East West University (Bangladesh)
Recent publications
In this paper, the economic sustainability of net billing and feed-in-tariff (all energy buy/sell) method is analyzed in comparison with the current net-metering for the industrial PV systems in Bangladesh. Three billing methods are compared according to plant capacity factor, excess energy transfer, levelized cost of energy (LCOE), net present value (NPV), payback period, and profitability index (PI) for the analysis of optimum billing scheme. The highest plant capacity factor and minimum LCOE are found for the large PV system in Chattogram. Out of three methods, all energy buy/sell exhibits the NPV which is USD 5.85 million and PI of 2.54 at the minimum 4.9 years discounted payback time for the large PV system in Chattogram. The sensitivity analysis of solar irradiation at six other regional areas, bill escalation rate, and discount factor is performed to observe the impact on annual energy production and NPV. For both systems, all three assessed methods are found economically feasible, but the net metering is attained as the least profitable in comparison with net billing and all energy buy/sell schemes. From various analyses, it is found that the adaption of these two schemes in the present guideline can enhance industrial PV production in Bangladesh.
Energy consumption is rising dramatically at the price of depleting fossil fuel supplies and rising greenhouse gas emissions. To resolve this crisis, barley waste, which is hazardous for the environment and landfill, was studied through thermochemical characterization and pyrolysis to use it as a feedstock as a source of renewable energy. According to proximate analysis, the concentrations of ash, volatile matter, fixed carbon, and moisture were 5.43%, 73.41%, 18.15%, and 3.01%, consecutively. The ultimate analysis revealed that the composition included an acceptable H/C, O/C, and (N+O)/C atomic ratio, with the carbon, hydrogen, nitrogen, sulfur, and oxygen amounts being 46.04%, 6.84%, 3.895%, and 0.91%, respectively. The higher and lower heating values of 20.06 MJ/kg and 18.44 MJ/kg correspondingly demonstrate the appropriateness and promise for the generation of biofuel effectively. The results of the morphological study of biomass are promising for renewable energy sources. Using Fourier transform infrared spectroscopy, the main link between carbon, hydrogen, and oxygen was discovered, which is also important for bioenergy production. The maximum degradation rate was found by thermogravimetric analysis and derivative thermogravimetry to be 4.27% per minute for pyrolysis conditions at a temperature of 366 °C and 5.41% per minute for combustion conditions at a temperature of 298 °C. The maximum yields of biochar (38.57%), bio-oil (36.79%), and syngas (40.14%) in the pyrolysis procedure were obtained at 400, 500, and 600 °C, respectively. With the basic characterization and pyrolysis yields of the raw materials, it can be concluded that barley waste can be a valuable source of renewable energy. Further analysis of the pyrolyzed products is recommended to apply in the specific energy fields.
Ethnopharmacological relevance: Acceleration of neurite outgrowth and halting neurodegeneration are the most critical factors that are negatively regulated in various neurodegenerative diseases or injuries in the central nervous system (CNS). Functional foods or nutrients are considered alternative sources of bioactive components to alleviate various CNS injuries by promoting neuritogenesis and synaptogenesis, while their exact molecular mechanism remains unexplored. Aim of the study: Coriandrum sativum L. (CS) is one of the popular herbs in the Apiaceae family, of which CNS modulating action is a well-documented traditionally but detailed study on memory boosting function yet remains unexplored. Consequently, this study aims to analyze the neurogenic and synaptogenic modulation of CS aqueous ethanol (CSAE) extract in the primary hippocampal neurons. Materials and methods: Primary hippocampal neurons were cultured and allowed to incubate with CSAE or vehicle. To observe the early neuronal differentiation, axonal and dendritic arborization, and synapse formation, neurons were immune-stained against indicated antibody or stained directly with a lipophilic dye (1, 1'-dioctadecyl-3, 3, 3', 3'-tetramethyl indocarbocyanine perchlorate, DiL). Meanwhile, Western blot was used to validate the synaptogenesis effect of CSAE compared to vehicle. Additionally, molecular docking and system pharmacology approach were applied to confirm the possible secondary metabolites and pathways in which CSAE play a role in neuritogenesis. Results: Results show that CSAE can induce neuritogenesis and synaptogenesis at 30 μg/mL concentrations. The treatment impacts early neuronal polarization, axonal and dendritic arborization, synaptogenesis, and synaptic plasticity via NMDARs expressions in primary neurons. In silico network pharmacology of CS metabolites show that the CSAE-mediated neurogenic effect is likely dependent on the NTRK2 (TrkB) mediated neurotrophin signaling pathway. Indeed, the observed neurogenic activity of CSAE is markedly reduced upon the co-treatment with a TrkB-specific inhibitor. Furthermore, molecular docking following binding energy calculation shows that one of the CS metabolites, scoparone, has a high affinity to bind in the BDNF mimetic site of TrkB, suggesting its role in TrkB activation. Scoparone was found to enhance neuritogenesis, but not to the same extent as CSAE. Moreover, the expression of TrkB signaling-related proteins (BCL2, CASP3, GSK3, and BDNF), which was found to be modulated by scoparone, was significantly affected by the co-treatment of TrkB inhibitor (ANA-12). These results further suggest that the modulation of neuritogenesis by scoparone is TrkB-dependent. Conclusions: This study provides deeper insights into the molecular mechanism of CS in boosting neuronal growth and memory function, which might implicate the prevention of many neurological disorders.
