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Quantum-dot Cellular Automata (QCA) has become one of the promising studies for nano-scale computing. QCA is one of the candidate technologies to be replaced with CMOS technology. QCA technology not only reduces power consumption and delay but also increases operating frequency and speed. The arithmetic logic unit is the essential component in a processor that performs arithmetic and logical operations. This paper presents a novel 5 × 5 reversible logic gate called the NRRG (Norouzi_Rasouli Reversible Gate) which can be used as the basic building block of 4:1 and 8:1 reversible multiplexers. Then, we have designed a RALU (reversible arithmetic and logic unit) using this gate. Our design can perform 20 operations such as AND, NAND, OR, XOR, XNOR, COPY, addition, and increment. The proposed QCA RALU requires 0.44 μm2 area, 480 QCA cells, and 10 clock phases. The proposed design needs less cell count, delay, and cost of QCA compared to previous works. The structure is implemented without any rotated cells and only uses one layer which improves the manufacturability of the design. The architectures are designed and simulated using QCA Designer 2.0.3. [ABSTRACT FROM AUTHOR]

This paper presents BubbleID, a sophisticated deep learning architecture designed to comprehensively identify both static and dynamic attributes of bubbles within sequences of boiling images. By amalgamating segmentation powered by Mask R-CNN with SORT-based tracking techniques, the framework is capable of analyzing each bubble's location, dimensions, interface shape, and velocity over its lifetime and capturing dynamic events such as bubble departure. BubbleID is trained and tested on boiling images across diverse heater surfaces and operational settings. This paper also offers a comparative analysis of bubble interface dynamics prior to and post-critical heat flux conditions. [ABSTRACT FROM AUTHOR]

There is a growing desire for inter-package modularity within the chemistry software community to reuse encapsulated code units across a variety of software packages. Most comprehensive efforts at achieving inter-package modularity will quickly run afoul of a very practical problem, being able to cohesively build the modules. Writing and maintaining build systems has long been an issue for many scientific software packages that rely on compiled languages such as C/C++. The push for inter-package modularity compounds this issue by additionally requiring binary artifacts from disparate developers to interoperate at a binary level. Thankfully, the de facto build tool for C/C++, CMake, is more than capable of supporting the myriad of edge cases that complicate writing robust build systems. Unfortunately, writing and maintaining a robust CMake build system can be a laborious endeavor because CMake provides few abstractions to aid the developer. The need to significantly simplify the process of writing robust CMake-based build systems, especially in inter-package builds, motivated us to write CMaize. In addition to describing the architecture and design of CMaize, the article also demonstrates how CMaize is used in production-level software. [ABSTRACT FROM AUTHOR]

Assistance systems have been widely adopted in the manufacturing sector to facilitate various processes and tasks in production environments. However, existing systems are mostly equipped with rigid functional logic and do not provide individual user experiences or adapt to their capabilities. This work integrates human factors in assistance systems by adjusting the hardware and instruction presented to the workers' cognitive and physical demands. A modular system architecture is designed accordingly, which allows a flexible component exchange according to the user and the work task. Gamification, the use of game elements in non-gaming contexts, has been further adopted in this work to provide level-based instructions and personalised feedback. The developed framework is validated by applying it to a manual workstation for industrial assembly routines. [ABSTRACT FROM AUTHOR]

Climate-responsive building design holds immense potential for enhancing comfort, energy efficiency, and environmental sustainability. However, many social, cultural, and economic obstacles might prevent the wide adoption of designing climate-adapted buildings. One of these obstacles can be removed by enabling practitioners to easily access, visualize and analyze local climate data. The CBE Clima Tool (Clima) is a free and open-source web application that offers easy access to publicly available weather files and has been created for building energy simulation and design. It provides a series of interactive visualizations of the variables contained in the EnergyPlus Weather Files and several derived ones like the UTCI or the adaptive comfort indices. It is aimed at students, educators, and practitioners in the architecture and engineering fields. Since its inception, Clima’s user base has exhibited robust growth, attracting over 25,000 unique users annually from across 70 countries. Our tool is poised to revolutionize climate-adaptive building design, transcending geographical boundaries and fostering innovation in the architecture and engineering fields.

AbstractAs the domain of artificial intelligence has advanced, the integration of deep learning techniques into terrain and landform analysis has become more prevalent. Nevertheless, many existing methods are fully supervised and designed for specific tasks; thus, their transferability is limited and massive annotated samples are required. This study introduces a geomorphic pretrained model (GeomorPM) capable of performing multiple tasks. First, an architecture was designed that combined a convolution-based Vector Quantised-Variational Autoencoder (VQVAE) with a Transformer-based masked autoencoder (MAE) framework, allowing it to autonomously learn local details and global patterns from large-scale digital elevation model (DEM) data. Subsequently, GeomorPM, based on the VQMAE architecture, was pretrained on massive DEM data and fine-tuned for three specific tasks: DEM void filling, DEM superresolution, and landform classification. GeomorPM outperformed the traditional and other deep learning methods in all three tasks, demonstrating the superior learning ability and transferability of the model. This study provides a practical framework for developing pretrained models based on DEMs that can be expanded to other continuous geoscientific data. [ABSTRACT FROM AUTHOR]

AbstractThis paper shows how and why it is ‘im-possible’—i.e. simultaneously possible and impossible—for humans to travel and stay underwater by narrating the story of how Jules’ Undersea Lodge was designed and how its guests can remain there for relatively long periods of time. Drawing from interviews with its designer, Ian Koblick, reflexive ethnographic material collected at Key Largo’s (Florida) Jules’ Undersea Lodge in the early spring of 2024, and an interview with Joseph Dituri—who in 2023 set a world record for the longest time spent in an underwater habitat – we conceptualize im-possibility as a dialectical tension between possibility and impossibility. [ABSTRACT FROM AUTHOR]

