Search results for: reaction wheels modeling

Authors: Maziar Ramezani, Thomas Neitzert, Timotius Pasang

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This paper aims at experimental and numerical investigation of springback behavior of sheet metals during L-bending process with emphasis on Stribeck-type friction modeling. The coefficient of friction in Stribeck curve depends on sliding velocity and contact pressure. The springback behavior of mild steel and aluminum alloy 6022-T4 sheets was studied experimentally and using numerical simulations with ABAQUS software with two types of friction model: Coulomb friction and Stribeck friction. The influence of forming speed on springback behavior was studied experimentally and numerically. The results showed that Stribeck-type friction model has better results in predicting springback in sheet metal forming. The FE prediction error for mild steel and 6022-T4 AA is 23.8%, 25.5% respectively, using Coulomb friction model and 11%, 13% respectively, using Stribeck friction model. These results show that Stribeck model is suitable for simulation of sheet metal forming especially at higher forming speed.

Keywords: friction, L-bending, springback, Stribeck curves

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Authors: Shivaji M. Sonawane, N. B. Chaure

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ZnTe is direct band gap, the P-type semiconductor with the high absorption coefficient of the order of 104cm-1 is suitable for solar cell development. It can be used as a low resistive ohmic contact to CdS/CdTe or tandem solar cell application. ZnTe and Cu-ZnTe thin film have been electrochemically synthesized on to fluorine-doped tin oxide coated glass substrates using three electrode systems containing Ag/AgCl, graphite and FTO as reference, counter and working electrode respectively were used to deposit the thin films. The aqueous electrolytic solution consist of 0.5M TeO2, 0.2M ZnSO4, and 0.1M Na3C6H5O7:2H2O, 0.1MC6H8O7:H2O and 0.1mMCuSO4 with PH 2.5 at room temperature was used. The reaction mechanism is studied in the cyclic voltammetry to identify the deposition potentials of ZnTe and Cu-ZnTe.The potential was optimized in the range -0,9 to -1,1 V. Vs Ag/AgCl reference electrode. The effect of deposition potential on the structural properties was studied by using X-ray diffraction. The X-ray diffraction result reveled cubic crystal structure of ZnTe with preferential (111) orientation with cubic structure. The surface morphology and film composition were analyzed by means of Scanning electron microscopy (SEM) and Energy Dispersive Analysis of X- Rays (EDAX). The optical absorption measurement has been analyzed for the band gap determination of deposited layers about 2.26 eV by UV-Visible spectroscopy. The drastic change in resistivity has been observed due to incorporation of copper probably due to the diffusion of Cu into grain boundaries.

Keywords: ohmic back contact, zinc telluride, electrodeposition, photovoltaic devices

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Authors: Sabrine Ammar, Mohamed Tahar Bhiri

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Automatic planning has a de facto standard language called Planning Domain Definition Language (PDDL) for describing planning problems. It aims to formalize the planning problems described by the concept of state space. PDDL-related dynamic analysis tools, namely planners and validators, are insufficient for verifying and validating PDDL descriptions. Indeed, these tools made it possible to detect errors a posteriori by means of test activity. In this paper, we recommend a formal approach coupling the two languages Event-B and PDDL, for automatic planning. Event-B is used for formal modeling by stepwise refinement with mathematical proofs of planning problems. Thus, this paper proposes a refinement strategy allowing to obtain reliable PDDL descriptions from an ultimate Event-B model correct by construction. The ultimate Event-B model, correct by construction which is supposed to be translatable into PDDL, is automatically translated into PDDL using our MDE Event-B2PDDL tool.

Keywords: code generation, event-b, PDDL, refinement strategy, translation rules

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Authors: Tachita Vlad-Bubulac, Ionela-Daniela Carja, Diana Serbezeanu, Corneliu Hamciuc, Vicente Javier Forrat Perez

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The present work describes the preparation of new organophosphorus compounds with high content of phosphorus followed by the incorporation of these compounds into epoxy resin systems in order to investigate the phosphorus effect in terms of thermal stability, flame-retardant and mechanical properties of modified epoxy resins. Thus, two new organophosphorus compounds have been synthesized and fully characterized. 6-Oxido-6H-dibenz[c,e][1,2]oxaphosphorinyl-phenylcarbinol has been prepared by the addition reaction of P–H group of 9,10-dihydro-9-oxa-10-phosphaphenanthrene-10-oxide to carbonyl group of benzaldehyde. By treating the phenylcarbinol derivative with POCl3 a new phosphorus compound was obtained, having a content of 12.227% P. The organophosphorus compounds have been purified by recrystallization while their chemical structures have been confirmed by melting point measurements, FTIR and HNMR spectroscopies. In the next step various flame-retardant epoxy resins with different content of phosphorus have been prepared starting from a commercial epoxy resin and using dicyandiamide (DICY) as a latent curing agent in the presence of an accelerator. Differential scanning calorimetry (DSC) has been applied to investigate the behavior and kinetics of curing process of thermosetting systems. The results showed that the best curing characteristic and glass transition temperature are obtained at a ratio of epoxy resin: DICY: accelerator equal to 94:5:1. The thermal stability of the phosphorus-containing epoxy resins was investigated by thermogravimetric analysis in nitrogen and air, DSC, SEM and LOI test measurements.

