Shift-invariant interpattern association neural network

A shift-invariant neural network that uses the translation-invariant property of the modulus Fourier spectra with the heteroassociation interpattern association memory is proposed. A binary encoding of a spectral sampling of the training set is used to preserve the main features. Computer simulations and experimental demonstrations are provided that show the shift-invariant property of the proposed optical neural network.     

我国爆炸品分级标准的研究制订及特点分析

文章阐述了研究制订“爆炸品分级程序”等两项国家标准的意义,过程和标准的主要特点,并提出加快实施标准的若干建议。     

基于开放光路FLRDS技术的C2H2和C2H6气体检测

磷酸铁锂电池因其较长的使用寿命和环保性,被广泛应用于储能系统。然而,近年来,储能电站安全事故频发,给电网稳定运行造成了威胁。在热失控过程中,磷酸铁锂电池会产生C₂H₂和C₂H₆等可燃气体,是造成燃烧、爆炸等灾害的重要原因。因此,实时准确监测储能预制舱内C₂H₂和C₂H₆等可燃气体的浓度是保障其安全稳定运行的关键。文中提出一种基于开放光路光纤环形腔衰荡光谱(fiber loop ring-down spectroscopy,FLRDS)技术的气体检测方法,可实现自由空间内微量气体在线监测。对梯度折射率(gradient index,GRIN)透镜的空间光耦合光学损耗进行理论和实验分析,建立插入损耗为0.95 dB的开放光路FLRDS气体检测系统。根据C₂H₂和C₂H₆红外光谱特性,开展激光光源性能测试。模拟开放空间气体环境研究了C₂H₂和C₂H₆的气体浓度检测方法,结果表明,该系统具有较好的稳定性,N₂背景下测量信号标准偏差S仅为平均值的0.156%。衰荡时间与气体浓度之间存在良好的线性关系,C₂H₂线性拟合度R²为0.998 32,C₂H₆线性拟合度R²为0.994 72。反演计算结果表明,C₂H₂和C₂H₆的最大相对误差分别为3.215%和4.72%,最大绝对误差分别为16.86 μL/L和12.74 μL/L,测量精度良好。     

A preliminary attempt to use radiomic features in the diagnosis of extra-articular long head biceps tendinitis

Magnetic Resonance Materials in Physics, Biology and Medicine - This study aims to present a radiomic application in diagnosing the long head of biceps (LHB) tendinitis. Moreover, we evaluated...     

低功耗SAR ADC的高性能比较器综述

目前逐次逼近型模数转换器(SAR ADC)已经成为低功耗数模混合集成电路中模数转换器的首选架构,其中的核心模块—高性能比较器的功耗大小直接决定了SAR ADC的整体功耗。文章从低功耗SAR ADC系统出发,聚焦高性能低功耗电压域和时间域比较器的发展历程与最新研究进展,总结了通过优化SAR逻辑实现低功耗比较器的技术方法。该综述为数模混合电路设计者了解并掌握SAR ADC中高性能低功耗比较器技术提供有力参考。     

基于引导调制的彩色点云无参考质量评价方法

彩色点云(color point cloud, CPC)作为三维场景和对象的有效描述形式,在虚拟现实、增强现实等许多领域得到重要应用。CPC在其采集、压缩、传输、重建等过程中会引入相应的失真,需要设计有效的评价方法对失真CPC质量进行评测。本文提出一种基于引导调制的CPC无参考质量评价方法。考虑到几何信息与彩色纹理信息的联合失真,利用引导调制的方法联立两者,以综合考虑几何失真、彩色纹理失真、联合失真。结合人眼的多通道性,利用剪切波变换提取特征。最后,将所有特征构成的特征向量输入到支持向量回归模型(support vector regression, SVR)学习预测点云质量。实验结果表明,所提出的方法与人类主观感知具有很好的一致性。     

Jointly improving energy efficiency and smoothing power oscillations of integrated offshore wind and photovoltaic power: a deep reinforcement learning approach

This paper proposes a novel deep reinforcement learning (DRL) control strategy for an integrated ofshore wind and photovoltaic (PV) power system for improving power generation efciency while simultaneously damping oscillations. A variable-speed ofshore wind turbine (OWT) with electrical torque control is used in the integrated ofshore power system whose dynamic models are detailed. By considering the control system as a partially-observable Markov decision process, an actor-critic architecture model-free DRL algorithm, namely, deep deterministic policy gradient, is adopted and implemented to explore and learn the optimal multi-objective control policy. The potential and efectiveness of the integrated power system are evaluated. The results imply that an OWT can respond quickly to sudden changes of the infow wind conditions to maximize total power generation. Signifcant oscillations in the overall power output can also be well suppressed by regulating the generator torque, which further indicates that complementary operation of ofshore wind and PV power can be achieved.     

