INTRODUCTION. Frontal collisions have been observed to cause the severe injuries on heavy goods vehicle occupants, and the lower extremities have been frequently injured. Injuries of knee joints are rarely life threatening, however they tend to give long-term consequences. AIM. Evaluate non-lethal frontal impacts towards the knee joint of Total Human Model for Safety (THUMS) v4.0 using a cylindrical barrier. The main objectives are to 1) create local injury risk functions of the knee ligaments restraining frontal impacts, 2) simulate frontal impacts towards the knee joints of THUMS and3) prepare the Hybrid III (HIII)-model for corresponding frontal impacts conducted on THUMS. The intention is for future HIII-simulations to be cross-correlated with the responses from THUMS for the ability to estimate knee ligament strains by investigating impacts on HIII. METHODS. 1) Ligament risk curves of PCL, MCL and LCL were formulated by assembling mean strain threshold values and standard deviations from literature. Virtual values were generated from these pooled strain thresholds, creating the risk curves. 2) THUMS lower body was impacted by a cylindrical steel barrier at four different locations - middle of patella, middle of knee joint, upper tibia and below tibia tuberositas. Four impact velocities ranging from 8-14 km/h were used at each location, giving a total of 16 impacts. 3) The HIII-model was prepared by removing the upper body and inserting the cylindrical steel barrier into the model file. RESULTS. The strain threshold at 50% rupture risk for PCL resulted in 23.6±4.4%, 34.2±6.0% for MCL and 26.6±6.5% for LCL. The simulated THUMS PCL strains reached between 36%-58% for the highest velocity at the impact locations where tibia was involved. Both MCL and LCL gave an approximate 5% strain outcome. The resultant knee displacement for these impacts ranged between 22 mm - 32 mm. The knee displacements at the PCL strain threshold ranged between 14 mm - 16 mm. DISCUSSION and CONCLUSION. Most of the maximal PCL strains exceeded the PCL threshold with large margins. However, the knee displacement at the PCL strain threshold resulted in outcomes comparable to the thresholds used for HIIImodel. These results supported the obtained PCL threshold to be within a reasonable range.

Bien que l’inhibition soit souvent considérée comme un concept uniforme, les habiletés d’inhibition se divisent en plusieurs types : l’inhibition spatiale et l’inhibition de réponse. L’inhibition spatiale réfère à l’atténuation de l’interférence de localisations contenant des stimuli saillants et non pertinents. Par exemple, l’inhibition spatiale guide la recherche visuelle de sorte à limiter la visite répétée de localisations déjà explorées. À l’opposé, l’inhibition de réponse est un processus de type moteur et permet l’adaptation du comportement à un contexte changeant lorsqu’un mouvement doit être altéré ou arrêté. Bien qu’il soit admis que les habiletés d’inhibition se subdivisent en plusieurs types, tel que l’inhibition spatiale et l’inhibition de réponse, les différents mécanismes les sous-tendant demeurent mal compris et sous explorés. L’objectif principal de la présente thèse était d’explorer les mécanismes communs et différents de l’inhibition spatiale et l’inhibition de réponse. Particulièrement, les mécanismes de suppression et de facilitation lors de la sélection de la cible furent décrits pour l’inhibition spatiale. De même, la perturbation des habiletés de l’inhibition spatiale et la préservation relative des habiletés d’inhibition de réponse à la suite à de lésions du cortex pariétal postérieur dorsal furent exemplifiées. Finalement, les apports spécifiques du cortex pariétal postérieur dorsal pour l’inhibition spatiale et l’inhibition de réponse furent définis. / While inhibition is often considered a uniform concept, inhibition abilities can be divided in many types: spatial inhibition and response inhibition. Spatial inhibition refers to the attenuation of the interference from locations containing salient and non-pertinent stimuli. For example, spatial inhibition guides visual search to limit the repeated visit of already explored locations. In contrast, response inhibition is motor-based and allows the adaptability of behaviour in a changing context when a movement must be prevented or altered. Although it is widely accepted that inhibition abilities can be divided in many types, such as spatial or response inhibition, the different mechanisms underlying them remain poorly understood and under-explored. The main aim of the present thesis was to explore the common and different mechanisms of spatial and response inhibition. Precisely, the mechanisms of suppression and enhancement during target selection were described during spatial inhibition. The alteration of spatial inhibition processes and the relative preservation of response inhibition abilities in patients with dorsal posterior parietal cortex lesions were also underlined. Finally, the specific contributions of the dorsal posterior parietal cortex for spatial and response inhibition were defined.

