Several epidemic and pandemic diseases have emerged over the last 20 years with increasing reach and severity. The current COVID-19 pandemic has affected most of the world’s population, causing ...millions of infections, hundreds of thousands of deaths, and economic disruption on a vast scale. The increasing number of casualties underlines an urgent need for the rapid delivery of therapeutics, prophylactics such as vaccines, and diagnostic reagents. Here, we review the potential of molecular farming in plants from a manufacturing perspective, focusing on the speed, capacity, safety, and potential costs of transient expression systems. We highlight current limitations in terms of the regulatory framework, as well as future opportunities to establish plant molecular farming as a global, de-centralized emergency response platform for the rapid production of biopharmaceuticals. The implications of public health emergencies on process design and costs, regulatory approval, and production speed and scale compared to conventional manufacturing platforms based on mammalian cell culture are discussed as a forward-looking strategy for future pandemic responses.
Smart Cities combine advances in Internet of Things, Big Data, Social Networks, and Cloud Computing technologies with the demand for cyber–physical applications in areas of public interest, such as ...Health, Public Safety, and Mobility. The end goal is to leverage the use of city resources to improve the quality of life of its citizens. Achieving this goal, however, requires advanced support for the development and operation of applications in a complex and dynamic environment. Middleware platforms can provide an integrated infrastructure that enables solutions for smart cities by combining heterogeneous city devices and providing unified, high-level facilities for the development of applications and services. Although several smart city platforms have been proposed in the literature, there are still open research and development challenges related to their scalability, maintainability, interoperability, and reuse in the context of different cities, to name a few. Moreover, available platforms lack extensive scientific validation, which hinders a comparative analysis of their applicability. Aiming to close this gap, we propose InterSCity, a microservices-based, open-source, smart city platform that enables the collaborative development of large-scale systems, applications, and services for the cities of the future, contributing to turn them into truly smart cyber–physical environments. In this paper, we present the architecture of the InterSCity platform, followed by a comprehensive set of experiments that evaluate its scalability. The experiments were conducted using a smart city simulator to generate realistic workloads used to assess the platform in extreme conditions. The experimental results demonstrate that the platform can scale horizontally to handle the highly dynamic demands of a large smart city while maintaining low response times. The experiments also show the effectiveness of the technique used to generate synthetic workloads.
•InterSCity platform microservices architecture provides elasticity and scalability.•Simulation-based method for realistic smart city workload generation.•Extensive analysis of the InterSCity architectural design points out its scalability.•Experimental results demonstrate the high scalability of the InterSCity platform.
In high performance computing environments, we observe an ongoing increase in the available number of cores. For example, the current TOP500 list reveals that nine clusters have more than 1 million ...cores. This development calls for re-emphasizing performance (scalability) analysis and speedup laws as suggested in the literature (e.g., Amdahl's law and Gustafson's law), with a focus on asymptotic performance. Understanding speedup and efficiency issues of algorithmic parallelism is useful for several purposes, including the optimization of system operations, temporal predictions on the execution of a program, the analysis of asymptotic properties, and the determination of speedup bounds. However, the literature is fragmented and shows a large diversity and heterogeneity of speedup models and laws. These phenomena make it challenging to obtain an overview of the models and their relationships, to identify the determinants of performance in a given algorithmic and computational context, and, finally, to determine the applicability of performance models and laws to a particular parallel computing setting. In this work, I provide a generic speedup (and thus also efficiency) model for homogeneous computing environments. My approach generalizes many prominent models suggested in the literature and allows showing that they can be considered special cases of a unifying approach. The genericity of the unifying speedup model is achieved through parameterization. Considering combinations of parameter ranges, I identify six different asymptotic speedup cases and eight different asymptotic efficiency cases. Jointly applying these speedup and efficiency cases, I derive eleven scalability cases, from which I build a scalability typology. Researchers can draw upon my suggested typology to classify their speedup model and to determine the asymptotic behavior when the number of parallel processing units increases. Also, the description of two computational experiments demonstrates the practical application of the model and the typology. In addition, my results may be used and extended in future research to address various extensions of my setting.
•We develop a generic speedup and efficiency model for computational parallelization.•The unifying model generalizes many prominent models suggested in the literature.•Asymptotic analysis extends existing speedup laws.•Asymptotic analysis allows explaining sublinear, linear and superlinear speedup.•Based upon asymptotic speedup and efficiency analyses, we build a scalability typology.
In this article, we propose the t-FDP model, a force-directed placement method based on a novel bounded short-range force (t-force) defined by Student's t-distribution. Our formulation is flexible, ...exerts limited repulsive forces for nearby nodes and can be adapted separately in its short- and long-range effects. Using such forces in force-directed graph layouts yields better neighborhood preservation than current methods, while maintaining low stress errors. Our efficient implementation using a Fast Fourier Transform is one order of magnitude faster than state-of-the-art methods and two orders faster on the GPU, enabling us to perform parameter tuning by globally and locally adjusting the t-force in real-time for complex graphs. We demonstrate the quality of our approach by numerical evaluation against state-of-the-art approaches and extensions for interactive exploration.
