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Air quality forecasting is crucial to reducing air pollution in China, which has detrimental effects on human health. Atmospheric chemical-transport models can provide air pollutant forecasts with high temporal and spatial resolution and are widely used for routine air quality predictions (e.g., 1–3 days in advance). However, the model’s performance is limited by uncertainties in the emission inventory and biases in the initial and boundary conditions, as well as deficiencies in the current chemical and physical schemes. As a result, experimentation with several new methods, such as machine learning, is occurring in the field of air quality forecasting. This study combined hourly PM 2.5 mass concentration forecasts from an operational air quality numerical prediction system (WRF-Chem) at the Shanghai Meteorological Service (SMS) with comprehensive near-surface measurements of air pollutants and meteorological conditions to develop a machine learning model that estimates the daily PM 2.5 mass concentration in Shanghai, China. With correlation coefficients that are higher by 50–100% and a standard deviation that is lower by 14–24 µg m –3 , the machine learning model provides significantly better daily forecasting of PM 2.5 than the WRF-Chem model. Thus, this research offers a new technique for enhancing air quality forecasting in China.

One major challenge often encountered during the production and transportation of crude oil in petroleum industries is the formation of extremely stable emulsions. This problem is particularly present during the production of heavy oils where steam is used to reduce the viscosity of heavy oil or where submersible pumps are used to artificially lift the produced fluids. This review elaborates on the mechanism of the formation and stability of heavy oil emulsions, discusses the effect of natural surfactants (resins, asphaltenes, solid particles, etc) on the stability of heavy oil emulsions, and summarizes the prominent and sustainable technological and methodological developments in the demulsification of heavy oil emulsions from an economical and environmental perspective. Ionic liquids have been reported as efficient and environmental demulsifiers, and the latest research results on this topic are discussed. Microwave and ultrasonic demulsification methods have been reported to be effective in treating heavy oil emulsions and are also recommended as substitutes in the further development of demulsification technology for treating heavy oil emulsions. This review elaborates on the mechanism of the formation and stability of heavy oil emulsions, discusses the effect of natural surfactants (resins, asphaltenes, solid particles, etc) on the stability of heavy oil emulsions, and summarizes the prominent and sustainable technological and methodological developments in the demulsification of heavy oil emulsions from an economical and environmental perspective.

A visibility forecast model called a boosting-based fusion model (BFM) was established in this study. The model uses a fusion machine learning model based on multisource data, including air pollutants, meteorological observations, moderate resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) data, and an operational regional atmospheric environmental modeling System for eastern China (RAEMS) outputs. Extreme gradient boosting (XGBoost), a light gradient boosting machine (LightGBM), and a numerical prediction method, i.e., RAEMS were fused to establish this prediction model. Three sets of prediction models, that is, BFM, LightGBM based on multisource data (LGBM), and RAEMS, were used to conduct visibility prediction tasks. The training set was from 1 January 2015 to 31 December 2018 and used several data pre-processing methods, including a synthetic minority over-sampling technique (SMOTE) data resampling, a loss function adjustment, and a 10-fold cross verification. Moreover, apart from the basic features (variables), more spatial and temporal gradient features were considered. The testing set was from 1 January to 31 December 2019 and was adopted to validate the feasibility of the BFM, LGBM, and RAEMS. Statistical indicators confirmed that the machine learning methods improved the RAEMS forecast significantly and consistently. The root mean square error and correlation coefficient of BFM for the next 24/48 h were 5.01/5.47 km and 0.80/0.77, respectively, which were much higher than those of RAEMS. The statistics and binary score analysis for different areas in Shanghai also proved the reliability and accuracy of using BFM, particularly in low-visibility forecasting. Overall, BFM is a suitable tool for predicting the visibility. It provides a more accurate visibility forecast for the next 24 and 48 h in Shanghai than LGBM and RAEMS. The results of this study provide support for real-time operational visibility forecasts.

