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IsoVISoR: Towards 3D Mesoscale Brain Mapping of Large Mammals at Isotropic Sub-micron Resolution

Neuroscience Bulletin 2025年第2期41卷 344-348页

作者： Chao-Yu Yang Yan Shen Xiaoyang Qi Lufeng Ding Yanyang Xiao Qingyuan Zhu Hao Wang Cheng Xu Pak-Ming Lau Pengcheng Zhou Fang Xu Guo-Qiang Bi Division of Life Sciences and Medicine CAS Key Laboratory of Brain Function and DiseaseUniversity of Science and Technology of ChinaHefei230026China Interdisciplinary Center for Brain Information Shenzhen-Hong Kong Institute of Brain ScienceShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055China Center for Integrative Imaging Hefei National Research Center for Physical Sciences at the MicroscaleUniversity of Science and Technology of ChinaHefei230027China National Engineering Laboratory for Brain-Inspired Intelligence Technology and Application MoE Key Laboratory of Brain-Inspired Intelligent Perception and CognitionSchool of Information Science and TechnologyUniversity of Science and Technology of ChinaHefei230026China Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial IntelligenceHefei Comprehensive National Science CenterHefei230088China Faculty of Life and Health Sciences Shenzhen University of Advanced TechnologyShenzhen518107China CAS Key Laboratory of Brain Connectome and Manipulation The Brain Cognition and Brain Disease InstituteShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055China

Dear Editor,The mammalian brain exhibits cross-scale complexity in neuronal morphology and connectivity,the study of which demands high-resolution morphological reconstruction of individual neurons across the entire brain[1-4].Current commonly used approaches for such mesoscale brain mapping include two main types of three-dimensional fluorescence microscopy:the block-face methods,and the lightsheet-based methods[5,6].In general,the high imaging speed and light efficiency of light-sheet microscopy make it a suitable tool for high-throughput volumetric imaging,especially when combined with tissue-clearing ***,large brain samples pose major challenges to this approach.

关键词： brain mapping three dimensional fluorescence microscopy mesoscale brain mapping lightsheet based methods block face methods high throughput volumetric imaging tissue clearing techniques

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Comparative analysis of data-driven models on detection and classification of electrical faults in transmission systems: Explainability, applicability and industrial implications

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Alexandria engineering Journal 2025年 127卷 75-91页

作者： Ukwuoma, Chibueze D. Cai, Dongsheng Ukwuoma, Chiagoziem C. Otuka, Chinedu I. Huang, Qi College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan 610059 China Sichuan Engineering Technology Research Center for Industrial Internet Intelligent Monitoring and Application Chengdu University of Technology Sichuan 610059 China OBU Sino-British Collaborative Education Chengdu University of Technology Sichuan 610059 China Information Engineering Department Southwest University of Science and Technology Sichuan Mianyang China

Most data-driven fault detection methods often face challenges in accuracy, adaptability, and real-time implementation, particularly in complex transmission networks. To address these issues, this study presents an in-depth comparative analysis of data-driven models, including machine learning, neural networks, and deep learning techniques, for detecting and classifying electrical faults in transmission lines. A few key gaps were identified to persist particularly in terms of the accuracy, computational complexity, explainability, applicability, and industrial implications of these models. Hence, a Quadratic Discriminant Analysis by Projection (QDA-P) model which effectively captures non-linear relationships between electrical fault features is proposed, enhancing classification accuracy, less computationally complex and explainable for both binary and multi-class scenarios. Using a publicly available simulated dataset generated through MATLAB Simulink to represent various fault types, the QDA-P model achieved binary classification scores of 0.988, 0.980, 0.982, 0.995, and 0.987 while recording a multi-class classification score of 0.982, 0.979, 0.982, 0.982, and 0.980 for accuracy, precision, specificity, recall, and F1-score, respectively. Feature importance analysis showed that voltage at the sending or receiving end of the transmission line for phase A was the most influential while the length of the transmission line in phase A had the lowest importance. In contrast, techniques such as SHAP, LIME, and PDP reveal that the length of the transmission line in Phases A & C significantly influenced Class 1 and Class 5 predictions, with the length of the transmission line in Phase C contributing positively and the length of the transmission line in phase A showing varied effects. An industrial applicability analysis using the Mahalanobis distance plot confirmed that the QDA-P model effectively captured underlying data patterns without significant outliers. These findi

