Search results for: miniature specimen testing

Authors: Joseph Marae Djouda, Ashraf Kasmi, François Hild

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Fused filament fabrication is one of the most widespread additive manufacturing techniques because of its low-cost implementation. Its initial development was based on part fabrication with thermoplastic materials. The influence of the manufacturing parameters such as the filament orientation through the nozzle, the deposited layer thickness, or the speed deposition on the mechanical properties of the parts has been widely experimentally investigated. It has been recorded the remarkable variations of the anisotropy in the function of the filament path during the fabrication process. However, there is a lack in the development of constitutive models describing the mechanical properties. In this study, integrated digital image correlation (I-DIC) is used for the identification of mechanical constitutive parameters of two configurations of ABS samples: +/-45° and so-called “oriented deposition.” In this last, the filament was deposited in order to follow the principal strain of the sample. The identification scheme based on the gap reduction between simulation and the experiment directly from images recorded from a single sample (single edge notched tension specimen) is developed. The macroscopic and mesoscopic analysis are conducted from images recorded in both sample surfaces during the tensile test. The elastic and elastoplastic models in isotropic and orthotropic frameworks have been established. It appears that independently of the sample configurations (filament orientation during the fabrication), the elastoplastic isotropic model gives the correct description of the behavior of samples. It is worth noting that in this model, the number of constitutive parameters is limited to the one considered in the elastoplastic orthotropic model. This leads to the fact that the anisotropy of the architectured 3D printed ABS parts can be neglected in the establishment of the macroscopic behavior description.

Keywords: elastic anisotropy, fused filament fabrication, Acrylonitrile butadiene styrene, I-DIC identification

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Authors: John Kabuba

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The study investigated the implementation of the Neural Network (NN) techniques for prediction of the loading of Cu ions onto clinoptilolite. The experimental design using analysis of variance (ANOVA) was chosen for testing the adequacy of the Neural Network and for optimizing of the effective input parameters (pH, temperature and initial concentration). Feed forward, multi-layer perceptron (MLP) NN successfully tracked the non-linear behavior of the adsorption process versus the input parameters with mean squared error (MSE), correlation coefficient (R) and minimum squared error (MSRE) of 0.102, 0.998 and 0.004 respectively. The results showed that NN modeling techniques could effectively predict and simulate the highly complex system and non-linear process such as ion-exchange.

Keywords: clinoptilolite, loading, modeling, neural network

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Authors: Yebeen Ji, Jimin Yun, Kwonhoo Kim

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Magnesium alloys were limited for industrial applications due to having a limited slip system and high plastic anisotropy. It has been known that specific textures were formed during processing (rolling, etc.), and These textures cause poor formability. To solve these problems, many researchers have studied controlling texture by adding rare-earth elements. However, the high cost limits their use; therefore, alternatives are needed to replace them. Although Pb addition doesn’t directly improve magnesium properties, it has been known to suppress the diffusion of other alloying elements and reduce grain boundary energy. These characteristics are similar to the additions of rare-earth elements, and a similar texture behavior is expected as well. However, there is insufficient research on this. Therefore, this study investigates the behavior of texture and microstructure development after adding Pb to magnesium. This study compared and analyzed AZ61 alloy and Mg-15wt%Pb alloy to determine the effect of adding solute elements. The alloy was hot rolled and annealed to form a single phase and initial texture. Afterward, the specimen was set to contraction and elongate parallel to the rolling surface and the rolling direction and then subjected to high-temperature plane strain compression under the conditions of 723K and 0.05/s. Microstructural analysis and texture measurements were performed by SEM-EBSD. The peak stress in the true strain-stress curve after compression was higher in AZ61, but the shape of the flow curve was similar for both alloys. For both alloys, continuous dynamic recrystallization was confirmed to occur during the compression process. The basal texture developed parallel to the compressed surface, and the pole density was lower in the Mg-15wt%Pb alloy. It is confirmed that this change in behavior is because the orientation distribution of recrystallized grains has a more random orientation compared to the parent grains when Pb is added.

Keywords: Mg, texture, Pb, DRX

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Authors: Zunaira Zaman, David Bohannon

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This study explores the application of machine learning (ML) for detecting security vulnerabilities in source code. The research aims to assist organizations with large application portfolios and limited security testing capabilities in prioritizing security activities. ML-based approaches offer benefits such as increased confidence scores, false positives and negatives tuning, and automated feedback. The initial approach using natural language processing techniques to extract features achieved 86% accuracy during the training phase but suffered from overfitting and performed poorly on unseen datasets during testing. To address these issues, the study proposes using the abstract syntax tree (AST) for Java and C++ codebases to capture code semantics and structure and generate path-context representations for each function. The Code2Vec model architecture is used to learn distributed representations of source code snippets for training a machine-learning classifier for vulnerability prediction. The study evaluates the performance of the proposed methodology using two datasets and compares the results with existing approaches. The Devign dataset yielded 60% accuracy in predicting vulnerable code snippets and helped resist overfitting, while the Juliet Test Suite predicted specific vulnerabilities such as OS-Command Injection, Cryptographic, and Cross-Site Scripting vulnerabilities. The Code2Vec model achieved 75% accuracy and a 98% recall rate in predicting OS-Command Injection vulnerabilities. The study concludes that even partial AST representations of source code can be useful for vulnerability prediction. The approach has the potential for automated intelligent analysis of source code, including vulnerability prediction on unseen source code. State-of-the-art models using natural language processing techniques and CNN models with ensemble modelling techniques did not generalize well on unseen data and faced overfitting issues. However, predicting vulnerabilities in source code using machine learning poses challenges such as high dimensionality and complexity of source code, imbalanced datasets, and identifying specific types of vulnerabilities. Future work will address these challenges and expand the scope of the research.

