Search results for: micro data

Authors: Tasawar Nawaz

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Promotion of socioeconomic justice through redistribution of wealth is one of the most salient features of Islamic economic system. Islamic financial institutions known as Islamic banks are used to implement this in practice under the guidelines of Islamic Shariah law. Islamic banking systems strive to promote and achieve financial inclusion among the society by offering interest-free banking and risk-sharing financing solutions. Shariah-compliant micro finance is one of the most popular financial instruments used by Islamic banks to enhance access to finance. Benevolent loan (or Qard-al-Hassanah) is one of the popular financial tools used by the Islamic banks to promote financial inclusion. This aspect of Islamic banking is empirically examined in this paper with specific reference to firm’s resources, largely defined here as intellectual capital. The paper finds that Islamic banks promote financial inclusion by exploiting available resources especially, the human intellectual capital.

Keywords: financial inclusion, intellectual capital, Qard-al-Hassanah, Islamic banking

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Authors: Chad Brown

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This paper establishes general conditions for the convergence rates of nonparametric sieve estimators with dependent data. We present two key results: one for nonstationary data and another for stationary mixing data. Previous theoretical results often lack practical applicability to deep neural networks (DNNs). Using these conditions, we derive convergence rates for DNN sieve estimators in nonparametric regression settings with both nonstationary and stationary mixing data. The DNN architectures considered adhere to current industry standards, featuring fully connected feedforward networks with rectified linear unit activation functions, unbounded weights, and a width and depth that grows with sample size.

Keywords: sieve extremum estimates, nonparametric estimation, deep learning, neural networks, rectified linear unit, nonstationary processes

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Authors: Y. K. Park, B. K. Kim, J. W. Lee, S. J. Lee

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To assess the risk of the underground structures and surrounding ground, we collect basic data by the engineering method of measurement, exploration and surveys and, derive the risk through proper analysis and each assessment for urban railroad underground structures and surrounding ground including station inflow. Basic data are obtained by the fiber-optic sensors, MEMS sensors, water quantity/quality sensors, tunnel scanner, ground penetrating radar, light weight deflectometer, and are evaluated if they are more than the proper value or not. Based on these data, we analyze the risk level of urban railroad underground structures and surrounding ground. And we develop the risk management system to manage efficiently these data and to support a convenient interface environment at input/output of data.

Keywords: urban railroad, underground structures, ground subsidence, station inflow, risk

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Authors: Yumi Okano

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The low level of English proficiency among Japanese people has been a problem for a long time. Japanese non-native English language teachers, under social or ideological constraints, feel a gap between government policy and their language proficiency and cannot maintain high self-esteem. This paper focuses on current Japanese policies and the social context in which teachers are placed and examines the measures necessary for their positive identity formation from a macro-meso-micro perspective. Some suggestions for achieving this are: 1) Teachers should free themselves from the idea of native speakers and embrace local needs and accents, 2) Teachers should be involved in student discussions as facilitators and individuals so that they can be good role models for their students, and 3) Teachers should invest in their classrooms. 4) Guidelines and training should be provided to help teachers gain confidence. In addition to reducing the workload to make more time available, 5) expanding opportunities for investment outside the classroom into the real world is necessary.

Keywords: language teacher identity, native speakers, government policy, critical pedagogy, investment

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Authors: Kiana Zeighami, Morteza Ozlati Moghadam

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Designing a high accuracy and high precision motion controller is one of the important issues in today’s industry. There are effective solutions available in the industry but the real-time performance, smoothness and accuracy of the movement can be further improved. This paper discusses a complete solution to carry out the movement of three stepper motors in three dimensions. The objective is to provide a method to design a fully integrated System-on-Chip (SOC)-based motion controller to reduce the cost and complexity of production by incorporating Field Programmable Gate Array (FPGA) into the design. In the proposed method the FPGA receives its commands from a host computer via wireless internet communication and calculates the motion trajectory for three axes. A profile generator module is designed to realize the interpolation algorithm by translating the position data to the real-time pulses. This paper discusses an approach to implement the linear interpolation algorithm, since it is one of the fundamentals of robots’ movements and it is highly applicable in motion control industries. Along with full profile trajectory, the triangular drive is implemented to eliminate the existence of error at small distances. To integrate the parallelism and real-time performance of FPGA with the power of Central Processing Unit (CPU) in executing complex and sequential algorithms, the NIOS II soft-core processor was added into the design. This paper presents different operating modes such as absolute, relative positioning, reset and velocity modes to fulfill the user requirements. The proposed approach was evaluated by designing a custom-made FPGA board along with a mechanical structure. As a result, a precise and smooth movement of stepper motors was observed which proved the effectiveness of this approach.

