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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]

In architectural education, informal interaction spaces play a significant role in experiential learning, allowing adult learners more flexibility and freedom. In India, the significance of these spaces has been heightened by the National Education Policy (NEP 2020) formulated by the Ministry of Human Resources, Government of India, which aligns with the activities promoted in educational institutes' informal interaction spaces. This research paper explores the role of informal interaction spaces in enhancing student-centered learning in architecture schools, presenting best practices for their design and utilization. The study employs mixed research methods, including literature review, case studies, and interviews, to gather perspectives from learners and instructors. The case study reveals a strong positive correlation between the percentage of informal interaction space and the learning outcomes of students. In addition to identifying challenges in informal interaction spaces, including interference and design issues, the literature study and respondents' views associate the cross-cultural discussions happening in these spaces with academic learning, life skill enhancement, and the development of collaboration skills, establishing their significance in facilitating effective and student-centered pedagogy. Varied preferences of respondents regarding design criteria to be adopted in designing informal interaction spaces emphasize the need for adaptable designs that cater to diverse student needs regarding placement, visual connection, and amenities. The conclusions underscore the multifaceted impact of these spaces, providing valuable insights for their refinement and contributing to ongoing discourse in architectural education.

Günümüzde fiziksel model, dijital çağdaşlarıyla birlikte mimarlık pratiğinin kullandığı etkin tasarım araçlarından biri olmayı sürdürür. Özellikle, müdahale edilebilir yapıları ile tasarım fikirlerinin deneysel bir düzlemde incelenebilmesini sağlayan çalışma maketleri, mekânsal olasılıkların fark edilmesine ve belgelenmesine olanak verir. Makalenin amacı, çalışma maketlerinin potansiyellerini, maketi oluşturan parçaların fiziksel dönüşümünün arketip kavramı ile çerçevelendiği kuramsal bir yöntemle ele almaktır. Makalede kullanılan arketip kavramı, maketin parçalarının temsil ettiği mimari elemanları mekânsal deneyimler üzerinden tanımlayarak birden fazla fizikselliğin bu tanımı karşılayabildiği bir çokluğa işaret etmektedir. Bu çokluğun mimari tasarım açısından önemini keşfedebilmek üzere, mimarlık pratiği içinde yer alan tasarım süreçlerinde üretilen çalışma maketleri, maketlerin biçim ve ölçek bilgilerine odaklanan bir yöntem ile biçimsel olarak analiz edilmektedir. Eisenman’a ait Karton Mimarlığı ile, OMA’nın Y2K Evi ve Casa de Musica yapısını olarak belirlenen iki vaka bünyesinde üretilmiş çalışma maketlerinin kullanıldığı bu analizlerde, arketipik yapıdaki çalışma maketlerinin tasarım sürecinde üretilen mekansal olasılıkların çeşitlendirilmesindeki rolü belirlenmeye çalışılmaktadır. Arketipik yapının, çalışma maketinin dönüşümü ile ortaya çıkan mekansal olasılıkları, akrabalıkları izlenebilen tasarım türevleri haline getirdiği ve bu türevler arasında biçimsel benzerlikler üzerinden tanımlanabilen bir ilişkisellik kurulmasını sağladığı görülmektedir.

In this study, a three-level static RAM cell with a single BL based on 16 nm BWG CNFET technology is proposed for use in high-performance IoT platforms. The proposed memory structure in a bit-interleaved architecture compatible design consists of a standard triple buffer gate (STB) based on the BWG CN-TGDI method and a pair of transmission gates as an access network. In the STB cell structure, the threshold voltage calibration by the flat band voltage parameter of the CNTs has been used to improve the static noise margin at logic level '1'. In addition, pseudo-resistor and stacked transistor structures have been used to control the single static current path during logic level '1' generation and cell leakage current, respectively. The simulation results based on the Monte Carlo method show that the hardware performance parameters PDP and EDP for the proposed design have been improved by 38.8% and 31%, respectively, compared to the counterpart structure. Moreover, the proposed architecture has shown a more reasonable result in terms of noise immunity than the best-performing design by 1.93 times. Finally, to investigate a real-world application, the triple memory structure has been used to store the colored QR code image with the data content of three values using the proposed hardware algorithm. The obtained results for the quality evaluation metrics, PSNR and MSSIM, emphasize the suitability of the proposed memory architecture for the development of next-generation SoCs. [ABSTRACT FROM AUTHOR]

