Search results for: online flood prediction system

Authors: Ziad Abdeldayem, Jakub Markiewicz, Kunal Kansara, Laura Edwards

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Airborne Laser Scanning (ALS) is one of the main technologies for generating high-resolution digital terrain models (DTMs). DTMs are crucial to several applications, such as topographic mapping, flood zone delineation, geographic information systems (GIS), hydrological modelling, spatial analysis, etc. Laser scanning system generates irregularly spaced three-dimensional cloud of points. Raw ALS data are mainly ground points (that represent the bare earth) and non-ground points (that represent buildings, trees, cars, etc.). Removing all the non-ground points from the raw data is referred to as filtering. Filtering heavily forested areas is considered a difficult and challenging task as the canopy stops laser pulses from reaching the terrain surface. This research presents an approach for removing non-ground points from raw ALS data in densely forested areas. Smoothing splines are exploited to interpolate and fit the noisy ALS data. The presented filter utilizes a weight function to allocate weights for each point of the data. Furthermore, unlike most of the methods, the presented filtering algorithm is designed to be automatic. Three different forested areas in the United Kingdom are used to assess the performance of the algorithm. The results show that the generated DTMs from the filtered data are accurate (when compared against reference terrain data) and the performance of the method is stable for all the heavily forested data samples. The average root mean square error (RMSE) value is 0.35 m.

Keywords: airborne laser scanning, digital terrain models, filtering, forested areas

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Authors: Ana Gabriela Diaz, Luis Fermín Córdova, Jr., Roberto Lamazares

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The island of Cuba, the largest of the Greater Antilles, is located in the tropical North Atlantic. It is annually affected by numerous weather events, which have caused severe damage to our coastal areas. In the same way that many other coastlines around the world, the beautiful beaches of the Hicacos Peninsula also suffer from erosion. This leads to a structural regression of the coastline. If measures are not taken, the hotels will be exposed to the advance of the sea, and it will be a serious problem for the economy. With the aim of studying the intensity of this type of activity, specialists of group of coastal and marine engineering from CIH, in the framework of the research conducted within the project MEGACOSTAS 2, provide their research to simulate extreme events and assess their impact in coastal areas, mainly regarding the definition of flood volumes and morphodynamic changes in sandy beaches. The main objective of this work is the evaluation of the process of Varadero beach erosion (the coastal sector has an important impact in the country's economy) on the Hicacos Peninsula for different paths of hurricanes. The mathematical model XBeach, which was integrated into the Coastal engineering system introduced by the project of MEGACOSTA 2 to determine the area and the more critical profiles for the path of hurricanes under study, was applied. The results of this project have shown that Center area is the greatest dynamic area in the simulation of the three paths of hurricanes under study, showing high erosion volumes and the greatest average length of regression of the coastline, from 15- 22 m.

Keywords: beach, erosion, mathematical model, coastal areas

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Authors: Adithya Jaikumar, Sudarsan Jayasingh

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The dramatic technology led changes that continue to take place at the market place has led to the emergence and implication of online brand pages on social media networks. The Facebook brand page has become extremely popular among different brands. The primary aim of this study was to identify the impact of post formats and content type on customer engagement in Facebook brand pages. Methodology used for this study was to analyze and categorize 9037 content messages posted by 20 automobile brands in India during April 2014 to March 2015 and the customer activity it generated in return. The data was obtained from Fanpage karma- an online tool used for social media analytics. The statistical technique used to analyze the count data was negative binomial regression. The study indicates that there is a statistically significant relationship between the type of post and the customer engagement. The study shows that photos are the most posted format and highest engagement is found to be related to videos. The finding also reveals that social events and entertainment related content increases engagement with the message.

Keywords: content analysis, customer engagement, digital engagement, facebook brand pages, social media

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Authors: Nato Kutaladze, George Mikuchadze, Giorgi Sokhadze

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Precipitation forecasts are one of the most demanding applications in numerical weather prediction (NWP). Georgia, as the whole Caucasian region, is characterized by very complex topography. The country territory is prone to flash floods and mudflows, quantitative precipitation estimation (QPE) and quantitative precipitation forecast (QPF) at any leading time are very important for Georgia. In this study, advanced research weather forecasting model’s skill in QPF is investigated over Georgia’s territory. We have analyzed several convection parameterization and microphysical scheme combinations for different rainy episodes and heavy rainy phenomena. We estimate errors and biases in accumulated 6 h precipitation using different spatial resolution during model performance verification for 12-hour and 24-hour lead time against corresponding rain gouge observations and satellite data. Various statistical parameters have been calculated for the 8-month comparison period, and some skills of model simulation have been evaluated. Our focus is on the formation and organization of convective precipitation systems in a low-mountain region. Several problems in connection with QPF have been identified for mountain regions, which include the overestimation and underestimation of precipitation on the windward and lee side of the mountains, respectively, and a phase error in the diurnal cycle of precipitation leading to the onset of convective precipitation in model forecasts several hours too early.

