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<jats:p>The mass production of uniform, high-quality polymer nanofibers remains a challenge. To enhance spinning yield, a multi-string standing wave electrospinning apparatus was developed by incorporating a string array into a standing wave electrospinning device. The process parameters such as string spacing, quantity, and phase difference were optimized, and their effects on the electric field distribution within the spinning area were analyzed using electric field simulations. When the string spacing was less than 40 mm or the number of strings exceeded two, the electric field strength significantly decreased due to electric field interference. However, this interference could be effectively mitigated by setting the string standing wave phase difference to half a period. The optimal string array parameters were identified as string spacing of 40 mm, two strings, and a phase difference of half a period. Multi-string standing wave electrospinning produced fibers with diameters similar to those obtained with single-string standing wave electrospinning (178 ± 72 nm vs. 173 ± 48 nm), but the yield increased by 88.7%, reaching 2.17 g/h, thereby demonstrating the potential for the large-scale production of nanofibers. This work further refined the standing wave electrospinning process and provided valuable insights for optimizing wire-type electrospinning processes.</jats:p>

Social Media Influencer (SMI) marketing represents a contemporary addition to the arsenal of digital advertising tools. Digital Content Creators are individuals who regularly share a variety of content, including visuals, audio recordings, and updates, across multiple social media platforms to shape consumers' perceptions of a brand and its products. The focus of this study is to examine how the credibility aspects of social media influencers (expertise, attractiveness, and trustworthiness) influence purchase intention and brand intimacy while also considering the mediating role of consumer engagement. This study used a quantitative, cross-sectional design with convenience sampling targeting social media-active individuals. Data were collected via a questionnaire distributed through email and social media, selecting participants who followed influencers. To gather data, 250 participants were engaged in an online questionnaire distributed via Google Forms. The findings indicate that the credibility dimensions of SMIs, particularly their attractiveness and trustworthiness, positively influence brand intimacy and purchase intention. Furthermore, consumer engagement serves as a critical mediator, connecting the authenticity of social media influencers with purchase intention and brand intimacy. In line with these results, it becomes evident that consumer engagement indirectly influences influencer credibility (trustworthiness and attractiveness), purchase intention, and brand intimacy. Notably, expertise does not exert any discernible impact on either brand intimacy or purchase intention. This study's outcomes provide valuable insights for marketing managers, underscoring the significance of partnering with influencers who possess a high level of trust within their respective marketing niches.

Traditional healthcare typically focuses on the treatment of illness and medical consultations (TAHIRU, 2021). However, new generations of health centers are moving beyond this traditional model. Nowadays, it has shifted to prioritizing preventive care, such as wellness programs, fitness classes, mental health support services and leisure activities to promote healthy lifestyles (Marizahn, 2020). Hence, this research focuses on exploring how an integrated health center approach could address the mental health challenges of youth in Macao.

In the first half of the 20th century, Macao’s three traditional handicraft industries were: making matches, firecrackers, and incense, and these three became important pillars of Macao's economy. These industries were supported by four other industries, namely: shipbuilding, wine, printing and the clothing that are integral to understanding the history of Macao. The purpose of this research has been to document the importance of Macao's seven traditional industries, in particular the firecracker factories with special reference to the Yick Loong factory. This dissertation makes a significance contribution to the subject of the historical development of Macao’s traditional handicraft industries in reviewing each of the traditional industries and in particular detailing the Yick Loong factories operations and the understanding of the local Macao people to the past importance of this operation. These industries need to be discussed so that society can have a deeper understanding of the importance of them, as up to now they have been little explored. So, by visiting the location of the Yick Loong Firecracker Factory, gathering knowledge, and conducting in-depth research and in books and through a survey about the Macao firecracker business, the author has been able to show that there appears to be limited understanding of the significance of the firecracker industry in Macao. Though, people most recall the Yick Loong factory’s name when prompted about naming a firecracker factory in Macao and associate firecrackers with the Lunar New Year celebrations. However, they seem to have limited understanding of the significance of the firecrackers to Macao, except for childhood memories of them being released.

