Search results for: geospatial data science

Authors: Abdel Rahman Khider Hassan

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This paper describes a multi-criteria geospatial model for prediction of landslide hazard zonation (LHZ) for Lake Naivasha catchment (Kenya), based on spatial analysis of integrated datasets of location intrinsic parameters (slope stability factors) and external landslides triggering factors (natural and man-made factors). The intrinsic dataset included: lithology, geometry of slope (slope inclination, aspect, elevation, and curvature) and land use/land cover. The landslides triggering factors included: rainfall as the climatic factor, in addition to the destructive effects reflected by proximity of roads and drainage network to areas that are susceptible to landslides. No published study on landslides has been obtained for this area. Thus, digital datasets of the above spatial parameters were conveniently acquired, stored, manipulated and analyzed in a Geographical Information System (GIS) using a multi-criteria grid overlay technique (in ArcGIS 10.2.2 environment). Deduction of landslide hazard zonation is done by applying weights based on relative contribution of each parameter to the slope instability, and finally, the weighted parameters grids were overlaid together to generate a map of the potential landslide hazard zonation (LHZ) for the lake catchment. From the total surface of 3200 km² of the lake catchment, most of the region (78.7 %; 2518.4 km²) is susceptible to moderate landslide hazards, whilst about 13% (416 km²) is occurring under high hazards. Only 1.0% (32 km²) of the catchment is displaying very high landslide hazards, and the remaining area (7.3 %; 233.6 km²) displays low probability of landslide hazards. This result confirms the importance of steep slope angles, lithology, vegetation land cover and slope orientation (aspect) as the major determining factors of slope failures. The information provided by the produced map of landslide hazard zonation (LHZ) could lay the basis for decision making as well as mitigation and applications in avoiding potential losses caused by landslides in the Lake Naivasha catchment in the Kenya Highlands.

Keywords: decision making, geospatial, landslide, multi-criteria, Naivasha

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Authors: Hemant Kumar, R. N. K. Sharma, A. P. Krishna

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The study has been carried out on the Saranda in Jharkhand and a part of Odisha state. Geospatial data of Hyperion, a remote sensing satellite, have been used. This study has used a wide variety of patterns related to image processing to enhance and extract the mining class of Fe and Mn ores.Landsat-8, OLI sensor data have also been used to correctly explore related minerals. In this way, various processes have been applied to increase the mineralogy class and comparative evaluation with related frequency done. The Hyperion dataset for hyperspectral remote sensing has been specifically verified as an effective tool for mineral or rock information extraction within the band range of shortwave infrared used. The abundant spatial and spectral information contained in hyperspectral images enables the differentiation of different objects of any object into targeted applications for exploration such as exploration detection, mining.

Keywords: Hyperion, hyperspectral, sensor, Landsat-8

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Authors: Akano Benedict Ubawuike

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This study investigated the effects of pre-laboratory discussion on physics students’ acquisition of science process skills. The study design was quasi-experimental and purposive sampling technique was applied in selecting two schools in Kontagora Town for the research based on the availability of a good physics laboratory. Intact classes already grouped by the school for the sake of small laboratory space and equipment, comprising Thirty (30) students, 15 for experimental group in School A and 15 for control in school B were the subjects for the research. The instrument used for data collection was the lesson prepared for pre – practical discussion and researcher made Science Process Skill Test (SPST ) and two (2) research questions, and two (2) research hypotheses were developed to guide the study. The data collected were analyzed using means and t-Test statistics at 0.05 level of significance. The study revealed that pre-laboratory discussion was found to be more efficacious in enhancing students’ acquisition of science process skills. It also revealed that gender, had no significant effect on students’ acquisition of science process skills. Based on the findings, it was recommended among others that teachers should encourage students to develop interest in practical activities by engaging them in pre-laboratory discussion and providing instructional materials that will challenge them to be actively involved during practical lessons. It is also recommended that Ministries of Education and professional organizations like Science Teachers' Association of Nigeria (STAN) should organize workshops, seminars and conferences for physics teachers and Physics concepts should be taught with practical activity so that the students will do science instead of learning about science.

