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A representative general polarimetric model-based decomposition framework was proposed by Chen et al., which implements a simultaneous full-parameter inversion by using complete polarimetric information and solves several limitations in previous decomposition methods. However, there are still shortcomings in Chen’s work. Firstly, only the real part of the parameter β in the generalized surface scattering model is considered. Secondly, inappropriate initial input values may lead to local optima in the nonlinear least squares optimization algorithm. Thirdly, the volume scattering component is underestimated in the volume scattering-dominated scene, but overestimated in buildings with large orientation (LOB) areas. Finally, nonlinear optimization is time-consuming computationally. To overcome those issues, an improved generalized polarimetric model-based decomposition method is proposed in this paper. The imaginary part of the parameter β is incorporated into the decomposition framework of the proposed method. Ingeniously utilizing the internal relationship in the generic equations composed of coherent matrix elements, the model parameters can be inversed by simplifying the nonlinear equations to linear equations. Therefore, compared with Chen’s method, the proposed method does not rely on the initial input values, and improves the computational efficiency. In addition, a hierarchical decomposition scheme is presented to solve the problem of underestimation or overestimation of volume scattering component mentioned above. The performance and advantages of this method are evaluated with L-band and C-band polarimetric synthetic aperture radar (PolSAR) data sets. Comparison studies are carried out with other model-based decomposition methods, demonstrating that the proposed method can further improve decomposition performance, especially in LOB areas.

In the electrical power system, a few transmission lines may have cascading failures and even lead to severe blackout accidents. In order to further prevent the electrical power system from cascading failures and being enlightened by Internet thinking, based on the complex network theory, this paper proposes the TL-SALSA (transmission line-stochastic approach for link structure analysis) algorithm combining Internet thinking and physical characteristics of the electrical power system for screening the important transmission lines. The TL-SALSA algorithm not only retains the low-computational complexity advantage of the SALSA (stochastic approach for link structure analysis) algorithm but considers the impact relational among transmission lines and the electrical power system operation mode. The IEEE 39-bus power system and IEEE 118-bus power system are simulated and compared with the electric betweenness algorithm, and the results further evidence the effectiveness of the TL-SALSA algorithm. The results also show that the TL-SALSA algorithm can improve the screening efficiency by nearly three orders of magnitude compared with the electric betweenness algorithm, which proves the potential of the TL-SALSLA algorithm for screening important transmission lines in large-scale electrical power systems.

This article addresses the resilient relative pose estimation problem for multiple mobile robot systems against abnormal sensor measurements. Motivated by the fact that in real implementations, sensors used for neighboring robot detection, such as stereo camera, laser range finder, etc., may suffer from unpredictable anomalies, a resilient relative pose estimation approach is proposed such that each robot can obtain satisfactory relative pose estimates of its neighbors for further coordination algorithm design. In the proposed approach, the optimal Kalman estimate is decomposed as a weighted sum of local state estimates, based on which a convex optimization problem is formulated to generate the resilient estimation. Unlike most of the existing approaches investigating similar problems, which assume that the statistics or the bound of anomalies is known in advance, our proposed approach is not limited by this assumption. The effectiveness of the proposed method has been validated by numerical simulations and real robot experiments. It has been demonstrated that the proposed approach is comparable to the Kalman filter in the absence of sensor anomalies, while boundedness of the relative pose estimation error can be guaranteed under abnormal observations.

With the rapid development of microgrids, voltage drop has become one of the focuses on the research of microgrid stability and grid-connected operation. In this article, an improved fuzzy voltage compensation control strategy (FVCC) is proposed to solve the bus voltage drop problem. This method takes the influence of feeder impedance into account, and there is no need to accurately measure the feeder impedance value. Since droop control and feeder impedance can lead to voltage drop and bus voltage instability, fuzzy control is applied to compensate bus voltage. Specifically, aiming at the voltage drop caused by droop control, an adaptive fuzzy controller is designed to automatically find suitable gain parameters to reduce the voltage difference. In addition, bus voltage gain module is added to compensate the voltage consumed by feeder impedance. The compensation only needs to measure the voltage at both ends of the line, without testing the line parameters. Finally, the simulation results show that the control strategy achieves the expected effect.

