Search results for: social learning

Authors: Ali Hamza

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Accurate segmentation of breast ultrasound images is of paramount importance in enhancing the diagnostic capabilities of breast cancer detection. This study presents an approach utilizing the U-Net architecture for segmenting breast ultrasound images aimed at improving the accuracy and reliability of mass identification within the breast tissue. The proposed method encompasses a multi-stage process. Initially, preprocessing techniques are employed to refine image quality and diminish noise interference. Subsequently, the U-Net architecture, a deep learning convolutional neural network (CNN), is employed for pixel-wise segmentation of regions of interest corresponding to potential breast masses. The U-Net's distinctive architecture, characterized by a contracting and expansive pathway, enables accurate boundary delineation and detailed feature extraction. To evaluate the effectiveness of the proposed approach, an extensive dataset of breast ultrasound images is employed, encompassing diverse cases. Quantitative performance metrics such as the Dice coefficient, Jaccard index, sensitivity, specificity, and Hausdorff distance are employed to comprehensively assess the segmentation accuracy. Comparative analyses against traditional segmentation methods showcase the superiority of the U-Net architecture in capturing intricate details and accurately segmenting breast masses. The outcomes of this study emphasize the potential of the U-Net-based segmentation approach in bolstering breast ultrasound image analysis. The method's ability to reliably pinpoint mass boundaries holds promise for aiding radiologists in precise diagnosis and treatment planning. However, further validation and integration within clinical workflows are necessary to ascertain their practical clinical utility and facilitate seamless adoption by healthcare professionals. In conclusion, leveraging the U-Net architecture for breast ultrasound image segmentation showcases a robust framework that can significantly enhance diagnostic accuracy and advance the field of breast cancer detection. This approach represents a pivotal step towards empowering medical professionals with a more potent tool for early and accurate breast cancer diagnosis.

Keywords: mage segmentation, U-Net, deep learning, breast cancer detection, diagnostic accuracy, mass identification, convolutional neural network

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Authors: Frederick Nyakudya

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In this paper, we examine the relationship between human capital, personal wealth and social capital to explain the differential start-up rates between female and male entrepreneurs. Since our dependent variable is dichotomous, we examine the determinants of these using a maximum likelihood logit estimator. We used the Global Entrepreneurship Monitor database covering the period 2006 to 2009 with 421 usable cases drawn from drawn from the Lower Layer Super Output Areas in the East Midlands in the United Kingdom. we found evidence that indicates that a female positively moderate the positive relationships between indicators of human capital, personal wealth and social capital with start-up activity. The findings have implications for programs, policies, and practices to encourage more females to engage in start-up activity.

Keywords: entrepreneurship, star-up, gender, GEM

Procedia PDF Downloads 114

Authors: Sangdeok Lee, Sangwon Han, Changtaek Hyun

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Construction defects are major components that result in negative impacts on project performance including schedule delays and cost overruns. Since construction defects generally occur when a few associated causes combine, a thorough understanding of defect causality is required in order to more systematically prevent construction defects. To address this issue, this paper uses association rule mining (ARM) to quantify the causality between defect causes, and social network analysis (SNA) to find indirect causality among them. The suggested approach is validated with 350 defect instances from concrete works in 32 projects in Korea. The results show that the interrelationships revealed by the approach reflect the characteristics of the concrete task and the important causes that should be prevented.

Keywords: causality, defect causes, social network analysis, association rule mining

Procedia PDF Downloads 371

Authors: Viacheslav Shkuratskyy, Aminu Bello Usman, Michael O’Dea, Saifur Rahman Sabuj

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This paper examines relationships between solar activity and earthquakes; it applied machine learning techniques: K-nearest neighbour, support vector regression, random forest regression, and long short-term memory network. Data from the SILSO World Data Center, the NOAA National Center, the GOES satellite, NASA OMNIWeb, and the United States Geological Survey were used for the experiment. The 23rd and 24th solar cycles, daily sunspot number, solar wind velocity, proton density, and proton temperature were all included in the dataset. The study also examined sunspots, solar wind, and solar flares, which all reflect solar activity and earthquake frequency distribution by magnitude and depth. The findings showed that the long short-term memory network model predicts earthquakes more correctly than the other models applied in the study, and solar activity is more likely to affect earthquakes of lower magnitude and shallow depth than earthquakes of magnitude 5.5 or larger with intermediate depth and deep depth.

Keywords: k-nearest neighbour, support vector regression, random forest regression, long short-term memory network, earthquakes, solar activity, sunspot number, solar wind, solar flares

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Authors: Nora Gavira Duron, Cinthya Moreda Gonzalez-Ortega, Reyna Susana Garcia Ruiz

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This paper objective is to analyze the mathematics performance in the Learning Evaluation National Plan (PLANEA for its Spanish initials: Plan Nacional para la Evaluación de los Aprendizajes), applied to Mexican students who are enrolled in the last elementary-school year over the 2017-2018 academic year. Such test was conducted nationwide in 3,573 schools, using a sample of 108,083 students, whose average in mathematics, on a scale of 0 to 100, was 45.6 points. 75% of the sample analyzed did not reach the sufficiency level (60 points). It should be noted that only 2% got a 90 or higher score result. The performance is analyzed while considering whether there are differences in gender, marginalization level, public or private school enrollment, parents’ academic background, and living-with-parents situation. Likewise, this variable impact (among other variables) on school performance by gender is evaluated, considering multivariate logistic (Logit) regression analysis. The results show there are no significant differences in mathematics performance regarding gender in elementary school; nevertheless, the impact exerted by mothers who studied at least high school is of great relevance for students, particularly for girls. Other determining variables are students’ resilience, their parents’ economic status, and the fact they attend private schools, strengthened by the mother's education.

