Miracle-dz/UniFAR-FTUs huggingface dataset:
Electrical Properties Evaluation on High Quality Hetero-Epitaxial 3C-SiC(001) for MOSFET Applications

The effect of the crystal quality and surface morphology on the electrical properties of MOS capacitors has been studied in devices manufactured on 3C-SiC epitaxial layers grown on Silicon (100) substrate. The interface state density, which represents one of the most important parameters for the 3C-SiC MOSFET development, has been determined through capacitance measurements. A cross-correlation between High Resolution X-ray Diffraction, AFM analysis and electrical conductance measurements has allowed determining the relationship between the crystalline quality and the interface state density. By improving the crystalline quality, a decrease of the interface state density down to 1010 cm-2 eV-1 was observed.
Sophisticated vs . Naive : The Disparate Effects of Misinformation Policies ∗

We use a simple model to analyze several policies currently proposed in the public sphere to reduce the effects of misinformation. We show that the efficacy of these policies crucially depends on the strategic sophistication and reasoning abilities in the population. We focus on the following policies: censorship, where news can be moderated by governments or social media platforms; content diversification, where agents are given news representing different viewpoints or are shown news that are counter to their prevailing beliefs; accuracy nudging, where agents are encouraged to think more critically about news they receive; and performance targets, where social media outlets try to regulate the amount of misinformation on their platforms. We show that policies that work well for naive agents can perform poorly or completely backfire for sophisticated agents and vice versa. This highlights the importance of sophistication as a factor that regulators should consider when deploying policies to fight misinformation.
Optimisation of a PID Controller for an Inverted Pendulum Using the Bees Algorithm

The inverted pendulum system is a challenging control problem in the control theory, which continually moves away from a stable state. The paper presents the design of a Proportional-Integral-Derivative (PID) controller for a single-input multi-output (SIMO) inverted pendulum system and using the Bees Algorithm (BA) to obtain optimal gains for PID controllers. The Bees Algorithm optimizes the gains so that the controller can move the cart to a desired position with the minimum amount of the change in the pendulum’s angle from the vertically upright position during the movement. The tuning aim is to minimize the control responses of the cart’s position and the pendulum’s angle in time domain. MATLAB/Simulink simulation has been performed to demonstrate that the effects on the system performance of PID controllers with optimal gains. The obtained results show that the tuning method by using the Bees Algorithm produced PID controllers successfully within the controller design criteria. Following a description of the inverted pendulum system and the Bees Algorithm, the paper gives the obtained simulation results for the system demonstrating the efficiency of the design.
‒ Comprehensive Driver Drowsiness Level Detection Model Combining Multimodal Information: For the Entire Range of Drowsiness Stages from Weak to Strong

We have successfully developed a drowsiness detection model that can detect early stage drowsiness more accurately than before by 16.5 % (from F1-score 0.358 to 0.523), by combining drivers’ blinking and posture information. Posture information was considered so as to reflect the loosening of the body’s muscle engagement when drivers get drowsy. This new technology can detect weak levels drowsiness that the drivers themselves are not aware of, as well as strong levels drowsiness. Future applications of this method include not only warning systems but also adaptive support systems tailored to the drivers’ circumstances.
COOPERATIVE GAME THEORY FOR MACHINE LEARNING TASKS Kooperativní teorie her pro úlohy strojového učení

The recent rise in popularity of complex machine learning models trained by numerical optimization has led to an increased interest in interpretation methods capable of explaining the decisions reached by those models. One of the most common interpretation method, and the centerpiece of this thesis, is based on the Shapley value from coalitional game theory. In this text we study its theoretical foundations from a number of perspectives and compare its results, both theoretically and on an applied example, to a different solution concept, the Banzhaf value. We propose a new way of interpreting categorical variables built upon axioms of coalitional game theory and show on a counterexample why the current way leads to wrong results. Finally, we end this text with an extensive analysis of a dataset containing measurements of brain activity where we use the Shapley value to discover interesting connections among the brain regions.
MICROSCOPIC MODELING OF TRAFFIC FLOW: INVESTIGATION OF COLLISION FREE VEHICLE DYNAMICS.

