Designing and Psychometric Assessment of the Questionnaire for Artificial Airway Patients’ Satisfaction with Nurse's Non-verbal Communication during Nursing Cares

Background & Aim: Verbal communication disorder is one of the most important problems of mechanically ventilated patients which can lead to anxiety and decrease satisfaction. The purpose of this study is designing and psychometric assessment of the questionnaire for artificial airway patients’ satisfaction with nurses nonverbal communication during nursing cares. Materials & Methods: This is a methodological study which was performed using Waltz 2010 method in 4 steps, namely conceptual model definition, determination of goals and tools design, compiling initial plan, and determining reliability and validity in 2016. The study population includes all patients with artificial airway in 3 hospitals under the supervision of Shiraz University of Medical Sciences. Totally, 240 patients were selected for the study, using convenience sampling. The questionnaire validity was evaluated using face, construct, and content validities. Pearson correlation coefficient and Cronbach's alpha were used to evaluate the external and internal reliabilities. SPSS Software V.19 was used for data analysis. Results: The initial version of questionnaire was designed with 27 items. After face and content validation process, the second version was designed in 24 items. The maximum score for all items was 1.5. The values of CVI and CVR were obtained at 0.89 and 0.88, respectively. For construct validity, the items were reduced to 12, based on explanatory factor analysis. The final questionnaire was obtained in 3 satisfaction dimensions namely providing physiologic, social, and emotional-psychological needs with predictive power of 47.706. The Cronbach's alpha value was calculated at 0.67. Pearson correlation coefficient was calculated at 0.67, which suggests the validity and reliability of the questionnaire. Conclusion: Considering the limitation of data availability for evaluating the satisfaction of artificial airway patients with nursing communication, the questionnaire can be an efficient tool for detecting the patient-nurse communicational challenges and patients’ needs in different areas as well as improving care services quality

Stitching Dynamic Movement Primitives and Image-based Visual Servo Control

Utilizing perception for feedback control in combination with Dynamic Movement Primitive (DMP)-based motion generation for a robot's end-effector control is a useful solution for many robotic manufacturing tasks. For instance, while performing an insertion task when the hole or the recipient part is not visible in the eye-in-hand camera, a learning-based movement primitive method can be used to generate the end-effector path. Once the recipient part is in the field of view (FOV), Image-based Visual Servo (IBVS) can be used to control the motion of the robot. Inspired by such applications, this paper presents a generalized control scheme that switches between motion generation using DMPs and IBVS control. To facilitate the design, a common state space representation for the DMP and the IBVS systems is first established. Stability analysis of the switched system using multiple Lyapunov functions shows that the state trajectories converge to a bound asymptotically. The developed method is validated by two real world experiments using the eye-in-hand configuration on a Baxter research robot.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

An Optimizing Method for Performance and Resource Utilization in Quantum Machine Learning Circuits

Quantum computing is a new and advanced topic that refers to calculations based on the principles of quantum mechanics. Itmakes certain kinds of problems be solved easier compared to classical computers. This advantage of quantum computingcan be used to implement many existing problems in different fields incredibly effectively. One important field that quantumcomputing has shown great results in machine learning. Until now, many different quantum algorithms have been presented toperform different machine learning approaches. In some special cases, the execution time of these quantum algorithms will bereduced exponentially compared to the classical ones. But at the same time, with increasing data volume and computationtime, taking care of systems to prevent unwanted interactions with the environment can be a daunting task and since thesealgorithms work on machine learning problems, which usually includes big data, their implementation is very costly in terms ofquantum resources. Here, in this paper, we have proposed an approach to reduce the cost of quantum circuits and to optimizequantum machine learning circuits in particular. To reduce the number of resources used, in this paper an approach includingdifferent optimization algorithms is considered. Our approach is used to optimize quantum machine learning algorithms forbig data. In this case, the optimized circuits run quantum machine learning algorithms in less time than the original onesand by preserving the original functionality. Our approach improves the number of quantum gates by 10.7% and 14.9% indifferent circuits and the number of time steps is reduced by three and 15 units, respectively. This is the amount of reduction forone iteration of a given sub-circuit U in the main circuit. For cases where this sub-circuit is repeated more times in the maincircuit, the optimization rate is increased. Therefore, by applying the proposed method to circuits with big data, both cost andperformance are improved