DEVELOPMENT AND VALIDATION OF THE ANTERIOR CRUCIATE LIGAMENT INJURY-RISK-ESTIMATION QUIZ (ACL-IQ)

Over 2 million Anterior Cruciate Ligament (ACL) injuries occur annually worldwide resulting in considerable economic and health burdens (e.g., suffering, surgery, loss of function, risk for re-injury, and osteoarthritis). Current screening methods are effective but they generally rely on expensive and time-consuming biomechanical movement analysis, and thus are impractical solutions. In this dissertation, I report on a series of studies that begins to investigate one potentially efficient alternative to biomechanical screening, namely skilled observational risk assessment (e.g., having experts estimate risk based on observations of athletes movements). Specifically, in Study 1 I discovered that ACL injury risk can be accurately and reliably estimated with nearly instantaneous visual inspection when observed by skilled and knowledgeable professionals. Modern psychometric optimization techniques were then used to develop a robust and efficient 5-item test of ACL injury risk prediction skill—i.e., the ACL Injury-Risk-Estimation Quiz or ACL-IQ. Study 2 cross-validated the results from Study 1 in a larger representative sample of both skilled (Exercise Science/Sports Medicine) and un-skilled (General Population) groups. In accord with research on human expertise, quantitative structural and process modeling of risk estimation indicated that superior performance was largely mediated by specific strategies and skills (e.g., ignoring irrelevant information), independent of domain general cognitive abilities (e.g., metal rotation, general decision skill). These cognitive models suggest that ACL-IQ is a trainable skill, providing a foundation for future research and applications in training, decision support, and ultimately clinical screening investigations. Overall, I present the first evidence that observational ACL injury risk prediction is possible including a robust technology for fast, accurate and reliable measurement—i.e., the ACL-IQ. Discussion focuses on applications and outreach including a web platform that was developed to house the test, provide a repository for further data collection, and increase public and professional awareness and outreach (www.ACL-IQ.org). Future directions and general applications of the skilled movement analysis approach are also discussed.

Reducing Uncertainties in Estimation of Wind Effects on Tall Buildings Using Aerodynamic Wind Tunnel Tests

Tall buildings are wind-sensitive structures and could experience high wind-induced effects. Aerodynamic boundary layer wind tunnel testing has been the most commonly used method for estimating wind effects on tall buildings. Design wind effects on tall buildings are estimated through analytical processing of the data obtained from aerodynamic wind tunnel tests. Even though it is widely agreed that the data obtained from wind tunnel testing is fairly reliable the post-test analytical procedures are still argued to have remarkable uncertainties. This research work attempted to assess the uncertainties occurring at different stages of the post-test analytical procedures in detail and suggest improved techniques for reducing the uncertainties. Results of the study showed that traditionally used simplifying approximations, particularly in the frequency domain approach, could cause significant uncertainties in estimating aerodynamic wind-induced responses. Based on identified shortcomings, a more accurate dual aerodynamic data analysis framework which works in the frequency and time domains was developed. The comprehensive analysis framework allows estimating modal, resultant and peak values of various wind-induced responses of a tall building more accurately. Estimating design wind effects on tall buildings also requires synthesizing the wind tunnel data with local climatological data of the study site. A novel copula based approach was developed for accurately synthesizing aerodynamic and climatological data up on investigating the causes of significant uncertainties in currently used synthesizing techniques. Improvement of the new approach over the existing techniques was also illustrated with a case study on a 50 story building. At last, a practical dynamic optimization approach was suggested for tuning structural properties of tall buildings towards attaining optimum performance against wind loads with less number of design iterations.

Online impedance spectroscopy estimation of a battery

Electrochemical impedance spectroscopy (EIS) is a helpful tool to understand how a battery is behaving and how it degrades. One of the disadvantages is that it is typically an 'off-line' process. This paper investigates an alternative method of looking at impedance spectroscopy of a battery system while it is on-line and operational by manipulating the switching pattern of the dc-dc converter to generate low frequency harmonics in conjunction with the normal high frequency switching pattern to determine impedance in real time. However, this adds extra ripple on the inductor which needs to be included in the design calculations. The paper describes the methodology and presents some experimental results in conjunction with EIS results to illustrate the concept.

