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Landslides represent a significant natural hazard in Sichuan Province, causing considerable property damage and loss of life. Utilizing machine learning (ML) for landslide susceptibility mapping (LSM) is an effective strategy for mitigating landslide risk. Nonetheless, individual ML models face certain limitations in interpreting landslide data, assigning weights to evaluation factors, and achieving high predictive accuracy. These limitations hinder the precision of landslide susceptibility classifications worldwide. To overcome these issues, this research proposes a hybrid ML model that combines information value (IVM) with ML, referred to as IVM-ML, to improve the precision of landslide susceptibility prediction (LSP). The dataset includes 7,840 historical landslide events and incorporates 14 landslide evaluation factors (LEFs), which were analyzed for their suitability through correlation and multicollinearity assessments. Low-susceptibility zones identified by the IVM model were used to select non-landslide samples, and six models—SVM, RF, LR, IVM-SVM, IVM-RF, and IVM-LR—were employed for LSP. The effectiveness of these models was evaluated based on Sensitivity (Se), Accuracy (Ac), Specificity (Sp), Precision(Pr), F1 Score(F1), Frequency ratio of landslide sites(Fr) and the area under the ROC curve (AUC). The findings reveal that the southeastern and eastern regions of the research region, which encompass approximately 30% of the entire area, exhibit a higher risk of landslides, while the western and northwestern areas, comprising approximately 45%, have a lower risk. The IVM-ML models, particularly IVM-RF, achieved notably higher predictive accuracy, with AUC values of 0.997, 0.996, and 0.998 for IVM-SVM, IVM-RF, and IVM-LR, respectively, outperforming the standard ML models. These results highlight the IVM-ML model’s potential for improving LSP accuracy, particularly in high-risk regions, contributing significantly to landslide hazard mitigation in Sichuan and globally. [ABSTRACT FROM AUTHOR]

Diversity in the plant gene pool provides breeders with the opportunity to improve targeted traits. Enhancing the genetic background through the introgression of desired gene(s) is an effective strategy to combat stresses like heat. Two field experiments were carried out in 2019–20 and 2020–21 at the research fields of Genetics and Plant Breeding at Hajee Mohammad Danesh Science and Technology University, Dinajpur, Bangladesh, to identify heat-tolerant wheat advance lines. In F4 generation, 60 advanced lines were selected from a pool of 160 line and in the F5 generation, 16 lines were selected from the previous selection. Analysis of variance revealed significant variation in both the F4 and F5 generations, with the F5 populations outperforming the check varieties. Grain weight demonstrated the highest heritability and genetic advance, while the plant yield exhibited moderate to high performance. Plant height had the most significant direct effect at the genotypic level, while grain weight had the highest impact at the phenotypic level. A total of 77 alleles were identified with an average of 4.53 alleles per locus. The genetic similarity analysis showed that Shatabdi and BARI Gom 25 had the highest similarity at 94%, followed by P3-18-10 with P3-11-3 at 90%, and P3-24-10 with P3-21-10 at 85%. The PCA, structural, and dendrogram analyses all indicated the same grouping of the selected lines. Therefore, selecting of these lines for the F6 generation would enhance resilience against heat stress. [ABSTRACT FROM AUTHOR]

Enhancement of dynamic characteristics of sand through bio-cementation is one of the prospective ground improvement techniques for sustainable development considering seismic loading scenarios. Microbially induced calcite precipitation (MICP) has already been established as an efficient and low-cost and sustainable bio-cementation technique. In the present study, engineering characteristics of poorly graded standard Ennore sand of India have been improved through the bio-cementation effects of Sporosarcina pasteurii bacteria using the MICP technique. Microstructure images obtained through scanning electron microscope and crystalline form of minerals present obtained through X-ray diffraction analyses are presented for the bio-cemented sand. Finally, dynamic characteristics of the bio-cemented sand are investigated through strain-controlled unconsolidated undrained cyclic triaxial testing varying the shear strain range from 0.3 to 1.5% with 1.0-Hz frequency. Variations of shear modulus and damping ratio of bio-cemented sand with shear strain are reported considering treatment durations of 7 and 14 days with treatment intervals of 12 and 24 h, respectively. It is observed that the improvement in shear modulus of bio-cemented sand for lesser strain rate approximately ranges from 37% to more than 80% compared to untreated sand. Furthermore, for strain higher than 1.0%, the margin of improvement varies from 50 to 70%. Moreover, the damping of bio-cemented sand was found to be lesser than untreated sand, and the variation is more significant for the higher number of loading cycles. This study is helpful in the assessment of dynamic characteristics of bio-cemented sand specifically applicable to seismic design. [ABSTRACT FROM AUTHOR]

The environmental burden of tannery wastewater, characterized by high levels of total dissolved solids (TDS) and other contaminants, presents a significant challenge for sustainable water management. This study addresses this issue by developing a novel polyvinyl alcohol (PVA) and polyvinyl chloride (PVC) composite membrane optimized for efficient TDS removal from tannery effluent. The membrane was fabricated using a solution casting technique, with glutaraldehyde employed as a crosslinking agent to enhance mechanical properties and stability. Characterization techniques, including Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), zeta potential analysis, and contact angle measurements, were used to evaluate the membrane's surface chemistry, morphology, and hydrophilicity, which are crucial for pollutant separation. Performance testing demonstrated that the membrane achieved a TDS removal efficiency of 91.73% at an optimal pH of 8 and a transmembrane pressure of 3.5 bar, with a permeability of 194 Lm−2 h−1 bar−1. Additionally, substantial reductions in turbidity (94.51%), chemical oxygen demand (COD, 91.91%), biological oxygen demand (BOD, 89.70%), salinity (80.57%), and total suspended solids (TSS, 96.45%) were observed. The membrane exhibited impressive mechanical properties, with a tensile strength of 44 ± 0.43 MPa, 150 ± 0.67% elongation at break, Young's modulus of 750 ± 0.47 MPa, and flexibility of 23 ± 0.53%, indicating its flexibility and durability. Its partial biodegradability and potential for scalable production contribute to its environmental sustainability. This work establishes the PVA-PVC composite membrane as a promising and cost-effective solution for industrial wastewater treatment, offering a sustainable approach to mitigating water pollution in the leather industry. Fabrication of PVA-PVC Composite Membrane for TDS removal from tannery wastewater [ABSTRACT FROM AUTHOR]

[18F]fluorocholine ([18F]FCH) is an effective tracer for the imaging of different tumors of human brain, lung, or prostate. The purpose of this study was to evaluate the radiochemical yield and quality of routinely synthesized [18F]FCH by a modified 2-deoxy-2-[18F]fluoro-d-glucose ([18F]FDG) synthesizer (GE TracerLab MX). The radiosynthesis of [18F]FCH was completed in two steps taking 49 min. First, [18F]fluorobromomethane ([18F]FCH2Br) was synthesized from [18F]fluoride and dibromethane. Then, [18F]FCH as bromide salt was produced by N-alkylation of N,N-dimethylaminoethanol (DMAE) with [18F]fluorobromomethane. [18F]FCH had > 99.99% radiochemical purity, with non-decay-corrected and decay-corrected yields of 12(± 3)% and 16(± 4)%, respectively (n = 8). [ABSTRACT FROM AUTHOR]

