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PubMed1996

First use of cognitive algorithms in investigations under compensated gravity.

In the present paper the use of cognitive algorithms for solving a wide spectrum of problems which often arise in investigations under compensated gravity is suggested. Applying such algorithms in the preparation and performance of experiments provides a substantial assistance to the experimentator as the behaviour of complex processes can be described and predicted correctly even when unexpected perturbations occur. Furthermore, an essential advantage of cognitive computing consists in the fact that the description and optimisation of the processes considered are possible also in such cases in which the corresponding basic equations are not known or not treatable practically. For convenience, the basic ideas of cognitive algorithms are discussed here. Due to their special relevance for investigations under compensated gravity algorithms based on fuzzy logic (FL) and artificial neuronal networks (ANN) are elucidated more in detail. In order to illustrate some advantages of cognitive computing exemplary results for the flow field induced by coaxial rotating disks are given. This represents the first attempt to use the benefits provided by cognitive algorithms in investigations under compensated gravity. The flow field between rotating disks plays an important role not only in experiments under compensated gravity but also in a wide range of terrestrial applications. A comparison of the results found by solving the Navier-Stokes equations and those from the prediction performed by ANN adequately trained shows an excellent agreement. However, the calculation times needed by the ANN are significantly smaller than that of the direct numerical simulation. Therefore, the real time prediction of the results from a running experiment seems to be possible.

Algorithms↗
PubMed1995

Multiple drug hemodynamic control by means of a supervisory-fuzzy rule-based adaptive control system: validation on a model.

A control device that uses an expert system approach for a two input-two output system has been developed and evaluated using a mathematical model of the hemodynamic response of a dog. The two inputs are the infusion rates of two drugs: sodium nitroprusside (SNP) and dopamine (DPM). The two controlled variables are the mean arterial pressure and the cardiac output. The control structure is dual mode, i.e., it has two levels: a critical conditions (coarse) control mode and a noncritical conditions (fine) control mode. The system switches from one to the other when threshold conditions are met. Different "controller parameters sets"-including the values for the threshold conditions-can be given to the system which will lead to different controller outputs. Both control modes are rule-based, and supervisory capabilities are added to ensure adequate drug delivery. The noncritical control mode is a fuzzy logic controller. The system includes heuristic features typically considered by anesthesiologists, like waiting periods and the observance of a "forbidden dosage range" for DPM infusion when used as an inotrope. An adaptation algorithm copes with the wide range of sensitivities to SNP found among different individuals, as well as the time varying sensitivity frequently observed in a single patient. The control device is eventually tested on a nonlinear model, designed to mimic the conditions of congestive heart failure in a dog. The test runs show a highest overshoot of 3 mmHg with nominal SNP sensitivity. When tested with different simulated SNP sensitivities, the controller adaptation produces a faster response to lower sensitivities, and reduced oscillations to higher sensitivities. The simulations seem to show that the system is able to drive and adequately keep the two hemodynamic variables within prescribed limits.

Adaptation, Physiological↗
PubMed2004

A hybrid fuzzy logic/constraint satisfaction problem approach to automatic decision making in simulation game models.

Possible techniques for representing automatic decision-making behavior approximating human experts in complex simulation model experiments are of interest. Here, fuzzy logic (FL) and constraint satisfaction problem (CSP) methods are applied in a hybrid design of automatic decision making in simulation game models. The decision processes of a military headquarters are used as a model for the FL/CSP decision agents choice of variables and rulebases. The hybrid decision agent design is applied in two different types of simulation games to test the general applicability of the design. The first application is a two-sided zero-sum sequential resource allocation game with imperfect information interpreted as an air campaign game. The second example is a network flow stochastic board game designed to capture important aspects of land manoeuvre operations. The proposed design is shown to perform well also in this complex game with a very large (billionsize) action set. Training of the automatic FL/CSP decision agents against selected performance measures is also shown and results are presented together with directions for future research.

Algorithms↗
PubMed2002

Expert system for the on-line diagnosis of anaerobic wastewater treatment plants.

