Preparation of MXene-based hybrids and their application in neuromorphic devices

The traditional von Neumann computing architecture has relatively-low information processing speed and high power consumption,making it difficult to meet the computing needs of artificial intelligence(AI).Neuromorphic computing systems,with massively parallel computing capability and low power consumption,have been considered as an ideal option for data storage and AI computing in the future.Memristor,as the fourth basic electronic component besides resistance,capacitance and inductance,is one of the most competitive candidates for neuromorphic computing systems benefiting from the simple structure,continuously adjustable conductivity state,ultra-low power consumption,high switching speed and compatibility with existing CMOS technology.The memristors with applying MXene-based hybrids have attracted significant attention in recent years.Here,we introduce the latest progress in the synthesis of MXene-based hybrids and summarize their potential applications in memristor devices and neuromorphological intelligence.We explore the development trend of memristors constructed by combining MXenes with other functional materials and emphatically discuss the potential mechanism of MXenes-based memristor devices.Finally,the future prospects and directions of MXene-based memristors are briefly described.

Knowledge Reasoning Method Based on Deep Transfer Reinforcement Learning:DTRLpath

In recent years,with the continuous development of deep learning and knowledge graph reasoning methods,more and more researchers have shown great interest in improving knowledge graph reasoning methods by inferring missing facts through reasoning.By searching paths on the knowledge graph and making fact and link predictions based on these paths,deep learning-based Reinforcement Learning(RL)agents can demonstrate good performance and interpretability.Therefore,deep reinforcement learning-based knowledge reasoning methods have rapidly emerged in recent years and have become a hot research topic.However,even in a small and fixed knowledge graph reasoning action space,there are still a large number of invalid actions.It often leads to the interruption of RL agents’wandering due to the selection of invalid actions,resulting in a significant decrease in the success rate of path mining.In order to improve the success rate of RL agents in the early stages of path search,this article proposes a knowledge reasoning method based on Deep Transfer Reinforcement Learning path(DTRLpath).Before supervised pre-training and retraining,a pre-task of searching for effective actions in a single step is added.The RL agent is first trained in the pre-task to improve its ability to search for effective actions.Then,the trained agent is transferred to the target reasoning task for path search training,which improves its success rate in searching for target task paths.Finally,based on the comparative experimental results on the FB15K-237 and NELL-995 datasets,it can be concluded that the proposed method significantly improves the success rate of path search and outperforms similar methods in most reasoning tasks.

面向并行文件系统的性能评估及相对预测模型

基于Lustre文件系统，对并行文件系统的性能评估和性能建模进行了研究．通过对性能因子的调研，进行了一系列性能评估实验，并提出性能相关性模~（PRModel）．在实验评估和PRModel分析中发现，在不同的性能因子之间存在着紧密的性能相关性，为了挖掘并利用这种相关性信息，提出了一种相对性能预测模（RPPModel）来预测不同性能因子条件下的性能．为了验证RPPModel的有效性，设计了大量实验用例．结果表明，预测结果的平均相对误差能够控制在170／o---28％的范围内，易于使用且具有较好的预测准确度．

Cr3轧辊局部堆焊修复工艺研究

Cr3钢是目前广泛使用的轧辊材料,其局部修复有重要意义。研究了不同预热温度和焊后缓冷对Cr3钢轧辊局部堆焊修复质量的影响。通过焊接工艺试验、无损探伤、焊接热模拟试验及金相分析,证明了在Cr3钢轧辊局部修复中,焊接热影响区的最大焊接收缩量ΔL、马氏体含量均随预热温度的下降而增加;在预热温度相同时,ΔL随冷速降低而降低。在350℃保温缓冷即可明显改善Cr3钢组织。通过缓冷可将常规400℃以上的预热温度降低到200℃而保证不裂。

Cr3轧辊局部修复工艺的焊接热模拟分析

利用焊接工艺试验和热模拟试验研究了预热温度和冷速对高碳含量的轧辊材料Cr3钢焊接性的影响。通过金相分析、X射线衍射分析及温度-体积变化关系的分析,研究了预热温度和冷速影响Cr3钢焊接性的机理,确定了Cr3钢在焊接冷速下的组织,得到了冷却速度与焊后组织、加热区最大收缩量和焊接残余应力的关系。研究表明,控制焊接时的冷却速度是保证这类轧辊材料堆焊修复质量的关键。

