Search results for: service learning

Authors: R. O. Osaseri, A. R. Usiobaifo

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The power transformer which is responsible for the voltage transformation is of great relevance in the power system and oil-immerse transformer is widely used all over the world. A prompt and proper maintenance of the transformer is of utmost importance. The dissolved gasses content in power transformer, oil is of enormous importance in detecting incipient fault of the transformer. There is a need for accurate prediction of the incipient fault in transformer oil in order to facilitate the prompt maintenance and reducing the cost and error minimization. Study on fault prediction and diagnostic has been the center of many researchers and many previous works have been reported on the use of artificial intelligence to predict incipient failure of transformer faults. In this study machine learning technique was employed by using gradient decent algorithms and Support Vector Machine (SVM) in predicting incipient fault diagnosis of transformer. The method focuses on creating a system that improves its performance on previous result and historical data. The system design approach is basically in two phases; training and testing phase. The gradient decent algorithm is trained with a training dataset while the learned algorithm is applied to a set of new data. This two dataset is used to prove the accuracy of the proposed model. In this study a transformer fault diagnostic model based on Support Vector Machine (SVM) and gradient decent algorithms has been presented with a satisfactory diagnostic capability with high percentage in predicting incipient failure of transformer faults than existing diagnostic methods.

Keywords: diagnostic model, gradient decent, machine learning, support vector machine (SVM), transformer fault

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Authors: Ammar Alali, Mahmoud Abughaban

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Cost reduction and drilling optimization is the goal of many drilling operators. Historically, stuck pipe incidents were a major segment of non-productive time (NPT) associated costs. Traditionally, stuck pipe problems are part of the operations and solved post-sticking. However, the real key to savings and success is in predicting the stuck pipe incidents and avoiding the conditions leading to its occurrences. Previous attempts in stuck-pipe predictions have neglected the local geology of the problem. The proposed predictive tool utilizes geophysical data processing techniques and Machine Learning (ML) algorithms to predict drilling activities events in real-time using surface drilling data with minimum computational power. The method combines two types of analysis: (1) real-time prediction, and (2) cause analysis. Real-time prediction aggregates the input data, including historical drilling surface data, geological formation tops, and petrophysical data, from wells within the same field. The input data are then flattened per the geological formation and stacked per stuck-pipe incidents. The algorithm uses two physical methods (stacking and flattening) to filter any noise in the signature and create a robust pre-determined pilot that adheres to the local geology. Once the drilling operation starts, the Wellsite Information Transfer Standard Markup Language (WITSML) live surface data are fed into a matrix and aggregated in a similar frequency as the pre-determined signature. Then, the matrix is correlated with the pre-determined stuck-pipe signature for this field, in real-time. The correlation used is a machine learning Correlation-based Feature Selection (CFS) algorithm, which selects relevant features from the class and identifying redundant features. The correlation output is interpreted as a probability curve of stuck pipe incidents prediction in real-time. Once this probability passes a fixed-threshold defined by the user, the other component, cause analysis, alerts the user of the expected incident based on set pre-determined signatures. A set of recommendations will be provided to reduce the associated risk. The validation process involved feeding of historical drilling data as live-stream, mimicking actual drilling conditions, of an onshore oil field. Pre-determined signatures were created for three problematic geological formations in this field prior. Three wells were processed as case studies, and the stuck-pipe incidents were predicted successfully, with an accuracy of 76%. This accuracy of detection could have resulted in around 50% reduction in NPT, equivalent to 9% cost saving in comparison with offset wells. The prediction of stuck pipe problem requires a method to capture geological, geophysical and drilling data, and recognize the indicators of this issue at a field and geological formation level. This paper illustrates the efficiency and the robustness of the proposed cross-disciplinary approach in its ability to produce such signatures and predicting this NPT event.

Keywords: drilling optimization, hazard prediction, machine learning, stuck pipe

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Authors: Arta Jashari, Enver Kutllovci

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Abstract— The purpose of this study is to examine the relationship of recruitment and selection practice and performance in medium service enterprises in Kosovo. A total of 110 managers from public and private sector was analyzed. Our empirical results show that enterprises in Kosovo use recruitment and selection practice and they know how important is to have the right people with skills and knowledge accordingly with the job requirements. The outcome of Pearson correlation analysis provides evidence that recruitment and selection practice, positively and significantly influence the financial performance. Also, our results show a significant relationship between the education of managers and the use of the recruitment and selection practice. From our results we can conclude and suggest that with a good recruiting and selection, the organization will fill with a group of potentially qualified candidates who will be able to fulfill the enterprises objective.

Keywords: Human Resource, Kosovo, Recruitment and Selection, Performance

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Authors: M. Assaad, J. Mäkiö, T. Mäkelä, M. Kankaanranta, N. Fachantidis, V. Dagdilelis, A. Reid, C. R. del Rio, E. V. Pavlysh, S. V. Piashkun

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To bring science and society together in Europe, thus increasing the continent’s international competitiveness, STEM (science, technology, engineering and mathematics) education must be more relatable to European youths in their everyday life. STIMEY (Science, Technology, Innovation, Mathematics, Engineering for the Young) project researches and develops a hybrid educational environment with multi-level components that is being designed and developed based on a well-researched pedagogical framework, aiming to make STEM education more attractive to young people aged 10 to 18 years in this digital era. This environment combines social media components, robotic artefacts, and radio to educate, engage and increase students’ interest in STEM education and careers from a young age. Additionally, it offers educators the necessary modern tools to deliver STEM education in an attractive and engaging manner in or out of class. Moreover, it enables parents to keep track of their children’s education, and collaborate with their teachers on their development. Finally, the open platform allows businesses to invest in the growth of the youths’ talents and skills in line with the economic and labour market needs through entrepreneurial tools. Thus, universities, schools, teachers, students, parents, and businesses come together to complete a circle in which STEM becomes part of the daily life of youths through a hybrid educational environment that also prepares them for future careers.

