Examination of Silicate Limitation of Primary Production in Jiaozhou Bay,North ChinaⅢ.Judgment Method,Rules and Uniqueness of Nutrient Limitation Among N,P,and Si

Analysis and comparison of Jiaozhou Bay data collected from May 1991 to February 1994(12 seasonal investigations) provided by the Ecological Station of Jiaozhou Bay revealed the characteristic spatiotemporal variation of the ambient concentration Si:DIN and Si:16P ratios and the seasonal variation of Jiaozhou Bay Si:DIN and Si:16P ratios showing that the Si:DIN ratios were < 1 throughout the year in Jiaozhou Bay; and that the Si:16P ratios were < 1 throughout Jiaozhou Bay in spring, autumn and winter. The results proved that silicate limited phytoplankton growth in spring, autumn and winter in Jiaozhou Bay. Analysis of the Si:DIN and Si:P ratios showed that the nutrient Si has been limiting the growth of phytoplankton throughout the year in some Jiaozhou Bay waters; and that the silicate deficiency changed the phytoplankton assemblage structure. Analysis of discontinuous 1962 to 1998 nutrient data showed that there was no N or P limitation of phytoplankton growth in that period. The authors consider that the annual cyclic change of silicate limits phytoplankton growth in spring, autumn and winter every year in Jiaozhou Bay; and that in many Jiaozhou Bay waters where the phytoplankton as the predominant species need a great amount of silicate, analysis of the nutrients N or P limitation of phytoplankton growth relying only on the N and P nutrients and DIN:P ratio could yield inaccurate conclusions. The results obtained by applying the rules of absolute and relative limitation fully support this view. The authors consider that the main function of nutrient silicon is to regulate and control the mechanism of the phytoplankton growth process in the ecological system in estuaries, bays and the sea. The authors consider that according to the evolution theory of Darwin, continuous environmental pressure gradually changes the phytoplankton assemblage's structure and the physiology of diatoms. Diatoms requiring a great deal of silicon either constantly decrease or reduce their requirement for silicon. This will cause a series of huge changes in the ecosystem so that the whole ecosystem requires continuous renewal, change and balancing. Human beings have to reduce marine pollution and enhance the capacity of continental sources to transport silicon to sustain the continuity and stability in the marine ecosystem.nt

Romanian legal management rules limit wood production in Norway spruce and beech forests

Background： The quantitative impact of forest management on forests＇ wood resource was evaluated for Picea and Fagus mixed forests. The effects on the productivity of tendering operations, thinnings and rotation length have seldom been directly quantified on landscape scale. Methods： Two sites of similar fertility but subject to contrasted forest management were studied with detailed inventories： one in Germany, the other in Romania, and compared with the respective national forest inventories. In Romania, regulations impose very long rotations, low thinnings and a period of no-cut before harvest. In contrast, tending and thinnings are frequent and intense in Germany. Harvests start much earlier and must avoid clear cutting but maintain a permanent forest cover with natural regeneration. While Germany has an average annual wood increment representative for Central Europe, Romania represents the average for Eastern Europe. Results： The lack of tending and thinning in the Romanian site resulted in twice as many trees per hectare as in the German site for the same age. The productivity in Romanian production forests was 20 % lower than in Germany despite a similar fertility. The results were supported by the data from the national forest inventory of each country, which confirmed that the same differential exists at country scale. Furthermore, provided the difference in rotation length, two crops are harvested in Germany when only one is harvested in Romania. The losses of production due to a lower level of management in Romania where estimated to reach 12.8 million m3.y-1 in regular mountain production forests, and to 15 million m3.y-1 if managed protection forest is included. Conclusions： The productivity of Picea and Fagus mountain forests in Romania is severely depressed by the lack of tending and thinning, by overly long rotations and the existence of a 25-years no-cut period prior to harvest. The average standing volume in Germany was 50 % lower than in Romania, but the higher harvesting rate resulted in more than doubling wood production. Considering the mitigation effects of climate change by forests, it emerges that the increase in standing volume of forests in Romania is smaller than the additional harvest in Germany which serves fossil fuel substitution.

