Software Architecture Research Articles

Evolving to find optimizations humans miss: using evolutionary computation to improve GPU code for bioinformatics applications

GPUs are used in many settings to accelerate large-scale scientific computation, including simulation, computational biology, and molecular dynamics. However, optimizing codes to run efficiently on GPUs requires developers to have both detailed understanding of the application logic and significant knowledge of parallel programming and GPU architectures. This paper shows that an automated GPU program optimization tool, GEVO, can leverage evolutionary computation to find code edits that reduce the runtime of three important applications, multiple sequence alignment, agent-based simulation and molecular dynamics codes, by 28.9%, 29%, and 17.8% respectively. The paper presents an in-depth analysis of the discovered optimizations, revealing that (1) several of the most important optimizations involve significant epistasis, (2) the primary sources of improvement are application-specific, and (3) many of the optimizations generalize across GPU architectures. In general, the discovered optimizations are not straightforward even for a GPU human expert, showcasing the potential of automated program optimization tools to both reduce the optimization burden for human domain experts and provide new insights for GPU experts.

Half-wavelength-pitch silicon optical phased array with a 180° field of view, high sidelobe suppression ratio, and complex-pattern beamforming

Solid-state optical beam steering devices desire a large field of view (FOV), good beam quality, and reconfigurable beamforming of complex patterns, which are not available in a single system yet. Having not been demonstrated, an active beamformer using an optical phased array (OPA) could potentially fulfill these requirements simultaneously, because it can control both the wavefront and beam pattern. Half-wavelength-pitch OPAs theoretically can achieve the three requirements concurrently, but suffer from crosstalk. Most previous efforts focus on mitigating/avoiding crosstalk. Instead, here we appreciate its existence and propose/demonstrate a programmable architecture to compensate for it. Using a tree of composite variable splitters with a full splitting-ratio range, we achieve arbitrary amplitude/phase modulation to pre-correct scrambled phase/amplitude by crosstalk. With comprehensive stray-light minimization strategies, the sidelobe suppression ratio (SLSR) is significantly improved. Our design achieves a 180∘ FOV, a peak SLSR of 24 dB, and complex-pattern beamforming simultaneously in a half-wavelength-pitch 64-waveguide array. Within the ±60∘ range, a SLSR of >20dB is achieved. Our OPA demonstrates Bayliss difference, pulse-shaped, and asymmetric three-beam patterns with high SLSRs of >20dB, ∼10dB, and >18dB, respectively. These performance metrics are important for various applications in light detection and ranging, imaging, and communication.

Movement Recognition and Injury Risk Assessment of Wushu Athletes Based on Computer Vision

This paper aims to study a method of movement recognition and injury risk assessment for Wushu athletes based on computer vision. Studying the optimization of Wushu athletes’ movement trajectory can effectively improve the athletes’ movement quality. Based on a hybrid real-time synchronization algorithm, the arm motion trajectory model of martial arts athletes is studied. A dynamic and static arm recognition algorithm is proposed, and the motion feature extraction method in Wushu movement is studied. Dynamic arm recognition typically relies on the collection of video or image sequences. These sequences contain the position, shape, and motion trajectory of the arm at different time points. Static arm recognition mainly relies on the acquisition of a single image or image frame. The dynamic arm recognition algorithm captures the motion trajectory and changes of the arm by processing video or image sequences, while the static arm recognition algorithm only processes individual images or image frames. On this basis, the design of the computer software architecture of the digital site system is completed. The real-time collection, analysis, display and storage of motion information and assembly configuration are realized. The simulation results show that the method has high accuracy and provides a powerful scientific basis for improving athletes’ movement injuries. To predict the risk of injury an athlete may suffer during training or competition.

Study on the Role of Student Feedback in The Value-added Assessment of Architecture Programmes

This study explores the important role of student feedback in value-added evaluation in architecture majors. It aims to reveal how to improve teaching quality through an effective feedback mechanism and provide a scientific basis for curriculum improvement. Value-added evaluation focuses on the growth and progress of students in the learning process, and student feedback, as a source of information that directly reflects the learning experience, can comprehensively support the assessment of teaching effectiveness. This study analyzes the existing student feedback mechanisms, explores their application methods and actual effects in value-added evaluation, and points out the deficiencies of the existing mechanisms and possible paths for optimization. The study found that feedback promotes continuous improvement of course design and teaching methods by constructing a closed-loop mechanism, and that this feedback mechanism shows significant advantages, especially in the field of architecture, which requires a high degree of practicality and creativity. The study concludes with recommendations for optimising the student feedback mechanism and looks ahead to the direction of future research, with a view to providing a feasible reference for improving the quality of education in the field of architecture.

