Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on a Text Topic Mining Algorithm Based on Big Data

Topic modeling has been widely used to mine topics from documents. However, a key disadvantage of topic modeling is that it requires large amounts of data (e.g. thousands of documents) to provide reliable statistics to generate coherent topics. However, in practice, many document collections do not have as many documents. Given a small number of documents, the topic generated by the classic topic model LDA is very poor. Even in the case of a large amount of data, unsupervised learning of the topic model will still produce unsatisfactory results. In recent years, knowledge-based topic models have been proposed which require human users to provide some prior domain knowledge to guide the model to generate better topics. Our research takes a completely different approach. We recommend learn like humans, i.e. retain the results learned in the past and use them to help future learning. When faced with a new task, we first mine some reliable (transcendental) knowledge from past learning/modeling results, and then use it to guide model inference to produce more coherent topics. This approach is possible because there is readily available big data on the Web. The algorithm mines two forms of knowledge: the chain must be (Meaning two words should be in the same topic) and cannot be linked (meaning two words should not be in the same topic). Two issues of automatic knowledge mining are discussed, namely the problem of erroneous knowledge and knowledge transferability. Use from the experimental results of the review documents of 100 product areas show that the method proposed in this paper has a significant improvement over the state-of-the-art baseline.
Towards Flexible Integration of 5G and IIoT Technologies in Industry 4.0: A Practical Use Case

The Industry 4.0 revolution envisions fully interconnected scenarios in the manufacturing industry to improve the efficiency, quality, and performance of the manufacturing processes. In parallel, the consolidation of 5G technology is providing substantial advances in the world of communication and information technologies. Furthermore, 5G also presents itself as a key enabler to fulfill Industry 4.0 requirements. In this article, the authors first propose a 5G-enabled architecture for Industry 4.0. Smart Networks for Industry (SN4I) is introduced, an experimental facility based on two 5G key-enabling technologies—Network Functions Virtualization (NFV) and Software-Defined Networking (SDN)—which connects the University of the Basque Country’s Aeronautics Advanced Manufacturing Center and Faculty of Engineering in Bilbao. Then, the authors present the deployment of a Wireless Sensor Network (WSN) with strong access control mechanisms into such architecture, enabling secure and flexible Industrial Internet of Things (IIoT) applications. Additionally, the authors demonstrate the implementation of a use case consisting in the monitoring of a broaching process that makes use of machine tools located in the manufacturing center, and of services from the proposed architecture. The authors finally highlight the benefits achieved regarding flexibility, efficiency, and security within the presented scenario and to the manufacturing industry overall.
Corporate Purpose and Value: Evidence from a Novel Measure of Environmental Awareness

We develop a novel metric of environmental corporate social responsibility (CSR) rivalry capturing the sustainability engagements of a firm relative to its “green” and “toxic” peers. We document that our measure has superior predictive power about firms’ future pollution levels when compared to alternative unitary environmental CSR scores. Our results show that firms with superior relative environmental performance exhibit higher sales growth, profitability, corporate valuation, and future stock returns, suggesting that firms compete in the market for environmental corporate responsibility ratings and that relative – in addition to unitary – environmental CSR metrics are linked to financial performance. We conclude by generalizing our findings to the broader domain of relative corporate social responsibility (CSR) engagements, documenting that environmental ratings drives the relation between a firm overall relative social profile and valuation.
Investigating self-regulated learning and academic achievement in an eLearning environment: The case of K-12 flipped classroom

Abstract This study aimed to investigate the impact of a flipped classroom on the self-regulated learning (SRL) and academic achievement of seventh-grade junior high school students. A quantitative approach was used to compare the traditional and flipped classroom approaches. The data were obtained using the Motivated Strategies for Learning Questionnaire (MSLQ) along with students achievement scores. Cognitive learning strategies and metacognitive self-regulation strategies were investigated as indicators of students SRL strategies. The results indicated that 64 seventh-grade participants demonstrated a good-to-high level of practicing SRL within the flipped classroom environment. Moreover, the student participants appeared to self-regulate their metacognitive learning strategies in the flipped classroom environment more than those in the traditional learning environment. In terms of their academic achievement, no statistically significant difference was detected between the traditional and flipped classrooms. Associations between the students SRL and academic achievement were identified, and several implications and recommendations were derived.
Statistical properties of Poisson-Voronoi tessellation cells in bounded regions

