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Abstract Quantum photonic integrated circuits are reshaping quantum networks and sensing by providing compact, efficient platforms for practical quantum applications. Despite continuous breakthroughs, integrating entangled registers into photonic devices on a CMOS-compatible platform presents significant challenges. Herein, we present single electron-nuclear spin entanglement and its integration into a silicon-carbide-on-insulator (SiCOI) waveguide. We demonstrate the successful generation of single divacancy electron spins and near-unity spin initialization of single 13C nuclear spins. Both single nuclear and electron spin can be coherently controlled and a maximally entangled state with a fidelity of 0.89 has been prepared under ambient conditions. Based on the nanoscale positioning techniques, the entangled quantum register has been further integrated into SiC photonic waveguides for the first time. We find that the intrinsic optical and spin characteristics of the register are well preserved and the fidelity of the entangled state remains as high as 0.88. Our findings highlight the promising prospects of the SiCOI platform as a compelling candidate for future scalable quantum photonic applications.

Abstract Quantum metrology promises unprecedented precision of parameter estimation, but it is often vulnerable to noise. While significant efforts have been devoted to improving the metrology performance in Markovian environments, practical control schemes specifically designed for non-Markovian noises are much less investigated. Here, we propose two control-enhanced quantum metrology schemes that are suitable for tackling general non-Markovian noises described by noise channels or noise spectra. We conduct experiments to verify the efficacy of these schemes on a nuclear magnetic resonance system. The experimental results involving multiqubit probes show that the parameter estimation precision can be greatly improved, significantly surpassing the standard quantum limit, with our schemes. At present, non-Markovian noises are widely encountered on diverse quantum devices, the proposed schemes are relevant for realistic metrology applications on these platforms.

The quantity and complexity of environmental data show exponential growth in recent years. High-quality big data analysis is critical for performing a sophisticated characterization of the complex network of environmental pollution. Machine learning (ML) has been employed as a powerful tool for decoupling the complexities of environmental big data based on its remarkable fitting ability. Yet, due to the knowledge gap across different subjects, ML concepts and algorithms have not been well-popularized among researchers in environmental sustainability. In this context, we introduce a new research paradigm—“ChatGPT + ML + Environment”, providing an unprecedented chance for environmental researchers to reduce the difficulty of using ML models. For instance, each step involved in applying ML models to environmental sustainability, including data preparation, model selection and construction, model training and evaluation, and hyper-parameter optimization, can be easily performed with guidance from ChatGPT. We also discuss the challenges and limitations of using this research paradigm in the field of environmental sustainability. Furthermore, we highlight the importance of “secondary training” for future application of “ChatGPT + ML + Environment”.

Being an essential component of organic chemistry, named reactions are indispensable in organic synthesis. More and more named reactions have been devised to create unique polymer backbones, which has significantly accelerated the creation of new polymer materials. Yet, the university chemical knowledge system is deficient in relevant introductions. The purpose of this study is to incorporate the specified reactions within the teaching of polymer chemistry. These reactions are divided into six categories: transition metal-catalyzed coupling reactions, condensation reactions, pericyclic reactions, multi-component reactions, free radical reactions, and other reactions. This work methodically covers these six types of reactions, which are critical for polymer backbone synthesis, and presents the practice in teaching. It can assist students to learn polymer chemistry in conjunction with organic chemistry and provide some reference for students and teachers.

The evaluation of the focal field of Cassegrain antennas is crucial for the design and optimization of the complex feeds in quasi-optical systems. However, employing traditional physical optics methods generates high computational complexity and is inefficient. An accurate and efficient calculation method of Cassegrain antennas’ focal fields that involves a range of incidence angles is proposed, which integrates ray tracing and vector diffraction integration (RT-VDI) theories. It can calculate the focal field in any given or predefined incident direction, not limited to the case of axial incidence. In addition, the use of the equivalent parabolic theory greatly simplifies the process of integral calculation. Moreover, ray tracing and integration operations are executed upon the calculation of the reflector to further improve efficiency. Numerical examples are presented to demonstrate the accuracy and efficiency of the proposed method.

