Search Results (96)

This study investigates how topic-specific expression by women delivery riders on digital platforms predicts their community engagement, emphasizing the mediating role of self-disclosure and the moderating influence of cognitive and emotional language features. Using unsupervised topic modeling (Top2Vec, Topical Vectors via Embeddings and Clustering) and psycholinguistic analysis (LIWC, Linguistic Inquiry and Word Count), the paper extracted eleven thematic clusters and quantified self-disclosure intensity, cognitive complexity, and emotional polarity. A moderated mediation model was constructed to estimate the indirect and conditional effects of topic probability on engagement behaviors (likes, comments, and views) via self-disclosure. The results reveal that self-disclosure significantly mediates the influence of topical content on engagement, with emotional negativity amplifying and cognitive complexity selectively enhancing this pathway. Indirect effects differ across topics, highlighting the heterogeneous behavioral salience of expressive themes. The findings support a statistically grounded, semantically interpretable framework for predicting user behavior in high-dimensional text environments. This approach offers practical implications for optimizing algorithmic content ranking and fostering equitable visibility for marginalized digital labor groups. Full article

Alzheimer’s disease (AD) involves the accumulation of amyloid-β (Aβ) plaques, whose quantification plays a central role in understanding disease progression. Automated segmentation of Aβ deposits in histopathological micrographs enables large-scale analyses but is hindered by the high cost of detailed pixel-level annotations. Weakly supervised learning offers a promising alternative by leveraging coarse or indirect labels to reduce the annotation burden. We evaluated a weakly supervised approach to segment and analyze thioflavin-S-positive parenchymal amyloid pathology in AD and age-matched brains. Our pipeline integrates three key components, each designed to operate under weak supervision. First, robust preprocessing (including retrospective multi-image illumination correction and gradient-based background estimation) was applied to enhance image fidelity and support training, as models rely more on image features. Second, class activation maps (CAMs), generated by a compact deep classifier SqueezeNet, were used to identify, and coarsely localize amyloid-rich parenchymal regions from patch-wise image labels, serving as spatial priors for subsequent refinement without requiring dense pixel-level annotations. Third, a patch-based convolutional neural network, U-Net, was trained on synthetic data generated from micrographs based on CAM-derived pseudo-labels via an extensive object-level augmentation strategy, enabling refined whole-image semantic segmentation and generalization across diverse spatial configurations. To ensure robustness and unbiased evaluation, we assessed the segmentation performance of the entire framework using patient-wise group k-fold cross-validation, explicitly modeling generalization across unseen individuals, critical in clinical scenarios. Despite relying on weak labels, the integrated pipeline achieved strong segmentation performance with an average Dice similarity coefficient (≈0.763) and Jaccard index (≈0.639), widely accepted metrics for assessing segmentation quality in medical image analysis. The resulting segmentations were also visually coherent, demonstrating that weakly supervised segmentation is a viable alternative in histopathology, where acquiring dense annotations is prohibitively labor-intensive and time-consuming. Subsequent morphometric analyses on automatically segmented Aβ deposits revealed size-, structural complexity-, and global geometry-related differences across brain regions and cognitive status. These findings confirm that deposit architecture exhibits region-specific patterns and reflects underlying neurodegenerative processes, thereby highlighting the biological relevance and practical applicability of the proposed image-processing pipeline for morphometric analysis. Full article

Although career choice is a kind of individual behavior, as the gatekeeper of the discipline, doctoral students’ academic career intention reflects the attractiveness of the academic labor market and determines the sustainable development of academic careers. An analysis of data (N = 1322) from a survey among Chinese doctoral students reveals that (1) environmental factors, such as departmental atmosphere and advisor support, cognitive factors like academic interest and research self-efficacy, as well as behavioral factors including research engagement and publication rates, significantly promote doctoral students’ academic career intentions; (2) female doctoral students and those from prestigious institutions show stronger academic career aspirations; (3) the influence of interest factors on doctoral students’ commitment to an academic career is particularly pronounced, especially in the field of fundamental science; (4) a clear understanding of career paths positively moderates the effect of interest on academic career intentions. Within increasingly severe competition in the global academic labor market, it is necessary to provide more support for doctoral students who are willing to engage in academic careers by enhancing career planning guidance for doctoral students and supporting them in making rational career plans and adequate preparations. Full article

