Application of Computer Vision and Mobile Systems in Education: A Systematic Review

The computer vision industry has experienced a significant surge in growth, resulting in numerous promising breakthroughs in computer intelligence. The present review paper outlines the advantages and potential future implications of utilizing this technology in education. A total of 84 research publications have been thoroughly scrutinized and analyzed. The study revealed that computer vision technology integrated with a mobile application is exceptionally useful in monitoring students’ perceptions and mitigating academic dishonesty. Additionally, it facilitates the digitization of handwritten scripts for plagiarism detection and automates attendance tracking to optimize valuable classroom time. Furthermore, several potential applications of computer vision technology for educational institutions have been proposed to enhance students’ learning processes in various faculties, such as engineering, medical science, and others. Moreover, the technology can also aid in creating a safer campus environment by automatically detecting abnormal activities such as ragging, bullying, and harassment

A 30-day follow-up study on the prevalence of SARS-COV-2 genetic markers in wastewater from the residence of COVID-19 patient and comparison with clinical positivity

Wastewater based epidemiology (WBE) is an important tool to fight against COVID-19 as it provides insights into the health status of the targeted population from a small single house to a large municipality in a cost-effective, rapid, and non-invasive way. The implementation of wastewater based surveillance (WBS) could reduce the burden on the public health system, management of pandemics, help to make informed decisions, and protect public health. In this study, a house with COVID-19 patients was targeted for monitoring the prevalence of SARS-CoV-2 genetic markers in wastewa-ter samples (WS) with clinical specimens (CS) for a period of 30 days. RT-qPCR technique was employed to target non-structural (ORF1ab) and structural-nucleocapsid (N) protein genes of SARS-CoV-2, according to a validated experimental protocol. Physiological, environmental, and biological parameters were also measured following the American Public Health Association (APHA) standard protocols. SARS-CoV-2 viral shedding in wastewater peaked when the highest number of COVID-19 cases were clinically diagnosed. Throughout the study period, 7450 to 23,000 gene copies/1000 mL were detected, where we identified 47 % (57/120) positive samples from WS and 35 % (128/360) from CS. When the COVID-19 patient number was the lowest (2), the highest CT value (39.4; i.e., lowest copy number) was identified from WS. On the other hand, when the COVID-19 patients were the highest (6), the lowest CT value (25.2 i.e., highest copy numbers) was obtained from WS. An advance signal of increased SARS-CoV-2 viral load from the COVID-19 patient was found in WS earlier than in the CS. Using customized primer sets in a traditional PCR approach, we confirmed that all SARS-CoV-2 variants identified in both CS and WS were Delta variants (B.1.617.2). To our knowledge, this is the first follow-up study to determine a temporal relationship be-tween COVID-19 patients and their discharge of SARS-CoV-2 RNA genetic markers in wastewater from a single house including all family members for clinical sampling from a developing country (Bangladesh), where a proper sewage system is lacking. The salient findings of the study indicate that monitoring the genetic markers of the SARS-CoV-2 virus in wastewater could identify COVID-19 cases, which reduces the burden on the public health system during COVID-19 pandemics.Peer reviewe

Wastewater-based epidemiological surveillance to monitor the prevalence of SARS-CoV-2 in developing countries with onsite sanitation facilities

