Gamers' Reasons for Purchasing Gaming Virtual Products: A Systematic Review

With the development of the gaming industry, the trend of gaming virtual goods consumption is gradually growing, and online gaming goods consumption has become a hot topic. Players spend a lot of money purchasing virtual items. Why do people buy these items? Therefore, we conduct a systematic literature review to investigate the reasons for purchasing virtual goods in games. We use the “The Preferred Reporting Items for Systematic reviews and Meta-Analyses” (PRISMA) statement to guide the data collection, analysis and reporting project for the systematic review. This article obtained 37 papers from the Web of Science (WOS) and Scopus databases. The article covers research during the period 2013-2023. We review the independent and dependent variables, research methods, theoretical background, and findings involved in the relevant literature. The study finds that the "enjoyment", "social attributes" and "functional value" of gaming products often play a key role in consumer purchases at certain times and among certain user groups. It is the hope of this paper that these findings will increase gaming companies' understanding of what drives players to purchase virtual items. Game designers can grasp the needs of game users and better convert ideas into payment points so that game companies can get higher profits

Research on Information Extraction of the Dongting Lake Ecological Wetland Based on Genetic Algorithm Optimized Convolutional Neural Network

Dongting Lake is an important lake wetland in China. How to quickly and accurately obtain the basic information of the Dongting Lake ecological wetland is of great + significance for the dynamic monitoring, protection, and sustainable utilization of the wetland. Therefore, this article proposes the information extraction of the Dongting Lake ecological wetland based on genetic algorithm optimized convolutional neural network (GA-CNN), an analysis model combining genetic algorithm (GA) and convolutional neural network (CNN). Firstly, we know the environmental information of Dongting Lake, take Gaofen-1 image as the data source, and use normalized vegetation index and normalized water body index as auxiliary data to preprocess the change detection of remote sensing images to obtain high-precision fitting images. GA-CNN is constructed to efficiently extract the information of the Dongting Lake ecological wetland, and the Relu excitation function is used to improve the phenomenon of gradient disappearance and convergence fluctuation so as to reduce the operation time. Logistic regression is used for feature extraction, and finally the automatic identification and information extraction of the Dongting Lake ecological wetland are realized. The research results show that the method proposed in this article can more deeply dig the information of ground objects, express depth features, and has high accuracy and credibility

EKF/UKF-based channel estimation for robust and reliable communications in V2V and IIoT

Cyber-physical systems (CPSs) are characterized by integrating computation, communication, and physical system. In typical CPS application scenarios, vehicle-to-vehicle (V2V) and Industry Internet of Things (IIoT), due to doubly selective fading and non-stationary channel characteristics, the robust and reliable end-to-end communication is extremely important. Channel estimation is a major signal processing technology to ensure robust and reliable communication. However, the existing channel estimation methods for V2V and IIoT cannot effectively reduce intercarrier interference (ICI) and lower the computation complexity, thus leading to poor robustness. Aiming at this challenge, according to the channel characteristics of V2V and IIoT, we design two channel estimation methods based on the Bayesian filter to promote the robustness and reliability of end-to-end communication. For the channels with doubly selective fading and non-stationary characteristics of V2V and IIoT scenarios, in the one hand, basis extended model (BEM) is used to further reduce the complexity of the channel estimation algorithm under the premise that ICI can be eliminated in the channel estimation. On the other hand, aiming at the non-stationary channel, a channel estimation and interpolation method based on extended Kalman filter (EKF) and unscented Kalman filter (UKF) Bayesian filters to jointly estimate the channel impulse response (CIR) and time-varying time domain autocorrelation coefficient is adopted. Through the MATLAB simulation, the robustness and reliability of end-to-end communication for V2V and IIoT are promoted by the proposed algorithms

Contention resolution in wi-fi 6-enabled internet of things based on deep learning

Internet of Things (IoT) is expected to vastly increase the number of connected devices. As a result, a multitude of IoT devices transmit various information through wireless communication technology, such as the Wi-Fi technology, cellular mobile communication technology, low-power wide-area network (LPWAN) technology. However, even the latest Wi-Fi technology is still ready to accommodate these large amounts of data. Accurately setting the contention window (CW) value significantly affects the efficiency of the Wi-Fi network. Unfortunately, the standard collision resolution used by IEEE 802.11ax networks is nonscalable; thus, it cannot maintain stable throughput for an increasing number of stations, even when Wi-Fi 6 has been designed to improve performance in dense scenarios. To this end, we propose a CW control strategy for Wi-Fi 6 systems. This strategy leverages deep learning to search for optimal configuration of CW under different network conditions. Our deep neural network is trained by data generated from a Wi-Fi 6 simulation system with some varying key parameters, e.g., the number of nodes, short interframe space (SIFS), distributed interframe space (DIFS), and data transmission rate. Numerical results demonstrated that our deep learning scheme could always find the optimal CW adjustment multiple by adaptively perceiving the channel competition status. The finalized performance of our model has been significantly improved in terms of system throughput, average transmission delay, and packet retransmission rate. This makes Wi-Fi 6 better adapted to the access of a large number of IoT devices. © 2014 IEEE

Multi-dimensional data indexing and range query processing via Voronoi diagram for internet of things

In a typical Internet of Things (IoT) deployment such as smart cities and Industry 4.0, the amount of sensory data collected from physical world is significant and wide-ranging. Processing large amount of real-time data from the diverse IoT devices is challenging. For example, in IoT environment, wireless sensor networks (WSN) are typically used for the monitoring and collecting of data in some geographic area. Spatial range queries with location constraints to facilitate data indexing are traditionally employed in such applications, which allows the querying and managing the data based on SQL structure. One particular challenge is to minimize communication cost and storage requirements in multi-dimensional data indexing approaches. In this paper, we present an energy- and time-efficient multidimensional data indexing scheme, which is designed to answer range query. Specifically, we propose data indexing methods which utilize hierarchical indexing structures, using binary space partitioning (BSP), such as kd-tree, quad-tree, k-means clustering, and Voronoi-based methods to provide more efficient routing with less latency. Simulation results demonstrate that the Voronoi Diagram-based algorithm minimizes the average energy consumption and query response time