Search results for: online training

Authors: Babesse Saad, Ameddah Djemeleddine

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In this paper, a learning algorithm using neuronal networks to improve the roll stability and prevent the rollover in a single unit heavy vehicle is proposed. First, LQR control to keep balanced normalized rollovers, between front and rear axles, below the unity, then a data collected from this controller is used as a training basis of a neuronal regulator. The ANN controller is thereafter applied for the nonlinear side force model, and gives satisfactory results than the LQR one.

Keywords: rollover, single unit heavy vehicle, neural networks, nonlinear side force

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Authors: Taieb Kherafa Noureddine

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The paper presents a new approach regarding the management in sports that is based on the principles of reengineering. Applying that modern and pure management system, called reengineering, in sports activity, we hope to get better and better results, in order to increase both the health state and the performances of trained athletes. The paper also presents the similarities between BPR (Business Process Reengineering) and sports managements, as well as the proposed solution for a proper implementation of such model of management. The five components of the basic BPR model are presented, together with their features for sports management.

Keywords: business process reengineering, great sports events, sports management, training activities

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Authors: Alejandro Paz Parra, Jose Luis Oslinger Gutierrez, Javier Olaya Ochoa

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In the present paper, five electric current based methods to analyze electric faults on the stator of induction motors (IM) are used and compared. The analysis tries to extend the application of the multiple reference frames diagnosis technique. An eccentricity indicator is presented to improve the application of the Park’s Vector Approach technique. Most of the fault indicators are validated and some others revised, agree with the technical literatures and published results. A tri-phase 3hp squirrel cage IM, especially modified to establish different fault levels, is used for validation purposes.

Keywords: motor fault diagnosis, induction motor, MCSA, ESA, Extended Park´s vector approach, multiparameter analysis

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Authors: Yeo Leng Leng

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Blended learning, with its combination of online and face-to-face instruction, presents a unique set of challenges and opportunities in terms of cultivating student agency. While it offers flexibility and personalized learning pathways, it also demands a higher degree of self-regulation and motivation from students. This paper presents the design of blended learning in a Chinese lesson and discusses the framework involved. It also talks about the Edtech tools adopted to engage the students. Some of the students’ works will be showcased. A qualitative case study research method was employed in this paper to find out more about students’ learning experiences and to give them a voice. The purpose is to seek improvement in the blended learning design of the Chinese lessons and to encourage students’ self-directed learning.

Keywords: blended learning, student agency, ed-tech tools, self-directed learning

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Authors: Robert Jedrys, Zdzislaw Kobos, Justyna Skrzynska, Zbigniew Wochynski

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Professions connected to aviation require an assessment of the suitability of health, psychological and psychomotor skills and overall physical fitness of the organism, who applies. Assessment of the physical condition is conducted by the committees consisting of aero-medical specialists in clinical medicine and aviation. In addition, psychological predispositions should be evaluated by specialized psychologists familiar with the specifics of the tasks and requirements for the various positions in aviation. Both, physical abilities and general physical fitness of candidates for aviation shall be assessed during the selection exams, which also test the ability to deal with stress what is very important in aviation. Hence, the mentioned exams in physical education not only help to judge on the ranking in candidates in terms of their efficiency and performance, but also allows to evaluate the functioning under stress measured using psychological tests. Moreover, before-test stress is a predictors of successfulness in the next stages of education and practical training in the aviation. The aim of the study was to evaluate the influence of temperamental traits on strategies used for coping with stress during selection exams in physical education, deciding on admission to aviation school. The study involved 30 candidates for fighter pilot training in aviation school . To evaluate the temperament 'The Formal Characteristics of Behavior-Temperament Inventory' (FCB-TI) by B. Zawadzki and J.Strelau was used. To determine the pattern of coping with stress 'The Coping Inventory for Stressful Situations' (CISS) to N. S. Endler and J. D. A. Parker were engaged. Study of temperament and styles of coping with stress was conducted directly before the exam selection of physical education. The results were analyzed with 'Statistica 9' program. The studies showed that:-There is a negative correlation between such a temperament feature as 'perseverance' and preferred style of coping with stress concentrated on the task (r = -0.590; p < 0.004); -There is a positive correlation between such a feature of temperament as 'emotional reactivity,' and preference to deal with a stressful situation with ‘style centered on emotions’ (r = 0.520; p <0.011); -There is a negative correlation between such a feature of temperament as ‘strength’ and ‘style of coping with stress concentrated on emotions’ (r = -0.580; p < 0.004). Studies indicate that temperament traits determine the perception of stress and preferred coping styles used during the selection, as during the exams in physical education.

