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Customer attrition in the banking industry occurs when consumers quit using the goods and services offered by the bank for some time and, after that, end their connection with the bank. Therefore, customer retention is essential in today’s extremely competitive banking market. Additionally, having a solid customer base helps attract new consumers by fostering confidence and a referral from a current clientele. These factors make reducing client attrition a crucial step that banks must pursue. In our research, we aim to examine bank data and forecast which users will most likely discontinue using the bank’s services and become paying customers. We use various machine learning algorithms to analyze the data and show comparative analysis on different evaluation metrics. In addition, we developed a Data Visualization RShiny app for data science and management regarding customer churn analysis. Analyzing this data will help the bank indicate the trend and then try to retain customers on the verge of attrition.

The rapid growth of urbanization and motorization has significantly increased traffic crashes, leading to both loss of life and diminished quality of life for crash survivors and their families. Identifying the factors influencing crash fatality is crucial for reducing such incidents. However, traffic crashes are inherently unpredictable, and crash fatality datasets are often imbalanced. This study provides a comprehensive evaluation of various machine learning (ML) techniques to analyze traffic crash fatality using an imbalanced dataset. It is the first to train eight distinct binary classification models: Classification and Regression Trees (CART), Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boost (XGBoost), Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes (NB) under three strategies: in isolation, with bagging, and with optimized bagging techniques (Grid Search CV, Random Search CV, and Bayesian Optimization). To handle data imbalance, eight resampling methods were employed, including SMOTE, Random Under-sampling (RUS), Random Over-sampling (ROS), ADASYN, Tomek Links, Near Miss, SMOTETomek, and SMOTEENN. Results show that GBM, combined with Bayesian optimized bagging and RUS, achieved the best performance with a G-mean score of 65.23 and an F1 score of 60.06. This study offers valuable insights into effective ML techniques, data resampling methods, and advanced optimization strategies for imbalanced crash severity datasets.

The dataset consists of survey data on pedestrian crosswalk usage behavior in high-density urban areas of a developing country, specifically collected from Dhaka, the capital city of Bangladesh. Data were gathered through a questionnaire survey conducted at twelve key locations, covering eight attributes related to crosswalk behavior and the demographic details of respondents. The survey yielded 682 valid responses, focusing on factors such as the suitability of crosswalk locations, guard rails, and lighting. The dataset is structured to support analyses using supervised machine learning techniques, facilitating reproducibility, secondary analysis, and policy development for pedestrian safety improvements. Furthermore, the dataset can be reused for cross-validation of future studies, comparison with pedestrian behavior in similar urban settings, and the development of predictive models to enhance pedestrian infrastructure in other developing regions.

Pedestrians are the most vulnerable road users and are over-represented in casualty statistics, particularly in low- and middle-income countries like Bangladesh. To ensure the safety of pedestrians, it is necessary to identify the factors underlying pedestrian behavior while crossing. Hence, this study aims to predict the pedestrian decision regarding crosswalks using supervised machine learning techniques namely, Classification and Regression Tree (CART), Random Forest (RF), and Extreme Gradient Boost (XGBoost). A questionnaire survey was conducted in twelve important locations of Dhaka, Bangladesh using 8 attributes related to crosswalk behavior. Analysis suggests RF model is the most effective in terms of prediction performances, specifically having a 96.00% F1 score and 95.83% MCC value. It has been found that unsuitability of crosswalk location, absence of guard rails on median, and inadequate lightning at night near crosswalks are the most important features for preferring to use crosswalks. The findings of the study will help policymakers and transport planners to plan accordingly in order to develop safe crosswalks.

Flash drought is characterized by rapid onset and short-duration drought conditions caused by a combination of factors, including high evaporation, high temperature, and prolonged periods of little to no precipitation, leading to a sudden and severe decrease in soil moisture levels. In comparison to conventional drought, it is more susceptible to the effects of global warming and has the potential to become a common drought phenomenon in the coming years, necessitating further research. In this paper, we focused on flash drought events, specifically in forest parts of northeastern China that are included within the Greater Khingan Mountains (GKM), Lesser Khingan Mountains (LKM), and Changbai Mountains (CM), using daily soil moisture data as well as SPOT- VEGETATION NDVI satellite data from 2000 to 2020 and determined their impact on the forest NDVI. Our major findings are as follows. (1) The forest within GKM had the maximum area being affected by flash drought events. (2) The frequency ranged from 1 to 2 times, whereas the total duration varied between 20 and 55 days over the study area in a 21-year period. (3) Flash drought was most common in the plant-growing seasons. (4) The flash drought events had a negative influence on the forest NDVI. Our study contributes to a deeper understanding of the flash drought dynamics in forest areas of northeast China for flash drought monitoring, prediction, and management strategies in this region.

