Application Of Factor Analysis Research Articles

Psoriatic arthritis phenotype clusters and their association with treatment response: a real-world longitudinal cohort study from the psoriatic arthritis research consortium

ObjectivesTo identify phenotype clusters and their trajectories in psoriatic arthritis (PsA) and examine the association of the clusters with treatment response in a real-world setting.MethodsIn the multicentre PsA Research Consortium...

Enhancing predictive accuracy of electoral outcomes: A comparative study of dimension reduction techniques in linear regression models

The effectiveness of Principal Component Analysis (PCA) and Factor Analysis (FA) in enhancing the predictive accuracy and interpretability of linear regression models for electoral outcomes is examined in this study, with a focus on the Croatian presidential election. Challenges such as multicollinearity and model complexity were addressed through the application of PCA and FA to socio-demographic and political variables, resulting in their transformation into a simpler, more manageable form. Multicollinearity was significantly reduced by PCA, and model stability was improved. Deeper insights into voter behavior were provided by FA through the identification of latent variables. Improvements were noted; however, issues with homoscedasticity were identified, which affected the predictive reliability of the models. The optimized linear regression model, specifically designed to address homoscedasticity (LR2), was found to demonstrate superior performance in terms of predictive accuracy and stability when compared to models that utilized PCA and FA alone. The importance of maintaining homoscedasticity in regression models is underscored by the findings, and the potential of dimension reduction techniques to improve electoral predictions is highlighted. The integration of these techniques in forecasting models is advocated for, with the aim of assisting political analysts and policymakers. It is suggested that future research could explore the combination of these methods with other advanced modeling approaches to further enhance predictive capabilities.

A Network Analytic Approach to Dissociation: New Insights from Clinical Data

ABSTRACT The transdiagnostic construct of dissociation, characterized by a disintegration of specific psychological functions such as consciousness, memory, identity, perception, body representation, and behavior, remains elusive to a unified conceptualization. Specifically, its dimensionality is a matter of ongoing controversy. Empirical approaches applying factor analyses to the Dissociative Experiences Scale (DES) have yielded inconsistent findings. This study adopts a novel methodological approach, utilizing Exploratory Graph Analysis (EGA) to address this issue. In a sample of 668 day-hospital patients undergoing psychotherapy for a variety of mental disorders, a Gaussian graphical model was estimated for the 28 items of the DES. Additionally, the stability of the results was ensured by bootstrap procedures. While both the original EGA and the bootstrap EGA suggested four dimensions, the structural consistency of this solution was low due to an instability of 12 items. After excluding 10 of these unstable items, re-analyses again revealed a four-factor structure, but boot EGA indicated that one factor had unsatisfactory structural consistency due to the multidimensionality of its two items. Upon removing these, our final network consisted of 16 items mapping onto 3 dimensions. Our study, using data from a diagnostically heterogeneous sample, replicates and extends previous findings on the dimensionality of dissociation as captured by the DES. The three dimensions identified correspond to segregated processes, derealization/depersonalization, and absorption. This solution aligns with a bipartite model of dissociation with two broader categories referring to either altered states of consciousness (often named detachment) or to non-integrated mental modules (labeled as compartmentalization).

Symptom Dimensions and Cognitive Impairments in Individuals at Clinical High Risk for Psychosis

BackgroundUnderstanding the intricate relationships between symptom dimensions, clusters, and cognitive impairments is crucial for early detection and intervention in individuals at clinical high risk for psychosis. This study delves into this complex interplay in a clinical high risk sample with the aim of predicting the conversion to psychosis. MethodsA comprehensive cognitive assessment was performed in 744 clinical high risk individuals. The study included a 3-year follow-up period to allow assessment of conversion to psychosis. Symptom profiles were determined using the Structured Interview for Prodromal Syndromes. By applying factor analysis, symptom dimensions were categorized as dominant negative symptoms (NSs), positive symptoms-stressful, and positive symptoms-odd. The factor scores were used to define 3 dominant symptom groups. Latent class analysis (LCA) and factor mixture model (FMM) were employed to identify discrete clusters based on symptom patterns. The 3-class solution was chosen for the LCA and FMM analysis. ResultsIndividuals in the dominant NS group exhibited significantly higher conversion rates to psychosis than those in the other groups. Specific cognitive variables, including performance on the Brief Visuospatial Memory Test–Revised (odds ratio = 0.702, p = .001) and Neuropsychological Assessment Battery Mazes Test (odds ratio = 0.776, p = .024), significantly predicted conversion to psychosis. Notably, cognitive impairments associated with NSs and positive symptoms-stressful affected different cognitive domains. LCA and FMM cluster 1, which was characterized by severe NSs and positive symptoms-odd, exhibited more impairments in cognitive domains than other clusters. No significant difference in the conversion rate was observed among the LCA and FMM clusters. ConclusionsThese findings highlight the importance of NSs in the development of psychosis and suggest specific cognitive domains that are affected by symptom dimensions.

