Search results for: real world driving data

Authors: Jinil Patel, Sarthak Patel, Sarthak Thakkar, Deepti Saraswat

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Recently, the COVID-19 outbreak has spread across the world, leading the World Health Organization to classify it as a global pandemic. To save the patient’s life, the COVID-19 symptoms have to be identified. But using an AI (Artificial Intelligence) model to identify COVID-19 symptoms within the allotted time was challenging. The RT-PCR test was found to be inadequate in determining the COVID status of a patient. To determine if the patient has COVID-19 or not, a Computed Tomography Scan (CT scan) of patient is a better alternative. It will be challenging to compile and store all the data from various hospitals on the server, though. Federated learning, therefore, aids in resolving this problem. Certain deep learning models help to classify Covid-19. This paper will have detailed work of certain deep learning models like VGG19, ResNet50, MobileNEtv2, and Deep Learning Aggregation (DLA) along with maintaining privacy with encryption.

Keywords: federated learning, COVID-19, CT-scan, homomorphic encryption, ResNet50, VGG-19, MobileNetv2, DLA

Procedia PDF Downloads 52

Authors: Chris Landorf, Stephen Ward

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This paper addresses a virtual environment approach to work integrated learning for students in construction-related disciplines. The virtual approach provides a safe and pedagogically rigorous environment where students can apply theoretical knowledge in a simulated real-world context. The paper describes the development of a 4-dimensional digital construction environment and associated learning activities funded by the Australian Office for Learning and Teaching. The environment was trialled with over 1,300 students and evaluated through questionnaires, observational studies and coursework analysis. Results demonstrate a positive impact on students’ technical learning and collaboration skills, but there is need for further research in relation to critical thinking skills and work-readiness.

Keywords: architectural education, construction industry, digital learning environments, immersive learning

Procedia PDF Downloads 387

Authors: Merve Sagiroglu

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The earthquake-resistant steel structure design is required taking into account the behavior of beam-column connections besides the basic properties of the structure such as material and geometry. Beam-column connections play an important role in the behavior of frame systems. Taking into account the behaviour of connection in analysis and design of steel frames is important due to presenting the actual behavior of frames. So, the behavior of the connections should be well known. The most important force which transmitted by connections in the structural system is the moment. The rotational deformation is customarily expressed as a function of the moment in the connection. So, the moment-rotation curves are the best expression of behaviour of the beam-to-column connections. The designed connections form various moment-rotation curves according to the elements of connection and the shape of placement. The only way to achieve this curve is with real-scale experiments. The experiments of some connections have been carried out partially and are formed in the databank. It has been formed the models using this databank to express the behavior of connection. In this study, theoretical studies have been carried out to model a real behavior of the top and seat angles connections with angles. Two stiffeners in the top and seat angle to increase the stiffness of the connection, and two stiffeners in the beam web to prevent local buckling are used in this beam-to-column connection. Mathematical models have been performed using the database of the beam-to-column connection experiments previously by authors. Using the data of the tests, it has been aimed that analytical expressions have been developed to obtain the moment-rotation curve for the connection details whose test data are not available. The connection has been dimensioned in various shapes and the effect of the dimensions of the connection elements on the behavior has been examined.

Keywords: top and seat angle connection, stiffener, moment-rotation curves, analytical study

Procedia PDF Downloads 163

Authors: Ya'u Saidu

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The introduction of the non-interest banking system in Nigeria was part of the regulators efforts to increase the inclusion of other stakeholders into the financial sector who have stayed out of the sector for some reasons. However, the concept has been misunderstood by various stakeholders within the country where some view it as a Muslim affair which exclude the non-Muslims from gaining despite its existence in advance countries of the world. This paper attempts to fill-in the gap created by the literature especially with regards to the proper education and enlightenment of the Nigerian citizens. Survey research method was employed where primary data was collected using questionnaire and convenience sampling was used to select 100 respondents. The data was analysed using Chi-square. It was found that lack of knowledge on Islamic banking has significant effect on its prospects.

Keywords: finance, non-interest, sustainability, enlightenment

Procedia PDF Downloads 418

Authors: Njeze Anthony

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In the world economy, entrepreneurship is an outstanding venture. Failures in the businesses of over 70% of Entrepreneurs can be attributed to lack of proper planning. For an entrepreneur to succeed, there are some vital planning strategies that will come into play such as organizational, operational, financial and marketing plans. When an entrepreneur lacks the above mentioned, such an entrepreneur is bound to encounter a catastrophic failure. An entrepreneur with an adequate plan will examine his/her own goals, know why he is in business, look at the venture resource base, have a sound knowledge of his proposed venture and identify obstacles that will be surmounted to achieve the desired goals. This work is aimed at identifying the organizational, operational, financial and marketing impact of entrepreneurship in the world economy and as well the important issues in global entrepreneurship, possible obstacles, and solutions.

