Unemployment and Innovation in Small and Medium-Sized Enterprises (SMEs) during the First Year of COVID-19 Pandemic in Metropolitan Lima, Peru

Due to the increase in unemployment caused by the COVID-19 pandemic in Metropolitan Lima (Peru), unemployment in 2020 rose to 16.5% (1.3 million unemployed) compared to the previous year. Through innovation, SMEs sought new strategies to continue growing in the highly competitive market, generating labour demand. Therefore, the research question proposed was: How has business innovation favoured the reduction of unemployment in SMEs during the pandemic caused by COVID-19 in Metropolitan Lima? In order to solve this problem, this research developed a qualitative approach using grounded theory. Data was collected by interviewing 17 key subjects, in addition to the observation of 12 businesses between the months of August and October 2021. The results show that the observed businesses that were able to successfully cope with the pandemic had to modify their structure or processes with new sales methods (home delivery), as well as novel promotion and advertising techniques. © 2023 Maldonado-Cueva et al.

A new ridge estimator for linear regression model with some challenging behavior of error term

Ridge regression is a variant of linear regression that aims to circumvent the issue of collinearity among predictors. The ridge parameter (Formula presented.) has an important role in the bias-variance tradeoff. In this article, we introduce a new approach to select the ridge parameter to deal with the multicollinearity problem with different behavior of the error term. The proposed ridge estimator is a function of the number of predictors and the standard error of the regression model. An extensive simulation study is conducted to assess the performance of the estimators for the linear regression model with different error terms, which include normally distributed, non-normal and heteroscedastic or autocorrelated errors. Based upon the criterion of mean square error (MSE), it is found that the new proposed estimator outperforms OLS, commonly used and closely related estimators. Further, the application of the proposed estimator is provided on the COVID-19 data of India. © 2023 Taylor & Francis Group, LLC.

COVID-19 Vaccines Analysis to Predict Its Impact Across the World

In recent years, due to the widespread of COVID-19 pandemic, a large amount of data set is available about the various types of vaccines used by different countries for the protection of their citizens. So it is very important and useful if one is able to perform effective analysis of the same to make the awareness and the effectiveness of each vaccine known to mankind. It is found that COVID-19 vaccines increase the immune system, prepare the body to fight against the virus, and reduce the probability of contracting COVID-19.this can be done with the help of regression techniques such as The MAE, MSE, RMSE values to predict and evaluate the observations with more efficiency. The RMSE technique measures the standard deviation of results and provides more accuracy. This analysis helps to find out how COVID-19 vaccines are provided in various countries and the countries where 80% of the population is vaccinated. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Model to Predict Heartbeat Rate Using Deep Learning Algorithms.

ECG provides critical information in a waveform about the heart's condition. This information is crucial to physicians as it is the first thing to be performed by cardiologists. When COVID-19 spread globally and became a pandemic, the government of Saudi Arabia placed various restrictions and guidelines to protect and save citizens and residents. One of these restrictions was preventing individuals from touching any surface in public and private places. In addition, the authorities placed a mandatory rule in all public facilities and the private sector to evaluate the temperature of individuals before entering. Thus, the idea of this study stems from the need to have a touchless technique to determine heartbeat rate. This article proposes a viable and dependable method to estimate an average heartbeat rate based on the reflected light on the skin. This model uses various deep learning tools, including AlexNet, Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), and ResNet50V2. Three scenarios have been conducted to evaluate and validate the presented model. In addition, the proposed approach takes its inputs from video streams and converts these streams into frames and images. Numerous trials have been conducted on volunteers to validate the method and assess its outputs in terms of accuracy, mean absolute error (MAE), and mean squared error (MSE). The proposed model achieves an average 99.78% accuracy, MAE is 0.142 when combing LSTMs and ResNet50V2, while MSE is 1.82. Moreover, a comparative measurement between the presented algorithm and some studies from the literature based on utilized methods, MAE, and MSE are performed. The achieved outcomes reveal that the developed technique surpasses other methods. Moreover, the findings show that this algorithm can be applied in healthcare facilities and aid physicians.

