AN EXPATRIATE MENTORING PLAN FOR ABU DHABI OIL AND GAS INDUSTRIES (GASCO)

Published ThesisMultinational Corporations (MNCs) are an essential part of the modern globalised business environment (Chen, Tzeng & Tang, 2005:278). Within an increasingly competitive global market, it is understandable that MNCs employ highly skilled expatriates from across the globe. Expatriates are individuals from one country who work and live in another country (Hill, 2005:618). According to Peiperl, Levy and Sorell (2014:45), skilled expatriates form the backbone of MNCs, and many MNCs fail due to a shortage of skilled labour (Abdullah & Jin, 2015:548-549). Purgal-Popiela (2011:27) attests, however, that one out of three expatriates perform below expectation. On average, 40% of expatriates who complete their foreign assignments are ineffective or marginally effective (Deresky, 2002:398). The US reports an annual loss of around $4 billion due to failed foreign assignments (Cole & Nesbeth, 2014:71). Numerous authors propose mentoring as a strategy to support expatriates in adjusting to the new environment (Cullen & Parboteeah, 2010; Feldman & Bolino, 1999; Forret, Turban & Dougherty, 1996; Gibb, 1999; Mezias & Scandura, 2005; Zhuanga et al., 2013). The majority of research on expatriates focuses on the mentor, but ignore the levels, methods and techniques that an expatriate mentoring plan should contain. The focus of this study was to develop a customised mentoring plan for Abu Dhabi Oil and Gas Industries (GASCO). The company has around 7150 expatriate employees from 19 different countries. As GASCO has also reported high expatriate failure rates, this investigation was aimed at ascertaining work-related adjustments, personal-level adjustments and the extent to which expatriates have formed a new identity. Mixed-method research (both quantitative and qualitative) was used in this study which consisted of two phases. Phase 1 involved a structured questionnaire that was administered to all GASCO’s expatriate employees who have been employed for two years or more. The quantitative findings confirmed that there was a lack of expatriate support pertaining to work-related and personal-level adjustments. Findings were supported by the inferential statistics performed on the data. This enabled the construction of a proposed mentoring plan for GASCO. During Phase 2 the mentoring plan was presented to a focus group for discussion and further refinement before finalisation

Pancreatic Enzyme Dosing and Gastrointestinal Symptoms in Children with Cystic Fibrosis

The aim of this study is to determine if higher than the recommended dosing for pancreatic enzyme replacement therapy (PERT) improves GI symptoms in children with Cystic fibrosis. Children with CF and exocrine pancreatic insufficiency (EPI) were categorized into two groups based on PERT dosing: less than 10,000 lipase units/kg/day, or greater than 10,000 lipase units/kg/day. Methods: 142 pediatric CF patients from the Nebraska Regional CF Center were included in the retrospective evaluation. Patients were split into two different groups based on their pancreatic enzyme dosing. Group 1 was greater than 10,000 lipase units/kg/day of enzymes, and group 2 was less than 10,000 lipase units/kg/day. Results: The p-value of the results was 0.5992, meaning that there was no statistical difference between the GI symptoms for those who take less or greater than 10,000 lipase units/kg/day of enzymes. The group that takes greater than 10,000 lipase units/kg/day are 1.194 times more likely to experience GI symptoms compared to the group with less than 10,000 lipase units per day. Conclusion: Based on the analysis that was completed on the data, there is no statistical significance between those that take greater or less than 10,000 lipase units/kg/day of pancreatic enzymes.https://digitalcommons.unmc.edu/surp2021/1018/thumbnail.jp

A model for the austenite-ferrite phase transition in steel including misfit stress

We present a thermodynamically consistent model to describe the austenite-ferrite phase transition in steel. We consider the influence of the mechanical displacement field due to eigenstrains caused by volumetric expansion. The model equations are derived in a systematical framework. They are based on the conservation laws for mass and momentum and the second law of thermodynamics. By means of numerical computations for a simplified interface controlled model, we examine the influence of the mechanical contributions to the transformation kinetics and the equilibrium states

Determination of stiffness and higher gradient coefficients by means of the embedded atom method: An approach for binary alloys

For a quantitative theoretical description of phase separation and coarsening reliable data of stiffness constants and the so called Higher Gradient Coefficients (HGCs) are required. For that reason pair potentials of the Lennard-Jones type were used in [1] to provide a theoretical tool for their quantitative determination. Following up on this work these quantities are now calculated by means of the Embedded-Atom Method (EAM), a recently developed approach to describe interatomic potentials in metals. This is done, first, to achieve a better agreement between predicted and experimentally observed stiffness data as well as to avoid artifacts, such as the Cauchy paradox, and, second, to increase the trustworthiness of the HGCs for which experimental data are rarely available. After an introduction to the fundamentals of EAM it is outlined how it can be used for calculating stiffness constants and HGCs. In particular, Johnson's modification of EAM for nearest neighbor interactions [3] is applied to present explicit numerical results for a case study alloy, Ag-Cu, which has a ``simple" face-centered-cubic crystal structure and where it is comparatively easy to obtain all the required analysis data from the literature and to experimentally compare the predictions of mechanical data

A higher gradient theory of mixtures for multi-component materials with numerical examples for binary alloys

A theory of mixture for multi-component materials is presented based on a novel, straightforward method for the exploitation of the Second Law of thermodynamics. In particular the constitutive equations for entropy, heat and diffusion flux as well as the stress tensor are formulated as a consequence of the non-negative entropy production. Furthermore we derive the established Gibbs equation as well as the Gibbs Duhem relation which also follow from the formalism. Moreover, it is illustrated, how local mechanical strains due to eigenstrains or external loadings, modify the free energy and, consequently, change the chemical potentials of the components. All consecutive steps are illustrated, first, for simple mixtures and, second, for a system containing two different phases. So-called higher gradients of the concentrations are considered, which take the nonuniform composition into account. It will also become apparent that more/other variables of modified/different physical pr oblems beyond the illustrated ones can be easily treated within the presented framework. This work ends with the specification to binary alloys and with the presentation of various numerical simulations

