Popular social media platforms, such as Twitter, have become an excellent source of information with their swift information dissemination. Individuals with different backgrounds convey their opinions through social media platforms. Consequently, these platforms have become a profound instrument for collecting enormous datasets. We believe that compiling, organizing, exploring, and analyzing data from social media platforms, such as Twitter, can offer various perspectives to public health organizations and decision makers in identifying factors that contribute to vaccine hesitancy. In this study, public tweets were downloaded daily from Tweeter using the Tweeter API. Before performing computation, the tweets were preprocessed and labeled. Vocabulary normalization was based on stemming and lemmatization. The NRCLexicon technique was deployed to convert the tweets into ten classes: positive sentiment, negative sentiment, and eight basic emotions (joy, trust, fear, surprise, anticipation, anger, disgust, and sadness). t-test was used to check the statistical significance of the relationships among the basic emotions. Our analysis shows that the p-values of joy–sadness, trust–disgust, fear–anger, surprise–anticipation, and negative–positive relations are close to zero. Finally, neural network architectures, including 1DCNN, LSTM, Multiple-Layer Perceptron, and BERT, were trained and tested in a COVID-19 multi-classification of sentiments and emotions (positive, negative, joy, sadness, trust, disgust, fear, anger, surprise, and anticipation). Our experiment attained an accuracy of 88.6% for 1DCNN at 1744 s, 89.93% accuracy for LSTM at 27,597 s, while MLP achieved an accuracy of 84.78% at 203 s. The study results show that the BERT model performed the best, with an accuracy of 96.71% at 8429 s.

Since the outbreak of coronavirus also known as COVID-19, there have been several studies on the disease. This study investigates patients’ electrocardiography (ECG) properties for an accurate prediction of this infectious disease. Our findings will be useful to medical practitioners in the accurate prognosis of COVID-19. We analyzed ECG datasets of patients who had tested positive for COVID-19 and Normal Persons who had tested negative. Using the analyzed dataset, we designed, developed, and evaluated twelve machine-learning models to predict COVID-19 with different combinations of learning algorithms and feature engineering techniques. Time, frequency, and time-frequency domain features were extracted using Time Series Feature Extraction Library (TSFEL). A combination of domains’ features comprising of Time, Frequency, and Time-Frequency attributes for prediction was also investigated. We deployed a T-test to determine the necessary and enough features for our predictive modeling. The ECG morphological features analysis was based on peak-to-peak detection, onset, and offset of the QRS complex. K-nearest neighbors (KNN), Random Forest, and Support Vector Machine (SVM) learning algorithms were deployed to classify the ECG signals for the prediction of COVID-19. For each feature domain, the Shapley Additive Explanations (SHAP) visualization tool was used to ’open’ the black box for explainability and interpretability of feature weights and importance. SHAP’s result suggests that extracted feature weight distribution in different algorithms shows consistency in feature importance per domain. However, the weight and the proportion are not the same. Performance evaluation of our classifiers was based on accuracy, sensitivity, precision, and F1 score. Our experimental results show that the classification encompassing all domain features (Temporal, Spectral, and wavelet) with Random Forest has a slight edge over other models. The proposed model has accuracy, sensitivity, precision, and F1 score of 97.09%, 98%, 97%, and 97%, respectively. COVID-19 continues to pose a threat to our world with its constant mutation, we believe that a study on its accurate prediction is critical for an effective universal mitigation strategy.

This review analyzes the relationship between Urban Heat Island (UHI) microclimate parameters and poor air quality in urban environments, focusing on how temperature variations, wind dynamics, and urban morphology influence pollutant concentrations. Experimental studies and numerical simulations emphasize the necessity of pedestrian-level sensing combined with computational fluid dynamics (CFD) simulations to accurately capture spatial variations in UHI-related parameters. Ozone concentrations have been observed to increase by up to 4 ppbv, while PM_2.5 levels rise by 1–2 µg/m³ in response to UHI effects. Additionally, ANSYS Fluent 2020.R1. simulations demonstrate a 0.93 °C error in temperature predictions and a 1.35 m/s error in wind speed estimations. These findings highlight the critical role of sustainable urban planning strategies in mitigating UHI effects and improving air quality in densely populated areas.

In this work, a phantom study is performed to investigate the feasibility of quantitative tissue stiffness assessment of breast cancer masses using transfer learning ultrasound elastography. Ultrasound elastography is an imaging modality developed to enhance the low specificity of conventional ultrasound, utilizing manual tissue compression, and tracked strain deformation to reconstruct a color-coded qualitative 2D tissue stiffness map (elastograms). Elastogram assessment in breast cancer screening is based on the working principle that normal tissue is less stiff than benign and malignant breast masses. A transfer learning ultrasound elastography model is developed to classify the breast masses into quantifiable Young's modulus (kilopascals, kP) values. The transfer learning model combines features of images from ultrasound elastography elastograms and from Google's deep learning model AlexNet. The elastogram features used in the training and validation of the transfer learning model were obtained from a CIRS-049 phantom with inclusions having various Young's moduli. The model was evaluated on a CIRS-059 breast phantom with spherical inclusions. Test results show that the transfer learning model yields greater than 88% validation accuracy. Therefore, with the computer-aided tool of transfer learning, ultrasound elastography has the potential of quantitatively differentiating benign and malignant breast masses based on tissue stiffness values.

During the intense heatwaves of late summer 2024, Washington, D.C.’s urban landscape revealed the powerful influence of urban morphology on microclimates and air quality. This study investigates the impact of building height-to-width (H/W) ratios on the urban heat island (UHI) effect, using a combination of field measurements and Computational Fluid Dynamics (CFD) simulations to understand the dynamics. Street-level data collected from late August to November 2024 across three sites in Washington, D.C., indicate that high H/W ratios (1.5–2.0) increased temperatures by approximately 2–3 °C and reduced wind speeds to around 0.8 m/s. These conditions led to elevated pollutant concentrations, with ozone (O₃) ranging from 1.8 to 7.3 ppb, nitrogen dioxide (NO₂) from 0.3 to 0.5 ppm, and carbon monoxide (CO) remaining relatively constant at approximately 2.1 ppm. PM_2.5 concentrations fluctuated between 2.8 and 0.4 $\mathsf{\mu }$g/m³. Meanwhile, lower H/W ratios (less than 1.5) demonstrated better air circulation and lower pollution levels. The CFD simulations are in agreement with the experimental data, yielding an RMSE of 0.75 for temperature, demonstrating its utility for forecasting UHI effects under varying urban layouts. These results demonstrate the potential of Computational Fluid Dynamics in not only modeling but also predicting UHI dynamics.

In this letter, an automatic detection algorithm for hypertrophic cardiomyopathy (HCM) is presented. Of particular interest is the algorithm's ability to differentiate HCM subjects and healthy volunteers from a single lead ECG dataset. Suspected HCM subjects are identified by the primary clinical abnormality associated with HCM: left ventricular hypertrophy (LVH). In total, n = 43 human subjects ECG datasets are investigated: n = 21 healthy volunteers and n = 22 LVH patients. Significant differences of p -value 0.01 and 0.04 were found for the respective ECG parameters, i.e., S-wave amplitude and ST-segment, when differentiating between the LVH patients and healthy human volunteers.