Keywords: code embeddings, neural networks, natural language processing, OS command injection, software security, code properties

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Authors: S. D. El Wakil, M. Pladsen

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Fiber reinforced polymeric (FRP) composites are finding wide-spread industrial applications because of their exceptionally high specific strength and specific modulus of elasticity. Nevertheless, it is very seldom to get ready-for-use components or products made of FRP composites. Secondary processing by machining, particularly drilling, is almost always required to make holes for fastening components together to produce assemblies. That creates problems since the FRP composites are neither homogeneous nor isotropic. Some of the problems that are encountered include the subsequent damage in the region around the drilled hole and the drilling – induced delamination of the layer of ply, that occurs both at the entrance and the exit planes of the work piece. Evidently, the functionality of the work piece would be detrimentally affected. The current work was carried out with the aim of eliminating or at least minimizing the work piece damage associated with drilling of FPR composites. Each test specimen involves a woven reinforced graphite fiber/epoxy composite having a thickness of 12.5 mm (0.5 inch). A large number of test specimens were subjected to drilling operations with different combinations of feed rates and cutting speeds. The drilling induced damage was taken as the absolute value of the difference between the drilled hole diameter and the nominal one taken as a percentage of the nominal diameter. The later was determined for each combination of feed rate and cutting speed, and a matrix comprising those values was established, where the columns indicate varying feed rate while and rows indicate varying cutting speeds. Next, the analysis of variance (ANOVA) approach was employed using Minitab software, in order to obtain the combination that would improve the drilling induced damage. Experimental results show that low feed rates coupled with low cutting speeds yielded the best results.

Keywords: drilling of composites, dimensional accuracy of holes drilled in composites, delamination and charring, graphite-epoxy composites

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Authors: Piush Verma, Y. R. Sood

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This paper provides the information on the diagnostics techniques for condition monitoring of power transformer (PT). This paper deals with the practical importance of the transformer diagnostic in the Electrical Engineering field. The life of the transformer depends upon its insulation i.e paper and oil. The major testing techniques applies on transformer oil and paper i.e dissolved gas analysis, furfural analysis, radio interface, acoustic emission, infra-red emission, frequency response analysis, power factor, polarization spectrum, magnetizing currents, turn and winding ratio. A review has been made on the modern development of this practical technology.

Keywords: temperature, condition monitoring, diagnostics methods, paper analysis techniques, oil analysis techniques

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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The widespread use of machine learning applications in production is significantly accelerated by improved computing power and increasing data availability. Predictive quality enables the assurance of product quality by using machine learning models as a basis for decisions on test results. The use of real Bosch production data based on geometric gauge blocks from machining, mating data from assembly and hydraulic measurement data from final testing of directional valves is a promising approach to classifying the quality characteristics of workpieces.

Keywords: machine learning, classification, predictive quality, hydraulics, supervised learning

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Authors: Saruhan Kartal, Ilker Kalkan

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The present paper reports the cracking moment estimates of a set of steel-reinforced, Fiber Reinforced Polymer (FRP)-reinforced and hybrid steel-FRP reinforced concrete beams, calculated from different analytical formulations in the codes, together with the experimental cracking load values. A total of three steel-reinforced, four FRP-reinforced, 12 hybrid FRP-steel over-reinforced and five hybrid FRP-steel under-reinforced concrete beam tests were analyzed within the scope of the study. Glass FRP (GFRP) and Basalt FRP (BFRP) bars were used in the beams as FRP bars. In under-reinforced hybrid beams, rupture of the FRP bars preceded crushing of concrete, while concrete crushing preceded FRP rupture in over-reinforced beams. In both types, steel yielding took place long before the FRP rupture and concrete crushing. The cracking moment mainly depends on two quantities, namely the moment of inertia of the section at the initiation of cracking and the flexural tensile strength of concrete, i.e. the modulus of rupture. In the present study, two different definitions of uncracked moment of inertia, i.e. the gross and the uncracked transformed moments of inertia, were adopted. Two analytical equations for the modulus of rupture (ACI 318M and Eurocode 2) were utilized in the calculations as well as the experimental tensile strength of concrete from prismatic specimen tests. The ACI 318M modulus of rupture expression produced cracking moment estimates closer to the experimental cracking moments of FRP-reinforced and hybrid FRP-steel reinforced concrete beams when used in combination with the uncracked transformed moment of inertia, yet the Eurocode 2 modulus of rupture expression gave more accurate cracking moment estimates in steel-reinforced concrete beams. All of the analytical definitions produced analytical values considerably different from the experimental cracking load values of the solely FRP-reinforced concrete beam specimens.

