Search results for: data mining applications and discovery

Authors: Ibrahim Rabiu Darazo

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The research has been conducted on the impact of E-banking on the operational efficiency of Banks in Nigeria, A case of some selected banks (Diamond Bank Plc, GTBankPlc, and Fidelity Bank Plc) in Nigeria. The research is a quantitative research which uses both primary and secondary sources of data collection. Questionnaire were used to obtained accurate data, where 150 Questionnaire were distributed among staff and customers of the three Banks , and the data collected where analysed using chi-square, whereas the secondary data where obtained from relevant text books, journals and relevant web sites. It is clear from the findings that, the use of e-banking by the banks has improved the efficiency of these banks, in terms of providing efficient services to customers electronically, using Internet Banking, Telephone Banking ATMs, reducing time taking to serve customers, e-banking allow new customers to open an account online, customers have access to their account at all the time 24/7.E-banking provide access to customers information from the data base and cost of check and postage were eliminated using e-banking. The recommendation at the end of the research include; the Banks should try to update their electronic gadgets, e-fraud(internal & external) should also be controlled, Banks shall employ qualified man power, Biometric ATMs shall be introduce to reduce fraud using ATM Cards, as it is use in other countries like USA.

Keywords: banks, electronic banking, operational efficiency of banks, biometric ATMs

Procedia PDF Downloads 337

Authors: Jennifer Leach, Umashanger Thayasivam

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The use of technology has benefited society in more ways than one ever thought possible. Unfortunately, though, as society’s knowledge of technology has advanced, so has its knowledge of ways to use technology to manipulate people. This has led to a simultaneous advancement in the world of fraud. Machine learning techniques can offer a possible solution to help decrease this advancement. This research explores how the use of various machine learning techniques can aid in detecting fraudulent activity across two different types of fraudulent data, and the accuracy, precision, recall, and F1 were recorded for each method. Each machine learning model was also tested across five different training and testing splits in order to discover which testing split and technique would lead to the most optimal results.

Keywords: data science, fraud detection, machine learning, supervised learning

Procedia PDF Downloads 201

Authors: O. O. Aiyelokun, O. A. Agbede

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Numerical modelling of groundwater flow can be susceptible to calibration errors due to lack of adequate ground-based hydro-metrological stations in river basins. Groundwater resources management in Southwest Nigeria is currently challenged by overexploitation, lack of planning and monitoring, urbanization and climate change; hence to adopt models as decision support tools for sustainable management of groundwater; they must be adequately calibrated. Since river basins in Southwest Nigeria are characterized by missing data, and lack of adequate ground-based hydro-meteorological stations; the need for adopting satellite-based data for constructing distributed models is crucial. This study seeks to evaluate the suitability of satellite-based data as substitute for ground-based, for computing boundary conditions; by determining if ground and satellite based meteorological data fit well in Ogun and Oshun River basins. The Climate Forecast System Reanalysis (CFSR) global meteorological dataset was firstly obtained in daily form and converted to monthly form for the period of 432 months (January 1979 to June, 2014). Afterwards, ground-based meteorological data for Ikeja (1981-2010), Abeokuta (1983-2010), and Oshogbo (1981-2010) were compared with CFSR data using Goodness of Fit (GOF) statistics. The study revealed that based on mean absolute error (MEA), coefficient of correlation, (r) and coefficient of determination (R²); all meteorological variables except wind speed fit well. It was further revealed that maximum and minimum temperature, relative humidity and rainfall had high range of index of agreement (d) and ratio of standard deviation (rSD), implying that CFSR dataset could be used to compute boundary conditions such as groundwater recharge and potential evapotranspiration. The study concluded that satellite-based data such as the CFSR should be used as input when constructing groundwater flow models in river basins in Southwest Nigeria, where majority of the river basins are partially gaged and characterized with long missing hydro-metrological data.

Keywords: boundary condition, goodness of fit, groundwater, satellite-based data

Procedia PDF Downloads 132

Authors: Hassan Irshad Bhatti, Saravanan Yuvaraja, Xiaohang Li

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GaN-based devices, such as high electron mobility transistors (HEMTs), offer superior characteristics for high-power, high-frequency, and high-temperature applications [1]. However, this exceptional electrical performance is compromised by undesirable self-heating effects under high-power applications [2, 3]. Some of the issues caused by self-heating are current collapse, thermal runway and performance degradation [4, 5]. Therefore, accurate and reliable methods for measuring the temperature of individual devices on a chip are needed to monitor and control the thermal behavior of GaN-based devices [6]. Temperature measurement at the micro/nanoscale is a challenging task that requires specialized techniques such as Infrared microscopy, Raman thermometry, and thermoreflectance. Recently, micro-thermocouples (MTCs) have attracted considerable attention due to their advantages of simplicity, low cost, high sensitivity, and compatibility with standard fabrication processes [7, 8]. A micro-thermocouple is a junction of two different metal thin films, which generates a Seebeck voltage related to the temperature difference between a hot and cold zone. Integrating MTC in a device allows local temperature to be measured with high sensitivity and accuracy [9]. This work involves the fabrication and integration of micro-thermocouples (MTCs) to measure the channel temperature of GaN HEMT. Our fabricated MTC (Platinum-Chromium junction) has shown a sensitivity of 16.98 µV/K and can measure device channel temperature with high precision and accuracy. The temperature information obtained using this sensor can help improve GaN-based devices and provide thermal engineers with useful insights for optimizing their designs.

