Search results for: ongoing training

Authors: Ninlawat Phuangchoke, Waraporn Viyanon, Setta Sasananan

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The most important process of the water treatment plant process is the coagulation using alum and poly aluminum chloride (PACL), and the value of usage per day is a hundred thousand baht. Therefore, determining the dosage of alum and PACL are the most important factors to be prescribed. Water production is economical and valuable. This research applies an artificial neural network (ANN), which uses the Levenberg–Marquardt algorithm to create a mathematical model (Soft Jar Test) for prediction chemical dose used to coagulation such as alum and PACL, which input data consists of turbidity, pH, alkalinity, conductivity, and, oxygen consumption (OC) of Bangkhen water treatment plant (BKWTP) Metropolitan Waterworks Authority. The data collected from 1 January 2019 to 31 December 2019 cover changing seasons of Thailand. The input data of ANN is divided into three groups training set, test set, and validation set, which the best model performance with a coefficient of determination and mean absolute error of alum are 0.73, 3.18, and PACL is 0.59, 3.21 respectively.

Keywords: soft jar test, jar test, water treatment plant process, artificial neural network

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Authors: Mariam Adel Hakim Fouad

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This quantitative examine investigates innovative arts teachers' perceptions regarding the implementation of an Inclusive innovative Arts curriculum. The study employs a descriptive method utilizing a 5-point Likert scale questionnaire comprising 15 objects to acquire data from innovative arts educators. The Census, with a disproportionate stratified sampling approach, became utilized to pick out 226 teachers from five academic circuits (Circuit A, B, C, D & E) within Offinso Municipality, Ghana. The findings suggest that most innovative arts instructors maintain a wonderful belief in enforcing an inclusive, innovative arts curriculum. Wonderful perceptions and attitudes amongst teachers are correlated with improved scholar engagement and participation in class sports. This has a look at recommends organizing workshops and in-carrier schooling periods centered on inclusive innovative arts schooling for creative Arts instructors. Moreover, it shows that colleges of education and universities accountable for trainer schooling integrate foundational guides in creative arts and special schooling into their number one schooling teacher training packages.

Keywords: arts-in-health, evidence based medicine, arts for health, expressive arts therapiesarts, cultural heritage, digitalization, ICTarts, design, font, identity

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Authors: Marta Moczulska

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One of the most important aspects of human management in an organization is the work engagement. In spite of the different perspectives of engagement, it is possible to see that it is expressed in the activity of the individual involved in the performance of tasks, the functioning of the organization. At the same time is considered not only in behavioural but also cognitive and emotional dimensions. Previous studies were related to sources, predictors of engagement and determinants, including organizational ones. Attention was paid to the importance of needs (including belonging, success, development, sense of work), values (such as trust, honesty, respect, justice) or interpersonal relationships, especially with the supervisor. Taking them into account and theories related to human acting, behaviour in the organization, interactions, it was recognized that engagement can be shaped through cooperation and competition. It was assumed that to shape the work engagement, it is necessary to simultaneously cooperate and compete in order to reduce the weaknesses of each of these activities and strengthen the strengths. Combining cooperation and competition is defined as 'coopetition'. However, research conducted in this field is primarily concerned with relations between companies. Intra-organizational coopetition is mainly considered as competing organizational branches or units (cross-functional coopetition). Less attention is paid to competing groups or individuals. It is worth noting the ambiguity of the concepts of cooperation and rivalry. Taking into account the terms used and their meaning, different levels of cooperation and forms of competition can be distinguished. Thus, several types of intra-organizational coopetition can be identified. The article aims at defining the potential for work engagement through intra-organizational coopetition. The aim of research was to know how levels of cooperation in competition conditions influence engagement. It is assumed that rivalry (positive competition) between teams (the highest level of cooperation) is a type of coopetition that contributes to working engagement. Qualitative research will be carried out among students of the University of Zielona Gora, realizing various types of projects. The first research groups will be students working in groups on one project for three months. The second research group will be composed of students working in groups on several projects in the same period (three months). Work engagement will be determined using the UWES questionnaire. Levels of cooperation will be determined using the author's research tool. Due to the fact that the research is ongoing, results will be presented in the final paper.

Keywords: competition, cooperation, intra-organizational coopetition, work engagement

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Authors: Valentina Zhang

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While facial expression is a complex and individualized behavior, all facial emotion recognition (FER) systems known to us rely on a single facial representation and are trained on universal data. We conjecture that: (i) different facial representations can provide different, sometimes complementing views of emotions; (ii) when employed collectively in a discussion group setting, they enable more accurate emotion reading which is highly desirable in autism care and other applications context sensitive to errors. In this paper, we first study FER using pixel-based DL vs semantics-based DL in the context of deepfake videos. Our experiment indicates that while the semantics-trained model performs better with articulated facial feature changes, the pixel-trained model outperforms on subtle or rare facial expressions. Armed with these findings, we have constructed an adaptive FER system learning from both types of models for dyadic or small interacting groups and further leveraging the synthesized group emotions as the ground truth for individualized FER training. Using a collection of group conversation videos, we demonstrate that FER accuracy and personalization can benefit from such an approach.

