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.

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.

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.

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).

Critical Success Factors for Executive Information Systems： A Case of Kenyan Organizations

In recent years, a number of organisations have implemented Executive Information Systems （EIS） including Computer-Based Information Systems （CBIS） in order to improve the performance of their executives＇ jobs though the majority of executives are unwilling to use EIS applications because of their design defects. Present researches on Executive Information Systems （EIS） are limited and less focused on the factors associated with EIS successful usage. This research adopts an extension of the Technology Acceptance Model （TAM） to derive useful variables to address the problem of the low usage of EIS by executives due to social factors, habits and facilitation conditions variables in Triandis＇ Framework. It reports on the adoption and usage of EIS by the executives in Organizations. The results show that experience in EIS usage was high with most respondents having used EIS for between 0 to 4 years （72.41%）, followed by the 5-9 years group with 25.86%. However, in terms of ability to use EIS, most executives were at the expert （knowledgeable）, casual user with 37.93%, followed by novice frequent user group with 31.03%. From this research, the authors propose a model that incorporates the habits, social factors and facilitating conditions in an Executive Information Systems.

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.

Critical Success Factors Influencing the Implementation of Sustainable Energy System in Uganda: A Case of Inter-University Council of East Africa Energy Project at the Head Quarters in Kampala, Uganda

The widespread usage of clean and sustainable energy sources is leading to a significant transformation of the world’s energy systems. Over-reliance on only the national grid energy system has made institutions fail to sustain energy systems. The council is only connected to the national grid electricity supply system, with diesel generators as the only alternative, which is unhealthy and unsafe. Surprisingly, even with such alternatives, power shortages have persisted despite government efforts to provide a solution to the shortages by installing numerous off-grid systems. Due to such a situation, the council would construct a sustainable energy system as a remedy. Thus, the purpose of this study was to establish critical success factors influencing the implementation of a sustainable energy system at the Inter-University Council of East Africa (IUCEA) Head Quarters, Kampala-Uganda. A cross-sectional survey design was used;a sample size of 84 participants was selected. Questionnaire survey and interview methods were utilized. The study found that the most significant (p < 0.05) critical factors in the implementation of sustainable energy in institutions are;the use of innovative technologies and infrastructure, the use of efficient zero emissions for heating and cooling, integration of renewable energy use in the existing buildings, building and renovating in an energy-efficient way, integrating regional energy systems, improving energy efficiency in the buildings, enhanced zero emission power technologies, energy efficient equipment in place and stakeholder empowerment in energy management. This study concludes that institutions like;the Inter-University Council of East Africa (IUCEA) need to clearly state policies and actions of energy management. The roles and responsibilities of each member have to be clearly stated while capturing the activities involved in energy conservation, energy security and energy efficiency.

Development of a distributed and integratable manufacturing execution system framework

Using remote method invocation (RMI) and a distributed object-oriented technique, this paper presents a systematic approach to developing a manufacturing execution system (MES) framework, which is open, modularized, distributed, configurable, interoperable and maintainable. Moreover, the design patterns for the framework .are developed and a variety of functional components are designed by inheriting appropriate patterns. And then an application is constructed by invoking corresponding methods of related components. An MES system implementing the framework and design patterns can be facilely integrated with other manufacturing applications, such as enterprise resource planning (ERP) and floor control system (FCS) .

Locality Improvement Technology for Auto-control System of Aquatic Seedling

[Objective] The aim was to improve part of auto-control system of aquatic seedling breeding and to lay foundation for highly-efficient and stable use of the sys- tem. [Method] The executive and drive modules of the auto-control system were im- proved, after which real-time monitor, data display and alarm modules were tested on time. [Result] In the test, for the improved system, stability and sensitivity were enhanced; different components matched better; transmitting electric resistanc6 was reduced. [Conclusion] The research provides references for auto-control of water temperature and dissolved oxygen, and faults clearing in aquatic seedling breeding.

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.

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.

