Search results for: data mapping

Authors: Fadzayi M.Maruza

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The conversation about diversity in the African academy focuses on the need for an international and ethnically diverse population of scholars and students. Operationalising the concept of cognitive diversity offers us an opportunity to broaden our conception of who can know and who can proclaim knowledge by availing new understandings of what knowledge is and how it is made. Limited attention is paid to the value of diversity generated by cognitive disabilities in the African academy. The inclusion of persons with minds labelled disabled in African academia requires an epistemology of disability to reform the still dominant notion of the expert and scholar as an able-bodied and hyper-rational in African academia. This review wants to explore how cognitive disabilities have been represented in higher education research in Africa or has the African academy reinforced ignorance by promoting an able-bodied academia. The review aims to tackle its exploratory objective by using Malcom Tights framework. The main questions this paper would focus on are: (I)What are the major disability themes and concerns discussed in the disability-related articles? (II)What are the major methods or methodologies used to address the topic in the papers? (III)What are the levels of analysis the papers focus on? (IV)How do higher education researchers define and represent cognitive disabilities in higher education research in Africa? To answer the exploratory questions that are aimed at mapping the disability-related higher education research landscape, Malcolm Tights’ framework is seen as most appropriate. In addition to a thematic categorization, that shall be made after reviewing of published empirical studies on disability in African higher education from the period 2010 – 2017. A synthesis of the findings and implications of African disability studies relating to students with cognitive disabilities in the African Academy will be provided using the categories suggested by Tight as a benchmark. Data for the proposed work shall be taken from well-reputed higher education journals between 2010 and 2017.Using the keyword ‘Disability’ in the titles, abstracts and keywords section of journal articles, a selection of disability-focused higher education articles shall be compiled for analysis regarding cognitive disability. It has to be noted as a limitation that the word Disability might not be sufficient to investigate the topic for there can be many more specific disabilities concerns the researchers would discuss. Therefore, the paper is only intended to give a bird’s eye view of cognitive disability in higher education research and therefore is not comprehensive. The paper is expected to shed some light for me, as a beginning researcher, and other researchers like myself as to what has been the focus of higher education researchers about cognitive disability in the African academy. Keywords: Cognitive diversity, cognitive disability, disability, higher education.

Keywords: cognitive disability, cognitive diversity, disability, higher education

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Authors: Ali Ulvi Uzer

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Data reduction is an important topic in the field of pattern recognition applications. The basic concept is the reduction of multitudinous amounts of data down to the meaningful parts. The Principal Component Analysis (PCA) method is frequently used for data reduction. The Support Vector Machine (SVM) method is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data, the algorithm outputs an optimal hyperplane which categorizes new examples. This study offers a hybrid approach that uses the PCA for data reduction and Support Vector Machines (SVM) for classification. In order to detect the accuracy of the suggested system, two boreholes taken from the soil sample was used. The classification accuracies for this dataset were obtained through using ten-fold cross-validation method. As the results suggest, this system, which is performed through size reduction, is a feasible system for faster recognition of dataset so our study result appears to be very promising.

Keywords: feature selection, sequential forward selection, support vector machines, soil sample

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Authors: Sabri Serkan Güllüoğlu

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Analysis can be defined as the process of gathering, recording and researching data related to products and services, in order to learn something. But for marketers, analyses are not only used for learning but also an essential and critical part of the business, because this allows companies to offer products or services which are focused and well targeted. Market analysis also identify market trends, demographics, customer’s buying habits and important information on the competition. Data mining is used instead of traditional research, because it extracts predictive information about customer and sales from large databases. In contrast to traditional research, data mining relies on information that is already available. Simply the goal is to improve the efficiency of supermarkets. In this study, the purpose is to find dependency on products. For instance, which items are bought together, using association rules in data mining. Moreover, this information will be used for improving the profitability of customers such as increasing shopping time and sales of fewer sold items.

Keywords: data mining, association rule mining, market basket analysis, purchasing

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Authors: Rizka Aisha Rahmi Hariadi, Chao Ou-Yang, Han-Cheng Wang, Rajesri Govindaraju

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As the increasing of medical check-up popularity, there are a huge number of medical check-up data stored in database and have not been useful. These data actually can be very useful for future strategic planning if we mine it correctly. In other side, a lot of patients come with unpredictable coming and also limited available facilities make medical check-up service offered by hospital not maximal. To solve that problem, this study used those medical check-up data to predict patient re-coming. Sequential pattern mining (SPM) and association rules method were chosen because these methods are suitable for predicting patient re-coming using sequential data. First, based on patient personal information the data was grouped into … groups then discriminant analysis was done to check significant of the grouping. Second, for each group some frequent patterns were generated using SPM method. Third, based on frequent patterns of each group, pairs of variable can be extracted using association rules to get general pattern of re-coming patient. Last, discussion and conclusion was done to give some implications of the results.

