Centre for Aromatic Plants

The research was conducted during 2024-25, aimed to assess the benefits of the Cluster Frontline Demonstration (CFLD) programme on mustard cultivation knowledge and yield among farmers in Uttar Pradesh (UP). By employing the quasi-experimental design, comparing non-beneficiaries (B0), previous beneficiaries 2021–2024 (B1) and current beneficiaries 2024-25 (B2) with 120 participants each and a total sample size of 360, across three Krishi Vigyan Kendras (KVKs) of UP. The data was collected through a personal interview. Knowledge scores (% correct) and yield (q ha-1) were measured and compared by using Kruskal-Wallis and Mann-Whitney U tests. Results showed a significant difference in yield, i.e. 14.00 q ha-1 (B0) and 18.61 q ha-1 (B2), and median knowledge increased significantly in variety (27.27% to 63.64%) and harvesting and threshing (50.00% to 100.00%) across three groups. All practices show a significant difference (p < 0.00625), with effect sizes up to 0.293. Results show that the CFLD programme significantly increases knowledge and yields, due to targeted training and resource support. Non-significant initial differences in some practices suggested overcoming barriers with sustained participation.

Modern wars is changing at an incredible pace and the major game changers are Drones. They offer remarkable precision and efficiency. These days, drone find application in many areas. Concept of IoT when extended to the drone networks, is termed IoD (Internet of Drones). These drone or IoD networks may have to work in an enemy territory and work over unreliable wireless networks. Blockchains can provide a natural solution to the security threats in a drone based implementation. Drone networks generate huge data. 5G networks offer QoS based fast services. So, we propose that communication channel between D2D, D2G, D2B are all 5G. This would ensure low latency and required QoS level to sustain real time services over these IoD networks. During this chapter different important aspects of above concepts i.e. drone networks, blockchain and 5G networks are covered. Finally, an integrated architectures are studied to sustain and secure the real time communications over IoD networks.

Nanomaterials (NMs) are a unique class of materials with at least one dimension between 1 and 100 nm, exhibiting exceptional properties distinct from their bulk counterparts. Their high surface area, as well as tunable physical, chemical, and magnetic behaviors make them valuable across diverse applications. Phase transitions, such as the emergence of magnetism at the nanoscale, highlight their distinctiveness. This review outlines the historical development of NMs and clarifies key terminology. It covers major synthesis techniques, including top-down and bottom-up approaches, and highlights the unique features that define nanoscale matter. A comprehensive overview of various NMs, such as fullerenes, graphene, carbon nanotubes, MXenes, and core–shell nanoparticles (NPs) is presented. The paper concludes with an overview of current challenges and future directions in the field of NMs.

Ransomware has become one of the most significant cybersecurity threats of the modern digital age, evolving in complexity and scale over time. This document provides a comprehensive analysis of ransomware, beginning with its background and a review of existing literature to understand its origins and early objectives. It evaluates early ransomware attacks and key milestones that have shaped its evolution.The study further explores modern ransomware tactics, including double extortion, Ransomware-as-a-Service (RaaS), and the targeting of critical infrastructure. It delves into the role of cryptocurrency in facilitating anonymous ransom payments and examines sophisticated delivery mechanisms used by attackers. The broader impacts of ransomware, encompassing financial, operational, and reputational damages, are assessed, highlighting the urgency for effective countermeasures. Various mitigation strategies are analysed, along with the challenges faced in their implementation. Legal implications of ransomware attacks are also discussed, emphasizing the need for stronger regulatory frameworks and international collaboration. The document concludes by exploring future trends in ransomware and offers actionable recommendations to strengthen defences against this evolving threat.

