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Traumatic aortic injury (TAI) is an acute, critical, and severe disease, and then combined with multiple organ damage, it is even more dangerous. TAI progresses very rapidly, with a pre-hospital mortality rate of 57%–80%, and even when arriving at the hospital, more than one-third of the patients die within 4 h, and it is the 2nd leading cause of death in individuals aged 4−34 years. In addition, the incidence of TAI combined with injury was 81.4%. Therefore, early diagnosis, expeditious surgery, and timely and effective multidisciplinary cooperation are essential for successful rescue. The authors report 2 patients with acute traumatic aortic dissection combined with multiple organ injuries and treated with emergency endovascular surgery to discuss their clinical characteristics and treatment experience, and to provide experience in the diagnosis and treatment of such patients.

Abstract It is crucial to precisely calculate temperature utilizing thermal models, which require the determination of thermal parameters that optimally align model outcomes with experimental data. In many instances, the refinement of these models is undertaken within space instruments. This paper introduces an optimization methodology for thermal network models, with the objective of enhancing the accuracy of temperature predictions for aerial cameras. The investigation of internal convective heat transfer coefficients for both cylindrical and planar structures provides an estimation of convective thermal parameters. Based on the identification of thermally sensitive parameters and the reliability evaluation of transient temperature data through the Monte-Carlo simulation, the genetic algorithm is employed to search for global optimal parameter values that minimize the root mean square error (RMSE) between calculated and measured node temperatures. As a result, the optimized model shows significantly improved accuracy in temperature prediction, attaining an RMSE of 1.07 ℃ and reducing the maximum relative error between predicted and experimental results from 33.8 to 3.1%. Furthermore, the flight simulation and thermal control experiments validate the robustness of the optimized model, demonstrating that discrepancies between the observed and predicted temperatures are within 2 °C after re-correcting the external convection heat transfer coefficient value.

Pulse pile-up presents a significant challenge in nuclear radiation measurements, particularly in neutron-gamma pulse shape discrimination, as it causes pulse distortion and diminishes identification accuracy. To address this, we propose an optimized Support Vector Regression (SVR) algorithm for correcting pulse pile-up. Initially, the Dung Beetle Optimizer (DBO) and Whale Optimization Algorithm (WOA) are integrated to refine the correction process, with performance evaluated using charge comparison methods (CCM) for pulse shape discrimination. Leveraging prior knowledge from simulated data, we further analyze the relationships between various types of pulse pile-ups, including their combinations, inter-peak distances, and the accuracy of corrections. Extensive experiments conducted in a mixed neutron-gamma radiation field using plastic scintillators demonstrate that the proposed method effectively corrects pulse pile-up and accurately discriminates between neutron and gamma. Moreover, our approach significantly improves the fidelity of pulse shape discrimination and enhances the overall reliability of radiation detection systems in high-interference environments.

Remote sensing object detection (RSOD) plays a crucial role in resource utilization, geological disaster risk assessment and urban planning. Deep learning-based object-detection algorithms have proven effective in remote sensing image studies. However, accurate detection of objects with small size, dense distribution and complex object arrangement remains a significant challenge in the remote sensing field. To address this, a refined and efficient object-detection algorithm (RE-YOLO) has been proposed in this paper for remote sensing images. Initially, a refined and efficient module (REM) was designed to balance computational complexity and feature-extraction capabilities, which serves as a key component of the RE_CSP block. RE_CSP block efficiently extracts multi-scale information, overcoming challenges posed by complex backgrounds. Moreover, the spatial extracted attention module (SEAM) has been proposed in the bottleneck of backbone to promote representative feature learning and enhance the semantic information capture. In addition, a three-branch path aggregation network (TBPAN) has been constructed as the neck network, which facilitates comprehensive fusion of shallow positional information and deep semantic information across different channels, enabling the network with a robust ability to capture contextual information. Extensive experiments conducted on two large-scale remote sensing datasets, DOTA-v1.0 and SCERL, demonstrate that the proposed RE-YOLO outperforms state-of-the-art other object-detection approaches and exhibits a significant improvement in generalization ability.