A Software-Defined Radio Platform for Teaching Beamforming Principles

This paper presents the development and validation of a hybrid beamforming system based on software-defined radio (SDR), designed for telecommunications engineering education. The system provides an agile and user-friendly platform that allows students to observe, test, and evaluate beamforming techniques in real time. The platform integrates a multichannel SDR device (USRP N310) with traditional radiofrequency equipment and open-source software, facilitating hands-on learning experiences. The paper details the proposed hardware and software architecture and documents the calibration and validation phases. The testing and validation processes were conducted using a 3.5 GHz antenna array in both indoor and outdoor environments. The results demonstrated the system’s effectiveness in achieving the desired beam orientations, with experimental results aligning closely with simulation and theoretical predictions. Significant differences in the radiation patterns observed between the indoor and outdoor measurements were documented, highlighting the impact of environmental factors on beamforming performance. The insights gained from this research provide valuable contributions to the education of future telecommunications engineers, enhancing their understanding of practical beamforming applications and the integration of modern SDR technology.

The Role of Spatial Ability in Academic Success: The Impact of the Integrated Hybrid Training Program in Architecture and Engineering Higher Education

Spatial ability (SA) is a critical cognitive skill across various disciplines, particularly in architecture and engineering. This research, comprising two main studies, investigates the relationship between SA and academic performance among first-year students. The first study examines the impact of the targeted Integrated Hybrid Training (IHT) SA intervention on the achievements of two main groups: engineering (electrical, software, and chemical) students and architecture students. The results indicate that, while the intervention significantly improved SA, the impact on academic performance varied. Architecture students, whose curriculum relies heavily on spatial reasoning, showed significant gains in courses such as studio design, with higher SA scores correlating with improved grades. Engineering students exhibited SA improvement without corresponding gains in the first-year core courses like calculus and physics. The second study focuses on architecture students who did not receive the intervention, revealing a strong positive correlation between inherent SA and success in architecture-specific courses. Correlation coefficients (r) ranged from 0.46 to 0.67, with adjusted p-values between 0.007 and 0.024, underscoring SA’s importance in architecture. These findings suggest that integrating SA training into architectural education can enhance academic performance, while disciplines might benefit from specialized SA training introduced or expanded in later years. This research contributes to educational theory by demonstrating SA’s impact across disciplines and supports the development of customized SA training programs. Future studies should explore long-term benefits, advanced SA training technologies, and individual differences in response to spatial interventions, providing insights for curriculum development in spatially demanding fields.

OpenPrep: Sensor Event-driven Open Column Chromatographic Sample Preparation System.

We propose a sensor event-driven, open-column chromatographic sample preparation system─OpenPrep. This system replaces the problematic flow selector valve with a noncontact built-in sensor, eliminating carryover and clogging issues, which are commonly encountered in pump-driven chromatographic systems. The innovative valveless column-flow design, combined with a compact gantry dispenser and motion stage-based fraction collector, reduces the sample flow path to a disposable component with a postcolumn dead volume of only 0.5 mL. This configuration is superior to conventional dynamic flow systems owing to its ability to minimize sample consumption, eluent loss, and cleaning waste generation. Thus, OpenPrep can be ideal for preventing cross-contamination. Owing to the rapid flow-transfer and stop approach, OpenPrep achieves higher reagent delivery accuracy and precision compared to ISO 8655-2 piston-operated pipettes, by minimizing liquid residue on the dispensing tip. Sensor-driven flow control ensures precise eluent collection and automatic regulation of the mobile-phase flow rate through an online calibration algorithm, meeting all the requirements for accurate and reproducible column flow control. OpenPrep features an open architecture for both hardware and software, offering high flexibility for analyzing samples with considerable variations in radioactivity and matrix compositions, such as radioactive waste. The proof-of-concept implementation of a multistage sequential separation strategy, using five resin columns to separate Re, Sr, Fe, and Ni from a single sample, demonstrates that OpenPrep provides enhanced flexibility and reconfigurability, improved operational efficiency, and cost-effective customization. Excellent chemical yields were obtained for Re, Sr, and Ni in the range of 83-97%, with less than 2.6% relative standard deviation at a flow rate three times faster than the gravity method. Additionally, improved recovery performance of Fe was achieved by applying a slower flow rate compared to gravity flow.