Thin-walled parts exhibit high flexibility, rendering them susceptible to chatter during milling, which can significantly impact machining accuracy, surface quality, and productivity. Therefore, chatter detection plays a crucial role in thin-wall milling. In this study, a chatter detection method based on multi-sensor fusion and a dual-stream convolutional neural network (CNN) is proposed, which can effectively identify the machining status in thin-wall milling. Specifically, the acceleration signals and cutting force signals are first collected during the milling process and transformed into the frequency domain using fast Fourier transform (FFT). Secondly, a dual-stream CNN is designed to extract the hidden features from the spectrum of multi-sensor signals, thereby avoiding confusion when learning the features of each sensor signal. Then, considering that the characteristics of each sensor are of different importance for chatter detection, a joint attention mechanism based on residual connection is designed, and the feature weight coefficients are adaptively assigned to obtain the joint features. Finally, the joint features feed into a machining status classifier to identify chatter occurrences. To validate the feasibility and effectiveness of the proposed method, a series of milling tests are conducted. The results demonstrate that the proposed method can accurately distinguish between stable and chatter under various milling scenarios, achieving a detection accuracy of up to 98.68%.

With the application of lithium batteries more and more widely, in order to accurately estimate the state of charge (SoC) of the battery, this paper uses the iterated extended Kalman filter (IEKF) algorithm to estimate the SoC. The Levenberg–Marquardt (LM) method is used to optimize the error covariance matrix of IKEF. Based on the hybrid pulse power characteristics experiment, a second-order Thevenin model with variable parameters is established on the MATLAB platform. The experimental results show that the proposed model is effective under the constant current discharge condition, the Federal Urban Driving Schedule (FUDS) condition, and the Beijing dynamic stress test (BJDST) condition. The results show that the simulation error of the improved LM-IEKF algorithm is less than 2% under different working conditions, which is lower than that of the IKEF algorithm. The improved algorithm has a fast convergence speed to the true value, and it has a good estimation accuracy in the case of large changes in external input current. Additionally, the fluctuation of error is relatively stable, which proves the reliability of the algorithm.

Among existing approaches to holonomic quantum computing, the adiabatic holonomic quantum gates (HQGs) suffer errors due to decoherence, while the non-adiabatic HQGs either require additional Hilbert spaces or are difficult to scale. Here, we report a systematic, scalable approach based on dynamical invariants to realize HQGs without using additional Hilbert spaces. While presenting the theoretical framework of our approach, we design and experimentally evaluate single-qubit and two-qubits HQGs for the nuclear magnetic resonance system. The single-qubit gates acquire average fidelity 0.9972 by randomized benchmarking, and the controlled-NOT gate acquires fidelity 0.9782 by quantum process tomography. Our approach is also platform-independent, and thus may open a way to large-scale holonomic quantum computation.

Topological quantum computation (TQC) is one of the most striking architectures that can realize fault-tolerant quantum computers. In TQC, the logical space and the quantum gates are topologically protected, i.e., robust against local disturbances. The topological protection, however, requires complicated lattice models and hard-to-manipulate dynamics; even the simplest system that can realize universal TQC—the Fibonacci anyon system—lacks a physical realization, let alone braiding the non-Abelian anyons. Here, we propose a disk model that can simulate the Fibonacci anyon system and construct the topologically protected logical spaces with the Fibonacci anyons. Via braiding the Fibonacci anyons, we can implement universal quantum gates on the logical space. Our disk model merely requires two physical qubits to realize three Fibonacci anyons at the boundary. By 15 sequential braiding operations, we construct a topologically protected Hadamard gate, which is to date the least-resource requirement for TQC. To showcase, we implement a topological Hadamard gate with two nuclear spin qubits, which reaches 97.18% fidelity by randomized benchmarking. We further prove by experiment that the logical space and Hadamard gate are topologically protected: local disturbances due to thermal fluctuations result in a global phase only. As a platform-independent proposal, our work is a proof of principle of TQC and paves the way toward fault-tolerant quantum computation.

Abstract Noise is ubiquitous in real quantum systems, leading to non-Hermitian quantum dynamics, and may affect the fundamental states of matter. Here we report in an experiment a quantum simulation of the two-dimensional non-Hermitian quantum anomalous Hall (QAH) model using the nuclear magnetic resonance processor. Unlike the usual experiments using auxiliary qubits, we develop a stochastic average approach based on the stochastic Schrödinger equation to realize the non-Hermitian dissipative quantum dynamics, which has advantages in saving the quantum simulation sources and simplifying the implementation of quantum gates. We demonstrate the stability of dynamical topology against weak noise and observe two types of dynamical topological transitions driven by strong noise. Moreover, a region where the emergent topology is always robust regardless of the noise strength is observed. Our work shows a feasible quantum simulation approach for dissipative quantum dynamics with stochastic Schrödinger equation and opens a route to investigate non-Hermitian dynamical topological physics.

