面向学生“学”的智能教育软件评估指标体系构建——《2023人工智能促进教育发展报告》节选五

当前智能教育软件市场样态纷繁,软件产品质量参差不齐,教育工作者难以根据教学场景需求选择合适的智能教育软件。通过梳理相关文献与资料,厘清当前智能教育软件评估的研究现状,对现有的教育软件评估指标框架体系进行分析,并根据当前面向学生“学”的智能软件应用场景形态,逐步构建涵盖课堂学习、自主学习、智能作业、社会实践、运动健康五大应用场景的智能教育软件评估指标系,以期帮助使用者在纷繁芜杂、良莠并存的智能教育软件中选到质量高且符合自己使用需求的产品,同时也为监管不同区域的智能教育软件建设和应用情况提供评估标准,规范智能教育软件的监管机制,促进我国人工智能教育应用的有效落地。

Distributed optimization of electricity-Gas-Heat integrated energy system with multi-agent deep reinforcement learning

The coordinated optimization problem of the electricity-gas-heat integrated energy system(IES)has the characteristics of strong coupling,non-convexity,and nonlinearity.The centralized optimization method has a high cost of communication and complex modeling.Meanwhile,the traditional numerical iterative solution cannot deal with uncertainty and solution efficiency,which is difficult to apply online.For the coordinated optimization problem of the electricity-gas-heat IES in this study,we constructed a model for the distributed IES with a dynamic distribution factor and transformed the centralized optimization problem into a distributed optimization problem in the multi-agent reinforcement learning environment using multi-agent deep deterministic policy gradient.Introducing the dynamic distribution factor allows the system to consider the impact of changes in real-time supply and demand on system optimization,dynamically coordinating different energy sources for complementary utilization and effectively improving the system economy.Compared with centralized optimization,the distributed model with multiple decision centers can achieve similar results while easing the pressure on system communication.The proposed method considers the dual uncertainty of renewable energy and load in the training.Compared with the traditional iterative solution method,it can better cope with uncertainty and realize real-time decision making of the system,which is conducive to the online application.Finally,we verify the effectiveness of the proposed method using an example of an IES coupled with three energy hub agents.

Engineering hollow core-shell hetero-structure box to induce interfacial charge modulation for promoting bidirectional sulfur conversion in lithium-sulfur batteries

Severe polysulfide shuttling and sluggish sulfur redox kinetics significantly decrease sulfur utilization and cycling stability in lithium-sulfur batteries(LSBs).Herein,we develop a hollow CoO/CoP-Box core-shell heterostructure as a model and multifunctional catalyst modified on separators to induce interfacial charge modulation and expose more active sites for promoting the adsorption and catalytic conversion ability of sulfur species.Theoretical and experimental findings verify that the in-situ formed core-shell hetero-interface induces the formation of P-Co-O binding and charge redistribution to activate surface O active sites for binding lithium polysulfides(LiPSs)via strong Li-O bonding,thus strongly adsorbing with Li PSs.Meanwhile,the strong Li-O bonding weakens the competing Li-S bonding in LiPSs or Li2S adsorbed on CoO/CoP-Box surface,plus the hollow heterostructure provides abundant active sites and fast electron/Li+transfer,so reducing Li2S nucleation/dissolution activation energy.As expected,LSBs with CoO/CoP-Box modified separator and traditional sulfur/carbon black cathode display a large initial capacity of 1240 mA h g^(-1)and a long cycling stability with 300 cycles(~60.1%capacity retention)at 0.5C.Impressively,the thick sulfur cathode(sulfur loading:5.2 mg cm^(-2))displays a high initial areal capacity of 6.9 mA h cm^(-2).This work verifies a deep mechanism understanding and an effective strategy to induce interfacial charge modulation and enhance active sites for designing efficient dual-directional Li-S catalysts via engineering hollow core-shell hetero-structure.

