A comparison of machine learning models for the mapping of groundwater spring potential

Groundwater resources are vitally important in arid and semi-arid areas meaning that spatial planning tools are required for their exploration and mapping. Accordingly, this research compared the predictive powers of five machine learning models for groundwater potential spatial mapping in Wadi az-Zarqa watershed in Jordan. The five models were random forest (RF), boosted regression tree (BRT), support vector machine (SVM), mixture discriminant analysis (MDA), and multivariate adaptive regression spline (MARS). These algorithms explored spatial distributions of 12 hydrological-geological-physiographical (HGP) conditioning factors (slope, altitude, profile curvature, plan curvature, slope aspect, slope length (SL), lithology, soil texture, average annual rainfall, topographic wetness index (TWI), distance to drainage network, and distance to faults) that determine where groundwater springs are located. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was employed to evaluate the prediction accuracies of the five individual models. Here the results were ranked in descending order as MDA (83.2%), RF (80.6%), SVM (80.2%), BRT (78.0%), and MARS (75.5%).The results show good potential for further use of machine learning techniques for mapping groundwater spring potential in other places where the use and management of groundwater resources is essential for sustaining rural or urban life

Impact of Nuts Consumption On Antioxidant Status and Pro-Oxidant Parameters in Healthy Human Volunteers

The aim of the current research was to study the effect of consumption of a mixture of almond and pistachio on antioxidants and pro-oxidants levels in healthy human volunteers.  This dietary intervention study was carried out during the period from February to March, 2013 on a total of 48 apparently healthy males students from Military Academy / Zakho, Kurdistan Region- Iraq. Blood samples were withdrawn from all volunteer who were living in a controlled environment and analyzed for the determination of serum antioxidants, pro-oxidants. Parameters were obtained at baseline, 3 and 6 weeks after daily consumption of 50 gm. of a mixture of almond and pistachio. The results of the current study demonstrated that daily consumption of 50 g of   almond and pistachio mixture for 3-6 weeks, significantly increased (P<0.05-0.005) the levels of total antioxidant, Ceruloplasmin, Superoxide dismutase and Glutathione reductase. On the other hand, the levels of the pro-oxidants Malonaldehyde and Peroxy- nitrite were significantly decreased (P<0.005) after 6 weeks of nuts consumption compared to the control. From this dietary intervention trial, it can be concluded that almonds and pistachio mixture improved anti-oxidants and pro-oxidants status compared with those of the healthy volunteers

Impact of Nuts Consumption on Blood Pressure, Glycemic Status, Lipid Profile and Total Protein in Healthy Human Volunteers

To investigate the advantage of consumption of a mixture of almond and pistachio on blood pressure, lipid profile, sugar and protein levels in healthy volunteers. Subjects and Methods:This dietary intervention study was carried out during the period from February to March 2013. A total of 48 apparently healthy males students from Military Academy / Zakho, Kurdistan Region of Iraq participated in this study. All the students were living in a controlled environment. Blood samples were taken and analyzed for the determination of serum glucose, lipid profile, total protein, albumin and globulin. Parameters were obtained at baseline, 3 and 6 weeks after daily consumption of 50gm of almond and pistachio mixture. Results: The results of the current study demonstrated that daily consumption of 50 gs of almond and pistachio mixture for 3-6 weeks significantly ( P<0.05 – 0.001) decreased the diastolic blood pressure and the level of blood glucose , whereas body mass index (BMI) was not influenced at all. Serum total protein, albumin and globulin levels were significantly increased (P<0.05-0.005). Finally, Total cholesterol (TC), triglycerides (TG), low density lipoprotein-cholesterol (LDL-ch), very low density lipoprotein cholesterol ( VLDL-ch), TC/HDL and LDL/HDL were significantly decreased (P<0.05-0.005) after 6 weeks of nuts consumption, where as high density lipoprotein- cholesterol, was significantly increased (P<0.01). Conclusion: This dietary intervention trial, demonstrated that almonds and pistachio mixture improved blood glucose , total protein, and lipid profile to much better levels than that obtained previously using each one separately in healthy volunteers

High-energy ball milling technique for ZnO nanoparticles as antibacterial material

