Life with a hemodialysis machine

The aim of the present study was to describe the experience of patients undergoing haemodialysis starting from their own perception. A qualitative perspective using Merleau Ponty's Existential Phenomenology was considered to be the most appropriate methodology for this study. Fifteen patients were interviewed in a haemodialysis unit at a Brazilian teaching hospital. Interviews were based on the question 'What does the experience of living with a haemodialysis machine mean?' Convergences in speeches were grouped into three categories: the machine, improvement in quality of life, reflection on patients' experience. These findings show the existential reality patients experience. A haemodialysis machine dictates their lives: they have to accept strict rules controlled by a team of healthcare providers. They realize it has to be so and there is no way out. It is the only way to get some relief from the symptoms of the disease. The feeling is mostly acceptance of the condition. Healthcare providers' dedication is recognized. Some participants complain bout painful procedures, others deny them, others fantasize the reality. An essential piece of information is the lack of future perspectives; few patients mentioned the possibility of a transplant or the possibility of carrying out their own care. The study may contribute in outlining new perspectives for nurses to understand the needs of patients undergoing haemodialysis. An approach accepting patients' views will probably bring awareness to patients as to the possibilities of helping with their own treatment.

Bond strength of a resin cement to high-alumina and zirconia-reinforced ceramics: The effect of surface conditioning

Purpose: The aim of this study was to evaluate the effect of two surface conditioning methods on the microtensile bond strength of a resin cement to three high-strength core ceramics: high alumina-based (In-Ceram Alumina, Procera AllCeram) and zirconia-reinforced alumina-based (In-Ceram Zirconia) ceramics. Materials and Methods: Ten blocks (5 ×6 × 8 mm) of In-Ceram Alumina (AL), In-Ceram Zirconia (ZR), and Procera (PR) ceramics were fabricated according to each manufacturer's instructions and duplicated in composite. The specimens were assigned to one of the two following treatment conditions: (1) airborne particle abrasion with 110-μm Al2O3 particles + silanization, (2) silica coating with 30 μm SiOx particles (CoJet, 3M ESPE) + silanization. Each ceramic block was duplicated in composite resin (W3D-Master, Wilcos, Petrópolis, RJ, Brazil) using a mold made out of silicon impression material. Composite resin layers were incrementally condensed into the mold to fill up the mold and each layer was light polymerized for 40 s. The composite blocks were bonded to the surface-conditioned ceramic blocks using a resin cement system (Panavia F, Kuraray, Okayama, Japan). One composite resin block was fabricated for each ceramic block. The ceramic-composite was stored at 37°C in distilled water for 7 days prior to bond tests. The blocks were cut under water cooling to produce bar specimens (n = 30) with a bonding area of approximately 0.6 mm2. The bond strength tests were performed in a universal testing machine (crosshead speed: 1 mm/min). Bond strength values were statistically analyzed using two-way ANOVA and Tukey's test (≤ 0.05). Results: Silica coating with silanization increased the bond strength significantly for all three high-strength ceramics (18.5 to 31.2 MPa) compared to that of airborne particle abrasion with 110-μm Al2O3 (12.7-17.3 MPa) (ANOVA, p < 0.05). PR exhibited the lowest bond strengths after both Al2O3 and silica coating (12.7 and 18.5 MPa, respectively). Conclusion: Conditioning the high-strength ceramic surfaces with silica coating and silanization provided higher bond strengths of the resin cement than with airborne particle abrasion with 110-μm Al2O3 and silanization.

Shear bond strength fatigue limit of rest seats made with dental restoratives

Purpose: This study compared the shear bond strength (SBS) to enamel of rest seats made with a glass-ionomer cement (Fuji IX GP Fast), a resin-modified glass-ionomer cement (Fuji II LC), and a composite resin (Z100 MP) under monotonic and cyclic loading. Materials and Methods: Rest seats were built up onto the lingual surfaces of 80 intact human mandibular incisors. Specimens (n=10) were stored in distilled water at 37°C for 30 days and subjected to shear forces in a universal testing machine (0.5 mm/min) until fracture. The SBS values were calculated (MPa) using the bonding area (9.62 mm2) delimited by adhesive tags. A staircase approach was used to determine the SBS fatigue limit of each material. Specimens were submitted to either 10,000 cycles (5 Hz) or until specimen fracture. A minimum of 15 specimens was tested for each material. Scanning electron microscopy was used to examine the mode of failure. Data were statistically analyzed with one-way ANOVA and Tukey HSD tests (α = 0.05). Results: Z100 MP yielded higher (p < 0.05) SBS (12.25 MPa) than Fuji IX GP Fast (7.21 MPa). No differences were found between Fuji II LC (10.29 MPa) and the other two materials (p > 0.05). Fuji II LC (6.54 MPa) and Z100 MP (6.26 MPa) had a similar SBS limit. Fuji IX GP Fast promoted the lowest (p < 0.05) SBS fatigue limit (2.33 MPa). All samples showed cohesive failure patterns. Conclusion: Fatigue testing can provide a better means of estimating the performance of rest seats made with dental restoratives.

