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Abstract Background Pulmonary fibrosis (PF) is a common and multidimensional devastating interstitial lung disease. The development of novel and more effective interventions for PF is an urgent clinical need. A previous study has found that miR-181a-5p plays an important role in the development of PF, and human amniotic mesenchymal stem cells (hAMSCs) exert potent therapeutic potential on PF. However, whether hAMSCs act on PF by delivering miR-181a-5p and its detailed mechanism still remain unknown. Thus, this study was designed to investigate the underlying possible mechanism of hAMSCs on PF in bleomycin (BLM)-induced mouse PF model, and a co-culture system of hAMSCs and A549 cells epithelial mesenchymal transition (EMT) model, focusing on its effects on collagen deposition, EMT, and epithelial cell cycle regulation. Methods hAMSCs with different miR-181a-5p expression levels were constructed. BLM (4 mg/kg) was used to create a PF model, while TGF-β1 was used to induce A549 cells to construct an EMT model. Furthermore, the effects of different miR-181a-5p expression in hAMSCs on collagen deposition and EMT during lung fibrosis were assessed in vivo and in vitro. Results We found that hAMSCs exerted anti-fibrotic effect in BLM-induced mouse PF model. Moreover, hAMSCs also exerted protective effect on TGFβ1-induced A549 cell EMT model. Furthermore, hAMSCs ameliorated PF by promoting epithelial cell proliferation, reducing epithelial cell apoptosis, and attenuating EMT of epithelial cells through paracrine effects. hAMSCs regulated EMT in PF through delivering miR-181a-5p targeting TGFBR1. Conclusions Our findings reveal for the first time that hAMSCs inhibit PF by promoting epithelial cell proliferation, reducing epithelial cell apoptosis, and attenuating EMT. Mechanistically, the therapeutic effect of hMASCs on PF is achieved through delivering miR-181a-5p targeting TGFBR1.

Objectives Human cancer is considered to be an important cause of death worldwide. Polypyrimidine tract binding protein 1 (PTBP1) is emerging as a powerful pro-oncogenic factor in bladder and liver cancer; however, no pan-cancer analysis is presently available. Our study aimed to explore PTBP1 expression profiles, prognostic immunological value, and biological functions across various cancers. Methods We conducted a comprehensive analysis using multi-omics bioinformatics from public databases, including TIMER, GEPIA2, ProteinAtlas, Kaplan-Meier Plotter, PrognoScan, cBioPortal, STRING, ENCORI, TargetScan, and DAVID. Results We found that PTBP1 was overexpressed across multiple cancer types. qRT-PCR results demonstrated that the PTBP1 mRNA was significantly up-regulated in lung adenocarcinoma (LUAD), colon cancer (COAD), and melanoma (SKCM) cell lines, as well as in melanoma-forming mouse models. Higher PTBP1 mRNA levels were associated with poorer survival probabilities in several cancer types. PTBP1 genetic alterations were related to amplification and mutation. PTBP1 significantly modulates tumor immunity by enhancing Tregs infiltration and reducing CD8 + T cell activity, promoting immune evasion and adversely affecting cancer prognosis. GO and KEGG pathway analyses implied that PTBP1 may participate in RNA metabolism, the spliceosome, the cell cycle, and the p53 signaling pathway in cancer development. Conclusion Our study is the first to demonstrate the oncogenic role of PTBP1 in a pan-cancer context. PTBP1 might serve as a new biomarker for prognostic prediction and immune cell infiltration across cancers in the future.

Abstract Obstruction and/or reflux compromise during venous emptying can facilitate different pathophysiologies in chronic venous insufficiency (CVI). We present a patient with persistent lower limb CVI edema caused by post-thrombotic syndrome (PTS), who responded well to femoral vein valve therapy via axillary vein bypass after unsuccessful valvuloplasty, and led a normal life. During a 12 month observation period, bridging vessels completely restored original anatomical structures. In a literature study, no similar surgeries were reported, but we show that this operation may be feasible in selected patients.

