Architectural Design Research Articles

Design and Architecture Definition for Advanced 3D Fan-Out Wafer-Level Packaging

Advanced 2.5D/3D wafer-level fan-out packaging technologies have been studied widely in literature and industry in recent years. Miniaturization, reduction in manufacturing costs, improvement in performance, and lower power consumption using packaging technologies drive increase in interconnect density and pitch scaling. Heterogeneous systems in package used in advanced packaging often include a combination of silicon, epoxy underfill or mold compounds, and organic or inorganic passivation or redistribution layers. This heterogeneity poses challenges with manufacturability, process selection, package architecture, and test vehicle design. The coefficient of thermal expansion mismatch, wafer warpage, and wafer yield are some of the critical process challenges. Delamination of silicon side wall or passivation to epoxy underfill or mold compound, joint fatigue are critical reliability challenges. This article explores various processes and reliability challenges with 3D wafer-level fan-out packaging and provides package design and architecture guidelines to overcome these failure modes. Stacked die-to-wafer test vehicles have been used for data collection. Different assembly materials, processes, and tooling have been evaluated to define comprehensive design rules.

디자인 씽킹을 적용한 친환경 건축 디자인교육 프로그램 개발 및 효과성 분석 - 중학교 1학년 자유학기제 예술·체육 활동 적용을 중심으로

The recently fast urban growth is leading to being deepened the severe environmental pollution and to being damaged the earth. According to the importance of the global sustainable power generation energy amid this destruction, the eco-friendly education is being carried out. Hence, in the wake of recognizing this issue, this study developed the eco-friendly architectural design education program by connecting the 2022 Revised Curriculum with the achievement standard for a free-semester system in middle school and applied it to the first grade of M middle school during the period of 5 weeks starting from April 29. And to verify effectiveness, the pre and post questionnaires and the student participation reviews were confirmed targeting 43 first graders of M middle school in Jungnang-gu, Seoul who take free semester arts and sports classes for 5 weeks from April 29, 2024 to May 20. As the pre and post survey questions include totally 3 realms such as ‘perception, problem-solving, environment’ in terms of the design thinking and the environmental problem, the item number corresponds to 10 pieces. As a result of t-test in the questions of all realms regarding ‘perception, problem-solving, environment,’ it could be known to have gotten higher after proceeding with the program at the significance level. This outcome shows that the implementation of a class with the application of design thinking leads to being capable of solving a problem by applying and using it even in daily life. It urgently needs the participation in social problem solving and the transformation of school education into self-directed experimental and practical education. In this situation, this study is expected to be likely able to be used as basic data that suggests the directivity of new design education.

Analysis of FPGA Architecture with Hybrid Logic Blocks Based on ULG and LUT

This paper analyzes the efficiency of different FPGA architectures consisting of Hybrid logic blocks (HLB) formed with Look-up Tables (LUTs) and Universal Logic Gates (ULGs). Pure LUT-based architectures induce area overhead and a longer delay. A ULG is designed to realize an efficient architecture compared to pure LUT-based architectures. Using this ULG and LUT in different proportions, HLBs are designed. The Field Programmable Gate Array (FPGA) architectures containing these proposed HLB structures are analyzed using various Microelectronics Centre of North Carolina (MCNC) benchmark circuits and compared with the performance of the existing designs. The result shows that the proposed architecture design can save a tremendous amount of time in overall flow and routing along with area improvements.

Nacre-Inspired Hybrid Multilayer Insulation Composites.

