Miracle-dz/UniFAR-FTUs huggingface dataset:
Volatile Compounds, Polyphenols and Sensory Quality in the Production of Tomato Vinegar

Fresh tomato and tomato products are widely consumed products. Their beneficial properties are not only present in fresh products but also in fermented ones. Therefore, the production of fresh tomato derivatives could be a good alternative to get a higher diversification and to make a good use of possible overproductions. Tomato vinegar from tomato paste by submerged culture fermentation has been developed. Final assays of maceration with rehydrated tomato paste (3, 5 and 7% v/v) in order to improve the final composition and organoleptic characteristics were also carried out. Polyphenols and volatile compounds have been determined along production and maceration processes. The statistical study (analysis of variance and principal component analysis) showed significant differences in volatile compounds and polyphenols according to matrix (rehydrated tomato paste, tomato wine and tomato vinegar) and specific maceration conditions (amount of rehydrated tomato paste and maceration time), which were also supported by sensory analysis. On the basis of the analytical and sensory results, 3 days of maceration and 5% v/v of rehydrated tomato paste were fixed as optimum conditions to obtain a tomato vinegar from tomato paste.
From 2D to VR Film: A Research on the Load of Different Cutting Rates Based on EEG Data Processing

Focusing on virtual reality (VR) and film cutting, this study compared and evaluated the effect of visual mode (2D, VR) and cutting rate (fast, medium, slow) on a load, to make an attempt for VR research to enter the cognitive field. This study uses a 2 × 3 experimental research design. Forty participants were divided into one of two groups randomly and watched films with three cutting rates. The subjective and objective data were collected during the experiment. The objective results confirm that VR films bring more powerful alpha, beta, theta wave activities, and bring a greater load. The subjective results confirm that the fast cutting rate brings a greater load. These results provide a theoretical support for further exploring the evaluation methods and standards of VR films and improving the viewing experience in the future.
Synthesizing Winning Strategies: What Differentiates Experienced Designers in Crowdsourcing Markets?

Prior studies of online crowdsourcing platforms have examined participants’ behaviors and found that experienced designers are more likely to win in crowdsourcing contests. However, what gives experienced designers an edge in these contests is not well understood. Our study seeks to understand what differentiates experienced designers from other participants, with a particular focus on how they leverage information in open design contests. We use a large-scale empirical analysis employing deep-learning algorithms and find that, while experienced designers are similar to less-experienced designers in a number of ways, experienced designers are more adept at integrating information from several prior highly-rated submissions from other designers within a contest, while less-experienced designers are more likely to excessively imitate individual prior highly-rated submissions. We also find that experienced designers whose submissions are closer in similarity to a synthesized image of several highly-rated prior submissions, are more likely to win. Our results are consistent with prior work on recombinant innovations which finds that a majority of innovations happen by a synthesis or recombination of prior innovations, and that inventors and designers, as they gain experience, learn optimal recombination strategies. Our findings provide new insights into the winning strategies of experienced designers in crowdsourcing platforms and have implications for the design of such markets.
GC-MS determination of diosgenin in rats plasma

Objective:To develop a GC-MS method for the determination of diosgenin in plasma.Methods:HP- 5MS capillary column(30m×0.25 mm×0.25μm),gas was He at a flow rate 1.0 mL·min~(-1),injector tempera- ture was 300℃,transfer temperature was 280℃,MS source temperature was 230℃,MS Quad temperature was 150℃,split injection,the colum temperature was 230～300℃ and rose gradually by the program as follow:initial temperature was 230℃,rose to 250℃ at the rate 4℃·min~(-1),then rose to 300℃ at the rate 25℃·min~(-1), maintained 23 min.EI ionization,electric energy was 70 eV,multiplier voltage was 2580 V.Selected ion monitoring (SIM) was used as detector.Ions m/z 139 and m/z 124 were used as target ions of the diosgenin and the internal standard,respectively.Ions m/z 300 and m/z 302 were used as assistant quantitative ions.Results:The linear range of the diosgenin standard curve was 0.013-1. 67μg·mL~(-1),The detection limit was 0.013μg·mL~(-1),The av- erage recoveries of diosgenin was 92.03%. Precision of determination were 2.9% (intra-day) and 2.4% (inter -day),respectively.Conclusion:The method of GC-MS for determination of diosgenin in rats plasma is simple and reliable.
The New Method to Calculate the Complex Effective Permittivity of Mixed Aqueous Electrolyte Solution at Microwave Frequency

