RESUMEN
There are many applications from computer hardware and sensors to thin films and coatings in which parts are fabricated in small sizes and low thicknesses. Most of these applications could undergo cyclic loading and unloading conditions during their operation. Therefore, cyclic and fatigue evaluations of these components are an essential topic and should be fully understood. In these cases, due to the dimensional limitations, conventional methods of the fatigue experiments encounter challenges and mostly are not accurate or applicable. Nano- and micro-indentation fatigue tests are considered non- or semi-destructive experiments that have opened a new approach to study the cyclic response of these small-sized specimens and thin films. The objective of the present review paper is to evaluate a convenient, reliable, and non-destructive testing approach in the assessment of fatigue (cyclic) response of materials on a small scale. Along with conventional bulk scale fatigue testing methods (i.e. reverse bending, pull-push, multi-axial bending), the depth-sensing indentation testing technique can be employed to study the cyclic behavior of metallic and non-metallic materials especially when a limited volume of the material is available. In this paper, we tried to cover most of the previous studies performed on indentation fatigue of composites, thin films, coatings, and ceramics along with associated discussions and main findings. We covered the physics behind the indentation and the difference between the indentation and conventional fatigue analyses. Followed by that, microstructural evaluations of some of the studies are provided to give readers more insights into this approach. In most applications, the indentation fatigue technique could be a reliable solution due to its accuracy, simplicity, and nondestructive approach in finding out the fatigue and cyclic behavior of materials having a small size or volume. It is worth noting that the loading mode in the indentation fatigue is completely different than the traditional (bulk-scale) fatigue as the tensile segment of the load cycle is not produced in the indentation fatigue (it is a compression-compression loading cycle). Therefore, the controlling mechanisms of failure between small-scale fatigue and bulk-scale fatigue may not be the same.
RESUMEN
OBJECTIVE: To evaluate the association between vitamin D status and diarrhoeal episodes by enterotoxigenic (ETEC), enteropathogenic (EPEC) and enteroaggregative (EAEC) E. coli in underweight and normal-weight children aged 6-24 months in urban Bangladesh. METHODS: Cohorts of 446 normal-weight and 466 underweight children were tested separately for ETEC, EPEC and EAEC from diarrhoeal stool samples collected during 5 months of follow-up while considering vitamin D status at enrolment as the exposure. Cox proportional hazards models with unordered failure events of the same type were used to determine diarrhoeal risk factors after adjusting for sociodemographic and concurrent micronutrient status. RESULTS: Vitamin D status was not independently associated with the risk of incidence of ETEC, EPEC and EAEC diarrhoea in underweight children, but moderate-to-severe retinol deficiency was associated with reduced risk for EPEC diarrhoea upon adjustment. Among normal-weight children, insufficient vitamin D status and moderate-to-severe retinol deficiency were independently associated with 44% and 38% reduced risk of incidence of EAEC diarrhoea, respectively. These children were at higher risk of ETEC diarrhoea with vitamin D deficiency status when adjusted for micronutrient status only. CONCLUSION: This study demonstrates for the first time that normal-weight children with insufficient vitamin D status have a reduced risk of EAEC diarrhoea than children with sufficient status. Moderate-to-severe deficiency of serum retinol is associated with reduced risk of EPEC and EAEC diarrhoea in underweight and normal-weight children.
RESUMEN
Presents a constructive algorithm for training cooperative neural-network ensembles (CNNEs). CNNE combines ensemble architecture design with cooperative training for individual neural networks (NNs) in ensembles. Unlike most previous studies on training ensembles, CNNE puts emphasis on both accuracy and diversity among individual NNs in an ensemble. In order to maintain accuracy among individual NNs, the number of hidden nodes in individual NNs are also determined by a constructive approach. Incremental training based on negative correlation is used in CNNE to train individual NNs for different numbers of training epochs. The use of negative correlation learning and different training epochs for training individual NNs reflect CNNEs emphasis on diversity among individual NNs in an ensemble. CNNE has been tested extensively on a number of benchmark problems in machine learning and neural networks, including Australian credit card assessment, breast cancer, diabetes, glass, heart disease, letter recognition, soybean, and Mackey-Glass time series prediction problems. The experimental results show that CNNE can produce NN ensembles with good generalization ability.