Electromagnetic metamaterials possess characteristics such as having large-scale, periodic, multi-media, and complex geometric structures, making them highly suitable for simulation using the finite element domain decomposition method (DDM). This work proposes a non-conformal DDM for simulating finite-period metamaterials, achieving simulation with a minimal number of domains while significantly reducing computational resource consumption. First, the computational domain of the periodic metamaterial is partitioned into several non-conformal hexahedral subdomains. Subsequently, a limited number of non-repetitive subdomain models with non-matching grids are constructed, leveraging the three-dimensional rotational and translational properties of these subdomains to facilitate simulation. By eliminating the necessity to construct models for every subdomain, a substantial reduction in memory consumption is achieved. Second, an iteration method for solving the matrix equations of subdomains is enhanced by introducing a multifrontal block incomplete Cholesky decomposition preconditioner, thereby enhancing the computational efficiency of matrix equations with a large number of unknowns. Meanwhile, parallel computing techniques are employed to accelerate the proposed method. Finally, we integrate the aforementioned method into a solver and leverage it to develop an electromagnetic simulation tool tailored for metamaterials. The tool is employed to simulate metamaterial structures of varying scales, resulting in notable reductions in both memory and time consumption while maintaining accuracy comparable to commercial software.

The precise thermal control of aerial cameras is crucial for the acquisition of high-resolution imagery, and an accurate temperature prediction is essential to achieve this. This paper presents a methodology for modifying thermal network models to improve the accuracy of temperature prediction for aerial cameras. Seven types of thermal parameters are extracted from the thermal network model, and a thermally sensitive analysis identifies eleven key parameters to streamline the processing time. Departing from traditional methods that rely on steady-state data, this study conducts transient thermal tests and leverages polynomial fitting to facilitate thorough parameter modification. To ensure data reliability, the Monte-Carlo algorithm is employed to explore the parameter spaces of key parameters, analyzing temperature errors. Subsequently, the Least-Squares method is utilized to obtain optimal estimates of the key parameter values. As a result, the updated model demonstrates significantly improved accuracy in temperature predictions, achieving a reduction in the maximum absolute error between the predicted and experimental results from 22 °C to 4 °C, and a lowering of the relative error from 33.8% to 6.1%. The proposed modification method validates its effectiveness in modeling and enhancing the precision of thermal network models for aerial cameras.

The quality of aerial remote sensing imaging is heavily impacted by the thermal distortions in optical cameras caused by temperature fluctuations. This paper introduces a lumped parameter thermal network (LPTN) model for the optical system of aerial cameras, aiming to serve as a guideline for their thermal design. By optimizing the thermal resistances associated with convection and radiation while considering the camera’s unique internal architecture, this model endeavors to improve the accuracy of temperature predictions. Additionally, the proposed LPTN framework enables the establishment of a heat leakage network, which offers a detailed examination of heat leakage paths and rates. This analysis offers valuable insights into the thermal performance of the camera, thereby guiding the refinement of heating zones and the development of effective active control strategies. Operating at a total power consumption of 26 W, the thermal system adheres to the low-power limit. Experimental data from thermal tests indicate that the temperatures within the optical system are maintained consistently between 19 °C and 22 °C throughout the flight, with temperature gradients remaining below 3 °C, satisfying the temperature requirements. The proposed LPTN model exhibits swiftness and efficacy in determining thermal characteristics, significantly facilitating the thermal design process and ensuring optimal power allocation for aerial cameras.

Abstract Tissues are highly complicated with spatial heterogeneity in gene expression. However, the cutting-edge single-cell RNA-seq technology eliminates the spatial information of individual cells, which contributes to the characterization of cell identities. Herein, we propose single-cell spatial position associated co-embeddings (scSpace), an integrative method to identify spatially variable cell subpopulations by reconstructing cells onto a pseudo-space with spatial transcriptome references (Visium, STARmap, Slide-seq, etc.). We benchmark scSpace with both simulated and biological datasets, and demonstrate that scSpace can accurately and robustly identify spatially variated cell subpopulations. When employed to reconstruct the spatial architectures of complex tissue such as the brain cortex, the small intestinal villus, the liver lobule, the kidney, the embryonic heart, and others, scSpace shows promising performance on revealing the pairwise cellular spatial association within single-cell data. The application of scSpace in melanoma and COVID-19 exhibits a broad prospect in the discovery of spatial therapeutic markers.

In the design of high-speed interconnection systems, it is often necessary to evaluate signal integrity and electromagnetic interference performance by extracting terminal network parameters and differential and common mode network parameters. However, in a multi-conductor transmission line, the characteristic parameters related to different conductors, which named terminal in terminal theory, are couped together in traditional model network parameters and could not be extracted directly. Therefore, in this paper, a three-dimension terminal simulation tool based on transfinite element method is developed to solve this problem. The vector finite element method is combined with the transfinite element method, and the electric field of the truncation surface is approximated by the expansion of the truncation surface mode function. As far as we know, the combination of the transfinite element method and the terminal theory is proposed for the first time. The terminal simulation tool has a human-computer interaction interface, as well as functions such as modeling, mesh generation, solution setting, and post-processing result display, which can accurately obtain the terminal network parameters and differential and common mode network parameters. Numerical examples are given to demonstrate the accuracy and efficiency of the developed tool compared with the results from HFSS.

