Miracle-dz/UniFAR-FTUs huggingface dataset:
The impact of a new genus on the molecular phylogeny of Hemisphaeriini (Hemiptera, Fulgoromorpha, Issidae)

Abstract A new genus, Retaldargen. nov. of the family Issidae (Hemisphaeriinae, Hemisphaeriini) is described from Guangxi Province of China. A revised molecular analysis for the Hemisphaeriini based on partial sequences of 18S, 28S, COXI and Cytb, provides evidence for a new lineage within the subtribe Mongolianina. With two subgroups of genera now identified, the monophyly of Mongolianina is discussed from both a morphological and a molecular basis.
Multiple paternity assessed in the cuttlefish Sepiella japonica (Mollusca, Cephalopoda) using microsatellite markers

Abstract Multiple paternity was demonstrated for seven clutches of eggs and 40 offspring sampled from these clutches in the cuttlefish Sepiella japonica from Fujian Shacheng Harbor Cultivation Base (Fujian Province, China), using four microsatellite DNA markers. It was observed that female cuttlefish copulated with different males. In this study, genotyping data suggest that at least three paternal allele genotypes were present in all seven clutches indicating that at least two males were responsible for each brood. Combined with behavioral observations, this study provides evidence for sperm competition and multiple paternity in S. japonica.
Evolutionary Federated Learning on EEG-data

With the spread of digitalization across every aspects of society and economy, the amount of data generated keeps increasing. In some domains, this generation happens in such a massively distributed fashion that poses challenges for even collecting the data to build machine learning (ML) models on it, not to mention the processing power necessary for training. An important aspect of processing information that has been generated at users is privacy concerns, that is, users might be unwilling to expose anything that would enable one to draw any conclusion regarding to confidential information they possess. In this work, we present a experiment on a genetic algorithm based federated learning (FL) algorithm, that reduces the data transfer from individual users to the learner to a single fitness value.
Towards Automatic Minuting of the Meetings

Many meetings of different kinds will potentially benefit from technological support like automatic creation of meeting minutes. To prepare a reasonable automation, we need to have a detailed understanding of common types of meetings, of the linguistic properties and commonalities in the structure of meeting minutes, as well as of methods for their automation. In this paper, we summarize the quality criteria and linguistic properties of meeting minutes, describe the available meeting corpora and meeting datasets and propose a classification of meetings and minutes types. Furthermore, we analyze the methods and tools for automatic minuting with respect to their use with existing types of datasets. We summarize the obtained knowledge with respect to our goal of designing automatic minuting and present our first steps in this direction.
Word Guessing Game with a Social Robotic Head

In this paper we address three limitations of our previous implementations in human-machine spoken interaction in Slovak: low prosodic variability, limited naturalness, and deployment in real acoustically challenging situations. We designed a word-guessing game in which subjects provide verbal cues for a target animal and social robotic head Furhat guesses the animal. We then deployed it in an open-air science festival with over 60 subjects playing the game. We describe the implementation and initial observations from the design, deployment, and user evaluation.
Drusen Quantification for Early Identification of Age Related Macular Degeneration

Age-related macular degeneration (AMD) is a degenerative disorder in people of age 50 and above, in developed nations, characterized on grading of color fundus images by the presence of pathologies such as drusen in macular area. Currently, there is no treatment which can cure irreversible blindness due to age-related macular degeneration. Therefore, the only feasible option is to prevent the incidence of age-related macular degeneration and avoid this unnecessary vision loss. This paper presents an automated method for early diagnosis of AMD by quantifying drusen on the basis of its size, number and area in macular region from standard color retinal images. Previously used methods, generating unsatisfactory results in some cases, are time consuming, complex and prone to error. Therefore, this paper provides a simple drusen detection and quantification method to detect the exact number of drusen , area and size as well as classify drusen into small, intermediate and soft or large which will further help in initial screening of early stage of age-related macular degeneration and its progression i.e. change in drusen area. The proposed method achieved 93.2% accuracy for drusen detection and 91.8% accuracy in small drusen, 98.66% in intermediate drusen and 92.91% in soft drusen quantification in order to grade the severity of AMD which outwits the other methods.
Experimental studies of a method of simultaneous contactless transmission of energy and information signals through a common inductive link

