Search

Hybrid renewable energy system-based reverse osmosis desalination has been identified as a reliable and sustainable solution for supplying potable water to the off-grid communities. In this respect, this study proposes a stand-alone hybrid energy system comprising solar PV, wind turbine, diesel generator, and a battery bank to power a reverse osmosis desalination plant. The proposed hybrid system is optimised using a novel metaheuristic optimisation technique named salp swarm algorithm (SSA) to minimise the three objective functions: levelised cost of energy (LCOE, $/kWh), life cycle emissions (LCE, CO2 kg-eq/yr.), and the excess energy (EE, kWh/yr.). Moreover, the performance of the novel algorithm is scrutinised using the benchmark solutions achieved from the widely utilised non-dominated sorting genetic algorithm II (NSGA-II) and particle swarm optimisation (PSO) techniques. In addition, the study also investigates the effects of water demand and the loss of power supply probability on the techno-economic performance. The results reveal that SSA outperforms NSGA-II and PSO algorithms towards achieving Pareto optimality hence provides financially rewarding solutions. The LCOE for the optimal HES is 0.179 $/kWh, and the LCE is 5921 CO2 kg-eq/yr. with an EE of 402 kWh/yr., whereas the cost of water for this system is 1.37 $/m3.

Objective: Genomic surveillance and seroprevalence of severe acute respiratory syndrome coronavirus-2 (SARS CoV-2) in Bangladesh is paramount for COVID-19 pandemic preparedness yet lagging the high-income countries due to limited resources. Methods: SARS-CoV-2 variants, COVID-19 symptoms, and serology were prospectively evaluated in a cross-sectional study of Bangladeshi adults testing RT-PCR positive in 2021 and 2022. Results: SARS CoV-2 Omicron variants of asymptomatic or mild COVID-19 in 2022 replaced Delta variant infections requiring hospitalization and oxygen support. The omicron XBB became predominant in July 2022 and associated with cough, headache or body ache and loss of smell; 47 of 68 (69 %), 30 of 68 (44 %) and 27 of 68 (40 %) respectively at higher frequency than BA.1/BA.2; 16 of 88 (18 %), 13 of 88 (15 %) and 0 of 88 (0 %) p

This study examines university students’ attitudes toward digitalizing higher education in Bangladesh. The objectives of the study are to examine the nature and the subjective causes of the student's attitudes and to examine the influence of gender, prior exposure to digitalization, and socio-economic background on the attitudes. This study uses the ABC Model of Attitudes and the Digital Divide Theory to prepare a questionnaire and analyze the data. A semi-structured interview questionnaire was developed based on the ABC Model of Attitudes to collect data from 32 interviewees. The interviewees from three public universities in Bangladesh were selected using random purposive sampling. Students from every socio-economic, gender, and level of digital experience category were selected to ensure heterogeneity of the sampling. The interview transcripts were analyzed using NVivo in conjugation with pen-and-paper thematic analysis. Data triangulation in the form of data analysis and data analyst triangulation was done to ensure the credibility and reliability of the research. The findings from the data analysis show that most of the students hold positive attitudes toward the digitalization of education. However, the gap in students’ access to digital facilities causes differences in students' attitudes. Students with digital accessibility generally hold positive attitudes. On the contrary, students who don't have access to digital facilities hold negative attitudes towards the digitalization of education, although they perceive the digitalization of education as essential and timely. In addition, the findings also confirm that gender, prior exposure to digitalization, and socio-economic background influence students’ attitudes significantly.

