Predictor−corrector inverse design scheme for property−composition prediction of amorphous alloys

In order to develop a generic framework capable of designing novel amorphous alloys with selected target properties,a predictor−corrector inverse design scheme(PCIDS)consisting of a predictor module and a corrector module was presented.A high-precision forward prediction model based on deep neural networks was developed to implement these two parts.Of utmost importance,domain knowledge-guided inverse design networks(DKIDNs)and regular inverse design networks(RIDNs)were also developed.The forward prediction model possesses a coefficient of determination(R^(2))of 0.990 for the shear modulus and 0.986 for the bulk modulus on the testing set.Furthermore,the DKIDNs model exhibits superior performance compared to the RIDNs model.It is finally demonstrated that PCIDS can efficiently predict amorphous alloy compositions with the required target properties.

Design Strategies Applied to Classroom's Daylight Design Optimization of Classrooms Design

This paper explains how the optimized classrooms were selected and the results that were achieved by the optimizations carried out and finalized.The context of the research is the city of Concepción,in Chile.Virtual models of classrooms were evaluated using the Radiance software.We used a methodology that allowed us to determine the luminous conditions under different types of skies,seasons of the year and times of the day.The evaluation of the typologies was performed based on three defined criteria,in order to achieve the stated design objectives.We defined the optimal solutions for each orientation and,finally,we stated design recommendations for daylit classrooms to ensure the visual comfort of the students.These recommendations link all that found in the initial analysis with that found in the optimization stage.

Design strategies and insights of flexible infrared optoelectronic sensors

Infrared optoelectronic sensing is the core of many critical applications such as night vision,health and medication,military,space exploration,etc.Further including mechanical flexibility as a new dimension enables novel features of adaptability and conformability,promising for developing next-generation optoelectronic sensory applications toward reduced size,weight,price,power consumption,and enhanced performance(SWaP^(3)).However,in this emerging research frontier,challenges persist in simultaneously achieving high infrared response and good mechanical deformability in devices and integrated systems.Therefore,we perform a comprehensive review of the design strategies and insights of flexible infrared optoelectronic sensors,including the fundamentals of infrared photodetectors,selection of materials and device architectures,fabrication techniques and design strategies,and the discussion of architectural and functional integration towards applications in wearable optoelectronics and advanced image sensing.Finally,this article offers insights into future directions to practically realize the ultra-high performance and smart sensors enabled by infrared-sensitive materials,covering challenges in materials development and device micro-/nanofabrication.Benchmarks for scaling these techniques across fabrication,performance,and integration are presented,alongside perspectives on potential applications in medication and health,biomimetic vision,and neuromorphic sensory systems,etc.

Seismic isolation design and resilience improvement of railway station considering the influence of near-fault pulse-like ground motions

To improve the resilience of railway stations,a typical station was selected as the research object,and an isolation design was introduced.Twenty-four groups of near-fault pulse-like ground motions were selected.The seismic resilience of the no-isolation railway stations(NIRS)and the isolation railway stations(IRS)were compared to provide a numerical result of the improvement in resilience.The results show that in the station isolation design,the station's functional requirements and structural characteristics should be considered and the appropriate placement of isolation bearings is under the waiting room.Under the action of a rare earthquake,the repair cost,repair time,rate of harm and death of the IRS were decreased by 8.04 million,18.30 days,6.93×10^(-3)and 1.21×10^(-3),respectively,when compared to the NIRS.The IRS received a seismic resilience grade of three-stars and the NIRS only one-star,indicating that rational isolation design improves the seismic resilience of stations.Thus,for the design of stations close to earthquake faults,it is suggested to utilize appropriate isolation techniques to improve their seismic resilience.