Keywords: multivariate regression analysis, academic performance, learning evaluation, mathematics result per gender

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Authors: Shreeyam, Ranjan Kumar Sah, Shivangi

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Recognizing and solving handwritten mathematical expressions can be a challenging task, particularly when certain characters are segmented and classified. This project proposes a solution that uses Convolutional Neural Network (CNN) and image processing techniques to accurately solve various types of equations, including arithmetic, quadratic, and trigonometric equations, as well as logical operations like logical AND, OR, NOT, NAND, XOR, and NOR. The proposed solution also provides a graphical solution, allowing users to visualize equations and their solutions. In addition to equation solving, the platform, called CNNCalc, offers a comprehensive learning experience for students. It provides educational content, a quiz platform, and a coding platform for practicing programming skills in different languages like C, Python, and Java. This all-in-one solution makes the learning process engaging and enjoyable for students. The proposed methodology includes horizontal compact projection analysis and survey for segmentation and binarization, as well as connected component analysis and integrated connected component analysis for character classification. The compact projection algorithm compresses the horizontal projections to remove noise and obtain a clearer image, contributing to the accuracy of character segmentation. Experimental results demonstrate the effectiveness of the proposed solution in solving a wide range of mathematical equations. CNNCalc provides a powerful and user-friendly platform for solving equations, learning, and practicing programming skills. With its comprehensive features and accurate results, CNNCalc is poised to revolutionize the way students learn and solve mathematical equations. The platform utilizes a custom-designed Convolutional Neural Network (CNN) with image processing techniques to accurately recognize and classify symbols within handwritten equations. The compact projection algorithm effectively removes noise from horizontal projections, leading to clearer images and improved character segmentation. Experimental results demonstrate the accuracy and effectiveness of the proposed solution in solving a wide range of equations, including arithmetic, quadratic, trigonometric, and logical operations. CNNCalc features a user-friendly interface with a graphical representation of equations being solved, making it an interactive and engaging learning experience for users. The platform also includes tutorials, testing capabilities, and programming features in languages such as C, Python, and Java. Users can track their progress and work towards improving their skills. CNNCalc is poised to revolutionize the way students learn and solve mathematical equations with its comprehensive features and accurate results.

Keywords: AL, ML, hand written equation solver, maths, computer, CNNCalc, convolutional neural networks

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Authors: Deepak Jugran

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Purpose: The purpose of this study is to identify how past Pandemics shaped social evolution and cities. Methodology: A historical and comparative analysis of major historical pandemics in human history their origin, transmission route, biological response and the aftereffects. A Comprehensive pre & post pandemic scenario and focuses selectively on major issues and pandemics that have deepest & lasting impact on society with available secondary data. Results: Past pandemics shaped the behavior of human societies and their cities and made them more resilient biologically, intellectually & socially endorsing the theory of “Survival of the fittest” by Sir Charles Darwin. Pandemics & Infectious diseases are here to stay and as a human society, we need to strengthen our collective response & preparedness besides evolving mechanisms for strict controls on inter-continental movements of people, & especially animals who become carriers for these viruses. Conclusion: Pandemics always resulted in great mortality, but they also improved the overall individual human immunology & collective social response; at the same time, they also improved the public health system of cities, health delivery systems, water, sewage distribution system, institutionalized various welfare reforms and overall collective social response by the societies. It made human beings more resilient biologically, intellectually, and socially hence endorsing the theory of “AGIL” by Prof Talcott Parsons. Pandemics & infectious diseases are here to stay and as humans, we need to strengthen our city response & preparedness besides evolving mechanisms for strict controls on inter-continental movements of people, especially animals who always acted as carriers for these novel viruses. Pandemics over the years acted like natural storms, mitigated the prevailing social imbalances and laid the foundation for scientific discoveries. We understand that post-Covid-19, institutionalized city, state and national mechanisms will get strengthened and the recommendations issued by the various expert groups which were ignored earlier will now be implemented for reliable anticipation, better preparedness & help to minimize the impact of Pandemics. Our analysis does not intend to present chronological findings of pandemics but rather focuses selectively on major pandemics in history, their causes and how they wiped out an entire city’s population and influenced the societies, their behavior and facilitated social evolution.

Keywords: pandemics, Covid-19, social evolution, cities

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Authors: Madifedile Thasi, Azola Mayeza

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In South Africa, provision of housing to the growing population has been described as one of the greatest challenges facing the government. Between 1994 to 2015, more than 2.5 million housing units were provided by the government for the poorest households and the low-income earners under the Reconstruction and Development Programme (RDP). Yet, the latest census figure revealed that about 2.1 million households still live in shacks and informal dwellings. Human settlements patterns remain dysfunctional across in South Africa because of rapid urbanisation. The housing backlog is growing at a rate of 178 000 units a year. The aforementioned predicament calls the need for innovative approaches to address the issue in a sustainable way and this need not be overemphasized. Aside from the issue of cost, the delivery of more housing units comes with environmental and sustainability issues. The prefabrication building technology has resulted into accelerated housing delivery to a satisfactory level in some countries such as Nigeria and Malaysia that are facing similar issue. It is therefore expected to be a foremost option to address the social housing backlog in South Africa and within the country housing sustainability agenda. This paper appraises the factors responsible for the limited implementation of prefabrication technology in South African housing projects. The objective is to recommend the method and materials that can be best sustained in the country in terms of local availability, cost effectiveness and environmental friendliness. It presents empirical data to support the hypothesis that a wider implementation of prefabrication technology in the social housing projects will be of significant benefit, by providing fast turnaround, cost-effective and sustainable solution that will dent the housing backlog, as well as improving the quality of the social housings. It was found that only 17 000 units of the RDP housings provided were constructed using alternative building technologies. Furthermore, there are variety of prefabricated technologies in the market but mostly have limited production capacity, minimal manufacturing capacity and most materials are imported, which leads to unavailability of the technology for large scale delivery and utilization despite its obvious advantages.

