Search Results (27,242)

To address the limitations of conventional reliability assessment methods in handling mesh distribution networks with flexible operation characteristics and complex topologies, namely their poor adaptability and low computational efficiency, this paper proposes a matrix-based analytical approach for reliability assessment of mesh distribution networks. First, a network configuration centered on the soft open points (SOP) is established. Through multi-feeder interconnection and flexible power flow control, a topology capable of fast fault transfer and service restoration is formed. Second, based on the restoration modes of load nodes under fault scenarios, three types of fault incidence matrices (FIM) are proposed. By means of matrix algebra, explicit analytical expressions are derived for the relationships among equipment failure probability, duration, impact range, and reliability indices. This overcomes the drawbacks of iterative search in conventional reliability assessments, significantly improving efficiency while ensuring accuracy. Finally, a modified 44 bus Taiwan test system is used for reliability assessment to verify the effectiveness of the proposed method. The results demonstrate that the proposed matrix-based analytical reliability assessment method enables explicit analytical calculation of both system-level and load-level reliability indices in mesh distribution networks, providing effective support for planning and operational optimization to enhance reliability. Full article

Background and Objectives: Neuromyelitis optica spectrum disorder (NMOSD) and MOG antibody-associated disease (MOGAD) are distinct autoimmune demyelinating disorders of the central nervous system, characterized by different pathological and clinical features. Reliable biomarkers are essential for accurate diagnosis and monitoring of disease activity. Glial fibrillary acidic protein (GFAP) and neurofilament light chain (NfL) are promising candidates, reflecting astrocytic and axonal damage, respectively. Materials and Methods: To investigate the relationship between astroglial (GFAP) and neuronal (NfL) protein levels in the peripheral blood, 89 plasma samples were analyzed using Simoa immunoassays. The concentrations of pNfL and pGFAP were measured in three groups: AQP4-IgG-positive NMOSD patients (n = 18), MOGAD patients (n = 12), and healthy controls (HCs, n = 19). Statistical analyses assessed group differences, correlations, and the predictive value of biomarkers for disease activity. Results: Both NMOSD and MOGAD patients exhibited elevated pNfL compared with controls, indicating neuroaxonal injury. No significant differences in pNfL, pGFAP, or pGFAP/pNfL ratios were observed between patient groups. The pGFAP levels and the pGFAP/pNfL ratio were significantly higher in NMOSD patients, particularly during attacks, indicating prominent astrocyte damage. Correlations revealed associations between biomarker levels, disability, and disease duration. pNfL demonstrated high accuracy in predicting recent relapses (AUC = 0.906), whereas pGFAP showed moderate predictive capacity (AUC = 0.638). Elevated pNfL and pGFAP levels were associated with an increased likelihood of relapse within six months. Conclusions: Plasma NfL and GFAP are promising biomarkers for assessing tissue injury and disease activity in NMOSD and MOGAD. NfL predicts relapses, while GFAP primarily reflects astrocytic damage in NMOSD. Longitudinal studies are warranted to validate these biomarkers and establish clinical thresholds for disease management. Full article

This study presents the design and performance assessment of suction anchor foundations for an integrated offshore wind–solar–aquaculture system located in Jiangsu Sheyang, China. The project represents one of the first practical demonstrations of coupling renewable energy production with large-scale marine aquaculture on a shared floating platform. Using three-dimensional numerical simulations in FLAC3D and ABAQUS, the study evaluates the anchors’ bearing capacity, structural safety, and fatigue performance under ultimate (ULS), accidental (ALS), and fatigue (FLS) limit states. The analysis incorporates site-specific geotechnical conditions, seabed scour, and installation deviations, providing a realistic framework for foundation design in layered coastal sediments. Results confirm that the suction anchor system meets international safety requirements (DNV, CCS) and maintains robust performance throughout its service life. The findings demonstrate that scour depth and installation accuracy are critical factors governing anchor reliability and offer practical insights for updating offshore foundation design standards in future multifunctional renewable–aquaculture developments. Full article

