Search results for: supervised machine learning algorithm

Authors: S. C. Sharma, Ankit Gambhir, Rajeev Arya

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In today’s era data security is an important concern and most demanding issues because it is essential for people using online banking, e-shopping, reservations etc. The two major techniques that are used for secure communication are Cryptography and Steganography. Cryptographic algorithms scramble the data so that intruder will not able to retrieve it; however steganography covers that data in some cover file so that presence of communication is hidden. This paper presents the implementation of Ron Rivest, Adi Shamir, and Leonard Adleman (RSA) Algorithm with Image and Audio Steganography and Data Encryption Standard (DES) Algorithm with Image and Audio Steganography. The coding for both the algorithms have been done using MATLAB and its observed that these techniques performed better than individual techniques. The risk of unauthorized access is alleviated up to a certain extent by using these techniques. These techniques could be used in Banks, RAW agencies etc, where highly confidential data is transferred. Finally, the comparisons of such two techniques are also given in tabular forms.

Keywords: audio steganography, data security, DES, image steganography, intruder, RSA, steganography

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Authors: Matthias Grunke

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In this qualitative study, we interviewed a young man with learning disabilities who played professional baseball for two years. Individuals with severe academic challenges constitute one of the most vulnerable groups of our society. Science has to find ways on how to arm them against life’s challenges and help them to cope with the many risk factors that they are usually confronted with. Team sports like baseball seem to be a suitable means for that purpose. In the interview, our participant talked about his life as a student with severe learning difficulties and related how his career in baseball made his academic challenges appear much less significant. He gave some meaningful insights into what helped him to build a happy and fulfilling life for himself, not only in spite of his challenges but also because of what he's learning disabilities taught him. Support from significant others, a sense of purpose, his fighting spirit ignited by sports, and the success that he experienced on the baseball field were among the most relevant factors. Overall, this study highlights the importance of finding an outlet for young people with learning disabilities where their academic difficulties retreat into the background and their talents are validated.

Keywords: baseball, inclusion, learning disabilities, resilience

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Authors: Shoba Bandi-Rao

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The lack of college readiness is one of the major contributors to low graduation rates at community colleges, especially among educationally and financially disadvantaged students. About 45% of underprepared high school graduates are required to complete ‘remedial’ reading/writing courses before they can begin taking college-level courses. Mobile apps present ‘bite-size’ learning materials that can be useful for practicing certain literacy skills, such as vocabulary learning. The convenience of mobile phones is ideal for a majority of students at community colleges who hold full or part-time jobs. Mobile apps allow students to learn during small ‘chunks’ of time available to them outside of the class—during subway commute, between classes, etc. Learning with mobile apps is a relatively new area in research, and their effectiveness for learning new words has been inconclusive. Using Mishra & Koehler’s TPCK theoretical framework, this study explored the effectiveness of the mobile app (Quizlet) for learning one hundred common college-level words in ‘remedial’ writing class over one semester. Each week, before coming to class, students studied a list of 10-15 words presented in context within sentences. Students came across these words in the article they read in class making their learning more meaningful. A pre and post-test measured the number of words students knew, learned and remembered. Statistical analysis shows that students performed better by 41% on the post-test indicating that the mobile app was helpful for learning words. Students also completed a short survey each week that sought to determine the amount of time students spent on the vocabulary app. A positive correlation was found between the amount of time spent on the mobile app and the number of words learned. The goal of this research is to capitalize on the convenience of smartphones to (1) better prepare them for college-level course work, and (2) contribute to current literature on mobile learning.

Keywords: mobile learning, vocabulary learning, literacy skills, Quizlet

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Authors: Samiah Alammari, Nassim Ammour

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When providing a massive number of tasks successively to a deep learning process, a good performance of the model requires preserving the previous tasks data to retrain the model for each upcoming classification. Otherwise, the model performs poorly due to the catastrophic forgetting phenomenon. To overcome this shortcoming, we developed a successful continual learning deep model for remote sensing hyperspectral image regions classification. The proposed neural network architecture encapsulates two trainable subnetworks. The first module adapts its weights by minimizing the discrimination error between the land-cover classes during the new task learning, and the second module tries to learn how to replicate the data of the previous tasks by discovering the latent data structure of the new task dataset. We conduct experiments on HSI dataset Indian Pines. The results confirm the capability of the proposed method.

