Search results for: collect data

Authors: Ermias Melake Tesfay

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The importance of evaluation practice and feedback to student advancement and retention has gained importance in the literature over the past ten years. So many issues and cases have been raised about the quality and types of evaluation carried out in higher education and the quality and quantity of student feedback. The aim of this study was to explore the students’ perspectives on the quality of course evaluation practice and feedback in College of Education and College of Science. The study used both quantitative and qualitative methods to collect data. Data were collected from third-year and fourth-year students of 13 departments in the College of Education and College of Science in Eritrea. A modified Service Performance (SERVPERF) questionnaire and focus group discussions were used to collect the data. The sample population comprised of 135 third-year and fourth-year students’ from both Colleges. A questionnaire using a 5 point Likert-scale was administered to all respondents whilst two focus group discussions were conducted. Findings from survey data and focus group discussions showed that the majority of students hold a positive perception of the quality of course evaluation practice but had a negative perception of methods of awarding grades and administrators’ role in listening to the students complain about the course. Furthermore, the analysis from the questionnaire showed that there is no statistically significant difference between third-year and fourth-year students, College of Education and College of Science and male and female students on the quality of course evaluation practice and feedback. The study recommends that colleges improve the quality of fairness and feedback during course assessment.

Keywords: evaluation, feedback, quality, students' perception

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Authors: Dragana Calic

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The openness, availability and the sheer volume of big data have provided, what some regard as, an invaluable and rich dataset. Researchers, businesses, advertising agencies, medical institutions, to name only a few, collect, share, and analyze this data to enable their processes and decision making. However, there are important ethical considerations associated with the use of big data. The rapidly evolving nature of online technologies has overtaken the many legislative, privacy, and ethical frameworks and principles that exist. For example, should we obtain consent to use people’s online data, and under what circumstances can privacy considerations be overridden? Current guidance on how to appropriately and ethically handle big data is inconsistent. Consequently, this paper focuses on two quite distinct but related ethical considerations that are at the core of the use of big data for research purposes. They include empowering the producers of data and empowering researchers who want to study big data. The first consideration focuses on informed consent which is at the core of empowering producers of data. In this paper, we discuss some of the complexities associated with informed consent and consider studies of producers’ perceptions to inform research ethics guidelines and practice. The second consideration focuses on the researcher. Similarly, we explore studies that focus on researchers’ perceptions and experiences.

Keywords: big data, ethics, producers’ perceptions, researchers’ perceptions

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Authors: David Ming Liu, Benjamin Hirsch, Bashir Aden

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Syndromic surveillance is to detect or predict disease outbreaks through the analysis of medical sources of data. Using social media data like tweets to do syndromic surveillance becomes more and more popular with the aid of open platform to collect data and the advantage of microblogging text and mobile geographic location features. In this paper, a Syndromic Surveillance Framework is presented with machine learning kernel using tweets data analytics. Influenza and the three cities Abu Dhabi, Al Ain and Dubai of United Arabic Emirates are used as the test disease and trial areas. Hospital cases data provided by the Health Authority of Abu Dhabi (HAAD) are used for the correlation purpose. In our model, Latent Dirichlet allocation (LDA) engine is adapted to do supervised learning classification and N-Fold cross validation confusion matrix are given as the simulation results with overall system recall 85.595% performance achieved.

Keywords: Syndromic surveillance, Tweets, Machine Learning, data mining, Latent Dirichlet allocation (LDA), Influenza

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Authors: Pierre van Tonder, Dinesh Muthoo, Kim twiname

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Many existing and newly built structures are constructed on the design basis of the engineer and the workmanship of the construction company. However, when considering larger structures where more people are exposed to the building, its structural integrity is of great importance considering the safety of its occupants (Raghu, 2013). But how can the structural integrity of a building be monitored efficiently and effectively. This is where the fourth industrial revolution step in, and with minimal human interaction, data can be collected, analysed, and stored, which could also give an indication of any inconsistencies found in the data collected, this is where the Fibre Bragg Grating (FBG) monitoring system is introduced. This paper illustrates how data can be collected and converted to develop stress – strain behaviour and to produce bending moment diagrams for the utilisation and prediction of the structure’s integrity. Embedded fibre optic sensors were used in this study– fibre Bragg grating sensors in particular. The procedure entailed making use of the shift in wavelength demodulation technique and an inscription process of the phase mask technique. The fibre optic sensors considered in this report were photosensitive and embedded in the slab and beams for data collection and analysis. Two sets of fibre cables have been inserted, one purposely to collect temperature recordings and the other to collect strain and temperature. The data was collected over a time period and analysed used to produce bending moment diagrams to make predictions of the structure’s integrity. The data indicated the fibre Bragg grating sensing system proved to be useful and can be used for structural health monitoring in any environment. From the experimental data for the slab and beams, the moments were found to be64.33 kN.m, 64.35 kN.m and 45.20 kN.m (from the experimental bending moment diagram), and as per the idealistic (Ultimate Limit State), the data of 133 kN.m and 226.2 kN.m were obtained. The difference in values gave room for an early warning system, in other words, a reserve capacity of approximately 50% to failure.

