Utilizing a Criminal Event Perspective, the analyses of this dissertation test a variety of relationships to the dependent variable: the Criminal Lethality Index. Data from the National Incident-Based Reporting System, the Census and American Community Survey, the American Trauma Society, and data derived from the Census’s mapping TIGER files are combined to create a database of 190 cities. This database is used to test road network connectivity (Gama Index), medical resources, criminal covariates and Social Disorganization variables in relation to a city’s Criminal Lethality Index. OLS regression demonstrates a significant and negative relationship between a city’s Gama Index and its Criminal Lethality Index. In addition, percent male, percent black, median income and percent of the population employed in diagnosing and treating medical professions were all consistently positively related to Criminal Lethality. The percent of males 16 to 24, percent of single parent households, and Concentrated Disadvantage Index were all consistently and negatively related to Criminal Lethality. Given these surprising results, additional diagnostic regressions are run using more traditional dependent variables such as the number of murders in a city and the proportion of aggravated assaults with major injuries per 100,000 population. These reveal the idiosyncratic nature of utilizing the Criminal Lethality Index. This dependent variable has proven useful in some circumstances and counterintuitive in others. The source of the seemingly unintuitive results is the fact that certain factors only reduce murders but many factors impact both murder and aggravated assaults, thereby creating difficultly when trying to predict patterns in Criminal Lethality

Walkability as a concept that captures the ability to walk from one place to another has multiple dimensions. Between traversability to being a proxy for better urban places, there are also numerous measurements of walkability that attempts to quantify certain or all aspects of walkability. It is, however, unclear, through a review of available literature, how these measurements of walkability relate to each other statistically. This methodology focuses on generating a framework for analysts to evaluate and prioritize pedestrian infrastructure. WalkScore™ (WS), HCM Pedestrian Level of Service (PLOS), Average Nodal Degree (AND), and Intersection Density are the four metrics selected for this analysis that focuses on distinctive aspects of walkability (proximity, amenity, network-connectivity, respectively). A sample of 51 street segments from the County of San Luis Obispo is selected according to their respective Average Daily Traffic (ADT) volumes. Pearson’s Correlations between the six combinations of relationships are measured, and the strongest correlation between the six relationships is between WalkScore™ and Intersection Density with an R2 of 0.44. A regression model that includes external factors such as population and adjacent land use is used to analyze and predict PLOS of the street segment. Although the model is not statistically significant, the goal of this research is to identify gaps in current and potential walkability of street segments in the sample. Therefore, this framework of using established walkability metrics to predict PLOS, and then distinguishing places for improvements is proposed as a result of this research to be used by government agencies to prioritize pedestrian infrastructure.

Acoustic telemetry is an important tool when studying the spatial ecology of marine animals. First, it is important to identify the challenges of using this tool in shallow tropical marine environments before tracking marine animals. One significant issue that can influence the effectiveness of acoustic telemetry is the efficiency or detectability of acoustic signals by receivers. Understanding factors influencing detection efficiency of acoustic tags is especially important for fine-scale positioning systems (such as the VEMCO positioning system, VPS) that use detections in an overlapping receiver network to calculate geographic positions of tagged fish. I modelled the efficiency of an acoustic array in a tropical reef flat in Culebra, Puerto Rico and then tested the capability of detecting and positioning tagged permit (Trachinotus falcatus) and great barracuda (Sphyraena barracuda). Detection efficiencies were analyzed for a fine-scale array comprised of 25 receivers and 17 sync tags deployed as a fine-scale array for nearly three years. I used a generalized linear mixed-effect model Template Builder Model (glmmTMB) that helped elucidate the environmental variables that play a role in influencing the ability to detect transmitter signals. Then, I evaluated how well the fine- scale array was at detecting tagged permit (n=1) and great barracuda (n=4) using the reef flat. Once, the model was created, it was used to predict the likeliness of hourly detections of sync tags and tagged animals. Finally, the fine-scale and broad-scale array consisting of 59 receivers were used to determine the spatial movement patterns of permit and great barracuda. A glmmTMB was created to determine what environmental variables were present when each species visited the fine-scale array in the coral reef flat. The dynamic Brownian-Bridge Movement Model (DBBMM) was used to determine the fishes’ home range within the coral reef flat using X Y locations. Network connectivity was used to determine each fishes’ spatial movement patterns and community structure related to the receivers they visited. Lastly, spatial patterns throughout each diel period and season were determined. Collectively, this study revealed the benefits and limitations of using fine-scale acoustic telemetry in shallow coral reef ecosystems.

