Search results for: R data science

Authors: Luiz Antonio Da Costa Rodrigues, Mara Aparecida De Castilho Lopes, Alexandre Santos Alencar

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Teaching of science must transcend simple transmission of fundamental concepts and allow scientific literacy, as a process for understanding the human being as an integral part of a complex and interdependent whole. In this context, approaching the theme ‘Antarctica’ in deaf education is an important advance for teaching, considering that that continent has direct interactions with the climatic and environmental system of the planet. Therefore, textbooks can be important tools to enable the Deaf Community to access the discussion about the natural environment. A specific script was used to analyze textbooks adopted by schools in the Rio de Janeiro Metropolitan Region. Results show that none of the 14 analyzed books have a specific chapter on the theme, some presents images of the continent without reference to their environmental importance, and the complementary texts present in the analyzed material do not address the theme either. It is concluded that the study on Antarctica and global changes in elementary education is still incipient and the material used by most Brazilian public schools does not contemplate that subject in an accessible way for the deaf person. This fact represents the distance between deaf students and their environment, denoting the need for actions to promote that and other neglected themes in Science teaching.

Keywords: Antarctica, deaf education, science teaching, textbook

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Authors: Nur Sahver Uslu, Ali̇ Hakan Büyüklü

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Maintenance, Repair, and Overhaul (MRO) costs are significant in the aviation industry. On the other hand, companies that provide MRO services to the aviation industry but are not dominant in the sector, need to determine the right strategies for sustainable profitability in a competitive environment. This study examined the operational real data of a small medium enterprise (SME) MRO company where analytical methods are not widely applied. The company's customers were divided into two categories: airline companies and non-airline companies, and the variables that best explained profitability were analyzed with Logistic Regression for each category and the results were compared. First, data reduction was applied to the transformed variables that went through the data cleaning and preparation stages, and the variables to be included in the model were decided. The misclassification rates for the logistic regression results concerning both customer categories are similar, indicating consistent model performance across different segments. Less profit margin is obtained from airline customers, which can be explained by the variables part description, time to quotation (TTQ), turnaround time (TAT), manager, part cost, and labour cost. The higher profit margin obtained from non-airline customers is explained only by the variables part description, part cost, and labour cost. Based on the two models, it can be stated that it is significantly more challenging for the MRO company, which is the subject of our study, to achieve profitability from Airline customers. While operational processes and organizational structure also affect the profit from airline customers, only the type of parts and costs determine the profit for non-airlines.

Keywords: aircraft, aircraft components, aviation, data analytics, data science, gini index, maintenance, repair, and overhaul, MRO, logistic regression, profit, variable clustering, variable reduction

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Authors: Ananya Gangavarapu

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Convolutional Neural Networks (CNN) based models are providing diagnostic capabilities on par with the medical specialists in many specialty areas. However, collecting the medical data for training purposes is very challenging because of the increased regulations around data collections and privacy concerns around personal health data. The gathering of the data becomes even more difficult if the capture devices are edge-based mobile devices (like smartphones) with feeble wireless connectivity in rural/remote areas. In this paper, I would like to highlight Federated Learning approach to mitigate data privacy and security issues.

Keywords: deep learning in healthcare, data privacy, federated learning, training in distributed environment

Procedia PDF Downloads 125

Authors: A. P. Mikhailovich, V. V. Fomin, E. M. Agapitov, V. E. Rogachev, E. A. Kostousova, E. S. Perekhodova

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Woody vegetation among the upper limit of its habitat is a sensitive indicator of biota reaction to regional climate changes. Quantitative assessment of temporal and spatial changes in the distribution of trees and plant biocenoses calls for the development of new modeling approaches based upon selected data from measurements on the ground level and ultra-resolution aerial photography. Statistical models were developed for the study area located in the Polar Urals. These models allow obtaining probabilistic estimates for placing Siberian Larch trees into one of the three age intervals, namely 1-10, 11-40 and over 40 years, based on the Weilbull distribution of the maximum horizontal crown projection. Authors developed the distribution map for larch trees with crown diameters exceeding twenty centimeters by deciphering aerial photographs made by a UAV from an altitude equal to fifty meters. The total number of larches was equal to 88608, forming the following distribution row across the abovementioned intervals: 16980, 51740, and 19889 trees. The results demonstrate that two processes can be observed in the course of recent decades: first is the intensive forestation of previously barren or lightly wooded fragments of the study area located within the patches of wood, woodlands, and sparse stand, and second, expansion into mountain tundra. The current expansion of the Siberian Larch in the region replaced the depopulation process that occurred in the course of the Little Ice Age from the late 13ᵗʰ to the end of the 20ᵗʰ century. Using data from field measurements of Siberian larch specimen biometric parameters (including height, diameter at root collar and at 1.3 meters, and maximum projection of the crown in two orthogonal directions) and data on tree ages obtained at nine circular test sites, authors developed a model for artificial neural network including two layers with three and two neurons, respectively. The model allows quantitative assessment of a specimen's age based on height and maximum crone projection values. Tree height and crown diameters can be quantitatively assessed using data from aerial photographs and lidar scans. The resulting model can be used to assess the age of all Siberian larch trees. The proposed approach, after validation, can be applied to assessing the age of other tree species growing near the upper tree boundaries in other mountainous regions. This research was collaboratively funded by the Russian Ministry for Science and Education (project No. FEUG-2023-0002) and Russian Science Foundation (project No. 24-24-00235) in the field of data modeling on the basis of artificial neural network.