Keywords: continual learning, data reconstruction, remote sensing, hyperspectral image segmentation

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Authors: Sara Rejeb, Catherine Duveau, Tabea Rebafka

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To monitor the production process of turbofan aircraft engines, multiple measurements of various geometrical parameters are systematically recorded on manufactured parts. Engine parts are subject to extremely high standards as they can impact the performance of the engine. Therefore, it is essential to analyze these databases to better understand the influence of the different parameters on the engine's performance. Self-organizing maps are unsupervised neural networks which achieve two tasks simultaneously: they visualize high-dimensional data by projection onto a 2-dimensional map and provide clustering of the data. This technique has become very popular for data exploration since it provides easily interpretable results and a meaningful global view of the data. As such, self-organizing maps are usually applied to aircraft engine condition monitoring. As databases in this field are huge and complex, they naturally contain multiple missing entries for various reasons. The classical Kohonen algorithm to compute self-organizing maps is conceived for complete data only. A naive approach to deal with partially observed data consists in deleting items or variables with missing entries. However, this requires a sufficient number of complete individuals to be fairly representative of the population; otherwise, deletion leads to a considerable loss of information. Moreover, deletion can also induce bias in the analysis results. Alternatively, one can first apply a common imputation method to create a complete dataset and then apply the Kohonen algorithm. However, the choice of the imputation method may have a strong impact on the resulting self-organizing map. Our approach is to address simultaneously the two problems of computing a self-organizing map and imputing missing values, as these tasks are not independent. In this work, we propose an extension of self-organizing maps for partially observed data, referred to as missSOM. First, we introduce a criterion to be optimized, that aims at defining simultaneously the best self-organizing map and the best imputations for the missing entries. As such, missSOM is also an imputation method for missing values. To minimize the criterion, we propose an iterative algorithm that alternates the learning of a self-organizing map and the imputation of missing values. Moreover, we develop an accelerated version of the algorithm by entwining the iterations of the Kohonen algorithm with the updates of the imputed values. This method is efficiently implemented in R and will soon be released on CRAN. Compared to the standard Kohonen algorithm, it does not come with any additional cost in terms of computing time. Numerical experiments illustrate that missSOM performs well in terms of both clustering and imputation compared to the state of the art. In particular, it turns out that missSOM is robust to the missingness mechanism, which is in contrast to many imputation methods that are appropriate for only a single mechanism. This is an important property of missSOM as, in practice, the missingness mechanism is often unknown. An application to measurements on one type of part is also provided and shows the practical interest of missSOM.

Keywords: imputation method of missing data, partially observed data, robustness to missingness mechanism, self-organizing maps

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Authors: Mohammad Vahed, Ana Sadeghitohidi, Majid Vahed, Hiroki Takahashi

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In the genomics field, the huge amounts of data have produced by the next-generation sequencers (NGS). Data volumes are very rapidly growing, as it is postulated that more than one billion bases will be produced per year in 2020. The growth rate of produced data is much faster than Moore's law in computer technology. This makes it more difficult to deal with genomics data, such as storing data, searching information, and finding the hidden information. It is required to develop the analysis platform for genomics big data. Cloud computing newly developed enables us to deal with big data more efficiently. Hadoop is one of the frameworks distributed computing and relies upon the core of a Big Data as a Service (BDaaS). Although many services have adopted this technology, e.g. amazon, there are a few applications in the biology field. Here, we propose a new algorithm to more efficiently deal with the genomics big data, e.g. sequencing data. Our algorithm consists of two parts: First is that BDaaS is applied for handling the data more efficiently. Second is that the hybrid method of MapReduce and Fuzzy logic is applied for data processing. This step can be parallelized in implementation. Our algorithm has great potential in computational analysis of genomics big data, e.g. de novo genome assembly and sequence similarity search. We will discuss our algorithm and its feasibility.

Keywords: big data, fuzzy logic, MapReduce, Hadoop, cloud computing

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Authors: Sarah K. Hagi, Majdi Alnowaimi

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In the last decade, there were immense advancements in the medical imaging modalities. These advancements can scan a whole volume of the lung organ in high resolution images within a short time. According to this performance, the physicians can clearly identify the complicated anatomical and pathological structures of lung. Therefore, these advancements give large opportunities for more advance of all types of lung cancer treatment available and will increase the survival rate. However, lung cancer is still one of the major causes of death with around 19% of all the cancer patients. Several factors may affect survival rate. One of the serious effects is the breathing process, which can affect the accuracy of diagnosis and lung tumor treatment plan. We have therefore developed a semi automated algorithm to localize the 3D lung tumor positions across all respiratory data during respiratory motion. The algorithm can be divided into two stages. First, a lung tumor segmentation for the first phase of the 4D computed tomography (CT). Lung tumor segmentation is performed using an active contours method. Then, localize the tumor 3D position across all next phases using a 12 degrees of freedom of an affine transformation. Two data set where used in this study, a compute simulate for 4D CT using extended cardiac-torso (XCAT) phantom and 4D CT clinical data sets. The result and error calculation is presented as root mean square error (RMSE). The average error in data sets is 0.94 mm ± 0.36. Finally, evaluation and quantitative comparison of the results with a state-of-the-art registration algorithm was introduced. The results obtained from the proposed localization algorithm show a promising result to localize alung tumor in 4D CT data.