Keywords: fibre bragg grating, structural health monitoring, fibre optic sensors, beams

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Authors: Tabitha Mberi, Tirus Wanyoike, Joseph Sevilla

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The use of Information and Communication Technology (ICT) in Government is gradually becoming a major requirement to transform delivery of services to its stakeholders by improving quality of service and efficiency. In Kenya, the devolvement of government from local authorities to county governments has resulted in many counties adopting online revenue collection systems which can be easily accessed by its stakeholders. Strathmore Research and Consortium Centre (SRCC) implemented a revenue collection system in Taita Taveta, a County in coastal Kenya. It consisted of two systems that are integrated; an online system dubbed “CountyPro” for processing county services such as Business Permit applications, General Billing, Property Rates Payments and any other revenue streams from the county. The second part was a Point of Sale(PoS) system used by the county revenue collectors to charge for market fees and vehicle parking fees. This study assesses the success and challenges in adoption of the integrated system. Qualitative and quantitative data collection methods were used to collect data on the adoption of the system with the researcher using focus groups, interviews, and questionnaires to collect data from various users of the system An analysis was carried out and revealed that 87% of the county revenue officers who are situated in county offices describe the system as efficient and has made their work easier in terms of processing of transactions for customers.

Keywords: e-government, counties, information technology, online system, point of sale

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Authors: Niyom Suwandej, Kevin Wongleedee

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The objectives of this research were to study an importance of tourists’ destination loyalty from the perspective of inbound tourists visiting Bangkok and to study their level of interest to recommend as a tourist destination to others. A probability random sampling of 200 inbound tourists was utilized. The design of sampling was to collect half male and half female sample. A Likert-five-scale questionnaire was developed to collect the data. In addition, in-depth interviews were also used to obtain their perspective and experience of visiting Thailand. The findings disclosed that positive tourist perception influenced destination loyalty. Also, the majority of respondents had a high level of loyalty. When examined in detail, the destination loyalty indicators can be ranked according to the mean average from high to low as follows: to recommend the destination, to say positive thing about the destination, to plan to visit the destination in the near future, to acquire more information about the destination, and to spend more money at the destination.

Keywords: destination loyalty, inbound tourists, impact, Thailand

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Authors: Eszter Siposne Nandori

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

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

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Authors: Poramatdha Chutimant

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The objective of the research was to propose a model of teacher leadership in history instruction for utilization. Everett M. Rogers’ Diffusion of Innovations Theory is applied as theoretical framework. Qualitative method is to be used in the study, and the interview protocol used as an instrument to collect primary data from best practices who awarded by Office of National Education Commission (ONEC). Open-end questions will be used in interview protocol in order to gather the various data. Then, information according to international context of history instruction is the secondary data used to support in the summarizing process (Content Analysis). Dendrogram is a key to interpret and synthesize the primary data. Thus, secondary data comes as the supportive issue in explanation and elaboration. In-depth interview is to be used to collected information from seven experts in educational field. The focal point is to validate a draft model in term of future utilization finally.

