Miracle-dz/UniFAR-FTUs huggingface dataset:
Teaching of heat transfer through annular fins of rectangular profile

The goal of the present paper is to disclose three different ways to determine the heat transfer rate in annular fins of rectangular profile employing the fin efficiency concept. The first way is evaluating the exact, analytical equation accounting for four modified Bessel functions I0(X), I1(X), K0(X), K1(X), the second way is employing the fin efficiency diagram designed by Gardner and the third way is evaluating the one-term empirical correlation equation developed by Schmidt.
No Tilt Angle Dependence of Grain Boundary on Mechanical Strength of Chemically Deposited Graphene Film

Although graphene has been successfully grown in large scale via chemical vapor deposition (CVD), it is still questionable whether the mechanical properties of CVD graphene are equivalent to those of exfoliated graphene. In addition, there has been an issue regarding how the tilt angle of the grain boundary (GB) affects the strength of graphene. We investigate the mechanical properties of CVD graphene with nanoindentation from atomic force microscopy and transmission electron microscopy. Surprisingly, the samples with GB angles of 10° and 26° yielded similar fracture stresses of ~ 80 and ~ 79 GPa, respectively. Even for samples with GB exhibiting a wider range, from 0° to 30°, only a slightly wider fracture stress range (~ 50 to ~ 90 GPa) was measured, regardless of tilt angle. The results are contrary to previous studies that have reported that GBs with a larger tilt angle yield stronger graphene film. Such a lack of angle dependence of GB can be attributed to irregular and well-stitched GB structures.
Automatic Recognition of Anatomical Regions in Computed Tomography Images

This paper presents a method that can recognize anatomy regions in Computed Tomography (CT) examinations. In this work the human body is divided into eleven regions from the foot to the head. The proposed method consists of two main parts. In the first step, a Convolutional Neural Network (CNN) is used to classify the axial slices of the CT exam. The accuracy of the initial classification is 93.4 %. As the neural network processes the axial slices independently from each other, no spatial coherence is guaranteed. To ensure the contentious labeling the initial classification step is followed by a post-processing method that incorporates the expected order and size of the anatomical regions to improve the labeling. In this way, the accuracy is increased to 94.0 %, the confusion of non-neighboring regions dropped from 1.5 % to 0.0 %. This means that a continuous and outlier free labeling is obtained. The method was trained on a set of 320 CT exams and evaluated on another set of 160 cases.
Research of the heat transfer crisis and limit state of the heat exchange surface covered by capillary porous medium

Model of the vapour bubbles formed at the solid surface in porous structures and steam generating wall (base) was developed. The model is based on filming and photography by high-speed camcorder SKS-1M. Heat flow extraction (up to 2.106 Wm−2) is ensured by the joint action of capillary and mass forces with application of intensifier. The analytical model is based on the theory of thermoelectricity. Limit state of the poor conductive porous coating and metal base are determined. Heat flows were calculated from the time of spontaneous birth of vapour bubble (10-8) to the material destruction time (102÷103s), i.e. the time interval was described from the relaxation process to macro process (destruction). The size of pulled particles at the moment of porous coating destruction in model gave a good match with trial at the optic stand.
Feasibility study of neutron production at SLRI beam test facility

SLRI Beam Test Facility (SLRI-BTF) has been constructed in order to increase usage of the Siam Photo Source injector. SLRI-BTF aims to provide electron test beams for calibration and testing of high-energy particle detectors and diagnostic instrumentations. The test-beam energy varies from 40 MeV to 1.2 GeV with adjustable intensity from a few to several millions of electrons per repetition rate. Since the test-beam energy is sufficiently large, the electron test beam can be used to produce photoneutrons resulted from collision of high-energy electrons to metal targets. Similar to the electron test beams, this neutron source will be utilized for calibration and testing of neutron detectors and instrumentations. In this work, the neutron production and its angular dependence has been investigated using the PHITS simulation code. The results show that photoneutrons generated by collision of the high-energy electron beam on a tungsten target are in range of intermediate and fast neutrons or 0.01 - 10 MeV with peak contribution at 0.6 ± 0.1 MeV and uniformly distributed in angular direction. For the primary beam of 108 electrons per spill at SLRI-BTF, the photoneutron flux is predicted to be around 50 neutrons/cm2. Based on this study, the possibility to service a neutron source for users in the future will be determined further.
Cycle-SUM: Cycle-consistent Adversarial LSTM Networks for Unsupervised Video Summarization

