面向集成电路先进制程的二维信息材料与器件

随着集成电路技术的发展至3 nm节点,摩尔定律接近其物理极限,传统芯片制程面临材料到器件的理论和技术瓶颈。二维信息材料凭借原子层厚度、低功耗等特性被产业界认为是1 nm及以下节点的核心材料,将助力芯片制程延续摩尔定律以及平面到三维的发展,与我国集成电路先进制程长期规划紧密相关。基于国家自然科学基金委员会第343期双清论坛,本文从材料—器件—异质集成多层次回顾了二维信息材料与器件的发展历史,总结了领域内所面临挑战,凝炼了未来5~10年的重大关键科学以及亟需布局的研究方向,进一步提出顶层设计的前沿研究方向和科学基金资助战略。

Organic amendments increase corn yield by enhancing soil resilience to climate change

A 22-year field experiment was conducted in Gongzhuling, Jilin province, China to investigate corn yield response to fertilization practice. Compared to an unfertilized control(CK), all fertilization treatments, including inorganic nitrogen fertilizer only(N), balanced inorganic fertilizers(NPK), NPK plus corn straw(SNPK), and NPK plus farmyard manure(MNPK), resulted in significant increases in corn yield. However, only organic matter amendments sustained increasing yield trends, with annual rates of 0.137 and 0.194 t ha-1for the SPNK and MNPK treatments, respectively(P < 0.05). During the 22 years, the daily mean, maximum and minimum temperatures increased by 0.50, 0.53, and 0.46 °C per decade, whereas precipitation displayed no significant change but showed large seasonal variation. According to a regression analysis, increased air temperature exerted positive effects on corn yields under the SNPK and the MNPK treatments. Under both treatments,soil organic carbon contents and soil nutrient availabilities increased significantly compared to their initial levels in 1990, whereas soil bulk density and total porosity changed slightly under the two treatments, which showed higher soil water storage than other treatments. In contrast, significant increases in soil bulk density and decreases in soil total porosity and soil nutrient availability were observed under the CK, N and NPK treatments. The contributions of soil fertility to corn yield were 28.4%, 37.9%, 38.4%, 39.0%,and 42.9% under CK, N, NPK, SNPK, and MNPK treatments, respectively, whereas climate changes accounted for 27.0%, 14.6%, 12.4%, 11.8%, and 10.8%. These results indicate that, in Northeast China, organic matter amendments can mitigate negative and exploit positive effects of climate change on crop production by enhancing soil quality.

治疗前外周血炎性标志物对EGFR突变阳性晚期非小细胞肺癌患者的预后价值研究

目的:探讨治疗前外周血炎性标志物在表皮生长因子受体(epidermal growth factor receptor,EGFR)阳性晚期非小细胞肺癌(non-small cell lung cancer,NSCLC)患者中的预后作用。方法:回顾性分析2015年1月至2018年10月中国人民解放军总医院第五医学中心142例EGFR突变阳性晚期NSCLC患者临床资料。通过受试者工作特征曲线(receiver operating characteristic curve,ROC)确定中性粒细胞与淋巴细胞计数比值(neutrophil-to-lymphocyte ratio,NLR)、血小板与淋巴细胞计数比值(platelet-to-lymphocyte ratio,PLR)、系统免疫炎症指数(systemic immune-inflammation index,SII)和淋巴细胞与单核细胞计数比值(lymphocyte-to-monocyte ratio,LMR)的最佳临界值,应用Kaplan-Meier方法进行生存分析。Cox比例风险模型评估各变量的预测价值。结果:NLR、PLR、SII和LMR的最佳临界值分别为2.60、167.32、687.39和3.13。根据最佳临界值分别将研究对象分为高值组和低值组,高NLR、PLR和SII组的中位无进展生存期(median progression-free survival,mPFS)及中位总生存期(median overall survival,mOS)均显著低于低值组中相应值,高LMR组mPFS及mOS较低LMR组延长。结论:治疗前升高的NLR、PLR、SII和降低的LMR可能与接受靶向治疗的EGFR突变阳性晚期NSCLC患者的不良预后相关。