Keywords: history study, nationalism, patriotism, responsible citizenship, teacher leadership

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Authors: M. Kobylanski, D. Buskulic, P.-H. Duron, D. Revuz, F. Ruggieri, E. Sandier, C. Tijus

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In order to design a cooperative e-learning platform, we observed teams of Teacher [T], Computer Scientist [CS] and exerciser's programmer-designer [ED] cooperating for the conception of a self-correcting exercise, but without the use of such a device in order to catch the kind of interactions a useful platform might provide. To do so, we first run a task analysis on how T, CS and ED should be cooperating in order to achieve, at best, the task of creating and implementing self-directed, self-paced, repeatable self-correcting exercises (RSE) in the context of open educational resources. The formalization of the whole process was based on the “objectives, activities and evaluations” theory of educational task analysis. Second, using the resulting frame as a “how-to-do it” guide, we run a series of three contrasted Hackathon of RSE-production to collect data about the cooperative process that could be later used to design the collaborative e-learning platform. Third, we used two complementary methods to collect, to code and to analyze the adequate survey data: the directional flow of interaction among T-CS-ED experts holding a functional role, and the Means-End Problem Solving analysis. Fourth, we listed the set of derived recommendations useful for the design of the exerciser as a cooperative e-learning platform. Final recommendations underline the necessity of building (i) an ecosystem that allows to sustain teams of T-CS-ED experts, (ii) a data safety platform although offering accessibility and open discussion about the production of exercises with their resources and (iii) a good architecture allowing the inheritance of parts of the coding of any exercise already in the data base as well as fast implementation of new kinds of exercises along with their associated learning activities.

Keywords: editorialization, open educational resources, pedagogical alignment, produsage, repeatable self-correcting exercises, team roles

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Authors: Hyun-Woo Cho

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Automatic process monitoring schemes are designed to give early warnings for unusual process events or abnormalities as soon as possible. For this end, various techniques have been developed and utilized in various industrial processes. It includes multivariate statistical methods, representation skills in reduced spaces, kernel-based nonlinear techniques, etc. This work presents a nonlinear empirical monitoring scheme for batch type production processes with incomplete process measurement data. While normal operation data are easy to get, unusual fault data occurs infrequently and thus are difficult to collect. In this work, noise filtering steps are added in order to enhance monitoring performance by eliminating irrelevant information of the data. The performance of the monitoring scheme was demonstrated using batch process data. The results showed that the monitoring performance was improved significantly in terms of detection success rate of process fault.

Keywords: batch process, monitoring, measurement, kernel method

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Authors: Mesfin Abebe Molla

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This research aims to examine the practices of inclusion of the hard of hearing and deaf students in regular schools. It also focuses on exploring strategies for optimal benefits of students with Hard of Hearing and Deaf (HH-D) from inclusion. Concurrent mixed methods research design was used to collect quantitative and qualitative data. The instruments used to gather data for this study were questionnaire, semi- structured interview, and observations. A total of 102 HH-D students and 42 primary and High School teachers were selected using simple random sampling technique and used as participants to collect quantitative data. Non-probability sampling technique was also employed to select 14 participants (4-school principals, 6-teachers and 4-parents of HH-D students) and they were interviewed to collect qualitative data. Descriptive and inferential statistical techniques (independent sample t-test, one way ANOVA and Multiple regressions) were employed to analyze quantitative data. Qualitative data were also analyzed qualitatively by theme analysis. The findings reported that there were individual principals’, teachers’ and parents’ strong commitment and efforts for practicing inclusion of HH-D students effectively; however, most of the core values of inclusion were missing in both schools. Most of the teachers (78.6 %) and HH-D students (75.5%) had negative attitude and considerable reservations about the feasibility of inclusion of HH-D students in both schools. Furthermore, there was a statistically significant difference of attitude toward to inclusion between the two school’s teachers and the teachers’ who had taken and had not taken additional training on IE and sign language. The study also indicated that there was a statistically significant difference of attitude toward to inclusion between hard of hearing and deaf students. However, the overall contribution of the demographic variables of teachers and HH-D students on their attitude toward inclusion is not statistically significant. The finding also showed that HH-D students did not have access to modified curriculum which would maximize their abilities and help them to learn together with their hearing peers. In addition, there is no clear and adequate direction for the medium of instruction. Poor school organization and management, lack of commitment, financial resources, collaboration and teachers’ inadequate training on Inclusive Education (IE) and sign language, large class size, inappropriate assessment procedure, lack of trained deaf adult personnel who can serve as role model for HH-D students and lack of parents and community members’ involvement were some of the major factors that affect the practicing inclusion of students HH-D. Finally, recommendations are made to improve the practices of inclusion of HH-D students and to make inclusion of HH-D students an integrated part of Ethiopian education based on the findings of the study.

