Search Results (13,942)

Inter-basin water transfer projects are core infrastructure for achieving sustainable water resource allocation and addressing regional water scarcity, and pumping station units, as their critical energy-consuming and operation-controlling components, are vital to the projects’ sustainable performance. With the growing complexity and scale of these projects, pumping station units have become more intricate, leading to a gradual rise in failure rates. However, existing fault diagnosis methods are relatively backward, failing to promptly detect potential faults—this not only threatens operational safety but also undermines sustainable development goals: equipment failures cause excessive energy consumption (violating energy efficiency requirements for sustainability), unplanned downtime disrupts stable water supply (impairing reliable water resource access), and even leads to water waste or environmental risks. To address this sustainability-oriented challenge, this paper focuses on the fault characteristics of pumping station units and proposes a comprehensive and accurate fault diagnosis model, aiming to enhance the sustainability of water transfer projects through technical optimization. The model utilizes advanced algorithms and data processing technologies to accurately identify fault types, thereby laying a technical foundation for the low-energy, reliable, and sustainable operation of pumping stations. Firstly, continuous wavelet transform (CWT) converts one-dimensional time-domain signals into two-dimensional time-frequency graphs, visually displaying dynamic signal characteristics to capture early fault features that may cause energy waste. Next, the multi-head attention mechanism (MHA) segments the time-frequency graphs and correlates feature-location information via independent self-attention layers, accurately capturing the temporal correlation of fault evolution—this enables early fault warning to avoid prolonged inefficient operation and energy loss. Finally, the improved convolutional neural network (CNN) layer integrates feature information and temporal correlation, outputting predefined fault probabilities for accurate fault determination. Experimental results show the model effectively solves the difficulty of feature extraction in pumping station fault diagnosis, considers fault evolution timeliness, and significantly improves prediction accuracy and anti-noise performance. Comparative experiments with three existing methods verify its superiority. Critically, this model strengthens sustainability in three key ways: (1) early fault detection reduces unplanned downtime, ensuring stable water supply (a core sustainable water resource goal); (2) accurate fault localization cuts unnecessary maintenance energy consumption, aligning with energy-saving requirements; (3) reduced equipment failure risks minimize water waste and environmental impacts. Thus, it not only provides a new method for pumping station fault diagnosis but also offers technical support for the sustainable operation of water conservancy infrastructure, contributing to global sustainable development goals (SDGs) related to water and energy. Full article

The strong uncertainty of renewable energy poses significant reliability and safety challenges for the coordinated operation of multi-integrated energy systems (MIES). To address this, a data-driven two-stage distributed robust collaborative optimization scheduling model for MIES is proposed, based on a spatiotemporal fusion conditional diffusion model (STF-CDM). First, to more accurately capture the uncertainty in renewable energy output, the model utilizes a scenario set generated by the STF-CDM model and reduced via the K-means clustering algorithm as the initial renewable energy scenarios for the distributed robust optimization set. The STF-CDM model employs a Temporal module component (TMC) unit composed of Transformer time-series modules and a Spatial module component (SMC) unit composed of CNN neural networks for feature extraction and fusion of time-series and spatial-series data. Second, a benefit allocation method based on multi-energy trading contribution rates is proposed to achieve equitable distribution of cooperative gains. Finally, to protect participant privacy and enhance computational efficiency, an alternating direction multiplier method (ADMM) coupled with parallelizable column and constraint generation (C&CG) is employed to solve the energy trading problem. The case analysis results demonstrate that the STF-CDM model proposed in this study exhibits superior performance in addressing the uncertainty of renewable energy output. Concurrently, the asymmetric Nash game mechanism and the ADMM-C&CG solution algorithm proposed in this study achieve a fair and reasonable distribution of benefits among all participants when handling energy transactions and cooperative gains. This is accomplished while ensuring system robustness, economic efficiency, and privacy. Full article