With the advent of the aging population, fall detection has gradually become a research hotspot. Aiming at the detection of human fall using millimeter-wave radar, a Range-Doppler heat map Sequence detection Network (RDSNet) model that combines the convolutional neural network and long short-term memory network is proposed in this study. First, feature extraction is performed using the convolutional neural network. After obtaining the feature vector, the feature vector corresponding to the dynamic sequence is inputted to the long short-term memory network. Subsequently, the time correlation information of the heat map sequence is learned. Finally, the detection results are obtained using the classifier. Moreover, diverse human movement information of different objects is collected using millimeter-wave radar, and a range-Doppler heat map dataset is built in this work. Comparative experiments show that the proposed RDSNet model can reach an accuracy of 96.67% and the calculation delay is not higher than 50 ms. The proposed RDSNet model has good generalization capabilities and provides new technical ideas for human fall detection and human posture recognition.

Universities are coming under increasing pressure to produce impact that contributes to socio-economic development. Previous studies have focused on university technology transfer (UTT) mechanisms that lead to the commercialisation of intellectual property. We find that off-late, the legitimacy of such mechanisms, owing to high-failure rates of IP-based start-ups, has come into question. Universities are being invited to widen their academic entrepreneurship (AE) practices to include other forms of knowledge transfer. The increased attention on contemporary concepts such as social entrepreneurship calls for broadening UTT performance indicators to include multiple bottom-lines. These bottom-lines should amplify the societal benefits of AE. This new emphasis raises high-level questions about the societal role of universities and UTT. The aim in this paper is to, therefore, reframe the debate on UTT processes by highlighting a revised agenda for further research. This is done by analysing the extant literature on UTT, academic entrepreneurship, entrepreneurial universities and innovation. The analysis includes a critical review of 96 peer-reviewed articles. As a result, a macro perspective is presented which highlights UTT as-a-mechanism for universities’ contribution to collective societal goals. Propositions are outlined for future research; these can be used for developing new understanding on how universities can actively contribute to socio-economic development agendas.

Exposure to ambient particulate matter is one of the leading global health risks. Here, the authors reveal, by means of chemical multi-fingerprinting, the presence of exogenous ultrafine particles with diverse species and morphology in non-occupational human serum and pleural effusion.

Background: Ciji-Hua’ai-Baosheng II Formula (CHB-II-F) is a traditional Chinese medicine formula, which specifically targets different aspects of chemotherapy-induced adverse effects in patients with cancer. In our clinical application, CHB-II-F significantly alleviated chemotherapy-induced anorexia (loss of appetite) and improved the quality of life for patients with tumor during and after chemotherapy. However, the mechanism of CHB-II-F in alleviation of chemotherapy-induced anorexia remains to be further investigated.Aim of Study: To explore the therapeutic effect and mechanism of CHB-II-F on chemotherapy-induced anorexia in the mice model of H22 hepatoma.Materials and Methods: A total of 72 Kunming mice of SPF grade were inoculated subcutaneously with H22 hepatoma cells into the right anterior armpit of the mice. After 1 week of seeding, mice were injected intraperitoneally with a high dose of 5-fluorouracil (200 mg/kg 5-FU) to establish the model of chemotherapy. The mice were randomly divided into six groups: untreated group, 5-FU group, 5-FU plus Yangzheng Xiaoji capsule (YZXJC) group, and three groups of 5-FU plus different concentrations of CHB-II-F. All the mice in each group were treated for 14 days. The body weight, food intake, tumor volume, and tumor weight of mice were measured, and pathological examinations of tumor tissue, stomach, and duodenum were carried out. Expressions of serum Leptin, Neuropeptide Y (NPY), epidermal cell growth factor (EGF), Motilin (MTL), Orexin A (OXA), Gastrin (GAS), Ghrelin, Prostaglandin E2 (PGE2), and jejunum superoxide dismutase (SOD) activity and malondialdehyde (MDA) content were examined. The protein and mRNA levels of proopiomelanocortin (POMC), Orexin receptor 1 (OX1R), neuropeptide Y (NPY), cocaine and amphetamine regulated transcript peptide (CART), Agouti gene-related protein (AgRP), Leptin receptor (Ob-R), and Ghrelin receptor (GHSR) were examined in hypothalamus, and the protein levels of substance P (SP) and 5-hydroxytryptamine (5-HT) in duodenum were measured.Results: The combination of CHB-II-F and 5-FU could enhance the inhibitory effect of 5-FU on tumor. The tumor inhibition rates of 5-FU group, YZXJC group, CHB-II-F(H) group, CHB-II-F(M) group, and CHB-II-F(L) group were 58.88, 28.08, 54.96, 37.69, and 28.61%, respectively. Compared with untreated group and 5-FU group, CHB-II-F significantly increased the body weight and food intake of tumor-bearing mice; increased the content of NPY, Orexin A, Ghrelin, GAS, MTL, EGF, and PGE2 in serum and the activity of SOD in jejunum; and decreased the content of Leptin in serum and the content of MDA in jejunum. Compared with untreated group and 5-FU group, CHB-II-F also enhanced the expression of OX1R, GHSR, NPY, and AgRP protein and gene and decreased the expression of Ob-R, POMC, and CART protein and gene in hypothalamus of mice, and the gene expression was consistent with the protein expression. In addition, CHB-II-F decreased the expression of 5-HT and SP protein in duodenum.Conclusion: In the murine model of H22 hepatocellular carcinoma (HCC) receiving chemotherapy, CHB-II-F enhances the inhibitory effect of 5-FU on tumor, significantly improves the pathological injury of gastrointestinal tract caused by chemotherapy, and regulates the secretion of gastrointestinal hormones. It may alleviate chemotherapy-induced anorexia by affecting appetite regulatory factors in the feeding area of hypothalamus central nervous system and peripheral appetite regulatory factors.

