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Assessment analysis of COVID-19 on the global economics and trades
No full textThe globalization of the pandemic has caused distortions in the global commerce and supply chain that have constituted a major danger to international trade. This study seeks to give a complete analysis of the anticipated trade effects of the pandemic. The research method used for the study was survey research, and participants were chosen at random for the survey. The recruited participants provided a total of 100 replies, all of which were verified as coming from Nigerian Customs Service employees. The study evaluates the impact of COVID-19 on global trade in terms of production and supply chain interruptions, transportation and logistics disruptions, and the influence on international trade agreements. The findings of the present study showed that the pandemic is likely to create new patterns of international trade, changing trade relations and globalization, with winners and losers among economies. Although some nations may gain from the disruptions, others may suffer substantial hurdles. The report stresses the need for policy makers to foresee and prepare for these changes and give assistance for firms to adjust to the new realities of the global economy. Since there is minimal academic work on the trade implications of COVID-19, this research adds by presenting a fresh and thorough assessment of the possible effect on international trade. The evaluations of this study might be valuable for policy-makers in planning for the future global order of international trade. The present study offers a platform for advanced analysis and government actions that might enable enterprises, sectors, and governments to adjust and adapt to the changing global economic environments
Analysis of Healthcare Systems Using Computational Approaches: Concepts, Methodologies, Tools and Applications
No full textA medical database of different diseases in the healthcare system was essential. The role of computer-supported systems is essential to accurately detect diseases by examining the various components of the human body using radiological or several X-ray, MRI, CT scans, etc. Although various approaches were used to improve healthcare systems. Several soft computing techniques were used to develop new diagnostic systems for any illness with improved performance, like an artificial neural networks approach, fuzzy logic, genetic algorithms etc. Based on disease diagnosis, the accuracy medicine was aimed at developing the powerful pharmaceutical drug system for health solutions. Therefore, it has focused on diverse approaches to artificial intelligence and machine learning in the updated data-centered era of the healthcare system. A vast quantity of health data is routinely collected and hard to obtain any helpful information every day. Currently, BDA offers several services satisfaction with the clinical system to identify censorious diseases at an early stage and deliver appropriate services to all patients on time. Various BDA tools play an essential role in quickly examining several clinical data
Diabetes Mellitius Detection and Self Management based on Machine Learning
Get PDFDiabetes Mellitus is considered to be a state evoked by unmonitored polygenic disorder which will cause various organs collapse in sufferers. An investigation of the identification, examination and autonomous methods of Diabetes Mellitus from six completely various sides viz. datasets of Diabetes Mellitus, preprocessing procedures, attribute extraction, machine learning based analysis, classifying and prediction of Diabetes Mellitus, and evaluating the results. Machine Learning Associate in Nursing computer science is advancing, which permits the first prediction and diagnosing the Diabetes Mellitus over an automatic method that is superior than a nonautomatic detection. There are various reports which are revealed on automated Diabetes Mellitus prediction, identification, examination and autonomous procedure through machine learning and artificial intelligence procedures and also three current analysis problems within the department of Diabetes Mellitus prediction are recorded. In this it provides the Diabetes Mellitus prediction procedures demonstrate importance to the research community utilized within a range of automated Diabetes Mellitus prediction and self supervision
Explainable Machine Learning for Data Extraction Across Computational Social System
Get PDFAbstract— This article addresses the explainable machine
learning for data extraction on diverse datasets. In many cases,
individual or specific approaches have been developed for feature selection (FS) on a certain dataset, but collecting the diversity dataset and demonstrating it through different FS methods are challenging. Thus, this article proposed multiapproaches for FS with the classification of diverse datasets. The pro-posed framework is developed using various methods, such as extendable particle swarm optimization (PSO), global and local searching, feature ranking, feature clustering, computational cost-based FS, and multiobjective optimization. We effectively used these methods in our proposed work in a single-setting framework. We focused on three essential computational items in our framework: classification accuracy, selected features, and computational times. Due to the diverse dataset, few methods have been considered challenging during computational evalua-tion for classification accuracy with test cost. We tried to manage the classification accuracy based on total cost and high accuracy with less cost. The proposed framework is experimented with the above methods and analyzed through comparative results on diversity datasets. For example, when regular parameter values are in the range of 2−13–2−6, the evaluation result affects all items, i.e., decreasing during this range; other values do not affect results. We used thresholds ranging from 0.6 to 0.9 for highly correlated feature pairs as per the support vector machine (SVM) method for recursive feature elimination