The continuous growth of road traffic volumes leads to significant environmental and economical problems. For more than four decades efforts have been done to understand the dynamics of traffic flow in order to find out how to optimize traffic with respect to a reduction of environmental impacts and economical loss due to congestion. A microsopic model of traffic flow is proposed, adding to the understanding of the different types of congestion found in traffic flow.
Patents as instruments for exploring innovation dynamics

The recently developed Cooperative Patent Classifications of the U.S. Patent and Trade Office (USPTO) and the European Patent Office (EPO) provide new options for an informed delineation of samples in both USPTO data and the Worldwide Patent Statistical Database (PatStat) of EPO. Among the ‘‘technologies for the mitigation of climate change’’ (class Y02), we zoom in on nine material technologies for photovoltaic cells; and focus on one of them (CuInSe2) as a lead case. Two recently developed techniques for making patent maps with interactive overlays—geographical ones using Google Maps and maps based on citation relations among International Patent Classifications (IPC)—are elaborated into dynamic versions that allow for online animations and comparisons by using split screens. Various forms of animation are discussed. The longitudinal development of Rao-Stirling diversity in the IPC-based maps provided us with a heuristics for studying technological diversity in terms of generations of the technology. The longitudinal patterns are clear in USPTO data more than in PatStat data because PatStat L. Leydesdorff (&) Amsterdam School of Communication Research (ASCoR), University of Amsterdam, PO Box 15793, 1001 NG Amsterdam, The Netherlands e-mail: loet@leydesdorff.net F. Alkemade G. Heimeriks Department of Innovation Studies, Faculty of Geosciences, Utrecht University, Heidelberglaan 2, 3584 CS Utrecht, The Netherlands e-mail: F.Alkemade@uu.nl G. Heimeriks e-mail: gheimeriks@gmail.com R. Hoekstra Department of Computer Science, VU University, De Boelelaan 1081, 1081 HV Amsterdam, The Netherlands e-mail: rinke.hoekstra@vu.nl R. Hoekstra Faculty of Law, University of Amsterdam, Amsterdam, The Netherlands 123 Scientometrics (2015) 102:629–651 DOI 10.1007/s11192-014-1447-8
An Analytical Literature Review of the Available Techniques for Relieving Transmission Line Bottleneck in Deregulated Power Systems

Relieving power transmission line bottleneck is one of the major tasks performed by system operators (SOs) to ensure the operation of transmission system within operating limits. With the advent of electric power market, the relieving bottleneck becomes extremely important challenge and it can impose a barrier to the electricity trading. In order to deal with the challenge, many researchers have recently proposed various techniques. The purpose of the this study is to provide a comprehensive review of the available techniques for relieving bottleneck that are applied to address power transmission line bottleneck issues in in deregulated power systems. The most up to date relevant options are described and categorized into specific clusters. A comparative analysis is carried out in which the advantages and disadvantages to each technique are assessed. Lastly, after the appraisement of the existing bottleneck management techniques, some conclusions and suggestions are put forward for relieving power transmission line bottleneck in the future.
Animal Breeding and Genetics Estimation of Live Weight From Linear Body M easurements of Three Afric Breeds of Cattle Under Extensive Management System in Nigeria

