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Abstract Spin–orbit couplings (SOCs), originating from the relativistic corrections in the Dirac equation, offer nonlinearity in the classical limit and are capable of driving chaotic dynamics. In a nanoscale quantum dot confined by a two-dimensional parabolic potential with SOCs, various quantum scar states emerge quasi-periodically in the eigenstates of the system, when the ratio of confinement energies in the two directions is nearly commensurable. The scars, displaying both quantum interference and classical trajectory features on the electron density, due to relativistic effects, serve as a bridge between the classical and quantum behaviors of the system. When the strengths of Rashba and Dresselhaus SOCs are identical, the chaos in the classical limit is eliminated as the classical Hamilton’s equations become linear, leading to the disappearance of all quantum scar states. Importantly, the quantum scars induced by SOCs are robust against small perturbations of system parameters. With precise control achievable through external gating, the quantum scar induced by Rashba SOC is fully controllable and detectable.

Abstract Background Immune thrombocytopenia (ITP), which is a well-known hemorrhagic disorder characterized by low platelet counts, has been shown to be associated with the risk of thrombosis. Thrombopoietic agents (TAs) are extensively used as second-line treatments for ITP, effectively reducing the risk of hemorrhage. However, thrombosis, a potential adverse effect of TAs, raises clinical challenges. Methods The MEDLINE(PubMed), Embase, and the Cochrane Library databases were systematically searched for relevant studies, including both single-arm trials and randomized controlled trials (RCTs), without language restrictions. Results A total of 17 RCTs comprising 2,105 patients and 29 single-arm trials comprising 3,227 patients were included. In the single-arm meta-analysis, the pooled rate of overall thrombotic events in ITP patients receiving TAs was 2.2% (95% CI 1.0% − 3.7%). In RCTs, a higher incidence of thrombosis (33/1425 vs. 4/680) and higher risk ratios (RR) of overall, arterial, and venous thrombotic events (1.73, 95% CI [0.88, 3.39], P = 0.113; RR 1.98, 95% CI [0.80, 4.92], P = 0.141; RR 1.06, 95% CI [0.46, 2.41], P = 0.895, respectively) were observed in the TAs group than in the control group, although the differences were not significant. Subgroup analysis demonstrated that hetrombopag was the only TA with no increased thrombotic risk (rate 0.3% 95% CI [0.0 − 1.5%]; RR 0.76, 95% CI [0.03, 18.41], P = 0.864) compared to eltrombopag, avatrombopag, romiplostim, and rhTPO. Subgroup analyses also revealed that ITP patients with advanced age (3.7% vs. 1.3%, P = 0.132) or with a thrombotic history (3.0% vs. 1.4%, P = 0.257), and patients who received TAs therapy for a long duration (4.7% vs. 0.1%, P

[Introduction] The purpose of the research is to propose a classification and fusion method for the data acquisition and system integration of various application systems of the intelligent power plant by studying the overall platform architecture of the intelligent power plant, and provide a reference for the integration scheme of the system and the big data platform in intelligent power plant. [Method] First, the division of four levels of the intelligent power plant was described. Then, the application system's data was collected and integrated according to the safety partition requirements, with the big data platform database as the core for interaction. Finally, according to the classification of three types of integration, the scheme of intelligent power plant application system was analyzed. [Result] The analysis and integration of the current architecture system of the intelligent power plant and data acquisition and application system greatly improved the efficiency and intelligence level of the big data platform. [Conclusion] With the gradual promotion and application of intelligent power plants in the China's power industry, technical research and planning in advance according to the project needs is conducive to building a highly integrated intelligent integration platform, which is conducive to big data analysis and interaction.

Two-dimensional material indium selenide (InSe) holds great promise for applications in electronics and optoelectronics by virtue of its fascinating properties. However, most multilayer InSe-based transistors suffer from extrinsic scattering effects from interface disorders and the environment, which cause carrier mobility and density fluctuations and hinder their practical application. In this work, we employ the non-destructive method of van der Waals (vdW) integration to improve the electron mobility of back-gated multilayer InSe FETs. After introducing the hexagonal boron nitride (h-BN) as both an encapsulation layer and back-gate dielectric with the vdW interface, as well as graphene serving as a buffer contact layer, the electron mobilities of InSe FETs are substantially enhanced. The vdW-integrated devices exhibit a high electron mobility exceeding 103 cm2 V−1 s−1 and current on/off ratios of ~108 at room temperature. Meanwhile, the electron densities are found to exceed 1012 cm−2. In addition, the fabricated devices show an excellent stability with a negligible electrical degradation after storage in ambient conditions for one month. Electrical transport measurements on InSe FETs in different configurations suggest that a performance enhancement with vdW integration should arise from a sufficient screening effect on the interface impurities and an effective passivation of the air-sensitive surface.

