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Accurate prediction of the rate of penetration (ROP) is a difficult issue in the drilling process, especially under complex formation conditions. Many methods, such as mechanism and machine learning, were introduced to investigate it. However, most of them are offline prediction methods which may not be capable of capturing the online trend of ROP. In this paper, a novel dynamic model for ROP prediction is proposed considering the process characteristics, which consists of three stages. In the first stage, the correlations between ROP and eight drilling parameters are analyzed, and the rotational speed, weight on bit, depth are selected as the model inputs. In the second stage, the drilling data are pre-processed by using the filtering and re-sampling techniques. In the last stage, the moving window strategy, extreme learning machine, and 10-fold cross validation are used to establish the ROP model. Our main idea of online prediction of ROP lies in this last stage. Specifically, two steps (modeling and prediction) are executed alternately in the moving drilling depth windows so as to predict the ROP more accurately. Finally, the proposed ROP prediction model is applied to the drilling well ZK3 in Xiangyang area, Central China. The prediction accuracy is improved by at least 7% compared with seven well-known ROP prediction methods, two online and five offline, which validates the effectiveness of the proposed method. It is believed that the proposed model provides a basis for intelligent optimization control in drilling process. • A novel intelligent dynamic model is proposed for the online prediction of drilling ROP. • The influence of the length of moving window and distance between two windows are investigated thoroughly. • The proposed model can learn autonomously in the drilling process and has a fast computation speed. • The proposed method has a higher prediction accuracy than two online and five offline well-known conventional methods. [ABSTRACT FROM AUTHOR]

[Display omitted] • Eco-efficiency of cultivated land use (EECLU) is evaluated. • Both carbon sequestration and carbon emissions are considered for measuring EECLU. • Super-efficiency SBM-DEA with undesirable output & panel data analysis are used. • Size has positive impact on the EECLU. • Shape complexity and patch fragmentation have negative impacts on the EECLU. With China's rapid urbanization and socioeconomic development, cultivated land has been experiencing continuous reduction, spatial shift, and ecological degradation in the past few decades, coupled with the decreasing and aging agricultural labor force, thus improving cultivated land utilization efficiency while minimizing associated environmental impacts is one of the major challenges facing China. This study first establishes an index system to access the eco-efficiency of cultivated land use (EECLU) considering both carbon sequestration and carbon emissions, next, employs the Super-SBM model to evaluate the spatial–temporal characteristics of EECLU, then, uses landscape metrics to describe landscape pattern dynamics of cultivated land, and finally conducts panel data analysis to investigate the relationship between landscape pattern changes and the EECLU variations. This framework was applied to Jiangsu Province, a major grain-producing area with a developed economy. The results showed that the average EECLU of the counties in rural Jiangsu from 2000 to 2020 slightly decreased, most counties were in the medium–low efficiency range (0.6–0.8), and Southern Jiangsu generally has the lowest EECLU values while Central Jiangsu has the highest. The total area and the average patch sizes of cultivated land have significantly positive impacts on EECLU, while the shape complexity and patch fragmentation have significantly negative impacts on EECLU. Therefore, comprehensive low-carbon strategies and better management and technology in the agricultural sector are needed to further improve EECLU. On the premise of protecting the total area of cultivated land, larger and more aggregated land patches should be promoted in the land consolidation process, and the cultivated land targeted for occupation or compensation should be able to simplify the overall shape of cultivated land patches. [ABSTRACT FROM AUTHOR]

The occurrence, spatiotemporal variations, influence factors and environmental risks of eight common neonicotinoids (NEOs), fipronil, and three fipronil metabolites (fipronil and its metabolites are collectively referred to as FIPs) in different seasons from the estuary to the inner area of Jiaozhou Bay, China were comprehensively investigated. First- and second-generation NEOs were found to be the predominant pesticides in this area. The average contents of ∑NEOs and ∑FIPs from the estuary to the inner bay decreased from 12.99 ng/L to 0.82 ng/L and from 1.10 ng/L to 0.17 ng/L, respectively. NEO and FIP concentrations were higher in summer and autumn. High ∑NEO content is distributed in main inflow rivers, such as Dagu River and Licun River, which are influenced by pesticide application. NEO concentrations in all rivers were high upstream and low downstream because of the influence of heavy rainfall and seawater dilution in summer. NEO concentrations were high along the coast and low at the mouth and center of Jiaozhou Bay in summer and autumn and evenly distributed in winter and spring. Temperature has a great influence on most NEOs and FIPs owing to its effect on their degradation. Nitrogen-containing nutrients have an important influence on the distribution of fipronil and acetamiprid, which may be due to the activity of nitrogen-containing functional groups in their structure. Only Licun River, Dagu River and Haibo river sewage treatment plant in summer posed a certain risk of chronic toxicity for NEOs using the new threshold established by the species sensitive distribution (SSD) method for Chinese native aquatic lives. These findings should arouse people's attention. [ABSTRACT FROM AUTHOR]