Keywords: e-learning, entrepreneurship, pedagogy, robotics, serious gaming, social media, STEM education

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Authors: Elena Maria Scalisi, Roberta Pecoraro, Martina Contino, Sara Ignoto, Carmelo Iaria, Santi Concetto Pavone, Gino Sorbello, Loreto Di Donato, Maria Violetta Brundo

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5G Technology is evolving to satisfy a variety of service requirements that may allow high data-rate connections (1Gbps) and lower latency times than current (<1ms). In order to support a high data transmission speed and a high traffic service for eMBB (enhanced mobile broadband) use cases, 5G systems have the characteristic of using different frequency bands of the radio wave spectrum (700 MHz, 3.6-3.8 GHz and 26.5-27.5 GHz), thus taking advantage of higher frequencies than previous mobile radio generations (1G-4G). However, waves at higher frequencies have a lower capacity to propagate in free space and therefore, in order to guarantee the capillary coverage of the territory for high reliability applications, it will be necessary to install a large number of repeaters. Following the introduction of this new technology, there has been growing concern over the past few months about possible harmful effects on human health. The aim of this preliminary study is to evaluate possible short term effects induced by 5G-millimeter waves on embryonic development and early life stages of Danio rerio by Z-FET. We exposed developing zebrafish at frequency of 27 GHz, with a standard pyramidal horn antenna placed at 15 cm far from the samples holder ensuring an incident power density of 10 mW/cm2. During the exposure cycle, from 6 h post fertilization (hpf) to 96 hpf, we measured a different morphological endpoints every 24 hours. Zebrafish embryo toxicity test (Z-FET) is a short term test, carried out on fertilized eggs of zebrafish and it represents an effective alternative to acute test with adult fish (OECD, 2013). We have observed that 5G did not reveal significant impacts on mortality nor on morphology because exposed larvae showed a normal detachment of the tail, presence of heartbeat, well-organized somites, therefore hatching rate was lower than untreated larvae even at 48 h of exposure. Moreover, the immunohistochemical analysis performed on larvae showed a negativity to the HSP-70 expression used as a biomarkers. This is a preliminary study on evaluation of potential toxicity induced by 5G and it seems appropriate to underline the importance that further studies would take, aimed at clarifying the probable real risk of exposure to electromagnetic fields.

Keywords: Biomarker of exposure, embryonic development, 5G waves, zebrafish embryo toxicity test

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Authors: Tina Wu, Andrew Martin

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The expansion of the Internet of Things (IoT) brings a new level of threat. Attacks on IoT are already being used by criminals to form botnets, launch Distributed Denial of Service (DDoS) and distribute malware. This opens a whole new digital forensic arena to develop forensic methodologies in order to have the capability to investigate IoT related crimes. However, existing proposed IoT forensic models are still premature requiring further improvement and validation, many lack details on the acquisition and analysis phase. This paper proposes an enhanced theoretical IoT digital forensic model focused on identifying and acquiring the main sources of evidence in a methodical way. In addition, this paper presents a theoretical acquisition framework of the different stages required in order to be capable of acquiring evidence from IoT devices.

Keywords: acquisition, Internet of Things, model, zoning

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Authors: Ivan Ashif Ardhana, Intan Mahanani

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Chemistry education tends to change from triplet representation among macroscopic, microscopic, and symbolic to tetrahedron shape. This change set the aspect of human element on the top of learning. Meaning that students are expected to solve the problems involving the ethic, morality, and humanity through the class. Ability to solve the problems connecting either theories or applications is called scientific literacy which have been implemented in curriculum 2013 implicitly. Scientific literacy has an aspect of nature science and critical thinking. Both can be integrated to learning using scientific approach and scientific inquiry. Unfortunately, students’ ability of scientific literacy in Indonesia is far from expectation. A survey from PISA had proven it. Scientific literacy of Indonesian students is always at bottom five position from 2002 till 2012. Improving a scientific literacy needs many efforts against them. Developing an instructional material based on scientific approach is one kind of that efforts. Instructional material contains both aspect of nature of science and critical thinking which is instructed explicitly to improve the students’ understanding about science. Developing goal is to produce a prototype and an instructional material using scientific approach whose chapter is chemical bonding and intermolecular forces for high school students grade ten. As usual, the material is subjected to get either quantitative mark or suggestion through validation process using validation sheet instrument. Development model is adapted from 4D model containing four steps. They are define, design, develop, and disseminate. Nevertheless, development of instructional material had only done until third step. The final step wasn’t done because of time, cost, and energy limitations. Developed instructional material had been validated by four validators. They are coming from chemistry lecture and high school’s teacher which two at each. The result of this development research shown the average of quantitative mark of students’ book is 92.75% with very proper in criteria. Given at same validation process, teacher’s guiding book got the average mark by 96.98%, similar criteria with students’ book. Qualitative mark including both comments and suggestions resulted from validation process were used as consideration for the revision. The result concluded us how the instructional materials using scientific approach to explicit nature of science and critical thinking on the topic of chemical bonding and intermolecular forces are very proper if they are used at learning activity.

Keywords: critical thinking, instructional material, nature of science, scientific literacy

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Authors: Ian Christopher Rocha

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This study aimed to determine the association of the quality of life (QOL) of the caregivers of children in need of special protection (CNSP) in child-caring institutions in Metro Manila with the levels of their role overload (RO) and role distress (RD). The CNSP in this study covered the orphaned, abandoned, abused, neglected, exploited, and mentally-challenged children. In this study, the domains of QOL included physical health (PH), psychological health, social health (SH), and living conditions (LC). It also intended to ascertain the association of their personal and work-related characteristics with their RO and RD levels. The respondents of this study were 130 CNSP caregivers in 17 residential child-rearing institutions in Metro Manila. A purposive non-probability sampling was used. Using a quantitative methodological approach, the survey method was utilized to gather data with the use of a self-administered structured questionnaire. Data were analyzed using both descriptive and inferential statistics. Results revealed that the level of RO, the level of RD, and the QOL of the CNSP caregivers were all moderate. Data also suggested that there were significant positive relationships between the RO level and the caregivers’ characteristics, such as age, the number of training, and years of service in the institution. At the same time, the findings revealed that there were significant positive relationships between the RD level and the caregivers’ characteristics, such as age and hours of care rendered to their care recipients. In addition, the findings suggested that all domains of their QOL obtained significant relationships with their RO level. For the correlations of their level of RO and their QOL domains, the PH and the LC obtained a moderate negative correlation with the RO level while the rest of the domains obtained weak negative correlations with RO level. For the correlations of their level of RD and the QOL domains, all domains, except SH, obtained strong negative correlations with the level of RD. The SH revealed to have a moderate negative correlation with RD level. In conclusion, caregivers who are older experience higher levels of RO and RD; caregivers who have more training and years of service experience the higher level of RO; and caregivers who have longer hours of rendered care experience the higher level of RD. In addition, the study affirmed that if the levels of RO and RD are high, the QOL is low, and vice versa. Therefore, the RO and RD levels are reliable predictors of the caregivers’ QOL. In relation, the caregiving situation in the Philippines revealed to be unique and distinct from other countries because the levels of RO and RD and the QOL of Filipino CNSP caregivers were all moderate in contrast with their foreign counterparts who experience high caregiving RO and RD leading to low QOL.

