Search results for: data space connector

Authors: Petr Jizba, Jan Korbel

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We combine an axiomatics of Rényi with the q-deformed version of Khinchin axioms to obtain a measure of information (i.e., entropy) which accounts both for systems with embedded self-similarity and non-extensivity. We show that the entropy thus obtained is uniquely solved in terms of a one-parameter family of information measures. The ensuing maximal-entropy distribution is phrased in terms of a special function known as the Lambert W-function. We analyze the corresponding ‘high’ and ‘low-temperature’ asymptotics and reveal a non-trivial structure of the parameter space.

Keywords: multifractals, Rényi information entropy, THC entropy, MaxEnt, heavy-tailed distributions

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Authors: Jamal Al-Zain, O. El Hajjaji, T. El Bardouni

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A (Th-U) O2 fuel pin benchmark made up of 25 w/o U and 75 w/o Th was used. In order to analyze the depletion and inventory of the fuel for the pressurized water reactor pin-cell model. The new version VIII.0 of the ENDF/B nuclear data library was used to create a data set in ACE format at various temperatures and process the data using the MAKXSF6.2 and NJOY2016 programs to process the data at the various temperatures in order to conduct this study and analyze cross-section data. The infinite multiplication factor, the concentrations and activities of the main fission products, the actinide radionuclides accumulated in the pin cell, and the total radioactivity were all estimated and compared in this study using the Monte Carlo N-Particle 6 (MCNP6.2) and DRAGON5 programs. Additionally, the behavior of the Pressurized Water Reactor (PWR) thorium pin cell that is dependent on burn-up (BU) was validated and compared with the reference data obtained using the Massachusetts Institute of Technology (MIT-MOCUP), Idaho National Engineering and Environmental Laboratory (INEEL-MOCUP), and CASMO-4 codes. The results of this study indicate that all of the codes examined have good agreements.

Keywords: PWR thorium pin cell, ENDF/B-VIII.0, MAKXSF6.2, NJOY2016, MCNP6.2, DRAGON5, fuel burn-up.

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Authors: Alexander Huang

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Online learning sessions have become an indispensable complement to in-classroom-learning sessions in the past two years due to the emergence of Covid-19. Due to social distance requirements, many courses and interaction-intensive sessions, ranging from music classes to debate camps, are online. However, online learning imposes a significant challenge for engaging students effectively during the learning sessions. To resolve this problem, Project PWR, a non-profit organization formed by high school students, developed an online kid-teach-kid learning environment to boost students' learning interests and further improve students’ engagement during online learning. Fundamentally, the kid-teach-kid learning model creates an affinity space to form learning groups, where like-minded peers can learn and teach their interests. The role of the teacher can also help a kid identify the instructional task and set the rules and procedures for the activities. The approach also structures initial discussions to reveal a range of ideas, similar experiences, thinking processes, language use, and lower student-to-teacher ratio, which become enriched online learning experiences for upcoming lessons. In such a manner, a kid can practice both the teacher role and the student role to accumulate experiences on how to convey ideas and questions over the online session more efficiently and effectively. In this research work, we conducted two case studies involving a 3D-Design course and a Speech and Debate course taught by high-school kids. Through Project PWR, a kid first needs to design the course syllabus based on a provided template to become a student-teacher. Then, the Project PWR academic committee evaluates the syllabus and offers comments and suggestions for changes. Upon the approval of a syllabus, an experienced and voluntarily adult mentor is assigned to interview the student-teacher and monitor the lectures' progress. Student-teachers construct a comprehensive final evaluation for their students, which they grade at the end of the course. Moreover, each course requires conducting midterm and final evaluations through a set of surveyed replies provided by students to assess the student-teacher’s performance. The uniqueness of Project PWR lies in its established kid-teach-kids affinity space. Our research results showed that Project PWR could create a closed-loop system where a student can help a teacher improve and vice versa, thus improving the overall students’ engagement. As a result, Project PWR’s approach can train teachers and students to become better online learners and give them a solid understanding of what to prepare for and what to expect from future online classes. The kid-teach-kid learning model can significantly improve students' engagement in the online courses through the Project PWR to effectively supplement the traditional teacher-centric model that the Covid-19 pandemic has impacted substantially. Project PWR enables kids to share their interests and bond with one another, making the online learning environment effective and promoting positive and effective personal online one-on-one interactions.

Keywords: kid-teach-kid, affinity space, online learning, engagement, student-teacher

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Authors: Bastian Haarmann, Likas Sikorski

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In this paper, we introduce an NLG application for the automatic creation of ready-to-publish texts from big data. The fully automatic generated stories have a high resemblance to the style in which the human writer would draw up a news story. Topics may include soccer games, stock exchange market reports, weather forecasts and many more. The generation of the texts runs according to the human language production. Each generated text is unique. Ready-to-publish stories written by a computer application can help humans to quickly grasp the outcomes of big data analyses, save time-consuming pre-formulations for journalists and cater to rather small audiences by offering stories that would otherwise not exist.

