Hi-gMISnet: generalized medical image segmentation using DWT based multilayer fusion and dual mode attention into high resolutionpGAN.

OBJECTIVE: Automatic medical image segmentation is crucial for accurately isolating target tissue areas in the image from background tissues, facilitating precise diagnoses and procedures. While the proliferation of publicly available clinical datasets led to the development of deep learning-based medical image segmentation methods, a generalized, accurate, robust, and reliable approach across diverse imaging modalities remains elusive. APPROACH: This paper proposes a novel high-resolution parallel generative adversarial network (pGAN)-based generalized deep learning method for automatic segmentation of medical images from diverse imaging modalities. The proposed method showcases better performance and generalizability by incorporating novel components such as partial hybrid transfer learning, discrete wavelet transform (DWT)-based multilayer and multiresolution feature fusion in the encoder, and a dual mode attention gate in the decoder of the multi-resolution U-Net-based GAN. With multi-objective adversarial training loss functions including a unique reciprocal loss for enforcing cooperative learning in pGANs, it further enhances the robustness and accuracy of the segmentation map. MAIN RESULTS: Experimental evaluations conducted on nine diverse publicly available medical image segmentation datasets, including PhysioNet ICH, BUSI, CVC-ClinicDB, MoNuSeg, GLAS, ISIC-2018, DRIVE, Montgomery, and PROMISE12, demonstrate the proposed method's superior performance. The proposed method achieves mean F1 scores of 79.53%, 88.68%, 82.50%, 93.25%, 90.40%, 94.19%, 81.65%, 98.48%, and 90.79%, respectively, on the above datasets, surpass state-of-the-art segmentation methods. Furthermore, our proposed method demonstrates robust multi-domain segmentation capabilities, exhibiting consistent and reliable performance. The assessment of the model's proficiency in accurately identifying small details indicates that the high-resolution generalized medical image segmentation network (Hi-gMISnet) is more precise in segmenting even when the target area is very small. SIGNIFICANCE: The proposed method provides robust and reliable segmentation performance on medical images, and thus it has the potential to be used in a clinical setting for the diagnosis of patients.

Technological Tools and Artificial Intelligence in Estrus Detection of Sows-A Comprehensive Review.

In animal farming, timely estrus detection and prediction of the best moment for insemination is crucial. Traditional sow estrus detection depends on the expertise of a farm attendant which can be inconsistent, time-consuming, and labor-intensive. Attempts and trials in developing and implementing technological tools to detect estrus have been explored by researchers. The objective of this review is to assess the automatic methods of estrus recognition in operation for sows and point out their strong and weak points to assist in developing new and improved detection systems. Real-time methods using body and vulvar temperature, posture recognition, and activity measurements show higher precision. Incorporating artificial intelligence with multiple estrus-related parameters is expected to enhance accuracy. Further development of new systems relies mostly upon the improved algorithm and accurate data provided. Future systems should be designed to minimize the misclassification rate, so better detection is achieved.

CAM-QUS guided self-tuning modular CNNs with multi-loss functions for fully automated breast lesion classification in ultrasound images.

Objective.Breast cancer is the major cause of cancer death among women worldwide. Deep learning-based computer-aided diagnosis (CAD) systems for classifying lesions in breast ultrasound images can help materialise the early detection of breast cancer and enhance survival chances.Approach.This paper presents a completely automated BUS diagnosis system with modular convolutional neural networks tuned with novel loss functions. The proposed network comprises a dynamic channel input enhancement network, an attention-guided InceptionV3-based feature extraction network, a classification network, and a parallel feature transformation network to map deep features into quantitative ultrasound (QUS) feature space. These networks function together to improve classification accuracy by increasing the separation of benign and malignant class-specific features and enriching them simultaneously. Unlike the categorical crossentropy (CCE) loss-based traditional approaches, our method uses two additional novel losses: class activation mapping (CAM)-based and QUS feature-based losses, to capacitate the overall network learn the extraction of clinically valued lesion shape and texture-related properties focusing primarily the lesion area for explainable AI (XAI).Main results.Experiments on four public, one private, and a combined breast ultrasound dataset are used to validate our strategy. The suggested technique obtains an accuracy of 97.28%, sensitivity of 93.87%, F1-score of 95.42% on dataset 1 (BUSI), and an accuracy of 91.50%, sensitivity of 89.38%, and F1-score of 89.31% on the combined dataset, consisting of 1494 images collected from hospitals in five demographic locations using four ultrasound systems of different manufacturers. These results outperform techniques reported in the literature by a considerable margin.Significance.The proposed CAD system provides diagnosis from the auto-focused lesion area of B-mode BUS images, avoiding the explicit requirement of any segmentation or region of interest extraction, and thus can be a handy tool for making accurate and reliable diagnoses even in unspecialized healthcare centers.