Orchestration Workflows in Distributed Systems: A Systematic Analysis of Efficiency Optimization and Service Coordination

This article presents a comprehensive analysis of orchestration workflows in modern software architectures, examining their role in optimizing service efficiency and coordination across distributed systems. Through a systematic investigation of implementation patterns and operational strategies, we demonstrate how orchestrated workflows enhance service integration, automate complex processes, and improve system reliability. The article employs a mixed-method approach, combining quantitative performance metrics from real-world implementations with qualitative analysis of architectural patterns across diverse enterprise environments. The findings reveal that organizations implementing robust orchestration workflows experienced a 47% reduction in service integration failures, 35% improvement in resource utilization, and 62% faster deployment cycles. The article also identifies critical success factors in orchestration implementation, including dynamic resource management, automated error handling, and comprehensive monitoring frameworks. Furthermore, The article proposes a novel framework for evaluating orchestration effectiveness in cloud-native environments, considering factors such as service mesh integration, container orchestration, and distributed tracing. This article contributes to the growing body of knowledge in service optimization by providing empirical evidence of orchestration benefits and offering practical guidelines for implementing scalable, resilient software systems through advanced workflow orchestration strategies.

The Pervasive Impact of Software Engineering and Architecture: A Multi-Industry Analysis

This article presents a comprehensive analysis of the ubiquitous role of software engineering and architecture across diverse industries in the modern digital landscape. Through a combination of literature review, industry case studies, and expert interviews, we examine how core software engineering principles—such as scalability, security, user experience, and data management—are applied and adapted in sectors ranging from e-commerce and fintech to healthcare, transportation, media, manufacturing, and government services. Our findings reveal both common challenges and industry-specific innovations in areas such as blockchain technology, telemedicine, autonomous vehicles, and industrial IoT. We also explore emerging trends, including the integration of artificial intelligence, edge computing, and quantum computing, and their potential impact on future software architectures. Furthermore, this article addresses critical ethical considerations and societal implications, including data privacy, job displacement, and the digital divide. By synthesizing these multifaceted perspectives, we provide valuable insights for both industry practitioners and academic researchers, highlighting the transformative power of software engineering in shaping the technological foundations of contemporary society and identifying key areas for future research and development.

DESIGN OF A CAMERA-ENABLED MOBILE ROBOT FOR IN-PIPE INSPECTION

The role of pipeline infrastructure is crucial in modern society, as it transports necessary resources such as water, gas, and oil. It is essential to prioritize the maintenance of these pipelines to prevent leaks, environmental harm, and economic losses. Traditional inspection techniques, which involve manual inspection, can be inefficient, laborious, and susceptible to mistakes. To overcome these obstacles, this article introduces a camera-equipped mobile robot specifically created for automated crack detection in pipelines. The robot, which is equipped with a high-resolution camera and advanced image processing algorithms, navigates inside pipelines on its own to capture detailed images of the pipe walls. These images are then processed using computer vision techniques to identify and categorize potential cracks. The algorithms utilize machine learning models that have been trained on a dataset of labelled crack images, enabling accurate and efficient crack detection. This paper provides a comprehensive overview of the robot system's design, development, and validation, detailing the critical hardware components such as the mobility platform, camera system, and sensor integration, as well as the software architecture, which encompasses image processing and machine learning frameworks. Field studies demonstrate the robot's ability to effectively detect cracks across various pipe materials and under diverse environmental conditions. Results indicate that the automated system not only improves inspection efficiency and accuracy but also significantly reduces the need for human intervention, thereby enhancing overall pipeline safety Keywords: pipeline inspection, crack detection, mobile robot, image processing, computer vision.

Integration of Artificial Vision and Image Processing into a Pick and Place Collaborative Robotic System

In the field of robotics, pick and place applications are becoming increasingly popular due to their ability to automate repetitive tasks that can create temporary or permanent injuries. To enhance the efficiency of these applications, object recognition using a fixed camera or one mounted on a robotic hand has been employed. This paper explores the possibilities of implementing a low-cost camera into a collaborative robotic system. A software architecture has been developed, including modules for perception, pick and place, and part transfer. A comprehensive overview of various intuitive drag-and-drop image processing technologies and their suitability for object recognition in a robotic context is provided. The challenges related to lighting and the effect of shadows in object recognition are discussed. A critical assessment is made of the architecture development platform as well as the study and the results are performed, and the effectiveness of the proposed solution based on the Niop architecture is verified.

