Search results for: decision based artificial neural network
31007 The Role of Temporary Migration as Coping Mechanism of Weather Shock: Evidence from Selected Semi-Arid Tropic Villages in India
Authors: Kalandi Charan Pradhan
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In this study, we investigate does weather variation determine temporary labour migration using 210 sample households from six Semi-Arid Tropic (SAT) villages for the period of 2005-2014 in India. The study has made an attempt to examine how households use temporary labour migration as a coping mechanism to minimise the risk rather than maximize the utility of the households. The study employs panel Logit regression model to predict the probability of household having at least one temporary labour migrant. As per as econometrics result, it is found that along with demographic and socioeconomic factors; weather variation plays an important role to determine the decision of migration at household level. In order to capture the weather variation, the study uses mean crop yield deviation over the study periods. Based on the random effect logit regression result, the study found that there is a concave relationship between weather variation and decision of temporary labour migration. This argument supports the theory of New Economics of Labour Migration (NELM), which highlights the decision of labour migration not only maximise the households’ utility but it helps to minimise the risks.Keywords: temporary migration, socioeconomic factors, weather variation, crop yield, logit estimation
Procedia PDF Downloads 22331006 Environmental Performance Measurement for Network-Level Pavement Management
Authors: Jessica Achebe, Susan Tighe
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The recent Canadian infrastructure report card reveals the unhealthy state of municipal infrastructure intensified challenged faced by municipalities to maintain adequate infrastructure performance thresholds and meet user’s required service levels. For a road agency, huge funding gap issue is inflated by growing concerns of the environmental repercussion of road construction, operation and maintenance activities. As the reduction of material consumption and greenhouse gas emission when maintain and rehabilitating road networks can achieve added benefits including improved life cycle performance of pavements, reduced climate change impacts and human health effect due to less air pollution, improved productivity due to optimal allocation of resources and reduced road user cost. Incorporating environmental sustainability measure into pavement management is solution widely cited and studied. However measuring the environmental performance of road network is still a far-fetched practice in road network management, more so an ostensive agency-wide environmental sustainability or sustainable maintenance specifications is missing. To address this challenge, this present research focuses on the environmental sustainability performance of network-level pavement management. The ultimate goal is to develop a framework to incorporate environmental sustainability in pavement management systems for network-level maintenance programming. In order to achieve this goal, this study reviewed previous studies that employed environmental performance measures, as well as the suitability of environmental performance indicators for the evaluation of the sustainability of network-level pavement maintenance strategies. Through an industry practice survey, this paper provides a brief forward regarding the pavement manager motivations and barriers to making more sustainable decisions, and data needed to support the network-level environmental sustainability. The trends in network-level sustainable pavement management are also presented, existing gaps are highlighted, and ideas are proposed for sustainable network-level pavement management.Keywords: pavement management, sustainability, network-level evaluation, environment measures
Procedia PDF Downloads 21131005 Uncertainty and Multifunctionality as Bridging Concepts from Socio-Ecological Resilience to Infrastructure Finance in Water Resource Decision Making
Authors: Anita Lazurko, Laszlo Pinter, Jeremy Richardson
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Uncertain climate projections, multiple possible development futures, and a financing gap create challenges for water infrastructure decision making. In contrast to conventional predict-plan-act methods, an emerging decision paradigm that enables social-ecological resilience supports decisions that are appropriate for uncertainty and leverage social, ecological, and economic multifunctionality. Concurrently, water infrastructure project finance plays a powerful role in sustainable infrastructure development but remains disconnected from discourse in socio-ecological resilience. At the time of research, a project to transfer water from Lesotho to Botswana through South Africa in the Orange-Senqu River Basin was at the pre-feasibility stage. This case was analysed through documents and interviews to investigate how uncertainty and multifunctionality are conceptualised and considered in decisions for the resilience of water infrastructure and to explore bridging concepts that might allow project finance to better enable socio-ecological resilience. Interviewees conceptualised uncertainty as risk, ambiguity and ignorance, and multifunctionality as politically-motivated shared benefits. Numerous efforts to adopt emerging decision methods that consider these terms were in use but required compromises to accommodate the persistent, conventional decision paradigm, though a range of future opportunities was identified. Bridging these findings to finance revealed opportunities to consider a more comprehensive scope of risk, to leverage risk mitigation measures, to diffuse risks and benefits over space, time and to diverse actor groups, and to clarify roles to achieve multiple objectives for resilience. In addition to insights into how multiple decision paradigms interact in real-world decision contexts, the research highlights untapped potential at the juncture between socio-ecological resilience and project finance.Keywords: socio-ecological resilience, finance, multifunctionality, uncertainty
Procedia PDF Downloads 12631004 Designing a Model to Increase the Flow of Circular Economy Startups Using a Systemic and Multi-Generational Approach
