Shannon Entropy in Stochastic Analysis of Some Mems

This work is focused on the numerical determination of Shannon probabilistic entropy for MEMS devices exhibiting some uncertainty in their structural response. This entropy is a universal measure of statistical or stochastic disorder in static deformation or dynamic vibrations of engineering systems and is available for both continuous and discrete distributions functions of structural parameters. An interval algorithm using Monte Carlo simulation and polynomial structural response recovery has been implemented to demonstrate an uncertainty propagation of the forced vibrations in some small MEMS devices. A computational example includes stochastic nonlinear vibrations described by the Duffing equation calibrated for some micro-resonators, whose damping is adopted as a Gaussian, uniformly and triangularly distributed input uncertainty source

Numerical and experimental evaluation of the magnetic interaction for frequency up-conversion in piezoelectric vibration energy harvesters

The purpose of this work is to improve the modelling process for the application of permanent magnets in a frequency up-conversion (FuC) mechanism for piezoelectric energy harvesters. More specifically, the aim is to avoid the burdensome finite element analyses (FEA) in the framework of electromechanical devices design. The analytical calculations are compared with experimental tests conducted by an ad-hoc set up and with FEA. After investigations on the interaction, an application of FuC mechanism is proposed on a meso-scale case study in which a low frequency seismic mass (LFM) interacts non-linearly, due to magnetic field, with an high frequency piezoelectric vibration energy harvester (PVEH). Numerical simulations have been carried out in the time domain (step-by-step analysis) under a harmonic low-frequency input acceleration signal. The peculiar behavior, due to non-linear dynamics, is investigated in both the repulsive and the attractive configurations of the magnets. The results confirm the effectiveness of magnetic FuC and show that the repulsive case allows the device to recover a larger amount of energy than the attractive configuration

Stochastic effects on the dynamics of a resonant MEMS magnetometer: a Monte Carlo investigation

In the design of Lorentz force MEMS magnetometers, the coupled thermo-electro-magneto-mechanical fields governing the dynamics of the relevant compliant structures can be appropriately exploited to enhance their performances. In recent works, we showed that reduced-order models for the dynamics of the said movable structures can be recast in the form of the Duffing equation, where nonlinear terms arise from the multi-physics governing the problem. As stochastic effects may play a role due to the micrometric dimensions of the device, an investigation of the link between the statistics of sensor imperfections and output is here carried out. The said imperfections at the microscopic length-scale are modeled in terms of: overetch thickness, assumed to feature a uniform distribution in a proper interval matching available experimental data; and elastic properties of the vibrating polycrystalline silicon film, as obtained through a numerical homogenization procedure over a representative film volume. To get insights into the effects of the parameters governing the nonlinear dynamics of the resonant structure, a Monte Carlo analysis is adopted

Attention Mechanism-Driven Sensor Placement Strategy for Structural Health Monitoring

Automated vibration-based structural health monitoring (SHM) strategies have been recently proven to be promising in the presence of aging and material deterioration threatening the safety of civil structures. Within such a framework, ensuring high-quality and informative data is a critical aspect that is highly dependent on the deployment of the sensors in the network and on their capability to provide damage-sensitive features to be exploited. This paper presents a novel data-driven approach to the optimal sensor placement devised to identify sensor locations that maximize the information effectiveness for SHM purposes. The optimization of the sensor network is addressed by means of a deep neural network (DNN) equipped with an attention mechanism, a state-of-the-art technique in natural language processing (NLP) that is useful in focusing on a limited number of important components in the information stream. The trained attention mechanism eventually allows for quantifying the relevance of each sensor in terms of the so-called attention scores, thereby enabling to identify the most useful input channels to solve the relevant downstream SHM task. With reference to the damage localization task, framed here as a classification problem handling a set of predefined damage scenarios, the DNN is trained to locate damage on labeled data that had been simulated to emulate the effects of damage under different operational conditions. The capabilities of the proposed method are demonstrated by referring to an eight-story shear building, characterized by damage states possibly located at any story and of unknown severity

Lifestyle interventions and prevention of suicide

Over the past years, there has been a growing interest in the association between lifestyle psychosocial interventions, severe mental illness, and suicide risk. Patients with severe mental disorders have higher mortality rates, poor health states, and higher suicide risk compared to the general population. Lifestyle behaviors are amenable to change through the adoption of specific psychosocial interventions, and several approaches have been promoted. The current article provides a comprehensive review of the literature on lifestyle interventions, mental health, and suicide risk in the general population and in patients with psychiatric disorders. For this purpose, we investigated lifestyle behaviors and lifestyle interventions in three different age groups: adolescents, young adults, and the elderly. Several lifestyle behaviors including cigarette smoking, alcohol use, and sedentary lifestyle are associated with suicide risk in all age groups. In adolescents, growing attention has emerged on the association between suicide risk and internet addiction, cyberbullying and scholastic and family difficulties. In adults, psychiatric symptoms, substance and alcohol abuse, weight, and occupational difficulties seems to have a significant role in suicide risk. Finally, in the elderly, the presence of an organic disease and poor social support are associated with an increased risk of suicide attempt. Several factors may explain the association between lifestyle behaviors and suicide. First, many studies have reported that some lifestyle behaviors and its consequences (sedentary lifestyle, cigarette smoking underweight, obesity) are associated with cardiometabolic risk factors and with poor mental health. Second, several lifestyle behaviors may encourage social isolation, limiting the development of social networks, and remove individuals from social interactions; increasing their risk of mental health problems and suicide

Numerical solution of the Duffing equation with random coefficients

The main aim of this work is numerical solution of the nonlinear vibrations of micro-resonators exhibiting bounded and Gaussian uncertainty in their parameters. The mechanical response in deterministic situation is described by the Duffing equation, whose numerical solution is obtained with the Runge–Kutta–Fehlenberg algorithm, while probabilistic analysis is carried out using the generalized stochastic perturbation technique enriched with automatic optimization of the approximating polynomial. Basic solution to this nonlinear vibration in the deterministic context is obtained with the use of the computer algebra system MAPLE, where all additional probabilistic procedures are also implemented. We compare each time expectations, coefficients of variation, skewness and kurtosis for the structural response to show probabilistic sensitivity of the MEMS accelerometer with respect to its design parameter expectation and coefficient of variation. An additional comparison of the proposed technique with the traditional Monte-Carlo sampling for the first four probabilistic moments is also provided

THE IMPLANT TREATMENT OF TWO PATIENTS SUFFERING FROM SJÖGREN’S SYNDROME WITH MULTIFACTORIAL REGENERATIVE PROTOCOL

The purpose of the work is to show that it is possible to rehabilitate with Multifatorial Regenerative Protocol (MFRP) also the patients with diseases considered to be absolute contraindications to the implant rehabilitation. For the rehabilitation it has been used the PBR rehabilitation technique, that allows to insert prosthetic roots not excessively traumatizing the bone tissue and to avoid the use of high speed rotary burs. Despite the disease and the poor bone quality, it has been possible to obtain the osteo-connection only in 45 days. Using the MFRP implants, it is possible to rehabilitate with a high percentage of success also patients with poor bone quality and density