Staining-based assays are widely used for cell analysis but are invasive, alter physiology, and prevent longitudinal monitoring. Label-free, morphology-based approaches could enable real-time, non-invasive drug testing, yet detection of subtle and dynamic changes has remained difficult. We developed a deep learning framework for stain-free monitoring of leukemia cell cultures using automated bright-field microscopy in a semi-automated culture system (AICE3, LABMaiTE, Augsburg, Germany). YOLOv8 models were trained on images from K562, HL-60, and Kasumi-1 cells, using an NVIDIA DGX A100 GPU for training and tested on GPU and CPU environments for real-time performance. Comparative benchmarking with RT-DETR and interpretability analyses using Eigen-CAM and radiomics (RedTell) was performed. YOLOv8 achieved high accuracy (mAP@0.5 > 98%, precision/sensitivity > 97%), with reproducibility confirmed on an independent dataset from a second laboratory and an AICE3 setup. The model distinguished between morphologically similar leukemia lines and reliably classified untreated versus differentiated K562 cells (hemin-induced erythroid and PMA-induced megakaryocytic; >95% accuracy). Incorporation of decitabine-treated cells demonstrated applicability to drug testing, revealing treatment-specific and intermediate phenotypes. Longitudinal monitoring captured culture- and time-dependent drift, enabling separation of temporal from drug-induced changes. Radiomics highlighted interpretable features such as size, elongation, and texture, but with lower accuracy than the deep learning approach. To our knowledge, this is the first demonstration that deep learning resolves subtle, drug-induced, and time-dependent morphological changes in unstained leukemia cells in real time. This approach provides a robust, accessible framework for label-free longitudinal drug testing and establishes a foundation for future autonomous, feedback-driven platforms in precision oncology. Ultimately, this approach may also contribute to more precise and adaptive clinical decision-making, advancing the field of personalized medicine. Full article

Background/Objectives: Occlusal analysis is an important component of oral rehabilitation with a determining role in the prognosis of restorations. Over time, several qualitative and quantitative occlusal analysis methods have been proposed, starting with occlusion wax up to the most advanced digital systems. The objective of the present study was to evaluate and compare the data obtained through dental occlusion analysis using the Medit i700 and Exocad Elefsina v3.2 in a group of subjects, in order to establish the reliability or compatibility between the two occlusal analysis systems. Methods: The study was conducted on 20 subjects, aged between 24 and 53 years, who presented in the Dental Prosthetics Clinic of the University of Medicine and Pharmacy of Craiova. Digital impressions were acquired using the Medit Link v.3.3.6 intraoral scanner, and the digital files were subsequently uploaded from the Medit i700 into the Medit Occlusion Analyzer application and the Dental CAD Exocad software. For the analysis of occlusion in dynamics, mandibular movements and data acquisition, positions of edge-to-edge in protrusion, edge-to-edge in right laterotrusion and edge-to-edge in left laterotrusion were recorded, using the corresponding print screens. The 2D occlusal contact images generated by the two software programs were converted into .jpeg format and subsequently imported into Adobe Photoshop CS6 (2021) for comparative analysis. The data were statistically processed for each software used and the obtained data were subsequently compared. Results: The occlusal surfaces recorded with the Medit Occlusion Analyzer application represent 94% of the occlusal surfaces recorded with the Exocad software for the maxilla and 90% of the occlusal surfaces recorded for the mandible. In maximum intercuspation, the highest values were recorded by the Medit i700 software, whereas in edge-to-edge protrusion and both right and left edge-to-edge laterotrusion positions, the highest values were reported by the Exocad software. The discrepancy between maxillary and mandibular values arises from the conversion of the data from a three-dimensional to a two-dimensional format during image processing. Conclusions: The occlusal areas recorded by the DentalCAD Exocad software show higher values than those provided by the Medit Link software with the Medit Occlusion Analyzer application. The differences in recorded values, in the case of the digital flow of prosthetic restorations, require the intervention of the dentist to perform clinical adjustments to optimize occlusal relationships after the fabrication and cementation of restorations. Full article

During the peak load period, there is a high level of imbalance between power supply and demand, which has become a critical challenge, leading to higher operational costs for power grids. To improve the accuracy of peak load forecasting, this study introduces a novel approach based on Extreme Gradient Boosting Trees (XGBoost) and Multiple Linear Regression (MLR) for daily peak load prediction. The proposed methodology first employs an improved version of the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) algorithm to decompose the raw load data, subsequently reconstructing each Intrinsic Mode Function (IMF) into high-frequency and stationary components. For the high-frequency components, XGBoost serves as the base predictor within a Bagging-based ensemble structure, while the Sparrow Search Algorithm (SSA) is employed to optimize hyperparameters automatically, ensuring efficient learning and accurate representation of complex peak load fluctuations. Meanwhile, the stationary components are modeled using MLR to provide fast and reliable estimations. The proposed framework was evaluated using actual daily peak load data from Western Inner Mongolia, China. The results indicate that the proposed method successfully captures the peak characteristics of the power grid, delivering both robust and precise predictions. When compared to the baseline model, the RMSE and MAPE are reduced by 54.4% and 87.3%, respectively, underscoring its significant potential for practical applications in power system operation and planning. Full article

