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The Mesozoic gold deposits in the North China Craton (NCC) were hosted by the Precambrian basement and Mesozoic intrusions. Thus, most researchers consider that these gold deposits were genetically linked to the Mesozoic intrusions. However, we suggest that a metamorphic devolatilization model provides an alternative based on a combined Fe and in-situ S isotopes study on auriferous pyrites from the Baiyun gold deposit in the NCC. The Triassic Baiyun gold deposit contains the quartz vein and altered rock ores that were developed in the Paleoproterozoic metavolcanic-sedimentary rocks (the Liaohe Group). Our in-situ S isotopic analyses show that pyrites from the quartz vein ores are characterized by negative δ34S values (-10.7 ∼ -5.5‰), while those from the altered rock ores have two distinct groups of δ34S values, one being positive (+13.5 ∼ +16.2‰) and the other negative (-10.6 ∼ -3.0‰). We suggest that pyrite grains with positive δ34S values should be relicts from the host rocks, because they show comparable δ34S values with those from the host rocks schists (+3.3 ∼ +16.1‰). Thus, only the negative δ34S values of pyrites in ores (-10.7 ∼ -3.0‰) and the Fe isotopes of the quartz vein ores (δ56Fe = +0.30 ∼ +0.48‰) can represent the isotopic characteristics of ore-forming fluids at Baiyun. Our study shows that the sulfur were probably from the pyritic volcanic-sedimentary sequences of the Liaohe Group, rather than from magmas. The calculated δ56Fe values of the ore-forming fluids (-0.78 ∼ -0.37‰; pyrite-fluid isotope fractionation) could be modelled in a metamorphic devolatilization model with Fe-species (pyrite&magnetite) of the Liaohe Group as sources. Therefore, our combined S- and Fe- isotope data indicate that the metamorphic devolatilization of the Liaohe Group could account for the genesis of the Baiyun gold deposit.

Text classification is an important topic in natural language processing, with the development of social network, many question-and-answer pairs regarding health-care and medicine flood social platforms. It is of great social value to mine and classify medical text and provide targeted medical services for patients. The existing algorithms of text classification can deal with simple semantic text, especially in the field of Chinese medical text, the text structure is complex and includes a large number of medical nomenclature and professional terms, which are difficult for patients to understand. We propose a Chinese medical text classification model using a BERT-based Chinese text encoder by N-gram representations (ZEN) and capsule network, which represent feature uses the ZEN model and extract the features by capsule network, we also design a N-gram medical dictionary to enhance medical text representation and feature extraction. The experimental results show that the precision, recall and F1-score of our model are improved by 10.25%, 11.13% and 12.29%, respectively, compared with the baseline models in average, which proves that our model has better performance.

This research focuses on Nike, Inc., an American multinational corporation and global leader in the design, marketing, and sale of athletic footwear, apparel, and equipment. The study executes a financial valuation of Nike with the objective of estimating its intrinsic value and comparing it with the market value as of May 31st, 2023. The analysis combines a review of the company's overall strategic positioning in the industry and its historical financial performance. The methodology involved the reorganization of financial statements, the calculation of key financial metrics (NOPLAT, FCF, ROIC), the estimation of the cost of capital (WACC) using CAPM, and the application of valuation models based on discounted cash flows (Enterprise DCF, APV, DEP). The results imply that, as of May 31st, 2023, Nike's estimated intrinsic value was between $54.94 and $59.23 per share. Comparing this to the market price of $105.26 on the same date, the analysis concludes that Nike shares were significantly overvalued. The sensitivity analysis highlighted the vulnerability of the valuation to changes in the market risk premium and the perpetual growth rate. It is concluded that, despite Nike's strong market position, effective management of external risks, and maintenance of financial discipline are crucial to its future value.

