Miracle-dz/UniFAR-FTUs huggingface dataset:
Microstructure and mechanical characterizations of graphene nanoplatelets-reinforced Mg–Sr–Ca alloy as a novel composite in structural and biomedical applications

In this study, the novel composites were fabricated by the introduction of Mg-0.3Sr-0.3Ca alloy as the matrix and addition of different amounts of graphene nanoplatelets (0.1, 0.2, and 0.4 wt.%) as reinforcement using a stir casting technique followed by homogenization and extrusion in order to improve the mechanical properties of the base alloy. Optimum weight percent of adding graphene nanoplatelets was 0.2 wt. %. The addition of 0.2 wt. % graphene nanoplatelets in the extruded Mg–Sr–Ca alloy led to the grain refinement (∼36%), the decrease of anisotropy (∼14%) and the lowest twin formation. Moreover, the tensile and compressive yield strengths and tensile and compressive fracture strains of extruded Mg-0.3Sr-0.3Ca/0.2GNP composite were enhanced by 22.8%, 66.7%, 43.1% and 28%, respectively. The load transfer was significant strengthening mechanism. The uniform dispersion of graphene nanoplatelets followed by the increase of non-basal slip and grain refinement improved tensile fracture strain. In addition to maintained factors, the increase of compressive fracture strain in the extruded Mg-0.3Sr-0.3Ca/0.2GNP composite was affected by local stresses caused by twins which resulted non-basal slip and conserved basal slip due to presence of twins. Simultaneously, enhancement of the strengthening and elongation efficiencies in both tensile and compressive tests was achieved in Mg-0.3Ca-0.3Sr/0.2GNP. The biocorrosion behavior of extruded Mg-0.3Sr-0.3Ca/0.2GNP composite was promoted by 11% compared with Mg-0.3Sr-0.3Ca alloy. Comparative plots indicated that the fabricated materials can be introduced as a new class of composites for the purpose of structural as well as biomedical applications.
RAPID IDENTIFICATION OF ABIOTIC STRESS (FROST) IN IN-FILED MAIZE CROP USING UAV REMOTE SENSING

Abstract. Stress in the crop not only decreases the production but can also have devastating consequences for farmers whose life depends upon the healthy crops. In recent time (January 2018) a such abiotic stress event (hoar frost) was experienced at ICAR research complex experimental filed, Ri-Bhoi district of Meghalaya on standing Maize crop. Therefore, remote sensing (Multispectral UAV- Unmanned Aerial Vehicle) technology were used to detect the effect of frost on in-filed Maize crop. Two set of multispectral data (before frost and after frost) with four advanced machine learning techniques viz. Random Forest (RF), Random Committee (RC), Support Vector Machine (SVM) and Artificial Neural Network were employed for detection of stress free crop and stressed crop due to frost. Results revealed that all the four methods of classification could able to identify / detect stress-free vs. stressed crops at satisfactory level. However, among the classifiers RF achieved relatively higher overall accuracy (OA&thinsp;=&thinsp;86.47%) with Kappa Indexanalysis (KIA&thinsp;=&thinsp;0.80) and found very cost effective in context of computational cost (time complexity&thinsp;=&thinsp;0.08 Seconds) to train the model. In addition, we have also recorded the area of each classes and found that after frost stress-free area (36.01% of all over filed) is decreased by 11% in comparison of before frost (25.036% of all over filed). Based on the results we can suggest that the RF ensemble classification method can be used for further other crop classification in order to estimate the yield, detect the condition, monitoring the health etc.
An Out-of-Order Packet Processing Algorithm of RoCE Based on Improved SACK

