Miracle-dz/UniFAR-FTUs huggingface dataset:
A facile route to mechanically robust graphene oxide fibers

Excellent mechanical, electrical, and thermal properties of graphene have been achieved at the macroscale by assembling individual graphene or graphene oxide (GO) particles. Wet-spinning is an efficient and well-established process that can provide GO assemblies in fiber form. The coagulation bath in the wet-spinning process has rarely been considered for the design of mechanically robust GO fibers (GOFs). In this study, locating the amidation reaction in the coagulation bath yielded mechanically improved GOFs. The imides 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide and N-hydroxysuccinimide were used to form covalent amide bonds between GO flakes and chitosan, thereby reinforcing the GOFs. Evidence and effects of the amidation reaction were systematically examined. The tensile strength and breaking strain of the GOFs improved by 41.6% and 75.2%, respectively, and the toughness almost doubled because of the optimized crosslinking reaction. Our work demonstrated that using a coagulation bath is a facile way to enhance the mechanical properties of GOFs.
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Free‐Standing 3D Electrodes for Electrochemical Detection of Hydrogen Peroxide

Electrochemical detection of hydrogen peroxide has achieved rapid development, due to the wide presence in industrial production, everyday life, bioprocess and green energy chemistry, etc. Many three‐dimensional structures have emerged as state‐of‐the‐art electrocatalysts due to their fascinating structures and electrochemical properties. This review summarizes free‐standing three‐dimensional electrocatalysts based on metal, metal oxide, carbon & silicon, and unconventional materials for detection of hydrogen peroxide with low limit of detection, wide range and fast response. The work also highlights their applications in near neutral conditions, especially their ability for single cell detection and mapping in biological environment. Further exploration into these materials together with devices design will facilitate the development of next‐generation detection technologies toward in situ and real‐time detection and contribute to wearable/portable smart detection electronics.
Effect of KAlF4 on the efficiency of hot metal desulphurisation with magnesium

At the hot metal desulphurisation (HMD) stations of Tata Steel’s BOS plant in Port Talbot (UK), KAlF4 is injected together with magnesium in order to increase the fluidity of the slag and thus to decrease the iron loss. Although KAlF4 is successfully contributing to a lower iron loss during HMD, it also has a negative effect on the desulphurisation efficiency of magnesium. Analysis of slag samples from the plant and FactSage calculations showed that the fluorine in KAlF4 reacts with the magnesium, thus preventing the magnesium from reacting with the sulphur.
Applications of the ant algorithm to combinatorial optimization

Ant algorithm is a newly emerged stochastic searching optimization algorithm in recent years. It has been paid much attention to since the successful application in the famous travelling salesman problem. This paper further extends the idea of this new biological optimization strategy to some other hard combinatorial optimization problems, including the multi attribute situation which lack of efficient solving methods. The ability of optimization for the algorithm is tested experimentally which give encouraging results.
Factors influencing the adoption of e-government services among Jordanian citizens

This work aims to study effective factors that could play an important role in the decision of Jordanian citizens to adopt e-government services. The study employed the UTAUT model with introducing new constructs, namely; 'website quality', 'trust of internet', 'trust of government', to study the adoption of e-government services in Jordan context. The data was collected using online survey from a total of 320 Jordanian citizen who are from both public and private sectors. The research model was evaluated using the structural equation modelling (SEM) technique. Based on the results, 'website quality', 'trust of internet', 'trust of government', 'performance expectancy', 'effort expectancy' and 'facilitating conditions' factors were shown to have a positive effect on behavioural intention to use e-government services. However, the influence of 'social influence' was found to be insignificant on the participants behavioural intention to use. Additionally, the 'website quality' factor was found to have a positive effect on the 'performance expectancy' of e-government services. The findings of this study will guide researchers, policy makers and professionals towards the Jordanian citizen' priorities in improving website design, website functions, website content quality, ease of use and security; thereby increasing the adoption of e-government services.
Carrier frequency offset estimation for OFDM systems

Orthogonal frequency division multiplexing (OFDM) is broadly considered as an effective approach for future high-speed wireless multimedia communications. However, it is very sensitive to carrier frequency offset between the transmitter and the receiver local oscillators, which causes intercarrier interference (ICI). Thus, in OFDM communication systems, the carrier frequency offset must be estimated and compensated at the receiver to minimize the effects of ICI and maintain orthogonality. We first derive a mathematic model of OFDM systems with cyclic prefix (CP) and virtual subcarrier. For the ESPRIT-like estimator, we propose a carrier frequency offset estimation algorithm using a DOA-MATRIX method. After the rough estimation, it can simultaneously acquire the frequency offset and matrix, including channel information, which are favorable for offset compensation and demodulation of the received signals. Numerical simulations illustrate the performance of the algorithm.
Determination of Quercetin and Rutin in Red Galangal Rhizomes (Alpinia purpurata) and White Galangal (Alpinia galanga) with High Performance Liquid Chromatography Method

