To explore the medication rule and pharmacological activity of the core combination of chrysanthemi flos,the drug frequency, complex network and association rules of Chrysanthemum prescriptions were analyzed. The network pharmacology research method was used to construct the "herbs-key target-disease" of core combination drugs. Combined with the forecast results, an in vitro cell model was used to evaluate the pharmacological effects of chrysanthemi flos drug combinations. A total of 92 prescriptions containing chrysanthemi flos were obtained, involving 212 herbs, and 32 herbs with frequency greater than or equal to 10.The association rules showed that the sustain and confidence degree of chrysanthemi flos-Glycyrrhizae radix et rhizoma were the highest, and chrysanthemi flos-glycyrrhizae radix et rhizoma-schizonepetae herba, chrysanthemi flos-glycyrrhizae radix et rhizoma-schizonepetae herba-chuanxiong rhizoma, chrysanthemi flos-glycyrrhizae radix et rhizome-schizonepetae herba-chuanxiong rhizome-saposhnikoviae radix were next to each other, respectively. Network pharmacology analysis showed that the core drug combination of chrysanthemi flos-glycyrrhizae radix et rhizoma could treat tumors, digestive system diseases, nervous system diseases and other diseases. In vitro cell activity study showed that the combination of chrysanthemi flos and glycyrrhizae radix et rhizoma had a better inhibition rate on NO levels than the single drug. Compared with the single use of chrysanthemi flos, the combination of drugs showed more significantactivity, reflecting the scientificity of compatibility of TCM in clinic.

The pharmacological method of traditional Chinese medicine serum was used to explore the effect of Banxiaxiexin Decoction (BXD) on the proliferation of different gastric cancer cells in vitro. Additionally,the preparation conditions of BXD drug-containing serums were examined toobserve the pharmacodynamics of gastric cancer cells. Blood was collected at intervals of 30, 60, 90, 120, and 150 minutes after high, medium, and low intragastric gavage of BXD to prepare drug-containing serums. Based on the selected gavage doses and blood collection times, MGC803, MKN45, AGS, and HGC27 cells were exposed to 5%~50% concentrations of drug-containing serum, and CCK8 assay was employed to detect the inhibitory effects of different gavage doses, blood collection times, and volume fractions of BXD drug-containing serum on the proliferation of gastric cancer cells. The test results showed that compared with other time points, all BXD drug-containing serums had the strongest inhibitory effect on gastric cancer cells at the blood collection time of 120 minutes. Compared with the low-dose BXD group, the medium-dose BXD group had a strong inhibitory effect on gastric cancer cells, and there was no significant difference between the medium-dose and high-dose BXD groups. Based on the analysis of the inhibitory effects of 10 concentrations ranging from 5% to 50% on different gastric cancer cells, the IC₅₀ values of all drug-containing serums collected 120 minutes after gavage in the medium-dose BXD group were 20%. Analysis of the inhibitory effects of different gavage doses, blood collection times, and volume fractions on different gastric cancer cells revealed that a medium dose of BXD, blood collection time of 120 minutes,and a volume fraction of 20% had the strongest inhibitory effect on gastric cancer cells. Therefore, the medicated serum with a blood collection time of 120 minutes and a volume fraction of 20% in the medium-dose BXD group had the best pharmacodynamic effect on gastric cancer cells.

