Is the iPhone an accurate and useful tool for the monitoring of spinal deformity?

The progression of spinal deformity is traditionally monitored by spinal surgeons using the Cobb method on hardcopy radiographs with a protractor and pencil. The rotation of the spine and ribcage (rib hump) in scoliosis is measured with a simple hand-held inclinometer (Scoliometer). The iPhone and other smart phones have the capability to accurately sense inclination, and can therefore be used to measure Cobb angles and rib hump angulation. The purpose of this study was to quantify the performance of the iPhone compared to a standard protractor for measuring Cobb angles and the Scoliometer for measuring rib humps. The study concluded that the iPhone is a clinically equivalent measuring tool to the traditional protractor and Scoliometer

Mathematical model and rule extraction for tool wear monitoring problem using nature inspired techniques

In this paper, pattern classification problem in tool wear monitoring is solved using nature inspired techniques such as Genetic Programming(GP) and Ant-Miner (AM). The main advantage of GP and AM is their ability to learn the underlying data relationships and express them in the form of mathematical equation or simple rules. The extraction of knowledge from the training data set using GP and AM are in the form of Genetic Programming Classifier Expression (GPCE) and rules respectively. The GPCE and AM extracted rules are then applied to set of data in the testing/validation set to obtain the classification accuracy. A major attraction in GP evolved GPCE and AM based classification is the possibility of obtaining an expert system like rules that can be directly applied subsequently by the user in his/her application. The performance of the data classification using GP and AM is as good as the classification accuracy obtained in the earlier study.

Tool wear monitoring using ant behaviour

In this paper we show the applicability of Ant Colony Optimisation (ACO) techniques for pattern classification problem that arises in tool wear monitoring. In an earlier study, artificial neural networks and genetic programming have been successfully applied to tool wear monitoring problem. ACO is a recent addition to evolutionary computation technique that has gained attention for its ability to extract the underlying data relationships and express them in form of simple rules. Rules are extracted for data classification using training set of data points. These rules are then applied to set of data in the testing/validation set to obtain the classification accuracy. A major attraction in ACO based classification is the possibility of obtaining an expert system like rules that can be directly applied subsequently by the user in his/her application. The classification accuracy obtained in ACO based approach is as good as obtained in other biologically inspired techniques.

Space-time monitoring of prescribed burnt soils performance – an effective tool for forest management

Among the most important measures to prevent wild forest fires is the use of prescribed and controlled burning actions in order to reduce the availability of fuel mass. However, the impact of these activities on soil physical and chemical properties varies according to the type of both soil and vegetation and is not fully understood. Therefore, soil monitoring campaigns are often used to measure these impacts. In this paper we have successfully used three statistical data treatments - the Kolmogorov-Smirnov test followed by the ANOVA and the Kruskall-Wallis tests – to investigate the variability among the soil pH, soil moisture, soil organic matter and soil iron variables for different monitoring times and sampling procedures.

Predicting origins of passerines migrating through Canadian migration monitoring stations using stable-hydrogen isotope analyses of feathers: a new tool for bird conservation

The Canadian Migration Monitoring Network (CMMN) consists of standardized observation and migration count stations located largely along Canada’s southern border. A major purpose of CMMN is to detect population trends of migratory passerines that breed primarily in the boreal forest and are otherwise poorly monitored by the North American Breeding Bird Survey (BBS). A primary limitation of this approach to monitoring is that it is currently not clear which geographic regions of the boreal forest are represented by the trends generated for each bird species at each station or group of stations. Such information on “catchment areas” for CMMN will greatly enhance their value in contributing to understanding causes of population trends, as well as facilitating joint trend analysis for stations with similar catchments. It is now well established that naturally occurring concentrations of deuterium in feathers grown in North America can provide information on their approximate geographic origins, especially latitude. We used stable hydrogen isotope analyses of feathers (δ²Hf) from 15 species intercepted at 22 CMMN stations to assign approximate origins to populations moving through stations or groups of stations. We further constrained the potential catchment areas using prior information on potential longitudinal origins based upon bird migration trajectories predicted from band recovery data and known breeding distributions. We detected several cases of differences in catchment area of species passing through sites, and between seasons within species. We discuss the importance of our findings, and future directions for using this approach to assist conservation of migratory birds at continental scales.

AC Biosusceptometry as a Tool for Monitoring the Gastrointestinal Transit of Multiparticulate Drug Delivery System

Drug delivery systems based on natural polysaccharides, such as chitosan (CS) and pectin (PC), rather than on synthetic polymers, have been widely studied. Some reasons for that are low toxicity and costs and high biodegradability of the formers. A multiparticulate system based on CS and PC was developed in our laboratories, including the addition of an enteric polymer, cellulose acetate phtalate (CAP). Such improvement promoted stronger gastric and enteric resistances, as assessed in vitro, making the systems more selective to enzymatic degradation in the colon. Although in vitro dissolution tests can simulate some properties concerning the gastrointestinal transit (GT), collaborating to characterize the systems behavior in the biological fluids, frequently they do not result in satisfactory in vitro/in vivo correlations. The objective of this work was to follow in vivo the GT of the particles developed by means of AC biosusceptometry (ACB), a non-invasive and of low cost methodology. The particles containing ferrite in powder form were prepared by complex coacervation using an ideal 3:1:1 mass ratio for PC:CS:CAP. The magnetic particles were administered to healthy volunteers by oral route. The GT was monitored by using multi-sensor ACB system and the signal acquisition was performed every IS min until the colonic region was reached. By means of ACB technique, it was possible to acquiring images generated by the magnetic particles within the whole gastrointestinal tract including the colonic region. Variable particles transit times were observed among the volunteers, but without interference on the mapping of the particles until the colonic region. The particles were able to produce magnetic field strong enough to generate signals adequate for mapping the particles. The results suggest that integral particles reached the colon, after they resisted against gastric and enteric media. Studies associating transit time and in vivo drug release are in development in order to confirm the efficiency of the systems.