Keywords: aviation, physical education, stress, temperamental traits

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Authors: R. L. Ujjwal, Abhishek Jain

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In the field of computer vision, most facial variations such as identity, expression, emotions and gender have been extensively studied. Automatic age estimation has been rarely explored. With age progression of a human, the features of the face changes. This paper is providing a new comparable study of different type of algorithm to feature extraction [Hybrid features using HAAR cascade & HOG features] & classification [KNN & SVM] training dataset. By using these algorithms we are trying to find out one of the best classification algorithms. Same thing we have done on the feature selection part, we extract the feature by using HAAR cascade and HOG. This work will be done in context of age group classification model.

Keywords: computer vision, age group, face detection

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Authors: Wen Huei Chou, Te Lung Pan, Tsu Wen Yeh

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New media have recently received increasing attention as a new communication form. The COVID-19 outbreak has pushed people’s lifestyles into the digital age, and the drug market has infiltrated formal e-commerce platforms. The self-media boom has fostered growth in online drug myths. To set the record straight, it is imperative to develop new media-based interventions. However, the usefulness of new media on this issue has not yet been fully examined. This study selected 13 articles on the development of new media-based interventions to prevent drug abuse from Airiti Library and Pub-Med as of October 3, 2021. The key conclusions are that (1) new media have a significantly positive influence on skills, self-efficacy, and behavior; (2) most interventions package traditional course learning into new media formats; and (3) new media can create a covert, interactive environment that cannot be replicated offline, which may merit attention in future research.

Keywords: drug abuse, interventions, new media, systematic review

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Authors: M. R. Babaei, S. M. Hosseini, S. Rahmani, L. Moradi

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Today, the quality of education and training systems and the effectiveness of the education systems of most concern to stakeholders and decision-makers of our country's development in each country. In Iran this is a double issue of concern to numerous reasons; So that governments, over the past decade have hardly even paid the running costs of education. ICT is claiming it has the power to change the structure of a program for training, reduce costs and increase quality, and do education systems and products consistent with the needs of the community and take steps to practice education. Own of the areas that the introduction of information technology has fundamentally changed is the field of education. The aim of this research is process reengineering management in schools simultaneously has been using field studies to collect data in the form of interviews and a questionnaire survey. The statistical community of this research has been the country of Iran and smart schools under the education. Sampling was targeted. The data collection tool was a questionnaire composed of two parts. The questionnaire consists of 36 questions that each question designates one of effective factors on the management of smart schools. Also each question consists of two parts. The first part designates the operating position in the management process, which represents the domain's belonging to the management agent (planning, organizing, leading, controlling). According to the classification of Dabryn and in second part the factors affect the process of managing the smart schools were examined, that Likert scale is used to classify. Questions the validity of the group of experts and prominent university professors in the fields of information technology, management and reengineering of approved and Cronbach's alpha reliability and also with the use of the formula is evaluated and approved. To analyse the data, descriptive and inferential statistics were used to analyse the factors contributing to the rating of (Linkert scale) descriptive statistics (frequency table data, mean, median, mode) was used. To analyse the data using analysis of variance and nonparametric tests and Friedman test, the assumption was evaluated. The research conclusions show that the factors influencing the management process re-engineering smart schools in school performance is affected.

Keywords: re-engineering, management process, smart school, Iran's school

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Authors: Airy Huang, Fuji Foo, Aries Prasetya Wibowo

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In recent years, the focus of Natural Language Processing (NLP) development has been gradually shifting from the semantics-based approach to deep learning one, which performs faster with fewer resources. Although it performs well in many applications, the deep learning approach, due to the lack of semantics understanding, has difficulties in noticing and expressing a novel business case with a pre-defined scope. In order to meet the requirements of specific robotic services, deep learning approach is very labor-intensive and time consuming. It is very difficult to improve the capabilities of conversational AI in a short time, and it is even more difficult to self-learn from experiences to deliver the same service in a better way. In this paper, we present an adaptive conversational AI algorithm that combines both semantic knowledge and deep learning to address this issue by learning new business cases through conversations. After self-learning from experience, the robot adapts to the business cases originally out of scope. The idea is to build new or extended robotic services in a systematic and fast-training manner with self-configured programs and constructed dialog flows. For every cycle in which a chat bot (conversational AI) delivers a given set of business cases, it is trapped to self-measure its performance and rethink every unknown dialog flows to improve the service by retraining with those new business cases. If the training process reaches a bottleneck and incurs some difficulties, human personnel will be informed of further instructions. He or she may retrain the chat bot with newly configured programs, or new dialog flows for new services. One approach employs semantics analysis to learn the dialogues for new business cases and then establish the necessary ontology for the new service. With the newly learned programs, it completes the understanding of the reaction behavior and finally uses dialog flows to connect all the understanding results and programs, achieving the goal of self-learning process. We have developed a chat bot service mounted on a kiosk, with a camera for facial recognition and a directional microphone array for voice capture. The chat bot serves as a concierge with polite conversation for visitors. As a proof of concept. We have demonstrated to complete 90% of reception services with limited self-learning capability.