Keywords: 3-axis linear interpolation, FPGA, motion controller, micro-stepping

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Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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The Intelligent transportation system is essential to build smarter cities. Machine learning based transportation prediction could be highly promising approach by delivering invisible aspect visible. In this context, this research aims to make a prototype model that predicts transportation network by using big data and machine learning technology. In detail, among urban transportation systems this research chooses bus system.  The research problem that existing headway model cannot response dynamic transportation conditions. Thus, bus delay problem is often occurred. To overcome this problem, a prediction model is presented to fine patterns of bus delay by using a machine learning implementing the following data sets; traffics, weathers, and bus statues. This research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data are gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is designed by the machine learning tool (RapidMiner Studio) and conducted tests for bus delays prediction. This research presents experiments to increase prediction accuracy for bus headway by analyzing the urban big data. The big data analysis is important to predict the future and to find correlations by processing huge amount of data. Therefore, based on the analysis method, this research represents an effective use of the machine learning and urban big data to understand urban dynamics.

Keywords: big data, machine learning, smart city, social cost, transportation network

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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 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 Ccomputing, data security, SAAS, PAAS, IAAS, Blowfish

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Authors: Jhoanne Pedres Boñgol, Zhang Liu, Yuyin Qiu, King Lun Yeung

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Flexible ceramic sorbent material can be a viable technology to capture and recover emulsified fats, oils, and grease (FOG) that often cause sanitary sewer overflows. This study investigates the sorption capacity and recovery rate of ceramic material in surfactant-stabilized oil-water emulsion by synthesizing silica aerogel: SiO₂–X via acid-base sol-gel method followed by ambient pressure drying. The SiO₂–X is amorphous, microstructured, lightweight, flexible, and highly oleophilic. It displays spring-back behavior apparent at 80% compression with compressive strength of 0.20 MPa and can stand a weight of 1000 times its own. The contact angles measured at 0° and 177° in oil and water, respectively, confirm its oleophilicity and hydrophobicity while its thermal stability even at 450 °C is confirmed via TGA. In pure oil phase, the qe,AV. of 1x1 mm SiO₂–X is 7.5 g g⁻¹ at tqe= 10 min, and a qe,AV. of 6.05 to 6.76 g g⁻¹ at tqe= 24 hrs in O/W emulsion. The filter ceramic can be reused 50 x with 75-80 % FOG recovery by manual compression.

Keywords: adsorption, aerogel, emulsion, FOG

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Authors: Lenka Markovičová, Viera Zatkalíková, Tomasz Garbacz

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The development of composite materials and the related design and manufacturing technologies is one of the most important advances in the history of materials. Composites are multifunctional materials having unprecedented mechanical and physical properties that can be tailored to meet the requirements of a particular application. Some composites also exhibit great resistance to high-temperature corrosion, oxidation, and wear. Polymers are widely used indoors and outdoors, therefore they are exposed to a chemical environment which may include atmospheric oxygen, acidic fumes, acidic rain, moisture heat and thermal shock, ultra-violet light, high energy radiation, etc. Different polymers are affected differently by these factors even though the amorphous polymers are more sensitive. Ageing is also important and it is defined as the process of deterioration of engineering materials resulting from the combined effects of atmospheric radiation, heat, oxygen, water, micro-organisms and other atmospheric factors.