A diagnosis system for anaerobic wastewater treatment processes is presented. The system is able to recognise the state and trend of the operation and suggest the appropriate control action. The on-line variables measured were gas flow rate and composition (methane and carbon monoxide), feed and recycling flow rates, temperature and pH, while the manipulable inputs are feed, recycling and buffer-addition flow rates. The diagnosis system comprises a structured rule base, incorporating expert knowledge using fuzzy logic features. The structure of the system is based on the classification of information related to the process in three categories: i) the state of the process, ii) its trend and iii) the recommended set-point values for the inputs manipulated: feeding, buffer and recycling pumps. The system was applied to diagnose the operation of a 1.1 m3 hybrid UASB-UAF treating wastewater from a fibreboard production factory under different conditions (overload and underload), corresponding to some of the typical causes of destabilisation in anaerobic wastewater treatment plants. These situations require control action in order to maintain the stability and the treatment capacity of the reactor. The application of the system developed for the purpose of managing the situation proved to be reliable for supplying the actual state and trend of the process, as well as the adequate set point values to recover stable operation and/or to avoid further destabilisation.

Bacteria, Anaerobic↗
PubMed1998

An expert system for EEG monitoring in the pediatric intensive care unit.

OBJECTIVES: was to design a warning system for the pediatric intensive care unit (PICU). The system should be able to make statements at regular intervals about the level of abnormality of the EEG. The warnings are aimed at alerting an expert that the EEG may be abnormal and needs to be examined. METHODS: A total of 188 EEG sections lasting 6 h each were obtained from 74 patients in the PICU. Features were extracted from these EEGs, and with the use of fuzzy logic and neural networks, we designed an expert system capable of imitating a trained EEGer in providing an overall judgment of abnormality about the EEG. The 188 sections were used in training and testing the system using the rotation method, thus separating training and testing data. RESULTS: The EEGer and the expert system classified the EEGs in 7 levels of abnormality. There was concordance between the two in 45% of cases. The expert system was within one abnormality level of the EEGer in 91% of cases and within two levels in 97%. CONCLUSIONS: We were therefore able to design a system capable of providing reliably an assessment of the level of abnormality of a 6 h section of EEG. This system was validated with a large data set, and could prove useful as a warning device during long-term ICU monitoring to alert a neurophysiologist that an EEG requires attention.

Algorithms↗
PubMed2003

The role of soft computing in intelligent machines.

An intelligent machine relies on computational intelligence in generating its intelligent behaviour. This requires a knowledge system in which representation and processing of knowledge are central functions. Approximation is a 'soft' concept, and the capability to approximate for the purposes of comparison, pattern recognition, reasoning, and decision making is a manifestation of intelligence. This paper examines the use of soft computing in intelligent machines. Soft computing is an important branch of computational intelligence, where fuzzy logic, probability theory, neural networks, and genetic algorithms are synergistically used to mimic the reasoning and decision making of a human. This paper explores several important characteristics and capabilities of machines that exhibit intelligent behaviour. Approaches that are useful in the development of an intelligent machine are introduced. The paper presents a general structure for an intelligent machine, giving particular emphasis to its primary components, such as sensors, actuators, controllers, and the communication backbone, and their interaction. The role of soft computing within the overall system is discussed. Common techniques and approaches that will be useful in the development of an intelligent machine are introduced, and the main steps in the development of an intelligent machine for practical use are given. An industrial machine, which employs the concepts of soft computing in its operation, is presented, and one aspect of intelligent tuning, which is incorporated into the machine, is illustrated.

Algorithms↗
PubMed1999

Optimal control of FES-assisted standing up in paraplegia using genetic algorithms.

A practical system for Functional Electrical Stimulation (FES) assisted standing up in paraplegia should involve only a minimum of manual set up and tuning. An improved tuning method, using a genetic algorithm (GA) is proposed and demonstrated using computer simulation. Specifically, the GA adjusts the parameters of fuzzy logic (FL) and gain-scheduling proportional integral derivative (GS-PID) controllers that electrically stimulate the hip and knee musculature during the sit-stand maneuver. These new GA designed controllers were found to be effective in coordinating volitional and FES control according to formulated criteria. The latter was based on the deviations from a desired trajectory of the knee and hip joints and the magnitude of the voluntary upper body forces. The magnitude of the average arm forces were slightly higher when compared with the open-loop maximal stimulation of the hip and knee musculature; however, the terminal knee velocities were significantly reduced to less than 10 degrees /s. For practical implementation, the number of trials required to optimize the FL and GS-PID controllers can be reduced by a proposed pre-training procedure using a computer model scaled to the individual. The GA designed controllers remain near optimal provided the model-subject mismatch is small.