Hypsometric or area-altitude analysis unveils the geomorphic evolution of a river basin and its interactions with tectonics, lithology, and climate. The results of hypsometric analysis i.e., hypsometric curve (HC) along with the hypsometric parameters (HP) namely, hypsometric integral (HI), skewness (SK), kurtosis (KU), density skewness (DSK), and density kurtosis (DKU) can also be further analyzed to evaluate the geological erosional status of a river basin. However, the spatial scale’s significance in such analyses is often underestimated, assuming consistency across various resolutions of Digital Elevation Models (DEMs). To address this oversight, this study compares HP values obtained from DEMs of different sources and resolutions—Shuttle Radar Topographic Mission (30 m), Global Multi-resolution Terrain Elevation Data 2010 (225 m), and Global 30 Arc-Second Elevation Data Set (1000 m). A total of 108 sub-watersheds spanning four large Indian River basins (Mahanadi, Godavari, Krishna, and Cauvery) underwent analysis using both parametric and non-parametric statistical tests, namely Shapiro-Wilk, Kolmogorov-Smirnov, and Anderson-Darling tests, to assess the distribution of hypsometric parameters. Subsequently, the null hypothesis regarding the robustness of HP across varying DEM resolutions was tested using non-parametric statistical tests such as the Mann-Whitney U test, Kruskal-Wallis H test, and Friedman test. The study’s findings challenge the prevalent notion of robustness, revealing that DEM resolution significantly impacts the results of hypsometric analysis. The Friedman test, in particular, highlighted significant differences in the full distribution of hypsometric parameters across different DEM resolutions, suggesting that variations in DEM resolution can alter the shape and variability of these parameters, not just their central tendencies. Higher-resolution DEMs, such as SRTM (30 m), provided more accurate representations of landscape features, especially in capturing subtle variations that influence erosion rates, sediment transport, and geomorphic classification. [ABSTRACT FROM AUTHOR]

Diabetes mellitus (DM) is a metabolic disease marked by an excessive rise in blood sugar (glucose) levels caused by a partial or total absence of insulin production, combined with alterations in the metabolism of proteins, lipids, and carbohydrates. The International Diabetes Federation estimates that 425 million individuals globally had diabetes in 2017 which will be 629 million by 2045. Several medications are used to treat DM, but they have limitations and side effects including weight gain, nausea, vomiting, and damage to blood vessels and kidneys. Therefore, it is essential to identify anti-diabetic drugs that have less or no side effects. Hence, the current study employed in silico approaches to discover new DPP-IV inhibitors that might be associated with diabetes. Thirty-four (34) co-crystalized DPP-IV enzymes were found from the protein data bank and the co-crystal ligands were docked into the active-site 6B1E protein to find out the hit compounds. From the docking results, we found two hit compounds (5T4E and 4J3J) which were used to find out the analogs from the experimental drug database using the DrugRep software. According to the results, twenty (20) analogs were found from the experimental drug database with the similarity score of ≥ 0.790 and docked once again into the active site of the DPP-IV (PDB ID: 6B1E) enzyme. Interestingly, DB02226 showed the best binding affinity (−10.3 kcal/mol) and prime MM/GBSA (−68.73 kcal/mol) compared to the reference drug (co-crystal ligand; −7.4 kcal/mol and −47.49 kcal/mol, respectively). Additionally, DB02226 has shown excellent reactivity, efficacy, and structural stability in the binding region of target proteins in studies using MD simulation, MM/GBSA, DFT, and MESP analysis. These findings can be utilized to support further in vitro, in vivo, pre-clinical and clinical research rather than definitively confirming anti-diabetic effectiveness. [ABSTRACT FROM AUTHOR]

Social media has become a platform for people to express emotions, including happiness and sadness, to their followers. Major Depressive Disorder (MDD), a common mental health disorder, is characterized by sadness and loss of interest in activities, leading to physical, emotional, cognitive, and social suicidal thoughts. Early detection and intervention of MDD are crucial for effective management and treatment. The study investigates the potential of detecting MDD on social media platforms like Facebook, Twitter and Reddit by analyzing text using advanced machine learning and deep learning algorithms. In order to collect dataset, we employed both web scraping techniques and publically existing datasets (Twitter, Reddit) that are available on the Kaggle website. Natural language processing (NLP) techniques are applied to preprocess and excerpt meaningful features from the textual data. Several machine learning algorithms are employed to make prophetic models for MDD discovery grounded on verbal patterns, sentiment analysis, and verbal labels associated with depressive symptoms. We analyse our models using three datasets. The two online datasets for which the LSTM algorithm performs best are Reddit with 93.72% accuracy, Twitter with 99.85% accuracy, and our dataset which is extracted using web scraping technologies from Reddit gets 96.47% accuracy utilizing Stacking ensemble. The model's performance is thoroughly assessed using a variety of criteria, such as accuracy, precision, recall, and F1-score. Additionally, We find an approach with a more effective ML framework for enhancing MDD detection. [ABSTRACT FROM AUTHOR]

The striped snakehead, Channa striata, is commercially and nutritionally important due to its medicinal properties, such as wound healing and antimicrobial abilities. This study investigated the reproductive biology of C. striata in relation to hydro-climatic changes using a fuzzy logic approach for long-term management in the wetland ecosystem (Gajner beel), Bangladesh. A total of 1200 C. striata individuals were collected monthly during January to December 2019 using various fishing methods. Measurements of total length (TL), body weight (BW), and gonad weight (GW) were taken with a precision of 0.01 cm and 0.01 g, respectively. The GSI (gonadosomatic index in %), MGSI (modified gonadosomatic index in %), and DI (Dobriyal index) were used to determine size at sexual maturity (Lm) and the spawning season. Results revealed a wide range of TL, ranging from 12.7 to 46 cm. The Lm was estimated between 21.7 and 25.33 cm based on various model as TL vs. GSI, MGSI, and DI; TL-fork length (FL) regressions; and maximum length (Lmax) for this species. The spawning period was observed from April to July, peaking in May, indicated by higher GSI, MGSI, and DI values. Eco-climatic factors showed a significant relationship with GSI, except for total dissolved solids (TDS). Rising temperatures and declining rainfall could delay spawning of C. striata. A fuzzy logic approach was employed to predict future GSI changes over extended periods (e.g., 50 or 60 years), enabling anticipation of shifts in the timing and duration of the spawning season. Finally, the findings of our research might be effective to carry out specific conservation efforts of C. striata in response to climate variability in sub-tropical water. [ABSTRACT FROM AUTHOR]

