Architectural Design Research Articles

Cloud-Native ETL: Integrating Databricks and Azure Data Factory for Scalable Data Processing in Enterprise Environments

This article examines the implementation of cloud-native ETL solutions leveraging Databricks and Azure Data Factory (ADF) for scalable data processing in enterprise environments. The article presents a comprehensive analysis of the architectural design, integration strategies, and performance optimization techniques for combining Databricks' powerful data transformation capabilities with ADF's robust workflow orchestration. Through a series of case studies and empirical evaluations, we demonstrate how this integrated approach addresses the challenges of big data processing, including scalability, flexibility, and cost-effectiveness. Our findings reveal significant improvements in processing efficiency and resource utilization, with observed reductions in ETL pipeline execution times by up to 40% and overall cloud infrastructure costs by 25%. The article also highlights best practices for data governance, security, and quality management within this framework. These insights provide valuable guidance for data engineers and IT professionals seeking to modernize their data processing infrastructure and harness the full potential of cloud-native ETL solutions.

Architecting Secure Microservices Deployments on AWS

In the rapidly evolving digital era, microservices deployments have become indispensable for businesses aiming for enhanced scalability, flexibility, and resilience. By fragmenting applications into smaller, autonomous services, organizations can scale individual components, optimize performance with specific technology stacks, and improve fault isolation, thereby minimizing the risk of comprehensive system failures. However, ensuring robust security is of utmost importance. This article delves into key security frameworks and architectural designs employed in AWS to provide comprehensive protection for microservices deployments, safeguarding them from potential threats and vulnerabilities.

Electroanalysis of Statin Drugs: A Review on the Electrochemical Sensor Architectures Ranging from Classical to Modern Systems.

An overview of the latest advances in the design of electrochemical sensor architectures dedicated to the determination of drugs from the statin class is presented in this review. Statins are drugs widely consumed for cholesterol control, and their determination in different matrices through the application of electroanalysis is growing considering advantages such as operational simplicity, lower cost and ease of sample preparation. Within the context of statins, electrochemical sensor architectures can be subdivided into conventional/classical electrodes such as glassy carbon electrodes, carbon paste electrodes, pencil graphite electrodes, boron-doped diamond electrodes and metallic electrodes, and more modern electrode systems, including the screen-printed electrodes and 3D-printed electrodes. Thus, different aspects related to the preparation of these electrochemical sensors and analytical performance are presented, also reflecting advances in terms of designs of new architectures and possible improvements not previously reviewed. Analyzed samples, advantages and disadvantages of different implemented sensor's modification strategies and perspectives for the electroanalysis of statins are also included throughout the work.

The Use and Development of Artificial Intelligence in Architectural Design Processes

Artificial intelligence is widely used as an interactive technology in various professional disciplines. The widespread use of these technologies, which we benefit from in most areas of our lives, in the education sector will provide important developments in the field of education. The main purpose of this study is based on analysing the existing studies in which the use of artificial intelligence helps in architectural design processes. In the study, identification, screening, eligibility, inclusion and data analysis processes were carried out in three search engines such as Web of Science, ScienceDirect and ULAKBIM. While reporting the research, ‘Systematic Literature Review’ and ‘Preferred Reporting Items for Meta-Analysis’ protocols were followed and a total of 35 relevant articles were identified. In the research, three popular Artificial Intelligence applications used in architectural design processes were identified as Generative Adversarial Networks (GAN), Machine Learning and Data Mining. In addition, Systematic Literature Review (SLR) outputs show that most researchers are supported by artificial intelligence applications in architectural design processes. As a result of the research, it was determined that artificial intelligence is widely used in architectural design processes, however, it has positive effects in 3D and animation parts.