Keywords: patient re-coming, medical check-up, health examination, data mining, sequential pattern mining, association rules, discriminant analysis

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Authors: Samiah Alammari, Nassim Ammour

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When providing a massive number of tasks successively to a deep learning process, a good performance of the model requires preserving the previous tasks data to retrain the model for each upcoming classification. Otherwise, the model performs poorly due to the catastrophic forgetting phenomenon. To overcome this shortcoming, we developed a successful continual learning deep model for remote sensing hyperspectral image regions classification. The proposed neural network architecture encapsulates two trainable subnetworks. The first module adapts its weights by minimizing the discrimination error between the land-cover classes during the new task learning, and the second module tries to learn how to replicate the data of the previous tasks by discovering the latent data structure of the new task dataset. We conduct experiments on HSI dataset Indian Pines. The results confirm the capability of the proposed method.

Keywords: continual learning, data reconstruction, remote sensing, hyperspectral image segmentation

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Authors: Bing-Shun Huang, Hung-Ju Chiu, Wen-Kai Hsieh, Hsiu-Chuan Lin, Ming-Lung Hung

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Taiwan's solar photovoltaic target in 2025 is 20 GW, of which the fish-electricity symbiosis target is 4 GW. In the future, many solar photovoltaic installations may cause local environmental or social impacts. Therefore, the Taiwan government inspects the fish-electricity symbiosis to reduce the impact of solar photovoltaics on the local environment or society. This stuy takes the symbiosis of fishery and electricity in Kaohsiung City as an example to explore Taiwan's environmental and social inspection practices. It mainly analyzes the two aspects of environmental ecology and social economy. The results show that the environmental inspection is mainly through site surveys, ecological information mapping, on-site interviews, and public consultation meetings. Social inspection mainly includes document analysis, on-site interviews, site surveys, expert discussions, and public consultations to identify possible local problems. Although the government had recognized the local issues, the future status may also change. It is recommended that future photoelectric companies should reconfirm the current situation of development sites when applying for the installation and propose countermeasures to solve the problem.

Keywords: taiwan, fish-electricity symbiosis, environment, society, inspection

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Authors: Veniamin Boiarkin, Bruno Bogaz Zarpelão, Muttukrishnan Rajarajan

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The manufacturing sector is a vital component of most economies, which leads to a large number of cyberattacks on organisations, whereas disruption in operation may lead to significant economic consequences. Adversaries aim to disrupt the production processes of manufacturing companies, gain financial advantages, and steal intellectual property by getting unauthorised access to sensitive data. Access to sensitive data helps organisations to enhance the production and management processes. However, the majority of the existing data-sharing mechanisms are either susceptible to different cyber attacks or heavy in terms of computation overhead. In this paper, a privacy-preserving data-sharing scheme for smart utilities is proposed. First, a customer’s privacy adjustment mechanism is proposed to make sure that end-users have control over their privacy, which is required by the latest government regulations, such as the General Data Protection Regulation. Secondly, a local differential privacy-based mechanism is proposed to ensure the privacy of the end-users by hiding real data based on the end-user preferences. The proposed scheme may be applied to different industrial control systems, whereas in this study, it is validated for energy utility use cases consisting of smart, intelligent devices. The results show that the proposed scheme may guarantee the required level of privacy with an expected relative error in utility.

Keywords: data-sharing, local differential privacy, manufacturing, privacy-preserving mechanism, smart utility

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Authors: Eszter Siposne Nandori

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The paper describes how the subjective interpretation of poverty changed during the COVID-19 pandemic. The results of data collection at the end of 2020 are compared to the results of a similar survey from 2019. The methods of systematic data collection are used to collect data about the beliefs of the population about poverty. The analysis is carried out in Borsod-Abaúj-Zemplén County, one of the most backward areas in Hungary. The paper concludes that poverty is mainly linked to material values, and it did not change from 2019 to 2020. Some slight changes, however, highlight the effect of the pandemic: poverty is increasingly seen as a generational problem in 2020, and another important change is that isolation became more closely related to poverty.