Estimation de cartes d'énergie de hautes fréquences ou d'irrégularité de périodicité de la marche humaine par caméra de profondeur pour la détection de pathologies

Ce travail présente deux nouveaux systèmes simples d'analyse de la marche humaine grâce à une caméra de profondeur (Microsoft Kinect) placée devant un sujet marchant sur un tapis roulant conventionnel, capables de détecter une marche saine et celle déficiente. Le premier système repose sur le fait qu'une marche normale présente typiquement un signal de profondeur lisse au niveau de chaque pixel avec moins de hautes fréquences, ce qui permet d'estimer une carte indiquant l'emplacement et l'amplitude de l'énergie de haute fréquence (HFSE). Le second système analyse les parties du corps qui ont un motif de mouvement irrégulier, en termes de périodicité, lors de la marche. Nous supposons que la marche d'un sujet sain présente partout dans le corps, pendant les cycles de marche, un signal de profondeur avec un motif périodique sans bruit. Nous estimons, à partir de la séquence vidéo de chaque sujet, une carte montrant les zones d'irrégularités de la marche (également appelées énergie de bruit apériodique). La carte avec HFSE ou celle visualisant l'énergie de bruit apériodique peut être utilisée comme un bon indicateur d'une éventuelle pathologie, dans un outil de diagnostic précoce, rapide et fiable, ou permettre de fournir des informations sur la présence et l'étendue de la maladie ou des problèmes (orthopédiques, musculaires ou neurologiques) du patient. Même si les cartes obtenues sont informatives et très discriminantes pour une classification visuelle directe, même pour un non-spécialiste, les systèmes proposés permettent de détecter automatiquement les individus en bonne santé et ceux avec des problèmes locomoteurs.

Indoor positioning system for mobile devices using radio frequency and perfect sequences

Recent advancements in the area of nanotechnology have brought us into a new age of pervasive computing devices. These computing devices grow ever smaller and are being used in ways which were unimaginable before. Recent interest in developing a precise indoor positioning system, as opposed to existing outdoor systems, has given way to much research heading into the area. The use of these small computing devices offers many conveniences for usage in indoor positioning systems. This thesis will deal with using small computing devices Raspberry Pi’s to enable and improve position estimation of mobile devices within closed spaces. The newly patented Orthogonal Perfect DFT Golay coding sequences will be used inside this scenario, and their positioning properties will be tested. After that, testing and comparisons with other coding sequences will be done.

Evolution of classifiers for pitch estimation of piano music using cartesian genetic programming

Pitch Estimation, also known as Fundamental Frequency (F0) estimation, has been a popular research topic for many years, and is still investigated nowadays. The goal of Pitch Estimation is to find the pitch or fundamental frequency of a digital recording of a speech or musical notes. It plays an important role, because it is the key to identify which notes are being played and at what time. Pitch Estimation of real instruments is a very hard task to address. Each instrument has its own physical characteristics, which reflects in different spectral characteristics. Furthermore, the recording conditions can vary from studio to studio and background noises must be considered. This dissertation presents a novel approach to the problem of Pitch Estimation, using Cartesian Genetic Programming (CGP).We take advantage of evolutionary algorithms, in particular CGP, to explore and evolve complex mathematical functions that act as classifiers. These classifiers are used to identify piano notes pitches in an audio signal. To help us with the codification of the problem, we built a highly flexible CGP Toolbox, generic enough to encode different kind of programs. The encoded evolutionary algorithm is the one known as 1 + , and we can choose the value for . The toolbox is very simple to use. Settings such as the mutation probability, number of runs and generations are configurable. The cartesian representation of CGP can take multiple forms and it is able to encode function parameters. It is prepared to handle with different type of fitness functions: minimization of f(x) and maximization of f(x) and has a useful system of callbacks. We trained 61 classifiers corresponding to 61 piano notes. A training set of audio signals was used for each of the classifiers: half were signals with the same pitch as the classifier (true positive signals) and the other half were signals with different pitches (true negative signals). F-measure was used for the fitness function. Signals with the same pitch of the classifier that were correctly identified by the classifier, count as a true positives. Signals with the same pitch of the classifier that were not correctly identified by the classifier, count as a false negatives. Signals with different pitch of the classifier that were not identified by the classifier, count as a true negatives. Signals with different pitch of the classifier that were identified by the classifier, count as a false positives. Our first approach was to evolve classifiers for identifying artifical signals, created by mathematical functions: sine, sawtooth and square waves. Our function set is basically composed by filtering operations on vectors and by arithmetic operations with constants and vectors. All the classifiers correctly identified true positive signals and did not identify true negative signals. We then moved to real audio recordings. For testing the classifiers, we picked different audio signals from the ones used during the training phase. For a first approach, the obtained results were very promising, but could be improved. We have made slight changes to our approach and the number of false positives reduced 33%, compared to the first approach. We then applied the evolved classifiers to polyphonic audio signals, and the results indicate that our approach is a good starting point for addressing the problem of Pitch Estimation.

