Search results for: correction factors for axisymmetric models

Authors: Shuaibu Hassan Usman, Ishaq Oyebisi Oyefolahan

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This study was aimed to explore the latent factors in the web 2.0 enabled knowledge sharing practices instrument. Seven latent factors were identified through a factor analysis with orthogonal rotation and interpreted based on simple structure convergence, item loadings, and analytical statistics. The number of factors retains was based on the analysis of Kaiser Normalization criteria and Scree plot. The reliability tests revealed a satisfactory reliability scores on each of the seven latent factors of the web 2.0 enabled knowledge sharing practices. Limitation, conclusion, and future work of this study were also discussed.

Keywords: factor analysis, latent factors, knowledge sharing practices, students, web 2.0 enabled

Procedia PDF Downloads 416

Authors: Mohd Adib Abd Muin

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Social entrepreneurship has become a new phenomenon in a country in order to reduce social problems and eradicate poverty communities. However, the study based on Islamic social entrepreneurship from the social entrepreneurial activity is still new especially in the Islamic perspective. In addition, this research found that is lacking of model on social entrepreneurship that focus on Islamic perspective. Therefore, the objective of this paper is to identify the issues and research gap based on Islamic perspective from existing models and to develop a concept of Islamic social entrepreneurship according to Islamic perspective and Maqasid Shari’ah. The research method used in this study is literature review and comparative analysis from 11 existing models of social entrepreneurship. The research finding shows that 11 existing models on social entrepreneurship has been analyzed and it shows that the existing models on social entrepreneurship do not emphasize on Islamic perspective.

Keywords: component, social entrepreneurship, Islamic perspective, research gap

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Authors: Ritu Sharma, Haifeng Shen

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Teaching introductory programming is a challenging task in tertiary education and various factors are believed to have influence on students’ learning of programming. However, these factors were largely studied independently in a chosen context. This paper aims to investigate whether interrelationships exist among the factors and whether the interrelationships are context-dependent. In this empirical study, two universities were chosen from two continents, which represent different cultures, teaching methodologies, assessment criteria and languages used to teach programming in west and east worlds respectively. The results reveal that some interrelationships are common across the two different contexts, while others appear context-dependent.

Keywords: introductory programming, tertiary education, factors, interrelationships, context, empirical study

Procedia PDF Downloads 340

Authors: Soheila Sadeghi

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Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: cost prediction, machine learning, project management, random forest, neural networks

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Authors: Fakhrosadat Sajjadian

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In the USA, about 5% of women diagnosed with breast cancer annually are affected by Inflammatory Breast Cancer (IBC), which is a highly aggressive type of Locally Advanced Breast Cancer (LABC). It is a type of LABC that is clinically and pathologically different, known for its rapid growth, invasiveness, and ability to promote the growth of blood vessels. Almost all women are found to have lymph nodes affected upon diagnosis, while around 36% show obvious distant metastases. Even with the latest improvements in multimodality therapies, the outlook for patients with IBC remains bleak, as the average disease-free survival time is less than 2.5 years. Recent research on the genetic factors responsible for the IBC phenotype has resulted in the discovery of genes that play a role in the advancement of this illness. The development of primary human cell lines and animal models has assisted in this research. These advancements offer new possibilities for future actions in identifying and treating IBC.

Keywords: breast cancer, inflammation, diagnosis, IBC, LABC

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Authors: M. Wigwe, J. G Akpa, E. N Wami

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Mathematical models of the absorber of a glycol dehydration facility was developed using the principles of conservation of mass and energy. Models which predict variation of the water content of gas in mole fraction, variation of gas and liquid temperatures across the parking height were developed. These models contain contributions from bulk and diffusion flows. The effect of diffusion on the process occurring in the absorber was studied in this work. The models were validated using the initial conditions in the plant data from Company W TEG unit in Nigeria. The results obtained showed that the effect of diffusion was noticed between z=0 and z=0.004 m. A deviation from plant data of 0% was observed for the gas water content at a residence time of 20 seconds, at z=0.004 m. Similarly, deviations of 1.584% and 2.844% were observed for the gas and TEG temperatures.

