Search results for: research data sharing

Authors: Anucha Wittayakorn-Puripunpinyoo

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The research objectives were 1) to study the situation and its trend of agricultural product export of Thailand 2) to study the impact of agricultural product export on income of Thai economy 3) the impact of agricultural product export on employment of Thai economy and 4) to find out the recommendations of agricultural product export policy of Thailand. In this research, secondary data were collected as yearly time series data from 1990 to 2016 accounted for 27 years. Data were collected from the Bank of Thailand database. Primary data were collected from the steakholders of agricultural product export policy of Thailand. Data analysis was applied descriptive statistics such as arithmetic mean, standard deviation. The forecasting of agricultural product was applied Mote Carlo Simulation technique as well as time trend analysis. In addition, the impact of agricultural product export on income and employment by applying econometric model while the estimated parameters were utilized the ordinary least square technique. The research results revealed that 1) agricultural product export value of Thailand from 1990 to 2016 was 338,959.5 Million Thai baht with its growth rate of 4.984 percent yearly, in addition, the forecasting of agricultural product export value of Thailand has increased but its growth rate has been declined 2) the impact of agricultural product export has positive impact on income in Thai economy, increasing in agricultural product export of Thailand by 1 percent would lead income increased by 0.0051 percent 3) the impact of agricultural product export has positive impact on employment in Thai economy, increasing in agricultural product export of Thailand by 1 percent would lead income increased by 0.079 percent and 4) in the future, agricultural product export policy would focused on finished or semi-finished agricultural product instead of raw material by applying technology and innovation in to make value added of agricultural product export. The public agricultural product export policy would support exporters in private sector in order to encourage them as agricultural exporters in Thailand.

Keywords: agricultural product export, income, employment, Thai economy

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Authors: Annette McArthur

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The traditional model of teaching and learning, where ICT sits as a separate entity is not a model for a 21st century school. It is imperative that teaching and learning embraces technological advancements. The challenge in schools lies in shifting the mindset of teachers so they see ICT as integral to their teaching, learning and curriculum rather than a separate E-Learning curriculum stream. This research project investigates how the effective, planned, intentional integration of ICT into a STEM curriculum, can enable the shift in the teacher mindset. The project incorporated: • Developing a professional coaching relationship with key STEM teachers. • Facilitating staff professional development involving student centered project based learning pedagogy in the context of a STEM curriculum. • Facilitating staff professional development involving digital literacy. • Establishing a professional community where collaboration; sharing and reflection were part of the culture of the STEM community. • Facilitating classroom support for the effective delivery innovative STEM curriculum. • Developing STEM learning spaces where technologies were used to empower and engage learners to participate in student-centered, project-based learning.

Keywords: e-learning, ICT, project based learning, STEM

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Authors: Aïcha Ben Salem, Faouzi Boufares, Sebastiao Correia

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The subject covered in this paper aims at assisting the user in its quality approach. The goal is to better extract, mix, interpret and reuse data. It deals with the semantic schema recognition of a data source. This enables the extraction of data semantics from all the available information, inculding the data and the metadata. Firstly, it consists of categorizing the data by assigning it to a category and possibly a sub-category, and secondly, of establishing relations between columns and possibly discovering the semantics of the manipulated data source. These links detected between columns offer a better understanding of the source and the alternatives for correcting data. This approach allows automatic detection of a large number of syntactic and semantic anomalies.

Keywords: schema recognition, semantic data profiling, meta-categorisation, semantic dependencies inter columns

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Authors: Abilio Avila, Orestis Terzidis

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A fundamental principle of Grounded Theory (GT) is to prevent the formation of preconceived theories. This implies the need to start a research study with an open mind and to avoid being absorbed by the existing literature. However, to start a new study without an understanding of the research domain and its context can be extremely challenging. This paper presents a research approach that simultaneously supports a researcher to identify and to focus on critical areas of a research project and prevent the formation of prejudiced concepts by the current body of literature. This approach comprises of four stages: Selection of secondary case studies, analysis of secondary case studies, development of an initial conceptual framework, development of an initial interview guide. The analysis of secondary case studies as a starting point for a research project allows a researcher to create a first understanding of a research area based on real-world cases without being influenced by the existing body of theory. It enables a researcher to develop through a structured course of actions a firm guide that establishes a solid starting point for further investigations. Thus, the described approach may have significant implications for GT researchers who aim to start a study within a given research area.

Keywords: grounded theory, interview guide, qualitative research, secondary case studies, secondary data analysis

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Authors: Shuai Zhao, Shalendra D. Sharma, Jin Xu

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This research uses advanced machine learning models to learn a number of hypotheses regarding political executives. Specifically, it analyses the impact these powerful leaders have on economic growth by using leaders’ data from the Archigos database from 1835 to the end of 2015. The data is processed by the AutoGluon, which was developed by Amazon. Automated Machine Learning (AutoML) and AutoGluon can automatically extract features from the data and then use multiple classifiers to train the data. Use a linear regression model and classification model to establish the relationship between leaders and economic growth (GDP per capita growth), and to clarify the relationship between their characteristics and economic growth from a machine learning perspective. Our work may show as a model or signal for collaboration between the fields of statistics and artificial intelligence (AI) that can light up the way for political researchers and economists.

