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The 2050 carbon‐neutral vision spawns a novel energy structure revolution, and the construction of the future energy structure is based on equipment innovation. Insulating material, as the core of electrical power equipment and electrified transportation asset, faces unprecedented challenges and opportunities. The goal of carbon neutral and the urgent need for innovation in electric power equipment and electrification assets are first discussed. The engineering challenges constrained by the insulation system in future electric power equipment/devices and electrified transportation assets are investigated. Insulating materials, including intelligent insulating material, high thermal conductivity insulating material, high energy storage density insulating material, extreme environment resistant insulating material, and environmental‐friendly insulating material, are categorised with their scientific issues, opportunities and challenges under the goal of carbon neutrality being discussed. In the context of carbon neutrality, not only improves the understanding of the insulation problems from a macro level, that is, electrical power equipment and electrified transportation asset, but also offers opportunities, remaining issues and challenges from the insulating material level. It is hoped that this paper envisions the challenges regarding design and reliability of insulations in electrical equipment and electric vehicles in the context of policies towards carbon neutrality rules. The authors also hope that this paper can be helpful in future development and research of novel insulating materials, which promote the realisation of the carbon‐neutral vision.

To investigate the predictive value of radiomics based on T1-weighted contrast-enhanced MRI (CE-MRI) in forecasting the recurrence of acute pancreatitis (AP). A total of 201 patients with first-episode of acute pancreatitis were enrolled retrospectively (140 in the training cohort and 61 in the testing cohort), with 69 and 30 patients who experienced recurrence in each cohort, respectively. Quantitative image feature extraction was obtained from MR contrast-enhanced late arterial-phase images. The optimal radiomics features retained after dimensionality reduction were used to construct the radiomics model through logistic regression analysis, and the clinical characteristics were collected to construct the clinical model. The nomogram model was established by linearly integrating the clinically independent risk factor with the optimal radiomics signature. The five best radiomics features were determined by dimensionality reduction. The radiomics model had a higher area under the receiver operating characteristic curve (AUC) than the clinical model for estimating the recurrence of acute pancreatitis for both the training cohort (0.915 vs. 0.811, p  = 0.020) and testing cohort (0.917 vs. 0.681, p  = 0.002). The nomogram model showed good performance, with an AUC of 0.943 in the training cohort and 0.906 in the testing cohort. The radiomics model based on CE-MRI showed good performance for optimizing the individualized prediction of recurrent acute pancreatitis, which provides a reference for the prevention and treatment of recurrent pancreatitis.

The conventional hand-picking of tea buds is inefficient and leads to inconsistent quality. Innovations in tea bud identification and automated grading are essential for enhancing industry competitiveness. Key breakthroughs include detection accuracy and lightweight model deployment. Traditional image recognition struggles with variable weather conditions, while high-precision models are often too bulky for mobile applications. This study proposed a lightweight YOLOV5 model, which was tested on three tea types across different weather scenarios. It incorporated a lightweight convolutional network and a compact feature extraction layer, which significantly reduced parameter computation. The model achieved 92.43% precision and 87.25% mean average precision (mAP), weighing only 4.98 MB and improving accuracy by 6.73% and 2.11% while reducing parameters by 2 MB and 141.02 MB compared to YOLOV5n6 and YOLOV5l6. Unlike networks that detected single or dual tea grades, this model offered refined grading with advantages in both precision and size, making it suitable for embedded devices with limited resources. Thus, the YOLOV5n6_MobileNetV3 model enhanced tea bud recognition accuracy and supported intelligent harvesting research and technology.

In order to promote sugarcane pre-cut seed good seed and good method planting technology, we combine the development of sugarcane pre-cut seed intelligent 0p99oposeed cutting machine to realize the accurate and fast identification and cutting of sugarcane stem nodes. In this paper, we proposed an algorithm to improve YOLOv4-Tiny for sugarcane stem node recognition. Based on the original YOLOv4-Tiny network, the three maximum pooling layers of the original YOLOv4-tiny network were replaced with SPP (Spatial Pyramid Pooling) modules, which fuse the local and global features of the images and enhance the accurate localization ability of the network. And a 1×1 convolution module was added to each feature layer to reduce the parameters of the network and improve the prediction speed of the network. On the sugarcane dataset, compared with the Faster-RCNN algorithm and YOLOv4 algorithm, the improved algorithm yielded an mean accuracy precision (MAP) of 99.11%, a detection accuracy of 97.07%, and a transmission frame per second (fps) of 30, which can quickly and accurately detect and identify sugarcane stem nodes. In this paper, the improved algorithm is deployed in the sugarcane stem node fast identification and dynamic cutting system to achieve accurate and fast sugarcane stem node identification and cutting in real time. It improves the seed cutting quality and cutting efficiency and reduces the labor intensity.

