Search results for: cognitive models

Authors: Nwachukwu Ihiejeto Celestine

Abstract:

Rural poverty in Nigeria has been the bake of the society. It has been a canker worm which has eaten deep into the fabric of Nigerian society. Different models and principles have been applied to eradicate it, such as operation feed the nation, green revolution, NAPEP etc. Little or nothing has been done in the area of entrepreneurship education to tame this monster. It is based on this that the author wants to x-ray the role entrepreneurship education which studies “the process of identifying, bringing a vision to life” could play in the eradication of rural poverty in Nigeria. This will go along in providing appropriate principles for poverty alleviation and eradication in Nigeria. Some selected states in the eastern Geo-political region could be x-rayed in this circumstance. It is hoped that policy makers etc will find the work cogent in formulating and implementing policy decisions.

Keywords: poverty, entrepreneurship, education, Nigeria

Procedia PDF Downloads 460

Authors: Mahesh Kothari, K. D. Gharde

Abstract:

The ANN and fuzzy logic (FL) models were developed to predict the runoff and sediment yield for catchment of Kal river, India using 21 years (1991 to 2011) rainfall and other hydrological data (evaporation, temperature and streamflow lag by one and two day) and 7 years data for sediment yield modelling. The ANN model performance improved with increasing the input vectors. The fuzzy logic model was performing with R value more than 0.95 during developmental stage and validation stage. The comparatively FL model found to be performing well to ANN in prediction of runoff and sediment yield for Kal river.

Keywords: transferred function, sigmoid, backpropagation, membership function, defuzzification

Procedia PDF Downloads 556

Authors: Payling Harn

Abstract:

LEGO® SERIOUS PLAY® is a kind of facilitated workshop of thinking and problem-solving approach. Participants built symbolic and metaphorical brick models in response to tasks given by the facilitator and presented these models to other participants. LEGO® SERIOUS PLAY® applied the positive psychological mechanism of Flow and positive emotions to help participants perceiving self-experience and unknown fact and increasing the happiness of life by building bricks and narrating story. At present, LEGO® SERIOUS PLAY® is often utilized for facilitating professional identity and strategy development to assist workers in career development. The researcher desires to apply LEGO® SERIOUS PLAY® to the career courses of college students in order to promote their career ability. This study aimed to use the facilitative method of LEGO® SERIOUS PLAY® to develop the career courses of college students, then explore the effects of Taiwanese college students' positive and negative emotions, career adaptabilities, and career sense of hope by LEGO® SERIOUS PLAY® career courses. The researcher regarded strength as the core concept and use the facilitative mode of LEGO® SERIOUS PLAY® to develop the 8 weeks’ career courses, which including ‘emotion of college life’ ‘career highlights’, ‘career strengths’, ‘professional identity’, ‘business model’, ‘career coping’, ‘strength guiding principles’, ‘career visions’,’ career hope’, etc. The researcher will adopt problem-oriented teaching method to give tasks which according to the weekly theme, use the facilitative mode of LEGO® SERIOUS PLAY® to guide participants to respond tasks by building bricks. Then participants will conduct group discussions, reports, and writing reflection journals weekly. Participants will be 24 second-grade college students. They will attend LEGO® SERIOUS PLAY® career courses for 2 hours a week. The researcher used’ ‘Career Adaptability Scale’ and ‘Career Hope Scale’ to conduct pre-test and post-test. The time points of implementation testing will be one week before courses starting, one day after courses ending respectively. Then the researcher will adopt repeated measures one-way ANOVA for analyzing data. The results revealed that the participants significantly presented immediate positive effect in career adaptability and career hope. The researcher hopes to construct the mode of LEGO® SERIOUS PLAY® career courses by this study and to make a substantial contribution to the future career teaching and researches of LEGO® SERIOUS PLAY®.

Keywords: LEGO® SERIOUS PLAY®, career courses, strength, positive and negative affect, career hope

Procedia PDF Downloads 245

Authors: Mohammed T. Shukri

Abstract:

The failure of bridges due to excessive local scour during floods poses a challenging problem to hydraulic engineers. The failure of bridges piers is due to many reasons such as localized scour combined with general riverbed degradation. In this paper, we try to estimate the temporal variation of scour depth at nonuniform cylindrical bridge pier, by experimental work conducted in hydraulic laboratories of Gaziantep University Civil Engineering Department on a flume having dimensions of 8.3 m length, 0.8 m width and 0.9 m depth. The experiments will be carried on 20 cm depth of sediment layer having d₅₀=0.4 mm. Three bridge pier shapes having different scaled models will be constructed in a 1.5m of test section in the channel.