Keywords: comparative politics, political executives, leaders’ characteristics, artificial intelligence

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Authors: Widyani Fatwa Dewi, Subroto Athor

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In a telecommunication service provider, quantity and interval of customer demand often difficult to predict due to high dependency on customer expansion strategy and technological development. Demand arrives when a customer needs to add capacity to an existing site or build a network in a new site. Because demand is uncertain for each period, and sometimes there is a null demand for several equipments, it is categorized as intermittent. This research aims to improve demand forecast quality in Indonesia's telecommunication service providers by using Artificial Neural Network. In Artificial Neural Network, the pattern or relationship within data will be analyzed using the training process, followed by the learning process as validation stage. Historical demand data for 36 periods is used to support this research. It is found that demand forecast by using Artificial Neural Network outperforms the existing method if it is reviewed on two criteria: the forecast accuracy, using Mean Absolute Deviation (MAD), Mean of the sum of the Squares of the Forecasting Error (MSE), Mean Error (ME) and service level which is shown through inventory cost. This research is expected to increase the reference for a telecommunication demand forecast, which is currently still limited.

Keywords: artificial neural network, demand forecast, forecast accuracy, intermittent, service level, telecommunication

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Authors: David A. Scott, Michelle G. Scott

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The problem of juvenile crime, school suspensions and oppositional behaviors indicates a need for a wide range of intervention programs for at-risk youth. Juvenile court systems and mental health agencies are examining alternative ways to deal with at-risk youth that will allow the adolescent to live within their home community. The previous trend that treatment away from home is more effective than treatment near one's community has shifted. Research now suggests that treatment be close to home for several reasons, such as increased treatment success, parental involvement, and reduced costs. Treatment options consist of a wide range of interventions, including outpatient, inpatient, and community-based services (therapeutic group homes, foster care and in-home preservation services). The juvenile justice system, families and other mental health agencies continue to seek the most effective treatment for at-risk youth in their communities. This research examines two possible treatment modalities, a multi-systemic outpatient program and a residential program. Research examining effective, evidence- based counseling will be discussed during this presentation. The presenter recently completed a three-year research grant examining effective treatment modalities for at-risk youth participating in a multi-systemic program. The presenter has also been involved in several research activities gathering data on effective techniques used in residential programs. The data and discussion will be broken down into two parts, each discussing one of the treatment modalities mentioned above. Data on the residential programs was collected on both a sample of 740 at- risk youth over a five-year period and also a sample of 63 participants during a one-year period residing in a residential programs. The effectiveness of these residential services was measured in three ways: services are evaluated by primary referral sources; follow-up data is obtained at various intervals after program participation to measure recidivism (what percentage got back into trouble with the Department of Juvenile Justice); and a more sensitive, "Offense Seriousness Score", has been computed and analyzed prior to, during and after treatment in the residential program. Data on the multi-systemic program was gathered over the past three years on 190 participants. Research will discuss pre and post test results, recidivism rates, academic performance, parental involvement, and effective counseling treatment modalities.

Keywords: at-risk youth, group homes, therapeutic group homes, recidivism rates

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Authors: Yassine Bouzouita, Oumaima Belghith, Cyrine Zitoun, Charles Bonneau

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Performance measurement framework (PMF) is an essential tool in any organization to assess the performance of its processes. It guides businesses to stay on track with their objectives and benchmark themselves from the market. With the growing trend of the digital transformation of business processes, led by innovations in artificial intelligence (AI) & Big Data applications, developing a mature system capable of capturing the impact of digital solutions across different industries became a necessity. Based on the conducted research, no such system has been developed in academia nor the industry. In this context, this paper covers a variety of methodologies on performance measurement, overviews the major AI and big data applications in the banking sector, and covers an exhaustive list of relevant metrics. Consequently, this paper is of interest to both researchers and practitioners. From an academic perspective, it offers a comparative analysis of the reviewed performance measurement frameworks. From an industry perspective, it offers exhaustive research, from market leaders, of the major applications of AI and Big Data technologies, across the different departments of an organization. Moreover, it suggests a standardized classification model with a well-defined structure of intelligent digital solutions. The aforementioned classification is mapped to a centralized library that contains an indexed collection of potential metrics for each application. This library is arranged in a manner that facilitates the rapid search and retrieval of relevant metrics. This proposed framework is meant to guide professionals in identifying the most appropriate AI and big data applications that should be adopted. Furthermore, it will help them meet their business objectives through understanding the potential impact of such solutions on the entire organization.

