A Study on the Mechanisms of Tension-Shear Failure and the Estimation Model for Key Parameters of Free-Face-Assisted Rock Breaking

One huge challenge TBM construction face is to improve the breaking ability of cutters for hard rocks, making the studies on new rock breaking approaches for cutters very important. Although a lot of previous tests have proved that it is feasible to reduce the cutter force by free-face-assisted rock breaking (FM), the mechanisms behind such feasibility and the estimation methods for key parameters involved, including the maximum free face distance, crushing angle, and cutting force, remain to be studied further, limiting its applications in cutterhead design and engineering construction practice. Based on the analysis of the phenomena and laws of FM tests, this paper proposes the tensile-shear failure mechanism of FM and a piecewise linear failure criterion, which could explain the reason for the reduction of cutting force. Subsequently, a series of estimation models for the parameters above are proposed, and a series of FM tests were performed. By comparing the data obtained from the tests and calculations of the estimation model and CSM model, the estimation model in this paper is verified for its feasibility and limitations, which offered some insights on these aspects in follow-up research.

Examining the Persistence Intentions of College Students of Color

This study used the Social Cognitive Career Theory—Career Self-Management Model (SCCT-CSM) to understand the process by which background variables impact students of color’s intentions to persist in college. Findings from 329 students of color revealed that perceived social status related positively to self-efficacy for self-regulated learning, that increased experiences of racism related negatively to self-efficacy for self-regulated learning, and that self-efficacy for self-regulated learning related positively to intentions to persist in college. Further, self-efficacy for self-regulated learning mediated the relationship between perceived social status and persistence intentions among this sample of college students of color. Lastly, SEM analyses provided support for several pathways of the SCCT-CSM model with students of color. Limitations of the current study are discussed. Implications and future directions for practice and research are presented.

INCORPORATING A DYNAMIC GENE-BASED PROCESS MODULE INTO A CROP SIMULATION MODEL

Dynamic crop simulation models are tools that predict plant phenotype grown in specific environments for genotypes using genotype-specific parameters (GSPs), often referred to as "genetic coefficients." These GSPs are estimated using phenotypic observations and may not represent "true" genetic information. Instead, estimating GSPs requires experiments to measure phenotypic responses when new cultivars are released. The goal of this study was to evaluate a new approach that incorporates a dynamic gene-based module for simulating time-to-flowering for common bean (Phaseolus vulgaris L.) into an existing dynamic crop model. A multi-environment study conducted in 2011 and 2012 included 187 recombinant inbred lines (RILs) from a bi-parental bean family to measure the effects of quantitative trait loci (QTL), environment (E), and QTLxE interactions across five sites. The dynamic mixed linear model from Vallejos et al. (2020) was modified in this study to create a dynamic module that was then integrated into the CSM-CROPGRO-Drybean model. This new hybrid crop model, with the gene-based flowering module replacing the original flowering component, requires allelic makeup of each genotype being simulated and daily E data. The hybrid model was compared to the original CSM model using the same E data and previously estimated GSPs to simulate time-to-flower. The integrated gene-based module simulated days of first flower agreed closely with observed values (root mean square error of 2.73 days and model efficiency of 0.90) across the five locations and 187 genotypes. The hybrid model with its gene-based module also described most of the G, E and GxE effects on time-to-flower and was able to predict final yield and other outputs simulated by the original CSM. These results provide the first evidence that dynamic crop simulation models can be transformed into gene-based models by replacing an existing process module with a gene-based module for simulating the same process.

Incorporating a dynamic gene-based process module into a crop simulation model

Dynamic crop simulation models are tools that predict plant phenotype grown in specific environments for genotypes using genotype-specific parameters (GSPs), often referred to as ‘genetic coefficients’. These GSPs are estimated using phenotypic observations and may not represent ‘true’ genetic information. Instead, estimating GSPs requires experiments to measure phenotypic responses when new cultivars are released. The goal of this study was to evaluate a new approach that incorporates a dynamic gene-based module for simulating time-to-flowering for common bean (Phaseolus vulgaris L.) into an existing dynamic crop model. A multi-environment study that included 187 recombinant inbred lines (RILs) from a bi-parental bean family was conducted in 2011 and 2012 to measure the effects of quantitative trait loci (QTLs), environment (E) and QTL × E interactions across five sites. A dynamic mixed linear model was modified in this study to create a dynamic module that was then integrated into the Cropping System Model (CSM)-CROPGRO-Drybean model. This new hybrid crop model, with the gene-based flowering module replacing the original flowering component, requires allelic make-up of each genotype that is simulated and daily E data. The hybrid model was compared to the original CSM model using the same E data and previously estimated GSPs to simulate time-to-flower. The integrated gene-based module simulated days of first flower agreed closely with observed values (root mean square error of 2.73 days and model efficiency of 0.90) across the five locations and 187 genotypes. The hybrid model with its gene-based module also described most of the G, E and G × E effects on time-to-flower and was able to predict final yield and other outputs simulated by the original CSM. These results provide the first evidence that dynamic crop simulation models can be transformed into gene-based models by replacing an existing process module with a gene-based module for simulating the same process.