Keywords: scour, local scour, bridge piers, scour depth

Procedia PDF Downloads 250

Authors: Mengistu G. Woldu, Hani Y. Zaki, Areeg Faggad, Badreldin E. Abdalla

Abstract:

Background: Type 2 diabetes mellitus (T2DM) is a complex, degenerative, and multi-factorial disease, which is culpable for huge mortality and morbidity worldwide. Even though relatively significant numbers of studies are conducted on the genetics domain of this disease in the developed world, there is huge information gap in the sub-Saharan Africa region in general and in Eritrea in particular. Objective: The principal aim of this study was to investigate the association of common variants of the Insulin Receptor Substrate 1 (IRS1) and Transcription Factor 7-Like 2 (TCF7L2) genes with T2DM in the Eritrean population. Method: In this cross-sectional case control study 200 T2DM patients and 112 non-diabetes subjects were participated and genotyping of the IRS1 (rs13431179, rs16822615, 16822644rs, rs1801123) and TCF7L2 (rs7092484) tag SNPs were carries out using PCR-RFLP method of analysis. Haplotype analyses were carried out using Plink version 1.07, and Haploview 4.2 software. Linkage disequilibrium (LD), and Hardy-Weinberg equilibrium (HWE) analyses were performed using the Plink software. All descriptive statistical data analyses were carried out using SPSS (Version-20) software. Throughout the analysis p-value ≤0.05 was considered statistically significant. Result: Significant association was found between rs13431179 SNP of the IRS1 gene and T2DM under the recessive model of inheritance (OR=9.00, 95%CI=1.17-69.07, p=0.035), and marginally significant association found in the genotypic model (OR=7.50, 95%CI=0.94-60.06, p=0.058). The rs7092484 SNP of the TCF7L2 gene also showed markedly significant association with T2DM in the recessive (OR=3.61, 95%CI=1.70-7.67, p=0.001); and allelic (OR=1.80, 95%CI=1.23-2.62, p=0.002) models. Moreover, eight haplotypes of the IRS1 gene found to have significant association withT2DM (p=0.013 to 0.049). Assessments made on the interactions of genotypes of the rs13431179 and rs7092484 SNPs with various parameters demonstrated that high density lipoprotein (HDL), low density lipoprotein (LDL), waist circumference (WC), and systolic blood pressure (SBP) are the best T2DM onset predicting models. Furthermore, genotypes of the rs7092484 SNP showed significant association with various atherogenic indexes (Atherogenic index of plasma, LDL/HDL, and CHLO/HDL); and Eritreans carrying the GG or GA genotypes were predicted to be more susceptible to cardiovascular diseases onset. Conclusions: Results of this study suggest that IRS1 (rs13431179) and TCF7L2 (rs7092484) gene polymorphisms are associated with increased risk of T2DM in Eritreans.

Keywords: IRS1, SNP, TCF7L2, type 2 diabetes

Procedia PDF Downloads 217

Authors: Rui Teixeira, Alan O’Connor, Maria Nogal

Abstract:

The statistical theory of extreme events is progressively a topic of growing interest in all the fields of science and engineering. The changes currently experienced by the world, economic and environmental, emphasized the importance of dealing with extreme occurrences with improved accuracy. When it comes to the design of offshore structures, particularly offshore wind turbines, the importance of efficiently characterizing extreme events is of major relevance. Extreme events are commonly characterized by extreme values theory. As an alternative, the accurate modeling of the tails of statistical distributions and the characterization of the low occurrence events can be achieved with the application of the Peak-Over-Threshold (POT) methodology. The POT methodology allows for a more refined fit of the statistical distribution by truncating the data with a minimum value of a predefined threshold u. For mathematically approximating the tail of the empirical statistical distribution the Generalised Pareto is widely used. Although, in the case of the exceedances of significant wave data (H_s) the 2 parameters Weibull and the Exponential distribution, which is a specific case of the Generalised Pareto distribution, are frequently used as an alternative. The Generalized Pareto, despite the existence of practical cases where it is applied, is not completely recognized as the adequate solution to model exceedances over a certain threshold u. References that set the Generalised Pareto distribution as a secondary solution in the case of significant wave data can be identified in the literature. In this framework, the current study intends to tackle the discussion of the application of statistical models to characterize exceedances of wave data. Comparison of the application of the Generalised Pareto, the 2 parameters Weibull and the Exponential distribution are presented for different values of the threshold u. Real wave data obtained in four buoys along the Irish coast was used in the comparative analysis. Results show that the application of the statistical distributions to characterize significant wave data needs to be addressed carefully and in each particular case one of the statistical models mentioned fits better the data than the others. Depending on the value of the threshold u different results are obtained. Other variables of the fit, as the number of points and the estimation of the model parameters, are analyzed and the respective conclusions were drawn. Some guidelines on the application of the POT method are presented. Modeling the tail of the distributions shows to be, for the present case, a highly non-linear task and, due to its growing importance, should be addressed carefully for an efficient estimation of very low occurrence events.

