Relationship between hip joint medial space ratio and collapse of femoral head in non-traumatic osteonecrosis: a retrospective study

ABSTRACT To retrospectively analyze the medial space ratio (MSR) of the hip joint to evaluate its efficacy in predicting osteonecrosis of femoral head (ONFH)-induced collapse and its impacts on the mechanical environment of necrotic femoral head. In this retrospective analysis of traditional Chinese medicine, non-traumatic ONFH (NONFH) patients from January 2008 to December 2013 were selected. The patients were divided into collapse group and non-collapse group based on whether the femoral head collapsed. The anatomical parameters including center–edge (CE) angle, sharp angle, acetabular depth ratio and MSR were evaluated. Receiver operating characteristic curves were estimated to evaluate the sensitivity and specificity of MSR and CE angle in collapse prediction. The results showed that 135 patients (151 hips) were included in this study. The differences in CE angle and MSR between collapse group and non-collapse group were statistically significant. The mean survival time of the hips of patients with MSR <20.35 was greater (P < 0.001) than that of patients with MSR >20.35. The ONFH patients with MSR >20.35 were prone to stress concentration. We could conclude that the hip joint MSR and CE angle strongly correlated with the collapse of NONFH. The specificity of MSR is higher than that of CE angle. When MSR is >20.35, the collapse rate of ONFH will increase significantly.

Voltage collapse prediction using artificial neural network

Unalleviated voltage instability frequently results in voltage collapse; which is a cause of concern in power system networks across the globe but particularly in developing countries. This study proposed an online voltage collapse prediction model through the application of a machine learning technique and a voltage stability index called the new line stability index (NLSI_1). The approach proposed is based on a multilayer feed-forward neural network whose inputs are the variables of the NLSI_1. The efficacy of the method was validated using the testing on the IEEE 14-bus system and the Nigeria 330-kV, 28-bus National Grid (NNG). The results of the simulations indicate that the proposed approach accurately predicted the voltage stability index with an R-value of 0.9975 with a mean square error (MSE) of 2.182415x10⁻⁵ for the IEEE 14-bus system and an R-value of 0.9989 with an MSE of 1.2527x10⁻⁷ for the NNG 28 bus system. The results presented in this paper agree with those found in the literature.

WILL FISH DISAPPEAR BY 2048?

In 2006, a tumult arose in the world of fisheries. A controversial paper titled “Impacts of biodiversity loss on ocean ecosystem services” by Worm et al. (2006) was published in Science. The paper was sensational since it alluded to a prediction that global populations of marine fish (finfish and invertebrates) will be 100% collapsed by 2048. The paper was written by a group of marine ecologists and economists in which Boris Worm from Dalhousie University Canada led the authorship. After the paper was published, the issue of fish disappearance in 2048 became hot topics in the world’s mass media. In fact, the Worm et al. paper triggered the debates among researchers. Over time the debates heated up. Surprisingly, a reconciliation took place in 2009, marked by a collaboration between Worm’s team and his critics, writing another paper in Science. The present essay reaffirms the invalidity of the global collapse prediction in 2048 as revealed by many researchers. It is also shown that the Worm et al. paper did not state that all fish will disappear and through the joint paper in 2009, Worm and colleagues have indirectly rectified the prediction already.

A KNN Based Collapse Methodology and Recent Qualification of Flexible Pipes in Deepwater Application

As the flexible pipe industry targets more on deepwater applications, collapse performance of flexible pipes becomes a key challenge due to the huge hydrostatic pressure during installation and service. The collapse strength of flexible pipes largely depends on the structural characteristics of carcass, pressure sheath and pressure armor layers. Therefore, the collapse prediction methodology involving a sound modeling of these layers is essential. Over the years, Baker Hughes have collected a large amount of collapse testing data. The prediction tool needs to be validated and calibrated against all the collapse tests for best accuracy. In this paper, the latest progress of the collapse prediction methodology and qualification tests are presented. A generalized collapse model was developed to predict the collapse pressure of flexible pipes. This model incorporates the advantages of both the weighted kNN regression technique and an analytical collapse model. It is able to reproduce the exact collapse pressure on the pipes tested and can predict the collapse pressure of other pipe designs not tested. As part of the qualification process, the capacity to prevent collapse must be demonstrated. Several flexible pipes were designed based on this generalized prediction methodology for deep water application, and pipe samples were manufactured using industrial production facilities for collapse tests. The results show that flexible pipes following current design guidelines are suitable for deepwater applications.