Miracle-dz/UniFAR-FTUs huggingface dataset:
The Mediating Effect of Perceived Control on the Relationship between the Mindfulness and Emotional Regulation in University Students

Mindfulness is being used as one of the techniques for emotional regulation. In emotional regulation, mindfulness is considered as an uncontrolled way in that it observes the present experience as it is, rather than attempting to reduce or eliminate negative affective experiences. However, in this study, the purpose of this study is to confirm that mindfulness can affect emotional regulation through perceived control. To confirm this, I verified whether perceived control had a mediating effect on the relationship between mindfulness and difficulty in emotional regulation. For this 366 university students (179 males, 187 females) were surveyed using the scales of mindfulness, perceived control, and difficulty in emotional regulation, and mediation was analysed using Hayes' PROCESS macro. As a result of the analysis, it was found that perceived control had a fully mediating effect in the relationship between mindfulness and emotional regulation difficulties. These results suggest that mindfulness affect emotional regulation through perceived control.
ANALYSIS OFWATER STRESS EFFECTS CAUSING SPATIAL YIELD VARIABILITY IN SOYBEANS

Soybean yields have been shown to be highly variable across fields. Past efforts to correlate yield in small sections of fields to soil type, elevation, fertility, and other factors in an attempt to characterize yield variability has had limited success. In this article, we demonstrate how a process oriented crop growth model (CROPGRO-Soybean) can be used to characterize spatial yield variability of soybeans, and to test hypotheses related to causes of yield variability. In this case, the model was used to test the hypothesis that variability in water stress corresponds well with final soybean yield variability within a field. Soil parameters in the model related to rooting depth and hydraulic conductivity were calibrated in each of 224 grids in a 16-ha field in Iowa using three years of yield data. In the best case, water stress explained 69% of the variability in yield for all grids over three years. The root mean square error was 286 kg ha–1 representing approximately 12% of the three-year mean measured yield. Results could further be improved by including factors that were not measured, such as plant population, disease, and accurate computation of surface water run on into grids. Results of this research show that it is important to include measurements of soil moisture holding capacity, and drainage characteristics, as well as root depth as data layers that should be considered in any data collection effort.
Post-harvest losses of citrus fruits and perceptions of farmers in marketing decisions

Citrus is a non-climacteric fruit that breaks easily. Poor postharvest handling methods have resulted in severe losses to farmers. This research was conducted in 2017 at Dau Subdistrict, Malang Regency, East Java, using a purposive sampling method to understanding the losses of citrus fruits and perceptions of farmers in marketing decisions. A total of 177 citrus farmers participated in this study. Citrus farmers experienced food loss and waste is 34% in total all activity. From the value chain activities carried out by most citrus farmers: production and harvesting, handling and storage, processing, and packaging, as well as distribution and markets, farmers experience food loss and waste of 13%, 10%, 4%, and 7% respectively. Postharvest losses were influenced by farmers' perceptions of selling citrus fruits, especially prices, cash payments and the frequency of delivery of citrus fruits. By knowing the critical point most detrimental, farmers can take action to minimize losses.
The design of high-efficiency line-start motors

In this paper, techniques for improving the efficiency of small, line-start motors are investigated. Through a combination of formal optimisation methods and standard design techniques, the efficiency of an induction motor is increased by 4%. This motor is then used as a basis for the design of a line start permanent magnet motor (LSPM). Steady-state and dynamic finite-element analysis of the LSPM indicates that significant efficiency improvements are obtained and that the motor will start when fully loaded.
Electrical and Thermal Performance of 1200 V, 100 A, 200°C 4H-SiC MOSFET-Based Power Switch Modules

