This study aimed to reveal the distribution pattern of antebrachial cutaneous nerves and provide a morphological basis for sensory reconstruction during flap transplantation. Forearm specimens containing skin and subcutaneous fat were obtained from 24 upper extremities of 12 adult cadavers. Cutaneous nerves were visualized using modified Sihler's staining. Then the data was used to show the distribution pattern and innervation area of the forearm cutaneous nerve. The anterior branch of lateral antebrachial cutaneous nerve innervates 26% of the medial anterior forearm; the posterior branch innervates 38.21% of the lateral anterior forearm and 24.46% of the lateral posterior forearm. The anterior branch of medial antebrachial cutaneous nerve innervates the medial aspect of the forearm covering 27.67% of the anterior region; the posterior branch the lateral part of the forearm covering 7.67% and 34.75% of the anterior and posterior regions, respectively. The posterior antebrachial cutaneous nerve covers 41.04% of the posterior forearm. Coaptations were found between the branches of these cutaneous nerves. The relatively dense secondary nerve branches were found in the middle 1/3 of the lateral anterior forearm and the middle 1/3 of the medial posterior forearm. The relatively dense tertiary nerve branches were the middle 1/3 and lower 1/3 of the medial anterior forearm. The intradermal nerve branches were the relatively dense in the middle 1/3 of the medial anterior and lateral posterior forearm. The middle 1/3 of the medial and lateral forearm had the relatively dense total nerve branches. These results can be used sensory matching while designing forearm flaps for reconstruction surgeries to obtain improved recovery of sensory.

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In the problem of multiple attributes group decision making (MAGDM), the probabilistic linguistic term sets (PLTSs) is an useful tool which can be more flexible and accurate to express the evaluation information of decision makers (DMs). However, due to the lack of time or knowledge, DMs tend to provide the evaluation information by incomplete PLTSs (InPLTSs) which contain missing information. The process to estimate the missing information of InPLTSs is essential, which is called the normalization of InPLTSs. By analyzing the previous methods, the existing defect is that the original uncertainty information of InPLTS can be hardly retained after normalizing. Moreover, the literature that considers the normalization method from perspective of entropy change is absent. Thus, to overcome the shortcoming and fill the research blank, we propose two optimization models based on minimum entropy change of InPLTSs, which can remain the original uncertainty information of InPLTSs to the greatest extent. Inspired by entropy measure of PLTSs, the novel concepts related to entropy measure of InPLTS are developed. In addition, based on the novel normalization method, a decision model is constructed to solve the MAGDM problem. To verify the feasibility and superiority of the proposed method and model, a case about the selection of five-star scenic spots is given and we conduct to have comparative analysis with other methods.

In recent years, large-scale group decision making (LSGDM) has been researched in various fields. Probabilistic linguistic term set (PLTS) is an useful tool to describe evaluation information of experts when solving the LSGDM problem. As decision-making becomes more complex, in most cases, decision makers are unable to give complete evaluations over alternatives, which leads to the lack of evaluation information. To estimate missing information, this paper proposes a new method based on knowledge-match degree with reliability that knowledge-match degree means the matching level between evaluation values provided by individual and ones from group. The possession of reliability associated with evaluation information depends on fuzzy entropy of PLTS. Compared with previous methods, this approach can enhance accuracy and reliability of estimated values of missing evaluation information. Based on this method, we develop a complete decision process of LSGDM including information collection, subgroup detecting, consensus reaching process (CRP), information aggregation and ranking alternatives. Subsequently, a case about pharmaceutical manufacturer selection is used to illustrate the proposed decision method. To verify effectiveness and superiority, we make a comparative analysis with other methods and finally draw a conclusion.

In the process of decision making, the probabilistic linguistic term set (PLTS) is a useful tool to express the evaluation information provided by decision makers (DMs). On the basis of PLTS, the probabilistic linguistic preference relation (PLPR) has been proposed, which can well describe the uncertainty of preferences when experts conduct pairwise comparison between any two alternatives. The consistency analysis is an essential process to check whether the preferences are reasonable and logical. For the consistency checking and improvement of PLPR, some methods have been developed to conduct the work. However, the previous methods seldom consider whether the information of original preferences is distorted after the adjustment of inconsistency preferences, and the adjustment processes are complicated in much of the literature. To overcome the defects of existing methods, we developed a novel PLPR consistency analysis model, and this paper mainly contains two sections. On the one hand, a new consistency index and the consistency checking method are proposed based on similarity measure, respectively. On the other hand, based on the idea of minimum adjustment, we constructed an optimization model to improve the consistency level and develop the process of decision making on the basis of consistency analysis. A numerical example about talent recruitment is given to verify the feasibility of the proposed method. We have a comparative analysis with Zhang’s method from many aspects including the decision results, consistency checking and improvement, as well as adjusted preferences, adjustment costs and consistence threshold. At length, the conclusion of this research is that the proposed consistency analysis model is superior to the previous method on the determination of adjustment parameter, as well as the adjustment cost and the retention of original preferences. To show the effectiveness and superiority, we have a comparative analysis with other approaches. At length, the conclusion of this study is drawn.