基于ENVI+HEC-RAS的苏北运河生态修复方法及改善效果研究

鉴于生态修复对维持河流健康生命十分重要,以提升植被覆盖度与水质为目标,对苏北运河进行生态修复。基于2018年研究区Landsat8遥感影像数据,运用ENVI软件对苏北运河全河段的土地利用情况进行处理分析,并在增加上游生态来水的情况下,使用HEC-RAS软件对泗阳新一水厂断面的COD水质指标浓度进行模拟。结果表明,苏北运河全河段植物覆盖度最大可提升至59.84%,且Kappa系数结果均大于0.6,精度符合要求;COD水质指标浓度在5、10、12月达到Ⅱ类水标准,其余月份较调控前均已达到Ⅲ类水标准,可见在增大生态来水作用下,运河水质得到提升。

基于HEC-RAS的沥青混凝土心墙坝溃坝洪水过程模拟分析

大坝溃口发展过程模拟是开展溃坝洪水分析的基础,不同坝型溃口发展过程不同,溃口流量(下泄洪水)过程差异很大。文章通过对某水电站沥青混凝土心墙坝溃口形式的研究,采用HEC-RAS(Hydrologic Engineering Centers-River Analysis System)软件进行溃坝洪水模拟,旨在探讨沥青混凝土心墙坝在水库溃坝风险中的关键因素,着重考虑沥青混凝土心墙对溃口发展的影响。结果显示,初始运行水位、洪峰流量的重现期、溃坝历时及泄水建筑的泄流方式对溃口流量产生显著影响。研究成果为未来水利工程的安全可靠建设提供了科学依据。

基于MIKE11和HEC-RAS的模拟水面线比较及敏感性分析

应用HEC-RAS和MIKE11模型对淮河踅孜镇到淮滨站河段进行水面线计算,采用相同河道、断面、流量数据、下游水位流量关系、糙率等,推算水面线并与真实数据对比,计算了模型的精度,并对模型中涉及的主要水文参数——河道坡度和河道糙率进行敏感性分析。计算结果表明,MIKE11水动力模型推算的水面线精确度更高,其水位结果也略高于HEC-RAS模型,更加适用于研究区,经计算,MIKE11模型对糙率更为敏感,两个模型对河道比降均不敏感。

Software Defect Prediction Method Based on Stable Learning

The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and labor.In previous software defect prediction studies,transfer learning was effective in solving the problem of inconsistent project data distribution.However,target projects often lack sufficient data,which affects the performance of the transfer learning model.In addition,the presence of uncorrelated features between projects can decrease the prediction accuracy of the transfer learning model.To address these problems,this article propose a software defect prediction method based on stable learning(SDP-SL)that combines code visualization techniques and residual networks.This method first transforms code files into code images using code visualization techniques and then constructs a defect prediction model based on these code images.During the model training process,target project data are not required as prior knowledge.Following the principles of stable learning,this paper dynamically adjusted the weights of source project samples to eliminate dependencies between features,thereby capturing the“invariance mechanism”within the data.This approach explores the genuine relationship between code defect features and labels,thereby enhancing defect prediction performance.To evaluate the performance of SDP-SL,this article conducted comparative experiments on 10 open-source projects in the PROMISE dataset.The experimental results demonstrated that in terms of the F-measure,the proposed SDP-SL method outperformed other within-project defect prediction methods by 2.11%-44.03%.In cross-project defect prediction,the SDP-SL method provided an improvement of 5.89%-25.46% in prediction performance compared to other cross-project defect prediction methods.Therefore,SDP-SL can effectively enhance within-and cross-project defect predictions.

HEC-RAS与MIKE溃坝洪水数值模拟研究进展

溃坝后洪水流速快、破坏力大、影响范围广,严重危害下游地区的人民生命财产安全。为了准确了解溃坝洪水造成的危害,对溃坝问题的研究背景、数值模拟常用软件及功能进行论述,并总结了溃坝模拟软件一维、二维和三维模型的特点,着重分析HEC-RAS和MIKE两款主流洪水数值模拟软件的国内外研究现状及特点。结果表明:HEC-RAS具有操作方便,界面简单等特点,MIKE具有效率高,应用广泛等特点,两款软件都可以准确进行溃坝模拟,促进了解溃坝机理,对维护大坝安全有着重要意义,研究成果可为后续的溃坝洪水研究提供参考。目前溃坝洪水数值模拟方法已经接近完善,但还存在对于泥沙、紊流等因素无法在数值模拟软件具体体现等问题,未来仍需进一步解决。