Keywords: epoxy resins, flame retardant properties, phosphorus-containing compounds, thermal stability

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Authors: Edwin Cartledge, Jack Clark, Mazaher Molaei-Chalchooghi

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The mixing in the crystallization step of active pharmaceutical ingredient manufacturers was studied via advanced modeling tools to enable a successful scale-up. A virtual representation of the vessel was created, and computational fluid dynamics were used to simulate multiphase flow and, thus, the mixing environment within this vessel. The study identified a significant dead zone in the vessel underneath the impeller and found that increasing the impeller speed and power did not improve the mixing. A series of sensitivity analyses found that to improve mixing, the vessel had to be redesigned, and found that optimal mixing could be obtained by adding two extra cylindrical baffles. The same two baffles from the simulated environment were then constructed and added to the process vessel. By identifying these potential issues before starting the manufacture and modifying the vessel to ensure good mixing, this study mitigated a failed crystallization and potential batch disposal, which could have resulted in a significant loss of high-value material.

Keywords: active pharmaceutical ingredient, baffles, computational fluid dynamics, mixing, modelling

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Authors: José Luis Carrillo-Medina, Roberto Latorre

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Experimental evidence has revealed that different living neural systems can sign their output signals with some specific neural signature. Although experimental and modeling results suggest that neural signatures can have an important role in the activity of neural networks in order to identify the source of the information or to contextualize a message, the functional meaning of these neural fingerprints is still unclear. The existence of cellular mechanisms to identify the origin of individual neural signals can be a powerful information processing strategy for the nervous system. We have recently built different models to study the ability of a neural network to process information based on the emission and recognition of specific neural fingerprints. In this paper we further analyze the features that can influence on the information processing ability of this kind of networks. In particular, we focus on the role that the duration of a refractory period in each neuron after emitting a signed message can play in the network collective dynamics.

Keywords: neural signature, neural fingerprint, processing based on signal identification, self-organizing neural network

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Authors: Palash Kumar Mollick, Leire Olazar, Laura Santamaria, Pablo Comendador, Gartzen Lopez, Martin Olazar

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Average calorific value of a mixure of waste plastic is approximately 38 MJ/kg. Present work aims to extract maximum possible energy from a mixure of waste plastic using a DC thermal plasma in a spouted bed reactor. Plasma pyrolysis and steam reforming process has shown a potential to generate hydrogen from plastic with much below of legal limit of producing dioxins and furans as the carcinogenic gases. A spouted bed pyrolysis rector can continuously process plastic beads to produce organic volatiles, which later react with steam in presence of catalyst to results in syngas. lasma being the fourth state of matter, can carry high impact electrons to favour the activation energy of any chemical reactions. Computational Fluid Dynamic (CFD) simulation using COMSOL Multiphysics software has been performed to evaluate performance of a plasma spouted bed reactor in producing contamination free hydrogen as a green energy from waste plastic beads. The simulation results will showcase a design of a plasma spouted bed reactor for converting plastic waste into green hydrogen in a single step process. The high temperature hydrodynamics of spouted bed with plastic beads and the corresponding temperature distribution inside the reaction chamber will be critically examined for it’s near future installation of demonstration plant.

Keywords: green hydrogen, plastic waste, synthetic gas, pyrolysis, steam reforming, spouted bed, reactor design, plasma, dc palsma, cfd simulation

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Authors: Abiodun Ladanu Ajala, Wuyi Oke

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Mathematical simulation and optimization models packaged within interactive computer programs provide a common way for planners and managers to predict the behaviour of any proposed water resources system design or management policy before it is implemented. Modeling presents a principal technique of predicting the behaviour of the proposed infrastructural designs or management policies. Models can be developed and used to help identify specific alternative plans that best meet those objectives. This study discusses various types of models, their development, architecture, data requirements, and applications in the field of engineering. It also outlines the advantages and limitations of each the optimization and simulation models presented. The techniques explored in this review include; dynamic programming, linear programming, fuzzy optimization, evolutionary algorithms and finally artificial intelligence techniques. Previous studies carried out using some of the techniques mentioned above were reviewed, and most of the results from different researches showed that indeed optimization and simulation provides viable alternatives and predictions which form a basis for decision making in building engineering structures and also in engineering planning and management.