Keywords: AI and Big Data applications, impact assessment, metrics, performance measurement

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Authors: Anung Style Bukhori, Ani Dijah Rahajoe

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Poverty poses a significant challenge in Indonesia, requiring an effective analytical approach to understand and address this issue. In this research, we applied the Naïve Bayes classification method to examine and classify poverty data in Indonesia. The main focus is on classifying data using RapidMiner, a powerful data analysis platform. The analysis process involves data splitting to train and test the classification model. First, we collected and prepared a poverty dataset that includes various factors such as education, employment, and health..The experimental results indicate that the Naïve Bayes classification model can provide accurate predictions regarding the risk of poverty. The use of RapidMiner in the analysis process offers flexibility and efficiency in evaluating the model's performance. The classification produces several values to serve as the standard for classifying poverty data in Indonesia using Naive Bayes. The accuracy result obtained is 40.26%, with a moderate recall result of 35.94%, a high recall result of 63.16%, and a low recall result of 38.03%. The precision for the moderate class is 58.97%, for the high class is 17.39%, and for the low class is 58.70%. These results can be seen from the graph below.

Keywords: poverty, classification, naïve bayes, Indonesia

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Authors: Jonathan Nash, Allen Hartt, Catherine Plante

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In many nations, community colleges, or similar institutions, play a crucial role in higher education. For example, in the United States more than half of all undergraduate students enroll in a community college at some point during their academic career. Similar statistics have been reported for Australia and Canada. Recognizing the important role these institutions play in educating future accountants, the American Accounting Association has called for research that contributes to a better understanding of these members of the academic community. Although previous literature has shown that community colleges and 4-year institutions differ on many levels, the extant literature has provided data on the characteristics of introductory accounting courses for four-year institutions but not for community colleges. We fill a void in the literature by providing data on the characteristics of introductory accounting courses offered by community colleges in the United States. Data are collected on several dimensions including: course size and staffing, pedagogical orientation, standardization of course elements, textbook selection, and use of technology-based course management tools. Many of these dimensions have been used in previous research examining four-year institutions thereby facilitating comparisons. The resulting data should be of interest to instructors, regulators and administrators, researchers, and the accounting profession. The data provide information on the introductory accounting courses completed by the average community college student which can help instructors identify areas where transfer students’ experiences might differ from their contemporaries at four-year colleges. Regulators and administrators may be interested in the differences between accounting courses offered by two- and four-year institutions when implementing standardized transfer programs. Researchers might use the data to motivate future research into whether differences between two- and four-year institutions affect outcomes like the probability of students choosing to major in accounting and their performance within the major. Accounting professionals may use our findings as a springboard for facilitating discussions related to the accounting labor supply.

Keywords: Accounting curricula, Community college, Descriptive study, Introductory accounting

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Authors: Marju Ben Sayed, Shigeko Haruyama

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Among all kinds of natural disaster, the flood is a common feature in rapidly urbanizing Dhaka city. In this research, assessment of flood risk of Dhaka metropolitan area has been investigated by using an integrated approach of GIS, remote sensing and socio-economic data. The purpose of the study is to measure the flooding risk concerning with the flood protection embankment in big flooding events (1988, 1998 and 2004) and urbanization of Dhaka metropolitan zone. In this research, we considered the Dhaka city into two parts; East Dhaka (outside the flood protection embankment) and West Dhaka (inside the flood protection embankment). Using statistical data, we explored the socio-economic status of the study area population by comparing the density of population, land price and income level. We have drawn the cross section profile of the flood protection embankment into three different points for realizing the flooding risk in the study area, especially in the big flooding year (1988, 1998 and 2004). According to the physical condition of the study area, the land use/land cover map has been classified into five classes. Comparing with each land cover unit, historical weather station data and the socio-economic data, the flooding risk has been evaluated. Moreover, we compared between DEM data and each land cover units to find out the relationship with flood. It is expected that, this study could contribute to effective flood forecasting, relief and emergency management for a future flood event in Dhaka city.

Keywords: land use, land cover change, socio-economic, Dhaka city, GIS, flood

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Authors: Nongyao Intasaso, Atchara Rattanama, Navarat Pewnual

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This research is survey research purposed to study the factor affected to knowledge management efficiency of personnel in Rajamangala University of Technology Srivijaya, and study the problem of knowledge management affected to knowledge development of personnel in the university. The tool used in this study is structures questioner standardize rating scale in 5 levels. The sample selected by purposive sampling and there are 137 participation calculated in 25% of population. The result found that factor affected to knowledge management efficiency in the university included (1) result from the organization factor found that the university provided project or activity that according to strategy and mission of knowledge management affected to knowledge management efficiency in highest level (x̅ = 4.30) (2) result from personnel factor found that the personnel are eager for knowledge and active to learning to develop themselves and work (Personal Mastery) affected to knowledge management efficiency in high level (x̅ = 3.75) (3) result from technological factor found that the organization brought multimedia learning aid to facilitate learning process affected to knowledge management efficiency in high level (x̅ = 3.70) and (4) the result from learning factor found that the personnel communicated and sharing knowledge and opinion based on acceptance to each other affected to knowledge management efficiency in high level (x̅ = 3.78). The problem of knowledge management in the university included the personnel do not change their work behavior, insufficient of collaboration, lack of acceptance in knowledge and experience to each other, and limited budget. The solutions to solve these problems are the university should be support sufficient budget, the university should follow up and evaluate organization development based on knowledge using, the university should provide the activity emphasize to personnel development and assign the committee to process and report knowledge management procedure.

