Search results for: Neural Processing Element (NPE)

Authors: Phakamas Rachamontree, Theerawut Phusantisampan, Natthakorn Woravutthikul, Peerapong Pornwongthong, Malinee Sriariyanun

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A total of 115 yeast strains isolated from local cassava processing wastes were measured for crude protein content. Among these strains, the strain MSY-2 possessed the highest protein concentration (>3.5 mg protein/mL). By using molecular identification tools, it was identified to be a strain of Pichia kudriavzevii based on similarity of D1/D2 domain of 26S rDNA region. In this study, to optimize the protein production by MSY-2 strain, Response Surface Methodology (RSM) was applied. The tested parameters were the carbon content, nitrogen content, and incubation time. Here, the value of regression coefficient (R2) = 0.7194 could be explained by the model, which is high to support the significance of the model. Under the optimal condition, the protein content was produced up to 3.77 g per L of the culture and MSY-2 strain contain 66.8 g protein per 100 g of cell dry weight. These results revealed the plausibility of applying the novel strain of yeast in single-cell protein production.

Keywords: single cell protein, response surface methodology, yeast, cassava processing waste

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Authors: Pejman Daryabor, Kamal Mohammadi

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In the present work, the study of vibration characteristics of a functionally graded thick hollow cylinder is investigated. The cylinder natural frequencies are obtained using Galerkin finite element method. The functionally graded cylinder is assumed to be made from many subcylinders. Each subcylinder is considered as an isotropic layer. Material’s properties in each layer are constant and functionally graded properties result by exponential function of layer radius in multilayer cylinder. To validate the FE results code, plane strain model of functionally graded cylinder are also modeled in ABAQUS. Analytical results are validated for both models. Also, a good agreement is found between the present results and those reported in the literature.

Keywords: natural frequency, functionally graded material, finite element method, thick cylinder

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Authors: Raj Banerjee, Y. M. Parulekar, Aniruddha Sengupta, J. Chattopadhyay

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This study proposes a Discrete Element Method (DEM) simulation using a commercial software PFC 2D (2019) for quantitatively simulating the monotonic and cyclic behaviour of sand using irregular shapes of sand grains. A preliminary analysis of the number of particles for optimal Representative Element Volume (REV) simulation of dimension 35mm x 35mm x 70mm using the scaled Grain Size Distribution (GSD) of sand is carried out. Subsequently, the effect of particle shape on the performance of sand during monotonic and cyclic bi-axial tests is assessed using numerical simulation. The validation of the numerical simulation for one case is carried out using the test results from the literature. Further numerical studies are performed in which the particles in REV are simulated by mixing round discs with irregular clumps (100% round disc, 75% round disc 25% irregular clump, 50% round disc 50% irregular clump, 25% round disc 75% irregular clump, 100% irregular clump) in different proportions using Dry Deposition (DD) method. The macro response for monotonic loading shows that irregular sand has a higher strength than round particles and that the Mohr-Coulomb failure envelope depends on the shape of the grains. During cyclic loading, it is observed that the liquefaction resistance curve (Cyclic Stress Ratio (CSR)-Number of cycles (N)) of sand is dependent on the combination of particle shapes with different proportions.

Keywords: biaxial test, particle shape, monotonic, cyclic

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Authors: Derek Brandon G. Yu, Clyde Vincent O. Pilapil, Christine F. Peña

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College students of Cebu province have been indoors since March 2020, and a challenge encountered is the sudden shift from face to face to online learning and with the lack of empirical data on online learning on Higher Education Institutions (HEIs) in the Philippines. Sentiments on face to face and online learning will be collected from University of San Carlos (USC), School of Arts and Sciences (SAS) students regarding Mathematics in the Modern World (MMW), a General Education (GE) course. Natural Language Processing with machine learning algorithms will be used to classify the sentiments of the students. Results of the research study are the themes identified through topic modelling and the overall sentiments of the students in USC SAS

Keywords: natural language processing, online learning, sentiment analysis, topic modelling

Procedia PDF Downloads 239

Authors: Vinod Kumar, Arun Kumar, Surjit Angra

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This paper addresses the junction stress analysis of orthogonally intersecting thin walled cylindrical shell and thin walled cylindrical nozzle subjected to external loading on nozzle. Junction stresses have been calculated theoretically by welding research council (WRC) bulletins 107 and 297 for different nozzle loads. WRC bulletins 107 and 297 have been used by design engineers for calculating nozzle-vessel junction stresses since their publication. They give simple empirical relations and easy in application. Also 3D FEA in which material is elastic has been done in ANSYS software with 8 node solid element model and results of FEA have been compared with WRC results. Stress intensities obtained by WRC 297 are generally slightly higher than obtained by WRC 107. Membrane stresses obtained by FEA are much higher than WRC and membrane plus bending stresses obtained by FEA are lower than WRC.