Keywords: Electrical Engineering, Thermal engineering, Power Devices, Semiconuctors

Procedia PDF Downloads 28

Authors: Abadou Yacine, Faid Hayette

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Significant investigations were performed on the use and impact on physical properties along with the mechanical strength of the recycled and reused E-glass waste powder. However, it has been modelled how recycled display e-waste glass may affect the characteristics and qualities of dune sand mortar. To be involved in this field, an investigation has been done with the substitution of dune sand for recycled E-glass waste and constant water-cement ratios. The linear relationship between the dune sand mortar and E-glass mortar mix % contributes to the model's reliability. The experimental data was exposed to regression analysis using JMP Statistics software. The regression model with one predictor presented the general form of the equation for the prediction of the five properties' characteristics of dune sand mortar from the substitution ratio of E-waste glass and curing age. The results illustrate that curing a long-term process produced an E-glass waste mortar specimen with the highest compressive strength of 68 MPa in the laboratory environment. Anova analysis indicated that the curing at long-term has the utmost importance on the sorptivity level and ultrasonic pulse velocity loss. Furthermore, the E-glass waste powder percentage has the utmost importance on the compressive strength and improvement in dynamic elasticity modulus. Besides, a significant enhancement of radiation-shielding applications.

Keywords: ANOVA analysis, E-glass waste, durability and sustainability, radiation-shielding

Procedia PDF Downloads 63

Authors: Kumara Jayapathma J. H. M. S. S., Dampegama S. D. P. J.

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Lands are valuable & limited resource which constantly changes with the growth of the population. An efficient and good land management system is essential to avoid conflicts associated with lands. This paper aims to design the prototype model of a Mobile GIS Land use and Land Information System in real-time. Homagama Divisional Secretariat Division situated in the western province of Sri Lanka was selected as the study area. The prototype model was developed after reviewing related literature. The methodology was consisted of designing and modeling the prototype model into an application running on a mobile platform. The system architecture mainly consists of a Google mapping app for real-time updates with firebase support tools. Thereby, the method of implementation consists of front-end and back-end components. Software tools used in designing applications are Android Studio with JAVA based on GeoJSON File structure. Android Studio with JAVA in GeoJSON File Synchronize to Firebase was found to be the perfect mobile solution for continuously updating Land use and Land Information System (LIS) in real-time in the present scenario. The mobile-based land use and LIS developed in this study are multiple user applications catering to different hierarchy levels such as basic users, supervisory managers, and database administrators. The benefits of this mobile mapping application will help public sector field officers with non-GIS expertise to overcome the land use planning challenges with land use updated in real-time.

Keywords: Android, Firebase, GeoJSON, GIS, JAVA, JSON, LIS, Mobile GIS, real-time, REST API

Procedia PDF Downloads 236

Authors: Neeti Nayak, Khalid Duri

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Despite the many advances made in transportation planning, the number of injuries and fatalities in the United States which involve motorized vehicles near intersections remain largely unchanged year over year. Data from the National Highway Traffic Safety Administration for 2018 indicates accidents involving motorized vehicles at traffic intersections accounted for 8,245 deaths and 914,811 injuries. Furthermore, collisions involving pedal cyclists killed 861 people (38% at intersections) and injured 46,295 (68% at intersections), while accidents involving pedestrians claimed 6,247 lives (25% at intersections) and injured 71,887 (56% at intersections)- the highest tallies registered in nearly 20 years. Some of the causes attributed to the rising number of accidents relate to increasing populations and the associated changes in land and traffic usage patterns, insufficient visibility conditions, and inadequate applications of traffic controls. Intersections that were initially designed with a particular land use pattern in mind may be rendered obsolete by subsequent developments. Many accidents involving pedestrians are accounted for by locations which should have been designed for safe crosswalks. Conventional solutions for evaluating intersection safety often require costly deployment of engineering surveys and analysis, which limit the capacity of resource-constrained administrations to satisfy their community’s needs for safe roadways adequately, effectively relegating mitigation efforts for high-risk areas to post-incident responses. This paper demonstrates how geospatial technology can identify high-risk locations and evaluate the viability of specific intersection management techniques. GIS is used to simulate relevant real-world conditions- the presence of traffic controls, zoning records, locations of interest for human activity, design speed of roadways, topographic details and immovable structures. The proposed methodology provides a low-cost mechanism for empowering urban planners to reduce the risks of accidents using 2-dimensional data representing multi-modal street networks, parcels, crosswalks and demographic information alongside 3-dimensional models of buildings, elevation, slope and aspect surfaces to evaluate visibility and lighting conditions and estimate probabilities for jaywalking and risks posed by blind or uncontrolled intersections. The proposed tools were developed using sample areas of Southern California, but the model will scale to other cities which conform to similar transportation standards given the availability of relevant GIS data.

