The human brain,a marvel of intricate connections,functions as a complex network comprising structurally and functionally inte-grated regions.This network orchestrates a multitude of complex patterns through high-level integration and continuous coopera-tion,essential for overall brain functionality[1].Understanding these communication patterns is vital for unraveling the complex-ities of brain functions and gaining insights into mental disorders caused by cerebral impairments[1].In this context,neuroimaging techniques like resting-state functional Magnetic Resonance Imag-ing(fMRI)have become indispensable tools.They enable research-ers to study functional connectivity among various brain regions,employing methods like seed-based,ICA-based,or clustering-based approaches to analyze resting-state brain networks[2].

An Electroencephalography (EEG) dataset utilizing rich text stimuli can advance the understanding of how the brain encodes semantic information and contribute to semantic decoding in brain-computer interface (BCI). Addressing the scarcity of EEG datasets featuring Chinese linguistic stimuli, we present the ChineseEEG dataset, a high-density EEG dataset complemented by simultaneous eye-tracking recordings. This dataset was compiled while 10 participants silently read approximately 13 hours of Chinese text from two well-known novels. This dataset provides long-duration EEG recordings, along with pre-processed EEG sensor-level data and semantic embeddings of reading materials extracted by a pre-trained natural language processing (NLP) model. As a pilot EEG dataset derived from natural Chinese linguistic stimuli, ChineseEEG can significantly support research across neuroscience, NLP, and linguistics. It establishes a benchmark dataset for Chinese semantic decoding, aids in the development of BCIs, and facilitates the exploration of alignment between large language models and human cognitive processes. It can also aid research into the brain's mechanisms of language processing within the context of the Chinese natural language.

Tissue segmentation of individual magnetic resonance imaging (MRI) is a fundamental step in building accurate head models for brain stimulation. In nonhuman primates (NHPs), due to limited sample size, site variability, and sub-optimal image quality, it is challenging to automate the tissue segmentation process. To overcome these challenges, we leveraged a recent transfer-learning framework for brain extraction and developed an automatic segmentation tool, DeepSeg, a U-Net model for NHP MRI data. We trained two DeepSeg models - a brain tissue model and a full head model - in a relatively large human dataset and then transferred them to limited macaque samples. We demonstrated that both full head and brain tissue models achieved good segmentation performance on the macaque test samples from the same training sites and also showed promising results on multi-site data (Dice coefficient mean±standard deviation for full-head within-site test sample: 0.88 ± 0.08; full-head out-of-site test sample: 0.72 ± 0.17). We further showed that the transferred brain tissue model outperformed a traditional template-driven approach, the prior-based ANTs segmentation (Dice coefficient mean±standard deviation for white matter: 0.90 ± 0.04 vs. 0.85 ± 0.03; gray matter: 0.82±0.07 vs. 0.81±0.04). We then discussed possible solutions to improve model generalizability. Overall, despite limited training samples, our preliminary results demonstrate that DeepSeg is a promising segmentation tool for NHP MRI data.

Open Science is becoming a mainstream scientific ideology in psychology and related fields. However, researchers, especially early-career researchers (ECRs) in developing countries, are facing significant hurdles in engaging in Open Science and moving it forward. In China, various societal and cultural factors discourage ECRs from participating in Open Science, such as the lack of dedicated communication channels and the norm of modesty. To make the voice of Open Science heard by Chinese-speaking ECRs and scholars at large, the Chinese Open Science Network (COSN) was initiated in 2016. With its core values being grassroots-oriented, diversity, and inclusivity, COSN has grown from a small Open Science interest group to a recognized network both in the Chinese-speaking research community and the international Open Science community. So far, COSN has organized three in-person workshops, 12 tutorials, 48 talks, and 55 journal club sessions and translated 15 Open Science-related articles and blogs from English to Chinese. Currently, the main social media account of COSN (i.e., the WeChat Official Account) has more than 23,000 subscribers, and more than 1,000 researchers/students actively participate in the discussions on Open Science. In this article, we share our experience in building such a network to encourage ECRs in developing countries to start their own Open Science initiatives and engage in the global Open Science movement. We foresee great collaborative efforts of COSN together with all other local and international networks to further accelerate the Open Science movement.

