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 (PM2.5, CH4, CO, NO2, SO2), and nighttime light EPs. Regression assessment and Principal Component Analysis (PCA) were applied to assess the relationships between urban expansion and EPs. The findings highlight the substantial growth of urban areas (0.067%-0.14%), a decline in soil moisture (16%-14%), water bodies (60%-22%), a nationwide increase of PM2.5 (44 µg/m3 to 73 µg/m3) and night light intensity (0.166-9.670) concentrations resulting in significant impacts on land, water, and air quality parameters. PCA showed vegetation cover, soil moisture, thermal comfort, PM2.5, and NO2 are highly impacted by urban expansion compared to other EPs. The results highlight the need for effective and sustainable interventions to mitigate environmental impacts using green innovations and urban development by applying mixed-use development, green space preservation, green building technologies, and implementing renewable energy approaches. The framework recommended for environmental management in this study provides a robust foundation for evidence-based policies and adaptive management practices that balance economic progress and environmental sustainability. It will also help policymakers and urban planners in making informed decisions and promoting resilient urban growth.

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, CH4, SO2, and NO2) under varying climatic conditions within a climate-sensitive nation, utilizing multiple regression models. Spatial and seasonal AP data were acquired via the Google Earth Engine platform, which indicated elevated AP concentrations in primarily urban areas. Remarkably, the average airborne particle levels were lower in 2020 than in 2019, though they escalated during winter. The study employed linear regression, Pearson's correlation (PC), Spearman rank correlation models, and Geographically Weighted Regression (GWR) models to probe the relationship between pollutant variability and climatic elements such as rainfall, temperature, and humidity. Across all seasons, APs showed a negative correlation with rainfall while displaying positive correlations with temperature and humidity. The GWR and PC models produced the most reliable results from all the models employed, with the GWR model superseding the rest. Moreover, heightened aerosol levels were detected within a rainfall range of 600 mm/season, a temperature range of 25-30 °C, and humidity levels of 75 %-85 %. Overall, this study emphasizes the growing levels of APs in correlation with meteorological changes. By adopting a comprehensive approach and considering multiple factors, this research provides a more sophisticated understanding of the relationship between AP variability and climatic shifts.

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. After analyzing the performance of several machine learning models, the six best models were selected based on their overall performances in every evaluation metric. The probability scores of each model were subsequently aggregated and used as input of our meta- model, called ANNprob-ACPs. Our model outperformed all others and its potential to lead to phenomenal identification of ACPs. The results of this study showed notable improvement in 10-fold cross-validation and independent test, with accuracy of 93.72% and 90.62%, respectively. Our proposed model, ANNprob-ACPs outperformed existing approaches in terms of accuracy and effectiveness in discovering ACPs. By using SHAP, this study obtained the physicochemical properties of QSO, and compositional properties of DPC, AAC, and PAAC are more impactful for our model's performances, which have a major impact on a drug's interactions and future discoveries. Consequently, this model is crucial for the future and has a high probability of detecting ACPs more frequently. We developed a web server of ANNprob-ACPs, which is accessible at ANNprob-ACPs webserver.

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. This study investigated the trends and variability of RH levels in Bangladesh and the influence of other meteorological factors over the past 40 years. Variabilities in the meteorological factors were identified by calculating descriptive statistics. Innovative trend analysis (ITA) and Mann-Kendall test (MK-test) methods were utilized to assess monthly, seasonal, and annual trends. The magnitude of temperature, rainfall, and windspeed influences on RH variability were identified using Pearson's correlation, Spearman rank correlation, and Kendall correlation model. Variability analysis showed higher spatial variations in RH levels across the country, and RH skewed negatively in all stations. Results reveal that daily, monthly, seasonal, and annual trends of RH exhibited positive trends in all stations, with an increasing rate of 0.083-0.53% per year in summer, 0.43-0.68% per year in winter, and 0.58-0.31% per year in the rainy season. Both ITA and MK-test provided consistent results, indicating no discrepancies in trend results. All three models indicate that temperature, rainfall, and windspeed have weak to moderate positive influences on changing RH levels in Bangladesh. The study will contribute to decision-making to improve crop yields, health outcomes, and infrastructure efficiency.

