High Level Architecture Research Articles

Decoding Spatial Tissue Architecture: A Scalable Bayesian Topic Model for Multiplexed Imaging Analysis.

Recent progress in multiplexed tissue imaging is advancing the study of tumor microenvironments to enhance our understanding of treatment response and disease progression. Cellular neighborhood analysis is a popular computational approach for these complex image data. Despite its popularity, there are significant challenges, including high computational demands that limit feasibility for large-scale applications and the lack of a principled strategy for integrative analysis across images. This absence hampers the precise and consistent identification of spatial features and tracking of their dynamics over disease progression. To overcome these challenges, we introduce SpaTopic, a spatial topic model designed to decode high-level spatial architecture across multiplexed tissue images. This algorithm integrates both cell type and spatial information within a topic modelling framework, originally developed for natural language processing and adapted for computer vision. Spatial information is incorporated into the flexible design of documents, representing densely overlapping regions in images. The model employs an efficient collapsed Gibbs sampling algorithm for both statistical and computational inference. We benchmarked the performance against five state-of-the-art algorithms through various case studies using different single-cell spatial transcriptomic and proteomic imaging platforms across different tissue types. Our findings demonstrate that SpaTopic consistently identifies biologically and clinically significant spatial "topics" such as tertiary lymphoid structures (TLSs) and tracks dynamic changes in spatial features over disease progression. Its computational efficiency and broad applicability across various molecular imaging platforms will enhance the analysis of large-scale tissue imaging datasets.

An Integrated Software-Defined Networking–Network Function Virtualization Architecture for 5G RAN–Multi-Access Edge Computing Slice Management in the Internet of Industrial Things

The Internet of Things (IoT), namely, the set of intelligent devices equipped with sensors and actuators and capable of connecting to the Internet, has now become an integral part of the most competitive industries, as it enables optimization of production processes and reduction in operating costs and maintenance time, together with improving the quality of products and services. More specifically, the term Industrial Internet of Things (IIoT) identifies the system which consists of advanced Internet-connected equipment and analytics platforms specialized for industrial activities, where IIoT devices range from small environmental sensors to complex industrial robots. This paper presents an integrated high-level SDN-NFV architecture enabling clusters of smart devices to interconnect and manage the exchange of data with distributed control processes and databases. In particular, it is focused on 5G RAN-MEC slice management in the IIoT context. The proposed system is emulated by means of two distinct real-time frameworks, demonstrating improvements in connectivity, energy efficiency, end-to-end latency and throughput. In addition, its scalability, modularity and flexibility are assessed, making this framework suitable to test advanced and more applications.

A Systematic Review of the Evolution of the Concept of Resilience in the Construction Industry

Despite the growing importance of resilience in the construction sector, the existing literature often focuses on specific systems or functions without integrating a holistic view of resilience. Hence, there is a pressing need for comprehensive research that synthesizes these dimensions to provide a clearer framework for resilience in the construction industry. To find the inherent patterns and trends of resilience, we carried out research from the perspective of three elements defining resilience (the subject of resilience, the influencing factors, and the subject’s ability). In this systematic review, 70 articles were rigorously handpicked from Web of Science, Scopus, and authoritative journals and divided into 6 major categories and 24 subcategories for quantitative and temporal analyses. The evolution of resilience in the construction industry is investigated in this review, shifting from specific systems facing defined impacts to addressing the complexities of diverse and unpredictable disruptions within VUCA (volatile, uncertain, complex, and ambiguous) environments. As Industry 4.0 and digital transformation progress, the significance of functional resilience across various system levels becomes apparent, necessitating a reconstitution into structural, operational, and supply chain resilience. This study encourages the use of advanced methodologies like big data, AI, and high-level architecture (HLA) federated simulations to predict disruptions and optimize resilience strategies, thus providing a robust foundation for handling future uncertainties.

Report on the 1st International Workshop on Designing Software (Designing 2024)

Software design is a set of activities and decisions involved in constructing a software system, ranging from high-level architecture and conceptual design to code design. Decisions made during the design process have a long-lasting impact on various qualities of a system, such as modularity, maintainability, scalability, robustness, security, usability, and performance. Despite its important role in software development, design is still considered something of an art-particularly in terms of design as an activity rather than design as a product or outcome. With the increasing societal impact of software and the potential use of AI in development, design has a new and larger role to play in the engineering of modern software systems. This new workshop aims to bring together researchers, practitioners, and educators who are interested in any aspect of software design, to discuss novel approaches to design, and to identify open challenges and future directions for the field.