Keywords: composites with glass fibers, mechanical properties, polyamides, UV degradation

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Authors: Wang Yichen, Haruka Yamashita

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In recent years, in the field of sports, decision making such as member in the game and strategy of the game based on then analysis of the accumulated sports data are widely attempted. In fact, in the NBA basketball league where the world's highest level players gather, to win the games, teams analyze the data using various statistical techniques. However, it is difficult to analyze the game data for each play such as the ball tracking or motion of the players in the game, because the situation of the game changes rapidly, and the structure of the data should be complicated. Therefore, it is considered that the analysis method for real time game play data is proposed. In this research, we propose an analytical model for "determining the optimal lineup composition" using the real time play data, which is considered to be difficult for all coaches. In this study, because replacing the entire lineup is too complicated, and the actual question for the replacement of players is "whether or not the lineup should be changed", and “whether or not Small Ball lineup is adopted”. Therefore, we propose an analytical model for the optimal player selection problem based on Small Ball lineups. In basketball, we can accumulate scoring data for each play, which indicates a player's contribution to the game, and the scoring data can be considered as a time series data. In order to compare the importance of players in different situations and lineups, we combine RNN (Recurrent Neural Network) model, which can analyze time series data, and NN (Neural Network) model, which can analyze the situation on the field, to build the prediction model of score. This model is capable to identify the current optimal lineup for different situations. In this research, we collected all the data of accumulated data of NBA from 2019-2020. Then we apply the method to the actual basketball play data to verify the reliability of the proposed model.

Keywords: recurrent neural network, players lineup, basketball data, decision making model

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Authors: Rajeev Kumar Bedi, Jaswinder Singh, Sunil Kumar Gupta

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The demand for cloud storage is increasing because users want continuous access their data. Cloud Storage revolutionized the way how users access their data. A lot of cloud storage service providers are available as DropBox, G Drive, and providing limited free storage and for extra storage; users have to pay money, which will act as a burden on users. To avoid the issue of limited free storage, the concept of Multi Cloud Storage introduced. In this paper, we will discuss the limitations of existing Multi Cloud Storage systems for mobile devices.

Keywords: cloud storage, data privacy, data security, multi cloud storage, mobile devices

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Authors: Wuttigrai Ngamsirijit

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Talent management in today’s modern organizations has become data-driven due to a demand for objective human resource decision making and development of analytics technologies. HR managers have been faced with some obstacles in exploiting data and information to obtain their effective talent management decisions. These include process-based data and records; insufficient human capital-related measures and metrics; lack of capabilities in data modeling in strategic manners; and, time consuming to add up numbers and make decisions. This paper proposes a framework of talent management through integration of talent value chain and human capital analytics approaches. It encompasses key data, measures, and metrics regarding strategic talent management decisions along the organizational and talent value chain. Moreover, specific predictive and prescriptive models incorporating these data and information are recommended to help managers in understanding the state of talent, gaps in managing talent and the organization, and the ways to develop optimized talent strategies.    

Keywords: decision making, human capital analytics, talent management, talent value chain

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Authors: Ouafa Amira, Jiangshe Zhang

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Clustering is an unsupervised machine learning technique; its aim is to extract the data structures, in which similar data objects are grouped in the same cluster, whereas dissimilar objects are grouped in different clusters. Clustering methods are widely utilized in different fields, such as: image processing, computer vision , and pattern recognition, etc. Fuzzy c-means clustering (fcm) is one of the most well known fuzzy clustering methods. It is based on solving an optimization problem, in which a minimization of a given cost function has been studied. This minimization aims to decrease the dissimilarity inside clusters, where the dissimilarity here is measured by the distances between data objects and cluster centers. The degree of belonging of a data point in a cluster is measured by a membership function which is included in the interval [0, 1]. In fcm clustering, the membership degree is constrained with the condition that the sum of a data object’s memberships in all clusters must be equal to one. This constraint can cause several problems, specially when our data objects are included in a noisy space. Regularization approach took a part in fuzzy c-means clustering technique. This process introduces an additional information in order to solve an ill-posed optimization problem. In this study, we focus on regularization by relative entropy approach, where in our optimization problem we aim to minimize the dissimilarity inside clusters. Finding an appropriate membership degree to each data object is our objective, because an appropriate membership degree leads to an accurate clustering result. Our clustering results in synthetic data sets, gaussian based data sets, and real world data sets show that our proposed model achieves a good accuracy.