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. The study used focus group discussions, key informant interviews, and a structured questionnaire survey to gather data, assessing the impact of tobacco farming on the environment, socioeconomic conditions, and human health using a five-point impact assessment scale. Results illustrated that tobacco cultivation contributes to the ecosystem and natural resource degradation, leading to a loss of habitat diversity and domestic animal death. Soil erosion, water pollution, and air pollution from excessive plowing and pesticide usage have also been observed, causing skin diseases and other health issues. Despite some economic benefits, social conditions have worsened due to drug addiction and conflicts among tobacco workers. The study will help policymakers and environmentalists by highlighting the need to take action in reducing the environmental and social impacts of tobacco farming in Bangladesh. It also informs the public about the potential tobacco production and consumption risks. This study provides important insights into the adverse effects of tobacco farming in Bangladesh and emphasizes the importance of implementing appropriate measures to reduce environmental and public health impacts.

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. This study aims to assess and predict the drought vulnerability over BT using Landsat satellite images from 1996 to 2031. Several indices, including Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Soil Moisture Content (SMC), Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Vegetation Health Index (VHI). VHI has been used to identify and predict DS based on VCI and TCI characteristics for 2026 and 2031 using Cellular Automata (CA)-Artificial Neural Network (ANN) algorithms. Results suggest an increasing patterns of DS accelerated by the reduction of healthy vegetation (19 %) and surface water bodies (26 %) and increased higher temperature (>5 °C) from 1996 to 2021. In addition, the VHI result signifies a massive increase in extreme drought conditions from 1996 (2 %) to 2021 (7 %). The DS prediction witnessed a possible expansion in extreme and severe drought conditions in 2026 (15 % and 13 %) and 2031 (18 % and 24 %). Understanding the possible impacts of drought will allow planners and decision-makers to initiate mitigating measures for enhancing the communities preparedness to cope with drought vulnerability.

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. Socioeconomic variables data like average monthly income, educational level, family size, age of family head, and average HSW generation per day were collected from 381 respondents through stratified random sampling with a 95% confidence level. Multi-criteria decision analysis (MCDA) was performed using variables like surface water, slope, road network, and land use through GIS and remote sensing to find suitable landfill sites. Results of the model show no multicollinearity as the variance inflation factor was estimated to be less than 2 for each independent variable. Furthermore, the model provides a moderate overall fit because of the coefficient of determination (R2 = 0.661), which denotes the independent variables' predictive capability. The results also demonstrate that family size and education are the most critical variables in predicting waste generation because of the values of coefficients 122.39 and - 184.72, respectively. This study also illustrated suitable landfill sites through MCDA, which can be a useful resource for the city authority to ensure environmental sustainability by implementing effective strategies for proper MSW management.

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. Due to the tremendous proliferation of research geared toward PVE, an extensive but disorganized knowledge review has accumulated in recent years. The review aims to discover several common themes and strategies across different disciplines and suggests resilience approaches might be the effective framework for PVE worldwide. METHODS: This study followed the guidelines provided by PRISMA. A systematic literature review on 81 articles was conducted in January 2022, with a screening approach starting from the title, abstract and finally, full articles. RESULTS: Seventeen studies were identified with a total sample of 2415 vulnerable young adults, age range: 16-29, male: 68.65% and female 31.35% mainly influenced VE pursuits through internet, TV and social media. In addition, the study identified that for PVE, individual actions would include ineffective approaches compared to a group approach starting from family to educational institutions. CONCLUSIONS: The effective PVE will be ensured by developing strategies for resilient individuals and dialoguing from the social-ecological perspective for taking practical actions in reducing VE activities.

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. METHODS: We obtained de-identified data for 200 patients among outpatient telehealth visits from Global Health Data Exchange as it captured telehealth use throughout Bangladesh. RESULTS: The analysis showed that male patients had a higher dependency on telehealth than female patients. 14% of the female patients opted for telehealth visits only with 57% cases of missed doses of medication, compared to males with 20% of them choosing telehealth visits and 29% missing their doses of medication. We found that the youngest age group, 16-25, had the highest dependence on telehealth compared to any other age group, and the lowest dependence was among the oldest age group of 45 years and above. CONCLUSIONS: There was a strong association between telehealth use and gender disparity with p value â€‹= â€‹0.02 â€‹< â€‹0.05. Longitudinal and geographical data are needed to understand more about the gender disparities and impact in telehealth utilizations.