Superinsulation aerogels are characterized by low tensile strength and brittleness due to their high porosity. To address these limitations, multiscale architectural design inspired by nacre can be employed. This materials design approach offers a promising strategy for enhancing the mechanical strength of aerogel thermal insulation. In this study, we present nacre-inspired multilayer cellulose-silica aerogel configurations. The cellulose "brick" network imparts structural strength to effectively redistribute energy, while the nanoporous "mortar" silica blocks heat transfer, maintaining insulation and fire retardance. The multilayer composites, with a layering configuration of five cellulose layers with four silica layers (5 + 4) and a cellulose layer thickness of 1.42 mm, exhibit a thermal conductivity of 31.3 mW/(m·K), a flexural modulus of 505 MPa, and an impact strength of 7.33 kJ/m2. The hydrophobic composite shows a water contact angle of 127°, enhanced soundproofing with a 27% noise reduction, and a carbon footprint of 0.49 kgCO2eq/kg. The multilayer cellulose-silica aerogel design provides a robust, eco-friendly thermal insulation solution for green building applications.

The Ecological Architecture Approach Analysis at Kusuma Agrotourism Resort & Convention Hotel

Resort areas are popular recreational destinations for urban populations, but rapid urban development often leads to significant environmental degradation in these areas. This degradation is evidenced by the decline in water, air, and soil quality, the extinction of local flora and fauna, and the destruction of natural ecosystems. As a response, the application of ecological architecture has become essential in designing and constructing city facilities, including resorts, to mitigate environmental damage. This study aims to examine the implementation of various principles of ecological architecture in resort areas and identify sustainable materials suitable for such applications. A qualitative descriptive method is employed, combining literature reviews and field observations to analyze the integration of ecological architectural principles in the resort area. The findings reveal that key principles such as the use of local materials, energy-efficient systems, and environmental management strategies have been applied. However, the study also highlights areas where improvements can be made to better preserve the ecosystem and minimize the negative impacts of architectural design.

Application of artificial intelligence technologies at substantiation of effective control algorithms for the electrical engineering complex of urban electric transport traction electrical equipment

Background. Studies on the current state and development trends of urban ground public electric transportation in Russia highlight the urgent need for innovative technologies. These innovations should focus on designing and operating new types of rolling stock, traction electrical equipment, and promising types of electric traction. At the same time, special attention should be paid to building autonomous control systems for electric transportation using artificial intelligence technologies. Aim. This study aims to explore the application of neural networks to develop algorithms for effectively controlling the electrical engineering complex of traction electrical equipment in urban ground rail electric transport. Materials and Methods. The research utilized data from studies on the traction electrical equipment modes of rolling stock. These studies were conducted through both computer simulations and field experiments under real operating conditions of urban electric transport using different control algorithms. By applying probability theory and mathematical statistics, the analysis identified the correlations between operational and energy parameters of rolling stock movement and the operational mode of its traction electrical equipment. Results. The identified correlation dependencies informed the design of an effective network architecture, including its size and complexity, as well as the composition of their training samples. This led to the development of an original, simplified algorithm for determining effective control parameters for the electrical complex of traction electrical equipment during the movement of a vehicle on a given section of track. Conclusion. The research concluded that using “simple” neural networks for calculating the parameters of effective control of traction electrical equipment operation modes in urban electric transport provides higher speed and sufficient accuracy compared to complex neural network models. These results are valuable for developers of intelligent control systems for streetcar transportation.

Evolving Adversarial Training (EAT) for AI-Powered Intrusion Detection Systems (IDS)

Intrusion Detection Systems (IDS) are crucial components of network security, yet traditional IDS models often fail to cope with rapidly evolving adversarial attacks that exploit their static nature. This study proposes a novel approach, Evolving Adversarial Training (EAT), to enhance the adaptability and robustness of AI-powered IDS against dynamic threats. The EAT framework integrates continuous model evolution with advanced adversarial training techniques, enabling the IDS to dynamically adjust to new attack patterns. Experimental results demonstrate that the EAT framework significantly enhances IDS performance, leading to increased detection accuracy and reduced false positive rates compared to conventional methods. These findings emphasize the potential of EAT in fortifying network defenses against evolving cyber threats, offering a promising avenue for future research in scalable and adaptive IDS solutions that can effectively combat the complexities of modern cyber adversaries. The research explores three key objectives: dynamic adaptation and adversarial training, continuous learning and enhanced threat detection, and robustness and generalization. By focusing on these objectives, the study aims to develop AI-powered IDS that can effectively navigate the ever-changing cyber threat landscape. The research methodology includes data collection, model architecture design, training and evaluation, continuous learning, simulation, and real-world testing, all aimed at enhancing the resilience of AI-powered IDS against adversarial attacks. By systematically following this framework, the study intends to enhance the security system of IDS through the effective implementation of EAT.

