Normal Regression Research Articles

대구 경북지역 병원종사자의 직무스트레스가 직무만족도에 미치는 영향에서 자아탄력성의 매개효과

Objectives The purpose of this study is to verify the mediating effect of ego resilience in the relationship between job stress and job satisfaction of hospital workers in the Daegu and Gyeongbuk regions. Methods An online survey was conducted targeting hospital workers working in the Daegu and Gyeongbuk regions from October 8, 2023 to October 15, 2023, and 86 copies were used in the final statistical analysis. The collected data were subjected to reliability analysis, frequency analysis, descriptive statistical analysis, multivariate normal distribution verification, correlation analysis, and regression analysis using SPSS V27.0, and the Sobel Test was performed to verify statistical significance. Results First, job stress of hospital workers in the Daegu and Gyeongbuk regions was found to have a significant negative effect on job satisfaction and ego resilience, and ego resilience was found to have a positive effect on job satisfaction. Second, ego resilience was found to mediate the relationship between job stress and job satisfaction among hospital workers in the Daegu and Gyeongbuk regions. Conclusions Through the results of this study, the relationship between job stress, job satisfaction, and ego resilience of hospital workers in the Daegu and Gyeongbuk regions was verified, and the relationship between major variables was confirmed theoretically and empirically, and key variables that affect hospital workers' job satisfaction were confirmed. It is meaningful in that it can provide basic data for early screening of hospital workers with low levels of job satisfaction and effective intervention by uncovering these factors. It is also significant in that it provided implications for the development of counseling and education programs related to job stress and ego resilience that can increase the job satisfaction of hospital workers in the Daegu and Gyeongbuk regions.

Plant biodiversity assessment of locally cultivated crops and household food security in Northern Iran

BackgroundLocally cultivated crops play an important role in the food security. The biodiversity of these crops can be important for the livelihood of households in current and future generations. This research aims to study the socio-economic, agronomic, and ecological aspects which contribute to the maintenance of crop diversification and food security in the study area.MethodBased on latitude and topography 10 villages were randomly selected, which was done in a study with 227 household farm managers. Food security was evaluated based on the species richness, and area under cultivation of food groups, and the probit logistic regression model was used for evaluation. Probit regression, also known as probit models, is used when the output or dependent variable of the model is bivariate. In inverse probit models, the standard normal distribution is modeled as a linear combination of predictor variables. In this situation, the application of normal regression methods is not applicable because the distribution occurred in two levels. In this study, it is assumed that the protection of biodiversity of local plants is related to food security, and for this reason, we have used this model.ResultsThe findings showed that socio-economic status of different households and farms in this coastal area is one of the key factors affecting the biodiversity of locally cultivated crops. Local cultivars, especially those of rice, are resistant to most environmental factors and contribute to family food security. Food security and rice abundance are significantly correlated in all of the research area’s communities.ConclusionsTo ensure the sustainability, and health of production, and to ensure food security, planting various crops in this study area is recommended. Future research is needed to focus on solutions and technologies rich in diversity tailored to the socio-economic and environmental factors of locally cultivated crops.

A study on the professional attention and career commitment of health management students.

The education process is based on leading the choice of profession in line with the interests and abilities of the individual and helping them with their career plans in these professions. The aim of this study is to investigate the effect of the occupational interest levels of the Health Management Department students on their career commitment. In the study, descriptive findings were included by using a quantitative research design. In the research, data were collected from 282 students studying Department of Health Management. SPSS 26.0 package program was used for statistical analysis of research data. Parametric tests (t-test, analysis of variance, Pearson correlation analysis, normal linear regression analysis) were utilized since the data fit the normal distribution. 57.1% of the individuals participating in the research are female, 47.8% have the age range of 21 to 22. There is a positive, moderate (r = 0.677 p < 0.01) significant relationship between professional interest and career commitment scales. The level of professional interest and career dedication varies according to gender, age, income status and the class of the student. It is very important that health management students have an interest in their profession. Because this interest will guide them in what kind of a career they should make in their business life after graduation.