Keywords: crosswalks, cyclist safety, geotechnology, GIS, intersection safety, pedestrian safety, roadway safety, transportation planning, urban design

Procedia PDF Downloads 112

Authors: Zhaopeng Zhu, Xianzhi Song, Gensheng Li, Shuo Zhu, Shiming Duan, Xuezhe Yao

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Accurate calculation of wellbore pressure is of great significance to prevent wellbore risk during drilling. The traditional mechanism model needs a lot of iterative solving procedures in the calculation process, which reduces the calculation efficiency and is difficult to meet the demand of dynamic control of wellbore pressure. In recent years, many scholars have introduced artificial intelligence algorithms into wellbore pressure calculation, which significantly improves the calculation efficiency and accuracy of wellbore pressure. However, due to the ‘black box’ property of intelligent algorithm, the existing intelligent calculation model of wellbore pressure is difficult to play a role outside the scope of training data and overreacts to data noise, often resulting in abnormal calculation results. In this study, the multi-phase flow mechanism is embedded into the objective function of the neural network model as a constraint condition, and an intelligent prediction model of wellbore pressure under the constraint condition is established based on more than 400,000 sets of pressure measurement while drilling (MPD) data. The constraint of the multi-phase flow mechanism makes the prediction results of the neural network model more consistent with the distribution law of wellbore pressure, which overcomes the black-box attribute of the neural network model to some extent. The main performance is that the accuracy of the independent test data set is further improved, and the abnormal calculation values basically disappear. This method is a prediction method driven by MPD data and multi-phase flow mechanism, and it is the main way to predict wellbore pressure accurately and efficiently in the future.

Keywords: multiphase flow mechanism, pressure while drilling data, wellbore pressure, mechanism constraints, combined drive

Procedia PDF Downloads 177

Authors: Chia-Chi Chang, Chuan-Bi Lin, Chia-Min Chan

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Most ZigBee sensor networks to date make use of nodes with limited processing, communication, and energy capabilities. Energy consumption is of great importance in wireless sensor applications as their nodes are commonly battery-driven. Once ZigBee nodes are deployed outdoors, limited power may make a sensor network useless before its purpose is complete. At present, there are two strategies for long node and network lifetime. The first strategy is saving energy as much as possible. The energy consumption will be minimized through switching the node from active mode to sleep mode and routing protocol with ultra-low energy consumption. The second strategy is to evaluate the energy consumption of sensor applications as accurately as possible. Erroneous energy model may render a ZigBee sensor network useless before changing batteries. In this paper, we present a ZigBee wireless sensor node with four key modules: a processing and radio unit, an energy harvesting unit, an energy storage unit, and a sensor unit. The processing unit uses CC2530 for controlling the sensor, carrying out routing protocol, and performing wireless communication with other nodes. The harvesting unit uses a 2W solar panel to provide lasting energy for the node. The storage unit consists of a rechargeable 1200 mAh Li-ion battery and a battery charger using a constant-current/constant-voltage algorithm. Our solution to extend node lifetime is implemented. Finally, a long-term sensor network test is used to exhibit the functionality of the solar powered system.

Keywords: ZigBee, Li-ion battery, solar panel, CC2530

Procedia PDF Downloads 378

Authors: Sidra Shaheen, Nadir Naveed Siddiqui, Shah Ali Ul Qader

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In recent years, significant progress has been made in discovering and developing new bacterial polysaccharides producing organisms possessing extremely functional properties. Levan is a natural biopolymer of fructose which is produced by transfructosylation reaction in the presence of levansucrase. It is one of the industrially promising enzymes that offer a variety of industrial applications in the field of cosmetics, foods and pharmaceuticals. Although levan has significant applications but the yield of levan produced is not equal to other biopolymers due to the inefficiency of producer microorganism. Among wide range of levansucrase producing microorganisms, Zymomonas mobilis is considered as a potential candidate for large scale production of this natural polysaccharide. The present investigation is concerned with the isolation of levansucrase producing natural isolate having maximum enzyme production. Furthermore, production parameters were optimized to get higher enzyme yield. Levansucrase was partially purified and characterized to study its applicability on industrial scale. The results of this study revealed that the bacterial strain Z. mobilis KIBGE-IB14 was the best producer of levansucrase. Bacterial growth and enzyme production was greatly influenced by physical and chemical parameters. Maximum levansucrase production was achieved after 24 hours of fermentation at 30°C using modified medium of pH-6.5. Contrary to other levansucrases, the one presented in the current study is able to produce high amount of products in relatively short period of time with optimum temperature at 35°C. Due to these advantages, this enzyme can be used on large scale for commercial production of levan and other important metabolites.