Keywords: FEA, junction stress, solid element, WRC 107, WRC 297

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Authors: Junior Nomani

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This paper presents a novel approach to modelling the metal cutting of duplex stainless steels, a two-phase alloy regarded as a difficult-to-machine material. Calculation and control of shear strain and stresses during cutting are essential to achievement of ideal cutting conditions. Too low or too high leads to higher required cutting force or excessive heat generation causing premature tool wear failure. A 2D finite element cutting model was created based on electron backscatter diffraction (EBSD) data imagery of duplex microstructure. A mesh was generated using ‘object-oriented’ software OOF2 version V2.1.11, converting microstructural images to quadrilateral elements. A virtual workpiece was created on ABAQUS modelling software where a rigid body toolpiece advanced towards workpiece simulating chip formation, generating serrated edge chip formation cutting. Model results found calculated stress strain contour plots correlated well with similar finite element models tied with austenite stainless steel alloys. Virtual chip form profile is also similar compared experimental frozen machining chip samples. The output model data provides new insight description of strain behavior of two phase material on how it transitions from workpiece into the chip.

Keywords: Duplex stainless steel, ABAQUS, OOF2, Chip formation

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Authors: Muhammad Ali Sarwar, Muhammad Farooq, Nayab Hassan, Hammad Hassan

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Pakistan is highly recognized for its agriculture and is well known for producing substantial amounts of wheat, cotton, and sugarcane. However, some factors contribute to a decline in crop quality and a reduction in overall output. One of the main factors contributing to this decline is the presence of weed and its late detection. This process of detection is manual and demands a detailed inspection to be done by the farmer itself. But by the time detection of weed, the farmer will be able to save its cost and can increase the overall production. The focus of this research is to identify and classify the four main types of weeds (Small-Flowered Cranesbill, Chick Weed, Prickly Acacia, and Black-Grass) that are prevalent in our region’s major crops. In this work, we implemented three different deep learning techniques: YOLO-v5, Inception-v3, and Deep CNN on the same Dataset, and have concluded that deep convolutions neural network performed better with an accuracy of 97.45% for such classification. In relative to the state of the art, our proposed approach yields 2% better results. We devised the architecture in an efficient way such that it can be used in real-time.

Keywords: deep convolution networks, Yolo, machine learning, agriculture

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Authors: Margaret Ryan, Linda Cupples, Lyndsey Nickels, Paul Sowman

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The current investigation explored the thematic roles of the nouns specified in the lexical entries of experiencer verbs. While prior experimental research assumes experiencer and theme roles for both subject-experiencer (SE) and object-experiencer (OE) verbs, syntactic theorists have posited additional agent and causer roles. Experiment 1 provided evidence for an agent as participants assigned a high degree of intentionality to the logical subject of a subset of SE and OE actives and passives. Experiment 2 provided evidence for a causer as participants assigned high levels of causality to the logical subjects of experiencer sentences generally. However, the presence of an agent, but not a causer, coincided with processing ease. Causality may be an aspect rather than a thematic role. The varying thematic roles amongst experiencer-verb sentences have important implications for stimulus selection because we cannot presume processing is similar across differing sentence subtypes.

Keywords: sentence comprehension, lexicon, canonicity, processing, thematic roles, syntax

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Authors: Martins Y. Otache, John J. Musa, Abayomi I. Kuti, Mustapha Mohammed

