Miracle-dz/UniFAR-FTUs huggingface dataset:
Generalized Frequency Dependent Small Signal Model for High Frequency Analysis of AlGaN/GaN MOS-HEMTs

Traditional lumped small signal equivalent circuit models of AlGaN/GaN metal oxide semiconductor high electron mobility transistors (MOS-HEMTs) are made up of constant valued circuit elements. Such models are unable to capture the high frequency behavior (above 20 GHz) of the device. In this work, a modified small signal equivalent circuit model of AlGaN/GaN MOS-HEMTs is presented. The key feature of the proposed model is that the values of the different circuit elements in the model are considered to be frequency dependent in nature and not constants. The frequency dependent value of each circuit element is mathematically represented using polynomial functions where the coefficients of the functions are determined via a least-square curve fitting approach. This frequency dependent attribute of the circuit element values ensures that the proposed model is very accurate at high frequencies without sacrificing the compactness of the model topology. The accuracy of the proposed model has been verified up to 50 GHz using experimentally measured Y-parameters of AlGaN/GaN MOS-HEMTs having a different gate dielectric and gate length.
Network Data Classification Mechanism for Intrusion Detection System

Intrusion detection system (IDS), as a network security device, monitors network data in real time and responds actively when it detects suspicious transmissions. However, suffered from the large amount of redundancy and high correlation of network data, the traditional IDS have defects in low detection rate and high computational overhead. In this paper, we propose a network data classification mechanism (CPEL). Data preprocessing of network traffic data is first performed using correlation-based feature selection (CFS) and principal component analysis (PCA). CFS selects the best features in the data, PCA to dimensionality reduction and denoising. Then, we use multiple classifiers to perform anomaly detection, and select the best three classifiers according to the classification effect. On this basis, we use the majority vote as the ensemble learning (EL) method of combination rules to further improve the performance of the classifier. Experimental results on the benchmark NSL-KDD and WSN-DS datasets indicate that this mechanism outperforms a single algorithm in accuracy and detection rate. In the meantime, computational overhead is remarkably reduced.
Analyzing and Predicting User Donations in Social Live Video Streaming

Social live video streaming has been a major internet phenomenon with the rise of platforms like Facebook-Live, Youtube-Live, and Twich. New features tailored by these platforms provide unprecedented opportunities for viewers to engage and for broadcasters to gain tangible rewards through user donation. In this article, we conduct an in-depth analysis of user donations, a distinctive feature and prominent phenomenon in social live streaming, which however we have very limited understandings with. Based on a publicly available (anonymized) dataset with detailed information on over 4 million donation relationships that cover over 2 million users and worth in total over 200 million US dollars, we quantitatively reveal donation disparity and dynamics of donation relationships. Among other results, we find that repeated donation relationships largely exist and the strength significantly increases with the repetition level. Finally, we adopt machine-learned classifiers to accurately predict future donations. Our findings shed lights on the user retention problem and the design of social live video streaming services.
Improved Twofish Algorithm: A Digital Image Enciphering Application

With the growth of networks, the Internet of Things (IoT) and new cyber attacks pose threats to privacy and security, secure communication is therefore becoming one of the most crucial concerns. For this purpose, symmetric algorithms namel; The Rijndael algorithm, the Serpent algorithm, and the TWOFISH algorithm pay equal attention. In this paper, the TWOFISH algorithm’s mathematical complexity is improved by using substitution boxes (S-boxes) drawn from a multiplicative group of units of chain ring $\sum \nolimits _{i=0}^{7} {u^{i}F_{2}} $ . As these S-boxes have the property of having copious generators, they, therefore, produce a rich algebraic complexity. Moreover, the time complexity of the proposed work is modified by processing the 64-bit block throughout the process and reducing the number of subkeys. To measure the strength of the proposed algorithm, various standard color digital images, with a size of $256\times 256$ , are encrypted and tested. The computation speed of the encryption is compared to the standard TWOFISH algorithm’s speed and found that the newly designed algorithm is quite fast. For security analysis and quality assessment, various statistical tests are performed on the standard encrypted images. The results recommend that the proposed algorithm is a strong candidate for digital image encryption.
Implementation of BFP-Typed Apertometer: Reference Location and Accuracy

This work is to investigate the instrumentation implementing issues of back focal plane (BFP)-typed apertometer. Firstly, we introduce a general instrument architecture of the BFP-typed apertometer, including the hardware (optics and mechanics) of the system, operation procedures of control and measurement system, and also the digital image processing algorithm/software. Secondly, we are the first to discuss the gradual rising effect of total-internal-reflection (TIR) phenomenon, the criteria on how to “exactly” locate the reference ring on BFP images, and the corresponding accuracies. As a significantly important complement, we further discuss three important issues: 1) the influence of Goos–Hänchen shift on numerical aperture (NA) measurement; 2) comparison of one-dimension and two-dimension identification algorithms; and 3) comparison of center locating using Fourier correlation analysis (FCA) and Hough transformation (HT) algorithms.
A Statistical Model for GPA Estimation Based on High School Grades and Standard Exams

