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Abstract Sentiment analysis is a pivotal tool in understanding public opinion, consumer behavior, and social trends, underpinning applications ranging from market research to political analysis. However, existing sentiment analysis models frequently encounter challenges related to linguistic diversity, model generalizability, explainability, and limited availability of labeled datasets. To address these shortcomings, we propose the Transformer and Attention-based Bidirectional LSTM for Sentiment Analysis (TRABSA) model, a novel hybrid sentiment analysis framework that integrates transformer-based architecture, attention mechanism, and recurrent neural networks like BiLSTM. The TRABSA model leverages the powerful RoBERTa-based transformer model for initial feature extraction, capturing complex linguistic nuances from a vast corpus of tweets. This is followed by an attention mechanism that highlights the most informative parts of the text, enhancing the model’s focus on critical sentiment-bearing elements. Finally, the BiLSTM networks process these refined features, capturing temporal dependencies and improving the overall sentiment classification into positive, neutral, and negative classes. Leveraging the latest RoBERTa-based transformer model trained on a vast corpus of 124M tweets, our research bridges existing gaps in sentiment analysis benchmarks, ensuring state-of-the-art accuracy and relevance. Furthermore, we contribute to data diversity by augmenting existing datasets with 411,885 tweets from 32 English-speaking countries and 7,500 tweets from various US states. This study also compares six word-embedding techniques, identifying the most robust preprocessing and embedding methodologies crucial for accurate sentiment analysis and model performance. We meticulously label tweets into positive, neutral, and negative classes using three distinct lexicon-based approaches and select the best one, ensuring optimal sentiment analysis outcomes and model efficacy. Here, we demonstrate that the TRABSA model outperforms the current seven traditional machine learning models, four stacking models, and four hybrid deep learning models, yielding notable gain in accuracy (94%) and effectiveness with a macro average precision of 94%, recall of 93%, and F1-score of 94%. Our further evaluation involves two extended and four external datasets, demonstrating the model’s consistent superiority, robustness, and generalizability across diverse contexts and datasets. Finally, by conducting a thorough study with SHAP and LIME explainable visualization approaches, we offer insights into the interpretability of the TRABSA model, improving comprehension and confidence in the model’s predictions. Our study results make it easier to analyze how citizens respond to resources and events during pandemics since they are integrated into a decision-support system. Applications of this system provide essential assistance for efficient pandemic management, such as resource planning, crowd control, policy formation, vaccination tactics, and quick reaction programs.

Objective This study aims to address the challenge of privacy-preserving Alzheimer’s disease classification using federated learning across various data distributions, focusing on real-world applicability. The goal is to improve the efficiency of classification by minimizing communication rounds between clients and the central server. Methods The proposed approach leverages two key strategies: increasing parallelism by utilizing more clients in each communication round and increasing computation per client during the intervals between rounds. To reflect real-world scenarios, data is divided into three distributions: identical and independently distributed, non-identical and independently distributed equal, and non-identical and independently distributed unequal. The impact of extreme quantity distribution skew is also examined. A convolutional neural network is used to evaluate the performance across these setups. Results The empirical study demonstrates that the proposed federated learning approach achieves a maximum accuracy of 84.75%, a precision of 86%, a recall of 85%, and an F1-score of 84%. Increasing the number of local epochs improves classification performance and reduces communication needs. The experiments show that federated learning is effective in handling heterogeneous datasets when all clients participate in each round of training. However, the results also indicate that extreme quantity distribution skew negatively impacts classification performance. Conclusions The study confirms that federated learning is a viable solution for Alzheimer’s disease classification while preserving data privacy. Increasing local computation and client participation enhances classification performance, though extreme distribution imbalances present a challenge. Further investigation is needed to address these limitations in real-world scenarios.

The increasing prevalence of diabetes necessitates the development of effective early detection methods to mitigate its health impacts. This paper investigates the impact of feature transformation and machine learning (ML) models on the early detection of diabetes using a binary tabular classification dataset. We explore three feature transformation techniques, no transformation, normalization, and min-max scaling, to assess their influence on the performance of various ML models. To comprehensively evaluate the effectiveness of these preprocessing techniques, we experimented with twelve different ML models, including both traditional algorithms and ensemble methods. A publicly available dataset has been used for this research, containing 768 samples and 8 features. To ensure their effectiveness, the models are assessed using several evaluation metrics, including accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Among the ML models, Light Gradient Boosting Machine (LGBM) achieved the highest accuracy of 82.91% when min-max scaling was applied to the data. Our results demonstrate the varying effectiveness of different combinations of feature transformation techniques and ML models in enhancing diabetes detection performance. Furthermore, it has been observed that the ensemble models generally achieved better performance than traditional ML models. These findings provide valuable insights for optimizing preprocessing and model selection strategies in the development of robust early diabetes detection systems.

Assessing children for specific language impairment (SLI) or other communication impairments can be challenging for doctors due to the extensive battery of tests and examinations required. Artificial intelligence and computer-aided diagnostics have aided medical professionals in conducting rapid, reliable assessments of children’s neurodevelopmental conditions concerning language comprehension and output. Previous research has shown differences between the vocal characteristics of typically developing (TD) children and those with SLI. This study aims to develop a natural language processing (NLP) system that can identify children’s early impairments using specific conditions. Our dataset contains examples of disorders, and this study seeks to (1) demonstrate the effectiveness of several classifiers in this regard and (2) select the most effective model from the classifiers. We utilized various machine learning (ML), deep learning (DL), and transformer models to achieve our objective. Our deep convolutional neural network (DCNN) model yielded excellent results, outperforming the competition with an accuracy of 90.47%, making it the top-performing model overall. To increase the accuracy and credibility of our most likely output, we have incorporated explainable AI approaches like SHAP and LIME. These approaches aid in interpreting and explaining model predictions, considering the significance and sensitivity of the topic. Additionally, we believe that our work can contribute to developing more accessible, effective methods for diagnosing language impairments in young children.

Abstract Background The birth cohort effect has been suggested to influence the rate of breast cancer incidence and the trends of associated reproductive and lifestyle factors. We conducted a cohort study to determine whether a differential pattern of associations exists between certain factors and breast cancer risk based on birth cohorts. Methods This was a cohort study using pooled data from 12 cohort studies. We analysed associations between reproductive (menarche age, menopause age, parity and age at first delivery) and lifestyle (smoking and alcohol consumption) factors and breast cancer risk. We obtained hazard ratios (HRs) with 95% confidence intervals (CIs) using the Cox proportional hazard regression analysis on the 1920s, 1930s, 1940s and 1950s birth cohorts. Results Parity was found to lower the risk of breast cancer in the older but not in the younger birth cohort, whereas lifestyle factors showed associations with breast cancer risk only among the participants born in the 1950s. In the younger birth cohort group, the effect size was lower for parous women compared to the other cohort groups (HR [95% CI] 0.86 [0.66–1.13] compared to 0.60 [0.49–0.73], 0.46 [0.38–0.56] and 0.62 [0.51–0.77]). Meanwhile, a higher effect size was found for smoking (1.45 [1.14–1.84] compared to 1.25 [0.99–1.58], 1.06 [0.85–1.32] and 0.86 [0.69–1.08]) and alcohol consumption (1.22 [1.01–1.48] compared to 1.10 [0.90–1.33], 1.15 [0.96–1.38], and 1.07 [0.91–1.26]). Conclusion We observed different associations of parity, smoking and alcohol consumption with breast cancer risk across various birth cohorts.