Keywords: physics, laboratory, discussion, students, acquisition, science process skills

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Authors: Olubiyi Johnson Ezekiel

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Basic science curriculum is an on-going effort towards developing the potential of manner to produce individuals in a holistic and integrated person, who are intellectually, spiritually, emotionally and physically balanced and harmonious. The main focus of this study is to determine the relationship between students’ achievement in junior school certificate examination (JSCE) and senior school basic science achievement test (SSBSAT) on the basis of all the components of basic science. The study employed the descriptive research of the survey type and utilized junior school certificate examination and senior school basic science achievement test(r = .87) scores as instruments. The data collected were subjected to Pearson product moment correlation, Spearman rank correlation, regression analysis and analysis of variance. The result of the finding revealed that the mean effects of the achievement in all the components of basic science on SSBSAT are significantly different from zero. Based on the results of the findings, it was concluded that the relationship between students’ achievement in JSCE and SSBSAT was weak and to achieve a unit increase in the students’ achievement in the SSBSAT when other subjects are held constant, we have to increase the learning of: -physics by 0.081 units; -chemistry by 0.072 units; -biology by 0.025 units and general knowledge by 0.097 units. It was recommended among others, that general knowledge aspect of basic science should be included in either physics or chemistry aspect of basic science.

Keywords: basic science curriculum, students’ achievement, science test, secondary school students

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Authors: Simangele Dlamini

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Smart city development is seen as a way of facing the challenges brought about by the growing urban population the world over. Research indicates that cities have a role to play in combating urban challenges like crime, waste disposal, greenhouse gas emissions, and resource efficiency. These solutions should be such that they do not make city management less sustainable but should be solutions-driven, cost and resource-efficient, and smart. This study explores opportunities on how the City of Johannesburg, South Africa, can use Geographic Information Systems, Big Data and the Internet of Things (IoT) in identifying opportune areas to initiate smart city initiatives such as smart safety, smart utilities, smart mobility, and smart infrastructure in an integrated manner. The study will combine Big Data, using real-time data sources to identify hotspot areas that will benefit from ICT interventions. The GIS intervention will assist the city in avoiding a silo approach in its smart city development initiatives, an approach that has led to the failure of smart city development in other countries.

Keywords: smart cities, internet of things, geographic information systems, johannesburg

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Authors: Deepak Singh, Rail Kuliev

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This research article explores the significance of advanced data visualization techniques in enhancing decision-making processes within the oil and gas exploration and production domain. With the oil and gas industry facing numerous challenges, effective interpretation and analysis of vast and diverse datasets are crucial for optimizing exploration strategies, production operations, and risk assessment. The article highlights the importance of data visualization in managing big data, aiding the decision-making process, and facilitating communication with stakeholders. Various advanced data visualization techniques, including 3D visualization, augmented reality (AR), virtual reality (VR), interactive dashboards, and geospatial visualization, are discussed in detail, showcasing their applications and benefits in the oil and gas sector. The article presents case studies demonstrating the successful use of these techniques in optimizing well placement, real-time operations monitoring, and virtual reality training. Additionally, the article addresses the challenges of data integration and scalability, emphasizing the need for future developments in AI-driven visualization. In conclusion, this research emphasizes the immense potential of advanced data visualization in revolutionizing decision-making processes, fostering data-driven strategies, and promoting sustainable growth and improved operational efficiency within the oil and gas exploration and production industry.

Keywords: augmented reality (AR), virtual reality (VR), interactive dashboards, real-time operations monitoring

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Authors: Kamal Prasad Acharya

