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In this paper, the enhanced adsorption of methylene blue (MB) dye ion on the activated carbon (AC) modified by three surfactants in aqueous solution was researched. Anionic surfactants—sodium lauryl sulfate (SLS) and sodium dodecyl sulfonate (SDS)—and cationic surfactant—hexadecyl trimethyl ammonium bromide (CTAB)—were used for the modification of AC. This work showed that the adsorption performance of cationic dye by activated carbon modified by anionic surfactants (SLS) was significantly improved, whereas the adsorption performance of cationic dye by activated carbon modified by cationic surfactant (CTAB) was reduced. In addition, the effects of initial MB concentration, AC dosage, pH, reaction time, temperature, real water samples, and additive salts on the adsorption were studied. When Na+, K+, Ca2+, NH4+, and Mg2+ were present in the MB dye solution, the effect of these cations was negligible on the adsorption (<5%). The presence of NO2- improved the adsorption performance significantly, whereas the removal rate of MB was reduced in the presence of competitive cation (Fe2+). It was found that the isotherm data had a good correlation with the Langmuir isotherm through analyzing the experimental data by various models. The dynamics of adsorption were better described by the pseudo-second-order model and the adsorption process was endothermic and spontaneous. The results showed that AC modified by anionic surfactant was effective for the adsorption of MB dye in both modeling water and real water.

Acute lung injury (ALI) is a life-threatening disorder with high rates of morbidity and mortality. Reactive oxygen species and epithelial apoptosis are involved in the pathogenesis of acute lung injury. Ferroptosis, an iron-dependent non-apoptotic form of cell death, mediates its effects in part by promoting the accumulation of reactive oxygen species. The inhibition of ferroptosis decreases clinical symptoms in experimental models of ischemia/reperfusion-induced renal failure and heart injury. This study investigated the roles of inhibitor of apoptosis-stimulating protein of p53 (iASPP) and Nrf2 in ferroptosis and their potential therapeutic effects in intestinal ischemia/reperfusion-induced acute lung injury. Intestinal ischemia/reperfusion-induced ALI was induced in wild-type and Nrf2 −/− mice. The mice were treated with erastin followed by liproxstatin-1. Ferroptosis-related factors in mice with ischemia/reperfusion-induced acute lung injury or in mouse lung epithelial-2 cells with hypoxia/regeneration (HR)-induced ALI were measured by western blotting, real-time PCR, and immunofluorescence. Ferroptosis contributed to intestinal ischemia/reperfusion-induced ALI in vivo. iASPP inhibited ferroptosis and alleviated intestinal ischemia/reperfusion-induced acute lung injury, and iASPP-mediated protection against ischemia/reperfusion-induced ALI was dependent on Nrf2 signaling. HR-induced acute lung injury enhanced ferroptosis in vitro in mouse lung epithelial-2 cells, and ferroptosis was modulated after the enhancement of intestinal ischemia/reperfusion in Nrf2 −/− mice. iASPP mediated its protective effects against acute lung injury through the Nrf2/HIF-1/TF signaling pathway. Ferroptosis contributes to intestinal ischemia/reperfusion-induced ALI, and iASPP treatment inhibits ferroptosis in part via Nrf2. These findings indicate the therapeutic potential of iASPP for treating ischemia/reperfusion-induced ALI.

Cholesterol gallstone disease is a worldwide common disease. Cholesterol supersaturation in gallbladder bile is the prerequisite for its pathogenesis, while the mechanism is not completely understood. In this study, we find enrichment of gut microbiota (especially Desulfovibrionales) in patients with gallstone disease. Fecal transplantation of gut microbiota from gallstone patients to gallstone-resistant strain of mice can induce gallstone formation. Carrying Desulfovibrionales is associated with enhanced cecal secondary bile acids production and increase of bile acid hydrophobicity facilitating intestinal cholesterol absorption. Meanwhile, the metabolic product of Desulfovibrionales , H 2 S increase and is shown to induce hepatic FXR and inhibit CYP7A1 expression. Mice carrying Desulfovibrionales present induction of hepatic expression of cholesterol transporters Abcg5/g8 to promote biliary secretion of cholesterol as well. Our study demonstrates the role of gut microbiota, Desulfovibrionales , as an environmental regulator contributing to gallstone formation through its influence on bile acid and cholesterol metabolism. Metabolic conditions associated with alterations of the gut microbiome, such as obesity and diabetes, predispose to gallstone disease. Here the authors demonstrate that the gut microbiome, in particular the genus Desulfovibrionale, contribute to gallstone formation in mice.

