The SUT‐NANOTEC‐SLRI beamline was constructed in 2012 as the flagship of the SUT‐NANOTEC‐SLRI Joint Research Facility for Synchrotron Utilization, co‐established by Suranaree University of Technology ...(SUT), National Nanotechnology Center (NANOTEC) and Synchrotron Light Research Institute (SLRI). It is an intermediate‐energy X‐ray absorption spectroscopy (XAS) beamline at SLRI. The beamline delivers an unfocused monochromatic X‐ray beam of tunable photon energy (1.25–10 keV). The maximum normal incident beam size is 13 mm (width) × 1 mm (height) with a photon flux of 3 × 108 to 2 × 1010 photons s−1 (100 mA)−1 varying across photon energies. Details of the beamline and XAS instrumentation are described. To demonstrate the beamline performance, K‐edge XANES spectra of MgO, Al2O3, S8, FeS, FeSO4, Cu, Cu2O and CuO, and EXAFS spectra of Cu and CuO are presented.
Detailed information is provided regarding the current status and performance of the SUT‐NANOTEC‐SLRI beamline, at the Synchrotron Light Research Institute (Thailand), for X‐ray absorption spectroscopy in the photon energy range 1.25–10 keV.
Abstract
Vγ9Vδ2+ T cell–targeted immunotherapy is of interest to harness its MHC-independent cytotoxic potential against a variety of cancers. Recent studies have identified heterodimeric ...butyrophilin (BTN) 2A1 and BTN3A1 as the molecular entity providing “signal 1” to the Vγ9Vδ2 TCR, but “signal 2” costimulatory requirements remain unclear. Using a tumor cell–free assay, we demonstrated that a BTN2A1/3A1 heterodimeric fusion protein activated human Vγ9Vδ2+ T cells, but only in the presence of costimulatory signal via CD28 or NK group 2 member D. Nonetheless, addition of a bispecific γδ T cell engager BTN2A1/3A1-Fc-CD19scFv alone enhanced granzyme B–mediated killing of human CD19+ lymphoma cells when cocultured with Vγ9Vδ2+ T cells, suggesting expression of costimulatory ligand(s) on tumor cells is sufficient to satisfy the “signal 2” requirement. These results highlight the parallels of signal 1 and signal 2 requirements in αβ and γδ T cell activation and demonstrate the utility of heterodimeric BTNs to promote targeted activation of γδ T cells.
An edge detection is important for its reliability and security that delivers a better understanding of object recognition in applications of computer vision such as pedestrian detection, face ...detection, and video surveillance. This paper introduced two fundamental limitations encountered in edge detection: edge connectivity and edge thickness, those have been used by various developments in the state-of-the-art. An optimal selection of the threshold for effectual edge detection has constantly been a key challenge in computer vision. Therefore, a robust edge detection algorithm using multiple threshold approach (B-Edge) is proposed to cover both the limitations. The majorly used canny edge operator focuses on two thresholds selections and still witnesses few gaps for optimal results. To handle the loopholes of canny edge operator our method selects the simulated triple thresholds that targets to the prime issues of edge detection: image contrast, effective edge pixels selection, errors handling and similarity to the ground truth. The qualitative and quantitative experimental evaluation demonstrates that our edge detection method outperforms competing algorithms for mentioned issues. The proposed approach endeavors an improvement for both grayscale and colored images. fails using local information around edge terminals for effective edge linking.
To satisfy the increasing demand of mobile data traffic and meet the stringent requirements of the emerging Internet-of-Things (IoT) applications such as smart city, healthcare, and augmented/virtual ...reality (AR/VR), the fifth-generation (5G) enabling technologies are proposed and utilized in networks. As an emerging key technology of 5G and a key enabler of IoT, multiaccess edge computing (MEC), which integrates telecommunication and IT services, offers cloud computing capabilities at the edge of the radio access network (RAN). By providing computational and storage resources at the edge, MEC can reduce latency for end users. Hence, this article investigates MEC for 5G and IoT comprehensively. It analyzes the main features of MEC in the context of 5G and IoT and presents several fundamental key technologies which enable MEC to be applied in 5G and IoT, such as cloud computing, software-defined networking/network function virtualization, information-centric networks, virtual machine (VM) and containers, smart devices, network slicing, and computation offloading. In addition, this article provides an overview of the role of MEC in 5G and IoT, bringing light into the different MEC-enabled 5G and IoT applications as well as the promising future directions of integrating MEC with 5G and IoT. Moreover, this article further elaborates research challenges and open issues of MEC for 5G and IoT. Last but not least, we propose a use case that utilizes MEC to achieve edge intelligence in IoT scenarios.
With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billions of devices and ...humans to bring promising advantages for us. With this growth, fog computing, along with its related edge computing paradigms, such as multi-access edge computing (MEC) and cloudlet, are seen as promising solutions for handling the large volume of security-critical and time-sensitive data that is being produced by the IoT. In this paper, we first provide a tutorial on fog computing and its related computing paradigms, including their similarities and differences. Next, we provide a taxonomy of research topics in fog computing, and through a comprehensive survey, we summarize and categorize the efforts on fog computing and its related computing paradigms. Finally, we provide challenges and future directions for research in fog computing.