Authors: Luís Marques, João Rocha, Andreia Fernandes, Maria Moura, Cláudia Caseiro, Filipa Figueiredo, João Nunes
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The implementation of circularity strategies other than recycling, such as reducing the amount of raw material, as well as reusing or sharing existing products, remains marginal. The European Commission announced that the transition towards a more circular economy could lead to the net creation of about 700,000 jobs in Europe by 2030, through additional labour demand from recycling plants, repair services and other circular activities. Efforts to create new circular business models in accordance with completely circular processes, as opposed to linear ones, have increased considerably in recent years. In order to create a societal Circular Economy transition model, it is necessary to include innovative solutions, where startups play a key role. Early-stage startups based on new business models according to circular processes often face difficulties in creating enough impact. The StartUp Zero Program designs a model and approach to increase the flow of startups in the Circular Economy field, focusing on a systemic decision analysis and multi-generational approach, considering Multi-Criteria Decision Analysis to support a decision-making tool, which is also supported by the use of a combination of an Analytical Hierarchy Process and Multi-Attribute Value Theory methods. We define principles, criteria and indicators for evaluating startup prerogatives, quantifying the evaluation process in a unique result. Additionally, this entrepreneurship program spanning 16 months involved more than 2400 young people, from ages 14 to 23, in more than 200 interaction activities.Keywords: circular economy, entrepreneurship, startups;, multi-criteria decision analysis
Procedia PDF Downloads 10531003 Reducing Energy Consumption and GHG Emission by Integration of Flare Gas with Fuel Gas Network in Refinery
Authors: N. Tahouni, M. Gholami, M. H. Panjeshahi
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Gas flaring is one of the most GHG emitting sources in the oil and gas industries. It is also a major way for wasting such an energy that could be better utilized and even generates revenue. Minimize flaring is an effective approach for reducing GHG emissions and also conserving energy in flaring systems. Integrating waste and flared gases into the fuel gas networks (FGN) of refineries is an efficient tool. A fuel gas network collects fuel gases from various source streams and mixes them in an optimal manner, and supplies them to different fuel sinks such as furnaces, boilers, turbines, etc. In this article we use fuel gas network model proposed by Hasan et al. as a base model and modify some of its features and add constraints on emission pollution by gas flaring to reduce GHG emissions as possible. Results for a refinery case study showed that integration of flare gas stream with waste and natural gas streams to construct an optimal FGN can significantly reduce total annualized cost and flaring emissions.Keywords: flaring, fuel gas network, GHG emissions, stream
Procedia PDF Downloads 34431002 Development of the Academic Model to Predict Student Success at VUT-FSASEC Using Decision Trees
Authors: Langa Hendrick Musawenkosi, Twala Bhekisipho
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The success or failure of students is a concern for every academic institution, college, university, governments and students themselves. Several approaches have been researched to address this concern. In this paper, a view is held that when a student enters a university or college or an academic institution, he or she enters an academic environment. The academic environment is unique concept used to develop the solution for making predictions effectively. This paper presents a model to determine the propensity of a student to succeed or fail in the French South African Schneider Electric Education Center (FSASEC) at the Vaal University of Technology (VUT). The Decision Tree algorithm is used to implement the model at FSASEC.Keywords: FSASEC, academic environment model, decision trees, k-nearest neighbor, machine learning, popularity index, support vector machine
Procedia PDF Downloads 20031001 Comparative Study of Scheduling Algorithms for LTE Networks
Authors: Samia Dardouri, Ridha Bouallegue
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Scheduling is the process of dynamically allocating physical resources to User Equipment (UE) based on scheduling algorithms implemented at the LTE base station. Various algorithms have been proposed by network researchers as the implementation of scheduling algorithm which represents an open issue in Long Term Evolution (LTE) standard. This paper makes an attempt to study and compare the performance of PF, MLWDF and EXP/PF scheduling algorithms. The evaluation is considered for a single cell with interference scenario for different flows such as Best effort, Video and VoIP in a pedestrian and vehicular environment using the LTE-Sim network simulator. The comparative study is conducted in terms of system throughput, fairness index, delay, packet loss ratio (PLR) and total cell spectral efficiency.Keywords: LTE, multimedia flows, scheduling algorithms, mobile computing
Procedia PDF Downloads 38331000 Sea of Light: A Game 'Based Approach for Evidence-Centered Assessment of Collaborative Problem Solving
Authors: Svenja Pieritz, Jakab Pilaszanovich
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Collaborative Problem Solving (CPS) is recognized as being one of the most important skills of the 21st century with having a potential impact on education, job selection, and collaborative systems design. Therefore, CPS has been adopted in several standardized tests, including the Programme for International Student Assessment (PISA) in 2015. A significant challenge of evaluating CPS is the underlying interplay of cognitive and social skills, which requires a more holistic assessment. However, the majority of the existing tests are using a questionnaire-based assessment, which oversimplifies this interplay and undermines ecological validity. Two major difficulties were identified: Firstly, the creation of a controllable, real-time environment allowing natural behaviors and communication between at least two people. Secondly, the development of an appropriate method to collect and synthesize both cognitive and social metrics of