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Gas chromatography-mass spectrometry (GC-MS) and ultra-high performance liquid chromatography (UPLC) were used to analyzed terpenes, fatty acids and cannabinoids in hemp oil, and the obtained data were analyzed by means of principal component analysis (PCA). The results showed that hemp oil produced in Yunnan contained rich characteristic flavor terpenes including β-caryophyllene, β-myrcene, and limonene. Also, it was rich in fatty acids; particularly the ratio of linolenic acid and linoleic acid was close to the golden ratio, indicating high nutritional value. Trace amounts of multiple cannabinoids were detected in hemp oil irrespective of cultivar or processing method. The content of cannabinoids in hemp oil was significantly reduced by dehulling, and cold pressing was more suitable for hemp oil processing. Moreover, this study discussed the safety of hemp oil for consumption in China and put forward some suggestions. Finally, four hemp oil samples were perfectly distinguished and identified by PCA.

Children with certain comorbidities and immunocompromising conditions are highly vulnerable to SARS-CoV-2 infection. Vaccination against SARS-CoV-2 is an important strategy to reduce death， critical illness and overall disease burden. With the evolving and increasing transmission of SARS-CoV-2， universal vaccination is essential to achieve this goal. Children with special medical conditions are considered as the priorities for SARS-CoV-2 vaccination. However， vaccine hesitancy towards the implementation of SARS-CoV-2 vaccination currently remains an urgent challenge. In order to promote the sustainable vaccination for those children in Shanghai as well as China， Shanghai municipal center for disease control and prevention， together with the national children’s medical center， children’s hospital of Fudan university and the expert group on immunization planning of the Shanghai preventive medicine association， organized a consensus expert working group to formulate the evidence-based recommendations and implementation suggestions for children with common chronic diseases， allergy history， diseases involving adverse events related to vaccination， and immunocompromising conditions， based on the published evidence of SARS-CoV-2 vaccination for populations and children with special medical conditions.

ObjectiveTo analyze the epidemiological characteristics of varicella in Minhang District， Shanghai from 2013 to 2022， and to evaluate the impact of adjusting the varicella vaccination program to a two-dose regimen and its inclusion in the Shanghai immunization program on the incidence of varicella.MethodsData on reported varicella cases from 2013 to 2022 in Minhang District were collected. Interrupted time series analysis and segmented regression models were used to analyze the changes in varicella incidence before and after the adjustment of varicella immunization strategies.ResultsFrom 2013 to 2022， the average annual incidence of varicella was 76.58/100 000， with the highest incidence in 2017 （119.21/100 000） and the lowest in 2022 （27.02/100 000）. The varicella incidence exhibited seasonal patterns with peaks occurring from March to June and October to January of the following year. Prior to the adjustment of varicella immunization strategies （2013‒2017）， the varicella incidence showed an upward trend （z=2.20， P=0.03）， while after the adjustment （2018‒2022）， a adownward trend was observed （2018‒2022） （z=-2.25， P=0.02）. Interrupted time series analysis showed that following the adjustment of varicella immunization strategies， an immediate change of -33.91/100 000 （t=-4.35， P

The neutron induced nuclear reaction cross section of fission products is related with the neutron flux and the reactor burnup, which plays an important role in accurately designing nuclear engineering. To predict (n, 2n) reaction cross sections especially those without experimental data, the relevant features were analyzed and the experimental data set were established on the basis of sorting out the experimental data recorded in EXFOR library. This work includes 5 294 (n, 2n) cross section measured results, among which a lot of experimental data concentrate around 14 MeV incident energy. Moreover, there may be divergence between measurements due to system error and negligence error. Faced with these real and defective data, researchers discovered laws behind them and established the compound nucleus reaction models. However this means a heavy workload. It would be surprising if machine learning could reach a quantitative level close to the evaluation results. The 8 features include the proton number Z, the mass number A, the single nucleon separation energy of both proton and neutron, the Casten factor, the level density, the pairing correction, and the incident energy. The back propagation artificial neural network (ANN) and decision tree (DT) models were built to learn the experimental data set, respectively, adopting PyTorch and XGBOOST toolboxes. Draw lessons from the variational auto-encoder network, the 2 sub-networks with the same internal structure, which contains 128 neurons in 2 hidden layers, were designed to learn the mean and variance respectively. The boosting model integrates 16 decision trees. The training set includes 4 000 uniform and randomly selected data, while the remaining data constitute the test set. The results show that both ANN and XGBOOST models describe the experimental cross section data well, moreover model gives a smooth and continuous curve, indicating a certain predictive ability. For the case of lack of experimental data, the predictions are also basically consistent with the evaluation nuclear reaction data libraries. Compared with the XGBOOST model, the ANN model has somewhat better generalization ability in the range of neutron incident energy above 20 MeV. In the test set, the ANN predictions with a mean absolute percentage error (MAPE) deviation less than 10% from the experimental data account for more than 85%. Therefore we successfully established machine learning models to analysis and predicate (n, 2n) reaction cross section. On the other hand, through traditional nuclear reaction models, one can intuitively understand the relationship among physical quantities and build a picture of the reaction mechanism. However, machine learning models are often regarded as difficult to understand black boxes, which is questionable for physicists. In future it is planned to further apply machine learning for the nuclear data research to verify the rationality of the method.

