Miracle-dz/UniFAR-FTUs huggingface dataset:
Increasing Depth of Field of Tilted Diffractive Lens in Image Classification Task

This work considers the influence of the tilt of a diffractive lens on the shape and size of the focused area. Analytical relations describing the geometry of the focused region for various spectral channels are given. The possibility of increasing by several times the size of the zone of accurate image classification using a neural network has been demonstrated. The results of the work can be used in machine vision units for robots and unmanned aerial vehicles.
Speech Emotion Identification Using Linear Predictive Coding and Recurrent Neural

Social, affective communication in recent years shows significant developments, especially in the verbal understanding of emotions. Human connection naturally adjusts to their responses based on the actions of their interlocutor in a particular matter. Previous research has shown that the use of neural network architecture can identify emotions based on speech, but the results of accuracy are not good due to the imbalance of data and problems with the design of the classification system. This study uses Linear Predictive Coding (LPC). LPC can represent the pronunciation of one’s dialogue. From 16 coefficient LPC is used as a vector feature as input for voice emotion identification using Recurrent Neural Network (RNN). Long Short Term Memory (LSTM) or Gated Recurrent Unit (GRU) architecture is used to overcome vanishing or exploding gradient. At the identification stage, that uses forward propagation with a softmax activation function. We have conducted a simulation using RNN as a method for making emotional identification. The results of this study RNNGRU using Adam optimization model with a learning rate of 0.001 get an accuracy of 90.93% and a losses value of 0.216. In comparison, the RNN-LSTM got an accuracy of 87.50% and losses value of 0.262. The experimental results show that the best model is achieved when using the RNN-GRU with the Adam optimization method. The F-Measure value obtained is 0.91.
The meta-GGA functional: Thermochemistry with a kinetic energy density dependent exchange-correlation functional

The meta-GGA functional recently proposed by Perdew et al. [Phys. Rev. Lett. 82, 2544 (1999)] goes beyond the generalized gradient approximations (GGAs) since it employs the noninteracting kinetic energy density in addition to the local density and the gradient of the local density. In this paper, we focus on thermochemistry and present an extensive assessment of the meta-GGA functional. We find that for atomization energies meta-GGA is as accurate as the computationally more involved Perdew–Burke–Ernzerhof (PBE) hybrid scheme. However, the geometries and frequencies obtained with meta-GGA are worse than those obtained with PBE or PBE hybrid. We give a detailed analysis of our results and propose explanations for the observed differences between PBE, PBE hybrid, and meta-GGA. Furthermore, we address the question whether the parameters in the meta-GGA functional are optimal for our benchmark set.
Giant polarization in organic heterostructures

Deep levels or polarization effects can influence the charge transport through organic layers. Both effects can be identified by charge deep-level transient spectroscopy (Q-DLTS). Here, deep levels or polarization effects cause the charge transient signal Q(t) to be dependent or independent on the bias voltage, respectively. Q-DLTS, accompanied by feedback charge capacitance (FCM) measurements, has been used in the present work to investigate an organic heterostructure grown on an inorganic semiconductor. GaAs(100) substrates (n=0.34×1018 cm−3) were sulfur passivated by wet chemical etching and additional annealing under ultrahigh vacuum (UHV) conditions. Organic molecular beam deposition was used for the growth of 20 nm of 3,4,9,10-perylenetetracarboxilic dianhydride (PTCDA, Lancaster) and 27 nm of tris-(8-hydroxyquinoline) aluminum (Alq3, Syntec). Silver was evaporated on the Alq3 film through a shadow mask resulting in an array of circular contacts with an area of A=2.1×10−7 m2. The back contact to the.
Study of Alkoxide Silica Gels by Infrared Spectroscopy

