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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.

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.

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.

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.

Abstract Background It has been suggested that the association between body mass index and breast cancer risk differs between Asian women and Western women. We aimed to assess the associations between body mass index and breast cancer incidence in East Asian women. Methods Pooled analyses were performed using individual participant data of 319,189 women from 13 cohort studies in Japan, Korea, and China. Participants’ height and weight were obtained by measurement or self-reports at cohort baseline. Breast cancer was defined as code C50.0-C50.9 according to the International Classification. Using a Cox proportional hazards model, hazard ratios of breast cancer were estimated for each body mass index category, with the reference group set as the group with a body mass index of 21 to

Abstract Metamaterial absorbers have sparked widespread interest due to their remarkable electromagnetic properties, which enable a wide range of applications in light absorption and manipulation. This study introduces a new three-layer nanomaterial absorber (NMA) unit cell composed of nickel (Ni), silicon dioxide (SiO2), and nickel (Ni) designed to operate across the entire visible spectrum (390–780 nm). We demonstrate the NMA’s exceptional absorption characteristics through rigorous numerical simulations using industry-standard software, achieving a mean absorption rate of 97.17% and a maximum absorption peak of 99.99% at 694.89 nm under standard angles. Furthermore, the NMA unit cell has good impedance matching, efficient coupling between capacitors and inductors, and significant plasmonic resonance properties. Fabrication feasibility and potential applications in solar energy harvesting, optical sensing, and light detection.

Objective: The study looked into the effects and techniques of feeding a traditional medicinal herb, honeyweed (Leonurus siribicus) to broiler chicks in a commercially operated poultry farm. Materials and Methods: Sixty chicks, each one day old, were partitioned into four distinct groups: T1 (Commercial Feed), T2 (formulated feed, FF with 5% (w/w) honeyweed powder), T3 (FF with 5% (w/v) honeyweed powder solution), and T4 (FF with 5% (w/v) honeyweed powder solution administered through drops). This arrangement followed a completely randomized design, with three (3) replications of five (5) birds each. The broiler chicks were fed either commercial feed or FF with a honeyweed supplemented diet for 30 days. Results: The body weight, feed conversion ratio (FCR), and death rate were computed at the conclusion of the trial. The T2 diet was found to have the lowest FCR, mortality rate, and maximum weight gain (p ≤ 0.05). Amazingly, honeyweed powder put into poultry feed dramatically reduced blood cholesterol, TG, and LDL and improved HDL (p ≤ 0.05). Remarkable differences in serum SGPT and SGOT levels have been found between the commercial feed and honeyweed-supplemented diets. Surprisingly, histological research revealed that 5% honeyweed-supplemented feed resulted in little fat formation in the liver. In addition, a 5% (w/w) honeyweed-supplemented diet reduced the total viable bacteria in the feces. We also conducted the study to observe the antimicrobial activity against Escherichia coli by using the methanol and water extracts of honeyweed in comparison to the synthetic antibiotic tetracycline in vitro. Intriguingly, tetracycline, methanol extract, and water extract of honeyweed showed inhibition zones of 17, 13, and 8 mm against E. coli, respectively. Conclusion: Our findings suggested that this novel-designed feed with 5% honeyweed might be used as a natural feed additive instead of dangerously manufactured antibiotics for safe poultry meat production. [Vet. Res. Notes 2024; 4(5.000): 56-64]

Complex structures can develop cracks and defects over time, which can compromise their long-term performance and safety. Structural Health Monitoring (SHM) systems are essential for detecting and measuring these defects by monitoring the load and deformation of the solid materials. This paper presents a simulation study of the frequency and strength of solid cylindrical bars made of aluminum and steel under different loads and crack conditions. Finite Element Method (FEM) and COMSOL Multiphysics software are used to perform the simulation, and a resonance model is used to analyze the results. The study investigates how cracks affect the frequency and deformation of the bars, and how different materials respond to load and bending. The results show that frequency varies linearly with load, cracks decrease the stiffness and increase the frequency at the crack location, and aluminum bars deform more than steel bars. The paper concludes that steel bars are more resistant to load and bending than aluminum bars for both cracked and uncracked case. Finally, it is found that steel bars are more resistant to load and bending than aluminum bars for both cracked and uncracked case.