基于Altium Designer软件的电子控制技术中电路设计探究

在正常的电路设计方案中,需要软硬件的互相搭配使用,普通的电路运用收稿方式或简单的办公辅助软件来完成绘制,但在面对复杂情况下的电路,专业的EDA辅助设计软件作用可以显而易见,以该辅助软件设计如SCH(电路原理图)与PCB(印制电路板)文件方面的工作。电子控制技术中电路随着时代的进步而不断创新,新型器件的不断迭代更新,其中的电路设计也变得复杂且繁琐,传统的电路设计已无法胜任最新电路的完善和使用。此时运用AD16(Altium Designer)这款辅助设计软件进行电路设计就可以事半功倍,AD都是复杂电路设计中的高效工具。

水田动力底盘逆向建模与质心验证——基于Geomagic Design X

针对目前农业机械仿真和优化模型精度不高的问题,提出了一种构建精确模型的逆向建模方法。应用光学扫描仪采集点云数据,基于Geomagic Design X处理点云数据并重构零部件模型,在SolidWorks中完成水田动力底盘的装配。以VP6D-CQ型水田动力底盘为例,将测得的实际质心位置与所建的模型质心位置进行比较,并对提出的逆向建模方法进行了试验验证以及误差分析。结果表明:总质量、x坐标、y坐标、z坐标的误差值分别为4.55%、3.62%、2.23%、4.81%,误差值均在5%内。此方法可构建准确的三维模型,为后续仿真优化数据精确性奠定了基础,与传统的研发相比较,可缩短农业机械研发周期、降低设计成本。

App Designer辅助教学方法探索与实践——以软件无线电课程为例

软件无线电作为通信学科的必修课程,具有承上启下的重要作用。因课程原理抽象、理论公式复杂,学员在学习过程存在理解掌握难、实践应用难等问题。通过对当前软件无线电教学过程中存在的不足与困难进行分析,提出利用MATLAB App Designer辅助课程教学,从教学准备、课堂互动、自主实验、反馈提升四个方面开展课程教学改革。实践表明,该方法对于提升学员的工程思维、系统思维具有显著效果,同时可以增强学员理论联系实际的能力。

Developing a process-based and remote sensing driven crop yield model for maize(PRYM–Maize) and its validation over the Northeast China Plain

Spatial dynamics of crop yield provide useful information for improving the production. High sensitivity of crop growth models to uncertainties in input factors and parameters and relatively coarse parameterizations in conventional remote sensing(RS) approaches limited their applications over broad regions. In this study, a process-based and remote sensing driven crop yield model for maize(PRYM–Maize) was developed to estimate regional maize yield, and it was implemented using eight data-model coupling strategies(DMCSs) over the Northeast China Plain(NECP). Simulations under eight DMCSs were validated against the prefecture-level statistics(2010–2012) reported by National Bureau of Statistics of China, and inter-compared. The 3-year averaged result could give more robust estimate than the yearly simulation for maize yield over space. A 3-year averaged validation showed that prefecture-level estimates by PRYM–Maize under DMCS8, which coupled with the development stage(DVS)-based grain-filling algorithm and RS phenology information and leaf area index(LAI), had higher correlation(R, 0.61) and smaller root mean standard error(RMSE, 1.33 t ha^(–1)) with the statistics than did PRYM–Maize under other DMCSs. The result also demonstrated that DVS-based grain-filling algorithm worked better for maize yield than did the harvest index(HI)-based method, and both RS phenology information and LAI worked for improving regional maize yield estimate. These results demonstrate that the developed PRYM–Maize under DMCS8 gives reasonable estimates for maize yield and provides scientific basis facilitating the understanding the spatial variations of maize yield over the NECP.

Integrating a novel irrigation approximation method with a process-based remote sensing model to estimate multi-years'winter wheat yield over the North China Plain

