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Recent advancements in network-connected devices have led to a rapid increase in the deployment of smart devices and enhanced grid connectivity, resulting in a surge in data generation and expanded deployment to the edge of systems. Classic cloud computing infrastructures are increasingly challenged by the demands for large bandwidth, low latency, fast response speed, and strong security. Therefore, edge computing has emerged as a critical technology to address these challenges, gaining widespread adoption across various sectors. This paper introduces the advent and capabilities of edge computing, reviews its state-of-the-art architectural advancements, and explores its communication techniques. A comprehensive analysis of edge computing technologies is also presented. Furthermore, this paper highlights the transformative role of edge computing in various areas, particularly emphasizing its role in power systems. It summarizes edge computing applications in power systems that are oriented from the architectures, such as power system monitoring, smart meter management, data collection and analysis, resource management, etc. Additionally, the paper discusses the future opportunities of edge computing in enhancing power system applications. [ABSTRACT FROM AUTHOR]

This paper presents a custom made small rover based surveying, mapping and building information modeling solution. Majority of the commercially available mobile surveying systems are larger in size which restricts their maneuverability in the targeted indoor vicinities. Furthermore their functional cost is unaffordable for low budget projects belonging to developing markets. Keeping in view these challenges, an economical indigenous rover based scanning and mapping system has developed using orthogonal integration of two low cost RPLidar A1 laser scanners. All the instrumentation of the rover has been interfaced with Robot Operating System (ROS) for online processing and recording of all sensorial data. The ROS based pose and map estimations of the rover have performed using Simultaneous Localization and Mapping (SLAM) technique. The perceived class 1 laser scans data belonging to distinct vicinities with variable reflective properties have been successfully tested and validated for required structural modeling. Systematically the recorded scans have been used in offline mode to generate the 3D point cloud map of the surveyed environment. Later the structural planes extraction from the point cloud data has been done using Random Sampling and Consensus (RANSAC) technique. Finally the 2D floor plan and 3D building model have been developed using point cloud processing in appropriate software. Multiple interiors of existing buildings and under construction indoor sites have been scanned, mapped and modelled as presented in this paper. In addition, the validation of the as-built models have been performed by comparing with the actual architecture design of the surveyed buildings. In comparison to available surveying solutions present in the local market, the developed system has been found faster, accurate and user friendly to produce more enhanced structural results with minute details. [ABSTRACT FROM AUTHOR]

With a large proportion of wind farms connected to the power grid, it brings more pressure on the stable operation of power systems in shorter time scales. Efficient and accurate scheduling, operation control and decision making require high time resolution power forecasting algorithms with higher accuracy and real-time performance. In this paper, we innovatively propose a high temporal resolution wind power forecasting method based on a light convolutional architecture—DC_LCNN. The method starts from the source data and novelly designs the dual-channel data input mode to provide different combinations of feature data for the model, thus improving the upper limit of the learning ability of the whole model. The dual-channel convolutional neural network (CNN) structure extracts different spatial and temporal constraints of the input features. The light global maximum pooling method replaces the flat operation combined with the fully connected (FC) forecasting method in the traditional CNN, extracts the most significant features of the global method, and directly performs data downscaling at the same time, which significantly improves the forecasting accuracy and efficiency of the model. In this paper, the experiments are carried out on the 1 s resolution data of the actual wind field, and the single-step forecasting task with 1 s ahead of time and the multi-step forecasting task with 1~10 s ahead of time are executed, respectively. Comparing the experimental results with the classical deep learning models in the current field, the proposed model shows absolute accuracy advantages on both forecasting tasks. This also shows that the light architecture design based on simple deep learning models is also a good solution in performing high time resolution wind power forecasting tasks. [ABSTRACT FROM AUTHOR]

Facing the continuous improvement of war complexity under the trend of system-of-systems confrontation, this paper introduces MBSE to the modeling design of complex system-of-systems combat. Adopting Unified Architecture Framework, a modeling method based on scenes is proposed by sorting out the modeling needs and internal mechanisms of system operations. Taking the task that searching and attacking multiple target as an example, various kinds of view is constructed including strategic, operating, resources and services. Then, logic verification is carried out based on the model. This method can provide a reference for system-of-system combat design and UAF application. [ABSTRACT FROM AUTHOR]

Facing the continuous improvement of war complexity under the trend of system-of-systems confrontation, this paper introduces MBSE to the modeling design of complex system-of-systems combat. Adopting Unified Architecture Framework, a modeling method based on scenes is proposed by sorting out the modeling needs and internal mechanisms of system operations. Taking the task that searching and attacking multiple target as an example, various kinds of view is constructed including strategic, operating, resources and services. Then, logic verification is carried out based on the model. This method can provide a reference for system-of-system combat design and UAF application. [ABSTRACT FROM AUTHOR]

The field of self-adaptive software is becoming increasingly crucial because software has to adapt its behavior at runtime to keep up with dynamic and constantly evolving environments. The ability of software to modify and adjust itself is known as adaptability, which has been acknowledged as an important quality attribute. As software architecture development represents the initial stage of the design process, architectural design has emerged as a critical development activity. The degree to which the architecture can adapt to changes will be the key factor in determining the adaptability of the software ultimately released to users. Existing metrics for software architectural adaptability typically view adaptability as a positive attribute, meaning that any possibility of change in architectural elements is perceived as an improvement in adaptability. However, the application of adaptability that leads to increased costs or complexity can only be seen as a negative attribute. In other words, the side effects brought about solely for the purpose of achieving adaptability are greatly overlooked; that is, the impacts caused by adaptability exhibit both symmetry and asymmetry for software architectural adaptability. On the other hand, the existing measures of adaptability only define it from the perspective of whether a module will change, without considering changes beyond the topological structure under multiple strategies. In this paper, we propose a comprehensive assessment approach to measure software architecture adaptability. Multiple factors are considered, including the number and types of strategies employed, the cost of completing the adaptation, the extent of changes made to the architectural elements, and the overall impact of the adaptation on the architecture. An extended UML (e-UML) component diagram is presented to model software architectural changes under various types of strategies. Finally, an experiment on the znn.com software is conducted to validate the practical feasibility of our proposed method. Our approach can assist software architects in making informed decisions regarding software architecture design options to improve the adaptability and longevity of their software systems. [ABSTRACT FROM AUTHOR]