Keywords: quality of life, caregivers, children in need of special protection, physical health, psychological health, social health, living conditions, role overload, role distress

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Authors: Youmen Chaaban, Maha Ellili-Cherif

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The purpose of this study was to examine the effects of teachers’ individual characteristics and perceptions of environmental factors that impact their technology integration into their EFL (English as a Foreign Language) classrooms. To this end, a national survey examining EFL teachers’ perceptions was conducted at Qatari Independent schools. 263 EFL teachers responded to the survey which investigated several factors known to impact technology integration. These factors included technology availability and support, EFL teachers’ perceptions of importance, obstacles facing technology integration, competency with technology use, and formal technology preparation. The impact of these factors on teachers’ and students’ educational technology use was further measured. The analysis of the data included descriptive statistics and a chi-square analysis test in order to examine the relationship between these factors. The results revealed important cultural factors that impact teachers’ practices and attitudes towards technology in the Qatari context. EFL teachers were found to integrate technology most prominently for instructional delivery and preparation. The use of technology as a learning tool received less emphasis. Teachers further revealed consistent perceptions about obstacles to integration, high levels of confidence in using technology, and consistent beliefs about the importance of using technology as a learning tool. Further analyses of the factors impacting technology integration can assist with Qatar’s technology advancement and development efforts by indicating the areas of strength and areas where additional efforts are needed. The results will lay the foundation for conducting context-specific professional development suitable for the needs of EFL teachers in Qatari Independent Schools.

Keywords: educational technology integration, Qatar, EFL, independent schools, ICT

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Authors: Arif Tri Setyanto, Aditya Nanda Priyatama, Nugraha Arif Karyanta, Fadjri Kirana A., Afia Fitriani, Rini Setyowati, Moh.Abdul Hakim

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The Basic Health Research Report by the Ministry of Health (2018) shows an increase in the prevalence of mental health disorders in the adolescent and early adult age ranges. Supporting this finding, data on the psychological examination of the student health service unit at one State University recorded 115 cases of moderate and severe health problems in the period 2016 - 2019. More specifically, the highest number of cases was experienced by clients in the age range of 21-23 years or equivalent, with the mid-semester stage towards the end. Based on the distribution of cases experienced and the disorder becomes a psychological problem experienced by students. A total of 29% or the equivalent of 33 students experienced anxiety disorders, 25% or 29 students experienced problems ranging from mild to severe, as well as other classifications of disorders experienced, including adjustment disorders, family problems, academics, mood disorders, self-concept disorders, personality disorders, cognitive disorders, and others such as trauma and sexual disorders. Various mental health disorders have a significant impact on the academic life of students, such as low GPA, exceeding the limit in college, dropping out, disruption of social life on campus, to suicide. Based on literature reviews and best practices from universities in various countries, one of the effective ways to prevent and treat student mental health disorders is to implement a mental health monitoring system in universities. This study uses a participatory action research approach, with a sample of 423 from a total population of 32,112 students. The scale used in this study is the Beck Depression Inventory (BDI) to measure depression and the Taylor Minnesota Anxiety Scale (TMAS) to measure anxiety levels. This study aims to (1) develop a digital-based health monitoring system for students' mental health situations in the mental health category. , dangers, or those who have mental disorders, especially indications of symptoms of depression and anxiety disorders, and (2) implementing a mental health monitoring system in universities at the beginning and end of each semester. The results of the analysis show that from 423 respondents, the main problems faced by all coursework, such as thesis and academic assignments. Based on the scoring and categorization of the Beck Depression Inventory (BDI), 191 students experienced symptoms of depression. A total of 24.35%, or 103 students experienced mild depression, 14.42% (61 students) had moderate depression, and 6.38% (27 students) experienced severe or extreme depression. Furthermore, as many as 80.38% (340 students) experienced anxiety in the high category. This article will review this review of the student mental health service system on campus.

Keywords: monitoring system, mental health, psychological problems, students

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Authors: Juan P. Novoa, Mario A. Rios

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This paper presents a transmission planning methodology that considers the conversion of HVAC transmission lines to HVDC as an alternative of expansion of power systems, as a consequence of restrictions for the construction of new lines. The transmission expansion planning problem formulates an optimization problem that minimizes the total cost that includes the investment cost to convert lines from HVAC to HVDC and possible required reinforcements of the power system prior to the conversion. The costs analysis assesses the impact of the conversion on the reliability because transmission lines are out of service during the conversion work. The presented methodology is applied to a test system considering a planning a horizon of 10 years.

Keywords: transmission expansion planning, HVDC, cost optimization, energy non-supplied

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Authors: Charlotte Brenner, Fisayo Latilo, McKenna Causey

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This study explores the transformation of teacher candidates' beliefs regarding inclusion and inclusive teaching practices during their instructional and practicum experiences in the Canadian context. With the increasing diversity of schools, the study investigates how teacher candidates' beliefs impact their implementation of inclusive teaching practices, which are essential for meeting diverse student needs. The research examines the influence of teacher education programs, transformative learning experiences, and inclusive practicum placements on teacher candidates' beliefs about inclusion. Using a multiple case study approach, the study assesses teacher candidates' initial beliefs, documents changes in these beliefs after coursework on inclusion, and explores the supports and constraints affecting belief development in both university and practicum settings. Preliminary findings suggest that teacher candidates generally hold positive beliefs about inclusion at the outset of their teacher education programs. However, coursework and practicum experiences significantly shape their understanding of diversity, strategies for inclusion, and awareness of broader social issues related to inclusive classrooms. The research underscores the critical role of teacher education programs in shaping teacher candidates' beliefs about inclusion and highlights the value of transformative learning experiences and inclusive practicum placements in enhancing their understanding of equity and inclusion. Continued research is necessary to identify specific elements within courses and practicum experiences that promote positive beliefs about inclusive teaching practices, ultimately contributing to the creation of more equitable classrooms and improved student outcomes.

