Smartphone and Our Students: Is It Being Good for Their Study?

The objectives of this study are to: (I) find out the discriminations or variations (if any) between the attentive and inattentive university students in terms of their purposes of using smartphones, (II) analyze the cause-effect relationship between “the purposes considered to have good or bad impact on study” and “the smartphone usage behavior of the attentive students”, and (III) analyze the cause-effect relationship between “the purposes considered to have good or bad impact on study” and “the smartphone usage behavior of the inattentive students”. 400 students (200 attentive and 200 inattentive) students are surveyed.  Based survey and statistical analysis results, it is found that attentive and inattentive student are differentiating from each other in terms of their purposes of using smartphones for learning and study, social networking and entertainment. Moreover, the reasons of using smartphones believed to be in favor of their learning activities have positive impact on the attentive students’ smartphones usage behavior, whereas inattentive students are not acting likewise. Corrective actions by the interested parties should be undertaken to reform this unexpected scenario. Keywords: Smartphone, Students, Education, Bangladesh

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

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

Distribution, Concentration, and Ecological Risk Assessment of Trace Metals in Surface Sediment of a Tropical Bangladeshi Urban River

Trace metal contamination in sediments is a global concern. This study aimed to assess the contamination level of trace metals, their sources, and ecological risk in surface sediments of Karnaphuli River—a tropical urban river in Bangladesh. Forty-five sediment samples were analyzed by atomic absorption spectrophotometry (AAS) for Cu, Fe, Zn, Pb, Cr, Cd, and Ni metals along with physicochemical parameters like pH and organic matter (OM). The pollution status and potential ecological risk were assessed by using the geo-accumulation index (Igeo), contamination factor (CF), and potential ecological risk index (PERI). Source identification of trace metals was performed by correlation analysis, cluster analysis, and principal component analysis (PCA). The results show that the range of Cu, Fe, Zn, Pb, Cr, Cd, and Ni concentrations were 0.62–1.61 mg/kg, 23.95–85.70%, 0.52–1.89 mg/kg, 7.99–12.90 mg/kg, 33.91–65.47 mg/kg, 0.77–1.17 mg/kg, and 2.73–5.36 mg/kg, respectively. The concentrations of Fe, Cd, and Cr were above the permissible limits while the contamination factor (CF) and geo-accumulation index (Igeo) values revealed that Fe and Cd were the most dominant pollutants. Cluster analysis and PERI exhibited significant anthropogenic intrusions of trace metals. A significant positive correlation between Fe-Cr, Cr-Ni, Fe-Ni, and Pb-Cd shows their common anthropogenic source and influences. PERI also revealed that Cr, Fe, and Cd have a significant contribution with a moderate to considerable potential threat

LLDNet: A Lightweight Lane Detection Approach for Autonomous Cars Using Deep Learning

Lane detection plays a vital role in making the idea of the autonomous car a reality. Traditional lane detection methods need extensive hand-crafted features and post-processing techniques, which make the models specific feature-oriented, and susceptible to instability for the variations on road scenes. In recent years, Deep Learning (DL) models, especially Convolutional Neural Network (CNN) models have been proposed and utilized to perform pixel-level lane segmentation. However, most of the methods focus on achieving high accuracy while considering structured roads and good weather conditions and do not put emphasis on testing their models on defected roads, especially ones with blurry lane lines, no lane lines, and cracked pavements, which are predominant in the real world. Moreover, many of these CNN-based models have complex structures and require high-end systems to operate, which makes them quite unsuitable for being implemented in embedded devices. Considering these shortcomings, in this paper, we have introduced a novel CNN model named LLDNet based on an encoder–decoder architecture that is lightweight and has been tested in adverse weather as well as road conditions. A channel attention and spatial attention module are integrated into the designed architecture to refine the feature maps for achieving outstanding results with a lower number of parameters. We have used a hybrid dataset to train our model, which was created by combining two separate datasets, and have compared the model with a few state-of-the-art encoder–decoder architectures. Numerical results on the utilized dataset show that our model surpasses the compared methods in terms of dice coefficient, IoU, and the size of the models. Moreover, we carried out extensive experiments on the videos of different roads in Bangladesh. The visualization results exhibit that our model can detect the lanes accurately in both structured and defected roads and adverse weather conditions. Experimental results elicit that our designed method is capable of detecting lanes accurately and is ready for practical implementation

Health Risk and Water Quality Assessment of Surface Water in an Urban River of Bangladesh

Despite significant contributions to the national economy of Bangladesh, various urban developments, massive industrial and growing shipping activities are making the water of many urban rivers, including Karnaphuli River, extremely polluted. To find out the pollution sources and their possible health effects, 45 water samples were collected from 15 sampling stations. Investigation of six physicochemical parameters (pH, temperature, total dissolved solids, conductivity, salinity, and turbidity) through in-situ measurements and eight heavy metals (Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn) status using atomic absorption spectrophotometer (AAS) was carried out in this research. Both the physicochemical parameters and heavy metals exceeded the World Health Organization (WHO)’s permeable threshold limit. The calculated hazard quotient (HQ) and hazard index (HI) ingestion values indicate non-carcinogenic risk both for adults and children, but dermal exposure was within the safety limit. Carcinogenic risk analysis revealed that Cd could cause a risk of cancer in those using the river water for a long period. Spatial analysis and metal pollution index (MPI) results exhibit that downstream of the river water is more polluted than upstream of the river. Overall, the findings of this study imply that polluted water is a threat to human health and the results will also help to undertake proper management strategies and incorporate monitoring programs that study river water for the implementation of safety measures to protect human health

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

Elevated serum malondialdehyde (MDA), insulin, follicle-stimulating hormone (FSH), luteinizing hormone (LH), and thyroid-stimulating hormone (TSH), and reduced antioxidant vitamins in polycystic ovarian syndrome patients

Elevated oxidative stress and hormonal imbalance have been suggested associate with polycystic ovarian syndromes (PCOS), a causal factor for unsuccessful pregnancy outcomes and other associated complications in women. The aim of this study was to compare the oxidative stress markers and different relevant hormone between pregnant women with and without PCOS. The levels of malondialdehyde (MDA), insulin, follicle-stimulating hormone (FSH), luteinizing hormone (LH), thyroid-stimulating hormone (TSH), vitamin A and vitamin C were measured in 80 pregnant women with PCOS and 80 healthy pregnancies. The mean MDA and insulin levels were significantly elevated in pregnant with PCOS compared to healthy controls (1.98±0.07 vs. 1.06±0.02 nmol/mL and 11.15±0.25 vs. 6.67±0.25 mIU/L, respectively with p<0.001 for both). Compared to healthy controls, the mean concentrations of FSH (3.65±0.16 vs. 1.75±0.10 IU/L) and LH (15.67±0.63 vs. 3.65±0.16 IU/L) were significantly higher in pregnant women with PCOS, p<0.001 for both comparisons. Similarly, the concentration of serum TSH was also higher in PCOS cases compared to controls (2.79±0.22 vs.2.34±0.06, p=0.048). In contrast, the levels of vitamin A and C were lower in PCOS cases compared to healthy pregnancy group, 0.45±0.01 vs. 1.05±0.01 and 0.26±0.01 vs. 0.53±0.02, respectively with p-values <0.001 for both comparations. In conclusion, in PCOS, serum MDA, insulin, FSH, LH and TSH levels elevated while the level of antioxidant vitamins lower compared to healthy pregnant women. Unusual hormonal imbalance and increase of oxidative stress markers during the pregnancy might important to establish the PCOS diagnosis

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