Keywords: neurodivergence care, facial emotion recognition, deep learning, ground truth for supervised learning

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Authors: Hanjun Hu, Jinxiang Zhang

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With the decline of "growthism", China's urbanization process has shifted from the stage of spatial expansion to the stage of optimization of built-up spaces, and urban renewal has gradually become a new wave of China's urban movement in recent years. The ongoing urban renewal movement in China not only needs to generate new motivation for urban development but also solve the backlog of social problems caused by rapid urbanization, which provides an opportunity for the transformation of China's urban governance model. Unlike previous approaches that focused on physical space and functional renewal, such as urban reconstruction, redevelopment, and reuse, the key challenge of urban renewal in the post-growth era lies in coordinating the complex interest relationships between multiple stakeholders. The traditional theoretical frameworks that focus on the structural relations between social groups are insufficient to explain the behavior logic and mutual cooperation mechanism of various groups and individuals in the current urban renewal practices. Therefore, based on the long-term tracking of the urban renewal practices in the Old City of Nanjing (OCN), this paper introduces the "collective action" theory to deeply analyze changes in the urban renewal governance model in OCN and tries to summarize the governance strategies that promote the formation of collective action within recent practices from a micro-scale. The study found that the practice in OCN experienced three different stages "government-led", "growth coalition" and "asymmetric game". With the transformation of government governance concepts, the rise of residents' consciousness of rights, and the wider participation of social organizations in recent years, the urban renewal in OCN is entering a new stage of "collective renewal action". Through the establishment of the renewal organization model, incentive policies, and dynamic negotiation mechanism, urban renewal in OCN not only achieves a relative balance between individual interests and collective interests but also makes the willingness of residents the dominant factor in formulating urban renewal policies. However, the presentation of "collective renewal action" in OCN is still mainly based on typical cases. Although the government is no longer the dominant role, a large number of resident-led collective actions have not yet emerged, which puts forward new research needs for a sustainable governance policy innovation in this action.

Keywords: urban renewal, collective action theory, governance, cooperation mechanism, China

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Authors: Abdel-Samad M. Ali, Ali A. Hadi Al-Shawi

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Discrimination against women and girls impairs progress in all domains of development articulated either in the framework of Millennium Development Goals (MDGs) or in the Post-2015 Development Agenda. Paper aspires to create greater awareness among researchers and policy makers of the challenges posed by gender gaps and the opportunities created by reducing them within the Arab region. The study reveals how Arab countries are closing in on gender-oriented targets of the third and fifth MDGs. While some countries can claim remarkable achievements particularly in girls’ equality in education, there is still a long way to go to keep Arab’s commitments to current and future generations in other countries and subregions especially in the economic participation or in the political empowerment of women. No country has closed or even expected to close the economic participation gap or the political empowerment gap. This should provide the incentive to keep moving forward in the Post-2015 Agenda. Findings of the study prove that while Arab states have uneven achievements in reducing maternal mortality, Arab women remain at a disadvantage in the labour market. For Arab region especially LDCs, improving maternal health is part of the unmet agenda for the post-2015 period and still calls for intensified efforts and procedures. While antenatal care coverage is improving across the Arab region, progress is marginal in LDCs. To achieve proper realization of gender equality and empowerment of women in the Arab region in the post-2015 agenda, the study presents critical key challenges to be addressed. These challenges include: Negative cultural norms and stereotypes; violence against women and girls; early marriage and child labour; women’s limited control over their own bodies; limited ability of women to generate their own income and control assets and property; gender-based discrimination in law and in practice; women’s unequal participation in private and public decision making autonomy; and limitations in data. However, in all Arab states, gender equality must be integrated as a goal across all issues, particularly those that affect the future of a country.

Keywords: gender, equity, millennium development goals, post-2015 development agenda

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Authors: Nadia Masood Khan, Muhammad Salman Khan, Gul Muhammad Khan

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Cardiologists perform cardiac auscultation to detect abnormalities in heart sounds. Since accurate auscultation is a crucial first step in screening patients with heart diseases, there is a need to develop computer-aided detection/diagnosis (CAD) systems to assist cardiologists in interpreting heart sounds and provide second opinions. In this paper different algorithms are implemented for automated heart sound classification using unsegmented phonocardiogram (PCG) signals. Support vector machine (SVM), artificial neural network (ANN) and cartesian genetic programming evolved artificial neural network (CGPANN) without the application of any segmentation algorithm has been explored in this study. The signals are first pre-processed to remove any unwanted frequencies. Both time and frequency domain features are then extracted for training the different models. The different algorithms are tested in multiple scenarios and their strengths and weaknesses are discussed. Results indicate that SVM outperforms the rest with an accuracy of 73.64%.

Keywords: pattern recognition, machine learning, computer aided diagnosis, heart sound classification, and feature extraction

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Authors: Jaspreet Singh, Gurvinder Singh, Prabhsimran Singh, Rajinder Singh, Prithvipal Singh, Karanjeet Singh Kahlon, Ravinder Singh Sawhney

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Morphological evaluation of Indian languages is one of the burgeoning fields in the area of Natural Language Processing (NLP). The evaluation of a language is an eminent task in the era of information retrieval and text mining. The extraction and classification of knowledge from text can be exploited for sentiment analysis and morphological evaluation. This study coalesce morphological evaluation and sentiment analysis for the task of classification of farmer suicide cases reported in Punjab state of India. The pre-processing of Punjabi text involves morphological evaluation and normalization of Punjabi word tokens followed by the training of proposed model using deep learning classification on Punjabi language text extracted from online Punjabi news reports. The class-wise accuracies of sentiment prediction for four negatively oriented classes of farmer suicide cases are 93.85%, 88.53%, 83.3%, and 95.45% respectively. The overall accuracy of sentiment classification obtained using proposed framework on 275 Punjabi text documents is found to be 90.29%.

