Simplified quantitative analysis method and its application in the insitu synthesized copper-based azide chips

Copper-based azide(Cu(N_(3))2 or CuN_(3),CA)chips synthesized by in-situ azide reaction and utilized in miniaturized explosive systems has become a hot research topic in recent years.However,the advantages of in-situ synthesis method,including small size and low dosage,bring about difficulties in quantitative analysis and differences in ignition capabilities of CA chips.The aim of present work is to develop a simplified quantitative analysis method for accurate and safe analysis of components in CA chips to evaluate and investigate the corresponding ignition ability.In this work,Cu(N_(3))2 and CuN_(3)components in CA chips were separated through dissolution and distillation by utilizing the difference in solubility and corresponding content was obtained by measuring N_(3)-concentration through spectrophotometry.The spectrophotometry method was optimized by studying influencing factors and the recovery rate of different separation methods was studied,ensuring the accuracy and reproducibility of test results.The optimized method is linear in range from 1.0-25.0 mg/L,with a correlation coefficient R^(2)=0.9998,which meets the requirements of CA chips with a milligram-level content test.Compared with the existing ICP method,component analysis results of CA chips obtained by spectrophotometry are closer to real component content in samples and have satisfactory accuracy.Moreover,as its application in miniaturized explosive systems,the ignition ability of CA chips with different component contents for direct ink writing CL-20 and the corresponding mechanism was studied.This study provided a basis and idea for the design and performance evaluation of CA chips in miniaturized explosive systems.

Rapid fabrication of modular 3D paper-basedmicrofluidic chips using projection-based 3D printing

Paper-based microchips have different advantages,such as better biocompatibility,simple production,and easy handling,making them promising candidates for clinical diagnosis and other fields.This study describes amethod developed to fabricate modular three-dimensional(3D)paper-based microfluidic chips based on projection-based 3D printing(PBP)technology.A series of two-dimensional(2D)paper-based microfluidic modules was designed and fabricated.After evaluating the effect of exposure time on the accuracy of the flow channel,the resolution of this channel was experimentally analyzed.Furthermore,several 3D paper-based microfluidic chips were assembled based on the 2D ones using different methods,with good channel connectivity.Scaffold-based 2D and hydrogel-based 3D cell culture systems based on 3D paper-based microfluidic chips were verified to be feasible.Furthermore,by combining extrusion 3D bioprinting technology and the proposed 3D paper-based microfluidic chips,multiorgan microfluidic chips were established by directly printing 3D hydrogel structures on 3D paperbased microfluidic chips,confirming that the prepared modular 3D paper-based microfluidic chip is potentially applicable in various biomedical applications.

Research on Multi-Core Processor Analysis for WCET Estimation

Real-time system timing analysis is crucial for estimating the worst-case execution time(WCET)of a program.To achieve this,static or dynamic analysis methods are used,along with targeted modeling of the actual hardware system.This literature review focuses on calculating WCET for multi-core processors,providing a survey of traditional methods used for static and dynamic analysis and highlighting the major challenges that arise from different program execution scenarios on multi-core platforms.This paper outlines the strengths and weaknesses of current methodologies and offers insights into prospective areas of research on multi-core analysis.By presenting a comprehensive analysis of the current state of research on multi-core processor analysis for WCET estimation,this review aims to serve as a valuable resource for researchers and practitioners in the field.

Variation-Aware Task Mapping on Homogeneous Fault-Tolerant Multi-Core Network-on-Chips

A variation-aware task mapping approach is proposed for a multi-core network-on-chips with redundant cores, which includes both the design-time mapping and run-time scheduling algorithms. Firstly, a design-time genetic task mapping algorithm is proposed during the design stage to generate multiple task mapping solutions which cover a maximum range of chips. Then, during the run, one optimal task mapping solution is selected. Additionally, logical cores are mapped to physically available cores. Both core asymmetry and topological changes are considered in the proposed approach. Experimental results show that the performance yield of the proposed approach is 96% on average, and the communication cost, power consumption and peak temperature are all optimized without loss of performance yield.

