The National Assessment and Accreditation Council (NAAC) is a government organisation in India that assesses and accredits Higher Education Institutions (HEIs). It is an autonomous body funded by the University Grants Commission and headquartered in Bangalore.
This paper examines the paradox of women’s educational achievement and their declining representation in the workforce, leadership, and STEM profession. The same is conceptualized here as the phenomenon of the “Missing Millions.” Drawing upon recent data from UNESCO GEM Report 2025, ASER 2023, AISHE 2021–22, the Economic Survey 2025–26, IMF estimates, and various national and international studies, the paper analyses how female students consistently outperform male students in school education and board examinations. However they remain underrepresented in competitive examinations, research professions, corporate leadership, and decision-making positions. The study argues that this “leaky pipeline” is not due to lack of talent, but emerges from a combination of structural inequalities in society. This includes patriarchal social norms, biased educational investments, workplace rigidities, unpaid care burdens, and institutional discrimination. The paper further highlights how skewed sex ratios, son preference, gender-biased stream selection, and early withdrawal from employment cause the erosion of women’s long-term socio-economic participation. By comparing global examples and policy interventions from India, the paper demonstrates that bridging the education-employment gap is not just a social justice matter. It is also a major economic necessity capable of increasing GDP, institutional integrity, and national productivity. In conclusion the paper proposes some radical structural reforms such as leadership reservation, women friendly workplace systems and rules, investment in care infrastructure, and long-term policy studies on impact of interventions—to reclaim the “missing millions”.
Hyperspectral remote sensing has been evolving as a multi-purpose spectral data sensing tool for various applications in agriculture. Asymptomatic disease detection in crops is vital for sensing technology-blended agronomic practices. Tomato, which forms a major portion of the global vegetable production, is susceptible to many diseases and pests. Fusarium wilt is one of the devastating diseases affecting tomato crops, with the risk of crop damage exceeding 70%. Various studies have attempted spectral discrimination of the tomato leaves affected by various diseases using hyperspectral data. However, the existence and continuity of the features of spectral discrimination observed at the in situ or laboratory level to the operationally useful natural farms at the regional level are yet to be understood. The objective of this research is the spectral detection and mapping of Fusarium wilt in tomatoes at plant and regional levels using hyperspectral data acquired from in situ, terrestrial, and satellite platforms. We acquired hyperspectral data from the ground (in situ and terrestrial) and the PRISMA satellite representing leaf, canopy, and field levels over a tomato growing region, Tumakur, India, in 2022. Identifying optimal spectral bands appropriate from leaf-level spectral measurements to field-level satellite-based hyperspectral imagery, we have calibrated and applied four different ML models for the detection and mapping of Fusarium wilt at five levels of disease severity (mildly infected with symptoms invisible to the human eye, infected but requiring expert inspection to localize, visibly infected, infected and extensively spread, and fully wilted) over a region of 263 sq. km. The results suggest model convergence of the disease detection at different levels of severity. At the ground level, all the four ML models used detect the Fusarium wilt at all the levels of disease intensity with accuracy varying from 85% to 88%. Upscaling the ML models to detect disease at the regional level, the quality of results indicates different degrees of success, ranging from 60% (invisible symptoms) to 93% (fully wilted). With an appropriate combination of spectral bands and ML algorithms, it is possible to detect and map Fusarium wilt in tomato with good prediction accuracy when the disease is at a mild stage. (c) 2026 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Background Rheumatoid arthritis (RA) is a chronic autoimmune disorder characterized by systemic inflammation and progressive joint degeneration. This survey was conducted to address a knowledge gap regarding the ethnomedicinal practices of indigenous communities in Kutch, Gujarat, specifically for the management of RA. Although RA is a globally prevalent condition, documentation of locally used medicinal plants and their preparation methods remains limited, particularly in arid regions where access to modern healthcare is restricted. Systematic documentation of this knowledge allows for the assessment of cultural consensus on plant use and the identification of species with therapeutic potential, which may guide