Search Results (66,421)

Background: Exercise interacts closely with the circadian system; however, whether long-term training elicits time-of-day-dependent metabolic adaptations in the context of obesity remains unclear. Methods: Male C57BL/6 mice were fed a high-fat diet and trained on a treadmill for 8 weeks during either the early rest phase (ZT3, Zeitgeber time) or the early active phase (ZT15). Sedentary mice served as controls. After the last session, animals were fasted for 4 h and sampled 48–49 h later. Plasma triglycerides (TGs) and glucose, as well as liver and epididymal white adipose tissue (EPI), were analyzed. Results: Plasma TGs showed a significant phase × exercise interaction (F(1, 25) = 5.25, p = 0.0307), with the lowest levels in ZT15-exe (27.22 mg/dL) compared with ZT15-sed (39.47 mg/dL, p < 0.01) and ZT3-exe (41.80 mg/dL, p < 0.01). Hepatic TG content was markedly lower in ZT3 than in ZT15 mice (F(1, 25) = 15.49, p < 0.001), and Oil Red O staining was associated with reduced lipid accumulation in exercised groups (p < 0.05). In EPI, Fasn expression was robustly decreased by exercise (F(1, 25) = 16.43, p = 0.0004, q = 0.0059), indicating long-term suppression of lipogenesis. In the liver, Cpt1a showed significant main effects of both phase (F(1, 25) = 10.11, p = 0.0039, q = 0.0158) and exercise (F(1, 25) = 13.42, p = 0.0012, q = 0.0353), being higher in ZT3 and under sedentary conditions, suggesting a circadian-dominant oxidative advantage in hepatic metabolism. Conclusions: Long-term exercise induced phase-dependent adaptations in lipid metabolism. Active-phase exercise promoted adipose lipid mobilization and lowered plasma TGs, while rest-phase training enhanced hepatic oxidative capacity. These results suggest a “tissue × time” framework of circadian-specific exercise responses, providing hypothesis-generating evidence for optimizing exercise timing in metabolic disorders. Full article

The development of high-performance MEMS-based eye trackers, crucial for next-generation medical diagnostics and human–computer interfaces, is often hampered by the mechanical instability and time-consuming recalibration of physical prototypes. To address this bottleneck, we present the development and rigorous validation of a high-fidelity digital twin (DT) designed to accelerate the design–test–refine cycle. We conducted a comparative study of a physical MEMS scanning system and its corresponding digital twin using a USAF 1951 test target under both static and dynamic conditions. Our analysis reveals that the DT accurately replicates the physical system’s behavior, showing a geometric discrepancy of <30 µm and a matching feature shift (1 µm error) caused by tracking dynamics. Crucially, the DT effectively removes mechanical vibration artifacts, enabling the precise analysis of system parameters in a controlled virtual environment. The validated model was then used to develop a pupil detection algorithm that achieved an accuracy of 1.80 arc minutes, a result that surpasses the performance of a widely used commercial system in our comparative tests. This work establishes a validated methodology for using digital twins in the rapid prototyping and optimization of complex optical systems, paving the way for faster development of critical healthcare technologies. Full article

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

Non-photosynthetic vegetation fractional cover (f_NPV) is a key indicator of vegetation decline and ecological health. Traditional inversion models assume identical spectral signatures for the same vegetation cover class across entire study areas. Spectral variations occur among regions due to divergent soil properties and vegetation types. To address this limitation, extensive ground sampling was conducted; ground observation data from multiple regions were utilized to establish localized spectral libraries, thereby enhancing spectral variability representation within the study area while concurrently optimizing vegetation indices across different sensor systems. The results reveal that, within the optimized spectral mixture analysis model, the coefficient of determination (R²) for f_NPV using the NPV soil separation index (NSSI) for Sentinel sensor is 0.6258, and that of f_PV using the modified soil adjusted vegetation index (MSAVI) is 0.8055. The MSAVI-NSSI achieved an R² of 0.7825 for f_NPV and 0.8725 for photosynthetic vegetation fractional cover (f_PV). Optimized vegetation indices also yielded favorable validation results. Landsat’s theoretical predictions improved by 0.1725, with validated results up by 0.1635. MODIS showed improvements of 0.1365 and 0.1923, respectively. This enhancement significantly improves the accuracy of NPV fractional cover identification, providing critical insights for vegetation ecological health assessment in arid and semi-arid regions under global warming. Furthermore, by optimizing the spectral constraint weights in remote sensing images, a solution is provided for the long-term monitoring of vegetation health status. Full article

