In the time since the first industrial revolution, technology, production, industry and a way of doing business have undergone a radical change. Due to industrial revolution, full automation and mass production activities have started. In recent years, due to the rapid developments in information technologies with Industry 4.0, countries have developed different strategies that can be applied in different sectors in order to adapt to change and gain competitive advantage. In this study, Turkey’s Industry 4.0 implementations were evaluated in order to ensure continuity and increased efficiency in the agricultural sector and for this purpose a multi-criteria model was suggested. Because with the use of Industry 4.0 technologies in agricultural sector, it is possible to obtain more output with less input in production. The technologies that provide this high output rate were structured with the proposed model and the most suitable alternative technology was determined by SWARA and EDAS methods, where multiple conflicting criteria can be evaluated together.

A new XSG or, the Xilinx System Generator based on the MEWO or, Modified Earthworm Optimization (MEWO) is suggested to evaluate the greatest power point within GIPV or, Grid integrated solar photovoltaic methodology of power generation. The suggested embedded controller functions with the aid of MEWO based algorithm and the procedures of XSG. Here, MEWO based algorithm is implemented in XSG controller for pulse optimization and the optimized switching pulses are utilized to control the cycle of duty of the SEPIC converter. An integral regulator is used with the suggested algorithm for automatically changing the duty cycle of the SEPIC converter. Here the switching pulses are produced with the help of this novel suggested MEWO based XSG embedded controller. The aim of the suggested innovative MPPT based controller is to get the highest possible power from within the PV array according to the constant and irregular solar irradiance (G) emanating from the PV array. The suggested method for the PV structure is implemented within the MATLAB/SIMULINK as also the PWM pulses are manufactured utilizing XSG controller. Finally, the simulation outcome of the suggested XSG based on the MEWO is assessed and contrasted with the traditional PWM technique and MJAYA algorithms.

The world is betting today on mobile phone applications to stopping the spread of deadly infectious diseases, include the COVID-19 pandemic. Since the start of the COVID-19 outbreak, a group of countries has launched contact-tracing apps aimed at stemming the spread of COVID-19. The app helps health authorities to track the movements of people diagnosed with COVID-19 which gives a chance to isolate them rather than isolate the whole population. When two people are near each other, their phones are exchanging tokens via a Bluetooth connection, recording that they\'ve had close contact. If then someone gets diagnosed with COVID-19, they can declare that fact and their phone will release the identifiers of all the other phones that they were close to over the past 14 days, so those people can self-quarantine if necessary. However, when two or more phones send simultaneously their tokens for looking for another phone, collisions occur.\nIn this paper, we aim to improve the Bluetooth network performance by proposing a ZigZag decoding mechanism. This mechanism contributes to enhanced Bluetooth network performance by reducing the loss rate of packets colliding. The goal of improvement in Bluetooth network performance is to make the contact tracing apps more efficient. A Markov chain is then constructed to evaluate the system performance in which the number of backlogged packets is taken as the system state. Numerical results demonstrate that the ZigZag decoding mechanism can significantly improve system performance compared to the current Bluetooth, and indeed improving the contact tracing apps performance.

Accordingly learning disabilities, just\nfound out the real-life issues which are going to happen in\nduring of the lectures in the class. Whenever mostly\nstudents act normal and like they do not have any diseases\nand any mentality issues even they got maximum marks\nand rewarded and got admission on graduation on the\nbases of merit base. They do not aspect what is reason\nbehind that to be occurring and during the semester they\nlost their marks.\nAn Arrangement Students with disability of learning With\nNaive Bays Classifier and Decision Tree is to\npreliminarily classify the student with learning disabilities\nbefore diagnostic physician using the classification\ntechniques, Naive Bayes Classifier and Decision Tree.\nChildren with learning disabilities cannot learn on their\nown side as good as the other defects. Even if the student\ncan see well any intelligence.\nIn the learning disability there is a prediction of some\ncauses that are happen certainly the below explain the\nhow to identify the disability of the students and how to\nresolve there are many reason to generate disability, it can\nbe appear from teacher side may or may not teachers do\nnot have the promptly ability of transferring a lecture or\nmaybe they have the ability to transfer lecture properly\nbut they don’t have lecture of exact topics .there can be\nmany reason to generate it.

The COVID-19 pandemic is an incomparable disaster triggering to a massive fatalities and security glitches. Under the pressure of these black clouds public frequently wear masks as safeguard to their lives. Since certain chunks of the face are concealed this makes face recognition a very challenging task. Primarily researchers focus to derive up with recommendations to tackle this problem through prompt and effective solution in this COVID-19 pandemic. This paper, presents a trustworthy method to address the problem of the masked face recognition process based on un-occluded and deep learning-based features. The first stage is to capture the non-obstructed face region. Than we extract the most significant features from the attained regions (forehead and eye) through pre-trained deep learning convolutional neural network (CovNet/CNN). On the feature maps of the CNN last convolutional layer we apply the Bag-of-word paradigm to quantize them and to get a minor illustration comparing to the CNN’s fully connected layer. Finally, for the classification process we applied Multilayer Perceptron (MLP). High recognition performance with significant accuracy on real world masked face dataset is seen in experimental results.

The rapid spread of the Coronavirus has boosted the researchers to find the reasons behind this spread. The relationship between nitrogen dioxide\'s (NO2) concentration as an air pollutant and the number of coronavirus infections and deaths has been suggested. This paper mainly seeks to determine whether there is a relationship between exposure to NO2 contamination and the confirmed COVID-19 deaths and exacerbations of infections in European, Asian, and African countries. The time series of the daily average of NO2 and COVID-19 reported data for March-June 2020 in Italy, China, and Egypt were analyzed. Also, the air quality modeling and analysis with Python is presented. Pearson\'s correlation coefficient (PCC) has been applied to evaluate this relationship. This paper aims to compare NO2 emissions\' concentration before and after the government\'s lockdown to combat the new pandemic using environmental data published by Copernicus Sentinel-5P satellite. The results show a positive relation between NO2 and COVID-19 infections, as the PCC values are 0.47, 0.048, and 0.78 for China, Italy, and Egypt. The PCC values of deaths have reached 0.29, 0.39, and 0.72 for China, Italy, and Egypt. The experimental results show that high levels of air pollution in Europe and Asia have a severe impact on the fulminate of COVID-19. The positive correlations of NO2 and COVID-19 cases are likely derived from bioaerosols\' distribution in the air. The lockdown activities have led to a significant decrease in NO2 emissions with additional benefits for the environment.