Enhancing urban development vitality and optimizing the allocation of regional industrial factors require a comprehensive analysis of listed companies, such as the overall distribution network, agglomeration evolution trend, industrialization layout, and driving mechanism. Using 1,624 A-share listed companies in China's Yangtze River Economic Belt as research area, this study integrated trend surface analysis, exploratory spatial data analysis, standard deviation ellipse, and spatial regression model methods. The main results are as follows: The overall quantity scale (...) of the listed companies in the Yangtze River Economic Belt has achieved significant growth, but the spatial difference of location selection persists. The spatial configuration formed a hierarchical urban distribution pattern with the Yangtze River Delta region as the agglomeration core and the provincial capitals as the fulcrum. Listed companies accelerate the expansion of the Yangtze River Delta region. Chengdu, Wuhan, Changsha, and other central-western provincial capitals gradually increased the region’s attractiveness. High-high and low-low agglomerations remain the main forms of agglomeration. There are significant differences in the location selection of listed companies with varying specialization levels, forming a relatively different alienated high-value distribution structure among various industry types. The levels of knowledge spillover, city scale, and policy support level are major factors affecting the location selection process of listed companies in the Yangtze River Economic Belt. For low-level city network with few listed companies, city scale and knowledge spillover levels are significant determinants for the development of headquarters’ economy. For high-level city network, along with the level of knowledge spillovers and policy support, globalization level has an important contribution to the shaping of the location advantages of attracting the layout of listed companies. (shrink)

With the emergence of big data, cloud computing, and other technologies, artificial intelligence technology has set off a new wave in the field of education. The application of AI technology to deep learning in university teachers' teaching and students' learning processes is an innovative way to promote the quality of teaching and learning. This study proposed the deep learning-based assessment to measure whether students experienced an improvement in terms of their mastery of knowledge, (...) development of abilities, and emotional experiences. It also used comparative analysis of pre-tests and post-tests through online questionnaires to test the results. The impact of technology on teachers' teaching and students' learning processes, identified the problems in the teaching and learning processes in the context of the application of AI technology, and proposed strategies for reforming and optimizing teaching and learning. It recommends the application of software and platforms, such as Waston and Knewton, under the orientation of AI technology to improve efficiency in teaching and learning, optimize course design, and engage students in deep learning. The contribution of this research is that the teaching and learning processes will be enhanced by the use of intelligent and efficient teaching models on the teachers' side and personalized and in-depth learning on the students' side. On the one hand, the findings are helpful for teachers to better grasp the actual conditions of in-class teaching in real time, carry out intelligent lesson preparations, enrich teaching methods, improve teaching efficiency, and achieve personalized and precision teaching. On the other hand, it also provides a space of intelligent support for students with different traits in terms of learning and effectively improves students' innovation ability, ultimately achieving the purpose of “artificial intelligence + education.”. (shrink)

Understanding students’ psychological pressure and bad emotional reaction can solve psychological problems as soon as possible and avoid affecting students’ normal study life. With the improvement of global scientific and technological strength, and the step-by-step in-depth research on deep learning and computational intelligence optimization. Now, we have enough conditions to build a psychological and emotional data set for the field of education, and build a mental health stress detection model with emotional analysis function. In (...) addition, a variety of experimental methods are used for comparison, which shows the superior performance of the model in practical application scenarios. The results show that: the data set constructed for the model is reasonable. Psychological stress test shows that the tested college students are in good health and have no positive performance. Schools need to pay special attention to obsessive–compulsive disorder and interpersonal sensitivity, and the average values of both indicators are higher than 0.9. For the optimization of ant colony algorithm computational intelligence, both the stability and the average execution time of the algorithm are obviously higher than those of other algorithms. This model has obvious performance advantages after using this algorithm. Using loss function value to measure the difference between simulated emotion analysis and real value. The difference of most emotion tests is less than 3%; the accuracy difference between sadness and fear is about 7%. Although the final results prove the feasibility of this method, there are still some shortcomings to be optimized. (shrink)

This article uses a research method that combines theoretical research and empirical analysis. It first introduces the relevant theories of energy service city distribution sites in the context of e-commerce and then the types of energy service city distribution sites and the composition of energy service city distribution systems. The network layout of the service city distribution site and the location objectives, principles, and processes of the model is studied to determine the network layout plan of the energy service (...) city distribution system in this paper. This paper fully considers the characteristics of the network operation mode of the energy service city distribution site and establishes an optimization model for the location selection and vehicle routing of the distribution center with the lowest total system cost under the simultaneous delivery service mode; based on the hierarchical solution strategy, a combination of deep learning is designed. Algorithms mainly include two-stage hybrid heuristic algorithm of cluster analysis, maximum coverage and genetic algorithm; simulation analysis is conducted to verify the effectiveness of the model and algorithm by data simulation, finally get the integrated optimization plan of distribution center location and routing, and put forward the operation strategy through the result expansion analysis. This paper studies the planning model based on the network layout planning of the energy service city distribution system under the e-commerce environment, aiming to promote the breakthrough development of urban smart logistics and prove the importance of the energy service city distribution station network layout planning. The purpose and results of the research are to reduce traffic and environmental pressures, achieve joint direct distribution, improve the efficiency of urban logistics and distribution, and solve the problem of the last mile of the city. (shrink)

Over the past two decades, China has launched a nationwide endeavor to push domestic firms up the value chain. This article explores why, in some localities, Chinese firms had significant success in upgrading, while in other localities, firms were paradoxically trapped in a race-to-the-bottom competition. Drawing on national economic census data, a firm-level survey, and in-depth interviews, the article conducts a controlled comparison of China’s largest electronics manufacturing bases in the Yangtze and the Pearl River Deltas. It (...) argues that the local government’s choice of global business allies shaped the upgrading behavior of domestic firms. When local governments allied with large multinational corporations at the top of the value chain, they reinforced the hierarchical structure of production, shrank the upgrading space for domestic firms, and squeezed them to the bottom of the value chain. In contrast, alliances with small foreign invested firms at the bottom helped break the hierarchical segregation and held more potential for local learning and innovation. The article sheds new light on the question of when industrial policies succeed or fail to facilitate domestic upgrading in a globalized era. (shrink)

Through a literature analysis, this study proposes that the difference between scientific innovation and technological innovation has been ignored in the current research on the level of scientific and technological innovation and its influencing factors. Combined with multidimensional proximity and knowledge type of current research, a theoretical induction has been carried on their corresponding relation with scientific innovation and technological innovation, research hypotheses were proposed the multidimensional proximity effect on the mode and degree of scientific innovation and (...) technological innovation, five theoretical factors, which are the economic development level, regional economic structure, the level of opening to the outside world, science and technology input and education input, are proposed to affect the level of scientific innovation and technological innovation. In this study, the Yangtze River Delta region of China from 2001 to 2018 is selected as the research sample, and the two hypotheses proposed are tested through a mixed method of exploratory spatial data analysis and spatial panel econometric model. The main conclusions are as follows: i) As an exogenous variable, geographical proximity has a small impact on the level of scientific innovation, but a large impact on the level of technological innovation; ii) As endogenous variables, theoretical influencing factors may not play a significant role in the actual environment due to the complex influence of multidimensional proximity; iii) Based on the idea of improving multidimensional proximity and the actual situation of the region and the city, we can formulate policies conducive to improving the regional and urban innovation environment. (shrink)

