bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimized neural network and adaptive neuro-fuzzy controlled dynamic voltage restorer for power quality performance
Authors: Kumar Prashant , Arya Sabha Raj , Mistry Khyati D. 
Published: April 2021

AbstractIn this article, a hybrid approach is implemented namely, neural network training (NNT) based machine learning (ML) estimator inspired by artificial neural network (ANN) and self-adaptive neuro-fuzzy inference system (ANFIS) to tackle the voltage aggravations in the power distribution network (DN). In this work, potential of swarm intelligence technique namely particle swam optimization (PSO) is analysed to obtain an optimum prediction model with certain modifications in training algorithm parameters. In practice, when the systems are continuously subjected to parametric changes or external disturbances, then ample time is dedicated to tune the system to regain its stable performance. To improve the dynamic performance of the system intelligence-based techniques are proposed to overcome the shortcomings of conventional controllers. So, gain tuning process based on the intelligence system is a desirable choice. The statistical tools are used to proclaim the effectiveness of the controllers. The obtained MSE, RMSE, ME,SDandRwere evaluated as 0.0015959, 0.039949, −0.00089838, 0.039941 and 1 in the training phase and 0.0015372, 0.039207, −0.0005657, 0.039203 and 1 in the testing phase, respectively. The results revealed that the ANFIS-PSO network model could accomplish a better DC voltage regulation performance when it is compared to the conventional PI. The proposed intelligence strategies confirm that the predicted DVR model based on NNT-ML and ANFIS has faster convergence speed and reliable prediction rate. Moreover, the simulation results show that the dynamic response is improved with proposed PSO based NNT based ML and ANFIS (Takagi-Sugeno) that significantly compensates the voltage based PQ issues. The proposed DVR is actualized in MATLAB/SIMULINK platform.
Modelling of hydrogen-air supersonic mixing and combustion in near-wall region
Authors: Solomatin Roman S. , Semenov Ilya V. 
Published: April 2021

Abstract
Turbulent mixing, ignition, and flame stabilization in the non-premixed supersonic hydrogen-air flow is numerically modelled in a near-wall region. Mixing algorithm based on the turbulence approach SARANS (Reynolds Averaged Navier–Stokes equations closed with Spalart–Allmaras turbulence model) with a diffusion model and a detailed kinetic model for hydrogen-air chemical reactions are employed. The system of governing equations that consists of basic conservation laws and the turbulence model equation is solved in a coupled manner with the LU–SGS–GMRES method. The model is applied to simulate the process of hydrogen injection into a M = 2.44 air flow with their subsequent mixing, ignition, and combustion in the Burrows– Kurkov chamber. The results are compared to available experimental and reference computational data. All calculations are carried out on the ‘MVS-10P’ JSCC RAS supercomputer cluster.
Ein korpuslinguistischer Beitrag zu Herkunft und Entwicklung des negativen Indefinitumskein
Authors: Pickl Simon 
Published: April 2021

AbstractThis article discusses the origin and historical development of the German n-indefinitekein, which is an unusual negator because it does not share the initialnthat marks virtually all other negatives in German. Despite the discussion about its origins going back to the nineteenth century, it is still unclear howkeinfirst emerged and out of which other forms it developed. In this paper, new light is shed on an old controversy using new data and modern corpus-linguistic tools, in this case theReferenzkorpus Mittelhochdeutsch(ReM). The article first summarises the current state of research before presenting and analysing the data. In combination with additional evidence, the results show that certain hypotheses that have to this day been treated as accurate are in fact not viable. Subsequently, a solution that combines some of the existing theses and is compatible with the data is presented: Morphological reanalysis and the ensuing back-formation ofkein’s predecessornehein – in combination with a phonologically conditioned sound substitution triggered by a shift of the syllable boundary – in the context of negative concord seems the most likely candidate for an accurate explanation of the emergence and early usage patterns ofkeinin Middle High German. Incongruent evidence from Swiss German, however, suggests that partially convergent developments ensuing from different indefinite forms have taken place in that variety.
„Du rotziger Blasebalckemacherischer Dieb! Solst du mich dutzen?“ – Funktionen des Personalpronomensduin Gryphius’ „Peter Squentz“
Authors: Gillmann Melitta , Imo Wolfgang 
Published: April 2021

