Search results for: cluster model approach

Authors: A. Novikova, L. Rocchi, G. Startiene

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Market and non-market outputs are produced jointly in agriculture. Their supply depends on the intensity and type of production. The role of agriculture as an economic activity and its effects are important for the Lithuanian case study, as agricultural land covers more than a half of country. Positive and negative externalities, created in agriculture are not considered in the market. Therefore, specific techniques such as stated preferences methods, in particular choice experiments (CE) are used for evaluation of non-market outputs in agriculture. The main aim of this paper is to present construction of the research path for evaluation of non-market farming system outputs in Lithuania. The conventional and organic farming, covering crops (including both cereal and industrial crops) and livestock (including dairy and cattle) production has been selected. The CE method and nested logit (NL) model were selected as appropriate for evaluation of non-market outputs of different farming systems in Lithuania. A pilot survey was implemented between October–November 2018, in order to test and improve the CE questionnaire. The results of the survey showed that the questionnaire is accepted and well understood by the respondents. The econometric modelling showed that the selected NL model could be used for the main survey. The understanding of the differences between organic and conventional farming by residents was identified. It was revealed that they are more willing to choose organic farming in comparison to conventional farming.

Keywords: choice experiments, farming system, Lithuania market outputs, non-market outputs

Procedia PDF Downloads 129

Authors: Mohammad Salah Abdullatif, Salam Hajjar, Paul Khanna

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The development of instrument grade mmWave transceivers comes with many challenges. A general rule of thumb is that the performance of the instrument must be higher than the performance of the unit under test in terms of accuracy and stability. The calibration and characterizing of mmWave transceivers are important pillars for testing commercial products. Using a Vector Network Analyzer (VNA) with a mixer option has proven a high performance as an approach to calibrate mmWave transceivers. However, this approach comes with a high cost. In this work, a reduced-cost method to calibrate mmWave transceivers is proposed. A comparison between the proposed method and the VNA technology is provided. A demonstration of significant challenges is discussed, and an approach to meet the requirements is proposed.

Keywords: mmWave transceiver, scalar characterization, coupler connection, magic tee connection, calibration, VNA, vector network analyzer

Procedia PDF Downloads 107

Authors: Amirhossein Chambari

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This article proposes a bi-objective model for the facility location problem subject to congestion (overcrowding). Motivated by implementations to locate servers in internet mirror sites, communication networks, one-server-systems, so on. This model consider for situations in which immobile (or fixed) service facilities are congested (or queued) by stochastic demand to behave as M/M/1/K queues. We consider for this problem two simultaneous perspectives; (1) Customers (desire to limit times of accessing and waiting for service) and (2) Service provider (desire to limit average facility idle-time). A bi-objective model is setup for facility location problem with two objective functions; (1) Minimizing sum of expected total traveling and waiting time (customers) and (2) Minimizing the average facility idle-time percentage (service provider). The proposed model belongs to the class of mixed-integer nonlinear programming models and the class of NP-hard problems. In addition, to solve the model, controlled elitist non-dominated sorting genetic algorithms (Controlled NSGA-II) and controlled elitist non-dominated ranking genetic algorithms (NRGA-I) are proposed. Furthermore, the two proposed metaheuristics algorithms are evaluated by establishing standard multiobjective metrics. Finally, the results are analyzed and some conclusions are given.

Keywords: bi-objective, facility location, queueing, controlled NSGA-II, NRGA-I

Procedia PDF Downloads 583

Authors: A. H. Elkholy, A. H. Falah

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A new approach has been developed to estimate the load share and stress distribution of worm gear sets. The approach is based upon considering the instantaneous tooth meshing stiffness where the worm gear drive was modelled as a series of spur gear slices, and each slice was analyzed separately using the well established formulae of spur gears. By combining the results obtained for all slices, the entire envolute worm gear set loading and stressing was obtained. The geometric modelling method presented, allows tooth elastic deformation and tooth root stresses of worm gear drives under different load conditions to be investigated. On the basis of the method introduced in this study, the instantaneous meshing stiffness and load share were obtained. In comparison with existing methods, this approach has both good analysis accuracy and less computing time.

