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Designing a critical success factors model for Chinese self-run e-business from resource orchestration theory

Journal of Management Analytics 2025年第1期12卷 174-199页

作者： Bingfeng Bai Yuan Yin Junjun Gao School of Business Jinling Institute of TechnologyNanjingPeople’s Republic of China School of Computer Science Nanjing University of Posts and TelecommunicationsNanjingPeople’s Republic of China Huishang Futures HefeiPeople’s Republic of China College of International Business Shanghai International Studies UniversityShanghaiPeople’s Republic of China Key Laboratory of Brain-Machine Intelligence for Information Behavior(Ministry of Education and Shanghai) Shanghai International Studies UniversityShanghaiPeople’s Republic of China School of Management Shanghai UniversityShanghaiPeople’s Republic of China

E-business develops rapidly and attracts a large number of merchants and consumers in the past 20 ***,the debate over e-business mode and its critical success factors(CSFs)is a research hotspot in the industry and the *** this study,we propose the CSFs framework of self-run e-business by combining systematic literature review,resource orchestration theory,and empirical firm *** quantitative method of content analysis is targeted at the interview data received from 90 managers of *** by software NVivo *** construct a self-run e-business CSFs framework and find out that the CSFs of self-run e-business are products,organization,and supply chain,*** addition,this study compares different characteristics and their impact on performance in platform-type and self-run *** managers can derive a better understanding and measurement of e-business activities.

关键词： self-run e-business firm operations critical success factors resource orchestration theory content analysis

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NeuSort: an automatic adaptive spike sorting approach with neuromorphic models

arXiv

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arXiv 2023年

作者： Yu, Hang Qi, Yu Pan, Gang State Key Lab of Brain-Machine Intelligence Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Affiliated Mental Health Center Hangzhou Seventh People’s Hospital Hangzhou China MOE Frontier Science Center for Brain Science and Brain-machine Integration Zhejiang University School of Medicine Hangzhou China

Objective. Spike sorting, a critical step in neural data processing, aims to classify spiking events from single electrode recordings based on different waveforms. This study aims to develop a novel online spike sorter, NeuSort, using neuromorphic models, with the ability to adaptively adjust to changes in neural signals, including waveform deformations and the appearance of new neurons. Approach. NeuSort leverages a neuromorphic model to emulate template-matching processes. This model incorporates plasticity learning mechanisms inspired by biological neural systems, facilitating real-time adjustments to online parameters. Results. Experimental findings demonstrate NeuSort’s ability to track neuron activities amidst waveform deformations and identify new neurons in real-time. NeuSort excels in handling non-stationary neural signals, significantly enhancing its applicability for long-term spike sorting tasks. Moreover, its implementation on neuromorphic chips guarantees ultra-low energy consumption during computation. Significance. NeuSort caters to the demand for real-time spike sorting in brain-machine interfaces through a neuromorphic approach. Its unsupervised, automated spike sorting process makes it a plug-and-play solution for online spike sorting. © 2023, CC BY-NC-ND.

关键词： Neural networks

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Plasma proteomics identifies proteins and pathways associated with incident depression in 46,165 adults

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science Bulletin 2025年第4期70卷 573-586页

作者： Jujiao Kang Liu Yang Tianye Jia Wei Zhang Lin-Bo Wang Yu-Jie Zhao Jia You Yue-Ting Deng Yi-Jun Ge Wei-Shi Liu Yi Zhang Yi-Lin Chen Xiao-Yu He Barbara J.Sahakian Yucheng TYang Xing-Ming Zhao Jin-Tai Yu Jianfeng Feng Wei Cheng Institute of Science and Technology for Brain-Inspired Intelligence(ISTBI) Fudan UniversityShanghai 200433China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan UniversityMinistry of EducationShanghai 200433China Department of Neurology and National Center for Neurological Disorders Huashan HospitalState Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain ScienceShanghai Medical CollegeFudan UniversityShanghai 200433China Department of Psychiatry University of CambridgeCambridge CB20SZUK Department of Computer Science University of WarwickCoventry CV47ALUK School of Data Science Fudan UniversityShanghai 200438China

