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With the formation of world markets, the competition between enterprises has focused on how to deal with the conict between scale economy with batch production and customers individual requirements in mass customization. An effective method is product family development and design based on product platforms. Considering the characteristics of the complex modular products, two types of network models including parts connection network and generic-modules connection network are built taking parts or generic-modules as nodes. Based on the network model and network properties, generic-modules are divided into basic modules, must-selected module and may-selected module, and the modular main structure of the product family is constructed layer-by-layer via breadth rst search. A layer-build model of the modular product platform is proposed and a bottom-up development method is provided for the industrial steam turbine as a case study.

Introduction Previous studies have found that white matter (WM) alterations might be correlated in Parkinson’s disease (PD) patients with cognitive impairment. This study aimed to investigate WM structural network connectome alterations in PD patients with mild cognitive impairment (PD-MCI) and assess the relationship between cognitive impairment and structural topological network changes in PD patients. Methods All 31 healthy controls (HCs) and 71 PD patients (43 PD-NC and 28 PD-MCI) matched for age, sex and education underwent 3.0 T MRI and diffusion tensor imaging (DTI) scan. Graph theoretical analyses and network-based statistical (NBS) analyses were performed to identify the structural WM networks and subnetwork changes in PD-MCI. Results PD-MCI patients showed significantly decreased global efficiency ( E glob ) and increased shortest path length ( L p ) compared with the HC group. Several nodal efficiencies showed significant differences in multiple brain regions among the three groups. The nodal efficiency of the orbitofrontal part was closely related to the overall cognitive ability and multiple sub-cognitive domains. Moreover, NBS analyses identified eight one-connect subnetworks, three two-connect subnetworks and two multi-connect subnetworks with reduced connectivity that characterizes the WM structural organization in PD-MCI patients. The two multi-connect subnetworks were located on the bilateral lobe, and both were centered on the orbitofrontal part. Conclusions This study provided new evidence that PD with cognitive dysfunction is associated with WM structural alterations. The nodal efficiency and sub-network analyses focusing on the orbitofrontal part might provide new ideas to explore the physiological mechanism of PD-MCI.

Despite growing scholarly attention on the role of urban networks for understanding regional dynamics, there has been limited research examining the impact of cities’ transportation network connections on regional market integration. This paper analyzes China’s four major urban agglomerations: the Yangtze River Delta, the Pearl River Delta, Beijing-Tianjin-Hebei, and Chengdu-Chongqing. Applying a spatial Durbin model to cross-sectional datasets for 2019, we provide insight into the role of cities’ transportation network connections in promoting regional market integration, considering both the potentially heterogeneous impact of network connections and the interplay between network and agglomeration externalities. Our results indicate that: (1) cities’ transportation network connections have an inverted ‘U’-shaped effect on regional market integration; (2) transportation network connections have spatial spillover effects; (3) the positive impact of transportation network connections on regional market integration becomes more pronounced as city size decreases; and (4) there are neither complementary nor substitution effects between network and agglomeration externalities. We reflect on the broader implications of our empirical findings for regional development strategies and discuss possible avenues for further research.

Identifying diffuse axonal injury (DAI) in patients with traumatic brain injury (TBI) presenting with normal appearing radiological MRI presents a significant challenge. Neuroimaging methods such as diffusion MRI and probabilistic tractography, which probe the connectivity of neural networks, show significant promise. We present a machine learning approach to classify TBI participants primarily with mild traumatic brain injury (mTBI) based on altered structural connectivity patterns derived through the network based statistical analysis of structural connectomes generated from TBI and age-matched control groups. In this approach, higher order diffusion models were used to map white matter connections between 116 cortical and subcortical regions. Tracts between these regions were generated using probabilistic tracking and mean fractional anisotropy (FA) measures along these connections were encoded in the connectivity matrices. Network-based statistical analysis of the connectivity matrices was performed to identify the network differences between a representative subset of the two groups. The affected network connections provided the feature vectors for principal component analysis and subsequent classification by random forest. The validity of the approach was tested using data acquired from a total of 179 TBI patients and 146 controls participants. The analysis revealed altered connectivity within a number of intra- and inter-hemispheric white matter pathways associated with DAI, in consensus with existing literature. A mean classification accuracy of 68.16%±1.81% and mean sensitivity of 80.0%±2.36% were achieved in correctly classifying the TBI patients evaluated on the subset of the participants that was not used for the statistical analysis, in a 10-fold cross-validation framework. These results highlight the potential for statistical machine learning approaches applied to structural connectomes to identify patients with diffusive axonal injury. [Display omitted] •Method to identify diffuse axonal injury in mild traumatic brain injury (mTBI) patients from structural connectivity patterns.•Network-based statistics (NBS) is used to find significant network differences in mTBI and controls.•Fractional anisotropy (FA) features of the different structural connections obtained from NBS used as features.•Random forest classifier discriminates between mTBI and controls based on FA features.•Discriminative and significant network differences obtained from feature importance of random forest.

