Xi Pei

Introduction: This study examined how eudaimonic life values (ELV) influence employment stress (ES) among Chinese higher education students, focusing on the mediating role of employment mindset (EM) and the moderating effects of subject major and educational background. Methods: A cross-sectional survey was conducted from February to April 2025 among students aged 18–25 from 18 higher education institutions in Shenzhen, China. A total of 9,264 valid responses were analyzed using mediation and moderated mediation models. Results: ELV significantly and negatively predicted both EM ( β = −0.073, p < .001) and ES ( β = −0.009, p < .001), while EM positively predicted ES ( β = 0.125, p < .001). The indirect effect of ELV on ES via EM was significant (95% CI [−0.0102, −0.0082]), indicating partial mediation, with 50.8% of the total effect mediated. Both direct and indirect effects were moderated by subject major and educational background, with stronger effects observed among natural sciences and engineering students and those with higher educational levels. Discussion: Findings highlight the role of cognitive-emotional processes in linking life values to employment stress and suggest that educational context shapes these relationships.

Amid the digital transformation of logistics and supply chains, Extended Reality (XR) technologies have emerged as promising tools for enhancing education and training. However, existing studies are fragmented, often limited to case-specific applications with minimal theoretical or longitudinal depth. This study conducts a systematic literature review of 1172 publications from 2009 to December 2024, using PRISMA protocols and VOSviewer-based text mining to identify trends and research gaps. A total of 59 peer-reviewed articles were selected for in-depth analysis based on relevance, methodological transparency, and educational scope. Five key themes emerged: immersive instructional innovation, XR-enabled safety training in high-risk logistics environments, simulation-based development of practical competencies, intelligent learning environments with personalized features, and competency alignment with Industry 4.0. These themes span higher education, vocational training, and community-based learning. A temporal analysis reveals a three-phase evolution: exploratory (2009–2013), applied implementation (2016–2020), and integrative innovation (2021–2024). Despite increasing interest, the field remains dominated by descriptive methods and lacks systematic evaluation frameworks. XR shows strong potential to bridge the theory–practice gap and support scalable, interdisciplinary education models. Future research should prioritize evidence-based frameworks and cross-contextual validation to support the effective adoption of XR in LSCM education.

With the increase in the number of undergraduate vocational colleges and universities, China’s education structure is undergoing profound changes. The establishment of an effective and smooth bridge for vocational education can help focus on the national development plan and major strategies. In this way, high-level, applied and technical talents can be cultivated, and more ‘national craftsmen’ can be forged. Vocational education at undergraduate level shoulders the important task of cultivating high-level technical and skilled talents adapted to the needs of new quality productivity. The article explores the connotation, characteristics, and talent cultivation requirements of the new quality productivity, and analyses the problems of non-adaptation and mismatch with the current vocational undergraduate economic and management professional talent cultivation. The article also puts forward optimization strategies and implementation paths in terms of talent cultivation positioning, curriculum system construction, faculty construction, and industry-teaching integration. This article provides powerful talent support for the development of new quality productivity.

In 2022, China’s total import and export of cross-border e-commerce reached 2 trillion Yuan for the first time, accounting for 4.9% of the country’s total import and export trade. With the booming development of cross-border e-commerce industry, the demand for cross-border e-commerce talents is increasing, and with the implementation of China’s going out and double-cycle strategy policy, higher vocational colleges and universities, as the main force of cross-border e-commerce talent cultivation, have carried out cross-border e-commerce courses based on the needs of the industry and have gradually become the important core courses for the majors of International Business, International Economics and Trade and Cross-Border E-commerce. Cross-Border e-commerce course as an emerging course, coincides with the full arrival of the digital economy era, AI and 5G technology are advancing rapidly, and how to cultivate applied high-quality cross-border e-commerce talents in the rapid development of information technology at the same time, is the challenge faced by each institution. Starting from the background of the era of comprehensive development of the digital economy, this paper analyzes the mission of the current cross-border e-commerce course system, analyzes the predicament faced, and explores the development principles and implementation path of course reform, which has important theoretical and practical value.

Purpose: One technical barrier to patient‐specific computed tomography (CT) dosimetry has been the lack of computational tools for the automatic patient‐specific multi‐organ segmentation of CT images and rapid organ dose quantification. When previous CT images are available for the same body region of the patient, the ability to obtain patient‐specific organ doses for CT — in a similar manner as radiation therapy treatment planning — will open the door to personalized and prospective CT scan protocols. This study aims to demonstrate the feasibility of combining deep‐learning algorithms for automatic segmentation of multiple radiosensitive organs from CT images with the GPU‐based Monte Carlo rapid organ dose calculation.Methods: A deep convolutional neural network (CNN) based on the U‐Net for organ segmentation is developed and trained to automatically delineate multiple radiosensitive organs from CT images. Two databases are used: The lung CT segmentation challenge 2017 (LCTSC) dataset that contains 60 thoracic CT scan patients, each consisting of five segmented organs, and the Pancreas‐CT (PCT) dataset, which contains 43 abdominal CT scan patients each consisting of eight segmented organs. A fivefold cross‐validation method is performed on both sets of data. Dice similarity coefficients (DSCs) are used to evaluate the segmentation performance against the ground truth. A GPU‐based Monte Carlo dose code, ARCHER, is used to calculate patient‐specific CT organ doses. The proposed method is evaluated in terms of relative dose errors (RDEs). To demonstrate the potential improvement of the new method, organ dose results are compared against those obtained for population‐average patient phantoms used in an off‐line dose reporting software, VirtualDose, at Massachusetts General Hospital.Results: The median DSCs are found to be 0.97 (right lung), 0.96 (left lung), 0.92 (heart), 0.86 (spinal cord), 0.76 (esophagus) for the LCTSC dataset, along with 0.96 (spleen), 0.96 (liver), 0.95 (left kidney), 0.90 (stomach), 0.87 (gall bladder), 0.80 (pancreas), 0.75 (esophagus), and 0.61 (duodenum) for the PCT dataset. Comparing with organ dose results from population‐averaged phantoms, the new patient‐specific method achieved smaller absolute RDEs (mean ± standard deviation) for all organs: 1.8% ± 1.4% (vs 16.0% ± 11.8%) for the lung, 0.8% ± 0.7% (vs 34.0% ± 31.1%) for the heart, 1.6% ± 1.7% (vs 45.7% ± 29.3%) for the esophagus, 0.6% ± 1.2% (vs 15.8% ± 12.7%) for the spleen, 1.2% ± 1.0% (vs 18.1% ± 15.7%) for the pancreas, 0.9% ± 0.6% (vs 20.0% ± 15.2%) for the left kidney, 1.7% ± 3.1% (vs 19.1% ± 9.8%) for the gallbladder, 0.3% ± 0.3% (vs 24.2% ± 18.7%) for the liver, and 1.6% ± 1.7% (vs 19.3% ± 13.6%) for the stomach. The trained automatic segmentation tool takes <5 s per patient for all 103 patients in the dataset. The Monte Carlo radiation dose calculations performed in parallel to the segmentation process using the GPU‐accelerated ARCHER code take <4 s per patient to achieve <0.5% statistical uncertainty in all organ doses for all 103 patients in the database.Conclusion: This work shows the feasibility to perform combined automatic patient‐specific multi‐organ segmentation of CT images and rapid GPU‐based Monte Carlo dose quantification with clinically acceptable accuracy and efficiency.