Cette thèse examine deux modèles alternatifs de prises de décision motrice à travers des données comportementales humaines et des données électrophysiologiques de singes obtenues lors d'une tâche de décision multi-attributs. Les théories psychologiques classiques suggèrent que la prise de décision soit une fonction de l'exécutif central (EC). En accord avec cela, de nombreuses études ont montré des modulations neuronales concernant les décisions dans le cortex préfrontal (PFC), renforçant la notion que les décisions sont prises à un niveau abstrait dans l'exécutif central du cerveau : le PFC. Cependant, de telles corrélations neuronales se trouvent également dans les régions sensorimotrices, qui étaient traditionnellement considérées externes à l’EC. Cela a conduit à un modèle alternatif de prise de décision dans un EC, impliquant plusieurs zones cérébrales, y compris les zones exécutives et sensorimotrices. Ce second modèle suggère qu'une décision est prise lorsque les compétitions au sein et entre les aires cérébrales arrivent à une résolution, ce qui permet d'atteindre un consensus distribué (CD). L'objectif principal de cette thèse est de tester les prédictions faites par ces deux modèles. Pour ce faire, nous avons conçu une tâche d'atteinte basée sur la valeur d'attributs multiples et créé une situation dans laquelle les deux modèles font des prédictions neuronales distinctes. Dans cette tâche, deux attributs visuels indépendants indiquaient le montant de la récompense associé à chaque cible. L'un était un degré de luminosité, information ascendante (BU pour "bottom-up"), ciblant le réseau de saillance par le biais de la voie visuelle dorsale. L'autre était un indice d'orientation de ligne, information descendante (TD pour "top-down"), ciblant le réseau de catégorisation basé sur la connaissance par le biais de la voie visuelle ventrale. Nous avons effectué des enregistrements dans la région d’atteinte pariétale (PRR) et le cortex pré-moteur dorsal (PMd) du singe, dont les activités neuronales ont été précédemment impliquées comme étant modulées par des attributs BU et TD similaires. Dans la plupart des essais, les deux attributs étaient congruents – tous les deux favorisant la même cible. Cependant, un sous-ensemble d'essais avait des cibles avec la même valeur de récompense totale, mais où les deux attributs étaient en conflit (les caractéristiques BU et TD favorisant des cibles opposées). Le modèle de l'EC prédit que dans ce cas, l’activité neuronale la plus précoce doit apparaître dans une région exécutive et que les régions sensorimotrices doivent recevoir la diffusion de cette décision. Ainsi, ce modèle prédit que la différence du temps de réaction entre le PRR et le PMd sera constante, quelle que soit la manière dont la décision est prise. En revanche, le modèle CD prédit que l’intervalle de décision doit refléter le rôle d'une région dans la décision en cours. Plus précisément, si PRR et PMd font tous deux parties du réseau de décision distribué et jouent un rôle dans l'évaluation des attributs BU et TD, un choix en faveur de l'attribut BU devrait apparaître d'abord dans le PRR et par la suite dans le PMd, tandis qu'un choix en faveur de l'attribut TD devrait apparaître dans l'ordre inverse. Notre étude démontre que le temps de réaction des participants humains était plus rapide dans les essais congruents et lors de l'utilisation de l'information BU par rapport à l'utilisation de l'information TD. La distribution ne reflétait pas linéairement la complexité de l'attribut et semblait plutôt suggérer une intégration incomplète des informations disponibles. Ainsi, le résultat n'était pas entièrement explicable par un modèle d'EC pur. Le temps de réaction des participants était également plus rapide lorsqu'ils choisissaient entre deux options de grande valeur par rapport aux options de faible valeur, ce qui suggère que la loi de Weber ne s'applique pas aux attributs visuels indiquant des informations de valeur. La distribution du temps de réaction de notre premier singe était similaire à celle des participants humains. Sur le plan neuronal, l’intervalle de décision du PMd était presque toujours plus rapide que celle du PRR et le PRR ne précédait jamais le PMd; aussi, la différence de l’intervalle de décision entre ces régions n'était pas constante. Le PMd a montré un biais de base pré-stimulus dans les essais de choix libre, alors que ce n’était pas le cas pour le PRR. La distribution de l’intervalle de décision dans le PMd variait également en fonction des conditions d'essai, tandis que celle du PRR ne distinguait que les cibles uniques des cibles multiples. Une tendance similaire a été observée dans les analyses préliminaires des potentiels de champ locaux (LFP). Enfin, les résultats préliminaires suggèrent des effets plus cohérents de la micro-stimulation dans le PMd que dans le PRR. Nos résultats soutiennent le rôle causal du PMd, mais pas celui du PRR. Nos résultats sont cohérents avec les rapports précédents sur l'activité neuronale liée au choix dans les régions pariétales, car