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In the process of rotating machinery fault diagnosis (RMFD), the lack of feature conditions leads to the problem of low accuracy of traditional rule-based reasoning methods FD. This paper proposed a knowledge graph (KG)-driven device FD method and applied it to RMFD. First, we proposed a multi-level KG construction method to get multi-source data based on each level and analyzed the levels that affected the system state. A single-level KG was constructed through data features, and a multilevel KG with a stereostructure was built using a multi-source data fusion model as data support for FD. Second, we proposed an approach based on multilevel KG and Bayes theory to detect the system state and located the source of faults by combining the KG reasoning based on relational paths, then used the relationships between the structures of rotating mechanical equipment for fault cause reasoning and used the KG as a knowledge base for a reason using machine learning. Finally, the proposed method was validated using a steelworks motor as an example and compared with other ways, such as rule-based FD. The results show that under the condition of missing input features, the accuracy of the proposed method reaches 91.1%, which is significantly higher than other methods and effectively solves the problem of low diagnostic accuracy.

Al–Si–Cu–Mg cast aluminum alloys have important applications in automobile lightweight due to their advantages such as high strength-to-weight ratio, good heat resistance and excellent casting performance. With the increasing demand for strength and toughness of automotive parts, the development of high strength and toughness Al–Si–Cu–Mg cast aluminum alloys is one of the effective measures to promote the application of cast aluminum alloys in the automotive industry. In this paper, the research progress of improving the strength and toughness of Al–Si–Cu–Mg cast aluminum alloys was described from the aspects of multi-component alloying and heat treatment based on the strengthening mechanism of Al–Si–Cu–Mg cast aluminum alloys. Finally, the development prospects of automotive lightweight Al–Si–Cu–Mg cast aluminum alloys is presented.

Purpose This study explores the relationship among lower limb dynamic balance, lower limb strength, explosive power, agility, and sports injuries in male volleyball players. Method The study involved thirty-one male volleyball athletes assessed for lower limb dynamic balance using the Y Balance Test Kit™. Muscle strength in the hip, knee, and ankle was measured using the Isomed 2000 isokinetic dynamometer. Power performance was evaluated through squat jump, countermovement (CMJ) jump, and drop jump tests using the Kistler force platform. Agility measurements were conducted using timing gates and a stopwatch. Results Our findings revealed a significant correlation between interlimb asymmetry in the anterior reach of the Y balance test and non-contact injuries ( r  = 0.597, P  < 0.01). Additionally, there were significant correlations between the Y balance test and lower limb strength ( r  = 0.356 to 0.715, P  < 0.05), vertical jumping performance ( r  = 0.357 to 0.672, P  < 0.05), and agility ( r = -0.379 to -0.702, P  < 0.05). Conclusion Based on these findings, It is recommended that interlimb asymmetry in the anterior reach direction of the Y Balance Test be considered as one of the indicators for potential non-contact lower limb injuries among elite male volleyball players. The lower limb muscle strength of the hip, knee, and ankle joints and power and agility are associated with lower limb dynamic balance capabilities. Additionally, dynamic balance may contribute to overall physical performance. Targeted strength training for unilateral muscles and incorporating various explosive exercise modes may support athletic performance and reduce the risk of sports-related injuries.

In remote-sensing images, a detected oil-spill area is usually affected by spot noise and uneven intensity, which leads to poor segmentation of the oil-spill area. This paper introduced a deep semantic segmentation method that combined a deep-convolution neural network with the fully connected conditional random field to form an end-to-end connection. On the basis of Resnet, it first roughly segmented a multisource remote-sensing image as input by the deep convolutional neural network. Then, we used the Gaussian pairwise method and mean-field approximation. The conditional random field was established as the output of the recurrent neural network. The oil-spill area on the sea surface was monitored by the multisource remote-sensing image and was estimated by optical image. We experimentally compared the proposed method with other models on the dataset established by the multisensory satellite image. Results showed that the method improved classification accuracy and captured fine details of the oil-spill area. The mean intersection over the union was 82.1%, and the monitoring effect was obviously improved.

Accurate forecasting is critical for effective warehouse network planning and inventory management in e-commerce. This study tackles these challenges by applying a differentiated forecasting strategy over a three-month period. The Autoregressive Integrated Moving Average (ARIMA) model is used for monthly inventory predictions, while the Long Short-Term Memory (LSTM) neural network is employed for daily sales forecasts. Experimental validation across 350 product categories demonstrates the efficacy of this approach. ARIMA effectively captured dynamic inventory trends (e.g., Category 1 showing gradual increases; Category 91 depleting from 3824 to 0). Concurrently, LSTM successfully modeled complex daily sales fluctuations (e.g., Category 61 peaking at 3693 units on 21 July; Category 31 consistently recording zero sales). This dual-model strategy, leveraging the complementary strengths of ARIMA for relatively stable monthly inventory series and LSTM for volatile daily sales patterns, provides a robust, data-driven basis for optimizing warehouse resource planning and product category allocation. Furthermore, visualization of categorized forecast results reveals distinct sales distribution patterns, thereby enabling enterprises to refine inventory and sales strategies with greater precision, leading to reduced redundant space investment and improved resource allocation efficiency. Future research will focus on incorporating multivariate interactions to further enhance model practicality and predictive power.