基于SWMM耦合HEC-RAS的人工建设水网防洪排涝模拟研究

对新建城区的水文、水动力过程进行准确模拟分析是人工水网规划建设及新建城区内涝防治的关键。应用SWMM分布式水文模型耦合HEC-RAS二维水动力SWE-ELW模型,对安徽省阜阳市城南水网进行模型模拟及防洪排涝分析。结果表明,SWMM模型中计算参数更多,排涝模数随汇水分区面积增大而减小,且与规划泵站排涝模数接近,计算结果更为合理。城南水网排涝标准30年一遇工况下,城南水网水系汛期最高水位为27.89 m,满足排涝方案要求;各闸门过流流量与其河道底宽成正比,各闸门各过程线波动程度与其河道底宽成反比;水网先蓄后排削峰57.21 m^(3)/s,错峰2.83 h,调蓄洪量169万m^(3),削峰率42.45%,防洪排涝效益显著。该方法具备一定的模拟效果及精度,可为相关人工建设水网的防洪排涝研究提供借鉴和指导。

基于HEC-RAS的竹根池水库溃坝洪水风险研究

为避免发生水库漫坝水淹周边电站厂房,采用HEC-RAS对向家坝电站周边的竹根池水库进行二维溃坝洪水模拟计算,分析其演进过程及最大的影响范围。结果表明,溃口洪峰大小取决于溃口底宽、溃决水头及溃决时间;竹根池溃坝对向家坝下游的机电设备仓库及金沙江大桥基本无影响,对几条进厂公路和上坝公路产生轻微影响。该分析结果可为竹根池水库及向家坝电厂的安全运行及防洪应急管理提供科学依据。

Open-Source Software Defined Networking Controllers:State-of-the-Art,Challenges and Solutions for Future Network Providers

Software Defined Networking(SDN)is programmable by separation of forwarding control through the centralization of the controller.The controller plays the role of the‘brain’that dictates the intelligent part of SDN technology.Various versions of SDN controllers exist as a response to the diverse demands and functions expected of them.There are several SDN controllers available in the open market besides a large number of commercial controllers;some are developed tomeet carrier-grade service levels and one of the recent trends in open-source SDN controllers is the Open Network Operating System(ONOS).This paper presents a comparative study between open source SDN controllers,which are known as Network Controller Platform(NOX),Python-based Network Controller(POX),component-based SDN framework(Ryu),Java-based OpenFlow controller(Floodlight),OpenDayLight(ODL)and ONOS.The discussion is further extended into ONOS architecture,as well as,the evolution of ONOS controllers.This article will review use cases based on ONOS controllers in several application deployments.Moreover,the opportunities and challenges of open source SDN controllers will be discussed,exploring carriergrade ONOS for future real-world deployments,ONOS unique features and identifying the suitable choice of SDN controller for service providers.In addition,we attempt to provide answers to several critical questions relating to the implications of the open-source nature of SDN controllers regarding vendor lock-in,interoperability,and standards compliance,Similarly,real-world use cases of organizations using open-source SDN are highlighted and how the open-source community contributes to the development of SDN controllers.Furthermore,challenges faced by open-source projects,and considerations when choosing an open-source SDN controller are underscored.Then the role of Artificial Intelligence(AI)and Machine Learning(ML)in the evolution of open-source SDN controllers in light of recent research is indicated.In addition,the challenges and limitations associated with deploying open-source SDN controllers in production networks,how can they be mitigated,and finally how opensource SDN controllers handle network security and ensure that network configurations and policies are robust and resilient are presented.Potential opportunities and challenges for future Open SDN deployment are outlined to conclude the article.

