Economic Policy Uncertainty,Heterogeneity of Executives and Enterprise Innovation

This study analyzes how economic policy uncertainty affects corporate innovation,and the moderating effects of executive heterogeneity.A threephase dynamic investment and financing model is first built to analyze the mechanism.Empirical analysis confirms that the increase in the degree of economic policy uncertainty promotes enterprise innovation.Further results show that this promotion effect is more significant in enterprises with male executives,low educational level,no financial experience and political background.Moreover,the positive impact is only found in enterprises with moderate executive ability,and the overconfidence of senior executives plays a positive regulating role in it.

Gender Equality and the Risk Assessment of Corporate Financialization:An Analysis of the Increase in the Proportion of Female Executives

This paper empirically investigates the impact of the proportion of female executives on the financialization of enterprises using a sample of listed companies in the Shanghai and Shenzhen stock markets from 2009-2018.Previous studies tend to conclude that female executives are risk averse,preventing firms from participating in high-risk financial investments.However,the results of this paper show that there is a positive relationship between the proportion of female executives and the degree of corporate financialization,with an increase of 1 percent in the proportion of female executives leading to an increase of 3.8 percent in the degree of corporate financialization.Further tests show that gender inequality is a possible mechanism influencing female executives'financial investment preferences in the unique gender culture context of China.This paper expands the research on the impact of non-institutional factors on corporate financialization,and also points out that unilaterally pursuing an increase in the proportion of female executives does not reduce the risk of corporate financialization,and that only an overall increase in the proportion of female executives can reduce the risk of corporate financialization.Improving gender equality at the social level can fundamentally reflect women's risk aversion characteristics and encourage enterprises to make more stable investment decisions.

Does top executives' US experience matter? Evidence from US-listed Chinese firms

Foreign firms face enormous obstacles in attracting investors and analysts when issuing securities in the United States.We use US-listed Chinese firms as our research sample and find that firms that hire top executives(i.e.,Chief Executive Officer[CEO]or Chief Financial Officer[CFO]) with work experience in the US or educational qualifications from the US attract more US institutional investors and analysts.Further,we find that CFOs' US experience dominates the results.Corroborating our results,we further find that firms with US-experienced CFOs are more likely to hold conference calls and voluntarily issue management forecasts,which suggests that CFOs with a US background are better at communicating with US investors and analysts and acting in alignment with US norms compared with Chinese CFOs.Collectively,our results suggest that hiring a CFO with a US background could facilitate cross-listed foreign firms to lower US investors' and analysts' information disadvantage.

Dopamine in the prefrontal cortex plays multiple roles in the executive function of patients with Parkinson's disease

Parkinson’s disease can affect not only motor functions but also cognitive abilities,leading to cognitive impairment.One common issue in Parkinson’s disease with cognitive dysfunction is the difficulty in executive functioning.Executive functions help us plan,organize,and control our actions based on our goals.The brain area responsible for executive functions is called the prefrontal co rtex.It acts as the command center for the brain,especially when it comes to regulating executive functions.The role of the prefrontal cortex in cognitive processes is influenced by a chemical messenger called dopamine.However,little is known about how dopamine affects the cognitive functions of patients with Parkinson’s disease.In this article,the authors review the latest research on this topic.They start by looking at how the dopaminergic syste m,is alte red in Parkinson’s disease with executive dysfunction.Then,they explore how these changes in dopamine impact the synaptic structure,electrical activity,and connection components of the prefrontal cortex.The authors also summarize the relationship between Parkinson’s disease and dopamine-related cognitive issues.This information may offer valuable insights and directions for further research and improvement in the clinical treatment of cognitive impairment in Parkinson’s disease.