In order to improve the efficiency of teaching and learning in Colleges and Universities, this work combines the Browser/Server framework with Model View Presenter technology to build a college student–oriented micro-video teaching system based on Deep Learning and Human–Computer Interaction technology. Firstly, it makes an in-depth analysis of the problems in the classroom teaching of Chinese CAUs. Three functional modules are designed for the micro-video online teaching platform: video management, user learning, and system (...) management. Then, it uses MVP technology to analyze the use-cases of these three functional modules in detail. Based on this, the micro-video online teaching platform is designed using the B/S framework. The teaching platform interface layer realizes the HCI between the platform and users. The business logic layer responds to the user requests submitted and returns the processing results to the interface layer. Finally, the function test and stress test of each module of the micro-video online teaching platform is carried out. The test results show that the response time of the proposed micro-video teaching platform increases with the number of users. Under the peak concurrent users, the system response time is 6 s, without abnormalities. Meanwhile, the proposed teaching platform has improved students' satisfaction with classroom teaching by nearly 15% and improved the compactness of the college classroom by nearly 12%. When the number of virtual users increases and the number of services increases linearly, the Random Access Memory and Central Processing Unit growth rate is significantly lower than that of the number of services. These outcomes indicate that many system resources are reused, and the system has good scalability, which can meet users' needs for the network video teaching system. The proposed teaching platform provides a new idea for applying DL and HCI technology in researching college students' micro-video teaching mode. (shrink)

Brain monitoring combined with automatic analysis of EEGs provides a clinical decision support tool that can reduce time to diagnosis and assist clinicians in real-time monitoring applications (e.g., neurological intensive care units). Clinicians have indicated that a sensitivity of 95% with specificity below 5% was the minimum requirement for clinical acceptance. In this study, a high-performance automated EEG analysis system based on principles of machine learning and big data is proposed. This hybrid architecture integrates hidden (...) Markov models (HMMs) for sequential decoding of EEG events with deep learning-based postprocessing that incorporates temporal and spatial context. These algorithms are trained and evaluated using the Temple University Hospital EEG, which is the largest publicly available corpus of clinical EEG recordings in the world. This system automatically processes EEG records and classifies three patterns of clinical interest in brain activity that might be useful in diagnosing brain disorders: (1) spike and/or sharp waves, (2) generalized periodic epileptiform discharges, (3) periodic lateralized epileptiform discharges. It also classifies three patterns used to model the background EEG activity: (1) eye movement, (2) artifacts and (3) background. Our approach delivers a sensitivity above 90% while maintaining a specificity below 5%. We also demonstrate that this system delivers a low false alarm rate, which is critical for any spike detection application. (shrink)

How to enable the computer to accurately analyze the emotional information and story background of characters in Qin opera is a problem that needs to be studied. To promote the artistic inheritance and cultural emotion color dissemination of Qin opera, an emotion analysis model of Qin opera based on attention residual network is presented. The neural network is improved and optimized from the perspective of the model, learning rate, network layers, and the network itself, and then multi-head attention (...) is added to the ResNet to increase the recognition ability of the model. The convolutional neural network is optimized from the internal depth, and the fitting ability and stability of the model are enhanced through the ResNet model. Combined with the attention mechanism, the expression of each weight information is strengthened. The multi-head attention mechanism is introduced in the model and a multi-head attention ResNet, namely, MHAtt_ResNet, is proposed. The network structure can effectively identify the features of the spectrogram, improve the weight information of spectrogram features, and deepen the relationship between distant information in long-time series. Through experiments, the proposed model has high emotional classification accuracy for Qin opera, and with the increase of the number of data sets, the model will train a better classification effect. (shrink)

Understanding religion requires explaining why supernatural beliefs, devotions, and rituals are both universal and variable across cultures, and why religion is so often associated with both large-scale cooperation and enduring group conflict. Emerging lines of research suggest that these oppositions result from the convergence of three processes. First, the interaction of certain reliably developing cognitive processes, such as our ability to infer the presence of intentional agents, favors—as an evolutionary by-product—the spread of certain kinds of counterintuitive concepts. Second, participation (...) in rituals and devotions involving costly displays exploits various aspects of our evolved psychology to deepen people's commitment to both supernatural agents and religious communities. Third, competition among societies and organizations with different faith-based beliefs and practices has increasingly connected religion with both within-group prosociality and between-group enmity. This connection has strengthened dramatically in recent millennia, as part of the evolution of complex societies, and is important to understanding cooperation and conflict in today's world. (shrink)

Deep learning developers typically rely on deep learning software frameworks (DLSFs)—simply described as pre-packaged libraries of programming components that provide high-level access to deep learning functionality. New DLSFs progressively encapsulate mathematical, statistical and computational complexity. Such higher levels of abstraction subsequently make it easier for deep learning methodology to spread through mimesis (i.e., imitation of models perceived as successful). In this study, we quantify this increase in abstraction and discuss its implications. Analyzing (...) publicly available code from Github, we found that the introduction of DLSFs correlates both with significant increases in the number of deep learning projects and substantial reductions in the number of lines of code used. We subsequently discuss and argue the importance of abstraction in deep learning with respect to ephemeralization, technological advancement, democratization, adopting timely levels of abstraction, the emergence of mimetic deadlocks, issues related to the use of black box methods including privacy and fairness, and the concentration of technological power. Finally, we also discuss abstraction as a symptom of an ongoing technological metatransition. (shrink)

In face recognition systems, highly robust facial feature representation and good classification algorithm performance can affect the effect of face recognition under unrestricted conditions. To explore the anti-interference performance of convolutional neural network reconstructed by deep learning framework in face image feature extraction and recognition, in the paper, first, the inception structure in the GoogleNet network and the residual error in the ResNet network structure are combined to construct a new deep reconstruction network algorithm, with the (...) random gradient descent and triplet loss functions as the model optimizer and classifier, respectively, and it is applied to the face recognition in Labeled Faces in the Wild face database. Then, the portrait pyramid segmentation and local feature point segmentation are applied to extract the features of face images, and the matching of face feature points is achieved using Euclidean distance and joint Bayesian method. Finally, Matlab software is used to simulate the algorithm proposed in this paper and compare it with other algorithms. The results show that the proposed algorithm has the best face recognition effect when the learning rate is 0.0004, the attenuation coefficient is 0.0001, the training method is SGD, and dropout is 0.1, and the algorithm proposed in this paper has the largest mean average precision compared to other CNN algorithms. The correct rate of face feature matching of the algorithm proposed in this paper is 84.72%, which is higher than LetNet-5, VGG-16, and VGG-19 algorithms, the correct rates of which are 6.94%, 2.5%, and 1.11%, respectively, but lower than GoogleNet, AlexNet, and ResNet algorithms. At the same time, the algorithm proposed in this paper has a faster matching time and a higher correct matching rate than the joint Bayesian method, indicating that the deep reconstruction network algorithm proposed in this paper can be used in face image recognition, FE, and matching, and it has strong anti-interference. (shrink)

Sentiment analysis is one of the important tasks of online opinion analysis and an important means to guide the direction of online opinion and maintain social stability. Due to the multiple characteristics of linguistic expressions, ambiguity, multiple meanings of words, and the increasing speed of new words, it is a great challenge for the task of text sentiment analysis. Commonly used machine learning methods suffer from inadequate text feature extraction, and the emergence of deep (...) class='Hi'>learning has brought a turnaround for this purpose. In this paper, we investigate the problem of text sentiment analysis using methods related to deep learning. In order to incorporate user and product information in a more diverse way in the model, this paper proposes a model based on a deep bidirectional long-and short-term memory network-self-attention mechanism-custom classifier. The model first identifies contextual associations and acquires deep text features through a deep bidirectional long- and short-term memory network and then captures important features in the text using a self-attentive mechanism. The model finally combines user information and product information to build a custom classifier module and uses context-aware attention mechanisms to assign specific parameters to user information and product information, which improves the performance of the model on public datasets compared with current common models. The results show that the accuracy of the algorithm in this paper is high, and it is about 5% lower than the traditional algorithm. The method can reduce the number of iterations and the running time of the algorithm. (shrink)

