Thermal inactivation kinetics of quality-related enzymes in cauliflower (<Emphasis Type="Italic">Brassica oleracea</Emphasis> var. <Emphasis Type="Italic">botrytis</Emphasis><Emphasis Type="Bold">)</Emphasis>

Thermal inactivation of quality-related enzymes in both cauliflower crude enzyme extracts and fresh tissue samples was studied in temperature range 50–100 °C. For crude enzyme extracts, several parameters, reaction rate constants (k) and activation energy (E _a) as well as decimal reduction time (D) and (z) values, were used to characterize the thermal stability. The rates of inactivation were found to follow first-order inactivation kinetics. Activation energies varied between 101.18 and 208.42 kJ mol⁻¹ with z values of 10.59–24.09 °C. The examined kinetics indicated that lipoxygenase was the most heat resistant followed by peroxidase, polyphenol oxidase, pectin methyl esterase and ascorbic acid oxidase. Furthermore, the obtained results from the blanched fresh tissues indicated that inactivation of lipoxygenase secured disappearing of any other enzyme activities. Therefore, this study recommends using lipoxygenase as an indicator enzyme to optimize the thermal treatments of cauliflower products.     

Bio-driven system-based virtual reality for prosthetic and rehabilitation systems

Current prosthetic and rehabilitation devices, used for those who are limbless or born with congenital defects or required rehabilitation, are difficult to use. The users have problems to adapt to their new hosts or receiving any bio-feedback despite rehabilitation process and retraining, particularly when working with electromyogram (EMG) signals. In characterizing virtual human limbs, as a potential prosthetic device in three dimensions (3D) virtual reality, patients are able to familiarize themselves with their new appendage and its capabilities or can see their movements’ intention in a Virtual Training Environment. This paper presents a virtual reality (VR)-based design and implementation of a below-shoulder 3D human arm capable of 10-class EMG-based motions driven system of biomedical EMG signal. The method considers a signal classification output as potential control stimulus to drive the virtual limb. A hierarchical design methodology is adopted based on anatomical structure to congruent with virtual reality modeling language (VRML) architecture used in order to progressively build the user interface model and its inherent functionality. The resulting simulation is based on a portable, self-contained VRML prototype implementation paired with an instrumental virtual control-select board capable of actuating any combinations of singular or paired kinematic of 10-class EMG motions. The simulation allows for multiple degree-of-freedom profiles as the classes can be activated independently, or in conjunction with others, allowing enhanced arm movement. Provisions for direct classified control inputs are built into the prototype for holistic system construction.     

Real-Time Differential Global Poisoning System Stability and Accuracy Improvement by Utilizing Support Vector Machine

Due to errors, accuracy of Global Positioning System is not so high. Therefore, the Real Time Differential Global Poisoning System (RTDGPS) which is based on the successive transmission message of RTCM protocol, is using in real-time applications. Stability and accuracy of the system, significantly depends to a fast transmission of correction messages. These messages come from the reference station to the user stations and affected by the errors related to each satellite. Receiving correction factors which are transmitted by the reference station are facing with time-lag problem, which can increase the error of the RTDGPS. To overcome this problem, prediction algorithms are used. In this research, support vector machine (SVM) model is used to predict the pseudo range correction. Unfortunately, the practical use of SVM is limited because the quality of SVM models depends on a proper setting of SVM and SVM kernel parameters. Therefore, to determine the main parameters of the SVM, both particle swarm optimization (PSO) and genetic algorithm (GA), as two optimization techniques, are used. The proposed methodology has been implemented by a 6-s predicts time step. Simulations showed that the accuracy of GA–SVM and PSO–SVM are equal to 0.186 and 0.154, respectively.     

A Comparative Study of Various Methods of Bearing Faults Diagnosis Using the Case Western Reserve University Data

Bearing is probably one of the most critical components of rotating machinery. They are employed to guide and support the shafts in rotating machinery. Therefore, any fault in the bearings can lead to losses on the level of production and equipments as well as potentially unsafe. For these reasons, the bearing fault diagnosis has received considerable attention from the research and engineering communities in recent years. The purpose of this study is to review the vibration analysis techniques and to explore their capabilities, advantages, and disadvantage in monitoring rolling element bearings.     

Sensitive and fast response ethanol chemical sensor based on as-grown Gd2O3 nanostructures

Well crystalline gadolinium oxide(Gd2O3) nanostructures were grown by annealing the hydrothermally as-prepared nanostructures without using any template. Microscopic studies of Gd2O3 nanostructures were recorded along the [111] direction due to the clearly resolved interplanar distance d(222)~0.31 nm of the cubic crystal structure Gd2O3. Sensing mechanism of Gd2O3 as efficient electron mediator for the detection of ethanol was explored. As-fabricated sensor demonstrated the high-sensitivity of ~0.266 μAm/M/cm2 with low detection limit(~52.2 μmol/L) and correlation coefficient(r2, 0.618). To the best of our knowledge, this was the first report for the detection of ethanol using as-grown(at 1000 oC) Gd2O3 nanostructures by simple and reliable I-V technique and rapid assessment of the reaction kinetics(in the order of seconds). The low cost of the starting reagents and the simplicity of the synthetic route made it a promising chemical sensor for the detection of various toxic analytes, which are not environmentally safe.     

Commutation modelling and sparks reduction based on coupled circuit method

The commutating machines have a notable effect on the exchanges in brush–commutator contact area, which is particularly obvious when determining the intensity of sparks located on the brush. With time, higher current density at the descending edge promote sparks excitation, which itself increases intensity of the electrical erosion, brush temperature and thus also the wear. So in order to make an analytical study of commutation phenomenon, the coupled circuit method was developed. Therefore, a generalized mathematical model of the commutation, for brush–commutator, is established and can be extended for any other types of commutation on the basis of electromagnetic field (e.g. transformers and phase shift transformer. This model provides a greater efficiency to explain the impact of the electromagnetic fluxes surrounding brush area (or switch), specially for the current transition of the commutation process. Successful commutation is defined as operation in normal service, with no serious damages to the commutator, brushes or switches due to sparking that might require abnormal maintenance. It is recognized that some visible sparking are not evidence of unsuccessful commutation. The recommendation to improve the commutation (to achieve longer brush life) is the implementation of the proposal (slotted brush), which provides a linear and a sweet transition of currents in the coils of commutation. Copyright © 2012 John Wiley & Sons, Ltd.