INTEGRATED ARCHITECTURAL AND ENGINEERING DESIGN STRATEGIES FOR A ZERO-ENERGY BUILDING:Illinois Institute of Technology’s Design Entry for the 2018 U.S.Department of Energy Race to Zero(Solar Decathlon Design Challenge)

This paper describes the design of InterTech,a zero-energy mixed-use student residence hall,developed in 2018 by an interdisciplinary team of Illinois Institute of Technology(Illinois Tech)students for the U.S.Department of Energy Solar Decathlon Design Challenge,formerly known as Race to Zero.The main focus is the team’s integrated and iterative approach,which blended architectural design and engineering concepts and led to achieving the high-performance goal.InterTech aims to provide an innovative housing solution to Illinois Institute of Technology’s graduate students and their families.Located along State Street in between Illinois Tech’s main campus and downtown Chicago,it offers a mix of living options providing both independence and access to the campus and to the city.In addition to the residential program,the project includes a small grocery/cafe con-nected to an outdoor public plaza,and an underground garage.Energy modeling was introduced in the early design stages.The potential of on-site renewable energy generation defined the project’s target Energy Use Intensity(EUI)of 37 kBtu/sqft.Several passive and active strategies were implemented to reduce the building’s total energy needs and meet the target EUI.The implementation of energy conservation measures led to a 25%reduction of the building’s cooling load and a 33%reduction of the heating load.A design EUI of 28 kBtu/sqft was calculated,validating that this design met and exceeded the zero-energy goal.

Equivalent protection factor of bi-layer ceramic metal structures

With increasing ballistic threat levels,there is ever more demand on developing ceramic armor designs with improved performance.This paper presents finite element simulations that investigate the performance of silicon carbide ceramic with steel 4340 backing material and titanium alloy,graphite as buffer layers when subjected to normal and oblique impacts by a tungsten alloy long rod projectile(LRP).Depth of penetration from experimental measurements is compared with simulations to confirm the validity of constitutive,failure model parameters.Titanium alloy cover plate and graphite interface weak layer laterally spread the impact shock away from the SiC tile and reduces the amplification of the stress accumulation at the front surface of the SiC tile.The dwelling time increases before it penetrates into ceramic armor.Further,using AUTODYN®numerical simulations detailed parametric study is carried out to identify the minimum areal density armor for a given ballistic limit velocity.The equivalent protection factor for the bi-layer armor is a simple function of the cosine of the angle of impact.

Effect of extrusion parameters on degradation of magnesium alloys for bioimplant applications:A review

Magnesium alloys,as a new generation temporary biomaterial,deserve the desirable biocompatibility and biodegradability,and also contribute to the repair of the damaged bone tissues.However,they do not possess the required corrosion resistance in human body fluid.Hot mechanical workings,such as extrusion,influence both the mechanical properties and bio-corrosion behavior of magnesium alloys.This review aims to gather information on how the extrusion parameters(extrusion ratio and temperature)influence the bio-corrosion performances of magnesium alloys.Their effects are mainly ascribed to the alteration of extruded alloy microstructure,including final grain size and uniformity of grains,texture,and the size,distribution and volume fraction of the second phases.Dynamic recrystallization and grain refinement during extrusion provide a more homogeneous microstructure and cause the formation of basal texture,resulting in improved strength and corrosion resistance of magnesium alloy.Extrusion temperature and extrusion ratio are reported as the influential factors in the degradation.The reports reveal that the increase in extrusion ratio and/or the reduction in extrusion temperature cause a decrease in the final grain size,leading to intensification of basal texture,in parallel side of the samples with extrusion line,and to lower volume fraction and size of precipitates in magnesium alloys.These all lead to improving the bio-corrosion resistance of the magnesium alloy implants.

Study on accuracy of casting flow simulation

In the field of casting flow simulation, the application of body-fitted coordinate(BFC) has not been widely used due to the difficulty and low efficiency of grid generation, despite the availability of good quality analysis results. Cartesian coordinates, on the other hand, have been used predominantly in casting process simulations because of their relatively easy and fast grid generation. However, Cartesian grid systems cannot obtain accurate results because they cannot express the geometries properly. In this study, Cut Cell method was applied to solve this problem. The three-dimensional incompressible viscous governing equation was analyzed using a function defined for the volume and area of the casting in the cutting cell. Using the Cut Cell method, accurate flow analysis results were also obtained in the Cartesian grid systems. The tests of simple shape and the applications of actual casting product have been tried with Cut Cell method.

