Uttarakhand Open University

Brain cancer remains one of the most challenging malignancies due to the blood-brain barrier (BBB), limited drug penetration, and resistance to conventional therapies. Recent advancements in magnetic nanoparticles (MNPs) have opened new avenues for targeted and efficient brain cancer treatment. MNPs offer multifunctionality, including magnetic hyperthermia therapy, targeted drug delivery, and enhanced imaging via magnetic resonance imaging (MRI). This review explores the latest progress in MNP-based theranostics, highlighting their physicochemical properties, functionalization strategies, and mechanisms of action in brain cancer therapy. Additionally, we discuss novel approaches such as stimuli-responsive nanocarriers, BBB penetration techniques, and multifunctional hybrid nanoparticles. Furthermore, preclinical and clinical studies are reviewed to assess the current status and translational challenges. Despite promising outcomes, toxicity, biodistribution, and long-term biocompatibility remain key hurdles in clinical applications. Addressing these limitations will pave the way for personalized nanomedicine-based brain cancer treatment, optimizing therapeutic efficacy and patient outcomes.

Abstract The solidarity economy, cultural heritage, and food sovereignty are interrelated concepts that can enhance the sustainability of food systems. The solidarity economy provides an alternative to the capitalist framework by prioritizing cooperation, social equity, and environmental sustainability. Cultural heritage is essential for preserving biodiversity and fostering ecological balance. Food sovereignty guarantees that food production and distribution honour cultural values and bolster local communities. This communication presents a critical analysis of components linked to the food sovereignty movement in the hill farming of Uttarakhand, India particularly concerning the solidarity economy and indigenous cultural practices.

Hedychium coronarium J. König, from the Zingiberaceae family, is a rhizomatous herb used in Ayurvedic medicine for its febrifuge, anti-rheumatic, and anthelmintic properties. This study characterizes the chemical diversity and biological activities of H. coronarium oleoresins collected from four locations in Uttarakhand: Pantnagar (HCPNOR), Bageshwar (HCBOR), Nainital (HCNOR) and Pithoragarh (HCPOR). GC-MS analysis identified key constituents, including n-Hexadecanoic acid (8.3-9.8%), photocitral B (4.8-27.6%), (Z)-9-eicosenoic acid (0.7-6.5%), α-pinene (2.5-3.5%) and trans-13-octadecenoic acid (7.5%). Heat map clustering, Venn diagrams, and PCA revealed compositional variations. The oleoresins showed potent nematicidal, insecticidal, herbicidal, and antifungal activities against Meloidogyne incognita, Spodoptera litura, Raphanus raphanistrum subsp. sativus, Fusarium oxysporum, and Curvularia lunata. These findings highlight the potential of H. coronarium oleoresins for pest and pathogen management, with chemical variation driven by environmental factors.

Nanocarriers, engineered nanoparticles designed to deliver fertilizers, pesticides, and genetic materials, are emerging as essential tools in modern agriculture. This chapter explores nanocarrier types, mechanisms, and applications in plant science. Key systems such as liposomes, polymeric, metallic, and silica nanoparticles enable targeted delivery, enhancing nutrient uptake, pest control, and stress resilience. By ensuring precise agrochemical release, nanocarriers reduce wastage, contamination, and application frequency, supporting sustainable farming. They also facilitate genetic transformation and gene editing for stress-tolerant, non-transgenic crops. However, challenges in cost, regulation, and environmental impact remain. With ongoing research, nanocarriers show promise in advancing food security and sustainable agriculture.

As the exponential increase of e-commerce and card-based transactions brings both benefits and risks— credit card fraud detection emerging as a crucial and consequential domain in financial technology. In order to help businesses make considerable financial savings and restore consumer confidence, accurate and reliable fraud detection systems are critical. Nonetheless, conventional machine learning models may encounter issues including but not limited to class imbalance, limited scalability and manual feature engineering dependence. Due to such limitations, it is required to introduce advanced techniques able to cope well with the complex datasets. To cope with these challenges, a Convolutional Neural Network (CNN) was used. This model uses convolutional layers to build a hierarchy of feature extraction, dropout for regularization on the parameters, and class weight to balance the dataset. The novel aspect of this study is that it is the first end-to-end deep learning-based study to delineate factors leading to Indian bank NPA with no manual feature extraction. This enabled the researchers to propose a CNN that achieved an accuracy of 98.76%, precision 91.34%, 93.89% recall, 92.60% F1-score, and 0.988 AUC-ROC score, outperforming traditional models. This shows that CNNs can be used for powerful and extendable fraud detection systems.