In this paper, we present a novel unsupervised video summarization model that requires no manual annotation. The proposed model termed Cycle-SUM adopts a new cycleconsistent adversarial LSTM architecture that can effectively maximize the information preserving and compactness of the summary video. It consists of a frame selector and a cycle-consistent learning based evaluator. The selector is a bi-direction LSTM network that learns video representations that embed the long-range relationships among video frames. The evaluator defines a learnable information preserving metric between original video and summary video and “supervises” the selector to identify the most informative frames to form the summary video. In particular, the evaluator is composed of two generative adversarial networks (GANs), in which the forward GAN is learned to reconstruct original video from summary video while the backward GAN learns to invert the processing. The consistency between the output of such cycle learning is adopted as the information preserving metric for video summarization. We demonstrate the close relation between mutual information maximization and such cycle learning procedure. Experiments on two video summarization benchmark datasets validate the state-of-theart performance and superiority of the Cycle-SUM model over previous baselines.
Comparison of Risk Categorization Methods in a Multisimulation Framework

In this article, we discuss various methods for risk categorization. We consider a statistical decision theory-based method, risk matrices, derivation of the risk acceptance level from fire data, and a comparative study. The values of risk used in the categorization process derive from multisimulation – a probabilistic fire risk assessment method. Following the theoretical description of the methods, a case study of a five story hotel building 20.000 m2 is elaborated and discussed as proof of concept. Based on the case study we reveal the shortcomings of the methods compared.
Electron Assisted Growth of Graphene atop Ir(111) supported Hexagonal Boron Nitride

Vertical heterostructures between graphene (Gr) and hexagonal boron nitride (hBN) have attracted a great deal of interest, due to their potential applications in the semiconductor industry as new and superior transistor materials. The direct growth of the heterostucture still remains challenging and in this bachelor thesis, I report a novel electron assisted growth technique, which is used to attempt to grow Gr atop hBN atop an Ir(111) single crystal surface. This technique has already proven to be successful in the opposite order of growing hBN atop Gr/Ir(111)[1]. To properly understand the growth and possible heterostructure formation, pure phases of both hBN and Gr on Ir(111) are characterized first using scanning tunneling microscopy (STM) and low energy electron diffraction (LEED). This structural characterization of the pure phases reveals a stronger binding of hBN than Gr to Ir(111) and, therefore, a favored stacking order of Gr atop hBN. In contrast to this expectation, my STM and LEED results of the electron assisted growth of Gr atop a full hBN layer show, that the temperature required for Gr formation is too high for hBN layer to sustain, and is already complicated by a low sticking coefficient of ethylene atop. After high temperature annealing, the hBN layer is determined to leave behind a (6x2) boron nanoribbon superstructure and no ordered Gr islands are observed. However, the effect of the electron beam in the growth technique is indicated to result in an uneven charging of the hBN layer on Ir(111), and this, in combination with its already highly corrugated pore-wire structure, calls for new research.
Does the direction of offshoring matter? Comparison of downward and upward offshoring strategies in changing consumers’ brand perception by brand tiers

Abstract Offshoring strategies raise a critical question to defining the country images of brands, as offshoring results in discrepancies between the country-of-brand origin (COB) and the country-of-manufacture (COM) by relocating COM from the home to another country. Based on cognitive dissonance theory and social exchange theory, this study assesses the outcomes of apparel brands’ differing types of offshoring strategies by tracing consumers’ brand perception as these change after the revelation of offshoring information, particularly by comparing those outcomes between an upscale brand and a midmarket brand. Results from an experimental study revealed that a brand originating from a developed country’s offshoring from a developing country can significantly decrease brand credibility and prestige, while a brand originating from an emerging country’s offshoring from a developed country does not significantly improve those brand assets in either the upscale or the midmarket brand contexts. The paper concludes with a discussion of theoretical and managerial implications along with suggestions for future research.
Preparation and application of multicomponent composite phase change materials in building energy conservation

In view of the problems existing in the practical application of phase change building materials, the cheap paraffin resources are used as phase change materials (PCM) to combine with ordinary building materials, and the mode analysis of the multi-phase composite of paraffin is carried out. Through ternary eutectic system theory of paraffin PCM composite, the temperature changes during the cooling process of ternary composite paraffin PCM is analyzed by cold curve test and differential scanning calorimetry (DSC) thermal property test. The results show that when the temperature range is 20-35°C, the temperature drop rate of composite PCM is obviously slowed down. The phase change of PCM begins to occur in this temperature range, and it lasts for a long time. It is concluded that a certain amount of latent heat of phase change is released during the process of PCM. In conclusion, the thermal performance of ternary composite paraffin PCM is better than binary composite system with solid-liquid mixing. Applying it to building roofing materials can effectively improve the thermal storage capacity of building materials.
Distributed Architecture for Managing Big Data in Smart Grid

