Static Architecture Research Articles

School buildings as performative machines: the new architectural devices of control

ABSTRACT The past two decades saw a growing attention to the role of design for the geography of education and simultaneously shifted architectural attention towards the understanding of different forms of pedagogy. Yet, careful empirical engagements with the material architecture of contemporary school buildings and the experiences they mediate are still scarce or missing. Focussing on how mechanisms of control are imagined and practiced in the design and use of school buildings, this article fleshes out a picture of the performative spatial machinery of schools. It will do so drawing on designers’ accounts, plans and visions for a Building Schools for the Future (BSF) building in Liverpool, UK and on accounts of the experiences of different school dwellers. Overcoming the dualist understanding of education as an activity that happens in objective frames of learning (the static architecture of the schools) or the subjective interpretations of users (the perception of teachers and students), we trace specific practices of ‘dwelling’ in the school building and identify architectural and designerly techniques for modulating control. Instead of dissipating or reducing control, or merely re-producing the classic forms of power, this versatile and porous type of architecture, we argue, multiplies and diversifies the forms of ‘polycentric’ control exercised through various intersecting lines of sight and sound.

Design for dependability — State of the art and trends

This paper presents an overview of design for dependability as a process involving three distinct but interrelated activities: risk analysis, risk mitigation, and risk assessment. Although these activities have been the subject of numerous works, few of them address the issue of their integration into rigorous design flows. Moreover, most existing results focus on dependability for small-size safety-critical systems with specific static architectures. They cannot be applied to large systems, such as autonomous systems with dynamic heterogeneous architectures and AI components. The overwhelming complexity and lack of interpretability of AI present challenges to model-based techniques and require empirical approaches. Furthermore, it is impossible to cope with all potential risks at design time; run-time assurance techniques are necessary to cost-effectively achieve the desired degree of dependability. The paper synthesizes the state of the art showing particularly the impact of new trends stemming from the integration of AI components in design flows. It argues that these trends will have a profound impact on design methods and the level of dependability. It advocates the need for a new theoretical basis for dependability engineering that allows the integration of traditional model-based approaches and data-driven techniques in the search for trade-offs between efficiency and dependability.

Gape drives regional variation in temporalis architectural dynamics in tufted capuchins.

Dynamic changes in jaw movements and bite forces depend on muscle architectural and neural factors that have rarely been compared within the same muscle. Here we investigate how regional muscle architecture dynamics-fascicle rotation, shortening, lengthening and architectural gear ratio (AGR)-vary during chewing across a functionally heterogeneous muscle. We evaluate whether timing in architecture dynamics relates to gape, food material properties and/or muscle activation. We also examine whether static estimates of temporalis fibre architecture track variation in dynamic architecture. Fascicle-level architecture dynamics were measured in three regions of the superficial temporalis of three adult tufted capuchins (Sapajus apella) using biplanar videoradiography and the XROMM workflow. Architecture dynamics data were paired with regional fine-wire electromyography data from four adult tufted capuchins. Gape accounted for most architectural change across the temporalis, but architectural dynamics varied between regions. Mechanically challenging foods were associated with lower AGRs in the anterior region. The timing of most dynamic architectural changes did not vary between regions and differed from regional variation in static architecture. Collectively these findings suggest that, when modelling temporalis muscle force production in extant and fossil primates, it is important to account for the effects of gape, regionalization and food material properties. This article is part of the theme issue 'Food processing and nutritional assimilation in animals'.

Dynamically Scalable NoC Architecture for Implementing Run-Time Reconfigurable Applications.

The paper proposes two architectures for a dynamically scalable network-on-chip (NoC) for dynamically reconfigurable intellectual properties (IPs) to save power. The first architecture is a run-time scalable column-based NoC, where the columns of the NoC are scaled up and down at run-time depending on the demands to connect reconfigurable IPs. The second architecture is an extension of the first, where both the rows and columns of the NoC are dynamically scaled up and down on demand. A robust control manager is developed to control the IP and sub-NoC reconfigurations by optimizing the reconfiguration costs. The proposed architectures have been implemented and tested in actual prototypes on a Virtex 6 FPGA mounted on the ML605 board. The results show that dynamically scalable architectures are capable of significant power reduction as compared to traditional static architectures for the same size of the NoC. It is anticipated that the scalable NoC can be very useful for sharing the FPGA resources among IPs at runtime.

