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1دورية أكاديمية
المؤلفون: Kaiming Han, Hong Liu, Madeline E. Rotella, Zeyu Xu, Lizhi Tao, Shufeng Chen, Marisa C. Kozlowski, Tiezheng Jia
المصدر: Nature Communications, Vol 15, Iss 1, Pp 1-9 (2024)
مصطلحات موضوعية: Science
الوصف: Abstract The unique features of the sulfenamides’ S(II)-N bond lead to interesting stereochemical properties and significant industrial functions. Here we present a chemoselective Chan–Lam coupling of sulfenamides to prepare N-arylated sulfenamides. A tridentate pybox ligand governs the chemoselectivity favoring C–N bond formation, and overrides the competitive C-S bond formation by preventing the S,N-bis-chelation of sulfenamides to copper center. The Cu(II)-derived resting state of catalyst is captured by UV-Vis spectra and EPR technique, and the key intermediate is confirmed by the EPR isotope response using 15N-labeled sulfenamide. A computational mechanistic study reveals that N-arylation is both kinetically and thermodynamically favorable, with deprotonation of the sulfenamide nitrogen atom occurring prior to reductive elimination. The origin of ligand-controlled chemoselectivity is explored, with the interaction between the pybox ligand and the sulfenamide substrate controlling the energy of the S-arylation and the corresponding product distribution, in agreement with the EPR studies and kinetic results.
وصف الملف: electronic resource
Relation: https://doaj.org/toc/2041-1723
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2دورية أكاديمية
المؤلفون: Zeyu Xu, Tiejun Wang, Andrew K. Skidmore, Richard Lamprey
المصدر: International Journal of Applied Earth Observations and Geoinformation, Vol 128, Iss , Pp 103732- (2024)
مصطلحات موضوعية: Biodiversity, Wildlife, Livestock, Remote sensing, Artificial intelligence, Object detection, Physical geography, GB3-5030, Environmental sciences, GE1-350
الوصف: Deep learning is an effective machine learning method that in recent years has been successfully applied to detect and monitor species population in remotely sensed data. This study aims to provide a systematic literature review of current applications of deep learning methods for animal detection in aerial and satellite images. We categorized methods in collated publications into image level, point level, bounding-box level, instance segmentation level, and specific information level. The statistical results show that YOLO, Faster R-CNN, U-Net and ResNet are the most used neural network structures. The main challenges associated with the use of these deep learning methods are imbalanced datasets, small samples, small objects, image annotation methods, image background, animal counting, model accuracy assessment, and uncertainty estimation. We explored possible solutions include the selection of sample annotation methods, optimizing positive or negative samples, using weakly and self-supervised learning methods, selecting or developing more suitable network structures. Future research trends we identified are video-based detection, very high-resolution satellite image-based detection, multiple species detection, new annotation methods, and the development of specialized network structures and large foundation models. We discussed existing research attempts as well as personal perspectives on these possible solutions and future trends.
وصف الملف: electronic resource
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3دورية أكاديمية
المؤلفون: Zeyu Xu, Gongjian Zhou
المصدر: IET Radar, Sonar & Navigation, Vol 17, Iss 10, Pp 1558-1580 (2023)
مصطلحات موضوعية: integration, object detection, radar detection, radar signal processing, Telecommunication, TK5101-6720
الوصف: Abstract Long‐time coherent integration (LTCI) is one of the most effective approaches to enhance radar detection ability of weak targets. The existing LTCI methods generally assume that the target motion satisfies polynomial models in range coordinates. However, it may be hard to provide precise description of common target motions, especially manoeuvring motions, using a polynomial with finite terms. In this study, LTCI for radar detection of weak manoeuvring targets is investigated. The range evolution rules over time are derived and employed to formulate the target signal for two common manoeuvring motions, constant acceleration (CA) and constant turn (CT) motions in Cartesian coordinates. Aiming at realising accurate energy accumulation and improving detection performance of CA and CT targets, two LTCI algorithms based on the derived range evolution model are proposed. By extracting target signal and compensating for phase fluctuation between samples in accordance with the accurate range evolution model, the coherent accumulation of target signal can be effectively accomplished, eliminating the complex range and Doppler frequency migration effects simultaneously. The use of accurate range evolution model guarantees effective energy integration regardless of target manoeuvrability and integration time as long as the target performs CA or CT motion. Moreover, more information of target kinematic characteristics such as target acceleration and turn rate, additional to range and Doppler, can be observed. Simulations are conducted to validate the effectiveness of the proposed algorithms. The cover image is based on the Research Article Long‐time coherent integration for radar detection of manoeuvring targets based on accurate range evolution model by Zeyu Xu et al., https://doi.org/10.1049/rsn2.12445.
