High fidelity full sized human patient simulation manikins: Effects on decision making skills of nursing students

Background: The continued use of high fidelity full sized human patient simulation manikins (HF-HPSMs) for developing decision making skills of nursing students has led to growing research focusing its value on student learning and decision making skills. Methods: In October 2012, a cross-sectional survey using the 24-item Nurse Decision-Making Instrument was used to explore the decision making process of 232 pre-registration nursing students (age 22.0 + 5.4; 83.2% female) in Singapore. Results: The independent samples t-tests demonstrated three significant predictive indicators. These indicators include: prior experience in high fidelity simulation based on pre-enrolled nursing course (t = 70.6, p = .001), actual hands-on practice (t = 69.66, p < .005) and active participation in debrief (t = 70.11, p < .005). A complete experience based on role-playing followed by active discussion in debrief was a significant contributor to the decision making process (t = 73.6667, p < .005). However, the regression model indicated active participation in debrief as a significant variable which explained its development (t = 12.633, p < .005). Conclusions: This study demonstrated the usefulness of active participation in simulation learning for an analytic- intuitive approach to decision making, however active participation in debrief was a more important influencing element than role-playing. In situations where resources are limited for students to experience hands-on role-playing, peer reviewing and feedback on others’ experiences could benefit students, just as much. However, further study is warranted to determine the development of HF-HPSMs as a pedagogic tool for enhancing the decision making process of nursing students

Caring behaviours of student nurses: Effects of pre-registration nursing education

In an increasing technologised and cost-constrained healthcare environment, the role of pre-registration nursing education in nurturing and developing the professional caring disposition of students is becoming far more critical than before. In view of this growing demand, the aim of this study was to evaluate the impact of Singapore's pre-registration nursing programmes on students' concept of caring. A descriptive quantitative cross-sectional survey collected data using the Caring Behaviour Inventory from first and final year student nurses, nurse lecturers and nurses in practice. The findings based on student surveys indicated a statistically significant reduction in the overall level of caring behaviour in first to final year students. When compared with the findings of lecturers and nurses, less variance to lecturers than to nurses was found amongst the first years' score, and the lowest variance to nurses was demonstrated amongst the final year. A greater reduction was evidenced amongst Singaporean students, which was exaggerated with exposure to pre-enrolled nursing education and magnified with caring job experience. This study indicates more effort is necessary to harness student caring attributes in students' entire educational journey so that expressive caring is not subsumed in the teaching of students to meet demands of complicated contemporary care

Exploring job interview skills of future engineers: application of appraisal analysis assessment and verbal impression management

The issue of employability among engineering graduates has been examined, debated and tried to be resolved by various organizations and researchers. The lack of employability skills especially communication skills causes graduates struggling to fulfill current work demands and professional expectations in order to succeed in today’s fast-changing and global working environments. Communication skills are a crucial factor of employability, thus there is a need for quality assessment especially in job interview to enhance communicative competence of undergraduates. This research aims to unfold and assess what and how job candidates perform in a mock job interview and how they are being judged by the interviewers. It also studies the underlying linguistic evidence in job interviews of future engineers under the conditions of English as a Second Language (ESL) by applying Appraisal analysis (Attitude subsystems) of Systemic Functional Linguistics and Verbal Impression Management. The findings revealed that future graduates who possess certain elements of linguistics competencies are better in answering and some are in dire need to be equipped with the skills of job interview in preparing them to be employable. It can be concluded that good communication skills especially the job interview skills as what the industry required and persisted are not only based on the fluency of English but the ability to present ideas explicitly and facts using appropriate words and positiveness. The study also recommends the need to assess the future engineering graduates’ linguistics abilities in preparing them to be employable

Properties of resin impregnated oil palm wood (Elaeis Guineensis Jack)

Oil palm wood (OPW) was treated with medium-molecular weight PF resin (mmw-PF) through a modified impregnation-compression method. The method consists of four steps, namely, drying, impregnation, heating, and hot pressing densification. The objective of the study was to optimize the impregnation variables. The overall density of the OPW increased, whereas the density gradient between the two OPW structural elements (namely, parenchyma tissues and vascular bundles) decreased. The weight percent gain (WPG) significantly increased even with a very short impregnation period (i.e. 1 hour). Young`s Modulus of the compression parallel to the grain increased by 15 times (from 170 to 2600 MPa) and the shear strength increased by 7 times (from 1.9 to 13 MPa). The strength of the samples was increased exponentially against density increment. The treatment also made the two OPW structural elements to be strongly bonded that helped in enhancing the durability and machining characteristics of the material

A genetically trained simplified ANFIS controller to control nonlinear MIMO systems

This paper presents a simplified ANFIS (Adaptive Neuro-Fuzzy Inference System) structure acting as a PID-like feedback controller to control nonlinear multi-input multi-output (MIMO) systems. Only few rules have been utilized in the rule base of this controller to provide the control actions, instead of the full combination of all possible rules. As a result, the proposed controller has several advantages over the conventional ANFIS structure particularly the reduction in execution time without sacrificing the controller performance, and hence, it is more suitable for real time control. In addition, the real-coded genetic algorithm (GA) has been utilized to train this MIMO ANFIS controller, instead of the hybrid learning methods that are widely used in the literature. Consequently, the necessity for the teaching signal required by other techniques has been eliminated. Moreover, the GA was used to find the optimal settings for the input and output scaling factors for this controller, instead of the widely used trial and error method. To demonstrate the accuracy and the generalization ability of the proposed controller, two nonlinear MIMO systems have been selected to be controlled by this controller. In addition, this controller robustness to output disturbances has been also evaluated and the results clearly showed the remarkable performance of this MIMO controller

