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Osteoporosis becomes a global public health concern due to its rising prevalence and substantial impact on life quality. Salvia miltiorrhiza Bunge (Salviae Miltiorrhizae Radix et Rhizoma, SM) has been firstly recorded in Shen Nong's Herbal Classic, and is frequently prescribed in conjunction with other herbs for the management of osteoporosis. This systematic review aims to comprehensively analyze the recent advances of SM on osteoporosis in traditional Chinese clinical uses and preclinical investigations. Literature encompassing pertinent studies were systematically retrieved across multiple databases, including the PubMed, Web of Science, Chinese National Knowledge Infrastructure, Chinese VIP Database, and Chinese Biomedical Literature Database. Original investigations spanning from February 2014 to March 2024, including traditional Chinese medicine (TCM) clinical trials and preclinical studies, were employed to analyze the effects and actions of SM on osteoporosis. Thirty-eight TCM clinical trials were identified to employ SM in combination with other herbs for the management of primary and secondary osteoporosis. The overall efficacy was between 77% and 96.67%. Forty preclinical studies were identified to investigate the effects and actions of SM and/or its ingredients on osteoporosis. The anti-osteoporosis actions of this herb may be attributed to inhibit osteoclastogenesis/bone resorption and promote osteoblastogenesis/osteogenesis. The ethanol extracts and its ingredients (tanshinones) inhibit osteoclastogenesis/bone resorption by inhibiting the MAPK/NF-κB/NFATc1 signaling pathway and cathepsin K-induced collagen degradation. Both ethanol extracts (tanshinones) and water extracts (Sal B and tanshinol) contribute to osteoblastogenesis by promoting osteogenesis and angiogenesis via activation of the Wnt/β-catenin/VEGF and ERK/TAZ pathways, and eliminating ROS production targeting Nrf2/ARE/HO-1 pathway. In conclusions, SM may offer a novel strategy for osteoporosis management. Well-designed clinical trials are still needed to evaluate the actions of this herb and its ingredients on bone remodeling. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to revise the Chen Internet Addiction Scale (CIAS-R) among older adults in China. Method: 1,107 participants were recruited through an online survey. The factorial structure, reliability, and validity of the CIAS-R were tested. Latent profile analysis (LPA) was employed to distinguish individuals' Internet addiction membership. Results: The first-order five-factor model was demonstrated to best explain the underlying structure of the CIAS-R among the elderly in China. Time spent online, sense of loneliness, and depression provided evidence for the criterion validity of the CIAS-R. The magnitude of the correlation coefficients indicated Internet addiction may lead to psychological problems. LPA analysis showed the elderly could be clustered into four types based on their Internet addiction severity. Conclusion: The CIAS-R is a reliable and valid measure of Internet addiction among the elderly. This study offers a feasible way to intervene in the addicted aged for social workers and clinicians. [ABSTRACT FROM AUTHOR]

Birth order differences in personality have been studied for almost a hundred years. However, most studies have been conducted in Western cultural contexts, while few studies have addressed the associations between birth order and personality in East Asian cultural contexts. In this study, we examined how birth order influences the Big Five personality traits using a representative Chinese sample (N = 14,148). Both between- and within-family designs were used to examine the associations between birth order and the Big Five personality traits. The results indicated that the impacts of birth order on the Big Five personality traits were not statistically significant and were negligible. We conclude that birth order has no lasting impact on the Big Five personality traits in the Chinese cultural context. Plain language summary: What factors cause children in a family to develop different personality traits? One popular belief in society is that birth order plays a significant role in shaping siblings' personality traits. Extensive research conducted by academics over the past century has explored the relationship between birth order and personality traits, but the findings remain inconclusive. It is important to note that most of the existing research has focused on Western cultural contexts. In an effort to shed light on this topic from a different cultural perspective, this study examined the impact of birth order on personality traits within the Chinese cultural context. The findings of this study indicated that birth order did not have any significant effect on Chinese personality traits. Therefore, the effect of birth order on personality traits may just be a social prejudice. [ABSTRACT FROM AUTHOR]

In this paper, we propose a low‐complexity expectation propagation (EP) detector for orthogonal time frequency space (OTFS) system with practical rectangular waveforms. In the high‐mobility scenario, OTFS is becoming a potential scheme for the sixth‐generation (6G) wireless communication system. However, the large size of the effective delay‐Doppler (DD) domain channel matrix brings unbearable computational complexity to the signal detection algorithm based on the matrix inversion. We propose a low‐complexity EP detector based on the sparsity and the block circulant structure of the effective channel covariance matrix in the DD domain. The proposed algorithm only requires log‐linear complexity. In addition, simulation results show that the proposed algorithm not only has the advantage of low complexity but also has good performance, which achieves a tradeoff between performance and complexity. [ABSTRACT FROM AUTHOR]