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. Cellular Automata (CA) and Artificial Neural Network (ANN) procedures were used to predict the LULC changes and seasonal LST variations for 2029 and 2039. In addition, Focus Group Discussions (FGDs) and Key Informants Interviews (KIIs) were conducted to identify the possible impacts of LULC change, LST shifts, and climate change on agricultural productivity and developed a sustainable land use management plan for the study area. Validation of the CA model demonstrated an excellent accuracy with a kappa value of 0.82. Similarly, the ANN model's validation using Mean Square Error (0.523 and 0.796 for summer) and Correlation coefficient (0.6023 and 0.831 for winter) values demonstrated a good prediction accuracy. The LULC prediction result indicated that the built-up area will be expanded by 58.03 km2 and 79.90 km2, respectively, from 2019 to 2029 and 2039. The predicted seasonal LST indicated that in 2029 and 2039, more than 23.30 % and 50.46 % of the summer and 3.02 % and 13.02 % of the winter seasons will likely be experienced LSTs greater than 35 °C. The results of public participation exposed that changes in LULC classes, variations in LST, and climate change significantly impact the regional biodiversity (loss of farmland and water bodies), reduce agricultural productivity, and increase extreme weather events (flood, heavy rainfall, and cold/warm temperature). This study provides the useful guidelines for agricultural officers, urban planners, and environmental engineers to understand the spatial configurations of built-up area enlargement and provide effective policy measures to conserve farming lands to ensure environmental sustainability.

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. Given the increasing connectedness that the internet and social media offer, it is crucial that the fight against CVE shift to the digital space. Extremists are increasingly adopting novel ways and means based on technology to draw unsuspecting youth to their cause. It is essential to establish effective implementable strategies to stop the CVE activities using social media in Bangladesh. This study aims to identify existing initiatives globally in the space of disruptive online technologies that have yielded some success in preventing CVE. Various publications such as journal and news articles, TV news, and blogs have been used as data sources for this study. Also, fifteen expert interviews have been conducted to identify the most effective strategies for CVE in Bangladesh. Through the content analysis, the study highlights successful efforts and explores technology-based initiatives that can be deployed in Bangladesh to minimize the impact of VE activities through online technology. Finally, recommendations for strategies to restrict VE activities through technologies have been suggested that can be potentially implemented by the Bangladesh government by coordinating with international donor agencies and CVE practitioners. The research output recommends that Bangladesh and other less developed countries can concurrently deal with CVE by successfully using cutting-edge online/digital technologies.

A perception-based study to explore COVID-19 pandemic stress and its factors in Bangladesh.

BACKGROUND AND AIMS: Worldwide the COVID-19 pandemic has accelerated sufferings of mental health and behaviour attitudes of people. Many countries, including Bangladesh, reported suicide as extreme consequences of the psychological burden influenced by COVID-19. The present study explores human stress and its factor influenced by COVID-19 in Bangladesh, which significantly affect the quality of life. METHODS: An online-based questionnaire survey was conducted among 651 adult Bangladeshi populations by capturing socio-demographic information, possible human stress, and consequences of the pandemic. A set of statistical tools such as Pearson's Correlation Matrix (PCM), T-test, Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were applied to identify the relationship between different factors and influential factors increasing human stress. RESULTS: More than 83% of the participants are facing COVID-19 related mental stress, which results in short temper, sleep disorder, and family chaos. PCA and HCA outcomes indicated a significant relationship between the respondents' opinions and human stress factors, which harmonized with the country's existing scenario. PCM results enlighten the relationship between human stress factors and found financial hardship, cutting back daily spending, and food crisis are interconnected together causes stress. Also, hampering students' formal education and future career plans significantly contribute to mental stress. CONCLUSION: Based on the above findings, it's crucial to introduce a time-oriented strategy and implement precaution monitoring plans for Bangladesh. The rescue plan will help people to manage the pandemic and improve mental health to fight against psychological challenges related to COVID-19 and future pandemics.