关键词： Discriminant analysis

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MCT-Net: a multi-branch hybrid CNN-transformer model for medical image segmentation

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Pattern Analysis and applications 2025年第2期28卷 1-21页

作者： Shen, Longfeng Diao, Liangjin Peng, Rui Chen, Jiacong Lu, Zhengtian Ge, Fangzhen Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior (ICACB) College of Computer Science and Technology Huaibei Normal University Huaibei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Anhui Big-Data Research Center on University Management Huaibei Normal University Huaibei China

In recent years, neural networks have demonstrated substantial progress in medical image segmentation. However, accurately segmenting objects in medical images is often restricted by edge blurring, which complicates the delineation of structures of interest. The purpose of this study is to develop a multi-branch hybrid CNN-transformer model to address the issue of edge blurring in medical image segmentation. This approach enables more precise edge detection and segmentation. Integrating hybrid CNN and transformer decoders facilitates detailed recovery while preserving global structures, thus minimizing errors in regions with blurred edges. Additionally, we incorporate a hybrid attention mechanism at the model’s bottleneck to enhance feature transfer between the encoder and decoder. The model was trained using the ACDC, Synapse, and polyp datasets, with a focus on improving segmentation accuracy in regions with blurry edges. Experimental results reveal that the proposed model surpasses existing state-of-the-art methods on the ACDC, Synapse, and polyp datasets, demonstrating its superior performance and effectiveness. These results suggest that the proposed model offers a significant improvement in handling edge blurring, with potential applications in enhancing medical image analysis and diagnostics.

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Additive Manufacturing of Ti6Al4V Using Directed Energy Deposition: An Elaborative Approach of Process Modelling via Computational Fluid Dynamics

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Lasers in Manufacturing and Materials Processing 2025年第1期12卷 201-225页

作者： Ur Rehman, Asif Azher, Kashif Rehman, Aqsa Shah, Mussadiq Mahmood, Muhammad Arif Tüfekci, Celal Sami Salamci, Metin Uymaz Department of Mechanical Engineering Gazi University Ankara Turkey Bartlett School of Sustainable Construction University College London LondonWC1E 6BT United Kingdom Department of Mechanical Engineering King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia Data Science and Analytics Institute The University of Oklahoma NormanOK73019 United States Institute of Chemical Technologies and Analytics TU Wien Getreidemarkt 9 Vienna1060 Austria Intelligent Systems Center Missouri University of Science and Technology RollaMO65409 United States Advanced Manufacturing Technologies Center of Excellence-URTEMM Ankara Turkey Additive Manufacturing Technologies Application and Research Center-EKTAM Ankara Turkey

Additive manufacturing (AM) has revolutionized many industries. Directed energy deposition (DED) offers remarkable utility in size and is an effective method in terms of energy efficiency. The interaction of powder with a laser before coming in contact with the substrate helps to attain fair melting and deposition. However, there is a problem associated with non-uniformity and accumulation of metallic melt on the edges of the manufactured parts which needs to be understood. Herein, the DED process of Ti6Al4V alloy is presented. The phenomenon of recoil pressure and Marangoni actions along the single tracks of the melt pool is defined via the computational fluid dynamics (CFD) analysis. The discrete element modeling (DEM) improves the simulation model output as variable particle diameters are considered, thus mimicking the actual powder size distribution. The simulation results disclosed intricate facts about the flow dynamics of molten material under varying conditions. The stream traces and thermal gradients are evaluated to recognize the effect of laser movement on material cooling and surface tension. The material accumulation occurred at curves and borders due to cooling-induced surface tension alterations, affecting the attribute of the deposited layers. The bi-directional flow patterns are prompted by Marangoni convection and recoil pressure, causing distinct features, including lateral menisci and depression modes. The cooling rates impacted the liquid portion and dilution levels, critical for attaining optimal bonding between deposited layers and substrate. Experimental endorsement using Synchrotron X-ray imaging favored the simulation results, confirming the simulated Marangoni convection effects. This comparison highlights the efficiency of CFD models in predicting molten material behavior, offering a worthy tool for adjusting DED printing parameters. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature

关键词： Titanium alloys

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Phase Error Correction Algorithm Based on Complementary Gray Code and Reverse Error Compensation 19th

Phase Error Correction Algorithm Based on Complementary Gray...

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19th Chinese Conference on Image and Graphics Technologies and applications, IGTA 2024

作者： Zhang, Pengjie Kong, Bin Wang, Shaoping Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei230031 China University of Science and Technology of China Hefei230026 China Anhui Engineering Research Center for Intelligent Driving Technology and Application Hefei230088 China

ISBN: (纸本)9789819799183

This study addresses the periodic phase error problem arising from the nonlinear response of instruments in structured light three-dimensional measurement systems. A novel phase error correction algorithm that integrates complementary Gray codes and reverse error compensation is proposed. The algorithm utilizes complementary Gray codes to assist in phase unwrapping, thereby reducing computational resources and improving the accuracy of the unwrapping process. Furthermore, effective phase error compensation is achieved through two measurements and their phase averaging, significantly enhancing the system's measurement accuracy. Simulation experiments are conducted to analyze the specific impact of nonlinear response on phase measurement, and theoretical derivations validate the effectiveness of the reverse compensation method in eliminating periodic phase errors. The experimental results demonstrate that, compared to traditional methods, the proposed algorithm improves measurement accuracy by more than 80%, confirming its efficacy. This research offers a new solution for enhancing structured light measurement systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Phase measurement

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Simple and flexible method for measuring the liquid refractive index with an electrically tunable lens

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Journal of the Optical Society of America A 2025年第5期42卷 664-671页

作者： Dingnan Deng Yalin Wang Danfeng Zhu Shaobin Qiu Junbo Chen Yuanlin Liu Yuancong Zhong Jinming Luo Key Laboratory of Conservation and Precision Utilization of Characteristic Agricultural Resources in Mountainous Areas of Guangdong Province and Jiaying University School of Physics and Electronic Engineering Jiaying University Meizhou 514015 China Meizhou Intelligent Photoelectric Detection Application Engineering Technology Research Center Meizhou 514015 China zsuljm@***

In the parallel-plate-based method for measuring the liquid refractive index (RI), the parallel light passes sequentially through a convex lens and a parallel plate containing the tested liquid, resulting in axial displacement of the emitted light. By moving the camera, the axial displacement caused by the introduction of the tested liquid can be obtained and used to calculate the liquid RI. To avoid the mechanical adjustment of the system, a simple and flexible method for measuring the liquid RI via an electrically tunable lens (ETL) is proposed. The convex lens is replaced with an ETL. By precisely controlling the driving current of the ETL, the focal position of the camera can remain unchanged after placing the tested liquid in the parallel plate. The focal length change, induced by variations in the driving current of the ETL, is equivalent to the axial displacement of the camera. Therefore, the RI of the liquid can be measured from the change in focal length, avoiding mechanical movement of the camera. The equivalence of the ETL is theoretically derived. The imaging characteristics of the ETL were thoroughly discussed based on Zemax simulations, validating the effectiveness of the proposed method. The experimental results of the RI for different liquids further demonstrate the validity of our proposal.