The Social Palace as a Medium for the Transfer and Transformation of Ideas in Early Socialism

ABSTRACT This essay offers an interpretation of Charles Fourier's phalanstery as a tool for transferring ideas. It examines how static architecture transforms into a concept and can act as a medium for the transcontinental travels of social ideas. Both buildings and their blueprints and illustrations are analysed as texts full of revolutionary political meanings. It examines attempts to use them in practice, in France and abroad, and the theoretical considerations on how they should be used. Material infrastructure was necessary to the establishing, functioning, and spreading of the propaganda of the political movement of the phalansterian school, which particularly distinguished early socialism and other ideological movements in the nineteenth century. This essay aims to contribute to the discussion on non-verbal speech acts in intellectual history.

Energy-Efficient Hierarchical Resource Allocation in Uplink–Downlink Decoupled NOMA HetNets

The dense deployment of small cell networks is a key feature of next-generation mobile networks aimed at providing the necessary capacity increase. It is noteworthy that small cell networks employ high-capacity backhaul links on millimeter-wave bands to develop multi-hop topologies in order to mitigate data transmission costs. The current static backhaul infrastructures cannot control severe fluctuating network traffic. To resolve this problem, this paper proposed a novel adaptive backhaul topology with the ability to adapt to different traffic patterns. Based on the graph theory, the adaptive system dynamically allows changes to the hybrid millimeter-wave backhaul architecture, and it also provides the possibility of effective channel allocation to each backhaul link to meet capacity and QoS demands. Also, regarding the importance of green networking in integrated-access-and-backhaul networks we proposed a dynamic optimization model which minimizes the overall energy consumption of UL/DL Decoupled NOMA heterogeneous networks in addition to providing the essential coverage and capacity. The proposed model optimizes user association/power utilization and presents an effective modular and scalable framework for analytical technology-oriented modeling of integrated multi-hop backhauls. The numerical results proved that the joint power optimization and hybrid backhaul architecture can increase the total network throughput by 18 percent compared to the current optimized static architectures. It can also reduce the energy consumption level by 30 percent, and enhance users’ quality satisfaction by 24.5 percent with respect to user distribution patterns.

Stress Detection Using Context-Aware Sensor Fusion From Wearable Devices

Wearable medical technology has become increasingly popular in recent years. One function of wearable health devices is stress detection, which relies on sensor inputs to determine a patient’s mental state. This continuous, real-time monitoring can provide healthcare professionals with vital physiological data and enhance the quality of patient care. Current methods of stress detection lack: (i) robustness—wearable health sensors contain high levels of measurement noise that degrades performance, and (ii) adaptation—static architectures fail to adapt to changing contexts in sensing conditions. We propose to address these deficiencies with SELF-CARE, a generalized selective sensor fusion method of stress detection that employs novel techniques of context identification and ensemble machine learning. SELF-CARE uses a learning-based classifier to process sensor features and model the environmental variations in sensing conditions known as the noise context. SELF-CARE uses noise context to selectively fuse different sensor combinations across an ensemble of models to perform robust stress classification. Our findings suggest that for wrist-worn devices, sensors that measure motion are most suitable to understand noise context, while for chest-worn devices, the most suitable sensors are those that detect muscle contraction. We demonstrate SELF-CARE’s state-of-the-art performance on the WESAD dataset. Using wrist-based sensors, SELF-CARE achieves 86.34% and 94.12% accuracy for the 3-class and 2-class stress classification problems, respectively. For chest-based wearable sensors, SELF-CARE achieves 86.19% (3-class) and 93.68% (2-class) classification accuracy. This work demonstrates the benefits of utilizing selective, context-aware sensor fusion in mobile health sensing that can be applied broadly to Internet of Things applications.

Linking muscle architecture and function in vivo: conceptual or methodological limitations?

