Progress on Transition Metal Ions Dissolution Suppression Strategies in Prussian Blue Analogs for Aqueous Sodium-/Potassium-Ion Batteries

Aqueous sodium-ion batteries(ASIBs)and aqueous potassium-ion batteries(APIBs)present significant potential for large-scale energy storage due to their cost-effectiveness,safety,and environmental compatibility.Nonetheless,the intricate energy storage mechanisms in aqueous electrolytes place stringent require-ments on the host materials.Prussian blue analogs(PBAs),with their open three-dimensional framework and facile synthesis,stand out as leading candidates for aqueous energy storage.However,PBAs possess a swift capacity fade and limited cycle longevity,for their structural integrity is compromised by the pronounced dis-solution of transition metal(TM)ions in the aqueous milieu.This manuscript provides an exhaustive review of the recent advancements concerning PBAs in ASIBs and APIBs.The dissolution mechanisms of TM ions in PBAs,informed by their structural attributes and redox processes,are thoroughly examined.Moreover,this study delves into innovative design tactics to alleviate the dissolution issue of TM ions.In conclusion,the paper consolidates various strategies for suppressing the dissolution of TM ions in PBAs and posits avenues for prospective exploration of high-safety aqueous sodium-/potassium-ion batteries.

Oilfield analogy and productivity prediction based on machine learning: Field cases in PL oilfield, China

In the early time of oilfield development, insufficient production data and unclear understanding of oil production presented a challenge to reservoir engineers in devising effective development plans. To address this challenge, this study proposes a method using data mining technology to search for similar oil fields and predict well productivity. A query system of 135 analogy parameters is established based on geological and reservoir engineering research, and the weight values of these parameters are calculated using a data algorithm to establish an analogy system. The fuzzy matter-element algorithm is then used to calculate the similarity between oil fields, with fields having similarity greater than 70% identified as similar oil fields. Using similar oil fields as sample data, 8 important factors affecting well productivity are identified using the Pearson coefficient and mean decrease impurity(MDI) method. To establish productivity prediction models, linear regression(LR), random forest regression(RF), support vector regression(SVR), backpropagation(BP), extreme gradient boosting(XGBoost), and light gradient boosting machine(Light GBM) algorithms are used. Their performance is evaluated using the coefficient of determination(R^(2)), explained variance score(EV), mean squared error(MSE), and mean absolute error(MAE) metrics. The Light GBM model is selected to predict the productivity of 30 wells in the PL field with an average error of only 6.31%, which significantly improves the accuracy of the productivity prediction and meets the application requirements in the field. Finally, a software platform integrating data query,oil field analogy, productivity prediction, and knowledge base is established to identify patterns in massive reservoir development data and provide valuable technical references for new reservoir development.

The Analogy between the Immune System and Human Life

The immune system operates as a complex organization with distinct roles and functions. Excitingly we recognized the similarities between the cellular dynamics of the immune system and our lives, activities, and behaviors. Observing the immune system can guide how to respond to various daily situations, including when to react, tolerate, or ignore. Recognizing this analogy between our lives and the immune system should motivate us to adopt a wisdom-based approach when investigating the mechanisms and future discoveries related to this system and to deepen our understanding of this complex system with newfound respect. In this context, the present review examines several integral biological processes of the immune system by drawing parallels between them and human life, activities, and behaviors to learn how we must behave based on the insights offered by this complex organization. The literature search was conducted in international databases such as PubMed/MEDLINE and Google Scholar search engine using English equivalent keywords from 1998 up to April 2023. The search strategy used the following subject heading terms: Immune system, analogy, human life, cellular dynamics, memory, tolerance, and ignorance. In conclusion, the immune system is a complex organization comprising various cells interacting within specific sites and networks, communicating, drawing experiences, and learning how to tolerate certain conditions that make it share certain similarities with human life.

Discovery of a geomorphological analog to Martian araneiforms in the Qaidam Basin, Tibetan Plateau

Araneiforms are spider-like ground patterns that are widespread in the southern polar regions of Mars.A gas erosion process driven by the seasonal sublimation of CO_(2) ice was proposed as an explanation for their formation,which cannot occur on Earth due to the high climatic temperature.In this study,we propose an alternative mechanism that attrib-utes the araneiform formation to the erosion of upwelling salt water from the subsurface,relying on the identification of the first terrestrial analog found in a playa of the Qaidam Basin on the northern Tibetan Plateau.Morphological analysis indicates that the structures in the Qaidam Basin have fractal features comparable to araneiforms on Mars.A numerical model is developed to investigate the araneiform formation driven by the water-diffusion mechanism.The simulation res-ults indicate that the water-diffusion process,under varying ground conditions,may be responsible for the diverse aranei-form morphologies observed on both Earth and Mars.Our numerical simulations also demonstrate that the orientations of the saltwater diffusion networks are controlled by pre-existing polygonal cracks,which is consistent with observations of araneiforms on Mars and Earth.Our study thus suggests that a saltwater-related origin of the araneiform is possible and has significant implications for water searches on Mars.