Binary Program Vulnerability Mining Based on Neural Network

Software security analysts typically only have access to the executable program and cannot directly access the source code of the program.This poses significant challenges to security analysis.While it is crucial to identify vulnerabilities in such non-source code programs,there exists a limited set of generalized tools due to the low versatility of current vulnerability mining methods.However,these tools suffer from some shortcomings.In terms of targeted fuzzing,the path searching for target points is not streamlined enough,and the completely random testing leads to an excessively large search space.Additionally,when it comes to code similarity analysis,there are issues with incomplete code feature extraction,which may result in information loss.In this paper,we propose a cross-platform and cross-architecture approach to exploit vulnerabilities using neural network obfuscation techniques.By leveraging the Angr framework,a deobfuscation technique is introduced,along with the adoption of a VEX-IR-based intermediate language conversion method.This combination allows for the unified handling of binary programs across various architectures,compilers,and compilation options.Subsequently,binary programs are processed to extract multi-level spatial features using a combination of a skip-gram model with self-attention mechanism and a bidirectional Long Short-Term Memory(LSTM)network.Finally,the graph embedding network is utilized to evaluate the similarity of program functionalities.Based on these similarity scores,a target function is determined,and symbolic execution is applied to solve the target function.The solved content serves as the initial seed for targeted fuzzing.The binary program is processed by using the de-obfuscation technique and intermediate language transformation method,and then the similarity of program functions is evaluated by using a graph embedding network,and symbolic execution is performed based on these similarity scores.This approach facilitates cross-architecture analysis of executable programs without their source codes and concurrently reduces the risk of symbolic execution path explosion.

Bibliographical Review on Rehabilitation of Executive Functions in Patients With Developmental Coordination Disorder(DCD)

The present study aims to establish a literature review on intervention programs for executive functions(EFs)through the use of fundamental motor skills,from a neuropsychopedagogical perspective in subjects with Developmental Coordination Disorder(DCD).An exploratory study was carried out through an integrative literature review.The research was carried out in the Scientific databases Electronic Library Online(SciELO),Latin American and Caribbean Literature in Health Sciences(LILACS),Virtual Health Library-Psychology Brazil(BVSPSI),Electronic Journals of Psychology(PePSIC),in the periodicals available in the Brazilian Digital Library of Theses and Dissertations(BDTD)and on the website of the Coordination for the Improvement of Higher Education Personnel(CAPES).The covering publications took place from 2018 to 2023,14 articles were selected for analysis.This literature review made it possible to create strategies for stimulating EF and Visuomotor Functions so that educators and other professionals can better deal with students with DCD.It was perceived the need to carry out and develop more empirical research regarding the intervention of EFs and Visuomotor Functions by educators and professionals,with a greater sampling amplitude,to increase the number of studies that enable interventions both in children and in teenagers with DCD.

Perspectives and Experiences of Education Stakeholders: A Quantitative Study on the Adoption of Artificial Intelligence in Executive Training Using Structural Equation Modeling

The recent increase in the use of artificial intelligence has led to fundamental changes in the development of training and teaching methods for executive education. However, the success of artificial intelligence in regional centers for teaching and training professions will depend on the acceptance of this technology by young executive trainees. This article discusses the potential benefits of adopting AI in executive training institutions in Morocco, specifically focusing on CRMEF Casablanca Settat. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003), this study proposes a model to identify the factors influencing the acceptance of artificial intelligence in regional centers for teaching professions and training in Morocco. To achieve this, a structural equation modeling approach was used to quantitatively describe the impact of each factor on AI adoption, utilizing data collected from 173 young executive trainees. The results indicate that perceived ease of use, perceived usefulness, trainer influence, and personal innovativeness influence the intention to use artificial intelligence. Our research provides managers of CRMEFs with a set of practical recommendations to enhance the implementation conditions of an artificial intelligence system. It aims to understand which factors should be considered in designing an artificial intelligence system within regional centers for teaching professions and training (CRMEFs).

Research on Multi-Core Processor Analysis for WCET Estimation

Real-time system timing analysis is crucial for estimating the worst-case execution time(WCET)of a program.To achieve this,static or dynamic analysis methods are used,along with targeted modeling of the actual hardware system.This literature review focuses on calculating WCET for multi-core processors,providing a survey of traditional methods used for static and dynamic analysis and highlighting the major challenges that arise from different program execution scenarios on multi-core platforms.This paper outlines the strengths and weaknesses of current methodologies and offers insights into prospective areas of research on multi-core analysis.By presenting a comprehensive analysis of the current state of research on multi-core processor analysis for WCET estimation,this review aims to serve as a valuable resource for researchers and practitioners in the field.

