Comparative Analysis of Different Systematic Odometry Error Correction Methods on a Differential Drive Indoor Service Robot

The COVID-19 pandemic has led to a surge of interest in research work involving the development of robotic systems that reduce human-to-human interaction, as such a technology can greatly benefit healthcare industries in preventing the spread of highly infectious diseases. An indoor service robot is built and equipped with wheel odometry and a 2D LiDAR. However, the presence of the systematic odometry errors is evident during field testing. Hence, the possibility of minimizing systematic odometry errors is inspected using various methods of calculation, namely: UMBmark, Lee's and Jung's. The methods all use the Bidirectional Square Path test, performed together with ROS. It is found that Jung's method is the most appropriate method showing a 20.4% improvement compared to the uncalibrated dead reckoning accuracy. Moreover, it is found that the presence of slippage, a nonsystematic error, greatly affects the return position errors of the robot. Consequently, it is recommended to improve the design of the wheelbase to minimize the effects of nonsystematic errors. © 2022 IEEE.

ANALYSIS OF THE EFFECT OF MACROPRUDENTIAL AND MACROPRUDENTIAL INDICATORS ON THE STOCK PRICE INDEX OF ISLAMIC BANKS IN INDONESIA USING THE ERROR CORRECTION MODEL

The experience of various crises that have occurred, including the impact of the Covid-19 pandemic, presents a challenge to implement macroprudential policies to ensure the financial system survives and continues to carry out its function in driving the economy. The existing macroprudential policies tend to be individual and focus on prudent banking and other financial institutions. Economic fluctuations that occur on the macro side will greatly impact, either directly or indirectly, the stock price index, as well as the company's internal indicators which are considered to have a major influence on the decisions of investors and potential investors to take action on the stock exchange. The type of research used in this research is quantitative research. The nature of this research is descriptive with a quantitative approach. The data collection technique in this research is Literature Study. The test carried out in this study is the multiple linear regression analysis test (multiple linear regression method), this study uses the ECM model to obtain the best model which includes the classical assumption test. The results of this study based on the partial short-term relationship test, it can be concluded that the Exchange Rate, Inflation, and TPF in the short term have no significant effect on the PNBS Stock Price Index. Meanwhile, short-term CAR has a significant positive effect on the PNBS Stock Price Index. Based on the results of the partial long-term relationship test, it can be concluded that in the long term, the Exchange Rate has a significant negative effect and TPF and CAR have a significant positive effect on the PNBS Stock Price Index while Inflation has no significant effect on the PNBS Stock Price Index. Based on the output results of the simultaneous short-term and long-term F test, it shows that all independent variables simultaneously have a significant effect on the PNBS Stock Price Index in the short term. Based on the provisions of the MUI DSN through the issued fatwas related to the Sharia capital market and Sharia shares, it is explained that Sharia stock investment to invest according to the perspective of Sharia economic law is allowed. [ FROM AUTHOR] Copyright of Jurnal Syntax Admiration is the property of Ridwan Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Assessing the Effect of the U.S. Vaccination Program on the Coronavirus Positivity Rate With a Multivariate Framework.

The factors influencing the incidence of COVID-19, including the impact of the vaccination programs, have been studied in the literature. Most studies focus on one or two factors, without considering their interactions, which is not enough to assess a vaccination program in a statistically robust manner. We examine the impact of the U.S. vaccination program on the SARS-CoV-2 positivity rate while simultaneously considering a large number of factors involved in the spread of the virus and the feedbacks among them. We consider the effects of the following sets of factors: socioeconomic factors, public policy factors, environmental factors, and non-observable factors. A time series Error Correction Model (ECM) was used to estimate the impact of the vaccination program at the national level on the positivity rate. Additionally, state-level ECMs with panel data were combined with machine learning techniques to assess the impact of the program and identify relevant factors to build the best-fitting models. We find that the vaccination program reduced the virus positivity rate. However, the program was partially undermined by a feedback loop in which increased vaccination led to increased mobility. Although some external factors reduced the positivity rate, the emergence of new variants increased the positivity rate. The positivity rate was associated with several forces acting simultaneously in opposite directions such as the number of vaccine doses administered and mobility. The existence of complex interactions, between the factors studied, implies that there is a need to combine different public policies to strengthen the impact of the vaccination program.

Intra-Host Co-Existing Strains of SARS-CoV-2 Reference Genome Uncovered by Exhaustive Computational Search.

