Smart Placement Research Articles

Smart placement of depolluting panels in urban areas as regards of the airflow using adjoint framework and district digital twin

A two-step numerical strategy based on a district digital twin is presented to efficiently deploy a limited number of depolluting panels in urban areas. In a diagnosis stage, a detailed pollutant concentration map is computed using CFD to identify critical highly polluted areas. Then, in a remediation stage, the optimal placement of depolluting panels as regards of urban airflow is determined to locally mitigate the air pollution in the exposed areas. For this purpose, a spatial sensitivity indicator calculated from an adjoint framework is proposed. The approach is applied to two real case studies: the full-scale laboratory district “Sense-City” under controlled conditions and a district area in Paris using realistic NOx traffic emission and wind conditions. In both case studies, it is shown that depolluting panels should be placed on a part of the sidewalks, the building facades and the roads adjacent to the sidewalks to reduce the high NOx concentration on some sidewalks and on first-floor building windows, thus preventing outdoor/indoor pollutant transfer. It is also proven that the proposed strategy is more efficient than a non-smart massive deployment of depolluting panels to improve the air quality in exposed areas. In addition to practical recommendations, this numerical strategy can provide a help-decision tool for city managers to design depolluting panels-based mitigation actions.

Electronic Technology for Wastewater Treatment and Clean Water Production

Water is essential for society. Due to excellent distribution systems for clean drinking water and wastewater, safe and reliable water transport is guaranteed. However, due to ageing network conditions, there is a need for extensive network monitoring and replacement strategies. There is a high demand for good insight into water mains and water distribution systems. A promising way to monitor our water transport involves various types of novel sensors, including strategies for the smart placement of these sensors, maximizing performance while minimizing costs. Furthermore, processing the increasingly large amount of sensor data can be done using Artificial Intelligence and sensor fusion techniques, yielding vastly increased information about the distribution mains.

The role of technology in assessment, documentation, and tracking of clinical competencies in radiography education

The role of technology in assessment, documentation, and tracking of clinical competencies in radiography education

Can smart nutrient applications optimize the plant's hidden half to improve drought resistance?

Global agriculture is challenged with achieving sustainable food security while the climate changes and the threat of drought increases. Much of the research attention has focused on above-ground plant responses with an aim to improve drought resistance. The hidden half, that is, the root system belowground, is receiving increasing attention as the interface of the plant with the soil. Because roots are a sensing organ for nutrients and moisture, we speculate that crop root system traits can be managed using smart nutrient application in order to increase drought resistance. Roots are known to be influenced both by their underlying genetics and also by responses to the environment, termed root plasticity. Though very little is known about the combined effect of water and nutrients on root plasticity, we explore the possibilities of root system manipulation by nutrient application. We compare the effects of different water or nutrient levels on root plasticity and its genetic regulation, with a focus on how this may affect drought resistance. We propose four primary mechanisms through which smart nutrient management can optimize root traits for drought resistance: (1) overall plant vigor, (2) increased root allocation, (3) influence specific root traits, and (4) use smart placement and timing to encourage deep rooting. In the longer term, we envision that beneficial root traits, including plasticity, could be bred into efficient varieties and combined with advanced precision management of water and nutrients to achieve agricultural sustainability.

Performance evaluation of distributed file systems for the phase-II upgrade of the ATLAS experiment at CERN

Over the next few years, the LHC will prepare for the upcoming High-Luminosity upgrade in which it is expected to deliver ten times more pp collisions. This will create a harsher radiation environment and higher detector occupancy. In this context, the ATLAS experiment, one of the general purpose experiments at the LHC, plans substantial upgrades to the detectors and to the trigger system in order to efficiently select events. Similarly, the Data Acquisition System (DAQ) will have to redesign the data-flow architecture to accommodate for the large increase in event and data rates. The Phase-II DAQ design involves a large distributed storage system that buffers data read out from the detector, while a computing farm (Event Filter) analyzes and selects the most interesting events. This system will have to handle 5.2 TB/s of input data for an event rate of 1 MHz and provide access to 3 TB/s of these data to the filtering farm. A possible implementation for such a design is based on distributed file systems (DFS) which are becoming ubiquitous among the big data industry. Features of DFS such as replication strategies and smart placement policies match the distributed nature and the requirements of the new data-flow system. This paper presents an up-to-date performance evaluation of some of the DFS currently available: GlusterFS, HadoopFS and CephFS. After characterization of the future data-flow systems workload, we report on small-scale raw performance and scalability studies. Finally, we conclude on the suitability of such systems to the tight constraints expected for the ATLAS experiment in phase-II and, in general, what benefits the HEP community can take from these storage technologies.