Keywords: Hungary, interpretation of poverty, pandemic, systematic data collection, subjective poverty

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Authors: Collins Ogbu, Godwin Johnny Akpan, James NDA Jacob

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The debilitating outcomes of violent conflict, consummated by rural armed banditry have nonetheless, occasioned the need for the mapping of crime zones in Nigeria. As a step towards understanding the scourge of armed bandits, ungoverned spaces have been uncovered as the most dominant excuse for rural crimes and fierce confrontations. From the creeks of the Niger Delta to the forest of Sambisa, Small Arms and Light Weapons (SALW) have proliferated to the vagaries of national insecurity. While the trends present indications of State fragility, the paucity of governance in these so-called ungoverned spaces has persistently reflected a Hobbesian state of nature, where the fittest survives. This study, therefore, interrogates the demographic implications of these ungoverned spaces by specifically identifying the most immediate features of the characters in the areas under investigation. The Farmers-Herders Crises, Niger-Delta Militancy, Boko-Haram Insurgency, Armed Robbery, Kidnapping and Cattle Rustling all define the major focus. In undertaking this study, anecdotal sources will be relied on, while extant information on the concept of ungoverned spaces will be content-analyzed. It is hoped that the knowledge gathered, as a result, will ultimately aid in proffering a dependable panacea to the crises of rural armed banditry in Nigeria.

Keywords: ungoverned spaces, rural armed banditry, state fragility, conflicts

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Authors: Nicodemus M. J. Mbwambo, Yu-Shan Sun, Murali Sitaraman, Joan Krone

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This paper presents a generic data abstraction that captures a navigable tree position. The mathematical modeling of the abstraction encapsulates the current tree position, which can be used to navigate and modify the tree. The encapsulation of the tree position in the data abstraction specification avoids the use of explicit references and aliasing, thereby simplifying verification of (imperative) client code that uses the data abstraction. To ease the tasks of such specification and verification, a general tree theory, rich with mathematical notations and results, has been developed. The paper contains an example to illustrate automated verification ramifications. With sufficient tree theory development, automated proving seems plausible even in the absence of a special-purpose tree solver.

Keywords: automation, data abstraction, maps, specification, tree, verification

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Authors: Z. Ezzouine, A. Nakheli

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This paper presents a high position electromagnetic sensor system (HPESS) that is applicable for moving object detection. The authors have developed a high-performance position sensor prototype dedicated to students’ laboratory. The challenge was to obtain a highly accurate and real-time sensor that is able to calculate position, length or displacement. An electromagnetic solution based on a two coil induction principal was adopted. The HPESS converts mechanical motion to electric energy with direct contact. The output signal can then be fed to an electronic circuit. The voltage output change from the sensor is captured by data acquisition system using LabVIEW software. The displacement of the moving object is determined. The measured data are transmitted to a PC in real-time via a DAQ (NI USB -6281). This paper also describes the data acquisition analysis and the conditioning card developed specially for sensor signal monitoring. The data is then recorded and viewed using a user interface written using National Instrument LabVIEW software. On-line displays of time and voltage of the sensor signal provide a user-friendly data acquisition interface. The sensor provides an uncomplicated, accurate, reliable, inexpensive transducer for highly sophisticated control systems.

Keywords: electromagnetic sensor, accurately, data acquisition, position measurement

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Authors: Gilang Maulana, Dhika Rahma Qomariah, Yasin Fadil

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Purpose: This research meant to measure the magnitude of the influence of the quality of the presentation to the targeted audience perception in catching information presentation. Design/Methodology/Approach: This research uses a quantitative research method. The kind of data that uses in this research is the primary data. The population in this research are students the economics faculty of Semarang State University. The sampling techniques uses in this research is purposive sampling. The retrieving data uses questionnaire on 30 respondents. The data analysis uses descriptive analysis. Result: The quality of presentation influential positive against perception of the audience. This proved that the more qualified presentation will increase the perception of the audience. Limitation: Respondents were limited to only 30 people.

