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PubMedDate not supplied

Conceptualization: knowledge and logic guiding empirical research.

This article analyzes functions and processes of research conceptualization. The most prevailing limitation of nursing research, as represented in its published literature, concerns the relevant use of knowledge and logic for the formulation of that which is to be studied. To that end, the components of the conceptual phase of research, as they develop progressively from the initiating question through the stipulation of the specific purpose of a study, are discussed. For each component emphasis is given to the function served by past knowledge toward the generation of new knowledge.

Concept Formation↗

PubMed2000

Quantum logic gates based on coherent electron transport in quantum wires.

It is shown that the universal set of quantum logic gates can be realized using solid-state quantum bits based on coherent electron transport in quantum wires. The elementary quantum bits are realized with a proper design of two quantum wires coupled through a potential barrier. Numerical simulations show that (a) a proper design of the coupling barrier allows one to realize any one-qbit rotation and (b) Coulomb interaction between two qbits of this kind allows the implementation of the CNOT gate. These systems are based on a mature technology and seem to be integrable with conventional electronics.

Electrons↗

PubMed2000

Hemodynamic management of congestive heart failure by means of a multiple mode rule-based control system using fuzzy logic.

A rule-based system was designed to control the mean arterial pressure (MAP) and the cardiac output (CO) of a patient with congestive heart failure (CHF), using two vasoactive drugs: sodium nitroprusside (SNP) and dopamine (DPM). The controller has three different modes, that engage according to the hemodynamic state. The critical conditions control mode (CCC) determines the initial infusion rates, and continues active if the MAP or the CO fall outside of the defined criticality thresholds: an upper and a lower boundary for the MAP and a lower boundary for the CO. Inside the boundaries the control is performed by noncritical conditions control modes (NCC's), which are fuzzy logic controllers. If the CO is within normal range and the MAP is close to the goal range, then the MAP is driven using only SNP, in a single-input-single-output mode (NCC-SISO). Otherwise the NCC multiple-input-multiple-output is active (NCC-MIMO). The goal values for the controlled variables are defined as a band of 5 mmHg for the MAP and 5 mL/kg/min for the CO, but there is little concern for this application if the CO is too high (i.e., in practical terms the CO only needs to achieve a necessary minimum rate). The NCC-MIMO includes a gain adaptation algorithm to cope with the wide variety in sensitivities to SNP. Supervisory capabilities to ensure adequate drug delivery complete the controller scheme. After extensive testing and tuning on a CHF-hemodynamics nonlinear model, the control system was applied in dog experiments, which led to further enhancements. The results show an adequate control, presenting a fast response to setpoint changes with an acceptable overshoot.

Algorithms↗

PubMed1998

A novel approach to microcalcification detection using fuzzy logic technique.

Breast cancer continues to be a significant public health problem in the United States. Approximately, 182,000 new cases of breast cancer are diagnosed and 46,000 women die of breast cancer each year. Even more disturbing is the fact that one out of eight women in the United States will develop breast cancer at some point during her lifetime. Since the cause of breast cancer remains unknown, primary prevention becomes impossible. Computer-aided mammography is an important and challenging task in automated diagnosis. It has great potential over traditional interpretation of film-screen mammography in terms of efficiency and accuracy. Microcalcifications are the earliest sign of breast carcinomas and their detection is one of the key issues for breast cancer control. In this study, a novel approach to microcalcification detection based on fuzzy logic technique is presented. Microcalcifications are first enhanced based on their brightness and nonuniformity. Then, the irrelevant breast structures are excluded by a curve detector. Finally, microcalcifications are located using an iterative threshold selection method. The shapes of microcalcifications are reconstructed and the isolated pixels are removed by employing the mathematical morphology technique. The essential idea of the proposed approach is to apply a fuzzified image of a mammogram to locate the suspicious regions and to interact the fuzzified image with the original image to preserve fidelity. The major advantage of the proposed method is its ability to detect microcalcifications even in very dense breast mammograms. A series of clinical mammograms are employed to test the proposed algorithm and the performance is evaluated by the free-response receiver operating characteristic curve. The experiments aptly show that the microcalcifications can be accurately detected even in very dense mammograms using the proposed approach.

Breast Neoplasms↗

PubMed1997

Applying fuzzy logic to control cycling movement induced by functional electrical stimulation.

