Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of Behavioral Organization Development Interventions on Employee Development and Organizational Performance. A Mixed Methods Approach

Corporate planners have found that organisational performance (OP) presents a significant strategic challenge and complication. They are looking for solutions to the problem of how to maintain their competitiveness in the global market while establishing a strategic position. Organizational development (OD) interventions provide solutions to these challenges and complications. We employed the mixed-methods explanatory sequential design and tested the effects of seven OD behavioral interventions on OP, including coaching, reward systems (RS), career development plans (CDP), financial assistance program (FAP), pay systems (PS), and training and development (T&D). The aim of this study was to support corporate planners and practitioners. We defined the OP as growth in financial performance (FP) (G). We employed employee development (knowledge, skills, and abilities) as a mediator. We sent 800 (sample size) questionnaires to HR heads, and 762 completed ones were returned. We use a stratified random probability sampling method to choose a sample of 800 firms from the population, which is all listed firms with the Security and Exchange Commission of Pakistan (SECP). We employed SPSS Version 20 to perform regression analysis. We calculated coefficient β values, t- and p-values, adjusted R2, and F-statistic values were computed and determine the causal influence. We conducted mediation analysis the four-step method proposed by Baron and Kenny (1986). Goodman and Sobel Preacher and Leonardelli (2001) used a web-based technique to conduct tests to confirm the mediations among variables. We conducted a focus group of ten OD professionals to gather qualitative data, conducted thematic analysis then merged the data to obtain insightful findings. The findings are valuable for OD practitioners and the study is a step towards a Green-OD Community.
Investigation of an extreme rainfall event during 8–12 December 2018 over central 1 Viet Nam – Part 2: An evaluation of predictability using a time-lagged cloud-resolving 2 ensemble system

: 9 This is the second part of a two-part study that investigates an extreme rainfall event that 10 occurred from 8 to 12 December 2018 over central Viet Nam (referred to as the D18 event). 11 In this part, the study aims to evaluate the predictability of the D18 event using a time-12 lagged cloud-resolving ensemble and a quantitative precipitation forecast system. To do 13 this study, 29 time-lagged (8 days in lead time) high resolution (2.5 km) members were 14 run, with the first members run at 12:00 UTC 3 December 2018, and the last member-run 15 at 12:00 UTC 10 December 2018. Between the first and the last members are multiple 16 members that run every 6-h. The evaluated results reveal that CReSS well predict the 17 rainfall fields at the short-range forecast (less than 3 days) for 10 December (rainiest day). 18 Particularly, results show CReSS has high skills in heavy-rainfall QPFs for the 24-h rainfall 19 of 10 Dec with the SSS scores greater than 0.5 for both the last five members and the last 20 nine members. These good results are due to the model having good predicts of other 21 meteorological variables, such as surface wind fields. However, these prediction skills are 22 reducing at extending lead time (longer than 3 days), and it is challenging to achieve the 23 prediction of QPF for rainfall thresholds greater than 100 mm with lead time longer than 6 24 days. Besides, the ensemble sensitivity analysis of 24-hour rainfall responds to the initial 25 conditions shows that the 24-hour rainfall is very sensitive with initial conditions, not only 26
Kinetic Characterization and Partial Purification of Peroxidase in Eucalyptus F1 Hybrids

A differential pattern of peroxidase (E.C. 1.1.11.7) has been observed in reciprocal F1 hybrids of Eucalyptus (FRI-4 and FRI-5) produced using parental combination of E. tereticornis and E. camaldulensis. The hybrids were characterized for the qualitative, kinetic parameters (total and specific activity, units, Km and Vmax) and partial purification of peroxidase. The qualitative study showed the presence of high molecular weight peroxidase isoforms i.e. 260 KD, 250 KD and 110KD bands. A strong maternal inheritance was observed for 260 KD loci in FRI-5. Kinetic characterization of peroxidase revealed that F1 genotypes had efficient peroxidase system over their parental combination resulting in 8 -10 times more heterotic vigour. The FRI-5 genotypes were found to be having efficient peroxidase system with lowest Km values (0.12mM-0.17mM) and higher Vmax values ranging from 0.07-0.14mM min-1mg prot-1. The study revealed the role of maternal inheritance and was found that better genotypes are produced when superior parents are used as seed parent. The results suggest the efficient use of peroxidase marker for the identification of the better genotypes in forest tree species.
Simple benchmark models as a basis for model efficiency criteria