Keywords: automated algorithm , computed tomography, lung tumor, tumor localization

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Authors: Adedolapo Taiwo Adeyemi, Success Ayodeji Fasanmi

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The emergence of the coronavirus pandemic and the rate of the spread affected a lot of activities across the world. This led to the introduction of online learning modes in several countries after institutions were shut down. Unfortunately, most public universities in Nigeria could not switch to the online mode because they were not prepared for it, as they do not have the technological capacity to support a full online learning mode. This study examines the readiness of university and the attitude of undergraduates towards online learning mode in Obafemi Awolowo University (OAU), Ile Ife. It investigated the skills and competencies of students for online learning as well as the university’s readiness towards online learning mode; the effort was made to identify challenges of online teaching and learning in the study area, and suggested solutions were advanced. OAU was selected because it is adjudged to be the leading Information and Communication Technology (ICT) driven institution in Nigeria. The descriptive survey research design was used for the study. A total of 256 academic staff and 1503 undergraduates were selected across six faculties out of the thirteen faculties in the University. Two set of questionnaires were used to get responses from the selected respondents. The result showed that students have the skills and competence to operate e-learning facilities but are faced with challenges such as high data cost, erratic power supply, and lack of gadgets, among others. The study found out that the university was not prepared for online learning mode as it lacks basic technological facilities to support it. The study equally showed that while lecturers possess certain skills in using some e-learning applications, they were limited by the unavailability of online support gadgets, poor internet connectivity, and unstable power supply. Furthermore, the assessment of student attitude towards online learning mode shows that the students found the online learning mode very challenging as they had to bear the huge cost of data. Lecturers also faced the same challenge as they had to pay a lot to buy data, and the networks were sometimes unstable. The study recommended that adequate funding needs to be provided to public universities by the government while the management of institutions must build technological capacities to support online learning mode in the hybrid form and on a full basis in case of future emergencies.

Keywords: universities, online learning, undergraduates, attitude

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Authors: Wafa' Alsharafat, Suhila Farhan Abu-Owida

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Genetic algorithm (GA) is a powerful evolutionary searching technique that is used successfully to solve and optimize problems in different research areas. Genetic Algorithm (GA) considered as one of optimization methods used to solve Travel salesman Problem (TSP). The feasibility of GA in finding a TSP solution is dependent on GA operators; encoding method, population size, termination criteria, in general. In specific, crossover and its probability play a significant role in finding possible solutions for Symmetric TSP (STSP). In addition, the crossover should be determined and enhanced in term reaching optimal or at least near optimal. In this paper, we spot the light on using a modified crossover method called modified sequential constructive crossover and its impact on reaching optimal solution. To justify the relevance of a parameter value in solving the TSP, a set comparative analysis conducted on different crossover methods values.

Keywords: genetic algorithm, crossover, mutation, TSP

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Authors: Atara Shriki, Ilana Lavy

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Teaching for the first time an online course dealing with the history of mathematics, we were struggling with questions related to the design of a proper learning environment (LE). Thirteen high school mathematics teachers, M.Ed. students, attended the course. The teachers were engaged in independent reading of mathematical texts, a task that is recognized as complex due to the unique characteristics of such texts. In order to support the learning processes and develop skills that are essential for succeeding in learning online (e.g. self-regulated learning skills, meta-cognitive skills, reflective ability, and self-assessment skills), the LE comprised of three components aimed at “scaffolding” the learning: (1) An online "self-feedback" questionnaires that included drill-and-practice questions. Subsequent to responding the questions the online system provided a grade and the teachers were entitled to correct their answers; (2) Open-ended questions aimed at stimulating critical thinking about the mathematical contents; (3) Reflective questionnaires designed to assist the teachers in steering their learning. Using a mixed-method methodology, an inquiry study examined the learning processes, the learners' difficulties in reading the mathematical texts and on the unique contribution of each component of the LE to the ability of teachers to comprehend the mathematical contents, and support the development of their learning skills. The results indicate that the teachers found the online feedback as most helpful in developing self-regulated learning skills and ability to reflect on deficiencies in knowledge. Lacking previous experience in expressing opinion on mathematical ideas, the teachers had troubles in responding open-ended questions; however, they perceived this assignment as nurturing cognitive and meta-cognitive skills. The teachers also attested that the reflective questionnaires were useful for steering the learning. Although in general the teachers found the LE as supportive, most of them indicated the need to strengthen instructor-learners and learners-learners interactions. They suggested to generate an online forum to enable them receive direct feedback from the instructor, share ideas with other learners, and consult with them about solutions. Apparently, within online LE, supporting learning merely with respect to cognitive aspects is not sufficient. Leaners also need an emotional support and sense a social presence.