Performance Limits and Advancements in Single 2D Transition Metal Dichalcogenide Transistor

Two-dimensional(2D)transition metal dichalcogenides(TMDs)allow for atomic-scale manipulation,challenging the conventional limitations of semiconductor materials.This capability may overcome the short-channel effect,sparking significant advancements in electronic devices that utilize 2D TMDs.Exploring the dimension and performance limits of transistors based on 2D TMDs has gained substantial importance.This review provides a comprehensive investigation into these limits of the single 2D-TMD transistor.It delves into the impacts of miniaturization,including the reduction of channel length,gate length,source/drain contact length,and dielectric thickness on transistor operation and performance.In addition,this review provides a detailed analysis of performance parameters such as source/drain contact resistance,subthreshold swing,hysteresis loop,carrier mobility,on/off ratio,and the development of p-type and single logic transistors.This review details the two logical expressions of the single 2D-TMD logic transistor,including current and voltage.It also emphasizes the role of 2D TMD-based transistors as memory devices,focusing on enhancing memory operation speed,endurance,data retention,and extinction ratio,as well as reducing energy consumption in memory devices functioning as artificial synapses.This review demonstrates the two calculating methods for dynamic energy consumption of 2D synaptic devices.This review not only summarizes the current state of the art in this field but also highlights potential future research directions and applications.It underscores the anticipated challenges,opportunities,and potential solutions in navigating the dimension and performance boundaries of 2D transistors.

Cosmology-Independent Photon Mass Limits from Localized Fast Radio Bursts by Using Artificial Neural Networks

A hypothetical photon mass m_(γ) can produce a frequency-dependent vacuum dispersion of light, which leads to an additional time delay between photons with different frequencies when they propagate through a fixed distance. The dispersion measure and redshift measurements of fast radio bursts(FRBs) have been widely used to constrain the rest mass of the photon. However, all current studies analyzed the effect of the frequency-dependent dispersion for massive photons in the standard ΛCDM cosmological context. In order to alleviate the circularity problem induced by the presumption of a specific cosmological model based on the fundamental postulate of the masslessness of photons, here we employ a new model-independent smoothing technique, artificial neural network(ANN), to reconstruct the Hubble parameter H(z) function from 34 cosmic-chronometer measurements.By combining observations of 32 well-localized FRBs and the H(z) function reconstructed by ANN, we obtain an upper limit of m_(γ) ≤ 3.5 × 10^(-51)kg, or equivalently m_(γ) ≤ 2.0 × 10^(-15)eV/c^(2)(m_(γ) ≤ 6.5 × 10^(-51)kg, or equivalently m_(γ) ≤ 3.6 × 10^(-15)eV/c_(2)) at the 1σ(2σ) confidence level. This is the first cosmology-independent photon mass limit derived from extragalactic sources.

Semantic Consistency and Correctness Verification of Digital Traffic Rules

The consensus of the automotive industry and traffic management authorities is that autonomous vehicles must follow the same traffic laws as human drivers.Using formal or digital methods,natural language traffic rules can be translated into machine language and used by autonomous vehicles.In this paper,a translation flow is designed.Beyond the translation,a deeper examination is required,because the semantics of natural languages are rich and complex,and frequently contain hidden assumptions.The issue of how to ensure that digital rules are accurate and consistent with the original intent of the traffic rules they represent is both significant and unresolved.In response,we propose a method of formal verification that combines equivalence verification with model checking.Reasonable and reassuring digital traffic rules can be obtained by utilizing the proposed traffic rule digitization flow and verification method.In addition,we offer a number of simulation applications that employ digital traffic rules to assess vehicle violations.The experimental findings indicate that our digital rules utilizing metric temporal logic(MTL)can be easily incorporated into simulation platforms and autonomous driving systems(ADS).