Keywords: extreme events, offshore structures, peak-over-threshold, significant wave data

Procedia PDF Downloads 261

Authors: Diana Islam, Juan Fang, Vito Fanelli, Bing Han, Julie Khang, Jianfeng Wu, Arthur S. Slutsky, Haibo Zhang

Abstract:

Introduction: MSC delivery in preclinical models of ARDS has demonstrated significant improvements in lung function and recovery from acute injury. However, the role of MSC delivery in ARDS associated pulmonary fibrosis is not well understood. Some animal studies using bleomycin, asbestos, and silica-induced pulmonary fibrosis show that MSC delivery can suppress fibrosis. While other animal studies using radiation induced pulmonary fibrosis, liver, and kidney fibrosis models show that MSC delivery can contribute to fibrosis. Hypothesis: The beneficial and deleterious effects of MSC in ARDS are modulated by the lung microenvironment at the time of MSC delivery. Methods: To induce ARDS a two-hit mouse model of Hydrochloric acid (HCl) aspiration (day 0) and mechanical ventilation (MV) (day 2) was used. HCl and injurious MV generated fibrosis within 14-28 days. 0.5x106 mouse MSCs were delivered (via both intratracheal and intravenous routes) either in the active inflammatory phase (day 2) or during the remodeling phase (day 14) of ARDS (mouse fibroblasts or PBS used as a control). Lung injury accessed using inflammation score and elastance measurement. Pulmonary fibrosis was accessed using histological score, tissue collagen level, and collagen expression. In addition alveolar epithelial (E) and mesenchymal (M) marker expression profile was also measured. All measurements were taken at day 2, 14, and 28. Results: MSC delivery 2 days after HCl exacerbated lung injury and fibrosis compared to HCl alone, while the day 14 delivery showed protective effects. However in the absence of HCl, MSC significantly reduced the injurious MV-induced fibrosis. HCl injury suppressed E markers and up-regulated M markers. MSC delivery 2 days after HCl further amplified M marker expression, indicating their role in myofibroblast proliferation/activation. While with 14-day delivery E marker up-regulation was observed indicating their role in epithelial restoration. Conclusions: Early MSC delivery can be protective of injurious MV. Late MSC delivery during repair phase may also aid in recovery. However, early MSC delivery during the exudative inflammatory phase of HCl-induced ARDS can result in pro-fibrotic profiles. It is critical to understand the interaction between MSC and the lung microenvironment before MSC-based therapies are utilized for ARDS.

Keywords: acute respiratory distress syndrome (ARDS), mesenchymal stem cells (MSC), hydrochloric acid (HCl), mechanical ventilation (MV)

Procedia PDF Downloads 662

Authors: Aya Rezgui, Lasaad Ajam, Belgacem Jalleli

Abstract:

The Tunis soft soils present a difficult challenge as construction sites and for Geotechnical works. Currently, different techniques are used to improve such soil properties taking into account the environmental considerations. One of the recent methods is involving plastic wastes as a reinforcing materials. The present study pertains to the development of a numerical model for predicting the behavior of Tunis Soft soil (TSS) improved with recycled Monobloc chair wastes.3D numerical models for unreinforced TSS and reinforced TSS aims to evaluate settlement reduction and the values of consolidation times in oedometer conditions.

Keywords: Tunis soft soil, settlement, plastic wastes, finte -difference, FLAC3D modeling

Procedia PDF Downloads 123

Authors: Abdallah A. Hlayel

Abstract:

Operations research science (OR) deals with good success in developing and applying scientific methods for problem solving and decision-making. However, by using OR techniques, we can enhance the use of computer decision support systems to achieve optimal management for institutions. OR applies comprehensive analysis including all factors that affect on it and builds mathematical modeling to solve business or organizational problems. In addition, it improves decision-making and uses available resources efficiently. The adoption of OR by universities would definitely contributes to the development and enhancement of the performance of OR techniques. This paper provides an understanding of the structures, approaches and models of OR in problem solving and decision-making.

Keywords: best candidates' method, decision making, decision support system, operations research

Procedia PDF Downloads 431

Authors: Łukasz Grabowski, Michał Gęca, Mirosław Wendeker

Abstract:

This paper presents the simulation results of a new opposed piston diesel engine to power a light aircraft. Created in the AVL Boost, the model covers the entire charge passage, from the inlet up to the outlet. The model shows fuel injection into cylinders and combustion in cylinders. The calculation uses the module for two-stroke engines. The model was created using sub-models available in this software that structure the model. Each of the sub-models is complemented with parameters in line with the design premise. Since engine weight resulting from geometric dimensions is fundamental in aircraft engines, two configurations of stroke were studied. For each of the values, there were calculated selected operating conditions defined by crankshaft speed. The required power was achieved by changing air fuel ratio (AFR). There was also studied brake specific fuel consumption (BSFC). For stroke S1, the BSFC was lowest at all of the three operating points. This difference is approximately 1-2%, which means higher overall engine efficiency but the amount of fuel injected into cylinders is larger by several mg for S1. The cylinder maximum pressure is lower for S2 due to the fact that compressor gear driving remained the same and boost pressure was identical in the both cases. Calculations for various values of boost pressure were the next stage of the study. In each of the calculation case, the amount of fuel was changed to achieve the required engine power. In the former case, the intake system dimensions were modified, i.e. the duct connecting the compressor and the air cooler, so its diameter D = 40 mm was equal to the diameter of the compressor outlet duct. The impact of duct length was also examined to be able to reduce the flow pulsation during the operating cycle. For the so selected geometry of the intake system, there were calculations for various values of boost pressure. The boost pressure was changed by modifying the gear driving the compressor. To reach the required level of cruising power N = 68 kW. Due to the mechanical power consumed by the compressor, high pressure ratio results in a worsened overall engine efficiency. The figure on the change in BSFC from 210 g/kWh to nearly 270 g/kWh shows this correlation and the overall engine efficiency is reduced by about 8%. Acknowledgement: This work has been realized in the cooperation with The Construction Office of WSK "PZL-KALISZ" S.A." and is part of Grant Agreement No. POIR.01.02.00-00-0002/15 financed by the Polish National Centre for Research and Development.