AbstractThis paper examines the variations and functions of the deictic address pronoundu(you) in the satirical Baroque drama “Absurda Comica. Oder Herr Peter Sequentz” written by Andreas Gryphius. Two types of address forms dominate in the drama, which vary in a highly systematic way. The most common one is the honorific formihr‘you’, which appears in addresses to humans of equal social status among both craftsmen and nobles. The more intimate pronoundumainly occurs when the characters address themselves, their body parts or feelings and deities. Even lovers use the polite pronounihr; they switch todusolely when the beloved one is either absent or dead. In addition, there is a switch toduwhen the characters get into a quarrel, which illustrates the well-known connection of address pronouns to the expression of social deixis and politeness as well as the local, context-sensitive mechanisms of pronoun choice. Finally, generic uses ofduare restricted to proverbs in the drama.
Die Verschriftungen in der Dialekterhebung Friedrich Maurers in Baden und im Elsass als Evidenz für die Verbreitung der Standardlautung
Authors: Strobel Maj-Brit 
Published: April 2021

AbstractThis paper examines the development of lay transcriptions of base dialects within a period of 50 years in the early 20th century in the Upper Rhine area in Alsace and Baden. I argue that the spread of spoken standard German can be documented by the development of these transcriptions. In alignment with Ganswindt (2017), it is assumed that the still not widespread standard competence at the beginning of this period is reflected in ‚misspellings‘ in the transcriptions of the dialect, since the Standard German grapheme was associated with the phonetics of the regional standard. The lay transcriptions of the Upper German delabialization by the participating teachers from Friedrich Maurer’s indirect dialect survey from 1941 and the survey of the so-called Wenkeratlas from 1887/88 serve as data.A place-by-place comparison via vector maps allows to localize variation patterns. It can be shown that the ‘misspellings’ – i. e. rounded variants in the area of delabialization – are scattered throughout the area and significantly decrease in time as the spoken German standard is spreading. This is especially the case in the area of Baden, where the amount of ‘misspellings’ diminishes from about 30–60 % to 5–30 %. In Alsace, on the other hand, the amount of ‘misspellings’ is generally much lower and consequently the decrease is as well. I argue that the reason for the difference between transcriptions from Baden and Alsace is to be found in the different repertoire structures with a diglossia with two written standard languages – French and German – in Alsace.
The Inhibitory Thermal Effects of Focused Ultrasound on an Identified, Single Motoneuron
Authors: Collins Morgan N. , Legon Wynn , Mesce Karen A. 
Published: April 2021

AbstractFocused ultrasound (US) is an emerging neuromodulation technology that has gained much attention because of its ability to modulate, noninvasively, neuronal activity in a variety of animals, including humans. However, there has been considerable debate about exactly which types of neurons can be influenced and what underlying mechanisms are in play. Are US-evoked motor changes driven indirectly by activated mechanosensory inputs, or more directly via central interneurons or motoneurons? Although it has been shown that US can mechanically depolarize mechanosensory neurons, there are no studies that have yet tested how identified motoneurons respond directly to US and what the underlying mechanism might be. Here, we examined the effects of US on a single, identified motoneuron within a well-studied and tractable invertebrate preparation, the medicinal leech,Hirudo verbana. Our approach aimed to clarify single neuronal responses to US, which may be obscured in other studies whereby US is applied across a diverse population of cells. We found that US has the ability to inhibit tonic spiking activity through a predominately thermal mechanism. US-evoked effects persisted after blocking synaptic inputs, indicating that its actions were direct. Experiments also revealed that US-comparable heating blocked the axonal conduction of spontaneous action potentials. Finally, we found no evidence that US had significant mechanical effects on the neurons tested, a finding counter to prevailing views. We conclude that a non-sensory neuron can be directly inhibited via a thermal mechanism, a finding that holds promise for clinical neuromodulatory applications.
Sexual Harassment Experience, Gender Harassment and Body Objectification Effect on Disordered Eating Tendencies
Authors: Aleksė Monika , Žardeckaitė-Matulaitienė Kristina 
Published: April 2021

