Modelling and diagnostic of an ultrasonic piezoelectric actuator

Modeling of piezoelectric motors is a difficult task because their characteristics are affected by various factors such as materials properties, electrical and mechanical boundary conditions. This work presents the modeling of piezoelectric motor via bond graph method and used for the diagnostic. This method is an innovative way to analyse the effects of different design variables on the objective function but can be also considered as an optimization stage of the study. The validation and the development of bond graph models are based on physical insight to aid in structural damage detection and use the technique of optimal sensors placement

Understanding others: a pilot investigation of cognitive and affective facets of social cognition in patients with 22q11.2 deletion syndrome (22q11DS)

Background Although significant impairments in the affective and cognitive facets of social cognition have been highlighted in patients with 22q11.2 deletion syndrome (22q11DS) in previous studies, these domains have never been investigated simultaneously within the same group of participants. Furthermore, despite theoretical evidence, associations between these two processes and schizotypal symptoms or social difficulties in this population have been scarcely examined. Methods Twenty-nine participants with 22q11DS and 27 typically developing controls (N = 5 siblings; N = 22 unrelated controls) aged between 11 and 21 years participated in the study. Both groups were matched for age and gender distribution. Two computerized social cognition tasks evaluating perspective and emotion recognition abilities were administered to all participants. The levels of schizotypal trait expression and social functioning were further investigated in both groups, based on a validated self-report questionnaire (Schizotypal Personality Questionnaire) and parental interview (Vineland Adaptive Behavior Scales). Results Participants with 22q11DS exhibited lower perspective-taking and emotion recognition capacities than typically developing controls. The two socio-cognitive dimensions investigated here were further correlated in healthy controls. The efficiency of perspective-taking processes (response time) was marginally related to the degree of schizotypal trait expression in patients with 22q11DS. Conclusions This study first provides support for significant deficits in two core facets of social cognition in 22q11DS. The associations observed between the experimental tasks and measures of social functioning or schizotypal symptoms in 22q11DS open promising research avenue, which should be more deeply investigated in future studies

Differentiation between Parkinson disease and other forms of Parkinsonism using support vector machine analysis of susceptibility-weighted imaging (SWI): initial results

Objectives: To diagnose Parkinson disease (PD) at the individual level using pattern recognition of brain susceptibility-weighted imaging (SWI). Methods: We analysed brain SWI in 36 consecutive patients with Parkinsonism suggestive of PD who had (1) SWI at 3T, (2) brain 123I-ioflupane SPECT and (3) extensive neurological testing including follow-up (16 PD, 67.4 ± 6.2years, 11 female; 20 OTHER, a heterogeneous group of atypical Parkinsonism syndromes 65.2 ± 12.5years, 6 female). Analysis included group-level comparison of SWI values and individual-level support vector machine (SVM) analysis. Results: At the group level, simple visual analysis yielded no differences between groups. However, the group-level analyses demonstrated increased SWI in the bilateral thalamus and left substantia nigra in PD patients versus other Parkinsonism. The inverse comparison yielded no supra-threshold clusters. At the individual level, SVM correctly classified PD patients with an accuracy above 86%. Conclusions: SVM pattern recognition of SWI data provides accurate discrimination of PD among patients with various forms of Parkinsonism at an individual level, despite the absence of visually detectable alterations. This pilot study warrants further confirmation in a larger cohort of PD patients and with different MR machines and MR parameters. Key Points: • Magnetic resonance imaging data offers new insights into Parkinson's disease • Visual susceptibility-weighted imaging (SWI) analysis could not discriminate idiopathic from atypical PD • However, support vector machine (SVM) analysis provided highly accurate detection of idiopathic PD • SVM analysis may contribute to the clinical diagnosis of individual PD patients • Such information can be readily obtained from routine MR dat

Effects of dorsolateral prefrontal cortex lesion on motor habit and performance assessed with manual grasping and control of force in macaque monkeys.

