Search Results (1,450)

As the core component of aircraft systems, aeroengines require accurate Remaining Useful Life (RUL) prediction to ensure flight safety, which serves as a key part of Prognostics and Health Management (PHM). Traditional RUL prediction methods primarily fall into two main categories: physics-based and data-driven approaches. Physics-based methods mainly rely on extensive prior knowledge, limiting their scalability, while data-driven methods (including statistical analysis and machine learning) struggle with handling high-dimensional data and suboptimal modeling of multi-scale temporal dependencies. To address these challenges and enhance prediction accuracy and robustness, we propose a novel hybrid deep learning framework (CLSTM-TCN) integrating 2D Convolutional Neural Network (2D-CNN), Long Short-Term Memory (LSTM) network, and Temporal Convolutional Network (TCN) modules. The CLSTM-TCN framework follows a progressive feature refinement logic: 2D-CNN first extracts short-term local features and inter-feature interactions from input data; the LSTM network then models long-term temporal dependencies in time series to strengthen global temporal dynamics representation; and TCN ultimately captures multi-scale temporal features via dilated convolutions, overcoming the limitations of the LSTM network in long-range dependency modeling while enabling parallel computing. Validated on the NASA C-MAPSS data set (focusing on FD001), the CLSTM-TCN model achieves a root mean square error (RMSE) of 13.35 and a score function (score) of 219. Compared to the CNN-LSTM, CNN-TCN, and LSTM-TCN models, it reduces the RMSE by 27.94%, 30.79%, and 30.88%, respectively, and significantly outperforms the traditional single-model methods (e.g., standalone CNN or LSTM network). Notably, the model maintains stability across diverse operational conditions, with RMSE fluctuations capped within 15% for all test cases. Ablation studies confirm the synergistic effect of each module: removing 2D-CNN, LSTM, or TCN leads to an increase in the RMSE and score. This framework effectively handles high-dimensional data and multi-scale temporal dependencies, providing an accurate and robust solution for aeroengine RUL prediction. While current performance is validated under single operating conditions, ongoing efforts to optimize hyperparameter tuning, enhance adaptability to complex operating scenarios, and integrate uncertainty analysis will further strengthen its practical value in aircraft health management. Full article

In the evolving discourse of affective urbanism, emotions are increasingly recognized as fundamental, systemic drivers shaping the social, perceptual, and symbolic dimensions of urban space. Meanwhile, advances in visual technologies and media aesthetics have transformed contemporary cities into visually saturated environments, where visual cues actively influence how urban space is perceived, navigated, and emotionally experienced. While prior research has addressed affective belonging and spatial identity, these studies often treat emotion and visual design as separate influences rather than examining their interdependent, systemic roles. To address this gap, this study develops an emotion-driven systemic model to analyze how visual design activates affective pathways that contribute to the sustainable construction of place branding. Drawing on survey data from 134 residents in Wuxi, China, we employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the interrelations among emotionally durable visual design, urban emotion, and place branding. The results reveal that visual attachment design (VAD) significantly strengthens place branding through emotional mediation, while visual behavior design (VBD) directly enhances sustainable branding by fostering participatory engagement even without emotional mediation. In contrast, visual function design (VFD) demonstrates limited impact, underscoring its insufficiency as a stand-alone strategy. These findings underscore the value of modeling emotionally durable visual communication as a system that links emotion, behavior, and identity in citizen-centered place branding. Full article

The increasing complexity and scale of service-oriented architectures in cloud computing have heightened the demand for intelligent, decentralized, and adaptive service composition techniques. This study proposes an advanced framework that integrates a Multi-Agent System (MAS) with a novel hybrid metaheuristic optimization method, the Integrated Particle-Ant Algorithm (IPAA), to achieve efficient, scalable, and Quality of Service (QoS)-aware service composition. The IPAA dynamically combines the global search capabilities of Particle Swarm Optimization (PSO) with the local exploitation strength of Ant Colony Optimization (ACO), thereby enhancing convergence speed and solution quality. The proposed system is structured into three logical layers—agent, optimization, and infrastructure—facilitating autonomous decision-making, distributed coordination, and runtime adaptability. Extensive simulations using a synthetic cloud service dataset demonstrate that the proposed approach significantly outperforms traditional optimization methods, including standalone PSO, ACO, and random composition strategies, across key metrics such as utility score, execution time, and scalability. Moreover, the framework enables real-time monitoring and automatic re-optimization in response to QoS degradation or Service-Level Agreement (SLA) violations. Through decentralized negotiation and minimal communication overhead, agents exhibit high resilience and flexibility under dynamic service availability. These results collectively suggest that the proposed IPAA-based framework provides a robust, intelligent, and scalable solution for service composition in complex cloud computing environments. Full article