Empirical study of Gum Ghatti as an alternative thickening agent in hydraulic fracturing
No full textGum ghatti (anogeissus latifolia) is being widely used as an emulsifier, thickener, stabilizer in food, pharmaceutical, and allied industries due to its shelf life, tolerance of heat, and pH stability. Considering the oil & gas industry application, it is ideal for a hydraulic fracturing fluid additive as a direct replacement for guar gum. Basically, unlike guar gum, it contains less residual hull and it is suitable for low permeability unconventional reservoir; mainly shale gas reservoir, where permeability counts trivial in amount. The polymer of ghatti aid exceptional rheological properties and help to produce higher molecular weight polymer; which has excellent proppant carrying capacity and fracture propagation. In this paper, the experimental study has been carried out in two different phases. This was achieved through optimization and characterization of hydraulic fracturing fluid which was embedded with gum matrices. In Phase-I, the study was carried out by using response surface methodology (RSM). Wherein, the relation between several explanatory and response variables have been measured. In Phase-II, the characterization was done by using a scanning electron microscope (SEM), differential scanning calorimeter (DSC), thermo-gravimetric analysis (TGA) and also, Fourier-transform infrared spectroscopy (FT-IR). This experimental study will potentially benefit for development of a new hydraulic fracturing fluid. Where gum ghatti observed as a satisfactory alternative agent for guar gum
A comprehensive review on potential pharmacological activities of Morinda citrifolia Linn. for the treatment of central nervous system disorders
Get PDFIn brazil, the plant Morinda citrifolia, popularly known as noni, is widely utilized in traditional medicine. Many components of the noni tree, including the roots, leaves, and seeds, are used in these traditions. Because of its high antioxidant activity and established health advantages Morinda citrifolia (Noni) has been widely utilized as a complementary and alternative medicine in many countries. The noni plant has an ancient legacy of being used to cure a range of diseases including CNS abnormalities. It has historically used as an antidepressant, anxiolytic, antiepileptic activity, antipsychotic, nootropic activity, anticraving activity aganist alcohol dependence, antiemetic activity, neuroprotective agent. Objective: Based on preclinical research published in the literature, the current study underlines noni therapeutic potential activities for the treatment of cns disorders.The literature was collected from research gate, Wikipedia, medline,scholarly articles, online databases and academic search.The present review targeted on mostly receptors,enzyme transporters and invivo, invitro and insilico methods for different cns disorders.Conclusion:The monoamine oxidase (MAO) A and B bioassays were used to evaluate the antidepressant effects of Morinda citrifolia (noni) fruit extracts invitro.Nonifruit has a synergistic impact because of its active components, involved in inhibiting MAOA and MAOB enzymes.The methanolic extract of noni shows antipsychotic activity by inhibiting dopaminergic receptors.Administration of benzodiazepine and MMC attenuated anxiolytic activityin mouse models.Noni inhibits acetycholine esterase enzyme and exhibits nootropic activity.Noni exhibits anticraving activity against alcoholdependence by CPP test.Noni exhibits neuroprotective activity by decreasing the brain damage and dysfunction caused by reperfusion injur
A Cross-sectional Study on the Development of Diabetic Cardiovascular Complications in Type 2 Diabetes Mellitus in a South Indian Tertiary Care Hospital
Get PDFBackground: Diabetic cardiovascular complication is a familiar macrovascular complication of Type 2 Diabetes mellitus (T2DM). Cardiovascular disease (CVD) is the major cause of morbidity and mortality for people with diabetes.
Objective: The aim of this study was to evaluate the parameters related to diabetic cardiovascular complication in patients with T2DM.
Methodology: This study was conducted on 530 subjects (171 with or 359 without diabetic cardiovascular complication). Prevalence of diabetic cardiovascular complication was measured, risk factors for diabetic cardiovascular complications, and drug utilization pattern was assessed.
Results: Cardiovascular complication was significantly higher in the subjects who are poorly educated, nature of work (house wives) and risk factors were pre-existing conditions (Hypertension, Cardiac, endocrine and other diseases), habit of smoking (past smoker), tea/coffee (twice without sugar), poor glycemic control, elevated triglyceride levels, elevated creatinine levels, duration of diabetes (5-10 years; >10 years).