This study examines the design of a rational stimulation pattern for electrical stimulation and a robust closed-loop control scheme to improve cycling system efficacy for subjects with paraplegia. The stimulation patterns were designed by analyzing gravitation potential needed for the cycling movement of the lower limbs against a frictionless cycling ergometer and the response delay of electrically stimulated muscles. To simplify the cycling control system, the stimulation patterns were fixed and only the single gain of the stimulation patterns was adjusted via a feedback control algorithm. To circumvent the complexity involved with exactly modeling a stimulated muscle and cycling ergometer, a model-free fuzzy logic controller (FLC) was adopted herein for our control scheme. Comparison between FLC and conventional proportional-derivative (PD) controllers demonstrated that the FLC with asymmetrical membership function enabled the subject with paraplegia to maintain varied desired cycling speeds, particularly at lower cycling speed. By incorporating the rational stimulation patterns, the FLC can produce a smooth and prolonged cycling movement deemed necessary for designing various training protocols.

Biomedical Engineering↗

PubMed2003

Reduction of interference in oscillometric arterial blood pressure measurement using fuzzy logic.

In oscillometry, oscillation amplitudes (OAs) embedded in the cuff pressure are drastically affected by a variety of artifacts and cardiovascular diseases, leading to inaccurate arterial blood pressure (ABP) measurement. The purpose of this paper is to improve the accuracy in the arterial pressure measurement by reducing interference in the OAs using a recursive weighted regression algorithm (RWRA). This method includes a fuzzy logic discriminator (FLD) and a recursive regression algorithm. The FLD is used to reduce the effect of artifacts caused by measurement motion disturbance or cardiovascular diseases, and to determine the truthfulness of the oscillation pulse. According to the truth degree, the relationship between the cuff pressure and OA is reconstructed using the regression algorithm. Because the regression method must utilize inverse matrix operation, which will be difficult to implement in an automatic or ambulatory monitor, the recursive regression method is proposed to solve this problem. To test the performance of this RWRA, 47 subjects underwent the ABP measurement using both the auscultation and the oscillometry combined with the RWRA. It was found that the average difference between the pooled blood pressures measured by the auscultation and those by the oscillometry combined with the RWRA was found to be only 4.9 mmHg. Clinical results demonstrated that the proposed RWRA is more robust than the traditional curve fitting algorithm (TCFA). We conclude that the proposed RWRA can be applied to effectively improve the accuracy of the oscillometric blood pressure measurement.

Adolescent↗

PubMed2004

Fuzzy logic control of FES rowing exercise in paraplegia.

An indoor personal rowing machine (Concept 2 Inc., Morrisville, VT) has been modified for functional electrical stimulation assisted rowing exercise in paraplegia. To successfully perform the rowing maneuver, the voluntarily controlled upper body movements must be coordinated with the movements of the electrically stimulated paralyzed legs. To achieve such coordination, an automatic controller was developed that employs two levels of hierarchy. A high level finite state controller identifies the state or phase of the rowing motion and activates a low-level state-dedicated fuzzy logic controller (FLC) to deliver the electrical stimulation to the paralyzed leg muscles. A pilot study with participation of two paraplegic volunteers showed that FLC spent less muscle energy, and produced smoother rowing maneuvers than the existing On-Off constant-level stimulation controller.

Aged↗

PubMed2003

A fuzzy logic based-method for prognostic decision making in breast and prostate cancers.

Accurate and reliable decision making in oncological prognosis can help in the planning of suitable surgery and therapy, and generally, improve patient management through the different stages of the disease. In recent years, several prognostic markers have been used as indicators of disease progression in oncology. However, the rapid increase in the discovery of novel prognostic markers resulting from the development in medical technology, has dictated the need for developing reliable methods for extracting clinically significant markers where complex and nonlinear interactions between these markers naturally exist. The aim of this paper is to investigate the fuzzy k-nearest neighbor (FK-NN) classifier as a fuzzy logic method that provides a certainty degree for prognostic decision and assessment of the markers, and to compare it with: 1) logistic regression as a statistical method and 2) multilayer feedforward backpropagation neural networks an artificial neural-network tool, the latter two techniques having been widely used for oncological prognosis. In order to achieve this aim, breast and prostate cancer data sets are considered as benchmarks for this analysis. The overall results obtained indicate that the FK-NN-based method yields the highest predictive accuracy, and that it has produced a more reliable prognostic marker model than the statistical and artificial neural-network-based methods.

Aged↗

PubMed2005

A heuristic fuzzy logic approach to EMG pattern recognition for multifunctional prosthesis control.

