Miracle-dz/UniFAR-FTUs huggingface dataset:
Effective coping strategies for stressed frontline employees in service occupations: outcomes and drivers

ABSTRACT Despite extensive research on the negative outcomes of role ambiguity in boundary-spanning positions, insight into how to prevent frontline employees from experiencing role ambiguity is sparse. This study addresses questions as to how the problem-focused coping strategies used by frontline employees can effectively reduce perceived role ambiguity. The gathered evidence indicates that only action coping is effective, whereas instrumental support seeking in fact enhances perceived role ambiguity. An examination of intrinsic and extrinsic coping resources as drivers of coping has revealed that conscientiousness and supervisor support are helpful coping resources. Contrastively, neuroticism drives insufficient coping and inhibits the effective use of coping resources. We find that managers of service firms should provide training to ensure effective supervisor support; also, in recruitment procedures they should consider potential employees’ personality traits, to reduce and prevent role ambiguity experienced by frontline employees.
A Novel Ensemble Classifier based Classification on Large Datasets with Hybrid Feature Selection Approach

Exploring and analyzing large datasets has become an active research area in the field of data mining in the last two decades. There had been several approaches available in the literature to investigate the large datasets that comprise of millions of data. The most important data mining approaches involved in this task are preprocessing, feature selection and classification. All the three approaches have their own importance in carrying out the task effectively. Most of the existing techniques suffer from drawbacks of high complexity and computationally costly on large data sets. Especially, the classification techniques do not provide consistent and reliable results for large datasets which makes the existing classification systems inefficient and unreliable. This study mainly focuses on develop a novel and efficient framework for analyzing and classifying a large dataset. This study proposes a novel classification approach on large datasets through the process of ensemble classification. Initially, efficient preprocessing approach based on enhanced KNN and feature selection based on genetic algorithm integrated with Kernal PCA are carried out which selects a subset of informative attributes or variables to construct models relating data. Then, Classification is carried on the selected features based on the ensemble approach to get accurate results. This research study presents two types of ensemble classifiers called homogenous and heterogeneous ensemble classifiers to evaluate the performance of the proposed system. Experimental results shows that the proposed approach provide significant results for various large datasets.
Selection of Relevant Features in Machine Learning

In this paper, we review the problem of selecting rele- vant features for use in machine learning. We describe this problem in terms of heuristic search through a space of feature sets, and we identify four dimensions along which approaches to the problem can vary. We consider recent work on feature selection in terms of this framework, then close with some challenges for future work in the area. 1. The Problem of Irrelevant Features accuracy) to grow slowly with the number of irrele- vant attributes. Theoretical results for algorithms that search restricted hypothesis spaces are encouraging. For instance, the worst-case number of errors made by Littlestone's (1987) WINNOW method grows only logarithmically with the number of irrelevant features. Pazzani and Sarrett's (1992) average-case analysis for WHOLIST, a simple conjunctive algorithm, and Lang- ley and Iba's (1993) treatment of the naive Bayesian classifier, suggest that their sample complexities grow at most linearly with the number of irrelevant features. However, the theoretical results are less optimistic for induction methods that search a larger space of concept descriptions. For example, Langley and Iba's (1993) average-case analysis of simple nearest neighbor indicates that its sample complexity grows exponen- tially with the number of irrelevant attributes, even for conjunctive target concepts. Experimental stud- ies of nearest neighbor are consistent with this conclu- sion, and other experiments suggest that similar results hold even for induction algorithms that explicitly se- lect features. For example, the sample complexity for decision-tree methods appears to grow linearly with the number of irrelevants for conjunctive concepts, but exponentially for parity concepts, since the evaluation metric cannot distinguish relevant from irrelevant fea- tures in the latter situation (Langley & Sage, in press). Results of this sort have encouraged machine learn- ing researchers to explore more sophisticated methods for selecting relevant features. In the sections that fol- low, we present a general framework for this task, and then consider some recent examples of work on this important problem.
Mental Health Treatment Involvement and Religious Coping among African American, Hispanic, and White Veterans of the Wars of Iraq and Afghanistan

Although racial/ethnic differences have been found in the use of mental health services for depression in the general population, research among Veterans has produced mixed results. This study examined racial/ethnic differences in the use of mental health services among 148 Operation Enduring/Iraqi Freedom (OEF/OIF) Veterans with high levels of depression and posttraumatic stress disorder (PTSD) symptoms and evaluated whether religious coping affected service use. No differences between African American, Hispanic, and Non-Hispanic white Veterans were found in use of secular mental health services or religious counseling. Women Veterans were more likely than men to seek secular treatment. After controlling for PTSD symptoms, depression symptom level was a significant predictor of psychotherapy attendance but not medication treatment. African American Veterans reported higher levels of religious coping than whites. Religious coping was associated with participation in religious counseling, but not secular mental health services.
Swertiamarin: An Active Lead from Enicostemma littorale Regulates Hepatic and Adipose Tissue Gene Expression by Targeting PPAR-γ and Improves Insulin Sensitivity in Experimental NIDDM Rat Model