Keywords: levansucrase, metabolites, polysaccharides, transfructosylation

Procedia PDF Downloads 499

Authors: Jan-Peter Mund, Christian Kind

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In view of the ongoing climate change and global warming, fires along railways in Germany are occurring more frequently, with sometimes massive consequences for railway operations and affected railroad infrastructure. In the absence of systematic studies within the infrastructure network of German Rail, little is known about the causes of such embankment fires. Since a further increase in these hazards is to be expected in the near future, there is a need for a sound knowledge of triggers and drivers for embankment fires as well as methodical knowledge of prediction tools. Two predictable future trends speak for the increasing relevance of the topic: through the intensification of the use of rail for passenger and freight transport (e.g..: doubling of annual passenger numbers by 2030, compared to 2019), there will be more rail traffic and also more maintenance and construction work on the railways. This research project approach uses satellite data to identify historical embankment fires along rail network infrastructure. The team links data from these fires with infrastructure and weather data and trains a machine-learning model with the aim of predicting fire hazards on sections of the track. Companies reflect on the results and use them on a pilot basis in precautionary measures.

Keywords: embankment fires, railway maintenance, machine learning, remote sensing, VIIRS data

Procedia PDF Downloads 94

Authors: Sharifah Mousli, Sona Taheri, Jiayuan He

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Autism spectrum disorder (ASD) is a complex developmental condition involving persistent difficulties with social communication, restricted interests, and repetitive behavior. The challenges associated with ASD can interfere with an affected individual’s ability to function in social, academic, and employment settings. Although there is no effective medication known to treat ASD, to our best knowledge, early intervention can significantly improve an affected individual’s overall development. Hence, an accurate diagnosis of ASD at an early phase is essential. The use of machine learning approaches improves and speeds up the diagnosis of ASD. In this paper, we focus on the application of unsupervised clustering methods in ASD as a large volume of ASD data generated through hospitals, therapy centers, and mobile applications has no pre-existing labels. We conduct a comparative analysis using seven clustering approaches such as K-means, agglomerative hierarchical, model-based, fuzzy-C-means, affinity propagation, self organizing maps, linear vector quantisation – as well as the recently developed optimization-based clustering (COMSEP-Clust) approach. We evaluate the performances of the clustering methods extensively on real-world ASD datasets encompassing different age groups: toddlers, children, adolescents, and adults. Our experimental results suggest that the COMSEP-Clust approach outperforms the other seven methods in recognizing ASD with well-separated clusters.

Keywords: autism spectrum disorder, clustering, optimization, unsupervised machine learning

Procedia PDF Downloads 121

Authors: Tyler Gill, Sophia Daniels

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The paper examines how accessing the spatial-temporal constrains of data will help inform policymakers and law enforcement officials. The authors utilize Chicago crime data from 2006-2016 to demonstrate how the Emerging Hot Spot Tool is an ideal hot spot clustering approach to analyze crime data. Traditional approaches include density maps or creating a spatial weights matrix to include the spatial-temporal constrains. This new approach utilizes a space-time implementation of the Getis-Ord Gi* statistic to visualize the data more quickly to make better decisions. The research will help complement socio-cultural research to find key patterns to help frame future policies and evaluate the implementation of prior strategies. Through this analysis, homicide trends and patterns are found more effectively and recommendations for use by non-traditional users of GIS are offered for real life implementation.

Keywords: crime mapping, emerging hot spot analysis, Getis-Ord Gi*, spatial-temporal analysis

Procedia PDF Downloads 249

Authors: Promothes Saha

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Recently, transportation engineering industry members at the study university showed concern that students lacked the skills needed to solve real-world engineering problems using spreadsheet data analysis. In response to the concerns shown by industry members, this study investigated how to engage students in a better way by incorporating spreadsheet analysis during class - also, help them learn the course topics. Helping students link theoretical knowledge to real-world problems can be a challenge. In this effort, in-class activities and worksheets were redesigned to integrate with Excel to solve example problems using built-in tools including cell referencing, equations, data analysis tool pack, solver tool, conditional formatting, charts, etc. The effectiveness of this technique was investigated using students’ evaluations of the course, enrollment data, and students’ comments. Based on the data of those criteria, it is evident that the spreadsheet activities may increase student learning.

Keywords: civil, engineering, active learning, transportation

Procedia PDF Downloads 140

Authors: Sabina Akter, Osiris Valdez Banda, Pentti Kujala, Jani Romanoff

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This paper examines the relationship between on-board environmental factors and customer overall satisfaction in the context of the cruise on-board experience. The on-board environmental factors considered are ambient, layout/design, social, product/service and on-board enjoyment factors. The study presents a data-driven framework and model for the on-board cruise experience. The data are collected from 893 respondents in an application of a self-administered online questionnaire of their cruise experience. This study reveals the cruise passengers’ on-board experience through the customer decision journey based on the publicly available data. Pearson correlation and regression analysis have been applied, and the results show a positive and a significant relationship between the environmental factors and on-board experience. These data help understand the cruise passengers’ on-board experience, which will be used for the ultimate decision-making process in cruise ship design.

