In this paper, we analyze the e-commerce data collected using Google Analytics from an IT components company and a tourism agency website located in the same county in Romania. Our aim is to analyze the COVID-19 lingering effects and seasonal effects. The data set was collected from January 2019, long before COVID-19, until mid-February 2023. To perform analyses, we grouped the metrics into categories that reflect site performance, access to the website, and users’ behavior for the IT company, and access to the website, users’ behavior, and marketing campaigns for the tourism agency. Additionally, the aim is to test five hypotheses we formulate on the immediate and persistent influence of COVID-19 on the activity of small businesses.

This study applies quantitative linguistics to explore how language distance plays an essential role in third language acquisition in terms of a morphosyntactic module. Data were drawn from 360 essays written in Japanese by low, middle and high levels of learners from 12 first-language (L1) backgrounds who acquire English as a second language (L2)-interlanguage and Japanese as a third language (L3). The findings indicate that (a) mean dependency distance is an efficient indicator for syntactic complexity of writing proficiency. In both elementary and intermediate groups, learners of highly agglutinative languages are likely to show higher dependency distance than learners from isolated-language and fusion-language backgrounds. (b) The frequency and dependency distance are distributed in Power Law Function. Fitting Right truncated Good to the dependency distances indicates that the values of the parameter p ascend as the degree of agglutination of learners’ mother tongue increases. (c) The syntactic complexity in multi-background Japanese learners’ essays highlights that no matter how diverse the learners’ native and target languages are, the syntax is always constrained by universal law, namely, minimizing dependency distance. This is in accordance with existing findings in second language acquisition of inflectional languages.

The assessment of power quality (PQ) could be a valuable tool to foster the efficiency of railway systems. Control of active and reactive power flow, suppression of harmonic currents,and compensation of the negative sequence component (NSC) are of major importance for improving power quality in electrified rail systems. Therefore, flexible AC transmission systems (FACTS) are the best means used to eliminate or minimize the above-mentioned problems. This paper presents a half-bridge reactive power railway power conditioner (HBRPC) with a Ynev balance transformer. The HBRPC is composed of four switching devices with two DC capacitors, the operating voltage of the DC link affects the performance of the compensator; any fluctuation or unbalance causes the compensator to operate unstably. The finality system is integrated with the fuzzy logic controller to optimize the performance of the DC voltage (constant, balance), which must be controlled to improve the stability of the system. The performance of the HBRPC is tested in the presence of a PI controller by comparing it with the fuzzy logic. The simulation results using MATLAB/Simulink software are used to show the validity of the control strategy. The effects are compared with the presence and absence of the HBRPC to increase the stability of the dynamic performance of the system as well as the power quality of the three-phase rail traction chain.

This paper focuses on the implementation of Visual SLAM (Simultaneous Localization and Mapping). Visual SLAM or vSLAM, is a technology able to build a map of an unknown environment and simultaneously localize itself in the partially built map, using just computer vision. Visual SLAM uses only visual inputs to perform location and mapping, meaning that the only sensor required is a camera that has to be mounted on board of the device. No other external sensors are required. Today, with the great improvements in automation and robotics, vSLAM is one of the most challenging open problems for developing autonomous robots and vehicles. One major potential opportunity for visual SLAM systems is to replace GPS tracking and navigation in certain applications. GPS systems are not useful indoors, or in big cities where the view of the sky is obstructed, and they are only accurate within a few meters. Visual SLAM systems solve each of these problems as they are not dependent on satellite information and they’re taking accurate measurements of the physical world around them. Visual SLAM technology has many potential applications and demand for this technology will likely increase as it helps augmented reality, autonomous vehicles and other products become more commercially viable.

Is it possible to know what is happening at a particular place\nprecisely without seeing video clips where we are physically absent.\nNowadays all of us being engaged with other works we don\'t have much\ntime to spend on seeing the full-length video to know what is happening. But there is an alternative option for this, i.e in the form audio clip which is exactly like a person narrating the scene. The main advantage of this is we can simultaneously save time and multi-task i.e doing our work by listening to the audio clip that is generated by getting the up to date information and also if any person suddenly falls which\nmay cause heavy injuries which can indirectly lead to a major medical\nissue for elderly people. So to prevent such emergencies, it will also provide an alarm system to detect human falls. This is possible by using image processing and computer vision to capture the live moments, RNN with LSTMs to process and analyse the captured ones and by using natural language processing we can describe what is going on. Audio clips are generated by using GTTS API and they are sent to\nusers.