Randomized comparison of lobaplatin plus etoposide and cisplatin plus etoposide chemotherapy in patients with extensive-stage small cell lung cancer

Objective： To compare the efficacy and safety of Lobaplatin plus Etoposide （EL） and Cisplatin plus Etoposide （EP） regimens in chemonaive with extensive-stage small-cell lung cancer （SCLC）. Methods： Between July 2010 and July 2011, a total of 62 patients with extensive-stage small-cell lung cancer who received initial treatment in our hospital and 309 hospital of PLA. 31 patients were randomly assigned to the EL Group： Lobaplatin was given intravenously at a dose of 30 mg/m2 on day 1 and Etoposide 100 mg/m2 on days 1 to 3 of 21-day cycles for a maximum of six cycles. Another 31 patients were assigned to the EP Group： Cisplatin was given intravenously at a dose of 75 mg/m2 on day 1 and Etoposide 100 mg/m2 on days 1 to 3 of 21-day cycles for a maximum of six cycles. We evaluated the efficacy, overall response rate （ORR）, disease control rate （DCR）, the progression-free survival （PFS） and toxicity between the patients of the two groups. Results： All 62 patients were eligible. In the EL group, 2 （6.5%） patients had complete response, 20 （64.5%） patients had partial response, 5 （16.1%） patients had stable disease and 4 （12.9%） patients had progress disease. In the EP group, 2 （6.5%） patients had complete response, 22 （70.9%） patients had partial response, 4 （12.9%） patients had stable disease and 3 （9.7%） patients had progress disease. The ORR of EL and EP group were 70.9% and 77.4%, respectively, showing no significant difference （P = 0.562）. The DCR of both groups were 87% and 90%, respectively, showing no significant difference （P = 0.688）. Median PFS of patients with EL and EP regimens were 5.5 months and 5 months, respectively, showing no significant difference （P = 0.637）. Adverse events were observed in all 62 patients. Grade 1 to 4 anemia was higher in the EP group than in EL group, showing significant difference （P = 0.02）. Grade 3 and 4 thrombocytopenia was seen in 4 patients （12.9%） in EL group and 1 patient （3.2%） in EP group. Although one patient had platelet transfusion owing to Grade 4 thrombocytopenia in EL group, no significant difference （P = 0.637） were shown. The incidence of nausea/vomiting was higher in the EP group than in the EL group （96.7% vs 51.6%, P = 0.00）. Conclusien： The EL regimen is an effective and low-toxicity chemotherapy and no inferior to EP regimen in treatment response, therefore, EL regimen maybe is a good choice for patients with extensive-stage SCLC.

Deep reinforcement learning based multi-level dynamic reconfiguration for urban distribution network:a cloud-edge collaboration architecture

With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provides reliable support for reconfiguration optimization in urban distribution networks.Thus,this study proposed a deep reinforcement learning based multi-level dynamic reconfiguration method for urban distribution networks in a cloud-edge collaboration architecture to obtain a real-time optimal multi-level dynamic reconfiguration solution.First,the multi-level dynamic reconfiguration method was discussed,which included feeder-,transformer-,and substation-levels.Subsequently,the multi-agent system was combined with the cloud-edge collaboration architecture to build a deep reinforcement learning model for multi-level dynamic reconfiguration in an urban distribution network.The cloud-edge collaboration architecture can effectively support the multi-agent system to conduct“centralized training and decentralized execution”operation modes and improve the learning efficiency of the model.Thereafter,for a multi-agent system,this study adopted a combination of offline and online learning to endow the model with the ability to realize automatic optimization and updation of the strategy.In the offline learning phase,a Q-learning-based multi-agent conservative Q-learning(MACQL)algorithm was proposed to stabilize the learning results and reduce the risk of the next online learning phase.In the online learning phase,a multi-agent deep deterministic policy gradient(MADDPG)algorithm based on policy gradients was proposed to explore the action space and update the experience pool.Finally,the effectiveness of the proposed method was verified through a simulation analysis of a real-world 445-node system.

