High ripening-stage temperatures strongly influence basmati rice yield, quality, and commercial value. This study investigated how different basmati varieties perform in yield and quality traits when exposed to varying temperatures by shifting the transplanting date. The response of four basmati varieties to early transplanting (5 July) and late transplanting (25 July) was evaluated under field conditions for two consecutive years. Across the varieties, basmati rice yield decreased for late transplanting (2.8 t /ha) as compared to early transplanting (3.5 t /ha), and the variety Pusa basmati-1509 (3.5 t /ha) produced higher grain yield than other tested varieties (2.9-3.1 t /ha). A wide variation in chalkiness (2–32%) was observed among the evaluated basmati rice varieties. Punjab basmati-3 and RYT-3390 exhibited markedly lower chalkiness than Pusa basmati-1121 and Pusa basmati-1509. Chalkiness showed a positive correlation with temperature during the ripening phase. Maximum temperatures ranged from 31.1 to 32.80C in the 5 July transplanting and 29.8 to 32.90C in the 25 July transplanting. Minimum temperature declined from 24.5 to 19.10C in the early transplanting and 23.9 to 16.7 0C in the late transplanting. The greatest reduction in temperature was observed under late transplanting during the later stages of grain filling, indicating a cooler thermal environment during grain development. Head rice recovery (HRR) was comparatively higher in Punjab basmati-3 (52–54%) and RYT-3390 (~57%) than in Pusa basmati-1121 (~49%) and Pusa basmati-1509(44–51%). In early-transplanted basmati, HRR was negatively associated with ripening-stage temperature. Varieties exhibiting high HRR together with low and stable chalkiness—such as Punjab basmati-3 and RYT-3390—may therefore serve as promising parental lines in breeding programs aimed at developing basmati varieties with improved grain appearance, quality, and HRR, particularly under warming climatic conditions.

This study optimized vermiwash production using Eisenia fetida from organic substrates rice straw + cow dung (1:1), wheat straw + cow dung (1:1), kitchen waste + cow dung (1:1), neem leaves + cow dung (1:1), and pure cow dung in a drip-irrigated earthen pot system at Punjab Agricultural University, Ludhiana. Kitchen waste vermiwash demonstrated superior nutrient enrichment (elevated NPK, micronutrients), microbiological (peak microbial populations, enzyme activities), and biochemical profiles (maximum proteins, pigments, sugars) across treatments (p<0.05). Kitchen waste's labile nutrients enhanced earthworm-microbial synergy over lignocellulosic straws and stabilized cow dung, positioning vermiwash as an optimal organic biostimulant for sustainable agriculture.

Myiasis, caused by dipteran larvae infesting living tissues, poses significant health and economic challenges in humans and livestock. The present study evaluates the larvicidal efficacy of crude leaf extracts of Ricinus communis L. (Euphorbiaceae) against third instar larvae of Sarcophaga ruficornis under laboratory conditions. Leaf extracts were prepared using suitable solvents and tested at different concentrations to determine their toxicity and growth-inhibitory effects. The treated larvae exhibited dose-dependent mortality, reduced feeding activity, developmental abnormalities, delayed pupation, and decreased adult emergence compared with untreated controls. Phytochemical constituents present in the extracts, including alkaloids, flavonoids, tannins, and phenolic compounds, are presumed to contribute to the observed bioactivity. The findings demonstrate that R. communis leaf extract possesses potent larvicidal properties and offers an environmentally safe, biodegradable, and sustainable alternative to synthetic insecticides. This ecofriendly botanical approach may serve as an effective strategy for controlling myiasis-causing flies and reducing their associated impacts.

The present study was aimed to document the diversity and conservation status of herpetofauna inhabiting the study region. A total of 38 species were recorded, representing 15 families of reptiles and amphibians. The annotated checklist provides species-specific details, including scientific and common names, conservation status under the Indian Wildlife (Protection) Act (IWPA), and global assessment through the IUCN Red List. Among the documented species, snakes formed a significant component with 16 species, reflecting the ecological richness and habitat heterogeneity of the area. The survey, however, did not record Telangana endemics such as Hemidactylus flavicaudus, H. xericolus, and H. aemulus, highlighting the need for further intensive exploration and long-term monitoring. The present checklist contributes to baseline information essential for regional biodiversity assessments and conservation planning. It serves as a reference for researchers, students, and conservation practitioners, while underlining the necessity of continued site-specific documentation to guide effective management strategies for herpetofauna diversity.

The present study provides the detailed spatio-temporal analysis and statistical assessment of three pertinent water quality parameters such as dissolved oxygen (DO), chemical oxygen demand (COD), and biochemical oxygen demand (BOD) in the urban reach of Gomti River at Lucknow for the period of 2023–2024. Four sites were selected based on the level of contamination due to combined anthropogenic, industrial and residential activities and monthly surface water samples were collected. The concentrations of the parameters were determined using standard analytical methods and the data were analyzed using statistical techniques such as multiple linear regression, descriptive analysis and Pearson correlation. The results revealed a high BOD (9.07 mg L⁻¹) and COD (39.86 mg L⁻¹) which indicates high extent of organic and chemical pollution and continuously low values of DO (mean: 3.15 mg L⁻¹) below the biological level required to support the aquatic organisms. During the summer months, dissolved oxygen was substantially reduced due to increased microbial activity, lower flow and warmer temperatures. BOD and COD were found to have a significant positive correlation (r = 0.734) which indicates their contribution to pollution severity. DO was found to have significant negative correlations with BOD (r = −0.404) and COD (r = −0.480). Regression analysis (R2 = 0.579, p < 0.001) shows that organic load has a significant influence on the COD variability, and BOD is the major determinant factor. Spatial variability revealed high levels of pollution due to untreated sewage and industrial effluents and the need for better treatment, regulation, monitoring and restoration.

