改进的tan■-展开法和几类非线性分数阶发展方程

运用改进的tan■-展开法,以一阶常系数微分方程为辅助方程,结合齐次平衡原理,研究了非线性分数阶Khokhlov-Zabolotskaya-Kuznetsov(KZK)方程、非线性分数阶foam drainage方程、非线性分数阶Jimbo-Miwa(JM)方程.借助符号计算系统Maple,求出了方程的多种精确解,这些解包括周期解、孤子解、有理函数解、指数函数解,扩大了解的范围.

No effect of invasive tree species on aboveground biomass increments of oaks and pines in temperate forests

Prunus serotina and Robinia pseudoacacia are the most widespread invasive trees in Central Europe.In addition,according to climate models,decreased growth of many economically and ecologically important native trees will likely be observed in the future.We aimed to assess the impact of these two neophytes,which differ in the biomass range and nitrogen-fixing abilities observed in Central European conditions,on the relative aboveground biomass increments of native oaks Qucrcus robur and Q.petraea and Scots pine Pinus sylvestris.We aimed to increase our understanding of the relationship between facilitation and competition between woody alien species and overstory native trees.We established 72 circular plots(0.05 ha)in two different forest habitat types and stands varying in age in western Poland.We chose plots with different abundances of the studied neophytes to determine how effects scaled along the quantitative invasion gradient.Furthermore,we collected growth cores of the studied native species,and we calculated aboveground biomass increments at the tree and stand levels.Then,we used generalized linear mixed-effects models to assess the impact of invasive species abundances on relative aboveground biomass increments of native tree species.We did not find a biologically or statistically significant impact of invasive R.pseudoacacia or P.serotina on the relative aboveground,biomass increments of native oaks and pines along the quantitative gradient of invader biomass or on the proportion of total stand biomass accounted for by invaders.The neophytes did not act as native tree growth stimulators but also did not compete with them for resources,which would escalate the negative impact of climate change on pines and oaks.The neophytes should not significantly modify the carbon sequestration capacity of the native species.Our work combines elements of the per capita effect of invasion with research on mixed forest management.

A Novel Incremental Attribute Reduction Algorithm Based on Intuitionistic Fuzzy Partition Distance

Attribute reduction,also known as feature selection,for decision information systems is one of the most pivotal issues in machine learning and data mining.Approaches based on the rough set theory and some extensions were proved to be efficient for dealing with the problemof attribute reduction.Unfortunately,the intuitionistic fuzzy sets based methods have not received much interest,while these methods are well-known as a very powerful approach to noisy decision tables,i.e.,data tables with the low initial classification accuracy.Therefore,this paper provides a novel incremental attribute reductionmethod to dealmore effectivelywith noisy decision tables,especially for highdimensional ones.In particular,we define a new reduct and then design an original attribute reduction method based on the distance measure between two intuitionistic fuzzy partitions.It should be noted that the intuitionistic fuzzypartitiondistance iswell-knownas aneffectivemeasure todetermine important attributes.More interestingly,an incremental formula is also developed to quickly compute the intuitionistic fuzzy partition distance in case when the decision table increases in the number of objects.This formula is then applied to construct an incremental attribute reduction algorithm for handling such dynamic tables.Besides,some experiments are conducted on real datasets to show that our method is far superior to the fuzzy rough set based methods in terms of the size of reduct and the classification accuracy.

DR-IS:Dynamic Response Incremental Scheduling in Time-Sensitive Network

Time-Sensitive Network(TSN)with deterministic transmission capability is increasingly used in many emerging fields.It mainly guarantees the Quality of Service(QoS)of applications with strict requirements on time and security.One of the core features of TSN is traffic scheduling with bounded low delay in the network.However,traffic scheduling schemes in TSN are usually synthesized offline and lack dynamism.To implement incremental scheduling of newly arrived traffic in TSN,we propose a Dynamic Response Incremental Scheduling(DR-IS)method for time-sensitive traffic and deploy it on a software-defined time-sensitive network architecture.Under the premise of meeting the traffic scheduling requirements,we adopt two modes,traffic shift and traffic exchange,to dynamically adjust the time slot injection position of the traffic in the original scheme,and determine the sending offset time of the new timesensitive traffic to minimize the global traffic transmission jitter.The evaluation results show that DRIS method can effectively control the large increase of traffic transmission jitter in incremental scheduling without affecting the transmission delay,thus realizing the dynamic incremental scheduling of time-sensitive traffic in TSN.

