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The United Nations Sustainable Development Goals (SDGs) prioritize affordable, clean energy solutions to tackle global issues like poverty, climate change, and environmental degradation. In Bangladesh, agricultural biomass residues present a renewable and underutilized alternative to fossil fuels. This study systematically characterizes the thermochemical and physical properties of key agricultural residues—rice husk, rice straw, wheat straw, sugarcane bagasse, jute stick, dhaincha, and water hyacinth—to assess their potential for sustainable energy production. Key analyses include higher heating value (HHV), proximate composition (volatile matter, fixed carbon, ash), and elemental makeup. Volatile matter content ranged from 71.52 % in rice husk to 90.83 % in sugarcane bagasse, while fixed carbon varied between 7.00 % in sugarcane bagasse and 16.45 % in dhaincha. Ash content was highest in rice straw (15.05 %) and lowest in sugarcane bagasse (2.17 %). The HHV ranged from 19.65 MJ/kg for dahincha to 11.95 MJ/kg for water hyacinth, highlighting dahincha’s high energy potential. However, elevated potassium in rice straw (1.62 %) and water hyacinth (2.84 %) suggests risks of slagging in thermal conversion systems. These findings underscore the value of region-specific biomass data in informing energy policy and advancing renewable energy initiatives. By identifying high-potential feedstocks and conversion needs, this study supports targeted biomass applications, contributing to cleaner energy solutions and reduced dependency on fossil fuels. Advanced conversion technologies and ash management strategies are recommended to optimize the energy efficiency of these residues, particularly in regions with abundant agricultural waste. The insights provided serve as a foundation for sustainable biomass utilization in Bangladesh, with potential replicability in other agrarian countries facing similar energy and environmental challenges.

Rice husks, abundant leftovers from rice production, offer immense potential as a renewable energy source through thermochemical conversion processes. However, their efficient utilization hinges on understanding their unique thermal properties and reaction kinetics. This study addresses a critical gap by meticulously analyzing four prominent Bangladeshi rice husk varieties: BR22, BRRI Dhan46, BRRI Dhan47, and BRRI Dhan49. Through rigorous experimentation, we unveil a comprehensive dataset encompassing their physical, chemical, and thermochemical characteristics. Our investigation reveals favorable alignment of moisture content with various conversion technologies, alongside suitable bulk density for efficient handling. Proximate analysis sheds light on crucial components like ash, volatile matter, and fixed carbon, vital for optimizing combustion efficiency. Furthermore, elemental analysis not only highlights the presence of ash-forming elements but also assures low nitrogen and sulfur content, suggesting potential environmental benefits compared to conventional fuels. Delving into thermochemical characteristics, we measured higher heating values ranging from 13.31 MJ/kg to 14.42 MJ/kg, confirming the viability of these rice husk varieties for energy conversion. Thermogravimetric and kinetic analyses further illuminate their unique decomposition behavior, with BRRI Dhan47 exhibiting the highest decomposition rate, emphasizing the distinct reactivity profiles of different varieties. This extensive dataset empowers researchers and industry professionals with valuable insights for informed decision-making. By understanding the unique attributes of each rice husk variety and their behavior during conversion, we can optimize operational parameters for various thermochemical methods. Ultimately, this study bridges a critical research gap and paves the way for more efficient and sustainable utilization of rice husks as a renewable energy source, contributing to a greener future.

AbstractClimate change has substantially increased both the occurrence and intensity of flood events, particularly in the Indian subcontinent, exacerbating threats to human populations and economic infrastructure. The present research employed novel ML models—LR, SVM, RF, XGBoost, DNN, and Stacking Ensemble—developed in the Python environment and leveraged 18 flood-influencing factors to delineate flood-prone areas with precision. A comprehensive flood inventory, obtained from Sentinel-1 Synthetic Aperture Radar (SAR) data using the Google Earth Engine (GEE) platform, provided empirical data for entire model training and validation. Model performance was assessed using precision, recall, F1-score, accuracy, and ROC-AUC metrics. The results highlighted Stacking Ensemble’s superior predictive ability (0.965), followed closely by, XGBoost (0.934), DNN (0.929), RF (0.925), LR (0.921), and SVM (0.920) respectively, establishing the feasibility of ML applications in disaster management. The maps depicting susceptibility to flooding generated by the current research provide actionable insights for decision-makers, city planners, and authorities responsible for disaster management, guiding infrastructural and community resilience enhancements against flood risks.