A qualitative Design and optimization of CIGS-based Solar Cells with Sn2S3 Back Surface Field: A plan for achieving 21.83 % efficiency.

Conventional Copper Indium Gallium Di Selenide (CIGS)-based solar cells are more efficient than second-generation technology based on hydrogenated amorphous silicon (a-Si: H) or cadmium telluride (CdTe). So, herein the photovoltaic (PV) performance of CIGS-based solar cells has been investigated numerically using SCAPS-1D solar simulator with different buffer layer and less expensive tin sulfide (Sn2S3) back-surface field (BSF). At first, three buffer layer such as cadmium sulfide (CdS), zinc selenide (ZnSe) and indium-doped zinc sulfide ZnS:In have been simulated with CIGS absorber without BSF due to optimized and non-toxic buffer. Then the optimized structure of Al/FTO/ZnS:In/CIGS/Ni is modified to become Al/FTO/ZnS:In/CIGS/Sn2S3/Ni by adding a Sn2S3 BSF to enhanced efficiency. The detailed analysis have been investigated is the influence of physical properties of each absorber and buffer on photovoltaic parameters including layer thickness, carrier doping concentration, bulk defect density, interface defect density. This study emphasizes investigating the reasons for the actual devices' poor performance and illustrates how each device's might vary open-circuit voltage (VOC), short-circuit current density (JSC), fill factor (FF), power conversion efficiency (PCE), and quantum efficiency (QE). The optimized structure offers outstanding power conversion efficiency (PCE) of 21.83 % with only 0.80 µm thick CIGS absorber. The proposed CIGS-based solar cell performs better than the previously reported conventional designs while also reducing CIGS thickness and cost.

Bakuchiol, a natural constituent and its pharmacological benefits.

Background and aims: Natural compounds extracted from medicinal plants have recently gained attention in therapeutics as they are considered to have lower Toxicity and higher tolerability relative to chemically synthesized compounds. Bakuchiol from Psoralea corylifolia L. is one such compound; it is a type of meroterpene derived from the leaves and seeds of Psoralea corylifolia plants. Natural sources of bakuchiol have been used in traditional Chinese and Indian medicine for centuries due to its preventive benefits against tumors and inflammation. It plays a strong potential role as an antioxidant with impressive abilities to remove Reactive Oxygen Species (ROS). This review has focused on bakuchiol's extraction, therapeutic applications, and pharmacological benefits. Methods: A search strategy has been followed to retrieve the relevant newly published literature on the pharmacological benefits of bakuchiol. After an extensive study of the retrieved articles and maintaining the inclusion and exclusion criteria, 110 articles were finally selected for this review. Results: Strong support of primary research on the protective effects via antitumorigenic, anti-inflammatory, antioxidative, antimicrobial, and antiviral activities are delineated. Conclusions: From ancient to modern life, medicinal plants have always been drawing the attention of human beings to alleviate ailments for a healthy and balanced lifestyle. This review is a comprehensive approach to highlighting bona fide essential pharmacological benefits and mechanisms underlying their therapeutic applications.

Prevention of Alzheimer's disease through diet: An exploratory review.