Improving Network Availability through Optimized Multipath Routing and Incremental Deployment Strategies

Currently,distributed routing protocols are constrained by offering a single path between any pair of nodes,thereby limiting the potential throughput and overall network performance.This approach not only restricts the flow of data but also makes the network susceptible to failures in case the primary path is disrupted.In contrast,routing protocols that leverage multiple paths within the network offer a more resilient and efficient solution.Multipath routing,as a fundamental concept,surpasses the limitations of traditional shortest path first protocols.It not only redirects traffic to unused resources,effectively mitigating network congestion,but also ensures load balancing across the network.This optimization significantly improves network utilization and boosts the overall performance,making it a widely recognized efficient method for enhancing network reliability.To further strengthen network resilience against failures,we introduce a routing scheme known as Multiple Nodes with at least Two Choices(MNTC).This innovative approach aims to significantly enhance network availability by providing each node with at least two routing choices.By doing so,it not only reduces the dependency on a single path but also creates redundant paths that can be utilized in case of failures,thereby enhancing the overall resilience of the network.To ensure the optimal placement of nodes,we propose three incremental deployment algorithms.These algorithms carefully select the most suitable set of nodes for deployment,taking into account various factors such as node connectivity,traffic patterns,and network topology.By deployingMNTCon a carefully chosen set of nodes,we can significantly enhance network reliability without the need for a complete overhaul of the existing infrastructure.We have conducted extensive evaluations of MNTC in diverse topological spaces,demonstrating its effectiveness in maintaining high network availability with minimal path stretch.The results are impressive,showing that even when implemented on just 60%of nodes,our incremental deployment method significantly boosts network availability.This underscores the potential of MNTC in enhancing network resilience and performance,making it a viable solution for modern networks facing increasing demands and complexities.The algorithms OSPF,TBFH,DC and LFC perform fast rerouting based on strict conditions,while MNTC is not restricted by these conditions.In five real network topologies,the average network availability ofMNTCis improved by 14.68%,6.28%,4.76%and 2.84%,respectively,compared with OSPF,TBFH,DC and LFC.

Hyperspectral Image Super-Resolution Network Based on Reinforcing Inter-Spectral Incremental Information

Hyperspectral images typically have high spectral resolution but low spatial resolution,which impacts the reliability and accuracy of subsequent applications,for example,remote sensingclassification and mineral identification.But in traditional methods via deep convolution neural net-works,indiscriminately extracting and fusing spectral and spatial features makes it challenging toutilize the differentiated information across adjacent spectral channels.Thus,we proposed a multi-branch interleaved iterative upsampling hyperspectral image super-resolution reconstruction net-work(MIIUSR)to address the above problems.We reinforce spatial feature extraction by integrat-ing detailed features from different receptive fields across adjacent channels.Furthermore,we pro-pose an interleaved iterative upsampling process during the reconstruction stage,which progres-sively fuses incremental information among adjacent frequency bands.Additionally,we add twoparallel three dimensional(3D)feature extraction branches to the backbone network to extractspectral and spatial features of varying granularity.We further enhance the backbone network’sconstruction results by leveraging the difference between two dimensional(2D)channel-groupingspatial features and 3D multi-granularity features.The results obtained by applying the proposednetwork model to the CAVE test set show that,at a scaling factor of×4,the peak signal to noiseratio,spectral angle mapping,and structural similarity are 37.310 dB,3.525 and 0.9438,respec-tively.Besides,extensive experiments conducted on the Harvard and Foster datasets demonstratethe superior potential of the proposed model in hyperspectral super-resolution reconstruction.

