical incisions, ESG promotes weight loss and can improve obesity-related comorbidities. However, patient responses to ESG can vary significantly. Materials and Methods: A comprehensive search was performed on PubMed, Embase, and Cochrane for studies with endoscopic sleeve gastroplasty; the main outcomes of interest are BMI, weight loss, and postinterventional complications. The search strategy employed a combination of keywords and Medical Subject Heading (MeSH) terms, including “endoscopic sleeve gastroplasty,” “endoscopy,” and “overweight”. To ensure the thoroughness of the review, additional manual searches of key journals and the reference lists of identified studies were performed. Grey literature, such as dissertations and conference abstracts, meta-analysis, and systematic reviews, was excluded to maintain a focus on peer-reviewed evidence. Duplicate records were identified and removed using Rayyan software to streamline the screening process. The I2 test was employed for heterogeneity assessment, while the risk of bias was evaluated utilizing ROBINS-I. Results: Our literature search resulted in the inclusion of 38 studies. Endoscopic sleeve gastroplasty for weight loss is important since it is more effective than pharmacological treatments and lifestyle changes and presents lower adverse event rates compared to bariatric surgery. Long-term weight loss outcomes varied, with total body weight loss ranging from 16% to 20.9% over a period from 2 to 5 years, while excess weight loss ranged from 13% to 79%. Revisional procedures showed higher failure rates, with up to 34.3% of patients experiencing insufficient weight loss. Most interventions led to clinically significant and sustained weight loss, though variability in outcomes highlights the need for further research to optimize long-term weight management strategies. Conclusions: Endoscopic sleeve gastroplasty (ESG) emerges as a promising minimally invasive option for weight loss, offering significant improvements in both weight reduction and obesity-related comorbidities, such as diabetes, hypertension, and dyslipidemia. Full article

Background: Aviation is one of the most demanding professions, exposing pilots to persistent stressors such as fatigue, irregular schedules, and high safety responsibility. These conditions heighten vulnerability to depression, anxiety, and stress (DAS), yet the protective mechanisms mitigating such effects remain less well understood. Objective: This study examined the roles of resilience, coping strategies, and fatigue in predicting DAS among commercial airline pilots. Method: A sample of 200 pilots completed validated self-report measures: the Connor–Davidson Resilience Scale (CD-RISC), the Coping Inventory for Stressful Situations (CISS), the Fatigue Severity Scale (FSS), and the Depression Anxiety Stress Scale (DASS-21). Data were analyzed using bivariate correlations, hierarchical multiple regression, and mediation/moderation analyses via the PROCESS macro. Results: Resilience was negatively correlated with total DAS scores (r = −0.46, p < 0.001), while fatigue (r = 0.42, p < 0.001) and avoidance coping (r = 0.38, p < 0.001) were positively correlated. The regression model accounted for 46% of the variance in DAS (R² = 0.46). Task-focused coping predicted lower stress levels, whereas avoidance coping predicted higher anxiety and depression. Resilience moderated the relationship between stress and depression, buffering the impact of stress on mood outcomes. Mediation analyses indicated that coping styles partially explained the protective effect of resilience. ANOVA results confirmed that pilots with high resilience reported significantly lower depression scores than those with medium or low resilience, F(2, 197) = 6.72, p < 0.01. Conclusions: Resilience emerged as both a direct and indirect buffer against psychological strain in aviation. These findings underscore the importance of promoting adaptive coping and resilience training, alongside effective fatigue management, to enhance pilot well-being and maintain safety in aviation systems. Full article