In this paper we report the electrical and thermal performance characteristics of 1200 V, 100 A, 200°C (Tj), SiC MOSFET power modules configured in a dual-switch topology. Each switch-diode pair was populated by 2 x 56 mm2 SiC MOSFETs and 2 x 32 mm2 SiC junction barrier Schottky (JBS) diodes providing the 100 A rating at 200°C. Static and dynamic characterization, over rated temperature and power ranges, highlights the performance potential of this technology for highly efficient drive and power conversion applications. Electrical performance comparisons were also made between SiC power modules and equivalently rated and packaged IGBT modules. Even at a modest Tj=125°C, conduction and dynamic loss evaluation for 20kHz, Id=100A operation demonstrated a significant efficiency advantage (38-43%) over the IGBT components. Initial reliability data also illustrates the potential for SiC technology to provide robust performance in harsh environments.
An approximate analytical solution for hydraulic fracture opening under non-uniform internal pressure

Because of the special non-uniform pressure distribution in hydraulic fracture, an especial hypothesis is proposed to get an approximate solution for the fracture opening, which satisfies the mixed boundary conditions on the fracture edges with the use of Bessel function integral properties. Error analysis of this approximate solution compared with the accurate solution is carried out. It is proved that the approximate solution is in good agreement with the accurate solution under the condition of this special pressure distribution in hydraulic-fracturing.
Hashing for Localization (HfL): A Baseline for Fast Localizing Objects in a Large-Scale Scene

Advanced remote-sensing instruments produce massively large scenes from the surface of the earth, with very high spatial resolution and dimensionality. Developing methods for efficiently localizing specific objects in a large-scale scene presents a significant challenge, mainly because of the high computational requirements involved. To tackle this issue, we propose a new hashing for localization (HfL) framework that efficiently searches for specific objects in the large-scale scene. It begins by dividing the scene into a large number of overlapping local patches. A lightweight deep hash model, referred to as a tiny hashing network (THNet), encodes the local patches into hash codes. The Hamming distances between the hash code of an object image, i.e., an image containing the specific class of objects to be localized in the scene, and those of all local patches are computed. Small values of the Hamming distance indicate local patches that are similar to the object image. The positions of these local patches in the large-scale scene reflect the regional locations of the specific objects. The hash codes are binary and do not take up much space, and the Hamming distance carries very low-computational overheads. Further, we exploit a class center loss as the THNet training objective, which can comprehensively manage multiple object classes. These features mean that the HfL framework can localize specific objects very quickly, regardless of the size of the scene. Extensive experiments validate the effectiveness and efficiency of the framework. For instance, HfL can find objects in a remote-sensing image of 19584 $\times$ 19584 pixels in only 4.388 s (on a single RTX2080ti), with remarkable localization results. The source codes and datasets are available at https://github.com/lrhan/HfL, together providing a baseline for fast localizing objects in a large-scale scene.
Crosstalk in erbium doped fibre amplifiers

Signal crosstalk measurements are presented for two signals amplified in an erbium-doped optical-fibre amplifier. The crosstalk is heavily dependent upon the modulating frequency and found to be negligible at frequencies above 100 kHz. The fibre amplifier may therefore be suitable for use in multichannel optical systems. >
COORDINATION DURING INITIAL ACQUISITION OF THREE-BALL JUGGLING

Bimanual coordination is critical for the performance of a number of continuous skills (e.g., walking, running, drumming, etc.). However, relatively little is known about how such coordination is developed and maintained. In the current study researchers sought to address this issue by examining coordination changes in bilateral elbow motion during initial acquisition of three-ball cascade juggling. Elbow motion was assessed using a set of both traditional (Average Amplitude; Avg. Amp) and more recently developed nonlinear time-series analyses (Approximate Entropy; ApEn – a measure of complexity within a single time series, and Average Mutual Information; AMI – the amount of information shared between two time-series) (Abarbanel, 1996; Pincus, 1995). It was hoped that together these analyses would lend new insights about bimanual coordination in continuous skills that could be used to develop more sophisticated biomechanical models of human movement and/or advance general motor theory
Low Overhead Beamtraining for Millimeter-Wave MIMO Systems: Machine Learning Approach Based on Path Parameters

