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Abstract Blue economy has the potential to promote economic growth, improve livelihoods, and create jobs while protecting marine ecosystems. This research uses a comprehensive analysis of secondary data sources to assess various blue economy sectors, including maritime transport, fisheries, aquaculture, offshore renewable energy, marine tourism, marine biotechnology, and ocean mining. By examining the blue economy experiences of developed nations like the United States, Canada, Japan, Norway, and Australia, the study identifies the best SDG practices and strategic lessons applicable to Bangladesh. In the case of Bangladesh, the research focuses on the blue economy initiatives, opportunities, and challenges associated with the Sustainable Development Goals (SDGs). The blue economy and SDGs nexus in the context of Bangladesh demonstrates that out of 17 goals, 12 SDGs (SDG 1, SDG 2, SDG 3, SDG 7, SDG 8, SDG 9, SDG 11, SDG 12, SDG 13, SDG 14, SDG 16 and SDG 17) are linked with blue economy practices in Bangladesh. However, in the case of developed countries, only six SDGs (SDG 7, SDG 8, SDG 9, SDG 12, SDG 13, SDG 14) are connected to the blue economy because of the diversity of blue economy practices across the countries. Situated along the Bay of Bengal, Bangladesh has significant potential to utilize its marine resources for sustainable development. However, it faces challenges such as inadequate infrastructure, regulatory gaps, environmental risks, and limited technological advancements. The study thus emphasizes the need for integrated policy frameworks, stakeholder coordination, investments in sustainable infrastructure, public–private partnerships, technological innovation, and community engagement.

Ensuring green, affordable, and available electricity is a challenge in the aftermath of the coronavirus pandemic. Meeting the criteria of Sustainable Development Goal 7 (SDG 7) and the pledges made at the Conference of Parties (COPs) is proving difficult for countries worldwide. Despite this, the untapped potential of the ocean as a readily available and reliable energy source remains largely unexplored. The South Asian Association for Regional Cooperation (SAARC) has been significantly impacted by energy crises. Surprisingly, countries in this area, despite having ocean renewable energy (ORE) potential, have been slow to embrace this option. This review focuses on summarizing the efforts and estimating the ORE potential in South Asia. It examines the future energy policies and plans of these countries in line with SDG 7 and COP commitments while also analyzing the barriers and drivers for ORE utilization. The review estimates that five SAARC nations have a wave energy reserve of over 41 GW, a tidal energy reserve of 8 GW, and an ocean thermal energy reserve of 31 GW. However, no studies have been conducted to estimate the salinity gradient energy. Academic and non-academic involvement in the sector is scarce in the region, and available data show inconsistencies. This work provides an overview of the current state of ocean energy research in and around the Indian Ocean, benefiting future researchers exploring the ORE potential of SAARC.

Abstract Hexachlorobutadiene (HCBD) is a concerning chemical that is included in the United States Toxic Substances Control Act, and the Stockholm Convention. Knowledge of the sources of HCBD is insufficient and is pivotal for accurate inventory and implementing global action. In this study, unintentional HCBD release and source emission factors of 121 full-scale industrial plants from 12 industries are investigated. Secondary copper smelting, electric arc furnace steelmaking, and hazardous waste incineration show potential for large emission reductions, which are found of high HCBD emission concentrations of > 20 ng/g in fine particulate matter in this study. The highest HCBD emission concentration is observed for the secondary copper smelting industry (average: 1380 ng/g). Source emission factors of HCBD for the 12 industries range from 0.008 kg/t for coal fire power plants to 0.680 kg/t for secondary lead smelting, from which an estimation of approximately 8452.8 g HCBD emissions annually worldwide achieved. The carcinogenic risks caused by HCBD emissions from countries and regions with intensive 12 industrial sources are 1.0-80 times higher than that without these industries. These results will be useful for formulating effective strategies of HCBD control.

The current understanding of the legacy and emerging organic pollutants in the soil of Bangladesh remains limited. Inadequate disposal practices, particularly in e-waste, landfills, and industrial operations, may lead to the pervasive presence of persistent organic pollutants (POPs) and polycyclic aromatic hydrocarbons (PAHs) in the country. This study aimed to systematically investigate the concentrations, sources, and risk assessment of halogenated flame retardants (HFRs), polychlorinated biphenyls (PCBs), polychlorinated naphthalenes (PCNs), organochlorine pesticides (OCPs), chlorinated paraffins (CPs), and PAHs in the contaminated soil of Dhaka's dumpsites, e-waste, and industrial areas collected in 2017. The total concentration of investigated pollutants averaged at 4790 ± 4040 ng g−1 dw, with the following order: ∑44CPs (4110 ± 4140 ng g−1 dw) > ∑16PAHs (422 ± 491 ng g−1 dw) > ∑16HFRs (190 ± 538 ng g−1 dw) > ∑209PCBs (43.5 ± 73.5 ng g−1 dw) > ∑50PCNs (17.7 ± 21.0 ng g−1 dw) > ∑27OCPs (9.41 ± 9.61 ng g−1 dw). The e-waste site exhibited the highest total concentration of the target pollutants, reaching 12,700 ng g−1 dw. CPs were the predominant contributors, accounting for averages of 81.8%, 70.5%, and 68.5% to waste landfills, e-waste, and industrial sites, respectively. A comprehensive analysis of 209 PCB congeners revealed their primary origin from Aroclor PCBs, with minor contributions from unintentionally produced PCBs. The highest incremental lifetime carcinogenic risk (ILCR) occurred in e-waste sites, contributed by dioxin-like PCBs, with a maximum value of 5.9 × 10−5 for adults, exceeding the limit 1.0 × 10−6 set by the U.S. EPA. The non-carcinogenic hazard quotients (HQs) were much lower, suggesting no significant risk from the investigated pollutants. Our findings highlight the importance of proper waste management and regulated e-waste recycling to mitigate potential hazardous risks to the Bangladeshi population.

