Browsing by Author "Bari, A.B.M. Mainul"

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A Fermatean fuzzy approach to analyze the drivers of digital transformation in the agricultural production sector: A pathway to sustainability for emerging economies
(Elsevier, 2025-07) Anam, Md. Zahidul; Islam, Md. Hasibul; Islam, Md. Tamzidul; Bari, A.B.M. Mainul; Raihan, Asif
The adoption of digital technologies in agriculture offers opportunities for efficiency and sustainability, particularly in emerging economies with resource and infrastructure constraints. However, challenges persist, exacerbated by crises such as COVID-19 and geopolitical instabilities, highlighting agricultural supply chains’ fragility. Industry 5.0-driven digital transformation (DT) can mitigate these challenges by enhancing food security, supply chain resilience, and environmental sustainability. This study identifies and analyzes key drivers of DT in agricultural production from a holistic perspective. Through a literature review and expert validation, 19 key drivers were identified in the context of Bangladesh. An integrated multi-criteria decision-making (MCDM) approach, combining the Fermatean fuzzy sets (FFS) with the decision-making trial and evaluation laboratory (DEMATEL) technique, was applied to examine the drivers and explore interrelations among them. The results indicate that the most influential drivers are ’commitment from regulatory bodies’, ’maximizing the use of dwindling resources’, ’fostering rural development’, and ’the need for safe food’, with prominence values of 4.175, 4.001, 3.999, and 3.888, respectively. Additionally, ’commitment from regulatory bodies’ emerges as the most impactful causal factor, having a causal weight of 1.848. These findings provide insights for policymakers and industry managers in emerging economies, supporting strategic decision-making to drive sustainable agricultural transformation and achieve the relevant sustainable development goals.
A New Approach from Public Behavioral Attitudes and Perceptions Towards Microplastics: Influencing Factors, and Policy Proposals
(Elsevier, 2024-07-01) Al Masud, Abdulla; Islam, Abu Reza Md Towfiqul; Al Mamun, Abdullah; Alam, G.M. Monirul; Arabameri, Alireza; Bari, A.B.M. Mainul; Pal, Subodh Chandra; Rakib, Md Refat Jahan; Senapathi, Venkatramanan; Bodrud-Doza, Md.; Idris, Abubakr M.; Malafaia, Guilherme
This research paper addresses the urgent environmental concern of microplastic (MP) emissions, focusing on the behavioral attitudes and perceptions of the general populace in Shyamnagar Upazila, Bangladesh. Against the backdrop of escalating MP pollution globally, this study investigates the level of awareness and the factors influencing public engagement in mitigating MP prevalence. Leveraging survey data from 350 respondents, the ordered logistic regression (OLR) and boosted regression tree (BRT) models are employed for comprehensive data analysis. The findings expose a concerning lack of awareness about MPs, as only 12% of respondents possessed prior knowledge, and a notable 63% remained uninformed about MP pollution. The OLR model reveals a positive correlation between heightened awareness of MPs and an increased willingness to take action. Gender differences become evident, with women exhibiting greater willingness than men to mitigate MP emissions, and environmental practitioners displaying heightened motivation. The BRT model underscores construction materials and industrial pollution as the primary influential factors amplifying MP pollution. These insights not only illuminate the existing scenario but also provide a basis for fostering favorable behavioral attitudes and perceptions to mitigate the prevalence of MPs within the coastal milieu.