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The related literature suggests that teaching and learning science at the basic level community schools in Nepal is based on book recitation. Consequently, the achievement levels and the understanding of basic science concepts is much below the policy expectations. In this context, this study intended to gain perception in the implementation practices of school gardens ‘One Garden One School’ for science learning and to meet the target of sustainable development goals that connects community wisdom regarding school gardening activities (SGAs) for science learning. This Participatory Action Research (PAR) study was done at the action school located in Province 3, Chitwan of Federal Nepal, supported under the NORHED/Rupantaran project. The purpose of the study was to connect the community wisdom related to gardening activities as contextual scaffolds for science learning. For this, in-depth interviews and focus group discussions were applied to collect data which were analyzed using a thematic analysis. Basic level students, science teachers, and parents reported having wonderful experiences such as active and meaningful engagement in school gardening activities for science learning as well as science teachers’ motivation in activity-based science learning. Overall, teachers, students, and parents reported that the school gardening activities have been found to have had positive effects on students’ science learning as they develop basic scientific concepts by connecting community wisdom as a contextual scaffold. It is recommended that the establishment of a school garden is important for science learning in community schools throughout Nepal.

Keywords: contextual scaffold, community wisdom, science and health learning, school garden

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Authors: Sunmaila Oyetunji Raimi, Olufemi Akinloye Bolaji, Abiodun Ezekiel Adesina

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The lingering poor performance of students in basic science spells doom for a vibrant scientific and technological development which pivoted the economic, social and physical upliftment of any nation. This calls for identifying appropriate strategies for imparting basic science knowledge and attitudes to the teaming youths in secondary schools. This study, therefore, determined the impact of computer mediated power point presentations on students’ achievement in basic science in Oyo State, Nigeria. A pre-test, posttest, control group quazi-experimental design adopted for the study. Two hundred and five junior secondary two students selected using stratified random sampling technique participated in the study. Three research questions and three hypotheses guided the study. Two evaluative instruments – Students’ Basic Science Attitudes Scale (SBSAS, r = 0.91); Students’ Knowledge of Basic Science Test (SKBST, r = 0.82) were used for data collection. Descriptive statistics of mean, standard deviation and inferential statistics of ANCOVA, scheffe post-hoc test were used to analyse the data. The results indicated significant main effect of treatment on students cognitive (F(1,200)= 171.680; p < 0.05) and attitudinal (F(1,200)= 34.466; p < 0.05) achievement in Basic science with the experimental group having higher mean gain than the control group. Gender has significant main effect (F(1,200)= 23.382; p < 0.05) on students cognitive outcomes but not significant for attitudinal achievement in Basic science. The study therefore recommended among others that computer mediated power point presentations should be incorporated into curriculum methodology of Basic science in secondary schools.

Keywords: basic science, computer mediated power point presentations, gender, students’ achievement

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Authors: Ulrica Pettersson

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Collection of information on incidents is regularly done through pre-printed incident report forms. These tend to be incomplete and frequently lack essential information. ne consequence is that reports with inadequate information, that do not fulfil analysts’ requirements, are transferred into the analysis process. To improve an incident reporting form, theory in design science, witness psychology and interview and questionnaire research has been used. Previously three experiments have been conducted to evaluate the form and shown significant improved results. The form has proved to capture knowledge, regardless of the incidents’ character or context. The aim in this paper is to describe how design science, in more detail a design hierarchy can be used to construct a collection form for improvements in safety science.

Keywords: data collection, design science, incident reports, safety science

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Authors: Jan-Peter Mund, Christian Kind

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

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

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Authors: Sajjad Baghernezhad, Saeideh Baghernezhad

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Nowadays considering human involved in increasing data development some methods such as data mining to extract science are unavoidable. One of the discussions of data mining is inherent distribution of the data usually the bases creating or receiving such data belong to corporate or non-corporate persons and do not give their information freely to others. Yet there is no guarantee to enable someone to mine special data without entering in the owner’s privacy. Sending data and then gathering them by each vertical or horizontal software depends on the type of their preserving type and also executed to improve data privacy. In this study it was attempted to compare comprehensively preserving data methods; also general methods such as random data, coding and strong and weak points of each one are examined.

