Knowledge Tracking Model Based on Learning Process

Knowledge tracking model has been a research hotspot in the field of educational data mining for a long time. Knowledge tracking can automatically discover students’ weak knowledge points, which helps to improve students’ self-motivation in learning and realize personalized guidance. The existing KT model has some shortcomings, such as the limitation of the calculation of knowledge growth, and the imperfect forgetting mechanism of the model. To this end, we proposed a new knowledge tracking model based on learning process (LPKT), LPKT applies the idea of Memory Augmented Neural Net-work(MANN).When we model the learning process of students, two additional important factors are considered. One is to consider the current state of knowledge of the students when updating the dynamic matrix of the neural network, and the other is to improve the forgetting mechanism of the model. In this paper we verified the effectiveness and superiority of LPKT through comparative experiments, and proved that the model can improve the effect of knowledge tracking and make the process of deep knowledge tracking easier to understand.

An Ultra-microporous Carbon Material Boosting Integrated Capacitance for Cellulose-Based Supercapacitors

A breakthrough in advancing power density and stability of carbon-based supercapacitors is trapped by inefficient pore structures of electrode materials.Herein,an ultramicroporous carbon with ultrahigh integrated capacitance fabricated via one-step carbonization/activation of dense bacterial cellulose(BC)precursor followed by nitrogen/sulfur dual doping is reported.The microporous carbon possesses highly concentrated micropores(~2 nm)and a considerable amount of sub-micropores(<1 nm).The unique porous structure provides high specific surface area(1554 m^2 g^-1)and packing density(1.18 g cm^-3).The synergistic effects from the particular porous structure and optimal doping effectively enhance ion storage and ion/electron transport.As a result,the remarkable specific capacitances,including ultrahigh gravimetric and volumetric capacitances(430 F g^-1 and 507 F cm^-3 at 0.5 A g^-1),and excellent cycling and rate stability even at a high current density of 10 A g^-1(327 F g^-1 and 385 F cm^-3)are realized.Via compositing the porous carbon and BC skeleton,a robust all-solid-state cellulose-based supercapacitor presents super high areal energy density(~0.77 mWh cm^-2),volumetric energy density(~17.8 W L^-1),and excellent cyclic stability.

Versatile bifunctional nitrogen-doped porous carbon derived from biomass in catalytic reduction of 4-nitrophenol and oxidation of styrene

The scarcity and weak durability of metal,especially precious metal catalysts are big obstacles for their large-scale application in many reactions.The state-of-the-art of the catalytic science prefers such type of catalysts,which can replace metal-based catalysts to alleviate energy and environmental crises and exhibit catalytic performance comparable to or even exceeding these metal catalysts.Herein,we report that N-doped porous carbon(NKC)derived from cheap and abundant radish can be employed as versatile and efficient bifunctional catalysts in both the catalytic reduction of 4-nitrophenol(NRR)and oxidation of styrene(SOR).The series of NKC catalysts were prepared with a simple and facile one-pot strategy by coupling the N-doping,carbonization and KOH activation processes.These catalysts show hierarchical porosity,with the specific surface area,total pore volume and N-doping content ranging from 918.9-3062.7 m^2 g^-1,1.01-2.04 cm^3 g^-1 and 1.29-15.3 at%,respectively.Interestingly,our finding suggests that the catalytic performance is not directly related to these parameters but correlates positively with the content of graphitic N dopants,which is the dominant contributor for impelling both the NRR and SOR.Another intriguing finding is that for both reactions,the optimal catalyst was found to be the NKC-3-800 which possesses the highest graphitic N content of 3.13 at%.In addition,to gain insight into the catalytic behavior,analyses of kinetics and thermodynamics were performed,and the catalytic mechanisms were postulated.This work paves the way for the construction of biomass-derived N-doped carbon catalysts for bi-or even multi-functional applications in various organic reactions.

