Search results for: reaction wheels modeling

Authors: Xiaohua Zhu, Lingling Ma, Yongguang Zhao

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Leaf Area Index (LAI) is a key structural characteristic of crops and plays a significant role in precision agricultural management and farmland ecosystem modeling. However, LAI retrieved from different resolution data contain a scaling bias due to the spatial heterogeneity and model non-linearity, that is, there is scale effect during multi-scale LAI estimate. In this article, a typical farmland in semi-arid regions of Chinese Inner Mongolia is taken as the study area, based on the combination of PROSPECT model and SAIL model, a multiple dimensional Look-Up-Table (LUT) is generated for multiple crops LAI estimation from unmanned aerial vehicle (UAV) hyperspectral data. Based on Taylor expansion method and computational geometry model, a scale transfer model considering both difference between inter- and intra-class is constructed for scale effect analysis of LAI inversion over inhomogeneous surface. The results indicate that, (1) the LUT method based on classification and parameter sensitive analysis is useful for LAI retrieval of corn, potato, sunflower and melon on the typical farmland, with correlation coefficient R2 of 0.82 and root mean square error RMSE of 0.43m2/m-2. (2) The scale effect of LAI is becoming obvious with the decrease of image resolution, and maximum scale bias is more than 45%. (3) The scale effect of inter-classes is higher than that of intra-class, which can be corrected efficiently by the scale transfer model established based Taylor expansion and Computational geometry. After corrected, the maximum scale bias can be reduced to 1.2%.

Keywords: leaf area index (LAI), scale effect, UAV-based hyperspectral data, look-up-table (LUT), remote sensing

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Authors: Yohannes Bisa Biramo

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This study examined the meditational role of surface acting and deep acting on the relationship between calling orientation and work engagement of teachers in secondary schools of Wolaita Zone, Wolaita, Ethiopia. A predictive non-experimental correlational design was performed among 300 secondary school teachers. Stratified random sampling followed by a systematic random sampling technique was used as the basis for selecting samples from the target population. To analyze the data, Structural Equation Modeling (SEM) was used to test the association between the independent variables and the dependent variables. Furthermore, the goodness of fit of the study variables was tested using SEM to see and explain the path influence of the independent variable on the dependent variable. Confirmatory factor analysis (CFA) was conducted to test the validity of the scales in the study and to assess the measurement model fit indices. The analysis result revealed that calling was significantly and positively correlated with surface acting, deep acting and work engagement. Similarly, surface acting was significantly and positively correlated with deep acting and work engagement. And also, deep acting was significantly and positively correlated with work engagement. With respect to mediation analysis, the result revealed that surface acting mediated the relationship between calling and work engagement and also deep acting mediated the relationship between calling and work engagement. Besides, by using the model of the present study, the school leaders and practitioners can identify a core area to be considered in recruiting and letting teachers teach, in giving induction training for newly employed teachers and in performance appraisal.

Keywords: calling, surface acting, deep acting, work engagement, mediation, teachers

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Authors: Luis C. Parra

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The significant wave height prediction is an issue of great interest in the field of coastal activities because of the non-linear behavior of the wave height and its complexity of prediction. This study aims to present a machine learning model to forecast the significant wave height of the oceanographic wave measuring buoys anchored at Mooloolaba of the Queensland Government Data. Modeling was performed by a multilayer perceptron neural network-genetic algorithm (GA-MLP), considering Relu(x) as the activation function of the MLPNN. The GA is in charge of optimized the MLPNN hyperparameters (learning rate, hidden layers, neurons, and activation functions) and wrapper feature selection for the window width size. Results are assessed using Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The GAMLPNN algorithm was performed with a population size of thirty individuals for eight generations for the prediction optimization of 5 steps forward, obtaining a performance evaluation of 0.00104 MSE, 0.03222 RMSE, 0.02338 MAE, and 0.71163% of MAPE. The results of the analysis suggest that the MLPNNGA model is effective in predicting significant wave height in a one-step forecast with distant time windows, presenting 0.00014 MSE, 0.01180 RMSE, 0.00912 MAE, and 0.52500% of MAPE with 0.99940 of correlation factor. The GA-MLP algorithm was compared with the ARIMA forecasting model, presenting better performance criteria in all performance criteria, validating the potential of this algorithm.

Keywords: significant wave height, machine learning optimization, multilayer perceptron neural networks, evolutionary algorithms

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Authors: Can Huang, Xiaoliang Wang, Qingquan Liu

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Soil‒water coupling is an important process in landslide-generated impulse waves (LGIW) problems, accompanied by large deformation of soil, strong interface coupling and three-dimensional effect. A meshless particle method, smooth particle hydrodynamics (SPH) has great advantages in dealing with complex interface and multiphase coupling problems. This study presents an improved soil‒water coupled model to simulate LGIW problems based on an open source code DualSPHysics (v4.0). Aiming to solve the low efficiency problem in modeling real large-scale LGIW problems, graphics processing unit (GPU) acceleration technology is implemented into this code. An experimental example, subaerial landslide-generated water waves, is simulated to demonstrate the accuracy of this model. Then, the Huangtian LGIW, a real large-scale LGIW problem is modeled to reproduce the entire disaster chain, including landslide dynamics, fluid‒solid interaction, and surge wave generation. The convergence analysis shows that a particle distance of 5.0 m can provide a converged landslide deposit and surge wave for this example. Numerical simulation results are in good agreement with the limited field survey data. The application example of the Huangtian LGIW provides a typical reference for large-scale LGIW assessments, which can provide reliable information on landslide dynamics, interface coupling behavior, and surge wave characteristics.

