Few have examined the distribution of the radial nerve branch to brachialis, generally believed to be motor, within the muscle. We examined the right brachialis muscles of six dissecting room cadavers and found that four received a supply from the radial nerve. The radial nerve branch(es) supplied the inferolateral region of the muscle and was overlapped proximodistally and mediolaterally by the intramuscular branches of the musculocutaneous nerve, which lay on a more superficial plane. The results have implications for the anterolateral approach to the humerus for orthopedic surgery. Anterior splitting of the muscle will almost certainly damage the most lateral branches of the musculocutaneous nerve.

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PurposeThe purpose of this paper is to enhance trust in e-commerce multi-agent systems by presenting a model, called RUU, to select the most trustworthy provider agent based on learning from previous interactions and computing reliability, unreliability and uncertainty.Design/methodology/approachThe methodology comprises analyzing the most representative existing trust models, while a new concept was proposed and measured as unreliability. To make decision about the agents, RUU integrated reliability, unreliability and uncertainty components and used the TOPSIS multi-criteria decision method to select the most trustworthy provider agent. To evaluate the RUU model, the experimentation was carried out in two stages. First, the average accuracy of the model was investigated by simulating RUU in a multi-agent environment. Second, the performance of the model was compared with other related trust models.FindingsThe experimental results revealed that RUU model outperforms current models in providing accurate credibility measurements and computing an accurate trust mechanism for agents, also presenting a decision-making process to choose the most trustworthy provider agent.Research limitations/implicationsThe model presented based on different mathematical computations that take time to be calculated, which is a big limitation of computational models.Practical implicationsRUU enables an agent to make effective and sound decisions in light of the uncertainty that exists in e-commerce multi-agent environments.Originality/valueThis paper is beneficial to enhance the fulfilment of purchasing between provider and requester agents. In fact, the proposed model can ensure critical transactions performed securely in e-commerce multi-agent environments.