Experimental and computational optimization of Prussian blue analogues as high-performance cathodes for sodium-ion batteries:A review

In this review,we discuss the electrochemical properties of Prussian blue(PB)for Na^(+)storage by combining structural engineering and electrolyte modifications.We integrated experimental data and density functional theory(DFT)in sodium-ion battery(SIB)research to refine the atomic arrangements and crystal lattices and introduce substitutions and dopants.These changes affect the lattice stability,intercalation,electronic and ionic conductivities,and electrochemical performance.We unraveled the intricate structure-electrochemical behavior relationship by combining experimental data with computational models,including first-principles calculations.This holistic approach identified techniques for optimizing PB and Prussian blue analog(PBA)structu ral properties for SIBs.We also discuss the tuning of electrolytes by systematically adjusting their composition,concentration,and additives using a combination of molecular dynamics(MD)simulations and DFT computations.Our review offers a comprehensive assessment of strategies for enhancing the electrochemical properties of PB and PBAs through structural engineering and electrolyte modifications,combining experimental insights with advanced computational simulations,and paving the way for next-generation energy storage systems.

An Adaptive Control Strategy for Energy Storage Interface Converter Based on Analogous Virtual Synchronous Generator

In the DC microgrid,the lack of inertia and damping in power electronic converters results in poor stability of DC bus voltage and low inertia of the DC microgrid during fluctuations in load and photovoltaic power.To address this issue,the application of a virtual synchronous generator(VSG)in grid-connected inverters control is referenced and proposes a control strategy called the analogous virtual synchronous generator(AVSG)control strategy for the interface DC/DC converter of the battery in the microgrid.Besides,a flexible parameter adaptive control method is introduced to further enhance the inertial behavior of the AVSG control.Firstly,a theoretical analysis is conducted on the various components of the DC microgrid,the structure of analogous virtual synchronous generator,and the control structure’s main parameters related to the DC microgrid’s inertial behavior.Secondly,the voltage change rate tracking coefficient is introduced to adjust the change of the virtual capacitance and damping coefficient flexibility,which further strengthens the inertia trend of the DC microgrid.Additionally,a small-signal modeling approach is used to analyze the approximate range of the AVSG’s main parameters ensuring system stability.Finally,conduct a simulation analysis by building the model of the DC microgrid system with photovoltaic(PV)and battery energy storage(BES)in MATLAB/Simulink.Simulation results from different scenarios have verified that the AVSG control introduces fixed inertia and damping into the droop control of the battery,resulting in a certain level of inertia enhancement.Furthermore,the additional adaptive control strategy built upon the AVSG control provides better and flexible inertial support for the DC microgrid,further enhances the stability of the DC bus voltage,and has a more positive impact on the battery performance.

High-Bandwidth,Low-Power CMOS Transistor Based CAB for Field Programmable Analog Array

This article presents an integrated current mode configurable analog block(CAB)system for field-programmable analog array(FPAA).The proposed architecture is based on the complementary metal-oxide semiconductor(CMOS)transistor level design where MOSFET transistors operating in the saturation region are adopted.The proposed CAB architecture is designed to implement six of thewidely used current mode operations in analog processing systems:addition,subtraction,integration,multiplication,division,and pass operation.The functionality of the proposed CAB is demonstrated through these six operations,where each operation is chosen based on the user’s selection in the CAB interface system.The architecture of the CAB system proposes an optimized way of designing and integrating only three functional cells with the interface circuitry to achieve the six operations.Furthermore,optimized programming and digital tuning circuitry are implemented in the architecture to control and interface with the functional cells.Moreover,these designed programming and tuning circuitries play an essential role in optimizing the performance of the proposed design.Simulation of the proposed CMOS Transistor Based CAB system is carried out using Tanner EDA Tools in 0.35μm standard CMOS technology.The design uses a±1.5 V power supply and results in maximum 3 dB bandwidth of 34.9 MHz and an approximate size of 0.0537 mm2.This demonstrates the advantages of the design over the current state-of-the-art designs presented for comparison in this article.Consequently,the proposed design has a clear aspect of simplicity,low power consumption,and high bandwidth operation,which makes it a suitable candidate for mobile telecommunications applications.