Based on bioinformatics, this study validates the mechanism of action of Haitongpi-Tougucao (compound Haitongpi) in inhibiting lipopolysaccharide (LPS)-induced ferroptosis in rat inflammatory chondrocytes. Bioinformatics tools were used to predict the mechanism of action of ferroptosis in osteoarthritis and identify pathways for validation. The key techniques used were as follows: the detection of ferrous ion content and reduced glutathione (GSH) content using relevant kits; the detection of cell viability and the levels of related cytokines IL-1β, IL-6, and TNF-α using the enzyme-linked immunosorbent assay (ELISA) after dosing; and the use of protein immunoblotting (western blot, WB) to detect the protein expression levels of NLRP3, Caspase-1, ASC, and GPX4, a gene that inhibits ferroptosis, related to the NLRP3 inflammasome pathway in each group. The results revealed that the ferrous ion content was significantly decreased,while the GSH content was significantly increased; the ELISA experiment showed that the levels of inflammatory factors IL-1β, IL-6, and TNF-α were decreased in each group administered with the drug compared with those in the model group; the WB results showed that the expression levels of NLRP3, Caspase-1, and ASC proteins were significantly decreased and GPX4 protein expression levels were significantly increased in each group administered with a specific dosage of the drug compared with those in the model group. Therefore, the compound Haitongpi can intervene in the ferroptosis of inflammatory chondrocytes by mediating the NLRP3 inflammasome pathway, thereby achieving the purpose of osteoarthritis treatment.

Based on network pharmacology, molecular docking, and in vitro experiments, this study explores the molecular mechanism of quercetin against colorectal cancer through the p53 signaling pathway. The drug targets quercetin, and the disease targets colorectal cancer, which was obtained via the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform and Gene Cards database, respectively. The common drug and disease targets were mapped using a Venn diagram, and the protein-protein interaction network map was constructed with the help of the String database and Cytoscape_v3.7.2 software. At the same time, GO and KEGG enrichment analysis, molecular docking, core target expression, and survival analysis were also performed. Finally, cell proliferation activity, level of apoptosis, cell cycle arrest, and changes in the expression of core targets and key proteins of the p53 pathway were detected through cellular experiments. Network pharmacology suggests that AKT1 and TP53 are the core targets of quercetin against colorectal cancer, GO and KEGG analysis demonstrate that quercetin is mainly involved in the PI3K/Akt and p53 signaling pathways, molecular docking demonstrates that quercetin exhibits strong binding activity with the core targets AKT1 and TP53, and TP53 is found to be both highly expressed in colorectal cancerand also affect the survival and prognosis of patients with colorectal cancer. The results of cellular experiments show that quercetin can inhibit the proliferation of HCT-116 cells, induce G₀/G₁ cell-cycle arrest in HCT-116 cells, and promote apoptosis. This mechanism may regulate core targets such as TP53 and AKT1, activate the p53 signaling pathway, participate in the proliferation and apoptosis of HCT-116 cells, and thus function to resist colorectal cancer.

To improve the corrosion resistance of traditional pure Zn coatings, we used 30 mm diameter Q195 welded pipes as the substrate and prepared a series of hot-dip galvanized alloy coatings by adding trace amounts of alloy elements such as Al, Ni, and Re to the Zn bath. First, the main factors affecting corrosion resistance were identified through a four-factor and three-level orthogonal experiment. Then, the experiment was further improved for the primary factors, and single-factor experiments were conducted to obtain the optimal parameter combination. Finally, the microstructure characteristics and corrosion resistance of the coating were studied and analyzed using methods such as high and low temperature humidity test, neutral salt spray test, metallographic analysis, and scanning electron microscopy. Results indicate that the introduction of alloying elements suppresses the growth of ζ layer, which makes the coating structure compact, and improves the corrosion resistance of the coating. The coating prepared in this study could remain rustless throughout a 72 h salt spray test and a 120 h humidity test. The process for preparing the alloy coating is same as the existing production process for traditional Zn coatings.

Although regression analysis can predict some drape indicators, they have problems such as low prediction accuracy and the inability to calculate some indicators. To overcome these issues, this study proposes a new method using genetic algorithm to optimize BP neural network (GA-BP neural network) to improve the prediction accuracy of real fabric drape. In this study, we designed a GA-BP neural network model, selected 100 pure cotton woven fabric samples from the fabric database, including 80 training samples, 10 test samples, and 10 validation samples, used the genetic algorithm to optimize the parameters of the neural network, and used correlation analysis to optimize sample input parameters to improve the prediction performance of the model. The results of the drape coefficient prediction for the 10 test samples show that compared with the traditional BP neural network, the average absolute percentage error of the BP neural network optimized by the genetic algorithm decreased from 12.74% to 7.03%. Furthermore, we used an empirical equation to identify error cycles and concluded that the optimal number of hidden layer nodes is 9. This study indicates that the GA-BP neural network can effectively improve the accuracy of fabric drape prediction and has important application value for the virtualization of fabric drape performance.

