Entertainment apps, limited attention and investment performance

With the advent of the “information age,” investors are now faced with the challenges of the “mobile age,” which has had a profound impact on the daily lives of people worldwide. Investors must process more information while experiencing increasing mobile phone-related distractions, particularly those generated by the fast-growing entertainment-type app industry. Attention is a limited cognitive resource that is vital for deliberate and thoughtful analysis. We analyzed data from an online peer-to-peer lending market to evaluate the impact of mobile distractions on investment performance. Our findings revealed that investors with a large number of mobile phone entertainment apps were more likely to exhibit higher default rates and lower investment returns. The results are robust, even when using exogenous internet service outage of the entertainment server and instrumental variables. We observed that the negative impact of distraction was more pronounced on Fridays and in regions with high-speed Internet access. A further examination of the mechanisms underlying this phenomenon revealed that investment decisions made while being distracted by mobile apps were influenced by information neglect and familiarity biases

A Unified Framework for Integrating Semantic Communication and AI-Generated Content in Metaverse

As the Metaverse continues to grow, the need for efficient communication and intelligent content generation becomes increasingly important. Semantic communication focuses on conveying meaning and understanding from user inputs, while AI-Generated Content utilizes artificial intelligence to create digital content and experiences. Integrated Semantic Communication and AI-Generated Content (ISGC) has attracted a lot of attentions recently, which transfers semantic information from user inputs, generates digital content, and renders graphics for Metaverse. In this paper, we introduce a unified framework that captures ISGC two primary benefits, including integration gain for optimized resource allocation and coordination gain for goal-oriented high-quality content generation to improve immersion from both communication and content perspectives. We also classify existing ISGC solutions, analyze the major components of ISGC, and present several use cases. We then construct a case study based on the diffusion model to identify an optimal resource allocation strategy for performing semantic extraction, content generation, and graphic rendering in the Metaverse. Finally, we discuss several open research issues, encouraging further exploring the potential of ISGC and its related applications in the Metaverse.Comment: 8 pages, 6 figure

A Unified Blockchain-Semantic Framework for Wireless Edge Intelligence Enabled Web 3.0

Web 3.0 enables user-generated contents and user-selected authorities. With decentralized wireless edge computing architectures, Web 3.0 allows users to read, write, and own contents. A core technology that enables Web 3.0 goals is blockchain, which provides security services by recording content in a decentralized and transparent manner. However, the explosion of on-chain recorded contents and the fast-growing number of users cause increasingly unaffordable computing and storage resource consumption. A promising paradigm is to analyze the semantic information of contents that can convey precisely the desired meanings without consuming many resources. In this article, we propose a unified blockchain-semantic ecosystems framework for wireless edge intelligence-enabled Web 3.0. Our framework consists of six key components to exchange semantic demands. We then introduce an Oracle-based proof of semantic mechanism to implement on-chain and off-chain interactions of Web 3.0 ecosystems on semantic verification algorithms while maintaining service security. An adaptive Deep Reinforcement Learning-based sharding mechanism on Oracle is designed to improve interaction efficiency, which can facilitate Web 3.0 ecosystems to deal with varied semantic demands. Finally, a case study is presented to show that the proposed framework can dynamically adjust Oracle settings according to varied semantic demands.Comment: 8 pages, 5 figures, 1 tabl

The food retail revolution in China and its association with diet and health

The processed food sector in low- and middle-income countries has grown rapidly. Little is understood about its effect on obesity. Using data from 14,976 participants aged two and older in the 2011 China Health and Nutrition Survey, this paper examines patterns of processed food consumption and their impacts on obesity while considering the endogeneity of those who purchase processed foods. A major assumption of our analysis of the impact of processed foods on overweight and obesity was that the consumption of processed foods is endogenous due to their accessibility and urbanicity levels. The results show that 74.5% of participants consumed processed foods, excluding edible oils and other condiments; 28.5% of participants' total daily energy intake (EI) was from processed foods. Children and teenagers in megacities had the highest proportion of EI (40.2%) from processed foods. People who lived in megacities or highly urbanized neighborhoods with higher incomes and educational achievement consumed more processed foods. When controlling for endogeneity, only the body mass index (BMI) and risk of being overweight of children ages two to eighteen are adversely associated with processed foods (+4.97 BMI units, 95% confidence interval (CI): 1.66–8.28; odds ratio (OR) = 3.63, 95% CI: 1.45–9.13). Processed food purchases represent less than a third of current Chinese food purchases. However, processed food purchases are growing at the rate of 50% per year, and we must begin to understand the implications for the future

The complete chloroplast genome of Agropyron pectinatum (M. Bieb.) P. Beauv.

