Search results for: switch-between-states training

Authors: Md Maruf Hossain Koli

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The issue of child labor is enormous and cannot be ignored in Bangladesh. The problem of child exploitation is a socio-economic reality of Bangladesh. This paper will indicate the causes, consequences, and possibilities of using microfinance as remedies of hazardous child labor in Bangladesh. Poverty is one of the main reasons for children to become child laborers. It is an indication of economic vulnerability, inadequate law, and enforcement system and cultural and social inequities along with the inaccessible and low-quality educational system. An attempt will be made in this paper to explore and analyze child labor scenario in Bangladesh and will explain holistic intervention of BRAC, the largest nongovernmental organization in the world to address child labor through promoting incubation support to youth-led enterprises. A combination of research methods were used to write this paper. These include non-reactive observation in the form of literature review, desk studies as well as reactive observation like site visits and, semi-structured interviews. Hazardous Child labor is a multi-dimensional and complex issue. This paper was guided by the answer following research questions to better understand the current context of hazardous child labor in Bangladesh, especially in Dhaka city. The author attempted to figure out why child labor should be considered as a development issue? Further, it also encountered why child labor in Bangladesh is not being reduced at an expected pace? And finally what could be a sustainable solution to eradicate this situation. One of the most challenging characteristics of child labor is that it interrupts a child’s education and cognitive development hence limiting the building of human capital and fostering intergenerational reproduction of poverty and social exclusion. Children who are working full-time and do not attend school, cannot develop the necessary skills. This leads them and their future generation to remain in poor socio-economic condition as they do not get a better paying job. The vicious cycle of poverty will be reproduced and will slow down sustainable development. The outcome of the research suggests that most of the parents send their children to work to help them to increase family income. In addition, most of the youth engaged in hazardous work want to get training, mentoring and easy access to finance to start their own business. The intervention of BRAC that includes classroom and on the job training, tailored mentoring, health support, access to microfinance and insurance help them to establish startup. This intervention is working in developing business and management capacity through public-private partnerships and technical consulting. Supporting entrepreneurs, improving working conditions with micro, small and medium enterprises and strengthening value chains focusing on youth and children engaged with hazardous child labor.

Keywords: child labour, enterprise development, microfinance, youth entrepreneurship

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Authors: Kevin Wongleedee

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This research paper was aimed to examine the reasons associated with hotel employees’ loyalty. This was a case study of 200 hotel employees in Bangkok, Thailand. The population of this study included all hotel employees who were working in Bangkok during January to March, 2014. Based on 200 respondents who answered the questionnaire, the data were complied by using SPSS. Mean and standard deviation were utilized in analyzing the data. The findings revealed that the average mean of importance was 4.40, with 0.7585 of standard deviation. Moreover, the mean average can be used to rank the level of importance from each factor as follows: 1) salary, service charge cut, and benefits, 2) career development and possible advancement, 3) freedom of working, thinking, and ability to use my initiative, 4) training opportunities, 5) social involvement and positive environment, 6) fair treatment in the workplace and fair evaluation of job performance, and 7) personal satisfaction, participation, and recognition.

Keywords: hotel employees, loyalty, reasons, case study

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Authors: M. Pellerone, S. G. Razza, L. Miano, A. Miccichè, M. Adamo

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The teacher’s attitude plays a decisive role in promoting the development of the non-native or disabled student and counteracting hypothetical negative attitudes and prejudice towards those who are “different”.The objective of the present research is to measure the relationship between teachers’ prejudices towards disabled and/or immigrant students as predictors of teaching-learning strategies. A cross-sectional study involved 200 Italian female teachers who completed an anamnestic questionnaire, the Assessment Teaching Scale, the Italian Modern and Classical Prejudices Scale towards people with ID, and the Pettigrew and Meertens’ Blatant Subtle Prejudice Scale. Confirming research hypotheses, data underlines the predictive role of prejudice on teaching strategies, and in particular on the socio-emotional and communicative-relational dimensions. Results underline that general training appears necessary, especially for younger generations of teachers.

Keywords: disabled students, immigrant students, instructional competence, prejudice, teachers

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Authors: T. S. Terence Ma, Irene T. Ho