The aim of this study is to evaluate the extent to which negotiated pedagogy as a decolonised pedagogy that calls for more social forms of learning is capable of enabling students to acquire skills for future practice in Nigeria. The curriculum of architectural education in Nigeria has been predicated on the British and American pedagogic learning models. These models do not reflect the diverse cultural practices and rarely enable students to acquire and develop capabilities to self-initiate projects and create jobs without waiting for commissions. The study utilised critical review of existing literature to expose the weaknesses of the current studio models in enabling students and future architects to acquire skills for future practice. The paper employed interview and case study to examine how negotiated pedagogies at the margins are opening up new ways of engaging in practice. The findings reveal that negotiated pedagogy challenges the orthodoxy of existing studio models by empowering today's graduating architecture students to develop a critical voice to question how their learning is equipping them for practice. By negotiating and integrating multiple approaches to learning and producing architecture, the line between education and practice, architects and users, educators and students and real and imagined is redrawn. [ABSTRACT FROM AUTHOR]

The success of deep learning in various applications depends on task-specific architecture design choices, including the types, hyperparameters, and number of layers. In computational biology, there is no consensus on the optimal architecture design, and decisions are often made using insights from more well-established fields such as computer vision. These may not consider the domain-specific characteristics of genome sequences, potentially limiting performance. Here, we present GenomeNet-Architect, a neural architecture design framework that automatically optimizes deep learning models for genome sequence data. It optimizes the overall layout of the architecture, with a search space specifically designed for genomics. Additionally, it optimizes hyperparameters of individual layers and the model training procedure. On a viral classification task, GenomeNet-Architect reduced the read-level misclassification rate by 19%, with 67% faster inference and 83% fewer parameters, and achieved similar contig-level accuracy with ~100 times fewer parameters compared to the best-performing deep learning baselines. Introducing GenomeNet-Architect, a neural architecture design framework that automatically optimises the overall layout of the architecture, the hyperparameters, and the training procedure of deep learning models for genome sequence data. [ABSTRACT FROM AUTHOR]

Numerous models for sleep stage scoring utilizing single-channel raw EEG signal have typically employed CNN and BiLSTM architectures. While these models, incorporating temporal information for sequence classification, demonstrate superior overall performance, they often exhibit low per-class performance for N1-stage, necessitating an adjustment of loss function. However, the efficacy of such adjustment is constrained by the training process. In this study, a pioneering training approach called separating training is introduced, alongside a novel model, to enhance performance. The developed model comprises 15 CNN models with varying loss function weights for feature extraction and 1 BiLSTM for sequence classification. Due to its architecture, this model cannot be trained using an end-to-end approach, necessitating separate training for each component using the Sleep-EDF dataset. Achieving an overall accuracy of 87.02%, MF1 of 82.09%, Kappa of 0.8221, and per-class F1-socres (W 90.34%, N1 54.23%, N2 89.53%, N3 88.96%, and REM 87.40%), our model demonstrates promising performance. Comparison with sleep technicians reveals a Kappa of 0.7015, indicating alignment with reference sleep stags. Additionally, cross-dataset validation and adaptation through training with the SHHS dataset yield an overall accuracy of 84.40%, MF1 of 74.96% and Kappa of 0.7785 when tested with the Sleep-EDF-13 dataset. These findings underscore the generalization potential in model architecture design facilitated by our novel training approach. [ABSTRACT FROM AUTHOR]