Ethical Decision-Making Framework Based on Incremental ILP Considering Conflicts

Humans are experiencing the inclusion of artificial agents in their lives,such as unmanned vehicles,service robots,voice assistants,and intelligent medical care.If the artificial agents cannot align with social values or make ethical decisions,they may not meet the expectations of humans.Traditionally,an ethical decision-making framework is constructed by rule-based or statistical approaches.In this paper,we propose an ethical decision-making framework based on incremental ILP(Inductive Logic Programming),which can overcome the brittleness of rule-based approaches and little interpretability of statistical approaches.As the current incremental ILP makes it difficult to solve conflicts,we propose a novel ethical decision-making framework considering conflicts in this paper,which adopts our proposed incremental ILP system.The framework consists of two processes:the learning process and the deduction process.The first process records bottom clauses with their score functions and learns rules guided by the entailment and the score function.The second process obtains an ethical decision based on the rules.In an ethical scenario about chatbots for teenagers’mental health,we verify that our framework can learn ethical rules and make ethical decisions.Besides,we extract incremental ILP from the framework and compare it with the state-of-the-art ILP systems based on ASP(Answer Set Programming)focusing on conflict resolution.The results of comparisons show that our proposed system can generate better-quality rules than most other systems.

Supplementation of Lycium barbarum residue increases the growth rate of Tan sheep by enhancing their feed intake and regulating their rumen microbiome and metabolome

Lycium barbarum residue(LBR),a by-product of L.barbarum processing,is packed with bioactive components and can be potentially utilized as a feed additive in animal husbandry.However,the fundamental understanding of its effectiveness on livestock animals is still lacking,particularly in ruminants.To explore the effects of LBR on the growth performance,rumen fermentation parameters,ruminal microbes and metabolites of Tan sheep,sixteen fattening rams(aged 4 mon)were fed a basal diet(CON,n=8)or a basal diet supplemented with 5%LBR(LBR,n=8).The experiment lasted for 70 d,with 10 d adaptation period and 60 d treatment period.The results showed that the LBR enhanced the average daily feed intake,average daily gain(P<0.05),and ruminal total volatile fatty acids(P<0.01)while decreasing ammonia-nitrogen concentration and rumen pH value(P<0.05).Additionally,the LBR improved the relative abundances of Prevotella,Succiniclasticum,Ruminococcus,Coprococcus,Selenomonas,and Butyrivibrio(P<0.05)and reduced the relative abundances of Oscillospira and Succinivibrio(P<0.05).The LBR altered the ruminal metabolome(P<0.01)by increasing the abundances of ruminal metabolites involved in amino acids(e.g.,L-proline,L-phenylalanine,L-lysine,and L-tyrosine),pyrimidine metabolism(e.g.,uridine,uracil,and thymidine),and microbial protein synthesis(e.g.,xanthine and hypoxanthine).In conclusion,LBR had positive effects on the growth rate of Tan sheep as well as on rumen fermentation parameters,rumen microbiome and rumen metabolome.

CNN-LSTM based incremental attention mechanism enabled phase-space reconstruction for chaotic time series prediction

To improve the prediction accuracy of chaotic time series and reconstruct a more reasonable phase space structure of the prediction network,we propose a convolutional neural network-long short-term memory(CNN-LSTM)prediction model based on the incremental attention mechanism.Firstly,a traversal search is conducted through the traversal layer for finite parameters in the phase space.Then,an incremental attention layer is utilized for parameter judgment based on the dimension weight criteria(DWC).The phase space parameters that best meet DWC are selected and fed into the input layer.Finally,the constructed CNN-LSTM network extracts spatio-temporal features and provides the final prediction results.The model is verified using Logistic,Lorenz,and sunspot chaotic time series,and the performance is compared from the two dimensions of prediction accuracy and network phase space structure.Additionally,the CNN-LSTM network based on incremental attention is compared with long short-term memory(LSTM),convolutional neural network(CNN),recurrent neural network(RNN),and support vector regression(SVR)for prediction accuracy.The experiment results indicate that the proposed composite network model possesses enhanced capability in extracting temporal features and achieves higher prediction accuracy.Also,the algorithm to estimate the phase space parameter is compared with the traditional CAO,false nearest neighbor,and C-C,three typical methods for determining the chaotic phase space parameters.The experiments reveal that the phase space parameter estimation algorithm based on the incremental attention mechanism is superior in prediction accuracy compared with the traditional phase space reconstruction method in five networks,including CNN-LSTM,LSTM,CNN,RNN,and SVR.

