Miracle-dz/UniFAR-FTUs huggingface dataset:
Stable Shortest Horizon FCS-MPC Output Voltage Control in Non-Minimum Phase Boost-Type Converters Based on Input-State Linearization

Finite Control Set Model Predictive Control (FCS-MPC) is an alternative to conventional linear controllers in power converters due to its attractive properties such as its conceptual simplicity, flexibility, direct consideration of non linearities as well as constraints, and fast control response. This last feature imposes computational constraints and short prediction horizons are therefore needed. In fact, in many applications an horizon one FCS-MPC controller is used. However, using FCS-MPC with a short prediction horizon in topologies that exhibit non-minimum phase behavior such as active-front-end rectifiers, dc-dc boost converters, and Z-source converters, among others, may lead to control issues. This work studies the origin of these control issues using nonlinear control theory considering a dc-dc boost converter as a case study. It is shown that the difficulty to directly control the converter output voltage using short horizon FCS-MPC is the resulting unstable internal dynamic, as the short horizon FCS-MPC controller acts like an input-output linearizing controller. To solve the problem of the unstable internal dynamic, a short horizon FCS-MPC controller based on input-state linearization is proposed allowing correct operation of the system. Experimental results show the feasibility of the proposal.
Multi-view Bearing Fault Diagnosis Method Based on Deep Learning

In recent years, the use of Time-frequency analysis to generate Time-frequency diagrams of vibration signals, and then the use of deep learning methods to classify them has become one of the mainstream methods for bearing fault diagnosis. However, a single Time-frequency analysis method usually cannot extract a complete vibration signal, so the accuracy of the model will be affected to a certain extent. Therefore, this paper proposes a multi-view bearing fault diagnosis method based on deep learning. The same segment of vibration signal is generated by different Time-frequency analysis methods, and then the pre-trained network is used to train the model. The Flatten layer was replaced by the Global Max Pooling (GMP) layer before the layer. The experimental results show that compared with the traditional feature fusion method, the method in this paper can not only achieve better accuracy, but also has stronger generalization.
HIGH EFFICIENCY MULTI-FUNCTIONAL ALL-OPTICAL LOGIC GATES BASED ON MIM PLASMONIC WAVEGUIDE STRUCTURE WITH THE KERR-TYPE NONLINEAR NANO-RING RESONATORS

In this paper, high efficiency multi-functional all-optical logic gates based on a metalinsulator-metal (MIM) plasmonic waveguide structure with Kerr-type nonlinear nano-ring resonators are proposed. The proposed structure consists of three straight input ports, eight nano-ring resonators filled with the Kerr-type nonlinear medium, and one straight output port. By fixing the input signal power and properly changing the control power, it can be used to design high efficiency multi-functional all-optical logic gates. The numerical results show that the proposed Kerr-type nonlinear plasmonic waveguide structures could really function as all-optical XOR/NXOR, AND/NAND, and OR/NOR logic gates in the optical communication spectral region. The transmission efficiency of the high logic state is higher than 95%, and that of the low logic state is about 0% at the wavelength 1310 nm. The performance of the proposed logic gates was analyzed and simulated by the finite element method (FEM).
Research on computer application software monitoring data processing technology based on NLP

In the process of using computer application software, the system may fail because of software defects. During the operation of the computer system, the running state of the system is also monitored. This paper mainly studies the monitoring data processing technology of computer application software based on NLP and presents a software monitoring data processing scheme based on NLP. First, the data were preprocessed by word segmentation and data cleaning. Then the named entity recognition and extraction information are accomplished by fusing CRF and rule-based methods. It can provide effective data support for system failure prediction by characterizing the software monitoring data.
Research on Application of Construction Model Based on 4D Visualization in Construction of Intelligent Substation

At present, the existing technology has not yet realized the conversion process from the digital design model of the substation project to the construction model. This paper provides a solution to this problem. After the design model GIM is loaded into unity, the vertices and materials of the GIM model are read and loaded into the scene through the company's independent research and development of three-dimensional design software (STD-R). The model level information returned by the terminal corresponds to the loaded model one by one, thereby obtaining a model tree structure. The research results of the thesis provide convenience for the design and construction of power grid substation projects.
The implementation of tpack framework in mathematics mobile edugame design: basic multiplication and division concept

Mobile edugames were popular and has grown rapidly due to their ease of access, affordability, and interactivity. There have been numerous arithmetic related mobile edugames developed and available online. However, there are not so many mathematics mobile edugames existed which emphasize the basic concepts in accordance with Indonesia available curriculum and optimalize specific learning methods that suitable for targeted learner have been developed, particularly in basic division and multiplication materials. Through this study, the design of mathematics mobile edugame about basic division and multiplication with repetitive addition and subtraction concept will be discussed using TPACK framework that generates design recommendation and its application in mathematics mobile edugame with discovery-based learning model as an outcome. It uses specific TPACK plot from PCK to TPACK. The result shows in optimization used of technology in mathematics mobile edugame design user cognitive psychology development, gameplay and applied learning method are the most important aspect that then generates an adventure-puzzle mathematics mobile edugame which utilizes storytelling as final result.
Context-aware security in the internet of things: a survey

