Radiographic arthrosis after elbow trauma : interobserver reliability

Purpose : This study measured observer variation in radiographic rating of elbow arthrosis.

Methods : Thirty-seven independent orthopedic surgeons graded the extent of elbow arthrosis in 20 consecutive sets of plain radiographs, according to the Broberg and Morrey rating system (grade 0, normal joint; grade 1, slight joint-space narrowing with minimum osteophyte formation; grade 2, moderate joint-space narrowing with moderate osteophyte formation; and grade 3, severe degenerative change with gross destruction of the joint). The kappa multirater measure (κ) was used to estimate reliability between observers, with 0 indicating no agreement above chance, and 1 indicating perfect agreement.

Results : There was fair agreement in arthrosis ratings between surgeons. Surgeons with more than 10 years of experience had greater agreement than did surgeons with less experience, and surgeons who treated more than 10 elbow fractures per year had better agreement than did those treating fewer fractures. In post hoc analyses, 2 simplified binary rating systems (eg, “none or mild” vs “moderate or severe” arthrosis) resulted in moderate agreement among observers.

Conclusions : The 4 grades of the Broberg and Morrey classification system have only fair interobserver reliability that is influenced by subspecialty and experience. Binary rating systems might be more reliable.

Simulation-based input loading condition optimisation of airport baggage handling systems

Scheduling check-in station operations are a challenging problem within airport systems. Prior to determining check-in resource schedules, an important step is to estimate the Baggage Handling System (BHS) operating capacity under non-stationary conditions. This ensures that check-in stations are not overloaded with bags, which would adversely affect the system and cause cascade stops and blockages. Cascading blockages can potentially lead to a poor level of service and in worst scenario a customer may depart without their bags. This paper presents an empirical study of a multiobjective problem within a BHS system. The goal is to estimate near optimal input operating conditions, such that no blockages occurs at check-in stations, while minimising the baggage travel time and maximising the throughput performance measures. We provide a practical hybrid simulation and binary search technique to determine a near optimal input throughput operating condition. The algorithm generates capacity constraint information that may be used by a scheduler to plan check-in operations based on flight arrival schedules.

Extension of bivariate means to weighted means of several arguments by using binary trees

ABSTRACTAveraging aggregation functions are valuable in building decision making and fuzzy logic systems and in handling uncertainty. Some interesting classes of averages are bivariate and not easily extended to the multivariate case. We propose a generic method for extending bivariate symmetric means to n-variate weighted means by recursively applying the specified bivariate mean in a binary tree construction. We prove that the resulting extension inherits many desirable properties of the base mean and design an efficient numerical algorithm by pruning the binary tree. We show that the proposed method is numerically competitive to the explicit analytical formulas and hence can be used in various computational intelligence systems which rely on aggregation functions.

A fast malware feature selection approach using a hybrid of multi-linear and stepwise binary logistic regression

Malware replicates itself and produces offspring with the same characteristics but different signatures by using code obfuscation techniques. Current generation anti-virus engines employ a signature-template type detection approach where malware can easily evade existing signatures in the database. This reduces the capability of current anti-virus engines in detecting malware. In this paper, we propose a stepwise binary logistic regression-based dimensionality reduction techniques for malware detection using application program interface (API) call statistics. Finding the most significant malware feature using traditional wrapper-based approaches takes an exponential complexity of the dimension (m) of the dataset with a brute-force search strategies and order of (m-1) complexity with a backward elimination filter heuristics. The novelty of the proposed approach is that it finds the worst case computational complexity which is less than order of (m-1). The proposed approach uses multi-linear regression and the p-value of each individual API feature for selection of the most uncorrelated and significant features in order to reduce the dimensionality of the large malware data and to ensure the absence of multi-collinearity. The stepwise logistic regression approach is then employed to test the significance of the individual malware feature based on their corresponding Wald statistic and to construct the binary decision the model. When the selected most significant APIs are used in a decision rule generation systems, this approach not only reduces the tree size but also improves classification performance. Exhaustive experiments on a large malware data set show that the proposed approach clearly exceeds the existing standard decision rule, support vector machine-based template approach with complete data and provides a better statistical fitness.