In the pursuit of bridging the energy demand gap and striving for a pristine environment, ammonia fuel has emerged as one of the most promising fuels of the future. Zero carbon emissions, high energy density, and low production and transportation costs make it a promising candidate. However, challenges persist regarding the overall efficiency of pure ammonia combustion. This paper proposes a regenerative cycle in an ammonia gas turbine that matches the reheat Rankine cycle, considering the maximum temperature of the exhaust gas from the turbine and phase transition temperature of liquid ammonia in the turbine cycle. We conducted a thermodynamic analysis and evaluated the system performance based on the first and second laws of thermodynamics and analyzed the influence of the inlet temperature and pressure of the ammonia gas turbine on the overall cycle performance. The results indicate that the combined cycle has improved the efficiency of the ammonia gas turbine by up to 33.38% and the maximum efficiency achieved by the combined thermodynamic cycle is 60.13%,when the inlet temperature of an ammonia gas turbine does not exceed 1 400 ℃ and the inlet pressure remains below 0.5 MPa. Furthermore, the combined cycle exhibits outstanding thermodynamic properties and energy recovery rates. Additionally, the efficiency of the regenerative cycle increases with increasing the inlet temperature and pressure of the ammonia gas turbine, provided that the inlet pressure does not exceed 5 MPa. New perspectives have been proposed to enhance the operational efficiency of ammonia-powered gas turbines and promote the efficient utilization of ammonia as a fuel. This study proposes novel perspectives towards enhancing the efficient utilization of ammonia fuel and the actual efficiency of ammonia gas turbine cycles, providing a forward-looking exploration for the energy utilization of ammonia gas turbine systems.

Currently, the steam pipelines in cigarette factories are characterized by numerous points, extensive lengths, and broad coverage. The thermal conversion factor of these pipelines is high, and their steam energy consumption accounts for a large proportion of the total energy consumption. Therefore, investigating the performance of the insulation layer of steam pipes is of considerable importance for improving steam utilization efficiency and reducing heat loss in the steam pipe network. In this study, the thermal conductivities of insulation layers made of four insulation materials were measured using the steady-state method at different temperatures to elucidate the relationship between the thermal conductivity of an insulation material and the steam temperature, thereby identifying the efficient insulation materials suitable for application scenarios. The appropriate insulation layer thickness was determined using the maximum allowable heat loss method and economic thickness method. Moreover, the thermal conductivities of insulation layers with different service lives were measured. Results indicate that the thermal conductivity increased linearly with the increasing service life. Factors causing the deterioration of insulation layer performance were incorporated into the model to study the relationship between the operating cost of an insulation layer and its outer diameter and service life. For insulation layers with different designed service lives, their optimal outer diameters and operating costs were calculated using the economic thickness method. Results show that considering material aging factors in the design of insulation layer thickness can reduce cumulative costs by 10.7% within the designed service life. However, when the service life expires, the operating cost of a design that considered the aging issue is higher than that of a design that did not consider the aging issue owing to increased heat loss as a result of aging of the insulation layer. The insulation layer can be designed to reduce steam heat loss and improve steam utilization efficiency as well as provide theoretical guidance for the green, low-carbon, and high-quality development of cigarette factories.

Supercritical CO₂ plays an important role in many applications such as nuclear power generation, solar power generation, cryogenic refrigeration, and aerospace. Currently, the majority of studies on supercritical CO₂ convective heat transfer in tubes focus on the temperature range near the critical point, while the heat transfer patterns at high temperature and pressure far from the critical point remain unclear and need to be further studied. In this study, numerical simulations were performed to analyze the effects of mass flow, inlet temperature, system pressure, heat flux density, and tube diameter on the convective heat transfer coefficient at high temperature and pressure, as well as the effects of buoyancy and flow acceleration caused by operating conditions on the heat transfer characteristics. The results show that the convective heat transfer coefficient increases with increasing mass flow, inlet temperature, system pressure, and heat flux density. The difference in convective heat transfer coefficient gradually grows along the flow direction under different heat flux densities. Convective heat transfer coefficient decreases with increasing tube diameter. Compared with the heat transfer patterns near the critical point, heat flux density and tube diameter exert different effects on the convective heat transfer coefficient. In general, the effects of pressure on the convective heat transfer coefficient are small. This study provides significant values to understand the law of supercritical fluid heat transfer and guide the design of efficient and safe heat exchanger.