Keywords: polymer reinforcement, four-point bending, hybrid use of reinforcement, cracking moment

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Authors: Yan Zhang, Jan Awrejcewicz, Lin Fu

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High-heeled shoes force the foot into plantar flexion position resulting in foot arch rising and disturbance of the articular congruence between the talus and tibiofibular mortice, all of which may increase the challenge of balance maintenance. Plantar pressure distribution of the stance limb during the star excursion balance test (SEBT) contributes to the understanding of potential sources of reaching excursions in SEBT. The purpose of this study is to evaluate the dynamic posture control while wearing high-heeled shoes using SEBT in a combination of plantar pressure measurement. Twenty healthy young females were recruited. Shoes of three heel heights were used: flat (0.8 cm), low (4.0 cm), high (6.6 cm). The testing grid of SEBT consists of three lines extending out at 120° from each other, which were defined as anterior, posteromedial, and posterolateral directions. Participants were instructed to stand on their dominant limb with the heel in the middle of the testing grid and hands on hips and to reach the non-stance limb as far as possible towards each direction. The distal portion of the reaching limb lightly touched the ground without shifting weight. Then returned the reaching limb to the beginning position. The excursion distances were normalized to leg length. The insole plantar measurement system was used to record peak pressure, contact area, and pressure-time integral of the stance limb. Results showed that normalized excursion distance decreased significantly as heel height increased. The changes of plantar pressure in SEBT as heel height increased were more obvious in the medial forefoot (MF), medial midfoot (MM), rearfoot areas. At MF, the peak pressure and pressure-time integral of low and high shoes increased significantly compared with that of flat shoes, while the contact area decreased significantly as heel height increased. At MM, peak pressure, contact area, and pressure-time integral of high and low shoes were significantly lower than that of flat shoes. To reduce posture instability, the stance limb plantar loading shifted to medial forefoot. Knowledge of this study identified dynamic posture control deficits while wearing high-heeled shoes and the critical role of the medial forefoot in dynamic balance maintenance.

Keywords: dynamic posture control, high-heeled shoes, plantar pressure, star excursion balance test.

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Authors: Tala Toonsi, Marah Alagha, Lina Alnowaiser, Hala Rajab

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This report is about the Photomath app, which is an AI application that uses image recognition technology, specifically optical character recognition (OCR) algorithms. The (OCR) algorithm translates the images into a mathematical equation, and the app automatically provides a step-by-step solution. The application supports decimals, basic arithmetic, fractions, linear equations, and multiple functions such as logarithms. Testing was conducted to examine the usage of this app, and results were collected by surveying ten participants. Later, the results were analyzed. This paper seeks to answer the question: To what level the artificial intelligence features are accurate and the speed of process in this app. It is hoped this study will inform about the efficiency of AI in Photomath to the users.

Keywords: photomath, image recognition, app, OCR, artificial intelligence, mathematical equations.

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Authors: Rekioua T., Rekioua D., Aissou S., Ouhabi A.

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Power provided by the photovoltaic array varies with solar radiation and temperature, since these parameters influence the electrical characteristic (Ipv-Vpv) of solar cells. In Scientific research, there are different methods to obtain these characteristics. In this paper, we present three methods. A simulation one using Matlab/Simulink. The second one is the standard experimental voltage method and the third one is by using LabVIEW software. This latter is based on an electronic circuit to test PV modules. All details of this electronic schemes are presented and obtained results of the three methods with a comparison and under different meteorological conditions are presented. The proposed method is simple and very efficiency for testing and measurements of electrical characteristic curves of photovoltaic panels.

Keywords: photovoltaic cells, measurement standards, temperature sensors, data acquisition

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Authors: М. Zh. Khazimov, А. B. Kudasheva

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The article presents the state of solid fuel reserves and their share in the world energy sector. The air pollution caused by the operation of heating devices using solid fuels is a significant problem. In order to improve the air quality, heating device producers take constant measures to improve their products. However, the emission results achieved during an initial test of heating devices in the laboratory may be much worse during operation in real operating conditions. The ways of increasing the efficiency of the boiler by improving its design for combustion in full-layer mode are shown. The results of the testing of the improved КВТС-0.2 hot water boiler is presented and the technical and economic indicators are determined, which indicate an increase in the efficiency of the boiler.

Keywords: boiler unit, grate, furnace, coal, ash

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Authors: Ulrich Wake, Eniman Syamsuddin

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The model for camera model identificaiton is trained using pretrained model ResNet43 and ResNet50. The dataset consists of 500 photos of each phone. Dataset is divided into 1280 photos for training, 320 photos for validation and 400 photos for testing. The model is trained using One Cycle Policy Method and tested using Test-Time Augmentation. Furthermore, the model is trained for 50 epoch using regularization such as drop out and early stopping. The result is 90% accuracy for validation set and above 85% for Test-Time Augmentation using ResNet50. Every model is also trained by slightly updating the pretrained model’s weights

Keywords: ​ One Cycle Policy, ResNet34, ResNet50, Test-Time Agumentation

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Authors: B. Vijay Antony Raj, Umarani Gunasekaran, R. Thiru Kumara Raja Vallaban

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Fibrous autoclaved aerated concrete (FAAC) is concrete containing fibrous material in it which helps to increase its structural integrity when compared to that of convention autoclaved aerated concrete (CAAC). These short discrete fibers are uniformly distributed and randomly oriented, which enhances the bond strength within the aerated concrete matrix. Conventional red-clay bricks create larger impact to the environment due to red soil depletion and it also consumes large amount to time for construction. Whereas, AAC are larger in size, lighter in weight and it is environmentally friendly in nature and hence it is a viable replacement for red-clay bricks. Internal micro cracks and corner cracks are the only disadvantages of conventional autoclaved aerated concrete, to resolve this particular issue it is preferable to make use of fibers in it.These fibers are bonded together within the matrix and they induce the aerated concrete to withstand considerable stresses, especially during the post cracking stage. Hence, FAAC has the capability of enhancing the mechanical properties and energy absorption capacity of CAAC. In this research work, individual fibers like glass, nylon, polyester and polypropylene are used they generally reduce the brittle fracture of AAC.To study the fibre’s surface topography and composition, SEM analysis is performed and then to determine the composition of a specimen as a whole as well as the composition of individual components EDAX mapping is carried out and then an experimental approach was performed to determine the effect of hybrid (multiple) fibres at various dosage (0.5%, 1%, 1.5%) and curing temperature of 180-2000 C is maintained to determine the mechanical properties of autoclaved aerated concrete. As an analytical part, the outcome experimental results is compared with fuzzy logic using MATLAB.