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Conducting adequate assessment of the challenges students with ASD (Autism Spectrum Disorder) face and the support they need is imperative for promoting their school adjustment. Students with ASD often show deficits in communication, social interaction, emotional regulation, and self-management in learning. While targeting these areas in intervention is often helpful, we argue that not enough attention has been paid to weak self-regulation being a key factor underlying their manifest difficulty in all these areas. Self-regulation refers to one’s ability to moderate their behavioral or affective responses without assistance from others. Especially for students with high functioning autism, who often show problems not so much in acquiring the needed skills but rather in applying those skills appropriately in everyday problem-solving, self-regulation becomes a key to successful adjustment in daily life. Therefore, a greater understanding of the construct of self-regulation, its relationship with other daily skills, and its role in school functioning for students with ASD would generate insights on how students’ school adjustment could be promoted more effectively. There were two focuses in this study. Firstly, we examined the extent to which self-regulation is a distinct construct that is differentiable from other daily skills and the most salient indicators of this construct. Then we tested a model of relationships between self-regulation and other daily school skills as well as their relative and combined effects on school adjustment. A total of 1,345 Grade1 to Grade 6 students with ASD attending mainstream schools in Hong Kong participated in the research. In the first stage of the study, teachers filled out a questionnaire consisting of 136 items assessing a wide range of student skills in social, emotional and learning areas. Results from exploratory factor analysis (EFA) with 673 participants and subsequent confirmatory factor analysis (CFA) with another group of 672 participants showed that there were five distinct factors of school skills, namely (1) communication skills, (2) pro-social behavior, (3) emotional skills, (4) learning management, and (5) self-regulation. Five scales representing these skill dimensions were generated. In the second stage of the study, a model postulating the mediating role of self-regulation for the effects of the other four types of skills on school adjustment was tested with structural equation modeling (SEM). School adjustment was defined in terms of the extent to which the student is accepted well in school, with high engagement in school life and self-esteem as well as good interpersonal relationships. A 5-item scale was used to assess these aspects of school adjustment. Results showed that communication skills, pro-social behavior, emotional skills and learning management had significant effects on school adjustment only indirectly through self-regulation, and their total effects were found to be not high. The results indicate that support rendered to students with ASD focusing only on the training of well-defined skills is not adequate for promoting their inclusion in school. More attention should be paid to the training of self-management with an emphasis on the application of skills backed by self-regulation. Also, other non-skill factors are important in promoting inclusive education.

Keywords: autism, assessment, factor analysis, self-regulation, school adjustment

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Authors: Azeez Ayodele Ayodele

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This paper investigated the problem of picture books design and the quality of the pictures in picture books. The research surveyed nursery and primary schools in four major cities in southwest of Nigeria. The instruments including the descriptive survey questionnaire and a structured interview were developed, validated and administered for collection of relevant data. Descriptive statistics was used in analyzing the data. The result of the study revealed that there were poor quality of pictures in picture books and this is due to scarcity of trained graphic designers who understand systems approach to picture books design and production. There is thus a need for more qualified graphic designers, given in-service professional training as well as a refresher course as criteria for upgrading by the stakeholders.

Keywords: pictures, picture books, pre-primary schools, trained graphic designers

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Authors: Jeerasak Wisatphan, Nara Samattapapong

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This study's goals were to enhance nitroglycerin manufacturing efficiency through simulation, recover nitroglycerin from the storage facility, and enhance nitroglycerine recovery and purge systems. It was found that the problem was nitroglycerin reflux. Therefore, the researcher created three alternatives to solve the problem. The system of Nitroglycerine Recovery and Purge was then simulated using the FlexSim program, and each alternative was tested. The results demonstrate that the alternative system-led Nitroglycerine Recovery and Nitroglycerine Purge System collaborate to produce Nitroglycerine, which is more efficient than other alternatives and can reduce production time. It can also improve the recovery of nitroglycerin. It also serves as a guideline for developing a real-world system and modeling it for training staff without wasting raw chemical materials or fuel energy.

Keywords: efficiency increase, nitroglycerine recovery and purge system, production improvement, simulation

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Authors: Dipanwita Chakravortty

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Officially, the Israel Defence Forces (IDF) follows a policy of 'gender equality and partnership' which institutionalises norms regarding equal duty towards the nation. It reiterates the equality in unbiased opportunities and resources for Jewish men and women to participate in the military as equal citizens. At the same time, as a military institution, the IDF supports gender biases and crystallises the same through various interactions among women soldiers, male soldiers and the institution. These biases are expressed through various stages and processes in the military institution like biased training, discriminatory postings of women soldiers, lack of combat training and acceptance of sexual harassment. The gender-military debates in Israel is largely devoted to female emancipation and converting the militarised women’s experiences into mainstream debates. This critical scholarship, largely female-based and located in Israel, has been consistently critical of the structural policies of the IDF that have led to continued discriminatory practices against women soldiers. This has compelled the military to increase its intake of women soldiers and make its structural policies more gender-friendly. Nonetheless, the continued thriving of gender discrimination in the IDF resulted in scholars looking deep into the failure of these policies in bringing about a change. This article looks into two research objectives, firstly to analyse existing gender relations in the IDF which impact the practices and prejudices in the institution and secondly to look beyond the structural discrimination as part of the gender debates in the IDF. The proposed research uses the structural-functional model as a framework to study the discourses and norms emerging out of the interaction between gender and military as two distinct social institutions. Changing gender-military debates will be discussed in great detail to understanding the in-depth relation between the Israeli society and the military due to the conscription model. The main arguments of the paper deal with the functional aspect of the military service rather than the structural component of the institution. Traditional stereotypes of military institutions along with cultural notions of a female body restrict the complete integration of women soldiers despite favourable legislations and policies. These result in functional discriminations like uneven promotion, sexual violence, restructuring gender identities and creating militarised bodies. The existing prejudices encourage younger women recruits to choose from within the accepted pink-collared jobs in the military rather than ‘breaking the barriers.’ Some women recruits do try to explore new avenues and make a mark for themselves. Most of them face stiff discrimination but they accept it as part of military life. The cyclical logic behind structural norms leading to functional discrimination which then emphasises traditional stereotypes and hampers change in the institutional norms compels the IDF to continue to strive towards gender equality within the institution without practical realisation.