Gap models across micro-to mega-scales of time and space:examples of Tansley’s ecosystem concept

Background:Gap models are individual-based models for forests.They simulate dynamic multispecies assemblages over multiple tree-generations and predict forest responses to altered environmental conditions.Their development emphases designation of the significant biological and ecological processes at appropriate time/space scales.Conceptually,they are with consistent with A.G.Tansley’s original definition of"the ecosystem".Results:An example microscale application inspects feedbacks among terrestrial vegetation change,air-quality changes from the vegetation’s release of volatile organic compounds(VOC),and climate change effects on ecosystem production of VOC’s.Gap models can allocate canopy photosynthate to the individual trees whose leaves form the vertical leaf-area profiles.VOC release depends strongly on leaf physiology by species of these trees.Leaf-level VOC emissions increase with climate-warming.Species composition change lowers the abundance of VOC-emitting taxa.In interactions among ecosystem functions and biosphere/atmosphere exchanges,community composition responses can outweigh physiological responses.This contradicts previous studies that emphasize the warming-induced impacts on leaf function.As a mesoscale example,the changes in climate(warming)on forests including pest-insect dynamics demonstrates changes on the both the tree and the insect populations.This is but one of many cases that involve using a gap model to simulate changes in spatial units typical of sampling plots and scaling these to landscape and regional levels.As this is the typical application scale for gap models,other examples are identified.The insect/climatechange can be scaled to regional consequences by simulating survey plots across a continental or subcontinental zone.Forest inventories at these scales are often conducted using independent survey plots distributed across a region.Model construction that mimics this sample design avoids the difficulties in modelling spatial interactions,but we also discuss simulation at these scales with contagion effects.Conclusions:At the global-scale,successful simulations to date have used functional types of plants,rather than tree species.In a final application,the fine-scale predictions of a gap model are compared with data from micrometeorological eddy-covariance towers and then scaled-up to produce maps of global patterns of evapotranspiration,net primary production,gross primary production and respiration.New active-remote-sensing instruments provide opportunities to test these global predictions.

Unsupervised Graph-Based Tibetan Multi-Document Summarization

Text summarization creates subset that represents the most important or relevant information in the original content,which effectively reduce information redundancy.Recently neural network method has achieved good results in the task of text summarization both in Chinese and English,but the research of text summarization in low-resource languages is still in the exploratory stage,especially in Tibetan.What’s more,there is no large-scale annotated corpus for text summarization.The lack of dataset severely limits the development of low-resource text summarization.In this case,unsupervised learning approaches are more appealing in low-resource languages as they do not require labeled data.In this paper,we propose an unsupervised graph-based Tibetan multi-document summarization method,which divides a large number of Tibetan news documents into topics and extracts the summarization of each topic.Summarization obtained by using traditional graph-based methods have high redundancy and the division of documents topics are not detailed enough.In terms of topic division,we adopt two level clustering methods converting original document into document-level and sentence-level graph,next we take both linguistic and deep representation into account and integrate external corpus into graph to obtain the sentence semantic clustering.Improve the shortcomings of the traditional K-Means clustering method and perform more detailed clustering of documents.Then model sentence clusters into graphs,finally remeasure sentence nodes based on the topic semantic information and the impact of topic features on sentences,higher topic relevance summary is extracted.In order to promote the development of Tibetan text summarization,and to meet the needs of relevant researchers for high-quality Tibetan text summarization datasets,this paper manually constructs a Tibetan summarization dataset and carries out relevant experiments.The experiment results show that our method can effectively improve the quality of summarization and our method is competitive to previous unsupervised methods.

Comparison of different land-surface perturbation methods in short-range ensemble forecasts

In order to compare the sensitivity of short-range ensemble forecasts to different land-surface parameters in the South China region,three perturbation experiments related to the land surface model(LSM),initial soil moisture(ISM),and land–atmosphere coupling coefficient(LCC)were designed,and another control experiment driven by the Global Ensemble Forecast System(GEFS)was also performed.All ensemble members were initiated at 0000 UTC each day,and integrated for 24 h for a total of 40 days from the period 1 April to 10 May 2019 based on the Weather Research and Forecasting model.The results showed that the perturbation experiment of the LSM(LSMPE)had the largest ensemble spread,as well as the lowest ensemble-mean root-mean-square error among the three sets of land-surface perturbed experiments,which indicated that it could represent more uncertainty and less error.The ensemble spread of the perturbation experiment of the ISM(ISMPE)was generally less than that of LSMPE but greater than that of LCCPE(the perturbation experiment of the LCC).In particular,although the perturbation of the LCC could not produce greater spread,it had an effective influence on the intensity of precipitation.However,the ensemble spread of all the land-surface perturbed experiments was smaller than that of GEFSPE(the control experiment).Therefore,in future,land-surface perturbations and atmospheric perturbations should be combined in the design of ensemble forecasting systems to make the model represent more uncertainties.

