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Abstract Rapid replacement of vegetated land with impermeable land (built-up areas) is a major factor in the increase in Land Surface Temperature (LST), while increased LST worsens the temperature in cities and creates the Surface Urban Heat Island (SUHI) effect. The study aims to measure vegetation loss and Land Surface Temperature of the Gazipur district between 2000 and 2020 and explore the relationship among Normalized Difference Vegetation Index (NDVI), LST, and Urban Thermal Field Variance Index (UTFVI). The Landsat TM/OLI images with minimum cloud coverage have been used to derive different indices. The mean NDVI values are 0.21, 0.16, and 0.22 in 2000, 2010, and 2020 respectively which indicates a general improvement in the health of the vegetation. Besides, the highest LST values throughout 20 years, represent a general increasing trend. As a consequence, different land covers have experienced fluctuations in mean temperature. The result shows that the mean temperature of bare land, buildup, vegetation, and waterbody has increased by 4.77, 2.01, 2.25, and 2.23 °C respectively from 2000 to 2020. The strongest SUHI zone’s area grew by about 28% between 2000 and 2020. Additionally, the highest index value of UTFVI was 0.39 in 2000 and grew to 0.43 in 2010. It changed to 0.49 in 2020, or ten years later. Thus, the SUHI effect’s increasing intensity is visible. Also, regression analysis has been used to explore the correlation between the derived indices. Stakeholders from different sectors like urban planners and policymakers may take insights from this study to work to promote greenery for a healthy urban environment.

Disperse dyes, primarily used for dyeing synthetic fibres like polyester, are characterized by their poor solubility in water, which necessitates the use of dispersing agents; however, environmental concerns arise due to their low biodegradability and potential toxicity, thus posing significant challenges in waste management and pollution control. This study explores the dyeing performance of knitted polyester with dispersed dyes, employing natural auxiliaries as substitutes for conventional commercial chemicals to promote an environmentally friendly approach. Citric acid (derived from lemon), sodium citrate (prepared from citric acid and sodium bicarbonate), and glucose (extracted from date fruits) replace commercial acetic acid, dispersing agent, and hydrolyze. Dyeing is conducted using the high-temperature and high-pressure methods with consistent parameters. A comparative study of the dyeing performance of the natural auxiliaries with the existing commercial chemicals was assessed concerning colour strength, different fastness properties, and the FT-IR diagram. Proton NMR is utilized for the structural determination of date fruit and lemon components. UV-Vis spectral data show that adding sodium citrate to the dispersed dye solution drops the absorbance value from 1.1457 to 0.58916, confirming sodium citrate's efficacy as a dispersing agent. With lower recipe costs, the environmentally friendly approach using natural auxiliaries exhibits comparable dyeing performance and excellent fastness properties, providing a sustainable alternative to traditional methods.

BackgroundDengue fever is a serious public health issue in Bangladesh, where its incidence rises with the monsoon. Meteorological variables are believed to be responsible factors among others. Therefore, this study examines the effects of meteorological variables (temperature, rainfall, and humidity) on dengue incidence in Bangladesh. While previous studies have examined the relationship between dengue and meteorological variables using single model approaches, this study employs advanced econometric techniques to capture dynamic interactions. Furthermore, in the case of Bangladesh, this type of analysis is necessary due to the fact that dengue outbreak become one of the major issues. However, the analysis related to this issue is not available.MethodsFor estimation purposes, the Augmented Dickey-Fuller (ADF) test, Vector Autoregressive (VAR) model, Granger causality tests, Impulse Response Function (IRF), Variance Decomposition (VDC), and Vector Error Correction Model (VECM) are employed.ResultsRainfall has a significant impact on dengue incidence compared to temperature and humidity. The Granger causality test demonstrates that rainfall and dengue incidence are causally related unidirectionally. Rainfall can potentially have a short-term and long-term effect on the incidence of dengue, as per the estimates of the VECM model.ConclusionsThese findings will assist policymakers in Bangladesh in developing a dengue fever early warning system depending on climate change. In order to efficiently avoid the spread of dengue in Bangladesh's dengue-endemic urban areas, this study suggests societal monitoring.