collaboration. This paper proposes a more holistic and automated approach to the assessment of CPS. To address these two difficulties, a multiplayer problem-solving game called Sea of Light was developed: An environment allowing students to deploy a variety of measurable collaborative strategies. This controlled environment enables researchers to monitor behavior through the analysis of game actions and chat. The according solution for the statistical model is a combined approach of Natural Language Processing (NLP) and Bayesian network analysis. Social exchanges via the in-game chat are analyzed through NLP and fed into the Bayesian network along with other game actions. This Bayesian network synthesizes evidence to track and update different subdimensions of CPS. Major findings focus on the correlations between the evidences collected through in- game actions, the participants’ chat features and the CPS self- evaluation metrics. These results give an indication of which game mechanics can best describe CPS evaluation. Overall, Sea of Light gives test administrators control over different problem-solving scenarios and difficulties while keeping the student engaged. It enables a more complete assessment based on complex, socio-cognitive information on actions and communication. This tool permits further investigations of the effects of group constellations and personality in collaborative problem-solving.Keywords: bayesian network, collaborative problem solving, game-based assessment, natural language processing
Procedia PDF Downloads 13230999 Chatbots in Education: Case of Development Using a Chatbot Development Platform
Authors: Dulani Jayasuriya
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This study outlines the developmental steps of a chatbot for administrative purposes of a large undergraduate course. The chatbot is able to handle student queries about administrative details, including assessment deadlines, course documentation, how to navigate the course, group formation, etc. The development window screenshots are that of a free account on the Snatchbot platform such that this can be adopted by the wider public. While only one connection to an answer based on possible keywords is shown here, one needs to develop multiple connections leading to different answers based on different keywords for the actual chatbot to function. The overall flow of the chatbot showing connections between different interactions is depicted at the end.Keywords: chatbots, education, technology, snatch bot, artificial intelligence
Procedia PDF Downloads 10430998 An Approach to Maximize the Influence Spread in the Social Networks
Authors: Gaye Ibrahima, Mendy Gervais, Seck Diaraf, Ouya Samuel
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In this paper, we consider the influence maximization in social networks. Here we give importance to initial diffuser called the seeds. The goal is to find efficiently a subset of k elements in the social network that will begin and maximize the information diffusion process. A new approach which treats the social network before to determine the seeds, is proposed. This treatment eliminates the information feedback toward a considered element as seed by extracting an acyclic spanning social network. At first, we propose two algorithm versions called SCG − algoritm (v1 and v2) (Spanning Connected Graphalgorithm). This algorithm takes as input data a connected social network directed or no. And finally, a generalization of the SCG − algoritm is proposed. It is called SG − algoritm (Spanning Graph-algorithm) and takes as input data any graph. These two algorithms are effective and have each one a polynomial complexity. To show the pertinence of our approach, two seeds set are determined and those given by our approach give a better results. The performances of this approach are very perceptible through the simulation carried out by the R software and the igraph package.Keywords: acyclic spanning graph, centrality measures, information feedback, influence maximization, social network
Procedia PDF Downloads 24830997 Optimal Design of the Power Generation Network in California: Moving towards 100% Renewable Electricity by 2045
Authors: Wennan Long, Yuhao Nie, Yunan Li, Adam Brandt
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To fight against climate change, California government issued the Senate Bill No. 100 (SB-100) in 2018 September, which aims at achieving a target of 100% renewable electricity by the end of 2045. A capacity expansion problem is solved in this case study using a binary quadratic programming model. The optimal locations and capacities of the potential renewable power plants (i.e., solar, wind, biomass, geothermal and hydropower), the phase-out schedule of existing fossil-based (nature gas) power plants and the transmission of electricity across the entire network are determined with the minimal total annualized cost measured by net present value (NPV). The results show that the renewable electricity contribution could increase to 85.9% by 2030 and reach 100% by 2035. Fossil-based power plants will be totally phased out around 2035 and solar and wind will finally become the most dominant renewable energy resource in California electricity mix.Keywords: 100% renewable electricity, California, capacity expansion, mixed integer non-linear programming
Procedia PDF Downloads 17130996 Performance Management of Tangible Assets within the Balanced Scorecard and Interactive Business Decision Tools
Authors: Raymond K. Jonkers
