A multi-fault diagnosis method for piston pump in construction machinery based on information fusion and PSO-SVM

Piston pumps are key components in construction machinery, the failure of which may cause long delay of the construction work and even lead to serious accident. Because construction machines are exposed to poor working conditions, multiple faults of piston pumps are most likely to occur simultaneously. When multiple faults occur together, it is difficult to detect. A multi-fault diagnosis method for piston pump based on information fusion and PSO-SVM is proposed in this thesis. Information fusion is used as fault feature extraction and PSO-SVM is applied as the fault mode classifier. According to the method, vibration signal and pressure signal of piston pump in normal state, single fault state and multi-fault state are collected at first. Then the empirical mode decomposition (EMD) is used to decompose vibration signals into different frequency band and energy features are extracted. These energy features extracted from vibration signals and time-domain features extracted from pressure signal are information fused at the feature layer and constitute the eigenvectors. Finally, these eigenvectors are put into support vector machine (SVM) and the working conditions of piston pump were classified. Particle swarm optimization (PSO) is applied to optimize two parameters of SVM. The experimental results show that the recognition accuracy of the normal state, three single failure modes and multi-fault modes are 98.3 %, 97.6 % and 94 % respectively. These recognition accuracies are higher than which using vibration signal or pressure signal alone. So, the proposed method can not only identify the single fault, but also effectively identify the multi-fault of piston pump

Appendix for Nonparametric Multivariate Probability Density Forecast in Smart Grids With Deep Learning

This paper proposes a nonparametric multivariate density forecast model based on deep learning. It not only offers the whole marginal distribution of each random variable in forecasting targets, but also reveals the future correlation between them. Differing from existing multivariate density forecast models, the proposed method requires no a priori hypotheses on the forecasted joint probability distribution of forecasting targets. In addition, based on the universal approximation capability of neural networks, the real joint cumulative distribution functions of forecasting targets are well-approximated by a special positive-weighted deep neural network in the proposed method. Numerical tests from different scenarios were implemented under a comprehensive verification framework for evaluation, including the very short-term forecast of the wind speed, wind power, and the day-ahead forecast of the aggregated electricity load. Testing results corroborate the superiority of the proposed method over current multivariate density forecast models considering the accordance with reality, prediction interval width, and correlations between different random variables

Smart Meters Integration in Distribution System State Estimation with Collaborative Filtering and Deep Gaussian Process

The problem of state estimations for electric distribution system is considered. A collaborative filtering approach is proposed in this paper to integrate the slow time-scale smart meter measurements in the distribution system state estimation, in which the deep Gaussian process is incorporated to infer the fast time-scale pseudo measurements and avoid anomalies. Numerical tests have demonstrated the higher estimation accuracy of the proposed method

Targeting oxidative stress in diabetic retinopathy: mechanisms, pathology, and novel treatment approaches

Diabetic retinopathy (DR) is a common and severe microvascular complication of diabetes, leading to vision impairment and blindness, particularly in working-age adults. Oxidative stress plays a central role in the pathogenesis of DR, with excessive reactive oxygen species (ROS) damaging retinal tissues, including blood vessels and neurons. This oxidative damage is exacerbated through various metabolic pathways, such as the polyol pathway, protein kinase C(PKC) activation, and advanced glycation end-product(AGE) formation. Additionally, mitochondrial dysfunction, retinal cell apoptosis, inflammation, and lipid peroxidation are key pathological processes associated with oxidative stress in DR. Epigenetic modifications, including DNA methylation and histone alterations, further contribute to gene expression changes induced by oxidative stress. To mitigate oxidative damage, therapeutic strategies targeting ROS production, neutralizing free radicals, and enhancing antioxidant defenses hold promise. Various natural antioxidant compounds, such as polyphenols (e.g., epigallocatechin-3-gallate, quercetin, resveratrol) and carotenoids (e.g., lutein, zeaxanthin), have demonstrated potential in reducing oxidative stress and improving retinal health in DR models. Moreover, activation of the Nrf2 and SIRT1 pathways has emerged as a promising approach to enhance the antioxidant response. Although preclinical studies show promising results, further clinical trials are necessary to validate the efficacy and safety of these therapeutic strategies. In conclusion, a better understanding of the molecular mechanisms underlying oxidative stress in DR and the development of multi-target therapies could provide more effective treatment options for DR patients

Control of sunroof buffeting noise by optimizing the flow field characteristics of a commercial vehicle

When a commercial vehicle is driving with the sunroof open, it is easy for the problem of sunroof buffeting noise to occur. This paper establishes the basis for the design of a commercial vehicle model that solves the problem of sunroof buffeting noise, which is based on computational fluid dynamics (CFD) numerical simulation technology. The large eddy simulation (LES) method was used to analyze the characteristics of the buffeting noise with different speed conditions while the sunroof was open. The simulation results showed that the small vortex generated in the cab forehead merges into a large vortex during the backward movement, and the turbulent vortex causes a resonance response in the cab cavity as the turbulent vortex moves above the sunroof and falls into the cab. Improving the flow field characteristics above the cab can reduce the sunroof buffeting noise. Focusing on the buffeting noise of commercial vehicles, it is proposed that the existing accessories, including sun visors and roof domes, are optimized to deal with the problem of sunroof buffeting noise. The sound pressure level of the sunroof buffeting noise was reduced by 6.7 dB after optimization. At the same time, the local pressure drag of the commercial vehicle was reduced, and the wind resistance coefficient was reduced by 1.55% compared to the original commercial vehicle. These results can be considered as relevant, with high potential applicability, within this field of researchThis work was supported by the National Natural Science Foundation of China (No. 52065013), the Guangxi Youth Science Fund Project (2018GXNSFBA281012), the Innovation-Driven Development Special Fund Project of Guangxi (Guike AA19182004), and the Liuzhou Scientific Research and Planning Development Project (2018AA20301)S

DrugRepo: a novel approach to repurposing drugs based on chemical and genomic features

The drug development process consumes 9–12 years and approximately one billion US dollars in costs. Due to the high finances and time costs required by the traditional drug discovery paradigm, repurposing old drugs to treat cancer and rare diseases is becoming popular. Computational approaches are mainly data-driven and involve a systematic analysis of different data types leading to the formulation of repurposing hypotheses. This study presents a novel scoring algorithm based on chemical and genomic data to repurpose drugs for 669 diseases from 22 groups, including various cancers, musculoskeletal, infections, cardiovascular, and skin diseases. The data types used to design the scoring algorithm are chemical structures, drug-target interactions (DTI), pathways, and disease-gene associations. The repurposed scoring algorithm is strengthened by integrating the most comprehensive manually curated datasets for each data type. At DrugRepo score ≥ 0.4, we repurposed 516 approved drugs across 545 diseases. Moreover, hundreds of novel predicted compounds can be matched with ongoing studies at clinical trials. Our analysis is supported by a web tool available at: http://drugrepo.org/.Peer reviewe