For patients with glioma located in or adjacent to the linguistic eloquent cortex, awake surgery with an emphasis on the preservation of language function is preferred. However, the brain network basis of postoperative linguistic functional outcomes remains largely unknown. In this work, 34 patients with left cerebral gliomas who underwent awake surgery were assessed for language function and resting-state network properties before and after surgery. We found that there were 28 patients whose language function returned to at least 80% of the baseline scores within 3 months after surgery or to 85% within 6 months after surgery. For these patients, the spontaneous recovery of language function synchronized with changes within the language and cognitive control networks, but not with other networks. Specifically, compared with baseline values, language functions and global network properties were the worst within 1 month after surgery and gradually recovered within 6 months after surgery. The recovery of connections was tumour location dependent and was attributed to both ipsihemispheric and interhemispheric connections. In contrast, for six patients whose language function did not recover well, severe network disruptions were observed before surgery and persisted into the chronic phase. This study suggests the synchronization of functional network normalization and spontaneous language recovery in postoperative patients with glioma.

Brain extraction (a.k.a. skull stripping) is a fundamental step in the neuroimaging pipeline as it can affect the accuracy of downstream preprocess such as image registration, tissue classification, etc. Most brain extraction tools have been designed for and applied to human data and are often challenged by non-human primates (NHP) data. Amongst recent attempts to improve performance on NHP data, deep learning models appear to outperform the traditional tools. However, given the minimal sample size of most NHP studies and notable variations in data quality, the deep learning models are very rarely applied to multi-site samples in NHP imaging. To overcome this challenge, we used a transfer-learning framework that leverages a large human imaging dataset to pretrain a convolutional neural network (i.e. U-Net Model), and then transferred this to NHP data using a small NHP training sample. The resulting transfer-learning model converged faster and achieved more accurate performance than a similar U-Net Model trained exclusively on NHP samples. We improved the generalizability of the model by upgrading the transfer-learned model using additional training datasets from multiple research sites in the Primate Data-Exchange (PRIME-DE) consortium. Our final model outperformed brain extraction routines from popular MRI packages (AFNI, FSL, and FreeSurfer) across a heterogeneous sample from multiple sites in the PRIME-DE with less computational cost (20s~10min). We also demonstrated the transfer-learning process enables the macaque model to be updated for use with scans from chimpanzees, marmosets, and other mammals (e.g. pig). Our model, code, and the skull-stripped mask repository of 136 macaque monkeys are publicly available for unrestricted use by the neuroimaging community at https://github.com/HumanBrainED/NHP-BrainExtraction.

Schizophrenia (SCZ) is a highly heterogeneous disorder with remarkable intersubject variability in clinical presentations. Previous neuroimaging studies in SCZ have primarily focused on identifying group-averaged differences in the brain connectome between patients and healthy controls (HCs), largely neglecting the intersubject differences among patients. We acquired whole-brain resting-state functional MRI data from 121 SCZ patients and 183 HCs and examined the intersubject variability of the functional connectome (IVFC) in SCZ patients and HCs. Between-group differences were determined using permutation analysis. Then, we evaluated the relationship between IVFC and clinical variables in SCZ. Finally, we used datasets of patients with bipolar disorder (BD) and major depressive disorder (MDD) to assess the specificity of IVFC alteration in SCZ. The whole-brain IVFC pattern in the SCZ group was generally similar to that in HCs. Compared with the HC group, the SCZ group exhibited higher IVFC in the bilateral sensorimotor, visual, auditory, and subcortical regions. Moreover, altered IVFC was negatively correlated with age of onset, illness duration, and Brief Psychiatric Rating Scale scores and positively correlated with clinical heterogeneity. Although the SCZ shared altered IVFC in the visual cortex with BD and MDD, the alterations of IVFC in the sensorimotor, auditory, and subcortical cortices were specific to SCZ. The alterations of whole-brain IVFC in SCZ have potential implications for the understanding of the high clinical heterogeneity of SCZ and the future individualized clinical diagnosis and treatment of this disease.