Keywords: knowledge management, efficiency, personnel, learning process

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Authors: Takahiro Nishigaki, Takashi Onoda

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In recent years, the number of document data has been increasing since the spread of the Internet. Many methods have been studied for extracting topics from large document data. We proposed Independent Topic Analysis (ITA) to extract topics independent of each other from large document data such as newspaper data. ITA is a method for extracting the independent topics from the document data by using the Independent Component Analysis. The topic represented by ITA is represented by a set of words. However, the set of words is quite different from the topics the user imagines. For example, the top five words with high independence of a topic are as follows. Topic1 = {"scor", "game", "lead", "quarter", "rebound"}. This Topic 1 is considered to represent the topic of "SPORTS". This topic name "SPORTS" has to be attached by the user. ITA cannot name topics. Therefore, in this research, we propose a method to obtain topics easy for people to understand by using the web search engine, topics given by the set of words given by independent topic analysis. In particular, we search a set of topical words, and the title of the homepage of the search result is taken as the topic name. And we also use the proposed method for some data and verify its effectiveness.

Keywords: independent topic analysis, topic extraction, topic naming, web search engine

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Authors: Peter Unoh Bassey

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The study examined the influence of Costa and McCrae’s Neo-PI Factor and early adolescent’s school social adjustment in Cross River State, Nigeria. The research adopted the causal-comparative design also known as the ex-post facto with about one thousand and eighteen (1,018) students who were randomly selected from one stream of JSS 1 classes in 19 schools out of seventy-three (73) in the study area. Data were collected using two instruments one is the NEO-PI scale, and students school social adjustment questionnaire. Three research questions and three research hypotheses were postulated and tested at 0.05 level of significance. The analysis of data was carried out using both the independent t-test statistics and the one-way analysis of variance (ANOVA). The analyzed result indicated that the five dimensions had a significant influence on students school social adjustment. A post hoc was equally carried out to show the relative significant difference among the study variables. In view of the above, it was recommended that teachers, parents and educational psychologists should be involved to enhance students the confidence to overcome their social adjustment problem.

Keywords: Costa and McCrae’s NEO-PI Factor, early adolescents, school, social adjustment

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Authors: Winfred Okoe Addy, Jean Jacques Dominique Beraud

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Access control systems (ACs) are some of the most important components in safety areas. Inaccuracies of regulatory frameworks make personal policies and remedies more appropriate than standard models or protocols. This problem is exacerbated by the increasing complexity of software, such as integrated Big Data (BD) software for controlling large volumes of encrypted data and resources embedded in a dedicated BD production system. This paper proposes a general access control strategy system for the diffusion of Big Data domains since it is crucial to secure the data provided to data consumers (DC). We presented a general access control circulation strategy for the Big Data domain by describing the benefit of using designated access control for BD units and performance and taking into consideration the need for BD and AC system. We then presented a generic of Big Data access control system to improve the dissemination of Big Data.

Keywords: access control, security, Big Data, domain

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Authors: Ruijuan Bi

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Discrimination in insurance pricing is a topic of increasing concern, particularly in the context of the rapid development of big data and artificial intelligence. There is a need to explore the various forms of discrimination, such as direct and indirect discrimination, proxy discrimination, algorithmic discrimination, and unfair discrimination, and understand their implications in insurance pricing models. This paper aims to analyze and interpret the definitions of discrimination in insurance pricing and explore measures to reduce discrimination. It utilizes a textual analysis methodology, which involves gathering qualitative data from relevant literature on definitions of discrimination. The research methodology focuses on exploring the various forms of discrimination and their implications in insurance pricing models. Through textual analysis, this paper identifies the specific characteristics and implications of each form of discrimination in the general insurance industry. This research contributes to the theoretical understanding of discrimination in insurance pricing. By analyzing and interpreting relevant literature, this paper provides insights into the definitions of discrimination and the laws and regulations surrounding it. This theoretical foundation can inform future empirical research on discrimination in insurance pricing using relevant theories of probability theory.

Keywords: algorithmic discrimination, direct and indirect discrimination, proxy discrimination, unfair discrimination, insurance pricing

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Authors: Iffat Ara Ebu, Fahmida Islam, Mohammad Abdus Shahid Rafi, Mahfuzur Rahman, Umar Iqbal, John Ball

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The application of multi-sensor fusion for enhanced distance estimation accuracy in dynamic environments is crucial for advanced driver assistance systems (ADAS) and autonomous vehicles. Limitations of single sensors such as cameras or radar in adverse conditions motivate the use of combined camera and radar data to improve reliability, adaptability, and object recognition. A multi-sensor fusion approach using an extended Kalman filter (EKF) is proposed to combine sensor measurements with a dynamic system model, achieving robust and accurate distance estimation. The research utilizes the Mississippi State University Autonomous Vehicular Simulator (MAVS) to create a controlled environment for data collection. Data analysis is performed using MATLAB. Qualitative (visualization of fused data vs ground truth) and quantitative metrics (RMSE, MAE) are employed for performance assessment. Initial results with simulated data demonstrate accurate distance estimation compared to individual sensors. The optimal sensor measurement noise variance and plant noise variance parameters within the EKF are identified, and the algorithm is validated with real-world data from a Chevrolet Blazer. In summary, this research demonstrates that multi-sensor fusion with an EKF significantly improves distance estimation accuracy in dynamic environments. This is supported by comprehensive evaluation metrics, with validation transitioning from simulated to real-world data, paving the way for safer and more reliable autonomous vehicle control.