Keywords: quality of presentation, presentation, audience, perception, semarang state university

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Authors: Julian Wise

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Newcrest Mining is one of the world’s top five gold and rare earth mining organizations by production, reserves and market capitalization in the world. This paper elaborates on the data acquisition processes employed by Newcrest in collaboration with Fortune 500 listed organization, Insight Enterprises, to standardize machine learning solutions which process data from over a hundred thousand distributed Internet of Things (IoT) devices located at mine sites globally. Through the utilization of software architecture cloud technologies and edge computing, the technological developments enable for standardized processes of machine learning applications to influence the strategic optimization of mineral processing. Target objectives of the machine learning optimizations include time savings on mineral processing, production efficiencies, risk identification, and increased production throughput. The data acquired and utilized for predictive modelling is processed through edge computing by resources collectively stored within a data lake. Being involved in the digital transformation has necessitated the standardization software architecture to manage the machine learning models submitted by vendors, to ensure effective automation and continuous improvements to the mineral process models. Operating at scale, the system processes hundreds of gigabytes of data per day from distributed mine sites across the globe, for the purposes of increased improved worker safety, and production efficiency through big data applications.

Keywords: mineral technology, big data, machine learning operations, data lake

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Authors: Sheikh Omar Sillah

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Introduction: Monitoring is an important means of ensuring the smooth implementation and quality of clinical trials. For many years, traditional site monitoring approaches have been critical in detecting data errors but not optimal in identifying fabricated and implanted data as well as non-random data distributions that may significantly invalidate study results. The objective of this paper was to provide recommendations based on best statistical monitoring practices for detecting data-integrity issues suggestive of fabrication and implantation early in the study conduct to allow implementation of meaningful corrective and preventive actions. Methodology: Electronic bibliographic databases (Medline, Embase, PubMed, Scopus, and Web of Science) were used for the literature search, and both qualitative and quantitative studies were sought. Search results were uploaded into Eppi-Reviewer Software, and only publications written in the English language from 2012 were included in the review. Gray literature not considered to present reproducible methods was excluded. Results: A total of 18 peer-reviewed publications were included in the review. The publications demonstrated that traditional site monitoring techniques are not efficient in detecting data anomalies. By specifying project-specific parameters such as laboratory reference range values, visit schedules, etc., with appropriate interactive data monitoring, statistical monitoring can offer early signals of data anomalies to study teams. The review further revealed that statistical monitoring is useful to identify unusual data patterns that might be revealing issues that could impact data integrity or may potentially impact study participants' safety. However, subjective measures may not be good candidates for statistical monitoring. Conclusion: The statistical monitoring approach requires a combination of education, training, and experience sufficient to implement its principles in detecting data anomalies for the statistical aspects of a clinical trial.

Keywords: statistical monitoring, data anomalies, clinical trials, traditional monitoring

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Authors: Yuqing Chen, Ying Xu, Renfa Li

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The development of matrix completion theory provides new approaches for data gathering in Wireless Sensor Networks (WSN). The existing matrix completion algorithms for WSN mainly consider how to reduce the sampling number without considering the real-time performance when recovering the data matrix. In order to guarantee the recovery accuracy and reduce the recovery time consumed simultaneously, we propose a new ALM algorithm to recover the weather monitoring data. A lot of experiments have been carried out to investigate the performance of the proposed ALM algorithm by using different parameter settings, different sampling rates and sampling models. In addition, we compare the proposed ALM algorithm with some existing algorithms in the literature. Experimental results show that the ALM algorithm can obtain better overall recovery accuracy with less computing time, which demonstrate that the ALM algorithm is an effective and efficient approach for recovering the real world weather monitoring data in WSN.

Keywords: wireless sensor network, matrix completion, singular value thresholding, augmented Lagrange multiplier

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Authors: Amir AlAmeeri, Mohamed Ibrahim

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Various data points are constantly being measured in the production system, and due to the nature of the wells, these data points, such as pressure, temperature, water cut, etc.., fluctuations are constant, which requires high frequency monitoring and collection. It is a very difficult task to analyze these parameters manually using spreadsheets and email. An automated system greatly enhances efficiency, reduce errors, the need for constant emails which take up disk space, and frees up time for the operator to perform other critical tasks. Various production data is being recorded in an oil field, and this huge volume of data can be seen as irrelevant to some, especially when viewed on its own with no context. In order to fully utilize all this information, it needs to be properly collected, verified and stored in one common place and analyzed for surveillance and monitoring purposes. This paper describes how data is recorded by different parties and departments in the field, and verified numerous times as it is being loaded into a repository. Once it is loaded, a final check is done before being entered into a production monitoring system. Once all this is collected, various calculations are performed to report allocated production. Calculated production data is used to report field production automatically. It is also used to monitor well and surface facility performance. Engineers can use this for their studies and analyses to ensure field is performing as it should be, predict and forecast production, and monitor any changes in wells that could affect field performance.