Durkheim’s classic Le Suicide provides the most astounding perspective in the sociological analysis of suicide. Using macro-level statistical data, he analyzed the suicide patterns of different populations and groups with reference to socio-cultural factors and social structures. His model comes with a four-fold typology of suicide: anomic, egoistic, altruistic, and fatalistic. This classification of suicide provides a means for analyzing the structural conditions in society with regard to the causes of suicide and the pathways for meaningful empirical research in sociology Moreover, suicide as a topic gradually waned from the mainstream sociological focus. Nonetheless, we attempted to understand the individual cases of suicide by explicating the essence form Durkheim’s four-fold schema. In doing so, we contextualized 20 male suicide cases from Bangladesh through qualitative semi-structured research interviews with persons close to deceased males and characterized their relevance to Durkheim’s typology. We conclude that explicating Durkheim’s model at the individual level has potential for rejuvenating the sociology of suicide in the field of suicide research.
The bifacial gain of various optimally-tilted, and tracking bifacial farms based on single-junction PERC and HIT technologies are well established. The solar module technology is, however, evolving rapidly with the commercial development of two, three, and four-terminal mono and bifacial HIT-Perovskite tandem cells underway. Given the complexity of current-matching in two-terminal tandem cells and significant variation of the weather conditions across the world, one wonders if the benefits of fixed-tilt and tracking cells obtained for single-junction solar cells would remain for tandem solar cells. In this paper, we use a detailed illumination and temperature-dependent bifacial solar farm model (supported by a detailed physical model for bifacial HIT-Perovskite tandem cells) to show that (a) row-to-row shading in solar arrays significantly suppresses the effective albedo collection and thereby the two-terminal (2T) tandem cell efficiency and relative gain compared to an optimal bifacial HIT cell, (b) the global energy yield potential of fixed-tilted and solar-tracking topologies would improve by adopting a 2T tandem design at optimal albedo, with maximum gain arising for tracking farms, (c) the 2T tandem cell/modules (subcell bandgaps, thickness) must be optimized for maximum benefit, and (d) even a relatively small deviation from the optimum will negate all benefits. Our results will broaden the scope and understanding of the emerging tandem bifacial technology by demonstrating global trends in energy gain for worldwide deployment and the need for location-specific tailoring of the module design.
Corona virus disease (COVID-19) is one of the deadliest scourge mankind have ever seen. It’s a highly infectious influenza virus which may transmit from one person to another without causing any symptoms. In compliance with WHO (World Health Organization) data, corona virus (COVID-19) was first found in China in 2019 and has spread swiftly to individuals in other countries, with an estimated total of 349,641,119 cases (till 25 January) globally. As counter measures to this condition, screening afflicted people is mandatory which requires time and is also costly. Radiological scanning is a plausible measure for achieving this. In this case, the chest X-Ray is the most at hand and cost-effective alternative. In this work, we present a Deep CNN (Convolutional Neural Network) based method for perceiving COVID-19 infected people by analyzing chest X-Ray images. Here, four pre-trained CNN models (AlexNet, VGG16, InceptionV3, and EfficientNetB4) are suggested to analyze chest X-ray radiographs. Among these models, EfficientNetB4 gives us the highest accuracy to detect COVID-19.