IntroductionThis study aims to assess individual flood preparedness in the flood-prone Islampur Union of Sunamganj District, Bangladesh, using an index-based Holistic Individual Preparedness Model (HIPM).MethodsA total of 510 respondents participated in a face-to-face field survey, which employed 21 flood preparedness indicators. The key objectives were to evaluate the extent of individual preparedness and its determinants. Statistical methods such as multiple linear regression and Spearman’s rank correlation were used to analyze the relationship between preparedness and various sociodemographic factors.Results and discussionThe results show that 91% of respondents felt insecure during floods and 86% experienced damage to their homes. A significant proportion (87%) rated flood warnings as inadequate, while 84% did not seek refuge in flood shelters. Notably, 54% of respondents demonstrated limited knowledge of flood preparedness measures. Factors like gender, housing type, monthly income, and evaluation of flood warnings were associated with preparedness levels, with females and residents of kacha (unsafe) houses being less prepared. The study underscores the need for targeted campaigns and educational initiatives to improve preparedness, particularly for vulnerable groups.

Social media and climate change are some of the most controversial issues of the 21st century. Despite numerous studies, our understanding of current social media trends, popular hot topics, and future challenges related to climate change remains significantly limited. This research presents a systematic review of climate change and social media for the first time. Review the studies published between 2009 and 2022 in places like Google Scholar, Science Direct, Web-of-Science, Scopus, ResearchGate, and others. For this systematic review, we found 1,057 articles. Forty-five articles were the most relevant according to our goals and study design, which followed the PRISMA framework. The results of this review demonstrate that Twitter is the most popular platform. Every year, we identify rising trends in the number of publications. Past studies often focused on just one social media site, like Twitter (n = 26) or Facebook (n = 5). Although most studies focus on the United States, the study area is primarily “all over the world.” This study offers a theoretical framework by examining the relationship between social media platforms and the discourse surrounding climate change. It looked into how social media trends influence public perception, raise awareness, and spur action on climate change. In practical terms, the study focuses on important and trending topics like nonbelievers and climate change. The contribution consists of synthesizing the body of research, providing insights into the state of the digital world, and suggesting future lines of inquiry for the field of social media and climate change studies. We highlighted the studies’ quality assessment result of “moderate quality.” This systematic review provides information about how climate change is now portrayed on social media and lays the groundwork for further study in this area.

Summary: Background: Bangladesh is making progress toward achieving zero dog-mediated rabies deaths by 2030, a global goal set in 2015. Methods: Drawing from multiple datasets, including patient immunisation record books and mass dog vaccination (MDV) databases, we conducted a comprehensive analysis between 2011 and 2023 to understand the effectiveness of rabies control programmes and predict human rabies cases in Bangladesh by 2030 using time-series forecasting models. We also compared rabies virus sequences from GenBank in Bangladesh and other South Asian countries. Findings: The estimated dog population in Bangladesh was determined to be 1,668,140, with an average dog population density of 12.83 dogs/km2 (95% CI 11.14–14.53) and a human-to-dog ratio of 86.70 (95% CI 76.60–96.80). The MDV campaign has led to the vaccination of an average of 21,295 dogs (95% CI 18,654–23,935) per district annually out of an estimated 26,065 dogs (95% CI 22,898–29,230). A declining trend in predicted and observed human rabies cases has been identified, suggesting that Bangladesh is poised to make substantial progress towards achieving the ‘Zero by 30’ goal, provided the current trajectory continues. The phylogenetic analysis shows that rabies viruses in Bangladesh belong to the Arctic-like-1 group, which differs from those in Bhutan despite sharing a common ancestor. Interpretation: Bangladesh's One Health approach demonstrated that an increase in MDV and anti-rabies vaccine (ARV) resulted in a decline in the relative risk of human rabies cases, indicating that eliminating dog-mediated human rabies could be achievable. Funding: The study was supported by the Communicable Disease Control (CDC) Division of the Directorate General of Health Services (DGHS) of the People's Republic of Bangladesh.

This study examines the impact, conditional correlation and volatility spillover effect of remittances, foreign direct investment and inflation rate on GDP in Bangladesh, Pakistan, and Sri-Lanka, three Asian nations that are particularly vulnerable. While numerous studies have examined the relationship between remittances, FDI and IR on GDP but this paper approaches advanced econometric techniques to capture spillover effect and the dynamic interactions between the variables. For estimation purposes the study employs different econometric techniques such as Augmented Dickey-Fuller (ADF) test, VAR model, Granger causality tests, Impulse Response function, Variance Decomposition and BEKK-GARCH model. Bangladesh and Sri Lanka's REM, FDI and IR have no significant effects on GDP according to the VAR model. BEKK-GARCH demonstrates that three countries have both unidirectional and bidirectional transmissions of volatility, with the exception of Pakistan's REM and Bangladesh's FDI. We find that VAR model may not be adequate in capturing the complex dynamics between variables, which can be better captured by BEKK-GARCH model. Our comparison research shows how these variables affect GDP differently and similarly in each of the three nations, giving policymakers information they can use to create customized policies that encourage economic growth.

