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The hot isostatic pressing process is a usual powder Ti2AlNb alloy preparation method to deeply study the influence of factors such as the powder-making process on the properties of Ti2AlNb powder alloy.Ti2AlNb pre-alloyed powders are prepared by plasma rotating electrode process and electrode induction melting gas atomization， respectively， and their mixed powders are characterized. Ti2AlNb alloy is prepared using a hot isostatic pressing process.The effects of the powder-preparing process， porosity， and inclusion on the microstructure and mechanical properties of the Ti2AlNb alloy are investigated. Optimized processes are employed for the forming of various Ti2AlNb powder metallurgy components. Experimental results show that the powder-making processes affect the durability of the powder alloy， the pore defects caused by slight capsule gas leakage significantly reduce the mechanical properties of the powder Ti2AlNb alloy， and the inclusions obviously affect the consistency and stability of the room-temperature tensile properties of the powder alloy.

［Objective］ Difficulty in easily obtaining fault data of various rail transit equipment leads to insufficient data to support the research on machine intelligence algorithms such as fault diagnosis and prediction. In order to meet the urgent need of intelligent rail transit operation and maintenance for a large amount of training data, it is necessary to design and verify the simulation data generator (hereinafter abbreviated as SD generator) of rail transit switch machine. ［Method］ The characteristics of S700K type switch machine power curves under normal operation and gradual fault conditions are analyzed, and causes of the faults are discussed. By comparing two simulation data generation methods, a switch machine SD generator is designed based on the Borderline-Smote algorithm. Through building a platform for SD generator, and using the time series features of learning the power data by LSTM (long short-term memory) prediction model, three characteristics of the generated gradual fault power data such as the crest factor, standard deviation, and variance are tested. ［Result & Conclusion］ The LSTM prediction model trained on the power data generated by SD generator can predict the power change trend of the S700K type switch machine. The root mean square errors of crest factor, standard deviation, and variance calculated by the LSTM prediction model and the periodic replication method are 0.335 5, 0.023 9, and 0.024 1 respectively. The relatively small errors prove the authenticity and feasibility of SD generator.

Hypersonic aircraft encounter challenges in adapting to complex external environments during missions. Conventional fixed-wing aircraft have limited optimal aerodynamic performance under specific conditions. In contrast，morphing aircraft can adapt to various mission environments by altering their aerodynamic shape to control their flight characteristics. Based on numerical simulations，the changes in aerodynamic characteristics of a hypersonic folding-wing aircraft before and after wing folding are analyzed in this paper for revealing the effects of wing folding on aerodynamic performance. Meanwhile，the influence of aeroelasticity in the wing on the aerodynamic characteristics of the aircraft during the wing folding process is discussed. The results show that the wing folding process has a little effect on the drag coefficient but significantly affects the lift coefficient and lift-to-drag ratio. Moreover，due to the presence of shock waves at the wingtips，stress is mainly concentrated in these areas，resulting in a relatively small impact of aeroelasticity on the aircraft's aerodynamic behavior.

AIM: To investigate the role of histidine-rich glycoprotein(HRG)in the neovascularization of diabetic retinopathy in rats.METHODS: Streptozocin(STZ)-induced diabetic Sprague-Dawley(SD)rats were utilized as an experimental model, the protein expression of HRG and vascular endothelial growth factor(VEGF)in the retinas of normal(Wild type, WT)and diabetic(diabetic mellitus, DM)groups was detected using Western blot(WB). The protein expression of HRG in high-glucose-induced human retinal microvascular endothelial cells(hRMECs)was verified by WB after transfection with HRG small interfering RNA(siRNA)low-expression sequences. The optimal si-HRG#298 sequence was selected for further experiments. In the animal experiment, HRG gene silencing was achieved using an adeno-associated virus(AAV)vector, with AAV2-sh-NC and AAV2-sh-HRG#298 serving as the HRG gene silencing group and the HRG empty vector control group, respectively. The protein expression of HRG and VEGF in each group was then detected by WB following the verification of HRG protein expression. Retinal structural changes were observed by HE staining, and neovascularization changes were observed by PAS staining.RESULTS: HE staining found that the retinal structure in the DM group was disordered, the number of cells in the ganglion cell layer decreased, the number of cells in the inner and outer nuclear layers decreased, and the total retinal thickness also decreased(P

Objective Currently, wireless environment detection for urban rail transit vehicle-wayside communication primarily relies on manual testing with instruments, which is labor-intensive and inefficient. To meet the needs for intelligent and routine monitoring of the vehicle-wayside wireless communication environment, it is necessary to construct a wireless fingerprint library for this issue to enable the real-time monitoring. Method The conceptualization and process of establishing the wireless fingerprint library for vehicle-wayside communication is elaborated. The wireless reception areas of metro lines are divided into numerous grids at appropriate intervals. Wireless monitoring equipment installed on metro trains collects real-time data on the vehicle-wayside communication wireless environment for each grid. By employing various encoding methods and introducing Mahalanobis distance, the characteristic data are processed, the wireless feature information for each grid is extracted and then recorded into the corresponding grid area to form wireless fingerprint library. The library utilizes the DeepFS algorithm, enabling adaptive updates of the wireless feature data for each grid. The application of the wireless fingerprint library in urban rail transit vehicle-wayside communication is illustrated through specific examples. Result & Conclusion By comparing real-time monitoring data with the wireless fingerprint information in the library, abnormalities in the vehicle-wayside wireless communication environment can be promptly detected. This effectively enhances the maintenance level of urban rail transit vehicle-wayside wireless communication environment.