Individual variability in human brain networks underlies individual differences in cognition and behaviors. However, researchers have not conclusively determined when individual variability patterns of the brain networks emerge and how they develop in the early phase. Here, we employed resting-state functional MRI data and whole-brain functional connectivity analyses in 40 neonates aged around 31-42 postmenstrual weeks to characterize the spatial distribution and development modes of individual variability in the functional network architecture. We observed lower individual variability in primary sensorimotor and visual areas and higher variability in association regions at the third trimester, and these patterns are generally similar to those of adult brains. Different functional systems showed dramatic differences in the development of individual variability, with significant decreases in the sensorimotor network; decreasing trends in the visual, subcortical, and dorsal and ventral attention networks, and limited change in the default mode, frontoparietal and limbic networks. The patterns of individual variability were negatively correlated with the short- to middle-range connection strength/number and this distance constraint was significantly strengthened throughout development. Our findings highlight the development and emergence of individual variability in the functional architecture of the prenatal brain, which may lay network foundations for individual behavioral differences later in life.

The recently proposed q-rung picture fuzzy set (q-RPFSs) can describe complex fuzzy and uncertain information effectively. The Hamy mean (HM) operator gets good performance in the process of information aggregation due to its ability to capturing the interrelationships among aggregated values. In this study, we extend HM to q-rung picture fuzzy environment, propose novel q-rung picture fuzzy aggregation operators, and demonstrate their application to multi-attribute group decision-making (MAGDM). First of all, on the basis of Dombi t-norm and t-conorm (DTT), we propose novel operational rules of q-rung picture fuzzy numbers (q-RPFNs). Second, we propose some new aggregation operators of q-RPFNs based on the newly-developed operations, i.e., the q-rung picture fuzzy Dombi Hamy mean (q-RPFDHM) operator, the q-rung picture fuzzy Dombi weighted Hamy mean (q-RPFDWHM) operator, the q-rung picture fuzzy Dombi dual Hamy mean (q-RPFDDHM) operator, and the q-rung picture fuzzy Dombi weighted dual Hamy mean (q-RPFDWDHM) operator. Properties of these operators are also discussed. Third, a new q-rung picture fuzzy MAGDM method is proposed with the help of the proposed operators. Finally, a best project selection example is provided to demonstrate the practicality and effectiveness of the new method. The superiorities of the proposed method are illustrated through comparative analysis.

With the rapid development of the global economy, the competition among manufacturing enterprises is becoming more and more fierce. Many enterprises begin to worry about the economic status and pay attention to the improvement and management of the production process existing in the production system. Taking a mobile phone loudspeaker production enterprise as the research object, after investigation and research for some days in the company, the paper analyzes the whole process of production. By using the value stream mapping analysis technology, the paper draws up the value stream mapping of production status, calculates takt time of this production line, and analyzes the bottleneck process existing in the production system of enterprises. With the knowledge of industrial engineering, the paper deletes and combines the working procedure, optimizes the problem of the bottleneck procedure, changes the push production into the pull production to improve the production line, then draws up the company's future production value stream mapping, in this way, the manager can intuitively see the implementation situation of improvement programs in the production line. Lastly the paper makes a comparison and analysis between the production status before and after improvement.

Deficit schizophrenia (DS), characterized by primary and enduring negative symptoms, has been considered as a pathophysiologically distinct schizophrenic subgroup. Neuroimaging characteristics of DS, especially functional brain network architecture, remain largely unknown. Resting-state functional magnetic resonance imaging and graph theory approaches were employed to investigate the topological organization of whole-brain functional networks of 114 male participants including 33 DS, 41 non-deficit schizophrenia (NDS) and 40 healthy controls (HCs). At the whole-brain level, both the NDS and DS group exhibited lower local efficiency (Eloc) than the HC group, implying the reduction of local specialization of brain information processing (reduced functional segregation). The DS, but not NDS group, exhibited enhanced parallel information transfer (enhanced functional integration) as determined by smaller characteristic path length (Lp) and higher global efficiency (Eglob). The Lp and Eglob presented significant correlations with Brief Psychiatric Rating Scale (BPRS) total score in the DS group. At the nodal level, both the NDS and DS groups showed higher functional connectivity in the inferior frontal gyrus and hippocampus, and lower connectivity in the visual areas and striatum than the controls. The DS group exhibited higher nodal connectivity in the right inferior temporal gyrus than the NDS and HC group. The diminished expression of Scale for the Assessment of Negative Symptoms (SANS) subfactors negatively correlated with nodal connectivity of right putamen, while asociality/amotivation positively correlated with right hippocampus across whole patients. We highlighted the convergence and divergence of brain functional network dysfunctions in patients with DS and NDS, which provides crucial insights into pathophysiological mechanisms of the 2 schizophrenic subtypes.