Keywords: separations, absorption, simulation, dehydration, water content, triethylene glycol

Procedia PDF Downloads 478

Authors: Khaoula Hidouri, Ali Benhmidene, Bechir Chouachi

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The purification of brackish seawater becomes a necessity and not a choice against demographic and industrial growth especially in third world countries. Two models can be used in this work: simple solar still and simple solar still coupled with a heat pump. In this research, the productivity of water by Simple Solar Distiller (SSD) and Simple Solar Distiller Hybrid Heat Pump (SSDHP) was determined by the orientation, the use of heat pump, the simple or double glass cover. The productivity can exceed 1.2 L/m²h for the SSDHP and 0.5 L/m²h for SSD model. The result of the global efficiency is determined for two models SSD and SSDHP give respectively 30%, 50%. The internal efficiency attained 35% for SSD and 60% of the SSDHP models. Convective heat coefficient can be determined by attained 2.5 W/m²°C and 0.5 W/m²°C respectively for SSDHP and SSD models.

Keywords: productivity, efficiency, convective heat coefficient, SSD model, SSDHPmodel

Procedia PDF Downloads 191

Authors: Soheila Sadeghi

Abstract:

Construction projects are complex and often subject to significant cost overruns due to the multifaceted nature of the activities involved. Accurate cost estimation is crucial for effective budget planning and resource allocation. Traditional methods for predicting overruns often rely on expert judgment or analysis of historical data, which can be time-consuming, subjective, and may fail to consider important factors. However, with the increasing availability of data from construction projects, machine learning techniques can be leveraged to improve the accuracy of overrun predictions. This study applied machine learning algorithms to enhance the prediction of cost overruns in a case study of a construction project. The methodology involved the development and evaluation of two machine learning models: Random Forest and Neural Networks. Random Forest can handle high-dimensional data, capture complex relationships, and provide feature importance estimates. Neural Networks, particularly Deep Neural Networks (DNNs), are capable of automatically learning and modeling complex, non-linear relationships between input features and the target variable. These models can adapt to new data, reduce human bias, and uncover hidden patterns in the dataset. The findings of this study demonstrate that both Random Forest and Neural Networks can significantly improve the accuracy of cost overrun predictions compared to traditional methods. The Random Forest model also identified key cost drivers and risk factors, such as changes in the scope of work and delays in material delivery, which can inform better project risk management. However, the study acknowledges several limitations. First, the findings are based on a single construction project, which may limit the generalizability of the results to other projects or contexts. Second, the dataset, although comprehensive, may not capture all relevant factors influencing cost overruns, such as external economic conditions or political factors. Third, the study focuses primarily on cost overruns, while schedule overruns are not explicitly addressed. Future research should explore the application of machine learning techniques to a broader range of projects, incorporate additional data sources, and investigate the prediction of both cost and schedule overruns simultaneously.

Keywords: resource allocation, machine learning, optimization, data-driven decision-making, project management

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Authors: Alireza Nikbakht Shahbazi

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There are various factors that affect climate changes; drought is one of those factors. Investigation of efficient methods for estimating climate change impacts on drought should be assumed. The aim of this paper is to investigate climate change impacts on drought in Karoon3 watershed located south-western Iran in the future periods. The atmospheric general circulation models (GCM) data under Intergovernmental Panel on Climate Change (IPCC) scenarios should be used for this purpose. In this study, watershed drought under climate change impacts will be simulated in future periods (2011 to 2099). Standard precipitation index (SPI) as a drought index was selected and calculated using mean monthly precipitation data in Karoon3 watershed. SPI was calculated in 6, 12 and 24 months periods. Statistical analysis on daily precipitation and minimum and maximum daily temperature was performed. LRAS-WG5 was used to determine the feasibility of future period's meteorological data production. Model calibration and verification was performed for the base year (1980-2007). Meteorological data simulation for future periods under General Circulation Models and climate change IPCC scenarios was performed and then the drought status using SPI under climate change effects analyzed. Results showed that differences between monthly maximum and minimum temperature will decrease under climate change and spring precipitation shall increase while summer and autumn rainfall shall decrease. The precipitation occurs mainly between January and May in future periods and summer or autumn precipitation decline and lead up to short term drought in the study region. Normal and wet SPI category is more frequent in B1 and A2 emissions scenarios than A1B.

