Architectural Design Research Articles

SfM Photogrammetry for Cost-Effective 3D Documentation and Rock Art Analysis of the Dombate Dolmen (Spain) and the Megalithic Sites of Chã dos Cabanos and Chã da Escusalha (Portugal)

SfM (structure from motion) photogrammetry is a technique developed in the field of computer vision that enables the generation of three-dimensional (3D) models from a set of overlapping images captured from disparate angles. The application of this technique in the field of cultural heritage, particularly in the context of megalithic monuments, is inherently challenging due to the spatial constraints of these environments and the usual limitations posed by their architectural design, which often results in poor lighting conditions. This article presents an accurate and cost-efficient methodology for the study and documentation of rock art, which has been applied to three megalithic monuments in the Iberian Peninsula: one in Spain and two in Portugal. The three working environments are complex, but the combination of techniques used and improvements such as rendering for the enhancement of engravings and the creation of 3D stop-motion models made it possible to integrate all the information in 3D formats that allow its universal dissemination. This not only preserves the heritage in graphic form but also makes it accessible to the public, both for study and for virtual visits.

Exploring the Facilitating Environment for Mindfulness Meditation: Establishing a Framework through Analysis of Means and Exploratory Factor Analysis

Abstract Objective While mindfulness meditation is an established practice for improving well-being, the physical environment for this activity awaits further exploration. This research aimed to explore how the physical environment facilitates mindfulness meditation and to establish a framework through Analysis of Means (ANOM) and Exploratory Factor Analysis (EFA). The framework was intended to provide guidance for organisations and individuals to better design or modify physical space to support their mindfulness meditations. Method This study used methods to establish an initial research framework from the literature on mindfulness, architectural design, and environmental psychology. A survey of UK mindfulness practitioners was conducted in 2021–2022 to provide insights from a quantitative perspective. The data were analysed using ANOM and EFA to obtain a comprehensive framework based on the responses. Results Through a review of the literature and empirical study, nine physical factors with controllable elements were identified, ranked by their relative importance. Quietness, the use of supportive tools, and natural sounds were found to be the most important. Conclusion This research suggests the priority elements for groups and individuals to consider when adapting the physical environment for mindfulness meditation. The results revealed quietness as the most beneficial controllable element for its facilitation. Additionally, the use of supportive meditation tools was also important. Considering these top elements first could support practitioners' mindfulness meditation more effectively and efficiently. Preregistration This study was not preregistered.

Application of mask R-CNN for building detection in UAV remote sensing images

This study aims to tackle the challenges of low accuracy in building feature extraction and insufficient details in three-dimensional (3D) modeling faced by traditional methods, particularly in complex backgrounds. To address these issues, a method for building feature extraction based on Mask Region-Convolutional Neural Network (Mask R-CNN) is proposed. This approach combines deep learning techniques with aerial images to ensure precise and efficient automatic detection and feature extraction. Urban building images are captured through aerial photography, and building outlines are annotated to create a comprehensive dataset of building features. The Mask R-CNN-based method efficiently processes and classifies the features of the dataset, generating candidate regions for further analysis. Additionally, this method demonstrates significant advantages in building feature extraction by employing the Mask R-CNN model to generate adaptive features. Comparative analysis with models such as Convolutional Neural Network (CNN), Region-based Convolutional Neural Network (R-CNN), Fast Region-based Convolutional Neural Network (Fast R-CNN), Faster Region-based Convolutional Neural Network (Faster R-CNN), and Generative Adversarial Network (GAN) indicates that Mask R-CNN exhibits superior performance in building feature extraction. The Mask R-CNN-based approach achieved approximately 95 % classification accuracy, while also showcasing strong stability and generalization capabilities. This study provides new methodologies and insights for enhancing feature extraction in aerial building imagery, offering significant reference value for the fields of architectural design and urban planning.

