Miracle-dz/UniFAR-FTUs huggingface dataset:
Process optimisation and exhibiting correlation in the exploitable variable of AWJM

Rare earth phosphate ceramic particles, when used as reinforcement, have considerably improved the toughness property. In the present work, lanthanum phosphate-yttrium composite synthesised by the Sol-Gel technique was taken as a study material. Abrasive waterjet machine (AWJM) is preferred to examine the machinability of the fabricated composite as ceramic particles possess low thermal and electrical conductivity. Irrespective of various operating parameters of AWJM, the dominant parameters such as jet pressure (JP), stand-off distance (SOD) and traverse speed (TS) are considered to measure the output performance characteristics of material removal rate (MRR), kerf angle (KA) and surface profile roughness (Ra). Silicon carbide and garnet are taken as the abrasive particles. The investigation shows that TS essentially characterise silicon carbide as abrasive shows 85.89%, accompanied by 7% JP and 6% SOD. On account of garnet, TS extraordinarily influences with a commitment of 64.84% followed by SOD of 20.11% and JP of 14.65%. The microstructure assessment on the cut surface reveals the plastic deformation surface for both abrasives and the surface erosion occurs by squashing and crushing of particles. Low MRR and KA are recorded for garnet and the superior Ra is therefore obtained when silicon carbide is used as abrasive.
NUC Optimization-Aided Hierarchical Modulation to Achieve Comparable Capacity as Layered Division Multiplexing

This article investigates the non-uniform constellation (NUC) optimization adapted for Hierarchical modulation (HM) without using Successive Interference Cancellation (SIC). This approach is promising to reduce system demod/decode delay in comparison to Layered Division Multiplexing (LDM). By maximizing the constellation constrained capacity of Enhanced Layer (EL) service in HM, while the capacity of Core Layer (CL) service are approximately the same in HM and LDM, the capacity achieved by HM using NUCs is comparable to LDM. Particle Swarm Optimization (PSO) algorithm is used to resolve this problem. To accelerate the optimization, initial constellation is selected from regular NUCs or the combination of CL and EL constellations of LDM in ATSC 3.0. The results imply that under certain capacity demands, especially when there is a large difference between the SNR thresholds of CL and EL or the power ratio of CL to EL is high (for example, 10 dB or higher), HM, with lower delay compared to LDM, can achieve the capacity comparable or even better than LDM with the help of NUCs. Even if the power ratio of CL to EL is relatively low (for example, 3 dB), the capacity loss can be reduced with properly designed NUCs and the SNR threshold loss of EL can be approximately 0.4 dB with respect to LDM. However, LDM is still superior to HM when the difference between the SNR thresholds of CL and EL is relatively low.
Factors Influencing the Use of Fintech Payment Services in Indonesia : Literature Review

—Fintech payment is a form of financial transformation that develops along with changing in people's lifestyles which are currently dominated by the use of information technology and demand of fast-paced life. Based on data from Bank Indonesia digital payment transactions through fintech, the trend raise to 77.6% on February 2019. This shows the increasing enthusiasm of Indonesian people to use fintech payments. According to the OJK, there are risks of using fintech payment, namely the potential for fraud and data privacy abuse from hackerattacks. This affect the trust of consumers to use fintech services. Furthermore fintech providers in Indonesia need to make efforts to maintain trust and service quality for their consumers. So this research aims to develop a model that affects consumers in Indonesia in using fintech payment. This study offers UTAUT2 development model by adding service quality and trust variables, where both of these variables in previous studies have been shown to increase Behavioral Intention. The results of this study provide recommendations for fintech payment service providers to improve their service users.
A Novel Image Retrieval Method with Improved DCNN and Hash

: In large-scale image retrieval, deep features extracted by Convolutional Neural Network (CNN) can effectively express more image information than those extracted by traditional manual methods. However, the deep feature dimensions obtained by Deep Convolutional Neural Network (DCNN) are too high and redundant, which leads to low retrieval efficiency. We propose a novel image retrieval method, which combines deep features selection with improved DCNN and hash transform based on high-dimension features reduction to gain low-dimension deep features and realizes efficient image retrieval. Firstly, the improved network is based on the existing deep model to build a more profound and broader network by adding multiple groups of different branches. Therefore, it is named DFS-Net (Deep Feature Selection Network). The adaptive learning deep features of the Network can effectively alleviate the influence of over-fitting and improve the feature expression of image content. Secondly, the information gain rate method is used to filter the extracted deep features to reduce the feature dimension and ensure the information loss is small. The last step of the method, hash Transform, sparsifies and binarizes this representation to reduce the computation and storage pressure while maintaining the retrieval accuracy. Finally, the scheme is based on the distinguished ResNet50, InceptionV3, and MobileNetV2 models, and studied and evaluated deeply on the CIFAR10 and Caltech256 datasets. The experimental results show that the novel method can train the deep features with stronger recognition ability on limited training samples, and improve the accuracy and efficiency of image retrieval effectively.
Radiationless Transitions in Polyatomic Molecules. I. Calculation of Franck—Condon Factors