Proteomic alterations preceding the onset of depression offer valuable insights into its development and potential *** data from 46,165 UK Biobank participants and 2920 plasma proteins profiled at baseline,we conducted a longitudinal analysis with a median follow-up of 14.5 years to explore the relationship between plasma proteins and incident *** regression was then used to assess associations between depression-related proteins and brain structures,genetic factors,and stress-related *** analysis identified 157 proteins associated with incident depression(P<1.71×10^(-5)),including novel associations with proteins such as GAST,PLAUR,LRRN1,BCAN,and ***,higher expression levels of GDF15(P=6.18×10-26)and PLAUR(P=2.88×10^(-14))were linked to an increased risk of depression,whereas higher levels of LRRN1(P=4.28×10^(-11))and ITGA11(P=3.68×10^(-9))were associated with a decreased *** of the 157 proteins is correlated with brain regions implicated in depression,including the hippocampus and middle temporal ***,these protein alterations were strongly correlated with stress-related events,including self-harm events,adult,and childhood *** pathway enrichment analysis highlighted the critical roles of the immune *** and TNF emerged as key proteins in the protein-protein interaction ***3A2,newly linked to incident depression(P=4.35×10^(-10)),was confirmed as a causal factor through Mendelian randomization *** summary,our research identified the proteomic signatures associated with the onset of depression,highlighting its potential for early intervention and tailored therapeutic avenues.

关键词： Plasma proteins Depression brain structure

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CEBRA Method: Decoding brain Activity for Advanced brain-computer Interface technology 46

CEBRA Method: Decoding Brain Activity for Advanced Brain-Com...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Yang, Jingcheng Kulwa, Frank Liu, Xuanwei Lu, Yiqing Fu, Yufa Li, Guanglin Huai, Yaping Zhang, Xin Li, Yongcheng Shenzhen Dapeng New District Nanao People's Hospital Guangdong Shenzhen518000 China Shenzhen Longhua District Central Hospital Department of Rehabilitation Medicine Guangdong Shenzhen518000 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Kunming University of Science and Technology Brain Cognition and Brain-Computer Intelligence Integration Group Kunming650500 China Cas Key Laboratory of Human-Machine Intelligence Synergy Systems Shenzhen518055 China University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Shenzhen518055 China Shandong Zhongke Advanced Technology Co. Ltd. Jinan250014 China

ISBN: (纸本)9798350371499

The emerging neurorehabilitation technology, brain-computer Interface (BCI), provides a novel prospect for stroke recovery. However, decoding brain activity during the movement present substantial challenges, and feature extraction is crutial to build a better decoder. In this study the CEBRA method were employed to extract features first based on electroencephalogram(EEG) data during Motor Execution(ME) and Motor lmagery (Ml) tasks for 20 participants (including 10 stroke patients). The results revealed that, in MI tasks, CEBRA-RF (Random Forests) achieved an average classification accuracy of 91.33%, with an average F1-score of 91.19%, and CEBRA-SVM (Support Vector machine) achieved an average classification accuracy of 91.32%, with an average F1-score of 90.83%. Compared to other conventional feature extraction methods, CEBRA shows significantly higher accuracy (t-test, p © 2024 IEEE.

关键词： Patient rehabilitation

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A Human-machine Joint Learning Framework to Boost Endogenous BCI Training