Classifying rice leaf diseases in agricultural technology helps to maintain crop health and to ensure a good yield. In this work, deep learning algorithms were, therefore, employed for the identification and classification of rice leaf diseases from images of crops in the field. The initial algorithmic phase involved image pre-processing of the crop images, using a bilateral filter to improve image quality. The effectiveness of this step was measured by using metrics like the Structural Similarity Index (SSIM) and the Peak Signal-to-Noise Ratio (PSNR). Following this, this work employed advanced neural network architectures for classification, including Cascading Autoencoder with Attention Residual U-Net (CAAR-U-Net), MobileNetV2, and Convolutional Neural Network (CNN). The proposed CNN model stood out, since it demonstrated exceptional performance in identifying rice leaf diseases, with test Accuracy of 98% and high Precision, Recall, and F1 scores. This result highlights that the proposed model is particularly well suited for rice leaf disease classification. The robustness of the proposed model was validated through k-fold cross-validation, confirming its generalizability and minimizing the risk of overfitting. This study not only focused on classifying rice leaf diseases but also has the potential to benefit farmers and the agricultural community greatly. This work highlights the advantages of custom CNN models for efficient and accurate rice leaf disease classification, paving the way for technology-driven advancements in farming practices.

There is increasing adoption of winter cover crops (WCCs) in corn and soybean production in Canada, primarily to reduce erosion and increase soil organic matter content. WCCs have the potential to influence nematode communities by increasing free-living nematodes and decreasing plant-parasitic nematodes or vice versa. However, the mechanism by which WCCs change nematode community assemblages still remains a key question in soil food web ecology. We tested the hypothesis that the long-term use of rye (Secale cereale), barley (Hordeum vulgare) and oat (Avena sativa) as monocultures or mixtures promotes nematode communities and improves overall soil health conditions compared to winter fallow. The results from this study revealed that the use of WCCs generally promoted a higher abundance and diversity of nematode communities, whereas plant parasitic nematodes were the most abundant in winter fallow. Moreover, the mixtures of WCCs had more similar nematode communities compared to rye alone and winter fallow. The structure and enrichment indices were higher with WCCs, indicating higher nutrient cycling and soil suppressiveness, which are signs of healthy soil conditions. Furthermore, WCCs significantly reduced the populations of root lesion nematode Pratylenchus, although their numbers recovered and increased during the main crop stages. Additionally, mixtures of WCCs promoted the highest abundance of the stunt nematode Tylenchorhynchus, whereas winter fallow had a higher abundance of the spiral nematode Helicotylenchus during the fallow period and the main crop stages. The results show that the long-term use of cover crops can have a positive impact on nematode communities and the soil food web, but these changes depend on the type of WCCs and how they are used.

IntroductionRoutine immunization programs have focused on increasing vaccination coverage, which is equally important for decreasing vaccine-preventable diseases (VPDs), particularly in low- and lower-middle-income countries (LMICs). We estimated the trends and projections of age-appropriate vaccination coverage at the regional and national levels, as well as place of residence and wealth index in LMICs.MethodsIn total, 174 nationally representative household surveys from 2000 to 2020 from 41 LMICs were included in this study. Bayesian hierarchical regression models were used to estimate trends and projections of age-appropriate vaccination.ResultsThe trend in coverage of age-appropriate Bacillus Calmette-Guérin (BCG), third dose of diphtheria, tetanus, and pertussis (DTP3), third dose of polio (polio3), and measles-containing vaccine (MCV) increased rapidly from 2000 to 2020 in LMICs. Findings indicate substantial increases at the regional and national levels, and by area of residence and socioeconomic status between 2000 and 2030. The largest rise was observed in East Africa, followed by South and Southeast Asia. However, out of the 41 countries, only 10 countries are estimated to achieve 90% coverage of the BCG vaccine by 2030, five of DTP3, three of polio3, and none of MCV. Additionally, by 2030, wider pro-urban and -rich inequalities are expected in several African countries.ConclusionSignificant progress in age-appropriate vaccination coverage has been made in LMICs from 2000 to 2020. Despite this, projections show many countries will not meet the 2030 coverage goals, with persistent urban–rural and socioeconomic disparities. Therefore, LMICs must prioritize underperforming areas and reduce inequalities through stronger health systems and increased community engagement to ensure high coverage and equitable vaccine access.

Assessing the quality of fuel is essential to comprehend its impact on the environment and human health. In this study, the evaluation of fuel quality standards at the consumer level was conducted by analyzing the motor fuels in Khulna, Bangladesh. A total of 32 samples of petrol (regular gasoline), and octane (premium gasoline) were collected from the fuel stations in the Khulna City Corporation area and analyzed with an FTIR-Fuel Analyzer. Fuel properties, such as research octane number (RON), motor octane number (MON), ethanol content, olefins content, and oxygen content were analyzed. For petrol, the average RON, MON, olefins, and oxygen content were 95.34, 85.70, 8.23 %v/v, and 0.78 %m/m, respectively, and for octane, they were 96.96, 85.39, 1.25 %v/v, and 0.09 %m/m, respectively. Almost all of these parameters complied with both Bangladesh standard and Euro 5 fuel specifications, and those that did not comply were very close to their standard values. However, benzene concentration, which was not specified in Bangladesh Standard, was the most alarming metric for octane since none of the samples matched the Euro 5 fuel specifications of the maximum concentration of 1 %v/v benzene; on average it was 3.70 %v/v. Although petrol benzene content (average 1.50 %v/v) was not as bad as it was for octane, it was still nowhere near good enough, with only 25% of the samples within the recommended level among the studied sample. This information holds significance in establishing the fuel profile and facilitating the identification of distinct samples linked to adulteration. Therefore, the analysis of motor fuel qualities is essential for maintaining the environment, human health, and the economy of a country.

Abstract Background Achievement of lipid targets is crucial in patients with type 2 diabetes mellitus (T2DM) to mitigate the risk of cardiovascular diseases (CVD). Data on lipid-control status among patients with T2DM in Bangladesh are scarce. This study was conducted to determine the lipid-control status among patients with T2DM who were on lipid-lowering drugs in the country. Methods This cross-sectional study was conducted in the diabetes outpatient departments of several tertiary hospitals in Bangladesh from January 2022 to December 2022. Adults of both sexes diagnosed with T2DM for at least one year and were on the lipid-lowering drug(s) for a minimum of 3 months were included in the study by consecutive sampling. Patients’ data were collected by face-to-face interviews, and blood samples were collected for fasting lipid profile. The lipid target was set at 40 mg/dL for high-density lipoprotein cholesterol (HDL-C), and

Human Activity Recognition (HAR), alongside Ambient Assisted Living (AAL), are integral components of smart homes, sports, surveillance, and investigation activities. To recognize daily activities, researchers are focusing on lightweight, cost-effective, wearable sensor-based technologies as traditional vision-based technologies lack elderly privacy, a fundamental right of every human. However, it is challenging to extract potential features from 1D multi-sensor data. Thus, this research focuses on extracting distinguishable patterns and deep features from spectral images by time-frequency-domain analysis of 1D multi-sensor data. Wearable sensor data, particularly accelerator and gyroscope data, act as input signals of different daily activities, and provide potential information using time-frequency analysis. This potential time series information is mapped into spectral images through a process called use of ’scalograms’, derived from the continuous wavelet transform. The deep activity features are extracted from the activity image using deep learning models such as CNN, MobileNetV3, ResNet, and GoogleNet and subsequently classified using a conventional classifier. To validate the proposed model, SisFall and PAMAP2 benchmark datasets are used. Based on the experimental results, this proposed model shows the optimal performance for activity recognition obtaining an accuracy of 98.4% for SisFall and 98.1% for PAMAP2, using Morlet as the mother wavelet with ResNet-101 and a softmax classifier, and outperforms state-of-the-art algorithms.