This paper investigates the infrastructure projects undertaken for the event initially known as the Semaine des sports d’hiver, which took place in Chamonix, France, from 25 January to 4 February 1924 and was later recognized as the first Winter Olympics. Although the already famous resort town was able to use its existing hotels to accommodate visitors and athletes, it also made a considerable investment in the construction of new sports infrastructure. Following an agreement signed just 9 months before the Games, these facilities included a large ice rink, a bobsleigh run and a ski jump. The project was entrusted to the Ponts et Chaussées engineers, who encouraged local firms to help with the construction. Archival analysis will be used to examine the relationship between the project and the changing environment. Our aim is to show how the work in Chamonix modified the environment by exploiting certain natural elements (e.g. water, soil and forests) and, conversely, how the natural (in particular, the geological and climatic) and historical (notably land ownership) components of the environment affected the execution of these works. [ABSTRACT FROM AUTHOR]

The discussion of "can machines think?" which started with the invention of the modern computer, brought along the question of "can machines design?" by researchers in the design field. These developments in information technologies have also affected the architecture. Artificial intelligence applications are encountered in many areas such as pricing estimation, energy conservation security systems of buildings, ventilation systems, user-oriented interactive design solutions, computer-aided programs used in the plan production phase and design process. When the literature on artificial intelligence applications in the architecture is reviewed, it can be seen that it generally includes shape grammars, graph theory, decision trees, constraint-based models, machine learning methods, RNN (Recursive Neural Networks), CNN (Convolutional Neural Network) and GAN (Generative Adversarial Network) algorithms. In this study, the use of artificial intelligence algorithms in architecture was examined, and an example was designed to determine the architectural structures of different periods by using CNN (Convolutional Neural Network). In the study, the open source TensorFlow library developed by Google and the Python programming language were used. Employing a statistical approach and utilizing convolutional neural networks (CNNs), a study has successfully classified the current flow patterns of buildings based on datasets comprising facades of Gothic, Modern, and Deconstructivist architectural styles. The findings demonstrate the efficacy of CNNs in accurately distinguishing the intricate details of diverse architectural styles. Recognizing elements from different periods using the CNN algorithm can examine not only individual buildings but also the relationship of buildings with their environments. It can also gain an important place in the field of conservation of the architectural discipline. The historical processes, aesthetic features and changes of protected buildings can be learned with the CNN algorithm and can guide restoration decisions. As a result of the study, the employed CNN-based model can correctly classify structures with 84.66% accuracy rate. [ABSTRACT FROM AUTHOR]

Designing ultrahigh energy density and mass-loading of electrocatalysts for hydrogen evolution is highly desirable but remains a great challenge. Herein, a 3D high-rise architectural structure consisting of dense Ni(OH) 2 nanoparticles on CuMoO 4 nanocube heterostructure arrays on Ni foam (Ni(OH) 2 /CuMoO 4 /NF) is developed via a hydrothermal reaction, calcination and electrodeposition method. Benefitting from the optimized electronic configuration, hierarchical high-rise architecture, and large specific surface area and mesoporous, the Ni(OH) 2 /CuMoO 4 /NF exhibits superior hydrogen evolution reaction (HER) activity in 1.0 M KOH solution. Ni(OH) 2 /CuMoO 4 /NF only requires low overpotential of 70 mV achieving a current density of 10 mA cm−2 with a low Tafel slope of 80 mV dec−1, and superior long-term stability toward HER. This work provides a new structural design to high-rise architectural structures for efficient catalytic application. [Display omitted] • A highly topological nickel hydroxide doped copper molybdate oxide composite was synthesized. • The high-topology structure with a heterogeneous interface enhances the conductivity. • The typical structure and proper components contribute to the electrochemical activity. [ABSTRACT FROM AUTHOR]

Three methods can be used to evaluate the colour used on architectural heritage: manual evaluation, instrumental measurement, and process restoration. Optical instruments that can obtain relatively accurate chromaticity data are ideal. As architectural heritage sites age, their colour fluctuates. Researchers must screen colour data and analyse the features based on experience with colour perception. This study combines quantitative colour analysis and a scaled perception survey to analyse the Wen Yuan Ge in the Forbidden City and provide a new reference for researching and designing colour in architectural heritage. [ABSTRACT FROM AUTHOR]