Grouping tree species to estimate basal area increment in temperate multispecies forests in Durango,Mexico

Multispecies forests have received increased scientific attention,driven by the hypothesis that biodiversity improves ecological resilience.However,a greater species diversity presents challenges for forest management and research.Our study aims to develop basal area growth models for tree species cohorts.The analysis is based on a dataset of 423 permanent plots(2,500 m^(2))located in temperate forests in Durango,Mexico.First,we define tree species cohorts based on individual and neighborhood-based variables using a combination of principal component and cluster analyses.Then,we estimate the basal area increment of each cohort through the generalized additive model to describe the effect of tree size,competition,stand density and site quality.The principal component and cluster analyses assign a total of 37 tree species to eight cohorts that differed primarily with regard to the distribution of tree size and vertical position within the community.The generalized additive models provide satisfactory estimates of tree growth for the species cohorts,explaining between 19 and 53 percent of the total variation of basal area increment,and highlight the following results:i)most cohorts show a"rise-and-fall"effect of tree size on tree growth;ii)surprisingly,the competition index"basal area of larger trees"had showed a positive effect in four of the eight cohorts;iii)stand density had a negative effect on basal area increment,though the effect was minor in medium-and high-density stands,and iv)basal area growth was positively correlated with site quality except for an oak cohort.The developed species cohorts and growth models provide insight into their particular ecological features and growth patterns that may support the development of sustainable management strategies for temperate multispecies forests.

Selective and Adaptive Incremental Transfer Learning with Multiple Datasets for Machine Fault Diagnosis

The visions of Industry 4.0 and 5.0 have reinforced the industrial environment.They have also made artificial intelligence incorporated as a major facilitator.Diagnosing machine faults has become a solid foundation for automatically recognizing machine failure,and thus timely maintenance can ensure safe operations.Transfer learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model,which typically involves two datasets.In response to the availability of multiple datasets,this paper proposes using selective and adaptive incremental transfer learning(SA-ITL),which fuses three algorithms,namely,the hybrid selective algorithm,the transferability enhancement algorithm,and the incremental transfer learning algorithm.It is a selective algorithm that enables selecting and ordering appropriate datasets for transfer learning and selecting useful knowledge to avoid negative transfer.The algorithm also adaptively adjusts the portion of training data to balance the learning rate and training time.The proposed algorithm is evaluated and analyzed using ten benchmark datasets.Compared with other algorithms from existing works,SA-ITL improves the accuracy of all datasets.Ablation studies present the accuracy enhancements of the SA-ITL,including the hybrid selective algorithm(1.22%-3.82%),transferability enhancement algorithm(1.91%-4.15%),and incremental transfer learning algorithm(0.605%-2.68%).These also show the benefits of enhancing the target model with heterogeneous image datasets that widen the range of domain selection between source and target domains.

CAN INCREMENTAL POLICIES SOLVE EMPLOYMENT PROBLEMS?

On October 8,the Information Office of the State Council of the People’s Republic of China held a press conference to outline the steps of“systematically implementing a package of incremental policies to solidly promote economic growth,structural optimization and sustained momentum of development”in the wake of the Third Plenary Session of the 20th Central Committee of the Communist Party of China.The series of policy measures mentioned at the press conference all demonstrated strong continuity,stability,and sustainability.The package of incremental policies reflects three“more attentions,”namely:more attention on improving the quality of economic development,more attention on supporting the healthy development of the real economy and business entities,and more attention on coordinating high-quality development and high-level security.One central goal is to promote high-quality full employment.

