Tabasum Rasool

Climate change, driven by human activities and increasing greenhouse gas emissions, is pushing Earth’s climate toward a warmer state, as evidenced by long-term observations. The frequency and intensity of unprecedented rainfall events have increased in recent years, underscoring the urgent need to revise design storms and depth-duration frequency (DDF) curves to better adapt to and mitigate the impacts of climate change. This study used a serial type of stochastic rainfall generator (SRG) that is capable of simulating daily rainfall series by embedding unprecedented events to study extreme precipitation scenarios under the changing climate. By perturbing values of power law tuning parameters in the SRG model, we developed thirty-six precipitation scenarios, some of which directly correlate with the current climate change scenario, while others represent very extreme conditions. High-performance computing is employed to run the computationally intensive SRG for simulating thirty-six scenarios across the entire Indian region. These simulated scenarios were analyzed to prepare rainfall return level maps and DDF curves. The findings reveal substantial increases in rainfall return levels across all frequencies when unprecedented events are considered, with pronounced impacts in coastal, northeastern, and Himalayan regions. The spatial pattern of simulated extreme precipitation was consistent across all generated scenarios from SRG irrespective of the return periods. Minimal spatial uncertainty in return level estimates across climate zones is observed which confirms the robustness of the SRG model and spatial clusters of extreme rainfall are identified irrespective of SRG being a point model. The analysis in this study based on SRG simulated climate change scenarios offers crucial insights for revising design storms and for devising climate resilience and flood management strategies.

Extended reality (XR) solutions are quietly maturing, and their novel use cases are already being investigated, particularly in the healthcare industry. By 2022, the extended reality market is anticipated to be worth $209 billion. Certain diseases, such as Alzheimer's, Schizophrenia, Stroke rehabilitation stimulating specific areas of the patient's brain, healing brain injuries, surgeon training, realistic 3D visualization, touch-free interfaces, and teaching social skills to children with autism, have shown promising results with XR-assisted treatments. Similar effects have been used in video game therapies like Akili Interactive's EndeavorRx, which has previously been approved by the Food and Drug Administration (FDA) as a treatment regimen for children with attention deficit hyperactivity disorder (ADHD). However, while these improvements have received positive feedback, the field of XR-assisted patient treatment is in its infancy. The growth of XR in the healthcare sphere has the potential to transform the delivery of medical services. Imagine an elderly patient in a remote setting having a consultation with a world-renowned expert without ever having to leave their house. Rather than operating on cadavers in a medical facility, a surgical resident does surgery in a virtual setting at home. On the first try, a nurse uses a vein finder to implant an IV. Through cognitive treatment in a virtual world, a war veteran recovers from post-traumatic stress disorder (PTSD). The paper discusses the potential impact of XR in transforming the healthcare industry, as well as its use cases, challenges, XR tools and techniques for intelligent health care, recent developments of XR in intelligent healthcare services, and the potential benefits and future aspects of XR techniques in the medical domain.

Connectivity and complexity are serious issues in the interconnection of networks. In this chapter, we have discussed various logical operators that are seen as very useful for the interconnection of networks boosting performance, reducing delay, revealing the best paths, high-speed and safe communiqué among processors, and multiple heterogeneous sensing devices. The work presented has the primary goal to build a smart healthcare scheme for a real-time 24 × 7 monitoring system for remote detection and care of various diseases. The system shows the way to improve connectivity problem, with optimal performance and rising reliability rates, among the smart sensors, connectivity layers, and decision-making fog layer. We discuss and analyse the same problem through the study of a perfect difference network using logical operators. The novelty of the proposed work is supported by the experimental results, and the numerical output results justified the performance of our proposal in terms of speed, reliability, delay, latency, sensitivity, and specificity rates.

Quantification of infiltration characteristics of soil is a complex problem because of its variability, selection of suitable infiltration model, and determination of infiltration model parameters which depend on various soil characteristics and land uses. In this study four different infiltration models, namely, Horton, Kostiakov, Modified Kostiakov and Philip were considered. Infiltration experiments, using double-ring infiltrometer, were conducted to measure infiltration rate (f) on three different land covers viz. farmland, built-up and shrubland under the soil textures of clay and sandy clay in an urban sub-basin of lesser Himalayas, India. Predictability of infiltration models for different land covers under given soil textures was assessed by comparing field measured and model-predicted f. The performance of different infiltration models was assessed by computing different statistical indicators, and an overall performance index (OPI) was computed to rank the models. The Horton model was found to be the most suitable infiltration model with highest OPI values of 0.94 for different land covers under the soils of clay and sandy clay texture followed by Philip model. The results of this study will be useful for selecting suitable infiltration model for estimating infiltration rate in water balance modelling, and planning and management of water resources in the study area.