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- Zhao, C., et al.
(författare)
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First performance results of the ALICE TPC Readout Control Unit 2
- 2016
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Ingår i: Journal of Instrumentation. - 1748-0221. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents the first performance results of the ALICE TPC Readout Control Unit 2 (RCU2). With the upgraded hardware typology and the new readout scheme in FPGA design, the RCU2 is designed to achieve twice the readout speed of the present Readout Control Unit. Design choices such as using the flash-based Microsemi Smartfusion2 FPGA and applying mitigation techniques in interfaces and FPGA design ensure a high degree of radiation tolerance. This paper presents the system level irradiation test results as well as the first commissioning results of the RCU2. Furthermore, it will be concluded with a discussion of the planned updates in firmware.
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- Abelev, Betty, et al.
(författare)
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Long-range angular correlations on the near and away side in p-Pb collisions at root S-NN=5.02 TeV
- 2013
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Ingår i: Physics Letters. Section B: Nuclear, Elementary Particle and High-Energy Physics. - : Elsevier BV. - 0370-2693. ; 719:1-3, s. 29-41
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Tidskriftsartikel (refereegranskat)abstract
- Angular correlations between charged trigger and associated particles are measured by the ALICE detector in p-Pb collisions at a nucleon-nucleon centre-of-mass energy of 5.02 TeV for transverse momentum ranges within 0.5 < P-T,P-assoc < P-T,P-trig < 4 GeV/c. The correlations are measured over two units of pseudorapidity and full azimuthal angle in different intervals of event multiplicity, and expressed as associated yield per trigger particle. Two long-range ridge-like structures, one on the near side and one on the away side, are observed when the per-trigger yield obtained in low-multiplicity events is subtracted from the one in high-multiplicity events. The excess on the near-side is qualitatively similar to that recently reported by the CMS Collaboration, while the excess on the away-side is reported for the first time. The two-ridge structure projected onto azimuthal angle is quantified with the second and third Fourier coefficients as well as by near-side and away-side yields and widths. The yields on the near side and on the away side are equal within the uncertainties for all studied event multiplicity and p(T) bins, and the widths show no significant evolution with event multiplicity or p(T). These findings suggest that the near-side ridge is accompanied by an essentially identical away-side ridge. (c) 2013 CERN. Published by Elsevier B.V. All rights reserved.
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- Alme, J., et al.
(författare)
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RCU2-The ALICE TPC readout electronics consolidation for Run2
- 2013
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Ingår i: Journal of Instrumentation. - 1748-0221. ; 8
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents the solution for optimization of the ALICE TPC readout for running at full energy in the Run2 period after 2014. For the data taking with heavy ion beams an event readout rate of 400 Hz with a low dead time is envisaged for the ALICE central barrel detectors during these three years. A new component, the Readout Control Unit 2 (RCU2), is being designed to increase the present readout rate by a factor of up to 2.6. The immunity to radiation induced errors will also be significantly improved by the new design.
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- Bhatti, A., et al.
(författare)
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Recognition and Classification of Handwritten Urdu Numerals Using Deep Learning Techniques
- 2023
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Ingår i: Applied Sciences. - : MDPI. - 2076-3417. ; 13:3
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Tidskriftsartikel (refereegranskat)abstract
- Urdu is a complex language as it is an amalgam of many South Asian and East Asian languages; hence, its character recognition is a huge and difficult task. It is a bidirectional language with its numerals written from left to right while script is written in opposite direction which induces complexities in the recognition process. This paper presents the recognition and classification of a novel Urdu numeral dataset using convolutional neural network (CNN) and its variants. We propose custom CNN model to extract features which are used by Softmax activation function and support vector machine (SVM) classifier. We compare it with GoogLeNet and the residual network (ResNet) in terms of performance. Our proposed CNN gives an accuracy of 98.41% with the Softmax classifier and 99.0% with the SVM classifier. For GoogLeNet, we achieve an accuracy of 95.61% and 96.4% on ResNet. Moreover, we develop datasets for handwritten Urdu numbers and numbers of Pakistani currency to incorporate real-life problems. Our models achieve best accuracies as compared to previous models in the literature for optical character recognition (OCR).