Dental panoramic X-ray imaging, due to its high cost-effectiveness and low radiation dose, has become a widely used diagnostic tool in dentistry. Accurate tooth segmentation is crucial for lesion analysis and treatment planning, helping dentists to quickly and precisely assess the condition of teeth. However, dental X-ray images often suffer from noise, low contrast, and overlapping anatomical structures, coupled with limited available datasets, leading traditional deep learning models to experience overfitting, which affects generalization ability. In addition, high-precision deep models typically require significant computational resources for inference, making deployment in real-world applications challenging. To address these challenges, this paper proposes a tooth segmentation method based on the pre-trained SAM2 model. We employ adapter modules to fine-tune the SAM2 model and introduce ScConv modules and gated attention mechanisms to enhance the model’s semantic understanding and multi-scale feature extraction capabilities for medical images. In terms of efficiency, we utilize knowledge distillation, using the fine-tuned SAM2 model as the teacher model for distilling knowledge to a smaller model named LightUNet. Experimental results on the UFBA-UESC dataset show that, in terms of performance, our model significantly outperforms the traditional UNet model in multiple metrics such as IoU, effectively improving segmentation accuracy and model robustness, particularly with limited sample datasets. In terms of efficiency, LightUNet achieves comparable performance to UNet, but with only 1.6% of its parameters and 24.0% of the inference time, demonstrating its feasibility for deployment on edge devices.

BackgroundBone metastases of lung cancer (BMLC) severely diminish patients’ quality of life due to bone-related events, and the lack of clear guidelines globally regarding medical and surgical treatment significantly reduces patient survival. While knowledge about BMLC has grown exponentially over the past two decades, a comprehensive and objective bibliometric analysis remains absent.MethodsA comprehensive bibliometric analysis was conducted on relevant literature on BMLC extracted from the Web of Science database from 2004 to 2023 by Biblioshiny, VOSviewer, Scimago Graphica, CiteSpace, and Microsoft Office Excel Professional Plus 2016 software. 936 papers related to BMLC were extracted from the Web of Science Core Collection (WoSCC). The number of publications, countries, institutions, global collaborations, authors, journals, keywords, thematic trends, and cited references were then visualized. Finally, the research status and development direction in the last 20 years were analyzed.ResultsThis study included a total of 936 papers on BMLC from 2004 to 2023. There has been a steady increase in global publications each year, peaking in 2021. China had the highest number of publications, followed by Japan and the United States. Additionally, China had the most citations with an H-index of 35, while the US followed with an H-index of 34, highlighting their significant contributions to the field. “Frontiers in Oncology” had the highest number of publications. CiteSpace analysis identified “lung cancer,” “bone metastasis,” and “survival” as the top high-frequency keywords, encapsulating the core research focus. Keyword clustering analysis revealed six main clusters representing the primary research directions. Burst analysis of keywords showed that “skeletal complications” had the highest burst intensity from 2005 to 2013, while recent research trends include “immunotherapy” and “denosumab,” with bursts from 2021 to 2023. Trend topic analysis indicated that “non-small cell lung cancer,” “immunotherapy,” and “immune checkpoint inhibitors” represent the cutting-edge research directions in this field.ConclusionThis article reveals the current status and trend of research on BMLC, which is increasing worldwide. China and the United States have contributed the most, but international cooperative research on BMLC should be strengthened. The pathogenesis, early prevention, and individualized treatment of BMLC need to be strengthened for further study, and immunotherapy is the next hotspot of lung cancer bone metastasis research.

The limitations of the existing catapults necessitate multiple batches of take-offs for carrier-based unmanned aerial vehicles (UAVs) to form a formation. Because of the differences in takeoff time and location of each batch of UAVs, ensuring the temporal and spatial consistency and rendezvous efficiency of the formation becomes crucial. Concerning the challenges mentioned above, a multi-batch formation rendezvous method based on improved sequential convex programming (SCP) is proposed. A reverse solution approach based on the multi-batch rendezvous process is developed. On this basis, a non-convex optimization problem is formulated considering the following constraints: UAV dynamics, collision avoidance, obstacle avoidance, and formation consistency. An SCP method that makes use of the trust region strategy is introduced to solve the problem efficiently. Due to the spatiotemporal coupling characteristics of the rendezvous process, an inappropriate initial solution for SCP will inevitably reduce the rendezvous efficiency. Thus, an initial solution tolerance mechanism is introduced to improve the SCP. This mechanism follows the idea of simulated annealing, allowing the SCP to search for better reference solutions in a wider space. By utilizing the initial solution tolerance SCP (IST-SCP), the multi-batch formation rendezvous algorithm is developed correspondingly. Simulation results are obtained to verify the effectiveness and adaptability of the proposed method. IST-SCP reduces the rendezvous time from poor initial solutions without significantly increasing the computing time.