Mixtures of logistic normal multinomial regression models for microbiome data

In the realm of bioinformatics, we frequently encounter discrete data, particularly microbiome taxa count data obtained through 16S rRNA sequencing. These microbiome datasets are commonly characterized by their high dimensionality and the ability to provide insights solely into relative abundance, necessitating their classification as compositional data. Analyzing such data presents challenges due to their confinement within a simplex. Additionally, microbiome taxa counts are subject to influence by various biological and environmental factors like age, gender, and diet. Thus, we have developed a novel approach involving regression-based mixtures of logistic normal multinomial models for clustering microbiome data. These models effectively categorize samples into more homogeneous subpopulations, enabling the exploration of relationships between bacterial abundance and biological or environmental covariates within each identified group. To enhance the accuracy and efficiency of parameter estimation, we employ a robust framework based on variational Gaussian approximations (VGA). Our proposed method's effectiveness is demonstrated through its application to simulated and real datasets.

The regressive surface of marine erosion generated by tides: A case study from a Pleistocene tidal sand ridge sequence, Calabria, Southern Italy

AbstractThe regressive surface of marine erosion is a key stratigraphic discontinuity used to identify episodes of forced regression in marine strata. Typically, it marks the base of shallow‐marine (e.g. shoreface and deltaic) deposits eroding over relatively deeper‐water (e.g. shelf and prodelta) lithofacies. While well‐documented in marginal‐marine areas, its occurrence in offshore or strait settings dominated by tidal currents is less understood. This study investigates lower Pleistocene outcrops in the Plio‐Quaternary Siderno Basin, Calabria, southern Italy, where tidal sand ridges developed in a Mediterranean strait. High‐resolution drone images reveal a basinward‐dipping basal surface marking the onset of tidally dominated sedimentation. This discontinuity separates underlying shelf fines from overlying cross‐stratified, tidal bioclastic/siliciclastic arenites, indicating a prolonged period of marine regression in a strait setting. The stratal architecture of the ridges shows cross‐strata aggradation in up‐dip sections, transitioning down‐dip into balanced aggrading/prograding strata, and further basinward into markedly prograding deposits. These features are interpreted to reflect an initial phase of normal regression, evolving into forced regression. This latter stage is characterised by a progressive deepening of the basal discontinuity, causing more erosional effects on the underlying beds, with a vertical basinward fall of about 60 m over 2 km. Internal foreset geometry (two‐dimensional vs. three‐dimensional cross strata) and their vertical and lateral repetition indicate stages of equilibrium and disequilibrium for tidal bedforms, reflecting varying current speeds and water depth changes. The regressive surface of marine erosion here is formed by tidal currents, rather than waves, suggesting a new type of sequence stratigraphic discontinuity associated with tide‐dominated settings.

Tests in skew–normal regression models

A skew-normal model is the first option to fit data with presence of skewness. It is also important not to forget the existence of small samples, which can come from experimental studies of areas such as health-related, biology, pharmacology, engineering, etc, and have as one of the most common characteristics the presence of skewness. Usual statistical tests, even those with good size and power properties, do not allow valid conclusions to be drawn when only small sample sizes are available, so it is necessary to consider some corrections of the statistical tests. Improvements to statistical tests for skew-normal models in small sample sizes have not yet been developed. This article presents a correction for the signed likelihood ratio test, a Bartlett bootstrap correction for the likelihood ratio test, and a Bartlett-type bootstrap correction for the gradient test. A Monte-Carlo simulation has been performed and allows us to conclude that the proposed corrections can significantly improve the performance of the statistical test in terms of size and power. A real data set was used to illustrate the methodology developed here from an experimental engineering study.

Analysis of factors influencing market participation among orange‐fleshed sweet potato smallholder farmers in southern region of Malawi: A case of RTC project

