Search results for: deep composite kernel

Authors: Angga S. Fajar, A. Aminullah, J. Kiyono, R. A. Safitri

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This paper discusses about the application of ANN for optimizing of bridge structure design. ANN has been applied in various field of science concerning prediction and optimization. The structural optimization has several benefit including accelerate structural design process, saving the structural material, and minimize self-weight and mass of structure. In this paper, there are three types of bridge structure that being optimized including PSC I-girder superstructure, composite steel-concrete girder superstructure, and RC bridge pier. The different optimization strategy on each bridge structure implement back propagation method of ANN is conducted in this research. The optimal weight and easier design process of bridge structure with satisfied error are achieved.

Keywords: bridge structures, ANN, optimization, back propagation

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Authors: Antônio P. de Araújo Neto, Ruy D. A. da Silva Neto, Juliana R. de Souza, Salete K. P. de Medeiros, João T. N. de Medeiros

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The tribological test with Pin-On-Disc configuration measures friction and wear properties in dry or lubricated sliding surfaces of a variety of materials and coatings. Polymeric matrix composites loaded with mineral filler were used, 1%, 3%, 10%, 30%, and 50% mass percentage of filler, to reduce the material cost by using mineral tailings. Using a pin-on-disc tribometer to quantify coefficient of friction and wear resistance of the specimens. The parameters known to performing the test were 300 rpm rotation, normal load of 16N and duration of 33.5 minutes. The composite with 10% mineral filler performed better, considering that the wear resistance was good when compared to the other compositions and an average low coefficient of friction, in the order of μ ≤ 0.15.

Keywords: microcomposites, microparticles tailings of scheelite, PTFE, tribology

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Authors: Laura Rodriguez Amaya

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Advances in science, technology, engineering, and mathematics (STEM) are viewed as important to countries’ national economies and their capacities to be competitive in the global economy. However, many countries experience low numbers of students entering these disciplines. To strengthen the professional STEM pipelines, it is important that students are retained in these disciplines at universities. Scholars agree that to retain students in universities’ STEM degrees, it is necessary that STEM course content shows the relevance of these academic fields to their daily lives. By increasing students’ understanding on the importance of these degrees and careers, students’ motivation to remain in these academic programs can also increase. An effective way to make STEM content relevant to students’ lives is the use of geospatial technologies and geovisualization in the classroom. The Geospatial Revolution, and the science and technology associated with it, has provided scientists and engineers with an incredible amount of data about Earth and Earth systems. This data can be used in the classroom to support instruction and make content relevant to all students. The purpose of this study was to find out the prevalence use of geospatial technologies and geovisualization as teaching practices in a USA university. The Teaching Practices Inventory survey, which is a modified version of the Carl Wieman Science Education Initiative Teaching Practices Inventory, was selected for the study. Faculty in the STEM disciplines that participated in a summer learning institute at a 4-year university in the USA constituted the population selected for the study. One of the summer learning institute’s main purpose was to have an impact on the teaching of STEM courses, particularly the teaching of gateway courses taken by many STEM majors. The sample population for the study is 97.5 of the total number of summer learning institute participants. Basic descriptive statistics through the Statistical Package for the Social Sciences (SPSS) were performed to find out: 1) The percentage of faculty using geospatial technologies and geovisualization; 2) Did the faculty associated department impact their use of geospatial tools?; and 3) Did the number of years in a teaching capacity impact their use of geospatial tools? Findings indicate that only 10 percent of respondents had used geospatial technologies, and 18 percent had used geospatial visualization. In addition, the use of geovisualization among faculty of different disciplines was broader than the use of geospatial technologies. The use of geospatial technologies concentrated in the engineering departments. Data seems to indicate the lack of incorporation of geospatial tools in STEM education. The use of geospatial tools is an effective way to engage students in deep STEM learning. Future research should look at the effect on student learning and retention in science and engineering programs when geospatial tools are used.

Keywords: engineering education, geospatial technology, geovisualization, STEM

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Authors: Tayo P. Ogundunmade, Adedayo A. Adepoju