Determinants of Young Polish Consumers’ Food Product Purchases

ABSTRACT This article investigates the factors influencing food product purchases by young Polish consumers aged 18–25 – a group important for the development of the economy. In the process of deciding to purchase specific food products, consumers pay attention to various economic and non-economic factors, including marketing. We carried out survey in October/November 2020 using the CAWI method (Google Forms) to test the importance of 17 factors on young Polish consumers’ food purchases, applying factor analysis and cluster analysis to the resulting data. The results reveal that price and product expiration date are young Polish consumers’ primary concerns when purchasing food products. The study also found that environmental sustainability attributes, although less critical than price or product quality, are increasingly influencing young Polish consumers' purchasing decisions.

Designing Package Labels for Upcycled Food Products in Japan: Case Study of Broccoli Stem Snack

ABSTRACT This study addresses the growing interest in upcycled foods as a solution to food loss by focusing on designing more appealing package labels in Japan. Through an online survey, we collected data on consumer attitudes and perceptions toward upcycled foods. We applied factor analysis to identify key factors and cluster analysis to segment consumers into distinct profiles. Using these insights, we conducted a choice-based conjoint analysis to extract the package design attributes most valued by consumers. Our results highlight key factors influencing purchase intentions, including food safety, innovation, and eco-friendliness. We identified two main consumer profiles: sustainability-focused and quality/safety-conscious, and innovative and socially engaged. Food safety emerged as the most crucial factor affecting purchase willingness, followed by product image and environmental considerations. The study emphasizes the importance of transparent, sustainable, and visually appealing package design in influencing consumer decisions and building long-term loyalty toward upcycled foods in Japan.

Unlocking organizational success in the hotel industry: the power of social exchange in HR practices

Purpose The purpose of this study is to examine the role of specific human resources (HR) practices (leadership, employee relations, employee safety, rewards and incentives and career opportunities) on organizational performance in the context of the Middle Eastern market, with a specific focus on the United Arab Emirates (UAE) hotel sector. Design/methodology/approach A comprehensive research methodology was used for this study. Two distinct questionnaires were crafted: one targeting 338 hotel employees who completed the HR practices survey, and the other for 109 general hotel managers who completed the organizational performance survey. All respondents were from five-star hotel establishments in the UAE. To validate the hypotheses, a multidimensional approach was adopted. This paper first applied factor analysis to understand correlations and groupings among the factors. This paper then undertook validity assessment by examining factor loadings, average variance extracted and the reliability values for all parameters. Discriminant validity was also carefully evaluated. Findings The results found that there is a direct correlation between leadership, employee relations and career opportunities, with boosted organizational performance. Contrary to prevalent assumptions, the data showed no discernible link between employee safety, rewards and incentives and the actual performance of organizations. The results highlight the crucial role of social exchange in interpreting the indirect relationship between HR practices and organizational performance. This insight offers a fresh perspective on the often-discussed 'black box' in HR research. Originality/value The novelty of this study lies in its intricate exploration of human resource management practices within a region experiencing swift socioeconomic transformations due to factors such as the significant expatriate presence and economic diversification. By underlining the significance of social exchange as a mediating factor, this research significantly contributes to the ongoing dialogue on HR-performance relationships, offering insights for both theoretical and practical applications in HR activities formulation in the context of the UAE.