Keywords: automation, oil production, Cheleken, exploration and production (E&P), Caspian Sea, allocation, forecast

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Authors: Iryna Biskub

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The interdisciplinary approach to modern linguistic studies is exemplified by the merge of various research methods, which sometimes causes complications related to the verification of the research results. This methodological confusion can be resolved by means of creating new techniques of linguistic analysis combining several scientific paradigms. Modern linguistics has developed really productive and efficient methods for the investigation of cognitive and communicative phenomena of which language is the central issue. In the field of discourse studies, one of the best examples of research methods is the method of Critical Discourse Analysis (CDA). CDA can be viewed both as a method of investigation, as well as a critical multidisciplinary perspective. In CDA the position of the scholar is crucial from the point of view exemplifying his or her social and political convictions. The generally accepted approach to obtaining scientifically reliable results is to use a special well-defined scientific method for researching special types of language phenomena: cognitive methods applied to the exploration of cognitive aspects of language, whereas communicative methods are thought to be relevant only for the investigation of communicative nature of language. In the recent decades discourse as a sociocultural phenomenon has been the focus of careful linguistic research. The very concept of discourse represents an integral unity of cognitive and communicative aspects of human verbal activity. Since a human being is never able to discriminate between cognitive and communicative planes of discourse communication, it doesn’t make much sense to apply cognitive and communicative methods of research taken in isolation. It is possible to modify the classical CDA procedure by means of mapping human cognitive procedures onto the strategic communicative planning of discourse communication. The analysis of the electronic petition 'Block Donald J Trump from UK entry. The signatories believe Donald J Trump should be banned from UK entry' (584, 459 signatures) and the parliamentary debates on it has demonstrated the ability to map cognitive and communicative levels in the following way: the strategy of discourse modeling (communicative level) overlaps with the extraction of semantic macrostructures (cognitive level); the strategy of discourse management overlaps with the analysis of local meanings in discourse communication; the strategy of cognitive monitoring of the discourse overlaps with the formation of attitudes and ideologies at the cognitive level. Thus, the experimental data have shown that it is possible to develop a new complex methodology of discourse analysis, where cognition would meet communication, both metaphorically and literally. The same approach may appear to be productive for the creation of computational models of human-computer interaction, where the automatic generation of a particular type of a discourse could be based on the rules of strategic planning involving cognitive models of CDA.

Keywords: cognition, communication, discourse, strategy

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Authors: Mohammad Muhebbullah Ibne Hoque, Zheng Jun, Wang Guoying

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Salinity stress is one of the most important abiotic factors contributing to crop growth and yield loss. Exploring the genetic basis is necessary to develop maize varieties with salinity tolerance. In order to discover the inherent basis for salinity tolerance traits in maize, 121 polymorphic SSR markers were used to analyze 163 F2 individuals derived from a single cross of inbred line B73 (a salt susceptible inbred line) and CZ-7 (a salt tolerant inbred line). A linkage map was constructed and the map covered 1195.2 cM of maize genome with an average distance of 9.88 cM between marker loci. Ten salt tolerance traits at seedling stage were evaluated for QTL analysis in maize seedlings. A total of 41 QTLs associated with seedling shoot and root traits were detected, with 16 and 25 QTLs under non-salinity and salinity condition, respectively. And only 4 major stable QTLs were detected in two environments. The detected QTLs were distributed on chromosomes 1, 2, 4, 5, 6, 7, 8, 9, and chromosome 10. Phenotypic variability for the identified QTLs for all the traits was in the range from 6.27 to 21.97%. Fourteen QTLs with more than 10% contributions were observed. Our results and the markers associated with the major QTL detected in this study have the potential application for genetic improvement of salt tolerance in maize through marker-assisted selection.

Keywords: salt tolerance, seedling stage, root shoot traits, quantitative trait loci, simple sequence repeat, maize

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Authors: Monika Chuchro

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This paper presents a preliminary attempt to apply classification of time series using meta-clusters in order to improve the quality of regression models. In this case, clustering was performed as a method to obtain a subgroups of time series data with normal distribution from inflow into waste water treatment plant data which Composed of several groups differing by mean value. Two simple algorithms: K-mean and EM were chosen as a clustering method. The rand index was used to measure the similarity. After simple meta-clustering, regression model was performed for each subgroups. The final model was a sum of subgroups models. The quality of obtained model was compared with the regression model made using the same explanatory variables but with no clustering of data. Results were compared by determination coefficient (R2), measure of prediction accuracy mean absolute percentage error (MAPE) and comparison on linear chart. Preliminary results allows to foresee the potential of the presented technique.

