Ocean processes are complex and have a high variability in both time and space. Thus, ocean scientists must collect data over long time periods to obtain synoptic views and resolve multidimensional spatiotemporal variability. In this paper, we present a methodology for incorporating time-varying currents into a Markov Decision Process for persistent path execution by underwater gliders. The application of an hybrid Gaussian distribution of ocean currents and a modified Markov Decision Process technique enables the incorporation of uncertainty from a deterministic ocean model. The proposed approach achieves improved navigational accuracy, and can extend the distance travelled over the duration of a mission. We present a derivation of our methodology, an outline of the proposed algorithms, and simulation predictions that are validated through experimental field trials.
Soil study plays a significant role in the cultivation of crops. To increase the productivity of any crop, one must know the soil type and properties of that soil. The conventional soil type identification, grid sampling and hydrometer method require expert intervention, more time and extensive laboratory experimentation. Digital soil mapping, while applying remote sensing, offers soil type information and has rapidity, low cost, and spatial resolution advantages. This study proposes a model to identify the soil type using remote sensing data. Spectral data of the Upper Indus Plain of Pakistan Pothwar region and Doabs were acquired using fifteen Landsat eight images dated between June 2020 to August 2020. Bare soil images were obtained to identify the soil type classes Silt Loam, Loam, Sandy Loam, Silty Clay Loam and Clay Loam. Spectral data of band values, reflectance band values, corrective reflectance band values and vegetation indices are practiced studying the reflectance factor of soil type. Regarding multi-class classification, Random Forest and Support Vector Machine are two popular techniques used in the research community. In the present work, we used these two techniques aided with Logistic Model Tree with 10-fold cross-validation. The classification with the best performance is achieved using the spectral data, with an overall accuracy of 86.61% and 84.41% for the Random Forest and Logistic Model Tree classification, respectively. These results may be applied for crop cultivation in specific areas and assist decision-makers in better agricultural planning.
In the wake of the COVID-19 pandemic, research indicates that the COVID-19 disease susceptibility varies among individuals depending on their ABO blood groups. Researchers globally commenced investigating potential methods to stratify cases according to prognosis depending on several clinical parameters. Since there is evidence of a link between ABO blood groups and disease susceptibility, it could be argued that there is a link between blood groups and disease manifestation and progression. The current study investigates whether clinical manifestation, laboratory, and imaging findings vary among ABO blood groups of hospitalized confirmed COVID-19 patients. This retrospective cohort study was conducted between March 1, 2020 and March 31, 2021 in King Faisal Specialist Hospital and Research Centre Riyadh and Jeddah, Saudi Arabia. Demographic information, clinical information, laboratory findings, and imaging investigations were extracted from the data warehouse for all confirmed COVID-19 patients. A total of 285 admitted patients were included in the study. Of these, 81 (28.4%) were blood group A, 43 (15.1%) were blood group B, 11 (3.9%) were blood group AB, and 150 (52.6%) were blood group O. This was almost consistent with the distribution of blood groups among the Saudi Arabia community. The majority of the study participants (79.6% [n = 227]) were asymptomatic. The upper respiratory tract infection ( P = .014) and shortness of breath showed statistically significant differences between the ABO blood group ( P = .009). Moreover, the incidence of the symptoms was highly observed in blood group O followed by A then B except for pharyngeal exudate observed in blood group A. The one-way ANOVA test indicated that among the studied hematological parameters, glucose ( P = .004), absolute lymphocyte count ( P = .001), and IgA ( P = .036) showed statistically significant differences between the means of the ABO blood group. The differences in both X-ray and computed tomography scan findings were statistically nonsignificant among the ABO age group. Only 86 (30.3%) patients were admitted to an intensive care unit, and the majority of them were blood groups O 28.7% (n = 43) and A 37.0% (n = 30). However, the differences in complications’ outcomes were statistically nonsignificant among the ABO age group. ABO blood groups among hospitalized COVID-19 patients are not associated with clinical, hematological, radiological, and complications abnormality.
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