Uttarakhand Technical University is a public university in the Indian state of Uttarakhand set up by the Government of Uttarakhand on 27 January 2005, through the Uttarakhand Technical University Act 2005. It has 8 constituent institutes and approximately 132 affiliated colleges spread all over the state..
Urban expansion in rapidly developing regions like Haldwani, Uttarakhand, necessitates accurate land cover monitoring to support sustainable planning. This study evaluates machine learning (ML) algorithms Random Forest (RF), Support Vector Machine (SVM), and XGBoost for land cover classification using Sentinel-2 multispectral data (B2, B3, B4, B8) and NDVI in R Studio. Five land cover classes (built-up, barren land, crops, forest, water) were classified using 200 manually annotated samples per class. XGBoost demonstrated superior performance with 95.9
For a new kind of electrical system, the smart grid is using more battery storage and clean energy sources compared to what is normally used by the power grid. Historically, big data and the Internet allowed scientists to find a solution that resulted in the wealth of ideas behind the energy Internet called the intelligent grid. With its unique characteristics, blockchain technology shows promise as a way to solve security problems and boost confidence in smart grid standards. This paper does a thorough analysis of blockchain applications with an emphasis on cybersecurity issues and energy data protection in smart grids. It explores the serious security issues that arise in smart grid situations and clarifies how big data and blockchain might provide practical answers. The study sheds light on the security concerns for smart grid systems by surveying current blockchain-based research endeavors from a variety of publications. Furthermore, useful ideas, tests, and advancements in this field are covered. The study's conclusion outlines important research questions and possible applications of blockchain technology to smart grid security challenges.
Nanoparticles (NPs) have begun substituting for more conventional cancer treatments in contemporary oncology, including radiation, chemotherapy, and surgery. Gold nanoparticles (GNPs) synthesized using Leucas cephalotes (Lc) leaf extract were developed via a green, eco-friendly route and evaluated for their anticancer potential. The formation of Leucas cephalotes–gold nanoparticles (Lc-GNPs) was confirmed by a distinct surface plasmon resonance peak at 524 nm, while XRD analysis revealed four prominent diffraction peaks, indicating their crystalline nature. SEM showed the spherical morphology and interaction of Lc-GNPs against cancer cell lines, and DLS revealed an average particle size of 20 nm with a narrow size distribution. Cytotoxic studies revealed dose-dependent inhibition of cancer cell viability, with IC₅₀ values of 26.91 µg/mL (MCF-7 breast cancer), 45.51 µg/mL (HeLa cervical cancer), and 17.33 µg/mL (A549 lung cancer). Lc-GNPs activity is statistically significant against all three MCF-7, HeLa, and A549 cancer cell lines. Lc-GNPs cancer activity compared with standard chemotherapeutic agents, literature-reported GNPs, and their combination. The Lc-GNPs demonstrated moderate potency but significantly lower expected systemic toxicity. These results indicate that Lc-derived GNPs possess promising, quantifiable anticancer efficacy and can serve as a sustainable nano biocompatible cancer therapeutic. The IC₅₀ values were determined from dose–response curves using nonlinear regression analysis. A549 cells exhibit the lowest viability and the highest cytotoxic response, confirming that Lc-GNPs possess the greatest potency against the A549 lung cancer cell line, followed by MCF-7 cells and HeLa.
Cars with zero or very low exhaust emissions are replacing fossil fuel-powered cars, which is a dramatic change in the global transportation scene. The surge of electric vehicles calls for a fast construction of a reliable charging infrastructure. The process of building this infrastructure depends on joining efforts from information technology, original energy generation designs, and favorable rules from the government. This article explores the key factors that must be taken into account. The conversation covers important aspects that affect how infrastructure for charging stations is designed and implemented, providing insight into planning techniques and technical developments. A thorough examination of how the electric vehicle market stands today is given, stressing the significance of optimum allocation provisioning for electric vehicles as well as the effect that EVs have on grid integration. Along with discussing issues and efforts to standardize the infrastructure in preparation for future upgrades, the study also looks at research and advances pertaining to charging station infrastructure. An important topic covered in the study is the best location for quick charging stations while taking grid effects and economic advantages into account. Adoption-related challenges are also covered, offering insights into the challenges that must be surmounted before EVs are widely accepted. The report expects that clean energy and EVs will be used more and will improve many aspects of our electric grid. All things considered, the document functions as a thorough manual, providing insightful information on the design, difficulties, and potential developments in electric car charging infrastructure.
Landslides pose a major threat to the Himalayan mountains, and their frequency is expected to rise under climate change. This study assesses present-day and climate change–driven landslide susceptibility in Kinnaur district, Himachal Pradesh, India, by integrating geomorphological and environmental conditioning factors with CMIP6 precipitation projections. A database of 12 landslide conditioning factor maps was prepared, including land use/land cover, elevation, slope, aspect, curvature, drainage density, geology, topographic wetness index (TWI), geomorphology, NDVI, soil texture, and rainfall. A Random Forest approach was used to generate the susceptibility map and was validated using Accuracy, Precision, Recall, MCC, Kappa, and AUC. The model performed strongly (Accuracy: 91.78 This graphical abstract presents a visual overview of the methodology and key outcomes of the study on climate-induced landslide susceptibility in the Kinnaur district of Himachal Pradesh, India. The graphical framework integrates geospatial datasets, machine learning techniques, and future climate projections to assess current and potential future landslide risks in a complex Himalayan terrain. The first component of the graphical abstract highlights the study area and the data sources used, including landslide inventory information, digital elevation models, satellite-derived environmental factors, and precipitation datasets. The second component illustrates the analytical workflow in which twelve landslide conditioning factors such as slope, elevation, geology, rainfall, land use/land cover, NDVI, drainage density, soil texture, geomorphology, curvature, aspect, and topographic wetness index are processed within a GIS environment. The central element of the graphical abstract depicts the Random Forest machine learning model used for landslide susceptibility mapping. This model analyses the nonlinear relationships between landslide occurrences and environmental conditioning factors to generate reliable susceptibility predictions. The results section visually represents the spatial distribution of susceptibility classes ranging from very low to very high across the Kinnaur district. Finally, the graphical abstract highlights the integration of CMIP6 precipitation projections under SSP245 and SSP585 scenarios to evaluate potential future changes in landslide susceptibility. Overall, the graphical abstract provides a concise and visually engaging summary of the research workflow and its findings, emphasizing the increasing landslide susceptibility associated with projected climate-driven precipitation changes in the Himalayan region. Landslide susceptibility in Kinnaur district was mapped using Random Forest model. Twelve landslide conditioning factors were considered for modelling. AUC value of 0.97 was achieved using this model. CMIP6 projections indicate increasing precipitation under SSP245 and SSP585 scenarios. Future climate scenarios show expansion of high landslide susceptibility zones.