Non-destructive evaluation tool for monitoring and detection of structural damage by using neural network.

This work studies the capability of generalization of Neural Network using vibration based measurement data aiming at operating condition and health monitoring of mechanical systems. The procedure uses the backpropagation algorithm to classify the input patters of a system with different stiffness ratios. It has been investigated a large set of input data, containing various stiffness ratios as well as a reduced set containing only the extreme ones in order to study generalizing capability of the network. This allows to definition of Neural Networks capable to use a reduced set of data during the training phase. Once it is successfully trained, it could identify intermediate failure condition. Several conditions and intensities of damages have been studied by using numerical data. The Neural Network demonstrated a good capacity of generalization for all case. Finally, the proposal was tested with experimental data.

The ACB technique: a biomagentic tool for monitoring gastrointestinal contraction directly from smooth muscle in dogs

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Specialist tool for monitoring the measurement degradation process of induction active energy meters

This paper presents a methodology and a specialist tool for failure probability analysis of induction type watt-hour meters, considering the main variables related to their measurement degradation processes. The database of the metering park of a distribution company, named Elektro Electricity and Services Co., was used for determining the most relevant variables and to feed the data in the software. The modeling developed to calculate the watt-hour meters probability of failure was implemented in a tool through a user friendly platform, written in Delphi language. Among the main features of this tool are: analysis of probability of failure by risk range; geographical localization of the meters in the metering park, and automatic sampling of induction type watt-hour meters, based on a risk classification expert system, in order to obtain information to aid the management of these meters. The main goals of the specialist tool are following and managing the measurement degradation, maintenance and replacement processes for induction watt-hour meters. © 2011 IEEE.

Defining a fish bio-assessment tool to monitoring the biological condition of a cascade reservoirs system in tropical area

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Tool Condition Monitoring of Single-Point Dresser Using Acoustic Emission and Neural Networks Models

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Rapid Assessment of Seed Removal and Frugivore Activity as a Tool for Monitoring the Health Status of Tropical Forests

Ecological processes in tropical forests are being affected at unprecedented rates by human activities. Yet, the continuity of ecological functions like seed dispersal is crucial for forest regeneration. It thus becomes increasingly urgent to be able to rapidly assess the health status of these processes in order to take appropriate management measures. We tested a method to rapidly evaluate seed removal rates on two animal-dispersed tree species, Virola kwatae and V.michelii (Myristicaceae), at three sites in French Guiana with increasing levels of anthropogenic disturbance. We counted fallen fruits, fruit valves, and seeds of each focal fruiting tree in a single 1m2 quadrat, and calculated two indices: the proportion of seeds removed and the proportion of fruits opened by mammals. They both provide an indirect and rapid assessment of frugivore activity. Our results showed a significant decrease in the proportion of removed seeds (16%) and fruits opened (19%) at the most impacted site in comparison with the other two sites (79% for seeds, 60% and 35% for fruits). This testifies to an increased impoverishment of the primate and toucan communities at the disturbed sites. This standardized protocol provides fast information about the health status of the community of seed dispersers and predators and of their seed removal services. It is time- and cost-effective and is not species-specific, allowing comparisons among sites or over time. We suggest using it with the pantropical Myristicaceae and any other capsule-producing family to rapidly assess the health status of seed removal processes across the tropics.

Tool condition monitoring of aluminum oxide grinding wheel in dressing operation using acoustic emission and neural networks

The grinding operation gives workpieces their final finish, minimizing surface roughness through the interaction between the abrasive grains of a tool (grinding wheel) and the workpiece. However, excessive grinding wheel wear due to friction renders the tool unsuitable for further use, thus requiring the dressing operation to remove and/or sharpen the cutting edges of the worn grains to render them reusable. The purpose of this study was to monitor the dressing operation using the acoustic emission (AE) signal and statistics derived from this signal, classifying the grinding wheel as sharp or dull by means of artificial neural networks. An aluminum oxide wheel installed on a surface grinding machine, a signal acquisition system, and a single-point dresser were used in the experiments. Tests were performed varying overlap ratios and dressing depths. The root mean square values and two additional statistics were calculated based on the raw AE data. A multilayer perceptron neural network was used with the Levenberg-Marquardt learning algorithm, whose inputs were the aforementioned statistics. The results indicate that this method was successful in classifying the conditions of the grinding wheel in the dressing process, identifying the tool as "sharp''(with cutting capacity) or "dull''(with loss of cutting capacity), thus reducing the time and cost of the operation and minimizing excessive removal of abrasive material from the grinding wheel.