Research of flavonoid determination (quercetin and rutin) in Alpinia purpurata and Alpinia galanga rhizomes using HPLC method has been conducted. This study aims to determine the qualitative and quantitative presence of rutin and quercetin compounds in Alpinia purpurata and Alpinia galanga rhizomes. Fractions of ethyl acetate and n-butanol have been obtained by TLC, followed by total flavonoid analysis with UV - Vis spectrophotometer method and quercetin and rutin determination with HPLC. The results obtained in this study are as follows: Total flavonoid from ethyl acetate fraction obtained is 46,48 mg EQ/gram in Alpinia purpurata and 70,60 mg EQ/gram in Alpinia galanga, and total flavonoid from nbutanol fraction obtained is 68,50 mg EQ/gram for Alpinia purpurata and 103,80 mg EQ/gram for Alpinia galanga. The ethyl acetate fraction of Alpinia purpurata detected quercetin content at 5469.64 mg / kg and rutin at 4955.59 mg / kg, while Alpinia galanga detected quercetin at 5764.10 mg / kg and rutin at 5327.93 mg / kg. From n-butanol fraction, quercetin are only detected at 6737.14 mg / kg in Alpini purpurata and 9098.74 mg / kg in Alpinia galanga. From these results, it can be concluded that HPLC method can be used to detect the presence of flavonoid quercetin and rutin in Alpinia purpurata and Alpinia galanga rhizomes.
Enforcing Security Policies On GPU Computing Through The Use Of Aspect-Oriented Programming Techniques

v CHAPTER 1: INTRODUCTION AND MOTIVATION 1 1.1 GPGPU Programming 3 1.2 Existing Attacks 4 1.3 Aspect-oriented Programming 6 1.4 Contributions 6 1.5 Outline of Writing 7 CHAPTER 2: RELATED WORK 8 2.1 Security Policy Theory 8 2.2 Policy Specification Languages 9 2.3 Aspect-Oriented Programming 11 2.4 Chapter Summary 13 CHAPTER 3: PROPOSED SYSTEM 14 3.1 The eGASP Policy Enforcement System 14 3.2 Utilizing eGASP in a Development Environment 17 3.3 Chapter Summary 18 CHAPTER 4: EMPIRICAL EVALUATION 19 4.1 System Configuration 19 4.2 Examples of Policies 20 4.2.1 Logging 20 4.2.2 Imposing Execution Limitations 21 4.2.3 Completing Missing Code 22 4.3 Performance Analysis 23 4.4 Chapter Summary 25 CHAPTER 5: IMPROVING THE POLICY ENFORCER 26 5.1 Join-points Prior to Calling CUDA Functions 26 5.2 Join-points After Returning from CUDA Functions 27 5.3 Modifying the Arguments of a CUDA Function Signature 27 5.4 Handling Classes and Types 29
Oscillatory behaviors of gene expression in escherichia coli by transcriptional and translational time delays

In the process of gene expression, time delays are unavoidable due primarily to the slow process of transcription, translation and translocation processes. It is observed that gene regulatory system may lose its stability even for very small delay. Motivated by the regulation of gene expression proposed in Shimoni et al. [Molecular Systems Biology, 2007], in this paper, a gene regulatory network mediated by small non-coding RNAs with both transcriptional and translational time delays is formulated. The nonlinear dynamic behaviors such as stability, bifurcations and oscillations are studied. It is shown that both transcriptional and translational time delays can induce gene expression in Escherichia coli to be oscillatory even when its deterministic counterpart exhibits no oscillations. Moreover, the obtained results are in consistence with experimental observations found in the biological literatures [Elowitz MB et al.: Nature, 2000, 403, pp.335–338; Lennart Hilbert et al.: Mol. BioSyst, 2011, 7, pp.2599–2607], which indicates that when time delays are significant, both transcription and translation should be taken into account in the regulation of gene expression.
Mobile Augmented Reality based CFD Simuation Post-Processor

