Weighted regularized extreme learning machine to model the discharge coefficient of side slots

In the current research, a modern learning machine algorithm named “Weighted Regularized Extreme Learning Machine (WRELM)" is implemented for the first time for the simulation of the coefficient of discharge of side slots. For this purpose, an effective variable on the coefficient of discharge of side slots is firstly introduced, then five distinctive WRELM models are produced by it for the estimation of the coefficient. In the next stage, a database is created for verification of WRELM results. it should be mentioned that 70% of the data are utilized for training the WRELM models, while the rest (i.e. 30%) for testing them. After that, the optimal number of hidden layer neurons as well as the best activation function of the WRELM algorithm are chosen. In addition, the best regularization parameter and also the weight function of the WRELM are achieved. By conducting a sensitivity analysis, the most effective variable for the simulation of the coefficient of discharge along with the WRELM superior model is introduced. The WRELM superior model estimates values of the coefficient of discharge with the maximum exactness and the highest correlation. For instance, the estimations of the correlation coefficient and scatter index for this model are computed to be 0.930 and 0.051, respectively. The sensitivity analysis shows that the ratio of the side slot crest height to its length and the Froude number should be considered as the most important input variables. A comparison between the WRELM with the ELM displays that the former works much better. Furthermore, an uncertainty analysis is executed for both models. Eventually, an equation is suggested for the estimation of the coefficient of discharge and a partial derivative sensitivity analysis is performed on it.     

Olfactory responses of Stegobium paniceum to different Chinese medicinal plant materials and component analysis of volatiles

Stegobium paniceum (L.) (Coleoptera: Anobiidae) is among the major pests of stored products, causing great damage to stored Chinese medicinal plant materials (CMPMs) in China. Effective control strategies are urgently needed. The aim of this study was to explore the role of volatile organic compounds in the host preference of S. paniceum. First, the olfactory behavioral responses of S. paniceum adults to volatiles from four CMPMs (Panax notoginseng, Angelica sinensis, Gastrodia elata, and Peucedanum praeruptorum) were tested in Y-tube olfactometer experiments. Then, the volatile composition of these plant materials were analyzed by gas chromatography-mass spectrometry (GC-MS). S. paniceum showed significant preferences for volatiles from the four CMPMs, compared with clean air (CA). When S. paniceum was presented with choices among different CMPMs, it showed the strongest preference for P. notoginseng, followed by A. sinensis, then G. elata, and P. praeruptorum. GC-MS analysis identified 43, 34, 28 and 60 components in the volatile profiles of P. notoginseng, A. sinensis, G. elata, and P. praeruptorum, respectively. Falcarinol (14.4%), 3-n-butyl phthalide (78.7%), p-cresol (40.1%), and β-pinene (29.1%) were the most abundant components of the volatiles of P. notoginseng, A. sinensis, G. elata, and P. praeruptorum, respectively. The olfactory responses of S. paniceum to the four CMPMs demonstrated that host-related volatiles play an important role in the host-searching process by adult beetles. This information will be useful for the development of safe and effective trapping strategies for this pest.     

Ground robotics in tunnels: Keys and lessons learned after 10 years of research and experiments

The work reported in this article describes the research advances and the lessons learned by the Robotics, Perception and Real‐Time group over a decade of research in the field of ground robotics in confined environments. This study has primarily focused on localization, navigation, and communications in tunnel‐like environments. As will be discussed, this type of environment presents several special characteristics that often make well‐established techniques fail. The aim is to share, in an open way, the experience, errors, and successes of this group with the robotics community so that those that work in such environments can avoid (some of) the errors made. At the very least, these findings can be readily taken into account when designing a solution, without needing to sift through the technical details found in the papers cited within this text.     