Parthenium hysterophorus L., commonly known as carrot grass (Gajar ghaas), is an annual plant in the Asteraceae family. This study aimed to examine the biological activity and chemical makeup of essential oils extracted from P. hysterophorus plants collected from Dehradun and Haridwar district, Garhwal region, Uttarakhand, India. The plant material underwent hydro-distillation using a Clevenger apparatus to extract the volatile oils. These essential oils were then analyzed for their chemical constituents using Gas Chromatography-Mass Spectrometry (GC-MS). The GC-MS analysis identified several major compounds in the essential oils from both districts, including β-cyperone (5.11-12.16%), 1-isopropenyl-4-methyl-1,2-cyclohexanediol (3.0-3.7%), 2,3-bornanediol (1.73-6.13%), isolongifolol (1.32-2.73%), saussurea lactone (0.76-7.81%), and neoclovene oxide (0.97-1.48%). Using Principal Component Analysis (PCA) and heat map clustering, the differences in biological activity and chemical composition of the oils were visualised. These analyses helped illustrate the differences and similarities in the chemical profiles and biological effects of the oils from the two districts. Both essential oils demonstrated significant allelopathic effects against Raphanus raphanistrum subsp. sativus (wild radish) seeds and showed moderate antifungal activity against Colletotrichum lindemuthianum and Curvularia lunata. These findings indicate that the essential oils from Parthenium hysterophorus L. possess notable allelopathic and antifungal properties, suggesting their potential applications in agriculture.

Climate change significantly impacts agriculture by increasing the frequency and intensity of environmental stresses, which can severely reduce agricultural yields. Adopting sustainable practices is crucial to mitigating these risks and enhancing crop resilience. Applying natural compounds and microorganisms as biostimulants has gained popularity as an eco-friendly approach to alleviating abiotic stress in agricultural plants. This study reviews the current research on applying biostimulants in horticulturally significant crops to boost their resistance to abiotic stressors such as salinity, drought, and high temperatures. It explores the mechanisms through which these stimulants offer protection, focusing on the roles of key bioactive substances in regulating physiological and molecular processes for stress adaptation. The study addresses biostimulant formulation, regulation, and application challenges. Future research directions are suggested to harness biostimulants’ potential fully, aiming to develop climate-resilient horticultural systems that follow sustainability principles. This comprehensive review underscores the use of biostimulants as a sustainable strategy to increase crop yields in the face of climate change, reducing reliance on synthetic agrochemicals.

Abstract Fiber reinforced polymer (FRP) has emerged as a significant advancement in construction, with design provisions outlined by codes such as GB/T 30022‐2013, CSA S806‐12 (R2017), and ACI 440:2015. While the use of FRP bars as alternatives to conventional reinforcement in columns has been extensively studied, their application in hollow concrete columns (HCCs) remains underexplored. This study investigates the behavior of FRP‐reinforced HCCs using advanced machine learning (ML) models, focusing on the prediction of two critical outputs: first peak load (Y1) and failure load (Y2), based on eight input parameters. Models evaluated include extreme gradient boosting (XGB), light gradient boosting (LGB), and categorical gradient boosting (CGB). A rigorous comparative analysis demonstrated that all models achieved high predictive accuracy, with deviations within ±10% of actual results, validating their reliability. Among the models, CGB exhibited superior generalization and robustness, emerging as the most reliable predictor for FRP‐reinforced HCC behavior. To enhance practicality, a user‐friendly graphical user interface was developed to allow engineers to input design parameters and instantly obtain predictions for Y1 and Y2. This study not only advances understanding of FRP‐reinforced HCCs but also bridges the gap between computational predictions and real‐world applications, contributing a robust predictive tool to structural engineering design.