Abstract Determining the degree of high groundwater arsenic (As) and fluoride (F−) risk is crucial for successful groundwater management and protection of public health, as elevated contamination in groundwater poses a risk to the environment and human health. It is a fact that several non-point sources of pollutants contaminate the groundwater of the multi-aquifers of the Ganges delta. This study used logistic regression (LR), random forest (RF) and artificial neural network (ANN) machine learning algorithm to evaluate groundwater vulnerability in the Holocene multi-layered aquifers of Ganges delta, which is part of the Indo-Bangladesh region. Fifteen hydro-chemical data were used for modelling purposes and sophisticated statistical tests were carried out to check the dataset regarding their dependent relationships. ANN performed best with an AUC of 0.902 in the validation dataset and prepared a groundwater vulnerability map accordingly. The spatial distribution of the vulnerability map indicates that eastern and some isolated south-eastern and central middle portions are very vulnerable in terms of As and F− concentration. The overall prediction demonstrates that 29% of the areal coverage of the Ganges delta is very vulnerable to As and F− contents. Finally, this study discusses major contamination categories, rising security issues, and problems related to groundwater quality globally. Henceforth, groundwater quality monitoring must be significantly improved to successfully detect and reduce hazards to groundwater from past, present, and future contamination.

Insect resistant genetically modified Bt cotton (containing a gene of Bacillus thuringiensis) has substantial potentiality of mounting cotton productivity. This study unveils an early insight on the economic viability of Bt cotton in Bangladesh. A total of 248 traditional cotton farmers and 8 Bt cotton experimental fields were surveyed in April 2022 for achieving the objectives. The data were analysed using descriptive statistics. Findings showed that the cost of Bt cotton production was slightly higher than that of conventional cotton. However, Bt cotton yielded a productivity increase of 0.81 t/ha. The cultivation of Bt cotton resulted in a higher net return (USD 2436/ha) compared to conventional cotton (USD 1624/ha). The results further indicated that the use of insecticides and pesticides in Bt cotton was significantly lower compared to traditional cotton, thereby contributing to the preservation of the natural environment. Overall, cultivation of Bt cotton is economically viable and may generate environmental benefits. Steps are warranted to disseminate and expand its cultivation.

Abstract Spatiotemporal rainfall trend analysis as an indicator of climatic change provides critical information for improved water resource planning. However, the spatiotemporal changing behavior of rainfall is much less understood in a tropical monsoon-dominated country like Bangladesh. To this end, this research aims to analyze spatiotemporal variations in rainfall for the period 1980–2020 over Bangladesh at seasonal and monthly scales using MAKESENS, the Pettitt test, and innovative trend analysis. Multilayer Perception (MLP) neural network was used to predict the next 8 years' rainfall changes nationally in Bangladesh. To investigate the spatial pattern of rainfall trends, the inverse distance weighting model was adopted within the ArcGIS environment. Results show that mean annual rainfall is 2432.6 mm, of which 57.6% was recorded from July to August. The Mann–Kendall trend test reveals that 77% of stations are declining, and 23% have a rising trend in the monthly rainfall. More than 80% of stations face a declining trend from November to March and August. There is a declining trend for seasonal rainfall at 82% of stations during the pre-monsoon, 75% during the monsoon, and 100% during the post-monsoon. A significant decline trend was identified in the north-center during the pre-monsoon, the northern part during the monsoon, and the southern and northwestern portions during the post-monsoon season. Predicted rainfall by MLP till 2030 suggests that there will be little rain from November to February, and the maximum fluctuating rainfall will occur in 2025 and 2027–2029. The ECMWF ERA5 reanalysis data findings suggested that changing rainfall patterns in Bangladesh may have been driven by rising or reducing convective precipitation rates, low cloud cover, and inadequate vertically integrated moisture divergence. Given the shortage of water resources and the anticipated rise in water demand, the study's findings have some implications for managing water resources in Bangladesh.