Dynamic Analogical Association Algorithm Based on Manifold Matching for Few-Shot Learning

At present,deep learning has been well applied in many fields.However,due to the high complexity of hypothesis space,numerous training samples are usually required to ensure the reliability of minimizing experience risk.Therefore,training a classifier with a small number of training examples is a challenging task.From a biological point of view,based on the assumption that rich prior knowledge and analogical association should enable human beings to quickly distinguish novel things from a few or even one example,we proposed a dynamic analogical association algorithm to make the model use only a few labeled samples for classification.To be specific,the algorithm search for knowledge structures similar to existing tasks in prior knowledge based on manifold matching,and combine sampling distributions to generate offsets instead of two sample points,thereby ensuring high confidence and significant contribution to the classification.The comparative results on two common benchmark datasets substantiate the superiority of the proposed method compared to existing data generation approaches for few-shot learning,and the effectiveness of the algorithm has been proved through ablation experiments.

Performance optimization of tri-gate junctionless FinFET using channel stack engineering for digital and analog/RF design

This manuscript explores the behavior of a junctionless tri-gate FinFET at the nano-scale region using SiGe material for the channel.For the analysis,three different channel structures are used:(a)tri-layer stack channel(TLSC)(Si-SiGe-Si),(b)double layer stack channel(DLSC)(SiGe-Si),(c)single layer channel(SLC)(S_(i)).The I−V characteristics,subthreshold swing(SS),drain-induced barrier lowering(DIBL),threshold voltage(V_(t)),drain current(ION),OFF current(IOFF),and ON-OFF current ratio(ION/IOFF)are observed for the structures at a 20 nm gate length.It is seen that TLSC provides 21.3%and 14.3%more ON current than DLSC and SLC,respectively.The paper also explores the analog and RF factors such as input transconductance(g_(m)),output transconductance(gds),gain(gm/gds),transconductance generation factor(TGF),cut-off frequency(f_(T)),maximum oscillation frequency(f_(max)),gain frequency product(GFP)and linearity performance parameters such as second and third-order harmonics(g_(m2),g_(m3)),voltage intercept points(VIP_(2),VIP_(3))and 1-dB compression points for the three structures.The results show that the TLSC has a high analog performance due to more gm and provides 16.3%,48.4%more gain than SLC and DLSC,respectively and it also provides better linearity.All the results are obtained using the VisualTCAD tool.

Structural Variation between the Western and Eastern Kuqa Fold-and-thrust Belt,China:Insights from Seismic Interpretation and Analog Modeling

The Kuqa fold-and-thrust belt exhibits apparent structural variation in the western and eastern zone.Two salt layer act as effective decollements and influence the varied deformation.In this study,detailed seismic interpretations and analog modeling are presented to construct the suprasalt and subsalt structures in the transfer zone of the middle Kuqa and investigate the influence of the two salt layers.The results reveal that the relationship of the two salt layers changes from separated to connected,and then overlapped toward the foreland in the transfer zone.Different structural models are formed in the suprasalt and subsalt units due to the interaction of the two salt layers.The imbricate thrust faults form two broom-like fault systems in the subsalt units.The suprasalt units develop detached folds terminating toward the east in the region near the orogenic belt.Whereas,two offset anticlines with different trends develop at the frontal edge of the lower salt layer and the trailing edge of the upper salt layer,respectively.According to exploration results in this region,the relationship between suprasalt and subsalt structures has an influence on hydrocarbon accumulation.We believe that the connected deformation contains high-risk plays while the decoupled deformation contains well-preserved plays.

A Low-Power 12-Bit SAR ADC for Analog Convolutional Kernel of Mixed-Signal CNN Accelerator

