Reliability analysis of the impact of thickness measurement accuracy on the longitudinal strength assessment of CAP

The reliability analysis of the longitudinal bending strength of ships is conducted using the total probability method, taking into account and analyzing the accuracy error in thickness measurement of transverse section members on existing servicing ships. A mathematical model for calculating failure probability CAP ratings is established. By calculating the CAP rating of an aged LPG carrier, it is observed that when the deterministic method reaches the target rating, the failure probability in rating calculation by the reliability analysis method is high. This indicates a significant influence of thickness measurement accuracy on CAP rating accuracy

Chromosome-level genome assembly of a high-altitude-adapted frog (Rana kukunoris) from the Tibetan plateau provides insight into amphibian genome evolution and adaptation

Background The high-altitude-adapted frog Rana kukunoris, occurring on the Tibetan plateau, is an excellent model to study life history evolution and adaptation to harsh high-altitude environments. However, genomic resources for this species are still underdeveloped constraining attempts to investigate the underpinnings of adaptation. Results The R. kukunoris genome was assembled to a size of 4.83 Gb and the contig N50 was 1.80 Mb. The 6555 contigs were clustered and ordered into 12 pseudo-chromosomes covering similar to 93.07% of the assembled genome. In total, 32,304 genes were functionally annotated. Synteny analysis between the genomes of R. kukunoris and a low latitude species Rana temporaria showed a high degree of chromosome level synteny with one fusion event between chr11 and chr13 forming pseudo-chromosome 11 in R. kukunoris. Characterization of features of the R. kukunoris genome identified that 61.5% consisted of transposable elements and expansions of gene families related to cell nucleus structure and taste sense were identified. Ninety-five single-copy orthologous genes were identified as being under positive selection and had functions associated with the positive regulation of proteins in the catabolic process and negative regulation of developmental growth. These gene family expansions and positively selected genes indicate regions for further interrogation to understand adaptation to high altitude. Conclusions Here, we reported a high-quality chromosome-level genome assembly of a high-altitude amphibian species using a combination of Illumina, PacBio and Hi-C sequencing technologies. This genome assembly provides a valuable resource for subsequent research on R. kukunoris genomics and amphibian genome evolution in general.Peer reviewe

Reliability analysis of the impact of thickness measurement accuracy on the longitudinal strength assessment of CAP

The reliability analysis of the longitudinal bending strength of ships is conducted using the total probability method, taking into account and analyzing the accuracy error in thickness measurement of transverse section members on existing servicing ships. A mathematical model for calculating failure probability CAP ratings is established. By calculating the CAP rating of an aged LPG carrier, it is observed that when the deterministic method reaches the target rating, the failure probability in rating calculation by the reliability analysis method is high. This indicates a significant influence of thickness measurement accuracy on CAP rating accuracy

Autonomous Air Combat Maneuver Decision-Making Based on PPO-BWDA

As Unmanned Combat Aerial Vehicle (UCAV) continue to play an increasingly pivotal role in modern aerial warfare, enhancing their intelligence levels is imperative for global military advancement. Despite notable progress in employing deep reinforcement learning for autonomous air combat maneuver decision-making, existing methods grapple with subpar performance, sluggish training, and susceptibility to local optima. Therefore, this paper proposes a new air combat maneuver decision algorithm based on Proximal Policy Optimization (PPO). Firstly, we establish a UCAV adversarial model and design a dual observation space. Secondly, we develop an Actor-Critic network based on Bidirectional Long Short-Term Memory (BiLSTM) and Multi-Head Self-Attention (MHSA), which better handles high-dimensional information with temporal correlations in air combat situations. Thirdly, we propose an action selection method based on Parallel Monte Carlo Tree Search with Watch the Unobserved (WU-PMCTS) to assist the algorithm in making more effective maneuver decisions. Fourthly, we design a Dynamic Reward Evaluation (DRE) method to dynamically adjust the weights of various rewards according to different adversarial situations, improving algorithm performance. Finally, we introduce an Advantage Prioritized Experience Replay (APER) to sample according to the sample advantage values, enhancing algorithm training efficiency. Experimental results from ablation and comparative experiments demonstrate the superiority of the proposed algorithm over PPO and other mainstream algorithms, with a 0.32 increase in average return and a 36% increase in win rate

Continuous Action Air Combat Maneuver Decision-Making Based on T-MGMM

In autonomous air combat, tactics are inherently complex, and control inputs are continuous. Traditional reinforcement learning (RL) algorithms often rely on discretization or independent Gaussian assumptions, which fail to capture correlations between control variables, limiting the expressiveness of strategies. Moreover, the highly dynamic and complex nature of battlefield scenarios poses significant challenges for conventional neural networks in modeling the long-term evolution of sequential data. To address these challenges, this paper proposes a novel algorithm, T-MGMM, which integrates Transformer networks with a Multivariate Gaussian Mixture Model (MGMM). The self-attention mechanism of Transformers effectively captures dependencies between variables and key situational information. Meanwhile, MGMM utilizes non-diagonal covariance matrices to account for correlations between actions, enhancing action modeling. This synergy ensures precise sequence modeling and flexible decision-making, making T-MGMM particularly well-suited for the complexities of air combat scenarios. To further improve optimization stability, we introduce internal Kullback-Leibler divergence regularization. Experimental results demonstrate that T-MGMM outperforms state-of-the-art algorithms, achieving higher Elo scores within the same training steps, and showcasing superior effectiveness and robustness in air combat decision-making

Application of Copper–Sulfur Compound Electrode Materials in Supercapacitors

Supercapacitors (SCs) are a novel type of energy storage device that exhibit features such as a short charging time, a long service life, excellent temperature characteristics, energy saving, and environmental protection. The capacitance of SCs depends on the electrode materials. Currently, carbon-based materials, transition metal oxides/hydroxides, and conductive polymers are widely used as electrode materials. However, the low specific capacitance of carbon-based materials, high cost of transition metal oxides/hydroxides, and poor cycling performance of conductive polymers as electrodes limit their applications. Copper–sulfur compounds used as electrode materials exhibit excellent electrical conductivity, a wide voltage range, high specific capacitance, diverse structures, and abundant copper reserves, and have been widely studied in catalysis, sensors, supercapacitors, solar cells, and other fields. This review summarizes the application of copper–sulfur compounds in SCs, details the research directions and development strategies of copper–sulfur compounds in SCs, and analyses and summarizes the research hotspots and outlook, so as to provide a reference and guidance for the use of copper–sulfur compounds