AbstractAgricultural markets remain major issue downgrading root and tuber crop farmers who form the mainstream of the agricultural producers in Malawi. Recent evidence however reveals that orange‐fleshed sweet potato (OFSP) (Ipomea batatas) is currently being sold in produce markets along with the non‐orange varieties, sometimes in differentiated form in Malawi. The main objective of this study was to examine factors affecting market participation of sweet potato farmers in southern region of Malawi. Using a double‐hurdle model, this paper analyzed both the factors affecting farmer decision to participate and the extent/intensity of participation in market. The first tier of the double hurdle focused on decision to participate in the market using probit model and the second tier addressed factors affecting intensity of participation using truncated normal regression model. The paper used data collected from 360 farmers in southern districts of Malawi. Out of 360, 146 farmers had grown OFSP in the season preceding the survey. Double hurdle results indicate that farm size, output, age of a farmer (household head), and early maturing trait significantly determined decision to participate in the market. Further, results on intensity of participation indicate that access to market training, farm size, asset value, and output positively determined the intensity and negatively determined by distance to main market and gender of a farmer (household head). Based on the research findings, higher output levels and early maturing trait were found to increase smallholder farmer market participation. It is therefore recommended that strategies aimed at increasing household capacity to produce surplus produce through market tailored extension messages and productivity enhancement technologies could be highly effective in fostering smallholders' participation in commercial markets. Again, enhanced capacity building of research institutions to breed sweet potato varieties based on market demand is pivotal for increased market participation. Furthermore, research suggests that improving smallholder farmers' access to market price information, asset value, and farm size can help increase their intensity in the market. Therefore, it is critical for policymakers to holistically comprehend these socioeconomic factors in order to address the issue of smallholder farm households' decisions about market participation and the degree to which they participate.

Urachal remnants: from embryology to clinical practice.

The urachus is a tubular structure that is apparent on the third week and connects the ventral cloaca to the yolk sac, as a progression from the allantois. Following the normal regression procedure, the urachus remains as the median umbilical ligament. Urachal remnants are present in 1.03% of paediatric patients while in 92.5% of cases represent incidental findings. Urachal anomalies are classified in four types as patent urachus (50-52%), urachal sinus (15%), urachal cyst (30%) and urachal diverticulum (3-5%). Ultrasound scan is the most commonly performed diagnostic imaging study. In case of symptomatic urachal remnants, surgical excision is indicated. Asymptomatic urachal remnants that are diagnosed at the neonatal period or early infancy should be watched up to 6 months of age, as they are likely to resolve. In persistent or symptomatic urachal remnants there is a risk of inflammation or even malignancy development, therefore we believe that there is indication for preventive surgical excision that may be performed either open or laparoscopically or by robot-assisted laparoscopy.

CritiCoder: An End-to-End Uncertain Regression Network for Robust Macroscopic Pressure Models in Water Distribution Systems

Due to the massive uncertain disturbances in water distribution systems (WDSs), it is rather challenging to construct robust macroscopic pressure models. In this article, we propose the CritiCoder, an end-to-end uncertain regression neural network, to build macroscopic pressure models in WDSs and estimate the distribution of uncertain disturbances. By separating the normal regression process into two parts, the CritiCoder decomposes the output into two parts brought by observable and unobservable variates separately. Two subnetworks, Coder and Critic, make up of the CritiCoder. Through the reconstruction of data flow, the Coder is expected to approximate ideal outputs from observable variates. Meanwhile, the loss function of the Critic is redesigned to consider the effectiveness of uncertain disturbances in output brought by unobservable variates. Experiments on a practical application of WDSs in a Chinese mega-city show superior performance of CritiCoder. Especially for pressure-monitoring nodes more severely impacted by disturbances, the performance decline of the CritiCoder is less compared with other baseline methods.

Parameter Estimation and Hypothesis Testing on Bivariate Log-Normal Regression Models

This study aims to introduce a bivariate Log-Normal regression model and to develop a technique for parameter estimation and hypothesis testing. We term the model Bivariate Log-Normal Regression (BLNR). The estimation procedure is conducted by the standard Maximum Likelihood Estimation (MLE) employing the Newton-Raphson method. To perform hypothesis testing, we adapt the Maximum Likelihood Ratio Test (MLRT) for simultaneous testing with test statistics which, for large n, follows Chi-Square distribution with degrees of freedom p. In addition, the partial testing is derived from a central limit theorem which results in a Z-test statistic.&#x0D; Keywords: parameter estimation, hypothesis testing, bivariate log, normal regression

Medium-term effects of rapid maxillary expansion on nasal cavity and pharyngeal airway volumes considering age as a factor: A retrospective study