The regulation of stand density has been studied in specific aspects of plantations with different age categories. A clearer understanding is still required of the extent to which stand density impacts multiple plantation attributes such as tree growth, understory vegetation diversity, and soil properties in Korean pine (Pinus koraeinsis Sieb. et Zucc) plantations. This study utilized a 43-year-old middle-aged Korean pine plantation in Qingping Forest Farm in northeast China to answer the research question posed. Three stand density levels, low stand density (LSD, 716 trees/ha), medium stand density (MSD, 850 trees/ha), and high stand density (HSD, 916 trees/ha) were studied for their effects on plantation attributes enumerated above. The results revealed a significant (p < 0.05) effect of stand density on mean stand volume (m3). Medium stand density had the highest mean stand volume of 26.16 (±0.27) m3 while the lowest stand volume was recorded for the low stand density at 14.90 (±1.72) m3. Also, significant differences in total nitrogen, total potassium, available nitrogen, available potassium, and percentage soil moisture content were observed among stand density levels. Additionally, stand density was found to have significant effects on species richness, Shannon–Wiener, and Simpson’s diversity indexes for the shrub and herb layers of the plantation. This study shows that a medium stand density is the most optimal for promoting tree growth and understory biodiversity, as well as enhancing the status of certain soil nutrients. Thus, medium density achieves a balance between growth, nutrient availability, and biodiversity in middle-aged Korean pine plantations after a short period of thinning. These findings provide valuable knowledge for forest management, emphasizing the significance of continuous, long-term, and site-specific research.

Building a well-rounded roster is crucial in sports, as it directly impacts a team's long-term success. However, the process can be complex, with various factors to consider, such as team dynamics, player skills, and financial limitations. This study aims to develop effective strategies for constructing teams in different sports, starting with American Football in the NFL and potentially expanding to other similar disciplines. In this paper, we propose an innovative tool that leverages genetic algorithm, signifying a method that balances between overall team positional strength, fosters collective team success, and encourages team's long-standing well-being. This data-driven approach also presents a generalizable solution that can be employed across similar team sports, transforming the pattern of roster optimisation strategies and team's performance augmentation.

This study aimed to develop a geochemical database by thoroughly analyzing groundwater and sediments from coastal aquifers of southwest Bangladesh. Moreover, we investigated the source of sediment deposition and the mechanisms behind the presence of arsenic and salinity in groundwater. The seasonal distribution patterns of arsenic among the shallow and deep coastal aquifers were found to be 45.12 µg/l and 20.65 µg/l during dry and wet seasons, respectively. Moreover, the groundwater salinity distribution ranged from 3262.88 mg/l to 1930.88 mg/l during the dry and wet seasons. Cored sediment samples showed fine to medium sands of 92%, with silt and clay particles. The petrographic study of authigenic and heavy minerals revealed that the mineral grains were subangular to angular, indicating their textural immaturity of coastal sediments. The reactivity of goethite (FeOOH) and siderite (FeCO3) minerals suggests that the aquifers were subjected to slightly oxidized to moderately reducing conditions, with ORP values ranging from +50.40 mv to −149.5 mv. Such redox conditions could potentially result in the enrichment and mobility of arsenic in the groundwater. Although arsenic concentrations in deep aquifers are relatively low, higher salinity values are found in both shallow and intermediate coastal aquifers.

Forest ecosystems are essential to the global carbon cycle because they are the biggest terrestrial carbon reserves. In the management of forests, thinning is a commonly employed strategy, impacting the respiration and biomass loss of trees, thereby modifying forest carbon dynamics. However, there is a lack of scientific research to confirm the impacts of thinning intensities on carbon storage in trees, soil layers, shrubs, and ground vegetation layers as well as its impact on wood production and growth rate. The goal of this study was to find the optimal thinning levels for increasing carbon sequestration during the growth stage of the Korean pine (Pinus koraiensis) middle-aged plantations in Northeast China. In this study, thinning intensity (0, 10, 11, 16, 18, and 22%) affected the carbon storage of trees, tree growth, volume, and, we suspected, soil layer, shrubs, and vegetation (herbs, litter, and grass) also. Specifically, after four years of thinning, the 18% treatment significantly increased total carbon storage, individual organ storage, growth, and tree volume (p < 0.05). These results give us abundant information about how thinning affects the dynamics of carbon storage, wood production, and the interactions between soil and plants in P. koraiensis plantations, contributing to multi-objective management strategies for optimizing carbon sequestration, wood production, and ecosystem health.

