An Automated Single Interface Flow System for the Spectrophotometric Determination of Ethanol in Beverages Based on Schlieren Effect

A new and expeditious automated procedure for the determination of ethanol content in alcoholic beverages (distilled liquors and wine) by using a single interface flow analysis (SIFA) system with spectrophotometric detection is presented. The developed approach was simply based on refractive index gradient measurements, known as Schlieren effect, produced when acidified beverages samples were introduced in a single-line SIFA system that used deionized water as carrier (trailing) stream and a computer-controlled spectrophotometer as detector. In the implemented SIFA system, no chromogenic reagent is used, being the refractive index gradient a consequence of the mutual interdispersion of sample and carrier zones at the formed single interface, and no predefined volumes of both sample and carrier are required, which greatly simplifies system operation and optimization. The proposed methodology allowed the determination of ethanol concentrations between 4% and 24% (v/v) with good precision (RSD < 5.2%, n = 3) and a sampling frequency of about 58 h⁻¹ with consumption of 400 μl of sulfuric acid 0.5 mol l⁻¹ and 400 μl of sample per measurement. The developed procedure was applied to whisky, gin, rum, vodka, the sugar cane-based beverage “cacha?a,” and white wine, and the obtained results were in a good agreement with those provided by the reference method (RD < 5.0%). Recovery values acquired in the analysis of spiked ethanol samples (between 90.0% and 111.0% of the added amount) were also satisfactory.     

The greatest co-authorships of finance theory literature (1896–2006): scientometrics based on complex networks

Scientometrics - We use some modern scientometrics tools to detect which articles written in co-authorship are the most influential in the finance theory literature from 1896 to 2006. To develop a...     

Virtual reality software package for implementing motor learning and rehabilitation experiments

Virtual reality games for rehabilitation are attracting increasing growth. In particular, there is a demand for games that allow therapists to identify an individual’s difficulties and customize the control of variables, such as speed, size, distance, as well as visual and auditory feedback. This study presents and describes a virtual reality software package (Bridge Games) to promote rehabilitation of individuals living with disabilities and highlights preliminary researches of its use for implementing motor learning and rehabilitation. First, the study presents seven games in the software package that can be chosen by the rehabilitation team, considering the patient’s needs. All game characteristics are described including name, function presentation, objective and valuable measurements for rehabilitation. Second, preliminary results illustrate some applications of two games, considering 343 people with various disabilities and health status. Based on the results, in the Coincident Timing game, there was a main effect of movement sensor type (in this instance the most functional device was the keyboard when compared with Kinect and touch screen) on average time reached by sample analyzed, F(2, 225) = 4.42, p < 0.05. Similarly, in the Challenge! game, a main effect was found for movement sensor type. However, in this case, touch screen provided better performance than Kinect and Leap Motion, F(2, 709) = 5.90, p < 0.01. Thus, Bridge Games is a possible software game to quantify motor learning. Moreover, the findings suggest that motor skills might be practiced differently depending on the environmental interface in which the game may be used.     

An efficient Neuro-Fuzzy approach to nuclear power plant transient identification

Transient identification in nuclear power plants (NPP) is often a computational very hard task and may involve a great amount of human cognition. The early identification of unexpected departures from steady state behavior is an essential step for the operation, control and accident management in NPPs. The bases for the transient identification relay on the evidence that different system faults and anomalies lead to different pattern evolution in the involved process variables. During an abnormal event, the operator must monitor a great amount of information from the instruments that represents a specific type of event. Recently, several works have been developed for transient identification. These works frequently present a non reliable response, using the “don´t know” as the system output. In this work, we investigate the possibility of using a Neuro-Fuzzy modeling tool for efficient transient identification, aiming to helping the operator crew to take decisions relative to the procedure to be followed in situations of accidents/transients at NPPs. The proposed system uses artificial neural networks (ANN) as first level transient diagnostic. After the ANN has done the preliminary transient type identification, a fuzzy-logic system analyzes the results emitting reliability degree of it. A validation of this identification system was made at the three loops Pressurized Water Reactor (PWR) simulator of the Human-System Interface Laboratory (LABIHS) of the Nuclear Engineering Institute (IEN/CNEN/Brazil). The obtained results show the potential of this new transient identification system to be used in an operational NPP in order to assist the operators to take decisions during transients/accidents.     

The Use of Ultrasonic Echo Pulse Velocity as an NDT Method to Predict the Concrete Strength and Uniformity

Russian Journal of Nondestructive Testing - The ultrasonic approach is one of the non-destructive testing methods that most technologically progressed in past few years. This study aims to validate...