Selecting environmental models to support water management decisions is challenging given the number and diversity of existing models. The Benchmark Models for the Water Framework Directive (BMW) project has identified the need for a priori qualitative criteria (e.g. model applicability and relevance for the management task) to select models. In addition to these qualitative criteria, model assessment should also include objective measures of performance to determine model credibility with regard to a specific application. To avoid misinterpretation of a model's simulation ability and to objectively assess the performance of different models on different catchments, model assessment should be comparative and include a benchmark model used as a reference in order to set a basic/minimum performance objective. This benchmark model can be a simple (baseline) model or a more complex one, depending on the performance level requested by the end-user. Relative evaluation criteria can then be established to assess model performance with reference to this benchmark. The formulation of such relative criteria can be general enough to be used in many fields of environmental sciences where models are applied to simulate time-series. However, the interpretation of relative criteria will remain conditioned by the nature and the formulation of the selected benchmark model. Choosing this baseline model can sometimes be difficult, especially when various types of catchments are studied.
Exploring the Spatiotemporal Pattern of Cyberbullying with Yik Yak

Cyberbullying is an emerging social issue along with the prevalence of social media. Previous studies have used extensive surveys or firsthand data primarily from conventional social networks1 (e.g., Twitter) to study cyberbullying, which often ignores the factor of anonymity and location. Considering the sensitive nature and contagious effect of cyberbullying, a better understanding of the spatiotemporal pattern in cyberbullying is sorely needed to develop effective policies to combat this toxic social behavior. Grounded in the dramaturgy theory and the emerging literature on technoself, this study aims to compare cyberbullying in the anonymous social media (Yik Yak) with the conventional social media (Twitter) and explore its spatiotemporal patterns. A support vector machine is used to help identify records with bullying content. Average nearest neighbor, kernel density, and Ripley's K-function are used to explore the spatiotemporal patterns of cyberbullying behavior. We have found that cyberbullying is more likely to occur in anonymous than conventional social media. We also detected a clustering pattern corresponding to the student population, which can be explained by the dramaturgy theory and recent studies on technoself. In addition to making suggestions to help reduce cyberbullying in the future, this article also sheds light on the need for future studies.
Analysing ICT and Development from the Perspective of the Capabilities Approach: A Study in South Brazil

Certain international agencies propose that Information and Communication Technology (ICT) allows development to be reached more efficiently and quickly. While there are success stories galore, there is acknowledgement of not all investments in ICT bringing positive effects towards development. Following the Capabilities Approach, this paper discusses how the use of ICT can promote a more effective development by studying the case of Sudotec (association for technological and industrial development), a non‐profit organization that saw in ICT the opportunity to change the local scenario. The results revealed positive effects of the use of ICT in social, economical and cultural spheres, but not presenting political effect.
The Influence of Earnings Volatility on the Association among Leverage and Real Earnings Management

The association between leverage (LEV) and Real Earnings Management (REM), as well as how the volatility of earnings affects this relationship, are examined in this paper. The study sample comprises all non-financial companies listed on the Pakistan Stock Exchange from 2004 to 2018. Due to the endogeneity issue between the variables and the two-step system Generalized Method of Moment (SYS-GMM) dynamic panel estimator, business risk will be used as an interaction term in the hypothetical model to examine the association between LEV and REM. The model will be further elaborated using agency theory and positive accounting theory. After examining the connection between LEV and REM, the study will examine how business risk affects earnings volatility for the Pakistan Stock Exchange.
A Stretchable Electromagnetic Interference Shielding Fabric with Dual‐Mode Passive Personal Thermal Management

Electromagnetic interference (EMI) shielding fabrics are crucial in addressing the increasingly serious electromagnetic pollution. To meet wearable requirements, stretchability and thermal comfortability are often desired, but which still are challenging. Herein, a stretchable EMI shielding fabric is fabricated via electrospinning coupled with biaxial pre‐stretching spraying, in which a block stacking wrinkled silver nanowire (AgNW)/Ti3C2Tx MXene network is coated on one side of electrospun thermoplastic polyurethane (TPU)/polydimethylsiloxane (PDMS) fabric. As expected, the wrinkled structure protects conductive network from fracture during stretching process, so as to realize the strain‐invariant electrical conductivity. Thus, the fabric exhibits a stretchable EMI shielding performance of over 40 dB when subjected to 10–50% uniaxial strains or 21–125% biaxial strains. More importantly, the white TPU/PDMS side and the black AgNW/MXene side enable the fabric passive radiative cooling and heating, respectively. The cooling side exhibits high mid‐infrared emissivity (97.5%) and solar reflectance (90%), thus reducing the skin temperature by ≈4.9 °C. The heating side with high solar absorptivity (86.6%) and photothermal effect increased the skin temperature by ≈5 °C. Therefore, the fabirc with stretchable EMI shielding and Janus‐type dual‐mode personal passive thermal management is promising in future wearable products.
The Application of Human Resource Analytics as a Strategy to Elevate the Corporate Performance