Keywords: aircraft, diesel, engine, simulation

Procedia PDF Downloads 201

Authors: Bilel Chalghaf, Mathieu Varin

Abstract:

Forest characterization in Quebec, Canada, is usually assessed based on photo-interpretation at the stand level. For species identification, this often results in a lack of precision. Very high spatial resolution imagery, such as DigitalGlobe, and Light Detection and Ranging (LiDAR), have the potential to overcome the limitations of aerial imagery. To date, few studies have used that data to map a large number of species at the tree level using machine learning techniques. The main objective of this study is to map 11 individual high tree species ( > 17m) at the tree level using an object-based approach in the broadleaf forest of Kenauk Nature, Quebec. For the individual tree crown segmentation, three canopy-height models (CHMs) from LiDAR data were assessed: 1) the original, 2) a filtered, and 3) a corrected model. The corrected CHM gave the best accuracy and was then coupled with imagery to refine tree species crown identification. When compared with photo-interpretation, 90% of the objects represented a single species. For modeling, 313 variables were derived from 16-band WorldView-3 imagery and LiDAR data, using radiance, reflectance, pixel, and object-based calculation techniques. Variable selection procedures were employed to reduce their number from 313 to 16, using only 11 bands to aid reproducibility. For classification, a global approach using all 11 species was compared to a semi-hierarchical hybrid classification approach at two levels: (1) tree type (broadleaf/conifer) and (2) individual broadleaf (five) and conifer (six) species. Five different model techniques were used: (1) support vector machine (SVM), (2) classification and regression tree (CART), (3) random forest (RF), (4) k-nearest neighbors (k-NN), and (5) linear discriminant analysis (LDA). Each model was tuned separately for all approaches and levels. For the global approach, the best model was the SVM using eight variables (overall accuracy (OA): 80%, Kappa: 0.77). With the semi-hierarchical hybrid approach, at the tree type level, the best model was the k-NN using six variables (OA: 100% and Kappa: 1.00). At the level of identifying broadleaf and conifer species, the best model was the SVM, with OA of 80% and 97% and Kappa values of 0.74 and 0.97, respectively, using seven variables for both models. This paper demonstrates that a hybrid classification approach gives better results and that using 16-band WorldView-3 with LiDAR data leads to more precise predictions for tree segmentation and classification, especially when the number of tree species is large.

Keywords: tree species, object-based, classification, multispectral, machine learning, WorldView-3, LiDAR

Procedia PDF Downloads 122

Authors: C. Pearcy, C. Rees

Abstract:

Cognitive behavioural therapy (CBT) and exposure and response prevention (ERP) are currently the most efficacious treatments for Obsessive-compulsive disorder (OCD). Many clients, however, remain symptomatic following treatment. As a result, refusal of treatment, withdrawal from treatment, and partial adherence to treatment are common amongst ERP. Such limitations have caused few professionals to actually engage in ERP therapy, which has warranted the exploration of alternative treatments. This study evaluated an online self-help treatment program for OCD (the OCD Doctor Online); a 4-week Metacognitive Therapy (MCT) program which has implemented strategies from Wells’ Metacognitive model of OCD. The aim of the present study was to investigate whether an online self-help treatment using MCT would reduce symptoms of OCD, reduce unhelpful metacognitions and improve quality of life. Treatment effectiveness was assessed using a case series methodology in 3 consecutively referred individuals. At post-treatment, all participants showed reductions in unhelpful metacognitive beliefs (MCQ-30) and improvements in quality of life (Q-LES-Q), which were maintained through to 4 week follow-up. Two of the three participants showed reductions in OCD symptomology (OCI-R), which were further reduced at 4-week follow-up. The present study suggests that internet-based self-help treatment may be an effective means of delivering MCT to adults with OCD.

Keywords: internet-based, metacognitive therapy, obsessive-compulsive disorder, self-help

Procedia PDF Downloads 425

Authors: Himanshi Allahabadi

Abstract:

Plants have found a widespread use in medicine traditionally, including the treatment of cognitive disorders, especially, neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease. In terms of indigenous medicine, it has been found that many potential drugs can be isolated from plant products, including those for dementia. Plant product is widely distributed in plant kingdom and forms a major antioxidant source in the human diet, is Polyphenols. There are four important groups of polyphenols: phenolic acids, flavonoids, stilbenes, and lignans. Due to their high antioxidant capacity, interest in their study has greatly increased. There are several methods for discovering and characterizing active compounds isolated from plant sources, now available. The results obtained so far seem fulfilling, but additionally, mechanism of functioning of polyphenols at the molecular level, as well as their application in human health need to be researched upon. Also, even though the neuroprotective effects of flavonoids have been much talked about, much of the data in support of this statement has come from animal studies rather than human studies. This review is based on a multi-faceted study of medicinal plants, i.e. phytochemicals, with special focus on flavanones and their relevance in remedy of Parkinson's disease.