Study on the Medication Rules of Traditional Chinese Medicine Prescriptions for the Treatment of New Crown Pneumonia Based on Data Mining Technology

[Objectives]This study was conducted to analyze the medication rules of clinical prescriptions of traditional Chinese medicine decoction pieces for the treatment of novel coronavirus pneumonia(COVID-19)during the epidemic in multiple regions based on data mining technology,so as to provide a reference for the treatment of COVID-19 with traditional Chinese medicine.[Methods]The traditional Chinese medicine prescriptions used since the outbreak of COVID-19 in Hubei Province during the fight against the epidemic from February 25,2020 to February 14,2022,the traditional Chinese medicine prescriptions used by Guizhou traditional Chinese medicine expert team aiding Hubei Province,the traditional Chinese medicine prescriptions for rehabilitation and conditioning of patients in Ezhou of Hubei Province after discharge,the traditional Chinese medicine prescriptions for the prevention and treatment of COVID-19 in Guizhou Province,and the traditional Chinese medicine prescriptions for the treatment of COVID-19 collected from the end of 2019 to the present from the Chinese database of CNKI were collected as the data of this study.Excel was used to establish a database and enter it into the TCM inheritance calculation platform V3.5,and the association rules and k-means clustering algorithm were used to analyze the frequency of herbal medicines in prescriptions during the treatment of COVID-19,the frequency of four natures,five flavors,meridian distribution,and drug combinations.[Results]A total of 1859 COVID-19 patients treated with traditional Chinese medicine were included,and the proportion of males was higher than that of females,and middle-aged and elderly people were the most common group.A total of 2170 prescriptions of traditional Chinese medicine were included,involving a total of 383 traditional Chinese medicines.High-frequency medicines included poria,Radix Bupleuri,Radix Scutellariae,Herba Pogostemonis,Fructus Forsythiae,Flos Loniceraeetc.The four natures were mainly concentrated in cold,warm and neutral,and the five flavors were mainly concentrated in bitter,pungent and sweet.The herbal medicines were mainly attributed to the lungs and stomach meridians,and were mainly of heat-clearing,exterior syndrome-relieving and diuresis-promoting and damp-clearing types.A total of 24 high-frequency herbal combinations and 35 association rule were excavated,and 3 types of formulas were obtained by cluster analysis.[Conclusions]The analysis results and medicine combinations obtained in the formulas are consistent with the traditional Chinese medicine treatment theory of COVID-19 caused by wind-heat filth accompanied with damp and toxin.

Density Clustering Algorithm Based on KD-Tree and Voting Rules

Traditional clustering algorithms often struggle to produce satisfactory results when dealing with datasets withuneven density. Additionally, they incur substantial computational costs when applied to high-dimensional datadue to calculating similarity matrices. To alleviate these issues, we employ the KD-Tree to partition the dataset andcompute the K-nearest neighbors (KNN) density for each point, thereby avoiding the computation of similaritymatrices. Moreover, we apply the rules of voting elections, treating each data point as a voter and casting a votefor the point with the highest density among its KNN. By utilizing the vote counts of each point, we develop thestrategy for classifying noise points and potential cluster centers, allowing the algorithm to identify clusters withuneven density and complex shapes. Additionally, we define the concept of “adhesive points” between two clustersto merge adjacent clusters that have similar densities. This process helps us identify the optimal number of clustersautomatically. Experimental results indicate that our algorithm not only improves the efficiency of clustering butalso increases its accuracy.

