Coal is the most extensively used fuel for thermal power generation, but burning fossil fuels is a major contributor to air pollution due to carbon dioxide emissions. Biomass has a significant potential energy source and can be blended with coal to reduce emissions of greenhouse gases for example CO2, NOx and SOx. Unlike fossil fuels, burning biomass does not increase the quantity of carbon dioxide in the atmosphere because the carbon consumed for growth is offset. The combination of biomass and coal can also have environmental benefits by helping to limit climate change. This study analysed the combustion of coal (C) and rice husk (RH) blends at different weight percentages (5
Entomopathogenic nematodes (EPNs) are important biological agents used to control various insect pests and can be applied in conjunction with different insecticides. Thus, the objective of this work was to evaluate the survival of infective juveniles (IJs) of two EPN species, Heterorhabditis indica and Steinernema carpocapsae, after exposure to fipronil and imidacloprid. The combination of these nematodes and insecticides at different rates for controlling the white grub Holotrichia serrata was evaluated both in the laboratory and in sugarcane fields. In laboratory assays, two insecticides, fipronil and imidacloprid, at different concentrations had no effect or a negligible effect on the survival of both nematode species, with mortality rates below 4.0 %. The combinations had a synergistic or additive effect on the third-instar grubs of H. serrata and caused faster and greater mortality than either an EPN species or an insecticide alone. Mortality and speed of kill were significantly increased in the combinations of H. indica-fipronil, H. indica-imidacloprid, and S. carpocapsae-imidacloprid, but nematode reproduction was also unaffected by these insecticides. However, both in the laboratory and in sugarcane fields, the degree of interaction varies with nematode species, being synergistic for H. indica-fipronil, H. indica-imidacloprid, and S. carpocapsae-imidacloprid against grubs. These three nematode-insecticide combinations produced significantly (P < 0.05) greater percentage reductions in Holotrichia serrata than did the chlorpyrifos treatment. We conclude that H. indica at 6.1 x 10(8) IJ ha(-1) combined with imidacloprid or fipronil is a practical strategy for the management of H. serrata in sugarcane production
The growing resistance to synthetic insecticides and Bt toxins, alongside persistent crop losses despite heavy pesticide application, highlights the urgent need for safer, sustainable and efficient pest management strategies. This review presents genome editing as a precise and versatile approach to reduce pest impact by altering fertility, feeding patterns or vulnerability, while protecting beneficial organisms. Among the genome editing tools, CRISPR/Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated protein 9) is one of the most promising genome editing techniques in insects. It facilitates targeted functional studies, integration with RNAi and dual-expression systems and gene drive applications. Deployment is envisioned in two phases, initial laboratory modification followed by regulated field release, with a strong emphasis on biosafety through terminator genes, marked individuals for gene flow monitoring, optimized dosages, stringent screening and long-term ecological surveillance, along with transparency and adherence to international safety protocols. Significant challenges encompass delivery efficiency, identification of edits, off-target mutations, dose-related efficacy and sterility, unstable transmission and resistance development. Innovations such as base and prime editing minimize unintended mutations by circumventing double-stranded breaks (DSBs), while paratransgenic strategies targeting gut symbionts offer supplementary avenues; plant-mediated insect gene editing emerges as a promising frontier. Overall, carefully regulated trials aligned with policy frameworks and stakeholder involvement are vital to assess effectiveness in natural environments and achieve targeted, dependable and ecologically responsible pest control.
This study explores the biochemical variability across four tuber regions (peel, cortex, medulla, and pith) in 14 potato varieties, focusing on dry matter, reducing sugars, sucrose, phenolics, and physiological traits like sprouting, rottage, and weight loss during storage at 0, 30, and 60 days. Initially, Kufri Lalima had the highest peel dry matter, while Kufri Chipsona-1 had the lowest. By 30 days, Kufri Jeevan exhibited high dry matter in the peel, cortex, and medulla, with Kufri Chipsona-1 leading in the pith. At 60 days, Kufri Arun showed significant dry matter variation, whereas Kufri Bahar experienced a slight decrease in the cortex. Reducing sugars varied, with Kufri Swarna having the highest levels initially and Kufri Chipsona-1 consistently the lowest. By 30 days, Kufri Bahar had high reducing sugars in the cortex and medulla, while Kufri Chipsona-1 remained low. Sprouting increased steadily, peaking at 60 days, with Kufri Swarna and Kufri Kumar showing the highest rates. Rottage was minimal at 30 days but increased significantly by 60 days, with Kufri Swarna and Kufri Giriraj being most affected. Weight loss was highest in Kufri Swarna at 30 days, while Kufri Surya and Kufri Arun retained the most moisture, indicating better storage potential. This analysis provides valuable insights into the biochemical and physiological changes in different potato varieties during storage, emphasizing varietal differences.
Reliable short-term forecasting of renewable energy generation is essential for efficient scheduling, optimized battery utilization, and stable operation of grid-connected microgrids, particularly under rapidly changing weather conditions. This study presents a hybrid, time-aware ensemble forecasting framework designed to improve the prediction accuracy of solar and wind power output. The proposed architecture integrates a Histogram-Based Gradient Boosting regressor as the primary forecasting model, supported by an Extra Trees algorithm for residual error correction. The primary model captures nonlinear relationships among meteorological and operational variables, including solar irradiance, wind speed, ambient temperature, and battery state of charge. The residual-learning component further enhances prediction quality by reducing unexplained variance and minimizing high-frequency forecasting errors during abrupt weather fluctuations. The framework was trained and validated using a real-world dataset containing 3,546 hourly observations representing renewable generation, battery management, and grid performance characteristics. Experimental analysis demonstrated strong predictive performance, with coefficient of determination values above 0.96 and an overall root mean square error of 2.665. For wind power forecasting, the model achieved an R2 value of 0.9613, with mean absolute error and root mean square error values of 2.439 and 2.513, respectively. These findings highlight the framework’s ability to deliver accurate, stable, and computationally efficient forecasts, making it adequate for real-time renewable energy prediction and intelligent energy coordination within Microgrid environments