Keywords: sensor fusion, EKF, MATLAB, MAVS, autonomous vehicle, ADAS

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Authors: A. Stifi, S. Gentes

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Undoubtedly from construction's perspective, the use of explosives will remove a large facility such as a 40-storey building , that took almost 3 to 4 years for construction, in few minutes. Usually, the reconstruction or decommissioning, the last phase of life cycle of any facility, is considered to be the shortest. However, this is proved to be wrong in the case of nuclear power plant. Statistics says that in the last 30 years, the construction of a nuclear power plant took an average time of 6 years whereas it is estimated that decommissioning of such plants may take even a decade or more. This paper is all about the decommissioning phase of a nuclear power plant which needs to be given more attention and encouragement from the research institutes as well as the nuclear industry. Currently, there are 437 nuclear power reactors in operation and 70 reactors in construction. With around 139 nuclear facilities already been shut down and are in different decommissioning stages and approximately 347 nuclear reactors will be in decommissioning phase in the next 20 years (assuming the operation time of a reactor as 40 years), This fact raises the following two questions (1) How far is the nuclear and construction Industry ready to face the challenges of decommissioning project? (2) What is required for a safety and reliable decommissioning project delivery? The decommissioning of nuclear facilities across the global have severe time and budget overruns. Largely the decommissioning processes are being executed by the force of manual labour where the change in regulations is respectively observed. In term of research and development, some research projects and activities are being carried out in this area, but the requirement seems to be much more. The near future of decommissioning shall be better through a sustainable development strategy where all stakeholders agree to implement innovative technologies especially for dismantling and decontamination processes and to deliever a reliable and safety decommissioning. The scope of technology transfer from other industries shall be explored. For example, remotery operated robotic technologies used in automobile and production industry to reduce time and improve effecincy and saftey shall be tried here. However, the innovative technologies are highly requested but they are alone not enough, the implementation of creative and innovative management methodologies should be also investigated and applied. Lean Management with it main concept "elimination of waste within process", is a suitable example here. Thus, the cooperation between international organisations and related industries and the knowledge-sharing may serve as a key factor for the successful decommissioning projects.

Keywords: decommissioning of nuclear facilities, innovative technology, innovative management, sustainable development

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Authors: Justine Jun

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This study aims to reveal migrant women English instructors' workplace learning experiences in Canadian post-secondary institutions in Ontario. Although many scholars have conducted research studies on internationally educated teachers and their professional and employment challenges, few studies have recorded migrant women English language instructors’ professional learning and support experiences in post-secondary English language programs in Canada. This study employs a qualitative research paradigm. Mezirow’s Transformative Learning Theory is an essential lens for the researcher to explain, analyze, and interpret the research data. It is a collaborative research project. The researcher and participants cooperatively create photographic or other artwork data responding to the research questions. Photovoice and arts-informed data collection methodology are the main methods. Research participants engage in the study as co-researchers and inquire about their own workplace learning experiences, actively utilizing their critical self-reflective and dialogic skills. Co-researchers individually select the forms of artwork they prefer to engage with to represent their transformative workplace learning experiences about the Canadian workplace cultures that they underwent while working with colleagues and administrators in the workplace. Once the co-researchers generate their cultural artifacts as research data, they collaboratively interpret their artworks with the researcher and other volunteer co-researchers. Co-researchers jointly investigate the themes emerging from the artworks. They also interpret the meanings of their own and others’ workplace learning experiences embedded in the artworks through interactive one-on-one or group interviews. The following are the research questions that the migrant women English instructor participants examine and answer: (1) What have they learned about their workplace culture and how do they explain their learning experiences?; (2) How transformative have their learning experiences been at work?; (3) How have their colleagues and administrators influenced their transformative learning?; (4) What kind of support have they received? What supports have been valuable to them and what changes would they like to see?; (5) What have their learning experiences transformed?; (6) What has this arts-informed research process transformed? The study findings implicate English language instructor support currently practiced in post-secondary English language programs in Ontario, Canada, especially for migrant women English instructors. This research is a doctoral empirical study in progress. This research has the urgency to address the research problem that few studies have investigated migrant English instructors’ professional learning and support issues in the workplace, precisely that of English instructors working with adult learners in Canada. While appropriate social and professional support for migrant English instructors is required throughout the country, the present workplace realities in Ontario's English language programs need to be heard soon. For that purpose, the conceptualization of this study is crucial. It makes the investigation of under-represented instructors’ under-researched social phenomena, workplace learning and support, viable and rigorous. This paper demonstrates the robust theorization of English instructors’ workplace experiences using Mezirow’s Transformative Learning Theory in the English language teacher education field.