An architectural design framework to promote healthy indoor-outdoor connections in Arctic housing

This research proposes biophilic intermediate spaces as a promising architectural solution to improve indoor-outdoor connections, occupant well-being, and energy efficiency in Arctic housing. Basic examples of intermediate spaces in Arctic housing models include porches and vestibules. However, the architecture of these spaces has not yet been optimized to adress extreme climatic conditions and occupants’ needs. Therefore, this research develops an architectural design framework to optimize the architecture of intermediate spaces for Arctic housing to meet occupants’ well-being needs and improve housing energy efficiency. The research methodology combines an archetypal approach, exploratory case study analysis, and scoping literature review. The archetypal study examines the historical development of Canadian Arctic housing models in Nunavut, revealing the typological evolution, features, potentials, and deficiencies of intermediate spaces in the Arctic. The study identifies of the main design variables and performance indicators of intermediate spaces corresponding to healthy and positive indoor-outdoor connections, thermal and visual comfort, and energy efficiency. A conceptual model of an intermediate space is then simulated as an exploratory case study for a public Canadian Arctic residential building. The architectural design framework is established based on findings from the literature, archetypal studies of Arctic housing, and simulation results of an integrated model for an intermediate space and a public Arctic housing model in Nunavut. The proposed framework includes main architectural variables such as physical adjacency, material, orientation, space depth, and transparency ratio which impact thermal, lighting, and energy performance. This design framework can serve as a reference for creating policies and decision-making processes that integrate biophilic intermediate spaces with Arctic building practices, contributing to Canada's strategic plan for energy efficiency and vegetable production in the Arctic.

Designing a Multi-Level Caching Forward Index for Recommendation System Services

This paper introduces a forward index based on microservices, which effectively addresses the issue of forward read amplification in recommendation systems through a multi-layer caching mechanism. This allows the forward service to handle a large number of concurrent requests with limited resources while ensuring service stability and final value rate. The paper provides a detailed description of the forward index's data structure design, traffic penetration calculation, and overall service architecture design, dividing the system into three parts: SDK, proxy service, and storage, corresponding to data query, data processing, and data storage respectively. In the SDK design, three components are employed: Cache Slot, synchronous queue, and timer, supporting synchronous, asynchronous, and flexible request forms. The proxy service design handles heterogeneous data and online computation, supporting batch and asynchronous designs, and provides a dynamic data protocol to adapt to the characteristic needs of different microservices. The storage layer design considers performance, cost, and maintainability, utilizing a Cache & SSD model to cope with high-concurrency scenarios.

Enhancing the Sustainability of AI Technology in Architectural Design: Improving the Matching Accuracy of Chinese-Style Buildings

Enhancing the Sustainability of AI Technology in Architectural Design: Improving the Matching Accuracy of Chinese-Style Buildings

Design of software system architecture for reading and publishing amateur literary works