l'activité du PRR reflétait le choix du singe dans notre tâche. Nos résultats sont également cohérents avec d'autres études montrant l'absence de preuves du rôle causal des régions pariétales dans la prise de décision, car l'ordre relatif de l'activité prédictive du choix dans le PRR et le PMd ne variait pas entre les différentes conditions. À la lumière de ces deux modèles, nos résultats suggèrent une troisième alternative, qui inclut potentiellement le PMd en tant que partie du réseau de décision, mais pas le PRR. / This thesis examines two alternative models of action decisions through human behavioural and monkey electrophysiological data obtained during a multi-attribute decision task. Classic psychological theories suggest that decision-making is a function of the Central Executive (CE). In line with this, many studies showed neural correlates of decision variables in the prefrontal cortex (PFC), strengthening the notion that decisions are made at an abstract level in the brain’s central executive: PFC. However, such neural correlates are also found in sensorimotor areas, which were traditionally considered outside the CE. This has led to an alternative model to the decision making in a CE, involving multiple brain areas including both executive and sensorimotor areas. This second model suggests that a decision is made when competitions within and across brain areas come to a resolution, thus a Distributed Consensus (DC) is achieved. The main objective of this thesis is to test the predictions made by these two models. To do so, we designed a multi-attribute value-based reaching task, and created a situation in which the two models made distinct neural predictions. In this task, two independent visual attributes indicated the amount of reward associated with each reach target. One was a “bottom-up” (BU) brightness, targeting the saliency network through the dorsal visual pathway. The other was a “top-down” (TD) line orientation cue, targeting the knowledge-based categorization network through the ventral visual pathway. We recorded from monkey parietal reach region (PRR) and dorsal premotor cortex (PMd), whose activities have previously been implied to be modulated by similar BU and TD attributes. In most trials, the two attributes were congruent – both favoring the same target. However, a subset of trials consisted of a conflict between the two attributes (BU and TD features favoring opposite targets), but the targets had the same total reward values. Here, the CE model predicted that the earliest choice-predictive activity should appear in an executive region, and sensorimotor regions were expected to be receiving this decision broadcast. Thus, the model predicted the latency difference between PRR and PMd to be constant, regardless of how the decision is made. In contrast, the DC model predicted choice latency should reflect a region’s role in the ongoing decision. Specifically, if both PRR and PMd are part of the distributed decision network and play a role in evaluating the BU and TD attributes, a choice in favor of the BU attribute should appear first in PRR and then in PMd, whereas a choice in favor of the TD attribute should appear in the opposite order. We report that human participants’ reaction time (RT) was faster in congruent trials and when using the BU information compared to when using the TD information. The RT distribution did not linearly reflect the attribute complexity, and instead suggested an incomplete integration of available information. Thus, the result was not fully explainable with a pure CE model. Their RT was also faster when choosing between two high-valued options compared to low-valued options, suggesting that Weber-Fechner law does not apply to visual attributes that indicate value. Our first monkey’s RT distribution was similar to that of human participants. Neurally, choice latency of PMd was almost always faster than that of PRR and PRR never preceded PMd, and the latency difference between these regions was not consistent. PMd showed a pre-stimulus baseline bias in free-choice trials, whereas PRR did not. The distribution of choice latency in PMd also varied with trial conditions, whereas that of PRR only discriminated single versus multiple targets. A similar trend was seen in preliminary analyses of local field potentials. Finally, preliminary results suggest more consistent effects of microstimulation in PMd than in PRR. Our results support the causal role of PMd, but do not support that of PRR. This is consistent with previous reports of choice-related neural activity in the parietal regions, as PRR activity did reflect the monkey’s choice in our task. Our results are also consistent with other studies showing the absence of evidence for parietal regions’ causal role in decision-making, as the relative order of choice-predictive activity in PRR and PMd did not vary between different conditions. In light of the two models, our results suggest a third alternative, which potentially includes PMd, but not PRR, as part of the decision network.