The textile industry is now focusing on sustainable approaches due to environmental concerns. The study explores an innovative approach to sustainable production utilizing recycled cotton sourced from hard waste. The article examines the characteristics of rotor yarns (recycled) manufactured from seven different percentages of recycled and virgin cotton, ranging from 0% to 100% recycled cotton. The study investigates the influence of cotton (recycled) percentage on yarn characteristics and results indicate when recycled cotton percentage in yarn increases, irregularity (CVm%), hairiness (H), and imperfections index (IPI/km) of yarns increase but strength and elongation% decrease. The study focuses on reducing the environmental issues of denim production by incorporating sustainable recycled cotton. Statistical analysis (Pearson correlation) confirms significant correlations (relationship) between recycled cotton proportion and yarn characteristics. The rotor yarns were tested in an air-jet weaving machine for denim production and found suitable for commercial production. The study gives significant insights into manufacturing sustainable yarns incorporating recycled fibres, focusing on environmental issues.

Consumer preferences for comfortable and well-fitting clothing have led to an increased demand for yarns (elastic) in denim fabric production. The study focuses on the manufacture of yarns (dual core) with various core components including T400® (Polyethylene terephthalate) / poly trimethylene terephthalate), PBT (Polybutylene terephthalate), Polyester (PES), and elastane (Lycra®) and their impact on denim fabric properties. The study involves the production of Ne 18/1 yarns (dual core) using an adjusted spinning method (ring) with elastane and different filaments in the core and additionally, twill (3/1 ‘Z’) fabric (denim) is manufactured using those yarns, and then fabric and yarn characteristics are evaluated. The results show significant variations in fabric and yarn characteristics based on the type of core material used. The yarn properties, such as the neps (+200), strength, thin place (-50), hairiness, elongation %, unevenness %, and thick place (+50) of yarns (dual core) are affected by the selection of filament. Fabric properties like weight, elasticity %, strength (tensile), strength (tearing), shrinkage %, and stiffness exhibit differences depending on the core filament. The statistical analysis employs one-way ANOVA to investigate the significance of differences in yarn and fabric properties within various core materials. The outcomes of this experimental work contribute valuable understandings of the production of yarns (dual core) with several filaments for enhancing fabric (denim) performance.

Identification of potential flood risk areas is crucial to reduce flood damage for the frequently flooded and low-lying South Asian developing countries. The present study has prepared district-level (District: second administrative unit of the country) flood risk map for the flood-prone Rangpur Division (Division: first administrative unit) of Bangladesh using the multi-criteria decision analysis along with the application of analytical hierarchy process (AHP) method. Eight physical factors such as elevation, slope, distance from river, drainage density, land cover, rainfall, height above nearest drainage (HAND), and topographic wetness index (TWI), and six social factors such as population density, dependent population, disabled population, female population, agriculture dependent population, and literacy have been assessed to create a final risk map. The flood risk map is divided into five risk zones: very low, low, moderate, high, and very high. Integration of the social factors along with the physical factors reflects the insight of the vulnerability and increases the authenticity of the generated risk map. This study found that 62.46% area of the Rangpur Division resides under the moderate to very high-risk zone of flooding. Using ROC (receiver operating characteristic)-AUC (area under the curve) curve, the risk map is validated with a score of 0.83 from the flood inventory map of 2020 generated from the Sentinel 1 image. This risk map will guide policymakers to easily identify the vulnerable area for flood hazards and suitable areas for development activities necessary to attain sustainable development.