This study examines the optimal sizing of an off-grid hybrid system comprising solar photovoltaic (PV), rice husk-based biomass, and lead-acid battery for meeting the electric demand of a rural community. Considering a selected remote village in Bangladesh as a case study, the proposed optimized system is primarily compared with the diesel generator and the micro gas turbine (MGT)-based options in techno-economic and environmental terms. The potential social benefits, such as the employment creation and the improvement in the human development index in the locality, have been investigated in this study. Moreover, the impacts of operational greenhouse gas emissions on the human health damage and the surrounding ecosystem have been examined. Additionally, an exergy analysis of the hybrid system and the components has been carried out. Results indicate that in addition to being the environmentally preferable option, the proposed PV/biomass/battery system offers a lower cost of energy of 0.314 $/kWh compared to the MGT-based system (0.377 $/kWh). Although the diesel-based system offers a marginally better economy (9.55% less energy cost), it comes with the expense of probable damages to human health and the ecosystem worth of $15,211 and $6,608, respectively, making biomass the best option with no such damages. Exergy analysis reveals higher loss from PV than biomass and 13.09% system exergy efficiency. The assessment of the social indicators testifies to the potential of promoting the human development index from its current value and the formation of 1.41 jobs to as high as 15.15 full-time permanent jobs with the installation of hybrid systems in the community.

Abstract The electricity demand is ever increasing, and the worldwide power generation is largely dependent on the rapidly dwindling fossil fuel resources. It is no different in Bangladesh as well where the prevailing dynamic in the energy sector projects to a certain lack of sustainability. This makes the performance evaluation to determine and improve the efficiency of energy conversion systems an unavoidable requisite. This study investigates the energetic and exergetic performance of a gas engine‐based (4 MW) combined heat and power system situated at Rajshahi District in Bangladesh. Different thermodynamic performance parameters along with the exergo‐environmental effect and the sustainability of the system are also assessed additionally. The overall system and individual components are analyzed based on the actual data measured on site at 48% engine load and a constant engine speed of 1500 rpm. The results reveal that the maximum energy loss and exergy destruction occur in the reciprocating engine with values of 656.54 kW and 1835.04 kW, respectively, followed by the heat recovery steam generator with 465.51 kW energy loss and 671.51 kW exergy destruction. The overall system has energy and exergy efficiency of 29.95% and 23.12%, respectively. The exergetic sustainability index of the system is found to be 1.30.

Despite being simpler, cheaper, and less energy-intensive than other desalination processes, solar stills have apparent shortcomings in terms of productivity. Such a limitation has been prompting the researchers to strive in upgrading this renewable-driven technology through innovative add-ons. With a similar goal, the present study looks into the combined effect of unique modifications of internal sidewall reflector (ISR), hollow circular fin (HCF), phase change material (PCM), and nanoparticle mixed PCM (nano-PCM) on the thermodynamic performance of double slope solar stills. One passive (conventional) and one active (modified) double slope stills are constructed to experimentally evaluate and compare selected energy, exergy, exergo-economic, environmental, and sustainability metrics. The results indicate that the cumulative action of ISR, HCF, and PCM (Case II) leads up to 51.8% augmented productivity compared to the conventional case (Case I) where there is an additional increase of 21.5% when PCM is mixed with nanoparticles (Case III). While both the modified cases experience sizeable improvements in energy and exergy efficiencies compared to the conventional one, nano-PCM, in particular, boosts these quantities by 20.1% and 25%, respectively. Furthermore, nano-PCM induces around 2% additional cost-saving per unit of water production while mitigating a maximum CO2 emission of 3.65 ton. In addition to these assessments, the exergetic sustainability of the stills is discussed using vital indicators such as waste exergy ratio, improvement potential, sustainability index, etc. For the three experimental cases, the values of improvement potential, environmental effect factor, and sustainability index are attained within the ranges of 1374–1469 kWh/yr, 0.529–1.137, and 1.006–1.016, respectively.