Steel/copper bimetallic structural materials combine the good strength and corrosion resistance of steel with the high thermal and wear resistance of copper and have been widely used in fusion reactors, conformal cooling channels, and heat transfer devices. Due to the large difference in thermal and physical properties of steel and copper, and the immiscibility of steel and copper, the bonding interface usually produces a large residual stress during the forming process, which leads to the formation of interface cracks. In order to combine CuCrZr with 316L stainless steel by using selective laser melting technology, IN718 was deposited on 316L stainless steel as a transition layer. The effects of the IN718 alloy transition layer on the microstructure, phase composition, and mechanical properties of the interface of 316L–CuCrZr were studied. IN718–316L had no defects such as pores and cracks at the interface and achieved good metallurgical bonding. The IN718–CuCrZr interface area and copper side had round key holes and relatively small pores. In the solidification process, IN718 preferentially solidified in the shape of fine particles, providing a nucleation point for the copper grain, thus forming equiaxed grains because of the high cooling rate of the interface. No macroscopic defects were observed at the bonding position of the 316L–IN718–CuCrZr samples, and the elements at the interface were fully diffused by each other. The tensile strength of the 316L–IN718–CuCrZr samples reached 403 MPa, and the interface strength was significantly improved upon the addition of the transition layer.

Relationship Extraction (RE) is a central task in information extraction. The use of entity mapping to address complex scenarios with overlapping triples, such as CasRel, is gaining traction, yet faces challenges such as inadequate consideration of sentence continuity, sample imbalance and data noise. This research introduces an entity mapping-based method CasRelBLCF building on CasRel. The main contributions include: A joint decoder for the head entity, utilizing Bi-LSTM and CRF, integration of the Focal Loss function to tackle sample imbalance and a reinforcement learning-based noise reduction method for handling dataset noise. Experiments on relation extraction datasets indicate the superiority of the CasRelBLCF model and the enhancement on model's performance of the noise reduction method.

Abstract Background WAC-antisense RNA1 (WAC-AS1) is a newly identified long non-coding RNA (lncRNA) implicated in the prognosis and development of a few types of tumors. However, the correlations of WAC-AS1 with immune infiltration and patient prognosis in pan-cancer remain unclear. In the present study, we aimed to investigate the prognostic value and immunological functions of WAC-AS1 across 33 different types of cancers. Methods To investigate the potential oncogenic roles of WAC-AS1, bioinformatics analyses were performed using the Cancer Genome Atlas (TCGA) and Genotype Tissue-Expression (GTEx) datasets. The correlations of WAC-AS1 with prognosis, clinical phenotype, tumor mutational burden (TMB), microsatellite instability (MSI), tumor regulation-related genes, tumor microenvironment, immune cell infiltration, and drug resistance to commonly used chemotherapy drugs in different types of tumors were explored. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed to explore the biological functions of WAC-AS1 in tumors. In situ hybridization (ISH) was performed in tissue microarray (TMA) to confirm the expression of WAC-AS1 in multiple tumor tissues. Results WAC-AS1 showed aberrant expression in most cancers when compared to the normal tissues. It also has prognostic value in multiple types of cancers. Elevated WAC-AS1 expression was associated with poor prognosis and overall survival in adrenocortical carcinoma (ACC), breast invasive carcinoma (BRCA), and liver hepatocellular carcinoma (LIHC). A significant negative correlation between WAC-AS1 expression and overall survival was observed in brain lower-grade glioma (LGG), pancreatic adenocarcinoma (PAAD), and skin cutaneous melanoma (SKCM). The expression of WAC-AS1 also showed a correlation with clinical stage in six types of tumors, and with tumor mutational burden and microsatellite instability in several different types of cancers. The immune scores of those cancers were found to be significant. Additionally, the effectiveness of fluorouracil and four other anticancer drugs was significantly different based on the expression of WAC-AS1 in these cancers. Moreover, the ISH results showed in six types of tumors, the expression of WAC-AS1 was consistent with the Pan-cancer analysis using TCGA and GTEx database. Conclusions These results indicate an intensive involvement of WAC-AS1 in the regulation of immune responses, immune cell infiltration, and malignant properties in various types of cancers, suggesting that WAC-AS1 may serve as a prognostic marker across diverse types of cancers.

