Task-driven Semantic-aware Green Cooperative Transmission Strategy for Vehicular Networks

Considering the infrastructure deployment cost and energy consumption, it is unrealistic to provide seamless coverage of the vehicular network. The presence of uncovered areas tends to hinder the prevalence of the in-vehicle services with large data volume. To this end, we propose a predictive cooperative multi-relay transmission strategy (PreCMTS) for the intermittently connected vehicular networks, fulfilling the 6G vision of semantic and green communications. Specifically, we introduce a task-driven knowledge graph (KG)-assisted semantic communication system, and model the KG into a weighted directed graph from the viewpoint of transmission. Meanwhile, we identify three predictable parameters about the individual vehicles to perform the following anticipatory analysis. Firstly, to facilitate semantic extraction, we derive the closed-form expression of the achievable throughput within the delay requirement. Then, for the extracted semantic representation, we formulate the mutually coupled problems of semantic unit assignment and predictive relay selection as a combinatorial optimization problem, to jointly optimize the energy efficiency and semantic transmission reliability. To find a favorable solution within limited time, we proposed a low-complexity algorithm based on Markov approximation. The promising performance gains of the PreCMTS are demonstrated by the simulations with realistic vehicle traces generated by the SUMO traffic simulator.Comment: Accepted by IEEE Transactions on Communication

STAR-RIS-Assisted Privacy Protection in Semantic Communication System

Semantic communication (SemCom) has emerged as a promising architecture in the realm of intelligent communication paradigms. SemCom involves extracting and compressing the core information at the transmitter while enabling the receiver to interpret it based on established knowledge bases (KBs). This approach enhances communication efficiency greatly. However, the open nature of wireless transmission and the presence of homogeneous KBs among subscribers of identical data type pose a risk of privacy leakage in SemCom. To address this challenge, we propose to leverage the simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) to achieve privacy protection in a SemCom system. In this system, the STAR-RIS is utilized to enhance the signal transmission of the SemCom between a base station and a destination user, as well as to covert the signal to interference specifically for the eavesdropper (Eve). Simulation results demonstrate that our generated task-level disturbance outperforms other benchmarks in protecting SemCom privacy, as evidenced by the significantly lower task success rate achieved by Eve

Semantic Change Driven Generative Semantic Communication Framework

The burgeoning generative artificial intelligence technology offers novel insights into the development of semantic communication (SemCom) frameworks. These frameworks hold the potential to address the challenges associated with the black-box nature inherent in existing end-to-end training manner for the existing SemCom framework, as well as deterioration of the user experience caused by the inevitable error floor in deep learning-based semantic communication. In this paper, we focus on the widespread remote monitoring scenario, and propose a semantic change driven generative SemCom framework. Therein, the semantic encoder and semantic decoder can be optimized independently. Specifically, we develop a modular semantic encoder with value of information based semantic sampling function. In addition, we propose a conditional denoising diffusion probabilistic mode-assisted semantic decoder that relies on received semantic information from the source, namely, the semantic map, and the local static scene information to remotely regenerate scenes. Moreover, we demonstrate the effectiveness of the proposed semantic encoder and decoder as well as the considerable potential in reducing energy consumption through simulation. The code is available at https://github.com/wty2011jl/SCDGSC.gi

A Dynamic Approach to Money Supply

Abstract: In this paper, we present the mechanism of money supply from a dynamic perspective, in which the behaviors of the sectors involved in the process of money creation and the interplay among them are taken into account. Specially, we introduce households' withdrawals of deposit and firms' repayments to loan, which are ignored in the conventional statement of money creation process. By deriving and analyzing the equilibrium solution to the dynamic equations which characterize the process, we can discuss the corresponding influence of each sector on the money supply

Entropy measures, entropy estimators, and their performance in quantifying complex dynamics: Effects of artifacts, nonstationarity, and long-range correlations