With the extensive development of intelligent transportation and eco-friendly travel, a low-energy task-offloading method based on edge computing in the internet of vehicles (IoV) is proposed to address the dual challenges of low-latency service demands and energy conservation in the IoV. In the context of multivehicle single-cell scenarios on public roads, this study explores the task-offloading requirements of vehicles in motion and systematically investigates the allocation of computational resources. To fully utilize computing resources, this study not only considers the computing power of vehicles but also introduces a new approach for offloading tasks to vehicle servers traveling in the same direction or parked along the roadside as well as to edge servers in roadside units. This enables the effective integration and efficient sharing of computing resources, thereby remarkably enhancing the processing capabilities of the IoV. Furthermore, this study employs an improved particle swarm optimization algorithm to optimize offloading power and task allocation ratios. Extensive simulation tests revealed that the proposed method significantly reduced the energy consumption of vehicle tasks and improved the service quality and energy efficiency of the IoV.It helps to promote green transportation and sustainable development, and lays a solid foundation for energy optimization and efficiency improvement of future intelligent transportation systems.

Energy-saving metro train control is closely related to the vertical track alignment (VTA) design, and both have a significant impact on operating costs. To further reduce operating costs based on optimized train control, this study proposed a collaborative optimization model for the VTA design phase. This model optimizes the bidirectional train control strategy and VTA of a metro section with the goal of minimizing energy consumption and maintenance costs simultaneously, while adhering to the constraints of scheduled train control and the requirements of the "Metro Design Code." Given the numerous factors affecting the maintenance costs of wheels and rails, a train-track dynamic simulation model was developed to calculate these costs. Based on this, an algorithm combining the pseudospectral method and brute force search was designed to solve the collaborative optimization model. The effectiveness of this optimization method was validated using three sections of the Guangzhou metro line. The results indicate that, compared to the method of optimizing scheduled train control alone on the actual VTA, the collaborative optimization model is more effective in saving operating costs, reducing the average operating costs by 21% across the studied sections. This study can provide novel approaches and theoretical support to further reduce metro operating costs, which contributes to promoting sustainable development of metro.

The prediction of highway tolls is affected by complex factors such as holidays and unexpected events. Traditional prediction methods often fail to fully account for intricate interactions between these multiple factors, resulting in less-than-ideal prediction accuracy. By leveraging the self-attention mechanism, large language models can better fit complex spatiotemporal data and have enhanced feature learning capabilities, making them highly effective for precise highway toll prediction. Therefore, this study proposes a highway toll prediction model based on iTransformer. This model embeds temporal information as an independent dimension into the input sequence and reverses the roles of the self-attention mechanism and feed-forward network, thereby allowing the model to more accurately capture the dynamic features of time series and correlations between multiple variables. Case studies show that the proposed model improves the average prediction accuracy by 23.47% and 17.84% compared with the SARIMA and LSTM models, respectively, in regular scenarios. In irregular scenarios, the model demonstrates even better predictive performance, improving the accuracy by 70.92% and 45.64%, respectively. A sensitivity analysis of the proposed model indicates that it is highly sensitive to the number of feed-forward network layers and stacked encoder layers but is less sensitive to changes in the number of attention heads. Thus, this study provides a new methodological approach for addressing the challenges associated with toll prediction in complex traffic environments and has significant implications in terms of improving the accuracy of highway toll predictions.

