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We make some remarks on the linear wave equation concerning the existence and uniqueness of weak solutions, satisfaction of the energy equation, growth properties of solutions, the passage from bounded to unbounded domains, and reconciliation of different representations of solutions. [ABSTRACT FROM AUTHOR]

The pursuit of autonomous driving relies on developing perception systems capable of making accurate, robust, and rapid decisions to interpret the driving environment effectively. Object detection is crucial for understanding the environment at these systems' core. While 2D object detection and classification have advanced significantly with the advent of deep learning (DL) in computer vision (CV) applications, they fall short in providing essential depth information, a key element in comprehending driving environments. Consequently, 3D object detection becomes a cornerstone for autonomous driving and robotics, offering precise estimations of object locations and enhancing environmental comprehension. The CV community's growing interest in 3D object detection is fueled by the evolution of DL models, including Convolutional Neural Networks (CNNs) and Transformer networks. Despite these advancements, challenges such as varying object scales, limited 3D sensor data, and occlusions persist in 3D object detection. To address these challenges, researchers are exploring multimodal techniques that combine information from multiple sensors, such as cameras, radar, and LiDAR, to enhance the performance of perception systems. This survey provides an exhaustive review of multimodal fusion-based 3D object detection methods, focusing on CNN and Transformer-based models. It underscores the necessity of equipping fully autonomous vehicles with diverse sensors to ensure robust and reliable operation. The survey explores the advantages and drawbacks of cameras, LiDAR, and radar sensors. Additionally, it summarizes autonomy datasets and examines the latest advancements in multimodal fusion-based methods. The survey concludes by highlighting the ongoing challenges, open issues, and potential directions for future research. [ABSTRACT FROM AUTHOR]

The purpose of the current study is to explore several correlates of adolescent students' preferences for at-home virtual or in-class in-person learning in a single case of a school that serves students with learning differences. Correlates of interest were the Big Five personality traits (Openness to Experience, Conscientiousness, Extroversion, Agreeableness, Neuroticism) and the students' self-reported learning engagement. Participants were recruited from a single independent school for students with neurodiversity and special learning needs, where they had high exposure to computer-/internet-assisted learning. Twenty-seven students responded to questionnaires measuring preferred learning modes, personality traits, and learning engagement. Despite teacher reports that some of these students thrived with virtual learning during the COVID-19 pandemic, 88.5% of this sample preferred in-class learning. The personality traits of Conscientiousness and Openness to Experience were related to a preference for in-class learning. A preference for in-class learning was related, in turn, to learning engagement. Learning engagement was associated with Conscientiousness, Agreeableness, and lower levels of Neuroticism. The strengths and limitations of this study and its implications for further research and practice are discussed. [ABSTRACT FROM AUTHOR]

The article "A Farewell, Steeped in Joy and Gratitude" by John David Ball discusses his departure as the Editor-in-Chief of Perceptual and Motor Skills (PMS) after 8 years, passing the responsibilities to Dr. Oliver Runswick. Ball expresses gratitude to mentors, colleagues, and contributors who supported him during his tenure. The article highlights the journal's focus on neuropsychological origins and its impact factor improvement under Ball's leadership. Ball encourages readers to continue supporting PMS under new leadership for further advancements in psychological research. [Extracted from the article]