Psychometric validation of diabetes distress scale in Bangladeshi population

Diabetes Distress (DD)—an emotional or affective state arise from challenge of living with diabetes and the burden of self-care—negatively impact diabetes management and quality of life of T2DM patients. Early detection and management of DD is key to efficient T2DM management. The study aimed at developing a valid and reliable instrument for Bangladeshi patients as unavailability such a tool posing challenge in diabetes care. Linguistically adapted, widely used, 17-item Diabetes Distress Scale (DDS), developed through forward–backward translation from English to Bengali, was administered on 1184 T2DM patients, from four diabetes hospitals in Bangladesh. Psychometric assessment of the instrument included, construct validity using principal component factor analysis, internal consistency using Cronbach’s α and discriminative validity through independent t-test and test–retest reliability using intraclass-correlation coefficient (ICC) and Kappa statistics. Factor analysis extracted 4 components similar to original DDS domains, confirms the construct validity. The scale demonstrated satisfactory internal consistency (α = 0.838), stability (test–retest ICC = 0.941) and good agreement across repeated measurements (Kappa = 0.584). Discriminative validity revealed that patients with complication (p < 0.001) and those are on insulin (p < 0.001) had significantly higher distress scores in all domains. Bengali version of DDS is a valid and reliable tool for assessing distress among Bangladeshi T2DM patients.

E-Commerce Credit Risk Assessment Based on Fuzzy Neural Network

In this paper, we propose a cooperative strategy-based self-organization mechanism to reconstruct the network. The mechanism includes a comprehensive evaluation algorithm and structure adjustment mechanism. The self-organization mechanism can be carried out simultaneously with the parameter optimization process. By calculating the similarity and independent contribution of normative neurons, the effectiveness of fuzzy rules can be jointly evaluated, and effective structural changes can be realized. Moreover, this mechanism should not set the threshold in advance in practical application. In order to optimize the parameters of SC-IR2FNN, we developed a parameter optimization mechanism based on an interaction strategy. The parameter optimization mechanism based on a joint strategy, namely multilayer optimization engine, can split SC-IR2FNN parameters into nonlinear and linear parameters for joint optimization. The nonlinear parameters are optimized by an advanced two-level algorithm, and the linear parameters are updated with the minimum biological multiplication. Two parameter optimization algorithms optimize nonlinear and linear parameters, reduce the computational complexity of SC-IR2FNN, and improve the learning rate. Using the principal component factor analysis method, seven representative common factors are selected to replace the original variables, which include the profitability factor of the financing enterprise, the solvency factor of the financing enterprise, the profitability factor of the core enterprise, the operation guarantee factor, and the growth ability of the financing enterprise. Factors, supply chain online degree factors, financing enterprise quality, and cooperation factors, can well measure the credit risk of online supply chains. The logistic model shows that the profitability factor of the financing company, the debt repayment factor of the financing company, and the profitability of the core company are three factors that have a significant impact on the credit risk of online supply chain finance. Based on the improved credit calculation model, we developed an online clue risk calculation. This method is based on site conditions and can evaluate credit risk. From the test results, the improved credit scoring system is the result of facing speculative and circular credit fraud and implies that the traders of risk commentators are in a leading position in each electronic device. The results show that risk analysis is effective in any case.

Mapping the Intellectual Structure of the Big Data Research in the IS Discipline

Big data (BD) is one of the emerging topics in the field of information systems. This article utilized citation and co-citation analysis to explore research articles in the field of BD to examine the scientific development in the area. The research data was retrieved from the WOS database from the period between 2005 and June 2016, which consists of 366 articles. In the citation analysis, this article relies on the degree centrality and betweenness centrality for identifying 38 important papers in BD. In the co-citation analysis, a principal component factor analysis of the co-citation matrix is employed for identifying six major research themes: foundations, BD applications, techniques and technologies, challenges, adoption and impacts and literature review. This literature review is one of the first studies to examine the knowledge structure of BD research in the information systems discipline by using evidence-based analysis methods. Recommendations for future research directions in BD are provided based on the analysis and results of this study.

