scholarly journals Application of single-level and multi-level modeling approach to examine geographic and socioeconomic variation in underweight, overweight and obesity in Nepal: findings from NDHS 2016

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Nipun Shrestha ◽  
Shiva Raj Mishra ◽  
Saruna Ghimire ◽  
Bishal Gyawali ◽  
Pranil Man Singh Pradhan ◽  
...  
2021 ◽  
Vol 13 (8) ◽  
pp. 1602
Author(s):  
Qiaoqiao Sun ◽  
Xuefeng Liu ◽  
Salah Bourennane

Deep learning models have strong abilities in learning features and they have been successfully applied in hyperspectral images (HSIs). However, the training of most deep learning models requires labeled samples and the collection of labeled samples are labor-consuming in HSI. In addition, single-level features from a single layer are usually considered, which may result in the loss of some important information. Using multiple networks to obtain multi-level features is a solution, but at the cost of longer training time and computational complexity. To solve these problems, a novel unsupervised multi-level feature extraction framework that is based on a three dimensional convolutional autoencoder (3D-CAE) is proposed in this paper. The designed 3D-CAE is stacked by fully 3D convolutional layers and 3D deconvolutional layers, which allows for the spectral-spatial information of targets to be mined simultaneously. Besides, the 3D-CAE can be trained in an unsupervised way without involving labeled samples. Moreover, the multi-level features are directly obtained from the encoded layers with different scales and resolutions, which is more efficient than using multiple networks to get them. The effectiveness of the proposed multi-level features is verified on two hyperspectral data sets. The results demonstrate that the proposed method has great promise in unsupervised feature learning and can help us to further improve the hyperspectral classification when compared with single-level features.


Author(s):  
Michelle Priante ◽  
David Tyrell ◽  
Benjamin Perlman

In train collisions, multi-level rail passenger vehicles can deform in modes that are different from the behavior of single level cars. The deformation in single level cars usually occurs at the front end during a collision. In one particular incident, a cab car buckled laterally near the back end of the car. The buckling of the car caused both lateral and vertical accelerations, which led to unanticipated injuries to the occupants. A three-dimensional collision dynamics model of a multi-level passenger train has been developed to study the influence of multi-level design parameters and possible train configuration variations on the reactions of a multi-level car in a collision. This model can run multiple scenarios of a train collision. This paper investigates two hypotheses that could account for the unexpected mode of deformation. The first hypothesis emphasizes the non-symmetric resistance of a multi-level car to longitudinal loads. The structure is irregular since the stairwells, supports for tanks, and draglinks vary from side to side and end to end. Since one side is less strong, that side can crush more during a collision. The second hypothesis uses characteristics that are nearly symmetric on each side. Initial imperfections in train geometry induce eccentric loads on the vehicles. For both hypotheses, the deformation modes depend on the closing speed of the collision. When the characteristics are non-symmetric, and the load is applied in-line, two modes of deformation are seen. At low speeds, the couplers crush, and the cars saw-tooth buckle. At high speeds, the front end of the cab car crushes, and the cars remain in-line. If an offset load is applied, the back stairwell of the first coach car crushes unevenly, and the cars saw-tooth buckle. For the second hypothesis, the characteristics are symmetric. At low speeds, the couplers crush, and the cars remain in-line. At higher speeds, the front end crushes, and the cars remain in-line. If an offset load is applied to a car with symmetric characteristics, the cars will saw-tooth buckle.


2016 ◽  
Vol 88 (1) ◽  
pp. 63-77
Author(s):  
Carolina Fortuna ◽  
Eli De Poorter ◽  
Primož Škraba ◽  
Ingrid Moerman

2016 ◽  
Vol 11 (1) ◽  
pp. 91-100 ◽  
Author(s):  
Gyan Bahadur Thapa ◽  
Sergei Silvestrov

The multi-level just-in-time sequencing problem is one of the challenging research areas in supply chain management. In this paper, we present brief review and some recent research developments of just-in-time production systems together with supply chain logistics. Observing production flows and supply chain synchronization in production process, we present the mathematical models of just-in-time (JIT) sequencing problem in multi-level and single-level as nonlinear integer programming in terms of discrepancy functions under the specified constraints. Discrete apportionment approach is briefly reported as an efficient frontier for single-level.  Journal of the Institute of Engineering, 2015, 11(1): 91-100


2016 ◽  
Vol 17 (1) ◽  
pp. 269-294 ◽  
Author(s):  
Muzaffar Igamberdiev ◽  
Georg Grossmann ◽  
Matt Selway ◽  
Markus Stumptner

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