Search results for: artificial social modeling

Authors: José Neves, M. Rosário Martins, Fátima Candeias, Diana Ferreira, Sílvia Arantes, Júlio Cruz-Morais, Guida Gomes, Joaquim Macedo, António Abelha, Henrique Vicente

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Some plants of genus Schinus have been used in the folk medicine as topical antiseptic, digestive, purgative, diuretic, analgesic or antidepressant, and also for respiratory and urinary infections. Chemical composition of essential oils of S. molle and S. terebinthifolius had been evaluated and presented high variability according with the part of the plant studied and with the geographic and climatic regions. The pharmacological properties, namely antimicrobial, anti-tumoural and anti-inflammatory activities are conditioned by chemical composition of essential oils. Taking into account the difficulty to infer the pharmacological properties of Schinus essential oils without hard experimental approach, this work will focus on the development of a decision support system, in terms of its knowledge representation and reasoning procedures, under a formal framework based on Logic Programming, complemented with an approach to computing centered on Artificial Neural Networks and the respective Degree-of-Confidence that one has on such an occurrence.

Keywords: artificial neuronal networks, essential oils, knowledge representation and reasoning, logic programming, Schinus molle L., Schinus terebinthifolius Raddi

Procedia PDF Downloads 538

Authors: Ferah Tesfaye Admasu

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Autism Spectrum Disorder (ASD) often presents significant communication challenges, particularly for non-verbal individuals who struggle to express their needs and emotions effectively. Assistive technologies (AT) have emerged as vital tools in enhancing communication abilities for this population. Recent advancements in artificial intelligence (AI) hold the potential to revolutionize the design and functionality of these technologies. This study explores the application of AI in developing intelligent, adaptive, and user-centered assistive technologies for non-verbal individuals with autism. Through a review of current AI-driven tools, including speech-generating devices, predictive text systems, and emotion-recognition software, this research investigates how AI can bridge communication gaps, improve engagement, and support independence. Machine learning algorithms, natural language processing (NLP), and facial recognition technologies are examined as core components in creating more personalized and responsive communication aids. The study also discusses the challenges and ethical considerations involved in deploying AI-based AT, such as data privacy and the risk of over-reliance on technology. Findings suggest that integrating AI into assistive technologies can significantly enhance the quality of life for non-verbal individuals with autism, providing them with greater opportunities for social interaction and participation in daily activities. However, continued research and development are needed to ensure these technologies are accessible, affordable, and culturally sensitive.

Keywords: artificial intelligence, autism spectrum disorder, non-verbal communication, assistive technology, machine learning

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Authors: Mojtaba Valinejadshoubi, Azin Shakibabarough, Ashutosh Bagchi

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Facilities such as residential buildings, office buildings, and hospitals house large density of occupants. Therefore, a low-cost facility management program (FMP) should be used to provide a satisfactory built environment for these occupants. Facility management (FM) has been recently used in building projects as a critical task. It has been effective in reducing operation and maintenance cost of these facilities. Issues of information integration and visualization capabilities are critical for reducing the complexity and cost of FM. Building information modeling (BIM) can be used as a strong visual modeling tool and database in FM. The main objective of this study is to examine the applicability of BIM in the FM process during a building’s operational phase. For this purpose, a seven-storey office building is modeled Autodesk Revit software. Authors integrated the cloud-based environment using a visual programming tool, Dynamo, for the purpose of having a real-time cloud-based communication between the facility managers and the participants involved in the project. An appropriate and effective integrated data source and visual model such as BIM can reduce a building’s operational and maintenance costs by managing the building life cycle properly.

Keywords: building information modeling, facility management, operational phase, building life cycle

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Authors: Hemin Yuan, Yun Wang, Xiangchun Wang