Keywords: inclusion, beliefs, teacher candidates, inclusive teaching practices

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Authors: Odd Jarl Borch, Marina Z. Solesvik

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The paper analyses partner selection approaches related to large scale R&D-based innovation projects at the different stages of development. We emphasize innovation projects in the maritime value chain and how partners are selected to improve quality according to high spec customer demands, and to reduce investment costs on new production technology such as advanced offshore service vessels. We elaborate on the differences in innovation approach and especially the role that purposive inflows and outflows of knowledge from external partners may be used to accelerate internal innovation. We present three cases related to different projects in terms of specificity and scope. We explore how the partner selection criteria change over time when the goals move from wide scope to a very specific R&D tasks.

Keywords: partner selection, innovation, offshore industry, concept design

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Authors: Chiahsu Yang, Peiling Wu, Ted Ho

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From emphasizing economic development to stressing public happiness, the international community mainly hopes to be able to understand whether the quality of life for the public is becoming better. The Better Life Index (BLI) constructed by OECD uses living conditions and quality of life as starting points to cover 11 areas of life and to convey the state of the general public’s well-being. In light of the BLI framework, the Directorate General of Budget, Accounting and Statistics (DGBAS) of the Executive Yuan instituted the Gross National Happiness Index to understand the needs of the general public and to measure the progress of the aforementioned conditions in residents across the island. Whereas living conditions consist of income and wealth, jobs and earnings, and housing conditions, health status, work and life balance, education and skills, social connections, civic engagement and governance, environmental quality, personal security. The ICT area consists of health care, living environment, ICT-enabled communication, transportation, government, education, pleasure, purchasing, job & employment. In the wake of further science and technology development, rapid formation of information societies, and closer integration between lifestyles and information societies, the public’s well-being within information societies has indeed become a noteworthy topic. the Board of Science and Technology of the Executive Yuan use the OECD’s BLI as a reference in the establishment of the Taiwan-specific ICT-Enabled Better Life Index. Using this index, the government plans to examine whether the public’s quality of life is improving as well as measure the public’s satisfaction with current digital quality of life. This understanding will enable the government to gauge the degree of influence and impact that each dimension of digital services has on digital life happiness while also serving as an important reference for promoting digital service development. The content of the ICT Enabled Better Life Index. Information and communications technology (ICT) has been affecting people’s living styles, and further impact people’s quality of life (QoL). Even studies have shown that ICT access and usage have both positive and negative impact on life satisfaction and well-beings, many governments continue to invest in e-government programs to initiate their path to information society. This research is the few attempts to link the e-government benchmark to the subjective well-being perception, and further address the gap between user’s perception and existing hard data assessment, then propose a model to trace measurement results back to the original public policy in order for policy makers to justify their future proposals.

Keywords: information and communications technology, quality of life, satisfaction, well-being

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Authors: Kumar Divyendra

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Underwater Sensor Networks (UWSNs) have wide applications in areas such as water quality monitoring, marine wildlife management etc. A typical UWSN system consists of a set of sensors deployed randomly underwater which communicate with each other using acoustic links. RF communication doesn't work underwater, and GPS too isn't available underwater. Additionally Automated Underwater Vehicles (AUVs) are deployed to collect data from some special nodes called Cluster Heads (CHs). These CHs aggregate data from their neighboring nodes and forward them to the AUVs using optical links when an AUV is in range. This helps reduce the number of hops covered by data packets and helps conserve energy. We consider the three-dimensional model of the UWSN. Nodes are initially deployed randomly underwater. They attach themselves to the surface using a rod and can only move upwards or downwards using a pump and bladder mechanism. We use graph theory concepts to maximize the coverage volume while every node maintaining connectivity with at least one surface node. We treat the surface nodes as landmarks and each node finds out its hop distance from every surface node. We treat these hop-distances as coordinates and use them for AUV navigation. An AUV intending to move closer to a node with given coordinates moves hop by hop through nodes that are closest to it in terms of these coordinates. In absence of GPS, multiple different approaches like Inertial Navigation System (INS), Doppler Velocity Log (DVL), computer vision-based navigation, etc., have been proposed. These systems have their own drawbacks. INS accumulates error with time, vision techniques require prior information about the environment. We propose a method that makes use of the earth's magnetic field values for navigation and combines it with other methods that simultaneously increase the coverage volume under the UWSN. The AUVs are fitted with magnetometers that measure the magnetic intensity (I), horizontal inclination (H), and Declination (D). The International Geomagnetic Reference Field (IGRF) is a mathematical model of the earth's magnetic field, which provides the field values for the geographical coordinateson earth. Researchers have developed an inverse deep learning model that takes the magnetic field values and predicts the location coordinates. We make use of this model within our work. We combine this with with the hop-by-hop movement described earlier so that the AUVs move in such a sequence that the deep learning predictor gets trained as quickly and precisely as possible We run simulations in MATLAB to prove the effectiveness of our model with respect to other methods described in the literature.

Keywords: clustering, deep learning, network backbone, parallel computing

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Authors: Tami Peterson

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Hyperbaric oxygen therapy (HBOT) has been hypothesized as an effective treatment for increasing verbal language skills in individuals on the autism spectrum. A child’s ability to effectively communicate with peers, parents, and caregivers impacts their level of independence and quality of personal relationships. This retrospective study will compare the speech development of participants aged 2-17 years that received 40 sessions of HBOT at 2.0 ATA to those who had not. Both groups will have a verbal assessment every six months. There were 31 subjects in the HBO group and 32 subjects in the non-HBO group. The statistical analysis will focus on whether hyperbaric oxygen therapy made a significant difference in Verbal Behavior Milestones Assessment and Placement Program (VB-MAPP) or Assessment of Basic Language and Learning Skills (ABLLS) results. The evidence demonstrates a strong correlation between HBOT and an increased change from baseline verbal scores compared to the control group, even in difficult to grasp areas such as spontaneous vocalization. We suggest this is due to the anti-inflammatory effects of hyperbaric oxygen therapy. Neuroinflammation causes hypoperfusion of critical central nervous system areas responsible for the symptoms described within the autism spectrum, such as problems with thought processing, memory, and speech. Decreasing the inflammation allows the brain to function properly, which results in improved verbal scores for the participants that underwent HBOT.