Background Peritoneal dialysis (PD) is a form of dialysis to replace the function of kidney, that uses the peritoneum as a dialysis membrane to remove metabolites and water retained in the body. Vitamin D deficiency is prevalent in patients treated with PD. This research investigated the correlation between serum 25-hydroxyvitamin D [25(OH)D] concentration and anemia, microinflammation, and oxidative stress in PD patients. Methods 62 PD patients and 56 healthy volunteers were recruited in this research. Serum concentrations of 25(OH)D and basic parameters of anemia were detected. The correlation between serum 25(OH)D concentration with anemia, oxidative stress, and microinflammatory state were analyzed. Results In the PD group, the concentration of 25(OH)D was lower than the healthy control (HC) group (p < 0.001). Hemoglobin, red blood cell count (RBC), and total iron binding capacity (TIBC) in the PD group was significantly lower (all p < 0.001), while high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), and tumor necrosis factor α (TNF-α) concentrations were significantly higher, than the HC group (all p < 0.001). In the PD group, malondialdehyde (MDA) concentration was higher than in the HC group (p < 0.001), while superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) were lower (both p < 0.001). Serum 25(OH)D exhibited positive correlation with hemoglobin (r = 0.4509, p = 0.0002), RBC (r = 0.3712, p = 0.0030), TIBC (r = 0.4700, p = 0.0001), SOD (r = 0.4992, p < 0.0001) and GSH-Px (r = 0.4312, p = 0.0005), and negative correlation with hs-CRP (r = − 0.4040, p = 0.0011), TNF-α (r = − 0.4721, p = 0.0001), IL-6 (r = − 0.5378, p < 0.0001) and MDA (r = − 0.3056, p = 0.0157). Conclusion In conclusion, reduced serum 25(OH)D concentrations in PD patients contribute to anemia, oxidative stress and microinflammatory state.

In order to improve the response capability of cross regional emergency material scheduling (CREMS), a CREMS algorithm based on seed optimization algorithm is proposed. Construct a segmented regional grid distribution model structure for CREMS, use a grid matching algorithm based on block link distribution to construct the optimization objective function during the emergency material scheduling process, use variable neighborhood search technology to solve the diversity problem of cluster optimization in CREMS, and combine seed optimization algorithms for combination control and recursive analysis in the emergency material scheduling process. Based on the combination of deep learning and reinforcement learning, the optimal route and configuration scheme design for CREMS process is achieved. The simulation results show that this method has better active configuration capability, better path optimization capability and stronger spatial regional planning capability for CREMS.

In response to the demand for hydrogen safety monitoring, this study proposes an optical hydrogen sensor based on surface plasmon resonance enhancement. Traditional palladium film optical sensors have problems such as weak response and susceptibility to interference. This design innovatively uses low refractive index magnesium fluoride as the substrate and combines it with a periodic palladium nanopore array structure. Finite-difference time-domain simulation shows that the structure exhibits a significant redshift in the reflection spectrum after hydrogen adsorption and an increase in reflectivity, with a sensitivity superior to that of traditional planar palladium films. The nanopore array enhances the optical response through local field enhancement and surface lattice resonance coupling. The magnesium fluoride substrate, with low optical loss and excellent light transmittance, further optimizes the plasmon resonance performance. This study provides a new approach for the development of highly sensitive, fast-response, and intrinsically safe hydrogen sensors.

A series of titanium-vanadium-tantalum (Ti-V-Ta) medium entropy alloys have been fabricated with all of them exhibiting a single-phase body-centered cubic structure in the homogenized states, and their thermal stability as well as their mechanical properties have been investigated. Among the four studied alloys, Ti 27 V 33 Ta 40 , Ti 33 V 33 Ta 34 , Ti 40 V 33 Ta 27 , and Ti 45 V 20 Ta 35 , the equi-atomic Ti 33 V 33 Ta 34 has shown the highest thermal stability, without secondary phases formed after annealed at 400–700°C for 48 h, while decompositions have been observed in the other three alloys. All the four alloys have exhibited a good compression strength of 394–527 MPa at 800°C, while, except Ti 27 V 33 Ta 40 , maintaining an acceptable tensile ductility with fracture strains of 7.6–13% at room temperature. Furthermore, the low neutron activity of the Ti-V-Ta alloys grant them application potentials in nuclear engineering.

Next-generation smart textiles are regarded as the most straightforward and effective solution to address the growing threats from environment including excessive electromagnetic radiation and global warming. Incorporation of novel materials using advanced fabrication technology has been the reliable technology in developing these smart textiles. Herein, a novel fabrication strategy integrating multi-layer spraying and mechanical compression is proposed to fabricate a liquid metal (LM) and carbon nanotubes (CNT)-decorated multifunctional cotton fabric (CF/LM/CNT). The results demonstrate that the double face-sprayed CF/LM/CNT possess electrical resistance of 0.07 Ω, which is primarily responsible for its unprecedented electromagnetic shielding effectiveness of about 85 dB over the X-band. Moreover, the CF/LM/CNT exhibits excellent heat dissipation and thermal insulation behavior, which is an essential prerequisite for effective thermal management. More importantly, the as-prepared CF/LM/CNT is recycled and reconfigured. This fundamental research work provides a facile and scalable approach to fabricate multifunctional textile materials.