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Background Diabetes nephropathy (DN) is a common complication of diabetes patients. The prediction and validation of its risk will help identify high-risk patients in advance and take intervention measures to avoid or delay the progress of nephropathy. Objective To analyze the risk factors affecting the complication of DN in patients with type 2 diabetes mellitus (T2DM) , construct a risk prediction model for the risk of DN in T2DM patients and validate it. Methods A total of 5 810 patients with T2DM admitted to the First Affiliated Hospital of Xinjiang Medical University from January 2016 to June 2021 were selected as the study subjects and divided into the DN group (n=481) and non-DN group (n=5 329) according to the complication of DN. A 1∶1 case-control matching was performed on 481 of these DN patients and non-DN patients by gender and age (±2 years) , and the matched 962 T2DM patients were randomly divided into the training group (n=641) and validation group (n=321) based on a 2∶1 ratio. Basic data of patients, such as clinical characteristics, laboratory test results and other related data, were collected. LASSO regression was applied to optimize the screening variables, and a nomogram prediction model was developed using multivariate Logistic regression analysis. The discriminability, calibration and clinical validity of the prediction model were evaluated by using the receiver operating characteristic (ROC) curve, Hosmer-Lemeshow calibration curve, and decision curve analysis (DCA) , respectively. Results There were significant differences in gender, age, BMI, course of diabetes, white blood cell count, total cholesterol, triacylglycerol, low-density lipoprotein cholesterol, serum creatinine, hypertension, systolic blood pressure, diastolic blood pressure, glycosylated hemoglobin, apolipoprotein B, 24-hour urinary micro total protein, qualitative urinary protein between the DN and non-DN group (P

This study aims to explore how the Metaverse platform could be applied to the field of online education, the benefits it brings, and the challenges posed by the application of Metaverse to online education. The study analyzes the benefits of immersive learning brought about by Metaverse and its potential to save education costs, enhance efficiency and students' interest in learning, and explores the application of Metaverse technology in education in relation to the characteristics of online education. The results show that Metaverse technology can support several aspects of online classrooms with realistic senses, personalized teaching models, realistic 3D identities, interactive communication, virtual reality (VR) technology, and gamified learning. This research also presents the problems faced by the Edu-Metaverse, including technology development difficulties, interaction issues, content production, game addiction, privacy, and ethics. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive switching anti-disturbance attitude control scheme based on improved snake optimizer (SO) is proposed for quadrotor attitude control when a quadrotor unmanned aerial vehicle is affected by measurement noise. The adaptive switching disturbance rejection controller (AWDRC) is composed of linear active disturbance rejection control and adaptive switching extended state observer which is used to achieve accurate signals reconstruction performance under measurement noise. Then, the improved SO (ISO) algorithm is developed with quadratic interpolation and comprehensive learning strategies to obtain the optimal parameters of the quadrotor attitude controller. The performance validity of ISO is demonstrated here by experiments on the CEC-2017 and the CEC-2020 benchmark functions with several state-of-the-art meta-heuristic algorithms. Secondly, the proposed ISO-based AWDRC algorithm is used in quadrotor attitude tracking control and compared with three other excellent active disturbance rejection controllers in a comparative experiment, and the experimental results show the effectiveness of the proposal. Finally, the robustness of the proposed method to parameters perturbation of the quadrotor attitude system is analyzed by Monte Carlo experiments. [ABSTRACT FROM AUTHOR]

This study explores how blockchain technology can be applied in the area of training in organizations, the benefits it brings, and the management challenges that blockchain technology raises. By exploring the decentralized, transparent, and intelligent contract nature of blockchain technology, we analyzed the application of blockchain technology to organizational training. The results show that organizations can achieve training resource sharing, supply and demand balancing, training certification, and intelligent contract enforcement based on blockchain technology. However, it also presents potential challenges, mainly regarding engagement across sectors, support from all members, global technology rollout, staff competency, data confidentiality, and return on investment. Case and industry analysis further validates the content of our research and demonstrates the potential and challenges of blockchain technology in the field of organizational training. [ABSTRACT FROM AUTHOR]

Distribution grid network has problems such as long mileage, large scale, complex surrounding environment, and aging of equipment. It is the development trend of power distribution network operation and maintenance to use unmanned aerial vehicles to patrol and combine with image processing technology for intelligent detection of equipment status. Image segmentation is well-known technique for extracting defect regions of equipment from distribution network inspection images. Therefore, this paper proposes an efficient a novel multilevel thresholding segmentation method to improve the fault diagnosis process with an improved weighted mean of vectors optimization (IINFO) algorithm. The IINFO algorithm adopts various measures to improve the optimization results, including Gaussian mutation to increase the local search ability and range of the optimal individual, Cauchy mutation to enhance the global search ability of its vector individual, reflective learning operators to strengthen self-learning and avoid local optimal solutions, and parallel operation to improve the utilization of computational resources. Moreover, two-dimensional Kapur cross-entropy is used as an objective function to solve the multilevel thresholding problem. The proposed method is evaluated using benchmark functions and distribution network inspection image datasets and is compared with 12 other metaheuristic algorithms. The results demonstrate that the proposed method has better performance and a higher ability to find optimal solutions compared to the other algorithms. These findings suggest that our method may be useful in improving the accuracy and efficiency of distribution network inspections and have significant potential for practical applications. [ABSTRACT FROM AUTHOR]

Animal cell culture technology in the production of slaughter-free meat offers ethical advantages with regards to animal welfare, rendering it a more socially acceptable approach for dog meat production. In this study, edible plant-derived scaffold was used as a platform for cell expansion to construct cell-cultured dog meat slices. Primary dog skeletal muscle satellite cells (MSCs) and adipose stem cells (ASCs) were isolated and cultured as seed cells, and 3D spheroid culture in vitro promoted MSCs and ASCs myogenic and adipogenic differentiation, respectively. Natural leaf veins (NLV) were produced as edible mesh scaffolds to create 3D engineered dog muscle and fat tissues. After MSCs and ASCs adhered, proliferated and differentiated on the NLV scaffolds, and muscle and fat slices were produced with cultured dog muscle fibers and adipocytes, respectively. These findings demonstrate the potential of plant-derived NLV scaffolds in the production of cultured dog meat., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)