Crop residue return is imperative to maintain soil health and productivity but some farmers resist adopting conservation tillage systems with residue return fearing reduced soil temperature following planting and crop yield. Soil temperatures were measured at 10 cm depth for one month following planting from 2004 to 2007 in a field experiment in Northeast China. Tillage treatments included mouldboard plough (MP), no till (NT), and ridge till (RT) with maize ( Zea mays L.) and soybean ( Glycine max Merr.) crops. Tillage had significant effects on soil temperature in 10 of 15 weekly periods. Weekly average NT soil temperature was 0–1.5 °C lower than MP, but the difference was significant ( P  < 0.05) only in 2007 when residue was not returned in MP the previous autumn. RT showed no clear advantage over NT in increasing soil temperature. Higher residue coverage caused lower soil temperature; the effect was greater for maize than soybean residue. Residue type had significant effect on soil temperature in 9 of 15 weekly periods with 0–1.9 °C lower soil temperature under maize than soybean residue. Both tillage and residue had small but inconsistent effect on soil temperature following planting in Northeast China representative of a cool to temperate zone.

An improved Secretary Bird Optimization Algorithm (ISSBOA) is proposed. First, an independent thinking mechanism (IM) enhances the algorithm’s ability to avoid local optima traps and broadens global exploration during the optimization process. Second, a sine-square step size mechanism (SM) dynamically adjusts the search step size, effectively balancing the performance deficiencies of the Secretary Bird Optimization Algorithm (SBOA) in both the exploration and exploitation phases. To validate the effectiveness of ISSBOA, simulations are conducted on the IEEE CEC2017 benchmark test suite, with comparisons made against 7 classic metaheuristic algorithms and seven recently proposed improved algorithms. The results demonstrate that ISSBOA achieves optimal performance in two sets of comparison experiments: when compared with the 7 standard algorithms, ISSBOA outperforms them in terms of average fitness value on 23 out of 30 test functions and in terms of variance on 16 functions, achieving an average Friedman test rank of 1.80 (securing first place); when compared with the 7 high-efficiency improved algorithms, it excels in average fitness value on 19 functions and in variance on 15 functions, with an average Friedman test rank of 2.73 (ranking first). This indicates that the proposed ISSBOA has both high optimization accuracy and strong robustness. Additionally, an adaptive transformation function is used to convert the continuous-domain ISSBOA into a binary version (BISSBOA) for discrete optimization tasks such as feature selection. To validate the performance of BISSBOA, a comprehensive evaluation is conducted using 20 public datasets with different dimensions, and comparisons are made against 7 high-performance feature selection algorithms. The results show that BISSBOA outperforms the other comparative algorithms across five evaluation metrics, thereby confirming its practicality and superiority in the field of feature selection.

In this study, an improved gravity model and social network analysis (SNA) are applied to analysis CO 2 emissions in China’s power sector, uniquely incorporating electricity and fossil fuel trade flows. It further explores the dynamic effect of energy transition on networks using a panel model, and clarifies the provincial roles in emission abatement and resource allocation. According to the findings, significant regional heterogeneities in CO 2 emissions from 2007 to 2022 can be observed. Coal-dependent provinces, such as Inner Mongolia and Shanxi, face high emissions and challenging transitions, while developed areas such as Beijing and Shanghai have decreased emissions through clean energy integration and enhanced power efficiency. Network analysis identifies Beijing and Jiangsu as central to resource management, empowered by robust policy and information-sharing capabilities, while most provinces demonstrate weaker coordination owing to constrained intermediary functions. In addition, the study observes that energy transitions increase network density (0.3512) and contacts (0.3545) yet decrease efficiency (− 0.1464), suggesting technical and coordinative obstacles. An increasing degree of transition strengthens interprovincial CO 2 connections, establishing provinces experiencing more rapid transitions as critical nodes. Greater closeness centrality (0.0186) signifies shorter collaborative pathways, accelerating the transition. These findings derive practical guidance for regional power collaborations and sustainable growth, offering novel perspectives for a green transition toward carbon neutrality.