In computer vision, object detection is the classical and most challenging problem to get accurate results in detecting objects. With the significant advancement of deep learning techniques over the past decades, most researchers work on enhancing object detection, segmentation and classification. Object detection performance is measured in both detection accuracy and inference time. The detection accuracy in two stage detectors is better than single stage detectors. In 2015, the real-time object detection system YOLO was published, and it rapidly grew its iterations, with the newest release, YOLOv8 in January 2023. The YOLO achieves a high detection accuracy and inference time with single stage detector. Many applications easily adopt YOLO versions due to their high inference speed. This paper presents a complete survey of YOLO versions up to YOLOv8. This article begins with explained about the performance metrics used in object detection, post-processing methods, dataset availability and object detection techniques that are used mostly; then discusses the architectural design of each YOLO version. Finally, the diverse range of YOLO versions was discussed by highlighting their contributions to various applications. [ABSTRACT FROM AUTHOR]

The World Health Organization has designated Pseudomonas aeruginosa as a critical pathogen for the development of new antimicrobials. Bacterial viruses, or bacteriophages, have been used in various clinical settings, commonly called phage therapy, to address this growing public health crisis. Here, we describe a high-resolution structural atlas of a therapeutic, contractile-tailed Pseudomonas phage, Pa193. We used bioinformatics, proteomics, and cryogenic electron microscopy single particle analysis to identify, annotate, and build atomic models for 21 distinct structural polypeptide chains forming the icosahedral capsid, neck, contractile tail, and baseplate. We identified a putative scaffolding protein stabilizing the interior of the capsid 5-fold vertex. We also visualized a large portion of Pa193 ~ 500 Å long tail fibers and resolved the interface between the baseplate and tail fibers. The work presented here provides a framework to support a better understanding of phages as biomedicines for phage therapy and inform engineering opportunities. Leveraging bioinformatics, proteomics, and cryogenic electron microscopy, this study deciphers the architecture and design principles of the therapeutic Pseudomonas phage Pa193. [ABSTRACT FROM AUTHOR]

If architecture is an expression of human creativity through multi-sensory embodiment, then learning, creating and experiencing architecture should also be multi-sensory and embodied. In this article, we challenge the separation of mind and body through Sheets-Johnstone's mindful bodies concept. We define a mindful body in architecture as one that documents, analyses and memory maps the moving body in different qualities of movement to create diverse spatial experiences. A mindful body approach to creating architecture involves: (i) engaging in meaningful movement and documenting the body, (ii) documenting embodied interactions with dynamic, animate elements in the built environment, (iii) connecting our body's movements with emotions and memories, (iv) designing spaces that produce diverse movements and atmospheres, and (v) designing architecture based on these spaces. We hypothesize that if designers engage in a mindful body approach to design, they can create spatial experiences that help us make sense of ourselves, others and the world. A mindful body approach to design can result in architectural spaces that activate our attentional switches, connect haptic experiences and memories and reveal wonders. This article is part of the theme issue 'Minds in movement: embodied cognition in the age of artificial intelligence'. [ABSTRACT FROM AUTHOR]

Personalization of different aspects of architectural designs is one of the most novel issues in the modern world. The present study aimed to predict the association between personality traits and architectural preferences. A total of 352 participants with architectural (experts) and nonarchitectural (nonexperts) education were asked to complete a demographic profile and the NEO Five-Factor Personality Inventory. They were then asked to choose their preferred images, which were previously rated based on three aesthetic variables, that is, color-contrast, abstractness-concreteness, and spatial openness, in a series of two-alternative forced-choice questions. By using a random forest classifier, an accuracy of 73 to 84% was achieved for the variables. Due to the complexity of rules in the random forest model, data were explored for more interpretable rules, and a rule-based classifier (Waikato Environment for Knowledge Analysis software) was used. Based on the findings, introverts had the opposite behavior compared to the general population; they preferred images of enclosed spaces and with high color contrast. They also preferred popular architectural styles to high-style designs. Otherwise, the preference for greater spatial openness was common in both expert and nonexpert groups, although it was more noticeable in female extroverts. Nonexperts with high levels of openness to experience were mostly attracted to abstract images. Experts and nonexperts showed similar preferences in terms of color contrast and spatial openness, while there was a significant difference between the two groups regarding their preferred abstract and concrete concepts. In conclusion, educational background and personality traits could influence aesthetic preferences. [ABSTRACT FROM AUTHOR]

Incorporating photoswitchable moieties into the molecular design of supramolecular architectures provides unique opportunities for controlling their morphology and functionality via optical stimuli. Harnessing geometrical and electrical changes in response to multiple external stimuli on the molecular level to modulate properties remains a fundamental challenge. Herein, the reversible formation of the aggregates of l‐tyrosine E‐azobenzene‐tetracarboxamide (E‐ABT) is shown to be finely controlled by light, solvent, or chemical additives. The resulting products differ not only in their overall morphology and supramolecular interactions, but also in their intrinsic chirality, that is, depending on the conditions applied, self‐assembly yields chiral columns or π‐stacked "achiral" oligomers. This report shows the potential of rational monomer design to achieve controlled self‐assembly by stimuli of choice and paves the way toward the use of multi‐responsive, sterically hindered azo‐benzene aggregates in materials chemistry and nanotechnology. [ABSTRACT FROM AUTHOR]