Smart grid is a technological advancement to the traditional power system that provides efficient and reliable utilization of energy resources. Large number of sensors are becoming part of the power network to improve its efficiency. These sensors enable communication between home appliances and power generators to enhance home appliance automation, monitoring and remote control capabilities. As smart power grid incorporates a large number of data-generating embedded sensors; key questions are where in the network to process and analyze the data, and how to perform the analysis. The data in smart grid can be processed either by one central processor or through multiple distributed processors. This thesis proposes a smart grid distributed architecture involving home sensors talking to a smart gateway in the home for local processing, which then passes processed data to a central processor for further analysis. As part of our work, a test bed is designed to highlight advantages of distributed smart grid architecture by comparing central and local processing of data. An open data set is used to feed power sensor data into the test setup. This thesis discusses variety of operations that can be performed in the distributed architecture of smart grid. It is shown that the local processing of data can improve efficiency by effectively utilizing available network bandwidth. Furthermore, local processing is favorable for smart grid applications that are time critical as local processing has less delay and jitters for data communication round trip time as compared to central processing.
Learning new Term Weighting Schemes with Genetic Programming

Text Classification (or Text Categorization) is a popular machine learning task which consists in assigning categories to documents. Feature weight methods are classic tools that are used in text categorization in order to assign a score to each term of a document based on a mathematical formula. In this paper, we are interested in automatically generating these formulas based on genetic programming. We experiment the generated formulas on three well-known benchmarks and state of the art classifiers.
Bibliometric study and mapping of a journal in the field of visualization and computer graphics

The study represents the first retrospective bibliometric analysis and mapping of the IEEE Transactions on Visualization and Computer Graphics (IEEE TVCG), one of the leading journals in the field of visualization and computer graphics, a discipline with expanding literature and emerging new areas. Using descriptive bibliometrics, the dynamics and trend patterns of publications, received citations and the impact factor of the journal were analyzed. The most productive and influential papers, authors, countries and institutions were identified. A mapping analysis, performed with VOSviewer software, visualized bibliometric networks from the perspective of keywords, authors, countries, institutions, cited references and cited journals, using co–occurrence, bibliographic coupling and co–citation analyses. From the results, the IEEE TVCG emerged as one of the leading journals in the field of visualization and computer graphics. The mapping of research themes revealed a range of established and evolving areas, including data visualizations, computer graphics, image synthesis and representation, scientific visualizations, interactive digital environments, such as virtual and augmented realities, human perception, animations and simulations. The rising number of published articles and citations, and a high journal impact factor demonstrated the growing interest of the academic community for the journal IEEE TVCG and the aforementioned area. The results and conclusions can be a valuable reference for academics, researchers and practitioners in computer graphics and visualization, as well as for authors and readers in the area of scientometrics.
Additive manufacturing by material extrusion with medical grade silicone elastomers and IR laser curing

Techniques of extrude and cure additive manufacturing for thermally cured, high viscosity and medical-grade silicone are investigated by using a small ram extruder and a near-infrared (IR) laser. The purpose of this study is to evaluate the process parameter effects on the stiffness of the final products. ,Process parameter effects on axial stiffness values and durometer are explored. Parameters such as extrusion layer height, laser speed, laser current, laser raster spacing and multiple laser passes were investigated and compared to traditional cast and cure methods. Dimensional changes were also recorded and compared. ,Tensile and durometer tests show that certain curing parameters give tensile stress and durometers within 10 per cent of bulk material specifications at 200 per cent strain. Parameters that had the highest impact on tensile stress at 200 per cent strain were layer height (0.73 per cent) followed by laser power (0.69 per cent), and then laser raster spacing (0.45 per cent). Parameters that had the highest impact on durometer were laser power (1.00 per cent), followed by layer height, (0.34 per cent) and then laser raster speed (0.32 per cent). Three-dimensional printed samples had about 11.2 per cent more shrinkage than the bulk cast samples in the longest dimension. ,This paper is one of the first that demonstrates near IR laser curing parameter effects on three-dimensional printed, commercial off-the-shelf, medical-grade and viscous silicone. The ability to cure very viscous thermosets locally enables interesting technologies such as wire encapsulation, high voltage actuators and drug delivery devices.
AI Technologies as a Method of Predictive Analytics

The article presents an analysis of currently used methods and tools for predictive analytics in the socio-economic space: inter-sectoral balance, econometrics, simulation models, expert systems. Identified their main advantages and disadvantages. The possible areas of their application are described. An alternative analysis and data processing based on the ideas and concepts of artificial intelligence is proposed. Its advantages in comparison with classical data analysis tools are presented.
Additive Manufacturing: A Novel Method for Developing an Acoustic Panel Made of Natural Fiber-Reinforced Composites with Enhanced Mechanical and Acoustical Properties

