Miracle-dz/UniFAR-FTUs huggingface dataset:
Decentralized Control of an Active Capacitor With NDO-Based Feedforward Compensation

This paper is about decentralized control of an active capacitor, which is intended to store a wide range of unknown low-frequency ripples. Only the local dc bus voltage is used for the controller design. The control algorithm is based on the indirect approach, with the buffering capacitor energy that is directly regulated, which in turn influences the dc bus voltage indirectly. With the proposed feedforward compensation on the reference signal, the attenuation around hundreds of hertz is further improved, resulting in a larger emulated capacitance. The feedforward signal is generated from a reference model of the active capacitor with disturbance current on the dc bus and dc bus voltage as its inputs. Rather than direct measurement via a current sensor, the disturbance current is estimated by a nonlinear disturbance observer (NDO). With the saved current sensor, the cost and volume of the active capacitor can be reduced. The control algorithm has been verified by both simulation and experiments, with the active capacitor being connected to a 350-W commercial power factor compensators (PFCs).
The Effect of Web Search Result Display on Users’ Perceptual Experience and Information Seeking Performance

ABSTRACT Adding multiple sources of information in the display of Web search results may negatively affect users’ perceptual experience and information-seeking behavior. This claim was established by investigating the impact of different Web search compositions on users’ ability to extract specific information. In this article, we assumed that the quantity and order of different compositions (areas) in the Web search results page may contribute to individual’s ability to find information relevant to their search queries. An eye-tracking device was used to observe and compare the perceptual behavior of 14 users in an information-seeking task. The results showed that the use of different compositions in the display of Web results page significantly influenced users’ perceptual experience by reducing their attention to the organic results area. The quantity of these compositions was found to greatly increase the cognitive load of users when attempting to retrieve information from the organic area, which negatively affects their information-seeking performance. Our finding provides a rationale for further studies to consider the impact of quantity and order of Web page compositions on individuals’ perceptual attention and cognitive load in information-seeking task settings.
Teaching Grammar Using a Parallel Concordancer: A feasibility study

Studies have illustrated several benefits of Data-driven learning (DDL), when students are researchers of the language using authentic language data (corpora), for learning grammar, including linking adverbials and phrasal verbs. However, few have examined the use of parallel concordancers, a tool that displays the same source-text in two languages side by side, although recent research points to potential benefits in helping learners notice L1/L2 differences. This study examined the feasibility of implementing a DDL approach to teaching L2 English grammar that presents a learning challenge to French L1 learners using a parallel concordancer, focusing on teachers’ and students’ perceptions on their training, the time needed, the completion of the tasks and the monitoring. Three intact Cegep (college) ESL classes, taught by two teachers, used a French-English parallel concordancer, Tradooit, over a 6-week period. The teachers were first trained, and then created and taught three laboratory tasks emphasizing L1/L2 differences between French and English. During those labs, students completed a discovery task, a proofreading task and an investigation task to correct L1 interference errors. Student performance was measured through accuracy scores on these tasks, and student and teacher perceptions were recorded using observation notes, teacher logs, a post-study questionnaire administered to students, and semi-structured interviews. The results suggest that DDL can be successfully implemented and that students and teachers are willing to use DDL in the future. However, both students and teachers would have liked more guidance for selecting keywords and finding grammar patterns. Students were able to complete the labs within the time allotted, but with varying degrees of accuracy; some grammar features such as the tense-aspect system seemed to be more challenging. Teachers could also have received more feedback on the labs. Implications include better training on pattern hunting and wording grammar patterns, and a better use of feedback.
Electronic government adoption in voluntary environments – a case study of Zimbabwe

