Central University of Rajasthan

Pearl millet [Pennisetum glaucum (L.) R. Br.] is a diploid, cross-pollinating and short-duration crop. It is the sixth leading crop after maize, sorghum, rice, wheat and staple food for more than 90 million poor farmers. It is adapted to survive under abiotic stresses such as drought, salt, high temperature and poor nutrient soil. Its productivity is limited by various challenges, including drought, salinity, heat, and low soil fertility, particularly a lack of nitrogen. Improving nitrogen use efficiency (NUE) and stress tolerance is essential for maintaining pearl millet production as the climate changes. Plant growth promoting rhizobacteria (PGPR) serve as a replacement for chemical fertilizers. They contribute to improved plant growth and resilience through multiple mechanisms like biological nitrogen fixation, producing phytohormones, solubilizing nutrients, releasing exopolysaccharides, and regulating antioxidant and stress-response pathways. In this review, we highlighted current research on how PGPR alleviate abiotic stress and improve nitrogen use efficiency in pearl millet with the key physiological, biochemical, and molecular responses. Although many studies demonstrated the potential of PGPR under controlled conditions, still there are significant research gaps persist. These include limited understanding of the specificity between PGPR and pearl millet host, inconsistence field outcomes, limited insights into how PGPR affects nitrogen uptake pathways and insufficient research on combined effects of multiple abiotic stresses in natural environments. Future studies should aim to develop robust stress-tolerant PGPR communities, alongside using advanced techniques to uncover the molecular processes that lead to PGPR-induced stress tolerance and improved NUE. Additionally, multi-location field trials are necessary to verify results and ensure they can be applied broadly. Combining PGPR strategies with improved nitrogen management practices will be vital for increasing pearl millet yields and fostering sustainable farming in challenging environments.

The widespread use of single-use plastics has resulted in their accumulation at landfills, leading to the release of hazardous additives such as bisphenol A (BPA). Rapid population growth has further contributed to residential and agricultural expansion near these unmanaged landfill sites, intensifying associated environmental and health risks. BPA, known as an endocrine-disrupting compound, exhibits multi-system toxicity, adversely affecting plants, animals, humans, and ecosystems by disrupting physiological, cellular, and ecological functions. This study assesses the toxicological impact of BPA leached from a landfill site, with concentrations in nearby soil and water bodies up to 770.8 ppm and 798.9 ppm, respectively. The phytotoxic effects of BPA-contaminated soil were investigated using Cicer arietinum as a model plant. Statistical analysis revealed a dose-dependent decline in key physiological and biochemical parameters, including seed germination, total pigment, total soluble protein, cell viability, and dehydrogenase activity was observed with reductions of 70–80

This study examines how artificial intelligence (AI) investments and governance quality affect economic growth and employment across 33 countries from 2012–2022. Using Principal Component Analysis (PCA) to construct governance indices and applying FMOLS and DOLS estimations, along with Fixed-Effects (FE) models with Driscoll–Kraay standard errors as a robustness check, the study finds that AI investments stimulate economic growth. However, the study also infers that AI’s ability to promote the objectives of SDG 8 for decent work is not guaranteed by growth alone, as its employment effects depend significantly on governance quality. Strong governance lowers the risk of job losses due to automation and helps people develop their skills. The findings provide policy insights for the formulation of governance frameworks that optimize the economic advantages of AI while fostering inclusive employment. Causality analysis reveals a bidirectional relationship between AI and governance, suggesting that while effective governance accelerates AI diffusion, AI adoption also reinforces institutional performance through greater transparency and accountability. This study proposes distinct policy recommendations: advanced and emerging economies should align AI investments with existing governance frameworks and adopt measures to reduce governance-related risks. At the same time, they must strengthen institutional capacity and governance structures to fully harness the developmental potential of AI.

Tropospheric formaldehyde (HCHO) serves as an effective tracer of volatile organic compound (VOC) oxidation and provides insight into regional photochemical regimes. In this study, satellite-derived HCHO column densities from the Ozone Monitoring Instrument (OMI) are analysed over the Indian subcontinent for the period 2007–2021 to examine spatial patterns, seasonal variability, long-term trends, and transport-driven enhancements. Seasonal mean HCHO fields are used to identify persistent hotspot regions, while day-to-day variability is evaluated using back-trajectory, residence-time, and potential source contribution function (PSCF) analyses at two hotspot locations in eastern (Nandapura, Odisha) and southern (Kuttampuzha, Kerala) India. The results reveal pronounced spatial and seasonal contrasts in HCHO distribution, with enhanced summer concentrations over forested regions and coastal zones, and reduced wintertime levels. Summer-to-winter HCHO ratios indicate strong biogenic influence across both hotspots, while trend analysis highlights divergent long-term behaviour, with a sustained increase in HCHO at Nandapura and comparatively stable or declining levels at Kuttampuzha. Analysis of the HCHO-NOx ratio demonstrates clear regional differences in ozone sensitivity, identifying NOx-limited conditions over the Malabar coast and VOC-limited to transitional regimes over eastern India, with distinct seasonal shifts at Nandapura. Trajectory-based diagnostics show that extreme HCHO events at both locations are associated with air masses that experience prolonged residence over emission-rich regions, including forested areas, coastal zones, shipping corridors, and power sector. Our results indicate that residence time within these source regions, rather than transport pathway alone, plays a critical role in determining peak HCHO column enhancements. Overall, this study demonstrates how long-term satellite observations, when combined with transport and chemical regime diagnostics, can be used to disentangle the roles of biogenic emissions, anthropogenic influence, and atmospheric transport in controlling HCHO variability and ozone sensitivity over India.

Brain tumors are said to be one of the deadliest diseases in the world. Determining the identity and classification of a brain tumor is an essential step in improving our understanding of its underlying causes. The location of brain cancers can be determined using a range of diagnostic imaging methods. The new paradigm of automated systems for medical picture recognition has been made possible by the advancement of deep learning techniques. Magnetic resonance imaging (MRI) is the most extensively employed tool of screening for brain tumors, although sonography, X-rays, and other techniques can also be utilized. The CNN models are a revolution in image classification tasks. However, optimizing them for lightweight applications is still a challenge. This study focuses on deploying a time-efficient CNN that can be deployed using limited resources. The proposed model showed 99