CHIP相关基因与MPN患者心脑血管事件的风险分析

目的:分析骨髓增殖性肿瘤(MPN)患者不确定潜能的克隆性造血(CHIP)相关基因突变谱和临床特征,探讨CHIP相关基因与其心脑血管事件(CCE)的相关性及可能作用机制。方法:回顾性分析2019年8月-2022年7月首都医科大学附属北京安贞医院血液科收治的73例MPN患者的临床资料和二代测序结果,采用Logistic回归分析CHIP相关基因、炎症细胞因子对MPN患者CCE的影响。结果:55例(75.3%)MPN患者检出CHIP相关基因,原发性血小板增多症(ET)和真性红细胞增多症(PV)患者CHIP相关基因各突变频率差异无统计学意义。CHIP相关基因突变以单基因形式为主,检出率从高至低依次为JAK2V617F(63.0%,46/73)、ASXL1(16.4%,12/73)、TET2(11.0%,8/73)、DNMT3A(9.6%,7/73)、SRSF2(6.9%,5/73)、SF3B1(4.1%,3/73)、TP53(1.4%,1/73)和PPMID(1.4%,1/73)。年龄>60岁患者CHIP相关基因检出率明显高于≤60岁者[91.7%(33/36)vs 59.5%(22/37)]。27例(37.0%)MPN患者伴CCE(MPN/CCE),2次CCE者5例,均为动脉事件。CCE组患者年龄(62.8±12.8 vs 53.9±15.8岁,P=0.015)、IL-1β水平(17.7±26.0vs 4.3±8.6,P=0.012)、IL-8水平(360.7±598.6 vs 108.3±317.0,P=0.045)、血栓形成史(29.6%vs 2.2%,P=0.020)和CHIP相关基因检出率(88.9%vs 67.4%,P=0.040)高于无CCE组。多因素Logistic回归分析结果显示,年龄(OR=0.917,95%CI:0.843-0.999,P=0.047)、血栓形成史(OR=34.148,95%CI:2.392-487.535,P=0.009)、任何1个CHIP相关基因突变(OR=16.065,95%CI:1.217-212.024,P=0.035)和IL-1β水平升高(OR=0.929,95%CI:0.870-0.992,P=0.027)均是MPN/CCE的独立危险因素;CHIP相关单基因突变与MPN/CCE无关,但DNMT3A(OR=88.717,95%CI:2.690-292.482,P=0.012)、ASXL1(OR=7.941,95%CI:1.045-60.353,P=0.045)突变是PV/CCE的独立危险因素。结论:MPN患者CHIP相关基因突变率高,尤其是60岁以上患者;高龄、血栓形成史、CHIP相关基因突变和IL-1β水平升高是MPN发生CCE的独立危险因素。DNMT3A、ASXL1单基因突变是PV患者CCE的独立危险因素。CHIP相关基因突变及炎症细胞因子IL-1β升高是MPN新的CCE危险因素。