Keywords: treeline, dynamic, climate, modeling

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Authors: Daniel Brian Thompson, Subarmaniam Kannan

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The huge weightage of knowledge in this world and within the repository of organizations has already reached immense capacity and is constantly increasing as time goes by. To accommodate these constraints, Big Data implementation and algorithms are utilized to obtain new or enhanced knowledge for decision-making. With the transition from data to knowledge provides the transformational changes which will provide tangible benefits to the individual implementing these practices. Today, various organization would derive knowledge from observations and intuitions where this information or data will be translated into best practices for knowledge acquisition, generation and sharing. Through the widespread usage of Big Data, the main intention is to provide information that has been cleaned and analyzed to nurture tangible insights for an organization to apply to their knowledge-creation practices based on facts and figures. The translation of data into knowledge will generate value for an organization to make decisive decisions to proceed with the transition of best practices. Without a strong foundation of knowledge and Big Data, businesses are not able to grow and be enhanced within the competitive environment.

Keywords: big data, knowledge management, data driven, knowledge creation

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Authors: Masese Chuma Benard, Martin Onsiro Ronald

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Businesses have been using cloud computing more frequently recently because they wish to take advantage of its advantages. However, employing cloud computing also introduces new security concerns, particularly with regard to data security, potential risks and weaknesses that could be exploited by attackers, and various tactics and strategies that could be used to lessen these risks. This study examines data security issues on cloud computing amongst sme’s in Nairobi county, Kenya. The study used the sample size of 48, the research approach was mixed methods, The findings show that data owner has no control over the cloud merchant's data management procedures, there is no way to ensure that data is handled legally. This implies that you will lose control over the data stored in the cloud. Data and information stored in the cloud may face a range of availability issues due to internet outages; this can represent a significant risk to data kept in shared clouds. Integrity, availability, and secrecy are all mentioned.

Keywords: data security, cloud computing, information, information security, small and medium-sized firms (SMEs)

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Authors: M. Strémy, P. Závacký, P. Cuninka, M. Juhás

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Main goal of this paper is to introduce our design of private cloud for storing large amount of data, especially pictures, and to provide good technological backend for data analysis based on parallel processing and business intelligence. We have tested hypervisors, cloud management tools, storage for storing all data and Hadoop to provide data analysis on unstructured data. Providing high availability, virtual network management, logical separation of projects and also rapid deployment of physical servers to our environment was also needed.

Keywords: cloud, glusterfs, hadoop, juju, kvm, maas, openstack, virtualization

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Authors: Amitha Puranik, V. S. Binu, Seena Biju

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Missing data is a common problem in spatial analysis especially at the aggregate level. Missing can either occur in covariate or in response variable or in both in a given location. Many missing data techniques are available to estimate the missing data values but not all of these methods can be applied on spatial data since the data are autocorrelated. Hence there is a need to develop a method that estimates the missing values in both response variable and covariates in spatial data by taking account of the spatial autocorrelation. The present study aims to develop a model to estimate the missing data points at the aggregate level in spatial data by accounting for (a) Spatial autocorrelation of the response variable (b) Spatial autocorrelation of covariates and (c) Correlation between covariates and the response variable. Estimating the missing values of spatial data requires a model that explicitly account for the spatial autocorrelation. The proposed model not only accounts for spatial autocorrelation but also utilizes the correlation that exists between covariates, within covariates and between a response variable and covariates. The precise estimation of the missing data points in spatial data will result in an increased precision of the estimated effects of independent variables on the response variable in spatial regression analysis.

Keywords: spatial regression, missing data estimation, spatial autocorrelation, simulation analysis

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Authors: K. Sangeeth, P. Rekha, P. Preeja, P. Divya, R. Arya, R. Maneesha

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Wireless sensor networks (WSN) are small or tiny devices that consists of different sensors to sense physical parameters like air pressure, temperature, vibrations, movement etc., process these data and sends it to the central data center to take decisions. The WSN domain, has wide range of applications such as monitoring and detecting natural hazards like landslides, forest fire, avalanche, flood monitoring and also in healthcare applications. With such different applications, it is being taught in undergraduate/post graduate level in many universities under department of computer science. But the cost and infrastructure required to purchase WSN nodes for having the students getting hands on expertise on these devices is expensive. This paper gives overview about the remote triggered lab that consists of more than 100 WSN nodes that helps the students to remotely login from anywhere in the world using the World Wide Web, configure the nodes and learn the WSN concepts in intuitive way. It proposes new way called over the air programming (OTAP) and its internals that program the 100 nodes simultaneously and view the results without the nodes being physical connected to the computer system, thereby allowing for sparse deployment.