Abstract Background National forest inventory and forest monitoring systems are more important than ever considering continued global degradation of trees and forests. These systems are especially important in a country like Bangladesh, which is characterised by a large population density, climate change vulnerability and dependence on natural resources. With the aim of supporting the Government’s actions towards sustainable forest management through reliable information, the Bangladesh Forest Inventory (BFI) was designed and implemented through three components: biophysical inventory, socio-economic survey and remote sensing-based land cover mapping. This article documents the approach undertaken by the Forest Department under the Ministry of Environment, Forests and Climate Change to establish the BFI as a multipurpose, efficient, accurate and replicable national forest assessment. The design, operationalization and some key results of the process are presented. Methods The BFI takes advantage of the latest and most well-accepted technological and methodological approaches. Importantly, it was designed through a collaborative process which drew from the experience and knowledge of multiple national and international entities. Overall, 1781 field plots were visited, 6400 households were surveyed, and a national land cover map for the year 2015 was produced. Innovative technological enhancements include a semi-automated segmentation approach for developing the wall-to-wall land cover map, an object-based national land characterisation system, consistent estimates between sample-based and mapped land cover areas, use of mobile apps for tree species identification and data collection, and use of differential global positioning system for referencing plot centres. Results Seven criteria, and multiple associated indicators, were developed for monitoring progress towards sustainable forest management goals, informing management decisions, and national and international reporting needs. A wide range of biophysical and socioeconomic data were collected, and in some cases integrated, for estimating the indicators. Conclusions The BFI is a new information source tool for helping guide Bangladesh towards a sustainable future. Reliable information on the status of tree and forest resources, as well as land use, empowers evidence-based decision making across multiple stakeholders and at different levels for protecting natural resources. The integrated socio-economic data collected provides information about the interactions between people and their tree and forest resources, and the valuation of ecosystem services. The BFI is designed to be a permanent assessment of these resources, and future data collection will enable monitoring of trends against the current baseline. However, additional institutional support as well as continuation of collaboration among national partners is crucial for sustaining the BFI process in future.

In this era of COVID19, proper diagnosis and treatment of pneumonia are very important. Chest X-Ray (CXR) image analysis plays a vital role in the reliable diagnosis of pneumonia. An experienced radiologist is required for this. However, even for an experienced radiographer, it is quite challenging and time-consuming to diagnose accurately due to the fuzziness of CXR images. Also, identification can be erroneous due to the involvement of human judgement. Hence, an authentic and automated system can play an important role here. In this era of cutting-edge technology, deep learning (DL) is highly used in every sector. There are several existing methods to diagnose pneumonia but they have accuracy problems. In this study, an automatic pneumonia detection system has been proposed by applying the extreme learning machine (ELM) on the Kaggle CXR images (Pneumonia). Three models have been studied: classification using extreme learning machine (ELM), ELM with a hybrid convolutional neural network-principal component analysis (CNN-PCA) based feature extraction, and CNN-PCA-ELM with the CXR images which are contrast-enhanced by contrast limited adaptive histogram equalization (CLAHE). Among these three proposed methods, the final model provides an optimistic result. It achieves the recall score of 98% and accuracy score of 98.32% for multiclass pneumonia classification. On the other hand, a binary classification achieves 100% recall and 99.83% accuracy. The proposed method also outperforms the existing methods. The outcome has been compared using several benchmarks that include accuracy, precision, recall, etc.

Background: The widespread increase in multiple variants of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) since 2020 is causing significant health concerns worldwide. While whole-genome sequencing (WGS) has played a leading role in surveillance programs, many local laboratories lack the expertise and resources. Thus, we aimed to investigate the circulating SARS-CoV-2 variants and evaluate the performance of multiplexed real-time reverse transcription-PCR (RT-PCR) for screening and monitoring the emergence of new SARS-CoV-2 variants in Bangladesh. Methods: A total of 600 confirmed SARS-CoV-2-positive cases were enrolled either prospectively or retrospectively from two divisions of Bangladesh. The samples were screened by variant RT-PCR targeting five mutations of the spike gene (N501Y, P681R, L452R, E484K, E484Q). A subsample of the study population was also selected for third-generation sequencing (TGS) and the results were compared to the variant RT-PCR screening. An in-depth comparison was made between the two methods in terms of congruence and cost-benefit. Result: Seven variants were detected among samples, with similar distributions of the variants across both divisions. Variant RT-PCR for the targeted mutations lead to a 98.5% call rate; only nine samples failed to be determined. No association was found regarding the demographic features, clinical criteria, or routine RT-PCR Ct values across the variants. The clade diversity of the sequenced subpopulation (n = 99) exhibited similar distributions across the two study sites and other epidemiologic variables. Variant RT-PCR successfully distinguished variants of concern (VOCs) and variants of interest (VOIs); however, 8% discrepancy was observed for the closest lineages. Moreover, the variant RT-PCR represented an ideal balance of cost, time, and accuracy that outweigh their limitations. Conclusion: Based on the strong agreement of variant RT-PCR with TGS, such rapid, easily accessible approaches of rapid strain typing are essential in the context of pandemic responses to guide both treatment decisions and public health measures. [ABSTRACT FROM AUTHOR]

