The Wood Strengthening and Decorative Automated Production Line ZY-06L-Type Manipulator Motion Analysis and Simulation

ZY-06L-type manipulator is the important components of Wood Surface layer Strengthening and Decorative Automated Production Line, designed for a variety of sheet metal or semi-finished components handling, distribution and laying work in the workshop. Through the establishment of the manipulator kinematics equations, combined the position coordinates of the manipulator and object in the actual work environment, and solving this equation to get the relationship between the manipulator end-effectors position and posture with the joint variables. The use of engineering software UG NX8.0 for modeling and simulation of the manipulator, and analysis the reasonableness of structural and workflow design. Provide the basis and reference to the manipulator structure optimization and control systems developments

The “unseen” tourism: Travel experience of people with visual impairment

The World Health Organization has estimated that globally, over 2.2 billion people live with some form of visual impairment. However, research into the tourism experiences of this large group of people remains limited. This paper employs embodiment theory and sensory compensation theory to examine aspects of the tourism experience from the perspective of visually impaired tourists. The analysis was based on travel notes written by Chinese visually impaired tourists. Seven unique types of tourism experience were identified including “Sensory Compensation” and “Barrier-free experiences”. The findings highlight opportunities to build a more comprehensive understanding of the tourism experiences of visually impaired tourists

Global Trends of Lipid Metabolism Research in Epigenetics Field: A Bibliometric Analysis from 2012–2021

Most common diseases are characterized by metabolic changes, among which lipid metabolism is a hotspot. Numerous studies have demonstrated a strong correlation between epigenetics and lipid metabolism. This study of publications on the epigenetics of lipid metabolism searched in the Web of Science Core Collection from 2012 to 2022, and a total of 3685 publications were retrieved. Much of our work focused on collecting the data of annual outputs, high-yielding countries and authors, vital journals, keywords and citations for qualitative and quantitative analysis. In the past decade, the overall number of publications has shown an upward trend. China (1382, 26.69%), the United States (1049, 20.26%) and Italy (206, 3.98%) were the main contributors of outputs. The Chinese Academy of Sciences and Yale University were significant potential cooperation institutions. Articles were mainly published in the “International Journal of Molecular Sciences”. In addition to typical liver-related diseases, “ferroptosis”, “diabetes” and “atherosclerosis” were identified as potential research topics. “NF-κB” and “oxidative stress” were referred to frequently in publications. METTL3 and ALKBH5 were the most discussed m6A-related enzymes in 2022. Our study revealed research hotspots and new trends in the epigenetics of lipid metabolism, hoping to provide significant information and inspiration for researchers to further explore new directions

The neuroanatomical basis of the Gambler's fallacy: A univariate and multivariate morphometric study

Human decision-making can be irrational, as in the case of the gambler's fallacy (GF). Converging evidence from behavioral and functional neuroimaging studies has suggested that a hyperactive cognitive system and a hypo-active affective system contribute to the false world model that generates the GF. However, the neuroanatomical basis underlying the GF remains unclear. The current study addressed this issue by collecting high-resolution magnetic resonance anatomical images from a large sample of 350 healthy Chinese adults. Univariate voxel-based morphometry (VBM) analysis suggested that the gray matter volume (GMV) in the anterior cingulate cortex (ACC) and medial temporal lobe (MTL) (two regions of the cognitive system) showed negative correlations with the degree of GF, while the GMV in the striatum and orbitofrontal cortex (OFC; two regions of the affective system) showed positive correlations. Further multivariate VBM analysis showed that the GMV in these regions could potentially predict the degree of GF. Moreover, a mediation analysis suggested that the GMV in MTL, ACC, and OFC mediated the relationships between the cognitive abilities or affective decision-making performance and the GF. Results of our study help us to understand the potential neural bases of the cognitive system's constructive role and the affective system's destructive role in decision making

The Small-Signal Stability of Offshore Wind Power Transmission Inspired by Particle Swarm Optimization

Voltage source converter-high-voltage direct current (VSC-HVDC) is the mainstream technology of the offshore wind power transmission, which has been rapidly developed in recent years. The small-signal stability problem is closely related to offshore wind power grid-connected safety, but the present study is relatively small. This paper established a mathematical model of the doubly fed induction generator (DFIG) integrated into the IEEE9 system via VSC-HVDC in detail, and small-signal stability analysis of offshore wind farm (OWF) grid connection is specially studied under different positions and capacities. By selecting two load nodes and two generator nodes in the system for experiments, the optimal location and capacity of offshore wind power connection are obtained by comparing the four schemes. In order to improve the weak damping of the power system, this paper presents a method to determine the parameters of the power system stabilizer (PSS) based on the particle swarm optimization (PSO) algorithm combined with different inertia weight functions. The optimal position of the controller connected to the grid is obtained from the analysis of modal control theory. The results show that, after joining the PSS control, the system damping ratio significantly increases. Finally, the proposed measures are verified by MATLAB/Simulink simulation. The results show that the system oscillation can be significantly reduced by adding PSS, and the small-signal stability of offshore wind power grid connection can be improved

A Machine Learning Approach for the Classification of Kidney Cancer Subtypes Using miRNA Genome Data

Kidney cancer is one of the deadliest diseases and its diagnosis and subtype classification are crucial for patients’ survival. Thus, developing automated tools that can accurately determine kidney cancer subtypes is an urgent challenge. It has been confirmed by researchers in the biomedical field that miRNA dysregulation can cause cancer. In this paper, we propose a machine learning approach for the classification of kidney cancer subtypes using miRNA genome data. Through empirical studies we found 35 miRNAs that possess distinct key features that aid in kidney cancer subtype diagnosis. In the proposed method, Neighbourhood Component Analysis (NCA) is employed to extract discriminative features from miRNAs and Long Short Term Memory (LSTM), a type of Recurrent Neural Network, is adopted to classify a given miRNA sample into kidney cancer subtypes. In the literature, only a couple of kidney subtypes have been considered for classification. In the experimental study, we used the miRNA quantitative read counts data, which was provided by The Cancer Genome Atlas data repository (TCGA). The NCA procedure selected 35 of the most discriminative miRNAs. With this subset of miRNAs, the LSTM algorithm was able to group kidney cancer miRNAs into five subtypes with average accuracy around 95% and Matthews Correlation Coefficient value around 0.92 under 10 runs of randomly grouped 5-fold cross-validation, which were very close to the average performance of using all miRNAs for classification