Keywords: linear programming, mutation, optimization, simulation

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Authors: Vivian Guo, Yan Qu

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Online marketplaces are not only digital places where consumers buy and sell merchandise, and they are also destinations for brands to connect with real consumers at the moment when customers are in the shopping mindset. For many marketplaces, brands have been important partners through advertising. There can be, however, a risk of advertising impacting a consumer’s shopping journey if it hurts the use experience or takes the user away from the site. Both could lead to the loss of transaction revenue for the marketplace. In this paper, we present user-based methods for cannibalization control by selectively turning off ads to users who are likely to be cannibalized by ads subject to business objectives. We present ways of measuring cannibalization of advertising in the context of an online marketplace and propose novel ways of measuring cannibalization through purchase propensity and uplift modeling. A/B testing has shown that our methods can significantly improve user purchase and engagement metrics while operating within business objectives. To our knowledge, this is the first paper that addresses cannibalization mitigation at the user-level in the context of advertising.

Keywords: cannibalization, machine learning, online marketplace, revenue optimization, yield optimization

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Authors: Deba Kumar Sarma, Sanjib Kr. Rajbongshi

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Surface finish is an important product quality in machining. At first, experiments were carried out to investigate the effect of the cutting tool nose radius (considering 1mm and 0.65mm) in prediction of surface finish with process parameters of cutting speed, feed and depth of cut. For all possible cutting conditions, full factorial design was considered as two levels four parameters. Commercial Mild Steel bar and High Speed Steel (HSS) material were considered as work-piece and cutting tool material respectively. In order to obtain functional relationship between process parameters and surface roughness, neural network was used which was found to be capable for the prediction of surface roughness within a reasonable degree of accuracy. It was observed that tool nose radius of 1mm provides better surface finish in comparison to 0.65 mm. Also, it was observed that feed rate has a significant influence on surface finish.

Keywords: full factorial design, neural network, nose radius, surface finish

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Authors: Palwinder Singh, Munish Sandhir, Tejinder Singh

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The ordinary differential equations (ODE) represent a natural framework for mathematical modeling of many real-life situations in the field of engineering, control systems, physics, chemistry and astronomy etc. Such type of differential equations can be solved by analytical methods or by numerical methods. If the solution is calculated using analytical methods, it is done through calculus theories, and thus requires a longer time to solve. In this paper, we compare the numerical accuracy of the solutions given by the three main types of one-step initial value solvers: Taylor’s Series Method, Euler’s Method and Runge-Kutta Fourth Order Method (RK4). The comparison of accuracy is obtained through comparing the solutions of ordinary differential equation given by these three methods. Furthermore, to verify the accuracy; we compare these numerical solutions with the exact solutions.

Keywords: Ordinary differential equations (ODE), Taylor’s Series Method, Euler’s Method, Runge-Kutta Fourth Order Method

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Authors: Roshanak Afrakhteh, Abdolrasoul Salman Mahini, Mahdi Motagh, Hamidreza Kamyab

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Urban Heat Islands (UHIs) are distinctive urban areas characterized by densely populated central cores surrounded by less densely populated peripheral lands. These areas experience elevated temperatures, primarily due to impermeable surfaces and specific land use patterns. The consequences of these temperature variations are far-reaching, impacting the environment and society negatively, leading to increased energy consumption, air pollution, and public health concerns. This paper emphasizes the need for simplified approaches to comprehend UHI temperature dynamics and explains how urban development patterns contribute to land surface temperature variation. To illustrate this relationship, the study focuses on the Guilan Plain, utilizing techniques like principal component analysis and generalized additive models. The research centered on mapping land use and land surface temperature in the low-lying area of Guilan province. Satellite data from Landsat sensors for three different time periods (2002, 2012, and 2021) were employed. Using eCognition software, a spatial unit known as a "city block" was utilized through object-based analysis. The study also applied the normalized difference vegetation index (NDVI) method to estimate land surface radiance. Predictive variables for urban land surface temperature within residential city blocks were identified categorized as intrinsic (related to the block's structure) and neighboring (related to adjacent blocks) variables. Principal Component Analysis (PCA) was used to select significant variables, and a Generalized Additive Model (GAM) approach, implemented using R's mgcv package, modeled the relationship between urban land surface temperature and predictor variables.Notable findings included variations in urban temperature across different years attributed to environmental and climatic factors. Block size, shared boundary, mother polygon area, and perimeter-to-area ratio were identified as main variables for the generalized additive regression model. This model showed non-linear relationships, with block size, shared boundary, and mother polygon area positively correlated with temperature, while the perimeter-to-area ratio displayed a negative trend. The discussion highlights the challenges of predicting urban surface temperature and the significance of block size in determining urban temperature patterns. It also underscores the importance of spatial configuration and unit structure in shaping urban temperature patterns. In conclusion, this study contributes to the growing body of research on the connection between land use patterns and urban surface temperature. Block size, along with block dispersion and aggregation, emerged as key factors influencing urban surface temperature in residential areas. The proposed methodology enhances our understanding of parameter significance in shaping urban temperature patterns across various regions, particularly in Iran.

