Application Architecture Research Articles

When Cybersecurity Meets Accessibility: A Holistic Development Architecture for Inclusive Cyber-Secure Web Applications and Websites

In today’s technology-centric era, digital transformation has made accessibility and cybersecurity critical components for developing inclusive web applications that are essential in daily operations. However, current development models often treat accessibility and cybersecurity separately, leading to solutions that may excel in one area while lacking in the other. This paper addresses this gap by proposing the Holistic Security and Accessibility Layered Architecture (HSALA), a novel architectural model that integrates accessibility and cybersecurity throughout the development process. HSALA consists of five interconnected layers, each paired with a corresponding security layer, ensuring that both accessibility and security are embedded at every level. We conduct a theoretical and analytical case study comparing HSALA with the traditional three-layered architecture, demonstrating HSALA’s superiority in addressing contemporary challenges related to accessibility, cybersecurity, scalability, and user experience. The findings indicate that HSALA provides a robust framework for developing secure and inclusive web applications, promoting a more equitable digital ecosystem. We outline a roadmap for future work, including empirical validation and practical implementation strategies. HSALA represents a significant advancement in web application development, fostering inclusivity and trust in an increasingly digital society.

FazBoard: An AI-Educational Hybrid Intelligent Teaching & Learning System

FazBoard, an avant-garde educational platform, seamlessly integrates artificial intelligence with contemporary educational methodologies to foster a dynamic, adaptive, and collaborative learning ecosystem. The platform’s linchpin comprises two components: an agile digital canvas that simulates interactive teaching and learning spaces, and an AI Assistant, incarnated as a digital humanoid, available round-the-clock for responsive academic support. The digital canvas is designed to cultivate an immersive and versatile environment, emulating the interactions of traditional classrooms without the constraints of time or location. The AI Assistant excels in providing instantaneous responses to queries, aggregating valuable learning analytics, and streamlining administrative tasks - all of which contribute to curriculum refinement and enhanced pedagogical efficacy. Furthermore, FazBoard is aimed at bolstering student engagement by creating an inclusive learning milieu, through adaptive learning strategies that cater to the diverse educational needs of its users. This paper delves into the architecture, functionalities, and far-reaching applications of FazBoard, heralding it as a quintessential model of integrating AI into education, thereby shaping the trajectory of 21st-century educational practices. Keywords: AI assistant, digital canvas, digital transformation, natural language processing, machine learning, instructional design, learning equity, connectedness

Pixel matching technique used for restoration of Mediterranean historical buildings

This paper aims to address the discovery of an architectural application using metric/pixel technique for re-establishing a profound restoration language within pre-contemporary constructed environment. We also aim to uncover the forgotten bridge of any visual gap in Mediterranean built environment by introducing a comprehensive and innovative approach of re-understanding such Mediterranean’s historic structures. The methodology involves analysing the statical arrangement of building blocks within the Mediterranean building. This work reflects out major reoccurrences of old Ottoman architectural code and the commonness that modular dimension as a means of gaining confidence in any future architectural applicability. The objective of this paper lies in the precise distinguish between the human and non-human factors of the core relationships between any of two elements of a preserved structure and derive the most truthful code of remedy that values the profound historical establishment reflected by the pixel nature of dimensional patterns. To substantiate the difference between human and non-human entities, it is clear that the persistent uncertainty surrounding the actual builder of Mediterranean destroyed historical buildings, coupled with the authors’ effort to develop the appropriate restoration pixel terminology while presenting below methodical assumptions, will be robustly supported by clear definition for each found factor.

Enhancing Microservice Security Through Vulnerability-Driven Trust in the Service Mesh Architecture

Cloud-native computing enhances the deployment of microservice architecture (MSA) applications by improving scalability and resilience, particularly in Beyond 5G (B5G) environments such as Sixth-Generation (6G) networks. This is achieved through the ability to replace traditional hardware dependencies with software-defined solutions. While service meshes enable secure communication for deployed MSAs, they struggle to identify vulnerabilities inherent to microservices. The reliance on third-party libraries and modules, essential for MSAs, introduces significant supply chain security risks. Implementing a zero-trust approach for MSAs requires robust mechanisms to continuously verify and monitor the software supply chain of deployed microservices. However, existing service mesh solutions lack runtime trust evaluation capabilities for continuous vulnerability assessment of third-party libraries and modules. This paper introduces a mechanism for continuous runtime trust evaluation of microservices, integrating vulnerability assessments within a service mesh to enhance the deployed MSA application. The proposed approach dynamically assigns trust scores to deployed microservices, rewarding secure practices such as timely vulnerability patching. It also enables the sharing of assessment results, enhancing mitigation strategies across the deployed MSA application. The mechanism is evaluated using the Train Ticket MSA, a complex open-source benchmark MSA application deployed with Docker containers, orchestrated using Kubernetes, and integrated with the Istio service mesh. Results demonstrate that the enhanced service mesh effectively supports dynamic trust evaluation based on the vulnerability posture of deployed microservices, significantly improving MSA security and paving the way for future self-adaptive solutions.