A Hybrid Optimization Approach of Single Point Incremental Sheet Forming of AISI 316L Stainless Steel Using Grey Relation Analysis Coupled with Principal Component Analysiss

We investigated the parametric optimization on incremental sheet forming of stainless steel using Grey Relational Analysis(GRA) coupled with Principal Component Analysis(PCA). AISI 316L stainless steel sheets were used to develop double wall angle pyramid with aid of tungsten carbide tool. GRA coupled with PCA was used to plan the experiment conditions. Control factors such as Tool Diameter(TD), Step Depth(SD), Bottom Wall Angle(BWA), Feed Rate(FR) and Spindle Speed(SS) on Top Wall Angle(TWA) and Top Wall Angle Surface Roughness(TWASR) have been studied. Wall angle increases with increasing tool diameter due to large contact area between tool and workpiece. As the step depth, feed rate and spindle speed increase,TWASR decreases with increasing tool diameter. As the step depth increasing, the hydrostatic stress is raised causing severe cracks in the deformed surface. Hence it was concluded that the proposed hybrid method was suitable for optimizing the factors and response.

AiTAN与涂鸦智能达成合作,加速构建东南亚智慧商业生态

1月27日,泰国领先的智慧解决方案提供商AiTAN与涂鸦智能在AiTAN品牌发布会上宣布达成合作。双方将联合打造AiTAN品牌在全屋&地产、酒店、租住、商照、办公等领域的全新智慧解决方案,为东南亚客户及用户带来更舒心的智慧体验。

面心立方结构TaN_δ薄膜功率电阻研究

TaN薄膜电阻由于具有高的微波承受功率、低的电阻温度系数以及良好的化学稳定性，是目前常用的功率电阻。在Ar／N2混合气氛下，利用直流反应溅射制备了面心立方结构的TaN薄膜功率电阻。实验发现，当Ta／N原子比在0．9～1．2之间时，可以形成比较好的面心立方结构TaN。其电阻温度系数约为-1．86‰／℃，对于6mm（长）×0．3mm（宽）×0．25μm（厚）的薄膜电阻，微波承受功率超过3W，而Ta薄膜电阻承受微波功率小于2W。

Incremental Placement Algorithm for Standard-Cell Layout

A new algorithm W ECOP is presented to effect incremental changes on a standard cell layout automatically.This algorithm deals with cell inserting and cell moving based on rows instead of on cells as most placement algorithms usually do.An integer programming problem is formulated to minimize the adjustment on the initial placement and a heuristic method is presented to search for a shifting path so as to optimize the wirelength.Test of W ECOP on a group of practical test cases shows that the algorithm can successfully accomplish incremental placement with good quality and high speed.

Correlation of eco-hydrographic benefit and height increment of Robinia pseudoacacia stand with climatic environmental factors in Yellow River Delta Wetland of China

The relationship between eco-hydrographic benefit of forest vegetation and climatic environmental factors is one of the focuses in the research on environmental protection and ecosystem countermeasures in Wetland. The runoff, sediment and soil moisture rate dynamics in Robinia pseudoacacia plantation and its clearcut area were investigated in the natural runoff experiment plots in Yellow River Delta Wet- land, Shandong Province, China. The correlation of height increment ofR. pseudoacacia with nine climate factors such as light, water, heat, etc. was analyzed by stepwise regression analysis. The results showed that the amounts of runoff and sediment in clearcut area of R. pseudoacacia were 53.9%-150.8% and 172.8%-387.1% higher than that in Robinia pseudoacacia plantation, respectively. The runoff peak value in R. pseudoacacia stand was obviously lower than that in clerarcut area, meantime, the occurrence of runoffpeak in R. pseudoacacia stand was 25 min later than in its clerarcut area. The soil moisture rates in R. pseudoacacia stand and its clearcut varied periodically with annual rainfall precipitation in both dry season and humid season. The annual mean soil moisture rate in R. pseudoacacia stand was 23.3%-25.6% higher than that in its clearcut area. Meanwhile, a regression model reflecting the correlation between the height increment of R. pseudoacacia and climatic factors was developed by stepwise regression procedure method. It showed that the light was the most important factor for the height increment ofR. pseudoacacia, followed by water and heat factors.