Surface ozone (O 3 ) substantially and adversely impacts public health and ecosystems. Despite China’s air quality improvements, pre-2013 ozone records in central-eastern China (CEC) remain scarce. To address this temporal gap, we constructed a 0.5° resolution daily maximum 8-h average (MDA8) ozone concentration dataset spanning 1980–2012 by employing a light gradient boosting machine (LightGBM, LGBM) model based on multisource datasets. Tenfold cross-validation (2013–2023) demonstrated robust spatiotemporal reconstruction capability, with 88% of grids exhibiting correlation coefficients (R 2 ) > 0.8 versus observations (42% exceeding 0.9) and root mean square errors (RMSE) of 7.5–7.9 μg⋅m − 3 . The reconstructed climatology revealed a regional mean of 87.6 μg⋅m − 3 with a north-south gradient and significant annual increase (0.14 μg⋅m − 3 ⋅yr − 1 , p < 0.01). μg⋅m − 3 Mechanistic attribution identified synergistic forcing from global warming (0.32 °C decade − 1 ), industrial expansion, and urbanization-induced emission growth as primary drivers of long-term ozone elevation. The LightGBM-reconstructed dataset exhibits exceptional stability (interannual variability < 5%), providing critical baseline data for quantifying multidecadal ozone pollution–climate interactions and informing regional air quality management strategies.

This systematic review and meta-analysis aimed to investigate the effects of transcranial direct current stimulation (tDCS) on static and dynamic postural control in older adults, with the goal of providing evidence-based support for tDCS interventions in fall prevention among the elderly. PubMed, Web of Science, Embase, Cochrane Library, Scopus and CNKI were searched from their inception to March 11, 2025, covering literature published in all languages. Eligible studies included randomized controlled trials or randomized crossover trials assessing the effects of tDCS on static or dynamic postural control in older adults. The methodological quality and risk of bias of included studies were assessed using the PEDro scale and the Cochrane Risk of Bias Tool, respectively. Meta-analysis was performed using Stata 14.0 with a random-effects model. Subgroup analyses and meta-regression were performed to explore potential moderators. A total of 19 studies were included in the systematic review, of which 14 were subjected to meta-analysis. Compared to control conditions, tDCS significantly improved following outcomes in older adults, static postural stability index (APSI :  < 0.001; MLSI :  < 0.001; OSI :  < 0.001), single-leg stance time (  = 0.004), center of pressure (COP) sway area during quiet standing (  = 0.044), COP path length (  = 0.03), dynamic postural stability index (APSI :  < 0.001; MLSI :  < 0.001; OSI :  < 0.001), Timed Up and Go test (TUGT;  = 0.003), and stride time variability during walking (  < 0.001). Subgroup analyses indicated that tDCS efficacy varied according to stimulation site and intervention duration. Meta-regression further revealed that the effect of tDCS on single-leg stance time was influenced by mean age. These findings suggested that tDCS can significantly improve static and dynamic postural control in older adults. However, due to the limited number of included studies and substantial heterogeneity observed in some analyses, the current conclusions require further validation through high-quality research. Based on the available evidence, it is recommended that future studies focus on the application of tDCS in fall-prevention interventions among older adults, in order to provide stronger evidence for its implementation in clinical practice. This systematic review was registered in PROSPERO (International Prospective Register of Systematic Reviews) (Unique Identifier: [registration number: CRD420251031377]). The protocol is publicly available at: [https://www.crd.york.ac.uk/PROSPERO/].

Aluminum foil sealing is widely employed in industrial packaging, and the quality of sealing plays a crucial role in ensuring product integrity and safety. Thermal infrared images frequently exhibit non-uniform heat distribution and indistinct boundaries within the sealing region. Additionally, variations in thermal response and local structural characteristics are observed across different defect types. Thus, traditional detection methods exhibit limitations regarding their stability and adaptability. In this paper, a novel thermal image recognition algorithm called EAC-Net is proposed for the classification and detection of sealing defects in thermal infrared images. In the proposed method, EfficientNet-B0 is utilized as the backbone network to improve its adaptability for industrial deployment. Furthermore, the Atrous Spatial Pyramid Pooling module is incorporated to enhance the multi-scale perception of defect regions, while the Channel–Spatial Attention Mixing with Channel Shuffle module is adopted to strengthen the focus on critical thermal features. Significant improvements in recognition performance were verified in experiments, while both computational complexity and inference latency were effectively kept at low levels. In the experiments, EAC-Net demonstrated an accuracy of 99.06% and a precision of 99.07%, indicating its high robustness and application potential.