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 Study on the Visual Perception of Cultural Value Characteristics of Traditional Southern Fujian Architecture Based on Eye Tracking

The traditional Southern Fujian architecture serves as a reflection of the region’s deeply rooted cultural values. However, research on the architectural and cultural heritage of Southern Fujian remains scarce. Accordingly, this study employs eye-tracking technology to experiment with 20 individuals with local cultural backgrounds and 20 without it to investigate the differences in the perception of the architectural heritage of Southern Fujian by individuals with disparate cultural backgrounds. The findings indicate that distinct groups evince disparate visual perceptions of the architectural and cultural heritage of Southern Fujian. The participants’ attention was divided into two categories: patterns and shapes and calligraphy and text. Non-locals were primarily drawn to patterns and shapes, whereas locals demonstrated a greater interest in the calligraphy and text category. The extraction of cultural information from calligraphic characters requires greater understanding and more time spent within the culture. Knowledge of local cultural backgrounds is helpful to better understand such information. This highlights the potential impact of Southern Fujian architecture on the local population. These findings provide a compelling rationale for the incorporation of multicultural elements in contemporary architectural design while also advocating for the preservation and advancement of Southern Fujian architectural heritage.

Comprehensive empirical evaluation of feature extractors in computer vision

Feature detection and matching are fundamental components in computer vision, underpinning a broad spectrum of applications. This study offers a comprehensive evaluation of traditional feature detections and descriptors, analyzing methods such as Scale Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), Binary Robust Independent Elementary Features (BRIEF), Oriented FAST and Rotated BRIEF (ORB), Binary Robust Invariant Scalable Keypoints (BRISK), KAZE, Accelerated KAZE (AKAZE), Fast Retina Keypoint (FREAK), Dense and Accurate Invariant Scalable descriptor for Yale (DAISY), Features from Accelerated Segment Test (FAST), and STAR. Each feature extractor was assessed based on its architectural design and complexity, focusing on how these factors influence computational efficiency and robustness under various transformations. Utilizing the Image Matching Challenge Photo Tourism 2020 dataset, which includes over 1.5 million images, the study identifies the FAST algorithm as the most efficient detector when paired with the ORB descriptor and Brute-Force (BF) matcher, offering the fastest feature extraction and matching process. ORB is notably effective on affine-transformed and brightened images, while AKAZE excels in conditions involving blurring, fisheye distortion, image rotation, and perspective distortions. Through more than 2 million comparisons, the study highlights the feature extractors that demonstrate superior resilience across various conditions, including rotation, scaling, blurring, brightening, affine transformations, perspective distortions, fisheye distortion, and salt-and-pepper noise.

Cultural Imprints on Urban Housing: A Spatial Analysis of Apartment Designs in Kenya, Ghana, and South Africa

Apartment design is often assumed to follow a standardized model, disregarding cultural influences. However, cultural context can significantly shape the spatial organization of living environments. A significant gap exists in addressing how cultural factors influence apartment design in rapidly urbanizing African cities. Modern architectural practices frequently overlook the sociocultural dynamics essential for creating living spaces that resonate with local populations. This study investigates the spatial configuration of apartment units in three African countries—Kenya, South Africa, and Ghana—challenging the notion of universally homogeneous apartment designs. By applying Space Syntax, a quantitative analysis tool, this study examines how cultural factors distinctly influence the layout of apartments. The findings reveal significant variations, particularly in the design of corridors, domestic servant quarters, and the separation of private zones. This research highlights the importance of incorporating cultural considerations into architectural design processes to promote housing solutions that are functionally efficient, culturally relevant, and responsive to local social norms. Future research could explore how these cultural influences manifest across different housing types, such as informal settlements and high-rise apartments, or examine the impact of migration and globalization on evolving housing needs.

Design Leadership and Sustainable Leadership in Sustainable Practice of an Indonesian Architectural Design Firm, Han Awal and Partners

Design Leadership and Sustainable Leadership in Sustainable Practice of an Indonesian Architectural Design Firm, Han Awal and Partners

