Research on dedicated rail power supply system for electric cars

in order to improve the endurance capacity and driving safety of electric vehicles, a special track power supply system for electric cars on expressways is studied. The working principle of the main components of the system, such as sliding contact charging track and mechanical charging arm, is simulated and analyzed by using SolidWorks software. The results show that the charging function of the contact track can provide unlimited endurance for electric vehicles, and the guidance function of the track can also ensure the safety of highspeed driving

The relationship between sleep quality and occupational well-being in employees: The mediating role of occupational self-efficacy

ObjectiveThis study aimed to examine the impact of sleep quality on occupational well-being in employees by primarily focusing on the mediating role of occupational self-efficacy.MethodsA total of 487 junior staff completed a set of questionnaires comprised Pittsburgh Sleep Quality Index scale, Occupational Self-efficacy Scale, and occupational well-being measurements.ResultsThe results revealed that both sleep quality and occupational self-efficacy were significantly correlated with occupational well-being. The structural equation modeling analysis and the bootstrap test indicated that occupational self-efficacy partially mediated the effect of poor sleep quality on occupational well-being.DiscussionThese findings expand upon existing research on the relationship between sleep quality and well-being among occupational workers, shed light on the correlation of poor sleep quality with occupational well-being, and are valuable in promoting the occupational well-being of employees

Correlation between diabetic retinopathy and diabetic nephropathy: a two-sample Mendelian randomization study

Rationale & objectiveA causal relationship concerning diabetic retinopathy (DR) and diabetic nephropathy (DN) has been studied in many epidemiological observational studies. We conducted a two-sample mendelian randomization study from the perspective of genetics to assess these associations.Methods20 independent single nucleotide polymorphisms (SNPs) associated with diabetic retinopathy were selected from the FinnGen consortium. Summary-level data for diabetic nephropathy were obtained from the publicly available genome-wide association studies (GWAS) database, FinnGen and CKDGen consortium. Inverse variance weighted (IVW) was selected as the primary analysis. MR-Egger, weighted median (WM), simple mode and weighted mode were used as complementary methods to examine causality. Additionally, sensitivity analyses including Cochran’s Q test, MR-Egger, MR-Pleiotropy Residual Sum and Outlier (MR-PRESSO), and leave-one-out analyses were conducted to guarantee the accuracy and robustness of our MR analysis.ResultsOur current study demonstrated positive associations of genetically predicted diabetic retinopathy with diabetic nephropathy (OR=1.32; P=3.72E-11), type 1 diabetes with renal complications (OR=1.96; P= 7.11E-11), and type 2 diabetes with renal complications (OR=1.26, P=3.58E-04). Further subtype analysis and multivariate mendelian randomization (MVMR) also reached the same conclusion. A significant casualty with DN was demonstrated both in non-proliferative DR (OR=1.07, P=0.000396) and proliferative DR (OR=1.67, P=3.699068E-14). All the findings were robust across several sensitivity analyses.ConclusionConsistent with previous clinical studies, our findings revealed a positive correlation between DR and DN, providing genetic evidence for the non-invasive nature of DR in predicting DN

Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning

Advances in deep learning have greatly improved structure prediction of molecules. However, many macroscopic observations that are important for real-world applications are not functions of a single molecular structure, but rather determined from the equilibrium distribution of structures. Traditional methods for obtaining these distributions, such as molecular dynamics simulation, are computationally expensive and often intractable. In this paper, we introduce a novel deep learning framework, called Distributional Graphormer (DiG), in an attempt to predict the equilibrium distribution of molecular systems. Inspired by the annealing process in thermodynamics, DiG employs deep neural networks to transform a simple distribution towards the equilibrium distribution, conditioned on a descriptor of a molecular system, such as a chemical graph or a protein sequence. This framework enables efficient generation of diverse conformations and provides estimations of state densities. We demonstrate the performance of DiG on several molecular tasks, including protein conformation sampling, ligand structure sampling, catalyst-adsorbate sampling, and property-guided structure generation. DiG presents a significant advancement in methodology for statistically understanding molecular systems, opening up new research opportunities in molecular science.Comment: 80 pages, 11 figure

First attempt of directionality reconstruction for atmospheric neutrinos in a large homogeneous liquid scintillator detector

The directionality information of incoming neutrinos is essential to atmospheric neutrino oscillation analysis since it is directly related to the oscillation baseline length. Large homogeneous liquid scintillator detectors, while offering excellent energy resolution, are traditionally very limited in their capabilities of measuring event directionality. In this paper, we present a novel directionality reconstruction method for atmospheric neutrino events in large homogeneous liquid scintillator detectors based on waveform analysis and machine learning techniques. We demonstrate for the first time that such detectors can achieve good direction resolution and potentially play an important role in future atmospheric neutrino oscillation measurements.Comment: Prepared for submission to PR