Theoretical analysis of serrated chip formation based on ideal models in high speed cutting

In this paper, two ideal formation models of serrated chips, the symmetric formation model and the unilateral right-angle formation model, have been established for the first time. Based on the ideal models and related adiabatic shear theory of serrated chip formation, the theoretical relationship among average tooth pitch, average tooth height and chip thickness are obtained. Further, the theoretical relation of the passivation coefficient of chip's sawtooth and the chip thickness compression ratio is deduced as well. The comparison between these theoretical prediction curves and experimental data shows good agreement, which well validates the robustness of the ideal chip formation models and the correctness of the theoretical deducing analysis. The proposed ideal models may have provided a simple but effective theoretical basis for succeeding research on serrated chip morphology. Finally, the influences of most principal cutting factors on serrated chip formation are discussed on the basis of a series of finite element simulation results for practical advices of controlling serrated chips in engineering application.

Nanoporous Graphitic-C3N4@Carbon Metal-Free Electrocatalysts for Highly Efficient Oxygen Reduction

Based on theoretical prediction, a g-C3N4@carbon metal-free oxygen reduction reaction (ORR) electrocatalyst was designed and synthesized by uniform incorporation of g-C3N4 into a mesoporous carbon to enhance the electron transfer efficiency of g-C3N4. The resulting g-C3N4@carbon composite exhibited competitive catalytic activity (11.3 mA cm–2 kinetic-limiting current density at −0.6 V) and superior methanol tolerance compared to a commercial Pt/C catalyst. Furthermore, it demonstrated significantly higher catalytic efficiency (nearly 100% of four-electron ORR process selectivity) than a Pt/C catalyst. The proposed synthesis route is facile and low-cost, providing a feasible method for the development of highly efficient electrocatalysts.

“Success breeds success” or “Pride goes before a fall”? ☆: Teams and individuals in multi-contest tournaments

We study the impact of progress feedback on players' performance in multi-contest team tournaments, in which team members' efforts are not directly substitutable. In particular, we employ a real-effort laboratory experiment to understand, in a best-of-three tournament, how players' strategic mindsets change when they compete on a team compared to when they compete individually. Our data corroborate the theoretical predictions for teams: Neither a lead nor a lag in the first component contest affects a team's performance in the subsequent contests. In individual tournaments, however, contrary to the theoretical prediction, we observe that leaders perform worse—but laggards perform better—after learning the outcome of the first contest. Our findings offer the first empirical evidence from a controlled laboratory of the impact of progress feedback between team and individual tournaments, and contribute new insights on team incentives.

The explicit hazard model – part 1: theoretical development

Modern Engineering Asset Management (EAM) requires the accurate assessment of current and the prediction of future asset health condition. Appropriate mathematical models that are capable of estimating times to failures and the probability of failures in the future are essential in EAM. In most real-life situations, the lifetime of an engineering asset is influenced and/or indicated by different factors that are termed as covariates. Hazard prediction with covariates is an elemental notion in the reliability theory to estimate the tendency of an engineering asset failing instantaneously beyond the current time assumed that it has already survived up to the current time. A number of statistical covariate-based hazard models have been developed. However, none of them has explicitly incorporated both external and internal covariates into one model. This paper introduces a novel covariate-based hazard model to address this concern. This model is named as Explicit Hazard Model (EHM). Both the semi-parametric and non-parametric forms of this model are presented in the paper. The major purpose of this paper is to illustrate the theoretical development of EHM. Due to page limitation, a case study with the reliability field data is presented in the applications part of this study.

Neural network learning:theoretical foundations

This important work describes recent theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Chapters survey research on pattern classification with binary-output networks, including a discussion of the relevance of the Vapnik Chervonenkis dimension, and of estimates of the dimension for several neural network models. In addition, Anthony and Bartlett develop a model of classification by real-output networks, and demonstrate the usefulness of classification with a "large margin." The authors explain the role of scale-sensitive versions of the Vapnik Chervonenkis dimension in large margin classification, and in real prediction. Key chapters also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient, constructive learning algorithms. The book is self-contained and accessible to researchers and graduate students in computer science, engineering, and mathematics

Prediction of pull-out force of multi-walled carbon nanotube (MWCNT) in sword-in-sheath mode

The pull-out force of some outer walls against other inner walls in multi-walled carbon nanotubes (MWCNTs) was systematically studied by molecular mechanics simulations. The obtained results reveal that the pull-out force is proportional to the square of the diameter of the immediate outer wall on the sliding interface, which highlights the primary contribution of the capped section of MWCNT to the pull-out force. A simple empirical formula was proposed based on the numerical results to predict the pull-out force for an arbitrary pull-out in a given MWCNT directly from the diameter of the immediate outer wall on the sliding interface. Moreover, tensile tests for MWCNTs with and without acid-treatment were performed with a nanomanipulator inside a vacuum chamber of a scanning electron microscope (SEM) to validate the present empirical formula. It was found that the theoretical pull-out forces agree with the present and some previous experimental results very well.