Algorithms↗
PubMed2006

Neuro-fuzzy modeling: an accurate and interpretable method for predicting bladder cancer progression.

PURPOSE: New methods are required to improve the prediction of cancer progression as traditional statistical tests have limited accuracy. Accurate predictions would allow physicians to offer specific treatment according to individual patient risk. While predictive improvements are obtained using ANN, the hidden nature of these networks prevents insight and has hindered their widespread implementation. NFM is an alternate form of artificial intelligence using fuzzy logic (which is a multivalued logic which provides reasoning under uncertainty). By defuzzification the NFM rule base becomes transparent to overcome the black box nature of ANN. MATERIALS AND METHODS: Combinations of clinicopathological (tumor stage and grade, patient age, gender, and smoking status) and molecular (immunohistochemical expression of p53 and methylation status of 11 loci) data from 117 patients were used to develop and compare predictive models of tumor progression using NFM, ANN and LR. RESULTS: NFM (88% to 100% sensitivity, 97% to 100% specificity and 94% to 100% accuracy) predicted the presence and timing of cancer progression more accurately than ANN (81% to 87%, 95% to 100% and 89% to 90%, p = 0.002) and LR 3%, 61% to 72% and 47% to 53%, p = 0.00005). NFM was able to interrogate the clinicopathological and molecular data, and select the most important parameters (age, grade, stage, smoking, methylation) for progression prediction. CONCLUSIONS: Intelligent systems and molecular biomarkers improved the accuracy of cancer progression predictions. NFM appeared superior to ANN in terms of accuracy, sensitivity, specificity and transparency. The use of NFM in routine clinical practice warrants further validation.

Aged↗
PubMed2006

Temporal representation and reasoning in medicine: Research directions and challenges.

OBJECTIVE: The main aim of this paper is to propose and discuss promising directions of research in the field of temporal representation and reasoning in medicine, taking into account the recent scientific literature and challenging issues of current interest as viewed from the different research perspectives of the authors of the paper. BACKGROUND: Temporal representation and reasoning in medicine is a well-known field of research in the medical as well as computer science community. It encompasses several topics, such as summarizing data from temporal clinical databases, reasoning on temporal clinical data for therapeutic assessments, and modeling uncertainty in clinical knowledge and data. It is also related to several medical tasks, such as monitoring intensive care patients, providing treatments for chronic patients, as well as planning and scheduling clinical routine activities within complex healthcare organizations. METHODOLOGY: The authors jointly identified significant research areas based on their importance as for temporal representation and reasoning issues; the subjects were considered to be promising topics of future activity. Every subject was addressed in detail by one or two authors and then discussed with the entire team to achieve a consensus about future fields of research. RESULTS: We identified and focused on four research areas, namely (i) fuzzy logic, time, and medicine, (ii) temporal reasoning and data mining, (iii) health information systems, business processes, and time, and (iv) temporal clinical databases. For every area, we first highlighted a few basic notions that would permit any reader--including those who are unfamiliar with the topic--to understand the main goals. We then discuss interesting and promising directions of research, taking into account the recent literature and underlining the yet unresolved medical/clinical issues that deserve further scientific investigation. The considered research areas are by no means disjointed, because they share common theoretical and methodological features. Moreover, subjects of imminent interest in medicine are represented in many of the fields considered. CONCLUSIONS: We propose and discuss promising subjects of future research that deserve investigation to develop software systems that will properly manage the multifaceted temporal aspects of information and knowledge encountered by physicians during their clinical work. As the subjects of research have resulted from merging the different perspectives of the authors involved in this study, we hope the paper will succeed in stimulating discussion and multidisciplinary work in the described fields of research.

Artificial Intelligence↗
PubMed2003

Automatic identification of motor unit action potential trains from electromyographic signals using fuzzy techniques.