Analysis of users’ electricity consumption behavior based on ensemble clustering

Due to the increase in the number of smart meter devices,a power grid generates a large amount of data.Analyzing the data can help in understanding the users’electricity consumption behavior and demands;thus,enabling better service to be provided to them.Performing power load profile clustering is the basis for mining the users’electricity consumption behavior.By examining the complexity,randomness,and uncertainty of the users’electricity consumption behavior,this paper proposes an ensemble clustering method to analyze this behavior.First,principle component analysis(PCA)is used to reduce the dimensions of the data.Subsequently,the single clustering method is used,and the majority is selected for integrated clustering.As a result,the users’electricity consumption behavior is classified into different modes,and their characteristics are analyzed in detail.This paper examines the electricity power data of 19 real users in China for simulation purposes.This manuscript provides a thorough analysis along with suggestions for the users’weekly electricity consumption behavior.The results verify the effectiveness of the proposed method.

Physicochemical properties and antioxidant activities of polysaccharides extracted from Panax ginseng C.A.Meyer with graded percipitation method

Ginseng polysaccharides were extracted by water decoction from Panax ginseng C.A.Meyer(Cultivated Ginseng),named CGPS.Four polysaccharide fractions,CGPS-20,CGPS-40,CGPS-60 and CGPS-80,were precipitated at final ethanol concentrations of 20%,40%,60%and 80%,respectively.Physicochemical properties,molecular weight,monosaccharide composition and antioxidant capacity of polysaccharide fractions were all investigated.The results indicated that changing the concentration of ethanol could precipitate polysaccharides into fractions with different molecular weights,functional group composition and physicochemical properties,eventually leading to differences in antioxidant activity,which would help to find a simple,efficient,and reliable method for rapid extraction and purification of antioxidant polysaccharides from Panax ginseng C.A.Meyer.Among the four polysaccharide fractions,CGPS-80 had lower molecular weight,higher contents of uronic acid and total phenolic,and stronger scavenging ability on DPPH∙and ABTS∙+radicals.

Exploring the Influences of Continuous Self-Management Education on the Self-Care Skills and Health Behavior of Patients with PICC Intubation

Objective:To analyze the effects of continuous self-management education on the selfcare ability and health behavior of patients with tumor through peripherally inserted central venous catheters(PICC).Methods:The period from August 2018 to August 2020 was used as the research time range,and the random number table method was used as the basis for grouping.80 patients with malignant tumors who regularly performed fixed catheter maintenance care in the PICC clinic of our hospital were admitted in the experimental group(given PICC specialist nursing,and implemented continuous self-management education),and 80 patients with PICC tube malignant tumors discharged from the superior hospital during this time range served as the control group(return to the original catheterization hospital from time to time or perform catheter maintenance care in the nursing clinic of our hospital).The self-care ability scores,health behavior scores,and complications during intubation between both groups were analyzed.Results:(1)There was no significant difference in self-care ability score and healthy behavior score between groups before the intervention,P>0.05;the self-care ability score and health behavior score of the research group were better than the control group after intervention,P<0.05;(2)After investigation,the incidence of complications in the research group(2.50%)was lower than that of the control group(10.00%),but there was no difference between the groups,P>0.05.Conclusion:Continuous self-management education has good effects on improving the self-care ability of tumor patients with PICC intubation.It can urge patients to maintain good health behaviors and reduce complications.It is worthy of promotion.

Effectiveness of Mobile-Assisted Language Learning in Enhancing the English Proficiency

In response to the significant impact of the widespread use of digital devices and mobile technologies on language teaching and learning in this time of Internet information technology,this study aims to investigate the effectiveness of Mobile-Assisted Language Learning(MALL)in enhancing the English proficiency of students,while exploring the potential advantages of mobile devices for assisted learning in the English learning environment in China and the potential for mobile applications to assist English learning to foster learner autonomy.Anchored with the design thinking approach,the researchers used the empirical analysis methodology in developing an efficient mobile-assisted language learning model.Usability testing was conducted using a case study of two mobile applications,WeLearn and Flipped English,in Heilongjiang University of Finance and Economics to measure the extent of usability and acceptability of MALL on English language acquisition among college students identified through surveys,interviews,and quantitative assessments.Mobile technology is a perfect tool for every student that enhances their experience and increases their joy while improving their English language skills.It adds new value and brings new opportunities for both English learners and the language education industry.Indeed,MALL is English learners’new ally.