A comparative study for COVID-19 cases forecasting with loss function as AIC and MSE in RNN family and ARIMA

Forecasting COVID-19 incidents is a trending research study in today's world. Since Machine learning models have been occupied in forecasting recently, this study focus on comparing statical and machine learning models such as ARIMA, RNN, LSTM, Seq2Seq, and Stacked LSTM. The performances were evaluated using two loss functions, namely, AIC and RMSE. The results showed that RNN performs with the lowest RMSE with-49.5% compared with the ARIMA. Seq2Seq scored the highest correlation of determination (R2) with 0.92. © 2022 IEEE.

Global Economic Effects of COVID-19: Analysis Based on Hybrid Model

Global economy has been destroyed by the COVID-19 pandemic, which has rendered many of the world's population impoverished. More uncertainties about the social policies will appear. Meanwhile, there are many researchers devoted themselves into using machine learning to analyze the economics. tarting from the decrease of population, the health crisis has translated to an economic crisis. The spread of the virus encouraged social distancing which led to the shutdown of financial markets, corporate offices, business and event. In this paper, we use the dataset provided by Kaggle platform to analyze the economic effects COVID-19 brings. We choose serval metrics, such as the Human Development Index, the total death caused by virus. The model is a hybrid one which combine AdaBoost and Linear Regression. AdaBoost is a kind of boosting model with an optimal performance. We also do the compared experiments using the metric: MSE, the result shows that our model owns the best performance with the lowest MSE score 7.23. The KNN, Random Forest are respectively 2.58 and 2.55 higher than that of our hybrid model. © 2022 SPIE. All rights reserved.

New Exponential Ratio Estimator in Ranked Set Sampling

In this study, we adapted the families of estimators from Unal and Kadilar (2021) using the exponential function for the population mean in case of non-response for simple random sampling for the estimation of the mean of the population with the RSS (ranked set sampling) method. The equations for the MSE (mean square error) and the bias of the adapted estimators are obtained for RSS and it in theory shows that the proposed estimator is additional efficient than the present RSS mean estimators in the literature. In addition, we support these theoretical results with real COVID-19 real data and conjointly the simulation studies with different distributions and parameters. As a result of the study, it was observed that the efficiency of the proposed estimator was better than the other estimators.

Modelling the impact of lock-downs in top-4 COVID-19 spreading states of India

A pandemic like COVID-19 being a highly infectious disease has severely affected mankind and business activities. Seeing the critical situation, the honourable Prime Minister of India has called for a lockdown in the entire country in order to suppress the spread of this pandemic. While there are many debates about the spread of disease and lockdown in the entire country, we wish to mathematically understand the diffusion of this pandemic in the context of four highly infected states of India. Moreover, through this paper, we wish to examine the impact of these lockdown periods in order to understand the spread of COVID-19.

Facilitating stress prevention in micro and small-sized enterprises: protocol for a mixed method study to evaluate the effectiveness and implementation process of targeted web-based interventions.