Background: Head and neck cancer patients frequently encounter nutritional deterioration, culminating in poor clinical and treatment-related outcomes and reduced quality of life. This systematic review and meta-analysis aim to examine the effects of non-invasive nutritional interventions on nutritional status and clinical, biochemical, and patient-reported outcomes. Methods: A comprehensive literature search across five databases (PubMed, CINAHL, ProQuest, Medline, and Scopus) was carried out to identify potentially relevant randomized control trials published in English between 2019 and 2024. Screening, extraction of data, and quality check were carried out separately by two reviewers. The Joanna Briggs Critical Appraisal tools assessed the quality of the included studies and evidence certainty was appraised using the GRADE framework. Depending on the amount of heterogeneity present, a random or fixed-effects model was used to conduct the meta-analysis. Results: Eleven studies were included, involving 1000 participants. Pooled estimates showed significant effects on weight (SMD = 0.171, 95%CI: 0.008, 0.335, p = 0.04), serum albumin (SMD = 0.539, 95%CI: 0.150, 0.927, p= 0.007), and patient-generated subjective global assessment score (SMD = −0.518, 95%CI: −0.931, −0.106, p = 0.014) in the intervention group compared to controls. Bias concerns were observed in some studies, largely stemming from inadequate blinding and deviations from intention-to-treat analysis. Evidence certainty ranged from moderate to very low. Conclusions: Non-invasive, patient-directed nutritional interventions may lead to clinically meaningful benefits in patients with head and neck cancer receiving chemoradiotherapy, particularly for the maintenance of body weight and nutritional status. However, robust, adequately powered trials with standardized reporting of intervention components and outcome measures are needed in the future to strengthen the evidence base for clinical application. Full article

Edge detection is a fundamental component of vision tasks, yet the fusion stage that combines multi-cue evidence has received limited attention. We explore the use of a family of Choquet-based fusion operators generalised by restricted dissimilarity functions for robust, training-free, single-scale edge detection on the BSDS500 dataset. Local cues are extracted from eight connected neighbours after Gaussian or Gravitational smoothing; ordered samples are aggregated with a fuzzy power measure using three operator families: d-CF, d-XC, and d-CC integrals. Binary edge maps are obtained through non-maximum suppression and Rosin thresholding. Evaluation follows the Bezdek framework for edge detection, utilising the Estrada–Jepson correspondence, and extracts precision, recall, and the F-score. All inferential statistics are restricted to within-family comparisons among our variants. The main results are that gravitational smoothing consistently improves performance, and the best performance is achieved with the absolute-difference restricted dissimilarity under gravitational smoothing. Under Gaussian smoothing, the best performance is obtained with the modulus of the squared difference and with the squared difference of the roots. These findings indicate that restricted-dissimilarity-based Choquet operators, particularly d-CC integrals with gravitational smoothing, form a straightforward and interpretable fusion mechanism, motivating further analysis of component interactions and multi-scale extensions. Full article

Aiming to improve the currently low accuracy of fault diagnosis due to the difficulty of extracting the non-stationary and nonlinear features of rolling bearing fault signals, a multi-condition fault diagnosis method for rolling bearings was proposed based on enhanced singular spectrum decomposition (ESSD), optimized multi-scale mean permutation entropy (MMPE), and support vector machine (SVM). Firstly, aiming to address the problem of singular spectrum decomposition (SSD) producing false components and signals with low energy proportions that cannot be accurately decomposed when the residual energy ratio is used as the final iteration termination condition, an enhanced singular spectral decomposition method is proposed. Secondly, the effect of the MMPE extraction of fault features depends on the selection of parameters, and after comprehensively considering the interaction between MMPE parameters, a method to optimize MMPE based on the particle swarm optimization (PSO) algorithm is proposed to maximize the performance of the extracted features. Finally, considering that the classification performance of SVM is affected by the penalty factor c and kernel function g, the fault characteristics proposed by ESSD + PSO - MMPE are identified by an SVM classifier model that is optimized by the particle swarm algorithm, so as to realize the effective diagnosis of multi-condition faults in rolling bearings. Using rolling bearing simulation signals, the Case Western Reserve University bearing dataset, and the online monitoring signal from the front bearings of a wind farm’s 1.5 MW wind turbine, the proposed method is compared with EMD + MMPE + SVM, SSD + MMPE + PSO - SVM, ESSD + MMPE + PSO - SVM, and other methods, and the results show that the proposed method can effectively identify multi-working faults in rolling bearings. Full article