Keywords: climate change impact, drought severity, drought frequency, Karoon3 watershed

Procedia PDF Downloads 221

Authors: Amirmehdi Dokhanchi, Babak Ziyae

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Studying and prioritizing effective factors on productivity of human resources through TOPSIS method is the main aim of the present research study. For this reason, while reviewing concepts existing in productivity, effective factors were studied. Managers, supervisors, staff and personnel of Tabriz Tractor Manufacturing Company are considered subject of this study. Of total individuals, 160 of them were selected through the application of random sampling method as 'subject'. Two questionnaires were used for collecting data in this study. The factors, which had the highest effect on productivity, were recognized through the application of software packages. TOPSIS method was used for prioritizing recognized factors. For this reason, the second questionnaire was put available to statistics sample for studying effect of each of factors towards predetermined indicators. Therefore, decision-making matrix was obtained. The result of prioritizing factors shows that existence of accurate organizational strategy, high level of occupational skill, application of partnership and contribution system, on-the-job-training services, high quality of occupational life, dissemination of appropriate organizational culture, encouraging to creativity and innovation, and environmental factors are prioritized respectively.

Keywords: productivity of human resources, productivity indicators, TOPSIS, prioritizing factors

Procedia PDF Downloads 311

Authors: Oulad Naoui Noureddine, Cherif ELAmine, Djehiche Abdelkader

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Water resources management includes several disciplines; the modeling of rainfall-runoff relationship is the most important discipline to prevent natural risks. There are several models to study rainfall-runoff relationship in watersheds. However, the majority of these models are not applicable in all basins of the world.  In this study, a new stochastic method called The Only Corresponding Competitor method (OCC) was used for the hydrological modeling of M’ZAB   Watershed (South East of Algeria) to adapt a few empirical models for any hydrological regime.  The results obtained allow to authorize a certain number of visions, in which it would be interesting to experiment with hydrological models that improve collectively or separately the data of a catchment by the OCC method.

Keywords: modelling, optimization, rainfall-runoff relationship, empirical model, OCC

Procedia PDF Downloads 247

Authors: Candida Petrogalli, Giovanni Incerti, Luigi Solazzi

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This paper describes three lumped parameters models for the study of the dynamic behaviour of a boom crane. The models proposed here allow evaluating the fluctuations of the load arising from the rope and structure elasticity and from the type of the motion command imposed by the winch. A calculation software was developed in order to determine the actual acceleration of the lifted mass and the dynamic overload during the lifting phase. Some application examples are presented, with the aim of showing the correlation between the magnitude of the stress and the type of the employed motion command.

Keywords: crane, dynamic model, overloading condition, vibration

Procedia PDF Downloads 553

Authors: N. Sena, C. Vasconcelos

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Portugal has a lower rate of people who exercise. Fitness centers are a widely recognized context for the performance of an exercise. Thus, the objective of this study is to analyze the motivational factors for the practice of exercise in a sample of Portuguese fitness center users. The sample consists of 34 users (23 men and 11 women), aged between 16 and 60 years old (24.7 ± 11,5 years old). The instrument used for data collection was the Motivation Questionnaire for Exercise (version translated and validated into Portuguese), consisting of forty-nine items grouped into ten motivational factors. Responses to the Exercise Motivation Questionnaire are given on a 6-point Likert scale (0="not at all true for me" to 5="completely true for me"). With regard to the results, it is possible to verify that the motivational factors considered most relevant by the sample of our study were “Well-being” (4.44 ± 0.28), followed by “Health” (4.29 ± 0.57) and “Stress Management” (4.06 ± 0.54). The factors “Affiliation” (3.11 ± 0.49) “Personal Appreciation” (2.26 ± 0.59) and “Medical History” (1.71 ± 0.74) were considered by the respondents to be the least important factors for performing the exercise. The conclusion of this study is that in the sample of this study, the factors that most motivated the practice of exercise were “Well-being”, “Health” and “Stress Management”. In the opposite direction, the factors that least motivated the individuals in this sample to practice exercise were “Affiliation”, “Personal Appreciation” and “Medical History”.

Keywords: exercise, fitness center users, motivational factors, Portugal

Procedia PDF Downloads 69

Authors: Linda Smail, Zineb Azouz

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Given a Bayesian network relative to a set I of discrete random variables, we are interested in computing the probability distribution P(S) where S is a subset of I. The general idea is to write the expression of P(S) in the form of a product of factors where each factor is easy to compute. More importantly, it will be very useful to give an interpretation of each of the factors in terms of conditional probabilities. This paper considers a semantic interpretation of the factors involved in computing marginal probabilities in Bayesian networks. Establishing such a semantic interpretations is indeed interesting and relevant in the case of large Bayesian networks.