Fine root decomposition is a physio-biochemical activity that is critical to the global carbon cycle (C) in forest ecosystems. It is crucial to investigate the mechanisms and factors that control fine root decomposition in forest ecosystems to understand their system-level carbon balance. This process can be influenced by several abiotic (e.g., mean annual temperature, mean annual precipitation, site elevation, stand age, salinity, soil pH) and biotic (e.g., microorganism, substrate quality) variables. Comparing decomposition rates within sites reveals positive impacts of nitrogen and phosphorus concentrations and negative effects of lignin concentration. Nevertheless, estimating the actual fine root breakdown is difficult due to inadequate methods, anthropogenic activities, and the impact of climate change. Herein, we propose that how fine root substrate and soil physiochemical characteristics interact with soil microorganisms to influence fine root decomposition. This review summarized the elements that influence this process, as well as the research methods used to investigate it. There is also need to study the influence of annual and seasonal changes affecting fine root decomposition. This cumulative evidence will provide information on temporal and spatial dynamics of forest ecosystems, and will determine how logging and reforestation affect fine root decomposition.

Despite the high potential for microplastics (MPs) pollution in Bangladesh, the presence of MPs in the industrial region has largely been unexplored in Bangladesh. So, this study was conducted to determine whether MP pollution is prevalent in the industrial soil of Bangladesh and the extent of its toxicity. To examine MPs, a total of 12 soil samples were collected from the industrial region of Narayanganj, and a stereoscopic microscope was used to visually identify the MPs. Prior to that the technique of density separation and sieving was applied to extract MPs from those 12 soil samples. Among the twelve investigated samples, a total of 151 MPs (Mean: 12.6 ± 7.9 particles kg−1) were identified, which were mostly white and ranged in size from 0.5 to 1 mm. Different types of MPs according to their shapes such as fibers (60.3%), fragments (19.2%), films (10.6%), and foam (9.9%) have been detected. 7 MPs (Mean: 0.58 ± 0.79) have been found in 3 urban farmland sites, 15 MPs (Mean: 1.87 ± 1.81) in two near metropolitan areas, and 129 MPs (Mean: 4.6 ± 4.39) in 7 industrial locations. Five polymers were identified by μ-FTIR, among which Polyamide predominated, followed by Polypropylene. According to risk assessments, the region falls under hazard categories II and III, suggesting a moderate to high risk. This paper gives thorough information on the toxicity of MP in an industrial location; therefore, it may be useful in the development of effective methods to address environmental issues.

DNA damage is a critical factor contributing to genetic alterations, directly affecting human health, including developing diseases such as cancer and age-related disorders. DNA repair mechanisms play a pivotal role in safeguarding genetic integrity and preventing the onset of these ailments. Over the past decade, substantial progress and pivotal discoveries have been achieved in DNA damage and repair. This comprehensive review paper consolidates research efforts, focusing on DNA repair mechanisms, computational research methods, and associated databases. Our work is a valuable resource for scientists and researchers engaged in computational DNA research, offering the latest insights into DNA-related proteins, diseases, and cutting-edge methodologies. The review addresses key questions, including the major types of DNA damage, common DNA repair mechanisms, the availability of reliable databases for DNA damage and associated diseases, and the predominant computational research methods for enzymes involved in DNA damage and repair.

Wearable antennas have grown in popularity in recent years as a result of their appealing features and prospects to actualize lightweight, compact, low-cost and adaptable wireless communications and surroundings. These antennas have to be conformal and made of lightweight materials in a low-profile arrangement when attached to various parts of the human body. Near-body operation of these antennas should be possible without degradation. When these characteristics are taken into account, the layout of wearable antennas become challenging, especially when textile substrates are investigated, high conductivity materials are used during manufacturing procedures and body binding scenarios have an impact on the design’s performance. Several of these issues arise in the context of body-worn deployment, despite modest changes in magnitude between implementations. This paper examines the multiple issues and obstacles encountered in the construction of wearable antennas as well as the range of materials used, and the Specific Absorption Rate (SAR) effects employed as well as the bending scheme. An overview of the innovative features and their separate approaches to addressing the difficulties lately raised by work in this field conducted by the scientific community is provided as an appendix.

