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To address the issues of image feature insensitivity, high requirement of expert experience, and low recognition accuracy of traditional machine vision methods in train wheelset bearing damage detection, this paper proposes an identification method based on the framework of convolutional and transformer fusion networks for identifying damage to train wheelset bearings. First, due to the complexity of train-bearing images, their category imbalance is more severe; an image preprocessing method called image enhancement category reorganization is used to improve the quality of the acquired image dataset and eliminate the effects of the imbalance dataset. Second, a convolutional neural network (CNN) has high model construction and training efficiency due to adopting a local sensing field and weight-sharing strategy, which can only sense local neighborhoods but has limited ability to capture global feature information. Transformer is a network model based on a self-attention mechanism. With strong parallel computing ability, it can learn the remote dependencies between image pixels in the global scope and has a more powerful global information extraction ability. However, the ability to mine the local features of the image is not sufficient. Therefore, this paper presents a VGG and transformer parallel fusion network that integrates the global contour features and local details of the image based on the fusion of convolution and self-attention. Furthermore, a multiscale dilation spatial pyramid convolution (MDSPC) module is constructed to fully mine the multiscale semantic features in the feature map using multiscale dilation convolution progressive fusion. The proposed method effectively solves the problem of feature information loss due to the mesh effect caused by the expansion convolution. Additionally, embedding coordinate attention (CA) after the MDSPC module can obtain remote dependencies and more precise positional relationships of feature images from two spatial directions, which can more accurately focus on specific regions in the feature map. Finally, experimental analyses were conducted using the NEU-DET image defect and self-constructed train wheelset bearing image datasets. The experimental results demonstrate that the proposed model has an accuracy of 99.44% and 98% for recognizing six types of defects and four types of images of wheelset bearings in NEU-DET data, respectively. The feature extraction capability of the proposed model was verified using model visualization methods. Compared with existing CNN models, ViT model with self-attention mechanism, and CNN-transformer fusion model, the proposed method shows significantly better evaluation metrics and accurately identifies different types of image samples without significantly increasing the model complexity.

Graphitic carbon nitride (g-C3N4)-based photocatalysts have garnered significant interest as a promising photocatalyst for hydrogen generation under visible light, to address energy and environmental challenges owing to their favorable electronic structure, affordability, and stability. In spite of that, issues such as high charge carrier recombination rates and low quantum efficiency impede its broader application. To overcome these limitations, structural and morphological modification of the g-C3N4-based photocatalysts is a novel frontline to improve the photocatalytic performance. Therefore, we briefly summarize the current preparation methods of g-C3N4. Importantly, this review highlights recent advancements in crafting high-performance g-C3N4-based photocatalysts, focusing on strategies like elemental doping, nanostructure design, bandgap engineering, and heterostructure construction. Notably, sophisticated doping techniques have propelled hydrogen production rates to a 104-fold increase. Ingenious nanostructure designs have expanded the surface area by a factor of 26, concurrently extending the fluorescence lifetime of charge carriers by 50%. Moreover, the strategic assembly of heterojunctions has not only elevated charge carrier separation efficiency but also preserved formidable redox properties, culminating in a dramatic hundredfold surge in hydrogen generation performance. This work provides a reliable and brief overview of the controlled modification engineering of g-C3N4-based photocatalyst systems, paving the way for more efficient hydrogen production.

Abstract Adjuvants are indispensable components of vaccines. Despite being widely used in vaccines, their action mechanisms are not yet clear. With a greater understanding of the mechanisms by which the innate immune response controls the antigen-specific response, the adjuvants’ action mechanisms are beginning to be elucidated. Adjuvants can be categorized as immunostimulants and delivery systems. Immunostimulants are danger signal molecules that lead to the maturation and activation of antigen-presenting cells (APCs) by targeting Toll-like receptors (TLRs) and other pattern recognition receptors (PRRs) to promote the production of antigen signals and co-stimulatory signals, which in turn enhance the adaptive immune responses. On the other hand, delivery systems are carrier materials that facilitate antigen presentation by prolonging the bioavailability of the loaded antigens, as well as targeting antigens to lymph nodes or APCs. The adjuvants’ action mechanisms are systematically summarized at the beginning of this review. This is followed by an introduction of the mechanisms, properties, and progress of classical vaccine adjuvants. Furthermore, since some of the adjuvants under investigation exhibit greater immune activation potency than classical adjuvants, which could compensate for the deficiencies of classical adjuvants, a summary of the adjuvant platforms under investigation is subsequently presented. Notably, we highlight the different action mechanisms and immunological properties of these adjuvant platforms, which will provide a wide range of options for the rational design of different vaccines. On this basis, this review points out the development prospects of vaccine adjuvants and the problems that should be paid attention to in the future.