Keywords: urban heat island, land surface temperature, LST modeling, GAM, Gilan province

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Authors: Yan Lyu, Yiqun Pan, Zhizhong Huang

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In the design stage of a new building, the energy model of this building is often required for the analysis of the performance on energy efficiency. In practice, a certain degree of geometric simplification should be done in the establishment of building energy models, since the detailed geometric features of a real building are hard to be described perfectly in most energy simulation engine, such as ESP-r, eQuest or EnergyPlus. Actually, the detailed description is not necessary when the result with extremely high accuracy is not demanded. Therefore, this paper analyzed the relationship between the error of the simulation result from building energy models and the geometric simplification of the models. Finally, the following two parameters are selected as the indices to characterize the geometric feature of in building energy simulation: the southward projected area and total side surface area of the building, Based on the parameterization method, the simplification from an arbitrary column building to a typical shape (a cuboid) building can be made for energy modeling. The result in this study indicates that this simplification would only lead to the error that is less than 7% for those buildings with the ratio of southward projection length to total perimeter of the bottom of 0.25~0.35, which can cover most situations.

Keywords: building energy model, simulation, geometric simplification, design, regression

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Authors: Mayar A. Omar, Ismail Gomaa, Heba Badawy, Hosam Moubarak

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Enterprise resource planning (ERP) is an integrated system that helps companies in managing their resources. There are two types of ERP systems, traditional ERP systems and cloud ERP systems. Cloud ERP systems were introduced after the development of cloud computing technology. This research aims to identify the factors that affect the adoption of cloud ERP in Egyptian companies. Moreover, the aim of our study is to provide guidance to Egyptian companies in the cloud ERP adoption decision and to participate in increasing the number of cloud ERP studies that are conducted in the Middle East and in developing countries. There are many factors influencing the adoption of cloud ERP in Egyptian organizations, which are discussed and explained in the research. Those factors are examined by combining the diffusion of innovation theory (DOI) and technology-organization-environment framework (TOE). Data were collected through a survey that was developed using constructs from the existing studies of cloud computing and cloud ERP technologies and was then modified to fit our research. The analysis of the data was based on structural equation modeling (SEM) using Smart PLS software that was used for the empirical analysis of the research model.

Keywords: cloud computing, cloud ERP systems, DOI, Egypt, SEM, TOE

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Authors: Y. Moldes-Diz, M. Gamallo, G. Eibes, C. Vazquez-Vazquez, G. Feijoo, J. M. Lema, M. T. Moreira

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Laccases (benzenediol oxygen oxidoreductases, EC 1.10.3.2) are excellent biocatalysts for biotechnological and environmental applications because of their high activity, selectivity, and specificity. Specifically, these characteristics allow them to perform the oxidation of recalcitrant compounds with simple requirements for the catalysis (presence of molecular oxygen). Nevertheless, the low stability under unfavorable conditions (pH, inactivating agents or temperature) and high production costs still limits their use for practical applications. Immobilization of enzymes has proven particularly valuable to avoid some of the aforementioned drawbacks. Magnetic nanoparticles (MNPs) have received increasing attention as carriers for enzyme immobilization since they can potentially provide an easy recovery of the biocatalyst from the reaction medium under an external magnetic field. In the present work, silica-coated magnetic nanoparticles (Fe3O4@SiO2) were prepared, characterized and used for laccase immobilization by covalent binding. The synthesis of Fe3O4@SiO2 was performed in a two-step procedure: co-precipitation and reverse microemulsion. The influence of immobilization conditions: concentrations of the functionalization agent (3-aminopropyl-triethoxy-silane) and the cross-linker (glutaraldehyde) as well as the influence of pH, T or inactivating agents were evaluated. In general, immobilized laccase showed superior stability compared to that of free enzyme. The reusability of the biocatalyst was demonstrated in successive batch reactions, where enzyme activity was maintained above 65% after 8 cycles of oxidation of the substrate 2,2'-azinobis-(3-ethylbenzothiazoline-6-sulfonate).

Keywords: silica-coated magnetic nanoparticles, laccase, immobilization, regeneration

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Authors: Getachew Tilahun, Oluwole Makinde, David Malonza

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We propose and analyze a non-linear mathematical model for the transmission dynamics of pneumonia disease in a population of varying size. The deterministic compartmental model is studied using stability theory of differential equations. The effective reproduction number is obtained and also the local and global asymptotically stability conditions for the disease free and as well as for the endemic equilibria are established. The model exhibit a backward bifurcation and the sensitivity indices of the basic reproduction number to the key parameters are determined. Using Pontryagin’s maximum principle, the optimal control problem is formulated with three control strategies; namely disease prevention through education, treatment and screening. The cost effectiveness analysis of the adopted control strategies revealed that the combination of prevention and treatment is the most cost effective intervention strategies to combat the pneumonia pandemic. Numerical simulation is performed and pertinent results are displayed graphically.