A technique is proposed that allows automatic decomposition of electromyographic (EMG) signals into their constituent motor unit action potential trains (MUAPTs). A specific iterative algorithm with a classification method using fuzzy-logic techniques was developed. The proposed classification method takes into account imprecise information, such as waveform instability and irregular firing patterns, that is often encountered in EMG signals. Classification features were determined by the combining of time position and waveform information. Statistical analysis of inter-pulse intervals and spike amplitude provided an accurate estimation of features used in the classification step. Algorithm performance was evaluated using simulated EMG signals composed of up to six different discharging motor units corrupted with white noise. The algorithm was then applied to real signals recorded by a high spatial resolution surface EMG device based on a Laplacian spatial filter. On six groups of 20 simulated signals, the decomposition algorithm performed with a maximum and an average mean error rate of 2.13% and 1.37%, respectively. On real surface EMG signals recorded at different force levels (from 10% to 40% of the maximum voluntary contraction), the algorithm correctly identified 21 MUAPTs, compared with the 29 MUAPTs identified by an experienced neurophysiologist. The efficiency of the decomposition on surface EMG signals makes this method very attractive for non-invasive investigation of physiological muscle properties. However, it can also be used to decompose intramuscularly recorded EMG signals.

Action Potentials↗
PubMed2005

Sequence signatures and the probabilistic identification of proteins in the Myc-Max-Mad network.

Accurate identification of specific groups of proteins by their amino acid sequence is an important goal in genome research. Here we combine information theory with fuzzy logic search procedures to identify sequence signatures or predictive motifs for members of the Myc-Max-Mad transcription factor network. Myc is a well known oncoprotein, and this family is involved in cell proliferation, apoptosis, and differentiation. We describe a small set of amino acid sites from the N-terminal portion of the basic helix-loop-helix (bHLH) domain that provide very accurate sequence signatures for the Myc-Max-Mad transcription factor network and three of its member proteins. A predictive motif involving 28 contiguous bHLH sequence elements found 337 network proteins in the GenBank NR database with no mismatches or misidentifications. This motif also identifies at least one previously unknown fungal protein with strong affinity to the Myc-Max-Mad network. Another motif found 96% of known Myc protein sequences with only a single mismatch, including sequences from genomes previously not thought to contain Myc proteins. The predictive motif for Myc is very similar to the ancestral sequence for the Myc group estimated from phylogenetic analyses. Based on available crystal structure studies, this motif is discussed in terms of its functional consequences. Our results provide insight into evolutionary diversification of DNA binding and dimerization in a well characterized family of regulatory proteins and provide a method of identifying signature motifs in protein families.

Amino Acid Sequence↗
PubMed2000

A new dose model for assessment of health risk due to contaminants in air.

The problem of making quantitative assessments of the risks associated with human exposure to toxic contaminants in the environment is a pressing one. This study demonstrates the capability of a new computational technique involving the use of fuzzy logic and neural networks to produce realistic risk assessments. The systematic analysis of human exposure involves the use of measurements and models, the results of which are sometimes used in regulatory decisions or in the drafting of legislation. Because of limited scientific understanding, however, interpretation of models often involves substantial uncertainty. Extensive measurement programs can be very expensive. The high complexity and inherent heterogeneity of exposure analysis is still a major challenge. The approach to this challenge tested here is to use a new model incorporating sophisticated artificial intelligence algorithms. Exposure assessment often requires that a number of factors be evaluated, including exposure concentrations, intake rates, exposure times, and frequencies. These factors are incorporated into a system that can "learn" the relevant relationships based on a known data set. The results can then be applied to new data sets and thus be applied widely without the need for extensive measurements. In this analysis, an example is developed for human health risk through inhalation exposure to benzene from vehicular emissions in the cities of Auckland and Christchurch, New Zealand. Risk factors considered were inhaled contaminant concentration, age, body weight, and activity patterns of humans. Three major variables affecting the inhaled contaminant concentration were emissions (mainly from motor vehicles), meteorology (wind speed, temperature, and atmospheric stability), and site factors (hilly, flat, etc.). The results are preliminary and used principally to demonstrate the technique, but they are very encouraging.

Age Factors↗
PubMed1999

Adaptive fuzzy control of electrically stimulated muscles for arm movements.