Static Analysis Techniques for Fixing Software Defects in MPI-Based Parallel Programs

The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed memorysystems.However, MPI implementations can contain defects that impact the reliability and performance of parallelapplications. Detecting and correcting these defects is crucial, yet there is a lack of published models specificallydesigned for correctingMPI defects. To address this, we propose a model for detecting and correcting MPI defects(DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blockingpoint-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defectsaddressed by the DC_MPI model include illegal MPI calls, deadlocks (DL), race conditions (RC), and messagemismatches (MM). To assess the effectiveness of the DC_MPI model, we performed experiments on a datasetconsisting of 40 MPI codes. The results indicate that the model achieved a detection rate of 37 out of 40 codes,resulting in an overall detection accuracy of 92.5%. Additionally, the execution duration of the DC_MPI modelranged from 0.81 to 1.36 s. These findings show that the DC_MPI model is useful in detecting and correctingdefects in MPI implementations, thereby enhancing the reliability and performance of parallel applications. TheDC_MPImodel fills an important research gap and provides a valuable tool for improving the quality ofMPI-basedparallel computing systems.

Towards Improving the Quality of Requirement and Testing Process in Agile Software Development:An Empirical Study

Software testing is a critical phase due to misconceptions about ambiguities in the requirements during specification,which affect the testing process.Therefore,it is difficult to identify all faults in software.As requirement changes continuously,it increases the irrelevancy and redundancy during testing.Due to these challenges;fault detection capability decreases and there arises a need to improve the testing process,which is based on changes in requirements specification.In this research,we have developed a model to resolve testing challenges through requirement prioritization and prediction in an agile-based environment.The research objective is to identify the most relevant and meaningful requirements through semantic analysis for correct change analysis.Then compute the similarity of requirements through case-based reasoning,which predicted the requirements for reuse and restricted to error-based requirements.Afterward,the apriori algorithm mapped out requirement frequency to select relevant test cases based on frequently reused or not reused test cases to increase the fault detection rate.Furthermore,the proposed model was evaluated by conducting experiments.The results showed that requirement redundancy and irrelevancy improved due to semantic analysis,which correctly predicted the requirements,increasing the fault detection rate and resulting in high user satisfaction.The predicted requirements are mapped into test cases,increasing the fault detection rate after changes to achieve higher user satisfaction.Therefore,the model improves the redundancy and irrelevancy of requirements by more than 90%compared to other clustering methods and the analytical hierarchical process,achieving an 80%fault detection rate at an earlier stage.Hence,it provides guidelines for practitioners and researchers in the modern era.In the future,we will provide the working prototype of this model for proof of concept.

HV Process Model of Software Development

Software Development Life Cycle (SDLC) is one of the major ingredients for the development of efficient software systems within a time frame and low-cost involvement. From the literature, it is evident that there are various kinds of process models that are used by the software industries for the development of small, medium and long-term software projects, but many of them do not cover risk management. It is quite obvious that the improper selection of the software development process model leads to failure of the software products as it is time bound activity. In the present work, a new software development process model is proposed which covers the risks at any stage of the development of the software product. The model is named a Hemant-Vipin (HV) process model and may be helpful for the software industries for development of the efficient software products and timely delivery at the end of the client. The efficiency of the HV process model is observed by considering various kinds of factors like requirement clarity, user feedback, change agility, predictability, risk identification, practical implementation, customer satisfaction, incremental development, use of ready-made components, quick design, resource organization and many more and found through a case study that the presented approach covers many of parameters in comparison of the existing process models. .

A Hybrid Model for Improving Software Cost Estimation in Global Software Development

Accurate software cost estimation in Global Software Development(GSD)remains challenging due to reliance on historical data and expert judgments.Traditional models,such as the Constructive Cost Model(COCOMO II),rely heavily on historical and accurate data.In addition,expert judgment is required to set many input parameters,which can introduce subjectivity and variability in the estimation process.Consequently,there is a need to improve the current GSD models to mitigate reliance on historical data,subjectivity in expert judgment,inadequate consideration of GSD-based cost drivers and limited integration of modern technologies with cost overruns.This study introduces a novel hybrid model that synergizes the COCOMO II with Artificial Neural Networks(ANN)to address these challenges.The proposed hybrid model integrates additional GSD-based cost drivers identified through a systematic literature review and further vetted by industry experts.This article compares the effectiveness of the proposedmodelwith state-of-the-artmachine learning-basedmodels for software cost estimation.Evaluating the NASA 93 dataset by adopting twenty-six GSD-based cost drivers reveals that our hybrid model achieves superior accuracy,outperforming existing state-of-the-artmodels.The findings indicate the potential of combining COCOMO II,ANN,and additional GSD-based cost drivers to transform cost estimation in GSD.