Keywords: cost effectiveness analysis, optimal control, pneumonia dynamics, stability analysis, numerical simulation

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Authors: Yu-Lin Shen, Ming-Kuen Chang

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In addition to a considerable amount of machinery and equipment, intricacies of the transmission pipeline exist in Petrochemical plants. Long term corrosion may lead to pipeline thinning and rupture, causing serious safety concerns. With the advances in non-destructive testing technology, more rapid and long-range ultrasonic detection techniques are often used for pipeline inspection, EMAT without coupling to detect, it is a non-contact ultrasonic, suitable for detecting elevated temperature or roughened e surface of line. In this study, we prepared artificial defects in pipeline for Electromagnetic Acoustic Transducer Testing (EMAT) to survey the relationship between the defect location, sizing and the EMAT signal. It was found that the signal amplitude of EMAT exhibited greater signal attenuation with larger defect depth and length.. In addition, with bigger flat hole diameter, greater amplitude attenuation was obtained. In summary, signal amplitude attenuation of EMAT was affected by the defect depth, defect length and the hole diameter and size.

Keywords: EMAT, artificial defect, NDT, ultrasonic testing

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Authors: Kaito Sugane, Mitsuhiro Shibata

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Self-healing polymers have attracted attention because their physical damage and cracks can be effectively repaired, thereby extending the lifetime of the materials. Self-healing polymers using host-guest interaction have the advantage that they are quickly repaired under mild temperature conditions when compared with self-healing polymer using dynamic covalent bonds such as Diels-Alder (DA)/retro-DA and disulfide metathesis reactions. Especially, it is known that hydrogels utilizing the host-guest interaction between cyclodextrin and various guest molecules are repeatedly self-repaired at room temperature. However, most of the works deal with hydrogels, and little attention has been paid for thermosetting resins as polyurethane, epoxy and unsaturated polyester resins. In this study, polyetherurethane networks (PUN-CD-Ads) incorporating cyclodextrin and adamantane moieties were prepared by the crosslinking reactions of β-cyclodextrin (CD), 1-adamantanol (AdOH), glycerol ethoxylate (GCE) and hexamethylene diisocyanate (HDI), and thermal, mechanical and self-healing properties of the polymer network films were investigated. Our attention was focused on the influences of molar ratio of CD/AdOH, GCE/CD and OH/NCO on the properties. The FT-IR, and gel fraction analysis revealed that the urethanization reaction smoothly progress to form polyurethane networks. When two cut pieces of the films were contacted at the cross-section at room temperature for 30 seconds, the two pieces adhered to produce a self-healed film. Especially, the PUN-CD-Ad prepared at GCE/CD = 5/1, CD/AdOH = 1/1, and OH/NCO = 1/1 film exhibited the highest healing efficiency for tensile strength. Most of the PUN-CD-Ads were successfully self-healed at room temperature.

Keywords: host-guest interaction, network polymer, polyurethane, self-healing

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Authors: Fahad Alahmadi, Norhan Abd Rahman, Mohammad Abdulrazzak, Zulikifli Yusop

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Runoff prediction of ungauged catchment is still a challenging task especially in arid regions with a unique land cover such as volcanic basalt rocks where geological weathering and fractures are highly significant. In this study, Bathan catchment in Madinah western Saudi Arabia was selected for analysis. The aim of this paper is to evaluate different rainfall loss methods; soil conservation Services curve number (SCS-CN), green-ampt and initial-constant rate. Different direct runoff methods were evaluated: soil conservation services dimensionless unit hydrograph (SCS-UH), Snyder unit hydrograph and Clark unit hydrograph. The study showed the superiority of SCS-CN loss method and Clark unit hydrograph method for ungauged catchment where there is no observed runoff data.

Keywords: urban runoff modelling, arid regions, ungauged catchments, volcanic rocks, Madinah, Saudi Arabia

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Authors: Salem B. Abdalla

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This study investigated the current Building Information Modelling (BIM) trends and practices in the UAE, particularly to shed light on a recently circulated Dubai BIM mandate. Two sets of surveys were mailed to the AEC industry and the corresponding academic sector within the UAE to collect up-to-date data on BIM awareness and utilization. The surveys showed startling results concerning the academic sector in the UAE where almost 70% of respondents were not aware of the BIM mandate. Among the rest, even when aware, the majority of mechanical and electrical engineering schools felt that BIM is not pertinent to their discipline. Therefore, the response to offering BIM in their curriculum was substantially low (35%). On the other hand, the industrial survey identified a large majority (76.5%) of the AEC industry in the UAE are using BIM. The results clearly indicate that the academia should include BIM in their curriculum to produce qualified graduates to support the market. However, the academia is also faced with several obstacles to implement BIM in their curriculum, where the main pretext is that there is “no room for new courses in existing curriculum”.

