966 resultados para Behavioral model


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This paper presents a behavior model for PLL Frequency Synthesizer. All the noise sources are modeled with noise voltages or currents in time-domain. An accurate VCO noise model is introduced, including both thermal noise and 1/f noise. The behavioral model can be co-simulated with transistor level circuits with fast speed and provides more accurate phase noise and spurs prediction. Comparison shows that simulation results match very well with measurement results.

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This paper contributes a new approach for developing UML software designs from Natural Language (NL), making use of a meta-domain oriented ontology, well established software design principles and Natural Language Processing (NLP) tools. In the approach described here, banks of grammatical rules are used to assign event flows from essential use cases. A domain specific ontology is also constructed, permitting semantic mapping between the NL input and the modeled domain. Rules based on the widely-used General Responsibility Assignment Software Principles (GRASP) are then applied to derive behavioral models.

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Abstract: Research has primarily focused on depression and mood disorders, but little research has been devoted to an examination of mental health services use amongst those with diagnosable anxiety disorder (Wittchen et al., 2002; Bergeron et al., 2005). This study examined the possible predicting factors for mental health services utilization amongst those with identifiable anxiety disorder in the Canadian population. The methods used for this study was the application of Andersen’s Behavioral Model of Health Services Use, where predisposing, need and enabling characteristics were regressed on the dependent variable of mental health services use. This study used the Canadian Community Health Survey (cycle 1.2: Mental Health and Well-Being) in a secondary data analysis. Several multiple logistics models predicted the likelihood to seek and use mental health services. Predisposing characteristics of gender and age, Enabling characteristics of education and geographical location, and those with co-occurring mood disorders were at the greatest increased likelihood to seek and use mental health services.

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"Weathering a Hidden Storm": An App~ication of Andersen's Behaviora~ Mode~ of Hea~th, and Hea~th Services Use for Those With Diagnosab~e Anxiety Disorder Research has primarily focused on depression and mood disorders, but little research has been devoted to an examination of mental health services use amongst those with diagnosable anxiety disorder (Wittchen et al., 2002; Bergeron et al., 2005). This study examined the possible predicting factors for mental health services utilization amongst those with identifiable anxiety disorder in the Canadian population. The methods used for this study was the application of Andersen's Behavioral Model of Health Services Use, where predisposing, need and enabling 111 characteristics were regressed on the dependent variable of mental health services use. This study used the Canadian Community Health Survey (cycle 1.2: Mental Health and Well- Being) in a secondary data analysis. Several multiple logistics models predicted the likelihood to seek and use mental health services. Predisposing characteristics of gender and age, Enabling characteristics of education and geographical location, and those with co-occurring mood disorders were at the greatest increased likelihood to seek and use mental health services.

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A integridade do sinal em sistemas digitais interligados de alta velocidade, e avaliada através da simulação de modelos físicos (de nível de transístor) é custosa de ponto vista computacional (por exemplo, em tempo de execução de CPU e armazenamento de memória), e exige a disponibilização de detalhes físicos da estrutura interna do dispositivo. Esse cenário aumenta o interesse pela alternativa de modelação comportamental que descreve as características de operação do equipamento a partir da observação dos sinais eléctrico de entrada/saída (E/S). Os interfaces de E/S em chips de memória, que mais contribuem em carga computacional, desempenham funções complexas e incluem, por isso, um elevado número de pinos. Particularmente, os buffers de saída são obrigados a distorcer os sinais devido à sua dinâmica e não linearidade. Portanto, constituem o ponto crítico nos de circuitos integrados (CI) para a garantia da transmissão confiável em comunicações digitais de alta velocidade. Neste trabalho de doutoramento, os efeitos dinâmicos não-lineares anteriormente negligenciados do buffer de saída são estudados e modulados de forma eficiente para reduzir a complexidade da modelação do tipo caixa-negra paramétrica, melhorando assim o modelo standard IBIS. Isto é conseguido seguindo a abordagem semi-física que combina as características de formulação do modelo caixa-negra, a análise dos sinais eléctricos observados na E/S e propriedades na estrutura física do buffer em condições de operação práticas. Esta abordagem leva a um processo de construção do modelo comportamental fisicamente inspirado que supera os problemas das abordagens anteriores, optimizando os recursos utilizados em diferentes etapas de geração do modelo (ou seja, caracterização, formulação, extracção e implementação) para simular o comportamento dinâmico não-linear do buffer. Em consequência, contributo mais significativo desta tese é o desenvolvimento de um novo modelo comportamental analógico de duas portas adequado à simulação em overclocking que reveste de um particular interesse nas mais recentes usos de interfaces de E/S para memória de elevadas taxas de transmissão. A eficácia e a precisão dos modelos comportamentais desenvolvidos e implementados são qualitativa e quantitativamente avaliados comparando os resultados numéricos de extracção das suas funções e de simulação transitória com o correspondente modelo de referência do estado-da-arte, IBIS.