Some models have been established to calculate the static dielectric constant of aqueous electrolyte solution.Others models based on the fitting parameters in Debye's equation by a lot of experiments and consequently were used to calculate the real and imaginary part of the complex effective permittivity of few aqueous electrolyte solutions,such as saline water and seawater.Here,the effective permittivity for various aqueous electrolyte solutions at microwave frequency has been gotten by the experiments,and we introduce the complex impact factor of different ion based on the experimental data and proposed a new model to calculate the complex effective permittivity of aqueous electrolyte solutions at microwave frequency.The calculated results are compared with the measured results at 915MHz and 2450MHz.The good agreement can be observed.This study offers base to calculate the distribution of electromagnetic field and temperature in the chemical reaction in dilute solution irradiated by microwaves and high potential applications of the method in the fields of geophysics,remote sensing,and inspection of water pollution.
A computational study of monolayer hexagonal WTe2 to metal interfaces

Monolayer (ML) hexagonal (2H) WTe2 is predicted to be the best channel material of tunnel field effect transistor (TFET) and metal–oxide–semiconductor field effect transistor (MOSFET) among ML transition‐metal dichalcogenides. Actual devices based on 2H WTe2 typically have a contact with metal. We explore for the first time the interfacial properties between ML 2H WTe2 and Sc, Ti, Pd, Pt, Ag, and Au by using ab initio electronic structure calculation and ab initio quantum transport simulations. The energy bands of ML 2H WTe2 on Sc, Ti, Pd, and Pt substrates are destroyed strongly due to strong adhesion of ML 2H WTe2 with metal substrates, and ML 2H WTe2–Sc, −Ti, −Pd, and −Pt systems are regarded as new metallic materials. Weak adhesion is formed between ML 2H WTe2 and the Ag and Au surfaces, with the electronic energy band of ML 2H WTe2 being identifiable. Ag and Au form n‐type Schottky contact with ML 2H WTe2 at the vertical direction with electron Schottky barrier height (SBH) of 0.24 and 0.49 eV, respectively. In contrast, Pd, Pt, and Ti form p‐type Schottky contact with ML 2H WTe2 in the lateral direction with hole SBH of 0.26, 0.40, and 0.63 eV, respectively. Our study not only presents a theoretical insight into the ML 2H WTe2–metal interfaces but also help in ML 2H WTe2 based device design.
A novel impedimetric aptasensor based on AuNPs–carboxylic porous carbon for the ultrasensitive detection of ochratoxin A

A novel aptasensor based on AuNPs–carboxylic porous carbon (cPC) is developed for the ultrasensitive detection of ochratoxin A (OTA) via electrochemical impedance spectroscopy. The prepared AuNPs–cPC, which combines the porous structure of cPC with the good conductivity of AuNPs, is used as an excellent carrier for the immobilization of capture DNA (cDNA), which enhances the loading of the aptamer and amplifies the impedimetric signal. When the target OTA is present, due to the complex effect of the three-dimensional macroporous structure of the AuNPs–cPC on the immobilization of cDNA, hybridization between the aptamer and cDNA and the conformational change of the OTA-aptamer, the OTA-aptamer complex may not be released from the electrode surface, which leads to an increase in the Rct signal. Through optimization of the important parameters, the optimal aptamer concentration of 10 μmol L−1 and optimal incubation time of 9 min are obtained. Under the optimal conditions, the ΔRct is linearly proportional to OTA concentration in a logarithmic way in the range of 1 × 10−8 to 0.1 ng mL−1, and the actual limit of detection is 1 × 10−8 ng mL−1.
Second order perturbation theory to determine the magnetic state of finite size aromatic hydrocarbons molecules