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The present study investigated approaches and techniques to enhance strategic management governance and decision making within the framework of a performance-based balanced scorecard. The review of best practices from strategic, program, process, and systems engineering management provided for a holistic approach toward effective outcome-based capability management. One technique, based on factorial experimental design methods, was used to develop an empirical model. This model predicted the degree of capability effectiveness and is dependent on controlled system input variables and their weightings. These variables represent business performance measures, captured within a strategic balanced scorecard. The weighting of these measures enhances the ability to quantify causal relationships within balanced scorecard strategy maps. The focus in this study was on the performance of tangible assets within the scorecard rather than the traditional approach of assessing performance of intangible assets such as knowledge and technology. Tangible assets are represented in this study as physical systems, which may be thought of as being aboard a ship or within a production facility. The measures assigned to these systems include project funding for upgrades against demand, system certifications achieved against those required, preventive maintenance to corrective maintenance ratios, and material support personnel capacity against that required for supporting respective systems. The resultant scorecard is viewed as complimentary to the traditional balanced scorecard for program and performance management. The benefits from these scorecards are realized through the quantified state of operational capabilities or outcomes. These capabilities are also weighted in terms of priority for each distinct system measure and aggregated and visualized in terms of overall state of capabilities achieved. This study proposes the use of interactive controls within the scorecard as a technique to enhance development of alternative solutions in decision making. These interactive controls include those for assigning capability priorities and for adjusting system performance measures, thus providing for what-if scenarios and options in strategic decision-making. In this holistic approach to capability management, several cross functional processes were highlighted as relevant amongst the different management disciplines. In terms of assessing an organization’s ability to adopt this approach, consideration was given to the P3M3 management maturity model.Keywords: management, systems, performance, scorecard
Procedia PDF Downloads 32230995 A Decision Making Tool for Selecting the Most Environmental Friendly Wastewater Treatment Plant for Small-Scale Communities
Authors: Mehmet Bulent Topkaya, Mustafa Yildirim
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Wastewater treatment systems are designed and used to minimize adverse impacts of the wastewater on the environment before discharging. Various treatment options for wastewater treatment have been developed, and each of them has different performance characteristics and environmental impacts (e.g. material and land usage, energy consumption, greenhouse gas emission, water and soil emission) during construction, operation or maintenance phases. Assessing the environmental impacts during these phases are essential for the overall evaluation of the treatment systems. In this study, wastewater treatment options, such as vegetated land treatment, constructed wetland, rotating biological contactor, conventional activated sludge treatment, membrane bioreactor, extended aeration and stabilization pond are evaluated. The comparison of the environmental impacts is conducted under the assumption that the effluents will be discharged to sensitive and less sensitive areas respectively. The environmental impacts of each alternative are evaluated by life cycle assessment (LCA) approach. For this purpose, data related to energy usage, land requirement, raw material consumption, and released emissions from the life phases were collected with inventory studies based on field studies and literature. The environmental impacts were assessed by using SimaPro 7.1 LCA software. As the scale of the LCA results is global, an MS-Excel based decision support tool that includes the LCA result is developed in order to meet also the local demands. Using this tool, it is possible to assign weight factors on the LCA results according to local conditions by using Analytical Hierarchy Process and finally the most environmentally appropriate treatment option can be selected.Keywords: analytical hierarchy process, decision support system, life cycle assessment, wastewater treatment
Procedia PDF Downloads 30130994 Introduction to Two Artificial Boundary Conditions for Transient Seepage Problems and Their Application in Geotechnical Engineering
Authors: Shuang Luo, Er-Xiang Song
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Many problems in geotechnical engineering, such as foundation deformation, groundwater seepage, seismic wave propagation and geothermal transfer problems, may involve analysis in the ground which can be seen as extending to infinity. To that end, consideration has to be given regarding how to deal with the unbounded domain to be analyzed by using numerical methods, such as finite element method (FEM), finite difference method (FDM) or finite volume method (FVM). A simple artificial boundary approach derived from the analytical solutions for transient radial seepage problems, is introduced. It should be noted, however, that the analytical solutions used to derive the artificial boundary are particular solutions under certain boundary conditions, such as constant hydraulic head at the origin or constant pumping rate of the well. When dealing with unbounded domains with unsteady boundary conditions, a more sophisticated artificial boundary approach to deal with the infinity of the domain is presented. By applying Laplace transforms and introducing some specially defined auxiliary variables, the global artificial boundary conditions (ABCs) are simplified to local ones so that the computational efficiency is enhanced significantly. The introduced two local ABCs are implemented in a finite element computer program so that various seepage problems can be calculated. The two approaches are first verified by the computation of a one-dimensional radial flow problem, and then tentatively applied to more general two-dimensional cylindrical problems and plane problems. Numerical calculations show that the local ABCs can not only give good results for one-dimensional axisymmetric transient flow, but also applicable for more general problems, such as axisymmetric two-dimensional cylindrical problems, and even more general planar two-dimensional flow problems for well doublet and well groups. An important advantage of the latter local boundary is its applicability for seepage under rapidly changing unsteady boundary conditions, and even the computational results on the truncated boundary are usually quite satisfactory. In this aspect, it is superior over the former local boundary. Simulation of relatively long operational time demonstrates to certain extents the numerical stability of the local boundary. The solutions of the two local ABCs are compared with each other and with those obtained by using large element mesh, which proves the satisfactory performance and obvious superiority over the large mesh model.Keywords: transient seepage, unbounded domain, artificial boundary condition, numerical simulation