The government service platform, exemplified by the government hotline, has to handle extensive volumes of business documents that contain rich and timely public opinion information and citizens' demands. However, manual processing struggles to process large-scale text data, adversely impacting operating costs and the quality of government services. This study proposes a comprehensive system for business document classification of the government hotline (BDCGHS) in China to address these challenges. BDCGHS leverages information entropy fused with term frequency-inverse document frequency (TF-IDF) weight to mine new words from business documents of the government hotline, and store them in a new word repository. These new words optimize Chinese word segmentation and text representation for text classification. We introduce a novel data structure called nested balanced binary tree to expedite new word mining, yielding a computational speed of almost five times than the Trie trees. Comparative experiments on the THUNews and government hotline datasets validate our proposed improvement BDCGHS algorithm's superior performance 3 % over text classification algorithms. Compared to the latest bidirectional encoder representations from the transformers (BERT) model, BDCGHS enhances the accuracy of order dispatch based on business documents by almost 3 %. It has also demonstrated stable operations in two Chinese cities for over a year, yielding favorable results. [Display omitted] • An embedded balanced binary tree structure is proposed for new word discovery. • A new word database is constructed for the government hotline. • The effects of mainstream classification algorithms are compared based on a new word database. • An intelligent text classification system for the government hotline is constructed and the results have promising results. [ABSTRACT FROM AUTHOR]

Humulus scanden, rich in flavonoids, is a traditional Chinese medicine. It is widely used in China to treat tuberculosis, dysentery and chronic colitis. In this study, Humulus scandens (H.S) was extracted in different solvents (water, n-butanol, acetic ether and chloroform) and the antioxidant, anti-inflammatory activities of extracts were investigated by four assays including DPPH, reducing power, xylene-induced mouse ear edema and cotton pellet-induced granuloma methods. And then, the acute oral toxicity of extracts and biochemical parameters such as superoxide dismutase (SOD) and catalase (CAT) in blood serum and edematous tissue of rats were also evaluated. Moreover, the active compounds were identified by LC-ESI-QTOF-MS-MS. Results showed that ethyl acetate and n-butanol extracts had the stronger antioxidant activities, while water extract appeared to be weakest among them. The anti-inflammatory test showed that the extract of n-butanol and chloroform had significant anti-inflammatory effects and could improve the activity of SOD and CAT in mice. In general, the site of n-butanol has a stronger antioxidant and anti-inflammatory activity, and 15 compounds were analyzed by LC/Q-TOF-MS/MS technology. The present study confirms the ethnobotanical use of H.S extracts as natural antioxidant, anti-inflammatory, and the composition of the active compound was preliminarily identified. [ABSTRACT FROM AUTHOR]

[Display omitted] • Electrostatic LBL self-assembly strategy is firstly employed to integrate MoS 2 with CMOF. • Superior rejection rates are increased above 27–55% more than that of monomer membranes for FQs. • Synergy of size exclusion, electrostatic repulsion, and hydrophilic interaction is explained. • High stability in harsh environments for 72 h and over 90% rejection against FQs in the real sample. Developing a novel nanofiltration membrane for effectively purifying antibiotic wastewater has been a daunting challenge in recent years. However, a convenient and controllable strategy for constructing membranes with two-dimensional nanomaterials (2DNMs) and metal–organic frameworks (MOFs) needs to be further studied. Herein, a loosely hierarchical architecture composite membrane has been fabricated, utilizing an electrostatic layer-by-layer (LBL) self-assembly method to intercalate chitosan crosslinked UiO-66-NH 2 MOFs (CMOF) into MoS 2 laminates. Through regulating the CMOF content, lower hydraulic-resisted channels and precise separation regions have been formed in the hierarchical architecture. The well-designed MoS 2 /CMOF composite membrane exhibited enhanced water permeance (78.98 ± 1.45 L·m-2h−1 bar−1) and superior antibiotic rejection capability (98.01 ± 1.12% for ciprofloxacin and 94.31 ± 0.68% for ofloxacin), which are increased by 27–55% more than monomer membranes. Detailed analysis results indicate the nanofiltration performance of the MoS 2 /CMOF composite membrane depends heavily on the synergistic mechanism, including the molecular sieving effect (size exclusion), electrostatic repulsion, and hydrophilic-hydrophobic interaction. Besides, the MoS 2 /CMOF composite membrane has emerged with excellent antifouling ability and long-term stability, which could maintain favorable rejection efficiency in continuous nanofiltration for 72 h under an alkaline and acidic environment. Furthermore, it presents over 90% antibiotic rejection rates for real water samples obtained from the municipal domestic sewage and livestock wastewater along the Xiangjiang River in China, indicating its good availability. In general, this work provides a new vision and a reasonable design strategy to construct 2DNMs/MOFs-based nanofiltration membranes with hierarchical architecture, further expanding their application in wastewater treatment and reclamation, in harmony with the global trend of green and low-carbon development. [ABSTRACT FROM AUTHOR]