The objective of the research is to investigate how refugees’ influx has altered the carbon dynamics of different land uses and the relationship between land use specific carbon emissions and land surface temperature (LST). Two upazilais of the Cox’s Bazar district, Bangladesh (i.e., Ukhiya and Teknaf), were mostly affected by the Rohingya refugee influx and are the focus of the study. The study classified the land use land cover (LULC) into four classes (e.g., agricultural, forest, settlement, and water) for two different time periods (i.e., before and after the influx of Rohingya refugees) using an artificial neural network algorithm and sentinel satellite imagery. Carbon emissions and absorptions specific to land use were calculated using classified land use land cover and coefficients. Again, two time series of Landsat 8 imagery were applied to estimate land surface temperature shifts. The area of forests was found to have decreased by 21.19 square miles (9.58 percent) and the area of settlements to have increased by 18.24 square miles (8.25 percent) between 2017 and 2021. There was a negative net land-use based carbon emission of -5187.02 tons per year in 2017. In 2021, it was predicted that annual net emissions would total 2208.24 tons. LST during the study period has increased as a result of human activities that release greenhouse gases into the atmosphere. The findings of this research will inform policymakers’ decisions about the conservation and sustainable development of natural resources in the region experiencing an influx of Rohingya refugees.

Abstract Immunoglobulin G4 (IgG4)‐related disease (IgG4‐RD) is a multi‐organ immune‐mediated fibroinflammatory disorder that may imitate malignancy, infectious or any other inflammatory disorder. IgG4‐related retroperitoneal fibrosis (IgG4‐RPF) is a rare form of IgG4‐RD, diagnosis of which is often relied on radiological technology. Herein, we describe a case of 60 year old male, presenting with low back pain and weight loss for a period of 2 months and 15 days. Imaging studies showed a retroperitoneal tumorous mass along with bilateral hydroureteronephrosis, which was later confirmed to be IgG4‐related retroperitoneal fibrosis on the basis of extensive histopathological analysis. Immunosuppressive therapy resulted in a decrease in fibrosis and restoration of renal function.

Human trafficking is the third most lucrative form of trafficking in the world (following drugs and counterfeit goods). Multiple outbreaks of unrest between October 2016 and August 2017 in the Rakhine State of Myanmar triggered ~745,000 influxes of Rohingyas crossing into Bangladesh through the border boundaries at Teknaf and Ukhiya sub-districts of Cox's Bazar. In this regard, the media confirmed that over a thousand Rohingya people, particularly women and girls, were victims of human trafficking. This research aims to explore the underlying causes of human trafficking (HT) during emergency responses and seeks to understand how the knowledge and capacity of the refugee, local administration, and law enforcement agencies in Bangladesh can be improved in promoting counter-trafficking (CT) and safe migration processes. In order to achieve the objectives, this study reviews acts, rules, policies, and action plans of the Government of Bangladesh on the HT, CT, and safe migration processes. Then, a case study has been applied to present the ongoing CT and safe migration programs of an NGO called Young Power in Social Action (YPSA), which received funding and technical support from the International Organization of Migration (IOM) for this purpose. This study also evaluates the effectiveness of the program through conducting key informant interviews (KIIs) and focus group discussions (FGDs) with the beneficiary and non-beneficiary participants including refugees, law-enforcing agencies (LEAs), and NGOs in Teknaf and Ukhyia. Thus, this study identifies program-level strengths and weaknesses in relation to the CT and safe migration process and provides key directions on how they can be improved. It concludes that non-state actors have a significant role in preventing HT and promoting CT and safe migration for Rohingyas in Bangladesh.

Obesity is a pandemic which adds greatly to health care costs through its association with type 2 diabetes, metabolic syndrome, cardiovascular disease, and cancers. The aim of this research is to investigate the effects of different concentrations of butyric acid on changes of body weight, blood biochemical parameters, and liver histo-texture in mice. Total twenty Swiss albino mice were divided into 4 groups viz Group A, B, C and D. Group A act as control while Group B, Group C and Group D were received 3 mM, 5 mM, and 7 mM/kg b.wt butyric acid respectively, throughout the experimental period. On the 60th day, blood and liver samples were collected for biochemical analysis and histopathology respectively. Results showed that body weight increased significantly (p [ J Adv Biotechnol Exp Ther 2021; 4(3.000): 415-423]