Diametral tensile strength of dual-curing resin cements submitted exclusively to autopolymerization.

OBJECTIVES: To evaluate, at different times, the diametral tensile strength (DTS) of dual-curing resin cements that were not photopolymerized. METHOD AND MATERIALS: Equal amounts of base and catalyst pastes of Panavia F (Kuraray), Variolink II (Vivadent), Rely X (3M ESPE), and Enforce (Dentsply) were mixed and inserted into cylindrical molds (4 x 2 mm) (n = 10). Cements were not photopolymerized. DTS test was performed in a testing machine at 30 minutes, 1 hour, 24 hours, and 7 days. The specimens were stored in light-proof containers with distilled water at 37 degrees C until the time of assay. An autopolymerizing resin cement (Cement-It, Jeneric Pentron) and a zinc phosphate cement served as controls. One-way analysis of variance (ANOVA) and Tukey test were performed separately for each cement and for each time (P <.05). RESULTS: All cements showed an increase in DTS when tested at 1 and 24 hours. Tests at 24 hours and 7 days revealed no statistically significant differences. In all groups, the zinc phosphate cement had the lowest DTS mean values (2.1 MPa, 3.6 MPa, 6.5 MPa, and 6.9 MPa), while Cement-It (35.1 MPa, 33.6 MPa, 46.9 MPa, and 46.3 MPa) and Enforce (31.9 MPa, 31.7 MPa, 43.4 MPa, and 47.6 MPa) presented the highest DTS mean values. CONCLUSION: All cements presented maximal strength at 24 hours. The dual-curing resin cements, even when nonphotopolymerized, demonstrated higher DTS than the zinc phosphate cement and similar or lower values than the autopolymerizing resin cement.

Bond strength durability of direct and indirect composite systems following surface conditioning for repair

Purpose: This study evaluated the effect of surface conditioning methods and thermocycling on the bond strength between a resin composite and an indirect composite system in order to test the repair bond strength. Materials and Methods: Eighteen blocks (5 x 5 x 4 mm) of indirect resin composite (Sinfony) were fabricated according to the manufacturer's instructions. The specimens were randomly assigned to one of the following two treatment conditions (9 blocks per treatment): (1) 10% hydrofluoric acid (HF) for 90 s (Dentsply) + silanization, (2) silica coating with 30-Ìm SiOx particles (CoJet) + silanization. After surface conditioning, the bonding agent was applied (Adper Single Bond) and light polymerized. The composite resin (W3D Master) was condensed and polymerized incrementally to form a block. Following storage in distilled water at 37°C for 24 h, the indirect composite/resin blocks were sectioned in two axes (x and y) with a diamond disk under coolant irrigation to obtain nontrimmed specimens (sticks) with approximately 0.6 mm2 of bonding area. Twelve specimens were obtained per block (N = 216, n = 108 sticks). The specimens from each repaired block were again randomly divided into 2 groups and tested either after storage in water for 24 h or thermocycling (6000 cycles, 5°C to 55°C). The microtensile bond strength test was performed in a universal testing machine (crosshead speed: 1 mm/min). The mean bond strengths of the specimens of each block were statistically analyzed using two-way ANOVA (α = 0.05). Results: Both surface conditioning (p = 0.0001) and storage conditions (p = 0.0001) had a significant effect on the results. After 24 h water storage, silica coating and silanization (method 2) showed significantly higher bond strength results (46.4 ± 13.8 MPa) than that of hydrofluoric acid etching and silanization (method 1) (35.8 ± 9.7 MPa) (p < 0.001). After thermocycling, no significant difference was found between the mean bond strengths obtained with method 1 (34.1 ± 8.9 MPa) and method 2 (31.9 ± 7.9 MPa) (p > 0.05). Conclusion: Although after 24 h of testing, silica coating and silanization performed significantly better in resin-resin repair bond strength, both HF acid gel and silica coating followed by silanization revealed comparable bond strength results after thermocycling for 6000 times.