This paper presents a heuristic fuzzy logic approach to multiple electromyogram (EMG) pattern recognition for multifunctional prosthesis control. Basic signal statistics (mean and standard deviation) are used for membership function construction, and fuzzy c-means (FCMs) data clustering is used to automate the construction of a simple amplitude-driven inference rule base. The result is a system that is transparent to, and easily "tweaked" by, the prosthetist/clinician. Other algorithms in current literature assume a longer period of unperceivable delay, while the system we present has an update rate of 45.7 ms with little postprocessing time, making it suitable for real-time application. Five subjects were investigated (three with intact limbs, one with a unilateral transradial amputation, and one with a unilateral transradial limb-deficiency from birth). Four subjects were used for system offline analysis, and the remaining intact-limbed subject was used for system real-time analysis. We discriminated between four EMG patterns for subjects with intact limbs, and between three patterns for limb-deficient subjects. Overall classification rates ranged from 94% to 99%. The fuzzy algorithm also demonstrated success in real-time classification, both during steady state motions and motion state transitioning. This functionality allows for seamless control of multiple degrees-of-freedom in a multifunctional prosthesis.

Adult↗

PubMed2005

Modeling gene expression networks using fuzzy logic.

Gene regulatory networks model regulation in living organisms. Fuzzy logic can effectively model gene regulation and interaction to accurately reflect the underlying biology. A new multiscale fuzzy clustering method allows genes to interact between regulatory pathways and across different conditions at different levels of detail. Fuzzy cluster centers can be used to quickly discover causal relationships between groups of coregulated genes. Fuzzy measures weight expert knowledge and help quantify uncertainty about the functions of genes using annotations and the gene ontology database to confirm some of the interactions. The method is illustrated using gene expression data from an experiment on carbohydrate metabolism in the model plant Arabidopsis thaliana. Key gene regulatory relationships were evaluated using information from the gene ontology database. A new regulatory relationship concerning trehalose regulation of carbohydrate metabolism was also discovered in the extracted network.

Animals↗

PubMed1992

On laying the foundations for an empirico-logical model of mental health nursing.

This paper argues that nursing is an elemental activity, the first concern of which is a person's experience of illness, not the causes of that experience. Any model of nursing should begin with an account of that experience and deduce from that what it is that nurses ought to do in order to explain what it is that they actually do. The logical form of such a model is delineated. An account of the experience of illness is offered and the nursing imperatives implied by such an account are stated in terms of securing client safety, obviating the client's experience by treatment and motivating the client to sustain or resume ordinary activities of life. These dimensions of nursing care--safety, treatment and motivation--were tested against the actual activities of mental health nurses in three services and were found to have utility as categories of nursing activity. A summary of that research is given and an outline of a model of mental health nursing suggested by the results is given.

Activities of Daily Living↗

PubMed1995

Fuzzy logic and control: principal approach and potential applications in medicine.

During the last few years intelligent machines appeared in nearly all technical areas, such as consumer electronics, robotics, and industrial control systems. There are for example washing machines that work very effectively, need comparably less power than in the past, and have short execution times because they adjust their washing cycles to each set of clothes and change their washing strategies as the clothes become clean. These intelligent systems are based on fuzzy control strategies, i.e., common sense rules are used to describe a system's behavior instead of complex mathematical models. We have applied this new technology to control problems as well as to reasoning problems in biomedical engineering where appropriate mathematical models could not be built due to the complexity of the problem. After a short introduction to the concepts of fuzzy logic two approaches in the field are described: a fuzzy control strategy for the pump rate adjustment of a novel total artificial heart and an intelligent alarm system based on fuzzy inference which supports the anesthetist in monitoring and evaluating the hemodynamic state of a patient undergoing cardiac surgery. These examples indicate the inherent reliability and stability of this technique in the field of complex dynamic systems. Such properties are highly significant especially in medical applications.

Anesthesia↗

PubMed1975

Diurnal variation in logical reasoning.

Subjects performed two tests of logical reasoning at each of six different times of day. In terms of speed, preformance on both tests was found to improve markedly from 08.00 to 14.00 and then to fall off fairly rapidly. Accuracy was found to decrease fairly linearly over the day. The results are interpreted as indicating that the different functions relating performance efficiency to time of day found by previous workers are due to differences in task demands rather than to individual differences. It is suggested that the larger the short-term memory component of a task the earlier in the day performance peaks.

Achievement↗

PubMed2005

An intelligent control method based on fuzzy logic for a robotic testing system for the human spine.