Determining the source of nanoparticles is critical for nanotechnology risk assessment. Here, the authors develop an approach that, by taking into account the isotopic signatures of both Si and O, may be able to distinguish between natural and engineered SiO2 nanoparticles, and even those synthesized by different manufacturers.

To address the defocusing problem faced by random stepped frequency (RSF) radars, this paper presents a complementary code cancellation (CCC) method to estimate the target velocity and achieve motion compensation. The proposed CCC method is capable of eliminating the coupling effect between range profile and target velocity. In this paper, we first give a block diagram of RSF radar modulated by the M-sequence generated pseudorandom code, and introduce the baseband sampling echo model of moving target. Then, the velocity estimation accuracy is derived to show the sensitivity of high-resolution range profile to target velocity. Subsequently, the CCC method is proposed and also investigated in the application of multi-target scenario. Performance analyses demonstrate that the proposed method can satisfy the estimation accuracy requirement and improve the signal to noise ratio by the velocity accumulation. Finally, simulations show that the method is effective and more computationally efficient than the existing popular methods.

With the increasing pressure of current life, fatigue caused by high-pressure work has deeply affected people and even threatened their lives. In particular, fatigue driving has become a leading cause of traffic accidents and deaths. This paper investigates electroencephalography (EEG)-based fatigue detection for driving by mining the latent information through the spatial-temporal changes in the relations between EEG channels. First, EEG data are partitioned into several segments to calculate the covariance matrices of each segment, and then we feed these matrices into a recurrent neural network to obtain high-level temporal information. Second, the covariance matrices of whole signals are leveraged to extract two kinds of spatial features, which will be fused with temporal characteristics to obtain comprehensive spatial-temporal information. Experiments on an open benchmark showed that our method achieved an excellent classification accuracy of 93.834% and performed better than several novel methods. These experimental results indicate that our method enables better reliability and feasibility in the detection of fatigued driving.

Quantum computation provides great speedup over its classical counterpart for certain tasks. Spin system is one of the most important candidates to realize quantum computations. The initialization, readout and quantum gate operations of spin-qubits can be accomplished by advanced spin resonance techniques, which include nuclear magnetic resonance, electron paramagnetic resonance and optically detected magnetic resonance. This review aims to summarize recent experimental progresses in spin-based quantum computing. Preserving quantum states, precise quantum operations, quantum algorithms and quantum simulations on spin systems are reviewed. The fast improvement of quantum technology motivated by quantum computations results in recent progress in spin magnetic resonance in nanoscale. This review also introduces the achievements in nanoscale magnetic resonance with Nitrogen vacancy centers in diamonds.

