Execute模型在提高低年资护士制度执行力中的应用

[目的]提高低年资护士制度执行力,提高护理工作质量及住院病人满意度。[方法]分析低年资护士制度执行力低的原因,运用Execute模型结合实际临床护理工作,提出解决方案,提高低年资护士的制度执行力。[结果]低年资护士制度执行力明显改善,护理工作质量及病人满意度提高。[结论]运用Execute模型,结合实际临床护理工作提出的解决方案,可以有效提高低年资护士制度执行力,各项工作指标达标率上升。

EXECUTER2 modulates the EXECUTER1 signalosome through its singlet oxygen-dependent oxidation

Oxidative post-translational modifications of specific chloroplast proteins contribute to the initiation of retrograde signaling.The Arabidopsis thaliana EXECUTER1(EX1)protein,a chloroplast-localized singlet oxygen(^(1)O_(2))sensor,undergoes tryptophan(Trp)643 oxidation by^(1)O_(2),a chloroplast-derived and light-dependent reactive oxygen species.The indole side chain of Trp is vulnerable to^(1)O_(2),leading to the generation of oxidized Trp variants and priming EX1 for degradation by a membrane-bound FtsH protease.The perception of^(1)O_(2)via Trp643 oxidation and subsequent EX1 proteolysis facilitate chloroplast-to-nucleus retrograde signaling.In this study,we discovered that the EX1-like protein EX2 also undergoes^(1)O_(2)-dependent Trp530 oxidation and FtsH-dependent turnover,which attenuates^(1)O_(2)signaling by decelerating EX1-Trp643 oxidation and subsequent EX1 degradation.Consistent with this finding,the loss of EX2 function reinforces EX1-dependent retrograde signaling by accelerating EX1-Trp643 oxidation and subsequent EX1 proteolysis,whereas overexpression of EX2 produces molecular phenotypes opposite to those observed in the loss-of-function mutants of EX2.Intriguingly,phylogenetic analysis suggests that EX2 may have emerged evolutionarily to attenuate the sensitivity of EX1 toward^(1)O_(2).Collectively,these results suggest that EX2 functions as a negative regulator of the EX1 signalosome through its own^(1)O_(2)-dependent oxidation,providing a new mechanistic insight into the regulation of EX1-mediated^(1)O_(2)signaling.

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.

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.

1O2-Mediated and EXECUTER-Dependent Retrograde Plastid-to-Nucleus Signaling in Norflurazon-Treated Seedlings of Arabidopsis thaliana

Chloroplast development depends on the synthesis and import of a large number of nuclear-encoded pro- teins. The synthesis of some of these proteins is affected by the functional state of the plastid via a process known as retrograde signaling. Retrograde plastid-to-nucleus signaling has been often characterized in seedlings of Arabidopsis thaliana exposed to norflurazon （NF）, an inhibitor of carotenoid biosynthesis. Results of this work suggested that, throughout seedling development, a factor is released from the plastid to the cytoplasm that indicates a perturbation of plastid homeostasis and represses nuclear genes required for normal chloroplast development. The identity of this factor is still under debate. Reactive oxygen species （ROS） were among the candidates discussed as possible retrograde signals in NF-treated plants. In the present work, this proposed role of ROS has been analyzed. In seedlings grown from the very beginning in the presence of NF, ROS-dependent signaling was not detectable, whereas, in seedlings first exposed to NF after light-dependent chloroplast formation had been completed, enhanced ROS production occurred and, among oth- ers, 1O2-mediated and EXECUTER-dependent retrograde signaling was induced. Hence, depending on the developmental stage at which plants are exposed to NF, different retrograde signaling pathways may be activated, some of which are also active in non-treated plants under light stress.

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.

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.

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.

GSDMB N-terminal assembles in plasma membrane to execute pyroptotic cell death

Human genome encodes six paralogous gasdermin genes:GSDMA,GSDMB,GSDMC,GSDMD,GSDME and DFNB59.1 Proteolytic cleavage of these gasdermin proteins liberates an N-terminal(NT)fragment from autoinhibition,which assembles in membrane to form pores and execute pyroptotic cell death in general.1 In contrast to other gasdermins,gasdermin B(GSDMB)is the only gasdermin gene that has not been identified in rodents.Zhou et al first shed light on the molecular mechanism by which cytotoxic lymphocyte-derived granzyme A(GZMA)cleaves GSDMB to execute pyroptosis in GSDMB-positive cells,especially in cancer cells.2 In this issue of Cell,Hansen et al reported a dynamic host pathogen S.flexneri prevents GSDMB-mediated lysis by secreting IpaH7.8,which targets and ubiquitinates GSDMB for 26S proteasome destruction.3 They showed that GSDMB implements bacteriocidic ability by recognition of the phospholipids on Gram-negative bacterial membranes rather than lysing host cells.Although their experimental design and data are clearly presented and straightforward,there are still some doubts to be clarified.

JNCDGICL to Execute Molybdenum Project

Jinzhou New China Dragon(Group)Indus-trial Co.,Ltd.(JNCDGICL)plans to to-tally invest RMB300 million to construct amolybdenum mining and deep processing project.It will

New Method Reveals How Protein-Mediated RNA Loops Orchestrate Gene Splicing

A research team led by Prof.XUE Yuanchao from the Institute of Biophysics of the Chinese Academy of Sciences has developed a new method for global profiling of in-situ RNA–RNA contacts associated with a specific RNA-binding protein(RBP)and revealed positional mechanisms by which PTBP1-associated RNA loops regulate cassette exon splicing.This study was published online in Molecular Cell on March 22.In eukaryotes,the same pre-mRNA can produce multiple protein isoforms to execute similar or different biological functions through alternative splicing.Several longstanding models proposed that RBPs may regulate alternative splicing by modulating long-range RNA–RNA interactions(RRI).However,direct experimental evidence was lacking.

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.

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.