Location, Proximity, Affinity – The key factors in FaaS

The Function-as-a-Service paradigm emerged not only as a pricing technique, but also as a programming model promising to simplify developing to the cloud. Interestingly, while placing functions across hosts under the service platform is believed to be flexible, currently the available platforms pay little attention to co-locate connected functions, or data with the respective processing function in order to improve performance. Even though the local function invocation and data access might be an order of magnitude faster than their remote intra-cloud counterparts. In this paper, we therefore propose a Function-asa- Service platform design that reaps the performance benefits of co-location. We build the platform on WebAssembly, a secure and flexible tool for efficient local function invocations, and on a distributed in-memory database, which allows arbitrary data placement. On top we advocate smart placement strategies for function executions and data, decoupled from the functions. Hence we envision good horizontal scaling of functions while keeping the experienced processing latency to that of a single machine case.

Surrogate-based black-box optimisation via domain exploration and smart placement

In the era of digital twins, the need for accurately mimicking the reality has given rise to complex, black-box, compute-intensive models that are vital for simulating, analysing, and optimising physicochemical systems. In this work, we propose a novel surrogate-assisted approach for black-box optimisation, which uses efficient domain exploration and smart adaptive sample placement to escape local valleys (traps) and obtain a global minimum efficiently. Our iterative algorithm comprises two stages. The first stage constructs sub-regions based on Delaunay triangulations and selects the best for exploration. The second stage adds a new sample point to the best sub-region via optimisation. The two stages together balance domain exploration versus exploitation. The algorithmic framework is illustrated using the six-hump camel back function. An extensive numerical evaluation using twenty test functions (up to six variables) shows that the proposed algorithm exhibits superior performance against seven well-known commercial global optimisation algorithms including a surrogate-based approach.

Modeling Flow and Pressure Fields in Porous Media with High Conductivity Flow Channels and Smart Placement of Branch Cuts for Variant and Invariant Complex Potentials

A long overdue distinction between so-called variant and invariant complex potentials is proposed here for the first time. Invariant complex potentials describe physical flows where a switch of the real and imaginary parts of the function will still describe the same type of physical flow (but only rotated by π/2). Such invariants can be formulated with Euler’s formula to depict the same flow for any arbitrary orientation with respect to the coordinate system used. In contrast, variant complex potentials, when swapping their real and imaginary parts, will result in two fundamentally different physical flows. Next, we show that the contour integrals of the real and imaginary part of simple variant and invariant complex potentials generally do not generate any discernable branch cut problems. However, complex potentials due to the multiple superpositions of simple flows, even when invariant, may involve many options for selecting the branch cut locations. Examples of such branch cut choices are given for so-called areal doublets and areal dipoles, which are powerful tools to describe the streamlines and pressure fields for flow in porous media with enhanced permeability flow channels. After a discussion of the branch cut solutions, applications to a series of synthetic and field examples with enhanced permeability flow channels are given with examples of the streamline and pressure field solutions.

Advancements in data management services for distributed e-infrastructures: the eXtreme-DataCloud project

The development of data management services capable to cope with very large data resources is a key challenge to allow the future einfrastructures to address the needs of the next generation extreme scale scientific experiments. To face this challenge, in November 2017 the H2020 eXtreme DataCloud - XDC project has been launched. Lasting for 27 months and combining the expertise of eight large European research organisations, the project aims at developing scalable technologies for federating storage resources and managing data in highly distributed computing environments. The targeted platforms are the current and next generation e-Infrastructures deployed in Europe, such as the European Open Science Cloud (EOSC), the European Grid Infrastructure (EGI), and the Worldwide LHC Computing Grid (WLCG). The project is use-case driven with a multidisciplinary approach, addressing requirements from research communities belonging to a wide range of scientific domains: High Energy Physics, Astronomy, Photon and Life Science, Medical research. XDC is aimed at implementing data management scalable services, combining already established data management and orchestration tools, to address the following high level topics: policy driven data management based on Quality-of-Service, Data Life-cycle management, smart placement of data with caching mechanisms to reduce access latency, meta-data with no predefined schema handling, execution of pre-processing applications during ingestion, data management and protection of sensitive data in distributed e-infrastructures, intelligent data placement based on access patterns. This contribution introduces the project, presents the foreseen overall architecture and the developments that are being carried on to implement the requested functionalities.

A Novel Decode-Aware Compression Technique for Improved Compression and Decompression

With compressed bit streams, more configuration information can be stored using the same memory. The access delay is also reduced, because less bits need to be transferred through the memory interface. To measure the efficiency of bit stream compression, compression ratio (CR) is widely used as a metric. it is a major challenge to develop an efficient compression technique that can significantly reduce the bit stream size without sacrificing the decompression performance. Our approach combines the advantages of previous compression techniques with good compression ratio and those with fast decompression. This paper makes three important contributions. First, it performs smart placement of compressed bit streams to enable fast decompression of variable-length coding. Next, it selects bitmask-based compression parameters suitable for bit stream compression. Finally, it efficiently combines run length encoding and bitmask-based compression to obtain better compression and faster decompression.