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Gas hydrate is drawing attention due to the fact that it has an enormous amount all over the world, which is almost twice the conventional hydrocarbon reserves, making it a potential alternative source of energy. It is widely distributed in permafrost and continental ocean shelves, and many countries have launched national programs for investigating the gas hydrate. Gas hydrate is mainly explored through seismic methods, which include bottom simulating reflectors (BSR), amplitude blanking, and polarity reverse. These seismic methods are effective at finding the gas hydrate formations but usually contain large uncertainties when applying to invert the micro-scale petrophysical properties of the formations due to lack of constraints. Rock physics modeling links the micro-scale structures of the rocks to the macro-scale elastic properties and can work as effective constraints for the seismic methods. A number of rock physics models have been proposed for gas hydrate modeling, which addresses different mechanisms and applications. However, these models are generally not well classified, and it is confusing to determine the appropriate model for a specific study. Moreover, since the modeling usually involves multiple models and steps, it is difficult to determine the source of uncertainties. To solve these problems, we summarize the developed models/methods and make four classifications of the models according to the hydrate micro-scale morphology in sediments, the purpose of reservoir characterization, the stage of gas hydrate generation, and the lithology type of hosting sediments. Some sub-categories may overlap each other, but they have different priorities. Besides, we also analyze the priorities of different models, bring up the shortcomings, and explain the appropriate application scenarios. Moreover, by comparing the models, we summarize a general workflow of the modeling procedure, which includes rock matrix forming, dry rock frame generating, pore fluids mixing, and final fluid substitution in the rock frame. These procedures have been widely used in various gas hydrate modeling and have been confirmed to be effective. We also analyze the potential sources of uncertainties in each modeling step, which enables us to clearly recognize the potential uncertainties in the modeling. In the end, we explicate the general problems of the current models, including the influences of pressure and temperature, pore geometry, hydrate morphology, and rock structure change during gas hydrate dissociation and re-generation. We also point out that attenuation is also severely affected by gas hydrate in sediments and may work as an indicator to map gas hydrate concentration. Our work classifies rock physics models of gas hydrate into different categories, generalizes the modeling workflow, analyzes the modeling uncertainties and potential problems, which can facilitate the rock physics characterization of gas hydrate bearding sediments and provide hints for future studies.

Keywords: gas hydrate, rock physics model, modeling classification, hydrate morphology

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Authors: Ashok Tejankar, Rohan K. Pathrikar

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Maharashtra is covered by heterogeneous flows of Deccan basaltic terrains of upper cretaceous to lower Eocene age. It consist mainly different types of basalt flow, having heterogeneous Geohydrological characters. The study area Aurangabad dist. lies in the central part of Maharashtra. The study area is typically covered by Deccan traps formation mainly basalt type of igneous volcanic rock. The area is located in the survey of India toposheet No. 47M and laying between 19° to 20° north latitudes and 74° to 76° east longitudes. Groundwater is the primary source for fresh water in the study area. There has been a growing demand for fresh water in domestic & agriculture sectors. Due to over exploitation and rainfall failure has been created an irrecoverable stress on groundwater in study area. In an effort to maintain the water table condition in balance, artificial recharge is being implemented. The selection of site for artificial recharge is a very important task in recharge basalt. The present study aims at sitting artificial recharge structure at village Pachod in basaltic terrain of the Godavari-Purna river basin in Aurangabad district of Maharashtra, India. where the average annual rainfall is 650mm. In this investigation, integrated remote sensing and GIS techniques were used and various parameters like lithology, structure, etc. aspect of drainage basins, landforms and other parameters were extracted from visual interpretation of IRS P6 Satellite data and Survey of India (SIO) topographical sheets, aided by field checks by carrying well inventory survey. The depth of weathered material, water table conditions, and rainfall data were been considered. All the thematic information layers were digitized and analyzed in Arc-GIS environment and the composite maps produced show suitable site, depth of bed rock flows for successful artificial recharge in village Pachod to increase groundwater potential of low laying area.

Keywords: hard rock, artificial recharge, remote sensing, GIS

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Authors: Ali S. Gargoum

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Graphical modeling plays an important role in providing efficient probability calculations in high dimensional problems (computational efficiency). In this paper, we address one of such problems where we discuss fragmenting puff models and some distributional assumptions concerning models for the instantaneous, emission readings and for the fragmenting process. A graphical representation in terms of a junction tree of the conditional probability breakdown of puffs and puff fragments is proposed.