Internet of Things applications encompass home-automation, health, transportation, etc. The main objective of these applications is to improve user’s daily lives. However, security and privacy threats and the lack of adapted security mechanisms could signiﬁcantly reduce their development and slow-down their adoption. Several researches have been conducted in order to ﬁnd solutions for securing the IoT systems and to reduce, even eliminate risks for user’s privacy. Oneoftheproposedsolutionsiscontext-awaresecurity,whichenablestoconsiderrelevantcontextualinformationwhileimplementingsecuritymechanisms.Inthis paper,wewillconductasurveyofthecontext-awaresecuritysolutionsthathavebeenproposedforsmartcityIoTapplications.Theseapplicationshaveagreat impactoncitizenslife.Foreachsolution,wewillprovidecriticalanalysisintermsofcontext-awaremanagement,securityservicesandprivacymechanisms. Then,wewillidentifythedifferentresearchdirectionsforbettercontext-awaresecurityintheseapplications.
EFFECT OF β ON EFFECTIVE MULTIPLICATION FACTOR IN 1/fβ SPECTRUM RANDOM SYSTEM

We investigated the effect of β on effective multiplication factor(keff) in the 1/fβ spectrum random system. The random system was generated by the 1/fβ noise model. The model is a continuous space model based on the Randomized Weierstrass function and describes the component spatial distribution with a power spectrum of 1/fβ, where f and β are the frequency domain variable and the characteristic parameter related to randomness, respectively. In this work, the two-group Monte Carlo calculations were performed to obtain the keff for a simple cubic geometry that consisted of two materials (fuel burned to 12 GWd/t and concrete). A large number of replicas having different spatial distribution and characterized by the representative β values were generated using the model, and the distribution on keff was analyzed. We found the dependency on β of standard deviation, skewness, and kurtosis of keff distribution. This result is expected to help to predict the keff distribution due to the randomizing model.
A new classification of deployable structures

Deployable lightweight structures are studied in the disciplines of architecture, civil engineering, aerospace engineering, mechanical engineering and other fields of their application. The research into these structures in individual fields resulted in a large amount of data sorted out by numerous classifications. The previously made classifications proposed by different authors are presented in this paper by the usage of reasonably unified tables that enabled a direct insight into the essential characteristics of these structures, their analysis and mutual comparison. One of the results of these analyses is the proposal for the unified classification given in a separate table in this paper. The results of interdisciplinary studies have been collected into a unified classification which could be applied for the research in different scientific fields, presenting the basic types of these structures, including individual elements and details with their characteristic features. The proposal of the unified classification of deployable structures is made according to the application of the basic elements used for structure forming. The suggested classification, with the review of the results of the present research, is a significant starting point for the scientists in different disciplines and it provides a detailed insight into the studied characteristics of these structures.
Comparison of Regarding Students’ Learning Motivation by Gender During the Online Learning

This study's objectives were to investigate and analyze comparisons of students' learning motivation during online learning based on gender. This quantitative study used a survey design. The subject study consisted of 331 students in the high school selected from science majors using the convenience sampling technique. The Instrument was adopted by The Motivated Strategies Learning Questionnaire (MSLQ), which consisted of five aspects (self-efficacy, intrinsic value, test anxiety, strategy use, self-regulation). The Instrument was tested valid and reliable to use. Data analyzed was used descriptive statistics and Mann Whitney test. These findings that the female student's mean score of learning motivation is higher than the male student's mean score. Based on the Mann-Whitney test results, there is a significant difference in the mean score of learning motivation between male and female students. Finally, we suggest further research on gender differences in learning motivation using a larger population and more extensive research about investigating the relationship or influence of students' learning motivation toward gender.
Hemocompatibility and ultrafiltration performance of PAN membranes surface‐modified by hyperbranched polyesters

Maleic anhydride was grafted onto a polyacrylonitrile (PAN) membrane surface via ultraviolet irradiation. Then, hyperbranched polyester, with varying numbers of hydroxyl end-groups (H20, H30, and H40), was grafted onto the PAN membrane surface by the reaction of hydroxyl groups with anhydride groups of maleic anhydride. The modified membranes were characterized by scanning electron microscopy, static water contact angle, and attenuated total reflectance-Fourier transform infrared spectroscopy measurements. The modified membranes showed a higher water flux and better antifouling properties than pristine PAN membranes, and their hydrophilicity was significantly improved. Membrane biocompatibility was characterized by platelet adhesion, and the results indicate that the modified membranes exhibited good biocompatibility. Copyright © 2016 John Wiley & Sons, Ltd.
Leverage and performance: the case of the U.S. hospitality industry