Keywords: cognitive and meta-cognitive skills, independent reading of mathematical texts, online learning environment, self-regulated learning skills

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Authors: Nirit Raichel, Dorit Alt

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Although university teaching is claimed to have a special task to support students in adopting ways of thinking and producing new knowledge anchored in scientific inquiry practices, it is argued that students' habits of learning are still overwhelmingly skewed toward passive acquisition of knowledge from authority sources rather than from collaborative inquiry activities. In order to overcome this critical inadequacy between current educational goals and instructional methods, the LLAF consortium is aimed at developing updated instructional practices that put a premium on adaptability to the emerging requirements of present society. LLAF has created a practical guide for teachers containing updated pedagogical strategies based on the constructivist approach for learning, arranged along Delors’ four theoretical ‘pillars’ of education: Learning to know, learning to do, learning to live together, and learning to be. This presentation will be limited to problem-based learning (PBL), as a strategy introduced in the second pillar. PBL leads not only to the acquisition of technical skills, but also allows the development of skills like problem analysis and solving, critical thinking, cooperation and teamwork, decision- making and self-regulation that can be transferred to other contexts. This educational strategy will be exemplified by a case study conducted in the pre-piloting stage of the project. The case describes a three-fold process implemented in a postgraduate course for in-service teachers, including: (1) learning about PBL (2) implementing PBL in the participants' classes, and (3) qualitatively assessing the contributions of PBL to students' outcomes. An example will be given regarding the ways by which PBL was applied and assessed in civic education for high-school students. Two 9th-grade classes have participated the study; both included several students with learning disability. PBL was applied only in one class whereas traditional instruction was used in the other. Results showed a robust contribution of PBL to students' affective and cognitive outcomes as reflected in their motivation to engage in learning activities, and to further explore the subject. However, students with learning disability were less favorable with this "active" and "annoying" environment. Implications of these findings for the LLAF project will be discussed.

Keywords: problem-based learning, higher education, pedagogical strategies

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Authors: Azim Khodadadi, Sahar Sadaat Vakili, Ebrahim Babaei

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This study presents a numerical method to optimize the failure rate and repair time of a typical radial distribution system. Failure rate and repair time are effective parameters in customer and energy based indices of reliability. Decrease of these parameters improves reliability indices. Thus, system stability will be boost. The penalty functions indirectly reflect the cost of investment which spent to improve these indices. Constraints on customer and energy based indices, i.e. SAIFI, SAIDI, CAIDI and AENS have been considered by using a new method which reduces optimization algorithm controlling parameters. Imperialist Competitive Algorithm (ICA) used as main optimization technique and particle swarm optimization (PSO), simulated annealing (SA) and differential evolution (DE) has been applied for further investigation. These algorithms have been implemented on a test system by MATLAB. Obtained results have been compared with each other. The optimized values of repair time and failure rate are much lower than current values which this achievement reduced investment cost and also ICA gives better answer than the other used algorithms.

Keywords: imperialist competitive algorithm, failure rate, repair time, radial distribution network

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Authors: M. Baghdasaryan, A. Sukiasyan

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An algorithm for estimating the stable operation conditions of the synchronous motor of the ore mill electric drive is proposed. The stable operation conditions of the synchronous motor are revealed, taking into account the estimation of the q angle change and the technological factors. The stability condition obtained allows to ensure the stable operation of the motor in the synchronous mode, taking into account the nonlinear character of the mill loading. The developed algorithm gives an opportunity to present the undesirable phenomena, arising in the electric drive system. The obtained stability condition can be successfully applied for the optimal control of the electromechanical system of the mill.

Keywords: electric drive, synchronous motor, ore mill, stability, technological factors

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Authors: M. Cangi, T. Dolar, C. Ersoy, Y. E. Aydogdu, A. I. Aydeniz, A. Mugan

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The drum is one of the critical parts in a washing machine in which the clothes are washed and spin by the rotational movement. It is activated by the drum shaft which is attached to an electric motor and subjected to dynamic loading. Being one of the critical components, failures of the drum require costly repairs of dynamic components. In this study, tolerance bands between the drum shaft and its two bearings were examined to develop a relationship between the fatigue life of the shaft and the interaction tolerances. Optimization of tolerance bands was completed in consideration of the fatigue life of the shaft as the cost function. The following methodology is followed: multibody dynamic model of a washing machine was constructed and used to calculate dynamic loading on the components. Then, these forces were used in finite element analyses to calculate the stress field in critical components which was used for fatigue life predictions. The factors affecting the fatigue life were examined to find optimum tolerance grade for a given test condition. Numerical results were verified by experimental observations.

Keywords: fatigue life, finite element analysis, tolerance analysis, optimization

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Authors: Ram A. Giri, Amna Bedri, Abdou Niane

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Exclusion in education on the basis of language in multilingual contexts operates at multiple levels. Learners of diverse ethnolinguistic backgrounds are often expected to learn through English and are pushed further down the learning ladder if they also have to access education through Information and Communication Technology (ICT). The paper explores marginalized children’s lived experiences in accessing technology and English in four low-income countries in Africa and Asia. Based on the findings of the first phase of a multinational qualitative research study, we report on the factors or barriers that affect children’s access, opportunities and motivation for learning through technology and English. ICT and English - the language of ICT and education - can enhance learning and can even be essential. However, these two important keys to education can also function as barriers to accessing quality education, and therefore as zones of exclusion. This paper looks into how marginalized children (aged 13-15) engage in learning through ICT and English and to what extent the restrictive access and opportunities contribute to the widening of the already existing gap in education. By applying the conceptual frameworks of “access and accessibility of learning” and “zones of exclusion,” the paper elucidates how the barriers prevent children’s effective engagement with learning and addresses such questions as to how marginalized children access technology and English for learning; whether the children value English, and what their motivation and opportunity to learn it are. In addition, the paper will point out policy and pedagogic implications.