This work has studied the co-addition of Sc and Zr elements into the Al-1.75wt%Fe-1.25wt%Ni eutectic alloy. The changes in the microstructure, electrical conductivity, and Vickers hardness of the Al-1.75wt%Fe-1.25wt%Ni-0.2wt%Sc-0.2wt%Zr alloy during heat treatment were studied. The results showed that two-step aging can effectively improve the aging response of the alloy over the single-step aging method. This was ascribed to the minimization of the diffusion difference between Sc and Zr elements. Furthermore, the homogenization treatment can also improve the aging response of the alloy by alleviating the uneven distribution of Sc and Zr. Nevertheless, the micro-alloyed elements exceeded the solid solubility limit in the Al-1.75wt%Fe-1.25wt%Ni-0.2wt%Sc-0.2wt%Zr alloy, and their strengthening effect has ever achieved the best prospect. Finally, both Sc and Zr contents were reduced simultaneously, and the aging response of the Al-1.75wt%Fe-1.25wt%Ni-0.15wt%Sc-0.1wt%Zr alloy was improved by optimized heat treatment. The underlying mechanisms for this alloy design and the corresponding microstructure–mechanical property relationship were analytically discussed.

IntroductionConductance-photosynthesis (Gs-A) models, accompanying with light use efficiency (LUE) models for calculating carbon assimilation, are widely used for estimating canopy stomatal conductance (Gs) and transpiration (Tc) under the two-leaf (TL) scheme. However, the key parameters of photosynthetic rate sensitivity (gsu and gsh) and maximum LUE (ϵmsu and ϵmsh) are typically set to temporally constant values for sunlit and shaded leaves, respectively. This may result in Tc estimation errors, as it contradicts field observations.MethodsIn this study, the measured flux data from three temperate deciduous broadleaved forests (DBF) FLUXNET sites were adopted, and the key parameters of LUE and Ball-Berry models for sunlit and shaded leaves were calibrated within the entire growing season and each season, respectively. Then, the estimations of gross primary production (GPP) and Tc were compared between the two schemes of parameterization: (1) entire growing season-based fixed parameters (EGS) and (2) season-specific dynamic parameters (SEA).ResultsOur results show a cyclical variability of ϵmsu across the sites, with the highest value during the summer and the lowest during the spring. A similar pattern was found for gsu and gsh, which showed a decrease in summer and a slight increase in both spring and autumn. Furthermore, the SEA model (i.e., the dynamic parameterization) better simulated GPP, with a reduction in root mean square error (RMSE) of about 8.0 ± 1.1% and an improvement in correlation coefficient (r) of 3.7 ± 1.5%, relative to the EGS model. Meanwhile, the SEA scheme reduced Tc simulation errors in terms of RMSE by 3.7 ± 4.4%.DiscussionThese findings provide a greater understanding of the seasonality of plant functional traits, and help to improve simulations of seasonal carbon and water fluxes in temperate forests.

