Search Results (24,934)

Myxomatous mitral valve disease (MMVD) is the most common acquired cardiac disoder in dogs and a relevant model for human mitral valve disease. However, the molecular drivers of disease progression remain unclear, and reliable biomarkers for early diagnosis still hamper clinical management. This study investigated microRNA (miRNA) expression directly in histologically characterized mitral valve tissues. Formalin-fixed paraffin-embedded samples were obtained from control dogs (n = 7), low-grade MMVD (n = 8), and high-grade MMVD (n = 5). A bioinformatics workflow identified candidate miRNAs converging on extracellular matrix remodeling and canonical signaling pathways, including TGF-β, PI3K–Akt, and MAPK. Selected candidates, let-7 family, miR-98, miR-21, miR-30b, miR-133b, and miR-103, were validated by qPCR. Results revealed a general upregulation of the panel in MMVD compared with controls, with stage-dependent differences between low- and high-grade lesions. In particular, miR-21, let-7b, and miR-133b were markedly increased in advanced disease, while miR-30b emerged as an early-stage marker with potential prognostic value. These findings provide molecular evidence linking miRNA dysregulation to progressive valvular degeneration. By combining histologically defined tissue analysis with stage-based comparisons, this study identifies miRNAs with potential diagnostic and prognostic utility for canine MMVD. Full article

In this paper, a hybrid fault-tolerant control (FTC) system for quadcopter unmanned aerial vehicles (UAVs) is proposed to counteract the deterioration of the performance of the quadcopter due to motor faults. A robust and adaptive approach to controlling fault conditions is simulated by combining an integral back-stepping controller for translational motion and a nonlinear observer-based sliding-mode controller for rotational motion, and then implemented on an FPGA. Finally, motor faults are treated as disturbances and are successfully compensated by the controller to ensure safe and high-performance flight. Simulations were taken at 0%, 10%, 30%, and 50% motor faults to test how effective the proposed FTC system is. After simulations, the controller’s real-time performance and reliability were validated through hardware-in-the-loop (HIL) experiments. The results validated that the proposed hybrid controller can guarantee stable flight and precision tracking of the desired trajectory when any single motor fails up to the order of 50%. It shows that the controller is of high fault tolerance and robustness, which will be a potential solution for improving the reliability of UAVs in fault-prone conditions. Full article

Weld lines in injection-molded plastics often act as structural weak points that reduce mechanical performance. Enhancing weld line strength is therefore essential to improve product reliability and service life. This study aims to develop and validate an injection mold system capable of localized cavity temperature control to strengthen weld line regions. A specialized mold with an integrated cooling layer was designed to enable rapid thermal response during molding. The Taguchi method was applied to optimize three key parameters—part thickness, melt temperature, and injection pressure—to maximize weld line flexural strength. Experiments based on an L25 orthogonal array revealed that weld line stress varied significantly across parameter combinations, with a maximum of 109.23 MPa. A subsequent validation test conducted under the optimal conditions (250 °C melt temperature, 1.5 mm part thickness, and 16 MPa injection pressure) achieved an enhanced weld line stress of 121.88 MPa, confirming the reliability of the Taguchi-based optimization. Among the factors studied, part thickness had the greatest influence, followed by injection pressure, while melt temperature had the smallest effect. These results demonstrate that combining cavity temperature control with systematic parameter optimization provides an effective strategy to enhance weld line strength in high-performance plastic components. Full article

Reliable quality of epicutaneous patch test (PT) preparations is a prerequisite for establishing a robust diagnosis in patients with suspected allergic contact dermatitis due to delayed-type sensitization. It is difficult to identify potential quality issues in daily practice, since confirmatory methods are lacking and assessment of PT-relevance is predominantly based on patients’ history and exposure. The quality of PT products can be affected, e.g., by the properties of the active substance, an insufficient development of the PT preparation or issues during manufacturing. Resulting quality deficiencies can cause both false-negative and false-positive test results. As PT preparations are medicinal products according to Directive 2001/83/EC, they require a marketing authorization (MA) entailing assessment of quality, safety and efficacy by the competent authorities. The corresponding product dossier is the basis for MA. It is continuously updated, e.g., upon change of a source material supplier, ensuring comparability of the respective product over time. Compliance with regulatory requirements is a crucial foundation for sustainable quality to prevent product deficiencies, ensuring reliable test results in practice. Harmonization across the EU is important to ensure the widespread availability of high-quality PT products. This review presents the MA requirements of PT preparations in the EU, as well as challenges previously reported by physicians. Full article