Metal cutting has been extensively studied over the years for improving its efficacy, yet, parasitic mechanisms like chatter and tool wear continue to generate higher forces and energy consumption with poor surface integrity. To address these parasitic mechanisms, a single-point turning cutter design is proposed based on the physics-of-machining including chatter theory to achieve reduced power consumption during the cutting of various metallic alloys like Al-6061, Ti-6Al-4V and others used by critical sectors such as aerospace and automotive. The current work focuses on aspects of machining that effectively reduce parasitic forces feeding into cutting power. The proposed cutter amalgamates features such as optimum side and end cutting edge angles, smaller nose radius and textured rake face into the cutter-body. Such a design is further proposed for use with a mechanochemical effect on a recently discovered plastic flow mode called sinuous flow, which has been reported to bring down cutting forces significantly. Experimental and analytical tests on the cutter design features validate reduction of cutting forces and through that alleviate the tendency to chatter as well as bring about energy savings for cutting of Al 6061. The potential for reduced real-time power consumption makes this design-framework significant for multipoint milling cutters too. It will greatly facilitate frugal manufacturing to account for sustainability in manufacturing operations. [ABSTRACT FROM AUTHOR]

Summary: In this work, a novel and model‐based artificial neural network (ANN) training method is developed supported by optimal control theory. The method augments training labels in order to robustly guarantee training loss convergence and improve training convergence rate. Dynamic label augmentation is proposed within the framework of gradient descent training where the convergence of training loss is controlled. First, we capture the training behavior with the help of empirical Neural Tangent Kernels (NTK) and borrow tools from systems and control theory to analyze both the local and global training dynamics (e.g., stability, reachability). Second, we propose to dynamically alter the gradient descent training mechanism via fictitious labels as control inputs and an optimal state feedback policy. In this way, we enforce locally ℋ2$$ {\mathscr{H}}_2 $$ optimal and convergent training behavior. The novel algorithm, Controlled Descent Training (CDT), guarantees local convergence. CDT unleashes new potentials in the analysis, interpretation, and design of ANN architectures. The applicability of the method is demonstrated on standard regression and classification problems. [ABSTRACT FROM AUTHOR]

Simple Summary: This literature review delves into the interdisciplinary field of neuroarchitecture, exploring the significant impact of architectural design on human behavior, emotions, and cognitive processes. It examines the roles of specific brain regions, such as the Anterior Cingulate Cortex (ACC) and the Parahippocampal Place Area (PPA), in perceiving and responding to architectural environments. The review also discusses the influence of mirror neurons in empathetic reactions to architecture, the emotional effects of design elements like natural light and color, and the importance of architectural features in spatial navigation and wayfinding. The paper aims to highlight the profound connection between architectural spaces and neurological functioning, emphasizing architecture's role in enhancing human well-being. The study of neuroarchitecture is concerned with the significant effects of architecture on human behavior, emotions and thought processes. This review explores the intricate relationship between the brain and perceived environments, focusing on the roles of the anterior cingulate cortex (ACC) and parahippocampal place area (PPA) in processing architectural stimuli. It highlights the importance of mirror neurons in generating empathetic responses to our surroundings and discusses how architectural elements like lighting, color, and space layout significantly impact emotional and cognitive experiences. The review also presents insights into the concept of cognitive maps and spatial navigation, emphasizing the role of architecture in facilitating wayfinding and orientation. Additionally, it addresses how neuroarchitecture can be applied to enhance learning and healing environments, drawing upon principles from the Reggio Emilia approach and considerations for designing spaces for the elderly and those with cognitive impairments. Overall, this review offers a neuroscientific basis for understanding how human cognition, emotions, spatial navigation, and well-being are influenced by architectural design. [ABSTRACT FROM AUTHOR]

