Impact of Islamic values on HRM practices: an empirical study of Jordanian universities

Purpose This study aims to present an in-depth theoretical and practical analysis of HRM practice in the light of Islamic values and principles. It looks at the four main HRM functions of recruitment and selection, training and development, performance appraisal, and rewards, from the Islamic perspective. Besides establishing a theoretical base for the influence of Islam on HRM, it analyses the key characteristics of HRM practice as applied in Jordanian universities and analyses the extent to which Islamic values are embedded in that practice. Design/methodology/approach The paper draws on a structured questionnaire distributed to 500 respondents working in four Jordanian universities which sought to elicit the respondents views on the influence of Islamic values within specific HR functions. Findings The findings indicate that there is a diffusion of Islamic values into HRM practice in the participating organisations. The extent of the diffusion varies between the organisations; there is a clear indication that explicitly Islamic values are being practiced, albeit to a limited extent. Originality/value The role of spirituality and/or religion in shaping the working of contemporary organisations is not sufficiently recognised in the literature. This paper is a response to the limited number of research studies assessing the extent of the absorption of religious values into the management of human resources. The study undertaken examines the current status of HRM practice in Jordanian universities and contributes to deepening the contemporary understanding of interactions between Islamic values and the core HR functions.

Anestésicos locais

Projeto de Pós-Graduação/Dissertação apresentado à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Mestre em Ciências Farmacêuticas

On the Effectiveness of Genetic Search in Combinatorial Optimization

In this paper, we study the efficacy of genetic algorithms in the context of combinatorial optimization. In particular, we isolate the effects of cross-over, treated as the central component of genetic search. We show that for problems of nontrivial size and difficulty, the contribution of cross-over search is marginal, both synergistically when run in conjunction with mutation and selection, or when run with selection alone, the reference point being the search procedure consisting of just mutation and selection. The latter can be viewed as another manifestation of the Metropolis process. Considering the high computational cost of maintaining a population to facilitate cross-over search, its marginal benefit renders genetic search inferior to its singleton-population counterpart, the Metropolis process, and by extension, simulated annealing. This is further compounded by the fact that many problems arising in practice may inherently require a large number of state transitions for a near-optimal solution to be found, making genetic search infeasible given the high cost of computing a single iteration in the enlarged state-space.

Reduction of malaria prevalence by indoor residual spraying: a meta-regression analysis.

Indoor residual spraying (IRS) has become an increasingly popular method of insecticide use for malaria control, and many recent studies have reported on its effectiveness in reducing malaria burden in a single community or region. There is a need for systematic review and integration of the published literature on IRS and the contextual determining factors of its success in controlling malaria. This study reports the findings of a meta-regression analysis based on 13 published studies, which were chosen from more than 400 articles through a systematic search and selection process. The summary relative risk for reducing malaria prevalence was 0.38 (95% confidence interval = 0.31-0.46), which indicated a risk reduction of 62%. However, an excessive degree of heterogeneity was found between the studies. The meta-regression analysis indicates that IRS is more effective with high initial prevalence, multiple rounds of spraying, use of DDT, and in regions with a combination of Plasmodium falciparum and P. vivax malaria.

An active learning approach for rapid characterization of endothelial cells in human tumors.

Currently, no available pathological or molecular measures of tumor angiogenesis predict response to antiangiogenic therapies used in clinical practice. Recognizing that tumor endothelial cells (EC) and EC activation and survival signaling are the direct targets of these therapies, we sought to develop an automated platform for quantifying activity of critical signaling pathways and other biological events in EC of patient tumors by histopathology. Computer image analysis of EC in highly heterogeneous human tumors by a statistical classifier trained using examples selected by human experts performed poorly due to subjectivity and selection bias. We hypothesized that the analysis can be optimized by a more active process to aid experts in identifying informative training examples. To test this hypothesis, we incorporated a novel active learning (AL) algorithm into FARSIGHT image analysis software that aids the expert by seeking out informative examples for the operator to label. The resulting FARSIGHT-AL system identified EC with specificity and sensitivity consistently greater than 0.9 and outperformed traditional supervised classification algorithms. The system modeled individual operator preferences and generated reproducible results. Using the results of EC classification, we also quantified proliferation (Ki67) and activity in important signal transduction pathways (MAP kinase, STAT3) in immunostained human clear cell renal cell carcinoma and other tumors. FARSIGHT-AL enables characterization of EC in conventionally preserved human tumors in a more automated process suitable for testing and validating in clinical trials. The results of our study support a unique opportunity for quantifying angiogenesis in a manner that can now be tested for its ability to identify novel predictive and response biomarkers.

