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Abstract In air combat missions, unmanned combat aerial vehicles (UCAVs) must take strategic actions to establish combat advantages, enabling effective tracking and attacking of enemy UCAVs. Currently, a lot of reinforcement learning algorithms are applied to the air combat mission of unmanned fighter aircraft. However, most algorithms can only select policies based on the current state of both sides. This leads to the inability to effectively track and attack when the enemy performs large angle maneuvering. Additionally, these algorithms cannot adapt to different situations, resulting in the unmanned fighter aircraft being at a disadvantage in some cases. To solve these problems, this paper proposes predictive air combat decision model with segmented reward allocation for air combat tracking and attacking. On the basis of the air combat environment, we propose the prediction soft actor-critic (Pre-SAC) algorithm, which combines the prediction of enemy states with the states of UCAV for model training. This enables the UCAV to predict the next move of the enemy UCAV in advance and establish a greater air combat advantage for us. Furthermore, by adopting a segmented reward allocation model and combining it with the Pre-SAC algorithm, we propose the segmented reward allocation soft actor-critic (Sra-SAC) algorithm, which solves the problem of UCAVs being unable to adapt to different situations. The results show that the prediction-based segmented reward allocation the Sra-SAC algorithm outperforms the traditional soft actor-critic (SAC) algorithm in terms of overall reward, travel distance, and relative advantage.

Abstract Background It’s unclear if excess visceral adipose tissue (VAT) mass in individuals with prediabetes can be countered by adherence to a Mediterranean lifestyle (MEDLIFE). We aimed to examine VAT mass, MEDLIFE adherence, and their impact on type 2 diabetes (T2D) and diabetic microvascular complications (DMC) in individuals with prediabetes. Methods 11,267 individuals with prediabetes from the UK Biobank cohort were included. VAT mass was predicted using a non-linear model, and adherence to the MEDLIFE was evaluated using the 25-item MEDLIFE index, encompassing categories such as “Mediterranean food consumption,” “Mediterranean dietary habits,” and “Physical activity, rest, social habits, and conviviality.” Both VAT and MEDLIFE were categorized into quartiles, resulting in 16 combinations. Incident cases of T2D and related DMC were identified through clinical records. Cox proportional-hazards regression models were employed to examine associations, adjusting for potential confounding factors. Results Over a median follow-up of 13.77 years, we observed 1408 incident cases of T2D and 714 cases of any DMC. High adherence to the MEDLIFE, compared to the lowest quartile, reduced a 16% risk of incident T2D (HR: 0.84, 95% CI: 0.71–0.98) and 31% for incident DMC (0.69, 0.56–0.86). Conversely, compared to the lowest quartile of VAT, the highest quartile increased the risk of T2D (5.95, 4.72–7.49) and incident any DMC (1.79, 1.36–2.35). We observed an inverse dose-response relationship between MEDLIFE and T2D/DMC, and a dose-response relationship between VAT and all outcomes (P for trend

ObjectiveTo evaluate the accuracy of contrast-enhanced ultrasound diagnostic reports for pancreatic lesions.MethodsIn this retrospective study, we included patients who underwent contrast-enhanced ultrasound examination of pancreatic lesions at Peking Union Medical College Hospital from January 2017 to December 2022 and received a confirmed pathological diagnosis. Using pathological diagnosis as the gold standard, the study evaluated the accuracy of contrast-enhanced diagnostic ultrasound. It also analyzed the misdiagnosis of contrast-enhanced ultrasound in diagnosing various pathological types of pancreatic lesions.ResultsOf the 859 patients who met inclusion and exclusion criteria, 489 were male(56.9%) and 370 were female(43.1%).Their age ranged from 16 to 85 years, with a median age of 60(53, 66) years. Except for 47 pancreatic space-occupying lesions that were difficult to categorize as either benign or malignant, a total of 812 cases were included in the diagnostic efficacy analysis of benign and malignant lesions. The results suggested that the diagnostic sensitivity and specificity of contrast-enhanced ultrasound for pancreatic malignant lesions were 98.3%(95% CI: 97.1%-99.1%) and 79.0%(95% CI: 70.1%-86.4%), AUC was 0.887(95% CI: 0.863-0.908), positive and negative predictive value were 96.9%(95% CI: 95.6%-97.9%) and 87.4%(95% CI: 79.6%-92.4%), positive and negative likelihood ratio were 4.69(95% CI: 3.24-6.80) and 0.02(95% CI: 0.01-0.04). The diagnostic accuracy rate of contrast-enhanced ultrasound for diagnosing benign and malignant pancreatic lesions was 95.8%. Of the 859 lesions examined, 48 cases were misdiagnosed by contrast-enhanced ultrasound, with a misdiagnosis rate of 5.6%(48/859), including 7 cases(0.8%) of undetermined diagnosis, while the diagnostic accuracy of pancreatic ductal adenocarcinoma was as high as 98.8%. No complications occurred in any of the patients.ConclusionsContrast-enhanced ultrasound is a safe and effective imaging method for evaluating microvascular perfusion in various pancreatic lesions. It has significant clinical value in diagnosing both benign and malignant pancreatic lesions, particularly in diagnosing pancreatic ductal adenocarcinoma.

