The performance of battery materials is determined by their composition and the processing conditions employed during commercial-scale fabrication, where raw materials undergo complex processing steps with various additives to yield final products. As the complexity of these parameters expands with the development of industry, conventional one-factor-at-a-time (OFAT) experiment becomes old fashioned. While domain expertise aids in parameter optimization, this traditional approach becomes increasingly vulnerable to cognitive limitations and anthropogenic biases as the complexity of factors grows. Herein, we introduce an iterative machine learning (ML) framework that integrates active learning to guide targeted experimentation and facilitate incremental model refinement. This method systematically leverages comprehensive experimental observations, including both successful and unsuccessful results, effectively mitigating human-induced biases and alleviating data scarcity. Consequently, it significantly accelerates exploration within the high-dimensional design space. Our results demonstrate that active-learning-driven experimentation markedly reduces the total number of experimental cycles necessary, underscoring the transformative potential of ML-based strategies in expediting battery material optimization.

Personal use of this material is permitted. Abstract --Virtual Platforms (VPs) enable early software validation of autonomous systems' electronics, reducing costs and time-to-market. While many VPs support both functional and non-functional simulation (e.g., timing, power), they lack the capability of simulating the environment in which the system operates. In contrast, robotics simulators lack accurate timing and power features. This twofold shortcoming limits the effectiveness of the design flow, as the designer can not fully evaluate the features of the solution under development. This paper presents a novel, fully open-source framework bridging this gap by integrating a robotics simulator (Webots) with a VP for RISC-V-based systems (MESSY). The framework enables a holistic, mission-level, energy-aware co-simulation of electronics in their surrounding environment, streamlining the exploration of design configurations and advanced power management policies. Virtual Platforms (VPs) enable comprehensive system modeling and simulation before physical production [1] and are thus a crucial resource in the design of modern embedded systems, characterized by heterogeneity and tight integration with the physical environment.

Recently, Wireless Rechargeable Sensor Networks (WRSNs) that leveraged the advantage of wireless energy transfer technology have opened a promising opportunity in solving the limited energy issue. However, an ineffective charging strategy may reduce the charging performance. Although many practical charging algorithms have been introduced, these studies mainly focus on optimizing the charging path with a fully charging approach. This approach may lead to the death of a series of sensors due to their extended charging latency. This paper introduces a novel partial charging approach that follows a bi-level optimized scheme to minimize energy depletion in WRSNs. We aim at optimizing simultaneously two factors: the charging path and time. To accomplish this, we first formulate a mathematical model of the investigated problem. We then propose two approximate algorithms in which the optimization of the charging path and the charging time are considered as the upper and lower level, respectively. The first algorithm combines a Multi-start Local Search method and a Genetic Algorithm to find a solution. The second algorithm adopts a nested approach that utilizes the advantages of the Multitasking and Covariance Matrix Adaptation Evolutionary Strategies. Experimental validations on various network scenarios demonstrate that our proposed algorithms outperform the existing works. Introduction A Wireless Sensor Network (WSN) consists of a collection of battery-powered sensor nodes deployed in a region of interest to monitor the physical environment and transfer the sensing information to the Base Station (BS) via multi-hop communication. However, limited energy issues remain as a major bottleneck phenomenon in WSNs. When a sensor's battery is exhausted, the sensor becomes a dead node and loses its monitoring and communicating ability causing a series of negative impacts on the whole network performance [1, 7]. Therefore, one of the most critical conditions in continuously maintaining the network's operation is to avoid the energy depletion of the sensor nodes. Energy-saving methods have been applied to prolong the sensor lifetime during the past decade [2, 8].

The integration of simultaneous wireless information and power transfer (SWIPT) technology in 6G Internet of Things (IoT) networks faces significant challenges in remote areas and disaster scenarios where ground infrastructure is unavailable. This paper proposes a novel unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system enhanced by directional antennas to provide both computational resources and energy support for ground IoT terminals. However, such systems require multiple trade-off policies to balance UAV energy consumption, terminal battery levels, and computational resource allocation under various constraints, including limited UAV battery capacity, non-linear energy harvesting characteristics, and dynamic task arrivals. To address these challenges comprehensively, we formulate a bi-objective optimization problem that simultaneously considers system energy efficiency and terminal battery sustainability. We then reformulate this non-convex problem with a hybrid solution space as a Markov decision process (MDP) and propose an improved soft actor-critic (SAC) algorithm with an action simplification mechanism to enhance its convergence and generalization capabilities. Simulation results have demonstrated that our proposed approach outperforms various baselines in different scenarios, achieving efficient energy management while maintaining high computational performance. Furthermore, our method shows strong generalization ability across different scenarios, particularly in complex environments, validating the effectiveness of our designed boundary penalty and charging reward mechanisms.

Microsoft has begun testing a new power-saving technology within Windows, as well as assigning AI actions to a right-click menu within File Explorer. Microsoft is also tweaking the way in which widgets are laid out, letting Copilot handle the decisions itself. Microsoft published the changes as part of the Windows 11 Insider Preview Build 26120.4151 By testing these features, Microsoft doesn't necessarily have to commit to eventually rolling them out, although many appear to be under consideration for a more general release. Under the hood, Microsoft said that it's testing out what it calls User Interaction-Aware CPU Power Management, "an OS-level enhancement that helps reduce power consumption and extend your battery life."

