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Background: As one of the adverse events after hip fracture surgery, hypoalbuminemia is usually treated using human serum albumin infusion. However, the application of human serum albumin may cause complications such as postsurgical infection and increased mortality. Aims: To examine the preoperative risk factors of human serum albumin infusion after hip fracture surgery, establish a nomogram prediction model, and verify its accuracy. Study Design: A retrospective cross-sectional study. Methods: Eligible patients who underwent hip fracture surgery were divided into the infusion and non-infusion groups according to whether human serum albumin was infused or not. All patients were divided randomly into a training set and a testing set in line with the ratio of 7:3. In the training set, independent risk factors of postoperative human serum albumin infusion were determined by univariate logistic regression analysis, LASSO regression, and multivariate logistic regression analysis. Then, a nomogram model was established. Furthermore, the receiver operating characteristic curve and calibration curve were plotted, and decision curve analysis was performed for the training and testing sets to assess the predictability, discriminative ability, and clinical usefulness of the model. Results: This study included a total of 1,339 eligible patients, 141 of whom were injected with human serum albumin postoperatively. Altogether, the training set incorporated 939 patients, and the testing set included 400 patients. Multivariate logistic analysis indicated five independent risk factors, including chronic lung disease (odds ratio, 95% confidence interval, 2.618, 1.413-4.849, p = 0.002), (albumin; odds ratio, 95% confidence interval, 0.842, 0.787-0.900, p < 0.001), prothrombin time (odds ratio, 95% confidence interval, 1.252, 1.071-1.463, p = 0.005), red blood cells (odds ratio, 95% confidence interval, 0.370, 0.228-0.602, p < 0.001), and type of anesthesia (odds ratio, 95% confidence interval, 0.553, 0.327-0.937, p = 0.028). Fracture type, a clinically significant factor, was also considered. Finally, the nomogram model was built based on these seven predictors. The areas under the curve of the nomogram were 0.854 (95% confidence interval, 0.811-0.898) and 0.767 (95% confidence interval, 0.686-0.847) in the training and testing sets separately. As shown in the calibration curve, the predicted result was consistent with the observed one. The decision curve analysis indicated that the nomogram has good clinical value. Conclusion: Low preoperative serum albumin levels, low preoperative red blood cell counts, prolonged preoperative prothrombin time, history of chronic lung disease, and general anesthesia were independent risk factors for postope...

A connected dominating set (CDS) performs a vital role in wireless ad-hoc sensor networks, which can establish virtual backbones and thus leads to less maintenance overhead and information exchanges in wireless communications. In the literature, many distributed algorithms applied in wireless ad-hoc sensor networks have been proposed. However, we find many of these algorithms are divided into different phases. The next phase stars to run until the previous phase completes in sequence. At this point the different phase must be synchronized before continuing with the next phase. Thus such distributed algorithms may suffer from high time delay for leaving sufficient time to end up the previous phase. In this paper, we present a distributed algorithm to constuct a CDS in wireless ad-hoc sensor networks. Our algorithm is an asynchronous distributed algorithm and converges quickly. According to the fact that a maximum independent set (MIS) is also a dominating set, the algorithm constructs a CDS by establishing an MIS first and then adds new nodes to the MIS to let subgraph induced by these nodes be connected. The algorithm requires only local information and iterative rounds of message exchanges among neighbors. Every node can obtain such necessary knowledge to form a CDS by 3-hop message relay asynchronously. As for the diameter of CDS proved as a significant factor, we also do some simulation work including the diameter of CDS generated by our algorithm besides the size of CDS.

A Connected Dominating Set (CDS) is a subset V of V for the graph G(V, E) and induces a connected subgraph, such that each node in V −V is at least adjacent to one node in V . CDSs have been proposed to formulate virtual backbones in wireless ad-hoc sensor networks to design routing protocols for alleviating the serious broadcast storms problem. It is not easy to construct the Minimum Connected Dominating Set (MCDS) due to the NP-hard nature of the problem. In this paper, we present an effective distributed algorithm to approach the MCDS. We first find an Maximal Independent Set (MIS) and then adds new nodes to the MIS to let subgraph induced by these nodes be connected. The dominators are selected into MIS based on effective degree. Default event is triggered to recalculate and update the node's effective degree after a predetermined amount of time. Many proposed algorithms suffers from high message complexity, thus, which confines the algorithms applied in large scale network. For our algorithm, we prove that it has a good performance in terms of message complexity with message complexity of O(Δ · n). We also analyse some other useful structural properties of CDS generated by our algorithm. Extensive simulations are also implemented to further evaluate the performance of the algorithm.