Science Progress最新文献

High-quality development represents a fundamental shift in China's economy from scale and speed to quality and efficiency. In this transition, environmental, social, and governance (ESG) practices are not only closely integrated with China's development strategy but have also become a core standard for measuring development quality. Nevertheless, research on how ESG practices specifically promote high-quality development in Chinese enterprises remains limited. Based on the environmental Kuznets curve (EKC) theory framework, this study uses data from the Huazheng ESG Index from 2009 to 2022 and employs fixed effect models, mediation effect models, and heterogeneity analysis to examine the specific impact of China's ESG practices on corporate high-quality development from multiple dimensions. The results show that there is a "U-shaped" relationship between the ESG practices of Chinese listed companies and green total factor productivity (GTFP). Initially, improving ESG practices may temporarily suppress GTFP, but as ESG levels further improve, they exhibit a significant positive impact on GTFP. Corporations significantly boost productivity by alleviating financing constraints and promoting green technological innovation, although the direct impact on technological advancement remains relatively limited. In China's western regions and industries such as manufacturing, electricity, heat, gas, and water production and supply, wholesale and retail, and information transmission, software, and information technology services, the positive effects of ESG on GTFP are particularly evident. Based on these findings, it is recommended that governments and financial institutions collaborate to promote green credit policies and increase support for the research and development of green technologies. Additionally, environmental education and resource use optimization should be strengthened, especially in industries such as agriculture, forestry, animal husbandry, and fisheries, and the exchange of ESG technologies and experiences between industries should be encouraged.

In the era of rapid internet expansion and technological progress, discerning real from fake news poses a growing challenge, exposing users to potential misinformation. The existing literature primarily focuses on analyzing individual features in fake news, overlooking multimodal feature fusion recognition. Compared to single-modal approaches, multimodal fusion allows for a more comprehensive and enriched capture of information from different data modalities (such as text and images), thereby improving the performance and effectiveness of the model. This study proposes a model using multimodal fusion to identify fake news, aiming to curb misinformation. The framework integrates textual and visual information, using early fusion, joint fusion and late fusion strategies to combine them. The proposed framework processes textual and visual information through data cleaning and feature extraction before classification. Fake news classification is accomplished through a model, achieving accuracy of 85% and 90% in the Gossipcop and Fakeddit datasets, with F1-scores of 90% and 88%, showcasing its performance. The study presents outcomes across different training periods, demonstrating the effectiveness of multimodal fusion in combining text and image recognition for combating fake news. This research contributes significantly to addressing the critical issue of misinformation, emphasizing a comprehensive approach for detection accuracy enhancement.

Climate change and plastic pollution are two main issues that our world is currently facing, and they are mainly linked through various processes, mechanisms, and chemical blueprint. Emerging issues related to microplastic (MP) contamination in freshwater are expanding and diverse research is being carried out globally. Factors causing climate change are increasing the frequency of extreme weather phenomena such as floods, drought, sea level rise, and heat waves, which can directly or indirectly influence the plastic/MP contamination in various ecosystems including groundwater environments. Here, we review the impacts of extreme weather events on MP contamination in freshwater with a specific focus on groundwater environments. This narrative review shows that flooding can have the most adverse effect on the MP pollution in groundwater environments through recharge events. Drought can also have major effects on MP pollution. Karst, alluvial, and coastal aquifers exhibit the highest levels of MP contamination among various aquifer types. Climate change's impact on different types of aquifers can vary depending on hydrogeological conditions and other factors in the groundwater environment. Prevention and comprehensive solutions are crucial for addressing MPs in the environment, with downstream measures being supplementary to upstream ones.

This study presents a novel approach to accurately predict the settlement of shallow foundations using advanced machine learning techniques while assessing the influence of key variables. Four machine learning models Gradient Boosting (GB), Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) are enhanced with Particle Swarm Optimization (PSO) for hyperparameter tuning, resulting in hybrid models GB-PSO, RF-PSO, SVM-PSO, and KNN-PSO. The experimental dataset comprises 189 samples, and model performance is rigorously evaluated through K-Fold Cross-Validation alongside R², RMSE, MAE, and MAPE metrics. The results indicate that PSO tuning does not consistently improve the prediction accuracy, with the original models, particularly GB and RF, outperforming their PSO-optimized counterparts. Sensitivity analysis via Shapley Additive Explanation (SHAP) highlights average Standard Penetration Test blow count (SPT) and footing width (B) as the most influential variables, with footing embedment ratio (D_f/B) and net applied pressure (q) also significantly impacting settlement predictions. The study offers a new Excel tool based on the GB model, facilitating practical applications for civil engineers, and providing a dependable, user-friendly tool to predict shallow foundation settlement.