Restricted by a metal-enclosed structure, the internal defects of large transformers are difficult to visually detect. In this paper, a micro-robot is used to visually inspect the interior of a transformer. For the micro-robot to successfully detect the discharge level and insulation degradation trend in the transformer, it is essential to segment the carbon trace accurately and rapidly from the complex background. However, the complex edge features and significant size differences of carbon traces pose a serious challenge for accurate segmentation. To this end, we propose the Hadamard production-Spatial coordinate attention-PixelShuffle UNet (HSP-UNet), an innovative architecture specifically designed for carbon trace segmentation. To address the pixel over-concentration and weak contrast of carbon trace image, the Adaptive Histogram Equalization (AHE) algorithm is used for image enhancement. To realize the effective fusion of carbon trace features with different scales and reduce model complexity, the novel grouped Hadamard Product Attention (HPA) module is designed to replace the original convolution module of the UNet. Meanwhile, to improve the activation intensity and segmentation completeness of carbon traces, the Spatial Coordinate Attention (SCA) mechanism is designed to replace the original jump connection. Furthermore, the PixelShuffle up-sampling module is used to improve the parsing ability of complex boundaries. Compared with UNet, UNet++, UNeXt, MALUNet, and EGE-UNet, HSP-UNet outperformed all the state-of-the-art methods on both carbon trace datasets. For dendritic carbon traces, HSP-UNet improved the Mean Intersection over Union (MIoU), Pixel Accuracy (PA), and Class Pixel Accuracy (CPA) of the benchmark UNet by 2.13, 1.24, and 4.68 percentage points, respectively. For clustered carbon traces, HSP-UNet improved MIoU, PA, and CPA by 0.98, 0.65, and 0.83 percentage points, respectively. At the same time, the validation results showed that the HSP-UNet has a good model lightweighting advantage, with the number of parameters and GFLOPs of 0.061 M and 0.066, respectively. This study could contribute to the accurate segmentation of discharge carbon traces and the assessment of the insulation condition of the oil-immersed transformer. [ABSTRACT FROM AUTHOR]

The rapid increase in new malware necessitates effective detection methods. While machine learning techniques have shown promise for malware detection, most research focuses on identifying malware through the content of executable files or full behavior logs collected from process start to finish. However, detecting threats like ransomware via full logs is redundant, as this malware type openly informs users of the infection. To address this, we present LEDA, a novel malware detection architecture designed to monitor process behavior during execution and to identify malicious actions in real time. LEDA dynamically learns the most relevant features for detection and optimally triggers model evaluations to minimize the performance impact perceived by users. We evaluated LEDA using a dataset of Windows malware and legitimate applications collected over a year, examining our model's temporal decay in effectiveness. [ABSTRACT FROM AUTHOR]

Purpose: The Sustainable Development Goals (SDGs) were established by the UN in 2015 as an international call to action to end poverty, protect the environment, and ensure that everyone will live in peace and prosperity by the year 2030. (UNDP, 2015). Out of the 17 Goals, Goal 4 talks about "ensuring inclusive and equitable quality education and promoting lifelong learning opportunities for all." The SDGs are essential to architectural education since buildings account for 40% of extracted materials used in construction and 39% of global energy-related carbon emissions. (Hendawy, 2023) To accomplish these objectives, the architecture curriculum must be up-to-date and efficient. It specifically takes into account the field of architecture. The purpose of this paper is to propose a framework for curriculum guidelines of B.Arch. design studios, focusing on the parameter designing for inclusivity (UIA2023CPH, 2020) of SDGs. Design/methodology/approach: Models, approaches and theories of curriculum development are studied. An evaluation matrix is made to evaluate different curriculum framework for government colleges on their incorporation of SDGs. A survey/questionnaire and interviews with academicians from architecture are conducted to analyze the incorporation of SDGs in design studios and find recommended solutions for its incorporation. Findings: Finally, this research paper proposes a set of Architecture curriculum guidelines based on the evaluation framework for B.Arch. and an exemplary curriculum design model for the Architecture design studio which is evaluated through the desk critique stage for analyzing its functionality and applicability. Originality/value: Despite the emphasis on SDGs in the present scenario, the "Council Of Architecture (Minimum Standards of Architecture Education in India) Regulations, 2020" does not include or mention SDGs as a key aspect (Architecture, 2020). [ABSTRACT FROM AUTHOR]

Piezoelectric energy harvesters (PEHs) have drawn considerable attention due to their unique ability to convert ambient mechanical energy to electrical energy. These devices are widely implemented in numerous applications such as wearable technology, structural health monitoring, and renewable energy systems. In this work, a novel approach that seamlessly integrates arbitrary metastructures into the substrate layer of cantilever beam‐based PEHs is presented. The corresponding performance of each PEH design is evaluated via an experimentally validated finite element model. This is the first systematic study to explore 3D metastructures with various unit cell configurations, unit cell numbers, and porosity levels. Compared with the existing PEH designs, implementation of a 3D auxetic metastructure with 85% porosity single unit cell design can demonstrate a substantial enhancement in output power, reaching 48.16 mW, and a high normalized power density (NPD) of 2.1131 µW mm−3 g−2 Hz−1. Results show that there are competing requirements for improving the performance of PEHs. On one hand, low metastructure stiffness is preferred to achieve high power output at low resonant frequency. On the other hand, metastructures designs with low stiffness may induce excessive distortion in the substrate layer, leading to mechanical energy loss. This deformation mechanism adversely affects the mechanical to electrical energy conversion efficiency. Detailed guidelines for designing and manufacturing high‐performance PEHs are discussed in this work. [ABSTRACT FROM AUTHOR]

From an architectural perspective, the purpose of this paper is to explore the dynamic relations between individuals and the environments using the concept of affordance. In three different cases of architectural research, the concept of affordance is used as an analytical tool – yet demonstrating different scopes and outcomes. A post-occupancy evaluation of an office space in transformation; a lighting assessment and intervention in low vision rehabilitation situated in private home environments; and an urban event at an architecture festival involving the intervention of a sound- and scentscape. The three cases show that the concept of affordance can embrace and operationalize the human perception of the environment, pointing at descriptive, prescriptive, and exploratory possibilities in relation to architectural design and research. The concept of affordance enables rich descriptions and explorations of qualities of architecture, related to multisensorial, emotional, and social interactional aspects of humans and their environments. [ABSTRACT FROM AUTHOR]

Architectural education encompasses a multidisciplinary curriculum including design, theory, history, technology, environmental considerations, urban planning, social aspects, and professional practice. This study aims to investigate the extent to which students can effectively integrate structural concepts into architectural design. This examination takes place within the framework of a third-year architectural design studio course, where students advance their design processes with input from architectural design instructors and experts in structural concepts. To facilitate this endeavour, a simple visual screening form was introduced to aid in the learning process. The study utilises a newly generated survey, namely 'Structural System Control Form', which is inspired by the rapid visual screening forms, to assess the students' comprehension of structural concepts during a 14-week architectural design studio. Employing a descriptive and correlational research approach, the study assesses the effectiveness of this integrated strategy in enhancing the students' understanding of structural principles. The findings revealed a notable development among students who participated in the integrated approach, highlighting the value of feedback and evaluation from instructors versed in construction. Furthermore, the use of simplified structural models in students' projects improved their understanding of structural concepts and their ability to incorporate them into their architectural representations. [ABSTRACT FROM AUTHOR]