arXiv

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arXiv 2023年

作者： Wang, Hanwen Qi, Yu Yao, Lin Wang, Yueming Farina, Dario Pan, Gang The College of Computer Science and Technology Zhejiang University Hangzhou China The Affiliated Mental Health Center Hangzhou Seventh People's Hospital The MOE Frontier Science Center for Brain Science and Brain-machine Integration Zhejiang University School of Medicine The State Key Lab of Brain-Machine Intelligence Hangzhou China The Department of Neurobiology Affiliated Mental Health Center Hangzhou Seventh People's Hospital Zhejiang University School of Medicine MOE Frontiers Science Center for Brain and Brain-Machine Integration Zhejiang University Department of Biomedical Engineering Zhejiang University College of Computer Science and Technology Hangzhou China The Qiushi Academy for Advanced Studies Zhejiang University Hangzhou China The Department of Bioengineering Imperial College London London United Kingdom The State Key Lab of Brain-Machine Intelligence The College of Computer Science and Technology Zhejiang University Hangzhou China The First Affiliated Hospital Zhejiang University Hangzhou China

brain-computer interfaces (BCIs) provide a direct pathway from the brain to external devices and have demonstrated great potential for assistive and rehabilitation technologies. Endogenous BCIs based on electroencephalogram (EEG) signals, such as motor imagery (MI) BCIs, can provide some level of control. However, mastering spontaneous BCI control requires the users to generate discriminative and stable brain signal patterns by imagery, which is challenging and is usually achieved over a very long training time (weeks/months). Here, we propose a human-machine joint learning framework to boost the learning process in endogenous BCIs, by guiding the user to generate brain signals towards an optimal distribution estimated by the decoder, given the historical brain signals of the user. To this end, we firstly model the human-machine joint learning process in a uniform formulation. Then a human-machine joint learning framework is proposed: 1) for the human side, we model the learning process in a sequential trial-and-error scenario and propose a novel "copy/new" feedback paradigm to help shape the signal generation of the subject toward the optimal distribution;2) for the machine side, we propose a novel adaptive learning algorithm to learn an optimal signal distribution along with the subject's learning process. Specifically, the decoder reweighs the brain signals generated by the subject to focus more on "good" samples to cope with the learning process of the subject. Online and psuedo-online BCI experiments with 18 healthy subjects demonstrated the advantages of the proposed joint learning process over co-adaptive approaches in both learning efficiency and effectiveness. © 2023, CC BY.

关键词： Electroencephalography

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Spin-UP: Spin Light for Natural Light Uncalibrated Photometric Stereo

Spin-UP: Spin Light for Natural Light Uncalibrated Photometr...

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Zongrui Li Zhan Lu Haojie Yan Boxin Shi Gang Pan Qian Zheng Xudong Jiang Rapid-Rich Object Search (ROSE) Lab Interdisciplinary Graduate Programme Nanyang Technological University School of Electrical and Electronic Engineering Nanyang Technological University The State Key Lab of Brain-Machine Intelligence Zhejiang University College of Computer Science and Technology Zhejiang University National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Natural Light Uncalibrated Photometric Stereo (NaUPS) relieves the strict environment and light assumptions in classical Uncalibrated Photometric Stereo (UPS) methods. However, due to the intrinsic ill-posedness and high-dimensional ambiguities, addressing NaUPS is still an open question. Existing works impose strong assumptions on the environment lights and objects' material, restricting the effectiveness in more general scenarios. Alternatively, some methods leverage supervised learning with intricate models while lacking interpretability, resulting in a biased estimation. In this work, we propose Spin Light Uncalibrated Photometric Stereo (Spin-UP), an unsupervised method to tackle NaUPS in various environment lights and objects. The proposed method uses a novel setup that captures the object's images on a rotatable platform, which mitigates NaUPS's ill-posedness by reducing unknowns and provides reliable priors to alleviate NaUPS's ambiguities. Leveraging neural inverse rendering and the proposed training strategies, Spin-UP recovers surface normals, environment light, and isotropic reflectance under complex natural light with low computational cost. Experiments have shown that Spin-UP outperforms other supervised / unsupervised NaUPS meth-ods and achieves state-of-the-art performance on synthetic and real-world datasets. Codes and data are available at https://***/LMozart/CVPR2024-SpinUP.

