Total factor productivity (TFP) serves as a critical indicator for measuring enterprise efficiency and technological progress. However, existing prediction methods often fail to distinguish genuine causal mechanisms from spurious correlations while neglecting inter-enterprise network dependencies. This study proposes a Causal-Temporal Graph Convolutional Network (CT-GCN) that integrates causal inference techniques with temporal graph convolutional networks for dynamic TFP prediction and optimization. The framework employs the Levinsohn-Petrin method for TFP estimation, double machine learning for causal effect identification, and constructs enterprise relationship graphs capturing supply chain linkages, geographic proximity, and technological similarity. Using panel data from 12,847 Chinese manufacturing enterprises spanning 2008-2022, empirical results demonstrate that CT-GCN achieves substantial prediction improvements over baseline models, with RMSE reductions exceeding 19%. The causal analysis identifies R&D investment, digital transformation, and human capital as genuine productivity drivers, with significant treatment effect heterogeneity across industry sectors, firm sizes, and regions. An optimization decision mechanism translates these insights into differentiated strategic recommendations. This research contributes a novel methodology bridging causal reasoning and deep learning for economic forecasting applications.
Corporate onboarding requires the effective transfer of complex organizational knowledge embedded in internal policies and procedural documents; however, existing artificial intelligence (AI)-driven course generation systems primarily target academic or public knowledge domains. This gap limits the scalability and consistency of enterprise training, particularly in regulated environments where factual accuracy is critical. In this study, we present a multi-agent pipeline that automatically generates Sharable Content Object Reference Model (SCORM) 1.2-compliant e-learning courses from heterogeneous enterprise documents using large language models and retrieval-augmented generation (RAG). The system integrates four stages: semantic document ingestion with structure-aware chunking and embedding, an autonomous ReAct-based architect agent for course design, a parallel content generation pipeline combining multi-query retrieval and neural reranking, and standards-compliant SCORM packaging for deployment in learning management systems. Evaluated using real-world occupational safety documents, the system produced a complete multi-module course with structured lessons and assessments within minutes, demonstrating end-to-end automation of instructional design grounded exclusively in source materials. By ensuring traceability of generated content to organizational knowledge, the approach reduces the risk of hallucinations.
Indonesian employees in state-owned enterprises (SOEs) often face psychological challenges after retirement due to strong emotional ties to their workplace. This study examined the effectiveness of coping strategy training in enhancing happiness among retired SOE personnel. Using a field-based randomized pretest-posttest design with a comparison group, 556 retirees were assigned to a training group (n = 278) or a comparison group (n = 278). The intervention integrated problem-focused and emotion-focused coping strategies, including laughter techniques and relaxation. Happiness was measured using the Oxford Happiness Questionnaire at pretest, posttest (1 month after the intervention), and follow-up assessments at 2 and 3 months, reflecting a longitudinal design to capture sustained effects. Mixed ANOVA results indicate a significant and sustained increase in happiness among participants who received the training. Qualitative findings showed improvements in emotional regulation, meaning-making, social connectedness, and engagement in daily activities, highlighting the potential of structured coping programs to support positive ageing.
暂无摘要（点击查看详情）
XXX.
Dyeing wastewater is a significant source of industrial pollution, with emerging contaminants posing risks to aquatic ecosystems and human health, yet integrated assessments of multiple contaminant classes in dyeing wastewater at the enterprise scale remain scarce. This study investigated the occurrence, removal efficiency, and ecological risk of polyfluoroalkyl substances (PFAS), bisphenols (BPs), and phthalates (PAEs) in wastewater samplesfrom 18 dyeing enterprises in Guangdong Province, China. Detected concentrations ranged from 1.20-111 ng/L for PFAS, 10.5-1.98 ×103 ng/L for BPs, and 15-3.27 × 104 ng/L for PAEs, revealing significant inter-enterprise variability. Removal efficiencies varied substantially across enterprises, with some compounds exhibiting negative removal rates, indicating possible transformation, desorption, or process-induced release during treatment. Ecological risk assessment identified bisphenol A (BPA) and diethylhexyl phthalate (DEHP) as priority contaminants. Given the persistence and bioaccumulation potential of PFAS, additional risk assessment focused on fish collected near discharge points. While PFAS concentrations in some samples exceeded European Union regulatory thresholds, overall levels suggest limited short-term health risk, although potential long-term exposure remains a concern. Given its high concentration of dyeing enterprises and substantial wastewater discharge intensity, Guangdong represents a critical case for understanding dyeing wastewater impacts in China. The findings and solutions derived from this region are therefore not only locally relevant but also provide a valuable reference for dyeing industries across China.
