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In this paper, Bayesian estimation and Coleman filtering are used to compose a multidimensional data fusion algorithm to study the cultivation of students’ personalized core literacy. A Trans E model is constructed based on the calculation of students’ specific course ratings using collaborative filtering algorithms. Combining the knowledge graph and collaborative filtering methods, the similarities between the course and user ratings are calculated respectively. The user’s rating situation can be predicted by combining similarity. Through the empirical analysis method, the overall and specific competency dimensions of students’ core literacy and the effect of core literacy cultivation, as well as students’ satisfaction, were analyzed, and the corresponding practice paths were proposed based on the analysis results. The mean value of students’ core literacy test is 46.25, and the number of students scoring between 50-60 points is the largest, but the number of excellent students is 0, which is poor in the cultivation of core literacy. The analysis of the effect of core literacy cultivation shows that in the correlation analysis of teaching effect and teaching support, the p-value is less than 0.01, but most of the correlation coefficients are less than 0.35, and there exists a weak relationship between the two, and the school authorities should strengthen the teaching support of core literacy.

In this paper, we use the support vector regression algorithm to minimize the loss function between the real value of sports tourism samples and the predicted value of the model, deal with the complex nonlinear problems through the MLP neural network model, and design a sports tourism development model based on the machine learning model. Focusing on the use of a support vector regression algorithm and MLP neural network model to predict the tourist flow, the panel threshold regression model is used to test whether the variables have a threshold effect on each other and the relationship between the sports industry and tourism development is deeply explored. Based on this basis, the current situation and development of sports tourism were analyzed using data from Province S and 29 provinces in China. The results show that there is a threshold effect on the influence of the Chinese sports industry on regional tourism development, and the estimated value of the threshold is 1.8293. The level of the sports industry in Chinese provinces can be divided into less developed regions of the sports industry ≤1.8293 and developed regions of the sports industry >1.8293. In the first model, which only includes the value of the level of the sports industry in the current period, the relationship between the sports industry and regional tourism development presents a “ λ Λ” shape.

Ferroelectric photoelectric devices show promising applications in artificial vision systems. However, this type of devices used in artificial vision systems exhibits low photoresponsivity and this limited cyclability of photocurrents, which hinders the progress of artificial vision systems. In this study, an artificial vision system is constructed with high photoresponsivity, fast photoresponse speed, and high read/write speed based on the Pd/PbZr0.4Ti0.6O3/La0.3Sr0.7MnO3/LaAlO3 ferroelectric photomemristor. The high optical response (18.86 nanoamperes) and read/write speeds (50 nanoseconds) of the device significantly improve the robustness and speed of the neuromorphic visual system while reducing power consumption for recognition. The artificial vision system developed in this study provides excellent real‐time image processing capabilities, including edge detection and classification with 100% accuracy for musical key patterns, and it is expected to offer great potential for the development of high‐performance self‐powered artificial vision systems. [ABSTRACT FROM AUTHOR]

This study investigated the impact of electric pulse treatment (EPT) on the microstructure and mechanical properties of laser powder bed fused Inconel 718 (IN718). Through a comprehensive experimental characterization, we found that EPT induced significant improvements in the microstructure of IN718. In the YOZ plane of EPT-700, the molten pool diminished and replaced by a grain boundary with granular Ni3Nb precipitates, and the dislocations increased while the irregular porosity decreased. Concurrently, enhanced mechanical properties of EPT-700 were obtained, including a hardness of 354.7 HV, an ultimate tensile strength of 930.21 MPa, and an elongation of 34.35%. Fractographic analysis revealed a transition in fracture mechanisms, highlighting the intricate relationship between microstructural modifications induced by EPT and mechanical response under load. These findings underscore the potential of EPT as a promising post-processing technique for optimizing the microstructure and mechanical properties of IN718 components fabricated via laser powder bed fusion additive manufacturing. This study contributes to the advancement of knowledge in the field of additive manufacturing and provides valuable insights for the development of high-performance metallic components. [ABSTRACT FROM AUTHOR]

Data collected from distributed sources or sites commonly have different distributions or contaminated observations. Active learning procedures allow us to assess data when recruiting new data into model building. Thus, combining several active learning procedures together is a promising idea, even when the collected data set is contaminated. Here, we study how to conduct and integrate several adaptive sequential procedures at a time to produce a valid result via several machines or a parallel‐computing framework. To avoid distraction by complicated modelling processes, we use confidence set estimation for linear models to illustrate the proposed method and discuss the approach's statistical properties. We then evaluate its performance using both synthetic and real data. We have implemented our method using Python and made it available through Github at https://github.com/zhuojianc/dsep. [ABSTRACT FROM AUTHOR]

Modern computing and communication technologies can make data collection procedures very efficient, while our ability to analyze and extract information from large data sets is hard-pressed to keep up with our capacity for data collection. If we are interested in learning a classification/prediction rule that was not considered in the original data collection procedure, then there is usually a lack of label information in the original data. Thus, how to start with minimum number of labeled data and aggressively selected most informative samples for being labeled becomes an important issue. The thoughts of active learning, with subjects to be selected sequentially without using label information, is a possible outlet for this situation. In addition, if we can identify variables, from the lengthy variable list of the data set, for constructing an effective classification rule with good interpretation ability will be better. Here, we propose an active learning procedure targeted the maximum area under the receiver operating characteristic curve via a commonly use logistic model, which can sequentially select the effective informative subjects and variables, simultaneously. We discuss the asymptotic properties of the proposed procedure and illustrate it with some synthesized and real data sets. [ABSTRACT FROM AUTHOR]

Neuro-symbolic artificial intelligence has garnered considerable attention amid increasing industry demands for high-performance neural networks that are interpretable and adaptable to previously unknown problem domains with minimal reconfiguration. However, implementing neuro-symbolic hardware is challenging due to the complexity in symbolic knowledge representation and calculation. We experimentally demonstrated a memristor-based neuro-fuzzy hardware based on TiN/TaO x /HfO x /TiN chips that is superior to its silicon-based counterpart in terms of throughput and energy efficiency by using array topological structure for knowledge representation and physical laws for computing. Intrinsic memristor variability is fully exploited to increase robustness in knowledge representation. A hybrid in situ training strategy is proposed for error minimizing in training. The hardware adapts easier to a previously unknown environment, achieving ~6.6 times faster convergence and ~6 times lower error than deep learning. The hardware energy efficiency is over two orders of magnitude greater than field-programmable gate arrays. This research greatly extends the capability of memristor-based neuromorphic computing systems in artificial intelligence.