A procedure for a mechanical evaluation of an undefined osteo-protective material

Falls represent a major care and cost problem to health and social services world-widely, since 50% of falls result in an injury. In this work, is proposed a methodology to evaluate protective pads materials and geometry performance, in order to reduce impact results in a fall event. Since the material properties and the pad geometry are the key factors to make the protection possible when a fall event occurs, our approach relies on the use of mechanical tests to evaluate the properties of the material and in the study of the pad response during a fall. For this, were used compression, tensile and instrumented falling weight tests, that allow a fully characterization of the materials that can be employed in the protective pads. Likewise, to gather precise information on falls events, in order to study the pad response during a fall, a set of laboratory fall trials were created using a camera-less inertial motion capture (mocap) system. This allow the acquisition of dynamic information of falls, namely acceleration and velocity that can be used to create a finite element analysis (FEA) model, where different segments from the human body can be evaluated when the protective pad is associated to it. Through the proposed methodology, different materials and pad geometries can be studied towards maximizing the performance of protection pads for falls. The mocap system allows the acquisition of fall data, and also the creation of a human body geometrical model, representative of the fall. From the mechanical trials, was showed that the spacer fabric embedded with silicone has the higher ability to reduce the peak force in case of impact when compared with all the other specimens. The compression and the tensile tests allow the mechanical definition of the material, and with this the material definition on the FEA model.     

Molecular dynamics computer simulation of scratch resistance testing of polymers: visualization

Experimental scratch resistance testing provides two numbers: the penetration depth R _p and the healing depth R _h. In molecular dynamics computer simulations, we create a material consisting of N statistical chain segments by polymerization; a reinforcing phase can be included. Then we simulate the movement of an indenter and response of the segments during X time steps. Each segment at each time step has three Cartesian coordinates of position and three of momentum. We describe methods of visualization of results based on a record of 6NX coordinates. We obtain a continuous dependence on time t of positions of each of the segments on the path of the indenter. Scratch resistance at a given location can be connected to spatial structures of individual polymeric chains.     

Thermomechanical environment characterisation in injection moulding and its relation to the mechanical properties of talc-filled polypropylene

This study is focused on the establishment of relationships between the injection moulding processing conditions, the applied thermomechanical environment (TME) and the tensile properties of talc-filled polypropylene, adopting a new extended concept of thermomechanical indices (TMI). In this approach, TMI are calculated from computational simulations of the moulding process that characterise the TME during processing, which are then related to the mechanical properties of the mouldings. In this study, this concept is extended to both the filling and the packing phases, with new TMI defined related to the morphology developed during these phases. A design of experiments approach based on Taguchi orthogonal arrays was adopted to vary the injection moulding parameters (injection flow rate, injection temperature, mould wall temperature and holding pressure), and thus, the TME. Results from analysis of variance for injection-moulded tensile specimens have shown that among the considered processing conditions, the flow rate is the most significant parameter for the Young’s modulus; the flow rate and melt temperature are the most significant for the strain at break; and the holding pressure and flow rate are the most significant for the stress at yield. The yield stress and Young’s modulus were found to be governed mostly by the thermostress index (TSI, related to the orientation of the skin layer), whilst the strain at break depends on both the TSI and the cooling index (CI, associated to the crystallinity degree of the core region). The proposed TMI approach provides predictive capabilities of the mechanical response of injection-moulded components, which is a valuable input during their design stage.     

In-Situ Thermal Resistance Evaluation of Walls Using an Iterative Dynamic Model

This paper proposes and validates, numerically and experimentally, an iterative model to evaluate the thermal resistance of multilayer walls in a dynamic state.

The paper first presents the analytical solution for simulating heat conduction in the frequency domain. The model is then modified by assuming a single-layer wall with unknown thermal properties. A nonlinear system is obtained by imposing temperatures and fluxes on the external surfaces. This is solved using an iterative approach based on the Newton–Raphson method. Finally, the model is applied to evaluate the thermal resistance of a wall in real conditions.     