An Analysis of Causes of Professional Embezzlement Committed by Executives of State-Owned Enterprise Based on the “Triangle Theory”

According to the fraud"triangle theory",the causes of professional embezzlements committed by executives of China’s state-owned enterprises are mainly from pressure,opportunities and excuses.Among them,pressure is a huge psychological burden and desire generated by the combined action of the self-interested value orientation of the economic man and the value-oriented market economy in the pursuit of wealth.Opportunity refers mainly to the weak restrain on the asset operation responsibility of the executives,the cultural atmosphere of advocating the status and authority in the enterprises,and the lack of necessary power balance and internal control,all of them are caused by the system defects of the company systems under the public ownership.Excuses mainly have such forms as"kickbacks","borrowed money"and unreasonable design of remuneration and incentive systems,etc.The effective way to prevent executives of state-owned enterprises from committing professional embezzlement is to select the personnel with excellent conduct and ability as executives,create the corporate culture of honesty and trustworthiness,perfect a variety of mechanisms and systems,urge the effective implementation of the system,and maintain the normal operation of the mechanism.

Review of Arbitral Award of Network Loan and Analysis of Construction of Implementation Review Rules

The network arbitration cases arising from the network lending disputes are pouring into the courts in large numbers.It is reported that the network arbitration system of some arbitration institutions even“can accept more than 10,000 cases every day,”while online lending is booming,it has also caused a lot of contradictions and disputes,and traditional dispute resolution methods have failed to effectively respond to the need for efficient and convenient resolution of online lending disputes.This paper tries to study the arbitral award of online loans and proposes the construction of implementation review rules.

How Flexible Should the Law Be Against Criminal Corporate Executives?

Against the backdrop of a widely spread-ing global economic crisis, coupled with the slowdown facing China’s own economy, cases of fraud and corruption are becoming a

Auto executives:Worried While Being Happy

Xu Liuping,director of the board of Chang'an Auto Group,had just come back from Japan and north America before the opening of this year's Shanghai auto show.He knew something about the financial crisis towards the overseas auto industry from reports and during this

Machine Learning Driven Latency Optimization for Internet of Things Applications in Edge Computing

Emerging Internet of Things(IoT)applications require faster execution time and response time to achieve optimal performance.However,most IoT devices have limited or no computing capability to achieve such stringent application requirements.To this end,computation offloading in edge computing has been used for IoT systems to achieve the desired performance.Nevertheless,randomly offloading applications to any available edge without considering their resource demands,inter-application dependencies and edge resource availability may eventually result in execution delay and performance degradation.We introduce Edge-IoT,a machine learning-enabled orchestration framework in this paper,which utilizes the states of edge resources and application resource requirements to facilitate a resource-aware offloading scheme for minimizing the average latency.We further propose a variant bin-packing optimization model that co-locates applications firmly on edge resources to fully utilize available resources.Extensive experiments show the effectiveness and resource efficiency of the proposed approach.

Adaptive Emulation Framework for Multi-Architecture IoT Firmware Testing

Internet of things(IoT)devices are being increasingly used in numerous areas.However,the low priority on security and various IoT types have made these devices vulnerable to attacks.To prevent this,recent studies have analyzed firmware in an emulation environment that does not require actual devices and is efficient for repeated experiments.However,these studies focused only on major firmware architectures and rarely considered exotic firmware.In addition,because of the diversity of firmware,the emulation success rate is not high in terms of large-scale analyses.In this study,we propose the adaptive emulation framework for multi-architecture(AEMA).In the field of automated emulation frameworks for IoT firmware testing,AEMA considers the following issues:(1)limited compatibility for exotic firmware architectures,(2)emulation instability when configuring an automated environment,and(3)shallow testing range resulting from structured inputs.To tackle these problems,AEMAcan emulate not onlymajor firmware architectures but also exotic firmware architectures not previously considered,such as Xtensa,ColdFire,and reduced instruction set computer(RISC)version five,by implementing a minority emulator.Moreover,we applied the emulation arbitration technique and input keyword extraction technique for emulation stability and efficient test case generation.We compared AEMA with other existing frameworks in terms of emulation success rates and fuzz testing.As a result,AEMA succeeded in emulating 864 out of 1,083 overall experimental firmware and detected vulnerabilities at least twice as fast as the experimental group.Furthermore,AEMAfound a 0-day vulnerability in realworld IoT devices within 24 h.