A socio-ecological approach to investigate the perception of green walls in cities: A comparative analysis of case studies in Turin and Lisbon

New urban greening practices are increasingly adopted to contrast and mitigate critical issues of urban areas. These strategies involve nature-inspired solutions to increase sustainability and restore natural processes in cities. In this framework, green walls play a pivotal role to reintroduce nature and provide both environmental and socio-cultural benefits in high densely cities. Despite green walls contribute to increase the aesthetic value of buildings and people's consideration of the area, methodologies to effectively assess their social benefits are still lacking.This study proposes a place-based approach to investigate the Restorative capacity of two outdoor green walls: a modular living walls system covering two facades of a public university building in Turin (Italy) and a plastic planter boxes based living wall located in an inner courtyard of a cultural centre in Lisbon (Portugal). Firstly, the two living walls were characterised through direct observation considering technical, spatial, and social aspects that influence the fruition and the biophilic perception of these public spaces. Then, a self-rating questionnaire based on the Perceived Restorativeness Scale model was given on-site to participants to investigate the influence of green walls on people's cognitive perception and well-being. The novel Green Wall Perceived Restorativeness Scale consists of 17 items that evaluate individuals' perceptions of green walls, emphasising their attractiveness, integrative role in the environment and contribution to the overall appeal and comfort of space. Similar results between sites suggest that common features such as development at maximum building height, symmetrical disposition, element repetition and plant variety are drivers of citizens perception of green walls. Outcomes support the recognised link between citizens aesthetic appreciation of green walls and their perceived sense of comfort and mental relief, related to the Being away factor. The spontaneous social gathering and citizens resting near both green walls creates evidence of the attractiveness of this nature-based solution as a landmark in the urban environment. These conclusions demonstrate the pivotal role of urban biophilic design to combine natural and artificial elements in architectural and landscape design creating an opportunity to increase the use of green walls to contribute to citizens well-being in urban settings.

Automatic Architecture Design for Distributed Quantum Computing

Abstract In distributed quantum computing (DQC), quantum hardware design mainly focuses on providing as many as possible high-quality inter-chip connections. Meanwhile, quantum software tries best to reduce the required number of remote quantum gates between chips. However, this "hardware first, software follows" methodology may not fully exploit the potential of DQC. Inspired by classical software-hardware co-design, this paper explores the design space of application-specific DQC architectures. More specifically, we propose AutoArch, an automated quantum chip network (QCN) structure design tool. With qubits grouping followed by a customized QCN design, AutoArch can generate a near-optimal DQC architecture suitable for target quantum algorithms. Experimental results show that the DQC architecture generated by AutoArch can outperform other general QCN architectures when executing target quantum algorithms.

Pioneering Emission Trading in Africa: An Architectural Design of a Blockchain-Powered Carbon Trading System

To achieve carbon neutrality by 2050 and reduce greenhouse gas (GHG) emissions to a range of 350–420 megatons of carbon dioxide equivalent, South Africa, the largest emitter of GHG in Africa, introduced a carbon taxation system in June 2019. However, within just 3 years, it has become evident that the system faces significant challenges, with less than 6% of the estimated tax returns reaching the government. This issue raises concerns about potential corruption and manipulation within the system. To address these challenges, this study presents a comprehensive framework for a carbon trading and taxation system leveraging blockchain technology (BCT). Designing this framework from the government’s perspective ensures efficient monitoring and oversight. Notably, the proposed system operates automatically, eliminating the need for third-party intermediaries. This study also identifies a crucial research gap and lays the foundation for future studies. It empirically implements the system as a decentralised application) using the Hedera and Solana blockchain networks. Complemented by JavaScript for the user interface. Python, through Django, was used to handle the server-side system implementation. Thus, this research aims to address the shortcomings of Africa’s current carbon taxation system through a novel, government-centric approach powered by BCT. The proposed system’s potential to enhance transparency and efficiency justifies further exploration in future studies.

Shaping Tomorrow: The Impact of AI on Architectural History and Interior Design Education

Artificial Intelligence (AI) techniques have become popular in architecture and design, and several studies have focused on using these technological advancements to solve various architectural problems. AI is used in various architectural design applications, from intelligent material composition to layout solutions, and it is also vital in supporting the architecture and design education mechanism. A comprehensive understanding of literature is necessary to use these powerful tools in education adequately. This is due to the large volume of research being created and disseminated on this subject as well as the increasing application of AI techniques to address various education design-related questions. This article offers a comprehensive and critical assessment of the study of artificial intelligence applications in architecture and interior design education as a course application in the history of architecture and interior design. The study's conclusions indicate that AI's implications in architecture and design re-imagination are beneficial; however, it was concluded that AI- in its current phase- cannot replace human input and perspective. The students' feedback indicates that critical thinking skills cannot be replaced, and AI complements but doesn't replace their intellect.