BackgroundSpinal cord injury (SCI) triggers motor, sensory, and autonomic impairments that adversely damage patients' quality of life. Its pathophysiological processes include inflammation, oxidative stress, and apoptosis, although existing treatment options have little success. Macrophages have a vital function in controlling inflammation in SCI, with their M1-type and M2-type macrophages dominating early inflammatory effects and late brain tissue repair and regeneration, respectively. However, there is a dearth of rigorous bibliometric study in this sector to explore its dynamics and trends. This study intends to examine the current status and trends of macrophage usage in SCI using bibliometric methodologies, which may drive novel therapeutic options.MethodsIn this study, the Web of Science Core Collection (WOSCC) was utilized to collect publications and reviews on macrophages in SCI from 2002 to 2023. Bibliometrics and visualization analyses were performed by VOSviewer, CiteSpace, the R package “bibliometrix”, and online analytic platforms. These analyses covered a variety of aspects, including countries and institutions, authors and co-cited authors, journals and co-cited journals, subject categories, co-cited references, and keyword co-occurrences, in order to provide insights into the research trends and hotspots in this field.Results1,775 papers were included in the study, comprising 1,528 articles and 247 reviews. Our research analysis demonstrates that the number of relevant studies in this sector is expanding, specifically the number of publications in the United States and China has risen dramatically. However, there are fewer collaborations between institutions in different nations, and international cooperation needs to be reinforced. Among them, Popovich PG became the leader in the field, and significant journals include Experimental Neurology, Journal of Neurotrauma, and Journal of Neuroscience. Research hotspots involve macrophage polarization, microglia, astrocytes, signaling, cytokines, inflammation, and neuroprotection.ConclusionsThis analysis gives, for the first time, a comprehensive overview of bibliometric studies on macrophages in SCI over the past 20 years. This study not only gives an extensive picture of the knowledge structure but also indicates trends in the subject. The systematic summarization gives a complete and intuitive understanding of the link between spinal cord damage and macrophages and provides a great reference for future related studies.

Constructing advanced nanostructures and combining various active materials are effective strategies to obtain high-performance anode materials for lithium-ion batteries (LIBs). Herein, we report a surface-coordinated polymerization method to synthesize ZIF-67 nanoparticles with a reuleaux tetrahedron morphology, and combine the electrostatic adsorption method and heat treatment, nitrogen-doped hollow porous Co3O4 (HP-Co3O4/NC) nanoparticles were anchored on reduced graphene oxide (rGO) to obtain HP-Co3O4/NC@rGO composites. This deliberate structural design can significantly improve the electrochemical performance of lithium (Li) storage. When applied as an anode material in LIBs, HP-Co3O4/NC@rGO nanocomposite renders a reversible capacity of 809 mAh g−1 after 200 charge/discharge cycles at a current density of 1000 mA g−1. The excellent electrochemical performance is mainly attributed to the synergistic effect of the hollow nanostructure and graphene network. A hollow nanostructure can provide abundant active sites and enhance structural stability. As a conductive network, graphene tightly wraps Co3O4, which can not only increase the electronic and ionic transport but also alleviates the volumetric changes of Co3O4 during the charge/discharge process, thereby improving the rate performance and cyclic stability.

Machine learning (ML) of phase transitions (PTs) has gradually become an effective approach that enables us to explore the nature of various PTs more promptly in equilibrium and nonequilibrium systems. Unlike equilibrium systems, non-equilibrium systems display more complicated and diverse features because of the extra dimension of time, which is not readily tractable, both theoretically and numerically. The combination of ML and most renowned nonequilibrium model, directed percolation (DP), led to some significant findings. In this study, ML is applied to $(1+1)$ -d, even offspring branching annihilating random walks (BAW), whose universality class is not DP-like. The supervised learning of $(1+1)$ -d BAW via convolutional neural networks (CNN) results in a more accurate prediction of the critical point than the Monte Carlo (MC) simulation for the same system sizes. The dynamic exponent z and spatial correlation length correlation exponent $\nu_{\perp}$ were also measured and found to be consistent with their respective theoretical values. Furthermore, the unsupervised learning of $(1+1)$ -d BAW via an autoencoder (AE) gives rise to a transition point, which is the same as the critical point. The latent layer of AE, through a single neuron, can be regarded as the order parameter of the system being properly re-scaled. Therefore, we believe that ML has exciting application prospects in reaction-diffusion systems such as BAW and DP.

Acute arterial thrombosis in a lower extremity is an unusual and serious complication in patients with nephrotic syndrome (NS) and coexisting antithrombin deficiency (ATD). Here we report the case of a patient with NS who was on steroid therapy, had a family history of pulmonary embolism, and had ATD that worsened after endovascular treatment of acute arterial disease in a lower extremity and was refractory to anticoagulant therapy. Thrombectomy and catheter-directed thrombolysis were unsuccessful and amputation was required. Vigilance is necessary in patients with refractory NS during steroid therapy, given the risk of thromboembolic complications that may require aggressive management. We believe that effective alternative anticoagulant therapy can promote recovery and improve the prognosis in patients with both NS and ATD.