Analyzing the Factors Influencing the Wind Energy Adoption in Bangladesh
(Elsevier, 2023-11-21) Debnath, Binoy; Shakur, Md Shihab; Siraj, Md Tanvir; Bari, A.B.M. Mainul; Islam, Abu Reza Md Towfiqul
The future of energy security has become a prominent concern for emerging economies due to the inevitable depletion of fossil fuels and the ongoing disruptions in their supply. The crippling effect of complete dependence on expensive fossil fuel imports is magnified by the ineffective policy response to the enduring energy crisis, impeding progress across various sectors and thwarting efforts to meet the demands of population growth and industrialization amid acute electricity shortages. Amidst the economic growth of a prominent emerging economy, Bangladesh, wind energy emerges as a transformative solution to effectively tackle the mounting challenges of electricity demand, environmental pollution, greenhouse gas emissions, and the depleting reserves of fossil fuels. Therefore, this study utilizes an integrated multi-criteria decision-making (MCDM) approach combining the inter-valued type 2 intuitionistic fuzzy (IVT2IF) theory with the decision-making trial and evaluation laboratory (DEMATEL) method aiming to identify, prioritize, and investigate the relationships among the factors that impact the sustainable adoption and growth of wind energy in an emerging economy like Bangladesh. Initially, the factors were derived from reviewing existing literature. After subsequent expert validation, sixteen factors were selected for analysis using the IVT2IF DEMATEL method. The findings of the study indicate that "Fossil fuel supply disruption," "Stable financial investment and resource mobilization," and "Geographical region" are the most significant factors influencing the adoption of wind energy for national grid support with prominence value 4.415, 4.406 and 4.339 respectively. Moreover, "Fossil fuel supply disruption" is also the most significant causal factor with a causal weight of 1.274, which is followed by "Stable financial investment and resource mobilization" and "Geographical region" with a causal weight of 1.029 and 0.794. The study's findings have the potential to aid decision-makers and policymakers in formulating long-term strategies and investment decisions to improve the sustainability of the national grid and achieve carbon neutrality.
Exploring the barriers to decarbonizing the transportation system: A pathway to a cleaner future in emerging economies
(Elsevier, 2025-08-01) Anam, Md Zahidul; Shakur, Md Shihab; Bari, A.B.M. Mainul; Debnath, Binoy; Bristy, Fahmida Tabassum
The transportation sector is a major contributor to global greenhouse gas (GHG) emissions, particularly due to the emissions released by motor vehicles, shipping, and aviation. Carbon emissions increase significantly as demand for transportation services rises, especially in emerging economies like Bangladesh. Substantial reduction of these emissions by this sector is essential for mitigating climate change. However, decarbonization strategies and initiatives face numerous challenges in emerging economies, where transport infrastructure is still developing and heavily reliant on fossil fuels. This study, therefore, identifies and examines the barriers that emerging economies like Bangladesh encounter when implementing decarbonization strategies. First, based on literature research and expert validation, the study identified sixteen of the most relevant barriers. Then, an integrated approach combining the Bayes theorem and the Best-Worst Method (BWM) was utilized to evaluate and rank these barriers. The results obtained from this study indicate that among the sixteen barriers assessed, the top three are investment risks, high dependence on fossil fuels, and poor purchasing and expenditure power of residents (having a global weight of 0.0972, 0.0863, and 0.0847, respectively). These findings are expected to assist policymakers in emerging economies in developing structured strategies and making more targeted investment decisions that promote decarbonization in the transportation sector. They will also facilitate a smoother transition towards a sustainable transportation system and a cleaner future.