Keywords: graphical models, influence diagrams, junction trees, Bayesian nets

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Authors: Cuneyt Yucelbas, Seral Ozsen, Sule Yucelbas, Gulay Tezel

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Due to the fact that there exist only a small number of complex systems in artificial immune system (AIS) that work out nonlinear problems, nonlinear AIS approaches, among the well-known solution techniques, need to be developed. Gaussian function is usually used as similarity estimation in classification problems and pattern recognition. In this study, diagnosis of breast cancer, the second type of the most widespread cancer in women, was performed with different distance calculation functions that euclidean, gaussian and gaussian-euclidean hybrid function in the clonal selection model of classical AIS on Wisconsin Breast Cancer Dataset (WBCD), which was taken from the University of California, Irvine Machine-Learning Repository. We used 3-fold cross validation method to train and test the dataset. According to the results, the maximum test classification accuracy was reported as 97.35% by using of gaussian-euclidean hybrid function for fold-3. Also, mean of test classification accuracies for all of functions were obtained as 94.78%, 94.45% and 95.31% with use of euclidean, gaussian and gaussian-euclidean, respectively. With these results, gaussian-euclidean hybrid function seems to be a potential distance calculation method, and it may be considered as an alternative distance calculation method for hard nonlinear classification problems.

Keywords: artificial immune system, breast cancer diagnosis, Euclidean function, Gaussian function

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Authors: Babar Sultan, Kiran Sultan, Waseem Khan, Ijaz Mansoor Qureshi

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In this paper, a novel idea for the performance enhancement of secondary network is proposed for Underlay Cognitive Radio Networks (CRNs). In Underlay CRNs, primary users (PUs) impose strict interference constraints on the secondary users (SUs). The proposed scheme is based on Artificial Bee Colony (ABC) optimization for relay selection and power allocation to handle the highlighted primary challenge of Underlay CRNs. ABC is a simple, population-based optimization algorithm which attains global optimum solution by combining local search methods (Employed and Onlooker Bees) and global search methods (Scout Bees). The proposed two-phase relay selection and power allocation algorithm aims to maximize the signal-to-noise ratio (SNR) at the destination while operating in an underlying mode. The proposed algorithm has less computational complexity and its performance is verified through simulation results for a different number of potential relays, different interference threshold levels and different transmit power thresholds for the selected relays.

Keywords: artificial bee colony, underlay spectrum sharing, cognitive radio networks, amplify-and-forward

Procedia PDF Downloads 578

Authors: Mohammad Sheikhalishahi, Vahid Ebrahimipour, Amir Hossein Radman-Kian

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This paper proposes an Ontology framework for knowledge modeling and representation of the equipment integrity process in a typical oil and gas production plant. Our aim is to construct a knowledge modeling that facilitates translation, interpretation, and conversion of human-readable integrity interpretation into computer-readable representation. The framework provides a function structure related to fault propagation using ISO 14224 and ISO 15926 OWL-Lite/ Resource Description Framework (RDF) to obtain a generic system-level model of asset integrity that can be utilized in the integrity engineering process during the equipment life cycle. It employs standard terminology developed by ISO 15926 and ISO 14224 to map textual descriptions of equipment failure and then convert it to a causality-driven logic by semantic interpretation and computer-based representation using Lite/RDF. The framework applied for an offshore gas riser. The result shows that the approach can cross-link the failure-related integrity words and domain-specific logic to obtain a representation structure of equipment integrity with causality inference based on semantic extraction of inspection report context.

Keywords: asset integrity modeling, interoperability, OWL, RDF/XML

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Authors: Phillip Garrad, Saritha Unnikrishnan

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The recent popularity of connected and autonomous vehicles (CAV) corresponds with an increase in the risk of cyber-attacks. These cyber-attacks have been instigated by both researchers or white-coat hackers and cyber-criminals. As Connected Vehicles move towards full autonomy, the impact of these cyber-attacks also grows. The current research details challenges faced in cybersecurity testing of CAV, including access and cost of the representative test setup. Other challenges faced are lack of experts in the field. Possible solutions to how these challenges can be overcome are reviewed and discussed. From these findings, a software simulated CAV network is established as a cost-effective representative testbed. Penetration tests are then performed on this simulation, demonstrating a cyber-attack in CAV. Studies have shown Artificial Intelligence (AI) to improve runtime, increase efficiency and comprehensively cover all the typical test aspects in penetration testing in other industries. There is an attempt to introduce similar AI models to the software simulation. The expectation from this implementation is to see similar improvements in runtime and efficiency for the CAV model. If proven to be an effective means of penetration test for CAV, this methodology may be used on a full CAV test network.

