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Evidence from real-world clinical settings is lacking with regard to first-line immunotherapy plus chemotherapy for the treatment of non-small cell lung cancer (NSCLC). Our aim was to describe outcomes for patients treated with first-line pembrolizumab-combination therapy for metastatic nonsquamous NSCLC in US oncology practices. Using an anonymized, nationwide electronic health record-derived database, we identified patients who initiated pembrolizumab plus pemetrexed-carboplatin in the first-line setting (May 2017 to August 2018) after diagnosis of metastatic nonsquamous NSCLC that tested negative for EGFR and ALK genomic aberrations. Eligible patients had ECOG performance status of 0–1. An enhanced manual chart review was used to collect outcome information. Time-to-event analyses were performed using the Kaplan–Meier method. Of 283 eligible patients, 168 (59%) were male; median age was 66 years (range 33–84); and the proportions of patients with PD-L1 tumor proportion score (TPS) of ≥ 50%, 1–49%, < 1%, and unknown were 28%, 27%, 28%, and 17%, respectively. At data cutoff on August 31, 2019, median patient follow-up was 20.3 months (range 12–28 months), and median real-world times on treatment (rwToT) with pembrolizumab and pemetrexed were 5.6 (95% CI 4.5–6.4) and 2.8 months (95% CI 2.2–3.5), respectively. Median overall survival (OS) was 16.5 months (95% CI 13.2–20.6); estimated 12-month survival was 59.5% (95% CI 53.3–65.0); rwProgression-free survival was 6.4 months (95% CI 5.4–7.8); and rwTumor response rate (complete or partial response) was 56.5% (95% CI 50.5–62.4). Median OS was 20.6, 16.3, 13.2, and 13.7 months for patient cohorts with PD-L1 TPS ≥ 50%, 1–49%, < 1%, and unknown, respectively. These findings demonstrate the effectiveness of pembrolizumab plus pemetrexed-carboplatin by describing clinical outcomes among patients with metastatic nonsquamous NSCLC who were treated at US oncology practices.

In recent years, the multimodal medical imaging assisted diagnosis and treatment technology has developed rapidly. In brain disease diagnosis, CT-SPECT, MRI-PET and MRI-SPECT fusion images are more favored by brain doctors because they contain both soft tissue structure information and organ metabolism information. Most of the previous medical image fusion algorithms are the migration of other types of image fusion methods and such operations often lose the features of the medical image itself. This paper proposes a multimodal medical image fusion model based on the residual attention mechanism of the generative adversarial network. In the design of the generator, we construct the residual attention mechanism block and the concat detail texture block. After source images are concatenated to a matrix , the matrix is put into two blocks at the same time to extract information such as size, shape, spatial location and texture details. The obtained features are put into the merge block to reconstruct the image. The obtained reconstructed image and source images are respectively put into two discriminators for correction to obtain the final fused image. The model has been experimented on the images of three databases and achieved good fusion results. Qualitative and quantitative evaluations prove that the model is superior to other comparison algorithms in terms of image fusion quality and detail information retention.

With the development of ecological paradigm coupled with the relentless implementation of myriad environmental policies in China, the rapid development of carbon emission trading and carbon trading market has had a vital impact on the financial performance of enterprises at the microlevel. This study has sampled the A-share listed companies in China, from 2009 to 2018, and adopted the difference-in-difference (DID) method to investigate the effect of the carbon emission trading on corporate financial performance from the microlevel. Evidence showed that the implementation of carbon emission trading effectively improved the total asset-liability ratio of enterprises, though it reduced the value of the current capital market. Moreover, in the regions under strict legal environment, the enhancement effect of the total asset-liability ratio was more obvious, whereas in the regions under loose legal environment, the reduction effect of the value of the capital market was more obvious. Further analysis showed that the implementation of carbon emission trading could not promote Chinese enterprises to increase R&D investment. Hence the implementation of carbon emission trading has improved the level of non-business income of enterprises incorporated into the trading system, but its impact on the investment income of enterprises was not significant.