The infrared spectra of silica gels made from alkoxides are different from those made from fumed silica, and from the infrared spectra of other amorphous silicas in which the SiO2 network is relatively complete. One important feature of these spectra is the occurrence of an absorption band at 960 cm−1 due to the vibration of dangling -Si-OH bonds in the alkoxide gels, which is not present in many other silicas. There also are less well-defined absorption bands due to structural defects for the alkoxide gels, but their interpretation is speculative. Dried gels made with more concentrated reactants contain more defects, and heat treatment reduces the concentration of dangling bonds, especially above 900°C. For gels containing fluorine, two absorption bands appear at 932 and 980 cm−1, and these are interpreted as arising from terminal -SiF and -SiF2 groups, respectively.
A DFT/TDDFT mission to probe push–pull vinyl coupled thiophene oligomers for optoelectronic applications

Vinyl coupled thiophene oligomers (VCTOs) are one of the active components in organic solar cells. In the present study, VCTOs with various acceptor groups (–CN, –NO2 and –COOH) have been considered and their optoelectronic properties have been evaluated using DFT/TDDFT calculations. In total, 17 VCTOs, including 3 already reported, have been considered. The computed results reveal that the reference VCTOs (VCTO1, 2 and 3) can be used as possible electron transport materials, and the newly designed VCTOs are found to be promising hole transport materials. Among these, VCTO4b is found to show a lower band gap, whereas VCTO3c has a higher band gap. Furthermore, the study explores the roles of donor and acceptor groups in the band gap, ionization potential, electron affinity, exciton binding energy and light harvesting efficiency of these VCTOs. The spectral analysis shows that the modelled VCTOs have strong n → π* transitions, while the reference VCTOs were found to show predominant π → π* transitions. In summary, 9 out of 14 of the designed VCTOs were found to show better optoelectronic properties than their reference molecules.
Understanding the tradeoffs of LoRaWAN for IoT-based Smart Irrigation

As LoRaWAN has been increasingly used in the Internet of Things (IoT) smart agriculture, efficacious deployments of this technology need a clear understanding of its performance and scalability tradeoffs. This paper proposes a two-step methodology to evaluate the performance of LoRaWAN based on simulation for understanding the behavior of the air interface and measurement for understanding the behavior of the IoT Platform. We conducted a performance analysis study in a smart irrigation scenario, varying the distance from sensors to the gateway, the sensor density (number of sensors), and the LoRaWAN spreading factor. Our results show that the LoRa air interface poses the most stringent scalability limits, mainly related to the number of sensors actively transmitting from a farm parcel to the gateway. The IoT Platform adds some delay but does not notably interfere with the overall performance of the solution.
Bifurcation Control in an Optimal Velocity Model via Double Time-Delay Feedback Method

In this paper, a double time-delay feedback control of an optimal velocity model (OVM) is investigated. Double time-delay means that there exist two different state feedback control signals in the controlled OVM system, which are related to the velocity difference and the optimal velocity difference, respectively. Through linear stability analysis, the critical condition of Hopf bifurcation for the controlled OVM is derived. Utilizing the characteristics of Hopf bifurcation and the improved definite integral method, appropriate double time-delay feedback control strategy is designed in term of the number of unstable eigenvalues of the characteristic equation to suppress the stop-and-go waves generated by the uncontrolled OVM. Note that when the number of unstable eigenvalues is equal to zero, the controlled OVM is stable, otherwise, it is unstable. Numerical simulations are executed to validate the accuracy and feasibility of the design of double time-delay control strategy. Finally, case studies approximating the actual traffic situation are given, and the appropriate combination of control parameters is selected through the verified design steps. In addition, the measured data from NGSIM are also considered.
Low Temperature Silicon Nitride and Silicon Dioxide Film Processing by Inductively Coupled Plasma Chemical Vapor Deposition