Keywords: deaf, hard of hearing, inclusion, regular schools

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Authors: Zerubabel Mihret

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Background: Work-related stress is a pattern of reactions to work demands unmatched by worker’s knowledge, skills, or abilities. Healthcare institutions are considered high-risk and intensive work areas for work-related stress. However, there is the nonexistence of clear and strong data about the magnitude of work-related stress on sanitation workers in hospitals in Ethiopia. The aim of this study was to determine the magnitude of work-related stress among sanitation workers in public hospitals during COVID-19 in Addis Ababa, Ethiopia. Methods: Institution-based cross-sectional study was conducted from October 2021 to February 2022 among 494 sanitation workers who were selected from 4 hospitals. HSE (Health and Safety Executive of UK) standard data collection tool was used, and an interviewer-administered questionnaire was used to collect the data using KOBO collect application. The collected data were cleaned and analyzed using SPSS version 20.0. Both binary and multivariable logistic regression analyses were done to identify important factors having an association with work-related stress. Variables with p-value ≤ 0.25 in the bivariate analysis were entered into the multivariable logistic regression model. A statistically significant level was declared at a p-value ≤ 0.05. Results: This study revealed that the magnitude of work-related stress among sanitation workers was 49.2% (95% CI 45-54). Significant proportions (72.7%) of sanitation workers were dissatisfied with their current job. Sex, age, experience, and chewing khat were significantly associated with work-related stress. Conclusion: Work-related stress is significantly high among sanitation workers. Sex, age, experience, and chewing khat were identified as factors associated with work-related stress. Intervention program focusing on the prevention and control of stress is desired by hospitals.

Keywords: work-related stress, sanitation workers, Likert scale, public hospitals, Ethiopia

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Authors: Jiankang Wang, Hongyang Yu

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This paper proposes a portable online 3D modeling method. The method first utilizes a depth camera to collect data and compresses the depth data using a frame-by-frame lossless data compression method. The color image is encoded using the H.264 encoding format. After the cloud obtains the color image and depth image, a 3D modeling method based on bundlefusion is used to complete the 3D modeling. The results of this study indicate that this method has the characteristics of portability, online, and high efficiency and has a wide range of application prospects.

Keywords: 3D reconstruction, bundlefusion, lossless compression, depth image

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Authors: Wong Yat Cheung Maggie

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Occupational Safety and Health Ordinance (OS&HO) (Chap 509) was enacted in 1997, OHS in HK should be growing and maturing, with a holistic approach to occupational health and safety in the workplace including physical, mental, social and spiritual well-being. The question is “How effective are OHSPs in meeting the current needs of HK health workers?” This study was designed to explore the issue for the first time, to empirically analyse the views of those who work in the system. The study employed a mixed method approach to collect various data from Occupational Health Service Providers (OHSPs), Occupational Health Service Consumers (OHSC): Registered nurses working in the hospital setting. This study was designed in two phases and two stages. Phase I Stage I was a paper survey to collect the data on OHSP. Then Phase I Stage II was a follow-up interview. Phase II Stage I was a paper survey to collect the data on OHSC. Then Phase II Stage II was a follow-up focus group study on OHSC for further clarification of the Phase II and Stage I result. The Phase I result reflects HK OHSPs point of view and their experience in the existing OHS practice in the local hospitals. It reflects various styles of reporting systems, staff profiles background and resource in providing OHS in HK hospitals. However, the basic OHS concern is similar between hospitals. In general, the OHS policies and procedures are available on site even though they may have different foci. The Phase II result is reflecting the HKs OHSCs echoes the OHSP feedback at providing of OHS, OHS concern and related policies and procedure are available on site. However, the most significant feedback from the OHSC at Phase II Stage II shows, nurses experienced various OHS concern most commonly work stress, workplace harassment and back strain without formal or official report to the related parties. The lack of reporting was due to the management handling attitude, stakeholders’ compliance and term of definition still have room to be improved even the related policies and procedures are available on site.

Keywords: occupational health service, registered nurse, Hong Kong hospital, mixed method

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Authors: Kamel Barka, Lyamine Guezouli, Assem Rezki

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In this article, we propose a protocol called DataGA-DRF (a protocol for Data collection using a Genetic Algorithm through Dynamic Reference Points) that collects data from Heterogeneous wireless sensor networks. This protocol is based on DGA (Destination selection according to Genetic Algorithm) to control the movement of the UAV (Unmanned aerial vehicle) between dynamic reference points that virtually represent the sensor node deployment. The dynamics of these points ensure an even distribution of energy consumption among the sensors and also improve network performance. To determine the best points, DataGA-DRF uses a classification algorithm such as K-Means.