Keywords: gear, load/stress distribution, worm, wheel, tooth stiffness, contact line

Procedia PDF Downloads 345

Authors: Mahsa Mohammadhosseini

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This study investigates the effectiveness of adapting the Reggio Emilia approach to online art education, specifically through White Ant Atelier (W.A.A), a virtual art initiative for Persian-speaking children. Employing an action research framework, the study examines the implementation of Reggio Emilia principles via the "Home" art project, which spanned four months and included 16 sessions. The analysis covers 50 artworks produced by participants, including 17 pieces created collaboratively by mothers and their children. The results demonstrate that integrating the Reggio Emilia approach into online platforms significantly improves children's creative expression and engagement. This finding illustrates that virtual education when integrated with child-centered methodologies like Reggio Emilia, can effectively address and reduce educational inequities among Persian-speaking children.

Keywords: Reggio Emilia, online education, art education, educational equity

Procedia PDF Downloads 18

Authors: Hengki Eko Putra, Dennish Ari Putro, Tri Wahyu Hadi, Edi Riawan, Junnaedhi Dewa Gede, Aditia Rojali, Fariza Dian Prasetyo, Yudhistira Satya Pribadi, Dita Fatria Andarini, Mila Khaerunisa, Raditya Hanung Prakoswa

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Catastrophe risk management can only be done if we are able to calculate the exposed risks. Jakarta is an important city economically, socially, and politically and in the same time exposed to severe floods. On the other hand, flood risk calculation is still very limited in the area. This study has calculated the risk of flooding for Jakarta using 2-Dimensional Model ANUGA. 2-Dimensional model ANUGA and 1-Dimensional Model HEC-RAS are used to calculate the risk of flooding from 13 major rivers in Jakarta. ANUGA can simulate physical and dynamical processes between the streamflow against river geometry and land cover to produce a 1-meter resolution inundation map. The value of streamflow as an input for the model obtained from hydrological analysis on rainfall data using hydrologic model HEC-HMS. The probabilistic streamflow derived from probabilistic rainfall using statistical distribution Log-Pearson III, Normal and Gumbel, through compatibility test using Chi Square and Smirnov-Kolmogorov. Flood event on 2007 is used as a comparison to evaluate the accuracy of model output. Property damage estimations were calculated based on flood depth for 1, 5, 10, 25, 50, and 100 years return period against housing value data from the BPS-Statistics Indonesia, Centre for Research and Development of Housing and Settlements, Ministry of Public Work Indonesia. The vulnerability factor was derived from flood insurance claim. Jakarta's flood loss estimation for the return period of 1, 5, 10, 25, 50, and 100 years, respectively are Rp 1.30 t; Rp 16.18 t; Rp 16.85 t; Rp 21.21 t; Rp 24.32 t; and Rp 24.67 t of the total value of building Rp 434.43 t.

Keywords: 2D hydrodynamic model, ANUGA, flood, flood modeling

Procedia PDF Downloads 275

Authors: Huda Al Shuaily, Karen Renaud

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Databases comprise the foundation of most software systems. System developers inevitably write code to query these databases. The de facto language for querying is SQL and this, consequently, is the default language taught by higher education institutions. There is evidence that learners find it hard to master SQL, harder than mastering other programming languages such as Java. Educators do not agree about explanations for this seeming anomaly. Further investigation may well reveal the reasons. In this paper, we report on our investigations into how novices learn SQL, the actual problems they experience when writing SQL, as well as the differences between expert and novice SQL query writers. We conclude by presenting a model of SQL learning that should inform the instructional material design process better to support the SQL learning process.