人-地系统动力学耦合模型研究的评述与展望

人-地系统动力学耦合模型研究是当前全球变化与地球系统科学领域的前沿科学问题,为可持续发展提供重要理论基础和研究工具.本文回顾总结了目前国内外“人-地系统耦合模型”的研究状况.分析认为:目前已有的人-地系统动力学耦合模型只是将人类社会经济系统模型和地球系统模式在计算机载体上的模拟耦合;这类耦合模型工作在全球变暖、未来CO_(2)排放趋势、土地利用变化、经济反馈方面取得了重要进展,但仍存在系统间反馈过程复杂性刻画不明确、时空尺度不匹配、极端事件的模拟能力差等难题.攻克以上难题,需要发展多样化反馈过程、采用中等复杂程度的地球系统建模思想、应用反映真实供求关系形成的狭义均衡化人类系统模型和极端事件响应化的人地系统耦合模型、构建统一数据集进行集合模拟试验.人-地系统动力学耦合模型的研究将为科学制定气候变化适应政策提供科学依据,有利于推动我国跻身地球系统科学的国际前沿.

伤口负压联合自体富血小板凝胶治疗糖尿病足及皮肤软组织难愈性溃疡的疗效探讨

该研究探讨糖尿病足及皮肤软组织难愈性溃疡行创面负压联合自体富血小板凝胶治疗(APG)的疗效。选取2019年10月至2021年11月广西壮族自治区人民医院糖尿病足治疗中心收治的50例糖尿病足及皮肤软组织难愈性溃疡患者作为研究对象。采用辅助式负压闭合引流治疗(VAC)2周后,按治疗方法不同分为VAC联合APG治疗组(25例,足踝以下创面18例,膝以下、踝以上7例;Wagner 2级12例,3级13例)、VAC联合敷料换药组(25例,失访3例,最终纳入22例,足踝以下创面21例,膝以下、踝以上1例;Wagner 2级10例,3级12例)。VAC联合APG治疗组采用自体富血小板凝胶治疗,VAC联合敷料换药组予每日或隔日泡沫敷料或(和)藻酸盐敷料换药,通过创面多维度几何评价指标的变化观察两组治疗后愈合情况。组间比较采用独立样本t检验、秩和检验和χ2检验。结果显示,两组治疗前创面面积、创面周长、创面周长面积比差异均无统计学意义(P>0.05)。治疗2周后,VAC联合APG治疗组和VAC联合敷料换药组的创面面积缩小率(分别为46.84%±18.10%和19.14%±11.33%,P<0.001)、周长缩小率[分别为24.51%(18.89%,32.24%)和13.68%(3.81%,20.56%),P=0.002]、平均愈合速度[分别为0.24(0.13,0.43)和0.14(0.08,0.26)cm2/d,P=0.034],完全上皮化时间[分别为(56.32±21.55)d和(86.09±49.54)d,P=0.014],差异有统计学意义。以上结果提示,采用VAC联合自体富血小板凝胶治疗糖尿病足及皮肤软组织难愈性溃疡,创面愈合效果优于敷料换药治疗,是一种安全、有效、操作简单的治疗方法。

Influence of the carbon cycle on the attribution of responsibility for climate change