Wireless power transfer and two-way transfer of information and control signals by using the same inductive link is a hot topic of research in recent years. Depending on the required transmitted power and the speed of the transmitted information and control signals, several methods have been outlined: a sequential energy and data transfer method, a simultaneous energy and data transfer method, using a change of the frequency inverter and the load resistance, and a simultaneous power and data transfer via frequency multiplexing method. The main focus in the present work is dedicated to the frequency multiplexing method. A mockup was developed to demonstrate the effectiveness of the method and to perform experimental research.
A study of Prediction models for football player valuations by quantifying statistical and economic attributes for the global transfer market

The global transfer market in the field of football has long been devoid of technology. Over the last two decades, the economic dynamics have completely been changed, and the amount of money which has been pumped has increased exponentially. Valuations for any physical entity with a well-defined set of parameters is a challenge in itself and depends on numerous other things. And doing the same for an individual with varying parameters, based on the source of data, is even more challenging. With this paper, we attempt to find the most optimum way of fetching data of football players and applying the right model on it so as to extract meaningful information, thus reducing the gap between the estimated prices and the Final price. We do so by crowdsourcing the data and applying regression techniques along with Opta index. We also try to find the results using Neural Networks and conclude with a comparison between our models
Design and Numerical Characterization of a Realistic Plasma Dipole

This work presents the numerical results for a plasma dipole antenna that exploits plasma technology to generate and receive electromagnetic fields. Two models have been considered, namely an ideal plasma dipole, and a realistic plasma dipole. The latter comprises part of the plasma generation equipment (e.g. metal terminations, inductors). The realistic plasma dipole was realized in practice and tested for the plasma density. Moreover, a preliminary test by means of a vector network analyzer with the plasma turned off was undertaken to verify the accuracy of the simulation models. Both the models can achieve good antenna performances. However, a downshift in the operational frequency can be observed when a more realistic design is considered. All the simulated results have been obtained through full-wave numerical simulations in CST Microwave Studio.
Moving RTS/CTS to the Frequency Domain: an Efficient Contention Scheme for 802.11ax Networks

In this paper, we propose a contention mechanism based on the execution of multiple contention rounds in the frequency domain (ReCHo), which is designed to offer high throughput performance and robustness with respect to imperfect carrier sensing. The main idea is using narrow tones as signalling messages for performing channel access contentions and allowing the Access Point (AP) to echo these signals, in order to extend the sensing capabilities to all the stations associated to the AP. In particular, we refer to the emerging IEEE 802.11ax standard, showing how our scheme can boost performance of random access with respect to the current version of IEEE 802.11ax OFDMA Back-Off (OBO), even when hidden node probability is very high.
Security Evaluation under N-1 for Active Distribution Networks Coordinated with Transmission Grid

With the rapid development of smart grid technology, the distribution networks become more active, and the coordination between transmission and distribution grids becomes more significant. To achieve an accurate analysis for distribution grids considering the impact of transmission grids, this paper presents a practical security evaluation process under N-1 for active distribution networks (ADNs) coordinated with transmission grids. A linearized ADNs load restoration model is given to calculate the restored load after N-1. In order to consider the coordinated operation of transmission and distribution networks, the distributed global power flow (GPF) based on the master-slave-splitting method is used to calculate the power flow. The index for security evaluation of ADNs is established in power-lost load and voltage deviation aspects. The proposed evaluation process was tested in an integrated transmission and distribution test system. Results of numerical simulation showed the rationality of load restoration model and the validity of the proposed evaluation method.
A Note on Stochastic Logistic Shocks and Economic Growth

We present an alternative to the geometric Brownian motion in order to model random shocks in economics, by focusing on the stochastic logistic process, which is a natural generalization of the geometric Brownian motion. We describe some potential applications in the context of economic growth, and show that its degree of tractability is very similar to that of the geometric Brownian motion, and thus its use can effectively improve the limits (related to the presence of a constant drift) of the geometric Brownian motion to model uncertainty.
Adaptive Simulation of Micro-Turbojet Engine Component Characteristics

In order to solve the problem that the micro-turbojet engine performance calculation accuracy is not high under the condition of unknown component characteristics map. This paper builds a component characteristics adaptive model of the micro-turbojet engine. According to the engine test data, based on the general characteristics map of the compressor, the weighting function and the correction factors are constructed. The weighting function is optimized on the iSIGHT platform by using the Downhill Simplex method to obtain the modified compressor characteristics map and more accurate engine performance. The results indicate that the adaptive model can improve the micro-turbojet engine performance calculation accuracy.
Double-layer Sixteenth-mode Substrate Integrated Waveguide Filter based on Defected Ground Structure

