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Accurately predicting the capacity of lithium-ion batteries is crucial for improving battery reliability and preventing potential incidents. Current prediction models for predicting lithium-ion battery capacity fluctuations encounter challenges like inadequate fitting and suboptimal computational efficiency. This study presents a new approach for fluctuation prediction termed ASW-DTW, which integrates Adaptive Sliding Window (ASW) and Dynamic Time Warping (DTW). Initially, this approach leverages Empirical Mode Decomposition (EMD) to preprocess the raw battery capacity data and extract local fluctuation components. Subsequent to this, DTW is employed to forecast the fluctuation sequence through pattern-matching methods. Additionally, to boost model precision and versatility, a feature recognition-based ASW technique is used to determine the optimal window size for the current segment and assist in DTW-based predictions. The study concludes with capacity fluctuation prediction experiments carried out across various lithium-ion battery models. The results demonstrate the efficacy and extensive applicability of the proposed method. [ABSTRACT FROM AUTHOR]

In this work, we propose combining a low work function anode metal and junction barrier Schottky structure to simultaneously achieve low turn-on voltage (Von) and high breakdown voltage (BV), which alleviates the dilemma that high BV requires high Schottky barrier height (SBH) and high Von. Molybdenum (Mo) is used to serve as the anode metal to reduce the SBH and facilitate fast turn-on to achieve a low Von. To resolve the low SBH related low BV issue, a p-NiO/n-Ga2O3-based heterojunction structure is used to enhance β-Ga2O3 sidewall depletion during the reverse state to improve the BV. With such a design, a low Von = 0.64 V(@1A/cm2) and a high BV = 2.34 kV as well as a specific on-resistance (Ron,sp) of 5.3 mΩ cm2 are demonstrated on a 10 μm-drift layer with a doping concentration of 1.5 × 1016 cm−3. β-Ga2O3 JBS diodes with low Von = 0.64 V and a power figure of merit of 1.03 GW/cm2 show great potential for future high-voltage and high-efficiency power electronics. [ABSTRACT FROM AUTHOR]

Morphine is usually preferred to treat moderate or severe pain for late-stage cancer patients. However, medically unindicated or excessive morphine use may result in respiratory depression and death. This essay contends that a clear distinction between relieving pain and performing active euthanasia in the use of morphine should be made in practice. By drawing on Confucian virtue resources, we construct a Confucian conception of human dignity, including both intrinsic and acquired dignity, to analyze the circumstances of morphine use in current China. We argue that not only the Confucian view of intrinsic dignity but also that of acquired dignity would not support morphine euthanasia. [ABSTRACT FROM AUTHOR]

Leveraging multi‐platform laser scanning systems offers a complete solution for 3D modelling of large‐scale urban scenes. However, the spatial inconsistency of point clouds collected by heterogeneous platforms with different viewpoints presents challenges in achieving seamless fusion. To tackle this challenge, this paper proposes a coarse‐to‐fine adjustment for multi‐platform point cloud fusion. First, in the preprocessing stage, the bounding box of each point cloud block is employed to identify potential constraint association. Second, the proposed local optimisation facilitates preliminary pairwise alignment with these potential constraint relationships, and obtaining initial guess for a comprehensive global optimisation. At last, the proposed global optimisation incorporates all the local constraints for tightly coupled optimisation with raw point correspondences. We choose two study areas to conduct experiments. Study area 1 represents a fast road scene with a significant amount of vegetation, while study area 2 represents an urban scene with many buildings. Extensive experimental evaluations indicate the proposed method has increased the accuracy of study area 1 by 50.6% and the accuracy of study area 2 by 44.7%. [ABSTRACT FROM AUTHOR]