Against the background of accelerated green energy development and the deep integration of intelligent sensing technologies, wind power forecasting is evolving toward a multimodal sensor collaborative perception paradigm within nonlinear multi-source integrated energy systems. To address the limitations of conventional methods, including the lack of dynamic importance modeling and constrained stability under complex wind conditions, a forecasting framework based on multimodal sensor importance perception is proposed. This study emphasizes the framework’s role in decoding the complex nonlinear dependencies between atmospheric drivers and turbine responses. Through a multimodal feature encoding architecture, unified temporal representations of meteorological environments and turbine operational states are established. A sensor-importance-aware attention mechanism and a cross-modal relational modeling strategy are introduced to adaptively allocate contributions under varying contexts. Furthermore, prediction compensation and uncertainty characterization modules are integrated to enhance robustness. Systematic experiments on real-world multi-source data validate the method. Overall performance comparisons demonstrate that MAE, RMSE, and MAPE reach 30.48, 42.37, and 9.16 percent, respectively, with the coefficient of determination R2 achieving 0.957, significantly outperforming the Transformer baseline. In multi-horizon tasks, the model exhibits superior error accumulation suppression, with twelve-step forecasting errors remaining at 41.27 and 56.48. These findings reveal that the framework captures the context-dependent nonlinear mapping of energy systems, providing effective technical support for green energy dispatch and intelligent sensing applications.

Environmental protection regulations adopted by governments affect the microeconomic behavior of enterprises. The Chinese government began piloting the outgoing leading officials’ accountability audit of natural resources assets (OANRA) in some regions in 2014. Based on this quasi-natural experimental setting, this paper chose heavy-polluting and resource-based enterprises in pilot regions of China from 2011 to 2016 as examples for studying the impact of the OANRA on enterprise innovation and further examines the role of government subsidies in this process. The study finds that the OANRA has no significant impact on enterprise innovation. However, with support from government subsidies, the OANRA dramatically accelerates enterprise innovation investment. The results are still seen after applying propensity matching analysis (PSM), balancing panel data and deleting special provinces. Further analysis shows that this effect is more obvious among small-scale, state-owned enterprises that are located in areas with high degrees of marketization and high bank credit constraints. This study advances the research of the OANRA’s effects on the microeconomic behavior of enterprises. Moreover, the adjustment effect of government subsidies also provides great reference value to making rational use of policy to cooperate with the OANRA.

With the rapid development of modern information technology, the health care industry is entering a critical stage of intelligence. Faced with the growing health care big data, information security issues are becoming more and more prominent in the management of smart health care, especially the problem of patient privacy leakage is the most serious. Therefore, strengthening the information management of intelligent health care in the era of big data is an important part of the long-term sustainable development of hospitals. This paper first identified the key indicators affecting the privacy disclosure of big data in health management, and then established the risk access control model based on the fuzzy theory, which was used for the management of big data in intelligent medical treatment, and solves the problem of inaccurate experimental results due to the lack of real data when dealing with actual problems. Finally, the model is compared with the results calculated by the fuzzy tool set in Matlab. The results verify that the model is effective in assessing the current safety risks and predicting the range of different risk factors, and the prediction accuracy can reach more than 90%.

Background Today’s biomedical imaging technology has been able to present the morphological structure or functional metabolic information of organisms at different scale levels, such as organ, tissue, cell, molecule and gene. However, different imaging modes have different application scope, advantages and disadvantages. In order to improve the role of medical image in disease diagnosis, the fusion of biomedical image information at different imaging modes and scales has become an important research direction in medical image. Traditional medical image fusion methods are all designed to measure the activity level and fusion rules. They are lack of mining the context features of different modes of image, which leads to the obstruction of improving the quality of fused images. Method In this paper, an attention-multiscale network medical image fusion model based on contextual features is proposed. The model selects five backbone modules in the VGG-16 network to build encoders to obtain the contextual features of medical images. It builds the attention mechanism branch to complete the fusion of global contextual features and designs the residual multiscale detail processing branch to complete the fusion of local contextual features. Finally, it completes the cascade reconstruction of features by the decoder to obtain the fused image. Results Ten sets of images related to five diseases are selected from the AANLIB database to validate the VANet model. Structural images are derived from MR images with high resolution and functional images are derived from SPECT and PET images that are good at describing organ blood flow levels and tissue metabolism. Fusion experiments are performed on twelve fusion algorithms including the VANet model. The model selects eight metrics from different aspects to build a fusion quality evaluation system to complete the performance evaluation of the fused images. Friedman’s test and the post-hoc Nemenyi test are introduced to conduct professional statistical tests to demonstrate the superiority of VANet model. Conclusions The VANet model completely captures and fuses the texture details and color information of the source images. From the fusion results, the metabolism and structural information of the model are well expressed and there is no interference of color information on the structure and texture; in terms of the objective evaluation system, the metric value of the VANet model is generally higher than that of other methods.; in terms of efficiency, the time consumption of the model is acceptable; in terms of scalability, the model is not affected by the input order of source images and can be extended to tri-modal fusion.

