This scholarly investigation conducts a rigorous exploration of Bangladesh's textile sector spanning the years 2011 to 2022, centering its attention primarily on the development of an intricate econometric framework. The study unveils profound insights into the sector's growth trajectories, the intricate dynamics of global demand, the undulating fluctuations of interest rates, and other pivotal economic gauges. The core component of this research, the econometric model, adeptly prognosticates textile exports through the incorporation of multifarious variables, encompassing the count of garment establishments, the magnitude of the workforce, market penetration, worldwide demand patterns, currency exchange rates, and interest rate fluctuations. Notably, the model attains a lofty degree of explicative potency, with an R-squared coefficient approximating 0.756, thereby attesting to its remarkable capacity to elucidate variances in textile export values. These discoveries carry substantial consequences for policymakers and stakeholders within the industry, as they bestow upon them a potent instrument for judicious decision-making and strategic blueprinting within Bangladesh's textile domain. The model accentuates the paramount significance of global demand and market share, concurrently accentuating the latent repercussions posed by fluctuations in interest rates.This research provides valuable insights into promoting the industry's sustainable growth, diversification, and resilience in the face of economic challenges.

Climate change causes weather extremes to rise in frequency and severity, which could have detrimental effects on human life, property and livelihood activity. There is significant uncertainty about the influences of anthropogenic climate change on the occurrence and severity of small-scale, sudden onset weather phenomena such as hailstorms and subsequent loss and damage. Yet, several studies indicate that there is an apparent stable connection between hailstorm activity and hailstorm damage. Severe hailstorm events are observed in Bangladesh in recent years, which are, in fact, rapid-onset disasters but low exposure in terms of giving government response and media consideration. Hence this study examines potential impacts and management strategies for loss and damage resulting from hailstorm events among smallholder farmers in Kurigram District's Phulbari Upazila of Bangladesh. Firstly, the direct and long term economic and non-economic loss and damage caused by the hailstorm on human well-beings and livelihoods were assessed. Then, the study evaluated the current adaptation, coping, management and response strategies at the institutional and community level in the context of such extreme events. Finally, a regulatory framework and implementation approaches had suggested achieving the country's resilience against disaster and climate change-induced loss and damage. Participatory Vulnerability Analysis, Key Informant Interviews and Sample Surveys accumulated the primary data for the study. In addition, secondary data were collected through analysis of literature, published and unpublished scientific articles and media reports, etc. This research outcome will help countries develop a guideline to address climate change and disaster-related loss and damage.

In recent years in Bangladesh, there has been regular cyclonic event, flooding and erratic pre-monsoons precipitation that has hampered production greatly and forced Bangladesh to import salt from abroad to manage market deficiency. There is much uncertainty about the effects of climate change on the frequency and intensity of small-scale, sudden onset weather phenomena such as heavy rainfall and subsequent loss and damage (L&D). But, several studies indicate that an obvious strong relationship exists between irregular rainfall and associated L&D. Nowadays, severe changing rainfall patterns are observed in Bangladesh, which is rapid-onset in nature, but low exposed in terms of response. The current study explored a ‘double-exposed’ burden combined of both climatic (e.g., uneven rainfall) and non-climatic governance factors (e.g., imperfect trade policy, the absence of risk transfer mechanisms) which are hindering salt production and pushing the country from the aspiration of salt exporting to the net buyer. This chapter mainly assesses the impacts of L&D due to climatic events that are causing overwhelming effects on the well-being of marginal salt farmers at Kutubdia Upazila of Bangladesh. Data were mainly collected through Participatory Vulnerability Analysis (PVA), Key informant interviews (KII), and Sample Surveys (SS). This study would provide insights for improved disaster management policy and an appropriate adaptive measure to address such extreme events as well as to initiate a further study for understanding the nexus of ‘nature and market’ in building resilience among the marginal salt farmers.