Thermophysiological comfort is a crucial aspect of human life, contributing to health and work performance. The current paper aims to enhance the understanding of current research, progress, and remaining challenges regarding clothing thermophysiological comfort from a textile science perspective. It provides a comprehensive review of several facets of clothing thermophysiological comfort, focusing on the history of thermophysiological comfort prediction models, heat and moisture transfer mechanisms in the skin–clothing–environment system, controlling factors of thermophysiological comfort, textile materials for superior thermophysiological comfort, and thermal comfort assessment techniques. The paper shows that previously developed thermophysiological comfort models were mainly based on the human thermoregulation process. However, the effect of the air gap size between the human skin and the cloth layer, i.e., the microclimate, on the heat and moisture transfer in the skin–clothing–environment system has been largely overlooked. In addition, thermophysiological comfort models of skin–clothing–environment systems generally only considered dry thermal resistance and evaporative resistance, yet many other fabric properties have effects on human thermophysiological comfort. Potential future directions are identified to fill some of the current gaps. A conceptual model of clothing comfort to contribute to a better understanding of thermophysiological comfort is also proposed.

Abstract Electronic textiles (e‐textiles) have emerged as a revolutionary solution for personalized healthcare, enabling the continuous collection and communication of diverse physiological parameters when seamlessly integrated with the human body. Among various methods employed to create wearable e‐textiles, printing offers unparalleled flexibility and comfort, seamlessly integrating wearables into garments. This has spurred growing research interest in printed e‐textiles, due to their vast design versatility, material options, fabrication techniques, and wide‐ranging applications. Here, a comprehensive overview of the crucial considerations in fabricating printed e‐textiles is provided, encompassing the selection of conductive materials and substrates, as well as the essential pre‐ and post‐treatments involved. Furthermore, the diverse printing techniques and the specific requirements are discussed, highlighting the advantages and limitations of each method. Additionally, the multitude of wearable applications made possible by printed e‐textiles is explored, such as their integration as various sensors, supercapacitors, and heated garments. Finally, a forward‐looking perspective is provided, discussing future prospects and emerging trends in the realm of printed wearable e‐textiles. As advancements in materials science, printing technologies, and design innovation continue to unfold, the transformative potential of printed e‐textiles in healthcare and beyond is poised to revolutionize the way wearable technology interacts and benefits.

Educational Data Mining (EDM) holds promise in uncovering insights from educational data to predict and enhance students' performance. This paper presents an advanced EDM system tailored for classifying and improving tertiary students' programming skills. Our approach emphasizes effective feature engineering, appropriate classification techniques, and the integration of Explainable Artificial Intelligence (XAI) to elucidate model decisions. Through rigorous experimentation, including an ablation study and evaluation of six machine learning algorithms, we introduce a novel ensemble method, Stacking-SRDA, which outperforms others in accuracy, precision, recall, f1-score, ROC curve, and McNemar test. Leveraging XAI tools, we provide insights into model interpretability. Additionally, we propose a system for identifying skill gaps in programming among weaker students, offering tailored recommendations for skill enhancement.

The rise of social media has changed how people view connections. Machine Learning (ML)-based sentiment analysis and news categorization help understand emotions and access news. However, most studies focus on complex models requiring heavy resources and slowing inference times, making deployment difficult in resource-limited environments. In this paper, we process both structured and unstructured data, determining the polarity of text using the TextBlob scheme to determine the sentiment of news headlines. We propose a Stochastic Gradient Descent (SGD)-based Ridge classifier (RC) for blending SGDR with an advanced string processing technique to effectively classify news articles. Additionally, we explore existing supervised and unsupervised ML algorithms to gauge the effectiveness of our SGDR classifier. The scalability and generalization capability of SGD and L2 regularization techniques in RCs to handle overfitting and balance bias and variance provide the proposed SGDR with better classification capability. Experimental results highlight that our string processing pipeline significantly boosts the performance of all ML models. Notably, our ensemble SGDR classifier surpasses all state-of-the-art ML algorithms, achieving an impressive 98.12% accuracy. McNemar's significance tests reveal that our SGDR classifier achieves a 1% significance level improvement over K-Nearest Neighbor, Decision Tree, and AdaBoost and a 5% significance level improvement over other algorithms. These findings underscore the superior proficiency of linear models in news categorization compared to tree-based and nonlinear counterparts. This study contributes valuable insights into the efficacy of the proposed methodology, elucidating its potential for news categorization and sentiment analysis.

Abstract Background Despite the wide acceptability of fasting lipid profiles in practice, emerging evidence suggests that random lipid profiles might be a convenient alternative for lipid measurement. The objective of the present study was to compare the fasting and random lipid profile among subjects with type 2 diabetes mellitus (T2DM). Methods The present cross-sectional study included 1543 subjects with T2DM visiting several endocrinology outpatient clinics throughout Bangladesh from January to December 2021. The fasting lipid profile was measured in the morning following 8–10 h of overnight fasting, and the random lipid profile was measured at any time of the day, irrespective of the last meal. The values of fasting and random lipids were compared using the Wilcoxon signed-rank test and Spearman rank correlation coefficients. Results In this study, a good level of correlation was observed between fasting and random lipid levels [r = 0.793, p

An optimized robot path-planning algorithm is required for various aspects of robot movements in applications. The efficacy of the robot path-planning model is vulnerable to the number of search nodes, path cost, and time complexity. The conventional A-star (A*) algorithm outperforms other grid-based algorithms because of its heuristic approach. However, the performance of the conventional A* algorithm is suboptimal for the time, space, and number of search nodes, depending on the robot motion block (RMB). To address these challenges, this paper proposes an optimal RMB with an adaptive cost function to improve performance. The proposed adaptive cost function keeps track of the goal node and adaptively calculates the movement costs for quickly arriving at the goal node. Incorporating the adaptive cost function with a selected optimal RMB significantly reduces the searches of less impactful and redundant nodes, which improves the performance of the A* algorithm in terms of the number of search nodes and time complexity. To validate the performance and robustness of the proposed model, an extensive experiment was conducted. In the experiment, an open-source dataset featuring various types of grid maps was customized to incorporate the multiple map sizes and sets of source-to-destination nodes. According to the experiments, the proposed method demonstrated a remarkable improvement of 93.98% in the number of search nodes and 98.94% in time complexity compared to the conventional A* algorithm. The proposed model outperforms other state-of-the-art algorithms by keeping the path cost largely comparable. Additionally, an ROS experiment using a robot and lidar sensor data shows the improvement of the proposed method in a simulated laboratory environment.

Summary: Electroconductive textile yarns are of particular interest for their use as flexible and wearable sensors without compromising the properties and comfort of usual textiles. However, the detection of fine actions of the human body is quite challenging since it requires both the relatively higher sensitivity and stability of the sensor. Herein, highly sensitive, ultra-stable, and extremely durable piezoresistive wearable sensors were prepared by loading N-doped rGO and polydopamine-coated carbon nanotubes into silicon rubber tube. The wearable sensors thus produced show an excellent ability to sense subtle movement or stimuli with good sensitivity and repeatability. Furthermore, by bending the straight conductive silicon rubber tube (CSRT) into three different patterns, its sensitivity was then dramatically increased. Finally, the CSRT was found capable of sensing cardiorespiratory signals, indicating that the sensor would be an important step toward realizing bio-signal sensing for next-generation personalized health care applications.