Keywords: dementia, parkinson's disease, flavanones, polyphenols, substantia nigra

Procedia PDF Downloads 294

Authors: Zunaira Zaman, David Bohannon

Abstract:

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

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

Procedia PDF Downloads 98

Authors: Sabyasachi Mukhopadhyay, Joseph Ogutu, Gundula Bartzke, Hans-Peter Piepho

Abstract:

Rainfall is a critical component of climate governing vegetation growth and production, forage availability and quality for herbivores. However, reliable rainfall measurements are not always available, making it necessary to predict rainfall values for particular locations through time. Predicting rainfall in space and time can be a complex and challenging task, especially where the rain gauge network is sparse and measurements are not recorded consistently for all rain gauges, leading to many missing values. Here, we develop a flexible Bayesian model for predicting rainfall in space and time and apply it to Narok County, situated in southwestern Kenya, using data collected at 23 rain gauges from 1965 to 2015. Narok County encompasses the Maasai Mara ecosystem, the northern-most section of the Mara-Serengeti ecosystem, famous for its diverse and abundant large mammal populations and spectacular migration of enormous herds of wildebeest, zebra and Thomson's gazelle. The model incorporates geographical and meteorological predictor variables, including elevation, distance to Lake Victoria and minimum temperature. We assess the efficiency of the model by comparing it empirically with the established Gaussian process, Kriging, simple linear and Bayesian linear models. We use the model to predict total monthly rainfall and its standard error for all 5 * 5 km grid cells in Narok County. Using the Monte Carlo integration method, we estimate seasonal and annual rainfall and their standard errors for 29 sub-regions in Narok. Finally, we use the predicted rainfall to predict large herbivore biomass in the Maasai Mara ecosystem on a 5 * 5 km grid for both the wet and dry seasons. We show that herbivore biomass increases with rainfall in both seasons. The model can handle data from a sparse network of observations with many missing values and performs at least as well as or better than four established and widely used models, on the Narok data set. The model produces rainfall predictions consistent with expectation and in good agreement with the blended station and satellite rainfall values. The predictions are precise enough for most practical purposes. The model is very general and applicable to other variables besides rainfall.

Keywords: non-stationary covariance function, gaussian process, ungulate biomass, MCMC, maasai mara ecosystem

Procedia PDF Downloads 285

Authors: Ali K. M. Al-Nasrawi, Uday A. Al-Hamdany, Sarah M. Hamylton, Brian G. Jones, Yasir M. Alyazichi

Abstract:

Surface elevation dynamics have always responded to disturbance regimes. Creating Digital Elevation Models (DEMs) to detect surface dynamics has led to the development of several methods, devices and data clouds. DEMs can provide accurate and quick results with cost efficiency, in comparison to the inherited geomatics survey techniques. Nowadays, remote sensing datasets have become a primary source to create DEMs, including LiDAR point clouds with GIS analytic tools. However, these data need to be tested for error detection and correction. This paper evaluates various DEMs from different data sources over time for Apple Orchard Island, a coastal site in southeastern Australia, in order to detect surface dynamics. Subsequently, 30 chosen locations were examined in the field to test the error of the DEMs surface detection using high resolution global positioning systems (GPSs). Results show significant surface elevation changes on Apple Orchard Island. Accretion occurred on most of the island while surface elevation loss due to erosion is limited to the northern and southern parts. Concurrently, the projected differential correction and validation method aimed to identify errors in the dataset. The resultant DEMs demonstrated a small error ratio (≤ 3%) from the gathered datasets when compared with the fieldwork survey using RTK-GPS. As modern modelling approaches need to become more effective and accurate, applying several tools to create different DEMs on a multi-temporal scale would allow easy predictions in time-cost-frames with more comprehensive coverage and greater accuracy. With a DEM technique for the eco-geomorphic context, such insights about the ecosystem dynamic detection, at such a coastal intertidal system, would be valuable to assess the accuracy of the predicted eco-geomorphic risk for the conservation management sustainability. Demonstrating this framework to evaluate the historical and current anthropogenic and environmental stressors on coastal surface elevation dynamism could be profitably applied worldwide.

Keywords: DEMs, eco-geomorphic-dynamic processes, geospatial Information Science, remote sensing, surface elevation changes,

Procedia PDF Downloads 262

Authors: Karoliina Nisula, Samuli Pekkola

Abstract:

Business education has been criticized for being too theoretical and distant from business life. Different types of experiential learning environments ranging from manual role-play to computer simulations and enterprise resource planning (ERP) systems have been used to introduce the realistic and practical experience into business learning. Each of these learning environments approaches business learning from a different perspective. The implementations tend to be individual exercises supplementing the traditional courses. We suggest combining them into a business skills laboratory resembling an actual workplace. In this paper, we present a concrete implementation of an ERP-supported business learning environment that is used throughout the first year undergraduate business curriculum. We validate the implementation by evaluating the learning outcomes through the different domains of Bloom’s taxonomy. We use the role-play oriented practice enterprise model as a comparison group. Our findings indicate that using the ERP simulation improves the poor and average students’ lower-level cognitive learning. On the affective domain, the ERP-simulation appears to enhance motivation to learn as well as perceived acquisition of practical hands-on skills.