Enicostemma littorale (EL) Blume is one of the herbs widely used for treating and alleviating the effects of both type I and type II diabetes. However, lack of understanding of mechanism precludes the use of the herb and its molecules. In this study, we attempt to unravel the molecular mechanism of action of swertiamarin, a compound isolated form EL, by comparing its molecular effects with those of aqueous EL extract in alleviating the insulin resistance in type II diabetes. We further investigated hypolipidemic and insulin sensitizing effect of swertiamarin in experimentally induced noninsulin dependent diabetes mellitus (NIDDM) in rats. Swertiamarin (50 mg/kg) and aqueous extract (15 grams dried plant equivalent extract/kg) were administered to rats orally for 40 days and tight regulation of serum glucose, insulin, and lipid profile was found in both groups. Their mode of action was by restoring G6Pase and HMG-CoA reductase activities to normal levels and restoring normal transcriptional levels of PEPCK, GK, Glut 2, PPAR-γ, leptin, adiponectin, LPL, SREBP-1c, and Glut 4 genes. This suggests that both treatments increased insulin sensitivity and regulated carbohydrate and fat metabolism. This is the first report on the role of SM in regulating the PPARγ-mediated regulation of candidate genes involved in metabolism in peripheral tissues in vivo.
ANALISIS KOMPETENSI GURU SMK BERDASARKAN TECHNOLOGICAL PEDAGOGICAL CONTENT KNOWLEDGE (TPACK) FRAMEWORK

Penelitian ini bertujuan untuk menganalisis kompetensi Technological Technological Pedagogical Content Knowledge (TPACK) guru SMK yang mengajar mata pelajaran produktif TKI dan guru SMK yang tidak mengajar mata pelajaran produktif TKI. Teknik sampling yang digunakan adalah convenience sampling sebanyak 18 orang guru produktif TKI dan 18 orang guru Non TKI dari empat Sekolah Menengah Kejuruan di Kota Bandung. Pengumpulan data dilakukan melalui pengisian instrument Self-Assessment TPACK, format observasi, dan kuisioner siswa. Berdasarkan hasil temuan di lapangan, Pada umumnya, guru sudah memiliki pengetahuan tentang TPACK. Hal ini ditunjukkan ketika pelaksanaan kegiatan belajar dan mengajar guru menyampaikan materi pembelajaran (Content Knowledge) dengan menggunakan metode pembelajaran tertentu (Pedagogical Knowledge) yang ditunjang dengan penggunaan teknologi, seperti projector dan personal computer. Guru Produktif TIK yang menganggap dirinya masih memerlukan tambahan pengetahuan mengenai komponen TPACK, menurut observer dianggap sudah memiliki pengetahuan yang cukup mengenai komponen TPACK. Kepuasan siswa terhadap Guru yang menganggap dirinya masih memerlukan tambahan pengetahuan mengenai komponen TPACK cukup baik. Rerata nilai Guru Produktif TIK lebih tinggi pada penguasaan Technological Knowledge (TK) jika dibandingkan dengan Guru Non TKI, yaitu 5,48. Sedangkan Guru Non TKI memiliki rerata nilai lebih tinggi pada penguasaan Pedagogical Knowledge (PK) dengan rerata 6,14. ------ This study aims to analyze the competence of Technological Technological Pedagogical Content Knowledge (TPACK) vocational teachers who teach productive subjects of TKI and vocational teachers who do not teach productive subjects of ICT. The sampling technique used is convenience sampling as much as 18 people productive teachers of ICT and 18 non-migrant teachers from four Vocational High School in Bandung. Data collection was done by filling TPACK Self-Assessment instrument, observation form, and student questionnaire. Based on the findings in the field, In general, teachers already have knowledge about TPACK. This is shown when the implementation of teaching and learning activities of teachers convey learning materials (Content Knowledge) by using a particular method of learning (Pedagogical Knowledge) is supported by the use of technology, such as projector and personal computer. ICT Productive Teachers who consider themselves still need additional knowledge about the TPACK component, according to the observer is considered to have enough knowledge about TPACK components. Student satisfaction towards teachers who consider themselves still need additional knowledge about TPACK component is good enough. The average of ICT Productive Teachers is higher in the mastery of Technological Knowledge (TK) when compared to Non-ICT Teachers, that is 5.48. While Non-ICT Teachers have higher mean value on Pedagogical Knowledge (PK) mastery with average of 6,11.
Presumptions and actions affecting an e-learning adoption by the educational system - Implementation using virtual private networks