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The performance of an artificial neural network (ANN) is contingent on a host of factors, for instance, the network optimisation scheme. In view of this, the study examined the general implications of the ANN training optimisation algorithm on its forecast performance. To this end, the Bayesian regularisation (Br), Levenberg-Marquardt (LM), and the adaptive learning gradient descent: GDM (with momentum) algorithms were employed under different ANN structural configurations: (1) single-hidden layer, and (2) double-hidden layer feedforward back propagation network. Results obtained revealed generally that the gradient descent with momentum (GDM) optimisation algorithm, with its adaptive learning capability, used a relatively shorter time in both training and validation phases as compared to the Levenberg- Marquardt (LM) and Bayesian Regularisation (Br) algorithms though learning may not be consummated; i.e., in all instances considering also the prediction of extreme flow conditions for 1-day and 5-day ahead, respectively especially using the ANN model. In specific statistical terms on the average, model performance efficiency using the coefficient of efficiency (CE) statistic were Br: 98%, 94%; LM: 98 %, 95 %, and GDM: 96 %, 96% respectively for training and validation phases. However, on the basis of relative error distribution statistics (MAE, MAPE, and MSRE), GDM performed better than the others overall. Based on the findings, it is imperative to state that the adoption of ANN for real-time forecasting should employ training algorithms that do not have computational overhead like the case of LM that requires the computation of the Hessian matrix, protracted time, and sensitivity to initial conditions; to this end, Br and other forms of the gradient descent with momentum should be adopted considering overall time expenditure and quality of the forecast as well as mitigation of network overfitting. On the whole, it is recommended that evaluation should consider implications of (i) data quality and quantity and (ii) transfer functions on the overall network forecast performance.

Keywords: streamflow, neural network, optimisation, algorithm

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Authors: Lusungu Ndovi

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Radio propagation from point-to-point is affected by the physical channel in many ways. A signal arriving at a destination travels through a number of different paths which are referred to as multi-paths. Research in this area of wireless communications has progressed well over the years with the research taking different angles of focus. By this is meant that some researchers focus on ways of reducing or eluding Multipath effects whilst others focus on ways of mitigating the effects of Multipath through compensation schemes. Baseband processing is seen as one field of signal processing that is cardinal to the advancement of software-defined radio technology. This has led to wide research into the carrying out certain algorithms at baseband. This paper considers compensating for Multipath for Frequency Modulated signals. The compensation process is carried out at Radio frequency (RF) and at Quadrature baseband (QBB) and the results are compared. Simulations are carried out using MatLab so as to show the benefits of working at lower QBB frequencies than at RF.

Keywords: quadrature baseband, qadio frequency, qultipath compensation, frequency qodulation, signal processing

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Authors: I. Gkolfinopoulos, N. Chijiwa

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To evaluate the remaining capacity in concrete tensioned members, it is important to accurately estimate damage in precast concrete tendons. In this research Johnson-Cook model and damage parameters of high-strength steel material were calculated by static and dynamic uniaxial tensile tests. Replication of experimental results was achieved through finite element analysis for both single 8-noded three-dimensional element as well as the full-scale dob-bone shaped model and relevant model parameters are proposed. Finally, simulation results in terms of strain and deformation were verified using digital image correlation analysis.

Keywords: DIC analysis, Johnson-Cook, quasi-static, dynamic, rupture, tendon

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Authors: Shreya Thusoo, Karan Modi, Ankit Kumar Jha, Rajesh Kumar

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Since the evolution of computational tools and simulation software, there has been considerable increase in research on Soil Structure Interaction (SSI) to decrease the computational time and increase accuracy in the results. To aid the designer with a proper understanding of the response of structure in different soil types, the presented paper compares the deformation, shear stress, acceleration and other parameters of multi-storey building for a specific input ground motion using Response-spectrum Analysis (RSA) method. The response of all the models of different heights have been compared in different soil types. Finite Element Simulation software, ANSYS, has been used for all the computational purposes. Overall, higher response is observed with SSI, while it increases with decreasing stiffness of soil.

Keywords: soil-structure interaction, response spectrum, analysis, finite element method, multi-storey buildings

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Authors: Maisarah Jaafar, Neil I. Ward

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Volcanic and geothermal activity can result in the release of arsenic (As), manganese (Mn), iron, selenium (Se), molybdenum (Mo) and uranium (U) into natural waters. Several studies have reported high levels of these elements in surface and groundwater in Argentina. The main focus has been on As associated with volcanic ash deposits. This study reports the trace element levels of groundwater from an agricultural region of south-eastern La Pampa and southern Buenos Aires provinces, Argentina which have reported high levels of human health problems (bone/teeth disorders, depression, arthritis, etc). Fifty-eight groundwater samples were collected from wells adjacent to Ruta 35 and an Agilent 7700x inductively coupled plasma mass spectrometer (ICP-MS) were used for total elemental analysis. Physicochemical analysis confirmed pH range of 7.05-8.84 and variable conductivity (988-3880 µS/cm) with total dissolved solid content of 502-1989 mg/l. The majority water samples are in an oxidizing environment (Eh= 45-146 mV). Total As levels ranged from (µg/l): 13.08 – 319.4 for La Pampa (LP) and 39.6 – 189.4 for Buenos Aires (BA); all above the WHO Guideline for Drinking Water, 10 µg/l As. Interestingly, Mo (LP: 1.85 – 85.39 µg/l; BA: 4.61– 55.55 µg/l;), Se (LP: 1.2 – 16.59 µg/l; BA: 0.3– 6.94 µg/l;) and U (LP: 1.85 – 85.39 µg/l; BA: 4.61– 55.55 µg/l;) levels are lower than reported values for northern La Pampa. Inter-elemental correlation displayed positive statistically significant between As-Mo, A-Se, As-U while negative statistically significant between As-Mn and As-Fe. This confirms that the source of the trace element is similar to that reported for other region of Argentina, namely volcanic ash deposition.

