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1.
Tanzila Saba Amjad Rehman Mirza Naveed Shahzad Rabia Latif Saeed Ali Bahaj Jaber Alyami 《Microscopy research and technique》2022,85(6):2083-2094
Early detection of post-traumatic stress disorder (PTSD) is essential for proper treatment of the patients to recover from this disorder. The aligned purpose of this study was to investigate the performance deviations in regions of interest (ROI) of PTSD than the healthy brain regions, to assess interregional functional connectivity and applications of machine learning techniques to identify PTSD and healthy control using resting-state functional magnetic resonance imaging (rs-fMRI). The rs-fMRI data of 10 ROI was extracted from 14 approved PTSD subjects and 14 healthy controls. The rs-fMRI data of the selected ROI were used in ANOVA to measure performance level and Pearson's correlation to investigate the interregional functional connectivity in PTSD brains. In machine learning approaches, the logistic regression, K-nearest neighbor (KNN), support vector machine (SVM) with linear, radial basis function, and polynomial kernels were used to classify the PTSD and control subjects. The performance level in brain regions of PTSD deviated as compared to the regions in the healthy brain. In addition, significant positive or negative functional connectivity was observed among ROI in PTSD brains. The rs-fMRI data have been distributed in training, validation, and testing group for maturity, implementation of machine learning techniques. The KNN and SVM with radial basis function kernel were outperformed for classification among other methods with high accuracies (96.6%, 94.8%, 98.5%) and (93.7%, 95.2%, 99.2%) to train, validate, and test datasets, respectively. The study's findings may provide a guideline to observe performance and functional connectivity of the brain regions in PTSD and to discriminate PTSD subject using only the suggested algorithms. 相似文献
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Post-traumatic stress disorder (PTSD) has a high lifetime prevalence and is one of the more serious challenges in mental health care. Fear-conditioned learning involving the amygdala has been thought to be one of the main causative factors; however, recent studies have reported abnormalities in the thalamus of PTSD patients, which may explain the mechanism of interventions such as eye movement desensitization and reprocessing (EMDR). Therefore, I conducted a miniature literature review on the potential contribution of the thalamus to the pathogenesis of PTSD and the validation of therapeutic approaches. As a result, we noticed the importance of the retinotectal pathway (superior colliculus−pulvinar−amygdala connection) and discussed therapeutic indicators. 相似文献
3.
Navid Khaleghimoghaddam Havva Alkan Bala G zin zmen &Scedil erefnur zt rk 《建筑学研究前沿(英文版)》2022,11(5):877-890
Environment psychologically affects individuals. According to the base of cognitive psychology, there is a direct relationship between human behavior, environment, and emotional process. Assuming that pleasantness and unpleasantness are associated with peripheral nervous system activation, the current study aims to explore if the pleasant or unpleasant architectural places can stimulate the brain regions engaged in emotions or not. As the main contribution, we used functional magnetic resonance imaging (fMRI) measuring blood oxygenation level-dependent (BOLD) changes to effectively detect the brain's region that mainly responds to the emotional-perceptual processes. Based on the results of examining the emotional assessment model of “Pleasure-Arousal” applied to 140 students, 30 most-rated images representing 15 pleasant and 15 unpleasant places were shown to 32 participants in a 1.5-T MRI scanner. After applying standard preprocessing steps (re-alignment, slice-timing, co-registration, segmentation, normalization, and smoothing) to functional MR images, first-level analysis was applied to each subject. The results were evaluated using statistical corrections at different levels for female and male participants with the second-level analysis. In conclusion, it has been shown that there is a significant linkage between environmental experience and brain activation so that the architectural qualities can change blood flow in specific brain regions. 相似文献
4.