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Terrorist attacks in liberal democracies bring about a few pessimistic results, for example, sabotaged public support in the governments they target, disturbing the peace of a protected environment underwritten by the state, and a limitation of individuals from adding to the advancement of the country, among others. Hence, seeking for techniques to understand the different factors involved in terrorism and how to deal with those factors in order to completely stop or reduce terrorist activities is the topmost priority of the government in every country. This research aim is to develop an efficient deep learning-based predictive model for the prediction of future terrorist activities in Nigeria, addressing low-quality prediction accuracy problems associated with the existing solution methods. The proposed predictive AI-based model as a counterterrorism tool will be useful by governments and law enforcement agencies to protect the lives of individuals in society and to improve the quality of life in general. A Heterogeneous Bayesian Neural Network (HETBNN) model was derived with Gaussian error normal distribution. Three primary transfer functions (HOTTFs), as well as two derived transfer functions (HETTFs) arising from the convolution of the HOTTFs, are namely; Symmetric Saturated Linear transfer function (SATLINS ), Hyperbolic Tangent transfer function (TANH), Hyperbolic Tangent sigmoid transfer function (TANSIG), Symmetric Saturated Linear and Hyperbolic Tangent transfer function (SATLINS-TANH) and Symmetric Saturated Linear and Hyperbolic Tangent Sigmoid transfer function (SATLINS-TANSIG). Data on the Terrorist activities in Nigeria gathered through questionnaires for the purpose of this study were used. Mean Square Error (MSE), Mean Absolute Error (MAE) and Test Error are the forecast prediction criteria. The results showed that the HETFs performed better in terms of prediction and factors associated with terrorist activities in Nigeria were determined. The proposed predictive deep learning-based model will be useful to governments and law enforcement agencies as an effective counterterrorism mechanism to understand the parameters of terrorism and to design strategies to deal with terrorism before an incident actually happens and potentially causes the loss of precious lives. The proposed predictive AI-based model will reduce the chances of terrorist activities and is particularly helpful for security agencies to predict future terrorist activities.

Keywords: activation functions, Bayesian neural network, mean square error, test error, terrorism

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Authors: Reza Fazaeli, Reza Eslami-Farsani, Hamid Targhagh

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Low density polyethylene (LDPE) nanocomposites with 3, 5 and 7 wt. % cobalt ferrite (CoFe2O4) nanopowder fabricated with extrusion mixing and followed up by hot press to reach compact samples. The transmission/reflection measurements were carried out with a network analyzer in the frequency range of 8-12 GHz. By increasing the percent of CoFe2O4 nanopowder, reflection loss (S11) increases, while transferring loss (S21) decreases. Reflectivity (R) calculations made using S11 and S21. Increase in percent of CoFe2O4 nanopowder up to 7 wt. % in composite leaded to higher reflectivity amount, and revealed that increasing the percent of CoFe2O4 nanopowder up to 7 wt. % leads to further microwave absorption in 8-12 GHz range.

Keywords: nanocomposite, cobalt ferrite, low density polyethylene, microwave absorption

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Authors: Deybith Venegas-Rojas, Sercan Keskin, Svenja Riekeberg, Sana Azim, Stephanie Manz, R. J. Dwayne Miller, Hoc Khiem Trieu

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Liquid Transmission Electron Microscopy (TEM) is a growing area with a broad range of applications from physics and chemistry to material engineering and biology, in which it is possible to image in-situ unseen phenomena. For this, a nanofluidic device is used to insert the nanoflow with the sample inside the microscope in order to keep the liquid encapsulated because of the high vacuum. In the last years, Si3N4 windows have been widely used because of its mechanical stability and low imaging contrast. Nevertheless, the pressure difference between the inside fluid and the outside vacuum in the TEM generates bulging in the windows. This increases the imaged fluid volume, which decreases the signal to noise ratio (SNR), limiting the achievable spatial resolution. With the proposed device, the membrane is fortified with a microstructure capable of stand higher pressure differences, and almost removing completely the bulging. A theoretical study is presented with Finite Element Method (FEM) simulations which provide a deep understanding of the membrane mechanical conditions and proves the effectiveness of this novel concept. Bulging and von Mises Stress were studied for different membrane dimensions, geometries, materials, and thicknesses. The microfabrication of the device was made with a thin wafer coated with thin layers of SiO2 and Si3N4. After the lithography process, these layers were etched (reactive ion etching and buffered oxide etch (BOE) respectively). After that, the microstructure was etched (deep reactive ion etching). Then the back side SiO2 was etched (BOE) and the array of free-standing micro-windows was obtained. Additionally, a Pyrex wafer was patterned with windows, and inlets/outlets, and bonded (anodic bonding) to the Si side to facilitate the thin wafer handling. Later, a thin spacer is sputtered and patterned with microchannels and trenches to guide the nanoflow with the samples. This approach reduces considerably the common bulging problem of the window, improving the SNR, contrast and spatial resolution, increasing substantially the mechanical stability of the windows, allowing a larger viewing area. These developments lead to a wider range of applications of liquid TEM, expanding the spectrum of possible experiments in the field.

