Search Results (4,061)

Industrial machine tools are both performance assets and environmental hotspots over their long service lives. Maintenance is traditionally optimized to safeguard availability, quality and cost. However, maintenance choices also determine the energy consumption, footprints, component duration and end-of-life pathways. In this study, we present a decision framework to compare performance-only maintenance (POM) with sustainability-aware maintenance (SAM) for machine tools. The framework integrates degradation and Remaining Useful Life (RUL) estimation, Life Cycle Assessment (LCA) and Life Cycle Costing (LCC). Outcomes are summarized with a Sustainable Maintenance Balance (SMB) index. We test the proposed approach on a horizontal machining center for aluminum, validated by running a Monte Carlo simulation over a 1000 h functional unit. Across empirical data and simulation, SAM—compared to POM—demonstrated an ability to improve availability, reduces downtime and scrap, and lower total LCC while cutting carbon emissions. The proposed method is proposed as readily deployable in real plants, supporting robust sustainable-production decisions. Full article

Fine-grained identification of crop planting structures provides key data for precision agriculture, thereby supporting scientific production and evidence-based policy making. This study selected a representative experimental farmland in Qingyang, Gansu Province, and acquired Unmanned Aerial Vehicle (UAV) multi-temporal data (six epochs) from multiple sensors (multispectral [visible–NIR], thermal infrared, and LiDAR). By fusing 59 feature indices, we achieved high-accuracy extraction of cropland and planting structures and identified the key feature combinations that discriminate among crops. The results show that (1) multi-source UAV data from April + June can effectively delineate cropland and enable accurate plot segmentation; (2) July is the optimal time window for fine-scale extraction of all planting-structure types in the area (legumes, millet, maize, buckwheat, wheat, sorghum, maize–legume intercropping, and vegetables), with a cumulative importance of 72.26% for the top ten features, while the April + June combination retains most of the separability (67.36%), enabling earlier but slightly less precise mapping; and (3) under July imagery, the SAM (Segment Anything Model) segmentation + RF (Random Forest) classification approach—using the RF-selected top 10 of the 59 features—achieved an overall accuracy of 92.66% with a Kappa of 0.9163, representing a 7.57% improvement over the contemporaneous SAM + CNN (Convolutional Neural Network) method. This work establishes a basis for UAV-based recognition of typical crops in the Qingyang sector of the Loess Plateau and, by deriving optimal recognition timelines and feature combinations from multi-epoch data, offers useful guidance for satellite-based mapping of planting structures across the Loess Plateau following multi-scale data fusion. Full article

Background: Microsurgical skill acquisition and development are complex processes, due to the often complex learning curve, limited training possibilities, and growing restrictions on working hours. Simulation-based training programmes, employing various models, have been proposed. Nevertheless, the extent to which these training programmes are supported by scientific evidence is unclear. The aim of this systematic review is to evaluate the extent and quality of the scientific evidence backing validated microsurgical training programmes. Methods: A systematic literature review was conducted, following a study protocol established a priori and in accordance with the PRISMA guidelines. The databases searched were the Web of Science Core Collection (Web of Knowledge), Medline (Ovid), Embase (Embase.com), and ERIC (Ovid). Studies were included if they described microsurgical training programmes and presented a form of validation of training effectiveness. Data extraction included the number of participants, training duration and frequency, validation type, assessment methods, outcomes, study limitations, and a detailed training regimen. The risk of bias and quality were assessed using the Medical Education Research Study Quality Instrument (MERSQI). Validity was assessed using an established validity framework (content, face, construct, and criterion encompassing both concurrent and predictive validity). The Level of Evidence (LoE) and Recommendation (LoR) were evaluated using the Oxford Centre for Evidence-Based Medicine (OCEBM). Results: A total of 25 studies met the inclusion criteria. Training programmes were classified into one-time intensive courses or longitudinal curricula. Face, content, and construct validity were the most commonly assessed aspects, while predictive validity was the least frequently assessed and not properly evaluated. Training models ranged from low-fidelity (silicone tubes, synthetic vessels) to high-fidelity (live animal models). The Global Rating Scale (GRS), the Structured Assessment of Microsurgery Skills (SAMS), and the Objective Structured Assessment of Technical Skills (OSATS) were the most frequently used objective assessment tools for evaluation methods within the programmes. The risk of bias MERSQI score was 12.96, ranging from 10.5 to 15.5, and LoE and LoR scores were moderated. Across the studies, 96% reported significant improvement in microsurgical skills among participants. However, most studies were limited by small sample sizes, heterogeneity in baseline skills, and a lack of long-term follow-up. Conclusions: While validated microsurgical training programmes improve skill acquisition, challenges remain in terms of standardisation and best cost-effective methods. Future research should prioritise evaluating predictive validity, creating standardised objective assessment tools, and focus on skill maintenance. Full article

