Superpixel-based appearance change prediction for long-term navigation across seasons

Changing environments pose a serious problem to current robotic systems aiming at long term operation under varying seasons or local weather conditions. This paper is built on our previous work where we propose to learn to predict the changes in an environment. Our key insight is that the occurring scene changes are in part systematic, repeatable and therefore predictable. The goal of our work is to support existing approaches to place recognition by learning how the visual appearance of an environment changes over time and by using this learned knowledge to predict its appearance under different environmental conditions. We describe the general idea of appearance change prediction (ACP) and investigate properties of our novel implementation based on vocabularies of superpixels (SP-ACP). Our previous work showed that the proposed approach significantly improves the performance of SeqSLAM and BRIEF-Gist for place recognition on a subset of the Nordland dataset under extremely different environmental conditions in summer and winter. This paper deepens the understanding of the proposed SP-ACP system and evaluates the influence of its parameters. We present the results of a large-scale experiment on the complete 10 h Nordland dataset and appearance change predictions between different combinations of seasons.

Prediction of GMA welding characteristic parameter by artificial neural network system

Nowadays, demand for automated Gas metal arc welding (GMAW) is growing and consequently need for intelligent systems is increased to ensure the accuracy of the procedure. To date, welding pool geometry has been the most used factor in quality assessment of intelligent welding systems. But, it has recently been found that Mahalanobis Distance (MD) not only can be used for this purpose but also is more efficient. In the present paper, Artificial Neural Networks (ANN) has been used for prediction of MD parameter. However, advantages and disadvantages of other methods have been discussed. The Levenberg–Marquardt algorithm was found to be the most effective algorithm for GMAW process. It is known that the number of neurons plays an important role in optimal network design. In this work, using trial and error method, it has been found that 30 is the optimal number of neurons. The model has been investigated with different number of layers in Multilayer Perceptron (MLP) architecture and has been shown that for the aim of this work the optimal result is obtained when using MLP with one layer. Robustness of the system has been evaluated by adding noise into the input data and studying the effect of the noise in prediction capability of the network. The experiments for this study were conducted in an automated GMAW setup that was integrated with data acquisition system and prepared in a laboratory for welding of steel plate with 12 mm in thickness. The accuracy of the network was evaluated by Root Mean Squared (RMS) error between the measured and the estimated values. The low error value (about 0.008) reflects the good accuracy of the model. Also the comparison of the predicted results by ANN and the test data set showed very good agreement that reveals the predictive power of the model. Therefore, the ANN model offered in here for GMA welding process can be used effectively for prediction goals.

Remaining useful life prediction of rotating equipment using covariate-based hazard models : industry applications

The ability to estimate the expected Remaining Useful Life (RUL) is critical to reduce maintenance costs, operational downtime and safety hazards. In most industries, reliability analysis is based on the Reliability Centred Maintenance (RCM) and lifetime distribution models. In these models, the lifetime of an asset is estimated using failure time data; however, statistically sufficient failure time data are often difficult to attain in practice due to the fixed time-based replacement and the small population of identical assets. When condition indicator data are available in addition to failure time data, one of the alternate approaches to the traditional reliability models is the Condition-Based Maintenance (CBM). The covariate-based hazard modelling is one of CBM approaches. There are a number of covariate-based hazard models; however, little study has been conducted to evaluate the performance of these models in asset life prediction using various condition indicators and data availability. This paper reviews two covariate-based hazard models, Proportional Hazard Model (PHM) and Proportional Covariate Model (PCM). To assess these models’ performance, the expected RUL is compared to the actual RUL. Outcomes demonstrate that both models achieve convincingly good results in RUL prediction; however, PCM has smaller absolute prediction error. In addition, PHM shows over-smoothing tendency compared to PCM in sudden changes of condition data. Moreover, the case studies show PCM is not being biased in the case of small sample size.