Pano-GAN: A Deep Generative Model for Panoramic Dental Radiographs

This paper presents the development of a generative adversarial network (GAN) for the generation of synthetic dental panoramic radiographs. While this is an exploratory study, the ultimate aim is to address the scarcity of data in dental research and education. A deep convolutional GAN (DCGAN) with the Wasserstein loss and a gradient penalty (WGAN-GP) was trained on a dataset of 2322 radiographs of varying quality. The focus of this study was on the dentoalveolar part of the radiographs; other structures were cropped out. Significant data cleaning and preprocessing were conducted to standardize the input formats while maintaining anatomical variability. Four candidate models were identified by varying the critic iterations, number of features and the use of denoising prior to training. To assess the quality of the generated images, a clinical expert evaluated a set of generated synthetic radiographs using a ranking system based on visibility and realism, with scores ranging from 1 (very poor) to 5 (excellent). It was found that most generated radiographs showed moderate depictions of dentoalveolar anatomical structures, although they were considerably impaired by artifacts. The mean evaluation scores showed a trade-off between the model trained on non-denoised data, which showed the highest subjective quality for finer structures, such as the mandibular canal and trabecular bone, and one of the models trained on denoised data, which offered better overall image quality, especially in terms of clarity and sharpness and overall realism. These outcomes serve as a foundation for further research into GAN architectures for dental imaging applications.

Ancient Era Hospitals “Asclepions” and their Heritage to the Day

Asclepios is the god of medicine and health in mythology. In ancient times, healing was sought in the worship of Asclepios. Ancient hospitals built in the name of Asclepios are called “Asclepion”. In ancient times, around three hundred and twenty Asclepions were built within the borders of Greece and Türkiye today. The most important of these ancient hospitals are located in Epidauros, Pergamon, Kos, Athens and Knidos. There was also a health center (an ancient hospital) in Allianoi. In this study, the most significant locations, architectural features, treatment methods, historical and cultural roles of the ancient hospitals among the Asclepions in Western Anatolia and Greece were investigated. When the treatment methods in Asclepions are examined, it is seen that holistic medical practices were applied with a biopsychosocial approach to the patients called guests. Diseases were not seen as one-dimensional but as the result of complex processes and negative environmental, social and psychological interactions. Patients were treated with respect as beings with mental, ethical, emotional, social, moral and natural characteristics. Centuries earlier in Asclepions, the first functional applications of hospital architecture and individual and holistic medical approaches are seen. We think that today's hospital architecture and treatment approaches have many features that can be learned from the heritage of ancient hospitals in history.

The Parallel Architecture—application and explanatory power for neurolinguistic research

Various linguistic models have been developed to systematize language processes and provide a structured framework for understanding the complex network of language production and reception. However, these models have often been developed in isolation from neurolinguistic research, which continues to provide new insights into the mental processes involved in language production and comprehension. Conversely, neurolinguists often neglect the potential benefits of incorporating contemporary linguistic models into their research, although these models could help interpret specific findings and make complex concepts more accessible to readers. This paper evaluates the utility of Jackendoff's Parallel Architecture as a generic framework for explaining language acquisition. It also explores the potential for incorporating neurolinguistic findings by mapping its components onto specific neural structures, functions, and processes within the brain. To this end, we reviewed findings from a range of neurolinguistic studies on language acquisition and tested how their results could be represented using the Parallel Architecture. Our results indicate that the framework is generally well-suited to illustrate many language processes and to explain how language systems are built. However, to increase its explanatory power, it would be beneficial to add other linguistic and non-linguistic structures, or to signal that there is the option of adding such structures (e.g., prosody or pragmatics) for explaining the processes of initiating language acquisition or non-typical language acquisition. It is also possible to focus on fewer structures to show very specific interactions or zoom in on chosen structures and substructures to outline processes in more detail. Since the Parallel Architecture is a framework of linguistic structures for modeling language processes rather than a model of specific linguistic processes per se, it is open to new connections and elements, and therefore open to adaptations and extensions as indicated by new findings in neuro- or psycholinguistics.