Keywords: conversational AI, chatbot, dialog management, semantic analysis

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Authors: Nugzar Todua

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The paper studies the role of food labeling in order to promote healthy eating issue in Georgia. The main focus of the research is directed to consumer attitudes regarding food labeling. The methodology of the paper is based on the focus group work, as well as online and face to face surveys. The data analysis has been provided through ANOVA. The study proves that the impact of variables such as the interest, awareness, reliability, assurance and satisfaction of consumers' on buying decision, is statistically important. The study reveals that consumers’ perception regarding to food labeling is positive, but their level of knowledge and ability is rather low. It is urgent to strengthen marketing promotions strategies in the process of implementations of food security policy in Georgia.

Keywords: food labeling, buying decision, Georgian consumers, marketing research

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Authors: Adrianna D. Hendricks

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This paper examines the factors that contribute to the re-traumatization of victims of sex trafficking within the Canadian context. Sex trafficking occurring domestically in Canada is severely under-researched, stigmatized, and under-prosecuted, leading to the re-traumatization of victims by various levels of government. This is in part due to the Canadian criminal justice system unethically utilizing prostitution laws in cases of sex trafficking and partially due to the unaddressed stigmatization victims face within the justice system itself. Utilizing evidence from a current literature review, personal correspondence, and personal life experiences, this paper will demonstrate the need for victim involvement in policy reform. The current literature review was done through an academic database search using the terms: “Sex Trafficking, Exploitation, Canada”, with the limitation of articles written within the last 5 years and written within the Canadian context. Overall, from the results, only eight articles precisely matched the criteria. The current literature argues strongly and unanimously for more research and education of professionals who have close contact with high-risk populations (doctors, police officers, social workers, etc.) to protect both minors and adults from being sexually trafficked. Additionally, for women and girls who do not have Canadian citizenship, the fear of deportation becomes a barrier to disclosing exploitation experiences to professionals. There is a desperate need for more research done in tandem with survivors and victims to inform policymaking in a meaningful way. The researcher is a survivor of sex trafficking both as a youth and as an adult, giving the researcher a unique insight into the realities of the criminal justice system for victims of sex trafficking. There is a clear need for professionals in positions of power to be re-educated about the realities of sex-trafficking, and what it means for the victims. Congruent to the current research the author calls for: standardized professional training for people in healthcare, police officers, court officials, and victim services; with the additional layers of victim involvement in creation of professional education training, and victim involvement in research. Justice for victims/survivors can only be obtained if they have been consulted and believed. Without meaningful consultation with survivors, victims who are both minors and adults will continue to fall through the cracks in policy.

Keywords: Canadian policy, re-traumatization, sex-trafficking, stigmatization

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Authors: Yassir Semmar

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During the past two decades, the topic of multicultural competence has gained much attention in the psychology field, most notably in the clinical and counseling specializations. While higher education institutions have been placing a premium on sensitizing their faculty, staff, and student bodies to various diversity and multicultural issues, little emphasis has been directed towards mandating multicultural training for graduate learners in the educational psychology specialty. Given the increasingly diverse student population, it is imperative for educational psychologists to become multiculturally competent particularly in instructional and mentoring contexts. Strategies and conditions for attaining multicultural competence are discussed.

Keywords: multicultural competence, instruction, pedagogical practices, mentoring

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Authors: Ksenia Volkova, Artur Petrosyan, Ignatii Dubyshkin, Alexei Ossadtchi