The 2020 began with a new threat to public health. A novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic outbreak was discovered in Wuhan, China on 12th December 2019. In 2020, the World Health Organization (WHO) introduced the name of the disease as ‘COVID-19’. On 10th June 2021, there is a sum of 175,171,038 infected cases of COVID-19; 3,776,988 death cases and 158,676,154 recovered cases that have been reported worldwide. In this review, the morphological, pathogenic mechanisms, causes, transmission, symptoms, treatments and drug potential, clinical researches on COVID-19 infection and prevention measures of SARS-CoV-2 were discussed. Although the vaccine is available, preventive measures are vital to curb the fast spread of SARS-CoV-2. The best solution should be ‘stay at home’ to break the pandemic chain. However, early preparations should be carried out for future pandemic outbreaks.

The heat transfer coefficient and consequently the Nusselt number for nanofluids depend on factors such as thermal conductivity, thermal capacity of the fluid and nanoparticles, flow pattern, nanofluid viscosity, volume fraction of suspended particles, particle shape, and dimensions. Since the parameters affecting the heat transfer coefficient of nanofluids are more than two or three parameters and optimization requires calculating the value of the function at different points and on the other hand, there are no suitable equations for the relation between these parameters, so for this purpose use existing experimental data were modeled using neural networks, after validating the created networks, using these networks, the optimal points in the existing information range have been determined using the genetic algorithm optimization method. All leading networks, with an intermediate layer, have the Levenberg-Marquardt training method, and the method of least sum of squares error has been used for validation. In addition, other networks were trained using data for all three nanofluids (Al2O3/water, CuO/water, and Cu/water nanofluids) by adding a particle size parameter. The obtained networks were also optimized in the genetic algorithm. The results indicated that for Al2O3/water nanofluids, with raising Reynolds (Re) from 729 to 1995 at a concentration of 1 wt.%, T= 96 °C, and Prandtl (Pr)=2.97, Nusselt increased by almost 82%. For CuO/water nanofluids, with increasing Reynolds from 617 to 2053 at a concentration of 3 wt.%, T=95.9 °C, and Pr=4.59, the variations of the ratio of convection heat transfer coefficient of nanofluid to water increased by almost 38%. For Cu/water nanofluids, with raising Reynolds from 626 to 1895 at a concentration of 3 wt.%, T= 65 °C, and Pr=2.95, with raising Reynolds from 626 to 1895, Nusselt increased by almost 77%.

This study aimed to analyze the researches conducted on business cycles in the international literature in terms of the bibliometric characteristics. The works, including articles, papers, notes, books, book reviews, book chapters, etc., published in the Web of Science, one significant bibliographic database of international literature, were examined in the study. On 1 March 2021, 601 open-access publications from 4650 academic publications on the Web of Science database were examined based on their \"headings.\" Bibliometric analysis was carried out with \"VOSviewer\" software. As a result of the study, it was determined that The United States was the most publishing country on the business cycles concept, most of the studies were in the English language, \"Federal Reserve System\" and \"National Bureau of Economic Research\" were the most-publishing institutions, CI Plosser and DR Osborn were the most-publishing authors. It was determined that most publications in the field took place in the journal of \"Monetary Economics.\" The total keywords number of studies was 999. The \"Business Cycle\" phrase was the most prevalent among the keywords. Executing necessary intervention by determining the leading indicators of business cycles causing fluctuations in economies, globalization\'s existence based on the business cycle, and the simultaneous high trade rate require a commercial synchronization. If synchronized economies have business cycles, they will make optimal decisions in all areas of macroeconomics. Otherwise, there will be instability in domestic demand, thus causing shocks.

Handwritten digit recognition allows the computer to recognize the digits written by humans. In general, it is a hard task for the computers to recognize the digits because the handwriting varies with the person and there is no standard handwriting. In some languages the letters overlap making the computer hard to recognize the characters.\nSo, Using Handwritten digit recognition technique we train the machine with different handwritten digits making the computer task easy. In this project a Convolutional Neural Network model was built. In order to train the model MNIST dataset is used as it consists of 60,000 training datasets. A graphical user interface is created which captures the image placed in front of the camera and passes the image as the input to the model so that the model predicts the output and the output is displayed in the result section of the graphical user interface.

CAPTCHA tests (Completely Automated Public Turing test to tell Computer and Humans Apart) are used by many services and websites. Recently, researchers have proposed interactive handwritten and text-based handwritten Arabic CAPTCHA schemes. Although both of them have shown promising results, this experimental study compared them in terms of security and usability for mobile device applications. The results demonstrated that the interactive scheme performs better than the text-based handwritten scheme in both usability and security