Traffic pattern prediction in cellular networks.

PhDIncreasing numbers of users together with a more use of high bit-rate services complicate radio resource management in 3G systems. In order to improve the system capacity and guarantee the QoS, a large amount of research had been carried out on radio resource management. One viable approach reported is to use semi-smart antennas to dynamically change the radiation pattern of target cells to reduce congestion. One key factor of the semi-smart antenna techniques is the algorithm to adjust the beam pattern to cooperatively control the size and shape of each radio cell. Methods described in the literature determine the optimum radiation patterns according to the current observed congestion. By using machine learning methods, it is possible to detect the upcoming change of the traffic patterns at an early stage and then carry out beamforming optimization to alleviate the reduction in network performance. Inspired from the research carried out in the vehicle mobility prediction field, this work learns the movement patterns of mobile users with three different learning models by analysing the movement patterns captured locally. Three different mobility models are introduced to mimic the real-life movement of mobile users and provide analysable data for learning. The simulation results shows that the error rates of predictions on the geographic distribution of mobile users are low and it is feasible to use the proposed learning models to predict future traffic patterns. Being able to predict these patterns mean that the optimized beam patterns could be calculated according to the predicted traffic patterns and loaded to the relevant base stations in advance

Coordination Strategy of Dual-Channel Supply Chain for Fresh Product Under the Fresh-Keeping Efforts

In the context of high loss in the storage and transportation of fresh agricultural products, in order to help company make reasonable fresh-keeping decisions and reduce losses, we established a leading supplier of fresh agricultural products in two level dual channel supply chain model based on consumer utility function, and using Stackelberg game method to solve the optimal pricing and optimal fresh-keeping decision of fresh agricultural supplier and retailer under centralized decision-making and decentralized decision-making model. Research shows: (1) Under centralized decision-making model, the highest profit does not affect the cooperation and achieve complete coordination regardless of the bargaining power of the retailer; (2) High cost factor of fresh-keeping efforts makes supplier and retailer more inclined to lower prices to attract consumers. (3)The “revenue sharing + fresh-keeping cost sharing” coordination strategy provided by the supplier can increase the respective profits of both parties and achieve complete coordination of the dual-channel supply chain of fresh agricultural products

Blockchain-driven dual-channel green supply chain game model considering government subsidies

In order to improve the performance of green supply chain and promote the adoption of blockchain, this paper establishes a dual-channel green supply chain consisting of a green manufacturer and a retailer, and builds Stackelberg game model considering different scenarios. We analyze the impact of blockchain operating costs and consumer uncertainty about the product greenness. Furthermore, we study the government subsidy for manufacturers' green costs and its impact on supply chain performance and blockchain adoption. Findings reveal that without blockchain technology, government subsidy can improve manufacturers' and retailers' profits. However, when blockchain is adopted, the subsidy effect depends on the blockchain operating costs. In case of higher blockchain operating cost, the product prices and greenness decrease as the green cost subsidies increase; In case of lower blockchain operating cost, the increase in green cost subsidies will lead to increased product prices and greenness; Green cost subsidies can raise profits and lower the blockchain adoption threshold

Prompt-Enhanced Software Vulnerability Detection Using ChatGPT

With the increase in software vulnerabilities that cause significant economic and social losses, automatic vulnerability detection has become essential in software development and maintenance. Recently, large language models (LLMs) like GPT have received considerable attention due to their stunning intelligence, and some studies consider using ChatGPT for vulnerability detection. However, they do not fully consider the characteristics of LLMs, since their designed questions to ChatGPT are simple without a specific prompt design tailored for vulnerability detection. This paper launches a study on the performance of software vulnerability detection using ChatGPT with different prompt designs. Firstly, we complement previous work by applying various improvements to the basic prompt. Moreover, we incorporate structural and sequential auxiliary information to improve the prompt design. Besides, we leverage ChatGPT's ability of memorizing multi-round dialogue to design suitable prompts for vulnerability detection. We conduct extensive experiments on two vulnerability datasets to demonstrate the effectiveness of prompt-enhanced vulnerability detection using ChatGPT. We also analyze the merit and demerit of using ChatGPT for vulnerability detection.Comment: 13 Pages, 4 figure

Type-II Ising Pairing in Few-Layer Stanene

Spin-orbit coupling has proven indispensable in realizing topological materials and more recently Ising pairing in two-dimensional superconductors. This pairing mechanism relies on inversion symmetry breaking and sustains anomalously large in-plane polarizing magnetic fields whose upper limit is expected to diverge at low temperatures, although experimental demonstration of this has remained elusive due to the required fields. In this work, the recently discovered superconductor few-layer stanene, i.e. epitaxially strained $\alpha$-Sn, is shown to exhibit a new type of Ising pairing between carriers residing in bands with different orbital indices near the $\Gamma$-point. The bands are split as a result of spin-orbit locking without the participation of inversion symmetry breaking. The in-plane upper critical field is strongly enhanced at ultra-low temperature and reveals the sought for upturn