Architectural Decisions Research Articles

An analytical framework for digital transformation in healthcare product supply chains

This paper presents an analytical framework for digital transformation in healthcare product supply chains. It aims to systematically analyze the impact of digital initiatives across the Supply Chain Operations Reference (SCOR) model's categories: Enable, Plan, Source, and Deliver. The study explores how digital technology enhances access, affordability, and adoption of healthcare products while influencing decision-making architecture and improving supply chain actor agency and accountability. The methods used in this study include a systematic analysis of digital initiatives within healthcare product supply chains, framed by the Supply Chain Operations Reference (SCOR) model, supported by case studies and real-world examples. The results revealed that digital technology has a profound impact on improving healthcare product supply chains in several key areas including enhancing product availability, improving affordability, and expediting the adoption of new health products. The paper indicates that digital solutions significantly enhance access to healthcare products by improving visibility and traceability throughout the supply chain, leading to better inventory management, fewer stockouts, and more efficient distribution. Furthermore, the findings suggest that digital interventions help reduce costs, contributing to affordability by streamlining procurement processes, minimizing transaction costs, and optimizing resource allocation. Additionally, the results indicate that digital tools accelerate the adoption of health products by enabling real-time data analytics, which supports faster decision-making and more efficient approval processes. The findings also reveal that digital technology reshapes the decision-making architecture within supply chains which allows for more decentralized, data-driven decisions that enhance the agility and responsiveness of supply chain actors. However, the results emphasize that the long-term effectiveness of digital solutions hinges on their ability to empower supply chain actors and improve accountability. The paper concludes that governments when developing digital transformation roadmaps, should prioritize areas where lack of information is the primary constraint.

The revolutionary impact of ai in the automotive industry

The automotive industry is undergoing a revolutionary transformation through the integration of Artificial Intelligence (AI) technologies. This article examines the multifaceted impact of AI across manufacturing, autonomous vehicle development, smart factory solutions, and customer experience enhancement. The article highlights significant improvements in manufacturing efficiency, with AI-driven quality control systems achieving high accuracy in defect detection and reducing production costs substantially. In autonomous vehicle development, AI systems have demonstrated remarkable capabilities, with object detection accuracy reaching exceptional levels under optimal conditions and decision-making architectures processing multiple discrete decisions per second. The implementation of AI in smart factories has led to considerable improvement in overall equipment effectiveness, while customer experience enhancements have resulted in a significant increase in satisfaction scores. The integration of AI in supply chain management and market intelligence has yielded substantial operational improvements, with demand forecasting accuracy reaching impressive levels for short-term predictions and enabling meaningful reduction in inventory costs.

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.

Determining Vulnerability Indicators of Buildings for Sea-Level Rise and Floods in Urban Coastal Areas

Projected sea-level rise and floods due to climate change impacts are the hazards threatening urban coastal areas. In the literature on mitigation and adaptation, it is determined that studies in the field of architectural design for the assessment of risks and vulnerabilities to these hazards are not yet at a sufficient level. This study aims to determine the vulnerability indicators of buildings due to architectural design decisions in the urban coastal areas facing the risk of sea-level rise and flood hazards. In this direction, it is argued that the decisions that are taken regarding the building and its environment during the architectural design process can be interpreted as vulnerability indicators in vulnerability assessments of buildings to be made in the context of these hazards. In this context, an indicator-based assessment framework is proposed as a method of examining the vulnerability and climate resilience capacity of design practices in urban coastal areas. The first stage of the research methodology includes the results of a literature review to identify indicators of building vulnerability. In the second stage, these indicators were presented for expert opinions and analyzed with the Delphi method and an assessment framework was created. This assessment framework is designed to serve as a decision-making tool or checklist for decision makers, facilitating the integration of vulnerability indicators into the design, implementation, and retrofitting of buildings in urban coastal areas. Due to its hierarchical, yet flexible, and adaptable structure, it can be used by architects, urban planners, and policy makers in terms of assessing buildings and its environments so that actions for adaptation can be implemented.