A Comprehensive Systematic Scoping Review of Self-Driving Vehicle Models

Self-driving vehicles (SDVs), also known as autonomous vehicles (AVs), are anticipated to revolutionize transportation by operating independently through the integration of machine learning algorithms, advanced processing units, and sensor networks. Numerous organizations globally are actively developing SDV models, prompting this paper’s objective to identify emerging trends and patterns in SDV development through a comprehensive systematic scoping review (SSR). This research involved selecting 85 relevant studies from an initial set of 551 records across multiple academic databases, utilizing well-defined inclusion and exclusion criteria along with snowballing techniques to ensure a thorough analysis. The findings emphasize critical technical specifications required for both full-scale and miniature SDV models, focusing on key software and hardware architectures, essential sensors, and primary suppliers. Additionally, the analysis explores publication trends, including publisher and venue distribution, authors’ affiliations, and the most active countries in SDV research. This work aims to guide researchers in designing their SDV models by identifying key challenges and exploring opportunities likely to shape future research and development in autonomous vehicle technology.

A Software Architecture Model to Manage Heterogeneous Data using Edge Computing in 5G Environment

With the improvement of 5G communication service, the need for edge computing has become more apparent. The Mobile Edge Computing service (MEC) has become one of the best solutions for mobile User Equipment (UE) data processing. MEC provides more context-aware local processing of UE's data. Meanwhile, the MEC needs to manage the heterogeneous nature of UEs and the type of data format they provide. Therefore, a Heterogeneous Data Service Bus is developed to manage different types of data formats and organize them in efficient manner. We propose a software architecture model to address the management of data heterogeneity within computing edges. The architecture discussion showcases its applicability and proper handling of heterogeneous data in the Edge Computing Environment.

Retriever: A view-based approach to reverse engineering software architecture models

Software systems, which have become an integral part of our daily lives, are constantly evolving and growing in complexity. The challenge of understanding and managing these systems has led to a shift towards automated extraction of models from software artifacts. However, extracting architectural models for web service or microservice systems is challenging due to the heterogeneity of formats, languages, and semantics. This challenge arises from the independent deployment and loose coupling of services, as well as the diverse technologies and platforms that comprise the heterogeneous artifacts. This paper addresses this issue with two key ideas: a knowledge representation model for diverse artifacts and a framework for integrating individual views into a unified architectural model. The former involves model-to-model transformations that consider technology-specific relationships and concepts. At the same time, the latter uses model transformation and composition techniques to create a unified model based on an existing metamodel. This work goes beyond the current state of the art by introducing an approach for reconstructing component-based software architectures and combining model-driven reverse engineering processes to capture information across multiple views. As empirical evidence of its applicability and accuracy, we report on its successful application in real projects of different sizes. Our evaluation shows scalability in generating architectural models and can reconstruct the components of a software system and their interactions with F1 values up to 0.94 for structural properties and up to 0.88 for behavioral properties.

Ipek Ozkaya on Generative AI for Software Architecture

Ipek Ozkaya on Generative AI for Software Architecture

The role of APIs in modern software development

Application Programming Interfaces (APIs) play a critical role in modern software development, enabling seamless communication between disparate systems and enhancing the scalability, modularity, and security of applications. This research investigates the impact of APIs on software architecture, focusing on their use in facilitating interoperability across distributed environments. Using a mixed-methods approach, the study combines a literature review, a survey of 50 software developers, and case studies from industry leaders such as Amazon Web Services (AWS), Google, and Facebook. The findings reveal that REST APIs are the most widely used (75%), while GraphQL is gaining popularity for its ability to optimize data retrieval in complex systems. Security challenges, particularly vulnerabilities in REST APIs, were highlighted by 35% of developers, underscoring the need for stronger authentication and encryption methods like OAuth. This study also explores how APIs foster innovation by enabling third-party integrations, driving ecosystem growth in cloud computing and other sectors. The research concludes that while APIs are vital for the future of software development, addressing security risks and improving API management practices will be essential for maximizing their benefits in emerging technologies such as IoT and AI.