Volume visualization plays a crucial role in both academia and industry, as volumetric data is extensively utilized in fields such as medicine, geosciences, and engineering. Addressing the complexities of volume rendering, neural rendering has emerged as a potential solution, facilitating the production of high-quality volume rendered images. In this paper, we propose TwineNet, a neural network architecture specifically designed for volume rendering. TwineNet combines features extracted from volume data, transfer functions, and viewpoints by utilizing twining skip connections across multiple feature layers. Building upon the TwineNet architecture, we introduce two neural networks, VolTFNet and PosTFNet, which leverage convolutional encoder–decoders and multilayer perceptrons to synthesize volume rendered images with novel transfer functions and viewpoints. Our experimental findings demonstrate the superiority of our models compared to state-of-the-art approaches in generating high-quality volume rendered images with novel transfer functions and viewpoints. This research contributes to advancing the field of volume rendering and showcases the potential of neural rendering techniques in scientific visualization. [ABSTRACT FROM AUTHOR]

Skeletal muscle architecture is a strong predictor of in vivo functional capacity and is evaluated in fixed tissues, accommodating the study of human muscles from cadaveric donors. Previous studies evaluating the pelvic floor muscles (PFMs) demonstrated that the rat is the most appropriate small animal model for the study of female PFM architecture, but the rat's suitability for the study of male PFMs is undetermined. We aimed to determine (1) whether PFM architecture exhibits sexual dimorphism in rats or humans, and (2) if the rat is also a suitable animal model for the study of male human PFMs. PFMs were fixed in situ and harvested en bloc from male and female cadaveric donors and 3-month-old male and female Sprague-Dawley rats. Three architectural parameters influenced by species size were used to compare male versus female PFMs within species, while four size-independent measures compared species within sex. All comparisons were made with two-way analysis of variances and Tukey's multiple comparisons tests post hoc. Sarcomere length (rats and humans, p = 0.016 and = 0.002) and normalized fiber length (rats, p < 0.001) were significantly larger in male PFMs. Three of the size-independent measures exhibited similar species trends in both sexes, while the size-independent sarcomere length measure (Ls/Lso) differed between male rats and humans (p < 0.001). Thus, sexual dimorphism is present in rat and human PFM architecture, and the male rat is suitable for studies of human male PFMs. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) networks comes with many challenges, especially in network architecture designs. IoT is populated by several kinds of devices with different characteristics that are autonomously managed. These devices do not have enough resources and they require to process data in real-time. Hence, there is a need to design suitable architectures for IoT networks that are as efficient as possible. Previously, Cloud Computing (CC) seemed to provide a good solution of processing data from IoT networks. Recently, Mobile Edge Computing (MEC) seems to be offering a better solution than CC by ensuring a better Quality of Services (QoS) provisioning. As a result, many MEC solutions have emerged for QoS improvement in IoT networks. These solutions mainly focus on device resource management without considering data routing from an end-user device to another, especially when the latter are mobile and need to communicate with each other. In this paper, we propose to design an adaptive routing protocol for a MEC-based network to manage efficiently, the end-user devices' energy consumption during data routing. The proposed adaptive Mobile Edge Computing-based protocol consists of two main phases: firstly, we subdivide the network's objects into clusters by exploiting a link quality prediction algorithm. Secondly, we route the data to their destination adaptively by considering the object's movement during the routing process. As presented in the simulation results, our protocol outperforms other existing routing protocols for IoT networks in terms of energy consumption. We then propose the use of our solution for data routing in IoT networks that require huge data processing and forwarding. [ABSTRACT FROM AUTHOR]

The evaluation of the effects of architecture and design in psychiatric hospitals primarily focuses on final outcomes, such as disease progression, and is made from the perspective of evidence-based medicine. Meanwhile, the evidence-informed, realist approach addresses how the intervention works. Understanding the underlying action mechanisms of the intervention is needed to facilitate its scaling-up and adaptation in new environments. This umbrella review reports in which ways architecture and design have an effect on patients' and staff experience in inpatient psychiatric hospital. The search was constructed around three key concepts (psychiatric hospital, design, and staff and patient outcomes) and was conducted across three reference databases (Embase, Medline, and PsychINFO). Academic and gray literature was analyzed. Information on design and architectural features in psychiatric hospitals, their effects on patients and staff experience, and the acting mechanisms enabling these effects were extracted. From 951 original references, 14 full texts were included in the analysis. Design and architectural features (e.g., aesthetic appeal of places, home-like environment) in psychiatric hospitals address patients' stress, boost social interaction, foster patients' autonomy and feelings of control, ensure respect for patient's privacy and dignity, and prevent under-and overstimulation. Using theory-driven evaluation may facilitate future hospital renovation and the evaluation of its effect. [ABSTRACT FROM AUTHOR]