Keywords: exclusion, inclusion, inclusive education, marginalization

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Authors: Yuqing Sun

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Chinese has been regarded as one of the most difficult languages in learning due to its complex spelling structure, difficult pronunciation, as well as its varying forms. Since vocabulary acquisition is the basic process to acquire a language, to express yourself, to compose a sentence, and to conduct a communication, so learning the vocabulary is of great importance. However, the vocabulary contains pronunciation, spelling, recognition and application which may seem as a huge work. This may pose a question for the language teachers (language teachers in China who teach Chinese to the foreign students): How to teach them in an effective way? Traditionally, teachers have no choice but teach it all by themselves, then with the development of technology, they can use computer as a tool to help them (Computer Assisted Language Learning or CALL). Now, they move into the Mobile Assisted Language Learning (MALL) method to guide their teaching, upon which the appraisal is convincing. It diversifies the learning material and the way of output, which can activate learners’ curiosity and accelerate their understanding. This paper will focus on actual case studies occurring in the universities in China of teaching the foreign students to learn Chinese, and the analysis of the utilization of WeChat channel as an example of MALL model to explore the active role of MALL to enhance the effectiveness of Chinese vocabulary acquisition.

Keywords: Chinese, vocabulary acquisition, MALL, case

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Authors: Zin Mar Lwin

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Brain Computer Interface (BCI) Systems have developed for people who suffer from severe motor disabilities and challenging to communicate with their environment. BCI allows them for communication by a non-muscular way. For communication between human and computer, BCI uses a type of signal called Electroencephalogram (EEG) signal which is recorded from the human„s brain by means of an electrode. The electroencephalogram (EEG) signal is an important information source for knowing brain processes for the non-invasive BCI. Translating human‟s thought, it needs to classify acquired EEG signal accurately. This paper proposed a typical EEG signal classification system which experiments the Dataset from “Purdue University.” Independent Component Analysis (ICA) method via EEGLab Tools for removing artifacts which are caused by eye blinks. For features extraction, the Time and Frequency features of non-stationary EEG signals are extracted by Matching Pursuit (MP) algorithm. The classification of one of five mental tasks is performed by Multi_Class Support Vector Machine (SVM). For SVMs, the comparisons have been carried out for both 1-against-1 and 1-against-all methods.

Keywords: BCI, EEG, ICA, SVM

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Authors: Kenneth N. Ohei, Roelien Brink

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For decades, teaching and learning processes have centered on the traditional approach (Web 1.0) that promoted teacher-directed pedagogical practices. Currently, there is a realization that the traditional approach is not adequate to effectively address and improve all student-learning outcomes. The subsequent incorporation of social software, Information, and Communication Technology (ICT) tools in universities may serve as complementary to support educational goals, offering students the affordability and opportunity to educational choices and learning platforms. Consequently, educators’ inability to incorporate these instructional ICT tools in their teaching and learning practices remains a challenge. This will signify that educators still lack the ICT skills required to administer lectures and bridging learning gaps. This study probes a methodological concept with the aim of developing a framework towards the adoption of social software in HES to help facilitate business processes and can build social presence among students. A mixed method will be appropriate to develop a comprehensive framework needed in Higher Educational System (HES). After research have been conducted, the adoption of social software will be based on the developed comprehensive framework which is supposed to impact positively on education and approach of delivery, improves learning experience, engagement and finally, increases educational opportunities and easy access to educational contents.

Keywords: blended and integrated learning, learning experience and engagement, higher educational system, HES, information and communication technology, ICT, social presence, Web 1.0, Web 2.0, Web 3.0

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Authors: Nikolina Ribarić

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E-learning is most commonly defined as a set of applications and processes, such as Web-based learning, computer-based learning, virtual classrooms, and digital collaboration, that enable access to instructional content through a variety of electronic media. The main goal of an e-learning system is learning, and the way to evaluate the impact of an e-learning system is by examining whether students learn effectively with the help of that system. Testmoz is a program for online preparation of knowledge evaluation assignments. The program provides teachers with computer support during the design of assignments and evaluating them. Students can review and solve assignments and also check the correctness of their solutions. Research into the increase of motivation by the practice of providing teaching content by applying online tests prepared in the Testmoz program was carried out with students of the 8th grade of Ljubo Babić Primary School in Jastrebarsko. The students took the tests in their free time, from home, for an unlimited number of times. SPSS was used to process the data obtained by the research instruments. The results of the research showed that students preferred to practice teaching content and achieved better educational results in chemistry when they had access to online tests for repetition and practicing in relation to subject content which was checked after repetition and practicing in "the classical way" -i.e., solving assignments in a workbook or writing assignments in worksheets.