當造成失敗的原因不只一種時，若各對象同一時間最多只經歷一種失敗原因，則這些失敗原因稱為競爭風險。然而，有些個體不會失敗或者經過治療之後已痊癒，我們稱這部分的群體為治癒群。本文考慮同時處理競爭風險及治癒率的混合模式，即競爭風險的治癒率模式，亦將解釋變數結合到治癒率、競爭風險的條件失敗機率，或未治癒下競爭風險的條件存活函數中，並以建立在完整資料上之擴充的概似函數為貝氏分析的架構。對於右設限對象則以插補方式決定是否會治癒或會因何種風險而失敗，並推導各參數的完全條件後驗分配及其性質。由於邊際後驗分配的數學形式無法明確呈現，再加上需對右設限者判斷其狀態，所以採用屬於馬可夫鏈蒙地卡羅法的Gibbs抽樣法及適應性拒絕抽樣法(adaptive rejection sampling) ，執行參數之模擬抽樣及設算右設限者之治癒或失敗狀態。實證部分，我們分析Klein and Moeschberger (1997)書中骨髓移植後的血癌病患的資料，並用不同模式之下的參數模擬值計算各對象之條件預測指標(CPO)，換算成各模式的對數擬邊際概似函數值(LPML)，比較不同模式的優劣。 / In case that there are more than one possible failure types, if each subject experiences at most one failure type at one time, then these failure types are called competing risks. Moreover, some subjects have been cured or are immune so they never fail, then they are called the cured ones. This dissertation discusses several mixture models containing competing risks and cure rate. Furthermore, covariates are associated with cure rate, conditional failure rate of each risk, or conditional survival function of each risk, and we propose the Bayesian procedure based on the augmented likelihood function of complete data. For right censored subjects, we make use of imputation to determine whether they were cured or failed by which risk and derive full conditional posterior distributions. Since all marginal posterior distributions don’t have closed forms and right censored subjects need to be identified their statuses, we take Gibbs sampling and adaptive rejection sampling of Markov chain Monte Carlo method to simulate parameter values. We illustrate how to conduct Bayesian analysis by using the bone marrow transplant data from the book written by Klein and Moeschberger (1997). To do model selection, we compute the conditional predictive ordinate(CPO) for every subject under each model, then the goodness is determined by the comparing the value of log of pseudo marginal likelihood (LMPL) of each model.