future pharmacological research and integrative treatment strategies. The present study aimed to systematically document and analyse the ethnomedicinal knowledge of plants used for RA among indigenous communities in Kutch, Gujarat, and to quantitatively evaluate their ethnobotanical significance, cultural consensus, and usage patterns using Informant Consensus Factor (ICF), Use Value (UV), and Fidelity Level (FL). Methods An ethnobotanical study was conducted in the Kutch district of Gujarat between December 2022 and December 2023, employing a cross-sectional survey design. Data were collected through semi-structured interviews with 162 participants, including 86 key informants recognized for their traditional knowledge of plant-based therapies for RA. Information was documented on plant species used, specific parts utilized, preparation methods, and routes of administration. Quantitative indices including Informant Consensus Factor (ICF), Use Value (UV) and Fidelity Level (FL)were calculated to assess ethnobotanical significance and cultural consensus. Results A total of 155 plant species were reported for the management of RA. The most frequently represented families were Fabaceae, Malvaceae, and Convolvulaceae. Whole plants (50.7%) and leaves (38.2%) were the most commonly used parts. Predominant preparation methods were decoction (Kwatha, 62.5%) and paste (Kalka, 40.97%). High ICF and FL values indicate strong cultural consensus and specificity of use among several species. Discussion This study underscores the rich ethnobotanical heritage of the Kutch region, particularly in relation to traditional plant-based therapies for RA. The documented knowledge reflects a culturally embedded, experience-based system with significant potential for developing integrative or complementary therapeutic strategies. However, comprehensive pharmacological, phytochemical, and toxicological evaluations are necessary to scientifically validate these traditional claims. Future interdisciplinary research may facilitate the translation of this indigenous knowledge into evidence-based applications in modern rheumatology.
Non-parametric and semi-parametric models allow us to predict the gender of individuals based on their survival time in lung-survival data. Primary, we used the North Central Cancer Treatment Group for patients with advanced lung survival dataset to apply the combination of survival models, along with the relevant confidence intervals. Based on the findings of this research, gender is statistically significant based on p-value, determinants in predicting coefficient of -0.5509, and hazard ratio of 0.5765 using cox proportional hazard with Breslow model. The results of these methods' analysis, chi 2 test, were utilized to ascertain whether a gender difference exists. The Kaplan-Meier curve for the entire dataset estimates a mean survival time of 327.5 days and a median survival time of 310 days; for males, the estimated median overall survival is 270 days, and for females, the median survival time is estimated to be 426 days. A study of the dataset indicates that sex significantly influences survival results. Gender is a determinant that impacts the duration of survival, with females often exhibiting a higher median survival time of 426 days. These findings emphasize the importance of considering age and gender when predicting survival outcomes and creating targeted interventions for certain subgroups.
Field-verifiable technologies for the detection and mapping of diseases in vegetable crops are vital for undertaking precision agriculture practices. The evolving hyperspectral sensors have the capacity to offer plant referenceable spectral data required to map and monitor crop diseases. Tomato is one of the most widely grown vegetable crops in India. Fusarium wilt is a fungal infection that causes severe damage to the growth and yield of tomato crops. As part of the research efforts on developing a regional-level remote sensing system for crop disease surveillance and monitoring, we have undertaken multiple studies pertaining to theoretical modelling, reference spectral data acquisition, and methods for the analyses of hyperspectral data. The objective of this work is the assessment of the spectral discrimination and classification of healthy and Fusarium wilt-infected tomato plants using hyperspectral data. In-situ reflectance spectra of healthy and infected plants over a tomato-growing region (Tumakuru, India) were measured, processed, and analyzed to differentiate between diseased and healthy tomato plants spectrally. We applied nine different methods belonging to machine learning, statistical, and spectral matching approaches considering five different levels of disease severity. Results suggest the existence of stable spectral features which differentiate healthy and diseased plants at distinct levels of disease severity. The prospect of discriminating healthy tomato plants against infected plants with different infection levels is reasonable, as indicated by the different accuracy metrics indicating about 80