Many governmental organisations across the world are progressively implementing electronic government systems to enhance their back-office operations and offer better and efficient services to citizens. Zimbabwe is not an exception to this e-government wave. Previous studies note that the acceptance and utilisation of e-government systems by citizens in Zimbabwe remains suboptimal, sluggish and problematic due to several factors. This study sought to establish the effect of seven predictor variables on citizens’ behavioural intentions to use e-government systems in Zimbabwe. Drawing from the extended Technology Acceptance Model (TAM2), extended Unified Theory of Acceptance and Use of Technology (UTAUT2), Framework for National and Donor Action, and e-Government Trust model as theoretical underpinnings, this study proposed a conceptual framework to predict citizens’ behavioural intentions on e-government. Survey data for testing the conceptual framework were collected from 247 respondents in Zimbabwe using structured questionnaires. Confirmatory factor analysis using IBM AMOS structural equation modelling method was conducted to establish the structural model fit of the proposed model. Findings of this study establish that eight of the hypothesised constructs explain 89% of the discrepancies of behavioural intention to demonstrate good predictive power of the proposed model in voluntary environments. Thus, level of education, facilitating conditions, e-government awareness, price value; privacy, security and trust; political self-efficacy and influence were all confirmed as salient predictors of e-government adoption. These findings provide invaluable insights and pointers to practitioners and policy-makers on e-government implementation and may guide further research on e-government adoption in voluntary environments.
Comparative Analysis of Face Detection Using Linear Binary Techniques and Neural Network Approaches

Facial recognition is now a days is very emerging topic. The most challenging task for normal human being is to follow the facial recognition retrieval model for correct match in the least running time. Especially while dealing with moving or non-static environment like live video, webcam recording, or accessing real-time video in which facial features are not clear as to take as input image. Comparison between two different approached has been presented in this paper, linear binary pattern Haar technique is compared by deep learning using neural networks, different images of different persons has been taken, deep learning approach is more accurate according to different angles the video taken or any distance the video captured either for moving or static objects either from Mobile Video Camera or CCTV Camera then LPBH approach.
Sentiment mining in a collaborative learning environment: capitalising on big data

ABSTRACT The ability to exploit students’ sentiments using different machine learning techniques is considered an important strategy for planning and manoeuvring in a collaborative educational environment. The advancement of machine learning technology is energised by the healthy growth of big data technologies. This helps the applications based on Sentiment Mining (SM) using big data to become a common platform for data mining activities. However, very little has been studied on the sentiment application using a huge amount of available educational data. Therefore, this paper has made an attempt to mine the academic data using different efficient machine learning algorithms. The contribution of this paper is two-fold: (i) studying the sentiment polarity (positive, negative and neutral) from students’ data using machine learning techniques, and (ii) modelling and predicting students’ emotions (Amused, Anxiety, Bored, Confused, Enthused, Excited, Frustrated, etc.) using the big data frameworks. The developed SM techniques using big data frameworks can be scaled and made adaptable for source variation, velocity and veracity to maximise value mining for the benefit of students, faculties and other stakeholders.
Analysis of Minerals in Foods: A Three-year Survey from Costa Rican Market Products

Developing and carrying out analyzes that allow nutritional profiling of foods has become increasingly necessary in the food industry, especially when essential nutrients, such as minerals, are involved. In addition, having this type of information makes it possible to characterize the food, corroborate labeling, monitor regulations, improve food quality, and take public health measures when there are deficiencies or excesses in the population level of any nutrient. During this survey, total ash, Cl, Ca, P, Mg, Fe, Zn, Cu, Na, and K, were analyzed in different foods (including meat, dairy, cocoa, baked products, fruits, vegetables, legumes, beverages, cocoa products), for a total of n = 2046, 190, 385, 101, 113, 718, 190, 79, 945, and 190 samples, respectively. These samples were compiled from January 2019 to December 2021 as part of routine surveillance of the food industry. Food mineral fraction was assessed by gravimetry, chloride by potentiometry, and the rest of the analytes by spectrometry. Descriptive statistics were produced to analyze the database, and the information was divided by type of food and minerals.
International Scientific Conference on Mobility and Transport Urban Mobility – Shaping the Future Together mobil . TUM 2018 , 13-14 June 2018 , Munich , Germany Analysis of the Travel Time of Various Transportation Systems in Urban Context

What’s the maximum detour for ride pooling that is accepted from the passengers’ point of view? We address this question by determining the accepted detour of conventional public line transport, which is being accepted by its passengers. This is done using the Google Maps Routing application programming interface (API) for analyzing the travel times of different means of transport in 13 German cities. From the identified travel time ratios of public transport and motorized individual transport conclusions are drawn about the maximum reasonable detour when using ride pooling.
Reciprocal Kit Build: Boundary Crossing with Concept Map for Collaborative Knowledge Construction

