The present study proposes a novel method for predicting the discharge capabilities of lithium-ion (Li-ion) batteries using a digital twin model in practice. By combining cutting-edge machine learning techniques, such as AdaBoost and long short-term memory (LSTM) network, with a semiempirical mathematical structure, the digital twin (DT)—a virtual representation that mimics the behavior of actual batteries in real time is constructed. Various metaheuristic optimization methods, such as antlion, grey wolf optimization (GWO), and improved grey wolf optimization (IGWO), are used to adjust hyperparameters in order to optimize the models. As indicators of performance, mean absolute error (MAE) and root-mean-square error (RMSE) are applied to the models after they have undergone extensive training and ten-fold cross-validation. The models are rigorously trained and cross-validated using the NASA battery aging dataset, a widely accepted benchmark dataset for battery research. The IGWO-AdaBoost digital twin model emerges as the standout performer, achieving exceptional accuracy in predicting the discharge capacity. This model demonstrates the lowest mean absolute error (MAE) of 0.01, showcasing its superior precision in estimating discharge capabilities. Additionally, the root mean square error (RMSE) for the IGWO-AdaBoost DT model is also the lowest at 0.01. The findings of this study offer insightful information about the potential utilization of the digital twin model to accurately predict the discharge capacity of batteries.

With high packing density and low leakage power, Spin Transfer Torque Random Access Memories (STT-RAM) are a promising alternative to replace traditional memory technologies such as SRAM and DRAM. Applications will continue to demand more memory for processing in the coming decades. To achieve higher cell density, Multi-Level Cell STT-RAM (MLC STT-RAM) that can store two or more bits in a single memory cell is preferred over Single Level Cell STT-RAM (SLC STT-RAM). But their multistep read and write operations lead to significant read and write latency. The multistep write operations are also affecting the durability of MLC STT-RAM. Specialised wear levelling techniques are not available for MLC STT-RAM. We propose ENDURA, a technique that could improve the lifetime and latency of MLC STT-RAMs. ENDURA extends the lifetime of 2MB and 4MB MLC STT-RAM L2 caches by 2.05x and 2.59x on a single-core system and reduces write latency by 9.6% and 8.06% respectively with minimal overhead.

Energy efficiency is one of the important parameters in cloud computing which is managed by the data centres. Data centres are computer warehouses that are responsible for storing large volumes of data to deal with the daily transaction handling needs of different productions. Effective scheduling for the execution of the request on machines is still a problem. In addition, the power consumption, as well as management of the node clusters is also a problematic situation when the CPU utilisation increases up to the limit. In this paper, efficient minimum execution and completion time scheduling are accomplished by using a machine learning approach for effectual CPU usage and service level agreement fulfilment in data centres, considered in terms of average accuracy which will reduce costs for the maintenance of the data centres in real-time scenarios. The simulation of the proposed work is achieved and the performance is evaluated in terms of power consumption and CPU usage. The proposed research utilises the neural network and linear regression analysis to perform the classification and compares the performance for the efficient CPU usage.