In previous biomechanical studies of the human spine, we implemented a hybrid controller to investigate load-displacement characteristics. We found that measurement errors in both position and force caused the controller to be less accurate than predicted. As an alternative to hybrid control, a fuzzy logic controller (FLC) has been developed and implemented in a robotic testing system for the human spine. An FLC is a real-time expert system that can emulate part of a human operator's knowledge by using a set of action rules. The FLC provides simple but robust solutions that cover a wide range of system parameters and can cope with significant disturbances. It can be viewed as a heuristic and modular way of defining a nonlinear, table-based control system. In this study, an FLC is developed which uses the force difference and the change in force difference as the input parameters, and the displacement as the output parameter. A rule-table based on these parameters is designed for the controller Experiments on a physical model composed of springs demonstrate the improved performance of the proposed method.

Algorithms↗

PubMed2006

The 0/0 problem in the fuzzy-logical model of perception.

The "Fuzzy-Logical Model of Perception" (FLMP) has often been questioned for its presumed ability to fit any data, but no clear-cut evidence has been presented yet. This paper demonstrates the ability of the FLMP to fit random data in the "McGurk region," that is in conditions involving conflicting stimuli. This is due to the so-called "0/0 problem," consisting in the fact that any audio-visual response can be fitted by the FLMP if the audio and visual stimuli provide at least one null probability in each possible category. The consequence is a high instability of the root mean square error in the region of the best fit.

Acoustic Stimulation↗

PubMed2000

Tests of auditory-visual integration efficiency within the framework of the fuzzy logical model of perception.

The fuzzy logical model of perception [FLMP, Massaro, Perceiving Talking Faces: From Speech Perception to a Behavioral Principle (MIT Press, Cambridge, MA, 1998)] has been extremely successful at describing performance across a wide range of ecological domains as well as for a broad spectrum of individuals. Because the model predicts optimal or maximally efficient integration, an important issue is whether this is the case for most individuals. Three databases are evaluated to determine to what extent a significant quantitative improvement in predictive ability can be obtained if integration is assumed to be somewhat inefficient. For the most part, there were no significant signs of inefficient integration. The previous differences found by Grant and Seitz [J. Acoust. Soc. Am. 104, 2438-2450 (1998)] must be due to their measures of efficiency, which appear to be invalid and/or conflate information with integration efficiency. Finally, the descriptive ability of the FLMP is shown to be theoretically informative and not simply the model's ability to describe any possible outcome.

Humans↗

PubMed2005

Real-time supervisor system based on trinary logic to control experiments with behaving animals and humans.

A new method is presented based on trinary logic able to check the state of different control variables and synchronously record the physiological and behavioral data of behaving animals and humans. The basic information structure of the method is a time interval of defined maximum duration, called time slice, during which the supervisor system periodically checks the status of a specific subset of input channels. An experimental condition is a sequence of time slices subsequently executed according to the final status of the previous time slice. The proposed method implements in its data structure the possibility to branch like an if-else cascade and the possibility to repeat parts of it recursively like the while-loop. Therefore its data structure contains the most basic control structures of programming languages. The method was implemented using a real-time version of LabVIEW programming environment to program and control our experimental setup. Using this supervision system, we synchronously record four analog data channels at 500 Hz (including eye movements) and the time stamps of up to six neurons at 100 kHz. The system reacts with a resolution within 1 ms to changes of state of digital input channels. The system is set to react to changes in eye position with a resolution within 4 ms. The time slices, experimental conditions, and data are handled by relational databases. This facilitates the construction of new experimental conditions and data analysis. The proposed implementation allows continuous recording without an inter-trial gap for data storage or task management. The implementation can be used to drive electrophysiological experiments of behaving animals and psychophysical studies with human subjects.

Animals↗

PubMed2003

Increasing the efficiency of fuzzy logic-based gene expression data analysis.

DNA microarray technology can accommodate a multifaceted analysis of the expression of genes in an organism. The wealth of spatiotemporal data generated by this technology allows researchers to potentially reverse engineer a particular genetic network. "Fuzzy logic" has been proposed as a method to analyze the relationships between genes and help decipher a genetic network. This method can identify interacting genes that fit a known "fuzzy" model of gene interaction by testing all combinations of gene expression profiles. This paper introduces improvements made over previous fuzzy gene regulatory models in terms of computation time and robustness to noise. Improvement in computation time is achieved by using a cluster analysis as a preprocessing method to reduce the total number of gene combinations analyzed. This approach speeds up the algorithm by a factor of 50% with minimal effect on the results. The model's sensitivity to noise is reduced by implementing appropriate methods of "fuzzy rule aggregation" and "conjunction" that produce reliable results in the face of minor changes in model input.

Animals↗