The identification of safflower filament targets and the precise localization of picking points are fundamental prerequisites for achieving automated filament retrieval. In light of challenges such as severe occlusion of targets, low recognition accuracy, and the considerable size of models in unstructured environments, this paper introduces a novel lightweight YOLO-SaFi model. The architectural design of this model features a Backbone layer incorporating the StarNet network; a Neck layer introducing a novel ELC convolution module to refine the C2f module; and a Head layer implementing a new lightweight shared convolution detection head, Detect_EL. Furthermore, the loss function is enhanced by upgrading CIoU to PIoUv2. These enhancements significantly augment the model's capability to perceive spatial information and facilitate multi-feature fusion, consequently enhancing detection performance and rendering the model more lightweight. Performance evaluations conducted via comparative experiments with the baseline model reveal that YOLO-SaFi achieved a reduction of parameters, computational load, and weight files by 50.0%, 40.7%, and 48.2%, respectively, compared to the YOLOv8 baseline model. Moreover, YOLO-SaFi demonstrated improvements in recall, mean average precision, and detection speed by 1.9%, 0.3%, and 88.4 frames per second, respectively. Finally, the deployment of the YOLO-SaFi model on the Jetson Orin Nano device corroborates the superior performance of the enhanced model, thereby establishing a robust visual detection framework for the advancement of intelligent safflower filament retrieval robots in unstructured environments. [ABSTRACT FROM AUTHOR]

This paper presents a new deep-learning architecture designed to enhance the spatial synchronization between CMOS and event cameras by harnessing their complementary characteristics. While CMOS cameras produce high-quality imagery, they struggle in rapidly changing environments—a limitation that event cameras overcome due to their superior temporal resolution and motion clarity. However, effective integration of these two technologies relies on achieving precise spatial alignment, a challenge unaddressed by current algorithms. Our architecture leverages a dynamic graph convolutional neural network (DGCNN) to process event data directly, improving synchronization accuracy. We found that synchronization precision strongly correlates with the spatial concentration and density of events, with denser distributions yielding better alignment results. Our empirical results demonstrate that areas with denser event clusters enhance calibration accuracy, with calibration errors increasing in more uniformly distributed event scenarios. This research pioneers scene-based synchronization between CMOS and event cameras, paving the way for advancements in mixed-modality visual systems. The implications are significant for applications requiring detailed visual and temporal information, setting new directions for the future of visual perception technologies. [ABSTRACT FROM AUTHOR]

Remote Direct Memory Access (RDMA) is a widely adopted optimization strategy in datacenter networking that surpasses traditional kernel-based TCP/IP networking through mechanisms such as kernel bypass and hardware offloading. However, RDMA also faces a scalability challenge with regard to connection management due to limited on-chip memory capacity in the RDMA Network Interface Card (RNIC). This necessitates the storage of connection context within RNIC's memory and induces considerable performance degradation when maintaining a large number of connections. In this paper, we propose a novel RNIC microarchitecture design that achieves peak performance and scales well with the number of connections. First, we model RNIC and identify two key factors that degrade performance when the number of connections grows large: head-of-line blocking when accessing the connection context and connection context dependency in transmission processing. To address the head-of-line blocking problem, we then combine a non-blocking connection requester and connection context management module to process prepared connections first, which achieves peak message rate when the number of connections grows large. Besides, to eliminate connection context dependency in RNIC, we deploy a latency-hiding connection context scheduling strategy, maintaining low latency when the number of connections increases. We implement and evaluate our design, demonstrating its successful maintenance of peak message rate (66.4 Mop/s) and low latency (3.89 µs) while scaling to over 50,000 connections with less on-chip memory footprint. [ABSTRACT FROM AUTHOR]

In the realm of offline handwritten text recognition, numerous normalization algorithms have been developed over the years to serve as preprocessing steps prior to applying automatic recognition models to handwritten text scanned images. These algorithms have demonstrated effectiveness in enhancing the overall performance of recognition architectures. However, many of these methods rely heavily on heuristic strategies that are not seamlessly integrated with the recognition architecture itself. This paper introduces the use of a Pix2Pix trainable model, a specific type of conditional generative adversarial network, as the method to normalize handwritten text images. Also, this algorithm can be seamlessly integrated as the initial stage of any deep learning architecture designed for handwritten recognition tasks. All of this facilitates training the normalization and recognition components as a unified whole, while still maintaining some interpretability of each module. Our proposed normalization approach learns from a blend of heuristic transformations applied to text images, aiming to mitigate the impact of intra-personal handwriting variability among different writers. As a result, it achieves slope and slant normalizations, alongside other conventional preprocessing objectives, such as normalizing the size of text ascenders and descenders. We will demonstrate that the proposed architecture replicates, and in certain cases surpasses, the results of a widely used heuristic algorithm across two metrics and when integrated as the first step of a deep recognition architecture. [ABSTRACT FROM AUTHOR]

The evolution of super-resolution (SR) technology has seen significant advancements through the adoption of deep learning methods. However, the deployment of such models by resource-constrained devices necessitates models that not only perform efficiently, but also conserve computational resources. Binary neural networks (BNNs) offer a promising solution by minimizing the data precision to binary levels, thus reducing the computational complexity and memory requirements. However, for BNNs, an effective architecture is essential due to their inherent limitations in representing information. Designing such architectures traditionally requires extensive computational resources and time. With the advancement in neural architecture search (NAS), differentiable NAS has emerged as an attractive solution for efficiently crafting network structures. In this paper, we introduce a novel and efficient binary network search method tailored for image super-resolution tasks. We adapt the search space specifically for super resolution to ensure it is optimally suited for the requirements of such tasks. Furthermore, we incorporate Libra Parameter Binarization (Libra-PB) to maximize information retention during forward propagation. Our experimental results demonstrate that the network structures generated by our method require only a third of the parameters, compared to conventional methods, and yet deliver comparable performance. [ABSTRACT FROM AUTHOR]

