Measurement Research Based on Bayesian Structural Equation Cognitive Model

The Bayesian structural equation model integrates the principles of Bayesian statistics, providing a more flexible and comprehensive modeling framework. In exploring complex relationships between variables, handling uncertainty, and dealing with missing data, the Bayesian structural equation model demonstrates unique advantages. Therefore, Bayesian methods are used in this paper to establish a structural equation model of innovative talent cognition, with the measurement of college students’ cognition of innovative talent being studied. An in-depth analysis is conducted on the effects of innovative self-efficacy, social resources, innovative personality traits, and school education, aiming to explore the factors influencing college students’ innovative talent. The results indicate that innovative self-efficacy plays a key role in perception, social resources are significantly positively correlated with the perception of innovative talents, innovative personality tendencies and school education are positively correlated with the perception of innovative talents, but the impact is not significant.

Visual Representation and Cognitive Models in Information Visualization

As a branch of computer science,information visualization aims to help users understand and analyze complex data through graphical interfaces and interactive technologies.Information visualization primarily includes various visual structures such as time-series structures,spatial relationship structures,statistical distribution structures,and geographic map structures,each with unique functions and application scenarios.To better explain the cognitive process of visualization,researchers have proposed various cognitive models based on interaction mechanisms,visual perception steps,and novice use of visualization.These models help understand user cognition in information visualization,enhancing the effectiveness of data analysis and decision-making.

Hierarchical Merging &Generalization Method of Three-Dimension City Model Group Based on the Theory of Spatial Visual Cognition

In order to simplify the three-dimensional building group model, this paper proposes a clustering generalization method based on visual cognitive theory. The method uses road elements to roughly divide scenes, and then uses spatial cognitive elements such as direction, area, height and their topological constraints to classify them precisely, so as to make them conform to the urban morphological characteristics. Delaunay triangulation network and boundary tracking synthesis algorithm are used to merge and summarize the models, and the models are stored hierarchically. The proposed algorithm should be verified experimentally with a typical urban complex model. The experimental results show that the efficiency of the method used in this paper is at least 20% higher than that of previous one, and with the growth of test data, the higher efficiency is improved. The classification results conform to human cognitive habits, and the generalization levels of different models can be relatively unified by adaptive control of each threshold in the clustering generalization process.

THEORY AND IMPLEMENTATION OF ICAI COGNITIVE MODEL BASED ON MEANING

Cognitive models must be able to adapt the students learning behaviors dynamically.In our point of view,the processes of learning and understanding are,in nature,the procedure that gains the meaning of the object to be learned.So,ICAI cognitive models should reflect the meaning structure of the domain knowledge in students mind.According to this view,we developed the meaning theory of Ludwig Wittgenstein,and proposed the concept of meaning conjoinism.On the basis of the meaning conjoinism we proposed a meaning oriented ICAI cognitive model and its corresponding teaching tactics.Furthermore,we developed an ICAI system named Thinking and the efficiency of our proposal has been demonstrated.

APPLICATION OF HUMAN COGNITIVE MODEL FOR TIME DEPENDENT OPERATOR BEHAVIOR IN CHINESE NPP

This paper discusses some issues on human reliability model of time dependent human behavior. Some results of the crew reliability experiment on Tsinghua training simulator in China are given, Meanwhile, a case of calculation for human error probability during anticipated transient without scram (ATWS) based on the data drew from the recent experiment is offered.

Research on cognitive model of English major writing course based on the Internet

With the development of Internet technology, network is more and more common in the teaching process. On the analysis of teaching status of English major writing course and in the guide of cognitive linguistics and constructivism, the paper studies the use of the internet in English major writing teaching cognitive model.

Mesenchymal stem cell-derived exosomes promote neurogenesis and cognitive function recovery in a mouse model of Alzheimer’s disease

Studies have shown that mesenchymal stem cell-derived exosomes can enhance neural plasticity and improve cognitive impairment.The purpose of this study was to investigate the effects of mesenchymal stem cell-derived exosomes on neurogenesis and cognitive capacity in a mouse model of Alzheimer’s disease.Alzheimer’s disease mouse models were established by injection of beta amyloid 1?42 aggregates into dentate gyrus bilaterally.Morris water maze and novel object recognition tests were performed to evaluate mouse cognitive deficits at 14 and 28 days after administration.Afterwards,neurogenesis in the subventricular zone was determined by immunofluorescence using doublecortin and PSA-NCAM antibodies.Results showed that mesenchymal stem cells-derived exosomes stimulated neurogenesis in the subventricular zone and alleviated beta amyloid 1?42-induced cognitive impairment,and these effects are similar to those shown in the mesenchymal stem cells.These findings provide evidence to validate the possibility of developing cell-free therapeutic strategies for Alzheimer’s disease.All procedures and experiments were approved by Institutional Animal Care and Use Committee(CICUAL)(approval No.CICUAL 2016-011)on April 25,2016.