__________________________________________________________________ The chemical composition, cholesterol content and consumer evaluation o f eggs from Japanese quails, guinea fowls, local chickens, and exotic chickens (Isa Brown) were determined. A total o f one hundred eggs, twenty five from each o f species were used to determine the chemical composition, cholesterol content and sensory evaluation (Five eggs fo r chemical composition, cholesterol content and twenty fo r sensory). On a dry matter basis, the local chicken had a significantly (P<0.05) lower value o f 23.20% than other species. The exotic chicken ranked highest with a mean value o f38.39%. The mean values recorded fo r ether extract showed no significant difference (P>0.05) amongst the four species. The ash content o f the local chicken was significantly (P<0.05) lower (4.33%)) than that o f Japanese quails, guinea fowl and exotic chickens with values o f4.84, 4.87 and 5.15 per cent, respectively. Values obtained fo r the nitrogen free extract also showed the local chicken to be significantly (P<0.05) lower (47.54%) than the other species whose values ranged between 51.39 52.06%. The exotic chicken had a significantly higher cholesterol value o f243.8 mg/dl while the values fo r the guinea fow l and the local chicken did not significantly (P>0.05) differ while the Japanese quail eggs had the least cholesterol value o f 80.60 mg/dl. Smell and texture were not significantly (P>0.05) influenced by egg type however the guinea fowl and local chicken eggs were the most preferred in terms o f colour and the quail with respect to taste. The lower cholesterol content o f quail eggs perhaps gave it the potential o f topping the consumers list o f preference and wider acceptability. It is also evident that consumer’s preference fo r one egg type over others might not be absolutely based on nutritional considerations but on psychological perceptions. _________________________________________________
Temporal Reasoning on Large-Scale Graphs

Many applications adopt graph data structures to model relations between entities, which continuously change over time. Reasoning on the temporal evolution of large-scale graphs is challenging, as it involves studying how events such as the addition or removal of an edge affect the structure and properties of the graph. We present our ongoing research on the deﬁnition of abstractions and processing mechanisms to reason on dynamic graphs. We propose a data and processing model and a system architecture that supports them. We discuss preliminary results and show our plans for future work.
Performance Analysis of Network Attack DetectionFrameworkusing Machine Learning

The network attack detection frameworks are developed to find out the access to computing systems that are unauthorizedly connected across the networks. The intrusion detection is one of such frameworks, developed by that has a higher accuracy for all majority attacks in comparison to existing works. The models deploy different classifiers to demonstrate that the approach is modular in structure. Intrusion detection model developed in this analytical research utilises various machine learning classifiers like Random Forest, SVM, K-Nearest Neighbor, and Naïve Bayes. Experimentation was conducted on dataset NSLKDD, The Performance of classifiers improved as dimensionality reduction and feature selection improves accuracy and reduces false alarm rate. A better generalization is also achieved while integrating multiple classifiers. High accuracy is obtained for all majority attacks in the NSLKDD datasets which is the widely available benchmark datasets for intrusion detection. © 2021
Improved accuracy of eddy-current sensor based carrier lifetime measurement using laser excitation

In the manufacturing of modern solar cells, the early-phase quality inspection is crucial to detect harmful impurities and to reduce production waste. Thus, the charge-carrier lifetime measurement is beneficial before slicing the silicon ingot into wafers. Optimization of the widely used eddy-current detected photoconductance decay (e-PCD) technique for lifetime measurements is necessary to keep the pace with the new challenges appearing in photovoltaics. We investigated the dependence of the accuracy of the measurable carrier lifetime on the excitation laser parameters in the entire relevant carrier lifetime range. The complex behaviour of surface-recombination phenomena and its time evolution is studied by computer simulation. We optimized the components and parameters of the measurement setup and found that a 1064 nm laser is rather optimal compared to the 980 nm laser used previously. Using a longer wavelength laser source with elevated photon flux results in an improved accuracy of carrier lifetime recorded at Δn = 1015 cm−3 excess carrier concentration. A convincing similarity is found by comparing results from as-sliced and passivated surfaces. The optimized laser e-PCD method was compared to the flash-lamp based PCD method (known as QSSPC). Results agree very well for longer carrier lifetimes for which both systems evaluates the PCD curves in transient mode. However, for lower carrier lifetimes the universal transient mode operation of the e-PCD method is advantageous in terms of consistency.
Phenotypic and Genotypic Characterization of Antimicrobial Resistance in Escherichia Coli Isolates from Chicken Meat