基于HEC-RAS的平陆运河河口段流速模拟及疏浚施工影响分析

针对河口段水流形态复杂多变,不利于组织河道疏浚施工的问题,以西部陆海新通道(平陆运河)为例,应用HEC-RAS一维非恒定流水力学计算模块,构建运河河口段水动力模型,并对模型糙率进行率定及模型精度进行验证。在此基础上,根据青年水闸(闸下)水位及流量特征,设定闸下4种流量(水位)工况,模拟分析河道流速的变化。结果表明:HEC-RAS模型在感潮河段模拟精度较高,具有较好的适用性;随断面流量的增加,石江墩河段S26一S32流速响应迅速,流速大值平均值较其他河段高约34.0%。研究成果可为运河河口段航道疏浚工程施工提供技术支撑。

Development and Evaluation of 3D Delivery Animation Software Designed to Improve the Mother’s and Spouse’s Satisfaction with Delivery

Purpose: To clarify the effectiveness of 3-D delivery animation software for the mother’s and husband’s satisfaction with delivery. Subjects and Method: We independently developed a software application used to display the pelvic region and explain the labor process. The study involved a collaboration with hospital staff who recruited 18 primiparous and 18 multiparous mothers who were hospitalized for delivery at Facility A. The midwife explained the process of delivery using the “Delivery Animation Software”. A self-administered, anonymous questionnaire was distributed and analyzed separately for primiparous and multiparous mothers and their husbands. Results: 1) For both primiparous and multiparous couples, both mothers and their husbands gained a significantly higher level of understanding after delivery than during pregnancy. 2) The Self-Evaluation Scale for Experience of Delivery results were as follows: “I did my best for the baby even if it was painful” was selected more often for “birth coping skills”;“reliable medical staff” was selected more often for “physiological birth process”;“the birth progressed as I expected” was selected frequently by primiparous mothers;and “the birth progressed smoothly” was selected often by multiparous mothers. 3) In terms of husbands’ satisfaction with the delivery, “I was satisfied with the delivery”, “I was given an easy-to-understand explanation”, and “They explained the process to me” was selected of primiparous and multiparous fathers. 4) All primiparous and multiparous mothers positively evaluated whether the delivery animation was helpful in understanding the process of delivery. Conclusion: The delivery animation was effective in improving the understanding and satisfaction of both the mothers and their husbands.

A Proposed Approach for Measuring Maturity Level of Software Delivery

Software delivery is vital for modern organizations, driving innovation and competitiveness. Measuring an organization’s maturity in software delivery is crucial for efficiency and quality. The Capability Maturity Model (CMM) framework provides a roadmap for improvement but assessing an organization’s CMM Level is challenging. This paper offers a quantitative approach tailored to the CMM framework, using Goal-Question-Metric (GQM) frame-works for each key process area (KPA). These frameworks include metrics and questions to compute maturity scores effectively. The study also refines practices into questions for a thorough assessment. The result is an Analysis Matrix that calculates weighted scores and an overall maturity score. This approach helps organizations assess and enhance their software delivery processes systematically, aiming for improved practices and growth.

A Tutorial on Federated Learning from Theory to Practice:Foundations,Software Frameworks,Exemplary Use Cases,and Selected Trends

When data privacy is imposed as a necessity,Federated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized environment.FL allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third parties.This paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data sources.By virtue of FL,models can be learned from all such distributed data sources while preserving data privacy.The aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software frameworks.Furthermore,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describing the main components of FL,from key elements to FL categories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL landscape.The ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scientists willing to explore the capabilities of FL in practical applications.

