Search Results (3,252)

Background: This study aimed to assess the impact of methacrylate-functionalized polyhedral oligomeric silsesquioxanes dispersed in nanosilica (MA/Ns-POSS) on the mechanical properties of light-curable dental resins and composites. The primary goal was to evaluate how different concentrations of MA/Ns-POSS (0.5–20 wt.%) affect the hardness, flexural strength, modulus, diametral tensile strength, polymerization shrinkage stress, and degree of conversion of these materials. Methods: A mixture of Bis-GMA, UDMA, TEGDMA, HEMA, and camphorquinone, with a tertiary amine as the photoinitiator, was used to create resin and composite samples, incorporating 45 wt.% silanized silica for the composites. Hardness (Vickers method, HV), flexural strength (FS), and flexural modulus (E_f) were assessed using three-point bending tests, while diametral tensile strength (DTS) polymerization shrinkage stresses (PSS), and degree of conversion (DC) analysis were analyzed for the composites. Results: The results showed that resins with 10 wt.% MA/Ns-POSS exhibited the highest E_f and FS values. Composite hardness peaked at 20 wt.% MA/Ns-POSS, while DTS increased up to 2.5 wt.% MA/Ns-POSS but declined at higher concentrations. PSS values decreased with increasing MA/Ns-POSS concentration, with the lowest values recorded at 15–20 wt.%. DC analysis also showed substantial improvement for 15–20 wt.% Conclusion: Incorporating MA/Ns-POSS improves the mechanical properties of both resins and composites, with 20 wt.% showing the best results. Further studies are needed to explore the influence of higher additive concentrations. Full article

The diffusion coefficient (DC) of CO₂ in brine is a key parameter in geological carbon sequestration and CO₂-Enhanced Oil Recovery (EOR), as it governs mass transfer efficiency and storage capacity. This study employs three machine learning (ML) models—Random Forest (RF), Gradient Boost Regressor (GBR), and Extreme Gradient Boosting (XGBoost)—to predict DC based on pressure, temperature, and salinity. The dataset, comprising 176 data points, spans pressures from 0.10 to 30.00 MPa, temperatures from 286.15 to 398.00 K, salinities from 0.00 to 6.76 mol/L, and DC values from 0.13 to 4.50 × 10⁻⁹ m²/s. The data was split into 80% for training and 20% for testing to ensure reliable model evaluation. Model performance was assessed using R², RMSE, and MAE. The RF model demonstrated the best performance, with an R² of 0.95, an RMSE of 0.03, and an MAE of 0.11 on the test set, indicating high predictive accuracy and generalization capability. In comparison, GBR achieved an R² of 0.925, and XGBoost achieved an R² of 0.91 on the test set. Feature importance analysis consistently identified temperature as the most influential factor, followed by salinity and pressure. This study highlights the potential of ML models for predicting CO₂ diffusion in brine, providing a robust, data-driven framework for optimizing CO₂-EOR processes and carbon storage strategies. The findings underscore the critical role of temperature in diffusion behavior, offering valuable insights for future modeling and operational applications. Full article

In recent years, significant attention has been directed toward the development of coating materials capable of tailoring surface properties for various functional applications. Transition metal nitrides, in particular, have garnered interest due to their superior physical and chemical properties, including high hardness, excellent wear resistance, and strong corrosion resistance. In this study, a fabrication process for CrN-based thin films was developed by combining reactive direct current magnetron sputtering (dcMS) with post-deposition annealing in air. CrN coatings were deposited by reactive dcMS using different argon-nitrogen (Ar:N₂) gas ratios (4:1, 3:1, 2:1, and 1:1), followed by annealing at 550 °C for 1.5 h in ambient air. XRD and EDS analysis revealed that this treatment results in the formation of a composite phase comprising CrN and Cr₂O₃. The resulting coating exhibited favorable mechanical and tribological properties, including a maximum hardness of 12 GPa, a low wear coefficient of 0.254 and a specific wear rate of 7.05 × 10⁻⁶ mm³/N·m, making it a strong candidate for advanced protective coating applications. Full article