With product customisation and emerging business opportunities, small and medium manufacturing enterprises (SMEs) must find ways to collaborate and share competency in a trustable manner to survive a ...turbulent market. Therefore, service industry turns to the manufacturing industry and SMEs migrate to cloud manufacturing (CM) and ubiquitous manufacturing. However, existing platforms use centralised networking, which suffers from security, scalability and big-data problems. In this paper, we propose a blockchain-based platform as a trustable network to eradicate third-party problems, which can improve the scalability, security and big-data problems for SMEs. Our proposed platform is developed based on a consortium blockchain which provides a peer-to-peer communication network between the end user and the service provider. We improve existing consensus mechanism and communication protocol based on a cyber-physical system (CPS), via an autonomous agent. Firstly, we provide a review of cloud manufacturing, ubiquitous manufacturing and blockchain-based manufacturing approaches by highlighting the main problems. Then, the proposed platform, blockchain ubiquitous manufacturing (BCUM), is explained, based on its architecture, consensus algorithm and CPS, with the help of autonomous agent communication. The proposed platform has been developed for 3D printing companies which are geographically distributed and tested based on network performance and three practical scenarios.
This article describes a soft robot based onboundary constrained modular subunits. The loop-shaped robot consists of a granule-filled elastic toroidal membrane with a series of modular subunit robots ...attached to its exterior. The robot can operate both as a soft robot to conform to external objects or navigate through narrow corridors and as a rigid robot by jamming its internal granules using a vacuum. The jammed state is useful for exerting forces on the environment in object manipulation or locomotion tasks. This article describes the robot's design, object handling capabilities, locomotion, shape formation, and ability to navigate narrow corridors. We also present computationally efficient control methodologies used for self-reconfiguration and target tracking, which enable scaling the number of subunits to create larger systems. The robot's scalability and the control methodologies are verified through simulation with ProjectChrono, a multibody dynamic simulation platform. All other results are obtained experimentally.
High-performance self-rectifying memristor (SRM)-based three-dimensional (3D) architecture with high integration density is an ideal hardware platform for 3D in- memory computing (IMC). In this work, ...we fabricated Pt/HfO2/TaO<inline-formula> <tex-math notation="LaTeX">_{X} </tex-math></inline-formula>/Ta SRM-based 2-layer <inline-formula> <tex-math notation="LaTeX">8\times16 </tex-math></inline-formula> vertical stacked 3D memristor arrays with split-cell structure. The specially designed structure of the 3D memristor array doubles the integration density of the traditional vertical-stacked resistive random access memory (V-RRAM) and further reduces the bit cost. The SRMs in the 3D memristor arrays show high uniformity,<inline-formula> <tex-math notation="LaTeX">10^{{4}} </tex-math></inline-formula> nonlinearity, and<inline-formula> <tex-math notation="LaTeX">10^{{4}} </tex-math></inline-formula> rectification ratio. The SRMs can be fast operated repeatedly at Set (4.5 V/200 ns) and Reset (−2 V/100 ns) pulses for more than <inline-formula> <tex-math notation="LaTeX">10^{{5}} </tex-math></inline-formula> cycles resulting in the <30 fJ switching energy. Excellent device-to-device uniformity verifies the high reliability and stability of our fabrication processes. Based on the measured data, we evaluate that, on the premise of 10% read margin, the maximum array size can reach 1.56 Gbit. Our work advances the development of 3D integration and even 3D IMC.
In this letter, to address the high-precision localization issue in wide-area three-dimensional (3D) scenes, we investigate a scalable fingerprint-based localization scheme for cell-free radio access ...network (CF-RAN). Based on the dynamic correlation mechanism, we first propose a scalable angle-space channel power matrix (SASCPM) fingerprint, which only consumes finite overhead and is closely related to 3D location. Then, based on the partially distributed collaboration mechanism, we design a scalable fingerprint-based localization (FedSLoc) framework for 3D CF-RAN. In FedSLoc, the centralized localization pressure in the cloud is sunk to multiple edge distributed units, which are equipped with local models to enable localization with limited resource consumption. Moreover, in FedSLoc, we use a modified federated block coordinate descent algorithm to solve the feature-partitioned collaborative learning issue with low communication overheads. By extensive simulations, the proposed framework is proven to achieve scalable high-precision localization.
Given a large graph, like a computer communication network, which k nodes should we immunize (or monitor, or remove), to make it as robust as possible against a computer virus attack? This problem, ...referred to as the node immunization problem, is the core building block in many high-impact applications, ranging from public health, cybersecurity to viral marketing. A central component in node immunization is to find the best k bridges of a given graph. In this setting, we typically want to determine the relative importance of a node (or a set of nodes) within the graph, for example, how valuable (as a bridge) a person or a group of persons is in a social network. First of all, we propose a novel `bridging' score Dλ, inspired by immunology, and we show that its results agree with intuition for several realistic settings. Since the straightforward way to compute Dλ is computationally intractable, we then focus on the computational issues and propose a surprisingly efficient way (O(nk 2 + m)) to estimate it. Experimental results on real graphs show that (1) the proposed `bridging' score gives mining results consistent with intuition; and (2) the proposed fast solution is up to seven orders of magnitude faster than straightforward alternatives.
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