A double-layer filter is proposed, designed, and implemented through a sixteen mode substrate integrated waveguide (SMSIW) with two defected ground structures (DGS) inside the middle metal layer. The proposed filter provides four transmission zeros on both sides of the pass-band. Through the appropriate design of the sixteenth mode and filter form, the transmission zeros can be controlled with the DGS position. The proposed filters also show a compact physical size as compared to conventional SIW filters. In order to validate the simulated results, the proposed concept is designed, manufactured and measured as an experimental example. The proposed SMSIW filter is a promising component for the design of high-performance telecommunication systems.
A Design of an Axial Field Permanent Magnet Generator for Small-Scale Water Turbines – Focus on Stacking Factor Effects on Performance

This paper presents the modelling and design of a small scale Axial Field Permanent Magnet Synchronous Generator driven by a low-head and low-speed water turbine. Firstly, the study suggests a water turbine that can be used to extract small-scale hydraulic power, then means to calculate the produced mechanical power and performance. Secondly, an overview of AFPM generators is presented. Afterwards, a generator is analytically designed through sizing equations, then its dimensions and performance is validated using FEM software. This generator is redesigned after decreasing the stacking factor to overcome disadvantages such as steel saturation and weight, which led to save steel consumption but with lower performance. Then, FEM-based sensitivity analysis is used to enhance the machine performance presenting a new hybrid machine comprising the advantages and disadvantages of cored and coreless ones.
A Comparative Study of Modal Methods for Photonic and Plasmonic Structures

We compare the performance in term of accuracy and convergence of numerical modal methods as applied to photonic and plasmonic structures. Both pure spectral problems and diffraction problems are investigated. The main challenge of any numerical modal method is to handle at best the interface conditions at the boundary of the different materials that constitute the structure and also radiation conditions in the case of isolated structures. That is the reason why we distinguish two kinds of modal methods according to whether they use full-domain or sub-domain basis expansion. In the first set we will consider the Fourier Modal Method, the B-spline modal methods, and in the second one Pseudo-Spectral modal methods and Polynomial modal methods.
Comparison of Classification Algorithms of the Autism Spectrum Disorder Diagnosis

ASD sufferers face difficulties in early development compared to normal humans. Various tools, clinical, and non-clinical approaches have been implemented but take a long time to produce a complete diagnosis. the solution by adopting machine learning. This study proposes the application of cross-validation techniques in the Decision Tree method, Linear Discriminant Analysis, Logistic Regression, SVM, and KNN and determines the best k value in each classification method because the shift of datasets when using cross-validation techniques in the classification method is one factor that can cause the estimate to be inaccurate. The results show that the decision tree provides an accuracy of 100% in each of the k values that have been determined previously. 96.9% on Linear Discriminant Analysis with $k=7, k=9$, and $k =10$. 99.7% in Logistic Regression with values of $k=2$ and $k= 3$. 99.9% in Support Vector Machine with values of $k=9$ and $k =1\theta$ and 94.2% for K-Nearest Neighbors with a value of $k=8$.
Intra-day Multi-sequential Generation Schedule Considering Energy Storage System

To adapt to the rapid development of new energy and the construction of large capacity energy storage power station, this paper designs the intra-day generation scheduling and real-time generation scheduling progressive coordination mode, and puts forward a two-stage optimization method in the intra-day and real-time generation scheduling, to eliminate the new energy and load prediction deviation sequentially, and also focus on the effect of energy storage system on this multi-sequential scheduling. The first stage is new energy and load prediction deviation distribution model: deviation is decomposed to the units according to adjustment strategies. On the basis of the calculation result in the first stage, a coordinated smoothing model is established to obtain the final optimization result in the second stage. A case based on actual operating data is utilized to show the validity of the proposed method.
An Investigation of Meta Heuristic Algorithms Applied on Capacitated Vehicle Routing Problem

In technical world, many organization and educational institutions need in the use of transportation but they are facing difficulties to give ideal courses to set of vehicles to serve given clients. In order to solve this situation, Capacitated Vehicle Routing Problem (CVRP) is introduced which provide optimal routes with minimum cost and time for set of vehicles with the subject of capacity constraints. Conventional algorithm is not suitable for large size instances. Therefore, scientists changed to meta-heuristic algorithm which is progressively appropriate for global search and large size occurrences. It has many variants like Ant Colony Optimization, Particle Swarm Optimization, Firefly Algorithm, Cuckoo Search Algorithm, Genetic Algorithm, etc. The objective of this article is to investigate about the various Meta-heuristics algorithm and key challenges in CVRP.
Combined Field/System Simulations of Low-Frequency Devices in Time Domain based on Adaptive Nonlinear Reduced Order Models

