High performance CaCO₃-based composites using sodium tripolyphosphate as phase controlling additive:Bamboo fiber driven high strength development

Due to the limited carbonation degree caused by the surface densification of carbonated products, the development of high-strength carbonated composites remains challenging. In this work, bamboo fiber (BFs) is utilized as a reaction reinforcing agent along with sodium tripolyphosphate (STPP) as a CaCO3 phase controlling additive to prepare high-strength bamboo fiber reinforced carbonated wollastonite composites (BFRCWs). The phase composition and microstructure are systematically investigated by multiscale physicochemical analysis, followed by the determination of macro properties and volume deformation. Results indicate that BF and STPP have a synergistic effect on the microstructural formation and macro performance of BFRCWs. STPP-treated BF (ST-BF) can serve as an internal curing agent and the porous structure of BF provides more channels for ion and CO2 transport, whereas CaCO3 phase composition and cementitious behavior is modified by STPP. The addition of ST-BF, particularly for long fibers, accelerates the carbonation reaction, resulting in an increased ratio of poorly crystalline CaCO3 and a refined pore structure. With increasing ST-BF dosage (0–3 vol%), the cementitious reaction is enhanced, but excessive fibers (3 vol%) incorporation introduces additional porosity, consequently reducing compressive strength. The desired pore structure with the optimal 2 vol% ST-BF (3–6 mm) shows the highest strength of 103.5 MPa at 28 days.</p

On-Demand Resource Management for 6G Wireless Networks Using Knowledge-Assisted Dynamic Neural Networks

On-demand service provisioning is a critical yet challenging issue in 6G wireless communication networks, since emerging services have significantly diverse requirements and the network resources become increasingly heterogeneous and dynamic. In this paper, we study the on-demand wireless resource orchestration problem with the focus on the computing delay in orchestration decision-making process. Specifically, we take the decision-making delay into the optimization problem. Then, a dynamic neural network (DyNN)-based method is proposed, where the model complexity can be adjusted according to the service requirements. We further build a knowledge base representing the relationship among the service requirements, available computing resources, and the resource allocation performance. By exploiting the knowledge, the width of DyNN can be selected in a timely manner, further improving the performance of orchestration. Simulation results show that the proposed scheme significantly outperforms the traditional static neural network, and also shows sufficient flexibility in on-demand service provisioning

Research On Trajectory Planning Control of Industrial Manipulator Based on ALO Algorithm

  Aiming at the shortcomings of the ant lion optimization algorithm (ALO) in industrial manipulator trajectory planning, such as long path length, time-consuming rotation time, and uneven path, an improved ALO (IALO) is proposed. Firstly, the population is initialized by cubic chaotic mapping to improve the quality of ant lion population. Secondly, the trust region mutation is used to improve the location update mode of ant lion population and balance the global search ability and local mining ability. Finally, the Gaussian mutation disturbance strategy is used to improve the location update mode of ant lion population and enhance the ability of the algorithm to jump out of local optimization. Taking trajectory length, rotation time, and redundancy rate as indicators, compared with the ABC algorithm and classic ALO, this algorithm has a shorter path length and less rotation time

Imperfect Digital Twin Assisted Low Cost Reinforcement Training for Multi-UAV Networks

Deep Reinforcement Learning (DRL) is widely used to optimize the performance of multi-UAV networks. However, the training of DRL relies on the frequent interactions between the UAVs and the environment, which consumes lots of energy due to the flying and communication of UAVs in practical experiments. Inspired by the growing digital twin (DT) technology, which can simulate the performance of algorithms in the digital space constructed by coping features of the physical space, the DT is introduced to reduce the costs of practical training, e.g., energy and hardware purchases. Different from previous DT-assisted works with an assumption of perfect reflecting real physics by virtual digital, we consider an imperfect DT model with deviations for assisting the training of multi-UAV networks. Remarkably, to trade off the training cost, DT construction cost, and the impact of deviations of DT on training, the natural and virtually generated UAV mixing deployment method is proposed. Two cascade neural networks (NN) are used to optimize the joint number of virtually generated UAVs, the DT construction cost, and the performance of multi-UAV networks. These two NNs are trained by unsupervised and reinforcement learning, both low-cost label-free training methods. Simulation results show the training cost can significantly decrease while guaranteeing the training performance. This implies that an efficient decision can be made with imperfect DTs in multi-UAV networks

Distilling Knowledge from Resource Management Algorithms to Neural Networks: A Unified Training Assistance Approach

