Lycophytes and seed plants constitute the typical vascular plants. Lycophytes have been thought to have no paleo-polyploidization although the event is known to be critical for the fast expansion of seed plants. Here, genomic analyses including the homologous gene dot plot analysis detected multiple paleo-polyploidization events, with one occurring approximately 13–15 million years ago (MYA) and another about 125–142 MYA, during the evolution of the genome of Selaginella moellendorffii, a model lycophyte. In addition, comparative analysis of reconstructed ancestral genomes of lycophytes and angiosperms suggested that lycophytes were affected by more paleo-polyploidization events than seed plants. Results from the present genomic analyses indicate that paleo-polyploidization has contributed to the successful establishment of both lineages—lycophytes and seed plants—of vascular plants.

Against the backdrop of the synergistic advancement of Industry 4.0 and the dual-carbon strategy, traditional university mathematics education struggles to meet the demands for cultivating engineering talents’ integrated competencies in mathematics, specialization, and application. The STEM education paradigm urgently needs innovation. Guided by sustainable development principles, this study explores integrated approaches to university mathematics teaching for advanced manufacturing. It constructs a four-stage cyclical framework, Concept–Algorithm–Equipment–Evaluation (CAEE), and integrates Fourier Transform systems into industrial inspection workflows, using silicon carbide wafer thickness measurement as a case study. Targeting second-year students in Measurement and Control Technology and Instrumentation, a comparative design involving an experimental and a control group was employed. Comprehensive evaluation utilized AI-powered dynamic questionnaires, multimodal eye-tracking and EEG data, along with mixed-methods research. Results indicate that the assessment tools achieved high reliability and validity (0.906). The experimental group demonstrated significantly superior performance in deep learning proficiency and subject-specific educational structure (effect size 0.67) compared to the control group, along with modest positive enhancements in cognitive engagement and social interaction dimensions. This pedagogical model transcends conventional ‘knowledge collage’ integration, transforming mathematics from an external auxiliary tool into an ‘endogenous variable’ within industrial systems. It establishes a replicable and scalable STEM education practice paradigm.

Seed plants and lycophytes constitute the extant vascular plants. As a model lycophyte, Selaginalla moellendroffii was deciphered its genome, previously proposed to have avoided polyploidies, as key events contributing to the origination and fast expansion of seed plants. Here, using a gold-standard streamline recently proposed to deconvolute complex genomes, we reanalyzed the S. moellendroffii genome. To our surprise, we found clear evidence of multiple paleo-polyploidies, with one being recent (~ 13-15 millions of years ago or Mya), another one occurring about ~125-142 Mya, during the evolution of lycophytes, and at least 2 or 3 events being more ancient. Besides, comparison of reconstructed ancestral genomes of lycophytes and angiosperms shows that lycophytes were likely much more affected by paleo-polyploidies than seed plants. The present analysis here provides clear and solid evidence that polyploidies have contributed the successful establishment of all vascular plants on earth.

Under the dual carbon strategy and the sweeping tide of digital transformation in education, higher education confronts an urgent imperative: cultivating talent equipped with interdisciplinary skills and sustainable decision-making capabilities. To meet this critical challenge, this study pioneers the PAMD (Patient Capital–Accounting–Matrix–Development) interdisciplinary teaching framework. Rooted firmly in Education for Sustainable Development (ESD) principles, PAMD uniquely weaves together patient capital, carbon asset accounting, and linear algebra matrix modeling. Utilizing a quasi-experimental design with undergraduate business students, we implemented “Carbon Asset Accounting and Low-Carbon Transition Investment Analysis” as a case study. We rigorously evaluated teaching effectiveness across academic performance, competency, and cognitive attitude dimensions using Welch’s t-test, Hedges’ g, and ANCOVA. After controlling for baseline scores, the experimental group significantly surpassed the control group in comprehensive decision-making (81.22 vs. 72.41, g = 0.71) and matrix modeling competency (3.74 vs. 3.22, g = 0.77). The experimental cohort also demonstrated consistent gains in carbon accounting reporting precision and data representation clarity. Cognitive assessments revealed moderate effect sizes for both low-carbon investment literacy and interdisciplinary learning interest. These compelling results demonstrate that embedding a long-term value orientation into accounting representation and matrix modeling powerfully cultivates students’ ability to transfer interdisciplinary knowledge and make sound sustainable decisions within complex contexts. This study offers a robust, evidence-based, and replicable pathway for driving sustainability-oriented interdisciplinary reform within business education.

Purpose: A theory–application–innovation (TAI) tri-integrated teaching model is developed, implemented and evaluated. The limited clinical relevance of traditional medical mathematics education is addressed. Quantitative reasoning skills for evidence-based health care are enhanced. Contributions are made to Sustainable Development Goals (SDG 4 and SDG 3). Design/methodology/approach: A mixed-method approach evaluates the model’s efficacy in fostering sustainable development capabilities. Pre- and post-course surveys track the evolution of students’ awareness of sustainability. Quantitative assessments measure mathematical and problem-solving skills. In-depth interviews explore student perspectives. A quasi-experimental design compares outcomes between a TAI-integrated course group and a traditional teaching group. Quantitative data are analyzed statistically. Qualitative responses are examined thematically to identify key learning patterns. Findings: The TAI model significantly improves theoretical mastery, with average scores rising from 75.82 to 80.39. Independent application of mathematics to medical problems increases from 35.2% to 78.7%. A shift from exam-focused “problem-solving” to clinical “medical modeling” is observed, indicating enhanced reasoning and professional readiness. Research limitations/implications: Validation across multiple institutions and examination of long-term clinical impacts are required. A replicable pedagogical framework for quantitative reasoning integration is provided. The clinical applicability of medical mathematics is enhanced, and a more adaptive health-care workforce is cultivated. Originality/value: This research is among the first to propose and validate a tri-integrated “TAI” model for advanced medical mathematics education. Novel evidence shows that pedagogical innovation supported by learning analytics and immersive strategies can simultaneously advance educational quality (SDG 4) and health workforce sustainability (SDG 3).

The transition toward Industry 5.0 requires safety management systems that are not only intelligent and data-driven but also human-centric, resilient, and aligned with sustainable operations. However, conventional safety risk assessment in steel manufacturing remains heavily dependent on expert judgment and often lacks the adaptability required to address complex and dynamic production environments. This study proposes a text-driven intelligent framework for sustainable safety risk governance by integrating natural language processing, topic modeling, objective indicator weighting, and Bayesian decision fusion. The framework establishes a closed-loop process encompassing risk identification, quantitative assessment, risk classification, and hierarchical control. It automatically extracts risk-related information from unstructured safety records, maps the identified hazards onto a human–machine–environment–management structure, quantifies multidimensional risk indicators, and translates assessment outcomes into differentiated control measures. The framework was evaluated using field safety records collected from Tianjin Iron and Steel Group. The topic modeling results identified four major dimensions of operational risk, while the CRITIC–Bayesian weighting mechanism combined data-driven indicator differentiation with context-sensitive probabilistic reasoning. Following its integration into the company’s intelligent safety management platform and one year of operational use, the framework reduced the time required to formulate safety inspection plans by 70%, supported dynamic four-level risk classification, and achieved a 97% task completion rate. The number of recorded safety accidents also decreased by 35% compared with the pre-deployment baseline. These findings demonstrate that unstructured safety text can be transformed into actionable risk intelligence, enhancing the proactive, systematic, and adaptive governance of safety risks. The proposed framework provides a practical pathway for advancing human-centric safety management, operational resilience, and sustainable production in the steel industry.