Keywords: assessment of basic language and learning skills, autism spectrum disorder, hyperbaric oxygen therapy, verbal behavior milestones assessment and placement program

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Authors: Salima R. Rajwani, Tazeen Ali, Rubina Barolia, Yasmin Parpio, Nasreen Alwani, Salima B. Virani

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Introduction: Nurses and physicians have a critical role in initiating lifesaving interventions during cardiac arrest. It is important that timely delivery of high quality Cardio Pulmonary Resuscitation (CPR) with advanced resuscitation skills and management of cardiac arrhythmias is a key dimension of code during cardiac arrest. It will decrease the chances of patient survival if the healthcare professionals are unable to initiate CPR timely. Moreover, traditional training will not prepare physicians and nurses at a competent level and their knowledge level declines over a period of time. In this regard, simulation training has been proven to be effective in promoting resuscitation skills. Simulation teaching learning strategy improves knowledge level, and skills performance during resuscitation through experiential learning without compromising patient safety in real clinical situations. The purpose of the study is to evaluate the effectiveness of simulation training in Advanced Cardiac Life Support Courses by using the selfefficacy tool. Methods: The study design is a quantitative research design and non-randomized quasi-experimental study design. The study examined the effectiveness of simulation through self-efficacy in two instructional methods; one is Medium Fidelity Simulation (MFS) and second is Traditional Training Method (TTM). The sample size was 220. Data was compiled by using the SPSS tool. The standardized simulation based training increases self-efficacy, knowledge, and skills and improves the management of patients in actual resuscitation. Results: 153 students participated in study; CG: n = 77 and EG: n = 77. The comparison was done between arms in pre and post-test. (F value was 1.69, p value is <0.195 and df was 1). There was no significant difference between arms in the pre and post-test. The interaction between arms was observed and there was no significant difference in interaction between arms in the pre and post-test. (F value was 0.298, p value is <0.586 and df is 1. However, the results showed self-efficacy scores were significantly higher within experimental group in post-test in advanced cardiac life support resuscitation courses as compared to Traditional Training Method (TTM) and had overall (p <0.0001) and F value was 143.316 (mean score was 45.01 and SD was 9.29) verses pre-test result showed (mean score was 31.15 and SD was 12.76) as compared to TTM in post-test (mean score was 29.68 and SD was 14.12) verses pre-test result showed (mean score was 42.33 and SD was 11.39). Conclusion: The standardized simulation-based training was conducted in the safe learning environment in Advanced Cardiac Life Suport Courses and physicians and nurses benefited from self-confidence, early identification of life-threatening scenarios, early initiation of CPR, and provides high-quality CPR, timely administration of medication and defibrillation, appropriate airway management, rhythm analysis and interpretation, and Return of Spontaneous Circulation (ROSC), team dynamics, debriefing, and teaching and learning strategies that will improve the patient survival in actual resuscitation.

Keywords: advanced cardiac life support, cardio pulmonary resuscitation, return of spontaneous circulation, simulation

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Authors: Nguyen Chan Hung, Mai Ngoc Anh, Nguyen Xuan Ha, Tran Xuan Duc, Dang Bao Lam, Nguyen Hoang Viet

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This paper presents a design and implementation of a navigation controller for a humanoid mobile robot platform to operate in indoor office environments. In order to fulfil the requirement of recognizing and approaching human to provide service while avoiding random obstacles, a behavior-based fuzzy logic controller was designed to simultaneously coordinate multiple behaviors. Experiments in real office environment showed that the fuzzy controller deals well with complex scenarios without colliding with random objects and human.

Keywords: behavior control, fuzzy logic, humanoid robot, mobile robot

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Authors: Yash Jain

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The US Center for Disease Control has recommended wearing masks to slow the spread of the virus. The research uses a video feed from a camera to conduct real-time classifications of whether or not a human is correctly wearing a mask, incorrectly wearing a mask, or not wearing a mask at all. Utilizing two distinct datasets from the open-source website Kaggle, a mask detection network had been trained. The first dataset that was used to train the model was titled 'Face Mask Detection' on Kaggle, where the dataset was retrieved from and the second dataset was titled 'Face Mask Dataset, which provided the data in a (YOLO Format)' so that the TinyYoloV3 model could be trained. Based on the data from Kaggle, two machine learning models were implemented and trained: a Tiny YoloV3 Real-time model and a two-stage neural network classifier. The two-stage neural network classifier had a first step of identifying distinct faces within the image, and the second step was a classifier to detect the state of the mask on the face and whether it was worn correctly, incorrectly, or no mask at all. The TinyYoloV3 was used for the live feed as well as for a comparison standpoint against the previous two-stage classifier and was trained using the darknet neural network framework. The two-stage classifier attained a mean average precision (MAP) of 80%, while the model trained using TinyYoloV3 real-time detection had a mean average precision (MAP) of 59%. Overall, both models were able to correctly classify stages/scenarios of no mask, mask, and incorrectly worn masks.