Keywords: cruise behavior, customer activities, on-board environmental factors, on-board experience, user or customer satisfaction

Procedia PDF Downloads 173

Authors: Ali Reza Sattarzadeh, Ronny J. Kutadinata, Pubudu N. Pathirana, Van Thanh Huynh

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Traffic congestion has become a severe worldwide problem, affecting everyday life, fuel consumption, time, and air pollution. The primary causes of these issues are inadequate transportation infrastructure, poor traffic signal management, and rising population. Traffic flow forecasting is one of the essential and effective methods in urban congestion and traffic management, which has attracted the attention of researchers. With the development of technology, undeniable progress has been achieved in existing methods. However, there is a possibility of improvement in the extraction of temporal and spatial features to determine the importance of traffic flow sequences and extraction features. In the proposed model, we implement the convolutional neural network (CNN) and long short-term memory (LSTM) deep learning models for mining nonlinear correlations and their effectiveness in increasing the accuracy of traffic flow prediction in the real dataset. According to the experiments, the results indicate that implementing Conv-LSTM networks increases the productivity and accuracy of deep learning models for traffic flow prediction.

Keywords: deep learning algorithms, intelligent transportation systems, spatiotemporal features, traffic flow prediction

Procedia PDF Downloads 178

Authors: Cinzia Carrodano, Dimitri Konstantas

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Risk is part of our lives. In today’s society risk is connected to our safety and safety has become a major priority in our life. Each person lives his/her life based on the evaluation of the risk he/she is ready to accept and sustain, and the level of safety he/she wishes to reach, based on highly personal criteria. The assessment of risk a person takes in a complex environment and the impact of actions of other people’actions and events on our perception of risk are alements to be considered. The concept of Holistic Risk Assessment (HRA) aims in developing a methodology and a model that will allow us to take into account elements outside the direct influence of the individual, and provide a personalized risk assessment. The concept is based on the fact that in the near future, we will be able to gather and process extremely large amounts of data about an individual and his/her environment in real time. The interaction and correlation of these data is the key element of the holistic risk assessment. In this paper, we present the HRA concept and describe the most important elements and considerations.

Keywords: continuous data, dynamic risk, holistic risk assessment, risk concept

Procedia PDF Downloads 131

Authors: Efthymios Chondrogiannis, Vassiliki Andronikou, Efstathios Karanastasis, Theodora Varvarigou

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A large amount of data is typically stored in relational databases (DB). The latter can efficiently handle user queries which intend to elicit the appropriate information from data sources. However, direct access and use of this data requires the end users to have an adequate technical background, while they should also cope with the internal data structure and values presented. Consequently the information retrieval is a quite difficult process even for IT or DB experts, taking into account the limited contributions of relational databases from the conceptual point of view. Ontologies enable users to formally describe a domain of knowledge in terms of concepts and relations among them and hence they can be used for unambiguously specifying the information captured by the relational database. However, accessing information residing in a database using ontologies is feasible, provided that the users are keen on using semantic web technologies. For enabling users form different disciplines to retrieve the appropriate data, the design of a Graphical User Interface is necessary. In this work, we will present an interactive, ontology-based, semantically enable web tool that can be used for information retrieval purposes. The tool is totally based on the ontological representation of underlying database schema while it provides a user friendly environment through which the users can graphically form and execute their queries.

Keywords: ontologies, relational databases, SPARQL, web interface

Procedia PDF Downloads 275

Authors: Salma Krafessi

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The financial markets have a multifaceted, intricate environment, and enormous volumes of data are produced every day. To find investment possibilities, possible fraudulent activity, and market oddities, accurate anomaly identification in this data is essential. Conventional methods for detecting anomalies frequently fail to capture the complex organization of financial data. In order to improve the identification of abnormalities in financial time series data, this study presents Tucker Decomposition as a reliable multi-way analysis approach. We start by gathering closing prices for the S&P 500 index across a number of decades. The information is converted to a three-dimensional tensor format, which contains internal characteristics and temporal sequences in a sliding window structure. The tensor is then broken down using Tucker Decomposition into a core tensor and matching factor matrices, allowing latent patterns and relationships in the data to be captured. A possible sign of abnormalities is the reconstruction error from Tucker's Decomposition. We are able to identify large deviations that indicate unusual behavior by setting a statistical threshold. A thorough examination that contrasts the Tucker-based method with traditional anomaly detection approaches validates our methodology. The outcomes demonstrate the superiority of Tucker's Decomposition in identifying intricate and subtle abnormalities that are otherwise missed. This work opens the door for more research into multi-way data analysis approaches across a range of disciplines and emphasizes the value of tensor-based methods in financial analysis.

