The Economy of Motion for Laparoscopic Ball Clamping Surgery: A Feedback Educational Tool.

The Ball Clamping module of the Laparoscopic Surgery Training Box involves the transfer of beads across the training board using laparoscopic tools. Fundamentals of Laparoscopic Surgery (FLS) requires practitioners to move their hands at as short a distance as possible to perform the functions in the shortest amount of time. This study introduces a feedback tool that presents to the student, after attempting their exam, the right direction (step by step) of obtaining the optimal pathway for minimizing distance traveled in the Ball Clamping Module of the Laparoscopic Surgery Training Box. The shortest distance tour for the ball clamping task is determined using the Traveling Salesman Model (TSM). A sensitivity analysis is conducted to assess the model's applicability to different types and settings of trainer boxes.•Find the best sequence of points resulting in the shortest distance tour for the ball clamping task.•The effects of adding or removing columns from the box cannot be intuitively predicted.

A new framework for warehouse assessment using a Genetic-Algorithm driven analytic network process.

A novel way of integrating the genetic algorithm (GA) and the analytic network process (ANP) is presented in this paper in order to develop a new warehouse assessment scheme, which is developed through various stages. First, we define the main criteria that influence a warehouse performance. The proposed algorithm that integrates the GA with the ANP is then utilized to determine the relative importance values of the defined criteria and sub-criteria by considering the interrelationships among them, and assign strength values for such interrelationships. Such an algorithm is also employed to linguistically present the relative importance and the strength of the interrelationships in a way that can circumvent the use of pairwise comparisons. Finally, the audit checklist that consists of questions related to the criteria is integrated with the proposed algorithm for the development of the warehouse assessment scheme. Validated on 45 warehouses, the proposed scheme has been shown to be able to identify the warehouse competitive advantages and the areas where more improvements can be achieved.

A predictive integrated framework based on the radial basis function for the modelling of the flow of pharmaceutical powders.

This study presents a modelling framework to predict the flowability of various commonly used pharmaceutical powders and their blends. The flowability models were trained and validated on 86 samples including single components and binary mixtures. Two modelling paradigms based on artificial intelligence (AI) namely, a radial basis function (RBF) and an integrated network were employed to model the flowability represented by the flow function coefficient (FFC) and the bulk density (RHOB). Both approaches were utilized to map the input parameters (i.e. particle size, shape descriptors and material type) to the flow properties. The input parameters of the blends were determined from the particle size, shape and material type properties of the single components. The results clearly indicated that the integrated network outperformed the single RBF network in terms of the predictive performance and the generalization capabilities. For the integrated network, the coefficient of determination of the testing data set (not used for training the model) for FFC was R2=0.93, reflecting an acceptable predictive power of this model. Since the flowability of the blends can be predicted from single component size and shape descriptors, the integrated network can assist formulators in selecting excipients and their blend concentrations to improve flowability with minimal experimental effort and material resulting in the (i) minimization of the time required, (ii) exploration and examination of the design space, and (iii) minimization of material waste.

Transparent predictive modelling of the twin screw granulation process using a compensated interval type-2 fuzzy system.

In this research, a new systematic modelling framework which uses machine learning for describing the granulation process is presented. First, an interval type-2 fuzzy model is elicited in order to predict the properties of the granules produced by twin screw granulation (TSG) in the pharmaceutical industry. Second, a Gaussian mixture model (GMM) is integrated in the framework in order to characterize the error residuals emanating from the fuzzy model. This is done to refine the model by taking into account uncertainties and/or any other unmodelled behaviour, stochastic or otherwise. All proposed modelling algorithms were validated via a series of Laboratory-scale experiments. The size of the granules produced by TSG was successfully predicted, where most of the predictions fit within a 95% confidence interval.