Keywords: extremal dependence index, false alarm, numerical weather prediction, quantitative precipitation forecasting

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Authors: Adeniran Adetunji, Babalola M. Florence, Akande Ademola

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In this paper, we made an overview on the concept of adaptive e-Learning system, enumerates the elements of adaptive learning concepts e.g. A pedagogical framework, multiple learning strategies and pathways, continuous monitoring and feedback on student performance, statistical inference to reach final learning strategy that works for an individual learner by “mass-customization”. Briefly highlights the motivation of this new system proposed for effective learning teaching. E-Review literature on the concept of adaptive e-learning system and emphasises on the Item Response Calibration, which is an important approach to developing an adaptive e-Learning system. This paper write-up is concluded on the justification of item response calibration/estimation towards designing a successful and effective adaptive e-Learning system.

Keywords: adaptive e-learning system, pedagogical framework, item response, computer applications

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Authors: Vedat Coskun, Fahri Soylemezgiller, Busra Ozdenizci, Kerem Ok

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In recent years, several smart card solutions for transportation services of cities with different technical infrastructures and business models has emerged considerably, which triggers new business and technical opportunities. In order to create a unique system, we present a novel, promising system called Multifunctional City Smart Card System to be used in all cities that provides transportation and loyalty services based on the MasterCard M/Chip Advance standards. The proposed system provides a unique solution for transportation services of large cities over the world, aiming to answer all transportation needs of citizens. In this paper, development of the Multifunctional City Smart Card System and system requirements are briefly described. Moreover, performance analysis results of M/Chip Advance Compatible Validators which is the system's most important component are presented.

Keywords: smart card, m/chip advance standard, city transportation, performance analysis

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Authors: Heichia Wang, Yalan Chao

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Social inequality is a persistent problem. One of the ways to solve this problem is through education. At present, vulnerable groups are often less geographically accessible to educational resources. However, compared with educational resources, communication equipment is easier for vulnerable groups. Now that information and communication technology (ICT) has entered the field of education, today we can accept the convenience that ICT provides in education, and the mobility that it brings makes learning independent of time and place. With mobile learning, teachers and students can start discussions in an online chat room without the limitations of time or place. However, because liquidity learning is quite convenient, people tend to solve problems in short online texts with lack of detailed information in a lack of convenient online environment to express ideas. Therefore, the ICT education environment may cause misunderstanding between teachers and students. Therefore, in order to better understand each other's views between teachers and students, this study aims to clarify the essays of the analysts and classify the students into several types of learning questions to clarify the views of teachers and students. In addition, this study attempts to extend the description of possible omissions in short texts by using external resources prior to classification. In short, by applying a short text classification, this study can point out each student's learning problems and inform the instructor where the main focus of the future course is, thus improving the ICT education environment. In order to achieve the goals, this research uses convolutional neural network (CNN) method to analyze short discussion content between teachers and students in an ICT education environment. Divide students into several main types of learning problem groups to facilitate answering student problems. In addition, this study will further cluster sub-categories of each major learning type to indicate specific problems for each student. Unlike most neural network programs, this study attempts to extend short texts with external resources before classifying them to improve classification performance. In short, by applying the classification of short texts, we can point out the learning problems of each student and inform the instructors where the main focus of future courses will improve the ICT education environment. The data of the empirical process will be used to pre-process the chat records between teachers and students and the course materials. An action system will be set up to compare the most similar parts of the teaching material with each student's chat history to improve future classification performance. Later, the function of short text classification uses CNN to classify rich chat records into several major learning problems based on theory-driven titles. By applying these modules, this research hopes to clarify the main learning problems of students and inform teachers that they should focus on future teaching.

Keywords: ICT education improvement, social equality, short text analysis, convolutional neural network

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Authors: H. J. T. Kevin Mozes, Dyah Purnamasari

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Background: Lung cancer accounted for the fourth most common cancer in Indonesia. 85% of all lung cancer cases are the Non-Small Cell Lung Cancer (NSCLC). The indistinct signs and symptoms of NSCLC sometimes lead to misdiagnosis. The gold standard assessment for the diagnosis of NSCLC is the histopathological biopsy, which is invasive. Cyfra 21-1 is a tumor marker, which can be found in the intermediate protein structure in the epitel. The accuracy of Cyfra 21-1 in diagnosing NSCLC is not yet known, so this report is made to seek the answer for the question above. Methods: Literature searching is done using online databases. Proquest and Pubmed are online databases being used in this report. Then, literature selection is done by excluding and including based on inclusion criterias and exclusion criterias. The selected literature is then being appraised using the criteria of validity, importance, and validity. Results: From six journals appraised, five of them are valid. Sensitivity value acquired from all five literature is ranging from 50-84.5 %, meanwhile the specificity is 87.8 %-94.4 %. Likelihood the ratio of all appraised literature is ranging from 5.09 -10.54, which categorized to Intermediate High. Conclusion: Serum Cyfra 21-1 is a sensitive and very specific tumor marker for diagnosis of non-small cell lung cancer (NSCLC).

Keywords: cyfra 21-1, diagnosis, nonsmall cell lung cancer, NSCLC, tumor marker

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Authors: Mohammad Soliman, Mohamed A. Abou-Shouk

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Knowledge management focuses on the development, storage, retrieval, and dissemination of information and expertise. It has become an important tool to improve performance in tourism enterprises. This includes improving decision-making, developing customer services, and increasing sales and profits. Knowledge management adoption depends on human, organizational and technological factors. This study aims to explore the concept of knowledge management in travel agents in Egypt. It explores the requirements of adoption and its impact on performance in these agencies. The study targets Category A travel agents in Egypt. The population of the study encompasses Category A travel agents having online presence. An online questionnaire is used to collect data from managers of travel agents. This study is useful for travel agents who are in urgent need to restructure their intermediary role and support their survival in the global travel market. The study sheds light on the requirements of adoption and the expected impact on performance. This could help travel agents identify their situation and the determine the extent to which they are ready to adopt knowledge management. This study is contributing to knowledge by providing insights from the tourism sector in a developing country where the concept of knowledge management is still in its infancy stages.

