Spatial/temporal indexing and information visualization genre for environmental digital libraries

Protecting and preserving our environmental systems require the ability to understand the spatio-temporal distri- bution of soils, parent material, topography, and land cover as well as the effects of human activities on ecosystems. Space-time modelling of ecosystems in an environmental digital library is essential for visualizing past, present, and future impacts of changes occurring within such landscapes (e.g., shift in land use practices). In this paper, we describe three novel features, spa- tio-temporal indexing, visualization, and geostatistical genre, for the environmental digital library, Environmental Visualization and Geographic Enterprise System (ENVISAGE), currently in progress at the University of Florida.

A DDoS Attack Information Fusion Method Based on CNN for Multi-Element Data

Traditional distributed denial of service(DDoS)detection methods need a lot of computing resource,and many of them which are based on single element have high missing rate and false alarm rate.In order to solve the problems,this paper proposes a DDoS attack information fusion method based on CNN for multi-element data.Firstly,according to the distribution,concentration and high traffic abruptness of DDoS attacks,this paper defines six features which are respectively obtained from the elements of source IP address,destination IP address,source port,destination port,packet size and the number of IP packets.Then,we propose feature weight calculation algorithm based on principal component analysis to measure the importance of different features in different network environment.The algorithm of weighted multi-element feature fusion proposed in this paper is used to fuse different features,and obtain multi-element fusion feature(MEFF)value.Finally,the DDoS attack information fusion classification model is established by using convolutional neural network and support vector machine respectively based on the MEFF time series.Experimental results show that the information fusion method proposed can effectively fuse multi-element data,reduce the missing rate and total error rate,memory resource consumption,running time,and improve the detection rate.

MULTILEVEL INDEX STRUCTURE FOR INFORMATION FILTERING BASED ON USER CHARACTERISTIC

Aimed at information overload and personalized characteristic of user information requirement, this letter presents a type of multilevel index structure and algorithm which is applied to large scale information filtering system and has better performance and stronger scalability.

Forecasting Model Based on Information-Granulated GA-SVR and ARIMA for Producer Price Index

The accuracy of predicting the Producer Price Index(PPI)plays an indispensable role in government economic work.However,it is difficult to forecast the PPI.In our research,we first propose an unprecedented hybrid model based on fuzzy information granulation that integrates the GA-SVR and ARIMA(Autoregressive Integrated Moving Average Model)models.The fuzzy-information-granulation-based GA-SVR-ARIMA hybrid model is intended to deal with the problem of imprecision in PPI estimation.The proposed model adopts the fuzzy information-granulation algorithm to pre-classification-process monthly training samples of the PPI,and produced three different sequences of fuzzy information granules,whose Support Vector Regression(SVR)machine forecast models were separately established for their Genetic Algorithm(GA)optimization parameters.Finally,the residual errors of the GA-SVR model were rectified through ARIMA modeling,and the PPI estimate was reached.Research shows that the PPI value predicted by this hybrid model is more accurate than that predicted by other models,including ARIMA,GRNN,and GA-SVR,following several comparative experiments.Research also indicates the precision and validation of the PPI prediction of the hybrid model and demonstrates that the model has consistent ability to leverage the forecasting advantage of GA-SVR in non-linear space and of ARIMA in linear space.

Studies on the Physical Properties of Alkanes Using Edge-adjacency Information Topological Index

Edge-adjacency index and information topological index for 82 molecules of alkanes have been constructed and calculated. The topological indices were used to correlate with seven physical properties of the alkanes. Some empirical equations were obtained through regression. The regression and calculation results show a good agreement of the topological indices and the properties.

A hybrid specific index-related process monitoring strategy based on a novel two-step information extraction method

A two-step information extraction method is presented to capture the specific index-related information more accurately.In the first step,the overall process variables are separated into two sets based on Pearson correlation coefficient.One is process variables strongly related to the specific index and the other is process variables weakly related to the specific index.Through performing principal component analysis(PCA)on the two sets,the directions of latent variables have changed.In other words,the correlation between latent variables in the set with strong correlation and the specific index may become weaker.Meanwhile,the correlation between latent variables in the set with weak correlation and the specific index may be enhanced.In the second step,the two sets are further divided into a subset strongly related to the specific index and a subset weakly related to the specific index from the perspective of latent variables using Pearson correlation coefficient,respectively.Two subsets strongly related to the specific index form a new subspace related to the specific index.Then,a hybrid monitoring strategy based on predicted specific index using partial least squares(PLS)and T2statistics-based method is proposed for specific index-related process monitoring using comprehensive information.Predicted specific index reflects real-time information for the specific index.T2statistics are used to monitor specific index-related information.Finally,the proposed method is applied to Tennessee Eastman(TE).The results indicate the effectiveness of the proposed method.

