Popecastro6041

Time perspective is conceptualized as a multidimensional construct that assesses individuals' feelings and thoughts about the past, present, and future. The current study examined relationships between feelings (time attitudes) and thoughts (time orientation) about time and substance use behaviors across three adolescent samples. Participants included a high-risk sample of adjudicated youth (N=124; M age =15.54, SD=1.69; 51.61% female) and two general population school samples (N=777; M age =15.82, SD=1.23; 53.41% female; N=1873; M age =15.87, SD=1.28; 52.22% female). Cross-sectional survey data were collected from samples in schools during 2010, 2016, and 2011, respectively. Poisson and negative binomial regression analyses indicated that overall, more positive feelings about time were associated with fewer substances used and, conversely, more negative feelings about time were associated with more substances used. These findings were particularly salient for participants with stronger positive and negative feelings toward the past and present time periods. Further, across the three samples, adolescents with a balanced time orientation (i.e., equal emphasis on all three time periods) generally reported less substance use than individuals who emphasized only one or two time periods. Findings highlight relationships between time perspective dimensions and substance use across diverse samples and illustrate opportunities for adapting time perspective-based substance use interventions for adolescents.

Hypertension is one of the leading causes of disability and death in both developed and developing countries including Ethiopia. Non-communicable diseases account for 42% of deaths in Ethiopia. However, it is still widely undetected and poorly controlled. Hence, this study aims to assess the lifestyle modification practices and related factors of adult hypertensive patients in the central Gondar region of northwestern Ethiopia.

Institutional based, cross-sectional study was conducted from April 10 up to May 10, 2021. A simple random sampling was used to select 629 study participants. Data were collected by using self-administered and structured questionnaire. Data were entered to EpiData 4.6 and exported to SPSS 20 for further analysis. A multivariable logistic regression analysis was employed to identify the factors associated with lifestyle modification. Adjusted Odds Ratio (AOR) with 95% confidence interval was used to show the strength of association, while a P-value <.05 of was used to declare thearea was found to be low among the hypertensive patients. Respondents' age, education status, wealth index, duration of diagnosis, and co morbidities were found to be significant factors related to good lifestyle modification practices. Therefore, more attention should be paid to providing nutrition counseling and health promotion to improve the practice of lifestyle modification in patients with hypertension.[This corrects the article DOI 10.2147/DMSO.S347830.].

Diabetes mellitus (DM) and thyroid dysfunction (TD) are two closely associated disorders. The objective of the present study was to investigate the thyroid status and the relationships between thyroid hormones, diabetic complications and metabolic parameters in hospitalized patients with newly diagnosed type 2 DM (T2DM).

This was an observational cross-sectional study, conducting on 340 patients with newly diagnosed T2DM who were admitted to ward of endocrinology department and 120 matched individuals without diabetes. Anthropometric, clinical and biochemical data were collected. Spearman correlation coefficients were calculated to evaluate the correlations between thyroid hormones and other variables. Factors associated with diabetic nephropathy (DN) was analyzed with multivariate logistic regression.

Levels of free triiodothyronine (FT3), free thyroxine (FT4) and thyroid stimulating hormone (TSH) were significantly lower in patients with T2DM as compared to control group without diabetes. The prevalens are related to thyroid hormone levels. Decreased FT3 is strongly correlated with the presence of DN.Emojis are small pictograms that are frequently embedded within micro-texts to more directly express emotional meanings. To understand the changes in the emoji usage of internet users during the COVID-19 outbreak, we analysed a large dataset collected from Weibo, the most popular Twitter-like social media platform in China, from December 1, 2019, to March 20, 2020. The data contained 38,183,194 microblog posts published by 2,239,472 unique users in Wuhan. We calculated the basic statistics of users' usage of emojis, topics, and sentiments and analysed the temporal patterns of emoji occurrence. After examining the emoji co-occurrence structure, we finally explored other factors that may affect individual emoji usage. We found that the COVID-19 outbreak greatly changed the pattern of emoji usage; i.e., both the proportion of posts containing emojis and the ratio of users using emojis declined substantially, while the number of posts remained the same. The daily proportion of Happy emojis significantly declined to approximately 32%, but the proportions of Sad- and Encouraging-related emojis rose to 24% and 34%, respectively. Despite a significant decrease in the number of nodes and edges in the emoji co-occurrence network, the average degree of the network increased from 34 to 39.8, indicating that the diversity of emoji usage increased. find more Most interestingly, we found that male users were more inclined towards using regular textual language with fewer emojis after the pandemic, suggesting that during public crises, male groups appeared to control their emotional display. In summary, the COVID-19 pandemic remarkably impacted individual sentiments, and the normal pattern of emoji usage tends to change significantly following a public emergency.

The online version contains supplementary material available at 10.1007/s10489-022-03195-y.