Keywords: big data, natural language generation, publishing, robotic journalism

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Authors: Ewa M. Laskowska, Jorn Vatn

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Industry 4.0 focuses on digital optimization of industrial processes. The idea is to use extracted data in order to build a decision support model enabling use of those data for real time decision making. In terms of predictive maintenance, the desired decision support tool would be a model enabling prognostics of system's health based on the current condition of considered equipment. Within area of system prognostics and health management, a commonly used health indicator is Remaining Useful Lifetime (RUL) of a system. Because the RUL is a random variable, it has to be estimated based on available health indicators. Health indicators can be of different types and come from different sources. They can be process variables, equipment performance variables, data related to number of experienced failures, etc. The aim of this study is the analysis of performance variables of emergency shutdown valves (ESV) used in oil and gas industry. ESV is inspected periodically, and at each inspection torque and time of valve operation are registered. The data will be analyzed by means of machine learning or statistical analysis. The purpose is to investigate whether the available data could be used as a health indicator for a prognostic purpose. The second objective is to examine what is the most efficient way to incorporate the data into predictive model. The idea is to check whether the data can be applied in form of explanatory variables in Markov process or whether other stochastic processes would be a more convenient to build an RUL model based on the information coming from registered data.

Keywords: emergency shutdown valves, health indicator, prognostics, remaining useful lifetime, RUL

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Authors: Kankeyanathan Kannan

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On approximation properties of group C* algebras is everywhere; it is powerful, important, backbone of countless breakthroughs. For a discrete group G, let A(G) denote its Fourier algebra, and let M₀A(G) denote the space of completely bounded Fourier multipliers on G. An approximate identity on G is a sequence (Φn) of finitely supported functions such that (Φn) uniformly converge to constant function 1 In this paper we prove that approximation property pass to free product.

Keywords: approximation property, weakly amenable, strong invariant approximation property, invariant approximation property

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Authors: James Newman

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Process mining is an emerging technology that looks to serialize enterprise data in time series data. It has been used by many companies and has been the subject of a variety of research papers. However, the majority of current efforts have looked at how to best create process mining from standard relational databases. This paper is the first pass at outlining a database custom-built for the minimal viable product of process mining. We present Block Miner, a blockchain protocol to store process mining data across a distributed network. We demonstrate the feasibility of storing process mining data on the blockchain. We present a proof of concept and show how the intersection of these two technologies helps to solve a variety of issues, including but not limited to ransomware attacks, tax documentation, and conflict resolution.

Keywords: blockchain, process mining, memory optimization, protocol

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Authors: Luciana Salomon

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The Andes hold one of the highest plant species diversity in the world. How such diversity originated is one of the most intriguing questions in studies addressing the pattern of plant diversity worldwide. Recently, the explosive adaptive radiations found in high Andean groups have been pointed as major triggers of this spectacular diversity. The Andes are one of the most species-rich area for the largest genus from the Asteraceae family, Senecio. There, the genus presents an incredible variation in growth form and ecological niche space. If this diversity of Andean Senecio can be explained by a monophyletic origin and subsequent radiation has not been tested up to now. Previous studies trying to disentangle the evolutionary history of some Andean Senecio struggled with the relatively low resolution and support of the phylogenies, which is indicative of recently radiated groups. Using Hyb-Seq, a powerful approach is available to address phylogenetic questions in groups whose evolutionary histories are recent and rapid. This approach was used for Senecio to build a phylogenetic backbone on which to study the mechanisms shaping its hyper-diversity in the Andes, focusing on Senecio ser. Culcitium, an exclusively Andean and well circumscribed group presenting large morphological variation and which is widely distributed across the Andes. Hyb-Seq data for about 130 accessions of Seneciowas generated. Using standard data analysis work flows and a newly developed tool to utilize paralogs for phylogenetic reconstruction, robustness of the species treewas investigated. Fully resolved and moderately supported species trees were obtained, showing Senecio ser. Culcitium as monophyletic. Within this group, some species formed well-supported clades congruent with morphology, while some species would not have exclusive ancestry, in concordance with previous studies showing a geographic differentiation. Additionally, paralogs were detected for a high number of loci, indicating duplication events and hybridization, known to be common in Senecio ser. Culcitium might have lead to hybrid speciation. The rapid diversification of the group seems to have followed a south-north distribution throughout the Andes, having accelerated in the conquest of new habitats more recently available: i.e., Montane forest, Paramo, and Superparamo.