Improved STNModels and Heuristic Rules for Cooperative Scheduling in Automated Container Terminals

Improving the cooperative scheduling efficiency of equipment is the key for automated container terminals to copewith the development trend of large-scale ships. In order to improve the solution efficiency of the existing spacetimenetwork (STN) model for the cooperative scheduling problem of yard cranes (YCs) and automated guidedvehicles (AGVs) and extend its application scenarios, two improved STN models are proposed. The flow balanceconstraints in the original model are decomposed, and the trajectory constraints of YCs and AGVs are added toacquire the model STN_A. The coupling constraint in STN_A is updated, and buffer constraints are added toSTN_A so that themodel STN_B is built.As the size of the problem increases, the solution speed of CPLEX becomesthe bottleneck. So a heuristic method containing three groups of heuristic rules is designed to obtain a near-optimalsolution quickly. Experimental results showthat the computation time of STN_A is shortened by 49.47% on averageand the gap is reduced by 1.69% on average compared with the original model. The gap between the solution ofthe heuristic rules and the solution of CPLEX is less than 3.50%, and the solution time of the heuristic rules is onaverage 99.85% less than the solution time of CPLEX. Compared with STN_A, the computation time for solvingSTN_B increases by 58.93% on average.

Learning Vector Quantization-Based Fuzzy Rules Oversampling Method

Imbalanced datasets are common in practical applications,and oversampling methods using fuzzy rules have been shown to enhance the classification performance of imbalanced data by taking into account the relationship between data attributes.However,the creation of fuzzy rules typically depends on expert knowledge,which may not fully leverage the label information in training data and may be subjective.To address this issue,a novel fuzzy rule oversampling approach is developed based on the learning vector quantization(LVQ)algorithm.In this method,the label information of the training data is utilized to determine the antecedent part of If-Then fuzzy rules by dynamically dividing attribute intervals using LVQ.Subsequently,fuzzy rules are generated and adjusted to calculate rule weights.The number of new samples to be synthesized for each rule is then computed,and samples from the minority class are synthesized based on the newly generated fuzzy rules.This results in the establishment of a fuzzy rule oversampling method based on LVQ.To evaluate the effectiveness of this method,comparative experiments are conducted on 12 publicly available imbalance datasets with five other sampling techniques in combination with the support function machine.The experimental results demonstrate that the proposed method can significantly enhance the classification algorithm across seven performance indicators,including a boost of 2.15%to 12.34%in Accuracy,6.11%to 27.06%in G-mean,and 4.69%to 18.78%in AUC.These show that the proposed method is capable of more efficiently improving the classification performance of imbalanced data.

Principles of Shiology——Revealing the Basic Rules of Human Shiance

The objective principles of shiology are mainly reflected in three fields as food acquisition, eaters' health and shiance order. Most of the objective principles in the field of food acquisition have been revealed by agronomy and foodstuff science. This research mainly focuses on 10 principles in the field of eaters' health and shiance order and in addition, there are also five lemmas that extend from the above principles. The 10 principles are the core theory of the shiology knowledge system, which play an important role in the objective principles revealed by human beings and constitute one of the basic principles of human civilization. Compared with the scientific principles of mathematics, physics, chemistry and economics, the principles of shiology have three characteristics as popularity, practicability and survivability. The principles of shiology in the field of eaters' health are all around us, and everyone can understand and master them. Using the principles of shiology can improve the healthy life span of 8 billion people. The principles of shiology in the field of shiance order is an important tool of social governance, which can reduce human social conflicts, reduce social involution, improve overall efficiency of social operation, and maintain the sustainable development of human beings.

Molecular Dynamics Numerical Simulation of Adsorption Characteristics and Exploitation Limits in Shale Oil Microscopic Pore Spaces

Microscopic pore structure in continental shale oil reservoirs is characterized by small pore throats and complex micro-structures.The adsorption behavior of hydrocarbons on the pore walls exhibits unique physical and chemical properties.Therefore,studying the adsorption morphology of hydrocarbon components in nanometer-sized pores and clarifying the exploitation limits of shale oil at the microscopic level are of great practical significance for the efficient development of continental shale oil.In this study,molecular dynamics simulations were employed to investigate the adsorption characteristics of various single-component shale oils in inorganic quartz fissures,and the influence of pore size and shale oil hydrocarbon composition on the adsorption properties in the pores was analyzed.The results show that different molecules have different adsorption capacities in shale oil pores,with lighter hydrocarbon components(C6H14)exhibiting stronger adsorption abilities.For the same adsorbed molecule,the adsorption amount linearly increases with the increase in pore diameter,but larger pores contribute more to shale oil adsorption.In shale pores,the thickness of the adsorption layer formed by shale oil molecules ranges from 0.4 to 0.5 nm,which is similar to the width of alkane molecules.Shale oil in the adsorbed state that is difficult to be exploited is mainly concentrated in the first adsorption layer.Among them,the volume fraction of adsorbed shale oil in 6 nm shale pores is 40.8%,while the volume fraction of shale oil that is difficult to be exploited is 16.2%.