Keywords: cybersecurity, connected vehicles, software simulation, artificial intelligence, penetration testing

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Authors: Mustapha Kamel Mihoubi, Hocine Dahmani

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Over recent years much progress has been achieved in the fields of numerical modeling shoreline processes: waves, currents, waves and current. However, there are still some problems in the existing models to link the on the first, the hydrodynamics of waves and currents and secondly, the sediment transport processes and due to the variability in time, space and interaction and the simultaneous action of wave-current near the shore. This paper is the establishment of a numerical modeling to forecast the sediment transport from development scenarios of harbor structure. It is established on the basis of a numerical simulation of a water-sediment model via a 2D model using a set of codes calculation MIKE 21-DHI software. This is to examine the effect of the sediment transport drivers following the dominant incident wave in the direction to pass input harbor work under different variants planning studies to find the technical and economic limitations to the sediment transport and protection of the harbor structure optimum solution.

Keywords: swell, current, radiation, stress, mesh, mike21, sediment

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Authors: Oleksii Bychkov

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The fundamental difference between randomness and vagueness is that the former requires statistical research. These issues were studied by Zadeh L, Dubois D., Prad A. The theory of possibility works with expert assessments, hypotheses, etc. gives an idea of the characteristics of the problem situation, the nature of the goals and real limitations. Possibility theory examines experiments that are not repeated. The article discusses issues related to the formalization of a fuzzy, uncertain idea of reality. The author proposes to expand the classical model of the theory of possibilities by introducing a measure of necessity. The proposed model of the theory of possibilities allows us to extend the measures of possibility and necessity onto a Boolean while preserving the properties of the measure. Thus, upper and lower estimates are obtained to describe the fact that the event will occur. Knowledge of the patterns that govern mass random, uncertain, fuzzy events allows us to predict how these events will proceed. The article proposed for publication quite fully reveals the essence of the construction and use of the theory of probability and the theory of possibility.

Keywords: possibility, artificial, modeling, axiomatics, intellectual approach

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Authors: Seyed Esmail Seyedi Bariran, Laysheng Ewe, Amy Ling

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Since supplier selection appears as a vital decision, selecting supplier based on the best and most accurate ways has a lot of importance for enterprises. In this study, a new Artificial Intelligence approach is exerted to remove weaknesses of supplier selection. The paper has three parts. First part is choosing the appropriate criteria for assessing the suppliers’ performance. Next one is collecting the data set based on experts. Afterwards, the data set is divided into two parts, the training data set and the testing data set. By the training data set the best structure of GEP and ANN are selected and to evaluate the power of the mentioned methods the testing data set is used. The result obtained shows that the accuracy of GEP is more than ANN. Moreover, unlike ANN, a mathematical equation is presented by GEP for the supplier selection.

Keywords: supplier selection, automotive supply chains, ANN, GEP

Procedia PDF Downloads 626

Authors: Min-Woo Kim, Ok-Kyun Na, Jun-Ho Byun, Jong-Hwan Park, Seung-Hwa Yang, Joon-Hong Park, Young-Chul Park

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This study is focused on the comparative analysis and improvement to grasp the flow characteristic of the Anti-Splash Device located under the P/V Valve and new concept design models using the CFD analysis and Artificial Neural Network. The P/V valve located upper deck to solve the pressure rising and vacuum condition of inner tank of the liquid cargo ships occurred oil outflow accident by transverse and longitudinal sloshing force. Anti-Splash Device is fitted to improve and prevent this problem in the shipbuilding industry. But the oil outflow accidents are still reported by ship owners. Thus, four types of new design model are presented by study. Then, comparative analysis is conducted with new models and existing model. Mostly the key criterion of this problem is flux in the outlet of the Anti-Splash Device. Therefore, the flow and velocity are grasped by transient analysis. And then it decided optimum model and design parameters to develop model. Later, it needs to develop an Anti-Splash Device by Flow Test to get certification and verification using experiment equipment.

Keywords: anti-splash device, P/V valve, sloshing, artificial neural network

Procedia PDF Downloads 586

Authors: Zahra Hamedani

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Many wonderful technological developments in recent years has opened up the possibility of using intelligent buildings for a number of important applications, ranging from minimizing resource usage as well as increasing building efficiency to maximizing comfort, adaption to inhabitants and responsiveness to environmental changes. The concept of an intelligent building refers to the highly embedded, interactive environment within which by exploiting the use of artificial intelligence provides the ability to know its configuration, anticipate the optimum dynamic response to prevailing environmental stimuli, and actuate the appropriate physical reaction to provide comfort and efficiency. This paper contains a general identification of the intelligence paradigm and its impacts on the architecture arena, that with examining the performance of artificial intelligence, a mechanism to analyze and finally for decision-making to control the environment will be described. This mechanism would be a hierarchy of the rational agents which includes decision-making, information, communication and physical layers. This multi-agent system relies upon machine learning techniques for automated discovery, prediction and decision-making. Then, the application of this mechanism regarding adaptation and responsiveness of intelligent building will be provided in two scales of environmental and user. Finally, we review the identifications of sustainability and evaluate the potentials of intelligent building systems in the creation of sustainable architecture and environment.

