Controlled product testing in the home - implications for sample size

Consumer product testing in the laboratory or using a Central Location Test (CLT) is a common approach to collect consumer responses to multiple products. The Covid-19 pandemic has challenged companies to adapt, with both sensory and consumer testing in home becoming a common way of working. Moving to the home for controlled product tests brings with it both practical and statistical considerations. To investigate the question, `What are the sample size implications of moving from traditional CLTs to Controlled Home Tests (CHT)?', 245 datasets from 16 organisations and covering 19 countries were combined to undertake a meta-analysis. Consumer tests were mainly in the food category, included an overall liking question and covered both monadic and sequential monadic tests. Test noise variation was examined looking at the impact of test type, number of products and number of respondents. The CHT was significantly less variable than the traditional CLT (Lab or Hall) with a greater than 9% drop in noise variation. It was also found that noise variation increases with number of products tested in sequential monadic testing. As a result, recommendations on appropriate consumer sample sizes for different scenarios were calculated.

An Efficient Automatic Face Mask Detection System for Human Safety Based on Deep Learning using Novel YOLOv3 in Comparison of YOLO with Improved Accuracy

The objective is to build an efficient face mask detector using Novel YOLOv3. The algorithm used to detect face masks is Novel YOLOv3 in comparison with YOLO, the dataset used was (Facemask Detection Dataset, no date) with the sample size was 136. Novel YOLOv3 gets an accuracy of 92% and in YOLO it is 88% the increase in accuracy is due to the use of Darknet53 neural network model, the novel YOLOv3 and YOLO are statistically satisfied with the independent sample t-test value (\mathrm{P}\unicode{x00A1}{0.001}) with confidence level of 95%. Face Mask detection in Novel Yolov3 has a significantly better accuracy than YOLO. © 2022 IEEE.

Optimization of sewage sampling for wastewater-based epidemiology through stochastic modeling

The proliferation of the SARS-CoV-2 global pandemic has brought to attention the need for epidemiological tools that can detect diseases in specific geographical areas through non-contact means. Such methods may protect those potentially infected by facilitating early quarantine policies to prevent the spread of the disease. Sampling of municipal wastewater has been studied as a plausible solution to detect pathogen spread, even from asymptomatic patients. However, many challenges exist in wastewater-based epidemiology such as identifying a representative sample for a population, determining the appropriate sample size, and establishing the right time and place for samples. In this work, a new approach to address these questions is assessed using stochastic modeling to represent wastewater sampling given a particular community of interest. Using estimates for various process parameters, inferences on the population infected are generated with Monte Carlo simulation output. A case study at the University of Oklahoma is examined to calibrate and evaluate the model output. Finally, extensions are provided for more efficient wastewater sampling campaigns in the future. This research provides greater insight into the effects of viral load, the percentage of the population infected, and sampling time on mean SARS-CoV-2 concentration through simulation. In doing so, an earlier warning of infection for a given population may be obtained and aid in reducing the spread of viruses. © 2023, The Author(s).

COVID-19 Detection in the Images of Chest CT Scan Using K-NN in Comparison with NB Classifier to Improve the Accuracy

This chapter is about the improvisation in the accuracy in COVID-19 detection using chest CT-scan images through K-Nearest Neighbour (K-NN) compared with Naive-Bayes (NB) classifier. The sample size considered for this detection is 20, for group 1 and 2, where G-power is 0.8. The value of alpha and beta was 0.05 and 0.2 along with a confidence interval at 95%. The K-NN classifier has achieved 95.297% of higher accuracy rate when compared with Naive Bayes classifier 92.087%. The results obtained were considered to be error-free since it was having the significance value of 0.036 (p < 0.05). Therefore, in this work K-Nearest Neighbor has performed significantly better than Naive Bayes algorithm in detection of COVID-19. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Effectiveness and policies analysis of pool testing method for COVID-19