Keywords: building information modeling, BIM adoption, UAE BIM industry survey, UAE BIM academia survey, Dubai BIM mandate, UK BIM mandate, BIM education, architecture education, engineering schools, BIM implementation, BIM curriculum

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Authors: Javad Abdi, Azam Famil Khalili

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Modeling emotions have attracted much attention in recent years, both in cognitive psychology and design of artificial systems. However, it is a negative factor in decision-making; emotions have shown to be a strong faculty for making fast satisfying decisions. In this paper, we have adapted a computational model based on the limbic system in the mammalian brain for control engineering applications. Learning in this model based on Temporal Difference (TD) Learning, we applied the proposed controller (termed BELBIC) for a simple model of a submarine. The model was supposed to reach the desired depth underwater. Our results demonstrate excellent control action, disturbance handling, and system parameter robustness for TDBELBIC. The proposal method, regarding the present conditions, the system action in the part and the controlling aims, can control the system in a way that these objectives are attained in the least amount of time and the best way.

Keywords: artificial neural networks, temporal difference, brain emotional learning based intelligent controller, heating- ventilating and air conditioning

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Authors: Nermin Mahmoud Gohar, Aisha Tarek Noour

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Learner satisfaction has been a vital field of interest in the literature. Accordingly, the paper will explore the reasons behind individual differences in motivation and satisfaction. This study examines the effect of both; Herzberg’s and Maslow’s theories on learners satisfaction. A self-administered questionnaire was used to collect data from learners who were geographically widely spread around the College of Maritime Transport and Technology (CMTT) at the Arab Academy for Science, Technology and Maritime Transport (AAST&MT) in Egypt. One hundred and fifty undergraduates responded to a questionnaire survey. Respondents were drawn from two branches in Alexandria and Port Said. The data analysis used was SPSS 22 and AMOS 18. Factor analysis technique was used to find out the dimensions under study verified by Herzberg’s and Maslow’s theories. In addition, regression analysis and structural equation modeling were applied to find the effect of the above-mentioned theories on maritime transport learners’ satisfaction. Concerning the limitation of this study, it used the available number of learners in the CMTT due to the relatively low population in this field.

Keywords: motivation, satisfaction, needs, education, Herzberg’s and Maslow’s theories

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Authors: A. A. Okafor, C. H. Achebe, J. L. Chukwuneke, C. G. Ozoegwu

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The performance of an engine whose basic design parameters are known can be predicted with the assistance of simulation programs into the less time, cost and near value of actual. This paper presents a comprehensive mathematical model of the performance parameters of four stroke spark ignition engine. The essence of this research work is to develop a mathematical model for the analysis of engine performance parameters of four stroke spark ignition engine before embarking on full scale construction, this will ensure that only optimal parameters are in the design and development of an engine and also allow to check and develop the design of the engine and it’s operation alternatives in an inexpensive way and less time, instead of using experimental method which requires costly research test beds. To achieve this, equations were derived which describe the performance parameters (sfc, thermal efficiency, mep and A/F). The equations were used to simulate and optimize the engine performance of the model for various engine speeds. The optimal values obtained for the developed bivariate mathematical models are: sfc is 0.2833kg/kwh, efficiency is 28.77% and a/f is 20.75.

Keywords: bivariate models, engine performance, injector engine, optimization, performance parameters, simulation, spark ignition

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Authors: E. Dehghan, R. Dehghan

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Generally, the slender structural walls have flexural behavior. Since behavior of bending members can be explained by moment–curvature relation, therefore, an analytical model is proposed based on moment–curvature relation for slender structural walls. The moment–curvature relationships of RC sections are constructed through section analysis. Governing equations describing the bond-slip behavior in walls are derived and applied to moment–curvature relations. For the purpose of removing the imprecision in analytical results, the plastic hinge length is included in the finite element modeling. Finally, correlation studies between analytical and experimental results are conducted with the objective to establish the validity of the proposed algorithms. The results show that bond-slip effect is more significant in walls subjected to larger axial compression load. Moreover, preferable results are obtained when ultimate strain of concrete is assumed conservatively.

Keywords: nonlinear analysis, slender structural walls, moment-curvature relation, bond-slip, plastic hinge length

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Authors: Rubens P. Costa, Marcelo A. Leigui de Oliveira