The somatic cell count (SCC) and differential somatic cell count (DSCC) are proxies for the udder health of dairy cattle, regarded as the criterion of mastitis identification with healthy, suspicious mastitis, mastitis, and chronic/persistent mastitis. However, SCC and DSCC are tested using flow cytometry, which is expensive and time-consuming, particularly for DSCC analysis. Mid-infrared spectroscopy (MIR) enables qualitative and quantitative analysis of milk constituents with great advantages, being cheap, non-destructive, fast, and high-throughput. The objective of this study is to develop a dairy cattle udder health status diagnostic model of MIR. Data on milk composition, SCC, DSCC, and MIR from 2288 milk samples collected in dairy farms were analyzed using the CombiFoss 7 DC instrument (FOSS, Hilleroed, Denmark). Three MIR spectral preprocessing methods, six modeling algorithms, and three different sets of MIR spectral data were employed in various combinations to develop several diagnostic models for mastitis of dairy cattle. The MIR diagnostic model of effectively identifying the healthy and mastitis cattle was developed using a spectral preprocessing method of difference (DIFF), a modeling algorithm of Random Forest (RF), and 1060 wavenumbers, abbreviated as “DIFF-RF-1060 wavenumbers”, and the AUC reached 1.00 in the training set and 0.80 in the test set. The other MIR diagnostic model of effectively distinguishing mastitis and chronic/persistent mastitis cows was “DIFF-SVM-274 wavenumbers”, with an AUC of 0.87 in the training set and 0.85 in the test set. For more effective use of the model on dairy farms, it is necessary and worthwhile to gather more representative and diverse samples to improve the diagnostic precision and versatility of these models. Full article

To address the issue of low accuracy in identifying the transition states of rolling bearing performance degradation when relying solely on vibration signals, this study proposed a vibration–temperature fusion-based adaptive method for bearing performance degradation assessments. First, a multidimensional time–frequency feature set was constructed by integrating vibration acceleration and temperature signals. Second, a novel composite sensitivity index (CSI) was introduced, incorporating the trend persistence, monotonicity, and signal complexity to perform preliminary feature screening. Mutual information clustering and regularized entropy weight optimization were then combined to reselect highly sensitive parameters from the initially screened features. Subsequently, an adaptive feature fusion method based on auto-associative kernel regression (AFF-AAKR) was introduced to compress the data in the spatial dimension while enhancing the degradation trend characterization capability of the health indicator (HI) through a temporal residual analysis. Furthermore, the entropy weight method was employed to quantify the information entropy differences between the vibration and temperature signals, enabling dynamic weight allocation to construct a comprehensive HI. Finally, a dual-criteria adaptive bottom-up merging algorithm (DC-ABUM) was proposed, which achieves bearing life-stage identification through error threshold constraints and the adaptive optimization of segmentation quantities. The experimental results demonstrated that the proposed method outperformed traditional vibration-based life-stage identification approaches. Full article

Accurate classification of breast cancer histopathology images is critical for early diagnosis and treatment planning. Yet, conventional deep learning models face significant challenges under limited annotation scenarios due to their reliance on large-scale labeled datasets. To address this, we propose Dynamic Cross-Scale Swin Transformer (DCS-ST), a robust and efficient framework tailored for histopathology image classification with scarce annotations. Specifically, DCS-ST integrates a dynamic window predictor and a cross-scale attention module to enhance multi-scale feature representation and interaction while employing a semi-supervised learning strategy based on pseudo-labeling and denoising to exploit unlabeled data effectively. This design enables the model to adaptively attend to diverse tissue structures and pathological patterns while maintaining classification stability. Extensive experiments on three public datasets—BreakHis, Mini-DDSM, and ICIAR2018—demonstrate that DCS-ST consistently outperforms existing state-of-the-art methods across various magnifications and classification tasks, achieving superior quantitative results and reliable visual classification. Furthermore, empirical evaluations validate its strong generalization capability and practical potential for real-world weakly-supervised medical image analysis. Full article