Keywords: liquid cell, liquid transmission electron microscopy, nanofluidics, nanofluidic cell, thin films

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Authors: D. Önen, N. Kızılcan, B. Yıldız, A. Akar

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In situ modified cyclohexanone-formaldehyde resins were prepared by addition of alendronic acid during resin preparation. Clay nanocomposites in ketonic resins were achieved by adding clay into the flask at the beginning of the resin preparation. The prepared resins were used for the synthesis of fire resistant polyurethanes foam. Both phosphorous containing modifier compound alendronic acid and nanoclay increases fire resistance of the cyclohexanone-formaldehyde resin thus polyurethane produced from these resins. The effect of the concentrations of alendronic acid and clay on the fire resistance and physical properties of polyurethanes was studied.

Keywords: alendronic acid, clay, ketonic resin, polyurethane

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Authors: A. Evcin, N. Çiçek Bezir, R. Duman, N. Duman

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Nowadays, a great concern is placed on the harmfulness of ultraviolet radiation (UVR) which attacks human bodies. Nanocarbon materials, such as carbon nanotubes (CNTs), carbon nanofibers (CNFs) and graphene, have been considered promising alternatives to shielding materials because of their excellent electrical conductivities, very high surface areas and low densities. In the present work, carbon nanofibers have been synthesized from solutions of Polyacrylonitrile (PAN)/ N,N-dimethylformamide (DMF) by electrospinning method. The carbon nanofibers have been stabilized by oxidation at 250 °C for 2 h in air and carbonized at 750 °C for 1 h in H2/N2. We present the fabrication and characterization of transparent and ultraviolet (UV) shielding CNF/polymer composites. The content of CNF filler has been varied from 0.2% to 0.6 % by weight. UV Spectroscopy has been performed to study the effect of composition on the transmittance of polymer composites.

Keywords: electrospinning, carbon nanofiber, characterization, composites, nanofiber, ultraviolet radiation

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Authors: Roli Saini, Pradeep Kumar

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A coagulation/flocculation process was adopted for the reduction of carbamate insecticide (carbofuran) from aqueous solution. Ferric chloride (FeCl₃) was used as a coagulant to treat the carbofuran. To exploit the reduction efficiency of pesticide concentration and COD, the jar-test experiments were carried out and process was optimized through response surface methodology (RSM). The effects of two independent factors; i.e., FeCl₃ dosage and pH on the reduction efficiency were estimated by using central composite design (CCD). The initial COD of the 30 mg/L concentrated solution was found to be 510 mg/L. Results exposed that the maximum reduction occurred at an optimal condition of FeCl₃ = 80 mg/L, and pH = 5.0, from which the reduction of concentration and COD 75.13% and 65.34%, respectively. The present study also predicted that the obtained regression equations could be helpful as the theoretical basis for the coagulation process of pesticide wastewater.

Keywords: carbofuran, coagulation, optimization, response surface methodology

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Authors: Piroska Biró, Mária Csernoch, János Máth, Kálmán Abari

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Testing the first year students of Informatics at the University of Debrecen revealed that students start their tertiary studies in programming with a low level of programming knowledge and algorithmic skills. The possible reasons which lead the students to this very unfortunate result were examined. The results of the test were compared to the students’ results in the school leaving exams and to their self-assessment values. It was found that there is only a slight connection between the students’ results in the test and in the school leaving exams, especially at intermediate level. Beyond this, the school leaving exams do not seem to enable students to evaluate their own abilities.

Keywords: deep and surface approaches, metacognitive abilities, programming and algorithmic skills, school leaving exams, tracking code

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Authors: Shirley Kuo

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The main purpose of this study is to develop sustainable indicators for Taiwan, and using the Delphi method to find that our tourist areas can progress in a sustainable way. We need a lot of infrastructures and policies to develop tourist areas, and with proper KPI indicators can reduce the destruction of the natural and ecological environment. This study will first study the foreign certification experiences, because Taiwan is currently in the development stage, and then the methodology will explain in-depth interviews using the Delphi method, and then there is discussion about which KPI indicators Taiwan currently needs. In this study current progress is a deep understanding of national sustainable tourism certification and KPI indicators.

Keywords: sustainable tourism, certification, KPI indicators, Delphi method

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Authors: Despoina Mouratidis, Katia Lida Kermanidis