Information Transfer Index-A Promising Measure of the Corticomusclar Interaction

It is generally believed that a major cause of motor dysfunction is the impairment in neural network that controls movement. But little is known about the underlying mechanisms of the impairment in cortical control or in the neural connections between cortex and muscle that lead to the loss of motor ability. So understanding the functional connection between motor cortex and effector muscle is of utmost importance. Previous study mostly relied on cross-correlation, coherence functions or model based approaches such as Granger causality or dynamic causal modeling. In this work the information transfer index (ITI) was introduced to describe the information flows between motor cortex and muscle. Based on the information entropy the ITI can detect both linear and nonlinear interaction between two signals and thus represent a very comprehensive way to define the causality strength. The applicability of ITI is investigated based on simulations and electroencephalogram (EEG), surface electromyography (sEMG) recordings in a simple motor task.

Optimal Hedging Strategies of Stock Index Futures Based on the Perspective of Information Asymmetry

Based on two different risk measurement criteria, this article studied the optimal hedging strategies of stock index futures in the case of asymmetric information, and discussed the influence of insider information on the hedging effect. Through simulation analysis, it can be shown that hedging people with insider information can save hedging costs to a certain extent, which also explains the reason why investors try to obtain corporate information in actual investment activities.

Information content of index inclusions： Evidence from China

This paper examines whether index inclusion has information content and the downward-sloping demand curve hypothesis in China. We investigate the stock price and volume effects when stocks are included in two major stock indexes, the Shanghai Stock Exchange 30 Index （SH30） and the Shenzhen Component 40 Index （SZ40）. Furthermore, we also study the performance changes after index inclusion. We find significant price and volume increases for the stocks selected by the SH30 when the index was created and announced. Thus, the original inclusion may not be an information-free event. For subsequent index inclusions, we observe significant abnormal returns but not abnormal trade volume around the announcement date. However, the stock returns quickly reversed at the post-announcement period. Moreover, the financial performance of index included firms does not improve. The evidence does not support the price pressure hypothesis in China.

A Review of Index Case Testing Induced Intimate Partner Violence and Electronic Management of Information in HIV Care

Intimate Partner Violence (IPV) is a form of Gender Base Violence (GBV) where an intimate partner perpetrates violence. In the HIV care continua which has the aim of achieving epidemic control based on the goals defined by UNAIDS, 95% of people living with HIV (PLHIV) have to know their HIV status, 95% initiated ARV treatment and 95% are virally suppressed in order to achieve epidemic control. One of the evidence-based strategies used for achieving an optimal number of PLHIV who know their HIV status is the Index Case Testing Strategy (ICT). While the ICT strategy helps the achievement of epidemic control, its implementation increases the incidence of IPV among either serodiscordant or concordant couples. Tackling information about IPV is very sensitive. A review of the literature on the management of HIV patient information has shown that shifting from paper-based management of HIV patient information to computerized Electronic Medical Records (EMR) systems, using software such as OPEN MRS has significantly improved the management of HIV patient information with high-level confidentiality of patient information. The reviews showed that the EMR systems put in place to manage HIV patient information need to integrate the stages used for the management of IPV among PLHIV.

Coverless Information Hiding Based on the Molecular Structure Images of Material

The traditional information hiding methods embed the secret information by modifying the carrier,which will inevitably leave traces of modification on the carrier.In this way,it is hard to resist the detection of steganalysis algorithm.To address this problem,the concept of coverless information hiding was proposed.Coverless information hiding can effectively resist steganalysis algorithm,since it uses unmodified natural stego-carriers to represent and convey confidential information.However,the state-of-the-arts method has a low hidden capacity,which makes it less appealing.Because the pixel values of different regions of the molecular structure images of material(MSIM)are usually different,this paper proposes a novel coverless information hiding method based on MSIM,which utilizes the average value of sub-image’s pixels to represent the secret information,according to the mapping between pixel value intervals and secret information.In addition,we employ a pseudo-random label sequence that is used to determine the position of sub-images to improve the security of the method.And the histogram of the Bag of words model(BOW)is used to determine the number of subimages in the image that convey secret information.Moreover,to improve the retrieval efficiency,we built a multi-level inverted index structure.Furthermore,the proposed method can also be used for other natural images.Compared with the state-of-the-arts,experimental results and analysis manifest that our method has better performance in anti-steganalysis,security and capacity.