Keywords: soil‒water coupling, landslide-generated impulse wave, large-scale, SPH

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Authors: Hamdi Beji, Toufik Kanit, Tanguy Messager

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This study aims to construct a predictive model proficient in foreseeing the linear elastic and thermal characteristics of composite materials, drawing on a multitude of influencing parameters. These parameters encompass the shape of inclusions (circular, elliptical, square, triangle), their spatial coordinates within the matrix, orientation, volume fraction (ranging from 0.05 to 0.4), and variations in contrast (spanning from 10 to 200). A variety of machine learning techniques are deployed, including decision trees, random forests, support vector machines, k-nearest neighbors, and an artificial neural network (ANN), to facilitate this predictive model. Moreover, this research goes beyond the predictive aspect by delving into an inverse analysis using genetic algorithms. The intent is to unveil the intrinsic characteristics of composite materials by evaluating their thermomechanical responses. The foundation of this research lies in the establishment of a comprehensive database that accounts for the array of input parameters mentioned earlier. This database, enriched with this diversity of input variables, serves as a bedrock for the creation of machine learning and genetic algorithm-based models. These models are meticulously trained to not only predict but also elucidate the mechanical and thermal conduct of composite materials. Remarkably, the coupling of machine learning and genetic algorithms has proven highly effective, yielding predictions with remarkable accuracy, boasting scores ranging between 0.97 and 0.99. This achievement marks a significant breakthrough, demonstrating the potential of this innovative approach in the field of materials engineering.

Keywords: machine learning, composite materials, genetic algorithms, mechanical and thermal proprieties

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Authors: Ljiljana Stojković, Manja Zec, Maja Zivkovic, Maja Bundalo, Marija Glibetić, Dragan Alavantić, Aleksandra Stankovic

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Cardiovascular disease (CVD) is associated with alterations in DNA methylation, the latter modulated by dietary polyphenols. The present pilot study (part of the original clinical study registered as NCT02800967 at www.clinicaltrials.gov) aimed to investigate the impact of 4-week daily consumption of polyphenol-rich Aronia melanocarpa juice on Long Interspersed Nucleotide Element-1 (LINE-1) methylation in peripheral blood leukocytes, in subjects (n=34, age of 41.1±6.6 years) at moderate CVD risk, including an increased body mass index, central obesity, high normal blood pressure and/or dyslipidemia. The goal was also to examine whether factors known to affect DNA methylation, such as folate intake levels, MTHFR C677T gene variant, as well as the anthropometric and metabolic parameters, modulated the LINE-1 methylation levels upon consumption of polyphenol-rich Aronia juice. The experimental analysis of LINE-1 methylation was done by the MethyLight method. MTHFR C677T genotypes were determined by the polymerase chain reaction-restriction fragment length polymorphism method. Folate intake was assessed by processing the data from the food frequency questionnaire and repeated 24-hour dietary recalls. Serum lipid profile was determined by using Roche Diagnostics kits. The statistical analyses were performed using the Statistica software package. In women, after vs. before the treatment period, a significant decrease in LINE-1 methylation levels was observed (97.54±1.50% vs. 98.39±0.86%, respectively; P=0.01). The change (after vs. before treatment) in LINE-1 methylation correlated directly with MTHFR 677T allele presence, average daily folate intake and the change in serum low-density lipoprotein cholesterol, while inversely with the change in serum triacylglycerols (R=0.72, R2=0.52, adjusted R2=0.36, P=0.03). The current results imply potential cardioprotective effects of habitual polyphenol-rich Aronia juice consumption achieved through the modifications of DNA methylation pattern in subjects at CVD risk, which should be further confirmed. Hence, the precision nutrition-driven modulations of DNA methylation may become targets for new approaches in the prevention and treatment of CVD.

Keywords: Aronia melanocarpa, cardiovascular risk, LINE-1, methylation, peripheral blood leukocytes, polyphenol

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Authors: Lethukuthula Ngobese, Abin Gupthar, Patrick Govender

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The ability of yeast cell wall-derived mannoproteins (glycoproteins) to positively contribute to oenological properties has been a key factor that stimulates research initiatives into these industrially important glycoproteins. In addition, and from a fundamental research perspective, yeast cell wall glycoproteins are involved in a wide range of biological interactions. To date, and to the best of our knowledge, our understanding of the fine molecular structure of these mannoproteins is fairly limited. Generally, the amino acid sequences of their protein moieties have been established from structural and functional analysis of the genomic sequence of these yeasts whilst far less information is available on the glycosyl moieties of these mannoproteins. A novel strategy was devised in this study that entails the genetic engineering of yeast strains that over-express and release cell wall-associated glycoproteins into the liquid growth medium. To this end, the Flo1p mannoprotein was overexpressed in Saccharomyces cerevisiae laboratory strains bearing a specific deletion in KNR4 and GPI7 genes involved in cell wall biosynthesis that have been previously shown to extracellularly hyper-secrete cell wall-associated glycoproteins. A polymerase chain reaction (PCR) -based cloning strategy was employed to generate transgenic yeast strains in which the native cell wall FLO1 glycoprotein-encoding gene is brought under transcriptional control of the constitutive PGK1 promoter. The modified Helm’s flocculation assay was employed to assess flocculation intensities of a Flo1p over-expressing wild type and deletion mutant as an indirect measure of their abilities to release the desired mannoprotein. The flocculation intensities of the transformed strains were assessed and all the strains showed similar intensities (>98% flocculation). To assess if mannoproteins were released into the growth medium, the supernatant of each strain was subjected to the BCA protein assay and the transformed Δknr4 strain showed a considerable increase in protein levels. This study has the potential to produce mannoproteins in sufficient quantities that may be employed in future investigations to understand their molecular structures and mechanisms of interaction to the benefit of both fundamental and industrial applications.

Keywords: glycoproteins, genetic engineering, flocculation, over-expression

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Authors: Juan Huang, Hugo Ninanya

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Oil has been used as a source of energy and supply to make materials, such as asphalt or rubber for many years. This is the reason why new technologies have been implemented through time. However, research still needs to continue increasing due to new challenges engineers face every day, just like unconventional reservoirs. Various numerical methodologies have been applied in petroleum engineering as tools in order to optimize the production of reservoirs before drilling a wellbore, although not all of these have the same efficiency when talking about studying fracture propagation. Analytical methods like those based on linear elastic fractures mechanics fail to give a reasonable prediction when simulating fracture propagation in ductile materials whereas numerical methods based on the cohesive zone method (CZM) allow to represent the elastoplastic behavior in a reservoir based on a constitutive model; therefore, predictions in terms of displacements and pressure will be more reliable. In this work, a hydro-geomechanical coupled model of horizontal wells in fractured rock was developed using ABAQUS; both extended element method and cohesive elements were used to represent predefined fractures in a model (2-D). A power law for representing the rheological behavior of fluid (shear-thinning, power index <1) through fractures and leak-off rate permeating to the matrix was considered. Results have been showed in terms of aperture and length of the fracture, pressure within fracture and fluid loss. It was showed a high infiltration rate to the matrix as power index decreases. A sensitivity analysis is conclusively performed to identify the most influential factor of fluid loss.