Keywords: economy, entrepreneurship, business, operation

Procedia PDF Downloads 435

Authors: Tan Swee Liang

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This paper estimates bank and stock market efficiency associations with real per capita GDP growth by examining panel-data across three different regions using Panel-Corrected Standard Errors (PCSE) regression developed by Beck and Katz (1995). Data from five economies in ASEAN (Singapore, Malaysia, Thailand, Philippines, and Indonesia), five economies in Asia (Japan, China, Hong Kong SAR, South Korea, and India) and seven economies in OECD (Australia, Canada, Denmark, Norway, Sweden, United Kingdom U.K., and United States U.S.), between 1990 and 2017 are used. Empirical findings suggest one, for Asia-5 high bank net interest margin means greater bank profitability, hence spurring economic growth. Two, for OECD-7 low bank overhead costs (as a share of total assets) may reflect weak competition and weak investment in providing superior banking services, hence dampening economic growth. Three, stock market turnover ratio has negative association with OECD-7 economic growth, but a positive association with Asia-5, which suggest the relationship between liquidity and growth is ambiguous. Lastly, for ASEAN-5 high bank overhead costs (as a share of total assets) may suggest expenses have not been channelled efficiently to income generating activities. One practical implication of the findings is that policy makers should take necessary measures toward financial liberalisation policies that boost growth through the efficiency channel, so that funds are efficiently allocated through the financial system between financial and real sectors.

Keywords: financial development, banking system, capital markets, economic growth

Procedia PDF Downloads 119

Authors: M. Khairudin

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This paper investigates the system identification and design quantitative feedback theory (QFT) for the robust control of a lathe spindle. The dynamic of the lathe spindle is uncertain and time variation due to the deepness variation on cutting process. System identification was used to obtain the dynamics model of the lathe spindle. In this work, real time system identification is used to construct a linear model of the system from the nonlinear system. These linear models and its uncertainty bound can then be used for controller synthesis. The real time nonlinear system identification process to obtain a set of linear models of the lathe spindle that represents the operating ranges of the dynamic system. With a selected input signal, the data of output and response is acquired and nonlinear system identification is performed using Matlab to obtain a linear model of the system. Practical design steps are presented in which the QFT-based conditions are formulated to obtain a compensator and pre-filter to control the lathe spindle. The performances of the proposed controller are evaluated in terms of velocity responses of the the lathe machine spindle in corporating deepness on cutting process.

Keywords: lathe spindle, QFT, robust control, system identification

Procedia PDF Downloads 520

Authors: Z. Ulukan, E. Demircioğlu,

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Facility location is a complex real-world problem which needs a strategic management decision. This paper provides a general review on studies, efforts and developments in Facility Location Problems which are classical optimization problems having a wide-spread applications in various areas such as transportation, distribution, production, supply chain decisions and telecommunication. Our goal is not to review all variants of different studies in FLPs or to describe very detailed computational techniques and solution approaches, but rather to provide a broad overview of major location problems that have been studied, indicating how they are formulated and what are proposed by researchers to tackle the problem. A brief, elucidative table based on a grouping according to “General Problem Type” and “Methods Proposed” used in the studies is also presented at the end of the work.

Keywords: discrete location problems, exact methods, heuristic algorithms, single source capacitated facility location problems

Procedia PDF Downloads 444

Authors: K. Rumchev, S. Gilbey

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Airborne particulate matter is a known hazard to human health, with a considerable body of evidence linking agricultural dust exposures to adverse human health effects in exposed populations. It is also known that agricultural workers are exposed to high levels of soil dust and other types of airborne particulate matter within the farming environment. The aim of this study was to examine exposure to agricultural dust among farm workers during the seeding season. Twenty-one wheat-belt farms consented to participate in the study with 30 workers being monitored for dust exposure whilst seeding or undertaking seeding associated tasks. Each farm was visited once and farmers’ were asked to wear a personal air sampler for a 4-hour sampling period. Simultaneous, real-time, tractor cabin air quality monitoring was also undertaken. Data for this study was collected using real-time aerosol dust monitors to determine in-tractor cabin PM exposure to five size fractions (total, PM10, respirable, PM2.5 and PM1), and personal sampling was undertaken to establish individual exposure to inhalable and respirable dust concentrations. The study established a significant difference between personal exposures and simultaneous real-time in-cabin exposures for both inhalable and respirable fractions. No significant difference was shown between in-cabin and personal inhalable dust concentrations during seeding and spraying tasks, although both in-cabin and personal concentrations were two times greater for seeding than spraying. Future research should focus on educating and providing farm owners and workers with more information on adopting safe work practices to minimise harmful exposures to agricultural dust.