This data article describes a dataset derived from a comprehensive household survey aimed at understanding the dynamics of agrarian transformation and its impacts on rural community food security within the Lower Gangetic Basin, specifically along the Arial Khan River, Bangladesh. The survey was conducted as part of a doctoral research project and encompasses data collected from 250 households across a defined Mouza within the basin. The dataset provides detailed insights into various dimensions such as household demographics, land ownership and use, agricultural practices and transformations, socio-cultural and environmental factors, power dynamics, food security status, and the strategies employed to enhance food security.The survey employed a structured questionnaire comprising 78 questions distributed across seven distinct sections. Each section aimed to capture specific aspects of the household's agricultural and food security-related characteristics. Data collection was carried out from September 2023 to March 2024, following ethical guidelines approved by the University Malaysia Sabah, which included obtaining informed consent from all participants.This dataset is unique as it offers a granular look at the interplay between agricultural practices and food security in a region highly susceptible to the impacts of environmental change and economic transformations. The data are presented in a structured format across multiple Microsoft Excel sheets, which are comprehensively labeled to facilitate ease of access and analysis. The dataset is significant for researchers and policymakers focusing on agricultural development, food security, and rural development in South Asia. It provides a valuable resource for comparative studies, policy formulation, and in educational settings, aiming to address issues of sustainability and community resilience in agrarian societies.

Due to technological advancement and modernization, the demand for Electric Vehicles (EVs) is rising. In Bangladesh, there is a growing demand for electric vehicles such as auto-rickshaws and easy bikes, and electric automobiles will be familiar soon. The demand for electrically powered vehicles is increasing in response to growing environmental concerns; it helps balance the greenhouse effect and global warming. Solar-based charging systems are becoming increasingly popular due to their low greenhouse gas (GHG) emissions. The CO2 emissions produced by solar-based EV charging systems are lower than those produced by coal-based chargers. Moreover, the Government of Bangladesh wants to accomplish Sustainable Development Goal (SDG) 7 by 2030 by increasing the proportion of accessible, cutting-edge, renewable energy sources. This study investigates and analyses the technological, economic, and ecological viability of a solar PV power plant in Bangladesh for charging EVs. This study examined solar-based EV charging stations, which generate 36,785.76 MWh of electricity for 20 years of their lifetime and are used to charge EVs. Furthermore, the net present value (NPV) is USD 652,656.80, the initial investment is USD 1,365,300, the Internal Rate of Return (IRR) is 11.70 %, and the Payback Period (PBP) is 7.2 years. Approximately per day, 250 battery-operated three-wheeler EVs can be charged using the generated electricity. In addition, the project will prevent the emission of 24,013.86 tons of CO2, 251.25 tons of SO2, 63.39 tons of NOx, and 12.55 tons of CO. Eliminating these GHG emissions may also aid Bangladesh in achieving SDG 13.

Significant wave height (SWH) is a key parameter for wave energy extraction, ship navigation, oil and gas extraction, coastal structure construction, etc. Direct measurements of SWH using buoys are expensive and accurate over a limited area while numerical models are computationally expensive, inaccurate, with limited generalizability. Thus, this work focuses on developing generalizable machine learning models for predicting SWH from wind parameters (speed, direction, and gust) and atmospheric parameters (temperature and pressure). Two deep learning models (Artificial Neural Network (ANN) and Self Normalizing Neural Network (SNN) and two gradient boosting tree-based models (XGBoost and LightGBM) have been used in this study. Three different data sets were collected from the National Data Buoy Center: Data Set-1 (DS1): 12 years of data from 47 stations; Data Set-2 (DS2): 14 months of additional data from 6 stations randomly selected from DS1; Data Set-3 (DS3): 13 years data from completely 6 new stations. DS1 was split into training, testing, and validation datasets. Training and hyper-parameter tuning was done on the train and validation dataset, while the performance of the models was evaluated on the DS1 test dataset, DS2, and DS3, employing three error metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-square score (R2). The collected data underwent data cleaning, preprocessing, and exploratory data analysis before modeling. The deep learning models have demonstrated superior fitting capacity to the tree-based models, achieving the lowest MSE (0.047), MAE (0.153), and highest R2 score (0.953) on test data. However, the gradient boosting models demonstrate better generalizing capacity than the deep learning models on DS2 and DS3. This study helps further Sustainable Development Goal 7 (SDG 7) by allowing fast and cheap assessment of wave height for ocean energy site development.

The unobstructed use of antibiotics in poultry production has emerged as a major driving force of antibiotic resistance and public health hazard, particularly in developing countries. This study aimed to determine the functional roles of lyophilized native probiotic based starter feed on performance, selective serum metabolites and meat quality of poultry. A total of 90 day-old birds (30 broilers, 30 layers and 30 ducks) were used as experimental birds which were divided into three treatment groups for each kind of bird. Isolated native probiotic strains from chicken intestine were used to prepare lyophilized probiotic samples. Growth performances were measured manually, serum biochemicals analysis were carried out using diagnostic kits, and meat quality was determined through Kjeldahl method and Soxhlet method. When compared to groups receiving antibiotics, the introduction of lyophilized probiotics in starter feed significantly (P

Deep learning models are widely used in healthcare systems. However, deep learning models are vulnerable to attacks themselves. Significantly, due to the black-box nature of the deep learning model, it is challenging to detect attacks. Furthermore, due to data sensitivity, such adversarial attacks in healthcare systems are considered potential security and privacy threats. In this paper, we provide a comprehensive analysis of adversarial attacks on medical image analysis, including two adversary methods, FGSM and PGD, applied to an entire image or partial image. The partial attack comes in various sizes, either the individual or combinational format of attack. We use three medical datasets to examine the impact of the model’s accuracy and robustness. Finally, we provide a complete implementation of the attacks and discuss the results. Our results indicate the weakness and robustness of four deep learning models and exhibit how varying perturbations stimulate model behaviour regarding the specific area and critical features.