Keywords: deep neural network, farmer suicides, morphological processing, punjabi text, sentiment analysis

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Authors: V. Saccà, A. Sarica, F. Novellino, S. Barone, T. Tallarico, E. Filippelli, A. Granata, P. Valentino, A. Quattrone

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The work aim was to evaluate how well Random Forest (RF) and Support Vector Machine (SVM) algorithms could support the early diagnosis of Multiple Sclerosis (MS) from resting-state functional connectivity data. In particular, we wanted to explore the ability in distinguishing between controls and patients of mean signals extracted from ICA components corresponding to 15 well-known networks. Eighteen patients with early-MS (mean-age 37.42±8.11, 9 females) were recruited according to McDonald and Polman, and matched for demographic variables with 19 healthy controls (mean-age 37.55±14.76, 10 females). MRI was acquired by a 3T scanner with 8-channel head coil: (a)whole-brain T1-weighted; (b)conventional T2-weighted; (c)resting-state functional MRI (rsFMRI), 200 volumes. Estimated total lesion load (ml) and number of lesions were calculated using LST-toolbox from the corrected T1 and FLAIR. All rsFMRIs were pre-processed using tools from the FMRIB's Software Library as follows: (1) discarding of the first 5 volumes to remove T1 equilibrium effects, (2) skull-stripping of images, (3) motion and slice-time correction, (4) denoising with high-pass temporal filter (128s), (5) spatial smoothing with a Gaussian kernel of FWHM 8mm. No statistical significant differences (t-test, p < 0.05) were found between the two groups in the mean Euclidian distance and the mean Euler angle. WM and CSF signal together with 6 motion parameters were regressed out from the time series. We applied an independent component analysis (ICA) with the GIFT-toolbox using the Infomax approach with number of components=21. Fifteen mean components were visually identified by two experts. The resulting z-score maps were thresholded and binarized to extract the mean signal of the 15 networks for each subject. Statistical and machine learning analysis were then conducted on this dataset composed of 37 rows (subjects) and 15 features (mean signal in the network) with R language. The dataset was randomly splitted into training (75%) and test sets and two different classifiers were trained: RF and RBF-SVM. We used the intrinsic feature selection of RF, based on the Gini index, and recursive feature elimination (rfe) for the SVM, to obtain a rank of the most predictive variables. Thus, we built two new classifiers only on the most important features and we evaluated the accuracies (with and without feature selection) on test-set. The classifiers, trained on all the features, showed very poor accuracies on training (RF:58.62%, SVM:65.52%) and test sets (RF:62.5%, SVM:50%). Interestingly, when feature selection by RF and rfe-SVM were performed, the most important variable was the sensori-motor network I in both cases. Indeed, with only this network, RF and SVM classifiers reached an accuracy of 87.5% on test-set. More interestingly, the only misclassified patient resulted to have the lowest value of lesion volume. We showed that, with two different classification algorithms and feature selection approaches, the best discriminant network between controls and early MS, was the sensori-motor I. Similar importance values were obtained for the sensori-motor II, cerebellum and working memory networks. These findings, in according to the early manifestation of motor/sensorial deficits in MS, could represent an encouraging step toward the translation to the clinical diagnosis and prognosis.

Keywords: feature selection, machine learning, multiple sclerosis, random forest, support vector machine

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Authors: Agastya Pratap Singh

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This review paper examines the emerging applications of generative adversarial networks (GANs) in the fields of image recognition and anomaly detection. With the rapid growth of digital image data, the need for efficient and accurate methodologies to identify and classify images has become increasingly critical. GANs, known for their ability to generate realistic data, have gained significant attention for their potential to enhance traditional image recognition systems and improve anomaly detection performance. The paper systematically analyzes various GAN architectures and their modifications tailored for image recognition tasks, highlighting their strengths and limitations. Additionally, it delves into the effectiveness of GANs in detecting anomalies in diverse datasets, including medical imaging, industrial inspection, and surveillance. The review also discusses the challenges faced in training GANs, such as mode collapse and stability issues, and presents recent advancements aimed at overcoming these obstacles.

Keywords: generative adversarial networks, image recognition, anomaly detection, synthetic data generation, deep learning, computer vision, unsupervised learning, pattern recognition, model evaluation, machine learning applications

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Authors: Kaoutar Ben Azzou, Hanaa Talei

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Human resources is an important department in each organization as it manages the life cycle of employees from recruitment training to retirement or termination of contracts. The recruitment process starts with a job opening, followed by a selection of the best-fit candidates from all applicants. Matching the best profile for a job position requires a manual way of looking at many CVs, which requires hours of work that can sometimes lead to choosing not the best profile. The work presented in this paper aims at reducing the workload of HR personnel by automating the preliminary stages of the candidate screening process, thereby fostering a more streamlined recruitment workflow. This tool introduces an automated system designed to help with the recruitment process by scanning candidates' CVs, extracting pertinent features, and employing machine learning algorithms to decide the most fitting job profile for each candidate. Our work employs natural language processing (NLP) techniques to identify and extract key features from unstructured text extracted from a CV, such as education, work experience, and skills. Subsequently, the system utilizes these features to match candidates with job profiles, leveraging the power of classification algorithms.