Estimation of speed-related car body acceleration limits with quantile regression

Purpose–This study aimed to facilitate a rapid evaluation of track service status and vehicle ride comfort based on car body acceleration.Consequently,a low-cost,data-driven approach was proposed for analyzing speed-related acceleration limits in metro systems.Design/methodology/approach–A portable sensing terminal was developed to realize easy and efficient detection of car body acceleration.Further,field measurements were performed on a 51.95-km metro line.Data from 272 metro sections were tested as a case study,and a quantile regression method was proposed to fit the control limits of the car body acceleration at different speeds using the measured data.Findings–First,the frequency statistics of the measured data in the speed-acceleration dimension indicated that the car body acceleration was primarily concentrated within the constant speed stage,particularly at speeds of 15.4,18.3,and 20.9 m/s.Second,resampling was performed according to the probability density distribution of car body acceleration for different speed domains to achieve data balance.Finally,combined with the traditional linear relationship between speed and acceleration,the statistical relationships between the speed and car body acceleration under different quantiles were determined.We concluded the lateral/vertical quantiles of 0.8989/0.9895,0.9942/0.997,and 0.9998/0.993 as being excellent,good,and qualified control limits,respectively,for the lateral and vertical acceleration of the car body.In addition,regression lines for the speedrelated acceleration limits at other quantiles(0.5,0.75,2s,and 3s)were obtained.Originality/value–The proposed method is expected to serve as a reference for further studies on speedrelated acceleration limits in rail transit systems.

Discovering hidden patterns:Association rules for cardiovascular diseases in type 2 diabetes mellitus

BACKGROUND It is increasingly common to find patients affected by a combination of type 2 diabetes mellitus(T2DM)and coronary artery disease(CAD),and studies are able to correlate their relationships with available biological and clinical evidence.The aim of the current study was to apply association rule mining(ARM)to discover whether there are consistent patterns of clinical features relevant to these diseases.ARM leverages clinical and laboratory data to the meaningful patterns for diabetic CAD by harnessing the power help of data-driven algorithms to optimise the decision-making in patient care.AIM To reinforce the evidence of the T2DM-CAD interplay and demonstrate the ability of ARM to provide new insights into multivariate pattern discovery.METHODS This cross-sectional study was conducted at the Department of Biochemistry in a specialized tertiary care centre in Delhi,involving a total of 300 consented subjects categorized into three groups:CAD with diabetes,CAD without diabetes,and healthy controls,with 100 subjects in each group.The participants were enrolled from the Cardiology IPD&OPD for the sample collection.The study employed ARM technique to extract the meaningful patterns and relationships from the clinical data with its original value.RESULTS The clinical dataset comprised 35 attributes from enrolled subjects.The analysis produced rules with a maximum branching factor of 4 and a rule length of 5,necessitating a 1%probability increase for enhancement.Prominent patterns emerged,highlighting strong links between health indicators and diabetes likelihood,particularly elevated HbA1C and random blood sugar levels.The ARM technique identified individuals with a random blood sugar level>175 and HbA1C>6.6 are likely in the“CAD-with-diabetes”group,offering valuable insights into health indicators and influencing factors on disease outcomes.CONCLUSION The application of this method holds promise for healthcare practitioners to offer valuable insights for enhancing patient treatment targeting specific subtypes of CAD with diabetes.Implying artificial intelligence techniques with medical data,we have shown the potential for personalized healthcare and the development of user-friendly applications aimed at improving cardiovascular health outcomes for this high-risk population to optimise the decision-making in patient care.