Abstract Apoptosis, also known as the programmed death of cells, is responsible for maintaining the homeostasis of tissues, and this function is carried out by caspases. The process of apoptosis is carried out via two distinct pathways: the extrinsic pathway, which is governed by death receptors, and the intrinsic pathway, also known as the mitochondrial pathway. The BCL-2 protein family encoded by the BCL-2 gene, located at the 18q21.33 chromosomal location, is in charge of regulating the intrinsic pathway, which is responsible for inducing cell death via the permeabilization of the mitochondrial membrane and the release of apoptosis-inducing components. The BCL-2 homology (BH1, BH2, BH3, BH4) domains of this family proteins are crucial for their functioning, and their common BH domains allow interactions between members of the same family and can also serve as indications of pro- or anti-apoptotic activity. A direct correlation may be shown between the overexpression of BCL-2 and the postponement of cell death. It has been determined that a change in the expression of BCL-2 is the root cause of a variety of malignancies, including lung, breast, melanoma, and chronic lymphocytic leukemia, multiple sclerosis, diabetes. In this review, we addressed the genetic information and structural homology of BCL-2 family members. Further, we elucidate the pro-apoptotic and anti-apoptotic roles of the family members. This review highlights the most recent developments in the BCL-2 protein family and presents evidence that targeting this family proteins may have a positive impact on the treatment of medical problems that are still underserved.

The generation of high-quality images from textual descriptions is a challenging task in computer vision and natural language processing. The goal of text-to-image synthesis, a current topic of research, is to produce excellent images from written descriptions. This study proposes a hybrid approach to evaluating a dataset consisting of various text-image pairs by efficiently combining conditional generative adversarial networks (C-GAN), attention mechanisms, and contrastive learning (C-GAN+ATT+CL). We suggest a two-step method to improve image quality that starts by utilizing generative adversarial networks (GANs) with attention mechanisms to create low-resolution images and then contrastive learning to improve. Contrastive learning modules train on a separate dataset of high-resolution pictures; GANs learn on datasets of low-resolution text and image pairs. The Conditional GAN with Attention Mechanism and Contrastive Learning Method provides state-of-the-art performance in terms of image quality, diversity, and visual realism, among the several methods. The results of this study demonstrate that the proposed approach works better than all other methods, achieving an Inception Score (IS) of 35.23, a Fréchet Inception Distance (FID) of 18.2, and an R-Precision of 89.14. Our findings demonstrate that our “C-GAN+ATT+CL” approach significantly improves image quality and diversity and offers exciting paths for further study.

Abstract Transport of rodlike particles in confinement environments of macromolecular networks plays crucial roles in many important biological processes and technological applications. The relevant understanding has been limited to thin rods with diameter much smaller than network mesh size, although the opposite case, of which the dynamical behaviors and underlying physical mechanisms remain unclear, is ubiquitous. Here, we solve this issue by combining experiments, simulations and theory. We find a nonmonotonic dependence of translational diffusion on rod length, characterized by length commensuration-governed unconventionally fast dynamics which is in striking contrast to the monotonic dependence for thin rods. Our results clarify that such a fast diffusion of thick rods with length of integral multiple of mesh size follows sliding dynamics and demonstrate it to be anomalous yet Brownian. Moreover, good agreement between theoretical analysis and simulations corroborates that the sliding dynamics is an intermediate regime between hopping and Brownian dynamics, and provides a mechanistic interpretation based on the rod-length dependent entropic free energy barrier. The findings yield a principle, that is, length commensuration, for optimal design of rodlike particles with highly efficient transport in confined environments of macromolecular networks, and might enrich the physics of the diffusion dynamics in heterogeneous media.