High‐density plasma technology is becoming increasingly attractive for the deposition of dielectric films such as silicon nitride and silicon dioxide. In particular, inductively coupled plasma chemical vapor deposition (ICPCVD) offers a great advantage for low‐temperature processing over plasma‐enhanced chemical vapor deposition (PECVD) for a range of devices including compound semiconductors and magnetic heads. In this paper, the development of low temperature (<200°C) silicon nitride and silicon dioxide films utilizing ICP technology is discussed. The material properties of these films have been investigated as a function of ICP source power, radio‐frequency chuck power, chamber pressure, gas chemistry, and temperature. The ICPCVD films are compared to PECVD films in terms of wet etch rate, stress, and other film characteristics. Two different gas chemistries, and , were explored for the deposition of ICPCVD silicon nitride. The ICPCVD silicon dioxide films were prepared from . The wet etch rates of both silicon nitride and silicon dioxide films are significantly lower than films prepared by conventional PECVD. This implies that ICPCVD films prepared at these low temperatures are of higher quality. The advanced ICPCVD technology can also be used for efficient void‐free filling of high aspect ratio (3:1) sub‐micrometer trenches. © 2000 The Electrochemical Society. All rights reserved.
Optical Fiber Refractive Index Sensor Based on the SPR Using a Multiple D-shaped Ag Nanowire

This study proposes the simulation of multiple D-shaped Ag nanowires (AgNWs) based on a highly sensitive surface plasmon resonance (SPR) optical fiber external refractive index (RI) sensor. The effect of SPR on the core-guided and plasmon modes of the designed multiple D-shaped AgNWs was used to clarify the changes in the external RI. The influencing parameters of the proposed sensor, such as the amount of residual cladding (D) and radius of the AgNW (ra), were optimized using the method of finite element (FEM) on the basis multiphysics of COMSOL. Simulation results verify that the maximum sensitivity of the SPR fiber sensor is 5.1 μm/RIU in the external RI range of 1.35 - 1.39, with 0.4 μm residual cladding (D) and 60 nm radius of the AgNW (ra). The results manifested the potential of the proposed SPR sensor-based multiple D-shaped AgNW structure of the biosensor applications.
Potential Use of Hyssopus officinalis and Borago officinalis as Curing Ingredients in Pork Meat Formulations

Simple Summary Health risks associated with nitrites as curing agents have led consumers to search for products without those additives. Herbs have been used in medicine for many years and are usually positively perceived by consumers. Good-quality products with medicinal plants may be an alternative for those who try to avoid additives other than salt and spices. Hyssopus officinalis and Borago officinalis were tested for their potential to be used as colour forming and antioxidant agents. Both herbs were used in pork meat formulations along with nitrate reducing bacteria. A colour formation similar to a control product containing nitrite was noted in all the samples. Borage had a stronger antioxidant effect. Those additives can be used as an alternative to nitrite cured pork products. Abstract The replacement of nitrites in pork meat products has been a studied issue for many years. Due to potential health threats associated with these additives, consumers tend to search for alternative meat curing methods. In this study, Hyssopus officinalis and Borago officinalis were tested for their potential to be used as colour-forming and antioxidant agents. Dry plant samples from various sources were tested for fat, protein, ash, polyphenol and nitrate content. There were significant differences between the herbs depending on source. Two control samples (containing curing salt and sodium chloride with nitrate reducing bacteria) and samples with herbs (hyssop, hyssop with nitrate reducing bacteria, borage, borage with nitrate reducing bacteria)—0.5% of the meat mass—were prepared and stored for 15 days. In the samples with herbs and bacterial cultures, a red colour was developed, the TBARS values were low and DPPH activity was strong. All the samples with herbs had lower residual nitrite levels compared to the samples with curing salt. Borage had a stronger influence on colour and antioxidant stability of the meat samples compared to hyssop. However, both herbs can be used as colour-forming and antioxidant agents along with nitrate-reducing bacteria.
Emerging role of artificial intelligence in therapeutics for COVID-19: a systematic review