A Tree-Based Approach for Efficient and Accurate Conjunction Analysis

Conjunction analysis is the study of possible collisions between objects in space.Conventional conjunction analysis algorithms are geared towards computing the collision probability between any two resident space objects.Currently,there are few heuristic methods available to select which objects should be considered for a detailed collision analysis.A simple all-on-all collision analysis results in an O(N2)procedure,which quickly becomes intractable for large datasets.The main objective of this research work is to preemptively determine which catalogued objects should be considered for a more detailed conjunction analysis,significantly reducing the number of object pairs to be investigated.The heart of the approach lies in the efficient kd-tree algorithm.It has been found that this binary search method significantly reduces computational cost to a tractable complexity of O(N logN).The conventional tree-based search is modified slightly by accounting for probabilistic nearest neighbors via the Hellinger Distance.Finally,the method is extended to account for Non-Gaussian errors via the inclusion of Gaussian Mixture Models.It has been found that the reduced computational complexity of the kd-tree is maintained,while the applicability of the method is extended to uncertain cases.

Tuning heterogeneous microstructures to enhance mechanical properties of nano-TiN particle reinforced Haynes 230 composites by laser powder bed fusion

Laser powder bed fusion(LPBF)is considered to be one of the most promising additive manufacturing technologies for producing components with geometries and high geometrical precision that are unattainable by traditional technologies.The superalloy exhibits exceptional mechanical and high-temperature performances,rendering it a prime candidate for advanced aero-engine applications.Despite the high demand for LPBF-manufactured superalloys,the superalloy is one of the materials manufactured difficultly by LPBF due to their large laser absorptivity fluctuation,poor molten pool stability and sharp temperature gradient.Hence,superalloys are characterized by severe pores,undesirable coarse columnar grains and poor mechanical properties.In this work,the effect of nano-TiN particles on defects,molten pool characteristics and microstructure and performance of the composites were investigated.The 4.5 wt%TiN/Haynes230 samples exhibited exceptional nanohardness and elastic modulus with maximum values reaching 5.53 GPa and 240.03 GPa,respectively.These superior mechanical properties were attributed to the combined effects of spatter and gas pore inhibition,grain refinement and duplex nanophases strengthening.Moreover,the stability of molten pool was enhanced,and spatter was effectively suppressed by adding nano-TiN particles,while grain refinement and columnar to equiaxed transitions were promoted.Furthermore,the matrix exhibited a high dislocation density due to a significant hindrance of dislocation movement caused by massive nano-phases(e.g.,TiN and M_(23)C_(6)),resulting in the formation of extensive dislocation tangles and rings.This work offers novel insights into the role of nanoparticles reinforced superalloy composites by LPBF.

Development of augmented reality serious games with a vibrotactile feedback jacket

Background In the past few years,augmented reality(AR)has rapidly advanced and has been applied in different fields.One of the successful AR applications is the immersive and interactive serious games,which can be used for education and learning purposes.Methods In this project,a prototype of an AR serious game is developed and demonstrated.Gamers utilize a head-mounted device and a vibrotactile feedback jacket to explore and interact with the AR serious game.Fourteen vibration actuators are embedded in the vibrotactile feedback jacket to generate immersive AR experience.These vibration actuators are triggered in accordance with the designed game scripts.Various vibration patterns and intensity levels are synthesized in different game scenes.This article presents the details of the entire software development of the AR serious game,including game scripts,game scenes with AR effects design,signal processing flow,behavior design,and communication configuration.Graphics computations are processed using the graphics processing unit in the system.Results/Conclusions The performance of the AR serious game prototype is evaluated and analyzed.The computation loads and resource utilization of normal game scenes and heavy computation scenes are compared.With 14 vibration actuators placed at different body positions,various vibration patterns and intensity levels can be generated by the vibrotactile feedback jacket,providing different real-world feedback.The prototype of this AR serious game can be valuable in building large-scale AR or virtual reality educational and entertainment games.Possible future improvements of the proposed prototype are also discussed in this article.

Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning

Understanding and controlling the self-assembly of vertically oriented carbon nanotube(CNT)forests is essential for realizing their potential in myriad applications.The governing process–structure–property mechanisms are poorly understood,and the processing parameter space is far too vast to exhaustively explore experimentally.We overcome these limitations by using a physics-based simulation as a high-throughput virtual laboratory and image-based machine learning to relate CNT forest synthesis attributes to their mechanical performance.Using CNTNet,our image-based deep learning classifier module trained with synthetic imagery,combinations of CNT diameter,density,and population growth rate classes were labeled with an accuracy of>91%.The CNTNet regression module predicted CNT forest stiffness and buckling load properties with a lower root-mean-square error than that of a regression predictor based on CNT physical parameters.These results demonstrate that image-based machine learning trained using only simulated imagery can distinguish subtle CNT forest morphological features to predict physical material properties with high accuracy.CNTNet paves the way to incorporate scanning electron microscope imagery for high-throughput material discovery.

Long-and short-range orders in 10-component compositionally complex ceramics

Neutron diffraction and total scattering are combined to investigate a series of single-phase 10-component compositionally complexfluorite-based oxides,[(Pr_(0.375)Nd_(0.375)Yb_(0.25))2(Ti_(0.5)Hf_(0.25)Zr_(0.25))_(2)O_(7)]_(1-x)[(DyHoErNb)O_(7)]_(x),denoted as 10CCFBOxNb.A long-range order-disorder transition(ODT)occurs at x=0.81±0.01 from the ordered pyrochlore to disordered defectfluorite.In contrast to ternary oxides,this ODT occurs abruptly without an observable two-phase region;moreover,the phase stability in 10CCFBOs deviates from the well-established criteria for simpler oxides.Rietveld refinements of neutron diffraction patterns suggest that this ODT occurs via the migration of oxygen anions from the position 48f to 8a,with a smallfinal jump at the ODT;however,the 8a oxygen occupancy changes gradually(without an observable discontinuous jump).We further discover diffuse scattering in Nb-rich compositions,which suggests the presence of short-range order.Using small-box modelling,four compositions near ODT(x=0.75,0.8,0.85,and 1)can be betterfitted by C2221 weberite ordering for the local polyhedral structure at nanoscale.Interestingly,10CCFBO_(0.75)Nb and 10CCFBO_(0.8)Nb possess both long-range pyrochlore order and short-range weberite-type order,which can be understood from severe local distortion of the pyrochlore polyhedral structure.Thus,weberite-type short-range order emerges before the ODT,coexisting and interacting with long-range pyrochlore order.After the ODT,the long-range pyrochlore order vanishes but the short-range weberite-type order persists in the long-range disordered defectfluorite structure.Notably,a drop in the thermal conductivity coincides with emergence of the short-range order,instead of the long-range ODT.

Modeling urban scale human mobility through big data analysis and machinelearning

In the United States,the buildings sector consumes about 76%of electricity use and 40% of all primary energy use and associated greenhouse gas emissions.Occupant behavior has drawn increasing research interests due to its impacts on the building energy consumption.However,occupant behavior study at urban scale remains a challenge,and very limited studies have been conducted.As an effort to couple big data analysis with human mobility modeling,this study has explored urban scale human mobility utilizing three months Global Positioning System(GPS)data of 93,o00 users at Phoenix Metropolitan Area.This research extracted stay points from raw data,and identified users'home,work,and other locations by Density-Based Spatial Clustering algorithm.Then,daily mobility patterns were constructed using different types of locations.We propose a novel approach to predict urban scale daily human mobility patterns with 12-hour prediction horizon,using Long Short-Term Memory(LSTM)neural network model.Results shows the developed models achieved around 85%average accuracy and about 86%mean precision.The developed models can be further applied to analyze urban scale occupant behavior,building energy demand and flexibility,and contributed to urban planning.