The purpose of this study was to investigate the impact of parents engaging in dialogic reading with their children while reading picture books on children's emotional understanding and parent-child relationships. The study also attempted to investigate the potential of picture books as useful instruments for parent-child communication, fostering meaningful parent-child connection and augmenting children's emotional comprehension. The study lasted for 11 weeks. The initial three weeks were focused on a parent-child reading session specifically designed for mothers, and the next eight weeks consisted of a picture book reading program in the participants' homes. Convenience sampling was employed to choose 11 families for participation in the study. Throughout the 8-week reading program, families actively participated in parent-child picture book reading sessions at home, ensuring a minimum frequency of once per week. The study used a combination of multiple case studies and a case study-mixed methods design. The data collection process involved gathering quantitative data through various means, including administering primary background surveys, utilizing the Child-Parent Relationship Scale (CPRS), and employing the Children’s Emotional Development Scale. The qualitative data consisted of interviews conducted before the intervention and records documenting parental reading observations. The analysis methodologies used in this study encompassed thematic and content analysis, which involved combining qualitative and quantitative findings to facilitate comparison. The quantitative data analysis of the Child Emotional Development Scale revealed notable disparities in emotional cognition, comprehension, expression, and overall emotional competence scores between the initial and final examinations. Although the CPRS results did not reveal any notable disparities in family intimacy and conflict; it is worth noting that seven families had higher post-test scores in family intimacy, indicating that mothers perceived an improved level of closeness with their children. Qualitative data analysis revealed that through shared reading of picture books on various emotional themes, children learned more emotional vocabulary and engaged in deeper parent-child conversations beyond daily interactions. This increased children's opportunities for emotional expression and helped mothers better understand their children's emotional needs, reflect on their parenting skills, and foster parent-child interaction and communication. In conclusion, this study demonstrated the significant impact of engaging in dialogic parent-child picture book reading on emotional understanding in preschool children. Furthermore, it is believed to be a valuable parent-child interaction strategy for dual-income families in Macau, enhancing parent-child relationships.

As cities continue to grow at an unprecedented rate, the need for efficient land use becomes even more critical. Mixed Use Architecture is essential for modern cities because of its efficiency in using land that is prevalent in the rapid urbanization we are facing today. Mixed-use development offers numerous advantages for communities and serves as a crucial approach in attaining sustainable ecosystems (Woo and Cho, 2018). Land scarcity in Macau has made mixed-use architecture essential to city expansion. Mixed-use developments in Macau typically consist of combination of housing and commercial towers, casino and hotel complexes and office buildings. This has improved land use in the city. However, these developments, however, unappetizing living conditions resulting sick building syndrome to its residents. The research question identified and addressed in this study is: How can Mixed Use Architecture respond to the underlying problems Urbanisation has brought? Based on this question, this dissertation develops a new type prototype for mixed use architecture that to resolve the issues prevalent in the current mixed use architecture models using Macau as context for this prototype. This study challenges traditional architecture and uses innovative approaches stemming from biophilic strategies and industry leading green technologies that improve quality of life and the environment. This study proposes a novel solution to urbanization's problems such Sick Building Syndrome, high population density, and land scarcity. This dissertation suggests a space-efficient architecture to meet the needs of rising populations and land scarcity while prioritizing resident well-being.

In today's rapidly changing educational landscape, parents and children demand personalised, innovative, and engaging learning experiences beyond the standard curriculum. Modern education places a premium on igniting curiosity, encouraging critical thinking, and cultivating creativity. Collaborative learning between parents and children is critical for improving academic performance and strengthening relationships. This article discusses the "Yay Island" platform's game-based technique, which combines Mandarin Chinese and English in a multicultural and multilingual setting. Initial study shows that digital technologies dramatically increase children's motivation and interest when paired with peer and parent participation and game-based teaching, resulting in a pleasant learning environment. Further research demonstrates that collaborative and game-based learning and teaching improves motivation, respects individual differences, and strengthens parent-child connections. As a game, "Yay Island" uses integrated learning, child psychology concepts, modern educational methodologies, and user-centred design. It seeks to develop a collaborative learning environment and encourage growth in children, parents, and educators. The game emphasises knowledge, personal discovery, and innovation, laying the groundwork for children's growth and future competition. Through constant research and optimisation, "Yay Island" solves digital transformation concerns in education by providing personalised, efficient, and entertaining learning experiences.

Macau, as a densely populated city, has been facing a prominent challenge in fulfilling the rapidly growing demand for land due to its small geographical size. The limited availability of land exacerbates the conflict between urban development needs and land supply, leading to an imbalance in land resource allocation. This scarcity of land not only hinders the city's ability to meet its growing population's housing needs but also contributes to a lack of social space. The absence of adequate outdoor gathering areas and communal spaces has resulted in the fragmentation of communities and a fading sense of community in the society of Macau. As the urban environment undergoes relentless development, the social fabric of Macau is undergoing a transformation. Communities are becoming fragmented into smaller groups, and chance encounters and serendipitous social interactions are being neglected or excluded. Social events primarily take place within dedicated indoor spaces that cater to groups, further isolating different segments of the population due to the limited and subpar outdoor gathering space available. This dissertation aims to examine the possibility of developing a new housing prototype that could provoke connections between segregated communities. By introducing a combination of communal spaces and vertical circulation spaces, the design seeks to create opportunities for social interaction and the revitalisation of community bonds that could eventually fit into a hyperdense city. To achieve this, the dissertation will begin with a comprehensive site analysis of the site, considering both the physical constraints and the potential benefits for social space creation. Furthermore, the study will draw insights from relevant literature and architectural projects that have addressed relevant challenges, exploring effective strategies for fostering community engagement and creating inclusive social spaces. The design process will emphasise the integration of communal spaces that encourage spontaneous interactions and vertical circulation spaces that facilitate movement and connectivity between different community segments.