Entropy measures are widely applied to quantify the complexity of dynamical systems in diverse fields. However, the practical application of entropy methods is challenging, due to the variety of entropy measures and estimators and the complexity of real-world time series, including nonstationarities and long-range correlations (LRC). We conduct a systematic study on the performance, bias, and limitations of three basic measures (entropy, conditional entropy, information storage) and three traditionally used estimators (linear, kernel, nearest neighbor). We investigate the dependence of entropy measures on estimator- and process-specific parameters, and we show the effects of three types of nonstationarities due to artifacts (trends, spikes, local variance change) in simulations of stochastic autoregressive processes. We also analyze the impact of LRC on the theoretical and estimated values of entropy measures. Finally, we apply entropy methods on heart rate variability data from subjects in different physiological states and clinical conditions. We find that entropy measures can only differentiate changes of specific types in cardiac dynamics and that appropriate preprocessing is vital for correct estimation and interpretation. Demonstrating the limitations of entropy methods and shedding light on how to mitigate bias and provide correct interpretations of results, this work can serve as a comprehensive reference for the application of entropy methods and the evaluation of existing studies

Enhancing the Quality of Low-Alcohol Navel Orange Wine through Simultaneous Co-Fermentation Using <i>Saccharomyces cerevisiae</i> SC-125, Angel Yeast SY, and <i>Lactiplantibacillus plantarum</i> BC114

To date, there has been limited research on the interactive effects of yeast and lactic acid bacteria (LAB) on the sensory qualities of navel orange wine. In this study, using Jintang navel orange juice as the raw material, multi-microbial fermentation was conducted with Saccharomyces cerevisiae SC-125 and Angel yeast SY, as well as Lactiplantibacillus plantarum BC114. Single yeast and co-fermentation with Lactiplantibacillus plantarum were used as the control groups. The research aimed to investigate the physicochemical parameters of navel orange wine during fermentation. Additionally, headspace solid-phase microextraction gas chromatography–mass spectrometry (HP-SPME-GC-MS) was employed to determine and analyze the types and levels of flavor compounds in the navel orange wines produced through the different fermentation methods. The co-fermentation using the three strains significantly enhanced both the quantity and variety of volatile compounds in the navel orange wine, concomitant with heightened total phenol and flavonoid levels. Furthermore, a notable improvement was observed in the free radical scavenging activity. A sensory evaluation was carried out to analyze the differences among the various navel orange wines, shedding light on the impact of different wine yeasts and co-fermentation with LAB on the quality of navel orange wines

Cross-reactivity between piperacillin–tazobactam and cefoperazone–sulbactam in drug-induced immune thrombocytopenia

Beta-lactam antibiotics commonly cause immune thrombocytopenia. Cross-reactivity in patients with drug-induced immune thrombocytopenia has rarely been reported. In this study, we describe the case of a 79-year-old man who developed thrombocytopenia after receiving piperacillin–tazobactam for an acute exacerbation of chronic obstructive pulmonary disease, and he was successfully treated with meropenem and cefotiam. However, thrombocytopenia recurred after cefoperazone–sulbactam administration. This indicated that cross-reactivity of platelet-specific antibodies occurred between piperacillin–tazobactam and cefoperazone–sulbactam. However, the responsible drug structures remain unknown, requiring further investigation. Likewise, chemical structure similarities among beta-lactam antibiotics must be examined to determine the risk of immune thrombocytopenia in the clinical setting

Toward autonomous vehicles: A survey on cooperative vehicle-infrastructure system

Summary: Cooperative vehicle-infrastructure system (CVIS) is an important part of the intelligent transport system (ITS). Autonomous vehicles have the potential to improve safety, efficiency, and energy saving through CVIS. Although a few CVIS studies have been conducted in the transportation field recently, a comprehensive analysis of CVIS is necessary, especially about how CVIS is applied in autonomous vehicles. In this paper, we overview the relevant architectures and components of CVIS. After that, state-of-the-art research and applications of CVIS in autonomous vehicles are reviewed from the perspective of improving vehicle safety, efficiency, and energy saving, including scenarios such as straight road segments, intersections, ramps, etc. In addition, the datasets and simulators used in CVIS-related studies are summarized. Finally, challenges and future directions are discussed to promote the development of CVIS and provide inspiration and reference for researchers in the field of ITS