关键词： Cylindrical lenses Imaging systems Laser light Liquid lenses Michelson interferometers Refractive index

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Personalized intelligent Job Recommendation Based on Deep Learning in Elasticsearch Environment

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第1期10卷 339-352页

作者： Cui, Xiu-Yan Hwang, David Hebei Software Institute Hebei Province University Intelligent Interconnection Equipment and Multimodal Big Data Application Technology Research and Development Center Baoding071000 China College of Engineering St. Paul University Philippines Tuguegarao3500 Philippines

Aiming at the issue that the jobs predicted by the existing job recommendation methods cannot be precisely matched with the job seekers, this paper designs a personalized intelligent job recommendation method based on deep learning in the Elas-ticsearch environment. Firstly, to deal with the data sparsity and cold start issue of traditional collaborative filtering algorithms, the similarity calculation formula of Pearson’s correlation in the algorithm is modified to enhance the accuracy of similarity calculation. Secondly, on the ground of the history of different job seekers, the keywords of job seekers are constructed in terms of various weights, and input into ElasticSearch to recall the set of jobs that are strongly related to the job seekers. Then adopting Convolutional Neural Network (CNN) to extract the text data features of job seekers and positions, and meanwhile, integrating the attention mechanism to get the auxiliary features of job seekers and positions, and finally, integrating the implicit feedback data with other at-tribute features of job seekers and positions as the input of the improved collaborative filtration algorithm, so that we can realize the intelligent recommendation of jobs for job seekers. Simulation outcome indicates that the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE) and Root Mean Square Percentage Error (RMSPE) of the suggested method are 0.0681, 0.0519, 0.0834 and 0.0628 respectively, which are lower than those of the comparison method, and the accuracy of the recommendation is effectively enhanced. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Convolutional neural networks

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Passive non-line-of-sight pedestrian imaging based on light transport matrix decomposition

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Optics and Lasers in engineering 2025年 192卷

作者： Chen, Mingyang Wang, Jiyue Liu, Mengge Xu, Ziqin Liu, Hao Xu, Mingliang School of Computer Science and Artificial Intelligence Zhengzhou University Henan Zhengzhou450001 China College of Advanced Interdisciplinary Studies National University of Defense Technology Hunan Changsha410073 China Engineering Research Center of Intelligent Swarm Systems Ministry of Education Zhengzhou University Henan Zhengzhou450001 China National Supercomputing Center in Zhengzhou Zhengzhou University Henan Zhengzhou450001 China

Passive non-line-of-sight (NLOS) imaging extends the observer's perceptual range, offering promising applications in fields such as autonomous driving and emergency rescue. However, mainstream passive NLOS imaging methods are generally limited to distances of less than 6 meters and often rely on a single simulated light transmission matrix, overlooking brightness and reflection characteristics. To overcome these limitations, we employ an infrared camera to capture relayed signals from long distances and introduce the NLOS Pedestrian Imaging Algorithm based on Light Transport Matrix Decomposition (NLOS-LTMD). This innovative algorithm simulates the light transport matrix in two parts: an illumination transport matrix for reconstructing brightness and a reflection transport matrix for enhancing contours. In addition, we propose an Illumination-Oriented Transformer (IO-Transformer) that utilizes threshold segmentation to identify the most informative regions based on the intensity of illumination information addressing the issue of introducing noise and low-SNR regions during contextual modeling, which can degrade imaging quality. Quantitative and qualitative experiments demonstrate that NLOS-LTMD markedly surpasses existing methods in both robustness and reconstruction quality. © 2025 Elsevier Ltd

关键词： Light transmission

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Predicting Treatment Response of Repetitive Transcranial Magnetic Stimulation in Major Depressive Disorder Using an Explainable Machine Learning Model Based on Electroencephalography and Clinical Features

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Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2025年