Keywords: women soldiers, Israel Defence Forces, gender-military debates, security studies

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Authors: Soyon Kim, Edward Kim

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In order to retrieve information from the massive stream of songs in the music industry, music search by title, lyrics, artist, mood, and genre has become more important. Despite the subjectivity and controversy over the definition of music genres across different nations and cultures, automatic genre classification systems that facilitate the process of music categorization have been developed. Manual genre selection by music producers is being provided as statistical data for designing automatic genre classification systems. In this paper, an automatic music genre classification system utilizing non-negative matrix factorization (NMF) is proposed. Short-term characteristics of the music signal can be captured based on the timbre features such as mel-frequency cepstral coefficient (MFCC), decorrelated filter bank (DFB), octave-based spectral contrast (OSC), and octave band sum (OBS). Long-term time-varying characteristics of the music signal can be summarized with (1) the statistical features such as mean, variance, minimum, and maximum of the timbre features and (2) the modulation spectrum features such as spectral flatness measure, spectral crest measure, spectral peak, spectral valley, and spectral contrast of the timbre features. Not only these conventional basic long-term feature vectors, but also NMF based feature vectors are proposed to be used together for genre classification. In the training stage, NMF basis vectors were extracted for each genre class. The NMF features were calculated in the log spectral magnitude domain (NMF-LSM) as well as in the basic feature vector domain (NMF-BFV). For NMF-LSM, an entire full band spectrum was used. However, for NMF-BFV, only low band spectrum was used since high frequency modulation spectrum of the basic feature vectors did not contain important information for genre classification. In the test stage, using the set of pre-trained NMF basis vectors, the genre classification system extracted the NMF weighting values of each genre as the NMF feature vectors. A support vector machine (SVM) was used as a classifier. The GTZAN multi-genre music database was used for training and testing. It is composed of 10 genres and 100 songs for each genre. To increase the reliability of the experiments, 10-fold cross validation was used. For a given input song, an extracted NMF-LSM feature vector was composed of 10 weighting values that corresponded to the classification probabilities for 10 genres. An NMF-BFV feature vector also had a dimensionality of 10. Combined with the basic long-term features such as statistical features and modulation spectrum features, the NMF features provided the increased accuracy with a slight increase in feature dimensionality. The conventional basic features by themselves yielded 84.0% accuracy, but the basic features with NMF-LSM and NMF-BFV provided 85.1% and 84.2% accuracy, respectively. The basic features required dimensionality of 460, but NMF-LSM and NMF-BFV required dimensionalities of 10 and 10, respectively. Combining the basic features, NMF-LSM and NMF-BFV together with the SVM with a radial basis function (RBF) kernel produced the significantly higher classification accuracy of 88.3% with a feature dimensionality of 480.

Keywords: mel-frequency cepstral coefficient (MFCC), music genre classification, non-negative matrix factorization (NMF), support vector machine (SVM)

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Authors: Karina Zaccari, Ernesto Cordeiro Marujo

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This paper presents a Machine Learning (ML) approach to support Meningitis diagnosis in patients at a children’s hospital in Sao Paulo, Brazil. The aim is to use ML techniques to reduce the use of invasive procedures, such as cerebrospinal fluid (CSF) collection, as much as possible. In this study, we focus on predicting the probability of Meningitis given the results of a blood and urine laboratory tests, together with the analysis of pain or other complaints from the patient. We tested a number of different ML algorithms, including: Adaptative Boosting (AdaBoost), Decision Tree, Gradient Boosting, K-Nearest Neighbors (KNN), Logistic Regression, Random Forest and Support Vector Machines (SVM). Decision Tree algorithm performed best, with 94.56% and 96.18% accuracy for training and testing data, respectively. These results represent a significant aid to doctors in diagnosing Meningitis as early as possible and in preventing expensive and painful procedures on some children.

Keywords: machine learning, medical diagnosis, meningitis detection, pediatric research

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Authors: Dora Martins, Susana Silva, Cândida Silva

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Human Resources Management (HRM) has been recognized by academics and practitioners as an important element in organizations. Therefore, this paper explores the best practices of HRM and seeks to understand the level of participation in the development of these practices by human resources managers in the hospitality industry and compare it with other industries. Thus, the study compared the HRM practices of companies in the hospitality sector with HRM practices of companies in other sectors, and identifies the main differences between their HRM practices. The results show that the most frequent HRM practices in all companies, independently of its sector of activity, are hiring and training. When comparing hospitality sector with other sectors of activity, some differences were noticed, namely in the adoption of the practices of communication and information sharing, and of recruitment and selection. According to these results, the paper discusses the major theoretical and practical implications. Suggestions for future research are also presented.