Keywords: knowledge management, knowledge management adoption, performance, travel agents

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Authors: Kawai Nobuaki

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This paper describes the design and implementation of web system for continuable and viable collaboration. This study proposes the improvement of the system based on a result of a certain practice. As contemporary higher education information environments transform, this study highlights the significance of university identity and college identity that are formed continuously through independent activities of the students. Based on these discussions, the present study proposes a practical media environment design which facilitates the processes of organizational identity formation based on a continuous and cyclical model. Even if users change by this system, the communication system continues operation and cooperation. The activity becomes the archive and produces new activity. Based on the result, this study elaborates a plan with a re-design by a system from the viewpoint of second-order cybernetics. Systems theory is a theoretical foundation for our study.

Keywords: collaborative work, learning management system, second-order cybernetics, systems theory, user generated contents, viable system model

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Authors: Fabio Marson, Revital Naor-Ziv, Patrizio Paoletti, Joseph Glicksohn, Filippo Carducci, Tal Dotan Ben-Soussan

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According to embodied cognition theories, higher-order cognitive functions and complex behaviors seems to be affected by bodily states. For example, the polyvagal theory suggests that the human autonomic nervous system evolved to support social interactions. Accordingly, integration and perception of information related to the physiological state arising from the peripherical nervous system (i.e., interoception) play a role in the regulation of social interaction by modulating emotional responses and prosocial behaviors. Moreover, recent studies showed that interoception is involved in the representations of conceptual knowledge, suggesting that the bodily information carried by the interoceptive system provides a perceptual basis for the embodiment of abstract concepts, especially those related to social and emotional domains. However, to the best of our knowledge, no studies explored the relationship between interoception, prosocial behaviors, and conceptual representations. Considering the privileged position of interoception in mediating higher-order cognition and social interaction, we designed a cross-cultural study to explore the relationship between interoception, the sensitivity of bodily functions, and empathy. We recruited Italian, English, and Hebrew participants, and we asked them to fill in a questionnaire about empathy (Empathy Quotient), a questionnaire about bodily perception (Body Perception Questionnaire), and to rate different concrete and abstract concepts for the extent such concepts can be experienced through vision, hearing, taste, smell, touch, and interoception. We observed that in all languages, interoception ratings for abstract concepts were greater than for concrete concepts. Importantly, interoception ratings for abstract concepts were positively correlated with empathy and sensitivity of bodily functions. Our results suggest that participants with higher empathy and sensitivity of bodily functions show also a greater embodiment of abstract concepts in interoception, providing further evidence for the importance of the interoceptive system in regulating prosocial behaviors and integrating conceptual representations.

Keywords: conceptual representations, embodiment, empathy, empathy quotient, interoception, prosocial behaviors

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Authors: Nuruddeen Usman, Mohammad Sani

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The performance of project delivery methods has been an issue of concern to various stakeholders in the construction industry. The contracting system of project delivery is the traditional system used in the delivery of most public projects in Nigeria. The direct labor system is used most times as an alternative to the traditional system. There were so many complain about the performance of contracting system and the suitability of direct labor as an alternative to the delivery of public projects. Therefore, this paper is aimed at investigating the effect of project size on the project delivery methods in the completed public buildings. Questionnaires were self-administered to managerial staff in the study area and analyzed using descriptive statistics. The findings reveals that contracting system was choosing for large size building construction project delivery with higher frequency (F) of 40 (76.9%) against direct labor with 12 (23.1%). While the small size project, the result revealed a frequency (F) of 26 (50%) for contracting system and direct labor system respectively. Base on the research findings, the contracting system, was recommended for all sizes of building construction project delivery while direct labor system can only use as an alternative for small size building construction projects delivery.

Keywords: construction size, contracting system, direct labour, effect

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Authors: Ju Peng Poh, Jun Yu Charles Lee, Jonathan Chew Hoe Khoo

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The emergence of digital twin technology, a digital replica of physical world, has improved the real-time access to data from sensors about the performance of buildings. This digital transformation has opened up many opportunities to improve the management of the building by using the data collected to help monitor consumption patterns and energy leakages. One example is the integration of predictive models for anomaly detection. In this paper, we use the GAM (Generalised Additive Model) for the anomaly detection of Air Handling Units (AHU) power consumption pattern. There is ample research work on the use of GAM for the prediction of power consumption at the office building and nation-wide level. However, there is limited illustration of its anomaly detection capabilities, prescriptive analytics case study, and its integration with the latest development of digital twin technology. In this paper, we applied the general GAM modelling framework on the historical data of the AHU power consumption and cooling load of the building between Jan 2018 to Aug 2019 from an education campus in Singapore to train prediction models that, in turn, yield predicted values and ranges. The historical data are seamlessly extracted from the digital twin for modelling purposes. We enhanced the utility of the GAM model by using it to power a real-time anomaly detection system based on the forward predicted ranges. The magnitude of deviation from the upper and lower bounds of the uncertainty intervals is used to inform and identify anomalous data points, all based on historical data, without explicit intervention from domain experts. Notwithstanding, the domain expert fits in through an optional feedback loop through which iterative data cleansing is performed. After an anomalously high or low level of power consumption detected, a set of rule-based conditions are evaluated in real-time to help determine the next course of action for the facilities manager. The performance of GAM is then compared with other approaches to evaluate its effectiveness. Lastly, we discuss the successfully deployment of this approach for the detection of anomalous power consumption pattern and illustrated with real-world use cases.