Keywords: clustering, fuzzy c-means, regularization, relative entropy

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Authors: Sarra Khelifi, Youcef Guerabli, Ahcene Boumaiza

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Advances in techniques for measuring individual crystallographic orientations have made it possible to investigate the role of local crystallography during the plastic deformation of materials. In this study, the change in crystallographic orientation distribution during deformation by deep drawing in carbon steel has been investigated in order to understand their role in propagation and arrest of crack. The results show that the change of grain orientation from initial recrystallization texture components of {111}<112> to deformation orientation {111}<110> incites the initiation and propagation of cracks in the region of {111}<112> small grains. Moreover, the misorientation profile and local orientation are analyzed in detail to discuss the change from {111}<112> to {111}<110>. The deformation of the grain with {111}<110> orientation is discussed in terms of stops of the crack in carbon steel during drawing. The SEM-EBSD technique was used to reveal the change of orientation; XRD was performed for the characterization of the global evolution of texture for deformed samples.

Keywords: fracture, heterogeneity, misorientation profile, stored energy

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Authors: Wookyong Kwon, Sangmoon Lee

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In this paper, a sampled-data model predictive tracking control method is presented for mobile robots which is modeled as constrained continuous-time linear parameter varying (LPV) systems. The presented sampled-data predictive controller is designed by linear matrix inequality approach. Based on the input delay approach, a controller design condition is derived by constructing a new Lyapunov function. Finally, a numerical example is given to demonstrate the effectiveness of the presented method.

Keywords: model predictive control, sampled-data control, linear parameter varying systems, LPV

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Authors: Nasser A. Al-Azri

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The effectiveness of passive cooling techniques is assessed based on bioclimatic charts that require the typical meteorological year (TMY) for a specified location for their development. However, TMYs are not always available; mainly due to the scarcity of records of solar radiation which is an essential component used in developing common TMYs intended for general uses. Since solar radiation is not required in the development of the bioclimatic chart, this work suggests developing TMYs based solely on the relevant parameters. This approach improves the accuracy of the developed TMY since only the relevant parameters are considered and it also makes the development of the TMY more accessible since solar radiation data are not used. The presented paper will also discuss the development of the TMY from the raw data available at the NOAA-NCDC archive of world weather data and the construction of the bioclimatic charts for some randomly selected locations around the world.

Keywords: bioclimatic charts, passive cooling, TMY, weather data

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Authors: Jeferson Gomes Dos Santos, Mirelle Cristina De Abreu Quintela

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Considering that after the 1988 Constitution, in Brazil, municipalities have acquired new roles in the face of a financial reality that jeopardizes more substantial actions, the Public Budget is essential for the establishment of guidelines for action, within each budgetary reality. Within this, the present work sought to understand the budget profile of the mining municipality of Ataleia, with a view to identifying its budget composition, in relation to the main sources of revenue and expenditure. To achieve the purposes of the study, information was collected on the municipality's finances, from the years 2000 to 2016, visualizing the progress of its revenues in terms of funding and origin, and expenses in terms of nature and purpose. It was evidenced that the municipality, having its budget revenue in the period, still shows great dependence on intergovernmental transfers, as the own collection was relatively low. The budget expenditure of the period was mainly influenced by social expenditures, but it must be said that the municipality complied with the limits of spending, minimum and maximum, established by law.