Background: Normal fusion of the upper lip and primary palate is a complex process involving a series of characteristic and orderly regulated cellular events. Cleft lip with or without palate (CL/P), one of the most common congenital malformations, may be induced by abnormalities in any of these events. However, less is known about the precise regulatory process in the fusion of the upper lip and primary palate.Methods: Lambdoidal junction tissues of mice from embryonic days 10.5, 11.5, and 12.5— three key fusion stages—were acquired for RNA sequencing.Results: Gene expression profiles in distinct fusion stages of mice were identified. Some of the differentially expressed genes (DEGs) have been reported to affect upper lip and primary palate development. However, other DEGs, such as Krt5, Pax1, Ambn, Hey2, and Tnmd, have not previously been investigated. Gene set enrichment analysis (GSEA) of these DEGs revealed the sequential intensification of Wnt, PI3K-Akt, MAPK, Hippo, and TGF-beta signaling pathways and identified relatively highly expressed genes including Tnn, Wnt3a, and Wnt16. We also observed substantial alternative splicing events during the fusion of the upper lip and primary palate and identified potentially important genes including Gtpbp8, Armcx1, Tle3, and Numa1. Protein-protein interaction (PPI) network analysis identified a series of hub genes, including Col1a2, Fos, Bmp2, Shh, Col1a1, Wnt3a, Anxa1, Gem, etc.Conclusion: Overall, the results of this study provided a comprehensive analysis of the development of the upper lip and primary palate. Our work provides insight into future studies of normal upper lip and primary palate development and the etiology of CL/P.

Environmental pollution caused by the use of fossil fuels is becoming increasingly serious, necessitating the adoption of clean energy solutions. Lithium-ion batteries (LIBs) have attracted great attention due to their high energy density and currently occupy a dominant commercial position. Metal oxide materials have emerged as promising anode materials for the next generation of LIBs, thanks to their high theoretical capacity. However, the practical application of these materials is hindered by their substantial volume expansion during lithium storage and poor electrical conductivity. In this work, a zinc/iron bimetallic hybrid oxide composite, ZnO/ZnFe2O4/NC, is prepared using ZIF-8 as a precursor (ZIF-8, one of the metal organic frameworks). The N-doped porous carbon composite improves the volume change and optimizes the lithium-ion and electron transport. Meanwhile, the ZnFe2O4 and ZnO synergistically enhance the electrochemical activity of the anode through the built-in heterojunction to promote the reaction kinetics at the interface. As a result, the material delivers an excellent cycling performance of 604.7 mAh g−1 even after 300 cycles of 1000 mA g−1. This study may provide a rational design for the heterostructure and doping engineering of anodes for high-performance lithium-ion batteries.

Accurate wind power data prediction is crucial to increase wind energy usage since wind power data are characterized by uncertainty and randomness, which present significant obstacles to the scheduling of power grids. This paper proposes a hybrid model for wind power prediction based on complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN), sample entropy (SE), bidirectional long short-term memory network (BiLSTM), and Markov chain (MC). First, CEEMDAN is used to decompose the wind power series into a series of subsequences at various frequencies, and then SE is employed to reconstruct the wind power series subsequences to reduce the model’s complexity. Second, the long short-term memory (LSTM) network is optimized, the BiLSTM neural network prediction method is used to predict each reconstruction component, and the results of the different component predictions are superimposed to acquire the total prediction results. Finally, MC is used to correct the model’s total prediction results to increase the accuracy of the predictions. Experimental validation with measured data from wind farms in a region of Xinjiang, and computational results demonstrate that the proposed model can better fit wind power data than other prediction models and has greater prediction accuracy and generalizability for enhancing wind power prediction performance.

In order to study the microstructure evolution of the formed parts in the axial vibration cold extrusion process， the microstructure evolution process of the blank in the axial cold extrusion process is simulated by using the cellular automata method. It is found that the average grain size of internal spline processed by cold extrusion with auxiliary vibration is significantly smaller than that without auxiliary vibration. The effects of vibration frequency，vibration amplitude and extrusion speed on the microstructure of the formed parts are analyzed by orthogonal test. The results of variance analysis show that the vibration amplitude has the most significant influence on the average grain size at the tooth root，and the most significant influence on the grain size at the pitch circle and the top of the tooth is the vibration frequency and extrusion speed， the vibration frequency has the most significant influence on the average grain uniformity at the root and pitch circle of the formed part， and the extrusion speed has the most significant influence on the grain uniformity of the tooth top. The mechanism of the effect of auxiliary vibration parameters on grain refinement is summarized.