Background: Healthcare 5.0 represents a shift toward intelligent, human-centric care systems. Intensive care units generate vast amounts of data that require real-time decisions, but current decision support systems lack comprehensive frameworks for safe integration of artificial intelligence. Objective: We developed and validated the Learn–Predict–Monitor–Detect–Correct (LPMDC) framework as a methodology for systematic artificial intelligence integration across the critical care workflow. The framework improves predictive analytics, continuous patient monitoring, intelligent alerting, and therapeutic decision support while maintaining essential human clinical oversight. Methods: Framework development employed systematic theoretical modeling integrating Healthcare 5.0 principles, comprehensive literature synthesis covering 2020–2024, clinical workflow analysis across 15 international ICU sites, technology assessment of mature and emerging AI applications, and multi-round expert validation by 24 intensive care physicians and medical informaticists. Each LPMDC phase was designed with specific integration requirements, performance metrics, and safety protocols. Results: LPMDC implementation and aggregated evidence from prior studies demonstrated significant clinical improvements: 30% mortality reduction, 18% ICU length-of-stay decrease (7.5 to 6.1 days), 45% clinician cognitive load reduction, and 85% sepsis bundle compliance improvement. Machine learning algorithms achieved an 80% sensitivity for sepsis prediction three hours before clinical onset, with false-positive rates below 15%. Additional applications demonstrated effectiveness in predicting respiratory failure, preventing cardiovascular crises, and automating ventilator management. Digital twins technology enabled personalized treatment simulations, while the integration of the Internet of Medical Things provided comprehensive patient and environmental surveillance. Implementation challenges were systematically addressed through phased deployment strategies, staff training programs, and regulatory compliance frameworks. Conclusions: The Healthcare 5.0-enabled LPMDC framework provides the first comprehensive theoretical foundation for systematic AI integration in critical care while preserving human oversight and clinical safety. The cyclical five-phase architecture enables processing beyond traditional cognitive limits through continuous feedback loops and system optimization. Clinical validation demonstrates measurable improvements in patient outcomes, operational efficiency, and clinician satisfaction. Future developments incorporating quantum computing, federated learning, and explainable AI technologies offer additional advancement opportunities for next-generation critical care systems. Full article

Currently, maritime navigation safety risks—particularly those related to ship navigation—are primarily assessed through traditional rule-based methods and expert experience. However, such approaches often suffer from limited accuracy and lack real-time responsiveness. As maritime environments and operational conditions become increasingly complex, traditional techniques struggle to cope with the diversity and uncertainty of navigation scenarios. Therefore, there is an urgent need for a more intelligent and precise risk prediction method. This study proposes a ship risk prediction framework that integrates a deep learning model based on Long Short-Term Memory (LSTM) networks with Bayesian risk evaluation. The model first leverages deep neural networks to process time-series trajectory data, enabling accurate prediction of a vessel’s future positions and navigational status. Then, Bayesian inference is applied to quantitatively assess potential risks of collision and grounding by incorporating vessel motion data, environmental conditions, surrounding obstacles, and water depth information. The proposed framework combines the advantages of deep learning and Bayesian reasoning to improve the accuracy and timeliness of risk prediction. By providing real-time warnings and decision-making support, this model offers a novel solution for maritime safety management. Accurate risk forecasts enable ship crews to take precautionary measures in advance, effectively reducing the occurrence of maritime accidents. Full article

Amid the increasing frequency and severity of major disasters, the rapid spread of online misinformation poses substantial risks to public safety, effective crisis management, and long-term societal sustainability. Current methods for managing disaster-related rumors rely on static, rule-based approaches that lack scalability, fail to capture nuanced misinformation, and are limited to reactive responses, hindering effective disaster management. To address this gap, this study proposes a novel framework that leverages large language models (LLMs) and event knowledge graphs (EKGs) to facilitate the sustainable agile identification and adaptive control of disaster-related online rumors. The framework follows a multi-stage process, which includes the collection and preprocessing of disaster-related online data, the application of Gaussian Mixture Wasserstein Autoencoders (GMWAEs) for sentiment and rumor analysis, and the development of EKGs to enrich the understanding and reasoning of disaster events. Additionally, an enhanced model for rumor identification and risk control is introduced, utilizing Graph Attention Networks (GATs) to extract node features for accurate rumor detection and prediction of rumor propagation paths. Extensive experimental validation confirms the efficacy of the proposed methodology in improving disaster response. This study contributes novel theoretical insights and presents practical, scalable solutions for rumor control and risk management during crises. Full article

The power and electricity (P & E) sector experiences a substantial number of occupational accidents, including in Nigeria. The implementation of a safety management system (SMS) to promote safety performance and mitigate occupational risks in this sector remains underreported. Therefore, we aimed to explore the factors influencing the safety performance of Nigeria’s P & E distribution companies by applying McGrath’s input–process–output model as a theoretical framework. We used SmartPLS 3.0 for structural equation modelling and SPSS Version 23 for preliminary data analysis. We included a sample of 222 organizations and found that management commitment to safety, safety communication, safety champions, and government regulations influence working conditions and safety performance to varying degrees. Employee involvement, safety training, and working conditions were significant factors affecting safety performance. Management commitment, employee involvement, safety communication, safety champions, and government regulations had significant indirect effects on safety performance through their influence on working conditions. Organizational and regulatory elements played a crucial role in shaping safety performance in high-risk environments. The results highlight vital areas to be considered when developing interventions to address P & E occupational accidents. The results can aid stakeholders in developing and implementing measures to improve workplace safety, including examining current SMSs and considering working conditions when implementing safety interventions. Full article