The article discusses the functionality and key requirements for a web application for reading and publishing amateur literary works. Against the backdrop of current changes in the book industry caused by the development of technology and the emergence of self-publishing, the need to create platforms that provide authors with the opportunity to publish content independently is emphasized. One of the main problems that authors face when publishing is the loss of the original formatting of their works when transferring them to text editors on web platforms. This paper proposes a solution to this problem by developing a web application with the function of importing the content of text files while preserving their original formatting. The article also contains a comparative analysis of popular web-based platforms for publishing works, such as Archive of Our Own, Wattpad, and Arkush, which helped to highlight the advantages and disadvantages of each of them. In particular, the difficulties associated with storing and processing unstructured data, such as images and text files, were investigated. To solve the problem of storing such data, a comparative analysis of three approaches was conducted: storing data in relational databases, local file systems, and cloud storage. Taking into account the results of this analysis, the web application under development was chosen to integrate with the Amazon Web Services cloud provider and use Amazon S3 cloud storage, which provides scalability, reliability, and convenience in managing large amounts of unstructured data. It was also decided to use the Amazon CloudFront CDN service to improve the speed of data access and increase overall system performance. In addition, the article describes the choice of a technology stack for implementing the web application, which includes Java, Spring Framework, MySQL, ORM Hibernate for the server side and HTML, CSS, React.js for the client side. The article highlights important aspects of designing a web application architecture aimed at scalability and resilience to large amounts of data and an increase in the number of active users, and provides a basis for further system improvement.

A three-tier approach to Internet of Things software architecture design

The Internet of Things (IoT) rapidly evolves, presenting challenges and opportunities. This study emphasises the critical role of software in advancing IoT technologies, focusing on machine learning (ML), fog computing, and process optimisation for security and resiliency. ML is pivotal in IoT for predicting equipment failures, evaluating process efficiency, and enabling informed decision- making through real-time data analysis. By integrating ML models directly into IoT devices (edge computing), latency is reduced, and data transmission needs are minimised. Fog computing and cloud computing address latency issues by moving computational resources closer to IoT devices, enhancing scalability and optimising network resource usage. Security remains a paramount concern due to the increasing number of connected devices and their vulnerabilities. IoT software must balance efficiency, security, and performance, employing deep learning for anomaly detection, blockchain for data transparency, and optimised encryption protocols. The trend towards distributed architectures like Edge Computing enhances system resilience by reducing latency and increasing fault tolerance. The proposed IoT system architecture is a three-tier structure consisting of Edge, Fog, and Cloud levels. At the Edge level, initial data processing occurs directly on IoT devices, reducing latency and network load. The Fog level processes data within the local network, utilising more powerful computational resources for complex tasks and ensuring security through advanced machine learning and encryption. The Cloud level serves as a central repository for long-term storage and global data analysis, leveraging containerisation and orchestration technologies for scalability and reliability. This multi-layered architecture ensures efficient data processing, high security, and adaptability, making it suitable for real-time applications. The study highlights the importance of software in optimising data processing across these levels, ensuring the IoT system’s resilience, scalability, and long-term sustainability.

Simulation of indoor space thermal energy circulation in architectural environment design detail features based on visual object tracking algorithm

The heat circulation of interior space plays a crucial role in the design of the built environment, affecting energy efficiency and residential comfort. With the development of science and technology, visual target tracking algorithm provides a new possibility for optimizing thermal energy cycle. The purpose of this paper is to study the simulation method of indoor space thermal energy cycle based on visual target tracking algorithm to improve the efficiency of thermal energy management in building environment design and ensure indoor comfort. The research uses laboratory Settings, combined with high-performance cameras and advanced visual target tracking algorithms, to monitor indoor heat source distribution and personnel movement in real time. Through data acquisition and analysis, the indoor heat cycle model was constructed and simulated by computational fluid dynamics (CFD) software to evaluate the influence of different design parameters on the heat cycle. Through the optimization of the design scheme, the efficiency of indoor heat use is significantly improved, and the comfort score of residents is significantly improved. Therefore, the simulation of indoor space thermal energy cycle based on visual target tracking algorithm provides an effective tool for building environment design, which can realize efficient energy management and improve living comfort.