Ford Motor Company (Ford) is an American car manufacturer and one of the leading automobile manufacturers with over a century of history in the auto industry. The Company is headquartered in the United States, in Dearborn, Michigan. However, it has operations in over 125 countries around the world, including Europe, Africa, Asia, and South America, offering a wide range of vehicles under the Ford and Lincoln brands. The Company has demonstrated resilience and adaptability in response to shifts according to consumer preferences, technology advancements, and government regulations. The company has been investing constantly to restructure and position itself and remain competitive. Therefore, finding strategies to boost sales and get high returns from invested capital is a must for the company to keep its market share, even though this is still a challenge due to the nature of the automotive industry, which brings intense competition from both traditional and new entrants, particularly EV manufacturers. In this study the focus is not only to estimate the intrinsic value of Ford Motor Company as of 31, December 2023, but also to analyze essential aspects of the company including SWOT, PESTEL, and Porter’s five forces analysis to get a framework of internal and external environment of the company which enables us to identify strategic opportunities, competitive advantage, vulnerabilities, and threats of the Company. After understanding the dynamics of the company and the economic overview, which are crucial to predict the impact of key assumptions when evaluating the company’s intrinsic value. For the second part of our study, the attention goes to the financial analysis of both historical and forecasted financial statements that are extremely important to apply for the Discounted Cash Flow valuation methods and later to perform a sensitivity analysis to understand the Company’s financial performance. Therefore, Ford Motor Company’s intrinsic value from the three discounted cash flow models leads us to conclude that the Ford stock’s price is currently undervalued, and it is expected to grow in the future.

Macula fovea detection is a crucial prerequisite towards screening and diagnosing macular diseases. Without early detection and proper treatment, any abnormality involving the macula may lead to blindness. However, with the ophthalmologist shortage and time-consuming artificial evaluation, neither accuracy nor effectiveness of the diagnose process could be guaranteed. In this project, we proposed a deep learning approach on ultra-widefield fundus (UWF) images for macula fovea detection. This study collected 2300 ultra-widefield fundus images from Shenzhen Aier Eye Hospital in China. Methods based on U-shape network (Unet) and Fully Convolutional Networks (FCN) are implemented on 1800 (before amplifying process) training fundus images, 400 (before amplifying process) validation images and 100 test images. Three professional ophthalmologists were invited to mark the fovea. A method from the anatomy perspective is investigated. This approach is derived from the spatial relationship between macula fovea and optic disc center in UWF. A set of parameters of this method is set based on the experience of ophthalmologists and verified to be effective. Results are measured by calculating the Euclidean distance between proposed approaches and the accurate grounded standard, which is detected by Ultra-widefield swept-source optical coherence tomograph (UWF-OCT) approach. Through a comparation of proposed methods, we conclude that, deep learning approach of Unet outperformed other methods on macula fovea detection tasks, by which outcomes obtained are comparable to grounded standard method.

Macau, Macau Business, MAG, MB, MB Featured, Opinion | The late psychologist, business and management consultant Edward de Bono gained a worldwide reputation for ‘lateral thinking’, which included his ‘six thinking hats’ and ‘tools for thinking’. Though his work is arguably only plausible pseudoscience, his ‘tools for thinking’ remain interesting. Consider some of these from his Cognitive Research Trust (CoRT), in approaching planning, e.g.: CAF (Consider All Factors); EBS (Examine Both Sides); and OPV (consider Other People’s Views). Here I apply them to Macau.

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.

Convolutional neural network (CNN) model based on deep learning has excellent performance for target detection. However, the detection effect is poor when the object is circular or tubular because most of the existing object detection methods are based on the traditional rectangular box to detect and recognize objects. To solve the problem, we propose the circular representation structure and RepVGG module on the basis of CenterNet and expand the network prediction structure, thus proposing a high-precision and high-efficiency lightweight circular object detection method RebarDet. Specifically, circular tubular type objects will be optimized by replacing the traditional rectangular box with a circular box. Second, we improve the resolution of the network feature map and the upper limit of the number of objects detected in a single detect to achieve the expansion of the network prediction structure, optimized for the dense phenomenon that often occurs in circular tubular objects. Finally, the multibranch topology of RepVGG is introduced to sum the feature information extracted by different convolution modules, which improves the ability of the convolution module to extract information. We conducted extensive experiments on rebar datasets and used AB-Score as a new evaluation method to evaluate RebarDet. The experimental results show that RebarDet can achieve a detection accuracy of up to 0.8114 and a model inference speed of 6.9 fps while maintaining a moderate amount of parameters, which is superior to other mainstream object detection models and verifies the effectiveness of our proposed method. At the same time, RebarDet’s high precision detection of round tubular objects facilitates enterprise intelligent manufacturing processes.