Compared to the distributed ground-based radar (DGBR), the distributed airborne radar (DAR) has been widely applied due to its stronger anti-damage ability, more degrees of freedom, and better detection view of targets. However, unlike DGBR, the premise for the normal operation of DAR is to maintain stable wireless communication between unmanned aerial vehicles (UAVs). This requires each UAV to make reasonable use of its electromagnetic domain resources. That is, to maximize radar detection performance while ensuring communication performance constraints. However, current research in the field of radar resource allocation has not taken this into account, which greatly limits the practical application of optimization algorithms. Moreover, the current research tends to adopt centralized optimization algorithms. When the baseline of the UAV swarm is long, applying multi-relay methods directly results in heavy communications overhead and long-time delay. Based on the above background, this article aimed to develop a fully distributed algorithm for the joint optimization of radar detection performance and communication transmission performance. This study first took the measurement angle of arrival (AOA) as an example to provide a system model with communication constraints. This model considers the impact of factors such as the UAV location error, UAV communication coverage, and dynamic communication topology of the UAV on joint optimization. A formal representation of the joint optimization is presented. Then, we proposed a joint radar-communication optimization (JRCO) algorithm to fully utilize the electromagnetic domain resources of each UAV. Finally, numerical simulations verified the effectiveness of the proposed JRCO algorithm to traditional radar resource allocation methods.

Quantum phase transitions with multicritical points are fascinating phenomena occurring in interacting quantum many-body systems. However, multicritical points predicted by theory have been rarely verified experimentally; finding multicritical points with specific behaviors and realizing their control remains a challenging topic. Here, we propose a system that a quantized light field interacts with a two-level atomic ensemble coupled by microwave fields in optical cavities, which is described by a generalized Dicke model. Multicritical points for the superradiant quantum phase transition are shown to occur. We determine the number and position of these critical points and demonstrate that they can be effectively manipulated through the tuning of system parameters. Particularly, we find that the quantum critical points can evolve into a Lifshitz point (LP) if the Rabi frequency of the light field is modulated periodically in time. Remarkably, the texture of atomic pseudo-spins can be used to characterize the quantum critical behaviors of the system. The magnetic orders of the three phases around the LP, represented by the atomic pseudo-spins, are similar to those of an axial next-nearest-neighboring Ising model. The results reported here are beneficial for unveiling intriguing physics of quantum phase transitions and pave the way towards to find novel quantum multicritical phenomena based on the generalized Dicke model.

Structural damage recognition is always the concerned focus in many fields like aerospace, petroleum and petrochemical industry, industrial production and civil life. For damage recognition in complex structure or structural interior, especially somewhere sensors can't go, minor damage is often hard identified by not only traditional nondestructive testing methods like ultrasonic testing, radiographic testing, magnetic particle testing, penetrant testing, eddy current testing, but also the current popular ultrasonic guided wave based on the piezoelectric wafer, electromagnetic acoustic transducer or magnetostrictive sensor, which is mainly because the response signals are always affected by many structural features. In this article, the advanced global search algorithm, quantum particle swarm optimization algorithm is first combined with the finite element method to accurately recognize the structural damage based on the conductance-frequency spectrum resulted from electromechanical impedance method. Meanwhile, the objective function is designed to compare the difference of peak frequency variations in the experiment and finite element calculation respectively. By adopting the stiffness reduction method of the elements near the structural damage, the identification efficiency is largely improved for no need to repeatedly partition the model grid. And after multiple iteration optimization of the artificial intelligence algorithm - quantum particle swarm optimization algorithm, the identification error of damage parameters including location and degree can be reduced to below 4 percent. Therefore, the combination of finite element method and quantum particle swarm optimization algorithm is quite effective for guaranteeing high accuracy and efficiency for damage parameters' recognition in complex structures.