Introduction: This exploratory review article describes about the genetic factors behind Alzheimer's disease (AD), their association with foods, and their relationships with cognitive impairment. It explores the dietary patterns and economic challenges in AD prevention. Methods: Scopus, PubMed and Google Scholar were searched for articles that examined the relationships between Diets, Alzheimer's Disease (AD), and Socioeconomic conditions in preventative Alzheimer's disease studies. Graphs and Network analysis data were taken from Scopus under the MeSH search method, including words, Alzheimer's, APoE4, Tau protein, APP, Amyloid precursor protein, Beta-Amyloid, Aß, Mediterranean Diet, MD, DASH diet, MIND diet, SES, Socioeconomic, Developed country, Underdeveloped country, Preventions. The network analysis was done through VOS viewer. Results: Mediterranean diet (MD) accurately lowers AD (Alzheimer's Disease) risk to 53% and 35% for people who follow it moderately. MIND scores had a statistically significant reduction in AD rate compared to those in the lowest tertial (53% and 35% reduction, respectively). Subjects with the highest adherence to the MD and DASH had a 54% and 39% lower risk of developing AD, respectively, compared to those in the lowest tertial. Omega-6, PUFA, found in nuts and fish, can play most roles in the clearance of Aß. Vitamin D inhibits induced fibrillar Aß apoptosis. However, the high cost of these diet components rise doubt about the effectiveness of AD prevention through healthy diets. Conclusion: The finding of this study revealed an association between diet and the effects of the chemical components of foods on AD biomarkers. More research is required to see if nutrition is a risk or a protective factor for Alzheimer's disease to encourage research to be translated into therapeutic practice and to clarify nutritional advice.

Acceptability of a head-mounted assistive mouse controller for people with upper limb disability: An empirical study using the technology acceptance model.

Due to limited motor capabilities, people with upper limb disabilities have trouble utilizing a typical mouse while operating a computer. Different wearable Assistive Mouse Controllers (AMCs) have been developed to overcome their challenges. However, these people may not be able to realize the importance, ease of use, and social approval of these AMCs due to their fear of new technology, lack of confidence, and lack of ingenuity. These may negatively affect their attitude and intention toward accepting AMCs for equitable human-computer interaction. This study presents the development of a sensor-based head-mounted AMC, followed by an empirical analysis of its acceptance using the Technology Acceptance Model (TAM) from the socioeconomic perspective of Bangladesh. In a similar vein, we examined the effects of three additional psychological constructs-technology anxiety, confidence, and innovation, on its acceptance along with the original components of the TAM. A total of 150 individuals with stroke-induced upper limb disability participated in an online survey, and their responses were analyzed using confirmatory factor analysis and structural equation modeling, following the general least square method. Analysis revealed, about 96.44% of the participants had positive attitude towards the AMC, and almost 88.56% of them had positive intentions to accept it. Furthermore, about 68.61% of them expressed signs of anxiety, 96.35% were confident, and 94.16% of them had an innovative mindset in terms of device usage. The findings imply that individuals with an innovative mentality are more capable of comprehending the practical implications of a new technology than those without one. It is also feasible to reduce technological anxiety and boost a user's confidence while using an AMC by combining an innovative mentality with straightforward device interaction techniques. Additionally, peer encouragement and motivation can significantly enhance their positive attitude towards accepting the AMC for facilitating their interaction with a computer.

Salmonella T3SS effector SseK1 arginine-glycosylates the two-component response regulator OmpR to alter bile salt resistance.

Type III secretion system (T3SS) effector proteins are primarily recognized for binding host proteins to subvert host immune response during infection. Besides their known host target proteins, several T3SS effectors also interact with endogenous bacterial proteins. Here we demonstrate that the Salmonella T3SS effector glycosyltransferase SseK1 glycosylates the bacterial two-component response regulator OmpR on two arginine residues, R15 and R122. Arg-glycosylation of OmpR results in reduced expression of ompF, a major outer membrane porin gene. Glycosylated OmpR has reduced affinity to the ompF promoter region, as compared to the unglycosylated form of OmpR. Additionally, the Salmonella ΔsseK1 mutant strain had higher bile salt resistance and increased capacity to form biofilms, as compared to WT Salmonella, thus linking OmpR glycosylation to several important aspects of bacterial physiology.

Melatonin-induced plant adaptation to cadmium stress involves enhanced phytochelatin synthesis and nutrient homeostasis in Solanum lycopersicum L.