Keywords: Argentina, groundwater, trace element, volcanic activity

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Authors: P. Chaipanya, R. Keawchai, W. Sombatsanongkhun, S. Jantaramporn

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To manage the demand of wireless communication that has been dramatically increased, switched beam antenna in smart antenna system is focused. Implementation of switched beam antennas at mobile terminals such as notebook or mobile handset is a preferable choice to increase the performance of the wireless communication systems. This paper proposes the low complexity switched beam antenna using single element of antenna which is suitable to implement at mobile terminal. Main beam direction is switched by changing the positions of short circuit on the radiating patch. There are four cases of switching that provide four different directions of main beam. Moreover, the performance in terms of Signal to Interference Ratio when utilizing the proposed antenna is compared with the one using omni-directional antenna to confirm the performance improvable.

Keywords: switched beam, shorted circuit, single element, signal to interference ratio

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Authors: Yi F. Wu, Ai Q. Li, Hao Wang

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Taking a novel type of bridge bearings with the diameter being 100mm as an example, the theoretical analysis, the experimental research as well as the numerical simulation of the bearing were conducted. Since the normal compression-shear machines cannot be applied to the small-size bearing, an improved device to test the properties of the bearing was proposed and fabricated. Besides, the simulation of the bearing was conducted on the basis of the explicit finite element software ANSYS/LS-DYNA, and some parameters of the bearing are modified in the finite element model to effectively reduce the computation cost. Results show that all the research methods are capable of revealing the fundamental properties of the small-size bearings, and a combined use of these methods can better catch both the integral properties and the inner detailed mechanical behaviors of the bearing.

Keywords: ANSYS/LS-DYNA, compression shear, contact analysis, explicit algorithm, small-size

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Authors: Chen ChihYu, Tang Wen Tzu, Chen Kuang Hui

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Aim —To compare the dismount skill between Taiwanese and elite international gymnastics in parallel bars following the 2017-2020 code of points. Methods—The gymnasts who advanced to the parallel bars event finals of these four competitions including World Championships, Universiade, the National Games of Taiwan, and the National Intercollegiate Athletic Games of Taiwan both 2017 and 2019 were selected in this study. The dismount skill of parallel bars was analyzed, and the average difficulty score was compared by one-way ANOVA. Descriptive statistics were applied to present the type of dismount skill and the difficulty of each gymnast in these four competitions. The data from World Championships and Universiade were combined as the international group (INT), and data of Taiwanese National Games and National Intercollegiate Athletic Games were also combined as the national group (NAT). The differences between INT and NAT were analyzed by the Chi-square test. The statistical significance of this study was set at α= 0.05. Results— i) There was a significant difference in the mean parallel bars dismount skill in these four competitions analyzed by one-way ANOVA. Both dismount scores of World Championships and Universiade were significantly higher than in Taiwanese National Games and National Intercollegiate Athletic Games (0.58±0.08 & 0.56±0.08 > 0.42±0.06 & 40±0.06, p < 0.05). ii) Most of the gymnasts in World Championships and Universiade selected the 0.6-point skill as the parallel bars dismount element, and for the Taiwanese National Games and the National Intercollegiate Athletic Games, most of the gymnasts performed the 0.4-point dismount skill. iii) The result of the Chi-square test has shown that there was a significant difference in the selection of parallel bars dismount skill. The INT group used the E or E+ difficulty element as the dismount skill, and the NAT group selected the D or D- difficulty element. Conclusion— The level of parallel bars dismount in Taiwanese gymnastics is inferior to elite international gymnastics. It is suggested that Taiwanese gymnastics must try to practice the F difficulty dismount (double salto forward tucked with half twist) in the future.