Keywords: Bayesian networks, D-Separation, level two Bayesian networks, factorization of computation

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Authors: Anuruddha Jayasuriya, Thanakorn Pheeraphan

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In this paper, a review of different mathematical models which can be used as prediction tools to assess the time to crack reinforced concrete (RC) due to corrosion is investigated. This investigation leads to an experimental study to validate a selected prediction model. Most of these mathematical models depend upon the mechanical behaviors, chemical behaviors, electrochemical behaviors or geometric aspects of the RC members during a corrosion process. The experimental program is designed to verify the accuracy of a well-selected mathematical model from a rigorous literature study. Fundamentally, the experimental program exemplifies both one-dimensional chloride diffusion using RC squared slab elements of 500 mm by 500 mm and two-dimensional chloride diffusion using RC squared column elements of 225 mm by 225 mm by 500 mm. Each set consists of three water-to-cement ratios (w/c); 0.4, 0.5, 0.6 and two cover depths; 25 mm and 50 mm. 12 mm bars are used for column elements and 16 mm bars are used for slab elements. All the samples are subjected to accelerated chloride corrosion in a chloride bath of 5% (w/w) sodium chloride (NaCl) solution. Based on a pre-screening of different models, it is clear that the well-selected mathematical model had included mechanical properties, chemical and electrochemical properties, nature of corrosion whether it is accelerated or natural, and the amount of porous area that rust products can accommodate before exerting expansive pressure on the surrounding concrete. The experimental results have shown that the selected model for both one-dimensional and two-dimensional chloride diffusion had ±20% and ±10% respective accuracies compared to the experimental output. The half-cell potential readings are also used to see the corrosion probability, and experimental results have shown that the mass loss is proportional to the negative half-cell potential readings that are obtained. Additionally, a statistical analysis is carried out in order to determine the most influential factor that affects the time to corrode the reinforcement in the concrete due to chloride diffusion. The factors considered for this analysis are w/c, bar diameter, and cover depth. The analysis is accomplished by using Minitab statistical software, and it showed that cover depth is the significant effect on the time to crack the concrete from chloride induced corrosion than other factors considered. Thus, the time predictions can be illustrated through the selected mathematical model as it covers a wide range of factors affecting the corrosion process, and it can be used to predetermine the durability concern of RC structures that are vulnerable to chloride exposure. And eventually, it is further concluded that cover thickness plays a vital role in durability in terms of chloride diffusion.

Keywords: accelerated corrosion, chloride diffusion, corrosion cracks, passivation layer, reinforcement corrosion

Procedia PDF Downloads 198

Authors: Khaled M. Alhawiti

Abstract:

This research study aims to present a retrospective study about speech recognition systems and artificial intelligence. Speech recognition has become one of the widely used technologies, as it offers great opportunity to interact and communicate with automated machines. Precisely, it can be affirmed that speech recognition facilitates its users and helps them to perform their daily routine tasks, in a more convenient and effective manner. This research intends to present the illustration of recent technological advancements, which are associated with artificial intelligence. Recent researches have revealed the fact that speech recognition is found to be the utmost issue, which affects the decoding of speech. In order to overcome these issues, different statistical models were developed by the researchers. Some of the most prominent statistical models include acoustic model (AM), language model (LM), lexicon model, and hidden Markov models (HMM). The research will help in understanding all of these statistical models of speech recognition. Researchers have also formulated different decoding methods, which are being utilized for realistic decoding tasks and constrained artificial languages. These decoding methods include pattern recognition, acoustic phonetic, and artificial intelligence. It has been recognized that artificial intelligence is the most efficient and reliable methods, which are being used in speech recognition.

Keywords: speech recognition, acoustic phonetic, artificial intelligence, hidden markov models (HMM), statistical models of speech recognition, human machine performance

Procedia PDF Downloads 458

Authors: S. Ghorbani, N. I. Polushin

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In this study, an approach to identify factors affecting on surface roughness in a machining process is presented. This study is based on 81 data about surface roughness over a wide range of cutting tools (conventional, cutting tool with holes, cutting tool with composite material), workpiece materials (AISI 1045 Steel, AA2024 aluminum alloy, A48-class30 gray cast iron), spindle speed (630-1000 rpm), feed rate (0.05-0.075 mm/rev), depth of cut (0.05-0.15 mm) and tool overhang (41-65 mm). A single decision tree (SDT) analysis was done to identify factors for predicting a model of surface roughness, and the CART algorithm was employed for building and evaluating regression tree. Results show that a single decision tree is better than traditional regression models with higher rate and forecast accuracy and strong value.