As deep learning techniques such as Convolutional Neural Networks(CNNs)are widely adopted,the complexity of CNNs is rapidly increasing due to the growing demand for CNN accelerator system-on-chip(SoC).Although conventional CNN accelerators can reduce the computational time of learning and inference tasks,they tend to occupy large chip areas due to many multiply-and-accumulate(MAC)operators when implemented in complex digital circuits,incurring excessive power consumption.To overcome these drawbacks,this work implements an analog convolutional filter consisting of an analog multiply-and-accumulate arithmetic circuit along with an analog-to-digital converter(ADC).This paper introduces the architecture of an analog convolutional kernel comprised of low-power ultra-small circuits for neural network accelerator chips.ADC is an essential component of the analog convolutional kernel used to convert the analog convolutional result to digital values to be stored in memory.This work presents the implementation of a highly low-power and area-efficient 12-bit Successive Approximation Register(SAR)ADC.Unlink most other SAR-ADCs with differential structure;the proposed ADC employs a single-ended capacitor array to support the preceding single-ended max-pooling circuit along with minimal power consumption.The SARADCimplementation also introduces a unique circuit that reduces kick-back noise to increase performance.It was implemented in a test chip using a 55 nm CMOS process.It demonstrates that the proposed ADC reduces Kick-back noise by 40%and consequently improves the ADC’s resolution by about 10%while providing a near rail-to-rail dynamic rangewith significantly lower power consumption than conventional ADCs.The ADC test chip shows a chip size of 4600μm^(2)with a power consumption of 6.6μW while providing an signal-to-noise-and-distortion ratio(SNDR)of 68.45 dB,corresponding to an effective number of bits(ENOB)of 11.07 bits.

采动岩层整体移动“类双曲线”理论模型及验证——从二维“类双曲线”到三维“类双曲面”模型

煤炭开采会引起上覆岩层自下而上变形、破断及移动,并导致地表沉陷等问题,其本质是一个力学过程,故亟需发展基于岩层破断力学机理的移动理论模型。首先简要回顾了国内外岩层移动理论模型,然后重点介绍了本团队近年来在岩层整体移动理论模型方面的研究进展。基于不同层位岩层的破断形态的力学分析和相似模拟试验结果,提出了采动覆岩整体移动“类双曲线”模型,其顶点大约位于上覆岩层主关键层的层位。进而建立了岩层移动内外“类双曲线”整体模型,给出了采动覆岩内外整体移动表达式及物理意义参数体系。针对不同覆岩和煤层赋存条件,分析了岩层整体移动“类双曲线”模型的演化规律。在此基础上,进一步将二维“类双曲线”拓展到三维“类双曲面”模型,建立了采动覆岩全空间“类双曲面”立体移动模型,该模型包含“类单叶”和“类双叶”双曲面。该理论模型是在我国很多前辈及学者的研究基础上,从系统科学的角度考虑岩层的整体移动规律,初步形成了采动覆岩整体移动“类双曲线(面)”理论模型框架,希望能为我国煤炭科学开采提供理论指导。

基于案例推理的法律人工智能建模——以海波案例系统的法律解析为中心

案例推理系统采取符号主义的路径进行法律人工智能建模,运用逻辑学的方法刻画案例推理的过程,但是,传统逻辑工具难以对案例推理过程中的可废止过程进行刻画和表达。运用论证理论中的论证型式工具,对经典案例推理系统海波的工作原理进行解析,可重构案例推理系统的逻辑模型,明确案例推理系统的智能界限。基于案例推理系统逻辑模型的解析,反思当下司法实践中的类案系统,可证立人工智能辅助司法的基本原则。

模拟集成电路设计课程实验项目驱动式教学改革

基于高校模拟集成电路设计课程实验项目分散且教学目标不明确,缺乏系统性和工程应用性,提出了项目驱动式教学改革。以与企业合作的工程项目为基础设立实验项目,并分解成由易及难的多个实验题目,分阶段、分步骤完成实验内容,最终完成模拟集成电路设计全流程实验,以“带隙基准电压源芯片全定制设计实验”为例阐述了实施步骤。课程实验项目采用驱动式教学加深了学生对课程内容的理解,有助于培养学生扎实的工程实践能力。

一流课程建设背景下勘探地震学课程教学改革探索与实践

高等教育是支撑、推动和引领国家现代化发展的重要力量,课程建设是高等教育发展的源泉,在以落实立德树人为根本任务,全面开展一流本科课程建设的背景下,开展教育教学改革是优化课程的重要驱动力。勘探地震学课程,从分析传统教学的局限性出发,提出“一引领,三结合,主体性,一体化”的教学改革理念,建设优质丰富的课程资源,实施“四维、六化、全程性评价”的创新改革举措,进行系统的教学模式改革,打造优质课程,培养复合型、高素质、创新性专门人才。该文为相关专业课程进行教学改革提供参考,对进行高质量课程建设具有一定的指导意义。