替普瑞酮通过E3泛素连接酶CHIP减轻LPS引起的心肌炎症反应和心功能障碍

目的:探究替普瑞酮又称香叶基香叶基丙酮,GGA)对脂多糖(LPS)诱导的心功能障碍的治疗作用及机制。方法:(1)取8周龄C57BL/6雄性野生型小鼠和热休克蛋白70(HSP70)羧基末端相互作用蛋白(CHIP)基因敲除小鼠,随机分为对照组、LPS组、LPS+GGA组和GGA组,每组8只。用腹腔注射LPS(25 mg/kg)的方法建立模型,于LPS刺激后1 h给予小鼠腹腔注射GGA(100 mg/kg)。利用小动物超声系统评估小鼠心脏功能;采集各组小鼠血清,检测血清肌酸激酶同工酶(CK-MB)和乳酸脱氢酶(LDH)水平;HE染色观察病理学改变;ELISA检测心脏组织中炎症因子肿瘤坏死因子α(TNF-α)和白细胞介素6(IL-6)的水平;Western blot检测各组心脏组织HSP70、CHIP、核转运蛋白α2(KPNA2)、髓过氧化物酶(MPO)、血管细胞黏附分子(VCAM)和细胞间黏附分子(ICAM)的蛋白表达以及细胞核NF-κB的水平。(2)利用小鼠心肌细胞HL-1,建立LPS刺激的离体细胞炎症模型。ELISA检测细胞上清中TNF-α和IL-6的水平;Western blot检测心肌细胞中HSP70、CHIP和KPNA2蛋白表达;免疫荧光染色观察细胞核NF-κB的表达。结果:(1)GGA有效改善LPS刺激小鼠的心脏功能,显著提高射血分数和左室短轴缩短率(P<0.01),减少血清CK-MB和LDH含量(P<0.01),减轻心肌损伤。(2)GGA显著减少LPS引起的TNF-α和IL-6炎症因子的释放(P<0.01),以及NF-κB的入核,降低心肌组织中KPNA2、MPO、VCAM和ICAM蛋白表达,增加心肌组织和细胞HSP70的水平(P<0.01)。(3)在CHIP基因敲除的心肌细胞和小鼠中,GGA不能抑制LPS引起的炎症反应,失去了改善LPS刺激小鼠心脏功能的作用。结论:GGA能够减轻LPS引起的心功能障碍,其作用机制与升高HSP70的表达,促进CHIP的活化,减少NF-κB的入核,抑制炎症因子的释放有关。CHIP的敲除使GGA丧失了减轻LPS诱导的炎症反应和心肌损伤的作用。

Hybridization of Metaheuristics Based Energy Efficient Scheduling Algorithm for Multi-Core Systems

The developments of multi-core systems(MCS)have considerably improved the existing technologies in thefield of computer architecture.The MCS comprises several processors that are heterogeneous for resource capacities,working environments,topologies,and so on.The existing multi-core technology unlocks additional research opportunities for energy minimization by the use of effective task scheduling.At the same time,the task scheduling process is yet to be explored in the multi-core systems.This paper presents a new hybrid genetic algorithm(GA)with a krill herd(KH)based energy-efficient scheduling techni-que for multi-core systems(GAKH-SMCS).The goal of the GAKH-SMCS tech-nique is to derive scheduling tasks in such a way to achieve faster completion time and minimum energy dissipation.The GAKH-SMCS model involves a multi-objectivefitness function using four parameters such as makespan,processor utilization,speedup,and energy consumption to schedule tasks proficiently.The performance of the GAKH-SMCS model has been validated against two datasets namely random dataset and benchmark dataset.The experimental outcome ensured the effectiveness of the GAKH-SMCS model interms of makespan,pro-cessor utilization,speedup,and energy consumption.The overall simulation results depicted that the presented GAKH-SMCS model achieves energy effi-ciency by optimal task scheduling process in MCS.

Shared Cache Based on Content Addressable Memory in a Multi-Core Architecture

Modern shared-memory multi-core processors typically have shared Level 2(L2)or Level 3(L3)caches.Cache bottlenecks and replacement strategies are the main problems of such architectures,where multiple cores try to access the shared cache simultaneously.The main problem in improving memory performance is the shared cache architecture and cache replacement.This paper documents the implementation of a Dual-Port Content Addressable Memory(DPCAM)and a modified Near-Far Access Replacement Algorithm(NFRA),which was previously proposed as a shared L2 cache layer in a multi-core processor.Standard Performance Evaluation Corporation(SPEC)Central Processing Unit(CPU)2006 benchmark workloads are used to evaluate the benefit of the shared L2 cache layer.Results show improved performance of the multicore processor’s DPCAM and NFRA algorithms,corresponding to a higher number of concurrent accesses to shared memory.The new architecture significantly increases system throughput and records performance improvements of up to 8.7%on various types of SPEC 2006 benchmarks.The miss rate is also improved by about 13%,with some exceptions in the sphinx3 and bzip2 benchmarks.These results could open a new window for solving the long-standing problems with shared cache in multi-core processors.