This paper outlines the robust construction of nonlinear reduced order models tailored for magneto(quasi)static approximations of Maxwell's equations in order to achieve a computationally efficient imprint into a coupled field/system simulation in time domain. To this end an adaptive, error bounded model order reduction technique based on spatial finite element discretizations has been developed. With regard to the nonlinear operator a two step procedure is proposed. The first step is an approximation of the nonlinearity itself in an affine form while the second step is applying a projection based model order reduction by means of a standard reduced basis scheme. The proposed technique will be verified by typical low-frequency applications, such as transformers.
Toeplitz vs. Hankel

This paper explores similarities and differences of different types of stochastic modeling, namely the traditional covariance modeling based on Schur-Levinson theory vs. partial moment matching. The first case leads to positive definite Toplitz ¨ matrices, while the second case handles positive definite Hankel matrices (or generalized versions ot those). In both cases a special type of interpolation problem is solved, from which general solutions can be derived. However, the differences between the two problems and their solution methods soon appear. In the multi-dimensional Hankel case, a joint pdf has to be determined, while in the (generalized) Schur-Levinson case, only second order data is handled. In contrast to the traditional Schur-Levinson approach, the non-linear, non-Gaussian estimation filter is a derivative of the model filter and not vice versa. Some results presented here for the multivariate Hankel case are believed to be new.
Subnet Addressing in Software Defined Wireless Sensor Networks

Software defined networking provides manageability and scalability to networks by decoupling network control and forwarding functions into separate layers. Though the concept is typically a subject for wired networks, recent research activities have also considered software defined networking in the context of wireless networking and especially in wireless sensor networking. Software defined networking in that context is, however, a very challenging task due to limited resources of the sensors. In particular, the number of entries in the flow table and the control message overhead is a limiting factor. This paper introduces a new approach to efficiently manage a software defined wireless sensor networks by means of a hierarchical addressing scheme. It is based on a tree of address masks which are used to split the network into subnets. Each subnet has a range of host addresses and a subnet head which acts as a gateway to its children. With that hierarchical structure, nodes need only to recognize the subnet where a packet is destined to and forward that packet to the subnet head. We evaluated the proposed approach using the Cooja simulation environment of Contiki. Compared to the state of the art our scheme reduces the amount of control messages, the size of path packets, the number of rules needed to be stored in the flow table and hence the processing time for these rules.
A 5-Channel Unipolar Fetal-ECG Readout IC for Patch-Based Fetal Monitoring

This letter presents a 5-channel unipolar fetal electrocardiogram readout IC for monitoring the health of a fetus during pregnancy. Each readout channel includes an instrumentation amplifier, a programable gain amplifier and a successive approximation register ADC. A unipolar, common half branch reuse topology is used to achieve low noise, low power, low crosstalk between the channels high input impedance and high CMRR at the same time. Each channel achieves an input referred noise of 0.47 <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>Vrms in 0.5 to 150 Hz, while consuming a power of 43.2 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula>. The 5-channel system provides a CMRR of 98 dB and an interchannel crosstalk rejection of 95 dB. The chip is implemented in a standard 55-nm CMOS process and occupies an area of 4.0 mm<sup>2</sup>. The whole chip, including five readout channels, leadoff detection, reference generation, autonomous data acquisition with on-chip sample storage and an interrupt-based serial peripheral host interface consumes a total power of 258 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula>.
QPSO Based Load Balancing Mechanism In Cloud Environment

We introducing a new algorithm called Quantum particle swarm optimization is a new Particle swarm optimization variant, which outperforms the particle swarm optimization search but few control parameters are there. The weighted attractors of new particle in QPSO algorithm are generated around the global best particle and previous best particles. The main reason for result in attracting faster. The strategy behind global search and local search to balance the exploration and exploitation. A Large amount of application data are stored in distributed data center in scientific workflow. A data manager will occupy smartly which are selected by data center in these data center. However, case of information are not have a location for hard and fast. A Challenge will be done in one task that need several data set located in large volumes of data while they are moving and located in different data center. Here we using four modules like data splitting, location awareness, node localization, load balancing to move the data’s more efficiently. QPSO algorithm considers data dependencies in cloud environment and data- intensive workflow features. The workflows use for data placement that can decrease the data transmission between data center effectively and also reduced the number of data movement.