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Wind power is one of the most accessible renewable energy. Wind speed forecasting with high accuracy, will be effective for the development of this power. This paper presents an appropriate solution for Wind speed forecasting problem, using three hybrid neural networks based on divide and conquer. The three networks are boosting by filtering (BF), mixture of expert (ME) and boosted mixture of experts (BME) respectively. In these networks, the problem spaces are divided between the base classifiers and then, with a determined approach arecombined. Tests based on actual wind data of Mahshahr show that the BME method can predict the wind speed with higher accuracy compared to other methods. In boosted mixture of experts at first, the problem space divided by boosting structure and then obtained weight from this structure, considered as the initial weight of the mixture. For main classifier of all structure, weused multilayer perceptron neural network (MLP).Also, both error criterion and performance have been used for assessing the results.

Purpose Measurement of vertebral axial rotation (VAR) is relevant for the assessment of scoliosis. Stokes method allows estimating VAR in frontal X-rays from the relative position of the pedicles and the vertebral body. This method requires identifying these landmarks for each vertebral level, which is time-consuming. In this work, a quasi-automated method for pedicle detection and VAR estimation was proposed. Method A total of 149 healthy and adolescent idiopathic scoliotic (AIS) subjects were included in this retrospective study. Their frontal X-rays were collected from multiple sites and manually annotated to identify the spinal midline and pedicle positions. Then, an automated pedicle detector was developed based on image analysis, machine learning and fast manual identification of a few landmarks. VARs were calculated using the Stokes method in a validation dataset of 11 healthy (age 6–33 years) and 46 AIS subjects (age 6–16 years, Cobb 10°–46°), both from detected pedicles and those manually annotated to compare them. Sensitivity of pedicle location to the manual inputs was quantified on 20 scoliotic subjects, using 10 perturbed versions of the manual inputs. Results Pedicles centers were localized with a precision of 84% and mean difference of 1.2 ± 1.2 mm, when comparing with manual identification. Comparison of VAR values between automated and manual pedicle localization yielded a signed difference of − 0.2 ± 3.4°. The uncertainty on pedicle location was smaller than 2 mm along each image axis. Conclusion The proposed method allowed calculating VAR values in frontal radiographs with minimal user intervention and robust quasi-automated pedicle localization. The authors are grateful to the ParisTech BiomecAM chair program on subject-specific musculoskeletal modeling for funding (with the support of ParisTech and Yves Cotrel Foundations, Société Générale, Proteor and Covea).

Renewable energy technologies around the world have become a priority today and among them, hydropower plants have the largest share. The most significant problem with small hydropower plants is the high cost of commercial turbines. The most economical way to generate small-scale energy is to use a pump as turbine (PAT) due to the cheapness and availability of the pump. In water distribution networks (WDNs), the PATs can be used instead of pressure reducing valves (PRVs) for both pressure reduction and energy production. The installed PAT must be capable of operating under different discharges due to fluctuations in rate of water consumption, which makes it challenging to select the appropriate pump. In this study, the process of PATs selection for PRVs replacement is optimized using particle swarm optimization (PSO) algorithm. Three different scenarios are performed for the optimization process, considering constant and variable speed PATs. It is worth mentioning that the wasted energy in the selected case study is about 494 kWh/day using PRVs. The results show, in scenario no. 3 (variable speed PATs), the total amount of produced energy is 182.15 kWh/day. Also, the penalty function is decreased by about 62% in comparison to other scenarios and as a result, the pressure of the critical nodes has better agreement to those values of PRVs utilization.