Keywords: English teacher education, professional learning, transformative learning theory, workplace learning

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Authors: Gabriel Odeh Ankeli

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The study determined how mentoring enhanced strategy can increase self-efficacy of secondary school students in physics in education zone C of Benue State, Nigeria. The study was guided by two research questions while two hypotheses were formulated and tested at 0.05 level of significance. The design of the study was the quasi-experimental, non-randomized, pre-test and post-test control groups. The population of the study consisted of 4,064 SS two physics students in the 94 schools in Education Zone C. The sample comprised 406 SS two physics students drawn from 10 schools using multi-stage sampling technique. The research instrument adapted and used for data collection was Students Self-Efficacy Scale (SSES). The research instrument was subjected to a reliability analysis using Cronbachs Alpha which yielded a reliability co-efficient of 0.84. Data collected were analyzed using discriptive statistics of mean and standard deviation to answer the research questions while inferential statistics of Analysis of Covariance (ANCOVA) was used to test the hypotheses. The findings revealed that students who were exposed to mentoring exhibited lower self-efficacy levels (F 1,405 = 2.751, P = 0.09˃0.05) than those students who were not exposed to mentoring. There was significant difference between male and female students’ self-efficacy level (F 1,211 = 5.496, P = 0.02˂0.05). Based on these findings, it was recommended among others that longer duration of mentoring period should be encouraged when using the mentoring strategy for better enhancement of Self-efficacy of students.

Keywords: physics, self-efficacy, mentoring enhanced strategy, students

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Authors: O. P. Popoola, O. A. Alawode, M. O. Olayiwola, A. M. Oladele

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This research work is based on using discriminant analysis to forecast crime rate in Nigeria between 1996 and 2008. The work is interested in how gender (male and female) relates to offences committed against the government, against other properties, disturbance in public places, murder/robbery offences and other offences. The data used was collected from the National Bureau of Statistics (NBS). SPSS, the statistical package was used to analyse the data. Time plot was plotted on all the 29 offences gotten from the raw data. Eigenvalues and Multivariate tests, Wilks’ Lambda, standardized canonical discriminant function coefficients and the predicted classifications were estimated. The research shows that the distribution of the scores from each function is standardized to have a mean O and a standard deviation of 1. The magnitudes of the coefficients indicate how strongly the discriminating variable affects the score. In the predicted group membership, 172 cases that were predicted to commit crime against Government group, 66 were correctly predicted and 106 were incorrectly predicted. After going through the predicted classifications, we found out that most groups numbers that were correctly predicted were less than those that were incorrectly predicted.

Keywords: discriminant analysis, DA, multivariate analysis of variance, MANOVA, canonical correlation, and Wilks’ Lambda

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Authors: Michael Gidey Gebru

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Most of Data Envelopment Analysis models operate in a static environment with input and output parameters that are chosen by deterministic data. However, due to ambiguity brought on shifting market conditions, input and output data are not always precisely gathered in real-world scenarios. Fuzzy numbers can be used to address this kind of ambiguity in input and output data. Therefore, this work aims to expand crisp Data Envelopment Analysis into Data Envelopment Analysis with fuzzy environment. In this study, the input and output data are regarded as fuzzy triangular numbers. Then, the Data Envelopment Analysis model with fuzzy environment is solved using a multi-objective method to gauge the Decision Making Units' efficiency. Finally, the developed Data Envelopment Analysis model is illustrated with an application on real data 50 educational institutions.

Keywords: efficiency, Data Envelopment Analysis, fuzzy, higher education, input, output

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Authors: Fikri, Rozmi Ismail, Fatimah Wati Halim, Sarah Waheeda

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The aim of this research is to investigate the influence of personality and driver behavior on road accident among bus driver who have the high risk behavior on road accident in Riau Province. The hypotheses proposed this research is there are has a significant influences of Treat of Personality and Driver Behavior among bus driver in Riau Province Indonesia. Subject participated in this research are 100 bus driver in Riau Province. This study using the purposive random sampling technique and quantitative design. The data is collected using the Big Five Personality questionnaires, Driver Behavior questionnaires and Road Accident Inventory. Research found that there are significant influence of personality and driver behavior on road accident among bus driver in Riau Province Indonesia.