Objective The current secondary positioning system of urban rail transit train beacons has a positioning accuracy of 300 meters, failing to meet the high-precision positioning requirements of wireless environment monitoring grid. Additionally, GPS (global positioning system) and the Beidou Satellite Navigation System are not suitable for underground rail positioning. Therefore, it is necessary to conduct in-depth research on continuous positioning technology for urban rail transit CBTC (communication-based train control) wireless environment monitoring grid. Method Building on the existing urban rail transit train beacon secondary positioning system, an inertial navigation technology enhanced by beacon-assisted training is adopted. By utilizing a multi-sensor fusion positioning algorithm, the sensor data from the inertial navigation module is integrated with the beacon data from the ATS (automatic train supervision) system, thus achieving the continuous positioning of wireless environment monitoring grid in urban rail transit. Additionally, a regression prediction algorithm is used for offline model training to enhance the accuracy of continuous positioning for the urban rail transit wireless environment monitoring grid. Result & Conclusion The proposed methods can achieve continuous and real-time positioning of wireless environment monitoring grid, supporting CBTC wireless environment monitoring grid in tunnels, beneath elevated structures, and in complex environments where metro train passing through areas with tall buildings and tree cover. The positioning accuracy reaches 10 m, meeting the positioning requirements of the CBTC wireless environment monitoring system, and fully ensuring the safety of the urban rail transit CBTC signaling system.

[Objective] Wind-water erosion crisscross region in Loess Plateau is a typical fragile ecosystem. Biological soil crusts (BSCs) are widely distributed in this region and play key roles in the accumulation and transformation of soil organic carbon (SOC). However, the effect of BSCs succession and microorganisms on enzymes related to organic carbon conversion is still unclear. [Methods] In this manuscript, SOC contents, microbial number and enzymes related with carbon conversion in different BSCs types were investigated. The relationships among SOC, microbial number and enzume activities related with carbon conversion were also analyzed by SEM model. [Results] (1) BSCs succession significantly inceased SOC content and enzyme activities related with carbon conversion. SOC and enzyme activities in BSCs layer(0—2 cm) were significantly higher than those in bare sand 2—5 cm soil layer (p

Milk is an excellent medium for the growth of microorganisms, which is easily contaminated by microorganisms from the environment. Psychrophilic bacteria are the most common microorganisms in cold-stored raw milk. Moreover, psychrophilic bacteria produce heat-resistant proteases and lipases in low-temperature environments, which remain active even after heat treatment, and decompose proteins and fats in liquid milk during storage, thereby affecting the quality of milk. The counting method for psychrophilic bacteria in milk specified by the agricultural industry standard of China takes 10 days to culture samples at 6.5 ℃ for 10 days, which is time-consuming and has low application value in actual production. Therefore, the development of detection technologies for psychrophilic bacteria and their heat-resistant enzymes is very important for ensuring the quality of milk and dairy products. This article reviews a variety of methods for the identification and analysis of psychrophilic bacteria in milk, including polymerase chain reaction (PCR), sequencing, fluorescence in situ hybridization, and loop-mediated isothermal amplification. It provides a reference for the development of new detection technologies for psychrophilic bacteria in milk.

The pancreas has always been one of the most challenging parts in medical image segmentation due to its complex anatomical structure and complex surrounding environment. Aiming at the above problems, a deep learning segmentation model combining dual decoding and global attention upsampling module (DGANet) is proposed. The model consists of an encoder and two decoders, where the latter realizes the full utilization of different depth feature information. The model applies the global attention upsampling module and high-level rich semantic information to guide the low-level selection of more accurate feature information. The data set provided by Changhai Hospital was used for experiments. The results showed that the average Dice similarity coefficient was 86.28%, Intersection-over-Union (IoU) was 0.77, and Hausdorff distance (HD) was 7.7 mm. The data confirmed the clinical value of this model in segmenting pancreatic cystic tumors.