Advances and challenges in learning from experience replay

From the first theoretical propositions in the 1950s to its application in real-world problems, Reinforcement Learning (RL) is still a fascinating and complex class of machine learning algorithms with overgrowing literature in recent years. In this work, we present an extensive and structured literature review and discuss how the Experience Replay (ER) technique has been fundamental in making various RL methods in most relevant problems and different domains more data efficient. ER is the central focus of this review. One of its main contributions is a taxonomy that organizes the many research works and the different RL methods that use ER. Here, the focus is on how RL methods improve and apply ER strategies, demonstrating their specificities and contributions while having ER as a prominent component. Another relevant contribution is the organization in a facet-oriented way, allowing different perspectives of reading, whether based on the fundamental problems of RL, focusing on algorithmic strategies and architectural decisions, or with a view to different applications of RL with ER. Moreover, we start by presenting a detailed formal theoretical foundation of RL and some of the most relevant algorithms and bring from the recent literature some of the main trends, challenges, and advances focused on ER formal basement and how to improve its propositions to make it even more efficient in different methods and domains. Lastly, we discuss challenges and open problems and present relevant paths to feature works.

Architecting Enterprise-Scale Data Products: A Framework for Advanced Data Science and AI/ML Operations

This article presents a comprehensive framework for building enterprise-scale data products that power modern Customer & Product Analytics, Data Science, artificial intelligence, and machine learning initiatives. The article examines the foundational architecture patterns, pipeline engineering strategies, and advanced distributed computing approaches in both on-prem and cloud. These are essential for developing robust data infrastructure capable of handling complex Data Analytics, Data Science, and AI/ML workflows. The article explores critical aspects of feature engineering at scale, real-time processing capabilities, and the implementation of feature stores, while addressing the challenges of data quality, governance, legal, and security in regulated environments. The article introduces a systematic approach to integrating data products with MLOps pipelines, emphasizing the importance of automated workflows, monitoring systems, and feedback loops in production environments. The findings demonstrate that successful implementation of scalable data products requires a careful balance of architectural decisions, technology selection, and operational practices. The article contributes to the field by providing actionable insights and architectural patterns that organizations can adopt to build resilient, scalable, and efficient data products for their Data Analytics, Data Science, and AI/ML use cases. This article establishes a foundational framework that bridges the gap between theoretical data architecture principles and practical implementation challenges in enterprise settings.

Architecture-Agnostic Untrained Network Priors for Image Reconstruction with Frequency Regularization.

Untrained networks inspired by deep image priors have shown promising capabilities in recovering high-quality images from noisy or partial measurements without requiring training sets. Their success is widely attributed to implicit regularization due to the spectral bias of suitable network architectures. However, the application of such network-based priors often entails superfluous architectural decisions, risks of overfitting, and lengthy optimization processes, all of which hinder their practicality. To address these challenges, we propose efficient architecture-agnostic techniques to directly modulate the spectral bias of network priors: 1) bandwidth-constrained input, 2) bandwidth-controllable upsamplers, and 3) Lipschitz-regularized convolutional layers. We show that, with just a few lines of code, we can reduce overfitting in underperforming architectures and close performance gaps with high-performing counterparts, minimizing the need for extensive architecture tuning. This makes it possible to employ a more compact model to achieve performance similar or superior to larger models while reducing runtime. Demonstrated on inpainting-like MRI reconstruction task, our results signify for the first time that architectural biases, overfitting, and runtime issues of untrained network priors can be simultaneously addressed without architectural modifications. Our code is publicly available .

Multidimensional Design Space Exploration for Evolving Manufacturing Systems: Interplay of Deployment, Topology, and Production Planning Architectural Decisions

Multidimensional Design Space Exploration for Evolving Manufacturing Systems: Interplay of Deployment, Topology, and Production Planning Architectural Decisions

Individualized clinical decisions within standard-of-care pragmatic clinical trials: Implications for consent.

Pragmatic clinical trials of standard-of-care interventions compare the relative merits of medical treatments already in use. Traditional research informed consent processes pose significant obstacles to these trials, raising the question of whether they may be conducted with alteration or waiver of informed consent. However, to even be eligible, such a trial in the United States must have no more than minimal research risk. We argue that standard-of-care pragmatic clinical trials can be designed to ensure that they are minimal research risk if the random assignment of an intervention in a pragmatic clinical trial can accommodate individualized, clinically motivated decision-making for each participant. Such a design will ensure that the patient-participants are not exposed to any risks beyond the clinical risks of the interventions, and thus, the trial will have minimal research risk. We explain the logic of this view by comparing three scenarios of standard-of-care pragmatic clinical trials: one with informed consent, one without informed consent, and one recently proposed design called Decision Architecture Randomization Trial. We then conclude by briefly showing that our proposal suggests a natural way to determine when to use an alteration versus a waiver of informed consent.