Keywords: clustering, data analysis, data mining, predictive models

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Authors: Theresia El Khoury, Georges Badr, Amir Hajjam El Hassani, Stéphane N’Guyen Van Ky

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The widespread adoption of machine learning and artificial intelligence across different domains can be attributed to the digitization of data over several decades, resulting in vast amounts of data, types, and structures. Thus, data processing and preparation turn out to be a crucial stage. However, applying these techniques to S1000D standard-based data poses a challenge due to its complexity and the need to preserve logical information. This paper describes SALERNO, an S1000d AppLicability dEpended pRocessiNg mOdel. This python-based model analyzes and converts the XML S1000D-based files into an easier data format that can be used in machine learning techniques while preserving the different logic and relationships in files. The model parses the files in the given folder, filters them, and extracts the required information to be saved in appropriate data frames and Excel sheets. Its main idea is to group the extracted information by applicability. In addition, it extracts the full text by replacing internal and external references while maintaining the relationships between files, as well as the necessary requirements. The resulting files can then be saved in databases and used in different models. Documents in both English and French languages were tested, and special characters were decoded. Updates on the technical manuals were taken into consideration as well. The model was tested on different versions of the S1000D, and the results demonstrated its ability to effectively handle the applicability, requirements, references, and relationships across all files and on different levels.

Keywords: aeronautics, big data, data processing, machine learning, S1000D

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Authors: Xiaogang Li, Jieqiong Miao

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As for the problem of the grey forecasting model prediction accuracy is low, an improved grey prediction model is put forward. Firstly, use trigonometric function transform the original data sequence in order to improve the smoothness of data , this model called SGM( smoothness of grey prediction model), then combine the improved grey model with support vector machine , and put forward the grey support vector machine model (SGM - SVM).Before the establishment of the model, we use trigonometric functions and accumulation generation operation preprocessing data in order to enhance the smoothness of the data and weaken the randomness of the data, then use support vector machine (SVM) to establish a prediction model for pre-processed data and select model parameters using genetic algorithms to obtain the optimum value of the global search. Finally, restore data through the "regressive generate" operation to get forecasting data. In order to prove that the SGM-SVM model is superior to other models, we select the battery life data from calce. The presented model is used to predict life of battery and the predicted result was compared with that of grey model and support vector machines．For a more intuitive comparison of the three models, this paper presents root mean square error of this three different models .The results show that the effect of grey support vector machine (SGM-SVM) to predict life is optimal, and the root mean square error is only 3.18%. Keywords: grey forecasting model, trigonometric function, support vector machine, genetic algorithms, root mean square error

Keywords: Grey prediction model, trigonometric functions, support vector machines, genetic algorithms, root mean square error

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Authors: Terungwa Simon Yange, Agana Moses A.

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This paper studied steganography and designed a simplistic approach to a steganographic tool for hiding information in image files with the view of addressing the security challenges with data by hiding data from unauthorized users to improve its security. The Structured Systems Analysis and Design Method (SSADM) was used in this work. The system was developed using Java Development Kit (JDK) 1.7.0_10 and MySQL Server as its backend. The system was tested with some hypothetical health records which proved the possibility of protecting data from unauthorized users by making it secret so that its existence cannot be easily recognized by fraudulent users. It further strengthens the confidentiality of patient records kept by medical practitioners in the health setting. In conclusion, this work was able to produce a user friendly steganography software that is very fast to install and easy to operate to ensure privacy and secrecy of sensitive data. It also produced an exact copy of the original image and the one carrying the secret message when compared with each.