Cognitive Principles for Remote Condition Monitoring Applied to a Rail Pantograph System

Remote condition monitoring (RCM) aims to ensure the availability of railway assets. Previous work has indicated the importance of a user-centred RCM design approach based on cognitive principles, but there has been no known demonstration of the application of these principles. The following paper takes this theory-based approach and applies it to the design of an RCM system for the rail pantograph/Overhead Line (OHL) system. The paper first presents a high-level conceptual architecture, based on four stages of cognitive decision-making (notification, acceptance, analysis and clearance), linked to the wider monitoring architecture. Second, the paper uses cognitive principles to propose demonstration Human–Machine Interface designs for the OHL system. These HMIs were presented in an evaluation with subject matter experts. The outcomes of the process generated user-centred design recommendations for RCM. Furthermore, the evaluation suggested the importance of multiple paths through the HMI dependent on the type and urgency of fault. Finally, the outcomes of the evaluation also highlighted the importance of considering context when deploying user-centred RCM.

Blockchain for Healthcare Management Systems : A Survey on Interoperability and Security

In recent years it has been shown that the secure exchange of medical information significantly benefits people’s life quality, improving their care and treatment. The interoperability of the entire healthcare ecosystem is a constant challenge, and even more, with all the risks posed to the security of healthcare information. Blockchain technology is emerging as one of the main alternatives when it comes to finding a balance in the healthcare ecosystem. However, the constant development of new Blockchain technologies and the evolution of healthcare systems make it difficult to find established proposals. From an architectural point of view, the design of blockchain-based solutions requires trade-offs e.g., security and interoperability. This paper focuses on two main objectives, in the first one, it was carried out a Systematic Literature Review for exploring architectural mechanisms used to support the interoperability and security of Blockchain-based Health Management Systems. Taking into account of results, a series of scenarios were generated where these mechanisms can be used along with their context, issues, and various architectural concerns (interoperability and security). In the second objective, a high-level architecture and its validation were proposed through an experiment for the whole process of developing a Domain Specific Language, using the Model Driven Engineering methodology for specific Smart Contracts.

How to Design Reinforcement Learning Methods for the Edge: An Integrated Approach toward Intelligent Decision Making

Extensive research has been carried out on reinforcement learning methods. The core idea of reinforcement learning is to learn methods by means of trial and error, and it has been successfully applied to robotics, autonomous driving, gaming, healthcare, resource management, and other fields. However, when building reinforcement learning solutions at the edge, not only are there the challenges of data-hungry and insufficient computational resources but also there is the difficulty of a single reinforcement learning method to meet the requirements of the model in terms of efficiency, generalization, robustness, and so on. These solutions rely on expert knowledge for the design of edge-side integrated reinforcement learning methods, and they lack high-level system architecture design to support their wider generalization and application. Therefore, in this paper, instead of surveying reinforcement learning systems, we survey the most commonly used options for each part of the architecture from the point of view of integrated application. We present the characteristics of traditional reinforcement learning in several aspects and design a corresponding integration framework based on them. In this process, we show a complete primer on the design of reinforcement learning architectures while also demonstrating the flexibility of the various parts of the architecture to be adapted to the characteristics of different edge tasks. Overall, reinforcement learning has become an important tool in intelligent decision making, but it still faces many challenges in the practical application in edge computing. The aim of this paper is to provide researchers and practitioners with a new, integrated perspective to better understand and apply reinforcement learning in edge decision-making tasks.

Automating Troubleshooting Network Issues with AIOps and Machine Learning

Network issues can significantly impact an organization's productivity and profitability. Traditional troubleshooting methods are often time-consuming and reactive, relying on human expertise to identify and resolve problems. The emerging field of AIOps offers a promising solution by leveraging machine learning and advanced analytics to automate the troubleshooting process. This paper explores the use of AIOps and machine learning in automating network troubleshooting, presenting real-life scenarios, a high-level architecture, and a comprehensive diagram to illustrate the approach.