Advances in microfluidic chips based on islet hormone-sensing techniques

Diabetes mellitus is a global health problem resulting from islet dysfunction or insulin resistance.The mechanisms of islet dysfunction are still under investigation.Islet hormone secretion is the main function of islets,and serves an important role in the homeostasis of blood glucose.Elucidating the detailed mechanism of islet hormone secretome distortion can provide clues for the treatment of diabetes.Therefore,it is crucial to develop accurate,real-time,laborsaving,high-throughput,automated,and cost-effective techniques for the sensing of islet secretome.Microfluidic chips,an elegant platform that combines biology,engineering,computer science,and biomaterials,have attracted tremendous interest from scientists in the field of diabetes worldwide.These tiny devices are miniatures of traditional experimental systems with more advantages of timesaving,reagent-minimization,automation,high-throughput,and online detection.These features of microfluidic chips meet the demands of islet secretome analysis and a variety of chips have been designed in the past 20 years.In this review,we present a brief introduction of microfluidic chips,and three microfluidic chipsbased islet hormone sensing techniques.We focus mainly on the theory of these techniques,and provide detailed examples based on these theories with the hope of providing some insights into the design of future chips or whole detection systems.

Simulation and fabrication of in vitro microfluidic microelectrode array chip for patterned culture and electrophysiological detection of neurons

To enable the detection and modulation of modularized neural networks in vitro,this study proposes a microfluidic microelectrode array chip for the cultivation,compartmentalization,and control of neural cells.The chip was designed based on the specific structure of neurons and the requirements for detection and modulation.Finite-element analysis of the chip’s flow field was conducted using the COMSOL Multiphysics software,and the simulation results show that the liquid within the chip can flow smoothly,ensuring stable flow fields that facilitate the uniform growth of neurons within the microfluidic channels.By employing MEMS technology in combination with nanomaterial modification techniques,the microfluidic microelectrode array chip was fabricated successfully.Primary hippocampal neurons were cultured on the chip,forming a well-defined neural network.Spontaneous electrical activity of the detected neurons was recorded,exhibiting a 23.7%increase in amplitude compared to neuronal discharges detected on an open-field microelectrode array.This study provides a platform for the precise detection and modulation of patterned neuronal growth in vitro,potentially serving as a novel tool in neuroscience research.

On-chip quantum NOON state sensing for temperature and humidity

A maximal photon number entangled state,namely NOON state,can be adopted for sensing with a quantum enhancedprecision.In this work,we designed silicon quantum photonic chips containing two types of Mach-Zehnder interferometerswherein the two-photon NOON state,sensing element for temperature or humidity,is generated.Compared with classicallight or single photon case,two-photon NOON state sensing shows a solid enhancement in the sensing resolution andprecision.As the first demonstration of on-chip quantum photonic sensing,it reveals the advantages of photonic chips forhigh integration density,small-size,stability for multiple-parameter sensing serviceability.A higher sensing precision isexpected to beat the standard quantum limit with a higher photon number NOON state.

A Fully-Integrated Memristor Chip for Edge Learning

It is still challenging to fully integrate computing in memory chip as edge learning devices.In recent work published on Science,a fully-integrated chip based on neuromorphic memristors was developed for edge learning as artificial neural networks with functionality of synapses,dendrites,and somas.A crossbar-array memristor chip facilitated edge learning including hardware realization,learning algorithm,and cycle-parallel sign-and threshold-based learning(STELLAR)scheme.The motion control and demonstration platforms were executed to improve the edge learning ability for adapting to new scenarios.