Keywords: personality, driver behavior, driver stress, road accident

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Authors: Luleka Dlamini, Olivier Crespo, Jos van Dam

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Accurately estimating crop growth and yield is pivotal for timely sustainable agricultural management and ensuring food security. Crop models and remote sensing can complement each other and form a robust analysis tool to improve crop growth and yield estimations when combined. This study thus aims to systematically evaluate how research that exclusively focuses on assimilating RS data into crop models varies among countries, crops, data assimilation methods, and farming conditions. A strict search string was applied in the Scopus and Web of Science databases, and 497 potential publications were obtained. After screening for relevance with predefined inclusion/exclusion criteria, 123 publications were considered in the final review. Results indicate that over 81% of the studies were conducted in countries associated with high socio-economic and technological advancement, mainly China, the United States of America, France, Germany, and Italy. Many of these studies integrated MODIS or Landsat data into WOFOST to improve crop growth and yield estimation of staple crops at the field and regional scales. Most studies use recalibration or updating methods alongside various algorithms to assimilate remotely sensed leaf area index into crop models. However, these methods cannot account for the uncertainties in remote sensing observations and the crop model itself. l. Over 85% of the studies were based on commercial and irrigated farming systems. Despite a great global interest in data assimilation into crop models, limited research has been conducted in resource- and data-limited regions like Africa. We foresee a great potential for such application in those conditions. Hence facilitating and expanding the use of such an approach, from which developing farming communities could benefit.

Keywords: crop models, remote sensing, data assimilation, crop yield estimation

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Authors: Luleka Dlamini, Olivier Crespo, Jos van Dam

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Accurately estimating crop growth and yield is pivotal for timely sustainable agricultural management and ensuring food security. Crop models and remote sensing can complement each other and form a robust analysis tool to improve crop growth and yield estimations when combined. This study thus aims to systematically evaluate how research that exclusively focuses on assimilating RS data into crop models varies among countries, crops, data assimilation methods, and farming conditions. A strict search string was applied in the Scopus and Web of Science databases, and 497 potential publications were obtained. After screening for relevance with predefined inclusion/exclusion criteria, 123 publications were considered in the final review. Results indicate that over 81% of the studies were conducted in countries associated with high socio-economic and technological advancement, mainly China, the United States of America, France, Germany, and Italy. Many of these studies integrated MODIS or Landsat data into WOFOST to improve crop growth and yield estimation of staple crops at the field and regional scales. Most studies use recalibration or updating methods alongside various algorithms to assimilate remotely sensed leaf area index into crop models. However, these methods cannot account for the uncertainties in remote sensing observations and the crop model itself. l. Over 85% of the studies were based on commercial and irrigated farming systems. Despite a great global interest in data assimilation into crop models, limited research has been conducted in resource- and data-limited regions like Africa. We foresee a great potential for such application in those conditions. Hence facilitating and expanding the use of such an approach, from which developing farming communities could benefit.

Keywords: crop models, remote sensing, data assimilation, crop yield estimation

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Authors: Atikah Nurhayati, Asep A. Handaka

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Indonesia is a world’s major fish producer which can feed not only its citizens but also the people of the world. Currently, the total annual production is 11 tons and expected to double by the year of 2050. Given the potential, fishery has been an important part of the national food security system in Indonesia. Despite such a potential, a big challenge is facing the Indonesians in making fish the reliable source for their food, more specifically source of protein intake. The long geographic distance between the fish production centers and the consumer concentrations has prevented effective supply chain from producers to consumers and therefore demands a good logistic system. This paper is based on our research, which aimed at analyzing the fish supply chain and is to suggest relevant improvement to the chain. The research was conducted in the Year of 2016 in selected locations of Java Island, where intensive transaction on fishery commodities occur. Data used in this research comprises secondary data of time series reports on production and distribution and primary data regarding distribution aspects which were collected through interviews with purposively selected 100 respondents representing fishers, traders and processors. The data were analyzed following the supply chain management framework and processed following logistic regression and validity tests. The main findings of the research are as follows. Firstly, it was found that improperly managed connectivity and logistic chain is the main cause for insecurity of availability and affordability for the consumers. Secondly, lack of quality of most local processed products is a major obstacle for improving affordability and connectivity. The paper concluded with a number of recommended strategies to tackle the problem. These include rationalization of the length of the existing supply chain, intensification of processing activities, and improvement of distribution infrastructure and facilities.

Keywords: fishery, food security, logistic, supply chain

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Authors: Jonathan Munyoro, Takawira Machimbidza, Stephen Mutula