Keywords: cutting condition, surface roughness, decision tree, CART algorithm

Procedia PDF Downloads 353

Authors: Hadi Kafil, Ali Ecder

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Heat transfer by natural convection in two-dimensional and three-dimensional axisymmetric enclosure fitted with partially heated vertical walls is investigated numerically. The range of Rayleigh number is varied from 10³ until convective flow becomes unstable. This research focuses on multiplicity and transition phenomena in natural convection and is based on a parametric analysis to study the onset of bifurcations. It is found that, even at low Rayleigh numbers, the flow undergoes a series of turning-point bifurcations which increase the rate of natural convention. On the other hand, by partially heating or cooling the walls, more effective results can be achieved for both heating and cooling applications, such as cooling of electronic devices and heating processes in solidification and crystal growth.

Keywords: natural convection, partial heated, onset of bifurcation, Rayleigh number

Procedia PDF Downloads 347

Authors: Tholokuhle T. Ntshakala, Seraphin D. Eyono Obono

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Nations are still finding it quite difficult to win mega sport competitions despite the major contribution of sport to society in terms of social and economic development, personal health, and in education. Even though the world of sports has been transformed into a huge global economy, it is important to note that the first step of sport is usually its introduction to children at school through physical education or PE. In other words, nations who do not win mega sport competitions also suffer from a weak and neglected PE system. This problem of the neglect of PE systems is the main motivation of this research aimed at examining the factors affecting the perceived awareness of physical education teachers on the ICT's that are adoptable for the teaching and learning of physical education. Two types of research objectives will materialize this aim: relevant theories will be identified in relation to the analysis of the perceived ICT awareness of PE teachers and subsequent models will be compiled and designed from existing literature; the empirical testing of such theories and models will also be achieved through the survey of PE teachers from the Camperdown magisterial district of the KwaZulu-Natal province of South Africa. The main hypothesis at the heart of this study is the relationship between the demographics of PE teachers, their behavior both as individuals and as social entities, and their perceived awareness of the ICTs that are adoptable for PE, as postulated by existing literature; except that this study categorizes human behavior under performance expectancy, computer attitude, and social influence. This hypothesis was partially confirmed by the survey conducted by this research in the sense that performance expectancy and teachers’ age, gender, computer usage, and class size were found to be the only factors affecting their awareness of ICT's for physical education.

Keywords: human behavior, ICT Awareness, physical education, teachers

Procedia PDF Downloads 245

Authors: Abiodun Ladanu Ajala, Wuyi Oke

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

Keywords: linear programming, mutation, optimization, simulation

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Authors: Lucie Cote, Sonia McFadden

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Mental health problems in the workplace are currently one of the main causes of absences. Research work has highlighted the importance of a collaborative process involving the stakeholders in the return-to-work process and has established the best management practices to ensure a successful return-to-work. However, very few studies have specifically explored the combination of various management models and determined whether they could satisfy the needs of the stakeholders. The objective of this study is to analyze two models for managing the return to work: the ‘medical-administrative’ and the ‘support of the worker’ in order to understand the actions and actors involved in these models. The study also aims to explore whether these models meet the needs of the actors involved in the management of the return to work. A qualitative case study was conducted in a Canadian federal organization. An abundant internal documentation and semi-directed interviews with six managers, six workers and four human resources professionals involved in the management of records of employees returning to work after a mental health problem resulted in a complete picture of the return to work management practices used in this organization. The triangulation of this data facilitated the examination of the benefits and limitations of each approach. The results suggest that the actions of management for employee return to work from both models of management ‘support of the worker’ and ‘medical-administrative’ are compatible and can meet the needs of the actors involved in the return to work. More research is needed to develop a structured model integrating best practices of the two approaches to ensure the success of the return to work.

Keywords: return to work, mental health, management models, organizations

Procedia PDF Downloads 198

Authors: Subhadip Biswas, Shivendra Maurya, Satish Chandra, Indrajit Ghosh

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Use of speed prediction models sometimes appears as a feasible alternative to laborious field measurement particularly, in case when field data cannot fulfill designer’s requirements. However, developing speed models is a challenging task specifically in the context of developing countries like India where vehicles with diverse static and dynamic characteristics use the same right of way without any segregation. Here the traffic composition plays a significant role in determining the vehicular speed. The present research was carried out to examine the effects of traffic volume and its composition on vehicular speed under mixed traffic conditions. Classified traffic volume and speed data were collected from different geometrically identical six lane divided arterials in New Delhi. Based on these field data, speed prediction models were developed for individual vehicle category adopting Kriging approximation technique, an alternative for commonly used regression. These models are validated with the data set kept aside earlier for validation purpose. The predicted speeds showed a great deal of agreement with the observed values and also the model outperforms all other existing speed models. Finally, the proposed models were utilized to evaluate the effect of traffic volume and its composition on speed.