Keywords: heterogeneous wireless networks, unmanned aerial vehicles, reference point, collect data, genetic algorithm

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

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

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

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Authors: Hui Zhang, Sherif Beskhyroun

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Structural Health monitoring systems (SHM) are commonly used to identify and assess structural damage. In terms of damage detection, SHM needs to periodically collect data from sensors placed in the structure as damage-sensitive features. This includes abnormal changes caused by the strain field and abnormal symptoms of the structure, such as damage and deterioration. Currently, deploying sensors on a large scale in a building structure is a challenge. In this study, a highly stretchable strain sensors are used in this study to collect data sets of strain generated on the surface of full-size reinforced concrete (RC) frames under extreme cyclic load application. This sensing sheet can be switched freely between the test bending strain and the axial strain to achieve two different configurations. On this basis, the deep neural network prediction model of the frame beam and frame column is established. The training results show that the method can accurately predict the strain value and has good generalization ability. The two deep neural network prediction models will also be deployed in the SHM system in the future as part of the intelligent strain sensor system.

Keywords: strain sensing sheets, deep neural networks, strain measurement, SHM system, RC frames

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Authors: Krishan Kumar, Mohit Mittal, Pramod Kumar

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Wireless sensor network is one of the most promising communication networks for monitoring remote environmental areas. In this network, all the sensor nodes are communicated with each other via radio signals. The sensor nodes have capability of sensing, data storage and processing. The sensor nodes collect the information through neighboring nodes to particular node. The data collection and processing is done by data aggregation techniques. For the data aggregation in sensor network, clustering technique is implemented in the sensor network by implementing self-organizing feature map (SOFM) neural network. Some of the sensor nodes are selected as cluster head nodes. The information aggregated to cluster head nodes from non-cluster head nodes and then this information is transferred to base station (or sink nodes). The aim of this paper is to manage the huge amount of data with the help of SOM neural network. Clustered data is selected to transfer to base station instead of whole information aggregated at cluster head nodes. This reduces the battery consumption over the huge data management. The network lifetime is enhanced at a greater extent.

Keywords: artificial neural network, data clustering, self organization feature map, wireless sensor network

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Authors: Komol Phaisarn, Anuphan Suttimarn, Vitchanan Keawtong, Kittisak Thongyoun, Chaiyos Jamsawang

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This article is intended to analyze insurance information which contains information on the customer decision when purchasing life insurance pay package. The data were analyzed in order to present new customers with Life Insurance Perfect Pay package to meet new customers’ needs as much as possible. The basic data of insurance pay package were collect to get data mining; thus, reducing the scattering of information. The data were then classified in order to get decision model or decision tree using Algorithm C4.5 (J-48). In the classification, WEKA tools are used to form the model and testing datasets are used to test the decision tree for the accurate decision. The validation of this model in classifying showed that the accurate prediction was 68.43% while 31.25% were errors. The same set of data were then tested with other models, i.e. Naive Bayes and Zero R. The results showed that J-48 method could predict more accurately. So, the researcher applied the decision tree in writing the program used to introduce the product to new customers to persuade customers’ decision making in purchasing the insurance package that meets the new customers’ needs as much as possible.

Keywords: decision tree, data mining, customers, life insurance pay package

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Authors: Sara Alshehri, Bayan Alenzi, Atheer Alshehri, Samia Chelloug, Zainab Almry, Hussah Albugmai

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This paper concerns body area networks sensor that collect signals around a human body. The black hole attacks are the main security challenging problem because the data traffic can be dropped at any node. The focus of our proposed solution is to efficiently route data packets while detecting black hole nodes.

Keywords: body sensor networks, security, black hole, routing, broadcasting, OMNeT++

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Authors: Ebrahim Fataei, Seyed Ali Hosseini, Zahra Arabi, Habib farhadi, Mehdi Aalipour Erdi, Seiied Taghi Seiied Safavian

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Solid waste production is an integral part of human life and management of waste require full scientific approach and essential planning. The allocation of most management cost to collection and transportation and also the necessity of operational efficiency in this system, by limiting time consumption, and on the other hand optimum collection system and transportation is the base of waste design and management. This study was done to optimize the exits collection and transportation system of solid waste in Meshkinshahr city. So based on the analyzed data of municipal solid waste components in seven zones of Meshkinshahr city, and GIS software, applied to design storage place based on origin recycling and a route to collect and transport. It was attempted to represent an appropriate model to store, collect and transport municipal solid waste. The result shows that GIS can be applied to locate the waste container and determine a waste collection direction in an appropriate way.