The carbon cycle is one of the fundamental climate change issues.Its long-term evolution largely affects the amplitude and trend of human-induced climate change,as well as the formulation and implementation of emission reduction policy and technology for stabilizing the atmospheric CO2concentration.Two earth system models incorporating the global carbon cycle,the Community Earth System Model and the Beijing Normal University-Earth System Model,were used to investigate the effect of the carbon cycle on the attribution of the historical responsibility for climate change.The simulations show that when compared with the criterion based on cumulative emissions,the developed(developing)countries’responsibility is reduced(increased)by 6%–10%using atmospheric CO2concentration as the criterion.This discrepancy is attributed to the fact that the developed world contributed approximately61%–68%(61%–64%)to the change in global oceanic(terrestrial)carbon sequestration for the period from 1850 to2005,whereas the developing world contributed approximately 32%–49%(36%–39%).Under a developed world emissions scenario,the relatively larger uptake of global carbon sinks reduced the developed countries’responsibility for carbon emissions but increased their responsibility for global ocean acidification(68%).In addition,the large emissions from the developed world reduced the efficiency of the global carbon sinks,which may affect the long-term carbon sequestration and exacerbate global warming in the future.Therefore,it is necessary to further consider the interaction between carbon emissions and the carbon cycle when formulating emission reduction policy.

Emerging low-dimensional materials for mid-infrared detection

Mid-infrared(IR)detectors based on the emerging low-dimensional(two-dimensional and quasi one-dimensional)materials offer unique characteristics including large bandgap tunability,optical polarization sensitivity and integrability with typical silicon process,which are not available in the mid-IR detectors based on traditional compound semiconductors.Here,we review the recent progress in study of mid-IR detectors based on the low-dimensional materials,including black phosphorus,black arsenic phosphorus,tellurene and BaTiS3,from the perspectives of crystal structure,material synthesis,optical properties,and the detector characteristics.The detector gain and detectivity are benchmarked,and the unique properties,such as the polarization sensitivity,are discussed.We also provide our perspective about key future research directions in this field.

Generalized spatial representation for digital modulation and its potential application

Constellation mapping has provided a great convenience to measure the performance of digital signal modulation in Euclid space. However, traditional in-phase and quadrature(IQ) plane is difficult to express the frequency modulation scheme such as minimum shift keying(MSK) and the time domain modulation such as cyclic code shift keying(CCSK). How to represent the digital signal modulation visually through constellation mapping is an attractive problem. To address this issue, in this paper, the combined frequency and phase modulation are utilized to define a new kind of constellation mapping, where the phase and frequency are quantized to the same elements. The uniform geometric construction for combined phase and frequency modulation is redefined in the 3D cylindrical coordinate system based on frequency(f), in-phase component(I) and quadrature component(Q). In the new coordinates, the quadrature frequency-phase shift keying(QFPSK) is produced by the QPSK with dimensional rotation matrix and denoted by the reduced dual quaternion. Furthermore, the spatial extension from QFPSK to chirp cyclic shift keying(Chirp CSK) is analyzed with bandwidth efficiency and energy efficiency. At last, the QFPSK is combined with the 2D OFDM, yielding the image OFDM system.Experimental results verify the effectiveness of QFPSK in the proposed system with the time-varying wireless channel and frequency selective fading channel respectively.

Enhanced electrical resistivity and mechanical properties in BCTZ-based composite ceramic

Environmental and human health concerns about lead toxicity have prompted the development of lead-free piezoceramics.Among them,(Ba_(0.85)Ca_(0.15))(Zr_(0.1)Ti_(0.9))O_(3)(BCTZ)with excellent piezoelectric properties has the most potential and attracts extensive attention.However,lack of concern toward electrical resistivity and mechanical properties has greatly hindered its practical application.Here,we report the achievement of enhanced insulation characteristics(grain electrical resistivity increased by one order of magnitude)and superior mechanical properties(Vickers hardness value increased by 40%)in Al_(2)O_(3)-added BCTZ composite ceramics.Such improvement can be attributed to specific composite microstructure,where the nonferroelectric second phase dispersed in the grain interior and grain boundary of BCTZ matrix results in blocking effect on the electric current paths as well as propagation of microcracks.These findings will pave a new way for the practical application of BCTZ ceramics.

