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The on-board identification of ore minerals during a cruise is often postponed until long after the cruise is over. During the M127 cruise, 21 cores with deep-seafloor sediments were recovered in the Trans-Atlantic Geotraverse (TAG) field along the Mid Atlantic Ridge (MAR). Sediments were analyzed on-board for physicochemical properties such as lightness (L*), pH and Eh. Selected samples were studied for mineral composition by X-ray powder diffraction (XRD). Based on XRD data, sediment samples were separated into high-, low- and non-carbonated. Removal of carbonates is a common technique in mineralogical studies in which HCl is used as the extraction agent. In the present study, sequential extraction was performed with sodium acetate buffer (pH 5.0) to remove carbonates. The ratio between the highest calcite XRD reflection in the original samples (Iorig) vs its XRD-reflection in samples after their treatment with the buffer (Itreat) was used as a quantitative parameter of calcite removal, as well as to identify minor minerals in carbonated samples (when Iorig/Itreat > 24). It was found that the lightness parameter (L*) showed a positive correlation with calcite XRD reflection in selected TAG samples, and this could be applied to the preliminary on-board determination of extraction steps with acetate buffer (pH 5.0) in carbonated sediment samples. The most abundant minerals detected in carbonated samples were quartz and Al- and Fe-rich clays. Other silicates were also detected (e.g., calcic plagioclase, montmorillonite, nontronite). In non-carbonated samples, Fe oxides and hydroxides (goethite and hematite, respectively) were detected. Pyrite was the dominant hydrothermal mineral and Cu sulfides (chalcopyrite, covellite) and hydrothermal Mn oxides (birnessite and todorokite) were mineral phases identified in few samples, whereas paratacamite was detected in the top 20 cm of the core. The present study demonstrates that portable XRD analysis makes it possible to characterize mineralogy at cored sites, in particular in both low- and high-carbonated samples, before the end of most cruises, thus enabling the quick modification of exploration strategies in light of new information as it becomes available in near-real time.

The Covid-19 pandemic evidenced the need Computer Aided Diagnostic Systems to analyze medical images, such as CT and MRI scans and X-rays, to assist specialists in disease diagnosis. CAD systems have been shown to be effective at detecting COVID-19 in chest X-ray and CT images, with some studies reporting high levels of accuracy and sensitivity. Moreover, it can also detect some diseases in patients who may not have symptoms, preventing the spread of the virus. There are some types of CAD systems, such as Machine and Deep Learning-based and Transfer learning-based. This chapter proposes a pipeline for feature extraction and classification of Covid-19 in X-ray images using transfer learning for feature extraction with VGG-16 CNN and machine learning classifiers. Five classifiers were evaluated: Accuracy, Specificity, Sensitivity, Geometric mean, and Area under the curve. The SVM Classifier presented the best performance metrics for Covid-19 classification, achieving 90% accuracy, 97.5% of Specificity, 82.5% of Sensitivity, 89.6% of Geometric mean, and 90% for the AUC metric. On the other hand, the Nearest Centroid (NC) classifier presented poor sensitivity and geometric mean results, achieving 33.9% and 54.07%, respectively.

The following study reflects and explores the dynamics of aesthetic experiences within drama improvisations. This arts-based research was carried out in Hong Kong with six Cantonese children who were aged 3?5?years. Data were collected from the video transcripts of five workshops and the researcher?s own research journal. Two significant milieus were observed: switching in-between roles and intuitive creativity is not talkback. I argue that because each of these two milieus provide the foreground for the complex ? and at times contradictory ? nature of children?s aesthetic experiences where Deleuzian power is at play, opportunities arise for both, challenging the traditional adult?child power relations, and in so doing, educators can be able to reconfigure and reconceptualise teaching goals and practices, both generally and specifically, within the context of early childhood education.

Macau, Macau Business, MAG, MB, MB Featured, Opinion | Business 101. When companies plan their business models, typically they must consider, amongst other matters: their products and/or services; demand; market(s) and their environments (economic, political, cultural, social); existing, potential, and likely competition; start-up and ongoing costs and expenses; emergency funding; marketing and promotion; income streams, revenue, and delayed profit; sales; distribution; cash flow; profit margins, gross and net; liabilities; risk analysis, evaluation, and handling; constraints and the ‘what if’ factor; flexibility, adaptability, and adjustment.

Dennis Zuev* The Belt and Road Initiative (BRI) has become the largest infrastructure program in history, and has become a symbol of growing significance of China and its power.