Asset health prediction using the explicit hazard model

The ability to estimate the asset reliability and the probability of failure is critical to reducing maintenance costs, operation downtime, and safety hazards. Predicting the survival time and the probability of failure in future time is an indispensable requirement in prognostics and asset health management. In traditional reliability models, the lifetime of an asset is estimated using failure event data, alone; however, statistically sufficient failure event data are often difficult to attain in real-life situations due to poor data management, effective preventive maintenance, and the small population of identical assets in use. Condition indicators and operating environment indicators are two types of covariate data that are normally obtained in addition to failure event and suspended data. These data contain significant information about the state and health of an asset. Condition indicators reflect the level of degradation of assets while operating environment indicators accelerate or decelerate the lifetime of assets. When these data are available, an alternative approach to the traditional reliability analysis is the modelling of condition indicators and operating environment indicators and their failure-generating mechanisms using a covariate-based hazard model. The literature review indicates that a number of covariate-based hazard models have been developed. All of these existing covariate-based hazard models were developed based on the principle theory of the Proportional Hazard Model (PHM). However, most of these models have not attracted much attention in the field of machinery prognostics. Moreover, due to the prominence of PHM, attempts at developing alternative models, to some extent, have been stifled, although a number of alternative models to PHM have been suggested. The existing covariate-based hazard models neglect to fully utilise three types of asset health information (including failure event data (i.e. observed and/or suspended), condition data, and operating environment data) into a model to have more effective hazard and reliability predictions. In addition, current research shows that condition indicators and operating environment indicators have different characteristics and they are non-homogeneous covariate data. Condition indicators act as response variables (or dependent variables) whereas operating environment indicators act as explanatory variables (or independent variables). However, these non-homogenous covariate data were modelled in the same way for hazard prediction in the existing covariate-based hazard models. The related and yet more imperative question is how both of these indicators should be effectively modelled and integrated into the covariate-based hazard model. This work presents a new approach for addressing the aforementioned challenges. The new covariate-based hazard model, which termed as Explicit Hazard Model (EHM), explicitly and effectively incorporates all three available asset health information into the modelling of hazard and reliability predictions and also drives the relationship between actual asset health and condition measurements as well as operating environment measurements. The theoretical development of the model and its parameter estimation method are demonstrated in this work. EHM assumes that the baseline hazard is a function of the both time and condition indicators. Condition indicators provide information about the health condition of an asset; therefore they update and reform the baseline hazard of EHM according to the health state of asset at given time t. Some examples of condition indicators are the vibration of rotating machinery, the level of metal particles in engine oil analysis, and wear in a component, to name but a few. Operating environment indicators in this model are failure accelerators and/or decelerators that are included in the covariate function of EHM and may increase or decrease the value of the hazard from the baseline hazard. These indicators caused by the environment in which an asset operates, and that have not been explicitly identified by the condition indicators (e.g. Loads, environmental stresses, and other dynamically changing environment factors). While the effects of operating environment indicators could be nought in EHM; condition indicators could emerge because these indicators are observed and measured as long as an asset is operational and survived. EHM has several advantages over the existing covariate-based hazard models. One is this model utilises three different sources of asset health data (i.e. population characteristics, condition indicators, and operating environment indicators) to effectively predict hazard and reliability. Another is that EHM explicitly investigates the relationship between condition and operating environment indicators associated with the hazard of an asset. Furthermore, the proportionality assumption, which most of the covariate-based hazard models suffer from it, does not exist in EHM. According to the sample size of failure/suspension times, EHM is extended into two forms: semi-parametric and non-parametric. The semi-parametric EHM assumes a specified lifetime distribution (i.e. Weibull distribution) in the form of the baseline hazard. However, for more industry applications, due to sparse failure event data of assets, the analysis of such data often involves complex distributional shapes about which little is known. Therefore, to avoid the restrictive assumption of the semi-parametric EHM about assuming a specified lifetime distribution for failure event histories, the non-parametric EHM, which is a distribution free model, has been developed. The development of EHM into two forms is another merit of the model. A case study was conducted using laboratory experiment data to validate the practicality of the both semi-parametric and non-parametric EHMs. The performance of the newly-developed models is appraised using the comparison amongst the estimated results of these models and the other existing covariate-based hazard models. The comparison results demonstrated that both the semi-parametric and non-parametric EHMs outperform the existing covariate-based hazard models. Future research directions regarding to the new parameter estimation method in the case of time-dependent effects of covariates and missing data, application of EHM in both repairable and non-repairable systems using field data, and a decision support model in which linked to the estimated reliability results, are also identified.