The starting torque of Self-lubricating Spherical Plain Bearings (SSPBs) has a significant impact on the reliability and service life of aircraft. Due to the low accuracy of the dynamic detection of the starting torque of the bearing, the starting torque cannot be measured accurately under high-frequency swinging conditions. Therefore, the problem of the dynamic detection accuracy of the starting torque of the bearing on a high-frequency swinging friction and wear tester was proposed to be investigated in this paper, and a dynamic simulation model of the swinging system of the tester was constructed. With the combination of the inertia torque test and the least square method, a mathematical model of the inertia torque was developed and the influence of the inertia torque on the results of the dynamic detection of the starting torque was revealed. At the same time, an error compensation procedure for the on-line dynamic detection of the starting torque was written. This research shows that the inertia torque of the swing system of the tester has a great influence on the detection accuracy of the starting torque. As the swing frequency increases, the inertia torque increases, and the dynamic detection accuracy of the starting torque is reduced. The dynamic detection error of the starting torque of the bearing can be efficiently compensated by the error compensation procedure, and then the detection accuracy can be improved. This research provides a good theory for the design of SSPBs and the reasonable control of the starting torque during the use of the bearings, and it is valuable for engineering practice. Full article

The Safety–Sustainability Governance (SSG) Framework is presented to address critical governance and safety challenges in the domestic ferry sector, particularly in developing countries. The sector faces persistent challenges, indicating the inadequacies in aligning national policies with international yardsticks despite the present global maritime safety standards. To foster an equilibrium between regulatory compliance and rights-based inclusivity, the SSG approach integrates Environmental, Social, and Governance (ESG) principles, socio-technical systems (STS), and maritime governance theory. Based on literature review and a wider range survey conducted across 48 countries, the study assesses the SSG approach through four key principles. These principles are proactive planning, vibrant governance policies, effective management and monitoring, and climate-resilient safety practices. The study employed a strong evaluation of governance metrics by using the Principles, Criteria, and Indicators (PCI) methodology which was supported by the Simple Multi-Attribute Rating Technique (SMART). In order to validate the reliability and consistency of findings, statistical tools, such as Pearson Correlation and Cronbach’s Alpha, were used. This not only unveiled high compliance in protective planning and operational monitoring but also highlighted shortcomings in policy enforcement, stakeholders’ engagement, and climate adaptation strategies. The SSG framework acts as an adaptable tool that enables stakeholders to execute targeted improvements and determine governance adequacy. The application of this framework emphasis the significance of stakeholder collaboration, advanced technologies, and regulatory alignment in promoting sustainability and safety in ferry operations. This research presents an innovative contribution by offering a practical model that links global safety standards with local operational realities by paving the technique for improved safety, governance, and sustainability in the domestic ferry industry. Full article

Background/Objectives: Immune checkpoint blockade (ICB) has emerged as a promising therapeutic option for hepatocellular carcinoma (HCC), yet reliable biomarkers to predict clinical outcomes remain limited. Tumor-associated macrophages (TAMs) are increasingly recognized as key regulators of the tumor immune microenvironment. Methods: We interrogated a publicly available HCC single-cell RNA sequencing (scRNA-seq) dataset to characterize intratumoral immune cell subpopulations. Through unsupervised clustering and gene signature analysis, we identified a distinct subset of SPP1 (secreted phosphoprotein 1, also known as osteopontin) and BCL2A1 (Bcl-2-related protein A1) double-positive TAMs. Their abundance was quantified and associated with patient outcomes. Further independent HCC transcriptomic datasets with annotated PD1-based ICB response status were used for examination. Results: Across the discovery (GSE149614; n = 10) cohort, elevated expression of intratumoral SPP1⁺BCL2A1⁺ TAMs was identified in HCC. In the ICB datasets (GSE151530; n = 4), patients with high SPP1⁺BCL2A1⁺ TAM expression further exhibited significantly poorer responses to ICB therapy. Further, the validation cohort (GSE206325; n = 18) confirmed these findings accordingly. Notably, these TAMs were expressed thoroughly within the immunosuppressive T-cell microenvironment in non-responders but were distinctly expressed among the cytotoxic T-cell responses in responders. Conclusions: Our findings identify SPP1⁺BCL2A1⁺ TAMs as a poor prognostic biomarker in HCC patients undergoing ICB therapy. By promoting an immunosuppressive microenvironment, SPP1⁺BCL2A1⁺ TAMs, which are survival-advantaged, may represent both a predictive marker and a potential therapeutic target to enhance the efficacy of immunotherapy. Full article

