The textile wet processing industry is considered a notorious polluter due to its widespread use of natural resources without proper replenishment. Bangladesh is one of the largest producers of textile products in the World, and therefore, it is vulnerable to environmental degradation. Bangladesh is predominantly a cotton processing country; therefore, reactive dyes are commonly used, and the dye combination is very water and chemical-intensive. There is a scarcity of information on the consumption of water, textile dyes and the generation of wastewater in the textile sector. Thus, this study aimed to estimate the amount of water use, wastewater generation, and chemical use in textile wet processing units. Therefore, a face-to-face in-depth questionnaire-based survey was conducted in 18 textile wet processing factories, including knit composite, knit dyeing, yarn dyeing, denim dyeing, and knit and yarn dyeing. The average specific groundwater consumption to process 1 Kg of textile materials was 164 L/Kg (SD ~ 81.8); dyehouse water was136 L/Kg (SD ~ 70.6), while corresponding wastewater was 119 L/Kg (SD ~ 73.0). This high consumption of groundwater is directly linked to the depletion of groundwater in the region, where textile industries are situated and also, causes water pollution through wastewater generation. The water used in the dyehouse water was usually soft water and found to be in a range of 68% to 100% that of groundwater extracted. For chemical use, a factory used 449 g of chemicals to process 1 Kg of textile materials, in which the most widely used chemicals were inorganic and basic chemical in nature. However, the chemical use varied from 152 g/Kg to 705 g/Kg of textile production. The total chemical consumption ranged from 954 tons to 4,525 tons a year. More than 50% of the wastewater treatment plants were biological, a quarter of combination and physico-chemical and biological, and the rest were chemical treatment plants in this study. Even though this study may not represent the whole textile wet processing industry of Bangladesh, however, This study provides baseline information on water and chemical consumption and wastewater generation. Our findings would be helpful for policy makers and researchers to identify transformative challenges required at the national level.

The COVID-19 pandemic has reshaped global infrastructure, highlighting the importance of effective infectious disease management. Identifying when and where infection trends change abnormally can aid strategic planning; yet, existing change point detection methods struggle due to the non-linear nature of infection trends, spatial and temporal dependencies, regional demographic and healthcare variations, and differing preventive measures. To address this issue, we propose a Bayesian method that can detect candidate regional disease-related change periods while overcoming these challenges. Specifically, we develop a Bayesian function-on-function regression model that learns from infection trends across multiple regions by incorporating both time-invariant features and the historical effect of time-dependent functional covariates. Temporal dependence in the covariate effects is captured through neighborhood-based spike-and-slab priors, whose latent binary inclusion indicators are, in turn, modeled by Ising priors. A Gibbs sampling framework is derived to approximate the joint posterior distribution of the model parameters. We compared the performance of the proposed framework against two widely used change-point detection methods, BCP and Segmented. In our simulation studies, BLIDE achieves an F1-score of $1.000$ under high signal-to-noise conditions and maintains an F1-score above $0.95$ even when noise dominates the trends, substantially outperforming BCP (F1-scores $0.454$ and $0.131$, respectively) and Segmented (F1-scores below $0.05$ across all scenarios).