Character in the workplace : self directedness mediates harm avoidance in the prediction of job performance

The purpose of this study was to test a model of the relationship between temperament, character and job performance, in order to better understand the cause of stable individual differences in job performance. Personality was conceptualized in terms of Cloninger, Svrakic and Przybeck’s (1993) theoretical framework of personality. It was expected that Self Directedness (character) would mediate Harm Avoidance and Persistence (temperament) in the prediction of job performance. In order to test the hypotheses, a sample of 94 employee/supervisor pairs was recruited from several organizations across Australia. Participants completed a number of questionnaires online, regarding their personality traits (completed by employees) and Job Performance (completed by Supervisors). Consistent with the hypothesis, Self Directedness was found to be a moderate, direct predictor of job performance. Also consistent with the hypothesis, Self Directedness mediated Harm Avoidance in the prediction of job performance. Results show that character (Self Directedness) is important in the prediction of job performance, and also suggests that fearful, avoidant individuals are less likely to perform well in the workplace, based on their low level of character development.

Do prediction markets produce well-calibrated probability forecasts?

This article presents new theoretical and empirical evidence on the forecasting ability of prediction markets. We develop a model that predicts that the time until expiration of a prediction market should negatively affect the accuracy of prices as a forecasting tool in the direction of a ‘favourite/longshot bias’. That is, high-likelihood events are underpriced, and low-likelihood events are over-priced. We confirm this result using a large data set of prediction market transaction prices. Prediction markets are reasonably well calibrated when time to expiration is relatively short, but prices are significantly biased for events farther in the future. When time value of money is considered, the miscalibration can be exploited to earn excess returns only when the trader has a relatively low discount rate.

Learning the learning rate for prediction with expert advice

Most standard algorithms for prediction with expert advice depend on a parameter called the learning rate. This learning rate needs to be large enough to fit the data well, but small enough to prevent overfitting. For the exponential weights algorithm, a sequence of prior work has established theoretical guarantees for higher and higher data-dependent tunings of the learning rate, which allow for increasingly aggressive learning. But in practice such theoretical tunings often still perform worse (as measured by their regret) than ad hoc tuning with an even higher learning rate. To close the gap between theory and practice we introduce an approach to learn the learning rate. Up to a factor that is at most (poly)logarithmic in the number of experts and the inverse of the learning rate, our method performs as well as if we would know the empirically best learning rate from a large range that includes both conservative small values and values that are much higher than those for which formal guarantees were previously available. Our method employs a grid of learning rates, yet runs in linear time regardless of the size of the grid.

Experimental and theoretical characterization of 5,10- diminoporphodimethenes: Dearomatized porphyrinoids from palladium-catalyzed hydrazinations of 5,10-diarylporphyrins

A new dearomatized porphyrinoid, 5,10-diiminoporphodimethene (5,10-DIPD), has been prepared by palladium-catalyzed hydrazination of 5,10-dibromo-15,20-bis(3,5-di-tert-butylphenyl)porphyrin and its nickel(II) complex, by using ethyl and 4-methoxybenzyl carbazates. The oxidative dearomatization of the porphyrin ring occurs in high yield. Further oxidation with 2,3-dichloro-5,6-dicyanobenzoquinone forms the corresponding 5,10-bis(azocarboxylates), thereby restoring the porphyrin aromaticity. The UV/visible spectra of the NiII DIPDs exhibit remarkable redshifts of the lowest-energy bands to 780 nm, and differential pulse voltammetry reveals a contracted electrochemical HOMO–LUMO gap of 1.44 V. Density functional theory (DFT) was used to calculate the optimized geometries and frontier molecular orbitals of model 5,10-DIPD Ni7c and 5,10-bis(azocarboxylate) Ni8c. The conformations of the carbamate groups and the configurations of the CNZ unit were considered in conjunction with the NOESY spectra, to generate the global minimum geometry and two other structures with slightly higher energies. In the absence of solution data regarding conformations, ten possible local minimum conformations were considered for Ni8c. Partition of the porphyrin macrocycle into tri- and monopyrrole fragments in Ni7c and the inclusion of terminal conjugating functional groups generate unique frontier molecular orbital distributions and a HOMO–LUMO transition with a strong element of charge transfer from the monopyrrole ring. Time-dependent DFT calculations were performed for the three lowest-energy structures of Ni7c and Ni8c, and weighting according to their energies allowed the prediction of the electronic spectra. The calculations reproduce the lower-energy regions of the spectra and the overall forms of the spectra with high accuracy, but agreement is not as good in the Soret region below 450 nm.