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The safety and reliability of chemical equipment are crucial to industrial production, as they directly impact production efficiency, environmental protection, and personnel safety. However, traditional fault detection techniques often exhibit limitations when applied to the complex operational conditions, varying environmental factors, and multimodal data encountered in chemical equipment. These conventional methods typically rely on a single signal source or shallow feature extraction, which makes it difficult to effectively capture the deep, implicit information within the equipment’s operating state. Moreover, their accuracy and robustness are easily compromised when confronted with noisy signals or large, diverse datasets. Therefore, designing an intelligent fault detection method that integrates multimodal data, efficiently extracts deep features, and demonstrates strong generalization capability has become a key challenge in current research.This paper proposes an innovative fault detection method for chemical equipment aimed at improving detection accuracy and efficiency, providing technical support for intelligent and predictive maintenance. The method combines Variational Mode Decomposition (VMD), Least Mean Squares (LMS) processing, an asymmetric attention mechanism, and a pre-activation ResNet-BiGRU model to create an efficient framework for multimodal data fusion and analysis. First, the VMD-LMS process handles complex non-stationary signals, addressing the issue of mode mixing. Next, an asymmetric attention mechanism optimizes the ResNet, enhancing feature representation capabilities through deep learning. The pre-activation mechanism introduced in the residual blocks of ResNet improves training efficiency and model stability. Subsequently, the BiGRU model is used to model the extracted features in the time domain, capturing complex temporal dependencies. Experimental results demonstrate that the proposed method performs exceptionally well in chemical equipment fault detection, significantly enhancing diagnostic timeliness and reliability, achieving a classification accuracy of 99.78%, and providing an effective fault detection solution for industrial production.

This paper proposes an optimized DenseNet-Transformer model based on FFT-VMD processing for bearing fault diagnosis. First, the original bearing vibration signal is decomposed into frequency-domain and time–frequency-domain components using FFT and VMD methods, extracting key signal features. To enhance the model’s feature extraction capability, the CBAM (Convolutional Block Attention Module) is integrated into the Dense Block, dynamically adjusting channel and spatial attention to focus on crucial features. The alternating stacking strategy of channel and spatial attention further improves the feature extraction ability at different scales. This optimized structure increases the diversity and discriminative power of feature representations, enhancing the model’s performance in fault diagnosis tasks. Furthermore, the Transformer module, replacing the LSTM, is employed to model long-term and short-term dependencies in the time series. Through its Self-Attention mechanism, Transformer efficiently captures the global relationships within the sequence, improving the model’s ability to handle complex temporal dependencies. Experimental results show that the proposed model achieves outstanding performance in bearing fault classification, with a classification accuracy of 99.68%, demonstrating excellent generalization ability. This model provides an effective and reliable solution for the health monitoring of chemical equipment.

Background The incidence of stage I and stage II lung adenocarcinoma (LUAD) is likely to increase with the introduction of annual screening programs for high-risk individuals. We aimed to identify a reliable prognostic signature with immune-related genes that can predict prognosis and help making individualized management for patients with early-stage LUAD. Methods The public LUAD cohorts were obtained from the large-scale databases including 4 microarray data sets from the Gene Expression Omnibus (GEO) and 1 RNA-seq data set from The Cancer Genome Atlas (TCGA) LUAD cohort. Only early-stage patients with clinical information were included. Cox proportional hazards regression model was performed to identify the candidate prognostic genes in GSE30219, GSE31210 and GSE50081 (training set). The prognostic signature was developed using the overlapped prognostic genes based on a risk score method. Kaplan–Meier curve with log-rank test and time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic value and performance of this signature, respectively. Furthermore, the robustness of this prognostic signature was further validated in TCGA-LUAD and GSE72094 cohorts. Results A prognostic immune signature consisting of 21 immune-related genes was constructed using the training set. The prognostic signature significantly stratified patients into high- and low-risk groups in terms of overall survival (OS) in training data set, including GSE30219 (HR = 4.31, 95% CI 2.29–8.11; P  = 6.16E−06), GSE31210 (HR = 11.91, 95% CI 4.15–34.19; P  = 4.10E−06), GSE50081 (HR = 3.63, 95% CI 1.90–6.95; P  = 9.95E−05), the combined data set (HR = 3.15, 95% CI 1.98–5.02; P  = 1.26E−06) and the validation data set, including TCGA-LUAD (HR = 2.16, 95% CI 1.49–3.13; P  = 4.54E−05) and GSE72094 (HR = 2.95, 95% CI 1.86–4.70; P  = 4.79E−06). Multivariate cox regression analysis demonstrated that the 21-gene signature could serve as an independent prognostic factor for OS after adjusting for other clinical factors. ROC curves revealed that the immune signature achieved good performance in predicting OS for early-stage LUAD. Several biological processes, including regulation of immune effector process, were enriched in the immune signature. Moreover, the combination of the signature with tumor stage showed more precise classification for prognosis prediction and treatment design. Conclusions Our study proposed a robust immune-related prognostic signature for estimating overall survival in early-stage LUAD, which may be contributed to make more accurate survival risk stratification and individualized clinical management for patients with early-stage LUAD.