The convergence of engineering and IT technology has brought many changes to the industry as well as academic research. In particular, computer simulation technology has evolved to a level that can accurately simulate actual physical phenomena and analyze them in real time. In this paper, we describe the CFD technology, which is mainly used in industry, and the post processor that uses the augmented reality which is emerging as the post-processing. Research on the visualization of fluid simulation results using AR technology is actively being carried out. However, due to the large size of the result data, it is limited to researches that are published in a desktop environment. Therefore, it is limitation that needs to be reviewed in actual space. In this paper, we discuss how to solve these problems. We analyze the fluid analysis results in the post-processing, and then perform optimizing data (more than 70%)to support operation in the mobile environment. In the visualization, lightweight data is used to perform real-time tracking using cloud computing, The analysis result is matched to the screen and visualized. This allows the user to review and analyze the fluid analysis results in an efficient and immersive manner in the various spaces where the simulation is performed.
Wavelet-based features for prognosis of degradation in rolling element bearing with non-linear autoregressive neural network

ABSTRACT In rotary machine, the frequently failure component is the rolling element bearing (REBs). The timely identifying the potential fault can prevent the breakdown and failure of rotary machine. A health assessment model based on Non-Linear Autoregressive Neural Network along with exponential value of Health Indicator (HIE) is proposed in this paper. The prognoses of bearing degradation estimates as: In first step, the friction torque transducer used to acquire vibration signal over the lifetime of bearing. After that, in second step, useful features are extracted after the processing of signals through continues wavelet transform (CWT). Then, exponential method is applied to remove the shortcomings into feature vectors. Finally, HIE features as input to the network; the prediction of bearing degradation is performed using optimal NAR and NARX network. An experimental result clarified that the performance of NARX network is better for the prediction of degradation. Outperform application of proposed method and conformation of degree of accuracy; it is compared with the published literature. The proposed methodology is more effective for a dataset of seeded defect and accelerated life test. The results indicate this methodology signifies the actual situation well and is able to precisely and efficiently predict the bearing degradation. Abbreviations: Bearing dynamics, Prognostics, Nonlinear Autoregressive (NAR) network, Nonlinear Autoregressive with exogenous inputs (NARX) network,Continuous wavelet transform (CWT)
Real-time Tracking Method for Eye Feature Points Based on Lucas-Kanada Optical Flow Algorithm

In order to realize the detection and tracking of moving targets,real-time eye feature points tracking method which combines Viola-Jones detecting algorithm,sub-pixel corner extracting with Lucas-Kanada optical flow algorithm is proposed in this paper.Viola-Jones algorithm is used to detect the eyes' rough region,and Harris operator with filtering mechanism is adopted to extract the accurate sub-pixel eye feature points in the target area,which can reduce the locating time but remain the locating accuracy.The Lucas-Kanada optical flow algorithm based on pyramid layer mechanism is introduced to capture the movement feature point.Taking into account the tracking for large scale and high speed objects,a switchable location windows method is presented and used in the last step.Experimental results show that this method can realize the eye feature points location and tracking for fast movement and large scale target with high precision,good real-time performance and strong robustness.
Networks of Canadian Business Elites: Historical Corporate Interlock Networks circa 1912

Abstract This paper provides details about a historical dataset of Canadian corporations and business elites who served on corporate boards circa 1912. The source of this corporate interlock data is the Directory of Directors in Canada, 1912, a public domain volume listing Canadian public companies in Canada. Because these data are thought to be of interest not only to network researchers, but also to business historians and management scholars, an attempt has been made to make the data as easy to use as possible. Supplementary information has also been added to the network files provided. All of the individuals and companies in the dataset have been geolocated. The proper 1911 Census Division a company was located in has also been added so that the networks can be combined with other publicly available data from the period. Two sets of graph files are provided in CSV format with other formats provided on the author’s website. The first file contains corporations as the nodes with directors as edges. The second file has the individual directors as nodes and edges connecting them are corporate boards individuals both sat on.
Efficient and Scalable Functional Dependency Discovery on Distributed Data-Parallel Platforms