Deep learning and artificial intelligence are jointly applied to concrete piano teaching for children to comprehensively promote modern piano teaching and improve the overall teaching quality. First, the teaching environment and the functions of the intelligent piano are expounded. Then, a piano note onset detection method is proposed based on the convolution neural network. The network can analyze the time-frequency of the input piano music signal by transforming the original time-domain waveform of the piano music signal into the (...) frequency distribution varying with time. Besides, it can detect the note onset at a stable state after 8 × 104 iterations. Moreover, an intelligent piano teaching method is designed to teach Jingle Bells for 40 preschool children aged 4–6 years. Finally, a questionnaire survey is performed to investigate the teaching situation, including the learning interest and learning effect of children and learning feedback from parents. The results show that 80% of children like smart music scores, 82% of children like intelligent piano lessons with games, and 84% of children can learn actively in the intelligent piano class. Besides, 85% of parents believe that their children are more interested in learning piano. In general, the intelligent piano teaching method effectively combines DL with AI to realize the overall optimization of piano performance. It is widely favored by preschool children and their parents and plays an important role in improving the interest of preschool children in piano learning. (shrink)

Improving the resilience of urban road networks suffering from various disruptions has been a central focus for urban emergence management. However, to date the effective methods which may mitigate the negative impacts caused by the disruptions, such as road accidents and natural disasters, on urban road networks is highly insufficient. This study proposes a novel adaptive signal control strategy based on a doubly dynamic learning framework, which consists of deep reinforcement learning and day-to-day traffic dynamic learning, (...) to improve the network performance by adjusting red/green time split. In this study, red time split is regarded as extra traffic flow to discourage drivers to use affected roads, so as to reduce congestion and improve the resilience when urban road networks are subject to different levels of disruptions. In addition, we utilize the convolution neural network as Q-network to approximate Q values, link flow distribution and link capacity are regarded as the state space, and actions are denoted as red/green time split. A small network is utilized as a numerical example, and a fixed time signal control and other two adaptive signal controls are employed for the comparisons with the proposed one. The results show that the proposed adaptive signal control based on deep reinforcement learning can achieve better resilience in most of the cases, particularly in the scenarios of moderate and severe disruptions. This study may shed light on the advantages of the proposed adaptive signal control dealing with major emergencies compared to others. (shrink)

Cultural development is often reflected in the emotional expression of various cultural carriers, such as literary works, movies, etc. Therefore, the cultural development can be analyzed through emotion analysis of the text, so as to sort out its context and obtain its development dynamics. This paper proposes a text emotion analysis method based on deep learning. The traditional neural network model mainly deals with the classification task of short texts in the form of word vectors, (...) which causes the model to rely too much on the accuracy of word segmentation. In addition, the short texts have the characteristics of short corpus and divergent features. A text emotion classification model combing the Bidirectional Encoder Representations from Transformers and Bi-directional Long Short-Term Memory is developed in this work. First, the BERT model is used to convert the trained text into a word-based vector representation. Then, the generated word vector is employed as the input of the BiLSTM to obtain the semantic representation of the context of the relevant word. By adding random dropout, the mechanism prevents the model from overfitting. Finally, the extracted feature vector is input to the fully connected layer, and the emotion category to which the text belongs is calculated through the Softmax function. Experiments show that in processing short texts, the proposed model based on BERT-BiLSTM is more accurate and reliable than the traditional neural network model using word vectors. The proposed method has a better analysis effect on the development of western culture. (shrink)

This paper aims to detect the methods of earnings management in Pearl River Delta region of China. The data were selected based on the list companies of Pearl River Delta region from 2008 to 2019 and the balanced panel analysis was adopted to pursue the results. After the random effect analysis, this study reaches the conclusion that downward earnings is through total assets, the difference between sales and receivables, fixed assets, and sales and upward (...) earnings is mainly through the changes of sales by the companies in Pearl River Delta region of China. (shrink)

This study investigates antecedents of corporate social responsibility in multinational corporations' subsidiaries. Using stakeholder theory and institutional theory that identify internal and external pressures for legitimacy in MNCs' subsidiaries, we integrate international business and CSR literatures to create a model depicting CSR practices in MNCs' subsidiaries. We propose that MNCs' subsidiaries will be likely to adapt to local practices to legitimize themselves if they operate in host countries with different institutional environments and demanding stakeholders. We also predict that MNCs' (...) subsidiaries will be likely to adapt to local practices to avoid spillover effects if their parent companies suffer major legitimacy problems at home or abroad. However, we speculate that MNCs' subsidiaries will be less likely to adapt to local practices if they are strongly annexed to their parent companies and the benefit to gain internal legitimacy outweighs external legitimacy. This article contributes to the discourse on CSR across borders by exploring the antecedents of CSR practices in MNCs' subsidiaries at social and organizational levels, and integrating institutional and stakeholder views. We provide a number of propositions for future studies and explore implications for practitioners. (shrink)

What is the interplay of high-tech industrial agglomeration and urban innovation? How does high-tech industrial agglomeration affect urban innovation? What are the heterogeneous effects of high-tech industry agglomeration on urban innovation in different conditions? To answer these questions, this paper analyzes the interrelationship between high-tech industry agglomeration and urban innovation based on panel data of China’s Yangtze River Delta urban agglomeration from 2010 to 2019. We discuss the influence mechanism of high-tech industrial agglomeration on urban innovation by (...) exploring the mediating effect of industrial structure optimization and the threshold effect of industrial attributes. The heterogeneous impact of high-tech industry agglomeration on urban innovation is also been further studied. We find that the interaction relationship between high-tech industry agglomeration and urban innovation output is positive. The advancement of industrial structure plays a positive intermediary role between high-tech industrial agglomeration and urban innovation output, while the rationalization of industrial structure shows a suppressing effect. There are different threshold effects between capital intensity and technology intensity. The influence of high-tech agglomeration on urban innovation is positive only when the capital intensity exceeds 1.125. However, the influence is always positive in different levels of technology intensity, significantly. When the technology intensity is higher than 9.012E − 06, the degree and significance of this positive impact would decrease. There are heterogeneous impacts of high-tech industry agglomeration on urban innovation output in the Yangtze River Delta urban agglomeration in different time stages, urban innovation development stages, and urban circles. (shrink)

This paper investigates if and to what point it is possible to trade on news sentiment and if deep learning, given the current hype on the topic, would be a good tool to do so. DL is built explicitly for dealing with significant amounts of data and performing complex tasks where automatic learning is a necessity. Thanks to its promise to detect complex patterns in a dataset, it may be appealing to those investors that are looking (...) to improve their trading process. Moreover, DL and specifically LSTM seem a good pick from a linguistic perspective too, given its ability to “remember” previous words in a sentence. After having explained how DL models are built, we will use this tool for forecasting the market sentiment using news headlines. The prediction is based on the Dow Jones industrial average by analyzing 25 daily news headlines available between 2008 and 2016, which will then be extended up to 2020. The result will be the indicator used for developing an algorithmic trading strategy. The analysis will be performed on two specific cases that will be pursued over five time-steps and the testing will be developed in real-world scenarios. (shrink)

