Cholangiopancreatography troubleshooting:the usefulness of endoscopic retrieval of migrated biliary and pancreatic stents

BACKGROUND:Stent migration in the hepatopancreatic duct might arise as one of the rare complications associated with biliary or pancreatic stenting.Although there are some procedures to retrieve the migrated stent,including surgical,percutaneous,and endoscopic approaches,endoscopy should be attempted first because it is least invasive.This study set out to evaluate the usefulness of endoscopic retrieval of migrated biliary and pancreatic stents.METHODS:Plastic stents that migrated in the bile duct(35 patients)or pancreatic duct(2)were retrieved with endoscopic retrograde cholangiopancreatography.Devices used were snare forceps,a basket catheter,grasping forceps,biopsy forceps,a balloon catheter,and the Soehendra stent retriever.RESULTS:Endoscopic retrieval of migrated stents was performed successfully in 36(97.0%)of the 37 patients.The devices utilized for successful treatment were basket catheter(13 patients),grasping forceps(10),snare forceps(8),balloon catheter(3),biopsy forceps(1),and the Soehendra stent retriever(1).The unsuccessfully treated patient with chronic pancreatitis underwent surgery since the guide wire did not move forward due to bile duct stenosis,and there was also duodenal stenosis.One patient developed mild pancreatitis after withdrawal of the stent;the pancreatitis was relieved with conservative treatment.CONCLUSIONS:Endoscopic retrieval of migrated biliary and pancreatic stents appears to be useful because of its safety and low invasiveness.However,various forceps should be prepared for the retrieval of a migrated stent.

Troubleshooting algorithm for solving assignment problem and its applications

A new troubleshooting algorithm for solving assignment problem based on existing algorithms is proposed, and an analysis on the related theory is given. By applying the new troubleshooting algorithm to the Lagrange relaxation algorithm of the multi-dimensional assignment problem of data association for multi-passive-sensor multi-target location systems, and comparing the simulation results with that of the Hungarian algorithm which is the classical optimal solving algorithm, and the multi-layer ordersearching algorithm which is a sub-optimal solving algorithm, the performance and applying conditions of the new algorithm are summarized. Theory analysis and simulation results prove the effectiveness and superiority of the new algorithm.

Comparing Machine Learning Algorithms for Improving the Maintenance of LTE Networks Based on Alarms Analysis

Mobile network operators are facing many challenges to satisfy their subscribers in terms of quality of service and quality of experience provided. To achieve this goal, technological progress and scientific advances offer good opportunities for efficiency in the management of faults occurring in a mobile network. Machine learning techniques allow systems to learn from past experiences and can predict, solutions to be applied to correct the root cause of a failure. This paper evaluates machine learning techniques and identifies the decision tree as a learning model that provides the most optimal error rate in predicting outages that may occur in a mobile network. Three machine learning techniques are presented in this study and compared with regard to accuracy. This study demonstrates that the appropriate machine learning technique improves the accuracy of the model. By using the decision tree as a machine learning model, it was possible to predict solutions to network failures, with an error rate less than 2%. In addition, the use of Machine Learning makes it possible to eliminate steps in the network failure processing chain;resulting in reduced service disruption time and improved the network availability which is a key network performance index.

Diagnostic Method for Beam Position Monitor Based on Clustering by Fast Search and Find of Density Peaks

Beam position monitors(BPMs)are important to monitor the beam moving steadily.Keeping the beam's normal motion is an important mission for Shanghai Synchrotron Radiation Facility.Effective diagnostic analysis is an important way to accomplish this task.This paper develops a new method based on clustering analysis to diagnose the healthy of BPMs with basic running data,i.e.,the β oscillation of X and Y directions and noise data.The analysis results showed that all beam position monitors(140 BPMs)can be classified into three groups:normal group,worse performance group,and fault group,respectively.In addition,the abnormal BPMs(including worse performance)could be marked.The new method showed its ability to handle faulty BPMs and it could instruct daily maintenance.On the other hand,it will be a useful supplement for data analysis in accelerator physics.