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Human Resource Management and Organisational Performance: Evidence from an Emerging Economy
Full text linkHR systems foster the development of human resource capability that organisations can leverage to create sustained competitive advantage. Grounded in this notion, the overarching goal of the three papers that comprise this thesis is to understand the processes through which HR systems influence individual and organisational performance in the context of an emerging economy. The first paper integrates the macro/micro divide in SHRM research by examining when and why unit-level high-commitment HR system relates to organisational and individual performance in a service context. Multisource and multi-wave data obtained from the hotel industry in Sri Lanka were used to test the hypothesized relationships. Results of the Mplus analysis reveal that serviced-oriented strategy moderates the unit-level high-commitment HR system-organisational performance relationships such that this relationship is stronger when service-oriented strategy is high but not low. Furthermore, unit-level high-commitment HR relates to emotional performance and supervisor-rated service quality indirectly through justice climate.Multisource and multi-wave data obtained from private sector organisations in Sri Lanka were used to test the hypothesized relationships proposed in the remaining two papers. As the two papers address different issues and are grounded in different theories, they could not be combined into a single paper. Drawing on the Ability, Motivation and Opportunity (AMO) framework, the second paper examines a revised version of the additive and multiplicative models whereby opportunity-enhancing HR system moderates the indirect influence of ability-enhancing and motivation-enhancing HR systems on organisational performance via the dual mediation of human capital and organizational-based psychological ownership. The Mplus results reveal that both HR systems relate to human capital and organisation-based psychological ownership (mediators) but only human capital transmits their respect influence on organisational performance. Furthermore, and contrary to our prediction, opportunity-enhancing HR system did not moderate the influence of the mediators on organisational performance.The third paper draws on social exchange theory to examine whether unit-level high-commitment HR system indirectly relates to operational effectiveness through the serial mediation of psychological need satisfaction and gratitude toward the organization. Additionally, it draws on contingency theory to examine whether organisational structure constitutes a first stage moderator of these relationships. Mplus results reveal that psychological need satisfaction and gratitude toward the organisation serially mediate the relationship between unit-level high-commitment HR system and operational effectiveness. Furthermore, and as predicted, the serial mediation uncovered is conditional upon organisational structure such that these relationships are stronger when structure is high (organic) but not low (mechanistic)
Materials and Molecular Modelling at the Exascale
Full text linkProgression of computational resources towards exascale computing makes possible simulations of unprecedented accuracy and complexity in the fields of materials and molecular modelling (MMM), allowing high fidelity in silico experiments on complex materials of real technological interest. However, this presents demanding challenges for the software used, especially the exploitation of the huge degree of parallelism available on exascale hardware, and the associated problems of developing effective workflows and data management on such platforms. As part of the UKs ExCALIBUR exascale computing initiative, the UK-led MMM Design and Development Working Group has worked with the broad MMM community to identify a set of high priority application case studies which will drive future exascale software developments. We present an overview of these case studies, categorized by the methodological challenges which will be required to realize them on exascale platforms, and discuss the exascale requirements, software challenges and impact of each application area
Deep Learning for Multi-Carrier Signal Reception
Full text linkWith the aim to meet the increasing demand of data rate, user capacity and qualityof services of networks, orthogonal frequency-division multiplexing (OFDM) systemshave been widely investigated and adopted in different communication scenarios duringthe past two decades, e.g., wireless local area networks (WLAN), long-term evolution(LTE) and 5G communications. It is appealing mainly in the sense that theinter-symbol interference (ISI) wireless channel is converted into a parallel of ISI-freesub-channels through Fourier transform with affordable computational complexity. Despitethe tremendous success that has been achieved, this well-investigated techniquefaces limit, e.g., it trades the computational complexity affordability off the achieveddetection performance. Recent advances in this research area lies in the developmentof deep learning algorithm for the design and optimisation on the multi-carrier system.This thesis investigates the deep learning for the multi-carrier signal reception techniquein various multi-carrier systems. Relying on the strong nonlinear processing capabilityof deep learning algorithms, a series of DNN architectures are first proposed to addressthe multiuser frequency synchronisation problem for the OFDMA uplink system. Theestablished DNN architectures are designed by relying on the conventional OFDMAsystem model. Then, a data-driven modular neural network (MNN), termed MCMNNetis proposed to address the coherent signal detection for various multiuser multicarriersystem. Moreover, with the aim of effectively reducing the offline trainingcomplexity, a transfer learning approach is tailored for the deep learning algorithmspresented in this thesis.The research in this thesis potentially offers the benefit of improved detection performance,reduced offine training complexity to the deep learning-enabled multi-carrierreceiver design. The ultimate goal is to pave the path towards better development andutilisation of deep learning for the wireless multi-carrier system design and optimisation