The COVID-19 pandemic caused by SARS-CoV-2 has had a severe impact on people worldwide. The reference genome of the virus has been widely used as a template for designing mRNA vaccines to combat the disease. In this study, we present a computational method aimed at identifying co-existing intra-host strains of the virus from RNA-sequencing data of short reads that were used to assemble the original reference genome. Our method consisted of five key steps: extraction of relevant reads, error correction for the reads, identification of within-host diversity, phylogenetic study, and protein binding affinity analysis. Our study revealed that multiple strains of SARS-CoV-2 can coexist in both the viral sample used to produce the reference sequence and a wastewater sample from California. Additionally, our workflow demonstrated its capability to identify within-host diversity in foot-and-mouth disease virus (FMDV). Through our research, we were able to shed light on the binding affinity and phylogenetic relationships of these strains with the published SARS-CoV-2 reference genome, SARS-CoV, variants of concern (VOC) of SARS-CoV-2, and some closely related coronaviruses. These insights have important implications for future research efforts aimed at identifying within-host diversity, understanding the evolution and spread of these viruses, as well as the development of effective treatments and vaccines against them.

The Differential Effects of Oil Prices on the Development of Renewable Energy in Oil-Importing and Oil-Exporting Countries in Africa

The shift to renewable sources of energy has become a critical economic priority in African countries due to energy challenges. However, investors in the development of renewable energy face problems with decision making due to the existence of multiple criteria, such as oil prices and the associated macroeconomic performance. This study aims to analyze the differential effects of international oil prices and other macroeconomic factors on the development of renewable energy in both oil-importing and oil-exporting countries in Africa. The study uses a panel vector error correction model (P-VECM) to analyze data from five net oil exporters (Algeria, Angola, Egypt, Libya and Nigeria) and five net oil importers (Kenya, Ethiopia, Congo, Mozambique and South Africa). The study finds that higher oil prices positively affect the development of renewable energy in oil-importing countries by making renewable energy more economically competitive. Economic growth is also identified as a major driver of the development of renewable energy. While high-interest rates negatively affect the development of renewable energy in oil-importing countries, it has positive effects in oil-exporting countries. Exchange rates play a crucial role in the development of renewable energy in both types of countries with a negative effect in oil-exporting countries and a positive effect in oil-importing countries. The findings of this study suggest that policymakers should take a holistic approach to the development of renewable energy that considers the complex interplay of factors, such as oil prices, economic growth, interest rates, and exchange rates.

A Time Series Forecast of COVID-19 Infections, Recoveries and Fatalities in Nigeria

The study investigated COVID-19 pandemic infections, recoveries, and fatalities in Nigeria to forecast future values of infections, recoveries, and fatalities and thus ascertain the extent to which the pandemic appeared to be converging with time. The prediction of COVID-19 infections, recoveries, and fatalities was necessitated by the impact that the pandemic had exerted in world economies since its outbreak in late 2019. The quantitative method was employed, and a longitudinal research design was applied. Data were obtained from the Nigeria Centre for Disease Control (NCDC). The least-squares test and autoregressive distributed lag (ARDL) tests were performed to forecast infections, recoveries, and fatalities. The results of the predicted infections for the last five months of the year (August–December 2020) shows that the cases of infections will narrow down within the period. The need for policymakers to implement complete unlocking of the economy for speedy economic recovery was suggested, among others.

Sustainability assessment of crops' production in India: empirical evidence from ARDL-ECM approach

PurposeThe study aims to evaluate the long- vs short-run relationships between crops' production (output) and crops' significant inputs such as land use, agricultural water use (AWU) and gross irrigated area in India during the period 1981–2018.Design/methodology/approachThe study applied the autoregressive distributed lag (ARDL) bounds testing approach to estimate the co-integration among the variables. The study uses the error correction model (ECM), which integrates the short-run dynamics with the long-run equilibrium.FindingsThe ARDL bounds test of co-integration confirms the strong evidence of the long-run relationship among the variables. Empirical results show the positive and significant relationship of crops' production with land use and gross irrigated area. The statistically significant error correction term (ECT) validates the speed of adjustment of the empirical models in the long-run.Research limitations/implicationsThe study suggests that the decision-makers must understand potential trade-offs between human needs and environmental impacts to ensure food for the growing population in India.Originality/valueFor a clear insight into the impact of climate change on crops' production, the current study incorporates the climate variables such as annual rainfall, maximum temperature and minimum temperature. Further, the study considered agro-chemicals, i.e. fertilizers and pesticides, concerning their negative impacts on increased agricultural production and the environment.