Segmenting small infrared targets presents a significant challenge for traditional image processing architectures due to the inherent lack of texture, minimal shape information, and their sparse pixel representation within images. The conventional UNet architecture, while proficient in general segmentation tasks, inadequately addresses the nuances of small infrared target segmentation due to its reliance on downsampling operations, such as pooling, which often results in the loss of critical target information. This paper introduces the Channel Spatial Attention Nested UNet (CSAN-UNet), an innovative architecture designed specifically to enhance the detection and segmentation of small infrared targets. Central to CSAN-UNet's design is the Cascaded Channel and Spatial Convolutional Attention Module (CSCAM), a novel component that adaptively enhances multi-level features and mitigates the loss of target information attributable to downsampling processes. Additionally, the Channel-priority and Spatial Attention Cascade Module (CPSAM) represents another pivotal advancement within CSAN-UNet, prioritizing channel-level adjustments alongside spatial attention mechanisms to efficiently extract deep semantic information pertinent to small infrared targets. Empirical validation conducted on two public datasets confirms that CSAN-UNet surpasses existing state-of-the-art algorithms in segmentation performance, while simultaneously reducing computational overhead. [ABSTRACT FROM AUTHOR]

The future is the first fundamental projection for architecture. Architecture has a duration in time, which often goes beyond the very life of its designer, so it is in itself a time machine, which must inexorably come to terms with projections of the future. Through a review of utopias in the field of architectural design over the last few centuries, this paper intends to explore utopian aspirations for the future spatial ideal of architectural education and the dialectic of its need to be 'realized'. The findings indicate that the futurescapes of architectural education requires cross-fertilization through interdisciplinarity, which emerges from the bottom up, generating contributing scenarios from the community; the adaptability of scenarios that are dynamic and in turn can generate variables in constant transformation; the temporal stratigraphy of the built environment, involving the coexistence of artifacts from different eras within the same space, and the need for reconciliation and sustainability; as well as the need for interdisciplinarity and "out of the box" thinking that contributes to a greater awareness of non-traditional approaches. From the perspective of design education, the futurescapes, through experimentation in literature research and educational practice, it is evident that in the educational practice of fostering the next generation of architects and designers, they need to be capable of more than assessing issues from the perspective of design and community, not only to face current challenges but also to design the future they desire for themselves and the communities in which people live. [ABSTRACT FROM AUTHOR]

This paper investigates the multifaceted realm of interdisciplinarity in architectural education, examining historical and contemporary perspectives from institutions such as the Bauhaus, the Ulm School of Design, TU Delft, and the University of Zagreb Faculty of Architecture. Acknowledging the challenges posed by complex urban issues, the study underscores the imperative for interdisciplinary approaches in addressing economic, social, and ecological crises. By tracing the evolution of the concept, the paper distinguishes between disciplinary and interdisciplinary approaches, exploring key definitions of the concept. The paper concludes by highlighting ongoing efforts in educational institutions, reflected in various courses, workshops, and summer schools. Despite the limitations inherent in examining a handful of examples, the findings offer valuable guidance for educational institutions aspiring to embrace or enhance interdisciplinary approaches in architectural education. The insights draw attention to the importance of holistic, collaborative models in preparing future architects to navigate the complexities of our urban environments. [ABSTRACT FROM AUTHOR]

This paper aims to deeply study the close relationship between architectural image and design intention in the field of architectural art design. Through an in-depth analysis of architectural works from different periods and styles throughout history, we will explore the key role that architectural images play in how they convey the designer's deep intentions, and how they shape the viewer's perceptions and emotions. This paper will make full use of examples to elaborate on how architectural images convey and embody designers' unique ideas and creativity through ingenious visual elements, unique spatial layouts and symbolic symbols. At the same time, we will also study how the audience can gain a deeper understanding and experience of the architectural works through the interpretation of these images. Through this study, we can more thoroughly understand the creative thinking process behind architectural art design, as well as the rich cultural value it contains. Since ancient times, architectural works have not only been practical structures but also carriers of culture, history and human thought. The discussion of this paper will help to reveal the profound meaning behind the architectural images, so as to provide a more comprehensive perspective for us to understand and interpret the multiple levels of architectural art. Finally, this research is expected to provide far-reaching implications for future architectural design and cultural research and promote the continuous innovation and development of the field of architectural art design. [ABSTRACT FROM AUTHOR]

This paper explains some design and architecture decisions around the GEODNET network and the GeoDAO decentralized autonomous organization, which aims to create and operate a truly decentralized public GNSS reference sensing network. This paper covers the motivation of the network, the capabilities of current and future reference stations, the blockchain and GEOD token mechanics, and how the network powers applications ranging from climate change monitoring to real-time centimeter-accurate positioning. [ABSTRACT FROM AUTHOR]

This paper proposes an integrated architecture for navigation and attitude control of low-cost suborbital launch vehicles, propelled by a solid motor. Single-nozzle, two Degrees-of-Freedom (DoF) Thrust Vector Control (TVC) actuation is adopted. For architecture design purposes, a non-linear, unstable, 6 DoF model for the generic thrust-vector-controlled launcher dynamics and kinematics is deduced, and a linear state-space representation is proposed. The navigation system provides full-state estimates resorting to novel complementary kinematic filters, whose design allows to establish an explicit relation with steady-state Kalman filtering. A globally stable estimation solution is obtained, apart from the singularities arising from the use of Euler angles. The attitude control law is derived from the Linear Quadratic Regulator (LQR) using the state-space models for each linearization point of the reference trajectory, with an integral action (LQI) added to improve robustness and to provide null steady state attitude tracking error. A correction method is proposed to allow for pitch and yaw control in the presence of spinning motion, precluding the need of a supplementary roll control system. The control system is implemented through gain scheduling, resorting to an altitude-based linear parametric varying method. The architecture is implemented in a realistic simulation environment, composed by the 6 DoF non-linear model, the Navigation and Control solutions, and the environmental disturbances, to assess its performance through Monte Carlo simulations. The navigation system is able to provide accurate estimates of the state vector, while the control system satisfies attitude tracking performance and robustness to both external disturbances and model parametric uncertainties. • An integrated architecture for navigation and attitude control of suborbital launch vehicles is proposed. • An original time-varying state-space representation is deduced. • Novel complementary kinematic filters are designed to provide accurate full-state estimation. • LQI-based control allows for accurate pitch and yaw reference tracking via thrust vectoring. • The architecture performance is assessed through extensive Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