Keywords: prefabrication technology, sustainable social housings, South Africa, housing delivery

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Authors: Rose Hattab

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Women have been an important dynamic in religious context and the state-building process of Arab countries throughout history. During the 1970s as the movement for women’s activism and rights developed, the Iraqi state under the Ba’ath Party began to provide Iraqi women with legal and civil rights. This was done to liberate women from the grasps of social traditions and was a tangible espousing of equality between men and women in the process of nation-building. Whereas women’s rights were stronger and more supported throughout the earliest years of the Ba’ath Regime (1970-1990), the aftermath of the Gulf War and economic sanctions on the conditions of Iraqi society laid the foundation for a division of women’s rights between civil and religious authorities. Personal status codes that were secured in 1959 were being pushed back by amendments made in coordination with religious leaders. Civil laws were present on paper, but religious authority took prominence in practice. The written legal codes were inclusive of women’s rights, but there is not an active or ensured practice of these rights within Iraqi society. This is due to many different factors, such as religious, sectarian, political and conservative reasons that hold back or limit the ability for Iraqi women to have autonomy in aspects such as participation in the workforce, getting married, and ensuring social justice. This paper argues that the Personal Status Code introduced in 1959 – which replaced Sharia-run courts with personal status courts – provided Iraqi women with equality and increased mobility in social and economic dynamics. The statewide crisis felt after the Gulf War and the economic sanctions imposed by the United Nations led to a stark shift in the Ba’ath party’s political ideology. This ideological turn guided the social system to the embracement of social conservatism and religious traditions in the 1990s. The effect of this implementation continued after the establishment of a new Iraqi government during 2003-2005. Consequently, Iraqi women's rights in employment, marriage, and family became divided into paper and practice by religious authorities and civil law from that period to the present day. This paper also contributes to the literature by expanding on the gap between legal codes on paper and in practice, through providing an analysis of Iraqi women’s rights in the Iraqi Constitution of 2005 and Iraq’s Penal Code. The turn to conservative and religious traditions is derived from the multiplicity of identities that make up the Iraqi social fabric. In the aftermath of a totalitarian regime, active wars, and economic sanctions, the Iraqi people attempted to unite together through their different identities to create a sense of security in the midst of violence and chaos. This is not an excuse to diminish the importance of women’s rights, but in the process of building a new nation-state, women were lost from the narrative. Thus, the presence of gender equity is found in the written text but is not practiced and upheld in the social context.

Keywords: civil rights, Iraqi women, nation building, religion and conflict

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Authors: Lama Ajinah

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Luxury brands witnessed continuous growth which followed women’s desire towards individual distinctiveness and social glare. Bags are a woman’s best friend either for aesthetic or functional purposes when she leaves her home for leisure or work. One way of women constant aspiration for being distinguished while reflecting their wealth is through handbags. Subsequently, the demand and attraction by consumers towards the dazzle of luxurious brands for personal pleasure and social status have flourished. According to the literature review, a visual analysis on luxury brands has been explored yet a focus on bags was not discussed in details. Hence, a deep analysis will be dedicated on the two segments by showcasing examples of high-end bag advertising. The research is conducted to understand advertising strategies used in promoting for luxurious products. Furthermore, the paper explores the definition of the term luxury, the condition in which it is used in, and the visual language used along with the term. As luxury is an indicator of superior satisfaction, it is obtained on two levels: a personal and a social level. The examples of luxury brand ads are selected from the last five years to uncover the latest, most common strategies used to promote for luxurious brands. The methods employed in this paper consist of literature review, semiotic analysis, and content analysis. The researcher concludes with revealing the methods used in advertising while categorizing them into various themes.

Keywords: advertising, brands, fashion, graphic design, luxury, semiotic analysis, semiology, visual analysis, visual communication

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Authors: Yu Ling Chang

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Since the United Nations announced Universal Declaration of Human Rights in 1948, people are paying more and more attention to the value of lives. On the other hand, life education is being vigorously pushed in different countries. Unfortunately, the results have been only moderately successful by reason that the concept is not implemented in everyone’s daily life. Even worse, animal abuse and killing events keep happening. This research is focused on generalizing a conclusion from different countries’ Animal Protection Act and actual execution by case studies, in order to make an approach of whether the number of animal abuse is directly influenced by different laws and regimes or not. It concludes the central notion and spirit of Animal Protection Act in German, Japan, and Taiwan. Providing the reference of specific schemes and analysis based on Taiwanese social culture.