Aquaculture Production in the Midst of GHG Emissions in South Africa

The study ascertained the relationship between aquaculture production and greenhouse gas (GHG) emissions in South Africa. The study used the Autoregressive Distributed Lag—Error Correction Model (ARDL-VECM) with time series data between 1990 and 2020. The results showed that the mean annual aquaculture production, GHG emissions, and Gross Domestic Product (GDP) in the period were 5200 tonnes, 412 tonnes, and US$447 billion, respectively. There was a long-run relationship between GHG emissions and GDP. In the short run, GHG emissions had a positive relationship with GDP and a negative relationship with beef production. Furthermore, there was a bi-directional relationship between aquaculture production and GHG emissions. In addition, beef production and GDP had a bi-directional relationship. Beef production also had a positive relationship with aquaculture production. The study concludes that aquaculture production is affected and tends to affect GHG emissions. Aquaculture legislation should consider GHG emissions in South Africa and promote sustainable production techniques.

Risk Measure between Exchange Rate and Oil Price during Crises: Evidence from Oil-Importing and Oil-Exporting Countries

This study investigates the risk spillover effect between the exchange rate of importing and exporting oil countries and the oil price. The analysis is supported by the utilization of a set of double-long memories. Thereafter, a multivariate GARCH type model is adopted to analyze the dynamic conditional correlations. Moreover, the Gumbel copula is employed to define the nonlinear structure of dependence and to evaluate the optimal portfolio. The conditional Value-at-Risk (CoVaR) is adopted as a risk measure. Findings indicate a long-run dependence and asymmetry of bidirectional risk spillover among oil price and exchange rate and confirm that the risk spillover intensity is different between the former and the latter. They show that the oil price has a stronger spillover effect in the case of oil exporting countries and the lowest spillover effect in the case of oil importing countries.

RECOVERY PERIOD OF AIR TRANSPORTATION: A FORECAST WITH VECTOR ERROR CORRECTION MODEL

Air transport is the primary module of civil aviation and because of its nature, air transport has been simultaneously affected by Pandemics and crises. The influence of COVID-19 was more devastating than the other Pandemics and crises due to its global effect. This effect has continued a long period that still this effect exists now with a slight trend. The aim of this study is to analyse the selected variables that shows the past and future trend of air transportation related to operational and financial status. These variables are the primary ones that can define the countries' general status in air transport. The forecasting results are examined by 9-months forecasting with Vector Error Correction Model. It is forecasted that slightly decreasing trend will proceed in the following 9-months for passenger transportation due to fall and winter seasons. It is forecasted that slightly upward trend will proceed in the following 3-months and slightly decreased in the other 6-months for cargo transportation due to potential economic crisis in 2023. The originality of this paper is the first research related to analyse passenger and freight transportation together with the operational and financial parameters that defined in the sample of data and methodology sections. © 2023 Journal of Cellular & Molecular Anesthesia. All rights reserved.

A Review on Recognition of Disguise Face Mask Using Novel Deep Convolutional Neural Network

Object recognition establishes a connection of different objects present in images or videos. Nowadays, this technology is widely used in transportation management systems, intelligence systems, military equipment acquisition, and also in surgical equipment to obtain a surgical guidance, etc. Wearing a facemask has become a mandate in public places to control the spread of coronavirus. This research study has developed a novel facemask detection model based on a single-shot detector (SSD) to collect real-time images. This process has been implemented in three modules: 1) A network of simple error correction features will be introduced based on SSD and partition in order to achieve a better access speed and satisfy the real-time requirements;2) Feature Enhancement Module (FEM) is used to strengthen the in-depth features learned by CNN models to improve the visibility of minor substances;3) A COVID-19-mask will be finally created by considering a large database of face mask images. Test results generate high accuracy while utilizing real-time acquisition and realization of the proposed algorithm. © 2022 IEEE.