Sepsis-associated encephalopathy (SAE) often manifests in severe diffuse cerebral dysfunction due to an aberrant systemic immune response to infection. The underlying pathophysiology of SAE is not entirely understood but is likely a multifactorial process that involves disruption in cell death mechanism. Ferroptosis is a novel form of programmed cell death characterized by iron accumulation and lipid peroxidation, leading to inflammatory cascade and glutamate release. We hypothesized that ferroptosis is involved in the glutamate-mediated excitotoxic neuron injury during the uncontrolled neural inflammatory process of SAE. Inhibiting ferroptosis with ferrostatin-1 (Fer-1) could alleviate glutamate excitotoxicity and reduce neuron death of SAE, potentially improving prognosis. We found that in the cecal ligation and puncture (CLP) sepsis model, ferroptosis occurred increasingly in the cerebrum, characterized by glutathione-dependent antioxidant enzyme glutathione peroxidase 4 (GPX4) inactivation, transferrin upregulation, mitochondria shrink and malondialdehyde (MDA) increased. Fer-1 treatment downregulated cerebral ferroptosis and alleviated glutamate excitotoxicity via dampening system xc-(SXC) and glutamate receptor N-methyl-D-asperate receptor subunit 2. Combined with an observed reduction in calcium transporter PLCG and PLCB activation, these processes ultimately protected the integrities of synapses and neurons during SAE. Fer-1 treatment also rescued sepsis-induced nuclear autophagy and improved the behaviors of tail suspension test and novel object recognition test in septic mice. Conclusively, our results suggested that inhibition of ferroptosis could attenuate glutamate excitotoxicity and SAE outcomes.

A photo-induced transition-metal-free regioselective hydroborylation of α,β-unsaturated carbonyl compounds is developed. The PhSSPh reagent was employed as the photocatalyst, and NHC-BH

A facil route to spirocyclic oxazole derivatives via the [3 + 2] cycloaddition reactions between 2-arylidene cycloalkanones and azaoxyallyl cations was developed.

The thermographic diagnostic of substation insulators can facility to detect their insulation status in time. However, the detection effect of infrared insulator images is usually poor due to the complex background of substation scene images. In order to solve this problem and improve the automatic detection capability of substation equipment, an infrared insulator image detection model based on the improved feature fusion single shot multibox detector is proposed in this paper. The model combines multi-scale feature maps to generate a new feature pyramid, and designs a new feature enhancement module in the shallow network of the model to improve its ability to extract features of infrared images of substation insulators. Meanwhile, the clustering algorithm is used to calculate the target aspect ratio information of the dataset to realize the adaptive change of the aspect ratio of the default box. Furthermore, the transfer learning is employed to further improve the learning efficiency of the model. Before the formal experiment, the design scheme of feature enhancement module is confirmed by ablation studies. Experiments are performed on the collected infrared insulator images, transformer images and Pascal VOC 2007 dataset. It is demonstrated experimentally that this model achieves excellent detection results.

We demonstrate how the Hong-Ou-Mandel (HOM) interference with polarization-entangled photons can be used to probe ultrafast dephasing. We can infer the optical properties like the real and imaginary parts of the complex susceptibility of the medium from changes in the position and the shape of the HOM dip. From the shift of the HOM dip, we are able to measure 22 fs dephasing time using a continuous-wave (CW) laser even with optical loss > 97%, while the HOM dip visibility is maintained at 92.3~\% (which can be as high as 96.7%). The experimental observations, which are explained in terms of a rigorous theoretical model, demonstrate the utility of HOM interference in probing ultrafast dephasing., 12 pages, 7 figures

Considering various cyberattacks aiming at the Internet of Vehicles (IoV), secure pose estimation has become an essential problem for ground vehicles. This paper proposes a pose estimation approach for ground vehicles under randomly occurring deception attacks. By modeling attacks as signals added to measurements with a certain probability, the attack model has been presented and incorporated into the existing process and measurement equations of ground vehicle pose estimation based on multisensor fusion. An unscented Kalman filter-based secure pose estimator is then proposed to generate a stable estimate of the vehicle pose states; i.e., an upper bound for the estimation error covariance is guaranteed. Finally, the simulation and experiments are conducted on a simple but effective single-input-single-output dynamic system and the ground vehicle model to show the effectiveness of UKF-based secure pose estimation. Particularly, the proposed scheme outperforms the conventional Kalman filter, not only by resulting in more accurate estimation but also by providing a theoretically proved upper bound of error covariance matrices that could be used as an indication of the estimator’s status.