As the Internet of Things (IoT) continues to revolutionize value-added services, its conventional architecture exhibits persistent scalability and security vulnerabilities, jeopardizing the trustworthiness of IoT-based services. These architectural limitations hinder the IoT's Sensor-as-a-Service (SEaaS) model, which enables the commercial transmission of sensed data through cloud platforms. This study proposes an innovative computational framework that integrates decentralized blockchain technology into the IoT architectural design, specifically enhancing SEaaS efficiency. This research contributes to an optimized IoT architecture with decentralized blockchain operations and simplified public key encryption. Furthermore, this study introduces an advanced SEaaS model featuring innovative trading operations for sensed data among diverse stakeholders. At its core, this model presents a unique blockchain-based data-sharing mechanism that manages multiple aspects, from enrollment to validation. Evaluations conducted in a standard Python environment indicate that the proposed SEaaS model outperforms existing blockchain-based data-sharing models, demonstrating approximately 40% less energy consumption, 18% increased throughput, 16% reduced latency, and a 25% reduction in algorithm processing time. Ultimately, integrating a lightweight authentication mechanism using simplified public key cryptography within the blockchain establishes the model's potential for efficient and secure data-sharing in IoT. [ABSTRACT FROM AUTHOR]

Through-wall radar human body pose recognition technology has broad applications in both military and civilian sectors. Identifying the current pose of targets behind walls and predicting subsequent pose changes are significant challenges. Conventional methods typically utilize radar information along with machine learning algorithms such as SVM and random forests to aid in recognition. However, these approaches have limitations, particularly in complex scenarios. In response to this challenge, this paper proposes a cross-modal supervised through-wall radar human body pose recognition method. By integrating information from both cameras and radar, a cross-modal dataset was constructed, and a corresponding deep learning network architecture was designed. During training, the network effectively learned the pose features of targets obscured by walls, enabling accurate pose recognition (e.g., standing, crouching) in scenarios with unknown wall obstructions. The experimental results demonstrated the superiority of the proposed method over traditional approaches, offering an effective and innovative solution for practical through-wall radar applications. The contribution of this study lies in the integration of deep learning with cross-modal supervision, providing new perspectives for enhancing the robustness and accuracy of target pose recognition. [ABSTRACT FROM AUTHOR]

Sustainability standards are integrated and applied in global supply chains through transnational commercial contracts that have come to play a regulatory function in addition to the conventional exchange task. The design of the architecture of contracting, the coordination among contracts, and the content of sustainability terms along the chain is the result of the cooperation of different actors, led by one or more transnational corporations. The paper describes the distribution of private regulatory power according to the structures of the chains- hierarchical, relational, market -and its impact on the compliance with sustainability standards. It considers the qualification system that regulates entry, exit, and permanence in the chain and the Suppliers codes and General principles of sustainability specified in the general terms and conditions. These represent complementary direct and indirect private regulatory instruments that can be deployed to ensure compliance. The choice of instruments depends on the distribution of regulatory power among the private actors and their exercise. Privity of contract makes it often difficult if not impossible direct regulation by the chain leader and obliges decentralization of regulatory power. The analysis shows that only the complementarity between the two sets of instruments enables control of compliance in complex chains whose actors operate across different jurisdictions with the use of sanctioning and remedial powers. The article analyzes how 26 principles of effectiveness, proportionality and dissuasiveness may contribute to a fair and non discriminatory exercise of private regulatory power related to the implementation of sustainability standards through contracts. [ABSTRACT FROM AUTHOR]

The present paper proposes a new reading of one of the most obscure passages of De Architectura; in I, 1,3 Vitruvius claims that in architecture there is always a signifier and a signified, but his approach differs from Quintilian's as presented in the latter's Institutio Oratoria. Vitruvius' is closer to Chrysippus approach, but he fails to mention the third constituent, the tynchanon. This omission is probably due to the fact that Vitruvius speaks of designs rather, than of existing buildings. What can successfully signify a proposed building is, according to my reading, the design and erection of a building that is easily recognized by the public as realizing the intentions of the community to erect the proposed building. Such successful signification constitutes the ultimate criterion for good architecture. [ABSTRACT FROM AUTHOR]