P-208 To assess or not to assess: reconsidering day 3 embryo quality in planned freeze-all blastocyst cycles

Abstract Study question Should day 3 embryo quality assessment be consider in planned freeze-all blastocyst cycles? Summary answer Day 3 embryo assessment may be unnecessary in planned freeze-all blastocyst cycles. What is known already Day 3 embryo quality is a predictor of in vitro fertilizastion (IVF) success rates in cleavage stage embryo transfer. However, the association between day 3 embryo quality and live birth in blastocyst transfer policy is largely unknown. Study design, size, duration This retrospective study included 1074 frozen-thawed single day 5/6 blastocyst transfer between January 2019 to December 2022. Three groups were assessed depending on whether the transferred blastocyst derived from a top-quality, good-quality or poor-quality embryo at day 3. The analysis was conducted independently for each blastocyst quality group (top, good, and poor) using multivariate logistic regression. We applied factor analysis of mixed data to reduce potential collinearity between covariates used in the model. Participants/materials, setting, methods All blastocysts included in this study were obtained from first ICSI freeze-all cycles. Cleavage stage and blastocyst evaluation were performed according to the Istanbul consensus. Cleavage and blastocysts stage were assessed between 67±0.5 (day-3), 115±0.5 (day-5) and 139±0.5 (day-6) hours post-insemination (hpi), respectively. Exclusion criteria were: i) ICSI-cycles performed with surgically retrieved or frozen sperm, (ii) gamete donation cycles, (iii) cryopreserved-thawed oocyte cycles, (iv) preimplantation genetic testing. The primary outcome was live birth rate. Main results and the role of chance After adjusting for the day of transfer (day 5 or day 6) and several confounding variables, no statistical differences in live birth were observed among same quality blastocysts derived from different day 3 embryo quality groups (top=A, good=B and poor=C). The analysis was stratified for different blastocyst quality groups: 1) top quality blastocyst [day 3 quality A =reference group; day 3 quality B= OR 1.04 95% CI (0.62 – 1.74), p value=0.871; day 3 quality C= OR 0.67 95% CI (0.34 – 1.33), p value= 0.254] 2) good quality blastocyst [A =reference group; B= OR 0.80 95% CI (0.49 – 1.30), p value=0.361; C= OR 0.74 95% CI (0.44 – 1.27), p value= 0.276] 3) poor quality blastocyst [A =reference group; B= OR 0.78 95% CI (0.29 – 2.10), p value=0.622; C= OR 0.80 95% CI (0.31 – 2.07), p value= 0.650]. Limitations, reasons for caution The study is limited by its retrospective design. Embryo assessment without time-lapse technology, to assess the blastocysts at the same developmental time-frame, is another limitation; although the static observation time very close and restricted to the Istanbul consensus guidelines may mitigate such a weakness. Wider implications of the findings Our findings showed that day 3 embryo quality assessment may be unnecessary in planned freeze-all blastocyst cycles. In the context of static embryo assessment, this can reduce the stress on embryos by avoiding unnecessary retrievals from incubators. Trial registration number not applicable

Validation of an abbreviated Big Five personality inventory at large population scale: Psychometric structure and associations with common psychiatric and neurological disorders

BackgroundThe five-factor model of personality, as quantified using instruments such as the Big Five Inventory, consists of broad personality domains including Extraversion, Agreeableness, Conscientiousness, Neuroticism (emotional instability), and Openness. Such instruments typically include >40 items. However, instruments with many items can be unwieldly and a cause of measurement error in clinical and cohort studies where multiple scales are sequenced. Conversely, established 5- and 10-item versions of the Big Five Inventory have poor reliability. Here, we developed and validated an abbreviated 18-item Big Five Inventory that balances efficiency, reliability and sensitivity. MethodWe analysed three datasets (N = 59,797, N = 21,177, and N = 87,983) from individuals who participated in the online Great British Intelligence Test (GBIT) study, a collaborative citizen science project with BBC2 Horizon. We applied factor analyses (FA), predictive normative modelling, and one-sample t-tests to validate the 18-item version of the Big Five and to investigate its associations with psychiatric and neurological conditions. ResultsThe 18-item version of the Big Five Inventory had higher validity and retest reliability compared to the other previously shortened versions in the literature, with comparable demographic associations to the full Big Five Inventory. It exhibited strong (i.e. large effect size) associations with psychiatric conditions, and moderate (small-medium) associations with neurological conditions. Neuroticism (emotional instability) was substantially higher in all psychiatric conditions, whereas Conscientiousness, Openness and Extraversion showed differential associations across conditions. ConclusionThe newly validated 18-item version of the Big Five provides a convenient means of measuring personality traits that is suitable for deployment in a range of studies. It retains psychometric structure, retest reliability and clinical-group sensitivity, as compared to the full original scale.