Recent advancements in Software Defined Networking (SDN) makes it possible to overcome the management challenges of traditional network by logically centralizing control plane and decoupling it from forwarding plane. Through centralized controllers, SDN can prevent security breach, but it also brings in new threats and vulnerabilities. Central controller can be a single point of failure. Hence, flow-based anomaly detection system in OpenFlow Controller can secure SDN to a great extent. In this paper, we investigated two different approaches of flow-based intrusion detection system in OpenFlow Controller. The first of which is based on machine-learning algorithm where NSL-KDD dataset with feature selection ensures the accuracy of 82% with Random Forest classifier using Gain Ratio feature selection evaluator. In the later phase, the second approach is combined with Gated Recurrent Unit Long Short-Term Memory based intrusion detection model based on Deep Neural Network (DNN) where we applied an appropriate ANOVA F-Test and Recursive Feature Elimination feature selection method to improve the classifier performance and achieved an accuracy of 88%. Substantial experiments with comparative analysis clearly show that, deep learning would be a better choice for intrusion detection in OpenFlow Controller.

The severe disease outbreak COVID-19 pandemic impacted public health and safety and the educational systems worldwide. For fear of the further spread of diseases, most educational institutions, including Bangladesh, have postponed their face-to-face teaching. Therefore, this study explores public university student's perceptions towards online classes during the COVID-19 pandemic in Bangladesh. Data were collected among students of Islamic University, Kushtia, Bangladesh, through an online survey. The study followed both a qualitative and quantitative approach, where the survey technique was used as an instrument of data collection. Results showed that most students were facing difficulty participating in virtual classes and could not communicate with their friends correctly during online classes. They faced challenges in online schooling, and the majority of the students preferred conventional types of learning to virtual classes and did not understand the content of virtual classes easily. The study also explored that most students did not feel comfortable in online classes. Still, considering the present pandemic situation, they decided to participate in online classes to continue schooling. Besides, the study discovered that female students showed better real perceptions than male students regarding online classes, and urban students have more optimistic appreciation than rural students. Moreover, laptop or personal computer users showed more positive perceptions towards online education than mobile users. Furthermore, Broadband/ Wi-Fi users have more positive perceptions than mobile network users. These findings would be an essential guideline for governments, policymakers, technology developers, and university authorities for making better policy choices in the future.

The contextual and risk perception of climate change plays a critical role in an individual’s decision-making process. It could also help people to respond appropriately to the consequences of global climate change and eventually take necessary adaptation actions. However, the perceptions of climate change are often gendered and vary among men and women. Therefore, this study explores different perceptions of climate change and its local adaptation options among ultra-poor vulnerable women, particularly in highly vulnerable flood-prone regions of Bangladesh. The research followed an empirical research methodology to collect primary and secondary information using qualitative and quantitative research tools. The study findings reveal that climate change perceptions at the individual level are relatively low (63%). Still, they have been observing significant changes in various climatic variables over the past 30 years. Moreover, this study identified some major adaptation options such as plinth raising (100%), livestock rearing (100%), homestead gardening (82%), seasonal migration (82%), and using indigenous knowledge (69%), and so on to tackle the adverse impacts of climate change-induced extreme events including flooding at the local level. For implementing these adaptation measures, the respondents spent a significant amount of financial resources from individual sources in the study area. Structural Equation Modeling (SEM) is used in addition to the statistical analyses to understand any connections between the climate change perceptions and other variables associated with the community under study. The SEM result shows that climate change will be a long–term problem, which offers a strong predictor in this model, considering standardized regression weight β= 0.56. It means, despite inadequate knowledge on climate change of the respondent’s, climate change is occurring and becoming the worst factor limiting cultural, economic, and environmental development in the study area.

Salinity is abiotic stress, which causes adverse environmental conditions for rice cultivation. In particular, local aromatic rice cultivation is heavily influenced by soil salinity stress, which has an impact on global food security. This study aimed to screen local aromatic rice genotypes in a hydroponics experiment using Yoshida solutions to evaluate the effect of increasing NaCl concentrations on the early growth stages of rice seedlings. Genetic diversity along with phylogenetic relationship was assessed using the random amplified polymorphic DNA (RAPD) markers. Out of 20 RAPD markers, 17 markers produced reproducible polymorphic bands. Individuals of all genotypes shared 88 (89.80%) of the 98 total RAPD elements amplified. The genetic distance-focused similarity index ranged from 0.05 to 0.94. The highest genetic distance (0.94) was discovered between genotypes Nayanmoni and Kalijira Barisal, and the lowest was between Badshabhog and Kataribhog (0.05). In addition, the OPS 3(510bp) and OPA 14(1100bp) markers could be used to identify salt-tolerant genotypes. According to genetic distance, the salt stress tolerant check genotype, Pokkali was genetically related to Chinigura as well as Kalijira Barisal. This study established a simple and consistent method for evaluating variability across various aromatic rice genotypes, which will benefit in genotype selection for breeding salinity stress tolerant aromatic rice varieties in Bangladesh.