Two key building characteristics, namely the number of stories and typology, is vital across various domains such as construction management and architectural design. These aspects are particularly critical for disaster risk assessment and infrastructure planning. Although deep learning models are adept at extracting this information from Street view images (SVIs), their success is contingent upon the availability of large and diverse data sets with high accuracy. Image augmentation presents an alternative method to artificially broaden data set diversity. However, the impact of image augmentation techniques on identifying building stories and typologies from SVIs has not been adequately explored. This study proposes a methodology employing eight distinct image augmentation techniques—brightness, contrast, perspective, rotation, scale, shearing, and translation augmentations—as well as a combined approach using all these methods. The study evaluates the efficacy of models trained with these techniques by comparing the accuracy of different classes and architectures for each task, both with and without the application of augmentation. The findings revealed that while most augmentation methods enhance model accuracy, their effectiveness is task-dependent. Furthermore, it was observed that the most effective augmentation techniques differ among building classes and architectures within each task. This suggests that augmentation strategies need to be custom-designed to align with the unique features of each class and architectures for precise estimation of the number of stories and building typologies. While the focus of this research is on specific tasks, the evaluated augmentation techniques could also extend to related areas, such as ascertaining the age of buildings or identifying window types. Practical Applications: In this study, the efficacy of augmentation techniques is explored within the framework of identifying the number of stories and building typologies. The models were assessed for average accuracy and class-specific accuracy across various architectures, comparing outcomes with and without the implementation of the proposed augmentation methods. A key finding is that the most effective augmentation method varies between architectures and individual classes. Contrary to common practice in deep learning, where applying multiple augmentation techniques is standard for accuracy enhancement, this study observed that such a strategy did not uniformly improve performance. Specifically, while combining augmentation methods generally resulted in higher average accuracy, this was not the case for some classes within MobileNetV3 when detecting the number of stories. Similarly, for ResNet-152, employing all augmentation techniques together led to the lowest accuracy in certain classes for building typology classification. These results indicate that augmentation strategies may require customization to cater to the distinct characteristics of each class and architecture for accurate estimation of number of stories and building typologies. [ABSTRACT FROM AUTHOR]

To enable off-site construction (OSC), architects must finalize construction details in their design deliverables as early as possible, particularly in a building information modeling (BIM) environment, to enhance design constructability and project efficiency. However, most architects lack construction knowledge and practical experience, which impedes their ability to incorporate design-specific construction input (i.e., manufacturing, logistics, assembly, and installation demands) into BIM processes at the early stages. This deficiency can lead to errors, rework, and ultimately increased construction costs. To address this challenge, this study introduces an innovative approach to incorporate constructability knowledge into architectural design processes for BIM-enabled OSC projects. The methodology involved retrospective case studies of two completed OSC projects utilizing a component-centric analysis framework. This analysis resulted in an ontology of construction-specific design features that identified 11 types of constructability issues and the characterization of nine design feature classes that were categorized into three groups: substance; intersection; and composition. These feature classes and their 29 attributes were rigorously characterized from the perspective of BIM-enabled architectural design. This feature-based approach not only extends the BIM vocabulary and semantics from an architectural standpoint but also encapsulates construction knowledge as an integral input to architectural design. Additionally, three strategies were identified, analyzed, and summarized to inform these design features and attributes and to evaluate their effectiveness of addressing constructability issues. The study's ontological approach assists architects in acquiring a comprehensive understanding of design-specific construction knowledge, enabling them to differentiate, locate, and integrate critical types of constructability information into BIM-enabled architectural design deliverables early in the project delivery process. This paper is a foundational step in the development of automated modeling, algorithms, and constructability assessment for BIM-enabled architectural design deliverables, aimed at achieving a more streamlined construction process. Practical Applications: The study presents several practical implications for the OSC industry, aimed at bridging the gap between architectural design and actual construction through BIM-enabled architectural design. First, the proposed construction-specific ontology of design features can assist architects in identifying and incorporating essential constructability information into BIM. Second, these design features and their attributes cover a broad spectrum of construction-specific design information, going beyond geometric details to include the semantics associated with constructability constraints and design considerations while exploring alternative design strategies and tools. Third, these design features and attributes act as a structured "construction information checklist," which systematically guides architects in the early design stages to ensure comprehensive inclusion of all necessary construction-specific design information. Essentially, this checklist offers architects a preliminary method for incorporating constructability knowledge within a BIM environment at the initial phases of the architectural design process for OSC. [ABSTRACT FROM AUTHOR]

Object detection algorithms are widely used but involve significant amounts of computations, posing challenges for adaptation to resource-constrained application scenarios, such as Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vehicles (USVs). To address these challenges, this paper proposes an object detection accelerator architecture with multi-channel parallel computation. In this architecture, the computationally intensive modules, including the convolution layer, pooling layer, and upsampling layer, are accelerated using hardware, and other modules are dealt with CPU embedded in the FPGA. The methods of pipeline design, loop expansion, data reordering, and other technologies are fully utilized to design hardware acceleration modules. A data transmission architecture is designed, incorporating multi-channel transmission along with ping-pong buffering and employing a blocking strategy for off-chip data access. Furthermore, the architecture incorporates multiple acceleration IP cores to minimize data transmission delays. Based on this architecture, the tiny-YOLOv4 model is optimized and implemented on FPGA as a hardware accelerator for object detection. The network model is enhanced by integrating the convolutional layer with normalization, and different attention mechanisms are applied to improve feature extraction, thereby reducing computational load and enhancing accuracy. The performance of the tiny-YOLOv4 FPGA-based accelerator is validated using the SIMD dataset. Experimental results demonstrate that the hardware accelerator performs exceptionally, consuming only 2.4W and surpassing existing alternatives. Such adaptability facilitates its integration into complex environments such as intelligent transportation systems. [ABSTRACT FROM AUTHOR]

This study enhances designers' ability to decode natural geometric patterns, simplify their descriptions, and apply them to the built environment using Voronoi diagram (VD). Therefore, VDs are examined using a literature review, establishing the D4G classification of VDs, which consists of five acronyms: dimension, domain, distribution, dominance, and generator. Additionally, we developed an analytical comparison matrix to conclude the VDs' application benefits at different design scales. Finally, we validate the proposed framework at three integration scales: interior, architectural, and urban design, to decode natural geometric patterns and generate various VDs using Rhino software and Grasshopper plug-in. The study concluded that a limitless number of decoding patterns can be deduced based on the inputs. Nevertheless, the analytical comparison matrix was applied to selected patterns using the D4G classification of VDs, and the D4G classification-based framework was evaluated in only three case studies. Moreover, the expected benefits were determined for each case. [ABSTRACT FROM AUTHOR]

