The impact of fluid balances in the first 48 hours on mortality in the critically ill patients

Introduction: There has been increasing evidence of detrimental effects of cumulative positive fluid balance in critically ill patients. The postulated mechanism of harm is the development of interstitial oedema, with resultant increase morbidity and mortality. We aim to assess the impact of positive fluid balance within the first 48 hours on mortality in our local ICU population. Methods: This was a secondary analysis of a single centre, prospective observational study. All ICU patients more than 18 years were screened for inclusion in the study. Admission of less than 48 hours, post-elective surgery and ICU readmission were excluded. Cumulative fluid balance either as volume or percentage of body weight from admission was calculated over 6, 24 and 48 hour period from ICU admission. Results: A total of 143 patients were recruited, of these 33 died. There were higher cumulative fluid balances at 6, 24 and 48 hours in non- survivors compared to survivors. However, after adjusted for severity of illness, APACHE II Score, they were not predictive of mortality. Sensitivity analysis on sub-cohort of patients with acute kidney injury (AKI) showed only an actual 48-hour cumulative fluid balance was independently predictive of mortality (1.21 (1.03 to 1.42)). Conclusions: Cumulative fluid balance was not independently predictive of mortality in a heterogenous group of critically ill patients. However, in subcohort of patients with AKI, a 48-hour cumulative fluid balance was independently predictive of mortality. An additional tile is thus added to the mosaic of findings on the impact of fluid balance in a hetergenous group of critically ill patients, and in sub- cohort of AKI patients

An investigation of emotional intelligence and counselling self-efficacy among counsellors-in-training

The challenges faced by the counsellors-in-training are enormous. Therefore, investigating emotional quotient (EQ) and counselling self-efficacy among counsellors-in-training is crucial. This study aimed to examine the EQ and counselling self-efficacy levels among counsellors-in-training and investigate the relationships between these variables. A total of 373 counselling students from three universities in the Northern Peninsular of Malaysia have participated in this study. This study involved a quantitative method using survey as a data collection technique and also employed a convenience sampling procedure. There were two questionnaires used in this study, namely Bar-On emotional quotient inventory (EQ-i) and counselling self-estimate inventory. The results showed that there was a significant correlation between EQ and counselling self-efficacy which all sub-constructs for both measures are well-associated with each other. In conclusion, these two variables are essential to ensure counselling students would move toward a better developing identity of counsellors-in-training and encourage the growth of professional competency among trainee counsellors in the future. Hence, it is vital for the government to ensure that quality counselling services should be provided for the community to deal with this problem

Opposition-sooty tern algorithm for fuzzy control optimization of an inverted pendulum system

This paper presents a novel Opposition-Sooty Tern Algorithm (OSTA) which is an improved version of the original Sooty- Tern Optimization Algorithm (STOA). An opposition scheme is incorporated into the STOA structure. This is to enhance the exploration and exploitation of all searching agents throughout a feasible search area. In solving a real-world problem, the algorithm is applied to optimize parameters of a fuzzy logic model for controlling cart's position and pendulum's angle of an inverted pendulum system. Result of the optimization test shows the OSTA has a better accuracy performance compared to its predecessor algorithm. For controlling the inverted pendulum, both OSTA and STOA acquired sufficiently good control performance for the system. However, the fuzzy control scheme optimized by OSTA has resulted in a better tracking and control performance for both cart's position and pendulum's angle

Spiral-sooty tern optimization algorithm for dynamic modelling of a twin rotor system

This paper presents a hybrid Spiral - Sooty Tern Algorithm (SSTA) which is an improved version of the original STOA. A spiral model is incorporated into the Sooty-Tern Optimization Algorithm (STOA) structure. A random switching is utilized to change from random-based to deterministic-based searching operations and vice versa. This is to balance between the exploration and exploitation of all searching agents throughout a feasible search area. For solving a real-world problem, the proposed SSTA algorithm in comparison to STOA is applied to optimize parameters of a linear Autoregressive-Exogenous (ARX) dynamic model for a twin rotor system. The dynamic modelling of the system is challenging in the presence of cross coupling effect between the main and tail rotors. 3000 pairs of captured input-output data from the system are used for the identification and optimization purpose. Result of the test has shown that the SSTA has achieved a better accuracy performance compared to the competing algorithm. For dynamic modelling of the nonlinear system, both SSTA and STOA have acquired a sufficiently good model for the twin rotor system

A multiobjective simulated Kalman filter optimization algorithm

This paper presents a new multiobjective type optimization algorithm known as a Multiobjective Optimization Simulated Kalman Filter (MOSKF). It is a further enhancement of a single-objective Simulated Kalman Filter (SKF) optimization algorithm. A synergy between SKF and Non-dominated Solution (NS) approach is introduced to formulate the multiobjective type algorithm. SKF is a random based optimization algorithm inspired from Kalman Filter theory. A Kalman gain is formulated following the prediction, measurement and estimation steps of the Kalman filter design. The Kalman gain is utilized to introduce a dynamic step size of a search agent in the SKF algorithm. A Non-dominated Solution (NS) approach is utilized in the formulation of the multiobjective strategy. Cost function value and diversity spacing parameters are taken into consideration in the strategy. Every single agent carries those two parameters in which will be used to compare with other solutions from other agents in order to determine its domination. A solution that has a lower cost function value and higher diversity spacing is considered as a solution that dominates other solutions and thus is ranked in a higher ranking. The algorithm is tested with various multiobjective benchmark functions and compared with Non-Dominated Sorting Genetic Algorithm 2 (NSGA2) multiobjective algorithm. Result of the analysis on the accuracy tested on the benchmark functions is tabulated in a table form and shows that the proposed algorithm outperforms NSGA2 significantly. The result also is presented in a graphical form to compare the generated Pareto solution based on proposed MOSKF and original NSGA2 with the theoretical Pareto solution