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The comprehension of features on the energy spectra, the chemical compositions, and the origins of Ultra-High Energy Cosmic Rays (UHECRs) - mainly atomic nuclei with energies above ~1.0 EeV (exa-electron volts) - are intrinsically linked to the problem of determining the magnitude of their deflections in cosmic magnetic fields on cosmological scales. In addition, as they propagate from the source to the observer, modifications are expected in their original energy spectra, anisotropy, and the chemical compositions due to interactions with low energy photons and matter. This means that any consistent interpretation of the nature and origin of UHECRs has to include the detailed knowledge of their propagation in a three-dimensional environment, taking into account the magnetic deflections and energy losses. The parameter space range for the magnetic fields in the universe is very large because the field strength and especially their orientation have big uncertainties. Particularly, the strength and morphology of the Extragalactic Magnetic Fields (EGMFs) remain largely unknown, because of the intrinsic difficulty of observing them. Monte Carlo simulations of charged particles traveling through a simulated magnetized universe is the straightforward way to study the influence of extragalactic magnetic fields on UHECRs propagation. However, this brings two major difficulties: an accurate numerical modeling of charged particles diffusion in magnetic fields, and an accurate numerical modeling of the magnetized Universe. Since magnetic fields do not cause energy losses, it is important to impose that the particle tracking method conserve the particle’s total energy and that the energy changes are results of the interactions with background photons only. Hence, special attention should be paid to computational effects. Additionally, because of the number of particles necessary to obtain a relevant statistical sample, the particle tracking method must be computationally efficient. In this work, we present an analysis of the propagation of ultra-high energy charged particles in the intergalactic medium. The EGMFs are considered to be coherent within cells of 1 Mpc (mega parsec) diameter, wherein they have uniform intensities of 1 nG (nano Gauss). Moreover, each cell has its field orientation randomly chosen, and a border region is defined such that at distances beyond 95% of the cell radius from the cell center smooth transitions have been applied in order to avoid discontinuities. The smooth transitions are simulated by weighting the magnetic field orientation by the particle's distance to the two nearby cells. The energy losses have been treated in the continuous approximation parameterizing the mean energy loss per unit path length by the energy loss length. We have shown, for a particle with the typical energy of interest the integration method performance in the relative error of Larmor radius, without energy losses and the relative error of energy. Additionally, we plotted the distance amplification from rectilinear propagation as a function of the traveled distance, particle's magnetic rigidity, without energy losses, and particle's energy, with energy losses, to study the influence of particle's species on these calculations. The results clearly show when it is necessary to use a full three-dimensional simulation.

Keywords: cosmic rays propagation, extragalactic magnetic fields, magnetic deflections, ultra-high energy

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Authors: S. O. Oyamakin, A. U. Chukwu,

Abstract:

We proposed a Hyperbolic Gompertz Growth Model (HGGM), which was developed by introducing a stabilizing parameter called θ using hyperbolic sine function into the classical gompertz growth equation. The resulting integral solution obtained deterministically was reprogrammed into a statistical model and used in modeling the height and diameter of Pines (Pinus caribaea). Its ability in model prediction was compared with the classical gompertz growth model, an approach which mimicked the natural variability of height/diameter increment with respect to age and therefore provides a more realistic height/diameter predictions using goodness of fit tests and model selection criteria. The Kolmogorov-Smirnov test and Shapiro-Wilk test was also used to test the compliance of the error term to normality assumptions while using testing the independence of the error term using the runs test. The mean function of top height/Dbh over age using the two models under study predicted closely the observed values of top height/Dbh in the hyperbolic gompertz growth models better than the source model (classical gompertz growth model) while the results of R2, Adj. R2, MSE, and AIC confirmed the predictive power of the Hyperbolic Monomolecular growth models over its source model.

Keywords: height, Dbh, forest, Pinus caribaea, hyperbolic, gompertz

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Authors: Mohieddine Benghersallah, Mohamed Zakaria Zahaf, Ali Medjber, Idriss Tibakh

Abstract:

The objective of this study is to analyse the effects of cutting parameters on surface roughness in dry face milling using statistical techniques. We studied the effect of the microstructure of AISI 52100 steel on machinability before and after hardening. The machining tests were carried out on a high rigidity vertical milling machine with a 25 mm diameter face milling cutter equipped with micro-grain bicarbide inserts with PVD (Ti, AlN) coating in GC1030 grade. A Taguchi L9 experiment plan is adopted. Analysis of variance (ANOVA) was used to determine the effects of cutting parameters (Vc, fz, ap) on the roughness (Ra) of the machined surface. Regression analysis to assess the machinability of steel presented mathematical models of roughness and the combination of parameters to minimize it. The recorded results show that feed per tooth has the most significant effect on the surface condition for both steel treatment conditions. The best roughnesses were obtained for the hardened AISI 52100 steel.