Conjugated system have complex behaviors when increasing the number of monomers, which is one of the reasons that makes long oligomers hard to be characterized by numerical methods. An ex- ample of this are fused-azulene, a molecule that has been reported to displays an increasing magnetic moment with system size. A similar system composed of symmetric fused-benzene rings is reported to be always no magnetic. Instead of the empiric parametrized Pariser-Parr-Pople (PPP) Hamiltonian, a standard model for conjugated molecules, we consider the Hubbard Hamiltonian to explore a range of low electronic correlation by means of perturbation theory (PT). We show that a simple second-order perturbation treatment of electronic correlations by means of Rayleigh-Schroedinger PT allow to accurately infer about the magnetic state of these long complex {\pi}-conjugated molecules. For fused-azulene our results supports the hypothesis that the high-spin ground state on azulene oligomers comes from the frustrated geometry of these chains. We validate this approach using Density Matrix Renormalization Group (DMRG) calculations. Our procedure procedure could be helpful to describe the magnetic ground state of a larger set of conjugated molecules.
ONE-STEP SYNTHESIS OF PYRIDINE AND 4H-PYRAN DERIVATIVES FROM BISARYLIDENECYCLOHEXANONE AND MALONONITRILE UNDER MICROWAVE IRRADIATION

Cyano 2 methoxylpyridine and 2 amino 3 cyano 4H pyran derivatives were synthesized in single step action 2,6 bisarylidenecyclohexanone on malononitrile in sodium hydroxide/methanol or piperidine/ethanol under microwave irradiation in good yields.
A novel MIMO-OFDM Alamouti architecture for 5G systems at 26 GHz

This paper introduces a novel 5G Millimetre-wave (mmWave) real-time Multiple-Input Multiple-Output (MIMO) testbed architecture aimed to tackle the 5G communication requirements. In this context, a $2\times2$ Alamouti coder and decoder is implemented in the VC707 Field Programmable Gate Array (FPGA) development board and its logical resources usage compared to those in the literature. In order to reduce the Hardware (HW) footprint of the proposed MIMO - Orthogonal Frequency Division Multiplexing (OFDM) Alamouti system, a pipelined modular architecture is considered, in order to boost HW utilisation and performance of MIMO-OFDM systems. A single pair of Inverse Fast Fourier Transform (IFFT)/ Fast Fourier Transform (FFT) is enough to process data for each MIMO antenna path. The achieved results show the importance of FPGA resource optimisation, demonstrating how depend coding and modulation are to hardware resources. Moreover, a Radio Frequency (RF) architecture is proposed to address $2 \times2$ MIMO communications at 26 GHz.
Design and Experimental Analysis of a Novel Compact and Flexible Super Wide Band Antenna for 5G

This paper investigates the design and practical implementation of a novel Super Wide Band (SWB) antenna on a flexible substrate. The antenna is designed on the Ultralam 3850 substrate having a compact dimension of $60\times 40\times 0.1$ mm3. This antenna has an operating frequency band from 1.74 to 100 GHz with a bandwidth (BW) ratio of approximately 57.47:1. Such an extended frequency coverage makes this antenna operable in a wide variety of wireless application areas, including 5G and the Internet of Things (IoT). The simulated performance of the designed antenna is analyzed here with respect to different antenna parameters, including reflection coefficient, radiation pattern, gain, efficiency, and surface current. The proposed antenna prototype is fabricated, and experimental validation is provided through the measurement using a programmable network analyzer (PNA).
Corpus and Baseline Model for Domain-Specific Entity Recognition in German

Transfer Learning approaches are a promising means to analyze low-resource domain specific texts. The German SmartData corpus is the first German corpus, annotated with entities from different domains, and thus allows to investigate transfer learning approaches for Named Entity Recognition (NER) on different domains. In order to prepare such investigations, this work includes a thorough analysis of the SmartData corpus, and a revision w.r. t. annotations and the split into training and test data, considering the distribution of document and entity types. Based on that a baseline model for NER using BiLSTM-CRF neural networks including hyperparameter optimization is presented.
Data Fusion for Intelligent Crowd Monitoring and Management Systems: A Survey