Keywords: data mining, distributed data mining, privacy protection, privacy preserving

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Authors: Genady Grabarnik, Serge Yaskolko

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Generative Artificial Intelligence (AI) has significantly expanded its applicability with the incorporation of Large Language Models (LLMs) and become a technology with promise to automate some areas that were very difficult to automate before. The paper describes the introduction of generative Artificial Intelligence into Introductory Computer and Data Science courses and analysis of effect of such introduction. The generative Artificial Intelligence is incorporated in the educational process two-fold: For the instructors, we create templates of prompts for generation of tasks, and grading of the students work, including feedback on the submitted assignments. For the students, we introduce them to basic prompt engineering, which in turn will be used for generation of test cases based on description of the problems, generating code snippets for the single block complexity programming, and partitioning into such blocks of an average size complexity programming. The above-mentioned classes are run using Large Language Models, and feedback from instructors and students and courses’ outcomes are collected. The analysis shows statistically significant positive effect and preference of both stakeholders.

Keywords: introductory computer and data science education, generative AI, large language models, application of LLMS to computer and data science education

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Authors: Sheila Shamuganathan, Mageswary Karpudewan

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This study investigated the effectiveness of green chemistry integrated with Science Writing Heuristic (SWH) in enhancing matriculation students’ responsible environmental behaviour. For this purpose 207 matriculation students were randomly assigned into experimental (N=118) and control (N=89) group. For the experimental group the chemistry concepts were taught using the instructional approach of green chemistry integrated with Science Writing Heuristic (SWH) while for the control group the same content was taught using green chemistry. The data was analysed using ANCOVA and findings obtained from the quantitative analysis reveals that there is significant changes in responsible environmental behaviour (F 1,204) = 32.13 (ηp² = 0.14) which favours the experimental group. The responses of the qualitative data obtained from an interview with the experimental group also further strengthen and indicated a significant improvement in responsible environmental behaviour. The outcome of the study suggests that using green chemistry integrated with Science Writing Heuristic (SWH) could be an alternative approach to improve students’ responsible environmental behaviour towards the environment.

Keywords: science writing heuristic, green chemistry, pro environmental behaviour, laboratory

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Authors: Branci Sarra, Branci Saadia

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Because of the multiplied data growth, many computer science tools have been developed to process and analyze these Big Data. High-performance computing architectures have been designed to meet the treatment needs of Big Data (view transaction processing standpoint, strategic, and tactical analytics). The purpose of this article is to provide a historical and global perspective on the recent trend of high-performance computing architectures especially what has a relation with Analytics and Data Mining.

Keywords: high performance computing, HPC, big data, data analysis

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Authors: Slim Namouchi, Bruno Vallet, Imed Riadh Farah

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3D urban models provide powerful tools for decision making, urban planning, and smart city services. The accuracy of this 3D based systems is directly related to the quality of these models. Since manual large-scale modeling, such as cities or countries is highly time intensive and very expensive process, a fully automatic 3D building generation is needed. However, 3D modeling process result depends on the input data, the proprieties of the captured objects, and the required characteristics of the reconstructed 3D model. Nowadays, producing 3D real-world model is no longer a problem. Remotely sensed data had experienced a remarkable increase in the recent years, especially data acquired using unmanned aerial vehicles (UAV). While the scanning techniques are developing, the captured data amount and the resolution are getting bigger and more precise. This paper presents a literature review, which aims to identify different methods of automatic 3D buildings extractions either from LiDAR or the combination of LiDAR and satellite or aerial images. Then, we present open source technologies, and data models (e.g., CityGML, PostGIS, Cesiumjs) used to integrate these models in geospatial base layers for smart city services.

Keywords: CityGML, LiDAR, remote sensing, SIG, Smart City, 3D urban modeling

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Authors: Nipawan Tharasak, Ladda Hirunyava

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The objective of the research was to study factors affecting working efficiency and the relationship between working environment, satisfaction to human resources management and operation employees’ working efficiency of Faculty of Management Science, Suan Sunandha Rajabhat University. The sample size of the research was based on 33 employees of Faculty of Management Science. The researcher had classified the support employees into 4 divisions by using Stratified Random Sampling. Individual sample was randomized by using Simple Random Sampling. Data was collected through the instrument. The Statistical Package for the Windows was utilized for data processing. Percentage, mean, standard deviation, the t-test, One-way ANOVA, and Pearson product moment correlation coefficient were applied. The result found the support employees’ satisfaction in human resources management of Faculty of Management Science in following areas: remuneration; employee recruitment & selection; manpower planning; performance evaluation; staff training & developing; and spirit & fairness were overall in good level.