The Trade-Off Between Performance and Security of Virtualized Trusted Execution Environment on Android

Nowadays,with the significant growth of the mobile market,security issues on the Android Operation System have also become an urgent matter.Trusted execution environment(TEE)technologies are considered an option for satisfying the inviolable property by taking advantage of hardware security.However,for Android,TEE technologies still contain restrictions and limitations.The first issue is that non-original equipment manufacturer developers have limited access to the functionality of hardware-based TEE.Another issue of hardware-based TEE is the cross-platform problem.Since every mobile device supports different TEE vendors,it becomes an obstacle for developers to migrate their trusted applications to other Android devices.A software-based TEE solution is a potential approach that allows developers to customize,package and deliver the product efficiently.Motivated by that idea,this paper introduces a VTEE model,a software-based TEE solution,on Android devices.This research contributes to the analysis of the feasibility of using a virtualized TEE on Android devices by considering two metrics:computing performance and security.The experiment shows that the VTEE model can host other software-based TEE services and deliver various cryptography TEE functions on theAndroid environment.The security evaluation shows that adding the VTEE model to the existing Android does not addmore security issues to the traditional design.Overall,this paper shows applicable solutions to adjust the balance between computing performance and security.

Privacy Preserving Demand Side Management Method via Multi-Agent Reinforcement Learning

The smart grid utilizes the demand side management technology to motivate energy users towards cutting demand during peak power consumption periods, which greatly improves the operation efficiency of the power grid. However, as the number of energy users participating in the smart grid continues to increase, the demand side management strategy of individual agent is greatly affected by the dynamic strategies of other agents. In addition, the existing demand side management methods, which need to obtain users’ power consumption information,seriously threaten the users’ privacy. To address the dynamic issue in the multi-microgrid demand side management model, a novel multi-agent reinforcement learning method based on centralized training and decentralized execution paradigm is presented to mitigate the damage of training performance caused by the instability of training experience. In order to protect users’ privacy, we design a neural network with fixed parameters as the encryptor to transform the users’ energy consumption information from low-dimensional to high-dimensional and theoretically prove that the proposed encryptor-based privacy preserving method will not affect the convergence property of the reinforcement learning algorithm. We verify the effectiveness of the proposed demand side management scheme with the real-world energy consumption data of Xi’an, Shaanxi, China. Simulation results show that the proposed method can effectively improve users’ satisfaction while reducing the bill payment compared with traditional reinforcement learning(RL) methods(i.e., deep Q learning(DQN), deep deterministic policy gradient(DDPG),QMIX and multi-agent deep deterministic policy gradient(MADDPG)). The results also demonstrate that the proposed privacy protection scheme can effectively protect users’ privacy while ensuring the performance of the algorithm.

An OP-TEE Energy-Efficient Task Scheduling Approach Based on Mobile Application Characteristics

Trusted Execution Environment(TEE)is an important part of the security architecture of modern mobile devices,but its secure interaction process brings extra computing burden to mobile devices.This paper takes open portable trusted execution environment(OP-TEE)as the research object and deploys it to Raspberry Pi 3B,designs and implements a benchmark for OP-TEE,and analyzes its program characteristics.Furthermore,the application execution time,energy consumption and energy-delay product(EDP)are taken as the optimization objectives,and the central processing unit(CPU)frequency scheduling strategy of mobile devices is dynamically adjusted according to the characteristics of different applications through the combined model.The experimental result shows that compared with the default strategy,the scheduling method proposed in this paper saves 21.18%on average with the Line Regression-Decision Tree scheduling model with the shortest delay as the optimization objective.The Decision Tree-Support Vector Regression(SVR)scheduling model,which takes the lowest energy consumption as the optimization goal,saves 22%energy on average.The Decision Tree-K-Nearest Neighbor(KNN)scheduling model with the lowest EDP as the optimization objective optimizes about 33.9%on average.

