Modeling Analysis of Factors Influencing Wind-Borne Seed Dispersal: A Case Study on Dandelion

A weed is a plant that thrives in areas of human disturbance, such as gardens, fields, pastures, waysides, and waste places where it is not intentionally cultivated. Dispersal affects community dynamics and vegetation response to global change. The process of seed disposal is influenced by wind, which plays a crucial role in determining the distance and probability of seed dispersal. Existing models of seed dispersal consider wind direction but fail to incorporate wind intensity. In this paper, a novel seed disposal model was proposed in this paper, incorporating wind intensity based on relevant references. According to various climatic conditions, including temperate, arid, and tropical regions, three specific regions were selected to establish a wind dispersal model that accurately reflects the density function distribution of dispersal distance. Additionally, dandelions growth is influenced by a multitude of factors, encompassing temperature, humidity, climate, and various environmental variables that necessitate meticulous consideration. Based on Factor Analysis model, which completely considers temperature, precipitation, solar radiation, wind, and land carrying capacity, a conclusion is presented, indicating that the growth of seeds is primarily influenced by plant attributes and climate conditions, with the former exerting a relatively stronger impact. Subsequently, the remaining two plants were chosen based on seed weight, yielding consistent conclusion.

Flexible Factor Model for Handling Missing Data in Supervised Learning

This paper presents an extension of the factor analysis model based on the normal mean-variance mixture of the Birnbaum-Saunders in the presence of nonresponses and missing data.This model can be used as a powerful tool to model non-normal features observed from data such as strongly skewed and heavy-tailed noises.Missing data may occur due to operator error or incomplete data capturing therefore cannot be ignored in factor analysis modeling.We implement an EM-type algorithm for maximum likelihood estimation and propose single imputation of possible missing values under a missing at random mechanism.The potential and applicability of our proposed method are illustrated through analyzing both simulated and real datasets.

基于多因素模糊聚类分析法的底板突水危险性评价

通过对新安煤矿的研究,利用多因素模糊聚类分析法,确定影响底板突水的主要因素及其权重,建立煤层底板突水模型,对煤层底板突水危险性进行分区,确定危险程度,为奥灰水的防治提供了重要依据,保证煤矿较安全开采。

On essential topics of BYY harmony learning： Current status, challenging issues, and gene analysis applications

As a supplementary of [Xu L. Front. Electr. Electron. Eng. China, 2010, 5（3）： 281-328], this paper outlines current status of efforts made on Bayesian Ying- Yang （BYY） harmony learning, plus gene analysis appli- cations. At the beginning, a bird＇s-eye view is provided via Gaussian mixture in comparison with typical learn- ing algorithms and model selection criteria. Particularly, semi-supervised learning is covered simply via choosing a scalar parameter. Then, essential topics and demand- ing issues about BYY system design and BYY harmony learning are systematically outlined, with a modern per- spective on Yin-Yang viewpoint discussed, another Yang factorization addressed, and coordinations across and within Ying-Yang summarized. The BYY system acts as a unified framework to accommodate unsupervised, su- pervised, and semi-supervised learning all in one formu- lation, while the best harmony learning provides novelty and strength to automatic model selection. Also, mathe- matical formulation of harmony functional has been ad- dressed as a unified scheme for measuring the proximity to be considered in a BYY system, and used as the best choice among others. Moreover, efforts are made on a number of learning tasks, including a mode-switching factor analysis proposed as a semi-blind learning frame- work for several types of independent factor analysis, a hidden Markov model （HMM） gated temporal fac- tor analysis suggested for modeling piecewise stationary temporal dependence, and a two-level hierarchical Gaus- sian mixture extended to cover semi-supervised learning, as well as a manifold learning modified to facilitate au- tomatic model selection. Finally, studies are applied to the problems of gene analysis, such as genome-wide asso- ciation, exome sequencing analysis, and gene transcrip- tional regulation.