Purpose: This research aims to figure out whether the pool testing method of SARS-CoV-2 for COVID-19 is effective and the optimal sample size is in one bunch. Additionally, since the infection rate was unknown at the beginning, this research aims to propose a multiple sampling approach that enables the pool testing method to be utilized successfully. Design/methodology/approach: The authors verify that the pool testing method of SARS-CoV-2 for COVID-19 is effective under the situation of the shortage of nucleic acid detection kits based on probabilistic modeling. In this method, the testing is performed on several samples of the cases together as a bunch. If the test result of the bunch is negative, then it is shown that none of the cases in the bunch has been infected with the novel coronavirus. On the contrary, if the test result of the bunch is positive, then the samples are tested one by one to confirm which cases are infected. Findings: If the infection rate is extremely low, while the same number of detection kits is used, the expected number of cases that can be tested by the pool testing method is far more than that by the one-by-one testing method. The pool testing method is effective only when the infection rate is less than 0.3078. The higher the infection rate, the smaller the optimal sample size in one bunch. If N samples are tested by the pool testing method, while the sample size in one bunch is G, the number of detection kits required is in the interval (N/G, N). Originality/value: This research proves that the pool testing method is not only suitable for the situation of the shortage of detection kits but also the situation of the overall or sampling detection for a large population. More importantly, it calculates the optimal sample size in one bunch corresponding to different infection rates. Additionally, a multiple sampling approach is proposed. In this approach, the whole testing process is divided into several rounds in which the sample sizes in one bunch are different. The actual infection rate is estimated gradually precisely by sampling inspection in each round. © 2021, Emerald Publishing Limited.

A Framework for Automatic Fake Content Identification

Fake news emerged as a challenge for society now a day. Easy accessibility and low cost to the internet makes the fake news propagation task easy. In the Covid-19 pandemic situation, it is required to reduce the proliferation of misleading content to reduce its severe impact. Many existing works are based on lexico-syntactic features using a small training sample size. To address this issue, this study used the Gossip-cop dataset for evaluation. Various supervised techniques of the ML model and advanced deep learning techniques are implemented for intense research. Dataset is crawled from Gossipcop fact-checking websites. The dataset consists of 4,947fake news with text and 16,694 real news. The result of these algorithms helps in differentiating false content from reliable news and improved the accuracy achieved using existing techniques. © 2021 IEEE.

Controlled Product Testing in the Home - Implications for Sample Size

Consumer product testing in the laboratory or using a Central Location Test (CLT) is a common approach to collect consumer responses to multiple products. The Covid-19 pandemic has challenged companies to adapt, with both sensory and consumer testing in home becoming a common way of working. Moving to the home for controlled product tests brings with it both practical and statistical considerations. To investigate the question, ‘What are the sample size implications of moving from traditional CLTs to Controlled Home Tests (CHT)?’, 245 datasets from 16 organisations and covering 19 countries were combined to undertake a meta-analysis. Consumer tests were mainly in the food category, included an overall liking question and covered both monadic and sequential monadic tests. Test noise variation was examined looking at the impact of test type, number of products and number of respondents. The CHT was significantly less variable than the traditional CLT (Lab or Hall) with a greater than 9% drop in noise variation. It was also found that noise variation increases with number of products tested in sequential monadic testing. As a result, recommendations on appropriate consumer sample sizes for different scenarios were calculated.

Imagining benefits and challenges for future hybrid workplace to enable reentry for women on career break

COVID-19 has shifted everyone to remote work. The paper discusses how employees have overcome various challenges to make most of the benefits of remote work. The benefits of remote work are flexible work arrangements, ease of working, access to global opportunities and positive impact on diversity hiring. The challenges of remote work are access to technology, tackling virtual distance and managing productivity. Exploratory research was conducted with a sample size of 93. Data was analysed using SPSS. The sample comprised of professionals who were working from home during the COVID-19 pandemic. The study verified the hypothesis using Spearman rank order correlation. The respondents are ready to overcome the challenges of remote work because of the benefits which remote work provides. Data verifies that hybrid workplace would enable to open doors for women on career break to reenter workforce. © 2022 Inderscience Enterprises Ltd.

Detection of Covid-19 in CXR: A Low Sample Size Deep Convolutional Neural Network Training Data Approach

Infectious illness Covid-19 is highly contagious and has claimed the lives of numerous individuals. To assist prevent the virus's transmission, it's critical to identify and isolate those who have been infected with the infection. The purpose of the study is to aid in the detection of Covid-19 alongside with RT-PCR test by utilizing a deep learning algorithm, specifically YOLOv3 as the technique to be used for it uses CNN, which then implements deep learning technique. The study has a promising detection to detect if the person's CXR has Covid-19, normal or viral pneumonia, obtaining an mAP value of 95.27% from model 14, which is the highest among the 12 models created. © 2022 IEEE.