Improving robustness under distributional shifts remains a central challenge in machine learning. Although Sharpness-Aware Minimization (SAM) has proven effective in finding flatter minima for better generalization, it overlooks the heterogeneity in sharpness across different subpopulations, which can exacerbate performance gaps for minority or vulnerable groups. To address this challenge, we propose Group-gap Guided SAM (G²-SAM), a new optimization framework that promotes distributional robustness by steering flatness-seeking directions according to intergroup loss disparities. Our method estimates group-wise sharpness and adaptively refines perturbation strategies to minimize the worst-group loss while preserving model consistency. Through comprehensive experiments across various datasets, we show that G²-SAM achieves superior Worst-Group Accuracy and robustness, outperforming previous baselines. These findings highlight the importance of addressing group-specific geometry in the loss landscape to build more reliable and equitable neural networks. Full article

The contribution of Higher Education Institutions (HEIs) in achieving UN-Sustainable Development Goals (SDGs) is critical. HEIs’ contribution to the SDGs is not only confined to education and research but also to their engagement with society, policy-ma-king, infrastructure establishment, and sustainability practice in the institution. By aligning their programs and projects to the SDGs, HEIs can share significantly, producing a more sustainable development for all. One of the notable investments of Davao Oriental State University (DOrSU) is the construction of the University Research Complex (UResCom). The structure contains the university’s research laboratories and working spaces for academic scholars to conduct cutting-edge research and scientific breakthroughs. The seed fund for the laboratory equipment and initial research participation was sponsored by project grants from the Department of Science and Technology—Philippine Council for Industry, Energy, and Emerging Technology Research and Development (DOST-PCIEERD). Partnering with established laboratories and institutions is also an effective tool to guide developing universities. This approach will strengthen more productive research engagements as one of the prime movers of the university’s development. It will enhance scientific research outputs and coverage, improve the faculty’s technological competence, enable partnerships with the industrial sector, and is a leverage for the submission of project proposals. This approach will significantly contribute to improving the country’s Global Innovation Index (GII) ranking and to the achievement of these goals. Full article

Selenium, an essential metalloid, plays a dual role in biological systems: while crucial for maintaining normal biological processes, excessive levels can be toxic. Organisms mitigate selenium toxicity through a biochemical process known as methylation, in which inorganic selenium species are enzymatically converted into less toxic, excretable organic metabolites. This review synthesizes recent biochemical and environmental findings (with an emphasis on the past decade) related to selenium methylation. It outlines the enzymatic mechanisms—particularly involving glutathione reductase, SAM-dependent methyltransferases, and selenocysteine lyase—through which selenite and selenate are reduced and methylated to intermediates such as hydrogen selenide (H₂Se), ultimately yielding MMSe, DMSe, and TMSe⁺. The role of enzymes such as selenocysteine lyase in processing organic selenium and factors affecting the efficiency of these processes, including environmental conditions, are discussed. The role of enzymes such as selenocysteine lyase in metabolizing organic selenium species is also discussed, along with how environmental conditions (e.g., soil composition, redox potential) and genetic variability influence methylation efficiency and selenium speciation. In conclusion, this paper explores selenium methylation in plants, focusing on rice and corn, and how their selenium uptake and metabolism are affected by environmental factors. It examines the conversion of selenium into organic forms like selenomethionine and selenocysteine, and the role of methylation in managing excess selenium. The findings offer insights into selenium chemistry, with implications for food safety, nutrition, and environmental management, addressing key knowledge gaps and enhancing our understanding of selenium’s biological and chemical roles. Full article

Hybrid battery systems, which combine high-energy and high-power cells, offer a promising solution for electrifying heavy-duty applications by balancing energy density, power capability, and cost. This paper presents a generic methodology for cost-optimal sizing of hybrid battery energy storage systems using a Mixed Integer Nonlinear Programming framework. A large-scale simulation study involving 10,000 load profiles replicating applications varying from road transportation to sea-going vessels is used to derive practical “rules of thumb” that guide when hybridization is beneficial, offering significant reductions in cost, weight, and volume compared to monotype battery configurations. Sensitivity analyses further validate the robustness of the method across varying cell costs and C-rates, making it applicable to a wide range of battery chemistries and use cases. Full article