The brain connectivity of resting-state fMRI (rs-fMRI) represents an intrinsic state of brain architecture, and it has been used as a useful neural marker for detecting psychiatric conditions such as autism spectrum disorder, as well as for predicting psychosocial characteristics such as age. However, most studies using brain connectivity have focused more on the strength of functional connectivity over time (static-FC) than temporal features of connectivity changes (connectome variability). The primary goal of the current study was to investigate the effectiveness of using the connectome variability in classifying an individual’s pathological characteristics from others and predicting psychosocial characteristics. In addition, the current study aimed to prove that benefits of the connectome variability are reliable across various analysis procedures. To this end, three open public large rs-fMRI datasets including ABIDE, COBRE, and NKI were used. The static-FC and the connectome variability metrics were calculated with various brain parcellations and parameters and then utilized for subsequent machine learning (ML) classification and prediction. The results demonstrated that including the connectome variability increased the ML performances significantly in most cases of analytical variations. In addition, including the connectome variability prevented ML performance deterioration when excessive components were used. In conclusion, the current finding proved the usefulness of the connectome variability and its reliability. / M.S. / Functional magnetic resonance imaging (fMRI) with functional connectivity (FC) analysis has been widely used to understand the human brain’s system and cognitive processes. Especially, the resting-state fMRI (rs-fMRI) has been regarded as a comprehensive map of the brain’s large-scale functional architecture. Previous seminal findings demonstrated that brain regions show synchronized patterns even without any external stimulus or task (Biswal et al., 1995; Power et al., 2011), and recent studies also demonstrated that functional network architecture during tasks can be formed based on resting-state network architecture primarily suggesting that the resting-state is an intrinsic and fundamental of brain organization functionally. At the early stage of fMRI FC studies, researchers commonly adopted static measure of connectivity (static-FC) such as Pearson correlation. However, the brain has a dynamic nature, thus the static approach does not capture temporal information of the brain. In this context, time-varying or dynamic-FC has been suggested as a promising substitute. The derived dynamic-FC usually has been used to distinguish several dynamic states by identifying repeated spatial dynamic-FC profiles. Another utilization is quantifying moment-to-moment changes of dynamic-FC (connectome variability) which can represent how much dynamic-FC is stable. Interestingly, although its importance of dynamic-FC temporal features, few studies have utilized connectome variability. In addition, only a few studies compared static-FC and connectome variability (Fong et al., 2019; Wang et al., 2018). Therefore, it is necessary to demonstrate the benefits of connectome variability and its reliability across various cognitive domains and analytic procedures. To this aim, this study used three large open fMRI datasets: ABIDE comprised of autism spectrum disorder and typical development, COBRE comprised of schizophrenia and control group, and NKI which is a developmental dataset across the lifespan. In individuals’ resting-state fMRI, brain signal time series was extracted using various parcellation methods including AAL2 atlas (Rolls et al., 2015), bilateralized AAL2 atlas, and LAIRD network atlas (Laird et al., 2011). To calculate static-FC, pairwise Pearson correlation was used. For the dynamic-FC, sliding-window correlation was used with 60 second window size. Additional 90 second and 120 second sliding window sizes were also used to test the reliability of the current study. The additional sliding window sizes showed almost identical results to that of the main sliding window size (60s). The derived dynamic-FC was used to calculate ‘connectome variability’ using mean square successive difference (MSSD). The calculated static-FC and the connectome variability were inputted to support vector machine (SVM) for group classifications or support vector regression (SVR) for predicting individuals’ characteristics. Before machine learning analysis (SVM, SVR), lasso regression was adopted as a feature selection method. The SVM results showed that including connectome variability increased group classification performances in ABIDE and COBRE datasets. Interestingly, including connectome variability improved the robustness of SVM classification when the number of components was controlled. Similarly, the SVR results also demonstrated that including connectome variability increased prediction performances for autism symptom severity score (ADOS), social responsiveness score (SRS), and individuals’ age. These benefits were consistent across three parcellation schemes. In conclusion, the current study demonstrated that the connectome variability is useful to classify different groups and to predict individuals’ characteristics. Such benefits were reliable across multiple cognitive domains and robust to several analytic procedures. These results emphasized that the connectome variability which has been usually overlooked reflects some aspects of functional brain architecture, and future fMRI studies should more attend connectome variability between brain regions.