The Effect of Mineral Composition and Quantity of Fines on the Atterberg Limits and Compaction Characteristics of Soils

Because of the various elements that come into play in natural soil formation, the impact of varied proportions of mineral composition and fines amount on Atterberg limits and compaction characteristics of soils is not well known. Three distinct soil samples were used in this investigation. The findings indicated the effect of varied mineral composition proportions and fines amount on the liquid limit, plastic limit, and plasticity index as assessed by the Casagrande test and hand-rolling method. The fluctuation of maximum dry density and optimal moisture content with these three soils has also been studied. Furthermore, correlations were established to indicate the compaction parameters and the amount of minerals and particles in the soil. The data show that the mineral content of the soil has a direct impact on the Atterberg limits and compaction characteristics. Soils containing larger percentages of expansive minerals, such as montmorillonite, have more flexibility and volume change capability. Mineral composition influences compaction parameters such as maximum dry density, ideal water content, axial strain, and axial stress. Soils with a larger proportion of fines, such as Soil 2 and Soil 3, have stronger flexibility and lower compaction qualities, with higher ideal water content and lower maximum dry density. Soil 1 has moderate flexibility and intermediate compaction qualities due to its low fines percentage. The effect of different mineral compositions and fines on the Atterberg limits and compaction characteristics of soils can be used to predict the behavior of compacted soils encountered in engineering practices, reducing the time and effort required to assess soil suitability for engineering use.

Verification and Application Evaluation of Intelligent Audit Rules for The UN9000 Urine Analysis System

Objective:To apply and verify the application of intelligent audit rules for urine analysis by Cui et al.Method:A total of 1139 urine samples of hospitalized patients in Tai’an Central Hospital from September 2021 to November 2021 were randomly selected,and all samples were manually microscopic examined after the detection of the UN9000 urine analysis line.The intelligent audit rules(including the microscopic review rules and manual verification rules)were validated based on the manual microscopic examination and manual audit,and the rules were adjusted to apply to our laboratory.The laboratory turnaround time(TAT)before and after the application of intelligent audit rules was compared.Result:The microscopic review rate of intelligent rules was 25.63%(292/1139),the true positive rate,false positive rate,true negative rate,and false negative rate were 27.66%(315/1139),6.49%(74/1139),62.34%(710/1139)and 3.51%(40/1139),respectively.The approval consistency rate of manual verification rules was 84.92%(727/856),the approval inconsistency rate was 0%(0/856),the interception consistency rate was 12.61%(108/856),and the interception inconsistency rate was 0%(0/856).Conclusion:The intelligence audit rules for urine analysis by Cui et al.have good clinical applicability in our laboratory.

Review of Arbitral Award of Network Loan and Analysis of Construction of Implementation Review Rules

The network arbitration cases arising from the network lending disputes are pouring into the courts in large numbers.It is reported that the network arbitration system of some arbitration institutions even“can accept more than 10,000 cases every day,”while online lending is booming,it has also caused a lot of contradictions and disputes,and traditional dispute resolution methods have failed to effectively respond to the need for efficient and convenient resolution of online lending disputes.This paper tries to study the arbitral award of online loans and proposes the construction of implementation review rules.

An empirical behavioral order‑driven model with price limit rules

We propose an empirical behavioral order-driven(EBOD)model with price limit rules,which consists of an order placement process and an order cancellation process.All the ingredients of the model are determined based on the empirical microscopic regularities in the order flows of stocks traded on the Shenzhen Stock Exchange.The model can reproduce the main stylized facts in real markets.Computational experiments unveil that asymmetric setting of price limits will cause the stock price to diverge exponentially when the up price limit is higher than the down price limit and to vanish vice versa.We also find that asymmetric price limits have little influence on the correlation structure of the return series and the volatility series,but cause remarkable changes in the average returns and the tail exponents of returns.Our EBOD model provides a suitable computational experiment platform for academics,market participants,and policy makers.