In some areas, such as Gansu in China and Texas in the USA, lots of wind power bases are located far away from load centers. Transmitting large amounts of wind power to load centers through long transmission lines will lead to wind turbines being integrated into a weak grid, which decreases the stability limits of wind turbines. To solve this problem, this study investigates the stability limits of a Doubly Fed Induction Generator (DFIG) connected to a weak grid in a DC-link voltage control timescale. To start with, a model of the DFIG in a DC-link voltage control timescale is presented for stability limit analysis, which facilitates profound physical understanding. Through steady-state stability analysis based on sensitivity evaluation, it is found that the critical factor restricting the stability limit of the DFIG connected to a weak grid is ∂P_e/∂ (−i_rd), changing from positive to negative. As ∂P_e/∂ (−i_rd) reaches zero, the system reaches its stability limit. Furthermore, by considering control loop dynamics and grid strength, the stability limit of the DFIG is investigated based on eigenvalue analysis with multiple physical scenarios. The results of root locus analysis show that, when the DFIG is connected to an extremely weak grid, reducing the bandwidth of the PLL or increasing the bandwidth of the AVC with equal damping can increase the stability limit. The aforesaid theoretical analysis is verified through both time domain simulation and physical experiments. Full article

The improvement of environmental conditions has become a priority for governments and legislators. New electrified mobility systems are increasingly present in our environment, as they enable the reduction of polluting emissions. Electric vehicles (EVs) are one of the fastest-growing alternatives to date, with exponential growth expected over the next few years. In this article, the various charging modes for EVs are explored, and the risks associated with charging technologies are analysed, particularly for charging systems in high-power DC with Lithium battery energy storage, given their long market deployment and characteristic behaviour. In particular, the Arc Flash (AF) risk present in high-power DC chargers will be studied, involving numerous simulations of the charging process. Subsequently, the Incident Energy (IE) analysis is carried out at different specific points of a commercial high-power ‘Mode 4’ charger. For this purpose, different analysis methods of recognised prestige, such as Doan, Paukert, or Stokes and Oppenlander, are applied, using the latest version of the ETAP^® simulation tool version 22.5.0. This study focuses on quantifying the potential severity (consequences) of an AF event, assuming its occurrence, rather than performing a probabilistic risk assessment according to standard methodologies. The primary objective of this research is to comprehensively quantify the potential consequences for workers involved in the operation, maintenance, repair, and execution of tasks related to EV charging systems. This analysis makes it possible to provide safe working conditions and to choose the appropriate and necessary personal protective equipment (PPE) for each type of operation. It is essential to develop this novel process to quantify the consequences of AF and to protect the end users of EV charging systems. Full article

In unmanned systems, especially in large-scale and complex ones, sensor and communication failures occur from time to time and are hard to avoid. Therefore, this paper studies the fault detection problem of a class of unknown nonlinear singular uncertain time-varying delay Markov jump systems (UNSUTVDMJSs). Firstly, the corresponding sliding mode controller (SMC) is designed by using the equivalent control principle, and the unknown nonlinearity is equivalently replaced by changing the system input. Then, a fault detection filter adapted to this system is designed, thereby obtaining the unknown nonlinear stochastic singular uncertain Augmented filter residual system (UNSSUAFRS) model. To obtain the sufficient conditions for the random admissibility of this augmented system, a weak infinitesimal generator was used to design the required Lyapunov-Krasovskii functional. With the help of the Lyapunov principle and $H_{\infty }$ performance analysis method, the sufficient conditions for the random admissibility of UNSSUAFRS under the $H_{\infty }$ performance index $\gamma$ were derived. Finally, with the aid of the designed residual evaluation function and threshold, simulation analysis was conducted on the examples of DC servo motors and numerical calculation examples to verify the effectiveness and practicability of this fault detection filter. Full article