Keywords: evolutionary radiations, andes, paralogy, hybridization, senecio

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Authors: S. Riadi, T. Hariyanto, A. Bawasir, M. Prabandaru, S. C. Navisa

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The era of digitalization and the advancement of information technology for mapping and modeling three-dimensional (3D) space has become an important factor in various sectors, including in land and building management. In Indonesia, the potential for utilizing 3D mapping technology can be used to optimize the absorption of Land and Building Tax (PBB) which is still not optimal. This is caused by various factors, one of which is the lack of accurate and comprehensive mapping. Aerial photo mapping is the main technology often used in 3D GIS (Geographic Information System) modeling. Visualization of the results of 3D modeling and digital twins in Kelurahan Jawa, Samarinda is modeled as an interactive tool for policy makers to improve operational efficiency in property tax management. The results of this study reveal how the geodatabase is compiled, the creation of 3D multipatch from oblique aerial photo acquisition to digital twin visualization with CE90 accuracy of 0.133 m and LE90 of 0.712 m. In addition, the quality of this accurate digital twin is indicated by mean difference 0.473 m and RMSE value of the building height of 0.561 m. The results of the photogrammetry method compared to BAPENDA data on an area of 92 Ha have a mean difference in building area of 73.98 m² and a total area difference of 114,747 m². Finally, this study was able to optimize regional income related to land and building tax from IDR 949,962,806 to IDR 1,112,471,515. This was able to optimize land and building tax in order to increase regional income by IDR 162,508,709 or 17.10%. This study clearly has a significant financial impact on the local government.

Keywords: digital twin, land and building tax, 3d mapping, photogrammetry

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Authors: Poonam Magu, Kumud Khanna, Premavathy Seetharaman

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The two important resources of any worker performing any type of work at any workplace are time and energy. These are important inputs of the worker and need to be utilised in the best possible manner. The kitchen is an important workplace where the homemaker performs many essential activities. Its layout should be so designed that optimum use of her resources can be achieved.Ideally, the shape of the kitchen, as determined by the physical space enclosed by the four walls, can be square, rectangular or irregular. But it is the shape of the arrangement of counter that one normally refers to while talking of the layout of the kitchen. The arrangement can be along a single wall, along two opposite walls, L shape, U shape or even island. A study was conducted in 50 kitchens belonging to middle income group families. These were DDA built kitchens located in North, South, East and West Delhi.The study was conducted in three phases. In the first phase, 510 non working homemakers were interviewed. The data related to personal characteristics of the homemakers was collected. Additional information was also collected regarding the kitchens-the size, shape , etc. The homemakers were also questioned about various aspects related to meal preparation-people performing the task, number of items cooked, areas used for meal preparation , etc. In the second phase, a suitable technique was designed for conducting time and motion study in the kitchen while the meal was being prepared. This technique was called Path Process Chart. The final phase was carried out in 50 kitchens. The criterion for selection was that all items for a meal should be cooked at the same time. All the meals were cooked by the homemakers in their own kitchens. The meal preparation was studied using the Path Process Chart technique. The data collected was analysed and conclusions drawn. It was found that of all the shapes, it was the kitchen with L shape arrangement in which, on an average a homemaker spent minimum time on meal preparation and also travelled the minimum distance. Thus, the average distance travelled in a L shaped layout was 131.1 mts as compared to 181.2 mts in an U shaped layout. Similarly, 48 minutes was the average time spent on meal preparation in L shaped layout as compared to 53 minutes in U shaped layout. Thus, the L shaped layout was more time and energy saving layout as compared to U shaped.

Keywords: kitchen layout, meal preparation, path process chart technique, workplace

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Authors: Aniedi A. Udo, Magnus U. Igboekwe, Rasaaq Bello, Francis D. Eyenaka, Michael C. Ohakwere-Eze

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Groundwater vulnerability to pollution was assessed in Akwa Ibom State, Southern Nigeria, with the aim of locating areas with high potentials for resource contamination, especially due to anthropogenic influence. The electrical resistivity method was utilized in the collection of the initial field data. Additional data input, which included depth to static water level, drilled well log data, aquifer recharge data, percentage slope, as well as soil information, were sourced from secondary sources. The initial field data were interpreted both manually and with computer modeling to provide information on the geoelectric properties of the subsurface. Interpreted results together with the secondary data were used to develop the DRASTIC thematic maps. A vulnerability assessment was performed using the DRASTIC model in a GIS environment and areas with high vulnerability which needed immediate attention was clearly mapped out and presented using an aquifer vulnerability map. The model was subjected to validation and the rate of validity was 73% within the area of study.

Keywords: groundwater, vulnerability, DRASTIC model, pollution

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Authors: Maryam Mousivand

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Subculture is one of the important concepts in social sciences and the field of cultural studies, which falls under the huge concept of culture. In general, subculture is a kind of movement and experience of collective resistance that is manifested by a population as a reaction against the acceptance of official identities approved by custom and society. Subcultures exist in the virtual world in the new era, and they emerged in various forms, such as the emergence of a subculture under common concepts and interests in the form of sites, channels, and groups of virtual space, which will be discussed in this article.