This project represents a comprehensive study of an interactive picture book employing augmented reality (AR) technology, focusing on the narratives of the A-Ma Temple and Nezha Temple in Macau. The target audience comprises children aged 6-9 years to enhance their concentration on aesthetic development and deepen their understanding of Macau's historical and cultural heritage. The study resulted in the creating of a picture book that integrates an interactive AR experience, resulting in highly satisfactory user feedback. The findings suggest the potential for further development of interactive picture books as a valuable medium for disseminating Macanese culture. Future efforts should prioritise continuous attention to user feedback and the AR technology's stability to ensure the work's long-term effectiveness and impact.

Traditional malls often suffer from isolated and inward-focused designs that disconnect them from their urban surroundings, hindering pedestrian integration and community engagement. This dissertation addresses these issues by exploring the concept of integrating streets and malls to create a more cohesive and vibrant urban environment. The research examines contemporary architectural approaches that emphasize seamless connections between exterior streets and mall interiors, blurring the lines to foster social interaction and enhance walkability. Central to this study is an architectural design project that illustrates the practical application of these concepts. Through a detailed analysis of urban design principles and case studies of successful integrated malls, this research investigates the potential benefits and challenges of such integration. The design project provides a tangible example of how thoughtful interventions can activate public spaces, promote pedestrian flow, and cultivate a strong sense of place. The research highlights best practices and achievements in the area of integrated mall designs, offering valuable insights for urban planners, architects, and stakeholders involved in mall and urban space development. Findings suggest that integrating streets and malls can lead to improved community engagement and create more vibrant urban destinations that meet the specific needs and aspirations of the local community. This study provides a comprehensive analysis of how these designs can enhance urban vitality, iii pedestrian activity, and social interaction. Future research could focus on additional case studies or pilot projects to further implement and evaluate this approach in different contexts, providing practical insights and refining the understanding of its benefits and challenges. By reimagining the traditional mall structure and its relationship with the urban environment, this dissertation contributes to the ongoing discourse on sustainable and inclusive urban development, proposing strategies that make urban retail spaces more responsive to contemporary needs. 傳統購物中心常常受到孤立和內向設計的困擾，這些設計使它們與城市環境脫節，阻礙了行人融入和社區參與。本論文透過探索整合街道和購物中心的概念來解決這些問題，以創造一個更有凝聚力和活力的城市環境。該研究探討了當代的建築方法，強調外部街道和購物中心內部之間的無縫連接，模糊界限以促進社會互動並增強步行能力。這項研究的核心是一個建築設計項目，它說明了這些概念的實際應用。透過對城市設計原則的詳細分析和成功綜合購物中心的案例研究，本研究探討了這種整合的潛在好處和挑戰。這個設計計畫提供了一個具體的例子，說明深思熟慮的干預措施如何激活公共空間、促進人流並培養強烈的地方感。該研究重點介紹了綜合購物中心設計領域的最佳實踐和成就，為參與購物中心和城市空間開發的城市規劃者、建築師和利益相關者提供了寶貴的見解。研究結果表明，整合街道和購物中心可以提高社區參與度，創造更有活力的城市目的地，滿足當地社區的特定需求和願望。這項研究對這些設計如何增強城市活力、行人活動和社會互動進行了全面分析。未來的研究可以專注於其他案例研究或試點項目，以在不同的背景下進一步實施和評估這種方法，提供實用的見解並加深對其好處和挑戰的理解。透過重新構想傳統的購物中心結構及其與城市環境的關係，本論文為可持續和包容性城市發展的持續討論做出了貢獻，提出了使城市零售空間更能滿足當代需求的策略。