Diabetes mellitus (DM) poses a major global healthcare challenge, highlighting the need for new treatments beyond current options. Currently available drugs have side effects including weight gain, nausea, vomiting, diarrhea, insulin resistance etc. Therefore, given the benefits of indole derivatives in diabetes and the lack of computational studies on bis-indole-based triazine derivatives with aldose reductase (AR), this study employs in-silico analysis to explore their potential as type-2 diabetes treatments. Based on the Differential Expression analysis, the human aldose reductase (HAR) encoding gene AKR1B1 showed overexpression in GSE30122 diabetes patients (Log2FC = 0.62, P < 0.01). Moreover, the compounds 2-((5,6-di(1H-indol-3-yl)-1,2,4-triazin-3-yl)thio)-1-(3-hydroxy-5-methylphenyl)ethan-1-one (4) and 2-((5,6-di(1H-indol-3-yl)-1,2,4-triazin-3-yl)thio)-1-(4-nitrophenyl)ethan-1-one (8) were identified as leading candidates, showing binding energies of − 62.12, − 81.73 kcal/mol and − 57.19, − 85.97 kcal/mol, respectively. Docking, MM/GBSA screening, molecular dynamics (MD) simulations, PCA, and post-MM/GBSA analysis confirmed their stability and favorable binding compared to the apo protein and control. Further in-vitro, in-vivo, and clinical studies are required to validate their therapeutic potential. [ABSTRACT FROM AUTHOR]

Background: Rice is subjected to various environmental stresses, resulting in significant production losses. Abiotic stresses, particularly drought and salinity, are the leading causes of plant damage worldwide. The High-affinity Potassium Transporter (HKT) gene family plays an important role in enhancing crop stress tolerance by regulating physiological and enzymatic functions. Methods and results: This study investigates the effect of overexpressing the rice HKT1;5 gene in Arabidopsis thaliana on its tolerance to salinity and drought. The OsHKT1;5 gene was introduced into Arabidopsis under the control of 35 S promoter of CaMV via floral dip transformation method. PCR confirmed the integration of the transgene into the Arabidopsis genome, while qPCR analysis showed its expression. Three transgenic lines of OsHKT1;5 were used for stress treatment and phenotypic studies. The overexpressed lines showed considerably higher germination rates, increased leaf counts, greater fresh and dry weights of the roots and shoots, higher chlorophyll contents, longer root lengths, and reduced Na+ levels together with increased K+ ions levels after salt and drought treatments, in comparison to wild-type plants. Furthermore, overexpressed lines exhibited higher antioxidant levels than wild-type plants under salinity and drought conditions. In addition, transgenic lines showed higher expression levels of the OsHKT1;5 gene in both roots and shoots compared to wild-type plants. Conclusions: In conclusion, this study revealed OsHKT1;5 as a promising candidate for enhancing tolerance to salinity and drought stresses in rice, marking a significant step toward developing a new rice variety with improved abiotic stress tolerance. [ABSTRACT FROM AUTHOR]

Mucuna (Family: Fabaceae), a herbaceous, indigenous plant, has been widely used worldwide due its nutritional value and medicinal properties. Mucuna gained recognition due to presence of L-DOPA (L-3, 4-dihydroxyphenylalanine), which is used in the treatment of Parkinson's disease and mental disorders. Traditionally, the plant is used for cuts, wound healing, and pain relief. Standardization of herbal drugs is of immense value to estimate drug efficacy. In the present work, an effective tincture of Mucuna imbricata (MI) DC. ex Baker was prepared through leaf maceration (for 10, 20, 30 days) using varying ethanol-water solvent systems (30%, 40%, 50%, 60%, 70%, 80%, 90% and 100%). A maximum yield (195 mg/10mL) was recorded on the 30th day of maceration in 30% v/v ethyl alcohol concentration, where the yellowish-black tincture had a pH of 5.2, conductivity of 900 µS sec− 1, and specific gravity of 0.99. The results of fluorescence analysis depicted the multifaceted fluorescence attribute of Mucuna imbricata (MI) leaves. Interestingly, the total crude protein (95 mg g− 1 dwt), total flavonoids (1.35 ± 0.51 mg g− 1 dwt) and total alkaloids (111.8 mg g− 1 dwt) contents were highest in 100% grain tincture after 30 days, while the total phenolics were found to be maximum (13.44 ± 0.53 mg g− 1) in 60% grain tincture after 30 days of maceration. 50% scavenging activity was observed at 50 µg mL− 1 of crude tincture after 30 days in 30% grain alcohol. Thus, the best physico-chemical attributes and moderate antioxidant activity were revealed with the longest duration of leaf tincture maceration. [ABSTRACT FROM AUTHOR]

Natural material-based non-volatile resistive switching (RS) memory devices are considered as one of the emerging technologies for the sustainable electronics. Here, Moringa Oleifera Lam. (MO) locally called Sajna leaves are used as an active switching layer for write-once-read-many (WORM) devices. Spin coating technique has been utilized to fabricate the MO leaves based active layer upon ITO. Observed WORM memory devices showed a high memory window (103), reliable read endurance (350 times) and stable data retention behaviour up to 1.5 × 103 s at room temperature. Fitting models revealed the switching mechanisms such as Schottky Emission (SE), trap-filled space charge limited conduction (TSCLC) and Ohmic conduction are involved in the RS process. Based on the obtained results, this work suggests that MO leaves based bio-WORM could be a promising substitute for the next generation non-volatile memory applications. [ABSTRACT FROM AUTHOR]

Garment products are international trade items and essential consumption items. The readymade garment industry of Bangladesh is the second largest garment exporter in the world. The garment industry of Bangladesh consumes one-third of the country's total industrial energy. Such industry consumes significant energy and contributes greenhouse gas (GHG) emissions into the atmosphere. Assessment of energy consumption and GHG emission of garment products helps to understand the baseline status of the production and benchmarking the energy and emission leveling of the product. This paper aims to investigate the energy consumption and GHG emissions of readymade garment (T-shirt) production in Bangladesh. Gate-to-gate production stages have been considered for the investigation. The strategies for the energy and GHG emission-saving potential have been also analyzed. The result showed that the total energy consumption in terms of natural gas, oil, and electricity found are 15,105 m3, 49.93 kg, and 36,265 kWh respectively per day. The total CO2 emission found is 56.65 tons per day. The specific energy consumption found is 12 MJ/piece and the specific emissions of the CO2, CO, CH4, NOx, and SOx are 0.94 kg/piece, 1.69 gm/piece, 3.53 gm/piece, 4.1 gm/piece, and 0.89gm/piece, respectively. The proposed strategies could save energy in the machinery section in the range of 22–70%. The energy saving potential of 25% is possible in the steam generation sector by employing the proposed electric steam generator. The steam generation section gives the highest CO2 emission and the compressor section gives the lowest emission. The specific energy consumption and carbon footprint of Bangladesh-manufactured garments are in very close agreement with the literature studied. [ABSTRACT FROM AUTHOR]

Tungsten carbide (WC) is one of the most robust mold materials in precision glass molding owing to its excellent hardness at high temperatures. However, the fabrication of a microlens array (MLA) shape on a WC mold is challenging. Electrical discharge machining (EDM) is highly effective in fabricating conductive materials regardless of their hardness. However, due to electrode wear, the machining accuracy and consistency of the MLA are gradually reduced in the EDM process. In this study, a micro copper ball–assembled array electrode (BAAE) was proposed and developed to achieve high efficiency, accuracy, and consistency in MLA mold manufacturing. The copper balls aligned in an array on the position template were used as the electrode to fabricate MLA in EDM. The general calculation method for the maximum service cycles of copper balls was developed to achieve maximum utilization of the copper balls. The in situ rotation of the copper ball updated the processing area, significantly improving the consistency of the lenslet morphology. The effects of two polarity processing were investigated, and the positive processing was chosen. The efficiency of BAAE was significantly improved through the implementation of array alignment and path planning. The effectiveness and efficiency of the ball rotation were confirmed by comparing the lenslet morphology with the ball end–formed electrode. The EDM using BAAE is proven to be capable of improving the efficiency, accuracy, and consistency of MLA mold manufacturing. [ABSTRACT FROM AUTHOR]