Procedia PDF Downloads 29430993 Mobile WiMAX Network based Wireless Communication on Rail: An Analysis
Authors: Vinod Kumar Jatav, Dr. Vrijendra Singh
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WiMAX is an emerging wireless technology designed by WiMAX forum. WiMAX technology delivers broadband internet access with QoS, mobility and robust security. WiMAX is among the prominent mobile broadband wireless technology which laid the foundation for the next generation networks (NGN). The next-generation communication system for railway should facilitate high level network availability, fast mobility for high speed trains with reliability, high handover rate, the firmness of train operations, and high QoS. The system should also be capable to provide various railway services by transmitting big data efficiently. One of the most promising technologies for the next generation railway wireless communication is Mobile WiMAX. This paper analyses some of the network architectures for railway wireless communication and considers the elementary concepts to facilitate the users with broadband internet access on trains. The paper aims to recognize the suitability of Mobile WiMAX technology for the special requirements of broadband internet facilities and wireless telecommunication services of Railways.Keywords: Broadband internet, IEEE 802.16e, mobile WiMAX, Railway wireless communication
Procedia PDF Downloads 52430992 Role and Impact of Artificial Intelligence in Sales and Distribution Management
Authors: Kiran Nair, Jincy George, Suhaib Anagreh
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Artificial intelligence (AI) in a marketing context is a form of a deterministic tool designed to optimize and enhance marketing tasks, research tools, and techniques. It is on the verge of transforming marketing roles and revolutionize the entire industry. This paper aims to explore the current dissemination of the application of artificial intelligence (AI) in the marketing mix, reviewing the scope and application of AI in various aspects of sales and distribution management. The paper also aims at identifying the areas of the strong impact of AI in factors of sales and distribution management such as distribution channel, purchase automation, customer service, merchandising automation, and shopping experiences. This is a qualitative research paper that aims to examine the impact of AI on sales and distribution management of 30 multinational brands in six different industries, namely: airline; automobile; banking and insurance; education; information technology; retail and telecom. Primary data is collected by means of interviews and questionnaires from a sample of 100 marketing managers that have been selected using convenient sampling method. The data is then analyzed using descriptive statistics, correlation analysis and multiple regression analysis. The study reveals that AI applications are extensively used in sales and distribution management, with a strong impact on various factors such as identifying new distribution channels, automation in merchandising, customer service, and purchase automation as well as sales processes. International brands have already integrated AI extensively in their day-to-day operations for better efficiency and improved market share while others are investing heavily in new AI applications for gaining competitive advantage.Keywords: artificial intelligence, sales and distribution, marketing mix, distribution channel, customer service
Procedia PDF Downloads 15430991 An Exploratory Sequential Design: A Mixed Methods Model for the Statistics Learning Assessment with a Bayesian Network Representation
Authors: Zhidong Zhang
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This study established a mixed method model in assessing statistics learning with Bayesian network models. There are three variants in exploratory sequential designs. There are three linked steps in one of the designs: qualitative data collection and analysis, quantitative measure, instrument, intervention, and quantitative data collection analysis. The study used a scoring model of analysis of variance (ANOVA) as a content domain. The research study is to examine students’ learning in both semantic and performance aspects at fine grain level. The ANOVA score model, y = α+ βx1 + γx1+ ε, as a cognitive task to collect data during the student learning process. When the learning processes were decomposed into multiple steps in both semantic and performance aspects, a hierarchical Bayesian network was established. This is a theory-driven process. The hierarchical structure was gained based on qualitative cognitive analysis. The data from students’ ANOVA score model learning was used to give evidence to the hierarchical Bayesian network model from the evidential variables. Finally, the assessment results of students’ ANOVA score model learning were reported. Briefly, this was a mixed method research design applied to statistics learning assessment. The mixed methods designs expanded more possibilities for researchers to establish advanced quantitative models initially with a theory-driven qualitative mode.Keywords: exploratory sequential design, ANOVA score model, Bayesian network model, mixed methods research design, cognitive analysis
Procedia PDF Downloads 17930990 Case-Based Reasoning for Build Order in Real-Time Strategy Games
Authors: Ben G. Weber, Michael Mateas
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We present a case-based reasoning technique for selecting build orders in a real-time strategy game. The case retrieval process generalizes features of the game state and selects cases using domain-specific recall methods, which perform exact matching on a subset of the case features. We demonstrate the performance of the technique by implementing it as a component of the integrated agent framework of McCoy and Mateas. Our results demonstrate that the technique outperforms nearest-neighbor retrieval when imperfect information is enforced in a real-time strategy game.Keywords: case based reasoning, real time strategy systems, requirements elicitation, requirement analyst, artificial intelligence