Keywords: chemistry class, e-learning, motivation, Testmoz

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Authors: Laura M. Schwarz

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WebQuests are an internet-based interactive teaching/learning tool and utilize a scaffolded methodology. WebQuests employ critical thinking, afford inquiry-based constructivist learning, and readily employ Bloom’s Taxonomy. WebQuests have generally been used as instructional technology tools in primary and secondary education and have more recently grown in popularity in higher education. The study of the efficacy of WebQuests as an instructional approach to learning, however, has been limited, particularly in the nursing education arena. The purpose of this mixed-methods study was to determine nursing students’ perceptions of the effectiveness of the Nursing WebQuest as a teaching/learning strategy for holistic nursing-related content. Quantitative findings (N=42) suggested that learners were active participants, used reflection, thought of new ideas, used analysis skills, discovered something new, and assessed the worth of something while taking part in the WebQuests. Qualitative findings indicated that participants found WebQuest positives as easy to understand and navigate; clear and organized; interactive; good alternative learning format, and used a variety of quality resources. Participants saw drawbacks as requiring additional time and work; and occasional failed link or link causing them to lose their location in the WebQuest. Recommendations include using larger sample size and more diverse populations from various programs and universities. In conclusion, WebQuests were found to be an effective teaching/learning tool as positively assessed by study participants.

Keywords: holistic nursing, nursing education, teaching/learning strategy, WebQuests

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Authors: Florin Leon, Silvia Curteanu

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The Bacterial Foraging Optimization (BFO) algorithm is inspired by the behavior of bacteria such as Escherichia coli or Myxococcus xanthus when searching for food, more precisely the chemotaxis behavior. Bacteria perceive chemical gradients in the environment, such as nutrients, and also other individual bacteria, and move toward or in the opposite direction to those signals. The application example considered as a case study consists in establishing the dependency between the reaction yield of hydrogels based on polyacrylamide and the working conditions such as time, temperature, monomer, initiator, crosslinking agent and inclusion polymer concentrations, as well as type of the polymer added. This process is modeled with a neural network which is included in an optimization procedure based on BFO. An experimental study of BFO parameters is performed. The results show that the algorithm is quite robust and can obtain good results for diverse combinations of parameter values.

Keywords: bacterial foraging, hydrogels, modeling and optimization, neural networks

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Authors: Zheng Yuxun

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This review critically assesses the advancements and prospective developments in defect detection methodologies within the semiconductor industry, an essential domain that significantly affects the operational efficiency and reliability of electronic components. As semiconductor devices continue to decrease in size and increase in complexity, the precision and efficacy of defect detection strategies become increasingly critical. Tracing the evolution from traditional manual inspections to the adoption of advanced technologies employing automated vision systems, artificial intelligence (AI), and machine learning (ML), the paper highlights the significance of precise defect detection in semiconductor manufacturing by discussing various defect types, such as crystallographic errors, surface anomalies, and chemical impurities, which profoundly influence the functionality and durability of semiconductor devices, underscoring the necessity for their precise identification. The narrative transitions to the technological evolution in defect detection, depicting a shift from rudimentary methods like optical microscopy and basic electronic tests to more sophisticated techniques including electron microscopy, X-ray imaging, and infrared spectroscopy. The incorporation of AI and ML marks a pivotal advancement towards more adaptive, accurate, and expedited defect detection mechanisms. The paper addresses current challenges, particularly the constraints imposed by the diminutive scale of contemporary semiconductor devices, the elevated costs associated with advanced imaging technologies, and the demand for rapid processing that aligns with mass production standards. A critical gap is identified between the capabilities of existing technologies and the industry's requirements, especially concerning scalability and processing velocities. Future research directions are proposed to bridge these gaps, suggesting enhancements in the computational efficiency of AI algorithms, the development of novel materials to improve imaging contrast in defect detection, and the seamless integration of these systems into semiconductor production lines. By offering a synthesis of existing technologies and forecasting upcoming trends, this review aims to foster the dialogue and development of more effective defect detection methods, thereby facilitating the production of more dependable and robust semiconductor devices. This thorough analysis not only elucidates the current technological landscape but also paves the way for forthcoming innovations in semiconductor defect detection.