Abstract Introduction: Respiratory viral infections, including SARS-CoV-2, respiratory syncytial virus (RSV), influenza, rhinovirus, and adenovirus, are major causes of acute respiratory illness (ARI) in children. Symptom-based predictive models are valuable tools for expediting diagnoses, particularly in primary care settings. This study assessed the effectiveness of machine learning-based models in estimating infection probabilities for these common pediatric respiratory viruses using symptom data. Methods: Data were collected from 868 children with ARI symptoms evaluated across 14 primary care centers, members of COPEDICAT (Coronavirus Pediatria Catalunya), from October 2021 to October 2023. Random Forest and Boosting models with 10-fold cross-validation were used, applying SMOTE-NC to address class imbalance. Model performance was evaluated via area under the curve (AUC), sensitivity, specificity, and Shapley Additive exPlanations (SHAP) values for feature importance. Results: The model performed best for RSV (AUC: 0.81, sensitivity: 0.64, specificity: 0.77) and effectively ruled out SARS-CoV-2 based on symptom absence, such as crackles and wheezing. Predictive performance was lower for non-enveloped viruses like rhinovirus and adenovirus, due to their nonspecific symptom profiles. SHAP analysis identified key symptoms patterns for each virus. Conclusions: The study demonstrated that symptom-based predictive models effectively identify pediatric respiratory infections, with notable accuracy for RSV, SARS-CoV-2 and influenza.

Rotator cuff injuries (RCIs) are a common musculoskeletal disability affecting daily activities and work performance for millions globally. Current conservative therapies are riddled with limitations. Therefore, this study aims to review the regenerative effects of exosomes derived from mesenchymal stem cells (MSCs) on RCIs and catalogue ongoing clinical trials investigating their effects. The main aim of this study is to compile findings from in vitro, preclinical, and clinical research articles that evaluate the regenerative potential of exosomes derived from different MSCs on injuries to the rotator cuff. Additionally, the secondary objective is to catalogue ongoing clinical trials registered in various clinical trial databases that explore the impact of exosomes on rotator cuff injuries. A systematic search across four extensive online journal databases until March 2024 was conducted following PRISMA guidelines. The search focused on articles in English using specific terms related to exosomes and RCIs. Eligible studies, including basic science, preclinical, and clinical research, and were assessed by two independent reviewers. Additionally, registered trials were identified from three international clinical trial databases. Four basic science, one basic science and preclinical, and twelve preclinical studies were included in this review. The studies demonstrated that exosomes derived from various stem cells have a strong regenerative capability, whose effects may be further enhanced by adding growth factors, glycoproteins, or other pro-regenerative markers. In vitro and preclinical studies reveal exosomes' regenerative potential for RCIs. Further research, including clinical trials, is needed to assess exosomes' safety and efficacy in RCI management.

Applications utilizing the Internet of Things (IoT) operate on a platform that works efficiently and has capabilities of handling vast volumes of data processing. The type of application will determine if this platform is Cloud, Edge, or Fog. On the Fog Computing platform, realtime applications require a faster response time. Nowadays most demanding area in the Fog-Cloud environment is task scheduling. Fog computing is therefore mostly used to schedule processes with the least amount of energy and time overhead. The most important element that forms the foundation for reducing energy consumption and makespan is the choice of task scheduling methodology. This paper presented a Hybrid Population-based Bi-Objective Task Scheduling (HPBOTS) algorithm that addresses the task scheduling problem by combining the principles of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The simulated experiment shows better performance of the proposed hybrid population-based bi-objective algorithm for makespan and energy efficiency compared to GA and PSO. Simulation results demonstrate a reduction in energy results by 8.28% and 11.21% when compared with PSO and GA. Also, the result shows proposed algorithm takes 4.02% less than the PSO makespan result and 2.08% less than the GA makespan result.

The Android operating system captures the largest global smartphone market share. However, its popularity and open-source nature have garnered the attention of cybercriminals. The landscape of Android malware has evolved significantly over time. Traditional techniques for detecting Android malware are encountering difficulties in keeping up with this evolution. Specifically, methods that rely on extracting various features from Android applications are becoming difficult to implement as high-dimensional feature sets incur huge computational overheads when employed with machine learning algorithms. Therefore, this research proposes using Bortua and BorutaShap feature selection algorithms to choose features that contribute to detecting malicious Android applications. It uses static and dynamic features of Android applications to create a detection model for verification and evaluation of the mentioned algorithms. Experimental results showed that Bortua and BorutaShap algorithms offer promising results by achieving the highest accuracy of approximately 99%.