Optical second-harmonic generation of Janus MoSSe monolayer

The transition metal dichalcogenides(TMD)monolayers have shown strong second-harmonic generation(SHG)ow-ing to their lack of inversion symmetry.These ultrathin layers then serve as the frequency converters that can be intergraded on a chip.Here,taking MoSSe as an example,we report the first detailed experimental study of the SHG of Janus TMD monolayer,in which the transition metal layer is sandwiched by the two distinct chalcogen layers.It is shown that the SHG effectively arises from an in-plane second-harmonic polarization under paraxial focusing and detection.Based on this,the orientation-resolved SHG spectroscopy is realized to readily determine the zigzag and armchair axes of the Janus crystal with an accuracy better than±0.6°.Moreover,the SHG intensity is wavelength-dependent and can be greatly enhanced(~60 times)when the two-photon transition is resonant with the C-exciton state.Our findings uncover the SHG properties of Janus MoSSe monolayer,therefore lay the basis for its integrated frequency-doubling applications.

Common (π,π) Band Folding and Surface Reconstruction in Fe As-Based Superconductors

High resolution angle-resolved photoemission spectroscopy(ARPES)measurements are carried out on CaKFe_4 As_4,KCa_2 Fe_4 As_4 F_2 and(Ba_(0.6)K_(0.4))Fe_2 As_2 superconductors.Clear evidence of band folding between the Brillouin zone center and corners with a(π,π)wave vector has been found from the measured Fermi surface and band structures in all the three kinds of superconductors.A dominant √2×√2 surface reconstruction is observed on the cleaved surface of CaKFe_4As_4 by scanning tunneling microscopy(STM)measurements.We propose that the commonly observed √2×√2 reconstruction in the FeAs-based superconductors provides a general scenario to understand the origin of the(π,π)band folding.Our observations provide new insights in understanding the electronic structure and superconductivity mechanism in iron-based superconductors.

Gate-controlled localization to delocalization transition of flat band wavefunction in twisted monolayer–bilayer graphene

Twisted graphene systems with flat bands have attracted much attention for they are excellent platforms to research novel quantum phases. Recently, transport measurements about twisted monolayer–bilayer graphene(t MBG) have shown the existence of correlated states and topological states in this system. However, the direct observations of the band structures and the corresponding spatial distributions are still not sufficient. Here we show that the distributions of flat bands in t MBG host two different modes by scanning tunneling microscopy and spectroscopy(STM/S). By tuning our t MBG device from the empty filling state to the full filling state through the back gate, we observe that the distributions of two flat bands develop from localized mode to delocalized mode. This gate-controlled flat band wavefunction polarization is unique to the t MBG system. Our work suggests that t MBG is promising to simulate both twisted bilayer graphene(TBG) and twisted double bilayer graphene(t DBG) and would be an ideal platform to explore novel moiré physics.

Bi-level Multi-leader Multi-follower Stackelberg Game Model for Multi-energy Retail Package Optimization

In the competitive energy market,energy retailers are facing the uncertainties of both energy price and demand,which requires them to formulate reasonable energy purchasing and selling strategies for improving their competitiveness in this market.Particularly,the attractive multi-energy retail packages are the key for retailers to increase their benefit.Therefore,combined with incentive means and price signals,five types of multi-energy retail packages such as peak-valley time-of-use(TOU)price package and day-night bundled price package are designed in this paper for retailers.The iterative interactions between retailers and end-users are modeled using a bi-level model of stochastic optimization based on multi-leader multi-follower(MLMF)Stackelberg game,in which retailers are leaders and end-users are followers.Retailers make decisions to maximize the profit considering the conditional value at risk(CVaR)while end-users optimize the satisfaction of both energy comfort and economy.Besides,a distributed algorithm is proposed to obtain the Nash equilibrium of above MLMF Stackelberg game model while the particle swarm optimization(PSO)algorithm and CPLEX solver are applied to solve the optimization model for each participant(retailer or end-user).Numeral results show that the designed retail packages can increase the overall profit of retailers,and the overall satisfaction of industrial users is the highest while that of residential users is the lowest after game interaction.

