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.

An improved model and performance analysis for grid-connected photovoltaic system in Oman.

The PV systems' sources are environmentally friendly, but at the same time, they are constantly changing with time. When evaluating solar energy resources, it is necessary to consider the variability and effects of different environmental operation parameters like solar irradiances, ambient temperature, and module temperature. The study introduces a method to simulate an existing photovoltaic system using a mathematical model that permits intelligent strategies to optimise the efficiency and adjust the most effective operational parameters for the solar energy systems.

The effect of customer satisfaction on parcel delivery operations using autonomous vehicles: An agent-based simulation study.

The quality of Third-Party Logistics (3PL) services represented by delivery time decides the outcome of customer satisfaction. The result of this satisfaction judges the type of Word of Mouth (WoM) that, if positive, plays a vital role in attracting non-customers who are willing in 3PL services to join as customers. In this paper, we investigate the effect of an essential factor represented by Word of Mouth on the number of customers in 3PL companies.

An adaptive backpropagation algorithm for long-term electricity load forecasting.

Artificial Neural Networks (ANNs) have been widely used to determine future demand for power in the short, medium, and long terms. However, research has identified that ANNs could cause inaccurate predictions of load when used for long-term forecasting. This inaccuracy is attributed to insufficient training data and increased accumulated errors, especially in long-term estimations.