The heightened frequency of unexpected and unknown disruptions has drawn the attention of academics and practitioners to uncover sustainable solutions to make organizations resilient. Grounding on the logic rooted in the conservation of resource (COR) theory, this study examined the influences of human resource support climate (HRSC) and innovative climate (IC) on organizational resilience (OR) via employee resilience (ER). We followed the deductive reasoning approach and collected 282 responses from business organizations. Our results showed that HRSC significantly influenced IC, and both IC and HRSC significantly influenced OR. The mediation analysis showed that the ER partially mediated the influences of IC and HRSC on OR. The study provides notable insights into the literature of OR and proposes solutions to the neutralization of post-COVID shocks. Theoretical and managerial implications and future research directions have been discussed. [ABSTRACT FROM AUTHOR]

The rapid expansion of varied network systems, including the Internet of Things (IoT) and the Industrial Internet of Things (IIoT), has led to an increasing range of cyber threats. Ensuring robust protection against these threats necessitates the implementation of an effective Intrusion Detection System (IDS). For more than a decade, researchers have delved into supervised machine learning techniques to develop IDS to classify normal and attack traffic. However, building effective IDS models using supervised learning requires a substantial number of benign and attack samples. To collect a sufficient number of attack samples from real-life scenarios is not possible since cyber attacks occur occasionally. Further, IDS trained and tested on known datasets fails in detecting zero-day or unknown attacks due to the swift evolution of attack patterns. To address this challenge, we put forth two strategies for semi-supervised learning-based IDS where training samples of attacks are not required: (1) training a supervised machine learning model using randomly and uniformly dispersed synthetic attack samples; (2) building a One Class Classification (OCC) model that is trained exclusively on benign network traffic. We have implemented both approaches and compared their performances using 10 recent benchmark IDS datasets. Our findings demonstrate that the OCC model based on the state-of-art anomaly detection technique called usfAD significantly outperforms conventional supervised classification and other OCC-based techniques when trained and tested considering real-life scenarios, particularly to detect previously unseen attacks. [ABSTRACT FROM AUTHOR]

Fraudulent transactions continue to pose a concern for financial institutions and organizations, necessitating the development of effective detection tools. Identification and prevention of fraudulent transactions depend heavily on the detection of credit card fraud. Even though instances of credit card fraud are uncommon, they can nonetheless cause significant financial losses because of the high cost of fraudulent transactions. When fraud is discovered early on, investigators can act quickly to stop additional losses. But because the investigation process takes a while, there are only so many warnings that can be looked through in detail in a given day. Thus, a fraud detection model's main goal is to minimize false alarms and missed fraud situations while producing accurate alerts. To improve fraud identification, we provide in this study an integrated multistage ensemble Machine Learning (IMEML) model that incorporates various multistage ensemble models intelligently, such as Ensemble Independent Classifier (EIC), Ensemble Bagging Classifier (EBC), and Ensemble ML Classifier (EMC). In order to overcome the problem of data imbalance, we use a number of methods-including Instant Hardness Threshold with EMC (IHT+EMC), Cluster Centroids (CC), and Randon Under Sampler (RUS)-that go beyond traditional methods. We run our studies on a 284,807-transaction credit card dataset that is made available to the public. The accuracy rates of 99.94%, 99.91%, 99.14%, 99.52%, and perfect 100% for accuracy, precision, recall, f1-score, and AUC score, respectively, are achieved by the suggested model, demonstrating remarkable performance scores. For real-world fraud detection applications, the EIBMC model sets a new benchmark for identifying fraudulent transactions in high-frequency scenarios by outperforming cutting-edge techniques. [ABSTRACT FROM AUTHOR]

The 2022 flood events in Quetta, Pakistan, caused severe damage to the economy, properties, and lives. Therefore, flood risk mapping to identify flood-prone areas is essential for planners and decision-makers to take critical protective measures to control the effects of flooding. This study focuses on mapping flood-prone regions in the Quetta district of Pakistan using an analytical hierarchy process (AHP) and a geographic information system (GIS). The factors influencing flood used in the present study were topographic witness index (TWI), elevation, slope, land use, land cover, precipitation, stream distance, drainage density, and soil type. Weights and ranks were allocated separately to all factors through AHP and were interpreted in a GIS environment. The produced flood hazard model of the study area depicted four zones. These zones ranged from low (19.49%), moderate (43.34%), high (28.30%), to very high (8.87%). The model was further validated through previous flood events in the study area. Around 90% of flood hazard events in the past took place mainly in the produced model's very high and high zones, which is why the current model is reliable. Finally, integrating geospatial approaches with AHP in flood hazard mapping is a quick, reliable, and affordable method that may be utilized in the area. [ABSTRACT FROM AUTHOR]

Financial institutions and businesses face an ongoing challenge from fraudulent transactions, prompting the need for effective detection methods. Detecting credit card fraud is crucial for identifying and preventing unauthorized transactions. While credit card fraud incidents are relatively rare, they can result in substantial financial losses, particularly due to the high monetary value associated with fraudulent transactions. Timely detection of fraud enables investigators to take swift actions to mitigate further losses. However, the investigation process is often time-consuming, limiting the number of alerts that can be thoroughly examined each day. Therefore, the primary objective of a fraud detection model is to provide accurate alerts while minimizing false alarms and missed fraud cases. In this paper, we introduce a state-of-the-art hybrid ensemble (ENS) dependable machine learning (ML) model that intelligently combines multiple algorithms with proper weighted optimization using grid search, including decision tree (DT), random forest (RF), K-nearest neighbor (KNN), and multilayer perceptron (MLP), to enhance fraud identification. To address the data imbalance issue, we employ the instant hardness threshold (IHT) technique in conjunction with logistic regression (LR), surpassing conventional approaches. Our experiments are conducted on a publicly available credit card dataset comprising 284,807 transactions. The proposed model achieves impressive accuracy rates of 99.66%, 99.73%, 98.56%, and 99.79%, and a perfect 100% for the DT, RF, KNN, MLP and ENS models, respectively. The hybrid ensemble model outperforms existing works, establishing a new benchmark for detecting fraudulent transactions in high-frequency scenarios. The results highlight the effectiveness and reliability of our approach, demonstrating superior performance metrics and showcasing its exceptional potential for real-world fraud detection applications. [ABSTRACT FROM AUTHOR]