Amelioration of Alzheimer's disease pathology and cognitive deficits by immunomodulatory agents in animal models of Alzheimer's disease

The most common age-related neurodegenerative disease is Alzheimer's disease(AD) characterized by aggregated amyloid-β(Aβ) peptides in extracellular plaques and aggregated hyperphosphorylated tau protein in intraneuronal neurofibrillary tangles,together with loss of cholinergic neurons,synaptic alterations,and chronic inflammation within the brain.These lead to progressive impairment of cognitive function.There is evidence of innate immune activation in AD with microgliosis.Classically-activated microglia(M1 state) secrete inflammatory and neurotoxic mediators,and peripheral immune cells are recruited to inflammation sites in the brain.The few drugs approved by the US FDA for the treatment of AD improve symptoms but do not change the course of disease progression and may cause some undesirable effects.Translation of active and passive immunotherapy targeting Aβ in AD animal model trials had limited success in clinical trials.Treatment with immunomodulatory/anti-inflammatory agents early in the disease process,while not preventive,is able to inhibit the inflammatory consequences of both Aβ and tau aggregation.The studies described in this review have identified several agents with immunomodulatory properties that alleviated AD pathology and cognitive impairment in animal models of AD.The majority of the animal studies reviewed had used transgenic models of early-onset AD.More effort needs to be given to creat models of late-onset AD.The effects of a combinational therapy involving two or more of the tested pharmaceutical agents,or one of these agents given in conjunction with one of the cell-based therapies,in an aged animal model of AD would warrant investigation.

Relationship between Contraceptive Knowledge,Cognition and Behaviors among Vocational High School Students

Objective To study the relationship between contraceptive knowledge, cognition and behaviors among vocational high school students. Methods A cross-sectional survey was anonymously self-administrated by way of computer to all the 1st graders＇ among three vocational high schools in a district of Shanghai. Four cognition indicators, including perceived benefits, perceived barriers, perceived risks and self eJficacy were applied according to Health Belief Model. Results The percentage of respondents who reported ever sexual practices and contraceptives use at first sex was 7.01% and 56.64% respectively. Condom （58. 70%） and oral contraceptive pills （27.78%） were used by the majority. Main reasons for non-use were ＂shy to buy it＂ （26.53%） and ＂partner don＇t want to use any＂ （18.37%）. Proportion of respondents who ever had sex regarded withdrawal and rhythm as effective were 68.35% and 68.42% respectively. Proportion of respondents having sex knew ＂If a person was infected with STDs, he/she might not have noticeable symptoms＂ were 22.86%. Proportion of respondents who reported non-use of contraceptives at first sex considered that ＂infrequent intercourse could not cause a girl pregnant even she had experienced her menstruation＂ were 40.63%. Proportion of respondents who had ever used condom got full score of knowledge on ＂how to correctly use condom＂ were 23,26%. Perceived risks were poorest among four cognition indicators. High level of contraceptive knowledge was connected with high rate of contraceptives use at first sex, whereas high level contraceptive cognition was connected with high rate of contraceptive use both at first and recent sex. Conclusions It is important to provide youth-friendly services as well as message on contraceptives among vocational school students.

Cold water swimming pretreatment reduces cognitive deficits in a rat model of traumatic brain injury

A moderate stress such as cold water swimming can raise the tolerance of the body to potentially injurious events. However, little is known about the mechanism of beneficial effects induced by moderate stress. In this study, we used a classic rat model of traumatic brain injury to test the hypothesis that cold water swimming preconditioning improved the recovery of cognitive functions and explored the mechanisms. Results showed that after traumatic brain injury, pre-conditioned rats（cold water swimming for 3 minutes at 4℃） spent a significantly higher percent of times in the goal quadrant of cold water swim, and escape latencies were shorter than for non-pretreated rats. The number of circulating endothelial progenitor cells was significantly higher in pre-conditioned rats than those without pretreatment at 0, 3, 6 and 24 hours after traumatic brain injury. Immunohistochemical staining and Von Willebrand factor staining demonstrated that the number of CD34~＋ stem cells and new blood vessels in the injured hippocampus tissue increased significantly in pre-conditioned rats. These data suggest that pretreatment with cold water swimming could promote the proliferation of endothelial progenitor cells and angiogenesis in the peripheral blood and hippocampus. It also ameliorated cognitive deficits caused by experimental traumatic brain injury.