Disability in Older People and Socio-Economic Deprivation in Italy: Effects on the Care Burden and System Resources

The sustainability of European Long-Term Care systems faces the demographic and socio-economic circumstances, mainly the increasing ageing of the population, with its chronic disease conditions, and the simultaneous economic general crises, exacerbated by the recent COVID-19 pandemic. Beyond the increase in general rate of relative poverty, there is a higher risk of poverty among elderly and families in a high demand of care, especially if situations of Activities Daily Living (ADL) disability are present. Italian welfare, which is based on family care regimes and regional strategies, and is oriented to private or public care, is a relevant case study with which to analyze such a relationship. This paper aims to study the relationship between ADL disability and the socio-economic deprivation of families, that is, household poverty. Variables came from the ISTAT Health for All Italian Database and the INAIL Disability Allowance Database. A pool of statistical methods, based on bivariate and multivariate analyses, from bivariate correlation, through multiple linear regression to principal component factor analysis, were used to reduce the number of the variables and compute the indicators. The multivariate analysis underlines how ADL disability impacts on a household’s poverty, confirming the existence of statistical correlation between them. Moreover, the study identifies and measures two answer capability models to cope with household poverty. The answer capability of the formal system is the main tool for reducing poverty due to one family member’s ADL disability. Integration and collaboration between the formal system and family capabilities remains the main solution.

Dietary patterns and birth outcomes in the ELSPAC pregnancy cohort

ObjectivesThe aim of this study was to identify dietary patterns in a Czech pregnancy cohort established in the early postcommunist era and investigate associations between dietary patterns, maternal characteristics and birth outcomes.MethodsPregnant women were recruited for the Czech part of the European Longitudinal Study of Pregnancy and Childhood. A self-reported questionnaire answered in late pregnancy was used to assess information about the weekly intake of 43 food items. Information about birth outcomes (birth weight, height, ponderal index, head circumference, cephalisation index, gestational length and Apgar score) was obtained from the National Registry of Newborns. Complete details on diet and birth outcomes were available for 4320 mother–infant pairs.Results and conclusionThe food items were aggregated into 28 variables and used for extraction of two dietary patterns by principal component factor analysis. The patterns were denoted ‘unhealthy’ and ‘healthy/traditional’ based on the food items with the highest factor loadings on each pattern. The ‘unhealthy’ pattern had high positive loadings on meat, processed food and confectionaries. In contrast, the ‘healthy/traditional’ pattern had high positive loadings on vegetables, dairy, fruits and wholemeal bread. Following adjustment for covariates, we found that high adherence to the unhealthy pattern (expressed as beta for 1 unit increase in pattern score), that is, the higher consumption of less healthy foods, was associated with lower birth weight: −23.8 g (95% CI −44.4 to −3.2) and length: −0.10 cm (95% CI −0.19 to −0.01) and increased cephalisation index: 0.91 μm/g (95% CI 0.23 to 1.60). The ‘healthy/traditional’ pattern was not associated with any birth outcomes. This study supports the recommendation to eat a healthy and balanced diet during pregnancy.

Factors Driving BIM Learning Performance: Research on China’s Sixth National BIM Graduation Design Innovation Competition of Colleges and Universities

With the popularization and rise in BIM technology usage, BIM education for undergraduate students in architecture, engineering, and construction (AEC) related disciplines has emerged as a priority. This study assesses the BIM learning outcomes of students participating in the National BIM Graduation Design Innovation Competition of Colleges and Universities. In total, 2777 valid questionnaire responses were obtained for this study. The Cronbach’s alpha coefficient method and principal component factor analysis method were used to verify the reliability of the data set (Cronbach’s alpha = 0.962, KMO = 0.965). The t-test (ANOVA) was used to verify that gender, school type, major, grade, study duration and use BIM related software, as well as other demographic attributes, displayed significant inter-group differences. Seven common factors affecting BIM learning performance were obtained by exploratory factor analysis: (1) ability of the instructor, (2) school (college) atmosphere, (3) teamwork, (4) individual ability, (5) understanding of BIM industry applications, (6) social environment incentives, and (7) achievement demand. Finally, the results of an ordered logistic regression revealed that the demographic attributes of participants, the comprehensive ability of the instructor, teamwork, individual ability, and achievement demand significantly affects BIM learning performance. Based on these findings, this paper puts forward suggestions for improving BIM learning performance and provides theoretical support for BIM education and learning in AEC related undergraduate majors.

Association between Dietary Patterns and Chronic Obstructive Pulmonary Disease in Korean Adults: The Korean Genome and Epidemiology Study

In addition to smoking, dietary habits may contribute to the development of chronic obstructive pulmonary disease (COPD). This study aimed to examine the association between dietary patterns and lung function in a Korean community cohort. A total of 5436 participants were included from the Ansan–Ansung cohort study. To identify the dietary patterns, we performed principal component factor analysis using the results of a semi-quantitative food frequency questionnaire. The forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), and FEV1/FVC ratio were measured by spirometry. Multiple logistic regression models were used to evaluate the association between dietary patterns and lung function after adjusting for confounders. We identified four major dietary patterns; ‘prudent’, ‘coffee, fat, and sweet’, ‘westernized’, and ‘white rice’. After adjusting for potential confounders, the ‘coffee, fat, and sweet’ dietary pattern was negatively associated with lung function, particularly the FEV1/FVC ratio. Participants with high scores for the ‘coffee, fat and sweet’ pattern had a higher risk of COPD among men but not women. Therefore, these results indicate that the ‘coffee, fat and sweet’ dietary pattern is inversely related to lung function in Korean adults. Our results indicate that dietary habits may be modifiable risk factors for COPD.

