Exact solutions of epidemic models are critical for identifying the severity and mitigation possibility for epidemics. However, solving complex models can be difficult when interfering conditions from the real-world are incorporated into the models. In this paper, we focus on the generally unsolvable adaptive susceptible-infected-susceptible epidemic model, a typical example of a class of epidemic models that characterize the complex interplays between the virus spread and network structural evolution. We propose two methods based on mean-field approximation, i.e., the first-order mean-field (...) approximation and higher-order mean-field approximation, to derive the exact solutions to ASIS models. Both methods demonstrate the capability of accurately approximating the metastable-state statistics of the model, such as the infection fraction and network density, with low computational cost. These methods are potentially powerful tools in understanding, mitigating, and controlling disease outbreaks and infodemics. (shrink)
The visual system is known to extract summary representations of visually similar objects which bias the perception of individual objects toward the ensemble average. Although vision plays a large role in guiding action, less is known about whether ensemble representation is informative for action. Motor behavior is tuned to the veridical dimensions of objects and generally considered resistant to perceptual biases. However, when the relevant grasp dimension is not available or is unconstrained, ensemble perception may be informative to behavior by (...) providing gist information about surrounding objects. In the present study, we examined if summary representations of a surrounding ensemble display influenced grip aperture and orientation when participants reached-to-grasp a central circular target which had an explicit size but importantly no explicit orientation that the visuomotor system could selectively attend to. Maximum grip aperture and grip orientation were not biased by ensemble statistics during grasping, although participants were able to perceive and provide manual estimations of the average size and orientation of the ensemble display. Support vector machine classification of ensemble statistics achieved above-chance classification accuracy when trained on kinematic and electromyography data of the perceptual but not grasping conditions, supporting our univariate findings. These results suggest that even along unconstrained grasping dimensions, visually-guided behaviors toward real-world objects are not biased by ensemble processing. (shrink)
Due to the COVID-19 outbreak, a series of chain reactions, like international trade breakdown, stock market collapse, and crude oil's collapse, have adversely affected the global economy, particularly small and medium-sized enterprises. As a result, the Chinese government issued many fiscal and financial policies to support SMEs. This paper analyzes SMEs' coping methods and conceptual changes affected by the epidemic and distinguishes “victims” and “beneficiaries.” Subsequently, based on extensive international experience and local government experience, it provides effective suggestions for the (...) Chinese government to deal with the post-epidemic era's economic changes, policy suggestions, and coping strategies for SMEs' short-term and long-term sustainable development. (shrink)
Understanding the neural basis of schizophrenia (SZ) is important for shedding light on the neurobiological mechanisms underlying this mental disorder. Structural and functional alterations in the anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC), hippocampus, and medial prefrontal cortex (MPFC) have been implicated in the neurobiology of SZ. However, the effective connectivity among them in SZ remains unclear. The current study investigated how neuronal pathways involving these regions were affected in first-episode SZ using functional magnetic resonance imaging (fMRI). Forty-nine patients (...) with a first-episode of psychosis and diagnosis of SZ—according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision—were studied. Fifty healthy controls (HCs) were included for comparison. All subjects underwent resting state fMRI. We used spectral dynamic causal modeling (DCM) to estimate directed connections among the bilateral ACC, DLPFC, hippocampus, and MPFC. We characterized the differences using Bayesian parameter averaging (BPA) in addition to classical inference (t-test). In addition to common effective connectivity in these two groups, HCs displayed widespread significant connections predominantly involved in ACC not detected in SZ patients, but SZ showed few connections. Based on BPA results, SZ patients exhibited anterior cingulate cortico-prefrontal-hippocampal hyperconnectivity, as well as ACC-related and hippocampal-dorsolateral prefrontal-medial prefrontal hypoconnectivity. In summary, spectral DCM revealed the pattern of effective connectivity involving ACC in patients with first-episode SZ. This study provides a potential link between SZ and dysfunction of ACC, creating an ideal situation to associate mechanisms behind SZ with aberrant connectivity among these cognition and emotion-related regions. (shrink)