Procedia PDF Downloads 44130989 Artificial Intelligence in Duolingo
Authors: Jwana Khateeb, Lamar Bawazeer, Hayat Sharbatly, Mozoun Alghamdi
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This research paper explores the idea of learning new languages through an innovative-mobile based learning technology. Throughout this paper we will discuss and examine a mobile-based application called Duolingo. Duolingo is a college standard application for learning foreign languages such as Spanish and English. It is a smart application where it uses smart adaptive technologies to advance the level of their students at each period of time by offering new tasks. Furthermore, we will discuss the history of the application and the methodology used within it. We have conducted a study in which we surveyed ten people about their experience using Duolingo. The results are examined and analyzed in which it indicates the effectiveness on Duolingo students who are seeking to learn new languages. Thus, the research paper will furthermore discuss the diverse methods and approaches in learning new languages through this mobile-based application.Keywords: Duolingo, AI, personalized, customized
Procedia PDF Downloads 28930988 Size Optimization of Microfluidic Polymerase Chain Reaction Devices Using COMSOL
Authors: Foteini Zagklavara, Peter Jimack, Nikil Kapur, Ozz Querin, Harvey Thompson
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The invention and development of the Polymerase Chain Reaction (PCR) technology have revolutionised molecular biology and molecular diagnostics. There is an urgent need to optimise their performance of those devices while reducing the total construction and operation costs. The present study proposes a CFD-enabled optimisation methodology for continuous flow (CF) PCR devices with serpentine-channel structure, which enables the trade-offs between competing objectives of DNA amplification efficiency and pressure drop to be explored. This is achieved by using a surrogate-enabled optimisation approach accounting for the geometrical features of a CF μPCR device by performing a series of simulations at a relatively small number of Design of Experiments (DoE) points, with the use of COMSOL Multiphysics 5.4. The values of the objectives are extracted from the CFD solutions, and response surfaces created using the polyharmonic splines and neural networks. After creating the respective response surfaces, genetic algorithm, and a multi-level coordinate search optimisation function are used to locate the optimum design parameters. Both optimisation methods produced similar results for both the neural network and the polyharmonic spline response surfaces. The results indicate that there is the possibility of improving the DNA efficiency by ∼2% in one PCR cycle when doubling the width of the microchannel to 400 μm while maintaining the height at the value of the original design (50μm). Moreover, the increase in the width of the serpentine microchannel is combined with a decrease in its total length in order to obtain the same residence times in all the simulations, resulting in a smaller total substrate volume (32.94% decrease). A multi-objective optimisation is also performed with the use of a Pareto Front plot. Such knowledge will enable designers to maximise the amount of DNA amplified or to minimise the time taken throughout thermal cycling in such devices.Keywords: PCR, optimisation, microfluidics, COMSOL
Procedia PDF Downloads 16130987 Using Single Decision Tree to Assess the Impact of Cutting Conditions on Vibration
Authors: S. Ghorbani, N. I. Polushin
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Vibration during machining process is crucial since it affects cutting tool, machine, and workpiece leading to a tool wear, tool breakage, and an unacceptable surface roughness. This paper applies a nonparametric statistical method, single decision tree (SDT), to identify factors affecting on vibration in machining process. Workpiece material (AISI 1045 Steel, AA2024 Aluminum alloy, A48-class30 Gray Cast Iron), cutting tool (conventional, cutting tool with holes in toolholder, cutting tool filled up with epoxy-granite), tool overhang (41-65 mm), spindle speed (630-1000 rpm), feed rate (0.05-0.075 mm/rev) and depth of cut (0.05-0.15 mm) were used as input variables, while vibration was the output parameter. It is concluded that workpiece material is the most important parameters for natural frequency followed by cutting tool and overhang.Keywords: cutting condition, vibration, natural frequency, decision tree, CART algorithm
Procedia PDF Downloads 33630986 The Role of Artificial Intelligence on Interior Space in College of Architecture and Design
Authors: Saif M. M. Obeidat
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This research investigates the impact of artificial intelligence (AI) on interior spaces within a college of Architecture and Design. Employing a qualitative approach, the study conducts in-depth interviews and reviews AI-integrated design projects within the academic setting. The key objectives include assessing AI integration in design processes, examining the influence of AI on user experience, exploring its role in architectural innovation, identifying challenges, and assessing educational implications. The study aims to provide a comprehensive understanding of AI's role in shaping interior spaces within academia. It anticipates improved efficiency in design processes, positive user feedback on functionality and experiences, the emergence of innovative design solutions, and the identification of challenges like ethical considerations and technical limitations. Additionally, the research expects insights into how educational programs may need to adapt to incorporate AI knowledge and skills, ensuring students are well-prepared for the evolving landscape of architecture and design practice. By addressing these objectives, the research contributes valuable insights into the evolving relationship between technology and the field of architecture, particularly within educational contexts.Keywords: interior design, artificial intelligence, academic settings, technology, education
Procedia PDF Downloads 9430985 Relation between Pavement Roughness and Distress Parameters for Highways
Authors: Suryapeta Harini
