In recent years, artificial intelligence (AI) and learning analytics (LA) have been introduced into the field of education, where their use has great potential to enhance the teaching and learning processes. Researchers have focused on applying these technologies to teacher education, as they see the value of technology for educating. Therefore, a systematic review of the literature on AI and LA in teacher education is necessary to understand their impact in the field. Our methodology follows the PRISMA guidelines, and 30 studies related to teacher education were identified. This review analyzes and discusses the several ways in which AI and LA are being integrated in teacher education based on the studies’ goals, participants, data sources, and the tools used to enhance teaching and learning activities. The findings indicate that (a) there is a focus on studying the behaviors, perceptions, and digital competence of pre- and in-service teachers regarding the use of AI and LA in their teaching practices; (b) the main data sources are behavioral data, discourse data, and statistical data; (c) machine learning algorithms are employed in most of the studies; and (d) the ethical clearance is mentioned by few studies. The implications will be valuable for teachers and educational authorities, informing their decisions regarding the effective use of AI and LA technologies to support teacher education.

Artificial intelligence (AI) and new technologies are having a pervasive impact on modern societies and communities. Given the potential of these new technologies to transform the way things are done, it is important to understand how they can be used to support inclusive education, particularly regarding minority students. This systematic review analyzes the advantages and challenges of using AI and new technologies in different sociocultural contexts, and their impact on minority students. In terms of advantages, this review found that AI and new technologies (a) improved student performance, (b) encouraged student interest in STEM/STEAM, (c) promoted student engagement, and (d) showed other advantages. This review also identifies the main challenges associated with the use of AI and new technologies for inclusive education: (a) technological challenges, (b) pedagogical challenges, (c) dataset limitations, (d) low satisfaction using technology, and (e) cultural differences. This review proposes some solutions to these challenges at the pedagogical, technological, and sociocultural levels, and also explores important aspects of inclusive education that address the students’ sociocultural diversity. The findings and implications will aid teachers, practitioners, and policymakers in making decisions on the effective use of AI and new technologies to support sociocultural inclusiveness in education.

As a key on-orbit service technology, relative measurement of non-cooperative spatial targets can give accurate relative pose for the unmanned rendezvous and docking of non-cooperative targets. With the rapid development of three-dimensional data acquisition equipment such as flash lidar in the field of non-cooperative targets’ pose measurement in recent years, research on the pose tracking technology based on the three-dimensional (3D) point cloud of non-cooperative targets has become more and more urgent. In this paper, we proposed an approach for autonomous recognition and pose tracking of non-cooperative targets based on point cloud features. For this method, the density, curvature and normal angle of a non-cooperative target 3D point cloud are first calculated as the local feature description (DCA) of the point cloud, and then the feature description is used to match and remove errors through RANSAC. Finally the Iterative Closest Point (ICP) algorithm is adopted to obtain the precise pose of the non-cooperative target. Experimental results have shown that this approach can effectively find the features of non-cooperative tumbling targets, get rid of the dependence on the target model and database, and realize the pose tracking of non-cooperative targets.

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Wireless sensor networks for obstacle detection, environment monitoring, and military surveillance have stringent accuracy requirements for target monitoring and demand long system lifetime. We define meeting such user requirements as confident sensing. In order to achieve confident sensing and energy balance among sensors, it is necessary to cluster sensors properly. But in most traditional approaches, such as sensor-target distance based methods, users' accuracy requirements on sensor correlation can hardly be fulfilled. Accordingly, we first make quantitative study on relationship between sensor-target distance and sensing accuracy as well as sensor correlation among the heterogeneous or homogeneous sensors. Their impacts on sensor collaboration provide guideline for confident sensing. Then, in order to meet user sensing accuracy requirements and obtain energy balance among sensors, we propose a collaborative sensor selection method named CSdT. Based on sensor-target distance and sensor correlation, CSdT scheme clusters right sensors in a distributed way. Besides, from perspective of mutual information, we explain the role of such correlation and sensor-target distance on confident sensing. Finally, we conduct extensive experiments on real data to validate and evaluate CSdT. Experimental results demonstrate the superior performance of our method over existing solutions in terms of meeting users' requirements and obtaining energy balance.

Time series analysis plays a critical role in informed decision-making across domains like energy management, transportation systems, and financial markets. Real-world time series data are inherently characterized by nonlinear dynamics and multi-scale temporal features. Nevertheless, existing methods face challenges such as insufficient nonlinear modeling, incomplete multi-scale feature separation, and ineffective time-frequency domain fusion. To tackle these issues, we put forward the WTConv-iKransformer framework. By incorporating the Kolmogorov-Arnold Network (KAN) into an improved nonlinear attention mechanism (KAN-attention), its nonlinear modeling capacity is enhanced. At the same time, the framework uses wavelet-based multi-frequency decomposition to clearly divide signals into trend, periodic, and noise components, and enhances feature representation via frequency-domain specific convolutions. Lastly, a gating network dynamically balances temporal and frequency-domain features to achieve cross-domain information integration. Experimental results show that the WTConv-iKransformer achieves an additional 3% error reduction compared with individual enhanced models and realizes an average 25% error decrease over mainstream methods (e.g., Informer, LSTM) on ETTh1, ETTm1, Electricity, and Traffic datasets.