Cadmium (Cd) pollution is an increasingly serious problem in crop production. Although significant progress has been made to comprehend the molecular mechanism of phytochelatins (PCs)-mediated Cd detoxification, the information on the hormonal regulation of PCs is very fragmentary. In the present study, we constructed TRV-COMT, TRV-PCS, and TRV-COMT-PCS plants to further assess the function of CAFFEIC ACID O-METHYLTRANSFERASE (COMT) and PHYTOCHELATIN SYNTHASE (PCS) in melatonin-induced regulation of plant resistance to Cd stress in tomato. Cd stress significantly decreased chlorophyll content and CO2 assimilation rate, but increased Cd, H2O2 and MDA accumulation in the shoot, most profoundly in PCs deficient TRV-PCS and TRV-COMT-PCS plants. Notably, Cd stress and exogenous melatonin treatment significantly increased endogenous melatonin and PC contents in non-silenced plants. Results also explored that melatonin could alleviate oxidative stress and enhance antioxidant capacity and redox homeostasis by conserving improved GSH:GSSG and ASA:DHA ratios. Moreover, melatonin improves osmotic balance and nutrient absorption by regulating the synthesis of PCs. This study unveiled a crucial mechanism of melatonin-regulated PC synthesis, persuaded Cd stress tolerance and nutrient balance in tomato, which may have potential implications for the enhancement of plant resistance to toxic heavy metal stress.

Antibiotics administration without prescription in Bangladesh.

Background: Antibiotic resistance is a global challenge. Therefore, adhering to proper antibiotic administration protocols is essential to mitigating the problem. This study investigated the prevalence, and factors associated with, antibiotics administration without prescription by registered doctors in Bangladesh. Method: This cross-sectional survey was carried out among 1102 adults. The outcome variable was antibiotics administration without prescription. The exploratory variables included the those relating to sociodemographics, attitudes, and knowledge (antibiotic-treatable diseases, types of disease specification, and antimicrobial drugs specifications). Descriptive and inferential statistics were performed, with a p-value of 0.05 considered significant with a 95% confidence interval. Results: The prevalence of antibiotics administration without prescription was found to be 37.02%. Age was significantly associated with the administration of antibiotics without prescription. Those who had taken antibiotics in the previous 2 months reported a significantly higher prevalence of antibiotics administration without prescription. The participants' attitudes toward antibiotics and knowledge of antibiotic-treatable diseases, types of disease, and antimicrobial drugs were significantly associated with antibiotics administration without prescription. Conclusion: These findings may assist in facilitating relevant initiatives to improve the magnitude of antibiotics utilization without prescription, and mitigate the emergence of antibiotic resistance in Bangladesh.

School-based cognitive-behavioural therapy for children and adolescents with social anxiety disorder and social anxiety symptoms: A systematic review.

BACKGROUND: Social anxiety disorder (SAD) is prevalent among children and adolescents. Cognitive-behavioural therapy (CBT) has been used as the first-line treatment. However, evaluation of CBT conducted in a school setting has been scarce. OBJECTIVES: This study aims to review the CBT and its effectiveness in the school setting for children and adolescents with SAD or social anxiety symptoms. Quality assessment on individual studies was conducted. METHODS: Studies were identified through the search in PsycINFO, ERIC, PubMed and Medline targeting CBT conducted in a school setting with an aim to treat children and adolescents with SAD or social anxiety symptoms. Randomised controlled trials and quasi-experimental studies were selected. RESULTS: A total of 7 studies met the inclusion criteria. Five studies were randomised controlled trials, and two were quasi-experimental studies with 2558 participants aged 6-16 years from 138 primary schools and 20 secondary schools. There were minor effects to reduce social anxiety symptoms for children and adolescents at post-intervention in 86% of the selected studies. Friend for Life (FRIENDS), Super Skills for Life (SSL) and Skills for Academic and Social Success (SASS) conducted in school were more effective than the control conditions. CONCLUSIONS: There is a lack of quality of the evidence for FRIENDS, SSL and SASS, due to inconsistencies on the outcome assessments, statistical analyses, and the fidelity measures adopted in individual studies. Insufficient school funding and workforce with relevant health background, and the low level of parental involvement in the intervention would be the major challenges in school-based CBT for children and adolescents with SAD or social anxiety symptoms.

A survey, review, and future trends of skin lesion segmentation and classification.

