Design,test,and verification of in-situ condition preserved coring and analysis system in lunar-based simulation environment

The lunar surface and its deep layers contain abundant resources and valuable information resources,the exploration and exploitation of which are important for the sustainable development of the human economy and society.Technological exploration and research in the field of deep space science,especially lunar-based exploration,is a scientific strategy that has been pursued in China and worldwide.Drilling and sampling are key to accurate exploration of the desirable characteristics of deep lunar resources.In this study,an in-situ condition preserved coring(ICP-Coring)and analysis system,which can be used to test drilling tools and develop effective sampling strategies,was designed.The key features of the system include:(1)capability to replicate the extreme temperature fluctuations of the lunar environment(-185 to 200℃)with intelligent temperature control;(2)ability to maintain a vacuum environment at a scale of 10^(-3) Pa,both under unloaded conditions within Ф580 mm×1000 mm test chamber,and under loaded conditions using Ф400 mm×800 mm lunar rock simulant;(3)application of axial pressures up to 4 MPa and confining pressures up to 3.5 MPa;(4)sample rotation at any angle with a maximum sampling length of 800 mm;and(5)multiple modes of rotary-percussive drilling,controlled by penetration speed and weight on bit(WOB).Experimental studies on the drilling characteristics in the lunar rock simulant-loaded state under different drill bit-percussive-vacuum environment configurations were conducted.The results show that the outgassing rate of the lunar soil simulant is greater than that of the lunar rock simulant and that a low-temperature environment contributes to a reduced vacuum of the lunar-based simulated environment.The rotary-percussive drilling method effectively shortens the sampling time.With increasing sampling depth,the temperature rise of the drilling tools tends to rapidly increase,followed by slow growth or steady fluctuations.The temperature rise energy accumulation of the drill bits under vacuum is more significant than that under atmospheric pressure,approximately 1.47 times higher.The real-time monitored drilling pressure,penetration speed and rotary torque during drilling serve as parameters for discriminating the drilling status.The results of this research can provide a scientific basis for returning samples from lunar rock in extreme lunar-based environments.

Fast design of catalyst layer with optimal electrical-thermal-water performance for proton exchange membrane fuel cells

The catalyst layer(CL)is the core component in determining the electrical-thermal-water performance and cost of proton exchange membrane fuel cell(PEMFC).Systemic analysis and rapid prediction tools are required to improve the design efficiency of CL.In this study,a 3D multi-phase model integrated with the multi-level agglomerate model for CL is developed to describe the heat and mass transfer processes inside PEMFC.Moreover,a research framework combining the response surface method(RSM)and artificial neural network(ANN)model is proposed to conduct a quantitative analysis,and further a rapid and accurate prediction.With the help of this research framework,the effects of CL composition on the electrical-thermal-water performance of PEMFC are investigated.The results show that the mass of platinum,the mass of carbon,and the volume fraction of dry ionomer has a significant impact on the electrical-thermal-water performance.At the selected points,the sensitivity of the decision variables is ranked:volume fraction of dry ionomer>mass of platinum>mass of carbon>agglomerate radius.In particular,the sensitivity of the volume fraction of dry ionomer is over 50%at these points.Besides,the comparison results show that the ANN model could implement a more rapid and accurate prediction than the RSM model based on the same sample set.This in-depth study is beneficial to provide feasible guidance for high-performance CL design.

气溶胶对降水影响的现象和机制

Aerosols greatly influence precipitation characteristics,thereby impacting the regional climate and human life.As an indispensable factor for cloud formation and a critical radiation budget regulator,aerosols can affect precipitation intensity,frequency,geographical distribution,area,and time.However,discrepancies exist among current studies due to aerosol properties,precipitation types,the vertical location of aerosols and meteorological conditions.The development of technology has driven advances in current research,but understanding the aerosol effects on precipitation remain complex and challenging.This paper revolves around the following topics from the two perspectives of Aerosol-Radiation Interaction(ARI)and Aerosol-Cloud Interaction(ACI):(1)the influence of different vertical locations of absorbing/scattering aerosols on the atmospheric thermal structure;(2)the fundamental theories of ARI reducing surface wind speed,redistributing water vapour and energy,and then modulating precipitation intensity;(3)different aerosol types(absorbing versus scattering)and aerosol concentrations causing different precipitation diurnal and weekly variations;(4)microphysical processes(cloud water competition,invigoration effect,and evaporation cooling)and observational evidence of different effects of aerosols on precipitation intensity,including enhancing,inhibiting,and transitional effects from enhancement to suppression;and(5)how meteorology,water vapor and dynamics influencing the effect of ACI and ARI on precipitation.In addition,this review lists the existing issues and future research directions for attaining a more comprehensive understanding of aerosol effects on precipitation.Overall,this review advances our understanding of aerosol effects on precipitation and could guide the improvement of weather and climate models to predict complex aerosol-precipitation interactions more accurately.