Currently, the digital transformation of the financial industry in China has moved from multiple breakthroughs into a new stage of deepening and high-quality development, which necessitates a collaborative governance mechanism that coordinates multiple parties including governments, enterprises, and individuals. In view of the new, complex, and potentially harmful financial risks arising in the context of digital transformation, the financial industry urgently needs to improve its financial risk monitoring and earlywarning capabilities to effectively protect financial security. This study analyzes the progress of digital transformation of the financial industry as well as the implications and characteristics of new financial risks through literature research and theoretical analysis. It also investigates the mainstream financial risk monitoring and early-warning technologies in China and abroad, and clarifies the prominent problems regarding risk characterization and recognition, transmission and tracking, and inference assessment. Furthermore, we propose the overall framework, innovative research methods, and improving paths for the financial risk monitoring and earlywarning system in the context of digital transformation. This study reveals that financial risks have new characteristics in the context of digital transformation, such as faster update and iteration, higher risk frequency, and stronger concealment. Existing financial risk monitoring and early-warning technologies have numerous deficiencies and face multiple challenges in dealing with new financial risks, such as difficulty in characterizing, tracking, and assessing risks. Therefore, to improve the financial risk prevention capability of China and guarantee national financial security, it is proposed to develop cross-industry sharing standards for financial data, establish a knowledge representation paradigm and a cross-industry transmission mechanism of financial risks, and build a large model regarding financial risk monitoring and early warning.

Based on the CICE sea ice model and the PDAF parallel data assimilation framework, this paper uses the local error subspace transform Kalman filter method (LESTKF) to assimilate the sea ice concentration, sea ice thickness and sea ice freeboard data into the model, and designs experiments to study the improvement of multi-parameter assimilation on the simulation of Arctic sea ice concentration and range. The results show that data assimilation has a good improvement effect on the simulation of Arctic sea ice concentration and range. The average deviation, root mean square error and mean absolute error of the assimilation experiment are significantly reduced compared with the control experiment. The assimilation experiment improves the simulation of sea ice concentration and range most obviously in summer. Multi-parameter assimilation can improve the prediction accuracy and reliability of Arctic sea ice change.

Objective To evaluate the impact of peripheral nerve aggression (PNI) on prognosis in patients with stage Ⅰ-Ⅲ early-onset colorectal cancer (EO-CRC) and to create a PNI-based nomogram prediction model. Methods This study analyzed data on 5 920 EO-CRC patients aged 50 years and younger enrolled in the U.S. Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. The patients were divided into the training group (n=4 164) and the validation group (n=1 756) by a ratio of 7∶3. Kaplan-Meier survival curve was drawn and Cox proportional risk model was used for univariate and multivariate prognosis analysis. Based on the significant variables of the multivariate model, a nomogram was constructed, and the predictive ability was evaluated by receiver operating characteristic curve (ROC), calibration curve and decision curve analysis (DCA). Results Compared with PNI negative patients, PNI positive patients tended to be young, lesions located in the left colon, with poorer differentiation, higher TNM stage, chemotherapy intervention, and higher CEA level (P＜0.05). Kaplan-Meier curve suggested that PNI positive group had a worse prognosis than PNI negative group. Cox univariate analysis suggested that age, sex, degree of tumor differentiation, TNM stage, T stage, N stage, whether to receive radiation therapy, whether to receive chemotherapy, CEA level, maximum tumor diameter and PNI status were variables affecting the survival and prognosis of patients. Multivariate analysis showed that PNI was an independent prognostic indicator for patients with stage Ⅰ to Ⅲ EO-CRC (HR=1.73, 95%CI: 1.44-2.08, P＜0.01). The area under ROC curve of nomogram model based on PNI was more than 0.7 in both 3-year and 5-year lifetime. Conclusion PNI can be used as an independent prognostic marker in patients with stage Ⅰ-Ⅲ EO-CRC, and PNI-based nomogram model can be used as an effective prognostic tool.

[Objective] Achieving non-destructive testing of sub-healthy watercore apples. [Methods] First, the logarithmic function method and the power function method proposed by this study were used to correct the sample spectra. Subsequently, the corrected data were converted into different images of gramian angular difference field (GADF), gramian angular summation field (GASF), markov transition field (MTF), recurrence plot (RP), symmetric dot pattern (SDP). Then, the ResNet50 network model with the convolutional block attention module (CBAM) was used to mine the deep image features related to the degree of watercore, which were downscaled by the t-distributed stochastic neighbor embedding (t-SNE) method and analyzed by clustering to determine the most suitable image transformation method. Finally, the most suitable image features were inputted into the improved particle swarm algorithm (IPSO) optimized support vector machine (SVM), extreme learning machine (ELM), k-nearest neighbour (KNN) and random forest (RF) classifier for the three-class classification of watercore apple. [Results] The results showed that the power function method was better than the logarithmic function method in eliminating the effect of diameter on the transmission spectrum. The silhouette coefficient (SC), the calinski harabasz score (CHS), and the davies-bouldin index (DBI) were 0.93, 0.88 and 0.24 after visualization of the image features in the GADF, better than the rest of the image transformation methods. ResNet50-IPSO-ELM achieved the highest classification accuracy of 96.8%. The overall discrimination accuracy of the three watercore classes apples in the test set reached 96.3%, and the stable precision (SP), stable recall (SR), and stable F1-score (SF) were 87.2%, 95.8%, and 92.3%, respectively. [Conclusion] The model maintains a high classification accuracy for the majority class of apples without watercore and healthy apples with watercore and a high discriminatory ability for the minority class of sub-healthy apples with watercore.