Keywords: artificial intelligence, intelligent building, responsiveness, adaption, sustainability

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Authors: Muhammad Fanseem E. M., Rama Satya Satish Kumar, Indrajeet Bhausaheb Bhavar, Deepak M.

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Keeping the right balance of electricity between the supply and demand sides of the grid is one of the most important objectives of electrical grid operation. Power generation and demand forecasting are the core of power management and generation scheduling. Large, centralized producing units were used in the construction of conventional power systems in the past. A certain level of balance was possible since the generation kept up with the power demand. However, integrating renewable energy sources into power networks has proven to be a difficult challenge due to its intermittent nature. The power imbalance caused by rising demands and peak loads is negatively affecting power quality and dependability. Demand side management and demand response were one of the solutions, keeping generation the same but altering or rescheduling or shedding completely the load or demand. However, shedding the load or rescheduling is not an efficient way. There comes the significance of virtual power plants. The virtual power plant integrates distributed generation, dispatchable load, and distributed energy storage organically by using complementing control approaches and communication technologies. This would eventually increase the utilization rate and financial advantages of distributed energy resources. Most of the writing on virtual power plant models ignored technical limitations, and modeling was done in favor of a financial or commercial viewpoint. Therefore, this paper aims to address the modeling intricacies of VPPs and their technical limitations, shedding light on a holistic understanding of this innovative power management approach.

Keywords: cost optimization, distributed energy resources, dynamic modeling, model quality tests, power system modeling

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Authors: Jungeun Cho

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Social finance has received increasing attention as a means to promote the growth of regional economies. Despite the plenty of research discussed their critical role and functions in regional economic development such as the financing and promotion of co-operatives or social enterprises and the offering credit to the financially excluded in the region, however, rarely are efforts made to measure the contribution of social finance in the regional economy. It is essential to establish an evaluation model in order to encourage social finance institutions to perform their supposed role and functions on regional economic development. The objective of this paper is to formulate an evaluation model of the contribution of social finance to the regional economy through an analytic hierarchy process (AHP) approach. This study is expected to provide useful guidelines for social finance institutions’ strategies and the policies of local or central government regarding social finance.

Keywords: social finance, regional economy, social economy, policies of local or central government

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Authors: César Enrique Jiménez Yañez, Yessica Martinez Soto

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In the present work, the phenomenon of undocumented migration in the northern border of Mexico is analyzed through the graphic representation of the experience of people who migrate in an undocumented way to the United States. 33 of them drew what it meant for them to migrate. Our objective is to analyze the social phenomenon of migration through the drawings of migrants, using the concepts of social imaginary and social representations, identifying the different significant elements with which this symbolically builds their experience. Drawing, as a methodological tool, will help us to understand the migratory experience beyond words.

Keywords: Mexico, social imaginary, social representations, undocumented migrants

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Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this work is to evaluate the advantages and disadvantages of various machine learning techniques in two key steps of seismic hazard and risk assessment of different types of structures. The first step is the development of ground-motion models, which are used for forecasting ground-motion intensity measures (IM) given source characteristics, source-to-site distance, and local site condition for future events. IMs such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available. Second, it is investigated how machine learning techniques could be beneficial for developing probabilistic seismic demand models (PSDMs), which provide the relationship between the structural demand responses (e.g., component deformations, accelerations, internal forces, etc.) and the ground motion IMs. In the risk framework, such models are used to develop fragility curves estimating exceeding probability of damage for pre-defined limit states, and therefore, control the reliability of the predictions in the risk assessment. In this study, machine learning algorithms like artificial neural network, random forest, and support vector machine are adopted and trained on the demand parameters to derive PSDMs for them. It is observed that such models can provide more accurate estimates of prediction in relatively shorter about of time compared to conventional methods. Moreover, they can be used for sensitivity analysis of fragility curves with respect to many modeling parameters without necessarily requiring more intense numerical response-history analysis.

