Government affiliation, real earnings management, and firm performance : the case of privately held firms

Using a moderated mediation model, we investigate the effects of government affiliation on the performance and real earnings management of privately held firms in China between 1998 and 2012. We find that politically affiliated firms tend to have superior accounting performance. The findings also suggest that politically affiliated firms are more likely than non-affiliated firms to engage in real activities to manipulate earnings. Furthermore, regional economic development moderates the relationships between political affiliation and real earnings management as well as firm performance. Finally, real earnings management mediates the effect of political affiliation on firm performance among privately held firms

Vaccine Adjuvant Delivery Systems Constructed Using Biocompatible Nanoparticles Formed through Self-Assembly of Small Molecules

Subunit vaccines are playing a critical role in controlling numerous diseases and attracting more and more research interests due to their numerous advantages over conventional whole microbe-based vaccines. However, subunit vaccines are weak immunogens and thus have limited capacity in eliciting the humoral and cellular immunity against pathogens. Recently, nanoparticles (NPs) formed with certain small molecules through self-assembly have been employed as an effective carrier for subunit vaccines to play roles of adjuvant, delivery and stabilization of antigens, thus engendering a vaccine adjuvant-delivery system (VADS), which shows promises to overcome the hurdles in developing subunit vaccines. In particular, the small molecule-self-assembled NPs as a VADS can not only deliver vaccine ingredients to immune cells but also influence the immunoresponse toward a Th1 (type 1 T helper cell) and Th2 balanced pathway to establish both humoral and cellular immunity. This chapter describes the innovative VADSs based on the small molecule-self-assembled NPs, such as metal NPs (mNPs), emulsions, liposomes, and ISCOMs, which are elaborately designed for the development of subunit vaccines

Research progress on the correlation between platelet aggregation and tumor progression

Platelets are generally considered as the main functional unit of the coagulation system. However, more and more studies have confirmed that platelets also have an important relationship with tumor progression. Tumor cells can utilize platelets to promote their own infiltration and hematogenous metastasis, and platelets are activated and aggregated in this process. Therefore, platelet aggregation may be a concomitant marker of tumor progression. This is of great significance for predicting tumor metastasis before timely treatments

RIS-aided Real-time Beam Tracking for a Mobile User via Bayesian Optimization

The conventional beam management procedure mandates that the user equipment (UE) periodically measure the received signal reference power (RSRP) and transmit these measurements to the base station (BS). The challenge lies in balancing the number of beams used: it should be large enough to identify high-RSRP beams but small enough to minimize reporting overhead. This paper investigates this essential performance-versus-overhead trade-off using Bayesian optimization. The proposed approach represents the first application of real-time beam tracking via Bayesian optimization in RIS-assisted communication systems. Simulation results validate the effectiveness of this scheme

DeepC2: AI-powered Covert Botnet Command and Control on OSNs

Botnets are one of the major threats to computer security. In previous botnet command and control (C&C) scenarios using online social networks (OSNs), methods for addressing (e.g., IDs, links, or DGAs) are hardcoded into bots. Once a bot is reverse engineered, the botmaster and C&C infrastructure will be exposed. Additionally, abnormal content from explicit commands may expose botmasters and raise anomalies on OSNs. To overcome these deficiencies, we proposed DeepC2, an AI-powered covert C&C method on OSNs. By leveraging neural networks, bots can find botmasters by avatars, which are converted into feature vectors and embedded into bots. Adversaries cannot infer botmasters' accounts from the vectors. Commands are embedded into normal contents (e.g., tweets and comments) using text data augmentation and hash collision. Experiments on Twitter show that command-embedded contents can be generated efficiently, and bots can find botmasters and obtain commands accurately. Security analysis on different scenarios show that DeepC2 is robust and hard to be shut down. By demonstrating how AI may help promote covert communication on OSNs, this work provides a new perspective on botnet detection and confrontation.Comment: 13 pages, 15 figures, 7 tables. Discussion on possible countermeasures update

Design of Reconfigurable Intelligent Surfaces for Wireless Communication: A Review

Existing literature reviews predominantly focus on the theoretical aspects of reconfigurable intelligent surfaces (RISs), such as algorithms and models, while neglecting a thorough examination of the associated hardware components. To bridge this gap, this research paper presents a comprehensive overview of the hardware structure of RISs. The paper provides a classification of RIS cell designs and prototype systems, offering insights into the diverse configurations and functionalities. Moreover, the study explores potential future directions for RIS development. Notably, a novel RIS prototype design is introduced, which integrates seamlessly with a communication system for performance evaluation through signal gain and image formation experiments. The results demonstrate the significant potential of RISs in enhancing communication quality within signal blind zones and facilitating effective radio wave imaging

Ultrafast-and-Ultralight ConvNet-Based Intelligent Monitoring System for Diagnosing Early-Stage Mpox Anytime and Anywhere

Due to the lack of more efficient diagnostic tools for monkeypox, its spread remains unchecked, presenting a formidable challenge to global health. While the high efficacy of deep learning models for monkeypox diagnosis has been demonstrated in related studies, the overlook of inference speed, the parameter size and diagnosis performance for early-stage monkeypox renders the models inapplicable in real-world settings. To address these challenges, we proposed an ultrafast and ultralight network named Fast-MpoxNet. Fast-MpoxNet possesses only 0.27M parameters and can process input images at 68 frames per second (FPS) on the CPU. To counteract the diagnostic performance limitation brought about by the small model capacity, it integrates the attention-based feature fusion module and the multiple auxiliary losses enhancement strategy for better detecting subtle image changes and optimizing weights. Using transfer learning and five-fold cross-validation, Fast-MpoxNet achieves 94.26% Accuracy on the Mpox dataset. Notably, its recall for early-stage monkeypox achieves 93.65%. By adopting data augmentation, our model's Accuracy rises to 98.40% and attains a Practicality Score (A new metric for measuring model practicality in real-time diagnosis application) of 0.80. We also developed an application system named Mpox-AISM V2 for both personal computers and mobile phones. Mpox-AISM V2 features ultrafast responses, offline functionality, and easy deployment, enabling accurate and real-time diagnosis for both the public and individuals in various real-world settings, especially in populous settings during the outbreak. Our work could potentially mitigate future monkeypox outbreak and illuminate a fresh paradigm for developing real-time diagnostic tools in the healthcare field