Keywords: animal abuse, Animal Management Act, Animal Protection Act, social culture

Procedia PDF Downloads 219

Authors: Ampai Muensit, Maniemai Thongyou, Patcharin Lapanun

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Teen mothers have been viewed as ‘a powerless’ facing numerous pressures including poverty, immaturity of motherhood, and especially social blame.This paper argues that, to endure as an agent, they keep struggling to overcome all difficulties in their everyday life by using certain symbols to negotiate the situations they encounter, and to obtain a social position without surrendering to the dominating socio-cultural structure. Guided by Bourdieu’s theory of practice, this study looks at how teen mothers use symbolic capital in their motherhood practices. Although motherhood practices can be found in different contexts with various types of capital utilization, this paper focuses on the use of symbolic capitals in teen mothers’ practices within the contexts of the community. The study employs a qualitative methodology; data was collected from 12 informants through life history, in-depth interview, observation and the content analytical method was employed for data analysis. The findings show that child and motherhood were key symbolic capitals in motherhood practices. Employing such capitals teen mothers can achieve an acceptance from community – particularly from the new community. These symbolic capitals were the important sources of teen mothers’ power to turn the tide by changing their status – from “the powerless” to be “the agent”. The use of symbolic capitals also related to habitus of teen mothers in better compromising for an appropriate social position.

Keywords: teen mother, motherhood practice, symbolic capital, community

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Authors: Sushish Baral, Pratibha Joshi, Yaman Maharjan

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Glaucoma is a chronic eye condition that causes vision loss that is irreversible. Early detection and treatment are critical to prevent vision loss because it can be asymptomatic. For the identification of glaucoma, multiple deep learning algorithms are used. Transformer-based architectures, which use the self-attention mechanism to encode long-range dependencies and acquire extremely expressive representations, have recently become popular. Convolutional architectures, on the other hand, lack knowledge of long-range dependencies in the image due to their intrinsic inductive biases. The aforementioned statements inspire this thesis to look at transformer-based solutions and investigate the viability of adopting transformer-based network designs for glaucoma detection. Using retinal fundus images of the optic nerve head to develop a viable algorithm to assess the severity of glaucoma necessitates a large number of well-curated images. Initially, data is generated by augmenting ocular pictures. After that, the ocular images are pre-processed to make them ready for further processing. The system is trained using pre-processed images, and it classifies the input images as normal or glaucoma based on the features retrieved during training. The Vision Transformer (ViT) architecture is well suited to this situation, as it allows the self-attention mechanism to utilise structural modeling. Extensive experiments are run on the common dataset, and the results are thoroughly validated and visualized.

Keywords: glaucoma, vision transformer, convolutional architectures, retinal fundus images, self-attention, deep learning

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Authors: Wan-I Chen, Tsung-I Pai

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Future the society aging of population will create the social problem has not had the good mechanism solution in the Asian country, especially in Taiwan. In the future ten year the people in Taiwan must facing the condition which is localization aging social problem. In this situation, how to use the spatial in eco way to development space use to solve the old age spatial demand is the way which might develop in the future Taiwan society. Over the next 10 years, taking care of the aging people will become part of the social problem of aging phenomenon. The research concentrate in the feasibility of spatial substitution, secondary use of spatial might solve out of spatial problem for aging people. In order to prove the space usable, the research required to review the project with the support system and infill system for space experiment, by using network grid way. That defined community level of space elements location relationship, make new definitions of space and return to cooperation. Research to innovation in the the appraisal space causes the possibility, by spatial replacement way solution on spatial insufficient suitable condition. To evaluation community spatial by using the support system and infill system in order to see possibilities of use in replacement inner space and modular architecture into housing. The study is discovering the solution on the Eco way to develop space use to figure out the old age spatial demand.

Keywords: sustainable use, space conversion, integration, replacement


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Authors: Vanessa Kummer, Maik Meusel

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Real estate demand estimates are typically derived from transaction data. However, in regions with excess demand, transactions are driven by supply and therefore do not indicate what people are actually looking for. To estimate the demand for housing in Switzerland, search subscriptions from all important Swiss real estate platforms are used. These data do, however, suffer from missing information—for example, many users do not specify how many rooms they would like or what price they would be willing to pay. In economic analyses, it is often the case that only complete data is used. Usually, however, the proportion of complete data is rather small which leads to most information being neglected. Also, the data might have a strong distortion if it is complete. In addition, the reason that data is missing might itself also contain information, which is however ignored with that approach. An interesting issue is, therefore, if for economic analyses such as the one at hand, there is an added value by using the whole data set with the imputed missing values compared to using the usually small percentage of complete data (baseline). Also, it is interesting to see how different algorithms affect that result. The imputation of the missing data is done using unsupervised learning. Out of the numerous unsupervised learning approaches, the most common ones, such as clustering, principal component analysis, or neural networks techniques are applied. By training the model iteratively on the imputed data and, thereby, including the information of all data into the model, the distortion of the first training set—the complete data—vanishes. In a next step, the performances of the algorithms are measured. This is done by randomly creating missing values in subsets of the data, estimating those values with the relevant algorithms and several parameter combinations, and comparing the estimates to the actual data. After having found the optimal parameter set for each algorithm, the missing values are being imputed. Using the resulting data sets, the next step is to estimate the willingness to pay for real estate. This is done by fitting price distributions for real estate properties with certain characteristics, such as the region or the number of rooms. Based on these distributions, survival functions are computed to obtain the functional relationship between characteristics and selling probabilities. Comparing the survival functions shows that estimates which are based on imputed data sets do not differ significantly from each other; however, the demand estimate that is derived from the baseline data does. This indicates that the baseline data set does not include all available information and is therefore not representative for the entire sample. Also, demand estimates derived from the whole data set are much more accurate than the baseline estimation. Thus, in order to obtain optimal results, it is important to make use of all available data, even though it involves additional procedures such as data imputation.