Keywords: agriculture, air quality, Australia, particulate matter

Procedia PDF Downloads 201

Authors: Juliana Sagretti, Susy Sabato

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Despite the poverty in the world, a third of all food produced in the world is wasted. FAO, the United Nations Organization of Agriculture and Food, points out the need to combine actions and new technologies to combat hunger and waste in contrast to the high production of food in the world. The energy of ionizing radiation in food brought many positive results, such as increased validity and insect infestation control. The food banks are organizations that act at various points of food chain to collect and distribute food to the needy. So, the aim of this study was to initiate a partnership between irradiation and the food bank through the development of a questionnaire to evaluate and disseminate the knowledge and acceptance of individuals in the food bank in Brazil. In addition, this study aimed to standardize a basis questionnaire for future research assessment of irradiated foods. For the construction of the questionnaire as a measuring instrument, a comprehensive and rigorous literature review was made. Its covered qualitative research, questionnaires, sensory evaluation and food irradiated. Three stages of pre - tests were necessary and related fields of experts were consulted. As a result, the questionnaire has three parts, personal issues, assertive issues and questions of multiple choices and finally an informative question. The questionnaire was applied in Ceagesp food bank in the biggest center of food in Brazil (data not shown).

Keywords: food bank, food irradiation, food waste, sustainability

Procedia PDF Downloads 307

Authors: Adriana Postal, Silvio C. Sampaio, Marcio A. Villas Boas, Josué P. Castro

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This study deals with the estimation of reference evapotranspiration (ET₀) in an agricultural context, focusing on efficient irrigation management to meet the growing interest in the sustainable management of water resources. Given the importance of water in agriculture and its scarcity in many regions, efficient use of this resource is essential to ensure food security and environmental sustainability. The methodology used involved the application of artificial intelligence techniques, specifically Multilayer Perceptron (MLP) Artificial Neural Networks (ANNs), to predict ET₀ in the state of Paraná, Brazil. The models were trained and validated with meteorological data from the Brazilian National Institute of Meteorology (INMET), together with data obtained from a producer's weather station in the western region of Paraná. Two optimizers (SGD and Adam) and different meteorological variables, such as temperature, humidity, solar radiation, and wind speed, were explored as inputs to the models. Nineteen configurations with different input variables were tested; amidst them, configuration 9, with 8 input variables, was identified as the most efficient of all. Configuration 10, with 4 input variables, was considered the most effective, considering the smallest number of variables. The main conclusions of this study show that MLP ANNs are capable of accurately estimating ET₀, providing a valuable tool for irrigation management in agriculture. Both configurations (9 and 10) showed promising performance in predicting ET₀. The validation of the models with cultivator data underlined the practical relevance of these tools and confirmed their generalization ability for different field conditions. The results of the statistical metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R²), showed excellent agreement between the model predictions and the observed data, with MAE as low as 0.01 mm/day and 0.03 mm/day, respectively. In addition, the models achieved an R² between 0.99 and 1, indicating a satisfactory fit to the real data. This agreement was also confirmed by the Kolmogorov-Smirnov test, which evaluates the agreement of the predictions with the statistical behavior of the real data and yields values between 0.02 and 0.04 for the producer data. In addition, the results of this study suggest that the developed technique can be applied to other locations by using specific data from these sites to further improve ET₀ predictions and thus contribute to sustainable irrigation management in different agricultural regions. The study has some limitations, such as the use of a single ANN architecture and two optimizers, the validation with data from only one producer, and the possible underestimation of the influence of seasonality and local climate variability. An irrigation management application using the most efficient models from this study is already under development. Future research can explore different ANN architectures and optimization techniques, validate models with data from multiple producers and regions, and investigate the model's response to different seasonal and climatic conditions.