Radiationless transitions between two electronic states are studied for a system consisting of a polyatomic molecule in a medium where vibrational relaxation is rapid. The transition rate is then governed by a vibronic matrix element and a vibrational overlap factor. Only the latter, known as the Franck—Condon factor and denoted by F, is investigated in detail. For harmonic oscillators F derives from shifts in equilibrium distance (displacements) and shifts in frequency (distortions). It is shown that for radiationless transitions involving large energy gaps (E), F is dominated by distortions, whereas for optical transitions it is dominated by displacements of the oscillators. These distortions lead to an approximately exponential decrease of F with increasing E. An isotope rule for F is derived which is valid for both displaced and distorted oscillators provided E is not too small. Since all formulas are of the form F (E), where E is the principal variable, comparison with experiment is only possible for.
CuWide: Towards Efficient Flow-Based Training for Sparse Wide Models on GPUs

Wide models such as generalized linear models and factorization-based models have been extensively used in various predictive applications, e.g., recommendation, CTR prediction, and image recognition. Due to the memory bounded property of the models, the performance improvement on CPU is reaching the limitation. GPU is known to have many computation units and high memory bandwidth, and becomes a promising platform for training machine learning models. However, the GPU training for the wide models is far from optimal due to the sparsity and irregularity in wide models. The existing GPU-based wide models are even slower than the ones using CPU. The classical training schema of the wide models does not optimized for the GPU architecture, which suffers from large amount of random memory accesses and redundant read/write of intermediate values. In this paper, we propose an efficient GPU-training framework for the large-scale wide models, named cuWide. To fully benefit from the memory hierarchy of GPU, cuWide applies a new flow-based schema for training, which leverages the spatial and temporal locality of wide models to drastically reduce the amount of communication with GPU global memory. To do so, we adopt a bigraph computation model to efficiently realize the flow-based schema and exploit three flexible interfaces for programming. Further, we use the 2D partition of mini-batch (in sample and feature dimensions) with proposed graph abstraction to optimize GPU memory access for sparse data, and apply several spatial-temporal caching mechanisms (importance-based model caching and cross-stage accumulation caching mechanisms) to achieve a high performance kernel. To efficiently implement cuWide, we also propose several GPU-oriented optimizations, including feature-oriented data layout to enhance the data locality, replication mechanism to reduce update conflicts in shared memory, and multi-stream scheduling to overlap data transferring and kernel computing. We show that cuWide can be up to more than 20× faster than the state-of-the-art GPU solutions and multi-core CPU solutions.
Procedure for Preparation of Lipid Vesicles (Liposomes) Using the Coacervation (Phase Separation) Technique

The preparation oflipid vesicles using simple coacervation is described in detail. The optimal coacervation conditions for the formation of lipid vesicles by the triangular phase diagram of the lipid-alcohol-water system were examined. Four different alcohols (methanol, ethanol, n-propanol, and 2-propanol) were used as a lipid solvent, and deionized, distilled water was used as a poor solvent for the lipids. The lamellarity and size homogeneity of the resulting lipid vesicles were observed by freeze-fracture electron microscopy and scanning electron microscopy, respectively, using a specific fixation technique with malachite green. The majority of vesicles formed by methanol as a lipid solvent appeared to be large and unilamellar, ranging from 100 to 1000 nm in diameter. However, when the vesicles were prepared using ethanol, concentric lamellae of multilamellar vesicles were recognizable. On the other hand, the use of propanol resulted in lipid vesicles that were homogeneous and unilamellar.
Modelling of nanocrystalline iron nitriding process — influence of specific surface area

Samples of iron catalysts of various specific surface areas for ammonia synthesis underwent nitriding with ammonia in a tubular reactor where continuous thermogravimetric measurement and measurements of hydrogen concentration in the gaseous phase were simultaneously performed. The nitriding process was performed under atmospheric pressure at 475°C. It was observed that, along with an increase in the mean size of iron nano-crystallites, the minimum nitriding potential (at which the iron nitriding reaction is initiated) of the gaseous phase also increased. The degree of nitriding of the catalyst samples increased with the increase in the mean size of iron crystallites. On the basis of the values of nitriding potential, nano-crystallite size distributions can be determined.
Conical Monopole Antenna With Integrated Tunable Notch Filters

