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China’s economy has entered a critical period of structural adjustment. The developing green industries and the transforming traditional industries have increasing demand for finance, making ""green finance"" increasingly essential. While China's green finance is in the development stage, some newly developed zones serve as pilots for the launch of green financial products. An example is Tongzhou District of Beijing, which aims to expand Beijing’s space, promote the coordinated development of Beijing-Tianjin-Hebei, and explore the optimal development mode of the densely populated economic areas. This thesis aims to study consumer acceptance of green financial technology (fintech) in the case of Tongzhou District. This thesis extended the commonly applied theoretical model for the problem of study, the Energy Augmented Technology Acceptance Model (EA-TAM), to analyze the impacts of perceived usefulness, perceived ease of use, attitude toward use, intention, usage intention, environmental awareness, and green knowledge on the acceptance of green fintech in Tongzhou District. The survey collected 403 valid responses from people that had been active in Tongzhou District. The quantitative analysis is based on structural equation modeling techniques, including reliability analysis, validity analysis, standard method deviation test, and hypothesis testing. The analytical results show that all the hypothesized factors are significant. In addition, the sample is divided into different gender groups and education groups, so that the impacts of the socio-demographic characteristics can be explored. Males’ environmental awareness and green knowledge are insignificant in determining their acceptance of green fintech. The low-educated group’s acceptance of green fintech does not significantly depend on environmental awareness and perceived usefulness
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The present study aimed to analyse the differences in the internalising problems (anxiety, depression, somatic complaints), assessed by different informants (teachers and students), according to the level of academic achievement and school adaptation level in secondary students. Furthermore, we examine the gender difference in the level of internalising symptoms. Finally, we analyzed the differences between teacher-rated and adolescents' self-reported internalising symptoms. The Achenbach System of Empirically Based Assessment (ASEBA) was used for collecting informants’ data. The sample consisted of 882 secondary students (349 males and 473 females), while 50 came from public schools and 772 from private schools. No significant differences are found in internalising problems according to the level of academic achievement from both teachers’ and students’ perspectives. Generally, students who are well-adapted to the school context have the least symptoms of internalising problems compared to average and less-adapted groups from the teachers' perspective. In addition, from students’ perspectives, adolescent females present more internalising problems than males. Finally, teachers rated fewer internalising problems when compared to the students. In conclusion, the low level of awareness of teachers towards the internalising problems of students arouse attention. It is suggested that teachers should attend professional development programs in order to address to students’ internalising problems
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Teacher turnover is a global issue that has not received much research attention in Macau despite studies indicating that teachers in the region experience high levels of stress and burnout. Given that private school teachers account for a significant proportion (88.6%) of the non-tertiary education system in Macau, this qualitative study focused on this specific group who voluntarily resigned from their positions. Through in-depth interviews with 13 former teachers from different kindergartens, primary, and secondary schools, the research identified 50 reasons categorized into 15 factors under three categories. Although schoolrelated factors account for the most, personal reasons were found to be the primary driver. The findings of the study highlight the complex nature of teacher turnover which can be attributed to both single and multiple factors, in both direct and indirect forms. The factors could also interplay in both unidirectional and mutual relationships. A conceptual framework for teacher turnover in Macau was developed to address the 15 contributing factors and the complex interplay of these factors. This study could fill the gap in the literature and serve as a valuable resource for policymakers and school leaders seeking to reduce teacher attrition rates in the region
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By using the Hamiltonian formulation, we demonstrate that the Merton-Garman equation emerges naturally from the Black-Scholes equation after imposing invariance (symmetry) under local (gauge) transformations over changes in the stock price. This is the case because imposing gauge symmetry implies the appearance of an additional field, which corresponds to the stochastic volatility. The gauge symmetry then imposes some constraints over the free-parameters of the Merton-Garman Hamiltonian. Finally, we analyze how the stochastic volatility gets massive dynamically via Higgs mechanism.
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By using the Hamiltonian formulation, we demonstrate that the Merton-Garman equation emerges naturally from the Black-Scholes equation after imposing invariance (symmetry) under local (gauge) transformations over changes in the stock price. This is the case because imposing gauge symmetry implies the appearance of an additional field, which corresponds to the stochastic volatility. The gauge symmetry then imposes some constraints over the free parameters of the Merton-Garman Hamiltonian. Finally, we analyze how the stochastic volatility gets massive dynamically via Higgs mechanism.
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By using both, the weak-value formulation as well as the standard probabilistic approach, we analyze the Hardy's experiment introducing a complex and dimensionless parameter ($\epsilon$) which eliminates the assumption of complete annihilation when both, the electron and the positron departing from a common origin, cross the intersection point $P$. We then find that the paradox does not exist for all the possible values taken by the parameter. The apparent paradox only appears when $\epsilon=1$; however, even in this case we can interpret this result as a natural consequence of the fact that the particles can cross the point $P$, but at different times due to a natural consequence of the energy-time uncertainty principle.