High-school grades (HSG) are often observed as unreliable measures for college admissions, and thought irrelevant to student’s college performance, because of the inconsistencies in grading standards across high schools. Many colleges and universities conduct their own admission exams along with some standard language tests such as IELTS and TOEFL, such examinations are seen more reliable in providing uniform and valid measures for assessing students’ capabilities and skills. This paper presents and tests three regression models for college Cumulative Grade Point Average (CGPA) based on several estimators, those are: student high school grades, student IELTS test results and Common Educational Proficiency Assessment (CEPA). The three models are: multiple linear regression model (MLR), logarithmic linear regression model (LLR) and polynomial expansion followed by spectral regression model (PESR). The study comes up with the following findings: 1) the best predictors for CGPA are IELTS writing scores, IELTS reading scores, high school averages and CEPA scores respectively according to their weights in the regression models. 2) Polynomial expansion followed by spectral regression model (PESR) gives better results in comparison with linear regression and logarithmic linear regression models.
Free Product Trials∗

With a pre-purchase product trial a seller discloses quality but also enables consumers to privately learn match values. This involves a tradeoff between increasing perceived quality, causing an upward shift in demand, and yielding information rents to consumers, causing a rotation in demand. In contrast to classic results, quality is revealed only when it is sufficiently high, and sometimes not at all. Fewer trials are offered when match is relatively more important than quality, and when there are fewer gains-fromtrade. “Cooling off” period rules that allow for free returns effectively require the seller to offer a product trial, and we demonstrate that these rules lead to higher consumer surplus, lower producer surplus, and an overall decrease in welfare when there are sufficient gains from trade. When a seller can commit to a trial policy before learning quality, a trial is only offered if quality is sufficiently low. (L15) (L5)
A Review on Antibacterial Effects of Fish Skin Mucus and Fish Lectins

Due to the constantly increasing demand for animal protein, aquaculture practices are intensified. Fishes grown in intense aquaculture systems are susceptible to diseases (Dash et al., 2018). Although there are various methods to prevent or combat diseases, these methods are often considered to possess a risk for the health of both the fish and the environment (Bilen et al., 2020). Therefore, to fight against diseases caused by microorganisms, recent studies have focused on natural antimicrobial agents (Nguafack et al., 2020; Palanikani et al., 2020; Elbeshti et al., 2020).
T. obliquus Cultivation Under Heterotrophic Conditions: Determination of Growth Parameters

Microalgae are a promising feedstock for the future of the industrial chemistry. Among microalgae, Tetradesmus obliquus (generally known as Scenedesmus obliquus) is one of the most studied species due to its robustness and its good performances for production of lipids, proteins and carbohydrates. However, the high production costs still limit large part of the possible applications. The exploitation of the heterotrophic metabolism for microalgae cultivation may potentially improve the biomass production sustainability, especially if organic substrates are obtained from wastewaters. However, this latter strategy has technical limits given by the issue of contamination by bacteria, which have growth rates ~ 10 folds higher than microalgae. The estimation of the microalgae growth parameters could allow optimization of the feeding strategy by which wastewaters are supplied to microalgae culture, and consequently, bacteria contamination minimization. In this work, an axenic culture of T. obliquus was isolated by a sonication pre-treatment followed by an incubation in solid media containing ampicillin (0.1 mg mL), penicillin (67 U mL) and streptomycin (0.07 mg mL). The axenic strain was then cultivated in heterotrophic condition under N-replete and N-starvation. Experimental data of biomass production and nitrogen consumption were well predicted by the Droop model. By means of the model fitting with experimental data, the optimal values for maximum theoretical growth rate (μ ), maximum nitrogen quota (qmax), minimum nitrogen quota (qmin) and maximum nitrogen uptake rate (ρmax) were estimated. By estimating, in the future, the growth parameters also for other substrates and for contaminant microorganisms, a more effective wastewater supply strategy can be developed.
SO4 interactions with dissolved organic matter: Implications of groundwater quality for CKDu incidence in Sri Lanka

1. State Key Joint Laboratory of Environmental Simulation and Pollution Control, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China 2. Department of Water Pollution Control Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China 3. University of Chinese Academy of Sciences, Beijing 100049, China 4. National Water Supply and Drainage Board, Katugastota, Sri Lanka 5. National Institute of Fundamental Studies, Hantana Road, Kandy 20000, Sri Lanka
Nanoparticle enhanced LIBS spectroscopy of liquid samples by using modified surface-enhanced Raman scattering substrates