Nanoparticles of zinc oxide (ZnO) are increasingly recognized for their utility in biological applications. In this study, the high-energy ball milling (HEBM) technique was used to produce nanoparticles of ZnO from its microcrystalline powder. Four samples were ball milled for 2, 10, 20, and 50 hours, respectively. The structural and optical modifications induced in the ‘as synthesized’ nanomaterials were determined by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscope (TEM), and photoluminescence emission spectra (PL). SEM and TEM results show a gradual decrease in particle size from around 600 to ∼30 nm, with increased milling time. The initial microstructures had random shapes, while the final shape became quite spherical. XRD analysis showed ZnO in a hexagonal structure, broadening in the diffracted peaks and going from larger to smaller particles along with a relaxation in the lattice constant c. The value of c was found to increase from 5.204 to 5.217 Å with a decrease in particle size (600 to ∼30 nm). PL result showed a new band at around 365 nm, whose intensity is found to increase as the particles size decreases. These remarkable structural and optical modifications induced in ZnO nanoparticles might prove useful for various applications. The increase in c value is an important factor for increasing the antibacterial effects of ZnO, suggesting that the HEBM technique is quite suitable for producing these nanoparticles for this purpose

Black Hole solutions in Einstein-Maxwell-Yang-Mills-Gauss-Bonnet Theory

We consider Maxwell and Yang-Mills (YM) fields together, interacting through gravity both in Einstein and Gauss-Bonnet (GB) theories. For this purpose we choose two different sets of Maxwell and metric ansaetze. In our first ansatz, asymptotically for $r\to 0$ (and $N>4$) the Maxwell field dominants over the YM field. In the other asymptotic region, $r\to \infty$, however, the YM field becomes dominant. For N=3 and N=4, where the GB term is absent, we recover the well-known Ba\U{f1}ados-Teitelboim-Zanelli (BTZ) and Reissner-Nordstr\U{f6}m (RN) metrics, respectively. The second ansatz corresponds to the case of constant radius function for $S^{N-2}$ part in the metric. This leads to the Bertotti-Robinson (BR) type solutions in the underlying theory.Comment: 20 pages, 5 figures, to be published in JCA

Antibiotics Prescriptions Pattern among Patients Visiting Primary Health Care Centers (PHCC) before and during COVID-19 Pandemic: A Cross-Sectional Population-Based Study from Qatar

Background: The COVID-19 pandemic, caused by the novel coronavirus 2 (SARS-CoV-2), has been associated with an increased risk of secondary bacterial infections. Numerous studies have reported a surge in antibiotic usage during the COVID-19 pandemic. This study aims to examine the impact of the COVID-19 pandemic on the frequency and patterns of antibiotic prescriptions at Primary Health Care Centers (PHCC) in Qatar, comparing the period before and during the pandemic. Methods: This population-based, cross-sectional study analyzed all antibiotic prescriptions issued in two-month intervals before COVID-19 (November and December 2019) and during the initial wave (June and July 2020) of COVID-19. The study included 27 PHCCs in Qatar. Results: Prior to the COVID-19 outbreak, the PHCCs dispensed a total of 74,909 antibiotic prescriptions in November and December. During the first wave of COVID-19, the number decreased to 29,273 prescriptions in June and July 2020. Antibiotics were most commonly prescribed for adults and least commonly for the elderly, both before and during the COVID-19 period. In the pre-COVID-19 period, Betalactams and macrolides accounted for the majority (73%) of all antibiotic prescriptions across all age groups. However, during the COVID-19 period, Betalactams and other antibiotics such as Nitrofurantoin and Metronidazole (73%) were the most frequently prescribed. Conclusion: The rate of antibiotic prescriptions during the first wave of COVID-19 was lower compared to the two months preceding the pandemic at the PHCC in Qatar.Scopu

A review of the use of terrestrial laser scanning application for change detection and deformation monitoring of structures