: Collaborative knowledge construction needs to be carefully designed to allow learners to collaborate, not only co-present. Researches show that individual learners need to make a continuous effort to construct and maintain group shared knowledge. As a representational tool, concept map has been employed to address such needs during the discourse. To promote active inquiries, prior studies also suggested extending the collaborative concept map with individual externalization, concept map sharing and reviewing activity. We introduce Reciprocal Kit Build activity where students in dyads are creating an individual and collaborative concept map, exchanging their ideas through reconstruction, and discussing their comprehension facilitated by shared and difference maps. Unlike existing studies, our design activity supports learning at boundaries, by boundary crossing and utilizing multiple types of boundary objects. In this paper, we also explain some principles underlying our proposed activity and how this activity may assist learners to move across boundaries.
A computational study on aging effect for facial expression recognition

: This work uses newly introduced variations of the sparse representation-based classifier (SRC) to challenge the issue of automatic facial expression recognition (FER) with faces belonging to a wide span of ages. Since facial expression is one of the most powerful and immediate ways to disclose individuals’ emotions and intentions, the study of emotional traits is an active research topic both in psychology and in engineering fields. To date, automatic FER systems work well with frontal and clean faces, but disturbance factors can dramatically decrease their performance. Aging is a critical disruption element, which is present in any real-world situation and which can finally be considered thanks to the recent introduction of new databases storing expressions over a lifespan. This study addresses the FER with aging challenge using sparse coding (SC) that represents the input signal as the linear combination of the columns of a dictionary. Dictionary learning (DL) is a subfield of SC that aims to learn from the training samples the best space capable of representing the query image. Focusing on one of the main challenges of SC, this work compares the performance of recently introduced DL algorithms. We run both a mixed-age experiment, where all faces are mixed, and a within-age experiment, where faces of young, middle-aged, and old actors are processed independently. We first work with the entire face and then we improve our initial performance using only discriminative patches of the face. Experimental results provide a fair comparison between the two recently developed DL techniques. Finally, the same algorithms are also tested on a database of expressive faces without the aging disturbance element, so as to evaluate DL algorithms’ performance strictly on FER.
A new construct of IS performance measurement: Consonance approach

The purpose o f this research is to create a subjective measurement system that can assist in the successful creation o f new information systems (IS). That entails moving from the commonly used objective measures o f success (time and money constraints) to subjective measures (people’s perceptions). Literature in the social perception area and goal congruence area has provided a basis for changing the measurement o f success. Due to major differences in how people react to stimuli and perceived outcomes the objective measures must evolve to include subjective observations. This research uses a questionnaire to gather data about the perceptions o f the stakeholders (IS personnel and IS end users) involved in production or use o f IS. A performance construct and satisfaction construct are used to investigate hypotheses regarding the differences in the perceptions o f IS end users and IS personnel. Discrepancy theory is the basis for depicting gaps in the perceptions o f the different stakeholders. By creating new constructs related to IS work, performance expectations and perceived outcomes can be measured for IS end users and IS personnel that are involved in creating and maintaining information systems. Current literature supports the involvement o f these two stakeholders and the movement to subjective measures. A process o f consonance is also included to insure consistence over time in creating common goals. Consonance is defined as harmony and understanding between stakeholders or people. Consonance is a process that can help achieve consistence in
Analysis of renewal input batch service queue with impatient customers and multiple working vacations

ABSTRACT This paper presents a renewal input single server batch service queue with impatient customers and multiple working vacations. An arriving customer may join the system with probability or balk with probability . The service is carried out in batches with a maximum threshold limit . The server takes a working vacation when there are no customers in the system, and continues taking multiple working vacations as long as there are no customers at working vacation completion epochs. Applying the embedded Markov chain approach and the displacement operator method, we find the probability distribution of the queue-length at pre-arrival epoch. With the help of the Markov renewal theory and semi-Markov processes, we compute the probability distribution of the queue-length at an arbitrary epoch. We also obtain the system-length distributions at both pre-arrival and arbitrary epochs and present a potential application of the model in cloud systems. Some corresponding results underneath special cases are found by putting suitable values. We present various performance measures and waiting-time distributions in the queue. Numerical results are demonstrated to emphasize the qualitative features of the model.
Investigating the Determinants of Students’ Intention to Use Business Simulation Games