Use of live streaming systems in field‐based learning

Field‐based learning is a key element in wildlife management curriculum as it is a valuable teaching tool for natural resource topics. There are multiple constraints that restrict use of field‐based learning techniques in wildlife programs that have been complicated by the COVID‐19 pandemic. Many academic programs were forced to rapidly transition to online instruction, but despite these difficulties, there was a need to provide interactive, in the field learning opportunities for students. This necessity resulted in the development of a live streaming system to provide an interactive learning experience (Leopold Live!). In this case study, we describe the technology used in Leopold Live! to augment an online, wildlife habitat management course at Texas A&M University, and the associated challenges and adjustments needed to improve delivery in the future. We conclude that Leopold Live! serves as a potential method to meet the challenge of providing interactive, field‐based learning in a distance education setting. [ABSTRACT FROM AUTHOR] Copyright of Wildlife Society Bulletin is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Does political risk matter for infrastructure investments? Empirical evidence

Infrastructure assets are vital for economic development and integration, but they also encompass political risks. In Africa, infrastructure assets have remained a paradox where there is great potential for opportunities but very few projects get to the final phases. Adequate infrastructure can propagate the attainment of the Sustainable Development Goals whilst supporting recovery from the Covid-19 pandemic. Drawing from a longitudinal data set from 2000 to 2021 for 35 African countries, the paper empirically examined the nexus between infrastructure and political risk. Several techniques were employed to determine the dynamic effect, cointegration and causality between infrastructure and political risk. Controlling for the potential endogeneity in infrastructure the system Generalized Method of Moments, the relationship between political risk and infrastructure was ascertained. Furthermore, the ARDL-PMG was employed to determine the cointegration and causal relationship between infrastructure and political risk. The results suggest a cointegration between infrastructure assets and political risk. Infrastructure adjusts to changes in political risk to its long-run equilibrium at a speed of adjustment of 16.9 per cent. Bridging infrastructure gaps in Africa requires an extensive set of actions. Thus, the policy derivatives of our findings, suggest controlling the proliferation of political risk to support infrastructure investment. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Towards a Multidimensional Model for Evaluating the Sustainable Effect of FDI on the Development of Host Developing Countries: Evidence from Africa

This study aims to comprehensively evaluate the sustainable impact of FDI on the development of host African countries. Previous empirical studies seem to have overestimated the impact of FDI by limiting its effects to one aspect or sub-aspect of sustainable development. This study focuses on the sustainable/net effect of FDI on development in Africa. To achieve this, a multidimensional model that combines two opposing views (mainstream theory of economic development and dependent theory) was tested. Panel data of 35 African countries with the PMG/ARDL approach were used to probe the sustainable effect of FDI from 1990 to 2020. The key findings of this study reveal that the overall estimated sustainable effect of FDI on real GDP per capita is statistically minuscule for the entire sample. Thus, the effect of FDI on the development of host African countries is not inherently more important. The most striking result that emerged from the data is that environmental degradation is the dominant variable that adversely influences overall development in Africa. Another striking finding that emerged from the data is that income inequality, in general, has a significant negative impact on real GDP per capita in the long run. More importantly, the results of this study confirm that CO2, GINI, and GOV play important roles in the relationship between FDI and African development. Estimates of the error correction term for each specific country are negative and statistically significant. The fastest speed of adjustment was observed in Morocco, while the lowest was recorded in South Africa. Furthermore, this study presents different policy implications based on the long-term results.

Frequency of convergence insufficiency among students at a tertiary care centre in rural Karnataka, India

As per our literature search studies on convergence insufficiency are being conducted in children or among the geriatric age group and before the pandemic affected the world. Since the commence of COVID 19 pandemic, increased screen time has increased with online teaching and work affecting their near work and increased convergence, refractive errors especially myopia. Hence the rate of change of convergence insufficiency might be possible. As per our knowledge this is one of first study being conducted among students on convergence insufficiency post Pandemic with increased near work as well as display time. The purpose of this study is to determine the frequency of convergence insufficiency and also determine the correlation between gender, refractive error (Corrected and uncorrected refractive error) and the amount of screen time among the students of same age group.

Hybrid Transfer Learning with ECOC Ensemble Configuration for COVID-19 CXR Detection

Coronavirus Disease 2019 (COVID-19) is a viral pneumonia that causes symptoms in the lungs of those infected. The presence of the symptoms must be diagnosed as soon as possible. If no test kits are available, the next best alternative is a computer-aided diagnostic of a patient's chest X-ray scan for a quick and accurate diagnosis. This paper proposes a hybrid transfer learning method with Error-Correction Output Codes (ECOC) by combining networks including GoogLeNet, ResNet-18, and ShuffleNet for feature extraction. X-ray input data are collected from open-source repositories. In this implementations, Support Vector Machine (SVM) as the base classifier. The proposed network attempts to categorize the input data into one of three categories: COVID-19, healthy, and non-COVID-19 pneumonia. The mean accuracy of our method is 96.21%, compared fine tuning existing pre-trained model which yielded 89.1% for GoogLeNet, 88.95% for ResNet-18, and 89.31% for ShuffleNet. © 2022 IEEE.