ASIC2 Synergizes with TRPV1 in the Mechano-Electrical Transduction of Arterial Baroreceptors

Mechanosensitive ion channels(MSCs)are key molecules in the mechano-electrical transduction of arterial baroreceptors.Among them,acid-sensing ion channel 2(ASIC2)and transient receptor potential vanilloid subfamily member 1(TRPV1)have been studied extensively and documented to play important roles.In this study,experiments using aortic arch-aortic nerve preparations isolated from rats revealed that both ASIC2 and TRPV1 are functionally necessary,as blocking either abrogated nearly all pressure-dependent neural discharge.However,whether ASIC2 and TRPV1 work in coordination remained unclear.So we carried out cell-attached patch-clamp recordings in HEK293T cells co-expressing ASIC2 and TRPV1 and found that inhibition of ASIC2 completely blocked stretch-activated currents while inhibition of TRPV 1 only partially blocked these currents.Immunofluorescence staining of aortic arch-aortic adventitia from rats showed that ASIC2 and TRPV1 are co-localized in the aortic nerve endings,and co-immunoprecipitation assays confirmed that the two proteins form a compact complex in HEK293T cells and in baroreceptors.Moreover,protein modeling analysis,exogenous co-immunoprecipitation assays,and biotin pull-down assays indicated that ASIC2 and TRPV1 interact directly.In summary,our research suggests that ASIC2 and TRPV1 form a compact complex and function synergisti-cally in the mechano-electrical transduction of arterial baroreceptors.The model of synergism between MSCs may have important biological significance beyond ASIC2 and TRPV 1.

Hierarchical nano-on-micro copper with enhanced catalytic activity towards electro-oxidation of hydrazine

The electrochemical properties of catalyst materials are highly dependent on the materials structure and architecture. Herein, nano-on-micro Cu electrodes are fabricated by growing Cu microcrystals on Ni foam substrate, followed by introducing Cu nanocrystals onto the surface of the Cu microcrystals. The introduction of Cu nanocrystals onto the surface of Cu microcrystals is shown to dramatically increase the electrochemically active surface area and thus significantly enhances the catalytic activity of the catalyst electrode towards electro-oxidation of hydrazine. The onset potential （-1.04 V vs. AglAgCI） of the nano-on-micro Cu electrode is lower than those of the reported Cu-based catalysts under similar testing conditions, and a current density of 16 mA-cm-2, which is 2 times that of the microsized Cu electrode, is achieved at a potential of -0.95 V vs. Ag/AgCh Moreover, the nano-on-micro Cu electrode demonstrates good long-term stability.

Framework to Identify Protein Complexes Based on Similarity Preclustering

Proteins interact with each other to form protein complexes, and cell functionality depends on both protein interactions and these complexes. Based on the assumption that protein complexes are highly connected and correspond to the dense regions in Protein-protein Interaction Networks（PINs）, many methods have been proposed to identify the dense regions in PINs. Because protein complexes may be formed by proteins with similar properties,such as topological and functional properties, in this paper, we propose a protein complex identification framework（KCluster）. In KCluster, a PIN is divided into K subnetworks using a K-means algorithm, and each subnetwork comprises proteins of similar degrees. We adopt a strategy based on the expected number of common neighbors to detect the protein complexes in each subnetwork. Moreover, we identify the protein complexes spanning two subnetworks by combining closely linked protein complexes from different subnetworks. Finally, we refine the predicted protein complexes using protein subcellular localization information. We apply KCluster and nine existing methods to identify protein complexes from a highly reliable yeast PIN. The results show that KCluster achieves higher Sn and Sp values and f-measures than other nine methods. Furthermore, the number of perfect matches predicted by KCluster is significantly higher than that of other nine methods.

Non-equilibrium and stochasticity influence the activation process of the yeast DNA damage pathway

Living cells are open systems that exist far away from a state of thermodynamical equilibrium. They utilize the high-grade chemical energy provided by food to produce ATP and re- lease ADP and Pi together with heat dissipation. Living cells exist in a non-equilibrium steady state （NESS）, they replicate themselves and respond to various environmental changes via signal transduction pathways. Because the majority of cells exist at room temperature, the stochasticity of chemical reac- tions in the cells is unavoidable. Recent research into fluores- cent proteins and microscopy techniques have enabled us to observe the dynamic process of mRNA and proteins in single living bacterial cells [1], and these have resulted in new in- sights into regulation mechanisms in molecular biology, i.e., in cellular signal transduction pathways.