Keywords: subculture, social media, cultural studies, culture

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Authors: Tonderai Muchenje, Xolani Mkhwanazi

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Precision agriculture uses a number of technologies, devices, protocols, and computing paradigms to optimize agricultural processes. Big data, artificial intelligence, cloud computing, and edge computing are all used to handle the huge amounts of data generated by precision agriculture. However, precision agriculture is still emerging and has a low level of security features. Furthermore, future solutions will demand data availability and accuracy as key points to help farmers, and security is important to build robust and efficient systems. Since precision agriculture comprises a wide variety and quantity of resources, security addresses issues such as compatibility, constrained resources, and massive data. Moreover, conventional protection schemes used in the traditional internet may not be useful for agricultural systems, creating extra demands and opportunities. Therefore, this paper aims at reviewing state of the art of precision agriculture security, particularly in open field agriculture, discussing its architecture, describing security issues, and presenting the major challenges and future directions.

Keywords: precision agriculture, security, IoT, EIDE

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Authors: Ahmed Soliman Nagiub, Mahmoud Fayez, Heba Khaled, Said Ghoniemy

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Accurate perception is a critical component in enabling autonomous vehicles to understand their driving environment. The acquisition of 3D information about objects, including their location and pose, is essential for achieving this understanding. This survey paper presents a comprehensive review of 3D object detection techniques specifically tailored for autonomous vehicles. The survey begins with an introduction to 3D object detection, elucidating the significance of the third dimension in perceiving the driving environment. It explores the types of sensors utilized in this context and the corresponding data extracted from these sensors. Additionally, the survey investigates the different types of datasets employed, including their formats, sizes, and provides a comparative analysis. Furthermore, the paper categorizes and thoroughly examines the perception methods employed for 3D object detection based on the diverse range of sensors utilized. Each method is evaluated based on its effectiveness in accurately detecting objects in a three-dimensional space. Additionally, the evaluation metrics used to assess the performance of these methods are discussed. By offering a comprehensive overview of 3D object detection techniques for autonomous vehicles, this survey aims to advance the field of perception systems. It serves as a valuable resource for researchers and practitioners, providing insights into the techniques, sensors, and evaluation metrics employed in 3D object detection for autonomous vehicles.

Keywords: computer vision, 3D object detection, autonomous vehicles, deep learning

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Authors: Nicolas Plamondon, Stuart Atkinson, Shuzi Zhou

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The insurance industry is usually not the first topic one has in mind when thinking about applications of data science. However, the use of data science in the finance and insurance industry is growing quickly for several reasons, including an abundance of reliable customer data, ferocious competition requiring more accurate pricing, etc. Among the top use cases of data science, we find pricing optimization, customer segmentation, customer risk assessment, fraud detection, marketing, and triage analytics. The objective of this paper is to present an application of the generalized additive model (GAM) on a commercial automobile insurance product: an individually rated commercial automobile. These are vehicles used for commercial purposes, but for which there is not enough volume to apply pricing to several vehicles at the same time. The GAM model was selected as an improvement over GLM for its ease of use and its wide range of applications. The model was trained using the largest split of the data to determine model parameters. The remaining part of the data was used as testing data to verify the quality of the modeling activity. We used the Gini coefficient to evaluate the performance of the model. For long-term monitoring, commonly used metrics such as RMSE and MAE will be used. Another topic of interest in the insurance industry is to process of producing the model. We will discuss at a high level the interactions between the different teams with an insurance company that needs to work together to produce a model and then monitor the performance of the model over time. Moreover, we will discuss the regulations in place in the insurance industry. Finally, we will discuss the maintenance of the model and the fact that new data does not come constantly and that some metrics can take a long time to become meaningful.

Keywords: insurance, data science, modeling, monitoring, regulation, processes

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Authors: Evridiki Papachristou, Nikolaos Bilalis

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Imagine an apparel industry where fashion design does not begin with a paper-and-pen drawing which is then translated into pattern and later to a 3D model where the designer tries out different fabrics, colours and contrasts. Instead, imagine a fashion designer in the future who produces that initial fashion drawing in a three-dimensional space and won’t leave that environment until the product is done, communicating his/her ideas with the entire development team in true to life 3D. Three-dimensional (3D) technology - while well established in many other industrial sectors like automotive, aerospace, architecture and industrial design, has only just started to open up a whole range of new opportunities for apparel designers. The paper will discuss the process of 3D simulation technology enhanced by high quality visualization of data and its capability to ensure a massive competitiveness in the market. Secondly, it will underline the most frequent problems & challenges that occur in the process chain when various partners in the production of textiles and apparel are working together. Finally, it will offer a perspective of how the Virtual Prototyping Technology will make the global textile and apparel industry change to a level where designs will be visualized on a computer and various scenarios modeled without even having to produce a physical prototype. This state-of-the-art 3D technology has been described as transformative and“disruptive”comparing to the process of the way apparel companies develop their fashion products today. It provides the benefit of virtual sampling not only for quick testing of design ideas, but also reducing process steps and having more visibility.A so called“digital asset” that can be used for other purposes such as merchandising or marketing.