Frequency domain analysis of stationary time series

This thesis provides a necessary and sufficient condition for asymptotic efficiency of a nonparametric estimator of the generalised autocovariance function of a Gaussian stationary random process. The generalised autocovariance function is the inverse Fourier transform of a power transformation of the spectral density, and encompasses the traditional and inverse autocovariance functions. Its nonparametric estimator is based on the inverse discrete Fourier transform of the same power transformation of the pooled periodogram. The general result is then applied to the class of Gaussian stationary ARMA processes and its implications are discussed. We illustrate that for a class of contrast functionals and spectral densities, the minimum contrast estimator of the spectral density satisfies a Yule-Walker system of equations in the generalised autocovariance estimator. Selection of the pooling parameter, which characterizes the nonparametric estimator of the generalised autocovariance, controlling its resolution, is addressed by using a multiplicative periodogram bootstrap to estimate the finite-sample distribution of the estimator. A multivariate extension of recently introduced spectral models for univariate time series is considered, and an algorithm for the coefficients of a power transformation of matrix polynomials is derived, which allows to obtain the Wold coefficients from the matrix coefficients characterizing the generalised matrix cepstral models. This algorithm also allows the definition of the matrix variance profile, providing important quantities for vector time series analysis. A nonparametric estimator based on a transformation of the smoothed periodogram is proposed for estimation of the matrix variance profile.

Novel methods of state estimation and parameter identification for high-performance motorcycles

This research activity aims at providing a reliable estimation of particular state variables or parameters concerning the dynamics and performance optimization of a MotoGP-class motorcycle, integrating the classical model-based approach with new methodologies involving artificial intelligence. The first topic of the research focuses on the estimation of the thermal behavior of the MotoGP carbon braking system. Numerical tools are developed to assess the instantaneous surface temperature distribution in the motorcycle's front brake discs. Within this application other important brake parameters are identified using Kalman filters, such as the disc convection coefficient and the power distribution in the disc-pads contact region. Subsequently, a physical model of the brake is built to estimate the instantaneous braking torque. However, the results obtained with this approach are highly limited by the knowledge of the friction coefficient (μ) between the disc rotor and the pads. Since the value of μ is a highly nonlinear function of many variables (namely temperature, pressure and angular velocity of the disc), an analytical model for the friction coefficient estimation appears impractical to establish. To overcome this challenge, an innovative hybrid solution is implemented, combining the benefit of artificial intelligence (AI) with classical model-based approach. Indeed, the disc temperature estimated through the thermal model previously implemented is processed by a machine learning algorithm that outputs the actual value of the friction coefficient thus improving the braking torque computation performed by the physical model of the brake. Finally, the last topic of this research activity regards the development of an AI algorithm to estimate the current sideslip angle of the motorcycle's front tire. While a single-track motorcycle kinematic model and IMU accelerometer signals theoretically enable sideslip calculation, the presence of accelerometer noise leads to a significant drift over time. To address this issue, a long short-term memory (LSTM) network is implemented.

ICE Emissions Estimation through Machine-learning techniques to support real-world R&D experiments

The emissions estimation, both during homologation and standard driving, is one of the new challenges that automotive industries have to face. The new European and American regulation will allow a lower and lower quantity of Carbon Monoxide emission and will require that all the vehicles have to be able to monitor their own pollutants production. Since numerical models are too computationally expensive and approximated, new solutions based on Machine Learning are replacing standard techniques. In this project we considered a real V12 Internal Combustion Engine to propose a novel approach pushing Random Forests to generate meaningful prediction also in extreme cases (extrapolation, very high frequency peaks, noisy instrumentation etc.). The present work proposes also a data preprocessing pipeline for strongly unbalanced datasets and a reinterpretation of the regression problem as a classification problem in a logarithmic quantized domain. Results have been evaluated for two different models representing a pure interpolation scenario (more standard) and an extrapolation scenario, to test the out of bounds robustness of the model. The employed metrics take into account different aspects which can affect the homologation procedure, so the final analysis will focus on combining all the specific performances together to obtain the overall conclusions.

Elevated Micronucleus Frequency In Patients With Type 2 Diabetes, Dyslipidemia And Periodontitis.