Keywords: faculty of management science, operational factors, practice performance, staff working

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Authors: H. Varol

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Although students’ interest level in pursuing Computer Science and related degrees are lower than previous decade, fundamentals of computers, specifically introductory level programming courses are either listed as core or elective courses for a number of non-computer science majors. Universities accommodate these non-computer science majored students either via creating separate sections of a class for them or simply offering mixed-body classroom solutions, in which both computer science and non-computer science students take the courses together. In this work, we demonstrated how we handle introductory level programming course at Sam Houston State University and also provide facts about our observations on students’ success during the coursework. Moreover, we provide suggestions and methodologies that are based on students’ major and skills to overcome the deficiencies of mix-body type of classes.

Keywords: computer science, non-computer science major, programming, programming education

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Authors: Beruk Berhanu Desalegn, Kebede Awgechew, Addisalem Mesfin

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Introduction: This study aimed to investigate and compare the energy and selected nutrient intakes of sport science and non-sport science University students of Southern Ethiopia. Method: Multiple-day dietary data were collected from 166 university students (76 sport science and 90 non-sport sciences). Average daily energy and nutrient intake, and inadequate intakes were calculated using NutriSurvey (NS). Results: There were significant differences (p < 0.05) in the median intakes of energy, total carbohydrate, and vitamin B1 between female students from the sport science and non-sport science groups, but only the median intake of iron was significantly different (p < 0.05) between the male sport and non-sport science students’ group. The prevalence of inadequate intake of vitamin B1 were significantly (p<0.05) higher in the male and female from the non-sport science groups compared to the male and female students’ groups in the sport science, respectively. Whereas, the prevalence of inadequate iron intake by the male sport science students’ group was significantly (p<0.05) higher compared to their counterparts. Similarly, the prevalence of inadequate energy among the females from the sport science group was significantly (p<0.05) higher compared to the female students from the non-sport science department group. The prevalence of inadequate intakes of dietary energy, and the majority of the nutrients (protein, fat, vitamin A, B1, B2, and magnesium) were high (>50%) in selected University students. Conclusion: The energy and majority of nutrient intakes by the students in the selected universities of southern Ethiopia were sub-optimal. Therefore, activities that will improve the dietary intake of University students should include weekly meal plan revision considering their average recommended nutrient intake (RNI).

Keywords: dietary intake, sport science, University students, Ethiopia

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Authors: M. Shaheed Hartley

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There has been increasing pressure on education researchers and practitioners at higher education institutions to focus on the development of South Africa’s rural and peri-urban communities and improving their quality of life. Many tertiary institutions are obliged to review their outreach interventions in schools. To ensure that the support provided to schools is still relevant, a systemic evaluation of science educator needs is central to this process. These prioritised needs will serve as guide not only for the outreach projects of tertiary institutions, but also to service providers in general so that the process of addressing educators needs become coordinated, organised and delivered in a systemic manner. This paper describes one area of a broader needs assessment exercise to collect data regarding the needs of educators in a district of 45 secondary schools in the Western Cape Province of South Africa. This research focuses on the needs and challenges faced by science educators at these schools as articulated by the relevant stakeholders. The objectives of this investigation are two-fold: (1) to create a data base that will capture the needs and challenges identified by science educators of the selected secondary schools; and (2) to develop a needs profile for each of the participating secondary schools that will serve as a strategic asset to be shared with the various service providers as part of a community of practice whose core business is to support science educators and science education at large. The data was collected by a means of a needs assessment questionnaire (NAQ) which was developed in both actual and preferred versions. An open-ended questionnaire was also administered which allowed teachers to express their views. The categories of the questionnaire were predetermined by participating researchers, educators and education department officials. Group interviews were also held with the science teachers at each of the schools. An analysis of the data revealed important trends in terms of science educator needs and identified schools that can be clustered around priority needs, logistic reasoning and educator profiles. The needs database also provides opportunity for the community of practice to strategise and coordinate their interventions.