Keywords: anomaly detection, digital twin, generalised additive model, GAM, power consumption, supervised learning

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Authors: Ali Isapour, Ramin Nateghi

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— Markov parameters are unique parameters of the system and remain unchanged under similarity transformations. Markov parameters from a power series that is convergent only if the system matrix’s eigenvalues are inside the unity circle. Therefore, Markov parameters of a stable discrete-time system are convergent. In this study, we aim to realize the system based on Markov parameters by using Artificial Neural Networks (ANN), and this end, we use Flexible Neural Networks. Realization means determining the elements of matrices A, B, C, and D.

Keywords: Markov parameters, realization, activation function, flexible neural network

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Authors: B. Garfolo, L. Kelpsh, R. Roak, R. Kuck

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Often, student ratings of instructors play a significant role in the career path of an instructor in higher education. So then, how does a student view the effectiveness of instructor teaching? This question has been address by literally thousands of studies found in the literature. Yet, why does this question still persist? A literature review reveals that while it is true that student evaluations of instructors can be biased, there is still a considerable amount of work that needs to be done in understanding why. As student evaluations of instructors can be used in a variety of settings (formative or summative) it is critical to understand the nature of the bias. The authors believe that not only is some bias possible in student evaluations, it should be expected for the simple reason that a student evaluation is a human activity and as such, relies upon perception and interpersonal judgment. As such, student ratings are affected by the same factors that can potentially affect any rater’s judgment, such as stereotypes based on gender, culture, race, etc. Previous study findings suggest that student evaluations of teacher effectiveness differ between male and female raters. However, even though studies have shown that instructor gender does play an important role in influencing student ratings, the exact nature and extent of that role remains the subject of debate. Researchers, in their attempt to define good teaching, have looked for differences in student evaluations based on a variety of characteristics such as course type, class size, ability level of the student and grading practices in addition to instructor and student characteristics (gender, age, etc.) with inconsistent results. If a student evaluation represents more than an instructor’s teaching ability, for example, a physical characteristic such as gender, then this information must be taken into account if the evaluation is to have meaning with respect to instructor assessment. While the authors concede that it is difficult or nearly impossible to separate gender from student perception of teaching practices in person, it is, however, possible to shield an instructor’s gender identity with respect to an online teaching experience. The online teaching modality presents itself as a unique opportunity to experiment directly with gender identity. The analysis of the differences of online behavior of individuals when they perceive that they are interacting with a male or female could provide a wealth of data on how gender influences student perceptions of teaching effectiveness. Given the importance of the role student ratings play in hiring, retention, promotion, tenure, and salary deliberations in academic careers, this question warrants further attention as it is important to be aware of possible bias in student evaluations if they are to be used at all with respect to any academic considerations. For experimental purposes, the author’s constructed and online class where each instructors operate under two different gender identities. In this study, each instructor taught multiple sections of the same class using both a male identity and a female identity. The study examined student evaluations of teaching based on certain student and instructor characteristics in order to determine if and where male and female students might differ in their ratings of instructors based on instructor gender. Additionally, the authors examined if there are differences between undergraduate and graduate students' ratings with respect to the experimental criteria.

Keywords: gender bias, ethics, student evaluations, student perceptions, online instruction

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Authors: Thor Alexis Sazon, Alexander Guzman-Urbina, Yasuhiro Fukushima

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This study outlines the method of how to develop a surrogate life cycle model based on fuzzy logic using three fuzzy inference methods: (1) the conventional Fuzzy Inference System (FIS), (2) the hybrid system of Data Analytics and Fuzzy Inference (DAFIS), which uses data clustering for defining the membership functions, and (3) the Adaptive-Neuro Fuzzy Inference System (ANFIS), a combination of fuzzy inference and artificial neural network. These methods were demonstrated with a case study where the Global Warming Potential (GWP) and the Levelized Cost of Energy (LCOE) of solar photovoltaic (PV) were estimated using Solar Irradiation, Module Efficiency, and Performance Ratio as inputs. The effects of using different fuzzy inference types, either Sugeno- or Mamdani-type, and of changing the number of input membership functions to the error between the calibration data and the model-generated outputs were also illustrated. The solution spaces of the three methods were consequently examined with a sensitivity analysis. ANFIS exhibited the lowest error while DAFIS gave slightly lower errors compared to FIS. Increasing the number of input membership functions helped with error reduction in some cases but, at times, resulted in the opposite. Sugeno-type models gave errors that are slightly lower than those of the Mamdani-type. While ANFIS is superior in terms of error minimization, it could generate solutions that are questionable, i.e. the negative GWP values of the Solar PV system when the inputs were all at the upper end of their range. This shows that the applicability of the ANFIS models highly depends on the range of cases at which it was calibrated. FIS and DAFIS generated more intuitive trends in the sensitivity runs. DAFIS demonstrated an optimal design point wherein increasing the input values does not improve the GWP and LCOE anymore. In the absence of data that could be used for calibration, conventional FIS presents a knowledge-based model that could be used for prediction. In the PV case study, conventional FIS generated errors that are just slightly higher than those of DAFIS. The inherent complexity of a Life Cycle study often hinders its widespread use in the industry and policy-making sectors. While the methodology does not guarantee a more accurate result compared to those generated by the Life Cycle Methodology, it does provide a relatively simpler way of generating knowledge- and data-based estimates that could be used during the initial design of a system.