The global food industry generates substantial waste, posing significant environmental, economic, and social challenges. This dissertation explores circular business strategies for food waste management, aiming to develop an efficient model that integrates circular economy principles and innovative technologies. Key research questions include: What are current food waste management practices? How can circular economy principles reduce food waste effectively? What role can technology play in improving these systems? The study also examines barriers to implementation and identifies gaps in existing literature. The methodology involves a comprehensive literature review, case studies, and the development of a detailed mathematical model. The literature review covers circular economy concepts, current food waste treatment technologies, machine learning and Al applications in waste management. Case studies from various countries provide insights into regulatory frameworks and innovative solutions. Central to this research is the mathematical modelling of food waste management systems. The model employs Hamiltonian and/or Lagrangian formulations to optimise waste transportation and processing. This approach allows for the simulation of various scenarios, helping to identify the most efficient pathways for food waste reduction and resource recovery. The model also incorporates phase transitions better to understand the dynamics of waste generation and treatment processes. Phase transitions mark changes on tendencies and in this case they help us to evaluate the viability of the construction of a fast track for the transportation of food waste in any city. Results indicate that adopting circular economy principles in food waste management is feasible and beneficial. Effective strategies include bioplastics, insectutilisation, and machine learning models for waste prediction and management. The developed mathematical model suggests efficient waste transportation through a coupled network approach, ensuring rapid and effective waste evacuation. The research highlights the importance of technological integration and cross-sector collaboration for sustainable food waste management. It also stresses the need for robust regulatory frameworks and consumer education to drive behavioural changes and support circular practices

The world we live in today is constantly changing, with new technologies and innovations being introduced all the time. Through the continuation and innovation of Chinese medicine theories, scientific research methods, and progress, Traditional Chinese Medicine continues to evolve and develop more innovative Modern Chinese Medicine. In this research, Molecular Chinese Medicine is studied as a new form of Chinese medicine, intended to provide a more pleasant and safe experience for consumers. Thus, the objective of this study is to identify both positive and negative perceptions that contribute to the adoption of innovative technologies and their related products. In order to reach this goal, a robust value-based theoretical model is used. To examine behavioural intentions to adopt Molecular Chinese Medicine (MCM) in Macau, this study combines quantitative and qualitative methods. Thus, a Value-based Acceptance Model (VAM) was used to determine Macau citizens' attitudes towards Molecular Chinese Medicine, including their perceptions of its usefulness, enjoyment, technicality, and perceived fee, in relation to the perceived value of the product, which may ultimately determine adoption intentions. Structural Equation Modeling (SEM) was used to process a sample of 194 Macau residents. The data analysis supported our model's explanatory and predictive power and helped describe the characteristics of the local population. Our results provide insight into the acceptance of innovative products that may be useful in designing more accurate strategies and facilitating the introduction of MCM to Macau and similar markets. It was found that low perceived fees demonstrated a relatively strong positive correlation with potential users' perceived value, whereas usefulness and enjoyment showed a medium-strong positive correlation. Also, a strong positive correlation was found between potential consumers' perception of the value of Molecular Chinese Medicine and their intention to adopt it

Validation of the Teachers’ Sense of Efficacy Scale (TSES) for use with teachers in Macao (SAR) was undertaken to determine its usefulness as a measure of teacher self-efficacy for inclusive education. This paper discusses the results found by analyzing various versions of the TSES and TSES-C in a Chinese format with 200 pre-service teachers in Macao (SAR).  Psychometric analyses were undertaken to investigate the validity of the existing scales and the three and two factor solutions. The results indicated a preferred 9-item version that produced improved factor loadings and reliabilities. The use of a relatively quick and short scale to measure such a complex phenomenon as teacher self-efficacy is discussed. Issues are raised regarding generalizability of scales and the impact of culture, demographics, and edifying issues that may impact on the usefulness of such scales.

Despite the levels of air pollution in Macao continuing to improve over recent years, there are still days with high-pollution episodes that cause great health concerns to the local community. Therefore, it is very important to accurately forecast air quality in Macao. Machine learning methods such as random forest (RF), gradient boosting (GB), support vector regression (SVR), and multiple linear regression (MLR) were applied to predict the levels of particulate matter (PM10 and PM2.5) concentrations in Macao. The forecast models were built and trained using the meteorological and air quality data from 2013 to 2018, and the air quality data from 2019 to 2021 were used for validation. Our results show that there is no significant difference between the performance of the four methods in predicting the air quality data for 2019 (before the COVID-19 pandemic) and 2021 (the new normal period). However, RF performed significantly better than the other methods for 2020 (amid the pandemic) with a higher coefficient of determination (R2) and lower RMSE, MAE, and BIAS. The reduced performance of the statistical MLR and other ML models was presumably due to the unprecedented low levels of PM10 and PM2.5 concentrations in 2020. Therefore, this study suggests that RF is the most reliable prediction method for pollutant concentrations, especially in the event of drastic air quality changes due to unexpected circumstances, such as a lockdown caused by a widespread infectious disease.