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Abstract. For nanomaterials, the concept of heat conductivity needs to be revisited. At this fine scale, heat is propagated using the phonon transport equation, an ab initio model derived from first principles. In this problem, a material’s thermal property is coded in a coefficient termed the relaxation time [Formula: see text], depending on the phonon frequency. We propose a novel inverse problem in this paper that infers the relaxation time using a material’s temperature response upon heat injection. This inverse problem is formulated in a PDE-constrained optimization and is numerically solved by the stochastic gradient descent method (SGD) and its variants. In the execution of SGD, the Fréchet derivative is computed and Lipschitz continuity is proved. This approach, in comparison to the earlier studies, enhances the ab initio concept of heat conduction in nanostructures associated to nanomaterials. 

We study the possibility of controlling kinetic plasma instabilities by using lasers to apply external transverse electromagnetic fields. We derive the dispersion relation for the corresponding mathematical description, a reduced Vlasov–Maxwell system, by extending the well-known Penrose condition. It is observed that, under very mild assumptions, the dispersion relation decouples into two parts. The first part is identical to the classic Penrose condition for the Vlasov–Poisson system, while the second part describes the influence of the laser on the transverse dynamics (e.g., a Weibel instability). In particular, this means that the longitudinal dynamics (e.g., a two-stream instability) cannot be stabilized in this manner as far as linear theory is concerned. We show that, however, nonlinear effects can be used to couple the two parts and achieve an effective control. This is done by determining the control parameters (i.e., the form of the external electric and magnetic fields) by solving a partial differential equation (PDE)-constrained optimization problem. 

Abstract We present FlareDB, a database that provides comprehensive magnetic field information, ultraviolet/extreme ultraviolet (UV/EUV) emissions, and white light continuum images for solar active regions (ARs) associated with 151 significant flares from May 2010 to May 2025. The data, sourced from the Solar Dynamics Observatory (SDO) via the Joint Science Operations Center (JSOC), were processed with SunPy and stored in standardized JSOC FITS format. FlareDB includes all M5.0 and larger flares within 50° of the solar disk center. Key features include (1) Atmospheric Imaging Assembly (AIA) AR patches in Helioprojective Cartesian(HPC) and Lambert Cylindrical Equal-Area (CEA) projections, aligned with corresponding HMI magnetogram patches; (2) quick-look movies with uniform value ranges that ensure consistent visualization, maintain data uniformity, and enhance readiness for machine learning studies; (3) a supplementary web interface that allows the entire dataset of a flare to be downloaded for large flare analysis. One of FlareDB’s primary objectives is to support scientists in predicting and understanding the onset of solar eruptions, including flares and coronal mass ejections. The data set is machine-learning ready for this purpose. 

Cooperation, the process through which individuals work together to achieve common goals, is fundamental to human and animal societies and increasingly critical in artificial intelligence (AI). In this study, we investigated cooperation in mice and AI systems, examining how they learn to actively coordinate their actions to obtain shared rewards. We identified key social behavioral strategies and decision-making processes in mice that facilitate successful cooperation. These processes are represented in the anterior cingulate cortex (ACC), and ACC activity causally contributes to cooperative behavior. We extended our findings to AI systems by training artificial agents in a similar cooperation task. The agents developed behavioral strategies and neural representations reminiscent of those observed in the biological brain, revealing parallels between cooperative behavior in biological and artificial systems.