In a gradually more interlinked world, the formation of collaborations with partners is increasingly regarded as an important driver for generating innovation. Although multidimensional proximities ...are important factors influencing interorganizational coinnovation performance, relevant empirical studies have not reached consistent conclusions. By focusing on organizational dyad and including intraorganizational collaboration network inefficiency as a moderating variable, we explore the effects of multidimensional proximities on interorganizational coinnovation performance. By reference to 5G patent data collected in China between 2011 and 2020, the research results based on the quadratic assignment procedure (QAP) model show that geographical proximity, cognitive proximity, and institutional proximity all improve interorganizational coinnovation performance. In addition, the inefficiency of intraorganizational collaboration networks decreases the positive effect of geographical proximity but increases the positive effects of cognitive and institutional proximity in this context. These findings have both theoretical and practical implications for organizational partner selection.
The objective of this study was to analyze the changes in connectivity between motor imagery (MI) and motor execution (ME) in the premotor area (PMA) and primary motor cortex (MA) of the brain, ...aiming to explore suitable forms of treatment and potential therapeutic targets.
Twenty-three inpatients with stroke were selected, and 21 right-handed healthy individuals were recruited. EEG signal during hand MI and ME (synergy and isolated movements) was recorded. Correlations between functional brain areas during MI and ME were compared.
PMA and MA were significantly and positively correlated during hand MI in all participants. The power spectral density (PSD) values of PMA EEG signals were greater than those of MA during MI and ME in both groups. The functional connectivity correlation was higher in the stroke group than in healthy people during MI, especially during left-handed MI. During ME, functional connectivity correlation in the brain was more enhanced during synergy movements than during isolated movements. The regions with abnormal functional connectivity were in the 18th lead of the left PMA area.
Left-handed MI may be crucial in MI therapy, and the 18th lead may serve as a target for non-invasive neuromodulation to promote further recovery of limb function in patients with stroke. This may provide support for the EEG theory of neuromodulation therapy for hemiplegic patients.
To identify potential treatment targets for spinal cord injury (SCI)-related neuropathic pain (NP) by analysing the differences in electroencephalogram (EEG) and brain network connections among SCI ...patients with NP or numbness.
The EEG signals during rest, as well as left- and right-hand and feet motor imagination (MI), were recorded. The power spectral density (PSD) of the θ (4-8 Hz), α (8-12 Hz), and β (13-30 Hz) bands was calculated by applying Continuous Wavelet Transform (CWT) and Modified S-transform (MST) to the data. We used 21 electrodes as network nodes and performed statistical measurements of the phase synchronisation between two brain regions using a phase-locking value, which captures nonlinear phase synchronisation.
The specificity of the MST algorithm was higher than that of the CWT. Widespread non-lateralised event-related synchronization was observed in both groups during the left- and right-hand MI. The PWP (patients with pain) group had lower θ and α bands PSD values in multiple channels of regions including the frontal, premotor, motor, and temporal regions compared with the PWN (patients with numbness) group (all
< 0.05), but higher β band PSD values in multiple channels of regions including the frontal, premotor, motor, and parietal region compared with the PWN group (all
< 0.05). During left-hand and feet MI, in the lower frequency bands (θ and α bands), the brain network connections of the PWP group were significantly weaker than the PWN group except for the frontal region. Conversely, in the higher frequency bands (β band), the brain network connections of the PWP group were significantly stronger in all regions than the PWN group.
The differences in the power of EEG and network connectivity in the frontal, premotor, motor, and temporal regions are potential biological and functional characteristics that can be used to distinguish NP from numbness. The differences in brain network connections between the two groups suggest that the distinct mechanisms for pain and numbness.
With the world moving towards low-carbon and environmentally friendly development, the rapid growth of new-energy vehicles is evident. The utilization of deep-learning-based license-plate-recognition ...(LPR) algorithms has become widespread. However, existing LPR systems have difficulty achieving timely, effective, and energy-saving recognition due to their inherent limitations such as high latency and energy consumption. An innovative Edge–LPR system that leverages edge computing and lightweight network models is proposed in this paper. With the help of this technology, the excessive reliance on the computational capacity and the uneven implementation of resources of cloud computing can be successfully mitigated. The system is specifically a simple LPR. Channel pruning was used to reconstruct the backbone layer, reduce the network model parameters, and effectively reduce the GPU resource consumption. By utilizing the computing resources of the Intel second-generation computing stick, the network models were deployed on edge gateways to detect license plates directly. The reliability and effectiveness of the Edge–LPR system were validated through the experimental analysis of the CCPD standard dataset and real-time monitoring dataset from charging stations. The experimental results from the CCPD common dataset demonstrated that the network’s total number of parameters was only 0.606 MB, with an impressive accuracy rate of 97%.
Spinal cord injury (SCI) may lead to impaired motor function, autonomic nervous system dysfunction, and other dysfunctions. Brain-computer Interface (BCI) system based on motor imagery (MI) can ...provide more scientific and effective treatment solutions for SCI patients.