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Road surface roughness is one of the essential aspects of the road's functional condition, indicating riding comfort in both the transverse and longitudinal directions. The government of India has made maintaining good surface evenness a prerequisite for all highway projects. Pavement distress data was collected with a Network Survey Vehicle (NSV) on a National Highway. It determines the smoothness and frictional qualities of the pavement surface, which are related to driving safety and ease. Based on the data obtained in the field, a regression equation was created with the IRI value and the visual distresses. The suggested system can use wireless acceleration sensors and GPS to gather vehicle status and location data, as well as calculate the international roughness index (IRI). Potholes, raveling, rut depth, cracked area, and repair work are all affected by pavement roughness, according to the current study. The study was carried out in one location. Data collected through using Bump integrator was used for the validation. The bump integrator (BI) obtained using deflection from the network survey vehicle was correlated with the distress parameter to establish an equation.Keywords: roughness index, network survey vehicle, regression, correlation
Procedia PDF Downloads 17630984 Colored Image Classification Using Quantum Convolutional Neural Networks Approach
Authors: Farina Riaz, Shahab Abdulla, Srinjoy Ganguly, Hajime Suzuki, Ravinesh C. Deo, Susan Hopkins
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Recently, quantum machine learning has received significant attention. For various types of data, including text and images, numerous quantum machine learning (QML) models have been created and are being tested. Images are exceedingly complex data components that demand more processing power. Despite being mature, classical machine learning still has difficulties with big data applications. Furthermore, quantum technology has revolutionized how machine learning is thought of, by employing quantum features to address optimization issues. Since quantum hardware is currently extremely noisy, it is not practicable to run machine learning algorithms on it without risking the production of inaccurate results. To discover the advantages of quantum versus classical approaches, this research has concentrated on colored image data. Deep learning classification models are currently being created on Quantum platforms, but they are still in a very early stage. Black and white benchmark image datasets like MNIST and Fashion MINIST have been used in recent research. MNIST and CIFAR-10 were compared for binary classification, but the comparison showed that MNIST performed more accurately than colored CIFAR-10. This research will evaluate the performance of the QML algorithm on the colored benchmark dataset CIFAR-10 to advance QML's real-time applicability. However, deep learning classification models have not been developed to compare colored images like Quantum Convolutional Neural Network (QCNN) to determine how much it is better to classical. Only a few models, such as quantum variational circuits, take colored images. The methodology adopted in this research is a hybrid approach by using penny lane as a simulator. To process the 10 classes of CIFAR-10, the image data has been translated into grey scale and the 28 × 28-pixel image containing 10,000 test and 50,000 training images were used. The objective of this work is to determine how much the quantum approach can outperform a classical approach for a comprehensive dataset of color images. After pre-processing 50,000 images from a classical computer, the QCNN model adopted a hybrid method and encoded the images into a quantum simulator for feature extraction using quantum gate rotations. The measurements were carried out on the classical computer after the rotations were applied. According to the results, we note that the QCNN approach is ~12% more effective than the traditional classical CNN approaches and it is possible that applying data augmentation may increase the accuracy. This study has demonstrated that quantum machine and deep learning models can be relatively superior to the classical machine learning approaches in terms of their processing speed and accuracy when used to perform classification on colored classes.Keywords: CIFAR-10, quantum convolutional neural networks, quantum deep learning, quantum machine learning
Procedia PDF Downloads 12930983 Packet Fragmentation Caused by Encryption and Using It as a Security Method
Authors: Said Rabah Azzam, Andrew Graham
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Fragmentation of packets caused by encryption applied on the network layer of the IOS model in Internet Protocol version 4 (IPv4) networks as well as the possibility of using fragmentation and Access Control Lists (ACLs) as a method of restricting network access to certain hosts or areas of a network.Using default settings, fragmentation is expected to occur and each fragment to be reassembled at the other end. If this does not occur then a high number of ICMP messages should be generated back towards the source host indicating that the packet is too large and that it needs to be made smaller. This result is also expected when the MTU is changed for certain links between devices.When using ACLs and packet fragments to restrict access to hosts or network segments it is possible that ACLs cannot be set up in this way. If ACLs cannot be setup to allow only fragments then it is a limitation of the hardware’s firmware holding back this particular method. If the ACL on the restricted switch can be set up in such a way to allow only fragments then a connection that forces packets to fragment should be allowed to pass through the ACL. This should then make a network connection to the destination machine allowing data to be sent to and from the destination machine. ICMP messages from the restricted access switch and host should also be blocked from being sent back across the link which will be shown in an SSH session into the switch.Keywords: fragmentation, encryption, security, switch
Procedia PDF Downloads 33630982 Enhancement of Capacity in a MC-CDMA based Cognitive Radio Network Using Non-Cooperative Game Model
Authors: Kalyani Kulkarni, Bharat Chaudhari