Lane detection is a remarkable practical application of computer vision technology in the field of transportation. However, existing semantic segmentation network models still face certain challenges such as insufficient accuracy and blurred edges in road semantic segmentation tasks. To address these issues, an improved lane segmentation network architecture based on the UNet model is proposed. First, a dual attention module (DAM) is introduced in the skip connections of the UNet model, which prioritizes the importance of lane lines and effectively reduces noise interference. Additionally, dynamic snake convolution (DSConv) is employed to replace traditional convolution methods, enhancing the network’s lane detection ability. To enhance the comprehensiveness and accuracy of lane detection in underexposed or dark backgrounds, an improved adaptive Gamma correction method is introduced in the image preprocessing stage. Furthermore, atrous spatial pyramid pooling (ASPP) technology is introduced at the end of the encoder to enhance network performance. Experimental results show that this model achieves an accuracy of 98.93% on the TuSimple dataset while meeting real-time requirements. Compared to five other semantic segmentation-based lane detection algorithms, the proposed algorithm demonstrates superior recognition performance, thus validating its effectiveness.

The Yellow River Delta is a dynamic-equilibrium wetland system formed via the complex interactions between the Yellow River and the ocean across multiple spatial and temporal scales. Owing to the frequent shifts in the Yellow River’s course, the deltaic wetlands have undergone a cyclical evolution involving rapid formation, development, erosion or succession, and disappearance or remnant persistence. Under the combined stresses of intensive human activities and climate change, the Yellow River Delta is facing a series of challenges, including water and sediment variability, vegetation degradation, species invasion, habitat fragmentation, and functional decline. Many existing ecological problems have emerged throughout the evolutionary process of the delta’s wetlands, characterized by overlapping impacts across multiple spatial and temporal dimensions. Consequently, conservation and restoration strategies based on isolated timeframes, specific sites, or individual elements are increasingly showing limitations in mitigating habitat fragmentation, biodiversity loss, and ecosystem degradation in the delta. This paper reviews extensive literature on ecological conservation and restoration in the Yellow River Delta, elucidating the influence mechanisms of biotic and abiotic disturbance factors on key ecological components, structures, and processes affecting the ecological functions of coastal wetlands. Moreover, it identifies the stability patterns of multifunctional wetland systems under multiple stressors, proposes an integrated optimization framework combining conservation, restoration, and regulation, and develops multiscale correlated and multiprocess coordinated conservation and restoration measures, thereby providing new insights for addressing ecosystem degradation in this region.

Bisphenol A (BPA) is an endocrine-disrupting chemical that is widely used in the production of epoxy resins and polycarbonate plastics. Due to its potential harm to human health, its use has been restricted in many materials that come into contact with the human body. Other bisphenol compounds have emerged as alternatives, but they have similar structures and characteristics, with varying degrees of estrogenic activity and toxicity. With the rapid economic development in China, the demand for bisphenol compounds has been increasing, and so has the production capacity. The sources and distribution of bisphenol pollutants are emerging pollutants in various environmental media and human exposure to them in China deserve in-depth research. In this study, we reviewed the literature published in the past ten years (2014—2024), systematically summarized the sources and distribution patterns, human exposure levels, and health risks of bisphenol pollutants in various environmental media (surface water, sediments, and soil), and proposed various control measures and policy recommendations for bisphenol pollutants in China, aiming to provide a reference for the environmental risk control and environmental governance of bisphenol pollutants in China.

Residues of pharmaceuticals and personal care products (PPCPs) are an emerging class of organic contaminants known for their endocrine-disrupting properties, pseudo-persistence, and ecotoxicity. Because of their wide application in the medical, animal husbandry, and cosmetic industries, PPCPs have been frequently detected in surface water, groundwater, industrial sludge, and even food such as milk in recent years, raising significant concerns about their safety. However, because PPCPs are present at very low concentrations in real samples and are affected by complex matrix effects, direct quantitative analysis is challenging. Therefore, before instrumental analysis, effective sample pretreatment methods are required for the enrichment analysis of PPCPs. In recent years, many novel materials have been developed for the extraction of trace contaminants. First, this paper provides a systematic introduction to the hazards and contamination status of PPCPs, followed by a detailed discussion of the contamination of two typical PPCPs: bisphenols and nonsteroidal anti-inflammatory drugs. Second, it lists several sample pretreatment techniques, highlighting the application of novel adsorbent materials in PPCP analysis and detection, while also exploring the development trends of these new adsorbent materials. Finally, the paper summarizes the obstacles and challenges that may be encountered in the analysis and detection of PPCPs.

