Market-oriented job skill valuation with cooperative composition neural network.
Ontology highlight
ABSTRACT:
SUBMITTER: Sun Y
PROVIDER: S-EPMC8012576 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Job security can never be taken for granted, especially in times of rapid, widespread and unexpected social and economic change. These changes can force workers to transition to new jobs. This may be because new technologies emerge or production is moved abroad. Perhaps it is a global crisis, such as COVID-19, which shutters industries and displaces labor en masse. Regardless of the impetus, people are faced with the challenge of moving between jobs to find new work. Successful transitions typically occur when workers leverage their existing skills in the new occupation. Here, we propose a novel method to measure the similarity between occupations using their underlying skills. We then build a recommender system for identifying optimal transition pathways between occupations using job advertisements (ads) data and a longitudinal household survey. Our results show that not only can we accurately predict occupational transitions (Accuracy = 76%), but we account for the asymmetric difficulties of moving between jobs (it is easier to move in one direction than the other). We also build an early warning indicator for new technology adoption (showcasing Artificial Intelligence), a major driver of rising job transitions. By using real-time data, our systems can respond to labor demand shifts as they occur (such as those caused by COVID-19). They can be leveraged by policy-makers, educators, and job seekers who are forced to confront the often distressing challenges of finding new jobs.
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Project description:PURPOSE:To examine the role of radiation oncology (RO) graduates' application patterns and personal preferences in current labor concerns. METHODS AND MATERIALS:An anonymous, voluntary survey was distributed to 665 domestic RO graduates from 2013 to 2017. Questions assessed graduates' regional (Northeast [NE]; Midwest [MW]; South [SO]; West [WT]) job type and population size preferences. Top regional choice was compared across other categorical and numerical variables using the ?2 test and analysis of variance, respectively. RESULTS:Complete responses were obtained from 299 (45.0% response rate) participants: 82 (27.4%), 74 (24.7%), 85 (28.4%), and 58 (19.4%) graduated from NE, MW, SO, and WT programs. The most to least commonly applied regions were SO (69.2%), MW (55.9%), and then NE/WT (55.2% each). The first and last regional choices were the WT (29.4%) and MW (15.7%), respectively. The most and least common application and top choice preferences were consistent in terms of city size: >500,000 (86.0% and 64.5%, respectively) and <100,001 (26.1% and 7.0%, respectively). The majority of applicants applied to both academic and nonacademic positions (60.9%), with top job type choice being equally split. The majority of respondents independently received a job offer in their preferred region (75.3%), city population size (72.6%) or job type (81.9%). Additionally, 52.5% received a job offer that included all three preferences. Those who underwent residency training (44.3% vs 62.0%-83.6%, P < .001) or medical schooling (50.7% vs 56.3%-75.6%, P < .001) or grew up in the MW (60.8% vs 70.0%-74.7%, P < .001) were least likely to choose this region as their top regional choice compared with other regions. CONCLUSIONS:The MW and jobs in smaller cities are less appealing to RO graduates, even if they receive training in the MW, which may contribute to current job market concerns. Nonetheless, the majority of respondents received a job offer in the region, population size, and job type of their top choice. Assessing prospective candidates' city size and geographic preferences and prioritizing applicants who are compatible with positions may help address potential job market discrepancies.
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Project description:Many postdoctoral researchers apply for faculty positions knowing relatively little about the hiring process or what is needed to secure a job offer. To address this lack of knowledge about the hiring process we conducted a survey of applicants for faculty positions: the survey ran between May 2018 and May 2019, and received 317 responses. We analyzed the responses to explore the interplay between various scholarly metrics and hiring outcomes. We concluded that, above a certain threshold, the benchmarks traditionally used to measure research success - including funding, number of publications or journals published in - were unable to completely differentiate applicants with and without job offers. Respondents also reported that the hiring process was unnecessarily stressful, time-consuming, and lacking in feedback, irrespective of outcome. Our findings suggest that there is considerable scope to improve the transparency of the hiring process.