In this paper, a reinforcement learning technique is employed to maximize the performance of a cognitive radio network (CRN). In the presence of primary users (PUs), it is presumed that two secondary users (SUs) access the licensed band within underlay mode. In addition, the SU transmitter is assumed to be an energy-constrained device that requires harvesting energy in order to transmit signals to their intended destination. Therefore, we propose that there are two main sources of energy; the interference of PUs' transmissions and ambient radio frequency (RF) sources. The SU will select whether to gather energy from PUs or only from ambient sources based on a predetermined threshold. The process of energy harvesting from the PUs' messages is accomplished via the time switching approach. In addition, based on a deep Q-network (DQN) approach, the SU transmitter determines whether to collect energy or transmit messages during each time slot as well as selects the suitable transmission power in order to maximize its average data rate. Our approach outperforms a baseline strategy and converges, as shown by our findings.

We propose a novel reinforcement learning (RL) design to optimize the charging strategy for autonomous mobile robots in large-scale block stacking warehouses. RL design involves a wide array of choices that can mostly only be evaluated through lengthy experimentation. Our study focuses on how different reward and action space configurations, ranging from flexible setups to more guided, domain-informed design configurations, affect the agent performance. Using heuristic charging strategies as a baseline, we demonstrate the superiority of flexible, RL-based approaches in terms of service times. Furthermore, our findings highlight a trade-off: While more open-ended designs are able to discover well-performing strategies on their own, they may require longer convergence times and are less stable, whereas guided configurations lead to a more stable learning process but display a more limited generalization potential. Our contributions are threefold. First, we extend SLAPStack, an open-source, RL-compatible simulation-framework to accommodate charging strategies. Second, we introduce a novel RL design for tackling the charging strategy problem. Finally, we introduce several novel adaptive baseline heuristics and reproducibly evaluate the design using a Proximal Policy Optimization agent and varying different design configurations, with a focus on reward.

W e discuss the challenges and propose a framework for evalua ting engineering artificial general intelligence ( eAGI) agents. W e consider eAGI as a specialization of artificial general intelligence (AGI), deemed capab le of addressing a broad range of problems in the engineering of physical systems and associated controllers. W e exclude software engineering for a tractable s coping of eAGI and expect dedicated software engineering AI agents to address the software implementation challenges. Similar to human engineers, eAGI agents should possess a unique blend of background knowledge (recall and retrieve) of facts and methods, demonstrate familiarity with tools and processes, exhibit deep understanding of industrial components and well-known design families, and be able to engage in creative problem solving (analyze and synthesize), transf erring ideas acquired in one context to another . Given this broad mandate, evaluatin g and qualifying the performance of eAGI agents is a challenge in itself and, arguably, a critical ena bler to developing eAGI agents. In this paper, we address this challenge by proposin g an extensible evaluation framework that specializes and gr ounds Bloom's taxonomy - a framework for evaluating human learning that has also been recently used for evaluating LLMs - in an engineering design context. Our p roposed framework advances the state of the art in benchmarking and evaluation of AI agents in terms of the following: (a) developing a rich taxonomy of evaluati on questions spanning from methodological knowledge to real-world design proble ms; (b) motivating a pluggable evaluation framework that can evaluate not only t extual responses but also evaluate structured design artifacts such as CAD model s and SysML models; and (c) outlining an automatable procedure to customize the evaluation benchmark to different engineering contexts.

Department of Electrical and Computer Engineering, National University of Singapore (Dated: June 13, 2025) We investigate work extraction protocols designed to transfer the maximum possible energy to a battery using sequential access to N copies of an unknown pure qubit state. The core challenge is designing interactions to optimally balance two competing goals: charging of the battery optimally using the qubit in hand, and acquiring more information by qubit to improve energy harvesting in subsequent rounds. Here, we leverage exploration-exploitation trade-off in reinforcement learning to develop adaptive strategies achieving energy dissipation that scales only poly-logarithmically in N . This represents an exponential improvement over current protocols based on full state tomography. Introduction --Given sequential access to finite, identical samples of an unknown quantum system, what is the optimal strategy for extracting work from them and charging a battery?

The state of health (SOH) of lithium-ion batteries (LIBs) is crucial for ensuring the safe and reliable operation of electric vehicles. Nevertheless, the prevailing SOH estimation methods often have limited generalizability. This paper introduces a data-driven approach for estimating the SOH of LIBs, which is designed to improve generalization. We construct a hybrid model named ACLA, which integrates the attention mechanism, convolutional neural network (CNN), and long short-term memory network (LSTM) into the augmented neural ordinary differential equation (ANODE) framework. This model employs normalized charging time corresponding to specific voltages in the constant current charging phase as input and outputs the SOH as well as remaining useful of life. The model is trained on NASA and Oxford datasets and validated on the TJU and HUST datasets. Compared to the benchmark models NODE and ANODE, ACLA exhibits higher accuracy with root mean square errors (RMSE) for SOH estimation as low as 1.01% and 2.24% on the TJU and HUST datasets, respectively.