The over-production of reactive oxygen species (ROS) can cause oxidative damage to a large number of molecules, including DNA, and has been associated with the pathogenesis of several disorders, such as diabetes mellitus (DM), dyslipidemia and periodontitis (PD). We hypothesise that the presence of these diseases could proportionally increase the DNA damage. The aim of this study was to assess the micronucleus frequency (MNF), as a biomarker for DNA damage, in individuals with type 2 DM, dyslipidemia and PD. One hundred and fifty patients were divided into five groups based upon diabetic, dyslipidemic and periodontal status (Group 1 - poor controlled DM with dyslipidemia and PD; Group 2 - well-controlled DM with dyslipidemia and PD; Group 3 - without DM with dyslipidemia and PD; Group 4 - without DM, without dyslipidemia and with PD; and Group 5 - without DM, dyslipidemia and PD). Blood analyses were carried out for fasting plasma glucose, HbA1c and lipid profile. Periodontal examinations were performed, and venous blood was collected and processed for micronucleus (MN) assay. The frequency of micronuclei was evaluated by cell culture cytokinesis-block MN assay. The general characteristics of each group were described by the mean and standard deviation and the data were submitted to the Mann-Whitney, Kruskal-Wallis, Multiple Logistic Regression and Spearman tests. The Groups 1, 2 and 3 were similarly dyslipidemic presenting increased levels of total cholesterol, low density lipoprotein cholesterol and triglycerides. Periodontal tissue destruction and local inflammation were significantly more severe in diabetics, particularly in Group 1. Frequency of bi-nucleated cells with MN and MNF, as well as nucleoplasmic bridges, were significantly higher for poor controlled diabetics with dyslipidemia and PD in comparison with those systemically healthy, even after adjusting for age, and considering Bonferroni's correction. Elevated frequency of micronuclei was found in patients affected by type 2 diabetes, dyslipidemia and PD. This result suggests that these three pathologies occurring simultaneously promote an additional role to produce DNA impairment. In addition, the micronuclei assay was useful as a biomarker for DNA damage in individuals with chronic degenerative diseases.

Frequency Of The Allelic Variant C.1150t > C In Exon 10 Of The Fibroblast Growth Factor Receptor 3 (fgfr3) Gene Is Not Increased In Patients With Pathogenic Mutations And Related Chondrodysplasia Phenotypes.

Mutations in the FGFR3 gene cause the phenotypic spectrum of FGFR3 chondrodysplasias ranging from lethal forms to the milder phenotype seen in hypochondroplasia (Hch). The p.N540K mutation in the FGFR3 gene occurs in ∼70% of individuals with Hch, and nearly 30% of individuals with the Hch phenotype have no mutations in the FGFR3, which suggests genetic heterogeneity. The identification of a severe case of Hch associated with the typical mutation c.1620C > A and the occurrence of a c.1150T > C change that resulted in a p.F384L in exon 10, together with the suspicion that this second change could be a modulator of the phenotype, prompted us to investigate this hypothesis in a cohort of patients. An analysis of 48 patients with FGFR3 chondrodysplasia phenotypes and 330 healthy (control) individuals revealed no significant difference in the frequency of the C allele at the c.1150 position (p = 0.34). One patient carrying the combination `pathogenic mutation plus the allelic variant c.1150T > C' had a typical achondroplasia (Ach) phenotype. In addition, three other patients with atypical phenotypes showed no association with the allelic variant. Together, these results do not support the hypothesis of a modulatory role for the c.1150T > C change in the FGFR3 gene.

Explosive Synchronization With Partial Degree-frequency Correlation.

Networks of Kuramoto oscillators with a positive correlation between the oscillators frequencies and the degree of their corresponding vertices exhibit so-called explosive synchronization behavior, which is now under intensive investigation. Here we study and discuss explosive synchronization in a situation that has not yet been considered, namely when only a part, typically a small part, of the vertices is subjected to a degree-frequency correlation. Our results show that in order to have explosive synchronization, it suffices to have degree-frequency correlations only for the hubs, the vertices with the highest degrees. Moreover, we show that a partial degree-frequency correlation does not only promotes but also allows explosive synchronization to happen in networks for which a full degree-frequency correlation would not allow it. We perform a mean-field analysis and our conclusions were corroborated by exhaustive numerical experiments for synthetic networks and also for the undirected and unweighed version of a typical benchmark biological network, namely the neural network of the worm Caenorhabditis elegans. The latter is an explicit example where partial degree-frequency correlation leads to explosive synchronization with hysteresis, in contrast with the fully correlated case, for which no explosive synchronization is observed.

Enhanced D1 And D2 Inhibitions Induced By Low-frequency Trains Of Conditioning Stimuli: Differential Effects On H- And T-reflexes And Possible Mechanisms.