Keywords: demand estimate, missing-data imputation, real estate, unsupervised learning

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Authors: M. R. Martinez-Torres, S. L. Toral, M. Olmedilla

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Typically, virtual communities exhibit the well-known phenomenon of participation inequality, which means that only a small percentage of users is responsible of the majority of contributions. However, the sustainability of the community requires that the group of active users must be continuously nurtured with new users that gain expertise through a participation process. This paper analyzes the time evolution of Open Source Software (OSS) communities, considering users that join/abandon the community over time and several topological properties of the network when modeled as a social network. More specifically, the paper analyzes the role of those users rejoining the community and their influence in the global characteristics of the network.

Keywords: open source communities, social network Analysis, time series, virtual communities

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Authors: Anne Allmrodt

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Regions of South-East Europe are characterised by high out-migration for decades. The reasons for leaving range from the hope of a better work situation to a better health care system and beyond. In Serbia, this high out-migration hits the rural areas in particular so that the population number is in the red repeatedly. It might not be hard to guess that this negative population growth has the potential to create different challenges for those who stay in rural areas. So how are they coping with the – statistically proven – high out-migration? Having this in mind, the study is investigating the people‘s individual awareness of the social phenomenon high out-migration and their daily life strategies in rural areas. Furthermore, the study seeks to find out the people’s resilient skills in that context. Is the condition of high out-migration conducive for resilience? The methodology combines a quantitative and a qualitative approach (mixed methods). For the quantitative part, a standardised questionnaire has been developed, including a multiple choice section and a choice experiment. The questionnaire was handed out to people living in rural areas of Serbia only (n = 100). The sheet included questions about people’s awareness of high out-migration, their own daily life strategies or challenges and their social network situation (data about the social network was necessary here since it is supposed to be an influencing variable for resilience). Furthermore, test persons were asked to make different choices of coping with high out-migration in a self-designed choice experiment. Additionally, the study included qualitative interviews asking citizens from rural areas of Serbia. The topics asked during the interview focused on their awareness of high out-migration, their daily life strategies, and challenges as well as their social network situation. Results have shown the following major findings. The awareness of high out-migration is not the same with all test persons. Some declare it as something positive for their own life, others as negative or not effecting at all. The way of coping generally depended – maybe not surprising – on the people’s social network. However – and this might be the most important finding - not everybody with a certain number of contacts had better coping strategies and was, therefore, more resilient. Here the results show that especially people with high affiliation and proximity inside their network were able to cope better and shew higher resilience skills. The study took one step forward in terms of knowledge about societal resilience as well as coping strategies of societies in rural areas. It has shown part of the other side of nowadays migration‘s coin and gives a hint for a more sustainable rural development and community empowerment.

Keywords: coping, out-migration, resilience, rural development, social networks, south-east Europe

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Authors: Milagros Jaén-Vargas, Javier García Martínez, Karla Miriam Reyes Leiva, María Fernanda Trujillo-Guerrero, Francisco Fernandes, Sérgio Barroso Gonçalves, Miguel Tavares Silva, Daniel Simões Lopes, José Javier Serrano Olmedo

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Human Activity Recognition (HAR) is having a growing impact in creating new applications and is responsible for emerging new technologies. Also, the use of wearable sensors is an important key to exploring the human body's behavior when performing activities. Hence, the use of these dispositive is less invasive and the person is more comfortable. In this study, a database that includes three activities is used. The activities were acquired from inertial measurement unit sensors (IMU) and motion capture systems (MOCAP). The main objective is differentiating the performance from four Deep Learning (DL) models: Deep Neural Network (DNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and hybrid model Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), when considering acceleration, velocity and position and evaluate if integrating the IMU acceleration to obtain velocity and position represent an increment in performance when it works as input to the DL models. Moreover, compared with the same type of data provided by the MOCAP system. Despite the acceleration data is cleaned when integrating, results show a minimal increase in accuracy for the integrated signals.

Keywords: HAR, IMU, MOCAP, acceleration, velocity, position, feature maps

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Authors: Tarmi A'Vard

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This paper discusses the transformative learning experience for students participating in the Inside-Out Prison Exchange Program (Inside-out) and explores the value this pedagogical approach may have in youth justice centers. Inside-Out is a semester-long university course which is unique as it takes 15 university students, with their textbook and theory-based knowledge, behind the walls to study alongside 15 incarcerated students, who have the lived experience of the criminal justice system. Inside-out is currently offered in three Victorian prisons, expanding to five in 2020. The Inside-out pedagogy which is based on transformative dialogic learning is reliant upon the participants sharing knowledge and experiences to develop an understanding and appreciation of the diversity and uniqueness of one another. Inside-out offers the class an opportunity to create its own guidelines for dialogue, which can lead to the student’s sense of equality, which is fundamental in the success of this program. Dialogue allows active participation by all parties in reconciling differences, collaborating ideas, critiquing and developing hypotheses and public policies, and encouraging self-reflection and exploration. The structure of the program incorporates the implementation of circular seating (where the students alternate between inside and outside), activities, individual reflective tasks, group work, and theory analysis. In this circle everyone is equal, this includes the educator, who serves as a facilitator more so than the traditional teacher role. A significant function of the circle is to develop a group consciousness, allowing the whole class to see itself as a collective, and no one person holds a superior role. This also encourages participants to be responsible and accountable for their behavior and contributions. Research indicates completing academic courses, like Inside-Out, contributes positively to reducing recidivism. Inside-Out’s benefits and success in many adult correctional institutions have been outlined in evaluation reports and scholarly articles. The key findings incorporate the learning experiences for the students in both an academic capability and professional practice and development. Furthermore, stereotypes and pre-determined ideas are challenged, and there is a promotion of critical thinking and evidence of self-discovery and growth. There is empirical data supporting positive outcomes of education in youth justice centers in reducing recidivism and increasing the likelihood of returning to education upon release. Hence, this research could provide the opportunity to increase young people’s engagement in education which is a known protective factor for assisting young people to move away from criminal behavior. In 2016, Tarmi completed the Inside-Out educator training in Philadelphia, Pennsylvania, and has developed an interest in exploring the pedagogy of Inside-Out, specifically targeting young offenders in a Youth Justice Centre.