This paper addresses the problem of the two-dimensional direction-of-arrival (2D DOA) estimation of low-elevation or non-low-elevation targets using L-shaped uniform and sparse arrays by analyzing the signal models’ features and their mapping to 2D DOA. This paper proposes a 2D DOA estimation algorithm based on the dilated convolutional network model, which consists of two components: a dilated convolutional autoencoder and a dilated convolutional neural network. If there are targets at low elevation, the dilated convolutional autoencoder suppresses the multipath signal and outputs a new signal covariance matrix as the input of the dilated convolutional neural network to directly perform 2D DOA estimation in the absence of a low-elevation target. The algorithm employs 3D convolution to fully retain and extract features. The simulation experiments and the analysis of their results revealed that for both L-shaped uniform and L-shaped sparse arrays, the dilated convolutional autoencoder could effectively suppress the multipath signals without affecting the direct wave and non-low-elevation targets, whereas the dilated convolutional neural network could effectively achieve 2D DOA estimation with a matching rate and an effective ratio of pitch and azimuth angles close to 100% without the need for additional parameter matching. Under the condition of a low signal-to-noise ratio, the estimation accuracy of the proposed algorithm was significantly higher than that of the traditional DOA estimation.

Acute kidney injury (AKI) in community-acquired septic patients is often associated with relatively high mortality rate. However, the appropriate timing for continuous renal replacement therapy (CRRT) initiation remains controversial. In the present study, we retrospectively analyzed 123 community-acquired septic patients with AKI admitted to the medical intensive care unit (ICU). The baseline patient characteristics and renal function parameters were compared between survivors and non-survivors. Then, we used the Cox proportional hazard analysis to identify the risk factors for ICU mortality. Moreover, we employed the area under the receiver operating characteristic curve analysis to determine the cutoff time for CRRT initiation. Finally, we used the cutoff time to separate the patients into early (treatment initiated earlier than the cutoff time) and late (treatment initiated later than the cutoff time) CRRT groups and performed the Kaplan–Meier survival analysis to assess the overall mortalities. At the time of ICU release, the mortality rate of the 123 patients was 48.8% (n = 60). We identified several baseline characteristics and renal function parameters that were significantly different between the survivors and the non-survivors. All of them were also identified as the risk factors for community-acquired sepsis. Importantly, the cutoff time point to distinguish the early and late CRRT initiation groups was determined to be 16 h after AKI onset. Based on such grouping, the mortality rate was significantly lower in the early CRRT initiation group at 30, 60 and 90 days. Our data suggest that initiating CRRT within 16 h may help improve the mortality rate of community-acquired septic patients.

The multi-modal image fusion plays an important role in various fields. In this paper, a novel multi-modal image fusion method based on robust principal component analysis (RPCA) is proposed, which consists of low-rank components fusion and sparse components fusion. In the low-rank components fusion part, a universal low-rank dictionary is constructed for sparse representation (SR) and the low-rank fusion is converted to sparse coefficients fusion by adopting the batch-OMP. In the sparse components fusion part, the anisotropic weight map is constructed to express salient structures of the images. Moreover, a multi-scale anisotropic guided measure is proposed to guide the fusion process, which can extract and preserve the scale-aware salient details of sparse components. Finally, the multi-modal fusion can be achieved by combining two fusion parts together. The experimental results validate that the proposed method outperforms nine state-of-the-art methods in multi-modal fusion both at gray-gray and gray-color scales, in terms of qualitative and quantitative evaluations.

Abstract Hepatocellular carcinoma (HCC) is a highly malignant disease with a poor prognosis and high mortality due to a low early diagnosis rate, resistance to systemic treatments and progression to late-stage liver disease. Owing to limitations in the detection of HCC and the lack of awareness of healthcare systems, fewer than 40% of HCC patients are eligible for surgery due to advanced stages of the disease at the time of diagnosis and the occurrence of multiple lesions in the cirrhotic or fibrotic liver. At present, the updated American Association for the Study of Liver Disease (AASLD) guidelines no longer recommend alpha-fetoprotein (AFP) testing as a part of diagnostic evaluation. Thus, it is imperative to establish a novel diagnostic strategy with high sensitivity and reliability to monitor risk factors to detect HCC at an early stage. In recent years, “liquid biopsy,” (including circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA)), has emerged as a technique for the characterization of circulating cells, providing a strong basis for the individualized treatment of patients. As a noninvasive detection method, liquid biopsy is expected to play an important role in the early diagnosis, dynamic monitoring of cancer patients and drug screening. In this review, we will focus on the clinical applications, recent studies and future prospects of liquid biopsy, particularly focusing on HCC.