A Low-Power Analog Integrated Euclidean Distance Radial Basis Function Classifier

This study introduces a low-power analog integrated Euclidean distance radial basis function classifier. The high-level architecture is composed of several Manhattan distance circuits in connection with a current comparator circuit. Notably, each implementation was designed with modularity and scalability in mind, effectively accommodating variations in the classification parameters. The proposed classifier’s operational principles are meticulously detailed, tailored for low-power, low-voltage, and fully tunable implementations, specifically targeting biomedical applications. This design methodology materialized within a 90 nm CMOS process, utilizing the Cadence IC Suite for the comprehensive management of both the schematic and layout design aspects. During the verification phase, post-layout simulation results were meticulously cross-referenced with software-based classifier implementations. Also, a comparison study with related analog classifiers is provided. Through the simulation results and comparative study, the design architecture’s accuracy and sensitivity were effectively validated and confirmed.

Empowering Electronic Health Record Systems (EHRs) with Cloud Storage Management

We present a novel method for building cloud based interoperable electronic health record (EHR) systems. All members of the healthcare ecosystem, including patients, providers, and payers, can benefit from cloud computing in a number of ways. The exchange of healthcare information among diverse stakeholders has been significantly hampered by the lack of standardised data interoperability. In order to solve this problem, semantic interoperability is used. With the use of a reference model that outlines a common set of data structures and an archetype model that details the characteristics of clinical data, we employ a general design technique. The loosely connected, asynchronously communicating components of the application are created utilising the cloud component model technique. This article discusses our methods for attaining semantic interoperability, data integration, and high-level architecture of our EHR system.

Fintech's Role in Green Finance: Procuring Funds for Sustainable Energy

Green financing is the furthermost significant name for the procurement of sustainable energy projects like renewable energy etc. Green bonds and green insurance are currently the two financing mechanisms for environmental businesses' sustainable procurement. There have been some problems that the issuing companies and the investors have encountered during the green bond procurement process. However, financial technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), cloud computing, blockchain, and robotics are promising solutions for green financing because they have already proven their significance and impact in a variety of fields such as finance and sustainable energy projects. The purpose of this study is to address and analyze the role and significance of fintech technologies in the procurement of greenfinanced sustainable infrastructure, as well as highlight research implications, limitations, and future work. In this study, we have followed inclusion and exclusion of the articles on the basis of key criteria. Following this, we have discussed the perspective of the different existing research on green finance, green bonds, insurance, fintech and sustainability for the sustainable energy procurement. The findings of the study are Fintech based high-level architecture proposed for the procurement process of funds for green financed sustainable energy projects. This study is the first attempt to group information from previously published research about green financing and integration of fintech for the smooth process of procurement of funds for sustainable energy projects.

High-level control architecture of lower limb exoskeleton: A review

As a rehabilitation robot for aiding in the movement of lower limbs, the lower limb exoskeleton is a beneficial device. In order to make the most effective use of the exoskeleton, the control strategy plays a crucial role. This review paper provides a background and classification of lower limb exoskeleton control strategies, such as model-based and hierarchy-based control. Further, we presented mainly the high-level control architecture of lower limb exoskeletons, which is aimed at detecting the intention of human movement. An in-depth discussion is provided in this paper regarding manual user input (MUI), surface electromyography (sEMG), and brain-computer interface (BCI). Many people need exoskeletons, which is why this review was written. Exoskeletons, however, are expensive and cannot be mass-produced, and their control methods are immature, making them ineffective. Thus, the objective of this review is to identify research gaps and common limitations in previous research to obtain future directions for improving the usability of the control mechanism. In an alternative approach, MUI and BCI are combined to reduce the time spent switching movement modes and the amount of concentration required to do so.