AbstractDespite the many fatalities and injuries, Bangladesh lacks holistic individual flood preparedness studies. This study evaluates flood preparedness in the flood-prone rural region of Dowarabazar Upazila, Sunamganj District, Bangladesh. The field survey had 596 respondents. As required, we implemented Spearman’s rank correlation and multiple linear regression. Flooding affected the study population, which made people feel extremely unsafe. Most respondents warned that floods might affect their homes. Many participants reported receiving flood-related information from locals. Most participants reported not being notified of flood forecasts and warnings in their area. The majority indicated that they sought refuge in flood shelters. A significant portion of the participants in the study were unaware of flood preparedness. Loss minimization and adaptability are deficiencies. Concerning flood preparedness, gender, age, occupation, monthly income, multiple sources of income, and house type are all significant determinants. According to field research, females are less prepared for flooding than males. Individuals with pucca housing are better equipped than those with unsafe housing (kacha). Authorities should thus take these factors into account while enhancing flood preparedness. In addition to implementing measures such as improving livelihood patterns and conducting educational campaigns, it is imperative to construct holistic flood preparedness.

India and Bangladesh, the two neighbouring economies of South Asia, are at the centre of attraction in the foreign trade literature. The present article analyses the pattern of trade between India and Bangladesh before and after joining the South Asian Free Trade Area (SAFTA). The study covers twenty-five years, from 1996 to 2020 and used indices, namely, the Index of Revealed Comparative Advantage (RCA)Index, Trade Intensity Index (TII) and Trade Complementarity Index (TCI), to analyses the bilateral trade. Although Bangladesh has a relative comparative advantage in some of the products, India has a maximum comparative advantage in relatively more product groups. The index value of the TII is more than one for both countries during the study period, which means that the bilateral trade flows are highly intensive compared to their trading partner of the rest of the world. Furthermore, at the bilateral level, the index value of trade complementarity reflects a partial match between each country’s export supply and import demand because the value of indices stays between 0 and 100 during the entire study period. It also reflects that India’s export complementarity with Bangladesh is reciprocating, indicating growing trade demand for Indian product in Bangladesh’s market and India’s export supply becomes more compatible to meet imports demand of Bangladesh. India and Bangladesh have huge potential for bilateral trade, because they belong to Indian subcontinent, have geographical proximity, abundant natural resources and pursed similar policies towards achieving economic development.

Urban flooding in Chittagong City usually occurs during the monsoon season and a rainwater harvesting (RWH) system can be used as a remedial measure. This study examines the feasibility of rain barrel RWH system at a distributed scale within an urbanized area located in the northwestern part of Chittagong City that experiences flash flooding on a regular basis. For flood modeling, the storm water management model (SWMM) was employed with rain barrel low-impact development (LID) as a flood reduction measure. The Hydrologic Engineering Center's River Analysis System (HEC-RAS) inundation model was coupled with SWMM to observe the detailed and spatial extent of flood reduction. Compared to SWMM simulated floods, the simulated inundation depth using remote sensing data and the HEC-RAS showed a reasonable match, i.e., the correlation coefficients were found to be 0.70 and 0.98, respectively. Finally, using LID, i.e., RWH, a reduction of 28.66% could be achieved for reducing flood extent. Moreover, the study showed that 10%–60% imperviousness of the subcatchment area can yield a monthly RWH potential of 0.04–0.45 m3from a square meter of rooftop area. The model can be used for necessary decision making for flood reduction and to establish a distributed RWH system in the study area.

Data stream clustering plays an important role in data stream mining for knowledge extraction. Numerous researchers have recently studied density-based clustering algorithms due to their capability to generate arbitrarily shaped clusters. However, most of the algorithms are either fully offline, hybrid online/offline, or cannot handle the property of evolving data stream. Recently, a fully online clustering algorithm for evolving data stream called CEDAS was proposed. However, similar to other density-based clustering algorithms, CEDAS requires predefining the global optimal radius of micro-clusters, which is a difficult task; in addition, an erroneous choice deteriorates cluster performance. Moreover, the algorithm ignores the presence of temporarily irrelevant micro-clusters, which may be relevant in the future. In this study, we present a fully online density-based clustering algorithm called buffer-based online clustering for evolving data stream (BOCEDS). This algorithm recursively updates the micro-cluster radius to its local optimal. It also introduces a buffer for storing irrelevant micro-clusters and a fully online pruning method for extracting the temporarily irrelevant micro-cluster from the buffer. In addition, BOCEDS proposes an online micro-cluster energy-updating function based on the spatial information of the data stream. Experimental results are compared with those of CEDAS and other alternative hybrid online/offline density-based clustering algorithms, and BOCEDS proves its superiority over the other clustering algorithms. The sensitivity of clustering parameters is also measured. The proposed algorithm is then applied to real-world weather data streams to demonstrate its capability to detect changes in data stream and discover arbitrarily shaped clusters. The proposed BOCEDS can be available in https://sites.google.com/view/md-manjur-ahmed and https://sites.google.com/view/kamrul-just. [ABSTRACT FROM AUTHOR]