This study analyzes the leverage and performance relationship in the context of the U.S. hospitality industry. We consider that, studying this traditional corporate finance issue in the context of the hospitality industry, is relevant due to its unique characteristics in terms of capital structure and value creation. In addition to Ordinary Least Squares (OLS) and Fixed-Random effects (FE-RE) estimations, we also employ System Generalized Method of Moments (GMM) panel data techniques to avoid the endogeneity issue. Thus, using a sample of 313 U.S. hospitality firms for the period 2001–2018, our primary results are consistent with the pecking order theory, suggesting a negative relationship between leverage and firm performance. The findings are robust to alternative variables description and econometric techniques. We also find an inverted U-shape relationship, but given the high indebtedness of hospitality firms, the negative impact on firm performance is prevalent. Our contribution to the literature is double. First, we highlight the importance of analyzing the capital structure issue in a certain industry and, second, we provide important policy implications for managers and investors.
Packaging and storage of cocoa beans fermented with Lactobacillus plantarum HL-15 in Agricultural Technology Park Nglanggeran, Yogyakarta

In Gunungkidul, there is Agricultural Techno Park (ATP) Nglanggeran which processes fermented cocoa beans from local farmers for making chocolate products. This research aimed to obtain a suitable packaging method for fermented cocoa beans during storage in ATP Nglanggeran. This research was carried out using a completely randomized factorial design. The first factor was the addition of starter (B), which consisted of B1 (with Lactobacillus plantarum HL 15) and B2 (without Lactobacillus plantarum HL 15). The second factor was the packaging methods (K), which consisted of K1 (vacuumed PP plastic 0.8 mm in thickness), K2 (non-vacuumed PP plastic 0.8 mm in thickness), K3 (plastic container), K4 (vacuumed PP plastic 0.8 mm in thickness stored within the plastic container), and K5 (nylon sack). The third factor was the storage time (P), which were P0 (0 month), P1 (1 month), and P2 (2 months). The result showed that the samples with the addition of the starter had a lower number of fungal colonies than those without the addition. Besides, the packaging using vacuumed PP plastic 0.8 mm in thickness stored within a plastic container gave the best result on minimizing the total fungal growth contamination and the peroxide value.
Performance evaluation of least-squares probabilistic classifier for corporate credit rating classification problem

The corporate credit rating classiﬁcation problem has attracted lots of research interests in the literature of ﬁnancial risk management. This article introduces the least-squares probabilistic classiﬁer to the problem in an attempt to provide a model with better explanatory power. Empirical results show that the least-squares probabilistic classiﬁer outperforms the logistic regression model, random forest, and the support vector machine in prediction accuracy ratios and F1 scores, for the samples of bond issuer ﬁrms in Japan
Speech-to-Text Conversion in Indonesian Language Using a Deep Bidirectional Long Short-Term Memory Algorithm

Nowadays, speech is used also for communication between humans and computers, which requires conversion from speech to text. Nevertheless, few studies have been performed on speech-to-text conversion in Indonesian language, and most studies on speech-to-text conversion were limited to the conversion of speech datasets with incomplete sentences. In this study, speech-to-text conversion of complete sentences in Indonesian language is performed using the deep bidirectional long short-term memory (LSTM) algorithm. Spectrograms and Mel frequency cepstral coefficients (MFCCs) were utilized as features of a total of 5000 speech data spoken by ten subjects (five males and five females). The results showed that the deep bidirectional LSTM algorithm successfully converted speech to text in Indonesian. The accuracy achieved by the MFCC features was higher than that achieved with the spectrograms; the MFCC obtained the best accuracy with a word error rate value of 0.2745% while the spectrograms were 2.0784%. Thus, MFCCs are more suitable than spectrograms as feature for speech-to-text conversion in Indonesian. The results of this study will help in the implementation of communication tools in Indonesian and other languages.
Tree-based intercropping may reduce, while fertilizer nitrate may increase, soil methane emissions1

Abstract: Tree-based intercropping (TBI) systems have shown some promise in mitigating greenhouse gas emissions, such as by sequestering carbon and decreasing soil nitrous oxide emissions. However, the effects of TBI on soil methane fluxes remain unknown. In a field study, we failed to show differences in soil CH4 production between TBI and conventional monocropping (CM) systems. Within TBI plots, however, we found significantly lower CH4 concentrations near the middle of the alleys than closer to tree rows. Soil CH4 concentrations also decreased with soil depth, even dipping below mean global atmospheric concentrations. Laboratory assays revealed a higher CH4 oxidation potential in soils collected from TBI plots compared with CM plots. These assays also revealed a decrease in CH4 oxidation potential after soils were amended with nitrate. We conclude that TBI could potentially reduce soil CH4 emissions, whereas fertilizer nitrate may increase them.
Around the clock ‐ capsule networks and image transformations