Traffic sign detection based on classic visual recognition models

This paper aims to assess the effectiveness of various object detection high-level architectures, including Faster R-CNN, R-FCN, SSD, and YOLO, in recognizing traffic signs. Since traffic sign recognition is a critical element of driver-assistance and autonomous driving systems, this study explores how five distinct architectures for feature extracting (ResNet V1 50, ResNet V1 101, Inception V2, Inception-ResNet-V2, and Darknet-19) can enhance the performance of these meta-architectures. To evaluate the models, the authors fine-tuned a pre-trained object recognition algorithm on the German Traffic Sign Detection Benchmark (GTSDB) dataset. While the Faster R-CNN Inception Resnet v2 proved to be the most accurate among the models, the unbalanced distribution of mandatory, prohibitory, and danger signs in the GTSDB dataset may have caused biased results in object detection. Overall, the result obtained from this study can help improve the accuracy of driver-assistance and autonomous driving systems by providing insights into the performance of various object detection models in recognizing traffic signs.

Robot Operating System 2 (ROS2)-Based Frameworks for Increasing Robot Autonomy: A Survey

Future challenges in manufacturing will require automation systems with robots that are increasingly autonomous, flexible, and hopefully equipped with learning capabilities. The flexibility of production processes can be increased by using a combination of a flexible human worker and intelligent automation systems. The adoption of middleware software such as ROS2, the second generation of the Robot Operating System, can enable robots, automation systems, and humans to work together on tasks that require greater autonomy and flexibility. This paper has a twofold objective. Firstly, it provides an extensive review of existing literature on the features and tools currently provided by ROS2 and its main fields of application, in order to highlight the enabling aspects for the implementation of modular architectures to increase autonomy in industrial operations. Secondly, it shows how this is currently potentially feasible in ROS2 by proposing a possible high-level and modular architecture to increase autonomy in industrial operations. A proof of concept is also provided, where the ROS2-based framework is used to enable a cobot equipped with an external depth camera to perform a flexible pick-and-place task.

THE WAYS OF INTRODUCING AI/ML-BASED PREDICTION METHODS FOR THE IMPROVEMENT OF THE SYSTEM OF GOVERNMENT SOCIO-ECONOMIC ADMINISTRATION IN UKRAINE

The objective of the article is to develop and test in practice a mechanism for constructing AI/ML-based predictions, adapted for use in the system of government socio-economic administration in Ukraine. Research design is represented by several methods like qualitative analysis in order to identify potential benefits of AI use in different spheres of government administration, synthesis to generate new datasets for the experiment, and abstraction to abstract from the current situation in Ukraine, population displacement, uneven statistics reporting. Among empirical methods are prediction and experimental methods to construct a mechanism for the implementation of AI/ML prediction methods in public administration, develop a high-level architecture of the AI/ML prediction system, and create and train the COVID-19 prediction neuron network. A holistic vision of the AI/ML-based prediction construction mechanism, depending on data taken from state official online platforms, is presented, in addition, the ways of its possible practical application for the improvement of the national system of state socio-economic administration are described. The main condition and guarantee of obtaining accurate results is access to quality data through platforms such as Diia, HELSI, national education platforms, government banks, etc. The findings of the research suggest that wide implementation of AI/ML-based prediction technologies will allow the government in perspective to increase the efficiency of the use of budgetary resources, the effectiveness of the government target programs, improve the quality of public administration and to better satisfy the citizens’ demand. Future studies should be done to overcome the limitations of the approach: find a way to protect and extract sensitive information from government platforms, fight neural network bias, and create a more perfect system that is able to make multiparameter predictions and is also self-improving on the basis of the obtained results.

Investigation of Secure Health Monitoring System Using IOT

The rapid progress of technology, particularly the Internet of Things (IoT), has introduced exciting opportunities for transforming the healthcare sector. One significant area where IoT has made a significant impact is in the creation of secure health monitoring systems. These systems utilize IoT devices and sensors to gather and transmit live health data, facilitating remote monitoring and individualized healthcare.The integration of IoT in healthcare monitoring offers numerous benefits, including improved patient outcomes, enhanced access to care, and increased efficiency in healthcare delivery.To develop you would typically follow a research methodology that involves several key steps. Clearly state the objectives of your research, such as designing and implementing a secure health monitoring system using IoT. Specify the aspects you want to focus on, such as data privacy, authentication, encryption, or device communication. Develop a high-level system architecture for your health monitoring system. Define the components, their functionalities, and how they interact with each other. Consider the security aspects, such as secure data transmission, authentication, access control, and data storage.By multiplying each of our goals by a weight provided by the user, we can scale our collection of goals into a single goal using the weighted sum approach. One of the most popular strategies is this one. Finding the appropriate weights to give each aim while using the weighted sum approach is a concern. Taken as alternative parameters for HMS1, HMS2, HMS3, HMS4, HMS5. Taken as evaluation parameters for Portability,Round-The-Clock Health Surveillance,ease of use,Reliability.HMS1 performance is good when compared to others so HMS 1 is preferred except HMS 1 performed better in secure health monitoring system using IIOD.