Traditional text classification models have some drawbacks, such as the inability of the model to focus on important parts of the text contextual information in text processing. To solve this problem, we fuse the long and short-term memory network BiGRU with a convolutional neural network to receive text sequence input to reduce the dimensionality of the input sequence and to reduce the loss of text features based on the length and context dependency of the input text sequence. Considering the extraction of important features of the text, we choose the long and short-term memory network BiLSTM to capture the main features of the text and thus reduce the loss of features. Finally, we propose a BiGRU-CNN-BiLSTM model (DCRC model) based on CNN, GRU and LSTM, which is trained and validated on the THUCNews and Toutiao News datasets. The model outperformed the traditional model in terms of accuracy, recall and F1 score after experimental comparison.

Artists are increasingly using blockchain as a tool for trading digital artwork as non-fungible tokens (NFTs); however, some are also beginning to experiment with the blockchain as a medium for generative art, using it as a seed for a generative process or to continuously modify an evolving piece. This paper surveys, reviews, and classifies the state-of-the-art in blockchain-interactive NFTs and presents a liberal-arts critique of the opportunities and threats posed by this technology, whilst addressing existing criticism on the broader topic of art-related NFTs. The paper examines some of the most experimental pieces minted on the Hic et Nunc (HEN) and Teia NFT marketplaces, for which a purpose-built research tool was developed. The survey reveals some reliance on centralised infrastructure, namely blockchain indexers, placing undesired trust on third parties which undermines the potential longevity of the artwork. The paper concludes with recommendations for artists and NFT platform designers for developing more resilient and economically sustainable architectures.

This contribution to the special issue is an historical account of Paulo Freire’s pedagogical and administrative praxis before his forced exile in 1964. It relies on interviews collected during a field trip in 1976, a conversation with Paulo Freire in Geneva one year later and on the secondary literature up to date. Being the head of the first Extension Service of a major Brazilian university in the early 1960s gave Freire and his collaborators the space and time to experiment with the today world famous literacy method bearing his name. The concept of ‘Field of Cultural Production’ (Bourdieu) is used to elucidate better Freire and his team’s avant-gardist production within the spaces opened up by Brazil’s popular movements in the early sixties. The contribution shows how the ‘Paulo Freire System’ developed in the praxis of a cultural movement and received its academic consecration in an incremental and eclectic style.

Sentiment analysis technologies have a strong impact on financial markets. In recent years there has been increasing interest in analyzing the sentiment of investors. The objective of this paper is to evaluate the current state of the art and synthesize the published literature related to the financial sentiment analysis, especially in investor sentiment for prediction of stock price. Starting from this overview the paper provides answers to the questions about how and to what extent research on investor sentiment analysis and stock price trend forecasting in the financial markets has developed and which tools are used for these purposes remains largely unexplored. This paper represents the comprehensive literature-based study on the fields of the investors sentiment analytics and machine learning applied to analyzing the sentiment of investors and its influencing stock market and predicting stock price.

<jats:p>This study aims to understand how companies address and integrate sustainability challenges in packaging design, as well as the motivations and processes that influence managers’ decisions when adopting sustainable practices. Semi-structured interviews were conducted with managers from five major Portuguese companies to gather qualitative data on the motivations and processes related to sustainable packaging strategies and actions. The list of questions was developed based on the literature review, from which the dimensions to be analyzed were identified. The results indicate that several factors influence companies’ decisions regarding sustainability in packaging. Despite some factors being beyond the control of companies, the interviews reveal that companies possess the necessary knowledge and are committed to adopting more sustainable packaging.</jats:p>

"Semantic studies of the Biblical Hebrew verb "whole" have been influenced by those of its most invoked nominal form "whole". In this volume Andrew Chin Hei Leong shows that the concepts of balance, alliance, and completeness form the basic semantic structure of "whole". Previous studies on "whole" employed either historical or textual methodology, which has been dominant in biblical lexical studies. In addition to these methods, in Leong develops a systematic semantic methodology from Cognitive Semantics and Frame Semantics, to demonstrate that it is balance, rather than completeness, that is the most central concept in holding the semantic network together"--

Macau, Macau Business, MAG, MB, MB Featured, Opinion | When the casino resorts applied for the first licenses to operate in Macau, one of the commitments that they made was to serve the Macau society. Many of them have honoured those commitments outstandingly well, and continue to do so, and in ways too many and diverse to list here.