Frontiers in Physics最新文献

The significance of agricultural information sharing in fostering agricultural development cannot be overstated. This practice plays a pivotal role in disseminating cutting-edge agricultural technologies, cultivation methods, and pest control strategies, empowering farmers with valuable knowledge to enhance crop yield and quality. Moreover, it aligns with government objectives of resource sharing and addressing gaps, contributing to the advancement of agricultural modernization and the development of the industry chain. Despite its inherent benefits, the practical implementation of agricultural information sharing faces challenges. Stakeholders engaged in information sharing often prioritize individual benefits, potentially leading to a decline in agricultural information quality and the inefficient use of experimental resources. To confront this issue, the present research establishes a three-party evolutionary game model comprising an agricultural product data sharing platform, agricultural data providers, and agricultural data consumers. Leveraging dynamic system theory, the model analyzes the evolutionary stable strategies of stakeholders and investigates the critical factors influencing the strategic choices of these three parties. Experimental findings underscore the pivotal role of participants’ initial strategies, regulatory intensity, reward and punishment mechanisms, and information feedback in shaping stakeholder decision-making behavior. Implementation of measures such as heightened scrutiny of information on the sharing platform and fostering consumer trust in data emerges as imperative for enhancing system stability. These actions are essential for constructing an efficient and reliable information-sharing ecosystem, thereby facilitating the sustainable development of modern agriculture.

The aim of this paper is to analyze the impact of the digital transformation of banks on their systemic risks. We find that the digital transformation of commercial banks can significantly inhibit the systemic risk of banks, and this conclusion is still valid after considering the endogeneity of the model. The bank’s digital transformation reduces its systemic risk by increasing its own competitiveness. Further analysis shows that the reduction of banks’ marginal costs due to digital transformation is a key factor in promoting banks’ competitiveness as the mechanism by which digital transformation reduces banks’ systemic risk. The role of bank digital transformation in reducing systemic risk is heterogeneous, which is more obvious in large commercial banks, commercial banks that have not established financial technology subsidiaries, and systemically important banks.

Currently, there is limited research on the influence of gas ionization on the pulse formation process in pulse power source-driven loads. This paper introduces a road-field-Particle-In-Cell (PIC)/Monte Carlo Collision (MCC) collaborative simulation method that can accurately simulate gas ionization in Linear Transformer Driver (LTD) electron beam generation (EBG). The method couples the electromagnetic field and charged particle simulated through PIC/MCC with the circuit modules, and the load's voltammetry characteristics can real-time feedback to the Blumlein Pulse Forming Network (BPFN) of the LTD. In contrast to prior simulations that used fitted ideal T-shaped pulse input waveforms to model the load, this method provides a clearer depiction of the influence of gas ionization on the pulse shape. Additionally, the paper conducts simulation studies on LTD electron beam generator operating at different argon gas pressures. The findings indicate that introducing gas can effectively increase current while reducing voltage amplitude, thereby lowering the diode impedance. A small amount of gas can slightly enhance peak power, but excessive gas diminishes peak power and significantly shortens voltage pulse width. This is attributed to the beneficial effect of a small amount of gas ionization-produced plasma on the device. However, an excessive amount of gas ionization-generated plasma can lead to impedance mismatch in the device, even resulting in a load short circuit. This phenomenon causes a decrease in pressure drop at the top, consequently shortening the pulse width.

The limited spatial sampling rates of conventional Shack–Hartmann wavefront sensors (SHWFSs) make them unable to sense higher-order wavefront distortion. In this study, by etching a known phase on each microlens to modulate sub-wavefront, we propose a higher-resolution wavefront reconstruction method that employs a modified modal Zernike wavefront reconstruction algorithm, in which the reconstruction matrix contains quadratic information that is extracted using a neural network. We validate this method through simulations, and the results show that once the network has been trained, for various atmospheric conditions and spatial sampling rates, the proposed method enables fast and accurate high-resolution wavefront reconstruction. Furthermore, it has highly competitive advantages such as fast dataset generation, simple network structure, and short prediction time.

X-ray phase-contrast micro computed tomography using synchrotron radiation (SR PhC-µCT) offers unique 3D imaging capabilities for visualizing microstructure of the human brain. Its applicability for unstained soft tissue is an area of active research. Acquiring images from a tissue block without needing to section it into thin slices, as required in routine histology, allows for investigating the microstructure in its natural 3D space. This paper presents a detailed step-by-step guideline for imaging unstained human brain tissue at resolutions of a few micrometers with SR PhC-µCT implemented at SYRMEP, the hard X-ray imaging beamline of Elettra, the Italian synchrotron facility. We present examples of how blood vessels and neurons appear in the images acquired with isotropic 5 μm and 1 µm voxel sizes. Furthermore, the proposed protocol can be used to investigate important biological substrates such as neuromelanin or corpora amylacea. Their spatial distribution can be studied using specifically tailored segmentation tools that are validated by classical histology methods. In conclusion, SR PhC-µCT using the proposed protocols, including data acquisition and image processing, offers viable means of obtaining information about the anatomy of the human brain at the cellular level in 3D.