Keywords: agricultural technology, neural networks in agriculture, water efficiency, water use optimization

Procedia PDF Downloads 27

Authors: Elizabeth M. Seabrook, Nikki S. Rickard

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Examining time-dependent measures of emotion such as variability, instability, and inertia, provide critical and complementary insights into mental health status. Observing changes in the pattern of emotional expression over time could act as a tool to identify meaningful shifts between psychological well- and ill-being. From a practical standpoint, however, examining emotion dynamics day-to-day is likely to be burdensome and invasive. Utilizing social media data as a facet of lived experience can provide real-world, temporally specific access to emotional expression. Emotional language on social media may provide accurate and sensitive insights into individual and community mental health and well-being, particularly with focus placed on the within-person dynamics of online emotion expression. The objective of the current study was to examine the dynamics of emotional expression on the social network platform Facebook for active users and their relationship with psychological well- and ill-being. It was expected that greater positive and negative emotion variability, instability, and inertia would be associated with poorer psychological well-being and greater depression symptoms. Data were collected using a smartphone app, MoodPrism, which delivered demographic questionnaires, psychological inventories assessing depression symptoms and psychological well-being, and collected the Status Updates of consenting participants. MoodPrism also delivered an experience sampling methodology where participants completed items assessing positive affect, negative affect, and arousal, daily for a 30-day period. The number of positive and negative words in posts was extracted and automatically collated by MoodPrism. The relative proportion of positive and negative words from the total words written in posts was then calculated. Preliminary analyses have been conducted with the data of 9 participants. While these analyses are underpowered due to sample size, they have revealed trends that greater variability in the emotion valence expressed in posts is positively associated with greater depression symptoms (r(9) = .56, p = .12), as is greater instability in emotion valence (r(9) = .58, p = .099). Full data analysis utilizing time-series techniques to explore the Facebook data set will be presented at the conference. Identifying the features of emotion dynamics (variability, instability, inertia) that are relevant to mental health in social media emotional expression is a fundamental step in creating automated screening tools for mental health that are temporally sensitive, unobtrusive, and accurate. The current findings show how monitoring basic social network characteristics over time can provide greater depth in predicting risk and changes in depression and positive well-being.

Keywords: emotion, experience sampling methods, mental health, social media

Procedia PDF Downloads 227

Authors: Tarek S. Duzan, Abdulaziz F. Ettir

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Wave velocities, Core and Log petrophysical data were collected from recently drilled four new wells scattered through-out the Dahra/Jofra (PL-5) Reservoir. The collected data were analyzed for the relationships of Wave Velocities with rock property such as Porosity, permeability and Bulk Density. Lots of Literature review reveals a number of differing results and conclusions regarding wave velocities (Compressional Waves (Vp) and Shear Waves (Vs)) versus rock petrophysical property relationships, especially in carbonate reservoirs. In this paper, we focused on the relationships between wave velocities (Vp , Vs) and the ratio Vp/Vs with rock properties for shallow marine libyan carbonate reservoir (Real Case). Upon data analysis, a relationship between petrophysical properties and wave velocities (Vp, Vs) and the ratio Vp/Vs has been found. Porosity and bulk density properties have shown exponential relationship with wave velocities, while permeability has shown a power relationship in the interested zone. It is also clear that wave velocities (Vp , Vs) seems to be a good indicator for the lithology change with true vertical depth. Therefore, it is highly recommended to use the output relationships to predict porosity, bulk density and permeability of the similar reservoir type utilizing the most recent seismic data.

Keywords: conventional core analysis (porosity, permeability bulk density) data, VS wave and P-wave velocities, shallow carbonate reservoir in D/J field

Procedia PDF Downloads 312

Authors: Richard Ren

Abstract:

Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

Procedia PDF Downloads 102

Authors: Etienne Alain Feukeu, L. W. Snyman

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Water scarcity is becoming a real issue nowadays. Most countries in the world are facing it in their own way based on their own geographical coordinate and condition. Many countries are facing a challenge of a growing water demand as a result of not only an increased population, economic growth, but also as a pressure of the population dynamic and urbanization. In view to mitigate some of this related problem, an accurate method of water estimation and future prediction, forecast is essential to guarantee not only the sufficient quantity, but also a good water distribution and management system. Beside the fact that several works have been undertaken to address this concern, there is still a considerable disparity between different methods and standard used for water prediction and estimation. Hence this work contrast and compare two well-defined and established methods from two countries (USA and South Africa) to demonstrate the inconsistency when different method and standards are used interchangeably.

Keywords: water scarcity, water estimation, water prediction, water forecast.

Procedia PDF Downloads 183

Authors: R. Biondi, G. Dys, G. Ferone, T. Renard, M. Zysman

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This paper describes an automated implementable system for impulsive signals detection and recognition. The system uses a Digital Signal Processing device for the detection and identification process. Here the system analyses the signals in real time in order to produce a particular response if needed. The system analyses the signals in real time in order to produce a specific output if needed. Detection is achieved through normalizing the inputs and comparing the read signals to a dynamic threshold and thus avoiding detections linked to loud or fluctuating environing noise. Identification is done through neuronal network algorithms. As a setup our system can receive signals to “learn” certain patterns. Through “learning” the system can recognize signals faster, inducing flexibility to new patterns similar to those known. Sound is captured through a simple jack input, and could be changed for an enhanced recording surface such as a wide-area recorder. Furthermore a communication module can be added to the apparatus to send alerts to another interface if needed.