High spatial quality (HQ) optical remote sensing images are very useful for target detection, target recognition and image classification. Due to the influence of imaging equipment accuracy and atmospheric environment, HQ images are difficult to acquire, while low spatial quality (LQ) remote sensing images are very easy to acquire. Hence, denoising and super-resolution (SR) reconstruction technology are the most important solutions to improve the quality of remote sensing images very effectively, which can lower the cost as much as possible. Most existing methods usually only employ denoising or SR technology to obtain HQ images. However, due to the complex structure and the large noise of remote sensing images, the quality of the remote sensing image obtained only by denoising method or SR method cannot meet the actual needs. To address these problems, a method of reconstructing HQ remote sensing images based on Generative Adversarial Network (GAN) named “Restoration Generative Adversarial Network with ResNet and DenseNet” (RRDGAN) is proposed, which can acquire better quality images by incorporating denoising and SR into a unified framework. The generative network is implemented by fusing Residual Neural Network (ResNet) and Dense Convolutional Network (DenseNet) in order to consider denoising and SR problems at the same time. Then, total variation (TV) regularization is used to furthermore enhance the edge details, and the idea of Relativistic GAN is explored to make the whole network converge better. Our RRDGAN is implemented in wavelet transform (WT) domain, since different frequency parts could be handled separately in the wavelet domain. The experimental results on three different remote sensing datasets shows the feasibility of our proposed method in acquiring remote sensing images.

The extraction of effective classification features from high-dimensional hyperspectral images, impeded by the scarcity of labeled samples and uneven sample distribution, represents a formidable challenge within hyperspectral image classification. Traditional few-shot learning methods confront the dual dilemma of limited annotated samples and the necessity for deeper, more effective features from complex hyperspectral data, often resulting in suboptimal outcomes. The prohibitive cost of sample annotation further exacerbates the challenge, making it difficult to rely on a scant number of annotated samples for effective feature extraction. Prevailing high-accuracy algorithms require abundant annotated samples and falter in deriving deep, discriminative features from limited data, compromising classification performance for complex substances. This paper advocates for an integration of advanced spectral–spatial feature extraction with meta-transfer learning to address the classification of hyperspectral signals amidst insufficient labeled samples. Initially trained on a source domain dataset with ample labels, the model undergoes transference to a target domain with minimal samples, utilizing dense connection blocks and tree-dimensional convolutional residual connections to enhance feature extraction and maximize spatial and spectral information retrieval. This approach, validated on three diverse hyperspectral datasets—IP, UP, and Salinas—significantly surpasses existing classification algorithms and small-sample techniques in accuracy, demonstrating its applicability to high-dimensional signal classification under label constraints.