Nuts, including tree nuts such as almonds, walnuts, cashews, and macadamias, as well as peanuts, are widely consumed for their health benefits owing to their high-quality protein content. Globally, the nut industry represents a multi-billion-dollar sector, with increasing demand driven by consumer interest in nutrition, functional foods, and plant-based diets. Recent advances in proteomic technologies have enabled comprehensive analyses of nut seed proteins, shedding light on their roles in nutrition, allergenicity, stress responses, and food functionality. Seed storage proteins such as 2S albumins, 7S vicilins, and 11S legumins, are central to nutrition and allergenicity. Their behavior during processing has important implications for food safety. Proteomic studies have also identified proteins involved in lipid and carbohydrate metabolism, stress tolerance, and defense against pathogens. Despite technical challenges such as high lipid content and limited genomic resources for many nut species, progress in both extraction methods and mass spectrometry has expanded the scope of nut proteomics. This review underscores the central role of proteomics in improving nut quality, enhancing food safety, guiding allergen risk management, and supporting breeding strategies for sustainable crop improvement. Full article

This study presents a data-driven framework that integrates lean management and digital business process modelling to enhance sustainability in textile manufacturing. Conducted in a company producing industrial safety textiles from Peru, this research applies lean tools within a digital BPM structure supported by real-time data tracking. The integrated approach led to increased production efficiency (from 79% to 86%), reduced setup times, and improved operational agility. The digital infrastructure empowered operators and supported informed decision-making. This work contributes to Industrial Engineering, Business Administration, and MIS by offering a holistic model that bridges lean principles with Industry 4.0 technologies. The findings, though context-specific, provide actionable insights for manufacturers aiming for smart and sustainable operations. Future research should validate the proposed framework across diverse industrial contexts and assess its longitudinal impact on lean performance outcomes. Full article

Structural health monitoring (SHM) plays a critical role in ensuring the safety and performance of aerospace structures throughout their lifecycle. As aircraft and spacecraft systems grow in complexity, the integration of machine learning (ML) into SHM frameworks is revolutionizing how damage is detected, localized, and predicted. This review presents a comprehensive examination of recent advances in ML-based SHM methods tailored to aerospace applications. It covers supervised, unsupervised, deep, and hybrid learning techniques, highlighting their capabilities in processing high-dimensional sensor data, managing uncertainty, and enabling real-time diagnostics. Particular focus is given to the challenges of data scarcity, operational variability, and interpretability in safety-critical environments. The review also explores emerging directions such as digital twins, transfer learning, and federated learning. By mapping current strengths and limitations, this paper provides a roadmap for future research and outlines the key enablers needed to bring ML-based SHM from laboratory development to widespread aerospace deployment. Full article