Feature selection is employed to reduce the feature dimensions and computational complexity by eliminating irrelevant and redundant features. A vast amount of increasing data and its processing generates many feature sets, which are reduced by the feature selection process to improve the performance in all types of classification, regression, clustering models. This study performs a detailed analysis of motivation and concentrates on the fundamental architecture of feature selection. This study aims to establish a structured formation related to popular methods such as filters, wrappers and, embedded into search strategies, evaluation criteria, and learning methods. Different methods organize a comparison of the benefits and drawbacks followed by multiple classification algorithms and standard validation measures. The diversity of applications in multiple domains such as data retrieval, prediction analysis, and medical, intrusion, and industrial applications is efficiently highlighted. This study focuses on some additional feature selection methods for handling big data. Nonetheless, new challenges have surfaced in the analysis of such data, which were also addressed in this study. Reflecting on commonly encountered challenges and clarifying how to obtain the absolute feature selection method are the significant components of this study.

Context: Telepathology is a promising tool for remote communities to receive pathology services where professional diagnosis services are inadequate. Aims: We aimed to clarify how effective telepathology was when compared with conventional pathology service among rural communities of Bangladesh. Methods and Materials: We conducted a cross-sectional study in suburban and rural areas of Bangladesh between June and August 2020. We enrolled 117 participants who received both telepathology services from Thakurgaon Eye Hospital and conventional pathology service experience. The participant's satisfaction with the accessibility and perceptions were statistically compared. In addition, we summarized descriptive statistics using the frequencies and percentages of participants' responses. Statistical Analysis Used: Wilcoxon's Signed-rank test using SPSS statistic software version 25.00. Results: Among the study participants, service cost, travel cost, travel time, waiting time, and travel distance were significantly higher for conventional pathology than telepathology (P < 0.001). The majority of participants (94%) were satisfied with the telepathology experience; however, one out of 117 participants was dissatisfied with this service when their travel distance was far away (≥50 km). Among the participants, 91.5% thought that telepathology service was effective for their treatment, and 98.3% wanted to continue this service in their community. On an average, participants saved 58% (95% CI, 53.4–61.5) of cost using telepathology rather than conventional pathology service. Conclusions: Remote under-resourced communities received professional pathology services with less time-consuming and significantly lower costs using the telepathology approach. Where pathology services are absent/insufficient, telepathology is efficacious for primary diagnosis, screening, and referral through professional pathologists for the satisfactory treatment of unreached communities.

Fat-associated lymphoid clusters (FALCs) are novel lymphoid tissues that have been reported in the mesenteric and mediastinal fat tissue of mouse and human. It plays role in the progression of respiratory and intestinal inflammation and parasitic infestations. However, their occurrence in the chicken air sacs and mesenteric adipose tissue has not yet been identified. Here we investigated the occurrence and distribution of FALCs in the air sacs (cervical, clavicular, thoracic, and abdominal) and mesenteric adipose tissue of healthy chicken. The latter air sacs and mesentery were immediately harvested after anesthesia and cutting the chicken heads then fixed in 4% paraformaldehyde fixative solution for histopathological examination. The degree of FALCs development among different specimens was measured and statistically analyzed. Our results revealed lymphoid clusters associating with the adipose tissues in mesentery, and air sacs (clavicular, thoracic, and abdominal), but not the cervical one. Interestingly, the thoracic air sacs showed significant higher FALCs size in comparison to that of other air sac types and the mesentery. Our findings suggested other possible immunological role of the air sacs and mesentery that could have impact on the progression of air sacculitis and mesenteritis- associate diseases. However, further investigations are required for clarification of air sacs and mesenteric FALCs in the progressions of respiratory and digestive tract diseases.

Abstract Graphene‐based wearable electronic textiles (e‐textiles) show promise for next‐generation personalized healthcare applications due to their non‐invasive nature. However, the poor performance, less comfort, and higher material cost limit their wide applications. Here a simple and scalable production method of producing graphene‐based electro‐conductive yarn that is further embroidered to realize piezoresistive sensors is reported. The multilayer structures improved the conductivity of the piezoresistive sensors, exhibiting good sensitivity with high response and recovery speed. Additionally, the potential applications of such wearable, ultraflexible and machine‐washable piezoresistive sensors as pressure and breathing sensors are demonstrated. This will be an important step toward realizing multifunctional applications of wearable e‐textiles for next‐generation personalized healthcare applications.

Abstract Electronic textiles (e‐textiles) have drawn significant attention from the scientific and engineering community as lightweight and comfortable next‐generation wearable devices due to their ability to interface with the human body, and continuously monitor, collect, and communicate various physiological parameters. However, one of the major challenges for the commercialization and further growth of e‐textiles is the lack of compatible power supply units. Thin and flexible supercapacitors (SCs), among various energy storage systems, are gaining consideration due to their salient features including excellent lifetime, lightweight, and high‐power density. Textile‐based SCs are thus an exciting energy storage solution to power smart gadgets integrated into clothing. Here, materials, fabrications, and characterization strategies for textile‐based SCs are reviewed. The recent progress of textile‐based SCs is then summarized in terms of their electrochemical performances, followed by the discussion on key parameters for their wearable electronics applications, including washability, flexibility, and scalability. Finally, the perspectives on their research and technological prospects to facilitate an essential step towards moving from laboratory‐based flexible and wearable SCs to industrial‐scale mass production are presented.

Object classification in hyperspectral images involves accurately categorizing objects based on their spectral characteristics. However, the high dimensionality of hyperspectral data and class imbalance pose significant challenges to object classification performance. To address these challenges, we propose a framework that incorporates dimensionality reduction and re-sampling as preprocessing steps for a deep learning model. Our framework employs a novel subgroup-based dimensionality reduction technique to extract and select the most informative features with minimal redundancy. Additionally, the data are resampled to achieve class balance across all categories. The reduced and balanced data are then processed through a hybrid CNN model, which combines a 3D learning block and a 2D learning block to extract spectral–spatial features and achieve satisfactory classification accuracy. By adopting this hybrid approach, we simplify the model while improving performance in the presence of noise and limited sample size. We evaluated our proposed model on the Salinas scene, Pavia University, and Kennedy Space Center benchmark hyperspectral datasets, comparing it to state-of-the-art methods. Our object classification technique achieves highly promising results, with overall accuracies of 99.98%, 99.94%, and 99.46% on the three datasets, respectively. This proposed approach offers a compelling solution to overcome the challenges of high dimensionality and class imbalance in hyperspectral object classification.

Abstract Background Equity is one of three dimensions of universal health coverage (UHC). However, Iraq has had capital-focused health services and successive conflicts and political turmoil have hampered health services around the country. Iraq has embarked on a new reconstruction process since 2018 and it could be time to aim for equitable healthcare access to realise UHC. We aimed to examine inequality and determinants associated with Iraq’s progress towards UHC targets. Methods We assessed the progress toward UHC in the context of equity using six nationally representative population-based household surveys in Iraq in 2000–2018. We included 14 health service indicators and two financial risk protection indicators in our UHC progress assessment. Bayesian hierarchical regression model was used to estimate the trend, projection, and determinant analyses. Slope and relative index of inequality were used to assess wealth-based inequality. Results In the national-level health service indicators, inequality indices decreased substantially from 2000 to 2030. However, the wide inequalities are projected to remain in DTP3, measles, full immunisations, and antenatal care in 2030. The pro-rich inequality gap in catastrophic health expenditure increased significantly in all governorates except Sulaimaniya from 2007 to 2012. The higher increases in pro-rich inequality were found in Missan, Karbala, Erbil, and Diala. Mothers’ higher education and more antenatal care visits were possible factors for increased coverage of health service indicators. The higher number of children and elderly population in the households were potential risk factors for an increased risk of catastrophic and impoverishing health payment in Iraq. Conclusions To reduce inequality in Iraq, urgent health-system reform is needed, with consideration for vulnerable households having female-heads, less educated mothers, and more children and/or elderly people. Considering varying inequity between and within governorates in Iraq, reconstruction of primary healthcare across the country and cross-sectoral targeted interventions for women should be prioritised.

