Configurable Architecture Research Articles

Designing Multi-Family Residences in the Post-COVID-19 Era: Addressing Workspace Privacy Needs

The COVID-19 pandemic profoundly affected every aspect of human life. Among the myriad changes, it catalyzed a significant shift in the way we perceive and use our living spaces. The home, traditionally seen as a refuge from work and external pressures, rapidly became a multifunctional space, doubling as an office, school, gym, and recreation center. Before the pandemic, open-plan living was on the rise, championing multifunctional spaces. However, with the sudden need for remote work, the demand for dedicated home offices or quiet corners became evident. People began to value having a distinct, quiet workspace that would not disturb other members of the household. In this paper, we highlight the importance of incorporating a separate room, distanced from the main private areas of a home, during the early design stages. This approach enhances privacy and subsequently boosts the psychological well-being of the household occupants. The research contributes to building-system integration by demonstrating how architectural configurations and spatial design elements can be optimized to enhance building performance, functionality, and occupant comfort in multi-family residential structures. The aim of this study is to explore the correlations between spatial design quality and occupants’ productivity while working from home. It employs a mixed-methods approach that combines various techniques to provide comprehensive analyses.

Research on Spatial Morphological Characteristics and Influencing Factors of Industrial Heritage: A Case Study of Nine Industrial Heritages in Guizhou Province

Industrial heritage, recognized as a significant aspect of historical and cultural heritage, has garnered considerable attention from scholars globally. To elucidate the spatial morphological characteristics and the underlying influencing factors of industrial heritage within karst regions, this study employs methods such as the interstice index, fractal dimension analysis, and spatial syntax. It conducts research on the spatial morphological characteristics of nine typical industrial heritages in Guizhou Province. The primary factors contributing to the variations in layout forms are the intricate karst topography and the functional requirements of production. The functional zoning of industrial heritage aligns with its layout, characterized by straightforward functional zones that have not developed into composite spaces. The overall connectivity of industrial heritage is relatively low, exhibiting weak integration, significant disparities in control values, low average depth values, and a deficiency in comprehensibility and diversity of options. This indicates that the internal connectivity of industrial heritage spaces is generally inadequate, with low accessibility, strong interrelations, average convenience, limited connectivity, and generally acceptable passage. The overall spatial, architectural, and roadway configurations of industrial heritage predominantly exhibit a uniform pattern. Importantly, industrial heritage reveals a highly variable overall spatial form, with an average fractal dimension of 1.57, complex architectural layouts (average fractal dimension of 1.50), and simplistic road network designs (average fractal dimension of 1.43), which collectively suggest high spatial complexity and irregular characteristics. This study can provide a reference for the analysis of spatial characteristics and influencing factors of other material cultural heritages, and it is of great significance for the systematic protection and revitalization of industrial heritage.

Statistical Analysis of nnU-Net Models for Lung Nodule Segmentation.

This paper aims to conduct a statistical analysis of different components of nnU-Net models to build an optimal pipeline for lung nodule segmentation in computed tomography images (CT scan). This study focuses on semantic segmentation of lung nodules, using the UniToChest dataset. Our approach is based on the nnU-Net framework and is designed to configure a whole segmentation pipeline, thereby avoiding many complex design choices, such as data properties and architecture configuration. Although these framework results provide a good starting point, many configurations in this problem can be optimized. In this study, we tested two U-Net-based architectures, using different preprocessing techniques, and we modified the existing hyperparameters provided by nnU-Net. To study the impact of different settings on model segmentation accuracy, we conducted an analysis of variance (ANOVA) statistical analysis. The factors studied included the datasets according to nodule diameter size, model, preprocessing, polynomial learning rate scheduler, and number of epochs. The results of the ANOVA analysis revealed significant differences in the datasets, models, and preprocessing.

Activated biochar production from young coconut waste (Cocos nucifera) as bioadsorbent: a pathway through Artificial Neural Network (ANN) optimization.