With the development of learning abroad, deep learning is used in research fields. On the basis of deep learning, this article studies physical education. First, this article analyzes and explains the related concepts and current situation of physical education, and explains the measurement and definition of the mental health. Then, the function analysis algorithm of deep learning is explained and analyzed, in which the algorithm of the convolution neural network of (...) class='Hi'>deep learning is mainly described. Finally, through experimental analysis, it shows that the research performance of deep learning in the physical education on college students’ mental health is relatively high. At the same time, through investigation and analysis, it is proposed that physical education in deep learning can improve mental health and social adaptability relatively high. And the content of physical education should focus on increasing physical psychological education and physical practice education, which can improve college students’ mental health and social adaptability compared with other teaching contents. Therefore, when introducing deep learning, universities should strengthen the physical education of college students. (shrink)

This work intends to solve the problem that the traditional education system cannot reasonably adjust the educational integration of children with the arrival of labor force in a short time, and support the education of migrant children in the education policy to integrate them into the local educational environment as soon as possible. Firstly, this work defines the surplus labor force and MC. Secondly, the principles of Artificial Intelligence and Deep Learning are introduced. Thirdly, it analyzes the (...) education of MC and relevant policies, and the data of the education effect of MC are collected and the evaluation effect model is built. Finally, the evaluation model of MC’s education effect is applied to test the effect of EP. The results show that using AI technology combined with DL technology to model the education effect of MC can establish an effective and accurate evaluation model of the education effect of MC, effectively evaluate the impact of local education policies on the education of MC, and give an effective effect analysis of relevant education policies in each period. The result of Adaptive Resonance Theory –Back Propagation algorithm is 65 ∼ 96%, which is much higher than the efficiency of traditional algorithms. This shows that the education integration evaluation model of MC based on AI technology and DL technology can effectively and accurately evaluate the integration effect of MC on the local education system, and then provide reference for local and even national adjustment of education policies. The results provide a new idea for the application of new technology in EP. (shrink)

We investigated family members’ lived experience of Parkinson’s disease aiming to investigate opportunities for well-being. A lifeworld-led approach to healthcare was adopted. Interpretative phenomenological analysis was used to explore in-depth interviews with people living with PD and their partners. The analysis generated four themes: It’s more than just an illness revealed the existential challenge of diagnosis; Like a bird with a broken wing emphasizing the need to adapt to increasing immobility through embodied agency; Being (...) together with PD exploring the kinship within couples and belonging experienced through support groups; and Carpe diem! illuminated the significance of time and fractured future orientation created by diagnosis. Findings were interpreted using an existential-phenomenological theory of well-being. We highlighted how partners shared the impact of PD in their own ontological challenges. Further research with different types of families and in different situations is required to identify services required to facilitate the process of learning to live with PD. Care and support for the family unit needs to provide emotional support to manage threats to identity and agency alongside problem-solving for bodily changes. Adopting a lifeworld-led healthcare approach would increase opportunities for well-being within the PD illness journey. (shrink)

The Philosophy of Rhetoric is widely regarded as the most important work of a theory of rhetoric produced in the 18th century. Campbell's work engages such themes in an attempt to formulate a universal theory of human communication. Campbell attempts to develop his theory by discovering deep principles in human nature that account for all instances and kinds of human communication. He seeks to derive all communication principles and processes empirically. In addition, all statements in discourse that have to (...) do with matters of fact and human affairs are likewise to be empirically derived. Thus, his theory of rhetoric is vastly wider than, and different from, such classical theories as those proposed by Aristotle, Cicero, and Quintilian, whose theories focused on discourse related to civic affairs. By attempting to elaborate a general theory of rhetoric through empirical procedures, Campbell's project reveals the limitations of his method. He cannot ground all statements empirically and it is at this point that his theological position comes into play. Inspection of his religious views shows that God's design of human nature, and God's revelations to humankind, make moral and spiritual truths known and quite secure to human beings, although not empirically. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Reviewed by:Ethics and Time: Ethos of Temporal Orientation in Politics and Religion of the Niger DeltaMelissa BrowningEthics and Time: Ethos of Temporal Orientation in Politics and Religion of the Niger Delta Nimi Wariboko Lanham, Md.: Lexington Books, 2010. 193 pp. $60.00In Ethics and Time: Ethos of Temporal Orientation in Politics and Religion of the Niger Delta, Nimi Wariboko offers a new definition of temporal orientation, arguing that (...) this new understanding of time can help us rethink social ethics, particularly economic ethics. He begins by citing literature showing that temporal orientation has always been used to speak about how a sociocultural group sees itself in light of a “time or origin” (such as a sacred past) or “time preference” (such as a preference for past, present, or future). Wariboko proposes a third way, redefining temporal orientation as a break or clearing in time between the past and the future—a kairos event. He goes on to say that understanding how members of a sociocultural group understand time is essential to understanding how they see themselves and their moral sources.Wariboko speaks of temporal orientation in terms of “time gaps,” contending that freedom and new beginnings are found within these gaps. Suggesting that these gaps are important to the process of defining subjectivity—since human beings live in a time-defined gap between birth and death—he coins the term “chronomethics” to refer to a new methodology where ethics is studied [End Page 213] through the lens of temporal orientation with attention to the analysis of cultural forms.Wariboko provides an exemplar of this methodology by doing an in-depth analysis of the Kalabari community of the Niger Delta in southern Nigeria. Through the use of ethnography, Wariboko explores politics, religious imagination, apocalypticism, and the philosophy of history in Kalabari beliefs and practices—setting African studies in dialogue with Western philosophy and theology. Here, and throughout the book, he draws heavily on the work of Hannah Arendt in his analysis.Although this ethnographic analysis makes an important contribution to ethical reflection, it is not without problems. Wariboko focuses almost exclusively on the chiefs of the Kalabari community, using both male language and male concepts. When Wariboko moves to a discussion of traditional Kalabari religion, he speaks mainly of the male or neuter aspect of God (which is called So, or so) and only briefly mentions the female aspect (Teme-órú). This leaves the reader to wonder whether the actors and agents Wariboko refers to include women. In sum, his descriptive analysis of Kalabari culture lacks a critique of patriarchy, which should be present in any analysis that seeks to make a contribution to Christian ethics.Perhaps the most significant contribution of the book is found in its chapter on imagination. Here Wariboko examines the ways in which the Kalabari people imagine their own society and construct a moral imagination. He pays significant attention to African conceptions of community and the ways in which individuals relate to their communities. Although Wariboko contributes an important original voice to this dialogue, this voice could be strengthened by incorporating the voices of other African philosophers and theologians rather than relying primarily on Western sources in his analysis of Kalabari culture.The book concludes by relating the newly proposed methodology (and to some extent the ethnography) to concrete applications within the realm of economic development. Here Wariboko focuses on the “kairos-ordering of society,” arguing that economic development will only be attained when the state maintains the rules of just conduct, nurtures kairos, develops human capabilities, embraces pluralism, and uses social practices to disperse knowledge. Although this final section is brief, it invites further reflection by delineating how temporal orientation could be used as a new methodology for Christian ethics. [End Page 214]Melissa BrowningLexington Theological SeminaryCopyright © 2012 The Society of Christian Ethics... (shrink)

Nowadays, the whole world is facing a pandemic situation in the form of coronavirus diseases. In connection with the spread of COVID-19 confirmed cases and deaths, various researchers have analysed the impact of temperature and humidity on the spread of coronavirus. In this paper, a deep transfer learning-based exhaustive analysis is performed by evaluating the influence of different weather factors, including temperature, sunlight hours, and humidity. To perform all the experiments, two data sets are used: one (...) is taken from Kaggle consists of official COVID-19 case reports and another data set is related to weather. Moreover, COVID-19 data are also tested and validated using deep transfer learning models. From the experimental results, it is shown that the temperature, the wind speed, and the sunlight hours make a significant impact on COVID-19 cases and deaths. However, it is shown that the humidity does not affect coronavirus cases significantly. It is concluded that the convolutional neural network performs better than the competitive model. (shrink)