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Note de l'éditeur : This article may not exactly replicate the final version published in the journal. It is not the copy of record. / Cet article ne constitue pas la version officielle, et peut différer de la version publiée dans la revue.

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Partner behavioral responses to pain can have a significant impact on patient pain and depression, but little is known about why partners respond in specific ways. Using a cognitive-behavioral model, the present study examined whether partner cognitions were associated with partner behavioral responses, which prior work has found to predict patient pain and depressive symptoms. Participants were 354 women with provoked vestibulodynia and their partners. Partner pain-related cognitions were assessed using the partner versions of the Pain Catastrophizing Scale and Extended Attributional Style Questionnaire, whereas their behavioral responses to pain were assessed with the Multidimensional Pain Inventory. Patient pain was measured using a numeric rating scale, and depressive symptoms were assessed using the Beck Depression Inventory–II. Path analysis was used to examine the proposed model. Partner catastrophizing and negative attributions were associated with negative partner responses, which were associated with higher patient pain. It was also found that partner pain catastrophizing was associated with solicitous partner responses, which in turn were associated with higher patient pain and depressive symptoms. The effect of partner cognitions on patient outcomes was partially mediated by partner behavioral responses. Findings highlight the importance of assessing partner cognitions, both in research and as a target for intervention. Perspective The present study presents a cognitive-behavioral model to partially explain how significant others' thoughts about pain have an effect on patient pain and depressive symptoms. Findings may inform cognitive-behavioral therapy for couples coping with PVD.

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We propose that key concepts from clinical psychotherapy can inform science-based initiatives aimed at building tolerance and community cohesion. Commonalities in social and clinical psychology are identified regarding (1) distorted thinking (intergroup bias and cognitive bias), (2) stress and coping (at intergroup level and intrapersonal level), and (3) anxiety (intergroup anxiety and pathological anxiety). On this basis we introduce a new cognitive-behavioral model of social change. Mental imagery is the conceptual point of synthesis, and anxiety is at the core, through which new treatment-based approaches to reducing prejudice can be developed. More generally, we argue that this integration is illustrative of broader potential for cross-disciplinary integration in the social and clinical sciences, and has the potential to open up new possibilities and opportunities for both disciplines.

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We explore theoretically and empirically whether corruption is contagious and whether conditional cooperation matters. We argue that the decision to bribe bureaucrats depends on the frequency of corruption within a society. We provide a behavioral model to explain this conduct: engaging in corruption results in a disutility of guilt. This disutility depends negatively on the number of people engaging in corruption. The empirical section presents evidence using two international panel data data sets, one at the micro and one at the macro level. Results indicate that corruption is influenced by the perceived activities of peers. Moreover, macro level data indicates that past levels of corruption impact current corruption levels.

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Purine compounds, such as caffeine, have many health-promoting properties and have proven to be beneficial in treating a number of different conditions. Theacrine, a purine alkaloid structurally similar to caffeine and abundantly present in Camellia kucha, has recently become of interest as a potential therapeutic compound. In the present study, theacrine was tested using a rodent behavioral model to investigate the effects of the drug on locomotor activity. Long Evans rats were injected with theacrine (24 or 48 mg/kg, i.p.) and activity levels were measured. Results showed that the highest dose of theacrine (48 mg/kg, i.p.) significantly increased locomotor activity compared to control animals and activity remained elevated throughout the duration of the session. To test for the involvement of adenosine receptors underlying theacrine's motor-activating properties, rats were administered a cocktail of the adenosine A₁ agonist, N⁶-cyclopentyladenosine (CPA; 0.1 mg/kg, i.p.) and A(2A) receptor agonist 2-p-(2-carboxyethyl)phenethylamino-5'-N-ethylcarboxamidoadenosine (CGS-21680; 0.2 mg/kg, i.p.). Pre-treatment with theacrine significantly attenuated the motor depression induced by the adenosine receptor agonists, indicating that theacrine is likely acting as an adenosine receptor antagonist. Next, we examined the role of DA D₁ and D₂ receptor antagonism on theacrine-induced hyperlocomotion. Both antagonists, D₁R SCH23390 (0.1 or 0.05 mg/kg, i.p.) and D₂R eticlopride (0.1 mg/kg, i.p.), significantly reduced theacrine-stimulated activity indicating that this behavioral response, at least in part, is mediated by DA receptors. In order to investigate the brain region where theacrine may be acting, the drug (10 or 20 μg) was infused bilaterally into nucleus accumbens (NAc). Theacrine enhanced activity levels in a dose-dependent manner, implicating a role of the NAc in modulating theacrine's effects on locomotion. In addition, theacrine did not induce locomotor sensitization or tolerance after chronic exposure. Taken together, these findings demonstrate that theacrine significantly enhances activity; an effect which is mediated by both the adenosinergic and dopaminergic systems.