VEXAS syndrome is a rare and severe systemic inflammatory disorder caused by somatic mutations in the X-linked UBA1 gene, primarily affecting men. Since its initial description in 2020, it has been recognized for its complex clinical phenotype and tendency to be misdiagnosed. We report a case of a 77-year-old Chinese man diagnosed with VEXAS syndrome. The patient presented with recurrent fever, elevated inflammatory markers, anemia (decreased hemoglobin), multifocal interstitial pneumonia, and cardiac arrhythmia. On the day of admission, the patient developed rapidly progressive respiratory distress with a marked worsening of inflammatory markers. While providing supportive symptomatic treatment, we performed next-generation sequencing (NGS), 18F-fluorodeoxyglucose positron emission tomograph–computed tomography (18FDG PET-CT), and whole-exome sequencing. Based on a presumed clinical diagnosis of small-vessel vasculitis, the patient was empirically treated with glucocorticoids combined with intravenous immunoglobulin (IVIG). Once the patient’s condition improved, whole-exome sequencing revealed a UBA1 splice-site mutation (c.118-1G>C), consistent with VEXAS syndrome. After reviewing related reports, we subsequently performed a bone marrow aspiration, which showed characteristic cytoplasmic vacuolization in myeloid precursor cells. Retrospective history review revealed that the patient had developed skin lesions one year before the onset of fever. The clinical presentation of VEXAS syndrome is heterogeneous and associated with high mortality. It can be difficult to distinguish VEXAS from other autoimmune diseases, hematologic malignancies, and infectious diseases. In this case, given the patient’s rapidly progressive interstitial pneumonia, we used NGS and 18FDG PET-CT to exclude infection and hematologic malignancy, and focused on empirical treatment for presumed small-vessel vasculitis, which quickly halted disease progression. Meanwhile, whole-exome sequencing ultimately identified the underlying cause.

Background and AimsChitinase 3-like protein 1 (CHI3L1) is a marker of liver fibrosis produced mainly by hepatic macrophages. However, few studies have assessed the relationship between CHI3L1 and liver fibrosis in autoimmune liver diseases (AILDs). We aimed to explore the diagnostic value of CHI3L1 for liver fibrosis in AILDs and to compare its application differences between AILDs and chronic hepatitis B (CHB) patients.MethodsThe fibrotic group was defined as liver stiffness measurement (LSM) > 9.70kPa. Serum CHI3L1 levels were measured by ELISA in 78 AILDs patients, 65 chronic hepatitis B patients. The diagnostic accuracy was evaluated by the area under the receiver operating characteristic curve (AUROC).ResultsSerum CHI3L1 levels in AILDs patients were positively correlated with LSM (r=0.750, p < 0.001). The AUROC for serum CHI3L1 in identifying significant liver fibrosis was 0.939 (95% CI: 0.891 - 0.988), which was higher than that of other non - invasive fibrosis scores (APRI, FIB - 4, GPR, AAR, NLP, and PLR). At the optimal cutoff value of 86.84 ng/mL, the sensitivity and specificity were 92.9% and 83.3%, respectively. Furthermore, in patients with no significant difference in LSM, serum CHI3L1 levels were higher in the autoimmune liver disease group than in the CHB group.ConclusionSerum CHI3L1 is an effective non-invasive indicator for assessing liver fibrosis in AILDs patients and may vary in different etiologies.