Keywords: solar photovoltaic, fuzzy logic, inference system, artificial neural networks

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Authors: Grace Omowunmi Soyebi

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A computerized medical record system is a collection of medical information about a person that is stored on a computer. One principal problem of most hospitals in rural areas is using the file management system for keeping records. A lot of time is wasted when a patient visits the hospital, probably in an emergency, and the nurse or attendant has to search through voluminous files before the patient's file can be retrieved; this may cause an unexpected to happen to the patient. This data mining application is to be designed using a structured system analysis and design method which will help in a well-articulated analysis of the existing file management system, feasibility study, and proper documentation of the design and implementation of a computerized medical record system. This computerized system will replace the file management system and help to quickly retrieve a patient's record with increased data security, access clinical records for decision-making, and reduce the time range at which a patient gets attended to.

Keywords: programming, data, software development, innovation

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Authors: B. G. C. Ahodode, S. Fekkaklouhail

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Following the triple helix theory, this study aims to evaluate the innovation system effect on African countries’ competitiveness by taking into account external contributions; according to the extent that developing countries (especially African countries) are characterized by weak innovation systems whose synergy operates more at the foreign level than domestic and global. To do this, we used the correlation test, parsimonious regression techniques, and panel estimation between 2013 and 2016. Results show that the degree of innovation synergy has a significant effect on competitiveness in Africa. Specifically, while the opening system (OPESYS) and social system (SOCSYS) contribute respectively in importance order to 0.634 and 0.284 (at 1%) significant points of increase in the GCI, the political system (POLSYS) and educational system (EDUSYS) only increase it to 0.322 and 0.169 at 5% significance level while the effect of the economic system (ECOSYS) is not significant on Global Competitiveness Index.

Keywords: innovation system, innovation, competitiveness, Africa

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Authors: Seree Chadcham, Niphon Sukvilai

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This study is the research and development which is intended to: 1) design of the Intelligent Scaffolding Learning Management System (ISLMS) for vocational education, 2) assess the suitability of the Design of Intelligent Scaffolding Learning Management System for Vocational Education. Its methods are divided into 2 phases. Phase 1 is the design of the ISLMS for Vocational Education and phase 2 is the assessment of the suitability of the design. The samples used in this study are work done by 15 professionals in the field of Intelligent Scaffolding, Learning Management System, Vocational Education, and Information and Communication Technology in education selected using the purposive sampling method. Data analyzed by arithmetic mean and standard deviation. The results showed that the ISLMS for vocational education consists of 2 main components which are: 1) the Intelligent Learning Management System for Vocational Education, 2) the Intelligent Scaffolding Management System. The result of the system suitability assessment from the professionals is in the highest range.

Keywords: intelligent, scaffolding, learning management system, vocational education

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Authors: Eddiebal P. Layco

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The purpose of this study is to describe students' virtual classroom relationships in terms of their relationship to their peers and teachers; academic resilience; and performance. Further, the researcher wants to examine if these virtual classroom relations predict students' resilience and performance in their academics. The data were collected from 720 junior and senior high school or grade 7 to 12 students in selected state universities and colleges (SUCs) in Region III offering online or virtual classes during S.Y. 2020-2021. Results revealed that virtual classroom relationships such as teacher-student and peer relationships predict academic resilience and performance. This implies that students' academic relations with their teachers and peers have something to do with their ability to bounce back and beat the odds amidst challenges they faced in the online or virtual learning environment. These virtual relationships significantly influence also their academic performance. Adequate teacher support and positive peer relations may lead to enhanced academic resilience, which may also promote a meaningful and fulfilled life academically. Result suggests that teachers should develop their students' academic resiliency and maintain good relationships in the classroom since these results in academic success.

Keywords: virtual classroom relationships, teacher-pupil relationship, peer-relationship, academic resilience, academic performance

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Authors: G. Alex Ambrose, Melissa Paulsen, Abrar Fitwi, Masud Akbari