Ultrasensitive Flexible Strain Sensor Based on the Synergistic Integration of Wrinkle and Microcrack Architectures for Wearable Applications

Wearable strain sensors have significant applications in important domains such as electronic skin, personalized health monitoring, and human–computer interaction. A prevailing objective in numerous research initiatives is to achieve a balance between high sensitivity and stretchability. A strain sensor that integrates a stretchable wrinkled structure with a highly sensitive microcrack sensor to address this is developed. This sensor employs wrinkled microstructures, constructed by thermally depositing a gold film onto a flexible substrate. Due to its unique microstructural design, the sensor demonstrates a maximum strain of 75% and a gauge factor reaching 6657. Additionally, the sensor exhibits outstanding mechanical stability, maintaining consistent performance through 1000 stretching–releasing cycles. The application of this wrinkle structure strain sensor in human motion tracking, physiological signal monitoring, and speech recognition is validated. These findings underscore its practical value in wearable technology and offer novel insights for the advancement of next‐generation wearable electronic devices.

Enhancing Site Reliability Engineering (SRE) Observability: A Comprehensive Approach

The subject of applying software engineering techniques to IT operations to create scalable and highly reliable software systems is known as site reliability engineering, or SRE for short. To preserve system’s health, it heavily emphasizes automation, ongoing improvement, and efficient incident response. Observability, is the tracking and examination of system outputs in the form of metrics, logs, and traces with the goal of providing insight into the system's internal state, is one of the fundamental principles of SRE. This keeps systems reliable and effective by enabling the early detection and resolution of problems. SRE and observability work together to help businesses achieve high IT operations performance and reliability. The contemporary IT environment is becoming more and more complex as industries growing infrastructure use on-premises and cloud hosting, adding microservice design along with traditional monolithic application architectures. In the pursuit of observability, this diversity presents unique difficulties for SRE. This article sheds light on the significance of SRE observability, tools and tactics used by enterprises, and how site reliability engineering could be improved across numerous industries with the use of an open-source framework like Open Telemetry to strengthen observability.

A Reference Architecture for Smart Car Parking Management Systems

Smart car parking management systems (SPMSs) have gained an ever-increasing popularity for the digital management of car parking processes. While various techniques and technologies have been proposed for SPMSs, the literature lacks in any generic software architecture design that can be reused systematically for the specifications of quality SPMS architectures. To bridge this gap, we propose a reference architecture (RA) for the SPMS product family after performing a comprehensive domain analysis. Our RA design offers a feature model that consists of the common and varying features for SPMSs. We offer multiple viewpoints for our RA, including context, module, component and connector, and allocation. The context viewpoint focuses on the stakeholders, the module viewpoint focuses on the software units, the component and connector viewpoint focuses on the layered architecture of SPMSs, and the allocation viewpoint focuses on mapping software units into the physical components. Each viewpoint can be re-used for specifying the application architecture of any SPMSs. We validated our RA with a real SPMS scenario specification and prototype development, where the former measures the reusability of RA and the latter measures the development performance. The RA design for SPMSs is expected to be useful for several stakeholders who research, develop, and sell SPMS solutions.

Decentralized Networking Models: Reshaping Digital Architecture

The emergence of decentralized networks represents a fundamental shift in digital infrastructure design and implementation. This comprehensive article explores the evolution of networking architectures from traditional centralized models to modern distributed systems, examining core components, implementation challenges, and future developments. It investigates how decentralized control systems and blockchain technologies are reshaping network topology design, routing protocols, and consensus mechanisms. Through detailed case studies of P2P file sharing, mesh networks, edge computing, and IoT implementations, the article demonstrates the practical applications and benefits of decentralized architectures. It encompasses critical aspects of network performance, including scalability considerations, resilience mechanisms, and resource optimization strategies. Special attention is given to emerging technologies such as AI-enhanced routing solutions, cross-network interoperability, and bio-inspired network designs. The article also addresses security challenges and their solutions in decentralized environments, providing insights into the development of robust and secure network architectures for future applications.

Valley-Forecast: Forecasting Coccidioidomycosis incidence via enhanced LSTM models trained on comprehensive meteorological data.