Smart Placement of Security Devices in Cloud Data Center Network

The extensive use of cloud services and newly-evolved security threats have made most cloud service providers deploy a variety of security devices such as firewalls, Internet Protocol Security, and intrusion detection systems to control resource access based on the security requirements of the data center. Therefore, security requirements are becoming more fine-grained where the control of access depends on heterogeneous partition levels like filtering network traffic, Internet Protocol Security encryption-based traffic forwarding, and payload inspection. However, today, cloud service providers are looking to systematically harden security by incorporating multiple security devices in the network in a cost-effective way. This requires evaluating several alternative security architectures to satisfy both organizational security requirements and business constraints. In this paper, we present an automated framework to synthesize data center security configurations by exploring various security design alternatives to provide better in-depth defense for the cloud infrastructure. The main design alternatives use different patterns of isolation for different segments of the cloud infrastructure. In this work, we take a dummy data center topology, cloud service provider security (connectivity and isolation) requirements and business constraints (usability and cost) as input ,and synthesize a correct and optimal data center security strategy by way of determining the optimal placement of different security devices in the data center.

SPA: Smart Placement Approach for Cloud-service Datacenter Networks

Abstract Business disciplines expand rapidly, so does the tendency to rely on computer applications and its varying services to support such expansion. Often, this is achieved through introducing physical network infrastructures that provide the appropriate environ- ments to run such applications. The required services change rapidly, and accordingly its resource requirements. In most cases, this may require building new physical networks which could lead to low utilization rates and high service-costs. A promising ap- proach that is becoming increasingly popular to overcome such a problem is known as Virtual Datacenter Networks (VDNs). These VDNs are usually hosted over physical networks; overlaying its resources to gain the dynamic required services. Cloud-service Datacenter Networks (CDNs) can provide such a service under a delivery model that is called Infrastructure as a Service (IaaS). In this paper, we present a Smart Placement Approach (SPA) that provides for smart placement maps of VDNs over CDNs. In this, we point out that choosing the placement maps for such VDNs should satisfy its requirements while: maintaining load-balancing over the hosting CDNs, guaranteeing its Quality of Service (QoS) levels, and assuring low placement costs.

Multi-exemplar inhomogeneous texture synthesis

This paper presents a new framework for the semi-automatic synthesis of an inhomogeneous texture from a theoretically infinite number of input exemplars. Our algorithm generates a result with natural multiway transitions, maintaining a reasonable synthesis time to keep interactivity. We introduce intermediate textures between multiple exemplars, called ‘transition textures’ which enable our algorithm to produce smooth transitions between input exemplar patches. We also propose ‘image index correction’ to achieve a synthesis speed that is independent of the number of input exemplars by smart placement of transition-texture samples. Our algorithm is semiautomatic, spatially deterministic and well suited to acceleration via parallel processors. Also, our algorithm can easily control the synthesis result to design a texture visual with user interactions.

Large-area high-throughput synthesis of monolayer graphene sheet by Hot Filament Thermal Chemical Vapor Deposition

We report hot filament thermal CVD (HFTCVD) as a new hybrid of hot filament and thermal CVD and demonstrate its feasibility by producing high quality large area strictly monolayer graphene films on Cu substrates. Gradient in gas composition and flow rate that arises due to smart placement of the substrate inside the Ta filament wound alumina tube accompanied by radical formation on Ta due to precracking coupled with substrate mediated physicochemical processes like diffusion, polymerization etc., led to graphene growth. We further confirmed our mechanistic hypothesis by depositing graphene on Ni and SiO2/Si substrates. HFTCVD can be further extended to dope graphene with various heteroatoms (H, N, and B, etc.,), combine with functional materials (diamond, carbon nanotubes etc.,) and can be extended to all other materials (Si, SiO2, SiC etc.,) and processes (initiator polymerization, TFT processing) possible by HFCVD and thermal CVD.

Decoding-Aware Compression of FPGA Bitstreams

Bitstream compression is important in reconfigurable system design since it reduces the bitstream size and the memory requirement. It also improves the communication bandwidth and thereby decreases the reconfiguration time. Existing research in this field has explored two directions: efficient compression with slow decompression or fast decompression at the cost of compression efficiency. This paper proposes a novel decode-aware compression technique to improve both compression and de compression efficiencies. The three major contributions of this paper are: 1) smart placement of compressed bitstreams that can significantly decrease the overhead of decompression engine; 2) selection of profitable parameters for bitstream compression; and 3) efficient combination of bitmask-based compression and run length encoding of repetitive patterns. Our proposed technique outperforms the existing compression approaches by 15%, while our decompression hardware for variable-length coding is capable of operating at the speed closest to the best known field-programmable gate array-based decoder for fixed-length coding.

Optical Routing for 3-D System-On-Package

In this paper, we present the first optical router for 3-D system-on-package (SOP). Recent advances in optical device integration for SOP offer drastic advantages over electrical interconnects. We propose efficient algorithms for the construction of timing and congestion-driven waveguides taking into account the optical resource constraints. Our experimental results suggest that smart placement of waveguides coupled with other routing techniques can reduce electrical wirelength by 11% and improve performance by 23%, when a single optical layer is introduced for every placement layer.