Despite the clear theoretical link between sarcomere arrangement and force production, the relationship between muscle architecture and function remain ambiguous in vivo. We used two frequently used ultrasound-based approaches to assess the relationships between vastus lateralis architecture parameters obtained in three common conditions of muscle lengths and contractile states, and the mechanical output of the muscle in twenty-one healthy subjects. The relationship between outcomes obtained in different conditions were also examined. Muscle architecture was analysed in panoramic ultrasound scans at rest with the knee fully extended and in regular scans at an angle close to maximum force (60°), at rest and under maximum contraction. Isokinetic and isometric strength tests were used to estimate muscle force production at various fascicle velocities. Measurements of fascicle length, pennation angle and thickness obtained under different experimental conditions correlated moderately with each other (r = 0.40-.74). Fascicle length measured at 60°at rest correlated with force during high-velocity knee extension (r = 0.46 at 400°s-1) and joint work during isokinetic knee extension (r = 0.44 at 200°s-1 and r = 0.57 at 100°s-1). Muscle thickness was related to maximum force for all measurement methods (r = 0.44-0.73). However, we found no significant correlations between fascicle length or pennation angle and any measures of muscle force or work. Most correlations between architecture and force were stronger when architecture was measured at rest close to optimal length. These findings reflect methodological limitations of current approaches to measure fascicle length and pennation angle in vivo. They also highlight the limited value of static architecture measurements when reported in isolation or without direct experimental context.

Dynamic System Diversification for Securing Cloud-based IoT Subnetworks

Remote exploitation attacks use software vulnerabilities to penetrate through a network of Internet of Things (IoT) devices. This work addresses defending against remote exploitation attacks on vulnerable IoT devices. As an attack mitigation strategy, we assume it is not possible to fix all the vulnerabilities and propose to diversify the open-source software used to manage IoT devices. Our approach is to deploy dynamic cloud-based virtual machine proxies for physical IoT devices. Our architecture leverages virtual machine proxies with diverse software configurations to mitigate vulnerable and static software configurations on physical devices. We develop an algorithm for selecting new configurations based on network anomaly detection signals to learn vulnerable software configurations on IoT devices, automatically shifting towards more secure configurations. Cloud-based proxy machines mediate requests between application clients and vulnerable IoT devices, facilitating a dynamic diversification system. We report on simulation experiments to evaluate the dynamic system. Two models of powerful adversaries are introduced and simulated against the diversified defense strategy. Our experiments show that a dynamically diversified IoT architecture can be invulnerable to large classes of attacks that would succeed against a static architecture.

Synthesis in presence of dynamic links

We consider the problem of distributed synthesis: Given a distributed architecture and a specification, can we generate a distributed algorithm that satisfies the specification?While previous work focused on static architectures and fixed message sizes, we assume that the communication network changes dynamically and that processes piggy-pack previously received messages onto current messages.Specifically, we address two processes communicating in rounds over a dynamic link. Given a network model, i.e., a set of link directions, an adversary picks, in each round, an arbitrary link from the network model. We show that the synthesis problem is decidable for a network model if and only if it does not contain the 'empty' link. For a more general setting allowing for sequences of links, the synthesis problem is decidable if and only if the number of consecutive empty links in all possible sequences is uniformly bounded from above.

Simulation analysis of a method to improve data-transmission performance of Nanshan 26m Radio Telescope based on Software-Defined Networks

Data Center of Xinjiang Astronomical Observatory (XAO-DC) commenced operating in 2015, and provides services including archiving, releasing and retrieving precious astronomical data collected by the Nanshan 26m Radio Telescope (NSRT) over the years, and realises the open sharing of astronomical observation data. The observation data from NSRT are transmitted to XAO-DC 100 km away through dedicated fiber for long-term storage. With the continuous increase of data, the static architecture of the current network cannot meet NSRT data-transmission requirements due to limited network bandwidth. To get high-speed data-transmission using the existing static network architecture, a method for reconstruction data-transmission network using Software-Defined Networks (SDN) is proposed. Benefit from the SDNʼs data and control plane separation, and open programmable, combined with the Mininet simulation platform for experiments, the TCP throughput (of single thread) was improved by ∼24.7%, the TCP throughput (of multi threads) was improved by ∼9.8%, ∼40.9%, ∼35.5% and ∼11.7%. Compared with the current network architecture, the Latency was reduced by ∼63.2%.

Comparative models for multi-step ahead wind speed forecasting applied for expected wind turbine power output prediction

This paper investigates six of the most widely used wind speed forecasting models for a combination of statistical and physical methods for the purpose of Wind Turbine Power Generation (WTPG) prediction in Cameroon. Statistical method based on both single static and dynamic neural networks architectures and two hybrid neural networks architectures in comparison to ARIMA model are employed for multi-step ahead wind speed forecasting in two Datasets in Bapouh, Cameroon. The physical method is used to estimate 1 day ahead expected WTPG for each Dataset using the previous predicted wind speed from better forecasting models. The obtained results of multi-step ahead forecasting showed that the ARIMA and nonlinear autoregression with exogenous input neural network (NARXNN) models perform well the wind speed forecasting than other forecasting models in both Datasets. The better performances of ARIMA are achieved with one-step ahead and two-step ahead forecasting, while NARXNN is better with one-step ahead forecasting. But NARXNN models have more computational time than other models such as ARIMA models. Furthermore, the effectiveness of employed hybrid method for WTPG prediction is proven.