Keywords: sound detection, impulsive signal, background noise, neural network

Procedia PDF Downloads 299

Authors: Rolando Barradas, Salviano Soares, António Valente, José Alberto Lencastre, Paulo Oliveira

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This article reviews the Ant Colony Optimization, a nature-inspired algorithm, and its implementation in the Scratch/m-Block programming environment. The Ant Colony Optimization is a part of Swarm Intelligence-based algorithms and is a subset of biological-inspired algorithms. Starting with a problem in which one has a maze and needs to find its path to the center and return to the starting position. This is similar to an ant looking for a path to a food source and returning to its nest. Starting with the implementation of a simple wall follower simulator, the proposed solution uses a dynamic graphical interface that allows young students to observe the ants’ movement while the algorithm optimizes the routes to the maze’s center. Things like interface usability, Data structures, and the conversion of algorithmic language to Scratch syntax were some of the details addressed during this implementation. This gives young students an easier way to understand the computational concepts of sequences, loops, parallelism, data, events, and conditionals, as they are used through all the implemented algorithms. Future work includes the simulation results with real contest mazes and two different pheromone update methods and the comparison with the optimized results of the winners of each one of the editions of the contest. It will also include the creation of a Digital Twin relating the virtual simulator with a real micromouse in a full-size maze. The first test results show that the algorithm found the same optimized solutions that were found by the winners of each one of the editions of the Micromouse contest making this a good solution for maze pathfinding.

Keywords: nature inspired algorithms, scratch, micromouse, problem-solving, computational thinking

Procedia PDF Downloads 104

Authors: Sandra T. Matarneh, Mark Danso-Amoako, Salam Al-Bizri, Mark Gaterell

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Today’s facilities are ever more sophisticated and the need for available and reliable information for operation and maintenance activities is vital. The key challenge for facilities managers is to have real-time accurate and complete information to perform their day-to-day activities and to provide their senior management with accurate information for decision-making process. Currently, there are various technology platforms, data repositories, or database systems such as Computer-Aided Facility Management (CAFM) that are used for these purposes in different facilities. In most current practices, the data is extracted from paper construction documents and is re-entered manually in one of these computerized information systems. Construction Operations Building information exchange (COBie), is a non-proprietary data format that contains the asset non-geometric data which was captured and collected during the design and construction phases for owners and facility managers use. Recently software vendors developed add-in applications to generate COBie spreadsheet automatically. However, most of these add-in applications are capable of generating a limited amount of COBie data, in which considerable time is still required to enter the remaining data manually to complete the COBie spreadsheet. Some of the data which cannot be generated by these COBie add-ins is essential for facilities manager’s day-to-day activities such as job sheet which includes preventive maintenance schedules. To facilitate a seamless data transfer between BIM models and facilities management systems, we developed a framework that enables automated data generation using the data extracted directly from BIM models to external web database, and then enabling different stakeholders to access to the external web database to enter the required asset data directly to generate a rich COBie spreadsheet that contains most of the required asset data for efficient facilities management operations. The proposed framework is a part of ongoing research and will be demonstrated and validated on a typical university building. Moreover, the proposed framework supplements the existing body of knowledge in facilities management domain by providing a novel framework that facilitates seamless data transfer between BIM models and facilities management systems.

Keywords: building information modeling, BIM, facilities management systems, interoperability, information management

Procedia PDF Downloads 98

Authors: Syed Iftikhar Hussain Shah, Vasilis Peristeras, Ioannis Magnisalis

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Organizations, including governments, generate (big) data that are high in volume, velocity, veracity, and come from a variety of sources. Public Administrations are using (big) data, implementing base registries, and enforcing data sharing within the entire government to deliver (big) data related integrated services, provision of insights to users, and for good governance. Government (Big) data ecosystem actors represent distinct entities that provide data, consume data, manipulate data to offer paid services, and extend data services like data storage, hosting services to other actors. In this research work, we perform a systematic literature review. The key objectives of this paper are to propose a robust definition of government (big) data ecosystem and a classification of government (big) data ecosystem actors and their roles. We showcase a graphical view of actors, roles, and their relationship in the government (big) data ecosystem. We also discuss our research findings. We did not find too much published research articles about the government (big) data ecosystem, including its definition and classification of actors and their roles. Therefore, we lent ideas for the government (big) data ecosystem from numerous areas that include scientific research data, humanitarian data, open government data, industry data, in the literature.