Effects of the information–knowledge–attitude–practice nursing model combined with predictability intervention on patients with cerebrovascular disease

BACKGROUND Cerebrovascular disease(CVD)poses a serious threat to human health and safety.Thus,developing a reasonable exercise program plays an important role in the long-term recovery and prognosis for patients with CVD.Studies have shown that predictive nursing can improve the quality of care and that the information–knowledge–attitude–practice(IKAP)nursing model has a positive impact on patients who suffered a stroke.Few studies have combined these two nursing models to treat CVD.AIM To explore the effect of the IKAP nursing model combined with predictive nursing on the Fugl–Meyer motor function(FMA)score,Barthel index score,and disease knowledge mastery rate in patients with CVD.METHODS A total of 140 patients with CVD treated at our hospital between December 2019 and September 2021 were randomly divided into two groups,with 70 patients in each.The control group received routine nursing,while the observation group received the IKAP nursing model combined with predictive nursing.Both groups were observed for self-care ability,motor function,and disease knowledge mastery rate after one month of nursing.RESULTS There was no clear difference between the Barthel index and FMA scores of the two groups before nursing(P>0.05);however,their scores increased after nursing.This increase was more apparent in the observation group,and the difference was statistically significant(P<0.05).The rates of disease knowledge mastery,timely medication,appropriate exercise,and reasonable diet were significantly higher in the observation group than in the control group(P<0.05).The satisfaction rate in the observation group(97.14%)was significantly higher than that in the control group(81.43%;P<0.05).CONCLUSION The IKAP nursing model,combined with predictive nursing,is more effective than routine nursing in the care of patients with CVD,and it can significantly improve the Barthel index and FMA scores with better knowledge acquisition,as well as produce high satisfaction in patients.Moreover,they can be widely used in the clinical setting.

Research on Protecting Information Security Based on the Method of Hierarchical Classification in the Era of Big Data

Big data is becoming increasingly important because of the enormous information generation and storage in recent years.It has become a challenge to the data mining technique and management.Based on the characteristics of geometric explosion of information in the era of big data,this paper studies the possible approaches to balance the maximum value and privacy of information,and disposes the Nine-Cells information matrix,hierarchical classification.Furthermore,the paper uses the rough sets theory to proceed from the two dimensions of value and privacy,establishes information classification method,puts forward the countermeasures for information security.Taking spam messages for example,the massive spam messages can be classified,and then targeted hierarchical management strategy was put forward.This paper proposes personal Information index system,Information management platform and possible solutions to protect information security and utilize information value in the age of big data.

Information content method for evaluating diagnostic tests and its comparison of power with other methods

This paper derives the variance of the information content and develops its statistical inference method. We describe the relations between information content and sensitivity, specificity, efficiency, prevalence rate. If sensitivity, specificity and efficiency are fixed, the closer to 0. 5 the prevalence rate is, the more the information content. If prevalence rate and efficiency are fixed, the closer to each other the sensitivity and specificity are, the more the information content. We compare the power of information content method, efficiecy test, Youden's index test and kappa coefficient method. The information content method has higher power than the other methods in most conditions. It is especially sensitive to the difference between two sensitivities. It comes to conclusion that the information content method has more virtues than the other methods mentioned in this paper.

Retrieval of canopy biophysical variables from remote sensing data using contextual information

In order to improve the accuracy of biophysical parameters retrieved from remotely sensing data, a new algorithm was presented by using spatial contextual to estimate canopy variables from high-resolution remote sensing images. The developed algorithm was used for inversion of leaf area index (LAI) from Enhanced Thematic Mapper Plus (ETM+) data by combining with optimization method to minimize cost functions. The results show that the distribution of LAI is spatially consistent with the false composition imagery from ETM+ and the accuracy of LAI is significantly improved over the results retrieved by the conventional pixelwise retrieval methods, demonstrating that this method can be reliably used to integrate spatial contextual information for inverting LAI from high-resolution remote sensing images.

The remote sensing composite information entropy and types of Nansha coral reef atolls

In the viempint that the coral reef atolls' growth index of the Nansha Islands is influenced by many factors, the measured remote sensing comopite information including some mutually related factors is divided into 10 geographic events as N1, N2 ..., N10, and the analysis of the atolls' information entropy is made. From the value of theentropy, the closed related factors with the index of the emerged atolls are shown. In proper order, the factors are reeftop's area(0. 319), lagoon's area(0. 324), open-degree of atoll(0. 336), trend of atoll(0. 551 ). On the basis of thiswork, a new description function of the emerged atoll growth index is proposed. This function can be used to identifythe open my of Nansha atoll growth.