Keywords: business simulations, experiential learning, ERP systems, learning environments

Procedia PDF Downloads 246

Authors: R. Mkahl, A. Nait-Sidi-Moh, M. Wack

Abstract:

Batteries of electric vehicles (BEV) are becoming more attractive with the advancement of new battery technologies and promotion of electric vehicles. BEV batteries are recharged on board vehicles using either the grid (G2V for Grid to Vehicle) or renewable energies in a stand-alone application (H2V for Home to Vehicle). This paper deals with the modeling, sizing and control of a photo voltaic stand-alone application that can charge the BEV at home. The modeling approach and developed mathematical models describing the system components are detailed. Simulation and experimental results are presented and commented.

Keywords: electric vehicles, photovoltaic energy, lead-acid batteries, charging process, modeling, simulation, experimental tests

Procedia PDF Downloads 428

Authors: Ashenafi Beyene Fanta, Daniel Makina

Abstract:

Economic theory predicts that finance promotes economic growth. Although the finance-growth link is among the most researched areas in financial economics, our understanding of the link between the two is still incomplete. This is caused by, among others, wrong econometric specifications, using weak proxies of financial development, and inability to address the endogeneity problem. Studies on the finance growth link in South Africa consistently report economic growth driving financial development. Early studies found that economic growth drives financial development in South Africa, and recent studies have confirmed this using different econometric models. However, the monetary aggregate (i.e. M2) utilized used in these studies is considered a weak proxy for financial development. Furthermore, the fact that the models employed do not address the endogeneity problem in the finance-growth link casts doubt on the validity of the conclusions. For this reason, the current study examines the finance growth link in South Africa using data for the period 1990 to 2011 by employing a generalized method of moments (GMM) technique that is capable of addressing endogeneity, simultaneity and omitted variable bias problems. Unlike previous cross country and country case studies that have also used the same technique, our contribution is that we account for the development of bond markets and non-bank financial institutions rather than being limited to stock market and banking sector development. We find that bond market development affects economic growth in South Africa, and no similar effect is observed for the bank and non-bank financial intermediaries and the stock market. Our findings show that examination of individual elements of the financial system is important in understanding the unique effect of each on growth. The observation that bond markets rather than private credit and stock market development promotes economic growth in South Africa induces an intriguing question as to what unique roles bond markets play that the intermediaries and equity markets are unable to play. Crucially, our results support observations in the literature that using appropriate measures of financial development is critical for policy advice. They also support the suggestion that individual elements of the financial system need to be studied separately to consider their unique roles in advancing economic growth. We believe that our understanding of the channels through which bond market contribute to growth would be a fertile ground for future research.

Keywords: bond market, finance, financial sector, growth

Procedia PDF Downloads 406

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

Abstract:

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

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

Procedia PDF Downloads 199

Authors: Hakimeh Sharififard, Mansooreh Soleimani

Abstract:

In this work, the magnetic activated carbon nanocomposite (Fe-CAC) has been synthesized by anchorage iron hydr(oxide) nanoparticles onto commercial activated carbon (CAC) surface and characterized using BET, XRF, SEM techniques. The influence of various removal parameters such as pH, contact time and initial concentration of vanadium on vanadium removal was evaluated using CAC and Fe-CAC in batch method. The sorption isotherms were studied using Langmuir, Freundlich and Dubinin–Radushkevich (D–R) isotherm models. These equilibrium data were well described by the Freundlich model. Results showed that CAC had the vanadium adsorption capacity of 37.87 mg/g, while the Fe-AC was able to adsorb 119.01 mg/g of vanadium. Kinetic data was found to confirm pseudo-second-order kinetic model for both adsorbents.

Keywords: magnetic activated carbon, remove, vanadium, nanocomposite, freundlich

Procedia PDF Downloads 447

Authors: Clifford Irikefe Gbeyonron

Abstract:

The Nigerian educational system, which has English language as a major medium of instruction, has been designed in such a way that the cognitive, psychomotor and affective endowments of the Nigerian learner could be explored. However, the human resources that would impart the desired knowledge, skills and values in the learners seem to be in short supply. This paucity is more manifest in the area of teachers of English. As a result, this research was conducted on the demand and supply of teachers of English at the junior secondary schools in Gashua, Yobe State. The results indicate that there was dearth of teachers of English the domain under review. This thus presents a challenge that should propel English language teacher education industries to produce more teachers of English. As a result, this paper recommends that the teacher production process should make use of qualified and enthusiastic teacher trainers that would be able to inculcate in-depth linguistic and communicative competence of English language and English language teaching skills in the potential teachers of English. In addition, English language education service providers should attract and retain the trained teachers of English in the business of English language teaching in such a way that all the states of Nigeria could experience educational development.