Yes / Sensing coverage and network connectivity are two of the most fundamental issues to ensure that there are effective environmental sensing and robust data communication in a WSN application. Random positioning of nodes in a WSN may result in random connectivity, which can cause a large variety of key parameters within the WSN. For example, data latency and battery lifetime can lead to the isolation of nodes, which causes a disconnection between nodes within the network. These problems can be avoided by using mobile data sinks, which travel between nodes that have connection problems. This research aims to design, test and optimise a data collection system that addresses the isolated node problem, as well as to improve the connectivity between sensor nodes and base station, and to reduce the energy consumption simultaneously. In addition, this system will help to solve several problems such as the imbalance of delay and hotspot problems. The effort in this paper is focussed on the feasibility of using the proposed methodology in different applications. More ongoing experimental work will aim to provide a detailed study for advanced applications e.g. transport systems for civil purposes. / European Union’s Horizon 2020 research and innovation programme under grant agreement H2020-MSCA-ITN-2016 SECRET-722424.

<div>The interplay between network topology and traffic dynamics in road networks impacts various performance measures. There are extensive existing researches focusing on link-level fundamental diagrams, traffic assignments under route choice assumptions. However, the underlying coupling of structure and dynamic makes network-level traffic not fully investigated. In this thesis, we build structural and dynamic models to deal with three challenges: 1) describing road network topology and understanding the difference between cities; 2) quantifying network congestion considering both road network topology and traffic flow information; 3) allocating transportation management resources to optimize the road network connectivity.</div><div><br></div><div>The first part of the thesis focuses on structural models for complex road networks. Online road map data platforms, like OpenStreetMap, provide us with reliable road network data of the world. To solve the duplicate node problem, an O(n) time complexity node merging algorithm is designed to pre-process the raw road network with n nodes. Hereafter, we define unweighted and weighted node degree distribution for</div><div>road networks. Numerical experiments present the heterogeneity in node degree distribution for Beijing and Shanghai road network. Additionally, we find that the power law distribution fits the weighted road network under certain parameter settings, extending the current knowledge that degree distribution for the primal road network is not power law.</div><div><br></div><div>In the second part, we develop a road network congestion analysis and management framework. Different from previous methods, our framework incorporates both network structure and dynamics. Moreover, it relies on link speed data only, which is more accessible than previously used link density data. Specifically, we start from the existing traffic percolation theory and critical relative speed to describe network-level traffic congestion level. Based on traffic component curves, we construct Aij for two road segments i and j to quantify the necessity of considering the two road segments in the same traffic zone. Finally, we apply the Louvain algorithm on defined road segment networks to generate road network partition candidates. These candidate partitions will help transportation engineers to control regional traffic.