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Road pavement cracks detection has been a hot research topic for quite a long time due to the practical importance of crack detection for road maintenance and traffic safety. Many methods have been proposed to solve this problem. This paper reviews the three major types of methods used in road cracks detection: image processing, machine learning and 3D imaging based methods. Image processing algorithms mainly include threshold segmentation, edge detection and region growing methods, which are used to process images and identify crack features. Crack detection based traditional machine learning methods such as neural network and support vector machine still relies on hand-crafted features using image processing techniques. Deep learning methods have fundamentally changed the way of crack detection and greatly improved the detection performance. In this work, we review and compare the deep learning neural networks proposed in crack detection in three ways, classification based, object detection based and segmentation based. We also cover the performance evaluation metrics and the performance of these methods on commonly-used benchmark datasets. With the maturity of 3D technology, crack detection using 3D data is a new line of research and application. We compare the three types of 3D data representations and study the corresponding performance of the deep neural networks for 3D object detection. Traditional and deep learning based crack detection methods using 3D data are also reviewed in detail. INDEX TERMS Crack detection, image processing, deep learning, 3D imaging.

There have been steady improvements in protein structure prediction during the past 2 decades. However, current methods are still far from consistently predicting structural models accurately with computing power accessible to common users. Toward achieving more accurate and efficient structure prediction, we developed a number of novel methods and integrated them into a software package, MUFOLD. First, a systematic protocol was developed to identify useful templates and fragments from Protein Data Bank for a given target protein. Then, an efficient process was applied for iterative coarse-grain model generation and evaluation at the Cα or backbone level. In this process, we construct models using interresidue spatial restraints derived from alignments by multidimensional scaling, evaluate and select models through clustering and static scoring functions, and iteratively improve the selected models by integrating spatial restraints and previous models. Finally, the full-atom models were evaluated using molecular dynamics simulations based on structural changes under simulated heating. We have continuously improved the performance of MUFOLD by using a benchmark of 200 proteins from the Astral database, where no template with >25% sequence identity to any target protein is included. The average root-mean-square deviation of the best models from the native structures is 4.28 Å, which shows significant and systematic improvement over our previous methods. The computing time of MUFOLD is much shorter than many other tools, such as Rosetta. MUFOLD demonstrated some success in the 2008 communitywide experiment for protein structure prediction CASP8.

MXenes are attractive candidates as surface-enhanced Raman scattering (SERS) substrates because of their metallic conductivity and abundant surface terminations. Herein, we report the facile synthesis of bimetallic solid-solution TiVC (MXene) and its application in SERS. The few-layered MXene nanosheets with high crystallinity were successfully prepared using a one-step chemical etching method without ultrasonic and organic solvent intercalation steps. SERS activity of the as-prepared MXene was investigated by fabricating free-standing TiVC film as the substrate. A SERS enhancement factor of 10 12 and femtomolar-level detection limit were confirmed using rhodamine 6G as a model dye with 532 nm excitation. The fluorescent signal of the rhodamine 6G dye was effectively quenched, making the SERS spectrum clearly distinguishable. Furthermore, we demonstrate that the TiVC-analyte system with ultrahigh sensitivity is dominated by the chemical mechanism (CM) based on the experimental and simulation results. The abundant density of states near the Fermi level of the TiVC and the strong interaction between the TiVC and analyte promote the intermolecular charge transfer resonance in the TiVC−analyte complex, resulting in significant Raman enhancement. Additionally, several other probe molecules were used for SERS detection to further verify CM-based selectivity enhancement on the TiVC substrates. This work provides guidance for the facile synthesis of 2D MXene and its application in ultrasensitive SERS detection.

Deep learning methods have achieved great success in analyzing traditional data such as texts, sounds, images and videos. More and more research works are carrying out to extend standard deep learning technologies to geometric data such as point cloud or voxel grid of 3D objects, real life networks such as social and citation network. Many methods have been proposed in the research area. In this work, we aim to provide a comprehensive survey of geometric deep learning and related methods. First, we introduce the relevant knowledge and history of geometric deep learning field as well as the theoretical background. In the method part, we review different graph network models for graphs and manifold data. Besides, practical applications of these methods, datasets currently available in different research area and the problems and challenges are also summarized. INDEX TERMS Convolutional neural networks, geometric deep learning, graph, manifold.