This study investigated the groundwater quality and its associated human health risks in the urban and peri-urban areas of Jashore, Bangladesh, where groundwater samples were collected from 67 randomly selected tube wells. The concentration of arsenic, iron, and manganese was analyzed by atomic absorption spectroscopy (AAS). The water quality index indicates that about 89 and 43% of groundwater samples are not consumable for the urban and peri-urban areas, respectively. All of the source water is significantly contaminated with Escherichia coli for urban (31 ± 17.77 CFU/100 mL) and peri-urban areas (76.12 ± 35.17 CFU/100 mL), where about 67 and 57% of water source has intermediate and high microbial risk of E. coli for urban and peri-urban areas, respectively. Children and adults face unacceptable non-carcinogenic health risks for the urban area (4.13–10.67 for adults; 9.65–24.91 for children) and peri-urban area (1.05–5.58 for adults; 2.46–13.03 for children) via oral ingestion. Both groups (e.g. children = 4.25E-03 to 1.10E-02 and adult = 1.82E-03 to 4.71E-03 for urban regions; children = 1E-03 to 5E-03 and adult = 4.29E0-04 to 2.14E-03 for peri-urban regions) face undesirable carcinogenic risks from arsenic. In addition, children are suspected to have 2.33 times higher non-carcinogenic and carcinogenic health risks than adults. HIGHLIGHTS About 89 and 43% of groundwater samples are not drinkable for the urban and peri-urban areas, respectively.; About 67 and 57% of water source has intermediate and high microbial risk by Escherichia coli for urban and peri-urban areas, respectively.; Children are suspected to have 2.33 times higher non-carcinogenic and carcinogenic health risks than adults.;

Background: Clear cell renal cell carcinoma (ccRCC) comprises the majority, approximately 70–80%, of renal cancer cases and often remains asymptomatic until incidentally detected during unrelated abdominal imaging or at advanced stages. Currently, standardized screening tests for renal cancer are lacking, which presents challenges in disease management and improving patient outcomes. This study aimed to identify ccRCC-specific volatile organic compounds (VOCs) in the urine of ccRCC-positive patients and develop a urinary VOC-based diagnostic model. Methods: This study involved 233 pretreatment ccRCC patients and 43 healthy individuals. VOC analysis utilized stir-bar sorptive extraction coupled with thermal desorption gas chromatography/mass spectrometry (SBSE-TD-GC/MS). A ccRCC diagnostic model was established via logistic regression, trained on 163 ccRCC cases versus 31 controls, and validated with 70 ccRCC cases versus 12 controls, resulting in a ccRCC diagnostic model involving 24 VOC markers. Results: The findings demonstrated promising diagnostic efficacy, with an Area Under the Curve (AUC) of 0.94, 86% sensitivity, and 92% specificity. Conclusions: This study highlights the feasibility of using urine as a reliable biospecimen for identifying VOC biomarkers in ccRCC. While further validation in larger cohorts is necessary, this study’s capability to differentiate between ccRCC and control groups, despite sample size limitations, holds significant promise.

Detecting tiny objects in aerial imagery presents a major challenge regarding their limited resolution and size. Existing research predominantly focuses on evaluating average precision (AP) across various detection methods, often neglecting computational efficiency. Furthermore, state-of-the-art techniques can be complex and difficult to understand. This paper introduces a comprehensive benchmarking analysis specifically tailored for enhancing small object detection within the DOTA dataset, focusing on one-stage detection methods. We propose a novel data-processing approach to enhance the overall AP for all classes in the DOTA-v1.5 dataset using the YOLOv8 framework. Our approach utilizes the YOLOv8’s darknet architecture, a proven effective backbone for object detection tasks. To optimize performance, we introduce innovative pre-processing techniques, including data formatting, noise handling, and normalization, in order to improve the representation of small objects and improve their detectability. Extensive experiments on the DOTA-v1.5 dataset demonstrate the superiority of our proposed approach in terms of overall class mean average precision (mAP), achieving 66.7%. Additionally, our method establishes a new benchmark regarding computational efficiency and speed. This advancement not only enhances the performance of small object detection but also sets a foundation for future research and applications in aerial imagery analysis, paving the way for more efficient and effective detection techniques.

Perfluoroalkyl carboxylic acids (PFCAs) are a significant group of per- and polyfluoroalkyl substances (PFASs). They are persistent organic chemicals manufactured for their resistance to heat, water, and stains. PFCAs are ubiquitous in the environment, particularly in surface water and wastewater, because they are widely used in everyday consumer products. This contamination poses a risk to drinking water supplies and human health, necessitating sensitive and effective analytical methods. Traditional liquid chromatography–tandem mass spectrometry (LC-MS/MS) is commonly used but involves complex sample handling and high costs. In this study, we developed an enhanced stir bar sorptive extraction (SBSE) method coupled with thermal desorption–gas chromatography–mass spectrometry (TD-GC-MS) for the analysis of PFCAs in water. This method demonstrates linearity, with R2 values from 0.9892 to 0.9988, and low limits of detection (LOD) between 21.17 ng/L and 73.96 ng/L. Recovery rates varied from 47 to 97%, suggesting efficient extraction. Compared to traditional methods, the developed SBSE technique requires only a 1 mL sample volume and minimal amounts of solvents, enhancing eco-friendliness and reducing potential contamination and handling errors. This method also demonstrated good precision and robustness across various water matrices. Overall, the developed method offers a precise, eco-friendly, and reliable approach for analyzing PFCAs in diverse water samples.