Objective: Polytrauma is a complex condition associated with poor outcomes and high mortality rates resulting from severe damage and complicated complications. This study sought to ascertain the incidence of chronic complications in polytrauma patients, as well as the early immune changes and risk factors.

Methods: A multicenter, prospective and observational cohort study was conducted at the emergency surgery or traumatic intensive care unit (TICU) of the Advanced Trauma Center from August 2020 to July 2023. A total of 2033 consecutive trauma patients were included in the study. In the first 1, 7, and 14 days after admission, flow cytometry and immunoassay kits were used to examine cytokine release and lymphocyte count.

Results: Trauma patients were reported 33.8% (687/2033) chronic complication rates, with monotrauma patients reported 8.1% (55/683) and polytrauma patients reported 59.4% (802/1350). And the four most frequent chronic complications in polytrauma patients were chronic musculoskeletal pain (30.4%), post-traumatic osteoarthritis (27.2%), chronic wound (21.6%), and chronic lung injury (14.1.%) .There were significant differences in lymphocyte ratios and cytokine levels, at 1, 7, and 14 day of admission between chronic complication groups (CCP) and not chronic complication groups (N-CCP) in polytrauma. Polytrauma patients with characteristics of higher ratio of Ts_7d ratio (95% CI: 2.01-6.21), Treg_14d (95% CI: 1.12-5.43) and level of IL-6_7d (95% CI: 1.22-4.43), TNF-α_7d (95% CI: 1.05-3.83), IL-10_14d (95% CI: 2.01-6.84) were found to have a higher likelihood of experiencing a chronic complication. Conversely, a higher ratio of Tc_1d (95% CI: 0.53-0.86), Th_1d (95% CI: 0.64-0.95) and Th/Ts_14d (95% CI: 0.21-0.64) were identified as independent protective factors against a chronic complication event.

Conclusion: Polytrauma patients exhibit a notable prevalence of chronic complications. Some immune and inflammatory indicators can be observed early in combination after injury to predict the risk of chronic complications after polytrauma.

Background: Benzodiazepines (BZDs) are commonly prescribed as adjunctive drugs for patients with cardiovascular diseases (CVDs), particularly those who experience anxiety or insomnia. However, the relationship between the use of BZDs and incident risk of sudden cardiac arrest (SCA) has not been well investigated. In this study, we aimed to examine the association between the use of BZDs and the incident risk of SCA among patients with CVD.

Method: In this retrospective cohort study, a total of 74,715 eligible patients with new-onset CVD as a primary cause of hospitalization between July 2016 and August 2022 were included from the health information platform in Shenzhen, China. Among them, 61,761 BZD non-initiators were identified and matched to 12,954 BZD initiators by propensity score at a maximum ratio of 5:1. Propensity score-matched Cox proportional hazard models were used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs).

Results: Over a 12-month follow-up period, 29 (2.24 per 1000 person-years) and 137 (2.22 per 1000 person-years) SCA cases occurred among propensity score-matched BZD initiators and non-initiators, respectively. Patients who initiated BZD treatment were associated with a 101% increased risk of SCA incidence compared with patients without BZD treatment (adjusted HR: 2.01, 95% CI: 1.42, 2.83). Furthermore, compared with the non-use (0 defined daily dose, DDD), the adjusted HR was 1.43 (95% CI: 1.32, 1.56) for the BZD consumption of ≤1 DDD and 2.58 (95% CI: 2.37, 2.81) for the BZD consumption of >1 DDD (P for trend < 0.001) within a 12-month follow-up period.

Conclusion: This study provides evidence that BZD initiation may be associated with an increased incident risk of SCA in patients with CVD. Our finding highlights the importance of cautious prescribing BZDs in the health management of patients with CVD.

