Prediction of Shear Bond Strength of Asphalt Concrete Pavement Using Machine Learning Models and Grid Search Optimization Technique

Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures.The study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Extra Trees(ET),and Light Gradient Boosting Machine(LGBM),to predict SBS based on easily determinable input parameters.Also,the Grid Search technique was employed for hyper-parameter tuning of the ML models,and cross-validation and learning curve analysis were used for training the models.The models were built on a database of 240 experimental results and three input variables:temperature,normal pressure,and tack coat rate.Model validation was performed using three statistical criteria:the coefficient of determination(R2),the Root Mean Square Error(RMSE),and the mean absolute error(MAE).Additionally,SHAP analysis was also used to validate the importance of the input variables in the prediction of the SBS.Results show that these models accurately predict SBS,with LGBM providing outstanding performance.SHAP(Shapley Additive explanation)analysis for LGBM indicates that temperature is the most influential factor on SBS.Consequently,the proposed ML models can quickly and accurately predict SBS between two layers of asphalt concrete,serving practical applications in flexible pavement structure design.

Stability Prediction in Smart Grid Using PSO Optimized XGBoost Algorithm with Dynamic Inertia Weight Updation

Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the grid.It also possesses a better impact on averting overloading and permitting effective energy storage.Even though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized loss.To overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning algorithms.To accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data features.Then,the pre-processed data are taken for training and testing.Following that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in SG.Since PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed characteristics.The hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on prediction.Regression results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.

Service-Oriented Advanced Metering Infrastructure for Smart Grids

Advanced Metering Infrastructure （AMI） enables smart grids to involve power consumers in the business process of power generation transmission, distribution and consumption. However, the participant of consumers challenges the current power systems with system integration and cooperation and security issues. In this paper, the authors introduce a service-oriented approach to AMI aiming at solving the intercommunication problem and meanwhile providing a trust and secure environment for smart grids. In this approach heterogeneous systems expose services to the network. System integration and cooperation are done through service composition. A generic service interfacing method is designed to develop standardized services for heterogeneous power systems. Moreover, role-based access control mechanism is used to guarantee the secure access to smart grids. With the seamless communication between consumers and power systems and among power systems themselves, this service-oriented AMI can associate consumers with actual system workload and furthermore support the intelligent running of power systems.

Shared and Service-oriented CNC Machining System for Intelligent Manufacturing Process

To improve efficiency, reduce cost, ensure quality effectively, researchers on CNC machining have focused on virtual machine tool, cloud manufacturing, wireless manufacturing. However, low level of information shared among different systems is a common disadvantage. In this paper, a machining database with data evaluation module is set up to ensure integrity and update. An online monitoring system based on internet of things and multi-sensors ＂feel＂ a variety of signal features to ＂percept＂ the state in CNC machining process. A high efficiency and green machining parameters optimization system ＂execute＂ service-oriented manufacturing, intelligent manufacturing and green manufacturing. The intelligent CNC machining system is applied in production. CNC machining database effectively shares and manages process data among different systems. The prediction accuracy of online monitoring system is up to 98.8% by acquiring acceleration and noise in real time. High efficiency and green machining parameters optimization system optimizes the original processing parameters, and the calculation indicates that optimized processing parameters not only improve production efficiency, but also reduce carbon emissions. The application proves that the shared and service-oriented CNC machining system is reliable and effective. This research presents a shared and service-oriented CNC machining system for intelligent manufacturing process.

Service-oriented weapon systems of system portfolio selection method

Weapon system portfolio selection is an important combinatorial problem that arises in various applications,such as weapons development planning and equipment procurement,which are of concern to military decision makers.However,the existing weapon system-of-systems(SoS)is tightly coupled.Because of the diversity and connectivity of mission requirements,it is difficult to describe the direct mapping relationship from the mission to the weapon system.In the latest service-oriented research,the introduction of service modules to build a service-oriented,flexible,and combinable structure is an important trend.This paper proposes a service-oriented weapon system portfolio selection method,by introducing service to serve as an intermediary to connect missions and system selection,and transferring the weapon system selection into the service portfolio selection.Specifically,the relation between the service and the task is described through the service-task mapping matrix;and the relation between the service and the weapon system is constructed through the servicesystem mapping matrix.The service collaboration network to calculate the flexibility and connectivity of each service portfolio is then established.Through multi-objective programming,the optimal service portfolios are generated,which are further decoded into weapon system portfolios.

