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Owing to the constant modernization and urbanization of China, most cities have subways, and the total power consumption of subway stations has been rising, which contradicts with China’s green goals. In this regard, energy consumption indicators for major energy use projects in subway stations should be established, comprehensive evaluation and detailed research on the current situation of subway operation should be conducted, and specific energy-saving measures should be taken from a diversified perspective. It is crucial to systematically sort out the principle of sub-item energy consumption in subway stations, and practice and explore the specific simplification measures of the principle model of sub-item energy consumption, so as to lay a solid foundation for achieving the goal of reducing energy consumption of subway stations.

Industrial cyber-physical systems (ICPSs) are deployed in many high-value facilities recently, and the monitoring of ICPS is more and more important. However, the prerequisite of ICPS monitoring is how to obtain an accurate network structure. In addition, the structure of ICPS may change over time and the observations data are limited and noisy. These situations make the ICPS network structure identification more difficult. In this article, we proposed the algorithm of temporal network identification from sparse data (ATNISD) to address these two issues simultaneously. First, we established the temporal network analysis model from the aspect of state equation and observation equation. Then, we analyze the characteristics of temporal networks in both time domain and space domain and propose a general framework of temporal networks structure identification, which is a combinatorial optimization problem. To improve the accuracy and alleviate the computational complexity, we decompose the combinatorial problem into small independent simple problems, which can be solved efficiently. The performance of the proposed algorithm is verified on synthetic evolutionary game dynamics on both homogeneous and heterogeneous temporal networks. The experimental results show that the proposed method can efficiently solve the problem of temporal networks structure identification from sparse data.

Background: How to reduce the radiation dose received from full-body CT scans during the follow-up of lymphoma patients is a concern. Objective: The aim of the study was to investigate the image quality and radiation dose of reduced-dose full-body computerized tomography (CT) in lymphoma patients during the follow-up. Methods: 121 patients were included and divided into conventional CT group (group 1, 120-kVp, n = 61) or reduced-dose CT group (group 2, 100-kVp combined dual-energy CT (DECT), n = 60). 140-kVp polychromatic images and 70-keV monochromatic images were reconstructed from DECT. The abdominal virtual non-enhanced (VNE) images were reconstructed from monochromatic images. Two radiologists rated the overall image quality with a five-point scale and graded the depiction of lesions using a four-point scale. The objective image quality was evaluated using image noise, signal-to-noise ratio, and contrast-to-noise ratio. The radiation dose and image quality were compared between the groups. Results: The comparable subjective image quality was observed between 70-keV and 120-kVp images in the neck, while 120-kVp images showed better objective image quality. 70-keV images showed better objective image quality in the chest. While the subjective image quality of abdominal VNE images was inferior to that of true non-enhanced images, the improved objective image quality was observed in VNE images. In the abdominal arterial phase, similar subjective image quality was observed between the groups. Abdominal 70-keV images in the arterial phase showed improved objective image quality. Similar image quality was obtained in the abdominal venous phase between the groups. The effective radiation dose in group 2 showed a significant reduction. Conclusion: The application of reduced-dose full-body CT can significantly reduce the radiation dose for lymphoma patients during the follow-up while maintaining or improving the image quality.

Aim: To investigate the computed tomography (CT) and clinical characteristics of immunotherapy-induced pneumonitis (IIP) in patients with advanced solid tumors. Patients & methods: CT and clinical data of 254 patients with advanced solid tumors treated with immune checkpoint inhibitors in our hospital were collected retrospectively. Results: The incidences of IIP in patients with non-small-cell lung cancer, lymphoma and gastrointestinal tumors were 19% (19/100), 9.8% (6/61) and 6.2% (4/65), respectively. The median onset time for all 31 IIP patients was 44 days (interquartile range: 24–65). Most IIP patients (21/31) had grade 1–2 disease. Multifocal ground-glass opacities (seen in 21/31 patients) were the main CT findings of IIP. Conclusion: Patients should be alerted to the risk of IIP, an adverse reaction that has a relatively low incidence but which is sometimes life-threatening.