Wastewater-based epidemiology (WBE) has emerged as a valuable approach for forecasting disease outbreaks in developed countries with a centralized sewage infrastructure. On the other hand, due to the absence of well-defined and systematic sewage networks, WBE is challenging to implement in developing countries like Bangladesh where most people live in rural areas. Identification of appropriate locations for rural Hotspot Based Sampling (HBS) and urban Drain Based Sampling (DBS) are critical to enable WBE based monitoring system. We investigated the best sampling locations from both urban and rural areas in Bangladesh after evaluating the sanitation infrastructure for forecasting COVID-19 prevalence. A total of 168 wastewater samples were collected from 14 districts of Bangladesh during each of the two peak pandemic seasons. RT-qPCR commercial kits were used to target ORF1ab and N genes. The presence of SARS-CoV-2 genetic materials was found in 98% (165/168) and 95% (160/168) wastewater samples in the first and second round sampling, respectively. Although wastewater effluents from both the marketplace and isolation center drains were found with the highest amount of genetic materials according to the mixed model, quantifiable SARS-CoV-2 RNAs were also identified in the other four sampling sites. Hence, wastewater samples of the marketplace in rural areas and isolation centers in urban areas can be considered the appropriate sampling sites to detect contagion hotspots. This is the first complete study to detect SARS-CoV-2 genetic components in wastewater samples collected from rural and urban areas for monitoring the COVID-19 pandemic. The results based on the study revealed a correlation between viral copy numbers in wastewater samples and SARS-CoV-2 positive cases reported by the Directorate General of Health Services (DGHS) as part of the national surveillance program for COVID-19 prevention. The findings of this study will help in setting strategies and guidelines for the selection of appropriate sampling sites, which will facilitate in development of comprehensive wastewater-based epidemiological systems for surveillance of rural and urban areas of low-income countries with inadequate sewage infrastructure.This research was supported by Water Aid Bangladesh, North South University, Dhaka, COVID-19 Diagnostic Lab, Department of Microbiology, Noakhali Science and Technology University (NSTU), Noakhali, Bangladesh, the International Training Network of Bangladesh University of Engineering and Technology (ITN-BUET) - Centre for Water Supply and Waste Management, and KTH Royal Institute of Technology, Sweden. We acknowledge the sincere help and support of the staff and volunteers of NSTU-COVID-19 Diagnostic Lab, Noakhali Science and Technology University, Bangladesh during the different phases of the study. PB and MTI acknowledge the Life Science Technology Platform, Science for Life Laboratory for the seed funding to initiate the wastewater-based epidemiological studies for SARS-CoV-2 in Bangladesh. We would also like to acknowledge the two anonymous reviewers for their critical comments as well as their thoughtful insights, which has significantly improved the manuscript.Peer reviewe

Starting up and operating an apparel brand in Bangladesh

The study was on the ready-to-wear clothing industry in Bangladesh in order to deter-mine what kinds of commercial opportunities are available to business people who want to launch a new company and satisfy the requirements of all of their target customers at the same time. As a direct consequence of the epidemic, many people suffered huge financial losses in their businesses. Some of these individuals are still exerting a great deal of effort in order to make up for the money that they have lost. In light of the recent adjustments, a new firm that opens in the ready-made garment sector of Bangladesh's economy may have problems effectively competing if the required processes are not followed. In the process of composing this thesis, a qualitative method is used. The findings obtained from the qualitative interviews conducted for the thesis demonstrated that Bangladesh is a market that offers lucrative potential for the creation of a clothing brand. There is a potential that new business owners who start their compa-nies during or after the epidemic may be liable to harsh repercussions. This is a possi-bility since there is a possibility. The new company has set as one of its long-term goals the accomplishment of firmly establishing itself as a prominent participant in the world of corporations

DeepRoadNet: A deep residual based segmentation network for road map detection from remote aerial image

Abstract The extraction of road networks is a critical activity in contemporary transportation networks. Deep neural networks have recently demonstrated excellent performance in the field of road segmentation. However, most of the convolutional neural network (CNN) based architectures could not verify their effectiveness in remote sensing images due to a smaller ratio of the targeted pixels, simple design, and fewer layers. In this study, a practical approach is assessed for road segmentation. The investigation was begun with basic encoder–decoder based segmentation models. Different state‐of‐the‐art segmentation models like U‐Net, V‐Net, ResUNet and SegNet were used for road network detection experiments in this research. A robust model named DeepRoadNet, a more complicated alternative, is proposed by utilizing a pre‐trained EfficientNetB7 architecture in the encoder and residual blocks as the decoder which mostly resembles the U‐Net segmentation process. The proposed model has been trained, validated as well as tested using the high‐resolution aerial image datasets and yielded good segmentation results with a mean intersection over union (mIoU) of 76%, a mean dice coefficient (mDC) of 73.18%, and an accuracy of 97.64% using Massachusetts road dataset. The proposed DeepRoadNet architecture overcomes the issues of lower mIoU, lower mDC, limited flexibility and interpretability already faced by existing models in the road segmentation field. The code is available at https://github.com/Imteaz1998/DeepRoadNet