This study investigates the asymmetric trade-off between cost and reliability in the optimal sizing of stand-alone Hybrid Renewable Energy Systems (HRESs) composed of photovoltaic panels (PV), wind turbines (WT), battery storage, a diesel generator (DG), and an inverter. The optimization is formulated as a multi-objective problem with Cost of Energy (CoE) and Loss of Power Supply Probability (LPSP) as conflicting objectives, highlighting that those small gains in reliability often require disproportionately higher costs. To ensure practical feasibility, the installation roof area limits both the number of PV panels, wind turbines, and batteries. Two metaheuristic algorithms—NSGA-II and MOPSO—are implemented in a Python-based framework with an Energy Management Strategy (EMS) to simulate operation under real-world load and resource profiles. Results show that MOPSO achieves the lowest CoE (0.159 USD/kWh) with moderate reliability (LPSP = 0.06), while NSGA-II attains a near-perfect reliability (LPSP = 0.0008) at a slightly higher cost (0.179 USD/kWh). Hypervolume (HV) analysis reveals that NSGA-II offers a more diverse Pareto front (HV = 0.04350 vs. 0.04336), demonstrating that explicitly accounting for asymmetric sensitivity between cost and reliability enhances the HRES design and that advanced optimization methods—particularly NSGA-II—can improve decision-making by revealing a wider range of viable trade-offs in complex energy systems. Full article

This critical review synthesizes findings from quantitative electroencephalography (QEEG) to bridge the gap between systems-level neurophysiology and the underlying cellular pathology of Attention-Deficit/Hyperactivity Disorder (ADHD). As a prevalent neurodevelopmental disorder, ADHD diagnosis is challenged by symptomatic heterogeneity, creating an urgent need for objective biological indicators. Analysis of QEEG data reveals consistent neurophysiological patterns in ADHD, primarily an excess of Theta-band activity and a deficit in Beta-band activity. These findings have led to the proposal of specific biomarkers, such as the Theta/Beta Ratio (TBR), and serve as the basis for neurofeedback interventions aimed at modulating brainwave activity. While not a standalone diagnostic tool, this review posits that QEEG-based biomarkers and Neurofeedback responses are systems-level manifestations of putative cellular and synaptic dysfunctions. By outlining these robust macro-scale patterns, this work provides a conceptual framework intended to guide future molecular and cellular research into the fundamental biology of ADHD. Full article

Background: Inflammation and nutritional status are known to affect outcomes in patients with chronic obstructive pulmonary disease (COPD). However, their prognostic relevance in critically ill COPD patients remains unclear. This study investigated whether C-reactive protein (CRP), serum albumin, and the CRP/albumin ratio (CAR) were associated with in-hospital mortality in ICU patients with COPD. Methods: We conducted a retrospective cohort study using data from the MIMIC-IV database. Adult ICU patients with a diagnosis of COPD were included. We analyzed CRP, albumin, CAR, glucose, lactate, and creatinine. The primary outcome was in-hospital mortality. Multivariable logistic regression was used to identify variables that were independently associated with in-hospital mortality. Subgroup analyses stratified by age and sex were performed. Results: We included 1000 ICU patients with COPD. In-hospital mortality was 19.6%. In univariate analyses, glucose, creatinine, and lactate levels were significantly higher in non-survivors. In multivariable models, only elevated creatinine (OR 1.60, 95% CI 1.01–2.53) remained independently associated with mortality, while glucose was no longer statistically significant. CRP, albumin, and CAR were not significantly associated with in-hospital mortality. Subgroup analyses showed consistent results across age and sex strata. Conclusion: In critically ill COPD patients, glucose and creatinine levels upon ICU admission were independently associated with in-hospital mortality, whereas inflammation- and nutrition-related markers, such as CRP, albumin, and CAR, were not. However, given that albumin is heavily influenced by systemic inflammation, it cannot serve as a standalone nutritional marker in the ICU setting. Composite nutritional scores such as the Nutritional Risk Screening (NRS-2002) or the Global Leadership Initiative on Malnutrition (GLIM), which were not available in the MIMIC-IV database, may provide more accurate assessments. These findings highlight the need for integrated risk models incorporating metabolic and renal parameters for early prognostication. Full article