关键词： Reflectivity Training Shape Supervised learning Anisotropic Rendering (computer graphics) Structural engineering

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Continuous Decoding of Movement Trajectory During Unimanual Movement Using Bilateral Motor Cortex Signals 23

Continuous Decoding of Movement Trajectory During Unimanual ...

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Proceedings of the 2023 10th International Conference on Biomedical and Bioinformatics Engineering

作者： Dongrong Lai Liangliang Chen Weihuang Chen Xiaoxiao Meng Yaoyao Hao Kedi Xu Department of Biomedical Engineering Qiushi Academy for Advanced Studies (QAAS) Zhejiang University China College of Computer Science and Technology Zhejiang University China College of Foreign Languages Zhejiang University China The State Key Lab of Brain-Machine Intelligence College of Computer Science and Technology Zhejiang University China Qiushi Academy for Advanced Studies (QAAS) Department of Biomedical Engineering Zhejiang University China

ISBN: (纸本)9798400708343

brain-computer interfaces (BCIs) provide a passway to connect the devices and the living brains by transforming the cortical signals to control commands to the devices. The ability of the contralateral primary motor cortex (M1) to represent sequentially changing kinematics has been proposed in previous intracortical BCI papers. However, it's not clear whether the ipsilateral M1 could be used to decode continuous trajectories like the contralateral one and whether the bilateral brain regions had similar levels of decoding performance. To that end, we recorded the single-unit activities from bilateral M1 during the center-out task performed by a monkey and predicted the 2-dimensional (2D) positions by using partial least squares (PLS). Our results revealed that the decoder with neurons recorded from ipsilateral M1 was able to decode continuous positions. When using the overall dataset, bilateral hemispheres showed an even contribution to the prediction. In addition, we found that the decoder using contralateral neurons outperformed that using ipsilateral neurons, which could be attributed to the stronger correlation of neuron pairs within contralateral M1 versus within ipsilateral M1. Our findings support that the movement kinematics are bihemispherically encoded in M1 and that the underlying structure within contralateral M1 may contribute to the outperformance in the prediction. These results improve our understanding of motor control in M1 and implicate the design of cutting-edge BCIs with a more precise prediction strategy.

关键词： bilateral hemispheres brain-computer interface (BCI) motor control neuronal connectivity primary motor cortex (M1)

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Development of yellow-light TiO2 integrated photonics

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Optics Letters 2025年第11期50卷 3489-3492页

作者： Chen, Zequn Tang, Yiheng Wei, Maoliang Li, Xiaojing Sun, Boshu Wu, Yingchun Zou, Sishuo Huang, Ji Si, Ke Gong, Wei Lin, Hongtao Li, Lan Institute of Advanced Technology Westlake Institute for Advanced Study Hangzhou310024 China Zhejiang Key Laboratory of 3D Micro Nano Fabrication and Characterization School of Engineering Westlake University Hangzhou310030 China The State Key Lab of Brain-Machine Intelligence College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China MOE Frontier Science Center for Brain Science & Brain-Machine Integration Zhejiang University Hangzhou310027 China Nanhu Brain-Computer Interface Institute Hangzhou311100 China College of Optical Science and Engineering Zhejiang University Hangzhou310027 China Westlake Institute for Optoelectronics Fuyang Zhejiang Hangzhou311421 China

Visible-light photonic integrated circuits (PICs) are rapidly emerging as a crucial technology to overcome the scaling challenges in quantum information processing and biosensing. Titanium dioxide (TiO2), with its high transparency in the visible range, is regarded as a promising material for facilitating the development of high-performance visible-light integrated systems. In this paper, we introduce a series of passive TiO2 integrated devices, which include waveguides, multimode interferometers (MMIs), and thermo-optic switches (TOSs), monolithically integrated for the first time to our knowledge at a working wavelength of 589 nm. A propagation loss of 5.46 ± 0.17 dB/cm demonstrates a significant improvement over previous reports on yellow-light TiO2 waveguides. This study marks the first fabrication of a TOS operating at 589 nm, featuring a TiO2 core and polymer cladding. Notably, it achieves a power consumption (Pπ) of 8.24 mW, which represents a threefold reduction compared to silicon nitride-based TOSs operating in the visible spectrum. These results offer new insight into the design and fabrication of visible waveguide-integrated devices, paving the way for future emerging applications, such as quantum information, sensors, and biomedical science. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial intelligence (AI) training, and similar technologies, are reserved.