Keywords: pattern, SQL, learning, model

Procedia PDF Downloads 254

Authors: Diandra Cristache

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The transition of traditional manuscript studies into the digital framework closely affects the methodological premises upon which manuscript descriptions are modeled, created, and questioned for the purpose of research. This paper intends to explore the issue by presenting a methodological investigation into the process of modeling, creating, and questioning manuscript metadata. The investigation is founded on a close observation of the Polonsky Greek Manuscripts Project, a collaboration between the Universities of Cambridge and Heidelberg. More than just providing a realistic ground for methodological exploration, along with a complete metadata set for computational demonstration, the case study also contributes to a broader purpose: outlining general methodological principles for making the most out of manuscript metadata by means of research-oriented digital outputs. The analysis mainly focuses on the scholarly approach to manuscript descriptions, in the specific instance where the act of metadata recording does not have a programmatic research purpose. Close attention is paid to the encounter of 'traditional' practices in manuscript studies with the formal constraints of the digital framework: does the shift in practices (especially from the straight narrative of free writing towards the hierarchical constraints of the TEI encoding model) impact the structure of metadata and its capability to respond specific research questions? It is argued that flexible structure of TEI and traditional approaches to manuscript description lead to a proliferation of markup: does an 'encyclopedic' descriptive approach ensure the epistemological relevance of the digital outputs to metadata? To provide further insight on the computational approach to manuscript metadata, the metadata of the Polonsky project are processed with techniques of distant reading and data networking, thus resulting in a new group of digital outputs (relational graphs, geographic maps). The computational process and the digital outputs are thoroughly illustrated and discussed. Eventually, a retrospective analysis evaluates how the digital outputs respond to the scientific expectations of research, and the other way round, how the requirements of research questions feed back into the creation and enrichment of metadata in an iterative loop.

Keywords: digital manuscript studies, digital outputs to manuscripts metadata, metadata interoperability, methodological issues

Procedia PDF Downloads 140

Authors: Phakamani Xaba, Robert Huberts, Bilainu Oboirien

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The present study was aimed at developing & comparing two neural-network-based predictive models to predict shelf-life/product age of South African margarine using free fatty acid (FFA), water droplet size (D3.3), water droplet distribution (e-sigma), moisture content, peroxide value (PV), anisidine valve (AnV) and total oxidation (totox) value as input variables to the model. Brick margarine products which had varying ages ranging from fresh i.e. week 0 to week 47 were sourced. The brick margarine products which had been stored at 10 & 25 °C and were characterized. JMP and MATLAB models to predict shelf-life/ margarine age were developed and their performances were compared. The key performance indicators to evaluate the model performances were correlation coefficient (CC), root mean square error (RMSE), and mean absolute percentage error (MAPE) relative to the actual data. The MATLAB-developed model showed a better performance in all three performance indicators. The correlation coefficient of the MATLAB model was 99.86% versus 99.74% for the JMP model, the RMSE was 0.720 compared to 1.005 and the MAPE was 7.4% compared to 8.571%. The MATLAB model was selected to be the most accurate, and then, the number of hidden neurons/ nodes was optimized to develop a single predictive model. The optimized MATLAB with 10 neurons showed a better performance compared to the models with 1 & 5 hidden neurons. The developed models can be used by margarine manufacturers, food research institutions, researchers etc, to predict shelf-life/ margarine product age, optimize addition of antioxidants, extend shelf-life of products and proactively troubleshoot for problems related to changes which have an impact on shelf-life of margarine without conducting expensive trials.

Keywords: margarine shelf-life, predictive modelling, neural networks, oil oxidation

Procedia PDF Downloads 197

Authors: Omar Boutkhoum, Mohamed Hanine, Abdessadek Bendarag

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Sustainable economic growth is nowadays driving firms to extend toward the adoption of many green supply chain management (GSCM) solutions. However, the evaluation and selection of these solutions is a matter of concern that needs very serious decisions, involving complexity owing to the presence of various associated factors. To resolve this problem, a comparative analysis approach based on multi-criteria decision-making methods is proposed for adequate evaluation of sustainable supply chain management solutions. In the present paper, we propose an integrated decision-making model based on FAHP (Fuzzy Analytic Hierarchy Process), TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and PROMETHEE (Preference Ranking Organisation METHod for Enrichment Evaluations) to contribute to a better understanding and development of new sustainable strategies for industrial organizations. Due to the varied importance of the selected criteria, FAHP is used to identify the evaluation criteria and assign the importance weights for each criterion, while TOPSIS and PROMETHEE methods employ these weighted criteria as inputs to evaluate and rank the alternatives. The main objective is to provide a comparative analysis based on TOPSIS and PROMETHEE processes to help make sound and reasoned decisions related to the selection problem of GSCM solution.