Change detection and deformation monitoring is an active area of research within the field of engineering surveying as well as overlapping areas such as structural and civil engineering. The application of Terrestrial Laser Scanning (TLS) techniques for change detection and deformation monitoring of concrete structures has increased over the years as illustrated in the past studies. This paper presents a review of literature on TLS application in the monitoring of structures and discusses registration and georeferencing of TLS point cloud data as a critical issue in the process chain of accurate deformation analysis. Past TLS research work has shown some trends in addressing issues such as accurate registration and georeferencing of the scans and the need of a stable reference frame, TLS error modelling and reduction, point cloud processing techniques for deformation analysis, scanner calibration issues and assessing the potential of TLS in detecting sub-centimetre and millimetre deformations. However, several issues are still open to investigation as far as TLS is concerned in change detection and deformation monitoring studies such as rigorous and efficient workflow methodology of point cloud processing for change detection and deformation analysis, incorporation of measurement geometry in deformation measurements of high-rise structures, design of data acquisition and quality assessment for precise measurements and modelling the environmental effects on the performance of laser scanning. Even though some studies have attempted to address these issues, some gaps exist as information is still limited. Some methods reviewed in the case studies have been applied in landslide monitoring and they seem promising to be applied in engineering surveying to monitor structures. Hence the proposal of a three-stage process model for deformation analysis is presented. Furthermore, with technological advancements new TLS instruments with better accuracy are being developed necessitating more research for precise measurements in the monitoring of structures

Identifying Immunological and Clinical Predictors of COVID-19 Severity and Sequelae by Mathematical Modeling

Since its emergence as a pandemic in March 2020, coronavirus disease (COVID-19) outcome has been explored via several predictive models, using specific clinical or biochemical parameters. In the current study, we developed an integrative non-linear predictive model of COVID-19 outcome, using clinical, biochemical, immunological, and radiological data of patients with different disease severities. Initially, the immunological signature of the disease was investigated through transcriptomics analysis of nasopharyngeal swab samples of patients with different COVID-19 severity versus control subjects (exploratory cohort, n=61), identifying significant differential expression of several cytokines. Accordingly, 24 cytokines were validated using a multiplex assay in the serum of COVID-19 patients and control subjects (validation cohort, n=77). Predictors of severity were Interleukin (IL)-10, Programmed Death-Ligand-1 (PDL-1), Tumor necrosis factors-α, absolute neutrophil count, C-reactive protein, lactate dehydrogenase, blood urea nitrogen, and ferritin; with high predictive efficacy (AUC=0.93 and 0.98 using ROC analysis of the predictive capacity of cytokines and biochemical markers, respectively). Increased IL-6 and granzyme B were found to predict liver injury in COVID-19 patients, whereas interferon-gamma (IFN-γ), IL-1 receptor-a (IL-1Ra) and PD-L1 were predictors of remarkable radiological findings. The model revealed consistent elevation of IL-15 and IL-10 in severe cases. Combining basic biochemical and radiological investigations with a limited number of curated cytokines will likely attain accurate predictive value in COVID-19. The model-derived cytokines highlight critical pathways in the pathophysiology of the COVID-19 with insight towards potential therapeutic targets. Our modeling methodology can be implemented using new datasets to identify key players and predict outcomes in new variants of COVID-19

Assessment of Eating Habits and Lifestyle during Coronavirus Pandemic in the MENA region: A Cross-Sectional Study

© The Authors 2020. The coronavirus disease (COVID-19) has rapidly spread globally, forcing countries to apply lockdowns and strict social distancing measures. The aim of this study was to assess eating habits and lifestyle behaviors among residents of the Middle East and North Africa (MENA) region during the lockdown. A cross-sectional study among adult residents of the MENA region was conducted using an online questionnaire designed on Google Forms during April 2020. A total of 2970 participants from 18 countries participated in the current study. During the pandemic, over 30% reported weight gain, 6.2% consumed five or more meals per day compared to 2.2% before the pandemic (p\u3c0.001), and 48.8% did not consume fruits on daily basis. Moreover, 39.1% did not engage in physical activity, over 35% spent more than five hours per day on screens. A significant association between the frequency of training during the pandemic and the reported change in weight was found (p \u3c 0.001). A significantly higher percentage of participants reported physical and emotional exhaustion, irritability, and tension either all the time or a large part of the time during the pandemic (p \u3c 0.001). Although a high percentage of participants reported sleeping more hours per night during the pandemic, 63% had sleep disturbances. The study highlights that the lockdown due to the COVID-19 pandemic caused a variety of lifestyle changes, physical inactivity, and psychological problems among adults in the MENA region