Business simulation games (BSGs) are educational tools that help students develop business management knowledge and skills. However, to date, relatively little research has investigated the factors that influence students’ BSG usage intention. Grounded on the extended unified theory of acceptance and use of technology, this study helped to fill this gap by exploring intention to use BSGs. Specifically, this study investigated the influence of performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, and price value on behavioral intention to use BSGs. Data collected from 141 useful respondents were tested against the research model using partial least square approach. The results of this study indicated that behavioral intention to use BSGs was influenced by facilitating conditions, hedonic motivation, and price value. Unexpectedly, performance expectancy, effort expectancy, and social influence were not predictive of students’ behavioral intention to use BSGs. These findings enhanced our understanding of students’ BSG usage behavior and provided several important theoretical and practical implications for the application of BSG in the context of business and management education.
Built Information Modeling for the 3D Reconstruction of Modern Railway Stations

The BIM process applied to the built environment represents a much debated topic in the last decade, but it still contains unanswered questions. National and international frameworks introduced standards mainly focused on the levels of detail definition related to new project, leaving a wide interpretation on the 3D reconstruction of existing building. On the other hand, the increase in the use of this modeling approach and the possible expansion of this application in the nearly future lead to predict a significant rise in built field, requiring a general assessment both on global methodology and on its peculiarities. Starting from the complete description and analysis of two modern railway architectures, based on integrated survey, 2D representation up to 3D modeling in BIM environments, the article tries to highlight the limits in the 3D BIM modeling applied on existing construction, suggesting possible solutions in relation with the obtained results. The process is critically evaluated in each passage, in order to focus the BIM research areas useful for built environment analysis.
Generalized Approximate Message Passing: Relations and Derivations

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A High-Power Multiphase Wireless Dynamic Charging System With Low Output Power Pulsation for Electric Vehicles

Wireless dynamic charging (WDC) of electric vehicles (EVs) is a new initiative aimed to increase EV uptake. However, pulsating output power along the driving direction is a major problem with this technology. Moreover, in order to deliver enough driving power for EVs, WDC systems must operate at high power. This article proposes a WDC system that has the capability of providing a low pulsating and high output power to EVs. The proposed WDC utilizes multiple primary windings that guarantee a homogeneous mutual magnetic flux for the receiver along the driving direction. This results in a constant induced voltage across the receiver, and hence, constant output power to charge the EV battery. High output power is realized by using multiple transmitter windings, which have been arranged in a novel winding method. The structure layout of the proposed WDC, including ferrite cores, windings, and resonant tanks, is presented. Furthermore, a theoretical analysis is conducted to determine the conditions of each winding’s current phase and amplitude for achieving constant output power. The crossing mutual inductances between the different transmitter’s phases are compensated by adding small capacitors in series with each transmitter winding. An optimization analysis using Maxwell 3-D simulation for both transmitter and receiver is carried out to achieve the highest coupling factor by using minimum ferrite material. The effectiveness of the proposed system is analytically demonstrated and experimentally verified using a 3-kW laboratory prototype. Finally, the performance of the proposed method is compared with similar systems presented in the literature. The comparison shows that the proposed system has the advantages of using simple control, it also eliminates communications between the primary and secondary side and delivers a normalized power of 2.25, which is 125% higher compared to conventional single-phase systems.
A Novel Electricity Price Forecasting Approach Based on Dimension Reduction Strategy and Rough Artificial Neural Networks

An accurate electricity price forecasting (EPF) plays a vital role in the deregulated energy markets and has a specific effect on optimal management of the power system. Considering all the potent factors in determining the electricity prices—some of which have stochastic nature—makes this a cumbersome task. In this article, first, Grey correlation analysis is applied to select the effective parameters in EPF problem and eliminate redundant factors based on low correlation grades. Then, a deep neural network with stacked denoising auto-encoders has been utilized to denoise data sets from different sources individually. After that, to detect the main features of the input data and putting aside the unnecessary features, dimension reduction process is implemented. Finally, the rough structure artificial neural network (ANN) has been executed to forecast the day-ahead electricity price. The proposed method is implemented on the data of Ontario, Canada, and the forecasted results are compared with different structures of ANN, support vector machine, long short-term memory—benchmarking methods in this field—and forecasting data reported by independent electricity system operator (IESO) to evaluate the efficiency of the proposed approach. Furthermore, the results of this article indicate that the proposed method is efficient in terms of reducing error criterion and improves the forecasting error about 5–10 percent in comparison with IESO. This is a remarkable achievement in EPF field.