Triboelectric materials can convert irregular mechanical stimuli from human motion or environmental sources into high surface charge densities and instantaneous electrical outputs. Their intrinsic properties, such as flexibility, stretchability, chemical tunability, and compatibility with diverse substrates, play a critical role in determining the efficiency and reliability of triboelectric devices. In the context of active health, triboelectric materials not only serve as the core functional layers for self-powered sensing but also enable real-time physiological monitoring, motion tracking, and human–machine interaction by directly transducing biomechanical signals into electrical information. Soft triboelectric sensors exhibit high sensitivity, wide operational ranges, excellent biocompatibility, and wearability, making them highly promising for active health monitoring applications. Despite these advantages, challenges remain in enhancing surface charge density, achieving effective signal multiplexing, and ensuring long-term stability. This review provides a comprehensive overview of triboelectric mechanisms, working modes, influencing factors, performance enhancement strategies, and wearable health applications. Finally, it systematically summarizes the key improvement approaches and future development directions of triboelectric materials for active health, offering valuable guidance for advancing wearable self-powered biosensors. Full article

Marine sediments enriched with rare earth elements (REEs) serve as a significant reservoir, particularly for heavy REEs. Conventional lab-based REE exploration restricts rapid and large-scale assessment, whereas hyperspectral imaging provides a promising approach for quantitative evaluation. This study evaluates the capacity of hyperspectral data for the quantitative determination of REEs in marine sediments. A total of 53 samples from various locations were analyzed to determine their chemical composition and spectral characteristics within the 380–1000 nm range under natural light. The influence of surface conditions on spectral integrity was evaluated, and multiple preprocessing and spectral feature extraction methods were applied to enhance data reliability. This study proposes a novel approach, termed Feature Importance within Pearson Correlation Coefficient-Based High-Correlation Spectral Range (PCCR-FI), designed for the identification of characteristic spectral bands associated with REEs. Machine learning models were subsequently constructed to estimate REE concentrations, and the following key findings were observed: (a) technical adjustments effectively addressed variations in sediment surface conditions, ensuring data reliability. (b) The PCCR-FI technique identified characteristic REEs spectral bands, enhancing processing efficiency and prediction accuracy. (c) The integration of the reciprocal logarithmic first derivative (TLOG-FD) technique with a multilayer perceptron (MLP) model, termed TLOG-FD-MLP, efficiently captured critical spectral features, resulting in improved prediction accuracy. For light REEs, the model achieved coefficient of determination (R²) values exceeding 0.60 and relative performance deviation (RPD) values exceeding 1.60, with some elements demonstrating R² values as high as 0.81 with RPD values surpassing 2.00. Furthermore, several heavy REEs exhibited moderate prediction performance, with R² values consistently exceeding 0.60. When considering the total REE content, an R² of 0.73 and an RPD of 1.97 were achieved. These findings demonstrate the use of hyperspectral imaging as a viable tool for quantitative evaluation of REE concentrations in marine sediments, providing valuable guidance for resource mapping and the exploration of seafloor polymetallic deposits. Full article

Atmospheric motion vector (AMV) products from EUMETSAT’s MetOp satellite series, including MetOp-B, MetOp-C, and the MetOp-B/C tandem (MetOp-Dual), have been assimilated at many numerical weather prediction centers worldwide. However, they have not yet been applied in the China Meteorological Administration’s Global Forecast System (CMA-GFS). This study addresses this gap by developing assimilation techniques, including quality control and thinning methods for MetOp AMVs. Based on these techniques, one-month assimilation and forecasting experiments reveal that MetOp AMVs increased the AMV volume in CMA-GFS by 25%, filling certain gaps over polar and oceanic areas. Notable and steady improvements in the background of CMA-GFS have been found, particularly in polar and high-latitude regions. The usable forecast lead time for the global 500 hPa geopotential height is extended by 0.22 days, enhancing the reliability of medium-range forecasts. Furthermore, the more substantial improvements in short-range (0–3 days) forecasting, potentially benefit severe weather alerting. This study marks the first to successfully apply MetOp-B, MetOp-C and MetOp-Dual products in CMA-GFS, confirming their value for improving the performance of the system. Full article

The accurate monitoring of crop water status is critical for optimizing irrigation strategies in winter wheat. Compared with satellite remote sensing, unmanned aerial vehicle (UAV) technology offers superior spatial resolution, temporal flexibility, and controllable data acquisition, making it an ideal choice for the small-scale monitoring of crop water status. During 2023–2025, field experiments were conducted to predict crop water status using UAV images in the North China Plain (NCP). Thirteen vegetation indices were calculated and their correlations with observed crop water content (CWC) and equivalent water thickness (EWT) were analyzed. Four machine learning (ML) models, namely, random forest (RF), decision tree (DT), LightGBM, and CatBoost, were evaluated for their inversion accuracy with regard to CWC and EWT in the 2024–2025 growing season of winter wheat. The results show that the ratio vegetation index (RVI, NIR/R) exhibited the strongest correlation with CWC (R = 0.97) during critical growth stages. Among the ML models, CatBoost demonstrated superior performance, achieving R² values of 0.992 (CWC) and 0.962 (EWT) in training datasets, with corresponding RMSE values of 0.012% and 0.1907 g cm⁻², respectively. The model maintained robust performance in testing (R² = 0.893 for CWC, and R² = 0.961 for EWT), outperforming conventional approaches like RF and DT. High-resolution (5 cm) inversion maps successfully identified spatial variability in crop water status across experimental plots. The CatBoost-RVI framework proved particularly effective during the booting and flowering stages, providing reliable references for precision irrigation management in the NCP. Full article