A Service-Oriented Architecture of Cyber-Physical System and Study on Two Key Issues

As the basis of designing and implementing a cyber-physical system (CPS), architecture research is very important but still at preliminary stage. Since CPS includes physical components, time and space constraints seriously challenge architecture study. In this paper, a service-oriented architecture of CPS was presented. Further, a two-way time synchronization algorithm for CPS service composition was put forward. And a formal method, for judging if actual CPS service meets space constraints, was suggested, which was based on space-π-calculus proposed. Finally, a case study was performed and CPS business process designed by the model and the proposed methods could run well. The application of research conclusion implies that it has rationality and feasibility.

Privacy Protection Framework with Defined Policies for Service-Oriented Architecture

Service-Oriented Architecture (SOA) is a computer systems design concept which aims to achieve reusability and integration in a distributed environment through the use of autonomous, loosely coupled, interoperable abstractions known as services. In order to interoperate, communication between services is very important due to their autonomous nature. This communication provides services with their functional strengths, but also creates the opportunity for the loss of privacy. In this paper, a Privacy Protection Framework for Service-Oriented Architecture (PPFSOA) is described. In this framework, a Privacy Service (PS) is used in combination with privacy policies to create privacy contracts that outline what can and cannot be done with a consumer’s personally identifiable information (PII). The privacy policy consists of one-to-many privacy rules, with each rule created from a set of six privacy elements: collector, what, purpose, retention, recipient and trust. The PS acts as an intermediary between the service consumer and service provider, to establish an unbiased contract before the two parties begin sending PII. It is shown how many Privacy Services work together to form the privacy protection framework. An examination of what current approaches to protecting privacy in an SOA environment is also presented. Finally, the operations the PS must perform in order to fulfill its tasks are outlined.

An agent-based service-oriented integration architecture for chemical process automation

In reality, traditional process control system built upon centralized and hierarchical structures presents a weak response to change and is easy to shut down by single failure. Aiming at these problems, a new agent-based service-oriented integration architecture was proposed for chemical process automation system. Web services were dynamically orchestrated on the internet and agent behaviors were built in them. Data analysis, model, optimization, control, fault diagnosis and so on were capsuled into different web services. Agents were used for service compositions by negotiation. A prototype system of poly(ethylene terephthalate) process automation was used as the case study to demonstrate the validation of the integration.

Describing Service-Oriented Architecture by Extended Darwin

Describing Service-Oriented Architecture （SOA） is critical in the development of Web based system, in this paper, an approach for describing SOA by extended Darwin is proposed. The requirements for describing SOA, which are different from that of ordinary architecture, are highlighted firstly, and then a solution for extending Darwin is presented. Using the extended Darwin, service components and connectors can be described explicit by the extended construct, as well as precise operational semantics of SOA by the π-calculus. Finally an example of supply-chain management system is given for manifesting the effect of the extended Darwin.

A Collaborative Framework for Mould Design Based on Service-Oriented Architecture

Aiming at improving the traditional object-oriented three-layer architecture for collaborative design, a service-oriented framework for mould design is established by adding a Web service layer to decoupling the direct linking of user interface to functional modules,making the system being platform independent and programming language independent,greatly facilitating the reuse of existing software and hardware resources. .NET technology is adopted to implement the framework. An example scenario is introduced to demonstrate how the system works.

Rapid Service-Oriented Business Process Generation Method Based on Ontology

Service-oriented business process generation is a key activity in the ServiceOriented Architecture(SOA)business lifecycle,and most of the other activities such as application execution depend on the business process being developed.After the business requirements are acquired,a developer has to use specific programming technologies to orchestrate web services to generate a deployable business process.It is time-consuming to specify all the business processes from lowlevel web services,especially for an enterprise that focuses on a series of similar businesses.This paper proposes a rapid service-oriented business process generation method with domain-specific assets specified in ontology systems.Assets with different levels of granularity are reused to refine the high level business process framework for executable business processes using the Business Process Executional Language(BPEL).The new methodology significantly simplifies service-oriented business process generation by reusing assets to construct business processes.A business process generation tool is also implemented to support the efficient visual design of SOA processes with the proposed method.This paper verifies the proposed method using a shipment tracking case.The studies show that the number of reusable assets increases significantly as these projects progress,and the business process generation speed also increases at the same time.