Various research on sexual harassment and gender harassment confirms the adverse effects on a person's physical and emotional health (Shrier, 1990), but so far, little is known about the impact of sexual harassment and gender harassment on one's body objectification and links to disordered eating behavior. Sexual harassment and gender harassment are not only based on gender stereotypes but also play an essential role in supporting gender norms in society by regulating what is seen as acceptable and unacceptable behavior, and appearance for gender. According to the theory of body objectification (Fredrickson, Roberts, 1997), both forms of gender discrimination can induce attention to one's body and appearance, which ultimately can lead to reduced satisfaction of one's body (Szymanski et al. 2011). Since body dissatisfaction is one of the leading causes of eating disorders (Brechan, Kvalem, 2015; Cruz-Sáez et al. 2018), it is important to have a better understanding of sexual harassment and gender harassment relationship with body objectification and disordered eating behavior. The study aims to assess the relationships between sexual harassment and gender harassment experiences, body-objectification, and disordered eating behavior tendencies. 181 (23 males, 158 females) aged 18-38 (M=24.12) participated in this research. Sexual Harassment Experience Questionnaire (Fitzgerald et al. 1998) was used to measure both Unwanted Sexual Attention (Cronbach α – 0,893) and Quid Pro Quo sexual harassment (Cronbach α – 0,876), and Gender Harassment experience (Cronbach α – 0,868). Objectified Body Consciousness Scale (McKinley et al. 1996) was used to measure body objectification: Body Shame (Cronbach α – 0,825) and Body Surveillance (Cronbach α – 0,804). The Eating Attitudes Test (Garner et al. 1979) was used to evaluate disordered eating behavior tendencies: Dieting (Cronbach α – 0,924), Bulimia and Food Preoccupation (Cronbach α – 0,725) and Oral Control (Cronbach α – 0,714). The results revealed significant sexual harassment and gender harassment experience differences between genders showing that women report significantly higher results of all forms of sexual harassment and gender harassment than men. Data analysis also revealed a statistically significant relationship between higher results of sexual harassment, gender harassment experience, and more pronounced disordered eating tendencies and higher body objectification. Data analysis has shown that gender harassment experience is a significant prognostic factor for higher body surveillance and body shame results, more frequent dieting.
Shade recognition of the color label based on the fuzzy clustering
Authors: Bobyr Maxim , Arkhipov Alexander , Yakushev Aleksey 
Published: April 2021

In this article the task of determining the current position of pneumatic actuators is considered. The solution to the given task is achieved by using a technical vision system that allows to apply the fuzzy clustering method to determine in real time the center coordinates and the displacement position of a color label located on the mechatronic complex actuators. The objective of this work is to improve the accuracy of the moving actuator’s of mechatronic complex by improving the accuracy of the color label recognition. The intellectualization of process of the color shade recognition is based on fuzzy clustering. First, a fuzzy model is built, that allows depending on the input parameters of the color intensity for each of the RGB channels and the color tone component, to select a certain color in the image. After that, the color image is binarized and noise is suppressed. The authors used two defuzzification models during simulation a fuzzy system: one is based on the center of gravity method (CoG) and the other is based on the method of area ratio (MAR). The model is implemented based on the method of area ratio and allows to remove the dead zones that are present in the center of gravity model. The method of area ratio determines the location of the color label in the image frame. Subsequently, when the actuator is moved longitudinally, the vision system determines the location of the color label in the new frame. The color label position offset between the source and target images allows to determine the moved distance of the color label. In order to study how noise affects recognition accuracy, the following digital filters were used: median, Gaussian, matrix and binomial. Analysis of the accuracy of these filters showed that the best result was obtained when using a Gaussian filter. The estimation was based on the signal-to-noise coefficient. The mathematical models of fuzzy clustering of color label recognition were simulated in the Matlab/Simulink environment. Experimental studies of technical vision system performance with the proposed fuzzy clustering model were carried out on a pneumatic mechatronic complex that performs processing, moving and storing of details. During the experiments, a color label was placed on the cylinder, after which the cylinder moved along the guides in the longitudinal direction. During the movement, video recording and image recognition were performed. To determine the accuracy of color label recognition, the PSNR and RMSE coefficients were calculated which were equal 38.21 and 3.14, respectively. The accuracy of determining the displacement based on the developed model for recognizing color labels was equal 99.7%. The defuzzifier speed has increased to 590 ns.
MODELING AND SIMULATION OF STATCOM FOR POWER QUALITY IMPROVEMENT
Authors: Et. al. Ch. Umamaheswararao, 
Published: April 2021