A novel competitive learning neural network based acoustictransmission system for oil-well monitoring

The optimal operation of an oil well requires the periodic measurement of temperature and pressure at the downhole. In this paper, acoustic waves are used to transmit data to the surface through the pipeline column of the well, making up a wireless transmission system. Binary data is transmitted in two frequencies, using frequency-shift keying modulation. Such transmission faces problems with noise, attenuation, and, at pipeline joints, multiple reflections and nonlinear distortion. Hence, conventional demodulation techniques do not work well in this case. The neural network presented here classifies signals received by the receiver to estimate transmitted data, using a linear-vector-quantization-based network, with the help of a preprocessing procedure that transforms time-domain incoming signals in three-dimensional images. The results have been successfully verified. The neural network estimation principles presented in this paper can be easily applied to other patterns and time-domain recognition applications     

Neural network-based estimation of power electronic waveforms

Artificial neural network techniques are indicating a lot of promise for application in power electronic systems. So far, these applications are mainly confined to control, identification, and diagnostic problems, but the application in estimation is fairly new. The paper explores the application of neural networks for estimation of power electronic waveforms. The distorted line current waveforms in a single-phase thyristor AC controller and a three-phase diode rectifier that feeds an inverter-machine load have been taken into consideration, and neural networks have been trained to estimate the total RMS current, fundamental RMS current, displacement factor, and power factor. The performance of the neural network-based estimators has been compared with the actual values, and excellent performance is indicated. Neural network-based estimation has the usual advantages of very fast and simultaneous response of all the outputs, noise, and fault-tolerant performance and can be easily implemented in dedicated analog or digital hardware chips, which can coexist with digital signal processor (DSP) and/or application-specific integrated circuit (ASIC) chips. The estimation techniques can be extended to more complex waveforms in power electronics     

Factors associated with alcohol and drug use among traffic crash victims in southern Brazil

Objective

To investigate the prevalence of and factors associated with alcohol- or drug-related traffic crashes (TC) in a sample of TC victims who were admitted to the two emergency rooms of Porto Alegre in southern Brazil.

Methods

A cross-sectional study with consecutive samples was used. Victims of non-fatal TCs (as drivers, passengers or pedestrians) who had presented at emergency rooms during the 45 days of data collection were selected. Subjects participated in a structured interview, were breathalyzed and underwent salivary drug testing. A multinomial logistic regression model was used to verify factors associated with alcohol or drug use.

Results

Of the 609 victims who participated in the interview, 72% were male, and the median age was 29 years (interquartile range 23.0–40.0 years). The drivers were mostly men (p < 0.001), with a higher binge drinking rate (p = 0.003) and marijuana use (p = 0.005) than seen in pedestrian and passengers. The prevalence of a positive blood alcohol concentration (BAC) ranged from 7.8% among the drivers to 9.2% among the pedestrians (p = 0.861), and the cannabis prevalence was 13.3% among the drivers. The variables associated with an alcohol-related accident were binge drinking in the prior 12 months (OR 2.4; CI 95% 1.1–5.1) and coming from a party/bar (OR 8.7; CI 95% 2.8–26.7). Alcohol abuse or dependence increased by 5.2-fold the chance of another substance-related TC.

Conclusion

The large number of individuals found in TC-related emergency room visits in a short time frame is evidence of the Brazilian epidemic of TC. The data showed that alcohol abuse or dependence also increases the risk of intoxication by other drugs, and they point to alcohol and drug use as a major problem requiring specific TC-related public policies and law enforcement.     

The influence of the dispersion method on the electrical properties of vapor-grown carbon nanofiber/epoxy composites

The influence of the dispersion of vapor-grown carbon nanofibers (VGCNF) on the electrical properties of VGCNF/Epoxy composites has been studied. A homogenous dispersion of the VGCNF does not imply better electrical properties. In fact, it is demonstrated that the most simple of the tested dispersion methods results in higher conductivity, since the presence of well-distributed nanofiber clusters appears to be a key factor for increasing composite conductivity.PACS: 72.80.Tm; 73.63.Fg; 81.05.Qk.