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Brain-computer interfaces are a growing research field producing many implementations that find use in different fields and are used for research and practical purposes. Despite the popularity of the implementations using non-invasive neuroimaging methods, radical improvement of the state channel bandwidth and, thus, decoding accuracy is only possible by using invasive techniques. Electrocorticography (ECoG) is a minimally invasive neuroimaging method that provides highly informative brain activity signals, effective analysis of which requires the use of machine learning methods that are able to learn representations of complex patterns. Deep learning is a family of machine learning algorithms that allow learning representations of data with multiple levels of abstraction. This study explores the potential of deep learning approaches for ECoG processing, decoding movement intentions and the perception of proprioceptive information. To obtain synchronous recording of kinematic movement characteristics and corresponding electrical brain activity, a series of experiments were carried out, during which subjects performed finger movements at their own pace. Finger movements were recorded with a three-axis accelerometer, while ECoG was synchronously registered from the electrode strips that were implanted over the contralateral sensorimotor cortex. Then, multichannel ECoG signals were used to track finger movement trajectory characterized by accelerometer signal. This process was carried out both causally and non-causally, using different position of the ECoG data segment with respect to the accelerometer data stream. The recorded data was split into training and testing sets, containing continuous non-overlapping fragments of the multichannel ECoG. A deep convolutional neural network was implemented and trained, using 1-second segments of ECoG data from the training dataset as input. To assess the decoding accuracy, correlation coefficient r between the output of the model and the accelerometer readings was computed. After optimization of hyperparameters and training, the deep learning model allowed reasonably accurate causal decoding of finger movement with correlation coefficient r = 0.8. In contrast, the classical Wiener-filter like approach was able to achieve only 0.56 in the causal decoding mode. In the noncausal case, the traditional approach reached the accuracy of r = 0.69, which may be due to the presence of additional proprioceptive information. This result demonstrates that the deep neural network was able to effectively find a representation of the complex top-down information related to the actual movement rather than proprioception. The sensitivity analysis shows physiologically plausible pictures of the extent to which individual features (channel, wavelet subband) are utilized during the decoding procedure. In conclusion, the results of this study have demonstrated that a combination of a minimally invasive neuroimaging technique such as ECoG and advanced machine learning approaches allows decoding motion with high accuracy. Such setup provides means for control of devices with a large number of degrees of freedom as well as exploratory studies of the complex neural processes underlying movement execution.

Keywords: brain-computer interface, deep learning, ECoG, movement decoding, sensorimotor cortex

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Authors: Maria Paula Santos, Ana Lucas

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Organizations support their operations and decision making on the data they have at their disposal, so the quality of these data is remarkably important and Data Quality (DQ) is currently a relevant issue, the literature being unanimous in pointing out that poor DQ can result in large costs for organizations. The literature review identified and described 24 Critical Success Factors (CSF) for Data Quality Management (DQM) that were presented to a panel of experts, who ordered them according to their degree of importance, using the Delphi method with the Q-sort technique, based on an online questionnaire. The study shows that the five most important CSF for DQM are: definition of appropriate policies and standards, control of inputs, definition of a strategic plan for DQ, organizational culture focused on quality of the data and obtaining top management commitment and support.

Keywords: critical success factors, data quality, data quality management, Delphi, Q-Sort

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Authors: Jayden R. Hunter, Brett A. Gordon, Stephen R. Bird, Amanda C. Benson

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The World Health Organisation recognises the workplace as an important setting for exercise promotion, with potential benefits including improved employee health and fitness, and reduced worker absenteeism and presenteeism. Despite these potential benefits to both employee and employer, there is a lack of evidence supporting the long-term effectiveness of workplace exercise programs. There is, therefore, a need for better-informed programs that cater to employee exercise preferences. Specifically, workplace exercise programs should address any time, motivation, internal and external barriers to participation reported by sub-groups of employees. This study sought to compare exercise participation to perceived barriers and facilitators to workplace exercise engagement of university employees. This information is needed to design and implement wider-reaching programs aiming to maximise long-term employee exercise adherence and subsequent health, fitness and productivity benefits. An online survey was advertised at an Australian university with the potential to reach 3,104 full-time employees. Along with exercise participation (International physical activity questionnaire) and behaviour (stage of behaviour change in relation to physical activity questionnaire), perceived barriers (corporate exercise barriers scale) and facilitators to workplace exercise participation were identified. The survey response rate was 8.1% (252 full-time employees; 95% white-collar; 60% female; 79.4% aged 30–59 years; 57% professional and 38% academic). Most employees reported meeting (43.7%) or exceeding (42.9%) exercise guidelines over the previous week (i.e. ⩾30 min of moderate-intensity exercise on most days or ⩾ 25 min of vigorous-intensity exercise on at least three days per week). Reported exercise behaviour over the previous six months showed that 64.7% of employees were in maintenance, 8.3% were in action, 10.9% were in preparation, 12.4% were in contemplation, and 3.8% were in the pre-contemplation stage of change. Perceived barriers towards workplace exercise participation were significantly higher in employees not attaining weekly exercise guidelines compared to employees meeting or exceeding guidelines, including a lack of time or reduced motivation (p < 0.001; partial eta squared = 0.24 (large effect)), exercise attitude (p < 0.05; partial eta squared = 0.04 (small effect)), internal (p < 0.01; partial eta squared = 0.10 (moderate effect)) and external (p < 0.01; partial eta squared = 0.06 (moderate effect)) barriers. The most frequently reported exercise facilitators were personal training (particularly for insufficiently active employees; 33%) and group exercise classes (20%). The most frequently cited preferred modes of exercise were walking (70%), swimming (50%), gym (48%), and cycling (45%). In conclusion, providing additional means of support such as individualised gym, swimming and cycling programs with personal supervision and guidance may be particularly useful for employees not meeting recommended moderate-vigorous volumes of exercise, to help overcome reported exercise barriers in order to improve participation, health, and fitness. While individual biopsychosocial factors should be considered when making recommendations for interventions, the specific barriers and facilitators to workplace exercise participation identified by this study can inform the development of workplace exercise programs aiming to broaden employee engagement and promote greater ongoing exercise adherence. This is especially important for the uptake of less active employees who perceive greater barriers to workplace exercise participation than their more active colleagues.