Keywords: Artistic Gymnastics World Championships, dismount, difficulty score, element

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Authors: Duncan Wallace, M-Tahar Kechadi

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In recent years, Machine Learning (ML) approaches have been successfully applied to an analysis of patient symptom data in the context of disease diagnosis, at least where such data is well codified. However, much of the data present in Electronic Health Records (EHR) are unlikely to prove suitable for classic ML approaches. Furthermore, as scores of data are widely spread across both hospitals and individuals, a decentralized, computationally scalable methodology is a priority. The focus of this paper is to develop a method to predict outliers in an out-of-hours healthcare provision center (OOHC). In particular, our research is based upon the early identification of patients who have underlying conditions which will cause them to repeatedly require medical attention. OOHC act as an ad-hoc delivery of triage and treatment, where interactions occur without recourse to a full medical history of the patient in question. Medical histories, relating to patients contacting an OOHC, may reside in several distinct EHR systems in multiple hospitals or surgeries, which are unavailable to the OOHC in question. As such, although a local solution is optimal for this problem, it follows that the data under investigation is incomplete, heterogeneous, and comprised mostly of noisy textual notes compiled during routine OOHC activities. Through the use of Deep Learning methodologies, the aim of this paper is to provide the means to identify patient cases, upon initial contact, which are likely to relate to such outliers. To this end, we compare the performance of Long Short-Term Memory, Gated Recurrent Units, and combinations of both with Convolutional Neural Networks. A further aim of this paper is to elucidate the discovery of such outliers by examining the exact terms which provide a strong indication of positive and negative case entries. While free-text is the principal data extracted from EHRs for classification, EHRs also contain normalized features. Although the specific demographical features treated within our corpus are relatively limited in scope, we examine whether it is beneficial to include such features among the inputs to our neural network, or whether these features are more successfully exploited in conjunction with a different form of a classifier. In this section, we compare the performance of randomly generated regression trees and support vector machines and determine the extent to which our classification program can be improved upon by using either of these machine learning approaches in conjunction with the output of our Recurrent Neural Network application. The output of our neural network is also used to help determine the most significant lexemes present within the corpus for determining high-risk patients. By combining the confidence of our classification program in relation to lexemes within true positive and true negative cases, with an inverse document frequency of the lexemes related to these cases, we can determine what features act as the primary indicators of frequent-attender and non-frequent-attender cases, providing a human interpretable appreciation of how our program classifies cases.

Keywords: artificial neural networks, data-mining, machine learning, medical informatics

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Authors: Phakamani Xaba, Robert Huberts, Bilainu Oboirien

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The present study was aimed at developing & comparing two neural-network-based predictive models to predict shelf-life/product age of South African margarine using free fatty acid (FFA), water droplet size (D3.3), water droplet distribution (e-sigma), moisture content, peroxide value (PV), anisidine valve (AnV) and total oxidation (totox) value as input variables to the model. Brick margarine products which had varying ages ranging from fresh i.e. week 0 to week 47 were sourced. The brick margarine products which had been stored at 10 & 25 °C and were characterized. JMP and MATLAB models to predict shelf-life/ margarine age were developed and their performances were compared. The key performance indicators to evaluate the model performances were correlation coefficient (CC), root mean square error (RMSE), and mean absolute percentage error (MAPE) relative to the actual data. The MATLAB-developed model showed a better performance in all three performance indicators. The correlation coefficient of the MATLAB model was 99.86% versus 99.74% for the JMP model, the RMSE was 0.720 compared to 1.005 and the MAPE was 7.4% compared to 8.571%. The MATLAB model was selected to be the most accurate, and then, the number of hidden neurons/ nodes was optimized to develop a single predictive model. The optimized MATLAB with 10 neurons showed a better performance compared to the models with 1 & 5 hidden neurons. The developed models can be used by margarine manufacturers, food research institutions, researchers etc, to predict shelf-life/ margarine product age, optimize addition of antioxidants, extend shelf-life of products and proactively troubleshoot for problems related to changes which have an impact on shelf-life of margarine without conducting expensive trials.

Keywords: margarine shelf-life, predictive modelling, neural networks, oil oxidation

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Authors: Nuo Duan, Yi Pik Cheng

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This paper presents the data of a series of two-dimensional Discrete Element Method (DEM) simulations of a large-diameter rigid monopile subjected to cyclic loading under a high gravitational force. At present, monopile foundations are widely used to support the tall and heavy wind turbines, which are also subjected to significant from wind and wave actions. A safe design must address issues such as rotations and changes in soil stiffness subject to these loadings conditions. Design guidance on the issue is limited, so are the availability of laboratory and field test data. The interpretation of these results in sand, such as the relation between loading and displacement, relies mainly on empirical correlations to pile properties. Regarding numerical models, most data from Finite Element Method (FEM) can be found. They are not comprehensive, and most of the FEM results are sensitive to input parameters. The micro scale behaviour could change the mechanism of the soil-structure interaction. A DEM model was used in this paper to study the cyclic lateral loads behaviour. A non-dimensional framework is presented and applied to interpret the simulation results. The DEM data compares well with various set of published experimental centrifuge model test data in terms of lateral deflection. The accumulated permanent pile lateral displacements induced by the cyclic lateral loads were found to be dependent on the characteristics of the applied cyclic load, such as the extent of the loading magnitudes and directions.