Early Changes in Pain Acceptance Predict Pain Outcomes in Interdisciplinary Treatment for Chronic Pain

Studies have shown that pain acceptance is associated with a better pain outcome. The current study explored whether changes in pain acceptance in the very early treatment phase of an interdisciplinary cognitive-behavioral therapy (CBT)-based treatment program for chronic pain predict pain outcomes. A total of 69 patients with chronic, non-malignant pain (at least 6 months) were treated in a day-clinic for four-weeks. Pain acceptance was measured with the Chronic Pain Acceptance Questionnaire (CPAQ), pain outcomes included pain intensity (Numeric Rating Scale, NRS) as well as affective and sensory pain perception (Pain Perception Scale, SES-A and SES-S). Regression analyses controlling for the pre-treatment values of the pain outcomes, age, and gender were performed. Early changes in pain acceptance predicted pain intensity at post-treatment measured with the NRS (B = -0.04 (SE = 0.02); T = -2.28; p = 0.026), affective pain perception at post-treatment assessed with the SES-A (B = -0.26 (SE = 0.10); T = -2.79; p = 0.007), and sensory pain perception at post-treatment measured with the SES-S (B = -0.19 (SE = 0.08); T = -2.44; p = 0.017). Yet, a binary logistic regression analysis revealed that early changes in pain acceptance did not predict clinically relevant pre-post changes in pain intensity (at least 2 points on the NRS). Early changes in pain acceptance were associated with pain outcomes, however, the impact was beneath the threshold defined as clinically relevant

Revealing Hidden Context Bias in Segmentation and Object Detection through Concept-specific Explanations

Applying traditional post-hoc attribution methods to segmentation or object detection predictors offers only limited insights, as the obtained feature attribution maps at input level typically resemble the models' predicted segmentation mask or bounding box. In this work, we address the need for more informative explanations for these predictors by proposing the post-hoc eXplainable Artificial Intelligence method L-CRP to generate explanations that automatically identify and visualize relevant concepts learned, recognized and used by the model during inference as well as precisely locate them in input space. Our method therefore goes beyond singular input-level attribution maps and, as an approach based on the recently published Concept Relevance Propagation technique, is efficiently applicable to state-of-the-art black-box architectures in segmentation and object detection, such as DeepLabV3+ and YOLOv6, among others. We verify the faithfulness of our proposed technique by quantitatively comparing different concept attribution methods, and discuss the effect on explanation complexity on popular datasets such as CityScapes, Pascal VOC and MS COCO 2017. The ability to precisely locate and communicate concepts is used to reveal and verify the use of background features, thereby highlighting possible biases of the model

Improving coding and primary care management for people with Chronic Kidney Disease: an observational controlled study in east London

Background: The UK national chronic kidney disease (CKD) audit in primary care shows diagnostic coding in the electronic health record for CKD averages 70%, with wide practice variation. Coding is associated with improvements to risk factor management; CKD cases coded in primary care have lower rates of unplanned hospital admission. Aim: To increase diagnostic coding of CKD (stages 3–5) and primary care management, including blood pressure to target and prescription of statins to reduce cardiovascular disease risk. Design and setting: Controlled, cross-sectional study in four East London clinical commissioning groups (CCGs). Method: Interventions to improve coding formed part of a larger system change to the delivery of renal services in both primary and secondary care in East London. Quarterly anonymised data on CKD coding, blood pressure values, and statin prescriptions were extracted from practice computer systems for 1-year pre- and post-initiation of the intervention. Results: Three intervention CCGs showed significant coding improvement over a 1 year period following the intervention (regression for post-intervention trend P<0.001). The CCG with highest coding rates increased from 76–90% of CKD cases coded; the lowest coding CCG increased from 52–81%. The comparison CCG showed no change in coding rates. Combined data from all practices in the intervention CCGs showed a significant increase in the proportion of cases with blood pressure achieving target levels (difference in proportion P<0.001) over the 2-year study period. Differences in statin prescribing were not significant. Conclusion: Clinically important improvements to coding and management of CKD in primary care can be achieved by quality improvement interventions that use shared data to track and monitor change supported by practice-based facilitation. Alignment of clinical and CCG priorities and the provision of clinical targets, financial incentives, and educational resource were additional important elements of the intervention

The Antimicrobial and Immunomodulatory Function of RNase 7 in Skin

The human ribonuclease RNase 7 has been originally isolated from human skin and is a member of the human RNase A superfamily. RNase 7 is constantly released by keratinocytes and accumulates on the skin surface. The expression of RNase 7 in keratinocytes can be induced by diverse stimuli such as cytokines, growth factors, and microbial factors. RNase 7 exhibits a potent broad spectrum of antimicrobial activity against various microorganisms and contributes to control bacterial growth on the skin surface. The ribonuclease and antimicrobial activity of RNase 7 can be blocked by the endogenous ribonuclease inhibitor. There is also increasing evidence that RNase 7 exerts immunomodulatory activities and may participate in antiviral defense. In this review, we discuss how these characteristics of RNase 7 contribute to innate cutaneous defense and highlight its role in skin infection and inflammation. We also speculate how a potential dysregulation of RNase 7 promotes inflammatory skin diseases and if RNase 7 may have therapeutic potential