Neutron-induced fission nuclear reaction data are critical for understanding nuclear physics, engineering, and technology. The accuracy of fission cross section of key fuel nuclides and sub-actinide nuclei is increasingly important with the development of new nuclear energy system concepts. Machine learning is a powerful tool for data analysis and modeling, and can be used to extract features from large amounts of data without requiring encoding for a specific task. Neural networks, a subset of machine learning, are particularly effective for mapping input to output for tasks with a sufficient number of target-valued data. In a recent study, researchers applied machine learning methods to analyze fission nuclear reaction cross section data. The team collected fission cross section data for the uranium isotope chain from the experimental nuclear reaction data (EXFOR) library and five sets of frequently used evaluation data from the evaluated nuclear data file (ENDF) library. The neutron-induced fission cross section data of 233-239U in the energy range from 2 to 20 MeV were selected as the training data set. To obtain fission cross sections for a given neutron number, proton number, and corresponding incident neutron energy, the feedforward neural network (FNN) was trained using fission cross section data from experimental measurement of the uranium isotope chain to achieve maximum agreement with experimental and evaluation data. The results show that the fission cross section data generated by the machine learning method can reproduce the step structure of the fission cross section well and is very close to the results of experimental and evaluation data. This provides a research basis for large-scale analysis of fission cross section data of easy-to-fission nuclei. The application of machine learning to nuclear physics and engineering is still in its early stages, and there are several challenges that need to be addressed. One major challenge is the lack of high-quality data sets, which are essential for training and testing machine learning algorithm. In addition, the complexity of nuclear reactions and the need for accurate modeling of the underlying physics present additional challenges. Despite these challenges, machine learning has the potential to revolutionize both the research and practice of nuclear physics and engineering. The ability to analyze and model large amounts of data can help researchers better understand the underlying physics of nuclear reactions and improve the accuracy of predictions. Machine learning algorithm can also be used to optimize the design and operation of nuclear reactors, leading to improved safety and efficiency. In conclusion, machine learning method is a promising approach for analyzing and modeling nuclear physics and engineering data. The application of neural networks to fission cross section data provides a new perspective on the understanding and prediction of nuclear reactions. Continued research and development of machine learning algorithm can contribute to the safe and efficient use of nuclear energy for generations to come.

To solve the problems of small local relief range, low continuity and high operational risk of rockburst, regional hydro-fracking and burst prevention system and engineering application research were carried out within thick and hard roof of a coal seam by well over thousand meters on and under the ground. The results show that, according to the loading conditions, the roof dominated rockburst can be divided into three types: superposed dynamic and static loading type, high static loading type and high static unloading type; The thick and hard roof is the main source of regional static and dynamic loading for rock burst initiation of mining surrounding rock. With the thick and hard roof as the target, which provides the source of dynamic and static load for rock burst initiation, a segmented fracturing pressure relief system for the roof with 1000 meter horizontal holes on and under the ground was established and fracking was carried out in this area. By destroying the integrity of the overlying roof in the burst prone area in advance on and under the ground, the load is transferred to the more intact roof area, so as to provide a low stress operational environment for underground excavation and extraction, and also make the rock burst initiation conditions inapplicable. The fracking and burst control tests on the roof area using the horizontal well drilled on the ground before the extraction of the panel, and underground drilling from the entries during the extraction were carried out respectively. Remarkable results have been achieved in practice, forming a new disaster prevention and control mode of “fracking before construction” for burst prone coal mines under construction, and “fracking before tunneling, fracturing before mining” for the producing coal mines.

