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Medical classification is affected by many factors, and the traditional medical classification is usually restricted by factors such as too long text, numerous categories and so on. In order to solve these problems, this paper uses word vector and word vector to mine the text deeply, considering the problem of scattered key features of medical text, introducing long-term and short-term memory network to effectively retain the features of historical information in long text sequence, and using the structure of CNN to extract local features of text, through attention mechanism to obtain key features, considering the problems of many diseases, by using hierarchical classification. To stratify the disease. Combined with the above ideas, a deep DLCF model suitable for long text and multi-classification is designed. This model has obvious advantages in CMDD and other datasets. Compared with the baseline models, this model is superior to the baseline model in accuracy, recall and other indicators.

Digital Factory (DF) planning is the key of intelligent factory construction, where intelligent production technologies of big data analysis, cloud computing, blockchain, Internet of Things, artificial intelligence, 5G, Time Sensitive Network (TSN), Digital Twin (DT), additive manufacturing are included. By applying the modern techniques, DF performs great advantages on the aspects of product lifecycle management, enterprise resource planning, operation management, supply chain management, real-time database construction, advanced process control, as well as the new technologies of distributed control system and fieldbus control system. This article delivers a review of key issues of DF top-level design and planning from the aspects of networking, precision, automation and digitalization. Solutions are explored based on 5G, TSN and DT advanced technologies, literately and practically. Additionally, the article describes the method and application of efficient big data comprehensive solution. Therefore, this study contributes valuable decision-making support for DF applications.

Environmental education (EE) has long been practiced worldwide, while Nature-based solutions (NBS) is a relatively new concept. This chapter aims to provide an overview of the EE and NBS practices in East Asia and evaluate how these two valuable applications can be used concurrently. East Asia has a well developed environmental education (EE) programs and activities, both in formal and informal education. These ranges from developing green schools and campuses to establishing policies and acts. While EE has been actively practiced for decades in the region, the adoption of NBS to address environmental and societal challenges is limited. The educational benefits and opportunities from NBS are also lacking. Although there are some projects that can be classified as NBS, like the use of wetlands for wastewater treatment, they are not clearly categorized as one. These projects are also not integrated into environmental education programs. Considering this, the region should develop innovative environmental education programs for schools, universities and communities, that integrate NBS projects. Integrating the two together will boost the effectiveness of environmental education in raising environmental awareness and changing the environmental attitude and behavior of people, which will also help address societal issues.

- Flipped classroom metodologiak eskaintzen dituen abantaila pedagogikoak (Ander Goikoetxea Pérez, Hannot Mintegia Beaskoa). - Conocimiento de la actualidad informativa a partir de la participación del alumnado que ejerce de periodista y lector crítico (María del Mar Rodríguez González, Iñigo Marauri Castillo, Guillermo Gurrutxaga Rekongo). - El papel del procesamiento dual de la información en la discriminación de noticias falsas (José Manuel Meza Cano, Cinthia Aranda-Solís, Blanca Olalde López de Arechavaleta, Santiago Palacios Navarro). - No trespassing: Arau, traba eta mugen bidezko metodologia sortzaile bat kazetaritza gradurako (Hannot Mintegia Beaskoa, Ander Goikoetxea Pérez). - Learning digital journalism: Analysing web media in comparative perspective to learn what is quality in digital communication (Javier Díaz-Noci). - El sistema híbrido vehículo de comunicación educativa (Antonio Vaquerizo Mariscal). - The pedagogical role of ethics and deontology for future professionals of communication and media: How to develop and nourish virtues (José Manuel Simões). - Desgaitasuna duten pertsonen Komunikazio ikasketetako prestakuntzari buruzko gogoetak (Terese Mendiguren, Jesús Ángel Pérez Dasilva, Koldobika Meso Ayerdi, Simón Peña, Ainara Larrondo, María Ganzabal). - Teaching Communication (and Journalism) History from Social History Theory: Some proposals (Javier Díaz-Noci).

This chapter presents a systematic review of research on human resources management (HRM) and employee relations (ER) in Angola to identify the main challenges and opportunities presented. To achieve that goal, this chapter characterises research conducted in the country, investigates its main findings, and proposes some directions for the future. Based on a bibliographic search in the EBSCO Discovery database of empirical articles about HRM and ER in Angola, we collected a sample of 28 studies published between 2009 and 2022. Most studies have focused on the development and retention of human resources. Other topics included diversity management, workplace attitudes and behaviours, scale validations, leadership and decision-making, performance appraisal, quality assessment, corporate social responsibility, and expatriates. We identified three main challenges and opportunities in HRM and ER in Angola. First, the policies and the planning, implementing, and evaluating processes of human resources development and retention strategies should be improved. Second, effective leadership and participation should be promoted while navigating the tensions between autocratic and participative leadership styles. Finally, positive ER and employee well-being should be promoted. Understanding these challenges and opportunities may contribute to the development of human capital in Angola and, ultimately, the country’s socioeconomic development.