Keywords: steganography, cryptography, encryption, decryption, secrecy

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Authors: Nushrat Khan, Mike Thelwall, Kayvan Kousha

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Making research data openly accessible has been mandated by most funders over the last 5 years as it promotes reproducibility in science and reduces duplication of effort to collect the same data. There are evidence that articles that publicly share research data have higher citation rates in biological and social sciences. However, how and whether shared data is being reused is not always intuitive as such information is not easily accessible from the majority of research data repositories. This study aims to understand the practice of data citation and how data is being reused over the years focusing on biodiversity since research data is frequently reused in this field. Metadata of 38,878 datasets including citation counts were collected through the Global Biodiversity Information Facility (GBIF) API for this purpose. GBIF was used as a data source since it provides citation count for datasets, not a commonly available feature for most repositories. Analysis of dataset types, citation counts, creation and update time of datasets suggests that citation rate varies for different types of datasets, where occurrence datasets that have more granular information have higher citation rates than checklist and metadata-only datasets. Another finding is that biodiversity datasets on GBIF are frequently updated, which is unique to this field. Majority of the datasets from the earliest year of 2007 were updated after 11 years, with no dataset that was not updated since creation. For each year between 2007 and 2017, we compared the correlations between update time and citation rate of four different types of datasets. While recent datasets do not show any correlations, 3 to 4 years old datasets show weak correlation where datasets that were updated more recently received high citations. The results are suggestive that it takes several years to cumulate citations for research datasets. However, this investigation found that when searched on Google Scholar or Scopus databases for the same datasets, the number of citations is often not the same as GBIF. Hence future aim is to further explore the citation count system adopted by GBIF to evaluate its reliability and whether it can be applicable to other fields of studies as well.

Keywords: data citation, data reuse, research data sharing, webometrics

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Authors: Chandra Gupt Porwal, Preeti C. Porwal

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Transient data reveals much about the machine's condition that steady-state data cannot. New technologies make this information much more available for evaluating the mechanical integrity of a machine train. Recent surveys at various stations indicate that simplicity is preferred over completeness in machine audits throughout the power generation industry. This is most clearly shown by the number of rotating machinery predictive maintenance programs in which only steady-state vibration amplitude is trended while important transient vibration data is not even acquired. Efforts have been made to explain what transient data is, its importance, the types of plots used for its display, and its effective utilization for analysis. In order to demonstrate the value of measuring transient data and its practical application in rotating machinery for resolving complex and persistent issues with turbine generators, the author presents a few case studies that highlight the presence of rotor instabilities due to the shaft moving towards the bearing centre in a 100 MM LMZ unit located in the Northern Capital Region (NCR), heavy misalignment noticed—especially after 2993 rpm—caused by loose coupling bolts, which prevented the machine from being synchronized for more than four months in a 250 MW KWU unit in the Western Region (WR), and heavy preload noticed at Intermediate pressure turbine (IPT) bearing near HP- IP coupling, caused by high points on coupling faces at a 500 MW KWU unit in the Northern region (NR), experienced at Indian power plants.

Keywords: transient data, steady-state-data, intermediate -pressure-turbine, high-points

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Authors: Avichal Malhotra, Jerome Frisch, Christoph van Treeck

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The utilization of semantic, cadastral and topological data from geographic information systems (GIS) has exponentially increased for building and urban-scale energy performance simulations. Urban planners, simulation scientists, and researchers use virtual 3D city models for energy analysis, algorithms and simulation tools. For dynamic energy simulations at city and district level, this paper provides an overview of the available GIS data models and their levels of detail. Adhering to different norms and standards, these models also intend to describe building and construction industry data. For further investigations, CityGML data models are considered for simulations. Though geographical information modelling has considerably many different implementations, extensions of virtual city data can also be made for domain specific applications. Highlighting the use of the extended CityGML models for energy researches, a brief introduction to the Energy Application Domain Extension (ADE) along with its significance is made. Consequently, addressing specific input simulation data, a workflow using Modelica underlining the usage of GIS information and the quantification of its significance over annual heating energy demand is presented in this paper.

Keywords: CityGML, EnergyADE, energy performance simulation, GIS

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Authors: Linnea Stenliden

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The aim of this paper is to understand emerging learning conditions, when a visual analytics is implemented and used in K 12 (education). To date, little attention has been paid to the role visual analytics (digital media and technology that highlight visual data communication in order to support analytical tasks) can play in education, and to the extent to which these tools can process actionable data for young students. This study was conducted in three public K 12 schools, in four social science classes with students aged 10 to 13 years, over a period of two to four weeks at each school. Empirical data were generated using video observations and analyzed with help of metaphors by Latour. The learning conditions are found to be distinguished by broad complexity characterized by four dimensions. These emerge from the actors’ deeply intertwined relations in the activities. The paper argues in relation to the found dimensions that novel approaches to teaching and learning could benefit students’ knowledge building as they work with visual analytics, analyzing visualized data.