</div><div><br></div><div>The last part formulates and solves a road network management resource allocation optimization. The objective is to maximize critical relative speed, which is defined from traffic component curves and is closely related to personal driving comfort. Budget upper bound serves as one of the constraints. To solve the simulation-based nonlinear optimization problem, we propose a simple allocation and a meta-heuristic method based on the genetic algorithm. Three applications demonstrate that the meta-heuristic method finds better solutions than simple allocation. The results will inform the optimal allocation of resources at each road segment in metropolitan cities to enhance the connectivity of road networks.</div>

Les oscillations jouent un rôle de premier plan dans la mise en place des réseaux cérébraux sains et pathologiques. En particulier, au niveau clinique, les activités oscillatoires sont d'une grande importance diagnostique en épilepsie. Par ailleurs, les méthodes non-invasives d'électrophysiologie sont particulièrement adaptées pour la compréhension des réseaux cérébraux à grande échelle. Cependant, la majorité des études en épilepsie a été dirigée vers les pointes intercritiques, qui sont des activités transitoires. Une question qui reste donc en suspens est le lien entre les pointes épileptiques et les activités oscillatoires épileptiques. Cette thèse a visé à résoudre deux problématiques complémentaires autour de cette question. La première problématique est la séparation adéquate entre les activités oscillatoires et transitoires. Il s'agit d'une tâche difficile surtout lors d'un grand chevauchement temporel, qui peut résulter en la contamination d'une activité par l'autre. Nous avons évaluée trois méthodes de filtrage : le filtre FIR (méthode classique), la transformé d'ondelette stationnaire et le filtrage parcimonieux par matching pursuit (MP, basé sur un dictionnaire). Sur des simulations, la SWT a donné de très bons résultats pour la reconstruction des transitoires et le MP pour les oscillations ; de plus, les deux méthodes ont donné un faible taux de faux positifs en détection automatique des oscillations. La SWT et le FIR ont donné les meilleurs résultats de filtrage sur les signaux réels, en particulier lors de la localisation de source. / The Oscillatory activities play a leading role in the development of healthy and pathological brain networks. In particular, at the clinical level, the oscillatory activities are of great importance in the diagnostic of epilepsy. In addition, the non-invasive electrophysiology methods are particularly suitable for understanding the large-scale brain networks. However, most studies in epilepsy have been directed to the interictal spikes, which are transitional activities. One issue that remains unresolved is the relationship between epileptic spikes and epileptic oscillatory activities. This thesis resolves two complementary problems. The first one is the suitable separation between the oscillatory and transitory activity, which is quite sensitive to the presence of the overlap in the time-frequency domain. This can lead to a contamination between the activities. We did evaluate three filtering methods: the FIR (classic methods), the stationary wavelet SWT and the parsimonious filter with the matching pursuit MP. The SWT gave good results in the reconstruction of transient activity and the MP in the reconstruction of oscillatory activity both for simulated data; also they provide a low false positive in automatic detection of oscillatory activity. The SWT and FIR gave the best results on real signals especially for source localization. In the simulated data, the MP is optimal since the atoms of the dictionary resembles to the simulated signals, which isn't guaranteed for real signals. The second problem is the comparison between network connectivity of transient and oscillatory activity, as measured in surface recordings (MEG) and invasive recordings SEEG.