Improper use of the antimicrobials as E. coli giving the microorganism multi-resistance against many antimicrobials by gene mutation on integrons, transposons and plasmids. Therefore, our aim in this study is to 1) examine antibiotics resistance phenotype and genotype in Escherichia coli, 2) identifying the structure of bacterial resistance genes on whole-genome sequencing against multi-drug resistant of Escherichia coli in marketed poultry meat. Samples collected, prepared and Bacteriological examination, Antimicrobial sensitivity test performed, Serological identification of Escherichia coli isolates. Results declared that; the prevalence of E. coli from tested chicken meat samples of 100 chicken meat samples surveyed against E. coli the result declared that about 40%. Antimicrobial susceptibility was; antibiotics of choice against E. coli Sulfonamides, Cephalosporins, Tetracyclines, Quinolones. Serologically, STEC (O157:H7) 30%, ETEC (O142) 10%, EHEC (O26:H11). The subunit B of shiga-like toxin (SLT) gene appeared as a homogenous band. Heat-labile toxin (LT) gene was screened in both genomic DNA and plasmid preps in tested strains. Control STEC as it represents a danger to the poultry consumers. We recommended to increase the hygienic measures during slaughtering, processing and/or handling of chicken carcasses and avoidance unnecessary usage of any antimicrobials to avoid appearance of new antimicrobials resistant.
Monitoring studies of the content of heterocyclic aromatic amines in second-course meals with chilled side dishes sold in the Russian Federation

The research carried out allowed establishing that during the industrial preparation of second-course meals with garnish in the meat components, heterocyclic aromatic amines are formed. Heterocyclic aromatic amines were found in all samples tested. However, the lack of information on the preparation technology of the selected samples does not allow a complete analysis of the results obtained. However, it is worth noting that the largest amount of heterocyclic aromatic amines was formed in samples with chicken meat cooked at the highest temperatures relative to other samples, judging by its appearance. The presence of heterocyclic aromatic amines in all studied samples indicates the potential harm of consuming such products for human health.
REVIEW: CURRENT TRENDS IN OAT PROTEIN RECOVERY AND UTILIZATION IN AQUEOUS FOOD SYSTEMS

Oat protein itself, as a substance, has extensively been studied providing information on its nutritional value, some functional properties and possible applicability in food systems. Chosen protein isolation methods and technological aspects define final composition of obtained oat protein product, its concentration, nutrition value and its functionality in food industry. Scientific data on oat protein recovery methods, typically relying on protein solubility or dry fractionation, provides an insufficient knowledge about the success in commercialization of oat protein recovery technologies and their derivatives in form of oat protein. The aim of the study was to analyse and summarize the research findings on oat protein extraction methods and functional properties of oat protein. Semi-systematic, monographic methods were used to analyse the oat protein isolation techniques, functional properties of oat protein in aqueous food systems, covering the latest information on oat protein extraction methods. Wet and dry isolation methods were demonstrated as main methods in oat protein extraction. Functional properties of oat protein, such as thermal stability, solubility, emulsification, water hydration capacity and foaming were reviewed and evaluated, identifying limitations and protein alterations which occur through the oat protein extraction process. The study provides recent trends in oat protein recovery technologies, along with an overview of current and potential oat protein utilization in food systems.
Horizontal Visibility Graph based on Correlation Coefﬁcient and Its Application

: In this paper, horizontal visibility graph based on correlation coefﬁcient is proposed, and its degree distribution is evaluated by combining the existing general visibility graph and horizontal visibility graph. The results show that the improved visibility graph algorithm can effectively extract the complex network features of white noise time series, periodic time series, fractal time series and chaotic time series, and the anti noise ability is more signiﬁcant. On this basis, the new visibility graph algorithm is applied to the carbon price data of the EU carbon market, and the dynamic evolution process and the linkage between them are obtained, which is convenient to make a more accurate prediction of the future EU carbon market.
A 0.5 V divider-by-2 design with optimization methods for wireless sensor networks