Keywords: artificial neural network, machine learning, random forest, seismic risk analysis, seismic hazard analysis, support vector machine

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Authors: Jing Yuan, Hongwei Yang

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This paper aims to understand U.S. middle school students’ mathematics achievements by examining relevant student and school-level predictors. Through a variance component analysis, the study first identifies evidence supporting the use of multilevel modeling. Then, a multilevel analysis is performed under Bayesian statistical inference where prior information is incorporated into the modeling process. During the analysis, independent variables are entered sequentially in the order of theoretical importance to create a hierarchy of models. By evaluating each model using Bayesian fit indices, a best-fit and most parsimonious model is selected where Bayesian statistical inference is performed for the purpose of result interpretation and discussion. The primary dataset for Bayesian modeling is derived from the Program for International Student Assessment (PISA) in 2012 with a secondary PISA dataset from 2003 analyzed under the traditional ordinary least squares method to provide the information needed to specify informative priors for a subset of the model parameters. The dependent variable is a composite measure of mathematics literacy, calculated from an exploratory factor analysis of all five PISA 2012 mathematics achievement plausible values for which multiple evidences are found supporting data unidimensionality. The independent variables include demographics variables and content-specific variables: mathematics efficacy, teacher-student ratio, proportion of girls in the school, etc. Finally, the entire analysis is performed using the MCMCpack and MCMCglmm packages in R.

Keywords: Bayesian multilevel modeling, mathematics education, PISA, multilevel

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Authors: Hye-Kyung Kang

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Social justice is a central principle of the social work profession and education. However, scholars have long questioned the profession’s commitment to putting social justice values into practice. Clinical social work has been particularly criticized for its lack of attention to social justice and for failing to address the concerns of the oppressed. One prominent criticism of clinical social work is that it often relies on individual intervention and fails to take on system-level changes or advocacy. This concern evokes the historical macro-micro tension of the social work profession where micro (e.g., mental health counseling) and macro (e.g., policy advocacy) practices are conceptualized as separate domains, creating a false binary for social workers. One contributor to this false binary seems to be that most clinical practice models do not prepare social work students and practitioners to make a clear link between clinical practice and social justice. This paper presents a model of clinical social work practice that clearly recognizes the essential and necessary connection between social justice, advocacy, and clinical practice throughout the clinical process: engagement, assessment, intervention, and evaluation. Contemporary relational theories, critical social work frameworks, and anti-oppressive practice approaches are integrated to build a clinical social work practice model that addresses the urgent need for mental health practice that not only helps and heals the person but also challenges societal oppressions and aims to change them. The application of the model is presented through case vignettes.

Keywords: social justice, clinical social work, clinical social work model, integrative model

Procedia PDF Downloads 80

Authors: Anahita Aris

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Geometry always plays a key role in natural structures, which can be a source of inspiration for architects and urban designers to create spaces. By understanding formative principles in nature, a variety of options can be provided that lead to freedom of formation. The main purpose of this paper is to analyze the geometrical order found in pomegranate to find formative principles explaining its complex structure. The point is how spherical arils of pomegranate pressed together inside the fruit and filled the space as they expand in the growing process, which made a self-organized system leads to the formation of each of the arils are unique in size, topology and shape. The main challenge of this paper would be using advanced architectural modeling techniques to discover these principles.

Keywords: advanced modeling techniques, architectural modeling, computational design, the geometry of natural formation, geometrical analysis, the natural order of pomegranate, voronoi diagrams

Procedia PDF Downloads 217

Authors: Vincent Iacobellis, Kamran Behdinan

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A finite element cohesive zone model is used to predict the temperature dependent material properties of a polyimide matrix composite with unidirectional carbon fiber arrangement. The cohesive zone parameters have been obtained from previous research involving an atomistic-to-continuum multiscale simulation of the fiber-matrix interface using the bridging cell multiscale method. The goal of the research was to both investigate the effect of temperature change on the composite behavior with respect to transverse loading as well as the validate the use of cohesive parameters obtained from atomistic-to-continuum multiscale modeling to predict fiber-matrix interfacial cracking. From the multiscale model cohesive zone parameters (i.e. maximum traction and energy of separation) were obtained by modeling the interface between the coarse-grained polyimide matrix and graphite based carbon fiber. The cohesive parameters from this simulation were used in a cohesive zone model of the composite microstructure in order to predict the properties of the macroscale composite with respect to changes in temperature ranging from 21 ˚C to 316 ˚C. Good agreement was found between the microscale RUC model and experimental results for stress-strain response, stiffness, and material strength at low and high temperatures. Examination of the deformation of the composite through localized crack initiation at the fiber-matrix interface also agreed with experimental observations of similar phenomena. Overall, the cohesive zone model was shown to be both effective at modeling the composite properties with respect to transverse loading as well as validated the use of cohesive zone parameters obtained from the multiscale simulation.