Microwave sensors offer appealing features such as susceptibility, quick response, and non-invasiveness, making them valuable tools for highly accurate measurements of material characterisation. A wide range of techniques, including cavity waveguide, planar transmission line, cavity waveguide perturbation, open-ended coaxial probe, and free-space transmission, have been employed to characterise materials that are essential for their cost-effectiveness, ease of manufacturing, high sensitivity, good quality factor (Q-factor), and compact size, allowing them to be applied to different material types. Among the microwave sensor types, the substrate-integrated waveguide (SIW) has emerged as a promising technology in order to characterise materials in an efficient manner. This paper presents a review of the current state and potential opportunities of SIW microwave sensors in the characterisation of dielectric materials. It provides insights into various design principles, techniques, and applications of SIW microwave sensors across different sectors, highlighting their advantages and limitations compared to conventional waveguide-based sensors. Furthermore, the paper summarises several fabrication methods that can be implemented for SIW microwave sensors to enable the production of efficient and reliable sensors. Additionally, the future directions provided in this paper aim to contribute to the ongoing development and optimisation of SIW-based microwave sensors for accurate and efficient dielectric material characterisation. Overall, this review article serves as a beneficial resource for new researchers seeking to understand the role of SIW microwave sensors in material characterisation. It outlines the current status, opportunities, and potential advancements of SIW sensors, shedding light on their significance and potential impact in the field of material characterisation.

Globally, with the growing importance of mangroves for providing ecosystem services and mitigating climate change, it is still uncertain whether planted mangroves can be the counterpart of natural mangroves, and thus the role of planted mangroves is still less understood. Therefore, this study aimed to assess the stand structure and carbon storage of a young mangrove plantation in Satkhira District, Bangladesh and to compare it with the natural mangrove forest (i.e., Sundarbans) to understand the potential of this young mangrove plantation as a nature-based solution to climate change. In addition, to better understand the spatial dynamics of afforested mangrove forests, we investigated and compared both above and below-ground carbon stocks between the landward and riverward sites. We collected both above (height and diameter at breast height, DBH) and belowground (i.e., coarse and fine root) data from 16 plots with a total area of 1600 m2 in two paired transects, eight plots each in landward and riverward sites. Aboveground carbon stocks were estimated using the species-specific allometric models from the biophysical tree parameters (i.e., height and DBH). Besides, belowground root (≥ 20 mm) carbon was measured by direct weighting after collecting soil samples through coring. Stand structural attributes (i.e., density, basal area, and DBH) showed a significant difference between the two sites, whereas Sonneratia apetala was found as the dominant species (IV = 188.7∼207.1) in both forest sites. The mean carbon stock of this plantation was 49.1 Mg C ha−1, while the mean aboveground carbon (AGC) was 37.3 Mg C ha−1. The landward site contributed significantly more AGC (40.1 Mg C ha−1) than the riverward (34.4 Mg C ha−1) site (p

Pollution generated by microplastics (MPs) has become an issue of global concern because to its severe effects on the general health of the ecosystem, especially the health of the terrestrial environment. There is a scarcity of data based on MP contamination research in Bangladesh that is currently available, and no work on MP contamination has previously been done in an industrial region of Bangladesh. As a result, this research was undertaken with the aim of determining whether or not MP contamination is present in the industrial area of the Barapukuria region in Bangladesh. The method of sieving and density separation was used in the process of extracting MPs from a total of 12 soil samples that were collected from the industrial area of Barapukuria. A stereomicroscope was utilized to accomplish the visual identification of the MPs. The method of sieving and density separation was used in the process of extracting MPs from 12 soil samples that were gathered from the industrial area of Barapukuria. A stereomicroscope was utilized in order to accomplish the visual identification of the MPs. The concentration of MPs accounted for 1–15 items/100g (Mean: 6.75 ± 5.3) in the 12 sampled regions, mostly white in color and ranging in size from 0.5–1 mm. Fibers have been found to be the most prevalent among the detected MPs (films, fiber, foam, and fragments). 8-types of MPs (Mean: 0.32 ± 0.69) were detected in 5 rural farmland locations, 11 MPs (Mean: 1.1 ± 1.73) in 2 sub-urban farmland sites, 11 MPs (Mean: 2.2 ± 3.19) in 1 urban farmland site, 24 MPs (Mean: 2.4 ± 1.89) in 2 industrial locations, and 27 MPs (Mean: 2.7 ± 3.05) in 2 near metropolitan areas. Based on the land use land cover analysis, higher contamination of MPs have been detected in the industrial and coal mine region of Barapukuria whereas relatively lower amount of MPs have been found in the rural and urban regions.