當母體中有部份對象因被治癒或免疫而不會失敗時，需考慮這群對象所佔的比率，即存活分率。本文主要在探討如何以貝氏方法對含存活分率之治癒率模式進行分析，並特別針對兩種含存活分率的迴歸模式，分別是Weibull迴歸模式以及對數邏輯斯迴歸模式，導出概似函數與各參數之完全條件後驗分配及其性質。由於聯合後驗分配相當複雜，各參數之邊際後驗分配之解析形式很難表達出。所以，我們採用了馬可夫鏈蒙地卡羅方法（MCMC）中的Gibbs抽樣法及Metropolis法，模擬產生參數值，以進行貝氏分析。實證部份，我們分析了黑色素皮膚癌的資料，這是由美國Eastern Cooperative Oncology Group所進行的第三階段臨床試驗研究。有關模式選取的部份，我們先分別求出各對象在每個模式之下的條件預測指標（CPO），再據以算出各模式的對數擬邊際概似函數值（LPML），以比較各模式之適合性。 / When we face the problem that part of subjects have been cured or are immune so they never fail, we need to consider the fraction of this group among the whole population, which is the so called survival fraction. This article discuss that how to analyze cure rate models containing survival fraction based on Bayesian method. Two cure rate models containing survival fraction are focused; one is based on the Weibull regression model and the other is based on the log-logistic regression model. Then, we derive likelihood functions and full conditional posterior distributions under these two models. Since joint posterior distributions are both complicated, and marginal posterior distributions don’t have closed form, we take Gibbs sampling and Metropolis sampling of Markov Monte Carlo chain method to simulate parameter values. We illustrate how to conduct Bayesian analysis by using the data from a melanoma clinical trial in the third stage conducted by Eastern Cooperative Oncology Group. To do model selection, we compute the conditional predictive ordinate (CPO) for every subject under each model, then the goodness is determined by the comparing the value of log of pseudomarginal likelihood (LPML) of each model.

We consider a class of periodic preventive maintenance (PM) optimization problems, for a single piece of equipment that deteriorates with time or use, and can be repaired upon failure, through corrective maintenance (CM). We develop analytical and simulation-based optimization models that seek an optimal periodic PM policy, which minimizes the sum of the expected total cost of PMs and the risk-averse cost of CMs, over a finite planning horizon. In the simulation-based models, we assume that both types of maintenance actions are imperfect, whereas our analytical models consider imperfect PMs with minimal CMs. The effectiveness of maintenance actions is modeled using age reduction factors. For a repairable unit of equipment, its virtual age, and not its calendar age, determines the associated failure rate. Therefore, two sets of parameters, one describing the effectiveness of maintenance actions, and the other that defines the underlying failure rate of a piece of equipment, are critical to our models. Under a given maintenance policy, the two sets of parameters and a virtual-age-based age-reduction model, completely define the failure process of a piece of equipment. In practice, the true failure rate, and exact quality of the maintenance actions, cannot be determined, and are often estimated from the equipment failure history. We use a Bayesian approach to parameter estimation, under which a random-walk-based Gibbs sampler provides posterior estimates for the parameters of interest. Our posterior estimates for a few datasets from the literature, are consistent with published results. Furthermore, our computational results successfully demonstrate that our Gibbs sampler is arguably the obvious choice over a general rejection sampling-based parameter estimation method, for this class of problems. We present a general simulation-based periodic PM optimization model, which uses the posterior estimates to simulate the number of operational equipment failures, under a given periodic PM policy. Optimal periodic PM policies, under the classical maximum likelihood (ML) and Bayesian estimates are obtained for a few datasets. Limitations of the ML approach are revealed for a dataset from the literature, in which the use of ML estimates of the parameters, in the maintenance optimization model, fails to capture a trivial optimal PM policy. Finally, we introduce a single-stage and a two-stage formulation of the risk-averse periodic PM optimization model, with imperfect PMs and minimal CMs. Such models apply to a class of complex equipment with many parts, operational failures of which are addressed by replacing or repairing a few parts, thereby not affecting the failure rate of the equipment under consideration. For general values of PM age reduction factors, we provide sufficient conditions to establish the convexity of the first and second moments of the number of failures, and the risk-averse expected total maintenance cost, over a finite planning horizon. For increasing Weibull rates and a general class of increasing and convex failure rates, we show that these convexity results are independent of the PM age reduction factors. In general, the optimal periodic PM policy under the single-stage model is no better than the optimal two-stage policy. But if PMs are assumed perfect, then we establish that the single-stage and the two-stage optimization models are equivalent. / text