Skill dependencies underlie career paths and have societal implications
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Skill dependencies uncover nested human capital
Modern economies require increasingly diverse and specialized skills, many of which depend on the acquisition of other skills first. Here we analyse US survey data to reveal a nested structure within skill portfolios, where the direction of dependency is inferred from asymmetrical conditional probabilities—occupations require one skill conditional on another. This directional nature suggests that advanced, specific skills and knowledge are often built upon broader, fundamental ones. We examine 70 million job transitions to show that human capital development and career progression follow this structured pathway in which skills more aligned with the nested structure command higher wage premiums, require longer education and are less likely to be automated. These disparities are evident across genders and racial/ethnic groups, explaining long-term wage penalties. Finally, we find that this nested structure has become even more pronounced over the past two decades, indicating increased barriers to upward job mobility.
Investigating the role of psychological elements in advancing IT skills among accounting students: insights from Saudi Arabia
Psychological factors are among the multiple influences on people’s daily behavior. The outcomes of various daily activities, ranging from success to failure, are often determined by these psychological aspects. The purpose of this research is to determine how psychological factors influence the skill of accounting students in Saudi Arabia with regard to information technology (IT). In order to achieve the research objectives, a descriptive and explanatory research design incorporating a quantitative approach is utilized. The study’s target population comprises accounting students from government universities in Saudi Arabia. Data collection employed a combination of convenient and snowball sampling strategies, ensuring broader applicability of the findings. A total of 306 accounting students from these universities participated in an online survey. Data analysis is conducted using partial least squares-structural equation modeling (PLS-SEM), and the significance of path coefficients is assessed through bootstrapping tests. Results indicated that motor skills, visual processing, fatigue, and stress positively influence IT skill development in these students. Conversely, ergonomics and cognitive abilities appeared to have no significant effect. The model accounted for approximately 65% of the variance in IT skill development among university students. These insights can guide educational institutions in formulating strategic plans for IT skill development, ensuring students acquire the necessary competencies on campus. Additionally, the findings offer valuable information for government bodies developing standards to foster IT skill growth.
Multi-country evidence on societal factors to include in energy transition modelling
Integrated assessment and energy system models are challenged to account for societal transformation dynamics, but empirical evidence is lacking on which factors to incorporate, how and to what extent this would improve the relevance of modelled pathways. Here we include six societal factors related to infrastructure dynamics, actors and decision-making, and social and institutional context into an open-source simulation model of the national power system transition. We apply this model in 31 European countries and, using hindcasting (1990–2019), quantify which societal factors improved the modelled pathways. We find that, if well-chosen and in most cases, incorporating societal factors can improve the hindcasting performance by up to 27% for modelled installed capacity of individual technologies. Public acceptance, investment risks and infrastructure lock–in contribute the most to model performance improvement. Our study paves the way to a systematic and objective selection of societal factors to be included in energy transition modelling.
Professional demand analysis for teaching Chinese to speakers of other languages: a text mining approach on internet recruitment platforms
The rapid development of international education in China highlights the growing importance of employment analysis in Teaching Chinese to Speakers of Other Languages (TCSOL). This study explores the enterprise demands for TCSOL professionals using text mining techniques to analyze recruitment data collected from four major platforms: Boss Zhipin, Zhaopin.com, 51job.com, and Liepin.com. Combining descriptive statistics, LDA topic modeling, BERT-BiLSTM-CRF-based named entity recognition, and co-occurrence network analysis were used. Results show that there is a high demand for TCSOL professionals, especially for small-scale enterprises located in first-tier cities such as Beijing, Shanghai, Guangzhou, and Shenzhen. Employers tend to favor candidates with at least a bachelor’s degree and 1–3 years of work experience. The topic model highlighted three central themes in job descriptions, emphasizing a shift toward a more diverse skill set. Named entity recognition identified essential attributes such as “communication ability”, “teaching experience”, “bachelor’s degree or above” and “responsibility” as core recruitment requirements. The co-occurrence network analysis revealed the importance of “teaching” and “priority” as core skill nodes. Time series analysis showed seasonal fluctuations in recruitment demand, peaking during spring recruitment and graduation periods. A hierarchical model of talent demand and development in TCSOL is proposed, integrating the perspectives of employers, job seekers, educators, and policymakers. This study provides valuable insights for aspiring TCSOL professionals, offering guidance to better align talent training with market needs and improve employment prospects.
Deep Bayesian active learning using in-memory computing hardware
Labeling data is a time-consuming, labor-intensive and costly procedure for many artificial intelligence tasks. Deep Bayesian active learning (DBAL) boosts labeling efficiency exponentially, substantially reducing costs. However, DBAL demands high-bandwidth data transfer and probabilistic computing, posing great challenges for conventional deterministic hardware. Here we propose a memristor stochastic gradient Langevin dynamics in situ learning method that uses the stochastic of memristor modulation to learn efficiency, enabling DBAL within the computation-in-memory (CIM) framework. To prove the feasibility and effectiveness of the proposed method, we implemented in-memory DBAL on a memristor-based stochastic CIM system and successfully demonstrated a robot’s skill learning task. The inherent stochastic characteristics of memristors allow a four-layer memristor Bayesian deep neural network to efficiently identify and learn from uncertain samples. Compared with cutting-edge conventional complementary metal-oxide-semiconductor-based hardware implementation, the stochastic CIM system achieves a remarkable 44% boost in speed and could conserve 153 times more energy.
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