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The purpose of this paper aims to assess the intake of dietary selenium among Jordanian adults in Madaba Governorate.

The authors conducted a cross-sectional study in Madaba Governorate. Convenience sampling was done at Madaba Governorate for this study. The subjects comprised 500 subjects (325 women and 175 men) aged from 18 to 60 years. A 24-h recall (24-HR) and Food Frequency Questionnaire (FFQ) were used to estimate the selenium intake in the studied population.

Mean of dietary selenium was more than the recommended amount by at least 2.5-fold in both methods FFQ and 24-HR (p = 0.004), while no significant difference was observed in selenium consumption per day for women and men respectively using FFQ. The results indicated that selenium consumption is higher than the RDA among Madaba population. A moderate correlation was found between both methods for all subjects, women and men, respectively (p < 0.05).

The authors conclude that the selenium intake in Jordanian population is significantly above the RDA. However, future studies are required to correlate this high intake with health benefits and exclude toxic effects of Selenium.

Job performance in the US Army is a complex construct, in part because of the stressors that soldiers face, both day-to-day and during deployment. This chapter critically reviews job performance, and the connections between performance and stress and health, discussing how findings may also be relevant within the specific context of the Army. We review established conceptualizations and metrics of job performance within the Army as well as the civilian sector. Then, we discuss the existing research on the associations between performance and stress, physical health, health behaviors, and mental health. Considering these findings, we discuss lessons learned for Army performance metrics, recommending that stress- and health-related issues be incorporated into unit and leader performance metrics, with two critical caveats: (1) data are aggregated at a company level and (2) non-reactive measures are used. Finally, we discuss how existing data repositories can facilitate future research and note potential constraints of using secondary data.

Based on virtual maintenance, this paper aims to propose a time prediction method of assembly and disassembly (A&D) actions of product maintenance process to enhance existing methods’ prediction accuracy, applicability and efficiency.

First, a framework of A&D time prediction model is constructed, which describes the time prediction process in detail. Then, basic maintenance motions which can comprise a whole A&D process are classified into five categories: body movement, working posture change, upper limb movement, operation and grasp/placement. A standard posture library is developed based on the classification. Next, according to motion characteristics, different time prediction methods for each motion category are proposed based on virtual maintenance simulation, modular arrangement of predetermined time standard theory and the statistics acquired from motion experiment. Finally, time correction based on the quantitative evaluation method of motion time influence factors is studied so that A&D time could be predicted with more accuracy.

Case study of time prediction of products’ various A&D processes is conducted by implementing the proposed method. The prediction process of diesel cooling fan disassemble time is presented in detail. Through comparison, the advantages and effectiveness of the method are demonstrated.

This paper proposes a more accurate, efficient and applicable product A&D time prediction method. It can help designers predict A&D time of a product maintenance accurately in early design phases without a physical prototype. It can also provide basis for the verification of maintainability, the balance of the design of product structure and system layout.

The purpose of this study is to target utilizing Human resources (HRs) data analytics that may enhance strategic business, but little study has examined how it affects components. Data analytics, HRM and strategic business require empirical investigations and how to over come HR data analytics implementation issues.

A semi-systematic methodology for its evaluation allows for a more complete examination of the literature that emerges theoretical framework and a structured survey questionnaire for quantitative data collection from IT sector personnel. SPSS analyses data.

Future research is essential for organisations to exploit HR data analytics’ performance-enhancing potential. Data analytics should complement human judgment, not replace it. This paper details these transitions, the important contributions to theory and practice and future research.

Data analytics has grown rapidly and might make HRM practices faster, more efficient and data-driven. HR data analytics may improve strategic business. HR data analytics on employee retention, engagement and organisational success is insufficient. HR data analytics may boost performance, but there is limited proof. The authors do not know how HRM data analytics influences firms and employees.

Data analytics offers HRM new opportunities, along with technical and ethical challenges. This study makes a significant contribution to HR data analytics, evidence-based practice and strategic business literature. In addition to estimating turnover risk, identifying engagement factors and planning interventions to increase retention and engagement, HR data analytics can also estimate the risk of employee attrition.