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UN TRŪKUMI ADVANTAGES AND DISADVANTAGES OF CLOUD COMPUTING

The paper defines cloud computing and describes the main cloud computing models, like software as a service (SaaS), platform as a service (PaaS) and infrastructure as a service (IaaS), than main advantages and disadvantages with concrete examples are given. The three advantages outlined in the paper are cost optimization, rapid access to computer resources and information sharing possibilities. The three disadvantages outlined are security, technology restrictions and unexpected service downtime.
Dealing with Reviews and Overcoming Rejection

This chapter addresses how to handle the reviews and action letter on your submitted paper. Our advice will doubtless overlap with that in other chapters in this volume. If you have followed advice in previous chapters on preparing your paper and making a compelling argument, your chances of publication are certainly increased. However, a large part of the process of academic publication involves rejection. Even the most esteemed researchers face rejection on a regular basis; indeed, they will often report that their most important papers were initially rejected, and sometimes they had to try two or three journals before gaining acceptance. In fact, probably the more exciting your paper is, making strong claims that generally upset the conventional wisdom, the more likely you are to be given a rough time in the reviewing process. After all, the reviewers believe the conventional wisdom; your new paper must convince them that they are wrong. A paper that is a landmark in retrospect might have originally suffered rejection for this very reason of going against the grain of the field (Sternberg, 1998, 2003). Some writers decry the conservatism of the “scientific establishment,” but actually this skeptical attitude is what makes the scientific process great (e.g., Gawande, 2016; Lilienfeld & Ammirati, 2012; Stemwedel, 2011) . As Richard Feynman famously remarked, “Science is a way of trying not to fool yourself. The first principle is that you must not fool yourself and you are the easiest person to fool” (Hutchins, 1985, p. 343). And, to add Carl Sagan’s (1996, p. 304) thought,
The future of metabolomics in ELIXIR [version 1; peer review: 2 approved, 1 approved with reservations]

Metabolomics, the youngest of the major omics technologies, is supported by an active community of researchers and infrastructure developers across Europe. To coordinate and focus efforts around infrastructure building for metabolomics within Europe, a workshop on the “Future of metabolomics in ELIXIR” was organised at Frankfurt Airport in Germany. This one-day strategic workshop involved representatives of ELIXIR Nodes, members of the PhenoMeNal consortium developing an e-infrastructure that supports workflow-based metabolomics analysis pipelines, and experts from the international metabolomics community. The workshop established metabolite identification as the critical area, where a maximal impact of computational metabolomics and data management on other fields could be achieved. In particular, the existing four ELIXIR Use Cases, where the metabolomics community - both industry and academia - would benefit most, and which could be exhaustively mapped onto the current five ELIXIR Platforms were discussed. This opinion article is a call for support for a new ELIXIR metabolomics Use Case, which aligns with and complements the existing and planned ELIXIR Platforms and Use Cases.
Polarization regulation characteristics of reflected waves at the interface of double topological insulators

The property of reflective polarization on the surface of two 3D strong topological insulators was studied, obtaining the generalized necessary conditions required for the complete polarization conversion of linearly polarized light. By analyzing the reflectivity, cross reflectivity, and polarization conversion ratios of the interface of two topological insulators, we found that the model can realize the complete polarization conversion by using the existing topological insulator material, breaking through the limitation that the complete conversion requires a new small dielectric constant topological insulator material. The process can be verified by the Kerr rotation angle. Finally, we show the design method of polarization conversion devices to realize super strong angular stability. The polarization control capability of topological insulators can also be verified by Kerr effect. This work provides a theoretical basis for the application of topological insulators in polarized devices.
Effects of anesthesia induction using dexmedetomidine in thoracoscopic pulmonary segmentectomy on the safety and protection of lung function

Objective: This study aimed to evaluate the effects of anesthesia induction using dexmedetomidine in thoracoscopic pulmonary segmentectomy in terms of safety and protection of lung function. Methods: A total of 76 patients were randomized into the control group (n = 38) and the observation group (n = 38) Anesthesia was induced in the observation group using dexmedetomidine. The intraoperative and postoperative data as well as complications were compared between the two groups. At the time of tracheal intubation (T0), 1 h after incubation (T1), and 30 min after extubation (T2) venous blood samples were extracted to determine the levels of cortisol (Cor) and angiotensin-II (A-II) using enzyme-linked immunosorbent assay. Moreover, the mean arterial pressure (MAP) and heart rate (HR) at T0, T1, and T2 were compared. On the 5th d after surgery, the visual analog scale (VAS) was used to evaluate pain in the two groups; lung function was also assessed and compared between the groups. Results: MAP and HR at T0 were different (P < 0.05) between the two groups. At T1 and T2, the observation group had lower Cor and A-II (P < 0.05) levels than the control group. Neither group showed a significant difference in postoperative VAS scores (P > 0.05) or forced vital capacity and forced expiratory volume in 1 second before treatment (P > 0.05). However, the lung function progressively deteriorated after the surgery (P < 0.05). The degradation was less severe in the observation group than in the control group (P < 0.05). Conclusions: The use of dexmedetomidine for inducing anesthesia in thoracoscopic pulmonary segmentectomy provides more safety and greater protection of lung function.
Wind spectral characteristics on fatigue responses of towerbase and moorings of a floating offshore wind turbine