This study deals with the successful exploitation of easily available and renewable potato peel waste (PPW) as an excellent feedstock in the production of PHA using Ralstonia eutropha. The process entailed the extraction of bioactive components from PPW by use of solvent-based procedures and screening of their antioxidant and antidiabetic activity. The extracted PPW biomass was subject to acid hydrolysis using different concentrations of sulfuric acid for hydrolysis and solubilization of sugar components. The obtained liquid (acid) hydrolysates were initially assessed to biosynthesize PHA. Activated charcoal-based detoxification of acid hydrolysates was observed to be more efficient in promoting bacterial growth and accumulation of PHA. Acid-pretreated PPW biomass was further enzymatically hydrolysed to accomplish full saccharification and used to produce PHA. The effects of provision of nutrients and employing stress state conditions were assessed to improve bacterial growth and PHA accumulation. In both hydrolysates under optimal conditions, R. eutropha demonstrated the highest biomass productivity of 7.41 g/L and 7.75 g/L, PHA accumulation of 66% and 67% and PHA yield of 4.85 g/L and 5.19 g/L, respectively. XRD, FT-IR, TGA and DSC analysis of produced PHA were studied. The results showed that the produced PHA displayed similar physicochemical and thermal properties to commercially available PHB. Overall, this work illustrates the possibilities of abundantly available PPW, which can be transformed into bioactive compounds and high-value bioplastics via a coupled bioprocess. This approach can develop process economics and sustainability within a cyclic biorefinery system and serve further industry applications. Full article

In the context of intensifying global efforts to mitigate climate change, methane emissions from the oil and gas sector have emerged as a critical environmental and regulatory challenge, given methane’s high global warming potential over short timeframes. This study investigates methane emissions from representative extraction and production of oil and gas facilities in Romania, focusing on fugitive emissions from wells and associated processing infrastructure. The research is grounded in the implementation of a comprehensive Leak Detection and Repair (LDAR) program, aligned with OGMP 2.0 standards, and utilizes advanced detection technologies such as Flame Ionization Detectors (FID), Optical Gas Imaging (OGI), and Quantitative Optical Gas Imaging (QOGI). A systematic inventory and screening of thousands of components enabled the precise identification and quantification of methane leaks, providing actionable data for maintenance and emissions management. The findings highlight that, although the proportion of leaking components is relatively low, cumulative emissions are significant, with block valves, connectors, and compressor shaft seals identified as the most frequent sources of major leaks. The study underscores the importance of rigorous preventive and corrective maintenance, rapid leak remediation, and the adoption of modern detection and continuous monitoring technologies. The approach developed offers a robust framework for regulatory compliance and supports the transition from inventory-based to measurement-based emissions reporting, in line with recent European regulations. Ultimately, effective methane management not only fulfills environmental obligations but also delivers economic benefits by reducing product losses and enhancing operational efficiency, contributing to the decarbonization and sustainability objectives of the energy sector. Full article

In this paper, we propose a mechanical fault diagnosis technology for circuit breakers based on the NGO-VMD, aiming to improve the accuracy and efficiency of fault diagnosis. The circuit breaker is a key protection device in power systems, and its operational status is crucial to grid security. This paper introduces the NGO-VMD method to decompose its vibration signals, aiming to improve the accuracy and efficiency of fault diagnosis. Failure to detect and address mechanical faults in circuit breakers can lead to equipment damage, power outages, and even personal injury. Therefore, it is of great significance to develop efficient and accurate mechanical fault diagnosis technology for after converting the mechanical fault signal of the vacuum circuit breaker in the distribution network into the IMF form, the modal information of the vibration signal under different faults of the circuit breaker is effectively extracted, and the singular value decomposition of the IMF signal component is carried out to make the information characteristics contained more obvious, Finally, LSTM is used to achieve precise identification of circuit breaker faults. In this paper, the experimental test is carried out on the basis of the actual vacuum circuit breaker in the distribution network, and the feasibility of the design scheme is verified by comprehensive analysis. The comparison and analysis with other methods can be obtained, and the scheme has the advantages of higher efficiency, stronger stability and more accuracy. Full article

Background/Objectives: Intensive care units (ICU) immobility and weakness impair recovery, yet practice for Physical Therapist (PT)-led therapeutic exercise and mobility varies in definitions, dosing, safety, and documentation, which limits comparability and complicates quality assessment. This study aims to integrate adult ICU evidence and present PT-led operational definitions, dose progression principles, safety parameters, outcome measurement, and a documentation minimum dataset. Methods: A scoping review following PRISMA-ScR is used. Eligibility used Population, Concept, and Context: adults in ICU; PT-led therapeutic exercise or mobility; and ICU-initiated or directed care. Primary studies and prespecified quality-improvement reports were included. Data were extracted with a standardized form and summarized descriptively without meta-analysis. Results: Sixty studies were included. Based on the extracted data, this review synthesizes current evidence to propose standardized PT-led operational definitions and a graded progression from in-bed exercise to ambulation. While the individual components are derived from the literature, the conceptual framework for safety parameters and the stop rules were integrated and elaborated to guide clinical decision-making. Adverse events were uncommon and manageable. Outcome measurement centered on validated mobility and function measures at prespecified time points. A concise electronic record minimum dataset specifies provider attribution, timing and duration, activity level with assistance or device, planned and delivered dose with progression, in-session responses, and adverse events, supporting unit-level quality review and comparisons across ICU. Conclusions: A PT-led, graded program that applies shared thresholds, uses validated outcome measures, and employs standardized electronic documentation is feasible and supports safe delivery, clinically meaningful change, and quality improvement across adult ICU. Full article