In the context of an autologous adult neural cell ecosystem (ANCE) transplantation study, four intact adult female macaque monkeys underwent a unilateral biopsy of the dorsolateral prefrontal cortex (dlPFC) to provide the cellular material needed to obtain the ANCE. Monkeys were previously trained to perform quantitative motor (manual dexterity) tasks, namely, the "modified-Brinkman board" task and the "reach and grasp drawer" task. The aim of the present study was to extend preliminary data on the role of the prefrontal cortex in motor habit and test the hypothesis that dlPFC contributes to predict the grip force required when a precise level of force to be generated is known beforehand. As expected for a small dlPFC biopsy, neither the motor performance (score) nor the spatiotemporal motor sequences were affected in the "modified-Brinkman board" task, whereas significant changes (mainly decreases) in the maximal grip force (force applied on the drawer knob) were observed in the "reach and grasp drawer" task. The present data in the macaque monkey related to the prediction of grip force are well in line with the previous fMRI data reported for human subjects. Moreover, the ANCE transplantation strategy (in the case of stroke or Parkinson's disease) based on biopsy in dlPFC does not generate unwanted motor consequences, at least as far as motor habit and motor performance are concerned in the context of a sequential grasping a small objects, which does not require the development of significant force levels

New design and comparative study via two techniques for wind energy conversion system

Introduction. With the advancements in the variable speed direct drive design and control of wind energy systems, the efficiency and energy capture of these systems is also increasing. As such, numerous linear controllers have also been developed, in literature, for MPPT which use the linear characteristics of the wind turbine system. The major limitation in all of those linear controllers is that they use the linearized model and they cannot deal with the nonlinear dynamics of a system. However, real systems exhibit nonlinear dynamics and a nonlinear controller is required to handle such nonlinearities in real-world systems. The novelty of the proposed work consists in the development of a robust nonlinear controller to ensure maximum power point tracking by handling nonlinearities of a system and making it robust against changing environmental conditions. Purpose. In the beginning, sliding mode control has been considered as one of the most powerful control techniques, this is due to the simplicity of its implementation and robustness compared to uncertainties of the system and external disturbances. Unfortunately, this type of controller suffers from a major disadvantage, that is, the phenomenon of chattering. Methods. So in this paper and in order to eliminate this phenomenon, a novel non-linear control algorithm based on a synergetic controller is proposed. The objective of this control is to maximize the power extraction of a variable speed wind energy conversion system compared to sliding mode control by eliminating the phenomenon of chattering and have a good power quality by fixing the power coefficient at its maximum value and the Tip Speed Ratio maintained at its optimum value. Results. The performance of the proposed nonlinear controllers has been validated in MATLAB/Simulink environment. The simulation results show the effectiveness of the proposed scheme, suppression of the chattering phenomenon and robustness of the proposed controller compared to the sliding mode control law.Вступ. З досягненнями у проектуванні та керуванні вітряними енергосистемами з регульованою швидкістю, зростають також ефективність та захоплення енергії цих систем. Так, в літературі також розроблено численні лінійні контролери для відстеження точки максимальної потужності, які використовують лінійні характеристики системи з вітряними турбінами. Основним обмеженням у всіх цих лінійних контролерах є те, що вони використовують лінеаризовану модель і не можуть мати справу з нелінійною динамікою системи. Однак реальні системи демонструють нелінійну динаміку, і для обробки таких нелінійностей у реальних системах необхідний нелінійний контролер. Новизна запропонованої роботи полягає у розробці надійного нелінійного контролера для забезпечення відстеження точки максимальної потужності шляхом обробки нелінійності системи та забезпечення її стійкості до змін умов навколишнього середовища. Мета. Спочатку управління ковзним режимом вважалося одним з найпотужніших методів управління, що пов’язано з простотою його реалізації та надійністю порівняно з невизначеністю системи та зовнішніми збуреннями. На жаль, цей тип контролера страждає від головного недоліку, а саме явища вібрування. Методи. Тому у цій роботі з метою усунення цього явища пропонується новий нелінійний алгоритм управління, заснований на синергетичному контролері. Завдання цього контролю – максимізувати відбір потужності системи перетворення енергії вітру зі змінною швидкістю порівняно із регулюванням ковзного режиму, усуваючи явище вібрування, і мати хорошу якість енергії, фіксуючи коефіцієнт потужності на його максимальному значенні та підтримуючи кінцевий коефіцієнт швидкості на його оптимальному значенні. Результати. Ефективність запропонованих нелінійних контролерів перевірена в середовищі MATLAB/Simulink. Результати моделювання показують ефективність запропонованої схеми, придушення явища вібрування та стійкість запропонованого контролера порівняно із законом управління ковзного режиму