Skin cancer is an exquisite disease globally nowadays. Because of the poor contrast and apparent resemblance between skin and lesions, automatic identification of skin cancer is complicated. The rate of human death can be massively reduced if melanoma skin cancer can be detected quickly using dermoscopy images. In this research, an anisotropic diffusion filtering method is used on dermoscopy images to remove multiplicative speckle noise and the fast-bounding box (FBB) method is applied to segment the skin cancer region. Furthermore, the paper consists of two feature extractor parts. One of the two features extractor parts is the hybrid feature extractor (HFE) part and another is the convolutional neural network VGG19 based CNN feature extractor part. The HFE portion combines three feature extraction approaches into a single fused feature vector: Histogram-Oriented Gradient (HOG), Local Binary Pattern (LBP), and Speed Up Robust Feature (SURF). The CNN method also is used to extract additional features from test and training datasets. This two-feature vector is fused to design the classification model. This classifier performs the classification of dermoscopy images whether it is melanoma or non-melanoma skin cancer. The proposed methodology is performed on two ordinary datasets and achieved the accuracy 99.85%, sensitivity 91.65%, and specificity 95.70%, which makes it more successful than previous machine learning algorithms.

Application of chemical pesticides to protect agricultural crops from pests and diseases is discouraged due to their harmful effects on human and environment. Therefore, alternative approaches for crop pro-tection through microbial or microbe originated pesticides have been gaining momentum. Wheat blast is a destructive fungal disease caused by Magnaporthe oryzae Triticum (MoT) pathotype, which poses a seri-ous threat to global food security. Screening of secondary metabolites against MoT revealed that antimy-cin A isolated from a marine Streptomyces sp. had significant inhibitory effect on mycelial growth in vitro. This study aimed to investigate the inhibitory effects of antimycin A on some critical life stages of MoT and evaluate the efficacy of wheat blast disease control by this natural product. Bioassay indicated that antimycin A suppressed mycelial growth, conidiogenesis, germination of conidia and formation of ap-pressoria in germinated conidia of MoT in a dose-dependent manner with minimum inhibitory concen-tration 0.005 μg/disk. If germinated, antimycin A induced abnormal germ tubes (4.8%) and suppressed the formation of appressoria. Interestingly, application of antimycin A significantly suppressed wheat blast disease in both seedling and heading stages of wheat supporting the results from invitro study. This is the first report on inhibition of mycelial growth, conidiogenesis, conidia germination, detrimental morphological alterations in germinated conidia, and suppression of wheat blast disease caused by a Triticum pathotype of M. Oryzae. Further study is required to unravel the precise mode of action of this promising natural compound for considering it as a biopesticide to combat wheat blast.

Globally, 2.2 billion urban residents are living without safely-managed sanitation, the majority of whom are slum residents. To improve the situation, Dhaka Water Supply and Sewerage Authority (DWASA) is implementing the Dhaka Sanitation Improvement Project (DSIP), mostly funded by the World Bank. This study assessed the feasibility of connecting low-income communities (LICs) within the proposed sewerage network by 2025. We conducted nine key-informant interviews from DWASA and City Corporation, and 23 focus-group discussions with landlords, tenants and Community Based Organisations (CBOs) from 16 LICs near the proposed catchment area. To achieve connections, LICs would require improved toilet infrastructures and have to be connected to main roads. Construction of large communal septic tanks is also required where individual toilet connections are difficult. To encourage connection in LICs, income-based or area-based subsidies were recommended. For financing maintenance, respondents suggested monthly fee collection for management of the infrastructure by dividing bill equally among sharing households, or by users per household. Participants also suggested the government's cooperation with development-partners/NGOs to ensure sewerage connection construction, operation and maintenance and prerequisite policy changes such as assuring land tenure.