The All of Us Research Program seeks to engage at least one million diverse participants to advance precision medicine and improve human health. We describe here the cloud-based Researcher Workbench that uses a data passport model to democratize access to analytical tools and participant information including survey, physical measurement, and electronic health record (EHR) data. We also present validation study findings for several common complex diseases to demonstrate use of this novel platform in 315,000 participants, 78% of whom are from groups historically underrepresented in biomedical research, including 49% self-reporting non-White races. Replication findings include medication usage pattern differences by race in depression and type 2 diabetes, validation of known cancer associations with smoking, and calculation of cardiovascular risk scores by reported race effects. The cloud-based Researcher Workbench represents an important advance in enabling secure access for a broad range of researchers to this large resource and analytical tools.

Textile industries are now focusing on sustainable issues in manufacturing operations to save the environment. The study focuses on the use of cotton fibers (recycled) sourced from fabric (knitted) waste (pre-consumer) to manufacture elastic yarn (dual-core) for denim fabric. The study involves the production of yarns (dual-core) using a redesigned ring spinning method with different elastomeric components, including T400® (Polyethylene terephthalate)/Polytrimethylene terephthalate), Polybutylene terephthalate (PBT), Polyester (PES), Lycra® (elastane), virgin cotton and cotton (recycled) fiber. The study investigates various yarn (Ne 18/1) characteristics such as strength, IPI (imperfection index), elongation %, unevenness %, and hairiness. It is noticed that the elongation and strength of recycled yarn (double core) are lower and IPI (Imperfection index), unevenness %, and hairiness values are higher than 100 % cotton (virgin) yarn (double core). One-way ANOVA (statistical analysis) is employed to assess the significance of differences among yarns manufactured from various core materials and found significant variation for all characteristics. Additionally, the article introduces the MOORA (multi-objective optimization based on ratio analysis) technique as a decision-making tool to determine the best yarn among three alternatives (PES yarn, PBT yarn, and T400 yarn) based on their properties, considering attributes and finding T400 filament containing yarn as the best option. The study introduces a sustainable approach using recycled cotton in yarn (double core) production and employs decision-making tools to assess and rank the performance of different yarn alternatives.

Recent studies have shown the potential of wearable sensors for objective detection of health and safety risks in construction workers through their collected physiological data. Body temperature, as the focus of the current study, is one of the most important physiological parameters that can help to detect various health and safety risks such as heat stress, physical fatigue, and infectious diseases. This study aims to assess the applicability and performance of off-the-shelf wearable sensor devices to monitor workers’ body temperature in construction sites by evaluating the accuracy of temperature measurements as well as the comfort of the devices.A total of nine off-the-shelf wearable sensor devices available on the market were initially trialed in the laboratory, and three devices were shortlisted considering a set of selection criteria for further assessment. Over three weeks, the shortlisted wearable sensors were tested on 26 workers in two large construction sites in Australia. The reliability/validity of the selected wearable sensors in measuring body temperature was investigated using Bland-Altman analysis. Human factors were also investigated in terms of the comfort of the devices, their impact on workers’ performance, and the acceptability of being worn for an extended period (i.e., 8 h or more). It was found that all selected devices measured body temperature with a bias of less than one indicating a slight difference in measurements compared to the reference hospital-grade thermometers. Two devices out of the three were also comfortable. The achieved results indicate that it is feasible to develop a continuous temperature monitoring platform using off-the-shelf wearable sensors to detect a range of significant health and safety risks in construction sites objectively. Considering the rapid advancements in manufacturing wearable sensors, future research can adopt a similar approach to include the newly introduced off-the-shelf temperature sensors and select the most appropriate device.

One of the biggest environmental worries in the world today is the risk of arsenic (As) contamination in groundwater. The Atomic Absorption Spectrometer (AAS) was used in this work to assess the As content in groundwater samples from 38 shallow (27 m) tubewells in northwest Bangladesh to determine the existing situation, potential source(s), and likely health risk of As and other important water quality parameters. The range of arsenic concentrations (μgL−1) was troublesome and greater than the WHO recommended level for drinking water, ranging from 0.50 to 164 (mean ± SD: 20.22 ± 36.46). In groundwater, the concentrations of Fe, and Mn vary from 0.04 to 52.75 mgL−1 (mean ± SD: 4.23 ± 9.68), and 0.23 to 3.27 mgL−1 (mean ± SD: 1.10 ± 0.67). The obtained groundwater samples have pH values ranging from 5.9 to 7.1, which indicates a somewhat acidic to neutral character. Major cations have an average abundance that is as follows: Ca2+ > Mg2+ > Na+ > K+, while major anions have an average abundance that is as follows: HCO3− > Cl− > SO42− > NO3−; Ca2+ and HCO3− are the main cation and anion, respectively. The groundwater in the Rajarampur village was deemed unfit for drinking or irrigation based on analyses of water quality performed using the entropy water quality index. The Ca–HCO3 type of water, in which Ca2+ and HCO3− are the main positive ions and negative ions, is suggested by the Piper tri-linear diagram. It was discovered that silicate weathering regulates the hydro-geochemical activities in groundwater using a bi-variate examination of several hydro-chemical variables. Four major clusters were observed for the water sample. According to reductive dissolution processes and principal component analysis, the arsenic in groundwater is geogenic in origin. Arsenic is discharged from sediment to groundwater by reductive dissolution of FeOOH and MnOOH, as shown by the modest connection between As, Fe, and Mn. The United Nations Environmental Protection Agency's (USEPA) suggested value for probable cancer risk assessment was 10−6, however the probable cancer risk assessment found a higher value, indicating that the population in the study region was at high risk for cancer. Remedial measures for arsenic mitigation include removing arsenic from groundwater after it is extracted, searching for alternative aquifers, and implementing various water-supply technologies such as dugwells, deep tubewells, pond-sand filters, and rainwater harvesting systems.