Keywords: WSN, over the air programming, virtual lab, AT45DB

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Authors: Barbara Covarrubias Venegas, Sabine Groblschegg, Bernhard Klaus, Julia Domnanovich

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Research Objectives: The roles and activities of human resource management (HRM) have changed a lot in the past years. Driven by a changing environment and therefore, new business requirements, the scope of human resource (HR) activities has widened. The extent to which these activities should focus on strategic issues to support the long-term success of a company has been discussed in science for many years. As many economies of Central and Eastern Europe (CEE) experienced a phase of transition after the socialist era and are now recovering from the 2008 global crisis it is needed to examine the current state of HR positioning. Furthermore, a trend in HR work developing from rather administrative units to being strategic partners of management can be noticed. This leads to the question of better understanding the underlying competencies which are necessary to support organisations. This topic was addressed by the international study “HR Competencies in international comparison”. The quantitative survey was conducted by the Institute for Human Resources & Organisation of FHWien University of Applied Science of WKW (A) in cooperation with partner universities in the countries Bosnia-Herzegovina, Croatia, Serbia and Slovenia. Methodology: Using the questionnaire developed by Dave Ulrich we tested whether the HR Competency model can be used for Austria, Bosnia and Herzegovina, Croatia, Serbia and Slovenia. After performing confirmatory and exploratory factor analysis for the whole data set containing all five countries we could clearly distinguish between four competencies. In a further step, our analysis focused on median and average comparisons between the HR competency dimensions. Conclusion: Our literature review, in alignment with other studies, shows a relatively rapid pace of development of HR Roles and HR Competencies in BCSS in the past decades. Comparing data from BCSS and Austria we still can notice that regards strategic orientation there is a lack in BCSS countries, thus competencies are not as developed as in Austria. This leads us to the tentative conclusion that HR has undergone a rapid change but is still in a State of Transition from being a rather administrative unit to performing the role of a strategic partner.

Keywords: comparative study, HR competencies, HRM, HR roles

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Authors: Sarra Senhadji, Imene Benzeguimi, Zohra Yagoub

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Big Data represents the recent technology of manipulating voluminous and unstructured data sets over multiple sources. Therefore, NOSQL appears to handle the problem of unstructured data. Association rules mining is one of the popular techniques of data mining to extract hidden relationship from transactional databases. The algorithm for finding association dependencies is well-solved with Map Reduce. The goal of our work is to reduce the time of generating of frequent itemsets by using Map Reduce and NOSQL database oriented document. A comparative study is given to evaluate the performances of our algorithm with the classical algorithm Apriori.

Keywords: Apriori, Association rules mining, Big Data, Data Mining, Hadoop, MapReduce, MongoDB, NoSQL

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Authors: Johannes Dregger, Michael Schmidt, Christian Prasse, Michael ten Hompel

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Most of the today’s companies face increasing need to operate efficiently. Driven by global trends like shorter product cycles, mass customization and the rising speed of delivery, manufacturing value chains are becoming more and more distributed. Manufacturing processes are becoming highly integrated, e.g. 3D printing. All these changes are affecting companies´ organization. They are leading towards individual, small scale, and ad-hoc logistics processes and structures, and finally, towards a significant increase in the importance of logistics itself since traditional value chains transform into agile value networks. In the past logistics has been following manufacturing but in the future industry, this role allocation might change. With this increase in the logistics practice of companies and businesses, the relevance of logistics research as the methodological foundation of logistics networks and processes is gaining importance. Logistics research is evolving into a central and highly interdisciplinary science for the future industry. Using the example of Germany, this paper discusses ways to establish logistics as a central scientific discipline of the future industry. About three million people work in the logistics sector in Germany. Only automotive and retail industry have more employees. Even though there is a bunch of logistics degree programs at more than 100 institutions of higher education, a common understanding of logistics as a research discipline is missing. In this paper an innovative approach will be presented, including; identified perspectives on logistics, such as process orientation, IT orientation or employees orientation, relevant scientific disciplines for logistics science, a concept for interdisciplinary research approaches to unify the perspectives of the different scientific disciplines on logistics and the methodological base of logistics science.

Keywords: logistics, logistics science, logistics management, future challenges

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Authors: Riaz A. Agha, Alexander J. Fowler, Seon-Young Lee, Buket Gundogan, Katharine Whitehurst, Harkiran K. Sagoo, Kyung Jin Lee Jeong, Douglas G. Altman, Dennis P. Orgill

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Introduction: Case Series are an important and common study type. Currently, no guideline exists for reporting case series and there is evidence of key data being missed from such reports. We propose to develop a reporting guideline for case series using a methodologically robust technique. The first step in this process is a systematic review of literature relevant to the reporting deficiencies of case series. Methods: A systematic review of methodological and reporting quality in surgical case series was performed. The electronic search strategy was developed by an information specialist and included MEDLINE, EMBASE, Cochrane Methods Register, Science Citation index and Conference Proceedings Citation index, from the start of indexing until 5th November 2014. Independent screening, eligibility assessments and data extraction was performed. Included articles were analyzed for five areas of deficiency: failure to use standardized definitions missing or selective data transparency or incomplete reporting whether alternate study designs were considered. Results: The database searching identified 2,205 records. Through the process of screening and eligibility assessments, 92 articles met inclusion criteria. Frequency of methodological and reporting issues identified was a failure to use standardized definitions (57%), missing or selective data (66%), transparency, or incomplete reporting (70%), whether alternate study designs were considered (11%) and other issues (52%). Conclusion: The methodological and reporting quality of surgical case series needs improvement. Our data shows that clear evidence-based guidelines for the conduct and reporting of a case series may be useful to those planning or conducting them.

