Osmania University is a collegiate public state university located in Hyderabad, Telangana, India. The university was founded by and named after Mir Osman Ali Khan, the 7th Nizam of Hyderabad in 1918. It is the third oldest university in southern India, and the first to be established in the erstwhile Kingdom of Hyderabad. It was the first Indian university to have Urdu as a medium of instruction — but with English as a compulsory subject. As of 2012, the university hosts 3,700 international students from more than 80 nations.The O.U. is one of the largest university systems in the world with over 300,000 students on its campuses and affiliated colleges. The Osmania Medical College was once a part of the O.U. System. However, it is now under the supervision of Kaloji Narayana Rao University of Health Sciences.U.
The fusion of Digital Twin (DT) and Artificial Intelligence (AI) is changing the face of maintenance practices for autonomous vehicles. This chapter offers an AI-based digital twin framework for predictive maintenance to ensure timely, efficient, and reliable maintenance decisions. The framework combines various data sources related to the vehicle for continuous monitoring of the condition of the components of the vehicle to detect any signs of failure. The chapter covers DT-based maintenance systems, intelligent data management, and AI-based anomaly detection for improved safety, reliability, and efficiency of the vehicles. The chapter also covers simulation-based analysis and practical scenario-based analysis to demonstrate the efficacy of the framework. The challenges of data heterogeneity, data synchronization, and data interoperability are also covered. The future of digital twin is also discussed for smarter maintenance practices for autonomous vehicles.
Wireless Mesh Networks (WMNs) are widely used in Industrial Internet of Things (IIoT) applications because they are flexible and easy to scale. However, managing congestion in these networks is difficult due to decentralized control, changing traffic patterns, and different types of devices. Most existing methods depend on centralized learning or react only after congestion happens, which limits their efficiency in dynamic environments. To solve this problem, this paper proposes a Collaborative Behavioral Fingerprinting Model (CBFM) for early congestion prediction in WMNs. In this approach, each node observes its recent behavior and calculates a simple Congestion Contribution Score (CCS) to estimate how much it may contribute to congestion. These scores are shared with nearby nodes using a federated signaling method, so that nodes can understand network conditions without sharing raw data. Based on this shared information, each node adjusts its transmission decisions in a distributed, adaptive way. The model works in four main steps: local behavior modeling, congestion scoring, score sharing, and adaptive decision-making. Simulation results show that the proposed method improves network performance, reducing end-to-end delay by up to 40% and increasing Packet Delivery Ratio (PDR) by 6– 11% under different network sizes and traffic conditions. Overall, the proposed approach provides a simple and effective way to manage congestion in dynamic IIoT networks while maintaining scalability and data privacy.
Abstract This study is on structural, thermal, and electrical properties of the composite solid electrolyte system [NaNO 3 ] 85 :[Sr(NO 3 ) 2 ] 15 (host matrix) dispersed with nano particles of zirconium oxide, whose size is less than 100 nm. XRD and FTIR studies on host and dispersed systems explored the three-phase coexistence of the host matrix and ZrO 2 , supporting that the zirconia nanoparticles continued as independent dispersed phase within the host system. SEM pictures clearly showed the closely contacted different size grains of host with evenly dispersed ZrO 2 nanoparticles, a morphology that helps to understand higher ionic conductivity in dispersed zirconia systems because of the enhanced movements in the in the interfaces of the phases . DSC results showed only a slight shift in melting behavior upon ZrO 2 addition, indicating that the nanoparticles do not affect the intrinsic phase transitions of the host but enhance ionic conductivity mainly through interfacial effects rather than thermal modification. The influence of ZrO 2 concentration on ionic transport was investigated through DC conductivity measurements. A notable increase in conductivity was observed with increasing ZrO 2 content, reaching a maximum at 11 mol%, followed by a reduction at higher mole percent. The conductivity enhancement is attributed to the formation of space-charge regions at the nanoparticle-electrolyte interface, which provides additional mobile ions and facilitate their movement through the matrix.
The article will present a Quantum-Inspired Federated Neural Network to improve risk predictions in international markets. The suggested methodology will combine the method of quantum computing with federated learning to resolve the problem of data privacy, scalability, and computational efficiency. The Quantum-Inspired Adaptive Histogram Equalization is used to preprocess financial data, which enhances feature contrast by making predictions more accurate. Quantum-Inspired Recursive Feature Elimination with Cross-Validation maximizes the use of features providing over a model only the most significant data to make predictions. Also, the Quantum-Enhanced Vision Transformer allows the model to learn and model non-linear relationships in financial time-series data that are complex and enhance the accuracy of the forecasts. The model was implemented with the help of IBM Quantum SDK and effectively employs quantum resources to minimize the computational overhead and still provide high accuracy in prediction. It is experimentally proved that quantum-inspired framework is highly efficient compared to traditional ones, is scalable and privacy-preserving, and provides a powerful tool in predictions-based risk forecasting in the dynamic market, which is one of the primary problems of the day
Wireless sensor networks (WSNs) possess numerous significant applications, encompassing both research and domestic utilization. The primary function of these entities is to gather and convey data originating from a designated region of interest (ROI) to a central station for the purpose of subsequent processing and analysis. As a result, it’s critical to guarantee both connectivity among the network’s nodes and maximum coverage of the selected region. The placement of sensors with the aim of ensuring optimum coverage and communication between sensor nodes is a significant issue in network architecture. The objective is to establish an ideal deployment that extends network lifespan and coverage rate while using the least amount of energy possible. Deploying sensor nodes randomly generates communication holes, which can degrade network performance. In order to solve optimization problems, this work introduces the interior search algorithm (ISA). The ISA reconstructs the network using minimal transmission lines from sink nodes to sensor nodes, using inspiration from interior design and aesthetics. This algorithm offers new insights for global optimization and minimizes end-to-end delay and energy consumption. Through network parameter optimization and a limit on the overall number of installed relays, the ISA-based deployment provides a prolonged lifespan. Simulations demonstrate that the suggested approach is successful in a range of issue complexity scenarios.