BACKGROUND: Workplace-related stress is a major risk factor for mental and physical health problems and related sickness absence and productivity loss. Despite evidence regarding the effectiveness of different workplace-based interventions, the implementation of stress prevention interventions is rare, especially in micro and small-sized enterprises (MSE) with fewer than 50 employees. The joint research project "PragmatiKK"+ aims to identify and address the specific barriers to the implementation of stress prevention interventions in MSE. This study protocol describes a mixed method study design to evaluate the effectiveness of adapted stress prevention interventions and the implementation process via an integrated web-based platform ("System P") specifically targeted at MSE. METHODS: First, we develop a web-based intervention, which accounts for the specific working conditions in MSE and addresses stress prevention at a structural and behavioral level. Second, we use common methods of implementation research to perform an effect and process evaluation. We analyze the effectiveness of the web-based stress prevention interventions by comparing depressive symptoms at baseline and follow-up (after 6 months and 12 months). Indicators for a successful implementation process include acceptability, adoption, feasibility, reach, dose, and fidelity, which we will measure with quantitative web-based questionnaires and qualitative interviews. We will also analyze the accumulated usage data from the web-based platform. DISCUSSION: Collecting data on the implementation process and the effectiveness of a web-based intervention will help to identify and overcome common barriers to stress prevention in MSE. This can improve the mental health of employees in MSE, which constitute more than 90% of all enterprises in Germany. + Full Project Name: "PragmatiKK - Pragmatische Lösungen für die Implementation von Maßnahmen zur Stressprävention in Kleinst- und Kleinbetrieben "(= Pragmatic solutions for the implementation of stress prevention interventions in micro and small-sized enterprises). TRIAL REGISTRATION: German Register of Clinical Studies (DRKS): DRKS00026154 , date of registration: 2021-09-16.

MODELING AND POTENTIAL PROGNOSIS OF THE NUMBER OF CASES COVID-19 PANDEMIC WITH LINEAR, NONLINEAR REGRESSION MODELS AND ARTIFICIAL NEURAL NETWORK MODELS

Coronavirus disease 2019 (COVID-2019) has been identified as a global threat, and many experiments are being performed using various mathematical models to forecast this epidemic's possible evolution. Many of the biggest wealth Economies are stressed because this disease is highly contagious and transmissible. Because of the rise in number of cases and their resulting burden on the government and health care practitioners, some predictive methods for predicting the number of cases in the future will be needed. We evaluated the performance of the linear, non-linear regression and artificial neural network models to forecast the cases reported daily COVID-19 in India 60 days ahead, and the impact of preventive measures such as social isolation, wearing mask and lockdown on COVID-19 spread. Predicting different parameters (number of positive cases, number of cases reported, number of deaths).

Entrepreneurial resilience in the covid-19 crisis: A qualitative study of micro and small entrepreneurs in poland

During global crises, small businesses suffer the most damage. At the same time, they are not sufficiently represented in the literature on entrepreneurship. At the outbreak of the pandemic, and then three months later, we conducted in-depth interviews with micro and small business owners operating in different industries in Poland. We focused on three levels of resilience: the owner, the company, and the environment. We found, among other matters, that the entrepreneurs differed in regard to the strategies they adopted in connection with the crisis and that the role of prior conceptualization in introducing new strategies was crucial. Our study contributes to the literature by providing insight into a crisis considered as an event and as a process. We also provide proposals for further research into entrepreneurial resilience. The results of this study could have practical implications for policymakers and those planning aid for entrepreneurs in a state of crisis. © 2021, Polish Sociological Association. All rights reserved.

Forecasting Brazilian Ethanol Spot Prices Using LSTM

Ethanol is one of the most used fuels in Brazil, which is the second-largest producer of this biofuel in the world. The uncertainty of price direction in the future increases the risk for agents operating in this market and can affect a dependent price chain, such as food and gasoline. This paper uses the architecture of recurrent neural networks—Long short-term memory (LSTM)—to predict Brazilian ethanol spot prices for three horizon-times (12, 6 and 3 months ahead). The proposed model is compared to three benchmark algorithms: Random Forest, SVM Linear and RBF. We evaluate statistical measures such as MSE (Mean Squared Error), MAPE (Mean Absolute Percentage Error), and accuracy to assess the algorithm robustness. Our findings suggest LSTM outperforms the other techniques in regression, considering both MSE and MAPE but SVM Linear is better to identify price trends. Concerning predictions per se, all errors increase during the pandemic period, reinforcing the challenge to identify patterns in crisis scenarios.

Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods.

