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PurposeHospital-acquired pressure ulcers are a significant cause of morbidity and consume considerable financial resources. Turn protocols (repositioning patients at regular intervals) are utilized to reduce incidence of pressure ulcers. Adherence to turn protocols is particularly challenging for nursing teams, given the high number of interventions in intensive care unit, and lack of widely available tools to monitor patient position and generate alerts. We decided to develop and evaluate usefulness of a continuous patient position monitoring system to assist nurses in improving turn protocol compliance.MethodsWe conducted a prospective, non-randomized, multiphase, multicentre trial. In Phase I (control group), the function of the device was not revealed to nurses so as to observe their baseline adherence to turn protocol, while Phase II (intervention group) used continuous patient position monitoring system to generate alerts, when non-compliant with the turn protocol. All consecutive patients admitted to one of the two intensive care units during the study period were screened for enrolment. Patients at risk of acquiring pressure ulcers (Braden score ResultsWe analysed over 1450 h of patient position data collected from 40 patients (Phase I (N = 20), Phase II (N = 20)). Turn protocol compliance was significantly higher in Phase II (80.15 ± 8.97%) compared to the Phase I (24.36 ± 12.67%); p ConclusionUsing a continuous patient position monitoring system to provide alerts significantly improved compliance with hospital turn protocol. Nurses found the system to be useful in providing automated turn reminders and prioritising tasks.

Water pollution due to agricultural and industrial processes may cause adverse biological effects in aquatic organisms such as fishes. The removal of brilliant green (BG) dye from aqueous solution using seaweed

Post-stroke rehabilitation monitoring provides key insights which can be used for development of customized treatment plans for patients. Rehabilitation monitoring systems available today are limited to observational measurements performed over a short period of time. Long term monitoring of stroke patients is necessary to keep track of stroke recovery and assess the patient’s response to the therapist’s treatment technique. This work is a case study that focuses on investigating the effects on muscle recruitment in bicep and calf muscles with and without orthotic intervention. A wireless surface Electromyography (sEMG) device is developed for monitoring muscle recruitment. Monitoring is done on a hemiplegic subject, before and after the physiotherapy treatment sessions, over duration of four months. An increase in sEMG peak frequency was observed after therapy in the absence of orthotic intervention while there was reduction in the peak frequency post therapy with orthotic intervention. Functional Independence Measurement scale, used to assess a patient’s level of disability as well as change in patient status in response to medical intervention is used as a reference measure to validate the sEMG device. The substantial changes in muscle recruitment due to regular therapy and orthotic intervention found in the study supports the use of the developed sEMG device as a surrogate to existing devices

Hospital acquired pressure ulcers (HAPUs) is a major problem that affects around one in twenty patients who are admitted in hospital with sudden illness. These ulcers often occur when patients have limited mobility and cannot change positions in bed on their own. Traditionally, the occurrence of HAPUs has been minimized by turning the patient every 2 hours to alternating lateral and supine positions, and by using pressure redistributing mattresses. In many healthcare facilities, such a patient repositioning schedule is not always maintained owing to low caregiver compliance to turning protocols. Difficulty in monitoring patient position continuously, lack of turn reminders/alerts and suboptimal caregiver staffing ratio increases the occurrence of HAPUs. A novel method to address the need for improved pressure ulcer prevention is presented. The proposed method consists of a wearable device which continuously monitors the patient's position and communicates wirelessly with a tablet which enables alerts to be sent to the caregiver when a patient turn is due in accordance with the protocol adopted by the hospital. The patient's position is continuously monitored and the turning procedure carried out is logged and updated on the hospital's cloud system, thereby enabling centralized monitoring. Under a controlled setting, system was able to continuously monitor patient's position and can accurately detect standard patient positions.

In this paper, Genetic Algorithm is utilized to optimize the coefficients of a decentralized PID controller for a nonlinear Multi-Input Multi-output process by minimizing the Integral Absolute Error (IAE).The controller is tuned at chosen operating points, which are selected to cover the nonlinear range of the process. The optimal PID controller parameters are gain scheduled using a Fuzzy Gain scheduler. The effectiveness of the proposed control scheme has been demonstrated by conducting simulation studies on a Continuous Stirred Tank Reactor (CSTR) process which exhibits dynamic nonlinearity

In this paper, Particle swarm optimization (PSO) is utilized to optimize the coefficients of decoupling controllers for a nonlinear process. The optimization criteria considered is the Integral Square Error (ISE) to minimize the tracking error. The controller is tuned at chosen operating points, which are selected to cover the nonlinear range of the process. The optimal PID controller parameters are gain scheduled using Sugeno Fuzzy Gain scheduler. Fuzzy gain scheduling is a special form of model-based fuzzy control that uses linguistic rule fuzzy reasoning to determine the controller parameter transition policy for a dynamic plant subject to large changes in the operating state. The effectiveness of the proposed control scheme has been demonstrated by conducting simulation studies on a Continuous Stirred Tank Reactor (CSTR) which exhibits dynamic nonlinearity.

