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With the continuous improvement of our country’s education level, the electrical design of teaching buildings has been paid more and more attention. If the safety measures of building facilities are not perfect, once there is a safety problem, it will inevitably bring irreparable huge losses to the personal safety of students and teachers and the property safety of the school. At the same time, taking into account school expenses, economy, energy saving is also a part that cannot be ignored, which requires full consideration of safety, environmental protection and other factors in the process of electrical system design.This paper mainly studies the electrical design of the teaching building, on the basis of strict compliance with national standards and norms, taking into account safety, economy, energy saving and other design factors, independently designed the power supply and distribution system, lighting system, lightning protection system, to ensure the safe operation of building electrical facilities and energy saving operation.

The contractile protein MYH9 (non-muscle myosin IIA) is an actin-binding protein that plays a fundamental role in cell adhesion, migration, and division. However, its distinct role in colorectal cancer (CRC) still remains unidentified. In this study, we detected significant MYH9 overexpression in CRC samples compared with paired normal tissues using western blotting and tissue microarray immunohistochemistry (IHC). Moreover, analysis of patient clinical information demonstrated that MYH9 overexpression was strongly correlated with lymph node metastasis and poor overall survival. Endogenous overexpression of MYH9 enhanced the ability of cell proliferation and migration in vitro, and accelerated CRC growth in mouse models. Silencing of MYH9 revealed repressive effects on CRC cells in vitro and in vivo. Furthermore, primary biomechanics that involved MAPK/AKT signaling mediated epithelial-mesenchymal transition (EMT) was uncovered underlying MYH9 dependent cell behaviors. Collectively, our data showed that MYH9 significantly promoted tumorigenesis by regulating MAPK/AKT signaling, and was remarkably correlated with poor prognosis in CRC. MYH9 may thus be a novel biomarker and drug target in the diagnosis and treatment of CRC.

AlGaN/GaN Schottky barrier diodes (SBDs) with a low work function W anode and a high work function Ni field plate on sapphire substrates are investigated in this Letter. The low work function metal W leads to the low turn-on voltage, while the high work function metal Ni maintains the leakage current and increases the breakdown voltage. An ultralow turn-on voltage of 0.23 V determined by the positive fixed charge at the AlGaN/Si3N4 interface is achieved in this W/Ni SBD. More importantly, benefitting from the dual-metal anode/field plate structure, the on-resistance decreases from 7.99 to 5.53 Ω mm, and the breakdown voltage increases from 493 to 1219 V. In addition, the turn-on voltage decreases less than 57%, and the on-resistance increases less than 186% at a high temperature up to 120 °C. The W/Ni SBD with a cathode–anode length of 18 μm and a field plate length of 4 μm shows a low turn-on voltage of 0.23 V, a low on-resistance of 5.53 Ω mm, a leakage current of 0.65 mA/mm, and a high breakdown voltage of 1.21 kV, indicating a great potential for power electronic applications. [ABSTRACT FROM AUTHOR]

In recent years, deep-learning methods have significantly improved the classification results in the field of plant-leaf recognition. However, limited by the model input, the original image needs to be compressed to a certain size before it can be input into the convolutional neural network. This results in great changes in the shape and texture information of some samples, thus affecting the classification accuracy of the model to a certain extent. Therefore, a minimum enclosing quadrate (MEQ) method is proposed to standardize the sample datasets. First, the minimum enclosing rectangle (MER) of the leaf is obtained in the original image, and the target area is clipped. Then, the minimum enclosing quadrate of the leaf is obtained by extending the short side of the rectangle. Finally, the sample is compressed to fit the input requirements of the model. In addition, in order to further improve the classification accuracy of plant-leaf recognition, an EC-ResNet50 model based on transfer-learning strategy is proposed and further combined with the MEQ method. The Swedish leaf, Flavia leaf, and MEW2012 leaf datasets are used to test the performance of the proposed methods, respectively. The experimental results show that using the MEQ method to standardize datasets can significantly improve the classification accuracy of neural networks. The Grad-CAM visual analysis reveals that the convolutional neural network exhibits a higher degree of attention towards the leaf surface features and utilizes more comprehensive feature regions during recognition of the leaf samples processed by MEQ method. In addition, the proposed MEQ + EC-ResNet50 method also achieved the best classification results among all the compared methods. This experiment provides a widely applicable sample standardization method for leaf recognition research, which can avoid the problem of sample deformation caused by compression processing and reduce the interference of redundant information in the image to the classification results to a certain degree. [ABSTRACT FROM AUTHOR]

