https://www.bmj.com/content/bmj/1/5125/836.2.full.pdf

Dictionary of psychology

This paper presents a novel bio-inspired optimization algorithm called Rat Swarm Optimizer (RSO) for solving the challenging optimization problems. The main inspiration of this optimizer is the chasing and attacking behaviors of rats in nature. This paper mathematically models these behaviors and benchmarks on a set of 38 test problems to ensure its applicability on different regions of search space. The RSO algorithm is compared with eight well-known optimization algorithms to validate its performance. It is then employed on six real-life constrained engineering design problems. The convergence and computational analysis are also investigated to test exploration, exploitation, and local optima avoidance of proposed algorithm. The experimental results reveal that the proposed RSO algorithm is highly effective in solving real world optimization problems as compared to other well-known optimization algorithms. Note that the source codes of the proposed technique are available at: http://www.dhimangaurav.com
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A novel algorithm for global optimization: Rat Swarm Optimizer

BACKGROUND
Contemporary research suggests that task and ego achievement goal orientations affect students' intrinsic motivation in physical education. This research has assessed intrinsic motivation as a unidimensional contruct, however, which is inconsistent with the more contemporary postulates of self-determination theory (Deci & Ryan, 1985, 1991) which states that intrinsic motivation is only one type of motivation. To date, research has not addressed whether different types of motivation at the situational level are influenced by the proneness to adopt task or ego involvement.


AIMS
To examine the relationship between achievement goal orientations and multidimensional situational motivation in PE.


SAMPLE
Middle school children (182 male, 136 female; M age = 13.2 years).


METHOD
Responded to questionnaires assessing their dispositional goal orientation (POSQ; Roberts, Treasure, & Balague, 1998) and situational motivation (SIMS; Guay, Vallerand, & Blanchard, 2000) in PE.


RESULTS
Task orientation was found to be positively associated with more self-determined types of situational motivation. Ego orientation was weakly related to less self-determined motivation. An extreme group split was conducted to create four goal groups and goal profile analyses conducted. A significant MANOVA was followed by univariate analyses, post hoc comparisons, and calculated effect sizes, which revealed that groups high in task orientation reported more motivationally adaptive responses than groups low in task orientation.


CONCLUSIONS
The results suggest that a high level of task orientation singularly or in combination with ego orientation fosters self-determined situational motivation in the context of PE.

Relation ship among achievement goal orientations and multidimensional situtional motivation in physical in education

Concept Development In Nursing Foundations Techniques And Applications

In the modern era, the cyberbullying (CB) is an intentional and aggressive action of an individual or a group against a victim via electronic media. The consequence of CB is increasing alarmingly, affecting the victim either physically or psychologically. This allows the use of automated detection tools, but research on such automated tools is limited due to poor datasets or elimination of wide features during the CB detection. In this paper, an integrated model is proposed that combines both the feature extraction engine and classification engine from the input raw text datasets from a social media engine. The feature extraction engine extracts the psychological features, user comments, and the context into consideration for CB detection. The classification engine using artificial neural network (ANN) classifies the results, and it is provided with an evaluation system that either rewards or penalizes the classified output. The evaluation is carried out using Deep Reinforcement Learning (DRL) that improves the performance of classification. The simulation is carried out to validate the efficacy of the ANN-DRL model against various metrics that include accuracy, precision, recall, and f-measure. The results of the simulation show that the ANN-DRL has higher classification results than conventional machine learning classifiers.

/pdf/nature-inspired-based-approach-for-automated-cyberbullying-19mqxk3s3w.pdf

Nature-Inspired-Based Approach for Automated Cyberbullying Classification on Multimedia Social Networking

At present, optimization algorithms are used extensively. One particular type of such algorithms includes random-based heuristic population optimization algorithms, which may be created by modeling scientific phenomena, like, for example, physical processes. The present article proposes a novel optimization algorithm based on Hooke’s law, called the spring search algorithm (SSA), which aims to solve single-objective constrained optimization problems. In the SSA, search agents are weights joined through springs, which, as Hooke’s law states, possess a force that corresponds to its length. The mathematics behind the algorithm are presented in the text. In order to test its functionality, it is executed on 38 established benchmark test functions and weighed against eight other optimization algorithms: a genetic algorithm (GA), a gravitational search algorithm (GSA), a grasshopper optimization algorithm (GOA), particle swarm optimization (PSO), teaching–learning-based optimization (TLBO), a grey wolf optimizer (GWO), a spotted hyena optimizer (SHO), as well as an emperor penguin optimizer (EPO). To test the SSA’s usability, it is employed on five engineering optimization problems. The SSA delivered better fitting results than the other algorithms in unimodal objective function, multimodal objective functions, CEC 2015, in addition to the optimization problems in engineering.