Functional dependencies (FDs) play a very important role in many data management tasks such as schema normalization, data cleaning, and query optimization. Meanwhile, there are ever-increasing application demands for efficient FD discovery on large-scale datasets. Unfortunately, due to huge runtime and memory overhead, the existing single-machine FD discovery algorithms are inefficient for large-scale datasets. Recently, distributed data-parallel computing has become the de facto standard for large-scale data processing. However, it is challenging to design an efficient distributed FD discovery algorithm. In this paper, we present SmartFD, which is an efficient and scalable algorithm for distributed FD discovery. First, we propose a novel attribute sorting-based algorithm framework. Next, to discover all the FDs grouped by a given attribute, we propose an efficient distributed algorithm Attribute-centric Functional Dependency Discovery (AFDD). In AFDD, we design an Fast Sampling and Early Aggregation (FSEA) mechanism to improve the efficiency of distributed sampling and propose a memory-efficient index-based method for distributed FD validation. Moreover, AFDD employs an attribute-parallel method to accelerate the pruning-and-generation of candidate FDs. Furthermore, we propose an adaptive switching strategy between distributed sampling and distributed validation based on the unified time-based efficiency metric. Also, we employ a distributed probing based method to make the switching strategy more accurate. Experimental results on Apache Spark reveal that SmartFD outperforms the state-of-the-art single-machine algorithm HyFD and the existing distributed algorithm HFDD with 3.2×–44.9× and 2.5×–455.7× speedup respectively. Moreover, SmartFD achieves good row scalability and column scalability. Additionally, SmartFD has sub-linear node scalability.
Development of novel method for prediction of gas density in operational conditions

Abstract The performance of gas industries is extensively function of gas properties such as gas density. Due to this importance in the present work, a novel grid partitioning based fuzzy inference system method applied to predict gas density base on pressure, temperature and molecular weight of gas. To this end, the required experimental data are collected from reliable sources. Different comparison scenarios are used to evaluate the ability of model. The coefficients of determination (R2) for training and testing phases are calculated as 0.9985 and 0.9980 respectively. The determined indexes and graphical evaluations show that predicting model can estimate gas density in high degree of accuracy. According to the obtained results, the predicting model can be used as a simple and powerful software in gas industries to predict different processes.
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MagicVO: An End-to-End Hybrid CNN and Bi-LSTM Method for Monocular Visual Odometry

For the robotic positioning and navigation, visual odometry (VO) system is widely used. However, the errors of the traditional VO accumulate when the robot moves. Besides, this paper proposes a new framework to solve the problem of monocular VO, called MagicVO. Based on the convolutional neural network (CNN) and the bi-directional LSTM (Bi-LSTM), MagicVO outputs a 6-DoF absolute-scale pose at each position of the camera with a sequence of continuous monocular images as input. It does not only utilize the outstanding performance of CNN in extracting the rich features of image frames fully but also learns the geometric relationship from image sequences pre and post through Bi-LSTM to get a more accurate prediction. A pipeline of the MagicVO is shown in this paper. The MagicVO is an end-to-end system, and the results of the experiments on the KITTI and ETH datasets show that MagicVO has a better performance than the traditional VO systems in the accuracy of pose and the generalization ability.
Development of a Synthetic Biology Tool to Quantify the Contribution of Protein Components and Protein Interactions Within Transcriptional Regulatory Complexes

We have formed the basis for a synthetic gene regulatory network in S. cerevisiae to assess the relative contribution of proteins and protein interactions to the function of transcriptional regulatory complexes. This gene network contains two synthetic transcriptional regulator (TR) fusion proteins that control transcriptional activation and repression of a reporter expressed by a synthetic promoter. This system can be modulated and quantified specifically by our manipulations since the transcriptional regulators (TRs) are only active when drug induced. In this thesis, I characterized and optimized my gene network to enable accurate measurements of transcriptional regulation. I demonstrated that my synthetic repressor interacts with a selected native transcriptional regulatory complex. Future mutation experiments can be performed to investigate the relative importance that the selected protein interaction plays on transcriptional regulation. New synthetic repressor fusion proteins can be created to study the relative importance of protein interactions between other TRs, making this tool versatile. This tool will allow us to quantify interactions and gain a deeper understanding of the complicated mechanisms controlling transcriptional regulation.
Application of Robust Estimation Methods for Detecting and Removing Gross Errors from Close-Range Photogrammetric Data

Systematic, random and gross errors are considered the main problems facing those working in the photo-triangulation processes. The influence of systematic errors on photo-measurements may include lens distortion, film deformation, refraction and other distortions. Usually, these types of errors can be solved by the calibration process. Meanwhile, the traditional least-squares method was used to adjust photogrammetric data in order to solve the problems of random errors. In case observations contain gross errors, the reliability of least-squares estimates is strongly affected. In this paper, two independent mathematical models (photo-variant self-calibration and robust estimation) are combined for solving and processing the problems of systematic and gross errors in one step. Also, this paper investigated the effectiveness of robust estimation models on solving gross errors in close-range photogrammetric data sets that require photo bundle adjustment solution. The results of investigation indicate that all robust methods have the advantage of detecting and removing gross errors over the least squares method especially in cases of observation contains large-sized errors. Moreover, the Modified M-estimator (IGGIII) method has the best performance and accuracy. Furthermore, gross error was also revealed in the residuals.