To address volatility and local optimality issues arising from multidimensional dynamic data in Human Resource Performance Evaluation (HRPE) within the tourism industry, this study proposes a Tourism Human Resource-Deep Momentum Performance Optimization Model (THR-DMPOM). The development of the model begins with the construction of a 24-item evaluation index system. This system was established through an extensive review of HRPE literature and interviewed with more than 300 managers and employees from four representative tourism enterprises. Following multiple rounds of expert screening, 24 key indicators were finalized. Methodologically, THR-DMPOM introduces recursive momentum accumulation, cross-layer weight smoothing, and adaptive gradient correction mechanisms. These components enable continuous dynamic weight updating and historical gradient propagation across multilayer networks, thereby improving stability in nonlinear and time-varying evaluation environments. Extensive experiments were conducted using datasets from four tourism enterprises. The results show that THR-DMPOM achieves high stability and accuracy in practical applications. The systemic deviation remained within ± 1.0, demonstrating strong robustness in handling complex performance data. In distribution consistency validation, the D-values of THR-DMPOM scores remained close to 1, with p-values consistently exceeding 0.7, indicating strong agreement between model-generated results and historical records. Comparative experiments under high-load and unstable network conditions further confirm the superiority of the proposed model. THR-DMPOM outperforms Fuzzy Analytic Hierarchy Process (FAHP) and the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (F-TOPSIS) in both accuracy and operational efficiency. Compared with traditional AHP, TOPSIS, and static machine learning methods, which struggle to adapt to dynamic performance fluctuations and nonlinear interdependencies among multiple criteria, THR-DMPOM demonstrates a key advantage in adaptive weight learning. The proposed mechanisms enable continuous weight evolution and historical information sharing across network layers, effectively mitigating local optima and weight oscillation issues in dynamic environments. Overall, the model provides a robust and high-precision evaluation framework for real-time performance management and resource optimization in tourism enterprises operating under dynamic conditions.
Emergency management higher education is a critical component of the emergency management enterprise. Since the first degree programs began in the 1980s, significant efforts have been undertaken to develop emergency management higher education to meet the growing and changing needs of practice. These efforts have historically been supported by the Federal Emergency Management Agency (FEMA), specifically the FEMA Higher-Education Program. Recent changes at FEMA have called into question how the agency intends to remain involved in the higher education community. In this moment of uncertainty and change, there is an opportunity for the broader emergency management higher education community to come together and reconsider our shared path forward. The history and current state of emergency management higher education are reviewed, followed by recommendations for the future of emergency management higher education.
In the context of Health China 2030, the Chinese government endorses the unique advantages of traditional Chinese medicine (TCM) within the public health care system and promotes the inheritance and development of TCM culture. Emerging digital formats-such as online literature, online audiovisual media, and digital games-have become important vehicles for promoting TCM culture and public health education. Consequently, Chinese digital games have increasingly incorporated elements of TCM, serving as new media for communicating health knowledge and cultural values to a broad player population. This study examines how exposure to digital games containing TCM culture, supported by national health promotion policies, influences players' willingness to accept TCM treatment, thereby contributing to the broader goal of improving residents' health welfare. However, empirical research examining how such games influence players' willingness to accept TCM-based medical treatment remains limited. This study aims to investigate the key factors and mechanisms through which digital game use motivations shape players' intention to seek TCM treatment. Based on data collected through an online questionnaire survey of 460 Chinese digital game players, PLS-SEM was utilized to examine the core factors and relationships between gaming motivations, TCM cultural identity, health self-efficacy, and TCM treatment intention. The findings reveal that three types of game use motivations-hedonic, social, and knowledge-seeking-indirectly enhance players' willingness to accept TCM treatment by strengthening their health self-efficacy and identification with TCM culture. Additionally, players' distrust of the TCM healthcare system negatively moderates the association between gaming motivations and TCM treatment intention. This study demonstrates the potential of digital games as effective tools for integrating cultural transmission with public health communication. By improving players' TCM treatment intention, digital games containing TCM culture contribute to the public health objectives outlined in the Health China 2030 strategy. The results provide both theoretical grounding and practical guidance for government agencies and enterprises seeking to leverage digital games to promote TCM cultural inheritance and enhance public health education as part of health welfare improvements.