The objective of this paper is to study and analyze an integrated anaerobic digester (AD)–solid oxide fuel cell (SOFC) system, to achieve an energy-efficient waste-to-energy solution. A detailed numerical modeling is developed for plant dimensioning and energy evaluations. The calculation pathway involves determining operational parameters based on specific variables such as the net electric power produced by the SOFC system or the amount of biogas produced by the AD. Three types of biomass—sewage sludge, slaughter waste, and the organic fraction of municipal solid waste (OFMSW)—are considered. The reactor volume required is approximately 24,000 m³ per 1 kg/s of biogas, processing a daily organic substrate of around 900 m³. The calculations reveal a SOFC electric efficiency of 51% and a thermal efficiency of 39%, under the most favorable conditions. In the integrated AD-SOFC layout, net electrical and thermal efficiencies of 47% and 35%, respectively, are achieved. The economic analysis evaluates the investment feasibility under current incentive schemes, considering both the standalone sale of biomethane and the sale of electricity and thermal energy through SOFC integration. A case study evaluates a biomethane facility producing 508 Sm³/h, integrated with an SOFC system capable of generating 2.36 MW_el and 1.74 MW_th of electric and thermal powers. Various scenarios are examined using net present value (NPV) and payback period (PB) analyses. Results show that the PB for the biomethane-only case is 6.46 years. When integrating the SOFC system, the PB is slightly longer—6.58 years in the most favorable scenario—while it increases to 11.55 years under the most likely scenario. Full article

This article presents an integrated system for automated diagnosis, combining convolutional neural networks (CNNs) for dermatological image analysis with a patient survey for clinical data collection. The goal is to evaluate the effectiveness of this hybrid approach compared to traditional diagnostic methods. The system was tested on a curated dataset composed of images collected from DermNet and publicly available dermatological image databases. The results demonstrate high diagnostic accuracy for inflammatory skin diseases, with the combined approach outperforming standalone methods. These findings highlight the potential of integrating machine learning with patient-reported data to enhance dermatological diagnostics. The proposed system can be implemented in clinical practice and integrated into existing medical platforms, aiding dermatologists in decision-making and improving patient care. Future research will focus on expanding the system to diagnose a broader range of skin conditions and incorporating additional clinical data to enhance its performance. Full article

High-dynamic airborne gravimetry faces critical challenges from platform-induced noise contamination. Conventional filtering methods exhibit inherent limitations in simultaneously achieving dynamic tracking capability and spectral fidelity. To overcome these constraints, this study proposes a Kalman–FIR fusion filtering (K-F) method, which is validated through engineering implementation on the GIPS-1A airborne gravimeter platform. The proposed framework employs a dual-stage strategy: (1) An adaptive state-space framework employing calibration coefficients (S_x, S_y, S_z) continuously estimates triaxial acceleration errors to compensate for gravity anomaly signals. This approach resolves aliasing artifacts induced by non-stationary noise while preserving low-frequency gravity components that are traditionally attenuated by conventional FIR filters. (2) A window-optimized FIR post-filter explicitly regulates cutoff frequencies to ensure spectral compatibility with downstream processing workflows, including terrain correction. Flight experiments demonstrate that the K-F method achieves a repeat-line internal consistency of 0.558 mGal at 0.01 Hz—a 65.3% accuracy improvement over standalone FIR filtering (1.606 mGal at 0.01 Hz). Concurrently, it enhances spatial resolution to 2.5 km (half-wavelength), enabling the recovery of data segments corrupted by airflow disturbances that were previously unusable. Implemented on the GIPS-1A system, K-F enables precision mineral exploration and establishes a noise-suppressed paradigm for extreme-dynamic gravimetry. Full article