Keywords: needs assessment, science and mathematics education, evaluation, teaching and learning, South Africa

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Authors: Vishnu Nagendra, Marten Van Der Veen, Stefania Giodini

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Effective humanitarian logistics operations are a cornerstone in the success of disaster relief operations. However, for effectiveness, they need to be demand driven and supported by adequate data for prioritization. Without this data operations are carried out in an ad hoc manner and eventually become chaotic. The current availability of geospatial data helps in creating models for predictive damage and vulnerability assessment, which can be of great advantage to logisticians to gain an understanding on the nature and extent of the disaster damage. This translates into actionable information on the demand for relief goods, the state of the transport infrastructure and subsequently the priority areas for relief delivery. However, due to the unpredictable nature of disasters, the accuracy in the models need improvement which can be done using remote sensing data from UAVs (Unmanned Aerial Vehicles) or satellite imagery, which again come with certain limitations. This research addresses the need for a framework to combine data from different sources to support humanitarian logistic operations and prediction models. The focus is on developing a workflow to combine data from satellites and UAVs post a disaster strike. A three-step approach is followed: first, the data requirements for logistics activities are made explicit, which is done by carrying out semi-structured interviews with on field logistics workers. Second, the limitations in current data collection tools are analyzed to develop workaround solutions by following a systems design approach. Third, the data requirements and the developed workaround solutions are fit together towards a coherent workflow. The outcome of this research will provide a new method for logisticians to have immediately accurate and reliable data to support data-driven decision making.

Keywords: unmanned aerial vehicles, damage prediction models, remote sensing, data driven decision making

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Authors: Sunghae Jun

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Technology management (TM) is important issue in a company improving the competitiveness. Among many activities of TM, technology analysis (TA) is important factor, because most decisions for management of technology are decided by the results of TA. TA is to analyze the developed results of target technology using statistics or Delphi. TA based on Delphi is depended on the experts’ domain knowledge, in comparison, TA by statistics and machine learning algorithms use objective data such as patent or paper instead of the experts’ knowledge. Many quantitative TA methods based on statistics and machine learning have been studied, and these have been used for technology forecasting, technological innovation, and management of technology. They applied diverse computing tools and many analytical methods case by case. It is not easy to select the suitable software and statistical method for given TA work. So, in this paper, we propose a methodology for quantitative TA using statistical computing software called R and data science to construct a general framework of TA. From the result of case study, we also show how our methodology is applied to real field. This research contributes to R&D planning and technology valuation in TM areas.

Keywords: technology management, R system, R data science, statistics, machine learning

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Authors: E. T. Svahn

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This article documents how open science policy is perceived in the scientific literature globally throughout the history. It also presents what policy needs are persistent to enable safe and effective dissemination of scientific knowledge. This information may be of interest to open science and science policy makers globally, especially in the view of recent adoption of supranational open science policies such as Plan S. Evaluation of open science policy landscape is in pressing need of assessment regarding its impact on the research community and society at wide as no previous literature review has been conducted on the topic. This study is a rigorous literature review based on constructivist grounded theory method on the full body of scientific open science policy publications. Selection of these articles has been conducted in 2019 and 2020 in major global knowledge databases. Through the analysis of these articles, two key themes emerged that are seen to shape the relationship between science and society. 1st is that of the policy enabling open science in a safe and effective way, and 2nd is that of the outcome of the science policy may have on the research community and the wider society. These findings accentuate that open science policies can have a major impact on not only research process and availability of knowledge but also on society itself. As an outcome of this study, a theoretical framework is constructed, and the need for further study on open science policy itself on a higher level becomes apparent.

Keywords: constructivist grounded theory, open science policy, rigorous literature review, science policy

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Authors: Joanne F. Hayek

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This study explores the use of gamification to promote citizen e-participation in urban planning. The research departs from a case study: the ‘Shape Your City’ web app designed and programmed by the author and presented as part of the 2021 Dubai Design Week to engage citizens in the co-creation of the future of their city through a gamified experience. The paper documents the design and development methodology of the web app and concludes with the findings of its pilot release. The case study explores the use of mobile interactive mapping, real-time data visualization, augmented reality, and machine learning as tools to enable co-planning. The paper also details the user interface design strategies employed to integrate complex cross-sector e-planning systems and make them accessible to citizens.