Drawing on the theory of planned behavior, we investigate the legitimacy of platform governance and whether consumers with greater ranges of risk propensity are more likely to purchase innovative products. This study develops a moderated mediation model involving risk propensity, cognitive legitimacy, purchase intention and perceived benefit. To examine our hypotheses, we conducted a survey of 315 consumers from Shanghai, China. The results reveal that risk propensity is positively related to consumers’ purchase intentions, in which cognitive legitimacy plays a mediating (...) role. Furthermore, the interaction suggests that perceived benefit moderates the relationship between risk propensity and cognitive legitimacy. (shrink)
In 2020, the sudden outbreak of coronavirus disease 2019 has had a great impact on the health and life of people all over the world, and the sports industry is facing unprecedented challenges due to its participation and strong clustering. Based on the questionnaire survey, literature analysis, and other research methods, this study introduces the stimulus-organism-response theory, takes the sports and consumption of Kunshan citizens as the research subject, and draws lessons from the structural equation model to build a theoretical (...) model of sports consumption characteristics and future consumption willingness. The results of empirical analysis show that physical sports consumption has been greatly affected by the epidemic, but because people realize the importance of sports, the willingness of residents to consume sports increases, and the venue and other factors affect the ornamental and participating sports consumption willingness decreases. At the same time, the restrictive factors, such as lower educational background, increased age, and lack of time, make the sports consumption willingness of this characteristic group significantly lower than that of other citizens. This study puts forward some suggestions for relevant government departments to improve the sports consumption willingness of citizens. In order to expand the development prospect of sports industry from a long-term perspective, it can provide reference for the development of sports consumption. (shrink)
ObjectiveThis study aimed to investigate the morphometric alterations in the cortical and subcortical structures in multiple system atrophy patients with mild cognitive impairment, and to explore the association with cognitive deficits.MethodsA total of 45 MSA patients and 29 healthy controls were recruited. FreeSurfer software was used to analyze cortical thickness, and voxel-based morphometry was used to analyze the gray matter volumes. Cortical thickness and gray matter volume changes were correlated with cognitive scores.ResultsCompared to healthy controls, both MSA subgroups exhibited widespread (...) morphology alterations of brain structures in the fronto-temporal regions. Direct comparison of MSA-MCI and MSA-only patients showed volume reduction in the left superior and middle temporal gyrus, while cortical thinning was found in the left middle and inferior temporal gyrus in MSA-MCI patients. Cortical thinning in the left middle temporal gyrus correlated with cognitive assessment and disease duration.ConclusionStructural changes in the brain occur in MSA-MCI patients. The alteration of brain structure in the left temporal regions might be a biomarker of cognitive decline in MSA-MCI patients. (shrink)
ObjectiveWe wished to explore Parkinson's disease subtypes by clustering analysis based on the multimodal magnetic resonance imaging indices amplitude of low-frequency fluctuation and gray matter volume. Then, we analyzed the differences between PD subtypes.MethodsEighty-six PD patients and 44 healthy controls were recruited. We extracted ALFF and GMV according to the Anatomical Automatic Labeling partition using Data Processing and Analysis for Brain Imaging software. The Ward linkage method was used for hierarchical clustering analysis. DPABI was employed to compare differences in ALFF (...) and GMV between groups.ResultsTwo subtypes of PD were identified. The “diffuse malignant subtype” was characterized by reduced ALFF in the visual-related cortex and extensive reduction of GMV with severe impairment in motor function and cognitive function. The “mild subtype” was characterized by increased ALFF in the frontal lobe, temporal lobe, and sensorimotor cortex, and a slight decrease in GMV with mild impairment of motor function and cognitive function.ConclusionHierarchical clustering analysis based on multimodal MRI indices could be employed to identify two PD subtypes. These two PD subtypes showed different neurodegenerative patterns upon imaging. (shrink)