Mechanically evoked reflexes have been postulated to be less sensitive to presynaptic inhibition (PSI) than the H-reflex. This has implications on investigations of spinal cord neurophysiology that are based on the T-reflex. Preceding studies have shown an enhanced effect of PSI on the H-reflex when a train of ~10 conditioning stimuli at 1 Hz was applied to the nerve of the antagonist muscle. The main questions to be addressed in the present study are if indeed T-reflexes are less sensitive to PSI and whether (and to what extent and by what possible mechanisms) the effect of low frequency conditioning, found previously for the H-reflex, can be reproduced on T-reflexes from the soleus muscle. We explored two different conditioning-to-test (C-T) intervals: 15 and 100 ms (corresponding to D1 and D2 inhibitions, respectively). Test stimuli consisted of either electrical pulses applied to the posterior tibial nerve to elicit H-reflexes or mechanical percussion to the Achilles tendon to elicit T-reflexes. The 1 Hz train of conditioning electrical stimuli delivered to the common peroneal nerve induced a stronger effect of PSI as compared to a single conditioning pulse, for both reflexes (T and H), regardless of C-T-intervals. Moreover, the conditioning train of pulses (with respect to a single conditioning pulse) was proportionally more effective for T-reflexes as compared to H-reflexes (irrespective of the C-T interval), which might be associated with the differential contingent of Ia afferents activated by mechanical and electrical test stimuli. A conceivable explanation for the enhanced PSI effect in response to a train of stimuli is the occurrence of homosynaptic depression at synapses on inhibitory interneurons interposed within the PSI pathway. The present results add to the discussion of the sensitivity of the stretch reflex pathway to PSI and its functional role.

Scaphoid Fracture Nonunion: Correlation Of Radiographic Imaging, Proximal Fragment Histologic Viability Evaluation, And Estimation Of Viability At Surgery: Diagnosis Of Scaphoid Pseudarthrosis.

The purpose of this study was to correlate the pre-operative imaging, vascularity of the proximal pole, and histology of the proximal pole bone of established scaphoid fracture non-union. This was a prospective non-controlled experimental study. Patients were evaluated pre-operatively for necrosis of the proximal scaphoid fragment by radiography, computed tomography (CT) and magnetic resonance imaging (MRI). Vascular status of the proximal scaphoid was determined intra-operatively, demonstrating the presence or absence of puncate bone bleeding. Samples were harvested from the proximal scaphoid fragment and sent for pathological examination. We determined the association between the imaging and intra-operative examination and histological findings. We evaluated 19 male patients diagnosed with scaphoid nonunion. CT evaluation showed no correlation to scaphoid proximal fragment necrosis. MRI showed marked low signal intensity on T1-weighted images that confirmed the histological diagnosis of necrosis in the proximal scaphoid fragment in all patients. Intra-operative assessment showed that 90% of bones had absence of intra-operative puncate bone bleeding, which was confirmed necrosis by microscopic examination. In scaphoid nonunion MRI images with marked low signal intensity on T1-weighted images and the absence of intra-operative puncate bone bleeding are strong indicatives of osteonecrosis of the proximal fragment.

Frequency of the different mutations causing spinocerebellar ataxia (SCA1, SCA2, MJD/SCA3 and DRPLA) in a large group of Brazilian patients

Spinocerebellar ataxia type 1 (SCA1), spinocerebellar ataxia type 2 (SCA2) and Machado-Joseph disease or spinocerebellar ataxia type 3 (MJD/SCA3) are three distinctive forms of autosomal dominant spinocerebellar ataxia (SCA) caused by expansions of an unstable CAG repeat localized in the coding region of the causative genes. Another related disease, dentatorubropallidoluysian atrophy (DRPLA) is also caused by an unstable triplet repeat and can present as SCA in late onset patients. We investigated the frequency of the SCA1, SCA2, MJD/SCA3 and DRPLA mutations in 328 Brazilian patients with SCA, belonging to 90 unrelated families with various patterns of inheritance and originating in different geographic regions of Brazil. We found mutations in 35 families (39%), 32 of them with a clear autosomal dominant inheritance. The frequency of the SCA1 mutation was 3% of all patients; and 6 % in the dominantly inherited SCAs. We identified the SCA2 mutation in 6% of all families and in 9% of the families with autosomal dominant inheritance. The MJD/SCA3 mutation was detected in 30 % of all patients; and in the 44% of the dominantly inherited cases. We found no DRPLA mutation. In addition, we observed variability in the frequency of the different mutations according to geographic origin of the patients, which is probably related to the distinct colonization of different parts of Brazil. These results suggest that SCA may be occasionally caused by the SCA1 and SCA2 mutations in the Brazilian population, and that the MJD/SCA3 mutation is the most common cause of dominantly inherited SCA in Brazil.