Modeling of cognitive framework in time-stressed decision making

An approach for modeling a human cognitive framework in time-stressed decision making is presented. The recognitive and metacognitive processes that represent the cognitive framework are modeled by the colored Petri nets （CPNs）. A structural and behavioral analysis method is adopted to obtain the static and dynamic property used to verify the CPNs model of the cognitive framework. Finally, an example from the command and control radar recognition system is used to evaluate the feasibility and availability of the CPNs model adopted in practical systems.

MODELING RADIO RESOURCE MANAGEMENT IN COGNITIVE RADIO SYSTEM BASED ON OFDMA USING COLORED PETRI NET

Cognitive Radio(CR) system based on Orthogonal Frequency Division Multiple Access(OFDMA),such as Wireless Regional Area Networks(WRAN) and Worldwide Interoperability for Microwave Access(WiMAX),often attempt to improve performance via dynamic radio resource management,which is characterized as concurrent processing of different traffic and nondeterministic system capacity.It is essential to design and evaluate such complex system using proper modeling and analysis tools.In the previous work,most of the communication systems were modeled as Markov Chain(MC) and Stochastic Petri Nets(SPN),which have the explicit limitation in evaluating adaptive OFDMA CR system with wide area traffic.In this paper,we develop an executable top-down hier-archical Colored Petri Net(CPN) model for adaptive OFDMA CR system,and analyze its performance using CPN tools.The results demonstrate that the CPN can model different radio resource manage-ment algorithms in CR Systems,and the CPN tools require less computational effort than Markov model using Matlab,with its flexibility and adaptability to the traffics which arrival interval and processing time are not exponentially distributed.

An enriched environment reduces hippocampal inflammatory response and improves cognitive function in a mouse model of stroke

An enriched environment is used as a behavio ral intervention therapy that applies sensory,motor,and social stimulation,and has been used in basic and clinical research of va rious neurological diseases.In this study,we established mouse models of photothrombotic stroke and,24 hours later,raised them in a standard,enriched,or isolated environment for 4 weeks.Compared with the mice raised in a standard environment,the cognitive function of mice raised in an enriched environment was better and the pathological damage in the hippocampal CA1 region was remarkably alleviated.Furthermore,protein expression levels of tumor necrosis factor receptor-associated factor 6,nuclear factorκB p65,interleukin-6,and tumor necrosis factorα,and the mRNA expression level of tumor necrosis factor receptor-associated factor 6 were greatly lower,while the expression level of miR-146a-5p was higher.Compared with the mice raised in a standard environment,changes in these indices in mice raised in an isolated environment were opposite to mice raised in an enriched environment.These findings suggest that different living environments affect the hippocampal inflammatory response and cognitive function in a mouse model of stro ke.An enriched environment can improve cognitive function following stroke through up-regulation of miR-146a-5p expression and a reduction in the inflammatory response.

Cognitive deficits in schizophrenia:from pharmacology to neurocognitive models

Schizophrenia is a typical mental disorder characterized by cognitive,social,and emotional impairments and by psychotic symptoms.For nearly a century,there have been ongoing discussions on the anatomical-functional connections between brain abnormalities and symptoms in patients with schizophrenia.Neuroimaging studies in such patients show abnormalities in the prefrontal cortex(PFC),a brain region that acts as an executive center in cognition processing.The disrupted brain connectivity between PFC and other brain structures(such as the limbic system,basal ganglia and thalamus)results in faulty information processing and cognition deficits.Dopamine receptors,which have historically acted as vital targets in schizophrenia therapies,have complex roles in cognition.Here we reviewed dopamine's role as a widespread neurotransmitter mediating the PFC-cognitive system.The imbalance of brain dopamine level,especially the abnormal D1/D2receptors ratio,leads to dysfunctions in brain connectivity in patients with schizophrenia.Recent neurocognitive modeling studies suggest the imbalance of dopamine receptors affects the internal noise within brain networks,which may lead to reduced signal-to-noise ratio in the PFC neuron populations.Going forward,more researches focusing on the relationship between pharmacology and neurocognitive models are needed,in an effort to identify more effective and efficient ways to treat cognitive impairment in patients with schizophrenia.