Keywords: 3D visualization, apparel, virtual prototyping, prototyping technology

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Authors: Wageeh A. Nafei

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This paper explores the role of Paradoxical Leadership (PL) in enhancing Organizational Success (OS) at private hospitals in Egypt. Based on the collected data from employees in private hospitals (doctors, nursing staff, and administrative staff). The researcher has adopted a sampling method to collect data for the study. The appropriate statistical methods, such as Alpha Correlation Coefficient (ACC), Confirmatory Factor Analysis (CFA), and Multiple Regression Analysis (MRA), are used to analyze the data and test the hypotheses. The research has reached a number of results, the most important of which are (1) there is a statistical relationship between the independent variable represented by PL and the dependent variable represented by Organizational Success (OS). The paradoxical leader encourages employees to express their opinions and builds a work environment characterized by flexibility and independence. Also, the paradoxical leader works to support specialized work teams, which leads to the creation of new ideas, on the one hand, and contributes to the achievement of outstanding performance on the other hand. (2) the mentality of the paradoxical leader is flexible and capable of absorbing all suggestions from all employees. Also, the paradoxical leader is interested in enhancing cooperation among them and provides an opportunity to transfer experience and increase knowledge-sharing. Also, the sharing of knowledge creates the necessary diversity that helps the organization to obtain rich external information and enables the organization to deal with a rapidly changing environment. (3) The PL approach helps in facing the paradoxical demands of employees. A paradoxical leader plays an important role in reducing the feeling of instability in the work environment and lack of job security, reducing negative feelings for employees, restoring balance in the work environment, improving the well-being of employees, and increasing the degree of job satisfaction of employees in the organization. The study referred to a number of recommendations, the most important of which are (1) the leaders of the organizations must listen to the views of employees and their needs and move away from the official method of control. The leader should give sufficient freedom to employees to participate in decision-making and maintain enough space among them. The treatment between the leaders and employees must be based on friendliness, (2) the need for organizational leaders to pay attention to sharing knowledge among employees through training courses. The leader should make sure that every information provided by the employee is valuable and useful, which can be used to solve a problem that may face his/her colleagues at work, (3) the need for organizational leaders to pay attention to sharing knowledge among employees through brainstorming sessions. The leader should ensure that employees obtain knowledge from their colleagues and share ideas and information among them. This is in addition to motivating employees to complete their work in a new creative way, which leads to employees’ not feeling bored of repeating the same routine procedures in the organization.

Keywords: paradoxical leadership, organizational success, human resourece, management

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Authors: Hamidreza Ghazvinian, Khosro Ghazvinian, Touba Khodaiean

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The importance of evaporation estimation in water resources and agricultural studies is undeniable. Pan evaporation are used as an indicator to determine the evaporation of lakes and reservoirs around the world due to the ease of interpreting its data. In this research, intelligent models were investigated in estimating pan evaporation on a daily basis. Shahroud and Mayamey were considered as the studied cities. These two cities are located in Semnan province in Iran. The mentioned cities have dry weather conditions that are susceptible to high evaporation potential. Meteorological data of 11 years of synoptic stations of Shahrood and Mayamey cities were used. The intelligent models used in this study are Artificial Neural Network (ANN), Least Squares Support Vector Machine (LSSVM), and M5 tree models. Meteorological parameters of minimum and maximum air temperature (Tmax, Tmin), wind speed (WS), sunshine hours (SH), air pressure (PA), relative humidity (RH) as selected input data and evaporation data from pan (EP) to The output data was considered. 70% of data is used at the education level, and 30 % of the data is used at the test level. Models used with explanation coefficient evaluation (R2) Root of Mean Squares Error (RMSE) and Mean Absolute Error (MAE). The results for the two Shahroud and Mayamey stations showed that the above three models' operations are rather appropriate.

Keywords: pan evaporation, intelligent methods, shahroud, mayamey

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Authors: Karen B. Ghazaryan

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Recently, the propagation of coupled electromagnetic and elastic waves in magneto-electro-elastic (MEE) structures attracted much attention due to the wide range of application of these materials in smart structures. MEE materials are a class of new artificial composites that consist of simultaneous piezoelectric and piezomagnetic phases. Magneto-electro-elastic composites are built up by combining piezoelectric and piezomagnetic phases to obtain a smart composite that presents not only the electromechanical and magneto-mechanical coupling but also a strong magnetoelectric coupling, which makes such materials highly valuable in technological usage. In the framework of quasi-static approach shear surface and localized waves are considered in magneto-electro-elastic piezo-active structure consisting of functionally graded 6mm hexagonal symmetry group crystals. Assuming that in a functionally graded material the elastic and electromagnetic properties vary in the same proportion in direction perpendicular to the MEE polling direction, special classes of inhomogeneity functions were found, admitting exact solutions for coupled electromagnetic and elastic wave fields. Based on these exact solutions, defining the coupled shear wave field in magneto-electro-elastic composites several modal problems are considered: shear surface waves propagation along surface of a MEE half-space, interfacial wave propagation in a MEE oppositely polarized bi-layer, Love type waves in a functionally graded MEE layer overlying a homogeneous elastic half-space. For the problems under consideration corresponding dispersion equations are deduced analytically in an explicit form and for the BaTiO₃–CoFe₂O₄ crystal numerical results estimating effects of inhomogeneity and piezo effect are carried out.