Over the past two decades, Bangladesh has experienced a rapid increase in access to electricity. Bangladesh has become more dependent on the burning of furnace oil for electricity generation instead of increasing the generation of renewable energy sources. There are a variety of energy resources in the country to achieve a carbon-neutral energy which have not yet been explored to their fullest potential. A lack of proper strategies would lead to the country failing to meet its emissions reduction target, for example lack of energy diversification policy. In order to reduce carbon emission, a country should have a well-defined relationship between economic growth to the energy diversification. In this paper a relationship between economic growth and energy diversification (Energy Mix Concentration Index) has been developed for the year of 200–2020. From this relationship, it is observable that most diversified outcome can be achievable for the next fiscal year by analyzing the past year reports. The of Effort has given in this review to assess the current state of the energy sector in Bangladesh with a specific focus on energy diversification and initiatives for clean energy production. The potential for clean energy sources and technologies to reduce carbon emissions were discussed. The evolution of energy diversification was evaluated using the energy mix concentration index. Providing empirical evidence for energy policies to enhance innovation skills in the renewable energy industry might help Bangladesh achieve long-term development in energy sector.

Background: The numbers of infections and death by COVID-19 have continued to rise worldwide and preventive practice has remained to be one of the most effective ways to prevent it. We conducted this study to survey the knowledge, attitude, perception and practice of university students during the early stage of the pandemic in Bangladesh. Methods: This was a cross-sectional study with 305 students from Rajshahi University, conducted between March 11 and March 19, 2020. We developed a questionnaire for collecting information regarding COVID-19 following the instructions and guidelines of WHO and used frequency distribution, Kolmogorov-Smirnov test and Mann-Whitney and Kruskal-Wallis (non-parametric) tests for data analysis. Statistical significance was accepted at p < 0.05. Results: The knowledge of the students on COVID-19 was of average level; however, more than one-third of them were unwilling to avoid public transport and travel public places with friends and family. Their practice of preventive measures was not at a satisfactory level. More than one-third of them were not keen to stay home and not hesitant to visit crowded places. About half of the students thought the pandemic would not affect their daily activities, and most of them felt it would not affect their study, financial status, fieldwork of their course, or meeting with their friends and relatives. Female students were found more knowledgeable than male ones. Conclusions: The general knowledge of students had an average level on COVID-19, but their attitude, perception and preventive practices were not consistent with their level of education. The authorities should consider the findings while promulgating policies and implementing measures for the prevention of COVID-19. Doi: 10.28991/SciMedJ-2021-03-SI-9 Full Text: PDF

Urban flooding in Chittagong City usually occurs during the monsoon season and a rainwater harvesting (RWH) system can be used as a remedial measure. This study examines the feasibility of rain barrel RWH system at a distributed scale within an urbanized area located in the northwestern part of Chittagong City that experiences flash flooding on a regular basis. For flood modeling, the storm water management model (SWMM) was employed with rain barrel low-impact development (LID) as a flood reduction measure. The Hydrologic Engineering Center's River Analysis System (HEC-RAS) inundation model was coupled with SWMM to observe the detailed and spatial extent of flood reduction. Compared to SWMM simulated floods, the simulated inundation depth using remote sensing data and the HEC-RAS showed a reasonable match, i.e., the correlation coefficients were found to be 0.70 and 0.98, respectively. Finally, using LID, i.e., RWH, a reduction of 28.66% could be achieved for reducing flood extent. Moreover, the study showed that 10%–60% imperviousness of the subcatchment area can yield a monthly RWH potential of 0.04–0.45 m3 from a square meter of rooftop area. The model can be used for necessary decision making for flood reduction and to establish a distributed RWH system in the study area.

Bisphenol- A (BPA) is a multi-functional chemical produced in large volumes that is extensively used primarily in the manufacture of polycarbonate plastics and epoxy resins. Black seed oil (BSO) has been employed for many century as one of the main sources of nutrition and healthcare both for humans and animals. The study was carried out to investigate the effects of BPA and BSO on blood, hormone profile, and histopathological changes of ovary in female mice. Total 15 female Swiss Albino mice, aged between 6-8 weeks with an average body weight of 27.4±1g were randomly divided into 3 groups (5 mice/group). Group A act as vehicle control. Group B was administered 50 mg BPA /kg bw daily, while group C received both BPA and BSO and dose rate was 50 mg BPA /kg/day and 1ml BSO /kg/day respectively. Results revealed that Hb concentration, TEC and PCV (p [ J Adv Biotechnol Exp Ther 2020; 3(1.000): 43-48]