—Machine learning has been widely used as a solution to deal with beam management training overhead for 5th generation wireless communication systems. However, the types of information adopted for the training base have not been sufficient to achieve a robust intelligent system. Channel path parameters provide valuable information that can increase the accuracy of this type of solution. In this work, we present initial results of the application of the Kalman filter to estimate path parameters aiming at a robust training base. Simulated results in 3D ray-tracing show promising results on obstruction conditions.
Probabilistic stability and "tall" wind profiles: theory and method for use in wind resource assessment

A model has been derived for calculating the aggregate effects of stability and the finite height of the planetary boundary layer upon the long-term mean wind profile. A practical implementation of this probabilistic extended similarity-theory model is made, including its incorporation within the European Wind Atlas (EWA) methodology for site-to-site application. Theoretical and practical implications of the EWA methodology are also derived and described, including unprecedented documentation of the theoretical framework encompassing vertical extrapolation, as well as some improvement to the methodology. Results of the modeling are shown for a number of sites, with discussion of the models' efficacy and the relative improvement shown by the new model, for situations where a user lacks local heat flux information, as well as performance of the new model using measured flux statistics. Further, the uncertainty in vertical extrapolation is characterized for the EWA model contained in standard (i.e., WAsP) wind resource assessment, as well as for the new model. Copyright © 2015 John Wiley & Sons, Ltd.
Sparsity-Exploiting Blind Receiver Algorithms for Unsourced Multiple Access in MIMO and Massive MIMO Channels

We propose new transmission schemes and receiver algorithms for unsourced multiple access (UMA) in MIMO and massive MIMO channels. Each active transmitter’s information bits are first channel encoded. The coded bits are divided into sub-blocks and each sub-block is modulated and transmitted. For both MIMO and massive MIMO channels, the conventional nonlinear modulation can be employed where each sub-block of coded bits is mapped to a transmitted signal vector. For the massive MIMO channel, we propose a new hybrid modulation scheme to reduce the receiver complexity, where the first sub-block is nonlinearly modulated, and the subsequent sub-blocks are linearly modulated and spread by the first sub-block signal. We also propose sparsity-exploiting blind receiver algorithms. Specifically, for the MIMO case, we exploit the codeword sparsity inherent in the UMA system, and a channel clustering technique, to estimate the channel and the transmitted signal of each transmitter. For the massive MIMO, in addition to the codeword sparsity, we further exploit the channel sparsity and user sparsity in estimating the channel and transmitted signal of each transmitter. The proposed receiver algorithms for both MIMO and massive MIMO channels output either hard or soft estimates of the coded bits, and therefore single-user channel decoding of the information bits can be performed for each transmitter. Extensive simulation results are provided to demonstrate the performances of the proposed algorithms.
Contribution to Research on Micro-Lensed Fibers for Modes Coupling

Abstract In this paper we present a review of different micro-lenses we have developed at the end of single-mode fibers for improving optical coupling efficiency in communication systems. These micro-lenses find their applications in laser-diodes or integrated waveguides to single mode fibers, fiber to detector coupling, or fiber-to-fiber interconnecting. More generally, the applications concern active or passive components to single mode fiber coupling. Three kinds of micro-lenses are considered whose fabrication process has been designed to be very simple and low cost. Some of them are produced by companies in Lannion such as IDIL Fibers Optiques.
Learning Informative Health Indicators Through Unsupervised Contrastive Learning