Keywords: automated recruitment, candidate screening, machine learning, human resources management

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Authors: Widyani Fatwa Dewi, Subroto Athor

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In a telecommunication service provider, quantity and interval of customer demand often difficult to predict due to high dependency on customer expansion strategy and technological development. Demand arrives when a customer needs to add capacity to an existing site or build a network in a new site. Because demand is uncertain for each period, and sometimes there is a null demand for several equipments, it is categorized as intermittent. This research aims to improve demand forecast quality in Indonesia's telecommunication service providers by using Artificial Neural Network. In Artificial Neural Network, the pattern or relationship within data will be analyzed using the training process, followed by the learning process as validation stage. Historical demand data for 36 periods is used to support this research. It is found that demand forecast by using Artificial Neural Network outperforms the existing method if it is reviewed on two criteria: the forecast accuracy, using Mean Absolute Deviation (MAD), Mean of the sum of the Squares of the Forecasting Error (MSE), Mean Error (ME) and service level which is shown through inventory cost. This research is expected to increase the reference for a telecommunication demand forecast, which is currently still limited.

Keywords: artificial neural network, demand forecast, forecast accuracy, intermittent, service level, telecommunication

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Authors: Meruert Serik, Nassipzhan Duisegaliyeva, Danara Tleumagambetova

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The article highlights and describes the characteristic features of real-time face detection in images and videos using machine learning algorithms. Students of educational programs reviewed the research work "6B01511-Computer Science", "7M01511-Computer Science", "7M01525- STEM Education," and "8D01511-Computer Science" of Eurasian National University named after L.N. Gumilyov. As a result, the advantages and disadvantages of Haar Cascade (Haar Cascade OpenCV), HoG SVM (Histogram of Oriented Gradients, Support Vector Machine), and MMOD CNN Dlib (Max-Margin Object Detection, convolutional neural network) detectors used for face detection were determined. Dlib is a general-purpose cross-platform software library written in the programming language C++. It includes detectors used for determining face detection. The Cascade OpenCV algorithm is efficient for fast face detection. The considered work forms the basis for the development of machine learning methods by future computer science teachers.

Keywords: algorithm, artificial intelligence, education, machine learning

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Authors: Farhad Asadi, Mohammad Javad Mollakazemi, Aref Ghafouri

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Recent research in neural networks science and neuroscience for modeling complex time series data and statistical learning has focused mostly on learning from high input space and signals. Local linear models are a strong choice for modeling local nonlinearity in data series. Locally weighted projection regression is a flexible and powerful algorithm for nonlinear approximation in high dimensional signal spaces. In this paper, different learning scenario of one and two dimensional data series with different distributions are investigated for simulation and further noise is inputted to data distribution for making different disordered distribution in time series data and for evaluation of algorithm in locality prediction of nonlinearity. Then, the performance of this algorithm is simulated and also when the distribution of data is high or when the number of data is less the sensitivity of this approach to data distribution and influence of important parameter of local validity in this algorithm with different data distribution is explained.

Keywords: local nonlinear estimation, LWPR algorithm, online training method, locally weighted projection regression method

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Authors: Pornpimol Chaiwuttisak

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The study aims to compare the performance of the logistics for Thailand’s wholesale and retail trade industries (except motor vehicles, motorcycle, and stalls) by using data (data envelopment analysis). Thailand Standard Industrial Classification in 2009 (TSIC - 2009) categories that industries into sub-group no. 45: wholesale and retail trade (except for the repair of motor vehicles and motorcycles), sub-group no. 46: wholesale trade (except motor vehicles and motorcycles), and sub-group no. 47: retail trade (except motor vehicles and motorcycles. Data used in the study is collected by the National Statistical Office, Thailand. The study consisted of four input factors include the number of companies, the number of personnel in logistics, the training cost in logistics, and outsourcing logistics management. Output factor includes the percentage of enterprises having inventory management. The results showed that the average relative efficiency of small-sized enterprises equals to 27.87 percent and 49.68 percent for the medium-sized enterprises.

Keywords: DEA, wholesales and retails, logistics, Thailand

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Authors: P. C. Li, R. H. Feng, S. J. Chen, Y. J. Yu, Y. L. Chen, X. Y. Fan

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The purpose of this study is to investigate the effect of mindfulness and wisdom comprehensive strategy intervention on addiction to the Internet of college students by engaging fourteen intensive full-day mindfulness-based wisdom retreat curriculum. Wisdom, one of the practice method from the threefold training. Internet addiction, a kind of impulse control disorder, which attract the attentions of society due to its high prevalence and harmfulness in the last decade. Therefore, the study of internet addiction intervention is urgent. Participants with internet addiction were Chinese college students and screened by internet addiction disorder diagnose questionnaire (IAD-DQ). A quasi-experimental pretest and posttest design was used as research design. The finding shows that the mindfulness-based wisdom intervention strategy appeared to be effective in reducing the Internet addiction. Moreover, semi-structure interview method was conducted and outcomes included five themes: the reduction of internet use, the increment of awareness on emotion, self-control, present concentration and better positive lifestyle, indicating that mindfulness could be an effective intervention for this group with internet addiction.