Energy management based on a fuzzy controller of a photovoltaic/fuel cell/Li-ion battery/supercapacitor for unpredictable, fluctuating, high-dynamic three-phase AC load

Introduction. Nowadays, environmental pollution becomes an urgent issue that undoubtedly influences the health of humans and other creatures living in the world. The growth of hydrogen energy increased 97.3 % and was forecast to remain the world’s largest source of green energy. It can be seen that hydrogen is one of the essential elements in the energy structure as well as has great potential to be widely used in the 21st century. Purpose. This paper aims to propose an energy management strategy based a fuzzy logic control, which includes a hybrid renewable energy sources system dedicated to the power supply of a three-phase AC variable load (unpredictable high dynamic). Photovoltaic (PV), fuel cell (FC), Li-ion battery, and supercapacitor (SC) are the four sources that make up the renewable hybrid power system; all these sources are coupled in the DC-link bus. Unlike usual the SC was connected to the DC-link bus directly in this research work in order to ensure the dominant advantage which is a speedy response during load fast change and loads transient. Novelty. The power sources (PV/FC/Battery/SC) are coordinated based on their dynamics in order to keep the DC voltage around its reference. Among the main goals achieved by the fuzzy control strategy in this work are to reduce hydrogen consumption and increase battery lifetime. Methods. This is done by controlling the FC current and by state of charge (SOC) of the battery and SC. To verify the fuzzy control strategy, the simulation was carried out with the same system and compared with the management flowchart strategy. The results obtained confirmed that the hydrogen consumption decreased to 26.5 g and the SOC for the battery was around 62.2-65 and this proves the desired goal.Вступ. В даний час забруднення навколишнього середовища стає актуальною проблемою, яка, безперечно, впливає на здоров’я людини та інших істот, які живуть у світі. Зростання водневої енергетики збільшилося на 97,3 %, і прогнозувалося, що вона залишиться найбільшим у світі джерелом зеленої енергії. Видно, що водень є одним із найважливіших елементів у структурі енергетики, а також має великий потенціал для широкого використання у 21 столітті. Мета. У цій статті пропонується стратегія управління енергоспоживанням, заснована на нечіткому логічному управлінні, яка включає гібридну систему відновлюваних джерел енергії, призначену для живлення трифазного змінного навантаження змінного струму (непередбачувана висока динаміка). Фотоелектричні (PV), паливні елементи (FC), літій-іонні батареї та суперконденсатори (SC) – це чотири джерела, з яких складається відновлювана гібридна енергосистема; всі ці джерела підключені до шини постійного струму. На відміну від звичайних застосувань,ув цій дослідницькій роботі SC був підключений до шини постійного струму безпосередньо, щоб забезпечити домінуючу перевагу, що полягає в швидкому реагуванні при швидкій зміні навантаження та перехідних режимах навантаження. Новизна. Джерела живлення (PV/FC/батареї/SC) координуються на основі їхньої динаміки, щоб підтримувати напругу постійного струму біля свого еталонного значення. Серед основних цілей, досягнутих стратегією нечіткого управління у цій роботі, - зниження споживання водню та збільшення терміну служби батареї. Методи. Це робиться шляхом керування струмом FC та станом заряду (SOC) батареї та SC. Для перевірки стратегії нечіткого управління було проведено моделювання з тією самою системою та порівняння зі стратегією блок-схеми керування. Отримані результати підтвердили, що споживання водню знизилося до 26,5 г, а SOC для батареї становило близько 62,2-65, що доводить досягнення бажаної мети