The elderly comprises over one-third of hepatocellular carcinoma (HCC) patients, however, they are not adequately represented in prognostic studies. The study aims to determine the prognostic significance of the preoperative prognostic nutritional index (PNI) and develop nomograms for predicting their recurrence-free and overall survival (RFS and OS). The study consisted of 282 elderly patients (aged ≥ 65 years) with early-stage HCC (China Liver Cancer Staging System: I-IIA) after curative resection (R0). They were randomly divided into a training (n = 197) and a test cohort (n = 85). The patients were stratified into two groups: PNI-low (PNI ≤ 49.05) and PNI-high (PNI > 49.05) based on a cut-off value. Most patients' demographics and perioperative outcomes were comparable, while patients in the PNI-high group were younger (P = 0.002), heavier (P < 0.001), and had lower comorbidity rates (P = 0.003). Although the tumor stages were earlier in the PNIlow group (P < 0.001), patients' OS (5-year OS: 48.9% vs. 93.1%) and RFS (5-year RFS: 27.3% vs. 75.7%) were significantly worse compared to the PNI-high group (both P < 0.0001). Patients' OS and RFS nomograms were developed by incorporating independent survival predictors including chronic obstructive pulmonary disease (COPD), age ≥ 75 years, PNI-low, tumor presence of satellite nodules, capsule, and microvascular invasion. The nomograms showed good calibration and discrimination, with all C-indexes ≥ 0.75 and calibration plots essentially coinciding with the diagonal. In conclusion, for elderly HCC patients, COPD, age ≥ 75 years, PNI-low, and tumor presence of satellite nodules, capsule, and microvascular invasion were independent prognostic factors. The nomogram could accurately predict the prognosis of these patients. [ABSTRACT FROM AUTHOR]

The prognostic significance of the signet-ring cell component in gallbladder carcinoma (GBC) has not been systematically evaluated. The aim of this study was to assess the similarities and differences between gallbladder signet-ring cell carcinoma (GBSRCA) and gallbladder adenocarcinoma (GBAC) in terms of clinicopathological features and long-term survival. Using the Surveillance, Epidemiology, and End Results (SEER) database, we analyzed 6,612 patients diagnosed with gallbladder cancer between 2000 and 2021. The cohort included 147 patients with GBSRCA and 6,465 with GBAC. Patients with GBSRCA were significantly younger, with 33.3% being age 60 or younger compared to 23.9% of patients with GBAC (P = 0.009). There was a higher proportion of females in the GBSRCA group (77.6%) compared to the GBAC group (70.1%, P = 0.049). GBSRCA was associated with a more advanced tumor stage (T3-T4: 56.5% vs. 44.4%, P = 0.004), higher rates of lymph node metastasis (43.5% vs. 28.0%, P < 0.001), and poorer differentiation status (poorly to undifferentiated: 80.3% vs. 29.7%, P < 0.001). Survival analysis revealed that patients with GBSRCA had significantly worse overall survival (OS) and cancer-specific survival (CSS) compared to patients with GBAC (P < 0.001). GBSRCA was an independent prognostic factor for OS (P = 0.001) in the entire cohort, while the T stage and N stage were independent prognostic factors for OS and CSS in patients with GBSRCA. Even after propensity score matching, patients with GBSRCA still had a poorer prognosis. [ABSTRACT FROM AUTHOR]

Intrahepatic bile duct mucinous adenocarcinoma (IHBDMAC) is a rare pathological subtype of intrahepatic cholangiocarcinoma (IHCC), and its tumor biological features and survival outcomes have rarely been explored, especially when compared to the most common subtype, intrahepatic bile duct adenocarcinoma (IHBDAC). Therefore, the aim of this study was to explore the clinical features and survival outcomes of IHBDAC and IHBDMAC using the Surveillance, Epidemiology, and End Results (SEER) database from 2000 to 2021. A total of 1,126 patients were included, with 1,083 diagnosed with IHBDAC and 43 diagnosed with IHBDMAC. Patients with IHBDMAC presented with a more advanced T stage (55.8% vs. 36.9%, P = 0.012) and higher rate of lymph node metastasis (37.2% vs. 24.9%, P = 0.070). Cox regression identified advanced T stage, lymph node metastasis, and distant metastasis as poor survival predictors, while chemotherapy and surgery were protective factors. Survival analyses revealed significantly worse overall survival (OS) and cancer-specific survival (CSS) for IHBDMAC compared to IHBDAC (P < 0.05). Even after matching, patients with IHBDMAC still had a worse prognosis than those with IHBDAC. These findings highlight the aggressive nature of IHBDMAC and the need for tailored therapeutic strategies. Future research should focus on prospective studies and molecular insights to develop targeted treatments for IHBDMAC. [ABSTRACT FROM AUTHOR]