In the recent years, air pollution has become an alarming issue for human health in many rapidly growing megacities including Dhaka and Narayanganj. Herein, pollution status and health risk have been assessed by the road dusts collected from landfills in Dhaka and Narayanganj through determination of heavy metals including Pb, Cd, Cr, As, Cu, Ni, Zn and Mn using atomic absorption spectroscopy. The collected dust samples have been grouped into three sizes, for example, 150, 75 and

Prior studies have identified the impact beliefs have on mathematics instructors' instructional practice, such as their choice to (or not to) lecture. However, the role of instructional context role in influencing beliefs and instruction has not been thoroughly researched. This paper explores how course context and beliefs could impact mathematics instructors' propensity to lecture by investigating two very different instructional contexts in undergraduate mathematics in the United States: Calculus and Abstract Algebra. The results of our regression analyses were significant in both data sets and, we did find beliefs in each context that predicted the amount of time spent lecturing. For instance, the more calculus instructors believed in the effectiveness of teacher-centered instructional practices, the more likely they were to lecture. Whereas the more abstract algebra instructors believed in their student's capacity to learn the less likely they were to lecture. However, while the regression model for the abstract algebra instructors accounted for 37.8% of the variability in the reported amount of time spent lecturing, the model for Calculus instructors only accounted for 2.7% of the variability. Thus our analyses indicate that there are contextual differences, such as course coordination, student demographics, and the job security of the instructors, that may be mitigating the extent to which beliefs impact instructional practice.

Abstract Previous literature has highlighted the importance of maternal behavior during the prenatal period for the upbringing of healthy adults. During pregnancy, fetal health assessments are mainly carried out non-invasively by monitoring fetal growth and heart rate (HR) or RR interval (RRI). Despite this, research entailing prediction of fHRs from mHRs is scarce mainly due to the difficulty in non-invasive measurements of fetal electrocardiogram (fECG). Also, so far, it is unknown how mHRs are associated with fHR over the short term. In this study, we used two machine learning models, support vector regression (SVR) and random forest (RF), for predicting average fetal RRI (fRRI). The predicted fRRI values were compared with actual fRRI values calculated from non-invasive fECG. fRRI was predicted from 13 maternal features that consisted of age, weight, and non-invasive ECG-derived parameters that included HR variability (HRV) and R wave amplitude variability. 156 records were used for training the models and the results showed that the SVR model outperformed the RF model with a root mean square error (RMSE) of 29 ms and an average error percentage (

The present study was conducted with the aim to reveal the effect of genotypes on reaction time, refractory period, semen index and liquid stored (at 4oC) semen quality of breeding bulls in Bangladesh. Semen was collected twice a week from Holstein Friesian, Sahiwal and Brahman breeding bulls. Reaction time and refractory period were examined for the breeding bulls. Semen volume, sperm concentration and total spermatozoa/ejaculate were measured immediately after collection. It was revealed that genotype had a significant (P [Fundam Appl Agric 2023; 8(3.000): 555-566]

In this study, methyl orange (MO) was removed from solution using rice husk charcoal (RHC) and acid modified rice husk charcoal (AMRHC). In batch adsorption mode, contact time (1–240 min), pH (3–10), adsorbent dose (1–30 g/L), and initial MO concentration (10–100 mg/L) were investigated. Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) were used to characterize the adsorbent's surface morphology and chemistry. At equilibrium, the highest removal of MO by RHC and AMRHC were 89 and 99%, respectively. Removal efficiency increased with increasing adsorbent dose, while the opposite was observed for adsorption capacity because of the availability of unsaturated adsorption sites. RHC and AMRHC were best described by the Freundlich and Langmuir isotherm models, with maximum adsorption capacities of 4.57 and 11.53 mg/g, respectively. The pseudo-second-order kinetic model fitted well for both adsorption and chemisorption, and the process was controlled by multi-step diffusion. Thermodynamic measurements proved that dye adsorption is a spontaneous endothermic process. HIGHLIGHTS MO adsorption was studied using RHC and AMRHC.; About 89 and 99% of MO dye were removed by RHC and AMRHC, respectively.; The maximum adsorption capacities were 4.57 and 11.53 mg/g, for RHC and AMRHC, respectively.; Kinetic data followed pseudo-second-order kinetics for both adsorbents.; The adsorption potential of both adsorbents for the treatment of MO dye was compared with other adsorbents which is cost effective.;