In power system, reactive power compensation is one of the important action to maintain better voltage profile, stability and decrease losses. STATCOM is feasible in terms of cost effective in wide range of problem solving capabilities among all Flexible AC Transmission system (FACTS) in both transmission and distribution levels. In this paper the synchronous rotating frame theory algorithm is used since it is easy to implement i.e. the rotating three phase quantities are converted into stationary components. So it requires less number of PI controllers and also calculations on the stationary quantities are easy than to do calculations on instantaneous quantities and the modeling of STATCOM is done. This project focuses on improvement of power quality in a three phase three wire system with a non-linear load i.e., three phase bridge rectifier and a parallel inductive load. Some power quality aspects like reactive power compensation of linear load, better Total Harmonic Distortion (THD) performance and the power factor improvement are achieved. The result shows the THD of input current achieved as per the IEEE 519-1992 standard. It is observed that STATCOM gives effective compensation for reactive power variation and hence the power quality of distribution systems improved.
Synergetic Control Based Fast-Converging MPPT Technique for Thermoelectric Generator Energy System
Authors: Et. al. Polamraju. V. S. Sobhan, 
Published: April 2021

Recently, waste heat energy recovery has attracted the attention of many researchers. The power conversion efficiency of Thermoelectric Generator (TEG) system is enhanced by designing suitable MPPT controller. In this study a robust nonlinear control technique based on synergetic control theory is designed to extract maximum power from Thermoelectric Generator system (TEG). The designed synergetic control law ensures the fast convergence towards maximum power operating point without any oscillations under the presence of system uncertainties and variable temperature conditions. The TEG system comprises of a Thermoelectric Generator module, power converter, maximum power point tracking algorithm and load. The simulation results show viability of proposed strategy in comparison with Perturb and Observe (P&O) method under variable temperature.
High Gain DC/DC Converter with IC MPPT Algorithm For DC Micro-Grid Applications
Authors: Et. al. Subbarao Mopidevi, 
Published: April 2021

High gain DC-DC converter with IC MPPT algorithm, solar & wind as inputs in conjunction with this converter are presented in this paper. Recently, due to increase in the green energy demand the renewable energy resources are widely used.DC micro grids are now of significant importance due to the rise in DC loads and the demand for high power quality. These DC loads require various levels of voltage based on their power ratings. The proposed step-up DC-DC converter therefore has a high voltage gain with optimum duty ratio characteristics. In addition, the additional benefit of delivering electricity at two distinct voltage levels is that it is more suitable for DC micro grid applications and all the switches are controlled using single control signal which reduces complexity. The proposed converter is implemented and theoretically verified in SIMULINK MATLAB software for two different load power ratings of 100W and 200W.
Fault Tolerance Mechanism for Internet of Things (IoT) by Solving Nature Inspired Computing Algorithm (NIC)
Authors: Et. al. A. Prasanth Rao, 
Published: April 2021