Keywords: datasets, classifier, mask-detection, real-time, TinyYoloV3, two-stage neural network classifier

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Authors: Asmamaw Yehun

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The name “TANA” is one of International Geodetic Service (IGS) Global Positioning System (GPS) station which is found in Bahir Dar University in Institute of Land Administration. The station name taken from one of big Lakes in Africa ,Lake Tana. The Institute of Land Administration (ILA) is part of Bahir Dar University, located in the capital of the Amhara National Regional State, Bahir Dar. The institute is the first of its kind in East Africa. The station is installed by cooperation of ILA and Sweden International Development Agency (SIDA) fund support. The Continues Operating Reference Station (CORS) is a network of stations that provide global satellite system navigation data to help three dimensional positioning, meteorology, space, weather, and geophysical applications throughout the globe. TANA station was as CORS since 2013 and sites are independently owned and operated by governments, research and education facilities and others. The data collected by the reference station is downloadable through Internet for post processing purpose by interested parties who carry out GNSS measurements and want to achieve a higher accuracy. We made a first observation on TANA, monitor stations on May 29th 2013. We used Leica 1200 receivers and AX1202GG antennas and made observations from 11:30 until 15:20 for about 3h 50minutes. Processing of data was done in an automatic post processing service CSRS-PPP by Natural Resources Canada (NRCan) . Post processing was done June 27th 2013 so precise ephemeris was used 30 days after observation. We found Latitude (ITRF08): 11 34 08.6573 (dms) / 0.008 (m), Longitude (ITRF08): 37 19 44.7811 (dms) / 0.018 (m) and Ellipsoidal Height (ITRF08): 1850.958 (m) / 0.037 (m). We were compared this result with GAMIT/GLOBK processed data and it was very closed and accurate. TANA station is one of the second IGS station for Ethiopia since 2015 up to now. It provides data for any civilian users, researchers, governmental and nongovernmental users. TANA station is installed with very advanced choke ring antenna and GR25 Leica receiver and also the site is very good for satellite accessibility. In order to test hydrostatic and wet zenith delay for positional data quality, we used GAMIT/GLOBK and we found that TANA station is the most accurate IGS station in East Africa. Due to lower tropospheric zenith and ionospheric delay, TANA and ADIS IGS stations has 2 and 1.9 meters 3D positional accuracy respectively.

Keywords: atmosphere, GNSS, neutral atmosphere, precipitable water vapour

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Authors: Maryam Fallahpoor, Biswajeet Pradhan

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Background: Artificial intelligence (AI) serves as a valuable tool in mitigating the scarcity of human resources required for the evaluation and categorization of vast quantities of medical imaging data. When AI operates with optimal precision, it minimizes the demand for human interpretations and, thereby, reduces the burden on radiologists. Among various AI approaches, deep learning (DL) stands out as it obviates the need for feature extraction, a process that can impede classification, especially with intricate datasets. The advent of DL models has ushered in a new era in medical imaging, particularly in the context of COVID-19 detection. Traditional 2D imaging techniques exhibit limitations when applied to volumetric data, such as Computed Tomography (CT) scans. Medical images predominantly exist in one of two formats: neuroimaging informatics technology initiative (NIfTI) and digital imaging and communications in medicine (DICOM). Purpose: This study aims to employ DL for the classification of COVID-19-infected pulmonary patients and normal cases based on 3D CT scans while investigating the impact of image format. Material and Methods: The dataset used for model training and testing consisted of 1245 patients from IranMehr Hospital. All scans shared a matrix size of 512 × 512, although they exhibited varying slice numbers. Consequently, after loading the DICOM CT scans, image resampling and interpolation were performed to standardize the slice count. All images underwent cropping and resampling, resulting in uniform dimensions of 128 × 128 × 60. Resolution uniformity was achieved through resampling to 1 mm × 1 mm × 1 mm, and image intensities were confined to the range of (−1000, 400) Hounsfield units (HU). For classification purposes, positive pulmonary COVID-19 involvement was designated as 1, while normal images were assigned a value of 0. Subsequently, a U-net-based lung segmentation module was applied to obtain 3D segmented lung regions. The pre-processing stage included normalization, zero-centering, and shuffling. Four distinct 3D CNN models (ResNet152, ResNet50, DensNet169, and DensNet201) were employed in this study. Results: The findings revealed that the segmentation technique yielded superior results for DICOM images, which could be attributed to the potential loss of information during the conversion of original DICOM images to NIFTI format. Notably, ResNet152 and ResNet50 exhibited the highest accuracy at 90.0%, and the same models achieved the best F1 score at 87%. ResNet152 also secured the highest Area under the Curve (AUC) at 0.932. Regarding sensitivity and specificity, DensNet201 achieved the highest values at 93% and 96%, respectively. Conclusion: This study underscores the capacity of deep learning to classify COVID-19 pulmonary involvement using real 3D hospital data. The results underscore the significance of employing DICOM format 3D CT images alongside appropriate pre-processing techniques when training DL models for COVID-19 detection. This approach enhances the accuracy and reliability of diagnostic systems for COVID-19 detection.

Keywords: deep learning, COVID-19 detection, NIFTI format, DICOM format

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Authors: Ahmed Khattab, Bassem Shetta

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Today, there are many branches of technology, one of them is the internet of things. In this paper, it's focused specifically on automating all the home appliances through E-mail using Node JS server, the server side stores, and processes this data. The server side contains user interface and notification system functionalities which is operated by Raspberry Pi. It will present the security requirements for the smart home. In this application, the privilege of home control including special persons to use it, using the hardware appliances through mobiles and tablets is achieved. The proposed application delivers high quality of service, long lifetime, low maintenance, fast deployment, and low power requirements with low cost needed for development.

Keywords: Raspberry Pi, E-mail, home automation, temperature sensor, PIR sensor, actuators, relay

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Authors: Anurag Saxena, Alkesh Agrawal, Dinesh Kumar

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The next generation mobile communication systems i.e. fourth generation (4G) was developed to accommodate the quality of service and required data rate. This project focuses on multiple access technique proposed in 4G communication systems. It is attempted to demonstrate the IDMA (Interleave Division Multiple Access) technology. The basic principle of IDMA is that interleaver is different for each user whereas CDMA employs different signatures. IDMA inherits many advantages of CDMA such as robust against fading, easy cell planning; dynamic channel sharing and IDMA increase the spectral efficiency and reduce the receiver complexity. In this, performance of IDMA is analyzed using QC-LDPC coding scheme further it is compared with LDPC coding and at last BER is calculated and plotted in MATLAB.