The dynamic characteristics of the traction gear transmission system have a great influence on the safety, comfort, and reliability of EMU (electric multiple units). Combining the methods of theoretical analysis, numerical simulation, and optimization design theory, establishing a parameterized gear modification model. Meanwhile, designing reasonable shape modification schemes and parameters. The dynamic characteristics, vibration response characteristics, and acoustic response characteristics of gear meshing of CRH380A high-speed EMU under continuous traction conditions are analyzed. The corresponding relationship between gear modification parameters and gear transmission radiation noise is approximated by finite element simulation data and RBF (radial basis function) neural network. Using a multi-island genetic algorithm to optimize gear modification parameters to minimize gear transmission noise, further seeking to meet the low-noise modification design of high-speed train traction helical gear transmission system under continuous operating conditions method.

The gut bacterium Akkermansia muciniphila has been increasingly recognized for its therapeutic potential in treating metabolic disorders, including obesity, diabetes, and metabolicdysfunction-associated fatty liver disease (MAFLD). However, its underlying mechanism involved in its well-known metabolic actions needs further evaluation. The present study explored the therapeutic effect and mechanism of A. muciniphila in intervening MAFLD by using a high-fat and high-cholesterol (HFC) diet induced obese mice model. Mice treated with A. muciniphila efficiently reversed MAFLD in the liver, such as hepatic steatosis, inflammatory, and liver injury. These therapeutic effects persisted after long-term drug withdrawal and were slightly weakened in the antibiotics-treated obese mice. A. muciniphila treatment efficiently increased mitochondrial oxidation and bile acid metabolism in the gut-liver axis, ameliorated oxidative stress-induced cell apoptosis in gut, leading to the reshaping of the gut microbiota composition. These metabolic improvements occurred with increased L-aspartate levels in the liver that transported from the gut. The administration of L-aspartate in vitro or in mice displayed the similar beneficial metabolic effects mentioned above and efficiently ameliorated MAFLD. Together, these data indicate that the anti-MAFLD activity of A. muciniphila correlated with lipid oxidation and improved gut–liver interactions through regulating the metabolism of L-aspartate. A. muciniphila could be a potential agent for clinical intervention in MAFLD.

In the face of the increasingly severe Botnet problem on the Internet, how to effectively detect Botnet traffic in realtimehas become a criticalproblem. Although the existing deepQnetwork (DQN) algorithminDeep reinforcement learning can solve the problem of real-time updating, its prediction results are always higher than the actual results. In Botnet traffic detection, although it performs well in the training set, the accuracy rate of predicting traffic is as high as%; however, in the test set, its accuracy has declined, and it is impossible to adjust its prediction strategy on time based on new data samples. However, in the new dataset, its accuracy has declined significantly. Therefore, this paper proposes a Botnet traffic detection system based on double-layer DQN (DDQN). Two Q-values are designed to adjust the model in policy and action, respectively, to achieve real-time model updates and improve the universality and robustness of the model under different data sets. Experiments show that compared with the DQN model, when using DDQN, the Q-value is not too high, and the detectionmodel has improved the accuracy and precision of Botnet traffic. Moreover, when using Botnet data sets other than the test set, the accuracy and precision of theDDQNmodel are still higher than DQN. [ABSTRACT FROM AUTHOR]

With information technology's rapid development, the Internet faces serious security problems. Studies have shown that malware has become a primary means of attacking the Internet. Therefore, adversarial samples have become a vital breakthrough point for studying malware. By studying adversarial samples, we can gain insights into the behavior and characteristics of malware, evaluate the performance of existing detectors in the face of deceptive samples, and help to discover vulnerabilities and improve detection methods for better performance. However, existing adversarial sample generation methods still need help regarding escape effectiveness and mobility. For instance, researchers have attempted to incorporate perturbation methods like Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and others into adversarial samples to obfuscate detectors. However, these methods are only effective in specific environments and yield limited evasion effectiveness. To solve the above problems, this paper proposes a malware adversarial sample generation method (PixGAN) based on the pixel attention mechanism, which aims to improve adversarial samples' escape effect and mobility. The method transforms malware into grey-scale images and introduces the pixel attention mechanism in the Deep Convolution Generative Adversarial Networks (DCGAN) model to weigh the critical pixels in the grey-scale map, which improves the modeling ability of the generator and discriminator, thus enhancing the escape effect and mobility of the adversarial samples. The escape rate (ASR) is used as an evaluation index of the quality of the adversarial samples. The experimental results show that the adversarial samples generated by PixGAN achieve escape rates of 97%, 94%, 35%, 39%, and 43% on the Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), Convolutional Neural Network and Recurrent Neural Network (CNN_RNN), and Convolutional Neural Network and Long Short Term Memory (CNN_LSTM) algorithmic detectors, respectively. [ABSTRACT FROM AUTHOR]