Keywords: case series, reporting quality, surgery, systematic review

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Authors: Melaku Tsehay

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The Consortium of Christian Relief and Development Associations (CCRDA), and the CORE Group Polio Partners (CGPP) Secretariat have been working with Global Alliance for Vac-cines and Immunization (GAVI) to improve the immunization data quality in Afar and Somali Regional States. The main aim of this study was to compare the quality of immunization data before and after the above interventions in health facilities in the pastoralist communities in Ethiopia. To this end, a comparative-cross-sectional study was conducted on 51 health facilities. The baseline data was collected in May 2019, while the end line data in August 2021. The WHO data quality self-assessment tool (DQS) was used to collect data. A significant improvment was seen in the accuracy of the pentavalent vaccine (PT)1 (p = 0.012) data at the health posts (HP), while PT3 (p = 0.010), and Measles (p = 0.020) at the health centers (HC). Besides, a highly sig-nificant improvment was observed in the accuracy of tetanus toxoid (TT)2 data at HP (p < 0.001). The level of over- or under-reporting was found to be < 8%, at the HP, and < 10% at the HC for PT3. The data completeness was also increased from 72.09% to 88.89% at the HC. Nearly 74% of the health facilities timely reported their respective immunization data, which is much better than the baseline (7.1%) (p < 0.001). These findings may provide some hints for the policies and pro-grams targetting on improving immunization data qaulity in the pastoralist communities.

Keywords: data quality, immunization, verification factor, pastoralist region

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Authors: Mitra Ghiasi, Roghayeh Ghorbani Bousari

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Nowadays, the aim of the organizations is to gain sustainable competitive. So, developing their intellectual capital, encouraging innovation, increasing suitable performance can be done by knowledge management. Knowledge turns into science if knowledge is used to improve decision making, decision quality and make effective decisions. The current research intends to investigate the relationship between cybernetics and knowledge management from the perspective of users who are members of electronic discussion groups (ALA, ALIA). The research methodology is survey method, and it is a type of correlation research. Cybernetics and knowledge management questionnaires used for collecting data. The questionnaire that was designed in electronic format, distributed among two electronic discussion groups during 30 days and completed by 100 members of each electronic discussion groups. The finding of this research showed that although cybernetics has an impact on knowledge management, there is no significant difference between the ALA and ALIA user's view regard to effect of cybernetics on knowledge management. The results also indicated that this conceptual model is consistent with the data collected from the sample.

Keywords: ALA discussion group, ALIA discussion group, cybernetics, knowledge management

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Authors: Haifeng Wang, Haili Zhang

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Most movie recommendation systems have been developed for customers to find items of interest. This work introduces a predictive model usable by small and medium-sized enterprises (SMEs) who are in need of a data-based and analytical approach to stock proper movies for local audiences and retain more customers. We used classification models to extract features from thousands of customers’ demographic, behavioral and social information to predict their movie genre preference. In the implementation, a Gaussian kernel support vector machine (SVM) classification model and a logistic regression model were established to extract features from sample data and their test error-in-sample were compared. Comparison of error-out-sample was also made under different Vapnik–Chervonenkis (VC) dimensions in the machine learning algorithm to find and prevent overfitting. Gaussian kernel SVM prediction model can correctly predict movie genre preferences in 85% of positive cases. The accuracy of the algorithm increased to 93% with a smaller VC dimension and less overfitting. These findings advance our understanding of how to use machine learning approach to predict customers’ preferences with a small data set and design prediction tools for these enterprises.

Keywords: computational social science, movie preference, machine learning, SVM

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Authors: R. Clancy, M. Ahern, D. O’Sullivan, K. Bruton