作者： Zhao, Zongya Ran, Xiangying Niu, Yanxiang Qiu, Mengyue Lv, Shiyang Zhu, Mingjie Wang, Junming Li, Mingcai Gao, Zhixian Wang, Chang Xu, Yongtao Ren, Wu Zhou, Xuezhi Fan, Xiaofeng Song, Jinggui Qi, Mingchao Yu, Yi School of Medical Engineering School of Mathematical Medicine Xinxiang Medical University Xinxiang China Second Affiliated Hospital of Xinxiang Medical University Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder Xinxiang China Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design Xinxiang China Henan Engineering Research Center of Medical Virtual Reality Intelligent Sensing Feedback Xinxiang China Henan Engineering Research Center of Physical Diagnostics and Treatment Technology for Mental and Neurological Diseases Henan China Engineering Technology Research Center of Neurosense and Control of Henan Province Xinxiang China Institute of Disaster and Emergency Medicine Tianjin University Tianjin China

Major depressive disorder (MDD) is highly heterogeneous in response to repetitive transcranial magnetic stimulation (rTMS), and identifying predictive biomarkers is essential for personalized treatment. However, most prior research studies have used either electroencephalography (EEG) or clinical features, lack interpretability, or have small sample sizes. This study included 74 patients with MDD who responded (responders) and 43 patients with MDD who did not respond (nonresponders) to rTMS. Eight baseline EEG metrics and clinical features were sent to 7 machine learning models to classify responders and nonresponders. Shapley additive explanations (SHAP) was used to interpret feature contributions. Combining phase locking value and clinical features with support vector machine achieved optimal classification performance (accuracy = 97.33%). SHAP revealed that delta and beta band functional connectivity (F3-P7, F3-P4, P3-P8, T7-Cz) significantly influenced predictions and differed between groups. This study developed an explainable predictive framework to predict rTMS response in MDD, enhancing the accuracy of rTMS response prediction and supporting personalized treatment in MDD. © 2025 Society of Biological Psychiatry

关键词： Biomarkers Machine learning Major depressive disorder Predicting treatment response rTMS

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Covalent Crosslinker-Free Photo-Curing 3D Printing of Liquid Metal Composite Hydrogels Based On SI-photoATRP

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Small 2025年 e2411688页

作者： Yan, Ge Zhou, Mengjie Zhang, Jun Zhang, Wenjie He, Yanjie Qiao, Xiaoguang Shi, Ge Pang, Xinchang School of Materials Science and Engineering Henan University of Science and Technology Luoyang471023 China Henan Joint International Research Laboratory of Living Polymerizations and Functional Nanomaterials Henan Key Laboratory of Advanced Nylon Materials and Application School of Materials Science and Engineering Zhengzhou University Zhengzhou450001 China College of Materials Engineering Henan International Joint Laboratory of Rare Earth Composite Materials Henan Engineering Technology Research Center for Fiber Preparation and Modification Henan University of Engineering Zhengzhou451191 China

Photocurable 3D printing (SLA or DLP) materials have garnered considerable attention due to their remarkable efficiency and precision in manufacturing. However, the presence of covalent crosslinking makes the recycling and reuse of printed materials extremely challenging. Here a novel approach to covalent crosslinker-free photo-curing 3D printing (via DLP) of liquid metal (LM) composite hydrogels is reported, leveraging surface-initiated photoinduced atom radical transfer polymerization (SI-photoATRP). The pre-synthesized PHEA-Br macroinitiators are grafted onto the surfaces of LM nanoparticles (LMNPs) by mechanical sonication, stabilizing the LMNPs within the resin solution while simultaneously generating active sites for SI-photoATRP. During the SI-photoATRP process, polymer chains of sufficient length form hydrogen bonds with multiple LMNPs, effectively transforming the LMNPs into crosslinking points. By integrating the aqueous photoATRP system catalyzed by carbon dots, LM@polymer composite hydrogel with complex structures are successfully established through DLP technology. The versatility of the 3D printed hydrogel is investigated by employing HEA, OEGA480, and AAm as the monomers in resin solution, respectively. Notably, all the LM@polymer composite hydrogels can be degraded in aqueous NaOH solution. Furthermore, LM@polymer-based networks exhibit self-repairing capabilities, serve as underwater adhesives, and conduct electricity. This work offers new insights into designing 3D printing materials and sustainable photocurable technology. © 2025 Wiley-VCH GmbH.

关键词： Crosslinking

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