GSDME as an executioner of chemotherapy-induced cell death

Cell death is a crucial process required for development, tissue homeostasis, and pathological cell loss in multicellu- lar organisms. Cell death mainly occurs in two alternative modes： apoptosis or necrosis. Apoptosis is a form of pro- grammed cell death with typical morphological features, including cell shrinkage, chromatin condensation, and DNA fragmentation （Degterev and Yuan, 2008）. The dying cell is eventually fragmented into membrane-bound apoptotic bodies that are engulfed by surrounding phagocytes.

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.

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.

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.

面向WCET估计的Cache分析研究综述

实时系统时间分析的首要任务是估计程序的最坏情况执行时间(worst-case execution time,简称WCET).程序的WCET通常受到硬件体系结构的影响,Cache则是其中最为突出的因素之一.对面向WCET计算的Cache分析研究进行了综述,介绍了经典Cache分析框架与Cache分析核心技术,并从循环结构分析、数据Cache分析、多级Cache分析、多核共享Cache分析、非LRU替换策略分析等角度介绍了Cache分析在不同维度上的研究问题与主要挑战,总结了现有技术的优缺点,展望了Cache分析研究的未来发展方向.

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) .

一种基于CPN的BPEL异常处理逻辑的开发方法

针对WS-BPEL在面向服务软件异常处理方面不够完善的问题,提出了一种基于着色Petri网描述的BPEL异常处理逻辑开发方法。该方法利用着色Petri网(CPN)形式化地描述BPEL的异常处理机制,建立BPEL的异常处理CPN模型,指导对BPEL的异常处理逻辑开发,并依据此建模思想,提出了一个BPEL的异常处理CPN模型的转换工具,用以实现将异常处理的BPEL着色Petri网模型转换成对应的带异常处理的BPEL代码。该工具可在输入的原始的没有异常处理功能的BPEL代码基础上,通过动态地添加BPEL的异常处理语言成份,形成带有异常处理功能的BPEL流程。最后通过引入供应商流程案例,阐述了该方法的具体使用过程。

基于WCET分析的实时系统轨迹获取技术

时序约束是判断实时系统运行是否正确的重要规约.为了减小测试时由于对系统进行插装而产生的对实时系统行为的影响,提出了一种混合式监控方法.它对系统的时间干扰比纯软件方式小,并支持对系统的完全测试.此外,还提出一种基于WCET(worst-caseexecutiontime)分析技术的目标系统时间补偿方法,在精确地计算插入断言对目标系统的时间影响基础上,给出时间补偿.

Optimization of RDF link traversal based query execution

Aiming at the problem that only some types of SPARQL （ simple protocal and resource description framework query language） queries can be answered by using the current resource description framework link traversal based query execution （RDF-LTE） approach, this paper discusses how the execution order of the triple pattern affects the query results and cost based on concrete SPARQL queries, and analyzes two properties of the web of linked data, missing backward links and missing contingency solution. Then three heuristic principles for logic query plan optimization, namely, the filtered basic graph pattern （FBGP） principle, the triple pattern chain principle and the seed URIs principle, are proposed. The three principles contribute to decrease the intermediate solutions and increase the types of queries that can be answered. The effectiveness and feasibility of the proposed approach is evaluated. The experimental results show that more query results can be returned with less cost, thus enabling users to develop the full potential of the web of linked data.

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.

Analysis and modeling of web services flow using π-calculus

In order to increase the effectiveness and the reliability of web services flow, the ~r-calculus formal method is introduced as a development language for web services flow. The π-calculus overcomes inabilities of web service flow languages in demonstrating the consistency, validating the correctness and so on. The π- calculus analysis and modeling of web services flow is presented, the dynamic actions and basic activities of WS-BPEL with π-calculus formally are described, and the mapping from π-calculus expression to WS-BPEL is built. The basic construction of web services flow with the π-calculus method after the analysis of the syntax of WS-BPEL and inter-description between WS-BPEL and π-calculus is expressed. Also discussed are the approaches to web services flow by modeling from different views, and the proposed approaches through the development and modeling of an e-commerce web service flow application are illustrated.

Method for test case selection and execution of web application regression testing

In order to improve the efficiency of regression testing in web application,the control flow graph and the greedy algorithm are adopted.This paper considers a web page as a basic unit and introduces a test case selection method for web application regression testing based on the control flow graph.This method is safe enough to the test case selection.On the base of features of request sequence in web application,the minimization technique and the priority of test cases are taken into consideration in the process of execution of test cases in regression testing for web application.The improved greedy algorithm is also raised resulting in optimization of execution of test cases.The experiments indicate that the number of test cases which need to be retested is reduced,and the efficiency of execution of test cases is also improved.