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The manufacturing industry is currently undergoing a digital transformation as part of the mega-trend Industry 4.0. As part of this phase of the industrial revolution, traditional manufacturing processes are being combined with digital technologies to achieve smarter and more efficient production. To successfully digitally transform a manufacturing facility, the processes must first be digitized. This is the conversion of information from an analogue format to a digital format. The objective of this study was to explore the research area of digitizing manufacturing data as part of the worldwide paradigm, Industry 4.0. The formal methodology of a systematic mapping study was utilized to capture a representative sample of the research area and assess its current state. Specific research questions were defined to assess the key benefits and limitations associated with the digitization of manufacturing data. Research papers were classified according to the type of research and type of contribution to the research area. Upon analyzing 54 papers identified in this area, it was noted that 23 of the papers originated in Germany. This is an unsurprising finding as Industry 4.0 is originally a German strategy with supporting strong policy instruments being utilized in Germany to support its implementation. It was also found that the Fraunhofer Institute for Mechatronic Systems Design, in collaboration with the University of Paderborn in Germany, was the most frequent contributing Institution of the research papers with three papers published. The literature suggested future research directions and highlighted one specific gap in the area. There exists an unresolved gap between the data science experts and the manufacturing process experts in the industry. The data analytics expertise is not useful unless the manufacturing process information is utilized. A legitimate understanding of the data is crucial to perform accurate analytics and gain true, valuable insights into the manufacturing process. There lies a gap between the manufacturing operations and the information technology/data analytics departments within enterprises, which was borne out by the results of many of the case studies reviewed as part of this work. To test the concept of this gap existing, the researcher initiated an industrial case study in which they embedded themselves between the subject matter expert of the manufacturing process and the data scientist. Of the papers resulting from the systematic mapping study, 12 of the papers contributed a framework, another 12 of the papers were based on a case study, and 11 of the papers focused on theory. However, there were only three papers that contributed a methodology. This provides further evidence for the need for an industry-focused methodology for digitizing and analyzing manufacturing data, which will be developed in future research.

Keywords: analytics, digitization, industry 4.0, manufacturing

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Authors: Rui Teixeira, Alan O’Connor, Maria Nogal

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The statistical theory of extreme events is progressively a topic of growing interest in all the fields of science and engineering. The changes currently experienced by the world, economic and environmental, emphasized the importance of dealing with extreme occurrences with improved accuracy. When it comes to the design of offshore structures, particularly offshore wind turbines, the importance of efficiently characterizing extreme events is of major relevance. Extreme events are commonly characterized by extreme values theory. As an alternative, the accurate modeling of the tails of statistical distributions and the characterization of the low occurrence events can be achieved with the application of the Peak-Over-Threshold (POT) methodology. The POT methodology allows for a more refined fit of the statistical distribution by truncating the data with a minimum value of a predefined threshold u. For mathematically approximating the tail of the empirical statistical distribution the Generalised Pareto is widely used. Although, in the case of the exceedances of significant wave data (H_s) the 2 parameters Weibull and the Exponential distribution, which is a specific case of the Generalised Pareto distribution, are frequently used as an alternative. The Generalized Pareto, despite the existence of practical cases where it is applied, is not completely recognized as the adequate solution to model exceedances over a certain threshold u. References that set the Generalised Pareto distribution as a secondary solution in the case of significant wave data can be identified in the literature. In this framework, the current study intends to tackle the discussion of the application of statistical models to characterize exceedances of wave data. Comparison of the application of the Generalised Pareto, the 2 parameters Weibull and the Exponential distribution are presented for different values of the threshold u. Real wave data obtained in four buoys along the Irish coast was used in the comparative analysis. Results show that the application of the statistical distributions to characterize significant wave data needs to be addressed carefully and in each particular case one of the statistical models mentioned fits better the data than the others. Depending on the value of the threshold u different results are obtained. Other variables of the fit, as the number of points and the estimation of the model parameters, are analyzed and the respective conclusions were drawn. Some guidelines on the application of the POT method are presented. Modeling the tail of the distributions shows to be, for the present case, a highly non-linear task and, due to its growing importance, should be addressed carefully for an efficient estimation of very low occurrence events.

Keywords: extreme events, offshore structures, peak-over-threshold, significant wave data

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Authors: Maria Paula Santos, Ana Lucas

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Organizations support their operations and decision making on the data they have at their disposal, so the quality of these data is remarkably important and Data Quality (DQ) is currently a relevant issue, the literature being unanimous in pointing out that poor DQ can result in large costs for organizations. The literature review identified and described 24 Critical Success Factors (CSF) for Data Quality Management (DQM) that were presented to a panel of experts, who ordered them according to their degree of importance, using the Delphi method with the Q-sort technique, based on an online questionnaire. The study shows that the five most important CSF for DQM are: definition of appropriate policies and standards, control of inputs, definition of a strategic plan for DQ, organizational culture focused on quality of the data and obtaining top management commitment and support.

Keywords: critical success factors, data quality, data quality management, Delphi, Q-Sort

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Authors: Nermin Mahmoud Gohar, Aisha Tarek Noour

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Learner satisfaction has been a vital field of interest in the literature. Accordingly, the paper will explore the reasons behind individual differences in motivation and satisfaction. This study examines the effect of both; Herzberg’s and Maslow’s theories on learners satisfaction. A self-administered questionnaire was used to collect data from learners who were geographically widely spread around the College of Maritime Transport and Technology (CMTT) at the Arab Academy for Science, Technology and Maritime Transport (AAST&MT) in Egypt. One hundred and fifty undergraduates responded to a questionnaire survey. Respondents were drawn from two branches in Alexandria and Port Said. The data analysis used was SPSS 22 and AMOS 18. Factor analysis technique was used to find out the dimensions under study verified by Herzberg’s and Maslow’s theories. In addition, regression analysis and structural equation modeling were applied to find the effect of the above-mentioned theories on maritime transport learners’ satisfaction. Concerning the limitation of this study, it used the available number of learners in the CMTT due to the relatively low population in this field.