Keywords: big data, big data ecosystem, classification of big data actors, big data actors roles, definition of government (big) data ecosystem, data-driven government, eGovernment, gaps in data ecosystems, government (big) data, public administration, systematic literature review

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Authors: Alaka Padhye, S. P. Kane

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The following study considers some variations made to the secretary problem (SP). In a multiple criteria secretary problem (MCSP), the selection of a unit is based on two independent characteristics. The units that appear before an observer are known say N, the best rank of a unit being N. A unit is selected, if it is better with respect to either first or second or both the characteristics. When the number of units is large and due to constraints like time and cost, the observer might want to stop earlier instead of inspecting all the available units. Let the process terminate at r2th unit where r1Keywords: joint distribution, marginal distribution, real ranks, secretary problem, selection criterion, two tailed secretary problem

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Authors: Abraham Motlhabane

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This research analysed the written views of science education students regarding the teaching of laboratory work using the online mode. The research adopted the qualitative methodology. The qualitative research was aimed at investigating small and distinct groups normally regarded as a single-site study. Qualitative research was used to describe and analyze the phenomena from the student’s perspective. This means the research began with assumptions of the world view that use theoretical lenses of research problems inquiring into the meaning of individual students. The research was conducted with three groups of students studying for Postgraduate Certificate in Education, Bachelor of Education and honors Bachelor of Education respectively. In each of the study programmes, the science education module is compulsory. Five science education students from each study programme were purposively selected to participate in this research. Therefore, 15 students participated in the research. In order to analysis the data, the data were first printed and hard copies were used in the analysis. The data was read several times and key concepts and ideas were highlighted. Themes and patterns were identified to describe the data. Coding as a process of organising and sorting data was used. The findings of the study are very diverse; some students are in favour of online laboratory whereas other students argue that science can only be learnt through hands-on experimentation.

Keywords: online learning, laboratory work, views, perceptions

Procedia PDF Downloads 123

Authors: Budi Nurani Ruchjana, Atje Setiawan Abdullah, I. Gede Nyoman Mindra Jaya, Eddy Hermawan

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In this paper, we propose the application package to estimate parameters of spatiotemporal model based on the multivariate time series analysis using the R open-source software. We build packages mainly to estimate the parameters of the Generalized Space Time Autoregressive (GSTAR) model. GSTAR is a combination of time series and spatial models that have parameters vary per location. We use the method of Ordinary Least Squares (OLS) and use the Mean Average Percentage Error (MAPE) to fit the model to spatiotemporal real phenomenon. For case study, we use oil production data from volcanic layer at Jatibarang Indonesia or climate data such as rainfall in Indonesia. Software R is very user-friendly and it is making calculation easier, processing the data is accurate and faster. Limitations R script for the estimation of model parameters spatiotemporal GSTAR built is still limited to a stationary time series model. Therefore, the R program under windows can be developed either for theoretical studies and application.

Keywords: GSTAR Model, MAPE, OLS method, oil production, R software

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Authors: M. Celeska, K. Najdenkoski, V. Dimchev, V. Stoilkov

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Accurate determination of wind turbine performance is necessary for economic operation of a wind farm. At present, the procedure to carry out the power performance verification of wind turbines is based on a standard of the International Electrotechnical Commission (IEC). In this paper, nonparametric statistical inference is applied to designing a simple, inexpensive method of verifying the power performance of a wind turbine. A statistical test is explained, examined, and the adequacy is tested over real data. The methods use the information that is collected by the SCADA system (Supervisory Control and Data Acquisition) from the sensors embedded in the wind turbines in order to carry out the power performance verification of a wind farm. The study has used data on the monthly output of wind farm in the Republic of Macedonia, and the time measuring interval was from January 1, 2016, to December 31, 2016. At the end, it is concluded whether the power performance of a wind turbine differed significantly from what would be expected. The results of the implementation of the proposed methods showed that the power performance of the specific wind farm under assessment was acceptable.

Keywords: canonical correlation analysis, power curve, power performance, wind energy

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Authors: Ferdinando Montemari, Antonio Vitale, Nicola Genito, Luca Garbarino, Urbano Tancredi, Domenico Accardo, Michele Grassi, Giancarmine Fasano, Anna Elena Tirri

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The development of guidance, navigation and control algorithms and avionic procedures requires the disposability of suitable analysis and verification tools, such as simulation environments, which support the design process and allow detecting potential problems prior to the flight test, in order to make new technologies available at reduced cost, time and risk. This paper presents a simulation environment for avionic software development and qualification, especially aimed at equipment for general aviation aircrafts and unmanned aerial systems. The simulation environment includes models for short and medium-range radio-navigation aids, flight assistance systems, and ground control stations. All the software modules are able to simulate the modeled systems both in fast-time and real-time tests, and were implemented following component oriented modeling techniques and requirement based approach. The paper describes the specific models features, the architectures of the implemented software systems and its validation process. Performed validation tests highlighted the capability of the simulation environment to guarantee in real-time the required functionalities and performance of the simulated avionics systems, as well as to reproduce the interaction between these systems, thus permitting a realistic and reliable simulation of a complete mission scenario.