With increasing radio spectrum being allocated to wireless communications, multiband and ultrawideband systems are becoming much more prevalent. Such broadband systems are susceptible to interference, or can inadvertently radiate out-of-band emissions, so significant filtering is required at RF front-ends. With this motivation, a broadband conical monopole antenna with integrated tunable notch cavity filters is presented. The antenna is a complex structure but it can be conveniently realized with lightweight perforated alloy surfaces using 3-D printing. This coax-fed antenna is demonstrated to maintain its omnidirectional azimuthal pattern with a very wide notch-tuning range, resulting in a versatile broadband antenna.
Three Colors Emission from S,N Co‐doped Graphene Quantum Dots for Visible Light H2 Production and Bioimaging

A facile solvothermal route to synthesize S,N co‐doped graphene quantum dots (S,N‐GQDs) with unique optical properties is demonstrated. Three absorption bands are observed at 338, 467, and 557 nm, which is different from any previous reports. The photoluminescent spectra display emissions in three primary colors that are independent of the excitation wavelength, within the excitation wavelength ranges of 340–420 nm, 460–540 nm, and 560–620 nm. The PL excitation spectra indicate that each emission is related to a single excitation band. It is proposed that three independent luminescent centers coexist in S,N‐GQDs because the doping with S and N may change the chemical environment of the GQDs. However, energy‐transfer processes usually do not occur among the independent luminescent centers under different wavelength light excitation. Heteroatom‐doping of GQDs provides an attractive means of effectively tuning their optical properties for the purpose of exploiting new applications in visible‐light photocatalytic and bioimaging. S,N‐GQDs/TiO2 composites exhibit better hydrogen production activities under visible light (λ > 420 nm) than commercial TiO2 (P25), owing to the presence of characteristic absorption bands in the visible region. Furthermore, the S,N‐GQDs have a pronounced biocompatibility and bioimaging ability under long‐wavelengths excitation for live A549 cells.
Fast and resource-efficient Deep Neural Network on FPGA for the Phase-II Level-0 muon barrel trigger of the ATLAS experiment

The Level-0 muon trigger system of the ATLAS experiment will undergo a full upgrade for the High Luminosity LHC to stand the challenging requirements imposed by the increase in instantaneous luminosity. The upgraded trigger system will send raw hit data to off-detector processors, where trigger algorithms run on a new generation of FPGAs. To exploit the flexibility provided by the FPGA systems, ATLAS is developing novel precision deep neural network architectures based on trained ternary quantisation, optimised to run on FPGAs for efficient reconstruction and identification of muons in the ATLAS “Level-0” trigger. Physics performance in terms of efficiency and fake rates and FPGA logic resource occupancy and timing obtained with the developed algorithms are discussed.
Developing WinXPRO: a software for determination of the multipole‐model‐based properties of crystals

The new release of the computer program package WinXPRO v.3x for determination of the crystal properties from parameters of the multipole-modeled experimental electron density and anharmonic atomic displacement coefficients is described. The set of properties is significantly extended by using the density functional and information theories. In addition, a built-in multi-functional viewer and programs to display the output data, including the mapping of the chosen functional bonding descriptors onto surfaces of the other properties, are included.
Biosurfactant-Mediated Biodegradation of Polycyclic Aromatic Hydrocarbons—Naphthalene

ABSTRACT The present study is aimed at the naphthalene degradation with and without biosurfactant produced from Pseudomonas aeruginosa isolated from oil-contaminated soil. The present study was carried out to isolate the bacterial strains for the naphthalene degradation and also for biosurfactant production. The isolated strains were screened for their ability to degrade the naphthalene by the methods of optimum growth rate test and for the production of biosurfactants by cetyltrimethylammonium bromide, blood agar medium, and thin-layer chromatography. The present study also focused on the effect of biosurfactant for the degradation of naphthalene by isolate-1. Two bacterial strains were isolated and screened, one for biodegradation and another for biosurfactant production. The second organism was identified as Pseudomonas aeruginosa by 16S rRNA analysis. The purified biosurfactant reduces the surface tension of water and also forms stable emulsification with hexadecane and kerosene. The end product of naphthalene degradation was estimated as salicylic acid equivalent by spectrophotometric method. The results demonstrated that Pseudomonas aeruginosa has the potential to produce biosurfactant, which enhances the biodegradation of naphthalene. The study reflects the potential use of biosurfactants for an effective bioremediation in the management of contaminated soils.
Nonequilibrium Lattice Fluids: A Predictive Model for the Solubility in Glassy Polymers

The calculation of sorption isotherms for gases and vapors in glassy polymers is approached through a nonequilibrium equation of state procedure. The basic peculiar feature of the system, represented by the nonequilibrium structure of the mixture, is accounted for by introducing an order parameter for an isotropic glass. By revisiting the lattice fluid model by Sanchez and Lacombe (Macromolecules 1978, 11, 1145.), an expression for the Gibbs free energy of nonequilibrium lattice fluids is obtained in which the polymer species density in the solid mixture is considered as an order parameter and it is thermodynamically treated as an internal state variable. The absence of adjustable parameters makes the resulting model entirely predictive for the solubility, once the pseudoequilibrium volumetric data are available. The comparison of the predicted isotherms with the data for CO2−poly(carbonate) systems at 35 °C, obtained by Fleming and Koros (Macromolecules 1990, 23, 1353.) under different polymer prehistori.
Generative Adversarial Neural Networks Model of Photonic Crystal Fiber Based Surface Plasmon Resonance Sensor