The aim of this study is primarily to demonstrate how earnings quality is an influential determinant of financial flexibility. Secondly, how earnings quality affects financial flexibility. And finally, to provide evidence of the role of corporate governance between earnings quality and financial flexibility composing overall corporate governance index (CG-INDEX). This study considered unbalanced panel data from the year 2007 to 2020 from the database CSMAR yielding 14,088 firm-year observations. This study used liquidity as the proxy of financial flexibility, and also used a comprehensive index of corporate governance constructed by adopting the principal component analysis and STATA has been used for analyzing data. The study used System GMM regression for analysis and controls endogeneity by applying lag financial flexibility as an instrumental variable. The empirical results reveal that poor earnings quality significantly negatively influences the level of corporate financial flexibility. The results also demonstrate that corporate governance can significantly positively moderate the relationship between earnings quality and financial flexibility. This suggests that when the earnings quality is poor, firms are less likely to be financially flexible in holding liquidity. More specifically, firms with poor earnings quality will reduce their financial flexibility of firms; hence, firms need to provide high-quality earnings in order to be more financially flexible. Earnings quality is an important factor, which led the author to examine how earnings quality influences financial flexibility. Under the views of agency theory and positive accounting theory, poor earnings quality is a source of amplified shareholder’s concern of increased informational asymmetry, which may adversely affect the firm’s financial flexibility. Conversely, higher earnings quality reduces the information asymmetry which leads to higher financial flexibility. This study provides a way how to achieve financial flexibility with the assistance of corporate governance which is essential to combat financial crises and smooth business operations successfully.
Objectives The prime objectives of the study were to measure the prevalence of facility delivery, assess socioeconomic inequalities and determine potential associated factors in the use of facility delivery in Bangladesh. Design Cross-sectional. Setting The study involved investigation of nationally representative secondary data from the Bangladesh Demographic and Health Survey between 2007 and 2017–2018. Participants The participants of this study were 30 940 (weighted) Bangladeshi women between the ages of 15 and 49. Methods Decomposition analysis and multivariable logistic regression were both used to analyse data to achieve the study objectives. Results The prevalence of using facility delivery in Bangladesh has increased from 14.48% in 2007 to 49.26% in 2017–2018. The concentration index for facility delivery utilisation was 0.308 with respect to household wealth status (p<0.001), indicating that use of facility delivery was more concentrated among the rich group of people. Decomposition analysis also indicated that wealth quintiles (18.31%), mothers’ education (8.78%), place of residence (7.75%), birth order (5.56%), partners’ education (4.30%) and antenatal care (ANC) seeking (8.51%) were the major contributors to the prorich socioeconomic inequalities in the use of facility delivery. This study found that women from urban areas, were overweight, had any level of education, from wealthier families, had ANC, and whose partners had any level of education and involved in business were more likely to have facility births compared with their respective counterparts. Conclusions This study found a prorich inequality in the use of facility delivery in Bangladesh. The socioeconomic disparities in facility delivery must be addressed if facility delivery usage is to increase in Bangladesh.
Brain tumor is a severe health condition that kills many lives every year, and several of those casualties are from rural areas. However, the technology to diagnose brain tumors at an early stage is not as efficient as expected. Therefore, we sought to create a reliable system that can help medical professionals to identify brain tumors. Although several studies are being conducted on this issue, we attempted to establish a much more efficient and error-free classification method, which is trained with a comparatively substantial number of real datasets rather than augmented data. Using a modified VGG-16 (Visual Geometry Group) architecture on 10,153 MRI (Magnetic Resonance Imaging) images with 3 different classes (Glioma, Meningioma, and Pituitary), the network performs significantly well. It achieved a precision of 99.4% for Glioma, 96.7% for Meningioma, and 100% for Pituitary, with an overall accuracy of 99.5%. It also attained better results than several other existing CNN architectures and state-of-the-art work.
