Technology Information, Forecasting and Assessment Council

This paper presents the development of a virtual traffic island-based approach for Urban Airspace Management (UAM) system to resolve simultaneous conflicts. Increasing usage of Unmanned Aerial Vehicle (UAV)-based transportation in urban environments requires a high-density conflict management solution that is both safe and implementable in real time. Existing state-of-the-art methods, including Control Barrier Function (CBF)-based approaches, can guarantee safety, but are not scalable when the number of UAVs is larger. In high-density traffic scenario, the simultaneous existence of multiple conflicts makes real-time execution of conflict resolution a challenging problem. This paper presents a scalable, safe, and implementable dynamic conflict resolution approach called the Virtual Traffic Island (VTI) approach for urban airspace management. The novel VTI approach employs an evolving virtual traffic island with changing radii to handle multiple conflicts simultaneously. In the proposed approach, since each UAV in conflict avoids a single virtual obstacle by solving a CBF-based quadratic programming (QP) problem, the computational requirement for each UAV remains the same, resulting in its easy scalability and safety. The proposed method has been validated for dynamic conflict resolution of a growing number of UAVs in the urban airspace. To validate the implementation aspects of the proposed approach in real time, a software-in-the-loop (SITL) simulation has also been presented. Furthermore, a comparison with other well-known existing methods shows that the novel VTI outperforms these in terms of UAVs reaching their goals and reduces computational complexity, resulting in an easier implementation.

As phishing attacks and malicious websites grow increasingly sophisticated, traditional blacklist-based solutions fall short in providing effective real-time protection. Therefore, a Secure Browser Extension that leverages a hybrid approach combining machine learning and deep learning for malicious URL detection has been presented in this research. The proposed framework focuses on URL-based analysis, utilizing a diverse set of features extracted from the URL itself, such as lexical, structural, and statistical attributes. The detection pipeline integrates multiple models: Random Forest for tabular feature classification, a CNN-LSTM network for sequential character-level analysis, TinyBERT for semantic and contextual understanding of URLs, and an Autoencoder for anomaly detection. An ensemble strategy, including stacking and weighted averaging, is employed to maximize detection performance by leveraging the strengths of each model. The system is implemented with a Python Flask backend serving real-time predictions to a browser extension, enabling seamless and user-friendly protection during web browsing. Experimental results on a comprehensive dataset demonstrate that the hybrid ensemble achieves high accuracy of up to 98.6%, strong recall, and low false positive rates, outperforming the individual models. This methodology provides a robust, adaptive, and scalable defense against evolving web-based threats, including obfuscated links and zero-day phishing attacks.

The computer-aided diagnosis helps medical professionals detect and classify lung diseases from chest X-rays by leveraging medical image processing and central server-based machine learning models. These technologies provide real-time assistance to analyze the input and help efficiently detect the abnormalities at the earliest. However, traditional learning models are not suitable for live scenarios that require privacy, data diversity, and decentralized processing. The Federated learning-based model facilitates the protection of medical data privacy while processing a large volume of medical images, aiming to improve the overall efficiency of the model. This paper proposes a Federated Learning based Ensemble Model (FLEM) framework for an efficient diagnosis of lung diseases. The FLEM utilizes explainable AI techniques, including SHAP, Grad-CAM, and Differential Privacy, to provide transparency and interpretability of predictions while maintaining the privacy and security of medical data. We applied InceptionV3, Conv2D, VGG16, and ResNet-50 models on the COVID-19, TB, and pneumonia datasets and analysed the performance of the models in FLEM and Central Server-based Learning Model (CSLM). The performance analysis shows that the FLEM model outperformed the traditional CSLM model in terms of accuracy, training time, and bandwidth consumption. CSLM witnesses a quicker convergence time than FLEM. Although the CSLM model converged after a considerable number of epochs, it resulted in a 5, 8, 9, and 10% accuracy reduction compared to the FLEM-based training of InceptionV3, Conv2D, VGG16, and ResNet50 that achieved accuracies of 91.8, 88, 92.5, and 95.5%, respectively.

The aim of the study is to undertake molecular docking simulations for different series of glycine/NMDA site antagonists in order to understand the dual allosteric mechanism. It also aims to propose newer antagonists in the light of comparative QSAR and molecular docking simulations.The results of molecular docking simulations based on Lamarckian Genetic Algorithm on five different series of trans-4-amido-2-carboxytetrahydroquinolines , 4-amino substituted-3-phenyl quinolin-2(1H)-ones, 4-substituted-3-phenylquinolin-2(1H)-ones, substituted 3-phenyl-4-hydroxy-2-quinolones, 3-nitro-3,4-dihydro-2 (1H)-quinolones as glycine/ NMDA antagonists indicate that molecules with aromatic substituents do not fit well into the hydrophobic cavity of the subsite with active site residues in rigid state. This may involve dual allosteric binding for fruitful interaction.

The penetration of renewable energy resources into the energy sector is gradually increasing. The major goal of the proposed work is to eliminate harmonic distortion and power quality problems in the solar PV system by designing, developing, and testing an 11-level multilevel inverter with contemporary power electronic switches. The issue of power quality has been a popular area of study in recent years as a result of the increasing use of power electronics converters to process power in various facets of our lives. To achieve adequate efficiency, the voltage level is raised in proportion to the power level. The multilayer power converter is becoming more and more common. Multilevel converter topologies have two main benefits: they reduce electromagnetic interference by having minimal deformation in output waveforms and restricted voltage load on the switching electronics. This work presents results based on both simulation studies as well as measurements on developed hardware set-up.