Typical fault scenarios such as actuator stuck, wingtip damage, and sudden center of gravity change usually bring great challenge to fault-tolerant flight control laws. In this paper, adaptive sliding mode disturbance observer (SMDO) is studied under the background of designing a fault-tolerant flight controller. This paper proposes a switched adaptive SMDO for observation of system uncertainties considering fault accommodation. It consists of two adaptive SMDOs: baseline gain-adaptation SMDO and fault-oriented gain-adaptation SMDO. The former is designed for observing slow-changing uncertainties, and can guarantee that the observer gains are small when disturbances are small; the latter aims at achieving fast observing and accommodating of sudden and large faults. Thereafter, a switching logic is developed for the switch between these two adaptive SMDO modes to ensure that the fast gain-adaptation SMDO only works in the case that the uncertainties are large and fast-changing. Finally, the switched adaptive SMDO is integrated with an incremental terminal sliding mode controller, and are validated using simulation experiments based on the nonlinear F-16 model.

For realizing high-accuracy short-term wind power prediction, a hybrid model considering physical features of data is proposed in this paper, with consideration of chaotic analysis and granular computing. First, considering the chaotic features of wind power time series physically, data reconstruction in chaotic phase space is studied to provide a low-dimensional input with more information in modeling. Second, considering that meteorological scenarios of wind development are various, complicated, and uncertain, typical chaotic time series prediction models and wind scenarios are analyzed correspondingly via granular computing (GrC). Finally, through granular rule-based modeling, a hybrid model combining reconstructed wind power data and different models is constructed for short-term wind power prediction. Data from real wind farms is taken for experiments, validating the feasibility and effectiveness of the proposed wind power prediction model.

With the rapid development of electric vehicle (EV) technology under the background of double carbon, a reasonable evaluation of the network planning of EV charging stations is conducive to the long-term stable development of the EV industry. Therefore, this paper proposes a comprehensive evaluation system and method for EV charging networks under the traffic network and power grid coupling. First, an EV travel model is developed based on the travel probability matrix to analyze the spatial and temporal characteristics of EVs. Secondly, the coupling relationship among users, charging network, road network, and the power grid is analyzed, four criteria of user feedback, charging network operation, road network operation impact, and power grid operation impact are proposed, and corresponding evaluation indexes are constructed under each criterion to form a comprehensive evaluation index system. Furthermore, the Analytic Hierarchy Process (AHP) and entropy weighting method are used to assign weights to the index layer and the criterion layer respectively, and the improved TOPSIS evaluation method is used to quantitatively analyze the impact of charging networks on different subjects for comprehensive evaluation. Finally, through the comprehensive evaluation of different charging network planning schemes, the effectiveness of the comprehensive evaluation method and model proposed in this paper is verified, which can provide a reference basis for the planning and improvement of EV charging station networks.

Submarine power cables must be reinforced and must possess suitable mechanical properties to meet operational requirements for complex marine environments. This study numerically investigated the mechanical properties of submarine power cables under tensile, torsional, and compressive loading. The optimal number of cores was determined according to the variation in tensile and torsional stiffness with radial compressive pressure. As the radial compressive pressure increased, the tensile and torsional stiffness of the four- and five-core cable models decreased marginally compared with those of the three-core cable model; thus, the five-core cable model was superior to the two-, three-, and four-core cable models in terms of radial deformation and contact stress. The two-core cable model was superior to the three-, four-, and five-core cable models in terms of tensile and torsional strength. The results of this study can serve as a reference in the design of submarine power cables.

In order to realize sensorless control of permanent magnet synchronous motor (PMSM) with high performance in low speed region, a novel rotor position observer scheme based on finite control set model predictive control (FCS-MPC) is presented in this paper. Firstly, the FCS-MPC is used to predict the current and drive the PMSM by selecting the optimal control quantity that minimizes the cost function. Next, an adaptive second-order generalized integrator (ASOGI) with adaptive center frequency adjustment was designed to replace the band-pass filter (BPF) in the rotor position observer. The ASOGI can calculate the high frequency value that can be used for position estimation by the controller switching frequency. The current ripple inherent in the FCS-MPC is considered as the response current obtained by the high frequency injection (HFI) method. The current ripple after ASOGI filtering is input to the phase-locked loop (PLL) for phase locking to obtain the estimated rotor position. In addition, adaptive linear (Adaline) neural networks are used to identify sensitive motor parameters online to avoid mismatch of model parameters, which causes degradation of control performance. Simulation experiments and hardware experiments show that this scheme is excellent in both static and dynamic conditions.