Agropyron pectinatum is a perennial forage widely cultivated in China, and it belongs to the Gramineous family. In this study, we assembled the complete chloroplast genome of A. pectinatum. The whole chloroplast genome of A. pectinatum is 135,041 bp in length, comprising a pair of inverted repeat (IR) regions (20,821 bp) that are separated by a large single copy (LSC) region (80,632 bp) and a small single copy (SSC) region (12,767 bp). The chloroplast genome of A. pectinatum contains 133 genes, and 87 of them are protein-coding genes, 38 are tRNA, and eight are rRNA genes. The chloroplast genome of A. pectinatum could provide valuable information for varieties identification and evolution of the Agropyron Gaertn

The Effects of Guided Imagery on Heart Rate Variability in Simulated Spaceflight Emergency Tasks Performers

Objectives. The present study aimed to investigate the effects of guided imagery training on heart rate variability in individuals while performing spaceflight emergency tasks. Materials and Methods. Twenty-one student subjects were recruited for the experiment and randomly divided into two groups: imagery group (n=11) and control group (n=10). The imagery group received instructor-guided imagery (session 1) and self-guided imagery training (session 2) consecutively, while the control group only received conventional training. Electrocardiograms of the subjects were recorded during their performance of nine spaceflight emergency tasks after imagery training. Results. In both of the sessions, the root mean square of successive differences (RMSSD), the standard deviation of all normal NN (SDNN), the proportion of NN50 divided by the total number of NNs (PNN50), the very low frequency (VLF), the low frequency (LF), the high frequency (HF), and the total power (TP) in the imagery group were significantly higher than those in the control group. Moreover, LF/HF of the subjects after instructor-guided imagery training was lower than that after self-guided imagery training. Conclusions. Guided imagery was an effective regulator for HRV indices and could be a potential stress countermeasure in performing spaceflight tasks

Intersection Fog-Based Distributed Routing for V2V Communication in Urban Vehicular Ad Hoc Networks

Due to the characteristics of urban vehicular ad hoc networks (VANETs), many difficulties exist when designing routing protocols. In this paper, we focus on designing an efficient routing strategy for vehicle-to-vehicle (V2V) communication in urban VANETs. Because, the characteristics of urban VANET routing performance are affected mainly by intersections, traffic lights, and traffic conditions, we propose an intersection-based distributed routing (IDR) strategy. In view of the fact that traffic lights are used to cause vehicles to stop at intersections, we propose an intersection vehicle fog (IVF) model, in which waiting vehicles dynamically form a collection or fog of vehicles at an intersection. Acting as infrastructure components, the IVFs proactively establish multihop links with adjacent intersections and analyze the traffic conditions on adjacent road segments using fuzzy logic. This approach offloads a large part of the routing work. During routing, the IVFs adjust the routing direction based on the real-time position of the destination, thus avoiding rerouting. Each time an IVF makes a distributed routing decision, the IDR model employs the ant colony optimization (ACO) algorithm to identify an optimal routing path whose connectivity is based on the traffic conditions existing in the multihop links between intersections. Because of the high connectivity of the routing path, the model requires only packet forwarding and not carrying when transmitting along the routing path, which reduces the transmission delay and increases the transmission ratio. The presented mathematical analyses and simulation results demonstrate that our proposed routing strategy is feasible and that it achieves relatively high performance.This work was supported in part by the National Natural Science Foundation of China under Grant 61571098, and in part by the 111 Project under Grant B14039

An Image Hashing Algorithm for Authentication with Multi-Attack Reference Generation and Adaptive Thresholding

Image hashing-based authentication methods have been widely studied with continuous advancements owing to the speed and memory efficiency. However, reference hash generation and threshold setting, which are used for similarity measures between original images and corresponding distorted version, are important but less considered by most of existing models. In this paper, we propose an image hashing method based on multi-attack reference generation and adaptive thresholding for image authentication. We propose to build the prior information set based on the help of multiple virtual prior attacks, and present a multi-attack reference generation method based on hashing clusters. The perceptual hashing algorithm was applied to the reference/queried image to obtain the hashing codes for authentication. Furthermore, we introduce the concept of adaptive thresholding to account for variations in hashing distance. Extensive experiments on benchmark datasets have validated the effectiveness of our proposed method