Influencing Factors of Spatial Ability for Architecture and Interior Design Students: A Fuzzy DEMATEL and Interpretive Structural Model

Spatial ability is not just a skill but a crucial element for architecture and interior design students, significantly impacting their proficiency in tasks involving 2D drawings, 3D components, and artistic expression. Despite extensive research in this area, a gap remains in the understanding of how to effectively cultivate spatial ability through educational interventions. This study, with its unique approach of identifying key influencing factors and their interrelationships within the fuzzy decision-making laboratory analysis method (Fuzzy-DEMATEL) and the interpretative structural model (ISM), fills this gap. The method visualizes cause-and-effect relationships within a structural model and captures the interdependencies between influencing factors. In a collaborative effort between nine universities in 2023–2024, 17 experts selected through purposeful sampling contributed to the development of a comprehensive list of potential influencing factors. After refinement through filtering, comparison with the existing literature, and expert consensus, seven influencing factors of spatial ability for architecture and interior design students from personal traits and STEAM disciplines were identified, which are sketching and hand drawing skills, mathematical skills, video game practice, descriptive geometry skills, augmented reality practice, spatial talk, and gesturing while talking. Sketching and hand drawing skills, mathematical skills, and video game practice come under cause factors of spatial ability, whereas the rest are effect factors. Proceeding with ISM analysis revealed that sketching and hand drawing skills and mathematical skills are located in the input layer and have a continuous impact on spatial ability. Descriptive geometry skills lie in the transition layer, which are considered as deep influencing factors, the rest of the factors lie in the effect layer. This study delves into the theoretical and practical implications of these findings, offering valuable insights for educational policy and practice.

Single glass and polymer coated phosphate glass microwire photoactuators with instant response times and large actuating angles

Over the years significant scientific attention has been given towards the design of advanced photoactuating architectures as they are important elements for various optical tweezers, grippers, and soft robot applications. Most of the currently available photoactuators, rely on the existence of a free-space illumination pathway from the light sources to the device or on the employment of bendable optical fibers, while consisting of two or more elements, i.e. the photoactuating material and the substrate. Herein, we report on a facile method of preparing single-component pristine microwires (MWs) that exhibit photoactuating features upon utilizing a soft phosphate glass doped with silver nanoparticles (AgNPs), without the need of any additional photoactuating element. The developed photoactuators exhibited bending angles of around 110°, within a couple of seconds. In addition, we have fabricated double-component polymer-coated phosphate glass MWs photoactuators, upon employing PDMS coatings on the surface of the so-formed glass MWs. The introduction of the polymer component boosts the bending angle of the photoactuating device to over 200° within the a few seconds upon modest laser irradiation. The presence of AgNPs within the glass MWs, play a key role on the remarkable performance of the developed photoactuators, both in terms of actuating angles as well as of the respective response times, since they assist on the effective transmission of laser irradiation energy to thermal energy. The fabrication method reported here appears promising for the development of high-performance and low-cost free-space, as well as fiber-based photoactuators.

An analytical model from physical parameters to minimum ranging time for photon-counting LiDARs

Long-distance light detection and ranging (LiDAR) has been highly demanded for applications on unmanned vehicles and drones. CMOS-fabricated single-photon avalanche diodes (SPADs) play a key role in the receiver end due to their high photo-sensitivity and readiness for system-on-chip integration. However, the large amounts of involved components together with the diverse ranging conditions make engineering and optimizing these modules a daunting challenge. In this work, we have developed an analytical model for calculating minimum ranging time from the physical parameters for a photon-counting LiDAR. The experimental verifications of the model have been performed and a good consistency has been obtained. Our work enables architecture design and optimization for making low-cost high-performance SPAD LiDARs.