Prediction of shrinkage and porosity during drying: Considering both material properties and process conditions

Drying of food materials offers a significant increase in the shelf life of food materials, along with the modification of quality attributes due to simultaneous heat and mass transfer. Shrinkage and variations in porosity are the common micro and microstructural changes that take place during the drying of mostly the food materials. Although extensive research has been carried out on the prediction of shrinkage and porosity over the time of drying, no single model exists which consider both material properties and process condition in the same model. In this study, an attempt has been made to develop and validate shrinkage and porosity models of food materials during drying considering both process parameters and sample properties. The stored energy within the sample, elastic potential energy, glass transition temperature and physical properties of the sample such as initial porosity, particle density, bulk density and moisture content have been taken into consideration. Physical properties and validation have been made by using a universal testing machine ( Instron 2kN), a profilometer (Nanovea) and a pycnometer. Apart from these, COMSOL Multiphysics 4.4 has been used to solve heat and mass transfer physics. Results obtained from models of shrinkage and porosity is quite consistent with the experimental data. Successful implementation of these models would ensure the use of optimum energy in the course of drying and better quality retention of dried foods.

Prediction of maximal surface electromyographically based voluntary contractions of erector spinae muscles from sonographic measurements during isometric contractions

Objectives Currently, there are no studies combining electromyography (EMG) and sonography to estimate the absolute and relative strength values of erector spinae (ES) muscles in healthy individuals. The purpose of this study was to establish whether the maximum voluntary contraction (MVC) of the ES during isometric contractions could be predicted from the changes in surface EMG as well as in fiber pennation and thickness as measured by sonography. Methods Thirty healthy adults performed 3 isometric extensions at 45° from the vertical to calculate the MVC force. Contractions at 33% and 100% of the MVC force were then used during sonographic and EMG recordings. These measurements were used to observe the architecture and function of the muscles during contraction. Statistical analysis was performed using bivariate regression and regression equations. Results The slope for each regression equation was statistically significant (P < .001) with R2 values of 0.837 and 0.986 for the right and left ES, respectively. The standard error estimate between the sonographic measurements and the regression-estimated pennation angles for the right and left ES were 0.10 and 0.02, respectively. Conclusions Erector spinae muscle activation can be predicted from the changes in fiber pennation during isometric contractions at 33% and 100% of the MVC force. These findings could be essential for developing a regression equation that could estimate the level of muscle activation from changes in the muscle architecture.

The effects of prediction, understanding, and control: A test of the stress antidote model

A study was undertaken to examine further the effects of perceived work control on employee adjustment. On the basis of the stress antidote model, it was proposed that high levels of prediction, understanding, and control of work-related events would have direct, indirect, and interactive effects on levels of employee adjustment. These hypotheses were tested in a short-term longitudinal study of 137 employees of a large retail organization. The stress antidote measures appeared to be indirectly related to employee adjustment, via their effects on perceptions of work stress. There was weak evidence for the proposal that prediction, understanding, and control would buffer the negative effects of work stress. Additional analyses indicated that the observed effects of prediction, understanding, and control were independent of employees' generalized control beliefs. However, there was no support for the proposal that the effects of the stress antidote measures would be dependent on employees' generalized control beliefs.

Use of Skin-Fold Thickness in Sri Lankan Children: Comparison of Several Prediction Equations

Objective There are many prediction equations available in the literature for the assessment of body composition from skinfold thickness (SFT). This study aims to cross validate some of those prediction equations to determine the suitability of their use on Sri Lankan children. Methods Height, weight and SFT of 5 different sites were measured. Total body water was assessed using the isotope dilution method (D2O). Percentage Fat mass (%FM) was estimated from SFT using prediction equations described by five authors in the literature. Results Five to 15 year old healthy, 282 Sri Lankan children were studied. The equation of Brook gave Ihe lowest bias but limits of agreement were high. Equations described by Deurenberg et al gave slightly higher bias but limits of agreement were narrowest and bias was not influence by extremes of body fat. Although prediction equations did not estimate %FM adequately, the association between %FM and SFT measures, were quite satisfactory. Conclusion We conclude that SFT can be used effectively in the assessment of body composition in children. However, for the assessment of body composition using SFT, either prediction equations should be derived to suit the local populations or existing equations should be cross-validated to determine the suitability before its application.