The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion analysis is an emerging field of research that has the potential to alleviate the burden and cost of skin cancer screening. Researchers have recently indicated increasing interest in developing such CAD systems, with the intention of providing a user-friendly tool to dermatologists to reduce the challenges encountered or associated with manual inspection. This article aims to provide a comprehensive literature survey and review of a total of 594 publications (356 for skin lesion segmentation and 238 for skin lesion classification) published between 2011 and 2022. These articles are analyzed and summarized in a number of different ways to contribute vital information regarding the methods for the development of CAD systems. These ways include: relevant and essential definitions and theories, input data (dataset utilization, preprocessing, augmentations, and fixing imbalance problems), method configuration (techniques, architectures, module frameworks, and losses), training tactics (hyperparameter settings), and evaluation criteria. We intend to investigate a variety of performance-enhancing approaches, including ensemble and post-processing. We also discuss these dimensions to reveal their current trends based on utilization frequencies. In addition, we highlight the primary difficulties associated with evaluating skin lesion segmentation and classification systems using minimal datasets, as well as the potential solutions to these difficulties. Findings, recommendations, and trends are disclosed to inform future research on developing an automated and robust CAD system for skin lesion analysis.

Melatonin mediates elevated carbon dioxide-induced photosynthesis and thermotolerance in tomato.

Increasing carbon dioxide (CO2 ) promotes photosynthesis and mitigates heat stress-induced deleterious effects on plants, but the regulatory mechanisms remain largely unknown. Here, we found that tomato (Solanum lycopersicum L.) plants treated with high atmospheric CO2 concentrations (600, 800, and 1000 µmol mol-1 ) accumulated increased levels of melatonin (N-acetyl-5-methoxy tryptamine) in their leaves and this response is conserved across many plant species, including Arabidopsis, rice, wheat, mustard, cucumber, watermelon, melon, and hot pepper. Elevated CO2 (eCO2 ; 800 µmol mol-1 ) caused a 6.8-fold increase in leaf melatonin content, and eCO2 -induced melatonin biosynthesis preferentially occurred through chloroplast biosynthetic pathways in tomato plants. Crucially, manipulation of endogenous melatonin levels by genetic means affected the eCO2 -induced accumulation of sugar and starch in tomato leaves. Furthermore, net photosynthetic rate, maximum photochemical efficiency of photosystem II, and transcript levels of chloroplast- and nuclear-encoded photosynthetic genes, such as rbcL, rbcS, rbcA, psaD, petB, and atpA, significantly increased in COMT1 overexpressing (COMT1-OE) tomato plants, but not in melatonin-deficient comt1 mutants at eCO2 conditions. While eCO2 enhanced plant tolerance to heat stress (42°C) in wild-type and COMT1-OE, melatonin deficiency compromised eCO2 -induced thermotolerance in comt1 plants. The expression of heat shock proteins genes increased in COMT1-OE but not in comt1 plants in response to eCO2 under heat stress. Further analysis revealed that eCO2 -induced thermotolerance was closely linked to the melatonin-dependent regulation of reactive oxygen species, redox homeostasis, cellular protein protection, and phytohormone metabolism. This study unveiled a crucial mechanism of elevated CO2 -induced thermotolerance in which melatonin acts as an essential endogenous signaling molecule in tomato plants.

The impacts of cooking and indoor air quality assessment in the southwestern region of Bangladesh.

The main objective of this study is to assess the impacts of cooking and indoor air quality (IAQ) in the southwestern region of Bangladesh. Here we report and compare the IAQ in considering a total of eight kitchens and living rooms of four selected households (HHs) in Jashore city and suburb area, the southwest district of Bangladesh. Air quality parameters, such as particulate matter (PM2.5) and volatile organic compounds (VOC), were assessed continuously for 24 h. In addition, Carbon dioxide (CO2) was evaluated in different phases during the study period. PM2.5, VOC, and CO2 levels were ranged from 18.52 to 207 µg/m3, 7.95-35.66 ppm, and 1061-2459 mg/m3, respectively, in the indoor cooking HHs. Conversely, while the average concentration was found between 20.63 and 23.72 µg/m3 PM2.5, 11.18-12.36 ppm VOC, and 1097-1747 mg/m3 CO2 in the outdoor cooking HHs. A significant increase in CO2 due to kitchen activities (cooking, frying, boiling) was observed that ranged between 5 and 77% compared to the background level. The calculated range of toxicity potential (TP) values was between 0.8 and 8.3 for PM2.5 in the HHs. In most of the observations, PM2.5, VOC, and CO2 exceed the standard values. The study reports that well ventilation systems and clean fuel use significantly reduce the indoor air contaminants level. Our study offers new insights about the IAQ of the southwest region of Bangladesh, particularly for suburbs and urban setups, and provides a background for further study, and decision-making. It will serve as a reference for the formulation and implementation of policies to improve air quality.