Abstract To elucidate the role of artificial intelligence (AI) in therapeutics for coronavirus disease 2019 (COVID-19). Five databases were searched (December 2019–May 2020). We included both published and pre-print original articles in English that applied AI, machine learning or deep learning in drug repurposing, novel drug discovery, vaccine and antibody development for COVID-19. Out of 31 studies included, 16 studies applied AI for drug repurposing, whereas 10 studies utilized AI for novel drug discovery. Only four studies used AI technology for vaccine development, whereas one study generated stable antibodies against SARS-CoV-2. Approx. 50% of studies exclusively targeted 3CLpro of SARS-CoV-2, and only two studies targeted ACE/TMPSS2 for inhibiting host viral interactions. Around 16% of the identified drugs are in different phases of clinical evaluation against COVID-19. AI has emerged as a promising solution of COVID-19 therapeutics. During this current pandemic, many of the researchers have used AI-based strategies to process large databases in a more customized manner leading to the faster identification of several potential targets, novel/repurposing of drugs and vaccine candidates. A number of these drugs are either approved or are in a late-stage clinical trial and are potentially effective against SARS-CoV2 indicating validity of the methodology. However, as the use of AI-based screening program is currently in budding stage, sole reliance on such algorithms is not advisable at this current point of time and an evidence based approach is warranted to confirm their usefulness against this life-threatening disease. Communicated by Ramaswamy H. Sarma
Property Prediction on Surfactant by Quantitative Structure‐Property Relationship: Krafft Point and Cloud Point

General empirical relationships have been developed for estimating the Krafft point of anionic surfactants and cloud point of nonionic surfactants using the quantitative structure‐property relationship (QSPR) method. For the Krafft point, a six‐parameter structure‐property relationship (R2=0.941) was obtained for a diverse set of 46 anionic hydrocarbon surfactants and a three‐parameter relationship (R2=0.864) for 19 linear fluorocarbon anionic surfactants by employing topological, thermodynamic, and structural descriptors. For the cloud point of 64 nonionic surfactants with six representative structures, the best multiple linear regression model involved five parameters and had a correlation coefficient of R2=0.921. From these relations Krafft point or cloud point of surfactants can be estimated based on their molecular structure.
NADS-RA: Network Anomaly Detection Scheme Based on Feature Representation and Data Augmentation

Network anomaly detection aims to identify network anomalies, and it has obtained many achievements using the supervised classification technique. Since the supervised classifier depends on the prior data, it is difficult to accurately classify the rare anomalies when they account less in the training set. Data augmentation can tackle the imbalanced training set problem through creating artificial rare anomaly samples. However, the existing data augmentation methods either ignore the data distribution or ignore the spatial knowledge between features. Therefore, this article addresses this issue by proposing a Network Anomaly Detection Scheme based on feature Representation and data Augmentation (NADS-RA). Re-circulation Pixel Permutation strategy is first designed as feature representation strategy to construct images, and it rotates each feature left by the times of feature number to maintain the spatial knowledge between original network traffic features. An image-based augmentation strategy is thus designed to produce augmented images according to the distribution characteristics of rare network anomaly images with the help of Least Squares Generative Adversarial Network, which alleviates the effect of imbalanced training set and avoids over-fitting. After that, NADS-RA is implemented on the Convolutional Neural Network classification model. We conduct experiments on five public benchmark datasets, including NSL-KDD and UNSW-NB15, and so on, and compare against 12 detection methods and 17 data generation methods. The experimental results demonstrate the superior effectiveness of our work to state-of-the-art methods and the general applicability in different scenarios.
Mesh-Network Equivalent Model for Unified Rectangular Microstrip Antenna Analysis