The significant others’ responses to trauma scale (SORTS): applying factor analysis and item response theory to a measure of PTSD symptom accommodation

ABSTRACT Background: Symptom accommodation by family members (FMs) of individuals with posttraumatic stress disorder (PTSD) includes FMs’ participation in patients’ avoidance/safety behaviours and constraining self-expression to minimise conflict, potentially maintaining patients’ symptoms. The Significant Others’ Responses to Trauma Scale (SORTS) is the only existing measure of accommodation in PTSD but has not been rigorously psychometrically tested. Objective: We aimed to conduct further psychometric analyses to determine the factor structure and overall performance of the SORTS. Method: We conducted exploratory and confirmatory factor analyses using a sample of N = 715 FMs (85.7% female, 62.1% White, 86.7% romantic partners of individuals with elevated PTSD symptoms). Results: After dropping cross-loading items, results indicated good fit for a higher-order model of accommodation with two factors: an anger-related accommodation factor encompassed items related largely to minimising conflict, and an anxiety-related accommodation factor encompassed items related primarily to changes to the FM’s activities. Accommodation was positively related to PTSD severity and negatively related to relationship satisfaction, although the factors showed somewhat distinct associations. Item Response Theory analyses indicated that the scale provided good information and robust coverage of different accommodation levels. Conclusions: SORTS data should be analysed as both a single score as well as two factors to explore the factors’ potential differential performance across treatment and relationship outcomes.

Machine Learning for short-term property rental pricing based on seasonality and proximity to food establishments

PurposeShort-term rentals (STRs) (like Airbnb) are reshaping social behaviour, notably in gastronomy, altering how people dine while travelling. This study delves into revenue management, examining the impact of seasonality and dining options near guests’ Airbnb. Machine Learning analysis of Airbnb data suggests owners enhance revenue strategies by adjusting prices seasonally, taking nearby food amenities into account.Design/methodology/approach This study analysed 220 Airbnb establishments from Madrid, Spain, using consistent monthly price data from Seetransparent and environment variables from MapInfo GIS. The Machine Learning algorithm calculated average prices, determined seasonal prices, applied factor analysis to categorise months and used cluster analysis to identify tourism-dwelling typologies with similar seasonal behaviour, considering nearby supermarkets/restaurants by factors such as proximity and availability of food options.FindingsThe findings reveal seasonal variations in three groups, using Machine Learning to improve revenue management: Group 1 has strong autumn-winter patterns and fewer restaurants; Group 2 shows higher spring seasonality, likely catering to tourists, and has more restaurants, while Group 3 has year-round stability, fewer supermarkets and active shops, potentially affecting local restaurant dynamics. Food establishments in these groups may need to adapt their strategies accordingly to capitalise on these seasonal trends.Originality/value Current literature lacks information on how seasonality, rental housing and proximity to amenities are interconnected. The originality of this study is to fill this gap by enhancing the STR price predictive model through a Machine Learning study. By examining seasonal trends, rental housing dynamics, and the proximity of supermarkets and restaurants to STR properties, the research enhances our understanding and predictions of STR price fluctuations, particularly in relation to the availability and demand for food options.