Learning in the classroom is mainly done through verbal interaction. Since science learning is a subject in which it is difficult for students to acquire knowledge on their own, teacher guidance in class is more important. This study aimed to compare teaching and learning concept networks for the ecosystem-related content taught in upper secondary schools in the Republic of Korea. This content is divided into two classes—namely, ‘Ecosystem Components’ and ‘Interactions between Ecosystem Components’. The key concept and connection networks for teaching and learning concepts were analysed. The study participants were 10th-grade students and three teachers who taught them. The teacher’s class content was recorded, while the students’ learning concepts were examined using a questionnaire. The collected data were analysed using NetMiner 4.0. The results of this study are as follows: First, the teachers and students predominantly shared the concept of the entire ecosystem; however, the detailed structures of the concept networks differed. Second, the teacher did not clearly teach the concepts of the ‘Ecosystem Components’ and ‘Interactions between Ecosystem Components’ classes and utilised the concepts repeatedly. Third, the follow-up learning content impacted the pre-learning. These findings suggest that teachers need to clearly divide concepts into each topic when teaching. Keywords: ecosystem composition, upper secondary school, teaching concept network, learning concept network, connection network

•DKI is feasible for SCN construction with improved classification performance.•SCN and FCN in PD were topologically impaired at levels of global, nodal and modular.•Patients with PD exhibited SCN-FCN decoupling across scales.•SCN-FCN coupling in PD was constrained by corresponding microstructural alterations. Parkinson's disease (PD) is manifested with disrupted topology of the structural connection network (SCN) and the functional connection network (FCN). However, the SCN and its interactions with the FCN remain to be further investigated. This multimodality study attempted to precisely characterize the SCN using diffusion kurtosis imaging (DKI) and further identify the neuropathological pattern of SCN-FCN decoupling, underscoring the neurodegeneration of PD. Diffusion-weighted imaging and resting-state functional imaging were available for network constructions among sixty-nine patients with PD and seventy demographically matched healthy control (HC) participants. The classification performance and topological prosperities of both the SCN and the FCN were analyzed, followed by quantification of the SCN-FCN couplings across scales. The SCN constructed by kurtosis metrics achieved optimal classification performance (area under the curve 0.89, accuracy 80.55 %, sensitivity 78.40 %, and specificity 80.65 %). Along with diverse alterations of structural and functional network topology, the PD group exhibited decoupling across scales including: reduced global coupling; increased nodal coupling within the sensorimotor network (SMN) and subcortical network (SN); higher intramodular coupling within the SMN and SN and lower intramodular coupling of the default mode network (DMN); decreased coupling between the modules of DMN-fronto-parietal network and DMN-visual network, but increased coupling between the SMN-SN module. Several associations between the coupling coefficient and topological properties of the SCN, as well as between network values and clinical scores, were observed. These findings validated the clinical implementation of DKI for structural network construction with better differentiation ability and characterized the SCN-FCN decoupling as supplementary insight into the pathological process underlying PD.

Objective: The shallow underwater environment is complex, with problems of color shift, uneven illumination, blurring, and distortion in the imaging process. These scenes are very unfavorable for the reasoning of the detection network. Additionally, typical object identification algorithms struggle to maintain high resilience in underwater environments due to picture domain offset, making underwater object detection problematic. Methods: This paper proposes a single-stage detection method with the double enhancement of anchor boxes and features. The feature context relevance is improved by proposing a composite-connected backbone network. The receptive field enhancement module is introduced to enhance the multi-scale detection capability. Finally, a prediction refinement strategy is proposed, which refines the anchor frame and features through two regressions, solves the problem of feature anchor frame misalignment, and improves the detection performance of the single-stage underwater algorithm. Results: We achieved an effect of 80.2 mAP on the Labeled Fish in the Wild dataset, which saves some computational resources and time while still improving accuracy. On the original basis, UWNet can achieve 2.1 AP accuracy improvement due to the powerful feature extraction function and the critical role of multi-scale functional modules. At an input resolution of 300 × 300, UWNet can provide an accuracy of 32.4 AP. When choosing the number of prediction layers, the accuracy of the four and six prediction layer structures is compared. The experiments show that on the Labeled Fish in the Wild dataset, the six prediction layers are better than the four. Conclusion: The single-stage underwater detection model UWNet proposed in this research has a double anchor frame and feature optimization. By adding three functional modules, the underwater detection of the single-stage detector is enhanced to address the issue that it is simple to miss detection while detecting small underwater targets.