A novel integrated microfluidic chip for on-demand electrostatic droplet charging and sorting

On-demand droplet sorting is extensively applied for the efficient manipulation and genome-wide analysis of individual cells.However,state-of-the-art microfluidic chips for droplet sorting still suffer from low sorting speeds,sample loss,and labor-intensive preparation procedures.Here,we demonstrate the development of a novel microfluidic chip that integrates droplet generation,on-demand electrostatic droplet charging,and high-throughput sorting.The charging electrode is a copper wire buried above the nozzle of the microchannel,and the deflecting electrode is the phosphate buffered saline in the microchannel,which greatly simplifies the structure and fabrication process of the chip.Moreover,this chip is capable of high-frequency droplet generation and sorting,with a frequency of 11.757 kHz in the drop state.The chip completes the selective charging process via electrostatic induction during droplet generation.On-demand charged microdroplets can arbitrarilymove to specific exit channels in a three-dimensional(3D)-deflected electric field,which can be controlled according to user requirements,and the flux of droplet deflection is thereby significantly enhanced.Furthermore,a lossless modification strategy is presented to improve the accuracy of droplet deflection or harvest rate from 97.49% to 99.38% by monitoring the frequency of droplet generation in real time and feeding it back to the charging signal.This chip has great potential for quantitative processing and analysis of single cells for elucidating cell-to-cell variations.

Single event effects evaluation on convolution neural network in Xilinx 28 nm system on chip

Convolutional neural networks(CNNs) exhibit excellent performance in the areas of image recognition and object detection, which can enhance the intelligence level of spacecraft. However, in aerospace, energetic particles, such as heavy ions, protons, and alpha particles, can induce single event effects(SEEs) that lead CNNs to malfunction and can significantly impact the reliability of a CNN system. In this paper, the MNIST CNN system was constructed based on a 28 nm systemon-chip(SoC), and then an alpha particle irradiation experiment and fault injection were applied to evaluate the SEE of the CNN system. Various types of soft errors in the CNN system have been detected, and the SEE cross sections have been calculated. Furthermore, the mechanisms behind some soft errors have been explained. This research will provide technical support for the design of radiation-resistant artificial intelligence chips.

Efficient stochastic parallel gradient descent training for on-chip optical processor

In recent years,space-division multiplexing(SDM)technology,which involves transmitting data information on multiple parallel channels for efficient capacity scaling,has been widely used in fiber and free-space optical communication sys-tems.To enable flexible data management and cope with the mixing between different channels,the integrated reconfig-urable optical processor is used for optical switching and mitigating the channel crosstalk.However,efficient online train-ing becomes intricate and challenging,particularly when dealing with a significant number of channels.Here we use the stochastic parallel gradient descent(SPGD)algorithm to configure the integrated optical processor,which has less com-putation than the traditional gradient descent(GD)algorithm.We design and fabricate a 6×6 on-chip optical processor on silicon platform to implement optical switching and descrambling assisted by the online training with the SPDG algorithm.Moreover,we apply the on-chip processor configured by the SPGD algorithm to optical communications for optical switching and efficiently mitigating the channel crosstalk in SDM systems.In comparison with the traditional GD al-gorithm,it is found that the SPGD algorithm features better performance especially when the scale of matrix is large,which means it has the potential to optimize large-scale optical matrix computation acceleration chips.

Effects of Using Softwood Pellet Biochar Prepared at Different Temperatures with Grass Chippings on Retention of Heavy Metals in Contaminated Soils

Heavy metals have been viewed as hazardous environmental pollutants, and anthropogenic activities due to their high toxicity and persistent nature in the environment. Anthropogenic activities such as artisanal mining, industrial activities, improper usage of fertilizers and pesticides, and indiscriminate open waste disposal bring about an increase in the presence of heavy metals in the environment. In the Keffi Metropolis, different elements lead to land contamination which debilitates soil quality, plant survival, human well-being, and the environment as a result of extensive dispersion or quantity of heavy metals in the soil and water. In recent years, biochar has emerged as a promising soil amendment for mitigating heavy metal pollution due to its unique physicochemical properties. This paper provides the effects of softwood pellet biochar on the retention of heavy metals in contaminated soils. A microcosm experiment was carried out to investigate the effects of biochar on the retention of heavy metals in contaminated soils. This research aimed to give an overview of the effects of softwood biochar at different temperatures (550˚C and 700˚C) on the retention of heavy metals and metalloids released from the soil during water inundation. The results show that the addition of organic matter (grass chippings) minimizes heavy metal mobilization. Also, biochar at high temperatures is more effective than those at low temperatures. The expected outcome of the research analysis includes providing insights into the role of biochar in retaining heavy metal contamination and further understanding the use of biochar as a sorbent for the management of contaminated soil.