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In any physical system, when we move from short to large scales, new spacetime symmetries emerge which help us to simplify the dynamics of the system. In this letter we demonstrate that certain variations on the symmetries of general relativity at large scales generate the effects equivalent to dark matter ones. In particular, we reproduce the Tully-Fisher law, consistent with the predictions proposed by MOND. Additionally, we demonstrate that the dark matter effects derived in this way are consistent with the predictions suggested by MOND, without modifying gravity.
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We review some general aspects about the Black–Scholes equation, which is used for predicting the fair price of an option inside the stock market. Our analysis includes the symmetry properties of the equation and its solutions. We use the Hamiltonian formulation for this purpose. Taking into account that the volatility inside the Black–Scholes equation is a parameter, we then introduce the Merton–Garman equation, where the volatility is stochastic, and then it can be perceived as a field. We then show how the Black–Scholes equation and the Merton–Garman one are locally equivalent by imposing a gauge symmetry under changes in the prices over the Black–Scholes equation. This demonstrates that the stochastic volatility emerges naturally from symmetry arguments. Finally, we analyze the role of the volatility on the decisions taken by the holders of the options when they use the solution of the Black–Scholes equation as a tool for making investment decisions.
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We prove the consistency of the different approaches for deriving the black hole radiation for the spherically symmetric case inside the theory of Massive Gravity. By comparing the results obtained by using the Bogoliubov transformations with those obtained by using the Path Integral formulation, we find that in both cases, the presence of the extra-degrees of freedom creates the effect of extra-particles creation due to the distortions on the definitions of time defined by the different observers at large scales. This, however, does not mean extra-particle creation at the horizon level. Instead, the apparent additional particles perceived at large scales emerge from how distant observers define their time coordinate, which is distorted due to the existence of extra-degrees of freedom.
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The Black-Scholes equation is famous for predicting values for the prices of Options inside the stock market scenario. However, it has the limitation of depending on the estimated value for the volatility. On the other hand, several Machine learning techniques have been employed for predicting the values of the same quantity. In this paper we analyze some fundamental properties of the Black-Scholes equation and we then propose a way to train its free-parameters, the volatility in particular. This with the purpose of using this parameter as the fundamental one to be learned by a Machine Learning system and then improve the predictions in the stock market.
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The primary research focus of this dissertation revolves around the concept of a "plugin" program. It raises a fundamental question about whether a building can attain long-term usability through metabolic flexibility (plugin units and their reconfigurable space), promoting adaptability (accommodating various program transfers), and meeting sustainable future criteria. Specifically, this dissertation inquires whether this "plugin" building design, with its reconfigurable units and metabolic system, can adapt to different spatial programs and become sustainable architecture
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We are delighted to present this special issue editorial for Neural Computing and Applications special issue on LatinX in AI research. This special issue brings together a collection of articles that explore machine learning and artificial intelligence research from various perspectives, aiming to provide a comprehensive and in-depth understanding of what LatinX researchers are working on in the field. In this editorial, we will introduce the overarching theme of the special issue, highlight the significance of the selected papers, and offer insights into the contributions made by the authors. The LatinX in AI organization was launched in 2018, with leaders from organizations in Artificial Intelligence, Education, Research, Engineering, and Social Impact with a purpose to together create a group that would be focused on “Creating Opportunity for LatinX in AI.” The main goal is to increase the representation of LatinX professionals in the AI industry. LatinX in AI Org and programs are volunteer-run and fiscally sponsored by the Accel AI Institute, 501(c)3 Non-Profit.
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Research in ubiquitous networked music systems has unveiled the potential of behavioural-driven interaction interfaces as an effective model to cope with network communication delays in remote musical performances. Most of the techniques developed under these premises are based on digital music interfaces implemented on laptop computers or tablet devices, where a certain degree of gestural control comes as an added dimension. The purpose of this paper is to present an implementation of such type of interfaces in the form of a physical tangible musical instrument, contemplating multiple expressive possibilities. This is viable at the current stage of technological development thanks to leveraging 3D printing and laser cutting technologies for effective prototyping and testing of such a device. The paper seeks to demonstrate that this approach opens a wide range of possibilities for creating musical instruments with versatility and expressiveness beyond what is usually accomplished in traditional instruments. This implementation, entitled “Radial String Chimes,” is presented with its advantages, the challenges it faces, and the methods used to create it. Finally, the paper will offer suggestions for further developing such an instrument to unlock its potential.
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The use of computational tools for medical image processing are promising tools to effectively detect COVID-19 as an alternative to expensive and time-consuming RT-PCR tests. For this specific task, CXR (Chest X-Ray) and CCT (Chest CT Scans) are the most common examinations to support diagnosis through radiology analysis. With these images, it is possible to support diagnosis and determine the disease’s severity stage. Computerized COVID-19 quantification and evaluation require an efficient segmentation process. Essential tasks for automatic segmentation tools are precisely identifying the lungs, lobes, bronchopulmonary segments, and infected regions or lesions. Segmented areas can provide handcrafted or self-learned diagnostic criteria for various applications. This Chapter presents different techniques applied for Chest CT Scans segmentation, considering the state of the art of UNet networks to segment COVID-19 CT scans and a segmentation experiment for network evaluation. Along 200 epochs, a dice coefficient of 0.83 was obtained.
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COVID-19 is a respiratory disorder caused by CoronaVirus and SARS (SARS-CoV2). WHO declared COVID-19 a global pandemic in March 2020 and several nations’ healthcare systems were on the verge of collapsing. With that, became crucial to screen COVID-19-positive patients to maximize limited resources. NAATs and antigen tests are utilized to diagnose COVID-19 infections. NAATs reliably detect SARS-CoV-2 and seldom produce false-negative results. Because of its specificity and sensitivity, RT-PCR can be considered the gold standard for COVID-19 diagnosis. This test’s complex gear is pricey and time-consuming, using skilled specialists to collect throat or nasal mucus samples. These tests require laboratory facilities and a machine for detection and analysis. Deep learning networks have been used for feature extraction and classification of Chest CT-Scan images and as an innovative detection approach in clinical practice. Because of COVID-19 CT scans’ medical characteristics, the lesions are widely spread and display a range of local aspects. Using deep learning to diagnose directly is difficult. In COVID-19, a Transformer and Convolutional Neural Network module are presented to extract local and global information from CT images. This chapter explains transfer learning, considering VGG-16 network, in CT examinations and compares convolutional networks with Vision Transformers (ViT). Vit usage increased VGG-16 network F1-score to 0.94.
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This chapter describes an AUTO-ML strategy to detect COVID on chest X-rays utilizing Transfer Learning feature extraction and the AutoML TPOT framework in order to identify lung illnesses (such as COVID or pneumonia). MobileNet is a lightweight network that uses depthwise separable convolution to deepen the network while decreasing parameters and computation. AutoML is a revolutionary concept of automated machine learning (AML) that automates the process of building an ML pipeline inside a constrained computing framework. The term “AutoML” can mean a number of different things depending on context. AutoML has risen to prominence in both the business world and the academic community thanks to the ever-increasing capabilities of modern computers. Python Optimised ML Pipeline (TPOT) is a Python-based ML tool that optimizes pipeline efficiency via genetic programming. We use TPOT builds models for extracted MobileNet network features from COVID-19 image data. The f1-score of 0.79 classifies Normal, Viral Pneumonia, and Lung Opacity.
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Although there is a substantial body of research on the second language acquisition of adults, there is little specific research on the learning experiences of senior and very senior adults. This thesis investigates and discovers the experience of being a senior from a traditional Confucian Heritage Culture aged between 55 and 75 years old, learning English as a foreign language through various interventions, including, the introduction of an adapted version of synthetic phonics to improve pronunciation, alongside the use of andragogical and geragogical principles to accommodate and encourage the development of agency and self-directed learning. This research adopted a case study methodology to investigate the lived experiences of seniors, and investigated the participants’ subjective constructions of the situation, learning experiences, challenges, circumstances, needs, and wants with regard to the situation. Therefore, an open and exploratory case study design was selected to understand the participants and report the findings. Furthermore, this thesis identifies the challenges faced by senior and very senior learners who are post-work and post-family rearing to make recommendations from the findings to complement, enhance and empower their learning
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"Student engagement is a catch-all term, irresistible to educators and policy makers, and serving many agendas and purposes. This ground-breaking book provides a powerful theory of student engagement, rooted in critical theory and social justice. It sets out a compelling argument for student engagement to promote social justice and to repel neoliberalism in, and through, higher education, addressing three key questions: -Student engagement in what? -Student engagement for what? -Student engagement for whom? The answers draw on Habermas, Honneth, Gramsci, Foucault, and Giroux in examining ideology, power, recognition, resistance, and student engagement, with examples drawn from across the world. It sets out key features, limitations and failures of neoliberalism in higher education, and indicates how student engagement can resist it. Student engagement calls for higher education institutions to be sites for challenge, debate on values and power, action for social justice, and for students to engage in the struggle to resist neoliberalism, taking action to promote social justice, democracy, and the public good. This book is essential reading for educators, researchers, managers and students in higher education, social scientists and social theorists. It is a call to reawaken higher education for social justice, human rights, democracy and freedoms"--
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