As a fundamental problem, numerous methods are dedicated to the optimization of signal-to-interference-plus-noise ratio (SINR), in a multi-user setting. Although traditional model-based optimization methods achieve strong performance, the high complexity raises the research of neural network (NN) based approaches to trade-off the performance and complexity. To fully leverage the high performance of traditional model-based methods and the low complexity of the NN-based method, a knowledge distillation (KD) based algorithm distillation (AD) method is proposed in this paper to improve the performance and convergence speed of the NN-based method, where traditional SINR optimization methods are employed as ``teachers" to assist the training of NNs, which are ``students", thus enhancing the performance of unsupervised and reinforcement learning techniques. This approach aims to alleviate common issues encountered in each of these training paradigms, including the infeasibility of obtaining optimal solutions as labels and overfitting in supervised learning, ensuring higher convergence performance in unsupervised learning, and improving training efficiency in reinforcement learning. Simulation results demonstrate the enhanced performance of the proposed AD-based methods compared to traditional learning methods. Remarkably, this research paves the way for the integration of traditional optimization insights and emerging NN techniques in wireless communication system optimization

Digital Twin-Assisted Efficient Reinforcement Learning for Edge Task Scheduling

Task scheduling is a critical problem when one user offloads multiple different tasks to the edge server. When a user has multiple tasks to offload and only one task can be transmitted to server at a time, while server processes tasks according to the transmission order, the problem is NP-hard. However, it is difficult for traditional optimization methods to quickly obtain the optimal solution, while approaches based on reinforcement learning face with the challenge of excessively large action space and slow convergence. In this paper, we propose a Digital Twin (DT)-assisted RL-based task scheduling method in order to improve the performance and convergence of the RL. We use DT to simulate the results of different decisions made by the agent, so that one agent can try multiple actions at a time, or, similarly, multiple agents can interact with environment in parallel in DT. In this way, the exploration efficiency of RL can be significantly improved via DT, and thus RL can converges faster and local optimality is less likely to happen. Particularly, two algorithms are designed to made task scheduling decisions, i.e., DT-assisted asynchronous Q-learning (DTAQL) and DT-assisted exploring Q-learning (DTEQL). Simulation results show that both algorithms significantly improve the convergence speed of Q-learning by increasing the exploration efficiency

Pore and fracture scale characterization of oil shale at different microwave temperatures

The spatial complexity of oil shale systems is manifested by microstructure, pore space randomness and extensive heterogeneity. A microwave pyrolysis device developed for this study was used to pyrolyze oil shale, and the microstructure before and after pyrolysis was visually examined and quantified. The internal structure of the rock and the extent of pore and fracture expansion are more accurately determined in this way. The microstructure of oil shale at different temperatures before and after microwave pyrolysis is identified by X-ray microcomputed tomography (μCT) with automatic ultra-high-resolution scanning electron microscopy (SEM), to observe the heterogeneous state of oil shale on 2D and 3D scales and define the distribution of internal pores and fractures by post-processing μCT visualization. The study found that fractures sized from microns to millimeters along with pore fractures were observed at increasing microwave temperatures. The fractures gradually expanded with increasing temperature in the direction of horizontal or vertical laminae and generated a more connected pore network. The kerogen gradually decreased with a rise in temperature. The porosity increased from 0.26% to 13.69% at the initial temperature. This research is essential for the qualitative as well as quantitative analysis of the internal structure of oil shales under microwave radiation

Experimental and numerical analysis of wind field effects on the dynamic responses of the 10 MW SPIC floating wind turbine concept

acceptedVersio

Constructing and Evaluating Digital Twins: An Intelligent Framework for DT Development

The development of Digital Twins (DTs) represents a transformative advance for simulating and optimizing complex systems in a controlled digital space. Despite their potential, the challenge of constructing DTs that accurately replicate and predict the dynamics of real-world systems remains substantial. This paper introduces an intelligent framework for the construction and evaluation of DTs, specifically designed to enhance the accuracy and utility of DTs in testing algorithmic performance. We propose a novel construction methodology that integrates deep learning-based policy gradient techniques to dynamically tune the DT parameters, ensuring high fidelity in the digital replication of physical systems. Moreover, the Mean STate Error (MSTE) is proposed as a robust metric for evaluating the performance of algorithms within these digital space. The efficacy of our framework is demonstrated through extensive simulations that show our DT not only accurately mirrors the physical reality but also provides a reliable platform for algorithm evaluation. This work lays a foundation for future research into DT technologies, highlighting pathways for both theoretical enhancements and practical implementations in various industries