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In 2016, a U.S. business college was awarded a sub grant to work with FHI360, a nonprofit human development organization, to support a university in Afghanistan funded by the State Department’s U.S. Agency for International Development (USAID). A newly designed Master’s Degree in Finance and Accounting is being implemented to support Afghanistan’s goal of 20% females in higher education and industry by 2020 and to use finance and accounting international standards to attract capital investment for economic development. This paper will present a case study to describe the co-construction of an approach to an International Flipped Faculty Development Model grounded in blended learning theory. Like education in general, faculty development is also evolving from the traditional face to face environment and interactions to the fully online and now to a best of both blends. Flipped faculty development is both a means and a model for careful integration of the strengths of the synchronous and asynchronous dynamics and technologies with the combination of intentional sequencing to pre-online interactions that prepares and enhances the face to face faculty development and mentorship residencies with follow-up post-online support. Initial benefits from this model include giving the Afghan faculty an opportunity to experience and apply modern teaching and learning strategies with technology in their own classroom. Furthermore, beyond the technological and pedagogical affordances, the reciprocal benefits gained from the mentor-mentee, face-to-face relationship will be explored. Evidence to support this model includes: empirical findings from pre- and post-Faculty Mentor/ Mentee survey results, Faculty Mentorship group debriefs, Faculty Mentorship contact logs, and student early/end of semester feedback. In addition to presenting and evaluating this model, practical challenges and recommendations for replicating international flipped faculty development partnerships will be provided.

Keywords: educational development, faculty development, international development, flipped learning

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Authors: Lawrence Kwaku Amoako, Parrendah Adwoa Kpeli

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This paper analyses the implications of the corruption and tax system on inclusive development. We employ a sample of 45 countries between 2007 and 2020. We test for two related hypotheses; first, corruption hinders the smooth mobilisation of revenue through the tax system. Second, a rise in corruption amidst a defective tax system impairs inclusive development. We expect that a rise in the level of corruption in the economy will distort the tax system, thus affecting efficient revenue mobilisation and, subsequently, inclusive development. By extension, these findings have important policy implications for governments in containing corruption and building an effective tax system as it will help promote inclusive development.

Keywords: corruption, development, tax systems, tax complexity

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Authors: Sabli, Eddy Rismunandar, Akhirudin, Nana Halim, Zulfikar, Nining Dwirosanti, Wila Ningsih, Pipih Siti Sofiah, Danik Dania Asadayanti, Dewi Eka Arini Algozi, Gita Mahardika Pamuji, Ajun, Mangasa Aritonang, Nanang Rukmana, Arief Rachman Wonodhipo, Victor Imanuel Nahumury, Lili Husada, Wawan Saepul Irwan, Al Mukhlas Fikri

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Covid-19 pandemic has widely impacted the lives. An adaptive strategy must be quickly formulated to maintain the quality of education, especially by vocational school which technical skill competencies are highly needed. This study aimed to evaluate the effectiveness of e-training on the attitude and skill competencies of vocational high school teachers in Indonesia. A total of 720 Indonesian vocational high school teachers with various programs, including hospitality, administration, online business and marketing, culinary arts, fashion, cashier, tourism, haircut, and accounting participated e-training for a month. The training used electronic learning management system to provide materials (modules, presentation slides, and tutorial videos), tasks, and evaluations. Tutorial class was carried out by video conference meeting. Attitude and skill competencies were evaluated before and after the training. Meanwhile, the teachers also gave satisfactory feedback on the quality of the organizer and tutors. Data analysis used paired sample t-test and Anova with Tukey’s post hoc test. The results showed that e-training significantly increased the score of attitude and skill competencies of the teachers (p <0,05). Moreover, the remarkable increase was found among hospitality (57,5%), cashier (50,1%), and online business and marketing (48,7%) teachers. However, the effect among fashion, tourism and haircut teachers was less obvious. In addition, the satisfactory score on the quality of the organizer and tutors were 88,9 (very good), and 93,5 (excellence), respectively. The study concludes that a well-organized e-training could increase the attitude and skill competencies of Indonesian vocational high school teachers during Covid-19 pandemic.

Keywords: E-training, skill, teacher, vocational high school

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Authors: Roxana D. Maiorescu-Murphy

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Companies have recently embraced political activism as an alleged responsibility toward the communities they operate in. As a result of its recency, there is little understanding of the impact of corporate activism on consumers. In addition, embracing corporate activism engenders polarizing opinions, potentially leading to a crisis of morality shown in past literature to flourish in online settings. The present study contributes to the literature on communication management, which currently lacks research on stakeholder perceptions toward corporate activism in general and from the perspective of the stakeholders’ emotions of brand hate versus a love that they display before a specific corporate act of activism. For this purpose, the study analyzed online reactions on Twitter following Disney’s stance against Florida’s House Bill 1577 enacted in April 2022. Dubbed the “Don’t Say Gay Bill” by the left wing and the “Parental Rights Bill” by the conservative movement, the legislation triggered polarizing opinions in society and among Disney’s stakeholders, as the company announce it was taking action against it. Given the scarcity of research on corporate political activism and crises of morality, the current study enacted the case study methodology. Consequently, it answered to the research questions of how online stakeholders responded to Disney’s stance as well as why they formed such an opinion. The data were collected from Twitter over a seven-day period of analysis, namely from March 28- April 3, 2022. The period of analysis started on the day Disney announced its stance (March 28, 2022) until the reactions to its announcement petered out significantly (April 3, 2022). The final sample of analysis consisted of N=1,344 and represented Twitter comments in response to the company’s political announcement. The data were analyzed using the grounded theory methodology, which implied multiple exposures to the text and the undertaking of an inductive-deductive approach that led to the emergence of several recurrent themes. The findings revealed that the stakeholders’ prior emotions toward the company (brand hate versus brand love) did not play a greater role in their (dis)agreement with the latter’s activism than the users’ political stances. Specifically, whether they despised or hated Disney prior to this incident was less significant than their personal political stances. Above all, users were more inclined to transition from brand love to brand hate and vice versa based on the political side they viewed Disney to fall under.