Keywords: exercise barriers, exercise facilitators, physical activity, workplace health

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Authors: Antonella Falanga, Armando Cartenì

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Today, an unprecedented amount of data coming from several sources, including mobile devices, sensors, tracking systems, and online platforms, characterizes our lives. The term “big data” not only refers to the quantity of data but also to the variety and speed of data generation. These data hold valuable insights that, when extracted and analyzed, facilitate informed decision-making. The 4Vs of big data - velocity, volume, variety, and value - highlight essential aspects, showcasing the rapid generation, vast quantities, diverse sources, and potential value addition of these kinds of data. This surge of information has revolutionized many sectors, such as business for improving decision-making processes, healthcare for clinical record analysis and medical research, education for enhancing teaching methodologies, agriculture for optimizing crop management, finance for risk assessment and fraud detection, media and entertainment for personalized content recommendations, emergency for a real-time response during crisis/events, and also mobility for the urban planning and for the design/management of public and private transport services. Big data's pervasive impact enhances societal aspects, elevating the quality of life, service efficiency, and problem-solving capacities. However, during this transformative era, new challenges arise, including data quality, privacy, data security, cybersecurity, interoperability, the need for advanced infrastructures, and staff training. Within the transportation sector (the one investigated in this research), applications span planning, designing, and managing systems and mobility services. Among the most common big data applications within the transport sector are, for example, real-time traffic monitoring, bus/freight vehicle route optimization, vehicle maintenance, road safety and all the autonomous and connected vehicles applications. Benefits include a reduction in travel times, road accidents and pollutant emissions. Within these issues, the proper transport demand estimation is crucial for sustainable transportation planning. Evaluating the impact of sustainable mobility policies starts with a quantitative analysis of travel demand. Achieving transportation decarbonization goals hinges on precise estimations of demand for individual transport modes. Emerging technologies, offering substantial big data at lower costs than traditional methods, play a pivotal role in this context. Starting from these considerations, this study explores the usefulness impact of big data within transport demand estimation. This research focuses on leveraging (big) data collected during the COVID-19 pandemic to estimate the evolution of the mobility demand in Italy. Estimation results reveal in the post-COVID-19 era, more than 96 million national daily trips, about 2.6 trips per capita, with a mobile population of more than 37.6 million Italian travelers per day. Overall, this research allows us to conclude that big data better enhances rational decision-making for mobility demand estimation, which is imperative for adeptly planning and allocating investments in transportation infrastructures and services.

Keywords: big data, cloud computing, decision-making, mobility demand, transportation

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Authors: Martin Klepek

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This paper presents results of primary quantitative research on viral advertising with focus on popularity and willingness to share viral video among Czech Internet population. It starts with brief theoretical debate on viral advertising, which is used for the comparison of the results. For purpose of collecting data, online questionnaire survey was given to 384 respondents. Statistics utilized in this research included frequency, percentage, correlation and Pearson’s Chi-square test. Data was evaluated using SPSS software. The research analysis disclosed high popularity of viral advertising video among Czech Internet population but implies lower willingness to share it. Significant relationship between likability of viral video technique and age of the viewer was found.