The rapidly growing demand of electric vehicle (EV) charging is one of the main challenges to modern electrical distribution systems. Accurate modelling of the EV charging load is crucial for charging load prediction and optimization. However, previous methods based on the charging behaviors of private EVs are hard to collect user’s private data. In this study, charging load data from 962 charging and battery swapping stations (CBSSs), classified into dedicated charging stations, public charging stations, and battery swapping stations, collected during 2021–2022, are analyzed to investigate seasonal variations in the charging coincidence factor. A data-driven probabilistic model of charging load, based on the Gaussian Mixture Model, is developed to address various scenarios, including new station construction, capacity expansions, and optimized charging strategies. This model is applicable to different types of CBSSs. A real-world 10 kV feeder system is employed as a case study to validate the model, and a delayed charging strategy is proposed. The results demonstrate that the proposed model accurately estimates charging load peaks after new construction and expansion in 2023, with an error rate under 3%. Furthermore, the delayed charging strategy achieved a 24.79% reduction in maximum load and a 31.96% decrease in the peak–valley difference. Its implementation in the real-world feeder significantly alleviated nighttime overloading in 2024. Full article

Barley (Hordeum vulgare L.) is a key cereal crop for malting and brewing, where grain plumpness and optimal grain protein concentration (GPC) are essential quality traits. This study investigated the combined effects of nitrogen fertilization strategies and a seaweed-based biostimulant (Ascophyllum nodosum extract) on malting barley production across four environments in Thessaly, Greece, over two growing seasons. Treatments included urea (U), urea with biostimulant (U + B), urea with urease inhibitor (UI), urea with urease inhibitor and biostimulant (UI + B), and a control (no fertilization). Applications were tested on genotype G20 at mid-development (Z30–33) and genotype G45 at an earlier stage (Z24–30). UI + B treatment consistently enhanced yield by up to 71%, thousand-grain weight by 27%, and spikelets per square meter by 75% relative to the control, with responses influenced by genotype and environment. Grain fractions > 2.8 mm increased by up to 22% under UI + B, while GPC remained within the optimal malting range (9.5–11.5%). Early-stage applications produced strong benefits overall. Principal component analysis distinguished treatment effects, with UI + B samples clustering consistently apart from controls. These results demonstrate that combining biostimulants with urease inhibitors can simultaneously improve yield, quality, and sustainability in malting barley, supporting reduced nitrogen input in Mediterranean systems. Full article

To address low energy utilization efficiency and severe exergy destruction from direct discharge of high-temperature turbine exhaust, this study proposes a supercritical CO₂ Brayton cogeneration system with a series-connected hot water heat exchanger for stepwise waste heat recovery. Based on finite-time thermodynamics, a physical model that provides a more realistic framework by incorporating finite temperature difference heat transfer, irreversible compression, and expansion losses is established. Aiming to maximize exergy output rate under the constraint of fixed total thermal conductance, the decision variables, including working fluid mass flow rate, pressure ratio, and thermal conductance distribution ratio, are optimized. Optimization yields a 16.06% increase in exergy output rate compared with the baseline design. The optimal parameter combination is a mass flow rate of 79 kg/s and a pressure ratio of 5.64, with thermal conductance allocation increased for the regenerator and cooler, while decreased for the heater. The obtained results could provide theoretical guidance for enhancing energy efficiency and sustainability in S-CO₂ cogeneration systems, with potential applications in industrial waste heat recovery and power generation. 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