Le but de ce travail de thèse consiste à étudier les propriétés physico-chimiques des verres chalcogénures afin de pouvoir les utiliser comme membranes de capteurs chimiques destinés pour le dosage des ions Hg²⁺. Dans un premier temps, les propriétés macroscopiques des systèmes vitreux AgY-As₂S₃ (Y = Br, I), HgS-GeS₂, AgI-HgS-As₂S₃ et AgI-HgS-GeS₂, telles que les densités et les températures caractéristiques (Tg et Tc) ont été mesurées et analysées selon les compositions des verres. Puis, dans un second temps, les propriétés de transport ont été étudiés à l'aide de la spectroscopie d'impédance complexe d'une part, ou d'autre part, par des mesures de la résistivité. Ces dernières montrent que les verres de chalcogénures dopés à l'halogénure d'argent présentent deux différents régimes de transports au-dessus du seuil de percolation xc ≈ 30 ppm : (i) domaine de percolation critique, et (ii) domaine contrôlé par modificateur. Vient ensuite la troisième partie, elle consiste à déchiffrer les relations composition/structure/propriété grâce à plusieurs études structurales. Des mesures par spectroscopie Raman, par diffraction de neutrons et de rayons X haute énergie, par diffusion des neutrons sous petits angles (SANS), ainsi que des modélisations RMC/DFT et AMID ont été réalisées. Enfin, la dernière partie de ce travail était une étude préliminaire des caractéristiques des nouveaux capteurs chimiques. Il a été consacré à l'étude des relations entre la composition et la sensibilité des membranes ainsi qu'aux limites de détection qui les définissent. / The aim of the thesis is to study the physicochemical properties of the silver halide doped chalcogenide glasses for the possibility to use them as chemical sensors for quantitative analysis of Hg²⁺ ions. First, the macroscopic properties of AgY-As₂S₃ (Y = Br, I), HgS-GeS₂, AgI-HgS-As₂S₃ and AgI-HgS-GeS₂ glassy systems such as the densities and the characteristic temperatures (Tg and Tc) were measured and analyzed according to the glass compositions. Second, the transport properties were studied using complex impedance and dc conductivity. Measurements show that the silver halide doped chalcogenide glasses exhibit two drastically different ion transport regimes above the percolation threshold at xc ≈ 30 ppm : (i) critical percolation, and (ii) modifier-controlled regimes. Third, to unveil the composition/structure/property relationships, various structural studies were carried out. Raman spectroscopy, high-energy X-ray diffraction, neutron diffraction and small-angle neutron scattering experiments, together with RMC/DFT and AMID modelling were employed. Finally, the last part was a preliminary study of the characteristics of new chemical sensors. It was devoted to study the relationship between the membranes' composition and sensitivity but also detection limits.

Ce travail a porté sur les remaniements corticaux précoces des circuits moteurs et extramoteurs dans la SLA grâce à l’étude des activations IRMf issues de tâches motrices et cognitives. La première partie de nos travaux nous a permis de montrer grâce des tâches simples d’activation motrice en IRMf (1) qu’alors que les patients SLA présentaient un déficit moteur discret, une augmentation des activations corticales est apparue dans les aires sensorimotrices bilatérales du cerveau. (2) Ces modifications précoces de l’activité neuronale étaient corrélées à la latéralisation du déficit moteur du membre ou la prédominance manuelle et surtout, (3) au taux de progression de la maladie à un an et à la survie, suggérant que ce remaniement de l’activité qui correspond probablement à de la plasticité cérébrale a des implications fonctionnelles. Enfin, (4) ce phénomène apparaissait actif puisqu’il s’est poursuivi pendant au moins onze mois. Dans la deuxième partie, nous avons montré lors d’une tâche de fluence verbale silencieuse que (1) les SLA présentaient une suractivation initiale des aires dévolues au processus sémantique avec un renforcement de la connectivité fonctionnelle entre les réseaux (CFR). (2) Au bout de onze mois et alors que l’atteinte des fluences était stable, ce phénomène de compensation s’épuisait avec une diminution conjointe de l’activation des réseaux et de la CFR. La tâche de 2-Back, réalisée alors que les patients ne présentaient pas d’atteinte de la mémoire de travail lors des tests psychométriques, nous a permis de montrer en outre que certains circuits non-moteurs se réorganisaient très précocement chez les patients, alors même qu’ils étaient asymptomatiques / In this work we used motor and cognitive tasks in an fMRI study to explore the early cortical reorganizations of the motor and extra-motor circuits in ALS patients. In a first part, using a simple motor task, we demonstrated (1) that increased cortical BOLD signal changes occurred in specific regions of the brain of ALS patients when their motor deficit was still moderate, and that this early signal changes correlated with (2) the