Keywords: expenses, municipal budget, planning, revenue

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Authors: M. A. El-Khateeb

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The growth of the olive oil production in Saudi Arabia peculiarly in Al Jouf region in recent years has been accompanied by an increase in the discharge of associated processing waste. Olive mill waste is produced throughout the extraction of oil from the olive fruit using the traditional mill and press process. Deterioration of the environment due to olive mill disposal wastes is a serious problem. When olive mill waste disposed into the soil, it affects soil quality, soil micro flora, and also toxic to plants. The aim of this work is to isolate microorganism (bacterial or fungal strains) from OMW capable of degrading phenols. Olive mill wastewater, olive mill waste and soil (beside oil production mill) contaminated with olive waste were used for isolation of phenol tolerant microorganisms. Four strains (two fungal and two bacterial) were isolated from olive mill waste. The isolated strains were Candida tropicalis and Phanerochaete chrysosporium (fungal strains) and Bacillus sp. and Rhodococcus sp. (bacterial strains). These strains were able to degrade phenols and could be used for bioremediation of olive mill waste.

Keywords: bioremediation, bacteria, fungi, Sakaka

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Authors: D. Nam Kim, D. Jin Kim, Jeonghwan Jeon

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Defense Defensive Modeling and Simulation (M&S) is a system which enables impracticable training for reducing constraints of time, space and financial resources. The necessity of defensive M&S has been increasing not only for education and training but also virtual fight. Soldiers who are using defensive M&S for education and training will obtain empirical knowledge and know-how. However, the obtained knowledge of individual soldiers have not been managed and utilized yet since the nature of military organizations: confidentiality and frequent change of members. Therefore, this study aims to develop a management system for the experience of defensive M&S based on data mining approach. Since individual empirical knowledge gained through using the defensive M&S is both quantitative and qualitative data, data mining approach is appropriate for dealing with individual empirical knowledge. This research is expected to be helpful for soldiers and military policy makers.

Keywords: data mining, defensive m&s, management system, knowledge management

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Authors: Hyun-Woo Cho

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The detection and identification of multivariate manufacturing processes are quite important in order to maintain good product quality. Unusual behaviors or events encountered during its operation can have a serious impact on the process and product quality. Thus they should be detected and identified as soon as possible. This paper focused on the efficient representation of process measurement data in detecting and identifying abnormalities. This qualitative method is effective in representing fault patterns of process data. In addition, it is quite sensitive to measurement noise so that reliable outcomes can be obtained. To evaluate its performance a simulation process was utilized, and the effect of adopting linear and nonlinear methods in the detection and identification was tested with different simulation data. It has shown that the use of a nonlinear technique produced more satisfactory and more robust results for the simulation data sets. This monitoring framework can help operating personnel to detect the occurrence of process abnormalities and identify their assignable causes in an on-line or real-time basis.

Keywords: detection, monitoring, identification, measurement data, multivariate techniques

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Authors: Thirunavoukkarasu Manikkannan, Karpanai Selvan Balasubramanian

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Textile industry is the one of the most important industrial sector in India. It accounts for 5% of total Gross Domestic Product (GDP) in the country. A Textile industry consumes large quantities of water (~250 m3/ton of product) and they generate almost ~90% of wastewater from its consumption. The problem is alarming and requires proper treatment process to acquire dual benefit of Zero Liquid Discharge and no contamination to the environment. Here we describe the process by which the textile wastewater can be reused. We have collected the textile wastewater in and around Ayyampettai area of Tamilnadu, India. Among different microalgal strains used, Desodesmus sp. collected at Manali, Chennai, Tamilnadu, India was able to lessen the colour of the waste water in 12-15 hrs of its growth, COD around 81.7%, Dissolved solid reduction was 28 ± 0.5 %, Suspended solid was reduced to 40.5 ± 0.3 %, Dye degradation was 50-78%. Further, Desodesmus sp. able to achieve the biomass of 0.9 ± 0.2 g/L (dry weight) in two weeks’ time, the Chl a content was 11 mg/L. It infers that this algal strain able to utilize the textile wastewater as source for growth and algal biomass production.