Many spatial statistics methods require neighbourhood structures such as the one determined by a Voronoi tessellation, so understanding statistical properties of Voronoi cells is crucial. While distributions of cell properties when data locations follow an unbounded homogeneous Poisson process have been studied, little attention has been given to how these properties change when a boundary is imposed. This is important when geographical data are gathered within a restricted study area, such as a national boundary or a coastline. We study the effects of imposing a boundary on the cell properties of a Poisson Voronoi tessellation. The area, perimeter and number of edges of individual cells with and without boundary conditions are investigated by simulation. Distributions of these properties differ substantially when boundaries are imposed, and these differences are affected by proximity to the boundary. We also investigate how changes in such properties when boundaries are imposed vary over two-dimensional space.
Experimental Study and Analysis of Thermal Comfort in a University Campus Building in Tropical Climate

This study presents the evaluation of the performance and acceptability of thermal comfort by students in the classrooms of a university building with minisplit-type air-conditioning systems, in a tropical climate. To carry out the study, temperature and humidity measurements were recorded, both outside and inside the selected classrooms, while the students were asked to complete thermal surveys on site. The survey model is based on the template proposed by Fanger and it was applied to a total number of 584 students. In each classroom, the Predicted Mean Vote (PMV) and the Predicted Percentage Dissatisfied (PPD) were estimated according to Fanger’s methodology, as well as the Thermal Sensation Vote (TSV) and the Actual Percentage Dissatisfied (APD), which were obtained from the measurements and the surveys. The results of this study showed that the PMV values, although they may vary with the insulation of the clothing, do not affect the TSV. Furthermore, comparing PMV vs. TSV scores, a 2 °C to 3 °C difference in operating temperature was found, whereby the thermal sensitivity for TSV was colder, so it could be assumed that the PMV model overestimates the thermal sensitivity of students in low-temperature conditions. In addition, an acceptability by 90% with thermal preferences between 23 °C and 24 °C were also found. These results indicate that it is possible to increase the temperature set point in minisplit-type air-conditioning system from 4 °C to 7 °C with respect to the currently set temperatures, without affecting the acceptability of the thermal environment to the students in the building.
Conditional Mixture Model and Its Application for Regression Model

Expectation maximization (EM) algorithm is a powerful mathematical tool for estimating statistical parameter when data sample contains hidden part and observed part. EM is applied to learn finite mixture model in which the whole distribution of observed variable is average sum of partial distributions. Coverage ratio of every partial distribution is specified by the probability of hidden variable. An application of mixture model is soft clustering in which cluster is modeled by hidden variable whereas each data point can be assigned to more than one cluster and degree of such assignment is represented by the probability of hidden variable. However, such probability in traditional mixture model is simplified as a parameter, which can cause loss of valuable information. Therefore, in this research I propose a so-called conditional mixture model (CMM) in which the probability of hidden variable is modeled as a full probabilistic density function (PDF) that owns individual parameter. CMM aims to extend mixture model. I also propose an application of CMM which is called adaptive regressive model (ARM). Traditional regression model is effective when data sample is scattered equally. If data points are grouped into clusters, regression model tries to learn a unified regression function which goes through all data points. Obviously, such unified function is not effective to evaluate response variable based on grouped data points. The concept “adaptive” of ARM means that ARM solves the ineffectiveness problem by selecting the best cluster of data points firstly and then evaluating response variable within such best cluster. In order words, ARM reduces estimation space of regression model so as to gain high accuracy in calculation.
Perception Model for Selecting Patentable Technologies

With the development of new technologies, it is necessary to develop new tools for verification and assessment for their protection by the institutions. Therefore, this study aims to build and validate the perception model for selecting patentable technologies. In relation to the methodology, a structured questionnaire was applied with the members of the National Institutes of Science and Technology (INCT), and structural equation modeling was used to examine the relationships between latent variables. The results show that five hypotheses were tested, all of which were tested and validated. Two complementary models were developed, the first being, to better adjust the model, the market construct was removed. The second model analyzed the four constructs, but it was noticed that, without the market construct, the adjustment indices are more adequate, according to what is presented in the literature as recommended indices. Thus, it is noted that the proposed model can contribute to improving the process of appreciating the technologies produced by Universities.
In vitro 3D Spheroid Culture Developed on the Parafilm Surface Using HEK-293 Cells