A modified adaptive Takagi-Sugeno (TS) fuzzy logic controller (FLC) is proposed that allows a simulated elbow-like biomechanical system to accurately track sigmoidal and sinusoidal trajectories in the sagittal plane. The work is a first effort towards the implementation of a system to restore elbow movements in quadriplegics using functional neuromuscular stimulation. The single-joint musculo-skeletal system is composed of a co-contractable pair of electrically stimulated muscles; the muscle model accounts for the increase in fatigue during the tracking exercise. In the proposed controller structure, a reinforcement learning scheme is used to accomplish the parameter tuning, and the parameter projection algorithm guarantees the system stability during the adaptation process. The controller performance is evaluated using computer simulation experiments and compared with the performance achievable when a standard proportional-integrative-derivative (PID) controller is employed for the same application. The modified adaptive TSFLC outperforms the PID controller in all tested situations, with a clear-cut advantage in the case of high-frequency sinusoidal trajectories (angular frequencies spanning the interval 8-12 rad s-1). The standard controller suffers from a dramatic increase in root mean square (RMS) tracking error above the value at 8 rad s-1, e.g. ERMS > or = 0.013, whereas the correlation coefficient between the actual and desired trajectory falls almost to zero, starting from the value rho approximately equal to 0.97 at 8 rad s-1. On the other hand, the adaptive TSFLC yields ERMS < or = 0.015, with rho > or = 0.78, over the whole range of tested angular frequencies.

Arm↗
PubMed2006

Cerebral state index during propofol anesthesia: a comparison with the bispectral index and the A-line ARX index.

BACKGROUND: The objective of this study was to prospectively test the Cerebral State Index designed for measuring the depth of anesthesia. The Cerebral State Index is calculated using a fuzzy logic combination of four subparameters of the electroencephalographic signal. The performance of the Cerebral State Index was compared with that of the Bispectral Index and the A-Line ARX Index. METHODS: This study applied raw data from two previously published clinical protocols. The patients in protocol 1 were given a continuous propofol infusion, 300 ml/h, until 80% of burst suppression occurred. In protocol 2, a stepwise increased target-controlled infusion of propofol was administered to patients until loss of response to noxious stimuli while the Observer's Assessment of Alertness and Sedation was registered every 4 min. The Cerebral State Index was calculated off-line from the recorded electroencephalographic data. The Spearman rank correlation coefficient between electronic indices and the effect site concentration of propofol was calculated along with the prediction probability of each index to predict the Observer's Assessment of Alertness and Sedation level. RESULTS: The Spearman rank correlation coefficients between the Cerebral State Index, Bispectral Index, and A-Line ARX Index and the propofol effect site concentration were -0.94, -0.89, and -0.82, respectively, in protocol 1, whereas the prediction probability values between the Cerebral State Index, Bispectral Index, and A-Line ARX Index and the Observer's Assessment of Alertness and Sedation score in protocol 2 were 0.92, 0.93, and 0.91, respectively. CONCLUSION: The Cerebral State Index detects well the graduated levels of propofol anesthesia when compared with the propofol effect site concentration and the Observer's Assessment of Alertness and Sedation score.

Adolescent↗
PubMed2005

Genetic fuzzy modelling and control of bispectral index (BIS) for general intravenous anaesthesia.

Based on an adaptive genetic fuzzy clustering algorithm, a derived fuzzy knowledge model is proposed for quantitatively estimating the systolic arterial pressure (SAP), heart rate (HR), and bispectral index (BIS) using 12 patients and it validates them according to pharmacological reasoning. Also, a genetic proportional integral derivative controller (GPIDC) to adaptive three controller parameters and a genetic fuzzy logic controller (GFLC) to adaptive controller rules using genetic algorithms (GAs) were simulated and compared each other in a patient model using the BIS value as a controlled variable. Each controller was tested using a set of 12 virtual patients undergoing a Gaussian random surgical disturbance repeated with BIS targets set at 40, 50, and 60. Controller performance was assessed using mean absolute error (MAE) of the BIS target, the percentage of time with acceptable BIS control (PTABC), and drug consumption (DC). It was found that the MAE value of the BIS target was significantly lower (P < 0.05) and the values of PTABC and DC of BIS target were significantly higher (P < 0.05) in BIS targets set at 40 than at 50 or 60 in both GPIDC and GFLC. However, when compared with two controllers in terms of the values of MAE, PTABC, and DC each other in BIS targets set at 40, 50, and 60, there were no significant differences (P > 0.05). Furthermore, when the simulation results in these two controllers were compared with routine standard practice of 12 clinical trials (i.e., manual control) in BIS target set at 50, the values of PTABC in both GPIDC and GFLC groups were significantly higher (P < 0.05) than in the manual control group. In contrast, there were no significant differences (P > 0.05) for these three groups in terms of drug consumption. This indicates that either GPIDC or GFLC can control the BIS target set at 50 better than manual control, although the similar drug consumption is used.

