Accurate measurement and monitoring of respiration is vital in patients affected by severe acute respiratory syndrome coronavirus – 2 (SARS-CoV-2). Patients with severe chronic diseases and pneumonia need continuous respiration monitoring and oxygenation support. Existing respiratory sensing techniques require direct contact with the human body along with expensive and heavy Holter monitors for continuous real-time monitoring. In this work, we propose a low-cost, non-invasive and reliable paper-based wearable screen printed sensor for human respiration monitoring as an effective alternative of existing sensing systems. The proposed sensor was fabricated using traditional screen printing of multi-walled carbon nanotubes (MWCNTs) and polydimethylsiloxane (PDMS) composite based interdigitated electrodes on paper substrate. The paper substrate was used as humidity sensing material of the sensor. The hygroscopic nature of paper during inhalation and exhalation causes a change in dielectric constant, which in turn changes the capacitance of the sensor. The composite interdigitated electrode configuration exhibited better response times with a rise time of 1.178s being recorded during exhalation and fall time of 0.88s during inhalation periods. The respiration rate of sensor was successfully examined under various breathing conditions such as normal breathing, deep breathing, workout, oral breathing, nasal breathing, fast breathing and slow breathing by employing it in a wearable mask, a mandatory wearable product during the current COVID-19 pandemic situation.Thus, the above proposed sensor may hold tremendous potential in wearable/flexible healthcare technology with good sensitivity, stability, biodegradability and flexibility at this time of need.

Personalized mobile healthcare integrated with various wearable devices has become a significant area of interest in the present era. In the current research work, a flexible, wearable and disposable paper-based continuous skin temperature monitoring sensor for early medical prognosis and accurate diagnosis of body temperature-related ailments, such as COVID-19, is proposed. Conventional screen-printing and drop-casting techniques were used to fabricate the proposed sensor using MWCNTs as the sensing material and paper as the substrate. The linearity, stability, repeatability and durability of the sensors were tested from 29°C (room temperature) to 60°C. A thin sheet of PET was laminated over the sensor surface to ascertain its stability toward environmental effects and physical movements, and a response time of ~13 s and a recovery time of ~38 s with a sensitivity of -0.0685% °C-1 were recorded. The efficacy of the proposed sensor was ascertained by placing it at different body locations on a human subject and comparing it with a standard thermocouple and IR sensor. The sensor even helped to effectively distinguish minimal temperature variations between various regions of the body. Furthermore, the feasibility of the fabricated temperature sensor as a temperature-based tactile sensor for robotics/artificial skin applications and as a noncontact breath monitoring device for use in personalized healthcare monitoring applications was investigated.

Flexible strain sensors have been used in myriad of applications with studies in human physiological monitoring, robotic operation, and human–machine interaction being intensively explored in recent years. Achieving a cost effective, high-performance flexible strain sensor with high sensitivity, and capable of large-scale production have great importance particularly for next generation of wearable electronics. In the present work, the authors have proposed a strain sensor which has been fabricated by simple screen printing of multi walled carbon nanotube ink over paper substrate in a facile and cost-effective manner. A thin sheet of polyethylene terephthalate. was laminated over the sensor surface to ascertain its stability towards environmental effects and physical movements. The characteristics of the printed strain sensor were systematically analyzed for its electrical performance with strain variations, repeatability, dynamic response and durability of the same was also investigated. The strain sensor exhibited a maximum gauge factor of 31.9 within the applied strain of 0%–1.72% and durability of 1000 bending cycles with a response and a recovery time of ∼13 ms and ∼17 ms respectively. Further, the fabricated strain sensors was also attached to various locations around human joints (knee, finger, elbow and wrist) and demonstrated significant sensing performance under different bending strains with superior mechanical robustness suitable for stable human motion monitoring for healthcare application.

Climate change is accelerating spatially complex transformations in land, water, coasts, cryosphere, and ecosystems, creating a critical need for reliable, scalable, and timely monitoring based on Earth observation imagery. Conventional approaches that rely on sparse in-situ measurements, manual image interpretation, and simple spectral indices or thresholding often fail to capture subtle, heterogeneous, and multiscale changes, and they do not scale to today's multi-sensor, multi-temporal satellite archives. This review synthesizes image processing and AI techniques applied to optical, SAR, thermal, and hyperspectral remote sensing for climate change detection, covering classical change detection methods, machine learning classifiers, deep learning architectures (including Siamese and segmentation networks), spatio-temporal models for satellite image time series, and multi-sensor fusion, across application domains such as land use/land cover (LULC) and deforestation, hydrology and flooding, coastal and mangrove dynamics, cryospheric change, urban heat, ecosystems, and natural hazards. In addition, we analyze how these methods are evaluated using common performance metrics–Overall Accuracy (OA), precision, recall, F1-score, Intersection over Union (IoU), Kappa coefficient, and error measures such as RMSE–and discuss key challenges related to data quality and annotation, domain shift and generalization, computational and operational constraints, interpretability, and integration with climate and impact models. The distinctive contribution of this review is a unified method-application taxonomy that explicitly links algorithm families to specific climate monitoring tasks, a systematic comparison of reported performance metrics that clarifies trade-offs between techniques under different data and class-imbalance conditions, and a practical decision framework to guide researchers and practitioners in selecting appropriate image processing and AI approaches for given sensors, regions, and operational requirements, while outlining promising future directions such as foundation models, standardized benchmarks, and interoperable climate decision-support systems. Across the reviewed literature, deep learning approaches consistently demonstrate higher accuracy (e.g., improved IoU and F1-scores) in complex and heterogeneous environments, while classical methods remain effective for large-scale and data-scarce applications. However, significant gaps persist in model generalization across regions, availability of labeled datasets, and integration of multi-sensor time-series data.

Background/Objectives: To evaluate the prediction accuracy of Neural Network algorithms for host CPU load prediction and evaluate their performance compared to actual values. Methods/Statistical Analysis: The speed of execution of job at the scheduled host is directly proportional to its CPU load. Therefore, target node load prediction plays an important role in job scheduling decisions. It is learnt that Neural Networks are capable of predicting the future values based on the training given on the past data. We designed a multilayer neural network and trained with learning algorithms for the input patterns collected from the load traces and predicted the future load statistics. The Mean and Standard Deviation of the predicted values are computed and analyzed against the Mean and Standard Deviation of actual values for all the ANN algorithms. Findings: We analyzed the prediction accuracy of Back Propagation, Quick Propagation, Back Propagation with Momentum and Resilient Propagation algorithm for the load traces collected from variety of computers connected in a network. Existing reports shows that Back Propagation algorithm exhibits better prediction accuracy compared to statistical approaches like linear regression and polynomial regression. In this paper, we have shown that Resilient Propagation algorithm has better prediction accuracy compared to other ANN algorithms. Application/Improvements: Job scheduling and resource selection algorithms can employ neural network algorithms to predict the load for the sharable resources connected in the network for more accurate and faster scheduling/resource selection decision.