This study presents the design and techno-economic optimization of a hybrid renewable energy system (HRES) for a coastal hotel in Manavgat, Türkiye. The system integrates photovoltaic (PV) panels, wind turbines (WT), pumped hydro storage (PHS), hydrogen storage (electrolyzer, tank, and fuel cell), batteries, a fuel cell-based combined heat and power (CHP) unit, and a boiler to meet both electrical and thermal demands. Within this broader optimization framework, six optimal configurations emerged, representing grid-connected and standalone operation modes. Optimization was performed in HOMER Pro to minimize net present cost (NPC) under strict reliability (0% unmet load) and renewable energy fraction (REF > 75%) constraints. The grid-connected PHS–PV–WT configuration achieved the lowest NPC ($1.33 million) and COE ($0.153/kWh), with a renewable fraction of ~96% and limited excess generation (~21%). Off-grid PHS-based and PHS–hydrogen configurations showed competitive performance with slightly higher costs. Hydrogen integration additionally provides complementary storage pathways, coordinated operation, waste heat utilization, and redundancy under component unavailability. Battery-only systems without PHS or hydrogen storage resulted in 37–39% higher capital costs and ~53% higher COE, confirming the economic advantage of long-duration PHS. Sensitivity analyses indicate that real discount rate variations notably affect NPC and COE, particularly for battery-only systems. Component cost sensitivity highlights PV and WT as dominant cost drivers, while PHS stabilizes system economics and the hydrogen subsystem contributes minimally due to its small scale. Overall, these results confirm the techno-economic and environmental benefits of combining seawater-based PHS with optional hydrogen and battery storage for sustainable hotel-scale applications. Full article

The gear transmission system is a safety-critical component in rail transit, typically designed for a service life exceeding 20 years. Failure analysis of such systems remains a key focus for railway engineers. This study systematically investigates four representative cases of premature gear failure in high-speed trains using a standardized analytical procedure that includes visual inspection, chemical analysis, metallographic examination, scanning electron microscopy, and hardness testing. The results identify four primary root causes: subsurface slag inclusions in raw materials, inadequate heat treatment leading to a non-martensitic layer (∼60 μm) at the tooth root, grinding-induced temper burns (crescent-shaped "black spots") accompanied by a hardness drop of ∼100–150 HV, and insufficient lubrication. The interdependencies between these factors and failure mechanisms, e.g., fatigue cracking, spalling, and thermal scuffing, are analyzed. This work provides an evidence-based framework for improving gear reliability and proposes targeted countermeasures, such as ultrasonic inclusion screening and real-time grinding temperature control, to extend operational lifespans. Full article

With location-based services becoming more common, smartphone global navigation satellite systems (GNSS) have begun to play a significant role in daily life. Providing reliable location information to smartphone users requires considering localization uncertainty, which varies with the surrounding environment. In this study, we developed an environmental context indicator (ECI) to provide interpretable, continuous information on GNSS observation quality using carrier-to-noise density ratio (C/N₀), the number of visible satellites, and positional dilution of precision (PDOP). The ECI was developed using a Samsung Galaxy S21+ and satellite signals from global positioning system (GPS) L1/L5, Galileo E1/E5, and BeiDou B1, consisting of three components: a real-valued indicator ranging from 0 to 6, an integer-valued indicator ranging from 1 to 5, and a probability density ratio representing the reliability of the integer-valued indicator. In experimental results, the ECI reflected the variations in the observation environment and corresponding quality changes. ECI values were lowest in open areas, increasing when approaching an urban area, and reaching their maximum in indoor environments where signal reception is severely limited. Consequently, ECI was influenced by building density, exhibiting large and frequent changes, particularly in urban areas. Full article