Capsule networks are a promising new model class for image processing tasks. We investigate and compare their classification performance with convolutional neural networks on a new entry level image data set. In our experiments the capsules are able to learn the effect of rotated input images from data and outperform a comparable convolutional architecture. The results also show that convolutional and capsule networks need structural adjustment to respond to transformations that are not included in the training set.
Image-Driven Spatial Interpolation With Deep Learning for Radio Map Construction

Radio maps are a promising technology that can boost the capability of wireless networks by enhancing spectrum efficiency. Since spatial interpolation is a critical challenge to construct a precise radio map, the latest works have proposed deep learning (DL)-based interpolation methods. However, a DL model that achieves enough estimation accuracy for practical uses has not yet been established due to the complexity of radio propagation characteristics. Therefore, we propose a novel DL framework that transforms the spatial interpolation problem into a shadowing adjustment problem suitable for DL-based approaches. We evaluate the performance using real measurement data in urban and suburban areas to show that the proposed framework outperforms the state-of-the-art deep learning models.
A high-efficient and low-cost secure AMBA framework utilizing configurable data encryption modeling against probe attacks

Inthispaper,ahigh-eﬃcientandlow-costsecureAMBAframe-work utilizing the bus data encryption modeling is proposed to resist the probe attacks. By encrypting the conﬁdential data ﬂowing through the bus, the proposed conﬁgurable encryption model meets the security requirement of total SoC. Further, a data encryption pipeline with the third-level branch predictor is proposed to accelerate the encryption process. Finally, an SoC with the 32-bit proposed AMBA framework is established and validated with 55nm technology. Experimental results show that the proposed frameworkachieves6152Mbpsthroughput,consumes39547um 2 area,and provides a stronger resistance compared to the other countermeasures.
Mozzarella Cheese Stretching: A Minireview

SUMMARY Mozzarella cheese stretching is a thermomechanical treatment influenced by factors such as pH, acidity, stretching time and temperature. The aim of this minireview is to provide information about the stretching step and the effect of the main factors on the functional properties of mozzarella. The presented studies show that stretching under higher temperatures promotes more interactions in the protein matrix, and changes occur in the calcium balance throughout the storage period that influence water mobility, proteolysis and lead to changes in mozzarella properties. Therefore, the information presented in this minireview may facilitate the production of mozzarella cheese with specific functional properties.
Resource Provisioning and Allocation in Function-as-a-Service Edge-Clouds

Edge computing has emerged as a new paradigm to bring cloud applications closer to users for increased performance. Unlike back-end cloud systems which consolidate their resources in a centralized data center location with virtually unlimited capacity, edge-clouds comprise distributed resources at various “computation spots”, each with very limited capacity. In this article, we consider Function-as-a-Service (FaaS) edge-clouds where application providers deploy their latency-critical functions to process user requests with strict response time deadlines. In this setting, we investigate the problem of resource provisioning and allocation. After formulating the optimal solution, we propose resource allocation and provisioning algorithms across the spectrum of fully-centralized to fully-decentralized. We evaluate the performance of these algorithms in terms of their ability to utilize CPU resources and meet request deadlines under various system parameters. Our results indicate that practical decentralized strategies, which require no coordination among computation spots, achieve performance that is close to the optimal fully-centralized strategy with coordination overheads.
Financial Distress and Bankruptcy Prediction in Conditions of Slovak Republic

Research background: Prediction of bankruptcy has an important place in financial analysis of an organization in the globalized economy. Ever since the first publication of a paper on bankruptcy prediction in 1932, the field of bankruptcy prediction was attracting researchers and scholars internationally. Over the years, there have been a great many models conceived in many different countries, such as Altman’s Z score or Ohlson’s model for use for managers and investors to assess the financial position of a company. Globalization in last few decades has made it even more important for all stakeholders involved to know the financial shape of the company and predict the possibility of bankruptcy. Purpose of the article: We aim in this article to examine the financial distress and bankruptcy prediction models used or developed for Slovakia to provide an overview of possibilities adjusted to specific conditions of the Slovak Republic in context of globalization. We will also look at the possibility of use of these prediction models for assessing financial status of non-profit organizations in the Slovak Republic. Methods: We will use analysis and synthesis of current research and theoretical background to compare existing models and their use. Findings & Value added: We hope to contribute with this paper to the theoretical knowledge in this field by summarizing and comparing existing models used.