Petroleum contamination in oil-field soils poses significant risks to both regional environmental safty and human health. The use of biotechnologies for the remediation of contaminated oil-field soils offers advantages such as ecological sustainability, economic feasibility, and high efficiency. This paper reviews the research progress on various biotechnologies used for the remediation of contaminated oil-field soils. By descrribing the mechanisms and methods of various bioremediation technogies, the study analyzes and evaluates microbial remediation, phytoremediation, and their combined applications with surfactants, chemical oxidation, and electrokinetic remediation. It also highlights the importance of assessing the effectiveness of bioremediation strategies. This study provides valuable guidance for the promotion and application of bioremediation technologies aimed at improving the quality of oil-field soils and restoring the ecological environment.

Key tasks in the construction of a “waste-free city” in Shandong Province are to formulate local regulations for solid-waste pollution prevention and control and to enhance the local standard system for solid-waste resource utilization. This study reviews the generation volume, comprehensive utilization rate, and industry-specific distribution of bulk industrial solid waste in Shandong Province in 2023. This study also investigates the current status of local standards for the resource utilization of bulk industrial solid waste across different provinces(autonomous regions) in China. Based on this comparative analysis, this study identifies existing issues in the local standard system for the resource utilization of bulk industrial solid waste in Shandong Province and proposes targeted countermeasures. These recommendations enhance pollution prevention and promote the establishment of a comprehensive local standard system for bulk industrial solid-waste resource utilization in Shandong Province.

Sea level rise and anthropogenic activities significantly affect the hydrological conditions of shallow groundwater in coastal wetlands. It is unclear how seed persistence responds to changes in the hydrological conditions of shallow groundwater. Seeds from four wild-resource plant species, Phragmites australis, Suaeda salsa, Chenopodium glaucum, and Cynanchum chinense, from coastal areas of the Yellow River Delta were selected for the study. Using indoor simulation methods, we investigated differences in seed persistence at two shallow groundwater levels (moist and saturated habitats), four gradients of shallow groundwater salinity, and under dry conditions based on mean germination time, germinability index, and viability index. Overall, mean germination time, seed germinability index, and viability index were consistent in evaluating seed persistence. The response trends of seed persistence of different plants to shallow groundwater level and salinity were different. Seed persistence was stronger in dry and saturated habitats than in moist habitats. Compared with storage in moist habitats, the seed persistence of P. australis, S. salsa, and C. glaucum was significantly stronger in saturated habitats (P<0.05). The effects of shallow groundwater salinity on seed persistence varied with changes in shallow groundwater level. In moist habitats, based on germinability index and viability index, seed persistence of S. salsa, C. glaucum, and C. chinense increased with the increase in shallow groundwater salinity. However, these trends did not exist when the seeds were in saturated habitats. The results will provide a scientific basis for the protection of resources in the degraded wetlands of the Yellow River Delta.

Bacillus velezensis, Bacillus thuringiensis and Brevibacterium frigoritolerans are three typical salt-tolerant plant growth-promoting rhizobacteria (ST-PGPR) known for their ability to alleviate salt stress in plants. To optimize the nutritional conditions for the growth and proliferation of these strains, experiment of single factor method was used to investigate the effects of different carbon-to-nitrogen ratios (n_C/n_N) on their development. The results showed that all three strains could grow and reproduce in a medium with a n_C/n_N of 3.25~6.00. However, the n_C/n_N had varying effects on their proliferation, i.e., the most significant impact was observed on the proliferation of velezensis, followed by thuringiensis and frigoritolerans. When the n_C/n_N was 4:1, velezensis exhibited the highest proliferation, with a viable bacterial count of 6.2 × 10⁸ CFU/mL. Similarly, thuringiensis achieved its highest proliferation at a n_C/n_N of 4:1, with a viable bacterial count of 5.1 × 10⁸ CFU/mL. Conversely, frigoritolerans achieved its highest proliferation at a n_C/n_N of 5:1, with a viable bacterial count of 3.5 × 10⁸ CFU/mL. In conclusion, the optimal n_C/n_N for the proliferation of velezensis and thuringiensis is 4:1, whereas that for frigoritolerans is 5:1.