The tower-platform interface and mooring system of floating offshore wind turbines (FOWTs) are some of the most critical components with significant influences on overall project costs. In addition to satisfying strength requirements, it is typical and vital to meet fatigue criteria for a service life of 25 years or more. Wind spectra characteristics considered in analysis can penalize fatigue designs, leading to unnecessary costs. The International Electrotechnical Commission (IEC, 2009) recommends the use of site-specific wind data (spectrum, turbulence intensity, etc.) in design of FOWTs, but for offshore sites it is often the case that such data is unavailable and land-based data are used as surrogates in design. For such scenarios, it is worth investigating whether such alternative approach is suitable and accurate, and understanding the consequence of the selection of wind spectral characteristics on fatigue design. This paper addresses the impact of the subsequent selection on fatigue responses of towerbase and mooring system in a FOWT, as a sequel to the paper by Udoh and Zou (2018) which focused on impacts on strength design. The 5 MW semi-submersible FOWT platform with six mooring lines implemented in the preceding study is applied in analysis. Results indicate significant variations in resulting fatigue life with considered wind parameters. Thus, it is critical to apply proper wind spectra characteristics for analysis and design of FOWTs to avoid unnecessary conservatism and costs. Based on the findings of this study, more explicit guidance on the application of turbulence intensities for IEC-recommended models in offshore sites could lead to more accurate load estimates in design of FOWTs.
HIL Evaluation of a Novel Real-time Energy Management System for an HEV with a Continuously Variable Transmission

One of the most important challenges facing automotive engineers is reducing vehicle fuel consumption and improving the drivability index. Modern hybrid electric powertrains play an important role in reducing fuel consumption. Continuously variable transmission (CVT) is an automatic transmission that can change the gear ratio seamlessly using a belt and pulleys. CVT performs with infinite gear ratios. Controlling and determining the optimal gear ratio, especially in a complex hybrid powertrain, is a major challenge. Therefore, a multi-parametric model predictive controller with real-time implementation capability is proposed that can handle the energy management task concurrently with gear shifting strategy in a parallel pre-transmission hybrid electric vehicle. The proposed controller hardware-in-the-loop (HIL) validation procedure on the high-fidelity Autonomie model shows significant improvement in fuel economy while maintaining drivability in three different driving schedules. HIL evaluation guarantees the real-time capability and the proposed controller readiness to be implemented to real-world control hardware.
The Supply Chain Integrated Inventory Model for Single Product, Multi-Buyer and Multi-Vendor

This paper presents an extended integrated inventory model with multi vendors that produce one item product and supply it to multi buyers. It is assumed that vendor production rates are not equal and the demands of buyers assumed to be normally distributed and independent. The model formulation was made for each buyer and vendor. Then, each cost component from the buyer and vendor is combined to form the total inventory costs. This model uses the service level as constraint. The final mathematical model consists of formulations of the total inventory costs with certain service level. Keywords— multi-vendor, multi-buyer, integrated inventory system, supply chain
Status of Certain Anthropometric Parameters of Males of Sasaram, India and Its Variation Due to Soybean Added Diet

The anthropometric parameters are regarded as sensitive indicators. The core elements of anthropometric parameters are bodyweight, height and body mass index. To assess certain anthropometric features of males of Sasaram in respect to the standards and the variations in these features due to 60 days feeding of soybean added diet was the purpose of the work. Experimental studies indicate that soybean added diet might facilitate loss of bodyweight. All subjects were observed for anthropometric measurements after feeding of routine diet and soybean added diet. The average bodyweight was 63.65±8.97kg of volunteers aged 20-59 years with a height of 162.0+6.0cm in controlled condition among 2127 males. The average height of volunteers of 162.0+6.0cm was found less than the present standard of the Bihar, India as well as the world. On the other hand, the average of body mass index was 24.88±3.01 kg/m2 among the volunteers of the above age group and was found less than the present standard of the world but more than the standard of India. Consumption of soybean added diet was related to a moderately significant decreased weight (p<0.01) and body mass index. The study helps in establishing the anthropometric features of people of this area in comparison to the standard of the state and the country.