Keywords: mindfulness, internet addiction, wisdom comprehensive intervention, cognitive-behavior therapy

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Authors: Ilma Robo, Saimir Heta, Rialda Xhizdari, Kers Kapaj

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Background: The ideal disinfectant for surfaces, instruments, air, skin, both in dentistry and in the fields of medicine, does not exist.This is for the sole reason that all the characteristics of the ideal disinfectant cannot be contained in one; these are the characteristics that if one of them is emphasized, it will conflict with the other. A disinfectant must be stable, not be affected by changes in the environmental conditions where it stands, which means that it should not be affected by an increase in temperature or an increase in the humidity of the environment. Both of these elements contradict the other element of the idea of an ideal disinfectant, as they disrupt the solubility ratios of the base substance of the disinfectant versus the diluent. Material and methods: The study aims to extract the constant of each disinfectant/antiseptic used during dental disinfection protocols, accompanied by the side effects of the surface of the skin or mucosa where it is applied in the role of antiseptic. In the end, attempts were made to draw conclusions about the best possible combination for disinfectants after a dental procedure, based on the data extracted from the basic literature required during the development of the pharmacology module, as a module in the formation of a dentist, against data published in the literature. Results: The sensitivity of the disinfectant to changes in the atmospheric conditions of the environment where it is kept is a known fact. The care against this element is always accompanied by the advice on the application of the specific disinfectant, in order to have the desired clinical result. The constants of disinfectants according to the classification based on the data collected and presented are for alcohols 70-120, glycols 0.2, aldehydes 30-200, phenols 15-60, acids 100, povidone iodine halogens 5-75, hypochlorous acid halogens 150, sodium hypochlorite halogens 30-35, oxidants 18-60, metals 0.2-10. The part of halogens should be singled out, where specific results were obtained according to the representatives of this class, since it is these representatives that find scope for clinical application in dentistry. Conclusions: The search for the "ideal", in the conditions where its defining criteria are also established, not only for disinfectants but also for any medication or pharmaceutical product, is an ongoing search, without any definitive results. In this mine of data in the published literature if there is something fixed, calculable, such as the specific constant for disinfectants, the search for the ideal is more concrete. During the disinfection protocols, different disinfectants are applied since the field of action is different, including water, air, aspiration devices, tools, disinfectants used in full accordance with the production indications.

Keywords: disinfectant, constant, ideal, side effects

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Authors: Huy Hieu Pham, Houssam Salmane, Louahdi Khoudour, Alain Crouzil, Pablo Zegers, Sergio Velastin

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We present a deep learning-based multitask framework for joint 3D human pose estimation and action recognition from color video sequences. Our approach proceeds along two stages. In the first, we run a real-time 2D pose detector to determine the precise pixel location of important key points of the body. A two-stream neural network is then designed and trained to map detected 2D keypoints into 3D poses. In the second, we deploy the Efficient Neural Architecture Search (ENAS) algorithm to find an optimal network architecture that is used for modeling the Spatio-temporal evolution of the estimated 3D poses via an image-based intermediate representation and performing action recognition. Experiments on Human3.6M, Microsoft Research Redmond (MSR) Action3D, and Stony Brook University (SBU) Kinect Interaction datasets verify the effectiveness of the proposed method on the targeted tasks. Moreover, we show that our method requires a low computational budget for training and inference.

Keywords: human action recognition, pose estimation, D-CNN, deep learning

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Authors: Jasper C. Dioco

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The study was done to investigate the relationship of taxi company safety climate and drivers’ safety motivation and knowledge on taxi drivers’ safety performance. Data were collected from three Taxi Companies with taxi drivers as participants (N = 84). The Hiligaynon translated version of Transportation Companies’ Climate Scale (TCCS), Safety Motivation and Knowledge Scale, Occupational Safety Motivation Questionnaire and Global Safety Climate Scale were used to study the relationships among four parameters: (a) Taxi company safety climate; (b) Safety motivation; (c) Safety knowledge; and (d) Safety performance. Correlational analyses found that there is no relation between safety climate and safety performance. A Hierarchical regression demonstrated that safety motivation predicts the most variance in safety performance. The results will greatly impact how taxi company can increase safe performance through the confirmation of the proximity of variables to organizational outcome. A strong positive safety climate, in which employees perceive safety to be a priority and that managers are committed to their safety, is likely to increase motivation to be safety. Hence, to improve outcomes, providing knowledge based training and health promotion programs within the organization must be implemented. Policy change might include overtime rules and fatigue driving awareness programs.