Decades before medical definitions of developmental normalcy revolved around statistical averages, nineteenth-century medical scientists identified a non-statistical "normal" that cohered within racial groups but was not generalizable across them. Using early child medicine treatises and anatomical, physiological, and ethnological texts, this article argues that nineteenth-century child development science was a race-making enterprise. The first sections analyze texts on child development and disease, identifying the racial parameters of developmental norms and the developmental events most frequently associated with racial differentiation: cranial suture closure and puberty. The following sections argue that variable rates of development and the degree of synchronicity between mental and physical development produced racial hierarchies and threatened racial degeneration. By examining "ethnic" classifications of idiocy, in which developmental timing considered typical for Black children was pathologized in White children, this article argues that developmental disorder posed a threat to racial order. As Darwinian evolutionism introduced scientific and social concerns about the historical contingency of racial progress, the clinical management of child development acquired newfound importance as a safeguard for White racial purity. Joining histories of nineteenth-century child development science and antebellum medical cultures with disability history, this article offers a pre-history of "developmental disability" as a racial category.
Healthcare systems face a persistent gap between the complexity of their adaptive challenges and the leadership behaviors applied to address them. Improvement efforts frequently plateau not for lack of commitment or tools, but because leadership behaviors remain primarily directive and technical in orientation. In this perspective, we synthesize current evidence and leadership theory to propose a practical framework for coaching as a core leadership behavior in complex health systems-applicable across clinical and operational domains, at every level of leadership. Coaching in this context does not refer to professional executive coaching services. It refers to a learnable, observable set of leadership behaviors-a coaching stance, organizational listening, and coaching questions-that shift organizations from episodic problem-solving to sustained adaptive capacity. These behaviors are distinct from existing literature on physician coaching as a wellness or remediation intervention; the argument here is that coaching must be embedded as a leadership infrastructure capability across the enterprise. A practical framework-three core leadership behaviors and two organizational routines-is proposed to guide health system leaders in embedding coaching capability regardless of organizational size or maturity. Early implementation at an academic health system informs the framework and will be the subject of subsequent outcome reporting.
Advanced Therapy Medicinal Products (ATMPs) can offer unprecedented therapeutic benefit for patients with limited treatment options. However, their clinical translation and early-stage clinical development remain highly complex, particularly for academic developers and small- and medium-sized enterprises (SMEs). This study presents a comprehensive analysis of the different types of challenges that ATMP developers face in initiating clinical trials, based on a scoping literature review, conducted according to the JBI methodology for scoping reviews and reported according to PRISMA-ScR. PubMed, Embase and Scopus were searched for peer-reviewed literature covering the last decade, focusing on publications discussing regulatory challenges for ATMP developers at the stage of clinical trial initiation. Our findings show that key challenges exist in four areas, comprising (a) preclinical evidence and product characteristics, (b) cell sourcing and manufacturing, (c) clinical trials and (d) regulatory landscape. Early engagement with regulators and the availability of up-to-date guidance documents emerged as key strategies to align developer efforts with regulatory expectations, while the review also identifies practical recommendations to support more efficient and harmonized clinical translation. In line with the objectives of JOIN4ATMP, these insights are valuable for developers and informative for regulators to foster more efficient and aligned ATMP development pathways.
Voluntary, Community and Social Enterprise (VCSE) programmes that create opportunities to move outdoors via a volunteer-led activity offer a novel context through which to understand issues of access, participation and (re)engagement in structured physical activity or movement-based programmes. Through ethnography, this paper offers an account of the moments leading up to walking or experiencing a bike ride together in two different VCSE programmes. We focus on what is involved within and beyond the volunteer-beneficiary encounter in these programmes, and the different configurations of care that made participation and continued re-engagement in movement successful. We find that these configurations of care shifted moments of access, bringing to the surface the transitions, technologies, temporalities, and touch required of the volunteer-beneficiary encounter. Our findings demonstrate the importance of research exploring the "edges" of structured, movement-based activities, by showing what these moments reveal about participation, engagement, access, and care. We conclude by proposing that an aesthetic mode of enquiry within qualitative research could help inform the design, delivery, and evaluation of movement-based programmes or interventions.