In the context of rapid industrialization, efficiently detecting metal corrosion areas has become a critical task in preventing material damage. Unlike conventional semantic segmentation targets, metal corrosion characteristics vary significantly in color, texture, and size. Traditional image segmentation methods need improvement in scenarios involving occlusions, shadows, and defects. This paper proposes a convolution and sequence encoding combined network, MCD-Net, for metal corrosion area segmentation. First, a visual Transformer sequence encoder is introduced into the convolutional encoder–decoder network to enhance global information processing capabilities and establish long-range feature dependencies. A feature fusion method based on an attention module is proposed to enhance the model’s ability to recognize corrosion boundaries, thereby enhancing segmentation accuracy and model robustness. Finally, in the model’s decoding stage, a score-based multi-scale feature enhancement method is employed to emphasize significant features in the corrosion areas. Experimental results indicate that this method attained an F1 score of 84.53% on a public corrosion dataset, demonstrating the model’s deeper understanding and reasoning capabilities for shadow and defect features, as well as excellent noise resistance performance.

Brevetoxins (PbTxs) are very potent marine neurotoxins that can cause an illness clinically described as neurologic shellfish poisoning (NSP). These toxins are cyclic polyether in chemistry and have increased their geographical distribution in the past 2 decades. However, the ethical problems as well as technical difficulties associated with currently employed analysis methods for marine toxins have spurred the quest for suitable alternatives to be applied in a regulatory monitoring regime. In this work, we reported the first instance of concurrent aptamer selection of Brevetoxin-1 (PbTx-1) and Brevetoxin-2 (PbTx-2) and constructed a biolayer interferometry (BLI) biosensor utilizing PbTx-1 aptamer as a specific recognition element. Through an in vitro selection process, we have, for the first time, successfully selected DNA aptamers with high affinity and specificity to PbTx-1 and PbTx-2 from a vast pool of random sequences. Among the selected aptamers, aptamer A5 exhibited the strongest binding affinity to PbTx-1, with an equilibrium dissociation constant (KD) of 2.56 μM. Subsequently, we optimized aptamer A5 by truncation to obtain the core sequence (A5-S3). Further refinement was achieved through mutations based on the predictions of a QGRS mapper, resulting in aptamer A5-S3G, which showed a significant increase in the KD value by approximately 100-fold. Utilizing aptamer A5-S3G, we fabricated a label-free, real-time optical BLI aptasensor for the detection of PbTx-1. This aptasensor displayed a broad detection range from 100 nM to 4000 nM PbTx-1, with a linear range between 100 nM and 2000 nM, and a limit of detection (LOD) as low as 4.5 nM. Importantly, the aptasensor showed no cross-reactivity to PbTx-2 or other marine toxins, indicating a high level of specificity for PbTx-1. Moreover, the aptasensor exhibited excellent reproducibility and stability when applied for the detection of PbTx-1 in spiked shellfish samples. We strongly believe that this innovative aptasensor offers a promising alternative to traditional immunological methods for the specific and reliable detection of PbTx-1.