Keywords: Desodesmus sp., microalgae, textile, treatment, wastewater

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Authors: Xiaochen Mu

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Data rights confirmation, as a key legal issue in the development of the digital economy, is undergoing a transition from a traditional rights paradigm to a more complex private-economic paradigm. In this process, data rights confirmation has evolved from a simple claim of rights to a complex structure encompassing multiple dimensions of personality rights and property rights. Current data rights confirmation practices are primarily reflected in two models: holistic rights confirmation and process rights confirmation. The holistic rights confirmation model continues the traditional "one object, one right" theory, while the process rights confirmation model, through contractual relationships in the data processing process, recognizes rights that are more adaptable to the needs of data circulation and value release. In the design of the data property rights system, there is a hierarchical characteristic aimed at decoupling from raw data to data applications through horizontal stratification and vertical staging. This design not only respects the ownership rights of data originators but also, based on the usufructuary rights of enterprises, constructs a corresponding rights system for different stages of data processing activities. The subjects of data property rights include both data originators, such as users, and data producers, such as enterprises, who enjoy different rights at different stages of data processing. The intellectual property rights system, with the mission of incentivizing innovation and promoting the advancement of science, culture, and the arts, provides a complete set of mechanisms for protecting innovative results. However, unlike traditional private property rights, the granting of intellectual property rights is not an end in itself; the purpose of the intellectual property system is to balance the exclusive rights of the rights holders with the prosperity and long-term development of society's public learning and the entire field of science, culture, and the arts. Therefore, the intellectual property granting mechanism provides both protection and limitations for the rights holder. This perfectly aligns with the dual attributes of data. In terms of achieving the protection of data property rights, the granting of intellectual property rights is an important institutional choice that can enhance the effectiveness of the data property exchange mechanism. Although this is not the only path, the granting of data property rights within the framework of the intellectual property rights system helps to establish fundamental legal relationships and rights confirmation mechanisms and is more compatible with the classification and grading system of data. The modernity of the intellectual property rights system allows it to adapt to the needs of big data technology development through special clauses or industry guidelines, thus promoting the comprehensive advancement of data intellectual property rights legislation. This paper analyzes data protection under the virtual property layer theory and two-fold virtual property rights system. Based on the “bundle of right” theory, this paper establishes specific three-level data rights. This paper analyzes the cases: Google v. Vidal-Hall, Halliday v Creation Consumer Finance, Douglas v Hello Limited, Campbell v MGN and Imerman v Tchenquiz. This paper concluded that recognizing property rights over personal data and protecting data under the framework of intellectual property will be beneficial to establish the tort of misuse of personal information.

Keywords: data protection, property rights, intellectual property, Big data

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Authors: Randy D. Colon

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Through a freedom of information request, data pertaining to Housing Choice Voucher (HCV) households has been obtained from the Chicago Housing Authority, including rent price and number of bedrooms per HCV household, community area, and zip code from 2013 to the first quarter of 2018. Similar data pertaining to the private rental market will be obtained through public records found through the United States Department of Housing and Urban Development. The datasets will be analyzed through statistical and mapping software to investigate the potential link between HCV households and distorted rent prices. Quantitative data will be supplemented by qualitative data to investigate the lived experience of Chicago residents. Qualitative data will be collected at community meetings in the Chicago Englewood neighborhood through participation in neighborhood meetings and informal interviews with residents and community leaders. The qualitative data will be used to gain insight on the lived experience of community leaders and residents of the Englewood neighborhood in relation to housing, the rental market, and HCV. While there is an abundance of quantitative data on this subject, this qualitative data is necessary to capture the lived experience of local residents effected by a changing rental market. This topic reflects concerns voiced by members of the Englewood community, and this study aims to keep the community relevant in its findings.

Keywords: Chicago, housing, housing choice voucher program, housing subsidies, rental market

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Authors: Amir Tosson, Mohammad Reza, Christian Gutt

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Metadata stands at the forefront of advancing data management practices within research communities, with particular significance in the realms of neutron and photon scattering. This paper introduces a groundbreaking approach—dynamic metadata schema—within the context of X-ray Photon Correlation Spectroscopy (XPCS). XPCS, a potent technique unravelling nanoscale dynamic processes, serves as an illustrative use case to demonstrate how dynamic metadata can revolutionize data acquisition, sharing, and analysis workflows. This paper explores the challenges encountered by the neutron and photon communities in navigating intricate data landscapes and highlights the prowess of dynamic metadata in addressing these hurdles. Our proposed approach empowers researchers to tailor metadata definitions to the evolving demands of experiments, thereby facilitating streamlined data integration, traceability, and collaborative exploration. Through tangible examples from the XPCS domain, we showcase how embracing dynamic metadata standards bestows advantages, enhancing data reproducibility, interoperability, and the diffusion of knowledge. Ultimately, this paper underscores the transformative potential of dynamic metadata, heralding a paradigm shift in data management within the neutron and photon research communities.