Dynamic microphysiological system chip platform for high-throughput,customizable,and multi-dimensional drug screening

Spheroids and organoids have attracted significant attention as innovative models for disease modeling and drug screening.By employing diverse types of spheroids or organoids,it is feasible to establish microphysiological systems that enhance the precision of disease modeling and offer more dependable and comprehensive drug screening.High-throughput microphysiological systems that support optional,parallel testing of multiple drugs have promising applications in personalized medical treatment and drug research.However,establishing such a system is highly challenging and requires a multidisciplinary approach.This study introduces a dynamic Microphysiological System Chip Platform(MSCP)with multiple functional microstructures that encompass the mentioned advantages.We developed a high-throughput lung cancer spheroids model and an intestine-liverheart-lung cancer microphysiological system for conducting parallel testing on four anti-lung cancer drugs,demonstrating the feasibility of the MSCP.This microphysiological system combines microscale and macroscale biomimetics to enable a comprehensive assessment of drug efficacy and side effects.Moreover,the microphysiological system enables evaluation of the real pharmacological effect of drug molecules reaching the target lesion after absorption by normal organs through fluid-based physiological communication.The MSCP could serves as a valuable platform for microphysiological system research,making significant contributions to disease modeling,drug development,and personalized medical treatment.

Single-pilot operations in commercial flight:Effects on neural activity and visual behaviour under abnormalities and emergencies

With cutting-edge technologies and considering airline human-resource-saving,a single pilot in commercial jets could be technically feasible.Investigating changes in captains’natural behaviours are initially required to comprehend the specific safe human performance envelope for safeguarding single-pilot flight,particularly in high-risk situations.This paper investigates how captains’performance transforms for fixing emergencies when operating from Dual-Pilot Operations(DPO)to Single-Pilot Operations(SPO)through a physiological-based approach.Twenty pilots flew an emergency-included flight with/without first officers’assistance.The neural activities and scanning behaviours were recorded using a 32-channel Electroencephalogram(EEG)and glasses-based eye tracker,with the observation and post-experiment questionnaires to evaluate the flight operations and pilots’perception.Flying alone,there was a significantly increased cortical activity in h and b waves over the frontal,parietal,and temporal lobes during the more complicated emergencies,and pilots focused less on the primary flight display while spending significantly more time scanning the other interfaces.The physiological fluctuating patterns associated with risky operations in SPO were highlighted by cross-correlating multimodal data.The experimental-based noteworthy insights may wish to inform commercial SPO measures to lessen the persistent physiological fluctuation,assisting airlines in creating SPO-oriented intelligent flight systems to give captains adequate support for assuring safer air transportation.

Application of ultrasonic fatigue technology in very-high-cycle fatigue testing of aviation gas turbine engine blade materials:A review

The need for very-high-cycle fatigue(VHCF)testing up to 1010cycles of aviation gas turbine engine blade materials under combined mechanical loads and complex environments has encouraged the development of VHCF testing instrumentation and technology.This article begins with a comprehensive review of the existing available techniques that enable VHCF testing.Recent advances in ultrasonic fatigue testing(UFT)techniques are highlighted,containing their new capabilities and methods for single load,multiaxial load,variable amplitude fatigue,and combined cycle fatigue.New techniques for conducting UFT in high-temperature,humid environments,and corrosive environments are summarized.These developments in mechanical loading and environmental building techniques provide the possibility of laboratory construction for real service conditions of blade materials.New techniques that can be used for in situ monitoring of VHCF damage are summarized.Key issues in the UFT field are presented,and countermeasures are collated.Finally,the existing problems and future trends in the field are briefly described.

Crafting chirality in three dimensions via a novel fabrication technique for bound states in the continuum metasurfaces

An additional deposition step was added to a multi-step electron beam lithographic fabrication process to unlock the height dimension as an accessible parameter for resonators comprising unit cells of quasi-bound states in the continuum metasurfaces,which is essential for the geometric design of intrinsically chiral structures.

Organoid intelligence:Integration of organoid technology and artificial intelligence in the new era of in vitro models

Organoid Intelligence ushers in a new era by seamlessly integrating cutting-edge organoid technology with the power of artificial intelligence.Organoids,three-dimensional miniature organ-like structures cultivated from stem cells,offer an unparalleled opportunity to simulate complex human organ systems in vitro.Through the convergence of organoid technology and AI,researchers gain the means to accelerate discoveries and insights across various disciplines.Artificial intelligence algorithms enable the comprehensive analysis of intricate organoid behaviors,intricate cellular interactions,and dynamic responses to stimuli.This synergy empowers the development of predictive models,precise disease simulations,and personalized medicine approaches,revolutionizing our understanding of human development,disease mechanisms,and therapeutic interventions.Organoid Intelligence holds the promise of reshaping how we perceive in vitro modeling,propelling us toward a future where these advanced systems play a pivotal role in biomedical research and drug development.