Batman University

About: Batman University is a education organization based out in Batman, Turkey. It is known for research contribution in the topics: Diesel fuel & Diesel engine. The organization has 300 authors who have published 810 publications receiving 10346 citations. The organization is also known as: Batman Üniversitesi.

Cannabinoids for sars-cov-2 and is there evidence of their therapeutic efficacy?

Abstract: To combat the coronaviruses and their novel variants, therapeutic drugs and the development of vaccines that are to be effective throughout human life are urgently needed. The endocannabinoid system (ECS) acts as a modulator in the activation of the microcirculation, immune system, and autonomic nervous system, along with controlling pharmacological functions such as emotional responses, homeostasis, motor functions, cognition, and motivation. The ECS contains endogenous cannabinoids, cannabinoid receptor (CBRs), and enzymes that regulate their biosynthesis, transport, and degradation. Moreover, phytocannabinoids and synthetic cannabinoids that mimic the action of endocannabinoids also play an essential role in the modulation of the ECS. Cannabinoids, the main constituents of cannabis (Cannabis sativa L.), are therapeutic compounds that have received international attention in the health field due to their therapeutic properties. Recently, they have been tested for the treatment of COVID-19 due to their antiviral properties. Indeed, cannabinoid-type compounds, and in particular cannabidiol (CBD), isolated from glandular trichomes found in the calyx of cannabis flowers with reported antiviral properties is hypothesized to be a therapeutic option in the ministration of SARS-CoV-2 consorted with COVID-19 disease. The relevant articles were determined from the database search published mainly in Web of Science, Google scholar, PubMed, Crossref, and ClinicalTrials.gov database during the pandemic period. The articles were evaluated for the therapeutic potentials, mechanisms of action of cannabinoids, the roles of the ECS in the immune system, impact of cannabinoids in SARS-CoV-2 septic, especially if they address the application of cannabinoids as drugs for the curability and management of SARS-CoV-2 and its novel variants. Although the evidence needed to be considered using cannabinoids in the control and treatment of viral diseases is currently in its infancy, they already offer an opportunity for clinicians due to their effects in relieving pain, improving appetite, and improving childhood epilepsy, especially in cancer and human immunodeficiency virus (HIV/AIDS) patients. In addition to these, the most recent scientific evidence emphasizes their use in the treatment of the coronavirus infected patients. In brief, all preclinic and clinic studies that have been reported show that, through the cannabinoid system, cannabinoids, particularly CBD, have many mechanisms that are effective in the treatment of patients infected by SARS-CoV-2. Thus, more extensive studies are necessary in this area to fully identify the effects of cannabinoids on SARS-CoV-2.

Improving Tracking Efficiency of Two-Axis Sun Tracking Systems

Abstract: Due to both reduction and insufficient of fossil fuel to supply current growing energy needs, investigation and employing of renewable resources has been accelerated. Besides, using fossil fuel affected the environment negatively. Therefore, renewable energy resources in the most studies are solar, wind and geothermal. In this study, electrical energy production methods from solar energy have been examined, a fixed and a two axis tracking system have been designed. Both systems are compared each other regarding to several factors by performing annual measurements. Energy consumption of the system is minimized by employing actuator motor in two axis solar tracking system. According to the efficiency of two-axis tracking system, the annual average has been calculated as 31.67% more. This efficiency has been calculated as 70% in winter, 11% in summer. As a result of these measurements several graphics of a year empirically daily, monthly and annual data have been contributed to the literature for Diyarbakir, one of the prominent cities of Southern east, having the most solar energy of Turkey. In the first section, literature review will be indicated. In the second section, solar angles, photovoltaic panels and systems, sample designs and solar tracking systems are examined. In the third section, photovoltaic two-axis solar tracking system and qualifications, work and advantages of fixed system which we designed are stated. In following section, obtained results will be given and in last section, financial analysis of fixed and tracking photovoltaic systems has been performed. Also, recommendations for increasing their efficiency have been noted.

Norwegian internet voting protocol revisited: ballot box and receipt generator are allowed to collude

Abstract: Norway experienced internet voting in 2011 and 2013 for municipal and parliamentary elections, respectively. Its security depends on the assumptions that the involving organizations are completely independent, reliable, and the receipt codes are securely sent to the voters. In this paper, we point out the following aspects: The vote privacy of the Norwegian scheme is violated if Ballot Box and Receipt Generator cooperate because the private key of Decryption Service can be obtained by the two former players. We propose a solution to avoid this issue without adding new players. To assure the correctness, the receipt codes are sent to the voters over a pre-channel (postal service) and a post-channel (Short Message Service [SMS]). However, by holding both SMS and the postal receipt code, a voter can reveal his vote even after the elections. Albeit revoting is a fairly well solution for coercion or concealment, intentional vote revealing is still a problem. We suggest SMS only for notification of vote submission. In case the codes are falsely generated or the pre-channel is not secure, a vote can be counted for a different candidate without detection. We propose a solution in which voters verify the integrity of the postal receipt codes. Copyright © 2016 John Wiley & Sons, Ltd.

Prediction of spontaneous coal combustion tendency using multinomial logistic regression.

Abstract: Spontaneous combustion of coal is a complex underground mining disaster, which mainly threats mine safety and efficiency. Several factors usually cause spontaneous combustion of coal, such as gas concentration, ventilation and coal properties. In this study, spontaneous combustion tendencies of coal mines were predicted considering the effective parameters for an underground coal mine in Turkey. Multinomial logistic regression, a multivariate statistical technique, was applied. Gas concentrations (CH4, CO, O2) and air velocity were defined as factors affecting spontaneous coal combustion. Fire hazard levels of the coal mines were determined as 'normal situation' and 'potential combustion'. It was observed that CH4 and CO variables and CH4 × CO interaction were effective in the formation of clusters. The results indicate that Mine I is more liable to spontaneous combustion than Mine II and Mine III. At the same time, the effects of variations in factors are examined in the study.

A joint generalized exemplar method for classification of massive datasets

Abstract: The proposed method depends on human learning. Therefore it is a natural way of classification.The main upgrade of JGE, which is derived from NGE, is to have adaptive boundaries.The obtained classification accuracies and speeds were acceptable depending upon NGE and other popular ML methods.The proposed method can be used with huge datasets and also can be used in real time application depending upon its simplicity and speed. Due to technological improvements, the number and volume of datasets are considerably increasing and bring about the need for additional memory and computational complexity. To work with massive datasets in an efficient way; feature selection, data reduction, rule based and exemplar based methods have been introduced. This study presents a method, which may be called joint generalized exemplar (JGE), for classification of massive datasets. This method aims to enhance the computational performance of NGE by working against nesting and overlapping of hyper-rectangles with reassessing the overlapping parts with the same procedure repeatedly and joining non-overlapped hyper-rectangle sections that falling within the same class. This provides an opportunity to have adaptive decision boundaries, and also employing batch data searching instead of incremental searching. Later, the classification was done in accordance with the distance between each particular query and generalized exemplars. The accuracy and time requirements for classification of synthetic datasets and a benchmark dataset obtained by JGE, NGE and other popular machine learning methods were compared and the achieved results by JGE found acceptable.