Ben shu fen wei yi ban ke xue zhe xue,Zi ran ke xue zhe xue yu shu xue zhe xue,She hui ke xue zhe xue,Ren zhi yu xin li xue zhe xue,Ke xue ji shu yu she hui wu ge bu fen,Ju ti nei rong bao kuo:gui fan xing wen ti de yu yi zhuan xiang yu yu yong jin lu,Gui ze zun xun yu yi yi de gui fan xing,Dang dai wu li xue zhe xue de yan jiu xian (...) zhuang ji qu shi,Guai ren jia she yu fu xian xiang lun deng. (shrink)
Du Weiming xian sheng shi dang dai yan jiu he chuan bo ru jia wen hua de zhong yao si xiang jia. Ta 1940 nian chu sheng yu Kunming, xian hou qiu xue yu Taiwan dong hai da xue he Meiguo Hafo da xue, ren jiao yu Pulinsidun da xue, Bokeli Jiazhou da xue. Zi 1981 nian, Du Weiming xian sheng yi zhi zai Hafo da xue Dong Ya xi dan ren li shi ji zhe xue jiao shou,qi jian huo (...) xuan Meiguo ren wen she hui ke xue yuan yuan shi, hai ceng dan ren Hafo Yanjing xue she she zhang. Chang qi yi lai, Du Weiming xian sheng zhi li yu ru xue di 3 qi fa zhan, quan shi Zhongguo wen hua, fan si xian dai jing shen, chang dao wen ming dui hua, zai hai nei wai xiang you hen gao de xue shu sheng yu. Du xian sheng fei chang re xin Zhongguo you xiu chuan tong wen hua zai ben tu de chuan bo, duo ci hui guo can jia jiao yu he xue shu jiao liu huo dong. Ta yu Beijing da xue you zhe te shu de yuan fen,zao zai 1985 nian jiu lai Beijing da xue jiang shou ru jia zhe xue. Zui jin Du Weiming xian sheng yi zheng shi shou pin wei Beijing da xue gao deng ren wen yan jiu yuan yuan zhang. (shrink)
Ben shu hui ji le zhu zhe de wei rao 20 shi ji zi ran ke xue yan jiu zhong suo she ji"ke xue ben yuan"yi ji"ke xue fang fa lun"deng ji ben ming ti,Zhen dui jin xie nian fa sheng zai guo nei wai ke xue sheng huo zhong mou xie zhong da shi jian suo zuo de ruo gan fan si.
For thousands of human’s history, we have learned how to fake or hide our genuine feelings and emotion to our surrounding folks intentionally or unconsciously. It is an irony that this is what we call emotion intelligence to get more interests, show our politeness, tackle the dilemma, or deal with many other complicated situations. Posed smiles are one of the most common faked expressions in our daily life. Indeed, it is a challenge for computer vision system to recognize genuine from (...) posed smile of an individual, not even for human brains sometimes. Recently, an interesting work by Guo et al.(Guo, et al., 2018) employed computer vision techniques to investigate the potential differences in duration, intensity, speed, symmetry of the lip corners, and certain irregularities between genuine and posed smiles based on the UvA-NEMO Smile Database. The results are quite rewarding since they found that genuine smiles were correlated with higher onset, offset, apex, and total durations, as well as offset displacement, and Irregularity-b, compared with posed smiles. In addition, posed smiles were correlated with higher onset and offset Speeds, Irregularity-a, Symmetry-a, and Symmetry-d.We can’t agree with the saying that only a handful of studies on the dynamic features of facial expressions have been conducted due to the lack of user-friendly analytic tools. For the past decades, hundreds of studies focused on dynamic features of facial expressions (Ko, 2018, Sandbach, et al., 201... (shrink)
Ben shu tong guo dui yu Li Zhuowu he Yangming xue de chan shi, dui yu Zhongguo qian jin dai bu tong yu xi fang de lu xiang jin xing le bian xi, ren wei"zai Zhongguo si xiang zhong cun zai zhe bu tong yu Ouzhou si xiang shi de zhan kai de Zhongguo du zi de si xiang shi de zhan kai", fan dui yi ban chang jian de, yi Ouzhou de li shi zhan kai he jia zhi guan (...) wei ji zhun de xi fang zhong xin lun li shi guan, cong er zai Zhongguo nei zai de si xiang li lu zhong xun zhao Zhongguo de"jin dai"ji qi"qu she yu fa zhan"; ta hai da po le zai Riben tong xing de dui yu Li Zhuowu tong xin shuo shi ge ren zhu yi biao xian de fu qian shuo fa, ba Li Zhuowu wan qi de fu za si xiang, te bie shi fo jiao si xiang zai ta shen shang de du te ti xian tui xiang dui yu ming dai si xiang ben shen de jie shi. (shrink)
Ben shu ji zhong fan ying le dang dai ri ben zhe xue jia dui ma ke si zhu yi zhe xue de li jie.Shu zhong shou lu le ti ming xiu zi ran li shi guo cheng de si xiang,Ping tian qing ming xun huan= ji lei li lun yu li shi ren shi,Yan yuan qing yi ri ben de1844 nian jing ji xue zhe xue shou gao yan jiu deng wen zhang.