This study aimed to develop cross-domain deep learning courses of artificial intelligence in vocational senior high schools and explore its impact on students’ learning effects. It initially adopted a literature review to develop a cross-domain SPOC-AIoT Course with SPOC and the Double Diamond 4D model in vocational senior high schools. Afterward, it adopted participatory action research and a questionnaire survey and conducted analyses on the various aspects of the technology acceptance model by SmartPLS. Further, this study (...) explored the impact on the effects of deep learning and knowledge-ability learning of artificial intelligence after 16 weeks of course teaching among 36 Grade I students from the electrical and electronic group of a vocational senior high school. This study revealed that the four stages of the SPOC-AIoT Teaching Mode of the Double Diamond 4D model may effectively guide students to learn AIoT knowledge and skills. Based on the technology acceptance model, the analysis of learning and participation in SmartPLS indicated that this model conformed to the academic fitness requirements of the overall model. After learning with the SPOC-AIoT Teaching Mode, the learning effects of students in AIoT have been significantly improved to a positive aspect. Finally, some suggestions were put forward to promote the development of the SPOC-AIoT Teaching Mode Course in the future. (shrink)

In recent years, multivariate pattern analysis has been hugely beneficial for cognitive neuroscience by making new experiment designs possible and by increasing the inferential power of functional magnetic resonance imaging, electroencephalography, and other neuroimaging methodologies. In a similar time frame, “deep learning” has produced a parallel revolution in the field of machine learning and has been employed across a wide variety of applications. Traditional MVPA also uses a form of machine learning, but most commonly (...) class='Hi'>with much simpler techniques based on linear calculations; a number of studies have applied deep learning techniques to neuroimaging data, but we believe that those have barely scratched the surface of the potential deep learning holds for the field. In this paper, we provide a brief introduction to deep learning for those new to the technique, explore the logistical pros and cons of using deep learning to analyze neuroimaging data – which we term “deep MVPA,” or dMVPA – and introduce a new software toolbox intended to facilitate dMVPA for neuroscientists everywhere. (shrink)

The study of cultural evolution has taken on an increasingly interdisciplinary and diverse approach in explicating phenomena of cultural transmission and adoptions. Inspired by this computational movement, this study uses Bayesian networks analysis, combining both the frequentist and the Hamiltonian Markov chain Monte Carlo (MCMC) approach, to investigate the highly representative elements in the cultural evolution of a Vietnamese city’s architecture in the early 20th century. With a focus on the façade design of 68 old houses (...) in Hanoi’s Old Quarter (based on 78 data lines extracted from 248 photos), the study argues that it is plausible to look at the aesthetics, architecture, and designs of the house façade to find traces of cultural evolution in Vietnam, which went through more than six decades of French colonization and centuries of sociocultural influence from China. The in-depth technical analysis, though refuting the presumed model on the probabilistic dependency among the variables, yields several results, the most notable of which is the strong influence of Buddhism over the decorations of the house façade. Particularly, in the top 5 networks with the best Bayesian Information Criterion (BIC) scores and p<0.05, the variable for decorations (DC) always has a direct probabilistic dependency on the variable B for Buddhism. The paper then checks the robustness of these models using Hamiltonian MCMC method and find the posterior distributions of the models’ coefficients all satisfy the technical requirement. Finally, this study suggests integrating Bayesian statistics in the social sciences in general and for the study of cultural evolution and architectural transformation in particular. (shrink)

A new theory about the origins of consciousness that finds learning to be the driving force in the evolutionary transition to basic consciousness. What marked the evolutionary transition from organisms that lacked consciousness to those with consciousness—to minimal subjective experiencing, or, as Aristotle described it, “the sensitive soul”? In this book, Simona Ginsburg and Eva Jablonka propose a new theory about the origin of consciousness that finds learning to be the driving force in the transition to basic (...) consciousness. Using a methodology similar to that used by scientists when they identified the transition from non-life to life, Ginsburg and Jablonka suggest a set of criteria, identify a marker for the transition to minimal consciousness, and explore the far-reaching biological, psychological, and philosophical implications. After presenting the historical, neurobiological, and philosophical foundations of their analysis, Ginsburg and Jablonka propose that the evolutionary marker of basic or minimal consciousness is a complex form of associative learning, which they term unlimited associative learning (UAL). UAL enables an organism to ascribe motivational value to a novel, compound, non-reflex-inducing stimulus or action, and use it as the basis for future learning. Associative learning, Ginsburg and Jablonka argue, drove the Cambrian explosion and its massive diversification of organisms. Finally, Ginsburg and Jablonka propose symbolic language as a similar type of marker for the evolutionary transition to human rationality—to Aristotle's “rational soul.”. (shrink)

The present proposal is an approach to the vision, cosmogony and philosophy of the Totonacapan people, and particularly with the inhabitants of the Totonacapan region in Veracruz Mexico, a town whose wisdom is manifested to this day, in the conservation of customs and traditions , as well as the hierarchy of collective desire that seeks health, well-being and peace in the region, are guides in the evolution of their cultural processes, where a closeness, respectful and deep (...) class='Hi'>with Mother Nature stands out. This wisdom by the Council of Elders, transmitted to the new generations, by the Supreme Council of Totonacapan who seek share the basis to understand and conceive the current world, from the ancestral vision and cosmogony to identify their space, thought, spirituality and in the building your social context. In the Totonacapan region, the first pre-hispanic population concentrations were located around 1259 to 1311. Already by the year 1450, there is a record of incursions and the conquest of Totonaca territory, by the Mexica, establishing a relationship of domain and subordination. As a result of the presence of "Mexica" in the Totonaco territory, there was an imposition of customs and language, since in some areas they begin to speak in Nahuatl, resulting in the presence of a bilingualism (Nahuatl-Totonac) , situation that persists, until the encounter with the Spaniards in the year of 1519. At present, the Totonacapan region includes the area that extends from the Cazones River to the north, to the towns of Gutiérrez Zamora and Tecolutla, in the State of Veracruz in Mexico. 2 The importance of the document is that it constitutes an invitation to deepen and understand the vision of the inhabitants of Totonacapan, in the construction of their social context and their connection with nature, as part of an ancient culture, which creates and recreates their vision, in the search for the feeling of belonging and as an inheritance of the children of thunder (Tajin), as well as the need to preserve the benefits that nature itself offers them, seeking a peaceful and harmonious coexistence, in the social and environmental environment. However, the preservation of its history, tradition and philosophy of life, contrast with public policy strategies, particularly in the social sphere, and which are aimed at achieving economic development in the fight against multidimensional poverty and marginalization, all Once they have not incorporated into them, the feeling and living of the relationship between individual and nature, as the guiding axis, of the philosophy of the Totonacapan people. What is sustainability for the people of Totonacapan? What is social justice in the search for sustainability? And what is your concept of justice in sustainability and human development? From the vision and philosophy of the people of Totonacapan, as part of the history and tradition of the children of thunder "Tajin" and in whose worldview, he is responsible for regulating the rain, climate and life of the people of the region. How does the people of Totonacapan live and live in the face of the maelstrom of actions that, from public policy, that seek human development? How do you adjust, your way of life and how do you adapt to the requirements, which from the perspective of justice, are required to contribute to the objectives of the 2030 development agenda? This document is an effort to learn and recognize one of the most important millenary cultures in Mexico, in the construction of a more inclusive vision of the social development of the Totonacapan people, which invites us to reflect on the direction and scope of this response. (shrink)