lateralisation of the motor deficit or hand predominance and, more importantly, (3) with the rate of disease progression at one year and survival time, suggesting that modulations of cerebral activity in ALS may have functional implications. Furthermore, (4) this brain plasticity was maintained with time and disease progression during at least eleven months. In a second part, we demonstrated during a silent verbal fluency task (1) that ALS patients presented initially an increased cortical activation of areas devolved to the semantic process with an intensification of the functional network connectivity (FNC). (2) After eleven months and while their performance in tests of verbal fluency was stable, this cerebral compensation ran out with a decrease of the previous cerebral activations and the FNC. A N-back working memory paradigm, realized while the patients did not present any deficit of their working memory, allowed us to show that certain non-motor circuits were reorganized prematurely while patients were still asymptomatic

Surveillance wireless sensor networks (WSNs) are highly vulnerable to the failure of the base station (BS) because attackers can easily render the network useless for relatively long periods of time by only destroying the BS. The time and effort needed to destroy the BS is much less than that needed to destroy the numerous sensing nodes. Previous works have tackled BS failure by deploying a mobile BS or by using multiple BSs, which requires extra cost. Moreover, despite using the best electronic countermeasures, intrusion tolerance systems and anti-traffic analysis strategies to protect the BSs, an adversary can still destroy them. The new BS cannot trust the deployed sensor nodes. Also, previous works lack both the procedures to ensure network reliability and security during BS failure such as storing then sending reports concerning security threats against nodes to the new BS and the procedures to verify the trustworthiness of the deployed sensing nodes. Otherwise, a new WSN must be re-deployed which involves a high cost and requires time for the deployment and setup of the new WSN. In this thesis, we address the problem of reliable recovery from a BS failure by proposing a new security architecture called Surveillance Security (SurvSec). SurvSec continuously monitors the network for security threats and stores data related to node security, detects and authenticates the new BS, and recovers the stored data at the new BS. SurvSec includes encryption for security-related information using an efficient dynamic secret sharing algorithm, where previous work has high computations for dynamic secret sharing. SurvSec includes compromised nodes detection protocol against collaborative work of attackers working at the same time where previous works have been inefficient against collaborative work of attackers working at the same time. SurvSec includes a key management scheme for homogenous WSN, where previous works assume heterogeneous WSN using High-end Sensor Nodes (HSN) which are the best target for the attackers. SurvSec includes efficient encryption architecture against quantum computers with a low time delay for encryption and decryption, where previous works have had high time delay to encrypt and decrypt large data size, where AES-256 has 14 rounds and high delay. SurvSec consists of five components, which are: 1. A Hierarchical Data Storage and Data Recovery System. 2. Security for the Stored Data using a new dynamic secret sharing algorithm. 3. A Compromised-Nodes Detection Algorithm at the first stage. 4. A Hybrid and Dynamic Key Management scheme for homogenous network. 5. Powerful Encryption Architecture for post-quantum computers with low time delay. In this thesis, we introduce six new contributions which are the followings: 1. The development of the new security architecture called Surveillance Security (SurvSec) based on distributed Security Managers (SMs) to enable distributed network security and distributed secure storage. 2. The design of a new dynamic secret sharing algorithm to secure the stored data by using distributed users tables. 3. A new algorithm to detect compromised nodes at the first stage, when a group of attackers capture many legitimate nodes after the base station destruction. This algorithm is designed to be resistant against a group of attackers working at the same time to compromise many legitimate nodes during the base station failure. 4. A hybrid and dynamic key management scheme for homogenous network which is called certificates shared verification key management. 5. A new encryption architecture which is called the spread spectrum encryption architecture SSEA to resist quantum-computers attacks. 6. Hardware implementation of reliable network recovery from BS failure. The description of the new security architecture SurvSec components is done followed by a simulation and analytical study of the proposed solutions to show its performance.