Condition monitoring is essential to operate industrial assets safely and efficiently. To achieve this goal, the development of robust health indicators has recently attracted significant attention. These indicators, which provide quantitative real-time insights into the health status of industrial assets over time, serve as valuable tools for fault detection and prognostics. In this study, we propose a novel and universal approach to learn health indicators based on unsupervised contrastive learning. Operational time acts as a proxy for the asset's degradation state, enabling the learning of a contrastive feature space that facilitates the construction of a health indicator by measuring the distance to the healthy condition. To highlight the universality of the proposed approach, we assess the proposed contrastive learning framework in two distinct tasks - wear assessment and fault detection - across two different case studies: a milling machines case study and a real condition monitoring case study of railway wheels from operating trains. First, we evaluate if the health indicator is able to learn the real health condition on a milling machine case study where the ground truth wear condition is continuously measured. Second, we apply the proposed method on a real case study of railway wheels where the ground truth health condition is not known. Here, we evaluate the suitability of the learned health indicator for fault detection of railway wheel defects. Our results demonstrate that the proposed approach is able to learn the ground truth health evolution of milling machines and the learned health indicator is suited for fault detection of railway wheels operated under various operating conditions by outperforming state-of-the-art methods. Further, we demonstrate that our proposed approach is universally applicable to different systems and different health conditions.
An Early Years' CPD initiative for mathematics: the power of collaborative, 'grassroots' learning

Local ‘grassroots’ Children’s Mathematics Network groups are initiated and ‘owned’ by teachers and practitioners and they explore and develop their understanding of children’s mathematical graphics (Carruthers & Worthington, 2005; 2006; DCSF, 2008) in their own ways. New research findings reveal the effectiveness of this form of ‘continuing professional development’ (CPD) and its impact on children’s mathematical thinking (NCETM, 2009). This paper explores the philosophy underpinning these groups, and their inter-connectedness with children’s mathematical graphics.
An Efficient Aspect based Sentiment Analysis Model by the Hybrid Fusion of Speech and Text Aspects

Aspect-based Sentiment Analysis (ABSA) is treated to be a challenging task in the domain of speech, as it needs the fusion of acoustic features and Linguistic features for information retrieval and decision making. The existing studies in speech are limited to speech and emotion recognition. The main objective of this work is to combine acoustic features in speech with linguistic features in text for ABSA. A deep learning and language model is implemented for acoustic feature extraction in speech. Different variants of text feature extraction techniques are used for aspect extraction in text. Trained Lexicons, Latent Dirichlet Allocation (LDA) model, Rule based approach and Efficient Named Entity Recognition (E-NER) guided dependency parsing approach has been used for aspect extraction. Sentiment with respect to the extracted aspect is analyzed using Natural Language Processing (NLP) techniques. The experimental results of the proposed model proved the effectiveness of hybrid level fusion by yielding improved results of 5.7% WER and 3% CER when compared with the traditional baseline individual linguistic and acoustic feature models.
Research on Railway Information Technology System Based on IOT

The domain of railway is the most promising field when IOT applies in practice. By means of expounding on the connotation of railway information technology, carrying out research on the components of railway information technology and classifying it into categories, this paper constructs railway information technology system based on Internet of Things (IOT). It buttresses further application of the IOT in railway field.
Diversity and Inclusion Practices of Selected German Multinational Firms in The Czech Republic

The paper investigates what specific diversity and inclusion strategies are applied across selected German multinational companies’ subsidiaries located on the territory of the Czech Republic. The study suggests that in the Czech Republic the majority of the applied diversity and inclusion managerial practices are based on historical footprints in the region. Furthermore, the paper reveals that diversity and inclusion practices are significantly affected by the cultural, social norms and values in the given country. Also, paper findings show that the respective firms’ subsidiaries in the Czech Republic are trying to westernize their diversity and inclusion strategies in order to attract more investors and to be more credible and prosperous. The study brings evidence that there is emerging interest and efforts by the subsidiaries in the selected emerging markets to adopt contemporary management approaches in dealing with its diverse employees in order to increase firm performance.
Analytic Models in Game Based Productive Systems