Anaerobic digestion of manures, crop residues, food waste, and sludge frequently yields biogas with elevated hydrogen sulfide concentrations, which accelerate corrosion and reduce biogas quality. Microaeration, defined as the controlled addition of oxygen at 1 to 5% of the biogas production rate, has been investigated as a low-cost desulfurization strategy. This review synthesizes studies from 2015 to 2025 spanning laboratory, pilot, and full-scale anaerobic digester systems. Continuous sludge digesters supplied with ambient air at 0.28–14 m³ h⁻¹ routinely achieved 90 to 99% H₂S removal, while a full-scale dairy manure system reported a 68% reduction at 20 m³ air d⁻¹. Pure oxygen dosing at 0.2–0.25 m³ O₂ (standard conditions) per m³ reactor volume resulted in greater than 99% removal. Reported methane yield improvements ranged from 5 to 20%, depending on substrate characteristics, operating temperature, and aeration control. Excessive oxygen, however, reduced methane yields in some cases by inhibiting methanogens or diverting carbon to CO₂. Documented benefits of microaeration include accelerated hydrolysis of lignocellulosic substrates, mitigation of sulfide inhibition, and stimulation of sulfur-oxidizing bacteria that convert sulfide to elemental sulfur or sulfate. Optimal redox conditions were generally maintained between −300 and −150 mV, though monitoring was limited by low-resolution oxygen sensors. Recent extensions of the Anaerobic Digestion Model No. 1 (ADM1), a mathematical framework developed by the International Water Association, incorporate oxygen transfer and sulfur pathways, enhancing its ability to predict gas quality and process stability under microaeration. Economic analyses estimate microaeration costs at 0.0015–0.0045 USD m⁻³ biogas, substantially lower than chemical scrubbing. Future research should focus on refining oxygen transfer models, quantifying microbial shifts under long-term operation, assessing effects on digestate quality and nitrogen emissions, and developing adaptive control strategies that enable reliable application across diverse substrates and reactor configurations. Full article

With the rapid development of unmanned aerial vehicle (UAV) technology, quadrotor UAVs have demonstrated extensive application potential in various fields. However, due to parameter uncertainties and strong coupling, the flight attitude of quadrotors is prone to external disturbances, posing challenges for achieving precise control and stable flight. In this paper, we address the tracking control problem under unknown command rate variations by proposing a Modified Linear Active Disturbance Rejection Control (LADRC) strategy, aiming to enhance flight stability and anti-disturbance capability in complex environments. First, based on the attitude dynamics model of quadrotors, an LADRC technique is adopted to realize three-channel decoupling-free control. By integrating a parameter estimator, the proposed method can compensate unknown disturbances in real time, thereby improving the system’s anti-disturbance ability and dynamic response performance. Second, to further enhance system robustness, a linear extended state observer (LESO) is designed to accurately estimate the tracking error rate and total disturbances. Additionally, a Levant differentiator is introduced to replace the traditional differentiation component for optimizing the response speed of command rate. Finally, a modified LADRC controller incorporating error rate estimation is constructed. Simulation results validate that the proposed scheme maintains good tracking accuracy under high-frequency disturbances, providing an effective solution for stable UAV flight in complex scenarios. Compared with traditional control methods, the modified LADRC strategy exhibits significant advantages in tracking performance, anti-disturbance capability, and dynamic response. This research not only offers a novel perspective and solution for quadrotor control problems but also holds important implications for improving UAV performance and reliability in practical applications. Full article

Humans form deep attachments to some nonhuman animals, yet these attachments are unequally distributed across the tree of life. Drawing on evolutionary biology, comparative cognition, neuroscience, and cultural anthropology, this narrative review explains why empathy and affective preference are typically stronger for phylogenetically closer species—especially mammals—than for distant taxa such as reptiles, fish, or arthropods. We synthesize evidence that signal recognizability (faces, gaze, vocal formants, biological motion) and predictive social cognition facilitate mind attribution to mammals; conserved neuroendocrine systems (e.g., oxytocin) further amplify affiliative exchange, particularly in domesticated dyads (e.g., dog–human). Ontogenetic learning and media narratives magnify these effects, while fear modules and disgust shape responses to some distant taxa. Notwithstanding this average gradient, boundary cases—cephalopods, cetaceans, parrots—show that perceived agency, sociality, and communicative transparency can overcome phylogenetic distance. We discuss measurement (behavioral, psychophysiological, neuroimaging), computational accounts in predictive-processing terms, and implications for animal welfare and conservation. Pragmatically, calibrated anthropomorphism, hands-on education, and messaging that highlights agency, parental care, or ecological function reliably broaden concern for under-represented taxa. Recognizing both evolved priors and cultural plasticity enables more equitable and effective science communication and policy. Expanding empathy beyond its ancestral anchors is not only an ethical imperative but a One Health necessity: safeguarding all species means safeguarding the integrity of our shared planetary life. Full article