Multimedia tablet or paper handout to supplement counseling during preterm birth hospitalization: a randomized controlled trial.

BACKGROUND: Parents of premature infants engage in shared decision-making regarding the care of their infant. The process of prenatal counseling typically involves a verbal conversation with a neonatal provider during hospitalization. Support people may not be available, and the pregnant person's memory is impaired by medications, pain, and stress. The American Academy of Pediatrics, American College of Obstetricians and Gynecologists, and the Eunice Kennedy Shriver National Institute of Child Health and Human Development have called for improvements to this process, including the development of educational aids. OBJECTIVE: This study aimed to investigate whether a multimedia tablet would be more effective than a paper handout in supplementing verbal clinician counseling during preterm birth hospitalization. STUDY DESIGN: This was a randomized controlled trial including English-speaking pregnant people aged ≥18 years and hospitalized at 22 to 33 weeks' gestation for preterm birth. Exclusion criteria were known fetal or chromosomal anomaly and delivery before study completion. Pregnant people received either a multimedia tablet or a paper handout before verbal clinician counseling. Preintervention assessment included demographics and State-Trait Anxiety Inventory, and postintervention assessment included the Parent Knowledge of Premature Birth Questionnaire and State-Trait Anxiety Inventory. Continuous variables were analyzed by t-test and categorical variables by Fisher exact test. RESULTS: A total of 122 pregnant people referred for counseling were screened; 76 were randomized, and 59 completed the study. Demographics were similar between groups, except that pregnant people in the handout group were older (mean 32 vs 29 years; P=.03). The multimedia tablet group (n=32) was less likely to report reviewing all the educational material than the paper handout group (n=27) (41% vs 72%; P=.037). Both groups correctly answered a similar number of knowledge items (P=.088). Postintervention state anxiety decreased in both groups (P<.0001), with no difference between groups. Computerized tracking showed that the multimedia group spent a median of 37 minutes reviewing the tablet. CONCLUSION: Contrary to our hypothesis, a paper handout and multimedia tablet were equally effective in the labor unit for supplementing verbal preterm birth counseling, and both decreased parental anxiety.

Electron beam technology for Re-processing of personal protective equipment.

Beginning with the outbreak of COVID-19 at the dawn of 2020, the continuing spread of the pandemic has challenged the healthcare market and the supply chain of Personal Protective Equipment (PPE) around the world. Moreover, the emergence of the variants of COVID-19 occurring in waves threatens the sufficient supply of PPE. Among the various types of PPE, N95 Respirators, surgical masks, and medical gowns are the most consumed and thus have a high potential for a serious shortage during such emergencies. Considering the unanticipated demand for PPE during a pandemic, re-processing of used PPE is one approach to continue to protect the health of first responders and healthcare personnel. This paper evaluates the viability and efficacy of using FDA-approved electron beam (eBeam) sterilization technology (ISO 11137) to re-process used PPE. PPEs including 3M N95 Respirators, Proxima Sirus gowns, and face shields were eBeam irradiated in different media (air, argon) over a dose range of 0-200 kGy. Several tests were then performed to examine surface properties, mechanical properties, functionality performance, discoloration phenomenon, and liquid barrier performance. The results show a reduction of filtration efficiency to about 63.6% in the N95 Respirator; however, charge regeneration may improve the re-processed efficiency. Additionally, mechanical degradation was observed in Proxima Sirus gown with increasing dose up to 100 kGy. However, no mechanical degradation was observed in the face shields after 10 times donning and doffing. Apart from the face shield, N95 Respirators and Proxima Sirus gown both show significant mechanical degradation with ebeam dose over sterilization doses (>25 kGy), indicating that eBeam technology is not appropriate for the re-processing these PPEs.