In this study, a long‐distance non‐contact current measurement method is proposed based on the non‐contact sensor array measurement technology, combined with a new current algorithm. The sensor array is a vertical array composed of three three‐axis magnetic field (MF) sensors that is placed on the ground plane below the measured overhead line. The proposed current algorithm can optimize the geometry of the overhead line using the spatial MF values measured by the sensor array and then the three‐phase currents can be calculated. To improve the accuracy of current measurement, the spatial MF model in the algorithm considers the influence of cable sag. It enables a more accurate coupling matrix between the measured overhead line and the sensor array to be established. Additionally, the current algorithm architecture is designed using a composite algorithm to more optimally obtain the best three‐phase current values. A scale‐down overhead‐line measurement system is developed to test the proposed current measurement method. The results show whether a cable sag exists when the three‐phase currents are approximately balanced, with errors less than 2.0%. Moreover, it can be determined whether cable sag exists when the three‐phase currents are unbalanced, with less than 3.4% errors. [ABSTRACT FROM AUTHOR]

Honeybees are essential to human society, providing pollination services globally as well as producing honey and other valuable products. Effective management of apiaries should not only rely on beekeeper knowledge and skill, but also incorporate new information technologies. The options to identify, predict and prevent beekeeping problems are becoming more affordable and applicable. The interdisciplinary Horizon 2020 project HIVEOPOLIS focuses on developing a new approach in beekeeping, by creating novel mechatronic beehives and implementing new bio-hybrid ideas. These intelligent beehives aim to help honeybees to cope with adverse environmental factors and increase the survival rate of the bee colonies. This paper focuses on the software architecture design for these intelligent beehives, providing infrastructure for data management and decision support system operation. The presented infrastructure is suitable for highly dynamic and diverse environments where a multitude of components interact and exchange information across technology domains (embedded, cloud, UIs) in a reliable and secure way. Besides user support, the decision support system built upon this infrastructure enables closed-loop automated decision making and control. • Intelligent beehives comprise diverse sensors and robotic actuators. • A data processing architecture for these futuristic hive systems is presented. • A system of intelligent hives integrated into a network of online microservices. • Remote decision support and automated actions are provided for beekeepers. [ABSTRACT FROM AUTHOR]

The purpose of this study is to extend the mileage by improving the efficiency of the electric vehicle (EV) driving and thermal management system (TMS). The powertrain system and the TMS must be considered simultaneously from the architecture stage as complementary systems to maintain the driving performance of the EV. This paper proposes an EV architectural design method and a multi-physical EV modeling technique. The main components of the EV are standardized differently from those of the internal combustion engine (ICE) vehicle, and the TMS is designed by thermal system configuration. Therefore, instead of the traditional continuous variable-based design methodology, optimal combination design based on the component database (DB) and the configuration plot presents the combinational design method. The performance of an EV varies depending on the performance of driving components and TMS, and weather. This paper presents a Modelica-based vehicle modeling technique to integrate multiple physical phenomena. Finally, we implemented an automated design using the optimal combination design method and the multi-physical integrated vehicle model design framework. The design of the C-segment EV was carried out to review the validity of the proposed method. 2880 laboratories were planned and designed using design variables with 10 levels of mixing. As a result of the design, we have confirmed that the mileage increases by 5% compared to the previous one in NEDC mode. [ABSTRACT FROM AUTHOR]

Fuel Cell Electric Vehicle (FCEV) powertrain layouts and control strategies have historically overlooked the asymmetric energy storage effect, despite its significant impact on system efficiency. In this study, we propose a novel FCEV powertrain layout using dual fuel cells to uncover hidden fuel efficiency improvement factors in comparison with the conventional Single Fuel Cell System (SFS). To address the issues of low efficiency operation in SFS and the limitations of existing energy management strategies that hinder high output performance, we present a minimum efficiency-based power control strategy. Additionally, we implement a partial system operation strategy to optimize efficiency according to the state of the power sources. This combined approach results in substantial improvements in both hardware and software efficiency, a possibility that was not previously achievable. Through this research, we demonstrate the potential for enhancing the fuel efficiency of the multi-stack system, a concept that has not been implemented yet. Moreover, we propose a new direction for the architectural design of FCEVs that overcomes the limitations of the SFS, thereby addressing potential malfunctions. [ABSTRACT FROM AUTHOR]