Keywords: internet advertising, internet population, promotion, marketing communication, viral advertising, viral video

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Authors: Pranav Gulati, Isha Sharma

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Hypertension and hypotension are known to have repercussions on the health of an individual, with hypertension contributing to an increased probability of risk to cardiovascular diseases and hypotension resulting in syncope. This prompts the development of a non-invasive, non-intrusive, continuous and cuff-less blood pressure monitoring system to detect blood pressure variations and to identify individuals with acute and chronic heart ailments, but due to the unavailability of such devices for practical daily use, it becomes difficult to screen and subsequently regulate blood pressure. The complexities which hamper the steady monitoring of blood pressure comprises of the variations in physical characteristics from individual to individual and the postural differences at the site of monitoring. We propose to develop a continuous, comprehensive cardio-analysis tool, based on reflective photoplethysmography (PPG). The proposed device, in the form of an eyewear captures the PPG signal and estimates the systolic and diastolic blood pressure using a sensor positioned near the temporal artery. This system relies on regression models which are based on extraction of key points from a pair of PPG wavelets. The proposed system provides an edge over the existing wearables considering that it allows for uniform contact and pressure with the temporal site, in addition to minimal disturbance by movement. Additionally, the feature extraction algorithms enhance the integrity and quality of the extracted features by reducing unreliable data sets. We tested the system with 12 subjects of which 6 served as the training dataset. For this, we measured the blood pressure using a cuff based BP monitor (Omron HEM-8712) and at the same time recorded the PPG signal from our cardio-analysis tool. The complete test was conducted by using the cuff based blood pressure monitor on the left arm while the PPG signal was acquired from the temporal site on the left side of the head. This acquisition served as the training input for the regression model on the selected features. The other 6 subjects were used to validate the model by conducting the same test on them. Results show that the developed prototype can robustly acquire the PPG signal and can therefore be used to reliably predict blood pressure levels.

Keywords: blood pressure, photoplethysmograph, eyewear, physiological monitoring

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Authors: Ioanna Skoura

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Past research shows that personal space has been investigated since the 1950s. Also, personality traits have been found to have a significant relationship with personal space. However, some of these studies have been criticized for being ethically inappropriate. In an attempt to avoid ethical issues, a new scale measuring desire for personal space has been created. The purpose of the present study is to investigate the impact of ethnicity on desire for personal space. Additionally, extraversion and neuroticism are expected to predict significantly desire for personal space. Furthermore, the study is looking for any impact of religiosity on desire for personal space. In order to test the previous hypotheses, 115 participants from three cultural groups (English, Greeks in Greece and Greeks in the UK) are recruited online. Results indicate that only extraversion and religiosity are significant predictors of desire for personal space. Implications of the findings are discussed and suggestions for future research are made.

Keywords: ethnicity, religiosity, personality, personal space

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Authors: Ábrám Zoltán

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Introduction: Climate change is now a major health risk, and its environmental and health effects have become frequently discussed topics. The consequences of climate change are clearly visible in natural disasters and excess deaths caused by extreme weather conditions. Global warming and the increasingly frequent extreme weather events have direct, immediate effects or long-term, indirect effects on health. For this reason, it is a need to teach the health effects of climate change in medical schools. Material and methods: We looked for various surveys, studies, and reports on the main pathways through which global warming affects health. Medical schools face the challenge of teaching the health implications of climate change and integrating knowledge about the health effects of climate change into medical training. For this purpose, there were organised World Café workshops for three target groups: medical students, academic staff, and practising medical doctors. Results: Among the goals of the research is the development of a detailed curriculum for medical students, which serves to expand their knowledge in basic education. At the same time, the project promotes the increase of teacher motivation and the development of methodological guidelines for university teachers; it also provides further training for practicing doctors. The planned teaching materials will be developed in a format suitable for traditional face-to-face teaching, as well as e-learning teaching materials. CLIMATEMED is a project based on the cooperation of six universities and institutions from four countries, the aim of which is to improve the curriculum and expand knowledge about the health effects of climate change at medical universities. Conclusions: In order to assess the needs, summarize the proposals, to develop the necessary strategy, World Café type, one-and-a-half to two-hour round table discussions will take place separately for medical students, academic staff, and practicing doctors. The CLIMATEMED project can facilitate the integration of knowledge about the health effects of climate change into curricula and can promote practical use. The avoidance of the unwanted effects of global warming and climate change is not only a public matter, but it is also a challenge to change our own lifestyle. It is the responsibility of all of us to protect the Earth's ecosystem and the physical and mental health of ourselves and future generations.

Keywords: climate change, health effects, medical schools, World Café, medical students

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Authors: Dae-Hee Son, Sang-Hee Kang, Soon-Ryul Nam

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In this paper, we develop a dynamic modeling of wind farms in the Jeju power system. The dynamic model of wind farms is developed to study their dynamic effects on the Jeju power system. PSS/E is used to develop the dynamic model of a wind farm composed of 1.5-MW doubly fed induction generators. The output of a wind farm is regulated based on pitch angle control, in which the two controllable parameters are speed and power references. The simulation results confirm that the pitch angle is successfully controlled, regardless of the variation in wind speed and output regulation.