This pioneering work explores the immense potential of young coconut waste, a continuously marginalized residue of the food and beverage industry, to serve as an indispensable feedstock in the production of biochar. Through an examination of the key carbonization factors that include time, temperature, and concentrations of the activating agent, KOH, the outcomes offer relevant insights that could be leveraged to maximize biochar production for tailored applications. This study stands out for its innovative use of Artificial Neural Network (ANN) approaches for predictive modeling. Fifty datasets, supplemented with secondary data obtained from the literature and experiments, were utilized for the purposes of training, testing, and validating the neural network model. Here, the datasets were processed utilizing the Deep Neural Network (DNN) framework, which was designed and implemented with the minimal loss function framework feasible. The architectural configuration comprises the following; an input layer, four hidden layers (128-neuron dense layer, batch normalization, and 64-neuron dense layer, batch normalization), a dropout layer, and an output layer. With an R2 of 0.8238 for biochar yield and 0.7324 for iodine number, the trained DNN model showed a relatively high degree of accuracy in making predictions.

A modified U-Net to detect real sperms in videos of human sperm cell.

This study delves into the crucial domain of sperm segmentation, a pivotal component of male infertility diagnosis. It explores the efficacy of diverse architectural configurations coupled with various encoders, leveraging frames from the VISEM dataset for evaluation. The pursuit of automated sperm segmentation led to the examination of multiple deep learning architectures, each paired with distinct encoders. Extensive experimentation was conducted on the VISEM dataset to assess their performance. Our study evaluated various deep learning architectures with different encoders for sperm segmentation using the VISEM dataset. While each model configuration exhibited distinct strengths and weaknesses, UNet++ with ResNet34 emerged as a top-performing model, demonstrating exceptional accuracy in distinguishing sperm cells from non-sperm cells. However, challenges persist in accurately identifying closely adjacent sperm cells. These findings provide valuable insights for improving automated sperm segmentation in male infertility diagnosis. The study underscores the significance of selecting appropriate model combinations based on specific diagnostic requirements. It also highlights the challenges related to distinguishing closely adjacent sperm cells. This research advances the field of automated sperm segmentation for male infertility diagnosis, showcasing the potential of deep learning techniques. Future work should aim to enhance accuracy in scenarios involving close proximity between sperm cells, ultimately improving clinical sperm analysis.

From Pursuit of the Universal AGI Architecture to Systematic Approach to Heterogeneous AGI (SAGI): Addressing Alignment, Energy & AGI Grand Challenges

Artificial intelligence (AI) faces a trifecta of grand challenges: the Energy Wall, the Alignment Problem and the Leap from Narrow AI to AGI. Contemporary AI solutions consume unsustainable amounts of energy during model training and daily operations. Making things worse, the amount of computation required to train each new AI model has been doubling every 2 months since 2020, directly translating to unprecedented increases in energy consumption. The leap from AI to AGI requires multiple functional subsystems operating in a balanced manner, which requires a system architecture. However, the current approach to AI lacks system design; even though system characteristics play a key role in the human brain; from the way it processes information to how it makes decisions. In this paper, we posit that system design is the missing piece in overcoming current AI the grand challenges. We present a Systematic Approach to AGI (SAGI) that utilizes system design principles to overcome the energy wall and the alignment challenges. This paper asserts that artificial intelligence can be realized through a multiplicity of design-specific pathways, rather than a singular, overarching AGI architecture. AGI systems may exhibit diverse architectural configurations and capabilities, contingent upon their intended use cases. We argue that AI alignment, the most difficult among the grand challenges, is not attainable without a way to reflect the complexity of the human moral system and its subsystems in the AGI architectures. We claim that AGI approaches such as symbolicism, connectionism and others are not fundamental to AGI but emergent from the system design processes. Hence, we focus on employing system design principles as a guiding framework, rather than solely concentrating on a universal AGI architecture.

Effect of Pure Dephasing Quantum Noise in the Quantum Search Algorithm Using Atos Quantum Assembly.

