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Sports play an important role in people’s physical health and mental regulation. The choice of sports venues is closely related to people’s age, income, and leisure time and consumption level. Traditional sports cultural halls have some drawbacks in operation time and pricing mode, which lead to waste of space resources, unbalanced net profit, and weak consumption intention. Based on operation mode of traditional sports cultural hall, this paper analyzes the game relationship between profit and consumption intention of sports cultural hall. By constructing a large data platform of the pricing model and using mathematical statistics and support vector machine, the dynamic pricing strategy of the spare time of sports cultural hall is formulated. By comparing different pricing strategies, the overall profit of sports cultural hall has been greatly improved. And the optimal strategy of adjusting pricing dynamically with time is obtained, which provides an effective method for research of the dynamic pricing strategy.

Rainfall-driven hazards such as landslides, debris flows, and earthen dam failures often arise when water changes the internal strain within sand. This study evaluates the ability of distributed acoustic sensing to monitor these strain changes in real time. We embed a fiber-optic cable in a sand-filled glass cylinder and run controlled dry- and wet-sand experiments to measure how strain develops as water infiltrates, saturates, and drains from the sand. The sensing system detects uneven water movement in dry sand and enables millimeter-scale estimates of infiltration rates, and in wet sand it tracks rising water levels, delayed strain peaks after saturation, and abrupt strain shifts during drainage. These results show that fiber-optic sensing captures subtle strain evolution throughout the full water-sand interaction cycle. The study demonstrates that fiber-optic sensing offers promising potential for real-time and cost-effective monitoring and early warning of rainfall-induced geohazards.

NiTi alloy has emerged as a promising bipolar plate (BP) material for proton exchange membrane fuel cells (PEMFCs), combining Ti-like corrosion resistance with Ni-like electrical conductivity through its intermetallic characteristics. However, its performance faces greater challenges under aggressive operating conditions (70 °C, F−-containing acidic solution with air bubbling). This study demonstrates that Nb alloying effectively enhances NiTi while preserving its balanced properties. The developed NiTiNb alloy exhibits improved performance with 26% lower corrosion current density (ic) and 29% reduced interfacial contact resistance (ICR) compared to conventional NiTi, effectively overcoming the conventional corrosion–conductivity trade-off in metallic BPs. The alloy also shows superior electrochemical stability and microhardness relative to pure Ti and Ni. These enhancements stem from a unique dual-phase microstructure comprising a NiTi (B2) matrix with continuous β-Nb grain boundary networks. During operation, this structure enables in situ formation of protective TiO2-Nb2O5 films while maintaining conductive Nb/Nb2O5 pathways and metallic Ni domains. The findings establish Nb alloying as a viable optimization strategy for NiTi-based BP substrate in demanding PEMFC applications.

Characterizing and monitoring geologic formations around a borehole are crucial for energy and environmental applications. However, conventional wireline sonic logging usually cannot be used in high-temperature environments nor is the tool feasible for long-term monitoring. We introduce and evaluate the feasibility of a source-free distributed-acoustic-sensing (DAS) logging method based on borehole DAS ambient noise. Our new logging method provides a next-generation borehole imaging tool. The tool is source free because it uses ever-present ambient noises as sources and does not need a borehole sonic source that cannot be easily re-inserted into a borehole after well completion for time-lapse monitoring. The receivers of our source-free DAS logging tool are fiber optic cables cemented behind casing, enabling logging in harsh, high-temperature environments, and eliminating the receiver repeatability issue of conventional wireline sonic logging for time-lapse monitoring. We analyze a borehole DAS ambient noise dataset to obtain root-mean-squares (RMS) amplitudes and use these amplitudes to infer subsurface elastic properties. We find that the ambient noise RMS amplitudes correlate well with anomalies in conventional logging data. The source-free DAS logging tool can advance our ability to characterize and monitor subsurface geologic formations in an efficient and cost-effective manner, particularly in high-temperature environments such as geothermal reservoirs. Further validation of the source-free DAS logging method using other borehole DAS ambient noise data would enable the new logging tool for wider applications.