Keywords: speed, Kriging, arterial, traffic volume

Procedia PDF Downloads 336

Authors: Shryas Bhurat, Aryan Vashistha, Sampreet Dinakar Nayak, Ayush Gupta

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As the technology is advancing more towards high computational resources, there is a paradigm shift in the usage of these resources to optimize the design process. This paper discusses the usage of ‘Generative Design using Artificial Intelligence’ to build better models that adapt the operations like selection, mutation, and crossover to generate results. The human mind thinks of the simplest approach while designing an object, but the intelligence learns from the past & designs the complex optimized CAD Models. Generative Design takes the boundary conditions and comes up with multiple solutions with iterations to come up with a sturdy design with the most optimal parameter that is given, saving huge amounts of time & resources. The new production techniques that are at our disposal allow us to use additive manufacturing, 3D printing, and other innovative manufacturing techniques to save resources and design artistically engineered CAD Models. Also, this paper discusses the Genetic Algorithm, the Non-Domination technique to choose the right results using biomimicry that has evolved for current habitation for millions of years. The computer uses parametric models to generate newer models using an iterative approach & uses cloud computing to store these iterative designs. The later part of the paper compares the topology optimization technology with Generative Design that is previously being used to generate CAD Models. Finally, this paper shows the performance of algorithms and how these algorithms help in designing resource-efficient models.

Keywords: genetic algorithm, bio mimicry, generative modeling, non-dominant techniques

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Authors: Ning Chang, Zelong Yuan, Yunpeng Wang, Jianchun Wang

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The utilization of Large Eddy Simulation (LES) has been extensive in turbulence research. LES concentrates on resolving the significant grid-scale motions while representing smaller scales through subfilter-scale (SFS) models. The deconvolution model, among the available SFS models, has proven successful in LES of engineering and geophysical flows. Nevertheless, the thorough investigation of how sub-filter scale dynamics and filter anisotropy affect SFS modeling accuracy remains lacking. The outcomes of LES are significantly influenced by filter selection and grid anisotropy, factors that have not been adequately addressed in earlier studies. This study examines two crucial aspects of LES: Firstly, the accuracy of direct deconvolution models (DDM) is evaluated concerning sub-filter scale (SFS) dynamics across varying filter-to-grid ratios (FGR) in isotropic turbulence. Various invertible filters are employed, including Gaussian, Helmholtz I and II, Butterworth, Chebyshev I and II, Cauchy, Pao, and rapidly decaying filters. The importance of FGR becomes evident as it plays a critical role in controlling errors for precise SFS stress prediction. When FGR is set to 1, the DDM models struggle to faithfully reconstruct SFS stress due to inadequate resolution of SFS dynamics. Notably, prediction accuracy improves when FGR is set to 2, leading to accurate reconstruction of SFS stress, except for cases involving Helmholtz I and II filters. Remarkably high precision, nearly 100%, is achieved at an FGR of 4 for all DDM models. Furthermore, the study extends to filter anisotropy and its impact on SFS dynamics and LES accuracy. By utilizing the dynamic Smagorinsky model (DSM), dynamic mixed model (DMM), and direct deconvolution model (DDM) with anisotropic filters, aspect ratios (AR) ranging from 1 to 16 are examined in LES filters. The results emphasize the DDM’s proficiency in accurately predicting SFS stresses under highly anisotropic filtering conditions. Notably high correlation coefficients exceeding 90% are observed in the a priori study for the DDM’s reconstructed SFS stresses, surpassing those of the DSM and DMM models. However, these correlations tend to decrease as filter anisotropy increases. In the a posteriori analysis, the DDM model consistently outperforms the DSM and DMM models across various turbulence statistics, including velocity spectra, probability density functions related to vorticity, SFS energy flux, velocity increments, strainrate tensors, and SFS stress. It is evident that as filter anisotropy intensifies, the results of DSM and DMM deteriorate, while the DDM consistently delivers satisfactory outcomes across all filter-anisotropy scenarios. These findings underscore the potential of the DDM framework as a valuable tool for advancing the development of sophisticated SFS models for LES in turbulence research.