Preclinical research to predict the effects of drugs and chemicals on humans is commonly carried out either by cell culture studies in vitro condition or on animals in vivo condition. While drug studies tested on cells cultured as a monolayer in plastic flasks are incompatible with realistic results, falsifying findings can also be achieved from in vivo studies performed on different species. In recent years, research on drug tests using spheroid cultures formed by growing cells in three-dimensional (3D) in vitro has attracted great interest. One of the techniques to form 3D spheroids structures is the hanging-drop technique (HDT) in which spheroids are formed by growing the cells in a drop suspended on superhydrophobic surfaces. In this study, HEK-293 cells were investigated on parafilm surfaces displaying superhydrophobic properties by growing in 2 μl volume using HDT in terms of spheroid formation. Light microscopy images from spheroid structures were taken on different incubation days and the area of spheroids was measured using the ImageJ program. Our study holds important findings for a chip platform that can be developed for use in vitro drug tests.
Academic Procrastination in Greek Higher Education: Shedding Light on a Darkened yet Critical Issue

Αcademic procrastination, characterized by self-regulation difficulties in delaying the start or completion of academic tasks (Ferrari, 2010), is widespread among university students. One of the most widely used measures of academic procrastination is Procrastination Assessment Scale Students (PASS, Solomon &Rotblum, 1984). However, there is a dearth of research investigating its factorial structure using confirmatory factor analysis. Greek studies on academic procrastination are also scarce. The present study investigated academic procrastination among Greek university students (n = 865),as well as the factorial structure of PΑSS. Results from a CFA supported a one factor solution. Moreover, 40.5% of students were characterized as frequent procrastinators, towards reading for the exams, writing essays or attending classes. The reasons students gave for procrastinating were “fear of failure”, “task aversion”, “fear of success /peer pressure” and “lack of assertiveness/ time management skills”. No major, age, or gender differences in academic procrastination were detected. Finally, most students wished to participate in a future anti-procrastination program. Findings increase the ecological validity of current literature and could be potentially useful for counselors and researchers.
The merging of the dielectric α- and β-relaxations in poly-(methyl methacrylate)

Using broad-band dielectric spectroscopy (10−2–109 Hz) the merging of the α- and β-relaxations has been investigated for a polymer, syndiotactic poly(methyl methacrylate) (PMMA), in which the dielectric losses are dominated by a strong β-relaxation. The asymmetrically shaped β-relaxation cannot be described by a Cole–Cole function, not even at low temperatures where the α- and β-relaxations are well separated in frequency. At higher temperatures close to Tg (=404 K), the weak α-relaxation enters our dynamic window and rapidly merges with the β-relaxation. To investigate this merging process we first used a simple addition of two Havriliak–Negami (HN) equations to fit the spectra. The obtained relaxation time for the β-relaxation then displays a kink in its temperature dependence close to Tg. To gain further understanding of the merging, the data were analyzed by means of a regularization method in order to calculate the corresponding distributions of relaxation times directly from the actual measurements .
High-Detail Animation of Human Body Shape and Pose From High-Resolution 4D Scans Using Iterative Closest Point and Shape Maps

In this article, we present a method of analysis for 3D scanning sequences of human bodies in motion that allows us to obtain a computer animation of a virtual character containing both skeleton motion and high-detail deformations of the body surface geometry, resulting from muscle activity, the dynamics of the motion, and tissue inertia. The developed algorithm operates on a sequence of 3D scans with high spatial and temporal resolution. The presented method can be applied to scans in the form of both triangle meshes and 3D point clouds. One of the contributions of this work is the use of the Iterative Closest Point algorithm with motion constraints for pose tracking, which has been problematic so far. We also introduce shape maps as a tool to represent local body segment deformations. An important feature of our method is the possibility to change the topology and resolution of the output mesh and the topology of the animation skeleton in individual sequences, without requiring time-consuming retraining of the model. Compared to the state-of-the-art Skinned Multi-Person Linear (SMPL) method, the proposed algorithm yields almost twofold better accuracy in shape mapping.
Performance Assessment of QoS-Aware LTE Sessions Offloading Onto LAA/WiFi Systems