Acute exercise is associated with specific executive functions in college students with ADHD:A preliminary study

Purpose： The relationship between acute exercise and executive functions in college students with attention deficit hyperactivity disorder （ADHD） has not been clearly established. The purpose of this preliminary study was to examine the difference in cognitive performance between college students with and without ADHD and to explore the effects of acute exercise on multiple aspects of executive functions and on serum brain derived neurotrophic factor （BDNF）. Methods： College students （normal： n = 10; ADHD： n = 10） performed the Stroop Test, Trail Making Test, and Digit Span Test prior to and after an acute exercise intervention. Blood samples were obtained prior to the pre-test cognitive test performance and then again after exercise and prior to the post-test cognitive test performance. Results： Students with ADHD exhibited impaired executive functions, particularly on inhibition. Additionally, while acute exercise improved all aspects of executive functions in those without ADHD, acute exercise only improved inhibitory performance for those with ADHD. Further, BDNF was not influenced by acute exercise regardless of the subjects＇ ADHD status. Conclusion： These results provide preliminary evidence for exercise as a potential adjunct treatment for benefitting inhibition in college students with ADHD.

Does exercise have a protective effect on cognitive function under hypoxia? A systematic review with meta-analysis

Objective:This study aimed to examine(1)the independent effects of hypoxia on cognitive function and(2)the effects of exercise on cognition while under hypoxia.Methods:Design:Systematic review with meta-analysis.Data sources:PubMed,Scopus,Web of Science,PsychInfo,and SPORTDiscus were searched.Eligibility criteria for selecting studies:randomized controlled trials and nonrandomized controlled studies that investigated the effects of chronic or acute exercise on cognition under hypoxia were considered(Aim 2),as were studies investigating the effects of hypoxia on cognition(Aim 1).Results:In total,18 studies met our inclusionary criteria for the systematic review,and 12 studies were meta-analyzed.Exposure to hypoxia impaired attentional ability(standardized mean difference(SMD)=-0.4),executive function(SMD=-0.18),and memory function(SMD=-0.26),but not information processing(SMD=0.27).Aggregated results indicated that performing exercise under a hypoxia setting had a significant effect on cognitive improvement(SMD=0.3,95%confidence interval:0.14-0.45,I^2=54%,p<0.001).Various characteristics(e.g.,age,cognitive task type,exercise type,exercise intensity,training type,and hypoxia level)moderated the effects of hypoxia and exercise on cognitive function.Conclusion:Exercise during exposure to hypoxia improves cognitive function.This association appears to be moderated by individual and exercise/hypoxia-related characteristics.

ARCHITECTURE OF MANUFACTURING EXECUTION SYSTEM IN THE EXTENDED ENTERPRISE

The extended enterprise is formed according to the philosophy of dispersednetworked manufacturing. Manufacturing execution system (MES) can close the information gap whichexists between device control system and production information management system. The functions andthe web-based architecture of the MES in the extended enterprise are introduced. Using thecooperating system models of object-oriented and distributed agents and CORBA, all objects keep touniform interface standards and are easily inserted to object request broker. The utilization ofdistributed MES in extended enterprise can adapt fast change of manufacturing environment andresource. It also can improve the independent management capability of manufacturing cell and theenterprise response capability to global economic competition.

Task Offloading Decision in Fog Computing System

Fog computing is an emerging paradigm of cloud computing which to meet the growing computation demand of mobile application. It can help mobile devices to overcome resource constraints by offloading the computationally intensive tasks to cloud servers. The challenge of the cloud is to minimize the time of data transfer and task execution to the user, whose location changes owing to mobility, and the energy consumption for the mobile device. To provide satisfactory computation performance is particularly challenging in the fog computing environment. In this paper, we propose a novel fog computing model and offloading policy which can effectively bring the fog computing power closer to the mobile user. The fog computing model consist of remote cloud nodes and local cloud nodes, which is attached to wireless access infrastructure. And we give task offloading policy taking into account executi+on, energy consumption and other expenses. We finally evaluate the performance of our method through experimental simulations. The experimental results show that this method has a significant effect on reducing the execution time of tasks and energy consumption of mobile devices.