Keywords: dialogic transformative learning, inside-out prison exchange program, prison education, youth justice

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Authors: Zaralyn Bernardo, Dante Boac, Annabelle Del Rosario

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Professional individuals who are in a primary role to impart learning with the new generation are alarmingly tend to have a vast decrease in their workforce due to stress at work. Thus, the study used mixed method research design to explore the ideal and actual coping patterns of college school teachers, both private and public, using Coping Response Inventory-Adult (CRI-Adult). It was suggested that in order for coping to be effective there must be a congruence or good match between coping efforts and preferred coping style. Results basically provided the same information on sources of teacher stress. However, workload and low salary were more likely heightened, for public and private school, respectively. There is also a significant difference between the ideal and actual coping style of college school teachers. Though the public school teachers leaned towards problem-focused as their ideal way of coping, both public and private teachers are somewhat inclined to use emotion-focused coping in actual situation. Results of FGD identified the factors that contribute to the incongruence or mismatch in their preferred style of coping and actual efforts to cope. Identified factors based on thematic analysis (TA) are clustered into themes such as affectivity and rehearsal of the preferred coping responses, sensitivity to pressure impairs coping efficacy, seeking for social acceptance and approval, indefinite appraisal of perceived stress, emotional dysregulation, and impulsivity, immediate desire to terminate negative emotion and adversity. Most of the factors somewhat provide partial elucidation on the engagement of the respondents on emotion-focused coping.

Keywords: coping responses subtypes, appraisal, teacher stress, ideal and actual coping

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Authors: Farshid Hajati, Federico Girosi, Shima Ghassempour

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We present a Deep Belief Network (DBN) method for clustering health trajectories. Deep Belief Network (DBN) is a deep architecture that consists of a stack of Restricted Boltzmann Machines (RBM). In a deep architecture, each layer learns more complex features than the past layers. The proposed method depends on DBN in clustering without using back propagation learning algorithm. The proposed DBN has a better a performance compared to the deep neural network due the initialization of the connecting weights. We use Contrastive Divergence (CD) method for training the RBMs which increases the performance of the network. The performance of the proposed method is evaluated extensively on the Health and Retirement Study (HRS) database. The University of Michigan Health and Retirement Study (HRS) is a nationally representative longitudinal study that has surveyed more than 27,000 elderly and near-elderly Americans since its inception in 1992. Participants are interviewed every two years and they collect data on physical and mental health, insurance coverage, financial status, family support systems, labor market status, and retirement planning. The dataset is publicly available and we use the RAND HRS version L, which is easy to use and cleaned up version of the data. The size of sample data set is 268 and the length of the trajectories is equal to 10. The trajectories do not stop when the patient dies and represent 10 different interviews of live patients. Compared to the state-of-the-art benchmarks, the experimental results show the effectiveness and superiority of the proposed method in clustering health trajectories.

Keywords: health trajectory, clustering, deep learning, DBN

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Authors: Mahfoodh Saleh Al Sabbagh, Amitabh Mishra, Anwar Al Sheyadi

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A tremendous transformation is taking place in the state of education in Sultanate of Oman. The vision 2040 for Higher Education focuses on both academic and technical sides of education aims at improving the quality of education as per higher international standards with emphasis on learning and innovation, creativity and scientific research. The objective is to achieve a proficient education system that keeps abreast of the recent development, the essentials of sustainable development and enhancing the national identity. Higher Education Institutes have contributed immensely to the growth of education in Oman, in this context; Business Organization represents the most complex social structure known today due to its dynamic nature. Employees are considered as one of the dynamic resources of the organization and through their commitment and involvement organization becomes competitive. Organization Culture can be promoted to facilitate the achievement of job satisfaction and employees commitment. The purpose of the research is to explore the impact of Higher Educational Institutions Culture on employee satisfaction, and commitment. Based on primary data, the study was conducted in Higher Education Institutions in the Sultanate of Oman. Data was collected through questionnaire consisting of 60 questions related to culture, satisfaction, and commitment. The sample consisted of 330 employees of leading Higher Education Institutes in the Sultanate of Oman. Structural Equation Modeling was carried out on the data through SPSS and AMOS. Results indicate that culture of organization is significantly related with employees’ satisfaction and commitment both in direct and indirect ways. Significant theoretical and practical implications are driven from the outcomes of the study.