Keywords: tucker decomposition, financial markets, financial engineering, artificial intelligence, decomposition models

Procedia PDF Downloads 75

Authors: King Kumire, Amos Kubeka

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The synergy of machine learning and astronomical technology advancement is giving rise to the new space age, which is pronounced by better habitability assessments. To initiate this discussion, it should be recorded for definition purposes that the symbiotic relationship between astronomy and improved computing has been code-named the Cis-Astro gateway concept. The cosmological fate of this phrase has been unashamedly plagiarized from the cis-lunar gateway template and its associated LaGrange points which act as an orbital bridge to the moon from our planet Earth. However, for this study, the scientific audience is invited to bridge toward the discovery of new habitable planets. It is imperative to state that cosmic probes of this magnitude can be utilized as the starting nodes of the astrobiological search for galactic life. This research can also assist by acting as the navigation system for future space telescope launches through the delimitation of target exoplanets. The findings and the associated platforms can be harnessed as building blocks for the modeling of climate change on planet earth. The notion that if the human genus exhausts the resources of the planet earth or there is a bug of some sort that makes the earth inhabitable for humans explains the need to find an alternative planet to inhabit. The scientific community, through interdisciplinary discussions of the International Astronautical Federation so far has the common position that engineers can reduce space mission costs by constructing a stable cis-lunar orbit infrastructure for refilling and carrying out other associated in-orbit servicing activities. Similarly, the Cis-Astro gateway can be envisaged as a budget optimization technique that models extra-solar bodies and can facilitate the scoping of future mission rendezvous. It should be registered as well that this broad and voluminous catalog of exoplanets shall be narrowed along the way using machine learning filters. The gist of this topic revolves around the indirect economic rationale of establishing a habitability scoping platform.

Keywords: machine-learning, habitability, exoplanets, supercomputing

Procedia PDF Downloads 93

Authors: King Kumire, Amos Kubeka

Abstract:

The synergy of machine learning and astronomical technology advancement is giving rise to the new space age, which is pronounced by better habitability assessments. To initiate this discussion, it should be recorded for definition purposes that the symbiotic relationship between astronomy and improved computing has been code-named the Cis-Astro gateway concept. The cosmological fate of this phrase has been unashamedly plagiarized from the cis-lunar gateway template and its associated LaGrange points which act as an orbital bridge to the moon from our planet Earth. However, for this study, the scientific audience is invited to bridge toward the discovery of new habitable planets. It is imperative to state that cosmic probes of this magnitude can be utilized as the starting nodes of the astrobiological search for galactic life. This research can also assist by acting as the navigation system for future space telescope launches through the delimitation of target exoplanets. The findings and the associated platforms can be harnessed as building blocks for the modeling of climate change on planet earth. The notion that if the human genus exhausts the resources of the planet earth or there is a bug of some sort that makes the earth inhabitable for humans explains the need to find an alternative planet to inhabit. The scientific community, through interdisciplinary discussions of the International Astronautical Federation so far, has the common position that engineers can reduce space mission costs by constructing a stable cis-lunar orbit infrastructure for refilling and carrying out other associated in-orbit servicing activities. Similarly, the Cis-Astro gateway can be envisaged as a budget optimization technique that models extra-solar bodies and can facilitate the scoping of future mission rendezvous. It should be registered as well that this broad and voluminous catalog of exoplanets shall be narrowed along the way using machine learning filters. The gist of this topic revolves around the indirect economic rationale of establishing a habitability scoping platform.

Keywords: exoplanets, habitability, machine-learning, supercomputing

Procedia PDF Downloads 121

Authors: Michal Brazda, Miroslav Urbanek, Martina Koukolikova

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This work focuses on the development of functional surfaces and edges for cutting and forming tools created through the Directed Energy Deposition (DED) technology. In the context of growing challenges in modern engineering, additive technologies, especially DED, present an innovative approach to manufacturing tools for forming and cutting. One of the key features of DED is its ability to precisely and efficiently deposit Fully dense metals from powder feedstock, enabling the creation of complex geometries and optimized designs. Gradually, it becomes an increasingly attractive choice for tool production due to its ability to achieve high precision while simultaneously minimizing waste and material costs. Tools created using DED technology gain significant durability through the utilization of high-performance materials such as nickel alloys and tool steels. For high-temperature applications, Nimonic 80A alloy is applied, while for cold applications, M2 tool steel is used. The addition of ceramic materials, such as tungsten carbide, can significantly increase the tool's resistance. The introduction of functionally graded materials is a significant contribution, opening up new possibilities for gradual changes in the mechanical properties of the tool and optimizing its performance in different sections according to specific requirements. In this work, you will find an overview of individual applications and their utilization in the industry. Microstructural analyses have been conducted, providing detailed insights into the structure of individual components alongside examinations of the mechanical properties and tool life. These analyses offer a deeper understanding of the efficiency and reliability of the created tools, which is a key element for successful development in the field of cutting and forming tools. The production of functional surfaces and edges using DED technology can result in financial savings, as the entire tool doesn't have to be manufactured from expensive special alloys. The tool can be made from common steel, onto which a functional surface from special materials can be applied. Additionally, it allows for tool repairs after wear and tear, eliminating the need for producing a new part and contributing to an overall cost while reducing the environmental footprint. Overall, the combination of DED technology, functionally graded materials, and verified technologies collectively set a new standard for innovative and efficient development of cutting and forming tools in the modern industrial environment.