BACKGROUND At present, the value of lipid indicators in evaluating the prognosis of colorectal cancer is still relatively limited. AIM To evaluate the value of a novel parameter for colorectal cancer (CRC) prognosis scoring based on preoperative serum lipid levels. METHODS Four key serum lipid factors, namely, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein A1 (ApoA1), and apolipoprotein B (ApoB), were detected. Two representative ratios, HDL-C-LDL-C ratio (HLR) and ApoA1-ApoB ratio (ABR) were calculated. The relationship of these parameters with the prognosis of CRC patients including progression-free survival (PFS) and overall survival (OS) was analyzed by Kaplan-Meier plot and Cox proportional hazards regression. A novel lipoprotein cholesterol-apolipoprotein (LA) score based on HLR and ABR was established and its value in prognosis evaluation for CRC patients was explored. RESULTS Multivariate Cox proportional hazards regression analysis of PFS and OS showed that HDL-C, ApoA1, HLR, and ABR were positively associated with the prognosis of CRC patients. LA score was independently associated with a good prognosis in resectable CRC patients. Data processing of a dummy variable showed that the prognosis of patients with higher LA scores is better than that with lower LA scores. CONCLUSION The newly established LA score might serve as a better predictor of the prognosis of resectable CRC patients.

Motives for writing is a new research area in the field of writing motivation. Recent studies have identified important motives for students’ writing. Adding to this nascent foundation, the current study examined relationships between writing motives, writing self-efficacy and time spent on writing using a sample of grade 4 Chinese students in Shanghai (n = 619) who completed a questionnaire assessing these constructs. Four alternative models, driven separately by key motives including social recognition, curiosity, competition and grades, depicted complex relationships between motives, self-efficacy and time on writing. The grades driven model best-fitted the data. This final model showed the importance of grades as a key motive for writing and how intrinsic and extrinsic motivational pathways were originated from this performance-focused motive, connecting with writing self-efficacy and time spent on writing. Cluster analyses classified these Chinese students into seven groups of beginning writers holding contrasting profiles of writing motives. Extremely-motivated and highly-motivated writers held multiple motives in their profiles. The motive profiles of curious and averagely-motivated writers were dominated by curiosity and involvement. Performance-focused and weakly-motivated writers focused predominantly on grades as a key motive. Finally, unmotivated writers did not hold any clear motives for writing. These clusters of writers differed in writing self-efficacy but not in their time spent on writing. Complementing the findings of path analyses, cluster analyses showed grades as a dominant motive for writing among different clusters of motivated writers.

In order to explore the surface state modulation mechanism of carbon dots (CDs) with high quantum yield (QY) and high product yield (PY), CDs were synthesized from different carbon sources with different contents of oxygen-containing functional groups and different silane coupling agents with nitrogen-containing functional groups. The highest QY of as-prepared CDs can reach 97.32% and the PY values of CDs are all high ranging from 46.33-58.76%. It is found that the high content of C[double bond, length as m-dash]O and pyrrolic N on the surface of CDs can endow CDs with high QY. Moreover, the PY of CDs not only depends on whether CDs have the crosslinked structure, but also is closely and positively correlated with pyridinic N. Consequently, our findings suggest that raw materials rich in carboxyl groups and amino groups are beneficial to the synthesis of CDs with high QY, and whether CDs with crosslinked structure and high content of pyridinic N decide the high PY of CDs. This work provides a theoretical guidance for large-scale synthesis of CDs with high QY and high PY.