Characterization of phytoconstituents and evaluation of antimicrobial activity of silver-extract nanoparticles synthesized from Momordica charantia fruit extract

Abstract Background Our present study was conducted to characterize the phytoconstituents present in the aqueous extract of Momordica charantia and evaluate the antimicrobial efficacy of silver-extract nanoparticles (Ag-Extract-NPs). Methods Silver nanoparticles (AgNPs) were prepared by reducing AgNO3; and NaBH4 served as reducing agent. After screening of phytochemicals; AgNPs and aqueous extract were mixed thoroughly and then coated by polyaniline. These NPs were characterized by using Visual inspection, UV spectroscopy, FTIR, SEM and TEM techniques. Antimicrobial activities were assessed against Staphylococcus aureus, Salmonella typhi, Escherichia coli and Pseudomonas aeruginosa. Results Aqueous extract of M. charantia fruits contain alkaloid, phenol, saponin etc. UV–Vis spectrum showed strong absorption peak around 408 nm. The presence of –CH, −NH, −COOH etc. stretching in FTIR spectrum of Ag-Extract-NPs endorsed that AgNPs were successfully capped by bio-compounds. SEM and TEM result revealed that synthesized NPs had particle size 78.5–220 nm. Ag-Extract-NPs showed 34.6 ± 0.8 mm zone of inhibition against E. coli compared to 25.6 ± 0.5 mm for ciprofloxacin. Maximum zone of inhibition for Ag-Extract-NPs were 24.8 ± 0.7 mm, 26.4 ± 0.4 mm, 7.4 ± 0.4 mm for S. aureus, P. aeruginosa and S. typhi. We found that Ag-Extract-NPs have much better antibacterial efficacy than AgNPs and M. charantia extract has individually. It is also noticed that gram negative bacteria (except S. typhi) are more susceptible to Ag-Extract-NPs than gram positive bacteria. Conclusion Ag-Extract-NPs showed strong antibacterial activity. In order to make a reliable stand for mankind, further study is needed to consider determining the actual biochemical pathway by which AgNPs-extracts exert their antimicrobial effect

Variation Theory in Teaching and Phenomenography in Learning : What’s Their Impact When Applied in Engineering Classrooms?

Although phenomenographic research approach has been widely used by education researchers to investigate students’ learning, little attention has been paid to the relationship between a pedagogical approach adopted by teachers and students’ learning outcomes, particularly in engineering education. This experimental study proposes integrating variation theory as a pedagogical approach to a face-to-face classroom environment for teaching complex engineering contents and adapting a phenomenographic approach to evaluate students’ learning outcomes. The teachers who participated in the experimental group incorporated the variation theory in their teaching process. In contrast, the teachers in the control group, being ignorant of the variation theory, taught the same content to achieve the same specific learning outcome. Drawing on data from students’ written responses both from experimental and control groups, this article illustrates how teachers implemented variation theory in the classroom and its impacts on student learning. The implementation of variation theory was confirmed by classroom observation, and the variation in understanding the topic was emerged from students’ written responses and interview data through phenomenographic analysis. The findings indicate that teachers informed by variation theory use variation and invariance that creates necessary conditions for learning. This study demonstrates how, by incorporating variation theory, a faculty member designed different pedagogical approaches, which helps students conceptualize complex engineering topics more systematically than those who do not discern variation. The study concludes with theoretical, empirical, and pedagogic implications for teacher education in engineering

Surface coatings analysis and their effects on reduction of tribological properties of coated aluminum under motion with ML approach