Early Prediction of Diabetes Using an Ensemble of Machine Learning Models.

Diabetes is one of the most rapidly spreading diseases in the world, resulting in an array of significant complications, including cardiovascular disease, kidney failure, diabetic retinopathy, and neuropathy, among others, which contribute to an increase in morbidity and mortality rate. If diabetes is diagnosed at an early stage, its severity and underlying risk factors can be significantly reduced. However, there is a shortage of labeled data and the occurrence of outliers or data missingness in clinical datasets that are reliable and effective for diabetes prediction, making it a challenging endeavor. Therefore, we introduce a newly labeled diabetes dataset from a South Asian nation (Bangladesh). In addition, we suggest an automated classification pipeline that includes a weighted ensemble of machine learning (ML) classifiers: Naive Bayes (NB), Random Forest (RF), Decision Tree (DT), XGBoost (XGB), and LightGBM (LGB). Grid search hyperparameter optimization is employed to tune the critical hyperparameters of these ML models. Furthermore, missing value imputation, feature selection, and K-fold cross-validation are included in the framework design. A statistical analysis of variance (ANOVA) test reveals that the performance of diabetes prediction significantly improves when the proposed weighted ensemble (DT + RF + XGB + LGB) is executed with the introduced preprocessing, with the highest accuracy of 0.735 and an area under the ROC curve (AUC) of 0.832. In conjunction with the suggested ensemble model, our statistical imputation and RF-based feature selection techniques produced the best results for early diabetes prediction. Moreover, the presented new dataset will contribute to developing and implementing robust ML models for diabetes prediction utilizing population-level data.

Mobile assisted language learning in learning English through social networking tools: An account of Instagram feed-based tasks on learning grammar and attitude among English as a foreign language learners.

Advancement of social media in the modern era provides a good incentive for researchers to unleash the potential of social networking (SN) tools in order to improve education. Despite the significant role of social media in affecting second/foreign language (L2) learning processes, few empirical studies have tried to find out how Instagram feed-based tasks affect learning grammar structure. To fill this lacuna of research, the current study set forth to delve into the influence of Instagram feed-based tasks on learning grammar among English as a foreign language (EFL) learners. In so doing, a sample of 84 intermediate EFL learners were randomly divided into experimental and control groups. The learners in the control group received regular online instruction via webinar platforms. In contrast, the learners in the experimental group were exposed to Instagram feed-based tasks. Data inspection applying one-way ANCOVA indicated that the learners in the experimental group outperformed their counterparts in the control group. The results highlighted the significant contributions of Instagram feed-based tasks in fostering learning grammar. Furthermore, EFL learners' positive attitudes toward using Instagram Feed-based Tasks in Learning Grammar was concluded. The implications of this study may redound to the benefits of language learners, teachers, curriculum designers, as well as policy makers in providing opportunities for further practice of Instagram feed-based tasks in language learning and teaching.

Social media and academic success: Impacts of using telegram on foreign language motivation, foreign language anxiety, and attitude toward learning among EFL learners.

Concerning the ubiquity of social media, this research tried to examine the impacts of using Telegram on Iranian EFL learners' foreign language motivation, foreign language anxiety, and attitude toward learning. To achieve these purposes, 60 Iranian EFL learners at the intermediate level were selected and randomly divided into two groups: experimental and control. After that, both groups were pretested on motivation and anxiety variables. After pretesting, the participants in the experimental class received treatment via using the Telegram application, and the control students were trained traditionally without using any social media. After an 18-session instruction, a post-test of motivation and a post-test of anxiety were given to both groups, and also an attitude questionnaire was distributed among the experimental group to inspect their attitudes toward the Telegram application in language learning. The results of using two one-way ANCOVA tests showed significant differences between the post-tests of the control and experimental groups in favor of the experimental group. The findings demonstrated that using the Telegram application increased the motivation of the experimental group and decreased their level of foreign language anxiety. Also, the results of one sample t-test showed that the participants of the experimental group held positive attitudes toward using the Telegram application in English language learning. The implications of this research can encourage both teachers and learners to use social media-based instruments in English teaching and learning.