Mars’ south polar cap hosts dynamic landforms known as Swiss cheese features (SCFs), which form through the sublimation of carbon dioxide (CO₂) ice driven by the planet’s extreme seasonal and diurnal solar insolation cycles. These shallow, rounded depressions—first identified by Mars Global Surveyor in 1999 and later monitored by the Mars Reconnaissance Orbiter (MRO)—have been observed to increase in size over time. However, large-scale analysis of SCF formation and growth has been limited by the slow and labor-intensive nature of manual mapping techniques. This study applies object-based image analysis (OBIA) to automate the detection and measurement of SCFs using High-Resolution Imaging Science Experiment (HiRISE) images spanning five Martian years. Results show that SCFs exhibit a near-linear increase in area, suggesting that summer sublimation consistently outpaces winter CO₂ deposition. Validation against manual digitization shows discrepancies of less than 1%, confirming the reliability of the OBIA method. Regression-based extrapolation of growth trends indicates that the current generation of SCFs likely began forming between Martian years 7 and 16, approximately corresponding to Earth years 1976 to 1998. These findings provide a quantitative assessment of SCF evolution and contribute to our understanding of recent climate-driven surface changes on Mars. HiRISE images were processed using the eCognition software to detect, classify, and measure SCFs, demonstrating that OBIA is a scalable and effective tool for analyzing dynamic planetary landforms. Full article

A Bayesian network (BN) is an uncertainty processing model that simulates human cognitive thinking on the basis of probability theory and graph theory. Its network topology is a directed acyclic graph (DAG) that can be manually constructed through expert knowledge or automatically generated through data learning. However, the acquisition of expert knowledge faces problems such as excessively high labor costs, limited expert experience, and the inability to solve large-scale and highly complex DAGs. Moreover, the current data learning methods also face the problems of low computational efficiency or decreased accuracy when dealing with large-scale and highly complex DAGs. Therefore, we consider mining fragmented knowledge from the data to alleviate the bottleneck problem of expert knowledge acquisition. This generated fragmented knowledge can compensate for the limitations of data learning methods. In our work, we propose a new binary stochastic fractal search (SFS) algorithm to learn DAGs. Moreover, a new feature selection (FS) method is proposed to mine fragmented knowledge. This fragmented prior knowledge serves as a soft constraint, and the acquired expert knowledge serves as a hard constraint. The combination of the two serves as guidance and constraints to enhance the performance of the proposed SFS algorithm. Extensive experimental analysis reveals that our proposed method is more robust and accurate than other algorithms. Full article

With the advancement of global economic restructuring and China’s economic transformation, structural employment contradictions have intensified amid increasingly competitive labor markets. The frequent occurrences of “degree devaluation” and talent “downskilling” have made perceived overqualification increasingly prevalent in organizations. This study, based on the Cognitive–Affective Personality System theory, investigates the differential mechanisms through which perceived overqualification drives approach and avoidance job crafting via cognitive and affective pathways. Data from a two-wave survey of 556 Chinese employees produced several key findings: (1) Perceived overqualification significantly enhances approach job crafting while suppressing avoidance job crafting by elevating role breadth self-efficacy (cognitive pathway), demonstrating a proactive breakthrough effect. (2) Perceived overqualification inhibits approach job crafting and exacerbates avoidance job crafting through triggering emotional exhaustion (affective pathway), revealing a passive exhaustion trap. (3) Perceived overqualification exerts a positive and significant overall indirect effect on approach job crafting through the combined mechanisms of cognitive gains from role breadth self-efficacy and affective costs from emotional exhaustion, whereas the overall indirect effect on avoidance job crafting is non-significant. (4) Idiosyncratic deals (i-deals) function as a dynamic boundary mechanism that amplifies the positive impact of role breadth self-efficacy and mitigates the negative effects of emotional exhaustion, while moderating the mediating roles of both pathways. This research develops a dual-path integrated model of perceived overqualification and job crafting by classifying job crafting categories, incorporating cognitive–affective pathways, and introducing i-deals as a contextual element. These findings respond to scholarly demands for elucidating the intricate connections between perceived overqualification and job crafting through integrative perspectives; in addition, they offer theoretical and practical insights for organizations to leverage the potential of overqualified individuals appropriately. Full article

While the transformation of and improvements in productivity are taking place under the guidance of new technologies, overtime work—which is still prevalent in the workplace—is simultaneously introducing substantial physical and mental burdens to workers. Based on baseline data from the China Labor Dynamics Survey (CLDS), we analyze employees’ willingness to work overtime and their overtime cognition and intensity using labor process theory. It is observed that skill control directly increases the probability of employees’ objective overtime work, mandatory overtime work, and unconscious overtime work; furthermore, de-skilling prolongs working hours while hiding the prevalence and blurring the boundaries of overtime work. De-skilling also results in reduced employee job autonomy and further extends overtime hours, increasing the probability of mandatory overtime. Bureaucratic control reinforces the relationship between de-skilling and voluntary overtime, unconscious overtime, and overtime intensity by fostering employees’ career development orientation. It is necessary to accurately grasp the characteristics of new technologies in the changing labor environment of the new era, strive to create an equal and open labor market, and respect and protect the legitimate rights and interests of workers. Full article