Ciji-Hua’ai-Baosheng II Formula (CHB-II-F) is a traditional Chinese medical formula that has been shown in clinical practice to relieve side effects of chemotherapy and improve quality of life for cancer patients. In order to understand the mechanism of its protective effects on chemotherapy, mice with transplanted H22 hepatocellular carcinoma were employed in this study. Ninety-two mice were injected subcutaneously with H22 HCC cell suspension into the right anterior armpit. After mice were treated with 5-fluorine pyrimidine (5-FU), they were divided into six groups as untreated group, 5-FU group, 5-FU plus Yangzheng Xiaoji Capsule group and three groups of 5-FU plus different concentrations of CHB-II-F. Twenty mice were euthanized after 7 days of treatment in untreated and medium concentration of CHB-II-F groups and all other mice were euthanized after 14 days of treatment. Herbal components/metabolites were analyzed by UPLC-MS. Tumors were evaluated by weight and volume, morphology of light and electron microscope, and cell cycle. Apoptosis were examined by apoptotic proteins expression by western blot. Four major components/metabolites were identified from serum of mice treated with CHB-II-F and they are β-Sitosterol, Salvianolic acid, isobavachalcone, and bakuchiol. Treatment of CHB-II-F significantly increased body weights of mice and decreased tumor volume compared to untreated group. Moreover, CHB-II-F treatment increased tumor cells in G0-G1 transition instead of in S phase. Furthermore, CHB-II-F treatment increased the expression of pro-apoptotic proteins and decreased the expression anti-apoptotic protein. Therefore, CHB-II-F could improve mice general condition and reduce tumor cell malignancy. Moreover, CHB-II-F regulates apoptosis of tumor cells, which could contribute its protective effect on chemotherapy.

In the Hamiltonian-based quantum dynamics, to estimate Hamiltonians from the measured data is a vital topic. In this work, we propose a recurrent neural network to learn the target Hamiltonians from the temporal records of single-qubit measurements, which does not require the ground states and only measures single-qubit observables. It is applicable on both time-independent and time-dependent Hamiltonians and can simultaneously capture the magnitude and sign of Hamiltonian parameters. Taking the Hamiltonians with the nearest-neighbor interactions as numerical examples, we trained our recurrent neural networks to learn different types of Hamiltonians with high accuracy. The study also shows that our method has good robustness against the measurement noise and decoherence effect. Therefore, it has widespread applications in estimating the parameters of quantum devices and characterizing the Hamiltonian-based quantum dynamics.

The brushless DC motor experiences operating safety problems due to the deterioration of its components following long-term operations, which are easily overlooked. To resolve these problems, failure mode, effects, and criticality analysis is utilized to characterize potential hazards in the motors. Hilbert–Huang transform is then employed to obtain the frequency-domain energy values of the vibration signals, which is defined as characteristic values that represent the performance degradation state. Second, gray model is selected to analyze the frequency-domain energy values and establish differential equations to predict the future vibration status, thereby achieving the vibration-based fault prediction. Furthermore, a gray safety assessment model is proposed to implement the safety assessment for the motor. The fault prediction and gray safety assessment are carried out based on historical data obtained from the brushless DC motor vibration experiment. The accuracy level of the gray model predictions is classified as Wonderful , thereby demonstrating the efficiency of gray model for the fault prediction. In addition, as low as reasonably practicable law is chosen to classify risk levels and formulate safety strategies in accordance with the results of safety assessment. Finally, the proposed safety effects of the methods and strategies are evaluated for microscopic and macroscopic levels.

This study aims at examining the dynamic response of the relationship between supply chain integration (SCI) and operational performance (OP) to demand uncertainty (DU). Based on a wide spectrum data sample with 357 participants in the China automotive supply chains, threshold regressions are used to examine the dynamic moderating effects. DU was found to moderate supplier integration (SI)–OP and customer integration (CI)–OP relationship. Internal integration (II)–OP relationship did not response to DU. The SI–OP relationship turned from negative to positive as DU increases, and CI–OP relationship responded to DU reversely compare to SI–OP relationship. Scholars now know the moderating effect of DU is not static and monotonic. Both of direction and magnitude of the correlations between SI, CI and OP change when DU changes. Managers of automotive supply chain recognize that their integrations’ strength should be properly managed subject to the level of DU for propose of achieving optimal OP. This study extends the current literature by delivering a field study of China and introducing dynamic capability theory for the first time to examine a dynamic response model that represents the SCI–OP relationships with respect to the DU as a moderating factor.