Keywords: fracture, hydro-geomechanical model, non-Newtonian fluid, numerical analysis, sensitivity analysis

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Authors: Armin Eilaghi, Ahmad Sedaghat, Hayder Abdurazzak, Fadi Alkhatib, Shiva Sadeghi, Martin Jaeger

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A summary of experiences, recommendations, and lessons learnt in the application of PBL in the course of “Engineering Skills” in the School of Engineering at Australian College of Kuwait in Kuwait is presented. Four projects were introduced as part of the PBL course “Engineering Skills” to 24 students in School of Engineering. These students were grouped in 6 teams to develop their skills in 10 learning outcomes. The learning outcomes targeted skills such as drawing, design, modeling, manufacturing and analysis at a preliminary level; and also some life line learning and teamwork skills as these students were exposed for the first time to the PBL (project based learning). The students were assessed for 10 learning outcomes of the course and students’ feedback was collected using an anonymous survey at the end of the course. Analyzing the students’ feedbacks, it is observed that 67% of students preferred multiple smaller projects than a single big project because it provided them with more time and attention focus to improve their “soft skills” including project management, risk assessment, and failure analysis. Moreover, it is found that 63% of students preferred to work with different team members during the course to improve their professional communication skills. Among all, 62% of students believed that working with team members from other departments helped them to increase the innovative aspect of projects and improved their overall performance. However, 70% of students counted extra time needed to regenerate momentum with the new teams as the major challenge. Project based learning provided a suitable platform for introducing students to professional engineering practice and meeting the needs of students, employers and educators. It was found that students achieved their 10 learning outcomes and gained new skills developed in this PBL unit. This was reflected in their portfolios and assessment survey.

Keywords: project-based learning, engineering skills, undergraduate engineering, problem-based learning

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Authors: Riad Benelmir, Muhammad Shoaib Ahmed Khan

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The socio-economic importance of the olive oil production is significant in the Mediterranean region, both in terms of wealth and tradition. However, the extraction of olive oil generates huge quantities of wastes that may have a great impact on land and water environment because of their high phytotoxicity. Especially olive mill wastewater (OMWW) is one of the major environmental pollutants in olive oil industry. This work projects to design a smart and sustainable integrated thermochemical catalytic processes of residues from olive mills by hydrothermal carbonization (HTC) of olive mill wastewater (OMWW) and fast pyrolysis of olive mill wastewater sludge (OMWS). The byproducts resulting from OMWW-HTC treatment are a solid phase enriched in carbon, called biochar and a liquid phase (residual water with less dissolved organic and phenolic compounds). HTC biochar can be tested as a fuel in combustion systems and will also be utilized in high-value applications, such as soil bio-fertilizer and as catalyst or/and catalyst support. The HTC residual water is characterized, treated and used in soil irrigation since the organic and the toxic compounds will be reduced under the permitted limits. This project’s concept includes also the conversion of OMWS to a green diesel through a catalytic pyrolysis process. The green diesel is then used as biofuel in an internal combustion engine (IC-Engine) for automotive application to be used for clean transportation. In this work, a theoretical study is considered for the use of heat from the pyrolysis non-condensable gases in a sorption-refrigeration machine for pyrolysis gases cooling and condensation of bio-oil vapors.

Keywords: biomass, olive oil extraction, adsorption cooling, pyrolisis

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Authors: Muhammad Umar Kiani, Muhammad Shahbaz

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Performance of any vehicle can be predicted by its design/modeling and optimization. Design optimization leads to efficient performance. Followed by horizontal launch, the air launch re-entry vehicle undergoes a launch maneuver by introducing a carefully selected angle of attack profile. This angle of attack profile is the basic element to complete a specified mission. Flight program of said vehicle is optimized under the constraints of the maximum allowed angle of attack, lateral and axial loads and with the objective of reaching maximum altitude. The main focus of this study is the endo-atmospheric phase of the ascent trajectory. A three degrees of freedom trajectory model is simulated in MATLAB. The optimization process uses evolutionary algorithm, because of its robustness and efficient capacity to explore the design space in search of the global optimum. Evolutionary Algorithm based trajectory optimization also offers the added benefit of being a generalized method that may work with continuous, discontinuous, linear, and non-linear performance matrix. It also eliminates the requirement of a starting solution. Optimization is particularly beneficial to achieve maximum advantage without increasing the computational cost and affecting the output of the system. For the case of launch vehicles we are immensely anxious to achieve maximum performance and efficiency under different constraints. In a launch vehicle, flight program means the prescribed variation of vehicle pitching angle during the flight which has substantial influence reachable altitude and accuracy of orbit insertion and aerodynamic loading. Results reveal that the angle of attack profile significantly affects the performance of the vehicle.

Keywords: endo-atmospheric, evolutionary algorithm, efficient performance, optimization process

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Authors: D. Nagalakshmi, S. Parashuramulu K. Sadasiva Rao, G. Aruna, L. Vikram

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The zinc (Zn) is the second most abundant trace element in mammals and birds, forming structural component of over 300 enzymes, playing an important role in anti-oxidant defense, immune response and reproduction. Organic trace minerals are more readily absorbed from the digestive tract and more biologically available compared with its inorganic salt. Thus, the present study was undertaken on 60 adult female Sprague Dawley rats (275±2.04 g) for experimental duration of 12 weeks to investigate the effect of dietary Zn supplementation from various organic sources on immunity, reproduction, oxidative defense mechanism and blood biochemical profile. The rats were randomly allotted to 30 replicates (2 per replicate) which were in turn randomly allotted to 5 dietary treatments varying in Zn source i.e., one inorganic source (Zn carbonate) and 4 organic sources (Zn-proteinate, Zn-propionate, Zn-amino acid complex and Zn-methionine) so as to supply NRC recommended Zn concentration (12 ppm Zn). Supplementation of organic Zn had no effect on various haematological and serum biochemical constituents compared to inorganic Zn fed rats. The TBARS and protein carbonyls concentration in liver indicative of oxidative stress was comparable between various organic and inorganic groups. The glutathione reductase activity in haemolysate (P<0.05) and reduced glutathione concentration in liver (P<0.01) was higher when fed organic Zn and RBC catalase activity was higher (P<0.01) on Zn methionine compared to other organic sources tested and the inorganic source. The humoral immune response assessed as antibody titres against sheep RBC was higher (P<0.05) when fed organic sources of zinc compared to inorganic source. The cell mediated immune response expressed as delayed type hypersensitivity reaction was higher (P<0.05) in rats fed Zn propionate with no effect of other organic Zn sources. The serum progesterone concentration was higher (P<0.05) in rats fed organic Zn sources compared to inorganic zinc. The data on ovarian folliculogenesis indicated that organic Zn supplementation increased (P<0.05) the number of graafian follicles and corpus luteum with no effect on primary, secondary and tertiary follicle number. The study indicated that rats fed organic sources of Zn had higher antioxidant enzyme activities, immune response and serum progesterone concentration with higher number of mature follicles. Though the effect of feeding various organic sources were comparable, rats fed zinc methionine had higher antioxidant activity and cell mediated immune response was higher in rats on Zn propionate.