RoCE (RDMA over commodity Ethernet) combines the go-back-N retransmission mechanism, priority-based flow control (PFC) and congestion control (CC) algorithms to achieve the low latency, low CPU overhead and high bandwidth required by data center networks (DCN). However, when faced with scenarios such as multi-path and adaptive routing that may lead to out-of-order packets, RoCE is prone to serious throughput degradation. This paper proposes an improved Selective Acknowledgement (SACK) algorithm for RoCE (SACK-RoCE) to solve the above problem. The SACK-RoCE includes three mechanisms: packet trace, out-of-order packet detection, and lost packets retransmission. In simulation, we test the performance of the SACK-RoCE in both single flow and real-world flows environment. The SACK-RoCE improves the throughput by 7 times compared to RoCE and can almost keep the number of retransmission times consistent with the number of lost packets. As for real loads, the SACK-RoCE outperforms the improved RoCE NIC (IRN) by 12.05%∼22.32% on average FCT, tail FCT and average slowdown metrics. In addition, the SACK-RoCE adds only 315 bits of memory usage per link, which makes the algorithm easy to deploy.
Fault Fiagnosis Method of Micro-service Link based on QoS Reliability Comparison

In recent years, cloud native technology represented by micro-services is the main technical way to help enterprises realize digital transformation. However, while micro-service technology provides various convenience benefits, it also brings various operation and maintenance problems such as difficult fault diagnosis. In view of the large number of micro-services and long call links, which make it difficult to locate user fault problems, this paper carries out research on the key technologies of micro-service link enhancement based on QoS measurement, focuses on the fault diagnosis of micro-service links, and puts forward an end-to-end service call link fault diagnosis method. Through the comparison of reliability similarity of micro-service links, it can realize the rapid location of fault types and fault locations, Experiments show that the accuracy of this method to judge the fault type and location is more than 90%, which can meet the needs of developers to locate faults quickly in the production environment.
Rigid Medical Image Registration Using Learning-Based Interest Points and Features

: For image-guided radiation therapy, radiosurgery, minimally invasive surgery, endoscopy and interventional radiology, one of the important techniques is medical image registration. In our study, we propose a learning-based approach named “FIP-CNNF” for rigid registration of medical image. Firstly, the pixel-level interest points are computed by the full convolution network (FCN) with self-supervise. Secondly, feature detection, descriptor and matching are trained by convolution neural network (CNN). Thirdly, random sample consensus (Ransac) is used to filter outliers, and the transformation parameters are found with the most inliers by iteratively fitting transforms. In addition, we propose “TrFIP-CNNF” which uses transfer learning and fine-tuning to boost performance of FIP-CNNF. The experiment is done with the dataset of nasopharyngeal carcinoma which is collected from West China Hospital. For the CT-CT and MR-MR image registration, TrFIP-CNNF performs better than scale invariant feature transform (SIFT) and FIP-CNNF slightly. For the CT-MR image registration, the precision, recall and target registration error (TRE) of the TrFIP-CNNF are much better than those of SIFT and FIP-CNNF, and even several times better than those of SIFT. The promising results are achieved by TrFIP-CNNF especially in the multimodal medical image registration, which demonstrates that a feasible approach can be built to improve image registration by using FCN interest points and CNN features.
Effect of Uniaxial Strain on Thermal Conductivity of Graphene

In this study, we investigate the effect of uniaxial strain on the thermal conductivity of zigzag graphene (ZG) and armchair graphene (AG). It is observed that the thermal conductivity reduces with strain in ZG and AG. For a given strain, the maximum reduction in thermal conductivity is observed in AG. The tensile strain causes a larger deformation of bonds in AG whereas a minor variation in bond angles is observed. This causes a high reduction of thermal conductivity in AG. DOS (Density of States) and Spectral Energy Density (SED) curves are calculated to study the phonon transport in graphene. It is found that due to strain, high frequency phonon modes undergo a red shift whereas blue shift is observed in the out-of-plane phonon modes. The phonon group velocity and lifetimes of in-plane acoustic modes get reduced because of strain and hence their contribution to the thermal conductivity reduces. But in the ZA modes, strain increases the phonon group velocities and phonon lifetimes. For unstrained graphene, in-plane acoustic modes contribute nearly 60-70% of thermal conductivity in ZG and AG. However, when graphene is strained their contribution sharply reduces and the contribution by ZA mode increases and is nearly 40-60% of the thermal conductivity. But the contribution by TA modes to the thermal conductivity is unaffected by strain. Phonon Mean Free Path (MFP) calculations indicate that MFP of phonon in strained graphene increases due to increase in the wavelength of low frequency ZA phonons. The MFP of phonons is found to be higher in AG than in ZG in both strained and unstrained conditions.
Enhancing Cyber Security in IoT Systems using FL-based IDS with Differential Privacy