Sensing as the key to the safety and sustainability of new energy storage devices

New energy storage devices such as batteries and supercapacitors are widely used in various felds because of their irreplaceable excellent characteristics. Because there are relatively few monitoring parameters and limited understanding of their operation, they present problems in accurately predicting their state and controlling operation, such as state of charge, state of health, and early failure indicators. Poor monitoring can seriously afect the performance of energy storage devices. Therefore, to maximize the efciency of new energy storage devices without damaging the equipment, it is important to make full use of sensing systems to accurately monitor important parameters such as voltage, current, temperature, and strain. These are highly related to their states. Hence, this paper reviews the sensing methods and divides them into two categories: embedded and non-embedded sensors. A variety of measurement methods used to measure the above parameters of various new energy storage devices such as batteries and supercapacitors are systematically summarized. The methods with diferent innovative points are listed, their advantages and disadvantages are summarized, and the application of optical fber sensors is emphasized. Finally, the challenges and prospects for these studies are described. The intent is to encourage researchers in relevant felds to study the early warning of safety accidents from the root causes.     

Promote Intratumoral Drug Release and Penetration to Counteract Docetaxel-Induced Metastasis by Photosensitizer-Modified Red Blood Cell Membrane-Coated Nanoparticle

The red blood cell membrane (RBCm) provides tight protection, lowers the immunogenicity, and prolongs the circulation time of drugs in vivo when acting as the coating of drug delivery systems. However, the cellular uptake and release of drugs are hindered by RBCm. Docetaxel (DTX) is the first-line medicine for treating triple-negative breast cancer (TNBC), but it induces tumor metastasis. To solve these dilemmas, in this study, the photosensitizer 1,1-dioctadecyl-3,3,3,3-tetramethylindotricarbocyanine iodide (DiR)-modified RBCm (DM) is prepared, which is coated onto a hybrid micelle consisting of the prodrugs of DTX and the anti-metastasis agent calcitriol (CTL), obtaining a nanoparticle, named HDC-DM. In a 4T1 tumor-bearing mouse model, after injecting HDC-DM, the intratumoral DTX and CTL concentrations are increased by 1.7 and 2.5 times compared with the free drug groups. After irradiating tumors with near-infrared laser, DiR elicits the photothermal effect, triggering the rupture of RBCm and drug release, promoting drug penetration in tumors, and inducing immunogenic cell death. The tumor growth inhibition rate is 77%, and the formation of lung metastases is reduced by 82%, with good biocompatibility. It is suggested that the combination of phototherapy, chemotherapy, and anti-metastatic therapy using HDC-DM is expected to be a powerful strategy for treating TNBC.     

Stabilizing Redox-Active Hexaazatriphenylene in a 2D Conductive Metal–Organic Framework for Improved Lithium Storage Performance

Organic redox-active materials are promising electrode candidates for lithium-ion batteries by virtue of their designable structure and cost-effectiveness. However, their poor electrical conductivity and high solubility in organic electrolytes limit the device's performance and practical applications. Herein, the π-conjugated nitrogen-containing heteroaromatic molecule hexaazatriphenylene (HATN) is strategically embedded with redox-active centers in the skeleton of a Cu-based 2D conductive metal–organic framework (2D c-MOF) to optimize the lithium (Li) storage performance of organic electrodes, which delivers improved specific capacity (763 mAh g⁻¹ at 300 mA g⁻¹), long-term cycling stability (≈90% capacity retention after 600 cycles at 300 mA g⁻¹), and excellent rate performance. The correlation of experimental and computational results confirms that this high Li storage performance derives from the maximum number of active sites (CN sites in the HATN unit and CO sites in the CuO₄ unit), favorable electrical conductivity, and efficient mass transfer channels. This strategy of integrating multiple redox-active moieties into the 2D c-MOF opens up a new avenue for the design of high-performance electrode materials.