Magnetic resonance imaging (MRI) is increasingly used to diagnose focal and diffuse liver disorders. Despite their enhanced efficacy, liver-targeted gadolinium-based contrast agents (GBCAs) raise safety concerns owing to the release of toxic Gd3+ ions. A π-conjugated macrocyclic chelate, Mn-NOTA-NP, was designed and synthesized as a non-gadolinium alternative for liver-specific MRI. Mn-NOTA-NP exhibits an r1 relaxivity of 3.57 mM−1 s−1 in water and 9.01 mM−1 s−1 in saline containing human serum albumin at 3 T, which is significantly greater than the clinically utilized Mn2+-based hepatobiliary drug, Mn-DPDP (1.50 mM−1 s−1), and comparable with that of GBCAs. Furthermore, the in vivo biodistribution and MRI enhancement patterns of Mn-NOTA-NP were similar to those of the Gd3+-based hepatobiliary agent, Gd-DTPA-EOB. Additionally, a 0.05 mmol/kg dose of Mn-NOTA-NP facilitated high-sensitivity tumor detection with tumor signal enhancement in a liver tumor model. Ligand-docking simulations further indicated that Mn-NOTA-NP differed from other hepatobiliary agents in their interactions with several transporter systems. Collectively, we demonstrated that Mn-NOTA-NP could be a new liver-specific MRI contrast agent.

The rapid increase in traffic volume on urban roads, over time, has altered the global traffic scenario. Additionally, it has increased the number of road crashes, some of which are severe and fatal in nature. The identification of hazardous roadway sections using the spatial pattern analysis of crashes and recognition of the primary and contributing factors may assist in reducing the severity of road traffic crashes (R.T.C.s). For crash severity prediction, along with spatial patterns, various machine learning models are used, and the spatial relations of R.T.C.s with neighboring areas are evaluated. In this study, tree-based ensemble models (gradient boosting and random forest) and a logistic regression model are compared for the prediction of R.T.C. severity. Sample data of road crashes in Al-Ahsa, the eastern province of Saudi Arabia, were obtained from 2016 to 2018. Random forest (R.F.) identifies significant features strongly correlated with the severity of the R.T.C.s. The analysis findings showed that the cause of the crash and the type of collision are the most crucial elements affecting the severity of injuries in traffic crashes. Furthermore, the target-specific model interpretation results showed that distracted driving, speeding, and sudden lane changes significantly contributed to severe crashes. The random forest (R.F.) method surpassed other models in terms of injury severity, individual class accuracies, and collective prediction accuracy when using k-fold (k = 10) based on various performance metrics. In addition to taking into account the machine learning approach, this study also included spatial autocorrelation analysis based on G.I.S. for identifying crash hotspots, and Getis Ord Gi* statistics were devised to locate cluster zones with high- and low-severity crashes. The results demonstrated that the research area’s spatial dependence was very strong, and the spatial patterns were clustered with a distance threshold of 500 m. The analysis’s approaches, which included Getis Ord Gi*, the crash severity index, and the spatial autocorrelation of accident incidents according to Moran’s I, were found to be a successful way of locating and rating crash hotspots and crash severity. The techniques used in this study could be applied to large-scale crash data analysis while providing a useful tool for policymakers looking to improve roadway safety.

While extreme heat events received wider attention in recent years, understanding heatwave vulnerability is still a challenging task that requires a clear understanding of a range of location-dependent climatic, socio-economic, physiological, and environmental parameters. This study investigates the spatial distribution of heatwave vulnerability in Chattogram City Corporation (CCC) — the commercial capital of Bangladesh. A heatwave vulnerability index (HVI) was developed, including various parameters related to three latent variables — exposure, sensitivity, and adaptive capacity — derived using remote sensing and socio-economic data. Factor analysis was performed to assess all parameters related to HVI. Besides, spatial autocorrelation, cluster and outlier analyses, and hot spot analysis were carried out to investigate the spatial distribution of HVI. This study shows a spatial distribution of HVI in CCC, which is spatially associated with various individual parameters. A total of seven wards (smallest administrative zone of CCC) were found to be very highly vulnerable to the heatwave. This study further reveals that heatwave vulnerability is randomly distributed throughout the city, although the high land surface temperature is concentrated in the existing built-up areas. It also identified three major hot spots of heatwave vulnerability in CCC. The methodology and findings of this study will be of interest to the policymakers and city planners to prepare mitigation plans, policies, and strategies to mitigate this hazard.