Keywords: motivation, satisfaction, needs, education, Herzberg’s and Maslow’s theories

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Authors: Sarah Baker, Jayati Chaudhuri

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Librarians have embraced the open access movement in all disciplines but their own. They are strong advocates on college campuses and curate institutional repositories, yet there are surprisingly few open access LIS journals. Presenters evaluated the open access availability of library and information science literature. After analyzing the top 100 library science journals (the top 50 journals from Scimago and JCR) and finding very few gold open access journals, they then investigated the availability of open access articles from the top 10 closed access journals. Presenters would like to generate a conversation on what type of proactive approach librarians can take to increase open access to literature within our discipline. Librarians like their colleagues in other disciplines are not motivated to submit their articles to their institutional repositories. Presenters have found a similar reluctance from their fellow colleagues regarding open access initiatives on campus. Presenters will describe Open Access Week activities as part of a campus-wide initiative and share some faculty comments, concerns, and misconceptions that came up as a part of this dialog. Presenters will discuss their personal experiences providing access to faculty publications through the California State University Los Angeles institutional repository.

Keywords: faculty scholarship, institutional repositories, library and information science journals, open access

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Authors: Juan Sebastián Rivera Soriano, Rosa Urbano Gutiérrez

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The Shodan House and the Sarabhai House (Ahmedabad, India, 1954 and 1955, respectively) are considered some of the most important works of Le Corbusier produced in the last stage of his career. There are some academic publications that study the compositional and formal aspects of their architectural design, but there is no in-depth investigation into how the climatic conditions of this region were a determining factor in the design decisions implemented in these projects. This paper argues that Le Corbusier developed a specific architectural design strategy for these buildings based on scientific research on climate in the Indian context. This new language was informed by a pioneering study and interpretation of climatic data as a design methodology that would even involve the development of new design tools. This study investigated whether their use of climatic data meets values and levels of accuracy obtained with contemporary instruments and tools, such as Energy Plus weather data files and Climate Consultant. It also intended to find out if Le Corbusier's office’s intentions and decisions were indeed appropriate and efficient for those climate conditions by assessing these projects using BIM models and energy performance simulations from Design Builder. Accurate models were built using original historical data through archival research. The outcome is to provide a new understanding of the environment of these houses through the combination of modern building science and architectural history. The results confirm that in these houses, it was achieved a model of low energy consumption. This paper contributes new evidence not only on exemplary modern architecture concerned with environmental performance but also on how it developed progressive thinking in this direction.

Keywords: bioclimatic architecture, Le Corbusier, Shodan, Sarabhai Houses

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Authors: Nadia Akbarpour

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Pancreatic ductal adenocarcinoma is a forceful and obliterating illness, which is portrayed by intrusiveness, fast movement, and significant protection from treatment. Advances in neurotic arrangement and malignant growth hereditary qualities have worked on our illustrative comprehension of this infection; be that as it may, significant parts of pancreatic disease science remain ineffectively comprehended. A superior comprehension of pancreatic disease science should lead the way to more viable medicines. In the course of the most recent couple of years, there have been significant advances in the sub-atomic and organic comprehension of pancreatic malignancy. This included comprehension of the genomic intricacy of the illness, the job of pancreatic malignant growth undifferentiated organisms, the importance of the growth microenvironment, and the one-of-a-kind metabolic transformation of pancreas disease cells to acquire supplements under hypoxic climate. Endeavors have been made towards the advancement of the practical answer for its treatment with compelled achievement due to its complicated science. It is grounded that pancreatic malignancy undifferentiated cells (CSCs), yet present in a little count, contribute extraordinarily to PC inception, movement, and metastasis. Standard chemo and radiotherapeutic choices, notwithstanding, grow general endurance, the connected aftereffects are a huge concern. In the midst of the latest decade, our understanding with regards to atomic and cell pathways engaged with PC and the job of CSCs in its movement has expanded massively. By and by, the center is to target CSCs. The natural items have acquired a lot of thought as of late as they, generally, sharpen CSCs to chemotherapy and target atomic flagging engaged with different cancers, including PC. Some arranged investigations have demonstrated promising outcomes recommending that assessments in this course bring a ton to the table for the treatment of PC. Albeit preclinical investigations uncovered the significance of natural items in lessening pancreatic carcinoma, restricted examinations have been led to assess their part in centers. The current survey gives another knowledge to late advances in pancreatic malignancy science, treatment, and the current status of natural items in its expectation.