Keywords: organization culture, employee satisfaction and commitment, higher education, Sultanate of Oman

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Authors: Elron Fouten

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Recently, debates surrounding the social relevance of psychology in South Africa have focussed on how the growing neoliberal rationality within academia has again resulted in the discipline catering to the needs of powerful social groupings to protect its own economic interests, rather than producing socially relevant knowledge. Consequently, this study aimed to conduct a content analysis of the recent research output of psychology masters students, to establish whether it has produced research that addresses local and national psychosocial issues and as such deemed socially relevant knowledge. The study sampled clinical, counselling, and research psychology masters theses from 16 South African universities submitted between 1998 and 2017. Overall, 2001 theses were sampled, which were analysed using qualitative content analysis predominantly based on the descriptive categories identified in similar studies using published journal articles. Results indicated that empirical qualitative theses, using systems-oriented theory and post-modern frameworks were most prevalent. Further, traditional topics within psychology had relatively more weighting compared to more social topics. Although a significant number of theses recruited participants from working-class or poor backgrounds, there was an overreliance on participants from urban areas located in some of the country’s wealthiest provinces. Despite a strong adult-centric focus, trends regarding participants’ race and gender roughly resembled current population demographics. Overall, the results indicate that psychology in South Africa, at least at university-level, is to some extent trying to engage with national psychosocial concerns. However, there are still several key areas which need to be addressed to ensure the continued social relevance of the discipline.

Keywords: adult-centric, content analysis, relevance, psychosocial

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Authors: Jaana Sepp, Ulvi Kõrgemaa, Kristi Puusepp, Õie Tähtla

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COVID-19 was officially recognized as a pandemic in late 2019 by the WHO, and it has led to changes in the education sector. Educational institutions were closed, and most schools adopted distance learning. Estonia is known as a digitally well-developed country. Based on that, in the pandemic time, nursing education continued, and new technological solutions were implemented. To provide nursing education, special focus was paid on quality and flexibility. The aim of this paper is to present administrative, digital, and technological solutions which support Estonian nursing educators to continue the study process in the pandemic time and to develop a sustainable solution for nursing education for the future. This paper includes the authors’ analysis of the documents and decisions implemented in the institutions through the pandemic time. It is a case study of Estonian nursing educators. Results of the analysis show that the implementation of distance learning principles challenges the development of innovative strategies and technics for the assessment of student performance and educational outcomes and implement new strategies to encourage student engagement in the virtual classroom. Additionally, hospital internships were canceled, and the simulation approach was deeply implemented as a new opportunity to develop and assess students’ practical skills. There are many other technical and administrative changes that have also been carried out, such as students’ support and assessment systems, the designing and conducting of hybrid and blended studies, etc. All services were redesigned and made more available, individual, and flexible. Hence, the feedback system was changed, the information was collected in parallel with educational activities. Experiences of nursing education during the pandemic time are widely presented in scientific literature. However, to conclude our study, authors have found evidence that solutions implemented in Estonian nursing education allowed the students to graduate within the nominal study period without any decline in education quality. Operative information system and flexibility provided the minimum distance between the students, support, and academic staff, and likewise, the changes were implemented quickly and efficiently. Institution memberships were updated with the appropriate information, and it positively affected their satisfaction, motivation, and commitment. We recommend that the feedback process and the system should be permanently changed in the future to place all members in the same information area, redefine the hospital internship process, implement hybrid learning, as well as to improve the communication system between stakeholders inside and outside the organization. The main limitation of this study relates to the size of Estonia. Nursing education is provided by two institutions only, and similarly, the number of students is low. The result could be generated to the institutions with a similar size and administrative system. In the future, the relationship between nurses’ performance and organizational outcomes should be deeply investigated and influences of the pandemic time education analyzed at workplaces.

Keywords: hybrid learning, nursing education, nursing, COVID-19

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Authors: Samrina Jafrin, Till Talaulicar

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This study investigates stakeholder perceptions of environmental, social, and governance (ESG) reporting patterns and their effectiveness in enhancing trust and transparency. Utilizing a multi-method approach, including experimental research and systematic literature review, insights are gathered from investors, employees, customers, suppliers, managers, and community members. The findings reveal diverse stakeholder expectations and perceptions and emphasize the importance of effective ESG reporting strategies in building credibility and trust. This research contributes to the academic discourse on corporate sustainability reporting and provides practical recommendations for optimizing ESG reporting practices.

Keywords: ESG reporting, stakeholder perceptions, corporate sustainability, transparency, trust

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Authors: Jonathan Gong

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deep learning, image processing, machine learning

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Authors: Mahmood Dehqan, Peyman Peyvasteh

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In recent years there has been a growing interest in issues concerning the impact of sociocultural theory (SCT) of learning on different aspects of second/foreign language learning. This study aimed to find the possible effects of sociocultural teaching techniques on reading strategy of EFL learners. Indeed, the present research compared the impact of peer and teacher scaffolding on EFL learners’ reading strategy use across two proficiency levels. To this end, a pre-test post-test quasi-experimental research design was used and two instruments were utilized to collect the data: Nelson English language test and reading strategy questionnaire. Ninety five university students participated in this study were divided into two groups of teacher and peer scaffolding. Teacher scaffolding group received scaffolded help from the teacher based on three mechanisms of effective help within ZPD: graduated, contingent, dialogic. In contrast, learners of peer scaffolding group were unleashed from the teacher-fronted classroom as they were asked to carry out the reading comprehension tasks with the feedback they provided for each other. Results obtained from ANOVA revealed that teacher scaffolding group outperformed the peer scaffolding group in terms of reading strategy use. It means teacher’s scaffolded help provided within the learners’ ZPD led to better reading strategy improvement compared with the peer scaffolded help. However, the interaction effect between proficiency factor and teaching technique was non-significant, leading to the conclusion that strategy use of the learners was not affected by their proficiency level in either teacher or peer scaffolding groups.