Trajectories of neurodevelopment of children in disadvantageous situations are greatly influenced by cognitive function of the caregiver during that period. We aimed to examine whether there is any association of maternal cognitive function with the neurodevelopmental outcomes of children living in slums in Bangladesh. We enrolled 207 mother-child (12 to 42 months) dyads. Rigorously trained testers assessed children's cognitive, language, and motor development using Bayley-III and behavior using Wolke's behavior ratings. Cognitive function of mothers was measured with the modified version of Mini-Mental State Examination (MMSE), and Digit Span scale. Other measures included home environment, parenting practices, self-esteem, anthropometry, and socio-economic status (SES). Maternal cognitive function was positively associated with cognitive, language, and motor performance of children with or without controlling for age of the children. Additionally, parenting practice was positively associated with the domains of neurodevelopment equally. Finally, multiple linear regression analysis divulged that MMSE is a significant predictor of both cognitive and language development after controlling for possible covariates, but not for the motor development. Moreover, better home stimulation predicted improved cognitive ability and higher self-esteem of mothers predicted improved motor ability of children. Maternal cognitive function has really a significant influence on the neurodevelopment of slum-dwelling children in Bangladesh. Therefore, intervention programs need to be emphasized on maternal cognition to improve neurodevelopment of children raised in adversity. [ABSTRACT FROM AUTHOR]

Cotton is a major cash crop in the agriculture of Bangladesh, primarily fueling its substantial textile industry. Although local production is limited, cotton has the potential to bolster the country's economic growth by supporting local economies and improving agricultural sustainability. An in-depth sustainability assessment of this integral crop is essential to ensure its long-term economic and environmental viability. This study aims to evaluate the sustainability of cotton production in Bangladesh and suggest strategic enhancements and policy measures to improve its environmental, economic, and social aspects. Data were collected from 60 farmers in two villages of Jashore district using a simple random sampling technique. A combination of descriptive, mathematical and statistical techniques was used to analyze the data. The findings highlighted a profitable benefit–cost ratio of 1.88 for cotton production. Positive impacts on cotton profitability were notably linked to the costs of labor, land preparation, and insecticides and pesticides. Despite this, 60% of farmers faced substantial challenges, including financial barriers, limited access to modern agricultural technologies, and a lack of training in sustainable practices. Economic constraints were a major barrier, although the environmental benefits of sustainable practices were widely acknowledged. To address these issues, the study recommends optimizing inputs like labor, land preparation, and pest management, and advocates for policy actions such as subsidies, tax incentives, and enhanced training programs to support the widespread adoption of sustainable practices across Bangladesh. [ABSTRACT FROM AUTHOR]

This study investigates the potential of renewable energy sources to address the growing energy demands of developing nations while ensuring environmental sustainability. Here, a model-based analysis to assess the feasibility of utilizing rice straw pyrolysis as a renewable energy source is investigated. The thermogravimetric analysis identified the optimal temperature range for pyrolysis (230–400 °C) and the Coats–Redfern model was employed for kinetic analysis. Diffusion models, particularly D3, exhibited the best fit with an activation energy of 16–18 kJ/mol. Thermodynamic analysis confirmed the endothermic nature of the process, with positive enthalpy and negative entropy changes indicating an increase in energy and a more ordered product. These findings suggest that rice straw pyrolysis holds promise as a viable renewable energy option. This study contributes to understanding the rice straw pyrolysis mechanism which could be helpful for its effective utilization at commercial scale. [ABSTRACT FROM AUTHOR]

Clonal hematopoiesis of indeterminate potential (CHIP) is linked to diverse aging-related diseases, including hematologic malignancy and atherosclerotic cardiovascular disease (ASCVD). While CHIP is common among older adults, the underlying factors driving its development are largely unknown. To address this, we performed whole-exome sequencing on 8,374 blood DNA samples collected from 4,187 Atherosclerosis Risk in Communities Study (ARIC) participants over a median follow-up of 21 years. During this period, 735 participants developed incident CHIP. Splicing factor genes (SF3B1, SRSF2, U2AF1, and ZRSR2) and TET2 CHIP grow significantly faster than DNMT3A non-R882 clones. We find that age at baseline and sex significantly influence the incidence of CHIP, while ASCVD and other traditional ASCVD risk factors do not exhibit such associations. Additionally, baseline synonymous passenger mutations are strongly associated with CHIP status and are predictive of new CHIP clone acquisition and clonal growth over extended follow-up, providing valuable insights into clonal dynamics of aging hematopoietic stem and progenitor cells. This study also reveals associations between germline genetic variants and incident CHIP. Our comprehensive longitudinal assessment yields insights into cell-intrinsic and -extrinsic factors contributing to the development and progression of CHIP clones in older adults. Clonal hematopoiesis of indeterminate potential (CHIP) is linked to diverse aging-related diseases but the underlying factors driving its development are largely unknown. Here, longitudinal assessment of 4,187 participants over 21 years provides insights into cell-intrinsic and -extrinsic factors contributing to the development and progression of CHIP clones in older adults. [ABSTRACT FROM AUTHOR]

This article presents a numerical analysis of a very thin concentric octagonal elliptical ring resonator (CORR) with a cylindrical rod optical nano-metamaterial absorber (ONMMA) that is designed to absorb solar energy in the infrared, visible, and ultraviolet ranges. The ONMMA is made of gold and nickel resonators on a quartz substrate with a gold plate ground material. The absorption rate of the ONMMA is over 90% from 593.84–829.28 THz, with an average absorption rate of 93.891%. This means that the ONMMA can absorb more than 80% of the energy in the infrared, visible, and a small portion of the ultraviolet range. It also has an absorption rate of over 75% from 226.64–853.04 THz, with an average absorption rate of 88.945%. This covers the entire visible spectrum, with a small portion in the infrared and ultraviolet regions. The design has an operational bandwidth of 200–920 THz, with an average absorption rate of 86.424%. The ONMMA achieves a maximum absorption rate of 99.866% at 705.44 THz. The design of the octagonal ONMMA ensures that it is not affected by the polarization of the incident light and can absorb energy even at oblique angles up to 60 degrees. The study also analyzed the effects of different materials and geometrical parameters on the absorption characteristics, as well as the distribution of surface current, magnetic field, electric field, and power loss. The proposed ONMMA has promising applications in infrared detection, solar cells, and multicolor imaging applications. [ABSTRACT FROM AUTHOR]

Based on the notions of an integrated approach, this study investigates the roles of people, plan, and process related activities in strategy implantation success in organizational contexts. Data were collected from 307 top- and mid-level managers working in public and private sector organizations in Kazakhstan. The findings revealed a significant influence of people-, plan-, and process-related activities on strategy implementation success and competitive advantages. However, the study found insignificant moderating roles of hierarchy culture and transactional leadership in strategy implementation and competitive advantage relationships. The findings reiterate the importance of an integrated approach in strategy implementation success and provide guidelines to initiate appropriate people, plan and process related activities in future strategic management practices. [ABSTRACT FROM AUTHOR]