Abstract Background Evidence suggests a possible link between diabetes and gastric cancer risk, but the findings remain inconclusive, with limited studies in the Asian population. We aimed to assess the impact of diabetes and diabetes duration on the development of gastric cancer overall, by anatomical and histological subtypes. Methods A pooled analysis was conducted using 12 prospective studies included in the Asia Cohort Consortium. Among 558 981 participants (median age 52), after a median follow‐up of 14.9 years and 10.5 years, 8556 incident primary gastric cancers and 8058 gastric cancer deaths occurred, respectively. Cox proportional hazard regression models were used to estimate study‐specific hazard ratios (HRs) and 95% confidence intervals (CIs) and pooled using random‐effects meta‐analyses. Results Diabetes was associated with an increased incidence of overall gastric cancer (HR 1.15, 95% CI 1.06–1.25). The risk association did not differ significantly by sex (women vs men: HR 1.31, 95% CI 1.07–1.60 vs 1.12, 1.01–1.23), anatomical subsites (noncardia vs cardia: 1.14, 1.02–1.28 vs 1.17, 0.77–1.78) and histological subtypes (intestinal vs diffuse: 1.22, 1.02–1.46 vs 1.00, 0.62–1.61). Gastric cancer risk increased significantly during the first decade following diabetes diagnosis (HR 4.70, 95% CI 3.77–5.86), and decreased with time (nonlinear p

In this paper, a novel metamaterial sensor with excellent sensitivity and quality factor for microwave sensing applications is presented. The designed metamaterial sensor is assembled on a 1.575 mm thickness of low-cost dielectric substrate material (Rogers RT5880), and the copper is used as a resonator. Computer Simulation Technology version 2019 (CST-2019) software is employed to design and analyze the proposed metamaterial sensor. In addition, the Advanced Design System version 2016 (ADS 2016) software is used to validate the CST simulated model. Subsequently, the simulated results were validated using laboratory measurements. The optimized cell is small; its dimension is 10 × 10 mm2, and the obtained resonances are 3.85 and 6.85 GHz with notches of −26.29 and −40.03 dB, respectively. The textile material is detected by the resonance frequency change, and this frequency is dependent on the material’s permittivity values. To test the developed sensor’s sensing capabilities, three types of textiles—wool, fleece, and denim—are used. The effective medium ratio, sensitivity, and Q-factor of the structure are evaluated, and the obtained values are 8.96, 14.57%, and 345, respectively. The sensor for detecting textile materials works in the S and C bands. The resonances are shifted 530 MHz between the air and wool, 420 MHz between the air and fleece, and 640 MHz between the air and denim. The simulated outcomes and laboratory results almost matched. The projected sensor can be employed in the apparel sector to identify textile materials because it is small, inexpensive, has a high quality factor, and has high sensitivity.

The use of polymeric passive samplers (often polydimethylsiloxane, PDMS, or low-density polyethylene, LDPE) to indicate water concentrations of hydrophobic organic compounds under environmental conditions generally requires two key parameters, a compound-specific polymer-water equilibrium partition coefficient (K pw ), and the degree of equilibration achieved in a given environmental exposure scenario. Herein, we have developed model to extrapolate equilibration of performance reference compounds between polymer and pore water across different compound classes that is also dependent upon accurate estimates of K pw . We have also developed quantitative structure-activity relationship (QSAR) models, to estimate K pw for PAHs, PCBs, DDx, and dioxin/furans for the two most common polymeric samplers, PDMS and LDPE. The QSAR models developed in this study provide high accuracy consistent estimates for K pw across the different HOC families. The root mean square error of the QSAR for K LDPE-w was 0.226 log units based upon measured values for 159 compounds and 0.184 log units for K PDMS-w based upon measured values for 131 compounds., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)