Keywords: surface shear waves, magneto-electro-elastic composites, piezoactive crystals, functionally graded elastic materials

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Authors: Rachida Benabbou, Sang Hun Park, Hee Chung Lee

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The world’s population is increasing with unprecedented speed, leading to fast growing urbanization pace. Cities since the Industrial revolution had evolved to fit the growing demand on infrastructure, roads, transportation, and housing. Through this evolution, cities had grown into grey, polluted, and vehicle-oriented urban areas with a significant lack of green spaces. Consequently, we ended up with low quality of life for citizens. Therefore, many cities, nowadays, are revising the way we think urbanism and try to grow into more livable and citizen-friendly, by creating change from the inside out. Thus, cities are trying to bring back nature in its crowded grey centers and regenerate many urban areas as green public spaces not only as a way to give new breath to the city, but also as a way to create change either in the environmental, social and economic levels. The city of Seoul is one of the fast growing global cities. Its population is over 12 million and it is expected to continue to grow to a point where the quality of life may seriously deteriorate. As most green areas in Seoul are located in the suburbs in form of mountains, the city’s urban areas suffer from lack of accessible green spaces in a walking distance. Understanding the gravity and consequences of this issue, Seoul city is undergoing major changes. Many of its projects are oriented to be green public spaces where citizens can enjoy the public life in healthy outdoors. The aim of this paper is to explore the results of urban conversions into green public spaces. Starting with different locations, nature, size, and scale, these conversions can lead to significant change in the surrounding areas, thus can be used as an efficient tool of regeneration for urban areas. Through a comparative analysis of three different types of urban conversions projects in the city of Seoul, we try to show the positive urban influence of the outcomes, in order to encourage cities to use green spaces as a strategic tool for urban regeneration and redevelopment.

Keywords: urban conversion, green public space, change, urban regeneration

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Authors: Allmin Susaiyah, Aki Härmä, Milan Petković

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Automatic generation of insights from data using insight mining systems (IMS) is useful in many applications, such as personal health tracking, patient monitoring, and business process management. Existing IMS face challenges in controlling insight extraction, scaling to large databases, and generalising to unseen domains. In this work, we propose a hybrid approach consisting of rule-based and neural components for generating insights from data while overcoming the aforementioned challenges. Firstly, a rule-based data 2CNL component is used to extract statistically significant insights from data and represent them in a controlled natural language (CNL). Secondly, a BERTSum-based CNL2NL component is used to convert these CNLs into natural language texts. We improve the model using task-specific and domain-specific fine-tuning. Our approach has been evaluated using statistical techniques and standard evaluation metrics. We overcame the aforementioned challenges and observed significant improvement with domain-specific fine-tuning.

Keywords: data mining, insight mining, natural language generation, pre-trained language models

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Authors: Vivien Chee Pei Wei

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Today we live in a scenario in which letters share space with images on screens that vary in size, shape, and style. The popularization of television, then the computer and now the e-readers, tablets, and smartphones made the electronic assume the role that previously was restricted to printed materials. Since the extensive use of new technologies to produce, disseminate, collect and access electronic publications began, the changes to reading has been intensified. To be able to read online, it involves more than just utilizing specific skills, strategies, and practices, but also in negotiating multiple information sources. In this study, different perspectives of digital reading are being explored in order to define the key aspects of the term. The focus is to explore how new technologies affect how undergraduates’ reading behavior, which in turn, gives readers different reading levels and engagement with the text and other support materials in the same media. There is also the importance of the relationship between reading platforms, reading levels and formats of electronic publications. The study looks at the online reading practices of about 100 undergraduates from a local university. The data collected using the survey and interviews with the respondents are analyzed thematically. Findings from this study found that both digital and traditional reading are interrelated, and should not be viewed as separate, but complementary to each other. However, reading online complicates some of the skills required by traditional reading. Consequently, in order to successfully read and comprehend multiple sources of information online, undergraduates need regular opportunities to practice and develop their skills as part of their natural reading practices.

