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Ajay K Sharma

Publications and source records attributed to Ajay K Sharma.

3 recordsLinked to original sources

Climate Driven Interactions Between Malaria Transmission and Diabetes Prevalence

Climate change is intensifying infectious and chronic diseases like malaria and diabetes, respectively, especially among the vulnerable populations. Global temperatures have risen by approximately $0.6^\circ$C since 1950, extending the window of transmission for mosquito-borne infections and worsening outcomes in diabetes due to metabolic stress caused by heat. People living with diabetes have already weakened immune defenses and, therefore, are at an alarmingly increased risk of contraction of malaria. However, most models rarely include both ways of interaction in changing climate conditions. In the paper, we introduce a new compartmental epidemiological model based on synthetic data fitted to disease patterns of India from 2019 to 2021. The framework captures temperature-dependent transmission parameters, seasonal variability, and different disease dynamics between diabetic and non-diabetic groups within the three-compartment system. Model calibration using Multi-Start optimization combined with Sequential Quadratic Programming allows us to find outstanding differences between populations. The odds of malaria infection in diabetic individuals were found to be 1.8--4.0 times higher, with peak infection levels in 35--36\%, as compared to 20--21\% in the non-diabetic ones. The fitted model was able to capture well the epidemiological patterns observed, while the basic reproduction number averaged around 2.3, ranging from 0.31 to 2.75 in different seasons. Given that India's diabetic population is set to rise to about 157 million people by 2050, these findings point to a pressing need for concerted efforts toward climate-informed health strategies and monitoring systems that address both malaria and diabetes jointly.

cs.MA

Realization of Graphene Quantum Dots for Innovative Biosensor Development and Diverse Applications

This paper investigates quantum dots (QDs), which are miniature semiconductor structures with remarkable optical and electrical properties due to quantum confinement processes. Traditional QDs, such as CdTe, have been extensively investigated; however, they frequently exhibit toxicity and stability issues. Graphene quantum dots (GQDs) are emerging as a safer and more stable alternative to traditional QDs. GQDs are honeycomb-lattice carbon atoms with unique electronic and optical properties that make them promising candidates for biomedical, electronic, and energy storage applications. GQD synthesis methods (top-down and bottom-up) and their advantages over standard QDs include better photostability, biocompatibility, and configurable band gaps. GQDs are perfect for real-world uses like sensitive biosensing, real-time food safety monitoring, and smart packaging because of their low toxicity, high sensitivity, and affordability. These uses are all essential for cutting down on food grain waste. This emphasizes the growing significance of GQDs in advancing nanotechnology and their potential integration with quantum technologies, paving the door for creative solutions in biosensing, food safety, environmental monitoring, and future quantum electronics.

quant-ph

Comparative Investigation for Energy Consumption of Different Chipsets Based on Scheduling for Wireless Sensor Networks

Rapid progress in microelectromechanical system (MEMS) and radio frequency (RF) design has enabled the development of low-power, inexpensive, and network-enabled microsensors. These sensor nodes are capable of capturing various physical information, such as temperature, pressure, motion of an object, etc as well as mapping such physical characteristics of the environment to quantitative measurements. A typical wireless sensor network (WSN) consists of hundreds to thousands of such sensor nodes linked by a wireless medium. In this paper, we present a comparative investigation of energy consumption for few commercially available chipsets such as TR1001, CC1000 and CC1010 based on different scheduling methods for two types of deployment strategies. We conducted our experiment within the OMNeT++ simulator.

cs.NI