Keywords: additive manufacturing, directed energy deposition, DED, laser, cutting tools, forming tools, steel, nickel alloy

Procedia PDF Downloads 54

Authors: M. Arun, A. Kannan

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Textile industries cater to varied customer preferences and contribute substantially to the economy. However, these textile industries also produce a considerable amount of effluents. Prominent among these are the azo dyes which impart considerable color and toxicity even at low concentrations. Azo dyes are also used as coloring agents in food and pharmaceutical industry. Despite their applications, azo dyes are also notorious pollutants and carcinogens. Popular techniques like photo-degradation, biodegradation and the use of oxidizing agents are not applicable for all kinds of dyes, as most of them are stable to these techniques. Chemical coagulation produces a large amount of toxic sludge which is undesirable and is also ineffective towards a number of dyes. Most of the azo dyes are stable to UV-visible light irradiation and may even resist aerobic degradation. Adsorption has been the most preferred technique owing to its less cost, high capacity and process efficiency and the possibility of regenerating and recycling the adsorbent. Adsorption is also most preferred because it may produce high quality of the treated effluent and it is able to remove different kinds of dyes. However, the adsorption process is influenced by many variables whose inter-dependence makes it difficult to identify optimum conditions. The variables include stirring speed, temperature, initial concentration and adsorbent dosage. Further, the internal diffusional resistance inside the adsorbent particle leads to slow uptake of the solute within the adsorbent. Hence, it is necessary to identify optimum conditions that lead to high capacity and uptake rate of these pollutants. In this work, commercially available activated carbon was chosen as the adsorbent owing to its high surface area. A typical azo dye found in textile effluent waters, viz. the monoazo Acid Orange 10 dye (CAS: 1936-15-8) has been chosen as the representative pollutant. Adsorption studies were mainly focused at obtaining equilibrium and kinetic data for the batch adsorption process at different process conditions. Studies were conducted at different stirring speed, temperature, adsorbent dosage and initial dye concentration settings. The Full Factorial Design was the chosen statistical design framework for carrying out the experiments and identifying the important factors and their interactions. The optimum conditions identified from the experimental model were validated with actual experiments at the recommended settings. The equilibrium and kinetic data obtained were fitted to different models and the model parameters were estimated. This gives more details about the nature of adsorption taking place. Critical data required to design batch adsorption systems for removal of Acid Orange 10 dye and identification of factors that critically influence the separation efficiency are the key outcomes from this research.

Keywords: acid orange 10, activated carbon, optimum adsorption conditions, statistical design

Procedia PDF Downloads 173

Authors: Deniz Karagöz

Abstract:

This study is attempt to comprehend the relationship between the spatial characteristics of cultural structure, activities and the tourism demand in Turkey. The analysis divided into four parts. The first part consisted of a cultural structure and cultural activity (CSCA) index provided by principal component analysis. The analysis determined four distinct dimensions, namely, cultural activity/structure, accessing culture, consumption, and cultural management. The exploratory spatial data analysis employed to determine the spatial models of cultural structure and cultural activities in 81 provinces in Turkey. Global Moran I indices is used to ascertain the cultural activities and the structural clusters. Finally, the relationship between the cultural activities/cultural structure and tourism demand was analyzed. The raw/original data of the study official databases. The data on the cultural structure and activities gathered from the Turkish Statistical Institute and the data related to the tourism demand was provided by the Republic of Turkey Ministry of Culture and Tourism.

Keywords: cultural activities, cultural structure, spatial characteristics, tourism demand, Turkey

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Authors: Sayor Ajfar Aaron, Sajjat Hossain Abir, Ashif Newaz, Mushfiqur Rahman

Abstract:

Investigating the convergence of blockchain technology and artificial intelligence, this paper examines their synergistic effects on data integrity and decision-making within smart contracts. By implementing AI-driven analytics on blockchain-based platforms, the research identifies improvements in automated contract enforcement and decision accuracy. The paper presents a framework that leverages AI to enhance transparency and trust while blockchain ensures immutable record-keeping, culminating in significantly optimized operational efficiencies in various industries.

Keywords: artificial intelligence, blockchain, data integrity, smart contracts

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Authors: Rowland Brucken

Abstract:

This paper argues that nation-building theories that prioritize democratic governance best explain the successful post-independence development of Botswana. Three main competing schools of thought exist regarding the sequencing of policies that should occur to re-build weakened or failed states. The first posits that economic development should receive foremost attention, while democratization and a binding sense of nationalism can wait. A second group of experts identified constructing a sense of nationalism among a populace is necessary first, so that the state receives popular legitimacy and obedience that are prerequisites for development. Botswana, though, transitioned into a multi-party democracy and prosperous open economy due to the utilization of traditional democratic structures, enlightened and accountable leadership, and an educated technocratic civil service. With these political foundations already in place when the discovery of diamonds occurred, the resulting revenues were spent wisely on projects that grew the economy, improved basic living standards, and attracted foreign investment. Thus democratization preceded, and therefore provided an accountable basis for, economic development that might otherwise have been squandered by greedy and isolated elites to the detriment of the greater population. Botswana was one of the poorest nations in the world at the time of its independence in 1966, with little infrastructure, a dependence on apartheid South Africa for trade, and a largely subsistence economy. Over the next thirty years, though, its economy grew the fastest of any nation in the world. The transparent and judicious use of diamond returns is only a partial explanation, as the government also pursued economic diversification, mass education, and rural development in response to public needs. As nation-building has become a project undertaken by nations and multilateral agencies such as the United Nations and the North Atlantic Treaty Organization, Botswana may provide best practices that others should follow in attempting to reconstruct economically and politically unstable states.