Traditional charging stations have a single function, which usually does not consider the construction of energy storage facilities, and it is difficult to promote the consumption of new energy. With the gradual increase in the number of new energy vehicles (NEVs), to give full play to the complementary advantages of source-load resources and provide safe, efficient, and economical energy supply services, this paper proposes the optimal operation strategy of a PV-charging-hydrogenation composite energy station (CES) that considers demand response (DR). Firstly, the operation mode of the CES is analyzed, and the CES model, including a photovoltaic power generation system, fuel cell, hydrogen production, hydrogen storage, hydrogenation, and charging, is established. The purpose is to provide energy supply services for electric vehicles (EVs) and hydrogen fuel cell vehicles (HFCVs) at the same time. Secondly, according to the travel law of EVs and HFCVs, the distribution of charging demand and hydrogenation demand at different periods of the day is simulated by the Monte Carlo method. On this basis, the following two demand response models are established: charging load demand response based on the price elasticity matrix and interruptible load demand response based on incentives. Finally, a multi-objective optimal operation model considering DR is proposed to minimize the comprehensive operating cost and load fluctuation of CES, and the maximum–minimum method and analytic hierarchy process (AHP) are used to transform this into a linearly weighted single-objective function, which is solved via an improved moth–flame optimization algorithm (IMFO). Through the simulation examples, operation results in four different scenarios are obtained. Compared with a situation not considering DR, the operation strategy proposed in this paper can reduce the comprehensive operation cost of CES by CNY 1051.5 and reduce the load fluctuation by 17.8%, which verifies the effectiveness of the proposed model. In addition, the impact of solar radiation and energy recharge demand changes on operations was also studied, and the resulting data show that CES operations were more sensitive to energy recharge demand changes.

Knowledge graph (KG) reasoning improves the perception ability of graph structure features, improving model accuracy and enhancing model learning and reasoning capabilities. This paper proposes a new GraphDIVA model based on the variational reasoning divergent autoencoder (DIVA) model. The network structures and calculation processes of the models are analyzed. The GraphSAGE algorithm is introduced into the path reasoning module to solve the inability of the original model to perceive the features of the graph structure, which leads to a decline in the accuracy rate. Hence, GraphDIVA can achieve a higher accuracy rate with fewer learning iterations. The experiments show the efficiency and effectiveness of our model and proves that our method has a better effect on the accuracy rate and training difficulty than the baseline model on the FB15k-237 and NELL-995 benchmark datasets.

In this study, we established a two-dimensional logistic differential equation model to study the number of visits in Chinese PHCIs and hospitals based on the behavior of patients. We determine the model's equilibrium points and analyze their stability and then use China medical services data to fit the unknown parameters of the model. Finally, the sensitivity of model parameters is evaluated to determine the parameters that are susceptible to influence the system. The results indicate that the system corresponds to the zero-equilibrium point, the boundary equilibrium point, and the positive equilibrium point under different parameter conditions. We found that, in order to substantially increase visits to PHCIs, efforts should be made to improve PHCI comprehensive capacity and maximum service capacity.

At present， the design and manufacture of the domestic spline cold roll forming machine are mainly based on the experimental data， and theoretical studies are still insufficient. Taking the vertical spline cold roll forming machine for example， the design and analysis methods are studied. Based on the calculation of the forming force， the overall layout and machine tool body structure of vertical spline cold roll forming machine are given. According to the forming force data obtained by simulation as well as the working conditions and force analysis for the sliding table and screw， the key parts of the transmission system are designed. The static and dynamic characteristics of the machine tool are also analyzed by strength and modal analysis for the vertical spline cold roll forming machine.