Keywords: corporate political advocacy, crisis management, brand hate, brand love

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Authors: Yuri V. Kim

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This article presents a new approach to the Functional Testing of Space Systems (SS). It can be considered as a generic test and used for a wide class of SS that from the point of view of System Dynamics and Control may be described by the ordinary differential equations. Suggested methodology is based on using semi-natural experiment- laboratory stand that doesn’t require complicated, precise and expensive technological control-verification equipment. However, it allows for testing system as a whole totally assembled unit during Assembling, Integration and Testing (AIT) activities, involving system hardware (HW) and software (SW). The test physically activates system input (sensors) and output (actuators) and requires recording their outputs in real time. The data is then inserted in laboratory PC where it is post-experiment processed by Matlab/Simulink Identification Toolbox. It allows for estimating system dynamics in form of estimation of system differential equations by the experimental way and comparing them with expected mathematical model prematurely verified by mathematical simulation during the design process.

Keywords: system dynamics, space system ground tests and space qualification, system dynamics identification, satellite attitude control, assembling, integration and testing

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Authors: Bitewulign Mekonnen

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Context: This scientific paper focuses on the use of near-infrared (NIR) spectroscopy to determine glucose concentration in aqueous solutions accurately and rapidly. The study compares six different machine learning methods for predicting glucose concentration and also explores the development of a deep learning model for classifying NIR spectra. The objective is to optimize the detection model and improve the accuracy of glucose prediction. This research is important because it provides a comprehensive analysis of various machine-learning techniques for estimating aqueous glucose concentrations. Research Aim: The aim of this study is to compare and evaluate different machine-learning methods for predicting glucose concentration from NIR spectra. Additionally, the study aims to develop and assess a deep-learning model for classifying NIR spectra. Methodology: The research methodology involves the use of machine learning and deep learning techniques. Six machine learning regression models, including support vector machine regression, partial least squares regression, extra tree regression, random forest regression, extreme gradient boosting, and principal component analysis-neural network, are employed to predict glucose concentration. The NIR spectra data is randomly divided into train and test sets, and the process is repeated ten times to increase generalization ability. In addition, a convolutional neural network is developed for classifying NIR spectra. Findings: The study reveals that the SVMR, ETR, and PCA-NN models exhibit excellent performance in predicting glucose concentration, with correlation coefficients (R) > 0.99 and determination coefficients (R²)> 0.985. The deep learning model achieves high macro-averaging scores for precision, recall, and F1-measure. These findings demonstrate the effectiveness of machine learning and deep learning methods in optimizing the detection model and improving glucose prediction accuracy. Theoretical Importance: This research contributes to the field by providing a comprehensive analysis of various machine-learning techniques for estimating glucose concentrations from NIR spectra. It also explores the use of deep learning for the classification of indistinguishable NIR spectra. The findings highlight the potential of machine learning and deep learning in enhancing the prediction accuracy of glucose-relevant features. Data Collection and Analysis Procedures: The NIR spectra and corresponding references for glucose concentration are measured in increments of 20 mg/dl. The data is randomly divided into train and test sets, and the models are evaluated using regression analysis and classification metrics. The performance of each model is assessed based on correlation coefficients, determination coefficients, precision, recall, and F1-measure. Question Addressed: The study addresses the question of whether machine learning and deep learning methods can optimize the detection model and improve the accuracy of glucose prediction from NIR spectra. Conclusion: The research demonstrates that machine learning and deep learning methods can effectively predict glucose concentration from NIR spectra. The SVMR, ETR, and PCA-NN models exhibit superior performance, while the deep learning model achieves high classification scores. These findings suggest that machine learning and deep learning techniques can be used to improve the prediction accuracy of glucose-relevant features. Further research is needed to explore their clinical utility in analyzing complex matrices, such as blood glucose levels.

Keywords: machine learning, signal processing, near-infrared spectroscopy, support vector machine, neural network

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Authors: Mia Françoise

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This report aims to develop a strategy for assisting short-term investors to benefit from the current economic climate in South Africa by utilizing technical analysis techniques and predictive analytics. As part of this research, value investing and technical analysis principles will be combined to maximize returns for South African investors while optimizing volatility. As an emerging market, South Africa offers many opportunities for high growth in sectors where other developed countries cannot grow at the same rate. Investing in South African companies with significant growth potential can be extremely rewarding. Although the risk involved is more significant in countries with less developed markets and infrastructure, there is more room for growth in these countries. According to recent research, the offshore market is expected to outperform the local market over the long term; however, short-term investments in the local market will likely be more profitable, as the Johannesburg Stock Exchange is predicted to outperform the S&P500 over the short term. The instabilities in the economy contribute to increased market volatility, which can benefit investors if appropriately utilized. Price prediction and portfolio optimization comprise the two primary components of this methodology. As part of this process, statistics and other predictive modeling techniques will be used to predict the future performance of stocks listed on the Johannesburg Stock Exchange. Following predictive data analysis, Modern Portfolio Theory, based on Markowitz's Mean-Variance Theorem, will be applied to optimize the allocation of assets within an investment portfolio. By combining different assets within an investment portfolio, this optimization method produces a portfolio with an optimal ratio of expected risk to expected return. This methodology aims to provide a short-term investment with a stock portfolio that offers the best risk-to-return profile for stocks listed on the JSE by combining price prediction and portfolio optimization.