Characterization of Distinct T Cell Receptor Repertoires in Tumor and Distant Non-tumor Tissues from Lung Cancer Patients

T cells and T cell receptors(TCRs)play pivotal roles in adaptive immune responses against tumors.The development of next-generation sequencing technologies has enabled the analysis of the TCRb repertoire usage.Given the scarce investigations on the TCR repertoire in lung cancer tissues,in this study,we analyzed TCRb repertoires in lung cancer tissues and the matched distant non-tumor lung tissues(normal lung tissues)from 15 lung cancer patients.Based on our results,the general distribution of T cell clones was similar between cancer tissues and normal lung tissues;however,the proportion of highly expanded clones was significantly higher in normal lung tissues than in cancer tissues(0.021%±0.002%vs.0.016%±0.001%,P=0.0054,Wilcoxon signed rank test).In addition,a significantly higher TCR diversity was observed in cancer tissues than in normal lung tissues(431.37±305.96 vs.166.20±101.58,P=0.0075,Mann-Whitney U test).Moreover,younger patients had a significantly higher TCR diversity than older patients(640.7±295.3 vs.291.8±233.6,P=0.036,Mann-Whitney U test),and the higher TCR diversity in tumors was significantly associated with worse cancer outcomes.Thus,we provided a comprehensive comparison of the TCR repertoires between cancer tissues and matched normal lung tissues and demonstrated the presence of distinct T cell immune microenvironments in lung cancer patients.

BIM-based automated design for HVAC system of office buildings-An experimental study

Although computer technologies have greatly advanced in recent years and help engineers improve work efficiency,the heating,ventilation,and air conditioning(HVAC)design process is still very time-consuming.In this paper,we propose a conceptual framework for automating the entire design process to replace current human-based HVAC design procedures.This framework includes the following automated processes:building information modeling(BIM)simplification,building energy modeling(BEM)generation&load calculation,HVAC system topology generation&equipment sizing,and system diagram generation.In this study,we analyze the importance of each process and possible ways to implement them using software.Then,we use a case study to test the automated design procedure and illustrate the feasibility of the new automated design approach.The purpose of this study is to simplify the steps in the traditional rule-based HVAC system design process by introducing artificial intelligence(Al)technology based on the traditional computer-aided design(CAD)process.Experimental results show that the automatic processes are feasible,compared with the traditional design process can effectively shorten the design time from 23.37 working hours to nearly 1 hour,and improve the efficiency.

An Agent-based Adaptive Mechanism for Efficient Job Scheduling in Open and Large-scale Environments

Agent-based scheduling refers to applying intelligent agents to autonomously allocate resources to jobs.Decentralized agent-based scheduling approaches have achieved good performance in open and dynamic environments because the relationships of agents are flexible.For new jobs and resources and unexpected events,decentralized agents can respond adaptively and flexibly.Besides,decentralized approaches are easy to be extended because there is no central control agent that limits the scalability.However,decentralized approaches might have low efficiency in large-scale environments because behaviors of agents may be self-interested and competitive,due to their local views during decision making.When interacting with a large number of agents,each agent may spend a considerable amount of time on failed attempts before reaching the final agreements with other agents.To improve the efficiency of decentralized agent-based scheduling approaches in large-scale environments,and to keep the flexibility and adaptability of decentralized agents for the decision-making on scheduling,this paper provides a new agent-based adaptive mechanism for efficient job scheduling.A new type of agent named host agent is introduced to coordinate self-interested behaviors of agents without participating in the decision making of agents during job scheduling.The proposed mechanism was developed in JADE and tested in open and large-scale environments.The experimental results indicate that the proposed mechanism is effective and efficient in open and large-scale environments.

Advances in understanding the mechanisms of Arctic amplification

The near-surface temperatures in the Arctic are increasing at more than twice the global average rate,a phenomenon known as Arctic amplification(AA).In recent years,numerous studies using ground-based and satellite observations,along with model simulations,have explored the potential mechanisms behind AA,offering a variety of observational evidence and theoretical explanations.Although the understanding of AA drivers has improved,significant uncertainties remain in quantifying the contributions of different influential factors.On the basis of the latest research,this article thoroughly examines the factors driving rapid warming in the Arctic,including local feedbacks,atmospheric circulation,ocean currents,and aerosols,and compares quantitative results across studies.The analysis highlights the complex interplay of multiple factors contributing to AA,with no clear consensus on the relative contributions of each driver.Finally,the article underscores key challenges in current research,emphasizing the need for more reliable observational data,a deeper understanding of AA mechanisms,improved model parameterizations,and the disentanglement of interactions among driving factors,all of which are essential for future investigations.