关键词： Titanium dioxide

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A 64-channel Fully Implantable brain-computer Microsystem with Custom Microelectrode Array

A 64-channel Fully Implantable Brain-computer Microsystem wi...

引用

IEEE Biomedical Circuits and Systems (BIOCAS)

作者： Yaoyao Hao Leixiao Han Youwei Jin Qin Chen Chenbin Yu Bowen Yu Penghai Li Jingzhuo Han Yueming Wang The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China Nanhu Brain-computer Interface Institute Hangzhou China Department of Biomedical Engineering Zhejiang University Hangzhou China Qiushi Academy for Advanced Studies Zhejiang University Hangzhou China MOE Frontier Science Center for Brain Science and Brain-Machine Integration Zhejiang University Hangzhou China

ISBN: (数字)9798350354959

ISBN: (纸本)9798350354966

This paper presents the development of a 64-channel fully implantable neural signal recording and processing system. The system is designed with hermetically sealed, commercial off-the-shelf components to enable neural signal acquisition, wireless transmitting and wireless charging. Additionally, an artificial neural network was implemented on the hardware to create a standalone, comprehensive brain signal decoding system. A silicon-based microelectrode array, fabricated with MEMS process and assembled into 3D floating form by stacking planar silicon probes, was attached to the system to sense neural signals. The whole system was militarized packaged into a medical-scale titanium case and embedded onto skull for brain-computer interface (BCI) applications. The study demonstrates the feasibility and potential of this advanced technology for neural signal recording and processing in a fully implantable system.

关键词： Micromechanical devices Wireless communication Microelectrodes Wireless sensor networks Three-dimensional displays Array signal processing Titanium Hardware Recording Decoding

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Personalized Sleep Staging Leveraging Source Free Unsupervised Domain Adaptation 39

Personalized Sleep Staging Leveraging Source Free Unsupervis...

引用

39th Annual AAAI Conference on Artificial intelligence, AAAI 2025

作者： Zhou, Yangxuan Zhao, Sha Wang, Jiquan Jiang, Haiteng Li, Shijian Luo, Benyan Li, Tao Pan, Gang State Key Laboratory of Brain-machine Intelligence Zhejiang University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Department of Neurobiology Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital Zhejiang University School of Medicine Hangzhou China MOE Frontier Science Center for Brain Science and Brain-machine Integration Zhejiang University Hangzhou China The First Affiliated Hospital College of Medicine Zhejiang University Hangzhou China

ISBN: (纸本)157735897X

Sleep staging is important for monitoring sleep quality and diagnosing sleep-related disorders. Recently, numerous deep learning-based models have been proposed for automatic sleep staging using polysomnography recordings. Most of them are trained and tested on the same labeled datasets which results in poor generalization to unseen target domains. However, they regard the subjects in the target domains as a whole and overlook the individual discrepancies, which limits the model’s generalization ability to new patients (i.e., unseen subjects) and plug-and-play applicability in clinics. To address this, we propose a novel Source-Free Unsupervised Individual Domain Adaptation (SF-UIDA) framework for sleep staging, leveraging sequential cross-view contrasting and pseudo-label based fine-tuning. It is actually a two-step subject-specific adaptation scheme, which enables the source model to effectively adapt to newly appeared unlabeled individual without access to the source data. It meets the practical needs in real-world scenarios, where the personalized customization can be plug-and-play applied to new ones. Our framework is applied to three classic sleep staging models and evaluated on three public sleep datasets, achieving the state-of-the-art performance. Copyright © 2025, Association for the Advancement of Artificial intelligence (***). All rights reserved.

关键词： Deep learning

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