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Project description:To better prevent the potential risks in Internet-based Supply Chain Financing (SCF) products, this paper optimizes and evaluates the Internet-based SCF-oriented Credit Risk Evaluation (CRE) method. Firstly, this paper summarizes 12 risk factors of SCF business, establishes a Risk Assessment Index System (RAIS) with good consistency and stability; then, the principles of Backpropagation (BP) Neural Network (NN) is expounded together with Support Vector Machines (SVM) and Genetic Algorithm (GA) model. Consequently, a CRE model is implemented by the NN tools in MATLAB based on the collection of multiple groups of SCF-oriented risk assessment samples. Subsequently, the assessment samples are trained and tested. Finally, the SCF-oriented CRE model is proposed and verified. The results show that the BP-GA model has presented high prediction consistency with the actual classification. According to the comparison of classification results of SVM, BP model, and BP-GA model, the classification accuracy of test samples of the proposed Internet-based SCF-oriented CRE system using BP-GA model reaches 97.19%; the Type I and Type II error rate of the CRE system based on BP-GA model is 7.2% and 14.21%, respectively. Therefore, a suitable SCF-oriented CRE method is put forward for China's commercial banks along with scientific and feasible suggestions to manage SCF-oriented credit risks more reasonably and effectively.
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Project description:Adolescents strive for peer approval, and an increased sensitivity to peers' opinions is normative. However, among vulnerable adolescents, peer evaluation can be detrimental, contributing to affective disorders. It is, therefore, critical to improve our understanding of neural underpinnings of peer evaluation. Prior research has investigated averaged neural responses to peer acceptance or rejection, neglecting to probe trial-by-trial computations that mirror real-time updating of daily activities. In non-social decision-making, a common neural valuation system centered on the medial prefrontal cortex (mPFC) has emerged, which evaluates different reward types on a common scale to guide choices. However, it is unclear whether the mPFC also tracks complex social scenarios involving peer feedback. To address this gap, we acquired fMRI data from 55 healthy adolescents during the Chatroom Task, which probes peer evaluation, and implemented a computational approach to characterize trial-by-trial social value, thereby allowing us to interrogate the neural correlates of social value. Consistent with our hypothesis, social value signals were encoded in the mPFC. Interestingly, analyses also revealed a wider social-specific valuation network including the precuneus and amygdala. Understanding how adolescents make social decisions and neural markers associated with it, may, ultimately, help us clarify promising targets for intervention.
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Project description:In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. Many factors such as political events, general economic conditions, and traders' expectations may have an influence on the stock market index. There are numerous research studies that use similar indicators to forecast the direction of the stock market index. In this study, we compare two basic types of input variables to predict the direction of the daily stock market index. The main contribution of this study is the ability to predict the direction of the next day's price of the Japanese stock market index by using an optimized artificial neural network (ANN) model. To improve the prediction accuracy of the trend of the stock market index in the future, we optimize the ANN model using genetic algorithms (GA). We demonstrate and verify the predictability of stock price direction by using the hybrid GA-ANN model and then compare the performance with prior studies. Empirical results show that the Type 2 input variables can generate a higher forecast accuracy and that it is possible to enhance the performance of the optimized ANN model by selecting input variables appropriately.
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Project description:How is effort integrated in value-based decision-making? Animal models and human neuroimaging studies primarily linked the anterior cingulate cortex (ACC) and ventral striatum (VS) to the integration of effort in valuation. Other studies demonstrated the role of these regions in invigoration to effort demands, thus it is hard to separate the neural activity linked to anticipation and subjective valuation from actual performance. Here, we studied the neural basis of effort valuation separated from performance. We scanned forty participants with fMRI, while they were asked to accept or reject monetary gambles that could be resolved with future performance of a familiar grip force effort challenge or a fixed risk prospect. Participants' willingness to accept prospective gambles reflected discounting of values by physical effort and risk. Choice-locked neural activation in contralateral primary sensory cortex and ventromedial prefrontal cortex (vmPFC) tracked the magnitude of prospective effort the participants faced, independent of choice time and monetary stakes. Estimates of subjective value discounted by effort were found to be tracked by the activation of a network of regions common to valuation under risk and delay, including vmPFC, VS and sensorimotor cortex. Together, our findings show separate neural mechanisms underlying prospective effort and actual effort performance.
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Project description:Interventions: None
Primary outcome(s): The primary outcome of the study is the accuracy of the CAD-CNN system for predicting histology of diminutive colorectal polyps (1-5mm) compared with the accuracy of the prediction of the endoscopist. Both the CAD-CNN system and the endoscopist will use NBI for their predictions.
Accuracy is defined as the percentage of correctly predicted optical diagnoses of the CAD-CNN system and/or endoscopist compared to the gold standard pathology. For the calculation of the accuracy, adenomas and SSLs will be dichotomised as neoplastic polyps, while HPs and other non-neoplastic histology are considered non-neoplastic.
Study Design: N/A: single arm study, Open (masking not used), N/A , unknown, Other
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