To design a quasi-automated three-dimensional reconstruction method of the spine from biplanar X-rays as the daily used method in clinical routine is based on manual adjustments of a trained operator and the reconstruction time is more than 10 min per patient. The proposed method of 3D reconstruction of the spine (C3–L5) relies first on a new manual input strategy designed to fit clinicians’ skills. Then, a parametric model of the spine is computed using statistical inferences, image analysis techniques and fast manual rigid registration. An agreement study with the clinically used method on a cohort of 57 adolescent scoliotic subjects has shown that both methods have similar performance on vertebral body position and axial rotation (null bias in both cases and standard deviation of signed differences of 1 mm and 3.5° around, respectively). In average, the solution could be computed in less than 5 min of operator time, even for severe scoliosis. The proposed method allows fast and accurate 3D reconstruction of the spine for wide clinical applications and represents a significant step towards full automatization of 3D reconstruction of the spine. Moreover, it is to the best of our knowledge the first method including also the cervical spine. These slides can be retrieved under electronic supplementary material.

Quantitative measurements of spine shape parameters on planar X-ray images is critical for clinical applications but remains tedious and with no fully-automated solution demonstrated on the whole spine. This study aims to limit manual input, while demonstrating precise vertebrae corners positioning and shape parameter measurements from sagittal radiographs of the cervical and lumbar regions, exploiting novel dedicated visual features and specialized classifiers. A database of manually annotated X-ray images is used to train specialized Random Forest classifiers for each spine regions and corner types. An original combination of local gradient characteristics, Haar-like features, and contextual features based on patch intensity and contrast is used as visual features. The proposed method is evaluated on 49 sagittal X-rays of asymptomatic and pathological subjects, from multiple imaging sites, and with a large age range (6 – 69 years old). Performance is first evaluated for positioning a 2D spine shape model, where precisely detected corners enable to adjust the model via an original multilinear statistical regression. Root-mean square errors (RMSE) of corners localization and vertebra orientations are reported, demonstrating state-of-the-art precision compared to existing methods, but with minimal manual input. The method is then evaluated for the extraction of additional vertebrae shape characteristics, such as centre positioning, endplate centres positioning and endplate length measures, rarely studied in previous literature. The proposed method enables, with minimal initialization, fast and precise individual vertebrae delineations on sagittal radiographs on normal and pathological cases, with a level of precision and robustness required for objective support for diagnosis and therapy decision making. BiomecAM chair program

This research has been carried out for determining the association of Social Support and Self-Efficacy with Academic Achievement and School Satisfaction among female junior high school students in Birjand. For this purpose, 240 students were selected from 10 schools using the Multi-Stage Sampling Method. The results revealed that two components, i.e. “Self-Regulation” and “Test Taking”, are meaningful predictors for Academic Achievement, where as the component, “Reading” and Social Support are not meaningful predictors for the same. Moreover, the same two components of “Self-Regulation” and “Test Taking” along with the component “Teacher” are seen to be meaningful predictors for School Satisfaction.

International audience; 3D reconstruction of the spine using biplanar X-rays remains approximate and requires human-machine interactions to adjust the position of important features such as vertebral corners and endplates. The purpose of this study is to develop a method to extract automatically the accurate position of lumbar vertebrae posterior corners. In the proposed method we select corner point candidates from an initial edge map. A dedicated pipeline is designed to discard unwanted candidates, involving polyline simplification, curvature thresholding and multiscale Haar filtering. Ultimately, we use a priori knowledge derived from an initial 3D spine model to define search areas and select the final corner points. The framework was tested on 21 biplanar X-rays from scoliotic children. Corner positions are compared with manual selections by two experts. The results report a localization accuracy between 0.7 and 1.6 mm, comparable to manual expert variability.

This research determines the relationship between Spiritual Intelligence (SI) and Emotional Intelligence (EI) with Life Satisfaction (LS) among gifted female high school students in Birjand. For this purpose, 123 students were selected considering the Simple Sampling Method. The results revealed that there is generally no meaningful relation between SI and LS, but a meaningful relation between EI and LS does exist. The results of regression analysis showed that “Moral Virtue“in SI and “Appraisal & Expression of Emotion“and “Regulation of Emotion“in EI are meaningful predictors for LS. Also SI together with EI have a meaningful relationship with LS.