Purpose: Algorithmic and computational thinking are necessary skills for designers in an increasingly digital world. Parametric design, a method to construct designs based on algorithmic logic and rules, has become widely used in architecture practice and incorporated in the curricula of architecture schools. However, there are few studies proposing strategies for teaching parametric design into architecture students, tackling software literacy while promoting the development of algorithmic thinking. Design/methodology/approach: A descriptive study and a prescriptive study are conducted. The descriptive study reviews the literature on parametric design education. The prescriptive study is centered on proposing the incomplete recipe as instructional material and a new approach to teaching parametric design. Findings: The literature on parametric design education has mostly focused on curricular discussions, descriptions of case studies or studio-long approaches; day-to-day instructional methods, however, are rarely discussed. A pedagogical strategy to teach parametric design is introduced: the incomplete recipe. The instructional method proposed provides students with incomplete recipes for parametric scripts that are increasingly pared down as the students become expert users. Originality/value: The article contributes to the existing literature by proposing the incomplete recipe as a strategy for teaching parametric design. The recipe as a pedagogical tool provides a means for both software skill acquisition and the development of algorithmic thinking. [ABSTRACT FROM AUTHOR]

Recently, an increasing number of people have employed do-it-yourself (DIY) and do-it-with-others (DIWO) techniques and processes to develop unique technology products. This trend is commonly called the maker movement and fosters the creation of own electronic and mechanical devices and tools. Oscilloscopes are really useful tools to diagnose problems and analyze electronic devices and electrical circuits, and thus they should not stay outside this trend. To contribute to this field, an architecture to make low-cost portable digital oscilloscopes is proposed. The proposal is mainly based on general-purpose microcontrollers and operational amplifiers. Following this approach, a portable oscilloscope with two input channels, a graphic display, a synchronism detector, internal and external triggers, and a digital signal analyzer function is designed. Furthermore, different options for the implementation are proposed and discussed. [ABSTRACT FROM AUTHOR]

This study discusses the application of AI technology in the design of traditional Chinese-style architecture, aiming to enhance AI's matching accuracy and sustainability. Currently, there are limitations in AI technology in generating details of traditional Chinese-style architecture, so this study proposes a method of fine-tuning AI pre-training models, by extracting samples of traditional architectural style elements, to enhance the trajectory and output accuracy of AI generation. The research method includes constructing AI pre-training models, using DreamBooth and ControlNet tools for personalized training and perspective control. Through experimental verification, this study found that pre-trained models can effectively enhance the accuracy and controllability of AI in the preliminary design of architecture. At the same time, the application of ControlNet technology has significantly improved the accuracy and realism of architectural rendering. The value of this study lies in proposing a new method that combines AI technology with the process of traditional Chinese architectural design, which can help architects better protect and inherit the culture of traditional Chinese architecture. Through this method, it can reduce the difficulty of learning traditional Chinese architectural design, optimize the design process, enhance design efficiency, and provide strong support for the sustainable development of traditional Chinese architecture. [ABSTRACT FROM AUTHOR]

Video-based person re-identification (Re-ID), a subset of retrieval tasks, faces challenges like uncoordinated sample capturing, viewpoint variations, occlusions, cluttered backgrounds, and sequence uncertainties. Recent advancements in deep learning have significantly improved video-based person Re-ID, laying a solid foundation for further progress in the field. In order to enrich researchers’ insights into the latest research findings and prospective developments, we offer an extensive overview and meticulous analysis of contemporary video-based person Re-ID methodologies, with a specific emphasis on network architecture design and loss function design. Firstly, we introduce methods based on network architecture design and loss function design from multiple perspectives, and analyzes the advantages and disadvantages of these methods. Furthermore, we provide a synthesis of prevalent datasets and key evaluation metrics utilized within this field to assist researchers in assessing methodological efficacy and establishing benchmarks for performance evaluation. Lastly, through a critical evaluation of the experimental outcomes derived from various methodologies across four prominent public datasets, we identify promising research avenues and offer valuable insights to steer future exploration and innovation in this vibrant and evolving field of video-based person Re-ID. This comprehensive analysis aims to equip researchers with the necessary knowledge and strategic foresight to navigate the complexities of video-based person Re-ID, fostering continued progress and breakthroughs in this challenging yet promising research domain. [ABSTRACT FROM AUTHOR]

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Traditional Chinese architecture boasts a unique style and profound cultural connotations. Accordingly, the related literary works attain value for international dissemination. The four-character structures in these works serve as concise expressions for describing architectural details and design concepts. However, due to their linguistic implicitness, grammatical structure diversity, and rich cultural images, their translation into English requires multidimensional shift, redundancy elimination, and the avoidance of cultural defaults. This research has developed an effective translation workflow that leverages the synergistic strengths of Large Language Models (LLMs) and the human translator. By strategically employing tailored methods and techniques, it addresses the challenges in the translation process. Analysis suggests: It is recommended that the technique of shift be employed to address the implicit content and complex grammatical structures in translating four-character structures in Chinese literary works on traditional architecture into English. Besides, the selective application of combination and omission can help deal with information redundancy. Furthermore, it is advisable to apply foreignization and a combined method of literal translation and paraphrasing in conveying cultural images. These approaches will, to a certain extent, convert the untranslatability of four-character structures into translatability, enhancing the dissemination of the essence of traditional Chinese architecture. [ABSTRACT FROM AUTHOR]

Why are certain organization design arrangements enacted in a project-based organization? Building on extant literature, this article addresses this fundamental question by explicating three distinct explanations of the raison d’etre of salient organization design arrangements: Functional, Legitimacy, and Agency. Conceiving these explanations as coexisting, interacting logics of organizing, the article argues to draw upon the powerful concept of logic multiplicity to pave the way for integrating diverse theoretical perspectives. Thus, the article contributes to the organization design stream of research in project studies by developing a typology of organization design logics and proposing a way forward for pursuing pluralistic theorizing. [ABSTRACT FROM AUTHOR]