Fuzzy Logic, Geostatistics, and Multiple Linear Models To Evaluate Irrigation Metrics and Their Influencing Factors in a Drought-Prone Agricultural Region
(Springer, 2023-10-01) Zihad, S.M. Rabbi Al; Islam, Abu Reza Md Towfiqul; Siddique, Md Abu Bakar; Mia, Md Yousuf; Islam, Md Saiful; Islam, Md Aminul; Bari, A.B.M. Mainul; Bodrud-Doza, Md.; Yakout, Sobhy M.; Senapathi, Venkatramanan; Chatterjee, Sumanta
The quality of water used for irrigation is one of the major threats to maintaining the long-term sustainability of agricultural practices. Although some studies have addressed the suitability of irrigation water in different parts of Bangladesh, the irrigation water quality in the drought-prone region has yet to be thoroughly studied using integrated novel approaches. This study aims to assess the suitability of irrigation water in the drought-prone agricultural region of Bangladesh using traditional irrigation metrics such as sodium percentage (NA%), magnesium adsorption ratio (MAR), Kelley's ratio (KR), sodium adsorption ratio (SAR), total hardness (TH), permeability index (PI), and soluble sodium percentage (SSP), along with novel irrigation indices such as irrigation water quality index (IWQI) and fuzzy irrigation water quality index (FIWQI). Thirty-eight water samples were taken from tube wells, river systems, streamlets, and canals in agricultural areas, then analyzed for cations and anions. The multiple linear regression model predicted that SAR (0.66), KR (0.74), and PI (0.84) were the primary important elements influencing electrical conductivity (EC). Based on the IWQI, all water samples fall into the “suitable” category for irrigation. The FIWQI suggests that 75% of the groundwater and 100% of the surface water samples are excellent for irrigation. The semivariogram model indicates that most irrigation metrics have moderate to low spatial dependence, suggesting strong agricultural and rural influence. Redundancy analysis shows that Na+, Ca2+, Cl−, K+, and HCO3− in water increase with decreasing temperature. Surface water and some groundwater in the southwestern and southeastern parts are suitable for irrigation. The northern and central parts are less suitable for agriculture because of elevated K+ and Mg2+ levels. This study determines irrigation metrics for regional water management and pinpoints suitable areas in the drought-prone region, which provides a comprehensive understanding of sustainable water management and actionable steps for stakeholders and decision-makers.
Impact of Climate Change on Vector-borne Diseases: Exploring Hotspots, Recent Trends and Future Outlooks in Bangladesh
(Elsevier, 2024-11-15) Jibon, Md. Jannatul Naeem; Ruku, S.M. Ridwana Prodhan; Islam, Abu Reza Md Towfiqul; Khan, Md. Nuruzzaman; Mallick, Javed; Bari, A.B.M. Mainul; Senapathi, Venkatramanan
Climate change is a significant risk multiplier and profoundly influences the transmission dynamics, geographical distribution, and resurgence of vector-borne diseases (VBDs). Bangladesh has a noticeable rise in VBDs attributed to climate change. Despite the severity of this issue, the interconnections between climate change and VBDs in Bangladesh have yet to be thoroughly explored. To address this research gap, our review meticulously examined existing literature on the relationship between climate change and VBDs in Bangladesh. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach, we identified 3849 records from SCOPUS, Web of Science, and Google Scholar databases. Ultimately, 22 research articles meeting specific criteria were included. We identified that the literature on the subject matter of this study is non-contemporaneous, with 68% of studies investing datasets before 2014, despite studies on climate change and dengue nexus having increased recently. We pinpointed Dhaka and Chittagong Hill Tracts as the dengue and malaria research hotspots, respectively. We highlighted that the 2023 dengue outbreak illustrates a possible shift in dengue-endemic areas in Bangladesh. Moreover, dengue cases surged by 317% in 2023 compared to 2019 records, with a corresponding 607% increase in mortality compared to 2022. A weak connection was observed between dengue incidents and climate drivers, including the El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). However, no compelling evidence supported an association between malaria cases, and Sea Surface Temperature (SST) in the Bay of Bengal, along with the NINO3 phenomenon. We observed minimal microclimatic and non-climatic data inclusion in selected studies. Our review holds implications for policymakers, urging the prioritization of mitigation measures such as year-round surveillance and early warning systems. Ultimately, it calls for resource allocation to empower researchers in advancing the understanding of VBD dynamics amidst changing climates.