The mechanism of furrow opener-soil interaction plays an important role in analyzing the process of no-till planting furrow opener. In order to study the disturbance effect of the furrow opener on the loam soil, firstly, the three-dimensional model of the furrow opener was established by using SolidWorks. Secondly, the 3D discrete element model of furrow opener-soil interaction was established by EDEM software. Combined with the indoor soil bin test bench and high-speed camera technology, the micro-disturbance and macro-disturbance behavior of the furrow opener on soil at different positions, speeds and operating depths were compared and analyzed. The results showed that, the disturbance range of soil was decreased with the increase of the distance between the furrow opener and the soil. At different locations, the disturbance range of soil from large to small was the surface layer, the shallow layer and the middle layer. Under the conditions of three different layouts of furrow openers, through the comparison of the soil trench test and the simulation test, it was determined that the furrow openers in a staggered layout would be beneficial to reduce the degree of soil disturbance. In the trenching process, the soil movement velocity was decreased with the increase of the distance between the soil and the furrow opener, and the distribution curves of the same-speed soil particles were basically consistent with the curves of the furrow opener. The average velocities of soil particles with different velocities and depths in different directions were the surface layer, the shallow layer and the middle layer. However, there were differences in the maximum velocities of soil particles in different directions. By comparing the data obtained from the simulation test and the soil bin test, it was found that the parameters obtained from the simulation and the test were basically consistent, and it was determined that the discrete element simulation could simulate the soil disturbance behavior of the furrow opener more accurately. The relative errors of cross-sectional area of the front furrow opener and the rear furrow opener were 2.48 % and 5.2 %, respectively. The relative errors of the dynamic soil rate of the front furrow opener and the rear furrow opener were 0.25 % and 5.12 %, respectively.

With the development of information and communication technologies, industrial cyber-physical systems (ICPSs) have accumulated a large amount of data, which enables us to convert data into industrial insight. However, since the industrial process of ICPS is always complicated and large-scale, the raw data only contains a few operation condition information, which brings challenges to process monitoring and control. Thus, an efficient operation condition division method for ICPS is necessary. Although many operation condition division methods have been proposed, they were mainly relying on the static characteristics and ignored how industrial process varies dynamically. Meanwhile, with industrial process running, there may exist some new operation conditions that make the operation condition division task even more difficult. In order to grasp the static and dynamic features simultaneously of industrial process and divide operation conditions accurately, we proposed an operation condition division method based on joint static and dynamic analysis with incremental learning. In detail, the slow feature analysis (SFA) and self-organizing map (SOM) network were proposed to extract the static and dynamic features jointly. Then, a division strategy was proposed to distinguish the operation condition changing points. For the new operating condition, we designed an incremental learning method based on SOM network, which can update the operation condition model in real-time. Extensive experiments including a numerical simulation, two benchmark processes and an industrial roasting process demonstrate that the proposed method can identify the operation conditions of the raw data in ICPS accurately and efficiently.

Tranexamic acid (TXA) has been proven to reduce perioperative blood loss and the incidence of allogeneic blood transfusion in total joint arthroplasty (TJA). However, there is limited literature on the use of TXA in patients undergoing hip hemiarthroplasty. Furthermore, combination effect of intraoperative and postoperative intravenous TXA has not been investigated extensively. The purpose of this study was to evaluate this combination effect on hip hemiarthroplasty by a propensity score matched analysis (PSMA).This is a retrospective cohort study involving hip hemiarthroplasty for femoral neck fracture. All patients received 1g intraoperative intravenous TXA. One group also received 1g intravenous TXA 6h after surgery, while the other group did not. Perioperative blood loss, the incidence of allogeneic blood transfusion and venous thromboembolism (VTE) were collected and compared.209 patients who underwent unilateral hip hemiarthroplasty were included. PSMA resulted in 66 matched pairs in each group. There were no significant differences in perioperative blood loss on postoperative day (POD) # 1, 3, 5 (637.2 ± 285.9 versus 653.6 ± 305.5, 726.6 ± 274.2 versus 676.3 ± 313.2, 795.5 ± 291.2 versus 759.9 ± 328.4), the incidence of allogeneic blood transfusion (9.1% versus 12.1%) and VTE (1.5% versus 1.5%) between the two groups (P value 05) for up to one year.Intraoperative and postoperative intravenous TXA have no combination effect on reducing perioperative blood loss, the incidence of allogeneic blood transfusion and VTE in patients undergoing hip hemiarthroplasty.

