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The optimization of the symmetry of MTDC systems after a contingency is crucial for the stable and economic operation of the MTDC systems. In this paper, a multi-objective optimal control method for the power flow symmetry of MTDC systems for the large-scale integration of offshore wind farms is proposed. A mirror relationship between the available headroom of DC lines and VSCs and their actual power flow distribution performance is established. A corresponding symmetry index is established for the MTDC network, and the multi-objective optimization problem is converted into a series of single-objective problems by the normal boundary intersection method, and solved by the original dyadic interior point method, so as to obtain the Pareto optimal solution with uniform distribution. The compromise optimal solution is decided according to the entropy weight double-basis point method, which provides decision-making guidance for the operators. The simulation results show that the normal boundary intersection method can solve the multi-objective dynamic optimal control problem of the VSC-HVDC system quickly and efficiently, and improve the symmetry of the power flow in an MTDC network. [ABSTRACT FROM AUTHOR]

As the share of intermittent and fluctuating renewable energy sources in the power system continues to increase, demand‐side resources must be utilized to enhance the real‐time balance of power generation and supply. Data centres, as typical energy‐intensive loads with bidirectional supply and demand characteristics, are crucial demand‐side resources. However, their operation is complex, making reliable regulation difficult. This paper establishes a refined process‐level load model for data centres by considering the temporal and spatial shifting characteristics of computational loads, effectively reflecting the intricate interactions between these loads and servers. Based on this model, a micro‐energy network is constructed that integrates renewable energy access and waste heat recovery from the data centre. This network also participates in carbon emission trading and green electricity certificate trading markets, applying an interaction mechanism. By considering data interaction and power sharing among multiple micro‐energy networks, the alternating direction method of multipliers algorithm is used to solve the system‐distributed optimization problem in two stages. This approach results in an optimal energy management scheme that simultaneously reduces total operation costs and carbon emissions. [ABSTRACT FROM AUTHOR]

This study investigated the enhancement of the mechanical and acoustic properties of polyurea (PU) composites by incorporating iron microparticles (Fe). The Fe/PU composites were prepared using a mixing process, where iron microparticles were evenly distributed within the polyurea matrix via mechanical stirring. The primary purpose of this study was to explore how varying volumes of iron microparticles influence the viscoelastic properties and acoustic performance of the composites, with a particular focus on applications in underwater acoustic stealth technology. Dynamic mechanical analysis was conducted to study the effect of the iron powder volume fraction on the mechanical properties of the composites, i.e., on the temperature-dependent storage moduli (E′), loss moduli (E″) and loss factor (tanδ). The intrinsic properties of the components and the nature of the interface between the phases determined the dynamic mechanical properties of the composite. It was found that the storage and loss moduli of the composite both increased as the iron volume fraction increased, which indicates that the incorporation of iron powder in a polyurea matrix appreciably induces reinforcing effects. Moreover, the tanδ curve of the Fe/PU composite generally had broader widths of the tanδ peak than those of pure polyurea, which showed better damping behavior. The height of the damping peaks depended on the iron powder content, and the glass transition temperature (measured as the temperature at the maximum value of the tanδ) shifted toward higher temperatures as the iron volume fraction increased. Also, the compression stress–strain curves of the composites obtained by a universal testing machine indicated that the addition of iron powder improved the compressive strength and the energy storage capacity of the composite. In addition, the acoustic absorption coefficient was found to increase with the addition of iron powder, and the increase in sample thickness was found to be the effective in improving low-frequency sound absorption. [ABSTRACT FROM AUTHOR]

Interaction behaviors are a crucial, distinctive feature of organisms in ecological systems. Inspired by nature, micro‐/nanorobots also show interaction behaviors with a significant number of novel and advanced functionalities. The micro‐/nanoscale size and self‐driven abilities of micro‐/nanorobots have attracted considerable attention for potential applications. The mission of micro‐/nanorobots has evolved from basic mechanical transfer or assistance to a diverse range of complex tasks, such as targeted drug delivery, biosensing, and environmental remediation. Herein, we focus on the interaction behavior of coexisting cooperative cognitive (Tri‐Co) micro‐/nanorobots, summarize recent research, and classify the interactions into three broad types: with the environment, with robot peers, and with human external control. [ABSTRACT FROM AUTHOR]