Nowadays, IoT networks and devices exist in our everyday life, capturing and carrying unlimited data. However, increasing penetration of connected systems and devices implies rising threats for cybersecurity with IoT systems suffering from network attacks. Artificial Intelligence (AI) and Machine Learning take advantage of huge volumes of IoT network logs to enhance their cybersecurity in IoT. However, these data are often desired to remain private. Federated Learning (FL) provides a potential solution which enables collaborative training of attack detection model among a set of federated nodes, while preserving privacy as data remain local and are never disclosed or processed on central servers. While FL is resilient and resolves, up to a point, data governance and ownership issues, it does not guarantee security and privacy by design. Adversaries could interfere with the communication process, expose network vulnerabilities, and manipulate the training process, thus affecting the performance of the trained model. In this paper, we present a federated learning model which can successfully detect network attacks in IoT systems. Moreover, we evaluate its performance under various settings of differential privacy as a privacy preserving technique and configurations of the participating nodes. We prove that the proposed model protects the privacy without actually compromising performance. Our model realizes a limited performance impact of only ∼ 7% less testing accuracy compared to the baseline while simultaneously guaranteeing security and applicability.
Path Selection Based Branching for Coarse Grained Reconfigurable Arrays

Coarse Grain Reconfigurable Arrays (CGRAs) are promising accelerators capable of achieving high performance at low power consumption. While CGRAs can efficiently accelerate loop kernels, accelerating loops with control flow (loops with if-then-else structures) is quite challenging. Techniques that handle control flow execution in CGRAs generally use predication. Such techniques execute both branches of an if-then-else structure and select outcome of either branch to commit based on the result of the conditional. This results in poor utilization of CGRA′s computational resources. Dual-issue scheme which is the state of the art technique for control flow fetches instructions from both paths of the branch and selects one to execute at runtime based on the result of the conditional. This technique has an overhead in instruction fetch bandwidth. In this thesis, to improve performance of control flow execution in CGRAs, I propose a solution in which the result of the conditional expression that decides the branch outcome is communicated to the instruction fetch unit to selectively issue instructions from the path taken by the branch at run time. Experimental results show that my solution can achieve 34.6% better performance and 52.1% improvement in energy efficiency on an average compared to state of the art dual issue scheme without imposing any overhead in instruction fetch bandwidth.
The LArge‐n Seismic Survey in Oklahoma (LASSO) Experiment

In 2016, the U.S. Geological Survey deployed >1800 vertical-component nodal seismometers in Grant County, Oklahoma, to study induced seismic activity associated with production of the Mississippi limestone play. The LArge‐n Seismic Survey in Oklahoma (LASSO) array operated for approximately one month, covering a 25 km by 32 km region with a nominal station spacing of ∼400 m. Primary goals of the deployment were to detect microseismic events not captured by the sparser regional network stations and to provide nearly unaliased records of the seismic wavefield. A more complete record of earthquakes allows us to map the spatiotemporal evolution of induced event sequences and illuminates the structures on which the events occur. Dense records of the seismic wavefield also provide improved measurements of the earthquake source, including focal mechanisms and stress drops. Taken together, we can use these findings to glean insights into the processes that induce earthquakes. Here, we describe the array layout, features of the nodal sensors, data recording configurations, and the field deployment. We also provide examples of earthquake waveforms recorded by the array to illustrate data quality and initial observations. LASSO array data provide a significant resource for understanding the occurrence of earthquakes induced by wastewater disposal.
Applying visual routines in a cognitive model of visual analysis

Through years of experience, expert image analysts acquire knowledge which allows them to perform an in-depth analysis of images quickly and efficiently. This research aims to build a humanlike framework and cognitive model of visual analysis based on the tacit visual knowledge of expert image analysts in medicine. Gaze tracking is used to discover the cognitive processes of radiologic technologists during their evaluation of x-ray scans. The resulting gaze patterns can be used to create visual routines which extract meaningful information from low-level image features and make them available to high-level cognitive processes. This method achieves a functional prototype of visual analysis within the biological, computational and memory limitations of the human visual system. The routines from this framework can be used to understand expert cognition, train new analysts, perform automated analysis, and potentially provide an informatics tool for medical error prevention.
Characterization of Ambient Noise