Text categorization based on fuzzy classification rules tree

To deal with the problem that arises when the conventional fuzzy class-association method applies repetitive scans of the classifier to classify new texts,which has low efficiency, a new approach based on the FCR-tree（fuzzy classification rules tree）for text categorization is proposed.The compactness of the FCR-tree saves significant space in storing a large set of rules when there are many repeated words in the rules.In comparison with classification rules,the fuzzy classification rules contain not only words,but also the fuzzy sets corresponding to the frequencies of words appearing in texts.Therefore,the construction of an FCR-tree and its structure are different from a CR-tree.To debase the difficulty of FCR-tree construction and rules retrieval,more k-FCR-trees are built.When classifying a new text,it is not necessary to search the paths of the sub-trees led by those words not appearing in this text,thus reducing the number of traveling rules.Experimental results show that the proposed approach obviously outperforms the conventional method in efficiency.

Reasearch on Occurrence Rules of Asian Corn Borer Ostrinia furnacalis in the Field of Waxy Corn

[Objective] This program aimed at providing basis for timely and effective control of Asian corn borer Ostrinia furnacalis in waxy corn fields through investigations and researching on occurrence and damage characteristics of Ostrinia furnacalis in the main producing area of waxy corn of Shanxi Province.[Method] Attraction and collection of the adults,fixed-points and random investigations on eggs and larvae were the main methods in this research.[Result] Ostrinia furnacalis appeared in two generations per year in the field of waxy corns,the generations were basically nonoverlapping with significant interval.The peak period of the overwintering generation adults in the field of waxy corn was early to middle of June,peak period of the first generation adults was from early August to late August;peak period of the first generation eggs was from the end of early June to the binning of late June,the peak period of the second generation eggs was from the middle of early August to late August.Egg-laying performances of Ostrinia furnacalis on waxy corns in different growth period suggested that the temptation of waxy corns to egg-laying of Ostrinia furnacalis had gradually increased with the continuation of growth stages before the heading of waxy corns;while after the heading of waxy corns,the temptation of waxy corns to egg-laying of Ostrinia furnacalis had gradually reduced with the continuation of growth stages.The amount of eggs on waxy corns was more than eggs on normal corns with the same growth period and growth conditions.The first generation of Ostrinia furnacalis caused more serious damages than the second generation.The first generation larvae caused more serious damages on waxy corns with early sowing period than with middle sowing period,while the late sowing fields basically uninjured.The early sowing and middle sowing corns were damaged but lightly,due to the slight occurrence of the first generation larvae.The second generation larvae caused more serious damages in late sowing waxy corn field than in middle sowing waxy corn field.As the early sowing waxy corns were harvested before the beginning of damage peak period of the second generation larvae,they were basically uninjured or slightly damaged by the second generation larvae in general years.Because the second generation larvae were in large-scale occurrence,they had caused serious damages to late and later middle sowing waxy corns.Incidence of ear rot disease on the female ears of waxy corns was closely related to the damages of Ostrinia furnacalis on ears,which was higher on the ears infested by Ostrinia furnacalis.[Conclusion] Comprehensive control on the second generation of Ostrinia furnacalis in the mid and late sowing field of waxy corn should be strengthened.

Study on association rules mining based on semantic relativity

An association rules mining method based on semantic relativity is proposed to solve the problem that there are more candidate item sets and higher time complexity in traditional association rules mining.Semantic relativity of ontology concepts is used to describe complicated relationships of domains in the method.Candidate item sets with less semantic relativity are filtered to reduce the number of candidate item sets in association rules mining.An ontology hierarchy relationship is regarded as a directed acyclic graph rather than a hierarchy tree in the semantic relativity computation.Not only direct hierarchy relationships,but also non-direct hierarchy relationships and other typical semantic relationships are taken into account.Experimental results show that the proposed method can reduce the number of candidate item sets effectively and improve the efficiency of association rules mining.