Urban subways have developed rapidly in recent years and has gradually become the primary choice of public transportation in big cities. Subways can effectively alleviate urban traffic congestion and improve passengers’ green travel experience, especially in the old urban areas where urban space is limited, community facilities are aging and historical features are gathered. Subways play a huge role in guiding the expansion of urban space form and improving urban landscape environment. As the only channel for passengers to get in and out of the station, the subway entrance and exit act as a bridge. It is not only the transition space between the subway station space and the urban above ground space, but also an important node for the exchange and circulation of various elements in the urban space. In this paper, the influencing factors of subway entrance and exit space environments in the old city were investigated, and relevant basis for future planning, design, and construction of subway entrances and exits were provided. Firstly, the research results and shortcomings of subway entrance and exit channels were outlined. Secondly, based on the perspective of residents and passengers, this paper uses the analytic hierarchy process to determine six first-class evaluation indexes, including safety, convenience, practicability and aesthetics. This paper constructs the evaluation system of spatial environment index of subway station entrances and exits in the old urban area of Xi’an. Lastly, according to the evaluation results, the paper puts forward the targeted optimization strategies for the current situation of the entrance and exit space environment of the subway stations in the Xi’an.

The main task of the impurity removal process is to control the oxidation reduction potential (ORP) within the range of the optimized set value. The impurity removal process is essentially an oxidation-reduction process. Oxidation reduction potential (ORP) is an external reflection of reaction state inside the impurity removal reactor. However, actual industry is time-varying, nonlinear and multimode. It is difficult to determine the appropriate dosage of impurity remover in practice. This will lead to large fluctuations in the operation mode, affecting the safety and stability of the process and the final product quality. To solve these problems, an intelligent dynamic setting control framework (IDSCF) for the multimode impurity removal process is proposed in this paper. It includes a preset module of the dosage of impurity remover based on impurity remover utilization (IRU) estimation, an operation mode detection module based on autoencoder, a normal mode adjustment module based on fuzzy logic, and an unsteady mode adjustment module based on case-based reasoning (CBR). The framework can determine the reasonable preset dosage of impurity remover and adjust the dosage according to the current operation mode of the impurity removal process. Because the operation mode is related to the residual dosage of impurity remover added over a period of time, that is, the accumulative effect of the large-scale metallurgical reactor. When calculating the preset dosage of impurity remover, the reactant accumulation ratio (RAR) is calculated, which makes the calculation of the preset value more reasonable. In addition, it can detect the unsteady modes causing large fluctuations in the process and adjust them in time. Experiments are carried out in accordance with the data of an actual cobalt removal process. The results show that this method can effectively improve the stability of the impurity removal process, control the ORP within the set range and cope with complex mode changes.

This study assesses BMSCs’ effect on bone strength and osteoblast activity in rats with osteoporosis. 40 SD female rats were assigned into normal (A) group, model (B) group, puerarin (C) group, and BMSCs (D) group followed by analysis of bone strength by small animal bone strength analyzer and the expression of ROCK1 protein in tibia by immunohistochemistry. The bone strength of group B was lower than group A (P < 0.05), and higher in groups C and D than group B (P < 0.05) with further higher in group D than group C (P < 0.05). Group A showed obvious mesh cancellous bone trabecular bone and continuity, compact structure, and group B cells appeared loose and irregular distribution, parts cancellous bone trabeculae decrease and part of the fracture, and arranged in sparse irregular distribution. The decrease of trabecular bone in group C and D is not more obvious than group B and the morphology in group D got better improved than in group C. Group B showed lower proliferation of osteoblasts and ALP activity than group A (P < 0.05) and the proliferation and ALP activity in groups C and D was increased significantly compared with group B (P < 0.05). ROCK1 expression was significantly lower in group B than group A (P < 0.05) higher in groups C and D than group B (P < 0.05). Bone marrow mesenchymal stem cells (BMSCs) can effectively increase the bone strength and activity of osteoblasts in rats with osteoporosis, and promote the activation of ROCK signal.

A fault diagnosis method based on a multi-scale feature fusion network (MSFF-CNN) is proposed for the problem that the vibration signals of wind turbine bearings are easily disturbed by noise, and feature extraction is harrowing. Compared with the traditional diagnosis method, which has two stages of manual feature extraction and fault classification, this method combines the two into one. First, based on the characteristics of the bearing vibration signal, the multi-scale kernel algorithm is used to learn features in parallel at different scales. Then, the features extracted at different scales are fused to obtain complementary and rich diagnostic information. Finally, the Softmax classifier is used to output the fault diagnosis results. The simulation is carried out through the bearing vibration data of Case Western Reserve University. The results show that the accuracy of bearing fault diagnosis reaches 99.17%, proving the proposed method’s high accuracy and effectiveness.