Adaptive levy flight distribution algorithm for solving a dynamic model of an electric heater

This paper presents an improved version of Levy Flight Distribution (LFD) algorithm. The original LFD is formulated based on the random walk strategy. However, it suffers a premature convergence due to imbalance exploration and exploitation. Consequently, the algorithm produces unsatisfactory performance in terms of its final accuracy achievement. As a solution to the problem, an adaptive scheme of search agents step size is incorporated into the original LFD algorithm. Moreover, a mating strategy is also adopted to improve its stochastic nature throughout the search process. The algorithm is applied to optimize a nonlinear dynamic model of an electric water heater. A fuzzy-based Hammerstein structure is adopted to represent the heater model. It comprises a combination of both linear and nonlinear equations so that it can capture the dynamic behavior of the heater satisfactorily. The proposed adaptive LFD algorithm is compared with the original LFD algorithm. The result shows that the proposed algorithm has attained a better accuracy. It also has captured the dynamic behavior of the heater more adequately

Identification of sharp edge non-slender delta wing aerodynamic coefficient using neural network

Delta wing formed a vortical flow on its surface which produced higher lift compared to conventional wing. The vortical flow is complex and non-linear which requires more studies to understand its flow physics. However, conventional flow analysis (wind tunnel test and computational flow dynamic) comes with several significant drawbacks. In recent times, application of neural network as alternative to conventional flow analysis has increased. This study is about utilization of Multi-Layer Perceptron (MLP) neural network to predict the coefficient of pressure (Cp) on a delta wing model. The physical model that was used is a sharp edge non-slender delta wing. The training data was taken from wind tunnel tests. 70% of data is used as training, 15% is used as validation and another 15% is used as test set. The wind tunnel test was done at angle of attack from 0°-18° with increment of 3°. The flow velocity was set at 25m/s which correspond to 800,000 Reynolds number. The inputs are angle of attack and location of pressure tube (y/cr) while the output is Cp. The MLP models were fitted with 3 different transfer functions (linear, sigmoid, and tanh) and trained with Lavenberg-Marquadt backpropagation algorithm. The results of the models were compared to determine the best performing model. Results show that large amount of data is required to produce accurate prediction model because the model suffer from condition called overfitting

Opposition based Spiral Dynamic Algorithm with an Application to a PID Control of a Flexible Manipulator

This paper presents an improved version of a Spiral Dynamic Algorithm (SDA). The original SDA is a relatively simple optimization algorithm. It uses a spiral strategy to move search agents within the feasible search space. However, SDA suffers from a premature convergence due to an unbalanced diversification and intensification throughout its search operation. Hence, the algorithm unable to acquire an optimal accuracy solution. An Opposition learning is adopted into SDA to improve the searching strategy of the SDA agents. Therefore in the proposed strategy, a random and a deterministic approaches are synergized and complement each other. The algorithm is tested on several benchmark functions in comparison to the original SDA. A statistical nonparametric Wilcoxon sign rank test is conducted to analyze the accuracy achievement of both algorithms. For solving a real world application, the algorithms are applied to optimize a PID controller for a flexible manipulator system. Result of the test on the benchmark functions shows that the Opposition based SDA outperformed the SDA significantly. For solving the PID control design, both algorithms acquire PID parameters and hence can control the flexible manipulator very well. However, the proposed algorithm shows a better control response

Quasi oppositional—Manta ray foraging optimization and its application to pid control of a pendulum system

This paper presents an improved version of Manta Ray Foraging Optimization (MRFO). MRFO is relatively a single objective optimization algorithm. It was inspired from the behavior of a cartilaginous fish called Manta Ray. Manta Ray applies three strategies in searching foods which are chain, cyclone and somersault foraging. From the study, MRFO is a relatively new developed algorithm and has low convergence rate. However, MRFO has potential to be improved in that aspect. In the meanwhile, Opposition-based Learning (OBL) is a well-known technique in increasing the convergence rate. Therefore, a type of OBL namely Quasi Oppositional-based Learning will be adopted into MRFO in order to increase the possibility of finding the solution by considering the opposite individual location of fitness. This version of MRFO is called as Oppositional-based MRFO (OMRFO). Further, OMRFO was performed on several benchmark function. A statistical non-parametric Wilcoxon Test was conducted to analyze the accuracy of MRFO and OMRFO. Furthermore, the proposed algorithm was applied to an inverted pendulum system. Result from shows that performance of OMRFO is significantly outperformed MRFO after tested in the benchmark functions