Rapid increase in scholarly publications on the web has posed a new challenge to the researchers in finding highly relevant and important research articles associated with a particular area of interest. Even a highly relevant paper is sometimes missed especially for novice researchers due to lack of knowledge and experience in finding and accessing the most suitable articles. Scholarly recommender system is a very appropriate tool for this purpose that can enable researchers to locate relevant publications easily and quickly. However, the main downside of the existing approaches is that their effectiveness is dependent on priori user profiles and thus, they cannot recommend papers to the new users. Furthermore, the system uses both public and non-public metadata and therefore, the system is unable to find similarities between papers efficiently due to copyright restrictions. Considering the above challenges, in this research work, a novel hybrid approach is proposed that separately combines a Content Based Filtering (CBF) recommender module and a Collaborative Filtering (CF) recommender module. Unlike previous CBF and CF approaches, public contextual metadata and paper-citation relationship information are effectively incorporated into these two approaches separately to enhance the recommendation accuracy. In order to verify the effectiveness of the proposed approach, publicly available datasets were employed. Experimental results demonstrate that the proposed approach outperforms the baseline approaches in terms of standard metrics (precision, recall, F1-measure, mean average precision, and mean reciprocal rank), indicating that the proposed approach is more efficient in recommending scholarly publications.

With rapid technological progress in the Internet of Things (IoT), it has become imperative to concentrate on its security aspect. This paper represents a model that accounts for the detection of botnets through the use of machine learning algorithms. The model examined anomalies, commonly referred to as botnets, in a cluster of IoT devices attempting to connect to a network. Essentially, this paper exhibited the use of transport layer data (User Datagram Protocol - UDP) generated through IoT devices. An intelligent novel model comprising Random Forest Classifier with Independent Component Analysis (ICA) was proposed for botnet detection in IoT devices. Various machine learning algorithms were also implemented upon the processed data for comparative analysis. The experimental results of the proposed model generated state-of-the-art results for three different datasets, achieving up to 99.99% accuracy effectively with the lowest prediction time of 0.12 seconds without overfitting. The significance of this study lies in detecting botnets in IoT devices effectively and efficiently under all circumstances by utilizing ICA with Random Forest Classifier, which is a simple machine learning algorithm.

Contamination of electroencephalogram (EEG) signals due to natural blinking electrooculogram (EOG) signals is often removed to enhance the quality of EEG signals. This paper discusses the possibility of using solely involuntary blinking signals for human authentication. The EEG data of 46 subjects were recorded while the subject was looking at a sequence of different pictures. During the experiment, the subject was not focused on any kind of blinking task. Having the blink EOG signals separated from EEG, 25 features were extracted and the data were preprocessed in order to handle the corrupt or missing values. Since spontaneous and voluntary blinks have different characteristics in terms of kinematic variables and because the previous studies’ control setup may have altered the type of blink from spontaneous to voluntary, a series of statistical analysis was carried out in order to inspect the changes in the multivariate probability distribution of data compared to the previous studies. Statistical significance shows that it is very likely that the blink features of both voluntary and involuntary blink signal are generated by Gaussian probability density function, although different than voluntary blink, spontaneous blink is not well discriminated with Gaussian. Despite testing several models, none managed to classify the data using only the information of a single spontaneous blink. Thereby, we examined the possibility of learning the patterns of a series of blinks using Gated Recurrent Unit (GRU). Our results show that individuals can be distinguished with up to 98.7% accuracy using only a reasonably short sequence of involuntary blinking signals.

The burgeoning growth of Big Data not only matures and improves the data management efficiency and useful information extraction techniques, but also motivates the computational science researchers to come up with a new method or solution that can be repurposed for problems across the domain. Computational Sustainability joins this movement for a transferrable computational technique for sustainable development and a better future. Internet-of-energy (IoE) - leveraging IoT to smart grids associated with advanced analytics - is one of the prominent efforts in this regard. This paper presents a qualitative analysis on the elements of the energy and power management ecosystem in the United States. This qualitative study includes the Grid Overview of the United States; Weather and Climate and its impact on the entire energy generation and consumption dynamics; Peak Load Forecasting and its techniques and burgeoning challenges; Variable Renewable Energy, its reliability challenges and how we can take advantage of this variability; Commodity Prices and its criticality; Energy Disaggregation and its impact on consumption-awareness; and Generation Expansion and Decision Analysis. Besides, IoE integration, associated trade-offs, challenges, research opportunities and transferable computational techniques are addressed in this communication. Furthermore, schematics and quantitative analysis are presented in support of this study.