Assessment of Seasonal and Spatial Variations of Coastal Water Quality Using Multivariate Statistical Techniques

This study investigates the seasonal and spatial trends in Kuwait’s coastal water’s physical, chemical, and biological parameters by applying multivariate statistical techniques, including cluster analysis (CA), principal component/factor analysis (PCA/FA), and the Pearson correlation (PC) method to the average daily reading of water quality parameters from fifteen stations over one year. The investigated parameters are pH, turbidity, chlorophyll-a, conductivity, dissolved oxygen (DO), phycoerythrin, salinity, and temperature. The results show that the coastal water of Kuwait is subjected to high environmental pressure due to natural and human interferences. During 2017, the DO levels were below the threshold limit, and at the same time, the water temperature and salinity were very high, causing a series of fish death events. CA resulted in three different regions based on the turbidity, including high, moderate, and low regions, and three seasons (winter, summer, and autumn). Spring is very short and overlaps with winter and summer. PCA/FA applied on the datasets assisted in extracting and identifying parameters responsible for the variations in the seasons and regions obtained from CA. Additionally, Pearson’s correlation resulted in a strong positive relation between chlorophyll and phycoerythrin in 7 out of the 15 stations. However, at high turbidity regions (stations 1 and 2), chlorophyll concentration was low. Additionally, the negative correlation between DO and temperature was observed at stations with rare human activities.

Development of a Scale to Remove Farmers’ Sustainability Barriers to Meteorological Information in Iran

Even with significant breakthroughs in the production and delivery of meteorological information, most farmers are not able to utilize such information properly and pertinently. Up to the present time, a standardized scale has not been developed to examine farmers’ sustainability barriers to meteorological information use (BMIU). Furthermore, there is no doubt that identifying indicators and dimensions of sustainability barriers to meteorological information and weather forecasts’ usage by farmers can play a major role in their adaptation and resilience to the risks of climate change. Therefore, the present study aimed to generate and validate a scale for BMIU by farmers through an eight-step approach. Accordingly, the statistical population included 9006 Iranian farmers, 368 of whom were selected as study samples. The principal component factor analysis (PCFA) and second-order confirmatory factor analysis (CFA) were further practiced to develop the scale for meteorological information and weather forecasts’ use. Factor analysis also led to the emergence of five latent factors including “educational–communicative barriers (ECBs)”, “normative barriers (NBs)”, “informational barriers (IBs)”, “infrastructural–political barriers (IPBs)”, and “professional–economic barriers (PEBs)”. The second-order CFA correspondingly confirmed these five factors and their 25 related indicators. Given the challenges facing academic scholars, decision makers, and authorities in the application and facilitation of meteorological information, the developed multidimensional scale in this study along with its implementation steps can be effective in examining the limitations of utilizing such information and measuring its impacts in different agricultural communities.

Breakfast Dietary Pattern Is Inversely Associated with Overweight/Obesity in European Adolescents: The HELENA Study

Obesity in children and adolescents is a public health problem and diet can play a major role in this condition. We aimed to identify sex-specific dietary patterns (DP) and to evaluate the association with overweight/obesity in European adolescents. We conducted a cross-sectional analysis with 2327 adolescents aged between 12.5 to 17.5 years from a multicenter study across Europe. The body mass index was categorized in “normal weight” and “overweight/obesity”. Two non-consecutive 24-h dietary recalls were collected with a computerized self-reported software. Principal component factor analysis was used to identify DP. Mixed-effect logistic regression models were used to evaluate the association between the sex-specific DP and overweight/obesity outcome. As a result, we found three DP in boys (snacking and bread, Mediterranean diet, and breakfast) and four DP in girls (convenience, plant-based and eggs, Western, and breakfast). The association between DP and overweight/obesity highlights that those adolescents with higher adherence to the breakfast DP had lower odds for overweight/obesity, even after the inclusion of covariables in the adjustments. In European adolescents, the breakfast DP positively characterized by breakfast cereals, fruit, milk, and dairy and negatively characterized by sugar-sweetened beverages in boys and negatively characterized by cereals (pasta, rice, and others) in girls, was inversely associated with overweight/obesity.