Agricultural by-products, such as tomato peels and sugar beet leaves, represent valuable sources of bioactive compounds that can be efficiently recovered using advanced extraction techniques. This study investigated the efficiency of high-voltage electrical discharge (HVED) extraction of bioactive compounds and antioxidant properties from tomato peel (TP) and sugar beet leaves (SBLs). The target compounds were total phenolic content (TPC), lycopene, β-carotene, and chlorophylls. HVED treatments of 1, 3, and 5 min were applied using 30% and 50% methanolic solutions. A 5 min treatment enhanced the extraction of lycopene (2.04 mg/100 mL) and β-carotene (1.14 mg/100 mL) in the 50% methanolic solution, while the shorter 3 min treatments increased TPC (0.117 mg GAE/mL in TP; 0.280 mg GAE/mL in SBLs) and chlorophyll content (25.47 mg/100 mL). For both TP and SBLs, the more concentrated methanolic solvent (50%) was more efficient in extracting bioactive components than the 30% solution. Electron paramagnetic resonance (EPR) spectroscopy confirmed increases in antioxidant activity in all treated samples, with the highest values of 45.27% for TP and 53.16% for SBLs. As a direct and sensitive technique for detecting free-radical scavenging, EPR proved highly suitable for evaluating the impact of HVED treatments. Overall, HVED demonstrated strong potential as a green and effective method for enhancing the recovery of valuable bioactives and antioxidant properties from tomato and sugar beet by-products. Full article

The mineral deposits in Namibia’s Erongo region are renowned and frequently associated with complex geological environments, including calcrete-hosted paleochannels and hydrothermal alteration zones. Mineral extraction is hindered by high operational costs, restricted accessibility and stringent environmental regulations. To address these challenges, this study proposes an integrated approach that combines satellite remote sensing and machine learning to map and identify mineralisation-indicative zones. Sentinel 2 Multispectral Instrument (MSI) and Landsat 8 Operational Land Imager (OLI) multispectral data were employed due to their global coverage, spectral fidelity and suitability for geological investigations. Normalized Difference Vegetation Index (NDVI) masking was applied to minimise vegetation interference. Spectral indices—the Clay Index, Carbonate Index, Iron Oxide Index and Ferrous Iron Index—were developed and enhanced using false-colour composites. Principal Component Analysis (PCA) was used to reduce redundancy and extract significant spectral patterns. Supervised classification was performed using Support Vector Machine (SVM), Random Forest (RF) and Maximum Likelihood Classification (MLC), with validation through confusion matrices and metrics such as Overall Accuracy, User’s Accuracy, Producer’s Accuracy and the Kappa coefficient. The results showed that RF achieved the highest accuracy on Landsat 8 and MLC outperformed others on Sentinel 2, while SVM showed balanced performance. Sentinel 2’s higher spatial resolution enabled improved delineation of alteration zones. This approach supports efficient and low-impact mineral prospecting in remote environments. Full article

Protein and starch are the two primary components of rice flour, significantly influencing their gelatinization and gel consistency. However, the role of protein in the gelatinization properties and gel consistency of high-starch starch remains unclear. Our study investigated the influence of protein on the gelatinization and gel consistency of high-amylose rice flour by analyzing six high-amylose rice varieties with differing protein levels. The results demonstrated that elevated protein content was associated with reduced breakdown (BD) and gel consistency. Additionally, a recombinant rice flour (RRF) system was developed by reintroducing extracted proteins into high-amylose rice flour in various ratios. The findings indicated that increasing protein proportions in the RRF system led to a marked decrease in gel consistency, accompanied by reductions in peak viscosity (PV), BD, final viscosity (FV), and setback (SB), while peak time (PeT) and pasting temperature (PaT) exhibited significant increases. Correlation analysis and microstructure observations support the hypothesis that proteins may enhance the stability of the paste by restricting the expansion of starch granules during gelatinization, which is related to the reduction in gel consistency. This study confirmed that protein content plays a significant role in determining the gel consistency of high-amylose rice, guiding the improvement of the quality of use or cultivating high-amylose rice suitable for processing. Full article