Keywords: GSCM solutions, multi-criteria analysis, decision support system, TOPSIS, FAHP, PROMETHEE

Procedia PDF Downloads 163

Authors: Sudha T., Naveen C.

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Dengue is a serious public health issue that causes significant annual economic and welfare burdens on nations. However, enhanced optimization techniques and quantitative modeling approaches can predict the incidence of dengue. By advocating for a data-driven approach, public health officials can make informed decisions, thereby improving the overall effectiveness of sudden disease outbreak control efforts. The National Oceanic and Atmospheric Administration and the Centers for Disease Control and Prevention are two of the U.S. Federal Government agencies from which this study uses environmental data. Based on environmental data that describe changes in temperature, precipitation, vegetation, and other factors known to affect dengue incidence, many predictive models are constructed that use different machine learning methods to estimate weekly dengue cases. The first step involves preparing the data, which includes handling outliers and missing values to make sure the data is prepared for subsequent processing and the creation of an accurate forecasting model. In the second phase, multiple feature selection procedures are applied using various machine learning models and optimization techniques. During the third phase of the research, machine learning models like the Huber Regressor, Support Vector Machine, Gradient Boosting Regressor (GBR), and Support Vector Regressor (SVR) are compared with several optimization techniques for feature selection, such as Harmony Search and Genetic Algorithm. In the fourth stage, the model's performance is evaluated using Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) as assistance. Selecting an optimization strategy with the least number of errors, lowest price, biggest productivity, or maximum potential results is the goal. In a variety of industries, including engineering, science, management, mathematics, finance, and medicine, optimization is widely employed. An effective optimization method based on harmony search and an integrated genetic algorithm is introduced for input feature selection, and it shows an important improvement in the model's predictive accuracy. The predictive models with Huber Regressor as the foundation perform the best for optimization and also prediction.

Keywords: deep learning model, dengue fever, prediction, optimization

Procedia PDF Downloads 65

Authors: Ndibarafinia Young Tobin, Simon Burnett

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In spite of the benefits of implementing whole life costing technique as a valuable approach for comparing alternative building designs allowing operational cost benefits to be evaluated against any initial cost increases and also as part of procurement in the construction industry, its adoption has been relatively slow due to the lack of tangible evidence, ‘know-how’ skills and knowledge of the practice, i.e. the lack of professionals in many establishments with knowledge and training on the use of whole life costing technique, this situation is compounded by the absence of available data on whole life costing from relevant projects, lack of data collection mechanisms and so on. This has proved to be very challenging to those who showed some willingness to employ the technique in a construction project. The knowledge generated from a project can be considered as best practices learned on how to carry out tasks in a more efficient way, or some negative lessons learned which have led to losses and slowed down the progress of the project and performance. Knowledge management in whole life costing practice can enhance whole life costing analysis execution in a construction project, as lessons learned from one project can be carried on to future projects, resulting in continuous improvement, providing knowledge that can be used in the operation and maintenance phases of an assets life span. Purpose: The purpose of this paper is to report an effective approach which can be utilised in capturing knowledge in whole life costing practice in a construction project. Design/methodology/approach: An extensive literature review was first conducted on the concept of knowledge management and whole life costing. This was followed by a semi-structured interview to explore the existing and good practice knowledge management in whole life costing practice in a construction project. The data gathered from the semi-structured interview was analyzed using content analysis and used to structure an effective knowledge capturing approach. Findings: From the results obtained in the study, it shows that the practice of project review is the common method used in the capturing of knowledge and should be undertaken in an organized and accurate manner, and results should be presented in the form of instructions or in a checklist format, forming short and precise insights. The approach developed advised that irrespective of how effective the approach to knowledge capture, the absence of an environment for sharing knowledge, would render the approach ineffective. Open culture and resources are critical for providing a knowledge sharing setting, and leadership has to sustain whole life costing knowledge capture, giving full support for its implementation. The knowledge capturing approach has been evaluated by practitioners who are experts in the area of whole life costing practice. The results have indicated that the approach to knowledge capture is suitable and efficient.

