In this study, we investigate the ionic transport behavior of potassium-based antiperovskites (K3HCh, Ch: S, Se, and Te) through a comprehensive machine learning framework. The workflow included 809 Inorganic electrolyte compounds, feature generation from elemental properties, selection of the top 50 features, and training and testing of several predictive models, and the XGBoost model achieves a mean absolute error (MAE) of approximately 0.45. To validate the model, we applied it to lithium-based antiperovskites (Li3HCh, Ch = S, Se, Te) at both high and low temperatures. The predicted ionic conductivities via the XGBoost model showed minimum errors of 7.34%, 2.65%, and 1.58% for Li3HS, Li3HSe, and Li3HTe, respectively, compared to experimental values. For potassium analogues, the predicted ionic conductivities at 100 degrees C were 7.64 & times; 10 -8, 3.5 & times; 10-8, and 1.9 & times; 10-7 S & centerdot;cm-1 for K3HS, K3HSe, and K3HTe, respectively. In addition, Nudged elastic band (NEB) calculations confirm that K3HTe possesses a low activation energy of 0.38 eV, highlighting its potential for efficient ion transport.
. This paper is concerned with a class of second-order evolution ory, and a constant time delay feedback within a real Hilbert space. While many previous studies have addressed stability in autonomous settings, this study focuses on the nonautonomous case of delayed systems. The main results presented in the paper are the well-posedness and stabilization of solutions under a globally Lipschitz continuous nonlinear source term and a constant delay. Under suitable assumptions on time-dependent operators, we prove the system is well-posed by semigroup approach. The stabilization is established following the construction of an appropriate Lyapunov functional and the application of the energy method. The result is new for such time-delayed nonautonomous systems with infinite memory and nonlinear effects.
In the landscape of Sixth-Generation (6G) networks, achieving efficient content delivery and caching in Unmanned Aerial Vehicle (UAV)-assisted terrestrial networks remains a critical challenge due to dynamic user demand, energy constraints, and redundant storage limitations. This paper proposes a novel framework integrating UAVs, Unmanned Ground Vehicles (UGVs), Reconfigurable Intelligent Surfaces (RISs), and Wireless Power Transfer (WPT) to eliminate the need for UAVs to return to the Base Station (BS) for energy replenishment and content reloading. A Multi-Agent Deep Reinforcement Learning (MADRL) approach is employed to optimize UAV and UGV movements, ensuring adaptive trajectory adjustments for uninterrupted connectivity and service continuity. Specifically, UAVs receive energy and data directly from the BS when a Line-of-Sight (LoS) link is available or, in the case of a Non-Line-of-Sight (NLoS) scenario, via UGV-mounted RISs that reflect signals to/from the BS, maximizing WPT efficiency and enhancing data relaying. A Softmax-based probabilistic caching strategy optimizes content placement based on request popularity while preventing redundant storage across UAVs, enabling them to autonomously update their caches and reposition according to expected demand patterns. When a requested content file is unavailable in a UAV's cache, the request is forwarded to the BS via RIS-assisted reflection or direct communication, ensuring low-latency content retrieval without requiring UAV trajectory deviations. The proposed framework significantly enhances cache efficiency and UAV mobility, enabling continuous operation. Comprehensive simulations confirm the efficacy of this method, showcasing significant gains in network sustainability, energy efficiency, and content availability over traditional UAV-assisted caching techniques.
In this paper, we investigate the well-posedness and establish lower and upper bounds for the blow-up time of solutions to a class of fractional Laplacian equations. The governing model includes a nonlinear source term and dissipative effects with variable exponents. The equation features a wave-like structure with a fractional diffusion term, a memory term involving a convolution with a relaxation kernel, a nonlinear damping term with a variable exponent, and a source term with another variable exponent. The kernel is assumed to be smooth, non-increasing, and satisfies a specific smallness condition on its total integral. We prove the existence of a solution and then derive lower and upper bounds for the blow-up time, which depend on the fractional exponent, the variable growth exponents in the damping and source terms, and the properties of the relaxation kernel.
Manganese dioxide (MnO2), a low-cost and environmentally benign material, has garnered significant attention as a potential photocatalyst and photoelectrode for sustainable hydrogen production. In this study, we investigate the photocatalytic and photoelectrochemical (PEC) performance of MnO2 nanostructures synthesized via hydrothermal method. The physicochemical properties of the prepared materials were systematically characterized using XRD, SEM, ATD/DTG, BET, FTIR, UV-Vis, XPS spectroscopies and electrochemical techniques. The photocatalytic hydrogen evolution was evaluated under visible light irradiation, while the PEC activity was assessed using linear voltammetry and Mott-Schottky analysis in a three-electrode system. The Mott-Schottky analysis confirmed n-type semiconducting behavior, with a flat band potential (Vfb) of -0.23 V vs. SCE, indicating favorable band alignment for water reduction. The conduction and valence band positions were estimated to be -0.21 V and + 1.78 eV vs. SCE, respectively. The potential for water reduction to hydrogen (VH2O/H2) was determined to be -0.98 V vs. SCE, confirming the thermodynamic feasibility of H2 evolution. Among the samples tested, MnO2-2 exhibited superior photocatalytic activity, achieving a hydrogen evolution amount similar to 600 mu mol after 35 min, while MnO2-4 recorded 320 mu mol under the same conditions. These results highlight the potential of MnO2-based materials as efficient, stable, and low-cost candidates for solar-driven hydrogen generation applications.