Keywords: ADS-B, avionics, NAVAIDs, real-time simulation, TCAS, UAS ground control station

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Authors: X. Cervantes-López, C. Arriaga-Canon, L. Contreras Espinosa

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The goal of this study is to validate the overexpression of the lncRNA GATA3-AS1 associated with resistance to neoadjuvant chemotherapy of female patients with locally advanced mammary adenocarcinoma of luminal B subtype This study involved a cohort of one hundred thirty-seven samples for which total RNA was isolated from formalin fixed paraffin embedded (FFPE) tissue. Samples were cut using a Microtome Hyrax M25 Zeiss and RNA was isolated using the RNeasy FFPE kit and a deparaffinization solution, the next step consisted in the analysis of RNA concentration and quality, then 18 µg of RNA was treated with DNase I, and cDNA was synthesized from 50 ng total RNA, finally real-time PCR was performed with SYBR Green/ROX qPCR Master Mix in order to determined relative gene expression using RPS28 as a housekeeping gene to normalize in a fold calculation ΔCt. As a result, we validated by real-time PCR that the overexpression of the lncRNA GATA3-AS1 is associated with resistance to neoadjuvant chemotherapy in locally advanced breast cancer patients of luminal B subtype.

Keywords: breast cancer, biomarkers, genomics, neoadjuvant chemotherapy, lncRNAS

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Authors: Dustin Dill

Abstract:

The physical movement of crossing over water points to both developing narrative tropes and innovative cinematography in World Cinema today. Two prime examples, Alamar (2009) by Pedro González-Rubio and Kaili Blues (2015) by Bi Gan, demonstrate how contemporary storytelling in a film not only rests upon these water shots but also emerges from them. The range of symbolism that these episodes in the story provoke goes hand in hand with the diverse filming sequences found in the respective productions. While González-Rubio decides to cut the scene into long and longer shots, Gan uses a single take. The differing angles depict equally unique directors and film projects: Alamar runs parallel to many definitions of the essay film, and Kaili Blues resonates much more with mystery and art film. Nonetheless, the crossing of water scenes influence the narratives’ subjects despite the generic consequences, and it is within the essay, mystery, and art film genres which allows for a better understanding of World Cinema. Tiago de Luca explains World Cinema’s prerogative of giving form to a certain type of spectator does not always line up. Given the immense number of interpretations of crossing water —the escape from suffering to find nirvana, rebirth, and colonization— underline the difficulty of categorizing it. If before this type of cross-genre was a trait that defined World Cinema in its beginning, this study observes that González-Rubio and Gan question the all-encompassing genre with their experimental shots of a universal narrative trope, the crossing of water.

Keywords: cinematography, genre, narrative, world cinema

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Authors: Atikah Nurhayati, Asep A. Handaka

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Indonesia is a world’s major fish producer which can feed not only its citizens but also the people of the world. Currently, the total annual production is 11 tons and expected to double by the year of 2050. Given the potential, fishery has been an important part of the national food security system in Indonesia. Despite such a potential, a big challenge is facing the Indonesians in making fish the reliable source for their food, more specifically source of protein intake. The long geographic distance between the fish production centers and the consumer concentrations has prevented effective supply chain from producers to consumers and therefore demands a good logistic system. This paper is based on our research, which aimed at analyzing the fish supply chain and is to suggest relevant improvement to the chain. The research was conducted in the Year of 2016 in selected locations of Java Island, where intensive transaction on fishery commodities occur. Data used in this research comprises secondary data of time series reports on production and distribution and primary data regarding distribution aspects which were collected through interviews with purposively selected 100 respondents representing fishers, traders and processors. The data were analyzed following the supply chain management framework and processed following logistic regression and validity tests. The main findings of the research are as follows. Firstly, it was found that improperly managed connectivity and logistic chain is the main cause for insecurity of availability and affordability for the consumers. Secondly, lack of quality of most local processed products is a major obstacle for improving affordability and connectivity. The paper concluded with a number of recommended strategies to tackle the problem. These include rationalization of the length of the existing supply chain, intensification of processing activities, and improvement of distribution infrastructure and facilities.