Keywords: metadata, FAIR, data analysis, XPCS, IoT

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Authors: M. Al Azri, M. Elzain, K. Bouziane, S. M. Chérif

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n-Type 6H-SiC(0001) substrates were implanted with three fluencies of Mn+ 5x1015 Mn/cm2 (Mn content: 0.7%), 1x1016 (~2 %), and 5x1016 cm–2 (7%) with implantation energy of 80 keV and substrate temperature of 365ºC. The samples were characterized using Rutherford Backscattering and Channeling Spectroscopy (RBS/C), High-Resolution X-Ray Diffraction technique (HRXRD), micro-Raman Spectroscopy (μRS), and Superconducting Quantum Interference Device (SQUID) techniques. The aim of our work is to investigate implantation induced defects with dose and to study any correlation between disorder-composition and magnetic properties. In addition, ab-initio calculations were used to investigate the structural and magnetic properties of Mn-doped 6H-SiC. Various configurations of Mn sites and vacancy types were considered. The calculations showed that a substitutional Mn atom at Si site possesses larger magnetic moment than Mn atom at C site. A model is introduced to explain the dependence of the magnetic structure on site occupation. The magnetic properties of ferromagnetically (FM) and antiferromagnetically (AFM) coupled pairs of Mn atoms with and without neighboring vacancies have also been explored.

Keywords: ab-initio calculations, diluted magnetic semiconductors, magnetic properties, silicon carbide

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Authors: Jae Young Park, Hwansu Jung, Jong Tae Kim

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Whether the data has been well parallelized is an important factor in the Solid-State-Drive (SSD) performance. SSD parallelization is affected by allocation scheme and it is directly connected to SSD performance. There are dynamic allocation and static allocation in representative allocation schemes. Dynamic allocation is more adaptive in exploiting write operation parallelism, while static allocation is better in read operation parallelism. Therefore, it is hard to select the appropriate allocation scheme when the workload is mixed read and write operations. We simulated conditions on a few mixed data patterns and analyzed the results to help the right choice for better performance. As the results, if data arrival interval is long enough prior operations to be finished and continuous read intensive data environment static allocation is more suitable. Dynamic allocation performs the best on write performance and random data patterns.

Keywords: dynamic allocation, NAND flash based SSD, SSD parallelism, static allocation

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Authors: Rashmi Raghunandan, Sanjana Shankar, Rakshitha K. Bhat

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Social media contributes a vast amount of data and information about individuals to the internet. This project will greatly reduce the need for unnecessary manual analysis of large and diverse social media profiles by filtering out and combining the useful information from various social media profiles, eliminating irrelevant data. It differs from the existing social media aggregators in that it does not provide a consolidated view of various profiles. Instead, it provides consolidated INFORMATION derived from the subject’s posts and other activities. It also allows analysis over multiple profiles and analytics based on several profiles. We strive to provide a query system to provide a natural language answer to questions when a user does not wish to go through the entire profile. The information provided can be filtered according to the different use cases it is used for.

Keywords: social network, analysis, Facebook, Linkedin, git, big data

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Authors: F. Brunke, L. Waalkes, C. Siemers

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Due to reduced stiffness, research on second generation titanium alloys for implant applications, like the metastable β-titanium alloy Ti-15Mo, become more and more important in the recent years. The machinability of these alloys is generally poor leading to problems during implant production and comparably large production costs. Therefore, in the present study, Ti 15Mo was alloyed with 0.8 wt.-% of the rare earth metals lanthanum (Ti-15Mo+0.8La) and neodymium (Ti-15Mo+0.8Nd) to improve its machinability. Their microstructure consisted of a titanium matrix and micrometer-size particles of the rare earth metals and two of their oxides. The particles stabilized the micro structure as grain growth was minimized. As especially the ductility might be affected by the precipitates, the behavior of Ti-15Mo+0.8La and Ti-15Mo+0.8Nd was investigated during static and dynamic deformation at elevated temperature to develop a processing route. The resulting mechanical properties (static strength and ductility) were similar in all investigated alloys.