Perceptions and Impacts of Local Education within the ICT Field

The primary aim of this research is to uncover valuable insights into the awareness, influences, and opinions pertaining to diverse educational approaches within the ICT sector. Specifically, the study seeks to appraise and analyse the disparities, commonalities, advantages, and limitations between the two prominent educational paths prevalent in the field: academic/traditional education and vocational education. A mixed-methods research design was adopted to encompass a triangulation of perspectives. The initial viewpoint is the lead researcher’s which involved scraping 200 ICT professional profiles from LinkedIn, extensive data cleaning and analyses using MaxQDA, and applying Factor Analysis for Mixed Data (FAMD). The second viewpoint entailed the general perspective of ICT students and professionals. The collection of this perspective was through 100 online survey participants. Lastly, the third viewpoint entailed the experience of eight ICT professionals through qualitative interviews. This methodological approach facilitates the acquisition of varied insights and observations in the ICT field, both within local educational contexts and beyond.Whilst each perspective provided insights and observations pertaining to the research aim, common theories emerged which showcased the strengths and benefits of viewpoint triangulation with the adoption of mixed methods. This study makes a significant contribution to the broader comprehension of the role and significance of local education in the realm of ICT. Additionally, it embraces the concept of industrial revolution 4.0 in universities, exploring new trends and ideas while promoting collaborative efforts between students and industries in creating high-quality education.

Factor Analysis on Possum Dataset to Simplify Many Independent Variables Into Fewer Factors

This research aims to apply factor analysis to possum data with the aim of simplifying many independent variables into fewer factors. The factor analysis steps begin by grouping the variables to be analyzed and compiling a correlation matrix using the Bartlett test and the Kaiser-Meyer-Olkin (KMO) test. From the test results, it was found that the variables had sufficient correlation to proceed to factor analysis. After that, factor extraction was carried out using three criteria, namely eigenvalues, diversity percentage, and scree plot, which concluded that the number of factors formed was two. Next, factor rotation was carried out using the varimax method to simplify interpretation. The results show that certain variables have high loadings on certain factors, making it easier to identify patterns. In conclusion, factor analysis succeeded in simplifying the relationship between variables into two factors that can be interpreted more easily.

Assessing heterogeneous groundwater systems: Geostatistical interpretation of well logging data for estimating essential hydrogeological parameters

This research presents an unsupervised learning approach for interpreting well-log data to characterize the hydrostratigraphical units within the Quaternary aquifer system in Debrecen area, Eastern Hungary. The study applied factor analysis (FA) to extract factor logs from spontaneous potential (SP), natural gamma ray (NGR), and resistivity (RS) logs and correlate it to the petrophysical and hydrogeological parameters of shale volume and hydraulic conductivity. This research indicated a significant exponential relationship between the shale volume and the scaled first factor derived through factor analysis. As a result, a universal FA-based equation for shale volume estimation is derived that shows a close agreement with the deterministic shale volume estimation. Furthermore, the first scaled factor is correlated to the decimal logarithm of hydraulic conductivity estimated with the Csókás method. Csókás method is modified from the Kozeny-Carman equation that continuously estimates the hydraulic conductivity. FA and Csókás method-based estimations showed high similarity with a correlation coefficient of 0.84. The use of factor analysis provided a new strategy for geophysical well-logs interpretation that bridges the gap between traditional and data-driven machine learning techniques. This approach is beneficial in characterizing heterogeneous aquifer systems for successful groundwater resource development.

Synoptic circulation factors associated with wintertime high-PM2.5 concentrations in seoul, Republic of Korea: Their interpretations and applications

Many studies have explored atmospheric circulation fields associated with high particulate matter (PM) events, but only a few studies have evaluated the relative importance of meteorological variables in the circulation fields. In this study, we applied factor analysis method to examine meteorological variables related to the synoptic pattern of high-PM2.5 episodes (i.e., days with daily averaged PM2.5 concentration exceeding 35 μg m−3) in Seoul, Republic of Korea, during the winter season of 2004–2018. Here, we highlight three independent key factors (F1, F2, and F3) associated with high-PM2.5 concentrations. F1 is related to temperature (T) and meridional wind (V) at 850 hPa and 1000 hPa (i.e., T850, T1000, V850, and V1000) and temperature and geopotential height (Z) at 500 hPa (i.e., T500 and Z500). It is the most strongly correlated with T850, indicating an increase in thermal stability over Korea during high-PM2.5 events due to vertically reduced atmospheric ventilation. Meanwhile, F2 is significantly related to the geopotential height and zonal wind (U) at 850 hPa and 1000 hPa (i.e., Z850, Z1000, U850, and U1000). This indicates a dynamically stable condition due to the anomalous easterlies caused by the vertically elongated anticyclone over the northern part of the Korean Peninsula. Finally, F3 is positively associated with zonal and meridional winds at 500 hPa (i.e., U500 and V500). This means that relatively strong winds in the low-to-mid troposphere contribute to high-PM2.5 concentrations by transporting pollutants from the industrial regions of eastern China to Korea. We also examined the possible influence of climate change on high-PM2.5 concentrations based on the aforementioned three factors. According to the various shared socioeconomic pathway (SSP) scenarios, F1 increases significantly for all SSP scenarios compared to F2 and F3, indicating that synoptic patterns related to F1 occur frequently, leading to a favorable condition for high-PM2.5 concentrations. Thus, our finding suggests that climate change alone could worsen air quality in the future even without changes in direct PM2.5 emissions.