Human exploration of Mars and beyond will demand unprecedented levels of self-sufficiency due to the exceedingly far distances from Earth and lengthy mission durations. As such, astronaut crews must be able to function in a progressively autonomous manner, as they will be unable to rely on timely logistical resupply and/or operational guidance from ground-based mission support teams. One means of increasing autonomy is by incorporating intelligent (i.e., smart) technologies into the mission architecture and habitat design. As plans for sending humans to deep space continue to develop and mature, identifying current and emerging smart technologies and which have the potential to enable these missions or maintain habitability for the crew can be used to explore design options prioritized for autonomous operations. This paper surveys select commercial-off-the-shelf (COTS) technologies currently available and marketed terrestrially for use in integrated smart homes and aligns them for consideration by their functional relevance toward potential Smart Habitat (SmartHab) applications in deep-space. • Self-sufficiency becomes increasingly necessary as distance from Earth prohibits timely communication and resupply capability. • Habitability affects a crew's physical, physiological, psychological, and social health, as well as human performance. • Self-sufficiency and habitability for deep space missions may benefit from utilizing 'smart systems'to enable autonomy. • Over 90 smart technologies were reviewed resulting in 97 space-relevant applications for deep space missions. [ABSTRACT FROM AUTHOR]

In the realm of modern architecture, the demand for materials that combine strength, durability, and aesthetic flexibility is ever-growing. Addressing this need, this paper presents a study on the innovative use of aluminum extrudates in construction. Focusing on the AA6005 alloy, which is known for its excellent balance of strength, corrosion resistance, and weldability, this research delves into the development of an extrusion process that yields thin-walled, lightweight, yet high-strength structural components. Using FEM simulations, a new extrudate of the AA6005A was developed. It is compatible with standard façade systems, with high-strength properties and a weight reduced by 20% compared to that of conventional extrudates made of the AA6063 alloy. Using CAD engineering and FEM simulations of aluminum extrusion process, an innovative die was designed for the extrusion process, ensuring uniform flow of metal from the bearing and minimizing the elastic deflection of the die. This resulted in an increase in the extrusion velocity of thin-walled extrudate from AA6005A by 24% compared to conventional profiles extruded from AA6063. As part of the research, a trial test was carried out in production conditions and the quality of the extrudates was tested by 3D optical scanning, mechanical and structural properties tests, and microstructure observation. [ABSTRACT FROM AUTHOR]

This paper proposes an image encryption algorithm based on combined 1D chaotic maps. First, a permutation technique was applied. It was then reorganized into 1D matrices along the rows and columns respectively, which were then shuffled by computing the substituted position indices to obtain the scrambled image. Subsequently, a method of confusion of the scrambled image was used through another generated data map, combined with random sub‐matrices for diffusion, then resulting in an encrypted image. Finally, the proposed cryptosystem was implemented in a single kernel platform developed using the Nios II Software Build Tools processor for Eclipse. A hardware architecture was designed using the Qsys‐built tool which is available in the Quartus II 13.0sp1 environment. The developed single‐core system was implemented using the Cyclone IV EP4CE6E22C8. Robustness evaluation of the cryptosystem was performed through security analysis tests such as histogram analysis, correlation coefficient, differential analysis, and key space analysis to prove that it is of good quality, efficient, fast, and successfully resisting brute force attacks. The hardware performance analysis was also carried out. Then the cryptosystem is compared with those in the literature both in the hardware and security performance aspects. [ABSTRACT FROM AUTHOR]

Artificial Neural Networks (ANNs) have been used in a multitude of real-world applications given their predictive capabilities, and algorithms based on gradient descent, such as Backpropagation (BP) and variants, are usually considered for their optimisation. However, these algorithms have been shown to get stuck at local optima, and they require a cautious design of the architecture of the model. This paper proposes a novel memetic training method for simultaneously learning the ANNs structure and weights based on the Coral Reef Optimisation algorithms (CROs), a global-search metaheuristic based on corals' biology and coral reef formation. Three versions based on the original CRO combined with a Local Search procedure are developed: (1) the basic one, called Memetic CRO; (2) a statistically guided version called Memetic SCRO (M-SCRO) that adjusts the algorithm parameters based on the population fitness; (3) and, finally, an improved Dynamic Statistically-driven version called Memetic Dynamic SCRO (M-DSCRO). M-DSCRO is designed with the idea of improving the M-SCRO version in the evolutionary process, evaluating whether the fitness distribution of the population of ANNs is normal to automatically decide the statistic to be used for assigning the algorithm parameters. Furthermore, all algorithms are adapted to the design of ANNs by means of the most suitable operators. The performance of the different algorithms is evaluated with 40 classification datasets, showing that the proposed M-DSCRO algorithm outperforms the other two versions on most of the datasets. In the final analysis, M-DSCRO is compared against four state-of-the-art methods, demonstrating its superior efficacy in terms of overall accuracy and minority class performance. [ABSTRACT FROM AUTHOR]

The arising of the Cyber–Physical Systems' vision and concepts drives technological evolution toward a new architectural design for the infrastructure of an environment referred to as a Smart Environment. This perspective alters the way systems within Smart City landscapes are conceived, designed, and ultimately realized. Modular architecture, resource-sharing techniques, and precise deployment approaches (such as microservices-oriented or reliant on the FaaS paradigm) serve as the cornerstones of a Smart City cognizant of multiple Cyber–Physical Systems composing it. This paper presents a framework integrating Digital Decisioning, encompassing the automated combination of human-derived knowledge and data-derived knowledge (e.g., business rules and machine learning), to enhance decision-making processes and application definition within the Smart City context. [ABSTRACT FROM AUTHOR]