Point neuron learning: a new physics-informed neural network architecture

Machine learning and neural networks have advanced numerous research domains, but challenges such as large training data requirements and inconsistent model performance hinder their application in certain scientific problems. To overcome these challenges, researchers have investigated integrating physics principles into machine learning models, mainly through (i) physics-guided loss functions, generally termed as physics-informed neural networks, and (ii) physics-guided architectural design. While both approaches have demonstrated success across multiple scientific disciplines, they have limitations including being trapped to a local minimum, poor interpretability, and restricted generalizability beyond sampled data range. This paper proposes a new physics-informed neural network (PINN) architecture that combines the strengths of both approaches by embedding the fundamental solution of the wave equation into the network architecture, enabling the learned model to strictly satisfy the wave equation. The proposed point neuron learning method can model an arbitrary sound field based on microphone observations without any dataset. Compared to other PINN methods, our approach directly processes complex numbers, offers better interpretability, and can be generalized to out-of-sample scenarios. We evaluate the versatility of the proposed architecture by a sound field reconstruction problem in a reverberant environment. Results indicate that the point neuron method outperforms two competing methods and can efficiently handle noisy environments with sparse microphone observations.

A Theoretical Design Framework of Contemporary Vernacular Architecture Based on a Scoping Review of the Best Practices Worldwide

This paper reviews the development of vernacular architecture design and practices worldwide and then focuses on Chinese rural development. It explores the evolution of vernacular architecture and related concepts from a longitudinal perspective, and indicates the dynamic adaptability of vernacular architecture. A scoping review based on archival materials and secondary source literature of the last 10 years is conducted. As a result, 41 design factors have been collected. With a cross-comparative study with related publications, five intangible design factors and ten key tangible design factors are discussed to build a theoretical design framework, which finally provides a foundation for related design practices and further research.

AI-Driven Production in Modular Architecture: An Examination of Design Processes and Methods

This study explores the incorporation of AI-driven production technologies within modular architecture, with an emphasis on optimizing design processes and facilitating practical application. By leveraging text-to-image technology via the Runway AI platform, a range of geometric moduls -including rhombic dodecahedra, cubes, hexagonal prisms, truncated octahedra, and triangular prisms-were utilized as prompts to generate architectural designs. The generated designs were subsequently assessed against established modular architecture criteria, with particular attention given to their structural integrity and construction feasibility. The AI-generated outputs exhibited notable versatility, yielding designs that met both structural and architectural standards. This methodological approach presents a new set of tools for architects, enabling them to explore intricate geometric forms while enhancing the efficiency of conceptual and design stages. The results indicate that the integration of AI-driven techniques into modular architecture has considerable potential to drive innovation, offering a more streamlined process for achieving creative and functional design outcomes. Moreover, the application of AI in this context not only expedites the generation of forms but also expands architects' capabilities in producing a wider array of high-quality architectural solutions.

A Systematic Mapping Study and a Review of the Optimization Methods of Structures in Architectural Design

Structural optimization has gained popularity in modern structural design, helping to reduce material consumption while maintaining the structural performance of buildings. This process also significantly influences the architectural appearance, affecting various aspects such as cross-section sizing, structural forms, and the layout of structural members. Beyond minimizing materials or costs, structural optimization can serve as a powerful tool for making architecture more visually appealing. However, with the wide variety of structural optimization methods proposed, gaining a comprehensive overview has become challenging. To address this, a systematic mapping study has been conducted, focusing on methods introduced over the past decade. The relevant journal articles are categorized based on several factors, including types of optimization, materials used, structural typologies, areas of application, and optimization objectives. The results of this study provide both a broad overview of recent developments in structural optimization and valuable insights into research-rich and under-explored areas. Moreover, the paper discusses which types of structural optimization are more relevant when applied as part of the architectural design process. It is suggested that future research should focus on identifying gaps and challenges in effectively applying structural optimization to architectural design, thus enhancing both efficiency and aesthetic potential.