A research paper on software design patterns

This article affords another exhaustive examination of software design patterns, discussing their importance, the classification system, and the influence on software architecture and design. It looks at how existing practices handle predictable problems, make things more easily serviceable, and increase developer collaboration. The article goes further and explains the use of design patterns and elaborates on real-life examples of design patterns and the difficulties and drawbacks of using them. Further, it expands on future possibilities, such as applying design patterns for cloud-new architectures, AI incorporation, serverless, and Agile/DevOps. From these trends, the article lays down the argument that design patterns are not insulated from change but are continually being adapted to fit into the software development processes of today's complex and sophisticated world and that to be relevant in the designing of today's complex, large and intelligent software-based systems, developers should be familiar with both the recognized and emergent design patterns.

BIM-based energy consumption analysis for residential buildings: effects of orientation, size, and type of windows

Buildings have a significant environmental impact due to their reliance on nonrenewable energy sources. To address this concern, computer software is utilized to estimate building energy requirements. Building Information Modeling (BIM) has emerged as a valuable tool in this process, with BIM 6D offering a particularly beneficial feature. BIM 6D provides a comprehensive energy model of the building, simulating its actual energy performance. By analyzing natural and artificial lighting systems, particularly daylighting, BIM 6D can assist in optimizing energy efficiency. Despite being underutilized, the potential of this feature to enhance building sustainability is noteworthy. BIM 6D facilitates well-informed design and operational decisions for both new constructions and the renovation of existing buildings. The study examines design criteria such as building orientation, window orientation, size, and type to optimize energy consumption in a bungalow in the Sangli region of Maharashtra, underscoring the importance of energy-efficient buildings to local communities. Energy analysis and optimization are conducted using the Autodesk Insight tool. The methodology involves creating 3D models with Autodesk Revit Architecture software and performing various analyses, including Building Energy Modeling (BEM) simulations, Illuminance Analysis, Solar Access, and Daylight Autonomy assessments. The analysis findings indicate that low-e glass windows of small size and oriented between 0° and 90° significantly enhance energy efficiency under certain conditions, while changing the window type does not affect the analysis. The paper concludes with recommendations for window orientation and size to enhance energy performance in residential buildings, contributing to the broader goal of sustainable and energy-efficient construction practices. This study provides valuable insights for designing energy-efficient residential buildings in rural areas, addressing awareness gaps, and promoting sustainable construction practices.

Cellular Vehicle-to-Everything Automated Large-Scale Testing: A Software Architecture for Combined Scenarios

As the commercialisation of Intelligent Connected Vehicles (ICVs) accelerates, Vehicle-to-Everything (V2X)-based general testing and assessment systems have emerged at the forefront of the research. Current field testing schemes mostly follow the norms of traditional vehicle tests. In contrast, Original Equipment Manufacturers (OEMs) have increasingly focused on the potential influence of V2X communication performance on the application response characteristics. Our previous work resulted in a C-V2X (Cellular-V2X) large-scale testing system (LSTS) for communication performance testing. However, when addressing the need to combine application and communication, the system software faces confronts heightened technical challenges. This paper proposes a layered software architecture for the automated C-V2X LSTS, which is tailored to combined scenarios. This architecture integrates scenario encapsulation technology with a large-scale node array deployment strategy, enabling communication and application testing under diversified scenarios. The experimental results demonstrate the scalability of the system, and a case study of Forward Collision Warning (FCW) validates the effectiveness and reliability of the system.

URBANISMO, DIGNIDADE E DIREITO À CIDADE, NA ERA DAS PPPs DOS GRANDES PROJETOS URBANOS

This work is the result of a research for the theoretical framework of a doctoral thesis, developed in the Graduate Program in Architecture and Urbanism of the Federal University of Bahia, in order to promote a discussion on the Right to the City. An exploratory research is carried out based on a bibliographic survey, which includes scientific articles and books that deal with the theme under discussion here. The discussion has been taking place for decades, since the period of struggle for Urban Reform in Brazil. The objective is to discuss the instruments of urban policy, relating the issue of the Right to the City and the role of the State as a mediator of a process where there are several actors, but which, together with the private sector, social movements and civil society organizations, constitutes one of the main agents of (re)production of urban space. It seeks to discuss the role of the State, through such instruments of urban policy, as well as their effectiveness and the processes of urban planning and management, in the sense of the social function of urban property, in the face of large urban projects, often gestated with PPPs.