Social Media Use and the Spread of COVID-19-Related Fake News among University Students in Bangladesh

Context: After its emergence in China in 2019, the coronavirus disease (COVID-19) emerged in Bangladesh on March 8, 2020. Since then, this virus has got wide coverage in print and electronic media. Rumours and fake news have also begun to spread across online and offline media. Objectives: This study aimed to discuss how social media is being used to spread fake news during the COVID-19 pandemic and the reasons behind this. In addition, this paper examines the overall use of social media by university students and their role in sharing fake news on social media. Methodology: An online survey was used to collect data from students who had a minimum of one social media account. Facebook groups, messengers and emails were used for data collection and 264 responses were recorded. Findings: This study found that 92.8% of students received COVID-19-related news on social media, 61% experienced fake news in many cases. This is because most users of social media share news without checking its authenticity and reliability, and without checking facts against reliable sources. It was also found that most students were fairly confident in detecting fake news and checked the authenticity of the news before sharing it on social media. Originality/Value: This is the initial effort in Bangladesh to recognise the role of social media in propagating false news during the COVID-19 pandemic. However, the sample size of this study was very small. Further studies with larger sample sizes may reveal a more evident understanding of this topic. © 2022 World Scientific Publishing Co.

Analysing the Changes in Shape of Lung using Pseudo Zernike Moments in Comparison to Invariant Moments

Aim: The aim of the analysis is to estimate the deformation in the shape of the lung due to incidence of COVID using pseudo Zernike moments in comparison to invariant moments. Materials and Methods: Images are obtained from Kaggle. Sample size of 176 acquired for the study using G power by considering factors effect size, standard error rate, algorithm power as 0.3, 0.05, 0.80 respectively. In this analysis the classification of normal and COVID subjects is made using seven invariant and pseudo-Zernike moment features. Classification is made using a neural network after extracting the feature values. Result: From the obtained results, the feature values of invariant moments were observed to be statistically significant (p<0.05) than pseudo-Zernike moments. The mean and standard deviation values of variance for normal and COVID subjects were (0.18\± 0.13,0.10± 0.13). For pseudo Zernike's M2 feature statistical values of normal and COVID subjects were (0.63± 0.22,0.56± 0.23). From the values, it is observed that the COVID subjects had loss in shape of lungs due to abnormality. Variance, skewness and kurtosis were found to be statistically significant in differentiating normal and COVID subjects. The accuracy and F1 score values of invariant moments were 0.98 and 0.97 respectively. Conclusion: Therefore, from this analysis it is observed that invariant moments provide significantly better classification between normal and COVID subjects when compared to pseudo Zernike moments. © 2022 IEEE.

A Deep Learning Based Novel Approach for Detection of Face Mask Wearing using Enhanced Single Shot Detector (SSD) over Convolutional Neural Network (CNN) with Improved Accuracy

The objective is to build an efficient face mask detector using Single Shot Detector (SSD). The algorithm used for face mask detection was a novel SSD and with the comparison of Convolutional Neural Network (CNN). The face mask detection dataset was usedand the ability of the algorithm was measured with the sample size of 136. SSD has achieved accuracy of 92.25% and for CNN it was 82.6%. By using a base architecture of VGG-16, SSD was able to outperform other object detectors like CNN without compromising speed and accuracy. The SSD and CNN are statistically satisfied with the independent sample t-test value (p<0.05) with a confidence level of 95%. Face mask detection using SSD was significantly better accurate than CNN. © 2022 IEEE.

A Deep Learning Based Approach for Detection of Face Mask Wearing using YOLO V3-tiny Over CNN with Improved Accuracy

Aim: The objective is to build an efficient face mask detector using YOLO V3-tiny. Materials and Methods: The algorithm used to detect face masks is novel YOLO V3-tiny in comparison with Convolutional Neural Network (CNN), the dataset used was ('Facemask Detection Dataset') the sample size was 136. Results: Novel YOLO V3-tiny gets accuracy of 95% and for CNN it was 84%. On the basis of the network's original two-scale prediction target, a scale is added to create a three-scale prediction, which can improve the accuracy of detecting small targets such as masks. The YOLO V3-tiny and CNN have a statistically significant independent sample t-test value (p0.001) with a 95 percent confidence level. Conclusion: face mask detection in YOLO V3-tiny has a significantly better accuracy than CNN. © 2022 IEEE.