Gliomas are among the most common types of central nervous system (CNS) tumors. A prompt diagnosis of the glioma subtype is crucial to estimate the prognosis and personalize the treatment strategy. The objective of this study was to develop a radiomics pipeline based on the clinical Magnetic Resonance Imaging (MRI) scans to noninvasively predict the glioma subtype, as defined based on the tumor grade, isocitrate dehydrogenase (IDH) mutation status, and 1p/19q codeletion status. A total of 212 patients from the public retrospective The Cancer Genome Atlas Low Grade Glioma (TCGA-LGG) and The Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) datasets were used for the experiments and analyses. Different settings in the radiomics pipeline were investigated to improve the classification, including the Z-score normalization, the feature extraction strategy, the image filter applied to the MRI images, the introduction of clinical information, ComBat harmonization, the classifier chain strategy, etc. Based on numerous experiments, we finally reached an optimal pipeline for classifying the glioma tumors. We then tested this final radiomics pipeline on the hold-out test data with 51 randomly sampled random seeds for reliable and robust conclusions. The results showed that, after tuning the radiomics pipeline, the mean AUC improved from 0.8935 (±0.0351) to 0.9319 (±0.0386), from 0.8676 (±0.0421) to 0.9283 (±0.0333), and from 0.6473 (±0.1074) to 0.8196 (±0.0702) in the test data for predicting the tumor grade, IDH mutation, and 1p/19q codeletion status, respectively. The mean accuracy for predicting the five glioma subtypes also improved from 0.5772 (±0.0816) to 0.6716 (±0.0655). Finally, we analyzed the characteristics of the radiomic features that best distinguished the glioma grade, the IDH mutation, and the 1p/19q codeletion status, respectively. Apart from the promising prediction of the glioma subtype, this study also provides a better understanding of the radiomics model development and interpretability. The results in this paper are replicable with our python codes publicly available in github.

Titanium (Ti) is generally considered as an ideal bipolar plate (BPP) material because of its excellent corrosion resistance, good machinability and lightweight nature. However, the easy-passivation property, which leads to increased interfacial contact resistance (ICR) and subsequently decreased cell performance, limits its large-scale commercial application in proton exchange membrane fuel cells (PEMFCs). In this paper, we proposed a NiTi alloy prepared by suction casting as a promising bipolar plate for PEMFCs. This NiTi alloy exhibits significantly decreased ICR values (16.8 mΩ cm2 at 1.4 MPa) compared with pure Ti (88.6 mΩ cm2 at 1.4 MPa), along with enhanced corrosion resistance compared with pure nickel (Ni). The superior corrosion resistance of NiTi alloy is accredited to the nobler open circuit potential and corrosion potential, coupled with low corrosion current densities and passive current densities. The improved ICR can be interpreted by the existence of high-proportioned metallic Ni in the passive film, which contributes to the reduced capacitance characteristic of the passive film (compared with Ti) and enhances charge conduction. This work provides a feasible option to ameliorate BPP material that may have desirable corrosion resistance and ICR.

Ferroptosis is a form of regulated cell death driven by the accumulation of iron-dependent lipid peroxides, and ferroptosis-mediated cancer therapy has gained considerable attention. Despite emerging evidence that ferroptosis induction effectively suppresses hepatocellular carcinoma (HCC) progression and enhances chemosensitivity, the development of resistance to ferroptosis-targeting therapies remains a critical challenge. Natural active compounds have great potential in cancer treatment. The impact of 6-ME on the cell viability of HCC cells was assessed using the Cell Counting Kit-8 (CCK-8) assay and colony formation assay. Furthermore, cellular morphology of HCC cells was visualized under inverted fluorescence microscopy. Intracellular reactive oxygen species (ROS) and lipid peroxidation levels were quantified using fluorescence probes and determined by flow cytometry analysis. The expression of ferroptosis-related proteins and genes was determined via Western blot and quantitative real-time PCR analyses. Here, we demonstrate that 6-Methoxydihydrosanguinarine (6-ME), an alkaloid from , exerts anti-tumor functions in HCC cells via ferroptosis. Stimulation with 6-ME induces intracellular ROS production, cell growth inhibition, and cell death in HCC cells, and these effects can be weakened by the ROS scavenger GSH or NAC and ferroptosis inhibitors deferoxamine mesylate (DFO) or ferrostatin-1 (Fer-1). Mechanistically, 6-ME downregulates the expression of the key ferroptosis defense enzyme GPX4 at the transcriptional level, leading to excessive lipid peroxidation and ferroptosis in HCC cells. Importantly, low concentrations of 6-ME also enhanced the ferroptosis sensitivity induced by RSL3 and IKE in HCC cells. These findings reveal that the natural product 6-ME exerts anti-tumor functions in HCC cells ferroptosis and underscore the potential of 6-ME administered alone or in combination with canonical ferroptosis inducers for the treatment of HCC patients.

