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Accurate coronary artery segmentation is essential for diagnosis and interventional planning, but conventional U-shaped networks often miss thin, low-contrast vessels and break vessel continuity. We propose Inverted Pyramid-Shaped Multi-resolution U-Net (IPSM-UNet), a dual U-Net++ architecture with multi-resolution feature interaction, feature aggregation, and layer-wise deep supervision. The method is evaluated on DRIVE, CHASE_DB1, DCA1, and an internal coronary angiography dataset. IPSM-UNet achieves competitive or better performance across datasets, including F1 = 0.8310 and Acc = 0.9707 on DRIVE, Se = 0.8792 and Acc = 0.9745 on CHASE_DB1, F1 = 0.8043 and Acc = 0.9793 on DCA1, and Se = 0.8741, F1 = 0.8590, and Acc = 0.9879 on the internal dataset. IPSM-UNet improves vessel continuity and overall segmentation quality, particularly for small-caliber vessels, and supports downstream coronary analysis.

This study uses deep reinforcement learning (DRL) combined with computer vision technology to investigate vehicle fuel economy. In a driving cycle with car-following and traffic light scenarios, the vehicle fuel-saving control strategy based on DRL can realize the cooperative control of the engine and continuously variable transmission. The visual processing method of the convolutional neural network is used to extract available visual information from an on-board camera, and other types of information are obtained through the vehicle’s inherent sensor. The various detected types of information are further used as the state of DRL, and the fuel-saving control strategy is built. A Carla–Simulink co-simulation model is established to evaluate the proposed strategy. An urban road driving cycle and highway road driving cycle model with visual information is built in Carla, and the vehicle power system is constructed in Simulink. Results show that the fuel-saving control strategy based on DRL and computer vision achieves improved fuel economy. In addition, in the Carla–Simulink co-simulation model, the fuel-saving control strategy based on DRL and computer vision consumes an average time of 17.55 ms to output control actions, indicating its potential for use in real-time applications.

Local planning is a critical and difficult task for intelligent vehicles in dynamic transportation environments. In this paper, a new method Suppress Q Deep Q Network (SQDQN) combining traditional deep reinforcement learning Deep Q Network (DQN) with information entropy is proposed for local planning in automatic driving. In the proposed approach, local planning strategy in complex traffic environment established by the actor–critic network based on DQN, the method adopts the way of execution action-evaluation action-update network to explore the optimal local planning strategy. Proposed strategy does not rely on accurate modeling of the scene, so it is suitable for complex and changeable traffic scenes. At the same time, evaluate the update process and determine the update range by using information entropy to solve a common problem in the network that over expectation of actions damage the performance of strategies. Use this approach to improve strategic performance. The trained local planning strategy is evaluated in three simulation scenarios: overtaking, following, driving in hazardous situations. The results illustrate the advantages of the proposed SQDQN method in solving local planning problem.

Social hierarchies emerged during evolution, and social rank influences behavior and health of individuals. However, the evolutionary mechanisms of social hierarchy are still unknown in amniotes. Here we developed a new method and performed a genome-wide screening for identifying regions with accelerated evolution in the ancestral lineage of placental mammals, where mammalian social hierarchies might have initially evolved. Then functional analyses were conducted for the most accelerated region designated as placental-accelerated sequence 1 (PAS1, P  = 3.15 × 10 −18 ). Multiple pieces of evidence show that PAS1 is an enhancer of the transcription factor gene Lhx2 involved in brain development. PAS1s isolated from various amniotes showed different cis -regulatory activity in vitro, and affected the expression of Lhx2 differently in the nervous system of mouse embryos. PAS1 knock-out mice lack social stratification. PAS1 knock-in mouse models demonstrate that PAS1s determine the social dominance and subordinate of adult mice, and that social ranks could even be turned over by mutated PAS1. All homozygous mutant mice had normal huddled sleeping behavior, motor coordination and strength. Therefore, PAS1- Lhx2 modulates social hierarchies and is essential for establishing social stratification in amniotes, and positive Darwinian selection on PAS1 plays pivotal roles in the occurrence of mammalian social hierarchies.