When we make museum exhibitions, we always do this in a particular architectural space. The question of how we deal with the aesthetic interrelations between museum architecture and exhibition design is, however, a contested one. Whereas some museums completely integrate architectural interiors and (permanent) display design, other museum interiors function as more 'neutral' containers, taking the shape of 'white cubes' and 'black boxes'. This article will investigate how the interfaces between museum architecture and exhibition design can be traced in current museum space conceptualisations. By focusing on three approaches to museum space that have been highly influential during the last decades, space syntax analysis, narrative approaches and the concept of atmosphere, this article will explore how museum space conceptualisations have integrated versus separated architectural and exhibition spaces, and how they have managed to capture the material interfaces between them. [ABSTRACT FROM AUTHOR]

New housing design has changed rapidly due to legislative requirements for energy-efficiency improvements. While housing energy efficiency is improving, little attention is given to individual indoor spaces, such as a domestic kitchen and associated occupant well-being. This study used secondary data from a building use survey (BUS) undertaken in 91 energy-efficient dwellings in five new social housing developments in Scotland to assess the occupant's views and experiences of their kitchen environments. The occupants' open-ended responses were explored using thematic analysis, identifying five themes: architectural design, building services, fixtures and fittings, change in diet and social outcomes. Satisfaction was reported for higher ceilings, good daylight, views and positive social engagement. Aspects considered poor related to construction quality, high internal temperature, low daylight levels, artificial lighting, and storage that negatively impacted the occupier's lived experience. The results highlight broad issues associated with kitchen environments that can affect the occupiers' physical and mental health. While an exploratory study, the outcomes emphasize the need for focused research of a larger sample size to explore the impact of kitchens on the resident's health and well-being in modern housing. [ABSTRACT FROM AUTHOR]

Performing music can be an enjoyable activity or a source of anxiety and discomfort. Previous studies suggest that it is possible to create spaces that promote well-being by applying knowledge from neuroscience to architectural design. However, this application remains unexplored in the context of music practice spaces. In this study, a virtual replica of a real conservatoire classroom was used to investigate the effect of window size and views through windows on the perception of the built environment. A sample of 61 musicians was recruited, and through questionnaires and eye tracking, participants' responses to environmental changes while playing their instruments were studied. The results of the experiment indicated a clear preference for larger windows, which significantly contributed to a more positive environment, perceived as more open, light, inviting, calming and spacious. In addition, eye tracking data suggested a reduction in anxiety levels, as evidenced by reduced gaze velocity and saccade ratios. These results demonstrate that windows and related variables such as daylight and views can have a significant impact on the experience of musicians during solo performance. This highlights the need to re-evaluate design considerations in music practice spaces, emphasising the crucial role of environmental elements in enhancing musicians' well-being. [ABSTRACT FROM AUTHOR]

Designing high-speed and energy-efficient blocks for image and digital signal processing (DSP) architecture is an evolving research field. This work designs a high-speed and energy-efficient multiply-accumulate (MAC) unit to augment the performance of field-programmable gate array (FPGA)-based accelerators and softcore processors. In this work, three discrete 32-bit fixed-point signed MAC architectures were designed in Verilog and synthesized for the Zynq 7000 ZedBoard to obtain efficient MAC architecture. The ultimate goal of this work is to design a fast and energy-efficient MAC unit that can achieve speed up to the DSP48 block to reduce the latency of IoT edge computing. Energy efficiency was achieved in PPG and partial product addition (PPA) for the proposed Booth radix-4 Dadda (BR4D)-based MAC. At PPG, the width of the partial product (PP) terms was optimized with Bewick's signed extension to reduce the power consumption. At PPA, the number of PP rows reduces the critical path delay (CPD) with Dadda-based PPA. The proposed BR4D MAC unit offers a reduction in dynamic power, CPD, power-delay product (PDP) and energy-delay product (EDP) by 22%, 9%, 29% and 36%, respectively, compared to standard Booth radix-4 Wallace tree (BR4WT) based MAC. Furthermore, hybrid MACs (BR4WT and BR4D) were compared with the current state-of-the-art (SoA) designs, and it was found that the proposed BR4D MAC is 47% faster compared to the same design in SoA. The proposed BR4D was tested for frequency scaling technique by reducing the frequency in steps of 10 MHz from a maximum usable frequency (MUF) of 64 MHz to 10 MHz to evaluate the performance for low-power applications. Reducing clock frequency by 84% will reduce the power consumption at the same proportion and speed by 38%. Additionally, the proposed design helps to improve the battery life of IoT end nodes with a reduction in energy consumption and EDP by 76% and 61%, respectively. [ABSTRACT FROM AUTHOR]

The use of multi-core chiplet systems is a promising technology for efficiently running large-scale hybrid applications, offering significant cost and performance advantages over the traditional 2D integrated circuits. However, incorrect task mapping and architecture design can lead to high power consumption and degraded performance. Current solutions mainly address these problems for 2D monolithic chips, which cannot be directly applied to 2.5D chiplet systems. To this end, this paper proposes a Multi-Application Hybrid Reconfigurable (MAHR) mechanism for task mapping and interconnection network construction, aiming to reduce energy consumption while optimizing communication latency. MAHR consists of three steps. In the first step, a Modified Adaptive Large Neighborhood Search (MALNS) algorithm is designed to solve the Chiplet Hybrid Applications Mapping Problem (CHAMP), allocating computational resources to application tasks; in the second step, the interconnection network is rebuilt using the results from the first step. This is accomplished through a novel reconfigurable interposer router that allows packets to bypass intermediate nodes; and in the third step, the results obtained in the second step are post-processed by checking all interposer nodes, restoring the router configurations of the nodes that have conflict links, and power-gating the unused routers. Compared to existing methods, simulation results demonstrate that MAHR reduces system power consumption and improves performance with little overhead in both synthetic and real-world scenarios. On average, MAHR achieves a 6.11% improvement in performance and an 11.3% reduction in power consumption, while increasing its area overhead by only 5.88%. [ABSTRACT FROM AUTHOR]