Food technologies provide numerous benefits to society and are extensively vetted for safety. However, many technological innovations still face high levels of skepticism from consumers. To promote development and use of food technologies, it is critical to understand the psychological and neurobiological processes associated with consumer acceptability concerns. The current study uses a neuroscience-based approach to understand consumer attitudes and perceptions of risk associated with food technologies and investigate how such attitudes impact consumer’s processing of information related to food technologies. We used functional magnetic resonance imaging (fMRI) to measure brain activation while participants processed infographics related to food technology topics. For technology topics perceived as riskier (antibiotics and hormones), activation was higher in areas of the lateral prefrontal cortex that are associated with decisional uncertainty. In contrast, technology topics that were viewed more favorably (sustainability and animal welfare) tended to activate the ventromedial prefrontal cortex, a region that processes positive affect and subjective value. Moreover, for information about hormones, the lateral PFC activation was associated with individual differences in resistance to change in risk perception. These results reveal how attitudes and risk perception relate to how the brain processes information about food technologies and how people respond to information about such technologies. 相似文献
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人脑效应连接网络刻画了脑区间神经活动的因果效应. 对不同人群的脑效应连接网络进行研究不仅能为神经精神疾病病理机制的理解提供新视角, 而且能为疾病的早期诊断和治疗评价提供新的脑网络影像学标记, 具有十分重要的理论意义和应用价值. 利用计算方法从功能磁共振成像(Functional magnetic resonance imaging, fMRI)数据中识别脑效应连接网络是目前人脑连接组学中一项重要的研究课题. 本文首先概括了从fMRI数据中进行脑效应连接网络识别的主要流程, 说明了其中的主要步骤和方法; 然后, 给出了一种脑效应连接网络识别方法的分类体系, 并对其中一些代表性的识别算法进行了阐述; 最后, 通过对该领域挑战性问题的分析, 预测了脑效应连接网络识别未来的研究方向, 以期对相关研究提供一定的参考. 相似文献
7.
为了研究说谎时的脑网络特征,采集了32个被试在说真话和说谎条件下的功能磁共振数据,预处理后利用AAL模板构建不同条件下的功能连接网络,再利用基于机器学习的多维模式分类器对说谎和说真话进行分类。该分类器取得了良好的分类正确率82.03%(说谎84.38%,说真话79.69%),并提取了辨别说谎和说真话的有效的功能连接模式。结果表明了使用大尺度的功能连接对说谎和说真话进行分类的良好性能,并且从脑网络角度揭示了说谎的特征。 相似文献
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9.
G. Petri P. Expert F. Turkheimer R. Carhart-Harris D. Nutt P. J. Hellyer F. Vaccarino 《Journal of the Royal Society Interface》2014,11(101)
Networks, as efficient representations of complex systems, have appealed to scientists for a long time and now permeate many areas of science, including neuroimaging (Bullmore and Sporns 2009 Nat. Rev. Neurosci.
10, 186–198. (doi:10.1038/nrn2618)). Traditionally, the structure of complex networks has been studied through their statistical properties and metrics concerned with node and link properties, e.g. degree-distribution, node centrality and modularity. Here, we study the characteristics of functional brain networks at the mesoscopic level from a novel perspective that highlights the role of inhomogeneities in the fabric of functional connections. This can be done by focusing on the features of a set of topological objects—homological cycles—associated with the weighted functional network. We leverage the detected topological information to define the homological scaffolds, a new set of objects designed to represent compactly the homological features of the correlation network and simultaneously make their homological properties amenable to networks theoretical methods. As a proof of principle, we apply these tools to compare resting-state functional brain activity in 15 healthy volunteers after intravenous infusion of placebo and psilocybin—the main psychoactive component of magic mushrooms. The results show that the homological structure of the brain''s functional patterns undergoes a dramatic change post-psilocybin, characterized by the appearance of many transient structures of low stability and of a small number of persistent ones that are not observed in the case of placebo. 相似文献
10.
《信息工程大学学报》2016,17(3)
近年来,机器学习技术广泛用于从功能磁共振成像(functional magnetic resonance imaging,fMRI)数据中解码视觉信息、精神状态、情绪和其它感兴趣的大脑感知和认知功能。然而,由于fMRI数据样本维数高,样本量少,一般需要利用特征提取方法去除多余的预测变量和实验噪声等信息,避免机器学习模型出现过拟合问题,提高模型的预测准确率和泛化能力。介绍和讨论了常用fMRI数据有监督特征提取方法的一般原理和研究现状,并着重分析其性能和可能改进方向,最后对特征提取方法在fMRI中的研究方向进行了展望。 相似文献