The application and promotion of biodegradable plastics are hindered by the imbalance between their mechanical and degradation performance imbalance, as well as high costs. Herein, a series of poly(ethylene adipate‐co‐terephthalate) (PEAT) copolyesters with excellent mechanical and degradation properties are synthesized by one‐step melt polycondensation. The influence of the proportion of aliphatic groups in the molecular chain on the structure and properties, especially the degradation performance, of the copolyesters was studied. The molecular chain flexibility and degradability of PEAT increases with increasing of adipic acid content and decreasing of ET fragment length (LET). The comprehensive mechanical properties of PEAT60 can be comparable to traditional packaging material poly(butylene adipateco‐terephthalate) (PBAT). The degradation performance of PEAT can be adjusted the molecular chain structure. PEAT with LET < 3 exhibit degradation performance, and PEAT with LET < 2.2 exhibit significant degradation acceleration. The excellent mechanical and degradable properties of PEAT copolyester make it expected to be used in degradable packaging and other fields to help solve plastic pollution, while its lower cost will aid in the promotion of degradable materials. [ABSTRACT FROM AUTHOR]

The time series data in the manufacturing process reflects the sequential state of the manufacturing system, and the fusion of temporal features into the industrial knowledge graph will undoubtedly significantly improve the knowledge process efficiency of the manufacturing system. This paper proposes a semantic-aware event link reasoning over an industrial knowledge graph embedding time series data. Its knowledge graph skeleton is constructed through a specific manufacturing process. NLTK is used to transform technical documents into a structured industrial knowledge graph. We employ deep learning (DL)-based models to obtain semantic information related to product quality prediction using time series data collected from IoT devices. Then the prediction information is attached to the specified node in the knowledge graph. Thus, the knowledge graph will describe the dynamic semantic information of manufacturing contexts. Meanwhile, a dynamic event link reasoning model that uses graph embedding to aggregate manufacturing processes information is proposed. The implicit information with industrial temporal knowledge can be further mined and inferred. The case study has shown that the proposed knowledge graph link reasoning reflects dynamic temporal characteristics. Compared to the classical knowledge graph prediction models, our model is superior to the baseline methods. [ABSTRACT FROM AUTHOR]

In order to effectively predict the changing trend of operating parameters in the pump unit and carry out fault diagnosis and alarm processes, a trend prediction model is proposed in this paper based on PCA-based multi-task learning (MTL) and an attention mechanism (AM). The multi-task learning method based on PCA was used to process the operating data of the pump unit to make full use of the historical data to extract the key common features reflecting the operating state of the pump unit. The attention mechanism (AM) is introduced to dynamically allocate the weight coefficient of common feature mapping for highlighting the key common features and improving the prediction accuracy of the model when predicting the trend of data change for new working conditions. The model is tested with the actual operating data of a pumping station unit, and the calculation results of different models are compared and analyzed. The results show that the introduction of multi-task learning and attention mechanisms can improve the stability and accuracy of the trend prediction model compared with traditional single-task learning and static common feature mapping weights. According to the threshold analysis of the monitoring statistical parameters of the model, a multi-stage alarm model of pump unit operation condition monitoring can be established, which provides a theoretical basis for optimizing operation and maintenance management strategy in the process of pump station management. [ABSTRACT FROM AUTHOR]

Sample clustering techniques play a crucial role in the data‐driven state evaluation of electromechanical equipment, and selecting an appropriate similarity measurement method for sample sets helps improve the clustering performance. The Jaccard coefficient is a commonly employed indicator of similarity for scalar set‐type samples. In this paper, we propose an incremental clustering algorithm for matrix‐type samples by defining an improved Jaccard coefficient. First, a new binary relation is formulated to derive a relationship matrix between samples. Second, an undirected graph is given by using the relationship matrix, and an improved pruning operation is provided to simplify the graph by eliminating redundant edges. Then, a new relationship matrix is generated according to the modified graph, which enables the calculation of the improved Jaccard coefficient. By using the improved Jaccard coefficient, the improved incremental clustering algorithm updates cluster centers by selecting a particular sample to maximize the sum of similarities between the selected sample and other samples within the same cluster. Finally, the effectiveness of the proposed incremental clustering algorithm is demonstrated in fault diagnosis and remaining useful life estimation application scenarios, respectively. The experimental results indicate that the improved algorithm outperforms traditional clustering methods. [ABSTRACT FROM AUTHOR]

Pumping stations have undergone significant modernization and digitalization in recent decades. However, traditional virtual inspections often prioritize the visual experience and fail to effectively represent the haptic physical properties of devices during inspections, resulting in poor immersion and interactivity. This paper presents a novel virtual inspection system for pumping stations, incorporating virtual reality interaction and haptic force feedback technology to enhance immersion and realism. The system leverages a 3D model, crafted in 3Ds Max, to provide immersive visualizations. Multimodal feedback is achieved through a combination of haptic force feedback provided by a haptic device and visual information delivered by a VR headset. The system's data platform integrates with external databases using Unity3D to display relevant information. The system provides immersive 3D visualizations and realistic force feedback during simulated inspections. We compared this system to a traditional virtual inspection method that demonstrated statistically significant improvements in task completion rates and a reduction in failure rates when using the multimodal feedback approach. This innovative approach holds the potential to enhance inspection safety, efficiency, and effectiveness in the pumping station industry. [ABSTRACT FROM AUTHOR]