A COMPREHENSIVE STUDY FOR DEVELOPING A FRAMEWORK FOR MICROSERVICES ARCHITECTURE MIGRATION

Building scalable and resilient systems has become a common practice using microservices architecture. Large and complex programs may be split up into manageable components that can be created and deployed separately. Migrating from a monolithic to a microservices architecture is a challenging process that needs careful planning and execution. In this article, we provide a method for migration to a microservices architecture that is based on accepted best practices and industry standards. Three essential phases make up our framework: assessment, design, and execution or Implementation. The assessment stage involves analyzing the existing monolithic application to identify potential microservices candidates. In the design stage, we create a high-level architecture and define microservices boundaries. The implementation stage focuses on building and deploying microservices, along with the necessary infrastructure and tooling. Additionally, we discuss best practices and lessons learned from practical microservices migration projects. Our framework offers a structured approach to organizations seeking to migrate to microservices architecture. Key Words: Monolithic Architecture, Microservices Architecture, Migration Framework

Melt dynamics with MEMENTO — Code development and numerical benchmarks

The new numerical implementation of the MEMOS-U physics model, fully validated in multiple EUROfusion sponsored experiments, is presented. The computational tool - MEMENTO (MEtallic Melt Evolution in Next-step TOkamaks)- is able to address fusion-relevant melting scenarios that feature complex plasma-facing component geometries, involve intricate plasma wetting patterns and are characterized by vast spatio-temporal scale separations. The high level architecture of the code is discussed and numerical benchmarks of the heat transfer, fluid dynamics and current propagation solvers are presented.

Design of tractor virtual test system based on high-level architecture technology.

The limitations of the tractor virtual test system are evident in various aspects, including model reuse, system expansion, offsite interconnection, and virtual reality verification. To address these challenges, a distributed virtual test system for tractors based on the high-level architecture (HLA) is proposed. Involve analyzing the hardware structure and the tractor virtual test system, constructing the system federation and its members, and designing the federated object model (FOM) and simulation object model (SOM) tables. The system integrates multi-domain commercial software and enables real-time virtual testing through TCP/IP interconnection of multiple machines. To evaluate the system's performance, a virtual test of the tractor's reversing clutch engagement performance is conducted. The system's simulation performance and data transmission delay are thoroughly tested and analyzed. The results indicate that when the system's data volume reaches 5000KB, the data delay is 9.7ms, which satisfies the requirement of not exceeding 10ms for tractor virtual testing delay. The virtual test of the reversing clutch power reversal process demonstrates that it lasts 0.7s, with the vehicle speed changing from -3.5km/h to 3.5km/h, the forward gear piston oil pressure increasing from 0MPa to 5MPa, and the peak impact degree reaching 17m/s3. The slip work during the reversing process is measured to be 21kJ. Furthermore, the gray correlation method is employed to compare the virtual test results with the bench test results, confirming their consistency. The power reversal process exhibits relatively smooth speed changes overall. Therefore, the tractor power shift transmission (PST) reversing clutch virtual test model operates effectively within the HLA-based tractor virtual test system.

Atmosim – An Atmosphere Simulation Package in the Tool Command Language (Tcl)

DLR’s SpaceLiner orbiter concept flight simulator gets a change from its commercial flight simulation software “X-Plane” dedicated solutionto a distributed system of systems high level architecture (HLA) based approach. For that purpose, a Tclatmosphere simulation package has been developed.Its configurable parametrization including the coverage of altitudes up to 109 meters is described. Package usage test results in a standalone application show anexecution time performancebelow 300 microseconds. Future options of different human-in-the-loop real-time system of systems integration capabilities are presented and discussed.