Recurrence after hiatal/paraesophageal hernia repair is variably defined, and radiographic criteria may overestimate clinically meaningful failure. We evaluated the incidence, timing, and operative predictors of recurrence, defined pragmatically as reoperation, in a large multi-hospital cohort. Retrospective cohort study of adults undergoing hiatal and/or paraesophageal hernia repair (April 2017-April 2025) using an enterprise administrative/operative database. Cases were identified using CPT codes supplemented by operative descriptors and nursing documentation. Patients with prior or concurrent bariatric surgery were excluded. The index repair was the first qualifying repair within the study window. The primary endpoint was clinically significant recurrence, defined as subsequent hiatal/paraesophageal hernia reoperation. Time-to-event was analyzed using survival models that incorporated operative factors and accounted for clustering. Among 2779 repairs, 900 bariatric-associated cases were excluded, leaving 1876 index repairs across 21 hospitals and 70 surgeons. Approach was laparoscopic in 49.3% and robotic in 48.0% (open 2.0%, thoracotomy 0.7%). Mesh was used in 51.1% and fundoplication in 39.4%. During follow-up, 49 reoperations (2.6%) occurred. Time to reoperation clustered early (median 348 days [IQR 107-623]), with follow-up extending to 3023 days. In unadjusted comparisons, reoperation was less frequent after fundoplication (1.86 vs. 2.99%) and more frequent with mesh reinforcement (3.04 vs. 2.03%). In adjusted time-to-event models accounting for clustering, fundoplication was independently associated with a lower hazard of reoperation, whereas mesh use was associated with a higher hazard. In routine practice, reoperation after hiatal/paraesophageal hernia repair was uncommon and concentrated within the first several postoperative years. Fundoplication was associated with lower risk of clinically significant recurrence, while mesh use identified higher-risk cases, likely reflecting operative complexity.
Achieving deep decarbonization of the power sector is essential for China's carbon neutrality goal and global climate mitigation. However, the coordination among emission reduction effectiveness, carbon market stability, and energy security remains unclear. This study develops a bottom-up multi-agent simulation model, Electricity and Carbon Coupling Multi-Agent System (ECMAS), integrating the power market with primary and secondary carbon markets to capture the adaptive behaviors of 2,241 heterogeneous power enterprises under alternative carbon market designs. Four policy scenarios are simulated to evaluate different pathways of quota tightening and auction introduction. Results show that rapidly synchronizing quota reductions with high auction shares imposes excessive carbon pressure, leading to carbon price collapse, premature fossil capacity retirement, and supply risks. In contrast, gradually introducing auctions alongside smooth quota tightening stabilizes carbon prices, supports phased low-carbon investment, and achieves sustained emission reductions. These findings provide evidence-based guidance for improving China's carbon market and offer transferable insights for global carbon market design under deep decarbonization.
To increase naloxone co-prescribing to ≥80% among patients prescribed opioids as outpatients by December 31, 2024, regardless of gender, race, or preferred language. We conducted a quality improvement initiative at the Children's Hospital of Philadelphia (CHOP) from October 2023 to June 2025. In 3 phases, we implemented an electronic health record (EHR)-based automated alert to co-prescribe naloxone, combined with education for providers, nurses, patients/families, and pharmacists. The primary outcome was the proportion of patients with an outpatient opioid prescription who were co-prescribed naloxone ("coverage"). We also tracked naloxone dispensing from CHOP pharmacies (outcome measure) and provider engagement with the alert (process measure). Balancing measures included provider-reported ease of prescribing naloxone and comfort offering naloxone. Compared with a 1-year baseline period, average monthly naloxone coverage increased from 3.0% to 84.1% enterprise-wide. Although early feedback revealed that perceived stigma and naloxone cost were barriers to dispensing, the proportion of orders for naloxone dispensed at our internal pharmacy during the initiative was 59.1%. Most providers reported that naloxone was easy to prescribe (81.8%, 36/44) and reported high levels of comfort with offering naloxone to families (86.4%, 38/44). EHR-based prompts, combined with education for providers, nurses, patients/families and pharmacists, increased naloxone co-prescribing across a diverse population of patients in a large pediatric health system.