Keywords: demand, supply, teachers of English, Yobe State

Procedia PDF Downloads 364

Authors: Margaret Boone Rappaport, Christopher J. Corbally

Abstract:

The ancient ape ancestral population from which living great ape and human species evolved had demographic features affecting their evolution. The population was large, had great genetic variability, and natural selection was effective at honing adaptations. The emerging populations of chimpanzees and humans were affected more by founder effects and genetic drift because they were smaller. Natural selection did not disappear, but it was not as strong. Consequences of the 'population crash' and the human effective population size are introduced briefly. The history of the ancient apes is written in the genomes of living humans and great apes. The expansion of the brain began before the human line emerged. Coalescence times for some genes are very old – up to several million years, long before Homo sapiens. The mismatch between gene trees and species trees highlights the anthropoid speciation processes, and gives the human genome history a fuzzy, probabilistic quality. However, it suggests traits that might form a foundation for capacities emerging later. A theoretical model is presented in which the genomes of early ape populations provide the substructure for the emergence of religious capacity later on the human line. The model does not search for religion, but its foundations. It suggests a course by which an evolutionary line that began with prosimians eventually produced a human species with biologically based religious capacity. The model of the sequential emergence of religious capacity relies on cognitive science, neuroscience, paleoneurology, primate field studies, cognitive archaeology, genomics, and population genetics. And, it emphasizes five trait types: (1) Documented, positive selection of sensory capabilities on the human line may have favored survival, but also eventually enriched human religious experience. (2) The bonobo model suggests a possible down-regulation of aggression and increase in tolerance while feeding, as well as paedomorphism – but, in a human species that remains cognitively sharp (unlike the bonobo). The two species emerged from the same ancient ape population, so it is logical to search for shared traits. (3) An up-regulation of emotional sensitivity and compassion seems to have occurred on the human line. This finds support in modern genetic studies. (4) The authors’ published model of morality's emergence in Homo erectus encompasses a cognitively based, decision-making capacity that was hypothetically overtaken, in part, by religious capacity. Together, they produced a strong, variable, biocultural capability to support human sociability. (5) The full flowering of human religious capacity came with the parietal expansion and smaller face (klinorhynchy) found only in Homo sapiens. Details from paleoneurology suggest the stage was set for human theologies. Larger parietal lobes allowed humans to imagine inner spaces, processes, and beings, and, with the frontal lobe, led to the first theologies composed of structured and integrated theories of the relationships between humans and the supernatural. The model leads to the evolution of a small population of African hominins that was ready to emerge with religious capacity when the species Homo sapiens evolved two hundred thousand years ago. By 50-60,000 years ago, when human ancestors left Africa, they were fully enabled.

Keywords: genetic drift, genomics, parietal expansion, religious capacity

Procedia PDF Downloads 331

Authors: Masayuki Higashino, Toshiya Kawato, Takao Kawamura

Abstract:

A monolithic service is based on the N-tier architecture in many cases. In order to divide a monolithic service into microservices, it is necessary to redefine a model as a new microservice by extracting and merging existing models across layers. Refactoring a monolithic service into microservices requires advanced technical capabilities, and it is a difficult way. This paper proposes a design and concept to ease the migration of a monolithic service to microservices using the mobile agent technology. Our proposed approach, mobile agents-based design and concept, enables to ease dividing and merging services.

Keywords: mobile agent, microservice, web service, distributed system

Procedia PDF Downloads 151

Authors: Valentin Nickolai, Florian Pfeffel, Christian Louis Kühner

Abstract:

In many industrialized nations, the demand for skilled workers rises, causing the current market for employees to be more candidate-driven than employer-driven. Therefore, losing highly skilled and experienced employees due to early or partial retirement negatively impacts firms. Therefore, finding new ways to incentivize older employees (Silver Workers) to stay longer with the company and in their job can be crucial for the success of a firm. This study analyzes how working remotely can be a valid incentive for experienced Silver Workers to stay in their job and instead work from home with more flexible working hours. An online survey with n = 684 respondents, who are employed in the service sector, has been conducted based on 13 constructs that influence job satisfaction. These have been further categorized into three groups “classic influencing factors,” “influencing factors changed by remote working,” and new remote working influencing factors,” and were analyzed using structural equation modeling (SEM). Here, Cronbach’s alpha of the individual constructs was shown to be suitable. Furthermore, the construct validity of the constructs was confirmed by face validity, content validity, convergent validity (AVE > 0.5: CR > 0.7), and discriminant validity. Additionally, confirmatory factor analysis (CFA) confirmed the model fit for the investigated sample (CMIN/DF: 2.567; CFI: 0.927; RMSEA: 0.048). It was shown in the SEM-analysis that the influencing factor on job satisfaction, “identification with the work,” is the most significant with β = 0.540, followed by “Appreciation” (β = 0.151), “Compensation” (β = 0.124), “Work-Life-Balance” (β = 0.116), and “Communication and Exchange of Information” (β = 0.105). While the significance of each factor can vary depending on the work model, the SEM-analysis also shows that the identification with the work is the most significant factor in all three work models mentioned above and, in the case of the traditional office work model, it is the only significant influencing factor. The study shows that employees between the ages of 56 and 65 years have the highest job satisfaction when working entirely from home or remotely. Furthermore, their job satisfaction score of 5.4 on a scale from 1 (very dissatisfied) to 7 (very satisfied) is the highest amongst all age groups in any of the three work models. Due to the significantly higher job satisfaction, it can be argued that giving Silver Workers the offer to work from home or remotely can incentivize them not to opt for early retirement or partial retirement but to stay in their job full-time Furthermore, these findings can indicate that employees in the Silver Worker age are much more inclined to leave their job for early retirement if they have to entirely work in the office.