Impact of Portable Executable Header Features on Malware Detection Accuracy

One aspect of cybersecurity,incorporates the study of Portable Executables(PE)files maleficence.Artificial Intelligence(AI)can be employed in such studies,since AI has the ability to discriminate benign from malicious files.In this study,an exclusive set of 29 features was collected from trusted implementations,this set was used as a baseline to analyze the presented work in this research.A Decision Tree(DT)and Neural Network Multi-Layer Perceptron(NN-MLPC)algorithms were utilized during this work.Both algorithms were chosen after testing a few diverse procedures.This work implements a method of subgrouping features to answer questions such as,which feature has a positive impact on accuracy when added?Is it possible to determine a reliable feature set to distinguish a malicious PE file from a benign one?when combining features,would it have any effect on malware detection accuracy in a PE file?Results obtained using the proposed method were improved and carried few observations.Generally,the obtained results had practical and numerical parts,for the practical part,the number of features and which features included are the main factors impacting the calculated accuracy,also,the combination of features is as crucial in these calculations.Numerical results included,finding accuracies with enhanced values,for example,NN_MLPC attained 0.979 and 0.98;for DT an accuracy of 0.9825 and 0.986 was attained.

Cognitive and Emotional Factors Related to Obstructive Sleep Apnea Diagnoses: A Brief Review

Sleep apnea is a clinical condition characterized by cessation of breathing in the sleeper due to pharyngeal airway closure. The reduction in air exchange results in decreased cerebral blood circulation with consequential behavioral deficits cognitively and emotionally. Untreated sleep apnea is associated with chronic illnesses of depression, cardiovascular disorder, obesity and diabetes mellitus. Measured cognitive behavior before and following CPAP treatment demonstrates the cognitive deficit as the effectiveness of CPAP treatment. Emotional factors related to sleep apnea diagnosis and adherence to treatment are facilitated in patients with cognitive behavior therapy (CBT) interventions by sleep specialists. This is a brief review paper that presents findings about cognition and emotional factors related to sleep apnea. This is a brief review paper.

Clinical study of different modes of repetitive transcranial magnetic stimulation in the treatment of post‑stroke executive dysfunction

Objective:To investigate the clinical efficacy of intermittent theta burst stimulation(iTBS)and high frequency repetitive transcranial magnetic stimulation(rTMS)on post‑stroke executive impairment(PSEI).Methods:Ninety patients with PSEI who were hospitalized in the rehabilitation department of Xuzhou Central Hospital and Xuzhou Rehabilitation Hospital from April 2021 to June 2022 were selected and divided into iTBS group,high‑frequency group and control group.All three groups of patients received routine rehabilitation training,given rTMS treatment with iTBS,10 Hz and shame stimulation for 4 weeks.Before and after treatment,all the patients were evaluated with the Montreal cognitive assessment(MoCA),the frontal assessment battery(FAB),troop color‑word test(SCWT),shape trails test(STT),digit span test(DST)and event related potential P300.Results:After treatment,MoCA,FAB,SCWT,STT,DST scores,P300 latency and amplitude were significantly better in the three groups than before treatment(P<0.05).MoCA,FAB,SCWT,STT‑B,DST scores,P300 latency and amplitude in the iTBS group and high‑frequency group were better than in the control group,with significant differences(P<0.05).The difference between iTBS group and high‑frequency group was not statistically significant(P>0.05).Conclusion:iTBS can improve PSEI,and the efficacy is comparable to 10Hz rTMS.iTBS takes less time with better efficiency,and it is worth popularizing and applying in clinic.