Jawaharlal Nehru Technological University, Hyderabad

The effect of laser source on the welding characteristics of Al alloys is studied for Li-ion battery applications. AA3003 sheets of 0.8 mm thickness used for Li-ion battery containers are welded using Pulsed wave (PW) laser and continuous wave (CW) laser, both having the same laser energy of 120 J and compared for their microstructure and mechanical properties. Microstructural analysis indicated that the PW weld has more surface defects and lower depth of fusion. The weld joint prepared by CW laser exhibit complete depth of fusion, higher strength and ductility than the PW laser due to higher heat input and more fusion time. PW laser weld samples exhibit higher hardness of the weld zone due to the higher cooling rates, which lead to quench cracks, lower strength and brittle failure. The cooling rate in PW weld has been calculated to be double that of CW weld which reduces the grain size by an order of magnitude in the PW weld. CW laser weld meets both the internal and external pressures exerted in Li-ion batteries and is found beneficial.

This paper presents a unique high-dimensional data-driven approach for enhancing the detection of cyber-physical attacks in photovoltaic (PV)-connected distribution power grids using Deep QNetworks (Deep-QNN). The increasing integration of renewable energy sources like PV systems to power grids poses significant cybersecurity argues, particularly from false data injection (FDI) attacks that can disrupt grid stability and operations. To address these challenges, a comprehensive dataset was generated through detailed simulations of PV-connected distribution grid systems using SIMULINK under various conditions, including normal operations, grid faults, PV inverter faults, and FDI into the reactive power reference values. The proposed Deep-QNN model was widely trained and tested on this dataset in MATLAB, achieving performance related to stateof-the-art machine learning techniques such as XGBoost, Binary Chimp Optimization Algorithm (BCOA), Particle Swarm Optimization-K Nearest Neighbour (PSO-KNN), Long Short-Term Memory (LSTM), and Support Vector Machine (SVM). The results show the efficacy of the Deep-QNN model in detecting false data and anomalies in key PV system parameters, such as PV voltage, PV current, duty cycle, AC voltage, and AC current. For training data (70 % of the dataset), the proposed Deep-QNN achieved an Accuracy of 99.03 %, Sensitivity of 99.36 %, Specificity of 98.62 %, F1 Score of 98.96 %, and an AUC Score of 94.31 % in PV voltage detection. Similar superior results were observed during testing (30 % of the dataset), with a Precision of 99.08 %, Sensitivity of 99.31 %, Specificity of 98.61 %, F1 Score of 98.93 %, and an AUC Score of 94.29 %. These performance metrics consistently outperformed other methods across all evaluation parameters and scenarios, particularly in detecting FDI attacks. The Deep-QNN model's ability to effectively handle high-dimensional, complex datasets and its robustness in non-linear environments make it a highly promising solution for securing modern distribution grids. Moreover, the integration of battery storage and flexible power exchange policies enhances grid operational reliability. Practical implementation challenges, such as computational requirements and scalability, are discussed, with proposed solutions to optimize deployment in real-world systems. This study focusses the transformative potential of Deep-QNN in enhancing the cybersecurity , resilience of PV-integrated distribution grids, paving the way for more secure and sustainable smart energy systems.

The modern cybersecurity landscape presents defenders with an excessive number of threats and critical workforce shortages. While traditional Security Orchestration, Automation, and Response (SOAR) systems offer remedies, they often demand high programming expertise, which most security providers lack. To address this, we introduce SOAR-Flow, an original low-code orchestration paradigm that opens complex automation to security analysts without requiring advanced coding knowledge. As a visual workflow engineering tool, SOAR-Flow manages multi-source threat intelligence and incident response through two major functional modules: the CVEIntel Pipeline for vulnerability checking and the Multi-Source Threat Aggregator for combining intelligence. It was empirically tested across twelve enterprise environments over a six-month period, processing 10,847 security events and eight threat intelligence feeds. Before SOAR-Flow’s implementation, the average Mean Time to Respond (MTTR) was 204 min, with 62

Concrete is one of the most versatile materials used in the construction industry in which cement is used as main ingredient playing a dominant role in gaining of strength. Despite of it, the production of cement leads to depreciation of natural materials. Regarding this aspect, in this study an attempt has been made to use GGBS (Ground Granulated Blast Furnace Slag) as a substitute material to cement with a percentage replacement of 50% .Specimens with various geometric shapes like cubes, cylinders and prisms were casted and tested with(High Volume Slag Concrete) and without replacements of GGBS, varying water-cement ratios of 0.55, 0.45, 0.36 & 0.27 for mechanical properties, Young’s Modulus of elasticity, impact strength with and without the addition of steel fibers and also abrasion resistance after 28 days and 90 days of curing.

Staphylococcus aureus is a major opportunistic pathogen and a leading cause of community and hospital-acquired infections worldwide, with a growing capacity to develop multidrug resistance (MDR). Among the genetic mechanisms driving this resistance, integrons play a critical role by capturing, rearranging, and expressing antimicrobial resistance gene cassettes through site-specific recombination. Although integrons are traditionally associated with Gram-negative bacteria, increasing evidence highlights their significant contribution to resistance dissemination in S. aureus, particularly in methicillin-resistant S. aureus (MRSA) strains. This review focuses on current knowledge on the structure, function, and types of integrons, and their role in prevalence and mechanistic role in S. aureus. Integrons interact with other mobile genetic elements like transposons and plasmids to enable horizontal gene transfer across strains and species. Among the four recognized classes of integrons, class 1 is the most prevalent in S. aureus frequently associated with plasmids and transposons and show resistance to β-lactams, aminoglycosides, fluoroquinolones, and tetracyclines. Class 2 integrons occur less commonly and exhibit limited cassette diversity, while class 3 integrons remain rare. Epidemiological studies report class 1 integron prevalence ranging from 40 to 70