Keywords: safety climate, safety knowledge, safety motivation, safety performance, taxi drivers

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Authors: Genevieve Lim, Arthur Shelley, Dongcheol Heo

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Abstract—Mobile technologies can enhance the learning process as it enables social engagement around concepts beyond the classroom and the curriculum. Early results in this ongoing research is showing that when learning interventions are designed specifically to generate new insights, mobile devices support regulated learning and encourage learners to collaborate, socialize and co-create new knowledge. As students navigate across the space and time boundaries, the fundamental social nature of learning transforms into mobile computer supported collaborative learning (mCSCL). The metacognitive interaction in mCSCL via mobile applications reflects the regulation of learning among the students. These metacognitive experiences whether self-, co- or shared-regulated are significant to the learning outcomes. Despite some insightful empirical studies, there has not yet been significant research that investigates the actual practice and processes of the new knowledge co-creation. This leads to question as to whether mobile learning provides a new channel to leverage learning? Alternatively, does mobile interaction create new types of learning experiences and how do these experiences co-create new knowledge. The purpose of this research is to explore these questions and seek evidence to support one or the other. This paper addresses these questions from the students’ perspective to understand how students interact when constructing knowledge in mCSCL and how students’ self-regulated learning (SRL) strategies support the co-creation of new knowledge in mCSCL. A pilot study has been conducted among international undergraduates to understand students’ perspective of mobile learning and concurrently develops a definition in an appropriate context. Using classroom action research (CAR) with multiple case studies, this study is being carried out in a private university in Thailand to narrow the research gaps in mCSCL and SRL. The findings will allow teachers to see the importance of social interaction for meaningful student engagement and envisage learning outcomes from a knowledge management perspective and what role mobile devices can play in these. The findings will signify important indicators for academics to rethink what is to be learned and how it should be learned. Ultimately, the study will bring new light into the co-creation of new knowledge in a social interactive learning environment and challenges teachers to embrace the 21st century of learning with mobile technologies to deepen and extend learning opportunities.

Keywords: mobile computer supported collaborative learning, mobile instant messaging, mobile learning, new knowledge co-creation, self-regulated learning

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Authors: Jane O`Halloran

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This research aims to identify the issues and challenges of teaching English as a Foreign Language to Japanese university students who have special learning needs. This study sought to investigate factors influencing the academic performance of students with special or additional needs in an inclusive education context. This study will focus on a consideration of the methods available to support those with hearing impairments. While the study population is limited, it is important to give classes to be inclusive places where all students receive equal access to content. Hearing impairments provide an obvious challenge to language learning and, therefore, second-language learning. However, strategies and technologies exist to support the instructor without specialist training. This paper aims to identify these and present them to other teachers of English as a second language who wish to provide the best possible learning experience for every student. Two case studies will be introduced to compare and contrast the experience of in-class teaching and the online option and to share the positives and negatives of the two approaches. While the study focuses on the situation in a university in Japan, the lessons learned by the author may have universal value to any classroom with a student with a hearing disability.

Keywords: inclusive learning, special needs, hearing impairments, teaching strategies

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Authors: Natalie Cutler, Lorna Moxham, Moira Stephens

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Safety is a priority in mental health services, and no more so than in the acute inpatient setting. Mental health service policies and accreditation frameworks commonly approach safety from a risk reduction or elimination perspective leading to service approaches that are arguably more focused on risk than on safety. An exploration what safety means for people who have experienced admission to an acute mental health inpatient unit is currently under way in Sydney, Australia. Using a phenomenographic research approach, this study is seeking to understand the meaning of safety from the perspective of people who use, rather than those who deliver mental health services. Preliminary findings suggest that the meanings of safety for users of mental health services vary from the meanings inherent in the policies and frameworks that inform how mental health services and mental health practice are delivered. This variance has implications for the physical and environmental design of acute mental health inpatient facilities, the policies and practices, and the education and training of mental health staff in particular nurses, who comprise the majority of the mental health workforce. These variances will be presented, along with their implications for the way quality and safety in mental health services are evaluated.

Keywords: acute inpatient, mental health, nursing, phenomenography, recovery, safety

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Authors: V. Reyes, P. Ferreira

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In response to high levels of competitiveness, industrial systems have evolved to improve productivity. As a consequence, a rapid increase in volume production and simultaneously, a customization process require lower costs, more variety, and accurate quality of products. Reducing time-cycle production, enabling customizability, and ensure continuous quality improvement are key features in advance intelligent industry. In this scenario, customers and producers will be able to participate in the ongoing production life cycle through real-time interaction. To achieve this vision, transparency, predictability, and adaptability are key features that provide the industrial systems the capability to adapt to customer demands modifying the manufacturing process through an autonomous response and acting preventively to avoid errors. The industrial system incorporates a diversified number of components that in advanced industry are expected to be decentralized, end to end communicating, and with the capability to make own decisions through feedback. The evolving process towards advanced intelligent industry defines a set of stages to empower components of intelligence and enhancing efficiency to achieve the decision-making stage. The integrated system follows an industrial cyber-physical system (CPS) architecture whose real-time integration, based on a set of enabler technologies, links the physical and virtual world generating the digital twin (DT). This instance allows incorporating sensor data from real to virtual world and the required transparency for real-time monitoring and control, contributing to address important features of the advanced intelligent industry and simultaneously improve sustainability. Assuming the industrial CPS as the core technology toward the latest advanced intelligent industry stage, this paper reviews and highlights the correlation and contributions of the enabler technologies for the operationalization of each stage in the path toward advanced intelligent industry. From this research, a real-time integration architecture for a cyber-physical system with applications to collaborative robotics is proposed. The required functionalities and issues to endow the industrial system of adaptability are identified.