Hierarchical Game Approach for Optimization of Regional Integrated Energy System Clusters Considering Bounded Rationality

Regional integrated energy system(RIES)cluster,i.e.,multi-source integration and multi-region coordination,is an effective approach for increasing energy utilization efficiency.The hierarchical architecture and limited information sharing of RIES cluster make it difficult for traditional game theory to accurately describe their game behavior.Thus,a hierarchical game approach considering bounded rationality is proposed in this paper to balance the interests of optimizing RIES cluster under privacy protection.A Stackelberg game with the cluster operator(CO)as the leader and multiple RIES as followers is developed to simultaneously optimize leader benefit and RIES utilization efficiency.Concurrently,a slight altruistic function is introduced to simulate the game behavior of each RIES agent on whether to cooperate or not.By introducing an evolutionary game based on bounded rationality in the lower layer,the flaw of the assumption that participants are completely rational can be avoided.Specially,for autonomous optimal dispatching,each RIES is treated as a prosumer,fexibly switching its market participation role to achieve cluster coordination optimization.Case studies on a RIES cluster verify effectiveness of the proposed approach.

Voltage-dependent P-Q Reserve Capacity Evaluation for TSO-DSO Interface Considering Uncertainties of DGs and FLs

Increasing distributed generators(DGs)and flexible loads(FLs)enable distribution systems to provide both active and reactive power reserves(P-Q reserves)in supporting the frequency and voltage regulations of transmission systems.However,such requirements at the interface between the transmission system operator(TSO)and distribution system operator(DSO)affect the distribution system operation security,considering the uncertainties of DGs and FLs.To exploit the reserve potential of distribution systems,this paper investigates the voltagedependent P-Q reserve capacity(V-PQRC)of such types of distribution systems.V-PQRC reflects the feasible space of PQ reserves that the DSO can provide to the TSO taking the voltage deviation limit at TSO-DSO interface into consideration,while ensuring the distribution system operation security under uncertainties of DGs and FLs.An evaluation method for VPQRC at the TSO-DSO interface is proposed.To improve the robust performance of the evaluation method,the DG uncertainty is captured by a generalized ambiguity set and the FL uncertainty is addressed by designing a constrained sliding mode controller(CSMC).Three objectives are considered in the evaluation,i.e.,P reserve capacity,Q reserve capacity,and the voltage deviation limit at the TSO-DSO interface.Then,a multiobjective optimization model integrating the generalized robust chance-constrained optimization and CSMC(GRCC-CSMC)is established for V-PQRC evaluation to obtain the Pareto optimal reserve schemes.Finally,a non-approximated selecting(NAS)method is proposed to build up a simplified V-PQRC linear model,which can be convenient to apply in the transmissiondistribution system coordination.Simulation results reveal that the V-PQRC evaluation method can achieve a good performance of accuracy and robustness against uncertainties.

英国脱欧对北爱尔兰族群问题的影响及其前景分析

英国脱欧带来的影响十分广泛深远,北爱尔兰作为英国的一部分,其边界地位、规则适用、和平进程和族群关系等都将因此面临重要调整和变化。本文以英国脱欧对北爱族群关系的影响为研究对象,分析了英国脱欧对居住于该地区,在民族起源、宗教信仰、政治主张、身份认同和国家归属等方面均呈现出明显差异性和矛盾性的两大族群所产生的重要影响。整体来看,英国脱欧有可能激化北爱两大族群之间的潜在紧张关系,甚至引发两大族群更加激烈的权力争夺和对立冲突,从而导致《贝尔法斯特协议》签署以来逐步形成的北爱和平局势出现较大的不确定性。与此同时,北爱族群问题还将因英国脱欧而产生一系列的溢出性影响,如助长英国国内的民族分离倾向、对其他国家民族分离运动产生示范作用,以及在地区和国际关系层面带来极为广泛的波及效应。