Human brain structural networks contain sets of centrally embedded hub regions that enable efficient information communication. However, it remains largely unknown about categories of structural brain hubs and their microstructural, functional and cognitive characteristics as well as contributions to individual identification. Here, we employed three multi-modal imaging data sets with structural MRI, diffusion MRI and resting-state functional MRI to construct individual structural brain networks, identify brain hubs based on eight commonly used graph-nodal metrics, and perform comprehensive validation analysis. We found three categories of structural hubs in the brain networks, namely, aggregated, distributed and connector hubs. Spatially, these distinct categories of hubs were primarily located in the default-mode system and additionally in the visual and limbic systems for aggregated hubs, in the frontoparietal system for distributed hubs, and in the sensorimotor and ventral attention systems for connector hubs. Importantly, these three categories of hubs exhibited various distinct characteristics, with the highest level of microstructural organization in the aggregated hubs, the largest wiring cost and topological vulnerability in the distributed hubs, and the highest functional associations and cognitive flexibility in the connector hubs, although they behaved better regarding these characteristics compared to non-hubs. Finally, all three categories of hub indices displayed high across-session spatial similarities and acted as a structural fingerprint with high predictive rates (100%, 100% and 84.2%) for individual identification. Collectively, our findings highlighted three categories of brain hubs with differential microstructural, functional and cognitive associations, which may shed light on the topological mechanisms of the human connectome.

In this paper, the author uses EXCEL to make classification statistic and quantitative analysis of the distributed situation of the years, periodicals, funds and subjects and author situation of 1,629 relevant papers in Chinese citation analysis research from 2005 to 2014 included in CNKI's Chinese Academic Journals Network Publishing Database, and makes analysis using Bibliometrics related laws such as the document growth law, Plath index, Brad Quantitative, Lokat's Law, Zipf's law and h index to explore the status and development trend of the Chinese citation analysis research, and reveal the pattern and characteristics of development to provide a reference for further research.Citation analysis belongs to the category of bibliometrics, and it is one of the most active research fields in current bibliometrics, scientometrics, webometrics, library and information science. It is increasingly widely used in the evaluation of journal, document, scientific research and talent, information service and knowledge management. The development of citation analysis was initiated by P. LK Gross et al.' s first research in 1927 and developed rapidly with the advent of the Science Citation Index. In 1981, Wang Chongde published the first citation analysis article in the China's Journal of Information Science, which marked the start of the research on the Chinese periodical citation([1]). During the transition period between the 20th century and the 21st century, Liu Yanping et al. and Zhou Yunping et al. statistically analyzed the 90' s Chinese citation analysis papers respectively([2] [3] [4].) In 2006, Ye Xiejie et al. and Guo Sang et al. made a quantitative analysis of the Chinese citation analysis research papers from 2000 to 2004 and 2007 to 2011 respectively([5]). This paper analyzes the status quo and development trend of the Chinese citation analysis research during the last ten years by using bibliometrics method. Compared with the existing documents, the research range is longer and more conducive to reveal the patent and characteristics of development of this field in China, thus providing the useful reference for the research in the future([1]).

In daily work, frequently related to large-scale manufacturing equipment update problem, How to solve equipment problems is very important. The reasonable renewal of equipment is an important subject to the technical transformation of steel plant and the feasibility of promoting technological progress. This paper introduces the principle and method of system analysis and decision making, and Tangshan steel equipment renewal decision for example through case analysis and comparison of economic analysis, for equipment renewal decision providing a theoretical and practical basis.