Keywords: cyber-physical systems, digital twin, sensor data, system integration, virtual model

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Authors: Wilawan Kanhachon, Yodchai Boonprakob, Uraiwon Chatchawan, Junichiro Yamauchi

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Introduction: Paslop is a traditional dance from Laos, which is popular in Laos and northeastern of Thailand. This unique type of Paslop dancing is to control body movement with the song. While dancing to the beat, dancers should contract their abdomen and back muscle all the time. Paslop may be a good alternative to improve strengthening, balance and flexibility. Objective: To investigate the effects of Paslop dance exercise on core strength, balance, and flexibility. Methods: Seven healthy participants (age, 20.57±1.13 yrs; height, 162.29±6.16 cm; body mass, 58.14±7.03 kg; mean± S.D.) were volunteered to perform the 45-minute Paslop dance exercise in three times a week for 8 weeks. Before, during and after the exercise period, core strength, balance and flexibility were measured with the pressure biofeedback unit (PBU), one-leg stance test (OLST), and sit and reach test (SAR), respectively. Result: PBU score for core strength increased from 2.12 mmHg in baseline to 6.34 mmHg at the 4th week and 10.10 mmHg at the 8th week after the Paslop dance training, while OLST and SAR did not change. Conclusion: The study demonstrates that 8-week Paslop dancing exercise can improve the core strength.

Keywords: balance, core strength, flexibility, Paslop

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Authors: Ahmed M. Ashteyat

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Artificial Neural Network (ANN) models have been widely used in material modeling, inter-correlations, as well as behavior and trend predictions when the nonlinear relationship between system parameters cannot be quantified explicitly and mathematically. In this paper, ANN was used to predict the residual modulus of elasticity (RME) of self compacted concrete (SCC) damaged by heat. The ANN model was built, trained, tested and validated using a total of 112 experimental data sets, gathered from available literature. The data used in model development included temperature, relative humidity conditions, mix proportions, filler types, and fiber type. The result of ANN training, testing, and validation indicated that the RME of SCC, exposed to different temperature and relative humidity levels, could be predicted accurately with ANN techniques. The reliability between the predicated outputs and the actual experimental data was 99%. This show that ANN has strong potential as a feasible tool for predicting residual elastic modulus of SCC damaged by heat within the range of input parameter. The ANN model could be used to estimate the RME of SCC, as a rapid inexpensive substitute for the much more complicated and time consuming direct measurement of the RME of SCC.

Keywords: residual modulus of elasticity, artificial neural networks, self compacted-concrete, material modeling

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Authors: Joseph Kimuli Balikuddembe

Abstract:

Road traffic incidents (RTIs) continue to be a serious public health and development burden around the globe. Compared to high-income countries (HICs), the low and middle-income countries (LMICs) bear the heaviest brunt of RTIs. Like other LMICs, Uganda, a country located in Eastern Africa, has been experiencing a worryingly high burden of RTIs and their associated impacts. Over the years, the highest number of all the total registered RTIs in Uganda has taken place in the Greater Kampala Metropolitan Area (GKMA). This places a tremendous demand on the few existing emergency medical services (EMS) to adequately respond to those affected. In this regard, the overall objective of the study was to risk map RTIs in the GKMA so as to help in the better planning of EMS for the victims of RTIs. Other objectives included: (i) identifying the factors affecting the exposure, vulnerability and EMS capacity for the victims of RTIs; (ii) identifying the RTI prone-areas and estimating their associated risk factors; (iii) identifying the weaknesses and capacities which affect the EMS systems for RTIs; and (iv) determining the strategies and priority actions that can help to improve the EMS response for RTI victims in the GKMA. To achieve these objectives, a mixed methodological approach was used in four phrases for approximately 15 months. It employed a systematic review based on the preferred reporting items for systematic reviews and meta-data analysis guidelines; a Delphi panel technique; retrospective data analysis; and a cross-sectional method. With Uganda progressing forward as envisaged in its 'Vision 2040', the GKMA, which is the country’s political and socioeconomic epicenter, is experiencing significant changes in terms of population growth, urbanization, infrastructure development, rapid motorization and other factors. Unless appropriate actions are taken, these changes are likely to worsen the already alarming rate of RTIs in Uganda, and in turn also to put pressure on the few existing EMS and facilities to render care for those affected. Therefore, road safety vis-à-vis injury prevention measures, which are needed to reduce the burden of RTIs, should be multifaceted in nature so that they closely correlate with the ongoing dynamics that contribute to RTIs, particularly in the GKMA and Uganda as a whole.