Accurate evaluation of peanut seedling emergence is critical for ensuring agronomic research accuracy and planting benefit efficiency, but traditional manual methods are limited by strong subjectivity and inconsistent batch inspection standards. In order to quickly and accurately evaluate the emergence rate and quality of peanuts, this study proposes an intelligent evaluation system for peanut seedling conditions, which is constructed based on an improved YOLOv11 combined with the Segment Anything Model (SAM) for peanut seedling emergence evaluation, using high-resolution images collected by Unmanned Aerial Vehicles as the data foundation. Experimental results show that the improved YOLOv11 model achieves a detection precision of 96.36%, a recall rate of 96.76%, and an mAP@0.5 of 99.03%. The segmentation performance of SAM is outstanding in terms of integrity. In practical applications, the detection time for a single image by the system is as low as 83.4 ms, and the efficiency of video counting is 6–10 times higher than that of manual counting. Without extensive data annotation, this method performs excellently in peanut seedling emergence quantity statistics and growth status classification, providing efficient, accurate technical support for refined peanut cultivation management and mechanical sowing quality evaluation. Full article

The rapid rise of generative AI tools such as ChatGPT has prompted significant shifts in how higher education institutions approach academic integrity. Many universities have implemented AI detection tools like Turnitin AI, GPTZero, Copyleaks, and ZeroGPT to identify AI-generated content in student work. This qualitative evidence synthesis draws on peer-reviewed journal articles published between 2021 and 2024 to evaluate the effectiveness, limitations, and ethical implications of AI detection tools in academic settings. While AI detectors offer scalable solutions, they frequently produce false positives and lack transparency, especially for multilingual or non-native English speakers. Ethical concerns surrounding surveillance, consent, and fairness are central to the discussion. The review also highlights gaps in institutional policies, inconsistent enforcement, and limited faculty training. It calls for a shift away from punitive approaches toward AI-integrated pedagogies that emphasize ethical use, student support, and inclusive assessment design. Emerging innovations such as watermarking and hybrid detection systems are discussed, though implementation challenges persist. Overall, the findings suggest that while AI detection tools play a role in preserving academic standards, institutions must adopt balanced, transparent, and student-centered strategies that align with evolving digital realities and uphold academic integrity without compromising rights or equity. Full article

Background/Objectives: The diagnosis of acute compartment syndrome (ACS) of the extremities is typically based on subjective clinical signs and symptoms, highlighting the need for user-friendly diagnostic tools to improve accuracy and reliability. This study evaluates the performance of two commercial devices, the MY01^® continuous pressure monitoring system and the Moxy Monitor near-infrared spectroscopy-based system, against a reference standard of continuous intracompartmental pressure (ICP) monitoring in a preclinical ACS model. Methods: ACS was induced in the anterior compartment of the distal hind limb in eight Yorkshire pigs using a balloon displacement model. ICP was incrementally elevated and maintained for four hours at >30 mmHg above mean arterial pressure. This was followed by balloon deflation and reperfusion. Final assessments were performed at 24 h post-injury. ICP measurements from the MY01^® and muscle oxygen saturation (SmO₂) data from the Moxy Monitor were compared to reference ICP measurements. Histologic analysis of muscle tissue was performed to assess the severity of necrosis. Results: The MY01^® provided accurate ICP measurements, with a mean bias of 2.21 ± 18.77 mmHg during pre-ischemia, 4.86 ± 10.43 mmHg during reperfusion, and 4.69 ± 3.28 mmHg 24 h post-injury, compared to reference probes. Correlation at 24 h post-injury was (r = 0.86, R² = 0.73, p < 0.0001). In contrast, the Moxy Monitor failed to detect significant differences in SmO₂ between injured and control limbs at 24 h post-injury, despite pronounced ICP differences. Our volumetric displacement ACS model demonstrated its efficacy as a testing platform by allowing for controlled, incremental elevation in ICP and sustaining elevated ICP levels after 24 h. Histologic evaluation confirmed extensive muscle damage, including edema and necrosis. Conclusions: The MY01^® provides accurate, continuous ICP monitoring, supporting its clinical utility in ACS diagnosis. However, the use of near-infrared spectroscopy-based systems such as the Moxy Monitor for ACS diagnosis and management should continue to be critically scrutinized. Full article

In this study, three Antarctic sponges of the genus Latrunculia were investigated, leading to the isolation of five unreported pyrroloiminoquinone alkaloids along with the known metabolite (+)-debromodiscorhabdin A (3). Three of the new metabolites were brominated, while the other two were found to have a C-5/C-8 sulfur bridge and a C-2/N-18 bridge. Three of the metabolites were shown to have a phenyl ketone substituent on C-14, not previously reported for discorhabdin derivatives. The cytotoxicity against the A549 cell lines was studied and compounds 1–4 showed activity of 4.3, 1.8, 1.0, and 23.9 µM, respectively, while no inhibition was found for 5 and 6. Full article