Keywords: whole life costing, knowledge capture, project review, construction industry, knowledge management

Procedia PDF Downloads 260

Authors: Samaneh Sadat Fattahi Massoom, Hossein Salimi Bajestani

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The study examines loneliness and depression in students who regularly care for themselves after school (latchkey students) in Mashhad and compares them with parent supervised students using a causal-comparative research method. The 270 participants, aged 7 -13, were selected using convenience and cluster random-assignment sampling. Independent t-test results showed significant differences between loneliness (-4.32, p ≤ 0.05) and depression (-3.02, p ≤0.05) among latchkey and non-latchkey students. Using the Pearson correlation test, significant correlation between depression and loneliness among latchkey students was also discovered (r=0.59, p ≤ 0.05). However, regarding non latchkey students, no significant difference between loneliness and depression was observed (r= 0.02. p ≥ 0.05). Multiple regression results also showed that depression variance can be determined by gender (22%) and loneliness (34%). The findings of this study, specifically the significant difference between latchkey and non-latchkey children regarding feelings of loneliness and depression, carries clear implications for parents. It can be concluded that mothers who spend most of their time working out of the house and devoid their children of their presence in the home may cause some form of mental distress like loneliness and depression. Moreover, gender differences affect the degree of these psychological disorders.

Keywords: loneliness, depression, self-care students, latchkey and non-latchkey students, gender

Procedia PDF Downloads 414

Authors: Muhammad Rafiq, Lamae Zulfiqar, Nazish Idrees Chaudhary

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A growing number of research focuses on healthy and unhealthy eating behaviors and their impact on health. It was intended to study the Islamic point of view on eating behavior, its impact on mental health and preventative strategies in the light of the Quran and Sunnah. Different articles and Islamic sayings related to eating behaviors and mental health were reviewed in detail. It was also revealed scientifically and through Islamic point of view that appropriate quantity, quality and timings of food have positive effects on mental health. Therefore, a 3Rs model of eating behaviors has been proposed.

Keywords: food intake, mental health, quality of food, quantity of food

Procedia PDF Downloads 239

Authors: Masayuki Higashino, Toshiya Kawato, Takao Kawamura

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

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

Procedia PDF Downloads 164

Authors: R. Boudjelthia

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The aim of this study is to determine the influence of mix composition of concrete as the content of water and cement, water–binder ratio, and the replacement of fly ash on the durability of self compacting concrete (SCC) by using artificial neural networks (ANNs). To achieve this, an ANNs model is developed to predict the durability of self compacting concrete which is expressed in terms of chloride ions permeability in accordance with ASTM C1202-97 or AASHTO T277. Database gathered from the literature for the training and testing the model. A sensitivity analysis was also conducted using the trained and tested ANN model to investigate the effect of fly ash on the durability of SCC. The results indicate that the developed model is reliable and accurate. the durability of SCC expressed in terms of total charge passed over a 6-h period can be significantly improved by using at least 25% fly ash as replacement of cement. This study show that artificial neural network have strong potentialas a feasible tool for predicting accurately the durability of SCC containing fly ash.

Keywords: artificial neural networks, durability, chloride ions permeability, self compacting concrete

Procedia PDF Downloads 378

Authors: Salman Naseer

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One of the main challenges of operating a smart city (SC) is collecting the massive data generated from multiple data sources (DS) and to transmit them to the control units (CU) for further data processing and analysis. These ever-increasing data demands require not only more and more capacity of the transmission channels but also results in resource over-provision to meet the resilience requirements, thus the unavoidable waste because of the data fluctuations throughout the day. In addition, the high energy consumption (EC) and carbon discharges from these data transmissions posing serious issues to the environment we live in. Therefore, to overcome the issues of intensive EC and carbon emissions (CE) of massive data dissemination in Smart Cities, we propose an energy efficient and carbon reduction approach by utilizing the daily mobility of the existing vehicles as an alternative communications channel to accommodate the data dissemination in smart cities. To illustrate the effectiveness and efficiency of our approach, we take the Auckland City in New Zealand as an example, assuming massive data generated by various sources geographically scattered throughout the Auckland region to the control centres located in city centre. The numerical results show that our proposed approach can provide up to 5 times lower delay as transferring the large volume of data by utilizing the existing daily vehicles’ mobility than the conventional transmission network. Moreover, our proposed approach offers about 30% less EC and CE than that of conventional network transmission approach.