This study proposes an Automatic Identification System (AIS)-based spatiotemporal allocation methodology to estimate catch distribution and fishing effort for large purse seine fisheries in Korean waters. AIS trajectory data from July 2019 to June 2022 were analyzed to identify fishing grounds, while carrier vessel port-entry records were used to estimate daily landings. These were allocated to specific fishing segments to derive spatially explicit catch quantities. Compared with periodic surveys or voluntary reports, the AIS-based approach significantly enhanced the accuracy of fishing ground identification and the reliability of catch estimation. The results showed that fishing activity peaked between November and February, with the highest catch densities observed south of Jeju Island and in adjacent East China Sea waters. Catch declined markedly from April to June due to the mackerel closed season. These findings demonstrate the method’s potential for evaluating the effectiveness of Total Allowable Catch (TAC) regulations, supporting dynamic and adaptive management frameworks, and strengthening IUU fishing monitoring. Although the current analysis is limited to TAC-regulated species, AIS-equipped vessels, and a three-year dataset, future studies could expand the timeframe, integrate environmental data, and apply this methodology to other fisheries to improve sustainable resource management. Full article

Background/Objectives: Lyme disease diagnosis remains challenging due to the limitations of current methods. While PCR-based assays are widely used, their sensitivity can be affected by sample type and the inhibition of host DNA. This study aimed to evaluate the feasibility and sensitivity of a CRISPR/Cas12-based detection system for Borrelia burgdorferi sensu lato, comparing its performance with real-time PCR. Methods: DNA from three Borrelia genospecies (B. burgdorferi, B. garinii, and B. afzelii) was amplified targeting the OspA gene. Detection was performed using a Cas12/crRNA system with a fluorescent ssDNA reporter. Sensitivity assays were conducted on serial dilutions of Borrelia DNA, with and without human genomic DNA, and results were compared with qPCR. Results: Direct detection of Borrelia DNA without amplification was not feasible. However, when combined with PCR, the Cas12/crRNA system reliably detected as few as 5 genome copies per reaction. End-point PCR extended to 60 cycles improved detection robustness for B. garinii and B. afzelii, although sensitivity decreased in the presence of human genomic DNA. Conclusions: The Cas12/crRNA-based system offers a sensitive and accessible alternative to qPCR, especially in settings lacking real-time PCR instrumentation. Future developments may include integration with isothermal amplification and microfluidic platforms to enhance direct detection capabilities. Full article

Amid China’s pursuit of its “dual carbon” goals, systematic theoretical and empirical research remains limited to the potential role of artificial intelligence (AI) in enhancing firms’ carbon emission performance (CEP). From a systems perspective, this study developed a dynamic learning game model that integrates a constant elasticity of substitution (CES) production function, an AI-enabled abatement function, and institutional constraints to analyze firms’ cleaner production and technology adoption under simultaneous budgetary and emission constraints. Empirically, we drew on panel data of 3404 Chinese A-share listed firms from 2013 to 2023 and employ a two-way fixed-effect model to examine the effect of AI empowerment on CEP. The results showed that AI significantly improves CEP overall, though its effect is potentially constrained by energy rebound effects. Robustness checks using alternative measures and specifications confirmed the reliability of the findings and further indicated that AI’s abatement effect became stronger after 2018, consistent with technological maturity and institutional improvement. Mechanism analysis suggests two plausible pathways: (1) improving ESG performance and strengthening environmental governance; and (2) stimulating green innovation to support low-carbon technology development and application. Heterogeneity analysis indicates that AI’s effects are more evident in regions with higher marketization, in private firms, and in non-pollution-intensive industries. By contrast, firms led by executives with overseas experience tend to exhibit weaker effects, a pattern consistent with institutional fit and localization considerations. This study contributes to cleaner production theory by highlighting firm-level mechanisms of AI-enabled carbon governance while offering practical insights for low-carbon transitions and digital decarbonization strategies in developing economies. Full article