Keywords: gamification, co-planning, citizen e-participation, mobile interactive mapping, real-time data visualization

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Authors: Aqsa Mehmood, Muhammad Ali Tahir, Hafiz Syed Hamid Arshad, Salman Atif, Ejaz Hussain, Gavin McArdle, Michela Bertolotto

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The evolution of smart cities in recent years has been developing dramatically. As urbanization increases, the demand for big data analytics and digital technology-based solutions for cities has also increased. Many cities around the world have now planned to focus on smart cities. To obtain a systematic overview of smart city models, we carried out a bibliometric analysis in the context of seven regions of the world to understand the main dimensions that characterize smart cities. This paper analyses articles published between 2017 and 2021 that were captured from Web of Science and Scopus. Specifically, we investigated publication trends to highlight the research gaps and current developments in smart cities research. Our survey provides helpful insights into the geographical distribution of smart city publications with respect to regions of the world and explores the current key topics relevant to smart cities and the co-occurrences of keywords used in these publications. A systematic literature review and keyword analysis were performed. The results have focused on identifying future directions in smart city development, including smart citizens, ISO standards, Open Geospatial Consortium and the sustainability factor of smart cities. This article will assist researchers and urban planners in understanding the latest trends in research and highlight the aspects which need further attention.

Keywords: smart cities, sustainability, regions, urban development, VOS viewer, research trends

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Authors: Shahnewaz Khan

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Machine learning (ML) techniques are often used in projects for creating data-driven applications. These tasks typically demand additional research and analysis. The proper technique and strategy must be chosen to ensure the success of data-driven projects. Otherwise, even exerting a lot of effort, the necessary development might not always be possible. In this post, an effort to examine the workflow of data-driven software development projects and its implementation process in order to describe how to manage a project successfully. Which will assist in minimizing the added workload.

Keywords: data, data-driven projects, data science, NLP, software project

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Authors: Enrique Barra, Aldo Gordillo, Juan Quemada

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This paper presents the Virtual Science Hub platform. It is an open source platform that combines a social network, an e-learning authoring tool, a video conference service and a learning object repository for science teaching enrichment. These four main functionalities fit very well together. The platform was released in April 2012 and since then it has not stopped growing. Finally we present the results of the surveys conducted and the statistics gathered to validate this approach.

Keywords: e-learning, platform, authoring tool, science teaching, educational sciences

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Authors: Avi Shrivastava

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In 2021, two heart-wrenching scenes, shown live on television screens across countries, painted a grim picture of refugees. One of them was of people clinging onto an airplane's wings in their desperate attempt to flee war-torn Afghanistan. They ultimately fell to their death. The other scene was the U.S. government authorities separating children from their parents or guardians to deter migrants/refugees from coming to the U.S. These events show the desperation refugees feel when they are trying to leave their homes in disaster zones. However, data paints a grave picture of the current refugee situation. It also indicates that a bleak future lies ahead for the refugees across the globe. Data and information are the two threads that intertwine to weave the shimmery fabric of modern society. Data and information are often used interchangeably, but they differ considerably. For example, information analysis reveals rationale, and logic, while data analysis, on the other hand, reveals a pattern. Moreover, patterns revealed by data can enable us to create the necessary tools to combat huge problems on our hands. Data analysis paints a clear picture so that the decision-making process becomes simple. Geopolitical and economic data can be used to predict future refugee hotspots. Accurately predicting the next refugee hotspots will allow governments and relief agencies to prepare better for future refugee crises. The refugee crisis does not have binary answers. Given the emotionally wrenching nature of the ground realities, experts often shy away from realistically stating things as they are. This hesitancy can cost lives. When decisions are based solely on data, emotions can be removed from the decision-making process. Data also presents irrefutable evidence and tells whether there is a solution or not. Moreover, it also responds to a nonbinary crisis with a binary answer. Because of all that, it becomes easier to tackle a problem. Data science and A.I. can predict future refugee crises. With the recent explosion of data due to the rise of social media platforms, data and insight into data has solved many social and political problems. Data science can also help solve many issues refugees face while staying in refugee camps or adopted countries. This paper looks into various ways data science can help solve refugee problems. A.I.-based chatbots can help refugees seek legal help to find asylum in the country they want to settle in. These chatbots can help them find a marketplace where they can find help from the people willing to help. Data science and technology can also help solve refugees' many problems, including food, shelter, employment, security, and assimilation. The refugee problem seems to be one of the most challenging for social and political reasons. Data science and machine learning can help prevent the refugee crisis and solve or alleviate some of the problems that refugees face in their journey to a better life. With the explosion of data in the last decade, data science has made it possible to solve many geopolitical and social issues.