Keywords: fiberous AAC, crack control, energy absorption, mechanical properies, SEM, EDAX, MATLAB

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Authors: Jonnalagadda Naveen, M. P. S. Tomar, Putluru Satish, Palanisamy Dharani

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Present investigation was carried out on a single specimen of the heart of yellow-bellied sea snake, which accidentally came to the seashore with the fisherman’s net. After the death, these specimens was preserved in 10% neutral buffered formalin and observe for its morphology. The literature cited revealed that meager information was available on the anatomy of the heart of this species of snake thus present study was planned on the gross anatomy of the heart of yellow-bellied sea snake. The heart of yellow-bellied sea snake was located between 28-35th rib in an oblique direction in the pericardial sac. It was three chambered with the complete division of atria but the ventricular cavity was incompletely divided. The apex did not show any gubernaculum cordis. The sinus venosus was the common cavity for confluence of anterior and posterior vana cava and the jugular vein was opened with anterior vena cava. The opening of posterior vena cava was slit-like and it was guarded by membranous valves whereas no valve could be observed at the opening of anterior vana cava and the jugular vein. Both the caval veins ran along the right border of the heart. Pulmonary vein was single which later divided into two branches. The length-width index for the atria was 1.33 whereas it was 1.67 for the ventricle. The atrioventricular canal was situated slightly towards the left of the midline of the heart and was divided into a right cavum pulmonale and left cavum arteriosum of which the right one was slightly larger and longer than the left. The cavum venosum was present in between the cavum pulmonale and the cavum arteriosum. The Ventricle was elongated triangle muscular compartment with ventrally located apex. Internally the cavity of ventricle was divided into two partial chambers dorsally by a muscular ridge and ventrally by an incomplete inter ventricular septum.

Keywords: aorta, atrium, heart, sea snake, sinus venosus, ventricle

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Authors: Mohammadreza Hedyehzadeh, Mahdi Yousefi

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Colon cancer is one of the most common cancer, which predicted to increase its prevalence due to the bad eating habits of peoples. Nowadays, due to the busyness of people, the use of fast foods is increasing, and therefore, diagnosis of this disease and its treatment are of particular importance. To determine the best treatment approach for each specific colon cancer patients, the oncologist should be known the stage of the tumor. The most common method to determine the tumor stage is TNM staging system. In this system, M indicates the presence of metastasis, N indicates the extent of spread to the lymph nodes, and T indicates the size of the tumor. It is clear that in order to determine all three of these parameters, an imaging method must be used, and the gold standard imaging protocols for this purpose are CT and PET/CT. In CT imaging, due to the use of X-rays, the risk of cancer and the absorbed dose of the patient is high, while in the PET/CT method, there is a lack of access to the device due to its high cost. Therefore, in this study, we aimed to estimate the tumor size and the extent of its spread to the lymph nodes using MR images. More than 1300 MR images collected from the TCIA portal, and in the first step (pre-processing), histogram equalization to improve image qualities and resizing to get the same image size was done. Two expert radiologists, which work more than 21 years on colon cancer cases, segmented the images and extracted the tumor region from the images. The next step is feature extraction from segmented images and then classify the data into three classes: T0N0، T3N1 و T3N2. In this article, the VGG-16 convolutional neural network has been used to perform both of the above-mentioned tasks, i.e., feature extraction and classification. This network has 13 convolution layers for feature extraction and three fully connected layers with the softmax activation function for classification. In order to validate the proposed method, the 10-fold cross validation method used in such a way that the data was randomly divided into three parts: training (70% of data), validation (10% of data) and the rest for testing. It is repeated 10 times, each time, the accuracy, sensitivity and specificity of the model are calculated and the average of ten repetitions is reported as the result. The accuracy, specificity and sensitivity of the proposed method for testing dataset was 89/09%, 95/8% and 96/4%. Compared to previous studies, using a safe imaging technique (MRI) and non-use of predefined hand-crafted imaging features to determine the stage of colon cancer patients are some of the study advantages.

Keywords: colon cancer, VGG-16, magnetic resonance imaging, tumor size, lymph node metastasis

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Authors: Lahari Ramya Pa, Sudhakar Ib, Madhu Vc, Madhusudhan Reddy Gd, Srinivasa Rao E.