Keywords: organic zinc, immune, rats, reproductive

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Authors: Amandeep Kaur, Ns Atri, Munruchi Kaur

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Mushrooms have shaped our environment in ways that we are only beginning to understand. The weather patterns, topography, flora and fauna of Punjab state in India create favorable growing conditions for thousands of species of mushrooms, but the complete region was unexplored when it comes to coprophilous mushrooms growing on herbivorous dung. Coprophilous mushrooms are the most specialized fungi ecologically, which germinate and grow directly on different types of animal dung or on manured soil. In the present work, the diversity of coprophilous mushrooms' of Family Psathyrellaceae of the order Agaricales is explored, their relationship to the human world is sketched out, and their supreme significance to life on this planet is revealed. During the investigation, different dung localities from 16 districts of Punjab state have been explored for the collection of material. The macroscopic features of the collected mushrooms were documented on the Field key. The hand cut sections of the various parts of carpophore, such as pileus, gills, stipe and the basidiospores details, were studied microscopically under different magnification. Various authentic publications were consulted for the identification of the investigated taxa. The classification, authentic names and synonyms of the investigated taxa are as per the latest version of Dictionary of Fungi and the MycoBank. The present work deals with the taxonomy of 81 collections belonging to 39 species spread over 05 coprophilous genera, namely Psathyrella, Panaeolus, Parasola, Coprinopsis, and Coprinellus of family Psathyrellaceae. In the text, the investigated taxa have been arranged as they appear in the key to the genera and species investigated. In this work, have been thoroughly examined for their macroscopic, microscopic, ecological, and chemical reaction details. The authors dig deeper to give indication of their ecology and the dung type where they can be obtained. Each taxon is accompanied by a detailed listing of its prominent features and an illustration with habitat photographs and line drawings of morphological and anatomical features. Taxa are organized as per their status in the keys, which allow easy recognition. All the taxa are compared with similar taxa. The study has shown that dung is an important substrate which serves as a favorable niche for the growth of a variety of mushrooms. This paper shows an insight what short-lived coprophilous mushrooms can teach us about sustaining life on earth!

Keywords: abundance, basidiomycota, biodiversity, seasonal availability, systematics

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Authors: Aulanni’am Aulanni’am

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Incidence of Diabetes Mellitus (DM) progressively increasing it became a serious problem in Indonesia and it is a disease that government is priority to be addressed. The longer a person is suffering from diabetes the more likely to develop complications particularly diabetic patients who are not well maintained. Therefore, Incidence of Diabetes Mellitus needs to be done in the early diagnosis of pre-phase of the disease. In this pre-phase disease, already happening destruction of pancreatic beta cells and declining in beta cell function and the sign autoimmunity reactions associated with beta cell destruction. Type 1 DM is a multifactorial disease triggered by genetic and environmental factors, which leads to the destruction of pancreatic beta cells. Early marker of "beta cell autoreactivity" is the synthesis of autoantibodies against 65-kDa protein, which can be a molecule that can be detected early in the disease pathomechanism. The importance of early diagnosis of diabetic patients held in the phase of pre-disease is to determine the progression towards the onset of pancreatic beta cell destruction and take precautions. However, the price for this examination is very expensive ($ 150/ test), the anti-GAD65 abs examination cannot be carried out routinely in most or even in all laboratories in Indonesia. Therefore, production-based Rapid Test Recombinant Human Protein GAD65 with "Reverse Flow Immunchromatography Technique" in Indonesia is believed to reduce costs and improve the quality of care of patients with diabetes in Indonesia. Rapid Test Product innovation is very simple and suitable for screening and routine inspection of GAD65 autoantibodies. In the blood serum of patients with diabetes caused by autoimmunity, autoantibody-GAD65 is a major serologic marker to detect autoimmune reaction because their concentration level of stability.GAD65 autoantibodies can be found 10 years before clinical symptoms of diabetes. Early diagnosis is more focused to detect the presence autontibodi-GAD65 given specification and high sensitivity. Autoantibodies- GAD65 that circulates in the blood is a major indicator of the destruction of the islet cells of the pancreas. Results of research in collaboration with Biofarma has produced GAD65 autoantibodies based Rapid Test had conducted the soft launch of products and has been tested with the results of a sensitivity of 100 percent and a specificity between 90 and 96% compared with the gold standard (import product) which worked based on ELISA method.

Keywords: diabetes mellitus, GAD65 autoantibodies, rapid test, sensitivity, specificity

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Authors: Abubakar Mukhtar Yakasai, Muhammad Tahir Jan

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Purpose: This paper is aimed at exploring factors influencing online shopping intention among the young consumers in Nigeria. Design/Methodology/approach: The paper adopted and extended Technology Acceptance Model (TAM) as the basis for literature review. Additionally, the paper proposed a framework with the inclusion of culture as a moderating factor of consumer online shopping intention among consumers in Nigeria. Findings: Despite high rate of internet penetration in Nigerian, as well as the rapid advancement of online shopping in the world, little attention was paid to this important revolution specifically among Nigeria’s consumers. Based on the review of extant literature, the TAM extended to include perceived risk and enjoyment (PR and PE) was discovered to be a better alternative framework for predicting Nigeria’s young consumers’ online shopping intention. The moderating effect of culture in the proposed model is shown to help immensely in ascertaining differences, if any, between various cultural groups among online shoppers in Nigeria. Originality/ value: The critical analysis of different factors will assist practitioners (like online retailers, e-marketing managers, website developers, etc.) by signifying which combinations of factors can best predict consumer online shopping behaviour in particular instances, thereby resulting in effective value delivery. Online shopping is a newly adopted technology in Nigeria, hence the paper will give a clear focus for effective e-marketing strategy. In addition, the proposed framework in this paper will guide future researchers by providing a tool for systematic evaluation and testing of real empirical situation of online shopping in Nigeria.