结肠癌组织中THBS2、SLC11A1基因表达及与TANs浸润的关系

目的分析结肠癌组织中血小板反应蛋白2(THBS2)溶质载体家族11成员1(SLC11A1)表达及与肿瘤相关性中性粒细胞(TANs)浸润的关系。方法选取2020年1月-2022年4月在泉州市第一医院进行手术切除的120例结肠癌患者的肿瘤组织及癌旁组织,用免疫组化法检测结肠癌组织及癌旁组织中THBS2、SLC11A1表达,收集患者临床病理特征,分析THBS2、SLC11A1表达与结肠癌患者临床病理特征的关系,分析THBS2、SLC11A1与TANs浸润密度的相关性。结果THBS2在结肠癌组织中主要表达于细胞质,SLC11A1在结肠癌组织中主要表达于细胞膜中,染色均呈现棕黄色;相较于癌旁正常组织,结肠癌组织中THBS2表达明显降低,SLC11A1表达明显升高。结肠癌组织THBS2表达与分化程度、淋巴结转移、TNM分期、血管浸润具有明显相关性(P<0.05);结肠癌组织SLC11A1表达与淋巴结转移、肿瘤浸润深度、病理分型、TNM分期、血管浸润具有明显相关性(P<0.05)。结肠癌组织中THBS2表达与TANs浸润密度呈负相关(r=-0.444,P<0.05),SLC11A1表达与TANs浸润密度呈正相关(r=0.355,P<0.05)。结论在结肠癌组织中THBS2、SLC11A1均呈现异常表达,与TANs浸润相互作用共同促进结肠癌的恶性进程,推测两指标可作为潜在的结肠癌临床预后因子。

Disentangling the factors that contribute to variation in forest biomass increments in the mid-subtropical forests of China

Mid-subtropical forests are the main vegetation type of global terrestrial biomes, and are critical for maintaining the global carbon balance. However, estimates of forest biomass increment in mid-subtropical forests remain highly uncertain. It is critically important to determine the relative importance of different biotic and abiotic factors between plants and soil, particularly with respect to their influence on plant regrowth. Consequently,it is necessary to quantitatively characterize the dynamicspatiotemporal distribution of forest carbon sinks at a regional scale. This study used a large, long-term dataset in a boosted regression tree（BRT） model to determine the major components that quantitatively control forest biomass increments in a mid-subtropical forested region（Wuyishan National Nature Reserve, China）. Long-term,stand-level data were used to derive the forest biomass increment, with the BRT model being applied to quantify the relative contributions of various biotic and abiotic variables to forest biomass increment. Our data show that total biomass（t） increased from 4.62 9 106 to 5.30 9 106 t between 1988 and 2010, and that the mean biomass increased from 80.19 ± 0.39 t ha-1（mean ± standard error） to 94.33 ± 0.41 t ha-1in the study region. The major factors that controlled biomass（in decreasing order of importance） were the stand, topography, and soil. Stand density was initially the most important stand factor, while elevation was the most important topographic factor. Soil factors were important for forest biomass increment but have a much weaker influence compared to the other two controlling factors. These results provide baseline information about the practical utility of spatial interpolationmethods for mapping forest biomass increments at regional scales.

Determination of Specific Refractive Index Increment (dn/dc)_μ at a Constant Chemical Potential,for Solutions of Polymer in Mixed Solvents by the GPC Method

A new chromatographic method is described for the determination of specific refractive index increment（dn/dc）μ at a constant chemical potential,for polymer/mixed solvent systems.In this method the（dn/dc）is obtained by measuring the areas of solvated-polymer peaks when the mixed solvent is used as an eluent.Values of（dn/dc）μ for the poly（dimethylsiloxane）（PDMS）-benzene-methanol system,determined by the proposed method are in good agreement with those determined by the conventional dialysis method.The new approach has the advantages of simplicity,fast speed,and high reproducibility.The experimental results for stearic acid-chloroform-methanol system show that this method can also be applied to nonpolymer/mixed solvent systems for the determination of（dn/dc）μ.