Keywords: municipal solid waste management, transportation, optimizing, GIS, Iran

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Authors: Reggie Chandra

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Trafficaccidents and traffic signal optimization are correlated. However, 70-90% of the traffic signals across the USA are not synchronized. The reason behind that is insufficient resources to create and implement timing plans. In this work, we will discuss the use of a breakthrough Artificial Intelligence (AI) technology to optimize traffic flow and collect 24/7/365 accurate traffic data using a vehicle detection system. We will discuss what are recent advances in Artificial Intelligence technology, how does AI work in vehicles, pedestrians, and bike data collection, creating timing plans, and what is the best workflow for that. Apart from that, this paper will showcase how Artificial Intelligence makes signal timing affordable. We will introduce a technology that uses Convolutional Neural Networks (CNN) and deep learning algorithms to detect, collect data, develop timing plans and deploy them in the field. Convolutional Neural Networks are a class of deep learning networks inspired by the biological processes in the visual cortex. A neural net is modeled after the human brain. It consists of millions of densely connected processing nodes. It is a form of machine learning where the neural net learns to recognize vehicles through training - which is called Deep Learning. The well-trained algorithm overcomes most of the issues faced by other detection methods and provides nearly 100% traffic data accuracy. Through this continuous learning-based method, we can constantly update traffic patterns, generate an unlimited number of timing plans and thus improve vehicle flow. Convolutional Neural Networks not only outperform other detection algorithms but also, in cases such as classifying objects into fine-grained categories, outperform humans. Safety is of primary importance to traffic professionals, but they don't have the studies or data to support their decisions. Currently, one-third of transportation agencies do not collect pedestrian and bike data. We will discuss how the use of Artificial Intelligence for data collection can help reduce pedestrian fatalities and enhance the safety of all vulnerable road users. Moreover, it provides traffic engineers with tools that allow them to unleash their potential, instead of dealing with constant complaints, a snapshot of limited handpicked data, dealing with multiple systems requiring additional work for adaptation. The methodologies used and proposed in the research contain a camera model identification method based on deep Convolutional Neural Networks. The proposed application was evaluated on our data sets acquired through a variety of daily real-world road conditions and compared with the performance of the commonly used methods requiring data collection by counting, evaluating, and adapting it, and running it through well-established algorithms, and then deploying it to the field. This work explores themes such as how technologies powered by Artificial Intelligence can benefit your community and how to translate the complex and often overwhelming benefits into a language accessible to elected officials, community leaders, and the public. Exploring such topics empowers citizens with insider knowledge about the potential of better traffic technology to save lives and improve communities. The synergies that Artificial Intelligence brings to traffic signal control and data collection are unsurpassed.

Keywords: artificial intelligence, convolutional neural networks, data collection, signal control, traffic signal

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Authors: Sudaporn Arundee

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The purposes of this research were to study in three areas: (1) to study political understanding and participating of the constitutional monarchy, (2) to study the level of participation. This paper drew upon data collected from 395 Dusit residents by using questionnaire. In addition, a simple random sampling was utilized to collect data. The findings revealed that 94 percent of respondents had a very good understanding of constitution monarchy with a mean of 4.8. However, the respondents overall had a very low level of participation with the mean score of 1.69 and standard deviation of .719.

Keywords: political participation, constitutional monarchy, management and social sciences

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Authors: Rima Shishakly, Mervyn Misajon

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

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

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Authors: Amir Taghizadeh Vahed, Adithya Thaduri

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Shovels are the main components in the mining transportation system. The productivity of the mines depends on the availability of shovels due to its high capital and operating costs. The unplanned failure/shutdowns of a shovel results in higher repair costs, increase in downtime, as well as increasing indirect cost (i.e. loss of production and company’s reputation). In order to mitigate these failures, predictive maintenance can be useful approach using failure prediction. The modern mining machinery or shovels collect huge datasets automatically; it consists of reliability and maintenance data. However, the gathered datasets are useless until the information and knowledge of data are extracted. Machine learning as well as data mining, which has a major role in recent studies, has been used for the knowledge discovery process. In this study, data mining and machine learning approaches are implemented to detect not only anomalies but also patterns from a dataset and further detection of failures.

