How Human Resource and Information Systems Practices Amplify the Returns on Information Technology Investments

This study examines the important roles that human resources (HR) for information technology (IT) professionals and information systems (IS) practices for all workers in an organization play in shaping returns on firms’ IT investments. In particular, we consider how incentives, autonomy, and training for IT professionals can enable a firm to better leverage the value of its IT investments. We argue that well-trained, motivated, and empowered IT professionals can help firms make better strategic choices in allocating IT investments and implementing IT projects. We also demonstrate how this moderating relationship depends upon collaborative IS and autonomy-enhancing IS practices that affect other knowledge workers in the firm. We leverage archival data for 228 firms with 736 firm-year observations and document two key findings. We find (1) that empowering HR practices for IT professionals positively moderate the effect of IT investments on firm performance, and (2) that the alignment between empowering HR practices for IT professionals and firm-wide collaborative IS practices enhances the value that firms derive from IT investments. Our results suggest that the business value of IT investments is linked to the rewards and opportunities offered to IT professionals, who have a pivotal role in the effective deployment of IT in organizations

Advances in Ecohydrology for Water Resources Optimization in Arid and Semi-Arid Areas

Conserving water resources is a current challenge that will become increasingly urgent in future due to climate change. The arid and semi-arid areas of the globe are expected to be particularly affected by changes in water availability. Consequently, advances in ecohydrology sciences, i.e., the interplay between ecological and hydrological processes, are necessary to enhance the understanding of the critical zone, optimize water resources’ usage in arid and semi-arid areas, and mitigate climate change. This Special Issue (SI) collected 10 original contributions on sustainable land management and the optimization of water resources in fragile environments that are at elevated risk due to climate change. In this context, the topics mainly concern transpiration, evapotranspiration, groundwater recharge, deep percolation, and related issues. The collection of manuscripts presented in this SI represents knowledge of ecohydrology. It is expected that ecohydrology will have increasing applications in the future. Therefore, it is realistic to assume that efforts to increase environmental sustainability and socio-economic development, with water as a central theme, will have a greater chance of success

Prevalence of chronic kidney disease and its associated risk factors: The first report from Iran using both microalbuminuria and urine sediment

Background: The incidence of major risk factors of chronic kidney disease (CKD) in the world is on the rise, and it is expected that this incidence and prevalence, particularly in developing countries, will continue to increase. Using data on urinary sediment and microalbuminuria, we aimed to estimate the prevalence of CKD in northeast Iran. Methods: In a cross-sectional study, the prevalence of CKD in a sample of 1557 regionally representative people, aged � 18 years, was analyzed. CKD was determined based on glomerular filtration rate (GFR) and microalbuminuria. Life style data, urine and blood samples were collected. Urine samples without any proteinuria in the initial dipstick test were checked for qualitative microalbuminuria. If the latter was positive, quantitative microalbuminuria was evaluated. Results: 1557 subjects with a mean age of 56.76 ± 12.04 years were enrolled in this study. Based on the modifcation of diet in renal disease (MDRD) equation, 137 subjects (8.89%) were categorized as CKD stages III-V. Based on urine abnormalities, the prevalence of combined CKD stages I and II was 10.63%, and based on macro- and microalbuminuria it was 14.53%. The prevalence of CKD was significantly associated with sex, age, marital status, education, diabetes mellitus (DM), hypertension (HTN), ischemic heart disease (IHD), waist to hip ratio, myocardial infarction (MI), and cerebrovascular accident (CVA). Conclusion: CKD and its main risk factors are common and represent a definite health threat in this region of Iran. Using and standardizing less expensive screening tests in low resource countries could be a good alternative that may improve the outcome through early detection of CKD

Molecular and Seroepidemiological Survey of Visceral Leishmaniasis among Humans and Domestic Dogs in Mazandaran Province, North of Iran

Background: New cases of visceral leishmaniasis (VL) have been reported recently in some parts of Mazandaran Province, north of Iran where the first human case of VL was reported in 1949. This study aimed to determine the present status of Leishmania infantum infection among humans and domestic dogs using serological and molecular methods in central parts of Mazandaran Province. Methods: In this cross-sectional study, blood samples were randomly collected from 402 humans and fortynine domestic dogs throughout 2009 and 2010 in the central part of Mazandaran Province including Semeskadeh and Kiakola districts where recent cases of human visceral leishmaniasis had been reported there. All the collected samples were tested by direct agglutination test (DAT) for the detection of anti-Leishmania infantum antibodies as well as convenience PCR assay on whole blood samples for detection of leishmanial infection and identification of Leishmania species. Results: None of 402 collected human (402) and dog (49) blood samples showed anti Leishmania infantum antibodies at titers 1:3200 and 1:320 as cut-off values of DAT, respectively but only 2 of domestic dogs (4.1 %) were found PCR-positive corresponding to L.infantum. Conclusion: This study confirms the circulation of L. infantum at least among domestic dogs an

A Geometry Preserving Kernel over Riemannian Manifolds

Abstract- Kernel trick and projection to tangent spaces are two choices for linearizing the data points lying on Riemannian manifolds. These approaches are used to provide the prerequisites for applying standard machine learning methods on Riemannian manifolds. Classical kernels implicitly project data to high dimensional feature space without considering the intrinsic geometry of data points. Projection to tangent spaces truly preserves topology along radial geodesics. In this paper, we propose a method for extrinsic inference on Riemannian manifold using kernel approach while topology of the entire dataset is preserved. We show that computing the Gramian matrix using geodesic distances, on a complete Riemannian manifold with unique minimizing geodesic between each pair of points, provides a feature mapping which preserves the topology of data points in the feature space. The proposed approach is evaluated on real datasets composed of EEG signals of patients with two different mental disorders, texture, visual object classes, and tracking datasets. To assess the effectiveness of our scheme, the extracted features are examined by other state-of-the-art techniques for extrinsic inference over symmetric positive definite (SPD) Riemannian manifold. Experimental results show the superior accuracy of the proposed approach over approaches which use kernel trick to compute similarity on SPD manifolds without considering the topology of dataset or partially preserving topology

Wake and power prediction of horizontal-axis wind farm under yaw-controlled conditions with machine learning

The main objective of this study is to employ the Extreme Gradient Boosting (XGBoost) machine learning algorithm to predict the power, wake, and turbulent characteristics of horizontal-axis wind farms under yaw-controlled conditions. For this purpose, a series of high-fidelity numerical simulations using LES method are performed over tandem NREL-5 MW wind turbines to generate the input data for training and testing in machine learning analysis. It is observed that XGBoost is more accurate for wake prediction of the yaw-controlled wind farms compared to ANN, which was used in previous studies. The results illustrate that XGBoost can predict the power with a mean deviation of 0.94 % for different yaw angles, while ANN can estimate the power generation with a mean deviation of 2.15 % for various tested yaw angles. At far wake regions (X > 2000 m) of the second wind turbine, the deviations reach below 1 %. Moreover, XGBoost requires a much shorter training time, 87.5 % faster than ANN. The power production of both wind turbines can be predicted more accurately with XGBoost compared to ANN. The wake prediction time of XGBoost is just 0.105 sec, while this time is 4.480 for the ANN model. In conclusion, XGBoost provides a significant reduction in error and training time compared to ANN and deep learning algorithms over yaw-misaligned wind farms