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Machine translation (MT) is a very important task in Natural Language Processing (NLP). MT evaluation is crucial in MT development, as it constitutes the means to assess the success of an MT system, and also helps improve its performance. Several methods have been proposed for the evaluation of (MT) systems. Some of the most popular ones in automatic MT evaluation are score-based, such as the BLEU score, and others are based on lexical similarity or syntactic similarity between the MT outputs and the reference involving higher-level information like part of speech tagging (POS). This paper presents a language-independent machine learning framework for classifying pairwise translations. This framework uses vector representations of two machine-produced translations, one from a statistical machine translation model (SMT) and one from a neural machine translation model (NMT). The vector representations consist of automatically extracted word embeddings and string-like language-independent features. These vector representations used as an input to a multi-layer neural network (NN) that models the similarity between each MT output and the reference, as well as between the two MT outputs. To evaluate the proposed approach, a professional translation and a "ground-truth" annotation are used. The parallel corpora used are English-Greek (EN-GR) and English-Italian (EN-IT), in the educational domain and of informal genres (video lecture subtitles, course forum text, etc.) that are difficult to be reliably translated. They have tested three basic deep learning (DL) architectures to this schema: (i) fully-connected dense, (ii) Convolutional Neural Network (CNN), and (iii) Long Short-Term Memory (LSTM). Experiments show that all tested architectures achieved better results when compared against those of some of the well-known basic approaches, such as Random Forest (RF) and Support Vector Machine (SVM). Better accuracy results are obtained when LSTM layers are used in our schema. In terms of a balance between the results, better accuracy results are obtained when dense layers are used. The reason for this is that the model correctly classifies more sentences of the minority class (SMT). For a more integrated analysis of the accuracy results, a qualitative linguistic analysis is carried out. In this context, problems have been identified about some figures of speech, as the metaphors, or about certain linguistic phenomena, such as per etymology: paronyms. It is quite interesting to find out why all the classifiers led to worse accuracy results in Italian as compared to Greek, taking into account that the linguistic features employed are language independent.

Keywords: machine learning, machine translation evaluation, neural network architecture, pairwise classification

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Authors: R. Khare, R. V. Gelamo, M. A. More, D. J. Late, Chandra Sekhar Rout

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Graphene emerges out as a promising material for various applications ranging from complementary integrated circuits to optically transparent electrode for displays and sensors. The excellent conductivity and atomic sharp edges of unique two-dimensional structure makes graphene a propitious field emitter. Graphene analogues of other 2D layered materials have emerged in material science and nanotechnology due to the enriched physics and novel enhanced properties they present. There are several advantages of using 2D nanomaterials in field emission based devices, including a thickness of only a few atomic layers, high aspect ratio (the ratio of lateral size to sheet thickness), excellent electrical properties, extraordinary mechanical strength and ease of synthesis. Furthermore, the presence of edges can enhance the tunneling probability for the electrons in layered nanomaterials similar to that seen in nanotubes. Here we report electron emission properties of multilayer graphene and effect of plasma (CO2, O2, Ar and N2) treatment. The plasma treated multilayer graphene shows an enhanced field emission behavior with a low turn on field of 0.18 V/μm and high emission current density of 1.89 mA/cm2 at an applied field of 0.35 V/μm. Further, we report the field emission studies of layered WS2/RGO and SnS2/RGO composites. The turn on field required to draw a field emission current density of 1μA/cm2 is found to be 3.5, 2.3 and 2 V/μm for WS2, RGO and the WS2/RGO composite respectively. The enhanced field emission behavior observed for the WS2/RGO nanocomposite is attributed to a high field enhancement factor of 2978, which is associated with the surface protrusions of the single-to-few layer thick sheets of the nanocomposite. The highest current density of ~800 µA/cm2 is drawn at an applied field of 4.1 V/μm from a few layers of the WS2/RGO nanocomposite. Furthermore, first-principles density functional calculations suggest that the enhanced field emission may also be due to an overlap of the electronic structures of WS2 and RGO, where graphene-like states are dumped in the region of the WS2 fundamental gap. Similarly, the turn on field required to draw an emission current density of 1µA/cm2 is significantly low (almost half the value) for the SnS2/RGO nanocomposite (2.65 V/µm) compared to pristine SnS2 (4.8 V/µm) nanosheets. The field enhancement factor β (~3200 for SnS2 and ~3700 for SnS2/RGO composite) was calculated from Fowler-Nordheim (FN) plots and indicates emission from the nanometric geometry of the emitter. The field emission current versus time plot shows overall good emission stability for the SnS2/RGO emitter. The DFT calculations reveal that the enhanced field emission properties of SnS2/RGO composites are because of a substantial lowering of work function of SnS2 when supported by graphene, which is in response to p-type doping of the graphene substrate. Graphene and 2D analogue materials emerge as a potential candidate for future field emission applications.

Keywords: graphene, layered material, field emission, plasma, doping

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Authors: Vinai K. Singh

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In Neural Network-based Learning techniques, there are several models of Convolutional Networks. Whenever the methods are deployed with large datasets, only then can their applicability and appropriateness be determined. Clinical and pathological pictures of lobular carcinoma are thought to exhibit a large number of random formations and textures. Working with such pictures is a difficult problem in machine learning. Focusing on wet laboratories and following the outcomes, numerous studies have been published with fresh commentaries in the investigation. In this research, we provide a framework that can operate effectively on raw photos of various resolutions while easing the issues caused by the existence of patterns and texturing. The suggested approach produces very good findings that may be used to make decisions in the diagnosis of cancer.

