Modelling of downslope displacement of failed soil blocks originating from submarine landslides

Submarine landslides are regarded as one of the major offshore geohazards that might affect offshore structures and associated facilities. The failed soil mass or debris that originate from a submarine landslide might travel hundreds of kilometres at a high speed and could affect offshore infrastructures. In the present study, numerical simulations are performed to investigate the velocity and run-out distance of the failed soil mass. The computational fluid dynamics (CFD) approach in ANSYS CFX is used for numerical simulation of the process, where the soft clay-rich sediments/debris are modelled as a non-Newtonian fluid. Large-deformation finite element (FE) simulations are also performed using the coupled Eulerian-Lagrangian (CEL) approach in Abaqus FE software. Similar to other large deformation FE analysis, Abaqus CEL and ANSYS CFX are computationally expensive. In offshore environments, the debris flows through water. The drag force resulting from water has a significant influence on the velocity of the debris and run-out distance. Modelling the downslope movement of an idealized soil block, it is shown that the pressure drag resulting from the pressure in front of the sliding block is the main source of the drag force. Progressive formation of additional shear planes, through localized plastic shear strain, might occur in the soil block during downslope displacements. A parametric study shows that the seabed slope angle and shear strength degradation due to undrained remoulding and/or water entrainment influence the failure patterns. For the cases analyzed, “flowslide” and “spread” type failures are obtained when the shear strength degradation of soil is considered. In terms of practical implications, the run-out distance will provide the information on whether an offshore structure will be affected by a failed soil mass resulting from a landslide. If so, the velocity will help to estimate the exerted force because it depends on the velocity of the moving soil block

Exploration of the drivers influencing the growth of hybrid electric vehicle adoption in the emerging economies: Implications towards sustainability and low-carbon economy

The heavy reliance of the transportation and power generation sector on fossil fuels is seriously impacting the environment. Transitioning towards more sustainable transportation modes is necessary to reduce this dependency on fossil fuels. Even though shifting toward electric vehicles (EVs) can reduce harmful emissions, due to the lack of adequate charging infrastructures, underdeveloped power transmission systems, and increased cost of power generation, it is difficult for a developing country to adopt and rely heavily on EVs. However, developing countries like Bangladesh can adopt a different strategy to address this issue. Harmful emission reduction is also possible by transitioning from conventional internal combustion engine (ICE) vehicles to hybrid electric vehicles (HEVs). The drivers that can promote the expansion of HEV adoption have not been extensively studied to date, which inspired the proposed study. This study explores the drivers for the growth of HEV adoption in emerging economies. First, the study identifies seventeen drivers from the literature review and expert feedback. Then the identified drivers were assessed using the Bayesian Best-Worst method (BWM). The study findings indicate that no requirement for a charging station, incentivizing consumers through policy measures, and enhanced fuel efficiency are the top three drivers influencing the growth of HEV adoption in developing or emerging economies. This study can help the decision-makers and end users in developing counties to gradually shift towards a low-carbon emission-based economy and ensure a greener and more sustainable future

Analyzing the factors influencing the wind energy adoption in Bangladesh: A pathway to sustainability for emerging economies

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

Evaluating barriers to sustainable boiler operation in the apparel manufacturing industry: Implications for mitigating operational hazards in the emerging economies.

The efficiency with which conventional boilers perform, in terms of sustainability, is affected by a variety of factors. Unsustainable boiler operating practices are still surprisingly frequent in developing countries, resulting in environmental liabilities and catastrophic accidents. It is a serious problem in developing countries like Bangladesh, where boilers are utilized extensively in the apparel manufacturing sector. However, no research has yet examined the challenges or barriers associated with sustainable boiler operation in the apparel manufacturing sector. This study, thereby, utilizes an integrated MCDM approach, combining the fuzzy theory and the decision-making trial and evaluation laboratory (DEMATEL) method, to identify, prioritize, and explore the relations among the barriers to sustainable boiler operation in the apparel manufacturing industry, from an emerging economy perspective. The barriers were initially identified from the literature and a visual survey of 127 factories. After expert validation, thirteen barriers were finally selected to be analyzed utilizing the fuzzy DEMATEL method. The study findings revealed that 'Absence of water treatment facilities', 'Fossil fuel burning and GHG emissions', and 'Excessive consumption of groundwater' are the three most prominent barriers to sustainable boiler operation. The cause-effect relations among the barriers suggest that 'Inadequate compliance with safety and hazard regulations' is the most influential and 'Fossil fuel burning and GHG emissions' is the most influenced barrier. This study is expected to guide the managers and policymakers of the apparel manufacturing sector in successfully overcoming the barriers to sustainable boiler operation, thus mitigating the operational hazards and achieving the sustainable development goals (SDGs)

Intelligent Vehicle Scheduling and Routing for a Chain of Retail Stores: A Case Study of Dhaka, Bangladesh