A Spring Search Algorithm Applied to Engineering Optimization Problems

Professional ethics refers to the use of logical and consistent communication, knowledge, clinical skills, emotions and values in nursing practice. This study aimed to explore and describe factors that affect professional ethics in nursing practice in Iran. This qualitative study was conducted using conventional content analysis approach. Thirty nurses with at least 5 years of experience participated in the study; they were selected using purposive sampling. Data were collected through semi-structured interviews and analyzed using thematic analysis. After encoding and classifying the data, five major categories were identified: individual character and responsibility, communication challenges, organizational preconditions, support systems, educational and cultural development. Awareness of professional ethics and its contributing factors could help nurses and healthcare professionals provide better services for patients. At the same time, such understanding would be valuable for educational administrators for effective planning and management.

/pdf/factors-affecting-professional-ethics-in-nursing-practice-in-1a42kes66w.pdf

Factors affecting professional ethics in nursing practice in Iran: a qualitative study

Aim
This study aimed to investigate the effects of a peer education on cardiac self-efficacy (CSE) and readmission of the patients undergoing bypass surgery.

Background
Self-efficacy is an antecedent vital factor in both initiating and maintaining healthy behaviours. It significantly improves after effects of heart attacks, anxiety and diminishes the possibility of readmission.

Design
This study is a randomized-controlled trial.

Method
Sixty patients undergoing bypass surgery were chosen and assigned equally into the control and intervention groups. While routine education was presented to the patients in the control group, intervention group were taught using the peer education in two sessions. CSE of all the selected patients was assessed orderly in 5 days, 4 weeks and 8 months after surgery. Moreover, their readmission was investigated after 8 months from surgery. Data was collected using demographic and the CSE scale. Data were also analysed by using χ2, Kolmogorov-Smirnov and repeated measures analysis of variance tests.

Results
The mean score of CSE in the intervention group was significantly different from the corresponding number in the control group in all three stages of data collecting (p < 0·001). Compared to the control group, a smaller number of the patients in the intervention group were readmitted to the hospitals after 8 months (p = 0·011).

Conclusions
Implementation of peer education has positive effects on CSE in patients who have bypass surgery and reduces their hospital readmission. It can be beneficial to apply this method as an educative-supportive approach in cardiac surgery fields.

Relevance to clinical practice
Implementation of peer education has positive effects on CSE in these patients and reduces their hospital readmission.

Effects of a peer education on cardiac self-efficacy and readmissions in patients undergoing coronary artery bypass graft surgery: a randomized-controlled trial

Optimization is a topic that has always been discussed in all different fields of science. One of the most effective techniques for solving such problems is optimization algorithms. In this paper, a new optimizer called Multi-Leader optimizer (MLO) is developed in which multiple leaders guide members of the population towards the optimal answer. MLO is mathematically modelled based on the process of advancing members of the population and following the leaders. MLO performance in optimization is examined on twenty-three standard objective functions. The results of this optimization are compared with the results of the other eight existing optimization algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Teaching-Learning-Based Optimization (TLBO), Gray Wolf Optimizer (GWO), Grasshopper Optimization Algorithm (GOA), Emperor Penguin Optimizer (EPO), Shell Game Optimization (SGO), and Hide Objects Game Optimization (HOGO). Based on the analysis of the simulation results on unimodal test functions to evaluate exploitation ability and multimodal test functions in order to evaluate exploration ability, it has been determined that MLO has a higher ability to solve optimization problems than existing optimization algorithms.

/pdf/mlo-multi-leader-optimizer-4c7y2fkciq.pdf

MLO: Multi Leader Optimizer

Nowadays, accident-based heuristic swarm algorithms are widely used for optimization. The important class of these algorithms has been developed by ideas of physical processes or behaviors of creatures. This paper introduced a new method to achieve semi-optimized solutions related to optimization problems in the various sciences. The proposed method of spring search algorithm is among the optimization algorithms developed by idea of laws of nature and the search factors are a set of objects. The paper presents the real version of the algorithm. The standard multi-function optimization results indicate efficiency and optimal performance of the proposed method.

Ali Dehghani

Papers

A Spring Search Algorithm Applied to Engineering Optimization Problems

Factors affecting professional ethics in nursing practice in Iran: a qualitative study

Effects of a peer education on cardiac self-efficacy and readmissions in patients undergoing coronary artery bypass graft surgery: a randomized-controlled trial

MLO: Multi Leader Optimizer

Spring search algorithm: A new meta-heuristic optimization algorithm inspired by Hooke's law