Keywords: lobular carcinoma, convolutional neural networks (CNN), deep learning, histopathological imagery scans

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Authors: Ahmad Kayvani Fard, Gordon Mckay, Muataz Hussien

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Adsorption/sorption is believed to be one of the optimal processes for the removal of heavy metals from water due to its low operational and capital cost as well as its high removal efficiency. Different materials have been reported in literature as adsorbent for heavy metal removal in waste water such as natural sorbents, organic polymers (synthetic) and mineral materials (inorganic). The selection of adsorbents and development of new functional materials that can achieve good removal of heavy metals from water is an important practice and depends on many factors, such as the availability of the material, cost of material, and material safety and etc. In this study we reported the synthesis of doped Activated carbon and Carbon nanotube (CNT) with different loading of metal oxide nanoparticles such as Fe2O3, Fe3O4, Al2O3, TiO2, SiO2 and Ag nanoparticles and their application in removal of heavy metals, hydrocarbon, and organics from waste water. Commercial AC and CNT with different loadings of mentioned nanoparticle were prepared and effect of pH, adsorbent dosage, sorption kinetic, and concentration effects are studied and optimum condition for removal of heavy metals from water is reported. The prepared composite sorbent is characterized using field emission scanning electron microscopy (FE-SEM), high transmission electron microscopy (HR-TEM), thermogravimetric analysis (TGA), X-ray diffractometer (XRD), the Brunauer, Emmett and Teller (BET) nitrogen adsorption technique, and Zeta potential. The composite materials showed higher removal efficiency and superior adsorption capacity compared to commercially available carbon based adsorbent. The specific surface area of AC increased by 50% reaching up to 2000 m2/g while the CNT specific surface area of CNT increased by more than 8 times reaching value of 890 m2/g. The increased surface area is one of the key parameters along with surface charge of the material determining the removal efficiency and removal efficiency. Moreover, the surface charge density of the impregnated CNT and AC have enhanced significantly where can benefit the adsorption process. The nanoparticles also enhance the catalytic activity of material and reduce the agglomeration and aggregation of material which provides more active site for adsorbing the contaminant from water. Some of the results for treating wastewater includes 100% removal of BTEX, arsenic, strontium, barium, phenolic compounds, and oil from water. The results obtained are promising for the use of AC and CNT loaded with metal oxide nanoparticle in treatment and pretreatment of waste water and produced water before desalination process. Adsorption can be very efficient with low energy consumption and economic feasibility.

Keywords: carbon nanotube, activated carbon, adsorption, heavy metal, water treatment

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Authors: P. Ravinder Reddy, Chaitanya Vishal Nalli, B. Tulja Lal, Anusha Kankanala

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The chassis frame of an automobile with different sections have been considered for different loads. The orthotropic materials are selected to get the stability by varying fiber angle, fiber thickness, laminates, fiber properties, matrix properties and elastic ratios. The geometric model of chassis frame is carried out with parametric modelling approach. The analysis of chassis frame is carried out with ANSYS FEA software. The static and dynamic analysis of chassis frame is carried out by varying geometric parameters, orthotropic properties, materials and various sections. The static and dynamic response is discussed in detail in different sections.

Keywords: chassis frame, dynamic response, geometric model, orthotropic materials

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Authors: Cheng-Jui Li, Chien-Chou Tseng