Accurate estimation of regional winter wheat yields is essential for understanding the food production status and ensuring national food security.However,using the existing remote sensing-based crop yield models to accurately reproduce the inter-annual and spatial variations in winter wheat yields remains challenging due to the limited ability to acquire irrigation information in water-limited regions.Thus,we proposed a new approach to approximating irrigations of winter wheat over the North China Plain(NCP),where irrigation occurs extensively during the winter wheat growing season.This approach used irrigation pattern parameters(IPPs)to define the irrigation frequency and timing.Then,they were incorporated into a newly-developed process-based and remote sensing-driven crop yield model for winter wheat(PRYM–Wheat),to improve the regional estimates of winter wheat over the NCP.The IPPs were determined using statistical yield data of reference years(2010–2015)over the NCP.Our findings showed that PRYM–Wheat with the optimal IPPs could improve the regional estimate of winter wheat yield,with an increase and decrease in the correlation coefficient(R)and root mean square error(RMSE)of 0.15(about 37%)and 0.90 t ha–1(about 41%),respectively.The data in validation years(2001–2009 and 2016–2019)were used to validate PRYM–Wheat.In addition,our findings also showed R(RMSE)of 0.80(0.62 t ha–1)on a site level,0.61(0.91 t ha–1)for Hebei Province on a county level,0.73(0.97 t ha–1)for Henan Province on a county level,and 0.55(0.75 t ha–1)for Shandong Province on a city level.Overall,PRYM–Wheat can offer a stable and robust approach to estimating regional winter wheat yield across multiple years,providing a scientific basis for ensuring regional food security.

Alloy design for laser powder bed fusion additive manufacturing:a critical review

Metal additive manufacturing(AM)has been extensively studied in recent decades.Despite the significant progress achieved in manufacturing complex shapes and structures,challenges such as severe cracking when using existing alloys for laser powder bed fusion(L-PBF)AM have persisted.These challenges arise because commercial alloys are primarily designed for conventional casting or forging processes,overlooking the fast cooling rates,steep temperature gradients and multiple thermal cycles of L-PBF.To address this,there is an urgent need to develop novel alloys specifically tailored for L-PBF technologies.This review provides a comprehensive summary of the strategies employed in alloy design for L-PBF.It aims to guide future research on designing novel alloys dedicated to L-PBF instead of adapting existing alloys.The review begins by discussing the features of the L-PBF processes,focusing on rapid solidification and intrinsic heat treatment.Next,the printability of the four main existing alloys(Fe-,Ni-,Al-and Ti-based alloys)is critically assessed,with a comparison of their conventional weldability.It was found that the weldability criteria are not always applicable in estimating printability.Furthermore,the review presents recent advances in alloy development and associated strategies,categorizing them into crack mitigation-oriented,microstructure manipulation-oriented and machine learning-assisted approaches.Lastly,an outlook and suggestions are given to highlight the issues that need to be addressed in future work.

Web Layout Design of Large Cavity Structures Based on Topology Optimization

Large cavity structures are widely employed in aerospace engineering, such as thin-walled cylinders, blades andwings. Enhancing performance of aerial vehicles while reducing manufacturing costs and fuel consumptionhas become a focal point for contemporary researchers. Therefore, this paper aims to investigate the topologyoptimization of large cavity structures as a means to enhance their performance, safety, and efficiency. By usingthe variable density method, lightweight design is achieved without compromising structural strength. Theoptimization model considers both concentrated and distributed loads, and utilizes techniques like sensitivityfiltering and projection to obtain a robust optimized configuration. The mechanical properties are checked bycomparing the stress distribution and displacement of the unoptimized and optimized structures under the sameload. The results confirm that the optimized structures exhibit improved mechanical properties, thus offering keyinsights for engineering lightweight, high-strength large cavity structures.

Design Principles and Mechanistic Understandings of Non-Noble-Metal Bifunctional Electrocatalysts for Zinc-Air Batteries

Zinc-air batteries(ZABs)are promising energy storage systems because of high theoretical energy density,safety,low cost,and abundance of zinc.However,the slow multi-step reaction of oxygen and heavy reliance on noble-metal catalysts hinder the practical applications of ZABs.Therefore,feasible and advanced non-noble-metal elec-trocatalysts for air cathodes need to be identified to promote the oxygen catalytic reaction.In this review,we initially introduced the advancement of ZABs in the past two decades and provided an overview of key developments in this field.Then,we discussed the work-ing mechanism and the design of bifunctional electrocatalysts from the perspective of morphology design,crystal structure tuning,interface strategy,and atomic engineering.We also included theoretical studies,machine learning,and advanced characterization technologies to provide a comprehensive understanding of the structure-performance relationship of electrocatalysts and the reaction pathways of the oxygen redox reactions.Finally,we discussed the challenges and prospects related to designing advanced non-noble-metal bifunctional electrocatalysts for ZABs.