Keywords: online shopping, perceived ease of use, perceived usefulness, perceived enjoyment, technology acceptance model, Nigeria

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Authors: Jingling Li, Yi Zhang, Wei Liang, Tao Cui, Jun Li

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Compared with terrestrial network, the traffic of spatial information network has both self-similarity and short correlation characteristics. By studying its traffic prediction method, the resource utilization of spatial information network can be improved, and the method can provide an important basis for traffic planning of a spatial information network. In this paper, considering the accuracy and complexity of the algorithm, the spatial information network traffic is decomposed into approximate component with long correlation and detail component with short correlation, and a time series hybrid prediction model based on wavelet decomposition is proposed to predict the spatial network traffic. Firstly, the original traffic data are decomposed to approximate components and detail components by using wavelet decomposition algorithm. According to the autocorrelation and partial correlation smearing and truncation characteristics of each component, the corresponding model (AR/MA/ARMA) of each detail component can be directly established, while the type of approximate component modeling can be established by ARIMA model after smoothing. Finally, the prediction results of the multiple models are fitted to obtain the prediction results of the original data. The method not only considers the self-similarity of a spatial information network, but also takes into account the short correlation caused by network burst information, which is verified by using the measured data of a certain back bone network released by the MAWI working group in 2018. Compared with the typical time series model, the predicted data of hybrid model is closer to the real traffic data and has a smaller relative root means square error, which is more suitable for a spatial information network.

Keywords: spatial information network, traffic prediction, wavelet decomposition, time series model

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Authors: Sijuade A. A., Oguntoye J. P., Awodoye O. O., Adedapo O. A., Wahab W. B., Okediran O. O., Omidiora E. O., Olabiyisi S. O.

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Electronic voting systems have been introduced to improve the efficiency, accuracy, and transparency of the election process in many countries around the world, including Nigeria. However, concerns have been raised about the security and integrity of these systems. One way to address these concerns is through the implementation of electronic forensic election audit systems. This study aims to evaluate voters' intention to the adoption of electronic forensic election audit systems using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. In the study, the UTAUT model which is a widely used model in the field of information systems to explain the factors that influence individuals' intention to use a technology by integrating performance expectancy, effort expectancy, social influence, facilitating conditions, cost factor and privacy factor to voters’ behavioural intention was proposed. A total of 294 sample data were collected from a selected population of electorates who had at one time or the other participated in at least an electioneering process in Nigeria. The data was then analyzed statistically using Partial Least Square Structural Equation Modeling (PLS-SEM). The results obtained show that all variables have a significant effect on the electorates’ behavioral intention to adopt the development and implementation of an electronic forensic election audit system in Nigeria.

Keywords: election Audi, voters, UTAUT, performance expectancy, effort expectancy, social influence, facilitating condition social influence, facilitating conditions, cost factor, privacy factor, behavioural intention

Procedia PDF Downloads 56

Authors: Thohaku Abdul Hadi, Hadyan Perdana Putra, Nugroho Wicaksono, Adhika Prajna Nandiwardhana, Onang Surya Nugroho, Heri Suryoatmojo, Soedibjo

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Access to electricity is now a basic requirement of mankind. Unfortunately, there are still many places around the world which have no access to electricity, such as small islands, where there could potentially be a factory, a plantation, a residential area, or resorts. Many of these places might have substantial potential for energy generation such us Photovoltaic (PV) and Wind turbine (WT), which can be used to generate electricity independently for themselves. Solar energy and wind power are renewable energy sources which are mostly found in nature and also kinds of alternative energy that are still developing in a rapid speed to help and meet the demand of electricity. PV and Wind has a characteristic of power depend on solar irradiation and wind speed based on geographical these areas. This paper presented a control methodology of hybrid small scale PV/Wind energy system that use a fuzzy logic controller (FLC) to extract the maximum power point tracking (MPPT) in different solar irradiation and wind speed. This paper discusses simulation and analysis of the generation process of hybrid resources in MPP and power conditioning unit (PCU) of Photovoltaic (PV) and Wind Turbine (WT) that is connected to the three-phase low voltage electricity grid system (380V) without battery storage. The capacity of the sources used is 2.2 kWp PV and 2.5 kW PMSG (Permanent Magnet Synchronous Generator) -WT power rating. The Modeling of hybrid PV/Wind, as well as integrated power electronics components in grid connected system, are simulated using MATLAB/Simulink.

Keywords: fuzzy MPPT, grid connected inverter, photovoltaic (PV), PMSG wind turbine

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Authors: Thewodros K. Geberemariam

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The rapid physical expansion of urbanization coupled with aging infrastructure presents a unique decision and management challenges for many big city municipalities. Cities must therefore upgrade and maintain the existing aging urban drainage infrastructure systems to keep up with the demands. Given the overall contribution of assets to municipal revenue and the importance of infrastructure to the success of a livable city, many municipalities are currently looking for a robust and smart urban drainage infrastructure asset management solution that combines management, financial, engineering and technical practices. This robust decision-making shall rely on sound, complete, current and relevant data that enables asset valuation, impairment testing, lifecycle modeling, and forecasting across the multiple asset portfolios. On this paper, predictive computational intelligence (CI) and multi-class machine learning (ML) coupled with online, offline, and historical record data that are collected from an array of multi-parameter sensors are used for the extraction of different operational and non-conforming patterns hidden in structured and unstructured data to determine and produce actionable insight on the current and future states of the network. This paper aims to improve the strategic decision-making process by identifying all possible alternatives; evaluate the risk of each alternative, and choose the alternative most likely to attain the required goal in a cost-effective manner using historical and near real-time urban drainage infrastructure data for urban drainage infrastructures assets that have previously not benefited from computational intelligence and machine learning advancements.