Due to the high water content of fresh wet noodles, it is easy for microorganisms to breed and spoil, and quickly loses its edible value. The effect of composite biopreservatives such as chitosan, Nisin and tea polyphenols and their formula on shelf-life and storage quality of fresh wet noodles was investigated in this study. The results showed that the prescription 0.25% chitosan, 0.025% Nisin and 0.012% tea polyphenols can prolong the fresh wet noodles shelf-life to 48 hours under room temperature and relative humidity 60%, and the total number of colonies was less than 5.0 lg (CFU/g), which complied with national standards. However, under the same conditions, the total number of colonies of CK group have exceeded the national standard within 12 hours and the water absorption rate decreased by 26.07% whose edible value was lost.

AIM: To study the early outcomes of anterior segment parameters after implantation of an implantable collamer lens with a central hole(ICL V4c)in patients with high myopia.METHODS:A total of 82 cases(160 eyes)with high myopia, including 42 males(82 eyes)and 40 females(78 eyes), aged 26.0±4.6(21 to 37)years, who underwent ICL V4c implantation at our institution from February 2019 to September 2022 and were followed up for 1 a, were included. The general characteristics of the anterior segment of the eye were measured preoperatively: spherical equivalent, mean horizontal corneal curvature, white-to-white(WTW), and axial length(AL); intraocular pressure(IOP), endothelial cell density(ECD), central anterior chamber depth(CACD), anterior chamber volume(ACV)and anterior chamber angle(ACA)were measured preoperatively and at 1 d, 1 wk, 1, 3 and 6 mo postoperatively. Furthermore, the distance from the centre of the posterior surface of the ICL V4c optical zone to the anterior surface of the lens(vault)was measured at 1 d, 1 wk, 1, 6 mo, and 1 a after surgery.RESULTS: The mean preoperative spherical equivalent of the patients was -7.56±2.55 D, mean horizontal corneal curvature was 42.89±1.47 D, WTW was 11.64±0.37 mm, and AL was 26.64±0.93 mm. The baseline IOP was 15.97±2.13 mmHg, and the differences in IOP at each time point after ICL V4c implantation compared to preoperative were not statistically significant(F=0.875, P=0.504); ECD was 2 989.30±140.78 cells/mm2 at baseline, and ECD at 6 mo after ICL V4c implantation was not statistically significant compared with preoperative ECD(t=1.475, P=0.142); CACD was 3.19±0.21 mm at baseline, and ACV was 210.30±27.7 mm3, and CACD and ACV were significantly lower than preoperative at all postoperative time points(F=111.10, 288.38, all P0.05).CONCLUSION: ICL V4c has certain safety and efficiency in 1 a postoperative follow-up, and the parameters of the anterior segment of the eye stabilized in the early period.

This study aims to classify and differentiate mucinous and serous cystic neoplasms of the pancreas using a multi-source feature classification model based on deep learning for preoperative auxiliary diagnosis. Deep learning features and radiomics features were extracted from segmented images using deep learning and radiomics technology, respectively. Clinical features were also evaluated and quantified. LASSO (least absolute shrinkage and selection operator) and cross-validation methods were applied to screen the features, and two multi-source feature models were constructed: the radiomics combined with deep learning (RAD_DL) model and the clinical feature combined with RAD_DL (Clinical_RAD_DL) model. Traditional radiomics (RAD) and deep learning (DL) models were used as controls. SVM (support vector machine), ADAboost (adaptive boosting), Random Forest, and Logistic were selected for classification. The Clinical_RAD_DL feature model shows the best classification performance, with the accuracy of 0.923 1, recall rate of 0.882 4, precision of 0.882 0, F1-score of 0.882 2, and AUC value of 0.912 6. The experimental results indicate that the multi-source feature classification model based on deep learning has good performance in classifying pancreatic serous cystic neoplasms and pancreatic mucinous cystic neoplasms, and can assist clinical accurate diagnosis and treatment.

