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This paper presents an optimization method for hybrid energy systems based on Model Predictive Control (MPC), Long Short-Term Memory (LSTM) networks, and Kolmogorov–Arnold Networks (KANs). The proposed method is applied to a high-altitude wind energy work umbrella control system, where it aims to enhance the stability and efficiency of energy utilization. The work umbrella system integrates wind and solar energy sources, with energy stored in a battery and used to control the umbrella's operations. The MPC framework is employed to optimize control actions by solving a finite-horizon optimization problem, ensuring the battery State of Charge (SOC) remains within an optimal range. The LSTM network provides accurate predictions of environmental conditions, including wind speed and solar irradiance, which are essential for MPC's decision-making process. To address complex nonlinearities in the system, the KAN is utilized to model and approximate these dynamics, refining the LSTM predictions. The integration of these advanced control strategies enables the system to handle varying operational conditions and maintain optimal performance. The case study demonstrates the effectiveness of the MPC-LSTM-KAN approach, revealing improvements in the SOC stability, energy efficiency, and operational endurance of the high-altitude wind energy work umbrella system. The results indicate that this hybrid optimization method offers a robust solution for managing hybrid energy systems in dynamic environments. [ABSTRACT FROM AUTHOR]

Mid-infrared (MIR) photonic integration is desirable in the development of MIR spectroscopy and "lab-on-a-chip" sensing. The germanium-on-silicon (GOS) platform offers a promising solution for MIR photonic integration, extending the operational wavelength to a longer band by eliminating the light-absorbing buried oxide layer. However, MIR photodetectors on the GOS platform remain undeveloped due to the challenging heterogeneous integration of active materials on silicon and inadequate light absorption in the photodetection region. Here, we demonstrate a photo-thermoelectric graphene photodetector on the GOS platform, taking advantage of zero-bias operation and easy heterogeneous integration of graphene. By employing split-gate architecture and plasmonic enhancement to strengthen the light-graphene interaction, we achieve a responsivity of 1.97 V W−1 and noise equivalent power of 2.8 nW Hz−1/2 at the wavelength of 3.7 µm. This work enables waveguide-integrated MIR photodetection on the GOS platform for the first time, and it holds great potential for on-chip MIR sensing and imaging applications. [ABSTRACT FROM AUTHOR]

Trailing oil is the tail section of contamination in oil pipelines. It is generated in batch transportation, for which one fluid, such as diesel oil follows another fluid, such as gasoline, and it has an effect on the quality of oil. This paper describes our analysis of the formation mechanism of trailing oil in pipelines and our study of the influence of dead-legs on the formation of trailing oil. We found that the oil replacement rate in a dead-leg is exponentially related to the flow speed, and the length of the dead-leg is exponentially related to the replacement time of the oil. To reduce the amount of mixed oil, the main flow speed should be kept at about 1.6 m/s, and the length of the dead-leg should be less than five times the diameter of the main pipe. In our work, the Reynolds time-averaged method is used to simulate turbulence. To obtain contamination-related experimental data, computational fluid dynamics (CFD) software is used to simulate different flow rates and bypass lengths. MATLAB software was used to perform multi-nonlinear regression for the oil substitution time, the length of the bypass, and the flow speed. We determined an equation for calculating the length of the trailing oil contamination produced by the dead-leg. A modified equation for calculating the length of the contamination was obtained by combining the existing equation for calculating the length of the contamination with new factors based on our work. The amounts of contamination predicted by the new equation is closer to the actual contamination amounts than predicted values from other methods suggested by previous scholars. [ABSTRACT FROM AUTHOR]

Sortase A (SrtA) is a cysteine transpeptidase and virulence factor from Staphylococcus aureus (S. aureus) that catalyses the attachment and display of surface proteins on the cell wall, thereby mediating bacterial adhesion to host tissues, host-cell entry and evasion of the immune response. As a result, SrtA has become an important target in the development of therapies for S. aureus infections. In this study, we used the new reference strain S. aureus Newman D2C to investigate the role of SrtA in a murine model of bloodstream infection, when the impact of coagulase and haemolysin is excluded. The results suggested that deletion of SrtA reduced the bacterial burden on the heart, liver and kidneys by blunting the host proinflammatory cytokine response at an early point in infection. Kidneys, but not heart or liver, formed abscesses on the sixth day following non-lethal infection, and this effect was diminished by SrtA mutation. These findings indicate that SrtA is a determining virulence factor in lethality and formation of renal abscesses in mice followed by S. aureus bloodstream infection. We have thus established a convenient in vitro and mouse model for developing SrtA-targeted therapeutic strategies., (© FEMS 2015. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)