Objective: This research aims to explore and highlight the growing importance of HR analytics in the corporate sector for data-driven decision-making and strategy execution. The primary objective is to present evidence of the increasing adoption and impact of HR analytics, both in academia and the actual corporate world. The study also intends to identify the requisite experience and competencies for HR analytics practices and to propose best practices and recommendations to enhance the effectiveness of HR analytics in organizations. Method: This research involves a comprehensive literature review, examining existing research and case studies on the utilization of HR analytics in the corporate sector. Various sources, including academic papers, industry reports, and real-world examples, were analyzed to gain insights into the current state and trends of HR analytics in practice. Results: The findings of the literature review demonstrate a significant surge in interest and implementation of HR analytics in the corporate world. The data-driven approach has shown considerable promise in aiding HR professionals in making informed decisions and aligning HR strategies with overall organizational goals. Evidence from both academic and practical sources supports the value of HR analytics as a crucial tool for optimizing HR processes and enhancing workforce productivity. Conclusion: Based on the research findings, it is evident that HR analytics has emerged as a pivotal component in the corporate sector, revolutionizing how HR professionals execute strategies and contribute to organizational success. The study substantiates the need for organizations to prioritize the development of HR analytics expertise and competencies among their HR teams. Moreover, the research highlights the importance of establishing best practices and guidelines for effective HR analytics implementation, ultimately leading to better talent management and organizational performance. As HR analytics continues to evolve, future research in this field should delve deeper into addressing competency requirements and refining the strategies for harnessing the full potential of HR analytics in the corporate landscape.
Geo-based technologies, tourists and bushfires in northern Australia

This paper analyses how the use of geo-based technologies can play a role in the safety of tourism operations and tourist travel—especially in the case of bushfires. The study uses data from 42 qualitative interviews with tourists, tourism operators and other stakeholders in the remote Kununurra area of Western Australia carried out in 2012 and 2013. We contend that the spatiotemporal nature of tourism has stimulated considerable development in a range of geo-based technologies. The paper argues that geo-based technologies are an integral part of fire suppression and mitigation practices, and that tourists’ familiarity with geo-based technologies makes these technologies an effective mode of fire safety information for independent tourists, especially those travelling in remote and regional areas of Australia. A key finding, from a thematic analysis of the data, is the importance and relevance of realtime fire warning information for tourist operators and independent tourists, along with an observation that tourists and tourism operators are particularly interested in using the new user-friendly MyFireWatch website. This finding draws a parallel with the high use of geospatial technologies in the tourism industry by both tourism operators and tourists themselves. Further, this paper also calls attention to outback tourism and the importance of a ‘loci of responsibility’ between emergency services, tourism operators and the independent tourist in times of bushfire and other emergencies in remote Australia.
Machine Learning Predictive Modeling for the Identification of Moderate Coronavirus Disease 2019 During the Pandemic: A Retrospective Study

Background: Timely differentiation of moderate COVID-19 cases from mild cases is beneficial for early treatment and saves medical resources during the pandemic. We attempted to construct a model to predict the occurrence of moderate COVID-19 through a retrospective study. Methods: In this retrospective study, clinical data from patients with COVID-19 admitted to Hainan Western Central Hospital in Danzhou, China, between August 1, 2022, and August 31, 2022, was collected, including sex, age, signs on admission, comorbidities, imaging data, post-admission treatment, length of stay, and the results of laboratory tests on admission. The patients were classified into a mild-to-moderate-type group according to WHO guidance. Factors that differed between groups were included in machine learning models such as Bernoulli Naïve Bayes (BNB), linear discriminant analysis, support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and logistic regression (LR) models. These models were compared to select the optimal model with the best predictive efficacy for moderate COVID-19. The predictive performance of the models was assessed using the area under the curve (AUC), sensitivity, specificity, and calibration plot. Results: A total of 231 patients with COVID-19 were included in this retrospective analysis. Among them, 152 (68.83%) were mild types, 72 (31.17%) were moderate types, and there were no patients with severe or critical types. A logistic regression model combined with age, respiratory rate (RR), lactate dehydrogenase (LDH), D-dimer, and albumin was selected to predict the occurrence of moderate COVID-19. The receiver operating characteristic curve (ROC) showed that AUC, sensitivity, and specificity in the model were 0.719, 0.681, and 0.635, respectively, in predicting moderate COVID-19. Calibration curve analysis revealed that the predicted probability of the model was in good agreement with the true probability. Stratified analysis showed better predictive efficacy after modeling for people aged ≤66 years (AUC = 0.7656) and a better calibration curve. Conclusion: The LR model, combined with age, RR, D-dimer, LDH, and albumin, can predict the occurrence of moderate COVID-19 well, especially for patients aged ≤66 years.
P-Glycoprotein Exacerbates Brain Injury Following Experimental Cerebral Ischemia by Promoting Proinflammatory Microglia Activation