In clustering approach the sensor nodes are grouped to form a cluster. The nodes of a clustering network have low powered battery capability and limited processing capabilities. These nodes continuously exchange the data to cluster head which is in turn transforming the data to its base station. Few of these nodes in network may be faulty or may not support life time processing data due to its low power battery. All these sensor nodes measure the temperature, humidity, sound and pollution from environment and collected data is send to cloud for further processing. The fault tolerance mechanism of these nodes is solved by applying genetic algorithm by implementing chromosome technique to identify and avoid fault nodes in the network. This proposed research work increases detection of fault nodes in a network, increase network efficiency, lifetime and reach energy optimization results in Internet of Things (IoT) concept. The performance evaluation shows that the data accuracy in Genetic Algorithm (GA) is higher when compared with Direct Diffusion (DD) Algorithm and Ad-hoc on demand Distance Vector (AODV) Algorithm.
ROBUST K-NEAREST NEIGHBOR CLASSIFIER AND NEAREST MEAN CLASSIFIER BASED INFORMATIVE KNOWLEDGE DISTILLATION FACE RECOGNITION
Authors: Et. al. M.Shanmuganathan, 
Published: April 2021

Human Face identification procedures have made tremendous progress in the last decade. Nevertheless, identifying faces with incomplete impediment is as yet challenging for the present face identifiers, and is very much required within certifiable application programs regarding reconnaissance and protection. Though great examination exertion has been dedicated to creating face de-impediment techniques, the greater part of them can just function admirably under obliged conditions. In this manuscript is proposed a Robust K-NNC (K-Nearest Neighbor Classifier) and NMC(Nearest Mean Classifier ) (RKNNC-NMC) prototype to efficiently reestablish incompletely occluded faces even in nature. This model comprises of two-stream, first introduced to perceive high-resolution faces and goal corrupted appearances with a student stream and a teacher stream, separately. A Teacher stream is signified by a Complex RKNNC-NMC for the sake of high-exactness recognition, and the student stream is signified by an a lot more straightforward RKNNC-NMC for low-unpredictability recognition. Broad examinations on synthetic and real datasets datasets of countenances with impediment plainly show the viability of RKNNC-NMC in eliminating various kinds of impediment in one’s face at different locations. The suggested technique additionally gives better behaviour gain than other de-occlusion strategies in advancing recognition execution through partially-occluded faces.
Effectiveness of Adapted Science Instruction for Teaching Learning of Science Temperature Concept among Students with Visual Impairment
Authors: Et. al. M.Revathi, 
Published: April 2021

The areas of science and mathematics have traditionally been inaccessible to students with Visual Impairment. Hence it needs adapted materials and instructional methods to understand and perform science experiments by visually impaired. This study aims to study the Effectiveness of Adapted Science Instruction on Leaning of Science Temperature Concept among students with Visual Impairment. The Investigator adapted the Science Temperature concept Activities as per the needs of Students with Visual Impairment. Visually Impaired sample from Grade VI to VIII were trained and effectivess of the Adapted Instruction was found.
Scalable Data Processing for Prediction, Batch Computation and Analysis and Response Times using Google BigQuery
Authors: Et. al. Gitanjali Sinha, 
Published: April 2021

Computing a complex dataset analysis is tedious task because distributed resource management is difficult task. Google disperses the registering utilized by BigQuery across process assets powerfully which implies that we don't need to oversee figure asset, for example, bunches, register motor, stockpiling structure. Fighting commitments customarily require custom estimating (and esteeming) of unequivocal procedure gatherings, and this can change after some time which can be trying. Since Google logically assigns resources, costs are dynamic too. Google offers both a compensation all the more just as costs emerge elective where you pay for the data brought into BigQuery and subsequently per question costs. Since BigQuery is a totally managed organization, the backend game plan and tuning is managed by Google. This is much more direct than battling plans that anticipate that you should pick a number and sort of gatherings to make and to administer after some time. BigQuery consequently recreates information between zones to empower high accessibility. It additionally naturally load adjusts to give ideal execution and to limit the effect of any equipment disappointments. So getting benefits of BigQuery we did complex data analysis in huge amount of data set within a friction of second. Our result is showing the capability of our research work in the field of scalable data processing.