Keywords: exploratory study, human resources management practices, human resources manager, hospitality companies, Portuguese companies

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Authors: Hassan Masoumi, Ahad Salimi, Nazanin Barhemmat, Babak Gholami

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Artificial neural networks have gained a lot of interest as empirical models for their powerful representational capacity, multi input and output mapping characteristics. In fact, most feed-forward networks with nonlinear nodal functions have been proved to be universal approximates. In this paper, we propose a new supervised method for color image classification based on self organizing feature maps (SOFM). This algorithm is based on competitive learning. The method partitions the input space using self-organizing feature maps to introduce the concept of local neighborhoods. Our image classification system entered into RGB image. Experiments with simulated data showed that separability of classes increased when increasing training time. In additional, the result shows proposed algorithms are effective for color image classification.

Keywords: classification, SOFM algorithm, neural network, neighborhood, RGB image

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Authors: Nutcha Taneepanichskul, Helen C. Hailes, Mark Miodownik

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Plastic waste has emerged as a critical global environmental challenge, primarily driven by the prevalent use of conventional plastics derived from petrochemical refining and manufacturing processes in modern packaging. While these plastics serve vital functions, their persistence in the environment post-disposal poses significant threats to ecosystems. Addressing this issue necessitates approaches, one of which involves the development of biodegradable plastics designed to degrade under controlled conditions, such as industrial composting facilities. It is imperative to note that compostable plastics are engineered for degradation within specific environments and are not suited for uncontrolled settings, including natural landscapes and aquatic ecosystems. The full benefits of compostable packaging are realized when subjected to industrial composting, preventing environmental contamination and waste stream pollution. Therefore, effective sorting technologies are essential to enhance composting rates for these materials and diminish the risk of contaminating recycling streams. In this study, it leverage hyperspectral imaging technology (HSI) coupled with advanced machine learning algorithms to accurately identify various types of plastics, encompassing conventional variants like Polyethylene terephthalate (PET), Polypropylene (PP), Low density polyethylene (LDPE), High density polyethylene (HDPE) and biodegradable alternatives such as Polybutylene adipate terephthalate (PBAT), Polylactic acid (PLA), and Polyhydroxyalkanoates (PHA). The dataset is partitioned into three subsets: a training dataset comprising uncontaminated conventional and biodegradable plastics, a validation dataset encompassing contaminated plastics of both types, and a testing dataset featuring real-world packaging items in both pristine and contaminated states. Five distinct machine learning algorithms, namely Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Logistic Regression, and Decision Tree Algorithm, were developed and evaluated for their classification performance. Remarkably, the Logistic Regression and CNN model exhibited the most promising outcomes, achieving a perfect accuracy rate of 100% for the training and validation datasets. Notably, the testing dataset yielded an accuracy exceeding 80%. The successful implementation of this sorting technology within recycling and composting facilities holds the potential to significantly elevate recycling and composting rates. As a result, the envisioned circular economy for plastics can be established, thereby offering a viable solution to mitigate plastic pollution.

Keywords: biodegradable plastics, sorting technology, hyperspectral imaging technology, machine learning algorithms

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Authors: Susmita Panda, Arnab Banerjee, Ajinkya Baxy, Bappaditya Manna

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The design of short to medium-span high-speed bridges in critical locations is an essential aspect of vehicle-bridge interaction. Due to dynamic interaction between moving load and bridge, mathematical models or finite element modeling computations become time-consuming. Thus, to reduce the computational effort, a universal approximator using an artificial neural network (ANN) has been used to evaluate the dynamic response of the bridge. The data set generation and training of surrogate models have been conducted over the results obtained from mathematical modeling. Further, the robustness of the surrogate model has been investigated, which showed an error percentage of less than 10% with conventional methods. Additionally, the dependency of the dynamic response of the bridge on various load and bridge parameters has been highlighted through a parametric study.

Keywords: artificial neural network, mode superposition method, moving load analysis, surrogate models

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Authors: Boudemagh Naime

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Automatic radar detection requires some methods of adapting to variations in the background clutter in order to control their false alarm rate. The problem becomes more complicated in non-Gaussian environment. In fact, the conventional approach in real time applications requires a complex statistical modeling and much computational operations. To overcome these constraints, we propose another approach based on artificial neural network (ANN-CMLD-CFAR) using a Back Propagation (BP) training algorithm. The considered environment follows a log-normal distribution in the presence of multiple Rayleigh-targets. To evaluate the performances of the considered detector, several situations, such as scale parameter and the number of interferes targets, have been investigated. The simulation results show that the ANN-CMLD-CFAR processor outperforms the conventional statistical one.