Background: Retail chains aim to maintain a competitive advantage by ensuring product availability and fulfilling customer demand on-time. However, inefficient scheduling and vehicle routing from the distribution center may cause delivery delays and, thus, stock-outs on the store shelves. Therefore, optimization of vehicle routing can play a vital role in fulfilling customer demand. Methods: In this research, a case study is formulated for a chain of retail stores in Dhaka City, Bangladesh. Orders from various stores are combined, grouped, and scheduled for Region-1 and Region-2 of Dhaka City. The ‘vehicle routing add-on’ feature of Google Sheets is used for scheduling and navigation. An android application, Intelligent Route Optimizer, is developed using the shortest path first algorithm based on the Dijkstra algorithm. The vehicle navigation scheme is programmed to change the direction according to the shortest possible path in the google map generated by the intelligent routing optimizer. Results: With the application, the improvement of optimization results is evident from the reductions of traveled distance (8.1% and 12.2%) and time (20.2% and 15.0%) in Region-1 and Region-2, respectively. Conclusions: A smartphone-based application is developed to improve the distribution plan. It can be utilized for an intelligent vehicle routing system to respond to real-time traffic; hence, the overall replenishment process will be improved

Analyzing the critical success factors to implement green supply chain management in the apparel manufacturing industry: Implications for sustainable development goals in the emerging economies

Green supply chain management (GSCM) is an emerging concept of modern supply chain management (SCM) that integrates eco-friendly and ethical environmental concerns with the traditional supply chain by reducing the negative impacts of unsustainable manufacturing practices. Developed countries have already adopted different sustainable SCM practices. However, despite being one of the significant sources of export earnings in emerging economies like Bangladesh, the apparel manufacturing industry is still lagging in the case of GSCM implementation. This study, thereby, utilized an integrated multi-criteria decision-making (MCDM) approach, including gray theory and decision-making trial and evaluation laboratory (DEMATEL) method to identify, prioritize, and examine the relations among the critical success factors (CSFs) to implement GSCM practices in the Bangladeshi apparel manufacturing industry. The study initially identified the CSFs from the literature review. After expert validation, sixteen significant CSFs were finally analyzed by the gray-DEMATEL method. The findings revealed that 'demand from buyers', 'economic and tax benefits', and 'government rules and regulations' are the three most prominent CSFs to implement GSCM practices in the apparel manufacturing industry. The cause-effect relations among the CSFs were later explored, which indicated 'Economic and tax benefits' to be the most influencing and 'Supplier training and cooperation' to be the most influenced CSF. The study insights can potentially guide apparel industry managers in successfully implementing GSCM practices toward achieving long-term sustainability and sustainable development goals (SDGs)

Gender-based vulnerability and adaptive capacity in the disaster-prone coastal areas from an intersectionality perspective

Households in the coastal areas are more vulnerable to various environmental, social, and economic disruptions in terms of an intersectionality point of view. As a first step in mitigating potential effects on families, knowing how susceptible they are and, ideally, fortifying themselves against existing and potential disruptions is essential. Vulnerability and adaptive capacity could not be uniformly distributed between households owing to gender-based socio-economic disparities and inequities. This research, thereby, examined the vulnerability and adaptive capacity variation between households headed by males and females in the two coastal areas of an emerging economy like Bangladesh. This study utilized the Evaluation-based on the Distance from Average Solution (EDAS) method and Boosted Regression Trees (BRT) technique to conduct the analysis. The EDAS method has been used to analyze the adaptive capacity index. Using BRT, an innovative approach in the area, we showed that male and female-headed households are different in terms of their capability to adapt. The findings from this study suggest that the households led by females are more vulnerable than those headed by males in the study region across a variety of dimensions (social, health, economic, housing, and land ownership) from an intersectionality perspective. The study findings can provide a new outlook for the decision-makers in the coastal region on the vulnerability and adaptive capacity differences among the residents and thus lead to more efficient disaster management practices

Assessing the critical success factors for implementing industry 4.0 in the pharmaceutical industry: Implications for supply chain sustainability in emerging economies.

The emerging technologies of Industry 4.0 (I4.0) are crucial to incorporating agility, sustainability, smartness, and competitiveness in the business model, enabling long-term sustainability practices in the pharmaceutical supply chain (PSC). By leveraging the latest technologies of I4.0, pharmaceutical companies can gain real-time visibility into their supply chain (SC) operations, allowing them to make data-driven decisions that improve SC performance, efficiency, resilience, and sustainability. However, to date, no research has examined the critical success factors (CSFs) that enable the pharmaceutical industry to adopt I4.0 successfully to enhance overall SC sustainability. This study, therefore, analyzed the potential CSFs for adopting I4.0 to increase all facets of sustainability in the PSC, especially from the perspective of an emerging economy like Bangladesh. Initially, sixteen CSFs were identified through a comprehensive literature review and expert validation. Later, the finalized CSFs were clustered into three relevant groups and analyzed using a Bayesian best-worst method (BWM)-based multi-criteria decision-making (MCDM) framework. The study findings revealed that "sufficient investment for technological advancement", "digitalized product monitoring and traceability", and "dedicated and robust research and development (R&D) team" are the top three CSFs to adopt I4.0 in the PSC. The study's findings can aid industrial practitioners, managers, and policymakers in creating effective action plans for efficiently adopting I4.0 in PSC to avail of its competitive benefits and ensure a sustainable future for the pharmaceutical industry