Open-pit mining often induces geological hazards such as slope instability, surface subsidence, and ground fissures. To support sustainable mine operations and safety, high-resolution monitoring and mechanism-based interpretation are essential tools for early warning, risk management, and compliant reclamation. This study focuses on the Baorixile open-pit coal mine in Inner Mongolia, China, where 48 Sentinel-1 images acquired between 3 March 2017 and 23 April 2021 were processed using the Small-Baseline Subset and Distributed-Scatterer Interferometric Synthetic Aperture Radar (SBAS-DS-InSAR) technique to obtain dense and reliable time-series deformation. Furthermore, a Trend–Periodic–Residual Subspace-Constrained Regression (TPRSCR) method was developed to decompose the deformation signals into long-term trends, seasonal and annual components, and residual anomalies. By introducing Distributed-Scatterer (DS) phase optimization, the monitoring density in low-coherence regions increased from 1055 to 338,555 points (approximately 321-fold increase). Deformation measurements at common points showed high consistency ($R^2$ = 0.97, regression slope = 0.88; mean rate difference = −0.093 mm/yr, standard deviation = 3.28 mm/yr), confirming the reliability of the results. Two major deformation zones were identified: one linked to ground compaction caused by transportation activities, and the other associated with minor subsidence from pre-mining site preparation. In addition, the deformation field exhibits a superimposed pattern of persistent subsidence and pronounced seasonality. TPRSCR results indicate that long-term trend rates range from −14.03 to 14.22 mm/yr, with a maximum periodic amplitude of 40 mm. Compared with the Seasonal-Trend decomposition using LOESS (STL), TPRSCR effectively suppressed “periodic leakage into trend” and reduced RMSEs of total, trend, and periodic components by 48.96%, 93.33%, and 89.71%, respectively. Correlation analysis with meteorological data revealed that periodic deformation is strongly controlled by precipitation and temperature, with an approximately 34-day lag relative to the temperature cycle. The proposed “monitoring–decomposition–interpretation” framework turns InSAR-derived deformation into sustainability indicators that enhance deformation characterization and guide early warning, targeted upkeep, climate-aware drainage, and reclamation. These metrics reduce downtime and resource-intensive repairs and inform integrated risk management in open-pit mining. Full article

New energy vehicles face a critical challenge in balancing the thermal safety management of high-specific-energy battery systems with the simultaneous improvement of energy density. With the large-scale application of high-energy-density systems such as silicon-based anodes and solid-state batteries, their inherent thermal runaway risks pose severe challenges to battery thermal management systems (BTMS). Currently, the thermal insulation performance, temperature resistance, and fire protection capabilities of flame-retardant materials (e.g., foam cotton, fiber felts) used in automotive batteries are inadequate to meet the demands of intense combustion and high temperatures generated during thermal failure in high-energy-density batteries. Against this backdrop, thermal insulation and fireproof aerogel materials are emerging as a revolutionary solution for the next generation of power battery thermal protection systems. Leveraging their nanoporous structure’s exceptional thermal insulation properties (thermal conductivity of 0.013–0.018 W/(m·K) at room temperature) and extreme fire resistance (temperature resistance > 1100 °C/UL94 V-0 flame retardancy), aerogels are gaining prominence. This article provides a systematic review of thermal runaway phenomena in automotive batteries and corresponding protective measures. It highlights recent breakthroughs in the selection of material systems, optimization of preparation processes, and fiber–matrix composite technologies for automotive fireproof aerogel composites. The core engineering values of these materials, such as blocking thermal runaway propagation, reducing system weight, and improving volumetric efficiency, are quantitatively validated. Furthermore, the paper explores future research directions, including the development of low-cost aerogel composites and the design of organic–inorganic hybrid composite structures, aiming to provide a foundation and industrial pathway for the research and development of next-generation high-performance battery thermal management systems. Full article

To address the challenge of coordinating economic cost control and low-carbon objectives in microgrid scheduling, while overcoming the performance limitations of the traditional Zebra Optimization Algorithm (ZOA) in complex problems, this paper proposes a Synergistic Zebra Optimization Algorithm (SZOA) and integrates it with innovative management concepts to enhance the microgrid scheduling process. The SZOA incorporates three core strategies: a multi-population cooperative search mechanism to strengthen global exploration, a vertical crossover–mutation strategy to meet high-dimensional scheduling requirements, and a leader-guided boundary control strategy to ensure variable feasibility. These strategies not only improve algorithmic performance but also provide technical support for innovative management in microgrid scheduling. Extensive experiments on the CEC2017 (d = 30) and CEC2022 (d = 10, 20) benchmark sets demonstrate that the SZOA achieves higher optimization accuracy and stability compared with those of nine state-of-the-art algorithms, including IAGWO and EWOA. Friedman tests further confirm its superiority, with the best average rankings of 1.20 for CEC2017 and 1.08/1.25 for CEC2022 (d = 10, 20). To validate practical applicability, the SZOA is applied to grid-connected microgrid scheduling, where the system model integrates renewable energy sources such as photovoltaic (PV) generation and wind turbines (WT); controllable sources including fuel cells (FC), microturbines (MT), and gas engines (GS); a battery (BT) storage unit; and the main grid. The optimization problem is formulated as a bi-objective model minimizing both economic costs—including fuel, operation, pollutant treatment, main-grid interactions, and imbalance penalties—and carbon emissions, subject to constraints on generation limits and storage state-of-charge safety ranges. Simulation results based on typical daily data from Guangdong, China, show that the optimized microgrid achieves a minimum operating cost of USD 5165.96, an average cost of USD 6853.07, and a standard deviation of only USD 448.53, consistently outperforming all comparison algorithms across economic indicators. Meanwhile, the SZOA dynamically coordinates power outputs: during the daytime, it maximizes PV utilization (with peak output near 35 kW) and WT contribution (30–40 kW), while reducing reliance on fossil-based units such as FC and MT; at night, BT discharges (−20 to −30 kW) to cover load deficits, thereby lowering fossil fuel consumption and pollutant emissions. Overall, the SZOA effectively realizes the synergy of “economic efficiency and low-carbon operation”, offering a reliable and practical technical solution for innovative management and sustainable operation of microgrid scheduling. Full article