Objective·To investigate the expression and clinical significance of geranylgeranyl diphosphate synthase1 (GGPS1) in lung squamous cell carcinoma (LUSC) by bioinformatics and immunohistochemistry.Methods·Firstly, the transcriptome data of LUSC tissues and paired normal tissues were downloaded from UCSC Xena platform. The data were standardized and differentially expressed by R language, and verified by UALCAN database. UALCAN and LinkedOmics databases were used to analyze the relationship between GGPS1 expression and clinicopathological features in LUSC patients. The Kaplan-Meier Plotter database was used to explore the effect of GGPS1 expression on prognosis in LUSC patients. The least absolute shrinkage and selection operator (LASSO) regression analyses were applied to screen gene correlation coefficients and risk scores. The diagnostic value of GGPS1 for LUSC was evaluated by nomogram and calibration curve. The protein-protein interaction (PPI) network of GGPS1 was constructed by using STRING and GeneMANIA databases. R language was used to select the differential genes related to GGPS1, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed. The expression of GGPS1 in LUSC patients was detected by immunohistochemistry, and its correlation with clinicopathological features and prognosis was analyzed.Results·Through the TIMER2.0 database, it was found that GGPS1 expression was increased in most tumors and was highly expressed in LUSC. The expression of GGPS1 in LUSC was higher than that in adjacent tissues in UCSC Xena and UALCAN databases (both P

In recent years, classical statistical models and machine learning models have been developed in parallel in the field of dam safety monitoring. However, there are some deficiencies in the predictive power of the former and the theoretical explanation of the latter. In this study, multiple linear regression, stepwise regression, and random forest algorithm were used to establish models for the crack opening degree of a concrete gravity dam based on the monitoring data of the crack opening degree of the concrete gravity dam. The results show that three models for predicting crack opening degree are successfully established based on the crack opening degree dataset measured in 2022. The random forest model has the best predictive ability (determination coefficient (R2) is 0.995; root mean square error (RMSE) and mean absolute error (MAE) are 0.174 mm and 0.124 mm, respectively), followed by the stepwise regression model (R2 is 0.989; RMSE and MAE are 0.192 mm and 0.151 mm). Three models both indicate that the temperature component is the main factor affecting the crack opening degree of the concrete gravity dam; by decomposing the multiple linear regression model item by item, the variation patterns of crack opening degree of the concrete gravity dam, temperature component, hydraulic pressure component, and time component are obtained. This study can provide a reference for the operation and management of the concrete gravity dam and the construction of the forecasting, early warning, drilling, and emergency plan (FEDE) platform, with a relatively high theoretical and practical significance.

Carbon-based materials strongly pertain to citizens’ daily life due to their versatile derivatives such as diamond, graphite, fullerenes, carbon nanotube, single-layer graphene, and amorphous carbon (a-C). Compared to other families, a-C exhibits reconfigurable electrical properties by triggering its sp2–sp3 transition and vice versa, which can be readily fabricated by conventional film deposition technologies. For above reasons, a-C has been adopted as a promising memristive material and has given birth to several physical and theoretical prototypes. To further help researchers comprehend the physics behind a-C-based memristors and push forward their development, here we first reviewed the classification of a-C-based materials associated with their respective electrical and thermal properties. Subsequently, several a-C -based memristors with different architectures were presented, followed by their respective memristive principles. We also elucidated the state-of-the-art modeling strategies of a-C memristors, and their practical applications on neuromorphic fields were also described. The possible scenarios to further mitigate the physical performances of a-C memristors were eventually discussed, and their future prospect to rival with other memristors was also envisioned.

This work applied the classification model of DenseNet combined with transfer learning to classify mucinous cystic tumor (MCN) from serous cystic tumor (SCN) of the pancreas. Firstly, the data of 65 MCNs and 107 SCNs from Changhai Hospital were augemented and preprocessed. Secondly, the classification model of DenseNet combined with transfer learning was constructed and fine-tuned, MCN and SCN were classified by 5-fold cross validation, and the proposed classification model was compared with AlexNet, VGG16, ResNet50 and other deep learning models. The classification model in this paper yielded the best recognition effect. The area under the ROC curve (AUC value), accuracy rate, recall rate and precision rate of the test set were 0.989, 0.943, 0.949 and 0.938 respectively. It proved that the classification model based on DenseNet combined with transfer learning has higher recognition accuracy for MCN and SCN and stronger learning ability than other deep learning models, which can help doctors in clinical diagnosis, and save manpower and material resources. It further confirmed the potential value and clinical significance of this model for the classification of pancreatic cystic tumors.

Abstract The stresses existing in power transformers lead to the generation of bubbles. They are the primary source that worsens and leads to insulation failure. Due to the complexity of bubble dynamics, it is challenging to figure out bubble mechanisms. However, creation, migration, and accumulation mechanisms require further explanation. A set of experiments has been conducted to provide a scientific overview of bubbles. The results revealed that buoyancy is dominant in low electric fields, while high electric fields affect bubbles' rise. Bubbles stretch and oscillate while moving. When the electric field is not strong, the AC field reduces the duration and volume of bubble detachment. Vibration stress on the other side is leading the bubble motion including detachment frequency and migration speed. Under the combination of AC and vibration stresses, the bubble distortion degree aggravates and bubble gathering formation is easy. This research can provide a deeper acumen of the bubble's actions beneath a power transformer under a combination of vibration and electric fields. It provides essential technique parameters to deal with while investigating bubble's problems and related engineering research areas.