Keywords: computational intelligence, machine learning, urban drainage infrastructure, machine learning, classification, prediction, asset management space

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Authors: Zhiyong Zheng, Xu Li, Liang Huang, Zhengliang Sun, Jianhua Xu

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Yaw angle plays a significant role in many vehicle safety applications, such as collision avoidance and lane-keeping system. Although the estimation of the yaw angle has been extensively studied in existing literature, it is still the main challenge to simultaneously achieve a reliable and cost-effective solution in complex urban environments. This paper proposes a multi-stage learning framework to estimate the yaw angle with a monocular camera, which can deal with the challenge in a more reliable manner. In the first stage, an efficient road detection network is designed to extract the road region, providing a highly reliable reference for the estimation. In the second stage, a variational auto-encoder (VAE) is proposed to learn the distribution patterns of road regions, which is particularly suitable for modeling the changing patterns of yaw angle under different driving maneuvers, and it can inherently enhance the generalization ability. In the last stage, a gated recurrent unit (GRU) network is used to capture the temporal correlations of the learned patterns, which is capable to further improve the estimation accuracy due to the fact that the changes of deflection angle are relatively easier to recognize among continuous frames. Afterward, the yaw angle can be obtained by combining the estimated deflection angle and the road direction stored in a roadway map. Through effective multi-stage learning, the proposed framework presents high reliability while it maintains better accuracy. Road-test experiments with different driving maneuvers were performed in complex urban environments, and the results validate the effectiveness of the proposed framework.

Keywords: gated recurrent unit, multi-stage learning, reliable estimation, variational auto-encoder, yaw angle

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Authors: Gong Zhilin, Jing Yang, Jian Yin

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The purpose of this paper is to develop a novel system for credit card fraud detection based on sequential modeling of data using hybrid deep learning models. The projected model encapsulates five major phases are pre-processing, imbalance-data handling, feature extraction, optimal feature selection, and fraud detection with an ensemble classifier. The collected raw data (input) is pre-processed to enhance the quality of the data through alleviation of the missing data, noisy data as well as null values. The pre-processed data are class imbalanced in nature, and therefore they are handled effectively with the K-means clustering-based SMOTE model. From the balanced class data, the most relevant features like improved Principal Component Analysis (PCA), statistical features (mean, median, standard deviation) and higher-order statistical features (skewness and kurtosis). Among the extracted features, the most optimal features are selected with the Self-improved Arithmetic Optimization Algorithm (SI-AOA). This SI-AOA model is the conceptual improvement of the standard Arithmetic Optimization Algorithm. The deep learning models like Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and optimized Quantum Deep Neural Network (QDNN). The LSTM and CNN are trained with the extracted optimal features. The outcomes from LSTM and CNN will enter as input to optimized QDNN that provides the final detection outcome. Since the QDNN is the ultimate detector, its weight function is fine-tuned with the Self-improved Arithmetic Optimization Algorithm (SI-AOA).

Keywords: credit card, data mining, fraud detection, money transactions

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Authors: Hayriye Anıl, Görkem Kar

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In addition to being rich in renewable energy resources, Turkey is one of the countries that promise potential in geothermal energy production with its high installed power, cheapness, and sustainability. Increasing imbalance penalties become an economic burden for organizations since geothermal generation plants cannot maintain the balance of supply and demand due to the inadequacy of the production forecasts given in the day-ahead market. A better production forecast reduces the imbalance penalties of market participants and provides a better imbalance in the day ahead market. In this study, using machine learning, deep learning, and, time series methods, the total generation of the power plants belonging to Zorlu Natural Electricity Generation, which has a high installed capacity in terms of geothermal, was estimated for the first one and two weeks of March, then the imbalance penalties were calculated with these estimates and compared with the real values. These modeling operations were carried out on two datasets, the basic dataset and the dataset created by extracting new features from this dataset with the feature engineering method. According to the results, Support Vector Regression from traditional machine learning models outperformed other models and exhibited the best performance. In addition, the estimation results in the feature engineering dataset showed lower error rates than the basic dataset. It has been concluded that the estimated imbalance penalty calculated for the selected organization is lower than the actual imbalance penalty, optimum and profitable accounts.

Keywords: machine learning, deep learning, time series models, feature engineering, geothermal energy production forecasting

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Authors: I. Wiedemann, A. R. Sharifi, A. Mählmeyer, C. Knorr

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A requirement for sperm motility is a morphologically intact flagellum with a central axoneme. The flagellar beating is caused by the varying activation and inactivation of dynein molecules which are located in the axoneme. DNAL4 (dynein, axonemal, light chain 4) is regarded as a possible functional candidate gene encoding a small subunit of the dyneins. In the present study, 5814bp of the porcine DNAL4 (GenBank Acc. No. AM284696.1, 6097 bp, 4 exons) were comparatively sequenced using three boars with a high motility (>68%) and three with a low motility (<60%). Primers were self-designed except for those covering exons 1, 2 and 3. Prior to sequencing, the PCR products were purified. Sequencing was performed with an ABI PRISM 3100 Genetic Analyzer using the BigDyeTM Terminator v3.1 Cycle Sequencing Reaction Kit. Finally, 23 SNPs were described and genotyped for 82 AI boars representing the breeds Piétrain, German Large White and German Landrace. The genotypes were used to assess possible associations with standard spermatological parameters (ejaculate volume, density, and sperm motility (undiluted (Motud), 24h (Mot1) and 48h (Mot2) after semen collection) that were regularly recorded on the AI station. The analysis included a total of 8,833 spermatological data sets which ranged from 2 to 295 sets per boar in five years. Only SNP g.1007A>G had a significant effect. Finally, the gene substitution effect using the following statistical model was calculated: Yijk= µ+αi+βj+αβij+b1Sijk+b2Aijk+b3T ijk + b4Vijk+b5(α*A)ijk +b6(β*A)ijk+b7(A*T)ijk+Uijk+eijk where Yijk is the semen characteristics, µ is the general mean, α is the main effect of breed, β is the main effect of season, S is the effect of SNP (g.1007A > G), A is the effect of age at semen collection, V is the effect of diluter, αβ, α*A, β*A, A*T are interactions between the fixed effects, b1-b7 are regression coefficients between y and the respective covariate, U is the random effect of repeated observation on animal and e is the random error. The results from the single marker regression analysis revealed highly significant effects (p < 0.0001) of SNP g.1007A > G on Mot1 resp. on Mot2, resulting in a marked reduction by 11.4% resp. 15.4%. Furthermore a loss of Motud by 4.6% was detected (p < 0.0178). Considering the SNP g.1007A > G as a main factor (dominant-recessive model), significant differences between genotypes AA and AG as well as AA and GG for Mot1 and Mot2 exist. For Motud there was a significant difference between AA and GG.