Researchers face difficulties in finding relevant papers to their research interest as the number of scientific publication is rapidly increasing on the web. Scientific paper recommenders have emerged as a leading solution to help researchers by automatically suggesting relevant and useful publications. Several approaches have been proposed on improving recommender systems. However, most existing approaches depend on priori user profiles, and thus they cannot recommend papers to new user. Furthermore, the existing approaches utilize non-public contextual information, and thus it cannot adequately find similarities between papers due to copyright restrictions. Also, the existing approaches consider only single level paper-citation relation to identify similarities between papers. Considering the above challenges, this paper presents a collaborative filtering based recommendation approach for scientific papers that does not depend on priori user profiles and which utilizes only public contextual information. Using citation context, we utilized 2-level paper-citation relations to find hidden associations between papers. The rational underlying this approach is that, two papers are co-occurred with same cited paper(s) and two papers are co-occurring with same citing paper(s) are significantly similar to some extent. To evaluate the performance of the proposed approach, publicly available datasets are used to conduct extensive experiments. The experimental results demonstrate that the proposed approach has significantly outperforms the baseline approaches in terms of precision, recall, F1, mean average precision, and mean reciprocal rank, which are commonly used information retrieval metrics. The novelty of this study is that, with the proposed approach, researchers are able to find relevant and useful publications over the internet regardless of their previous research experiences and research area.

Groundwater is a vital source of fresh water for drinking and agricultural purposes in the region of Hatiya Island, Bangladesh, yet it is subject to contamination by natural and anthropogenic activities. Over time, the contamination has resulted in a variety of negative effects on human health. This research aims to examine groundwater hydrogeochemical characterization and suitability for irrigation and drinking purposes on different indices utilizing a combined geochemical method, multivariate statistical approaches, and geospatial analysis. Results of hydrogeochemical analyses of 15 groundwater samples from the intermediate aquifer (60–90 m depth) represent the order of cations, anions, and metal concentrations as Na+ > Mg2+ > Ca2+ > K+, Cl− > SO42− > HCO3− > NO3−, and Fe > I > Br > Mn > As, respectively. Saltwater intrusion has been determined to be the most important hydrogeochemical process influencing the chemistry of groundwater. Principal component analysis, Pearson’s correlation matrix, and hierarchical cluster analysis indicate that groundwater quality is highly governed by geogenic influences. This information has been evaluated using a drinking water quality index map, which found that six (40%) of the groundwater samples are suitable for human consumption, whereas the other nine (60%) are unfit for drinking. The analysis of irrigation quality parameters revealed that eight (54%) of the groundwater samples are in excellent or good condition for agricultural uses; however, the other seven (46%) samples are unsuitable.

Bagasse is produced as a waste in the sugar production process, which is used as fuel to stoke boilers in the sugar mills. The concluding product of this burning is residual sugarcane bagasse ash (BA), which is normally dumped or used as low-quality fertilizer. The ash for this study was collected from a reputed sugar mill located in the northern region of Bangladesh. Type I Portland cement (PC) was partially replaced with that finely ground bagasse ash without any pretreatment. The ground BA was used as a replacement for Portland cement at 5, 10, 15, 20, 25 and 30% of BA, respectively, in structural mortar. In addition, chemical characterization, specific gravity, X-ray diffraction (XRD), scanning electron microscopy (SEM), setting time, a strength activity index, compressive strength, water absorption, density and durability in a chloride environment of mortar were determined. The strength activity index result indicates that the used BA has the pozzolanic properties to be used as a partial cement replacement. The results showed that, at the age of 56 days, the mortar samples containing 5–15% ground bagasse ash had higher compressive strengths than the control mixture (mortar without ground bagasse ash). Mortar containing 15% ground bagasse ash had the highest mechanical and durability properties. Therefore, the substitution of 15% BA is acceptable for producing good quality structural mortar in the civil engineering construction field except in chloride environments.

The alarmingly aggravating incidents of sepsis and septic shock, and associated mortality, morbidity, and annual treatment costs among ICU admissions are an increasing concern. SepINav is a medical informatics endeavor that helps ICU practitioners and researchers to monitor and intervene on the existing sepsis patients more efficiently and interactively and conduct retrospective studies to seek rationales to different sepsis scenarios in the ICU. Moreover, Bayesian Online Changepoint Detection will help the practitioners understand the structural changes in patients’ vital sign regimes that may harbinger prior to septic shock. Besides, several additional features are added to this data-driven software tool to promise efficient monitoring and intervention and address confounding medical interventions in the ICU.

Autism Spectrum Disorder (ASD) is a critical problem worldwide; however, low and middle-income countries (LMICs) often suffer more from it due to the lack of contextual research and effective care infrastructure. Moreover, ASD in LMICs offers unique challenges as cultural misperceptions and social practices often impede effective care there. However, the recent growth in mobile phone adoption in many LMICs has created a timely opportunity for improving ASD care practices through digital means. We present qualitative findings on the challenges of designing mobile assistive technologies for ASD in Bangladesh. Our data reveals a set of technical, social, and cultural challenges with caregivers and care professionals for designing a meaningful technology for them. We show how those challenges are deeply rooted in the broader infrastructural and cultural tensions of the country. We also present embedded design implications to overcome these challenges.