Measurement of pressure profiles in pneumatic conveying pipelines

Accurate design of two-phase air-solid pipelines requires data from flow and pressure measurements, requiring the appropriate positioning and selection of sensors as well as judicious processing of signals. This paper shows how detailed measurements of pressure profiles have been obtained for use in design of improved pneumatic conveying pipelines.

Genome sequence of Stenotrophomonas maltophilia PML168 which displays Baeyer-Villiger Monooxygenase activity

Stenotrophomonas maltophilia PML168 was isolated from Wembury Beach on the English Coast from a rock pool following growth and selection on agar plates. Here we present the permanent draft genome sequence, which has allowed prediction of function for several genes encoding enzymes relevant to industrial biotechnology, including a novel flavoprotein monooxygenase.

SuperWASP-N extrasolar planet candidates from fields 06h

TheWide Angle Search for Planets (WASP) survey currently operates two installations, designated SuperWASP-N and SuperWASP-S, located in the Northern and Southern hemispheres, respectively. These installations are designed to provide high time-resolution photometry for the purpose of detecting transiting extrasolar planets, asteroids, and transient events. Here, we present results from a transit-hunting observing campaign using SuperWASP-N covering a right ascension (RA) range of 06h < RA < 16h. This paper represents the fifth and final in the series of transit candidates released from the 2004 observing season. In total, 729 335 stars from 33 fields were monitored with 130 566 having sufficient precision to be scanned for transit signatures. Using a robust transit detection algorithm and selection criteria, six stars were found to have events consistent with the signature of a transiting extrasolar planet based on the photometry, including the known transiting planet XO-1b. These transit candidates are presented here along with discussion of follow-up observations and the expected number of candidates in relation to the overall observing strategy.

A Hybrid Evolutionary Approach to the Nurse Rostering Problem

Nurse rostering is a difficult search problem with many constraints. In the literature, a number of approaches have been investigated including penalty function methods to tackle these constraints within genetic algorithm frameworks. In this paper, we investigate an extension of a previously proposed stochastic ranking method, which has demonstrated superior performance to other constraint handling techniques when tested against a set of constrained optimisation benchmark problems. An initial experiment on nurse rostering problems demonstrates that the stochastic ranking method is better in finding feasible solutions but fails to obtain good results with regard to the objective function. To improve the performance of the algorithm, we hybridise it with a recently proposed simulated annealing hyper-heuristic within a local search and genetic algorithm framework. The hybrid algorithm shows significant improvement over both the genetic algorithm with stochastic ranking and the simulated annealing hyper-heuristic alone. The hybrid algorithm also considerably outperforms the methods in the literature which have the previously best known results.

Avandia (TM) - From molecule to market for type 2 diabetes.

Type 2 diabetes is a disease fast approaching epidemic proportions throughout the world. From insulin sensitizers to PPARg agonists, the lecture will outline the successful medicinal chem. and biol. strategy underpinning the discovery and selection of Avandia, an innovative new medicine for this disease.

Research strategies to improve honeybee health in Europe

Understanding the fundaments of colony losses and improving the status of colony health will require cross-cutting research initiatives including honeybee pathology, chemistry, genetics and apicultural extension. The 7th framework of the European Union requested research to empirically and experimentally fill knowledge gaps on honeybee pests and diseases, including 'Colony Collapse Disorder' and the impact of parasites, pathogens and pesticides on honeybee mortality. The interactions among these drivers of colony loss will be studied in different European regions, using experimental model systems including selected parasites (e. g. Nosema and Varroa mites), viruses (Deformed Wing Virus, Black Queen Cell Virus, Israeli Acute Paralysis Virus) and model pesticides (thiacloprid, tau-fluvalinate). Transcriptome analyses will be used to explore host-pathogen-pesticide interactions and identify novel genes for disease resistance. Special attention will be given to sublethal and chronic exposure to pesticides and will screen how apicultural practices affect colony health. Novel diagnostic screening methods and sustainable concepts for disease prevention will be developed resulting in new treatments and selection tools for resistant stock. Research initiatives will be linked to various national and international ongoing European, North-and South-American colony health monitoring and research programs, to ensure a global transfer of results to apicultural practice in the world community of beekeepers.