Methods for Population-Based eQTL Analysis in Human Genetics

Gene expression is a critical process in biological system that is influenced and modulated by many factors including genetic variation. Expression Quantitative Trait Loci（e QTL） analysis provides a powerful way to understand how genetic variants affect gene expression. For genome wide e QTL analysis, the number of genetic variants and that of genes are large and thus the search space is tremendous. Therefore, e QTL analysis brings about computational and statistical challenges. In this paper, we provide a comprehensive review of recent advances in methods for e QTL analysis in population-based studies. We first present traditional pairwise association methods, which are widely used in human genetics. To account for expression heterogeneity, we investigate the methods for correcting confounding factors. Next, we discuss newly developed statistical learning methods including Lasso-based models. In the conclusion, we provide an overview of future method development in analyzing e QTL associations. Although we focus on human genetics in this review, the methods are applicable to many other organisms.

Evaluation of ICUs and weight of quality control indicators:an exploratory study based on Chinese ICU quality data from 2015 to 2020

This study aimed to explore key quality control factors that affected the prognosis of intensive care unit(ICU)patients in Chinese mainland over six years(2015–2020).The data for this study were from 31 provincial and municipal hospitals(3425 hospital ICUs)and included 2110685 ICU patients,for a total of 27607376 ICU hospitalization days.We found that 15 initially established quality control indicators were good predictors of patient prognosis,including percentage of ICU patients out of all inpatients(%),percentage of ICU bed occupancy of total inpatient bed occupancy(%),percentage of all ICU inpatients with an APACHE II score≥15(%),three-hour(surviving sepsis campaign)SSC bundle compliance(%),six-hour SSC bundle compliance(%),rate of microbe detection before antibiotics(%),percentage of drug deep venous thrombosis(DVT)prophylaxis(%),percentage of unplanned endotracheal extubations(%),percentage of patients reintubated within 48 hours(%),unplanned transfers to the ICU(%),48-h ICU readmission rate(%),ventilator associated pneumonia(VAP)(per 1000 ventilator days),catheter related blood stream infection(CRBSI)(per 1000 catheter days),catheter-associated urinary tract infections(CAUTI)(per 1000 catheter days),in-hospital mortality(%).When exploratory factor analysis was applied,the 15 indicators were divided into 6 core elements that varied in weight regarding quality evaluation:nosocomial infection management(21.35%),compliance with the Surviving Sepsis Campaign guidelines(17.97%),ICU resources(17.46%),airway management(15.53%),prevention of deep-vein thrombosis(14.07%),and severity of patient condition(13.61%).Based on the different weights of the core elements associated with the 15 indicators,we developed an integrated quality scoring system defined as F score=21.35%xnosocomial infection management+17.97%xcompliance with SSC guidelines+17.46%×ICU resources+15.53%×airway management+14.07%×DVT prevention+13.61%×severity of patient condition.This evidence-based quality scoring system will help in assessing the key elements of quality management and establish a foundation for further optimization of the quality control indicator system.

Chemical characteristics and source apportionment of PM_(10) during a brown haze episode in Harbin, China

This study investigates the correlation between PM10 and meteorological factors such as wind speed, atmospheric visibility, dew point, relative humidity, and ambient temperature during a brown haze episode. In order to identify the potential sources of PMlo during brown haze episode, respirable par- ticulate matter （PM10） was collected during both non-haze days and haze days and further analyzed for metallic elements, ionic species, and carbonaceous contents. Among them, ionic species contributed 45-64% to PM10, while metallic elements contributed 7-21% to PM10 which was smaller than the other chemical constituents. The average OC/EC ratio （42） in haze days was about three times of the average OC/EC ratio （14） in non-haze days. By using chemical mass balance （CMB） receptor model, the major sources were apportioned, including traffics, incinerators, coal combustion, steel industry, petrochemical industry, and secondary aerosols, etc. The contribution to PM10 concentration of each source was calcu- lated for all the samples collected. The results showed that coal combustion was the major source of PM10 in non-haze days and secondary aerosols were the major source in haze days, followed by petrochemical industry, incinerators, and traffics, while other sources had negligible effect.