Objectives. This study examined fatal machinery crushing accidents in South Korea's manufacturing sector using official government investigation reports, identifying phase-specific vulnerability patterns and their policy implications. Methods. Analysis included 272 fatal accident reports (2016-2019) systematically coded through Reason's Swiss cheese model framework and interaction effect quantification using Cramér's V coefficients. Key variables comprise safeguard status, work phase, enterprise size, lock/tagout (LOTO) compliance and machinery type. Methods encompassed descriptive statistics, χ2 tests and Cramér's V analysis, exposure-adjusted risk ratios and Pareto intervention simulation. Results. Among 132 legally mandated safeguard cases, 87.1% showed non-compliance, with 59.8% occurring during maintenance phases (safeguard-maintenance interaction: V = 0.35, p < 0.001). Small enterprises (<50 employees) accounted for 65% of fatalities despite representing only 40% of manufacturing employment (risk ratio = 2.8, 95% confidence interval [2.1-3.7]). Pareto analysis identified 18 priority machinery types responsible for 82% of fatalities. Conclusion. Maintenance-phase systemic failures predominate through safeguard-procedure misalignment. Targeted interventions - mandatory LOTO audits, small and medium-sized enterprise support programs, priority machinery decommissioning - offer substantial fatality prevention. Findings inform Korean industrial safety policy while contributing phase-specific evidence to global machinery accident prevention.
The rapid advancement of artificial intelligence (AI) is reshaping talent management by enabling data-driven approaches to recruitment, skill development, and workforce planning. This study introduces the InsightConnect AI Empowerment System, an integrated digital platform designed to optimize talent-project matching, recruitment forecasting, and knowledge sharing through predictive analytics, natural language processing (NLP), and graph-based learning. Grounded in a Design Science Research (DSR) framework, the system was developed and validated using anonymized datasets comprising 10,000 user profiles and approximately 2,000 enterprise projects (1,840 completed projects used for evaluation).The hybrid recommendation model, combining content-based, collaborative, and graph-embedding techniques, achieved a 12.7% improvement in precision and a 10.4% increase in recall over traditional baselines, while the predictive module attained a Root Mean Square Error (RMSE) of 0.083, indicating strong forecasting accuracy. Prototype deployment results revealed a 24% rise in successful talent-project matches and a 30% reduction in search time, enhancing both organizational efficiency and user satisfaction.The findings highlight how AI-enabled ecosystems can advance workforce intelligence, improve data-informed decision-making, and support policy innovation for sustainable human capital development.
Accurate prediction of fly ash generation from municipal solid waste incineration is critical for source reduction and cost-effective disposal. However, conventional models fail to capture the complex nonlinear relationships governing fly ash formation. In this study, a machine-learning-based prediction framework was developed using six algorithms (Lasso, KNN, DT, SVM, RF, and XGBoost), with XGBoost identified as the optimal model, achieving an R2 of 0.896, RMSE of 178.49, and MAE of 101.44 on the test set. Model interpretability analysis using SHapley Additive exPlanations and Partial Dependence-Individual Conditional Expectation analysis methods revealed that incinerator type, designed disposal capacity, operating load, and lime injection rate were the dominant factors influencing fly ash yield. Based on these insights, optimized operational ranges were proposed, including an operating load of 95 ∼ 105 %, deacidification efficiency of 85 ∼ 95 %, denitrification efficiency of 60 ∼ 70 %, high-temperature flue gas residence time of 2.7 ∼ 3.0 s, average furnace temperature of 860 ∼ 870 °C, and lime injection rate of 50 ∼ 100 kg/h. By applying Particle Swarm Optimization to optimize incineration parameters in a specific case study, the fly ash yield was reduced by 22.40 %, corresponding to a maximum annual reduction of 1.229 thousand tons. This optimization could lower the enterprise's annual fly ash disposal costs by approximately 2.089 ∼ 2.458 million CNY, while achieving a minimum reduction of 682 tons of CO2 emissions per year.
Understanding how domestic and foreign-invested firms across China's provinces participate in domestic and international production networks has become increasingly important in both scientific research and policy analysis. However, existing data infrastructures, most notably input-output (IO) datasets, does not adequately support this line of inquiry. Conventional inter-country IO (ICIO) databases treat China as a single, homogeneous economy and therefore overlook substantial variation at the provincial level. Meanwhile, China's inter-provincial IO tables provide no direct linkages between individual provinces and their foreign trading partners. Moreover, firm ownership heterogeneity is largely absent in most available IO datasets. To address these limitations, we construct a new ICIO dataset that integrates China's inter-provincial IO tables-disaggregated by domestic and foreign-invested firms-with the OECD's Activities of Multinational Enterprises-ICIO data and detailed Chinese customs statistics, all within a globally consistent accounting framework. The resulting database comprises 26 sectors, 107 regions, and 2 ownership categories for the benchmark years 2007, 2012, and 2017. These data offer a new foundation for empirical work in economics, environmental assessments, network analyses, and their interdisciplinary applications, particularly for studies examining how firms with different ownership structures across Chinese provinces engage in global supply chains.