In this article, a meander line with parasitic resonator based on ENG metamaterial is introduced, with nonlinear regression of sensitivity for sensing applications. The unit cell is manufactured on a dielectric substrate of FR-4 with a thickness of 1.5 mm. The electrical dimension of the optimized cell is 0.098λ×0.094λ, where λ is the calculated wavelength at 7.2 GHz resonance frequency. A sensor has been designed by the unit cell to measure the thickness of the polypropylene. The offered metamaterial architecture exhibits resonance frequency 7.2 GHz at C-band with the amplitude of −21.72 dB. The NRW approach based MATLAB software shows properties with an ENG behavior and permeability close to zero. For the analysis procedure, a frequency domain solver with the frequency range of 2–12 GHz was used. ADS software verified the scattering parameters (coefficient of transmission) of the equivalent circuit for the corresponding unit cell with the CST result. The result indicates that this design has SNG features at C-band. The EMR (effective medium ratio) of this suggested unit cell is 10.60 at 7.2 GHz resonance frequency. The bandwidth of the optimized cell is 6.75–7.36 GHz, and the negative permittivity region is 7.2–7.8 GHz 1 × 2, 2 × 2, 4 × 4, and 16 × 16 array of unit cell results have been examined. All the array results show the same with the unit cell. Substrate material has also been changed to see the effect of resonance frequency in the parametric study. Finally, the proposed meander line structure performance was further simulated numerically and experimentally investigated for sandwich combination thickness sensing. It shows a narrow bandwidth sensing range between 6.25 and 6.47 GHz with significant sensitivity (S) and accuracy. The design complies with superior effective medium parameters and affords miniaturization factor for the size. Due to its low profile and high EMR, the planned ENG metamaterial can be a suitable C-band aspirant for sensing applications.

Summary: Wearable e-textiles have gained huge tractions due to their potential for non-invasive health monitoring. However, manufacturing of multifunctional wearable e-textiles remains challenging, due to poor performance, comfortability, scalability, and cost. Here, we report a fully printed, highly conductive, flexible, and machine-washable e-textiles platform that stores energy and monitor physiological conditions including bio-signals. The approach includes highly scalable printing of graphene-based inks on a rough and flexible textile substrate, followed by a fine encapsulation to produce highly conductive machine-washable e-textiles platform. The produced e-textiles are extremely flexible, conformal, and can detect activities of various body parts. The printed in-plane supercapacitor provides an aerial capacitance of ∼3.2 mFcm−2 (stability ∼10,000 cycles). We demonstrate such e-textiles to record brain activity (an electroencephalogram, EEG) and find comparable to conventional rigid electrodes. This could potentially lead to a multifunctional garment of graphene-based e-textiles that can act as flexible and wearable sensors powered by the energy stored in graphene-based textile supercapacitors.

Modern contraceptive methods are effective tools for controlling fertility and reducing unwanted pregnancies. Yet, the unmet need for modern contraception (UNMC) remains high in most of the developing countries of the world. This study aimed to compare the coverage of modern contraceptive usage and the UNMC among the young married women of low- and lower-middle-income countries (LMICs) of Asia and Sub-Saharan Africa, and further examined the likelihood of UNMC across these regions. This cross-sectional study used Demographic and Health Survey (DHS) data on family planning from 32 LMICs of South Asia (SA), Southeast Asia (SEA), West-Central Africa (WCA), and Eastern-Southern Africa (ESA). Multilevel logistic regression models were used to investigate the relationship between UNMC and women's socioeconomic status. Out of 1,00,666 younger married women (15-24 years old), approximately 37% used modern contraceptives, and 24% experienced UNMC. Regionally, women from SA reported higher modern contraceptive usage (44.7%) and higher UNMC (24.6%). Socioeconomic factors like- higher education (in SA and WCA), unemployment (in SA and ESA), no media exposure (in SA and ESA), and higher decision-making autonomy (except SEA) showed positive and significant association with UNMC. Poorest households were positively associated with UNMC in SA and ESA, while negatively associated with UNMC in SEA. UNMC was highly reported among the SA young married women, followed by WCA, SEA, and ESA regions. Based on this study findings, versatile policies, couples counseling campaigns, and community-based outreach initiatives might be undertaken to minimize UNMC among young married women in LMICs.

Summary: Natural fiber reinforced composites (FRC) are of great interests, because of their biodegradability, recyclability, and environmental benefits over synthetic FRC. Natural jute FRC could provide an environmentally sustainable, light weight, and cost-effective alternative to synthetic FRC. However, the application of natural jute FRC is limited because of their poor mechanical and interfacial properties. Graphene and its derivatives could potentially be applied to modify jute fiber surface for manufacturing natural FRC with excellent mechanical properties, and lower environmental impacts. Here, we review the physical and chemical treatments, and graphene-based modifications of jute fibers, and their effect on mechanical properties of jute FRC. We introduce jute fiber structure, chemical compositions, and their potential applications first. We then provide an overview of various surface treatments used to improve mechanical properties of jute FRC. We discuss and compare various graphene derivative-based surface modifications of jute fibers, and their impact on the performance of FRC. Finally, we provide our future perspective on graphene-based jute fibers research to enable next generation strong and sustainable FRC for high performance engineering applications without conferring environmental problems.

This paper presents a comparative performance study of single-stage desalination processes with major configurations of membrane distillation (MD) modules. MD modules covered in this study are (a) direct contact MD (DCMD), (b) vacuum MD (VMD), (c) sweeping gas MD (SGMD), and (d) air gap MD (AGMD). MD-based desalination processes are simulated with rigorous theoretical MD models supported by molecular thermodynamic property models for the accurate calculation of performance metrics. The performance metrics considered in MD systems are permeate flux and energy efficiency, i.e., gained output ratio (GOR). A general criterion is established to determine the critical length of these four MDs (at fixed width) for the feasible operation of desalination in a wide range of feed salinities. The length of DCMD and VMD is restricted by the feed salinity and permeate flux, respectively, while relatively large AGMD and SGMD are allowed. The sensitivity of GOR flux with respect to permeate conditions is investigated for different MD configurations. AGMD outperforms other configurations in terms of energy efficiency, while VMD reveals the highest permeate production. With larger MD modules, utilization of thermal energy supplied by the hot feed for evaporation is in the order of VMD > AGMD > SGMD > DCMD. Simulation results highlight that energy efficiency of the overall desalination process relies on the efficient recovery of spent for evaporation, suggesting potential improvement in energy efficiency for VMD-based desalination.

Hyperspectral images (HSIs) are one of the most successfully used tools for precisely and potentially detecting key ground surfaces, vegetation, and minerals. HSIs contain a large amount of information about the ground scene; therefore, object classification becomes the most difficult task for such a high-dimensional HSI data cube. Additionally, the HSI’s spectral bands exhibit a high correlation, and a large amount of spectral data creates high dimensionality issues as well. Dimensionality reduction is, therefore, a crucial step in the HSI classification pipeline. In order to identify a pertinent subset of features for effective HSI classification, this study proposes a dimension reduction method that combines feature extraction and feature selection. In particular, we exploited the widely used denoising method minimum noise fraction (MNF) for feature extraction and an information theoretic-based strategy, cross-cumulative residual entropy (CCRE), for feature selection. Using the normalized CCRE, minimum redundancy maximum relevance (mRMR)-driven feature selection criteria were used to enhance the quality of the selected feature. To assess the effectiveness of the extracted features’ subsets, the kernel support vector machine (KSVM) classifier was applied to three publicly available HSIs. The experimental findings manifest a discernible improvement in classification accuracy and the qualities of the selected features. Specifically, the proposed method outperforms the traditional methods investigated, with overall classification accuracies on Indian Pines, Washington DC Mall, and Pavia University HSIs of 97.44%, 99.71%, and 98.35%, respectively.