To automatically map source code entities to architectural modules with Naive Bayes

Background:The process of mapping a source code entity onto an architectural module is to a large degree a manual task. Automating this process could increase the use of static architecture conformance checking methods, such as reflexion modeling, in industry. Current techniques rely on user parameterization and a highly cohesive design. A machine learning approach would potentially require less parameters and better use of the available information to aid in automatic mapping. Aim:We investigate how a classifier can be trained to map from source code to architecture modules automatically. This classifier is trained with semantic and syntactic dependency information extracted from the source code and from architecture descriptions. The classifier is implemented using multinomial naive Bayes and evaluated. Method:We perform experiments and compare the classifier with three state-of-the-art mapping functions in eight open-source Java systems with known ground-truth-mappings. Results:We find that the classifier outperforms the state-of-the-art in all cases and that it provides a useful baseline for further research in the area of semi-automatic incremental clustering. Conclusions:We conclude that machine learning is a useful approach that performs better and with less need for parameterization compared to other approaches. Future work includes investigating problematic mappings and a more diverse set of subject systems.

Blockchain‐enabled balise data security for train control system

The train control system is the railway system's brain, ensuring the train protection and the railway network's operating performance. This control system relies on the balise data received via near-field wireless communication to localize the train and update real-time operating conditions and constraints. The attackers can capture these data through their balise antenna and corrupt them through replay or false data injection attacks. This corrupted data can compromise train safety and jeopardize the entire train fleet operations. To overcome these attacks and to ensure the trustworthiness of balise and data integrity, this study proposes the permissioned blockchain-enabled two novel architectures for data transactions. Besides, each balise is equipped with a novel built-in blockchain cryptography algorithm to create its secret and public keys to provide the key integrity without needing third-party Certification Authority of conventional Public-Key Infrastructure based security systems. Each balise can create the unique signature using a ‘nonce’ signal sent by the train control system in the static architecture or using a self-generated ‘k-once’ secret key per train control system in dynamic architecture. Finally, the case studies are carried out to confirm the security sufficiency of the proposed architectures.

Elucidating the Mechanical Energy for Cyclization of a DNA Origami Tile

DNA origami has emerged as a versatile method to synthesize nanostructures with high precision. This bottom-up self-assembly approach can produce not only complex static architectures, but also dynamic reconfigurable structures with tunable properties. While DNA origami has been explored increasingly for diverse applications, such as biomedical and biophysical tools, related mechanics are also under active investigation. Here we studied the structural properties of DNA origami and investigated the energy needed to deform the DNA structures. We used a single-layer rectangular DNA origami tile as a model system and studied its cyclization process. This origami tile was designed with an inherent twist by placing crossovers every 16 base-pairs (bp), corresponding to a helical pitch of 10.67 bp/turn, which is slightly different from that of native B-form DNA (~10.5 bp/turn). We used molecular dynamics (MD) simulations based on a coarse-grained model on an open-source computational platform, oxDNA. We calculated the energies needed to overcome the initial curvature and induce mechanical deformation by applying linear spring forces. We found that the initial curvature may be overcome gradually during cyclization and a total of ~33.1 kcal/mol is required to complete the deformation. These results provide insights into the DNA origami mechanics and should be useful for diverse applications such as adaptive reconfiguration and energy absorption.

Can Business Architecture Modeling be Adaptive?

Businesses find the need to adapt to changes in the dynamic and competitive environment. However, they are confronted by static business architecture (BA) modeling and artefacts, which are less likely to adapt and, thus, quickly become obsolete. This article proposes a dynamic analytics-enabled adaptive BA modeling framework to address this concern. This research is performed using an action design research (ADR) method in collaboration with an Australian organization. The proposed approach has been implemented and evaluated using a banking organization as a case study.