Quantum computing is tipped to lead the future of global technological progress. However, the obstacles related to quantum software development are an actual challenge to overcome. In this scenario, this work presents an implementation of the quantum search algorithm in Atos Quantum Assembly Language (AQASM) using the quantum software stack my Quantum Learning Machine (myQLM) and the programming development platform Quantum Learning Machine (QLM). We present the creation of a virtual quantum processor whose configurable architecture allows the analysis of induced quantum noise effects on the quantum algorithms. The codes are available throughout the manuscript so that readers can replicate them and apply the methods discussed in this article to solve their own quantum computing projects. The presented results are consistent with theoretical predictions and demonstrate that AQASM and QLM are powerful tools for building, implementing, and simulating quantum hardware.

Advances in neural architecture search.

Automated machine learning (AutoML) has achieved remarkable success in automating the non-trivial process of designing machine learning models. Among the focal areas of AutoML, neural architecture search (NAS) stands out, aiming to systematically explore the complex architecture space to discover the optimal neural architecture configurations without intensive manual interventions. NAS has demonstrated its capability of dramatic performance improvement across a large number of real-world tasks. The core components in NAS methodologies normally include (i) defining the appropriate search space, (ii) designing the right search strategy and (iii) developing the effective evaluation mechanism. Although early NAS endeavors are characterized via groundbreaking architecture designs, the imposed exorbitant computational demands prompt a shift towards more efficient paradigms such as weight sharing and evaluation estimation, etc. Concurrently, the introduction of specialized benchmarks has paved the way for standardized comparisons of NAS techniques. Notably, the adaptability of NAS is evidenced by its capability of extending to diverse datasets, including graphs, tabular data and videos, etc., each of which requires a tailored configuration. This paper delves into the multifaceted aspects of NAS, elaborating on its recent advances, applications, tools, benchmarks and prospective research directions.

Refining adaptive models for overheating evaluation: Evidence-driven comparative analysis of residential buildings in China's cold and severe cold regions

China's residential buildings in cold and severe cold regions grapple with unaddressed summer overheating issues owing to the lack of tailored regional standards, necessitating a critical examination through globally recognized thermal comfort benchmarks. In this study, 27 bedrooms and 47 living rooms in 13 representative cities were monitored from May to September in the years 2021–2023 to investigate the summer indoor operative temperature of residential buildings in 5 sub-regions. The accuracy of adaptive models from ASHRAE 55, EN 16798–1, and GB/T 50785 for overheating evaluations was tested using measured data. Further, the Chinese Thermal Comfort Database (CTCD) was expanded in this study to enable the development of a preliminary Summer Residential Adaptive Model (SRAM) specifically designed by considering the distinct characteristics of China's cold and severe cold regions. Analysis of the monitoring results showed that 73.1 % of living rooms and all bedrooms were overheated according to CIBSE TM59. Moreover, the slope of SRAM is 0.40, which is lower than 0.73–0.77 of GB/T 50785 and higher than 0.31 and 0.33 of ASHRAE 55 and EN 16798–1, respectively. Further comparative assessments revealed inconsistencies with actual human sensations of heat. The results of this study highlight the inadequacy of CIBSE TM59 and existing thermal comfort standards in accurately assessing building overheating in China's cold and severe cold regions. To improve evaluation, future research must integrate regional climate modeling, various architectural configurations, and thermal sensation indicators to refine the developed SRAM, thus enabling the establishment of more holistic thermal comfort standards.