The shale oil reservoirs of Member 7 of the Triassic Yanchang Formation in the Longdong Area of the Ordos Basin have attracted widespread attention due to their unique geological characteristics and enormous development potential. As the core factor controlling reservoir storage capacity and hydrocarbon flow efficiency, the precise characterization and quantitative analysis of pore structure are the prerequisite and key for reservoir evaluation and development plan optimization. All samples selected in this study were collected from the shale of Member 7 of the Triassic Yanchang Formation and were classified into two categories: medium-organic-rich shales (total organic carbon, TOC: 2–6%; TOC refers to the total organic carbon content in rocks, indicating organic matter abundance; unit: %) and high-organic-rich shales (TOC: >6%). The mineral composition and organic geochemical parameters of the shale were determined via X-ray diffraction (XRD) and Rock-Eval pyrolysis experiments, respectively. Meanwhile, pore structure characteristics were analyzed by combining low-temperature nitrogen adsorption–desorption experiments before and after extraction, and multifractal analysis was used to systematically investigate the differences in pore heterogeneity of shale and their influencing factors. The results show that the specific surface area (SSA) and total pore volume (TPV) of shale increased after extraction, while the change in average pore diameter (APD) varied. Multifractal analysis indicates that the micropores of shale both before and after extraction exhibit significant multifractal characteristics; after extraction, pore connectivity is improved, but the changes in pore heterogeneity are inconsistent. The pore connectivity of shale first increases and then decreases with the increase in TOC content and pyrolysis parameter S₂ content. The better the pore connectivity of shale, the lower the content of light-component saturated hydrocarbons and the relatively higher the content of heavy-component resins in the extractable organic matter (EOM). Brittle minerals can provide a rigid framework to inhibit compaction and are prone to forming natural microfractures under tectonic stress, thereby promoting pore connectivity. In contrast, clay minerals, due to their plasticity, are prone to deformation and filling pore throats during compaction, thus reducing pore connectivity. This study provides a theoretical basis for the evaluation and development of shale reservoirs in the Longdong Area. Full article

To address the limited performance of transfer fault diagnosis for planetary gearboxes under cross-operating conditions, which is caused by the heterogeneous feature distribution of vibration data and insufficient feature extraction. An improved domain-adversarial neural network (IDANN) model based on a joint-adaptive-domain alignment component and a dual-branch feature extractor is proposed. Firstly, a joint domain adaptation alignment approach, integrating maximum mean discrepancy (MMD) and CORrelation ALignment (CORAL), is proposed to realize the correlation structure matching of features between the source and target domains of IDANN. Secondly, a dual-branch feature extractor composed of ResNet18 and Swin Transformer is proposed with an attention-weighted fusion mechanism to enhance feature extraction. Finally, validation experiments conducted on public planetary gearbox fault datasets show that the proposed method attains high accuracy and stable performance in cross-operating-condition transfer fault diagnosis. Full article

Decarbonizing building energy use requires efficient heat pumps and low-impact geothermal exchangers. A novel standalone TRNSYS Type was developed for a patented shallow horizontal ground heat exchanger (HGHE), called flat-panel (FP), designed at the University of Ferrara. Beyond simulating the FP in isolation, the Type enables coupling with other components within heat-pump configurations, allowing performance assessments that reflect realistic operating conditions. The Type was implemented in TRNSYS models of a ground-source heat pump (GSHP) and of a dual air and ground source heat pump (DSHP) to verify Type reliability and evaluate potential DSHP advantages over GSHP in terms of efficiency and ground-loop downsizing. The performance of the system was analyzed under varying HGHE lengths and DSHP control strategies, which were based on onset temperature differential $DT$. The results highlighted that shorter HGHE lines yielded higher specific HGHE performance, while higher $DT$ reduced HGHE operating time. Concurrently, the total energy extracted from the ground decreased with increasing $DT$ and reduced length, thus supporting long-term thermal preservation and allowing HGHE to operate under more favorable conditions. Exploiting air as an alternative or supplemental source to the ground allows significant reduction of the HGHE length and the related installation costs, without compromising the system performance. Full article