Keywords: smart city, delay tolerant network, infrastructure offloading, opportunistic network, vehicular mobility, energy consumption, carbon emission

Procedia PDF Downloads 142

Authors: Irfan Ali, Srikant Gupta, Aquil Ahmed

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Every country needs a sustainable development (SD) for its economic growth by forming suitable policies and initiative programs for the development of different sectors of the country. This paper is comprised of modeling and optimization of different sectors of India that form a multi-criterion model. In this paper, we developed a fractional goal programming (FGP) model that helps in providing the efficient allocation of resources simultaneously by achieving the sustainable goals in gross domestic product (GDP), electricity consumption (EC) and greenhouse gasses (GHG) emission by the year 2030. Also, a weighted model of FGP is presented to obtain varying solution according to the priorities set by the policy maker for achieving future goals of GDP growth, EC, and GHG emission. The presented models provide a useful insight to the decision makers for implementing strategies in a different sector.

Keywords: sustainable and economic development, multi-objective fractional programming, fuzzy goal programming, weighted fuzzy goal programming

Procedia PDF Downloads 223

Authors: Saba Arif, Anam Nadeem, Roman Kalvin, Tanzeel Rashid, Burhan Ali, Juntakan Taweekun

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Currently, the energy crisis is taking serious attention. Globally, industries and building are major share takers of energy. 72% of total global energy is consumed by residential houses, markets, and commercial building. Additionally, in appliances air conditioners are major consumer of electricity; about 60% energy is used for cooling purpose in houses due to HVAC units. Energy demand will aid in determining what changes will be needed whether it is the estimation of the required energy for households or instituting conservation measures. Bottom-up model is one of the most famous methods for forecasting. In current research bottom-up model of air conditioners' energy consumption in all income classes in comparison with seasonal variation and hourly consumption is calculated. By comparison of energy consumption of all income classes by usage of air conditioners, total consumption of actual demand and current availability can be seen.

Keywords: air conditioning, bottom up model, income classes, energy demand

Procedia PDF Downloads 249

Authors: Manouchehr Amiri

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This article introduces a general notion of physical bit information that is compatible with the basics of quantum mechanics and incorporates the Shannon entropy as a special case. This notion of physical information leads to the Binary data matrix model (BDM), which predicts the basic results of quantum mechanics, general relativity, and black hole thermodynamics. The compatibility of the model with holographic, information conservation, and Landauer’s principles are investigated. After deriving the “Bit Information principle” as a consequence of BDM, the fundamental equations of Planck, De Broglie, Beckenstein, and mass-energy equivalence are derived.

Keywords: physical theory of information, binary data matrix model, Shannon information theory, bit information principle

Procedia PDF Downloads 171

Authors: Elsheikh Asser

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Time and cost are the main goals of the construction project management. The first schedule developed may not be a suitable schedule for beginning or completing the project to achieve the target completion time at a minimum total cost. In general, there are trade-offs between time and cost (TCT) to complete the activities of a project. This research presents genetic algorithms (GAs) multi-objective model for project scheduling considering different scenarios such as least cost, least time, and target time.

Keywords: genetic algorithms, time-cost trade-off, multi-objective model, project scheduling