Objective To observe the damage effect of irradiation on intestine of C57 mice and investigate the protective effect and role of human α-defensin 5 (HD5) in the injury. Methods Intestinal α-defensin knockout mice (MMP7-/- mice) were constructed based on C57 mice, and HD5 gene was transferred into MMP7-/- mice to construct and breed a HD5 transgenic mice (HD5tg mice). The experiment was divided into wild-type C57 mice group (WT group), MMP7-/- mice group and HD5tg mice group. After the mice were irradiated with 5 Gy X-ray (1.26 Gy/min), the survival status of mice within 30 d was observed and statistically analyzed, and the ileal tissues were collected at 0, 2 and 10 d. The pathological injury of the ileum was observed with HE staining, the cell apoptosis in the mice intestinal mucosa was evaluated by TUNEL, and the proliferation and differentiation of ileum intestinal stem cells were analyzed with immunofluorescence assay and PAS staining. The protein expression of TLR4, MyD88 and NF-κB was detected with Western blotting, and the transcription levels of IL-1, IL-6 and IL-10 in intestinal mucosa were measured with RT-qPCR. Results MMP7-/- mice and HD5tg mice were successfully constructed in this study. At 30 d after irradiation, the survival rates of WT, MMP7-/- and HD5tg groups were 50% (10/20), 35% (7/20) and 55% (11/20), respectively. The survival rates of WT and HD5tg groups were significantly higher than that of MMP7-/- group (P < 0.05), but there was no statistic difference between WT group and HD5tg group. When compared with MMP7-/- group, HD5 transfer significantly improved the pathological damage of villi and crypt structure in the small intestine of mice after irradiation, inhibited the apoptosis of intestinal mucosal cells, promoted the proliferation and differentiation of intestinal stem cells, and decreased transcription levels of inflammatory factors via inhibiting TLR4-MyD88-NF-κB signaling pathway (P < 0.05). Conclusion HD5 promotes the renewal of intestinal mucosal cells and tissue repair and inhibit inflammatory injury by improving intestinal epithelial barrier dysfunction induced by irradiation, and thus, enhance the anti-irradiation ability of C57 mice.

Fatty acid oxidation (FAO) has been proven to be an accomplice in tumor progression. Carnitine palmitoyltransferase 1C (CPT1C), a rate-limiting enzyme in FAO, mainly functions to catalyze fatty acid carnitinylation and guarantee subsequent entry into the mitochondria for FAO in colorectal cancer (CRC). Gene expression data and clinical information extracted from The Cancer Genome Atlas (TCGA) database show significantly higher expression of CPT1C in patients with metastatic CRC ( P=0.005). Moreover, overexpression of CPT1C is correlated with worse relapse-free survival in CRC (HR 2.1, P=0.0006), while no statistical significance is indicated for CPT1A and CPT1B. Further experiments demonstrate that downregulation of CPT1C expression leads to a decrease in the FAO rate, suppression of cell proliferation, cell cycle arrest and repression of cell migration in CRC, whereas opposite results are obtained when CPT1C is overexpressed. Furthermore, an FAO inhibitor almost completely reverses the enhanced cell proliferation and migration induced by CPT1C overexpression. In addition, analysis of TCGA data illustrates a positive association between CPT1C expression and HIF1α level, suggesting that CPT1C is a transcriptional target of HIF1α. In conclusion, CPT1C overexpression indicates poor relapse-free survival of patients with CRC, and CPT1C is transcriptionally activated by HIF1α, thereby promoting the proliferation and migration of CRC cells.

Thermostat plays an important role in people′s production, life and scientific research, such as the measurement and compensation of temperature compensated crystal oscillator parameters. This paper designs and implements a software-controlled portable thermostat control system with low fluctuation. This thermostat uses a method to control the power of ceramic heating chip or semiconductor refrigeration chip by dynamically adjusting the working voltage, so as to realize the constant control of temperature. This thermostat has advantages of low temperature fluctuation, supporting temperature curve software control, small and portable. After testing, the temperature control range of the thermostat realized in this design is from -15 ℃ to 80 ℃, and the temperature fluctuation in stable state is ±0.2 ℃. In addition, compared with the thermostat using pulse width modulation control method, this thermostat has lower system electromagnetic radiation and higher refrigeration efficiency. Under the same power input condition, the temperature can be further reduced by 12.9 ℃.