Recently, with the rapid development of logistics industry, the construction of logistics distribution center has become an essential mainstream in the engineering. There is no doubt that the site selection of distribution center is the basis of enterprise's development. However, many risk factors still remain during site selection process. How to recognize these risk factors comprehensively and accurately is very important. This paper mainly adopts the WBS-RBS method during tobacco distribution center site selection process in Tangshan in risk identification.

Recent studies have suggested that the brain's structural and functional networks (i.e., connectomics) can be constructed by various imaging technologies (e.g., EEG/MEG; structural, diffusion and functional MRI) and further characterized by graph theory. Given the huge complexity of network construction, analysis and statistics, toolboxes incorporating these functions are largely lacking. Here, we developed the GRaph thEoreTical Network Analysis (GRETNA) toolbox for imaging connectomics. The GRETNA contains several key features as follows: (i) an open-source, Matlab-based, cross-platform (Windows and UNIX OS) package with a graphical user interface (GUI); (ii) allowing topological analyses of global and local network properties with parallel computing ability, independent of imaging modality and species; (iii) providing flexible manipulations in several key steps during network construction and analysis, which include network node definition, network connectivity processing, network type selection and choice of thresholding procedure; (iv) allowing statistical comparisons of global, nodal and connectional network metrics and assessments of relationship between these network metrics and clinical or behavioral variables of interest; and (v) including functionality in image preprocessing and network construction based on resting-state functional MRI (R-fMRI) data. After applying the GRETNA to a publicly released R-fMRI dataset of 54 healthy young adults, we demonstrated that human brain functional networks exhibit efficient small-world, assortative, hierarchical and modular organizations and possess highly connected hubs and that these findings are robust against different analytical strategies. With these efforts, we anticipate that GRETNA will accelerate imaging connectomics in an easy, quick and flexible manner. GRETNA is freely available on the NITRC website.

[This corrects the article on p. 386 in vol. 9, PMID: 26175682.].

In this article, by choosing highly frequent keywords from core journals in the field of 1992–2013 knowledge discovery in CNKI database, counting the number of two frequent keywords co-occurrences in the same journal, then constructing the highly frequent keywords matrix, and transforming the highly frequent keywords matrix into the correlation matrix and the dissimilarity matrix, then we analyzed the dissimilarity matrix based on the use of factor analysis, cluster analysis. Finally, after discussing the results of the analysis, we found that the current hotspots in the field of domestic knowledge discovery have focused on following six aspects, the knowledge discovery based on data research, knowledge discovery algorithm optimization research, the model of knowledge discovery and research of literature study, knowledge management based on domain ontology, expert system construction research, and applied research of knowledge discovery.

Functional near-infrared spectroscopy (fNIRS), a promising noninvasive imaging technique, has recently become an increasingly popular tool in resting-state brain functional connectivity (FC) studies. However, the corresponding software packages for FC analysis are still lacking. To facilitate fNIRS-based human functional connectome studies, we developed a MATLAB software package called “functional connectivity analysis tool for near-infrared spectroscopy data” (FC-NIRS). This package includes the main functions of fNIRS data preprocessing, quality control, FC calculation, and network analysis. Because this software has a friendly graphical user interface (GUI), FC-NIRS allows researchers to perform data analysis in an easy, flexible, and quick way. Furthermore, FC-NIRS can accomplish batch processing during data processing and analysis, thereby greatly reducing the time cost of addressing a large number of datasets. Extensive experimental results using real human brain imaging confirm the viability of the toolbox. This novel toolbox is expected to substantially facilitate fNIRS-data-based human functional connectome studies.

At present, the most of data mining system are independent from database system, and data loading, data conversing and algorithm operating will cost much time. Aiming at how to manage the source data, intermediate data and result data in the process of data mining effectively, the view of embedding a database into data mining system is put forward innovatively in this paper. Analyze the reason of using embedded database in data mining system, then embed Derby database into data mining system in Eclipse plug-in form. It ensures good portability and improves the efficiency of data mining greatly. The embedded data mining system and un-embedded data mining system are used for data mining respectively, making use of two typical data mining algorithms in the application of managing credit card risk to verify the advantage of embedded database in data mining.