ObjectivesThe medium-term effects of rapid maxillary expansion (RME) on nasal cavity (NC) and upper airway (UA) dimensions based on chronological age are still unclear. This retrospective study evaluated the medium-term changes occurring in the NC and pharyngeal airways (PA) after RME in two distinct age-based cohorts of patients. MethodsThis retrospective study included 48 subjects who underwent RME grouped in two cohorts: a 6–9-year-old group (EEG group: early expansion group - 25 subjects) and an 11–14-year-old group (LEG group: late expansion group - 23 subjects). NC and PA volumes were analyzed from CBCT imaging segmentation before RME (T0) and twelve months after RME (T1). The amount of maxillary expansion (PW) and minimal cross-sectional area (CSmin) were also considered. ResultsAll PAs’ volumetric sub-regions, CSmin and PW showed a significant volumetric increment (p < 0.05). Inter-group comparisons showed significant differences (p < 0.05) for nasopharynx and CSmin parameters (p < 0.05), while no significant changes were recorded for the other UA's sub-regions and PW (p > 0.05). According to a deviation analysis, part of the UA increase (more marked for the nasopharynx area) may have occurred due to reduced adenotonsillar tissues, which were larger in the EEG group. ConclusionsTwelve months after treatment, clinicians should not expect changes in the UAs dimensions to be solely related to treatment effects of RME; instead, normal craniofacial growth changes and spontaneous regression of the adenotonsillar tissue could represent the most significant factors influencing UAs changes. Clinical SignificanceFrom the clinical perspective, the results of the present study encourage caution when considering the therapeutic effects of RME on airways dimensions.

Surviving from transgression to regression of Lake Pannon: Fan deltas of the Nemčiňany Fm. persisted across the rifting until the post-rift stage of the Danube Basin, western Slovakia

Successions deposited under rifting and post-rift settings of an isolated epicontinental basin often exhibit contrasting characteristics. Facies linked to transgression during basin rifting are typically locally sourced fan deltas transporting coarse-grained sediment, whereas the post-rift setup generally involves a normal regression marked by more extensive catchments, sediment sourced from greater distances, and an overall decrease in average grain size compared to the transgressive facies. In this study, we present a specific scenario based on a sedimentological, stratigraphic, and authigenic 10Be/9Be analysis of the Nemčiňany Formation, an Upper Miocene fan deltaic succession in the eastern Danube Basin, Slovakia. Deposition of the Nemčiňany Fm. was initiated shortly after ~11.6 Ma by the fourth rifting phase of the Pannonian Basin System, triggering the transgression of Lake Pannon. The depositional system persisted until ~9.6 Ma, likely balancing the increased accommodation rate with sediment supply in the shallow lake area situated on a basement high. Subsequently, a relative decrease in the accommodation rate prompted the progradation of shelf-slope scale clinoforms, originating from the Nemčiňany depositional system, toward the center of the Komjatice depression at ~9.4 Ma, resulting in a normal regression of Lake Pannon. The persistence of the Nemčiňany fan delta system, unlike common stratigraphic patterns, was associated to the high sediment supply yielded by the paleo-Hron river, which entered the basin in the study area. These observations underscore the need for caution in predicting provenance shifts during changes in geodynamic stages of a basin.

Factors driving FIFA world cup 2022 viewership ratings in mainland China: marketing outlooks for FIFA world cup 2026.

The FIFA World Cup is not only the most lucrative athletic event globally, but it also functions as a platform for promoting peace owing to FIFA's new vision. Nevertheless, the determinants of TV viewership ratings, especially in the Chinese market, which is a critical revenue stream for FIFA TV broadcasting, are still unsolved. Using a distributional regression, this study aimed to quantify the dynamics of viewership ratings for the FIFA World Cup 2022 in mainland China. The CCTV viewership ratings were modeled using 12 covariables related to Chinese TV consumer behavior. Given the data structure, a Logit Normal regression model was chosen to fit the location and dispersion parameters of viewership ratings to explanatory variables. In the fitted heteroscedastic model, the viewership ratings dynamics in mainland China were driven by the match kick-off time: . In addition, the model captures the factors that influence the variations in viewership ratings: (if, FIFA World Cup champion = "Yes") + 0.004 × FIFA world ranking]. Thus, it shows that the FIFA World Cup champions tamp down such variations, leading to a more stable viewing behavior among Chinese consumers. Time- and team-sensitive strategies are proposed to aid in crafting uncertainty-suppressing business decisions for the FIFA World Cup 2026. Ultimately, in the more insecure 2020s, a broader live coverage of the FIFA World Cup would be invaluable for promoting peace.

