Leveraging machine learning algorithms in dynamic modeling of urban expansion, surface heat islands, and carbon storage for sustainable environmental management in coastal ecosystems.

Climate change and rapid urbanization are dramatically altering coastal ecosystems worldwide, with significant implications for land surface temperatures (LST) and carbon stock concentration (CSC). This study investigates the impacts of day and night time LST dynamics on CSC in Cox's Bazar, Bangladesh, from 1996 to 2021, with future projections to 2041. Using Landsat and MODIS imagery, we found that mean daytime LST increased by 3.

The urban air quality nexus: Assessing the interplay of land cover change and air pollution in emerging South Asian cities.

Air quality degradation presents a significant public health challenge, particularly in rapidly urbanizing regions where changes in land use/land cover (LULC) can dramatically influence pollution levels. This study investigates the association between LULC changes and air pollution (AP) in the five fastest-growing cities of Bangladesh from 1998 to 2021. Leveraging satellite data from Landsat and Sentinel-5P, the analysis reveals a substantial increase in urban areas and sparse vegetation, with declines in dense vegetation and water bodies over this period.

Harnessing urban analytics and machine learning for sustainable urban development: A multidimensional framework for modeling environmental impacts of urbanization in Saudi Arabia.

Sustainable urban development is crucial for managing natural resources and mitigating environmental impacts induced by rapid urbanization. This study demonstrates an integrated framework using machine learning-based urban analytics techniques to evaluate spatiotemporal urban expansion in Saudi Arabia (1987-2022) and quantify impacts on leading land, water, and air-related environmental parameters (EPs). Remote sensing and statistical techniques were applied to estimate vegetation health, built-up area, impervious surface, water bodies, soil characteristics, thermal comfort, air pollutants (PM, CH, CO, NO, SO), and nighttime light EPs.

Decoding seasonal variability of air pollutants with climate factors: A geostatistical approach using multimodal regression models for informed climate change mitigation.

In response to changes in climatic patterns, a profound comprehension of air pollutants (AP) variability is vital for enhancing climate models and facilitating informed decision-making in nations susceptible to climate change. Earlier research primarily depended on limited models, potentially neglecting intricate relationships and not fully encapsulating associations. This study, in contrast, probed the spatiotemporal variability of airborne particles (CO, CH, SO, and NO) under varying climatic conditions within a climate-sensitive nation, utilizing multiple regression models.

ANNprob-ACPs: A novel anticancer peptide identifier based on probabilistic feature fusion approach.

Anticancer Peptides (ACPs) offer significant potential as cancer treatment drugs in this modern era. Quickly identifying active compounds from protein sequences is crucial for healthcare and cancer treatment. In this paper ANNprob-ACPs, a novel and effective model for detecting ACPs has been implemented based on nine feature encoding techniques, including AAC, CC, W2V, DPC, PAAC, QSO, CTDC, CTDT, and CKSAAGP.

Spatiotemporal characterization of relative humidity trends and influence of climatic factors in Bangladesh.

The frequency and intensity of climate change and resulting impacts are more prevalent in South Asian countries, particularly in Bangladesh. Relative humidity (RH) is a crucial aspect of climate, and higher RH variability has far-reaching impacts on human health, agriculture, environment, and infrastructure. While temperature and rainfall have gained much research attention, RH studies have received scant attention in the research literature.

Environmental impact assessment of tobacco farming in northern Bangladesh.

Tobacco farming in Bangladesh has significant and far-reaching environmental impacts, affecting the land, water, and air. While the country has implemented tobacco control measures, the lack of monitoring and enforcement has resulted in environmental degradation and public health concerns. This study aims to document the environmental impact of tobacco farming in Bangladesh, adopting a qualitative approach to collect and analyze data.

Assessment and prediction of index based agricultural drought vulnerability using machine learning algorithms.

The consequences of droughts are far-reaching, impacting the natural environment, water quality, public health, and accelerating economic losses. Applications of remote sensing techniques using satellite imageries can play an influential role in identifying drought severity (DS) and impacts for a broader range of areas. The Barind Tract (BT) is a region of Bangladesh located northwest of the country and considered one of the hottest, semi-arid, and drought-prone regions.

Estimating solid waste generation and suitability analysis of landfill sites using regression, geospatial, and remote sensing techniques in Rangpur, Bangladesh.

Municipal solid waste (MSW) management has been a growing problem in fast-developing cities. A considerable amount of solid waste is generated daily and disposed anywhere, which creates an unhealthy environment. This study aims to develop a model to determine household solid waste (HSW) generation using multiple linear regression and identify suitable landfill sites to ensure proper MSW disposal in Rangpur City, Bangladesh.

A systematic literature review on preventing violent extremism.

Introduction: In recent years, violent extremism (VE) attacks have escalated worldwide. More schools and students are being attacked. Examining and addressing VE core causes through preventing VE (PVE) strategies can help avoid future atrocities.

Gender disparity in telehealth usage in Bangladesh during COVID-19.

Background And Aims: Telehealth allows healthcare workers to see patients virtually in locations that were not accessible previously, which has reduced cost and time and saved lives. The research aims to examine gender disparity among telehealth usage during the pandemic in 2020. This study will leverage a timely national experiment to evaluate the users of telehealth across the Bangladeshi population.

Predicting changes in land use/land cover and seasonal land surface temperature using multi-temporal landsat images in the northwest region of Bangladesh.

Land use/land cover (LULC) variations are accelerated by rapid urbanization and significantly impacted global Land Surface Temperature (LST). The dynamic increase in LST results in the Urban Heat Island (UHI) effect. In this study, future LULC change scenarios, seasonal (summer & winter) LST variations, and LST distribution over different LULC classes were predicted using Landsat satellite images for 1999, 2009, and 2019 in Rajshahi District, Bangladesh.

Countering violent extremism using social media and preventing implementable strategies for Bangladesh.

Globally, more than 85% of youth use social media daily in the medium of Facebook, Youtube, Twitter, etc., which is more than 70% for Bangladesh. The young population of Bangladesh is rapidly embracing social media through the internet and afflicted with the malaise of countering violent extremism (CVE), often through Facebook.