Planar reconstruction detects planar segments and deduces their 3D planar parameters (normals and offsets) from the input image; this has significant potential in the fields of digital preservation of cultural heritage, architectural design, robot navigation, intelligent transportation, and security monitoring. Existing methods mainly employ multiple-view images with limited overlap for reconstruction but lack the utilization of the relative position and rotation information between the images. To fill this gap, this paper uses two views and their relative camera pose to reconstruct indoor scene planar surfaces. Firstly, we detect plane segments with their 3D planar parameters and appearance embedding features using PlaneRCNN. Then, we transform the plane segments into a global coordinate frame using the relative camera transformation and find matched planes using the assignment algorithm. Finally, matched planes are merged by tackling a nonlinear optimization problem with a trust-region reflective minimizer. An experiment on the Matterport3D dataset demonstrates that the proposed method achieves 40.67% average precision of plane reconstruction, which is an improvement of roughly 3% over Sparse Planes, and it improves the IPAA-80 metric by 10% to 65.7%. This study can provide methodological support for 3D sensing and scene reconstruction in sparse view contexts. [ABSTRACT FROM AUTHOR]

Spiking neural networks (SNNs) are gaining increasing attention for their biological plausibility and potential for improved computational efficiency. To match the high spatial-temporal dynamics in SNNs, neuromorphic chips are highly desired to execute SNNs in hardware-based neuron and synapse circuits directly. This paper presents a large-scale neuromorphic chip named Darwin3 with a novel instruction set architecture, which comprises 10 primary instructions and a few extended instructions. It supports flexible neuron model programming and local learning rule designs. The Darwin3 chip architecture is designed in a mesh of computing nodes with an innovative routing algorithm. We used a compression mechanism to represent synaptic connections, significantly reducing memory usage. The Darwin3 chip supports up to 2.35 million neurons, making it the largest of its kind on the neuron scale. The experimental results showed that the code density was improved by up to 28.3× in Darwin3, and that the neuron core fan-in and fan-out were improved by up to 4096× and 3072× by connection compression compared to the physical memory depth. Our Darwin3 chip also provided memory saving between 6.8× and 200.8× when mapping convolutional spiking neural networks onto the chip, demonstrating state-of-the-art performance in accuracy and latency compared to other neuromorphic chips. [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]

The AutoICE challenge, organized by multiple national and international agencies, seeks to advance the development of near-real-time sea ice products with improved spatial resolution, broader spatial and temporal coverage, and enhanced consistency. In this paper, we present a detailed description of our solutions and experimental results for the challenge. We have implemented an automated sea ice mapping pipeline based on a multi-task U-Net architecture, capable of predicting sea ice concentration (SIC), stage of development (SOD), and floe size (FLOE). The AI4Arctic dataset, which includes synthetic aperture radar (SAR) imagery, ancillary data, and ice-chart-derived label maps, is utilized for model training and evaluation. Among the submissions from over 30 teams worldwide, our team achieved the highest combined score of 86.3 %, as well as the highest scores on SIC (92.0 %) and SOD (88.6 %). Notably, the result analysis and ablation studies demonstrate that instead of model architecture design, a collection of strategies/techniques we employed led to substantial enhancement in accuracy, efficiency, and robustness within the realm of deep-learning-based sea ice mapping. Those techniques include input SAR variable downscaling, input feature selection, spatial–temporal encoding, and the choice of loss functions. By highlighting the various techniques employed and their impacts, we aim to underscore the scientific advancements achieved in our methodology. [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 crosscultural 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. [ABSTRACT FROM AUTHOR]

As demands for the quality of living environments increase, thermal comfort in furniture design is garnering attention. Studies indicate that furniture should not only meet basic functional and aesthetic requirements but also influence indoor thermal environments and interact with human thermal exchanges, which are crucial for optimizing living comfort. This research aims to explore how thermal comfort in furniture design can be integrated with the building thermal environment to enhance indoor comfort and energy efficiency. Existing studies predominantly focus on surface treatments of furniture, with a lack of comprehensive analysis on the material's contact thermal sensation, the thermal properties of furniture, and their performance under varying building thermal conditions. Addressing this gap, this paper proposes a comprehensive research framework consisting of three main components: first, analyzing the impact of the building thermal environment on furniture thermal comfort; second, designing furniture with consideration for material's contact temperature sensation; and finally, developing furniture heat release rate curves suitable for different building thermal environments. This series of studies not only enhances the scientific and practical aspects of furniture design but also provides new theories and methods for the field of architectural design. [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]

Artificial Neural Networks (ANNs) have been used in a multitude of real-world applications given their predictive capabilities, and algorithms based on gradient descent, such as Backpropagation (BP) and variants, are usually considered for their optimisation. However, these algorithms have been shown to get stuck at local optima, and they require a cautious design of the architecture of the model. This paper proposes a novel memetic training method for simultaneously learning the ANNs structure and weights based on the Coral Reef Optimisation algorithms (CROs), a global-search metaheuristic based on corals' biology and coral reef formation. Three versions based on the original CRO combined with a Local Search procedure are developed: (1) the basic one, called Memetic CRO; (2) a statistically guided version called Memetic SCRO (M-SCRO) that adjusts the algorithm parameters based on the population fitness; (3) and, finally, an improved Dynamic Statistically-driven version called Memetic Dynamic SCRO (M-DSCRO). M-DSCRO is designed with the idea of improving the M-SCRO version in the evolutionary process, evaluating whether the fitness distribution of the population of ANNs is normal to automatically decide the statistic to be used for assigning the algorithm parameters. Furthermore, all algorithms are adapted to the design of ANNs by means of the most suitable operators. The performance of the different algorithms is evaluated with 40 classification datasets, showing that the proposed M-DSCRO algorithm outperforms the other two versions on most of the datasets. In the final analysis, M-DSCRO is compared against four state-of-the-art methods, demonstrating its superior efficacy in terms of overall accuracy and minority class performance. [ABSTRACT FROM AUTHOR]

TiCp/steel composites are conventionally produced via powder metallurgy. In this paper, a liquid pressure infiltration method was developed to prepare a kind of spherical hierarchical architectured composite, in which spherical TiCp-rich hard phase regions were uniformly dispersed in TiCp-free soft phase region. The microstructure and mechanical properties of the architectured composites were carefully studied and compared with the common composite, as well as the effect of TiCp fraction on the properties. The results show that architecturual design can effectively improve both the toughness and strength of the composites. With TiCp content increasing from 30% to 50%, both the bending strength and the impact toughness of the architectured composites first increase, then decrease, and reach the highest at 40% TiCp. The highest impact toughness reaches 21.2 J/cm2, being 6.2 times that of the common composite and the highest strength being 67% higher. The pressure infiltration method possesses adaptability to varying shapes and sizes of the products, allowing for large-scale preparation. Therefore, for the first time, the combination of pressure infiltration preparation and architectural design was applied to TiCp/steel composites. [ABSTRACT FROM AUTHOR]