Real-time Operation Optimization in Active Distribution Networks Based on Multi-agent Deep Reinforcement Learning

The increasing integration of intermittent renewable energy sources(RESs)poses great challenges to active distribution networks(ADNs),such as frequent voltage fluctuations.This paper proposes a novel ADN strategy based on multiagent deep reinforcement learning(MADRL),which harnesses the regulating function of switch state transitions for the realtime voltage regulation and loss minimization.After deploying the calculated optimal switch topologies,the distribution network operator will dynamically adjust the distributed energy resources(DERs)to enhance the operation performance of ADNs based on the policies trained by the MADRL algorithm.Owing to the model-free characteristics and the generalization of deep reinforcement learning,the proposed strategy can still achieve optimization objectives even when applied to similar but unseen environments.Additionally,integrating parameter sharing(PS)and prioritized experience replay(PER)mechanisms substantially improves the strategic performance and scalability.This framework has been tested on modified IEEE 33-bus,IEEE 118-bus,and three-phase unbalanced 123-bus systems.The results demonstrate the significant real-time regulation capabilities of the proposed strategy.

A Projection and Decomposition Approach for Multi-agent Coordinated Scheduling in Power Systems

Aiming at multi-agent coordinated scheduling problems in power systems under uncertainty,a generic projection and decomposition(P&D)approach is proposed in this letter.The canonical min-max-min two-stage robust optimization(TSRO)model with coupling constraints is equivalent to a concise robust optimization(RO)model in the version of mixed-integer linear programming(MILP)via feasible region projection.The decentralized decoupling of the non-convex MILP problem is realized through a dual decomposition algorithm,which ensures the fast convergence to a high-quality solution in the distributed optimization.Numerical tests verify the superior performance of the proposed P&D approach over the existing distributed TSRO method.

5d电子材料新奇物性研究中的关键科学问题

基于国家自然科学基金委员会第298期“双清论坛”,本文介绍了5d电子相关材料的新奇物性以及相关材料体系的研究意义,从理论、制备、表征、器件四个主要方向回顾了这一研究领域的研究现状以及面临的主要挑战,并进一步提出了亟需关注和解决的重要基础科学问题和重点研究方向。

Review and prospect of active distribution system planning

The approach to planning,design and operation of distribution networks have significantly changed due to the proliferation of distributed energy resources(DERs)together with load growth,energy storage technology advancements and increased consumer expectations.Planning of active distribution systems(ADS)has been a very hot topic in the 21st Century.A large number of studies have been done on ADS planning.This paper reviews the state of the art of current ADS planning.Firstly,the influences of DERs on the ADS planning are addressed.Secondly,the characteristics and objectives of ADS planning are summarized.Then,up to date planning model and some related research are highlighted in different areas such as forecasting load and distributed generation,mathematical model of ADS planning and solution algorithms.Finally,the paper explores some directions of future research on ADS planning including planning collaboratively with all elements combined in ADS,taking into account of joint planning in secondary system,coordinating goals among different layers,integrating detailed operation simulations and regular performance based reviews into planning,and developing advanced planning tools.