A 0.5 V static master-slave D flip-flop (DFF) divider-by-2 is implemented with a 0.13 μm 1P8M RF-mixed signal CMOS process. Low-threshold transistors in a deep-N well with forward-body bias technology are used in the circuit. Each of the D-latch with source coupled logic consists of sensing and latching circuits. To increase the maximum operating frequency and decrease power consumption, the latching current is one half of the sensing current. The circuit optimization methods are described in this paper. The measured maximum operating frequency is 6.5 GHz and the minimum input singled-signal amplitude is 0.15 V.
Diagnosis of Major Depressive Disorder with Neural Network Models

Artificial neural network is widely used in diagnosing diseases. One of the advantages of this network can be traced to the lack of fatigue or burnout. Present paper is aimed to achieve practical and tangible approach in the field of neural networks has been developed. So that Psychiatrists with little knowledge about computers and programming by reading the content and efficiency of artificial neural networks realize and are interested in using them. In this article trying to design the models of neural network such as RBF and SVM, then compare the error of models for select the best model for diagnosis of major depressive. The SVM model with the 14.16% error is better for training the neural network.
Comparative analysis of two methodological approaches to the study of endocrine disruptor alpha-cypermethrin reproductive toxicity

L. I. Medved’s Research Center of Preventive Toxicology, Food and Chemical Safety, Heroiv Oborony st., 6, Kyiv, 03127, Ukraine. Tel. :+38-063-067-68-78. E-mail: kolyanchuk.yana @ gmail.com Shepelska, N. R. , Prodanchuk, M. G. , & Kolianchuk, Y. V. (2021). Comparative analysis of two methodological approaches to the study of endocrine disruptor alpha-cypermethrin reproductive toxicity. Regulatory Mechanisms in Biosystems, 12(4), 724–732. doi:10.15421/0221100
The Electrical System Expansion Planning for the Eastern Electrical Region, Saudi Arabia

This paper presents an optimal electrical system expansion planning for the Electrical Eastern Region (EER), Saudi Arabia. The load demand forecast is presented. The load duration curve has been determined. The existing and committed generation system has been obtained. By using screening curve, the most appropriate candidate units for expansion planning have been chosen. The optimal genera- tion expansion has been established, as well as the loss of load prob- ability.
The Arctic sea-ice navigability index

A rapidly changing sea-ice regime in the Arctic demands an index that could provide a priori information to icebreakers for hazard-free navigation through ice across the Arctic. This study presents a new sea-ice navigability index, solely based on daily available remote-sensing products. The index has a numerical scale from 0 to 100 that represents the navigability of icebreakers through sea ice. High mobility of sea ice implies that there may be fragmented ice floes that are susceptible to quick displacement, thus allowing an icebreaker to navigate through the ice.
Design of Resources Allocation in 6G Cybertwin Technology Using the Fuzzy Neuro Model in Healthcare Systems

In 6G edge communication networks, the machine learning models play a major role in enabling intelligent decision-making in case of optimal resource allocation in case of the healthcare system. However, it causes a bottleneck, in the form of sophisticated memory calculations, between the hidden layers and the cost of communication between the edge devices/edge nodes and the cloud centres, while transmitting the data from the healthcare management system to the cloud centre via edge nodes. In order to reduce these hurdles, it is important to share workloads to further eliminate the problems related to complicated memory calculations and transmission costs. The effort aims mainly to reduce storage costs and cloud computing associated with neural networks as the complexity of the computations increases with increasing numbers of hidden layers. This study modifies federated teaching to function with distributed assignment resource settings as a distributed deep learning model. It improves the capacity to learn from the data and assigns an ideal workload depending on the limited available resources, slow network connection, and more edge devices. Current network status can be sent to the cloud centre by the edge devices and edge nodes autonomously using cybertwin, meaning that local data are often updated to calculate global data. The simulation shows how effective resource management and allocation is better than standard approaches. It is seen from the results that the proposed method achieves higher resource utilization and success rate than existing methods. Index Terms are fuzzy, healthcare, bioinformatics, 6G wireless communication, cybertwin, machine learning, neural network, and edge.