The automated replacement of empty tubes in the yarn bank is a critical step in the process of automatic winding machines with yarn banks, as the real-time detection of depleted yarn on spools and accurate positioning of empty tubes directly impact the production efficiency of winding machines. Addressing the shortcomings of traditional methods, such as poor adaptability and low sensitivity in optical and visual tube detection, and aiming to reduce the computational and detection time costs introduced by neural networks, this paper proposes a lightweight yarn spool detection model based on YOLOv8. The model utilizes Darknet-53 as the backbone network, and due to the dense spatial distribution of yarn spool targets, it incorporates large selective kernel units to enhance the recognition and positioning of dense targets. To address the issue of excessive focus on local features by convolutional neural networks, a bi-level routing attention mechanism is introduced to capture long-distance dependencies dynamically. Furthermore, to balance accuracy and detection speed, a FasterNeck is constructed as the neck network, replacing the original convolutional blocks with Ghost convolutions and integrating with FasterNet. This design minimizes the sacrifice of detection accuracy while achieving a significant improvement in inference speed. Lastly, the model employs weighted IoU with a dynamic focusing mechanism as the bounding box loss function. Experimental results on a custom yarn spool dataset demonstrate a notable improvement over the baseline model, with a high-confidence mAP of 94.2% and a compact weight size of only 4.9 MB. The detection speed reaches 223FPS, meeting the requirements for industrial deployment and real-time detection.

In the field of fabric defect detection, the development of algorithms has been hindered by issues such as poor quality and limited quantity of open-source datasets. Traditional data augmentation methods offer limited improvements in model performance, while generative data augmentation methods are plagued by difficulties in training generative models, susceptibility to artifacts, and the need for re-labeling. To address these challenges, this paper proposes a blind super-resolution algorithm for fabric defect data augmentation. The model is based on Real-ESRGAN and has been optimized specifically for the resolution degradation module to better adapt to the resolution degradation process in fabric images. Subsequently, a novel loss function named Local Blur Discrimination Loss is designed to address the local blur phenomenon and suppress the generation of fabric artifacts during the super-resolution process. Finally, both subjective evaluations of super-resolution effects and objective comparisons of data augmentation performance were conducted during the experimental phase. The subjective assessments demonstrate that the proposed method outperforms the baseline model. Additionally, in terms of objective performance, augmenting the DAGM2007 dataset using the proposed model, the detection model's accuracy (P) increased by 7.4%, recall (R) increased by 1.0%, and the mean average precision (mAP) increased by 2.5%, surpassing commonly used traditional vision-based data augmentation algorithms.

Existing research on the quality assurance of transnational higher education (TNHE) has examined institutional, national, and international actors separately, with limited understanding of how these levels interact in practice. This study examines how quality assurance is negotiated across governance levels within a Sino-foreign joint institution. Drawing on the glonacal agency heuristic, it presents a qualitative case analysis of X Business School at Shanghai University, a long-standing partnership with the University of Technology Sydney. Data were collected through focus group interviews with institutional leaders and administrators, and complemented by documentanalysis of institutional, national and international quality assurance framework. The findings show that the quality assurance operates through a hybrid governance arrangement shaped by the interaction of national regulation, institutional partnership governance, and global accreditation regimes. They also demonstrate the institutional agency in mediating these governance expectations through locally adapted teaching practices , coordinating internal quality mechanisms, and strategic engagement with international accreditation. This study contributes to TNHE scholarship by showing how quality assurance is shaped through the interaction of different forces and enacted through institutional agency, and offers insights into the governance dynamics of Sino-foreign joint institutions within the broader landscape of TNHE.