Electricity production from photovoltaic (PV) systems has accelerated in the last few decades. Numerous environmental factors, particularly the buildup of dust on PV panels have resulted in a significant loss in PV energy output. To detect the dust and thus reduce power loss, several techniques are being researched, including thermal imaging, image processing, sensors, cameras with IoT, machine learning, and deep learning. In this study, a new dataset of images of dusty and clean panels is introduced and applied to the current state-of-the-art (SOTA) classification algorithms. Afterward, a new convolutional neural network (CNN) architecture, SolNet, is proposed that deals specifically with the detection of solar panel dust accumulation. The performance and results of the proposed SolNet and other SOTA algorithms are compared to validate its efficiency and outcomes where SolNet shows a higher accuracy level of 98.2\%. Hence, both the dataset and SolNet can be used as benchmarks for future research endeavors. Furthermore, the classes of the dataset can also be expanded for multiclass classification. At the same time, the SolNet model can be fine-tuned by tweaking the hyperparameters for further improvements.
Most recently, monkeypox virus (MPXV) has emanated as a global public health threat. Unavailability of effective medicament against MPXV escalates demand for new therapeutic agent. In this study, in silico strategies were conducted to identify novel drug against the A36R protein of MPXV. The A36R protein of MPXV is responsible for the viral migration, adhesion, and vesicle trafficking to the host cell. To block the A36R protein, 4893 potential antiviral peptides (AVPs) were retrieved from DRAMP and SATPdb databases. Finally, 57 sequences were screened based on peptide filtering criteria, which were then modeled. Likewise, 31 monkeypox virus A36R protein sequences were collected from NCBI protein database to find consensus sequence and to predict 3D protein model. The refined and validated models of the A36R protein and AVP peptides were used to predict receptor-ligand interactions using DINC 2 server. Three peptides that showed best interactions were SATPdb10193, SATPdb21850, and SATPdb26811 with binding energies −6.10, −6.10, and −6.30 kcal/mol, respectively. Small molecules from drug databases were also used to perform virtual screening against the A36R protein. Among databases, Enamine-HTSC showed strong affinity with docking scores ranging from −8.8 to 9.8 kcal/mol. Interaction of target protein A36R with the top 3 peptides and the most probable drug (Z55287118) examined by molecular dynamic (MD) simulation. Trajectory analyses (RMSD, RMSF, SASA, and Rg) confirmed the stable nature of protein-ligand and protein-peptide complexes. This work suggests that identified top AVPs and small molecules might interfere with the function of the A36R protein of MPXV.
This work focuses on the prediction of an air pollutant called particulate matter (PM2.5) across the Paso Del Norte region. Outdoor air pollution causes millions of premature deaths every year, mostly due to anthropogenic fine PM2.5. In addition, the prediction of ground-level PM2.5 is challenging, as it behaves randomly over time and does not follow the interannual variability. To maintain a healthy environment, it is essential to predict the PM2.5 value with great accuracy. We used different supervised machine learning algorithms based on regression and classification to accurately predict the daily PM2.5 values. In this study, several meteorological and atmospheric variables were retrieved from the Texas Commission of Environmental Quality’s monitoring stations corresponding to 2014–2019. These variables were analyzed by six different machine learning algorithms with various evaluation metrics. The results demonstrate that ML models effectively detect the effect of other variables on PM2.5 and can predict the data accurately, identifying potentially risky territory. With an accuracy of 92%, random forest performs the best out of all machine learning models.
Background Around 5.29% of the world population is suffering from ADHD, and 60 million people are suffering from CVS, with an increasing rate of prevalence of these disorders. This study aimed to determine the prevalence rate of ADHD and CVS symptoms among the Bangladeshi population. Results To assess the aim of the study, a cross-sectional survey was conducted online through stratified sampling, and 197 responses were collected from the participants. Our survey method follows these criteria where the ARSV1.1 standard questionnaire was followed for the ADHD questionnaire, and a self-administered questionnaire was established based on the symptoms of CVS. The male age ranges from 18–24 have the highest value of ADHD (34%) coincided with > 6 h digital device usage (51%), and the Stroop effect is significantly correlated with the ADHD score (0.498, p < 0.01). The Stroop effect value is also higher among the males aged 18–24, digital device users for > 6 h (48%). Conclusions With the advent of science, it is impossible to avoid digital devices as necessary. Notwithstanding, safe and appropriate use of digital media is a must for healthy living.