Adaptive hierarchical origami-based metastructures

Shape-morphing capabilities are crucial for enabling multifunctionality in both biological and artificial systems. Various strategies for shape morphing have been proposed for applications in metamaterials and robotics. However, few of these approaches have achieved the ability to seamlessly transform into a multitude of volumetric shapes post-fabrication using a relatively simple actuation and control mechanism. Taking inspiration from thick origami and hierarchies in nature, we present a hierarchical construction method based on polyhedrons to create an extensive library of compact origami metastructures. We show that a single hierarchical origami structure can autonomously adapt to over 103 versatile architectural configurations, achieved with the utilization of fewer than 3 actuation degrees of freedom and employing simple transition kinematics. We uncover the fundamental principles governing theses shape transformation through theoretical models. Furthermore, we also demonstrate the wide-ranging potential applications of these transformable hierarchical structures. These include their uses as untethered and autonomous robotic transformers capable of various gait-shifting and multidirectional locomotion, as well as rapidly self-deployable and self-reconfigurable architecture, exemplifying its scalability up to the meter scale. Lastly, we introduce the concept of multitask reconfigurable and deployable space robots and habitats, showcasing the adaptability and versatility of these metastructures.

Automatic blockchain network configuration and optimisation for credible and authorised data sharing in social manufacturing networks

The management of social manufacturing networks (SMN) faces sophisticated situations due to the customer's personalisation demands, leading to reduced trust among entities. Therefore, credible and authorised data sharing among different participants becomes vital to operate SMN well. Although blockchain has been applied to SMN to ensure credibility and authority, there is little work on how to configure and optimise a blockchain network for SMN, especially when the structure of SMN changes over time. This paper discusses an automatic blockchain network configuration and optimisation mechanism to enable credible and authorised data sharing in SMN. First, a system architecture for automatic blockchain network configuration and optimisation is built. Then, the structure of SMN and blockchain network model are established using complex network and hypergraph. Then, an automatic blockchain network configuration mechanism is studied to configure blockchain networks from certain SMN structures. Afterward, an automatic blockchain network optimisation mechanism is studied to optimise blockchain networks for SMN when dynamic events occur. Finally, the mechanisms are validated through a case study of SMN to produce air conditioners. The results show that our mechanisms can help core enterprises efficiently deploy a blockchain network for credible and authorised data sharing among different participants in the SMN.

Investigating the Actuation of Sidewall Sprinkler in an Atrium Using CFD Simulation

This study investigates the actuation of sidewall sprinklers in large-scale buildings with high-ceilinged atriums, addressing the challenges of unique architectural configurations. Compliance with NFPA 101 requires automatic sprinkler systems, including atrium areas, in these buildings. To maintain aesthetic considerations, design engineers, particularly in the middle east, often propose sidewall sprinklers as an alternative to traditional ceiling sprinklers. This research assesses whether the sidewall sprinklers would actuate during a fire using Fire Dynamic Simulator (FDS). The findings indicate that sidewall sprinklers will fail to actuate if the fire is located at the centre of the atrium, even if the edge of the fire area is below the sprinklers. Furthermore, the study emphasizes the importance of using an FDS mesh resolution (D*/dx) of 6 or finer resolution when measuring temperatures near the flame or fire plume to ensure accurate evaluations of sprinkler activation. These findings provide valuable insights for design engineers and authorities, assisting in decision-making processes related to fire safety measures, system designs, and regulatory compliance.

Dual-Slope Path Loss Model for Integrating Vehicular Sensing Applications in Urban and Suburban Environments.

The development of intelligent transportation systems (ITS), vehicular ad hoc networks (VANETs), and autonomous driving (AD) has progressed rapidly in recent years, driven by artificial intelligence (AI), the internet of things (IoT), and their integration with dedicated short-range communications (DSRC) systems and fifth-generation (5G) networks. This has led to improved mobility conditions in different road propagation environments: urban, suburban, rural, and highway. The use of these communication technologies has enabled drivers and pedestrians to be more aware of the need to improve their behavior and decision making in adverse traffic conditions by sharing information from cameras, radars, and sensors widely deployed in vehicles and road infrastructure. However, wireless data transmission in VANETs is affected by the specific conditions of the propagation environment, weather, terrain, traffic density, and frequency bands used. In this paper, we characterize the path loss based on the extensive measurement campaign carrier out in vehicular environments at 700 MHz and 5.9 GHz under realistic road traffic conditions. From a linear dual-slope path loss propagation model, the results of the path loss exponents and the standard deviations of the shadowing are reported. This study focused on three different environments, i.e., urban with high traffic density (U-HD), urban with moderate/low traffic density (U-LD), and suburban (SU). The results presented here can be easily incorporated into VANET simulators to develop, evaluate, and validate new protocols and system architecture configurations under more realistic propagation conditions.