This study evaluated the clinical efficacy, safety, and side effects of intramuscular administration of 15(S) methyl F2 alpha for midtrimester pregnancy termination. 25 healthy women, generally in the 14-6th weeks of pregnancy, were given repeated doses of 250 mcg of 15(S) followed by 300-600 mcg at 2-3 hour intervals, depending upon the uterine contractions and side effects. In addition, 15 women were given Lomotil tablets before prostaglandin administration to counteract gastrointestinal side effects. The mean required dosage of prostaglandin was 2.3 mg. The induction-abortion interval was 5-10 hours in 32%, 11-15 hours in 52%, 16-20 hours in 8%, and 21-25 hours in 8%, with a mean abortion time of 13.04 hours. 23 women (92%) has complete and spontaneous abortion. Women treated with Lomotil experienced fewer episodes of vomiting and diarrhea. Other side effects included nausea in 40% of cases, cough in 12%, and fever in 16%. These side effects were mild and well tolerated by the patients, however. It is concluded that serial intramuscular injection of prostaglandin 15(S) methyl F2 alpha is an effective method for midtrimester abortion. In view of the minimal blood loss and asepsis associated with this method, it is particularly suitable for centers where blood transfusion facilities are inadequate.

The gradual atraumatic dilatation of required extent of cervix for obstetrical procedure, especially in termination of pregnancy is the essential phenomena before evacuation of first and late first trimester of pregnancy. The methods available now are associated with serious side effects, causing an increased morbidity. So our experience with the clinical trial of “ISAPTENT (DILEX-C)” in 60 patients of first trimester abortion has been reported. The satisfactory, atraumatic dilatation of cervix was achieved in 92 per cent of the patients with an average of 16.66 mm. dilatation by using single Isaptent (DILEX-C) 20 hours prior to termination of pregnancy. The dilatation was found to have no relations to the age, parity and gestational period of the patients. There was no damage to the cervix. The Vaginal flora remained Unaltered.

In the present work the artificial neural network linked genetic algorithm was applied for the optimization of fermentation media components like carbon and nitrogen sources for L-asparaginase production by Enterobacter aerogenes MTCC 2823 in submerged fermentation. Artificial neural network (ANN) based back propagation algorithm was used to train and test the neural network using the experimental activity obtained by central composite design. Higher value of coefficient of determination (R2=0.984) of artificial neural network justified an excellent correlation between the media components and L-asparaginase activity, the artificial neural network model fitted well with high statistical reliability and significance than RSM model (R2=0.871) developed by central composite design. The predicted optimum concentration of the media components using artificial neural network linked genetic algorithm was sodium citrate 2.09%, DAHP 0.25% and L-asparagine 0.92% with the maximum predicted L-asparaginase activity of 18.59 IU/mL which was close to the experimental L-asparaginase activity of 18.72 IU/mL at simulated optimum conditions.

ZnS-Ag nanoballs were synthesized using chemical precipitation method at room temperature aided with the capping agent cetyl trimethyl ammonium bromide. The synthesized nanoballs were characterized by UV–Visible spectrophotometer, diffuse reflectance spectroscopy, X-ray diffraction, high resolution scanning electron microscopy, and energy dispersive X-ray. Photocatalytic activity of ZnS and ZnS-Ag nanoballs was evaluated by irradiating the solution of organic dye methylene blue (MB) in presence of visible light. The different parameters affecting degradation, such as dye concentration, catalyst loading and pH, were studied. Reusability of catalyst was evaluated up to three cycles. Kinetics study was done for the degradation of MB by ZnS and ZnS-Ag nanoballs. It was found that ZnS-Ag nanoballs show higher degradation efficiency than ZnS.

The comprehensive study of smoke deposited nano sized MgO as a catalyst for biodiesel production was investigated. The transesterification reaction was studied under constant ultrasonic mixing for different parameters like catalyst quantity, methanol oil molar ratio, reaction temperature and reaction time. An excellent result of conversion was obtained at 1.5 wt% catalyst; 5:1 methanol oil molar ratio at 55°C, a conversion of 98.7% was achieved after 45 min. The conversion was three to five times higher than those are reported for laboratory MgO in literature. This was mainly due to the enhancement of surface area of the catalyst and the activity of ultrasonic waves. Catalyst is easily recovered and reused up to eight times with easy regeneration steps. © 2013 BCREC UNDIP. All rights reservedReceived: 18th March 2013; Revised: 20th August 2013; Accepted: 9th September 2013[How to Cite: Sivakumar, P., Sankaranarayanan, S., Renganathan, S., Sivakumar, P. (2013). Studies on Sono-Chemical Biodiesel Production Using Smoke Deposited Nano MgO Catalyst. Bulletin of Chemical Re-action Engineering & Catalysis, 8 (2): 89-96.(doi:10.9767/bcrec.8.2.4628.89-96)][Permalink/DOI: http://dx.doi.org/10.9767/bcrec.8.2.4628.89-96]