Keywords: radat detection, ANN-CMLD-CFAR, log-normal clutter, statistical modelling

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Authors: Pocholo Rodriguez, Anne Bernadine Ocampo, Ian Benedict Chan, Janric Micah Gray

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The state of an electric power system may be classified as either stable or unstable. The borderline of stability is at any condition for which a slight change in an unfavourable direction of any pertinent quantity will cause instability. Voltage instability in power distribution systems could lead to voltage collapse and thus power blackouts. The researchers will present an intelligent system using back propagation algorithm that can detect voltage instability and output voltage of a power distribution and classify it as stable or unstable. The researchers’ work is the use of parameters involved in voltage instability as input parameters to the neural network for training and testing purposes that can provide faster detection and monitoring of the power distribution system.

Keywords: back-propagation algorithm, load instability, neural network, power distribution system

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Authors: A. Chenafa, S. Boulenouar, M. Zitouni, M. Boukouria

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The sterilization of medical devices constitutes for all the medical professions, an inescapable obligation. It has for objective to prevent the infectious risk, both for the patient and for the medical team. The Dental surgeon as every healthcare professional has to master perfectly this subject and to train his staff to the various techniques of sterilization. It is the only way to assure the patients all the security for which they are entitled to wait when they undergo a dental care. It’s for it, that we undertook to lead an investigation aiming at estimating the sterilization practice at the dental surgeon of Sidi bel Abbes. The survey result showed a youth marked with the profession with a majority use of autoclave with cycle B and an almost total absence of the sterilization controls (test of Bowie and Dick). However, the majority of the dentists control and validate their sterilizers. Finally, our survey allowed us to describe some practices which must be improved regarding control, regarding qualification and regarding staff training. And suggestions were made in this sense.

Keywords: dental surgeon, medical devices, sterilization, survey

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Authors: Baha Eddine Aissou, Aichouche Belhadj Aissa

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Light detection and ranging (LiDAR) is an active remote sensing technology used for several applications. Airborne LiDAR is becoming an important technology for the acquisition of a highly accurate dense point cloud. A classification of airborne laser scanning (ALS) point cloud is a very important task that still remains a real challenge for many scientists. Support vector machine (SVM) is one of the most used statistical learning algorithms based on kernels. SVM is a non-parametric method, and it is recommended to be used in cases where the data distribution cannot be well modeled by a standard parametric probability density function. Using a kernel, it performs a robust non-linear classification of samples. Often, the data are rarely linearly separable. SVMs are able to map the data into a higher-dimensional space to become linearly separable, which allows performing all the computations in the original space. This is one of the main reasons that SVMs are well suited for high-dimensional classification problems. Only a few training samples, called support vectors, are required. SVM has also shown its potential to cope with uncertainty in data caused by noise and fluctuation, and it is computationally efficient as compared to several other methods. Such properties are particularly suited for remote sensing classification problems and explain their recent adoption. In this poster, the SVM classification of ALS LiDAR data is proposed. Firstly, connected component analysis is applied for clustering the point cloud. Secondly, the resulting clusters are incorporated in the SVM classifier. Radial basic function (RFB) kernel is used due to the few numbers of parameters (C and γ) that needs to be chosen, which decreases the computation time. In order to optimize the classification rates, the parameters selection is explored. It consists to find the parameters (C and γ) leading to the best overall accuracy using grid search and 5-fold cross-validation. The exploited LiDAR point cloud is provided by the German Society for Photogrammetry, Remote Sensing, and Geoinformation. The ALS data used is characterized by a low density (4-6 points/m²) and is covering an urban area located in residential parts of the city Vaihingen in southern Germany. The class ground and three other classes belonging to roof superstructures are considered, i.e., a total of 4 classes. The training and test sets are selected randomly several times. The obtained results demonstrated that a parameters selection can orient the selection in a restricted interval of (C and γ) that can be further explored but does not systematically lead to the optimal rates. The SVM classifier with hyper-parameters is compared with the most used classifiers in literature for LiDAR data, random forest, AdaBoost, and decision tree. The comparison showed the superiority of the SVM classifier using parameters selection for LiDAR data compared to other classifiers.

Keywords: classification, airborne LiDAR, parameters selection, support vector machine

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Authors: Nitin Joseph

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Objectives: This study was done to assess emotional intelligence levels and to find out its association with socio demographic variables and perceived stress among medical students. Material and Methods: This study was done among first and second year medical students. Data was collected using a self-administered questionnaire. Results: Emotional intelligence scores was found to significantly increase with age of the participants (F=2.377, P < 0.05). Perceived stress was found to be significantly more among first year (t=1.997, P=0.05). Perceived stress was found to significantly decrease with increasing emotional intelligence scores (r = – 0.226, P < 0.001). Conclusion: First year students were found to be more vulnerable to stress than their seniors probably due to lesser emotional intelligence. As both these parameters are related, ample measures to improve emotional intelligence needs to be supported in the training curriculum of beginners so as to make them more stress free during early student life.