A hyperspectral image (HSI), which contains a number of contiguous and narrow spectral wavelength bands, is a valuable source of data for ground cover examinations. Classification using the entire original HSI suffers from the “curse of dimensionality” problem because (i) the image bands are highly correlated both spectrally and spatially, (ii) not every band can carry equal information, (iii) there is a lack of enough training samples for some classes, and (iv) the overall computational cost is high. Therefore, effective feature (band) reduction is necessary through feature extraction (FE) and/or feature selection (FS) for improving the classification in a cost-effective manner. Principal component analysis (PCA) is a frequently adopted unsupervised FE method in HSI classification. Nevertheless, its performance worsens when the dataset is noisy, and the computational cost becomes high. Consequently, this study first proposed an efficient FE approach using a normalized mutual information (NMI)-based band grouping strategy, where the classical PCA was applied to each band subgroup for intrinsic FE. Finally, the subspace of the most effective features was generated by the NMI-based minimum redundancy and maximum relevance (mRMR) FS criteria. The subspace of features was then classified using the kernel support vector machine. Two real HSIs collected by the AVIRIS and HYDICE sensors were used in an experiment. The experimental results demonstrated that the proposed feature reduction approach significantly improved the classification performance. It achieved the highest overall classification accuracy of 94.93% for the AVIRIS dataset and 99.026% for the HYDICE dataset. Moreover, the proposed approach reduced the computational cost compared with the studied methods.

Objectives Relative to the attention given to improving the measurement of adequacy of antenatal care (ANC) in South Asian (SA) region, the influence of women’s empowerment and socioeconomic status (WESES) on adequate ANC services has hardly received any attention. This study aimed to investigate the present scenario of adequacy of ANC in SA and how its adequacy was associated with WESES.Setting and participants Using the Demographic and Health Survey data set of five SA countries, that is, Afghanistan, Bangladesh, India, Nepal and Pakistan, 48 107 women were selected in this study who received at least one ANC component and had at least one live birth in the 3 or 5 years preceding the survey.Analysis Multilevel logistic regression models were used to investigate the relationship between adequacy of ANC and WESES.Results Only 30% women received adequate ANC in SA, ranging from 8.4% (95% CI 7.1% to 9.9%) in Afghanistan to 39.8% (95% CI 37.4% to 42.2%) in Nepal. The poor utilisation of adequate ANC services was most prevalent among the women residing in rural areas and that of poor families as well as low empowerment status in SA countries. Different levels of WESES, that is, highly empowered but poor (adjusted OR (AOR): 1.33; 95% CI 1.18 to 1.49), lowly empowered but rich (AOR: 2.07; 95% CI 1.84 to 2.32) and highly empowered and rich women (AOR: 3.07; 95% CI 2.75 to 3.43), showed significant positive association with adequate ANC services than the poor and low empowered women, after adjusting the potential covariates.Conclusion As unsatisfactory level of adequate ANC services has been observed in SA region, this study suggests a nationwide comprehensive improvement of women’s empowerment status as well as establishment of necessary healthcare centres in remote areas is essential to ensure long-term and sustainable adequacy of ANC services.

BackgroundMost of national schistosomiasis elimination programmes in Asia are relying on stool examination, particularly Kato Katz stool examination technique for regular transmission monitoring. However, the Kato-Katz technique has shown low sensitivity for the detection of light-intensity infections, and therefore highly sensitive diagnostic tools are urgently required to monitor prevalence of infection in low transmission settings. The objective of this systematic review was to evaluate and synthesize the performance of diagnostic tests for detecting Schistosoma japonicum and S. mekongi infection in people living in endemic areas.Methodology/principal findingsWe comprehensively searched these nine electronic databases and other resources until July 2019, with no language or publication limits: PubMed, EMBASE, MEDLINE, Web of Science, BIOSIS Citation Index, HTA, CINAHL PLUS, The Cochrane Library, and PsycINFO. We included original studies that assessed diagnostic performance using antibody, antigen, and molecular tests with stool examination test as a reference standard. Two reviewers independently extracted a standard set of data and assessed study quality. We estimated the pooled estimates of sensitivity and specificity for each index test. We used diagnostic odds ratio to determine the overall accuracy and hierarchical summary receiver operating characteristics (HSROC) curve to assess the index tests performance. Fifteen studies (S. japonicum [n = 13] and S. mekongi [n = 2]) testing 15,303 participants were included in the review. Five studies reported performance of enzyme-linked immunosorbent assay (ELISA), seven studies reported indirect hemagglutination assay (IHA), and four studies reported polymerase chain reaction (PCR) for detecting S. japonicum. The pooled sensitivity and specificity were 0.93 (95% CI: 0.84-0.98) and 0.40 (95% CI: 0.29-0.53) for ELISA, 0.97 (95% CI: 0.90-0.99) and 0.66 (95% CI: 0.58-0.73) for IHA, and 0.89 (95% CI: 0.71-0.96) and 0.49 (95% CI: 0.29-0.69) for PCR respectively. A global summary indicated the best performance for IHA, closely followed by ELISA. We were unable to perform meta-analysis for S. mekongi due to insufficient number of studies.Conclusions/significanceIHA showed the highest detection accuracy for S. japonicum. Further studies are needed to determine the suitable diagnostic methods to verify the absence of transmission of S. mekongi and also to compare detection accuracy against more sensitive reference standards such as PCR.

Cotton rats are one of the experimental rodents used for testing different infectious and non-infectious diseases, including gastrointestinal tract pathology. However, their intestinal morphological characteristics are still poorly understood. Here, we clarified the anatomical and histological characteristics of the cecum and ascending colon (AC) of young (1–3-month old), adult (4–6-month old), and old (10–12-month old) cotton rats. The large intestine (LI) in cotton rats is composed of the cecum, AC, transverse and descending colons, and rectum, and is similar to that of other mammals. The AC begins with a double or triple spiral loop-like flexure (SLLF) and ends with a coupled horseshoe-like flexure (HSLF). A single longitudinal mucosal fold (SLMF) was found at the beginning of the AC along the mesentery line and developed with age. Furthermore, the SLMF contained several lymphatic nodules (LNs), indicating their role in digestive and immunological functions. Small and large protuberant LNs were found in the cecum and SLLF, respectively, whereas thin and flat LNs were observed in the HSLF and transverse colon, respectively. Regarding sex-related differences, adult females had a significantly longer AC with a higher number of SLLFs compared to males. The SLMF length and LN number were also longer and higher, respectively, in adult females compared to adult males. These are crucial findings, indicating the presence of sex-related differences in the morphology of the LI in cotton rats, and ours is the first study to discover a sex difference in the mammalian LI lining. Our study clarified the unique morphology of the LI in cotton rats, which could serve as the principal model for elucidating species-specific digestive tract functions and gastrointestinal disorders.