In this paper we present a model of e-learning suitable for teacher training sessions. The main purpose of our work is to define the components of the educational system which influences the successful adoption of e-learning in the field of education. We also present the factors of the readiness of e-learning mentioned in the literature available and classifies them into the 3 major categories that constitute the components of every organization and consequently that of education. Finally, we present an implementation model of e-learning through the use of virtual private networks, which lends an added value to the realization of e-learning.
Social Class and Racial Prejudice: A Re-examination of the “Working-Class Authoritarianism” Hypothesis

Author(s): Moskowitz, Jake Philip | Advisor(s): Piff, Paul K | Abstract: Social class, a multifaceted construct implicated in the formation of social values and interpersonal behavior, has long been debated as either a driver or inhibitor of racial prejudice. Here, I test the relationship between various components of social class and racial prejudice, predicting that prejudice will be positively correlated with social class when operationalized as income or subjective rank, and negatively correlated with social class when operationalized as educational attainment. An internal meta-analysis of 6 studies (N = 77,574) provided support for the first hypothesis, but not the second: income and subjective rank were found to be positively associated with racial prejudice, operationalized through a variety of survey measures of prejudice and the IAT. Educational attainment, in contrast, was not significantly associated with prejudice. Three hypothesized mechanism variables (power, SDO, and intergroup contact) provided inconsistent evidence of mediation of the relationship between income/subjective rank and racial prejudice. These findings contribute to the existing literature on social class and prejudice, suggesting that in certain social contexts, greater material wealth and subjective rank are associated with increased prejudice against racial minorities. These findings also illuminate the importance of studying the ways in which the various components of social class denote subtle differences in their downstream psychological effects. Implications of these findings for societies characterized by increasing racial socioeconomic stratification are discussed.
The Effect of Role Ambiguity and Role Conflict on Dysfunctional Audit Behaviour: Evidence from Jordan

This paper explores dysfunctional audit behaviour among internal auditors specifically on its antecedents. The applicable literature is used to predict the relationships among stressors and dysfunctional audit behaviour. These relationships were then tested based on the survey outcomes from 187 internal auditors serving in Jordan. The results of structural equation model demonstrate the appropriate fit of the proposed conceptual model towards the data. The results demonstrate premature sign-off as the direct outcomes of role ambiguity and role conflict. This study also highlights the implications of these findings towards the management of companies and the potential future research.
Electronic Journal of Sociology (2008)

This paper applies Max Weber’s proposition regarding the differences between the ‘sciences’ to the ‘historicist controversy’: the problems emerging from opposing approaches to understanding the past. The historiography in question is the development of the ‘biomedical model’ of health and disease, and the rise of ‘medicine’ in the course of 19th century Europe and Britain. While Weber’s theoretical framework does not answer the questions posed by present-day scholars about specific historical events, it enables a critique of the process through which history is ‘constructed’, and offers an alternative approach to the ‘transformation’ of 19th century medicine.
Semantic networks and competition: Election year winners and losers in U.S. televised presidential debates, 1960-2004

Drawing on network theory, this study considers the content of U.S. presidential debates and how candidates’ language differentiates them. Semantic network analyses of all U.S. presidential debates (1960–2004) were conducted. Results reveal that regardless of party affiliation, election winners were more central in their semantic networks than losers. Although the study does not argue causation between debating and electoral outcomes, results show a consistent pattern: Candidates who develop coherent, central, semantically structured messages in debates seem to be victorious on election day. An argument is made for employing semantic networks in studying debates and political discourse.
Development of cost effective ECG data acquisition system for clinical applications using LabVIEW

The main objective of this work is to develop a portable and cost effective data acquisition (DAQ) system for clinical applications. This DAQ consists of several modules such as power supply, analog to digital converter (ADC), amplifiers, isolators, filters and interfacing circuits. The complete data acquisition circuit has been developed using This system mainly aims to collect the ECG signals of frequency between 0.05 Hz and 113 Hz with a gain of 3113. This frequency information from the ECG signal is highly useful clinical applications such as SCA prediction, cardiovascular disease (CVD) detection, etc. ECG signals will be collected from the subjects using 3 leads system and given to DAQ for recording the ECG signal. The acquired signal through this DAQ will then be transferred to the Notebook through NI6008 data acquisition card. This DAQ interface is used to convert the input analog signal to digital signal output and to save the ECG data in the notebook using Labview software. This acquired signal from Labview software is used for further clinical investigation. We also developed a Graphical User Interface (GUI) in LabVIEW software to continuously monitor the ECG signal traces and to record the ECG data with higher precision. The morphology of the acquired ECG signal in the system is highly precise and useful for clinical diagnosis. Furthermore, this proposed system is used for developing sudden cardiac arrest (SCA) prediction in our university.
Investigating the Psychometric Properties of a New Survey Instrument Measuring Factors Related to Upward Transfer in STEM Fields