Keywords: maintenance, machine learning, shovel, conditional based monitoring

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Authors: Mou Juliet Rebeiro, S. M. Abul Kalam Azad

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This study was carried out to see the anxiety and depression in caregivers of autistic children. The objectives of the research were to assess depression and anxiety among caregivers of autistic children and to find out the experience of caregivers. For this purpose, the research was conducted on a sample of 39 caregivers of autistic children. Participants were taken from a special school. To collect data for this study each of the caregivers were administered questionnaire comprising scales to measure anxiety and depression and some responses of the participants were taken through interview based on a topic guide. Obtained quantitative data were analyzed by using statistical analysis and qualitative data were analyzed according to themes. Mean of the anxiety score (55.85) and depression score (108.33) is above the cutoff point. Results showed that anxiety and depression is clinically present in caregivers of autistic children. Most of the caregivers experienced behavior, emotional, cognitive and social problems of their child that is linked with anxiety and depression.

Keywords: anxiety, autism, caregiver, depression

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Authors: K. Y. Chen, Y. N. Jia, H. Chua, C. W. Kan

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As the largest developing country in the world, China is now facing the problem arising from the environment. Thus, China government increases the environmental investment yearly. In this study, we will analyse the effect of the public environmental investment on the sustainable development in China. Firstly, we will review the current situation of China's environmental issue. Secondly, we will collect the yearly environmental data as well as the information of public environmental investment. Finally, we will use the collected data to analyse and project the SWOT of public environmental investment in China. Therefore, the aim of this paper is to provide the relationship between public environmental investment and sustainable development in China. Based on the data collected, it was revealed that the public environmental investment had a positive impact on the sustainable development in China as well as the GDP growth. Acknowledgment: Authors would like to thank the financial support from the Hong Kong Polytechnic University for this work.

Keywords: China, public environmental investment, sustainable development, analysis

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Authors: Prapoj Na Bangchang

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This research aimed to study the satisfaction of university students on the Suan Sunandha Rajabhat University television station. The sample were 250 undergraduate students from Year 1 to Year 4. The tool used to collect data was a questionnaire. Statistics used in data analysis were percentage, mean and standard deviation. The results showed that student satisfaction on the University's television station location received high score, followed by the number of devices, and the content presented received the lowest score. Most students want the content of the programs to be improved especially entertainment content, followed by sports content.

Keywords: student satisfaction, university TV channel, media, broadcasting

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Authors: Khelifa Benahmed, Tarek Benahmed

Abstract:

There are diverse applications of wireless sensor networks (WSNs) in the real world, typically invoking some kind of monitoring, tracking, or controlling activities. In an application, a WSN is deployed over the area of interest to sense and detect the events and collect data through their sensors in a geographical area and transmit the collected data to a Base Station (BS). This paper presents an overview of the research solutions available in the field of environmental monitoring applications, more precisely the problems of critical area monitoring using wireless sensor networks.

Keywords: critical infrastructure monitoring, environment monitoring, event region detection, wireless sensor networks

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Authors: Antonio Gioiosa, Simone Doanti, Eric Flumerfelt, Luca Morescalchi, Elena Pedreschi, Gianantonio Pezzullo, Ryan A. Rivera, Franco Spinella

Abstract:

The Mu2e experiment at Fermilab aims to measure the charged-lepton flavour violating neutrino-less conversion of a negative muon into an electron in the field of an aluminum nucleus. With the expected experimental sensitivity, Mu2e will improve the previous limit of four orders of magnitude. The Mu2e data acquisition (DAQ) system provides hardware and software to collect digitized data from the tracker, calorimeter, cosmic ray veto, and beam monitoring systems. Mu2e’s trigger and data acquisition system (TDAQ) uses otsdaq as its solution. developed at Fermilab, otsdaq uses the artdaq DAQ framework and art analysis framework, under-the-hood, for event transfer, filtering, and processing. Otsdaq is an online DAQ software suite with a focus on flexibility and scalability while providing a multi-user, web-based interface accessible through the Chrome or Firefox web browser. The detector read out controller (ROC) from the tracker and calorimeter stream out zero-suppressed data continuously to the data transfer controller (DTC). Data is then read over the PCIe bus to a software filter algorithm that selects events which are finally combined with the data flux that comes from a cosmic ray veto system (CRV).

Keywords: trigger, daq, mu2e, Fermilab

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