Search

A high-precision hypernuclear experiment has been commissioned at the Mainz Microtron (MAMI) to determine the hypertriton Λ binding energy via decay-pion spectroscopy. The method has been successfully pioneered with 4ΛH studies in the last decade. The experiment makes use of a novel high luminosity lithium target with a length of 45mm while being only 0.75mm thick to keep momentum smearing of the decay pions low. The target-to-beam alignment as well as the observation of the deposited heat is achieved with a newly developed thermal imaging system. Together with a precise beam energy determination via the undulator light interference method a recalibration of the magnetic spectrometers will be done to obtain a statistical and systematic error of about 20 keV. The experiment started in the summer of 2022 and initial optimization studies for luminosity and data quality are presented.

In these proceedings, we present the measurement of the charge symmetry breaking in A = 4 hypernuclei in Au+Au collisions at √sNN = 3 GeV. The signal reconstruction and binding energy measurements of 4ΛH and 4ΛHe, including corrections on momentum, are discussed. Our result of Λ binding energy difference for ground states is ΔBΛ(0+) = 0.16 ± 0.14(stat.) ± 0.10(syst.) MeV. Combined with the energy levels of excited states, the difference for excited states is ΔBΛ(1+) = −0.16 ± 0.14(stat.) ± 0.10(syst.) MeV which shows a negative sign with a magnitude comparable to the result of ground states. These results are compared with previous measurements and theoretical model calculations.

The International Maritime Organization (IMO) has been progressively implementing stricter regulations on ship carbon emissions, leading to many vessels adopting the virtual arrival (VA) method to reduce their carbon footprint. However, the effectiveness of the traditional VA method often varies in busy ports with complex traffic organization scenarios. To address this, our study presents a novel, comprehensive model that integrates vessel scheduling with the VA approach. This model is designed to achieve a dual objective: reducing carbon emissions through virtual arrival while simultaneously minimizing vessel waiting times. In addition to these goals, it incorporates essential aspects of safety, efficiency, and fairness in port management, utilizing the NSGA-2 algorithm to find optimal solutions. This model has been tested and validated through a case study at Ningbo-Zhoushan port, employing its dataset. The results demonstrate that our innovative model and algorithm significantly outperform traditional scheduling methods, such as First-Come-First-Serve (FCFS) and Virtual-Arrival Last-Serve (VALS), particularly in terms of operational efficiency and reduction in vessel carbon emissions. [ABSTRACT FROM AUTHOR]

Fuzzy neural networks have both the interpretability of fuzzy systems and the self-learning ability of neural networks, but they will face the challenge of "rule explosion" when dealing with high-dimensional data. Moreover, the structure and parameter identifications of models are generally performed in two stages, and this always attends to one thing and loses another in terms of interpretability and predictive performance. In this paper, a fuzzy neural network regression method (FNNR) that coordinates structure identification and parameter identification is proposed. To alleviate the problem of rule explosion, the structure identification and parameter identification are coordinated in the training process, and the numbers of fuzzy rules and fuzzy partitions are effectively limited, while the parameters of fuzzy rules are optimized. The symmetrical architecture of the FNNR is designed for automatic structure identification. An alternate training strategy is adopted by treating discrete and continuous parameters differently, and thus the convergence efficiency of the algorithm is improved. To enhance interpretability, regularized terms are designed from fuzzy rule level and fuzzy partition level to guide the model to learn fuzzy rules with simple structures and clear semantics. The experimental results show that the proposed method has both a compact structure and high precision. [ABSTRACT FROM AUTHOR]

As an important part of the intelligent planning technology, hierarchical task network (HTN) is successfully applied to the task planning in unmanned aerial vehicle platform, emergency plan formulation and other fields. Due to effect the increasing uncertain factors on the implementation the influence of the plan, it is urgent to use replanning technology, thus the replanning technology based on HTN has become a hotspot in recent years. First of all, a three-layer replan framework based on HTN is proposed by summarizing the current research status. Then, the corresponding technical route and implementation details are represented from three levels of plan repair, local replanning and global replanning in the framework respectively, and the advantages and disadvantages of the current method are pointed out for different levels. Finally, the future research direction of this problem is prospected, and the key problems to be solved and the solutions are putting forward. [ABSTRACT FROM AUTHOR]