Longitudinal Relationships Between Reflective Functioning, Empathy, and Externalizing Behaviors During Adolescence and Young Adulthood

Reflective functioning (RF) refers to the understanding of one’s own and others’ behaviors in terms of mental states, whereas empathy entails the abilities to understand (cognitive empathy) and to share (affective empathy) the emotions of others. Low RF and low empathy have been previously related to externalizing behaviors, such as aggression and rule breaking. However, few longitudinal studies have simultaneously examined the relationships between these variables during adolescence. The aim of the present study is to investigate the longitudinal effects of both RF and empathy on potential changes in externalizing behaviors over time, in a group of 103 adolescents and young adults from the general population assessed repeatedly up to four times. We conducted multilevel analysis in order to examine the effects of RF and empathy on the initial levels and the trajectories of externalizing behaviors over time, while accounting for other variables previously associated with externalizing behaviors, such as age, gender, internalizing problems, and cognitive abilities. The results suggest that the ability to reflect on behaviors in terms of mental states predicted a sharper decrease in externalizing behaviors over time. Moreover, externalizing behaviors at the first assessment were associated with RF impairments and low affective empathy. Age, gender, cognitive abilities, and cognitive empathy were not associated with externalizing behaviors. We discuss how our results, based on a typically developing population, might inform primary or indicated prevention strategies for externalizing behaviors by focusing on socio-cognitive processes such as RF and affective empathy

Comparison of machine learning and semi-quantification algorithms for (I123)FP-CIT classification: the beginning of the end for semi-quantification?

Background Semi-quantification methods are well established in the clinic for assisted reporting of (I123) Ioflupane images. Arguably, these are limited diagnostic tools. Recent research has demonstrated the potential for improved classification performance offered by machine learning algorithms. A direct comparison between methods is required to establish whether a move towards widespread clinical adoption of machine learning algorithms is justified. This study compared three machine learning algorithms with that of a range of semi-quantification methods, using the Parkinson’s Progression Markers Initiative (PPMI) research database and a locally derived clinical database for validation. Machine learning algorithms were based on support vector machine classifiers with three different sets of features: Voxel intensities Principal components of image voxel intensities Striatal binding radios from the putamen and caudate. Semi-quantification methods were based on striatal binding ratios (SBRs) from both putamina, with and without consideration of the caudates. Normal limits for the SBRs were defined through four different methods: Minimum of age-matched controls Mean minus 1/1.5/2 standard deviations from age-matched controls Linear regression of normal patient data against age (minus 1/1.5/2 standard errors) Selection of the optimum operating point on the receiver operator characteristic curve from normal and abnormal training data Each machine learning and semi-quantification technique was evaluated with stratified, nested 10-fold cross-validation, repeated 10 times. Results The mean accuracy of the semi-quantitative methods for classification of local data into Parkinsonian and non-Parkinsonian groups varied from 0.78 to 0.87, contrasting with 0.89 to 0.95 for classifying PPMI data into healthy controls and Parkinson’s disease groups. The machine learning algorithms gave mean accuracies between 0.88 to 0.92 and 0.95 to 0.97 for local and PPMI data respectively. Conclusions Classification performance was lower for the local database than the research database for both semi-quantitative and machine learning algorithms. However, for both databases, the machine learning methods generated equal or higher mean accuracies (with lower variance) than any of the semi-quantification approaches. The gain in performance from using machine learning algorithms as compared to semi-quantification was relatively small and may be insufficient, when considered in isolation, to offer significant advantages in the clinical context