Examining the Career Self-Management Model Among Native American Students With STEM Career Goals

Native Americans are highly underrepresented in science, technology, engineering, and math (STEM) careers; however, little research exists concerning how to promote Native Americans’ participation in STEM. In this study, we address this gap by examining variables hypothesized to promote participation using the career self-management (CSM) model among Native American college students with STEM career goals. Results of stepwise regressions demonstrated that academic achievement along with the problem-solving aspects of career self-management (CSM) self-efficacy and instrumental assistance from parents, peers, and others in students’ schools and communities predicts clearer, more specific, and more personally congruent goals; and that these goals along with self-efficacy and instrumental assistance predict career exploration. Contrary to hypotheses, neither STEM outcome expectations nor gender was related to goals or exploration. These findings suggest that CSM can be used to guide research regarding the STEM career development of Native American college students.

Rethinking the “social” in “social media”: Insights into topology, abstraction, and scale on the Mastodon social network

Online interactions are often understood through the corporate social media (CSM) model where social interactions are determined through layers of abstraction and centralization that eliminate users from decision-making processes. This study demonstrates how alternative social media (ASM)—namely Mastodon—restructure the relationship between the technical structure of social media and the social interactions that follow, offering a particular type of sociality distinct from CSM. Drawing from a variety of qualitative data, this analysis finds that (1) the decentralized structure of Mastodon enables community autonomy, (2) Mastodon’s open-source protocol allows the internal and technical development of the site to become a social enterprise in and of itself, and (3) Mastodon’s horizontal structure shifts the site’s scaling focus from sheer number of users to quality engagement and niche communities. To this end, Mastodon helps us rethink “the social” in social media in terms of topology, abstraction, and scale.

Fluid–Structure Interaction Simulations of a Wind Gust Impacting on the Blades of a Large Horizontal Axis Wind Turbine

The effect of a wind gust impacting on the blades of a large horizontal-axis wind turbine is analyzed by means of high-fidelity fluid–structure interaction (FSI) simulations. The employed FSI model consisted of a computational fluid dynamics (CFD) model reproducing the velocity stratification of the atmospheric boundary layer (ABL) and a computational structural mechanics (CSM) model loyally reproducing the composite materials of each blade. Two different gust shapes were simulated, and for each of them, two different amplitudes were analyzed. The gusts were chosen to impact the blade when it pointed upwards and was attacked by the highest wind velocity due to the presence of the ABL. The loads and the performance of the impacted blade were studied in detail, analyzing the effect of the different gust shapes and intensities. Also, the deflections of the blade were evaluated and followed during the blade’s rotation. The flow patterns over the blade were monitored in order to assess the occurrence and impact of flow separation over the monitored quantities.

A Deep Learning Trained Clear-Sky Mask Algorithm for VIIRS Radiometric Bias Assessment

Clear-sky mask (CSM) is a crucial influence on the calculating accuracy of the sensor radiometric biases for spectral bands of visible, infrared, and microwave regions. In this study, a fully connected deep neural network (FCDN) was proposed to generate CSM for the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard Suomi National Polar-Orbiting Partnership (S-NPP) and NOAA-20 satellites. The model, well-trained by S-NPP data, was used to generate both S-NPP and NOAA-20 CSMs for the independent data, and the results were validated against the biases between the sensor observations and Community Radiative Transfer Model (CRTM) calculations (O-M). The preliminary result shows that the FCDN-CSM model works well for identifying clear-sky pixels. Both O-M mean biases and standard deviations were comparable with the Advance Clear-Sky Processor over Ocean (ACSPO) and were significantly better than a prototype cloud mask (PCM) and the case without a clear-sky check. In addition, by replacing CRTM brightness temperatures (BTs) with the atmosphere air temperature and water vapor contents as input features, the FCDN-CSM exhibits its potential to generate fast and accurate VIIRS CSM onboard follow-up Joint Polar Satellite System (JPSS) satellites for sensor calibration and validation before the physics-based CSM is available.

Integrating Job Loss, Unemployment, and Reemployment With Social Cognitive Career Theory

Job loss and recovery remain critical challenges in the United States and Europe in the wake of the Great Recession. However, the experience of unemployment is poorly integrated in theories of vocational psychology. In this article, we explore how job loss and recovery can be understood through the lens of social cognitive career theory’s career self-management (SCCT-CSM) model. We apply the SCCT-CSM model to understand the critical importance of person-cognitive variables, individual differences, and contextual affordances to the experiences of job loss and job recovery. Implications for future research, including research with particular groups of unemployed persons, are discussed. Overall, our analysis indicates that the SCCT-CSM model is a fruitful perspective for organizing future scholarship related to job loss and recovery.

The Selection Method of Parts in Product Family Evolution

In order to solute the problems of selection and improvement of parts when the product family architecture to adapt to the diverse needs, the selection method of parts in product family evolution was proposed herein. The part’s performance and the Matching degree of demand characteristics were analyzed. The parts CSM model in product family was defined and established, the clustering method based on fuzzy C-means (FCM) for parts was used in CSM coupling space of parts. Finally, the wheel loader’s manipulator product family as an example was employed to demonstrate the feasibility of the proposed method.