Search

Considering that vascular endothelial cell dysfunction is the pathological basis of erectile dysfunction (ED), and recognizing the beneficial effects of 25-hydroxyvitamin D (25(OH)D) on vascular endothelial cell protection, the researchers diligently investigated the causal relationship between serum 25(OH)D levels and ED. However, inconsistent clinical evidence has left the association between serum 25(OH)D levels and ED unclear. The objective of this work was to employ Mendelian randomization (MR) analysis to ascertain the potential causal relationship between serum 25(OH)D levels and ED. We conducted a two-sample MR analysis utilizing data from publicly available genome-wide association studies (GWASs). The primary analysis method for the MR analysis was the inverse-variance weighted (IVW) method, supplemented by the MR-Egger and weighted median methods. In addition, we evaluated heterogeneity with Cochran’s Q test, assessed pleiotropy using the MR-Egger intercept test, and performed a leave-one-out analysis to identify single-nucleotide polymorphisms (SNPs) with potential effects. Outliers were detected using MR-pleiotropy residual sum and outlier (MR-PRESSO). Genetically predicted serum 25(OH)D levels were not found to be causally associated with ED in IVW method (OR = 1.028, 95% CI = 0.845–1.250, P= 0.785), MR-Egger method (OR = 1.057, 95% CI = 0.782–1.430, P= 0.720), and weighted median method (OR = 1.225, 95% CI = 0.920–1.633, P= 0.165). The results of sensitivity analyses reinforced our conclusion, indicating no evidence of heterogeneity or directional pleiotropy. In summary, our findings do not substantiate a genetic-level causal link between serum 25(OH)D levels and the prevalence of ED. Nonetheless, future research, including larger MR studies, clinical trials, and additional observational studies, is essential to validate and reinforce the outcomes of our present study.

With the development of China's economy, the urban floating population is also increasing, resulting in a sharp increase in the amount of urban waste. How to recycle and dispose of municipal waste more efficiently has become the top concern of municipalities and other relevant departments. In this article, the above problem is transformed into the municipal waste collection vehicle routing problem (MWCVRP) to solve the problem with the minimum total waste transportation cost. Because the carrying capacity of different models is different, this article introduces a cost calculation criterion that combines the total mileage of different models of transport vehicles and the number of station services. A multi-model garbage truck path optimization model is established, and then a heuristic-based task dynamic assignment algorithm is designed to solve the problem. The Solomon dataset is used to verify the feasibility and effectiveness of the model and algorithm through experiments.

Many real-world applications require prediction that takes the most advantage of data. Classic data mining mechanisms tend to feed a prediction model pivotal data to achieve a promising result, which needs to be adjusted in different application scenarios. Recent studies have shown the potential of I Ching mechanism to improve prediction capacity. However, the I Ching prediction mechanism has several issues, including underutilized I Ching knowledge and incomplete data conversion. To address these issues, the authors designed a model to leverage I Ching knowledge and transform traditional I Ching prediction processing into data mining. The authors' investigation revealed promising results in the stock market compared to popular machine learning and deep learning algorithms such as support vector machine (SVM), extreme gradient boosting (XGBoost), and long short-term memory (LSTM).

In this article, a practical electrothermal SPICE model is proposed based on finite differential method. Other than the conventional Fourier model and the Hefner model, the distribution of excess carriers can be accurately solved by a finite differential method in the SPICE simulation tool. In this method, the electrothermal behavior of the device is modeled by using a semi-mathematic model. In order to verify the modeling results, a self-packaging insulated-gate bipolar transistor (IGBT) module is tested in both static characteristics and dynamic characteristics, and the simulation results of the presented model fit well with the experimental results under different junction temperatures. More importantly, the finite differential method provided in this article is an approach for modeling bipolar devices, and it can also be applied to model widebandgap devices such as a SiC IGBT and a SiC BJT.

The China Internet Network Information Centre (CNNIC) published that internet users around the world mostly spent 10-16 hours per week online. For effective advertising and social information publishing on the internet, how to dig out the commercial value from users' online behaviour becomes a new challenge compared with the traditional recommendation system. In this paper, we propose a novel system named 'online commercial intention (OCI) detection system' using users' global web browsing history to predict potential purchasing products on an online shopping platform. A 'commercial keyword dictionary (KD)' that reveals the relationship between user queries and product categories is firstly set up by analysing the click distribution of billion queries on the shopping platform. Footprints of millions of internet users are gathered and the raw page contents are crawled. Keywords in these pages are extracted using N-gram algorithm and commercial probabilities are estimated with query frequency (QF), inverse category frequency (ICF), etc. The page OCI is estimated by merging the KD matrices of its commercial keywords. In order to increase categories' coherence and accuracy, we provide a category similarity model to observe the distance between top N categories. The experiment results show that category prediction accuracy reaches 86% with manual evaluation.

The acoustic data remotely measured by microphones are widely used to investigate monitoring and diagnose integrity of ball bearing in rotational machines. Early fault diagnosis is very difficult for acoustic emission. We propose a new method using a cross-correlation of frequency spectrum to classify various faults with fine grit. Principal component analysis (PCA) is used to separate the primary frequency spectrum into main frequency and residual frequency. Different with conventional classification using the PCA eigenvalue, we introduce the general cross-correlation (GCC) of main frequency and residual frequency spectrums between a basic signal vector and monitoring signal. Multi-classification strategy based on binary-tree support vector machine (SVM) is applied to perform faults diagnosis. In order to remove noise interference and increase robustness, a normalization method is proposed during time generation. Experiment results show that PCA–GCC–SVM method is able to diagnose various faults with high sensitivity.

A method based on the deep learning algorithm is proposed to accurately capture the posture of the human body. It is one of the important means to improve athletes’ competitive level in modern sports to accurately analyze the posture of sports training by technical means. Aiming at the application demand of using artificial intelligence technology to accurately analyze and predict the motion training posture, a motion posture analysis and prediction system based on deep learning is designed in this paper. Based on the Arduino embedded development board and equipped with multiple IMU sensors, the scheme established a system to collect accurate human movement data such as speed and acceleration by using stepper motors and obtained accurate human movement data. The experimental results show that these models have been trained with H3.6 m data sets. The sampling frequency was reduced to 25 Hz, and the joint angles were converted into exponential graphs. When the time window covers approximately 1 660 ms, the loop network will be initialized to 40 frames, equivalent to 1 600 ms. For each action, a separate pretrained recursive model is used. It is proved that the method based on deep learning can reduce the prediction error of fine-tuning specific movements and effectively classify and predict the movements not included in the original training data.