Modelling the link between Covid-19 cases, hospital admissions and deaths in England

Analysing the mass of time series data accumulating daily and weekly from the coronavirus pandemic has become ever more important as the pandemic has progressed through its numerous phases. Econometric techniques are particularly suited to analysing this data and research using these techniques is now appearing. Much of this research has focused on short-term forecasting of infections, hospital admissions and deaths, and on generalising to stochastic settings compartmental epidemiological models, such as the well-known "susceptible (S), infected (I) and recovered or deceased (R)", or SIR, model. The focus of the present paper is rather different, however, in that it investigates the changing dynamic relationship between infections, hospital admissions and deaths using daily data from England. It does this using two approaches, balanced growth models and autoregressive distributed lag/error correction models. It is found that there has been a substantial decrease over time in the number of deaths and hospital admissions associated with an increase in infections, with patients being kept alive longer, as clinical practice has improved and the vaccination program rolled out. These responses may be tracked and monitored through time to ascertain whether such improvements have been maintained.

Teaching Writing in Emergency Distance Education: The Case of Primary School Teachers

This is a case study examining the writing teaching processes of primary school teachers during the COVID-19 pandemic. Fifty-four primary school teachers who conduct their writing teaching practices by distance education took part in the study. The data were gathered through a semi-structured interview form developed by the researcher under the guidance of an expert. The interviews were conducted via video chat programs. After having the participant's consent, the interviews were recorded. In the analysis of the transcribed texts, descriptive and content analysis methods were used. The results were divided into five groups after the data analysis, and these are: first writing experiences, situations originating from the teacher, situations originating from the student, situations originating from the student's parents, and recommendations. The results revealed that sufficient significance was not given to the writing tasks during the pandemic, and these efforts were postponed to the next semester when it was expected that face-to-face education would begin. In the process, the study revealed that student motivation was poor, there were issues in the management of the classroom environment, and writings of students could not be provided with the necessary feedback and corrections. Besides, what stands out that for teaching writing, parent support is required, and parents do not pay desired attention to the writing. However, primary school teachers have provided some suggestions for more effective writing teaching.

Preliminary Indicators of EFL Essay Writing for Teachers' Feedback Using Automatic Text Analysis

During the pandemic of Coronavirus disease 2019 (COVID-19), English as a foreign language (EFL) students have to study and submit their assignments and quizzes through online systems using electronic files instead of hardcopies. This has created an opportunity for teachers to use computer tools to conduct preliminary assessment of the students' writing performance and then give advice to them timely. Hence, this paper proposed some indicators which were essay readability scored by Flesch Reading Ease (FRE), length of essays, errors in writing and a method to assist the teachers in providing writing feedback to the students. The results showed a large difference in FRE, the number of words, sentences, paragraphs and errors. The K-means clustering findings were applied to classify groups of students based on writing proficiency indicators. The findings also revealed that the number of words and sentences in the essays indicated some deficiencies. The concept of paragraph should be reinforced while some specific errors such as misspelling, grammatical and typographical errors found need to be eliminated. This study showcased that the computer tools should be integrated to process the students' essays so that the teachers can pinpoint the problems and make suggestions to their students in appropriate time. Lastly, the results can be served as the guidelines for the teachers to develop and adjust teaching materials pertinent to writing to enhance the writing performance of EFL learners.

Forecasting Recovery Period of the Airfreight Transportation from Covid-19 Pandemic by using Time Series Modelling

COVID-19 has a dramatically negative effect globally, so all transportation modes also airfreight have been affected negatively. This study aims to forecast the airfreight load factor by applying time series to the selected variables. After providing general information about COVID-19, the forecasting results apply to the time series modeling finding the getting back time into the recovery period. It analyzes between January 2016-May 2021 with available tonne-kilometer, revenue tonne-kilometer, load factor, gross domestic product, domestic and international freight. The findings show that the cargo load factor is affected by domestic transportation in the long-term and international transport in the short-term periods. So, airfreight is firstly affected by international transport due to its global position. The forecast results show that the recovery period started in February 2021 and will continue with a robust growth trend in July 2021 due to the changing airlines' focus on freight transportation. After the completion of vaccination, primarily related to passenger transportation, airfreight transportation also benefits from this growth trend with the configuration change of aircraft'. This paper's contribution shows the necessity to minimize the economic damage by using passenger aircraft for freight transport to increase the speed of the recovery period in terms of GDP.