大花银莲花愈伤组织诱导及再生体系的建立

为建立野生大花银莲花(Anemone silvestris)组培再生体系,分别以无菌苗上、下胚轴、叶片和叶柄为外植体,探讨不同浓度植物生长调节剂对不同外植体的愈伤组织诱导、不定芽分化、增殖与生根的影响。结果表明,4种外植体均可诱导出不定芽,其中上胚轴诱导效果最佳,其在1/2MS+2.0mg·L^(–1)6-BA+0.1mg·L^(–1) NAA培养基中诱导率最高,为86.67%;最适增殖培养基为1/2MS+1.0mg·L^(–1)6-BA+0.05mg·L^(–1) NAA,增殖系数为3.67;最佳生根培养基为1/2MS+0.3mg·L^(–1) IBA,生根率为100%;在草炭:蛭石=2:1(v/v)的栽培基质中,组培苗的移栽成活率最高,为98.33%。该研究有效解决了野生大花银莲花在园林及药用生产上的种质资源紧缺难题,为工厂化育苗提供了技术支撑。

Preventive Control for Power System Transient Security Based on XGBoost and DCOPF with Consideration of Model Interpretability

This paper proposes a new approach for online power system transient security assessment(TSA)and preventive control based on XGBoost and DC optimal power flow(DCOPF).The novelty of this proposal is that it applies the XGBoost and data selection method based on the 1-norm distance in local feature importance evaluation which can provide a certain model interpretability.The method of SMOTE+ENN is adopted for data rebalancing.The contingency-oriented XGBoost model is trained with databases generated by time domain simulations to represent the transient security constraint in the DCOPF model,which has a relatively fast speed of calculation.The transient security constrained generation rescheduling is implemented with the differential evolution algorithm,which is utilized to optimize the rescheduled generation in the preventive control.Feasibility and effectiveness of the proposed approach are demonstrated on an IEEE 39-bus test system and a 500-bus operational model for South Carolina,USA.

Spatio-temporal Convolutional Network Based Power Forecasting of Multiple Wind Farms

The rapidly increasing wind power penetration presents new challenges to the operation of power systems.Improving the accuracy of wind power forecasting is a possible solution under this circumstance.In the power forecasting of mul-tiple wind farms,determining the spatio-temporal correlation of multiple wind farms is critical for improving the forecasting accuracy.This paper proposes a spatio-temporal convolutional network(STCN)that utilizes a directed graph convolutional structure.A temporal convolutional network is also adopted to characterize the temporal features of wind power.Historical data from 15 wind farms in Australia are used in the case study.The forecasting results show that the proposed model has higher accuracy than the existing methods.Based on the structure of the STCN,asymmetric spatial correlation at different temporal scales can be observed,which shows the effectiveness of the proposed model.

Renewable Scenario Generation Using Controllable Generative Adversarial Networks with Transparent Latent Space

With the growing penetration of renewable energysources in power systems, it becomes increasingly important tocharacterize their inherent variability and uncertainty. Scenariogeneration is a key approach to provide a series of possible powerscenarios in the future for the system planner and operator tomake decisions. In this paper, a data-driven method is presentedfor renewable scenario generation using stable and controllablegenerative adversarial networks with transparent latent space(ctrl-GANs). The machine learning based algorithm can capturethe nonlinear and dynamic renewable patterns without the needfor modeling assumptions and complicated sampling techniques.The orthogonal regularization and spectral normalization areadopted to improve the training stabilization of the GAN model.To control the generation process, a relationship is built betweenfeatures of the generated scenarios and latent vectors on themanifold. Moreover, several new metrics for GANs are used toevaluate the quality of the scenarios. The proposed approachis applied to generate realistic time series data of wind andphotovoltaic power. The results demonstrate that our methodhas a better performance on numerical stabilization and is ableto control the generation process with latent space.