وصف الملف: electronic resource
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4دورية أكاديمية
المؤلفون: Zeyu Xu, Wenbin Yu, Chengjun Zhang, Yadang Chen
المصدر: Information, Vol 15, Iss 4, p 175 (2024)
مصطلحات موضوعية: quantum computing, long short-term memory, variational quantum algorithm, quantum convolutional neural network, noise issues, Information technology, T58.5-58.64
الوصف: In the era of noisy intermediate-scale quantum (NISQ) computing, the synergistic collaboration between quantum and classical computing models has emerged as a promising solution for tackling complex computational challenges. Long short-term memory (LSTM), as a popular network for modeling sequential data, has been widely acknowledged for its effectiveness. However, with the increasing demand for data and spatial feature extraction, the training cost of LSTM exhibits exponential growth. In this study, we propose the quantum convolutional long short-term memory (QConvLSTM) model. By ingeniously integrating classical convolutional LSTM (ConvLSTM) networks and quantum variational algorithms, we leverage the variational quantum properties and the accelerating characteristics of quantum states to optimize the model training process. Experimental validation demonstrates that, compared to various LSTM variants, our proposed QConvLSTM model outperforms in terms of performance. Additionally, we adopt a hierarchical tree-like circuit design philosophy to enhance the model’s parallel computing capabilities while reducing dependence on quantum bit counts and circuit depth. Moreover, the inherent noise resilience in variational quantum algorithms makes this model more suitable for spatiotemporal sequence modeling tasks on NISQ devices.
وصف الملف: electronic resource
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5دورية أكاديمية
المؤلفون: Zeyu Xu, Yu Han, Dianbo Zhao, Ke Li, Junguang Li, Junyi Dong, Wenbo Shi, Huijuan Zhao, Yanhong Bai
المصدر: Foods, Vol 13, Iss 3, p 469 (2024)
مصطلحات موضوعية: livestock and poultry meat, quality detection, machine vision, hyperspectral, multi-source information fusion, Chemical technology, TP1-1185
الوصف: Presently, the traditional methods employed for detecting livestock and poultry meat predominantly involve sensory evaluation conducted by humans, chemical index detection, and microbial detection. While these methods demonstrate commendable accuracy in detection, their application becomes more challenging when applied to large-scale production by enterprises. Compared with traditional detection methods, machine vision and hyperspectral technology can realize real-time online detection of large throughput because of their advantages of high efficiency, accuracy, and non-contact measurement, so they have been widely concerned by researchers. Based on this, in order to further enhance the accuracy of online quality detection for livestock and poultry meat, this article presents a comprehensive overview of methods based on machine vision, hyperspectral, and multi-sensor information fusion technologies. This review encompasses an examination of the current research status and the latest advancements in these methodologies while also deliberating on potential future development trends. The ultimate objective is to provide pertinent information and serve as a valuable research resource for the non-destructive online quality detection of livestock and poultry meat.