A quantitative analysis of the fault transient is critical for system resilience assessment and protection coordination. Focusing on hybrid modular multilevel converter (MMC)-based HVDC architecture with enhanced fault ride-through (FRT) capability, this study develops a mathematical calculation framework to quantify how controller configurations influence fault current profiles. Unlike conventional static topologies (e.g., RLC or fixed-voltage RL circuits), the proposed model integrates an RL network with a time-variant controlled voltage source, which can emulate closed-loop control response during the FRT transient. Then, the quantitative relationship is established to map the parameters of DC controllers to the fault current across diverse FRT strategies, including scenarios where control saturation dominates the transient response. Simulation studies conducted on a two-terminal MMC-HVDC architecture substantiate the efficacy and precision of the developed methodology. The proposed method enables the evaluation of DC fault behavior for hybrid MMCs, concurrently appraising FRT control strategies. Full article

This paper proposes an energy-efficient current control strategy for drive modules of permanent magnetic actuators (PMAs) to reduce the cost and volume of DC-link capacitors. The drive module of the PMA does not receive the input power from an external power source during operation. Instead, the externally charged DC-link capacitors are used as internal backup power sources to guarantee the reliable operation even in the case of an emergency. Therefore, it is important to use the charged energy efficiently within the limited DC-link capacitors. However, conventional control strategies using a voltage open loop have trouble reducing the energy waste. This is because the drive module with the voltage open loop uses unnecessary energy even after the PMA mover has finished its movement. To figure it out, the proposed control strategy adopts a current control loop to save energy even if the displacement of the PMA mover is unknown. In addition, the proposed strategy can ensure the successful operation of the PMA by using the driving force analysis. The efficacy of the proposed strategy is verified through the experimental test. It would be expected that the proposed strategy can reduce the cost and volume of the PMA drive system. Full article

Oxysterols such as 7-ketocholesterol (7KCh) contribute to the pathogenesis of autoimmune and chronic inflammatory diseases by inducing oxidative stress and promoting pro-inflammatory immune cell activation. Dendritic cells (DCs) play a central role in maintaining immune tolerance, and their dysregulation is a key driver of autoimmunity. Targeting DCs by using natural compounds offers a promising strategy to restore redox balance and suppress aberrant immune responses. This study investigated the immunomodulatory and antioxidant properties of Lupeol, a natural triterpenoid, in human monocyte-derived DCs exposed to 7KCh. Flow cytometry and cytokine profiling demonstrated that Lupeol preserved the immature, tolerogenic phenotype of DCs by promoting a dose-dependent increase in the anti-inflammatory cytokine IL-10. Lupeol also inhibited the 7KCh-induced upregulation of maturation markers (CD83, CD86) and suppressed the release of pro-inflammatory cytokines IL-1β and IL-12p70. Functionally, Lupeol-treated DCs directed T cell polarization toward an anti-inflammatory and regulatory profile while dampening the inflammatory responses triggered by 7KCh. This immunoregulatory effect was further supported by the decreased secretion of the pro-inflammatory cytokines IL-1β and IL-12p70 in DC culture supernatants. Mechanistic analyses using immunofluorescence showed that Lupeol alone significantly increased nuclear NRF2 levels and upregulated HO-1 expression. Western blot analysis further confirmed Lupeol’s ability to activate the KEAP1-NRF2 signaling pathway, as evidenced by increased expression of NRF2 and its downstream target, NQO1. The use of ML385, a selective NRF2 inhibitor, in ROS and cytokine assays supported the involvement of NRF2 in mediating the Lupeol antioxidant and anti-inflammatory effects in DCs. Notably, the oxidative burden induced by 7KCh limited the full activation of NRF2 signaling triggered by Lupeol. Furthermore, docking and MM/PBSA analyses revealed the specific interactions of Lupeol with the kelch domain of KEAP1. These findings suggest that Lupeol may serve as a promising orally available immunomodulatory agent capable of promoting tolerogenic DCs, offering potential applications in autoimmune and other chronic inflammatory diseases. Full article