Terminalia chebula Retz, commonly known as ‘Haritaki/Myrobalan,’ has been utilised as a traditional medicine for a long time. It has been extensively exercised in various indigenous medicine practices like Unani, Tibb, Ayurveda, and Siddha to remedy human ailments such as bleeding, carminative, dysentery, liver tonic, digestive, antidiarrheal, analgesic, anthelmintic, antibacterial and helpful in skin disorders. Studies on the pharmacological effects of T. chebula and its phytoconstituents documented between January, 1996 and December, 2021 were explored using various electronic databases. During the time mentioned above, several laboratory approaches revealed the biological properties of T. chebula, including antioxidative, antiproliferative, anti-microbial, proapoptotic, anti-diabetic, anti-ageing, hepatoprotective, anti-inflammatory, and antiepileptic. It is also beneficial in glucose and lipid metabolism and prevents atherogenesis and endothelial dysfunction. Different parts of T. chebula such as fruits, seeds, galls, barks extracted with various solvent systems (aqueous, ethanol, methanol, chloroform, ethyl-acetate) revealed major bioactive compounds like chebulic acid, chebulinic acid, and chebulaginic acid, which in turn proved to have valuable pharmacological properties through broad scientific investigations. There is a common link between chebulagic acid and chebulanin with its antioxidant property, antiaging activity, antiinflammatory, antidiabetic activity, and cardioprotective activity. The actions may be through neutralizing the free radicals responsible for producing tissue damage alongside interconnecting many other diseases. The current review summarises the scientifically documented literature on pharmacological potentials and chemical compositions of T. chebula, which is expected to investigate further studies on this subject.

Background: The assessment of antibody responses to severe acute respiratory syndrome coronavirus-2 is potentially confounded by exposures to flaviviruses. The aims of the present research were to determine whether anti-dengue antibodies affect the viral load and the detection of anti-coronavirus nucleocapsid (N)-protein antibodies in coronavirus infectious disease 2019 (COVID-19) in Bangladesh. Methods: Viral RNA was evaluated in swab specimens from 115 COVID-19 patients by real-time reverse transcription polymerase chain reaction (rT-PCR). The anti-N-protein antibodies, anti-dengue virus E-protein antibodies and the dengue non-structural protein-1 were determined in serum from 115 COVID-19 patients, 30 acute dengue fever pre-COVID-19 pandemic and nine normal controls by ELISA. Results: The concentrations of viral RNA in the nasopharyngeal; Ct median (95% CI); 22 (21.9–23.3) was significantly higher than viral RNA concentrations in oropharyngeal swabs; and 29 (27–30.5) p < 0.0001. Viral RNA concentrations were not correlated with-dengue IgG levels. The anti-nucleocapsid antibodies were IgA 27% positive and IgG 35% positive at days 1 to 8 post-onset of COVID-19 symptoms versus IgA 0% and IgG 0% in dengue patients, p < 0.0001. The levels of anti- nucleocapsid IgA or IgG versus the levels of anti-dengue IgM or IgG revealed no significant correlations. Conclusions: Viral RNA and anti-nucleocapsid antibodies were detected in COVID-19 patients from dengue-endemic regions of Bangladesh, independently of the dengue IgG levels.