Parameters and barriers affecting the marketability of energy efficient residential buildings: a case study in Turkey

ABSTRACT The data indicate that merely improving energy efficiency within energy-efficient buildings (EEBs) is insufficient to significantly reduce the energy demand of the building sector. To achieve substantial reductions, strategies must also focus on increasing the number of EEBs within the existing building stock. However, research in this area remains limited. Therefore, this study aims to explore the current market demand and barriers for EEBs, examine the impact of various design decisions on housing prices, identify the factors influencing the marketability of residential buildings, and analyze customer purchasing decisions in Turkey (Türkiye). For this purpose, a comprehensive literature review was first made. It was observed that factors such as macroeconomic conditions, sales price, housing opportunities, location, brand, customer demographics, energy efficiency, housing features, and aesthetics were frequently discussed in the literature. Based on the literature review and preliminary study results, among other factors, aesthetic appreciation is considered the most applicable design strategy, and windows and balconies are identified as building components that can simultaneously affect energy efficiency, marketability, and aesthetics. Then, as a preliminary study to determine major marketing strategies, the housing marketing strategies were surveyed and analyzed from the real estate sector in different cities as provided in the advertisements posted on their websites. Furthermore, a comprehensive e-mail survey was conducted with 351 different real estate companies amongst 22 cities of Türkiye. Based on the findings, enhancing architectural designs could potentially elevate the perceived value of energy-efficient homes by up to 18% among consumers and this can increase the marketability.

Predicting the Influence of Vibration from Trains in the Throat Area of a Metro Depot on Over-Track Buildings

Urban land resources are scarce in China. To utilize land effectively and economically, many cities are developing over-track buildings above metro depots. The vibration from the entrance and exit lines of metro depots under an over-track platform would significantly impact over-track buildings. To study the influence of train vibration in the throat area of a metro depot on over-track buildings, a simulation model was established using a finite element method. The reasonability of the simulation method and parameter settings was verified through comparing the vibration simulation results with vibration test results in the throat area of a metro depot. Furthermore, the impact of parameters of over-track platform and building on indoor vibration induced by a train was quantitatively studied. According to simulation results, a prediction model was developed to predict the impact of train vibration on over-track buildings in metro depots. From the perspective of architectural planning and design, this study provides a theoretical and technical basis for the prevention and control of indoor vibrations in over-track buildings of urban metro depots.

Application of multimodal learning in daylight provision and view quality assessment of residential building layouts

Optimizing the layout of residential buildings based on daylight performance and view quality is crucial to visual comfort and well-being of building occupants. Machine Learning (ML) methods offer valuable support for performance-based decision-making process at the early-stage building design. In this study, a novel workflow is introduced to integrate ML models into the architectural design process. With the designer’s input floor layout designs, the presented multimodal ML model predicts daylight provision and view quality, which are then translated into practical visual representations by a post-processing step. This approach allows input designs to be evaluated by the ML model, leading to enhanced design decisions while preserving the designer’s autonomy. Results for the best-performing model, implementing ResNet50 and a fully connected network, led to a Mean Square Error (MSE) of 0.0440 and 0.0478, and an R2 score of 0.7411 and 0.7815 for the daylight and view metrics, respectively. The results of the daylight and view predictive models are further interpreted according to different apartment categories and at various resolutions. These results indicate that the method could be viable for predicting daylight provision and view quality in early design tools, providing designers with faster feedback that supports informed decision-making during design iterations. Ultimately, the challenges of the study and further improvements are discussed.

Research on Public Space Interior Environmental Planning and Design Method Based on BIM Topological Relationships

With the wide application of building information modeling (BIM) technology in the field of architectural design, this study aims to explore the planning and design method of public space internal environment based on BIM topological relationship. Through in-depth analysis of the data structure and spatial topological relationships of BIM models, this study proposes a public space design framework that combines user needs and environmental standards. Collect and convert relevant data of public spaces into BIM models, use BIM technology to establish detailed models of spatial elements and their topological relationships, combine spatial connectivity, line of sight, and flow simulation analysis, and consider key factors such as spatial layout, lighting, and ventilation. Realize the planning and design of indoor environments in public spaces. The experimental results indicate that the method can effectively improve the quality of public space design and promote the sustainable development of the environment. This study not only highlights the application potential of BIM in public space design, but also provides a new design methodology for the internal environmental planning of public space.