Objective·To systematically evaluate the application of return-to-work intervention programs to young and middle-aged cancer survivors and its effectiveness in improving work-related outcomes in cancer survivors.Methods·An initial literature search of eight electronic databases, including PubMed, Cochrane Library, Embase, CNKI and Wanfang was conducted with a time frame from inception to Aug 2nd, 2022 to collect randomized controlled trials (RCTs) and quasi-experimental studies of return-to-work interventions on work-related outcomes among cancer survivors. Two researchers independently conducted literature screening, data extraction, and methodological quality evaluation using the JBI (Joanna Briggs Institute) Methodology Quality Evaluation Tool.Results·A total of 13 original studies were included, involving 11 return-to-work intervention programs, containing 2 045 cancer survivors. The results of the systematic evaluation showed that 10 of the intervention programs were multidisciplinary, with intervention modules on disease/mental health education, group discussions, physical rehabilitation, work capacity exercises, multidisciplinary team meetings and counselling. Among them, three interventions also included employers in the support program. The duration of the interventions ranged from 7 d to one year. Eight of the included interventions took return-to-work rates as a primary outcome, but only three reported that the difference was statistically significant, two of which were quasi-experimental studies without control group. Besides, no clear positive effects were found on work-related outcome such as length of return-to-work, change of work status, work ability, and work meaning.Conclusion·The content, format, intensity and frequency of return-to-work intervention programs for cancer survivors vary widely, and the effects of interventions on work-related outcomes are still unclear. Researchers should further explore the mechanisms that influence cancer survivors′ return-to-work, and develop multidisciplinary intervention programs based on this to effectively help young and middle-aged cancer survivors return-to-work and society. In addition, due to the limitations of study quality and intervention program heterogeneity, more high-quality experimental studies are needed to further validate the above findings.

To accurately characterize the different operating states of the rotor system, a modified variational mode decomposition（MVMD） combined with instantaneous energy distribution permutation entropy（IEDPE） is proposed to quantify and extract rotor fault features. The parameters of the variational mode decomposition（VMD） decomposition layer are determined using correlation threshold strategy and the MVMD algorithm is designed. Then, the acquired vibration signals are input into the MVMD for decomposition and the instantaneous energy distribution of each mode component is calculated. Finally, the IEDPE of each mode component is extracted as the fault quantization feature. Compared with MVMD+PE, EMD+IEDPE, and EEMD+IEDPE, the results show that the fault features extracted by the proposed method are more effective in characterizing significant differences between rotor fault states, allowing more accurate recognition of fault categories.

Hypergraph, as a generalization of the notions of graph and simplicial complex, has gained a lot of attention in many fields. It is a relatively new mathematical model to describe the high-dimensional structure and geometric shapes of data sets. In this paper,we introduce the neighborhood hypergraph model for graphs and combine the neighborhood hypergraph model with the persistent (embedded) homology of hypergraphs. Given a graph,we can obtain a neighborhood complex introduced by L. Lovász and a filtration of hypergraphs parameterized by aweight function on the power set of the vertex set of the graph. Theweight function can be obtained by the construction fromthe geometric structure of graphs or theweights on the vertices of the graph. We show the persistent theory of such filtrations of hypergraphs. One typical application of the persistent neighborhood hypergraph is to distinguish the planar square structure of cisplatin and transplatin. Another application of persistent neighborhood hypergraph is to describe the structure of small fullerenes such as C20. The bond length and the number of adjacent carbon atoms of a carbon atom can be derived from the persistence diagram. Moreover, our method gives a highly matched stability prediction (with a correlation coefficient 0.9976) of small fullerene molecules.

To ensure maritime search and rescue safety, this study combined a geographic information system and a fuzzy analytic hierarchy process to map and evaluate the difficulties of maritime search and rescue in the South China Sea. To construct a maritime search and rescue difficulty index system, nine factors related to the natural environment and human power that may influence maritime search and rescue were selected, and difficulty indices were calculated according to the corresponding calculation method. The fuzzy analytic hierarchy process was then used to determine the weight of each index, and the linear weighting method was used to obtain the maritime search and rescue difficulty value of the South China Sea. The difficulty values were divided into 10 levels and the spatial distribution characteristics were analyzed. The results show that: 1) under the influence of the natural environment, the difficulty of search and rescue in the sea area of the South China Sea had the characteristics of a V-shaped distribution, and the difficulty level gradually decreased from northeast to southwest. 2) Under the influence of humanistic search and rescue forces, the overall difficulty of search and rescue in the study area was the "NE-SW belt" type. The area with high difficulty levels was mainly located on the line connecting the northeast to the southwest of the study area, and the difficulty decreased in an irregular circle on both sides of the connecting line. 3) Considering these two factors, the difficulty of search and rescue was relatively high around some islands in the South China Sea, because of the influence of severe weather events, such as typhoons, and their large distance from the search and rescue base. The difficulty values in the far seas have "NE-SW decreasing" spatial distribution pattern, with the highest values in the areas extending from the northeast to the southwest of the study area, then decreasing to the northwest and southeast. According to statistics, approximately 21.1% of the sea areas in the South China Sea have search and rescue difficulties greater than level 6, and the construction of search and rescue forces should be optimized accordingly.

This paper focuses on niobium applications in the steel industry, aerospace industry, electronics industry, superconducting alloys, medical and other fields. As the demand of niobium resources is increasing year by year, this paper also introduces the distribution and characteristics of niobium resources in the world, and the characteristics of niobium resources in Bayan Obo. As well as the current situation and main methods of niobium resources development and utilization.