Agricultural greenhouses (AGs) are an important component of modern facility agriculture, and accurately mapping and dynamically monitoring their distribution are necessary for agricultural scientific management and planning. Semantic segmentation can be adopted for AG extraction from remote sensing images. However, the feature maps obtained by traditional deep convolutional neural network (DCNN)-based segmentation algorithms blur spatial details and insufficient attention is usually paid to contextual representation. Meanwhile, the maintenance of the original morphological characteristics, especially the boundaries, is still a challenge for precise identification of AGs. To alleviate these problems, this paper proposes a novel network called high-resolution boundary refined network (HBRNet). In this method, we design a new backbone with multiple paths based on HRNetV2 aiming to preserve high spatial resolution and improve feature extraction capability, in which the Pyramid Cross Channel Attention (PCCA) module is embedded to residual blocks to strengthen the interaction of multiscale information. Moreover, the Spatial Enhancement (SE) module is employed to integrate the contextual information of different scales. In addition, we introduce the Spatial Gradient Variation (SGV) unit in the Boundary Refined (BR) module to couple the segmentation task and boundary learning task, so that they can share latent high-level semantics and interact with each other, and combine this with the joint loss to refine the boundary. In our study, GaoFen-2 remote sensing images in Shouguang City, Shandong Province, China are selected to make the AG dataset. The experimental results show that HBRNet demonstrates a significant improvement in segmentation performance up to an IoU score of 94.89%, implying that this approach has advantages and potential for precise identification of AGs.

The standard crow search algorithm suffers from low convergence accuracy, insufficient stability, and susceptibility to getting stuck in local optima. To tackle these formidable challenges, this paper proposes a novel multi-strategy improved crow search algorithm (MSICSA) specifically designed for multi-level image segmentation. The proposed approach incorporates three key enhancements: firstly, opposition-based learning (OBL) is utilized to improve the quality of initial solutions within MSICSA; secondly, an adaptive awareness probability mechanism is introduced to better balance the trade-off between exploration and exploitation; lastly, two differential mutation operators are developed to enhance global search capabilities, increase population diversity, and reduce the risk of converging on local optima. To validate the performance of the proposed algorithm, two sets of experiments are conducted. In the first set of experiments, CEC 2020 benchmark test functions are selected to compare the performance of MSICSA with other group intelligent optimization algorithms. In the second set of experiments, Otsu’s method and fuzzy entropy are employed as objective functions for performing multilevel threshold segmentation on twelve grayscale images. The experimental results demonstrate that MSICSA outperforms seven comparison algorithms in terms of both convergence speed and segmentation quality.

The massively and rapidly spreading disinformation on social network platforms poses a serious threat to public safety and social governance. Therefore, early and accurate detection of rumors in social networks is of vital importance before they spread on a large scale. Considering the small-world property of social networks, the source tweet-word graph is decomposed from the global graph of rumors, and a rumor detection method based on graph attention network of source tweet-word graph is proposed to fully learn the structure of rumor propagation and the deep representation of text contents. Specifically, the proposed model can adequately capture the contextual semantic association representation of source tweets during the propagation and extract semantic features. For the data sparseness of the early stage of information dissemination, text attention mechanism based on opinion similarity can aggregate and capture more tweet propagation structure features to help improve the efficiency of early detection of rumors. Through the analysis of the experimental results on real public datasets, the rumor detection performance of the proposed method is better than that of other baseline methods. Especially in the early rumor detection tasks, the proposed method can detect rumors with an accuracy of nearly 90% in the early stage of information dissemination. And it still has good robustness with noise interference.

Rock mass deformation and failure are macroscopic manifestations of crack initiation, propagation, and coalescence. However, simulating the transition of rocks from continuous to discontinuous media under cyclic dynamic loading remains challenging. This study proposes a hybrid finite-discrete element method (HFDEM) to model crack propagation, incorporating a frequency-dependent cohesive-zone model. The mechanical properties of standard sandy mudstone under quasi-static and cyclic dynamic loading were simulated using HFDEM, and the method's reliability was verified through experimental comparison. The comparative analysis demonstrates that HFDEM successfully captures crack interaction mechanisms and accurately simulates the overall failure behavior of specimens. Additionally, the effects of pre-existing flaw inclination angle and dynamic loading frequency on rock failure mechanisms were investigated. The numerical results reveal that rock samples exhibit significantly higher compressive strength under dynamic loading compared to quasi-static loading, with compressive strength increasing with higher cyclic dynamic load frequencies. Furthermore, by analyzing the strength characteristics, crack propagation, and failure modes of the samples, insights into the failure mechanisms of rocks under different frequency loads were obtained. This study provides valuable insights into crack development and failure of rocks under seismic loads, offering guidance for engineering practices.