Comparative analysis of neural network architectures for short-term FOREX forecasting

This document outlines the analysis, design, implementation, and benchmarking of various neural network architectures in a short-term frequency prediction system for the FOREX market. Our objective is to emulate the decision-making process of a human expert (technical analyst) through a system that swiftly adapts to market condition changes, thereby optimizing short-term trading strategies. We have designed and implemented a series of LSTM neural network architectures that take exchange rate values as input to generate short-term market trend forecasts. Additionally, we developed a custom ANN architecture based on simulators for technical analysis indicators. We performed a comparative analysis of the results and came to useful conclusions regarding the suitability of each architecture and the cost in terms of time and computational power to implement them. The ANN custom architecture produces better prediction quality with higher sensitivity using fewer resources and spending less time than LSTM architectures. The ANN custom architecture appears to be ideal for use in low-power computing systems and for use cases that need fast decisions with the least possible computational cost.

ARCHITECTURE DESIGN OF HEALTH ASSET DETECTION SYSTEM IN HOSPITAL

Efficient management of hospital assets is essential to ensure that operations can run optimally and the quality of health services is good. However, the recording and management of assets in hospitals carried out manually often causes data errors, information mismatches, and also assets are only known by the manager without being explicitly recorded. In overcoming this problem, the researcher aims to develop a hospital asset detection system architecture using an iterative and incremental methodology approach. The stages of this system development include identification of needs and conceptual models, logical architecture design, conceptual architecture design, logical architecture, physical architecture, technology selection, and evaluation. This system utilizes YOLO model reading technology for asset detection and identification, storing detection results into a local database using SQLite3, sending data to a central server via API, and post-processing data by selecting the highest confidence score stored in a MySQL database and then using the data to manage asset management and asset visualization. The implementation of this system successfully reduces manual recording time, improves asset visibility, and optimizes resource usage, thus contributing to the improvement of efficiency and quality of health services.

Explaining Crisis Situations via a Cognitive Model of Attention

Decision making in critical situations is a complex process. There are many processes to consider. This paper describes a theoretical approach to researching attentional processes and automatic unconscious processes in terms of metacognition. An application of the approach is presented to explain decision making and metacognition as a solution for ineffective cognitive biases during a crisis situation. Evidence is presented from studies on neuropsychology, cognitive control, and cognitive architectures. An application of the recently formulated semiotic methodology is implemented that allows the design of conceptual models of Attention as Action. The formulation of a general model of attentional processes is based on a set of rules. The crisis phenomenon, as the crisis situation trigger, is semiotically described and applied as insight for a crisis information system design that prompts its users toward self-aware internal decision making. The research conducted evidently shows how the approach can explain the design of several cognitive architectures. Pointing toward metacognition as a solution to a crisis phenomenon and cognitive biases, the paper shows that understanding human cognitive and behavioral processes can significantly improve management in a critical infrastructure crisis situation.

Architectural Design Policy Framework within Digital Tools and Techniques for Safe Sustainable Green Tourism Facilities at Post

Architectural Design Policy Framework within Digital Tools and Techniques for Safe Sustainable Green Tourism Facilities at Post

Needle-Plug/Piston-Based Modular Mesoscopic Design Paradigm Coupled With Microfluidic Device for Point-of-Care Pooled Testing.

Emerging diagnostic scenarios, such as population surveillance by pooled testing and on-site rapid diagnosis, highlight the importance of advanced microfluidic systems for in vitro diagnostics. However, the widespread adoption of microfluidic technology faces challenges due to the lack of standardized design paradigms, posing difficulties in managing macro-micro fluidic interfaces, reagent storage, and complex macrofluidic operations. This paper introduces a novel modular-based mesoscopic design paradigm, featuring a core "needle-plug/piston" structure with versatile variants for complex fluidic operations. These structures can be easily coupled with various microfluidic platforms to achieve truly self-contained microsystems. Incorporated into a "3D extensible" design architecture, the mesoscopic design meets the demands of function integration, macrofluid manipulations, and flexible throughputs for point-of-care nucleic acid testing. Using this approach, an ultra-sensitive nucleic acid detection system is developed with a limit of detection of ten copies of SARS-CoV-2 per mL. This system efficiently conducts large-scale pooled testing from 50 pharyngeal swabs in a tube with an uncompromised sensitivity, enabling a truly "sample-in-answer-out" microsystem with exceptional performance.