Anesthesia, General↗
PubMed2001

Bayes factor of model selection validates FLMP.

The fuzzy logical model of perception (FLMP; Massaro, 1998) has been extremely successful at describing performance across a wide range of ecological domains as well as for a broad spectrum of individuals. An important issue is whether this descriptive ability is theoretically informative or whether it simply reflects the model's ability to describe a wider range of possible outcomes. Previous tests and contrasts of this model with others have been adjudicated on the basis of both a root mean square deviation (RMSD) for goodness-of-fit and an observed RMSD relative to a benchmark RMSD if the model was indeed correct. We extend the model evaluation by another technique called Bayes factor (Kass & Raftery, 1995; Myung & Pitt, 1997). The FLMP maintains its significant descriptive advantage with this new criterion. In a series of simulations, the RMSD also accurately recovers the correct model under actual experimental conditions. When additional variability was added to the results, the models continued to be recoverable. In addition to its descriptive accuracy, RMSD should not be ignored in model testing because it can be justified theoretically and provides a direct and meaningful index of goodness-of-fit. We also make the case for the necessity of free parameters in model testing. Finally, using Newton's law of universal gravitation as an analogy, we argue that it might not be valid to expect a model's fit to be invariant across the whole range of possible parameter values for the model. We advocate that model selection should be analogous to perceptual judgment, which is characterized by the optimal use of multiple sources of information (e.g., the FLMP). Conclusions about models should be based on several selection criteria.

Bayes Theorem↗
PubMed1998

Automatic tuning of myoelectric prostheses.

This paper is concerned with the development of a software package for the automatic tuning of myoelectric prostheses. The package core consists of Fuzzy Logic Expert Systems (FLES) that embody skilled operator heuristics in the tuning of prosthesis control parameters. The prosthesis system is an artificial arm-hand system developed at the National Institute of Accidents at Work (INAIL) laboratories. The prosthesis is powered by an electric motor that is controlled by a microprocessor using myoelectric signals acquired from skin-surface electrodes placed on a muscle in the residual limb of the subject. The software package, Microprocessor Controlled Arm (MCA) Auto Tuning, is a tool for aiding both INAIL expert operators and unskilled persons in the controller parameter tuning procedure. Prosthesis control parameter setup and subsequent recurrent adjustments are fundamental for the correct working of the prosthesis, especially when we consider that myoelectric parameters may vary greatly with environmental modifications. The parameter adjustment requires the end-user to go to the manufacturer's laboratory for the control parameters setup because, generally, he/she does not have the necessary knowledge and instruments to do this at home. However, this procedure is not very practical and involves a waste of time for the technicians and uneasiness for the clients. The idea behind the MCA Auto Tuning package consists in translating technician expertise into an FLES knowledge database. The software interacts through a user-friendly graphic interface with an unskilled user, who is guided through a step-by-step procedure in the prosthesis parameter tuning that emulates the traditional expert-aided procedure. The adoption of this program on a large scale may yield considerable economic benefits and improve the service quality supplied to the users of prostheses. In fact, the time required to set the prosthesis parameters are remarkably reduced, as is the technician's working time. This is interpreted as minor costs for prostheses manufacturers and suppliers.

Arm↗
PubMed1990

Real-time expert-system-based fuzzy control of mean arterial pressure in pigs with sodium nitroprusside infusion.

This paper deals with real-time fuzzy control of mean arterial pressure (MAP) in pigs by regulating the infusion rate of the vasodilator drug, sodium nitroprusside (SNP). The fuzzy controller was based on the parallel firing mode of a general-purpose Fuzzy LOgic Production System shell, FLOPS [2,3,16,20]. One of the major advantages of this fuzzy control drug delivery system over other existing automatic drug delivery systems is that the fuzzy control system may be designed by using experts' knowledge and experience without any explicit mathematical models involved. Mean arterial pressure in pigs was controlled satisfactorily in real-time by the fuzzy control system. It comes to the conclusion that it may be feasible to employ this fuzzy controller in the management of mean arterial pressure of patients clinically. Also, various applications of the fuzzy controller may exist in industry and in biomedical engineering.

Animals↗