Systemic therapies using programmed death-1 (PD-1) and programmed death ligand 1 (PD-L1) inhibitors have demonstrated commendable efficacy in some patients with advanced hepatocellular carcinoma (HCC); however, other individuals do not respond favorably. Hence, identifying the biomarkers, the prognostic factors, and their underlying mechanisms is crucial. In this review, we summarized the latest advancements in this field. Within the tumor microenvironment, PD-L1 expression is commonly utilized to predict response. Moreover, the characteristics of tumor-infiltrating lymphocytes are associated with the effectiveness of immunotherapy. Preclinical studies have identified stimulatory dendritic cells, conventional dendritic cells, and macrophages as potential biomarkers. The emergence of single-cell sequencing and spatial transcriptomics has provided invaluable insights into tumor heterogeneity through the lens of single-cell profiling and spatial distribution. With the widespread adoption of next-generation sequencing, certain genomic characteristics, including tumor mutational burden, copy number alterations, specific genes (TP53, CTNNB1, and GZMB), and signaling pathways (WNT/β-catenin) have been found to correlate with prognosis. Furthermore, clinical features such as tumor size, number, and metastasis status have demonstrated prognostic value. Notably, common indicators such as the Child-Pugh score and Eastern Cooperative Oncology Group score, which are used in patients with liver diseases, have shown potential. Similarly, commonly employed laboratory parameters such as baseline transforming growth factor beta, lactate dehydrogenase, dynamic changes in alpha-fetoprotein (AFP) and abnormal prothrombin, CRAFITY score (composed of C-reactive protein and AFP), and immune adverse events have been identified as predictive biomarkers. Novel imaging techniques such as EOB-MRI and PET/CT employing innovative tracers also have potential. Moreover, liquid biopsy has gained widespread use in biomarker studies owing to its non-invasive, convenient, and highly reproducible nature, as well as its dynamic monitoring capabilities. Research on the gut microbiome, including its composition, dynamic changes, and metabolomic analysis, has gained considerable attention. Efficient biomarker discovery relies on continuous updating of treatment strategies. Next, we summarized recent advancements in clinical research on HCC immunotherapy and provided an overview of ongoing clinical trials for contributing to the understanding and improvement of HCC immunotherapy.

Background The grass-roots civil servants are the final implementers and executors of a series of government policies and the fundamental force for social stability and harmonious development. However, the mental health problems of grass-roots civil servants have not got full attention. This study aimed to assess the impact of resilience on anxiety and depression among grass-roots civil servants in China. Method From Oct to Dec 2019, 302 civil servants completed a series of questionnaires. The Civil Servants Stress Scale (CSSS) was used to assess the stress of civil servants. The Self-rating Depression Scale (SDS) and the Self-rating Anxiety Scale (SAS) were used to evaluate the depression and anxiety of participants, respectively. The resilience of civil servants evaluates by the Connor-Davidson Resilience Scale (CD-RSCI). We conducted the moderating and mediating analysis on the impact of resilience on depression and anxiety in grass-roots civil servants. Results There were significant differences in gender, education, position, relationship with coworkers, physical exercise, and monthly income for stress in grass-roots civil servants ( P  < 0.05). Resilience can negatively regulate the stress of grass-roots civil servants, and an effective mediator and moderator in the relationship between stress and anxiety and depression and the mediating effect ratios of 7.77 and 22.79%. Conclusion Resilience has moderating and mediating effects on the relationship between stress and depression, and anxiety. The negative effects of stress on depression and anxiety of grass-roots civil servants can be buffered by resilience as a dynamic moderator directly and indirectly. These findings contribute to society and government better understand the mental health status of grass-roots civil servants and provide references and guidance for the formulation of corresponding management and prevention measures.