Ben shu tong guo dui Kang Youwei zhi "qun" yu"du" guan nian de kao cha, lai ba wo Kang Youwei dui ge ren, guo jia nai zhi tian xia zhi xu de shen ke si kao. Zhu yao nei rong you: yi, Jin dai"qun-du" guan nian de chu xian ji qi zou xiang, er, Kang Youwei "qun-du" guan nian chan sheng de si xiang bei jing, san, "Du ren" yu Kang Youwei dui xian dai xing ge ti sheng cun zhi (...) ling hui, si, "Qun" yu guo jia wei xin, wu, "qun-du" guan lian yu chong tu: min quan yu guo jia guan nian zhi jian de zhang li, liu, Shi jie fan wei zhi "du" yu "qun" : guo jia yu "da tong"deng. (shrink)
Ben shu tong guo li shi shang de yi ge ge xiao gu shi lai shuo li,Rang du zhe kan dao le yi ge zhong min ai min,Shou min ai dai de yan zi,Yi ge qin zheng lian jie,Chi shen li jie de yan zi,Yi ge chong shang jie jian,Fan dui she hua de yan zi.
Ben shu nei rong bao kuo:zhong hua min zu dao de sheng huo zong lun,Yuan gu zhi zhan guo dao de sheng huo de ji ben zhuang kuang,Dao de guan xi ji wu zhong ji ben lun chang de que li,Li yi gui fan he li wen hua de xing qi yu hong yang,De hua tian xia yu dao de jiao yu zhi kai zhan deng.
Ben shu cong da liang xi fang zhe xue de chuan bo fen fan xian xiang zhong xun chu ma ke si zhu yi zhe xue zai zhong guo cheng wei li shi fa zhan zhi bi ran, qi ta zhe xue si xiang dou zai zhong guo chuan bo guo, zhi you ma ke si zhu yi cai you wu bi qiang da de sheng ming li.
Xue xi he yan jiu Zhongguo zhe xue, yi ban lai shuo, Feng xian sheng shi ke chao er bu ke yue de. Yi si shi, hou ren wan quan ke neng er qie ye ying dang sheng guo Feng xian sheng, dan shi que bu neng rao guo Feng xian sheng. Rao guo Feng xian sheng, bu dan bi ran yao duo fei li qi, er qie rong yi zou wan lu er nan yu shen ru tang ao. Feng (...) Youlan zuo wei wo guo jin dai zhe xue de yi dai ju bo, zai mou zhong yi yi shang, ying xiang liao zheng ge jin dai Zhongguo zhe xue de ge ju. Er dui yu Feng Youlan zhe xue ji qi xue shu sheng ya de yan jiu, ye sui zhe dang dai guo xue yan jiu de xing qi, shou dao yue lai yue duo de guan zhu. Minguo shi qi de Zhongguo xue shu jie, ying gai shuo shi hui ju le yi da pi kua shi dai de xue shu jing ying, te bie shi zai chuan tong xue shu ling yu, kan cheng yi chang si xiang de sheng yan.ben shu zuo zhe zai hao ru yan hai de Minguo wen xian zhong, jing xin zheng li chu dang shi xue jie dui Feng Youlan zhe xue de hui ying he dui hua, qi zhong bao kuo zhu ru Chen Yinke, Jin Yuelin, Hu Shi, Zhang Dainian, Cai Shangsi deng yi da pi xue jie tai dou de wen zhang, zi liao xing fei chang qiang, bu jin fan ying le zhu wei xue zhe ge ren de du dao jian jie, geng cong yi ge ce mian fan ying le Minguo shi qi Feng Youlan zhe xue yan jiu de sheng kuang, dui dang xia yan jiu Feng Youlan zhe xue si xiang ju you zhong yao de can kao jia zhi. (shrink)
Ben shu zhi zai yong zhe xue yan jiu de fang shi zheng ti lun shu fo xue ti xi. wei le shi pu tong da zhong neng chu kui wan zheng de fo jiao xi tong, zuo zhe fen bie cong fo jiao de ge di yu pai bie,zhe xue si xiang guan dian,si miao jian zhu yi ji fo jing shu ji lun zhu deng fang mian jin xing liao xiang jin de xu shu ji tao lun. shu (...) zhong chu le dui fo jiao wen hua zhi shi de zheng li yu chuan hang, hai hui jie he you zuo zhe zai de zhe xue ji Zhongguo wen hua ling yu zhong di yan jiu cheng guo. ben shu zun zhong zong jiao ben shen, zai xie zuo shou fa shang shi fen yan jin, fo jing ji xue zhe guan dian jie zhi yi yin yong xing shi lie chu, bing bu zuo ping lun ; dui zhuan men de shu yu hui dai you jiao zhu,jian suo shen zhi shi fan wen yuan wen de shuo ming, ke du xing qiang bing yi yu cha yue. (shrink)
Ben shu zhu zai zhang xian ru jia yi li zai xiu dao fan chou de te se, Bao kuo wang chuan shan liang duan yi zhi lun yan yi, Bian zheng si wei yu shi jian zhi he, Zhong guo ren wen chuan tong yu xian dai jiao yu.