In recent years, the application of pretrained models in specialized domains has become increasingly important. Traditionally, adapting these models involved fine-tuning their parameters and structures through retraining. However, these fine-tuning methods can be inefficient, particularly when addressing data from specific domains or when modifications are needed in the lower layers of large-scale pretrained models. This study aims to investigate the effectiveness of using pretrained models with frozen weights for downstream tasks in the context of railway track detection, particularly focusing on the railway system. To achieve this, we employed a large-scale semantic segmentation model that had been pretrained on extensive datasets. The models utilized were kept with fixed weights, eliminating the need for retraining. We conducted a comparative analysis of various pretrained models sourced from different datasets to identify the most suitable model for the track detection system. The findings from our experiments revealed the performance metrics of the selected pretrained models, highlighting their effectiveness in the specific domain of railway track detection. Overall, this research demonstrates the practical applicability of pretrained models with frozen weights in specialized fields such as railway systems, offering insights into their usefulness and potential for improving detection algorithms in this domain.

Due to the advantages of high stiffness, high precision, high load capacity and large workspace, hybrid robots are applicable to drilling and milling of complicated components with large sizes, for instance car panels. However, the difficulty in establishing an exact dynamic model and external disturbances affect the high accuracy control directly, which will decrease the machining accuracy and thereby affect the machining quality and efficiency of the system. Sliding mode control is an effective approach for high-order nonlinear dynamic systems since that it is very insensitive to disturbances and parameter variations. However, chattering may exist in traditional sliding mode control with fixed parameters, which results from a constant approaching speed. Besides, the approaching speed will affect the chattering strength directly. To solve these problems, a modified sliding mode controller with self-adaptive parameters is proposed to enhance the trajectory-tracking performance of a 5-degree-of-freedom hybrid robot. Firstly, the kinematic model of the robot is established. Then adopting the principle of virtual work, a rigid dynamic model of the robot is built. Based on the built dynamic model, a modified sliding mode control method is developed, of which the approaching speed is dependent on the system state. Finally, the sliding mode controller with self-adaptive parameters is created for a hybrid robot. The proposed sliding mode controller can achieve a rapid approaching speed and suppress chattering simultaneously. Simulation results demonstrate that the proposed modified sliding mode controller can achieve a comparatively accurate and smooth trajectory, which owns good robustness to external disturbances.

Due to the discrete and non-homogeneous of similar materials and the inability to realize large-size and original scale modeling, it is difficult to restore the structure and stress state of underground coal and rock mass in similar simulation tests. To solve this problem, a lightweight and suitable for large-scale modeling similar material, rock-like porous material has been developed. The quasi-static uniaxial compression experiment was carried out by using the large tonnage multi-module electronic control test system. And the influencing factors of controllable mechanical behavior of rock-like porous materials were studied. The results showed that, under uniaxial compression conditions, the material stress-strain curve exhibits three phases: elastic stage, failure stage, and platform stage. The uniaxial compressive strength, elasticity modulus, stress drop, and softening modulus of rock-like porous materials basically increase with the increase of density. The stress after peak strength changes from a slow decrease to a "stepped" or even "cliff like" downward trend. Polypropylene fibers have the effect of enhancing the uniaxial compressive strength, elasticity modulus, stress drop, softening modulus, shear deformation, and residual strength stability of rock-like porous materials. The rock-like porous material has a critical loading velocity, and it increases with density. At the critical loading velocity, the material shows obvious shear failure, and the shear inclination angle is the largest, and so is the uniaxial compressive strength. Through the experimental research, the influence laws of density, polypropylene fiber, and loading velocity on the failure mode, mechanical parameters, and mechanical behavior of the material are clarified, and the quantitative relationship between density and each mechanical parameter is obtained. The research is helpful to realize the accurate control of mechanical behavior of rock-like porous materials and further inverts the deformation and failure mechanism of underground coal and rock structures through indoor similar simulation tests.

Rheumatoid arthritis (RA) is often treated with anti-tumor necrosis factor α (anti-TNF-α) medications. While these drugs can cause common side effects such as injection-site and infusion reactions, rare cases of Guillain-Barré syndrome (GBS) have been reported. It's a potentially life-threatening condition characterized by progressive, ascending weakness of the extremities and areflexia, with an incidence of about 1.5 cases per 100,000 annually and a mortality rate of around 5%. It has been linked to various triggers, including infections, vaccinations, and medications like TNF inhibitors. Anti-TNF-α treatments may induce GBS by activating latent infections, increasing susceptibility, triggering autoimmune responses, or disrupting the balance of TNF-α in the peripheral nervous system. We report a 39-year-old female with a 26-year history of RA, initially treated with methotrexate until it was discontinued due to myelosuppression. She was then prescribed etanercept. A few weeks later, she developed numbness and burning pain in her limbs. GBS was suspected based on her symptoms, and nerve conduction studies confirmed the diagnosis. She was successfully treated with plasmapheresis.