Keywords: dynamic model, Jeju power system, online limitation, pitch angle control, wind farm

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Authors: Mohammad Reza Babaei, Sanaz Kamrani

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The main problems That arise In adopting most Enterprise resources planning (ERP) strategies come from organizational, complex information systems like the ERP integrate the data of all business areas within the organization. The implementation of ERP is a difficult process as it involves different types of end users. Based on literature, we proposed a conceptual framework and examined it to find the effect of some of the individual, organizational, and technological factors on the usage of ERP and its impact on the end user. The results of the analysis suggest that computer self-efficacy, organizational support, training, and compatibility have a positive influence on ERP usage which in turn has significant influence on panoptic empowerment and individual performance.

Keywords: factor, influencing, enterprise, system

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Authors: B.-Y.-S. Lau, Y.-C.-T. Jonathan, M.-S. Alias

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Heritage trees are natural large, individual trees with exceptionally value due to association with age or event or distinguished people. In Malaysia, there is an abundance of tropical heritage trees throughout the country. It is essential to set up a repository of heritage trees to prevent valuable trees from being cut down. In this cross domain study, a web-based online expert system namely the Heritage Tree Expert Assessment and Classification (HTEAC) is developed and deployed for public to nominate potential heritage trees. Based on the nomination, tree care experts or arborists would evaluate and verify the nominated trees as heritage trees. The expert system automatically rates the approved heritage trees according to pre-defined grades via Delphi technique. Features and usability test of the expert system are presented. Preliminary result is promising for the system to be used as a full scale public system.

Keywords: arboriculture, Delphi, expert system, heritage tree, urban forestry

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Authors: Leena Alfarani

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Although mobile devices are ubiquitous on university campuses, teacher-readiness for mobile learning has yet to be fully explored in the non-western nations. This study shows that two main factors affect the adoption and use of m-learning among female teachers within a university in Saudi Arabia—resistance to change and perceived social culture. These determinants of the current use and intention to use of m-learning were revealed through the analysis of an online questionnaire completed by 165 female faculty members. This study reveals several important issues for m-learning research and practice. The results further extend the body of knowledge in the field of m-learning, with the findings revealing that resistance to change and perceived social culture are significant determinants of the current use of and the intention to use m-learning.

Keywords: blended learning, mobile learning, technology adoption, devices

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Authors: Lazlo Fauth, Andreas Ligocki

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For cost-effective use of harvesters, expensive repairs and unplanned downtimes must be reduced as far as possible. The predictive detection of failing systems and the calculation of intelligent service intervals, necessary to avoid these factors, require in-depth knowledge of the machines' behavior. Such know-how needs permanent monitoring of the machine state from different technical perspectives. In this paper, three approaches will be presented as they are currently pursued in the publicly funded project PreForst at Ostfalia University of Applied Sciences. These include the intelligent linking of workshop and service data, sensors on the harvester, and a special online hydraulic oil condition monitoring system. Furthermore the paper shows potentials as well as challenges for the use of these data in the conception of a predictive maintenance system.

Keywords: predictive maintenance, condition monitoring, forest harvesting, forest engineering, oil data, hydraulic data

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Authors: Orhun Vural, Oguz Bayat, Rustu Akay, Osman N. Ucan

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This study presents a new parallel approach clustering of GPS data. Evaluation has been made by comparing execution time of various clustering algorithms on GPS data. This paper aims to propose a parallel based on neighborhood K-means algorithm to make it faster. The proposed parallelization approach assumes that each GPS data represents a vehicle and to communicate between vehicles close to each other after vehicles are clustered. This parallelization approach has been examined on different sized continuously changing GPS data and compared with serial K-means algorithm and other serial clustering algorithms. The results demonstrated that proposed parallel K-means algorithm has been shown to work much faster than other clustering algorithms.

Keywords: parallel k-means algorithm, parallel clustering, clustering algorithms, clustering on flowing data

Procedia PDF Downloads 222

Authors: Joel Niklaus, Matthias Sturmer

Abstract:

