154 research outputs found
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
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