Abstract Background Pulmonary hypertension (PH) in chronic obstructive pulmonary disease (COPD) is suggested as the consequence of emphysematous destruction of vascular bed and hypoxia of pulmonary microenvironment, mechanisms underpinning its pathogenesis however remain elusive. The dysregulated expression of nicotinamide adenine dinucleotide phosphate (NADPH)-oxidases and superoxide generation by pulmonary vasculatures have significant implications in the hypoxia-induced PH. Methods In this study, the involvement of NADPH oxidase subunit 4 (NOX4) in pulmonary arteriolar remodeling of PH in COPD was investigated by ascertaining the morphological alteration of pulmonary arteries and pulmonary blood flow using cardiac magnetic resonance imaging (cMRI), and the expression and correlation of NOX4 with pulmonary vascular remodeling and pulmonary functions in COPD lungs. Results Results demonstrated that an augmented expression of NOX4 was correlated with the increased volume of pulmonary vascular wall in COPD lung. While the volume of distal pulmonary arteries was inversely correlated with pulmonary functions, despite it was positively associated with the main pulmonary artery distensibility, right ventricular myocardial mass end-systolic and right ventricular myocardial mass end-diastolic in COPD. In addition, an increased malondialdehyde and a decreased superoxide dismutase were observed in sera of COPD patients. Mechanistically, the abundance of NOX4 and production of reactive oxygen species (ROS) in pulmonary artery smooth muscle cells could be dynamically induced by transforming growth factor-beta (TGF-β), which in turn led pulmonary arteriolar remodeling in COPD lungs. Conclusion These results suggest that the NOX4-derived ROS production may play a key role in the development of PH in COPD by promoting distal pulmonary vascular remodeling.

Solar photovoltaic (PV) power generation has strong intermittency and volatility due to its high dependence on solar radiation and other meteorological factors. Therefore, the negative impact of grid-connected PV on power systems has become one of the constraints in the development of large scale PV systems. Accurate forecasting of solar power generation and flexible planning and operational measures are of great significance to ensure safe, stable, and economical operation of a system with high penetration of solar generation at transmission and distribution levels. In this paper, studies on the following aspects are reviewed: (1) this paper comprehensively expounds the research on forecasting techniques of PV power generation output. (2) In view of the new challenge brought by the integration of high proportion solar generation to the frequency stability of power grid, this paper analyzes the mechanisms of influence between them and introduces the current technical route of PV power generation participating in system frequency regulation. (3) This section reviews the feasible measures that facilitate the inter-regional and wide-area consumption of intermittent solar power generation. At the end of this paper, combined with the actual demand of the development of power grid and PV power generation, the problems that need further attention in the future are prospected.

This paper studies the unbalanced radial force of in-wheel switched reluctance motors effect on vehicle performance under stability condition. First, the IWM-EV vehicle model which highlights the new factors of the rotor, stator, SRM vertical force, and airgap deflection was adopted. And the vehicle comparison models were established to make a contrastive study about IWM-EV new structure effects. Then, Routh’s stability criterion method was adopted to judge the vehicle system’s stability condition. It includes two parts: characteristic polynomial of the vehicle system which is served to calculate the IWM-EV system Routh array and Routh’s stability criterion which is used to judge the vehicle system’s stability condition. The effect of the new structure on vehicle performance was further discussed under stability condition. It mainly includes two aspects: vehicle vibration mode analysis which is used to study vehicle vibration characteristics and amplitude-frequency analysis which is served to research transfer response characteristics of the vehicle system. The result shows that the new structure of IWM-EV has a negative effect on vehicle stability performance. The SRM vertical force will change vehicle stability characteristic. In some severe cases, it will even destroy IWM-EV’s stability.

Switched reluctance motor (SRM) has got great attention in in-wheel motor electric vehicle (IWM-EV), but SRM vertical force, the vertical component of SRM unbalanced radial force, yields SRM vertical vibration and does harm to dynamic performance of IWM-EV. In order to reduce the SRM vertical vibration, electromagnetic active suspension and a linear quadratic Gaussian (LQG) controller were used to suppress the unbalanced radial force in this paper. All the models and the controller were constructed in Matlab/Simulink R2015b. The controller considers five performance indexes: vehicle body acceleration, SRM airgap eccentricity, SRM stator acceleration, suspension dynamic deflections, and tyre deformation. Analytic Hierarchy Process (AHP) was used to calculate the weighted coefficients of performance indexes. Simulations indicate that this electromagnetic active suspension can reduce SRM vertical vibration obviously and improve dynamic performance of IWM-EV.

Substantial increase in the amount of civil aviation traffic and other factors have led to frequent congestion and adverse effects in terminal area. Therefore, the landing sequence of aircrafts need to be managed so as to reduce adverse effects. This type of problem is called Aircraft Landing Problem (ALP), and is an optimization problem. In this paper, the current situation of ALP research is analysed. The specific process of landing is described, and the existing research problems are classified. At the same time, on the aspect of optimization, this paper discusses the optimization processing of ALP, including the establishment of optimization model and the design of optimization algorithm. In the last, the future research trend of ALP is proposed as conclusions.