The popularity of coated aluminum is gaining significant attention in numerous sectors in the industry due to its specific strength, corrosion resistance, and recyclability. However, because of friction, its lifetime reduces which causes a billion-dollar loss every year to our property. Many types of research are going around the world on how friction and wear loss can be reduced. This research focuses on the tribological study of coated aluminum in different conditions in the experiments, lubricant is used to find its efficiency, and coating materials have also its self-lubricating properties. Both reciprocating motion of pin and simultaneous motion of pin and disc applied. The combined effects of lubrication and motions are correlated with the reduction of tribological properties to a certain extent. The velocity of both pin and disc is also varied. Applied loads are changed in different experiments as well. Roughness analysis has also been done to observe the effect of lubricant, motion, and applied load on the surface of the specimens. SEM, EDX, XRD, and FTIR tests are also performed to check the morphology of the specimens. The experiments show that comparatively less friction and wear are in at lubricated, reciprocating, and less velocity of pin and disc conditions. Less coefficient of friction is observed at higher applied load but less wear is produced at lower applied load. The Machine Learning (ML) approach is used to detect patterns automatically in datasets and create models to predict future data or other outcomes

Genome-wide study of globally distributed respiratory syncytial virus (RSV) strains implicates diversification utilizing phylodynamics and mutational analysis

Abstract Respiratory syncytial virus (RSV) is a common respiratory pathogen that causes mild cold-like symptoms and severe lower respiratory tract infections, causing hospitalizations in children, the elderly and immunocompromised individuals. Due to genetic variability, this virus causes life-threatening pneumonia and bronchiolitis in young infants. Thus, we examined 3600 whole genome sequences submitted to GISAID by 31 December 2022 to examine the genetic variability of RSV. While RSVA and RSVB coexist throughout RSV seasons, RSVA is more prevalent, fatal, and epidemic-prone in several countries, including the United States, the United Kingdom, Australia, and China. Additionally, the virus's attachment glycoprotein and fusion protein were highly mutated, with RSVA having higher Shannon entropy than RSVB. The genetic makeup of these viruses contributes significantly to their prevalence and epidemic potential. Several strain-specific SNPs co-occurred with specific haplotypes of RSVA and RSVB, followed by different haplotypes of the viruses. RSVA and RSVB have the highest linkage probability at loci T12844A/T3483C and G13959T/C2198T, respectively. The results indicate that specific haplotypes and SNPs may significantly affect their spread. Overall, this analysis presents a promising strategy for tracking the evolving epidemic situation and genetic variants of RSV, which could aid in developing effective control, prophylactic, and treatment strategies

Application of Mathematical Modeling and Computational Tools in the Modern Drug Design and Development Process

The conventional drug discovery approach is an expensive and time-consuming process, but its limitations have been overcome with the help of mathematical modeling and computational drug design approaches. Previously, finding a small molecular candidate as a drug against a disease was very costly and required a long time to screen a compound against a specific target. The development of novel targets and small molecular candidates against different diseases including emerging and reemerging diseases remains a major concern and necessitates the development of novel therapeutic targets as well as drug candidates as early as possible. In this regard, computational and mathematical modeling approaches for drug development are advantageous due to their fastest predictive ability and cost-effectiveness features. Computer-aided drug design (CADD) techniques utilize different computer programs as well as mathematics formulas to comprehend the interaction of a target and drugs. Traditional methods to determine small-molecule candidates as a drug have several limitations, but CADD utilizes novel methods that require little time and accurately predict a compound against a specific disease with minimal cost. Therefore, this review aims to provide a brief insight into the mathematical modeling and computational approaches for identifying a novel target and small molecular candidates for curing a specific disease. The comprehensive review mainly focuses on biological target prediction, structure-based and ligand-based drug design methods, molecular docking, virtual screening, pharmacophore modeling, quantitative structure–activity relationship (QSAR) models, molecular dynamics simulation, and MM-GBSA/MM-PBSA approaches along with valuable database resources and tools for identifying novel targets and therapeutics against a disease. This review will help researchers in a way that may open the road for the development of effective drugs and preventative measures against a disease in the future as early as possible