Biodiesel from green energy source is gaining tremendous attention for ecofriendly and economically aspect. In this investigation, a two-step process was developed for the production of biodiesel from microalgae Spirulina maxima and determined best operating conditions for the steps. In the first stage, acid esterification was conducted to lessen acid value (AV) from 10.66 to 0.51 mgKOH/g of the feedstock and optimal conditions for maximum esterified oil yielding were found at molar ratio 12:1, temperature 60°C, 1% (wt%) H2SO4, and mixing intensity 400 rpm for a reaction time of 90 min. The second stage alkali transesterification was carried out for maximum biodiesel yielding (86.1%) and optimal conditions were found at molar ratio 9:1, temperature 65°C, mixing intensity 600 rpm, catalyst concentration 0.75% (wt%) KOH for a reaction time of 20 min. Biodiesel were analyzed according to ASTM standards and results were within standards limit. Results will helpful to produce third generation algal biodiesel from microalgae Spirulina maxima in an efficient manner.

State departments of transportation recognize the need to incorporate pavement structural condition in their pavement performance models and/or decision processes used to select candidate projects for preservation, rehabilitation, or reconstruction at the network level. However, pavement structural condition data are costly to obtain. To this end, this paper develops and evaluates the effectiveness of two machine learning methods, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost), for predicting a flexible pavement's structural condition. The aim is to be able to predict whether a pavement section's structural condition is poor or not based on Annual Average Daily Traffic (AADT), truck percentage, and speed limit. The structural condition of a pavement is considered poor if the Surface Curvature Index (SCI12) is above 3.3. The models are developed using 950 miles of Traffic Speed Deflectometer (TSD) data collected along 8 primary routes in South Carolina. The performance of the machine learning models was compared with that of a logistic regression model. When the trained models are applied to the test data, the prediction results indicated that the XGBoost and RF models outperform the logistic regression model by 12% and 8%, respectively. XGBoost outperformed RF by 4%. With XGBoost found to be the best among the three models evaluated, its performance was examined using other poor structural condition threshold values; its prediction accuracy is found to be robust across the different scenarios. AADT and truck percentages are found to be significant factors whereas speed limit has no effect on a pavement's structural condition. [ABSTRACT FROM AUTHOR]

With rapid technological progress in the Internet of Things (IoT), it has become imperative to concentrate on its security aspect. This paper represents a model that accounts for the detection of botnets through the use of machine learning algorithms. The model examined anomalies, commonly referred to as botnets, in a cluster of IoT devices attempting to connect to a network. Essentially, this paper exhibited the use of transport layer data (User Datagram Protocol - UDP) generated through IoT devices. An intelligent novel model comprising Random Forest Classifier with Independent Component Analysis (ICA) was proposed for botnet detection in IoT devices. Various machine learning algorithms were also implemented upon the processed data for comparative analysis. The experimental results of the proposed model generated state-of-the-art results for three different datasets, achieving up to 99.99% accuracy effectively with the lowest prediction time of 0.12 seconds without overfitting. The significance of this study lies in detecting botnets in IoT devices effectively and efficiently under all circumstances by utilizing ICA with Random Forest Classifier, which is a simple machine learning algorithm. [ABSTRACT FROM AUTHOR]

Cholesterol granuloma is a rare benign tumour and is described as inflammatory granulation that occurs in response to the deposition of cholesterol crystals. It is found most commonly in the paranasal sinuses or temporal bones, but there are also rare reports of their occurrence in the peritoneum, parotid gland, lymph nodes, thyroglossal duct, kidney, liver, and spleen. Involvement of the ribs has rarely been described previously. We report a case of cholesterol granuloma involving chest wall in second intercostals space of a 74-year-old male who presented with a slowly growing lesion of the anterior aspect of right side of the chest. [ABSTRACT FROM AUTHOR]

In this letter, the performance of a 25-Gb/s $1 \times 2$ multimode interference (MMI) coupler-based monolithic differential phase shift keying (DPSK) receiver integrated with Germanium (Ge) on silicon-on-insulator (SOI) photodetector (PD) is presented. Theoretical analysis suggests that the device exhibits more uniform extinction ratio over the C-band compared with a DPSK demodulator based on 2\times 2$ MMI coupler. Electrical scattering parameters (S-parameters) investigated through component level simulation comparing the fabrication tolerance shows power penalty of 1.2 and 5 dB for unbalanced and balanced detection DPSK receiver, respectively. The SOI 1\times 2$ MMI coupler-based unbalanced DPSK receiver has a sensitivity of 0.41 dBm at a bit error rate of without post-electrical amplification. The PD integrated single-end detection-based receiver has a total footprint area of 0.39 mm ^{2} . [ABSTRACT FROM PUBLISHER]

