Defence Electronics Research Laboratory

Thiazolotriazole is gaining attention in medicinal chemistry due to its wide spectrum of biological activity. It is a fused heterocyclic compound formed by the fusion of 1,3-thiazole and 1,2,4-triazole, and the type of ring fusion results in the formation of isomeric thiazolotriazoles-[3,2-b] or [2,3-c] isomers. The synthesis of both ring systems has been carried out by various methodologies ranging from conventional methods such as cyclization and annulation to the use of metal catalysts, microwave radiation, photochemical and multicomponent reactions. In drug discovery, thiazolotriazole derivatives have been primarily investigated for their antibacterial, anticancer, anti-inflammatory, and antifungal properties. Recent years have seen significant advancements in anticancer drug research, revealing that these molecules are potential anticancer agents interacting with specific targets or biochemical pathways responsible for apoptosis and proliferation. In addition, thiazolotriazole also exhibits analgesic, anticonvulsant, antidiabetic, and antioxidant activities. Furthermore, thiazolotriazoles have also demonstrated the potential to inhibit enzymes such as carbonic anhydrase, urease, cyclooxygenase, and butyrylcholinesterase, which are meant to have particular biological functions. In the context of various applications, a review that describes biological activities with a particular focus on structural attributes that contribute to the activity will be helpful to better understand structure-activity relationship (SAR) and guide for further design of bioactive thiazolotriazoles. This review explains the biological activity of thiazolotriazole highlighting SAR and drug targets for specific disease conditions which will be helpful to better understand the scaffold and apply this knowledge to future drug discovery on thiazolotriazoles.

The project “Predictive Maintenance of Power Transformers using Machine Learning” aims to create a solution to predict potential transformer failures, reducing maintenance costs and operational downtime. By analyzing both historical and real-time data using machine learning algorithms like support vector machines (SVM) the system can identify early signs of failure in critical transformer parameters like temperature, oil quality, load data, and insulation health. The model provides actionable insights to optimize maintenance scheduling and prevent unexpected outages. Technologies like Pandas are used for data processing, while Matplotlib and Plotly generate visual insights. Additionally, Streamlit is used to create an interactive interface, allowing users to visualize predictions.

Exciting new communication and sensing methods and imaging technologies come with the inclusion of terahertz (THz) frequency bands in the spectrum of electromagnetic waves. The design of new radio antennas that can be used on different frequencies at the same time is the basic part of the technological process of today. The paper proposes a novel, compact triple-band metamaterial-inspired antenna for handling the mentioned requirements of THz applications that often need high efficiency along with multiband functionality control. The submitted license's entourage is crafted so that different structural metamaterial elements such as periodic split-ring resonators and complementary resonant inclusions are used to deliver simultaneous resonance at three different frequencies of THz. The plan involves true optimization of effectual variables such as thinking clearly about the gain, bandwidth, and efficiencies of the radiations using sophisticated electromagnetic simulation tools. The comparison between the results of THz antennas and the classical way in terms of return loss, gain stability, and radiation patterns is easily perceivable. The design is multi-dimensional and can be used for ultra-fast communication devices and non-invasive medical imaging as well as high-resolution industrial sensing. This work creates a new platform for the inclusion of metamaterials in future THz systems where the metamaterials utilization is enhanced over the traditional THz antennas that show higher limits in design.

In this paper, a four-area interconnected multi area system with hybrid generating units is modelled for the study of automatic generation control (AGC). In systems where renewable energy are present, the inertia of the system decreases due to the absence of rotor or rotating force causing system instability. A virtual inertia control is introduced to the system to compensate the loss of inertia using the proposed novel controller named tilt double integral derivative with filter controller (TI-IDN). The proposed controller is tuned with Zebra optimization algorithm to improve the system response like settling time (ST), peak overshoot (POS) and peak undershoot (PUS). The superiority of the proposed controller is observed and damping ratio is improved by 93.31 %, 89.97 % and 31.03 % respectively by comparing with Integral, Tilt-Integral and Tilt integral derivative controller. Analysis reveals that VIC coordinated control enhances system dynamic performance. Finally, the robustness of the proposed control methodology is validated by performing sensitivity analysis during wide variations of system loading and system with communication Time delay.

Huge applications of the notch band featured antennas have been recently designed for radar systems. The gain and radiation performance analysis is an essential part of any antenna system design. Therefore, this paper aimed to investigate the gain of the internally matched auto mesh Vivaldi antenna design for two dielectric materials. Ultimate aim is to design the 2.4 GHz Vivaldi notch antenna. The radiation characteristics are plotted at this frequency range. Then, as an experiment, the performance of the gain characteristic is compared across a wide range of UWB frequencies from 2 to 11 GHz for FR4 or Teflon dielectric materials. The optimal operating range is determined based on the analysis.

This research work is devoted to the researching of an effective framework for brain tumor identification with Deep Belief based Networks (DBNs), an excellent supervisor learning method. Brain tumors are a serious health issue, and early detection is essential for the best patient results. Traditional methods of diagnosing, like a manual MRI study, take a long time and can be wrong due to human errors. In a demographic dataset, DBN-based method automates the identification process by deep learning complex patterns, resulting in the improvement of accuracy and efficiency in tumor detection. The DBN model, that consists of stacked Restricted Boltzmann Machines (RBMs), reaches the precision of 96.4%, thus outstripping the average of conventional CNN-based models, which stands at 92.8%. Additionally, the model displays a precision of 94.7%, a result that impressively outperforms previously held traditional method which had been performing at 91.2%. In addition, the recall rate of 95.5% helps to minimize false negatives and, in this way, to reduce the unrecognized tumor risk compared to the previous 89.6% recall rate. Moreover, the F1-score of 95.1% underlines the model's good overall performance in classifying tasks. The research enhances a compelling case for using DBNs' generative capabilities in the optimization of the models for increased diagnosis accuracy. The findings will mostly shape the area of medical imaging and bring a properly automated brain tumor detection system that will be the fastest and most precise for diagnosing.