Can Likert scales predict choices? Testing the congruence between using Likert scale and comparative judgment on measuring attribution

Comparative study of biomodels manufactured using 3D printing techniques for surgical planning and medical training

OBJECTIVES

To obtain 3D printed bone models with a haptic sensation similar to that of the real bone, which will help the surgeon to learn and improve based on practice.

METHODS

From computed tomography, 3 digital anatomical models of the human proximal femur were created and, by modifying the printing parameters, both cortical and trabecular tissues were simulated, which were combined in a different cortico-cancellous interface depending on the bone segment. The 3 equivalent models obtained were compared with a commercial Sawbone synthetic model and subjected to a series of blind surgical practice trials performed by 5 TOC specialists from a hospital, each of them with different degrees of expertise. A statistical analysis of the qualitative data collected based on the Wilcoxon test, the Spearman correlation matrix, and the Validity Ratio Coefficient was performed.

RESULTS

The deviations observed in the dimensional study are less than 0.2 millimeter, which confirms the validity of the 3DP-FFF technology to geometrically recreate personalized biomodels with high anatomical precision.

CONCLUSIONS

The reproductions obtained have given rise to a reliable method that professionals can refine to plan operations with the consequent reduction of time and risks for the patient, as well as for medical training.

A hybrid algorithm for adjusting the input parameters of the wirecut EDM machine in order to obtain maximum customer satisfaction

This study presents an algorithm to optimally adjust the input parameters of the wirecut to align its output with the customer’s expectations. For this, AHP and QFD are used to identify and prioritize customer needs in the form of a desirability function. Then, using the Taguchi method, variance analysis, and regression, a fitness function is prepared and optimized by the multi-objective genetic algorithm. Through a case study, the proposed method is validated in terms of flexibility, simplicity, speed, cost-effectiveness, and updateability. Also, customer satisfaction is calculated for two groups of 45 people, with and without using the proposed method. The growth of the customer satisfaction index (CSAT) from 57.6 to 70.3, and the customer satisfaction score from 30.2 to 54.2, show the positive performance of the method. This converts regular customers into loyal ones. It also makes them encourages others to use the mentioned services and widen the customer network. It is clearly seen in the growth of the net promoter score from 6.67 to 31.11. All in all, it can be said that this algorithm helps the survival, profitability, and expansion of an industrial organization.

Risk Management of Island Petrochemical Park: Accident Early Warning Model Based on Artificial Neural Network

Island-type petrochemical parks have gradually become the ‘trend’ in establishing new parks because of the security advantages brought by their unique geographical locations. However, due to the frequent occurrence of natural disasters and difficulties in rescue in island-type parks, an early warning model is urgently needed to provide a basis for risk management. Previous research on early warning models of island-type parks seldom considered the particularity. In this study, the early warning indicator system is used as the input parameter to construct the early warning model of an island-type petrochemical park based on the back propagation (BP) neural network, and an actual island-type petrochemical park was used as a case to illustrate the model. Firstly, the safety influencing factors were screened by designing questionnaires and then an early warning indicator system was established. Secondly, particle swarm optimization (PSO) was introduced into the improved BP neural network to optimize the initial weights and thresholds of the neural network. A total of 30 groups of petrochemical park data were taken as samples—26 groups as training samples and 4 groups as test samples. Moreover, the safety status of the petrochemical park was set as the output parameter of the neural network. The comparative analysis shows that the optimized neural network is far superior to the unoptimized neural network in evaluation indicators. Finally, the Zhejiang Petrochemical Co., Ltd., park was used as a case to verify the accuracy of the proposed early warning model. Ultimately, the final output result was 0.8324, which indicates that the safety status of the case park was “safer”. The results show that the BP neural network introduced with PSO can effectively realize early warning, which is an effective model to realize the safety early warning of island-type petrochemical parks.