The role of time pressure and different psychological safety climate referents in the prediction of nurses’ hand hygiene compliance

In this study of 638 Australian nurses, compliance to hand hygiene (HH), as defined by the “five moments” recommended by the World Health Organisation (2009), was examined. Hypotheses focused on the extent to which time pressure reduces compliance and safety climate (operationalised in relation to HH using colleagues, manager, and hospital as referents) increases compliance. It also was proposed that HH climate would interact with time pressure, such that the negative effects of time pressure would be less marked when HH climate is high. The extent to which the three HH climate variables would interact among each other, either in the form of boosting or compensatory effects, was tested in an exploratory manner. A prospective research design was used in which time pressure and the HH climate variables were assessed at Time 1 and compliance was assessed by self-report two weeks later. Compliance was high but varied significantly across the 5 HH Moments, suggesting that nurses make distinctions between inherent and elective HH and also seemed to engage in some implicit rationing of HH. Time pressure dominated the utility of HH climate to have its positive impact on compliance. The most conducive workplace for compliance was one low in time pressure and high in HH climate. Colleagues were very influential in determining compliance, more so than the manager and hospital. Manager and hospital support for HH enhanced the positive effects of colleagues on compliance. Providing training and enhancing knowledge was important, not just for compliance, but for safety climate.

Adjustable stiffness, external fixator for the rat femur osteotomy and segmental bone defect models

The mechanical environment around the healing of broken bone is very important as it determines the way the fracture will heal. Over the past decade there has been great clinical interest in improving bone healing by altering the mechanical environment through the fixation stability around the lesion. One constraint of preclinical animal research in this area is the lack of experimental control over the local mechanical environment within a large segmental defect as well as osteotomies as they heal. In this paper we report on the design and use of an external fixator to study the healing of large segmental bone defects or osteotomies. This device not only allows for controlled axial stiffness on the bone lesion as it heals, but it also enables the change of stiffness during the healing process in vivo. The conducted experiments have shown that the fixators were able to maintain a 5 mm femoral defect gap in rats in vivo during unrestricted cage activity for at least 8 weeks. Likewise, we observed no distortion or infections, including pin infections during the entire healing period. These results demonstrate that our newly developed external fixator was able to achieve reproducible and standardized stabilization, and the alteration of the mechanical environment of in vivo rat large bone defects and various size osteotomies. This confirms that the external fixation device is well suited for preclinical research investigations using a rat model in the field of bone regeneration and repair.

Vertically-aligned graphene flakes on nanoporous templates: morphology, thickness, and defect level control by pre-treatment

Various morphologies of the vertically-aligned graphene flakes were fabricated on the nanoporous templates treated with metal ions in solutions, as well as coated with a thin gold layer and activated in the low-temperature Ar plasma. The thickness and level of structural defects in the graphene flakes could be effectively controlled by a proper selection of the pre-treatment method. We have also demonstrated that various combinations of the flake thickness and defect levels can be obtained, and the morphology and density of the graphene pattern can be effectively controlled. The result obtained could be of interest for various applications requiring fabrication of large graphene networks with controllable properties.

Histopathological features of bone regeneration in a canine segmental ulnar defect model

Background Today, finding an ideal biomaterial to treat the large bone defects, delayed unions and non-unions remains a challenge for orthopaedic surgeions and researchers. Several studies have been carried out on the subject of bone regeneration, each having its own advantages. The present study has been designed in vivo to evaluate the effects of cellular auto-transplantation of tail vertebrae on healing of experimental critical bone defect in a dog model. Methods Six indigenous breeds of dog with 32 ± 3.6 kg average weight from both sexes (5 males and 1 female) received bilateral critical-sized ulnar segmental defects. After determining the health condition, divided to 2 groups: The Group I were kept as control I (n = 1) while in Group II (experimental group; n = 5) bioactive bone implants were inserted. The defects were implanted with either autogeneic coccygeal bone grafts in dogs with 3-4 cm diaphyseal defects in the ulna. Defects were stabilized with internal plate fixation, and the control defects were not stabilized. Animals were euthanized at 16 weeks and analyzed by histopathology. Results Histological evaluation of this new bone at sixteen weeks postoperatively revealed primarily lamellar bone, with the formation of new cortices and normal-appearing marrow elements. And also reformation cortical compartment and reconstitution of marrow space were observed at the graft-host interface together with graft resorption and necrosis responses. Finally, our data were consistent with the osteoconducting function of the tail autograft. Conclusions Our results suggested that the tail vertebrae autograft seemed to be a new source of autogenous cortical bone in order to supporting segmental long bone defects in dogs. Furthermore, cellular autotransplantation was found to be a successful replacement for the tail vertebrae allograft bone at 3-4 cm segmental defects in the canine mid- ulna. Clinical application using graft expanders or bone autotransplantation should be used carefully and requires further investigation.