COVID-19, a worldwide pandemic that has affected many people and thousands of individuals have died due to COVID-19, during the last two years. Due to the benefits of Artificial Intelligence (AI) in X-ray image interpretation, sound analysis, diagnosis, patient monitoring, and CT image identification, it has been further researched in the area of medical science during the period of COVID-19. This study has assessed the performance and investigated different machine learning (ML), deep learning (DL), and combinations of various ML, DL, and AI approaches that have been employed in recent studies with diverse data formats to combat the problems that have arisen due to the COVID-19 pandemic. Finally, this study shows the comparison among the stand-alone ML and DL-based research works regarding the COVID-19 issues with the combinations of ML, DL, and AI-based research works. After in-depth analysis and comparison, this study responds to the proposed research questions and presents the future research directions in this context. This review work will guide different research groups to develop viable applications based on ML, DL, and AI models, and will also guide healthcare institutes, researchers, and governments by showing them how these techniques can ease the process of tackling the COVID-19.
Because petroleum‐related natural organic matter (NOM), such as humic substances, is extremely heterogeneous and complex, characterizing and identifying its chemical structure at the molecular level is attracting considerable attention. Among the different techniques for mass analysis, ultrahigh‐resolution mass spectrometry methods, such as FT‐ICR MS and its coupling with other complementary methods, have proven to be crucial for structurally characterizing complex environmental mixtures owing to their excellent analytical power. In particular, their ultrahigh resolution, unrivaled mass accuracy, remarkable ionization specificity, excellent ion activation, and high degree of flexibility for combination with hybrid instruments offer a reliable platform for analyzing petroleum‐NOM molecules. This account, aimed at contributing to the advancement of petroleum‐NOM molecule analysis, summarizes the application of FT‐ICR MS with different ionization sources and its coupling with other complementary methods developed by our research group. This account reviews recent progress in method development and application of ultrahigh resolution MS to find the chemicals in the natural organic mixture at the molecular level. Due to their extreme heterogeneity and complexity, understanding compositions and reactivity of the mixtures has been scarce. However, there has been significant progress recently thanks to analytical method development.
Climate-induced disasters affect a wide range of communities in Bangladesh. Among them, women are the most affected groups. Climate change increases their socioeconomic vulnerabilities by directly impacting their families’ food security, water consumption, health, and overall livelihood. Unlike in many patriarchal societies, Bangladeshi women often face challenges of unequal social relations and hierarchies, which enforce gender differented vulnerabilities. In this context, this paper intends to reveal what barriers Bangladeshi women face and how they cope with many uncertainties relating to changing contexts, particularly at the household, community, and institutional levels. The paper draws from the author’s Ph.D. research which was carried out in four districts of Bangladesh, focused on four types of climate change impacts and adaptation practices. Qualitative data collection methods were used such as focus group discussions, in-depth Interviews with women, and observations of their household and community-level activities for the study. In addition to that, key informant interviews were conducted with local and national level experts, government officials, and development workers to reveal institutional barriers confronted by women. Findings explore women’s gendered struggles in preparing for and responding to climate change through their lived experiences. Moreover, the study sheds light on the limited gender-responsive environment drawing attention to the need for strategies and actions for gender-transformative approaches to develop climate-resilient households and communities with women at the center.
The coronavirus (COVID-19) epidemic has transmuted the business environment and disordered supply chains worldwide. Over the last decade, online social networks (OSNs) have impacted every element of human life and corporate organization. Deploying OSNs services in supply chain networks is currently a pressing need. This study developed a model based on the IS success model, resource-based view, and absorptive capacity to explore the strategic value of OSNs in the supply chain network during COVID-19. Structural Equation Modeling (SEM) analyzes 220 data collected using online questionnaires. Information, system, and service quality emerged as influential features of online social networks that enhance the absorptive capacity and supply chain visibility and agility. The performance of the supply chain is positively impacted by each of OSN’s strategic values, including visibility, agility, and absorptive capacity during COVID-19. In addition, absorptive capacity and supply chain visibility and agility mediate the association of information, system, and service quality features with supply chain performance. Supply chain managers and policymakers will better grasp OSNs adoption necessities and features during pandemic situations. It will also contribute to operation management, information systems, and social media by presenting OSNs in a supply chain environment.