Offshore sites show greater potential for wind energy utilization than most onshore sites. When planning an offshore wind power farm, the speed of offshore wind is used to estimate various operation parameters, such as the power output, extreme wind load, and fatigue load. Accurate speed prediction is crucial to the running of wind power farms and the security of smart grids. Unlike onshore wind, offshore wind has the characteristics of random, intermittent, and chaotic, which will cause the time series of wind speeds to have strong nonlinearity. It will bring greater difficulties to offshore wind speed predictions, which traditional recurrent neural networks cannot deal with for lacking in long-term dependency. An offshore wind speed prediction method is proposed by using a clockwork recurrent network (CWRNN). In a CWRNN model, the hidden layer is subdivided into several parts and each part is allocated a different clock speed. Under the mechanism, the long-term dependency of the recurrent neural network can be easily addressed, which can furthermore effectively solve the problem of strong nonlinearity in offshore speed winds. The experiments are performed by using the actual data of two different offshore sites located in the Caribbean Sea and one onshore site located in the interior of the United States, to verify the performance of the model. The results show that the prediction model achieves significant accuracy improvement.

Climate change has proven to have a profound impact on the growth of vegetation from various points of view. Understanding how vegetation changes and its response to climatic shift is of vital importance for describing their mutual relationships and projecting future land–climate interactions. Arid areas are considered to be regions that respond most strongly to climate change. Xinjiang, as a typical dryland in China, has received great attention lately for its unique ecological environment. However, comprehensive studies examining vegetation change and its driving factors across Xinjiang are rare. Here, we used the remote sensing datasets (MOD13A2 and TerraClimate) and data of meteorological stations to investigate the trends in the dynamic change in the Normalized Difference Vegetation Index (NDVI) and its response to climate change from 2000 to 2019 across Xinjiang based on the Google Earth platform. We found that the increment rates of growth-season mean and maximum NDVI were 0.0011 per year and 0.0013 per year, respectively, by averaging all of the pixels from the region. The results also showed that, compared with other land use types, cropland had the fastest greening rate, which was mainly distributed among the northern Tianshan Mountains and Southern Junggar Basin and the northern margin of the Tarim Basin. The vegetation browning areas primarily spread over the Ili River Valley where most grasslands were distributed. Moreover, there was a trend of warming and wetting across Xinjiang over the past 20 years; this was determined by analyzing the climate data. Through correlation analysis, we found that the contribution of precipitation to NDVI (R2 = 0.48) was greater than that of temperature to NDVI (R2 = 0.42) throughout Xinjiang. The Standardized Precipitation and Evapotranspiration Index (SPEI) was also computed to better investigate the correlation between climate change and vegetation growth in arid areas. Our results could improve the local management of dryland ecosystems and provide insights into the complex interaction between vegetation and climate change.