Keywords: machinability, heat treatment, microstructure, surface roughness, Taguchi method

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Authors: David F. Nettleton, Elodie Bugnicourt, Christian Wasiak, Alejandro Rosales

Abstract:

In this presentation, preliminary results are given for the modeling and calibration of two different industrial winding MIMO (Multiple Input Multiple Output) processes using machine learning techniques. In contrast to previous approaches which have typically used ‘black-box’ linear statistical methods together with a definition of the mechanical behavior of the process, we use non-linear machine learning algorithms together with a ‘white-box’ rule induction technique to create a supervised model of the fitting error between the expected and real force measures. The final objective is to build a precise model of the winding process in order to control de-tension of the material being wound in the first case, and the friction of the material passing through the die, in the second case. Case 1, Tension Control of a Winding Process. A plastic web is unwound from a first reel, goes over a traction reel and is rewound on a third reel. The objectives are: (i) to train a model to predict the web tension and (ii) calibration to find the input values which result in a given tension. Case 2, Friction Force Control of a Micro-Pullwinding Process. A core+resin passes through a first die, then two winding units wind an outer layer around the core, and a final pass through a second die. The objectives are: (i) to train a model to predict the friction on die2; (ii) calibration to find the input values which result in a given friction on die2. Different machine learning approaches are tested to build models, Kernel Ridge Regression, Support Vector Regression (with a Radial Basis Function Kernel) and MPART (Rule Induction with continuous value as output). As a previous step, the MPART rule induction algorithm was used to build an explicative model of the error (the difference between expected and real friction on die2). The modeling of the error behavior using explicative rules is used to help improve the overall process model. Once the models are built, the inputs are calibrated by generating Gaussian random numbers for each input (taking into account its mean and standard deviation) and comparing the output to a target (desired) output until a closest fit is found. The results of empirical testing show that a high precision is obtained for the trained models and for the calibration process. The learning step is the slowest part of the process (max. 5 minutes for this data), but this can be done offline just once. The calibration step is much faster and in under one minute obtained a precision error of less than 1x10-3 for both outputs. To summarize, in the present work two processes have been modeled and calibrated. A fast processing time and high precision has been achieved, which can be further improved by using heuristics to guide the Gaussian calibration. Error behavior has been modeled to help improve the overall process understanding. This has relevance for the quick optimal set up of many different industrial processes which use a pull-winding type process to manufacture fibre reinforced plastic parts. Acknowledgements to the Openmind project which is funded by Horizon 2020 European Union funding for Research & Innovation, Grant Agreement number 680820

Keywords: data model, machine learning, industrial winding, calibration

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Authors: Tseng-Lung Huang

Abstract:

Sensory breadth experiences, such as visualization, a sense of self-location, and haptic experiences, are critical in an online try-on environment. This research adopts an emotional appeal perspective, including concrete and abstract effects, to clarify the relationship between sensory experience and consumer's behavior intention in an online try-on context. This study employed an augmented reality interactive technology (ARIT) in an online clothes-fitting context and applied snowball sampling using e-mail to invite online consumers, first to use ARIT for trying on online apparel and then to complete a questionnaire. One hundred sixty-eight valid questionnaires were collected, and partial least squares (PLS) path modeling was used to test our hypotheses. The results showed that sensory breadth, by arousing concrete effect, induces impulse buying intention and willingness to pay a price premium of online shopping. Parasocial presence, as an abstract effect, diminishes the effect of concrete effects on willingness to pay a price premium.

Keywords: sensory breadth, impulsive behavior, price premium, emotional appeal, online try-on context

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Authors: Jiazhen Liao, Xiaolan Zeng

Abstract:

A series of BiOCl nanoplates with different oxygen vacancies (OVs) concentrations were successfully synthesized via a facile solvothermal method. The concentration of OVs of BiOCl can be tuned by the ratios of water/ethylene glycol. Such nanoplates containing oxygen vacancies served as an efficient visible-light-driven photocatalyst for NO oxidation. Compared with pure BiOCl, the enhanced photocatalytic performance was mainly attributed to the introduction of OVs, which greatly enhanced light absorption, promoted electron transfer, activated oxygen molecules. The present work could provide insights into the understanding of the role of OVs in photocatalysts for reference. Combined with characterization analysis, such as XRD（X-ray diffraction）, XPS（X-ray photoelectron spectroscopy), TEM（Transmission Electron Microscopy）, PL（Fluorescence Spectroscopy）, and DFT (Density Functional Theory) calculations, the effect of vacancies on photoelectrochemical properties of BiOCl photocatalysts are shown. Furthermore, the possible reaction mechanisms of photocatalytic NO oxidation were also revealed. According to the results of in situ DRIFTS （ Diffused Reflectance Infrared Fourier Transform Spectroscopy）, various intermediates were produced during different time intervals of NO photodegradation. The possible pathways are summarized below. First, visible light irradiation induces electron-hole pairs on the surface of OV-BOC (BiOCl with oxygen vacancies). Second, photogenerated electrons form superoxide radical with the contacted oxygen. Then, the NO molecules adsorbed on the surface of OV-BOC are attacked by superoxide radical and form nitrate instead of NO₂ (by-products). Oxygen vacancies greatly improve the photocatalytic oxidation activity of NO and effectively inhibit the production of harmful by-products during the oxidation of NO.

Keywords: OVs-BiOCl nanoplate, oxygen vacancies, NO oxidation, photocatalysis

Procedia PDF Downloads 127