Accelerated design of high-performance Mg-Mn-based magnesium alloys based on novel bayesian optimization

Magnesium(Mg),being the lightest structural metal,holds immense potential for widespread applications in various fields.The development of high-performance and cost-effective Mg alloys is crucial to further advancing their commercial utilization.With the rapid advancement of machine learning(ML)technology in recent years,the“data-driven''approach for alloy design has provided new perspectives and opportunities for enhancing the performance of Mg alloys.This paper introduces a novel regression-based Bayesian optimization active learning model(RBOALM)for the development of high-performance Mg-Mn-based wrought alloys.RBOALM employs active learning to automatically explore optimal alloy compositions and process parameters within predefined ranges,facilitating the discovery of superior alloy combinations.This model further integrates pre-established regression models as surrogate functions in Bayesian optimization,significantly enhancing the precision of the design process.Leveraging RBOALM,several new high-performance alloys have been successfully designed and prepared.Notably,after mechanical property testing of the designed alloys,the Mg-2.1Zn-2.0Mn-0.5Sn-0.1Ca alloy demonstrates exceptional mechanical properties,including an ultimate tensile strength of 406 MPa,a yield strength of 287 MPa,and a 23%fracture elongation.Furthermore,the Mg-2.7Mn-0.5Al-0.1Ca alloy exhibits an ultimate tensile strength of 211 MPa,coupled with a remarkable 41%fracture elongation.

The Process-based Approach in English Writing Class

English writing is gaining high attention and emphasis in our college syllabus. It is a complicated process of written communication. In the process, the four basic skills—speaking, reading, listening and writing are organically integrated together. This paper is about the process-based approach in English writing class and its aim to emphasize the importance of process-based approach to writing. There are four parts in this paper. I start with a brief introduction to the importance of writing. Next, I will introduce my practice in English writing class. Then I will comment on the process approach and then make a little revision.

Inverse design of mechanical metamaterial achieving a prescribedconstitutive curve

Besides exhibiting excellent capabilities such as energy absorption,phase-transforming metamaterials offer a vast design space for achieving nonlinear constitutive relations.This is facilitated by switching between different patterns under deformation.However,the related inverse design problem is quite challenging,due to the lack of appropriate mathematical formulation and the convergence issue in the post-buckling analysis of intermediate designs.In this work,periodic unit cells are explicitly described by the moving morphable voids method and effectively analyzed by eliminating the degrees of freedom in void regions.Furthermore,by exploring the Pareto frontiers between error and cost,an inverse design formulation is proposed for unit cells.This formulation aims to achieve a prescribed constitutive curve and is validated through numerical examples and experimental results.The design approach presented here can be extended to the inverse design of other types of mechanical metamaterials with prescribed nonlinear effective properties.

Rationally designing electrolyte additives for highly improving cyclability of LiNi_(0.5)Mn_(1.5)O_(4)/Graphite cells

High voltage is necessary for high energy lithium-ion batteries but difficult to achieve because of the highly deteriorated cyclability of the batteries.A novel strategy is developed to extend cyclability of a high voltage lithium-ion battery,LiNi_(0.5)Mn_(1.5)O_(4)/Graphite(LNMO/Graphite)cell,which emphasizes a rational design of an electrolyte additive that can effectively construct protective interphases on anode and cathode and highly eliminate the effect of hydrogen fluoride(HF).5-Trifluoromethylpyridine-trime thyl lithium borate(LTFMP-TMB),is synthesized,featuring with multi-functionalities.Its anion TFMPTMB-tends to be enriched on cathode and can be preferentially oxidized yielding TMB and radical TFMP-.Both TMB and radical TFMP can combine HF and thus eliminate the detrimental effect of HF on cathode,while the TMB dragged on cathode thus takes a preferential oxidation and constructs a protective cathode interphase.On the other hand,LTFMP-TMB is preferentially reduced on anode and constructs a protective anode interphase.Consequently,a small amount of LTFMP-TMB(0.2%)in 1.0 M LiPF6in EC/DEC/EMC(3/2/5,wt%)results in a highly improved cyclability of LNMO/Graphite cell,with the capacity retention enhanced from 52%to 80%after 150 cycles at 0.5 C between 3.5 and 4.8 V.The as-developed strategy provides a model of designing electrolyte additives for improving cyclability of high voltage batteries.