Coccidioidomycosis (cocci), or more commonly known as Valley Fever, is a fungal infection caused by Coccidioides species that poses a significant public health challenge, particularly in the semi-arid regions of the Americas, with notable prevalence in California and Arizona. Previous epidemiological studies have established a correlation between cocci incidence and regional weather patterns, indicating that climatic factors influence the fungus's life cycle and subsequent disease transmission. This study hypothesizes that Long Short-Term Memory (LSTM) and extended Long Short-Term Memory (xLSTM) models, known for their ability to capture long-term dependencies in time-series data, can outperform traditional statistical methods in predicting cocci outbreak cases. Our research analyzed daily meteorological features from 2001 to 2022 across 48 counties in California, covering diverse microclimates and cocci incidence. The study evaluated 846 LSTM models and 176 xLSTM models with various fine-tuning metrics. To ensure the reliability of our results, these advanced neural network architectures are cross analyzed with Baseline Regression and Multi-Layer Perceptron (MLP) models, providing a comprehensive comparative framework. We found that LSTM-type architectures outperform traditional methods, with xLSTM achieving the lowest test RMSE of 282.98 (95% CI: 259.2-306.8) compared to the baseline's 468.51 (95% CI: 458.2-478.8), demonstrating a reduction of 39.60% in prediction error. While both LSTM (283.50, 95% CI: 259.7-307.3) and MLP (293.14, 95% CI: 268.3-318.0) also showed substantial improvements over the baseline, the overlapping confidence intervals suggest similar predictive capabilities among the advanced models. This improvement in predictive capability suggests a strong correlation between temporal microclimatic variations and regional cocci incidences. The increased predictive power of these models has significant public health implications, potentially informing strategies for cocci outbreak prevention and control. Moreover, this study represents the first application of the novel xLSTM architecture in epidemiological research and pioneers the evaluation of modern machine learning methods' accuracy in predicting cocci outbreaks. These findings contribute to the ongoing efforts to address cocci, offering a new approach to understanding and potentially mitigating the impact of the disease in affected regions.

Machine Learning Quantum Mechanical/Molecular Mechanical Potentials: Evaluating Transferability in Dihydrofolate Reductase-Catalyzed Reactions.

Integrating machine learning potentials (MLPs) with quantum mechanical/molecular mechanical (QM/MM) free energy simulations has emerged as a powerful approach for studying enzymatic catalysis. However, its practical application has been hindered by the time-consuming process of generating the necessary training, validation, and test data for MLP models through QM/MM simulations. Furthermore, the entire process needs to be repeated for each specific enzyme system and reaction. To overcome this bottleneck, it is required that trained MLPs exhibit transferability across different enzyme environments and reacting species, thereby eliminating the need for retraining with each new enzyme variant. In this study, we explore this potential by evaluating the transferability of a pretrained ΔMLP model across different enzyme mutations within the MM environment using the QM/MM-based ML architecture developed by Pan, X. J. Chem. Theory Comput. 2021, 17(9), 5745-5758. The study includes scenarios such as single point substitutions, a homologous enzyme from different species, and even a transition to an aqueous environment, where the last two systems have MM environment that is substantially different from that used in MLP training. The results show that the ΔMLP model effectively captures and predicts the effects of enzyme mutations on electrostatic interactions, producing reliable free energy profiles of enzyme-catalyzed reactions without the need for retraining. The study also identified notable limitations in transferability, particularly when transitioning from enzyme to water-rich MM environments. Overall, this study demonstrates the robustness of the Pan et al.'s QM/MM-based ML architecture for application to diverse enzyme systems, as well as the need for further research and the development of more sophisticated MLP models and training methods.

Perencanaan Arsitektur Sistem Informasi Menggunakan Metode Enterprise Architecture Planning di CV. Furqon Buana Gemilang

CV. Furqon Buana Gemilang currently does not have an architectural plan so that the business processes that occur are still not optimal. CV. Furqon Buana Gemilang has business process activities, such as Tour & Travel which are in the form of processes in selling airline tickets, vacation programs, hotel reservations, and outbound. In its marketing process, it only uses social media such as Instagram and WhatsApp as the main media in promoting this travel product brand. And for the website, it is still in the development process and has not been able to fully process. The modeling used in the architectural planning to be made is by using the Enterprise Architecture Planning (EAP) method which consists of several stages starting from initialization of planning, business modeling, current systems and technologies, data architecture, application architecture, technology architecture, and implementation plans. This research will produce information system architecture planning at CV. Furqon Buana Gemilang Cikancung using the Enterprise Architecture Planning method which includes 7 stages. In the Company's business process there are 5 main activities, namely airline ticket sales, vacation programs, hotel reservations, outbound, and consumer services, as well as 3 supporting activities, namely HR management, financial management, and information system development. With this architectural planning, the Company can follow the stages and schedules designed to ensure that the implementation runs in a structured manner.