Keywords: cohesive zone model, fiber-matrix interface, microscale damage, multiscale modeling

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Authors: Onyebuchi Mogbo, Abdulsalam Mohammed, Salsabila Wali

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This research discusses the design and modeling of an amphibious building. The amphibious building is a house with the function of floating during a flood event. Over the years, houses have been built to resist flood events some of which have failed. The floating house is designed to work with nature and not against it. In the event of a flood, the house will rise with the increasing water level and protect the house from sinking. For the design and modeling of this house an estimated cost of N250, 000, approximately $700, will be needed. It is expected that the house will rise when lightweight materials are incorporated in the design, and the concrete dock (in form of a hollow box) carrying the entire house in its hollow space is well designed. When there is flooding the water will fill up the concrete dock, and the house will rise upwards with vertical guides preventing it from moving side to side or out of its boundary. Architectural and Structural designs will be used in this project.

Keywords: amphibious building, flood, housing, design and modelling

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Authors: M. Novak, E. Munoz, A. Jana, M. Nelemans

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Cities and local governments the world over are interested to employ circular strategies as a means to bring about food security, create employment and increase resilience. The selection and implementation of circular strategies is facilitated by modeling the effects of strategies locally and understanding the impacts such strategies have had in other (comparable) cities and how that would translate locally. Urban areas are heterogeneous because of their geographic, economic, social characteristics, governance, and culture. In order to better understand the effect of circular strategies on urban systems, we create a dataset for over 300 cities around the world designed to facilitate circular strategy scenario modeling. This new dataset integrates data from over 20 prominent global national and urban data sources, such as the Global Human Settlements layer and International Labour Organisation, as well as incorporating employment data from over 150 cities collected bottom up from local departments and data providers. The dataset is made to be reproducible. Various clustering techniques are explored in the paper. The result is sets of clusters of cities, which can be used for further research, analysis, and support comparative, regional, and national policy making on circular cities.

Keywords: data integration, urban innovation, cluster analysis, circular economy, city profiles, scenario modelling

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Authors: Neslihan Şahin Çelik, Ali Eraslan

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In recent years, mathematics educators have frequently stressed the necessity of instructing students about models and modeling approaches that encompass cognitive and metacognitive thought processes, starting from the first years of school and continuing on through the years of higher education. The purpose of this study is to examine the thought processes of 4th-grade primary school students in their modeling activities and to explore the difficulties encountered in these processes, if any. The study, of qualitative design, was conducted in the 2015-2016 academic year at a public state-school located in a central city in the Black Sea Region of Turkey. A preliminary study was first implemented with designated 4th grade students, after which the criterion sampling method was used to select three students that would be recruited into the focus group. The focus group that was thus formed was asked to work on the model eliciting activity of the Paper Airplane Problem and the entire process was recorded on video. The Paper Airplane Problem required the students to determine the winner with respect to: (a) the plane that stays in the air for the longest time; (b) the plane that travels the greatest distance in a straight-line path; and (c) the overall winner for the contest. A written transcript was made of the video recording, after which the recording and the students' worksheets were analyzed using the Blum and Ferri modeling cycle. The results of the study revealed that the students tested the hypotheses related to daily life that they had set up, generated ideas of their own, verified their models by making connections with real life, and tried to make their models generalizable. On the other hand, the students had some difficulties in terms of their interpretation of the table of data and their ways of operating on the data during the modeling processes.