Conclusion: Combination of Glimepiride and Metformin (35.10%), Metformin (34.04%), combination of insulin isophane and insulin regular (23.40%), Insulin Regular (11.70%) were the anti-diabetic drugs widely prescribed to the T2DM patients with cardiovascular complications. Significant risk factors for development of diabetic cardiovascular complication were multiple
Non-covalent functionalization of triazine framework decorated over reduced graphene oxide as a novel anode catalyst support for glycerol oxidation
No full textAbstract
The electrocatalytic performance of platinum-gold(Pt-Au) nanoparticles decorated non-covalent functionalization of triazine framework derived from poly(cyanuric chloride-co-biphenyl) over reduced graphene oxide (Poly(CC-co-BP)-RGO) was carried out for glycerol in basic medium and their oxidized products were analysed to support the enhanced activity. The surface morphology and the composition of the catalyst were obtained using X-ray diffraction, transmission electron microscopy and energy-dispersive X-ray spectroscopy. The electrooxidation results illustrate that the Pt-Au/Poly(CC-co-BP)-RGO catalyst exhibits improved catalytic activity and stability when compared to that of Pt/Poly(CC-co-BP)-RGO, Pt/Poly(CC-co-BP) and Pt/RGO catalysts. The better performed Pt-Au/Poly(CC-co-BP)-RGO catalyst was used as electrode material for the fabrication of single test direct alkaline glycerol fuel cell. The fuel cell performance was tested by varying the concentration of glycerol and the temperature of the cell. The maximum power density of 122.96 mWcm−2 was obtained for Pt-Au/Poly(CC-co-BP)-RGO catalyst in single direct alkaline glycerol fuel cell under the optimum concentration of 2.0 M glycerol at 70 °C
Non-dominated Sorting Genetic Algorithm II and Particle Swarm Optimization for design optimization of Shell and Tube Heat Exchanger
No full textOptimization methods are applied in Shell and Tube Heat Exchanger (STHE) design to reduce the cost of the device. Various existing optimization techniques such as Particle Swarm Optimization (PSO), Adaptive Range Genetic Algorithm (ARGA) are applied in the design of STHE. Existing optimization methods used in STHE design, have the limitation of lower convergence and easily trap into local optima. In this research, the hybrid method of Non-dominated Sorting Genetic Algorithm II (NSGA II) and PSO method is proposed to reduce the cost
in STHE design. The NSGA II method is applied to improve the exploration and PSO method is applied to improve exploitation of search process. The hybrid method has objective function of total cost and overall heat transfer of the model to improve the performance. The NSGA II has strong exploration in the search due to the nondominated search process and also provides good convergence. The PSO method is applied in the best solution of NSGA II and the PSO method has the advantage of strong exploitation that escapes from the local optima.
The hybrid NSGA II-PSO method is tested on three test cases and is compared with existing optimization methods to analyze its performance. The result shows that the hybrid NSGA II-PSO method has a 4.85% lesser total cost in case 1 and 1.51% lesser total cost in case 2, when compared to the ARGA method
Assessment of Bioprocess Development-Based Modeling and Simulation in a Sustainable Environment
Get PDFModeling and simulation help us gain a better knowledge of chemical systems and develop obstacles and improvement
opportunities. In the initial stages of systems integration, the time and money constraints prevent more precise estimates, basic simulation software that provides a reasonable approximation of energy and material usage and procedure exhaust is typically useful. Every next era of technicians will confront a new set of difficulties, including developing new biochemical reactions with high sensitivity and selectivity for pharmaceutical industries and manufacturing lesser chemicals from biomass resources. This job will need the use of operational process systems integration development tools. The existing toolsneed improvement so that they could be used to examine operations against sustainability principles as well as profitability. Eventually, characteristic models for substances that aren’t presently in collections will be necessary. In the field of integrated bioprocesses, there will undoubtedly be a plethora of new prospects for process systems engineering. The financial and environmental evaluations were based on a generic methodology for collecting first-estimate stock levels. The time it takes to do the evaluation may be cut in half, and a wider number of choices could be explored. A valuable commitment to sustainability bioprocess modeling and evaluation can be made by using a first-approximation numerical method as the basis for financial and environmental evaluation