Geospatial data, especially remote sensing (RS) data, are of significant importance for public services and production activities. Expertise is critical in processing raw data, generating geospatial information, and acquiring domain knowledge and other remote sensing applications. However, existing geospatial service platforms are more oriented towards the professional users in the implementation process and final application. Building appropriate geographic applications for non-professionals remains a challenge. In this study, a geospatial data service architecture is designed that links desktop geographic information system (GIS) software and cloud-based platforms to construct an efficient user collaboration platform. Based on the scalability of the platform, four web apps with different themes are developed. Data in the fields of ecology, oceanography, and geology are uploaded to the platform by the users. In this pilot phase, the gap between non-specialized users and experts is successfully bridged, demonstrating the platform's powerful interactivity and visualization. The paper finally evaluates the capability of building spatial data infrastructures (SDI) based on GeoNode and discusses the current limitations. The support for three-dimensional data, the improvement of metadata creation and management, and the fostering of an open geo-community are the next steps. [ABSTRACT FROM AUTHOR]

As the in-depth expansion and integrated application of information technology, smart city is one of the important directions for nurturing breakthroughs in the new generation of information technology, and an important part of the development of global strategic emerging industries. In the context of world peace, the world economy is developing rapidly and the process of urbanization is accelerating. The construction of a city not only reflects the economic strength of the city, but also is closely related to the life of the citizens. Therefore, this paper aimed to explore a new way of urban cultural creative design. This paper focused on the application of human–computer interaction virtual reality (VR) technology in urban cultural creative design. For this reason, this paper designed an interactive and immersive urban design simulation platform based on VR technology. In order to choose the viewing angle freely and control the design module freely, this paper proposed improved gesture recognition algorithm and anti-collision algorithm. The algorithm designed in this paper can enable designers to have better presentation effects when working on the platform, making architectural design more intuitive. Experiments show that the average accuracy rate of the gesture recognition algorithm designed in this paper reaches 97.3%, which is 4.1% higher than that of the traditional algorithm; and when the anti-collision algorithm in this paper is set to the optimal parameters, the accuracy rate is as high as 95%. These results fully demonstrate that the human–computer interaction VR technology design platform proposed in this paper has good design performance and can be applied to urban cultural creative design. [ABSTRACT FROM AUTHOR]

In recent years, the need for affordable sustainable housing has increased. At the same time, there has been a gradual rising interest in compact living. With the mounting impacts of climate change, a new way of thinking is needed to develop more resilient and climate responsive ways of living that are compact, affordable, and climate-conscious. In response to this need, the idea of a 'Z-Free Home' was born. The 'Z-Free Home' is a tiny mobile house equipped with essential passive and eco-cycle systems that achieves nine zero targets. The main design and construction concept is based on circular design and a return to nature life cycle principles. In this paper, the architectural design concept, building energy modelling, and simulation for the Z-Free Home design proposal is discussed. This paper describes the concept design and design development phases together with building modelling and simulation. A focus was made on the use of virtual reality in design development assessment as a new method for evaluating passive and eco-cycle systems. The results show that it's possible to achieve nine different zero goals while the analysis illustrates the challenges in achieving them. The paper also described the next steps planned for the proof of concept, i.e., the 1:1 house model. The project is ongoing, and it aims at a full-scale physical prototype as a proof of concept for the zero targets. The 'Z-Free Home' is designed for the cold Swedish climate but could be more widely applicable in other mild climates as well as hot climates. [ABSTRACT FROM AUTHOR]

As the in-depth expansion and integrated application of information technology, smart city is one of the important directions for nurturing breakthroughs in the new generation of information technology, and an important part of the development of global strategic emerging industries. In the context of world peace, the world economy is developing rapidly and the process of urbanization is accelerating. The construction of a city not only reflects the economic strength of the city, but also is closely related to the life of the citizens. Therefore, this paper aimed to explore a new way of urban cultural creative design. This paper focused on the application of human–computer interaction virtual reality (VR) technology in urban cultural creative design. For this reason, this paper designed an interactive and immersive urban design simulation platform based on VR technology. In order to choose the viewing angle freely and control the design module freely, this paper proposed improved gesture recognition algorithm and anti-collision algorithm. The algorithm designed in this paper can enable designers to have better presentation effects when working on the platform, making architectural design more intuitive. Experiments show that the average accuracy rate of the gesture recognition algorithm designed in this paper reaches 97.3%, which is 4.1% higher than that of the traditional algorithm; and when the anti-collision algorithm in this paper is set to the optimal parameters, the accuracy rate is as high as 95%. These results fully demonstrate that the human–computer interaction VR technology design platform proposed in this paper has good design performance and can be applied to urban cultural creative design. [ABSTRACT FROM AUTHOR]

Abstract: This article uses lightweight J2EE tools based on B/S architecture to design an auto-generated paper management system. This system includes several modules, namely: user management, subject management, classification management, question entry, question management, paper generation, and paper management. The design process performs the analysis and composes the examination paper using an efficient algorithm with a high possibility of success. With this algorithm, the user needs to identify the subject, the question type and the difficulty level. From this input, the examination paper will be generated automatically. On the basis of a study of the JSP and Microsoft Word interface, we find that the system combines with Word very well. Therefore the editing of questions and answers is performed using Word and the final paper may be stored as “.doc” files. The system has characteristics such as easy operation, a good interface, good usability, high security, high stability, and reliability. [Copyright &y& Elsevier]