Keywords: analytical reasoning, complexity, data use, problem space, visual analytics, visual storytelling, translation

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Authors: A. D. Tayal

Abstract:

The energy industry is undergoing significant disruption. This research outlines that, whilst challenging; this disruption is also an emerging opportunity for electricity utilities. One such opportunity is leveraging the developments in data analytics and machine learning. As the uptake of renewable energy technologies and complimentary control systems increases, electricity grids will likely transform towards dense microgrids with high penetration of renewable generation sources, rich in network and customer data, and linked through intelligent, wireless communications. Data digitisation and analytics have already impacted numerous industries, and its influence on the energy sector is growing, as computational capabilities increase to manage big data, and as machines develop algorithms to solve the energy challenges of the future. The objective of this paper is to address how far the uptake of renewable technologies can go given the constraints of existing grid infrastructure and provides a qualitative assessment of how higher levels of renewable energy penetration can be facilitated by incorporating even broader technological advances in the fields of data analytics and machine learning. Western Australia is used as a contextualised case study, given its abundance and diverse renewable resources (solar, wind, biomass, and wave) and isolated networks, making a high penetration of renewables a feasible target for policy makers over coming decades.

Keywords: data, innovation, renewable, solar

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Authors: Apurva Saxena, Sunita Gond

Abstract:

Demand of computation, data storage in large amount are rapidly increases day by day. Cloud computing technology fulfill the demand of today’s computation but this will lead to high power consumption in cloud data centers. Initiative for Green IT try to reduce power consumption and its adverse environmental impacts. Paper also focus on various green computing techniques, proposed models and efficient way to make cloud greener.

Keywords: virtualization, cloud computing, green computing, data center

Procedia PDF Downloads 554

Authors: Dmitry Yu. Korulkin, Raissa A. Muzychkina, Evgenii N. Kojaev

Abstract:

In review the generalized data about biological activity of anthraquinone-containing plants and specimens on their basis is presented. Data of traditional medicine, results of bioscreening and clinical researches of specimens are analyzed.

Keywords: anthraquinones, physiologically active substances, phytopreparation, Ramon

Procedia PDF Downloads 376

Authors: Veli Durmus, Mert Uydaci

Abstract:

Every patient who needs to get a medical treatment should share health-related personal data with healthcare providers. Therefore, personal health data plays an important role to make health decisions and identify health threats during every encounter between a patient and caregivers. In other words, health data can be defined as privacy and sensitive information which is protected by various health laws and regulations. In many cases, the data are an outcome of the confidential relationship between patients and their healthcare providers. Globally, almost all nations have own laws, regulations or rules in order to protect personal data. There is a variety of instruments that allow authorities to use the health data or to set the barriers data sharing across international borders. For instance, Directive 95/46/EC of the European Union (EU) (also known as EU Data Protection Directive) establishes harmonized rules in European borders. In addition, the General Data Protection Regulation (GDPR) will set further common principles in 2018. Because of close policy relationship with EU, this study provides not only information on regulations, directives but also how they play a role during the legislative process in Turkey. Even if the decision is controversial, the Board has recently stated that private or public healthcare institutions are responsible for the patient call system, for doctors to call people waiting outside a consultation room, to prevent unlawful processing of personal data and unlawful access to personal data during the treatment. In Turkey, vast majority private and public health organizations provide a service that ensures personal data (i.e. patient’s name and ID number) to call the patient. According to the Board’s decision, hospital or other healthcare institutions are obliged to take all necessary administrative precautions and provide technical support to protect patient privacy. However, this application does not effectively and efficiently performing in most health services. For this reason, it is important to draw a legal framework of personal health data by stating what is the main purpose of this regulation and how to deal with complicated issues on personal health data in Turkey. The research is descriptive on data protection law for health care setting in Turkey. Primary as well as secondary data has been used for the study. The primary data includes the information collected under current national and international regulations or law. Secondary data include publications, books, journals, empirical legal studies. Consequently, privacy and data protection regimes in health law show there are some obligations, principles and procedures which shall be binding upon natural or legal persons who process health-related personal data. A comparative approach presents there are significant differences in some EU member states due to different legal competencies, policies, and cultural factors. This selected study provides theoretical and practitioner implications by highlighting the need to illustrate the relationship between privacy and confidentiality in Personal Data Protection in Health Law. Furthermore, this paper would help to define the legal framework for the health law case studies on data protection and privacy.

Keywords: data protection, personal data, privacy, healthcare, health law

Procedia PDF Downloads 224