The rapid advancement of artificial intelligence (AI) and digital transformation is reshaping labor markets, emphasizing creativity as a core competency in entrepreneurship education. Large Language Models (LLMs) provide personalized learning experiences through natural language processing (NLP), enhancing real-time feedback and problem-solving skills. However, research on how LLMs foster creativity in entrepreneurship education remains limited. This study analyzed the technical characteristics and educational impact of LLMs, focusing on their applications in entrepreneurship education and their role in fostering creativity-driven learning environments. Specifically, it explores the educational effects of LLMs, their integration into entrepreneurship education, and the ways in which they enhance learners’ creative thinking. A systematic literature review using the PRISMA methodology was conducted to analyze existing studies. Findings suggest that LLMs improve self-efficacy, cognitive engagement, and creative problem-solving, supporting entrepreneurship education in areas such as business model development, market analysis, and multicultural communication. Despite these benefits, concerns remain regarding over-reliance, ethical risks, and the need for critical thinking frameworks. This study proposes a hybrid model integrating LLMs with traditional pedagogies to maximize creativity. Future research should explore long-term effects, cross-cultural applications, and ethical challenges to ensure responsible implementation. Full article

The changing nature of the labor market demands lifelong development of employees. This places a new responsibility on education to prepare students for a future of lifelong development. An important aspect of this preparation is the development of career competencies. Career competencies are examined from different perspectives. Two meta-studies, analyzing 77 and 80 international studies, highlight two key theories for understanding career competency development: the Intelligent Career Theory (ICT) and the Social Cognitive Career Theory (SCCT). This article aims to provide deeper insight into career competencies for students by analyzing them conceptually through various theoretical lenses and linking them to research on educational practice in the Dutch context, where development of career competencies is a mandatory part of the pre-vocational and secondary educational curriculum. The ultimate goal is to develop recommendations for designing a learning environment that fosters career competency development in students. Full article

This study estimates the labor market returns to non-cognitive skills among the youth under 30 years old during the early career stage. Using data from the Russian Longitudinal Monitoring Survey (RLMS-HSE) for 2016 and 2019, it examines the effects of the Big Five personality traits (openness, conscientiousness, extraversion, agreeableness, and emotional stability) on hourly wages. To account for potential heterogeneity in the effect of non-cognitive skills along the wage distribution, a quantile linear mixed model is employed, estimating returns at the 10th, 25th, 50th, 75th, and 90th percentiles while controlling for repeated observations with random intercepts at the individual level. Inverse probability weighting is applied to address the selection of employment. The results indicate that openness yields the highest returns for young workers, though its effect diminishes after controlling for educational attainment. By controlling for education, the model identifies the effect of conscientiousness below the median wage level, and that of extraversion above. Finally, the study finds that the impact of non-cognitive skills on wages evolves over the life course. First, the effects of non-cognitive skills on wages vary a lot in the youth group and the entire working population (ages 16–65). Furthermore, breaking the data down by age cohorts reveals how their significance and magnitude shift at different career stages. Full article

Background: With the increasingly severe trend of population aging, the well-being of the elderly is receiving growing attention. This study aimed to investigate the association between cognitive social capital in the community (familiarity with community members, trust in community members, and sense of security in the community where they live) and the mental health among older individuals in China and to examine how age, education, and income moderate this relationship. Methods: To achieve this, we utilized nationally representative data (n = 2301) from the China Labor-Force Dynamics Survey (CLDS) 2018, and we assessed whether older adults’ mental health was associated with cognitive social capital in the community. Cognitive social capital includes familiarity with and trust in other members living in the same community together with the sense of security within the community where the older individuals reside. The marginal effect was applied to analyze how age, education, and income moderate the impact of community-based cognitive social capital on the mental health of older individuals. Results: Our findings indicate that the cognitive social capital within communities is significantly linked to the mental health of older adults. Additionally, age, education, and income serve as crucial moderators in this relationship. Conclusions: Strategies to enhance the cognitive social capital of older adults in the community are beneficial for their mental health, which deserves policymakers’ further attention. Full article