Microglia are activated following cerebral ischemic insult. P-glycoprotein (P-gp) is an efflux transporter on microvascular endothelial cells and upregulated after cerebral ischemia. This study evaluated the effects and possible mechanisms of P-gp on microglial polarization/activation in mice after ischemic stroke. P-gp-specific siRNA and adeno-associated virus (p-AAV) were used to silence and overexpress P-gp, respectively. Middle cerebral artery occlusion/reperfusion (MCAO/R) and oxygen-glucose deprivation/reoxygenation (OGD/R) were performed in mice and cerebral microvascular endothelial cells (bEnd.3) in vitro, respectively. OGD/R-injured bEnd.3 cells were cocultured with mouse microglial cells (BV2) in Transwell. Influences on acute ischemic stroke outcome, the expression of inflammatory cytokines, and chemokines and chemokines receptors, microglial polarization, glucocorticoid receptor (GR) nuclear translocation, and GR-mediated mRNA decay (GMD) activation were evaluated via reverse transcription real-time polymerase chain reaction, western blot, or immunofluorescence. Silencing P-gp markedly alleviated experimental ischemia injury as indicated by reduced cerebral infarct size, improved neurological deficits, and reduced the expression of interleukin-6 (IL-6) and IL-12 expression. Silencing P-gp also mitigated proinflammatory microglial polarization and the expression of C-C motif chemokine ligand 2 (CCL2) and its receptor CCR2 expression, whereas promoted anti-inflammatory microglia polarization. Additionally, P-gp silencing promoted GR nuclear translocation and the expression of GMD relative proteins in endothelial cells. Conversely, overexpressing P-gp via p-AAV transfection offset all these effects. Furthermore, silencing endothelial GR counteracted all effects mediated by silencing or overexpressing P-gp. Elevated P-gp expression aggravated inflammatory response and brain damage after ischemic stroke by augmenting proinflammatory microglial polarization in association with increased endothelial CCL2 release due to GMD inhibition by P-gp.
Relationship Between Mental Health Literacy Level and Treatment Motivation in Alcohol and Drug Addicts

The primary objective of this research is to explore how the level of Mental Health Literacy influences the motivation for treatment among individuals dealing with alcohol or substance addiction. The study involved 128 subjects undergoing inpatient treatment at the Alcohol and Substance Abuse Treatment Center. To gather data, researchers utilized a Personal Information Form, Mental Health Literacy Scale, and Treatment Motivation (TM) survey. The scores obtained from the Treatment Motivation Questionnaire and Mental Health Literacy Scale were 94.21±12.2 and 108.03±11.7, respectively. The analysis revealed a statistically significant, albeit weak, positive correlation between the participants' Mental Health Literacy Scale-Knowledge of How to Seek Mental Health Information subscale mean score and both their total Treatment Motivation Questionnaire score and their Interpersonal Help-Seeking subscale mean scores (r=0.284). Conversely, a statistically significant weak negative correlation emerged between the participants' Mental Health Literacy Scale-Knowledge of Professional Help Available subscale mean score and their Treatment Motivation Questionnaire-Distrust in Treatment subscale mean score (r=−0.230). Conclusions drawn from the study indicate that a lack of knowledge regarding addiction and accessing professional assistance, coupled with concerns surrounding stigma, pose as significant barriers to motivation for seeking treatment.
Digital health education as a topic for PE: Relevance, practical implications, and teachers’ perspectives

Digital health technologies and social networking services have become an inherent part of physical activity, exercise routines and fitness training. Adolescents obtain significant parts of their health-related knowledge and beliefs from such technologies and social media, which in turn can influence how they approach sports and physical activity. These processes have been accelerated and intensified by the Covid-19 pandemic. Against this background, this paper aims to discuss the relevance of digital health education as a topic for the subject PE. First, potential benefits and risks of digital health technologies and networks are explained with a focus on adolescents as the target group for digital health education. In this context ideas and recommendations to implement digital health education in teaching practices are illustrated. It is argued that young people should be supported to develop their own critical viewpoints and attitudes towards the use of digital health technologies and networks to pursue individual health-related goals. Second, results from a qualitative interview study with 32 PE teachers in Austria regarding their perspectives on digital health education in PE are presented. The most significant findings of the survey are that asked teachers see digital technology mainly as a tool for functional health promotion and are aware of normative body representations in social networks. Some PE teachers also orchestrate PE as a counterpart to an increasingly digitalized world excluding digital technology form their lessons. However, a holistic approach to digital health education requires that the digital culture of health, body, exercise and fitness is experienced and discussed from different perspectives. In doing so, students can be enabled to use digital health technologies and networks in an informed, responsible and self-determined way.