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This paper addresses the issue of resource allocation in the emerging cognitive technology. Focusing the quality of service (QoS) of primary users (PU), a novel method is proposed for the resource allocation of secondary users (SU). In this paper, we propose the unique utility function in the game theoretic model of Cognitive Radio which can be maximized to increase the capacity of the cognitive radio network (CRN) and to minimize the interference scenario. The utility function is formulated to cater the need of PUs by observing Signal to Noise ratio. The existence of Nash equilibrium is for the postulated game is established.Keywords: cognitive networks, game theory, Nash equilibrium, resource allocation
Procedia PDF Downloads 48030981 Decellularized Brain-Chitosan Scaffold for Neural Tissue Engineering
Authors: Yun-An Chen, Hung-Jun Lin, Tai-Horng Young, Der-Zen Liu
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Decellularized brain extracellular matrix had been shown that it has the ability to influence on cell proliferation, differentiation and associated cell phenotype. However, this scaffold is thought to have poor mechanical properties and rapid degradation, it is hard for cell recellularization. In this study, we used decellularized brain extracellular matrix combined with chitosan, which is naturally occurring polysaccharide and non-cytotoxic polymer, forming a 3-D scaffold for neural stem/precursor cells (NSPCs) regeneration. HE staining and DAPI fluorescence staining confirmed decellularized process could effectively vanish the cellular components from the brain. GAGs and collagen I, collagen IV were be showed a great preservation by Alcain staining and immunofluorescence staining respectively. Decellularized brain extracellular matrix was well mixed in chitosan to form a 3-D scaffold (DB-C scaffold). The pore size was approximately 50±10 μm examined by SEM images. Alamar blue results demonstrated NSPCs had great proliferation ability in DB-C scaffold. NSPCs that were cultured in this complex scaffold differentiated into neurons and astrocytes, as reveled by NSPCs expression of microtubule-associated protein 2 (MAP2) and glial fibrillary acidic protein (GFAP). In conclusion, DB-C scaffold may provide bioinformatics cues for NSPCs generation and aid for CNS injury functional recovery applications.Keywords: brain, decellularization, chitosan, scaffold, neural stem/precursor cells
Procedia PDF Downloads 32030980 Real-Time Generative Architecture for Mesh and Texture
Abstract:
In the evolving landscape of physics-based machine learning (PBML), particularly within fluid dynamics and its applications in electromechanical engineering, robot vision, and robot learning, achieving precision and alignment with researchers' specific needs presents a formidable challenge. In response, this work proposes a methodology that integrates neural transformation with a modified smoothed particle hydrodynamics model for generating transformed 3D fluid simulations. This approach is useful for nanoscale science, where the unique and complex behaviors of viscoelastic medium demand accurate neurally-transformed simulations for materials understanding and manipulation. In electromechanical engineering, the method enhances the design and functionality of fluid-operated systems, particularly microfluidic devices, contributing to advancements in nanomaterial design, drug delivery systems, and more. The proposed approach also aligns with the principles of PBML, offering advantages such as multi-fluid stylization and consistent particle attribute transfer. This capability is valuable in various fields where the interaction of multiple fluid components is significant. Moreover, the application of neurally-transformed hydrodynamical models extends to manufacturing processes, such as the production of microelectromechanical systems, enhancing efficiency and cost-effectiveness. The system's ability to perform neural transfer on 3D fluid scenes using a deep learning algorithm alongside physical models further adds a layer of flexibility, allowing researchers to tailor simulations to specific needs across scientific and engineering disciplines.Keywords: physics-based machine learning, robot vision, robot learning, hydrodynamics
Procedia PDF Downloads 6630979 Anomaly Detection Based on System Log Data
Authors: M. Kamel, A. Hoayek, M. Batton-Hubert
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With the increase of network virtualization and the disparity of vendors, the continuous monitoring and detection of anomalies cannot rely on static rules. An advanced analytical methodology is needed to discriminate between ordinary events and unusual anomalies. In this paper, we focus on log data (textual data), which is a crucial source of information for network performance. Then, we introduce an algorithm used as a pipeline to help with the pretreatment of such data, group it into patterns, and dynamically label each pattern as an anomaly or not. Such tools will provide users and experts with continuous real-time logs monitoring capability to detect anomalies and failures in the underlying system that can affect performance. An application of real-world data illustrates the algorithm.Keywords: logs, anomaly detection, ML, scoring, NLP
Procedia PDF Downloads 9430978 Integrated Grey Rational Analysis-Standard Deviation Method for Handover in Heterogeneous Networks
Authors: Mohanad Alhabo, Naveed Nawaz, Mahmoud Al-Faris
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The dense deployment of small cells is a promising solution to enhance the coverage and capacity of the heterogeneous networks (HetNets). However, the unplanned deployment could bring new challenges to the network ranging from interference, unnecessary handovers and handover failures. This will cause a degradation in the quality of service (QoS) delivered to the end user. In this paper, we propose an integrated Grey Rational Analysis Standard Deviation based handover method (GRA-SD) for HetNet. The proposed method integrates the Standard Deviation (SD) technique to acquire the weight of the handover metrics and the GRA method to select the best handover base station. The performance of the GRA-SD method is evaluated and compared with the traditional Multiple Attribute Decision Making (MADM) methods including Simple Additive Weighting (SAW) and VIKOR methods. Results reveal that the proposed method has outperformed the other methods in terms of minimizing the number of frequent unnecessary handovers and handover failures, in addition to improving the energy efficiency.Keywords: energy efficiency, handover, HetNets, MADM, small cells
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