Introducing circularity in early architectural design education

PurposeWith around 40% of global waste attributed to the built environment sector, architects play a significant role in how resources are consumed, produced and wasted. UK architectural educators have made good progress to embed climate emergency issues in design curricula but the challenges of resource use and waste, and the opportunities afforded by circular economy design principles are less well-considered. The purpose of this paper is to provide new insights on how to introduce circular design principles to early-stage architectural design students within university curricula.Design/methodology/approachThe study described took an experimental approach to designing design project briefs across several interlinked studio design projects for 1st-year Architecture students. Structured as a case study, each section describes the project, learning objectives, teaching methods and project reflections informed by a qualitative assessment of student development, outputs and feedback gathered through a questionnaire and focus group.FindingsIntroducing circular design early is highly beneficial to the development of knowledge on sustainability issues, critical design thinking and creative solution generation. Examples of beneficial teaching approaches include building systems thinking, facilitating collaboration, supporting learning-through-making, using simple analogies and referencing best-practice examples.Originality/valueThis research builds on limited existing circular design literature for built environment fields and through practical insights fills a significant knowledge gap on ways to introduce a complex and dynamic topic such as the circular economy to early-stage architectural design students as they develop fundamental discipline-specific knowledge, skills and competences.

Uncertainty Estimation for Dual View X-ray Mammographic Image Registration Using Deep Ensembles.

Techniques are developed for generating uncertainty estimates for convolutional neural network (CNN)-based methods for registering the locations of lesions between the craniocaudal (CC) and mediolateral oblique (MLO) mammographic X-ray image views. Multi-view lesion correspondence is an important task that clinicians perform for characterizing lesions during routine mammographic exams. Automated registration tools can aid in this task, yet if the tools also provide confidence estimates, they can be of greater value to clinicians, especially in cases involving dense tissue where lesions may be difficult to see. A set of deep ensemble-based techniques, which leverage a negative log-likelihood (NLL)-based cost function, are implemented for estimating uncertainties. The ensemble architectures involve significant modifications to an existing CNN dual-view lesion registration algorithm. Three architectural designs are evaluated, and different ensemble sizes are compared using various performance metrics. The techniques are tested on synthetic X-ray data, real 2D X-ray data, and slices from real 3D X-ray data. The ensembles generate covariance-based uncertainty ellipses that are correlated with registration accuracy, such that the ellipse sizes can give a clinician an indication of confidence in the mapping between the CC and MLO views. The results also show that the ellipse sizes can aid in improving computer-aided detection (CAD) results by matching CC/MLO lesion detects and reducing false alarms from both views, adding to clinical utility. The uncertainty estimation techniques show promise as a means for aiding clinicians in confidently establishing multi-view lesion correspondence, thereby improving diagnostic capability.

PDQRRFF: Poisson-distributed quantum random reversible flip flop generator for BIST

Reversible logic has gained popularity recently because it allows circuits to use significantly less power. Due to the inherent reversibility of quantum operation, there is enormous interest in designing and optimizing reversible circuits. In this work, a new gate named as transvidkan gate, Quantum Random Reversible Flip Flop, Sequence generator, and Build in Self-Test (BIST) has been proposed. Quantum Random Reversible Flip Flop (QRRFF) is an emerging technology that is used in a variety of apps that use modern security and encryption systems. For creating a random string, typical approaches combine an entropy source with an elimination or bit-generation system. Quantum computing reversible logic chips and Low-power design are emerging as intriguing research topics. A classical logic-based 8-bit reversible comparator is represented using existing reversible gates. This study provides a BIST-based architecture for a comparator design that reduces the garbage outputs, quantum cost, and constant inputs. According to simulation result, the proposed approach outperforms traditional methods in terms of hardware complexity and quantum cost.