This paper aims to examine the association between green innovation and the cost of equity in China. This study relies on the investors’ base perspective and shareholders’ perceived risk perspective to investigate the relation between green innovation and the cost of equity in China. The paper uses firm-fixed effect regression for a sample of Chinese public companies for the period 2008–2018. The authors find a negative relationship between green innovation and the cost of equity capital. This negative association is found to be more pronounced for less financially constrained firms, during periods of high economic policy uncertainty, and for firms with a strong internal control environment. Finally, the paper shows that the negative association became more pronounced after the passage of the Environmental Protection Law of China in 2012. The results remain robust to possible endogeneity concerns. This study contributes to the green innovation literature by documenting that shareholders favorably view firms implementing green innovation policies. The study also has policy implications for Chinese regulators in improving the green credit policy.

This study aims to develop a robust and adaptable real-time forecasting system for coastal and estuarine regions, considering the challenges posed by the limited or unavailable forecast data. A real-time forecast system has been designed to handle three distinct scenarios: forecast data from global models are available, unavailable, or intermittently unavailable. To address the challenge of data unavailability, this research proposes the application of a deep learning model (DLM). This study involves the construction of a high-resolution numerical model nested within global models. The performance of the DLM is evaluated by comparing the simulation results of the nested model generated using DLM-predicted data against those obtained using data from global models. The results demonstrate a high correlation and around 90% accuracy for up to the initial 5 days of the forecast. Even in the absence of hindcast data up to 4 days prior to the forecast beginning time, the real-time forecast with DLM can achieve an accuracy exceeding 70% of the forecast data from global models. However, direct application of the developed DLM to an unknown domain results in significantly lower performance, highlighting the importance of retraining the DLM with local data. Overall, this study presents a comprehensive approach to constructing a real-time forecasting system for coastal and estuarine regions that adeptly handles various data availability scenarios through the integration of machine learning techniques.

Capacitive deionization (CDI) is an emerging technology which is being developed as a promising desalination alternative for charged species from salt water, being electrode materials as key drivers for highly efficient process. Here, we describe synthesis of highly porous activated carbons from chicken feathers using pyrolysis followed by chemical activation with potassium hydroxide in a 1:4 ratio to produce sustainable, scalable, and sustainable carbon electrodes for CDI. Poly (vinyl alcohol) (PVA) was used as binder to modify chicken feather activated carbons (CF-AC), which were subsequently crosslinked with glutaraldehyde (GA) to produce CF-AC-PVA-GA polymers through acetylation reaction. This resulted from improving hydrophilicity of CF-AC-PVA-GA polymers to raise the electrodes' resistance. Prepared materials were characterized using scanning electron microscopy, Fourier transform infrared, X-ray diffraction, Brunauer-Emmett-Teller and cyclic voltammetry. Fabricated CDI electrodes were used to investigate their performance for desalination, and exhibited different electrosorption capacity at different applied potentials. Modified electrodes possessed good stability in shear conditions and CDI process was stable and reproducible around 16 electrosorption cycles. Salt removal capacity of the modified electrodes (CF-AC-PVA-GA) was found to be 3.89 mg g−1. The obtained outcomes offer important considerations of ions electrosorption and help advancing CDI system for water treatment and desalination. HIGHLIGHTS Activated carbons (ACs) were prepared from chicken feathers via pyrolysis using KOH.; Poly (vinyl alcohol) and glutaraldehyde were used for modification of ACs.; Modified carbon materials were used for capacitive deionization (CDI) electrodes.; The CDI electrodes retained good stability around 16 electrosorption cycles.; Salt removal capacity of the fabricated CDI electrodes was found to be 3.89 mg g−1.;

Abstract With the popularization of low-cost mobile and wearable sensors, several studies have used them to track and analyze mental well-being, productivity, and behavioral patterns. However, there is still a lack of open datasets collected in real-world contexts with affective and cognitive state labels such as emotion, stress, and attention; the lack of such datasets limits research advances in affective computing and human-computer interaction. This study presents K-EmoPhone, a real-world multimodal dataset collected from 77 students over seven days. This dataset contains (1) continuous probing of peripheral physiological signals and mobility data measured by commercial off-the-shelf devices, (2) context and interaction data collected from individuals’ smartphones, and (3) 5,582 self-reported affect states, including emotions, stress, attention, and task disturbance, acquired by the experience sampling method. We anticipate the dataset will contribute to advancements in affective computing, emotion intelligence technologies, and attention management based on mobile and wearable sensor data.