Here, we describe the synthesis, characterization, and in vitro biological evaluation of a series of transition metal complexes containing benzothiazole aniline (BTA). We employed BTA, which is known for its selective anticancer activity, and a salen-type Schiff-based ligand to coordinate several transition metals to achieve selective and synergistic cytotoxicity. The compounds obtained were characterized by NMR spectroscopy, mass spectrometry, Fourier transform infrared spectroscopy, and elemental analysis. The compounds L, MnL, FeL, CoL, and ZnL showed promising in vitro cytotoxicity against cancer cells, and they had a lower IC50 than that of the clinically used cisplatin. In particular, MnL had synergistic cytotoxicity against liver, breast, and colon cancer cells. Moreover, MnL, CoL, and CuL promoted the production of reactive oxygen species in HepG2 tumor cell lines. The lead compound of this series, MnL, remained stable in physiological settings, and docking results showed that it interacted rationally with the minor groove of DNA. Therefore, MnL may serve as a viable alternative to platinum-based chemotherapy.

Road traffic crashes (RTCs) are a major problem for authorities and governments worldwide. They incur losses of property, human lives, and productivity. The involvement of teenage drivers and road users is alarmingly prevalent in RTCs since traffic injuries unduly impact the working-age group (15–44 years). Therefore, research on young people’s engagement in RTCs is vital due to its relevance and widespread frequency. Thus, this study focused on evaluating the factors that influence the frequency and severity of RTCs involving adolescent road users aged 15 to 44 in fatal and significant injury RTCs in Al-Ahsa, Saudi Arabia. In this study, firstly, descriptive analyses were performed to justify the target age group analysis. Then, prediction models employing logistic regression and CART were created to study the RTC characteristics impacting the target age group participation in RTCs. The most commonly observed types of crashes are vehicle collisions, followed by multiple-vehicle and pedestrian crashes. Despite its low frequency, the study area has a high severity index for RTCs, where 73% of severe RTCs include individuals aged 15 to 44. Crash events with a large number of injured victims and fatalities are more likely to involve people in the target age range, according to logistic regression and CART models. The CART model also suggests that vehicle overturn RTCs involving victims in the target age range are more likely to occur as a result of driver distraction, speeding, not giving way, or rapid turning. As compared with the logistic regression model, the CART model was more convenient and accurate for understanding the trends and predicting the involvement probability of the target age group in RTCs; however, this model requires a higher processing time for its development.

The data herein presented concerns the article entitled “Evaluation of hydrochemical properties and groundwater suitability for irrigation uses in southwestern zones of Jashore, Bangladesh”. Data was collected during 2018-2019 in the southwestern zones of Jashore, Bangladesh. One hundred groundwater samples (boreholes and tube wells) were collected to evaluate groundwater quality, using the irrigation water quality index (IWQI) as an indicator. Fourteen hydrochemical parameters (pH, EC, TDS, NO3N, pH, EC, Ca2+, Mg2+, Na+, K+, Cl−, HCO3−, SO42− and Fe2+) were used to calculate irrigation water quality indices (KI, Na%, PI, SAR, SSP, MH, and TH). Statistical methods such as Viper diagrams, USSL, and Wilcox diagrams were used to visualize datasets. The attained data can be used to assess the hydrogeochemistry of the sampled sites and groundwater quality for irrigation purposes. The findings of this work can be used in the optimization of management and treatment procedures and in the implementation of sustainable water development.

Abstract Background Salmonella spp. is one of the most common pathogens associated with fresh produce related foodborne illness. This study aimed to determine Salmonella spp. contamination level in betel leaf, internalization potential and possible decontamination process. Results A total of 77% betel leaf sample collected from local market was found to be contaminated with Salmonella spp. Of all the Salmonella spp. isolated and identified, 28.5% belong to Salmonella enterica subsp. enterica serovar Enteritidis, 19.5% belong to Salmonella Typhimurium, 15.6% to Salmonella Paratyphi, 10.4% to Salmonella Schottmuelleri, 9.1% to Salmonella Gallinarum, 10.4% to Salmonella Choleraesuis and 6.5% belong to Salmonella Bongori. Internalized Salmonella spp. showed moderate resistance to commonly used antibiotics. Treatment with common surface food disinfectants could not remove Salmonella spp. completely from betel leaf indicating the possibility that the bacteria may be in internal tissue of the leaf. Assessment of internalization potential showed that Salmonella spp. isolated from inner part of betel leaf pose better internalization potential (6.7–7.4 logCFU/gm) comparing with ATCC (American Type Culture Collection) strains (0.86–0.6 logCFU/gm). The isolates also showed better survivability in internalized condition, biofilm formation ability and motility than ATCC strains. Prevalence and expression of invasion (sefA and invA) and type 3 secretion system (TTSS) associated genes (hila, avrA and sopE) were high in internalized Salmonella isolates. Commercial disinfectants as well as H2O2 were found to have poor efficacy (log reduction around 2 CFU/gm) against internalized Salmonella. Ozonated water showed better decontamination efficiency (log reduction around 3 CFU/gm) whereas ethanolic extract of Terminalia arjuna stem bark showed higher decontamination (log reduction around 4.5 CFU/gm) of internalized Salmonella. Conclusion Salmonella spp. can ingress into betel leaf and better decontamination treatment is needed to be established.