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This study adopts Computational Fluid Dynamics (CFD) technique to build the multiphase flow numerical model where the interface between the flue gas and desulfurization liquid can be traced by Eulerian-Eulerian model. Inside the tower, the contact of the desulfurization liquid flow from the spray nozzles and flue gas flow can trigger chemical reactions to remove the sulfur dioxide from the exhaust gas. From experimental observations of the industrial scale plant, the desulfurization mechanism depends on the mixing level between the flue gas and the desulfurization liquid. In order to significantly improve the desulfurization efficiency, the mixing efficiency and the residence time can be increased by perforated sieve trays. Hence, the purpose of this research is to investigate the flow structure of sieve trays for the flue gas desulfurization by numerical simulation. In this study, there is an outlet at the top of FGD tower to discharge the clean gas and the FGD tower has a deep tank at the bottom, which is used to collect the slurry liquid. In the major desulfurization zone, the desulfurization liquid and flue gas have a complex mixing flow. Because there are four perforated plates in the major desulfurization zone, which spaced 0.4m from each other, and the spray array is placed above the top sieve tray, which includes 33 nozzles. Each nozzle injects desulfurization liquid that consists of the Mg(OH)2 solution. On each sieve tray, the outside diameter, the hole diameter, and the porosity are 0.6m, 20 mm and 34.3%. The flue gas flows into the FGD tower from the space between the major desulfurization zone and the deep tank can finally become clean. The desulfurization liquid and the liquid slurry goes to the bottom tank and is discharged as waste. When the desulfurization solution flow impacts the sieve tray, the downward momentum will be converted to the upper surface of the sieve tray. As a result, a thin liquid layer can be developed above the sieve tray, which is the so-called the slurry layer. And the volume fraction value within the slurry layer is around 0.3~0.7. Therefore, the liquid phase can't be considered as a discrete phase under the Eulerian-Lagrangian framework. Besides, there is a liquid column through the sieve trays. The downward liquid column becomes narrow as it interacts with the upward gas flow. After the flue gas flows into the major desulfurization zone, the flow direction of the flue gas is upward (+y) in the tube between the liquid column and the solid boundary of the FGD tower. As a result, the flue gas near the liquid column may be rolled down to slurry layer, which developed a vortex or a circulation zone between any two sieve trays. The vortex structure between two sieve trays results in a sufficient large two-phase contact area. It also increases the number of times that the flue gas interacts with the desulfurization liquid. On the other hand, the sieve trays improve the two-phase mixing, which may improve the SO2 removal efficiency.

Keywords: Computational Fluid Dynamics (CFD), Eulerian-Eulerian Model, Flue Gas Desulfurization (FGD), perforated sieve tray

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Authors: Alexis Aldo Mendoza Villarroel, Ademir Clemente Villena Zevallos, Cristian Jose Lopez Del Alamo

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With the advancement of technology it is now possible to scan entire objects and obtain their digital representation by using point clouds or polygon meshes. However, some objects may be broken or have missing parts; thus, several methods focused on this problem have been proposed based on Geometric Deep Learning, such as GCNN, ACNN, PointNet, among others. In this article an approach from a different paradigm is proposed, using metric data structures to index global descriptors in the spectral domain and allow the recovery of a set of similar models in polynomial time; to later use the Iterative Close Point algorithm and recover the parts of the incomplete model using the geometry and topology of the model with less Hausdorff distance.

Keywords: 3D reconstruction method, point cloud completion, shape completion, similarity search

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Authors: Man Bahadur Shahu

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The secularism is a controversial, debatable and misinterpreted issue since its endorsement in the 2007 constitution in Nepal. The unprecedented acts have been seen favoring and disfavoring against the secularism within the public domain—which creates the fallacies and suspicions in the rationalization and modernization process. This paper highlights three important points: first, the secularization suddenly ruptures the silence and institutional decline of religion within the state. Second, state effort on secularism simultaneously fosters the state neutrality and state separation from religious institutions that amplify the recognition of all religious groups through the equal treatment in their festivity, rituals, and practices. Third, no state would completely secular because of their deep-rooted mindset and disposition with their own religious faiths and beliefs that largely enhance intergroup conflict, dispute, riot and turbulence in post-secular period in the name of proselytizing and conversion.

Keywords: conflict, proselytizing, religion, secular

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Authors: Sumit Kumar, Sunil Kumar, Chandan Deep Singh

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This paper presents modeling and simulation of flexible robot in an underwater environment. The underwater environment completely contrasts with ground or space environment. The robot in an underwater situation is subjected to various dynamic forces like buoyancy forces, hydrostatic and hydrodynamic forces. The underwater robot is modeled as Rayleigh beam. The developed model further allows estimating the deflection of tip in two directions. The complete dynamics of the underwater robot is analyzed, which is the main focus of this investigation. The control of robot trajectory is not discussed in this paper. Simulation is performed using Symbol Shakti software.

Keywords: bond graph modeling, dynamics. modeling, rayleigh beam, underwater robot

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Authors: Turin Datta, Kisor K. Sahu