Background: Currently, food price inflation is a widespread issue in Bangladesh as well as the rest of the world. Malnutrition is a common issue among children that can have long-lasting effects on their development and overall health. There have been lots of studies conducted to identify the factors responsible for child malnutrition, but inflation is rarely considered a factor in child malnutrition. We aimed to determine the relationship between food price inflation and stunting (Height-for-Age Z-score (HAZ)) in children under five years of age in Bangladesh. Method: The study utilized food price data from the World Food Programme database and malnutrition (stunting) information from the 2014 and 2017-18 Bangladesh Demographic Health Surveys (BDHS). This includes the total study period from 2009 to 2018. Food prices were linked to the BDHS dataset using each child's birth month. For each child, the average food prices from 9 months prior to 5 months post-birth, including their birth month, were recorded to calculate month-to-month inflation. This inflation was computed for rice (coarse), oil, wheat flour, and lentils by comparing the price sum of each item from one month to the previous month and dividing by the total price of the preceding month. A generalized linear regression model was used to assess the relationship between food price inflation and stunting, with stunting as the dependent variable. Other explanatory variables included wealth index, sex of the child, height, weight, mother's education, respondent's current pregnancy, and breastfeeding status. Results: Our study has revealed that food price inflation has a significant negative effect on stunting, with a coefficient of -0.127 (p < 0.001). Furthermore, we have identified several other factors that have also significantly negative associations with stunting, including the wealth index (p < 0.001), mother's education level (p < 0.001), mother's pregnancy status (p < 0.001), breastfeeding (p < 0.001), child's age (p < 0.001). child's weight (p < 0.001) has significantly positive effect on stunting. However, we did not find any significant differences in stunting between boys and girls. Conclusion: In conclusion, the findings of this study underscore the significant negative impact of food price inflation on child stunting, emphasizing the need to acknowledge this factor alongside others. These results highlight the critical role of addressing food price inflation as a key determinant of stunting, in conjunction with various other contributing factors, in efforts to combat childhood malnutrition. [ABSTRACT FROM AUTHOR]

The k-Nearest Neighbors (kNN) method, established in 1951, has since evolved into a pivotal tool in data mining, recommendation systems, and Internet of Things (IoT), among other areas. This paper presents a comprehensive review and performance analysis of modifications made to enhance the exact kNN techniques, particularly focusing on kNN Search and kNN Join for high-dimensional data. We delve deep into 31 kNN search methods and 12 kNN join methods, providing a methodological overview and analytical insight into each, emphasizing their strengths, limitations, and applicability. An important feature of our study is the provision of the source code for each of the kNN methods discussed, fostering ease of experimentation and comparative analysis for readers. Motivated by the rising significance of kNN in high-dimensional spaces and a recognized gap in comprehensive surveys on exact kNN techniques, our work seeks to bridge this gap. Additionally, we outline existing challenges and present potential directions for future research in the domain of kNN techniques, offering a holistic guide that amalgamates, compares, and dissects existing methodologies in a coherent manner. [ABSTRACT FROM AUTHOR]

Female labor migration for livelihood is becoming a common phenomenon in migrant-sending countries. Although female workers migrate with a temporary contract for a better livelihood, they are experienced with various exploitations and difficulties while working abroad. Therefore, many female migrants returned to the country from the Middle East, and they faced enormous challenges in reintegration. Thus, this paper critically reviews the role of migration-related NGOs in the reintegration of returned female migrants in Bangladesh. It also explores the critical limitations of the NGO's approach to this reintegration. The paper is qualitative research that includes primary and secondary data. Primary data were collected through face-to-face in-depth interviews with 27 participants. The paper also employs secondary data from articles, books, and reports of migration-focused NGOs. Based on the findings from data, the paper argues that the migration-focused NGOs in Bangladesh positively impact the reintegration of Bangladeshi returned female migrants by providing economic assistance, social, psychological, and legal support to returned women, entrepreneurship training, social mobilization strategies, and grassroots-level activities. The data also show that the migration-focused NGOs in Bangladesh face challenges in the reintegration of many returned women due to the NGOs' aid dependency, a lack of comprehensive actions, shortage of funds, required resources, lack of coordination, socioeconomic structure, stigma, and religious issues. Finally, the paper includes some policy suggestions that would be helpful for the NGO's approach to reintegrating returned women migrants. [ABSTRACT FROM AUTHOR]

The principal goal of this research revolves around imparting antimicrobial activities on printed cotton fabric utilizing silver nanoparticles. Two colorants, dyes, and pigments are used for printing of 100% scoured and bleached cotton, fabric, and both fabrics are assessed to get antimicrobial activity of printed cloth. Screen-printing techniques are used here to apply the pigment and reactive dye onto the fabric. The outcomes of the study reflect that the addition of silver nanoparticles to the reactive dye-printed and pigment-printed fabric significantly decreases bacterial growth up to 99.99%. In addition, Silver nanoparticles increase color intensity by around 25% in pigment printing, however, reduce it in reactive printing. Both methods enhance color fastness by increasing resistance to fading, staining, and rubbing. The scanning electron microscopy (SEM) analysis reveals that the print paste and sodium alginate have an impact on the distribution of nanoparticles. Through X-ray diffraction (XRD), it has been determined that the sample maintains a crystalline form and an average size of 29.185 nm of silver nanoparticles. Fourier Transform Infrared Spectroscopy (FTIR) analysis indicates no notable molecular-level alterations. [ABSTRACT FROM AUTHOR]

This research extensively used different progressive machine learning (ML) techniques to predict the compressive strength (CS) and tensile strain (TSt) of engineered cementitious composites (ECC) with 14 input variables and six algorithms. Specifically, random forest (RF), support vector machine, extreme gradient boosting (XGBoost), light gradient boosting machine, categorical gradient boosting (CatBoost), and natural gradient boosting techniques were used in the present study, to understand mechanical properties of ECC meanwhile these properties are crucial for design codes and developing new reliable models for mixtures. The discrepancy between the ML technique and specific ECC expected outputs is novel in this study and will aid researchers in better understanding of ECC features. To estimate the CS and TSt of the ECC, 2535 and 1469 input data points, respectively, were incorporated based on the material ratio, W/B, and different properties of the fibers. In addition, hyperparameter optimization techniques have also been used in ML to improve over fitting and make the model more accurate and robust. Moreover, an error analysis was highlighted between the actual and predicted CS and TSt of the ECC with each ML technique. Also, the significance and influence of the variable inputs that affect the CS and TSt were explained using the Shapley additive explanation (SHAP) approach. Among all approaches, CatBoost and XGBoost predicted the CS and TSt of ECC with greater accuracy than other techniques in terms of the coefficient of determination (R2), mean square error, mean absolute error, root mean square error, and symmetric mean absolute percentage error. The training and testing R2 values of CatBoost and XGBoost for predicting the CS and TSt of ECC were 0.96, 0.89, 0.89, and 0.76, respectively. SHAP analysis revealed that W/B and fiber elongation were the most significant input variables for the CS and TSt of the ECC. [ABSTRACT FROM AUTHOR]

The primary priority of bridge construction over a waterway is to support and confirm constant communication. Conversely, it may locally disrupt the health of the river which will have an adverse influence on the hydrological as well as morphological behavior of nearby regions by way of the river's narrowing. The current study evaluated the effect of bridge structure on river morphological characteristics for Teesta River. Hence to do, this work robbed two approaches. Firstly, the Landsat images from a pre-road bridge (2001 and 2006) and post-road bridge construction (2011, 2016, and 2021) are processed and used for bar formation and bank line shifting. Secondly, the bar properties, differences in channel width, and changes in river bank were evaluated using geospatial technology. The outcomes revealed a recurrent change of bar area and channel width at the upstream side of the bridges and likewise dominating at the downstream side. Throughout the post-road bridge period, the average bar area has increased noticeably by 32.45 sq.km which is 7.75% of the total river area and the downstream bar area has also increased considerably. Besides, both bank lines of the river were dominated by erosion in the post-road bridge construction era. Accordingly, the Teesta Road bridge's existence has had a significant impact on the morphological modification in recent years. The study also reveals that the bars and islands of Teesta River undergo a drastic change and the river can be treated as a braided one, and also showed the minor trend of meandering. The findings of this research may be supportive for sustainable and long-term planning and development of the rivers and neighboring floodplains in Bangladesh. [ABSTRACT FROM AUTHOR]