Leveraging complex event processing for monitoring and automatically detecting anomalies in Ethereum-based blockchain networks

Blockchain is a relatively recent technology that provides immutability, traceability and transparency of information, thus building trust in the digital society. Blockchain networks generate a large amount of logs which capture and describe data flowing through the network in the form of transactions, blocks and events. Monitoring these blockchain data from the off-chain world is needed to detect anomalies with the aim of mitigating the risks that may arise as a result of using blockchain technology. However, the real-time monitoring of these logs by off-chain systems has become a challenge from the beginning of 2018 when the blockchain networks reached a high number of daily transactions. In this paper, we propose a portable, maintainable and easily configurable architecture integrating blockchain and complex event processing technologies that allows for both the real-time monitoring of logs generated in Ethereum Virtual Machine (EVM)-compatible blockchain networks and the automatic detection of anomalies in these networks by matching event patterns. This architecture was tested by using vast amounts of blockchain data already publicly registered in Ethereum and Polygon networks. The results demonstrate that the proposed architecture is able to automatically detect anomalies which occur in different blockchain networks, making analytics of blockchain data possible by off-chain systems.

Improved Drycooler control by custom hybrid controller

Refrigeration devices designed for use in industrial environments are typically equipped with universal control algorithms, which require a minimal number of signals and parameters to ensure satisfactory device operation. These algorithms are typically of the PID type. This study elaborates upon the impact of using a dedicated hybrid controller, which was designed with specific consideration of the operating conditions of a given refrigeration device. The identified nonlinear Drycooler characteristics were used to support the controller at steady-state operating conditions. The system dynamics were supervised by a dedicated, experience-based designed hybrid fuzzy logic Mamdani type controller for both freecooling and compressor modes. The switchable configuration of the MISO control architecture results in a reduction in overshoots and oscillations in the system, a decrease in the time necessary for stabilization, a reduction in CO2, and an increase in control quality and energy efficiency.

Cortejos na avenida: Anhangabaú, São João e Tiradentes (1968-1990)

This article deals with the samba school parade locations in the city of São Paulo since its officialization, which occurred in 1968, before the construction of the sambódromo, opened in 1991. The objective is to analyze the transformations that the festivities underwen while it was based, respectively, in Vale do Anhangabaú (1968 to 1972), on Avenida São João (1973 to 1977) and on Avenida Tiradentes (1977 to 1990). For this purpose, the main sources consulted were photographic and video images produced, mainly, for journalistic coverage of the event. Our objective is to identify how changes occurred in the relationships between the audience and paraders, the successive resizing of the parade track and the stands and point out how the architectural configurations of the parade catwalk were adapting to different stimuli and needs and, at the same time, induced transformations in the procession itself.

TCN-Inception: Temporal Convolutional Network and Inception modules for sensor-based Human Activity Recognition

The field of Human Activity Recognition (HAR) has experienced a significant surge in interest due to its essential role across numerous areas, including human–computer interaction (HCI), healthcare, smart homes, and various Internet of Things (IoT) applications. The power of deep learning methods in performing various classification tasks, including HAR, has been well-demonstrated. In light of this, our paper presents an efficient HAR system developed using a unique deep-learning architecture called TCN-Inception, which is designed for multivariate time series tasks like HAR data, by combining Temporal Convolutional Network (TCN) and Inception modules. The network starts with an Inception module that uses parallel convolution layers with various kernel sizes for feature extraction. It then includes a TCN module with dilated convolutions to grasp extended temporal patterns. Features are merged from different channels, and the use of residual connections and batch normalization improves training and deepens the architecture. We use four public datasets, UCI-HAR, MobiAct, Daphnet, and DSADS to assess the performance of the TCN-Inception model, and it obtains an average accuracy of 96.15%, 98.86%, 92.63%, and 99.50% for each dataset, respectively. Moreover, we compare the TCN-Inception to several deep learning frameworks to verify its performance. Finally, we implement an ablation study using several architectural configurations of the TCN-Inception model.