Abstract:We investigated the psychometric properties of six latent factors in a new survey instrument measuring factors related to transfer in STEM from 2-year to 4-year institutions: initial attitudes toward math, initial attitudes toward science, self-efficacy in math, self-efficacy in science, active learning, and transfer-oriented interaction. We demonstrated the utility of combining confirmatory factor analysis and item response theory in assessing both the overall factor structure and individual items' capability to capture respondent characteristics across all levels of a given latent trait. Our study expands the methodological repertoire for higher education survey research while illuminating potential mechanisms associated with transfer in STEM.
The Postprandial Effects of a Moderately High-Fat Meal on Lipid Profiles and Vascular Inflammation in Alzheimer’s Disease Patients: A Pilot Study

Objective Alzheimer’s disease (AD) is a neurodegenerative disease of aging with unknown causative factors. Accumulating evidence suggests that inflammation and neurovascular dysfunction play important roles in AD. The postprandial period following a moderately high-fat meal is associated with vascular inflammation in young, healthy individuals; however, this relationship has not been investigated in Alzheimer’s patients despite their exaggerated inflammatory state. Methods Patients with AD and age-matched control subjects were recruited through the UC Davis Alzheimer’s Disease Center. All subjects consumed a moderately high-fat breakfast meal. Fasting and postprandial blood samples were collected for lipid, lipoprotein, and oxylipin analyses, as well as assays for cytokine levels and monocyte activation. Results The plasma lipid analyses revealed similar levels of triglycerides and esterified oxylipins between groups, but there was an interaction between postprandial non-esterified fatty acid (NEFA) levels and body mass index in the AD group compared to the control subjects. The AD group also had increased behenic acid and decreased linoleic and oleic acids in the postprandial period; however, these were not significantly different. Inflammatory assays revealed elevated fasting levels of interleukin (IL)-10 and IL-12 p70, but no change in monocyte activation in the AD group. Conclusion The postprandial period following a moderately high-fat meal is not associated with an exaggerated inflammatory state in Alzheimer’s patients, and basal esterified oxylipin profiles do not indicate elevated oxidative stress. However, the baseline inflammatory state during fasting in AD patients includes elevated levels of plasma IL-10 and IL-12 p70, which may indicate a balance between immune responses mediated by these interleukins.
Large-scale environmental correlates of forest tree distributions in Catalonia (NE Spain)

Aim To explore the environmental correlates of tree species distributions in Catalonia according to the chorological status of the species. Location The study area is the region of Catalonia, in northeastern Spain. Methods We used presence-absence data for 24 species, sampled in random plots distributed throughout forests of Catalonia. A climate model for the Catalonia region provided environmental variables. We used classification tree analysis to explore the environmental correlates of the realized niches of tree species. The predictive accuracy of the models was assessed using the ROC curve approach. Potential distribution maps of tree species were generated for the whole Catalonia region. Results Models were ranked from low to high accuracy for the 24 species. Differences in accuracy among species were related to the chorological status of species. Zonal species, or species at the core of their range (Mediterranean and SubMediterranean species), were generally well predicted, while extrazonal species, or species at the edge of their range, were predicted only moderately well. Mediterranean species distributions showed good correlations with extreme temperatures and annual precipitation. Main conclusions The above trends confirmed the difficulty of identifying the realized niche of species at the edges of their ranges. In contrast, Mediterranean and Sub-Mediterranean species, which were at the core of their range, were well-predicted, confirming the importance of extremes of temperature and annual precipitation as effective surrogates for variables having more direct physiological roles in limiting the ability of plants to survive and grow. Maps of potential tree distributions allowed us to define suitable habitats and to highlight areas where species have been planted outside their natural distribution.
Educational Enrollment and Attainment in India : Household Wealth , Gender , Village , and State Effects

This paper uses the National Family Health Survey (NFHS) data collected in 1992-93 to estimate the determinants of child (aged 6 to 14) enrollment and educational attainment of a recent cohort (aged 15 to 19) in India. The analysis produces five major results. First, using an index of assets as a proxy for household wealth shows enormous gaps between the enrollment and attainment of children from rich and poor households. While 82 percent of the children from the richest 20 percent complete grade 8 only 20 percent of children from the poorest 40 percent of households do. Second, the wealth gaps vary widely across states of India. Third, gender differences exacerbate these differences, so while 80 percent of girls from households in the top 20 percent complete grade 8 only 9.5 percent of girls from the poorest 40 percent do so. Fourth, the physical presence or absence of school facilities in the rural villages explains only a very small part of enrollment differences. Fifth, there are huge gaps in the enrollment rates of observationally equivalent households across states, especially among the poor. For instance, enrollment rates are 44 percentage points higher in Kerala than for an observationally equivalently poor household in Bihar. We conclude with an examination of the state specific policies that could account for such differences. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent.