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Having navigated up to two years of online course delivery worldwide as a result of the Covid-19 pandemic. Education systems are now in a better position to leverage the benefits of technology in facilitating the language acquisition process more effectively. Regardless of the student population served, there is no longer a concern as to whether students have access to facilities necessary for online delivery as the smartphone has become a standard household necessity. This conceptual literature review introduces a potential model for second language instruction utilizing a flipped classroom approach based on evidence gained through empirical research interpreted through the lens of present reality. Technology enables learners to explore the form and structure of language through the use of online autonomous learning units with no limitation of time or accessibility, while scheduled classroom engagement allows opportunity for authentic language practice and refinement. The implications of this study add value to second and foreign language instruction, providing language teachers with a pragmatic approach to enhance their instructional delivery. © 2025 selection and editorial matter, Leung Sze Ming and Chan Sin-wai; individual chapters, the contributors.

<jats:title>Abstract</jats:title> <jats:p>Under the agreement signed with Portugal, which defined the terms of the handover to China, Macau became a Special Administrative Region on 20 December 1999. China undertook to maintain the way of life, the rights and freedoms of the residents and the essence of the laws previously in force, and guarantee the inapplicability of the socialist system. Events in Hong Kong since 2019 and the concerns of the Central Government have led to changes in the national security law and electoral laws which, among other things, have imposed political screening on candidates for the Legislative Assembly and Chief Executive, which can lead to their exclusion without appeal, while criminalising calls for blank votes, null votes, and abstentions. This article answers the question of whether these changes are compatible with the guarantees provided, the Luso-Chinese Joint Declaration and Macau’s Basic Law.</jats:p>

Accurate classification of brain tumors from MRI is critical for effective diagnosis and treatment. In this study, we introduce Trans-EffNet, a hybrid model combining pre-trained EfficientNet architectures with a transformer encoder to enhance brain tumor classification accuracy. By leveraging EfficientNet's deep CNN capabilities for localized feature extraction and the transformer encoder for capturing global contextual relationships, our model improves the identification of intricate tumor characteristics. Fine-tuned with ImageNet-derived weights and utilizing extensive data augmentation, Trans-EffNet was validated on both multi-class and binary datasets. Trans-EffNetB1 achieved 99.49 % accuracy on the multi-class dataset, while Trans-EffNetB2 recorded 99.83 % accuracy on the binary dataset, with perfect precision, recall, and F1-Score. These results underscore Trans-EffNet's robustness and potential as a significant advancement in brain tumor detection and classification.

This proposed study looks into the popular TikTok app and its impact on the identity formation and expression of young people. It investigates how TikTok enables young individuals to create, share, and consume diverse and authentic content that reflects their interests, values, and experiences. People on the app can work together, take part in trends, and interact with others. Also, the paper dives into how TikTok gives people a place to chase their dreams and find out what other possibilities their lives could hold. It indicates how TikTok can have some good and bad effects on young generations in terms of culture and personal development. These youngsters could be the ones leading in the future. However, we also need to think about the possible dangers TikTok could pose to young people. This includes the chance that they come across something damaging, feel like they have to live up to hard-to-reach standards, have their private information leaked, or fall victim to someone with bad intentions. Because of these risks, it's very important to teach youngsters how to use TikTok in a way that's safe and responsible. The dissertation highlights the need for responsible usage, awareness of potential challenges, and the development of strategies to support the safe and healthy engagement of young people with TikTok and similar platforms.

<jats:p>The use of artificial intelligence (AI) tools in writing and proofreading is beginning to develop. Studies show that AI tools can positively influence students' writing and proofreading skills. This study presents the perceptions of vocational education students regarding the assessments and suggestions for improvement provided by the AI assistant Curipod and followed by students in the proofreading phase. It centres on a case study, with data collected using a survey with open and closed questions, participant observation, and an interview. The students positively perceived the feedback they received from the AI assistant on their initial text and consider that it helped them to revise and improve the final versions of the texts written on paper and digitally. The students are interested in using tools like these in writing revision activities, as they see the potential they have for the classroom and autonomous learning.</jats:p>

Objetivo: Explorar a aplicação de inteligência artificial (IA) na predição da idade óssea a partir de imagens de raios-X. Método: Utilizou-se a Metodologia Interdisciplinar para o Desenvolvimento de Tecnologias em Saúde (MIDTS) para desenvolver uma ferramenta de predição. O treinamento foi realizado com redes neurais convolucionais (CNNs) usando um conjunto de dados de 14.036 imagens de raios-X. Resultados: A ferramenta alcançou um coeficiente de determinação (R²) de 0,94807 e um Erro Médio Absoluto (MAE) de 6,97, destacando sua precisão e potencial de aplicação clínica. Conclusão: O projeto demonstrou grande potencial para aprimorar a predição da idade óssea, com possibilidades de evolução conforme a base de dados aumenta e a IA se torna mais sofisticada.

<jats:title>Abstract</jats:title> <jats:p> <jats:italic>Objective.</jats:italic> Mild cognitive impairment (MCI) is a precursor stage of dementia characterized by mild cognitive decline in one or more cognitive domains, without meeting the criteria for dementia. MCI is considered a prodromal form of Alzheimer’s disease (AD). Early identification of MCI is crucial for both intervention and prevention of AD. To accurately identify MCI, a novel multimodal 3D imaging data integration graph convolutional network (GCN) model is designed in this paper. <jats:italic>Approach.</jats:italic> The proposed model utilizes 3D-VGGNet to extract three-dimensional features from multimodal imaging data (such as structural magnetic resonance imaging and fluorodeoxyglucose positron emission tomography), which are then fused into feature vectors as the node features of a population graph. Non-imaging features of participants are combined with the multimodal imaging data to construct a population sparse graph. Additionally, in order to optimize the connectivity of the graph, we employed the pairwise attribute estimation （PAE） method to compute the edge weights based on non-imaging data, thereby enhancing the effectiveness of the graph structure. Subsequently, a population-based GCN integrates the structural and functional features of different modal images into the features of each participant for MCI classification. <jats:italic>Main results.</jats:italic> Experiments on the AD Neuroimaging Initiative demonstrated accuracies of 98.57%, 96.03%, and 96.83% for the normal controls (NC)-early MCI (EMCI), NC-late MCI (LMCI), and EMCI-LMCI classification tasks, respectively. The AUC, specificity, sensitivity, and F1-score are also superior to state-of-the-art models, demonstrating the effectiveness of the proposed model. Furthermore, the proposed model is applied to the ABIDE dataset for autism diagnosis, achieving an accuracy of 91.43% and outperforming the state-of-the-art models, indicating excellent generalization capabilities of the proposed model. <jats:italic>Significance.</jats:italic> This study demonstrate<jats:bold>s</jats:bold> the proposed model’s ability to integrate multimodal imaging data and its excellent ability to recognize MCI. This will help achieve early warning for AD and intelligent diagnosis of other brain neurodegenerative diseases.</jats:p>

This work provides a comprehensive systematic review of optimization techniques using artificial intelligence (AI) for energy storage systems within renewable energy setups. The primary goals are to evaluate the latest technologies employed in forecasting models for renewable energy generation, load forecasting, and energy storage systems, alongside their construction parameters and optimization methods. The review highlights the progress achieved, identifies current challenges, and explores future research directions. Despite the extensive application of machine learning (ML) and deep learning (DL) in renewable energy generation, consumption patterns, and storage optimization, few studies integrate these three aspects simultaneously, underscoring the significance of this work. The review encompasses studies from Web of Science, Scopus, and Science Direct up to December 2023, including works scheduled for publication in 2024. Each study related to renewable energy storage was individually analyzed to assess its objectives, methodology, and results. The findings reveal useful insights for developing AI models aimed at optimizing storage systems. However, critical areas need further exploration, such as real-time forecasting, long-term storage predictions, hybrid neural networks for demand-based generation forecasting, and the evaluation of various storage scales and battery technologies. The review also notes a significant gap in research on large-scale storage systems in Brazil and Latin America. In conclusion, the study emphasizes the need for continued research and the development of new algorithms to address existing limitations in the field.

Artificial intelligence (AI) and deep learning (DL) are advancing in stock market prediction, attracting the attention of researchers in computer science and finance. This bibliometric review analyzes 525 articles published from 1991 to 2024 in Scopus-indexed journals, utilizing VOSviewer software to identify key research trends, influential contributors, and burgeoning themes. The bibliometric analysis encompasses a performance analysis of the most prominent scientific contributors and a network analysis of scientific mapping, which includes co-authorship, co-occurrence, citation, bibliographical coupling, and co-citation analyses enabled by the VOSviewer software. Among the 693 countries, significant hubs of knowledge production include China, the US, India, and the UK, highlighting the global relevance of the field. Various AI and DL technologies are increasingly employed in stock price predictions, with artificial neural networks (ANN) and other methods such as long short-term memory (LSTM), Random Forest, Sentiment Analysis, Support Vector Machine/Regression (SVM/SVR), among the 1399 keyword counts in publications. Influential studies such as LeBaron (1999) and Moghaddam (2016) have shaped foundational research in 8159 citations. This review offers original insights into the bibliometric landscape of AI and DL applications in finance by mapping global knowledge production and identifying critical AI methods advancing stock market prediction. It enables finance professionals to learn about technological developments and trends to enhance decision-making and gain market advantage.

Construction projects are complex endeavours, with potential obstacles that can cause delays which can have particularly profound implications potentially impacting on company's financial health, business continuity and reputation. It is becoming increasingly recognised that delays are context-specific and multifaceted, requiring more industry-oriented perceptions. This work proposes the exploratory use of Machine Learning based on Classification and Regression Trees (CART) Decision Trees (DT) to assess the predictive analysis of these approaches, considering surveys (primary data) collected from 100 specialists with different backgrounds and experiences in the construction industry. Survey responses are discussed, followed by the CART DTs, which are used as predictor for clarifying underneath relationship among different variables in a project environment. The major issue presented is related to Project Design, with "The firm is not allowed to apply for an extension of contract period", with two possible predictors, firstly, as the main factor it is found "Mistakes, inconsistencies, and ambiguities in specification and drawing", while other aspect highlights "Poor site supervision and management by the contractor". The results indicate that the correct use of Artificial Intelligence techniques with relevant data are potential tools to support the analysis of scenarios and avoidance of project delays in Project Management.

<jats:title>Abstract</jats:title><jats:p>Speaking truth ought to be normative in churches, and yet when it does, the foundations and structures of power are often shaken to the core. This paper explores the issues of identity and integrity in ecclesiology and is concerned with the ethical paradigms and moral frameworks that need to be in place if churches are to be places where honesty and truthfulness can be normative. Churches often fail as institutions because they presume they can conduct their affairs as organizations might. Churches become anger-averse, resisting the voices and experiences of victims, in order that the flow of power and its structures are unimpeded. At that point, churches become inherently committed to re-abusing victims and are unable to hear their pain and protests, which only leads to the perpetration of further abuse.</jats:p>

With the rise of the awareness of human rights, students with special educational needs (SEN) are receiving more attention. As many mainstream schools adopt inclusive education, society is focusing on teachers' professional awareness and attitudes toward inclusive education. This study examines the professional awareness and attitudes of post-90s early childhood teachers in Macau regarding inclusive education, aiming to improve policies and training programs. The research employs both qualitative and quantitative methods to explore teachers' views on inclusive education. The qualitative research consists of semi- structured interviews to understand teachers' readiness, attitudes, and the challenges they face in implementing inclusive education. The quantitative aspect uses a questionnaire adapted from the ""Survey on Equal Learning Opportunities for Students with Special Educational Needs under the Hong Kong Inclusive Education System,"" tailored for Macau. By combining interview findings and data, the study analyzes and summarizes the results. The results indicate that the professional awareness of inclusive education among post-90s early childhood teachers in Macau is at a relatively low to moderate level, and their willingness to engage in inclusive education is not high. Factors influencing teachers' willingness to implement inclusive education include a lack of professional knowledge (such as development history, relevant regulations, and guidance methods), insufficient resources, and unclear attitudes from schools toward the implementation of inclusive education. 隨著人權意識的掘起，特殊教育需要學生(SEN 學生)的學習受到關注， 澳門的融合教育逐漸受到關注，許多主流學校開始實施融合教育。就著學校教 育的轉型，教師對融合教育的知識和開展的態度受到社會所關注。因此，是次 研究以質量結合的方式進行調查，為廣泛地、深入地暸解澳門 90 後新生代幼兒 教師在融合教育方面的專業認知和態度，從而對澳門融合教育政策和教師課程 提出建議，促進融合教育的發展。 質性研究以半結構性的深度訪談暸解教師對實施融合教育的預備度和態 度，並探討當中的影響因素和教師在教學中的困擾與需求。量化的研究則參考 了《香港融合教育制度下有特殊教育需要學生的平等學習機會調查》的教師問 卷，向澳門 90 後新生代幼兒教師對融合教育認知和態度作調查。研究透過結合 訪談資料與數據，對研究問題作分析、討論與歸納，並撰寫出研究結果。 研究結果指出澳門 90 後新生代幼兒教師的融合教育專業認知處中等偏低的 水平，同時對融合教育開展的意願不高。而影響教師融合教育開展意願的因素 包括教師缺乏融合教育專業知識(如:發展歷史、相關法規和輔導方式)、資 源不足和學校對融合教育開展的態度不明確。

This research focuses on common misconceptions about the factors driving women to purchase footwear impulsively. Its primary objective is to explore how emotional and social triggers specifically influence women's purchasing decisions, contrasting with the traditionally rational consumer models.,An online questionnaire was administered to a sample of women, yielding 199 useable responses.,The findings reveal the key determinants of women's impulsive retail footwear purchases, which include self-regulation, hedonic motivations and the influence of the retail store environment. This research challenges the prevailing assumption that women's passion for shopping is driven solely by inherent characteristics and suggests that external factors substantially shape their impulsive buying behaviour. In summary, the stereotypical portrayal of women as compulsive retail footwear shoppers may result more from external stimuli and environmental factors rather than an intrinsic trait.,This study improves the existing knowledge of women’s impulsive buying behaviour by unveiling the determinants of women's impulsive footwear purchases and assessing whether prevailing stereotypes hold true.

This article sets a theoretical foundation to transformative mixed methods research that is rooted in the critical theory of Habermas and Honneth. This addresses Habermas’s knowledge-constitutive interests and communicative action for redressing societal pathologies, and Honneth’s work on (mis)recognition, (dis)respect, and social justice. In doing so, the article argues for broadening the scope and embrace of mixed methods research, to go beyond being empirical research only or largely, and to include theorisation, critical theoretical discourse and its analysis, and ideology critique, as legitimate methods for (transformative) mixed methods research. The article makes a case for these methods as constituting important research methods in themselves in the portfolio of mixed methods research, moving the boundaries of mixed methods research beyond solely empirical studies, and providing emancipatory lenses and consciousness-raising in recognising that transformation takes many forms.

PDF | Purpose Whilst the majority of academic studies have focused on the for-profit business-to-consumer type of sharing economy, the community-based... | Find, read and cite all the research you need on ResearchGate

Introduction: SARS-CoV-2, a virus responsible for the emergence of the life-threatening disease known as COVID-19, exhibits a diverse range of clinical manifestations. The spectrum of symptoms varies widely, encompassing mild to severe presentations, while a considerable portion of the population remains asymptomatic. COVID-19, primarily a respiratory virus, has been linked to cardiovascular complications in some patients. Notably, cardiac issues can also arise after recovery, contributing to post-acute COVID-19 syndrome, a significant concern for patient health. The present study intends to evaluate the post-acute COVID-19 syndrome cardiovascular effect through ECG by comparing patients affected with cardiac diseases without COVID-19 diagnosis report (class 1) and patients with cardiac pathologies who present post-acute COVID-19 syndrome (class 2). Methods: From 2 body positions, a total of 10 non-linear features, extracted every 1 second under a multi-band analysis performed by Discrete Wavelet Transform (DWT), have been compressed by 6 statistical metrics to serve as inputs for an individual feature analysis by the means of Mann-Whitney U-test and XROC classification. Results and Discussion: 480 Mann-Whitney U-test statistical analyses and XROC discrimination approaches have been done. The percentage of statistical analysis with significant differences (p<0.05) was 30.42% (146 out of 480). The best overall results were obtained by approximating the feature Energy, with the data compressor Kurtosis in the body position Down. Those results were 83.33% of Accuracy, 83.33% of Sensitivity, 83.33% of Specificity and 87.50% of AUC. Conclusions: The results show that the applied methodology can be a way to show changes in cardiac behaviour provoked by post-acute COVID-19 syndrome.