Keywords: home office, remote work instead of early or partial retirement, silver worker, structural equation modeling

Procedia PDF Downloads 64

Authors: Lei Ren, Michael Hartnett, Stephen Nash

Abstract:

The abundant measured surface current from HF radar system in coastal area is assimilated into model to improve the modeling forecasting ability. A simple sequential data assimilation scheme, Direct Insertion (DI), is applied to update model forecast states. The influence of Direct Insertion data assimilation over time is analyzed at one reference point. Vector maps of surface current from models are compared with HF radar measurements. Root-Mean-Squared-Error (RMSE) between modeling results and HF radar measurements is calculated during the last four days with no data assimilation.

Keywords: data assimilation, CODAR, HF radar, surface current, direct insertion

Procedia PDF Downloads 558

Authors: Abdelfetah Laouzai, Rachid Ouafi

Abstract:

In a traffic network, users usually choose their way so that it reduces their travel time between pairs origin-destination. This behavior might seem selfish as it produces congestions in different parts of the network. The traffic assignment problem (TAP) models the interactions between congestion and user travel decisions to obtain vehicles flows over each axis of the traffic network. The resolution methods of TAP serve as a tool allows predicting users’ distribution, identifying congesting points and affecting the travelers’ behavior in the choice of their route in the network following dynamic data. In this article, we will present a review about specific resolution approach of TAP. A comparative analysis is carried out on those approaches so that it highlights the characteristics, advantages and disadvantages of each.

Keywords: network traffic, travel decisions, approaches, traffic assignment, flows

Procedia PDF Downloads 460

Authors: Obafemi Richard Jegede

Abstract:

The relationship between adults and children is pivotal for the social, emotional, and cognitive development of the latter. This paper explores the multifaceted dynamics of adult-child relationships, emphasizing their significance in fostering positive outcomes for children's well-being. It delves into dimensions such as attachment, communication, and parenting styles, addressing their impact on children's mental health and development. Furthermore, the role of supportive environments and interventions in enhancing adult-child relationships is examined. Understanding the complexities of these relationships is crucial for promoting healthy and nurturing interactions that contribute to children's holistic development. Positive interactions with caring adults promote children's self-regulation, empathy, and resilience, while negative or inconsistent relationships can lead to emotional distress and impaired social skills. Creating supportive environments that prioritize positive adult-child relationships is essential for promoting children's well-being. By comprehensively understanding the factors that shape adult-child relationships, we can better support children's development and well-being. This paper aims to provide insights into the complexities of adult-child relationships and their profound impact on children's development and overall well-being.

Keywords: impact on children's development, supportive environments and interventions, parenting style, communication between adult and children

Procedia PDF Downloads 51

Authors: Mamouni Mohammed Dhiya Eddine

Abstract:

This work aims at studying the exploitation of high-speed networks of clusters for distributed storage. Parallel applications running on clusters require both high-performance communications between nodes and efficient access to the storage system. Many studies on network technologies led to the design of dedicated architectures for clusters with very fast communications between computing nodes. Efficient distributed storage in clusters has been essentially developed by adding parallelization mechanisms so that the server(s) may sustain an increased workload. In this work, we propose to improve the performance of distributed storage systems in clusters by efficiently using the underlying high-performance network to access distant storage systems. The main question we are addressing is: do high-speed networks of clusters fit the requirements of a transparent, efficient and high-performance access to remote storage? We show that storage requirements are very different from those of parallel computation. High-speed networks of clusters were designed to optimize communications between different nodes of a parallel application. We study their utilization in a very different context, storage in clusters, where client-server models are generally used to access remote storage (for instance NFS, PVFS or LUSTRE). Our experimental study based on the usage of the GM programming interface of MYRINET high-speed networks for distributed storage raised several interesting problems. Firstly, the specific memory utilization in the storage access system layers does not easily fit the traditional memory model of high-speed networks. Secondly, client-server models that are used for distributed storage have specific requirements on message control and event processing, which are not handled by existing interfaces. We propose different solutions to solve communication control problems at the filesystem level. We show that a modification of the network programming interface is required. Data transfer issues need an adaptation of the operating system. We detail several propositions for network programming interfaces which make their utilization easier in the context of distributed storage. The integration of a flexible processing of data transfer in the new programming interface MYRINET/MX is finally presented. Performance evaluations show that its usage in the context of both storage and other types of applications is easy and efficient.

Keywords: distributed storage, remote file access, cluster, high-speed network, MYRINET, zero-copy, memory registration, communication control, event notification, application programming interface

Procedia PDF Downloads 211

Authors: Yutae Lee

Abstract:

In this paper, we consider the performance and availability of a redundancy model. The redundancy model is a form of resilience that ensures service availability in the event of component failure. This paper considers a 2N redundancy model. In the model there are at most one active service unit and at most one standby service unit. The active one is providing the service while the standby is prepared to take over the active role when the active fails. We design our analysis model using Stochastic Reward Nets, and then evaluate the performance and availability of 2N redundancy model using Stochastic Petri Net Package (SPNP).

Keywords: availability, performance, stochastic reward net, 2N redundancy

Procedia PDF Downloads 405