Photonic crystal fibers (PCF) for specific applications are designed and optimized by both industry experts and researchers. However, the potential number of design combinations and flexible propagation features are offering a wide range of application areas, resulting in an exponential increase in the search space. This issue combined with the speed of the commonly used full vectorial finite element method (FV-FEM) simulators causes the task to take a significant amount of time. As stated in the previous works, artificial neural networks (ANN) can be employed to predict the result of numerical simulations much faster. However, there are two issues with the methods proposed previously: the required number of samples for training and the generality of these methods. In this article, we propose the use of generative adversarial networks (GAN) to augment the real dataset to train an ANN model. The experimental analysis suggested that the proposed combination not only accurately predicts the confinement loss even with limited amounts of data but also GAN can be used to improve existing methods in the literature. Moreover, the proposed system can also predict the confinement loss over a range of analytes and wavelengths in a completely new geometric configuration and generic enough not to require any additional tuning when used in a new dataset. This property is demonstrated by experimenting on an existing PCF dataset, which the proposed system surpassed the accuracy of the method proposed for it.
Track Reconstruction with PANDA at FAIR

The future PANDA experiment at FAIR aims to investigate a wide range of physics processes in antiproton-proton collisions at rates of up to 20 MHz, while employing a purely software-based event filter. To educate the trigger decisions, a full event reconstruction has to be carried out in real time. This challenge is amplified when considering tracks from particles with long lifetimes and displaced decay vertices, which add to the complexity of the reconstruction algorithms. Here, we present modifications to a cellular automatonbased track finder taking detector time-stamps into account in addition to spatial information, as well as several pattern recognition methods for longitudinal track reconstrucion with PANDA’s Straw Tube Tracker.
Electrical Measurement Techniques in Atomic Force Microscopy

A conductive tip in an atomic force microscope (AFM) has extended the capability from conventional topographic imaging to electrical surface characterization. The conductive tip acts as a voltage electrode to provide stimuli and monitor electrical surface properties. In this review article, we have organized the AFM electrical techniques based on whether the electrical properties are monitored at the cantilever tip or across the sample. Furthermore, the techniques are organized based on probe detection signal. A number of acronyms are used in the literature, and the more commonly used ones are identified. The principle of each technique is described, and representative applications are presented. A better understanding of the spectrum of techniques should serve as the driver to expand the application of electrical techniques to study interdisciplinary phenomena at the nanoscale.
New poly(4-chloro)maleimides. I. Synthesis and characterization of poly[ether-ester-(4-chloro)maleimides]

Abstract New poly[N-(ester)-3-(ether)-4-(chloro)]maleimides were synthesized by the reaction of N-(4-chlorocarbonylphenyl)-3,4-dichloromaleimide (3) with various bisphenols such as hydroquinone (8a), 2,2-bis-(4-hydroxyphenyl)propane (8b), 4,4′ -(hexafluoroisopropylidene)diphenol (8c), and 2,5-bis(4-hydroxybenzylidene)cyclopentanone (8d). The structures of the resulted polymers were confirmed by IR and elemental analyses. A series of model compounds (4–7) was synthesized to facilitate confirmation of the structure of the polymers. The polymer containing hydroquinone units (9a) exhibited LC behavior in the melt, as observed by PLM measurements.
DroidClone: Attack of the android malware clones - a step towards stopping them

Code clones are frequent in use because they can be created fast with little effort and expense. Especially for malware writers, it is easier to create a clone of the original than writing a new malware. According to the recent Symantec threat reports, Android continues to be the most targeted mobile platform, and the number of new mobile malware clones grew by 54%. There is a need to develop techniques and tools to stop this attack of Android malware clones. To stop this attack, we propose DroidClone that exposes code clones (segments of code that are similar) in Android applications to help detect malware. DroidClone is the first such effort uses specific control flow patterns for reducing the effect of obfuscations and detect clones that are syntactically different but semantically similar up to a threshold. DroidClone is independent of the programming language of the code clones. When evaluated with real malware and benign Android applications, DroidClone obtained a detection rate of 94.2% and false positive rate of 5.6%. DroidClone, when tested against various obfuscations, was able to successfully provide resistance against all the trivial (Renaming methods, parameters, and nop insertion, etc) and some non-trivial (Call graph manipulation and function indirection, etc.) obfuscations.