According to the interaction between brain regions, a coherence-based graph convolutional network (C-GCN) method is proposed to extract the temporal-frequency-spatial features and functional connectivity information of EEG signals. The proposed algorithm constructs multi-channel EEG features based on coherence networks as graphical signals and then classifies MI tasks. Different from the traditional graphical convolutional neural network (GCN), the C-GCN method uses the coherence network of EEG signals to determine MI-related functional connections, which are used to represent the intrinsic connections between EEG channels in different rhythms and different MI tasks. EEG data of SCI patients and healthy subjects have been analyzed, where healthy subjects served as the control group.
The experimental results show that the C-GCN method can achieve the best classification performance with certain reliability and stability, the highest classification accuracy is 96.85%.
The proposed framework can provide an effective theoretical basis for the rehabilitation treatment of SCI patients.
As a medium for developing brain-computer interface systems, EEG signals are complex and difficult to identify due to their complexity, weakness, and differences between subjects. At present, most of ...the current research on sleep EEG signals are single-channel and dual-channel, ignoring the research on the relationship between different brain regions. Brain functional connectivity is considered to be closely related to brain activity and can be used to study the interaction relationship between brain areas.
Phase-locked value (PLV) is used to construct a functional connection network. The connection network is used to analyze the connection mechanism and brain interaction in different sleep stages. Firstly, the entire EEG signal is divided into multiple sub-periods. Secondly, Phase-locked value is used for feature extraction on the sub-periods. Thirdly, the PLV of multiple sub-periods is used for feature fusion. Fourthly, the classification performance optimization strategy is used to discuss the impact of different frequency bands on sleep stage classification performance and to find the optimal frequency band. Finally, the brain function network is constructed by using the average value of the fusion features to analyze the interaction of brain regions in different frequency bands during sleep stages.
The experimental results have shown that when the number of sub-periods is 30, the α (8-13 Hz) frequency band has the best classification effect, The classification result after 10-fold cross-validation reaches 92.59%.
The proposed algorithm has good sleep staging performance, which can effectively promote the development and application of an EEG sleep staging system.
Hemiplegia is a common motor dysfunction caused by a stroke. However, the dynamic network mechanism of brain processing information in post-stroke hemiplegic patients has not been revealed when ...performing motor imagery (MI) tasks. We acquire electroencephalography (EEG) data from healthy subjects and post-stroke hemiplegic patients and use the Fugl-Meyer assessment (FMA) to assess the degree of motor function damage in stroke patients. Time-varying MI networks are constructed using the adaptive directed transfer function (ADTF) method to explore the dynamic network mechanism of MI in post-stroke hemiplegic patients. Finally, correlation analysis has been conducted to study potential relationships between global efficiency and FMA scores. The performance of our proposed method has shown that the brain network pattern of stroke patients does not significantly change from laterality to bilateral symmetry when performing MI recognition. The main change is that the contralateral motor areas of the brain damage and the effective connection between the frontal lobe and the non-motor areas are enhanced, to compensate for motor dysfunction in stroke patients. We also find that there is a correlation between FMA scores and global efficiency. These findings help us better understand the dynamic brain network of patients with post-stroke when processing MI information. The network properties may provide a reliable biomarker for the objective evaluation of the functional rehabilitation diagnosis of stroke patients.
Innovation network formulated by the collaborations between entities (firms, universities and research institutes) facilitates the technology spillovers, which usually act as a key factor that boosts ...indigenous innovation capability. This study analyzes the collaborative innovation network from both intra- and inter-regional dimensions. We use the Chinese biomedical patent co-inventing data between entities to construct the intra- and inter-regional collaborative innovation networks, and examine how they generate the spillover effect by moderating the relationship between R&D input and output, as well as how they moderate the technology spillovers from overseas countries. The empirical study shows that collaborative innovation network generates a significant moderating effect on the relationship between R&D personnel and innovation output, while this effect is insignificant for R&D investment. In the intra-regional collaborative innovation network dimension, clustering coefficient and network density generate positive moderating effects by amplifying the positive relationship between R&D personnel and innovation output, network density also positively moderates the correlation between overseas technology spillover and innovation output; In the inter-regional dimension, only the clustering coefficient positively moderates the relationship between R&D personnel and innovation output. Our study is also relevant to policy makers.
This study delves into the bidirectional relationship between collaboration network stability (CS) and technological innovation performance (TIP), and the moderating effect of knowledge network ...characteristics. Using patent data gathered from China's biotechnology industry between 1985 and 2022, we develop a simultaneous equation model and employ a three‐stage least squares estimation to explore the bidirectional relationship between CS and TIP, as well as to reveal the underlying moderating mechanisms. The results indicate that CS and TIP affect each other. The impact of CS on TIP has an inverted U‐shape, and TIP exerts a negative impact on CS. Knowledge network stability (KS) and diversity (KD) weaken the inverted U‐shaped influence of CS on TIP.