Natural fibers and their composites are being widely used in almost all the applications in this modern era. However, the properties of natural fibers have to be enhanced in order to compete with synthetic fibers. This review paper opens up additive manufacturing, as a novel method for developing an acoustic panel using natural fiber composites with enhanced mechanical and acoustical properties. This approach will help to replace synthetic-based acoustic absorbers with biodegradable composite panels in acoustic applications. This review also covers, poly(lactic acid) as a polymer matrix and its advantages, the available variety of natural fibers as reinforcement in terms of mechanical and acoustical properties. The natural fiber-based filaments used in additive manufacturing and acoustic panels made from the available natural fibers are also elaborated here. This review shows the importance of additive manufacturing and its application to develop novel acoustic panels made of agricultural waste.
A Capillarity-dependent Model for Prediction of Chloride Penetration Process into Concrete in Artificial Splash Test

In order to better simulate the penetration process of chloride ions in the splash zone of concrete, this paper modified existing chloride ion transport model on the basis of capillarity-dependent model under unsaturated and non-pressure conditions to make it applicable to the calcualtion of choride flow flux. The artificial splash tests is firstly designed and conducted under varied conditions of water to cement ratio, splash time duratinoa and splash period. Then the original chloride constitutive model is improved by enhancement of convection term calculation. At last, the authors simulate the chloride ions penetration process under different conditions by reasonble design of equivalent chloride and moisture boundary to verify the effectiveness of capillarity-dependent model.
Design and Experimental Validation of a Wire-Bond-Less 10-kV SiC MOSFET Power Module

Wide bandgap (WBG) power devices with voltage ratings exceeding 10 kV have the potential to revolutionize medium- and high-voltage systems due to their high-speed switching and lower ON-state losses. However, the present power module packages are limiting the performance of these unique switches. The objective of this article is to push the boundaries of high-density, high-speed, 10-kV power module packaging. The proposed package addresses the well-known electromagnetic and thermal challenges, as well as the more recent and prominent electrostatic and electromagnetic interference (EMI) issues associated with high-speed, 10-kV devices. The module achieves low and balanced parasitic inductances, resulting in a record switching speed of 250 V/ns with negligible ringing and voltage overshoot. An integrated screen reduces the common-mode (CM) current that is generated by these fast voltage transients by ten times. This screen connection simultaneously increases the partial discharge inception voltage (PDIV) by more than 50%. A compact, medium-voltage termination and system interface design is also proposed in this article. With the integrated jet-impingement cooler, the power module prototype achieves a power density of 4 W/mm3. This article presents the design, prototyping, and testing of this optimized package for 10-kV SiC MOSFETs.
Approach to Integration of Data Mining Techniques in Simulation Results Analysis

The research is carried out in the area of analysis of simulation results by using data mining techniques. The goal of the research is to explore the applicability of data mining techniques in the area of simulation results analysis, to offer an application scheme of data mining techniques in the analysis of simulation results, as well as to demonstrate the usage of these techniques in the analysis of experimental data. As a result of the theoretical study, an approach is proposed, consisting of two stages and combining the fundamental techniques of data farming and knowledge discovery. A variety of data mining techniques, such as correlation analysis, clustering and several visualization mechanisms of results, are used for knowledge discovery. The proposed approach is applied to the analysis of experimental data. The performance of a queueing system is analysed, and knowledge and decision rules are obtained from simulation results.
Robust Control Of Dc Motor Using Fuzzy Sliding Mode Control And Genetic Algorithm

Wide amplitude, DC motor's speed and their facile control cause its great application in industries. Generally the DC motors gain speed by armature voltage control or field control. In this paper, by using a combination of Fuzzy Sliding Mode methods and Genetic Algorithms, we have tired to optimally control the inverted pendulum by nonlinear equations. The results of this simulation have been mentioned in the conclusion. It seems that the results be acceptable results.
Design and Analysis of Efficient Maximum/Minimum Circuits for Stochastic Computing

In stochastic computing (SC), a real-valued number is represented by a stochastic bit stream, encoding its value in the probability of obtaining a one. This leads to a significantly lower hardware effort for various functions and provides a higher tolerance to errors (e.g., bit flips) compared to binary radix representation. The implementation of a stochastic max/min function is important for many areas where SC has been successfully applied, such as image processing or machine learning (e.g., max pooling in neural networks). In this work, we propose a novel shift-register-based architecture for a stochastic max/min function. We show that the proposed circuit has significantly higher accuracy than state-of-the-art architectures for uncorrelated bit streams at comparable hardware costs. Moreover, we analytically proof the correctness of the proposed circuit and provide a new error analysis, based on the individual bits of the stochastic streams. Interestingly, the analysis reveals that for a certain practical bit stream length a finite optimal shift register length exists and it allows to determine the optimal length.