Within the hazardous construction industry, roofers represent one of the most at-risk workforces, with high fatalities and injury rates largely driven by Work-Related Musculoskeletal Disorders (WMSDs). The primary precursor to these disorders is muscle fatigue, yet its objective assessment remains a significant challenge for implementing proactive safety management. To address this gap, this study details the implementation and validation of an AI-driven predictive analytics framework for automated fatigue detection using surface electromyography (sEMG) signals. Data was collected as participants (novice roofers) performed strenuous, simulated roofing tasks involving sustained standing, stooping, and kneeling postures. A key innovation is a data-driven labeling methodology using Weak Monotonicity (WM) trend analysis to automate the generation of objective labels. After a feature selection process yielded seven significant features, an evaluation of standard models confirmed that their classification performance was highly posture-dependent, motivating a more robust, hybrid solution. The framework culminates in a high-performance hybrid machine learning model. This architecture synergistically combines a Transformer–LSTM network for deep feature extraction with an XGBoost classifier. The model outperformed all standalone approaches, achieving over 82% accuracy across all postures with consistently strong fatigue F1-scores (0.77–0.78). The entire framework was validated using a stringent Leave-One-Subject-Out (LOSO) cross-validation protocol to ensure subject-independent generalizability. This research provides a validated component for AI-enhanced safety management systems. Future work should prioritize field validation with professional workers to translate this framework into practical, real-world ergonomic monitoring systems. Full article

This study examines the impact of catchment characteristics and design on the performance of urban lakes in terms of water quality and stormwater harvesting potential. Two urban lake systems in Western Sydney, Australia, were selected for comparison: Wattle Grove Lake, a standalone constructed lake, and Woodcroft Lake, part of an integrated wetland–lake system. Both systems receive runoff from surrounding residential catchments of differing sizes and land uses. Over a one-year period, continuous monitoring was conducted to evaluate water quality parameters, including turbidity, total suspended solids (TSS), nutrients (total nitrogen and total phosphorus), pH, dissolved oxygen, and biochemical oxygen demand. The results reveal that the lake with an integrated wetland significantly outperformed the standalone lake in terms of water quality, particularly in terms of turbidity and total suspended solids (TSS), achieving up to 70% reduction in TSS at the outlet compared to the inlet. The wetland served as an effective pre-treatment system, reducing pollutant loads before water entered the lake. Despite this, nutrient concentrations in both systems remained above the thresholds set by the Australian and New Zealand Environment and Conservation Council (ANZECC) Guidelines (2000), indicating persistent challenges in nutrient retention. Notably, the larger catchment area and shallow depth of Wattle Grove Lake likely contributed to higher turbidity and nutrient levels, resulting from sediment resuspension and algal growth. Hydrological modelling using the Model for Urban Stormwater Improvement Conceptualisation (MUSIC) software (version 6) complemented the field data and highlighted the influence of catchment size, hydraulic retention time, and lake depth on pollutant removal efficiency. While both systems serve important environmental and recreational functions, the integrated wetland–lake system at Woodcroft demonstrated greater potential for safe stormwater harvesting and reuse within urban settings. The findings from the study offer practical insights for urban stormwater management and inform future designs that enhance resilience and water reuse potential in growing cities. Full article

Nuclear energy has undergone a significant transformation over the past decades, driven by technological innovation, shifting safety priorities, and the urgent need to mitigate climate change. This study presents a comprehensive review of the historical evolution, current developments, and future prospects of nuclear energy as a strategic low-carbon resource. A structured literature review was conducted following Kitchenham’s methodology, covering peer-reviewed articles and institutional reports from 2000 to 2025. Key advances examined include the deployment of Small Modular Reactors, Generation IV technologies, and fusion systems, along with progress in safety protocols, waste management, and regulatory frameworks. Comparative environmental data confirm nuclear power’s low life-cycle CO₂ emissions and high energy density relative to other generation sources. However, major challenges remain, including high capital costs, long construction times, complex waste disposal, and issues of public acceptance. The analysis underscores that nuclear energy, while not a standalone solution, is a critical component of a diversified and sustainable energy mix. Its successful integration will depend on adaptive governance, international cooperation, and enhanced social engagement. Overall, the findings support the role of nuclear energy in achieving global decarbonization targets, provided that safety, equity, and environmental responsibility are upheld. Full article