The publication of judicial proceedings is a cornerstone of many democracies. It enables the court system to be made accountable by ensuring that justice is made in accordance with the laws. Equally important is privacy, as a fundamental human right (Article 12 in the Declaration of Human Rights). Therefore, it is important that the parties (especially minors, victims, or witnesses) involved in these court decisions be anonymized securely. Today, the anonymization of court decisions in Switzerland is performed either manually or semi-automatically using primitive software. While much research has been conducted on anonymization for tabular data, the literature on anonymization for unstructured text documents is thin and virtually non-existent for court decisions. In 2019, it has been shown that manual anonymization is not secure enough. In 21 of 25 attempted Swiss federal court decisions related to pharmaceutical companies, pharmaceuticals, and legal parties involved could be manually re-identified. This was achieved by linking the decisions with external databases using regular expressions. An automated re-identification system serves as an automated test for the safety of existing anonymizations and thus promotes the right to privacy. Manual anonymization is very expensive (recurring annual costs of over CHF 20M in Switzerland alone, according to an estimation). Consequently, many Swiss courts only publish a fraction of their decisions. An automated anonymization system reduces these costs substantially, further leading to more capacity for publishing court decisions much more comprehensively. For the re-identification system, topic modeling with latent dirichlet allocation is used to cluster an amount of over 500K Swiss court decisions into meaningful related categories. A comprehensive knowledge base with publicly available data (such as social media, newspapers, government documents, geographical information systems, business registers, online address books, obituary portal, web archive, etc.) is constructed to serve as an information hub for re-identifications. For the actual re-identification, a general-purpose language model is fine-tuned on the respective part of the knowledge base for each category of court decisions separately. The input to the model is the court decision to be re-identified, and the output is a probability distribution over named entities constituting possible re-identifications. For the anonymization system, named entity recognition (NER) is used to recognize the tokens that need to be anonymized. Since the focus lies on Swiss court decisions in German, a corpus for Swiss legal texts will be built for training the NER model. The recognized named entities are replaced by the category determined by the NER model and an identifier to preserve context. This work is part of an ongoing research project conducted by an interdisciplinary research consortium. Both a legal analysis and the implementation of the proposed system design ESRA will be performed within the next three years. This study introduces the system design of ESRA, an end-to-end system for re-identification and anonymization of Swiss court decisions. Firstly, the re-identification system tests the safety of existing anonymizations and thus promotes privacy. Secondly, the anonymization system substantially reduces the costs of manual anonymization of court decisions and thus introduces a more comprehensive publication practice.

Keywords: artificial intelligence, courts, legal tech, named entity recognition, natural language processing, ·privacy, topic modeling

Procedia PDF Downloads 148

Authors: Mohamed A. Abou-Shouk, Mahamoud M. Hewedi

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This study extends technology acceptance model (TAM) to investigate the antecedents and consequences of social media adoption by tourists and travel agents. It compares their perceptions on social media adoption and its consequences. Online survey was addressed to tourists and travel agents for data collection purposes. Structural equation modelling was employed for analysis purposes. The findings revealed that the majority of tourists and travel agents involved in the study believe in the usefulness of social media adoption for travel planning and marketing purposes. They agree that adopting social media could change the attitude of tourists towards specific destination or attraction and influence their purchasing decisions. This study contributes to knowledge by extending TAM and provides some managerial implication to marketers.

Keywords: TAM, social media, travel and tourism, travel agents

Procedia PDF Downloads 413

Authors: Hamad Aljassmi, Sangwon Han

Abstract:

Various solutions for preventing construction defects have been suggested. However, a construction company may have difficulties adopting all these suggestions due to financial and practical constraints. Based on this recognition, this paper aims to identify the most significant defect causes and formulate their defect generation mechanism in order to help a construction company to set priorities of its defect prevention strategies. For this goal, we conducted a questionnaire survey of 106 industry professionals and identified five most significant causes including: (1) organizational culture, (2) time pressure and constraints, (3) workplace quality system, (4) financial constraints upon operational expenses and (5) inadequate employee training or learning opportunities.

Keywords: defect, quality, failure, risk

Procedia PDF Downloads 627

Authors: Hazri Jamil, Munir Shuib, Farhah Muhammad

Abstract:

In line with the 27th ASEAN (Association of Southeast Asian Nations) Summit 2015 principles in Kuala Lumpur on higher education, synergised collaboration is aimed to promote resilience and vibrancy between institutions and academia. Hence, this paper aims to discuss matters concerning collaboration among ASEAN Universities derived from the perspectives of academics from the universities in ASEAN countries. The data were collected from 234 respondents of nine universities in ASEAN using questionnaires and online survey analyzed using purposive sampling. The findings revealed that more than half of the respondents in this survey were optimistic that the ASEAN universities have a great potential in collaboration among academics in ASEAN countries. The findings also indicated that collaboration among ASEAN universities will have a positive impact on the ASEAN economy and society. Finally, to enhance collaboration among the universities in ASEAN, educational improvement and exchanges as well as environmental issues are among the noteworthy aspects which need to be taken into account.

Keywords: academics, ASEAN, collaboration, higher education, universities

Procedia PDF Downloads 301