Solvothermal-Assisted Exfoliation Process to Produce Graphene with High Yield and High Quality

Monolayer and bilayer graphene sheets have been produced by a solvothermal-assisted exfoliation process in a highly polar organic solvent,acetonitrile,using expanded graphite(EG)as the starting material.It is proposed that the dipole-induced dipole interactions between graphene and acetonitrile facilitate the exfoliation and dispersion of graphene.The facile and effective solvothermal-assisted exfoliation process raises the low yield of graphene reported in previous syntheses to 10 wt%12 wt%.By means of centrifugation at 2000 rpm for 90 min,monolayer and bilayer graphene were separated effectively without the need to add a stabilizer or modifi er.Electron diffraction and Raman spectroscopy indicate that the resulting graphene sheets are high quality products without any signifi cant structural defects.

A Two-stage Robust Optimal Allocation Model of Distributed Generation Considering Capacity Curve and Real-time Price Based Demand Response

Demand response, the reactive power output of distributed generation(DG), and network reconfiguration have significant impacts on a DG allocation strategy. In this context, a novel real-time price-based demand response formulation is integrated into the allocation model of DG. The tariff is regulated by the difference between the load and active power of renewable energy. Meanwhile, network reconfiguration and the capacity curve describing the active and reactive power limits of DG are included in the optimization model for promoting the allocation of DG.With these measures, the optimal allocation model of DG is established with the goal of maximizing the net annual profit while guaranteeing the efficient utilization of renewable energy. In addition, the uncertainties of renewable energy are considered on the basis of a two-stage robust optimization method. Finally, the entire optimization model is solved by the column and constraint generation algorithm in the IEEE 33-bus distribution system and a practical 99-bus distribution system. Numerical simulations show that the proposed model is effective in terms of improving both the usage of renewable energy and net annual profit.

Multi-objective Dynamic Reconfiguration for Urban Distribution Network Considering Multi-level Switching Modes

The increasing integration of photovoltaic generators(PVGs) and the uneven economic development in different regions may cause the unbalanced spatial-temporal distribution of load demands in an urban distribution network(UDN). This may lead to undesired consequences, including PVG curtailment, load shedding, and equipment inefficiency, etc. Global dynamic reconfiguration provides a promising method to solve those challenges. However, the power flow transfer capabilities for different kinds of switches are diverse, and the willingness of distribution system operators(DSOs) to select them is also different. In this paper, we formulate a multi-objective dynamic reconfiguration optimization model suitable for multi-level switching modes to minimize the operation cost, load imbalance, and the PVG curtailment. The multi-level switching includes feeder-level switching, transformer-level switching, and substation-level switching. A novel load balancing index is devised to quantify the global load balancing degree at different levels. Then, a stochastic programming model based on selected scenarios is established to address the uncertainties of PVGs and loads. Afterward, the fuzzy c-means(FCMs) clustering is applied to divide the time periods of reconfiguration. Furthermore, the modified binary particle swarm optimization(BPSO)and Cplex solver are combined to solve the proposed mixed-integer second-order cone programming(MISOCP) model. Numerical results based on the 148-node and 297-node systems are obtained to validate the effectiveness of the proposed method.

Bounded Rationality Based Multi-VPP Trading in Local Energy Markets:A Dynamic Game Approach with Different Trading Targets

It is expected that multiple virtual power plants(multi-VPPs)will join and participate in the future local energy market(LEM).The trading behaviors of these VPPs needs to be carefully studied in order to maximize the benefits brought to the local energy market operator(LEMO)and each VPP.We propose a bounded rationality-based trading model of multiVPPs in the local energy market by using a dynamic game approach with different trading targets.Three types of power bidding models for VPPs are first set up with different trading targets.In the dynamic game process,VPPs can also improve the degree of rationality and then find the most suitable target for different requirements by evolutionary learning after considering the opponents’bidding strategies and its own clustered resources.LEMO would decide the electricity buying/selling price in the LEM.Furthermore,the proposed dynamic game model is solved by a hybrid method consisting of an improved particle swarm optimization(IPSO)algorithm and conventional largescale optimization.Finally,case studies are conducted to show the performance of the proposed model and solution approach,which may provide some insights for VPPs to participate in the LEM in real-world complex scenarios.