وصف الملف: electronic resource
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6دورية أكاديمية
المؤلفون: Di Zang, Xiangyu Zhao, Yuanfang Qiao, Jiayu Huo, Xuehai Wu, Zhe Wang, Zeyu Xu, Ruizhe Zheng, Zengxin Qi, Ying Mao, Lichi Zhang
المصدر: Brain Informatics, Vol 10, Iss 1, Pp 1-15 (2023)
مصطلحات موضوعية: Structural imaging, Brain parcellation, Hydrocephalus, Traumatic brain injury, Consciousness, Computer applications to medicine. Medical informatics, R858-859.7, Computer software, QA76.75-76.765
الوصف: Abstract Brain network analysis based on structural and functional magnetic resonance imaging (MRI) is considered as an effective method for consciousness evaluation of hydrocephalus patients, which can also be applied to facilitate the ameliorative effect of lumbar cerebrospinal fluid drainage (LCFD). Automatic brain parcellation is a prerequisite for brain network construction. However, hydrocephalus images usually have large deformations and lesion erosions, which becomes challenging for ensuring effective brain parcellation works. In this paper, we develop a novel and robust method for segmenting brain regions of hydrocephalus images. Our main contribution is to design an innovative inpainting method that can amend the large deformations and lesion erosions in hydrocephalus images, and synthesize the normal brain version without injury. The synthesized images can effectively support brain parcellation tasks and lay the foundation for the subsequent brain network construction work. Specifically, the novelty of the inpainting method is that it can utilize the symmetric properties of the brain structure to ensure the quality of the synthesized results. Experiments show that the proposed brain abnormality inpainting method can effectively aid the brain network construction, and improve the CRS-R score estimation which represents the patient’s consciousness states. Furthermore, the brain network analysis based on our enhanced brain parcellation method has demonstrated potential imaging biomarkers for better interpreting and understanding the recovery of consciousness in patients with secondary hydrocephalus.
وصف الملف: electronic resource
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7دورية أكاديمية
المؤلفون: Shiyao Chen, Chun Guo, Jian Guo, Li Liu, Meiqin Wang, Puwen Wei, Zeyu Xu
المصدر: IACR Transactions on Symmetric Cryptology, Vol 2023, Iss 2 (2023)
مصطلحات موضوعية: Symmetric-Key, Cryptanalysis, Proof, MPC/FHE/ZK-Friendly Primitives, Generalized Feistel, GMiMC, Computer engineering. Computer hardware, TK7885-7895
الوصف: Symmetric-key primitives designed over the prime field Fp with odd characteristics, rather than the traditional Fn2 , are becoming the most popular choice for MPC/FHE/ZK-protocols for better efficiencies. However, the security of Fp is less understood as there are highly nontrivial gaps when extending the cryptanalysis tools and experiences built on Fn2 in the past few decades to Fp. At CRYPTO 2015, Sun et al. established the links among impossible differential, zero-correlation linear, and integral cryptanalysis over Fn2 from the perspective of distinguishers. In this paper, following the definition of linear correlations over Fp by Baignères, Stern and Vaudenay at SAC 2007, we successfully establish comprehensive links over Fp, by reproducing the proofs and offering alternatives when necessary. Interesting and important differences between Fp and Fn2 are observed. - Zero-correlation linear hulls can not lead to integral distinguishers for some cases over Fp, while this is always possible over Fn2 proven by Sun et al.. - When the newly established links are applied to GMiMC, its impossible differential, zero-correlation linear hull and integral distinguishers can be increased by up to 3 rounds for most of the cases, and even to an arbitrary number of rounds for some special and limited cases, which only appeared in Fp. It should be noted that all these distinguishers do not invalidate GMiMC’s security claims. The development of the theories over Fp behind these links, and properties identified (be it similar or different) will bring clearer and easier understanding of security of primitives in this emerging Fp field, which we believe will provide useful guides for future cryptanalysis and design.
وصف الملف: electronic resource
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8دورية أكاديمية
المؤلفون: Zeyu Xu, Wenbo Shi, Dianbo Zhao, Ke Li, Junguang Li, Junyi Dong, Yu Han, Jiansheng Zhao, Yanhong Bai
المصدر: Foods, Vol 12, Iss 18, p 3451 (2023)
مصطلحات موضوعية: food raw materials, low damage grasping, robot gripper, control strategies, Chemical technology, TP1-1185
الوصف: The sorting and processing of food raw materials is an important step in the food production process, and the quality of the sorting operation can directly or indirectly affect the quality of the product. In order to improve production efficiency and reduce damage to food raw materials, some food production enterprises currently use robots for sorting operations of food raw materials. In the process of robot grasping, some food raw materials such as fruits, vegetables and meat have a soft appearance, complex and changeable shape, and are easily damaged by the robot gripper. Therefore, higher requirements have been put forward for robot grippers, and the research and development of robot grippers that can reduce damage to food raw materials and ensure stable grasping has been a major focus. In addition, in order to grasp food raw materials with various shapes and sizes with low damage, a variety of sensors and control strategies are required. Based on this, this paper summarizes the low damage grasp principle and characteristics of electric grippers, pneumatic grippers, vacuum grippers and magnetic grippers used in automated sorting production lines of fruit, vegetable and meat products, as well as gripper design methods to reduce grasp damage. Then, a grasping control strategy based on visual sensors and tactile sensors was introduced. Finally, the challenges and potential future trends faced by food robot grippers were summarized.
