Automatic Task Matching and Negotiated Vehicle Scheduling for Intelligent Logistics System

The decision-making of logistics vehicle scheduling is difficult under varying constraints, multiple disturbances and strong time-variation. The multi-agent system (MAS) is a new approach to investigate the real-time decision-making of logistics vehicle scheduling. It satisfies the various requirements of the logistics system, such as the geographical distribution of vehicles, the dynamic changes of information, and the constant changes in consumer orders. In view of the theoretical and practical significance of the MAS, this paper explores the decision-making of logistics vehicle scheduling based on the MAS, and relies on two-level planning modelling method to construct the mathematical model of outsourcing-based container port vehicle scheduling problem. Then, an effectively exchange neighbourhood tabu search algorithm was designed to solve the model. Through the research, it is concluded that the proposed hierarchical decomposition method of logistics distribution task can reduce the overall scheduling difficulty and reduce the actual planning error effectively; the established MAS-based intelligent logistics scheduling model can minimize the total distribution cost through continuous adjustment of resources according to the distribution task. Finally, the feasibility of the proposed algorithm was verified by the results of a calculation example

Institutional Ownership, Audit Committee and Risk Disclosure – Evidence from Australian Stock Market

This study investigates the influence of institutional ownership and audit committees corporate risk disclosures. Focusing on analysing firms’ risk disclosures make in their 2009 annual reports, our sample constitutes a sample of 66 Australian listed firms. We divide institutional shareholders into dedicated-type institutional block shareholders and transient-type institutional block shareholders. We find that while there is no significant relationship between dedicated-type institutional block shareholders and risk disclosure, there is a positive relationship between transient-type institutional block shareholders and risk disclosures. Our result is consistent with a principal that wields limited monitoring resources while achieving high resource dependency over management. We also find a significant and positive relationship between audit committee independence and risk disclosures, showing the positive role played by audit committee in improving the information transparency and reducing information asymmetry in capital market

Janus icosahedral particles: amorphization driven by three-dimensional atomic misfit and edge dislocation compensation

Icosahedral nanoparticles composed of fivefold twinned tetrahedra have broad applications. The strain relief mechanism and angular deficiency in icosahedral multiply twinned particles are poorly understood in three dimensions. Here, we resolved the three-dimensional atomic structures of Janus icosahedral nanoparticles using atomic resolution electron tomography. A geometrically fivefold face consistently corresponds to a less ordered face like two hemispheres. We quantify rich structural variety of icosahedra including bond orientation order, bond length, strain tensor; and packing efficiency, atom number, solid angle of each tetrahedron. These structural characteristics exhibit two-sided distribution. Edge dislocations near the axial atoms and small disordered domains fill the angular deficiency. Our findings provide new insights how the fivefold symmetry can be compensated and the geometrically-necessary internal strains relived in multiply twinned particles.Comment: 30 pages, 5 figure

Artificial Intelligence-aided OFDM Receiver: Design and Experimental Results

Orthogonal frequency division multiplexing (OFDM) is one of the key technologies that are widely applied in current communication systems. Recently, artificial intelligence (AI)-aided OFDM receivers have been brought to the forefront to break the bottleneck of the traditional OFDM systems. In this paper, we investigate two AI-aided OFDM receivers, data-driven fully connected-deep neural network (FC-DNN) receiver and model-driven ComNet receiver, respectively. We first study their performance under different channel models through simulation and then establish a real-time video transmission system using a 5G rapid prototyping (RaPro) system for over-the-air (OTA) test. To address the performance gap between the simulation and the OTA test caused by the discrepancy between the channel model for offline training and real environments, we develop a novel online training strategy, called SwitchNet receiver. The SwitchNet receiver is with a flexible and extendable architecture and can adapts to real channel by training one parameter online. The OTA test verifies its feasibility and robustness to real environments and indicates its potential for future communications systems. At the end of this paper, we discuss some challenges to inspire future research.Comment: 29 pages, 13 figures, submitted to IEEE Journal on Selected Areas in Communication

Classification of knee osteoarthritis based on quantum-to-classical transfer learning

Quantum machine learning takes advantage of features such as quantum computing superposition and entanglement to enable better performance of machine learning models. In this paper, we first propose an improved hybrid quantum convolutional neural network (HQCNN) model. The HQCNN model was used to pre-train brain tumor dataset (MRI) images. Next, the quantum classical transfer learning (QCTL) approach is used to fine-tune and extract features based on pre-trained weights. A hybrid quantum convolutional network structure was used to test the osteoarthritis of the knee dataset (OAI) and to quantitatively evaluate standard metrics to verify the robustness of the classifier. The final experimental results show that the QCTL method can effectively classify knee osteoarthritis with a classification accuracy of 98.36%. The quantum-to-classical transfer learning method improves classification accuracy by 1.08%. How to use different coding techniques in HQCNN models applied to medical image analysis is also a future research direction