The state of HRM in the Middle East:Challenges and future research agenda

Based on a robust structured literature analysis, this paper highlights the key developments in the field of human resource management (HRM) in the Middle East. Utilizing the institutional perspective, the analysis contributes to the literature on HRM in the Middle East by focusing on four key themes. First, it highlights the topical need to analyze the context-specific nature of HRM in the region. Second, via the adoption of a systematic review, it highlights state of development in HRM in the research analysis set-up. Third, the analysis also helps to reveal the challenges facing the HRM function in the Middle East. Fourth, it presents an agenda for future research in the form of research directions. While doing the above, it revisits the notions of “universalistic” and “best practice” HRM (convergence) versus “best-fit” or context distinctive (divergence) and also alternate models/diffusion of HRM (crossvergence) in the Middle Eastern context. The analysis, based on the framework of cross-national HRM comparisons, helps to make both theoretical and practical implications

Standardisation of a new model of H9N2/Escherichia coli challenge in broilers in the Lebanon

Primary infection by low pathogenic avian influenza (LPAI) predisposes for secondary infection by Escherichia coli in poultry, leading to significant economic losses. Future research in control of this ailment requires the establishment of a successful controlled challenge by avian influenza virus (AIV)/E. coli. Six groups of broilers (6 birds/group) were included for the standardisation of the controlled challenge by AIV/E. coli. Birds in groups 1, 2, 3, 4 and 5 received an intra-tracheal challenge of 0.5 ml of two haemagglutinating units of H9N2 virus at 20 days of age. At the age of 23 days, birds in group 1 received an intra-thoracic (right air sac)-E. coli challenge equivalent to 1.6 × 109 colony-forming units (cfu)/0.5 ml/bird, while birds in groups 2, 3, 4 and 5 received E. coli by the same route and in the following respective decreasing order of viable cells: 1.6 × 106, 1.6 × 105, 1.6 × 104 and 1.6 × 103 cfu. Birds in control group 6 were deprived of H9N2 and E. coli challenge. Results showed significant early mortality in group 1 that was challenged with the highest number of E. coli, in comparison to groups 2-6 (p0.05). The frequencies of four signs at 2 days and at 5 days post E. coli challenge (conjunctivitis, diarrhoea, ocular exudates and rales) in the surviving birds of groups 2-5 were most often higher than those observed in control group 6 (p<0.05). These four signs and five gross lesions (abdominal airsacculitis, left thoracic airsacculitis, pericarditis, right thoracic airsacculitis and tracheitis) had a decreasing pattern of frequency related to a decrease in the E. coli count used in the challenge

pH-gated succinate secretion regulates muscle remodeling in response to exercise

In response to skeletal muscle contraction during exercise, paracrine factors coordinate tissue remodeling, which underlies this healthy adaptation. Here we describe a pH-sensing metabolite signal that initiates muscle remodeling through exercise. In mice and humans, exercising skeletal muscle releases the mitochondrial metabolite succinate into the local interstitium and circulation. Selective secretion of succinate is facilitated by its transient protonation, which occurs upon muscle cell acidification. In the protonated monocarboxylic form, succinate is rendered a transport substrate for monocarboxylate transporter 1, which facilitates pH-gated release. Upon secretion, succinate signals via its cognate receptor SUCNR1 in non-myofibrillar cells in muscle tissue to control muscle-remodeling transcriptional programs. This succinate-SUCNR1 signaling is required for paracrine regulation of muscle innervation, muscle matrix remodeling, and muscle strength in response to exercise training. In sum, we define a bioenergetic sensor in muscle that utilizes intracellular pH and succinate to coordinate tissue adaptation to exercise

Machine learning in recycling business: an investigation of its practicality, benefits and future trends

Machine learning (ML) algorithms, such as neural networks, random forest, and more recent deep learning, are illustrating their utility for waste recycling. The increasing computational power of ML makes waste generation prediction, even at municipal level, possible with satisfying accuracy. ML is so critical and efficient and yet it is severely under-researched in recycling business. Also, the ML application in the recycling business is still a niche area judged by the limitations in its literature sources, the research domains, the ML algorithms’ use and benefits involved or reported in the literature. To unlock the value of ML in recycling business, this paper reviewed 51 related articles systematically and presents the current obstacles and future directions in applying ML to waste recycling industries