Keywords: peer scaffolding, proficiency level, reading strategy, sociocultural theory, teacher scaffolding

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Authors: Shimaa Nagro, Russell Campion

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Mobile devices are becoming ever more widely available, with growing functionality, and they are increasingly used as enabling technology to give students access to educational material anytime and anywhere. However, the design of educational material's user interfaces for mobile devices is beset by many unresolved research problems such as those arising from constraints associated with mobile devices or from issues linked to effective learning. The proposed research aims to produce: (i) a method framework for the design and evaluation of educational material’s interfaces to be delivered on mobile devices, in multimedia form based on Human Computer Interaction strategies; and (ii) a software tool implemented as a fast-track alternative to use the method framework in full. The investigation will combine qualitative and quantitative methods, including interviews and questionnaires for data collection and three case studies for validating the method framework. The method framework is a framework to enable an educational designer to effectively and efficiently create educational multimedia interfaces to be used on mobile devices by following a particular methodology that contains practical and usable tools and techniques. It is a method framework that accepts any educational material in its final lesson plan and deals with this plan as a static element, it will not suggest any changes in any information given in the lesson plan but it will help the instructor to design his final lesson plan in a multimedia format to be presented in mobile devices.

Keywords: mobile learning, M-Learn, HCI, educational multimedia, interface design

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Authors: Xu Heng

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Since the financial crisis of 2008 and the resulting Great Recession, the number of China’s unemployed college graduate reached over 500 thousand in 2011. Following the severe situation of college graduate employment, there has been growing public concern about college graduates, especially those with the less-privileged background, and their working and living condition in metropolises. Previous studies indicate that well-educated urban migrants with less-privileged background tend to obtain temporary occupation with less financial income and lower social status. Those vulnerable young migrants are described as ‘Ant Tribe’ by some scholars. However, since the implementation of a new urbanization project, together with the relaxed Hukou system and the acceleration of socio-economic development in middle/small cities, some researchers described well-educated urban migrant’s situation and the prospect of upward social mobility in urban areas in an overly optimistic light. In order to shed more lights on the underlying tensions encountered by China’s well-educated urban migrants in their upward social mobility pursuit, this research mainly focuses on 10 well-educated urban migrants’ life trajectories between their university-to-work transition and their current situation. All selected well-educated urban migrants are young adults with rural background who have already received higher education qualification from first-tier universities of Wuhan City (capital of Hubei Province). Drawing on the in-depth interviews with 10 participants and Inspired by Lahire’s Theory of Plural Actor, this study yields the following preliminary findings; 1) For those migrants who move to super-mega cities (i.e., Beijing, Shenzhen, Guangzhou) or stay in Wuhan after college graduation, their inadequacies of economic and social capital are the structural factors which negatively influence their living condition and further shape their plan for career development. The incompatibility between the sub-fields of urban life and the disposition, which generated from their early socialization, is the main cause for marginalized position in the metropolises. 2) For those migrants who move back to middle/small cities located in their hometown regions, the inconsistency between the disposition, which generated from college life, and the organizational habitus of the workplace is the main cause for their sense of ‘fish out of water’, even though they have obtained the stable occupation of local government or state-owned enterprise. On the whole, this research illuminates how the underlying the structural forces shape well-educated urban migrants’ life trajectories and hinder their upward social mobility under the context of new urbanization project.

Keywords: life trajectory, social mobility, urbanization, well-educated urban migrant

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Authors: Joane Adeclas, Yoonyoung Kim, Taekyun Hur

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This study investigates individual’s intrinsic motivation to practice their leisure activities, as well as, how the cultural environment may influence their motivation to practice their activities. Focused on the positive psychology, the present study proposed redefinition of leisure activities considering two factors. First, leisure activities could be as any activities that provide pleasure or meaning to individuals. Second, they can be practiced alone or in groups. In fact, based on this definition, a four-dimensional model of leisure activities was developed, to measure individual’s perception of their leisure experience, based on four factors that are: personal pleasure, social pleasure, personal meaning and social meaning. Furthermore, recent studies have argued that leisure activities can be interpreted and understood differently across cultures. Therefore, the present study proposed to examine the possible role of the cultural context of individual’s leisure practices. To do so, two cultural groups (Koreans vs. French) were compared in terms of the four-dimensional model of leisure activities. Three hundred Koreans and three hundred French participants were asked to answer an online survey about their leisure activities. Participants had to respond to questions related to several aspects of leisure practices as followed: the reason why their practice their leisure activities, the reason why they fail to practice their leisure, and their obsession relate to their leisure activities. Factor analyses based on participant’s responses proposed a moderate fit of the four-dimensional model of leisure activities. Furthermore, significant cultural differences were also found. As a result, the cultural context seems to influence the reason why individuals practice their leisure activities based on our model. In fact, Koreans explained more than French, the practice of their leisure activities with social-pleasurable reasons. At a contrary, French explained more than Koreans, the practice of their leisure activities with social-meaningful reasons. The two cultural groups also significantly differ on their perception of failure. The results showed that French participants used more meaningful social factors to explain why they failed to practice their leisure activities than did Koreans participants. Finally, Koreans and French significantly differed regarding their obsession on their leisure activities. In general, French tend to have more obsession than Koreans about their leisure activities. Those results validated the four-dimensional model of leisure, as well as, the cultural differences in leisure practices. However, further studies are needed to validate this model at an individual and cultural level.

Keywords: culture, leisure, meaning, pleasure

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