Procedia PDF Downloads 413

Authors: Radul Shishkov, Orlin Davchev

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The digital era has revolutionized architectural practices, with building information modeling (BIM) emerging as a pivotal tool for architects, engineers, and construction professionals. However, the transition from traditional methods to BIM-centric approaches poses significant challenges, particularly in the context of existing structures. This research introduces a technical approach to bridge this gap through the development of algorithms that facilitate the automated transformation of 3D point cloud data into detailed BIM models. The core of this research lies in the application of algorithmic modeling and computational design methods to interpret and reconstruct point cloud data -a collection of data points in space, typically produced by 3D scanners- into comprehensive BIM models. This process involves complex stages of data cleaning, feature extraction, and geometric reconstruction, which are traditionally time-consuming and prone to human error. By automating these stages, our approach significantly enhances the efficiency and accuracy of creating BIM models for existing buildings. The proposed algorithms are designed to identify key architectural elements within point clouds, such as walls, windows, doors, and other structural components, and to translate these elements into their corresponding BIM representations. This includes the integration of parametric modeling techniques to ensure that the generated BIM models are not only geometrically accurate but also embedded with essential architectural and structural information. Our methodology has been tested on several real-world case studies, demonstrating its capability to handle diverse architectural styles and complexities. The results showcase a substantial reduction in time and resources required for BIM model generation while maintaining high levels of accuracy and detail. This research contributes significantly to the field of architectural technology by providing a scalable and efficient solution for the integration of existing structures into the BIM framework. It paves the way for more seamless and integrated workflows in renovation and heritage conservation projects, where the accuracy of existing conditions plays a critical role. The implications of this study extend beyond architectural practices, offering potential benefits in urban planning, facility management, and historic preservation.

Keywords: BIM, 3D point cloud, algorithmic modeling, computational design, architectural reconstruction

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Authors: Iftikhar U. Sikder

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This paper illustrates an application of granular computing approach, namely rough set theory in data mining. The paper outlines the formalism of granular computing and elucidates the mathematical underpinning of rough set theory, which has been widely used by the data mining and the machine learning community. A real-world application is illustrated, and the classification performance is compared with other contending machine learning algorithms. The predictive performance of the rough set rule induction model shows comparative success with respect to other contending algorithms.

Keywords: concept approximation, granular computing, reducts, rough set theory, rule induction

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Authors: Mahesh Kothari, K. D. Gharde

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

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

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Authors: Prachi Sharma, Mukesh Kumar Rana

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In the present study, the simple sequence repeat(SSR) markers have been used in analysis of genetic diversity of 37 genotypes of Triticum aestivum. The DNA was extracted using cTAB method. The DNA was quantified using the fluorimeter. The annealing temperatures for 27 primer pairs were standardized using gradient PCR, out of which 16 primers gave satisfactory amplification at temperature ranging from 50-62⁰ C. Out of 16 polymorphic SSR markers only 10 SSR primer pairs were used in the study generating 34 reproducible amplicons among 37 genotypes out of which 30 were polymorphic. Primer pairs Xgwm533, Xgwm 160, Xgwm 408, Xgwm 120, Xgwm 186, Xgwm 261 produced maximum percent of polymorphic bands (100%). The bands ranged on an average of 3.4 bands per primer. The genetic relationship was determined using Jaccard pair wise similarity co-efficient and UPGMA cluster analysis with NTSYS Pc.2 software. The values of similarity index range from 0-1. The similarity coefficient ranged from 0.13 to 0.97. A minimum genetic similarity (0.13) was observed between VL 804 and HPW 288, meaning they are only 13% similar. More number of available SSR markers can be useful for supporting the genetic diversity analysis in the above wheat genotypes.

Keywords: wheat, genetic diversity, microsatellite, polymorphism

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Authors: Ayşe Dilek Maden

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For a given a simple connected graph, we present some new bounds via a new approach for a special topological index given by the sum of the real number power of the non-zero normalized Laplacian eigenvalues. To use this approach presents an advantage not only to derive old and new bounds on this topic but also gives an idea how some previous results in similar area can be developed.

Keywords: degree Kirchhoff index, normalized Laplacian eigenvalue, spanning tree, simple connected graph

Procedia PDF Downloads 366

Authors: Akash Verma, Sujit Kumar Samanta

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This study examines a discrete-time Geo/G/1 queueing-inventory system attached with (s, Q) inventory policy. Assume that the customers follow the Bernoulli process on arrival. Each customer demands a single item with arbitrarily distributed service time. The inventory is replenished by an outside supplier, and the lead time for the replenishment is determined by a geometric distribution. There is a single server and infinite waiting space in this facility. Demands must wait in the specified waiting area during a stock-out period. The customers are served on a first-come-first-served basis. With the help of the embedded Markov chain technique, we determine the joint probability distributions of the number of customers in the system and the number of items in stock at the post-departure epoch using the Matrix Analytic approach. We relate the system length distribution at post-departure and outside observer's epochs to determine the joint probability distribution at the outside observer's epoch. We use probability distributions at random epochs to determine the waiting time distribution. We obtain the performance measures to construct the cost function. The optimum values of the order quantity and reordering point are found numerically for the variety of model parameters.