An Architecture of Decision Support System for Visual-Auditory-Kinesthetic (VAK) Learning Styles Detection Through Behavioral Modelling

Learning style (LS) is a description of the attitudes and behaviors which determine an individual’s preferred way of learning. Since each student has different LS, it is important for the teacher to recognize the differences in LS. Thus, an appropriate technique to detect students' LS, improve the motivation and academic achievement are required. The common approach using questionnaires to identify LS is less accurate due to complete the questionnaire is a tedious task for students and tend to choose answers randomly without understanding the questions. Emotions such as anger, sadness, and happiness resulting the different questionnaire answers. Due to the approach constrains, this study has focused on automated approaches that identify student LS from student behavior in the learning process. Implementation of decision support system (DSS) as automated application systems is needed to help teachers make decisions in determining students' LS. Thus, the objective of this study is to propose the architecture of LS detection automatically using decision support system. The development of the architecture is applying the behavioral modelling, that are contained student’s behavior parameters for visual-auditory-kinesthetic (VAK) model. Evaluation of the architecture is tested with the precision DSS engine. The accuracy of the rule technique achieves significant 80% accuracy. This study aims to help teachers to identify the ability of the student through the learning style (LS) in order to create effectiveness of learning and improving student’s achievement indirectly. Keywords— decision support system, reasoning engines, learning style detection, user behavior, visual-auditory-kinesthetic (VAK) mode

Process Control of Pink Guava Puree Pasteurization Process: Simulation and Validation by Experiment

Recently, process control has been applied extensively in many food processes include pasteurization process. The purpose is to control and maintain the product temperature at desired value. In order to be able to control the process properly, the model of the process needs to be obtained. This research aims to obtain the empirical model and to determine the best control strategy in pasteurization process of pink guava puree. The PID controller tuned by different tuning methods was simulated using Simulink and closed loop responses were observed. Simulation results revealed that PID controller tuned by minimizing of integral absolute error (IAE) method were satisfactory adaptable in this process in term of faster settling time, less overshoot, smallest values of IAE and ISE that less than 1. Then, experiment was performed using this method in order to validate simulation results. In general, a good agreement was achieved between experimental data and dynamic simulation result in control of pasteurization temperature process with  R2=0.83. As the conclusion, the results obtained can be used as the recommendation for a suitable control strategy for the pasteurization process of pink guava puree in the industry

Utilizing global-best harmony search to train a PID-like ANFIS controller

This paper presents a PID-like adaptive neuro-fuzzy inference system (ANFIS) controller that can be trained by the global-best harmony search (GHS) technique to control nonlinear systems. Instead of the hybrid learning methods that are widely used in the literature to train the ANFIS structure, the GHS technique alone is used to train the ANFIS as a feedback controller, and hence, the necessity for the teaching signal required by other techniques has been eliminated. Moreover, the input and output scaling factors for this controller are also determined by the GHS. To show the effectiveness of this controller and its learning method, two nonlinear plants, including the continuous stirred tank reactor (CSTR), were used to test its performance in terms of generalization ability and reference tracking. In addition, this controller robustness to output disturbances has been also tested and the results clearly indicate the remarkable performance of this controller

An exploratory study of a research culture development by administrators, lecturers and clinical specialists in nursing

Context: Escalating healthcare demands combined with diminishing resources underline the importance of ensuring nurses in leading roles, having the capacity to conduct evidence-based research to inform practice. Aims: This study explored the perceptions of research knowledge and experiences of nurses in administrative, teaching and clinical specialist positions to highlight gaps in research provision within educational institutions and healthcare organisations in Singapore. Design/Methods: A mixed-method exploratory descriptive design, using a questionnaire with open and closed questions was employed to obtain the views of nurses on their capacity in conducting research. Convenience sampling was employed in 3 research seminars in Singapore between July-August 2011. Results: Forty seven nurses were recruited and they confirmed good research knowledge and skills but indicated the need for enhanced educational preparation and organisational support to fully embrace a research culture. Conclusions: Research in nursing requires prioritisation and support in educational training and healthcare settings. Otherwise, conducting research would continue to be a lesser priority for nurses, even if they were in teaching or clinical positions which provided significant opportunities to lead or facilitate research. Given that role modeling enhances research culture in nursing, within education and clinical settings, nurses in leadership positions require confidence in conducting research. However, without prioritising research, and filtering this down through the nursing hierarchical system to promote a research culture, new knowledge to improve practice will remain elusive

Neuro-fuzzy modeling of a conveyor-belt grain dryer

The grain drying process is one of the most critical post-harvest operations in modern agricultural production. Development of a reliable control strategy for this process plays an important role in improving the overall efficiency and productivity of the drying process. In control system design, the first problem to be addressed is the availability of a relatively simple and accurate model of the process to be controlled. However, the majority of the models developed for the grain drying process and the numerical methods required to solve them are characterized by their highly complex nature, and thus they are not suitable to be utilized in control system design. This paper presents an application of a neuro-fuzzy system, in particular the adaptive neuro-fuzzy inference system (ANFIS), to develop a data-driven model for a conveyor-belt grain dryer. This model can be easily used in control system design to develop a reliable control strategy for the drying process. By conducting a real-time experiment to dry paddy grains, a set of input-output data were collected from a laboratory-scale conveyor-belt grain dryer. These data were then presented to the ANFIS network in order to learn the nonlinear functional relationship between the input and output data by this network. Based on utilizing a clustering method to identify the structure of the ANFIS network, the resulting ANFIS model has shown a remarkable modeling performance to represent the drying process. In addition, the modeling result achieved by this ANFIS model was compared with those of an autoregressive with exogenous input (ARX) model and an artificial neural network (ANN) model, and the results clearly showed the superiority of the ANFIS model