Detecting Paroxysmal Atrial Fibrillation from Normal Sinus Rhythm in Equine Athletes using Symmetric Projection Attractor Reconstruction and Machine Learning
No full textBackground: Atrial fibrillation (AF) is a common cardiac arrhythmia in both human and equine populations. It is associated with adverse outcomes in humans, and decreased athletic performance in both populations. Paroxysmal atrial fibrillation (PAF) presents with intermittent, self-terminating AF episodes, and is difficult to diagnose once sinus rhythm resumes.Objective: We aimed to detect PAF subjects from normal sinus rhythm equine ECGs using the Symmetric Projection Attractor Reconstruction (SPAR) method to encapsulate the waveform morphology and variability as the basis of a machine learning classification.Methods: We obtained ECG signals from 139 active equine athletes (120 control, 19 with a PAF diagnosis). The SPAR method was applied to nine short (20-second) ECG strips for each subject. An optimal SPAR feature set was determined by forward feature selection for input to a machine learning model ensemble of three different classifiers (k-NN, linear SVM and RBF kernel SVM). Imbalanced data was handled by upsampling the minority (PAF) class. A final subject classification was made by taking a majority vote over results from the nine ECG strips.Results: Our final cross-validated classification for a subject gave an accuracy of 89.0%, sensitivity of 94.8%, specificity of 87.1% and ROC AUC of 0.98, taking PAF as the positive class.Conclusion: The SPAR method and machine learning generated a final model with high sensitivity, suggesting that PAF can be discriminated from short equine ECG strips. This preliminary study indicated that SPAR analysis of human ECG could support patient monitoring, risk stratification and clinical decision-making.</p
Jurisdictional Objections in International Courts and Tribunals
Full text linkObjections to jurisdiction have been long embedded in the practice of international adjudication and form an integral part of the proceedings. Following the traditional practice of determination of the jurisdiction prior to the merits, preliminary objections to jurisdiction were regulated for the first time by the PCIJ in its Rules of Procedure in the Mavrommatis case. The ICJ is considered the backbone of international adjudication and thus its procedures on preliminary objections are emulated in both permanent and ad hoc adjudicative bodies. Throughout the years, the increase in recourse to international adjudication along with the existence of the Optional Clause at the ICJ as well as the compromissory clauses in institutionalised arbitration at the PCA, ICSID, and ITLOS, have allowed for unilateral applications that in turn gave rise to litigation tactics being employed around the application and the administration of the rules regarding objections to jurisdiction. Evidently, the ICJ has does not only provide a consistent point of reference for the rest of the courts and tribunals when it comes to procedural questions, but it could also benefit from their corresponding practices. Also, procedural principles such as procedural fairness, good administration of justice and equality of the parties have been challenged by the parties to the disputes however, these principles have also provided good grounds to the courts and tribunals to develop and adjust their Rules of Procedures accordingly
Impact of corporate social (ir)responsibility on volume and valence of online employee reviews: Evidence from the tourism and hospitality industry
Full text linkCorporate social responsibility (CSR) and irresponsibility (CSI) can influence employee voice behavior in online review platforms. This study utilizes online employee review (OER), builds upon ethical climate theory, and hypothesizes the independent and joint effects of CSR and CSI on two aspects of employee voice – OER volume and OER valence. Using novel OER data of US tourism and hospitality firms, we perform a panel data regression with industry and year fixed effects. The results indicate that firm CSR engagement increases both the volume and valence of OER, whereas CSI accelerates (attenuates) the positive CSR effect on OER volume (valence). These findings can help tourism and hospitality firms implement CSR strategies for enhancing employees’ word of mouth from both volume and valence perspectives
Vitamin D Status in Saudi Arabia: Metabolic and Genetic Association Study
Full text linkVitamin D (vitD) deficiency is a key public health issue worldwide. Despite the abundance of sunlight exposure in countries in the Middle East, including Saudi Arabia, vitD deficiency is still highly prevalent in these regions and particularly in postmenopausal women. VitD is mainly bound to vitD binding protein (VDBP), while the unbound form of vitD (˂1%) is said to be free. Free 25(OH)D is suggested to be more precise than total 25(OH)D in evaluating vitD status, especially in a population with different ethnic groups. Furthermore, the association between vitD and type 2 diabetes mellitus (T2DM), and the influence of genetic polymorphisms in genes involved in vitD metabolism on 25(OH)D levels seem to differ between the ethnic groups. Therefore, the aims of this PhD project were as follows: (i) Determine vitD status (total and free 25(OH)D) and its association with metabolic health parameters and specific single-nucleotide polymorphisms (SNP) in vitD related genes in a multi-ethnic vitD deficiency high risk cohort, particularly postmenopausal women in Saudi Arabia; (ii) Look for known or novel genetic variations in genes involved in vitD metabolism in families diagnosed with vitD deficiency in Saudi Arabia; (iii) Investigate the association between vitD and glycaemic parameters in a multi-ethnic cohort of postmenopausal women with T2DM in Saudi Arabia.Study (i) included