Construction of an easy-to-use predictive model for ultrasound-detected tophi to improve the detection of hidden tophi

Background:Musculoskeletal ultrasound is used in clinical practice to evaluate gout patients and is an effective imaging tool for the detection of tophi.The aim of this study was to analyze the factors associated with ultrasound-detected tophi in gout patients and to construct a clinical model to predict its occurrence and improve the detection of hidden tophi.Methods:Data of gout patients admitted to Beijing Jishuitan Hospital from January 2015 to December 2021 were collected.The complete and detailed information from gout cases with completed musculoskeletal ultrasound was included in the analysis.Univariate and multivariate analyses were used to identify independent factors associated with ultrasound-detected tophi.A nomogram was used to visualize the clinical predictive models.Results:Among 517 gout patients,rheumatologists found that 67 patients(13.0%)had subcutaneous tophi by visual observation,while musculoskeletal ultrasound revealed that 123 patients(23.8%)had ultrasound-detected tophi with odds ratio[OR](95%confidence intervals[CIs])=2.20(1.81-2.67).Disease duration,upper limb joint flare(ULJF),persistent joint pain(PJP),uric acid,and homocysteine levels were independently associated with ultrasounddetected tophi,and they had ORs(95%CIs)of 1.092(1.050-1.136),3.732(2.312-6.025),1.864(1.086-3.200),1.003(1.001-1.004),and 1.015(1.000-1.030),respectively.After balancing the complexity and accuracy of the model,Model 2(incorporating disease duration,ULJF,PJP,and uric acid)was chosen to create a nomogram to predict the occurrence of ultrasound-detected tophi.The nomogram had good discrimination(consistency index[C-index]=0.774)and excellent calibration,demonstrated by calibration curves.Conclusion:Using easily available indicators,such as disease duration,the nature of the joint pain,and uric acid levels,we successfully developed an easy-to-use clinical model to improve the detection of hidden tophi.

Flexible Wolf Pack Algorithm for Dynamic Multidimensional Knapsack Problems

Optimization problems especially in a dynamic environment is a hot research area that has attracted notable attention in the past decades.It is clear from the dynamic optimization literatures that most of the efforts have been devoted to continuous dynamic optimization problems although the majority of the real-life problems are combinatorial.Moreover,many algorithms shown to be successful in stationary combinatorial optimization problems commonly have mediocre performance in a dynamic environment.In this study,based on binary wolf pack algorithm(BWPA),combining with flexible population updating strategy,a flexible binary wolf pack algorithm(FWPA)is proposed.Then,FWPA is used to solve a set of static multidimensional knapsack benchmarks and several dynamic multidimensional knapsack problems,which have numerous practical applications.To the best of our knowledge,this paper constitutes the first study on the performance of WPA on a dynamic combinatorial problem.By comparing two state-of-the-art algorithms with the basic BWPA,the simulation experimental results demonstrate that FWPA can be considered as a feasibility and competitive algorithm for dynamic optimization problems.

Review of Dynamic Task Allocation Methods for UAV Swarms Oriented to Ground Targets

Dynamic task allocation of unmanned aerial vehicle swarms for ground targets is an important part of unmanned aerial vehicle(UAV)swarms task planning and the key technology to improve autonomy.The realization of dynamic task allocation in UAV swarms for ground targets is very difficult because of the large uncertainty of swarms,the target and environment state,and the high real-time allocation requirements.Hence,dynamic task allocation of UAV swarms oriented to ground targets has become a key and difficult problem in the field of mission planning.In this work,a dynamic task allocation method for UAV swarms oriented to ground targets is comprehensively and systematically summarized from two aspects:the establishment of an allocation model and the solution of the allocation model.First,the basic concept and trigger scenario are introduced.Second,the research status and the advantages and disadvantages of the two allocation models are analyzed.Third,the research status and the advantages and disadvantages of several common dynamic task allocation algorithms,such as the algorithm based on market mechanisms,intelligent optimization algorithm,and clustering algorithm,are evaluated.Finally,the specific problems of the current UAV swarm dynamic task allocation method for ground targets are highlighted,and future research directions are established.This work offers important reference significance for fully understanding the current situation of UAV swarm dynamic task allocation technology.