Human responses to high levels of carbon dioxide and air temperature

In this study, 30 subjects were exposed to different combinations of air temperature (T_a: 24, 27, and 30°C) and CO₂ level (8000, 10 000, and 12 000 ppm) in a high-humidity (RH: 85%) underground climate chamber. Subjective assessments, physiological responses, and cognitive performance were investigated. The results showed that as compared with exposure to T_a = 24°C, exposure to 30°C at all CO₂ levels caused subjects to feel uncomfortably warm and experience stronger odor intensity, while increased mental effort and greater intensity of acute health symptoms were reported. However, no significant effects of T_a on task performance or physiological responses were found. This indicated that subjects had to exert more effort to maintain their performance in an uncomfortably warm environment. Increasing CO₂ from 8000 to 12 000 ppm at all T_a caused subjects to report higher rates of headache, fatigue, agitation, and feeling depressed, although the results were statistically significant only at 24 and 27°C. The text typing performance and systolic blood pressure (SBP) decreased significantly at this exposure, whereas diastolic blood pressure (DBP) and thermal discomfort increased significantly. These effects suggest higher arousal/stress. No significant interaction effect of T_a and CO₂ concentration on human responses was identified.     

Efficient estimation of extreme quantiles using adaptive kriging and importance sampling

This study considers an efficient method for the estimation of quantiles associated to very small levels of probability (up to O(10⁻⁹)), where the scalar performance function J is complex (eg, output of an expensive-to-run finite element model), under a probability measure that can be recast as a multivariate standard Gaussian law using an isoprobabilistic transformation. A surrogate-based approach (Gaussian Processes) combined with adaptive experimental designs allows to iteratively increase the accuracy of the surrogate while keeping the overall number of J evaluations low. Direct use of Monte-Carlo simulation even on the surrogate model being too expensive, the key idea consists in using an importance sampling method based on an isotropic-centered Gaussian with large standard deviation permitting a cheap estimation of small quantiles based on the surrogate model. Similar to AK-MCS as presented in the work of Schöbi et al., (2016), the surrogate is adaptively refined using a parallel infill criterion of an algorithm suitable for very small failure probability estimation. Additionally, a multi-quantile selection approach is developed, allowing to further exploit high-performance computing architectures. We illustrate the performances of the proposed method on several two to eight-dimensional cases. Accurate results are obtained with less than 100 evaluations of J on the considered benchmark cases.     

Lubricating and physico-chemical properties of CI- 4 plus engine oil dispersed with surface modified multi-walled carbon nanotubes

This paper studies the effect of surface modification of multiwall carbon nanotubes (MWCNTs) prior to dispersion in engine oil to improve the tribological properties. The MWCNTs are stabilised in the lubricant with two different surfactants cetrimonium bromide (CTAB) and sorbitan monooleate (SPAN 80) and the effect of surfactants on the tribological properties has been studied. Pristine and surface modified MWCNTs in weight per cent range of 0·5% are dispersed in CI4 plus diesel engine oil. The foaming tendency and other physico-chemical properties of test lubricant have been studied to investigate the effect of nano materials and surfactants. The anti-wear and anti-friction properties are tested on a four ball wear tester and the comparison is made to assess the relative performance of pristine MWCNTs over surface modified MWCNTs. A strong influence of the surface modification technique is found on lubricating and physico-chemical properties. Both CTAB and SPAN 80 could keep the MWCNTs stable in the lubricant without compromising the foaming tendency of lubricant and other physico-chemical properties. The friction and wear characteristics of lubricants have improved with the dispersion of surface modified MWCNTs while there is no improvement in the properties of lubricant dispersed with pristine MWCNTs.     

Modeling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search

This study presents the optimization of biodiesel engine performance that can achieve the goal of fewer emissions, low fuel cost and wide engine operating range. A new biodiesel engine modeling and optimization framework based on extreme learning machine (ELM) is proposed. As an accurate model is required for effective optimization result, kernel-based ELM (K-ELM) is used instead of basic ELM because K-ELM can provide better generalization performance, and the randomness of basic ELM does not occur in K-ELM. By using K-ELM, a biodiesel engine model is first created based on experimental data. Logarithmic transformation of dependent variables is used to alleviate the problems of data scarcity and data exponentiality simultaneously. With the K-ELM engine model, cuckoo search (CS) is then employed to determine the optimal biodiesel ratio. A flexible objective function is designed so that various user-defined constraints can be applied. As an illustrative study, the fuel price in Macau is used to perform the optimization. To verify the modeling and optimization framework, the K-ELM model is compared with a least-squares support vector machine (LS-SVM) model, and the CS optimization result is compared with particle swarm optimization and experimental results. The evaluation result shows that K-ELM can achieve comparable performance to LS-SVM, resulting in a reliable prediction result for optimization. It also shows that the optimization results based on CS is effective.