Bipolar power supply can effectively reduce line losses and optimize power transmission. This paper proposes a low-power bipolar DC–DC converter with voltage self-balancing, which not only achieves bipolar output but also automatically balances the inter-pole voltage under load imbalance conditions without requiring additional voltage balancing control. This paper first elaborates on the derivation process of the proposed converter, then analyzes its working principles and performance characteristics. A 400 W experimental prototype is built to validate the correctness of the theoretical analysis and the voltage self-balancing capability. Finally, loss analysis and conclusions are presented. Full article

Background/Objectives: Atractylodes lancea (Thunb.) DC. [Asteraceae] (ALR)-derived exosome-like nanoparticles (ALR-ELNs) exhibit anti-neuroinflammatory effects in microglial cells. However, the associated mechanisms and pathways are unknown. We aimed to characterize the effects of ALR-ELNs on inflammatory responses of BV-2 microglial cells to lipopolysaccharide (LPS) using RNA sequencing. Methods: ALR-ELNs were fractionated from ALR. BV-2 microglial murine cells were stimulated with LPS after treatment with ALR-ELNs. RNA sequencing was performed to analyze variations in mRNA levels. Ingenuity pathway analysis (IPA) was performed to investigate the mechanism of action of ALR-ELNs. mRNA expression was assessed using real-time quantitative polymerase chain reaction (qPCR). Results: The expression of 651 genes was downregulated, whereas that of 1204 genes was upregulated in LPS-stimulated BV2 cells pretreated with ALR-ELNs. The IPA showed that the effects of ALR-ELNs on inflammation took place through pathogen-influenced signaling. Network analysis via IPA showed that the Toll-like receptor (TLR) is involved in the suppression of inflammation by ALR-ELNs. The qPCR analysis showed that pretreatment with ALR-ELNs significantly reduced TLR4 mRNA expression. Conclusions: ALR-ELNs suppress the release of inflammatory mediators by downregulating TLR4 expression, which is a novel mechanism by which ALR-ELNs act on microglia. Identifying active ingredients in ALR-ELNs that downregulate TLR4 expression can advance the development of therapeutic drugs for neuroinflammatory diseases. Full article

The growing demand for low-emission maritime transport and efficient onboard energy management has intensified research into advanced control strategies for hybrid shipboard microgrids. These systems integrate both AC and DC power domains, incorporating renewable energy sources and battery storage to enhance fuel efficiency, reduce greenhouse gas emissions, and support operational flexibility. However, integrating renewable energy into shipboard microgrids introduces challenges, such as power fluctuations, varying line impedances, and disturbances caused by AC/DC load transitions, harmonics, and mismatches in demand and supply. These issues impact system stability and the seamless coordination of multiple distributed generators. To address these challenges, we proposed a hierarchical control strategy that supports sustainable operation by improving the voltage and frequency regulation under dynamic conditions, as demonstrated through both MATLAB/Simulink simulations and real-time hardware validation. Simulation results show that the proposed controller reduces the frequency deviation by up to 25.5% and power variation improved by 20.1% compared with conventional PI-based secondary control during load transition scenarios. Hardware implementation on the NVIDIA Jetson Nano confirms real-time feasibility, maintaining power and frequency tracking errors below 5% under dynamic loading. A comparative analysis of the classical PI and sliding mode control-based designs is conducted under various grid conditions, such as cold ironing mode of the shipboard microgrid, and load variations, considering both the AC and DC loads. The system stability and control law formulation are verified through simulations in MATLAB/SIMULINK and practical implementation. The experimental results demonstrate that the proposed secondary control architecture enhances the system robustness and ensures sustainable operation, making it a viable solution for modern shipboard microgrids transitioning towards green energy. Full article