Machine learning for membrane design and discovery

Membrane technologies are becoming increasingly versatile and helpful today for sustainable development.Machine Learning(ML),an essential branch of artificial intelligence(AI),has substantially impacted the research and development norm of new materials for energy and environment.This review provides an overview and perspectives on ML methodologies and their applications in membrane design and dis-covery.A brief overview of membrane technologies isfirst provided with the current bottlenecks and potential solutions.Through an appli-cations-based perspective of AI-aided membrane design and discovery,we further show how ML strategies are applied to the membrane discovery cycle(including membrane material design,membrane application,membrane process design,and knowledge extraction),in various membrane systems,ranging from gas,liquid,and fuel cell separation membranes.Furthermore,the best practices of integrating ML methods and specific application targets in membrane design and discovery are presented with an ideal paradigm proposed.The challenges to be addressed and prospects of AI applications in membrane discovery are also highlighted in the end.

OptoGPT: A foundation model for inverse design in optical multilayer thin film structures

Optical multilayer thin film structures have been widely used in numerous photonic applications.However,existing inverse design methods have many drawbacks because they either fail to quickly adapt to different design targets,or are difficult to suit for different types of structures,e.g.,designing for different materials at each layer.These methods also cannot accommodate versatile design situations under different angles and polarizations.In addition,how to benefit practical fabrications and manufacturing has not been extensively considered yet.In this work,we introduce OptoGPT(Opto Generative Pretrained Transformer),a decoder-only transformer,to solve all these drawbacks and issues simultaneously.

Review on the design of high-strength and hydrogen-embrittlement-resistant steels

Given the carbon peak and carbon neutrality era,there is an urgent need to develop high-strength steel with remarkable hydrogen embrittlement resistance.This is crucial in enhancing toughness and ensuring the utilization of hydrogen in emerging iron and steel materials.Simultaneously,the pursuit of enhanced metallic materials presents a cross-disciplinary scientific and engineering challenge.Developing high-strength,toughened steel with both enhanced strength and hydrogen embrittlement(HE)resistance holds significant theoretical and practical implications.This ensures secure hydrogen utilization and further carbon neutrality objectives within the iron and steel sector.Based on the design principles of high-strength steel HE resistance,this review provides a comprehensive overview of research on designing surface HE resistance and employing nanosized precipitates as intragranular hydrogen traps.It also proposes feasible recommendations and prospects for designing high-strength steel with enhanced HE resistance.

Glucocorticoids-based prodrug design:Current strategies and research progress

Attributing to their broad pharmacological effects encompassing anti-inflammation,antitoxin,and immunosuppression,glucocorticoids(GCs)are extensively utilized in the clinic for the treatment of diverse diseases such as lupus erythematosus,nephritis,arthritis,ulcerative colitis,asthma,keratitis,macular edema,and leukemia.However,longterm use often causes undesirable side effects,including metabolic disorders-induced Cushing's syndrome(buffalo back,full moon face,hyperglycemia,etc.),osteoporosis,aggravated infection,psychosis,glaucoma,and cataract.These notorious side effects seriously compromise patients'quality of life,especially in patients with chronic diseases.Therefore,glucocorticoid-based advanced drug delivery systems for reducing adverse effects have received extensive attention.Among them,prodrugs have the advantages of low investment,low risk,and high success rate,making them a promising strategy.In this review,we propose the strategies for the design and summarize current research progress of glucocorticoid-based prodrugs in recent decades,including polymer-based prodrugs,dendrimer-based prodrugs,antibody-drug conjugates,peptide-drug conjugates,carbohydrate-based prodrugs,aliphatic acid-based prodrugs and so on.Besides,we also raise issues that need to be focused on during the development of glucocorticoid-based prodrugs.This review is expected to be helpful for the research and development of novel GCs and prodrugs.