Propagation Modelling Using Integral Equation Methods to Enable Co-existence and Address Physical Layer Security Issues in Cognitive Radio

In this paper it is envisaged that cognitive radios (CRs) consult a supporting network infrastructure for per-mission to transmit. The network server either grants or rejects these requests by estimating, from the CR’s geo-location and antenna features, the likely impact its transmission would have on incumbents and other CR devices. This decision would be based on a real-time radio environment map [1] which would be kept up to date with readings from CRs, sensors and dynamic radio propagation prediction. By this means coexistence with incumbents and other CRs can be satisfied. It is maintained here that integral-equation (IE) - based al-gorithms are suitable candidates for the propagation engine given their ‘automatic’ nature and that they can be implemented to give results arbitrarily close to the exact numerical solution. IE methods based on the Fast Multipole Method are examined as a likely route to achieve the accuracy and speed necessary for real-time propagation mapping. It is concluded that the results obtained using one of the most recent of these, the Field Extrapolation Method (FEXM) [2], are promising for rural/suburban profiles and could serve to enable co-existence, for example, in IEEE802.22 networks. It is also explained how dynamic propagation prediction can address some fundamental security threats to CR networks.

Natural Scene Classification Inspired by Visual Perception and Cognition Mechanisms

The process of human natural scene categorization consists of two correlated stages: visual perception and visual cognition of natural scenes.Inspired by this fact,we propose a biologically plausible approach for natural scene image classification.This approach consists of one visual perception model and two visual cognition models.The visual perception model,composed of two steps,is used to extract discriminative features from natural scene images.In the first step,we mimic the oriented and bandpass properties of human primary visual cortex by a special complex wavelets transform,which can decompose a natural scene image into a series of 2D spatial structure signals.In the second step,a hybrid statistical feature extraction method is used to generate gist features from those 2D spatial structure signals.Then we design a cognitive feedback model to realize adaptive optimization for the visual perception model.At last,we build a multiple semantics based cognition model to imitate human cognitive mode in rapid natural scene categorization.Experiments on natural scene datasets show that the proposed method achieves high efficiency and accuracy for natural scene classification.

A Cognitive Model for Multi-Agent Collaboration

In multi-agent system, agents work together for solving complex tasks and reaching common goals. In this paper, we propose a cognitive model for multi-agent collaboration. Based on the cognitive model, an agent architecture will also be presented. This agent has BDI, awareness and policy driven mechanism concurrently. These approaches are integrated in one agent that will make multi-agent collaboration more practical in the real world.

Markov Model Based Jamming and Anti-Jamming Performance Analysis for Cognitive Radio Networks

In this paper, we conduct a cross-layer analysis of both the jamming capability of the cognitiveradio-based jammers and the anti-jamming capability of the cognitive radio networks (CRN). We use a Markov chain to model the CRN operations in spectrum sensing, channel access and channel switching under jamming. With various jamming models, the jamming probabilities and the throughputs of the CRN are obtained in closed-form expressions. Furthermore, the models and expressions are simplified to determine the minimum and the maximum CRN throughput expressions under jamming, and to optimize important anti-jamming parameters. The results are helpful for the optimal anti-jamming CRN design. The model and the analysis results are verified by simulations.

On Constructing the Cognitive Model of Knowledge Acquisition in Globalization-Oriented Study

This paper which is directed to constructing the cognitive model of knowledge acquisition in view of the increasing need of globalization-oriented study, points out features of globalization-oriented study, and proposes the importance and necessity for constructing the CMKAGS （cognitive model of knowledge acquisition in globalization-oriented study） which includes attention, background knowledge, and chunking memory that involves semantic chunking, information encoding as well as choice of information encoding. The cognitive model of knowledge acquisition in question aims at helping e-learners to memorize their learned knowledge and improve their studies effectively and efficiently, whether they study in enhanced conditions or in natural conditions

On Evaluating the Efficacy of Predictive Models for Cognitive Radio Spectrum Availability in Nigeria

In order to avoid the undesired interference with the activities of the primary users in cognitive radio networks, the secondary users are required to be able to predict the behavior of the primary users so as to leave the channel before the arrival of such licensed owner of the spectrum. While a number of existing literatures on cognitive radio spectrum prediction employ the use of propagation curves for predicting the spectrum holes otherwise known as TV white space, these models are built based on measurements conducted in regions that are different from Nigeria, suitability in terms of usage may therefore vary due to environmental factors and terrain profile. This work evaluates the efficacy of the developed model in predicting the cognitive spectrum availability in Nigeria. Models capable of predicting spectrum occupancy in the time domain using discrete-time two-state Markov chain with an appropriate Duty Cycle (DC) model and also a modified m-bell shaped exponential equation were formulated. The result obtained in all cases considered shows that the formulated models are appropriate to be used in any environment if the parameters were carefully extracted from the data. This work has also demonstrated that the accuracy of Markov chain models depends on the level of usage of a spectrum under consideration and may therefore not give desirable results when employed in some other spectrum.