Keywords: deconvolution model, large eddy simulation, subfilter scale modeling, turbulence

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Authors: Nessreen Y. Ibrahim

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Designing the future according to realistic analytical study for the futuristic market needs can be a milestone strategy to make a huge improvement in developing countries economics. In developing countries, access to high technology and latest science approaches is very limited. The financial problems in low and medium income countries have negative effects on the kind and quality of imported new technologies and application for their markets. Thus, there is a strong need for shifting paradigm thinking in the design process to improve and evolve their development strategy. This paper discusses future possibilities in developing countries, and how they can design their own future according to specific future models FDM (Future Design Models), which established to solve certain economical problems, as well as political and cultural conflicts. FDM is strategic thinking framework provides an improvement in both content and process. The content includes; beliefs, values, mission, purpose, conceptual frameworks, research, and practice, while the process includes; design methodology, design systems, and design managements tools. In this paper the main objective was building an innovative economic model to design a chosen possible futuristic scenario; by understanding the market future needs, analyze real world setting, solve the model questions by future driven design, and finally interpret the results, to discuss to what extent the results can be transferred to the real world. The paper discusses Egypt as a potential case study. Since, Egypt has highly complex economical problems, extra-dynamic political factors, and very rich cultural aspects; we considered Egypt is a very challenging example for applying FDM. The paper results recommended using FDM numerical modeling as a starting point to design the future.

Keywords: developing countries, economic models, future design, possible futures

Procedia PDF Downloads 250

Authors: Zhu Xinxin, Wang Hui, Yang Kai

Abstract:

Heat flux is one of the most important test parameters in the ground thermal protection test. Slug calorimeter is selected as the main sensor measuring heat flux in arc wind tunnel test due to the convenience and low cost. However, because of excessive lateral heat transfer and the disadvantage of the calculation method, the heat flux measurement error of the slug calorimeter is large. In order to enhance measurement accuracy, the heat insulation structure and heat flux calculation method of slug calorimeter were improved. The heat transfer model of the slug calorimeter was built according to the energy conservation principle. Based on the heat transfer model, the insulating sleeve of the hollow structure was designed, which helped to greatly decrease lateral heat transfer. And the slug with insulating sleeve of hollow structure was encapsulated using a package shell. The improved insulation structure reduced heat loss and ensured that the heat transfer characteristics were almost the same when calibrated and tested. The heat flux calibration test was carried out in arc lamp system for heat flux sensor calibration, and the results show that test accuracy and precision of slug calorimeter are improved greatly. In the meantime, the simulation model of the slug calorimeter was built. The heat flux values in different temperature rise time periods were calculated by the simulation model. The results show that extracting the data of the temperature rise rate as soon as possible can result in a smaller heat flux calculation error. Then the different thermal contact resistance affecting calculation error was analyzed by the simulation model. The contact resistance between the slug and the insulating sleeve was identified as the main influencing factor. The direct comparison calibration correction method was proposed based on only heat flux calibration. The numerical calculation correction method was proposed based on the heat flux calibration and simulation model of slug calorimeter after the simulation model was solved by solving the contact resistance between the slug and the insulating sleeve. The simulation and test results show that two methods can greatly reduce the heat flux measurement error. Finally, the improved slug calorimeter was tested in the arc wind tunnel. And test results show that the repeatability accuracy of improved slug calorimeter is less than 3%. The deviation of measurement value from different slug calorimeters is less than 3% in the same fluid field. The deviation of measurement value between slug calorimeter and Gordon Gage is less than 4% in the same fluid field.

Keywords: correction method, heat flux calculation, heat insulation structure, heat transfer model, slug calorimeter

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Authors: Debjyoti Bhakat, Indranil Mondal, Asish K. Mukhopadayay, Nabendu S. Chatterjee