Private fifth generation (5G) networks have emerged as a cornerstone for ultra-reliable, low-latency connectivity across mission-critical domains such as industrial automation, healthcare, and smart cities. Compared to conventional technologies like 4G or Wi-Fi, they provide clear advantages, including enhanced service continuity, higher reliability, and customizable security controls. However, these benefits come with new security challenges, particularly regarding the confidentiality, integrity, and availability of data and services. This article presents a review of security vulnerabilities in private 5G networks. The review pursues four objectives: (i) to identify and categorize key vulnerabilities, (ii) to analyze threats that undermine core security principles, (iii) to evaluate mitigation strategies proposed in the literature, and (iv) to outline gaps that demand further investigation. The findings offer a structured perspective on the evolving threat landscape of private 5G networks, highlighting both well-documented risks and emerging concerns. By mapping vulnerabilities to mitigation approaches and identifying areas where current solutions fall short, this study provides critical insights for researchers, practitioners, and policymakers. Ultimately, the review underscores the urgent need for robust and adaptive security frameworks to ensure the resilience of private 5G deployments in increasingly complex and high-stakes environments. Full article

The operational state of high-voltage disconnectors plays a critical role in ensuring the safety, stability, and power supply reliability of electrical systems. To enable accurate identification of the operational status of high-voltage disconnectors, this paper proposes a fault diagnosis method based on a Multi-Scale Residual Depthwise Separable Convolutional Neural Network (MSRDSN). First, wavelet transform is applied to vibration signals to perform multi-scale analysis and enhance detail resolution. Then, a novel network architecture, referred to as RDSN, is constructed to extract discriminative high-level features from vibration signals by integrating residual learning blocks and depthwise separable convolution blocks. Furthermore, a combined loss function is introduced to optimize the RDSN, which simultaneously maximizes inter-class distance, minimizes intra-class distance, and reduces feature redundancy. Experimental results show that the proposed method achieves a top accuracy of 99.44% on a balanced dataset, outperforming the sub-optimal approach by 1.11%. This study offers a novel and effective solution for fault diagnosis in high-voltage disconnectors. Full article

Depth completion aims to achieve high-quality dense depth prediction from a pair of synchronized sparse depth map and RGB image, and it plays an important role in many intelligent applications, including urban mapping, scene understanding, autonomous driving, and augmented reality. Although the existing convolutional neural network (CNN)-based deep learning architectures have obtained state-of-the-art depth completion results, depth ambiguities in large areas with extremely sparse depth measurements remain a challenge. To address this problem, an efficient hierarchical feature fusion network (HFF-Net) is proposed for producing complete and accurate depth completion results. The key components of HFF-Net are the hierarchical depth completion architecture for predicting a robust initial depth map, and the multi-level spatial propagation network (MLSPN) for progressively refining the predicted initial depth map in a coarse-to-fine manner to generate a high-quality depth completion result. Firstly, the hierarchical feature extraction subnetwork is adopted to extract multi-scale feature maps. Secondly, the hierarchical depth completion architecture that incorporates a hierarchical feature fusion module and a progressive depth rectification module is utilized to generate an accurate and reliable initial depth map. Finally, the MLSPN-based depth map refinement subnetwork is adopted, which progressively refines the initial depth map utilizing multi-level affinity weights to achieve a state-of-the-art depth completion result. Extensive experiments were undertaken on two widely used public datasets, i.e., the KITTI depth completion and NYUv2 datasets, to validate the performance of HFF-Net. The comprehensive experimental results indicate that HFF-Net produces robust depth completion results on both datasets. Full article

This study investigates fiber content and orientation in prefabricated slab elements made of ultra-high-performance fiber-reinforced concrete (UHPFRC), using novel non-destructive measurement using a coil’s quality factor, where the coil is put to one side of the specimen only. This allows the analysis of slab specimens of arbitrary size. That then allows an accurate quality control of elements made in the prefabrication industry. This study presents an experimental campaign designed to evaluate the non-destructive principle’s accuracy and practical feasibility. Twenty-five large slab specimens were made in an industrial prefabrication plant using various casting methods to introduce different flow-induced fiber parameters. The slabs were subjected to this non-destructive testing, then destructive bending tests and CT scanning to tie the results together and validate the non-destructive results. The results showed that the coil’s quality factor values correspond well to the distribution (concentration) and orientation of fibers, and the method reliably reveals potential defects of the material and can predict the element’s mechanical properties. Full article