This paper represents a tri-band left-handed meta-atom with high effective medium ratio, which is applicable for tri-band microwave based communication applications. The proposed meta-atom consists of modified complementary split ring resonator (CSRR) with two pi shaped metal strip loaded in the inner side. It shows the behavior of negative permeability and permittivity at 2.85 GHz, 4.83 GHz, 8.39 GHz, and 11.42 GHz resonance frequencies and extracted the double negative (DNG) metamaterial properties. The designed meta-atom dimension is 0.076λ×0.076λ at lower resonance frequency. To develop and analyze the offered meta-atom, the electromagnetic (EM) simulator CST Microwave Studio and MATLAB software were used. The equivalent circuit with RLC components of the proposed metamaterial unit cell is also investigated through ADS simulator and comparing S parameter (transmission coefficients) results with CST results. Analysis and comparison have been carried out with different configurations, such as various design structures, distinct substrate, changing the gap of the splits, and various array measures. All the array configurations proved that the frequency of operation is in the S-, C-, and X-bands. The designed meta-atom has double negative regions of 2.89–2.96 GHz, 4.84–4.99 GHz, 8.42–8.49 GHz, and 11.43–12.32 GHz, with the regions of effective negative refractive index 2.85–3.09 GHz, 4.83–5.36 GHz, 8.40–8.57 GHz, and 11.40–12.55 GHz, respectively. The effective medium ratio is 13.16; this implies that the proposed miniaturized meta-atom design is efficient and compact. The scattering parameter dimension of the meta-atom promising for the microwave based applications at S-, C-, and X-bands.

BACKGROUND:Because of the rapid increase of non-communicable diseases (NCDs) and high burden of healthcare-related financial issues in Bangladesh, there is a concern that out-of-pocket (OOP) payments related to illnesses may become a major burden on household. It is crucial to understand what are the major illnesses responsible for high OPP at the household level to help policymakers prioritize key areas of actions to protect the household from 100% financial hardship for seeking health care as part of universal health coverage. OBJECTIVES:We first estimated the costs of illnesses among a population in urban Bangladesh, and then assessed the household financial burden associated with these illnesses. METHOD:A cross-sectional survey of 1593 randomly selected households was carried out in Bangladesh (urban area of Rajshahi city), in 2011. Catastrophic expenditure was estimated at 40% threshold of household capacity to pay. We employed the Bayesian two-stage hurdle model and Bayesian logistic regression model to estimate age-adjusted average cost and the incidence of household financial catastrophe for each illness, respectively. RESULTS:Overall, approximately 45% of the population of Bangladesh had at least one episode of illness. The age-sex-adjusted average medical expenses and catastrophic health care expenditure among the households were TK 621 and 8%, respectively. Households spent the highest amount of money 7676.9 on paralysis followed by liver disease (TK 2695.4), injury (TK 2440.0), mental disease (TK 2258.0), and tumor (TK 2231.2). These diseases were also responsible for higher incidence of financial catastrophe. Our study showed that 24% of individuals who suffered typhoid incurred catastrophic expenditure followed by liver disease (12.3%), tumor (12.1%), heart disease (8.4%), injury (7.9%), mental disease (7.9%), cataract (7.1%), and paralysis (6.5%). CONCLUSION:The study findings suggest that chronic illnesses were responsible for high costs and high catastrophic expenditures in Bangladesh. Effective risk pooling mechanism might reduce household financial burden related to illnesses. Chronic illness related to NCDs is the major cause of OOP. It is also important to consider prioritizing vulnerable population by subsidizing the high health care cost for some of the chronic illnesses.

IntroductionAnemia is a common problem in children particularly in developing countries and taking steps to tackle it is one of the major public health challenges for Nepal. The objective of this study is to investigate the prevalence of individual, household and community level determinants of childhood anemia in Nepal.MethodsData was taken from a nationally representative sample of 1,942 Nepalese children aged from 6-59 months. The Chi-square test was used to determine the bivariate relationship between the selected variables and childhood anemia and a multilevel logistic regression model with a random intercept at household and community level was used to identify important determinants of this kind of anemia.ResultsThe results showed that 52.6% (95% CI: 49.8%-55.4%) of the children were anemic while 26.6% (95% CI: 24.0%-29.3%) of them were moderate to severe. The prevalence of overall anemia was higher among children aged less than 11 months as well as in underweight children, children of underweight, anemic and uneducated mothers and those in the terrain ecological regions. Multivariable analysis showed that children aged less than 11 months, who were underweight and had anemic mothers were more likely to have moderate or severe anemia. Children in the hilly ecological region were less likely to have it compared to mountain and terrain ecological regions. Children in middle-class families and children of mothers who completed secondary education were more likely to have anemia.ConclusionNepal is facing a serious public health problem due to the high prevalence of childhood anemia. This adverse situation occurs due to socio-demographic and geographical factors such as age, malnutrition status, mother's anemia status, socio-economic status and regional variations. Prevention of childhood anemia should be given top priority in Nepal and should be considered as a major public health intervention.

Colorectal cancer (CRC) is the second most common cause of death worldwide, affecting approximately 1.9 million individuals in 2020. Therapeutics of the disease are not yet available and discovering a novel anticancer drug candidate against the disease is an urgent need. Thymidylate synthase (TS) is an important enzyme and prime precursor for DNA biosynthesis that catalyzes the methylation of deoxyuridine monophosphate (dUMP) to deoxythymidine monophosphate (dTMP) that has emerged as a novel drug target against the disease. Elevated expression of TS in proliferating cells promotes oncogenesis as well as CRC. Therefore, this study aimed to identify potential natural anticancer agents that can inhibit the activity of the TS protein, subsequently blocking the progression of colorectal cancer. Initially, molecular docking was implied on 63 natural compounds identified from Catharanthus roseus and Avicennia marina to evaluate their binding affinity to the desired protein. Subsequently, molecular dynamics (MD) simulation, ADME (Absorption, Distribution, Metabolism, and Excretion), toxicity, and quantum chemical-based DFT (density-functional theory) approaches were applied to evaluate the efficacy of the selected compounds. Molecular docking analysis initially identified four compounds (PubChem CID: 5281349, CID: 102004710, CID: 11969465, CID: 198912) that have better binding affinity to the target protein. The ADME and toxicity properties indicated good pharmacokinetics (PK) and toxicity ability of the selected compounds. Additionally, the quantum chemical calculation of the selected molecules found low chemical reactivity indicating the bioactivity of the drug candidate. The global descriptor and HOMO-LUMO energy gap values indicated a satisfactory and remarkable profile of the selected molecules. Furthermore, MD simulations of the compounds identified better binding stability of the compounds to the desired protein. To sum up, the phytoconstituents from two plants showed better anticancer activity against TS protein that can be further developed as an anti-CRC drug.

Inter-robot communication and high computational power are challenging issues for deploying indoor mobile robot applications with sensor data processing. Thus, this paper presents an efficient cloud-based multirobot framework with inter-robot communication and high computational power to deploy autonomous mobile robots for indoor applications. Deployment of usable indoor service robots requires uninterrupted movement and enhanced robot vision with a robust classification of objects and obstacles using vision sensor data in the indoor environment. However, state-of-the-art methods face degraded indoor object and obstacle recognition for multiobject vision frames and unknown objects in complex and dynamic environments. From these points of view, this paper proposes a new object segmentation model to separate objects from a multiobject robotic view-frame. In addition, we present a support vector data description (SVDD)-based one-class support vector machine for detecting unknown objects in an outlier detection fashion for the classification model. A cloud-based convolutional neural network (CNN) model with a SoftMax classifier is used for training and identification of objects in the environment, and an incremental learning method is introduced for adding unknown objects to the robot knowledge. A cloud–robot architecture is implemented using a Node-RED environment to validate the proposed model. A benchmarked object image dataset from an open resource repository and images captured from the lab environment were used to train the models. The proposed model showed good object detection and identification results. The performance of the model was compared with three state-of-the-art models and was found to outperform them. Moreover, the usability of the proposed system was enhanced by the unknown object detection, incremental learning, and cloud-based framework.