ResGEM: Multi-scale Graph Embedding Network for Residual Mesh Denoising.

Mesh denoising is a crucial technology that aims to recover a high-fidelity 3D mesh from a noise-corrupted one. Deep learning methods, particularly graph convolutional networks (GCNs) based mesh denoisers, have demonstrated their effectiveness in removing various complex real-world noises while preserving authentic geometry. However, it is still a quite challenging work to faithfully regress uncontaminated normals and vertices on meshes with irregular topology. In this paper, we propose a novel pipeline that incorporates two parallel normal-aware and vertex-aware branches to achieve a balance between smoothness and geometric details while maintaining the flexibility of surface topology. We introduce ResGEM, a new GCN, with multi-scale embedding modules and residual decoding structures to facilitate normal regression and vertex modification for mesh denoising. To effectively extract multi-scale surface features while avoiding the loss of topological information caused by graph pooling or coarsening operations, we encode the noisy normal and vertex graphs using four edge-conditioned embedding modules (EEMs) at different scales. This allows us to obtain favorable feature representations with multiple receptive field sizes. Formulating the denoising problem into a residual learning problem, the decoder incorporates residual blocks to accurately predict true normals and vertex offsets from the embedded feature space. Moreover, we propose novel regularization terms in the loss function that enhance the smoothing and generalization ability of our network by imposing constraints on normal consistency. Comprehensive experiments have been conducted to demonstrate the superiority of our method over the state-of-the-art on both synthetic and real-scanned datasets.

Multivariate contaminated normal mixture regression modeling of longitudinal data based on jointmean‐covariancemodel

AbstractOutliers are common in longitudinal data analysis, and the multivariate contaminated normal (MCN) distribution in model‐based clustering is often used to detect outliers and provide robust parameter estimates in each subgroup. In this paper, we propose a method, the mixture of MCN (MCNM), based on the joint mean‐covariance model, specifically designed to analyze longitudinal data characterized by mild outliers. Our model can automatically detect outliers in longitudinal data and provide robust parameter estimates in each subgroup. We use iteratively expectation‐conditional maximization (ECM) algorithm and Aitken acceleration to estimate the model parameters, achieving both algorithm acceleration and stable convergence. Our proposed method simultaneously clusters the population, identifies progression patterns of the mean and covariance structures for different subgroups over time, and detects outliers. To demonstrate the effectiveness of our method, we conduct simulation studies under various cases involving different proportions and degrees of contamination. Additionally, we apply our method to real data on the number of people infected with AIDS in 49 countries or regions from 2001 to 2021. Results show that our proposed method effectively clusters the data based on various mean progression trajectories. In summary, our proposed MCNM based on the joint mean‐covariance model and MCD of covariance matrices provides a robust method for clustering longitudinal data with mild outliers. It effectively detects outliers and identifies progression patterns in different groups over time, making it valuable for various applications in longitudinal data analysis.

A new extended normal quantile regression model: properties and applications

We propose the exponentiated odd log-logistic normal quantile regression model relating the covariates to the parameters through two systematic components, and adopt the maximum likelihood method to estimate the parameters. We report Monte Carlo simulations to verify the accuracy of the maximum likelihood estimators. The usefulness of the new regression is proved using three applications to real data. It and its special cases proved to be interesting alternatives to model data in different shapes, and obtain important information on the response variable from the analysis of different quantiles.