Sketch‐based architectural design tasks require considerable time and effort. However, developing an artificial intelligence agent to color and fuse architectural sketches is challenging due to the imaginative nature of the task and technical limitations. Here, we first introduce Y‐shaped generative adversarial networks (GANs), which can color sketches on the basis of temporarily specified reference images by embedding an attention mechanism model that can match spatial and color information and by maintaining a type of triangular equilibrium state. Then, we develop practicable methods for coloring and fusing the given sketches simultaneously using a combination of our Y‐shaped GANs and the denoising diffusion implicit model, yielding results with high quality and veracity. A novel interpolation process is developed as a necessary trick for realizing the fusion function. The testing results indicate that this method works better than the existing methods for the tasks of coloring and fusing architectural sketches. [ABSTRACT FROM AUTHOR]

The increasing diversity of materials creates many possibilities and constraints that designers have to consider when selecting materials for projects. While the literature has investigated materials selection in engineering, product, and architectural design, knowledge is still lacking in interior design. Accordingly, this study focused on interior finishing materials with three objectives: (1) explore the determinants of materials selection in interior design, and within the residential design context, (2) identify designers' finishing material preferences and selection criteria for floors, walls, and ceilings; (3) investigate designers' criteria prioritizations while selecting materials. For this we conducted one-to-one interviews with architects and interior designers specializing in residential spaces. We first explored their material selection considerations in general. Second, we documented their material designations in residential project entry halls they had designed along with their selection criteria. Third, we presented an entry hall of a residential space for them to choose the materials while we questioned them about their materials selection priorities. The results reveal that the main determinants of materials selection include material-related, project-related, and designer-related factors. Moreover, materials choices and selection criteria vary between surfaces in space. Finally, the designers give the most priority to sensorial properties and the least to ecological properties. These findings expand our knowledge about materials in interior design, enhance the knowledge base for materials education, and have implications for designers and manufacturers regarding selecting and designing finishing materials. [ABSTRACT FROM AUTHOR]

Aim: The architecture of neonatal units plays a key role in developmental strategies and preterm outcomes. The aim was to evaluate the design of Spanish neonatal units and its impact on the participation of parents in neonatal care. Methods: A web‐based survey was sent to all level III Spanish neonatal units, including questions about hospital data, architectural design, facilities and family participation. Results: The study included 63 units. Most units (87%) had part or all the intensive care patients located in open bay units, while 54% had at least one individual patient cubicle. Single family rooms, defined as those including enough space and furniture for family members to stay with the infant without restrictions, were available in 8 units (13%). Eighteen units (29%) had a structured programme of family education. Units with single family rooms were more likely to have parental participation in rounds (p < 0.01), safety protocols (p = 0.02), oxygen management (p < 0.01) and nasogastric tube feeding (p = 0.02), as well as to allow siblings to participate in kangaroo care (p < 0.01). Conclusion: Widely variable architectural designs and policies were found in Spanish neonatal units. The presence of single family rooms may have impacted the participation of parents in neonatal care. [ABSTRACT FROM AUTHOR]

Quantum dot Cellular Automata (QCA) may be viewed as the poten- tial digital logic design alternative to supplant the current CMOS Technol- ogy. The logical design is based on the polarization of electrons to transfer information which results in a huge improved performance metric in terms of speed, power & area. This paper demonstrates a Reversible Arithmetic Processor (RAP) architecture design with Reversible Multiplexer (RM) and Reversible Functional Gate in QCA. The QCA Designer-E simulation tool has been used to design and verify all the proposed archi- tecture and the energy dissipation has been simulated using a coherent vector energy engine setup. The total and average energy dissipation per cycle of our proposed RAP using QCA are 3.91 × 10−1 eV and 3.56 × 10−2 eV respectively. Moreover, the power dissipation of proposed RAP is 1.3 nW. Regarding cell count, area, latency, and energy dissipation, the proposed architecture beats out the existing architecture. [ABSTRACT FROM AUTHOR]