Constructing Knowledge Graphs for New Species in the Greater Mekong Subregion

Purpose: This study presents a methodology for developing a knowledge graph by integrating World Wildlife Fund (WWF) species discovery reports in the Greater Mekong Subregion. Design/methodology/approach: The research employs a four-phase approach: constructing a taxonomic data model, implementing a graph database, developing a web application architecture, and creating an interactive user interface. Findings: The knowledge graph integrates 2,783 species across 27 taxonomic groups, comprising 12,089 nodes and 30,303 connections. The system enables complex queries through SPARQL endpoints, revealing patterns in species distribution and conservation status from 1997 to 2022. Research limitations: Primary limitations include geographical constraints to the Greater Mekong Subregion and limited integration with external biodiversity databases. Practical implications: The system provides researchers with a graph search engine for exploring species data, supporting scientific research and conservation planning. Originality/value: This study presents a novel approach to organizing biodiversity data by combining taxonomic hierarchies with conservation metrics, establishing a framework for integrating species discovery data.

Elastic robotic structures: a multidisciplinary framework for the design and control of shape-morphing elastic system for architectural and design applications

The design of adaptive structures and objects takes place at the intersection of design, architecture, robotics and engineering. Evolving from 1960s cybernetics to today’s interactive projects, technological advancements shape new visions for adaptive systems. A key challenge in this field is developing human-scale, shape-morphing structures. Elastic materials offer a promising solution for creating lightweight systems capable of large transformations with minimal components and energy, unlike conventional rigid systems. This approach requires methodologies for designing and controlling complex material deformations. While architectural and structural design methods focus on large-scale but static elastic structures, soft robotics explores dynamic behaviors. However these approaches are limited for complex shapes and large-scale, as their focus is on specialized applications. To address these issues, this research introduces a multidisciplinary framework for the design and control of shape-morphing elastic system for architectural and design applications. It also presents the concept of elastic robotic structures (ERS), which refers to a body of work developed with the framework. ERS are defined as large-scale elastic systems that are robotically actuated and can achieve multiple geometrical states, interacting with humans and adapting to diverse conditions. The multidisciplinary framework is presented for ERS design, characterization and control, showing how it leverages the integration of architecture, engineering and robotics to overcome the limitations of discipline-specific traditional approaches. The framework is applied in the realization of different types of ERS, which are presented and categorized. Combining the flexibility and interactivity of design methodologies with the reliability of robotic solutions will enable designers and engineers to develop innovative elastic shape-changing systems and program their behaviors.

Understanding Agentic Frameworks in AI Development: A Technical Analysis

This technical article examines the evolution and implementation of agentic frameworks in artificial intelligence development, focusing on their transformative impact across multiple industries. The article explores the fundamental architectural components, implementation methodologies, and practical applications of these frameworks in manufacturing, financial services, and healthcare sectors. By investigating the core components, including perception systems and decision architectures, alongside the Belief-Desire-Intention model and advanced learning mechanisms, this article provides comprehensive insights into how agentic frameworks are revolutionizing autonomous decision-making capabilities. The article also addresses critical technical challenges in scalability and safety while offering potential solutions and future directions for development, highlighting the growing importance of these frameworks in shaping the future of AI technology.

Optimizing transparent quantum-dot LEDs on plastic substrates: enhancing performance through dielectric/metal/dielectric electrode design

Abstract This study explores the optimization of transparent quantum-dot light-emitting diodes (TQLEDs) on polyethylene naphthalate substrates, focusing on dielectric/metal/dielectric electrode structures. By employing MoOₓ/Ag/MoOₓ and WOₓ/Ag/WOₓ as the cathode and anode, respectively, we demonstrated enhanced device performance through precise control of the WOₓ layer thickness, significantly improving luminance and efficiency. These TQLEDs exhibit superior light emission comparable to opaque quantum-dot light-emitting diodes while ensuring transparency, which is crucial for automotive and architectural applications. Our findings highlight TQLEDs' potential as a next-generation technology for flexible, transparent displays.

Efficient Coarse-Grained Reconfigurable Array architecture for machine learning applications in space using DARE65T library platform

Efficient Coarse-Grained Reconfigurable Array architecture for machine learning applications in space using DARE65T library platform

A Comprehensive Analysis of Real-time Data Processing Architectures for High-throughput Applications

A Comprehensive Analysis of Real-time Data Processing Architectures for High-throughput Applications