Assessment of Pedestrians Crossing Behaviour at Mid-Blocks Under Mixed Traffic Conditions in Urban Contexts

Pedestrians are an integral part of urban transportation infrastructure systems, which is also sustainable mode of transportation system. But they are particularly at risk in isolated mid-block sites that have different levels of traffic. The purpose of this study is to investigate, analyze, and simulate pedestrian behavior at Uncontrolled Midblock Crossings in Hyderabad (Hitech city to Habsiguda) under Mixed Traffic Conditions. Particularly, the Mid-Block Crossing Behavior and Pedestrian Crossing Speed. A field survey needs to be conducted using during the project by using any kind of survey techniques, such as Video-graphic technique, manually. Different locations are to be identified, and to be chosen in accordance to Pedestrian traffic and Pedestrians Crossing at Midblock. The Survey should be carried out as per the standards and done during the peak hours. The overall Pedestrian Speed should be determined by using the pedestrian field survey. An Interview method Survey was done to determine the exact Age of Pedestrians at the Study Locations. The data that is required for analysis of pedestrian behaviour at the Mid-Block Crossings are included with Speed of vehicles, Crossing Speeds of Pedestrians, individual characteristics (Gender, Age etc.) as well as the type of Crossing Behaviour. The Pedestrian Crossing Speeds and the Cumulative Speeds were determined and the variations in accordance to Age and Gender were obtained for the selected locations. And further it can be extended to predict the Gap size of the locations by using the log Normal Regression Model and A Binary Logit Model can be examined to find the effects of behavioural aspects of parameters on the Gender (Male and Female) coded as a discrete variable.

Exploratory analysis of the associations between neonicotinoids insecticides and serum lipid profiles among US adults: A cross-sectional, population-based study

Neonicotinoid insecticides (NNIs) are widely used in agriculture, horticulture, forestry, and household environment, but their potential impact on human health remains a subject of concern. This study aimed to investigate the relationship between NNIs and their metabolites in urine with serum lipid profiles in adults using data from the National Health and Nutrition Examination Survey (NHANES) 2015–2016. The study included 1192 participants aged over 20 years with urinary NNIs levels, serum lipid parameter levels and potential confounders. Urinary concentrations of NNIs, including imidacloprid, acetamiprid, clothianidin, thiacloprid, N-desmethyl-acetamiprid, and 5-hydroxy-imidacloprid, were quantified. Serum lipids profiles, such as total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and apolipoprotein B (Apo-B), were assessed. Considering the effects of lipid-lowering medications, the censored normal regression model was used to explore the associations between urinary NNIs and TC, TG, HDL-C, LDL-C and Apo-B levels. The results revealed a significant increase of 9.0 mg/dL (95%CI: 2.0, 16.1) in TC levels among participants with detectable N-desmethyl-acetamiprid compared to those with undetectable levels. Stratified analysis indicated that the association between N-desmethyl-acetamiprid and HDL-C levels was more pronounced among participants aged ≥ 46 years compared to those aged between 20 and 45 years with undetectable N-desmethyl-acetamiprid (pinteraction=0.044). Additionally, there were marginal effect modification of BMI on the association between N-desmethyl-acetamiprid and LDL-C (pinteraction=0.097) and Apo-B (pinteraction=0.052) levels. Specifically, participants with BMI ≥ 25 kg/m² and detectable N-desmethyl-acetamiprid tended to have higher LDL-C and Apo-B levels compared to those with BMI < 25 kg/m² and undetectable N-desmethyl-acetamiprid. However, no significant associations were observed between other NNIs and lipid profiles in the present study. To validate these findings, further longitudinal studies with larger sample sizes should be conducted, particularly within populations characterized by a high detection rate of NNIs.

Marginalised Normal Regression: Unbiased curve fitting in the presence of x-errors

Marginalised Normal Regression: Unbiased curve fitting in the presence of x-errors

Bayesian profile regression for clustering analysis involving a longitudinal response and explanatory variables.

The identification of sets of co-regulated genes that share a common function is a key question of modern genomics. Bayesian profile regression is a semi-supervised mixture modelling approach that makes use of a response to guide inference toward relevant clusterings. Previous applications of profile regression have considered univariate continuous, categorical, and count outcomes. In this work, we extend Bayesian profile regression to cases where the outcome is longitudinal (or multivariate continuous) and provide PReMiuMlongi, an updated version of PReMiuM, the R package for profile regression. We consider multivariate normal and Gaussian process regression response models and provide proof of principle applications to four simulation studies. The model is applied on budding yeast data to identify groups of genes co-regulated during the Saccharomyces cerevisiae cell cycle. We identify 4 distinct groups of genes associated with specific patterns of gene expression trajectories, along with the bound transcriptional factors, likely involved in their co-regulation process.