Against the backdrop of growing environmental problems, the sustainable development of enterprises in all countries is facing serious challenges. Restricted by capital and costs, some enterprises risk breaking the law by producing or disposing of raw materials and wastes in order to make temporary profits, thus bringing about a series of environmental problems. Small and medium-sized enterprises (SMEs), which are vital for economic progress, have prompted the government to devise an array of initiatives and strategies aimed at fostering their environmentally sustainable growth. This paper explores the factors that increase the willingness of enterprises to bear green costs and how the government’s measures affect their choices by constructing a “government-firm” evolutionary game model. It is found that the government’s environmental policies, subsidies and penalties have a significant impact on SMEs’ green cost decisions. When the government’s subsidies are high enough and penalties are strong enough, SMEs are more inclined to increase their green costs to achieve a win-win situation between economic and environmental benefits.

It is well-known that the use of a conical die in cylindrical deep drawing can enhance the formability compared with conventional process. The most commonly observed failure mode in the process is fracture near the punch corner or wrinkling in the flange region. In order to obtain the limiting drawing ratio (LDR), a cylindrical deep drawing including only the conical die and flat punch is discussed based on numerical simulation in the paper. A finite element model for the deep drawing with a conical die is developed, and the accuracy of the model is verified by experiments results. The investigation shows that in the cylindrical deep drawing with a conical die, the types of failure above, which can obtain for thinner sheet materials, are obviously related to the sheet thickness and the conical angle, and the optimum conical angle is also found.

Staphylococcus epidermidis (S. epidermidis) is a clinically important conditioned pathogen that can cause a troublesome chronic implant-related infection once a biofilm is formed. The nitric oxide synthase ( NOS ) gene, which is responsible for endogenous nitric oxide synthesis, has already been found in the genome of S. epidermidis ; however, the specific mechanisms associated with the effects of NOS on S. epidermidis pathogenicity are still unknown. The purpose of the current study was to investigate whether the NOS gene has an impact on biofilm formation in S. epidermidis . Bioinformatics analysis of the NOS gene was performed, and homologous recombination was subsequently employed to delete this gene. The effects of the NOS gene on biofilm formation of S. epidermidis and its underlying mechanisms were analyzed by bacterial growth assays, biofilm semiquantitative determination, Triton X-100-induced autolysis assays, and bacterial biofilm dispersal assays. Additionally, the transcription levels of fbe , aap , icaA , icaR and sigB , which are related to biofilm formation, were further investigated by qRT-PCR following NOS deletion. Phylogenetic analysis revealed that the NOS gene was conserved between bacterial species originating from different genera. The NOS deletion strain of S. epidermidis 1457 and its counterpart were successfully constructed. Disruption of the NOS gene resulted in significantly enhanced biofilm formation, slightly retarded bacterial growth, a markedly decreased autolysis rate, and drastically weakened bacterial biofilm dispersal. Our data showed that the fbe , aap and icaA genes were significantly upregulated, while the icaR and sigB genes were significantly downregulated, compared with the wild strain. Therefore, these data strongly suggested that the NOS gene can negatively regulate biofilm formation in S. epidermidis by affecting biofilm aggregation and dispersal.

Air pollution has become one of the most challenging problems in China, especially in economically developed and densely populated regions such as Shanghai. In this study, the long short-term memory (LSTM) model is introduced for the application in extended-range forecasting of PM 2.5 in Shanghai by incorporating three members of the Subseasonal-to-Seasonal Prediction project (S2S) forecasting, moderate-resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) data, and large-scale circulation factors derived from ERA-5 reanalysis. Therefore, an accurate ∼40-day PM 2.5 prediction model over Shanghai was developed, providing new insights for air pollution extended-range forecasting. The new model not only exhibited much better accuracy but also captured the pollution process more closely than traditional methods, such as multiple regression (MLR). The prediction root-mean-square errors (RMSEs) based on the China Meteorological Administration (CMA), the U.K. model, and the European Centre for Medium-Range Weather Forecasts (ECMWF) were 24.84, 24.35, and 22.27 μg m −3 , respectively, and their Heidke Skill Scores (HSSs) were between 0.1 and 0.5. As a result, the S2S-LSTM model for extension period pollution prediction with higher accuracy developed in this study could further burst the hot spots of pollution extended-range prediction research. However, limitations of the prediction model are still in existence, especially in dealing with only a single site instead of a two-dimensional prediction, which requires further investigation in future studies.