基于FSSA-ELM的模拟电路故障诊断方法

在大规模电路中,模拟电路的故障率高达80%。针对模拟电路故障诊断方法准确率低、耗时长的问题,提出了一种分数阶麻雀搜索算法结合极限学习机(FSSA-ELM)的模拟电路故障诊断方法。利用核主成分分析与局部线性嵌入(KPCA-LLE)联合方式对电路故障数据进行特征提取,通过分数阶与麻雀搜索算法(SSA)相融合,对极限学习机(ELM)的权重和阈值进行寻优,将提取后的特征数据输入到FSSA-ELM模型中进行训练和测试。T型反馈网络反相比例运算电路诊断实例表明,FSSA-ELM的故障诊断用时相较于SSA-ELM缩短了891 s,单故障诊断准确率可达972%,比SSA-ELM和ELM分别提高了19%和28%;双故障诊断准确率可达95%,分别提高了04%和10%。该故障诊断方法准确率高、耗时短,具有较强的模拟电路故障检测能力。

关于改善中枢性性早熟患儿身高获益的思考

随着遗传和环境等多种因素的改变,儿童性早熟的发病率逐渐增加,以改善身高为目的的性早熟治疗是临床关注的重点问题之一。目前,促性腺激素释放激素类似物(gonadotropin-releasing hormone analogs,GnRHa)仍是首选治疗药物,但其对身高的改善作用受多种因素影响,可能导致身高获益低于预期。联合重组人生长激素(recombinant human growth hormone,rhGH)治疗是提高GnRHa疗效的可选治疗策略,然而联用时机等尚无明确推荐。基于性早熟治疗现状,重新审视单用GnRHa及联合rhGH治疗对身高的改善作用至关重要。该文探索了联合治疗适应证等策略,以期指导临床用药,帮助性早熟患儿实现更理想的身高获益。

基于IHHO-BP神经网络的模拟电路故障诊断

针对模拟电路故障类型多、故障状态不稳定以及故障数据冗余,使得模拟电路故障诊断困难的问题,提出利用改进哈里斯鹰算法(improved Harris Hawks optimization, IHHO)优化反向传播(back propagation, BP)神经网络,实现模拟电路故障特征选择与诊断。首先,将非线性自适应因子、柯西变异和随机差分扰动引入哈里斯鹰算法,实现收敛速度和精度的提升;其次,采用IHHO对模拟电路的单一故障和组合故障仿真数据进行特征选择,完成数据预处理;最后,采用IHHO-BP算法,对预处理后的故障数据进行训练和测试,实现模拟电路故障诊断。诊断结果表明,所提方法的诊断精度相较于其他算法提升了5.5%。

A 5MS/s 12-Bit Successive Approximation Analog-to-Digital Converter

With the continuous development of science and technology, digital signal processing is more and more widely used in various fields. Among them, the analog-to-digital converter (ADC) is one of the key components to convert analog signals to digital signals. As a common type of ADC, 12-bit sequential approximation analog-to-digital converter (SAR ADC) has attracted extensive attention for its performance and application. This paper aims to conduct in-depth research and analysis of 12-bit SAR ADC to meet the growing demands of digital signal processing. This article designs a 12-bit, successive approximation analog-to-digital converter (SAR ADC) with a sampling rate of 5 MS/s. The overall circuit adopts a fully differential structure, with key modules including DAC capacitor array, comparator, and control logic. According to the DAC circuit in this paper, a fully differential capacitor DAC array structure is proposed to reduce the area of layout DAC. The comparator uses a digital dynamic comparator to improve the ADC conversion speed. The chip is designed based on the SMIC180 nm CMOS process. The simulation results show that when the sampling rate is 5 MS/s, the effective bit of SAR ADC is 11.92 bit, the SNR is 74.62 dB, and the SFDR is 89.24 dB.

基于加窗随机解调架构的电网超谐波信号检测方法

针对电网超谐波检测采样压力大、数据处理困难等问题,提出一种基于加窗随机解调架构的超谐波信号检测方法。该方法首先利用模拟信息转换技术结合加窗思想,在采样端对超谐波信号进行加窗压缩采样,得到压缩采样后的数据;随后,在重构端采用逐级二分的思想,将残差的一阶微分作为收敛阈值,改进稀疏度自适应匹配追踪(SAMP)算法,实现对采样数据的快速重构。大量的仿真实验和测试结果表明,方法可以在减少90%数据量的情况下实现超谐波信号的频率幅值精确检测,其中频幅值检测的相对误差≤2%,频率检测误差≤3 Hz,为更准确测量超谐波提供了新思路。