Hidden in Plain Sight: A Data-Driven Approach to Safety Risk Management for Highway Traffic Officers

Highway traffic officers (HTOs) are often exposed to life-threatening workplace incidents while performing their duties. However, scant research has been undertaken to address these safety concerns. This research explores case study data from highway incident reports (held by National Highways, a UK government company) and employs deep neural network (DNN) in unearthing patterns which inform safety decision makers on pertinent safety challenges confronting HTOs. A mixed philosophical stance of positivism and interpretivism was adopted to synthesise the findings made. A four-phase sequential method was implemented to evaluate the validity of the research viz.: (i) architectural design; (ii) data exploration; (iii) predictive modelling; and (iv) performance evaluation. The DNN model’s predictive performance is benchmarked against three other machine learning models, namely Support Vector Machines (SVM), Random Forest (RF), and Naïve Bayes (NB). The DNN model outperformed the other three models. Findings from the data exploration also show that most work operations undertaken by HTOs have a medium risk level with night shifts posing the greatest risk challenges. Carriageways and traffic management enclosures had the highest incident occurrence. This is the first study to uncover such hidden patterns and predict risk levels using a database specifically for HTOs. This study presents evidence-based information for proactive risk management for HTOs.

A High Scalability Memory NoC with Shared-Inside Hierarchical-Groupings for Triplet-Based Many-Core Architecture

Innovative processor architecture designs are shifting towards Many-Core Architectures (MCAs) to meet the future demands of high-performance computing as the limits of Moore’s Law have almost been reached. Many-core processors utilize shared memory hierarchies to achieve high-speed memory systems, improving memory access efficiency. However, as the number of cores multiplies, the scalability of this system is significantly constrained by the increased proportion of long-distance and Non-Uniform Memory Access (NUMA). Improving the scalability of MCAs is crucial for achieving large/super-scale general-purpose many-core processors. This work proposes a high scalability memory Network-on-Chip (NoC) for Triplet-Based Many-Core Architecture (TriBA), named TriBA-mNoC. TriBA-mNoC maintains a consistent core-to-core spacing as the network scale increases, effectively preventing increased long-distance memory access latency. Moreover, it leverages an inherent advantage of shared-inside hierarchical-groupings, alleviating common NUMA issues in the NoC design. Evaluations of static network characteristics show that TriBA-mNoC outperforms most classical NoCs in network diameter, average distance, and cost. TriBA-mNoC can be integrated with TriBA in the same silicon die with a tile-like floorplan, forming a novel NoC called TriBA-NoC, which can combine the strengths of both networks to maximize the architecture performance. We evaluated the memory access performance and scalability of TriBA-NoC using the mathematical evaluation models and actual simulations with real traffic (PARSEC 3.0 and SPLASH-2) at different network scales. The mathematical evaluation results indicate that TriBA-NoC achieves an aggregate speedup of approximately 3x compared with 2D-Mesh for a similar number of cores. Furthermore, TriBA-NoC’s single-core speedup efficiency remains stable as the number of cores increases under the same cache hit ratio, while 2D-Mesh experiences a rapid decline, highlighting TriBA-NoC’s exceptional scalability. Finally, the actual traffic simulation results show that TriBA-NoC achieves an average memory access latency and time reduction of 25.90% − 40.50% and 5.61% − 31.69% respectively, compared with 2D-Mesh.

Fluid Grey 2: How Well Does Generative Adversarial Networks Learn Deeper Topology Structure in Architecture That Matches Images?

Taking into account the regional characteristics of ‘intrinsic’ and ‘extrinsic’ properties of space is an essential issue in architectural design and urban renewal, which is often achieved step by step using image and graph-based GANs. However, each model nesting and data conversion may cause information loss, and it is necessary to streamline the tools to facilitate architects and users to participate in the design. Therefore, this study hopes to prove that I2I GAN also has the potential to recognize topological relationships autonomously. Therefore, this research proposes a method for quickly detecting the ability of pix2pix to learn topological relationships, which is achieved by adding two Grasshopper-based detection modules before and after GAN. At the same time, quantitative data is provided and its learning process is visualized, and changes in different input modes such as greyscale and RGB affect its learning efficiency. There are two innovations in this paper: 1) It proves that pix2pix can automatically learn spatial topological relationships and apply them to architectural design. 2) It fills the gap in detecting the performance of Image-based Generation GAN from a topological perspective. Moreover, the detection method proposed in this study takes a short time and is simple to operate. The two detection modules can be widely used for customizing image datasets with the same topological structure and for batch detection of topological relationships of images. In the future, this paper may provide a theoretical foundation and data support for the application of architectural design and urban renewal that use GAN to preserve spatial topological characteristics.