Glioblastoma multiforme (GBM) is one of the most common aggressive, resistant, and invasive primary brain tumors that share neurodegenerative actions, resembling many neurodegenerative diseases. Although multiple conventional approaches, including chemoradiation, are more frequent in GBM therapy, these approaches are ineffective in extending the mean survival rate and are associated with various side effects, including neurodegeneration. This review proposes an alternative strategy for managing GBM and neurodegeneration by targeting heat shock protein 90 (Hsp90). Hsp90 is a well-known molecular chaperone that plays essential roles in maintaining and stabilizing protein folding to degradation in protein homeostasis and modulates signaling in cancer and neurodegeneration by regulating many client protein substrates. The therapeutic benefits of Hsp90 inhibition are well-known for several malignancies, and recent evidence highlights that Hsp90 inhibitors potentially inhibit the aggressiveness of GBM, increasing the sensitivity of conventional treatment and providing neuroprotection in various neurodegenerative diseases. Herein, the overview of Hsp90 modulation in GBM and neurodegeneration progress has been discussed with a summary of recent outcomes on Hsp90 inhibition in various GBM models and neurodegeneration. Particular emphasis is also given to natural Hsp90 inhibitors that have been evidenced to show dual protection in both GBM and neurodegeneration.
Vibrio parahaemolyticus, an aquatic pathogen, is a major concern in the shrimp aquaculture industry. Several strains of this pathogen are responsible for causing acute hepatopancreatic necrosis disease as well as other serious illness, both of which result in severe economic losses. The genome sequence of two pathogenic strains of V. parahaemolyticus, MSR16 and MSR17, isolated from Bangladesh, have been reported to gain a better understanding of their diversity and virulence. However, the prevalence of hypothetical proteins (HPs) makes it challenging to obtain a comprehensive understanding of the pathogenesis of V. parahaemolyticus. The aim of the present study is to provide a functional annotation of the HPs to elucidate their role in pathogenesis employing several in silico tools. The exploration of protein domains and families, similarity searches against proteins with known function, gene ontology enrichment, along with protein-protein interaction analysis of the HPs led to the functional assignment with a high level of confidence for 656 proteins out of a pool of 2631 proteins. The in silico approach used in this study was important for accurately assigning function to HPs and inferring interactions with proteins with previously described functions. The HPs with function predicted were categorized into various groups such as enzymes involved in small-compound biosynthesis pathway, iron binding proteins, antibiotics resistance proteins, and other proteins. Several proteins with potential druggability were identified among them. In addition, the HPs were investigated in search of virulent factors, which led to the identification of proteins that have the potential to be exploited as vaccine candidate. The findings of the study will be effective in gaining a better understanding of the molecular mechanisms of bacterial pathogenesis. They may also provide an insight into the process of evaluating promising targets for the development of drugs and vaccines against V. parahaemolyticus.
Ganoderma lucidum is known as lingzhi mushroom, which is said to have medicinal properties by the local residents. This research was focused to assess the antidepressant, anxiolytic, and sedative activities of the mentioned mushroom extracts by means of in vivo and in silico approaches. The antidepressant, anxiolytic, and sedative properties of the methanol extracts of G. lucidum (MEGL) were assessed using the forced swim test hole board, open field test, elevated plus maze, hole cross test, and thiopental sodium-induced sleeping time. The extracts revealed significant antidepressant, anxiolytic, and sedative activities in a dose-dependent manner. Rutin and quercetin were found to be the most effective enzyme inhibitors in the molecular docking study. According to the findings of in vivo and molecular docking study, it could be forecast that, the extract could have substantial antidepressant, anxiolytic, and sedative characteristics and deep molecular strategies on this extracts might create a target for the development of novel therapeutics. Further investigations are needed to appraise the molecular mechanisms implicated and isolate the bioactive components.
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2,872 members
M. Ruhul Amin
  • Department of Mathematical and Physical Sciences
Chowdhury Faiz Hossain
  • Department of Pharmacy
Mozammel Khan
  • Department of Computer Science and Engineering
Md Mobarak Hossain Khan
  • Department of Social Relations
Basanta Kumar Barmon
  • Institutional Quality Assurance Cell (IQAC)
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