Keywords: Botswana, democratization, economic development, nation-building

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Authors: Mahdi Daghmhehci Firoozjaei, Minchang Kim, Dima Alhadidi

Abstract:

In this paper, we present a power profiling model for smart grid consumers based on real time load data acquired smart meters. It profiles consumers’ power consumption behaviour using the dynamic time warping (DTW) clustering algorithm. Due to the invariability of signal warping of this algorithm, time-disordered load data can be profiled and consumption features be extracted. Two load types are defined and the related load patterns are extracted for classifying consumption behaviour by DTW. The classification methodology is discussed in detail. To evaluate the performance of the method, we analyze the time-series load data measured by a smart meter in a real case. The results verify the effectiveness of the proposed profiling method with 90.91% true positive rate for load type clustering in the best case.

Keywords: power profiling, user privacy, dynamic time warping, smart grid

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Authors: Chulsang Yoo, Gildo Kim

Abstract:

Dual polarization radar provides comprehensive information about rainfall by measuring multiple parameters. In Korea, for the rainfall estimation, JPOLE and CSU-HIDRO algorithms are generally used. This study evaluated the local applicability of JPOLE and CSU-HIDRO algorithms in Korea by using the observed rainfall data collected on August, 2014 by the Biseulsan dual polarization radar data and KMA AWS. A total of 11,372 pairs of radar-ground rain rate data were classified according to thresholds of synthetic algorithms into suitable and unsuitable data. Then, evaluation criteria were derived by comparing radar rain rate and ground rain rate, respectively, for entire, suitable, unsuitable data. The results are as follows: (1) The radar rain rate equation including KDP, was found better in the rainfall estimation than the other equations for both JPOLE and CSU-HIDRO algorithms. The thresholds were found to be adequately applied for both algorithms including specific differential phase. (2) The radar rain rate equation including horizontal reflectivity and differential reflectivity were found poor compared to the others. The result was not improved even when only the suitable data were applied. Acknowledgments: This work was supported by the Basic Science Research Program through the National Research Foundation of Korea, funded by the Ministry of Education (NRF-2013R1A1A2011012).

Keywords: CSU-HIDRO algorithm, dual polarization radar, JPOLE algorithm, radar rainfall estimation algorithm

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Authors: Ryan Messina, Mehedi Hasan

Abstract:

This research is to examine the impacts of using data to generate performance requirements for automation in visual inspections using machine vision. These situations are intended for design and how projects can smooth the transfer of tacit knowledge to using an algorithm. We have proposed a framework when specifying machine vision systems. This framework utilizes varying levels of automation as contingency planning to reduce data processing complexity. Using data assists in extracting tacit knowledge from those who can perform the manual tasks to assist design the system; this means that real data from the system is always referenced and minimizes errors between participating parties. We propose using three indicators to know if the project has a high risk of failing to meet requirements related to accuracy and reliability. All systems tested achieved a better integration into operations after applying the framework.

Keywords: automation, contingency planning, continuous engineering, control theory, machine vision, system requirements, system thinking

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Authors: Yongde Xia, Laicong Deng, Zhuxian Yang

Abstract:

Developing cost-effective electrocatalysts for oxygen reduction reaction (ORR), oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) is vital in energy conversion and storage applications. Herein, we report a simple method for the synthesis of graphene-reinforced cobalt sulfide/carbon nanocomposites and the evaluation of their electrocatalytic performance for typical electrocatalytic reactions. Nanocomposites of cobalt sulfide embedded in N, S co-doped porous carbon and graphene (CoS@C/Graphene) were generated via simultaneous sulfurization and carbonization of one-pot synthesized graphite oxide-ZIF-67 precursors. The obtained CoS@C/Graphene nanocomposite was characterized by X-ray diffraction, Raman spectroscopy, Thermogravimetric analysis-Mass spectroscopy, Scanning electronic microscopy, Transmission electronic microscopy, X-ray photoelectron spectroscopy and gas sorption. It was found that cobalt sulfide nanoparticles were homogenously dispersed in the in-situ formed N, S co-doped porous carbon/Graphene matrix. The CoS@C/10Graphene composite not only shows excellent electrocatalytic activity toward ORR with high onset potential of 0.89 V, four-electron pathway and superior durability of maintaining 98% current after continuously running for around 5 hours, but also exhibits good performance for OER and HER, due to the improved electrical conductivity, increased catalytic active sites and connectivity between the electrocatalytic active cobalt sulfide and the carbon matrix. This work offers a new approach for the development of novel multifunctional nanocomposites for the next generation of energy conversion and storage applications.

Keywords: MOF derivative, graphene, electrocatalyst, oxygen reduction reaction, oxygen evolution reaction, hydrogen evolution reaction

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