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The study investigates the current status of Intellectual Property (IP) information services in Zimbabwe's university libraries. Specifically, the study assesses the current IP information services offered in Zimbabwe’s university libraries, identifies challenges to the development of comprehensive IP information services in Zimbabwe’s university libraries, and suggests solutions for the development of IP information services in Zimbabwe’s university libraries. The study is born out of a realisation that research on IP information services in university libraries has received little attention, especially in developing country contexts, despite the fact that there are calls for heightened participation of university libraries in IP information services. In Zimbabwe, the launch of the National Intellectual Property Policy and Implementation Strategy 2018-2022 and the introduction of the Education 5.0 concept are set to significantly change the IP landscape in the country. Education 5.0 places more emphasis on innovation and industrialisation (in addition to teaching, community service, and research), and has the potential to shift the focus and level of IP output produced in higher and tertiary education institutions beyond copyrights and more towards commercially exploited patents, utility models, and industrial designs. The growing importance of IP commercialisation in universities creates a need for appropriate IP information services to assist students, academics, researchers, administrators, start-ups, entrepreneurs, and inventors. The critical challenge for university libraries is to reposition themselves and remain relevant in the new trajectory. Designing specialised information services to support increased IP generation and commercialisation appears to be an opportunity for university libraries to stay relevant in the knowledge economy. However, IP information services in Zimbabwe’s universities appear to be incomplete and focused mostly on assisting with research publications and copyright-related activities. Research on the existing status of IP services in university libraries in Zimbabwe is therefore necessary to help identify gaps and provide solutions in order to stimulate the growth of new forms of such services. The study employed a quantitative approach. An online questionnaire was administered to 57 academic librarians from 15 university libraries. Findings show that the current focus of the surveyed institutions is on providing scientific research support services (15); disseminating/sharing university research output (14); and copyright activities (12). More specialised IP information services such as IP education and training, patent information services, IP consulting services, IP online service platforms, and web-based IP information services are largely unavailable in Zimbabwean university libraries. Results reveal that the underlying challenge in the development of IP information services in Zimbabwe's university libraries is insufficient IP knowledge among academic librarians, which is exacerbated by inadequate IP management frameworks in university institutions. The study proposes a framework for the entrenchment of IP information services in Zimbabwe's university libraries.

Keywords: academic libraries, information services, intellectual property, IP knowledge, university libraries, Zimbabwe

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Authors: A. Uludağ, F. G. Tufekci, N. Ceviz

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Aim: The research has been carried out in order to evaluate caregiver burden, life satisfaction and received social support level of the parents whose children have congenital heart disease; to examine the relationship between the social supports received by them and caregiver burden and life satisfaction. Material and Method: The research which is descriptive and which is searching a relationship has been carried out between the dates June 7, 2012- June 30, 2014, in Erzurum Ataturk University Research and Application Hospital, Department of Pediatrics and Children Cardiology Polyclinic. In the research, it was collaborated with the parents (N = 157) who accepted to participate in, of children who were between the ages of 3 months- 12 years. While gathering the data, a questionnaire, Zarit Caregiver Burden, Life Satisfaction and Social Support Scales have been used. The statistics of the data acquired has been produced by using percentage distribution, mean, and variance and correlation analysis. Ethical principles are followed in the research. Results: In the research, caregiver burden, life satisfaction and social support level received from family (p < 0.05), have been determined higher in the parents whose children have serious congenital heart disease than that of parents whose children have slight disease and social support received from friends has been found lower. It has been determined that there is a strong relation (p < 0.001) through negative direction between both social support levels and caregiver burden of parents; and that there is a strong relation (p < 0.001) through positive direction between both support levels and life satisfaction. Conclusion: That Social Support is in a strong relation with Caregiver Burden through a negative direction and a strong relation with Life Satisfaction through positive direction in parents of all the children who have congenital heart disease requires social support systems to be reinforced. Parents can be led or guided so as to prompt social support systems more.

Keywords: congenital heart disease, child, parents, caregiver burden, life satisfaction, social support

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Authors: Mahesh Balan U, Rohith Srinivaas Mohanakrishnan, Venkat Subramanian

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Positive and unlabeled learning (PU) has gained more attention in both academic and industry research literature recently because of its relevance to existing business problems today. Yet, there still seems to be some existing challenges in terms of validating the performance of PU learning, as the actual truth of unlabeled data points is still unknown in contrast to a binary classification where we know the truth. In this study, we propose a novel PU learning technique based on the Pseudo-F measure, where we address this research gap. In this approach, we train the PU model to discriminate the probability distribution of the positive and unlabeled in the validation and spy data. The predicted probabilities of the PU model have a two-fold validation – (a) the predicted probabilities of reliable positives and predicted positives should be from the same distribution; (b) the predicted probabilities of predicted positives and predicted unlabeled should be from a different distribution. We experimented with this approach on a credit marketing case study in one of the world’s biggest fintech platforms and found evidence for benchmarking performance and backtested using historical data. This study contributes to the existing literature on semi-supervised learning.

Keywords: PU learning, semi-supervised learning, pseudo f measure, classification

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Authors: Gabriele Borg, Alexei Debono, Charlie Abela

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There on a constant rise in the availability of high volumes of data gathered from multiple sources, resulting in an abundance of unprocessed information that can be used to monitor patterns and trends in user behaviour. Similarly, year after year, Malta is also constantly experiencing ongoing population growth and an increase in mobilization demand. This research takes advantage of data which is continuously being sourced and converting it into useful information related to the traffic problem on the Maltese roads. The scope of this paper is to provide a methodology to create a custom dataset (MalTra - Malta Traffic) compiled from multiple participants from various locations across the island to identify the most common routes taken to expose the main areas of activity. This use of big data is seen being used in various technologies and is referred to as ITSs (Intelligent Transportation Systems), which has been concluded that there is significant potential in utilising such sources of data on a nationwide scale. Furthermore, a series of traffic prediction graph neural network models are conducted to compare MalTra to large-scale traffic datasets.

Keywords: graph neural networks, traffic management, big data, mobile data patterns

Procedia PDF Downloads 110