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- Gadallah, Adel S., et al.
(författare)
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Anti-Inflammatory Principles from Tamarix aphylla L. : A Bioassay-Guided Fractionation Study
- 2020
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Ingår i: Molecules. - : MDPI AG. - 1431-5157 .- 1420-3049. ; 25:13
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Tidskriftsartikel (refereegranskat)abstract
- Natural products have served as primary remedies since ancient times due to their cultural acceptance and outstanding biodiversity. To investigate whether Tamarix aphylla L. modulates an inflammatory process, we carried out bioassay-guided isolation where the extracts and isolated compounds were tested for their modulatory effects on several inflammatory indicators, such as nitric oxide (NO), reactive oxygen species (ROS), proinflammatory cytokine; tumour necrosis factor (TNF-alpha), as well as the proliferation of the lymphocyte T-cells. The aqueous ethanolic extract of the plant inhibited the intracellular ROS production, NO generation, and T-cell proliferation. The aqueous ethanolic crude extract was partitioned by liquid-liquid fractionation using n-hexane (n-C6H6), dichloromethane (DCM), ethyl acetate (EtOAc),n-butanol (n-BuOH), and water (H2O). The DCM and n-BuOH extracts showed the highest activity against most inflammatory indicators and were further purified to obtain compounds 1-4. The structures of 3,5-dihydroxy-4',7-dimethoxyflavone (1) and 3,5-dihydroxy-4-methoxybenzoic acid methyl ester (2) from the DCM extracts; and kaempferol (3), and 3-hydroxy-4-methoxy-(E)-cinnamic acid (4) from then-BuOH extract were elucidated by different spectroscopic tools, including MS, NMR, UV, and IR. Compound 2 inhibited the production of ROS and TNF-alpha, whereas compound 3 showed inhibitory activity against all the tested mediators. A better understanding of the potential aspect of Tamarix aphylla L. derivatives as anti-inflammatory agents could open the door for the development of advanced anti-inflammatory entities.
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- Anjum, Asad Ur Rehman, et al.
(författare)
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Sensitivity Analysis of Mathematical Model to Study the Effect of T Cells Infusion in Treatment of CLL
- 2020
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Ingår i: Mathematics. - : MDPI AG. - 2227-7390. ; 8:4
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we considered a mathematical model concerned with the treatment of Chronic Lymphocytic Leukemia (CLL) taking into account the effect of superficially infused T cells in this particular type of tumor. The model is described thoroughly by the system of non-linear differential equations explaining the interaction of naive, infected, cancer and immune cell population. The detailed sensitivity analysis with the application is the major part of this paper. The basic objective is to provide insight to how parameters' behavior varies model results by elaborating the results obtained from the application of sensitivity analysis. The sensitivity of the model was evaluated not only theoretically, but also with the help of a numerical approach, producing graphs providing better imminent of results. We argue that the application of the sensitivity analysis method endows an insight into how and which parameters are of primary significance in controlling the spread of leukemia.
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- Haq, M. U., et al.
(författare)
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Capsule Network with Its Limitation, Modification, and Applications—A Survey
- 2023
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Ingår i: Machine Learning and Knowledge Extraction. - : Multidisciplinary Digital Publishing Institute (MDPI). - 2504-4990. ; 5:3, s. 891-921
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Tidskriftsartikel (refereegranskat)abstract
- Numerous advancements in various fields, including pattern recognition and image classification, have been made thanks to modern computer vision and machine learning methods. The capsule network is one of the advanced machine learning algorithms that encodes features based on their hierarchical relationships. Basically, a capsule network is a type of neural network that performs inverse graphics to represent the object in different parts and view the existing relationship between these parts, unlike CNNs, which lose most of the evidence related to spatial location and requires lots of training data. So, we present a comparative review of various capsule network architectures used in various applications. The paper’s main contribution is that it summarizes and explains the significant current published capsule network architectures with their advantages, limitations, modifications, and applications.
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