Nuclear data are fundamental data for nuclear energy, nuclear engineering, nuclear technology applications and nuclear science research, and have always received national attention. In particular, the importance of neutroninduced fission nuclear reaction data is unquestionable and the theoretical evaluation of nuclear data is required for a long period of time. The reaction mechanism of neutron induced fission reaction is very complicated and needs to be described by several different nuclear reaction models, such as optical potential model and compound nuclei model and so on. Nowadays, the most famous international nuclear reaction program systems are TALYS and EMPIRE. In China, the program FUNF is a fission nuclear reaction program system with our own independent intellectual property rights, which was developed by Prof. Zhang Jingshang of the China Nuclear Data Center. Each of these program systems need more than 40 parameters to be adjusted. Therefore, it is necessary to optimize a large number of parameters at the same time in order to correctly describe the nuclear fission reaction data. In this work, the FUNF program system was used to calculate the fission reaction data of neutron induced 238U. The fast neutron reaction data of fissile materials with incident energy from about 1 keV up to 20 MeV were calculated by FUNF code. It consists of the spherical optical model, the HauserFeshbach model, and the angular momentum dependent exciton model to describe the emission from compound nucleus to the discrete levels of the residual nuclei in preequilibrium process, meanwhile equilibrium reaction process was described by the HauserFeshbach model with width fluctuation correction. Based on the FUNF nuclear fission reaction model, the parameters of the FUNF program system was adjusted by the advanced optimization method MINUIT. MINUIT is a numerical minimization software library in the Fortron by CERN staff Fred James in the 1970 s. Then, combined with the parallel calculation method of MPI (message passing interface), preliminary calculations for the neutron induced fission reaction of 238U were performed. The results show that the FUNF parameters obtained by the MINUIT optimization method can well describe the fission reaction data of 238U. Next, the procedure will continue to be optimized to further improve the efficiency of the tuning parameters and the accuracy of the parameters. And the application to the relevant fission nuclear reactions will be developed to improve the accuracy of our fission nuclear reaction data.

Neutron induced reaction cross sections play an important role in nuclear science and technology research, such as national defense, nuclear energy construction and development, nuclear medicine, radiation protection and nuclear safety. With the rapid development of nuclear technology, there are increasing demand for the variety and accuracy of neutron data. As an important structure material nuclide, the accuracy of neutron induced reaction cross section on 56Fe is directly related to the design and operation of reactors, spent fuel disposal and miniaturization design, and also of great significance to nuclear physics basic research. The 56Fe(n,p)56Mn reaction is usually used as the standard to monitor the neutron field flux in the experiment. It greatly influences the results of nuclear data measurement, and also has guiding significance for upon carry the determination of the parameters in the nuclear reaction model. Due to the obvious differences existing in the previous experimental data and inconsistency between the evaluation databases, the experimental measurement data evaluation and covariance analysis were carried out for the 56Fe(n,p)56Mn reaction cross section in this paper. Firstly, the existing experimental measurement data of 56Fe(n,p)56Mn reaction cross section in EXFOR and literature were downloaded and systematically collected, then summarized, contrasted, and classified in terms of neutron source, measurement method, detector kinds, etc. Secondly, the data with low reliability were discarded with checking the original papers, and the uncertainty mentioned in these papers was also analyzed. Additionally, previous experimental results which using the imprecise values were adjusted and modified with the more accurate data such as recently ratios, supervised reaction standard cross sections, and isotopic abundances. The data with significant divergence near 14 MeV were normalized, which made it more concentrated and less divergent. The polynomial curve fitting was applied to obtain the excitation curve of 56Fe(n,p)56Mn reaction between 29535 MeV which was derived from the corrected and normalized results. Following that, the correlation covariance matrix was calculated for the suggested experimental data after evaluation. Meanwhile, the energy density, optical potential parameters, energy level density parameters, and other parameters of 56Fe(n,p)56Mn reaction were adjusted within the physical model calculated by the nuclear reaction simulation program TALYS. Finally, the reaction cross section of 56Fe(n,p)56Mn was computed theoretically, compared with experimental data and evaluation databases. This paper broadened the existing evaluation method of neutron activation reaction cross section experimental data, and also improved the accuracy of the assessment data for neutroninduced 56Fe(n,p)56Mn reaction cross section measured data below 35 MeV. The recommended assessment value and its covariance of the 56Fe(n,p)56Mn reaction excitation function were given based on the evaluted results of experimental data. Excitation function input file for 56Fe(n,p)56Mn reaction derived by adjusting the TALYS parameters agree well with the experimental and evaluation data. A set of standard assessment techniques were constructed, which serves as a guide for theoretical calculation and database construction of related nuclear data.