东北林蛙抗菌肽dybowskin-1ST的结构预测及生物学活性分析

运用生物信息学方法进行东北林蛙抗菌肽dybowskin-1ST的进化、结构及抗原表位预测,分析其抑菌机理及结构性质,应用小鼠伤口愈合实验及体外抑菌实验进行活性验证。同时为改良亲本肽、进行新型衍生肽的研发提供理论基础。使用软件MEGAX对dybowskin-1ST及其他蛙类抗菌肽进行同源性比对并绘制系统进化树;通过在线软件ProtParam、ProtScale、PeptideCutter、SignalP、TMHMM Server分别预测抗菌肽dybowskin-1ST的理化参数、亲/疏水性、剪切位点、信号肽及跨膜区域;分别应用SOPMA、Jpred4及DNAstar Protean软件多重分析预测dybowskin-1ST的二级结构,利用SWISS-MODEL和I-TASSER软件进行三级结构预测。通过在线软件ABCpred和SYFPEITHI进行T/B抗原表位预测。构建小鼠伤口模型,观察dybowskin-1ST促进伤口愈合活性。应用纸片法及96孔板法,确定dybowskin-1ST的抑菌活性。抗菌肽dybowskin-1ST含有59个氨基酸,其中亮氨酸占16.9%,分子式为C_(318)H_(510)N_(80)O_(93)S_(2),理论等电点为5.10,电荷量为-2。抗菌肽dybowskin-1ST与东北林蛙抗菌肽dybowskin-1CDYa亲缘较近。三种方法二级结构预测结果相似,dybowskin-1ST中α-螺旋、延伸链、β-转角、无规卷曲,所占比例分别为44.07%、16.95%、3.39%、35.39%。三级结构预测中显示该抗菌肽大部分为α-螺旋结构,抗菌肽dybowskin-1ST总体预测为亲水性蛋白,具有信号肽序列。亚细胞定位分析显示,其分泌线粒体靶向肽的可能性为0.944。该蛋白属于膜外蛋白,无跨膜结构区,有7个磷酸化位点,3个T细胞抗原表位和8个B细胞抗原表位。dybowskin-1ST具有促进伤口愈合的作用,能够有效抑制大肠杆菌和金黄色葡萄球菌活性,但对真菌及耐药菌的抑菌活性有限。dybowskin-1ST结构中虽富含α-螺旋,但验证实验表明其抑菌能力仍有待加强,原因可能是由于其带负电荷且为亲水性蛋白,以提高正电荷数及改变疏水性为基本思路进行氨基酸改造可获得活性升级的衍生肽。

Load Shedding Control Strategy in Power Grid Emergency State Based on Deep Reinforcement Learning

In viewing the power grid for large-scale new energy integration and power electrification of power grid equipment,the impact of power system faults is increased,and the ability of anti-disturbance is decreased,which makes the power system fault clearance more dificult.In this paper,a load shedding control strategy based on artificial intelligence is proposed,this action strategy of load shedding,which is selected by deep reinforcement learning,can support autonomous voltage control.First,the power system operation data is used as the basic data to construct the network training dataset,and then a novel reward function for voltage is established.This value function,which conforms to the power grid operation characteristics,will act as the reward value for deep reinforcement learning,and the Deep Deterministic Policy Gradient algorithm(DDPG)algorithm,with the continuous action strategy,will be adopted.Finally,the deep reinforcement learning network is continuously trained,and the load shedding strategy concerning the grid voltage control problem will be obtained in the power system emergency control situation,and this strategy action is input into the Pypower module for simulation verification,thereby realizing the joint drive of data and model.According to the numerical simulation analysis,it shows that this method can effectively determine the accurate action selection of load shedding,and improve the stable operational ability of the power system.

Modeling and Control of an Isolated Module Multilevel DC/DC Converter for DC Grid

This paper presents an isolated DC/AC/DC converter using a middle frequency transformer coupling two modular multilevel converters(MMC),suitable for interconnecting DC transmission lines of different voltage levels in high voltage direct current(HVDC)system.The basic operational principle of the isolated module multilevel DC/DC converter(IMMDCC)is analyzed.The dynamic model of IMMDCC is studied in detail and the transient relationship between DC side and AC side of IMMDCC is revealed,which is physically straightforward for understanding the power transfer in IMMDCC.The control strategy in D-Q coordinate system is put forward,and the fault characteristic and corresponding protection method is analyzed.Finally,computer simulation using Matlab/Simulink is performed to verify the dynamic model and the proposed control strategy.The simulation results show good performances and the quick response ability of the proposed control strategy.