Operational decisions significantly influence organizational performance and individual well-being. Decision mining offers a method to discover and analyze decision logic from decision logs, enhancing decision-making processes. However, evaluating the quality of decision discovery algorithms remains a challenge. While precision, fitness, generalization, and simplicity are well-established quality dimensions in process mining, their adaptation to the decision mining domain is underexplored. This study adapts these four dimensions to the necessary characteristics of decision models, providing a framework for evaluating decision discovery algorithms. Using a design science research approach, we develop tailored metrics and functions and demonstrate their application through a practical example of environmental permit management modeled in Decision Model and Notation (DMN). Precision measures how the discovered decision model reproduces the observed fact types and values from the decision log, detecting over-specification in the decision model. Fitness evaluates how completely the decision model covers the behavior in the decision log, identifying missing or under-specified elements in the decision model. Generalization assesses the model’s robustness to unseen decision cases by quantifying how well the discovered decision model performs beyond the training data. Simplicity captures the complexity in the discovered decision model in relation to a human actor-specified threshold. These insights guide decision model improvements, contributing to higher transparency, accountability, and fairness in operational decision-making processes. This research bridges a gap in the body of knowledge by providing a concrete methodology for evaluating decision discovery algorithms. The results support organizations in aligning decision models with regulatory requirements and public values, while also laying a foundation for future research. Full article

The COVID-19 pandemic has increased interest in wastewater-based epidemiology (WBE) as a reliable and cost-effective framework for monitoring the spread of microbes. However, WBE frameworks have rarely been applied to the study of fecal–oral transmissible diseases, except for poliomyelitis. Here, we investigated the presence of hepatitis A virus (HAV) and hepatitis E virus (HEV) in wastewater in Munich. We collected wastewater samples between July 2020 and November 2023. A total of 186 samples were processed using centrifugation and analyzed for HAV- and HEV-RNA using RT-qPCR. As a reference, we used notification data from clinically or laboratory-diagnosed hepatitis A and E cases. Lockdown stringency levels were derived from official documentation. Our results show that 87.6% of wastewater samples were positive for HEV at concentrations of 9.0 × 10¹ to 2.5 × 10⁵ copies/L, while HAV was only detectable in 7.5% of the samples at viral loads of 4.6 × 10¹ to 2.4 × 10³ copies/L. We also detected differences in HEV concentrations but not in case numbers when comparing lockdown and no-lockdown periods. This study covers all but the first lockdowns in Bavaria. We present a unique real-world dataset evaluating the impact of lockdown interventions on hepatitis A and E case numbers, as well as on the concentrations of HAV and HEV in wastewater. Person-to-person spread and eating out appear to have contributed to the transmission of HEV. In addition, the consistently high HEV concentrations in sewage support the findings of serological studies, indicating a substantial burden of undetected subclinical infections. Full article

This study introduces a novel hybrid framework that integrates machine learning (ML) with fractional-order differential equations (FDE) to enhance the prediction and clinical management of psoriasis, leveraging real-world data from the UCI Dermatology Dataset. By optimizing ML models, particularly the Voting Ensemble, to inform FDE parameters, and developing a user-friendly graphical user interface (GUI) for real-time diagnostics, the approach bridges computational efficiency with physiological realism, capturing memory-dependent disease progression beyond traditional integer-order models. Key findings reveal that the Voting Ensemble achieves a precision of 0.986 ± 0.007 and an AUC of 0.992 ± 0.005. At the same time, the fractional-order model, with an optimized order of 0.6781 and a mean square error (MSE) of 0.0031, accurately simulates disease trajectories, closely aligning with empirical trends for features such as Age and SawToothRete. The GUI effectively translates these insights into clinical tools, demonstrating probabilities ranging from 0% to 100% based on input features, supporting early detection and personalized planning. The framework’s robustness and potential for broader application to chronic conditions highlight its significance in advancing healthcare. Full article

This study evaluates coke for iron ore sintering manufactured from Ekibastuz coal fines (fraction 0–3 mm), spent anode material (SAM) from aluminum electrolysis, and coal tar pitch. Laboratory coking was performed at 1000 °C (60 min hold), followed by sintering trials using coke containing 10 wt% and 20 wt% SAM. Microstructural (SEM/EDS) and spectral data indicate an optimal SAM range of 10–20 wt%: higher additions (≥30 wt%) lead to structural degradation of coke, accompanied by reduced mechanical integrity. The produced coke shows C = 85%, S = 0.9–1.1%, ash ≈ 19%, volatiles = 1.5–2.5%, and moisture (Wr) ≤ 1%, which is acceptable for sintering use. In sintering tests, the yield of usable sinter reached 72.4% (10 wt% SAM) and 73.5% (20 wt% SAM); impact strength was 83% and 78%, respectively. XRF of sinter showed Fe_total > 51%, CaO ≈ 5.5–6.8%, SiO₂ ≈ 6.6–7.2%, and S = 0.40–0.45%, meeting technological requirements for blast-furnace practice. Overall, using spent anode material within 10–20 wt% increases fixed-carbon content, enables valorization of aluminum industry waste, and delivers coke for agglomeration performance without compromising key chemical or mechanical indices. Full article