Unmanned aerial vehicle (UAV) swarms is an emerging technology that will significantly expand the application areas and open up new possibilities for UAVs, while also presenting new requirements for the robustness and reliability of the UAV swarming system. However, its complex and dynamic characteristics make it extremely challenging and uncertain to model such a system. In this study, to reach a full understanding of the swarming system, a modeling framework based on complex network theory is presented. First, the scope of work is identified from the point of view of control algorithms considering the dynamics and research novelty of the development of UAV swarming control strategy and three control structures consisting of three interdependent network layers are proposed. Second, three algorithms that systematically build the modeling framework considering all characteristics of the system are also developed. Finally, some network measurements are introduced by adjusting the fundamental ones into the UAV swarming system. The proposed framework is applied to a case study to illustrate the visualization models and estimate the statistical characteristics of the proposed networks with static and dynamic topology analysis. Furthermore, a simple demonstration of the robustness evaluation of the network is also presented. The networks obtained from this framework can be used to further analyze the robustness or reliability of a UAV swarming system in a high-confrontation battlefield environment the effect of cascading failure in ad-hoc network on system.

The Leggett–Garg (LG) test of macroscopic realism involves a series of dichotomic non-invasive measurements that are used to calculate a function which has a fixed upper bound for a macrorealistic system and a larger upper bound for a quantum system. The quantum upper bound depends on both the details of the measurement and the dimension of the system. Here we present an LG experiment on a three-level quantum system, which produces a larger theoretical quantum upper bound than that of a two-level quantum system. The experiment is carried out in nuclear magnetic resonance and consists of the LG test as well as a test of the ideal assumptions associated with the experiment, such as measurement non-invasiveness. The non-invasive measurements are performed via the modified ideal negative result measurement scheme on a three-level system. Once these assumptions are tested, the violation becomes small, despite the fact that the LG value itself is large. Our results showcase the advantages of using the modified measurement scheme that can reach higher LG values, since these leave a larger margin for violating the inequality in the face of experimental imperfections.

Anyons, quasiparticles living in two-dimensional spaces with exotic exchange statistics, can serve as the fundamental units for fault-tolerant quantum computation. However, experimentally demonstrating anyonic statistics is a challenge due to the technical limitations of current experimental platforms. Here, we take a state perpetration approach to mimic anyons in the toric code using a seven-qubit nuclear magnetic resonance quantum simulator. Anyons are created by dynamically preparing the ground and excited states of a seven-qubit planar version of the toric code, and are subsequently braided along two distinct, but topologically equivalent paths. We observe that the phase acquired by the anyons is independent of the path, and coincides with the ideal theoretical predictions when decoherence and implementation errors are taken into account. As the first demonstration of the topological path independence of anyons, our experiment helps to study and exploit the anyonic properties towards the goal of building a topological quantum computer.

The ability to post-select the outcomes of an experiment is a useful theoretical concept and experimental tool. In the context of weak measurements, post-selection can lead to surprising results such as complex weak values outside the range of eigenvalues. Usually post-selection is realized by a projective measurement, which is hard to implement in ensemble systems such as NMR. We demonstrate the first experiment of a weak measurement with post-selection on an NMR quantum information processor. Our setup is used for measuring complex weak values and weak values outside the range of eigenvalues. The scheme for overcoming the problem of post-selection in an ensemble quantum computer is general and can be applied to any circuit-based implementation. This experiment paves the way for studying and exploiting post-selection and weak measurements in systems where projective measurements are hard to realize experimentally.

Objective: To observe the treatment of severe preeclampsia in newborns with enoxaparin sodium combined with magnesium sulfate. Methods: A retrospective analysis was conducted on the clinical data of 80 patients with severe preeclampsia admitted to Hefei Second People's Hospital, China from January 2019 to December 2020. Treatment records showed that 40 cases received magnesium sulfate treatment (single group), and 40 cases received enoxaparin sodium combined with magnesium sulfate treatment (combination group). Levels of D-dimer, soluble fms-like tyrosine kinase 1 (sFlt-1), placental growth factor (PLGF), Apgar scores of newborns delivered before and after treatment were compared. Gestation weeks and incidence of adverse reactions were analyzed. Results: After treatment, levels of D-dimer, sfit-1 and adverse reactions in the combination group were significantly lower than those in the single group (P<0.05), and the level of PLGF, newborn Apgar score and length of gestation were significantly higher than those in the single group (P<0.05). Conclusion: Compared to magnesium sulfate alone, the combination of enoxaparin sodium and magnesium sulfate in the treatment of pregnant women with severe preeclampsia can more effectively regulate the cytokine level of patients, improve pregnancy outcome, and improve neonatal Apgar score. The incidence of adverse reactions is low, making it a safe and efficient treatment modality. [ABSTRACT FROM AUTHOR]