Singhania University was established by the Government of [6 of 2007. It is a UGC recognized private university as per Sec. 2f of the UGC Act 1956. It is in Pacheri Bari, Jhunjhunu in the northern Indian state of Rajasthan. 2f of the UGC Act 1956..
The rapid growth of 6G networks has intensified the need for ultra-reliable low-latency communication (URLLC) and intelligent task offloading in multi-edge computing environments. This study proposes an enhanced Hybrid Double Deep Q-Network–Fuzzy SARSA (DDQN–Fuzzy SARSA) model integrating experience replay memory to achieve stable, interpretable, and real-time offloading decisions. The model combines the long-term action-value optimization of DDQN with the adaptive rule-based reasoning of Fuzzy SARSA ( λ ), while a task-failure-aware reward function incorporating Task Failure Rate (TFR) ensures rapid avoidance of congested edge nodes and reinforces QoS guarantees. A comprehensive system model considering MAN/WAN delays, VM utilisation, server coordinates, and heterogeneous IoT workloads was developed and evaluated through extensive MATLAB simulations. Results demonstrate that the proposed framework significantly surpasses standalone DQN, Fuzzy SARSA, PPO, A3C, and PSO baselines, achieving 0.35 ms latency, 99.9999
The Huisgen azide-alkyne cycloaddition method has been used to synthesise the titled molecule, Ethyl 1-((2-chlorothiazol-5-yl)methyl)-1H-1,2,3-triazole-4-carboxylate (E2CMT). This structure was confirmed by 1H NMR, 13C NMR, single crystal XRD, FTIR and HRMS. The theoretical study has been performed for comparative study of E2CMT with the experimental data. The theoretical findings including FTIR, UV-Visible, NMR, Hirshfeld analysis, ligand protein binding and molecular dynamic simulation (MDS) compared with experimental results have shown excellent agreements. Hirshfeld surface analysis depicting the inter and intramolecular interactions through various parameters such as dnorm and de ranges fell between -0.1970 to 1.1689 and 1.0028 to 2.5761 respectively. The UV-visible spectra were recorded in six different solvents including EtOH (250 nm), MeOH (259 nm) and were compared with the theoretical TD-DFT values, revealing the minimum solvent influence. Molecular electrostatic potential (MEP) mapped in six solvents revealed good nucleophilic reactivity on the surface of the titled molecule, and the best reactivity was obtained in MeOH solvent. The E2CMT was docked with eleven proteins to explore best ligand-protein interaction and the minimum binding energy obtained was -7.3 kcal/mol indicating good ligand-protein interaction. Molecular dynamic simulation performed at 100ns confirmed the biomolecular stability of E2CMT. Theoretical calculations of drug-likeness, chemical hardness, electrophilicity index and bioavailability score revealed a promising bioactivity profile.
The rapid advancement of Artificial Intelligence (AI) has transformed cybersecurity by enabling intelligent threat detection, automated incident response, and predictive risk assessment. However, the same technological progress has also introduced a new generation of cyber threats in which malicious actors leverage AI to automate attacks, evade traditional security mechanisms, and conduct highly targeted cybercrimes. AI-powered bots, autonomous malware, intelligent phishing campaigns, deepfake-based social engineering, and adaptive ransom ware have significantly increased the sophistication, scale, and impact of cyberattacks across governments, financial institutions, healthcare organizations, educational sectors, and critical infrastructure. This research investigates the evolution of Artificial Intelligence-driven cybersecurity crimes, emphasizing the role of AI bots in executing autonomous attacks, reconnaissance, credential theft, distributed denial-of-service operations, and misinformation campaigns. The study further evaluates advanced AI-based cybersecurity defense strategies, including machine learning-based intrusion detection systems, behavioral analytics, federated learning, explainable artificial intelligence, zero trust security architecture, threat intelligence platforms, and autonomous incident response mechanisms. A comprehensive experimental framework was developed using benchmark cybersecurity datasets to compare traditional machine learning algorithms with advanced deep learning models in detecting AI-generated cyber threats. Experimental findings demonstrate that hybrid deep learning architectures significantly outperform conventional approaches in identifying sophisticated AI-assisted attacks while reducing false-positive rates and improving detection speed.
The current work examines the surface modification and improvement of tribological performance of AISI-304 stainless steel via gas and salt bath nitriding under dry sliding conditions. Although austenitic types of stainless steels have good resistance to corrosion, their surface hardness is relatively low and they have poor wear properties, thereby they are undergone with surface engineering techniques. Nitriding by gas is done for 6 h, 12 h, 18 h and salt bath treatment is done for 1 h, 2 h, 3 hrs to a temperature of 550 °C to 570 °C. Their effect on the hardness, case depth and resistance to wear is determined. SEM microstructural analysis, hardness and wear test under a load of 10 N at a speed of 1000 rpm were undergone for this experimental work. The outcomes proved the rise in hardness from 219 Hv (untreated) to 245 Hv after 3 hours of salt bath nitirding, 284 Hv after 18 h of gas nitriding with a corresponding loss in wear between 6.812 mm3 and 1.721 mm3. It was found that diffusion layer increases with the temperature and time, proving the enrichment of nitrogen and the appearance of nitrides. The experiment finds that gas nitriding at 570o C in 18 h offers the best surface qualities whereby a thick and uniform layer of nitride was formed that increases wear resistance and surface hardness significantly without loss of structural integrity.
Celiac disease (CD) is a chronic immune disorder which is triggered by the ingestion of gluten. There are increased chances of having autoimmune disorders (Type 1 Diabetes Mellitus, Thyroid Disorder, and Rheumatoid Arthritis and others) in people who have CD. However, there is limited research available till date on understanding the combined effect of lifestyle factors, physical, sociodemographic characteristics and immune-hematological profiles of autoimmune disorders who also had CD. This study aims to bridge this gap on understanding of associations of these parameters and help identify immune-hematological parameters that may serve as shared indicator across autoimmune conditions. This study utilised the large scale database from UK Biobank which comprises over 502,132 participants biomedical, lifestyle, diet and sociodemographic data of aged 40–70 years. To find the statistically quantifiable associations between sociodemographic, physical, and lifestyle factors and the likelihood of CD among individuals with Type 1 Diabetes Mellitus, Thyroid Disorders, and Rheumatoid Arthritis, multivariate logistic regression models were used. Independent samples t-tests (significance level: 0.05) were conducted to compare the means of immune-hematological parameters between individuals with CD alone and those with coexisting autoimmune diagnoses. CD was consistently associated with female sex, lower BMI, and recent illness-related dietary changes across all autoimmune subgroups. Minority Ethnic Origin was inversely associated with CD diagnosis. Immune-hematological parameters revealed a high degree of similarity across the examined disease groups. Notably, the immune-hematological parameters, including monocytes, eosinophils, and basophils percentage, were found to be significantly associated between individuals with isolated CD and those with coexisting autoimmune diagnoses. The observed convergence in immune-hematological profiles across autoimmune diseases supports their potential utility as baseline indicators for early diagnosis and longitudinal monitoring. Parameters demonstrating consistent similarity across disease states—such as monocytes, eosinophils, and basophils percentage—could enhance diagnostic precision and facilitate more standardized approaches to screening in at-risk populations.