An Air Force sponsor is interested in improving an acoustic detection model by providing better estimates on how to characterize the background noise of various environments. This would inform decision makers on the probability of acoustic detection of different systems of interest given different levels of noise. Data mining and statistical learning techniques are applied to a National Park Service acoustic summary data set to find overall trends over varying environments. Linear regression, conditional inference trees, and random forest techniques are discussed. Findings indicate only sixteen geospatial variables at different resolutions are necessary to characterize the first ten 1 3 octave band frequencies of the L90 band using just the linear regression. The accuracy of the regression model is within 2 to 6 decibels and depends on the frequency of interest. This research is the first of its kind to apply multiple linear regression and a conditional inference tree to the national park service acoustic dataset for insights on predicting noise levels with dramatically less variables than needed in random forest algorithms. Recommended next steps are to supplement the national park service dataset with more geographic information system variables in common global databases, not unique to the United States.
Lens-free on-chip microscopy: theory, advances, and applications

Wide field-of-view (FOV) and high-resolution is one of the goals of optical microscopy. However, limited by the optical design in traditional optical microscopes, the space bandwidth product (SBP) is generally in the order of megapixels, and thus, high -resolution and wide FOV cannot be achieved at the same time. On the other hand, complex optical systems have also made microscopes increasingly expensive, cumbersome, complex and difficult to maintain, greatly limiting their promotion and application. Lensfree on-chip microscopy is a new computational imaging technology: without the imaging lens to focus, the sample is directly attached to the imaging sensor to record the diffraction patterns and the object information can be achieved with the corresponding reconstructed method. Due to its wide FOV, high-resolution, label-free detection, low-cost, perfect portability and three-dimensional (3D) imaging, the lensfree on -chip microscope is expected to expand the boundaries of traditional 第 48卷第 6期 红外与激光工程 2019年 6月 Vol.48 No.6 Infrared and Laser Engineering Jun. 2019 收稿日期院2019-01-10曰 修订日期院2019-02-20 基金项目院 国 家 自 然 科 学 基 金 (61722506,11574152)曰 江 苏 省 杰 出 青 年 基 金 (BK20170034)曰 江 苏 省 重 点 研 发 计 划 项 目 (BE2017162)曰 江 苏 省 野333 工 程 冶 科 研 项 目 资 助 计 划 (BRA2016407)曰 南 京 理 工 大 学 自 主 科 研 基 金 (30917011204) 作者简介院 张 佳 琳 (1993-)袁 女 袁 博 士 生 袁 主 要 从 事 无 透 镜 显 微 成 像 方 面 的 研 究 遥 Email: JialinZhang@njust.edu. cn 导师简介院 陈 钱 (1964-)袁 男 袁 教 授 袁 博 士 生 导 师 袁 博 士 袁 主 要 从 事 光 谱 成 像 与 智 能 感 知 等 方 面 的 研 究 遥 Email: chenqian@njust.edu. cn 通讯作者院 左 超 (1987-)袁 男 , 教 授 袁 博 士 生 导 师 袁 博 士 袁 主 要 从 事 计 算 光 学 显 微 成 像 与 快 速 三 维 传 感 等 方 面 的 研 究 遥 Email: zuochao@njust.edu. cn 红外与激光工程 第 6期 www.irla. cn 第 48卷
Development and Application of Information Verification and Transmission Experimental System based on Haiming Verification Code

In this paper, we study how to realize online simulation experiment based on Web in the experimental teaching of computer composition principle course. Taking the information verification and transmission experiment based on Haiming verification code as an example, this paper introduces the system development and design technology, and the design of experimental steps, so that students can master the processing process of information input, transcoding storage, verification encoding and decoding, and interference transmission in an interactive and immersive way, and visualize the internal information processing process of components. The idea of new teaching, new experiment and new future is realized through virtual simulation experiment teaching system.
Shielding effectiveness of reverberant enclosures containing printed circuit boards