Beneficiaries Behavioural Intention Towards Primary Agricultural Co-operative Credit Society – A Quantitative ABCD Analysis

Purpose: The initial effort of the ABCD Analysis Framework is to come across the variables that affect members 'behavioural intentions towards Primary Agricultural Credit Co-operative Society, and to evaluate the fundamental determinants that strengthen the model's advantages, benefits, challenges, and disadvantages. A commandeer statistics can be given for each component utilizing the ABCD model. Moreover, the framework intends to apply factor analysis and elementary analysis to get supplementary comprehension of the subject matter. Determinants are a factor which decisively affects the outcome of something or controls what happens in a particular situation. Design: For this study, the published articles relating to the ABCD analytical framework have undergone a thorough evaluation. The study was able to pinpoint the main influencing factors and crucial constituent elements for the beneficiaries' behavioural intention toward agricultural co-operative credit society by properly weighting critical features and applying quantitative analysis through a group of interacting individuals. They use the group and its interaction to gain information about the specific topic and the focused issues. Findings: Based on the ABCD analysis framework, it was discovered that the motivational factors that influence a given behaviour where the stronger the intention the behaviour will be performed. Many agricultural co-operative credit societies and individuals adopt this new kind of approach the ability to maintain or support a process continuously overtime. It helps to overcome their many problems and relax. Through focus group discussions, the ABCD framework's assessment of agricultural credit co-operative members' intentions to active part in society’s activities, programmes and utilisation of available crop credit in a systematic manner. Therefore, the results of this model will help in the systematic, efficient, and effective utilisation of the available society funds for cultivation purposes. Originality/Value: The ABCD analysis method is utilized to carefully examine customers' intents not to diversify the society's available crop growing credit. The research makes use of determinant factors, important qualities, factor analysis, and elementary analysis based on crucial constituent elements in order to understand the various factors impacting members' behavioural intention to take out crop loans. This study will assist the policymakers in developing a comprehensive perspective in relation to the long-term sustenance of PACS, which is one of the most significant but underappreciated entities in India. Paper Type: Empirical analysis.

Research on the Integration Strategy of Civic and Political Education in College Students’ Career Guidance under the Perspective of Factor Analysis

Abstract The integration of Civic and Political Education and Employment Guidance can enhance the effectiveness and validity of employment guidance so that students can better solve the problems in job development and, employment, and entrepreneurship under the dual influence of employment guidance and Civic and Political Education. In analyzing the influencing factors of college students’ employability, this paper first initially identifies the relevant factors affecting college students’ employability based on survey interviews, and then applies factor analysis to explore the potential structure in the data. This study is based on doing questionnaire survey on employment influencing factors for some graduates and current students of Z university, using SPSS software to factor analyze the sample data, downgrading 10 employment influencing factors to 3 common factors, named as employment guidance factors, civic education factors, and enterprise factors, and obtaining the weights of the common factors through the normalization process. The degree of influence on college students’ employment is in descending order, with employment guidance factors, civic and political education factors, and enterprise factors being the most important factors. Employment guidance and ideological education are the key factors affecting the employment of college students, and this paper proposes relevant strategies for integrating and developing these two factors.