Revisiting Meta-Heuristic Optimization Method in solving Stochastic System Design Problem

Optimization is an important issue in the real world, and most problems can be transformed into optimization problems. However, such stochastic optimization problems are always accompanied by uncertainty, especially in the industries of innovative technologies (i.e., wearable devices and sensors on healthcare), integrated supply chain, and sustainable operations management. Due to the outbreak of COVID-19 pandemics last year, it has become quite difficult for industries to quickly obtain their supplies and optimize their operations. Therefore, a Particle Swarm Optimization Retrospective Approximation (PSORA) algorithm is proposed to solve and validate the problem using a unimodal example and sensitivity analysis. PSORA uses the framework of Retrospective approximation (RA) to iteratively solve a sequence of sample path approximation problems with increasing sample sizes;each sample path problem is solved by the improved PSO algorithm. When the sample size approaches infinite, the improved PSO algorithm solves the sample path problem to approximately identify the real objective function. Our simulation results show that PSORA is robust, and converges quickly. The result of the developed optimal model can provide marginal insights to decision-makers in problem-solving. © 2021 IEEE.

COVID-19 pandemic and Online Teaching from the Lenses of K-12 STEM Teachers in Albania

The aim of this study was to gain a better understanding of the attitudes and challenges faced by STEM teachers during the implementation of online teaching in the midst of the unprecedented Covid-19 pandemic. The approach involved collection and extraction of relevant data from two survey questionnaires. A sample size of 98 STEM teachers was recorded for the surveys. Responses were obtained from 98 STEM teachers working in different schools in Albania. The survey was comprised of two parts, namely teachers' preparedness for online teaching and teachers' pedagogical approaches in the online teaching. The results of the study revealed a general positive tendency towards the online learning and the integration of technological advances into young learner online classes. The results showed that although most teachers enjoyed the online teaching experience, they were unable to actively engage the young learners in the online classes. Moreover, teachers experienced issues with regard to classroom management and the design and management of online assessment tasks. The findings of this study encourage researchers to carry out further research on the utilisation of technological tools to increase young learners' active participation in STEM classes in the online platform. Finally, this study discusses pedagogical implications that emphasise the need for teachers' continuous professional development to benefit from technological advances and utilise them efficiently in online teaching. © 2021 IEEE.

Antecedents and consequences of telecommuting on public organizations in Iran: the case of west Azerbaijan province

Purpose: Telecommuting can reduce traffic congestion, energy consumption, prevalence and a death toll of COVID-19 among employees due to less transportation and fewer physical contacts among employees, on the one hand, and efficiently develop their use of information and communications technology, on the other hand. In this regard, the present study aims to explore antecedents and consequences of telecommuting in public organizations. Design/methodology/approach: The study used a descriptive survey method to collect data. The statistical population includes all employees of government organizations in West Azerbaijan province in 2020, which according to the collected information, their number is equal to 63,079 employees. Based on Cochran's formula, a sample size of 686 people was obtained;stratified random sampling was used to select sampling. The process of calculating the sample volume was such that after referring to the preliminary sample and processing the collected data, the variance of the given answers was approximately 0.446. After obtaining the variance of the data, assuming a maximum acceptable error of 5% and a significance level of 0.05, the Cochran's formula calculated the sample size to be 686 people. In order to collect and measure data for the study, a standard questionnaire and the collected data were analyzed using structural equation modeling. Findings: Findings indicate that there is no meaningful relationship between the employees' physical job conditions or the quality of their life with telecommuting and that telecommuting does not have a significant effect on their life. However, job burnout, training and telecommuting experience have a significant positive effect on telecommuting, which in turn has a positive and significant effect on job security, job flexibility, organizational performance and overall productivity of employees. Research limitations/implications: This research is a cross-sectional study, and its data have been collected in a certain period of time, while longitudinal research can provide a richer result. Future research can benefit from the impact of employee isolation and telecommuter organizational commitment. Originality/value: This study hopes to contribute to the increase of the scientific knowledge in the telecommuting field and to allow organizations to rethink the telecommuting strategies to optimize resources and costs and to improve the organization's productivity without harming the quality of life and well-being of their workers. © 2021, Emerald Publishing Limited.