原位构建具有铋合金纳米片的三维导电核壳纳米网络和电子结构用于高效电催化CO_(2)还原

由于CO_(2)传质的限制,在H型电池中,具有高法拉第效率(>90%)和大电流密度(>150 mA cm^(-2))的电催化CO_(2)还原(CO_(2)RR)制备HCOOH燃料的过程非常具有挑战性.在本文中,我们报道了一种在泡沫Cu上原位构建具有三维(3D)多孔网络核壳纳米线结构的优异CO_(2)RR电催化剂.核壳结构由Cu纳米线核和Sb-Bi合金壳组成(Cu@Sb_(x)Bi_(y)NWs/Cu).制备的Cu@Sb_(x)Bi_(y)NWs/Cu具有171.3mA cm^(-2)的高电流密度以及92%的HCOOH法拉第效率,就电流密度而言优于几乎所有报道的铋基催化剂.理论研究表明,Sb的引入使Bi的电子态提高到接近费米能级,与纯Bi表面相比,^(*)OCHO中间体更易吸附在Sb-Bi界面上,从而加速CO_(2)还原.此外,Sb_(0.1)Bi_(1)与纯Bi相比具有更强的键能,这有利于催化剂在反应过程中的稳定性.Sb_(0.1)Bi_(1)合金和3D导电核壳纳米网络的形成更有利于快速电子转移,并在反应过程中暴露出更多的活性位点,从而获得更好的催化活性.这项工作为设计用于能量转换的高活性铋基催化剂提供了理论依据.

Successive MISOCP Algorithm for Islanded Distribution Networks with Soft Open Points

An optimal operation scheme is of great significance in islanded distribution networks to restore critical loads and has recently attracted considerable attention.In this paper,an optimal power flow(OPF)model for islanded distribution networks equipped with soft open points(SOPs)is proposed.Unlike in the grid-connected mode,the adequacy of local power generation in distribution networks is critical for islanded systems.The proposed approach utilizes the power output of local distributed generations(DGs)and the benefits of reactive power compensation provided by SOPs to allow maximum loadability.To exploit the available resources,an optimal secondary droop control strategy is introduced for the islanded distribution networks,thereby minimizing load shedding.The formulated OPF problem is essentially a mixed-integer nonlinear programming(MINLP)model.To guarantee the computation efficiency and accuracy.A successive mixed-integer second-order cone programming(SMISOCP)algorithm is proposed for handling the nonlinear islanded power flow formulations.Two case studies,incorporating a modified IEEE 33-bus system and IEEE 123-bus system,are performed to test the effectiveness of the proposed approach.

Inflammation responsive tofacitinib loaded albumin nanomedicine for targeted synergistic therapy in ulcerative colitis

Patients with ulcerative colitis(UC)often loss responses over long term usage of conventional therapies.Tofacitinib,a pan-Janus kinases(JAK)inhibitor is approved for moderate to severe UC treatment,while dose-limiting systemic side effects including infections,cancers and lymphoma limit its popularity of clinical application.This study sought to construct an anti-mucosal vascular addressin cell-adhesion molecule-1(anti-MAdCAM-1)antibody modified reactive oxygen species(ROS)responsive human serum albumin-based nanomedicine denoted as THM,to improve the therapeutic efficacy of tofacitinib for UC treatment.THM has the drug releasing properties in response to ROS stimulation.In vitro studies show that THM selectively adhered to the endothelial cells and had obvious anti-inflammatory effect on macrophages.Meanwhile,the nanomedicine can inhibit the phenotypic switching of M1 macrophages and promote M2 polarization to produce anti-inflammatory medicators during wound healing.In addition,in vivo fluorescence imaging verified that THM exhibited enhanced preferential accumulation and extended retention in inflamed colon.Moreover,THM significantly reduced the production of proinflammatory cytokines in the colon and suppressed the homing of T cells to the gut in dextran sodium sulfate induced experimental colitis.This work elucidates that the inflamed colon-targeted delivery of tofacitinib by nanomedicine is promising for UC treatment and sheds light on addressing the unmet medical need.