Keywords: association, DNAL4, porcine, sperm traits

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Authors: Choupani Andisheh, Temucin Elif Sevval, Bediz Bekir

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Due to their various advantages compared to many other drug delivery systems such as hypodermic injections and oral medications, microneedle arrays (MNAs) are a promising drug delivery system. To achieve enhanced performance of the MN, it is crucial to develop numerical models, optimization methods, and simulations. Accordingly, in this work, the optimized design of dissolvable MNAs, as well as their manufacturing, is investigated. For this purpose, a mechanical model of a single MN, having the geometry of an obelisk, is developed using commercial finite element software. The model considers the condition in which the MN is under pressure at the tip caused by the reaction force when penetrating the skin. Then, a multi-objective optimization based on non-dominated sorting genetic algorithm II (NSGA-II) is performed to obtain geometrical properties such as needle width, tip (apex) angle, and base fillet radius. The objective of the optimization study is to reach a painless and effortless penetration into the skin along with minimizing its mechanical failures caused by the maximum stress occurring throughout the structure. Based on the obtained optimal design parameters, master (male) molds are then fabricated from PMMA using a mechanical micromachining process. This fabrication method is selected mainly due to the geometry capability, production speed, production cost, and the variety of materials that can be used. Then to remove any chip residues, the master molds are cleaned using ultrasonic cleaning. These fabricated master molds can then be used repeatedly to fabricate Polydimethylsiloxane (PDMS) production (female) molds through a micro-molding approach. Finally, Polyvinylpyrrolidone (PVP) as a dissolvable polymer is cast into the production molds under vacuum to produce the dissolvable MNAs. This fabrication methodology can also be used to fabricate MNAs that include bioactive cargo. To characterize and demonstrate the performance of the fabricated needles, (i) scanning electron microscope images are taken to show the accuracy of the fabricated geometries, and (ii) in-vitro piercing tests are performed on artificial skin. It is shown that optimized MN geometries can be precisely fabricated using the presented fabrication methodology and the fabricated MNAs effectively pierce the skin without failure.

Keywords: microneedle, microneedle array fabrication, micro-manufacturing structural optimization, finite element analysis

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Authors: Silvano Salvador, Maryam Zarrinderakht, Alan Martin

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Open pits, which are the result of mining, are filled by water over time until the water reaches the elevation of the local water table and generates mine pit lakes. There are several specific regulations about the water quality of pit lakes, and mining operations should keep the quality of groundwater above pre-defined standards. Therefore, an accurate, acceptable numerical model predicting pit lakes’ water balance and water quality is needed in advance of mine excavation. We carry on analyzing and developing the model introduced by Crusius, Dunbar, et al. (2002) for pit lakes. This model, called “PitMod”, simulates the physical and geochemical evolution of pit lakes over time scales ranging from a few months up to a century or more. Here, a lake is approximated as one-dimensional, horizontally averaged vertical layers. PitMod calculates the time-dependent vertical distribution of physical and geochemical pit lake properties, like temperature, salinity, conductivity, pH, trace metals, and dissolved oxygen, within each model layer. This model considers the effect of pit morphology, climate data, multiple surface and subsurface (groundwater) inflows/outflows, precipitation/evaporation, surface ice formation/melting, vertical mixing due to surface wind stress, convection, background turbulence and equilibrium geochemistry using PHREEQC and linking that to the geochemical reactions. PitMod, which is used and validated in over 50 mines projects since 2002, incorporates physical processes like those found in other lake models such as DYRESM (Imerito 2007). However, unlike DYRESM PitMod also includes geochemical processes, pit wall runoff, and other effects. In addition, PitMod is actively under development and can be customized as required for a particular site.

Keywords: pit lakes, mining, modeling, hydrology

Procedia PDF Downloads 131

Authors: Annamária Käferné Rácz, Bence Jáger, Balázs Kövesdi, László Dunai

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Due to the numerous advantages of steel corrugated web girders, its application field is growing for bridges as well as for buildings. The global stability behavior of such girders is significantly larger than those of conventional I-girders with flat web, thus the application of the structural steel material can be significantly reduced. Design codes and specifications do not provide clear and complete rules or recommendations for the determination of the lateral torsional buckling (LTB) resistance of corrugated web girders. Therefore, the authors made a thorough investigation regarding the LTB resistance of the corrugated web girders. Finite element (FE) simulations have been performed to develop new design formulas for the determination of the LTB resistance of trapezoidally corrugated web girders. FE model is developed considering geometrical and material nonlinear analysis using equivalent geometric imperfections (GMNI analysis). The equivalent geometric imperfections involve the initial geometric imperfections and residual stresses coming from rolling, welding and flame cutting. Imperfection sensitivity analysis was performed to determine the necessary magnitudes regarding only the first eigenmodes shape imperfections. By the help of the validated FE model, an extended parametric study is carried out to investigate the LTB resistance for different trapezoidal corrugation profiles. First, the critical moment of a specific girder was calculated by FE model. The critical moments from the FE calculations are compared to the previous analytical calculation proposals. Then, nonlinear analysis was carried out to determine the ultimate resistance. Due to the numerical investigations, new proposals are developed for the determination of the LTB resistance of trapezoidally corrugated web girders through a modification factor on the design method related to the conventional flat web girders.