This study develops a novel multi-agent augmented NSGA-II architecture specifically designed to efficiently handle high-dimensional multi-objective optimization challenges in building energy-efficient design. In this paper, the share Q method is abandoned, and a novel crowding evaluation and comparison mechanism is adopted to ensure comprehensive coverage of the quasi-Pareto frontier while maintaining the diversity of the population. After integrating fast non-dominated sorting, the computational pressure of the algorithm can be effectively reduced. The integration of elite strategies further expands the solution space and prevents the omission of optimal solutions, thereby improving the operating efficiency and stability of the algorithm. After an in-depth analysis of 50 actual building examples, the results show that compared with the conventional NSGA-II method, our method optimizes the quality and diversity of Pareto solutions, with an average improvement of 12% and 15% respectively, while significantly shortening the calculation time, bringing an innovative and efficient optimization path to the energy-saving practice of building design. [ABSTRACT FROM AUTHOR]

The current Indian stock market structure is fully centralized, with each party's government, financial institutions, and private sector relying on one another to authenticate transactions. Because of the prior party's reliance, the settlement in client Demat accounts takes T + 2. Most of the time, the T + 2 rule causes retail short-term investors or traders to lose money or fail to make a profit. The current settlement method is extremely difficult to comprehend, resulting in anomalies in customer Demat accounts. Blockchain is an immutable append-only data chain that is always developing. The SHA-256 cryptographic technique prevents data from being removed or changed after it has been inserted. There is no central database to maintain the chain; instead, each node stores a copy and processes data locally, updating it in milliseconds. In the form of fresh blocks, new information is added to the chain, ensuring that all parties have the same picture of the blockchain at all times. Everyone has access to the information, so they may verify and confirm transactions, in the end, resulting in complete transparency for all transactions. This work proposes a decentralized stock exchange architecture based on permissioned blockchain to address the T + 2 settlement cycle rule problem. Before designing this architecture it considers every aspect so, it excludes the traditional party name custodian member party because of its traditional functionality so that our architecture becomes cost-efficient at the time of implementation. [ABSTRACT FROM AUTHOR]

This article recounts 6 years of empirical research in a humanitarian context on spatial behaviour using the behaviour settings theory. This research journey details the shortcomings of conventional architectural processes and the subsequent development of a human-centred behaviour setting methodology that drives behaviour change for adaptable spaces. The research work puts Barker's theory of behaviour settings into practice to show its significant methodological abilities in shaping behaviours through spaces. While the original theory was solely an analytical account of existing behaviours in certain settings, this study marks the first pragmatic exploration of the theory into both residential and refugee contexts. The methodology that is subsequently proposed is a complementary tool to account for the deficiencies of conventional architectural design processes. A method that enables one to fully immerse themselves in the environment, recognize specific architectural interventions, assess their effects and reiterate. It is a proposal for humanizing architecture, sympathizing its processes and personalizing its results for the users of any space. This article is part of the theme issue 'People, places, things, and communities: expanding behaviour settings theory in the twenty-first century'. [ABSTRACT FROM AUTHOR]

Translation abstract Dark discoloration in the lower wall area on exposed concrete surfaces – Interactions between formwork vibrations and concrete technologyThe use of exposed concrete construction represents an irreplaceable architectural design element, particularly for the creation of represented concrete buildings. Despite an increasing variety of design possibilities, such as with colored or textured elements, many planners and builders still prefer smooth and uniformly light‐colored exposed concrete surfaces created for using in‐situ concrete constructions. Despite significant and technical advancements, optical irregularities like dark discolorations, are repeatedly observed in the production of such exposed concrete components. These discolorations cannot be fully explained by existing scientific approaches and cannot be technically eliminated so far. As a part of a research project jointly conducted by the Institute of Building Material Science of Leibniz University of Hannover and the German Concrete and Construction Technology Association (Deutscher Beton‐ und Bautechnik‐Verein E. V.), the mechanisms leading to dark discolorations in lower wall areas of exposed concrete components were systematically investigated. In Particular, a direct influence of formwork vibrations in interaction with concrete technological properties was identified as a central factor for dark discolorations. The likelihood of such discolorations significantly increases when stiffening concrete (i. e. concrete whose age is approaching the initial setting time) are either directly or indirectly set into vibration by the formwork. A significant interaction was found between the timing of vibration initiation, the concrete technological properties and the vibration parameters (frequency and amplitude). [ABSTRACT FROM AUTHOR]

The development of electric vehicles (EVs) reduces dependence on fossil fuels, promotes energy conservation and emissions reduction, and facilitates the transition to clean energy sources in the power grid. However, subjective charging behavior among EV owners can lead to blind charging practices, compromising the reliability of the distribution network by widening the peakto-valley difference. To address safety concerns during the charging process, this paper proposes hardware and software systems for an experimental verification system. The network architecture, focused on charging safety, is examined. Analysis of the system's operation data reveals that it enables bidirectional interaction between electric vehicles and the power grid. This solution proves ef-fective for integrating a large number of EVs in peak-shaving and valley-filling efforts, laying a technical foundation for their inclusion in the power grid for peak shaving, valley filling, as well as providing standby and frequency regulation services. [ABSTRACT FROM AUTHOR]

Hospitals often evoke negative feelings due to their antiseptic architecture and personal memories. Biophilic hospital design can reduce stress and enhance health and wellness for patients and caregivers, creating sustainable therapeutic environments. This research paper explores these environments and presents case studies demonstrating the positive outcomes of biophilic design on health and wellbeing. This paper systematically reviews and critically assesses literature using secondary data from peer-reviewed journals and reliable sources on sustainable design from 2010-2023. The results show that biophilic design in hospitals reduces hospitalization time, patient mortality, pain levels, and stress for healthcare providers. It alleviates anxiety, improves experiences for patients, families, and staff, reduces patient harm, and supports faster recovery. Overall, it positively influences the psychological and physiological responses of patients and staff. Future research should analyze the impact of individual biophilic design elements separately and explore implementation challenges. It should also quantify benefits such as reduced staff absenteeism and increased productivity. This study fills the gap of the limitation of holistic studies on biophilic design's impact on patient care and service delivery in hospital settings. [ABSTRACT FROM AUTHOR]