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Selection of suitable armor materials for defense applications is very crucial with respect to increasing mobility of the systems as well as maintaining safety. Therefore, determining the material with the lowest possible areal density that resists the predefined threat successfully is required in armor design studies. A number of light metal and alloys are come in to forefront especially to substitute the armour grade steels. AA5083 aluminium alloy which fit in to the military standards imposed by USA army is foremost nonferrous alloy to consider for possible replacement of steel to increase the mobility of armour vehicles and enhance fuel economy. Growing need of AA5083 aluminium alloy paves a way to develop supplement aluminium alloys maintaining the military standards. It has been witnessed that AA 2xxx aluminium alloy, AA6xxx aluminium alloy and AA7xxx aluminium alloy are the potential material to supplement AA5083 aluminium alloy. Among those cited aluminium series alloys AA7xxx aluminium alloy (heat treatable) possesses high strength and can compete with armour grade steels. Earlier investigations revealed that layering of AA7xxx aluminium alloy can prevent spalling of rear portion of armour during ballistic impacts. Hence, present investigation deals with fabrication of hard layer (made of boron carbide) i.e. layer on AA 7075 aluminium alloy using friction stir processing with an intention of blunting the projectile in the initial impact and backing tough portion(AA7xxx aluminium alloy) to dissipate residual kinetic energy. An analytical approach has been adopted to unfold the ballistic performance of projectile. Penetration of projectile inside the armour has been resolved by considering by strain energy model analysis. Perforation shearing areas i.e. interface of projectile and armour is taken in to account for evaluation of penetration inside the armour. Fabricated surface composites (targets) were tested as per the military standard (JIS.0108.01) in a ballistic testing tunnel at Defence Metallurgical Research Laboratory (DMRL), Hyderabad in standardized testing conditions. Analytical results were well validated with experimental obtained one.

Keywords: AA7075 aluminium alloy, friction stir processing, boron carbide, ballistic performance, target

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Authors: Chukwuemeka Daniel Ezeliora, Chukwuebuka Lawrence Ezeliora

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The research focused on the appraisal of impact strength on mild steel cladding weld metal geometry. Over the years, poor welding has resulted in failures in engineering components, poor material quality, the collapse of welded materials, and failures in material strength. This is as a result of poor selection and combination of welding input process parameters. The application of the Tungsten Inert Gas (TIG) welding method with weld specimen of length 60; width 40, and thickness of 10 was used for the experiment. A butt joint method was prepared for the welding, and tungsten inert gas welding process was used to perform the twenty (20) experimental runs. A response surface methodology was used to model and to analyze the system. For an adequate polynomial approximation, the experimental design was used to collect the data. The key parameters considered in this work are welding current, gas flow rate, welding speed, and voltage. The range of the input process parameters was selected from the literature and the design. The steps followed to achieve the experimental design and results is the use of response surface method (RSM) implemented in central composite design (CCD) to generate the design matrix, to obtain quadratic model, and evaluate the interactions in the factors as well as optimizing the factors and the response. The result expresses that the best impact strength of the mild steel cladding weld metal geometry is 115.419 Joules. However, it was observed that the result of the input factors is; current 180.4 amp, voltage 23.99 volt, welding speed 142.7 mm.s and gas flow rate 10.8 lit/min as the optimum of the input process parameters. The optimal solution gives a guide for optimal impact strength of the weldment when welding with tungsten inert gas (TIG) under study.

Keywords: mild steel, impact strength, response surface, bead geometry, welding

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Authors: Suwaree Yordchim, Toby J. Gibbs

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The linguistic competence of Thai university students majoring in Business English was examined in the context of knowledge of English language inflection, and also various linguistic elements. Errors analysis was applied to the results of the testing. Levels of errors in inflection, tense and linguistic elements were shown to be significantly high for all noun, verb and adjective inflections. Findings suggest that students do not gain linguistic competence in their use of English language inflection, because of interlanguage interference. Implications for curriculum reform and treatment of errors in the classroom are discussed.

Keywords: interlanguage, error analysis, inflection, second language acquisition, Thai students

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Authors: Yun Yu Stephanie Law

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Sociometer Theory is well tested among young adults in western population, however, limited research is found for other age groups, like adolescent and middle-adulthood in Asia population. Thus, one of the main purposes of this study is to verify the validity of Sociometer Theory in different age groups among Asian. To be specific, we hypothesized that an increase in one’s perceived social rejection is associated to a decrease in his/her state self-esteem among all age groups in Asian population. And we expected that this association can be found among all age groups including adolescent, young adults and middle-adults group in our first study. In this way, we can verify the validity of Sociometer Theory across different age groups as well as its significance in Asian population. Furthermore, those participants who received rejection about ‘mate-role’ would also receive some negative feedbacks regarding their current/future capacity of being a good mate. Results suggested that participants’ state self-esteem sensitivity for mating-capacity rejection is higher when comparing to that of friend-capacity rejection, i.e. greater drop in state self-esteem when receiving mating-capacity feedbacks then receiving friend-capacity feedbacks. These results, however, is just applicable on young adults. Thus, the main purpose of study two would be testing the state self-esteem sensitivity towards social rejection in different domains among three age groups. We hypothesized that group differences would be found for three age groups regarding state self-esteem sensitivity. Research question 1: perceived social rejection is associated to decrease in state self-esteem, is applicable among different age groups in Asia population. Research question 2: there are significant group differences for three age groups regarding state self-esteem sensitivity. Methods: 300 subjects are divided into three age groups, adolescents group, young adult group and middle-adult group, with 100 subjects in each group. Two questionnaires were used in testing this fundamental concept. Subjects were then asked to rate themselves on questionnaire in measuring their current state self-esteem in order to obtain the baseline measurements for later comparison. In order to avoid demand characteristics from subjects, other unrelated tasks like word matching were also given after the first test. Results: A positive correlation between scores in questionnaire 1 and questionnaire 2 among all age groups. Conclusion: State self-esteem decrease to both imagined social rejection (study1) and experienced social rejection (study2). Moreover, level of decrease in state self-esteem vary when receiving different domains of social rejection. Implications: a better understanding of self-esteem development for various age group might bring insights for education systems and policies for teaching approaches and learning methods among different age groups.

