The chick retina can discern both the sign and the magnitude of optical defocus. Chick eyes were able to integrate blur cues from simultaneously presented images focused either side of the photoreceptors and to modulate their refractive development accordingly. This implies that the complex nature of defocus in the visual environment may play a critical role in the pathogenesis of myopia. The results suggest a rational method for arresting or reversing the development of myopia, which may be useful in the treatment of human myopia if the primate retina is also capable of responding to simultaneously presented opposing defocus cues.

Large area flexible electronics rely on organic or hybrid materials prone to degradation limiting the device lifetime. For many years, photo-oxidation has been thought to be one of the major degradation pathways. However, intense illumination may lead to a burn-in or a rapid decrease in performance for devices completely isolated from corrosive elements as oxygen or moisture. The experimental studies we present in here indicate that a plausible triggering for the burn-in is a spin flip after a UV photon absorption leading to the accumulation of electrostatic potential energy that initiates a rapid destruction of the nanomorpholgy in the fullerene phase of a polymer cell. To circumvent this and achieve highly stable and efficient devices, we induce a robust nano-crystalline ordering in the PCBM phase prior to UV illumination. In that event, PTB7-Th:PC 71 BM cells are shown to exhibit T 80 lifetimes larger than 1.6 years under a continuous UV-filtered 1-sun illumination, equivalent to 7 years for sunlight harvesting at optimal orientation and 10 years for vertical applications.

OH can be successfully treated by endoscopic surgery. CT and MR examination provide characteristic findings for prediction and careful surgical planning.

For the artificial ground freezing (AGF) projects in highly permeable formations, the effect of groundwater flow cannot be neglected. Based on the heat transfer and seepage theory in porous media with the finite element method, a fully coupled numerical model was established to simulate the changes of temperature field and groundwater flow field. Firstly, based on the classic analytical solution for the frozen temperature field, the model’s ability to solve phase change problems has been validated. In order to analyze the influences of different parameters on the closure time of the freezing wall, we performed the sensitivity analysis for three parameters of this numerical model. The analysis showed that, besides the head difference, the thermal conductivity of soil grain and pipe spacing are also the key factors that control the closure time of the frozen wall. Finally, a strengthening project of a metro tunnel with AGF method in South China was chosen as a field example. With the finite element software COMSOL Multiphysics® (Stockholm, Sweden), a three-dimensional (3D) numerical model was set up to simulate the change of frozen temperature field and groundwater flow field in the project area as well as the freezing process within 50 days. The simulation results show that the freezing wall appears in an asymmetrical shape with horizontal groundwater flow normal to the axial of the tunnel. Along the groundwater flow direction, freezing wall forms slowly and on the upstream side the thickness of the frozen wall is thinner than that on the downstream side. The actual pipe spacing has an important influence on the temperature field and closure time of the frozen wall. The larger the actual pipe spacing is, the slower the closing process will be. Besides this, the calculation for the average temperature of freezing body (not yet in the form of a wall) shows that the average temperature change of the freezing body coincides with that of the main frozen pipes with the same trend.

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Hip bone fracture is one of the most important causes of morbidity and mortality in the elder adults. It is necessary to establish a prediction model to provide suggestions for elders. A total of 725 subjects were involved, including 228 patients with first low-trauma hip fracture and 497 ages-, sex-, and living area-matched controls (215 from the same hospital and 282 from community). All the subjects were interviewed with the same questionnaire, and the answers of the interviewees were recorded to be the database. Three-layer back-propagation Artificial Neural Networks (ANN) models were applied for females and males separately in this study to predict the risk of hip bone fracture for elders. Furthermore, to improve the accuracies and the generalizations of the models, the ensemble ANNs method was applied. To understand variables contributions and find the important variables for predicting hip fracture, sensitivity analysis and connection weights approach were applied. In this study, three ANNs prediction models were tested with different architectures. With the fivefold crossvalidation method evaluating the performances, one of the three models turned out to be the best prediction model and achieved a big success of prediction. The best area under the receiver operating characteristic (ROC) curve and the accuracy of the prediction model are 0.91 ± 0.028 (mean ± SD) and 0.85 ± 0.029 for females, while for males are 0.99 ± 0.015 and 0.93 ± 0.020. With the method of sensitivity analysis and connection weights, input variables were ranked according to contributions/importance, and the top 10 variables show great proportion of contribution to predict hip fracture. The top 10 important variables causing hip fracture for both females and males are similar to our previous results got from logistic regression model and other related researches. In conclusion, ANNs has successfully been to establish prediction models for predicting the risk of hip bone fracture for both female and male elder adults respectively and identified the top 10 important variables from 74 input variables to predict hip bone fracture of elders. This study verified the performance of ANNs to be a highly complex prediction model. Sept 2014Dear Prof. R. Allen, I use the electronic version to send this manuscript to you. The manuscript title is: "Ensemble back-propagation neural networks for predicting the risk of hip bone fracture for elders in Taiwan". We are submitting this material for possible publication in "Biomedical Signal Processing and Control". This material has not been submitted for publication or published elsewhere in whole or part. We believe this manuscript represents an original and significant contribution to the field of "Neural networks for predicting the risk of hip bone fracture" and therefore would like to be considered for publication in "Original Articles". AbstractHip bone fracture is one of the most important causes of morbidity and mortality in the elder adults. It is necessary to establish a prediction mod...