In this paper, Particle Swarm Optimization (PSO) is utilized to optimize the coefficients of a decentralized PID controller for a nonlinear process by minimizing the Integral Square Error (ISE).The controller is tuned at chosen operating points, which are selected to cover the nonlinear range of the process. The optimal PID controller parameters are gain scheduled using a Fuzzy Gain scheduler. The effectiveness of the proposed control scheme has been demonstrated by conducting simulation studies on a Continuous Stirred Tank Reactor (CSTR) process which exhibits dynamic nonlinearity. It is shown that the proposed controller provides better set point tracking and load disturbance rejection than the Internal Model Control (IMC) based conventional control scheme.

This paper deals with the development of an unsupervised pattern classification technique that exploits the searching capability of genetic algorithms for automatic clustering of a given set of feature vectors into an appropriate number of clusters using the Kohonen self organizing map (SOM) technique and adaptive resonant theory network (ART). Since the number of clusters is not known a priori, a modified string representation is used in order to encode the variable number of clusters. The percentage of correct classifications is used as a measure of fitness of a chromosome. In addition, wavelets are used for data pre-processing. The effectiveness of the evolutionary classification scheme is demonstrated for 7 classes of Brodatz texture. The performance with SOM and ART clustering is evaluated and the results show the efficiency of the genetic algorithm in selecting various parameters and clusters in developing an automatic texture classification method.

Soft computing is a new approach to construct intelligent systems. The complex real world problems require intelligent systems that combine knowledge, techniques and methodologies from various sources. Neuro fuzzy is the combination of the neural network and fuzzy logic. Neural networks recognize patterns and adapt themselves to cope with changing environments. Fuzzy inference systems incorporate human knowledge and perform inferencing and decision making. Noise is an unwanted energy, which interferes with the desired signal. It can be suppressed with adaptive filters using signal processing. But if the noise frequency is same as the original signal then sometimes it also eliminates the desired signal. Therefore, noise cancellation is used which will not affect the desired signal. The basic principle of noise cancellation using neuro fuzzy is to filter out an interference component by identifying the nonlinear model between a measurable interference (J.S.R. Jang, et al., 1997). The matlab command called 'ANFIS' (adaptive neuro fuzzy inference system) is used to demonstrate how noise cancellation can be applied as interference canceling in ECG [Fuzzy logic tool box].

A discrete adaptive observer based on a weighted moving identification technique for an unknown linear SISO system is presented. It is an alternative to the scheme based on the exponentially weighted least squares method. This adaptive observer is then implemented in an adaptive pole placement controller which uses a state feedback control law for arbitrarily positioning all the poles of the overall system. In the weighted moving identification technique, two sets of weighting factors are used. One is for discarding the old data and the other is for adding new data. These weighting factors are so chosen to ensure that the proposed scheme exhibits a rapid convergence and tracking property for the linear time varying plant parameters. This scheme is demonstrated by computer simulations of a second-order plant. >

In the recent past man has created a large number of artificial automatic systems classified as discrete event dynamic systems (DEDS). The operation of such systems depend on a number of discrete events and the time of their occurrences. Any change in the parameters of the system may have different effects on the behaviour of the system. For better understanding and control of such systems it is necessary to have the sensitivity information for the various parameters of the system. In this paper the sensitivity analysis of the parameters of a computer communication and control system using the fibre distributed data interface (FDDI) media access control (MAC) scheme for factory automation is described. The important parameters of such a system are detailed in general and the mathematical dependencies of the parameters for the system under consideration are derived. The sensitivity equations for the performance measures like the latency ratio /spl alpha/, the resource utilisation p, the arrival rate /spl lambda/, the computer process response time (CPRT), and the critical bandwidth C/sub crit/ are obtained and discussed.

A stochastic Petri net (SPN) model of a computer control and communication system for factory automation using a fibre distributed data interface (FDDI) is applied to controller synthesis for a stochastic discrete event dynamic system (SDEDS). This system is modelled using stochastic Petri nets (SPN) as the various parameters associated with the communication system are stochastic in nature. They have the memoryless property and hence a Markov chain description can be used for modelling and analysis of the system. A transition rate matrix is obtained for the above system and using this matrix, it is shown that the controller can be synthesised for the specified operation of the stochastic DEDS.