Handwriting analysis is playing an important role in user authentication or online writer identification for more than a decade. It has a significant role in different applications such as e-security, signature biometrics, e-health, gesture analysis, diagnosis system of Parkinson’s disease, Attention-deficit/hyperactivity disorders, analysis of vulnerable people (stressed, elderly, or drugged), prediction of gender, handedness and so on. Classical authentication systems are image-based, text-dependent, and password or fingerprint-based where the former one has the risk of information leakage. Alternatively, image processing and pattern-analysis-based systems are vulnerable to camera attributes, camera frames, light effect, and the quality of the image or pattern. Thus, in this paper, we concentrate on real-time and context-free handwriting data analysis for robust user authentication systems using digital pen-tablet sensor data. Most of the state-of-the-art authentication models show suboptimal performance for improper features. This research proposed a robust and efficient user identification system using an optimal feature selection technique based on the features from the sensor’s signal of pen and tablet devices. The proposed system includes more genuine and accurate numerical data which are used for features extraction model based on both the kinematic and statistical features of individual handwritings. Sensor data of digital pen-tablet devices generate high dimensional feature vectors for user identification. However, all the features do not play equal contribution to identify a user. Hence, to find out the optimal features, we utilized a hybrid feature selection model. Extracted features are then fed to the popular machine learning (ML) algorithms to generate a nonlinear classifier through training and testing phases. The experimental result analysis shows that the proposed model achieves more accurate and satisfactory results which ensure the practicality of our system for user identification with low computational cost.

Most mammalian ovarian follicles contain only a single oocyte having a single nucleus. However, two or more oocytes and nuclei are observed within one follicle and one oocyte, respectively, in several species, including cotton rat (CR, Sigmodon hispidus). The present study compared ovarian histology, focusing on folliculogenesis, between two inbred CR strains, HIS/Hiph and HIS/Mz. At 4 weeks of age, ovarian sections from both the strains were analyzed histologically. Multi-oocyte follicles (MOFs) and double-nucleated oocytes (DNOs) were observed in all stages of developing follicles in HIS/Hiph, whereas HIS/Mz had MOFs up to secondary stages and lacked DNOs. The estimated total follicles in HIS/Mz were almost half that of HIS/Hiph, but interstitial cells were well developed in HIS/Mz. Furthermore, immunostaining revealed no clear strain differences in the appearance of oocytes positive for Ki67, PCNA, and p63 in MOF or DNOs; no cell death was observed in these oocytes. Ultrastructural analysis revealed more abundant mitochondrial clouds in oocytes of HIS/Hiph than HIS/Mz. Thus, we clarified the strain differences in the CR ovary. These findings indicate that early events during folliculogenesis affect the unique ovarian phenotypes found in CRs, including MOFs or DNOs, and their strain differences.

Human Activity Recognition (HAR) using embedded sensors in smartphones and smartwatch has gained popularity in extensive applications in health care monitoring of elderly people, security purpose, robotics, monitoring employees in the industry, and others. However, human behavior analysis using the accelerometer and gyroscope data are typically grounded on supervised classification techniques, where models are showing sub-optimal performance for qualitative and quantitative features. Considering this factor, this paper proposes an efficient and reduce dimension feature extraction model for human activity recognition. In this feature extraction technique, the Enveloped Power Spectrum (EPS) is used for extracting impulse components of the signal using frequency domain analysis which is more robust and noise insensitive. The Linear Discriminant Analysis (LDA) is used as dimensionality reduction procedure to extract the minimum number of discriminant features from envelop spectrum for human activity recognition (HAR). The extracted features are used for human activity recognition using Multi-class Support Vector Machine (MCSVM). The proposed model was evaluated by using two benchmark datasets, i.e., the UCI-HAR and DU-MD datasets. This model is compared with other state-of-the-art methods and the model is outperformed.

This article concerns smoke detection in the early stages of a fire. Using the computer-aided system, the efficient and early detection of smoke may stop a massive fire incident. Without considering the multiple moving objects on background and smoke particles analysis (i.e., pattern recognition), smoke detection models show suboptimal performance. To address this, this paper proposes a hybrid smoke segmentation and an efficient symmetrical simulation model of dynamic smoke to extract a smoke growth feature based on temporal frames from a video. In this model, smoke is segmented from the multi-moving object on the complex background using the Gaussian’s Mixture Model (GMM) and HSV (hue-saturation-value) color segmentation to encounter the candidate smoke and non-smoke regions in the preprocessing stage. The preprocessed temporal frames with moving smoke are analyzed by the dynamic smoke growth analysis and spatial-temporal frame energy feature extraction model. In dynamic smoke growth analysis, the temporal frames are segmented in blocks and the smoke growth representations are formulated from corresponding blocks. Finally, the classifier was trained using the extracted features to classify and detect smoke using a Radial Basis Function (RBF) non-linear Gaussian kernel-based binary Support Vector Machine (SVM). For validating the proposed smoke detection model, multi-conditional video clips are used. The experimental results suggest that the proposed model outperforms state-of-the-art algorithms.

Human activity recognition (HAR) techniques are playing a significant role in monitoring the daily activities of human life such as elderly care, investigation activities, healthcare, sports, and smart homes. Smartphones incorporated with varieties of motion sensors like accelerometers and gyroscopes are widely used inertial sensors that can identify different physical conditions of human. In recent research, many works have been done regarding human activity recognition. Sensor data of smartphone produces high dimensional feature vectors for identifying human activities. However, all the vectors are not contributing equally for identification process. Including all feature vectors create a phenomenon known as ‘curse of dimensionality’. This research has proposed a hybrid method feature selection process, which includes a filter and wrapper method. The process uses a sequential floating forward search (SFFS) to extract desired features for better activity recognition. Features are then fed to a multiclass support vector machine (SVM) to create nonlinear classifiers by adopting the kernel trick for training and testing purpose. We validated our model with a benchmark dataset. Our proposed system works efficiently with limited hardware resource and provides satisfactory activity identification.

Hand gesture-based sign language recognition is a prosperous application of human− computer interaction (HCI), where the deaf community, hard of hearing, and deaf family members communicate with the help of a computer device. To help the deaf community, this paper presents a non-touch sign word recognition system that translates the gesture of a sign word into text. However, the uncontrolled environment, perspective light diversity, and partial occlusion may greatly affect the reliability of hand gesture recognition. From this point of view, a hybrid segmentation technique including YCbCr and SkinMask segmentation is developed to identify the hand and extract the feature using the feature fusion of the convolutional neural network (CNN). YCbCr performs image conversion, binarization, erosion, and eventually filling the hole to obtain the segmented images. SkinMask images are obtained by matching the color of the hand. Finally, a multiclass SVM classifier is used to classify the hand gestures of a sign word. As a result, the sign of twenty common words is evaluated in real time, and the test results confirm that this system can not only obtain better-segmented images but also has a higher recognition rate than the conventional ones.

This paper proposes an online fault diagnosis system for bearings that detect emerging fault modes and then updates the diagnostic system knowledge (DSK) to incorporate information about the newly detected fault modes. New fault modes are detected using k-means clustering along with a new cluster evaluation method, i.e., multivariate probability density function’s cluster distribution factor (MPDFCDF). In this proposed model, a heterogeneous pool of features is constructed from the signal. A hybrid feature selection model is adopted for selecting optimal feature for learning the model with existing fault mode. The proposed online fault diagnosis system detects new fault modes from unknown signals using k-means clustering with the help of proposed MPDFCDF cluster evaluation method. The DSK is updated whenever new fault modes are detected and updated DSK is used to classify faults using the k-nearest neighbor (k-NN) classifier. The proposed model is evaluated using acoustic emission signals acquired from low-speed rolling element bearings with different fault modes and severities under different rotational speeds. Experimental results present that the MPDFCDF cluster evaluation method can detect the optimal number of fault clusters, and the proposed online diagnosis model can detect newly emerged faults and update the DSK effectively, which improves the diagnosis performance in terms of the average classification performance.