Keywords: financial stocks, optimized asset allocation, prediction modelling, South Africa

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Authors: Vladimir Stojanović, Marko D. Petković

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The vibration of a complex bogie system that moves on along the high order shear deformable beam on a viscoelastic foundation is studied. The complex bogie system has been modeled by elastically connected rigid bars on an identical supports. Elastic coupling between bars is introduced to simulate rigidly or flexibly (transversal or/and rotational) connection. Identical supports are modeled as a system of attached spring and dashpot to the bar on one side and interact with the beam through the concentrated mass on the other side. It is assumed that the masses and the beam are always in contact. New analytically determined critical velocity of the system is presented. It is analyzed the case when the complex bogie system exceeds the minimum phase velocity of waves in the beam when the vibration of the system may become unstable. Effect of an elastic coupling between bars on the stability of the system has been analyzed. The instability regions are found for the complex bogie system by applying the principle of the argument and D-decomposition method.

Keywords: Reddy-Bickford beam, D-decomposition method, principle of argument, critical velocity

Procedia PDF Downloads 303

Authors: Gabriela R. Silveira, Claudia S. Rocha, Lais S. Vitti, Jeane L. Borges, Helen B. Durgante

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Emerging adulthood occurs after adolescence in a period that maybe be marked by experimentation, identity reconfigurations, labor life demands, and insertion in the work environment, which tends to generate stress and emotional instability. Health promotion programs for the development of strengths and virtues, based on Positive Psychology, for emerging adults are sparse in Brazil. The aim of this study was to evaluate the preliminary effects of an online multi-component Positive Psychology program for the health promotion of emerging adults based on Cognitive Behavioural Therapy and Positive Psychology. The program included six online (synchronous) weekly group sessions of approximately two hours each and homework (asynchronous) activities. The themes worked were Values and self-care/Prudence, Optimism, Empathy, Gratitude, Forgiveness, and Meaning of life and work. This study presents data from a longitudinal, pre-experimental design with pre (T1) and post-test (T2) evaluation in the intervention group. 47 individuals aged between 19-30 years old participated, mean age of 24.53 years (SD=3.13), 37 females (78.7%). 42 (89.4%) self-defined as heterosexual, four (8.5%) as homosexual, and one (2.5%) as bisexual. 33 (70.2%) had incomplete higher education, four (8.5%) completed higher education, and seven (14.9%) had a graduate level of education. 27 participants worked (57.4%), out of which 25 were health workers (53.2%). 14 (29.8%) were caregivers, 27 (57.4%) had a spiritual belief, 36 (76.6%) had access to leisure, and 38 (80.9%) had perceived social support. The instruments used were a sociodemographic questionnaire, the 10-item Perceived Stress Scale, and the 12-item General Health Questionnaire. The program was advertised on social networks and interested participants filled out the Consent Form and the evaluation protocol at T1 and T2 via Google Docs form. The main research was approved (CEP n.1,899,368; 4,143,219; CAAE: 61997516.5.0000.5334) and complied with sanitary and Ethics criteria in research with human beings. Wilcoxon statistics revealed significant improvements in indicators of perceived stress between T1 (X=22.21, SD=6.79) and T2 (X=15.10, SD=5.82); (Z=-4.353; p=0.001) as well as depression and anxiety symptoms (T1:X=26.72, SD=8.84; T2: X=19.23, SD=4.68); (Z=-3.945, p=0.001) of the emerging adults after their participation in the programme. The programme has an innovative character not only for presenting an online Positive Psychology approach but also for being based on an intervention developed, evaluated, and manualized in Brazil. By focusing on emerging adults, this study contributes to advancing research on a relatively new field in developmental studies. As a limitation, this is a pre-experimental and pilot study, requiring an increase in sample size for greater statistical robustness, also qualitative data analysis is crucial for methodological complementarity. The importance of investing efforts to accompany this age group and provide advances in longitudinal research in the area of health promotion and disease prevention is highlighted.

Keywords: emerging adults, disease prevention, health promotion, online program

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Authors: Chika Catherine Ayogu

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Rehabilitation plays an important and integral part in the life of roadway rehabilitation management system. It is a systematic method for inspection and rating the roadway condition in a given area. The system performs a cost effective analysis of various maintenance and rehabilitation strategies. Finally the system prioritize and recommend roadway rehabilitation and maintenance to maximize results within a given budget amount. During execution of maintenance activity, the system also tracks labour, materials, equipment and cost for activities performed. The system implements physical assessment field inspection and rating of each street segment which is then entered into a database. The information is analyzed using a software, and provide recommendations and project future conditions. The roadway management system provides a deterioration curve for each segment based on input then assigns the most cost-effective maintenance strategy based on conditions, surface type and functional classification, and available budget. This paper investigates the roadway management system and its capabilities to assist in applying the right treatment to the right roadway at the right time so that expected service life of the roadway is extended as long as possible with acceptable cost.

Keywords: effectiveness, rehabilitation, roadway, software system

Procedia PDF Downloads 142