Study on Construction of Service-oriented Government at Grass-roots Level and Development of Farmers' Professional Cooperatives

Features of farmers' professional cooperatives in China are introduced.The establishment of these organizations is spontaneous.Their operation shows self-government.And their targets are highly pertinent.Then,I analyze the existing problems in farmers' professional cooperatives and vacancy of government functions.These cooperatives are single in content,narrow in service field,not scientific in management mode,and low in efficiency.In addition,government policy support is not put in place for these cooperatives.In view of these situations,firstly,it is proposed to speed up construction of service-oriented government at grass-roots level and promote development of farmers' professional cooperatives.Secondly,it is suggested to set up awareness of service-oriented government and enhance guidance and service for farmers' professional cooperatives.Thirdly,we should increase financial support and guide these organizations to achieve a benign development.Fourthly,we should strengthen propaganda to raise popularity and influence power of these organizations.Fifthly,it is recommended to make clear the guidance and leading functions of regional supply and marketing cooperatives to raise service efficiency.Finally,we should establish scientific evaluation and incentive system to push forward healthy development of farmers' professional cooperatives.

Service-oriented air combat simulation architecture

Service-oriented air combat simulation architecture is proposed.The core design goal is high agility which represents the ability to accommodate the simulation requirements change.Its main idea is to design model units as services that can communicate and interoperate with any other services at runtime.A service is autonomous and is fully defined by a description contract which contains some combination of syntactic,semantic,and behavioral information.Based on the architecture,air combat simulation system can be described as an abstract composition of description contracts.It becomes concrete at run time as services that implement the constituent description contracts are discovered and bind.The whole process is a continuous run-time activity that responds to simulation needs and the availability of services.This provides benefits of implementation transparency and minimal dependency between models.Thus,simulation system can minimize the impact of change on it and increase the overall efficiency to respond to requirements change.

Performance Assessment of a Real PV System Connected to a Low-Voltage Grid

The generation of photovoltaic(PV)solar energy is increasing continuously because it is renewable,unlimited,and clean energy.In the past,generation systems depended on non-renewable sources such as oil,coal,and gas.Therefore,this paper assesses the performance of a 51 kW PV solar power plant connected to a low-voltage grid to feed an administrative building in the 6th of October City,Egypt.The performance analysis of the considered grid-connected PV system is carried out using power system simulator for Engineering(PSS/E)software.Where the PSS/E program,monitors and uses the power analyzer that displays the parameters and measures some parameters such as current,voltage,total power,power factor,frequency,and current and voltage harmonics,the used inverter from the type of grid inverter for the considered system.The results conclude that when the maximum solar radiation is reached,the maximum current can be obtained from the solar panels,thus obtaining the maximum power and power factor.Decreasing total voltage harmonic distortion,a current harmonic distortion within permissible limits using active harmonic distortion because this type is fast in processing up to 300 microseconds.The connection between solar stations and the national grid makes the system more efficient.

Customization and Performance of Service-Oriented Manufacturing Information System: The Mediating Effect of Information System Flexibility

The purpose of this paper is to provide empirical evidence for the validity of the relationship between service-oriented manufacturing information system (SMIS) customization and performance from three aspects: data flexibility, process flexibility and system flexibility. We select some questionnaires from the third round of High-performance manufacturing (HPM) data to construct the construct, verify the reliability and validity of the construct, extract principal components, and analyze the mediating effect by using multiple chain linear regression and structural equation model. The results show that SMIS customization has a significant impact on its performance, and this effect works through its flexibility. More specifically, it is the multiple chain mediation effect composed of data flexibility, process flexibility and system flexibility. The importance of SMIS customization and flexibility to the organization is made clear, which helps practitioners understand the internal mechanism that affects SMIS performance, so as to use limited resources to improve system performance.

Assessing Internal Software Quality Attributes of the Object-Oriented and Service-Oriented Software Development Paradigms: A Comparative Study

Service-Oriented Architecture (SOA) is becoming the dominant approach for developing and organizing distributed enterprise-wide applications. Although the concepts of SOA have been extensively described in the literature and in-dustry, the effects of adopting SOA on software quality are still unclear. The aim of the paper is to analyze how adopt-ing SOA can affect software quality as opposed to the Object-Oriented (OO) paradigm and expose the differential implications of adopting both paradigms on software quality. The paper provides a brief introduction of the architectural differences between the Service-Oriented (SO) and OO paradigms and a description of internal software quality metrics used for the comparison. The effects and differences are exposed by providing a case study architected for both paradigms. The quantitative measure concluded in the paper showed that a software system developed using SOA approach provides higher reusability and lower coupling among software modules, but at the same time higher complexity than those of the OO approach. It was also found that some of the existing OO software quality metrics are inapplicable to SOA software systems. As a consequence, new metrics need to be developed specifically to SOA software systems.