Keywords: state self-esteem, social rejection, stage theory, self-feelings

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Authors: Kanyarat Bussaban, Kunyanuth Kularbphettong

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This research aims to create a model for analysis of student behavior using Library resources based on data mining technique in case of Suan Sunandha Rajabhat University. The model was created under association rules, apriori algorithm. The results were found 14 rules and the rules were tested with testing data set and it showed that the ability of classify data was 79.24 percent and the MSE was 22.91. The results showed that the user’s behavior model by using association rule technique can use to manage the library resources.

Keywords: behavior, data mining technique, a priori algorithm, knowledge discovery

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Authors: Oluwaseun Ijiwade, Chris Davison, Kelvin Gregory

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In Australia, like other parts of the world, the debate on how to enhance teachers using assessment data to inform teaching and learning of English as an Additional Language (EAL, Australia) or English as a Foreign Language (EFL, United States) have occupied the centre of academic scholarship. Traditionally, this approach was conceptualised as ‘Formative Assessment’ and, in recent times, ‘Assessment for Learning (AfL)’. The central problem is that teacher-made tests are limited in providing data that can inform teaching and learning due to variability of classroom assessments, which are hindered by teachers’ characteristics and assessment literacy. To address this concern, scholars in language education and testing have proposed a uniformed large-scale computer-based assessment program to meet the needs of teachers and promote AfL in language education. In Australia, for instance, the Victoria state government commissioned a large-scale project called 'Tools to Enhance Assessment Literacy (TEAL) for Teachers of English as an additional language'. As part of the TEAL project, a tool called ‘Reading and Vocabulary assessment for English as an Additional Language (RVEAL)’, as a diagnostic language assessment (DLA), was developed by language experts at the University of New South Wales for teachers in Victorian schools to guide EAL pedagogy in the classroom. Therefore, this study aims to provide qualitative evidence for understanding beliefs about the diagnostic language assessment (DLA) among EAL teachers in primary and secondary schools in Victoria, Australia. To realize this goal, this study raises the following questions: (a) How do teachers use large-scale assessment data for diagnostic purposes? (b) What skills do language teachers think are necessary for using assessment data for instruction in the classroom? and (c) What factors, if any, contribute to teachers’ beliefs about diagnostic assessment in a large-scale assessment? Semi-structured interview method was used to collect data from at least 15 professional teachers who were selected through a purposeful sampling. The findings from the resulting data analysis (thematic analysis) provide an understanding of teachers’ beliefs about DLA in a classroom context and identify how these beliefs are crystallised in language teachers. The discussion shows how the findings can be used to inform professional development processes for language teachers as well as informing important factor of teacher cognition in the pedagogic processes of language assessment. This, hopefully, will help test developers and testing organisations to align the outcome of this study with their test development processes to design assessment that can enhance AfL in language education.

Keywords: beliefs, diagnostic language assessment, English as an additional language, teacher cognition

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Authors: Young-Kwan Choi, Han-Sang Jeong, Yeon-Ho Ok, Jae-Ho Choi

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The purpose of this study is to verify validity of design by testing output of induction generator through finite element analysis before manufacture of induction generator designed. Characteristics in the operating domain of induction generator can be understood through analysis of magnetic field according to load (rotational speed) of induction generator. Characteristics of induction generator such as induced voltage, current, torque, magnetic flux density (magnetic flux saturation), and loss can be predicted by analysis of magnetic field.

Keywords: electromagnetic analysis, induction generator, small hydro power generator, small francis turbine generator

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Authors: Adriano Bessa Albuquerque, Francisco Jose Barreto Nunes

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Software vulnerabilities are increasing and not only impact services and processes availability as well as information confidentiality, integrity and privacy, but also cause changes that interfere in the development process. Security test could be a solution to reduce vulnerabilities. However, the variety of test techniques with the lack of real case studies of applying tests focusing on software development life cycle compromise its effective use. This paper offers an overview of how a Systematic Mapping Study (MS) about security verification, validation and test (VVT) was performed, besides presenting general results about this study.

Keywords: software test, software security verification validation and test, security test institutionalization, systematic mapping study

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Authors: Suraj Makkar, Asis Isor, Jeetendra Gupta, Simran Bareja, Maushumi K. Talukdar