The Kallada River, a major west-flowing river in Kerala, serves as a critical source of drinking water, irrigation, fisheries, and ecological support. Increasing anthropogenic pressures, including sand mining, agricultural runoff, sewage discharge, pilgrimage activities, and riparian degradation, have adversely impacted its water quality. This study evaluated the physicochemical and bacteriological characteristics of the Kallada River at eight sampling stations during pre-monsoon, monsoon, and post-monsoon seasons from November 2022 to July 2023. Water samples were analyzed for temperature, pH, total dissolved solids (TDS), electrical conductivity, alkalinity, hardness, free carbon dioxide, nitrate, phosphate, sulfate, sodium, potassium, and total coliform counts using standard analytical procedures. Results indicated seasonal variations in water quality parameters, with most physicochemical parameters remaining within permissible limits established by Indian standards. However, elevated phosphate concentrations and exceptionally high total coliform counts reflected significant anthropogenic contamination. Site 1 consistently exhibited greater deterioration in water quality, primarily due to intensive human activities associated with the Sabarimala pilgrimage. Pearson correlation analysis revealed significant relationships among physicochemical parameters across seasons, underscoring the influence of nutrient enrichment, runoff, organic matter decomposition, and mineralization processes. Although the river water generally meets criteria for Class C surface water, it is unsuitable for direct consumption without treatment and disinfection. Continuous monitoring and implementation of effective management strategies are necessary to safeguard the ecological integrity and water quality of the Kallada River.

Curcumin, the primary bioactive curcuminoid from Curcuma longa, displays potent antioxidant activity yet is highly susceptible to oxidation, impacting its stability, bioavailability, and utility in environmental applications such as pollutant scavenging and natural preservative systems. This review comprehensively examines kinetic studies on curcumin oxidation up to 2025, encompassing oxidants including hydrogen peroxide, peroxydisulfate, hypochlorite, transition metal ions, reactive oxygen species (ROS), and autoxidation across diverse solvents (aqueous buffers, ethanol, DMSO, acetonitrile, and lipid media). Predominantly first-order or pseudo-first-order kinetics are observed, with rate constants spanning several orders of magnitude depending on pH, solvent polarity, temperature, and oxidant type. Organic protic solvents generally confer greater stability compared to alkaline aqueous media. The review incorporates data tables, comparative graphs, and Arrhenius insights derived from primary literature. Environmental implications for green chemistry and gaps in real-matrix studies are highlighted.

This study analyses spatial distribution and common places of abode for internally displaced persons (IDPs) in camp settings across Greater Yola, Adamawa State, Nigeria, from 2015 to 2019, amid Boko Haram insurgency displacement. Using Displacement Tracking Matrix (DTM) data from the International Organization for Migration (IOM), we mapped 15 camps across eight wards, revealing that 85-88% of IDPs (peaking at 29,000 in 2017) concentrated in peripheral fringes due to land availability and affordability, while core urban wards hosted only 12-15%. Descriptive spatial analysis and demographic surveys (n=269 household heads) highlight vulnerabilities, including 57% female respondents, 86% under age 50, and low education levels exacerbating livelihood challenges.Findings demonstrate non-uniform camp placement, with clustering in low-density outskirts (Namtari, Girei wards), driven by infrastructure constraints rather than security or services. This first ward-level DTM assessment in Adamawa underscores policy gaps in equitable displacement management. We recommend zoning reforms for balanced facility distribution to mitigate urban congestion and enhance resilience, informing scalable interventions amid Nigeria's >3 million IDPs.

Artificial Intelligence (AI) has emerged as one of the most transformative technologies of the twenty-first century, reshaping industries, enhancing productivity, and enabling solutions to complex global challenges. Yet its environmental footprint — encompassing energy consumption, carbon emissions, water usage, electronic waste (e-waste), and mineral extraction — remains largely invisible to end users and policymakers alike. This paper critically examines the environmental implications of AI development and large-scale deployment, with particular emphasis on large language models (LLMs) and generative AI systems. Drawing on the most current quantitative benchmarks (IEA, 2025; UN University, 2025; Global E-Waste Monitor, 2024), we situate AI's resource demands within the broader context of global industrial energy consumption, updating prior analyses with 2024–2026 data. Data centre electricity consumption reached 415 TWh globally in 2024, with AI-specific workloads surging 50% in 2025 alone; by 2030, the sector is projected to consume 945–1,300 TWh annually. We further evaluate mitigation strategies under the emerging paradigm of Green AI — encompassing algorithmic efficiency, renewable energy procurement, carbon-aware scheduling, and transparent reporting standards. The study concludes that while AI is not yet among the largest absolute environmental threats, its compound growth trajectory demands immediate, systemic sustainability interventions to ensure technological advancement and environmental stewardship remain aligned.