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Structural materials used in today’s oil and gas exploration and drilling of both onshore and offshore oil and gas wells must possess superior tensile properties, excellent resistance to corrosive degradation that includes general, localized (pitting and crevice) and environment assisted cracking such as stress corrosion cracking and hydrogen embrittlement. The High Pressure and High Temperature (HPHT) wells are typically operated at temperature and pressure that can exceed 300-3500F and 10,000psi (69MPa) respectively which necessitates the use of exotic materials in these exotic sources of natural resources. This research investigation is focussed on the evaluation of tensile properties and corrosion behavior of AISI 4140 High-Strength Low Alloy Steel (HSLA) possessing tempered martensitic microstructure and Duplex 2205 Stainless Steel (DSS) having austenitic and ferritic phase. The selection of this two alloys are primarily based on economic considerations as 4140 HSLA is cheaper when compared to DSS 2205. Due to the harsh aggressive chemical species encountered in deep oil and gas wells like chloride ions (Cl-), carbon dioxide (CO2), hydrogen sulphide (H2S) along with other mineral organic acids, DSS 2205, having a dual-phase microstructure can mitigate the degradation resulting from the presence of both chloride ions (Cl-) and hydrogen simultaneously. Tensile properties evaluation indicates a ductile failure of DSS 2205 whereas 4140 HSLA exhibit quasi-cleavage fracture due to the phenomenon of ‘tempered martensitic embrittlement’. From the potentiodynamic polarization testing, it is observed that DSS 2205 has higher corrosion resistance than 4140 HSLA; the former exhibits passivity signifying resistance to localized corrosion while the latter exhibits active dissolution in all the environmental parameters space that was tested. From the Scanning Electron Microscopy (SEM) evaluation, it is understood that stable pits appear in DSS 2205 only when the temperature exceeds the critical pitting temperature (CPT). SEM observation of the corroded 4140 HSLA specimen tested in aqueous 3.5 wt.% NaCl solution reveals intergranular cracking which appears due to the adsorption and diffusion of hydrogen during polarization, thus, causing hydrogen-induced cracking/hydrogen embrittlement. General corrosion testing of DSS 2205 in acidic brine (pH~3.0) solution at ambient temperature using coupons indicate no weight loss even after three months whereas the corrosion rate of AISI 4140 HSLA is significantly higher after one month of testing.

Keywords: DSS 2205, polarization, pitting, SEM

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Authors: Daumantas Zidanavicius

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In order to investigate the bond between concrete and steel in the circular steel tube column filled with concrete, the 7 series of specimens were tested with the same geometrical parameters but different concrete properties. Two types of specimens were chosen. For the first type, the expansive additives to the concrete mixture were taken to increase internal forces. And for the second type, mechanical components were used. All 7 series of the short columns were modeled by FEM and tested experimentally. In the work, big attention was taken to the bond-slip models between steel and concrete. Results show that additives to concrete let increase the bond strength up to two times and the mechanical anchorage –up to 6 times compared to control specimens without additives and anchorage.

Keywords: concrete filled steel tube, push-out test, bond slip relationship, bond stress distribution

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Authors: Shih-Ting Yang, Chih-Yun Lin, Ming-Yu Li, Jhong-Ting Syue, Wei-Ming Huang

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Global positioning system (GPS) has become increasing precise in recent years, and the location-based service (LBS) has developed rapidly. Take the example of finding a parking lot (such as Parking apps). The location-based service can offer immediate information about a nearby parking lot, including the information about remaining parking spaces. However, it cannot provide expected search results according to the requirement situations of users. For that reason, this paper develops a “Location-based Search Approach according to Users’ Application Scenario” according to the location-based search and demand determination to help users obtain the information consistent with their requirements. The “Location-based Search Approach based on Users’ Application Scenario” of this paper consists of one mechanism and three kernel modules. First, in the Information Pre-processing Mechanism (IPM), this paper uses the cosine theorem to categorize the locations of users. Then, in the Information Category Evaluation Module (ICEM), the kNN (k-Nearest Neighbor) is employed to classify the browsing records of users. After that, in the Information Volume Level Determination Module (IVLDM), this paper makes a comparison between the number of users’ clicking the information at different locations and the average number of users’ clicking the information at a specific location, so as to evaluate the urgency of demand; then, the two-dimensional space is used to estimate the application situations of users. For the last step, in the Location-based Search Module (LBSM), this paper compares all search results and the average number of characters of the search results, categorizes the search results with the Manhattan Distance, and selects the results according to the application scenario of users. Additionally, this paper develops a Web-based system according to the methodology to demonstrate practical application of this paper. The application scenario-based estimate and the location-based search are used to evaluate the type and abundance of the information expected by the public at specific location, so that information demanders can obtain the information consistent with their application situations at specific location.

Keywords: data mining, knowledge management, location-based service, user application scenario

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Authors: Lin Cheng, Zijiang Yang

Abstract:

Program synthesis is the task to automatically generate programs based on user specification. In this paper, we present a framework that synthesizes programs from flow charts that serve as accurate and intuitive specification. In order doing so, we propose a deep neural network called GRCNN that recognizes graph structure from its image. GRCNN is trained end-to-end, which can predict edge and node information of the flow chart simultaneously. Experiments show that the accuracy rate to synthesize a program is 66.4%, and the accuracy rates to recognize edge and node are 94.1% and 67.9%, respectively. On average, it takes about 60 milliseconds to synthesize a program.