Classification of petroleum well drilling operations using Support Vector Machine (SVM)

During the petroleum well drilling operation many mechanical and hydraulic parameters are monitored by an instrumentation system installed in the rig called a mud-logging system. These sensors, distributed in the rig, monitor different operation parameters such as weight on the hook and drillstring rotation. These measurements are known as mud-logging records and allow the online following of all the drilling process with well monitoring purposes. However, in most of the cases, these data are stored without taking advantage of all their potential. On the other hand, to make use of the mud-logging data, an analysis and interpretationt is required. That is not an easy task because of the large volume of information involved. This paper presents a Support Vector Machine (SVM) used to automatically classify the drilling operation stages through the analysis of some mud-logging parameters. In order to validate the results of SVM technique, it was compared to a classification elaborated by a Petroleum Engineering expert. © 2006 IEEE.

Neural network approach for surface roughness prediction in surface grinding

Several systems are currently tested in order to obtain a feasible and safe method for automation and control of grinding process. This work aims to predict the surface roughness of the parts of SAE 1020 steel ground in a surface grinding machine. Acoustic emission and electrical power signals were acquired by a commercial data acquisition system. The former from a fixed sensor placed near the workpiece and the latter from the electric induction motor that drives the grinding wheel. Both signals were digitally processed through known statistics, which with the depth of cut composed three data sets implemented to the artificial neural networks. The neural network through its mathematical logical system interpreted the signals and successful predicted the workpiece roughness. The results from the neural networks were compared to the roughness values taken from the worpieces, showing high efficiency and applicability on monitoring and controlling the grinding process. Also, a comparison among the three data sets was carried out.

Estimation of electrical machine speed using sensorless technology and neural networks

The use of sensorless technologies is an increasing tendency on industrial drivers for electrical machines. The estimation of electrical and mechanical parameters involved with the electrical machine control is used very frequently in order to avoid measurement of all variables related to this process. The cost reduction may also be considered in industrial drivers, besides the increasing robustness of the system, as an advantage of the use of sensorless technologies. This work proposes the use of a recurrent artificial neural network to estimate the speed of induction motor for sensorless control schemes using one single current sensor. Simulation and experimental results are presented to validate the proposed approach. ©2008 IEEE.

Analysis of electric torque in single machine-infinite bus system using phase shifters

ResumoThe main idea of this work is based on the analysis of the electric torque through the acting of the PS in the power system, provided of a control for the compensation degree (PSC). A linear model of the single machine-infinite bus system is used with a PS installed (SMIB/PS system). The variable that represents the presence of PS in the net is associated to the phase displacement introduced in the terminal voltage of the synchronous machine by PS. For the input signals of the PSC are evaluated variations of the angular speed of the rotor, the current magnitude and the active power through the line where the PS is located. The simulations are accomplished to analyze the influence of the PS in the torque formation (synchronizing and damping), of the SMIB/PS system. The analysis are developed in the time and frequency domain.

Precipitates segmentation from scanning electron microscope images through machine learning techniques

The presence of precipitates in metallic materials affects its durability, resistance and mechanical properties. Hence, its automatic identification by image processing and machine learning techniques may lead to reliable and efficient assessments on the materials. In this paper, we introduce four widely used supervised pattern recognition techniques to accomplish metallic precipitates segmentation in scanning electron microscope images from dissimilar welding on a Hastelloy C-276 alloy: Support Vector Machines, Optimum-Path Forest, Self Organizing Maps and a Bayesian classifier. Experimental results demonstrated that all classifiers achieved similar recognition rates with good results validated by an expert in metallographic image analysis. © 2011 Springer-Verlag Berlin Heidelberg.

Characterization of texture in image of skin lesions by support vector machine

Due to the increased incidence of skin cancer, computational methods based on intelligent approaches have been developed to aid dermatologists in the diagnosis of skin lesions. This paper proposes a method to classify texture in images, since it is an important feature for the successfully identification of skin lesions. For this is defined a feature vector, with the fractal dimension of images through the box-counting method (BCM), which is used with a SVM to classify the texture of the lesions in to non-irregular or irregular. With the proposed solution, we could obtain an accuracy of 72.84%. © 2012 AISTI.