Multi-Tenant Architecture: A Comprehensive Framework for Building Scalable SaaS Applications

Multi-tenant architecture has emerged as a fundamental paradigm in modern software development, particularly in Software as a Service (SaaS) applications where multiple organizations share computing resources while maintaining data isolation. This article presents a comprehensive framework for understanding and implementing multi-tenant systems, focusing on essential architectural decisions and design patterns that ensure scalability, security, and resource efficiency. The article examines the evolution from single-tenant to multi-tenant architectures, analyzes various data partitioning strategies, and explores critical aspects of tenant isolation, authentication, and authorization mechanisms. The article addresses key challenges in performance optimization, resource allocation, and security implementation, providing practical insights into database design approaches and caching strategies. Through a structured approach, this article bridges the gap between theoretical concepts and practical implementation, offering developers and architects a foundation for building robust multi-tenant systems. The findings emphasize the importance of balanced architectural decisions that accommodate both technical requirements and business objectives while maintaining system integrity and tenant isolation. This article contributes to the growing body of knowledge in cloud computing and distributed systems, providing practitioners with actionable insights for developing scalable multi-tenant applications.

Optimal Configuration of Spatial Planning for Energy-Efficient Buildings

The project phase is where the life-cycle of a building starts. The best decisions are made during the design or pre-project step. In terms of both economic resources and time, changes to specific design decisions made at this step are inexpensive compared to subsequent steps of architectural planning, not to mention the course of the construction's operation itself. The choices made during the design phase determine to a large extent whether the architectural design decisions of a building are achieved, whether the building and site can be used appropriately, and whether the project is economically viable. With BIM, building spatial planning is possible. As a result, architects can evaluate the proposed structure, its impact on the ecology, and the ecology's impact on the structure more comprehensively and at an earlier stage. This research proposes an energy modeling approach for the BIM-based spatial planning phase of a construction. The proposed method will result in an energy model for specific sites and building resolutions when utilized to create a spatial modelling for a construction. The energy model can then be used for new architectural creations. In this study, 36 different alternative scenarios were designed in terms of the rate of construction height to construction spacing, orientation factor, and form factor. With the help of BIM and GBS softwares, the energy consumption values of the alternative scenarios in cooling and heating load conditions were compared, and the alternative scenario with the minimum energy consumption was tried to be determined with spatial planning parameters.Ključne riječi

Net-Rrjet XI: Temporary intervention and future architectural practice

AbstractThis innovative research adopts a comprehensive approach to delve into temporary intervention. It aims to gain a deeper and more accurate understanding of architectural decisions related to long-term interventions. The study is based on several compelling case studies, revealing that temporary interventions achieve a more sustainable outcome and showcase remarkable flexibility and adaptability.Projects curated by the author serve as practical research tools, providing a deeper and more accurate insight based on user experiences. The study concludes that the strategic use of temporary intervention is a proven and effective method in architectural design. It promotes community engagement and sustainable urban development and addresses the issue of abandoned permanent structures.

Dopamine builds and reveals reward-associated latent behavioral attractors

Phasic variations in dopamine levels are interpreted as a teaching signal reinforcing rewarded behaviors. However, behavior also depends on the motivational, neuromodulatory effect of phasic dopamine. In this study, we reveal a neurodynamical principle that unifies these roles in a recurrent network-based decision architecture embodied through an action-perception loop with the task space, the MAGNet model. Dopamine optogenetic conditioning in mice was accounted for by an embodied network model in which attractors encode internal goals. Dopamine-dependent synaptic plasticity created “latent” attractors, to which dynamics converged, but only locally. Attractor basins were widened by dopamine-modulated synaptic excitability, rendering goals accessible globally, i.e. from distal positions. We validated these predictions optogenetically in mice: dopamine neuromodulation suddenly and specifically attracted animals toward rewarded locations, without off-target motor effects. We thus propose that motivational dopamine reveals dopamine-built attractors representing potential goals in a behavioral landscape.