CaO-SiO2-B2O3 terpolymer system is used as the basic raw material to synthesize other glass and melt. Doping Pr, Eu and other rare earth elements can get special performance glass materials with good luminous performance, CaO-SiO2-B2O3 terpolymer system is widely used in packaging substrate and dielectric materials. As an ideal substitute for fluorine, boron in borosilicate has become a new research hotspot of fluorine-free protective slag technology due to its advantages of economic cost and fluxing effect. Raman spectroscopy can be used for real-time observation and microsturcture analysis of materials, which provides a powerful means for the study of high-temperature melt and glass. In this work, CaO-SiO2-B2O3 ternary glass was prepared and Raman spectrum of glass was measured, which provided the necessary basis for the subsequent analysis of its properties, structure and the relationship between its components.

CaO-SiO 2 -B 2 O 3 terpolymer system is used as the basic raw material to synthesize other glass and melt. Doping Pr, Eu and other rare earth elements can get special performance glass materials with good luminous performance, CaO-SiO 2 -B 2 O 3 terpolymer system is widely used in packaging substrate and dielectric materials. As an ideal substitute for fluorine, boron in borosilicate has become a new research hotspot of fluorine-free protective slag technology due to its advantages of economic cost and fluxing effect. Raman spectroscopy can be used for real-time observation and microsturcture analysis of materials, which provides a powerful means for the study of high-temperature melt and glass. In this work, CaO-SiO 2 -B 2 O 3 ternary glass was prepared and Raman spectrum of glass was measured, which provided the necessary basis for the subsequent analysis of its properties, structure and the relationship between its components.

In this paper, we propose using a chaotic system composed of nanolasers (NLs) as a physical entropy source. Combined with post-processing technologies, this system can produce high-quality physical random number sequences. We investigated the parameter range for achieving time-delay signature (TDS) concealment in the chaotic system. This study demonstrates that NLs exhibit noticeable TDS only under optical feedback. As mutual injection strength between the master NLs (MNLs) increases, the TDS of the MNLs is gradually suppressed until they are completely concealed. Compared to MNLs, the slave NL (SNL) exhibits better TDS suppression performance. Additionally, we investigated the chaotic and highly unpredictable regions of the SNL, demonstrating that high-quality chaotic signals can be produced over a wide range of parameters. Using TDS hidden and highly unpredictable chaotic signals as the source of random entropy, the effects of different post-processing techniques on random number extraction were compared. The results indicate that effective post-processing can enhance the unpredictability of the random sequence. This study successfully utilized NLs for random number generation, showcasing the potential and application prospects of NLs in the field of random numbers.

Significant progress has been made in the research of all-optical neural networks in recent years. In this paper, we theoretically explore the properties of a neural system composed of semiconductor ring lasers (SRLs). Our study demonstrates that external optical signals generated by a tunable laser (TL) are injected into the first semiconductor ring laser photonic neuron (SRL1). Subsequently, the responses of SRL1 in the clockwise (CW) and counterclockwise (CCW) directions are unidirectionally injected into the CW and CCW directions of the second semiconductor ring laser photonic neuron (SRL2), respectively, which then exhibits similar spiking inhibition behaviors. Numerical simulations reveal that the spiking inhibition behavior of the SRL response can be precisely controlled by adjusting the perturbation time and intensity of the external injection signal, and this behavior is highly repeatable. Most importantly, we successfully achieve the stable transmission of these responses between the two SRL photonic neurons. These inhibition behaviors are analogous to those of biological neurons, but with a response speed reaching the sub-nanosecond level. Additionally, we indicate that SRL photonic neurons undergo a refractory-period-like phenomenon when subjected to two consecutive perturbations. These findings highlight the immense potential for the design and implementation of future all-optical neural networks, providing critical theoretical foundations and support for them.

In this paper, a method of generating controllable spikes utilizing symmetric semiconductor ring lasers (SRLs) is investigated, and various optical behaviors of biological neurons are successfully emulated on a faster timescale. We demonstrate the synchronized spike phenomena in two directions, generated in both the clockwise (CW) and counterclockwise (CCW) modes of the tunable laser (TL)-injected SRL. The size of the peaks and the interval between them can be manipulated by adjusting the output complex amplitude of the TL and bias current. At the same time, we also analyzed the CW mode of the TL-injected SRL and successfully replicated the four distinct discharge patterns of biological neurons. These findings offer promising prospects for future neuromorphic research.