Intelligent Crowd Monitoring and Management Systems (ICMMSs) have become effective resources for strengthening safety and security along with enhancing early-warning capabilities to manage emergencies in crowded situations of smart cities and massive gatherings events. The main advantage of such systems is their ability to detect multiple features associated with the crowd gathering, as they enable multi-source sensors, multi-modal data, and powerful intelligent and analytical methods. Unlike traditional crowd monitoring systems, which make use of simplex forms of different data types, data and information associated with crowded scenarios can be collected, fused, processed and analyzed in large quantities for accurate global assessment and enhanced decision making processes in an ICMMS. Therefore, data fusion is introduced as an enabler to decrease data quantity, reduce data dimensions, and improve data quality. In this paper, we first survey the literature on data fusion application in crowd monitoring systems as we are developing a state-of-the-art ICMMS with data fusion as a major platform enabler. Next, we discuss some popular data fusion architectures and classifications from different perspectives. Based on this, we propose a multi-sensor, multi-modal, and dimensional ICMMS architecture based on data fusion. Then, we identify the data fusion processes in the ICMMS and classify them into sensor fusion, feature-based data fusion, and decision fusion. Relevant algorithms, applications and examples of three classes are elaborated. Finally, future data fusion research directions are discussed.
Online Extremism Detection: A Systematic Literature Review With Emphasis on Datasets, Classification Techniques, Validation Methods, and Tools

Social media platforms are popular for expressing personal views, emotions and beliefs. Social media platforms are influential for propagating extremist ideologies for group-building, fund-raising, and recruitment. To monitor and control the outreach of extremists on social media, detection of extremism in social media is necessary. The existing extremism detection literature on social media is limited by specific ideology, subjective validation methods, and binary or tertiary classification. A comprehensive and comparative survey of datasets, classification techniques, validation methods with online extremism detection tool is essential. The systematic literature review methodology (PRISMA) was used. Sixty-four studies on extremism research were collected, including 31 from SCOPUS, Web of Science (WoS), ACM, IEEE, and 33 thesis, technical and analytical reports using Snowballing technique. The survey highlights the role of social media in propagating online radicalization and the need for extremism detection on social media platforms. The review concludes lack of publicly available, class-balanced, and unbiased datasets for better detection and classification of social-media extremism. Lack of validation techniques to evaluate correctness and quality of custom data sets without human interventions, was found. The information retrieval unveiled that contemporary research work is prejudiced towards ISIS ideology. We investigated that deep learning based automated extremism detection techniques outperform other techniques. The review opens the research opportunities for developing an online, publicly available automated tool for extremism data collection and detection. The survey results in conceptualization of architecture for construction of multi-ideology extremism text dataset with robust data validation techniques for multiclass classification of extremism text.
Compact Dual-Band Differential Bandpass Filter with Flexible Frequency Ratio Using SIR and EMSIW Technology

This letter presents a compact dual-band differential BPF based on SIR and EMSIW technology with flexible frequency ratio. The proposed filter is composed of three metal layers. Two half-wavelength SIRs are distributed along the symmetry plane on the top layer which constructs the first DM passband. A hybrid slot feed is embedded in the middle layer to couple signals to/from the resonators. The middle metal layer is also regarded as a circuit grounding. The EMSIW cavity placed in the bottom layer has been utilized to generate the second passband. Moreover, the thru-via array which usually provides the side-wall effect is not required in the EMSIW cavity. In addition, the two DM center frequencies could be controlled separately which makes this novel design more attractive. A prototype of the proposed filter operating at 3.3 and 8.3 GHz is fabricated and measured to demonstrate the design idea. The measurements match well with the simulated ones.
Experimental Validation of Interface Aiding Human Depth Prediction on Images in terms of Usability