Grid System Analysis of Urban Flora of Bukhara City (Uzbekistan)

This article presents information on the study of the flora of Uzbekistan based on grid system mapping. The urban flora of the city of Bukhara was researched in it. As a result of research, the territory of Bukhara city was divided into 85 indexes based on 1 × 1 km2 grid mapping system. The diversity and density of species in the indexes are determined. The influence of anthropogenic factors on the diversity of species in the indexes is determined.

RoGRUT: A Hybrid Deep Learning Model for Detecting Power Trapping in Smart Grids

Electricity theft is a widespread non-technical issue that has a negative impact on both power grids and electricity users.It hinders the economic growth of utility companies,poses electrical risks,and impacts the high energy costs borne by consumers.The development of smart grids is crucial for the identification of power theft since these systems create enormous amounts of data,including information on client consumption,which may be used to identify electricity theft using machine learning and deep learning techniques.Moreover,there also exist different solutions such as hardware-based solutions to detect electricity theft that may require human resources and expensive hardware.Computer-based solutions are presented in the literature to identify electricity theft but due to the dimensionality curse,class imbalance issue and improper hyper-parameter tuning of such models lead to poor performance.In this research,a hybrid deep learning model abbreviated as RoGRUT is proposed to detect electricity theft as amalicious and non-malicious activity.The key steps of the RoGRUT are data preprocessing that covers the problem of class imbalance,feature extraction and final theft detection.Different advanced-level models like RoBERTa is used to address the curse of dimensionality issue,the near miss for class imbalance,and transfer learning for classification.The effectiveness of the RoGRUTis evaluated using the dataset fromactual smartmeters.A significant number of simulations demonstrate that,when compared to its competitors,the RoGRUT achieves the best classification results.The performance evaluation of the proposed model revealed exemplary results across variousmetrics.The accuracy achieved was 88%,with precision at an impressive 86%and recall reaching 84%.The F1-Score,a measure of overall performance,stood at 85%.Furthermore,themodel exhibited a noteworthyMatthew correlation coefficient of 78%and excelled with an area under the curve of 91%.

A Long-Time-Step-Permitting Tracer Transport Model on the Regular Latitude–Longitude Grid

If an explicit time scheme is used in a numerical model, the size of the integration time step is typically limited by the spatial resolution. This study develops a regular latitude–longitude grid-based global three-dimensional tracer transport model that is computationally stable at large time-step sizes. The tracer model employs a finite-volume flux-form semiLagrangian transport scheme in the horizontal and an adaptively implicit algorithm in the vertical. The horizontal and vertical solvers are coupled via a straightforward operator-splitting technique. Both the finite-volume scheme's onedimensional slope-limiter and the adaptively implicit vertical solver's first-order upwind scheme enforce monotonicity. The tracer model permits a large time-step size and is inherently conservative and monotonic. Idealized advection test cases demonstrate that the three-dimensional transport model performs very well in terms of accuracy, stability, and efficiency. It is possible to use this robust transport model in a global atmospheric dynamical core.

Physics-Constrained Robustness Enhancement for Tree Ensembles Applied in Smart Grid

With the widespread use of machine learning(ML)technology,the operational efficiency and responsiveness of power grids have been significantly enhanced,allowing smart grids to achieve high levels of automation and intelligence.However,tree ensemble models commonly used in smart grids are vulnerable to adversarial attacks,making it urgent to enhance their robustness.To address this,we propose a robustness enhancement method that incorporates physical constraints into the node-splitting decisions of tree ensembles.Our algorithm improves robustness by developing a dataset of adversarial examples that comply with physical laws,ensuring training data accurately reflects possible attack scenarios while adhering to physical rules.In our experiments,the proposed method increased robustness against adversarial attacks by 100%when applied to real grid data under physical constraints.These results highlight the advantages of our method in maintaining efficient and secure operation of smart grids under adversarial conditions.