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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.

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.

In the face of the Covid-19 pandemic, the fashion industry was surprised and quickly had to adapt to digital media. However, the relationship between fashion and the multiplicity of screens is not new. Fashion emerged and took its first steps with Cinema, in Modernity. Although there are times when these two systems are further apart from each other, the alliance survived. To analyse contemporaneity, we take as main reference the studies of Gilles Lipovetsky, and his reflections on aesthetic capitalism. The fashion system has many Western fields of life, including art and technology. In this article we discuss how this relationship of fashion adapts and develops with aesthetic capitalism and post-digital art while we analyse representative artefacts from/about fashion. We propose to put the recent digital fashion artefacts in dialogue with post-digital aesthetics theories, discussing the blurred boundaries between the digital and the post-digital, and proposing the instantiation of a post-digital creation cycle applied to fashion artefacts.

In this essay we argue that, based on current scientific data, the most prudential course of future actions that an American conservative can take, is one that assumes what we call climate change alarmism. In order to establish this thesis, we first provide a basic overview of the relevant climate change science, as well as give an analysis of the alarmist and lukewarming dialectic (the two primary interpretations of the data). We then move to develop our environmental wager. Finally, following Roger Scruton, we end this work by proposing what sort of policies conservatives should endorse going further.

In this essay, we put forth a novel solution to Plantinga’s Evolutionary Argument Against Naturalism, utilizing recent work done by Duncan Pritchard on radical skepticism. Key to the success of Plantinga’s argument is the doubting of the reliability of one’s cognitive faculties. We argue (viz. Pritchard and Wittgenstein) that the reliability of one’s cognitive faculties constitutes a hinge commitment, thus is exempt from rational evaluation. In turn, the naturalist who endorses hinge epistemology can deny the key premise in Plantinga’s argument and avoid the dilemma posed on belief in the conjunction of naturalism and evolution.

There are a large number of symptom consultation texts in medical and healthcare Internet communities, and Chinese health segmentation is more complex, which leads to the low accuracy of the existing algorithms for medical text classification. The deep learning model has advantages in extracting abstract features of text effectively. However, for a large number of samples of complex text data, especially for words with ambiguous meanings in the field of Chinese medical diagnosis, the word-level neural network model is insufficient. Therefore, in order to solve the triage and precise treatment of patients, we present an improved Double Channel (DC) mechanism as a significant enhancement to Long Short-Term Memory (LSTM). In this DC mechanism, two channels are used to receive word-level and char-level embedding, respectively, at the same time. Hybrid attention is proposed to combine the current time output with the current time unit state and then using attention to calculate the weight. By calculating the probability distribution of each timestep input data weight, the weight score is obtained, and then weighted summation is performed. At last, the data input by each timestep is subjected to trade-off learning to improve the generalization ability of the model learning. Moreover, we conduct an extensive performance evaluation on two different datasets: cMedQA and Sentiment140. The experimental results show that the DC-LSTM model proposed in this paper has significantly superior accuracy and ROC compared with the basic CNN-LSTM model.

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.

With the fifth generation (5G) communication technology, the mobile multiuser networks have developed rapidly. In this paper, the performance analysis of mobile multiuser networks which utilize decode-and-forward (DF) relaying is considered. We derive novel outage probability (OP) expressions. To improve the OP performance, we study the power allocation optimization problem. To solve the optimization problem, we propose an intelligent power allocation optimization algorithm based on grey wolf optimization (GWO). We compare the proposed GWO approach with three existing algorithms. The experimental results reveal that the proposed GWO algorithm can achieve a smaller OP, thus improving system efficiency. Also, compared with other channel models, the OP values of the 2-Rayleigh model are increased by 81.2% and 66.6%, respectively.

Classification of electroencephalogram (EEG) is a key approach to measure the rhythmic oscillations of neural activity, which is one of the core technologies of brain-computer interface systems (BCIs). However, extraction of the features from non-linear and non-stationary EEG signals is still a challenging task in current algorithms. With the development of artificial intelligence, various advanced algorithms have been proposed for signal classification in recent years. Among them, deep neural networks (DNNs) have become the most attractive type of method due to their end-to-end structure and powerful ability of automatic feature extraction. However, it is difficult to collect large-scale datasets in practical applications of BCIs, which may lead to overfitting or weak generalizability of the classifier. To address these issues, a promising technique has been proposed to improve the performance of the decoding model based on data augmentation (DA). In this article, we investigate recent studies and development of various DA strategies for EEG classification based on DNNs. The review consists of three parts: what kind of paradigms of EEG-based on BCIs are used, what types of DA methods are adopted to improve the DNN models, and what kind of accuracy can be obtained. Our survey summarizes the current practices and performance outcomes that aim to promote or guide the deployment of DA to EEG classification in future research and development.

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.