Four Models of Wastewater-Based Monitoring for SARS-CoV-2 Complementing Individual Screening in Jail Settings.

Objectives. To describe 4 unique models of operationalizing wastewater-based surveillance (WBS) for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in jails of graduated sizes and different architectural designs. Methods. We summarize how jails of Cook County, Illinois (average daily population [ADP] 6000); Fulton County, Georgia (ADP 3000); Middlesex County, Massachusetts (ADP 875); and Washington, DC (ADP 1600) initiated WBS between 2020 and 2023. Results. Positive signals for SARS-CoV-2 via WBS can herald a new onset of infections in previously uninfected jail housing units. Challenges implementing WBS included political will and realized value, funding, understanding the building architecture, and the need for details in the findings. Conclusions. WBS has been effective for detecting outbreaks of SARS-CoV-2 in different sized jails, those with both dorm- and cell-based architectural design. Public Health Implications. Given its effectiveness in monitoring SARS-CoV-2, WBS provides a model for population-based surveillance in carceral facilities for future infectious disease outbreaks. (Am J Public Health. 2024;114(11):1232-1241. https://doi.org/10.2105/AJPH.2024.307785).

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 x 40 centimeters 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.

Noise in the Postanesthesia Care Unit: Identifying Potential Causes for Increased Noise Levels

PurposeWith increasingly complex care, monitor alarms, and high patient traffic, the postanesthesia care unit (PACU) is one of the loudest patient care areas in the hospital. High noise levels lead to worse outcomes and impede recovery. The objective of this study is to measure the sound levels in the PACU and identify potential causes for increased sound levels. DesignAn observational study in the 24-unit PACU in the Ghent University Hospital was conducted. MethodsSound levels were measured every second continuously at 3 separate locations using noise dosimeters over a period of 11 days. During the sound measurements, the patient and nurse census in the PACU was measured. FindingsThe mean A weighted equivalent sound level pressure (LAeq) was 59.0 A-weighted decibel (dBA) (standard deviation 6.5), exceeded the noise levels recommended by the World Health Organization (40 dBA LAEQ). With an average LAEQ of 62.4 dBA (6.1), the morning shift is the noisiest. We evaluated the effect of patient census and nurse census on LAEQ. The overall regression was statistically significant (P < .001). Nurse census and patient census significantly predicted noise levels (P < .001). ConclusionsNoise levels in the PACU were consistently above the levels recommended by the World Health Organization. Nurse census and patient census seemed to significantly impact the mean noise levels. Our results suggest that the most important sources of ambient noise are consistently present sources such as computers, ventilation systems, air-conditioning. These factors, along with the architectural design and nurse and patient census, might play a very important role in noise control.

Natural Landscapes and the Psychological Impact of Environment on Architecture: Examining the Synergy Between Nature and Spatial Design

One of the most important characteristics of architectural art is the quality and type of connection it establishes with nature. Nature has a historical depth spanning hundreds of millions of years, having been shaped, evolved, and grown over time. It presents a structured and regulated form, within which a profound harmony can be observed in its diverse components. This homogeneous and dynamic structure has created a coherent language, which requires an understanding and decoding of this symbolic language to establish an interactive and constructive relationship with nature. This research examines the connection between natural landscapes and the psychological impacts of the environment on architectural design. The main objective of the study is to analyze the synergy between nature and designed spaces, investigating how this interaction can affect the quality of human life. The results indicate that the presence of natural elements in living and working spaces reduces stress, increases satisfaction, and improves mental health. Additionally, sustainable design that considers environmental principles and human needs can lead to the creation of spaces that provide a sense of tranquility and pleasure for users. Despite the economic and cultural challenges in implementing these approaches, collaboration among designers, engineers, and local communities is essential. Ultimately, this paper emphasizes the importance of considering the psychological impacts of the environment and the necessity of creating spaces that promote the synergy between nature and architecture.