Managing the Invisible Threat of Microplastics in Marine Ecosystems: Lessons From Coast of the Bay of Bengal
(IEEE, 2023-09-01) Mubin, Al-Nure; Arefin, Shahoriar; Mia, Md. Sonir; Islam, Abu Reza Md. Towfiqul; Bari, A.B.M. Mainul; Islam, Md. Saiful; Ali, Mir Mohammad; Siddique, Md. Abu Bakar; Rahman, M. Safiur; Senapathi, Venkatramanan; Idris, Abubakr M.; Malafaia, Guilherme
Invisible microplastics (MP) have become a significant problem worldwide in recent years. Although many studies have highlighted the sources, effects, and fate of MPs pollution on various ecosystems in developed countries, there is limited information on MPs in the marine ecosystem along the northeastern coast of the Bay of Bengal (BoB). Coastal ecosystems along the BoB coasts are critical to a biodiverse ecology that supports human survival and resource extraction. However, the multi-environmental hotspots, ecotoxicity effects, transport mechanisms, fates, and intervention measures to control MP pollution initiatives along the BoB coasts have received little attention. Therefore, this review aims to highlight the multi-environmental hotspots, ecotoxicity effects, sources, fates, and intervention measures of MP in the northeastern BoB to understand how MP spreads in the nearshore marine ecosystem. This study critically evaluates the hotspots and ecotoxic effects of pollution from MP on the coastal multi-environment, e.g., soil, sediment, salt, water, and fish, as well as current intervention measures and additional mitigation recommendations. This study identified the northeastern part of the BoB as a hotspot for MP. In addition, the transport mechanisms and fate of MP in different environmental compartments are highlighted, as are research gaps and potential future research areas. Research on the ecotoxic effects of MP on BoB marine ecosystems must be a top priority, given the increasing use of plastics and the presence of significant marine products worldwide. The knowledge gained from this study would inform decision-makers and stakeholders in a way that could reduce the impact of the legacy of micro- and nanoplastics in the area. This study also proposes structural and non-structural measures to mitigate the effects of MPs and promote sustainable management.
Personal Protective Equipment-Derived Pollution During COVID-19 Era: A Critical Review of Ecotoxicology Impacts, Intervention Strategies, and Future Challenges
(Elsevier, 2023-05-13) Hasan, Mehedi; Islam, Abu Reza Md. Towfiqul; Jion, Most. Mastura Munia Farjana; Rahman, Md. Naimur; Peu, Susmita Datta; Das, Arnob; Bari, A.B.M. Mainul; Islam, Md. Saiful; Pal, Subodh Chandra; Islam, Aznarul; Choudhury, Tasrina Rabia; Rakib, Md. Refat Jahan; Idris, Abubakr M.; Malafaia, Guilherme
During the COVID-19 pandemic, people used personal protective equipment (PPE) to lessen the spread of the virus. The release of microplastics (MPs) from discarded PPE is a new threat to the long-term health of the environment and poses challenges that are not yet clear. PPE-derived MPs have been found in multi-environmental compartments, e.g., water, sediments, air, and soil across the Bay of Bengal (BoB). As COVID-19 spreads, healthcare facilities use more plastic PPE, polluting aquatic ecosystems. Excessive PPE use releases MPs into the ecosystem, which aquatic organisms ingest, distressing the food chain and possibly causing ongoing health problems in humans. Thus, post-COVID-19 sustainability depends on proper intervention strategies for PPE waste, which have received scholarly interest. Although many studies have investigated PPE-induced MPs pollution in the BoB countries (e.g., India, Bangladesh, Sri Lanka, and Myanmar), the ecotoxicity impacts, intervention strategies, and future challenges of PPE-derived waste have largely gone unnoticed. Our study presents a critical literature review covering the ecotoxicity impacts, intervention strategies, and future challenges across the BoB countries (e.g., India (162,034.45 tons), Bangladesh (67,996 tons), Sri Lanka (35,707.95 tons), and Myanmar (22,593.5 tons). The ecotoxicity impacts of PPE-derived MPs on human health and other environmental compartments are critically addressed. The review's findings infer a gap in the 5R (Reduce, Reuse, Recycle, Redesign, and Restructure) Strategy's implementation in the BoB coastal regions, hindering the achievement of UN SDG-12. Despite widespread research advancements in the BoB, many questions about PPE-derived MPs pollution from the perspective of the COVID-19 era still need to be answered. In response to the post-COVID-19 environmental remediation concerns, this study highlights the present research gaps and suggests new research directions considering the current MPs' research advancements on COVID-related PPE waste. Finally, the review suggests a framework for proper intervention strategies for reducing and monitoring PPE-derived MPs pollution in the BoB countries.