Retained products of conception (RPOC) represent a significant cause of morbidity in the post-abortive and postpartum periods, potentially leading to abnormal uterine bleeding, pelvic pain, infections, and intrauterine adhesions. Accurate diagnosis is crucial to avoid unnecessary surgical interventions and to preserve future fertility. Transvaginal ultrasound constitutes the primary imaging modality for identifying RPOC, but the lack of standardized diagnostic criteria complicates clinical decision-making. This narrative review explores the current literature on sonographic findings associated with RPOC, focusing on the diagnostic value of endometrial thickness (ET), the presence of intrauterine echogenic masses, and the use of Color Doppler imaging. Although an ET ≥15 mm is frequently used to suspect RPOC, the variability in cut-off thresholds and limited specificity reduce its diagnostic reliability. The detection of an echogenic intrauterine mass appears to be the most sensitive and specific sonographic feature. Color Doppler assessment, particularly the presence of enhanced myometrial vascularity (EMV) and classification systems like the Gutenberg score, offers further insight by stratifying hemorrhagic risk and guiding therapeutic choices. However, vascular parameters such as peak systolic velocity (PSV) and resistive index (RI) demonstrate a substantial overlap between benign and pathological conditions, limiting their stand-alone utility. The review also addresses the differential diagnosis of RPOC, including blood clots, arteriovenous malformations, placental polyps, gestational trophoblastic disease, and endometrial osseous metaplasia. The role of three-dimensional ultrasound remains limited in clinical practice, offering no significant advantage over two-dimensional imaging. Finally, the timing of follow-up ultrasound after medical treatment with misoprostol is critical: delayed assessment reduces overtreatment by allowing time for spontaneous resolution. In conclusion, despite advances in ultrasound technology, the diagnosis of RPOC remains challenging due to heterogeneity in imaging findings and inter-observer variability. A multimodal approach integrating grayscale and Doppler ultrasound with clinical evaluation is essential for optimal management. Full article

Condition assessment of power equipment is crucial for optimizing maintenance strategies. However, knowledge-driven approaches rely heavily on manual alignment between equipment failure characteristics and guideline information, while data-driven methods predominantly depend on on-site experiments to detect abnormal conditions. Both face challenges in terms of inefficiency and timeliness limitations. With the growing integration of information systems, a significant portion of condition assessment-related information is represented in textual formats, such as system alerts and experimental records. Although Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) show promise in processing such text-based information, their practical application is constrained by LLMs’ hallucinations and RAG’s coarse-grained retrieval mechanisms, which struggle with semantically similar but contextually distinct guideline items. To address these issues, this paper proposes an enhanced RAG framework that integrates hierarchical and global retrieval mechanisms (IHGR-RAG). The framework comprehensively incorporates three optimization strategies: a query rewriting mechanism based on few-shot learning prompt engineering, an integrated approach combining hierarchical and global retrieval mechanisms, and a zero-shot chain-of-thought generation optimization pipeline. Additionally, a Task-Specific Quantitative Evaluation Benchmark is developed to rigorously evaluate model performance. Experimental results indicate that IHGR-RAG achieves accuracy improvements of $4.14\%$ and $5.12\%$ in the task of matching the solely correct guideline item, compared to conventional RAG and standalone hierarchical methods, respectively. Ablation studies confirm the effectiveness of each module. This work advances dynamic health monitoring for power equipment by balancing interpretability, accuracy, and domain adaptability, providing a cost-effective optimization pathway for scenarios with limited annotated data. Full article

This study investigates the optimal sizing of photovoltaic (PV) and battery storage (BAT) systems for Estonian households operating under grid constraints that prevent selling surplus energy. We develop and compare three sizing models of increasing complexity, ranging from a simple heuristic to a full simulation based optimization. Their performance is evaluated using a multi-criteria decision analysis (MCDA) framework that integrates Net Present Value (NPV), Internal Rate of Return (IRR), Profitability Index Ratio (PIR), and payback period. Sensitivity analyses are used to test the robustness of each configuration against electricity price shifts and market volatility. Our findings reveal that standalone PV-only systems are the most economically robust investment. They consistently outperform combined PV + BAT and BAT-only configurations in terms of investment efficiency and overall financial attractiveness. Key results demonstrate that the simplest heuristic-based model (Model 1) identifies configurations with a better balance of financial returns and capital efficiency than the more complex simulation-based approach (Model 3). While the optimization model achieves the highest absolute NPV, it requires significantly higher investment and results in lower overall efficiency. The economic case for batteries remains weak, with viability depending heavily on price volatility and arbitrage potential. These results provide practical guidance, suggesting that for grid constrained households, a well-sized PV-only system identified with a simple model offers the most effective path to cost savings and energy self-sufficiency. Full article