Remote phenology: Applying machine learning to detect phenological patterns in a cerrado savanna

Plant phenology has gained importance in the context of global change research, stimulating the development of new technologies for phenological observation. Digital cameras have been successfully used as multi-channel imaging sensors, providing measures of leaf color change information (RGB channels), or leafing phenological changes in plants. We monitored leaf-changing patterns of a cerrado-savanna vegetation by taken daily digital images. We extract RGB channels from digital images and correlated with phenological changes. Our first goals were: (1) to test if the color change information is able to characterize the phenological pattern of a group of species; and (2) to test if individuals from the same functional group may be automatically identified using digital images. In this paper, we present a machine learning approach to detect phenological patterns in the digital images. Our preliminary results indicate that: (1) extreme hours (morning and afternoon) are the best for identifying plant species; and (2) different plant species present a different behavior with respect to the color change information. Based on those results, we suggest that individuals from the same functional group might be identified using digital images, and introduce a new tool to help phenology experts in the species identification and location on-the-ground. ©2012 IEEE.

The assessment of the quality of sugar using electronic tongue and machine learning algorithms

The correct classification of sugar according to its physico-chemical characteristics directly influences the value of the product and its acceptance by the market. This study shows that using an electronic tongue system along with established techniques of supervised learning leads to the correct classification of sugar samples according to their qualities. In this paper, we offer two new real, public and non-encoded sugar datasets whose attributes were automatically collected using an electronic tongue, with and without pH controlling. Moreover, we compare the performance achieved by several established machine learning methods. Our experiments were diligently designed to ensure statistically sound results and they indicate that k-nearest neighbors method outperforms other evaluated classifiers and, hence, it can be used as a good baseline for further comparison. © 2012 IEEE.

Development of a support vector machine-based navigation system for driving a mobile robot through paths in plantations

The use of mobile robots turns out to be interesting in activities where the action of human specialist is difficult or dangerous. Mobile robots are often used for the exploration in areas of difficult access, such as rescue operations and space missions, to avoid human experts exposition to risky situations. Mobile robots are also used in agriculture for planting tasks as well as for keeping the application of pesticides within minimal amounts to mitigate environmental pollution. In this paper we present the development of a system to control the navigation of an autonomous mobile robot through tracks in plantations. Track images are used to control robot direction by preprocessing them to extract image features. Such features are then submitted to a support vector machine in order to find out the most appropriate route. The overall goal of the project to which this work is connected is to develop a real time robot control system to be embedded into a hardware platform. In this paper we report the software implementation of a support vector machine, which so far presented around 93% accuracy in predicting the appropriate route. © 2012 IEEE.

Effect of different methods of cutting fluid application on turning of a difficult-to-machine steel (SAE EV-8)

In this study, different methods of cutting fluid application are used in turning of a difficult-to-machine steel (SAE EV-8). Initially, a semisynthetic cutting fluid was applied using a conventional method (i.e. overhead flood cooling), minimum quantity of cutting fluid, and pulverization. A lubricant of vegetable oil (minimum quantity of lubricant) was also applied using the minimum quantity method. Thereafter, a cutting fluid jet under high pressure (3.0 MPa) was singly applied in the following regions: chip-tool interface, top surface of the chip (between workpiece and chip) and tool-workpiece contact. Moreover, two other methods were used: an interflow between conventional application and chip-tool interface jet (combined method) and, finally, three jets simultaneously applied. In order to carry out these tests, it was necessary to set up a high-pressure system using a piston pump for generating a cutting fluid jet, a venturi for fluid application (minimum quantity of cutting fluid and minimum quantity of lubricant) and a nozzle for cutting fluid pulverization. The output variables analyzed included tool life, surface roughness, cutting tool temperature, cutting force, chip form, chip compression rate and machined specimen microstructure. Among the results, it can be observed that the tool life increases and the cutting force decreases with the application of cutting fluid jet, mainly when it is directed to the chip-tool interface. Excluding the methods involving jet fluid, the conventional method seems to be more efficient than other methods of low pressure, such as minimum quantity of volume and pulverization, when considering just the cutting tool wear. © 2013 IMechE.