Assessing Student-User Experience in Building Design Studios Using Advanced Intelligent Tools: A Pilot Study at the University

Recent advancements in technology have revolutionized various fields, including architecture education. However, studies exploring immersive realities in architectural design studios remain limited. This research addresses this gap by conducting a pilot study at the Architectural Engineering Intermediate Design Studio, which thoroughly explores the use of Virtual Reality (VR) to help architectural students enhance their design skills, detect design flaws and clashes between different systems, and provide valuable insights for the advancement of architectural education and practice. The study will adopt a qualitative methodology that utilizes in-depth interviews conducted with the students while they explore their designs using VR hardware. The research findings revealed the effectiveness of VR in identifying the following: Enhancing the visualization of design challenges, providing a comprehensive building assessment, and holistically detecting design qualities and system integration problems. The pilot study recommends integrating the use of VR into the curriculum of the intermediate design studio level at the University. The research findings will contribute to the current knowledge base and guide future advancements in immersive design technologies.

Research on the Design of Medical Imaging Assisted Diagnosis System Based on Machine Learning

The design and application of medical imaging diagnostic assistance systems have become an indispensable facet of modern medicine. Technologies based on machine learning can handle vast and intricate medical imaging data, offering precise diagnostic support and significantly enhancing the efficiency and accuracy of clinical diagnoses. This study focuses on the development and implementation of an efficient medical imaging diagnostic assistance system utilizing machine learning algorithms such as Principal Component Analysis (PCA), Support Vector Machines (SVM), and Multi-Layer Perceptrons (MLP). Through the architectural design of intelligent diagnostic assistance systems, data preprocessing, and feature vectorization, combined with the application of classification algorithms, a robust diagnostic support system capable of addressing diverse medical scenarios has been constructed. Performance evaluation experiments indicate that this system demonstrates high accuracy and robustness in processing medical imaging data, holding potential for clinical implementation. The findings of this study provide new insights for the advancement of intelligent diagnostic systems in medical imaging and lay a solid foundation for the development of future clinical diagnostic tools.

Signal Crosstalk in a Flip-Chip Quantum Processor

Quantum processors require a signal-delivery architecture with high addressability (low crosstalk) to ensure high performance already at the scale of dozens of qubits. Signal crosstalk causes inadvertent driving of quantum gates, which will adversely affect quantum gate fidelities in scaled-up devices. Here, we demonstrate packaged flip-chip superconducting quantum processors with signal-crosstalk performance competitive with those reported in other platforms. For capacitively coupled qubit-drive lines, we find on-resonant crosstalk better than −27 dB (average −37 dB). For inductively coupled magnetic-flux-drive lines, we find less than 0.13% direct-current flux crosstalk (average 0.05%). These observed crosstalk levels are adequately small and indicate a decreasing trend with increasing distance, which is promising for further scaling up to larger numbers of qubits. We discuss the implications of our results for the design of a low-crosstalk on-chip signal-delivery architecture, including the influence of a shielding tunnel structure, potential sources of crosstalk, and estimation of crosstalk-induced qubit-gate error in scaled-up quantum processors. Published by the American Physical Society 2024

Fast rough mode decision algorithm and hardware architecture design for AV1 encoder

Fast rough mode decision algorithm and hardware architecture design for AV1 encoder

Teacher collaboration and innovative learning spaces in New Zealand

ABSTRACT The changing architectural design of the spaces where schooling occurs can impact on the types of pedagogical practices that were once solely viewed within the traditional single teacher classroom. Innovative learning environments are being built where there can be multiple teachers with larger cohorts of students. This may provide more opportunities for collaboration amongst teachers which may enhance learning opportunities. This New Zealand study looks at the views of ten primary school teachers in innovative learning environments and the results from a national survey in regard to primary teachers’ and principals’ perceptions on teacher collaboration and innovative learning environments. Specifically, we highlight the views of principals and teachers situated within the New Zealand education system and the range of views they bring to this change to the types of differing structural spaces of learning and teaching. The findings suggest that a continuing challenge for teachers is the level of noise and the wide range of distractions within innovative learning environment. The attitudes and adaptability of teachers to adjust to change, and their opportunities for professional development to prepare for this change are key influential factors.