Applicability of 3D concrete printing technology in building construction with different architectural design decisions in housing

3D Printing (3DP) technology, one of the layered production methodologies where design and construction configurations can be made with increasing digitalization and Industry 4.0, is one of the leading techniques of the period. 3D Concrete Printing (3DCP) also comes to the fore in the construction industry. The study aims to observe the impact of architectural design decisions on the 3DCP process by developing controlled and consistent design alternatives. Architectural designs developed with different geometric forms in the housing function were subjected to digital planning, 3DCP preparation, and 3DCP prototype final product stages. In the integration of design and 3D concrete printing (3DCP), two key aspects were investigated: (i) the impact of different geometric forms (square, rectangle, hexagon, octagon, circle, and ellipse) on the buildability performance of 3D-printed structures, and (ii) how variations in design details influence printing time, material consumption, cost, and the feasibility of mass production in architectural applications. For the 3DCP process, 3D-printable cementitious mixture was developed, and a 3D printer with dimensions of 100 x 100 × 40 cm was used. The results obtained during the printing process were analyzed in comparison with the building evaluation parameters. As a result, the house with circular geometry showed the optimum behavior according to the parameters of buildability, cost, and mass producibility. The circle geometric form provides a 4.2 % advantage over the octagon in terms of buildability, a 16.6 % advantage over the ellipse in terms of cost, and a 20 % advantage over the ellipse in terms of mass producibility.

Subterranean eco-dystopia in Istanbul: the concreted segregation of Tarlabaşı

ABSTRACT The underground layers of cities are constantly concreted with urban transformation projects. The studies on built environment mainly focus on the morphological changes, related dispossessions and observable aspects of urban landscape. However, the environmental costs of transformations are not limited to the impacts of above-ground interventions. In Istanbul, the major impact of subterranean interventions is the destruction of water and soil ecosystems as well as its multi-layered heritage as the large-scale urban transformation decisions are primarily applied to historic neighbourhoods where the existing building stock was appropriated by the urban precariat. The urban transformation projects in historic neighbourhoods of Istanbul come into question with socio-economic dispossessions rather than environmental justice. In this manuscript, the main aim is to address the socio-ecological impacts of planning and architectural design decisions disregarding subterranean construction density, through the case of Tarlabaşı in Istanbul. Tarlabaşı represents one of the most emblematic case in Istanbul’s urban resistance history and evokes eco-dystopias, which may be attributed to the decisions that justify stigmatisation. With the context, conceptual frame and scope set by these problem definitions, while considering urban inequality a pre-condition of environmental justice, this manuscript examines the correlation between environmental policies, social stigmatisation and flexed environmental values in planning and architecture culture via local political ecology. The methodology employed in the study is based on qualitative analysis of the stages and legal decisions involved. Archival documents on the study area were used to identify the local variables contributing to the deterioration of socio-ecological unity for future discussions.

A model-driven, metrics-based approach to assessing support for quality aspects in MLOps system architectures

In machine learning (ML) and machine learning operations (MLOps), automation serves as a fundamental pillar, streamlining the deployment of ML models and representing an architectural quality aspect. Support for automation is especially relevant when dealing with ML deployments characterised by the continuous delivery of ML models. Taking automation in MLOps systems as an example, we present novel metrics that offer reliable insights into support for this vital quality attribute, validated by ordinal regression analysis. Our method introduces novel, technology-agnostic metrics aligned with typical Architectural Design Decisions (ADDs) for automation in MLOps. Through systematic processes, we demonstrate the feasibility of our approach in evaluating automation-related ADDs and decision options. Our approach can itself be automated within continuous integration/continuous delivery pipelines. It can also be modified and extended to evaluate any relevant architectural quality aspects, thereby assisting in enhancing compliance with non-functional requirements and streamlining development, quality assurance and release cycles.