In order to better assist investors in the evaluation and decision-making of financial data, this paper puts forward the need to build a reliable and effective financial data prediction model and, on the basis of financial data analysis, integrates deep learning algorithm to analyze financial data and completes the financial data analysis system based on deep learning. This paper introduces the implementation details of the key modules of the platform in detail. The user interaction (...) module obtains and displays the retrieval results through data parsing, calling the background, and computing engine. The data cleaning module fills, optimizes, and normalizes the data through business experience; the calculation engine module uses the algorithm and extracts the database information to get the similar time series and matching financial model. Finally, the data acquisition module fills the database with historical data at the initialization stage and updates the database every day. The data analysis platform for quantitative trading designed and implemented in this paper has carried out demand analysis, design, implementation, and test. From the perspective of function test and performance test, two functions of similar stock search and financial matching model are selected and tested, and the results are in line with the expected results. (shrink)

As a product of the tourism performing arts industry in culture–tourism integration development, to develop a featured culture–tourism town is a new trend for tourism development in the new era. To analyze the social benefit of the culture–tourism industry, in this study, an artificial intelligence model for social benefit evaluation is constructed based on backpropagation neural network and fuzzy comprehensive analysis, with Yiyang Town taken as an example. The criterion layer in the model includes three indexes, and the (...) index layer contains 11 indexes. The weight values of cultural inheritance and protection, ecological environment improvement, and commercial economy development to the social benefit of the town are 0.522, 0.570, and 0.424, respectively. For G1, 41.20% is excellent; for G2, 39.5% is excellent; and for G3, 40.5% is good. In general, 30.76% of the total social benefit is excellent, with 37.69% being good, 21.48% being qualified, and 10.07% being unqualified. It is inferred that the total social benefit level of Yiyang Town is good according to the constructed model. Therefore, the culture inheritance and protection, the ecological environment improvement, and the commercial economy development are the key evaluation factors of social benefit. (shrink)

In recent years, the family of algorithms collected under the term ``deep learning'' has revolutionized artificial intelligence, enabling machines to reach human-like performances in many complex cognitive tasks. Although deep learning models are grounded in the connectionist paradigm, their recent advances were basically developed with engineering goals in mind. Despite of their applied focus, deep learning models eventually seem fruitful for cognitive purposes. This can be thought as a kind of biological exaptation, where (...) a physiological structure becomes applicable for a function different from that for which it was selected. In this paper, it will be argued that it is time for cognitive science to seriously come to terms with deep learning, and we try to spell out the reasons why this is the case. First, the path of the evolution of deep learning from the connectionist project is traced, demonstrating the remarkable continuity, and the differences as well. Then, it will be considered how deep learning models can be useful for many cognitive topics, especially those where it has achieved performance comparable to humans, from perception to language. It will be maintained that deep learning poses questions that cognitive sciences should try to answer. One of such questions is the reasons why deep convolutional models that are disembodied, inactive, unaware of context, and static, are by far the closest to the patterns of activation in the brain visual system. (shrink)

Entrepreneurship may be taught, and entrepreneurship education is flourishing at colleges and universities. However, previous documents show that entrepreneurship education is inconsistent with the research conclusions of entrepreneurial intention, which is a lack of discussion on the mediating effect of government subsidies from external resources. Based on the cognitive behavior theory, a mediating effect of entrepreneurship education on entrepreneurial strategy and entrepreneurial intention is built. By collecting the data of 334 questionnaires of college students in Pearl River (...) class='Hi'>Delta in China, a structural equation is used for empirical analysis. The result indicates that entrepreneurship education does not have a significant influence on entrepreneurial intention; exploration innovation and exploitation innovation have a positive influence on entrepreneurial intention, and exploration innovation and exploitation innovation have a mediating effect on entrepreneurship education and entrepreneurial intention; government subsidies have a positive regulating effect on exploration innovation, exploitation innovation, and entrepreneurial intention. In this article, the application of the cognitive behavior theory in the field of entrepreneurship research is expanded to provide the theoretical basis for building the entrepreneurship education ecosystem, which is conductive to innovation and entrepreneurship to promote regional economic development. (shrink)

This paper aims to explore coevolution of emotional contagion and behavior for microblog sentiment analysis. Accordingly, a deep learning architecture is proposed for the target microblog. Firstly, the coevolution of emotional contagion and behavior is described by the tie strength between microblogs, that is, with the spread of emotional contagion, user behavior such as emotional expression will be affected. Then, based on user interaction and the correlation with target microblog, the Hawkes process is adopted to (...) quantify the tie strength between microblogs so as to build the corresponding weighted network. Secondly, in the weighted network, the Deepwalk algorithm is used to build the sequence representation of microblogs which are similar to the target microblog. Next, a CNN-BiLSTM-Attention network is designed to analyze the sentiment analysis of target and similar microblogs. Finally, the experimental results on two real Twitter datasets demonstrate that the proposed MSA-UITC has advanced performance compared with the existing state-of-the-art methods. (shrink)

With their focus on human production and consumption activities, cities incur massive energy consumption and CO2 emissions. An intercity connection is a typical complex system in which the interaction between cities is crucial for developing low-carbon outputs within the urban agglomeration. This paper presents the construction of the CO2 emission network of an urban agglomeration in the Yangtze River middle reaches megalopolis, based on the gravity model. Combined with social network analysis, a multilevel analysis (...) framework is proposed to deal with the complexity, spatiality, and visualization of the CO2 emission network with reference to the network features, structural equivalence, and the rich-club phenomenon. The following results emerged: firstly, the spatial structure of the CO2 emissions was characterized by low robustness and compactness, indicating disunity among the studied cities. Secondly, there was found to be a strong correlation between regionalism and intercity connections, with geographically close cities playing a similar role in the network. Thirdly, the “rich-club” cities, including Wuhan, Changsha, Xiaogan, and Zhuzhou, dominated the connections, covering more than 87.1% of the network in the Yangtze River Middle Reaches Megalopolis. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Reviewed by:Being-Moved: Rhetoric as the Art of Listening by Daniel M. GrossNathaniel A. RiversBeing-Moved: Rhetoric as the Art of Listening. By Daniel M. Gross. Oakland: University of California Press, 2020. 260 pp. Paper $34.95. ISBN: 9780520340466.September 29, 2008. Radiohead front man Thom Yorke sits frustrated at his piano. Live on stage. He is trying to start a song, but something is tripping him up. The song is “Videotape,” and (...) he cannot start playing it. “Temporary loss of information,” he mumbles to an expectant crowd.The song “Videotape” is syncopated, meaning there is a “placement of rhythmic stresses or accents where they wouldn’t normally occur” (Wikipedia 2021). Practically, it means that Yorke is joining a song already in progress: there is a beat before the beat that “starts” the song. Yorke, then, is starting the song not on the down beat but after the down beat—after the song has already started. He needs to hear something before he can play anything. He needs to hear the beat before he can sing. “Give me the fuckin’ hi-hats only,” he asks.Yorke is moved by the drum and a beat not his alone. It is a beat preceding him to which he must become subject. He needs to listen, but it is a particular kind of listening: a passive listening that makes him, as it were, “subject to the instruction of others” (Gross 137).Like Yorke on stage in 2008, I struggled to start and write this review. Not for any fault in the book, which is clear and concise, complex and compelling, but because I wanted to write a review that practiced the art of listening Gross cultivates:Active listening [“auditor-as-judge”], as it is understood by theoreticians and practitioners of persuasion from classical antiquity through today, only takes off at dusk like Hegel’s owl of Minerva, leaving behind obscurities of our daily lives including our susceptibility [End Page 190] to advertising, our political apathy, our immersion in common-sense, our lovely credulity, our vulnerability to others, our very capacity to learn and change. We have much work ahead when it comes to the theoretical and practical nuances of listening in its passive dimensions.(137)This review practices listening to this call in this way. The worry remains that the genre of the book review tends toward what Gross identifies as active listening: the judge, the critic. I should probably be the “active listener-as-judge” (83). Surely, a good reviewer should protect future readers from a “bad teacher” (131). But how should a book review practicing passive listening read? Does it aim for learning? Surely. Credulity? Why not. Subjection? Hmm.... It is, after all, subjection that lies at the heart of Gross’s book. Subjection is the beat before the beat that is rhetoric, an art forever syncopated.There are many aspects of Gross’s argument, which I will hear out below, but key for me, and crucial for Gross’s argument, is his emphasis on passive dispositions (e.g., apathy, adherence, suggestibility, attentiveness, etc.) crucial to political formations and so vital to rhetoric. Being moved, toward which rhetoric (sacred rhetoric especially) bends, must admit not only to the prowess and power of the rhetor but also, necessarily, to the “basic vulnerability that lies at the heart of political agency itself ” (1). Indeed, “Rhetoric as a life science depends upon those lives affected” (8). Because of this dependence (and dependencies saturate the arts of listening), “rhetoric offers much more detail because it is the traditional domain where subjection is both theorized and practiced” (3).To articulate this offer, Gross works through what he describes as the “orphaned materials of modernity [that] often turn out to be vital strains of a different geology altogether” (12). Gross is here describing his own historiographic methodology. There are other things to hear in and about rhetoric. The core of his argument isn’t simply that listening is a practice important to rhetoric, with listening understood as a kind of critical facility—what Gross calls “active listener-as-judge” (83–84). Listening, for Gross, through his approach to Heidegger, bears upon being and becoming... (shrink)