Keywords: 4G, QC-LDPC, CDMA, IDMA

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Authors: Rabie Fadil, Parshuram Aarotale, Shubha Majumder, Bijay Guargain

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Hemorrhage is the loss of blood from the circulatory system and leading cause of battlefield and postpartum related deaths. Early detection of hemorrhage remains the most effective strategy to reduce mortality rate caused by traumatic injuries. In this study, we investigated the physiological changes via non-invasive cardiac signals at rest and under different hemorrhage conditions simulated through graded lower-body negative pressure (LBNP). Simultaneous electrocardiogram (ECG), photoplethysmogram (PPG), blood pressure (BP), impedance cardiogram (ICG), and phonocardiogram (PCG) were acquired from 10 participants (age:28 ± 6 year, weight:73 ± 11 kg, height:172 ± 8 cm). The LBNP protocol consisted of applying -20, -30, -40, -50, and -60 mmHg pressure to the lower half of the body. Beat-to-beat heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean aerial pressure (MAP) were extracted from ECG and blood pressure. Systolic amplitude (SA), systolic time (ST), diastolic time (DT), and left ventricle Ejection time (LVET) were extracted from PPG during each stage. Preliminary results showed that the application of -40 mmHg i.e. moderate stage simulated hemorrhage resulted significant changes in HR (85±4 bpm vs 68 ± 5bpm, p < 0.01), ST (191 ± 10 ms vs 253 ± 31 ms, p < 0.05), LVET (350 ± 14 ms vs 479 ± 47 ms, p < 0.05) and DT (551 ± 22 ms vs 683 ± 59 ms, p < 0.05) compared to rest, while no change was observed in SA (p > 0.05) as a consequence of LBNP application. These findings demonstrated the potential of cardiac signals in detecting moderate hemorrhage. In future, we will analyze all the LBNP stages and investigate the feasibility of other physiological signals to develop a predictive machine learning model for early detection of hemorrhage.

Keywords: blood pressure, hemorrhage, lower-body negative pressure, LBNP, machine learning

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Authors: Ayşe Coban, Orhan Coban, Haldun Soydal, Sükrü Sürücü

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While health is a source of prosperity for individuals, it is also one of the most important determinants of economic growth for a country. Health, by increasing the productivity of labor, contributes to economic growth. Therefore, countries should give the necessary emphasis to health services. The primary aim of this study is to analyze the changes occurring in health services in Turkey by examining the developments in the sector. In this scope, the second aim of the study is to reveal the place of health expenditures in the Turkish economy. As a result of the analysis in the dataset, in which the 1999-2013 periods is considered, it was determined that some increase in health expenditures took place and that the increase in the share of health expenditures in GDP was too small. Furthermore, analysis of the results points out that in financing health expenditures, the public sector is prominent compared to the private sector.

Keywords: health, health service, health expenditures, Turkey

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Authors: Carolin Schulze

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Characterized by a positive interdependence of learners, cooperative learning (CL) is one possibility of successfully dealing with the increasing heterogeneity of students. Various positive effects of CL on the mental, physical and social health of students have already been documented. However, this structure is still rarely used in physical education (PE). Moreover, there is a lack of information about factors that determine the successful implementation of CL in PE. Therefore, the objective of the current study was to find out factors that determine the successful implementation of CL in PE using a digital questionnaire that was conducted from November to December 2022. In addition to socio-demographic data (age, gender, teaching experience, and education level), frequency of using CL, implementation strategies (theory-led, student-centred), and positive and negative effects of CL were measured. Furthermore, teachers were asked to rate the success of implementation on a 6-point rating scale (1-very successful to 6-not successful at all). For statistical analysis, multiple linear regression was performed, setting the success of implementation as the dependent variable. A total of 224 teachers (mean age=44.81±10.60 years; 58% male) took part in the current study. Overall, 39% of participants stated that they never use CL in their PE classes. Main reasons against the implementations of CL in PE were no time for preparation (74%) or for implementation (61%) and high heterogeneity of students (55%). When using CL, most of the reported difficulties are related to uncertainties about the correct procedure (54%) and the heterogeneous performance of students (54%). The most frequently mentioned positive effect was increased motivation of students (42%) followed by an improvement of psychological abilities (e.g. self-esteem, self-concept; 36%) and improved class cohesion (31%). Reported negative effects were unpredictability (29%), restlessness (24%), confusion (24%), and conflicts between students (17%). The successful use of CL is related to a theory-based preparation (e.g., heterogeneous formation of groups, use of rules and rituals) and a flexible implementation tailored to the needs and conditions of students (e.g., the possibility of individual work, omission of CL phases). Compared to teachers who solely implemented CL theory-led or student-adapted, teachers who switched from theory-led preparation to student-centred implementation of CL reported more successful implementation (t=5.312; p<.001). Neither frequency of using CL in PE nor the gender, age, the teaching experience, or the education level of the teacher showed a significant connection with the successful use of CL. Corresponding to the results of the current study, it is advisable that teachers gather enough knowledge about CL during their education and to point out the need to adapt the learning structure according to the diversity of their students. In order to analyse implementation strategies of teachers more deeply, qualitative methods and guided interviews with teachers are needed.

Keywords: diversity, educational technology, physical education, teaching styles

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Authors: Aliya Kamilyevna Salahova

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This study examines the effectiveness of the Kids Science Labs intervention program, based on STEM, in fostering independent thinking among preschool and elementary school children and its influence on their academic achievement. Through a comprehensive methodology involving interviews, surveys, observations, case studies, and statistical tests, data were collected from various sources to accurately analyze the program's effects. The findings indicate a significant positive impact on children's independent thinking abilities, leading to improved academic performance in mathematics and science, enhanced learning motivation, and a propensity to critically evaluate problem-solving approaches. This research contributes to the theoretical understanding of how STEM activities can foster independent thinking and academic success in young children, providing valuable insights for the development of educational programs. Introduction: The goal of this study is to investigate the influence of the Kids Science Labs intervention program, grounded in STEM, on the development of independent thinking skills among preschool and elementary school children. By addressing this objective, we aim to explore the program's potential to enhance academic performance in mathematics and science. The study's findings have theoretical significance as they shed light on the ways in which STEM activities can foster independent thinking in young children, thus enabling educators to design effective learning programs that promote academic success. Methodology: This study employs a robust methodology that includes interviews, surveys, observations, case studies, and statistical tests. These methods were carefully selected to collect comprehensive data from multiple sources, such as documents and records, ensuring a thorough analysis of the program's effects. The use of diverse data collection and analysis procedures facilitated an in-depth exploration of the research questions and yielded reliable results. Results: The results indicate that children participating in the Kids Science Labs program experienced a sustained positive impact on their independent thinking abilities. Moreover, these children demonstrated improved academic performance in mathematics and science, displaying higher learning motivation and the capacity to critically evaluate problem-solving methods and seek optimal solutions. Theoretical Importance: This study contributes significantly to the existing theoretical knowledge by elucidating how STEM activities can foster independent thinking and enhance academic success in preschool and elementary school children. The findings have practical implications for educators, empowering them to develop learning programs that stimulate independent thinking, leading to improved academic performance in young children. Discussion: The findings of this research affirm that the Kids Science Labs intervention program is highly effective in fostering independent thinking among preschool and elementary school children. The program's positive impact extends to improved academic performance in mathematics and science, highlighting its potential to enhance learning outcomes. Educators can leverage these findings to develop educational programs that promote independent thinking and elevate academic achievement in young children. Conclusion: In conclusion, the Kids Science Labs intervention program has been found to be highly effective in fostering independent thinking among preschool and elementary school children. Furthermore, participation in the program correlates with improved academic performance in mathematics and science. The study's outcomes underscore the importance of developing educational initiatives that stimulate independent thinking in young children, thereby enhancing their academic success.