Keywords: corrugated web, lateral torsional buckling, critical moment, FE modeling

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Authors: Goksel Ezgi Guzey, Bihrat Onoz

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The scarcity of streamflow gauging stations and the increasing effects of global warming cause designing water management systems to be very difficult. This study is a significant contribution to assessing regional regression models for estimating streamflow. In this study, simulated meteorological data was related to the observed streamflow data from 1971 to 2020 for 33 stream gauging stations of the Euphrates-Tigris Basin. Ordinary least squares regression was used to predict flow for 2020-2100 with the simulated meteorological data. CORDEX- EURO and CORDEX-MENA domains were used with 0.11 and 0.22 grids, respectively, to estimate climate conditions under certain climate scenarios. Twelve meteorological variables simulated by two regional climate models, RCA4 and RegCM4, were used as independent variables in the ordinary least squares regression, where the observed streamflow was the dependent variable. The variability of streamflow was then calculated with 5-6 meteorological variables and watershed characteristics such as area and height prior to the application. Of the regression analysis of 31 stream gauging stations' data, the stations were subjected to a clustering analysis, which grouped the stations in two clusters in terms of their hydrometeorological properties. Two streamflow equations were found for the two clusters of stream gauging stations for every domain and every regional climate model, which increased the efficiency of streamflow estimation by a range of 10-15% for all the models. This study underlines the importance of homogeneity of a region in estimating streamflow not only in terms of the geographical location but also in terms of the meteorological characteristics of that region.

Keywords: hydrology, streamflow estimation, climate change, hydrologic modeling, HBV, hydropower

Procedia PDF Downloads 113

Authors: Marco Camilli

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This article examines the impact of the pandemic outbreak of COVID-19 in the tourism sector in Italy, analyzing the current scenario, the government decisions and the private company reaction for the summer season 2020. The framework of the data analyzed shows how massive it’s the impact of the pandemic outbreak in the tourism revenue, and the weaknesses of the measures proposed. Keywords Travel &Tourism, Transportation, Sustainability, COVID-19, Businesses Introduction The current COVID-19 scenario shows a shocking situation for the tourism and transportation sectors: it could be the most affected by the Coronavirus in Italy. According to forecasts, depending on the duration of the epidemic outbreak and the lockdown strategy applied by the Government, businesses in the supply chain could lose between 24 and 66 billion in turnover in the period of 2020-21, with huge diversified impacts at the national and regional level. Many tourist companies are on the verge of survival and if there are no massive measures by the government they risk closure. Data analysis The tourism and transport sector could be among the sectors most damaged by Covid-19 in Italy. Considering the two-year period 2020-21, companies operating in the travel & tourism sector (Tour operator, Travel Agencies, Hotel, Guides, Bus Company, etc..) could in suffer losses in revenues of 24 to 64 billion euros, especially in the sectors such as the travel agencies, hotel and rental. According to Statista Research Department, from April 2020 estimated that the coronavirus (COVID-19) pandemic will have a significant impact on revenues of the tourism industry in Italy. Revenues are expected to decrease by over 40 billion euros in the first semester of 2020, compared to the same period of the previous year. According to the study, hotel and non-hotel accommodations will experience the highest loss. Revenues of this sector are expected to decrease by 13 billion euros compared to the first semester of 2019 when accommodations registered revenues for about 17 billion euros. According to Statista.com, in 2020, Italy is expected to register a decrease of roughly 28.5 million tourist arrivals due to the impact of coronavirus (COVID-19) on the country's tourist sector. According to the estimate, the region of Veneto will record the highest drop with a decrease of roughly 4.61 million arrivals. Similarly, Lombardy is expected to register a decrease of about 3.87 million arrivals in 2020.

Keywords: travel and tourism, sustainability, COVID-19, businesses, transportation

Procedia PDF Downloads 186

Authors: Mauricio Cruz, Andrés Díaz García, Martha Isabel Gómez, Juan Carlos Serrato Bermúdez

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The disadvantages of using natural substrates in SSF processes have been well recognized and mainly are associated to gradual decomposition of the substrate, formation of agglomerates and decrease of porosity bed generating limitations in the mass and heat transfer. Additionally, in several cases, materials with a high agricultural value such as sour milk, beets, rice, beans and corn have been used. Thus, the use of economic inert supports (natural or synthetic) in combination with a nutrient suspension for the production of biocontrol microorganisms is a good alternative in SSF processes, but requires further studies in the fields of modeling and optimization. Therefore, the aim of this work is to compare the performance of two inert supports, a synthetic (polyurethane foam) and a natural one (rice husk), identifying the factors that have the major effects on the productivity of T. asperellum Th204 and the maximum specific growth rate in a PROPHYTA L05® fixed bed bioreactor. For this, the six factors C:N ratio, temperature, inoculation rate, bed height, air moisture content and airflow were evaluated using a fractional design. The factors C:N and air flow were identified as significant on the productivity (expressed as conidia/dry substrate•h). The polyurethane foam showed higher maximum specific growth rate (0.1631 h-1) and productivities of 3.89 x107 conidia/dry substrate•h compared to rice husk (2.83x106) and natural substrate based on rice (8.87x106) used as control. Finally, a quadratic model was generated and validated, obtaining productivities higher than 3.0x107 conidia/dry substrate•h with air flow at 0.9 m3/h and C:N ratio at 18.1.

Keywords: bioprocess, scale up, fractional design, C:N ratio, air flow

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Authors: Joonas Pääkkönen

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In sports, individuals and teams are typically interested in final rankings. Final results, such as times or distances, dictate these rankings, also known as places. Places can be further associated with ordered random variables, commonly referred to as order statistics. In this work, we introduce a simple, yet accurate order statistical ordinal regression function that predicts relay race places with changeover-times. We call this function the Fenton-Wilkinson Order Statistics model. This model is built on the following educated assumption: individual leg-times follow log-normal distributions. Moreover, our key idea is to utilize Fenton-Wilkinson approximations of changeover-times alongside an estimator for the total number of teams as in the notorious German tank problem. This original place regression function is sigmoidal and thus correctly predicts the existence of a small number of elite teams that significantly outperform the rest of the teams. Our model also describes how place increases linearly with changeover-time at the inflection point of the log-normal distribution function. With real-world data from Jukola 2019, a massive orienteering relay race, the model is shown to be highly accurate even when the size of the training set is only 5% of the whole data set. Numerical results also show that our model exhibits smaller place prediction root-mean-square-errors than linear regression, mord regression and Gaussian process regression.

Keywords: Fenton-Wilkinson approximation, German tank problem, log-normal distribution, order statistics, ordinal regression, orienteering, sports analytics, sports modeling

Procedia PDF Downloads 114