The aim of this paper is to discuss the radical shift which emerges in the 1990s and enhances architecture in the 2000s by turning it into a less sculptural more intellectual field of design. Hence, architects rather focus on ground than figure in design projects. This leads them to interrogate the conventional relationships between figure and ground enabling figure to dominate the ground in architecture for decades. They discover the mutual relationships between figure and ground, and design grounded structures instead of ungrounded sculptures. These artificial structures seem like the extensions of the natural landscape, as such the conceptual and categorical distinction between artificial and natural blurs in architecture. Another conceptual blurring emerges between the concepts of landscape, ground, and field. These are generally used as interchangeable concepts, but landscape encompasses ground and field, making it a more comprehensive concept for architects. It is revealed in the paper that landscape is a reemerging concept which refers to the conceptual shift from form and function to flow and force in architecture. Landscape, therefore, awaits to be explored as a field of flows and forces by even more architects in this century in which cities are characterized by sculptural forms and objects. [ABSTRACT FROM AUTHOR]

The architectural design patterns of buildings have varied throughout the ages according to economic, social, cultural, and other variables to suit the function of the building, the design module is one of the most important means that organize and arrange the architectural design to enhance its functional, structural, and other performance. This research paper studies the concept behind the module and the analysis and classification of its types in the design of interior spaces, mass formation, structure, and composition of architectural. The research aims to reach a criterion for selection of the appropriate module, especially in the processes of development and elevation, to avoid the scattering that occurs in the mass formation, the structure, the composition of architectural, as well as the interior spaces. In addition, the research aims to help to coordinate the urban spaces of the architectural group to achieve integration between them and the mass, space, and elements of formation to raise their efficiency, especially those created in different periods. The research paper concluded that the provision of treatments and solutions using the architectural module, and methods of architectural integration to reach integrated buildings that optimally achieve the objectives and meet their function . [ABSTRACT FROM AUTHOR]

In recent years, many image denoising methods have been proposed based on convolutional neural networks (CNNs). While these methods have shown continuous performance improvement by introducing various mechanisms and structures, their computational cost tends to become increasingly expensive, owing to the resulting complex network architectures. This paper aims at winning the trade-off between computational efficiency and denoising performance for CNN-based image denoisers. Towards this end, we draw inspirations from traditional variational models with wavelet analysis operators for CNN architecture design. A model-inspired CNN is proposed with four key modules: iterative encoding-decoding units inspired by the iterative denoising process, directional convolutions inspired by the separable wavelet filters, inception modules inspired by the multi-scale analysis of wavelets, and stage-wise connections inspired by the adding noise back operation. In experiments, our CNN shows high computational efficiency in both training and test, with competitive results to state-of-the-art approaches. • A neural network inspired by the key ingredients of wavelet analysis model. • A sequential encoding-decoding network for image denoising. • A deep image denoiser well balancing effectiveness and computational efficiency. [ABSTRACT FROM AUTHOR]

In order to cope with the need for fast and adaptive voltage regulation in the face of large-scale disturbance in future smart grid EV grid-connected converters, a control strategy based on deep reinforcement learning is proposed in this paper. For electric vehicle grid-connected dual active bridge (DAB) DC–DC converter, a deep deterministic strategy gradient (DDPG) reinforcement learning intelligent controller based on an actor-critic architecture is designed. Through real-time online learning and training by collecting a large amount of system data, the intelligent controller can automatically adjust the power parameters of the DAB converter to ensure that the DC converter has strict stability in the face of various system disturbances. Simulation results show that the control strategy is effective. This research provides an innovative and feasible solution, which has important practical application value to the voltage regulation of electric vehicle grid-connected converters. [ABSTRACT FROM AUTHOR]

Time and resource restrictions limit the architect's design scope. Computational design methods can offer support to overcome these limitations. Design exploration has been a long‐established task in computational‐aided generative design. However, conventional objective‐ and performance‐based systems have restrictions pertaining to the exploration scope. Without a quasi‐global cognition of the conceptual design space, the exploration scope is bound to be limited. This paper is a proposal for an epistemic shift toward the interpretation of conceptual design space per se. This topic receives limited attention in the current literature due to the scarcity of interpretation tools. Using a customized large‐scale architectural image database with high‐level visual diversity and latent data space coverage, this paper serves as a first attempt to investigate the possibilities of leveraging disentangled representation learning to structurally interpret architectural conceptual design space in both supervised and unsupervised manner. Various schemes of supervised disentanglement are tested, with analytical comparisons indicating discrepant structural traits of different latent spaces. The unsupervised interpretation scheme shows the preliminary capability of automatic feature disentanglement. Our long‐term objective is to offer designers a broader spectrum of creative design through innovative design systems. [ABSTRACT FROM AUTHOR]

Cyber Threat intelligence (CTI) systems offer new capabilities in the arsenal of information security experts, who can explore new sources of data that were partially exploited during the past decades. This paper deals with the exploitation of discussion forums as a source of raw data for a cyber threat intelligence process. Specifically, it analyzes the discussion forums' characteristics and investigates their relationship with CTI. It proposes a semantic schema for the representation of data collected from discussion forums. Then, it applies a systematic methodology to design the reference architecture of the SECDFAN system, which handles the creation of CTI products following a comprehensive approach from the source selection to CTI product sharing and security experts' collaboration. The final product of this work is the SECDFAN reference architecture. The contribution of this paper is the development of a CTI reference architecture of a system that, by design, handles all CTI-related issues for creating CTI products by analyzing the content of discussion forums. [ABSTRACT FROM AUTHOR]

Human resource data sharing is very important for the cooperation of human resource institutions. Since the human resource data-sharing service needs to take into account the needs of individuals and talent service institutions, it faces issues such as the security of information sharing and the traceability of information. Therefore, this paper constructs a human resource data-sharing service system based on blockchain technology. Its trust mechanism is based on the Fabric alliance chain, and the system makes full use of its advantages of decentralization and consensus. Our research mainly includes data sharing architecture design, consensus mechanism analysis, smart contract design, data sharing process, and blockchain construction. The contribution of this paper is mainly in two aspects. On the one hand, it explores the trust mechanism of human resource data sharing and gives the scheme of the Fabric alliance chain. On the other hand, the overall architecture and smart contract design are given on the construction of the blockchain, which provides a reference for future research on human resource data sharing. [ABSTRACT FROM AUTHOR]