Keywords: discrete-time queueing inventory model, matrix analytic method, waiting-time analysis, cost optimization

Procedia PDF Downloads 43

Authors: Rodrigo T. Sepulveda-Hirose, Ana B. Carrera-Aguilar, Francisco Andree Ramirez-Casas, Alondra Orozco-Gomez, Miguel Angel Sanchez-Caro, Carlos Herrera-Ventosa

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High concentrations of particulate matter in the atmosphere pose a threat to human health, especially over areas with high concentrations of population; however, field air pollution monitoring is expensive and time-consuming. In order to achieve reduced costs and global coverage of the whole urban area, remote sensing can be used. This study evaluates PM2.5 concentrations, over the Mexico City´s metropolitan area, are estimated using atmospheric reflectance from LANDSAT 8, satellite imagery and historical PM2.5 measurements of the Automatic Environmental Monitoring Network of Mexico City (RAMA). Through the processing of the available satellite images, a preliminary model was generated to evaluate the optimal bands for the generation of the final model for Mexico City. Work on the final model continues with the results of the preliminary model. It was found that infrared bands have helped to model in other cities, but the effectiveness that these bands could provide for the geographic and climatic conditions of Mexico City is still being evaluated.

Keywords: air pollution modeling, Landsat 8, PM2.5, remote sensing

Procedia PDF Downloads 196

Authors: Alia Abdul Ghaffar, Tom Richardson

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A model reference adaptive control and a fixed gain LQR control were implemented in the height controller of a quadrotor that has parametric uncertainties due to the act of picking up an object of unknown dimension and mass. It is shown that an adaptive control, unlike a fixed gain control, is capable of ensuring a stable tracking performance under such condition, although adaptive control suffers from several limitations. The combination of both adaptive and fixed gain control in the controller architecture results in an enhanced tracking performance in the presence of parametric uncertainties.

Keywords: UAV, quadrotor, robotic arm augmentation, model reference adaptive control, LQR control

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Authors: B.J. Khoza, B. Kembo

Abstract:

Global socioeconomic developments and ever-growing technological advancements of the art and design industry indicate the pivotal importance of lifelong learning. There exists a discrepancy between competencies, personal ambition, and workplace requirements. There are few , if at all, institutions of higher learning in South Africa which offer Short Learning Programmes (SLP) in Art and Design Education. Traditionally, Art and Design education is delivered face to face via a hands-on approach. In this way the enduring perception among educators is that art and design education does not lend itself to online delivery. Short Learning programmes (SLP) are a concentrated approach to make revenue and lure potential prospective students to embark on further education study, this is often of weighted value to both students and employers. SLPs are used by Higher Education institutions to generate income in support of the core academic programmes. However, there is a gap in terms of the translation of art and design studio pedagogy into SLPs which provide quality education, are adaptable and delivered via a blended mode. In our paper, we propose a conceptual framework drawing on secondary research to analyse existing research to SLPs for arts and design education. We aim to indicate a new dimension to the process of using a design-based research approach for short learning programmes in art and design education. The study draws on a conceptual framework, a qualitative analysis through the lenses of Herrington, McKenney, Reeves and Oliver (2005) principles of the design-based research approach. The results of this study indicate that design-based research is not only an effective methodological approach for developing and deploying arts and design education curriculum for 1st years in Higher Education context but it also has the potential to guide future research. The findings of this study propose that the design-based research approach could bring theory and praxis together regarding a common purpose to design context-based solutions to educational problems.

Keywords: design education, design-based research, digital literacies, multi-literacies, short learning programme

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