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Monitoring signals such as fetal heart rate (FHR) are important indicators of fetal well-being. Computer-assisted analysis of FHR patterns has been successfully used as a decision support tool. However, the absence of a gold standard for the building blocks decision-making in the systems design process impairs the development of new solutions. Here we propose a prognostic model based on advanced signal processing techniques and machine learning algorithms for the fetal state assessment within a comprehensive evaluation process. Feature-engineering-based and time-series-based machine learning classifiers were modeled into three data segmentation schemas for CTU-UHB, HUFA, and DB-TRIUM datasets and the generalization performance was assessed by a two-way cross-dataset evaluation. It has been shown that the feature-based algorithms outperformed the time-series ones on data-limited scenarios. The Support Vector Machines (SVM) obtained the best results on the datasets individually: specificity (85.6% ) and sensitivity (67.5%). On the other hand, the most effective generalization results were achieved by the Multi-layer perceptron (MLP) with a specificity of 71.6% and sensitivity of 61.7%. The overall process provided a combination of techniques and methods that increased the final prognostic model performance, achieving relevant results and requiring a smaller amount of data when compared to the state-of-the-art fetal status assessment solutions.

The place of theology is under threat in the modern university. It is denied a place, except insofar as it is useful in the training of religious professionals or as a phenomenon in its own right, on the grounds that relate to an unscientific scientism that both makes metaphysical assumptions it itself does not recognise as scientific or denies its own epistemological commitments. This article argues that the notion of education in ‘liberal knowledge’ or ‘universal knowledge’, the idea at the heart of John Henry Newman’s The Idea of a University provides a sufficiently robust counter to these assaults on the place of theology proper in the modern university and that refusing such a place to it undermines the claim of universities to use the name at all. It is precisely the uselessness of theology that guarantees its place in the university committed to universal knowledge and universal enquiry.

This essay presents a mapping of the historical concepts that contributed to the emergence of post-digital aesthetics and their connections to the concept of post-media in historical terms. It also analyzes the transition from techno-positivism to discourse of resistance against the effects of the capital technological industrial complex and how these advances in technology influence artistic discourses, practices and are the leverage of art and technology which is nothing more than a representation of the aesthetics of capital. Following art and capitalism as an ideology of innovation. Is proposed an unstinting theory about technology, geology, and the importance of these conditions to the post-digital aesthetics in terms of material disponible and conceptual articulation. Producing a reconfiguration of the post-digital conceptual approach as I propose beyond the dysfunctional aesthetics and connected with the concept of radical ecology centered in the usability of electronic garbage and technical obsolescent technologies in the arts.

The occurrence of endocrine disrupting chemicals (EDCs) is a major issue for marine and coastal environments in the proximity of urban areas. The occurrence of EDCs in the Pearl River Delta region is well documented but specific data related to Macao is unavailable. The levels of bisphenol-A (BPA), estrone (E1), 17α-estradiol (αE2), 17β-estradiol (E2), estriol (E3), and 17α-ethynylestradiol (EE2) were measured in sediment samples collected along the coastline of Macao. BPA was found in all 45 collected samples with lower BPA concentrations associated to the presence of mangrove trees. Biodegradation assays were performed to evaluate the capacity of the microbial communities of the surveyed ecosystems to degrade BPA and its analogue BPS. Using sediments collected at a WWTP discharge point as inoculum, at a concentration of 2 mg l−1 complete removal of BPA was observed within 6 days, whereas for the same concentration BPS removal was of 95% after 10 days, which is particularly interesting since this compound is considered recalcitrant to biodegradation and likely to accumulate in the environment. Supplementation with BPA improved the degradation of bisphenol-S (BPS). Aiming at the isolation of EDCs-degrading bacteria, enrichments were established with sediments supplied with BPA, BPS, E2 and EE2, which led to the isolation of a bacterial strain, identified as Rhodoccoccus sp. ED55, able to degrade the four compounds at different extents. The isolated strain represents a valuable candidate for bioremediation of contaminated soils and waters.

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.

Robotics are being used in the intervention with children with Autism Spectrum Disorder (ASD) in many places and already for many years. Many robots were developed and different studies are being made in order to evaluate its effectiveness. “Socially Assistive Robotics” is shown to be effective in different areas mainly in social and emotional development. Milo, a robot developed by a team led by Richard Margolin for the Robots4Autism program (RoboKind, 2020), is one of the robots whose use is reported to be successful. In Macao there is no report of studies or experiences on the use of robots in the intervention with children with ASD. In a collaboration between the Macao Science Centre, the Macao Autism Association (MAA) and the University of Saint Joseph, an exploratory study was developed to understand the applicability of Milo to the work with children with ASD in Macao. The study showed that the robot is able to facilitate social and emotional competences of children with ASD. However, several limitations including language, cultural differences, the inexperienced facilitators and the level of sessions are too simple for the participants to be aware of that may affect the effectiveness of the intervention. It is important to show that the adoption of Milo in Macao for intervening children with ASD can be further implemented, with better practical solutions.

To solve the problem of one-sided pursuit of the shortest distance but ignoring the tourist experience in the process of tourism route planning, an improved ant colony optimization algorithm is proposed for tourism route planning. Contextual information of scenic spots significantly effect people’s choice of tourism destination, so the pheromone update strategy is combined with the contextual information such as weather and comfort degree of the scenic spot in the process of searching the global optimal route, so that the pheromone update tends to the path suitable for tourists. At the same time, in order to avoid falling into local optimization, the sub-path support degree is introduced. The experimental results show that the optimized tourism route has greatly improved the tourist experience, the route distance is shortened by 20.5% and the convergence speed is increased by 21.2% compared with the basic algorithm, which proves that the improved algorithm is notably effective.

In this essay, we respond to Dustin Crummett’s argument that one cannot consistently appeal to body count reasoning to justify being a single-issue pro-life voter if one is also committed to the usual response to the embryo rescue case. Specifically, we argue that a modified version of BCR we call BCR* is consistent with the usual response. We then move to address concerns about the relevance of BCR* to Crummett’s original thesis.

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.

China’s return to social work education, after a nearly 35-year absence, opened the door for partnerships like the 2012 China Collaborative partnership between the Council on Social Work Education’s (CSWE) Katherine A. Kendall Institute, the China Association of Social Work Education (CASWE) and the International Association of Schools of Social Work (IASSW). The University of Alabama School of Social Work (UA SSW) was selected to participate in the collaborative and was connected to the Southwest China Region, specifically partnered with Yunnan University. This manuscript will share the strategies used to engage faculty and students from each partnering institution. Data collected by UA SSW over the five-year partnership will be utilised to contribute to the discussion of the extent to which Western knowledge and theory about social work education might usefully be applied to the Chinese context.