Coal gasification slag is a solid waste generated during coal chemical production and accounts for a notable proportion of solid wastes. Leveraging the natural properties (e.g., high specific surface area and pore volume) and compositional characteristics (rich in carbon) of coal gasification slag, this study used the mechanical ball milling method to composite coal gasification slag using a conventional photocatalyst titanium dioxide (TiO₂) for broadening the photocatalytic response range of TiO₂. Dye wastewater was used as the treatment object to evaluate the photocatalytic performance of the resulting composite material. Characterization techniques such as X-ray diffraction, Fourier Transform infrared spectroscopy, and scanning electron microscopy were used to investigate the optimal process conditions for the catalytic degradation of a methylene blue (MB) solution by the TiO₂/coal gasification slag composite material. Results show that under visible-light conditions, the degradation efficiency of the developed composite material (TiO₂∶slag ratio of 90∶10) is higher than those of anatase TiO₂, P25, and the coal gasification slag/P25 composite material. Infrared characterization and free-radical quenching experiments indicated that coal gasification slag and TiO₂ effectively bonded through Ti—O—Si bonds, expanding the photocatalytic response range of TiO₂ and increasing the photocatalytic reaction contact area. In addition, hydroxyl radicals were identified as the primary active substances responsible for degrading MB. Compared with anatase TiO₂, the catalytic efficiency of the composite material increased by 4.96 times. Furthermore, its catalytic degradation efficiency remained above 90% after three cycles, indicating that the TiO₂/coal gasification slag composite material has excellent degradation efficiency and stability.

The nine provinces(autonomous regions)along the Yellow River are important supporting regions for the economic and social development of China. The coordinated advancement of their green low-carbon and digital economies holds strategic significance for China to achieve its “dual-carbon” goals and high-quality economic development. Considering the nine provinces(autonomous regions) along the Yellow River from 2013 to 2022 as the research object, this study constructs an evaluation index system for the digital economy and green low-carbon systems from six dimensions: resource consumption, pollution emissions, governance effectiveness, communication capabilities, the Internet, and the information industry. The spatiotemporal coupling relationship and obstacle factors between the two were analyzed using entropy weight method, coupling coordination degree model, obstacle degree model, and gray prediction. The results demonstrated that the green low-carbon level of the nine provinces(autonomous regions) along the Yellow River has significantly improved, following a spatial pattern of “upper reaches green low-carbon level<lower reaches green low-carbon level<middle reaches green low-carbon level”. The evaluation indices of the digital economy systems exhibit regional imbalances, showing a gradually increasing trend from top to bottom. The coupling coordination degree of the digital economy and green low-carbon systems as a whole shows a steady upward trend, and the overall state of the nine provinces(autonomous regions) along the Yellow River has changed from being on the verge of disorder to an initially coordinated state. The information industry is the main obstacle to coupling coordination. The insufficient development of the digital information industry in the Yellow River Basin severely hinders the coordinated development of the digital economy and green low-carbon systems in these provinces(autonomous regions). Gray prediction analysis suggests that the coupling state of the digital economy and green low-carbon systems in the nine provinces(autonomous regions) along the Yellow River from 2025 to 2029 will steadily improve to an intermediate coordination level. On the basis of the research results, targeted suggestions are put forward to promote the coupling coordination of the green low-carbon systems and digital economy in the nine provinces(autonomous regions) along the Yellow River for them to advance to a higher stage of development. These insights provide a scientific basis and decision-making reference for the sustainable development of the region.