Keywords: program synthesis, flow chart, specification, graph recognition, CNN

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Authors: Duy Cuong Nguyen, Ali Makke, Guillaume Montay

Abstract:

This work presents an improved single fiber pull-out test for fiber/matrix interface characterization. This test has been used to study the Inter-Facial Shear Strength ‘IFSS’ of hemp fibers reinforced polypropylene (PP). For this aim, the fiber diameter has been carefully measured using a tomography inspired method. The fiber section contour can then be approximated by a circle or a polygon. The results show that the IFSS is overestimated if the circular approximation is used. The Influence of the molding temperature on the IFSS has also been studied. We find a molding temperature of 183°C leads to better interface properties. Above or below this temperature the interface strength is reduced.

Keywords: composite, hemp, interface, pull-out, processing, polypropylene, temperature

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Authors: Houssam A. Toutanji, Meng Han

Abstract:

This study focuses on the development of a fracture mechanics based-model that predicts the debonding behavior of FRP strengthened RC beams. In this study, a database includes 351 concrete prisms bonded with FRP plates tested in single and double shear were prepared. The existing fracture-mechanics-based models are applied to this database. Unfortunately the properties of adhesive layer, especially a soft adhesive layer, used on the specimens in the existing studies were not always able to found. Thus, the new model’s proposal was based on fifteen newly conducted pullout tests and twenty four data selected from two independent existing studies with the application of a soft adhesive layers and the availability of adhesive properties.

Keywords: carbon fiber composite materials, interface response, fracture characteristics, maximum shear stress, ultimate transferable load

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Authors: Tao Qu, Chandra Prakash, Vikas Tomar

Abstract:

The interfaces between organic and inorganic phases in natural materials have been shown to be a key factor contributing to their high performance. This work analyzes crack propagation in a 2-ply laminate subjected to uniaxial tensile mode-I crack propagation loading that has laminate properties derived based on biological material constituents (marine exoskeleton- chitin and calcite). Interfaces in such laminates are explicitly modeled based on earlier molecular simulations performed by authors. Extended finite element method and cohesive zone modeling based simulations coupled with theoretical analysis are used to analyze crack propagation. Analyses explicitly quantify the effect that interface mechanical property variation has on the delamination as well as the transverse crack propagation in examined 2-ply laminates.

Keywords: chitin, composites, interfaces, fracture

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Authors: Danilo López, Nelson Vera, Luis Pedraza

Abstract:

This paper analyzes fundamental ideas and concepts related to neural networks, which provide the reader a theoretical explanation of Long Short-Term Memory (LSTM) networks operation classified as Deep Learning Systems, and to explicitly present the mathematical development of Backward Pass equations of the LSTM network model. This mathematical modeling associated with software development will provide the necessary tools to develop an intelligent system capable of predicting the behavior of licensed users in wireless cognitive radio networks.

Keywords: neural networks, multilayer perceptron, long short-term memory, recurrent neuronal network, mathematical analysis

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Authors: Hadi Rezghimaleki, Masatoshi Kubouchi, Yoshihiko Arao

Abstract:

The use of natural fiber composites (NFCs) is growing at a fast rate regarding industrial applications and principle researches due to their eco-friendly, renewable nature, and low density/costs. Drilling is one of the most important machining operations that are carried out on natural fiber composites. Delamination is a major concern in the drilling process of NFCs that affects the structural integrity and long-term reliability of the machined components. Flax fiber reinforced epoxy composite laminates were prepared by hot press technique. In this research, we evaluated drilling-induced delamination of flax/epoxy composites by X-ray computed tomography (CT), ultrasonic testing (UT), and optical methods and compared the results.

Keywords: natural fiber composites, flax/epoxy, X-ray CT, ultrasonic testing

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Authors: Yoshimoto Kurihara, Tad Gonsalves

Abstract:

Currently, in the field of object posture estimation, there is research on estimating the position and angle of an object by storing a 3D model of the object to be estimated in advance in a computer and matching it with the model. However, in this research, we have succeeded in creating a module that is much simpler, smaller in scale, and faster in operation. Our 6D pose estimation model consists of two different networks – a classification network and a regression network. From a single RGB image, the trained model estimates the class of the object in the image, the coordinates of the object, and its rotation angle in 3D space. In addition, we compared the estimation accuracy of each camera position, i.e., the angle from which the object was captured. The highest accuracy was recorded when the camera position was 75°, the accuracy of the classification was about 87.3%, and that of regression was about 98.9%.

Keywords: 6D posture estimation, image recognition, deep learning, AlexNet

Procedia PDF Downloads 140