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During the exploratory testing phase of an offshore oil and gas well, when the tubing string was pulled out after production testing, it was observed that there was visible corrosion/pitting in a few of the 3-1/2” API 5 CT L-80 Grade tubing. The area of corrosion was at the same location in all the tubing, i.e., just above the pin end. Since the corrosion was observed in the tubing within two months of their installation, it was a matter of concern, as it could lead to premature failures resulting in leakages and production loss and thus affecting the integrity of the asset. Therefore, the tubing was analysed to ascertain the mechanism of the corrosion occurring on its surface. During the visual inspection, it was observed that the corrosion was totally external, which was near the pin end, and no significant internal corrosion was observed. The chemical compositional analysis and mechanical properties (tensile and impact) show that the pipeline material was conforming to API 5 CT L-80 specifications. The metallographic analysis of the tubing revealed tempered martensitic microstructure. The grain size was observed to be different at the pin end as compared to the microstructure at base metal. The microstructures of the corroded area near threads reveal an oriented microstructure. The clearly oriented microstructure of the cold-worked zone near threads and the difference in microstructure represents inappropriate heat treatment after cold work. This was substantiated by hardness test results as well, which show higher hardness at the pin end in comparison to hardness at base metal. Scanning Electron Microscope (SEM) analysis revealed the presence of round and deep pits and cracks on the corroded surface of the tubing. The cracks were stress corrosion cracks in a corrosive environment arising out of the residual stress, which was not relieved after cold working, as mentioned above. Energy Dispersive Spectroscopy (EDS) analysis indicates the presence of mainly Fe₂O₃, Chlorides, Sulphides, and Silica in the corroded part indicating the interaction of the tubing with the well completion fluid and well bore environment. Thus it was concluded that residual stress after the cold working of male pins during threading and the corrosive environment acted in synergy to cause this pitting corrosion attack on the highly stressed zone along the circumference of the tubing just below the threaded area. Accordingly, the following suitable recommendations were given to avoid the recurrence of such corrosion problems in the wells. (i) After any kind of hot work/cold work, tubing should be normalized at full length to achieve uniform microstructure throughout its length. (ii) Heat treatment requirements (as per API 5 CT) should be part of technical specifications while at the procurement stage.

Keywords: pin end, microstructure, grain size, stress corrosion cracks

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Authors: Eiman Kattan, Hong Wei

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In using Convolutional Neural Network (CNN) for classification, there is a set of hyperparameters available for the configuration purpose. This study aims to evaluate the impact of a range of parameters in CNN architecture i.e. AlexNet on land cover classification based on four remotely sensed datasets. The evaluation tests the influence of a set of hyperparameters on the classification performance. The parameters concerned are epoch values, batch size, and convolutional filter size against input image size. Thus, a set of experiments were conducted to specify the effectiveness of the selected parameters using two implementing approaches, named pertained and fine-tuned. We first explore the number of epochs under several selected batch size values (32, 64, 128 and 200). The impact of kernel size of convolutional filters (1, 3, 5, 7, 10, 15, 20, 25 and 30) was evaluated against the image size under testing (64, 96, 128, 180 and 224), which gave us insight of the relationship between the size of convolutional filters and image size. To generalise the validation, four remote sensing datasets, AID, RSD, UCMerced and RSCCN, which have different land covers and are publicly available, were used in the experiments. These datasets have a wide diversity of input data, such as number of classes, amount of labelled data, and texture patterns. A specifically designed interactive deep learning GPU training platform for image classification (Nvidia Digit) was employed in the experiments. It has shown efficiency in both training and testing. The results have shown that increasing the number of epochs leads to a higher accuracy rate, as expected. However, the convergence state is highly related to datasets. For the batch size evaluation, it has shown that a larger batch size slightly decreases the classification accuracy compared to a small batch size. For example, selecting the value 32 as the batch size on the RSCCN dataset achieves the accuracy rate of 90.34 % at the 11th epoch while decreasing the epoch value to one makes the accuracy rate drop to 74%. On the other extreme, setting an increased value of batch size to 200 decreases the accuracy rate at the 11th epoch is 86.5%, and 63% when using one epoch only. On the other hand, selecting the kernel size is loosely related to data set. From a practical point of view, the filter size 20 produces 70.4286%. The last performed image size experiment shows a dependency in the accuracy improvement. However, an expensive performance gain had been noticed. The represented conclusion opens the opportunities toward a better classification performance in various applications such as planetary remote sensing.

Keywords: CNNs, hyperparamters, remote sensing, land cover, land use

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Authors: N. D'Amico, E. Grossi, B. Colombo, F. Rigiroli, M. Buscema, D. Fazzini, G. Cornalba, S. Papa

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Purpose: To characterize, through a radiomic approach, the nature of nodules considered non-specific by expert radiologists, recognized in magnetic resonance mammography (MRm) with T1-weighted (T1w) sequences with paramagnetic contrast. Material and Methods: 47 cases out of 1200 undergoing MRm, in which the MRm assessment gave uncertain classification (non-specific nodules), were admitted to the study. The clinical outcome of the non-specific nodules was later found through follow-up or further exams (biopsy), finding 35 benign and 12 malignant. All MR Images were acquired at 1.5T, a first basal T1w sequence and then four T1w acquisitions after the paramagnetic contrast injection. After a manual segmentation of the lesions, done by a radiologist, and the extraction of 150 radiomic features (30 features per 5 subsequent times) a machine learning (ML) approach was used. An evolutionary algorithm (TWIST system based on KNN algorithm) was used to subdivide the dataset into training and validation test and to select features yielding the maximal amount of information. After this pre-processing, different machine learning systems were applied to develop a predictive model based on a training-testing crossover procedure. 10 cases with a benign nodule (follow-up older than 5 years) and 18 with an evident malignant tumor (clear malignant histological exam) were added to the dataset in order to allow the ML system to better learn from data. Results: NaiveBayes algorithm working on 79 features selected by a TWIST system, resulted to be the best performing ML system with a sensitivity of 96% and a specificity of 78% and a global accuracy of 87% (average values of two training-testing procedures ab-ba). The results showed that in the subset of 47 non-specific nodules, the algorithm predicted the outcome of 45 nodules which an expert radiologist could not identify. Conclusion: In this pilot study we identified a radiomic approach allowing ML systems to perform well in the diagnosis of a non-specific nodule at MR mammography. This algorithm could be a great support for the early diagnosis of malignant breast tumor, in the event the radiologist is not able to identify the kind of lesion and reduces the necessity for long follow-up. Clinical Relevance: This machine learning algorithm could be essential to support the radiologist in early diagnosis of non-specific nodules, in order to avoid strenuous follow-up and painful biopsy for the patient.