Keywords: fishery, food security, logistic, supply chain

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Authors: Yusri Yusof, Kamran Latif

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Computer Numeric Control (CNC) machine has been widely used in the industries since its inception. Currently, in CNC technology has been used for various operations like milling, drilling, packing and welding etc. with the rapid growth in the manufacturing world the demand of flexibility in the CNC machines has rapidly increased. Previously, the commercial CNC failed to provide flexibility because its structure was of closed nature that does not provide access to the inner features of CNC. Also CNC’s operating ISO data interface model was found to be limited. Therefore, to overcome that problem, Open Architecture Control (OAC) technology and STEP-NC data interface model are introduced. At present the Personal Computer (PC) has been the best platform for the development of open-CNC systems. In this paper, both ISO data interface model interpretation, its verification and execution has been highlighted with the introduction of the new techniques. The proposed is composed of ISO data interpretation, 3D simulation and machine motion control modules. The system is tested on an old 3 axis CNC milling machine. The results are found to be satisfactory in performance. This implementation has successfully enabled sustainable manufacturing environment.

Keywords: CNC, ISO 6983, ISO 14649, LabVIEW, open architecture control, reconfigurable manufacturing systems, sustainable manufacturing, Soft-CNC

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Authors: Riktesh Srivastava

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Online Shopping have established an astonishing evolution in the last few years. And it is now apparent that B2C architecture is becoming progressively imperative channel for even traditional brick and mortar type traders as well. In this completion knowing customers and predicting behavior are extremely important. More important, when any customer logs onto the B2C architecture, the traces of their buying patterns can be stored and used for future predictions. Such a prediction is called Customer Lifetime Value (CLV). Earlier, we used Net Present Value to do so, however, it ignores two important aspects of B2C architecture, “market risks” and “big amount of customer data”. Now, we use RFM- Recency, Frequency and Monetary Value to estimate the CLV, and as the term exemplifies, market risks, is well sheltered. Big Data Analysis is also roofed in RFM, which gives real exploration of the Big Data and lead to a better estimation for future cash flow from customers. In the present paper, 6 factors (collected from varied sources) are used to determine as to what attracts the customers to the B2C architecture. For these 6 factors, RFM is computed for 3 years (2013, 2014 and 2015) respectively. CLV and Revenue are the two parameters defined using RFM analysis, which gives the clear picture of the future predictions.

Keywords: CLV, RFM, revenue, recency, frequency, monetary value

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Authors: Chiara Barone

Abstract:

In Naples, the visible architecture is only what emerges from the underground. Caves and tunnels cross it in every direction, intertwining with each other. They are not natural caves but spaces built by removing what is superfluous in order to dig a form out of the material. Architects, as sculptors of space, do not determine the exterior, what surrounds the volume and in which the forms live, but an interior underground space, perceptive and sensitive, able to generate new emotions each time. It is an intracorporeal architecture linked to the body, not in its external relationships, but rather with what happens inside. The proposed aims to reflect on the design of underground spaces in the Neapolitan city. The idea is to intend the underground as a spectacular museum of the city, an opportunity to learn in situ the history of the place along an unpredictable itinerary that crosses the caves and, in certain points, emerges, escaping from the world of shadows. Starting form the analysis and the study of the many overlapping elements, the archaeological one, the geological layer and the contemporary city above, it is possible to develop realistic alternatives for underground itineraries. The objective is to define minor paths to ensure the continuity between the touristic flows and entire underground segments already investigated but now disconnected: open-air paths, which abyss in the earth, retracing historical and preserved fragments. The visitor, in this way, passes from real spaces to sensitive spaces, in which the imaginary replaces the real experience, running towards exciting and secret knowledge. To safeguard the complex framework of the historical-artistic values, it is essential to use a multidisciplinary methodology based on a global approach. Moreover, it is essential to refer to similar design projects for the archaeological underground, capable of guide action strategies, looking at similar conditions in other cities, where the project has led to an enhancement of the heritage in the city. The research limits the field of investigation, by choosing the historic center of Naples, applying bibliographic and theoretical research to a real place. First of all, it’s necessary to deepen the places’ knowledge understanding the potentialities of the project as a link between what is below and what is above. Starting from a scientific approach, in which theory and practice are constantly intertwined through the architectural project, the major contribution is to provide possible alternative configurations for the underground space and its relationship with the city above, understanding how the condition of transition, as passage between the below and the above becomes structuring in the design process. Starting from the consideration of the underground as both a real physical place and a sensitive place, which engages the memory, imagination, and sensitivity of a man, the research aims at identifying possible configurations and actions useful for future urban programs to make the underground a central part of the lived city, again.

Keywords: underground paths, invisible ruins, imaginary, sculpted forms, sensitive spaces, Naples

Procedia PDF Downloads 85