Most current online distributed machine learning algorithms have been studied in a data-parallel architecture among agents in networks. We study online distributed machine learning from a different perspective, where the features about the same samples are observed by multiple agents that wish to collaborate but do not exchange the raw data with each other. We propose a distributed feature online gradient descent algorithm and prove that local solution converges to the global minimizer with a sublinear rate O2T. Our algorithm does not require exchange of the primal data or even the model parameters between agents. Firstly, we design an auxiliary variable, which implies the information of the global features, and estimate at each agent by dynamic consensus method. Then, local parameters are updated by online gradient descent method based on local data stream. Simulations illustrate the performance of the proposed algorithm.

Aiming at high performance requirements of snake-like robots under complex environment, we present a control system of our proposed design which utilizes a STM32 as the core processor and incorporates real-time image acquisition, multisensor fusion, and wireless communication technology. We use Solidworks to optimize the design of head, body, and tail joint structure of the snake-like robot. The system is a real-time system with a simple-circuit structure and multidegrees of freedom are attributed to the flawless design of control system and mechanical structure. We propose a control method based on our simplified CPG model. Meanwhile, we improve Serpenoid control function and then investigate how different parameters affect the motion gait in terms of ADAMS emulation. Finally, experimental results show that the snake-like robot can tackle challenging problems including multi-information acquisition and processing, multigait stability, and autonomous motion and further verify the reliability and accuracy of the system in our combinatory experiments.

The navigation accuracy of the inertial navigation system (INS) can be greatly improved when the inertial measurement unit (IMU) is effectively calibrated and compensated, such as gyro drifts and accelerometer biases. To reduce the requirement for turntable precision in the classical calibration method, a continuous dynamic self-calibration method based on a three-axis rotating frame for the hybrid inertial navigation system is presented. First, by selecting a suitable IMU frame, the error models of accelerometers and gyros are established. Then, by taking the navigation errors during rolling as the observations, the overall twenty-one error parameters of hybrid inertial navigation system (HINS) are identified based on the calculation of the intermediate parameter. The actual experiment verifies that the method can identify all error parameters of HINS and this method has equivalent accuracy to the classical calibration on a high-precision turntable. In addition, this method is rapid, simple and feasible.

Fast and elitist nondominated sorting generic algorithm (NSGA2) is an improved multiobjective genetic algorithm with good convergence and robustness. The Pareto optimal solution set using NSGA2 has the character of uniform distribution. This paper builds a time-of-use (TOU) pricing mathematical model considering actual constraint conditions and puts forward a new method which realizes multiobjective TOU pricing optimization using NSGA2. A variety of objective TOU pricing schemes can be provided for decision makers compared with traditional method. Furthermore, the multiple attribute decision making theory is applied in processing the Pareto optimal solution set to calculate the optimal compromise price scheme. The simulation results have shown that the TOU pricing scheme determined by the method proposed above can achieve a better effect of clipping the peak load to fill the valley load. Consequently, the study in this paper is innovative and is a successful exploration of coordinating the relation of various objective functions concerned in TOU pricing optimization problem.

This study aimed to examine the clinical and prognostic significance of cell-cycle progression gene 1 (CCPG1) in hepatocellular carcinoma (HCC). We firstly analyzed CCPG1 expression in various cancers using The Cancer Genome Atlas and the Genotype-Tissue Expression project databases. The relative expression levels of CCPG1 were determined in 164 paired HCC and adjacent tissues using immunohistochemistry. The correlation between CCPG1 and clinicopathological characteristics of HCC was analyzed. Cox proportional models were used to identify the prognostic factors for overall survival (OS) and disease-free survival (DFS). The expression of CCPG1 was lower in HCC tissues than in adjacent non-tumor liver tissues. The expression of CCPG1 was significantly correlated with tumor number (p = 0.02) and tumor differentiation (p = 0.04) in HCC. Lower expression of CCPG1 in HCC patients was associated with poor OS and DFS (p < 0.01). Relative low expression of CCPG1 in HCC is significantly correlated with the poor prognosis of HCC patients after surgical resection, suggesting its possible role as a potential prognostic marker for HCC. [ABSTRACT FROM AUTHOR]