The evolving labor environment underscores the critical role of cognitive and non-cognitive (soft) skills in fostering workforce adaptability and enhancing labor market outcomes. This study investigates the combined influence of these skills on the probability of employment, focusing on the Kenyan labor market, where high youth unemployment and job market mismatches persist despite government interventions and education sector reforms. Traditionally, emphasis has been placed on cognitive skills, with limited integration of non-cognitive skills into educational curricula, exacerbating the disconnect between youth competencies and market demands. Using binary logistic regression, this study evaluates factors influencing youth employment, highlighting the complementarity of cognitive and non-cognitive skills. Findings reveal that individuals possessing a blend of these skills have higher employment prospects, with notable improvements for young women possessing agreeableness and digital literacy. Additionally, factors such as marital status and higher education levels positively influence employability. These results underscore the equal importance of personality traits and cognitive abilities in labor market success. Policymakers are urged to prioritize curriculum reforms that integrate non-cognitive skill development and encourage employers to include assessments of these skills in hiring practices to address persistent labor market mismatches. Full article

This article provides a systematic review of the role of social capital in employability models. Although social capital is recognized as a key resource in employment and society, its role in academic research on employability is frequently neglected. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this systematic review reveals an underrepresentation of social capital within employability models, as empirical studies do not attribute the same significance to it as observed in everyday life. The analysis found social capital was identified as a determinant in only 16 out of 47 empirical employability models. In less than half of these models, social capital is included as an independent variable, while, in the remaining models, it is incorporated within another explanatory factor. Notably, only in four models are all three dimensions of social capital (structural, cognitive, relational) included. This raises questions about the validity of existing employability models, emphasizes the necessity of social capital inclusion, and calls for future empirical research. Fostering social capital in employability is pivotal for the economic and social sustainability of aging societies, as it mitigates labor shortages, ensures fiscal stability, supports innovation, and enhances social sustainability through inclusivity and intergenerational equity. Full article

Humanity stands at a pivotal moment of technological revolution, with artificial intelligence (AI) reshaping fields traditionally reliant on human cognitive abilities. This transition, driven by advancements in artificial neural networks, has transformed data processing and evaluation, creating opportunities for addressing complex and time-consuming tasks with AI solutions. Convolutional networks (CNNs) and the adoption of GPU technology have already revolutionized image recognition by enhancing computational efficiency and accuracy. In radiology, AI applications are particularly valuable for tasks involving pattern detection and classification; for example, AI tools have enhanced diagnostic accuracy and efficiency in detecting abnormalities across imaging modalities through automated feature extraction. Our analysis reveals that neuroimaging and chest imaging, as well as CT and MRI modalities, are the primary focus areas for AI products, reflecting their high clinical demand and complexity. AI tools are also used to target high-prevalence diseases, such as lung cancer, stroke, and breast cancer, underscoring AI’s alignment with impactful diagnostic needs. The regulatory landscape is a critical factor in AI product development, with the majority of products certified under the Medical Device Directive (MDD) and Medical Device Regulation (MDR) in Class IIa or Class I categories, indicating compliance with moderate-risk standards. A rapid increase in AI product development from 2017 to 2020, peaking in 2020 and followed by recent stabilization and saturation, was identified. In this work, the authors review the advancements in AI-based imaging applications, underscoring AI’s transformative potential for enhanced diagnostic support and focusing on the critical role of CNNs, regulatory challenges, and potential threats to human labor in the field of diagnostic imaging. Full article

In this volatile, uncertain, complex, and ambiguous (VUCA) era, resilient and sustainable construction methods, such as prefabricated construction, are essential for addressing the planet’s sustainability challenges. However, disruptions in the prefabricated construction supply chain (PCSC) frequently arise, seriously impeding the performance of prefabricated building projects. Therefore, this study aims to identify the factors influencing the prefabricated construction supply chain (RPCSC) and analyze their intrinsic interconnections. Initially, an exhaustive literature review was conducted to identify the primary factors affecting the RPCSC. Subsequently, the Delphi technique was applied to validate and refine the list of factors, resulting in the identification of 11 key concepts. Finally, the impact of these concepts on the RPCSC, along with their interactions, was assessed using the fuzzy cognitive map (FCM) approach. The results indicate that these factors can be ranked by their degree of effect on the RPCSC: information exchange/sharing, research and development, the performance of prefabricated components, decision alignment, the construction of prefabricated buildings, relationship quality among members, professional management personnel/labor quality, supply–demand consistency, cost/profit sharing, policies and regulations, and transport risk. Furthermore, this study elucidates both the individual and synergistic effects of these factors on the RPCSC by constructing a pathway map. Full article