Objectives To investigate the effects of total parenteral nutrition (TPN) on immune function and systemic inflammation in young rats. Methods Eighteen young male SD rats aged 6 to 8 weeks were randomly divided into 3 groups: normal group, control group and TPN group, with 6 rats in each group. The rats in the normal group were not catheterized, and those in the control group were treated with normal saline after right jugular vein catheterization. The rats in the control group and normal group were fed standard food and water, and those in the TPN group were treated with TPN after right jugular vein catheterization, fasting and water abdication. All the young rats were sacrificed in 14 d later. The blood routine of peripheral blood was detected by automatic flow cytometry. The expression of HLA-DR in spleen cells, the proportion of Treg cells in peripheral blood, the ability of endotoxin-induced monocytes to produce TNF-α, apoptosis of lymphocytes (CD4+, CD8+ T cells) and the ability of IFN-γ production after stimulation by phytohemagglutinin (PHA) were detected by flow cytometry. The levels of IL-1β, TNF-α, IL-10 and IL-4 in peripheral blood plasma were determined by ELISA. Results Compared with normal group and control group, the number of venous blood lymphocytes was decreased (P < 0.05), the HLA-DR expression level in spleen cells was reduced (P < 0.05), the proportion of Treg cells in peripheral blood was significantly higher in the TPN group (8.54±1.20) than that in the normal group (3.02±1.49) and the control group (3.00±0.75) (P < 0.05). The proportion of TNF-α induced by endotoxin was significantly lower in the TPN group (8.73±3.11) than the normal group (39.07±16.17) and control group (43.23±18.40). The percentage of IFN-γ produced by CD4+T lymphocytes stimulated by PHA was significantly lower in the TPN group (0.65±0.46) than the normal group (2.94±1.62) and the control group (2.97±1.39), and the difference was statistically significant (P < 0.05). The TPN group had increased apoptosis of lymphocytes (CD4+ CD8+ T cells) at late stage (P < 0.05), increased IL-1β and TNF-α levels (P < 0.05) while decreased IL-10 and IL-4 levels (P < 0.05). Conclusion TPN sustained for 2 weeks can lead to congenital and acquired immune impairment and increase of pro-inflammatory factors in young mice. Early initiation of enteral nutrition is of great significance to reduce immune injury and systemic inflammation.

Objective To investigate the anti-tumor activity and mechanism of lapatinib, paclitaxel and their combination on esophageal cancer cells EC109. Methods MTT assay was used to detect the effects of lapatinib (1, 2, 4, 8 μmol/L), paclitaxel (5, 10, 20, 40 μg/L) and their combination on the proliferation of esophageal cancer cells EC109. We tested the effects of 2 μmol/L lapatinib, 10 μg/L paclitaxel and their combination on the invasion, cell cycle, apoptosis and EGFR, HER2 and downstream signaling pathways of EC109 cells. Results Lapatinib combined with paclitaxel synergistically inhibited the proliferation of EC109 cells, and the combined action index was greater than 1.15. The number of invaded cells in the combination group (62.0±9.5) was significantly less than those in the lapatinib group (152.4±16.1) and the paclitaxel group (103.6±12.7) (P < 0.05). G2/M cells in the combination group ((43.4±3.1)%) was higher than those in lapatinib group ((20.3±2.5)%) and paclitaxel group ((26.6±2.8)%) (P < 0.05). The apoptosis rate of the combination group was (47.3±8.4)%, higher than those of lapatinib ((12.7±2.3)%) and paclitaxel group ((21.4±5.2)%) (P < 0.05). Lapatinib combined with paclitaxel could synergistically inhibit the expression of phosphorylated EGFR, HER2 and AKT proteins. Conclusion Lapatinib combined with paclitaxel exert synergistic antitumor activity by synergistically inhibiting the proliferation and invasion of esophageal cancer cell line EC109, arresting cell cycle, inducing apoptosis and inhibiting the transduction of EGFR/HER downstream signaling pathway.

In view of the notice issued by the State Administration of Coal Mine Safety Supervision on the acceptance specifications for the Upgrading and Transformation of Coal Mine Safety Supervision and Control Systems, as well as the requirements of AQ 6201-2019 on technical indexes such as digitalization, anti-electromagnetic interference, inspection cycle, remote control, etc., a low-power all digital optical interface substation based on multi-bus architecture is designed. The substation has RS485, CAN, optical interface and other bus structures, and the key technology of man-computer interaction for reducing power consumption in substation are discussed, through the independent design of the traditional human-computer interaction display function, the power consumption is reduced obviously. Through various tests submitted by the system for inspection, all indexes of the sub-station are higher than the standard requirements, and the electrical fast pulse group immunity test of system has reached level 3 A, and the surge level 4 B, with high electromagnetic compatibility level, stable and low power consumption and other characteristics.