Under the “dual carbon” goals, high carbon emitting enterprises in industries, such as petrochemicals, are undergoing a low-carbon transformation and are reducing their carbon emissions, which have grown to become an important development trend. Oil and gas losses account for a large proportion of the total energy consumption in the petrochemical industry. In this study, focusing on the inability to automatically identify the high-value points of loss because of the change in the law of oil and gas loss data, a method for automatically identifying the high-value points of oil and gas loss based on the peak over threshold (POT) model is proposed. First, accounting of oil and gas losses was conducted in 35 links of an oil producing reservoir in Shengli Oilfield. Second, according to the leptokurtic distribution characteristics of the accounting data, the oil and gas loss tail data were segmented using the POT model and fitted with its probability distribution function (PDF). The corresponding high-value points of oil and gas losses in the PDF were identified according to the 3σ principle. Finally, the results determine the threshold of high-value points to be 869.34 m³/d, and the identification accuracy of high-value points to be 0.986, accuracy greater than other traditional methods. Therefore, the proposed method is conducive to the efficient development of loss control methods.

An ecological environmental damage compensation system is a key mechanism for promoting ecological protection and high-quality development in the Lower Yellow River Basin. This study focuses on 2 371 ecological environmental damage compensation cases across nine prefecture-level cities in the Lower Yellow River Basin in Shandong province, analyzes their characteristics, and uses the LMDI model to determine factors for these cases and thus identify the directions and degrees of the main drivers. The findings provide strategies for preventing and controlling ecological environmental damage in the Lower Yellow River Basin. This study reveals that air pollution cases are the highest (75.6%) in the Lower Yellow River Basin, and the compensation intensity and cost rate of ecological environmental damage are the primary factors inhibiting an increase in the number of cases. Specifically, the most notable inhibitory effect of the compensation intensity is observed in Dezhou city, with a contribution of -993.71%, while the highest inhibitory effect of the cost rate of ecological environmental damage is noticed in Jinan city, with a contribution of -301.96%. The economic development level and population size positively drive the increase in the number of cases, although the contribution of the population size to this increase is relatively small. The highest positive driving effect of the economic development level is observed in Jining city, with a contribution of 23.71%. Based on these findings, measures such as promoting green economic transformation and diversifying compensation models can help control ecological environmental damage. The findings of this study provide valuable insights for implementing the ecological environmental damage compensation system in the Lower Yellow River Basin.

Ecological effect evaluation is a critical component in ecological and environmental damage assessment, providing an essential foundation for ecological damage compensation and restoration efforts. We systematically analyze ecological effect evaluation indictors, assessment methodologies, and damage identification pathways through a comprehensive literature review. Case studies validate the effectiveness of the established model in scientifically assessing the impact of pollutants on ecosystem service value, offering both methodological references and practical insights for similar research in other regions, with significant theoretical and applied implications. To address existing challenges, we propose improvements in optimizing ecological evaluation methodologies, refining technical frameworks, establishing cross-sectoral coordination mechanisms, and developing professional expertise. The principles and strategies proposed in this paper aim to enhance the scientific rigor and accuracy of ecological assessments, advance the standardization of ecological and environmental damage assessment systems, and provide valuable references for future research in related fields.

With the rapid economic and social development, environmental pollution and ecological damage have become increasingly severe. Under the guidance of Xi Jinping Thought on Ecological Civilization, China has initiated the establishment of an ecological and environmental damage compensation system and made significant progress. Biodiversity is fundamental to human survival and development, and environmental pollution is a major cause of the loss of plant diversity. This study examines the impact of environmental pollution on plant damage assessment and identification, highlighting key challenges in the assessment process, including causality judgment, quantification of physical damage, and valuation of damage. It also discusses the critical aspects of plant damage assessment and proposed recommendations, such as strengthening fundamental research, innovating damage assessment techniques, and improving the assessment system. The study aims to enhance the scientific rigor and accuracy of plant damage assessment, thus contributing to the development of an ecological and environmental damage assessment standard system, safeguarding public environmental rights, and promoting ecological civilization.