The east coast of India, especially the coastal region of Odisha, is highly threatened by tropical cyclones. This study develops a detailed risk map for tropical cyclones in the coastal districts of Odisha at the micro level, focusing on the assessment of risk factors at the block level. Using a multi-criteria decision making (MCDM) approach, the study considers four primary risk components: Exposure, vulnerability, susceptibility, and mitigation options. The Explainable Artificial Intelligence (XAI) framework, which uses the XGBoost model in conjunction with SHAP values, is applied to identify and elucidate the factors influencing risk levels in 69 blocks. Results indicate that about 65% of the area is at high risk to tropical cyclone, especially in the northeastern and central regions. In particular, 32 blocks are classified as high to very high-risk zones. The study shows a contrast in risk levels, with blocks in the northeast and southeast at higher risk, while blocks in the southern regions such as Ganjam and Puri and in the central parts of Kendrapara and Baleswar districts are at lower risk. The findings from this study are crucial for local authorities to identify vulnerable areas and improve cyclone preparedness and risk management strategies in Odisha.

A study of the extended desertification due to anthropogenic causes under climate change (CC) associated with its impact is presented here. Desertification, the main environmental issue, severely impacts agricultural output, causing poverty and economic instability in a nation like India. The regional distribution of desertification was determined using the RF and MaxEnt models. The western, central, and southern portions of the nation are very high, high, and moderately susceptible to desertification, respectively, according to the RF model. The MaxEnt model indicates that the western, central, and southern parts of the country exhibit a significant susceptibility to desertification, with the eastern parts also showing a moderate level of vulnerability. The remaining portion of this region, mainly in the north, east, and northeast, is particularly resistant to desertification. The outcome demonstrated that the country's desertification process had expanded from the west to the south. However, there are some spatial differences associated with the mentioned part of the country. This relevant information is crucial for decision maker of this country to take suitable remedies in regard to the reduction of the intensity of desertification.

In this study, we designed and synthesized a highly stable manganese (Mn2+)-based hepatobiliary complex by tethering an ethoxybenzyl (EOB) moiety with an ethylenediaminetetraacetic acid (EDTA) coordination cage as an alternative to the well-established hepatobiliary gadolinium (Gd3+) chelates and evaluated its usage as a T1hepatobiliary magnetic resonance imaging (MRI) contrast agent (CA). This new complex exhibits higher r1relaxivity (2.3 mM–1s–1) than clinically approved Mn2+-based hepatobiliary complex Mn-DPDP (1.6 mM–1s–1) at 1.5 T. Mn-EDTA-EOB shows much higher kinetic inertness than that of clinically approved Gd3+-based hepatobiliary MRI CAs, such as Gd-DTPA-EOB and Gd-BOPTA. In addition, in vivo biodistribution and MRI enhancement patterns of this new Mn2+chelate are comparable to those of Gd3+-based hepatobiliary MRI CAs. The diagnostic efficacy of the new complex was demonstrated by its enhanced tumor detection sensitivity in a liver cancer model using in vivo MRI.

Over the past two decades, Bangladesh has experienced a rapid increase in access to electricity. Bangladesh has become more dependent on the burning of furnace oil for electricity generation instead of increasing the generation of renewable energy sources. There are a variety of energy resources in the country to achieve a carbon-neutral energy which have not yet been explored to their fullest potential. A lack of proper strategies would lead to the country failing to meet its emissions reduction target, for example lack of energy diversification policy. In order to reduce carbon emission, a country should have a well-defined relationship between economic growth to the energy diversification. In this paper a relationship between economic growth and energy diversification (Energy Mix Concentration Index) has been developed for the year of 200–2020. From this relationship, it is observable that most diversified outcome can be achievable for the next fiscal year by analyzing the past year reports. The of Effort has given in this review to assess the current state of the energy sector in Bangladesh with a specific focus on energy diversification and initiatives for clean energy production. The potential for clean energy sources and technologies to reduce carbon emissions were discussed. The evolution of energy diversification was evaluated using the energy mix concentration index. Providing empirical evidence for energy policies to enhance innovation skills in the renewable energy industry might help Bangladesh achieve long-term development in energy sector.

Jute (Corchorus sp.) is one of the most important sources of natural fibre, covering ∼80% of global bast fibre production1. Only Corchorus olitorius and Corchorus capsularis are commercially cultivated, though there are more than 100 Corchorus species2in the Malvaceae family. Here we describe high-quality draft genomes of these two species and their comparisons at the functional genomics level to support tailor-designed breeding. The assemblies cover 91.6% and 82.2% of the estimated genome sizes for C. olitorius and C. capsularis, respectively. In total, 37,031 C. olitorius and 30,096 C. capsularis genes are identified, and most of the genes are validated by cDNA and RNA-seq data. Analyses of clustered gene families and gene collinearity show that jute underwent shared whole-genome duplication ∼18.66 million years (Myr) ago prior to speciation. RNA expression analysis from isolated fibre cells reveals the key regulatory and structural genes involved in fibre formation. This work expands our understanding of the molecular basis of fibre formation laying the foundation for the genetic improvement of jute.