Novel technique for cost reduction in mutation testing

Aimed at the problem of expensive costs in mutation testing which has hampered its wide use,a technique of introducing a test case selection into the process of mutation testing is proposed.For each mutant,a fixed number of test cases are selected to constrain the maximum allowable executions so as to reduce useless work.Test case selection largely depends on the degree of mutation.The mutation distance is an index describing the semantic difference between the original program and the mutated program.It represents the percentage of effective test cases in a test set,so it can be used to guide the selection of test cases.The bigger the mutation distance is,the easier it is that the mutant will be killed,so the corresponding number of effective test cases for this mutant is greater.Experimental results suggest that the technique can remarkably reduce execution costs without a significant loss of test effectiveness.

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.

Material-flow Modeling Technology and Its Application in Manufacturing Execution Systems of Petrochemical Industry

The management and control of material flow forms the core of manufacturing execution systems （MES） in the petrochemical industry. The bottleneck in the application of MES is the ability to match the material-flow model with the production processes. A dynamic material-flow model is proposed in this paper after an analysis of the material-flow characteristics of the production process in a petrochemical industry. The main material-flow events are described, including the movement, storage, shifting, recycling, and elimination of the materials. The spatial and temporal characters of the material-flow events are described, and the material-flow model is constructed. The dynamic material-flow model introduced herein is the basis for other subsystems in the MES. In addition, it is the subsystem with the least scale in MES. The dynamic-modeling method of material flow has been applied in the development of the SinoMES model. It helps the petrochemical plant to manage the entire flow information related to tanks and equipments from the aspects of measurement, storage, movement, and the remaining balance of the material. As a result, it matches the production process by error elimination and data reconciliation. In addition, it facilitates the integration of application modules into the MES and guarantees the potential development of SinoMES in future applications.

Visual feedback therapy for restoration of upper limb function of stroke patients

Objective:To investigate the effects of mirror neuron theory-based visual feedback therapy(VFT)on restoration of upper limb function of stroke patients and motor-related cortical function using functional magnetic resonance imaging(fMRI).Methods:Hemiplegic stroke patients were randomly divided into two groups:a VFT group and a control(CTL)group.Sixteen patients in the VFT group received conventional rehabilitation(CR)and VFT for 8 weeks,while 15 patients in the CTL group received only CR.The Barthel Index(BI)was used to assess the activities of daily living at baseline and the 8th week of the recovery training period.The Fugl-Meyer assessment(FMA)scale,somatosensory evoked potential(SEP),and fMRI were used to evaluate the recovery effect of the training therapies.The latencies and amplitudes of N9 and N20 were measured.Before recovery training,fMRI was performed for all patients in the VFT and CTL groups.In addition,17 patients(9 in the VFT group and 8 in the CTL group)underwent fMRI for follow-up 2 months after treatment.Qualitative data were analyzed using the x2 test.The independent sample t-test was used to compare normally distributed data among different groups,the paired sample t-test was used to compare data between groups,and the non-parametric test was used to comparing data without normal distribution among groups.Results:There were no significant differences between the VFT and CTL group in all indexes.However,after 8 weeks of recovery training,these indexes were all significantly improved(P<0.05).As compared with the CTL group,the FMA scores,BI,and N9/N20 latencies and amplitudes of SEP in the VFT group were significantly improved(P<0.05).Two months after recovery training,fMRI showed that the degree of activation of the bilateral central anterior gyrus.parietal lobe,and auxiliary motor areas was significantly higher in the VFT group than the CTL group(P<0.05).Conclusions:VFT based on mirror neuron theory is an effective approach to improve upper extremity motor function and daily activity performance of stroke patients.The therapeutic mechanism promotes motor relearning by activating the mirror neuron system and motor cortex.SEP amplitudes increased only for patients who participated in visual feedback.VFT promotes sensory-motor plasticity and behavioral changes in both the motor and sensory domains.

Distributed tasks-platforms scheduling method to holonic-C2 organization

To solve the problem of distributed tasks-platforms scheduling in holonic command and control(C2) organization,the basic elements of the organization are analyzed firstly and the formal description of organizational elements and structure is provided. Based on the improvement of task execution quality,a single task resource scheduling model is established and the solving method based on the m-best algorithm is proposed. For the problem of tactical decision-holon cannot handle tasks with low priority effectively, a distributed resource scheduling collaboration mechanism based on platform pricing and a platform exchange mechanism based on resource capacities are designed. Finally,a series of experiments are designed to prove the effectiveness of these methods. The results show that the proposed distributed scheduling methods can realize the effective balance of platform resources.

Event-driven process execution model for process virtual machine

Current orchestration and choreography process engines only serve with dedicate process languages.To solve these problems,an Event-driven Process Execution Model(EPEM) was developed.Formalization and mapping principles of the model were presented to guarantee the correctness and efficiency for process transformation.As a case study,the EPEM descriptions of Web Services Business Process Execution Language(WS-BPEL) were represented and a Process Virtual Machine(PVM)-OncePVM was implemented in compliance with the EPEM.