Current computer-aided design (CAD) applications deliver limited user experience due to their complex functionality and step-by-step evolution; on the contrary, games offer memorable and formative experiences to their users. Hence, the article focuses on the architecture characteristics of games and CAD with a particular interest in developing and evaluating a CAD system embedded with game mechanics for mechanical engineering design. To establish the benefit of game-enriched engineering design ecosystem preliminary experiments were conducted to investigate engineer affect whilst carrying out parametric design tasks in a game-based CAD UI. By analysing their psycho-physiological signals and action log data an interaction model was established to identify which feedback game mechanics contributed the most in the user’s design experience.
Preliminary Studies on Glycine Max Ethanol Extract (GMEE)

Present study deals with the preliminary study of Glycine Max Ethanol Extract (GMEE). GMEE stacks more macro and micro nutrients with many pharmacological and nutraceutical standards. GMEE was preliminary screened by simple test methods and instrumentation methods such as RP-HPLC, IR and GC-MS. The obtained results from IR predicted the presence of different functional groups such as OH, CH 2 , C=O, C-O and cyclic ring. While, the RP-HPLC and GC-MS profiles of GMEE predicted the presence of lipids, polyphenols, alkaloids and flavonoids in the extract. The IR spectrum of GMEE confirms that presence of stretching vibrational band for OH group, aromatic C-H group, coordinated carbonyl (C=O) stretching of CHO group and the bending form C-H aliphatic groups.
Compact quad band monopole antenna

In this article, a compact quad band monopole antenna is demonstrated. Asymmetric coplanar strip feeding is applied in the design in order to compact the size. The proposed antenna fabricated on a FR4 substrate has an overall size of 30–30 mm2. By integrating an invert‐C slot and a modified invert‐F strip into an invert‐C patch, multiple resonant frequencies can be excited at 1.1, 2.35, 3.6, and 5.4 GHz individually for global positioning system, wireless local area network, Bluetooth/ISM, and Wi‐MAX applications. The measured result has shown a good agreement with the simulation results. © 2011 Wiley Periodicals, Inc. Microwave Opt Technol Lett 53:1272–1276, 2011; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.25994
An Improved Equivalent Circuit Model of SiC MOSFET and Its Switching Behavior Predicting Method

Wide-bandgap (WBG) power devices have better dynamic characteristics and higher working temperatures compared to traditional Si devices and are more suitable for high-frequency and high-voltage applications because of fast turn-on and turn-off speeds. The main obstacle for the use of WBG devices is the high-frequency oscillation (HF-Osc) in voltage and current during the switching process. In this article, a new method of predicting the SiC mosfet switching behavior (including HF-Osc and slow switching) is proposed. First, the models of nonlinear capacitances are improved and parasitic elements are taken into account for improving the RLC equivalent circuit of the mosfet switching transient. On this basis, the LC equivalent frequency is obtained. Then, a new method is proposed to obtain the SiC mosfet instantaneous frequency. The comparison between them in the time domain makes it possible to analyze the SiC mosfet switching behavior. The prediction method is verified using a CREE SiC mosfet via a double pulse test. Experimental and simulation results show that the prediction method is effective in describing the HF-Osc characteristics. The proposed method can be used to effectively improve the mosfet HF-Osc, which provides guidance for the application of SiC mosfets.
Curadoria de chatbots: conceptualização, estratégias e indicadores de desempenho

Nos últimos anos o surgimento e expansão de inovações tecnológicas, como os chatbots, tem ocasionado a formação de novas áreas de desempenho e/ou novos perfis profissionais. Nesse sentido a presente pesquisa teve o objetivo de analisar o fenômeno da curadoria de chatbots, para o qual foram realizadas 10 entrevistas semiestruturadas a profissionais experientes no desenvolvimento e na curadoria de chatbots. Os resultados foram organizados em três apartados: 1) a conceptualização da curadoria de chatbots, 2) estratégias utilizadas na curadoria e 3) indicadores de desempenho. Por último, as conclusões finais sintetiza os principais achados do estudo, discute-se sobre a relevância da curadoria de chatbots e compartilha possíveis linhas de pesquisa.