Architecture of an object-oriented software implementation of algorithms for a full cycle of optical images of an unknown object processing

Formulation of the problem. In the process of software processing of highly distorted optical images (OI) of distant objects formed in ground-based telescopes, it becomes necessary to solve the problems of estimating the parameters of background radiation, filtering images of information objects, isolating them on the original OIs, and classifying them. This situation is typical when processing images of both a single object and moving space objects in images of the starry sky. In this regard, the authors, within the framework of the object-oriented approach, have developed a universal design pattern that makes it possible to implement the necessary processing from a single point of view.Objective. Development of a prototype for software implementation of algorithms for solving the aforementioned problems within the framework of the design pattern proposed by the authors. Examining the possibility of its use in the development of software architecture, with examples of OI processing obtained in natural conditions.Results. The article presents the results of the development of the software implementation algorithms architecture for processing distorted OIs of remote space objects formed in ground-based telescopes. Within the framework of the objectoriented programming paradigm using the Universal Modeling Language (UML), a description of the main components of static and dynamic architecture models is provided. For the static model, a new representation of the OI is proposed using a set of Boolean matrices. Within the framework of the dynamic model, a universal template (interface) has been developed for the implementation of algorithms for processing distorted OIs using UML activity diagrams. Its particularization is given when solving the problems of highlighting individual structural elements in the image of a single object and highlighting the tracks of moving objects in the image of the starry sky.Practical significance. The developed software implementations of low-quality optical image processing algorithms can be used in the modernization of software systems for ground-based telescopes.

Runtime verification for dynamic architectures

The architecture of a system captures important design decisions for the system. Over time, changes in a system's implementation may lead to violations of specific design decisions. This problem is common in industry and known as architectural erosion. Since it may have severe consequences on the quality of a system, research has focused on the development of tools and techniques to address the presented problem. As of today, most of the approaches to detect architectural erosion employ static analysis techniques. While these techniques are well-suited for the analysis of static architectures, they reach their limit when it comes to dynamic architectures. Thus, in this paper, we propose an alternative approach based on runtime verification: We describe techniques to formally specify constraints for dynamic architectures and algorithms to translate such specifications to instrumentation code and corresponding monitors. The approach is implemented in Eclipse/EMF, demonstrated through a running example, and evaluated using two case studies.

Analytics-Enabled Adaptive Business Architecture Modeling

In a changing competitive business landscape, organizations are challenged by traditional processes and static document-driven business architecture models or artifacts. This marks the need for a more adaptive and analytics-enabled approach to business architecture. This article proposes a framework for adaptive business architecture modeling to address this critical concern. This research is conducted in an Australian business architecture organization using the action design research (ADR) method. The applicability of the proposed approach was demonstrated through its use in a health insurance business architecture case study using the Tableau and Jalapeno business architecture modeling platform. The proposed approach seems feasible to process business architecture data for generating essential insights and actions for adaptation.

A hybrid model of generalized regression neural network and radial basis function neural network for wind power forecasting in Indian wind farms

Wind power generation is a major part of renewable energy but it suffers from uncertainty and variable nature of wind speed. In conquering the uncertainty, forecasting of wind power generation emerges as best solution. Accordingly, wind power forecasting assists the power grid to increase the grid reliability. In this paper, a generalized regression neural network (GRNN), a neural network of radial basis function (RBFN) and a hybrid of GRNN and RBFN are applied for estimation of wind power and their performance is compared in respect to short-term wind power forecasting. In comparison to RBFN, GRNN has simple and static architecture. Historical wind power and meteorological wind speed data of the year 2014 from Indian wind farms are employed for training and testing the neural networks. The research work carried in this article interprets that GRNN has consistently performed better than RBFN. The hybrid GRNN-RBFN has also depicted good accuracy in a week ahead wind power forecasting. In such cases, either GRNN or RBFN predicts with high errors the hybrid GRNN-RBFN is helpful in accurate wind power forecasting. The errors are calculated in terms of MAPE & RMSE. For GRNN approach MAPE ranges from 0.48 % to 10.53 % and RMSE ranges from 0.01 KW to 3.31 KW. Whereas the maximum MAPE is 25.6 % for RBFN and the maximum RMSE is much higher i.e 11.61KW. In fact, for any forecasting approach, reliability is a major concern. The work carried also includes reliability analysis of all three forecasting models proposed. To ensure the best performance of GRNN, confidence intervals of MAPE are computed for all three models implemented. Among all, GRNN is well perceived to be the best one with narrowest confidence intervals.