National Institute of Technology Delhi

The use of calcium carbide (CaC2) as a ripening agent for fruits is banned in most countries across the world, including India (under the prevention of Food Adulteration Act (PFA: 44 AA), 1955 and Food Safety and Standards Regulation Act, 2011). Yet, in our country; it is a common practice to use industrial-grade Calcium Carbide for ripening of climacteric fruits like mango, banana, papaya etc. which leaves behind traces of arsenic and phosphorus, along with heavy metals like Fe, Co, Hg, Pb etc. CaC2 reacts with moisture to produce acetylene gas, which accelerates the ripening process, but the byproducts of this process pose a severe threat to human health. Hence, there is a need of accessible and economically feasible methods for detecting CaC2 induced ripening in fruits, for all stakeholders in the fruit supply chain, especially the end consumer. This review investigates the utility, limitations, and underlying principles of key methods. While laboratory-based techniques like gas chromatography-mass spectrometry are highly accurate, they are time-consuming and destructive. Sensor-based and colorimetric methods target residues of CaC2, arsenic, and the VOC (volatile organic compound) profile of fruit. Spectroscopy based methods have been explored, including the more accurate hyperspectral imaging and the low-cost, portable sensors in the visible-near infrared wavelength band, which show potential for hand-held applications.

Malaria and type 2 diabetes represent two grow- ing global health challenges that are increasingly intersecting in tropical and subtropical regions. Climate change further complicates this scenario by expanding mosquito habitats and worsening metabolic complications in diabetic individuals. Re- search shows that diabetic patients face 46 percent higher malaria infection risk and significantly elevated mortality rates compared to non-diabetic individuals. This review examines the evolution of mathematical models for malaria transmission over the past century, from the foundational Ross model (1911) to contemporary frameworks incorporating climate forcing and host heterogeneity. We trace how successive generations of models have added biological realism through compartmental structures, age-dependent immunity, vector population dynamics, and environmental factors. Despite substantial progress, current models largely assume uniform host susceptibility and recovery rates, overlooking the profound impact of metabolic disorders on infection dynamics. We identify critical research gaps and emphasise the urgent need for integrated modelling approaches that explicitly account for differential transmission probabilities and recovery rates in diabetic versus non-diabetic populations under climate change scenarios. Such models are essential for protecting vulnerable communities facing the converging threats of infectious and chronic diseases in a warming world.

Health is a primary concern for humans, and good air quality enables to live healthier. The Air Quality Index (AQI) was developed to better understand the air quality situation, which comprises parameters such as particulate matter and harmful gases. Currently several AQI monitoring systems are available to measure or forecast AQI. After a survey of existing system designs, two significant challenges were identified while developing an AQI monitoring system: first, making it low-cost, and second, achieving better efficiency in terms of experiencing lesser missing values. Addressing first challenge, two low-cost programming boards were used to sense and communicate AQI data over a cloud database; however, the setup experienced some delay in serial communication among the programming boards, resulting in missing values. To minimise missing values (the second challenge), low-cost AQI monitoring system design with a delay synchronization (LcDs) algorithm has been proposed to measure AQI values. The LcDs comprises a data acquisition unit to collect sensor data of AQI, a data processing and communication unit to calibrate and apply the proposed delay synchronization algorithm, and a data visualization unit to see the results in real-time. The algorithm synchronizes the communication between two programming boards and sends the data over the cloud data repository. On average, the efficiency of receiving correct sensor data was increased from 85.7% to 96.28% with the proposed delay synchronization algorithm. The proposed low-cost AQI setup design will further help disseminate AQI data from various locations in smart cities to monitor air quality and attain sustainability.