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Stretch-denim is a unique, trendy, and sturdy fabric produced with coarser cotton yarns requiring high consumption of cotton fiber. With the increasing expenses of cotton and the adverse ecological effects linked to its cultivation, it became necessary to investigate an alternative fiber. In this context, a novel elastic jute yarn was created by incorporating elastane in the core, marking the first instance of such innovation, aimed at producing articles with a pleasant stretch for enhanced comfort. To accurately predict the elastane draw ratio, an I-optimal design, which is a part of the response surface methodology, was employed using design expert software. The study involved nine experimental runs to produce 276 tex elastic jute core-spun yarns at different elastane draw ratios ranging from 1.4 to 4.6. Surface response models were developed and found statistically significant for mass variation, imperfection, hairiness, tenacity, and elongation of yarn, with p-values below 0.05. Using the optimized formulation obtained from the response models, the optimum elastane draw ratio was determined to be 3.42, which was then used to produce optimized core-spun yarn. The quality parameters of produced yarn closely matched the predicted values, suggesting the effectiveness of the response model in navigating and predicting the characteristics of jute core-spun yarn. The newly developed elastic jute yarn is expected to widen the applications of jute yarns where elasticity is necessary, especially in producing stretch denim products by replacing the cotton counterpart. [ABSTRACT FROM AUTHOR]

Historically, the analysis of stimulus-dependent time–frequency patterns has been the cornerstone of most electroencephalography (EEG) studies. The abnormal oscillations in high-frequency waves associated with psychotic disorders during sensory and cognitive tasks have been studied many times. However, any significant dissimilarity in the resting-state low-frequency bands is yet to be established. Spectral analysis of the alpha and delta band waves shows the effectiveness of stimulus-independent EEG in identifying the abnormal activity patterns of pathological brains. A generalized model incorporating multiple frequency bands should be more efficient in associating potential EEG biomarkers with first-episode psychosis (FEP), leading to an accurate diagnosis. We explore multiple machine-learning methods, including random-forest, support vector machine, and Gaussian process classifier (GPC), to demonstrate the practicality of resting-state power spectral density (PSD) to distinguish patients of FEP from healthy controls. A comprehensive discussion of our preprocessing methods for PSD analysis and a detailed comparison of different models are included in this paper. The GPC model outperforms the other models with a specificity of 95.78% to show that PSD can be used as an effective feature extraction technique for analyzing and classifying resting-state EEG signals of psychiatric disorders. [ABSTRACT FROM AUTHOR]

The stringent global environmental regulations and human health concerns associated with synthetic dyes pave the way for utilizing biodegradable dyes from natural resources for coloring textiles. The coloration of silk fabric using the leaf extracts of natural dye henna to impart an attractive and durable color effect was studied in this research. The henna-dyed silk fabric was then functionalized with antimicrobial properties by padding with betel leaf extracts. A brilliantly colored antibacterial fabric was, therefore, developed due to the combined treatment of henna and betel leaf extracts. The treated silk fabric exhibited an adequate resistance against washing, perspiration, and rubbing. The morphological investigation revealed smooth surface characteristics with no deterioration of fiber structure after high temperature dyeing. The existence of henna and betel leaf extracts in silk fiber was confirmed from their respective characteristic peaks of the FT-IR spectroscopy. The improved germicidal activity against staphylococcus aureus (S. aureus) microbes has also been evidenced due to the concomitant treatment of henna dye and betel leaf on the fabric. Besides, the satisfactory moisture management profile conferred favorable thermal comfort properties of dyed silk fabrics worn in hot and moist environment. The consecutive dyeing and padding of silk fabric with henna and betel leaf extract are, therefore, expected to provide attractive color with excellent antibacterial activity and satisfactory moisture management properties for wearing in a hot and humid condition with better comfort. [ABSTRACT FROM AUTHOR]

Copyright of Low-Carbon Materials & Green Construction is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Electrode wear (EW) during the electrical discharge machining (EDM) process determines surface topography accuracy. In this paper, an automatic evaluation method is proposed by integrating image segmentation and three-dimensional (3D) registration (IS-3DR) to evaluate the EW more accurately. First, the original two-dimensional (2D) image and the 3D point cloud were derived from a laser microscope image, and the 2D worn area was determined by automatic recognition and segmentation. Then, 3D registration between the 3D point cloud and the 2D worn area was utilized to qualitatively define the 3D EW area. Finally, the 3D EW volume was obtained quantitatively through micro-element calculation based on the 3D EW area and the shape fitting of the original electrode. The results showed that the intersection over union (IOU) of image segmentation was higher than 97.5%, compared with the manually labeled image segmentation method. Compared with IS-3DR, the random error of the two-dimensional evaluation method could reach 27.08%, indicating higher reliability and stability of IS-3DR. The EDM experiment was carried out to investigate the effect of parameters on EW. The experimental results showed that the effects of current and servo voltage were most significant, followed by pulse width. [ABSTRACT FROM AUTHOR]

In the domain of cyber-physical systems, wireless sensor networks (WSNs) play a pivotal role as infrastructures, encompassing both stationary and mobile sensors. These sensors self-organize and establish multi-hop connections for communication, collectively sensing, gathering, processing, and transmitting data about their surroundings. Despite their significance, WSNs face rapid and detrimental attacks that can disrupt functionality. Existing intrusion detection methods for WSNs encounter challenges such as low detection rates, computational overhead, and false alarms. These issues stem from sensor node resource constraints, data redundancy, and high correlation within the network. To address these challenges, we propose an innovative intrusion detection approach that integrates machine learning (ML) techniques with the Synthetic Minority Oversampling Technique Tomek Link (SMOTE-TomekLink) algorithm. This blend synthesizes minority instances and eliminates Tomek links, resulting in a balanced dataset that significantly enhances detection accuracy in WSNs. Additionally, we incorporate feature scaling through standardization to render input features consistent and scalable, facilitating more precise training and detection. To counteract imbalanced WSN datasets, we employ the SMOTE-Tomek resampling technique, mitigating overfitting and underfitting issues. Our comprehensive evaluation, using the wireless sensor network dataset (WSN-DS) containing 374,661 records, identifies the optimal model for intrusion detection in WSNs. The standout outcome of our research is the remarkable performance of our model. In binary classification scenarios, it achieves an accuracy rate of 99.78%, and in multiclass classification scenarios, it attains an exceptional accuracy rate of 99.92%. These findings underscore the efficiency and superiority of our proposal in the context of WSN intrusion detection, showcasing its effectiveness in detecting and mitigating intrusions in WSNs. [ABSTRACT FROM AUTHOR]