A generalized mesh-network equivalent model for a microstrip rectangular patch antenna (RPA), which is simple yet efficient is proposed, studied, and demonstrated in this article. The mesh-network equivalent model is established through the segmentation of RPA width and radiation parameters into $M$ sections, as well as the segmentation of RPA length into $N$ sections. A general design procedure to realize the mesh-network equivalent model is addressed and described for any number of RPA segmentations, as desired by specific applications. Subsequently, the proposed mesh-network equivalent model is used to accurately evaluate the input impedance responses at various feed positions along the length of RPA. In addition, to include the effects of a slot etched inside the RPA geometry, a generalized microstrip coupled line equivalent model is proposed, analyzed, and discussed in detail. Eventually, the microstrip coupled line model in conjunction with the proposed mesh-network equivalent model can accurately estimate the input impedance response of a slotted RPA, and has been verified for RPA with center slot, edge slot, and multiple slots as examples in this work. Two cases with multiport excitation of RPA are also analyzed and presented as a part of this work. For demonstration purposes, all these prototypes are designed, fabricated, and measured at 28 GHz. The proposed equivalent model-based results are validated by measured and simulated counterparts. Furthermore, the extension of the proposed mesh-network model to accommodate slots of smaller size inside RPA, RPA loaded with via, inverted-F antenna, and multimode operation are demonstrated and discussed through a comparison between mesh-network modeling results and simulation results.
Adaptive Terminal Sliding Mode Backstepping Control for Virtual Synchronous Generators

A novel control strategy based on microgrid (MG) is proposed in this paper to improve the low inertia characteristics of traditional voltage source three-phase converters (VSC). The proposed control strategy consists of the Virtual Synchronous Generator (VSG), backstepping control and adaptive terminal sliding mode control. The VSG can improve the virtual inertia of the system in the application of inverter, the sliding mode method can improve the robust performance of the system, and the adaptive control method can be used to compensate the parameter transient error of the system. The low inertia characteristic of MG system in traditional VSC is improved. The simulation results has shown that the designed controller has better antiinterference and inertia performance.
Association Between Angiotensin II Receptor Blockers and the Risk of Lung Cancer Among Patients With Hypertension From the Korean National Health Insurance Service-National Health Screening Cohort

Objectives: The objective of this study was to estimate the risk of lung cancer in relation to angiotensin II receptor blocker (ARB) use among patients with hypertension from the Korean National Health Insurance Service-National Health Screening Cohort. Methods: We conducted a retrospective cohort study of patients with hypertension who started to take antihypertensive medications and had a treatment period of at least 6 months. We calculated the weighted hazard ratios (HRs) and their 95% confidence intervals (CIs) of lung cancer associated with ARB use compared with calcium channel blocker (CCB) use using inverse probability treatment weighting. Results: Among a total of 60 469 subjects with a median follow-up time of 7.8 years, 476 cases of lung cancer were identified. ARB use had a protective effect on lung cancer compared with CCB use (HR, 0.75; 95% CI, 0.59 to 0.96). Consistent findings were found in analyses considering patients who changed or discontinued their medication (HR, 0.50; 95% CI, 0.32 to 0.77), as well as for women (HR, 0.56; 95% CI, 0.34 to 0.93), patients without chronic obstructive pulmonary disease (HR, 0.75; 95% CI, 0.56 to 1.00), never-smokers (HR, 0.64; 95% CI, 0.42 to 0.99), and non-drinkers (HR, 0.69; 95% CI, 0.49 to 0.97). In analyses with different comparison antihypertensive medications, the overall protective effects of ARBs on lung cancer risk remained consistent. Conclusions: The results of the present study suggest that ARBs could decrease the risk of lung cancer. More evidence is needed to establish the causal effect of ARBs on the incidence of lung cancer.
Project Management Methodology for Automating Performance Monitoring Processes Based on Mobile Technologies

Over the past few years mobile technologies have been rapidly developed through improved wireless communications, expanded mobile functionality, and the advent of the open source mobile platforms. Today mobile devices can be used not only for communication, but also for educational, commercial purposes and, of course, in the production of goods and services. And this is the real progress! The article considers the results of the study on the use of mobile technologies to automate the monitoring processes of production indices in rolling production. The subject of the study was the process of developing a mobile application for operational monitoring and control of technological indicators of production. And the goal is to increase the efficiency of the processes for the development of automation solutions for the operational monitoring and control of technological indicators of production based on mobile technologies. However, due to the limitation of the volume of the article, only one task was discussed in detail: the method of managing the project for automating the processes of monitoring the production indicators in rolling production.