Electronic structure of exfoliated millimeter-sized monolayer WSe2 on silicon wafer

The mono layer WSe2 is in teresting and important for future application in nanoelectronics,spintronics and valleytronics devices,because it has the largest spin splitting and Ion gest valley coherence time among all the known monolayer transition-metal dichalcogenides(TMDs).Toobtain the large-area monolayer TMDs'crystal is the first step to manu facture scalable and high-performance electronic devices.In this letter,we have successfully fabricated millimeter-sized mono layer WSe2 single crystals with very high quality,based on our improved mecha nicalexfoliation method.With such superior samples,using standard high resolution angle-resolved photoemission spectroscopy,we didcomprehe nsive electronic band structure measurements on our mono layer WSe2.The overall band features point it to be a 1.2 eV direct bandgap semico nductor.Its spin splitting of the valence band at K point is found as 460 meV,which is 30 meV less than the corresponding band splitting in its bulk counterpart.The effective hole masses of valence bands are determined as 2.344 me atГ,and 0.529 me as well as 0.532 meat K for the upper and lower branch of splitting ban ds,respectively.And screening effect from substrate is shown to substa ntially impact onthe electronic properties.Our results provide importa nt insights into band structure engineering in mono layer TMDs.Our mono layer WSe2 crystals may constitute a valuable device platform.

Decision-making Method of Sharing Mode for Multi-microgrid System Considering Risk and Coordination Cost

Power sharing can improve the benefit of the multi-microgrid(MMG)system.However,the information disclosure may appear during the sharing process,which would bring privacy risk to a local microgrid.Actually,the risk and coordination cost are different in different sharing modes.Therefore,this paper develops a decision-making method to decide the most suitable one of three mostly used sharing modes(i.e.,cooperative game with complete information,cooperative game with incomplete information,and noncooperative game).Firstly,power sharing paradigms and coordination mechanisms in the three modes are formulated in detail.Particularly,different economic operation models of MMG system are included to analyze the economic benefit from different sharing modes.Based on the different disclosed information,the risk cost is evaluated by using the simplified fuzzy analytic hierarchy process(FAHP).And the coordination cost for different sharing modes is expressed in different functions.In addition,a hierarchical evaluation system including three decision-making factors(e.g.,economics,risk,and coordination)is set up.Meanwhile,a combination weighting method(e.g.,the simplified FAHP combined with the anti-entropy weight method)is applied to obtain the weight of each factor for comprehensive evaluation.Finally,the optimal sharing solution of MMG system is decided by comparing and analyzing the difference among the three sharing modes.Numerical results validate that the proposed method can provide a reference to deciding a suitable sharing mode.

A Data-driven Distributionally Robust Operational Model for Urban Integrated Energy Systems

A multi-energy conversion can effectively increase the utilization of renewable energy in the urban integrated energy system(UIES).Meanwhile,the uncertainties of renewable energy resources(e.g.,wind energy)also bring increased challenges to the operation of UIES.In this study,a typical two-stage datadriven distributionally robust operation(DDRO)model based on finite scenarios is proposed for UIES including power,gas and heat networks to obtain a salient strategy from both an economic and robustness perspective.In the first stage,the forecasted information for wind power is especially included to improve the economic aspect of robust decisions.The worst probability distribution for the selected known real-time wind power scenarios can be identified in the second stage where the power differences caused by the real-time uncertainties of wind power can be mitigated by flexible regulation of energy purchasing and coupling units(such as gas turbine,power to gas equipment,electric boiler and gas boiler).Moreover,norm-1 and norm-inf co-constraints are utilized to construct a confidence set for the probability distributions of uncertain wind power.The whole two-stage model is solved by the column-and-constraint generation(CCG)algorithm.Finally,case studies are conducted to show the performance of the proposed model and various approaches.Index Terms-Data-driven methods,distributionally robust optimization,urban integrated energy system,wind power.