Predicting fatigue crack growth rate in a welded butt joint: The role of effective R ratio in accounting for residual stress effect

A simple and efficient method is presented in this paper for predicting fatigue crack growth rate in welded butt joints. Three well-known empirical crack growth laws are employed using the material constants that were obtained from the base material coupon tests. Based on the superposition rule of the linear elastic fracture mechanics, welding residual stress effect is accounted for by replacing the nominal stress ratio (R) in the empirical laws by the effective stress intensity factor ratio (Reff). The key part of the analysis process is to calculate the stress intensity factor due to the initial residual stress field and also the stress relaxation and redistribution due to crack growth. The finite element method in conjunction with the modified virtual crack closure technique was used for this analysis. Fatigue crack growth rates were then calculated by the empirical laws and comparisons were made among these predictions as well as against published experimental tests, which were conducted under either constant amplitude load or constant stress intensity factor range. Test samples were M(T) geometry made of aluminium alloy 2024-T351 with a longitudinal weld by the variable polarity plasma arc welding process. Good agreement was achieved

Automatic Segmentation of Multiparty Dialogue

In this paper, we investigate the problem of automatically predicting segment boundaries in spoken multiparty dialogue. We extend prior work in two ways. We first apply approaches that have been proposed for predicting top-level topic shifts to the problem of identifying subtopic boundaries. We then explore the impact on performance of using ASR output as opposed to human transcription. Examination of the effect of features shows that predicting top-level and predicting subtopic boundaries are two distinct tasks: (1) for predicting subtopic boundaries, the lexical cohesion-based approach alone can achieve competitive results, (2) for predicting top-level boundaries, the machine learning approach that combines lexical-cohesion and conversational features performs best, and (3) conversational cues, such as cue phrases and overlapping speech, are better indicators for the top-level prediction task. We also find that the transcription errors inevitable in ASR output have a negative impact on models that combine lexical-cohesion and conversational features, but do not change the general preference of approach for the two tasks

Predicting Human Cooperation

The Prisoner's Dilemma has been a subject of extensive research due to its importance in understanding the ever-present tension between individual self-interest and social benefit. A strictly dominant strategy in a Prisoner's Dilemma (defection), when played by both players, is mutually harmful. Repetition of the Prisoner's Dilemma can give rise to cooperation as an equilibrium, but defection is as well, and this ambiguity is difficult to resolve. The numerous behavioral experiments investigating the Prisoner's Dilemma highlight that players often cooperate, but the level of cooperation varies significantly with the specifics of the experimental predicament. We present the first computational model of human behavior in repeated Prisoner's Dilemma games that unifies the diversity of experimental observations in a systematic and quantitatively reliable manner. Our model relies on data we integrated from many experiments, comprising 168,386 individual decisions. The computational model is composed of two pieces: the first predicts the first-period action using solely the structural game parameters, while the second predicts dynamic actions using both game parameters and history of play. Our model is extremely successful not merely at fitting the data, but in predicting behavior at multiple scales in experimental designs not used for calibration, using only information about the game structure. We demonstrate the power of our approach through a simulation analysis revealing how to best promote human cooperation.Comment: Added references. New inline citation style. Added small portions of text. Re-compiled Rmarkdown file with updated ggplot2 so small aesthetic changes to plot

Predicting occupational strain and job satisfaction: the role of stress, coping, personality, and affectivity variables

Four studies employed path analysis to examine how measures of occupational stressors, coping resources, and negative affectivity (NA) and positive affectivity (PA) interact to predict occupational strain. The Occupational Stress Inventory (Osipow & Spokane, 1987) was used to measure stress, strain, and coping. The Positive and Negative Affectivity Schedule (Watson, Clark, & Tellegen, 1988) was used for the affectivity variables. The hypothesised model showed NA and PA as background dispositional variables that influenced relations among stress, strain, and coping while still allowing stress and coping to have a direct influence on strain. Goodness of fit indices were acceptable with the model predicting 15 per cent of the variance in stress, 24 per cent of coping, and 70 per cent of strain. Study 2 replicated these findings. Study 3 added a positive outcome variable, job satisfaction (JSI: Brayfield & Rothe, 1951) to the model. The expanded model again fit the data well. A fourth study added a global measure of personality (NEO-FFI: Costa & McCrae, 1991) to the model tested in Study 3. Results indicated that personality measures did not add anything to the prediction of job satisfaction and strain in a model that already included measures of stressors, coping resources, NA and PA. The series of four studies yielded a reliable structural model that highlights the influence of organizational and dispositional variables on occupational strain and job satisfaction

Predicting Stellar Angular Sizes

Reliable prediction of stellar diameters, particularly angular diameters, is a useful and necessary tool for the increasing number of milliarcsecond resolution studies being carried out in the astronomical community. A new and accurate technique of predicting angular sizes is presented for main sequence stars, giant and supergiant stars, and for more evolved sources such as carbon stars and Mira variables. This technique uses observed $K$ and either $V$ or $B$ broad-band photometry to predict V=0 or B=0 zero magnitude angular sizes, which are then readily scaled to the apparent angular sizes with the $V$ or $B$ photometry. The spread in the relationship is 2.2% for main sequence stars; for giant and supergiant stars, 11-12%; and for evolved sources, results are at the 20-26% level. Compared to other simple predictions of angular size, such as linear radius-distance methods or black-body estimates, zero magnitude angular size predictions can provide apparent angular sizes with errors that are 2 to 5 times smaller.Comment: 28 pages, 4 figures, accepted by PAS

Factors predicting the scientific wealth of nations

It has been repeatedly demonstrated that economic affluence is one of the main predictors of the scientific wealth of nations. Yet, the link is not as straightforward as is often presented. First, only a limited set of relatively affluent countries is usually studied. Second, there are differences between equally rich countries in their scientific success. The main aim of the present study is to find out which factors can enhance or suppress the effect of the economic wealth of countries on their scientific success, as measured by the High Quality Science Index (HQSI). The HQSI is a composite indicator of scientific wealth, which in equal parts considers the mean citation rate per paper and the percentage of papers that have reached the top 1% of citations in the Essential Science Indicators (ESI; Clarivate Analytics) database during the 11-year period from 2008 to 2018. Our results show that a high position in the ranking of countries on the HQSI can be achieved not only by increasing the number of high-quality papers but also by reducing the number of papers that are able to pass ESI thresholds but are of lower quality. The HQSI was positively and significantly correlated with the countries’ economic indicators (as measured by gross national income and Research and Development expenditure as a percentage from GDP), but these correlations became insignificant when other societal factors were controlled for. Overall, our findings indicate that it is small and well-governed countries with a long-standing democratic past that seem to be more efficient in translating economic wealth into high-quality science