Keywords: semiconductor defect detection, artificial intelligence in semiconductor manufacturing, machine learning applications, technological evolution in defect analysis

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Authors: Salinee Thumronglaohapun

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The proper number and appropriate locations of service centers can save cost, raise revenue and gain more satisfaction from customers. Establishing service centers is high-cost and difficult to relocate. In long-term planning periods, several factors may affect the service. One of the most critical factors is uncertain demand of customers. The opened service centers need to be capable of serving customers and making a profit although the demand in each period is changed. In this work, the capacitated location-allocation problem with stochastic demand is considered. A mathematical model is formulated to determine suitable locations of service centers and their allocation to maximize total profit for multiple planning periods. Two heuristic methods, a local search and genetic algorithm, are used to solve this problem. For the local search, five different chances to choose each type of moves are applied. For the genetic algorithm, three different replacement strategies are considered. The results of applying each method to solve numerical examples are compared. Both methods reach to the same best found solution in most examples but the genetic algorithm provides better solutions in some cases.

Keywords: location-allocation problem, stochastic demand, local search, genetic algorithm

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Authors: Yashi Ye

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Principles play vital roles in shaping the school culture and promoting teachers' professional learning by exerting their leadership. In the changing time of the 21st century, the creative leadership of school leaders is increasingly important in cultivating the professional learning communities of teachers for eventually improving student performance in every continent. This study examines under what conditions and how principal creative leadership contributes to teachers’ professional learning and school culture. Data collected from 632 teachers in 30 primary and middle schools in the cities of Chengdu and Chongqing in mainland China are analyzed using structural equation modeling and bootstrapping tests. A moderated mediation model of principle creative leadership effects is used to analyze professional teacher learning and school culture in which the mediator will be school culture and the moderator will be power distance orientation. The results indicate that principal creative leadership has significant direct and indirect effects on teachers' professional learning. A positive correlation between principal creative leadership, professional teacher learning, and school culture is observed. Further model testing found that teacher power distance orientation moderated the significant effect of principal creative leadership on school culture. When teachers perceived higher power distance in teacher-principal relations, the effects of principal creative leadership were stronger than for those who perceived low power distance. The results indicate the “culture change” in the young generation of teachers in China, and further implications to understanding the cultural context in the field of educational leadership are discussed.

Keywords: power distance orientation, principal creative leadership, school culture, teacher professional learning

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Authors: Aleksander Nawrat, Karol Jędrasiak, Artur Ryt, Dawid Sobel

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The goal of the article is to present a novel Multimedia Firearms Training System. The system was developed in order to compensate for major problems of existing shooting training systems. The designed and implemented solution can be characterized by five major advantages: algorithm for automatic geometric calibration, algorithm of photometric recalibration, firearms hit point detection using thermal imaging camera, IR laser spot tracking algorithm for after action review analysis, and implementation of ballistics equations. The combination of the abovementioned advantages in a single multimedia firearms training system creates a comprehensive solution for detecting and tracking of the target point usable for shooting training systems and improving intervention tactics of uniformed services. The introduced algorithms of geometric and photometric recalibration allow the use of economically viable commercially available projectors for systems that require long and intensive use without most of the negative impacts on color mapping of existing multi-projector multimedia shooting range systems. The article presents the results of the developed algorithms and their application in real training systems.

Keywords: firearms shot detection, geometric recalibration, photometric recalibration, IR tracking algorithm, thermography, ballistics

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Authors: Shiwei Deng, Yang Bao

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In this paper, we present a model-based regression test suite reducing approach that uses EFSM model dependence analysis and probability-driven greedy algorithm to reduce software regression test suites. The approach automatically identifies the difference between the original model and the modified model as a set of elementary model modifications. The EFSM dependence analysis is performed for each elementary modification to reduce the regression test suite, and then the probability-driven greedy algorithm is adopted to select the minimum set of test cases from the reduced regression test suite that cover all interaction patterns. Our initial experience shows that the approach may significantly reduce the size of regression test suites.

Keywords: dependence analysis, EFSM model, greedy algorithm, regression test

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Authors: C. Iraklis, G. Evmiridis, A. Iraklis

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Renewable energy sources and distributed power generation units already have an important role in electrical power generation. A mixture of different technologies penetrating the electrical grid, adds complexity in the management of distribution networks. High penetration of distributed power generation units creates node over-voltages, huge power losses, unreliable power management, reverse power flow and congestion. This paper presents an optimization algorithm capable of reducing congestion and power losses, both described as a function of weighted sum. Two factors that describe congestion are being proposed. An upgraded selective particle swarm optimization algorithm (SPSO) is used as a solution tool focusing on the technique of network reconfiguration. The upgraded SPSO algorithm is achieved with the addition of a heuristic algorithm specializing in reduction of power losses, with several scenarios being tested. Results show significant improvement in minimization of losses and congestion while achieving very small calculation times.