For modern enterprises, computer integrated manufacturing (CIM) utilises supervisory control of a large scale real-time system. To be able to implement a supervisory control (dynamical network management), a reduced model of the large scale computing and communication system is a prerequisite. A reduced model is obtained in this work using the cell structure of a modern enterprise, which leads to a hierarchical modelling methodology of the information flow and the system parameter information flow. In the hierarchical modelling methodology, the development of one model of a communication system cell is sufficient for the supervisory control of the enterprise which is a widely distributed computing and communication system. The methodology reported in this work is sufficient for the modelling and analysis of a factory-wide fibre distributed data interface (FDDI) based communications system for integral plant automation.

Stochastic Petri net (SPN) models are successfully used to study the dynamics of stochastic discrete events dynamic systems (SDEDS). This paper deals with a state dependent automatic observer synthesis for SDEDS, used as a module for supervisory control of DEDS. The underlying theory arises naturally in the field of performability analysis of stochastic system. In order to explain the procedure a digital computer control and communication system is considered for which the SPN model is obtained. For this model the transition probabilities are assumed to be exponentially distributed. The model order is reduced using the knowledge of the system and the standard procedure. For this reduced order model the state transition rate matrix is obtained. This matrix is state dependent and the transition rate matrix is used to synthesise the state observer. Results obtained on the system are included.

This paper deals with the synthesis of a robust adaptive controller considering the nonlinearities of SISO system. Processes such as blast furnace, cement kiln, digesters etc. are nonlinear and their order of mathematical model is not known. These processes are nonlinear to the extent that they can be controlled only by expert human operator. A fuzzy interface is introduced in this paper to model such a process into piecewise linear system online. This model is identified and is used by an adaptive pole placement controller to make the system robust. Simulation results are shown for verification. >

In this paper, Genetic Algorithm (GA) is used for identifying two bioprocesses. Bioprocesses are highly nonlinear systems and hence conventional techniques such as Recursive Least Squares (RLS) cannot be used to identify these systems. Systems chosen for identification are fed batch fermentor and bioreactor. Simulation results are presented for the above cases and are found to be satisfactory.

In the normal fuzzy logic control (FLC) system, both the membership functions and the rule sets are usually decided upon subjectively, case by case. The application of Genetic Algorithm(GA) could lead to proper selection of membership functions and rule base objectively. In this paper, the optimisation of membership functions of a FLC for a fed-batch fermentor is carried out with help of Genetic Algorithm (GA). Results are found to be satisfactory.

The estimation of parameters of process dynamics is an important phase in the modeling of systems and in the design of many instrumentation systems. This problem has been taken up by many researchers and many methods and algorithms have been formulated. To the best of the author's knowledge, a compact and simple recursive method is not available in the literature for a fixed memory type estimator from the measurements of input and output data sets. In this proposed paper a simple and straightforward on-line method for parameter estimation of multivariable discrete-data systems is presented. The parameters are determined taking into consideration the present and n + m − 1 past measurements for a nth-order process with m inputs. To reduce the computational burden, a recursive scheme is detailed for updating the estimate of the parameters as soon as the new data arrives. This recursive scheme can be started with arbitrary parameter values. But to have an easy starting procedure, a simple starting matrix with a recursive computational procedure is also given. This method is suitable for situations involving tracking of slowly time-varying parameters that remain unchanged at least for n + m iterations.

Data acquisition of fast time varying transient phenomena relevant to structural dynamics and aerodynamic wind tunnel testing pose a number of problems such as speed, retrieval of data for analysis, number of channels, etc. To overcome these limitations, a software-based high-speed Multichannel Transient Data Acquisition system has been designed and developed. This system is built around the Burr-Brown SDM-853 Module. The data are stored in 16-kbyte MOS RAM memory. The stored data are retrieved by a low-speed system clock through a digital-to-analog converter (DAC) onto an X-Y/t plotter for hard copy.

A weak-base anion-exchange resin based on Styrene-furfural polymer has been prepared and characterized. A suitable composition to the repeating unit in the polymer has been proposed and supported by experimental evidences.

Methods for recursive computation of the pseudo-inverse of a sequence of horizontal rectangular matrices in which the successive matrices differ by: (i) one column only, and (ii) one row only, are described. In both the above cases, the pseudo-inverse of the first matrix of the sequence is assumed to be known. The proposed methods avoid the direct inversion of matrices. These new methods can also be used to compute the pseudo-inverse of any arbitrary matrix iteratively. The two distinct cases mentioned above are considered in two parts in this paper. These situations arise in the fixed memory adaptive identification of discrete-data systems. ? 1982.

The experiment featured in this article throws light on the usefulness of ion-exchange as a tool for recovery of valuable materials from waste, and further the whole process imparts a sound trainin...

A model of an octahedron useful in teaching coordination chemistry can be easily made by following the steps illustrated here.