Software Vulnerability Mining and Analysis Based on Deep Learning

In recent years,the rapid development of computer software has led to numerous security problems,particularly software vulnerabilities.These flaws can cause significant harm to users’privacy and property.Current security defect detection technology relies on manual or professional reasoning,leading to missed detection and high false detection rates.Artificial intelligence technology has led to the development of neural network models based on machine learning or deep learning to intelligently mine holes,reducing missed alarms and false alarms.So,this project aims to study Java source code defect detection methods for defects like null pointer reference exception,XSS(Transform),and Structured Query Language(SQL)injection.Also,the project uses open-source Javalang to translate the Java source code,conducts a deep search on the AST to obtain the empty syntax feature library,and converts the Java source code into a dependency graph.The feature vector is then used as the learning target for the neural network.Four types of Convolutional Neural Networks(CNN),Long Short-Term Memory(LSTM),Bi-directional Long Short-Term Memory(BiLSTM),and Attention Mechanism+Bidirectional LSTM,are used to investigate various code defects,including blank pointer reference exception,XSS,and SQL injection defects.Experimental results show that the attention mechanism in two-dimensional BLSTM is the most effective for object recognition,verifying the correctness of the method.

基于HEC-RAS的千河流域宝鸡段溃坝洪水风险评估

水库是大量蓄水的水利工程,对生产生活提供便利,但同时溃坝洪水一般具有极高伤害性,进行洪水溃坝风险评估对保障下游居民安全有重要意义.以千河流域宝鸡段的冯家山水库为例,利用HEC-RAS模型,模拟了溃坝洪水淹没进程,并对淹没范围和流速分布进行分析.洪水演进数据结合灾害系统理论构建风险评估模型,从危险性、易损性角度,通过GIS方法实现风险评估并分为五个等级.结果表明:①100年一遇洪水淹没总面积为274.581km^(2),危险水深占全部水深93%,河道最高流速可达到14.34m/s.②洪水危险性自西向东逐渐降低,整体危险性中度偏下.③易损性在河道附近两侧较低,距离河道较远的部分易损性提高,整体是处于中低水平.④溃坝洪水风险水平中等,对淹没区各乡镇风险划分,危险区明显集中,主要在贾村镇、长青镇、千河镇以及揉谷镇.研究成果对千河流域宝鸡段下游洪水避难预警系统建设和人口转移有一定参考意义.

SCIRD: Revealing Infection of Malicious Software in Edge Computing-Enabled IoT Networks

The Internet of Things(IoT)has characteristics such as node mobility,node heterogeneity,link heterogeneity,and topology heterogeneity.In the face of the IoT characteristics and the explosive growth of IoT nodes,which brings about large-scale data processing requirements,edge computing architecture has become an emerging network architecture to support IoT applications due to its ability to provide powerful computing capabilities and good service functions.However,the defense mechanism of Edge Computing-enabled IoT Nodes(ECIoTNs)is still weak due to their limited resources,so that they are susceptible to malicious software spread,which can compromise data confidentiality and network service availability.Facing this situation,we put forward an epidemiology-based susceptible-curb-infectious-removed-dead(SCIRD)model.Then,we analyze the dynamics of ECIoTNs with different infection levels under different initial conditions to obtain the dynamic differential equations.Additionally,we establish the presence of equilibrium states in the SCIRD model.Furthermore,we conduct an analysis of the model’s stability and examine the conditions under which malicious software will either spread or disappear within Edge Computing-enabled IoT(ECIoT)networks.Lastly,we validate the efficacy and superiority of the SCIRD model through MATLAB simulations.These research findings offer a theoretical foundation for suppressing the propagation of malicious software in ECIoT networks.The experimental results indicate that the theoretical SCIRD model has instructive significance,deeply revealing the principles of malicious software propagation in ECIoT networks.This study solves a challenging security problem of ECIoT networks by determining the malicious software propagation threshold,which lays the foundation for buildingmore secure and reliable ECIoT networks.

ProTSA: A Testing Process for Automotive Software Domain

This study evaluates the development of a testing process for the automotive software domain, highlighting challenges stemming from the absence of adequate processes. The research demonstrates the application of Design Science Research methodology in developing, an automotive software testing process—ProTSA, using six functional testing modules. Additionally, the study evaluates the benefits of implementing ProTSA in a specific Original Equipment Manufacturer (OEM) using an experimental single-case approach with industry professionals’ participation through a survey. The study concludes that combining testing techniques with effective communication and alignment is crucial for enhancing software quality. Furthermore, survey data indicates that implementing ProTSA leads to productivity gains by initiating tests early, resulting in time savings in the testing program and increased productivity for the testing team. Future work will explore implementing ProTSA in cybersecurity, over-the-air software updates, and autonomous vehicle testing processes. .