Cryogenic heat treatment is an advanced heat treatment process employed to modify the microstructure of a cutting bit to enhance its properties. This research study aims to investigate the effects of deep cryogenic treatment under different soaking times on the tribological performance of rock cutting bits. Cryogenic treatment was applied for 12-, 18-, 24-, 30-, and 36-h soaking duration, and their corresponding microstructural modifications were identified through SEM and XRD analysis. In accordance with the mechanical properties of deep cryogenic treated WC, the hardness initially strengthened up to 24 h (52.49%). Further increase in cryogenic treatment (36 h) leads to drop in hardness (CT 36: 32.46%). The microstructural observation confirms the conversion of martensitic phase from fcc to hcp-Co phase. The atomic percentage of Co was found to be higher (35.42%) for CT 24 bit as compared to UT bit. The variation in properties is due to the formation of η carbides (Co3W3C, Co6W6C) and the martensitic transformation of α-Co to ε-Co phase. Wear tests were conducted under dry condition using CT and UT bits. Multi-criteria decision-making optimization has been carried out through the grey fuzzy integrated Taguchi technique to obtain the optimized soaking duration along with load and speed. To predict the value of GFRG, an ANFIS model was developed, which resulted in 94.57% prediction accuracy. The treatment time influences 86.29% of the output responses. According to the results obtained, CT 24 bit exhibited best optimized results as compared to other bits, and hence, 24-h cryogenic holding time can be recommended for rock cutting applications. [ABSTRACT FROM AUTHOR]

The shifting of tannery industries from Hazaribagh to Savar adjacent to Dhaleshwari River might have inevitable adverse impacts, especially the heavy metal contamination of both terrestrial and aquatic ecosystems. The study was carried out to investigate the concentrations of four heavy metals in the soil, water, and plant samples collected from around Dhaleshwari River adjacent to the Bangladesh Small & Cottage Industries Corporation Tannery Industrial Estate, Dhaka. This study revealed that average concentrations of cadmium and chromium in soil exceeded the maximum permissible limit of World Health Organization (1996) and average concentrations of lead, cadmium, and chromium in water exceeded the maximum permissible limit of World Health Organization (2011) and Environmental Conservation Rules (1997). The average concentrations of lead, cadmium, and chromium in the roots and shoots of both Eichhornia crassipes and Cynodon dactylon exceeded the maximum permissible limit of Food and Agriculture Organization/ World Health Organization (2016). Ecological risk assessment using indices model for soil pollution indicated that soil is mostly contaminated with cadmium and chromium which can pose strong ecological risk Health risk assessment using indices model for water pollution revealed the high degree of contamination and unacceptable non-carcinogenic risk and carcinogenic risk for adults as well as children through ingestion of water. Average bioconcentration factor and bioaccumulation factor were higher in Eichhornia crassipes than Cynodon dactylon for lead, cadmium, and chromium. Average translocation factor was also higher in Eichhornia crassipes for all the metals except cadmium. It is suggested remedial and mitigation measures be instituted to control environmental degradation of the newly established Tannery Industrial Estate. [ABSTRACT FROM AUTHOR]

This study uses the rational Sine-Gordon expansion (RSGE) method to investigate the dynamical behavior of traveling wave solutions of the water wave phenomena for the time-fractional phi-four equation and the (2 + 1) dimensional Calogero-Bogoyavlanskil schilf (CBS) equation based on the conformable derivative. The technique uses the sine-Gordon equation as an auxiliary equation to generalize the well-known sine-Gordon expansion. It adopts a more broad strategy, a rational function rather than a polynomial one, of the solutions of the auxiliary equation, in contrast to the traditional sine-Gordon expansion technique. Several explanations for hyperbolic functions may be produced using the previously stated approach. The approach mentioned above is employed to provide diverse solutions of the time-fractional phi-four equation and the (2 + 1) dimensional CBS equations involving hyperbolic functions, such as soliton, single soliton, multiple-soliton, kink, cusp, lump-kink, kink double-soliton, and others. The RSGE approach enhances our comprehension of nonlinear processes, offers precise solutions to nonlinear equations, facilitates the investigation of solitons, propels the development of mathematical tools, and is applicable in many scientific and technical fields. The solutions are graphically shown in three-dimensional (3D) surface and contour plots using MATLAB software. All screens display the absolute wave configurations in the resolutions of the equation with the proper parameters. Furthermore, it can be deduced that the physical properties of the found solutions and their characteristics may help us comprehend how shallow water waves move in nonlinear dynamics. [ABSTRACT FROM AUTHOR]

The unique physical and chemical properties of silica fume enhance the compressive strength and water resistance of cement mortar. The study aims to investigate the effect of silica fume in cement mixes in terms of strength-gaining characteristics and water-resistance attributes. The silica fume was added in amounts of 0%, 10%, 20%, and 25% as a partial cement replacement for the production of mortar. A total of 120 mortar cubes were prepared with steel molds of 50 × 50 × 50 mm dimensions. Two water-cement ratios (w/c) of 0.4 and 0.5 were utilized to investigate the water-resistance properties. A water-resistant property was calculated using the compressive strength ratio in the saturated surface dry (SSD) state to the oven-dry condition. The compressive strength ratios in SSD and the oven-dry state were measured after curing in water for 3, 7, 28, 45, and 90 days. After 90 days of curing, the sample with a water-cement ratio of 0.4 exhibited the greatest compressive strength and the highest ratio of SSD to the oven-dry state. The higher SSD to oven-dry strength ratio shows the silica fume possesses superior water resistance. This study concludes that silica fume possesses great pozzolanic activity and excellent resistance to water. The incorporation of silica fume creates a concrete composition that enables the construction of durable concrete structures. [ABSTRACT FROM AUTHOR]

Copper Indium Gallium Sulfide Selenide (CIGSSe)-based solar cells, featuring Al/ZnO/ZnMnO/CIGSSe/Cu2O/Ni layers, are optimized using the solar cell capacitance simulator (SCAPS) for enhanced photovoltaic (PV) performance. The solar cell design incorporates a CIGSSe absorber layer, a zinc manganese oxide (ZnMnO) buffer layer, and a zinc oxide (ZnO) window layer. The upper/top and back contacts are made of aluminum (Al) and nickel (Ni), respectively, with an electron-reflected-hole transport layer (ER-HTL) of cuprous oxide (Cu2O). The performance of the proposed structure can be improved by adjusting the thicknesses of the absorber, buffer, and window layers, along with the acceptor and donor concentrations of the absorber and buffer layers, series and shunt resistance, and temperature. The configuration improves the cell structure's open-circuit voltage (VOC), short-circuit current (JSC), fill factor (FF), and power conversion efficiency (PCE). For optimal outcomes, set the acceptor and donor concentrations in the absorber and buffer layers to 1017 and 1018 cm–3, respectively. Furthermore, keep the thicknesses of the absorber layer at 2000 nm, the window and buffer layers at 50 nm, and the ER-HTL at 10 nm. The optimized model demonstrates PV performance characteristics of 1.0642 V for VOC, 36.10 mA/cm2 for JSC, 81.06% for FF, and 31.15% for PCE under the AM1.5G spectrum. Furthermore, it exhibits a quantum efficiency of around 95.23% at visible wavelengths. [ABSTRACT FROM AUTHOR]