In the original article, there is an error in Fig. 1. Following is the corrected Fig. 1. [ABSTRACT FROM AUTHOR]

Abstract Over the last decade, human resource (HR) analytics has been widely discussed in the landscape of human resource management due to its dynamic capacity to transform into a data-driven decision-making system for optimizing workforce management. The aim of this paper is to provide a comprehensive summary of the emerging trends and themes of HR analytics drawn from previous literature and offer valuable insight into academia, researchers, practitioners, and policymakers. This paper employs a bibliometric methodology while using RStudio, Biblioshiny, and VOSviewer tools to collect and analyze 102 articles from the Scopus database from January 2008 to September 2023. The findings of the paper reveal current state-of-the-art research in the HR analytics domain while exploring key themes and areas for further study. This study offers practical guidelines for policymakers and contributes to the existing knowledge domain of HR analytics.

The rapid evolution of Artificial Intelligence (AI) and its widespread adoption have given rise to a critical need for understanding the underlying factors that shape users' behavioral intentions. Therefore, the main objective of this study is to explain user perceived behavioral intentions and use behavior of AI technologies for academic purposes in a developing country. This study has adopted the unified theory of acceptance and use of technology (UTAUT) model and extended it with two dimensions: trust and privacy. Data have been collected from 310 AI users including teachers, researchers, and students. This study finds that users' behavioral intention is positively and significantly associated with trust, social influence, effort expectancy, and performance expectancy. Privacy, on the other hand, has a negative yet significant relationship with behavioral intention unveiling that concerns over privacy can deter users from intending to use AI technologies which is a valuable insight for developers and educators. In determining use behavior, facilitating condition, behavioral intention, and privacy have significant positive impact. This study hasn't found any significant relationship between trust and use behavior elucidating that service providers should have unwavering focus on security measures, credible endorsements, and transparency to build user confidence. In an era dominated by the fourth industrial revolution, this research underscores the pivotal roles of trust and privacy in technology adoption. In addition, this study sheds light on users' perspective to effectively align AI-based technologies with the education system of developing countries. The practical implications encompass insights for service providers, educational institutions, and policymakers, facilitating the smooth adoption of AI technologies in developing countries while emphasizing the importance of trust, privacy, and ongoing refinement.

Recently, clathrate hydrate-based CO2 separation is considered as one of the most attractive processes for reducing CO2 emissions because of its effective energy utilization, environmental friendliness, and economic viability. The ionic semi-clathrate hydrate, a quaternary salt used to facilitate hydrate formation, is particularly shown interest because it can boost CO2 capture by improving the physical and chemical interactions between host lattice and guest molecules for hydrate formation. A reduced pressure of 1 MPa or less is high enough for effective gas trapping at 280 K using a semi-clathrate hydrate. The operating parameters, ionic hydrate structure, and promoter concentration affect CO2 capture. The efficiency of the CO2 separation process can be significantly reduced by inhibitory effects at a particular salt concentration. Research has been conducted using tetra-n-butyl-ammonium and phosphonium salts because their crystal structure and morphology are favorable to form semi-clathrate hydrates. However, only a few lookups on environment-friendly and appropriate characterization strategies of the hydrates and novel promoters, and their design, and operability have been performed. This review addresses the mechanisms involving the size of CO2 molecules in an ionic hydrate network, the characterization methods of the hydrates, promoter integration and their overall performance analysis. In addition to that, operational strategies of the semi-clathrate hydrate-based CO2 capture processes, the drawbacks and future routes to research CO2 capture using semi-clathrate hydrates have been addressed.

Abstract This paper aims to investigate government policy intervention for mitigating the impacts of COVID-19 on small and medium enterprises (SMEs). This paper has utilized a methodology comprising of bibliometrics, network, and content analyses on 831 documents extracted from the Scopus database to explore the extant literature on this domain. A systematic literature review has also been performed to propose the government policy intervention during the crisis framework. The analysis and review discerned that government policy interventions yielding the greatest success display a pronounced industry focus, regardless of a country’s economic context. Notably efficacious interventions encompass government support for market access and promotion, regulatory flexibility, wage subsidies, supply chain bolstering, sustainable long-term support, training and development, digitization aid, and industry-specific access to credit and capital. Collaborative efforts between governments and financial institutions also proved instrumental in expediting SME recovery. Furthermore, initiatives like knowledge-sharing, capacity-building, digitalization of SMEs, and crisis management training played pivotal roles in ensuring SME survival during the COVID-19 pandemic. This paper has also provided guidelines for future research direction.