Keywords: pancreatic, genomic, organic, cancer

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Authors: Saidalavi Kannattippadi

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It is a culture based study on revealing how the indebtedness of the west belongs to the moral and scientific culture of Islam, even to such a way to be said there was no room for renaissance and the enlightment of the west without the active intervention of the Islamic culture in thoughts and activities of the European thinkers. The study focuses on the exact causes that led the west to the renaissance and goes through analyzing each of historical evidences for confirming the continuous cultural assimilations that occurred between east and west, through transmissions of knowledge, translations of unique treatises, study trips and so on. The west had deeply influenced by the thought and culture of Islam after having a long bitter experience from the blind rituals and customs introduced by the church and was expecting for a movement that can raise them upwards from the bankruptcy of morality and spirituality. The sequence of crusades and voyages of thinkers from west to eastern wards made the western people aware of the best culture ever found in the world as in name of Islam and they become ready to assimilate its notable cultural values and to borrow its cultural achievements. The west had two types of influences from the Islam; moral and scientific. the uprooting of untouchablitlity and racism from western society and their accepting the ideologies of equality and fraternity are moral influence and the innumerable inventions and discoveries found in modern science and technology are the scientific influences. Without the frantic efforts of Muslims in translating, modifying and commenting the science and philosophy of the Greek the west would not have even a chance to peep to the cultural values of the Greek. Here the Muslims are the guides and channels through which the west got educated and well cultured. The study also briefly sheds light on the cultural achievements of Muslims in material science, human science, etc.

Keywords: cultural assimilation, culture and civilization, indebtedness, Muslim world, west, translation, transmission

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Authors: Mahdi Biabanaki

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Religion is present in all human societies and has been for tens of thousands of years. What is noteworthy is that although religious traditions vary in different societies, there are considerable similarities in their religious beliefs. In all human cultures, for example, there is a widespread belief in the afterlife. Cognitive science of Religion (CSR), an emerging branch of cognitive science, searches for the root of these widespread similarities and the widespread prevalence of beliefs such as beliefs in the afterlife in common mental structures among humans. Accordingly, the cognitive architecture of the human mind has evolved to produce such beliefs automatically and non-reflectively. For CSR researchers, belief in the afterlife is an intuitive belief resulting from the functioning of mental tools. Our purpose in this article is to extract and evaluate the cognitive explanations presented in the CSR field for explaining beliefs in the afterlife. Our research shows that there are two basic theories in this area of CSR, namely "intuitive dualism" and "simulation constraint" theory. We show that these two theories face four major challenges and limitations in explaining belief in the afterlife: inability to provide a causal explanation, inability to explain cultural/religious differences in afterlife belief, the lack of distinction between the natural and the rational foundations of belief in the afterlife and disregarding the supernatural foundations of the afterlife belief.

Keywords: afterlife, cognitive science of religion, intuitive dualism, simulation constraint

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Authors: Namiiro Shirat Umar

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The science of animals and veterinary medicine is a multidisciplinary field dedicated to understanding, managing, and enhancing the health and welfare of animals. This field encompasses a broad spectrum of disciplines, including animal physiology, genetics, nutrition, behavior, and pathology, as well as preventive and therapeutic veterinary care. Veterinary science focuses on diagnosing, treating, and preventing diseases in animals, ensuring their health and well-being. It involves the study of various animal species, from companion animals and livestock to wildlife and exotic species. Through advanced diagnostic techniques, medical treatments, and surgical procedures, veterinarians address a wide range of health issues, from infectious diseases and injuries to chronic conditions and reproductive health. Animal science complements veterinary medicine by providing a deeper understanding of animal biology and behavior, which is essential for effective health management. It includes research on animal breeding, nutrition, and husbandry practices aimed at improving animal productivity and welfare. Incorporating modern technologies and methodologies, such as genomics, bioinformatics, and precision farming, the science of animals and veterinary medicine continually evolves to address emerging challenges. This integrated approach ensures the development of sustainable practices, enhances animal welfare and contributes to public health by monitoring zoonotic diseases and ensuring the safety of animal products. Animal nutrition is a cornerstone of animal and veterinary science, focusing on the dietary needs of animals to promote health, growth, reproduction, and overall well-being. Proper nutrition ensures that animals receive essential nutrients, including macronutrients (carbohydrates, proteins, fats) and micronutrients (vitamins, minerals), tailored to their specific species, life stages, and physiological conditions. By emphasizing a balanced diet, animal nutrition serves as a preventive measure against diseases and enhances recovery from illnesses, reducing the need for pharmaceutical interventions. It addresses key health issues such as metabolic disorders, reproductive inefficiencies, and immune system deficiencies. Moreover, optimized nutrition improves the quality of animal products like meat, milk, and eggs and enhances the sustainability of animal farming by improving feed efficiency and reducing environmental waste. The integration of animal nutrition into veterinary practice necessitates a collaborative approach involving veterinarians, animal nutritionists, and farmers. Advances in nutritional science, such as precision feeding and the use of nutraceuticals, provide innovative solutions to traditional veterinary challenges. Overall, the focus on animal nutrition as a primary aspect of veterinary care leads to more holistic, sustainable, and effective animal health management practices, promoting the welfare and productivity of animals in various settings. This abstract is a trifold in nature as it traverses how education can put more emphasis on animal nutrition as an alternative for improving animal health as an important issue espoused under the discipline of animal and veterinary science; therefore, brief aspects of this paper and they are as follows; animal nutrition, veterinary science and animals.