Digital Transformation in the Health Product Supply Chain: A Framework for Analysis

ABSTRACT Well-functioning supply chains for medicines and other health products are vital for a health system’s goals of ensuring access, quality, and efficiency. However, in several countries the performance of government-run supply chains for health products remains subpar. The widespread adoption of digital technology presents new opportunities for enhancing the performance of the health product supply chain. This paper aims to provide a practical and systematic analysis of digital initiatives within health product supply chains. It provides examples of successful digital interventions in each of the Enable, Plan, Source, and Deliver categories of the Supply Chain Operations Reference model. The examples provide clear evidence that the use of digital technology in the health supply chain can improve access and affordability; in some instances, use of digital technology can lead to faster health product adoption and alter the overall architecture of decision making. While many digital interventions in the public sector supply chain target the collection of data and its analysis and use for reporting, the long-term effectiveness of digital solutions hinges on their ability to enhance the agency of supply chain actors. A thorough and systematic inquiry about the logic model of how a particular digital solution enhances agency and improves accountability is essential at the outset. In developing roadmaps to prioritize and sequence digital solutions in health supply chains, governments should start by asking where lack of information is the primary constraint impeding supply chain performance.

Digital twin-enhanced opportunistic maintenance of smart microgrids based on the risk importance measure

Smart microgrids face more diverse and frequent risks than traditional grids due to their complexity and reliance on distributed generation. Ensuring the reliable operation of smart microgrids requires effective maintenance. Traditional maintenance methods, based on periodic inspections and fault diagnosis, struggle to adapt to the dynamics and complexity of microgrid systems. The introduction of digital twin technology provides a new solution for the opportunistic maintenance of microgrid systems. This paper presents a digital twin microgrid architecture for real-time monitoring and decision-making in opportunistic maintenance. Meanwhile, this paper introduces a risk importance measure to aid to optimize opportunistic maintenance strategies when resources are limited. Finally, a wind-solar-storage microgrid is used to illustrate the proposed method. Experimental results show that the proposed method significantly reduces maintenance costs and improves system reliability, effectively supporting microgrid maintenance.

Leveraging AWS and Java Microservices: An Analysis of Amazon\'s Scalable E-commerce Architecture

Abstract: This article presents a comprehensive case study of Amazon's journey in building a robust and scalable e-commerce platform capable of handling millions of daily transactions while maintaining high availability and performance. We examine the critical architectural decisions that facilitated Amazon's transition from a monolithic structure to a microservices-based architecture, leveraging Java and various AWS cloud services. The article explores key components of Amazon's scalable infrastructure, including the implementation of DynamoDB for high-performance database needs and the use of Elastic Load Balancing to ensure fault tolerance. We analyze the challenges encountered during this transformation and the solutions developed to address them. The article also discusses the resulting improvements in scalability, reliability, and cost-efficiency that have contributed to Amazon's position as the world's largest online retailer. Our findings provide valuable insights into best practices for architecting large-scale, cloud-native e-commerce platforms and offer implications for future developments in the field of distributed systems and cloud computing

Architecture decisions in quantum software systems: An empirical study on Stack Exchange and GitHub

Context:Quantum computing provides a new dimension in computation, utilizing the principles of quantum mechanics to potentially solve complex problems that are currently intractable for classical computers. However, little research has been conducted about the architecture decisions made in quantum software development, which have a significant influence on the functionality, performance, scalability, and reliability of these systems. Objective:The study aims to empirically investigate and analyze architecture decisions made during the development of quantum software systems, identifying prevalent challenges and limitations by using the posts and issues from Stack Exchange and GitHub. Methods:We used a qualitative approach to analyze the obtained data from Stack Exchange Sites and GitHub projects — two prominent platforms in the software development community. Specifically, we collected data from 385 issues (from 87 GitHub projects) and 70 posts (from 3 Stack Exchange sites) related to architecture decisions in quantum software development. Results:The results show that in quantum software development (1) architecture decisions are articulated in six linguistic patterns, the most common of which are Solution Proposal and Information Giving, (2) the two major categories of architectural decisions are Implementation Decision and Technology Decision, (3) Software Development Tools are the most common application domain among the twenty application domains identified, (4) Maintainability is the most frequently considered quality attribute, and (5) Design Issues and High Error Rates are the major limitations and challenges that practitioners face when making architecture decisions in quantum software development. Conclusions:Our results show that the limitations and challenges encountered in architecture decision-making during the development of quantum software systems are strongly linked to the particular features (e.g., quantum entanglement, superposition, and decoherence) of those systems. These issues mostly pertain to technical aspects and need appropriate measures to address them effectively.