Sugar content is an essential indicator for evaluating crisp pear quality and categorization, being used for fruit quality identification and market sales prediction. In this study, we paired a support vector machine (SVM) algorithm with genetic algorithm optimization to reliably estimate the sugar content in crisp pears. We evaluated the spectral data and actual sugar content in crisp pears, then applied three preprocessing methods to the spectral data: standard normal variable transformation (SNV), multivariate scattering correction (MSC), and convolution smoothing (SG). Support vector regression (SVR) models were built using processing approaches. According to the findings, the SVM model preprocessed with convolution smoothing (SG) was the most accurate, with a correlation coefficient 0.0742 higher than that of the raw spectral data. Based on this finding, we used competitive adaptive reweighting (CARS) and the continuous projection algorithm (SPA) to select key representative wavelengths from the spectral data. Finally, we used the retrieved characteristic wavelength data to create a support vector machine model (GASVR) that was genetically tuned. The correlation coefficient of the SG–GASVR model in the prediction set was higher by 0.0321 and the root mean square prediction error (RMSEP) was lower by 0.0267 compared with those of the SG–SVR model. The SG–CARS–GASVR model had the highest correlation coefficient, at 0.8992. In conclusion, the developed SG–CARS–GASVR model provides a reliable method for detecting the sugar content in crisp pear using hyperspectral technology, thereby increasing the accuracy and efficiency of the quality assessment of crisp pear.

Background This study evaluated the diagnostic value of complement levels in neonatal sepsis and their roles in disease progression. Methods This diagnostic accuracy study, conducted in Guangdong Province, China (January 2021-February 2022), involved 41 neonates with sepsis and 41 controls matched by sex and gestational age. Cases included neonates with culture-positive or clinically diagnosed sepsis, while controls consisted of neonates hospitalized for non-septic conditions, confirmed by negative blood cultures. Ten complement components (C1q, C3, C3c, C3b, C4, C5, C5a, H, B, mannose-binding lectin [MBL]) were quantified using residual specimens from routine clinical tests. Descriptive statistics, logistic regression, ROC curve analysis, and correlation assessments were introduced in this study. Results The neonatal sepsis group had significantly higher levels of C3c (0.68 vs. 0.49 ng/mL, P  = 0.007) and MBL (518.81 vs. 397.06 pg/mL, P  < 0.001) compared to controls. In contrast, C5a levels were significantly lower in neonates with sepsis (51.18 vs. 57.25 ng/mL, P  = 0.042). C5a demonstrated limited individual performance (AUC = 0.63, 95% CI: 0.51–0.75; sensitivity = 65.9%, specificity = 65.9% at 55.63 ng/mL), while MBL showed moderate accuracy (AUC = 0.75, 95% CI: 0.64–0.85; specificity = 90.2%, sensitivity = 53.7% at 512.86 pg/mL). Notably, their combined C5a + MBL indicator achieved exceptional discrimination (AUC = 0.92, 95% CI: 0.85–0.99) with 85.4% sensitivity and 97.6% specificity, yielding 97.2% positive predictive value (PPV) and 87.0% negative predictive value (NPV).Positive correlations were found between C4 levels and C-reactive protein (CRP), and C3 levels and neutrophil percentage (Neut%), while negative correlations were observed between C5 and MBL levels and total cholesterol (TCH). Conclusions This study highlights the diagnostic significance of combined C5a + MBL indicator in neonatal sepsis and underscores the association between complement levels and disease progression.

Background This study aimed to analyze the effects of anterior descending mandible (ADM) and free superficial musculoaponeurotic system (SMAS) folding flaps on post-parotidectomy facial depression. Methods This retrospective study examined the effects of sex, age, surgical sample size, method, duration, and blood loss on postoperative complications in 65 patients. Results No significant differences involving sex, age, or sample size for surgical resection were observed between the two groups. The incidence of Frey syndrome ( P  = 0.175) and the locations of facial nerve injuries and tumors were not significantly different between the two groups. However, a statistically significant difference was observed in postoperative facial depression between the groups ( P  = 0.045, P  < 0.05). No significant difference was found between facial nerve injury and Frey’s sign in subgroup analysis of facial depression deformities. Within the facial depression group, tumor locations were significantly different ( P  = 0.021, P  < 0.05). In the cases of facial depression after partial parotid resection, no significant difference was observed between ADM implantation and SMAS flap placement. A significant difference was noted between the ADM implantation and SMAS flap groups in the total parotidectomy group ( P  = 0.046 and P  < 0.05, respectively). Conclusion Women are more likely to experience facial depression after parotid surgery. Facial depression is most likely to occur after parotid resection if the tumor is located in the deep lobes of the parotid gland. The use of SMAS flaps can prevent facial depression, and both ADM and SMAS flaps can prevent Frey’s syndrome. Partial parotid resection reduces the risk of facial nerve injury and facial depression.