Among the numerous anthropogenic pollutants, nitrogen dioxide (NO₂) is one of the leading contaminants mainly released by burning fossil fuels in industrial and transport sectors. This study evaluates the impact of COVID-19 lockdown on the growing trend of NO₂ emissions in South Asia. Satellite imagery data of Sentinel-5 Precursor with Tropomi instrument was employed in this study. The analysis was performed using time series data from February–May 2019 and February–May 2020. The time frame from February–May 2020 was further divided into two sub-time-frames, i.e., from 1 February–20 March (pre-lockdown) and from 21 March–May 2020 (lockdown). Results show the concentration of NO₂ pollutants over the region declined by 6.41% from February–May 2019 to February–May 2020. Interestingly, an increasing trend of NO₂ concentration by 6.58% occurred during the pre-lockdown phase in 2020 (1 February–20 March) compared to 2019 (February–May). However, the concentration of NO₂ pollutants reduced considerably by 21.10% during the lockdown phase (21 March–10 May) compared to the pre-lockdown phase in 2020. Furthermore, the country-specific detailed analysis demonstrates the significant impact of COVID-19-attributed lockdown on NO₂ concentration in South Asia.

Cuffless blood pressure (BP) estimation is critical for managing growing concerns about hypertension and cardiovascular diseases. Despite recent advancements in multimodal (ECG and PPG) BP estimation methods, which have achieved varying degrees of success, several challenges remain to be addressed. These include capturing the full spectrum of BPrelevant information, redundant feature spaces, and handling the multigrade classification. To address these issues, we propose a Multimodal Fusion BP Network (MuFuBP-Net), featuring a novel dual-feature pipeline architecture designed to extract hierarchical and modality-specific features from both ECG and PPG signals. Additionally, the Cascading Cross-Feature Enhancer (CCFE) module integrates multiple fusion strategies with a squeeze-and-excitation mechanism to apply channel-wise attention to spatial features, enabling dynamic re-weighting. We also employed a Sequence Context Network (SCN) module to capture global sequential features. Subsequently, a Probabilistic Feature Encoder (PFE) encodes the multilevel features from both pipelines into a compact latent space, preserving their discriminative characteristics. Our approach achieved MAE ± SDE of 2.99 ± 4.37 mmHg (SBP) and 2.63 ± 4.19 mmHg (DBP) on MIMIC-II, and 2.27 ± 4.15 mmHg (SBP) and 1.63 ± 2.96 mmHg (DBP) on MIMIC-III dataset, meeting AAMI, BHS, and IEEE grade A standards. The proposed approach demonstrated competitive results compared to existing techniques, highlighting its significance as a reliable solution for cuffless BP monitoring.

Sleep monitoring is essential for assessing sleep quality and understanding its broader implications for overall health. Although electroencephalography (EEG) remains the gold standard for sleep analysis, multichannel techniques are often cumbersome and impractical for real-world application. As a more feasible alternative, single-channel EEG offers greater practicality but still faces several persistent challenges, including reduced spatial resolution, feature instability, and limited clinical interpretability. To address these limitations, we propose KAleep-Net (Kolmogorov-Arnold based Sleep Network) for sleep stage classification. It employs a Multispectral Feature Pipeline to extract both fine-grained and coarse-grained features from single-channel EEG signals. It integrates a Temporal Sequencing Network with Flash Attention to capture rich and stable features effectively. The proposed approach achieved an accuracy of 86.5%, an F1-score of 79.6%, and a Cohen’s $\kappa $ of 79.9% on the Sleep-EDF-20 dataset, along with a 41.7% improvement in training speed. For the Sleep-EDF-78 dataset, it attained 85.0% accuracy, 77.0% F1-score, 78.0% $\kappa $ , and a 67.5% gain in training efficiency. On the SHHS dataset, the model achieved 86.4% accuracy, an F1-score of 0.79, and a $\kappa $ of 0.81, with an 8.18% improvement in training speed. For interpretability, an integrated gradient technique was adopted to enhance decision transparency and promote clinical adoption. The framework offers an efficient solution for sleep staging in resource-constrained environments with clinically trusted insights for single-channel EEG-based sleep monitoring.

A considerable fraction of the female workforce worldwide is making ends meet by doing various jobs informally at home or in nearby places, rather than at employers’ premises. The contribution of these female home-based workers (FHBWs) is significant to the country’s economic growth. FHBWs are often confronted with numerous occupational diseases due to a lack of awareness of occupational safety and health measures, and unhealthy living and working conditions. The informality of FHBWs prevents them from getting proper healthcare, safety, and other dispensations enjoyed by formal employees. Despite their undeniable importance, health issues of FHBWs are still overlooked. This study is an attempt to discover the frequent co-occurring occupational diseases encountered by FHBWs in Punjab, a province of Pakistan. Frequent itemset mining (FIM) or co-occurrence grouping is a technique of data science that identifies the associations among different entities in the data. Based on FIM, the D-GENE algorithm is applied in this study to efficiently discover frequent co-occurring diseases in the data obtained from the Punjab Home-based Workers Survey (2016). The far-reaching goal of the study is to bring awareness of the occupational health issues and safety risks to the health authorities as well as to the FHBWs.

Radio Frequency (RF) microwave imaging is emerging as a modality for breast tumor diagnosis due to its non-invasive, non-ionizing, cost-effective, and wearable nature. Deep learning has shown great potential for imaging applications compared to traditional and complex image processing methods. But, deep learning algorithms require substantial amounts of labeled RF data for training, which are often limited in breast tumor diagnostic tasks such as detection, localization, and size classification. In the presence of limited labeled datasets, data augmentation becomes a crucial preprocessing strategy to enhance training effectiveness. We propose RF-BreastNet, an augmented hierarchical feature extraction framework for multi-task breast tumor detection, localization, and size classification in MRI-derived signals from female breast phantom. Our proposed framework effectively processed the dielectric properties of breast tissue with minimal error by leveraging the multimodal spatial, temporal, and metadata features of S11 and S21 parameters. In addition, we introduced a biologically informed augmentation strategy to improve generalization and reduce overfitting during training. The results indicate that RF-BreastNet framework demonstrated enhanced performance, with 97.19% accuracy in detection, 93.37% R² Score in localization, and 98.35% accuracy in size classification. The outcome of this study will facilitate early diagnosis and provide continuous breast tumor monitoring with high accuracy.