Keywords: animal nutrition as a way to enhance growth, animal science as a study, veterinary science dealing with health of the animals, animals healthcare dealing with proper sanitation

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Authors: Aliya K. Salahova

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Scientific thinking, characterized by purposeful knowledge-seeking and the harmonization of theory and facts, holds a crucial role in preparing young minds for an increasingly complex and technologically advanced world. This abstract presents a research study aimed at fostering scientific thinking in early childhood, focusing on children aged 5 to 9 years, through experiential learning in Kids Science Labs (STEM). The study utilized a longitudinal exploration design, spanning 240 weeks from September 2018 to April 2023, to evaluate the effectiveness of the Kids Science Labs program in developing scientific thinking skills. Participants in the research comprised 72 children drawn from local schools and community organizations. Through a formative psychology-pedagogical experiment, the experimental group engaged in weekly STEM activities carefully designed to stimulate scientific thinking, while the control group participated in daily art classes for comparison. To assess the scientific thinking abilities of the participants, a registration table with evaluation criteria was developed. This table included indicators such as depth of questioning, resource utilization in research, logical reasoning in hypotheses, procedural accuracy in experiments, and reflection on research processes. The data analysis revealed dynamic fluctuations in the number of children at different levels of scientific thinking proficiency. While the development was not uniform across all participants, a main leading factor emerged, indicating that the Kids Science Labs program and formative experiment exerted a positive impact on enhancing scientific thinking skills in children within this age range. The study's findings support the hypothesis that systematic implementation of STEM activities effectively promotes and nurtures scientific thinking in children aged 5-9 years. Enriching education with a specially planned STEM program, tailoring scientific activities to children's psychological development, and implementing well-planned diagnostic and corrective measures emerged as essential pedagogical conditions for enhancing scientific thinking abilities in this age group. The results highlight the significant and positive impact of the systematic-activity approach in developing scientific thinking, leading to notable progress and growth in children's scientific thinking abilities over time. These findings have promising implications for educators and researchers, emphasizing the importance of incorporating STEM activities into educational curricula to foster scientific thinking from an early age. This study contributes valuable insights to the field of science education and underscores the potential of STEM-based interventions in shaping the future scientific minds of young children.

Keywords: Scientific thinking, education, STEM, intervention, Psychology, Pedagogy, collaborative learning, longitudinal study

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Authors: Carmen Georgeta Dumitrache

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Measurements of terrestrial science aims to study the totality of operations and computing, which are carried out for the purposes of representation on the plan or map of the land surface in a specific cartographic projection and topographic scale. With the development of society, the metrics have evolved, and they land, being dependent on the achievement of a goal-bound utility of economic activity and of a scientific purpose related to determining the form and dimensions of the Earth. For measurements in the field, data processing and proper representation on drawings and maps of planimetry and landform of the land, using topographic and geodesic instruments, calculation and graphical reporting, which requires a knowledge of theoretical and practical concepts from different areas of science and technology. In order to use properly in practice, topographical and geodetic instruments designed to measure precise angles and distances are required knowledge of geometric optics, precision mechanics, the strength of materials, and more. For processing, the results from field measurements are necessary for calculation methods, based on notions of geometry, trigonometry, algebra, mathematical analysis and computer science. To be able to illustrate topographic measurements was established for the lifting of property located near the locality of Gircov, Romania. We determine this total surface of the plan (T30), parcel/plot, but also in the field trace the coordinates of a parcel. The purpose of the removal of the planimetric consisted of: the exact determination of the bounding surface; analytical calculation of the surface; comparing the surface determined with the one registered in the documents produced; drawing up a plan of location and delineation with closeness and distance contour, as well as highlighting the parcels comprising this property; drawing up a plan of location and delineation with closeness and distance contour for a parcel from Dave; in the field trace outline of plot points from the previous point. The ultimate goal of this work was to determine and represent the surface, but also to tear off a plot of the surface total, while respecting the first surface condition imposed by the Act of the beneficiary's property.

Keywords: topography, surface, coordinate, modeling

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Authors: Djamila Benhaddouche, Abdelkader Benyettou

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In this paper, we used data mining to extract biomedical knowledge. In general, complex biomedical data collected in studies of populations are treated by statistical methods, although they are robust, they are not sufficient in themselves to harness the potential wealth of data. For that you used in step two learning algorithms: the Decision Trees and Support Vector Machine (SVM). These supervised classification methods are used to make the diagnosis of thyroid disease. In this context, we propose to promote the study and use of symbolic data mining techniques.

Keywords: biomedical data, learning, classifier, algorithms decision tree, knowledge extraction

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Authors: Usama Ahmed

Abstract:

Data mining is the process of analyze data which are used to predict helpful information. It is the field of research which solve various type of problem. In data mining, classification is an important technique to classify different kind of data. Diabetes is most common disease. This paper implements different classification technique using Waikato Environment for Knowledge Analysis (WEKA) on diabetes dataset and find which algorithm is suitable for working. The best classification algorithm based on diabetic data is Naïve Bayes. The accuracy of Naïve Bayes is 76.31% and take 0.06 seconds to build the model.

Keywords: data mining, classification, diabetes, WEKA

Procedia PDF Downloads 133