Purpose: This study was designed to develop and validate a machine learning-based, multimodality fusion (MMF) model using 18F-fluorodeoxyglucose (FDG) PET/CT radiomics and kernelled support tensor machine (KSTM), integrated with clinical factors and nuclear medicine experts' diagnoses to individually predict peritoneal metastasis (PM) in advanced gastric cancer (AGC). Methods: A total of 167 patients receiving preoperative PET/CT and subsequent surgery were included between November 2006 and September 2020 and were divided into a training and testing cohort. The PM status was confirmed via laparoscopic exploration and postoperative pathology. The PET/CT signatures were constructed by classic radiomic, handcrafted-feature-based model and KSTM self-learning-based model. The clinical nomogram was constructed by independent risk factors for PM. Lastly, the PET/CT signatures, clinical nomogram, and experts' diagnoses were fused using evidential reasoning to establish the MMF model. Results: The MMF model showed excellent performance in both cohorts (area under the curve [AUC] 94.16% and 90.84% in training and testing), and demonstrated better prediction accuracy than clinical nomogram or experts' diagnoses (net reclassification improvement p < 0.05). The MMF model also had satisfactory generalization ability, even in mucinous adenocarcinoma and signet ring cell carcinoma which have poor uptake of 18F-FDG (AUC 97.98% and 89.71% in training and testing). Conclusions: The 18F-FDG PET/CT radiomics-based MMF model may have significant clinical implications in predicting PM in AGC, revealing that it is necessary to combine the information from different modalities for comprehensive prediction of PM. [ABSTRACT FROM AUTHOR]

Aims: To develop a nomogram to provide a screening tool for recognising patients at risk of post‐operative pain undergoing abdominal operations. Background: Risk prediction models for acute post‐operative pain can allow initiating prevention strategies, which are valuable for post‐operative pain management and recovery. Despite the increasing number of studies on risk factors, there were inconsistent findings across different studies. In addition, few studies have comprehensively explored predictors of post‐operative acute pain and built prediction models. Design: A prospective observational study. Methods: A total of 352 patients undergoing abdominal operations from June 2022 to December 2022 participated in this investigation. A nomogram was developed for predicting the probability of acute pain after abdominal surgery according to the results of binary logistic regression. The nomogram's predictive performance was assessed by discrimination and calibration. Internal validation was performed via Bootstrap with 1000 re‐samplings. Results: A total of 139 patients experienced acute post‐operative pain following abdominal surgery, with an incidence of 39.49%. Age <60, marital status (unmarried, divorced, or widowed), consumption of intraoperative remifentanil >2 mg, indwelling of drainage tubes, poor quality sleep, high pain catastrophizing, low pain self‐efficacy, and PCIA not used were predictors of inadequate pain control in patients after abdominal surgery. Using these variables, we developed a nomogram model. All tested indicators showed that the model has reliable discrimination and calibration. Conclusions: This study established an online dynamic predictive model that can offer an individualised risk assessment of acute pain after abdominal surgery. Our model had good differentiation and calibration and was verified internally as a useful tool for risk assessment. Relevance to Clinical Practice: The constructed nomogram model could be a practical tool for predicting the risk of experiencing acute post‐operative pain in patients undergoing abdominal operations, which would be helpful to realise personalised management and prevention strategies for post‐operative pain. Reporting Method: The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines were adopted in this study. Patient or Public Contribution: Before the surgery, research group members visited the patients who met the inclusion criteria and explained the purpose and scope of the study to them. After informed consent, they completed the questionnaire. The patients' pain scores (VAS) were regularly assessed and documented by the bedside nurse for the first 3 days following surgery. Other information was obtained from medical records. [ABSTRACT FROM AUTHOR]