Keywords: congestion, distribution networks, loss reduction, particle swarm optimization, smart grid

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Authors: Mohammed Abu Shquier

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This paper introduces a word segmentation method using the novel BP-LSTM-CRF architecture for processing semantic output training. The objective of web morphological analysis tools is to link a formal morpho-syntactic description to a lemma, along with morpho-syntactic information, a vocalized form, a vocalized analysis with morpho-syntactic information, and a list of paradigms. A key objective is to continuously enhance the proposed system through an inductive learning approach that considers semantic influences. The system is currently under construction and development based on data-driven learning. To evaluate the tool, an experiment on homograph analysis was conducted. The tool also encompasses the assumption of deep binary segmentation hypotheses, the arbitrary choice of trigram or n-gram continuation probabilities, language limitations, and morphology for both Modern Standard Arabic (MSA) and Dialectal Arabic (DA), which provide justification for updating this system. Most Arabic word analysis systems are based on the phonotactic morpho-syntactic analysis of a word transmitted using lexical rules, which are mainly used in MENA language technology tools, without taking into account contextual or semantic morphological implications. Therefore, it is necessary to have an automatic analysis tool taking into account the word sense and not only the morpho-syntactic category. Moreover, they are also based on statistical/stochastic models. These stochastic models, such as HMMs, have shown their effectiveness in different NLP applications: part-of-speech tagging, machine translation, speech recognition, etc. As an extension, we focus on language modeling using Recurrent Neural Network (RNN); given that morphological analysis coverage was very low in dialectal Arabic, it is significantly important to investigate deeply how the dialect data influence the accuracy of these approaches by developing dialectal morphological processing tools to show that dialectal variability can support to improve analysis.

Keywords: NLP, DL, ML, analyser, MSA, RNN, CNN

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Authors: Nurwati Djaman, Suradi Tahmir, Nurdin Arsyad

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The major objective of this study was to implement and evaluate the use of the implementation of social responsibility with the approach of Indonesian Realistic Mathematics Education (PMRI) in teaching and learning mathematics on students’ engagement and learning. The research problems investigated in this research: 1) What were the effects of the implementation of social responsibility with PMRI approach to learning mathematics? 2) What were the effects of the approach to students’ engagement? An action research and grounded theory methodology were adopted for the study. This study used mixed methods to collect, describe, and interpret the data. The data were collected through focus group discussion, classroom observations, questionnaire, interview, and students’ work. The participants in this study consisted of 45 students. The study revealed that the approach has given students the opportunity to develop their understanding of concepts and procedures, problem-solving ability, and communication ability. Also, students’ involvement in the approach improved their engagement in learning mathematics in the three domains of cognitive engagement, effective engagement, and behavioral engagement. In particular, the data collection from the focus group, classroom observations, and interviews suggest that, during this study, the students became more active participants in the mathematics lessons.

Keywords: Indonesian Realistic Mathematics Education, PMRI, learning mathematics, social responsibility, students' engagement

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Authors: Abubakar Rashid, Muhammad Saad, Faheem Ahmed

Abstract:

This paper provides a comprehensive study pertaining to diagnosis of intermittent high vibrations on an industrial gas turbine using detailed vibrations analysis, followed by its rectification. Engro Polymer & Chemicals Limited, a Chlor-Vinyl complex located in Pakistan has a captive combined cycle power plant having two 28 MW gas turbines (make Hitachi) & one 15 MW steam turbine. In 2018, the organization faced an issue of high vibrations on one of the gas turbines. These high vibration peaks appeared intermittently on both compressor’s drive end (DE) & turbine’s non-drive end (NDE) bearing. The amplitude of high vibration peaks was between 150-170% on the DE bearing & 200-300% on the NDE bearing from baseline values. In one of these episodes, the gas turbine got tripped on “High Vibrations Trip” logic actuated at 155µm. Limited instrumentation is available on the machine, which is monitored with GE Bently Nevada 3300 system having two proximity probes installed at Turbine NDE, Compressor DE &at Generator DE & NDE bearings. Machine’s transient ramp-up & steady state data was collected using ADRE SXP & DSPI 408. Since only 01 key phasor is installed at Turbine high speed shaft, a derived drive key phasor was configured in ADRE to obtain low speed shaft rpm required for data analysis. By analyzing the Bode plots, Shaft center line plot, Polar plot & orbit plots; rubbing was evident on Turbine’s NDE along with increased bearing clearance of Turbine’s NDE radial bearing. The subject bearing was then inspected & heavy deposition of carbonized coke was found on the labyrinth seals of bearing housing with clear rubbing marks on shaft & housing covering at 20-25 degrees on the inner radius of labyrinth seals. The collected coke sample was tested in laboratory & found to be the residue of lube oil in the bearing housing. After detailed inspection & cleaning of shaft journal area & bearing housing, new radial bearing was installed. Before assembling the bearing housing, cleaning of bearing cooling & sealing air lines was also carried out as inadequate flow of cooling & sealing air can accelerate coke formation in bearing housing. The machine was then taken back online & data was collected again using ADRE SXP & DSPI 408 for health analysis. The vibrations were found in acceptable zone as per ISO standard 7919-3 while all other parameters were also within vendor defined range. As a learning from subject case, revised operating & maintenance regime has also been proposed to enhance machine’s reliability.

Keywords: ADRE, bearing, gas turbine, GE Bently Nevada, Hitachi, vibration

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