Ben shu zhu yao yan yong bing zun xun "li shi yu luo ji xiang tong yi" de yuan ze he fang fa, xiang xi shu li, jian tao zuo wei ming dai zhong qi si xiang jia de Huang Wan, qi yi sheng dao xue si xiang jin zhan guo cheng zhong cong Song ru zhi xue (li xue) dao Wang Yangming liang zhi xue (xin xue), zai dao hui gui jing dian (jing xue) de yan bian li cheng, (...) yi qi lun zheng qi xue shu yan bian fan shi zhi yu Ming Qing xue shu zhuan xing de "xian dao xing" dian fan yi yi. (shrink)
Ben shu wei "Song Ming shi qi Chang Jiang zhong you de ru xue yan jiu" cong shu zhi yi: ben cong shu shi ren wen she hui ke xue chong dian yan jiu ji di-Wuhan da xue Zhongguo chuan tong wen hua yan jiu zhong xin tou biao, jing jiao yu bu zu zhi zhuan jia ping shen tong guo, zheng shi pi zhun de jiao yu bu ren wen she hui ke xue 2001 nian du zhong da yan (...) jiu xiang mu, xiang mu pi zhun hao shi 01JAZJD720008, ding yu 2004 nian qian hou wan cheng. ke ti zu zhu chi ren wei Guo Qiyong jiao shou, "Jiangxi zhi xue"shi Zhu Xi ti chu de yi ge gai nian, zhu yao zhi chu zi yu Jiangxi Jinxi Lu shi jia zu yi men de xue shuo, qi nei he shi Lu Jiuyuan de xin xue; di 2 quan ze shi you "Song Yuan xue an" suo zai zhi Xiangshan xiong Zhangfu, Suoshan de xue shuo; di 3 quan wei Xiangshan xiong di de di zi men de si xiang. Ben shu quan mian kao cha Jiangxi zhi xue zai Song dai Chang Jiang zhong you xing qi de xue mo yuan yuan, zhu yao xue shuo, yu Luo xue Min xue Zhejiang zhu xue zhe (Lü Zuqian, Yong Shangsi xian sheng deng) zhi jian de guan xi, yi ji qi dui Wang (Yangming) xue xing qi de nei zai guan lian xing, bing yan ji dui xian dai xin ru xue de tan tao. ben shu de xie zuo jiang zhu zhong si xiang kao gu de fang fa, zai quan mian yan jiu you guan dian ji de ji chu shang, dui you guan ren wu de chu sheng di, huo dong de di qu (ru jia zu suo zai cun luo, xue zhe shu xin wang lai, jiang xue, hui jiang, shu yuan deng) jin xing shi di de kao cha, guang fan di yun yong fang zhi, nian pu, shu yuan zhi, jia pu deng cai liao, ba si xiang jia huan yuan yu ju ti huan jing zhi zhong, zai ju ti she hui de li shi de jia zu de bei jing xia kao cha qi si xiang de nei han, jie shi qi jing shen suo zai, bing jin er yin shen chu qi yi yi yu jia zhi. (shrink)