Federated Learning (FL) heralds a paradigm shift in the training of artificial intelligence (AI) models by fostering collaborative model training while safeguarding client data privacy. In sectors where data sensitivity and AI model security are of paramount importance, such as fintech and biomedicine, maintaining the utility of models without compromising privacy is crucial with the growing application of AI technologies. Therefore, the adoption of FL is attracting significant attention. However, traditional FL methods are susceptible to Deep Leakage from Gradients (DLG) attacks, and typical defensive strategies in current research, such as secure multi-party computation and differential privacy, often lead to excessive computational costs or significant decreases in model accuracy. To address DLG attacks in FL, this study introduces VAEFL, an innovative FL framework that incorporates Variational Autoencoders (VAEs) to enhance privacy protection without undermining the predictive prowess of the models. VAEFL strategically partitions the model into a private encoder and a public decoder. The private encoder, remaining local, transmutes sensitive data into a latent space fortified for privacy, while the public decoder and classifier, through collaborative training across clients, learn to derive precise predictions from the encoded data. This bifurcation ensures that sensitive data attributes are not disclosed, circumventing gradient leakage attacks and simultaneously allowing the global model to benefit from the diverse knowledge of client datasets. Comprehensive experiments demonstrate that VAEFL not only surpasses standard FL benchmarks in privacy preservation but also maintains competitive performance in predictive tasks. VAEFL thus establishes a novel equilibrium between data privacy and model utility, offering a secure and efficient FL approach for the sensitive application of FL in the financial domain.

Cancer is a form of inflammation, which is different from common inflammation in that it is uncontrollable and difficult to identify pathogenic microorganisms. Most tumors are related to specific pathogenic microorganisms, especially viral infections. The relationship between gliomas and human cytomegalovirus (HCMV) infection has been debated for decades. This article puts forward the author’s own point of view as an interpretation on the basis of objectively expounding the positive and negative points of view, and put forward a further solution to the dispute, for clinical reference.

The vibration signals of the bearings of coal mine machanical equipment under the working conditions of strong impact and heavy load show strong transient non-stationary and local nonlinear features. It is difficult to identify the fault features by the classical time-domain statistical analysis method and the global domain transformation method. The traditional order tracking method has the problems of inconvenient equipment installation and difficulty in obtaining instantaneous frequency. The traditional keyless phase order tracking method estimates the instantaneous frequency with low precision under the condition of severe speed fluctuation. This leads to poor fault identification effect. To solve these problems, a new method of bearing fault diagnosis based on harmonic matching compensation and keyless phase order tracking is proposed. Firstly, the time-frequency analysis method based on harmonic matching compensation is used to process the bearing vibration signal and estimate the instantaneous frequency accurately. Secondly, the Vold-Kalman filtering method is used to adaptively extract the harmonic component signal. Thirdly, the Hilbert transform is used to calculate the instantaneous phase of the harmonic. The mapping relationship between the time domain and angle domain is obtained, so as to complete the resampling of the original time domain signal in the angle domain. Finally, the resampled signals are processed by fast Fourier transform (FFT). The fault features of the bearing are identified by analyzing the envelope order spectrum. The simulation and experimental results show that the maximum relative error between the estimated instantaneous frequency and the actual value is less than 1%. The feature order of bearing fault is accurate and obvious, which can effectively diagnose the bearing fault.

The engine start requests are from the subsystems like the driver, the energy management, the engine system. There is no prioritization between different subsystems engine requests, which indicates that all system need to approve the stop and one single system can start the engine. When the final engine start request is decided, the engine start request should be considered first, then the transmission status should be focused on. The vehicle mode is dependent on the drive cycle mode, the engine status and the motor torque path. The vehicle test result shows that the engine status changes to the stopping mode after the motor gear is equal to the requested gear and both of the clutch is neutral to avoid the power loss. The result also shows that the smooth control of motor path changing progress improves the system efficiency and drivability performance.

To investigate the effects of chronic restraint stress on cognition and the probable molecular mechanism in mice.

Objective: To investigate the major risk factors for coronary heart disease (CHD) in elderly patients (≥75 years old) using coronary angiography and develop relevant diagnostic model. Methods: Retrospective analysis was conduc-ted on 548 elderly patients with suspected diagnosis of CHD(≥75 years old) underwent coronary angiography (CAG) treatment in Ruijin Hospital from June 2018 to December 2019. According to CAG results, the patients were divided into two groups :CHD group and control group. The risk factors were analyzed by multivariate Logistic regression, and the predictive value of these factors for the occurrence of CHD in the elderly was evaluated by receiver operator characteristic curve (ROC curve). Results: There were 408 cases in the CHD group and 140 cases in the control group based on results of CAG. The multivariate Logistic regression analysis after baseline analysis showed that elevated level of troponin I(OR=6.828, 95%CI:3.834-12.160, P