a multi-ethnic cohort of 459 postmenopausal healthy women aged between 50 and 81 years, randomly recruited from seven primary health care centers scattered in Jeddah, Saudi Arabia. Blood samples were collected from subjects for measurement of serum levels of total 25(OH)D, directly measured free 25(OH)D, VDBP, metabolic bone parameters, lipid profile and other serum biochemical tests .Around 49% of the participants had optimal total 25(OH)D level (≥20 ng/ml) according to Institute of Medicine. Total 25(OH)D level was associated with rs7041 SNP in GC gene (P=0.023). A positive correlation was found between directly measured free and total 25(OH)D (r=0.61, P<0.0001). The total, but not free 25(OH)D showed significant association with serum intact parathyroid hormone (PTH), blood pressure, waist and hip circumferences (P<0.05); whilst free 25(OH)D but not total 25(OH)D showed a significant association with total cholesterol and LDL-C (P=0.027 and P=0.022; respectively). These observed total 25(OH)D significant associations were significantly affected by ethnic group.Study (ii) involved 21 families with vitD deficiency (n=39) in Saudi Arabia. WES was performed for DNA samples, then resulting WES data was filtered and a number of variants were prioritized and validated by Sanger DNA sequencing. We were able to find a novel mutation in DHCR7 (rs143587828) in two subjects from one family, and a polymorphism in LRP2 (rs2075252) in 3 families (2 subjects from each family, n=6). When these variants were validated, they were not observed in controls (n=100) which may suggest that these genetic variants might affect vitD levels and influence vitD status. Further studies are now required to confirm the association of these variants with vitD deficiency.Study (iii) involved a multi-ethnic cohort of postmenopausal females (n = 173, age ≥ 50 years) with T2DM. Several biochemical parameters were measured including total 25(OH)D, glycosylated hemoglobin, insulin, glucose, c-peptide and insulin sensitivity indices. VitD status was inversely associated with insulin resistance and anthropometric measures in this cohort [fasting glucose (r=-0.165, P=0.037), insulin (r=-0.184, P=0.02), C-peptide (r=-0.19, P=0.015), HOMA2- IR C-peptide (r=-0.23,P=0.004), body weight (r=-0.173 P=0.028), waist and hip circumferences (r=-0.167, P=0.033; r=-0.22, P=0.004 respectively)]. Our findings also showed that vitD associations with insulin resistance and anthropometric measures in women with white ethnicity were significant (P<0.05); unlike those from black/Asian ethnic backgrounds
CPInformer for Efficient and Robust Compound-Protein Interaction Prediction
Full text linkRecently, deep learning has become the mainstream methodology for Compound-Protein Interaction (CPI) prediction. However, the existing compound-protein feature extraction methods have some issues that limit their performance. First, graph networks are widely used for structural compound feature extraction, but the chemical properties of a compound depend on functional groups rather than graphic structure. Besides, the existing methods lack capabilities in extracting rich and discriminative protein features. Last, the compound-protein features are usually simply combined for CPI prediction, without considering information redundancy and effective feature mining. To address the above issues, we propose a novel CPInformer method. Specifically, we extract heterogeneous compound features, including structural graph features and functional class fingerprints, to reduce prediction errors caused by similar structural compounds. Then, we combine local and global features using dense connections to obtain multi-scale protein features. Last, we apply ProbSparse self-attention to protein features, under the guidance of compound features, to eliminate information redundancy, and to improve the accuracy of CPInformer. More importantly, the proposed method identifies the activated local regions that link a CPI, providing a good visualisation for the CPI state. The results obtained on five benchmarks demonstrate the merits and superiority of CPInformer over the state-of-the-art approaches
Swiss Banking – Quo Vadis? An Empirical Survival Analysis of the Swiss Banking Industry
Full text link"The recent global financial crisis of 2007-2009 is widely regarded as the worst since the great depression andthreatened the global financial system with a total collapse. Healthy banks are important for every economy.Financial distress has a negative impact on the prosperity of a country and is prone to spread beyond the bankingsector. Hence, the development of an adequate early warning system for bank failures is essential. This thesisdistinguishes itself from the vast literature of bankruptcy, bank failure and bank exit prediction models byintroducing novel categorical parameters inspired by Switzerland’s banking landscape. This thesis evaluates datafrom 274 banks in Switzerland over the period from 2007 to 2017 using a Generalised Linear Model (GLM) withlogit link function and a Multinomial Logistic Regression (MNL) to evaluate determinants of corporaterestructuring and financial distress. For model comparison, it presents a robustness test via a Bayesian frameworkwith Markov Chain Monte Carlo-methods featuring a basic Gaussian random-walk Metropolis-Hastingsalgorithm. The findings suggest that total assets and net interest margin impact bank exit and mergers andacquisitions (M&A) categories. Furthermore, the study reveals that cost-to-income has a positive relationship tobank exit. Therefore, both net interest margin and cost-to-income are the main key performance indicators in thefield of retail banking and wealth management. Furthermore, the findings suggest that the two macroeconomiccovariates, gross domestic product and unemployment rate, are unrelated to bank exit. Specifically, for the Swissarea the results indicate that banks operating in the Zurich cluster have a higher exit likelihood and become a M&Atarget more frequently, than banks operating in the Geneva area.
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