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Behavioral models capture operational principles of real-world or designed systems. Formally, each behavioral model defines the state space of a system, i.e., its states and the principles of state transitions. Such a model is the basis for analysis of the system’s properties. In practice, state spaces of systems are immense, which results in huge computational complexity for their analysis. Behavioral models are typically described as executable graphs, whose execution semantics encodes a state space. The structure theory of behavioral models studies the relations between the structure of a model and the properties of its state space. In this article, we use the connectivity property of graphs to achieve an efficient and extensive discovery of the compositional structure of behavioral models; behavioral models get stepwise decomposed into components with clear structural characteristics and inter-component relations. At each decomposition step, the discovered compositional structure of a model is used for reasoning on properties of the whole state space of the system. The approach is exemplified by means of a concrete behavioral model and verification criterion. That is, we analyze workflow nets, a well-established tool for modeling behavior of distributed systems, with respect to the soundness property, a basic correctness property of workflow nets. Stepwise verification allows the detection of violations of the soundness property by inspecting small portions of a model, thereby considerably reducing the amount of work to be done to perform soundness checks. Besides formal results, we also report on findings from applying our approach to an industry model collection.

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This paper describes a dynamic voltage frequency control scheme for a 256 X 64 SRAM block for reducing the energy in active mode and stand-by mode. The DVFM control system monitors the external clock and changes the supply voltage and the body bias so as to achieve a significant reduction in energy. The behavioral model of the proposed DVFM control system algorithm is described and simulated in HDL using delay and energy parameters obtained through SPICE simulation. The frequency range dictated by an external controller is 100 MHz to I GHz. The supply voltage of the complete memory system is varied in steps of 50 mV over the range of 500 mV to IV. The threshold voltage range of operation is plusmn100 mV around the nominal value, achieving 83.4% energy reduction in the active mode and 86.7% in the stand-by mode. This paper also proposes a energy replica that is used in the energy monitor subsystem of the DVFM system.

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There is a growing amount of experimental evidence that suggests people often deviate from the predictions of game theory. Some scholars attempt to explain the observations by introducing errors into behavioral models. However, most of these modifications are situation dependent and do not generalize. A new theory, called the rational novice model, is introduced as an attempt to provide a general theory that takes account of erroneous behavior. The rational novice model is based on two central principals. The first is that people systematically make inaccurate guesses when they are evaluating their options in a game-like situation. The second is that people treat their decisions similar to a portfolio problem. As a result, non optimal actions in a game theoretic sense may be included in the rational novice strategy profile with positive weights.

The rational novice model can be divided into two parts: the behavioral model and the equilibrium concept. In a theoretical chapter, the mathematics of the behavioral model and the equilibrium concept are introduced. The existence of the equilibrium is established. In addition, the Nash equilibrium is shown to be a special case of the rational novice equilibrium. In another chapter, the rational novice model is applied to a voluntary contribution game. Numerical methods were used to obtain the solution. The model is estimated with data obtained from the Palfrey and Prisbrey experimental study of the voluntary contribution game. It is found that the rational novice model explains the data better than the Nash model. Although a formal statistical test was not used, pseudo R^2 analysis indicates that the rational novice model is better than a Probit model similar to the one used in the Palfrey and Prisbrey study.

The rational novice model is also applied to a first price sealed bid auction. Again, computing techniques were used to obtain a numerical solution. The data obtained from the Chen and Plott study were used to estimate the model. The rational novice model outperforms the CRRAM, the primary Nash model studied in the Chen and Plott study. However, the rational novice model is not the best amongst all models. A sophisticated rule-of-thumb, called the SOPAM, offers the best explanation of the data.

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Consumption of addictive substances poses a challenge to economic models of rational, forward-looking agents. This dissertation presents a theoretical and empirical examination of consumption of addictive goods.

The theoretical model draws on evidence from psychology and neurobiology to improve on the standard assumptions used in intertemporal consumption studies. I model agents who may misperceive the severity of the future consequences from consuming addictive substances and allow for an agent's environment to shape her preferences in a systematic way suggested by numerous studies that have found craving to be induced by the presence of environmental cues associated with past substance use. The behavior of agents in this behavioral model of addiction can mimic the pattern of quitting and relapsing that is prevalent among addictive substance users.

Chapter 3 presents an empirical analysis of the Becker and Murphy (1988) model of rational addiction using data on grocery store sales of cigarettes. This essay empirically tests the model's predictions concerning consumption responses to future and past price changes as well as the prediction that the response to an anticipated price change differs from the response to an unanticipated price change. In addition, I consider the consumption effects of three institutional changes that occur during the time period 1996 through 1999.