This research proposed and validated an LC-MSMS method for five reduction and hydroxylation metabolites of Mequindox (MEQ) as well as the precursor in holothurian samples. Specially, three hydroxylation metabolites (2-isoethanol-mequindox, M3, 2-isoethanol-1-desoxymequindox, M4 and 2-isoethanol-4-desoxymequindox, M5) are novel for analysis. Target compounds were extracted with methanol and ethyl acetate in turn without any complicated acidolysis, alkaline hydrolysis or enzymolysis steps. Samples were further purified with C18 solid-phase extraction cartridges for LC-MSMS analysis. Mean recoveries in spiked samples ranged from 81 to 107% with intra-day relative standard deviation (RSD) and inter-day RSD

Background: Novel coronavirus disease 2019 (COVID-19) pneumonia remains a matter of concern. Chest CT findings of COVID-19 pneumonia have been reported widely, while there is relatively rare research on chest X-ray (CXR). Objective: The study was aimed to compare the CXR and chest CT findings of patients with confirmed COVID-19 infection and to explore their respective clinical values. Methods: 28 inpatients with COVID-19 pneumonia who underwent both CXR and CT were included. The pulmonary manifestations of the lesions were recorded. Ground-glass opacity (GGO), consolidation, and fibrosis were quantified in CXR and chest CT separately. Consistency was analyzed using Fleiss' kappa and intraclass correlation coefficient. The stages of the disease in CXR and chest CT were evaluated. Results: Approximately 67.9% (19/28) of subjects had abnormal findings on CXR. The common manifestations in CXR were ground-glass opacities (GGO) (100%, 19/19) and consolidation (68.4%, 13/19). 92.9% (26/28) of patients had abnormal manifestations on CT. The common manifestations in CT were GGO (88.5%, 23/26), consolidation (69.2%, 18/26), reticular opacity (69.2%, 18/26) and nodule (46.2%, 12/26). Among the abnormalities between CXR and CT, only consolidation was consistent (κ=0.510). GGO (ICC=0.501) and consolidation (ICC=0.431) scores were consistent in CXR and chest CT. The results of staging were the same in 14 cases, most of them were in stage I and stage II. While in other cases with inconsistent results, CT was more advanced in the disease stage than CXR, mainly stage III and stage IV. Conclusion: CXR is helpful to observe the change of the pulmonary lesions in patients with confirmed COVID-19 pneumonia. CT can be used for early diagnosis and staging of lesions.

The multi-scale flexibility coordination of multiple storages is a key technology to enhance the diversified regulation ability of the power system.This paper first considered the interaction mechanism of multi-type storage peak regulation time sequences based on the Euclidian distance, Dynamic time warping distance, and storage correlation distance. A matching index was proposed to consider the temporal correlation, overall distribution characteristics, and dynamic characteristics of the net load and energy storage. The multitype storage coordination mode, including battery storage, pumped storage, and electric vehicles, was formulated, and a collaborative optimal scheduling system architecture of source-grid-load-storage (SGLS) was constructed. To attain a low-carbon economy, a collaborative optimal scheduling model of SGLS considering the dynamic time-series complementarity of multiple energy storage systems was constructed. The Nash equilibrium theory was used to achieve friendly interaction among the source, grid, load, and storage. Then, an improved transfer reinforcement learning algorithm for SGLS was proposed, which used reinforcement learning and transfer learning algorithms combined with K-means clustering and dual-structure experience pool technology. The test results of actual regional power grid data indicated that the proposed strategy can effectively reduce the economic and carbon treatment costs of the system and improve the absorption capacity of renewable energy.

Objectives A retrospective study was performed to summarize the experience of transcatheter closure of patent ductus arteriosus (PDA) through the right femoral vein under the guidance of transesophageal echocardiography (TEE). Methods From January 2019 to September 2021, 75 children who underwent PDA closure through the right femoral vein under the guidance of TEE were included. The guide wire and delivery sheath were inserted through the ductus arteriosus into the descending aorta via the right femoral vein, and the occluder was subsequently deployed. After discharge, all patients were required for outpatient follow-ups at 1, 3, 6 and 12 months. Results In this group, patients were older than 10 months of age and body weight greater than 8 kg. Among 75 cases with PDA, 63 were tubular type and 12 were conical type. The mean operative time was 40.2 ± 7.3 min. The size of PDA occluder ranged from 4–6 to 12–14 mm. The mean hospital stay was 5.5 ± 0.5 days. One month after discharge, there were 4 cases with a mild residual shunt. Eventually, the residual shunt was not observed during 3, 6, and 12 months of follow-up. Conclusions PDA closure under the guidance of TEE can be performed through the right femoral vein successfully and effectively. This procedure has no contrast agent usage, radiation exposure, or open incisions.