The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread worldwide, leading to an ongoing pandemic, imposed restrictions and costs to many countries. Predicting the number of new cases and deaths during this period can be a useful step in predicting the costs and facilities required in the future. The purpose of this study is to predict new cases and deaths rate one, three and seven-day ahead during the next 100 days. The motivation for predicting every n days (instead of just every day) is the investigation of the possibility of computational cost reduction and still achieving reasonable performance. Such a scenario may be encountered in real-time forecasting of time series. Six different deep learning methods are examined on the data adopted from the WHO website. Three methods are LSTM, Convolutional LSTM, and GRU. The bidirectional extension is then considered for each method to forecast the rate of new cases and new deaths in Australia and Iran countries. This study is novel as it carries out a comprehensive evaluation of the aforementioned three deep learning methods and their bidirectional extensions to perform prediction on COVID-19 new cases and new death rate time series. To the best of our knowledge, this is the first time that Bi-GRU and Bi-Conv-LSTM models are used for prediction on COVID-19 new cases and new deaths time series. The evaluation of the methods is presented in the form of graphs and Friedman statistical test. The results show that the bidirectional models have lower errors than other models. A several error evaluation metrics are presented to compare all models, and finally, the superiority of bidirectional methods is determined. This research could be useful for organisations working against COVID-19 and determining their long-term plans.

Impact of Covid-19 pandemic on electricity demand in the UK based on multivariate time series forecasting with Bidirectional Long Short Term Memory.

Due to lockdown measures taken by the UK government during the Coronavirus disease 2019 pandemic, the national electricity demand profile presented a notably different performance. The Coronavirus disease 2019 crisis has provided a unique opportunity to investigate how such a landscape-scale lockdown can influence the national electricity system. However, the impacts of social and economic restrictions on daily electricity demands are still poorly understood. This paper investigated how the UK-wide electricity demand was influenced during the Coronavirus disease 2019 crisis based on multivariate time series forecasting with Bidirectional Long Short Term Memory, to comprehend its correlations with containment measures, weather conditions, and renewable energy supplies. A deep-learning-based predictive model was established for daily electricity demand time series forecasting, which was trained by multiple features, including the number of coronavirus tests (smoothed), wind speed, ambient temperature, biomass, solar & wind power supplies, and historical electricity demand. Besides, the effects of Coronavirus disease 2019 pandemic on the Net-Zero target of 2050 were also studied through an interlinked approach.

Recurrent neural network based prediction of number of COVID-19 cases in India.

COVID-19 has become the most devastating disease of the current century and is pandemic. As per WHO report, there are globally 31,174,627 confirmed cases including 962,613 deaths as of 22nd September,2020. The disease is spreading through outbreaks despite the availability of latest technologies for treatment of patients. In this paper, we proposed a neural network-based prediction of number of cases in India due to COVID-19. Recurrent neural network (RNN) based LSTM is applied on India dataset for prediction. LSTM networks are a type of RNN capable of learning order dependence in sequence forecasting problems. We analyze the performance of the network and then compare it with two parameter reduced variants of LSTM, obtained by elimination of hidden unit signals, bias and input signal. For performance evaluation, we used the MSE measure.

Examining the Emergency Medical Treatment and Active Labor Act: impact on telemedicine for neurotrauma.

The Emergency Medical Treatment and Active Labor Act (EMTALA) protects patient access to emergency medical treatment regardless of insurance or socioeconomic status. A significant result of the COVID-19 pandemic has been the rapid acceleration in the adoption of telemedicine services across many facets of healthcare. However, very little literature exists regarding the use of telemedicine in the context of EMTALA. This work aimed to evaluate the potential to expand the usage of telemedicine services for neurotrauma to reduce transfer rates, minimize movement of patients across borders, and alleviate the burden on tertiary care hospitals involved in the care of patients with COVID-19 during a global pandemic. In this paper, the authors outline EMTALA provisions, provide examples of EMTALA violations involving neurosurgical care, and propose guidelines for the creation of telemedicine protocols between referring and consulting institutions.