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.

Planning,monitoring and replanning techniques for handling abnormity in HTN-based planning and execution

A framework that integrates planning,monitoring and replanning techniques is proposed.It can devise the best solution based on the current state according to specific objectives and properly deal with the influence of abnormity on the plan execution.The framework consists of three parts:the hierarchical task network(HTN)planner based on Monte Carlo tree search(MCTS),hybrid plan monitoring based on forward and backward and norm-based replanning method selection.The HTN planner based on MCTS selects the optimal method for HTN compound task through pre-exploration.Based on specific objectives,it can identify the best solution to the current problem.The hybrid plan monitoring has the capability to detect the influence of abnormity on the effect of an executed action and the premise of an unexecuted action,thus trigger the replanning.The norm-based replanning selection method can measure the difference between the expected state and the actual state,and then select the best replanning algorithm.The experimental results reveal that our method can effectively deal with the influence of abnormity on the implementation of the plan and achieve the target task in an optimal way.

Robust Malicious Executable Detection Using Host-Based Machine Learning Classifier

The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leadsto wide losses for various organizations. These dangers have proven that signature-based approaches are insufficientto prevent emerging and polymorphic attacks. Therefore, this paper is proposing a Robust Malicious ExecutableDetection (RMED) using Host-based Machine Learning Classifier to discover malicious Portable Executable (PE)files in hosts using Windows operating systems through collecting PE headers and applying machine learningmechanisms to detect unknown infected files. The authors have collected a novel reliable dataset containing 116,031benign files and 179,071 malware samples from diverse sources to ensure the efficiency of RMED approach.The most effective PE headers that can highly differentiate between benign and malware files were selected totrain the model on 15 PE features to speed up the classification process and achieve real-time detection formalicious executables. The evaluation results showed that RMED succeeded in shrinking the classification timeto 91 milliseconds for each file while reaching an accuracy of 98.42% with a false positive rate equal to 1.58. Inconclusion, this paper contributes to the field of cybersecurity by presenting a comprehensive framework thatleverages Artificial Intelligence (AI) methods to proactively detect and prevent cyber-attacks.

Improving Prediction Efficiency of Machine Learning Models for Cardiovascular Disease in IoST-Based Systems through Hyperparameter Optimization

This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST(Internet of Sensing Things)device.Ten distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter tuning.Significant improvements were observed across various models,with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score,recall,and precision.The study underscores the critical role of tailored hyperparameter tuning in optimizing these models,revealing diverse outcomes among algorithms.Decision Trees and Random Forests exhibited stable performance throughout the evaluation.While enhancing accuracy,hyperparameter optimization also led to increased execution time.Visual representations and comprehensive results support the findings,confirming the hypothesis that optimizing parameters can effectively enhance predictive capabilities in cardiovascular disease.This research contributes to advancing the understanding and application of machine learning in healthcare,particularly in improving predictive accuracy for cardiovascular disease management and intervention strategies.

Cerebral arterial blood flow,attention,and executive and cognitive functions in depressed patients after acute hypertensive cerebral hemorrhage

BACKGROUND Intracerebral hemorrhage mainly occurs in middle-aged and elderly patients with hypertension,and surgery is currently the main treatment for hypertensive cerebral hemorrhage,but the bleeding caused by surgery will cause damage to the patient's nerve cells,resulting in cognitive and motor dysfunction,resulting in a decline in the patient's quality of life.AIM To investigate associations between cerebral arterial blood flow and executive and cognitive functions in depressed patients after acute hypertensive cerebral hemorrhage.METHODS Eighty-nine patients with depression after acute hypertensive cerebral hemorrhage who were admitted to our hospital between January 2019 and July 2021 were selected as the observation group,while 100 patients without depression who had acute hypertensive cerebral hemorrhage were selected as the control group.The attention span of the patients was assessed using the Paddle Pin Test while executive function was assessed using the Wisconsin Card Sorting Test(WCST)and cognitive function was assessed using the Montreal Cognitive Assessment Scale(MoCA).The Hamilton Depression Rating Scale(HAMD-24)was used to evaluate the severity of depression of involved patients.Cerebral arterial blood flow was measured in both groups.RESULTS The MoCA score,net scores I,II,III,IV,and the total net score of the scratch test in the observation group were significantly lower than those in the control group(P<0.05).Concurrently,the total number of responses,number of incorrect responses,number of persistent errors,and number of completed responses of the first classification in the WCST test were significantly higher in the observation group than those in the control group(P<0.05).Blood flow in the basilar artery,left middle cerebral artery,right middle cerebral artery,left anterior cerebral artery,and right anterior cerebral artery was significantly lower in the observation group than in the control group(P<0.05).The basilar artery,left middle cerebral artery,right middle cerebral artery,left anterior cerebral artery,and right anterior cerebral artery were positively correlated with the net and total net scores of each part of the Paddle Pin test and the MoCA score(P<0.05),and negatively correlated with each part of the WCST test(P<0.05).In the observation group,the post-treatment improvement was more prominent in the Paddle Pin test,WCST test,HAMD-24 score,and MoCA score compared with those in the pre-treatment period(P<0.05).Blood flow in the basilar artery,left middle cerebral artery,right middle cerebral artery,left anterior cerebral artery,and right anterior cerebral artery significantly improved in the observation group after treatment(P<0.05).CONCLUSION Impaired attention,and executive and cognitive functions are correlated with cerebral artery blood flow in patients with depression after acute hypertensive cerebral hemorrhage and warrant further study.

The Mediating Effect of Digital Transformation on the Link Between Executive Overconfidence and Corporate Green Innovation

Green technological innovation is crucial for the manufacturing industry’s green transformation and sustainable development.This study examines the impact of executive overconfidence on corporate green innovation,focusing on the internal drivers of corporate innovation and using a sample of Shanghai and Shenzhen A-share listed manufacturing companies from 2013 to 2020.We further examine the mediating role of digital transformation and the moderating role of external attention.The findings indicate that executive overconfidence promotes corporate green technological innovation.Overconfident executives enhance green innovation by accelerating digital transformation.Moreover,external attention from analysts and media positively moderates the relationship between executive overconfidence and corporate green innovation.Heterogeneity analysis reveals that the positive impact of executive overconfidence on green innovation is more significant in non-state-owned enterprises,high-tech firms,and enterprises with lower pollution levels.

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.

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.

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.

Multi-User MmWave Beam Tracking via Multi-Agent Deep Q-Learning

Beamforming is significant for millimeter wave multi-user massive multi-input multi-output systems.In the meanwhile,the overhead cost of channel state information and beam training is considerable,especially in dynamic environments.To reduce the overhead cost,we propose a multi-user beam tracking algorithm using a distributed deep Q-learning method.With online learning of users’moving trajectories,the proposed algorithm learns to scan a beam subspace to maximize the average effective sum rate.Considering practical implementation,we model the continuous beam tracking problem as a non-Markov decision process and thus develop a simplified training scheme of deep Q-learning to reduce the training complexity.Furthermore,we propose a scalable state-action-reward design for scenarios with different users and antenna numbers.Simulation results verify the effectiveness of the designed method.

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.

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.

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.

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.