We describe the synthesis, characterization, molecular modeling, and in vitro anticancer activity of three benzothiazole aniline (BTA) ligands and their corresponding platinum (II) complexes. We designed the compounds based on the selective antitumor properties of BTA, along with three types of metallic centers, aiming to take advantage of the distinctive and synergistic activity of the complexes to develop anticancer agents. The compounds were characterized using nuclear magnetic resonance spectrometry, Fourier transform infrared spectroscopy, mass spectrometry, elemental analysis, and tested for antiproliferative activity against multiple normal and cancerous cell lines. L1, L2, and L1Pt had better cytotoxicity in the liver, breast, lung, prostate, kidney, and brain cells than clinically used cisplatin. Especially, L1 and L1Pt demonstrated selective inhibitory activities against liver cancer cells. Therefore, these compounds can be a promising alternative to the present chemotherapy drugs.

The COVID pandemic forced higher educational institutions to transition from in-person lectures to online lectures. In Bangladesh, this transition was steep, with no prior experience of this mode of delivery. This study aimed to understand and compare the experiences of students, academic staff and support staff within the area of veterinary science in Bangladesh, when comparing in-person and online lectures. We conducted a mixed methods survey to learn about the perceived experiences, strengths and limitations of each mode of teaching from students and staff. Our findings highlighted that students, academic staff and support staff all preferred in-person lectures when compared to online lectures. However, the extent of preference varied, with support staff strongly favouring the in-person mode of delivery, followed by academic staff and then students. Overall, these findings are essential to aid policymakers and university authorities on some of the strengths and barriers of in-person and online lectures.

This study has been attempted to investigate the effects of garlic and turmeric on blood cholesterol level. A total of 20 male guinea pigs of unknown breed weighing 400–600 g were randomly divided into four equal groups (A, B, C, D) with free access to food and water for 30 days. Three groups of animals (B, C and D) were fed either with turmeric or garlic supplemented basal diet and were defined as treated groups while remaining one group (A) was fed only with basal diet and considered control group. The body weight increased significantly (p>0.01) in all the animals with the advancement of age. The administration of turmeric or garlic or both reduced the blood cholesterol level of the animals significantly (p>0.01). At the end of the experiment, the animals were killed and liver was examined for gross and histological changes. Some gross lesions were identified in liver of treated animals but there were no histological changes.

In this study, we explored the optimal performance of perovskite solar cells (PSCs) using the tin-halide material Rb2SnI6. This study focuses exclusively on the electrical properties of the devices, as simulated using SCAPS-1D software (solar capacitance simulator). The SCAPS-1D was employed to improve the device in the Rb2SnI6-based PSC, which utilized tungsten disulfide (WS2) as the electron transport layer and cadmium telluride (CdTe) as the hole transport layer (HTL). To identify the most suitable electron transport layer (ETL), we initially investigated WS2, SnS2, PCBM, and C60. The ITO/WS2/ Rb2SnI6/CdTe/Ni structure proved to be the most effective ETL after extensive investigation, demonstrating a power conversion efficiency (PCE) of 24.95%, a Voc of 1.0896 V, a Jsc of 44.6795 mA cm2, and an FF of 82.71%. Subsequently, we evaluated the impact of the absorber thickness, ETL thickness, and defect density on the device's effectiveness in the Rb2SnI6, WS2, and CdTe layers. We further investigated the effect of adjusting the interfacial defect densities at the CdTe/Rb2SnI6 and Rb2SnI6/WS2 interfaces to optimize the device's capabilities further. Additionally, we examined the proposed PSC's quantum efficiency (QE), current density–voltage (J-V), shunt resistance, series resistance, capacitance–voltage, working temperature, and generation-recombination parameters. The results of these simulations provide valuable information for the excellent scientific fabrication of an inorganic PSC that is based on Rb2SnI6. [ABSTRACT FROM AUTHOR]