Keywords: Ti 15Mo, titanium alloys, rare earth metals, free machining alloy

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Authors: Rima Shishakly, Mervyn Misajon

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Education is similar to other businesses and industries. Achieving data integrity is essential in order to attain a significant supporting for all the stakeholders in the educational sector. Efficient data collect, flow, processing, storing and retrieving are vital in order to deliver successful solutions to the different stakeholders. Ministry of Education (MOE) in United Arab Emirates (UAE) has adopted ‘Education 2020’ a series of five-year plans designed to introduce advanced education management information systems. As part of this program, in 2010 MOE implemented Student Information Systems (SIS) to manage and monitor the students’ data and information flow between MOE and international private schools in UAE. This paper is going to discuss data integrity concerns between MOE, and private schools. The paper will clarify the data integrity issues and will indicate the challenges that face private schools in UAE.

Keywords: education management information systems (EMIS), student information system (SIS), United Arab Emirates (UAE), ministry of education (MOE), (KHDA) the knowledge and human development authority, Abu Dhabi educational counsel (ADEC)

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Authors: Tomofumi Kubota, Mitsuhiro Okayasu

Abstract:

In recent years, environmental problems and energy problems typified by global warming are intensifying, and transportation devices are required to reduce the weight of structural materials from the viewpoint of strengthening fuel efficiency regulations and energy saving. Carbon fiber reinforced plastic (CFRP) used in this research is attracting attention as a structural material to replace metallic materials. Among them, thermoplastic CFRP is expected to expand its application range in terms of recyclability and cost. High formability and weldability of the unidirectional CFRP sheets conducted by a proposed hot stamping process were proposed, in which the carbon fiber reinforced plastic sheets are heated by a designed technique. In this case, the CFRP sheets are heated by the high electric voltage applied through carbon fibers. In addition, the electric voltage was controlled by the area ratio of exposed carbon fiber on the sample surfaces. The lower exposed carbon fiber on the sample surface makes high electric resistance leading to the high sample temperature. In this case, the CFRP sheets can be heated to more than 150 °C. With the sample heating, the stamping and welding technologies can be carried out. By changing the sample temperature, the suitable stamping condition can be detected. Moreover, the proper welding connection of the CFRP sheets was proposed. In this study, we propose a fusion bonding technique using thermoplasticity, high current flow, and heating caused by electrical resistance. This technology uses the principle of resistance spot welding. In particular, the relationship between the carbon fiber exposure rate and the electrical resistance value that affect the bonding strength is investigated. In this approach, the mechanical connection using rivet is also conducted to make a comparison of the severity of welding. The change of connecting strength is reflected by the fracture mechanism. The low and high connecting strength are obtained for the separation of two CFRP sheets and fractured inside the CFRP sheet, respectively. In addition to the two fracture modes, micro-cracks in CFRP are also detected. This approach also includes mechanical connections using rivets to compare the severity of the welds. The change in bond strength is reflected by the destruction mechanism. Low and high bond strengths were obtained to separate the two CFRP sheets, each broken inside the CFRP sheets. In addition to the two failure modes, micro cracks in CFRP are also detected. In this research, from the relationship between the surface carbon fiber ratio and the electrical resistance value, it was found that different carbon fiber ratios had similar electrical resistance values. Therefore, we investigated which of carbon fiber and resin is more influential to bonding strength. As a result, the lower the carbon fiber ratio, the higher the bonding strength. And this is 50% better than the conventional average strength. This can be evaluated by observing whether the fracture mode is interface fracture or internal fracture.

Keywords: CFRP, hot stamping, weliding, deforamtion, mechanical property

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