Flip Chip技术在集成电路封装中的应用

阐述从集成电路封装发展现状、Flip Chip技术内涵、Flip Chip技术在集成电路封装中的应用剖析、市场发展展望等多个角度,探讨在集成电路封装中,应用Flip Chip技术的必要性和重要性。

An electrochemiluminescent magneto-immunosensor for ultrasensitive detection of hs-cTnI on a microfluidic chip

Sensitive detection and precise quantitation of trace-level crucial biomarkers in a complex sample matrix has become an important area of research.For example,the detection of high-sensitivity cardiac troponin I (hs-cTnI) is strongly recommended in clinical guidelines for early diagnosis of acute myocardial infarction.Based on the use of an electrode modified by single-walled carbon nanotubes (SWCNTs) and a Ru(bpy)32+-doped silica nanoparticle (Ru@SiO2)/tripropylamine (TPA) system,a novel type of electrochemiluminescent (ECL) magnetoimmunosensor is developed for ultrasensitive detection of hs-cTnI.In this approach,a large amount of[Ru(bpy)3]2+is loaded in SiO2(silica nanoparticles) as luminophores with high luminescent efficiency and SWCNTs as electrode surface modification material with excellent electrooxidation ability for TPA.Subsequently,a hierarchical micropillar array of microstructures is fabricated with a magnet placed at each end to efficiently confine a single layer of immunomagnetic microbeads on the surface of the electrode and enable 7.5-fold signal enhancement In particular,the use of transparent SWCNTs to modify a transparent ITO electrode provides a two-order-of-magnitude ECL signal amplification.A good linear calibration curve is developed for hs-cTnI concentrations over a wide range from 10 fg/ml to 10 ng/ml,with the limit of detection calculated as 8.720 fg/ml (S/N=3).This ultrasensitive immunosensor exhibits superior detection performance with remarkable stability,reproducibility,and selectivity.Satisfactory recoveries are obtained in the detection of hs-cTnI in human serum,providing a potentia analysis protocol for clinical applications.

Chiplet技术推动半导体产业可持续发展的研究

随着半导体产业面临莫尔定律的瓶颈,寻找新的技术推动力变得至关重要。文章将焦点放在Chiplet技术,这是一种可突破物理和经济限制、进一步提升性能的新兴技术。Chiplet技术利用模组化策略,不仅能提供更高的效能,且符合可持续发展的要求。文章深入探讨了Chiplet技术如何在莫尔定律遇到挑战的情况下推动半导体产业的可持续发展,并期望提供新的思维和发展方向。

社会资本与中国农村养老保险——基于CHIPS数据的实证研究

党的十八大以来,习近平总书记多次发表重要论述,阐明自己的"民生观",指出中国养老问题的重要性。改革开放以来,中国经济、社会、政治都发生了巨大变化。福利一词也越来越多为人所谈论。本文以农村养老保险为切入口,研究社会资本、党员身份与农村养老保险的参与程度、知情程度、未来参与意愿之间的关系,从一个侧面反映中国农村的福利情况。通过文献综述,提出了六个假设。根据中国家庭收入调查(CHIPS)数据,建立Logistic回归模型,发现社会资本对是否参加农村养老保险、是否知道个人账户有多少钱、将来是否加入农村养老保险都有积极作用;党员身份与是否参加农村养老保险、是否知道自己的个人账户有多少钱、将来是否可能参加无关。结果表明,提高村民的社会资本,加强他们的社会交往有助于他们获取信息,改善福利,所以可以提高村民小组活动和村民大会的频率以增加农民的社会资本。