This study addresses the absence of maximum residue limits (MRLs) for tebuconazole and trifloxystrobin in edible rose petals in China by systematically evaluating the residue behavior and dietary exposure risks of these fungicides. An analytical method based on QuEChERS sample preparation coupled with UPLC–MS/MS was developed for the simultaneous determination of tebuconazole, trifloxystrobin, and its metabolite CGA321113 in fresh and dried rose petals. Field trials under the highest application conditions (184 g a.i./hm², applied twice) were conducted to investigate residue dissipation dynamics, storage stability, processing concentration effects, and transfer behavior during brewing. Results indicated that the target compounds remained stable in rose petals for 12 months at –20 °C ± 2 °C. The drying process significantly concentrated residues due to the hydrophobic nature of the compounds, with enrichment factors ranging from 3.0 to 3.9. Brewing tests further confirmed low transfer rates of tebuconazole, trifloxystrobin, and CGA321113 into the infusion, consistent with their low water solubility and high log K_ow values. Residue dissipation followed first-order kinetics, with half-lives of 1.9–2.9 days for tebuconazole and 1.2–2.7 days for trifloxystrobin. Dietary risk assessment showed an acceptable risk for trifloxystrobin (RQ = 22.7%) but a high risk for tebuconazole (RQ = 175.1%). It is recommended to set the MRL for both tebuconazole and trifloxystrobin in edible roses at 15.0 mg/kg. This standard ensures consumer safety while accommodating agricultural needs and aligns with international regulations. For the high-risk pesticide tebuconazole, measures such as optimizing application strategies and promoting integrated management should be implemented to mitigate residue risks. Full article

In this study, we propose an architectural model for path optimization in cluster order picking within warehouse robotics, utilizing a hybrid approach that combines symbolic control and metaheuristic techniques. Among the optimization strategies, we incorporate bio-inspired metaheuristic algorithms such as the Walrus Optimization Algorithm (WOA), Puma Optimization Algorithm (POA), and Flying Foxes Algorithm (FFA), which are grounded in behavioral models observed in nature. We consider large-scale warehouse robotic systems, partitioned into clusters. To manage shared resources between clusters, the set of clusters is first formulated as a symbolic control design task within a discrete synthesis framework. Subsequently, the desired control goals are integrated into the model, encoded using parallel synchronous dataflow languages; the resulting controller, derived using our safety-focused and optimization-based synthesis approach, serves as the manager for the cluster. Safety objectives address the rigid system behaviors, while optimization objectives focus on minimizing the traveled path of the warehouse robots through the constructed cost function. The metaheuristic algorithms contribute at this stage, drawing inspiration from real-world animal behaviors, such as walruses’ cooperative movement and foraging, pumas’ territorial hunting strategies, and flying foxes’ echolocation-based navigation. These nature-inspired processes allow for effective solution space exploration and contribute to improving the quality of cluster-level path optimization. Our hybrid approach, integrating symbolic control and metaheuristic techniques, demonstrates significantly higher performance advantage over existing solutions, with experimental data verifying the practical effectiveness of our approach. Our proposed algorithm achieves up to 3.01% shorter intra-cluster paths compared to the metaheuristic algorithms, with an average improvement of 1.2%. For the entire warehouse, it provides up to 2.05% shorter paths on average, and even in the worst case, outperforms competing metaheuristic methods by 0.28%, demonstrating its consistent effectiveness in path optimization. Full article