Predicting Groundwater Phosphate Levels in Coastal Multi-aquifers A Geostatistical and Data-driven Approach
(Elsevier, 2024-11-15) Abdullah-Al Mamun, Md.; Islam, Abu Reza Md Towfiqul; Aktar, Mst. Nazneen; Uddin, Md Nashir; Islam, Md. Saiful; Pal, Subodh Chandra; Islam, Aznarul; Bari, A.B.M. Mainul; Idris, Abubakr M.; Senapathi, Venkatramanan
Even if you want to make a profit from cryptocurrency, you are worried that you will lose money, and it is difficult to afford it. There are a vast number of papers that study such unpredictable price fluctuations of cryptocurrency. Currently, it is mainstream to use learning deep to predict the price of cryptocurrency. The goal of this research is to predict the price of cryptocurrency over the long-term using deep learning. The algorithms used are LSTM, GRU, and Bi-LSTM. The targeted cryptocurrencies are Bitcoin, Ethereum, Litecoin, and Cardano. Finally, we will compare it with previous research and verify the performance of our model.
Predicting Groundwater Phosphate Levels in Coastal Multi-aquifers: A Geostatistical and Data-driven Approach
(Elsevier, 2024-11-25) Abdullah-Al Mamun, Md.; Islam, Abu Reza Md Towfiqul; Aktar, Mst.Nazneen; Uddin, Md Nashir; Islam, Md. Saiful; Pal, Subodh Chandra; Islam, Aznarul; Bari, A.B.M. Mainul; Idris, Abubakr M.; Senapathi, Venkatramanan
The groundwater (GW) resource plays a central role in securing water supply in the coastal region of Bangladesh and therefore the future sustainability of this valuable resource is crucial for the area. However, there is limited research on the driving factors and prediction of phosphate concentration in groundwater. In this work, geostatistical modeling, self-organizing maps (SOM) and data-driven algorithms were combined to determine the driving factors and predict GW phosphate content in coastal multi-aquifers in southern Bangladesh. The SOM analysis identified three distinct spatial patterns: K+single bondNa+single bondpH, Ca2+single bondMg2+single bondNO₃−, and HCO₃−single bondSO₄2−single bondPO43−single bondF−. Four data-driven algorithms, including CatBoost, Gradient Boosting Machine (GBM), Long Short-Term Memory (LSTM), and Support Vector Regression (SVR) were used to predict phosphate concentration in GW using 380 samples and 15 prediction parameters. Forecasting accuracy was evaluated using RMSE, R2, RAE, CC, and MAE. Phosphate dissolution and saltwater intrusion, along with phosphorus fertilizers, increase PO43− content in GW. Using input parameters selected by multicollinearity and SOM, the CatBoost model showed exceptional performance in both training (RMSE = 0.002, MAE = 0.001, R2 = 0.999, RAE = 0.057, CC = 1.00) and testing (RMSE = 0.001, MAE = 0.002, R2 = 0.989, RAE = 0.057, CC = 0.998). Na+, K+, and Mg2+ significantly influenced prediction accuracy. The uncertainty study revealed a low standard error for the CatBoost model, indicating robustness and consistency. Semi-variogram models confirmed that the most influential attributes showed weak dependence, suggesting that agricultural runoff increases the heterogeneity of PO43− distribution in GW. These findings are crucial for developing conservation and strategic plans for sustainable utilization of coastal GW resources.