Keywords: primary school students, model eliciting activity, mathematical modeling, modeling process, paper airplane problem

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Authors: Thiago Fabrizio Gavilano Farje, Marcelo Patricio Herrera Malpartida

Abstract:

This study examines the influence of artificial intelligence (AI) on the work efficiency of administrative employees in the financial sector of Metropolitan Lima, Peru, during the year 2024. Focusing on the relationship between AI implementation and work efficiency, it addresses specific variables such as decision-making, motivation, and employee productivity. To accomplish the analysis between AI and work efficiency within the financial sector of Metropolitan Lima, it is necessary to evaluate how AI optimizes time in administrative tasks, examine how AI impacts the agility of the process of making decisions, and investigate the influence of AI on the satisfaction and motivation of employees. The research adopts a correlational and explanatory approach, designed to establish and understand the connections between AI and work efficiency. A survey design adapted from an OECD study is used, applying questionnaires to a representative sample of administrative workers in the financial sector who incorporate AI into their functions. The target population includes administrative workers in the financial sector of Metropolitan Lima, estimated at 73,097 employees based on data from the Censo Nacional de Empresas y Establecimientos and studies by the BCRP. The sample, selected through simple random sampling, comprises 246 workers.

Keywords: business management, artificial intelligence, decision making, labor efficiency, financial sector

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Authors: Samantha Clarke

Abstract:

The term social justice is central to social work; however, the meaning behind this term is not as simple as defining the term itself. This is because the meaning of social justice is relative since its origin and development is based on evolving political perspectives. Political perspectives provide numerous lenses to view social justice in social work; however, the realities of changing society have meant that social justice has assumed different values, definitions, and understandings over time and in different geopolitical and cultural contexts. There are many competing and convincing theories of social justice that are relevant to social work practice. Exploring the term is not an idle preoccupation because the meaning of the term is not as crucial as the meaning of the worldview, as it is the worldview that positions social justice as crucial in the emancipation of people marginalized from oppression. The many political assumptions that underlie the term social justice are explored and connected to the contemporary discussions about social justice in social work. These connections are then interrogated in the Canadian Social Works Code of Ethics, and in micro, mezzo, and macro approaches. To be remiss in interrogating the underlying political assumptions of the worldview of social justice is to entrench oppression and to preserve oppressive structures in contemporary Canadian social work. The concept of social justice is unable to withstand closer scrutiny about its emancipatory qualities in Canadian social work when we interrogate the many political assumptions that frame its understanding. In order to authenticate social justice as an emancipatory central organizing principle, Canadian social workers must engage in deeper discussions about the political implications of social justice in their everyday practices based on diverse worldviews and geopolitical contexts. Social workers are well positioned to develop an understanding of social justice that is emancipatory based on their everyday practices because as social and political actors they are positioned to work for and with individuals and toward the greater good of those who are marginalized from oppression.

Keywords: Canadian social work, political analysis, social justice, social work practice

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Authors: Mohammad Yousefikia, Sara Moridpour, Ehsan Mazloumi

Abstract:

Perceiving track geometry deterioration decisively influences the optimization of track maintenance operations. The effective management of this deterioration and increasingly utilized system with limited financial resources is a significant challenge. This paper provides a review of degradation models relevant for railroad tracks. Furthermore, due to the lack of long term information on the condition development of tram infrastructures, presents the methodology which will be used to derive degradation models from the data of Melbourne tram network.

Keywords: deterioration modeling, asset management, railway, tram

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Authors: Assem M. F. Sallam, Ah. El-S. Makled

Abstract:

This paper presents a study on the trajectory of a two stage launch vehicle. The study includes dynamic responses of motion parameters as well as the variation of angles affecting the orientation of the launch vehicle (LV). LV dynamic characteristics including state vector variation with corresponding altitude and velocity for the different LV stages separation, as well as the angle of attack and flight path angles are also discussed. A flight trajectory study for the drop zone of first stage and the jettisoning of fairing are introduced in the mathematical modeling to study their effect. To increase the accuracy of the LV model, atmospheric model is used taking into consideration geographical location and the values of solar flux related to the date and time of launch, accurate atmospheric model leads to enhancement of the calculation of Mach number, which affects the drag force over the LV. The mathematical model is implemented on MATLAB based software (Simulink). The real available experimental data are compared with results obtained from the theoretical computation model. The comparison shows good agreement, which proves the validity of the developed simulation model; the maximum error noticed was generally less than 10%, which is a result that can lead to future works and enhancement to decrease this level of error.

Keywords: launch vehicle modeling, launch vehicle trajectory, mathematical modeling, Matlab- Simulink

Procedia PDF Downloads 272