How educators and students process and respond to emotions can either enhance or impede the development of the whole child. Social and emotional learning refers to the processes of developing social and emotional competencies, which depend on individuals’ capacity to recognize, understand, and manage emotions. Consensus across disciplines about the importance of EI highlights the need to advance the science of how to teach SEL. RULER, an evidence-based approach to teaching EI, provides an educational framework that encompasses a set (...) of practices for comprehensive SEL integration across a school or district. In this article, we describe RULER, explain how it teaches EI, and summarize evidence of its effectiveness. (shrink)

Here, a streamlined, scalable, laboratory approach is discussed that enables medium‐to‐large dataset analysis. The presented approach combines data management, artificial intelligence, containerization, cluster orchestration, and quality control in a unified analytic pipeline. The unique combination of these individual building blocks creates a new and powerful analysis approach that can readily be applied to medium‐to‐large datasets by researchers to accelerate the pace of research. The proposed framework is applied to a project that counts the number of plasmonic nanoparticles bound (...) to peripheral blood mononuclear cells in dark‐field microscopy images. By using the techniques presented in this article, the images are automatically processed overnight, without user interaction, streamlining the path from experiment to conclusions. (shrink)

Cardiovascular disease is a major complication of type 2 diabetes mellitus. In addition to traditional risk factors, psychological determinants play an important role in CVD risk. This study applied Deep Neural Network to develop a CVD risk prediction model and explored the bio-psycho-social contributors to the CVD risk among patients with T2DM. From 2017 to 2020, 834 patients with T2DM were recruited from the Department of Endocrinology, Affiliated Hospital of Harbin Medical University, China. In this cross-sectional study, (...) the patients' bio-psycho-social information was collected through clinical examinations and questionnaires. The dataset was randomly split into a 75% train set and a 25% test set. DNN was implemented at the best performance on the train set and applied on the test set. The receiver operating characteristic curve analysis was used to evaluate the model performance. Of participants, 272 were diagnosed with CVD. The developed ensemble model for CVD risk achieved an area under curve score of 0.91, accuracy of 87.50%, sensitivity of 88.06%, and specificity of 87.23%. Among patients with T2DM, the top five predictors in the CVD risk model were body mass index, anxiety, depression, total cholesterol, and systolic blood pressure. In summary, machine learning models can provide an automated identification mechanism for patients at CVD risk. Integrated treatment measures should be taken in health management, including clinical care, mental health improvement, and health behavior promotion. (shrink)

While the term “metacognition” is sometimes used to refer to any form of thinking about thinking, in cognitive psychology, it is typically reserved for thinking about one’s own thinking, as opposed to thinking about others’ thinking. How metacognition in this more specific sense relates to other-directed mindreading is one of the main theoretical issues debated in the literature. This article considers the idea that we make use of the same or a largely similar package of resources in conceptually interpreting our (...) own mind as we do in interpreting others’. I assume that a capacity for other-directed mindreading is minimally shared with our great-ape relatives, but I argue that the architecture of this system had to be substantially modified before it could efficiently and adaptively be turned inwards on one’s own mind. I contend that an important piece of the overall evolutionary explanation here likely concerns selection pressures arising from the domain of conversational interaction. Specifically, drawing on work carried out in the human interaction studies tradition (e.g., conversation analysis), I argue that the smooth to-and-fro of conversational interaction can be seen to heavily depend on metacommunicative capacities, which, in turn, are underpinned by metacognitive capacities. I conclude with a thumbnail sketch of an evolutionary account of the emergence of these metacognitive capacities in the human line. Their appearance and spread – whether via genes, cultural learning, or more likely, some combination of the two – helps to explain the transition from great-ape communication to human conversation. (shrink)

Diet directly affects children’s physical and mental development. Nonetheless, how food insecurity and household food consumption impact the cognitive performance of children at risk of social exclusion remains poorly understood. In this regard, children in Guatemala face various hazards, mainly related to the socioeconomic difficulties that thousands of families have in the country. The main objective of this study was to analyze the differences in cognitive performance considering food insecurity and household food consumption in a sample of rural and urban (...) Guatemalan children and adolescents at risk of social exclusion. Child cognitive performance was assessed in 134 children and adolescents from rural and urban settings. Language, attention, and executive functions were assessed using neuropsychological tasks. Differences in cognitive performance in each level of food insecurity and household diet consumption were compared using the Mann–Whitney U test. A stepwise multivariate regression analysis was conducted to determine which factors may influence cognitive scores. The results showed that rural and urban groups did not differ in terms of food insecurity. However, considering just rural areas, differences were found between groups with food security and insecurity in attention and executive function tasks. Moreover, differences were found in food consumption for certain groups of food. Regarding regressions, protein food consumption, which is related to having a more balanced diet, was a relevant factor in executive performance. Contrary to what we expected, performance in attentional tasks was not related to the consumption of any food group. These findings could help politicians and decision-makers to select actions focused on improving diet balance and food security in families at risk of social exclusion. It is necessary to carry out more specific studies on the factors related to diet that affect the cognitive development of minors at risk of social exclusion. In addition, it is necessary to study the implementation of alternative interventions that include low-cost nutrients, thus ensuring that minors have access to a more balanced diet. (shrink)

This study aims to achieve the goal of cultivating and reserving emerging professional talents in social security law, improve the curriculum and mechanism of entrepreneurship education, and improve students’ entrepreneurial willingness and entrepreneurial ability. Deep learning technology is used to study the psychological effects of entrepreneurship education for college students majoring in social security law. Firstly, the concept of entrepreneurial psychology is elaborated and summarized. A related model is designed using the theory of proactive personality and planned behavior (...) through questionnaire survey and regression analysis to explore the relationship between students’ entrepreneurial psychology and entrepreneurial intention. Secondly, an entrepreneurship education method based on deep learning is proposed, and a teaching model of multi-dimensional collaborative entrepreneurship education practice is constructed. On this basis, the deep learning algorithm combines the characteristics of the personalized recommendation algorithm to construct an efficient Problem-Based Learning learning resource recommendation algorithm. Finally, the proposed method is tested. The results show that the Significant value of students who have participated in PBL deep learning courses is less than 0.05, indicating that PBL significantly improves students’ learning ability and the ability to deal with entrepreneurial environments. The results verify the impact of entrepreneurial learning on entrepreneurial intentions. The research on PBL online learning recommendation system shows that the proposed recommendation algorithm is superior to the traditional recommendation algorithm in both roots mean square error value and mean absolute error value on both datasets. The proposed method provides a new idea of reform and innovation to cultivate social security law professionals and the cultivation of the reserve model. (shrink)