Keywords: cyclic loading, DEM, numerical modelling, sands

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Authors: Meihua Fang, Tao Fei

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Post-processing can effectively improve the resistance to laser damage in multilayer films used in a high power laser system. In this work, HfO₂/SiO₂ multilayer films are prepared by e-beam evaporation and then β-ray irradiation is employed as the post-processing method. The particle irradiation affects the laser induced damage threshold (LIDT), which includes defects, surface roughness, packing density, and residual stress. The residual stress that is relaxed during irradiation changes from compressive stress into tensile stress. Our results indicate that appropriate tensile stress can improve LIDT remarkably. In view of the fact that LIDT rises from 8 J/cm² to 12 J/cm², i.e., 50% increase, after the film has been irradiated by 2.2×10¹³/cm² β-ray, the particle irradiation can be used as a controllable and desirable post-processing method to improve the resistance to laser induced damage.

Keywords: β-ray irradiation, multilayer film, residual stress, laser-induced damage threshold

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Authors: Benkouiten Imene, Chabli Ouerdia, Boutoutaou Hamid, Kadri Nesrine, Bouledroua Omar

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Algeria is actively enhancing gas productivity by augmenting the supply flow. However, this effort has led to increased internal pressure, posing a potential risk to the pipeline's integrity, particularly in the presence of corrosion defects. Sonatrach relies on a vast network of pipelines spanning 24,000 kilometers for the transportation of gas and oil. The aging of these pipelines raises the likelihood of corrosion both internally and externally, heightening the risk of ruptures. To address this issue, a comprehensive inspection is imperative, utilizing specialized scraping tools. These advanced tools furnish a detailed assessment of all pipeline defects. It is essential to recalculate the pressure parameters to safeguard the corroded pipeline's integrity while ensuring the continuity of production. In this context, Sonatrach employs symbolic pressure limit calculations, such as ASME B31G (2009) and the modified ASME B31G (2012). The aim of this study is to perform a comparative analysis of various limit pressure calculation methods documented in the literature, namely DNV RP F-101, SHELL, P-CORRC, NETTO, and CSA Z662. This comparative assessment will be based on a dataset comprising 329 burst tests published in the literature. Ultimately, we intend to introduce a novel approach grounded in the finite element method, employing ANSYS software.

Keywords: pipeline burst pressure, burst test, corrosion defect, corroded pipeline, finite element method

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Authors: Racha El Kadiri, Mohamed Sultan, Henrique Momm, Zachary Blair, Rachel Schultz, Tamer Al-Bayoumi

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The Mediterranean Basin is a very diverse region of nationalities and climate zones, with a strong dependence on agricultural activities. Predicting long term (with a lead of 1 to 12 months) rainfall, and future droughts could contribute in a sustainable management of water resources and economical activities. In this study, an integrated approach was adopted to construct predictive tools with lead times of 0 to 12 months to forecast rainfall amounts over nine subzones of the Mediterranean Basin region. The following steps were conducted: (1) acquire, assess and intercorrelate temporal remote sensing-based rainfall products (e.g. The CPC Merged Analysis of Precipitation [CMAP]) throughout the investigation period (1979 to 2016), (2) acquire and assess monthly values for all of the climatic indices influencing the regional and global climatic patterns (e.g., Northern Atlantic Oscillation [NOI], Southern Oscillation Index [SOI], and Tropical North Atlantic Index [TNA]); (3) delineate homogenous climatic regions and select nine pilot study zones, (4) apply data mining methods (e.g. neural networks, principal component analyses) to extract relationships between the observed rainfall and the controlling factors (i.e. climatic indices with multiple lead-time periods) and (5) use the constructed predictive tools to forecast monthly rainfall and dry and wet periods. Preliminary results indicate that rainfall and dry/wet periods were successfully predicted with lead zones of 0 to 12 months using the adopted methodology, and that the approach is more accurately applicable in the southern Mediterranean region.