Keywords: concepts, digital reading, literacy, traditional reading

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Authors: Ana Magana

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Youth are entering the juvenile justice system at alarming rates. For the youth of color entering the system, the outcomes are far worse than for their white counterparts. In fact, the youth of color are more likely to be arrested and sentenced for longer periods of time than white youth. Race disproportionality in the juvenile justice system is evident, but what happens to the youth that exit the juvenile justice system? Who supports them after they are incarcerated and who can prevent them from re-offending? There are several diversion programs that have been implemented in the US to aid the reduction of juvenile incarceration and help reduce recidivism. The program interviewed for this study is a community-based diversion program (CBDP). The CBDP is a pre-filing diversion non-profit organization based in South Seattle. The objective of this exploratory research study is to provide a space and platform for the CBDP team to speak about their background experiences and the influence their background has on their current approach and practice with juveniles. A qualitative, exploratory study was conducted. Interviews were conducted with staff and provided oral consent. The interview included six open-ended, semi-structured questions. Interviews were digitally recoded and transcribed. The aim of this study was to understand how the influence of the participant’s backgrounds and previous experiences impact their current practice approaches with the CBDP youth and young adults. Ecological systems theory was the guiding framework for analysis. After careful analysis, three major themes emerged: 1) strong influence of participant’s background, 2) participants belonging to community and 3) strong self-identity with the CBDP. Within these three themes, subthemes were developed based on participant’s responses. It was concluded that the participant’s approach is influenced by their background experiences. This corresponds to the ecological systems theory and the community-based lens which underscores theoretical analysis. The participant’s approach is grounded in interpersonal relationships within the client’s systems, meaning that the participants understand and view their clients within an ecological systems perspective. When choosing participants that reflect the population being served, the clients receive a balanced, inclusive and caring approach. Youth and young adults are searching for supportive adults to be there for them, it is essential for diversion programs to provide a space for shared background experiences and have people that hold similar identities. Grassroots organizations such as CBDP have the tools and experience to work with marginalized populations that are constantly being passed on. While articles and studies focus on the reduction of recidivism and re-offending it is important to question the reasons behind this data. For instance, there can be a reduction in statistics, but at whose expense. Are the youth and young adults truly being supported? Or is it just a requirement that they are completing in order to remove their charge? This research study can serve as the beginning of a series of studies conducted at CBDP to further understand and validate the need to employ individuals with similar backgrounds as the participants CBDP serves.

Keywords: background experience, diversion, ecological systems theory, relationships

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Authors: Manh-Dung Ho, Van-Giap Pham, Van-Doanh Ho, Quang-Thien Tran, Tuan-Anh Tran

Abstract:

The k0-factors and related nuclear data, i.e. the Q0-factors and effective resonance energies (Ēr) of the selected radionuclides which are used in the k0-based neutron activation analysis (k0-NAA), were critically reviewed to be integrated in the “k0-DALAT” software. The k0- and Q0-factors of some short-lived radionuclides: 46mSc, 110Ag, 116m2In, 165mDy, and 183mW, were experimentally determined at the Dalat research reactor. The other radionuclides selected are: 20F, 36S, 49Ca, 60mCo, 60Co, 75Se, 77mSe, 86mRb, 115Cd, 115mIn, 131Ba, 134mCs, 134Cs, 153Gd, 153Sm, 159Gd, 170Tm, 177mYb, 192Ir, 197mHg, 239U and 239Np. The reviewed data as compared with the literature data were biased within 5.6-7.3% in which the experimental re-determined factors were within 6.1 and 7.3%. The NIST standard reference materials: Oyster Tissue (1566b), Montana II Soil (2711a) and Coal Fly Ash (1633b) were used to validate the new reviewed data showing that the new data gave an improved k0-NAA using the “k0-DALAT” software with a factor of 4.5-6.8% for the investigated radionuclides.

Keywords: neutron activation analysis, k0-based method, k0 factor, Q0 factor, effective resonance energy

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Authors: Tayyibah Khanam, Mohammad Saad Alam, Sanchari Deb, Yasser Rafat

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Electric vehicles are considered as viable replacements to gasoline cars since they help in reducing harmful emissions and stimulate power generation through renewable energy sources, hence contributing to sustainability. However, one of the significant obstacles in the mass deployment of electric vehicles is the charging time anxiety among users and, thus, the subsequent large waiting times for available chargers at charging stations. Data analytics, on the other hand, has revolutionized the decision-making tasks of management and operating systems since its arrival. In this paper, we attempt to optimize the choice of EV charging stations for users in their vicinity by minimizing the time taken to reach the charging stations and the waiting times for available chargers. Time taken to travel to the charging station is calculated by the Google Maps API and the waiting times are predicted by polynomial regression of the historical data stored. The proposed framework utilizes real-time data and historical data from all operating charging stations in the city and assists the user in finding the best suitable charging station for their current situation and can be implemented in a mobile phone application. The algorithm successfully predicts the most optimal choice of a charging station and the minimum required time for various sample data sets.