Keywords: STEM in preschool, STEM in elementary school, kids science labs, independent thinking, STEM activities in early childhood education

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Authors: Latif Adnane, Benaatou Wafa, Pla Vicent

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Handover is of great significance to achieve seamless connectivity in wireless networks. This paper gives an impression of the main factors which are being affected by the soft and the hard handovers techniques. To know and understand the handover process in The Universal Mobile Telecommunications System (UMTS) network, different statistics are calculated. This paper focuses on the quality of service (QoS) of soft and hard handover in UMTS network, which includes the analysis of received power, signal to noise radio, throughput, delay traffic, traffic received, delay, total transmit load, end to end delay and upload response time using OPNET simulator.

Keywords: handover, UMTS, mobility, simulation, OPNET modeler

Procedia PDF Downloads 321

Authors: Li-hsing Shih, Wei-Jen Hsu

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Recently, the sale of electrical vehicles (EVs) has increased dramatically due to maturing technology development and decreasing cost. Governments of many countries have made regulations and policies in favor of EVs due to their long-term commitment to net zero carbon emissions. However, due to uncertain factors such as the future price of EVs, forecasting the future market share of EVs is a challenging subject for both the auto industry and local government. This study tries to forecast the market share of EVs using conjoint models and Monte Carlo simulation. The research is conducted in three phases. (1) A conjoint model is established to represent the customer preference structure on purchasing vehicles while five product attributes of both EV and internal combustion engine vehicles (ICEV) are selected. A questionnaire survey is conducted to collect responses from Taiwanese consumers and estimate the part-worth utility functions of all respondents. The resulting part-worth utility functions can be used to estimate the market share, assuming each respondent will purchase the product with the highest total utility. For example, attribute values of an ICEV and a competing EV are given respectively, two total utilities of the two vehicles of a respondent are calculated and then knowing his/her choice. Once the choices of all respondents are known, an estimate of market share can be obtained. (2) Among the attributes, future price is the key attribute that dominates consumers’ choice. This study adopts the assumption of a learning curve to predict the future price of EVs. Based on the learning curve method and past price data of EVs, a regression model is established and the probability distribution function of the price of EVs in 2030 is obtained. (3) Since the future price is a random variable from the results of phase 2, a Monte Carlo simulation is then conducted to simulate the choices of all respondents by using their part-worth utility functions. For instance, using one thousand generated future prices of an EV together with other forecasted attribute values of the EV and an ICEV, one thousand market shares can be obtained with a Monte Carlo simulation. The resulting probability distribution of the market share of EVs provides more information than a fixed number forecast, reflecting the uncertain nature of the future development of EVs. The research results can help the auto industry and local government make more appropriate decisions and future action plans.

Keywords: conjoint model, electrical vehicle, learning curve, Monte Carlo simulation

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Authors: Nadjet Mekaouche, Hanane Zitouni, Fatma Boudia, Habiba Fetati, A. Saleh, A. Lardjam, H. Geniaux, A. Coubret, H. Toumi

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Medicines have undeniably played a major role in prolonging shelf life and improving quality. The absolute efficacy of the drug remains a lever for innovation, its benefit/risk balance is not always assured and it does not always have the expected effects. Prior to marketing, knowledge about adverse drug reactions is incomplete. Once on the market, phase IV drug studies begin. For years, the drug was prescribed with less care to a large number of very heterogeneous patients and often in combination with other drugs. It is at this point that previously unknown adverse effects may appear, hence the need for the implementation of a pharmacovigilance system. Pharmacovigilance represents all methods for detecting, evaluating, informing and preventing the risks of adverse drug reactions. The most severe adverse events occur frequently in hospital and that a significant proportion of adverse events result in hospitalizations. In addition, the consequences of hospital adverse events in terms of length of stay, mortality and costs are considerable. It, therefore, appears necessary to develop ‘hospital pharmacovigilance’ aimed at reducing the incidence of adverse reactions in hospitals. The most widely used monitoring method in pharmacovigilance is spontaneous notification. However, underreporting of adverse drug reactions is common in many countries and is a major obstacle to pharmacovigilance assessment. It is in this context that this study aims to describe the experience of the pharmacovigilance service at the University Hospital of Oran (EHUO). This is a retrospective study extending from 2011 to 2017, carried out on archived records of declarations collected at the level of the EHUO Pharmacovigilance Department. Reporting was collected by two methods: ‘spontaneous notification’ and ‘active pharmacovigilance’ targeting certain clinical services. We counted 217 statements. It involved 56% female patients and 46% male patients. Age ranged from 5 to 78 years with an average of 46 years. The most common adverse reaction was drug toxidermy. For the drugs in question, they were essentially according to the ATC classification of anti-infectives followed by anticancer drugs. As regards the evolution of declarations by year, a low rate of notification was noted in 2011. That is why we decided to set up an active approach at the level of some services where a resident of reference attended the staffs every week. This has resulted in an increase in the number of reports. The declarations came essentially from the services where the active approach was installed. This highlights the need for ongoing communication between all relevant health actors to stimulate reporting and secure drug treatments.

Keywords: adverse drug reactions, hospital, pharmacovigilance, spontaneous notification

Procedia PDF Downloads 175