Keywords: rainfall, neural networks, climatic indices, Mediterranean

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Authors: R. J. Kuo, W. C. Cheng, W. C. Lien, T. J. Yang

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Taiwan is a hyper endemic area for the Hepatitis B virus (HBV). The estimated total number of HBsAg carriers in the general population who are more than 20 years old is more than 3 million. Therefore, a case record review is conducted from January 2003 to June 2007 for all patients with a diagnosis of acute hepatitis who were admitted to the Emergency Department (ED) of a well-known teaching hospital. The cost for the use of medical resources is defined as the total medical fee. In this study, principal component analysis (PCA) is firstly employed to reduce the number of dimensions. Support vector regression (SVR) and artificial neural network (ANN) are then used to develop the forecasting model. A total of 117 patients meet the inclusion criteria. 61% patients involved in this study are hepatitis B related. The computational result shows that the proposed PCA-SVR model has superior performance than other compared algorithms. In conclusion, the Child-Pugh score and echogram can both be used to predict the cost of medical resources for patients with acute hepatitis in the ED.

Keywords: acute hepatitis, medical resource cost, artificial neural network, support vector regression

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Authors: Galal Omer, Onisimo Mutanga, Elfatih M. Abdel-Rahman, Elhadi Adam

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Threatened tree species (TTS) play a significant role in ecosystem functioning and services, land use dynamics, and other socio-economic aspects. Such aspects include ecological, economic, livelihood, security-based, and well-being benefits. The development of techniques for mapping and monitoring TTS is thus critical for understanding the functioning of ecosystems. The advent of advanced imaging systems and supervised learning algorithms has provided an opportunity to classify TTS over fragmenting landscape. Recently, vegetation maps have been produced using advanced imaging systems such as WorldView-2 (WV-2) and robust classification algorithms such as support vectors machines (SVM) and artificial neural network (ANN). However, delineation of TTS in a fragmenting landscape using high resolution imagery has widely remained elusive due to the complexity of the species structure and their distribution. Therefore, the objective of the current study was to examine the utility of the advanced WV-2 data for mapping TTS in the fragmenting Dukuduku indigenous forest of South Africa using SVM and ANN classification algorithms. The results showed the robustness of the two machine learning algorithms with an overall accuracy (OA) of 77.00% (total disagreement = 23.00%) for SVM and 75.00% (total disagreement = 25.00%) for ANN using all eight bands of WV-2 (8B). This study concludes that SVM and ANN classification algorithms with WV-2 8B have the potential to classify TTS in the Dukuduku indigenous forest. This study offers relatively accurate information that is important for forest managers to make informed decisions regarding management and conservation protocols of TTS.

Keywords: artificial neural network, threatened tree species, indigenous forest, support vector machines

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Authors: Maged Assem Soliman Mossallam

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Thermal vacuum testing target is to qualify the space system and ensure its operability under harsh space environment. The functionality of the cubesat was checked at extreme orbit conditions. Test was performed for operational and nonoperational modes. Analysis is done to simulate the cubesat thermal cycling inside thermal vacuum chamber. Comsol Multiphysics finite element is used to solve three dimensional problem for the cubesat inside TVAC. Three dimensional CAD model is done using Autodesk Inventor program. The boundary conditions were applied from the actual shroud temperature. The input heat load variation with time is considered to solve the transient three dimensional problem. Results show that the simulated temperature profiles are within an acceptable range from the real testing data.

Keywords: cubesat, thermal vacuum test, testing simulation, finite element analysis

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Authors: Fayçal Benyahia, Abdelmohsen Albedah, Bel Abbes Bachir Bouiadjra

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In this study the Finite element method is used to analyse the effect of the thermal residual stresses resulting from adhesive curing on the performances of the bonded composite repair in aircraft structures. The stress intensity factor at the crack tip is chosen as fracture criterion in order to estimate the repair performances. The obtained results show that the presence of the thermal residual stresses reduces considerably the repair performances and consequently decreases the fatigue life of cracked structures. The effects of the curing temperature, the adhesive properties and the adhesive thickness on the Stress Intensity Factor (SIF) variation with thermal stresses are also analysed.

Keywords: bonded composite repair, residual stress, adhesion, stress transfer, finite element analysis

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Authors: Aaron Aboshio

Abstract:

Numerical study of the static response of homogeneous clay stratum considering a wide range of cohesion and subject to foundation loads is presented. The linear elastic–perfectly plastic constitutive relation with the von Mises yield criterion were utilised to develop a numerically cost effective finite element model for the soil while imposing a rigid body constrain to the foundation footing. From the analyses carried out, estimate of the bearing capacity factor, Nc as well as the ultimate load-carrying capacities of these soils, effect of cohesion on foundation settlements, stress fields and failure propagation were obtained. These are consistent with other findings in the literature and hence can be a useful guide in design of safe foundations in clay soils for buildings and other structure.