وصف الملف: electronic resource
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9دورية أكاديمية
المؤلفون: Andrea Buccellato, Di Zang, Federico Zilio, Javier Gomez-Pilar, Zhe Wang, Zengxin Qi, Ruizhe Zheng, Zeyu Xu, Xuehai Wu, Patrizia Bisiacchi, Alessandra Del Felice, Ying Mao, Georg Northoff
المصدر: NeuroImage, Vol 265, Iss , Pp 119802- (2023)
مصطلحات موضوعية: Electroencephalography, Intrinsic neural timescales, Alpha peak frequency, Disorders of consciousness, Anaesthesia, Temporal input processing, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
الوصف: Our brain processes the different timescales of our environment's temporal input stochastics. Is such a temporal input processing mechanism key for consciousness? To address this research question, we calculated measures of input processing on shorter (alpha peak frequency, APF) and longer (autocorrelation window, ACW) timescales on resting-state high-density EEG (256 channels) recordings and compared them across different consciousness levels (awake/conscious, ketamine and sevoflurane anaesthesia, unresponsive wakefulness, minimally conscious state). We replicate and extend previous findings of: (i) significantly longer ACW values, consistently over all states of unconsciousness, as measured with ACW-0 (an unprecedented longer version of the well-know ACW-50); (ii) significantly slower APF values, as measured with frequency sliding, in all four unconscious states. Most importantly, we report a highly significant correlation of ACW-0 and APF in the conscious state, while their relationship is disrupted in the unconscious states. In sum, we demonstrate the relevance of the brain's capacity for input processing on shorter (APF) and longer (ACW) timescales - including their relationship - for consciousness. Albeit indirectly, e.g., through the analysis of electrophysiological activity at rest, this supports the mechanism of temporo-spatial alignment to the environment's temporal input stochastics, through relating different neural timescales, as one key predisposing factor of consciousness.
وصف الملف: electronic resource
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10دورية أكاديمية
المؤلفون: Jia Guo, Zeyu Xu, Rachel C. Gunderson, Baohui Xu, Sara A. Michie
المصدر: Biomolecules, Vol 13, Iss 8, p 1194 (2023)
مصطلحات موضوعية: leukocyte function-associated antigen-1, intercellular adhesion molecule-1, lymphocytes, migration, lymphatics, Microbiology, QR1-502
الوصف: Lymphocyte function-associated antigen-1 (LFA-1) and its endothelial ligand intercellular adhesion molecule-1 (ICAM-1) are important for the migration of lymphocytes from blood vessels into lymph nodes. However, it is largely unknown whether these molecules mediate the homeostatic migration of lymphocytes from peripheral tissues into lymph nodes through lymphatic vessels. In this study, we find that, in naive mice, ICAM-1 is expressed on the sinus endothelia of lymph nodes, but not on the lymphatic vessels of peripheral tissues. In in vivo lymphocyte migration assays, memory CD4+ T cells migrated to lymph nodes from peripheral tissues much more efficiently than from blood vessels, as compared to naive CD4+ T cells. Moreover, ICAM-1 deficiency in host mice significantly inhibited the migration of adoptively transferred wild-type donor lymphocytes from peripheral tissues, but not from blood vessels, into lymph nodes. The migration of LFA-1-deficient donor lymphocytes from peripheral tissues into the lymph nodes of wild-type host mice was also significantly reduced as compared to wild-type donor lymphocytes. Furthermore, the number of memory T cells in lymph nodes was significantly reduced in the absence of ICAM-1 or LFA-1. Thus, our study extends the functions of the LFA-1/ICAM-1 adhesion pathway, indicating its novel role in controlling the homeostatic migration of lymphocytes from peripheral tissues into lymph nodes and maintaining memory T cellularity in lymph nodes.
وصف الملف: electronic resource
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