In 2020, the World Health Organization declared the Coronavirus Disease 19 a global pandemic. While detecting COVID-19 is essential in controlling the disease, prognosis prediction is crucial in reducing disease complications and patient mortality. For that, standard protocols consider adopting medical imaging tools to analyze cases of pneumonia and complications. Nevertheless, some patients develop different symptoms and/or cannot be moved to a CT-Scan room. In other cases, the devices are not available. The adoption of ambulatory monitoring examinations, such as Electrocardiography (ECG), can be considered a viable tool to address the patient’s cardiovascular condition and to act as a predictor for future disease outcomes. In this investigation, ten non-linear features (Energy, Approximate Entropy, Logarithmic Entropy, Shannon Entropy, Hurst Exponent, Lyapunov Exponent, Higuchi Fractal Dimension, Katz Fractal Dimension, Correlation Dimension and Detrended Fluctuation Analysis) extracted from 2 ECG signals (collected from 2 different patient’s positions). Windows of 1 second segments in 6 ways of windowing signal analysis crops were evaluated employing statistical analysis. Three categories of outcomes are considered for the patient status: Low, Moderate, and Severe, and four combinations for classification scenarios are tested:  (Low vs. Moderate, Low vs. Severe, Moderate vs. Severe) and 1 Multi-class comparison  (All vs. All)). The results indicate that some statistically significant parameter distributions were found for all comparisons.  (Low vs. Moderate—Approximate Entropy p-value = 0.0067 < 0.05, Low vs. Severe—Correlation Dimension p-value = 0.0087 < 0.05, Moderate vs. Severe—Correlation Dimension p-value = 0.0029 < 0.05, All vs. All—Correlation Dimension p-value = 0.0185 < 0.05. The non-linear analysis of the time-frequency representation of the ECG signal can be considered a promising tool for describing and distinguishing the COVID-19 severity activity along its different stages.

The continuous development of robust machine learning algorithms in recent years has helped to improve the solutions of many studies in many fields of medicine, rapid diagnosis and detection of high-risk patients with poor prognosis as the coronavirus disease 2019 (COVID-19) spreads globally, and also early prevention of patients and optimization of medical resources. Here, we propose a fully automated machine learning system to classify the severity of COVID-19 from electrocardiogram (ECG) signals. We retrospectively collected 100 5-minute ECGs from 50 patients in two different positions, upright and supine. We processed the surface ECG to obtain QRS complexes and HRV indices for RR series, including a total of 43 features. We compared 19 machine learning classification algorithms that yielded different approaches explained in a methodology session.

This chapter describes how intellectual capital comprising human capital, structural capital, and relational capital are being created for school development and quality assurance in Macau. Macau has aimed to catch up with the global education reform by subsidising majorities of the non-tertiary sectors and promulgating Decree Laws regarding education policies and development. Despite the significance of the intangible assets of the intellectual capital, the chapter also attempts to analyse the issues and challenges towards the management of intellectual capital emerging simultaneously in the transition process in the educational context of Macau. It suggests capitalising on the accumulated school knowledge for school effectiveness. This chapter depicts the chronological development of Macau's education reform by analysing how Macau has attempted to emancipate its education institutions from the period of quasi-closed system to that of the open system by creating different types of intellectual capital in school. It discusses the emerging issues and challenges simultaneously in the transition process of educational development in Macau, namely before and after returning its sovereignty to the Chinese government.

Toda a obra e pensamento do Padre Manuel Antunes se revestem de características de grande abrangência e de capacidade de abertura à inovação, na perspetiva de que o pensamento crítico, sendo perscrutador do desconhecido, enquanto questiona o conhecimento adquirido ou em pesquisa, não se pode fechar em si mesmo ou separar partes do conhecimento de um todo que constitui o universo, e o homem como parte deste, já que se objetiva a compreensão última do Todo. Encontramos, portanto, traços de transdisciplinaridade na obra e pensamento do Padre Manuel Antunes indicando um pioneirismo relativamente ao movimento da transdisciplinaridade que arranca com o primeiro congresso da área e a respetiva carta daí resultante. Neste artigo, os autores propõem uma análise crítica da obra do Padre Manuel Antunes à luz dos princípios fundacionais encontrados na Carta da Transdisciplinaridade de 1994.

Even with more than 12 billion vaccine doses administered globally, the Covid-19 pandemic has caused several global economic, social, environmental, and healthcare impacts. Computer Aided Diagnostic (CAD) systems can serve as a complementary method to aid doctors in identifying regions of interest in images and help detect diseases. In addition, these systems can help doctors analyze the status of the disease and check for their progress or regression. To analyze the viability of using CNNs for differentiating Covid-19 CT positive images from Covid-19 CT negative images, we used a dataset collected by Union Hospital (HUST-UH) and Liyuan Hospital (HUST-LH) and made available at the Kaggle platform. The main objective of this chapter is to present results from applying two state-of-the-art CNNs on a Covid-19 CT Scan images database to evaluate the possibility of differentiating images with imaging features associated with Covid-19 pneumonia from images with imaging features irrelevant to Covid-19 pneumonia. Two pre-trained neural networks, ResNet50 and MobileNet, were fine-tuned for the datasets under analysis. Both CNNs obtained promising results, with the ResNet50 network achieving a Precision of 0.97, a Recall of 0.96, an F1-score of 0.96, and 39 false negatives. The MobileNet classifier obtained a Precision of 0.94, a Recall of 0.94, an F1-score of 0.94, and a total of 20 false negatives.