Keywords: emotional intelligence, medical students, perceived stress, socio demographic variables

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Authors: Manuel Alves, Angelica Reis, Armindo Lobo, Valdemar Leiras

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In industry 4.0, it is common to have a lot of sensor data. In this deluge of data, hints of possible problems are difficult to spot. The digital twin concept aims to help answer this problem, but it is mainly used as a monitoring tool to handle the visualisation of data. Failure detection is of paramount importance in any industry, and it consumes a lot of resources. Any improvement in this regard is of tangible value to the organisation. The aim of this paper is to add the ability to forecast test failures, curtailing detection times. To achieve this, several anomaly detection algorithms were compared with a symbolic regression approach. To this end, Isolation Forest, One-Class SVM and an auto-encoder have been explored. For the symbolic regression PySR library was used. The first results show that this approach is valid and can be added to the tools available in this context as a low resource anomaly detection method since, after training, the only requirement is the calculation of a polynomial, a useful feature in the digital twin context.

Keywords: anomaly detection, digital twin, industry 4.0, symbolic regression

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Authors: Sikiru Ademola Adewale, Joe Thomas, Bolanle Hafiz Matti, Tosin Ige

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This project demonstrates the implementation and use of an encoder-decoder model to perform a many-to-many mapping of video data to text captions. The many-to-many mapping occurs via an input temporal sequence of video frames to an output sequence of words to form a caption sentence. Data preprocessing, model construction, and model training are discussed. Caption correctness is evaluated using 2-gram BLEU scores across the different splits of the dataset. Specific examples of output captions were shown to demonstrate model generality over the video temporal dimension. Predicted captions were shown to generalize over video action, even in instances where the video scene changed dramatically. Model architecture changes are discussed to improve sentence grammar and correctness.

Keywords: decoder, encoder, many-to-many mapping, video captioning, 2-gram BLEU

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Authors: Teimuraz Manjapharashvili, Ketevani Manjaparashvili

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Fuzzy logic is a modeling method for complex or ill-defined systems and is a relatively new mathematical approach. Its basis is to consider overlapping cases of parameter values and define operations to manipulate these cases. Fuzzy logic can successfully create operative automatic management or appropriate advisory systems. Fuzzy logic techniques in various operational control technologies have grown rapidly in the last few years. Fuzzy logic is used in many areas of human technological activity. In recent years, fuzzy logic has proven its great potential, especially in the automation of industrial process control, where it allows to form of a control design based on the experience of experts and the results of experiments. The engineering of chemical technological processes uses fuzzy logic in optimal management, and it is also used in process control, including the operational control of continuous cycle chemical industrial, technological processes, where special features appear due to the continuous cycle and correct management acquires special importance. This paper discusses how intelligent systems can be developed, in particular, how fuzzy logic can be used to build knowledge-based expert systems in chemical process engineering. The implemented projects reveal that the use of fuzzy logic in technological process control has already given us better solutions than standard control techniques. Fuzzy logic makes it possible to develop an advisory system for decision-making based on the historical experience of the managing operator and experienced experts. The present paper deals with operational control and management systems of continuous cycle chemical technological processes, including advisory systems. Because of the continuous cycle, many features are introduced in them compared to the operational control of other chemical technological processes. Among them, there is a greater risk of transitioning to emergency mode; the return from emergency mode to normal mode must be done very quickly due to the impossibility of stopping the technological process due to the release of defective products during this period (i.e., receiving a loss), accordingly, due to the need for high qualification of the operator managing the process, etc. For these reasons, operational control systems of continuous cycle chemical technological processes have been specifically discussed, as they are different systems. Special features of such systems in control and management were brought out, which determine the characteristics of the construction of control and management systems. To verify the findings, the development of an advisory decision-making information system for operational control of a lime kiln using fuzzy logic, based on the creation of a relevant expert-targeted knowledge base, was discussed. The control system has been implemented in a real lime production plant with a lime burn kiln, which has shown that suitable and intelligent automation improves operational management, reduces the risks of releasing defective products, and, therefore, reduces costs. The special advisory system was successfully used in the said plant both for the improvement of operational management and, if necessary, for the training of new operators due to the lack of an appropriate training institution.