Shielded enclosures are a convenient and widely used method of protecting electronic equipment from an undesirable electromagnetic environment. The amount of protection the enclosure provides is known as Shielding Effectiveness (SE). Generally, the SE is given for an empty enclosure. However, the contents of an enclosure, such as Printed Circuit Boards (PCBs), will change the SE. In this thesis, the absorption and transmission of a set of PCBs has been investigated and this information used to predict the SE of populated enclosures. Power balance modelling was used to predict the SE of a number of configurations of a single cavity and two cavity enclosure with and without contents. These results were compared to measurements made in a reverberation chamber (RC). Measurements and analysis were made of the absorption of individual PCBs and the absorption was shown to change when PCBs were stacked together or positioned close to the RC wall. The transmission through a set of PCBs was measured and found to be low enough to ignore in the models. The results presented in this thesis have shown how the absorption of a PCB can change in different situations and how this information can be used in power balance modelling to estimate the SE of an enclosure. Further investigation into the absorption and transmission of PCBs will refine the models for more accurate results. It is important to know the SE of an enclosure and its contents as a whole system as under or over estimating the amount of SE could lead to equipment malfunctions. If the SE is underestimated additional, unnecessary, shielding may be used which will add weight, cost and space to the equipment. Too little shielding and equipment may not be fully protected.
Single-molecule Aflatoxin B1 Sensing via Pyrrole-based Molecular Quantum Dot

We investigate through ab-initio simulations the gold-8PyrroleDiThiol-gold (Au-8PyDT) molecular quantum dot as an amperometric single-molecule sensor for the aflatoxin B1 (AFB1) detection. We study the adsorption of AFB1 onto the Au-8PyDT and we analyze the transport characteristics for the most probable adsorption configuration. We find that a significant current modulation occurs, with around 80% of current decrease in presence of AFB1. Interestingly, the investigated sensor exhibits a voltage-dependent response, that we motivate through a transmission properties analysis. Our results, considering the synthesis simplicity of PolyPyrroles and their non-toxicity, motivate future research efforts in this direction.
Identificación de niveles confiables de penetración de generación fotovoltaica

This paper presents the development of a methodology to identify reliable penetration levels of non-conventional renewable generation, such as photovoltaic generation in the Paraguayan Transmission System. Therefore, this methodology is developed for the quantification of current stability margins, the recognition of critical areas of the system, and the identification of candidate busbars for possible system reinforcements such as the insertion of solar photovoltaic generation, thus obtaining an evaluation of the technical performance of the system. For this, some classical voltage stability tools are applied, such as the Load Margin, obtained by means of the PV curves and the Tangent Vector. Likewise, this analysis allows to obtain the positive impacts of a higher photovoltaic penetration such as a significant increase in the load margin, as well as eventual negative effects considering the decrease of the rotational inertia of the system. Finally, it is concluded that this method is valid for the proposed analysis and that, in the Paraguayan system, up to 15% of non-conventional renewable generation can be safely inserted, based on dynamic studies.
Statistics of dislocations at low temperature in pure metals with body centered cubic symmetry

In situ straining tests in high purity α-Fe thin-foils at low temperatures have demonstrated that crystalline defects, called dislocations, have a jerky type of motion made of intermittent long jumps of several nanometers. Such an observation is in conflict with the standard Peierls mechanism for plastic deformation in bcc crystals, where the screw dislocation jumps are limited by inter-reticular distances, i.e. of a few Angstroms. Employing atomic-scale simulations, we show that although the short jumps are initially more favorable, their realization requires the propagation of a kinked profile along the dislocation line which yields coherent atomic vibrations acting as traveling thermal spikes. Such local heat bursts favor the thermally assisted nucleation of new kinks in the wake of primary ones. The accumulation of new kinks leads to long dislocation jumps like those observed experimentally. Our study constitutes an important step toward predictive atomic-scale theory for materials deformation.
Extraction and Analysis of Vector Flow Imaging Data in a Pediatric Population

Follow this and additional works at: http://scholarworks.uark. edu/bmeguht Part of the Bioimaging and Biomedical Optics Commons, Biomechanics and Biotransport Commons, Biomedical Devices and Instrumentation Commons, Cardiology Commons, Cardiovascular Diseases Commons, Congenital, Hereditary, and Neonatal Diseases and Abnormalities Commons, Other Biomedical Engineering and Bioengineering Commons, and the Pediatrics Commons