<jats:p>Antibiotic pollution poses a serious environmental concern worldwide, posing risks to ecosystems and human well-being. Transforming waste activated sludge into adsorbents for antibiotic removal aligns with the concept of utilizing waste to treat waste. However, the adsorption efficiency of these adsorbents is currently limited. This study identified KOH modification as the most effective method for enhancing tetracycline (TC) adsorption by sludge biochar through a comparative analysis of acid, alkali, and oxidant modifications. The adsorption characteristics of TC upon unmodified sludge biochar (BC) as well as KOH-modified sludge biochar (BC-KOH) were investigated in terms of equilibrium, kinetics, and thermodynamics. BC-KOH exhibited higher porosity, greater specific surface area, and increased abundance of oxygen-based functional groups compared to BC. The TC adsorption on BC-KOH conformed the Elovich and Langmuir models, with a maximum adsorption capacity of 243.3 mg/g at 298 K. The adsorption mechanisms included ion exchange, hydrogen bonding, pore filling, and electrostatic adsorption, as well as π-π interactions. Interference with TC adsorption on BC-KOH was observed with HCO3−, PO43−, Ca2+, and Mg2+, whereas Cl−, NO3−, and SO42− ions exhibited minimal impact on the adsorption process. Following three cycles of utilization, there was a slight 5.94% reduction in the equilibrium adsorption capacity, yet the adsorption capacity remained 4.5 times greater than that of unmodified sludge BC, underscoring its significant potential for practical applications. This research provided new insights to the production and application of sludge biochar for treating antibiotic-contaminated wastewater.</jats:p>

<jats:p>In this study, sixteen Sprague Dawley (SD) female rats and eight SD male rats were co-housed to mate. Pregnant SD female rats were fed with a control diet or an MA diet. Breast milk, maternal ileum, and intestinal samples of the offspring were collected at the day of birth and ten days afterwards. The results showed that the impact of MA was more obvious on the microbiota of mature milk (p = 0.066) than on that of colostrum. In addition, MA additive did not significantly affect maternal ileal microbiota, but affected offsprings’ colonic microbiota significantly ten days after birth (p = 0.035). From the day of giving birth to ten days afterwards, in addition to the increase in microbial richness and diversity, at genus level, the dominant bacteria of breastmilk changed from Pseudomonas veronii to Bacillus and Lactococcus. Different from breastmilk microbiota, ten days after giving birth, the maternal ileal microbiota and the offsprings’ intestinal microbiota were dominated by Lactobacillus. Instead of ileal microbiota, offsprings’ colonic microbiota is a key action site of maternal MA additive. Therefore, the current findings have significant implications for the development of maternal feed aimed at modulating the intestinal microbiota of offspring, ultimately leading to improved health outcomes for both mothers and their offspring.</jats:p>

The stock market's inherent volatility and complexity pose significant challenges for investors seeking to optimize their strategies. This thesis addresses the critical need for improved forecasting methods in stock price prediction by proposing a hybrid approach that combines traditional machine learning (ML) techniques, specifically Support Vector Machines (SVM) and Long Short-Term Memory (LSTM) networks, with sentiment analysis derived from financial news and social media platforms. The research establishes a theoretical framework integrating quantitative data, such as historical stock prices, with qualitative sentiment data to enhance prediction accuracy. The study involves the collection of a comprehensive dataset covering stock prices and sentiment scores from various sources, including news articles and social media posts, from January 2010 to December 2023. Rigorous data preprocessing techniques, including normalization and feature engineering, are employed to prepare the data for analysis. A comparative analysis of the SVM and LSTM models uses multiple performance metrics, including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and classification accuracy. The findings reveal that the LSTM model significantly outperforms the SVM model in predictive accuracy, demonstrating its capability to capture complex temporal dependencies inherent in financial time series data. Furthermore, integrating sentiment analysis significantly enhances the predictive performance of both models. Notably, transformer-based sentiment analysis techniques, such as BERT and DistilBERT, provide superior sentiment classifications compared to traditional methods like VADER and TextBlob. The empirical results indicate that incorporating sentiment data leads to an average accuracy improvement of 12.8% over models that rely solely on historical price data. This research contributes to the evolving field of financial forecasting by emphasizing the importance of a hybrid approach that amalgamates quantitative and qualitative data. The implications of these findings extend beyond academic research, offering valuable insights for investors and financial analysts seeking to leverage advanced predictive models to navigate market uncertainties. Ultimately, this dissertation advocates adopting sophisticated hybrid models that enhance stock investment strategies and decision-making processes in the finance sector.

<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>

Predicting stock prices is difficult because of their multiple input variables, volatility, and unpredictable nature. To provide a suitable model for forecasting the global stock market, this study conducts an exploratory analysis comparing two models based on Artificial Intelligence: Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) Neural Networks. The work considers a publicly accessible dataset and uses feature engineering to extract time-series features. Stock price predictions are made using the SVM and LSTM algorithms. For this purpose, Accuracy (ACC) and Root Mean Squared Error (RMSE) are considered accuracy and performance measures. According to the results, LSTM with mean accuracy (ACC) = 0.9061 achieved better accuracy than SVM with mean accuracy (ACC) = 0.881. SVM with mean RMSE = 0.729 achieved better performance and the degree of fit to the data than LSTM with mean RMSE = 427.1. According to the results, the study demonstrates the effectiveness and applicability of machine learning methods for estimating the values of the global stock market and providing valuable models for researchers, analysts, and investors.

<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>

<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>