To achieve efficient size tuning of printed microstructures on insulating substrates, an integrated process parameter intelligent optimization design framework for alternating current pulse modulation electrohydrodynamic (AC-EHD) printing is proposed for the first time. The framework is comprised of two stages: the construction of a prediction model and the acquisition of process parameters. The first stage employs the elk herd optimizer(EHO)-artificial neural network(ANN) to establish a mapping relationship between printing process parameters and the size of deposited droplets. The analysis of the prediction performance of the EHO-ANN model across various datasets reveals that the model exhibits commendable accuracy and robustness in predicting printed droplet size. In the second stage, the process parameters of AC-EHD printing are intelligently determined by utilizing the error between the model output and the desired droplet size as the fitness value for EHO. By comparing three sets of experimental cases with varying droplet sizes, it is observed that the actual printed droplet sizes closely align with the desired values, thus validating the effectiveness of this framework. The framework proposed in this paper mitigates the time and material wastage caused by adjusting AC-EHD printing process parameters on insulating substrates, thereby significantly enhancing the usability of the technology., (© 2025 Wiley‐VCH GmbH.)

This paper presents an improved gas sensor based on the dual-excitation of quartz-enhanced photothermal spectroscopy (QEPTS) using a single quartz tuning fork (QTF) for signal detection. The silver coating on one side of the QTF was chemically etched to increase the laser power interacted with QTF for QEPTS signal excitation. By etching the silver coating on one side of QTF, the reflection structure between the silver coating of the other side of QTF and the external flat mirror was established. The device uses an absorption gas cell with an optical range length of 3 m, making the laser beam interact with the gas more completely and posing more gas concentration information. Acetylene was selected as the target gas to verify the performance of the sensor. The experimental results show that the signal amplitude with a flat mirror was 1.41 times that without a flat mirror, and 2.47 times that of traditional QEPTS sensor. The system has a minimum detection limit (MDL) of 1.10 ppmv, corresponding to a normalized noise equivalent absorption coefficient (NNEA) of 7.14 × 10 -9  cm -1 ·W·Hz -1/2 . Allan variance analysis results show that when the integration time is 700 s, the MDL of the system is 0.21 ppmv. The proposed gas sensor can play an important role on detecting trace gas in many fields.

Objective. Relative biological effectiveness (RBE) plays a vital role in carbon ion radiotherapy, which is a promising treatment method for reducing toxic effects on normal tissues and improving treatment efficacy. It is important to have an effective and precise way of obtaining RBE values to support clinical decisions. A method of calculating RBE from a mechanistic perspective is reported. Approach. Ratio of dose to obtain the same number of double strand breaks (DSBs) between different radiation types was used to evaluate RBE. Package gMicroMC was used to simulate DSB yields. The DSB inductions were then analyzed to calculate RBE. The RBE values were compared with experimental results. Main results. Furusawa's experiment yielded RBE values of 1.27, 2.22, 3.00 and 3.37 for carbon ion beam with dose-averaged LET of 30.3 keV μ m -1 , 54.5 keV μ m -1 , 88 keV μ m -1 and 137 keV μ m -1 , respectively. RBE values computed from gMicroMC simulations were 1.75, 2.22, 2.87 and 2.97. When it came to a more sophisticated carbon ion beam with 6 cm spread-out Bragg peak, RBE values were 1.61, 1.63, 2.19 and 2.36 for proximal, middle, distal and distal end part, respectively. Values simulated by gMicroMC were 1.50, 1.87, 2.19 and 2.34. The simulated results were in reasonable agreement with the experimental data. Significance. As a mechanistic way for the evaluation of RBE for carbon ion radiotherapy by combining the macroscopic simulation of energy spectrum and microscopic simulation of DNA damages, this work provides a promising tool for RBE calculation supporting clinical applications such as treatment planning., (© 2024 Institute of Physics and Engineering in Medicine.)