Objectives
To determine predictors for long‐term outcome in high‐risk patients undergoing transcatheter edge‐to‐edge mitral valve repair (TMVR) for severe mitral regurgitation (MR).
Background
There ...is no data on predictors of long‐term outcome in high‐risk real‐world patients.
Methods
From August 2009 to April 2011, 126 high‐risk patients deemed inoperable were treated with TMVR in two high‐volume university centers.
Results
MR could be successfully reduced to grade ≤2 in 92.1% of patients (116/126 patients). Long‐term clinical follow‐up up to 5 years (95.2% follow‐up rate) revealed a mortality rate of 35.7% (45/126 patients). Repeat mitral valve treatment (surgery or intervention) was needed in 19 patients (15.1%). Long‐term clinical improvement was demonstrated with 69% of patients being in NYHA class ≤II. In a multivariable Cox regression analysis, the post‐procedural grade of MR (hazard ratio HR 1.55 per grade, P = 0.035), the left ventricular ejection fraction (HR 0.58 for difference between 75th and 25th percentile, P = 0.031) and the glomerular filtration rate (HR 0.33 for 75th vs 25th percentile, P < 0.001) were independent predictors for long‐term mortality. Patients with primary MR and a post‐procedural MR grade ≤1 had the most favorable long‐term outcome.
Conclusions
This study determines predictors of long‐term clinical outcome after TMVR and demonstrates that the grade of residual MR determines long‐term survival. Our data suggest that it might be of benefit reducing residual MR to the lowest possible MR grade using TMVR—especially in selected high‐risk patients with primary MR who are not considered as candidates for surgical MVR.
► A method for fully-automatic crack detection from pavement images. ► A geodesic shadow-removal algorithm that can remove the pavement shadows while preserve the cracks. ► A sequential ...implementation of ball voting and stick voting that enhances the crack curves. ► An MST construction and edge pruning for reducing false positives. ► A collection of 206 pavement images for performance evaluation.
Pavement cracks are important information for evaluating the road condition and conducting the necessary road maintenance. In this paper, we develop
CrackTree, a fully-automatic method to detect cracks from pavement images. In practice, crack detection is a very challenging problem because of (1) low contrast between cracks and the surrounding pavement, (2) intensity inhomogeneity along the cracks, and (3) possible shadows with similar intensity to the cracks. To address these problems, the proposed method consists of three steps. First, we develop a geodesic shadow-removal algorithm to remove the pavement shadows while preserving the cracks. Second, we build a crack probability map using tensor voting, which enhances the connection of the crack fragments with good proximity and curve continuity. Finally, we sample a set of crack seeds from the crack probability map, represent these seeds by a graph model, derive minimum spanning trees from this graph, and conduct recursive tree-edge pruning to identify desirable cracks. We evaluate the proposed method on a collection of 206 real pavement images and the experimental results show that the proposed method achieves a better performance than several existing methods.
Nowadays, benefit from more powerful edge computing devices and edge artificial intelligence (edge-AI) could be introduced into Internet of Things (IoT) to find the knowledge derived from massive ...sensory data, such as cyber results or models of classification, and detection and prediction from physical environments. Heterogeneous edge-AI devices in IoT will generate isolated and distributed knowledge slices, thus knowledge collaboration and exchange are required to complete complex tasks in IoT intelligent applications with numerous selfish nodes. Therefore, knowledge trading is needed for paid sharing in edge-AI enabled IoT. Most existing works only focus on knowledge generation rather than trading in IoT. To address this issue, in this paper, we propose a peer-to-peer (P2P) knowledge market to make knowledge tradable in edge-AI enabled IoT. We first propose an implementation architecture of the knowledge market. Moreover, we develop a knowledge consortium blockchain for secure and efficient knowledge management and trading for the market, which includes a new cryptographic currency knowledge coin, smart contracts, and a new consensus mechanism proof of trading. Besides, a noncooperative game based knowledge pricing strategy with incentives for the market is also proposed. The security analysis and performance simulation show the security and efficiency of our knowledge market and incentive effects of knowledge pricing strategy. To the best of our knowledge, it is the first time to propose an efficient and incentive P2P knowledge market in edge-AI enabled IoT.
Mobile target tracking with artificial intelligence (AI) approaches such as deep reinforcement learning (DRL) in edge-assisted Internet of Things (Edge-IoT) platform can be promising. In this ...article, we propose DRLTrack , a framework for target tracking with a collaborative DRL called C-DRL in Edge-IoT with the aim to obtain two major objectives: high quality of tracking (QoT) and resource-efficient network performance. In DRLTrack , a huge number of IoT devices are employed to collect data about a target of interest. One or two edge devices in the network coordinate with a group of IoT devices and collaboratively detect the target by using the C-DRL approach and form an area around the target by the group of IoT devices. To maintain such an area during the tracking time, we employ a deep Q-network to track the target from one group to another. An EdgeAI sitting on the top of the edge devices has the control of the C-DRL approach during tracking and can identify a sequence of tracks. DRLTrack is said to be trustworthy as it shows trustworthy performance in terms of QoT, dynamic environments, and even under certain cyberattacks. We validate the performance of DRLTrack considering the objectives through simulations and it demonstrates superior performance compared with existing work.
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