Keywords: charging data, electric vehicles, machine learning, waiting times

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Authors: R. Shamsi, F. Sharifi

Abstract:

In this paper, we obtain the projection of inefficient units in data envelopment analysis (DEA) in the case of stochastic inputs and outputs using the multi-objective programming (MOP) structure. In some problems, the inputs might be stochastic while the outputs are deterministic, and vice versa. In such cases, we propose a multi-objective DEA-R model because in some cases (e.g., when unnecessary and irrational weights by the BCC model reduce the efficiency score), an efficient decision-making unit (DMU) is introduced as inefficient by the BCC model, whereas the DMU is considered efficient by the DEA-R model. In some other cases, only the ratio of stochastic data may be available (e.g., the ratio of stochastic inputs to stochastic outputs). Thus, we provide a multi-objective DEA model without explicit outputs and prove that the input-oriented MOP DEA-R model in the invariable return to scale case can be replaced by the MOP-DEA model without explicit outputs in the variable return to scale and vice versa. Using the interactive methods for solving the proposed model yields a projection corresponding to the viewpoint of the DM and the analyst, which is nearer to reality and more practical. Finally, an application is provided.

Keywords: DEA-R, multi-objective programming, stochastic data, data envelopment analysis

Procedia PDF Downloads 111

Authors: Amani A. Saad, Ahmed A. El-Farag, El-Sayed A. Helali

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Cloud computing is an important and promising field in the recent decade. Cloud computing allows sharing resources, services and information among the people of the whole world. Although the advantages of using clouds are great, but there are many risks in a cloud. The data security is the most important and critical problem of cloud computing. In this research a new security model for cloud computing is proposed for ensuring secure communication system, hiding information from other users and saving the user's times. In this proposed model Blowfish encryption algorithm is used for exchanging information or data, and SHA-2 cryptographic hash algorithm is used for data integrity. For user authentication process a simple user-name and password is used, the password uses SHA-2 for one way encryption. The proposed system shows an improvement of the processing time of uploading and downloading files on the cloud in secure form.

Keywords: cloud computing, data security, SAAS, PAAS, IAAS, Blowfish

Procedia PDF Downloads 361

Authors: Miguel Cañon, Darwin Mena, Ivan Cabeza

Abstract:

In this work was compared and evaluated the applicability of statistical methods for the estimation of missing precipitations data in the basin of the river Lenguazaque located in the departments of Cundinamarca and Boyacá, Colombia. The methods used were the method of simple linear regression, distance rate, local averages, mean rates, correlation with nearly stations and multiple regression method. The analysis used to determine the effectiveness of the methods is performed by using three statistical tools, the correlation coefficient (r2), standard error of estimation and the test of agreement of Bland and Altmant. The analysis was performed using real rainfall values removed randomly in each of the seasons and then estimated using the methodologies mentioned to complete the missing data values. So it was determined that the methods with the highest performance and accuracy in the estimation of data according to conditions that were counted are the method of multiple regressions with three nearby stations and a random application scheme supported in the precipitation behavior of related data sets.

Keywords: statistical comparison, precipitation data, river subbasin, Bland and Altmant

Procedia PDF Downloads 470

Authors: Evy Latifah, Eli Korlina, Hanik Anggraeni, Kuntoro Boga, Joko Mariyono

Abstract:

During the rainy season, the tomato plants are vulnerable to various diseases. A disease that attacks the leaves of tomato plants (foliar diseases) such as late blight (Phytophtora infestans) and spotting bacteria (bacterial spot / Xanthomonas sp.) In addition, there is a disease that attacks the roots such as fusarium and bacterial wilt. If not immediately anticipated, it will decrease the quality and quantity of crop yields. In fact, it can lead to crop failure. The aim of this research is to know the production of tomato grafting by using Timoty and CLN 3024 tomatoes at rain shelter during rainy season in lowland. Data were analyzed using analysis of variance and tested further by Least Significant Difference (LSD) level of 5 %. The parameters measured were plant height (cm), stem diameter (cm), number of fruit space, canopy extended, number of branches, number of productive branches, and the number of stem segments. The results show at the beginning of growth until the end of the treatment without grafting with relative rain shelter displays the highest plant height. This was followed by extensive crop canopy. For tomato grafting and non-grafting using rain shelter able to produce the number of branches and number of productive branches at most. While at the end of the growth in the number of productive branches generated as much. Highest production of tomatoes produced by tomato dig rafting to use the shelter.

Keywords: field trail, wet and dry season, production, diseases, rain shelter

Procedia PDF Downloads 234

Authors: Li Qingjian, Li Ke, He Chun, Huang Yong

Abstract:

In this paper, the method of combining the Pohl Seidman's deep belief network with the self-organizing neural network is proposed to classify the target. This method is mainly aimed at the high nonlinearity of the hyperspectral image, the high sample dimension and the difficulty in designing the classifier. The main feature of original data is extracted by deep belief network. In the process of extracting features, adding known labels samples to fine tune the network, enriching the main characteristics. Then, the extracted feature vectors are classified into the self-organizing neural network. This method can effectively reduce the dimensions of data in the spectrum dimension in the preservation of large amounts of raw data information, to solve the traditional clustering and the long training time when labeled samples less deep learning algorithm for training problems, improve the classification accuracy and robustness. Through the data simulation, the results show that the proposed network structure can get a higher classification precision in the case of a small number of known label samples.

Keywords: DBN, SOM, pattern classification, hyperspectral, data compression

Procedia PDF Downloads 344