The purpose of this study is to maximize the remote participation experience of disabled and elderly people who have mobility problems. We reviewed the prior art on visual interfaces from two aspects and proposed a stereo matching interface to improve their combined performance. The first aspect is hedonic quality as it relates to reality of going-out experience and usability. The second aspect is that of utility. Our visual interface is a human-assisted sensing system that enables the user to move the remote-controlled robot as intended by allowing the user to predictably recognize remote depth information. Depth recognition of an arbitrary point on the display is possible just using a planar image from the robotic perspective. In this study, performances of specific tasks resulting from use of the interface were experimentally validated. The results of the stereo matching task on the display between the target feature point and its depth index as summarized by edging showed that the slower the relative approach velocity was, the easier the stereo matching task was. However, various designs created to achieve the relative approach velocity conflicted with design constraints meant to improve usability or hedonic quality.
Parkinson’s Disease Prediction using Adaptive Quantum Computing

Adaptability is the most generous thing we need to acquire to solve any kind of prediction model design and implementation. Dementia is the most dangerous disease which will affect the human nervous system. Parkinson’s is one of the most occupied space in dementia. It will affect complete operational behavior of the patient. Using machine learning and the quantum computing the proposed system is working on implementing the speech signal-based implementation on the Parkinson’s disease prediction. The prediction involves the four major algorithms of the machine learning like Naïve Bayes, K-NN, Decision trees and Artificial Neural Networks. Some of the ensemble learning models in machine learning used for the increment of the accuracy of the models by combining several combinations of the models. The performance of the model will be decided using the standard dataset from UCI machine learning repository. The ensemble models overcome the accuracy of the most accurate method like neural networks. The proposed system consists of the multi-layer perceptron which is one of the most relevant optimization methods in the machine learning.
Anomalous Transport of Infectious Diseases in Structured Populations

Structural properties of populations strongly influence dynamic transport and interaction such as the transmission of infectious diseases. In this paper a hierarchical block model is investigated, that schematically describes the configuration of social communities. It is shown that these models can produce super-diffusive spread and that the balance between intra- and inter-community contacts is a suitable factor for controlling the expression of this feature. The qualitative characteristics of epidemics simulated with this model are reproduced in numerical simulations of a basic fractional-in-space differential equation SIR model. Based on a stochastic formulation of the block structure, a preliminary connection between both models can be derived. The results of this paper confirm that certain structural characteristics of interacting populations can be simulated in aggregated models by employing fractional derivatives.
Compact Dual-Polarized Dipole Antenna Design for Base Station Applications

A small-size dual-polarized dipole antenna for base station applications is presented in this paper. The proposed antenna consists of three parts: main radiator, feeding baluns, and reflector. By optimizing the shape of the slot of the dipole, it is possible to obtain compact high-gain antenna designs because antenna aperture efficiency might be improved through adjusting the current distribution. Meanwhile, four bent arms could be used to further decrease the antenna size through extending the current path. Measured results show that the return loss of the proposed antenna is more than 14.5 dB over the band of 3.4 GHz-3.6 GHz, and the isolation is higher than 27 dB. Moreover, antenna gain is higher than 8 dBi, and half power beamwidth (HPBW) is 65°±5°. The proposed antenna can be used in the fifth generation (5G) mobile communication systems.
Angular Velocity Measurement With Improved Scale Factor Based on a Wideband-Tunable Optoelectronic Oscillator

Optoelectronic oscillator (OEO) has received growing attentions from researchers in the field of demodulations for optical sensors because of its excellent performance of combining optics and electronics in signal processing. In this article, we present a wideband-tunable OEO for angular velocity measurement. The OEO consists of two cascaded microwave photonic filters (MPFs), namely, a dispersion-induced MPF for mainly determining the OEO’s oscillating frequency and a two-tap MPF for the fine selection of the oscillating frequency. With proper control of polarization of the optical signal, the angular velocity is first mapped into the central frequency shift of the dispersion-induced MPF, which will then cause an oscillating frequency shift of the OEO. This approach ensures a large-scale factor for the angular velocity measurement due to the fact that a small phase change in the optical domain causes a large frequency shift in the electrical domain. An experiment is conducted to validate the scheme. The experimental results show that the oscillating frequency shift can reach nearly 700 MHz when the system rotates at about 1 rad/s by using a 560-m Sagnac loop. In addition, the measurement range can be achieved to 5.24 rad/s with a 2.5-GHz oscillating frequency tuning range and the maximum measurement error is 0.06 rad/s.