This paper designs a kind of power integrated regional fire-fighting Internet of Things optical port substation, introduces the system architecture, discusses the analog signal isolation anti-interference, switching value and communication isolation circuit, power integrated power supply strategy, network optical port and other key technologies. Take STM32F429 as the control core, combined with the output circuit and control strategy of the sensor, the substation realizes the regional fire-fighting function of full isolation, patrol detection and early warning of the object connection, and integration of power supply and photoelectric port, which improves the anti-interference ability of the monitoring system.

A kind of Internet of things intrinsic safety rescue watch with fast networking and detection is designed. Taking NRF52832 as the control core, Bluetooth 5.0 and mesh networking technology are used to realize fast networking of mesh network after disaster and fast detection of RSSI signal strength area and realize equipment interconnection, intelligent perception, early warning and patrol inspection through mesh network. The test results show that the device can effectively improve the safety and efficiency of the first-line miners, and has practical significance for rapid detection and rescue.

Objective: To explore the computed tomographic (CT) and pathological findings of small intrahepatic cholangiocarcinoma and improve its understanding., Methods: A total of 20 patients with bile duct non-dilatation peripheral small intrahepatic cholangiocarcinoma were retrospectively analyzed. All of them were examined by plain and enhanced CT and pathological studies., Results: CT plain scan:there were 15 cases of low-density lesions with fibers and necrotic tissue and surviving tumor tissue in peripheral or central portions. There were 4 cases of lesions with mixed high and low densities. Abundant necrotic tissues were found with a rare vascular distribution.Or abundant fibers and connective tissues around tumor were manifested as hardened cholangiocarcinoma.One lesion showed equal density with abundant necrotic tissue and fibrous connective tissue with peripheral tumor tissue; dynamic contrast-enhanced CT: (1) there were 6 cases of lesion with peripheral thin ring-enhanced and tumor margin of viable tumor tissue. And internal tumor was fibrous tissue; (2) there were 7 cases of lesions with peripheral thick ring-enhanced and enhanced portion was surviving tumor tissue, no enhanced central portion was mainly necrotic tissue mixed with little fibrous tissue; (3) there were 2 cases of whole tumors without enhancement. There were significant hemorrhage and necrosis with few surviving tumor cells in tumor and normal liver tissue; (4) there were 5 cases of lesions with heterogeneous enhancement and the heterogeneous enhancement portion was surviving tumor tissue and little fibrous connective tissue. And some necrotic tissue remained; (5) only one case had overall enhancement of same-sized tumor cells without necrosis., Conclusion: CT scanning of peripheral small cholangiocarcinoma lacks distinguishing characteristics. However the findings of contrast-enhanced CT have certain characteristics. Due to different pathological types, each attribute has corresponding characteristic with different pathological features.

Objective: To explore the protection mechanisms of telmisartan on inflammation and fibrosis after myocardial infarction in rats., Methods: The model of acute myocardial infarction (AMI) was established by ligating left anterior descending coronary artery. The surviving rats were divided into AMI (AMI) and telmisartan treatment (telmisartan) groups. And another sham operation group (sham) was designated (n = 8). At the end of study, total heart weight (THW), left ventricular weight (LVW) and weight index were measured; myocardial infarction and inflammatory reactions detected by hematoxylin and eosin and Masson staining; the serum levels of C-reactive protein (CRP), tumor necrosis factor-alpha (TNFα), monocyte chemotactic protein-1 (MCP-1), interleukin-6 (IL-6) and interleukin-1 beta (IL-1β) by enzyme-linked immunosorbent assay (ELISA); the levels of transforming growth factor 1 (TGFβ1), collagen I, collagen III and MMP9 mRNA in myocardial tissue by reverse transcription-polymerase chain reaction (RT-PCR); the expressions of TGFβ1, collagen I, collagen III, matrix metallopeptidase 9 (MMP9) and nuclear factor-kappa B (NF-κB) by Western blot., Results: Compared with sham group, significant pathological changes of myocardium occurred in AMI group. The serum levels of CRP [(472 ± 132) vs (104 ± 28) ng/ml], TNFα [(229 ± 41) vs (18 ± 5) pg/ml], MCP-1[(558 ± 116) vs (158 ± 20) pg/ml], IL-6 [(404 ± 63) vs (21 ± 4) pg/ml] and IL-1β [(625 ± 145) vs (189 ± 34) pg/ml] increased (P < 0.05). RT-PCR analysis showed that the expression levels of TGFβ1, collagen I, collagen III and MMP9 increased significantly. The results of Western blot were consistent and NF-κB was activated significantly (P < 0.05). Compared with AMI group, the above-mentioned indicators decreased obviously in telmisartan group (P < 0.05)., Conclusion: Telmisartan may regulate inflammation and myocardial fibrosis after acute myocardial infarction by signaling pathways of NF-κB and TGFβ in rats.