The accurate estimation of aboveground biomass (AGB) and leaf area index (LAI) is critical to characterize crop growth status and predict grain yield. Unmanned aerial vehicle (UAV) -based remote sensing has attracted significant interest due to its high flexibility and easiness of operation. The mixed effect model introduced in this study can capture secondary factors that cannot be captured by standard empirical relationships. The objective of this study was to explore the potential benefit of using a linear mixed-effect (LME) model and multispectral images from a fixed-wing UAV to estimate both AGB and LAI of rice. Field experiments were conducted over two consecutive years (2017–2018), that involved different N rates, planting patterns and rice cultivars. Images were collected by a compact multispectral camera mounted on a fixed-wing UAV during key rice growth stages. LME, simple regression (SR), artificial neural networks (ANN) and random forests (RF) models were developed relating growth parameters (AGB and LAI) to spectral information. Cultivar (C), growth stage (S) and planting pattern (P) were selected as candidates of random effects for the LME models due to their significant effects on rice growth. Compared to other regression models (SR, ANN and RF), the LME model improved the AGB estimation accuracy for all stage groups to varying degrees: the R2 increased by 0.14–0.35 and the RMSE decreased by 0.88–1.80 t ha−1 for the whole season, the R2 increased by 0.07–0.15 and the RMSE decreased by 0.31–0.61 t ha−1 for pre-heading stages and the R2 increased by 0.21–0.53 and the RMSE decreased by 0.72–1.52 t ha−1 for post-heading stages. Further analysis suggested that the LME model also successfully predicted within the groups when the number of groups was suitable. More importantly, depending on the availability of C, S, P or combinations thereof, mixed effects could lead to an outperformance of baseline retrieval methods (SR, ANN or RF) due to the inclusion of secondary effects. Satisfactory results were also obtained for the LAI estimation while the superiority of the LME model was not as significant as that for AGB estimation. This study demonstrates that the LME model could accurately estimate rice AGB and LAI and fixed-wing UAVs are promising for the monitoring of the crop growth status over large-scale farmland.

Background Microsatellite instability-high (MSI-H) is a unique genomic status in many cancers. However, its role in the genomic features and immunotherapy in cholangiocarcinoma (CCA) is unclear. This study aimed to systematically investigate the genomic characterization and immunotherapy efficacy of MSI-H patients with CCA. Methods We enrolled 887 patients with CCA in this study. Tumor samples were collected for next-generation sequencing. Differences in genomic alterations between the MSI-H and microsatellite stability (MSS) groups were analyzed. We also investigated the survival of PD-1 inhibitor-based immunotherapy between two groups of 139 patients with advanced CCA. Results Differential genetic alterations between the MSI-H and MSS groups included mutations in ARID1A , ACVR2A , TGFBR2 , KMT2D , RNF43 , and PBRM1 which were enriched in MSI-H groups. Patients with an MSI-H status have a significantly higher tumor mutation burden (TMB) (median 41.7 vs. 3.1 muts/Mb, P  < 0.001) and more positive programmed death ligand 1 (PD-L1) expression (37.5% vs. 11.9%, P  < 0.001) than those with an MSS status. Among patients receiving PD-1 inhibitor-based therapy, those with MSI-H had a longer median overall survival (OS, hazard ratio (HR) = 0.17, P  = 0.001) and progression-free survival (PFS, HR = 0.14, P  < 0.001) than patients with MSS. Integrating MSI-H and PD-L1 expression status (combined positive score ≥ 5) could distinguish the efficacy of immunotherapy. Conclusions MSI-H status was associated with a higher TMB value and more positive PD-L1 expression in CCA tumors. Moreover, in patients with advanced CCA who received PD-1 inhibitor-based immunotherapy, MSI-H and positive PD-L1 expression were associated with improved both OS and PFS. Trial registration This study was registered on ClinicalTrials.gov on 07/01/2017 (NCT03892577).

Building geometry design decisions are important for energy efficiency and daylight performance. Sensitivity analysis, coupled with optimization, is an important approach to investigate and optimize building geometry in the early design stage. Incorporating skylights is an important daylighting strategy in commercial buildings; however, skylight-to-floor ratio (SFR) is often the only design variable evaluated in precedent studies. More design variables related to skylight geometry, clerestory geometry, skylight material, and building geometry need to be evaluated. This study investigates the skylight design of a 2000-square-meter commercial building. Eighteen design variables are evaluated according to their influence on building energy and daylight performance. One-at-a-time (OAT), linear regression, and Morris sensitivity analysis approaches are utilized to identify the most influential variables. Seven of the twelve building geometry variables and two of the six building material variables are considered as important. Then, a multi-objective optimization with genetic algorithms is processed to find out the optimal design solution. The three objectives are energy use intensity (EUI), daylight autonomy (DA), and daylight uniformity (DU). After the optimization, five candidate design options are picked from the Pareto front. Discussions are made on the features of these designs, and one design is selected as the optimal solution.