Keywords: emergency medical services, Kampala, risk mapping, road traffic incidents

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Authors: M. Bartal

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A large empirical evidence points to sizable lifetime earnings losses associated with the displacement of tenured workers. The causes of these losses are still not well-understood. Existing explanations are heavily based on human capital depreciation during non-employment spells. In this paper, a new avenue is explored. Evidence on the role of household liquidity constraints in accounting for the persistence of post-displacement earning losses is provided based on SIPP data. Then, a directed search and matching model with endogenous human capital and wealth accumulation is introduced. The model is computationally tractable thanks to its block-recursive structure and highlights a non-trivial, yet intuitive, interaction between wealth and human capital. Constrained workers tend to accept jobs with low firm-sponsored training because the latter are (endogenously) easier to find. This new channel provides a plausible explanation for why young (highly constrained) workers suffer persistent scars after displacement. Finally, the model is calibrated on US data to show that the interplay between wealth and human capital is crucial to replicate the observed lifecycle pattern of earning losses. JEL— E21, E24, J24, J63.

Keywords: directed search, human capital accumulation, job displacement, wealth accumulation

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Authors: Usman Ahmed Adam, Umar Ibrahim, Ezra S. Gbaje

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This study is posed to explore the practice of Public Access Computers (PACs) in academic libraries in Kaduna State, Nigeria. The study aimed to determine the computers and other tools available, their services and challenges of the practices. Three questions were framed to identify number of public computers and tools available, their services and problems faced during the practice. The study used qualitative research design along with semi-constructed interview and observation as tools for data collection. Descriptive analysis was employed to analyze the data. The sample size of the study comprises 52 librarian and IT staff from the seven academic institutions in Kaduna State. The findings revealed that, PACs were provided for access to the Internet, digital resources, library catalogue and training services. The study further explored that, despite the limit number of the computers, users were not allowed to enjoy many services. The study recommends that libraries in Kaduna state should provide more public computers to be able to cover the population of their users; libraries should allow users to use the computers without limitations and restrictions.

Keywords: academic libraries, computers in library, digital libraries, public computers

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Authors: John E. Foley, Martin Miele, Raul Fonda, Jon Jacobson

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This paper presents development and implementation of new and innovative data collection and analysis methodologies based on deployment of total field magnetometer arrays. Our research has focused on the development of a vertically-integrated suite of platforms all utilizing common data acquisition, data processing and analysis tools. These survey platforms include low-altitude helicopters and ground-based vehicles, including robots, for terrestrial mapping applications. For marine settings the sensor arrays are deployed from either a hydrodynamic bottom-following wing towed from a surface vessel or from a towed floating platform for shallow-water settings. Additionally, sensor arrays are deployed from tethered remotely operated vehicles (ROVs) for underwater settings where high maneuverability is required. While the primary application of these systems is the detection and mapping of unexploded ordnance (UXO), these system are also used for various infrastructure mapping and geologic investigations. For each application, success is driven by the integration of magnetometer arrays, accurate geo-positioning, system noise mitigation, and stable deployment of the system in appropriate proximity of expected targets or features. Each of the systems collects geo-registered data compatible with a web-enabled data management system providing immediate access of data and meta-data for remote processing, analysis and delivery of results. This approach allows highly sophisticated magnetic processing methods, including classification based on dipole modeling and remanent magnetization, to be efficiently applied to many projects. This paper also briefly describes the initial development of magnetometer-based detection systems deployed from low-altitude helicopter platforms and the subsequent successful transition of this technology to the marine environment. Additionally, we present examples from a range of terrestrial and marine settings as well as ongoing research efforts related to sensor miniaturization for unmanned aerial vehicle (UAV) magnetic field mapping applications.

Keywords: dipole modeling, magnetometer mapping systems, sub-surface infrastructure mapping, unexploded ordnance detection

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Authors: Tamar Trop, Boris Portnov

Abstract:

Public Space Lighting (PSL) of outdoor urban areas promotes comfort, defines spaces and neighborhood identities, enhances perceived safety and security, and contributes to residential satisfaction and wellbeing. However, if excessive or misdirected, PSL leads to unnecessary energy waste and increased greenhouse gas emissions, poses a non-negligible threat to the nocturnal environment, and may become a potential health hazard. At present, PSL is designed according to international, regional, and national standards, which consolidate best practice. Yet, knowledge regarding the optimal light characteristics needed for creating a perception of personal comfort and safety in densely populated residential areas, and the factors associated with this perception, is still scarce. The presented study suggests a paradigm shift in designing PSL towards a user-centered approach, which incorporates pedestrians' perspectives into the process. The study is an ongoing joint research project between China and Israel Ministries of Science and Technology. Its main objectives are to reveal inhabitants' perceptions of and preferences for PSL in different densely populated neighborhoods in China and Israel, and to develop a model that links instrumentally measured parameters of PSL (e.g., intensity, spectra and glare) with its perceived comfort and quality, while controlling for three groups of attributes: locational, temporal, and individual. To investigate measured and perceived PSL, the study employed various research methods and data collection tools, developed a location-based mobile application, and used multiple data sources, such as satellite multi-spectral night-time light imagery, census statistics, and detailed planning schemes. One of the study’s preliminary findings is that higher sense of safety in the investigated neighborhoods is not associated with higher levels of light intensity. This implies potential for energy saving in brightly illuminated residential areas. Study findings might contribute to the design of a smart and adaptive PSL strategy that enhances pedestrians’ perceived safety and comfort while reducing light pollution and energy consumption.

Keywords: energy efficiency, light pollution, public space lighting, PSL, safety perceptions

Procedia PDF Downloads 129