Keywords: refugee crisis, artificial intelligence, data science, refugee camps, Afghanistan, Ukraine

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Authors: Janis E. Campbell

Abstract:

Most of what is known about complementary, alternative or traditional medicine (CATM) in the United States today is known from either the National Health Interview Survey a cross-sectional survey with a few questions or from small cross-sectional or cohort studies with specific populations. The broad geographical distribution in CATM use or providers is not known. For this project, we used geospatial cluster analysis to determine if there were clusters of CATM provider by county in the US. In this analysis, we used the National Provider Index to determine the geographic distribution of providers in the US. Of the 215,769 CAMT providers 211,603 resided in the contiguous US: Acupuncturist (26,563); Art, Poetry, Music and Dance Therapist (2,752); Chiropractor (89,514); Doula/Midwife (3,535); Exercise (507); Homeopath (380); Massage Therapist (36,540); Mechanotherapist (1,888); Naprapath (146); Naturopath (4,782); Nutrition (42,036); Reflexologist (522); Religious (2,438). ESRI® spatial autocorrelation was used to determine if the geographic location of CATM providers were random or clustered. For global analysis, we used Getis-Ord General G and for Local Indicators of Spatial Associations with an Optimized Hot Spot Analysis using an alpha of 0.05. Overall, CATM providers were clustered with both low and high. With Chiropractors, focusing in the Midwest, religious providers having very small clusters in the central US, and other types of CAMT focused in the northwest and west coast, Colorado and New Mexico, the great lakes areas and Florida. We will discuss some of the implications of this study, including associations with health, economic, social, and political systems.

Keywords: complementary medicine, alternative medicine, geospatial, United States

Procedia PDF Downloads 150

Authors: Adisai Thovicha, Jiranan Pattaphong

Abstract:

The objective of this research is to study the satisfaction of the disbursement of the Faculty of Science and Technology, Suan Sunandha Rajabhat University. The sample of this study consisted of 98 participants who are faculty members and staff of the Faculty of Science and Technology. Sample was drawn by systematic random sampling technique. Questionnaire was used to collect data. Analysis involves frequency, percentage, mean and standard deviation. It was found that: (1) Most of the 98 faculty members and staff are female, aged between 31-40 years and they have been working at the university for 1-5 years. (2) The satisfaction level of the disbursement of the Faculty of Science and Technology, Suan Sunandha Rajabhat University is high. When each aspect is considered, the satisfaction level of faculty members and staff of the Faculty of Science and Technology is high in service providing staff, process and facilitation.

Keywords: satisfaction of disbursement, petition financing, faculty members, staff

Procedia PDF Downloads 411

Authors: Kazuo Nishimura, Junichi Hirata, Tadashi Yagi, Junko Urasaka

Abstract:

This paper is an investigation of the effect of science education during the high school education how science graduates of universities are appreciated in the labor market in Japan. We conducted a survey utilizing the internet and analyzed the subjects they were good at and their annual income. The results confirm that among science graduates, workers adept at physics tend to have higher incomes compared to workers good at other subjects. Generational analysis based on the curriculum guideline amendments reveals that the generational difference is small among science majors who are good at physics.

Keywords: curriculum, income, physics, science

Procedia PDF Downloads 284