Keywords: breast, machine learning, MRI, radiomics

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Authors: Marinko Skare, Malgorzata Porada Rochon

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This paper study the link between energy justice and economic growth. The link between energy justice and growth has not been extensively studied. Here we study the impact and importance of energy justice, as a part of the energy transition process, on economic growth. Our study shows energy justice growth is an important determinant of economic growth and development that should be addressed at the industry and economic levels. We use panel data modeling and causality testing to research the empirical link between energy justice and economic growth. Industry and economy-level policies designed to support energy justice initiatives are beneficial to economic growth. Energy justice is a necessary condition for green growth and sustainability targets.

Keywords: energy justice, economic growth, panel data, energy transition

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Authors: Cherry Yieng Siang Ling, Joong Hee Lee, Myung Hwan Yun

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The quality of a product to be usable has become the basic requirement in consumer’s perspective while failing the requirement ends up the customer from not using the product. Identifying usability issues from analyzing quantitative and qualitative data collected from usability testing and evaluation activities aids in the process of product design, yet the lack of studies and researches regarding analysis methodologies in qualitative text data of usability field inhibits the potential of these data for more useful applications. While the possibility of analyzing qualitative text data found with the rapid development of data analysis studies such as natural language processing field in understanding human language in computer, and machine learning field in providing predictive model and clustering tool. Therefore, this research aims to study the application capability of text processing algorithm in analysis of qualitative text data collected from usability activities. This research utilized datasets collected from LG neckband headset usability experiment in which the datasets consist of headset survey text data, subject’s data and product physical data. In the analysis procedure, which integrated with the text-processing algorithm, the process includes training of comments onto vector space, labeling them with the subject and product physical feature data, and clustering to validate the result of comment vector clustering. The result shows 'volume and music control button' as the usability feature that matches best with the cluster of comment vectors where centroid comments of a cluster emphasized more on button positions, while centroid comments of the other cluster emphasized more on button interface issues. When volume and music control buttons are designed separately, the participant experienced less confusion, and thus, the comments mentioned only about the buttons' positions. While in the situation where the volume and music control buttons are designed as a single button, the participants experienced interface issues regarding the buttons such as operating methods of functions and confusion of functions' buttons. The relevance of the cluster centroid comments with the extracted feature explained the capability of text processing algorithms in analyzing qualitative text data from usability testing and evaluations.

Keywords: usability, qualitative data, text-processing algorithm, natural language processing

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Authors: David X. Dong, Qingming Zhang, Meng Lu

Abstract:

Nitrites enter waterways as runoff from croplands and are discharged from many industrial sites. Excessive nitrite inputs to water bodies lead to eutrophication. On-site rapid detection of nitrite is of increasing interest for managing fertilizer application and monitoring water source quality. Existing methods for detecting nitrites use spectrophotometry, ion chromatography, electrochemical sensors, ion-selective electrodes, chemiluminescence, and colorimetric methods. However, these methods either suffer from high cost or provide low measurement accuracy due to their poor selectivity to nitrites. Therefore, it is desired to develop an accurate and economical method to monitor nitrites in environments. We report a low-cost optical sensor, in conjunction with a machine learning (ML) approach to enable high-accuracy detection of nitrites in water sources. The sensor works under the principle of measuring molecular absorptions of nitrites at three narrowband wavelengths (295 nm, 310 nm, and 357 nm) in the ultraviolet (UV) region. These wavelengths are chosen because they have relatively high sensitivity to nitrites; low-cost light-emitting devices (LEDs) and photodetectors are also available at these wavelengths. A regression model is built, trained, and utilized to minimize cross-sensitivities of these wavelengths to the same analyte, thus achieving precise and reliable measurements with various interference ions. The measured absorbance data is input to the trained model that can provide nitrite concentration prediction for the sample. The sensor is built with i) a miniature quartz cuvette as the test cell that contains a liquid sample under test, ii) three low-cost UV LEDs placed on one side of the cell as light sources, with each LED providing a narrowband light, and iii) a photodetector with a built-in amplifier and an analog-to-digital converter placed on the other side of the test cell to measure the power of transmitted light. This simple optical design allows measuring the absorbance data of the sample at the three wavelengths. To train the regression model, absorbances of nitrite ions and their combination with various interference ions are first obtained at the three UV wavelengths using a conventional spectrophotometer. Then, the spectrophotometric data are inputs to different regression algorithm models for training and evaluating high-accuracy nitrite concentration prediction. Our experimental results show that the proposed approach enables instantaneous nitrite detection within several seconds. The sensor hardware costs about one hundred dollars, which is much cheaper than a commercial spectrophotometer. The ML algorithm helps to reduce the average relative errors to below 3.5% over a concentration range from 0.1 ppm to 100 ppm of nitrites. The sensor has been validated to measure nitrites at three sites in Ames, Iowa, USA. This work demonstrates an economical and effective approach to the rapid, reagent-free determination of nitrites with high accuracy. The integration of the low-cost optical sensor and ML data processing can find a wide range of applications in environmental monitoring and management.

Keywords: optical sensor, regression model, nitrites, water quality

Procedia PDF Downloads 60