Isogeometric Topology Optimization of Multi-patch Shell Structures

Shell structures refer to structural elements that derive strength and load-bearing capacity from their thin and curved geometry. In practical applications, shell structures are commonly composed of multiple patches to represent intricate and diverse architectural configurations faithfully. Nevertheless, the design of multi-patch shell structures holds considerable promise. However, most of the previous work is devoted to the numerical analysis of multi-patch shell structures without further optimization design. The work proposes an inverse design framework, specifically focusing on multi-patch configurations based on Reissner–Mindlin theory. First, reparameterization and global refinement operations are employed on the provided multi-patch shell structures. Renumbering the indices of control points with shared degrees of freedom at the interface naturally ensures C0-continuity between patches. Subsequently, this study investigates the amalgamation of Isogeometric Analysis (IGA) and the Solid Isotropic Material with Penalization (SIMP) method for topology optimization of shell structures. The proposed approach is validated through numerical examples, emphasizing its capacity to enhance multi-patch shell structure design, showcasing robustness and efficiency.

Cloud Computing and Its Security in Real applications

Cloud computing is one of the revolutionary technology that offers efficient means to remotely access and store data in different locations through the internet network. Cloud computing provides a shared pool of computing resources such as storage devices, servers and network. All these resources can provision and released with minimal management efforts or service provider interactions. Cloud computing is a framework that allows users to access computing resources over the internet instead of using all computers facilities that are available locally on site. The purpose of this paper is to come up with the new knowledge related to the wide range of architectural configurations, (memories management, number of processors, and nodes), relevant new trends of storing, processing and accessing remote data. Cloud computing is especially powerful for businesses. This paper has been including cloud computing benefits in addition to the challenges with their impacts on information technology that may happen in the future. Despite the challenges there are good benefits related to the saving, scalability and high performance of cloud over the internet. The providers have the responsibilities to ensure the confidentiality and security of the customers’ data in addition to the security measure that have been taken by the customers themselves. Cloud computing may face the same security problems for the net work. All these matters have explained in details in this paper.

Physical Properties and Biochemical Composition of Extracellular Matrix-Derived Hydrogels Dictate Vascularization Potential in an Organ-Dependent Fashion.

The inherent extracellular matrix (ECM) originating from a specific tissue impacts the process of vascularization, specifically vascular network formation (VNF) orchestrated by endothelial cells (ECs). The specific contribution toward these processes of ECM from highly disparate organs such as the skin and lungs remains a relatively unexplored area. In this study, we compared VNF and ECM remodeling mediated by microvascular ECs within gel, lung, and combinations thereof (hybrid) ECM hydrogels. Irrespective of the EC source, the skin-derived ECM hydrogel exhibited a higher propensity to drive and support VNF compared to both lung and hybrid ECM hydrogels. There were distinct disparities in the physical properties of the three types of hydrogels, including viscoelastic properties and complex architectural configurations, including fiber diameter, pore area, and numbers among the fibers. The hybrid ECM hydrogel properties were unique and not the sum of the component ECM parts. Furthermore, cellular ECM remodeling responses varied with skin ECM hydrogels promoting matrix metalloproteinase 1 (MMP1) secretion, while hybrid ECM hydrogels exhibited increased MMP9, fibronectin, and collagen IV deposition. Principal component analysis (PCA) indicated that the influence of a gel's mechanical properties on VNF was stronger than the biochemical composition. These data indicate that the organ-specific properties of an ECM dictate its capacity to support VNF, while intriguingly showing that ECs respond to more than just the biochemical constituents of an ECM. The study suggests potential applications in regenerative medicine by strategically selecting ECM origin or combinations to manipulate vascularization, offering promising prospects for enhancing wound healing through pro-regenerative interventions.