Secondary structure element alignment kernel method for prediction of protein structural classes

In this paper, we aim at predicting protein structural classes for low-homology data sets based on predicted secondary structures. We propose a new and simple kernel method, named as SSEAKSVM, to predict protein structural classes. The secondary structures of all protein sequences are obtained by using the tool PSIPRED and then a linear kernel on the basis of secondary structure element alignment scores is constructed for training a support vector machine classifier without parameter adjusting. Our method SSEAKSVM was evaluated on two low-homology datasets 25PDB and 1189 with sequence homology being 25% and 40%, respectively. The jackknife test is used to test and compare our method with other existing methods. The overall accuracies on these two data sets are 86.3% and 84.5%, respectively, which are higher than those obtained by other existing methods. Especially, our method achieves higher accuracies (88.1% and 88.5%) for differentiating the α + β class and the α/β class compared to other methods. This suggests that our method is valuable to predict protein structural classes particularly for low-homology protein sequences. The source code of the method in this paper can be downloaded at http://math.xtu.edu.cn/myphp/math/research/source/SSEAK_source_code.rar.

Prediction of the fate of oxytetracycline and oxolinic acid in a fish pond using simulation model: A preliminary study

The fate of two popular antibiotics, oxytetracycline and oxolinic acid, in a fish pond were simulated using a computational model. The VDC model, which is designed based on a model for predicting pesticide fate and transport in paddy fields, was modified to take into account the differences between the pond and the paddies as well as those between the fish and the rice plant behaviors. The pond conditions were set following the typical practice in South East Asia aquaculture. The two antibiotics were administered to the animal in the pond through medicated feed during a period of 5 days as in actual practice. Concentrations of oxytetracycline in pond water were higher than those of oxolinic acid at the beginning of the simulation. Dissipation rate of oxytetracycline is also higher as it is more readily available for degradation in the water. For the long term, oxolinic acid was present at higher concentration than oxytetracycline in pond water as well as pond sediment. The simulated results were expected to be conservative and can be useful for the lower tier assessment of exposure risk of veterinary medicine in aquaculture industry but more data are needed for the complete validation of the model.

Ability of commonly used prediction equations to predict resting energy expenditure in children with inflammatory bowel disease

Background: Paediatric onset inflammatory bowel disease (IBD) may cause alterations in energy requirements and invalidate the use of standard prediction equations. Our aim was to evaluate four commonly used prediction equations for resting energy expenditure (REE) in children with IBD. Methods: Sixty-three children had repeated measurements of REE as part of a longitudinal research study yielding a total of 243 measurements. These were compared with predicted REE from Schofield, Oxford, FAO/WHO/UNU, and Harris-Benedict equations using the Bland-Altman method. Results: Mean (±SD) age of the patients was 14.2 (2.4) years. Mean measured REE was 1566 (336) kcal per day compared with 1491 (236), 1441 (255), 1481 (232), and 1435 (212) kcal per day calculated from Schofield, Oxford, FAO/WHO/UNU, and Harris-Benedict, respectively. While the Schofield equation demonstrated the least difference between measured and predicted REE, it, along with the other equations tested, did not perform uniformly across all subjects, indicating greater errors at either end of the spectrum of energy expenditure. Smaller differences were found for all prediction equations for Crohn's disease compared with ulcerative colitis. Conclusions: Of the commonly used equations, the equation of Schofield should be used in pediatric patients with IBD when measured values are not able to be obtained. (Inflamm Bowel Dis 2010;) Copyright © 2010 Crohn's & Colitis Foundation of America, Inc.