Fifth-generation (5G) cellular systems are expected to orchestrate a variety of air interfaces. In this heterogeneous setting 4G, LTE systems will remain of special importance providing service over a relatively large area not covered with the 3GPP new radio technology. However, the scarcity of radio resources below 6 GHz as well as constantly increasing user traffic demands call for efficient offloading strategy from the LTE network. In this paper, we consider and compare two LTE session offloading strategies, including LAA and WiFi offloading. Although by design, the LAA system supports quality-of-service (QoS) guarantees in terms of minimum throughput, the assured performance might still be violated by resource reallocations when satisfying the compulsory fairness criteria. To assess and compare the performance of these schemes, we develop an analytical framework that takes into account specifics of duty-cycle schedule-based Licensed Assisted LTE Access (LAA) system with connection admission control (CAC), resource reallocation mechanism, and realistic elastic traffic nature. As a special case of the developed framework, we also address the offloading of QoS-aware LTE sessions into conventional WiFi. Our numerical results indicate that WiFi offloading is characterized by a significantly greater number of offloaded sessions. However, this advantage comes at the expense of severe performance degradation in terms of minimum throughput guarantees. Implementing CAC procedures, LAA efficiently addresses this problem. We conclude that WiFi offloading of QoS-aware LTE sessions can be used in light traffic conditions only. On the other hand, offloading to the schedule-based LAA system allows preserving QoS over a wide range of arrival traffic statistics.
Time‐varying parameters identification and synchronization of switching complex networks using the adaptive fuzzy‐impulsive control with an application to secure communication

In this paper, secure communication using the synchronization of complex networks has been studied. The method of secure communication is based on chaotic parameter modulation. The adaptive update rules are obtained for the identification of parameters and messages in the receiver. Considered networks have time‐varying parameters. Also, the switching topology has been considered for the networks. Different topologies are selected for the transmitter and receiver networks. Using a fuzzy inference engine, an impulsive controller is designed for synchronization. Finally, the simulation results show the effectiveness of the proposed method.
Morphology-controlled synthesis of ZnO 3D hierarchical structures and their photocatalytic performance

ZnO 3D hierarchical structures assembled by different low-dimensional building blocks (nanosheet, nanoneedle) have been successfully synthesized at room temperature without the addition of organic agents. The morphology of the hierarchical structures can be well controlled by simply changing the concentration of the reactant. On the basis of the experimental results, a possible mechanism for the formation of the ZnO 3D hierarchical structures is proposed. The room temperature photoluminescence spectra of the ZnO 3D hierarchical structures show that the relative intensity of UV emission increases from nanosheets to the thin needle flowers and can be tuned by controlling the morphology. Importantly, the thin needle flowers sample exhibits an enhanced photocatalytic performance as compared with the other ZnO nanostructures due to structural features and good optical quality.
Modeling evaporation effects in conditional moment closure for spray autoignition

Simulations of an n-heptane spray autoigniting under conditions relevant to a diesel engine are performed using two-dimensional, first-order conditional moment closure (CMC) with full treatment of spray terms in the mixture fraction variance and CMC equations. The conditional evaporation term in the CMC equations is closed assuming interphase exchange to occur at the droplet saturation mixture fraction values only. Modeling of the unclosed terms in the mixture fraction variance equation is done accordingly. Comparison with experimental data for a range of ambient oxygen concentrations shows that the ignition delay is overpredicted. The trend of increasing ignition delay with decreasing oxygen concentration, however, is correctly captured. Good agreement is found between the computed and measured flame lift-off height for all conditions investigated. Analysis of source terms in the CMC temperature equation reveals that a convective–reactive balance sets in at the flame base, with spatial diffusion terms being important, but not as important as in lifted jet flames in cold air. Inclusion of droplet terms in the governing equations is found to affect the mixture fraction variance field in the region where evaporation is the strongest, and to slightly increase the ignition delay time due to the cooling associated with the evaporation. Both flame propagation and stabilization mechanisms, however, remain unaffected.
Design and Performance of an Elbow Assisting Mechanism

An elbow assisting device is presented as based on a cable-driven parallel mechanism with design solutions that are improvements from a previous original design. The new mechanism, ideal for domestic use, both for therapies and exercises, is characterized by low-cost, portable, easy-to-use features that are evaluated through numerical simulations and experimental tests whose results are reported with discussions.
Excitation temperature and analytical parameters for an ethanol-loaded inductively coupled plasma atomic emission spectrometer

The effect of ethanol concentrations on the excitation temperature of an inductively coupled plasma was determined from hydrogen emission signals. Increasing the ethanol concentration resulted in an initial increase in excitation temperature. This temperature reached a maximum when 15% v/v ethanol was nebulized, then decreased with further increase in ethanol concentration. The analytical parameters of sensitivity, detection limits and background equivalent concentration for Pb, Cd, Al, Cr, Fe, Na, Mn, Mo and V were correlated to the effects caused by the ethanol within the plasma. The sensitivity in the determination of all the elements increased with increasing ethanol to a maximum of 15% ethanol, the amount of increase depending on the element. The flow rate of the aerosol carrier gas was of greater importance when ethanol was present in the solvent than when pure water was the solvent. Improved detection limits were obtained when the plasma was operating under optimum conditions and the samples were in 15% ethanol solutions.