Classical novae from the POINT-AGAPE microlensing survey of M31 - I. The nova catalogue

The POINT-AGAPE (Pixel-lensing Observations with the Isaac Newton Telescope-Andromeda Galaxy Amplified Pixels Experiment) survey is an optical search for gravitational microlensing events towards the Andromeda galaxy (M31). As well as microlensing, the survey is sensitive to many different classes of variable stars and transients. Here we describe the automated detection and selection pipeline used to identify M31 classical novae (CNe) and we present the resulting catalogue of 20 CN candidates observed over three seasons. CNe are observed both in the bulge region as well as over a wide area of the M31 disc. Nine of the CNe are caught during the final rise phase and all are well sampled in at least two colours. The excellent light-curve coverage has allowed us to detect and classify CNe over a wide range of speed class, from very fast to very slow. Among the light curves is a moderately fast CN exhibiting entry into a deep transition minimum, followed by its final decline. We have also observed in detail a very slow CN which faded by only 0.01 mag d(-1) over a 150-d period. We detect other interesting variable objects, including one of the longest period and most luminous Mira variables. The CN catalogue constitutes a uniquely well-sampled and objectively-selected data set with which to study the statistical properties of CNe in M31, such as the global nova rate, the reliability of novae as standard-candle distance indicators and the dependence of the nova population on stellar environment. The findings of this statistical study will be reported in a follow-up paper.

On Regression Methods for Virtual Metrology in Semiconductor Manufacturing

Virtual metrology (VM) aims to predict metrology values using sensor data from production equipment and physical metrology values of preceding samples. VM is a promising technology for the semiconductor manufacturing industry as it can reduce the frequency of in-line metrology operations and provide supportive information for other operations such as fault detection, predictive maintenance and run-to-run control. The prediction models for VM can be from a large variety of linear and nonlinear regression methods and the selection of a proper regression method for a specific VM problem is not straightforward, especially when the candidate predictor set is of high dimension, correlated and noisy. Using process data from a benchmark semiconductor manufacturing process, this paper evaluates the performance of four typical regression methods for VM: multiple linear regression (MLR), least absolute shrinkage and selection operator (LASSO), neural networks (NN) and Gaussian process regression (GPR). It is observed that GPR performs the best among the four methods and that, remarkably, the performance of linear regression approaches that of GPR as the subset of selected input variables is increased. The observed competitiveness of high-dimensional linear regression models, which does not hold true in general, is explained in the context of extreme learning machines and functional link neural networks.

Antimicrobial resistance in the respiratory microbiota of people with cystic fibrosis

Cystic fibrosis is characterised by chronic polymicrobial infection and inflammation in the airways of patients. Antibiotic treatment regimens, targeting recognised pathogens, have substantially contributed to increased life expectancy of patients with this disease. Although the emergence of antimicrobial resistance and selection of highly antibiotic-resistant bacterial strains is of major concern, the clinical relevance in cystic fibrosis is yet to be defined. Resistance has been identified in recognised cystic fibrosis pathogens and in other bacteria (eg, Prevotella and Streptococcus spp) detected in the airway microbiota, but their role in the pathophysiology of infection and inflammation in chronic lung disease is unclear. Increased antibiotic resistance in cystic fibrosis might be attributed to a range of complex factors including horizontal gene transfer, hypoxia, and biofilm formation. Strategies to manage antimicrobial resistance consist of new antibiotics or localised delivery of antimicrobial agents, iron sequestration, inhibition of quorum-sensing, and resistome analysis. Determination of the contributions of every bacterial species to lung health or disease in cystic fibrosis might also have an important role in the management of antibiotic resistance. 

Efficiency assessment of electricity power generation on Ireland’s Single Electricity Market by Data Envelopment Analysis

The efficiency of generation plants is an important measure for evaluating the operating performance. The objective of this paper is to evaluate electricity power generation by conducting an All-Island-Generator-Efficiency-Study (AIGES) for the Republic of Ireland and Northern Ireland by utilising a Data Envelopment Analysis (DEA) approach. An operational performance efficiency index is defined and pursued for the year 2008. The economic activities of electricity generation units/plants examined in this paper are characterized by numerous input and output indicators. Constant returns to scale (CRS) and variable returns to scale (VRS) type DEA models are employed in the analysis. Also a slacks based analysis indicates the level of inefficiency for each variable examined. The findings from this study provide a general ranking and evaluation but also facilitate various interesting efficiency comparisons between generators by fuel type.