Ferroptosis, a recently elucidated style of regulated cell death, has emerged as a significant area of investigation in cancer biology. Natural active compounds that have anti-cancer effects are promising candidates for cancer prevention. Iberverin, a natural compound derived from Brassica oleracea var. capitata, has been shown to exert anti-tumor activities in some cancers. However, its role in hepatocellular carcinoma (HCC) cells and the molecular mechanisms are still poorly understood. In this study, we proved that iberverin can induce intracellular reactive oxygen species (ROS) generation to inhibit cell proliferation and initiate ferroptotic cell death in HCC cells, which can be eradicated by the ferroptosis inhibitor ferrostatin-1 (Fer-1) or deferoxamine mesylate (DFO) and ROS scavenger (GSH or NAC). Mechanistically, iberverin treatment can simultaneously downregulate SLC7A11 mRNA level and degrade GPX4 through the ubiquitination pathway, leading to lipid peroxidation and ferroptotic cell death in HCC cells. Significantly, a low dose of iberverin can remarkably increase the sensitivity of HCC cells to ferroptosis induced by canonical ferroptosis inducers RSL3 and imidazole ketone erastin (IKE). This study uncovers a critical function of iberverin in preventing HCC through ferroptosis and provides a promising strategy for HCC treatment either via iberverin alone or in combination with canonical ferroptosis inducers in the future.

The natural product auraptene can influence tumor cell proliferation and invasion, but its effect on hepatocellular carcinoma (HCC) cells is unknown. Here, we report that auraptene can exert anti-tumor effects in HCC cells via inhibition of cell proliferation and ferroptosis induction. Auraptene treatment induces total ROS and lipid ROS production in HCC cells to initiate ferroptosis. The cell death or cell growth inhibition of HCC cells induced by auraptene can be eliminated by the ROS scavenger NAC or GSH and ferroptosis inhibitor ferrostatin-1 or Deferoxamine Mesylate (DFO). Mechanistically, the key ferroptosis defense protein SLC7A11 is targeted for ubiquitin–proteasomal degradation by auraptene, resulting in ferroptosis of HCC cells. Importantly, low doses of auraptene can sensitize HCC cells to ferroptosis induced by RSL3 and cystine deprivation. These findings demonstrate a critical mechanism by which auraptene exhibits anti-HCC effects via ferroptosis induction and provides a possible therapeutic strategy for HCC by using auraptene or in combination with other ferroptosis inducers.

Background Early diagnosis of syphilis is vital for its effective control. This study aimed to develop an Artificial Intelligence (AI) diagnostic model based on radiomics technology to distinguish early syphilis from other clinical skin lesions. Methods The study collected 260 images of skin lesions caused by various skin infections, including 115 syphilis and 145 other infection types. 80% of the dataset was used for model development with 5-fold cross-validation, and the remaining 20% was used as a hold-out test set. The exact lesion region was manually segmented as Region of Interest (ROI) in each image with the help of two experts. 102 radiomics features were extracted from each ROI and fed into 11 different classifiers after deleting the redundant features using the Pearson correlation coefficient. Different image filters like Wavelet were investigated to improve the model performance. The area under the ROC curve (AUC) was used for evaluation, and Shapley Additive exPlanations (SHAP) for model interpretation. Results Among the 11 classifiers, the Gradient Boosted Decision Trees (GBDT) with the wavelet filter applied on the images demonstrated the best performance, offering the stratified 5-fold cross-validation AUC of 0.832 ± 0.042 and accuracy of 0.735 ± 0.043. On the hold-out test dataset, the model shows an AUC and accuracy of 0.792 and 0.750, respectively. The SHAP analysis shows that the shape 2D sphericity was the most predictive radiomics feature for distinguishing early syphilis from other skin infections. Conclusion The proposed AI diagnostic model, built based on radiomics features and machine learning classifiers, achieved an accuracy of 75.0%, and demonstrated potential in distinguishing early syphilis from other skin lesions. Highlights Radiomics is highly effective for detecting sexually transmitted infection lesions. Integrating the radiomics model with machine learning achieves high AUC for early syphilis detection. Important radiomics features extracted from models reveal lesion shape details crucial for early syphilis detection.