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Enterotoxigenic Escherichia coli is one of the leading causes of diarrhea in infants and travelers in developing countries. Colonization factors play an important role in pathogenesis and are one of the main targets for Enterotoxigenic Escherichia coli (ETEC) vaccine development. However, ETEC vaccines had poorly performed in the past, as the prevalence of colonization factors is region-dependent. There are more than 25 classical colonization factors presently known to be expressed by ETEC, although all are not expressed together. Further, there are other multiple non-classical virulence factors that are also identified. Here the presence and expression of common classical and non-classical virulence factors were studied. Further studies were done on the expression of prevalent colonization factors in different strains. For the prevalence determination, multiplex polymerase chain reaction (PCR) was employed, which was confirmed by simplex PCR. Quantitative RT-PCR was done to study the RNA expression of these virulence factors. Strains negative for colonization factors expression were confirmed by SDS-PAGE. Among the clinical isolates, the most prevalent toxin was est+elt, followed by est and elt, while the pattern was reversed in the control strains. There were 29% and 40% strains negative for any classical colonization factors (CF) or non-classical virulence factors (NCVF) among the clinical and control strains, respectively. Among CF positive ETEC strains, CS6 and CS21 were the prevalent ones in the clinical strains, whereas in control strains, CS6 was the predominant one. For NCVF genes, eatA was the most prevalent among the clinical isolates and etpA for control. CS6 was the most expressed CF, and eatA was the predominantly expressed NCVF for both clinical and controlled ETEC isolates. CS6 expression was more in strains having CS6 alone. Different strains express CS6 at different levels. Not all strains expressed their respective virulence factors. Understanding the prevalent colonization factor, CS6, and its nature of expression will contribute to designing an effective vaccine against ETEC in this region of the globe. The expression pattern of CS6 also will help in examining the relatedness between the ETEC subtypes.

Keywords: classical virulence factors, CS6, diarrhea, enterotoxigenic escherichia coli, expression, non-classical virulence factors

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Authors: Showkat Ahmad Bhat, M. S. Bhatt

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The study evaluates the ex-post impact of institutional credit on agricultural output. It first examines the key factors that influence the accessibility of institutional credit by farm households. For quantitative analysis both program participant and non-participant respondents were drawn and cross-sectional survey data were collected from 412 households in Pulwama District of Jammu & Kashmir (India). Propensity Score Matching Method was employed to analyze the impact of the institutional credit on agricultural output. Results show that institutional credit has a positive and significant impact on the agricultural output measured in terms of farm income and crop productivity. To estimate the accessibility of credit, an examination of both demand side and supply side factors were carried out. The demand for credit was measured with respect to respondents who applied for credit. Supply side credit allocation measured in terms of the proportion of ‘credit amount’ farmers obtained. Logit and Two-limit Tobit Regression Models were used to investigate the determinants that influence the accessibility of formal credit for Demand for and supply of credit respectively. The estimated results suggested that the demand for credit is positively and significantly affected by the factors such as: age of the household head, formal education, membership, cash crop grown, farm size and saving account. All the variables were found significantly increasing the household’s likelihood to demand for and supply of credit from banks. However, the impact of these factors varies considerably across the credit markets. Factors which were found negatively and significantly influencing the accessibility of credit were: ‘square of the age’, household assets and rate of interest. The credit constraints analysis suggested that square of the age; household assets and rate of interest were the three most important factors that increased the probability of being constrained. The study finally discusses these results in detail and draws some recommendations.

Keywords: institutional credit, agriculture, propensity score matching logit model, Tobit model

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Authors: Verónica Diaz Quezada

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This research examines the teaching models used by secondary math teachers when teaching logarithmic, quadratic and exponential functions. For this, descriptive case studies have been carried out on 5 secondary teachers. These teachers have been chosen from 3 scientific-humanistic and technical schools, in Chile. Data have been obtained through non-participant class observation and the application of a questionnaire and a rubric to teachers. According to the results, the didactic model that prevails is the one that starts with an interactive strategy, moves to a more content-based structure, and ends with a reinforcement stage. Nonetheless, there is always influence from teachers, their methods, and the group of students.

Keywords: teaching models, math teachers, functions, secondary education

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Authors: Shayem Saleh ALresheedi, Lu Song Feng, Abdulaziz Abdulwahab M. Fatani

Abstract:

Cloud computing is an emerging concept in the technological sphere. Its development enables many applications to avail information online and on demand. It is becoming an essential element for businesses due to its ability to diminish the costs of IT infrastructure and is being adopted in Saudi Arabia. However, there exist many factors that affect its adoption. Several researchers in the field have ignored the study of the TAM model for identifying the relevant factors and their impact for adopting of cloud computing. This study focuses on evaluating the acceptability of cloud computing and analyzing its impacting factors using Technology Acceptance Model (TAM) of technology adoption in Saudi Arabia. It suggests a model to examine the influential factors of the TAM model along with external factors of technical support in adapting the cloud computing. The proposed model has been tested through the use of multiple hypotheses based on calculation tools and collected data from customers through questionnaires. The findings of the study prove that the TAM model along with external factors can be applied in measuring the expected adoption of cloud computing. The study presents an investigation of influential factors and further recommendation in adopting cloud computing in Saudi Arabia.

Keywords: cloud computing, acceptability, adoption, determinants

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