Аналітичні методи дослідження якості ринку праці України

In the context of diversification of characteristics and conditions of employment, qualitative parameters of labor market development are recognized by international organizations as a priority, which is reflected in numerous conceptions (for example, decent work) and methodological approaches – both scientific and institutional (research on the quality of work, quality of employment). Quality assessment is based on analytical approaches, the implementation of which in Ukraine faces a number of difficulties: lack of information support, no uniform approaches to assessment, insufficient methodical elaboration of a complex category of quality within the framework of qualiology and qualimetry. To overcome these gaps in the study, it is proposed to apply factor analysis (main components, with the rotation of solutions of factors), while using the information obtained as a result of processing microdata files of surveys on the labor force of the regions of Ukraine. The inclusion of qualitative features of the labor market development (gender parity, opportunities to combine work and family responsibilities, guarantees of formal employment, decent wages and acceptable working hours, in particular for persons with a high level of education, and others) in the factor analysis allows to identify problem processes that have the greatest impact on the formation of the quality of the labor market. The results of the calculations indicate the decisive influence of the existing deprivations on the formation of the quality of the labor market in Ukraine. The deprivations refer to compliance with decent wages and the existence of informal employment, including among people with higher education, opportunities for economic activity, as well as gender restrictions in the labor market. The problem of acceptable working hours, in particular for married women, is also significant. Such results should be taken into account when developing a policy of labor market regulation in the context of Ukraine’s post-war recovery, aimed at forming a qualitative maturity of the labor market.

Job Stress and Employees' Satisfaction in the Hospitality and Tourism Sector of North East India

In the modern times, the demand for hotel and tourism services is increasing rapidly. As a result, hospitality employees face enormous professional pressure at work. This work pressure causes them stress at work. Since job satisfaction is a prerequisite for providing the highest quality services, it largely depends on the level of stress in the workplace. In this study, determinants of job stress have been identified first and then an attempt has been made to study the impact of job stress on employees’ satisfaction. The study has been conducted with the help of primary data collected from the sample respondents from the study region. The study applies factor analysis and multiple regression analysis. The study confirmed that Eustress has direct positive relations with the job satisfaction of employees. Since, the study reveals that Duration of Work (DW), Job Profile (JP), and Expectation of Management (EM) have positively influenced job satisfaction. HR Policy (HRP) and Work Pressure (WP) negatively affected job satisfaction and these aspects should be taken care of by the management.

Identifying subgroups of childhood obesity by using multiplatform metabotyping.

Introduction: Obesity results from an interplay between genetic predisposition and environmental factors such as diet, physical activity, culture, and socioeconomic status. Personalized treatments for obesity would be optimal, thus necessitating the identification of individual characteristics to improve the effectiveness of therapies. For example, genetic impairment of the leptin-melanocortin pathway can result in rare cases of severe early-onset obesity. Metabolomics has the potential to distinguish between a healthy and obese status; however, differentiating subsets of individuals within the obesity spectrum remains challenging. Factor analysis can integrate patient features from diverse sources, allowing an accurate subclassification of individuals. Methods: This study presents a workflow to identify metabotypes, particularly when routine clinical studies fail in patient categorization. 110 children with obesity (BMI > +2 SDS) genotyped for nine genes involved in the leptin-melanocortin pathway (CPE, MC3R, MC4R, MRAP2, NCOA1, PCSK1, POMC, SH2B1, and SIM1) and two glutamate receptor genes (GRM7 and GRIK1) were studied; 55 harboring heterozygous rare sequence variants and 55 with no variants. Anthropometric and routine clinical laboratory data were collected, and serum samples processed for untargeted metabolomic analysis using GC-q-MS and CE-TOF-MS and reversed-phase U(H)PLC-QTOF-MS/MS in positive and negative ionization modes. Following signal processing and multialignment, multivariate and univariate statistical analyses were applied to evaluate the genetic trait association with metabolomics data and clinical and routine laboratory features. Results and Discussion: Neither the presence of a heterozygous rare sequence variant nor clinical/routine laboratory features determined subgroups in the metabolomics data. To identify metabolomic subtypes, we applied Factor Analysis, by constructing a composite matrix from the five analytical platforms. Six factors were discovered and three different metabotypes. Subtle but neat differences in the circulating lipids, as well as in insulin sensitivity could be established, which opens the possibility to personalize the treatment according to the patients categorization into such obesity subtypes. Metabotyping in clinical contexts poses challenges due to the influence of various uncontrolled variables on metabolic phenotypes. However, this strategy reveals the potential to identify subsets of patients with similar clinical diagnoses but different metabolic conditions. This approach underscores the broader applicability of Factor Analysis in metabotyping across diverse clinical scenarios.