The online version contains supplementary material available at 10.1007/s10489-022-03195-y.Today, due to the widespread outbreak of the deadly coronavirus, popularly known as COVID-19, the traditional classroom education has been shifted to computer-based learning. Students of various cognitive and psychological abilities participate in the learning process. However, most students are hesitant to provide regular and honest feedback on the comprehensiveness of the course, making it difficult for the instructor to ensure that all students are grasping the information at the same rate. The students' understanding of the course and their emotional engagement, as indicated via facial expressions, are intertwined. This paper attempts to present a three-dimensional DenseNet self-attention neural network (DenseAttNet) used to identify and evaluate student participation in modern and traditional educational programs. With the Dataset for Affective States in E-Environments (DAiSEE), the proposed DenseAttNet model outperformed all other existing methods, achieving baseline accuracy of 63.59% for engagement classification and 54.27% for boredom classification, respectively. Besides, DenseAttNet trained on all four multi-labels, namely boredom, engagement, confusion, and frustration has registered an accuracy of 81.17%, 94.85%, 90.96%, and 95.85%, respectively. In addition, we performed a regression experiment on DAiSEE and obtained the lowest Mean Square Error (MSE) value of 0.0347. Finally, the proposed approach achieves a competitive MSE of 0.0877 when validated on the Emotion Recognition in the Wild Engagement Prediction (EmotiW-EP) dataset.Online learning is playing an increasingly important role in education. Massive open online course (MOOC) platforms are among the most important tools in online learning, and record historical learning data from an extremely large number of learners. To enhance the learning experience, a promising approach is to apply sequential pattern mining (SPM) to discover useful knowledge in these data. In this paper, mining sequential patterns (SPs) with flexible constraints in MOOC enrollment data is proposed, which follows that research approach. Three constraints are proposed the length constraint, discreteness constraint, and validity constraint. They are used to describe the effect of the length of enrollment sequences, variance of enrollment dates, and enrollment moments, respectively. To improve the mining efficiency, the three constraints are pushed into the support, which is the most typical parameter in SPM, to form a new parameter called support with flexible constraints (SFC). SFC is proved to satisfy the downward closure property, and two algorithms are proposed to discover SPs with flexible constraints. They traverse the search space in a breadth-first and depth-first manner. The experimental results demonstrate that the proposed algorithms effectively reduce the number of patterns, with comparable performance to classical SPM algorithms.This paper proposes a new generalization of the Gull Alpha Power Family of distribution, namely the exponentiated generalized gull alpha power family of distribution abbreviated as (EGGAPF) with two additional parameters. This proposed family of distributions has some well known sub-models. Some of the basic properties of the distribution like the hazard function, survival function, order statistics, quantile function, moment generating function are investigated. In order to estimate the parameters of the model the method of maximum likelihood estimation is used. To assess the performance of the MLE estimates a simulation study was performed. It is observed that with increase in sample size, the average bias, and the RMSE decrease. A distribution from this family is fitted to two real data sets and compared to its sub-models. It can be concluded that the proposed distribution outperforms its sub-models.Authigenic molybdenum (Mo) accumulation in marine sediments has often been used as a qualitative indicator of hypoxic bottom water. To investigate its use as a quantitative indicator of hypoxic exposure, sediment cores were collected from water quality monitoring sites in Narragansett Bay (RI, USA) that experience varying periods of hypoxia. Total Mo concentrations in surficial (0-1 cm) sediments were determined by total digestion and ICP-MS analysis. Lithogenic contributions to total Mo concentrations were estimated by multiplying measured concentrations of aluminum (Al) by the mean crustal MoAl ratio and subtracting them from the total concentrations to yield the authigenic fraction. 210Pb dating was used to determine sediment accumulation rates at each site. Mean annual periods of hypoxia in bottom waters were determined from continuous monitoring data for the years coinciding with the top 1 cm of sediment. Results indicated a linear relationship between authigenic Mo concentrations and frequency of hypoxia, although the relationships differed between different sampling periods. These results demonstrate the potential of sedimentary Mo as a tool for assessing the spatial and temporal extent of hypoxia in coastal waters.Nowadays, there is a problem related to wastewater handling which is released from different activities. The electrocoagulation method has been a dominant treatment method for wastewater treatment. There are different forms of electrocoagulation methods for wastewater treatment. Nevertheless, there was no comparison made for the removal efficiency of the sonoalternate current (SAC), alternate current (AC), sonodirect current (SDC), and direct current (DC) electrocoagulation process. The efficiency of electrocoagulation methods was compared for their removal of chemical oxygen demand (COD) from Jimma University domestic wastewater. Batch Reactor DC/AC electrocoagulation cell was used to determine the removal efficiency. During the comparison, the response surface methodology (RSM) was used to analyze and optimize the data taken from the laboratory. Besides, ANOVA was used to analyze the interaction effects of different parameters. The removal of COD from domestic wastewater was achieved with DCE, ACE, SDCE, and SACE which were 82.