Keywords: chemical process control systems, continuous cycle industrial technological processes, fuzzy logic, lime kiln

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Authors: Rosemary Saunders, Courtney Glass, Karla Seaman, Karen Gullick, Julie Andrew, Anne Wilkinson, Ashwini Davray

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Current literature demonstrates that most Australians receive end-of-life care in a hospital setting, despite most hoping to die within their own home. The necessity for high quality end-of-life care has been emphasised by the Australian Commission on Safety and Quality in Health Care and the National Safety and Quality in Health Services Standards depict the requirement for comprehensive care at the end of life (Action 5.20), reinforcing the obligation for continual organisational assessment to determine if these standards are suitably achieved. Limited research exploring clinical staff perspectives of end-of-life care delivery has been conducted within an Australian private health context. This study aimed to investigate clinical staff member perceptions of end-of-life care delivery at a private hospital in Western Australia. The study comprised of a multi-faceted mixed-methods methodology, part of a larger study. Data was obtained from clinical staff utilising surveys and focus groups. A total of 133 questionnaires were completed by clinical staff, including registered nurses (61.4%), enrolled nurses (22.7%), allied health professionals (9.9%), non-palliative care consultants (3.8%) and junior doctors (2.2%). A total of 14.7% of respondents were palliative care ward staff members. Additionally, seven staff focus groups were conducted with physicians (n=3), nurses (n=26) and allied health professionals including social workers (n=1), dietitians (n=2), physiotherapists (n=5) and speech pathologists (n=3). Key findings from the surveys highlighted that the majority of staff agreed it was part of their role to talk to doctors about the care of patients who they thought may be dying, and recognised the importance of communication, appropriate training and support for clinical staff to provide quality end-of-life care. Thematic analysis of the qualitative data generated three key themes: creating the setting which highlighted the importance of adequate resourcing and conducive physical environments for end-of-life care and to support staff and families; planning and care delivery which emphasised the necessity for collaboration between staff, families and patients to develop care plans and treatment directives; and collaborating in end-of-life care, with effective communication and teamwork leading to achievable care delivery expectations. These findings contribute to health professionals better understanding of end-of-life care provision and the importance of collaborating with patients and families in care delivery. It is crucial that health care providers implement strategies to overcome gaps in care, so quality end-of-life care is provided. Findings from this study have been translated into practice, with the development and implementation of resources, training opportunities, support networks and guidelines for the delivery of quality end-of-life care.

Keywords: clinical staff, end-of-life care, mixed-methods, private hospital.

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Authors: Wentian Shi, Daming Shi, Maysam Orouskhani, Feng Tian

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Whereas currently the most prevalent deep learning methods require a large amount of data for training, few-shot learning tries to learn a model from limited data without extensive retraining. In this paper, we present a loss function based on triplet loss for solving few-shot problem using metric based learning. Instead of setting the margin distance in triplet loss as a constant number empirically, we propose an adaptive margin distance strategy to obtain the appropriate margin distance automatically. We implement the strategy in the deep siamese network for deep metric embedding, by utilizing an optimization approach by penalizing the worst case and rewarding the best. Our experiments on image recognition and co-segmentation model demonstrate that using our proposed triplet loss with adaptive margin distance can significantly improve the performance.

Keywords: few-shot learning, triplet network, adaptive margin, deep learning

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Authors: Sheldon McCall, Miao Yu, Liyun Gong, Shigang Yue, Stefanos Kollias

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Falls are a significant health concern for older adults globally, and prompt identification is critical to providing necessary healthcare support. Our study proposes a new fall detection method using computer vision based on modern deep learning techniques. Our approach involves training a trans- former model on a large 2D pose dataset for general action recognition, followed by transfer learning. Specifically, we freeze the first few layers of the trained transformer model and train only the last two layers for fall detection. Our experimental results demonstrate that our proposed method outperforms both classical machine learning and deep learning approaches in fall/non-fall classification. Overall, our study suggests that our proposed methodology could be a valuable tool for identifying falls.

Keywords: healthcare, fall detection, transformer, transfer learning

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Authors: Narges Solaymani

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Definition of Nurse: Nurse: A person who is educated and skilled in the field of scientific principles and professional skills of health care, treatment, and medical training of patients. Nursing is a very important profession in the societies of the world. Although in the past, all caregivers of the sick and disabled were called nurses, nowadays, a nurse is a person who has a university education in this field. There are nurses in bachelor's, master's, and doctoral degrees in nursing. New courses have been launched in the master's degree based on duty-oriented nurses. A nurse cannot have an independent treatment center but is a member of the treatment team in established treatment centers such as hospitals, clinics, or offices. Nurses can establish counseling centers and provide nursing services at home. According to the standards, the number of nurses should be three times the number of doctors or twice the number of hospital beds, or there should be three nurses for every thousand people. Also, international standards show that in the internal and surgical department, every 4 to 6 patients should have a nurse.

Keywords: nurse, intensive care, CPR, bandage

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Authors: Bin Liu

Abstract:

Protein remote homology detection and fold recognition are two most important tasks in protein sequence analysis, which is critical for protein structure and function studies. In this study, we combined the profile-based features with various string kernels, and constructed several computational predictors for protein remote homology detection and fold recognition. Experimental results on two widely used benchmark datasets showed that these methods outperformed the competing methods, indicating that these predictors are useful computational tools for protein sequence analysis. By analyzing the discriminative features of the training models, some interesting patterns were discovered, reflecting the characteristics of protein superfamilies and folds, which are important for the researchers who are interested in finding the patterns of protein folds.