Keywords: bearing capacity factors, finite element method, safe bearing pressure, structure-soil interaction

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Authors: Rilwan Kayode Apalowo, Dimitrios Chronopoulos

Abstract:

An approach for identifying the geometric and material characteristics of layered composite structures through an inverse wave and finite element methodology is proposed. These characteristics are obtained through multi-frequency single shot measurements. However, it is established that the frequency regime of the measurements does not matter, meaning that both ultrasonic and structural dynamics frequency spectra can be employed. Taking advantage of a full FE (finite elements) description of the periodic composite, the scheme is able to account for arbitrarily complex structures. In order to demonstrate the robustness of the presented scheme, it is applied to a sandwich composite panel and results are compared with that of experimental characterization techniques. Excellent agreement is obtained with the experimental measurements.

Keywords: structural identification, non-destructive evaluation, finite elements, wave propagation, layered structures, ultrasound

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Authors: Binger Lu

Abstract:

It remains unclear whether second language (L2) speakers could use discourse context cues to predict upcoming information as native speakers do during online comprehension. Some researchers propose that L2 learners may have a reduced ability to generate predictions during discourse processing. At the same time, there is evidence that discourse-level cues are weighed more heavily in L2 processing than in L1. Previous studies showed that L1 prediction is sensitive to the global validity of predictive cues. The current study aims to explore whether and to what extent L2 learners can dynamically and strategically adjust their prediction in accord with the global validity of predictive cues in L2 discourse comprehension as native speakers do. In a self-paced reading experiment, Chinese native speakers (N=128), C-E bilinguals (N=128), and English native speakers (N=128) read high-predictable (e.g., Jimmy felt thirsty after running. He wanted to get some water from the refrigerator.) and low-predictable (e.g., Jimmy felt sick this morning. He wanted to get some water from the refrigerator.) discourses in two-sentence frames. The global validity of predictive cues was manipulated by varying the ratio of predictable (e.g., Bill stood at the door. He opened it with the key.) and unpredictable fillers (e.g., Bill stood at the door. He opened it with the card.), such that across conditions, the predictability of the final word of the fillers ranged from 100% to 0%. The dependent variable was reading time on the critical region (the target word and the following word), analyzed with linear mixed-effects models in R. C-E bilinguals showed reliable prediction across all validity conditions (β = -35.6 ms, SE = 7.74, t = -4.601, p< .001), and Chinese native speakers showed significant effect (β = -93.5 ms, SE = 7.82, t = -11.956, p< .001) in two of the four validity conditions (namely, the High-validity and MedLow conditions, where fillers ended with predictable words in 100% and 25% cases respectively), whereas English native speakers didn’t predict at all (β = -2.78 ms, SE = 7.60, t = -.365, p = .715). There was neither main effect (χ^²(3) = .256, p = .968) nor interaction (Predictability: Background: Validity, χ^²(3) = 1.229, p = .746; Predictability: Validity, χ^²(3) = 2.520, p = .472; Background: Validity, χ^²(3) = 1.281, p = .734) of Validity with speaker groups. The results suggest that prediction occurs in L2 discourse processing but to a much less extent in L1, witha significant effect in some conditions of L1 Chinese and anull effect in L1 English processing, consistent with the view that L2 speakers are more sensitive to discourse cues compared with L1 speakers. Additionally, the pattern of L1 and L2 predictive processing was not affected by the global validity of predictive cues. C-E bilinguals’ predictive processing could be partly transferred from their L1, as prior research showed that discourse information played a more significant role in L1 Chinese processing.

Keywords: bilingualism, discourse processing, global validity, prediction, self-paced reading

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Authors: Pilar Rey del Castillo

Abstract:

This document describes the building of an average indicator of the general sentiments about the future exposed in the newspapers in Spain. The raw data are collected through the scraping of the Digital Periodical and Newspaper Library website. Basic tools of natural language processing are later applied to the collected information to evaluate the sentiment strength of each word in the texts using a polarized dictionary. The last step consists of summarizing these sentiments to produce daily indices. The results are a first insight into the applicability of these techniques to produce periodic sentiment indicators.

Keywords: natural language processing, periodic indicator, sentiment analysis, web scraping

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