This essay explores connections between bacteriology and the disciplinary evolution of biochemistry in this country during the 1930s. Many features of intermediary metabolism, a central component of biochemistry, originated as attempts to answer fundamental bacteriological questions. Thus, many bacteriologists altered their research programs to answer these questions. In so doing they changed their disciplinary focus from bacteriology to biochemistry. Chester Hamlin Werkman's (1893-1962) Iowa State career illustrates the research perspective that many bacteriologists adopted. As a junior faculty member in (...) the Bacteriology Department in the late 1920s, Werkman faced a powerful professional dilemma: establishing a research identity that distinguished him from his colleagues with flourishing national and international reputations. His solution was to radically alter his research program from traditional bacteriology to a biochemistry program, which reflected the influence of the Dutch microbiologist/biochemist, Albert Jan Kluyver (1888-1956). Werkman was extremely successful in this career change. His laboratory made significant contributions to biochemistry, and Werkman achieved a notable degree of personal success. His career began in the shadow of his departmental bacteriological colleagues; within a decade he became the department's dominant research figure, as a biochemist. Werkman's personal success, however, had profound consequences for the disciplinary future of bacteriology at Iowa State. (shrink)

Reactions to the target article included requests for extensions and elaborations of the schema we proposed and discussions of apparent shortcomings of our approach. In general, we welcome suggestions for extension of the schema to additional kinds of social behavior and to forms of learning other than Pavlovian conditioning. Many of the requested elaborations of the schema are consistent with our approach, but some may limit its generality. Many of the apparent shortcomings that commentators discussed do not seem (...) problematic. Our schema encourages a broad view of the behavioral consequences of Pavlovian conditioning – including learned modifications of responding to the unconditioned stimulus. Costs and benefits addressed by our schema are the long-range reproductive consequences of learning – not the immediate reinforcing consequences of particular conditioned responses. Our approach allows the evolution of learning to yield maladaptive behavior and can be extended to characterize dynamic social interactions. We clarify that ours is not a homeostatic model involving ideal set points, and we clarify and defend our application of Pavlovian concepts to the analysis of social play. (shrink)

Nowadays, the development of human society and daily life are inseparable from the power supply. Therefore, people also put forward higher requirements for the reliability of distribution network, but power companies can only passively deal with distribution network failures, which is a bottleneck for the improvement of distribution network reliability. The Internet of Things is the best solution for online equipment status monitoring and basic data sharing for large, widely distributed, relatively fixed, and large numbers of equipment. The construction (...) of the IoT for power distribution equipment faces many important problems, including the selection of networking, equipment selection, and interaction standards. When researching the implementation plan, research on the distribution of IoT market was carried out. Based on the grid, the idea of optimizing the investment selection plan of the power distribution using IoT was discussed, and a result verification model was established. After the completion of the theoretical part, a case study of medium-voltage distribution grid equipment management and medium-voltage distribution network equipment management based on the grid was carried out by taking a real enterprise application situation as an example. Realizing fault diagnosis of distribution network will not only provide decision support for operation and maintenance of distribution network for power companies, but also have great economic and social benefits. Aiming at the shortcomings of single data mining method in distribution network fault diagnosis, hybrid data mining method is proposed. First, rough set theory is used to reduce the original fault data and form a simplified rule set. Because of the non-linearity of distribution network fault and the strong learning ability, adaptability, and robustness of Bayesian network, Bayesian network can be used to classify distribution network faults. Therefore, a simplified fault diagnosis system is established in this paper, and its correctness is confirmed. Then, the learning and training are carried out by using Bayesian network to call the simplest rule set, which has the characteristics of short learning and training time and high diagnostic accuracy. (shrink)

In the process of children’s psychological development, various levels of psychological distress often occur, such as attention problems, emotional problems, adaptation problems, language problems, and motor coordination problems; these problems have seriously affected children’s healthy growth. Scene matching in the treatment of psychological distress can prompt children to change from a third-person perspective to a first-person perspective and shorten the distance between scene contents and child’s perceptual experience. As a part of machine learning, deep learning can perform (...) mapping transformations in huge data, process huge data with the help of complex models, and extract multilayer features of scene information. Based on the summary and analysis of previous research works, this paper expounded the research status and significance of the scene matching method for children’s psychological distress, elaborated the development background, current status, and future challenges of deep learning algorithm, introduced the methods and principles of depth spatiotemporal feature extraction algorithm and dynamic scene understanding algorithm, constructed a scene matching model for children’s psychological distress based on deep learning algorithm, analyzed the scene feature extraction and matching function construction of children’s psychological distress, proposed a scene matching method for children’s psychological distress based on deep learning algorithm, performed scene feature matching and information processing of children’s psychological distress, and finally conduced a simulation experiment and analyzed its results. The results show that the deep learning algorithm can have a deep and abstract mining on the characteristics of children’s psychological distress scenes and obtain a large amount of more representative characteristic information through training on large-scale data, thereby improving the accuracy of classification and matching of children’s psychological distress scenes. The study results of this paper provide a reference for further researches on the scene matching method for children’s psychological distress based on deep learning algorithm. (shrink)

Text emotion analysis is an effective way for analyzing the emotion of the subjects’ anomie behaviors. This paper proposes a text emotion analysis framework based on word embedding and splicing. Bi-direction Convolutional Word Embedding Classification Framework can express the word vector in the text and embed the part of speech tagging information as a feature of sentence representation. In addition, an emotional parallel learning mechanism is proposed, which uses the temporal information of the parallel structure calculated by (...) Bi-LSTM to update the storage information through the gating mechanism. The convolutional layer can better extract certain components of sentences, which play a more significant role in the expression of emotion. To take advantage of convolution, a Convolutional Long Short-Term Memory network is designed to further improve the classification results. Experimental results show that compared with traditional LSTM model, the proposed text emotion analysis model has increased 3.3 and 10.9% F1 score on psychological and news text datasets, respectively. The proposed CBDM model based on Bi-LSTM and ConvLSTM has great value in practical applications of anomie behavior analysis. (shrink)

Repetitive practice is one of the most important factors in improving the performance of motor skills. This paper focuses on the analysis and classification of forearm gestures in the context of violin playing. We recorded five experts and three students performing eight traditional classical violin bow-strokes: martelé, staccato, detaché, ricochet, legato, trémolo, collé, and col legno. To record inertial motion information, we utilized the Myo sensor, which reports a multidimensional time-series signal. We synchronized inertial motion recordings with audio (...) data to extract the spatiotemporal dynamics of each gesture. Applying state-of-the-art deep neural networks, we implemented and compared different architectures where convolutional neural networks models demonstrated recognition rates of 97.147%, 3DMultiHeaded_CNN models showed rates of 98.553%, and rates of 99.234% were demonstrated by CNN_LSTM models. The collected data contained sufficient information to distinguish the bowing techniques studied, and deep learning methods were capable of learning the movement patterns that distinguish these techniques. Each of the learning algorithms investigated produced high classification accuracies which supported the feasibility of training classifiers. The resulting classifiers may provide the foundation of a digital assistant to enhance musicians' time spent practicing alone, providing real-time feedback on the accuracy and consistency of their musical gestures in performance. (shrink)