The increasing ubiquity of wearable computing and multimodal interaction technologies has created unprecedented opportunities for natural and seamless human–computer interaction. However, most existing systems adapt only to external user actions such as speech, gesture, or gaze, without considering internal cognitive or affective states. This limits their ability to provide intelligent and empathetic adaptations. This paper addresses this critical gap by proposing the Neuro-Adaptive Multimodal Architecture (NAMI), a principled, modular, and reproducible framework designed to integrate behavioral and neurophysiological signals in real time. NAMI combines multimodal behavioral inputs with lightweight EEG and peripheral physiological measurements to infer cognitive load and engagement and adapt the interface dynamically to optimize user experience. The architecture is formally specified as a three-layer pipeline encompassing sensing and acquisition, cognitive–affective state estimation, and adaptive interaction control, with clear data flows, mathematical formalization, and real-time performance on wearable platforms. A prototype implementation of NAMI was deployed in an augmented reality Java programming tutor for postgraduate informatics students, where it dynamically adjusted task difficulty, feedback modality, and assistance frequency based on inferred user state. Empirical evaluation with 100 participants demonstrated significant improvements in task performance, reduced subjective workload, and increased engagement and satisfaction, confirming the effectiveness of the neuro-adaptive approach. 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

Precise online measurement of large structural components is urgently needed in modern manufacturing and intelligent construction, requiring a measurement range over 1 m, near-millimeter accuracy, second-level measurement speed, and adaptability to complex environments. In this paper, three mainstream measurement technologies, namely the image method, line laser scanning method, and structured light method, are comparatively analyzed. The structured light method exhibits remarkable comprehensive advantages in terms of accuracy and speed; however, it suffers from the issue of occlusion during contour measurement. To tackle this problem, multi-camera stitching is employed, wherein the accuracy of camera calibration plays a crucial role in determining the quality of point cloud stitching. Focusing on the cable tightening scenario of meter-diameter cables in cable-stayed bridges, this study develops a contour measurement system based on the collaboration of multiple structured light cameras. Measurement indicators are optimized through modeling analysis, system construction, and performance verification. During verification, four structured light scanners were adopted, and measurements were repeated 11 times for the test workpieces. Experimental results demonstrate that although the current measurement errors have not yet been stably controlled within the millimeter level, this research provides technical exploration and practical experience for high-precision measurement in the field of intelligent construction, thus laying a solid foundation for subsequent accuracy improvement. 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

In this paper, the rail mean vertical alignment is estimated through double integration of wheel–rail contact forces measured using dynamometric wheelsets on a dedicated track recording vehicle (TRV). A simplified three degrees of freedom (DOF) linear model of half a train coach has been developed for this purpose. The model’s ability to simulate the average left and right longitudinal level has been tested using vertical contact force recordings from a constant speed track section, as measured by the TRV. The results are compared with available track geometry (TG) data, recorded by the optical system of the same vehicle, used for condition monitoring of the Italian railway infrastructure. Model parameters, such as masses, stiffness, and damping of the suspensive system have been optimized. An error analysis has been conducted on results. A good agreement is found between simulated and recorded vertical alignment at the D1 level, suggesting the feasibility of using contact forces measured with instrumented wheelsets for railway TG condition monitoring. This computationally efficient approach highlights the potential of strain gauges and instrumented wheelsets as alternative or complementary technologies to the widely adopted accelerometers, rate gyros, and optical devices for railway condition monitoring. Given its low computational cost, embedded and real-time TG estimation could be further investigated. Full article

A better understanding of diabetic kidney disease (DKD) will help optimize its management. Few animal models replicate human DKD characteristics as closely as ZSF1 male rats. To address the male-specific focus in murine model systems, we aimed to characterize the manifestation of DKD in ZSF1 females and compare them with ZSF1 males and control rats (CD). ZSF1 males become obese at an early age. ZSF1 females are fatter and heavier than CD females but remain smaller, lighter, and more active than ZSF1 males throughout their lives. Male, but not female, ZSF1 rats become hypertensive with age. ZSF1 females have a higher heart rate in early life, which reduces significantly with age. ZSF1 males exhibit significant hyperglycemia from an early age. In contrast, female ZSF1 are not overly hyperglycemic; however, their blood glucose levels trend higher than those of CD females, and the difference is statistically significant. Both ZSF1 males and females develop progressive proteinuria. ZSF1 females, therefore, display various features of DKD: higher-trending blood glucose levels, hyperlipidemia, and progressive proteinuria, but not hypertension. Thus, ZSF1 female rats may be a suitable model for studying DKD without hypertension and for testing the effects of DKD-relevant drug responses in females. Full article