Keywords: protein remote homology detection, protein fold recognition, profile-based features, Support Vector Machines (SVMs)

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Authors: Jeff A. Tysinger, Dawn P. Tysinger

Abstract:

There has been considerable growth in online learning. Researchers continue to explore the impact various methods of delivery. Podcasting is a popular method for sharing information. The purpose of this study was to examine the impact of student motivation and the perception of the acquisition of knowledge in an online environment of a skill-based class. 25 students in a school psychology graduate class completed a pretest and posttest examining podcast use and familiarity. In addition, at the completion of the course they were administered a modified version of the Instructional Materials Motivation Survey. The four subscales were examined (attention, relevance, confidence, and satisfaction). Results indicated that students are motivated, they perceive podcasts as positive instructional tools, and students are successful in acquiring the needed information. Additional benefits of using podcasts and recommendations in school psychology training are discussed.

Keywords: motivation, online learning, pedagogy, podcast

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Authors: I. Derek Kaka’an, P. Smolianov, D. Koh Choon Lian, S. Dion, C. Schoen, J. Norberg

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This study was designed to examine mass participation and elite football performance in Nigeria with reference to advance international football management practices. Over 200 sources of literature on sport delivery systems were analyzed to construct a globally applicable model of elite football integrated with mass participation, comprising of the following three levels: macro- (socio-economic, cultural, legislative, and organizational), meso- (infrastructures, personnel, and services enabling sport programs) and micro-level (operations, processes, and methodologies for development of individual athletes). The model has received scholarly validation and showed to be a framework for program analysis that is not culturally bound. The Smolianov and Zakus model has been employed for further understanding of sport systems such as US soccer, US Rugby, swimming, tennis, and volleyball as well as Russian and Dutch swimming. A questionnaire was developed using the above-mentioned model. Survey questions were validated by 12 experts including academicians, executives from sport governing bodies, football coaches, and administrators. To identify best practices and determine areas for improvement of football in Nigeria, 120 coaches completed the questionnaire. Useful exemplars and possible improvements were further identified through semi-structured discussions with 10 Nigerian football administrators and experts. Finally, content analysis of Nigeria Football Federation’s website and organizational documentation was conducted. This paper focuses on the meso-level of Nigerian football delivery, particularly infrastructures, personnel, and services enabling sport programs. This includes training centers, competition systems, and intellectual services. Results identified remarkable achievements coupled with great potential to further develop football in different types of public and private organizations in Nigeria. These include: assimilating football competitions with other cultural and educational activities, providing favorable conditions for employees of all possible organizations to partake and help in managing football programs and events, providing football coaching integrated with counseling for prevention of antisocial conduct, and improving cooperation between football programs and organizations for peace-making and advancement of international relations, tourism, and socio-economic development. Accurate reporting of the sports programs from the media should be encouraged through staff training for better awareness of various events. The systematic integration of these meso-level practices into the balanced development of mass and high-performance football will contribute to international sport success as well as national health, education, and social harmony.

Keywords: football, high performance, mass participation, Nigeria, sport development

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Authors: Chunfang Zhou

Abstract:

The growing issues of sustainability have increased the discussions on how to foster “green engineers” from diverse perspectives in both contexts of education and organizations. As creativity has been considered as the first stage of innovation process that can also be regarded as a path to sustainability, this paper will particularly propose creativity as a scientific literacy meaning a collection of awareness, ability, and skills about sustainability. From this sense, creativity should be an element in IT engineering education and organizational learning programmes, since IT engineers are one group of key actors in designing, researching and developing social media products that are most important channels of improving public awareness of sustainability. This further leads this paper to discuss by which pedagogical strategies and by which training methods in organizations, creativity and sustainability can be integrated into IT engineering education and IT enterprise innovation process in order to meeting the needs of ‘creative engineers’ in the society changes towards sustainability. Accordingly, this paper contributes to future work on the links between creativity, innovation, sustainability, and IT engineering development both theoretically and practically.

Keywords: creativity, innovation, IT engineers, sustainability

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Authors: Sameil Hanna Samweil Botros

Abstract:

The variation of social networking websites inside higher training has garnered enormous hobby in recent years, with numerous researchers thinking about it as a possible shift from the conventional lecture room-based learning paradigm. However, this boom in research and carried out research, but the adaption of SNS-based modules has not proliferated inside universities. This paper commences its contribution with the aid of studying the numerous fashions and theories proposed in the literature and amalgamates together various effective aspects for the inclusion of social technology within e-gaining knowledge. A three-phased framework is similarly proposed, which informs the important concerns for the hit edition of SNS in improving the student's mastering experience. This suggestion outlines the theoretical foundations as a way to be analyzed in sensible implementation across worldwide university campuses.

Keywords: eLearning, institutionalization, teaching and learning, transformation vtuber, ray tracing, avatar agriculture, adaptive, e-learning, technology eLearning, higher education, social network sites, student learning

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