Non-noble-metal-based chalcogenides are promising candidates for hydrogen evolution reaction (HER) by harnessing the architectural design and the synergistic effect between the elements. Herein, a porous bimetallic selenide (NiFeSe) nanocube deposited on carbon fiber paper (NiFeSe/CFP) was synthesized through a facile selenization reaction based on Prussian blue analogues (PBAs) as precursors. The NiFeSe/CFP exhibited excellent HER activity with an overpotential of just 186 mV for a current density of 10 mA cm−2 in 1.0 M KOH at ambient temperature, similar to most of the state-of-the-art transition metal chalcogenides. The corresponding Tafel slope was calculated to be 52 mV dec−1, indicating fast discharge of the proton during the HER. Furthermore, the catalyst could endure long-term catalytic tests and showed remarkable durability. The enhanced electrocatalytic performance of NiFeSe/CFP is attributed to the unique 3D porous configuration inherited from the PBA templates, enhanced charge transfer occurring at the heterogeneous interface due to the synergistic effect between the bimetallic phases, and the high conductivity improved by the formation of amorphous carbon shells during the selenization. These findings prove that the combination of inexpensive metal–organic framework precursors and hybrid metallic compounds is a feasible way to realize the performance enhancement of non-noble-metal-based chalcogenides towards alkaline HER. [ABSTRACT FROM AUTHOR]

Square-root unscented Kalman filter (SRUKF) is a widely used state estimator for several state of-the-art, highly nonlinear, and critical applications. It improves the stability and numerical accuracy of the system compared to the non-square root formulation, the unscented Kalman filter (UKF). At the same time, SRUKF is less computationally intensive compared to UKF, making it suitable for portable and battery-powered applications. This paper proposes a low-complexity and power-efficient architecture design methodology for SRUKF presented with a use case of the simultaneous localization and mapping (SLAM) problem. Implementation results show that the proposed SRUKF methodology is highly stable and achieves higher accuracy than the extensively used extended Kalman filter and UKF when developed for highly critical nonlinear applications such as SLAM. The design is synthesized and implemented on resource constraint Zynq-7000 XC7Z020 FPGA-based Zedboard development kit and compared with the state-of-the-art Kalman filter-based FPGA designs. Synthesis results show that the architecture is highly stable and has significant computation savings in DSP cores and clock cycles. The power consumption was reduced by 64 % compared to the state-of-the-art UKF design methodology. ASIC design was synthesized using UMC 90-nm technology, and the results for on-chip area and power consumption results have been discussed. [ABSTRACT FROM AUTHOR]

This paper explores the implementation of strategic design in sustainable landscape development through a case study of the Neckar Landscape Park Student Competition. Drawing upon previous research on sustainable land use development, the study emphasizes the importance of integrated approaches and transdisciplinary thinking in managing landscapes. The strategic design process, characterized by analysis, synthesis and evaluation, is applied to address the socio-ecological and spatial challenges of the Neckar river valley. The findings highlight the potential of strategic design to create resilient and sustainable landscapes by combining scientific analysis and imaginative problem-solving. The case study exemplifies the value of strategic design in landscape architecture and offers insights for future landscape management. [ABSTRACT FROM AUTHOR]

Comprehensive neural network applications led to the customization of a scheme to accelerate the computation on ASIC implementation. Hence, the determination of activation function in a neural network is an indispensable requisite. However, the specific design architecture of an activation function in a digital network encounters several difficulties as these activation functions demand additional hardware resources due to their non-linearity. This paper proposed an efficient hyperbolic tangent (tanh) function, wholly based on stochastic Computing methodology. The Hyperbolic tangent implementation is backed by the clock gating technique to curtail the dynamic power dissipation. The results are derived by implementing two different clock gating techniques on the proposed hardware. In this work, the proposed clock gating-based stochastic design for the implementation of activation function is efficient in terms of performance parameters such as area, power, and delay with negligible accuracy loss. MNIST dataset has been used for checking accuracy on LeNeT benchmark architecture. Furthermore, post-synthesis results show that the proposed clock gating design area is reduced by ≈ 70.62 % , power is reduced by ≈ 58.19 % , and delay is reduced by ≈ 98.87 % compared to the state of the art. [ABSTRACT FROM AUTHOR]

Finishing materials play an important role in architectural design. Since ancient times, claddings made of noble materials (mainly stone) convey grandeur. Clearly, there was a technology to transform quarry stones into slabs using stone sawing machines. An example of this comes from the excavation work carried out in 1930 at the Temple of Artemis in Jerash (6th century AD). The archaeologists found a facility which was classified as a stone sawmill. The efficiency of such machines, their construction method and their resistance characteristics have not been studied as yet. This paper aims to define such parameters for the stone sawing machine located in Jerash. [ABSTRACT FROM AUTHOR]

RISC-V set architecture is playing an increasingly important role in processor technology due to its open instructions which allow researchers to build and improve computing systems. However, many RISC-V architectures exist in multi-core architecture with complex designs, large area, and high-power consumption. This paper studies an open-source multi-core RISC-V processor in a simple design with less power consumption. The processor depends on an open-source single RISC-V core processor, Taiga. Two cores of Taiga are integrated on a single chip while addressing issues related to cache coherence, interconnect, and memory design. A solution has been developed to achieve data coherence between implemented caches and the main memory; its architecture depends on the snoopy protocol. A hardware-customized peripheral unit has been implemented to achieve the management process among operated cores' tasks. For more consistent and highly controlled memory storage, the main memory unit has been designed in dual-port based on a specific protocol in the interface, and 8192 lines and word addressable unit. As the UART is common in several devices and processors for communication, the UART as a peripheral customized device has been appended for communication with other devices. The processor has been implemented in System Verilog HDL and extensively tested on various testbenches to ensure correct functionality. Hence, the system performance has been evaluated using the CoreMark benchmark, and achieved 4.605 CoreMark/MHz on Zedboard (FPGA Xilinx family) with a maximum operating frequency 98 MHz. The results indicate that the processor performs comparably to state-of-the-art multi-core processors, while offering a simpler and more power-efficient design. Overall, the research demonstrates the potentialof RISC-V architecture in creating a simple and power-efficient multi-core RISC-V processor. [ABSTRACT FROM AUTHOR]