Facilitating transparency in spinal cord injury studies using data standards and ontologies

Progress in developing robust therapies for spinal cord injury （SCI）, trau- matic brain injury （TBI） and peripheral nerve injury has been slow. A great deal has been learned over the past 30 years regarding both the intrinsic factors and the environmental factors that regulate axon growth, but this large body of information has not yet resulted in clinically available thera- peutics. This therapeutic bottleneck has many root causes, but a consensus is emerging that one contributing factor is a lack of standards for experi- mental design and reporting. The absence of reporting standards, and even of commonly accepted definitions of key words, also make data mining and bioinformatics analysis of neural plasticity and regeneration difficult, if not impossible. This short review will consider relevant background and poten- tial solutions to this problem in the axon regeneration domain.

Commentary on:“Facilitating transparency in spinal cord injury studies using data standards and ontologies”

Commentary Most would agree that providing comprehensive detail in scientific reporting is critical for the development of mean- ingful therapies and treatments for diseases. Such stellar practices 1） allow for reproduction of experiments to con- firm results, 2） promote thorough analyses of data, and 3） foster the incremental advancement of valid approaches. Unfortunately, most would also agree we have far to go to reach this vital goal （Hackam and Redelmeier, 2006; Prinz et al., 2011; Baker et al., 2014）.

Progress of Geological Survey Using Airborne Hyperspectral Remote Sensing Data in the Gansu and Qinghai Regions

Hyperspectral remote sensing is now a frontier of the remote sensing technology. Airborne hyperspectral remote sensing data have hundreds of narrow bands to obtain complete and continuous ground-object spectra. Therefore, they can be effectively used to identify these grotmd objects which are difficult to discriminate by using wide-band data, and show much promise in geological survey. At the height of 1500 m, have 36 bands in visible to the CASI hyperspectral data near-infrared spectral range, with a spectral resolution of 19 nm and a space resolution of 0.9 m. The SASI data have 101 bands in the shortwave infrared spectral range, with a spectral resolution of 15 nm and a space resolution of 2.25 m. In 2010, China Geological Survey deployed an airborne CASI/SASI hyperspectral measurement project, and selected the Liuyuan and Fangshankou areas in the Beishan metallogenic belt of Gansu Province, and the Nachitai area of East Kunlun metallogenic belt in Qinghai Province to conduct geological survey. The work period of this project was three years.

Use of Data Mining to Support the Development of Knowledge Intensive CAD

In order to compete in the global manufacturing mar ke t, agility is the only possible solution to response to the fragmented market se gments and frequently changed customer requirements. However, manufacturing agil ity can only be attained through the deployment of knowledge. To embed knowledge into a CAD system to form a knowledge intensive CAD (KIC) system is one of way to enhance the design compatibility of a manufacturing company. The most difficu lt phase to develop a KIC system is to capitalize a huge amount of legacy data t o form a knowledge database. In the past, such capitalization process could only be done solely manually or semi-automatic. In this paper, a five step model fo r automatic design knowledge capitalization through the use of data mining is pr oposed whilst details of how to select, verify and performance benchmarking an a ppropriate data mining algorithm for a specific design task will also be discuss ed. A case study concerning the design of a plastic toaster casing was used as an illustration for the proposed methodology and it was found that the avera ge absolute error of the predictions for the most appropriate algorithm is withi n 17%.

Satellite Data Reduction Using Entropy-preserved Image Compression Technique

In this paper, three techniques, line run coding, quadtree DF (Depth-First) representation and H coding for compressing classified satellite cloud images with no distortion are presented. In these three codings, the first two were invented by other persons and the third one, by ourselves. As a result, the comparison among their compression rates is. given at the end of this paper. Further application of these image compression technique to satellite data and other meteorological data looks promising.

On Accurate Detection of Oceanic Features from Satellite IR Data Using ICSED Method

ICSED (Improved Cluster Shade Edge Detection) algorithm and other various methods to accurately and efficiently detect edges on satellite data are presented. Error rate criterion is used to statistically evaluate the performances of these methods in detecting oceanic features for both noise free and noise contaminated AVHRR (Advanced Very High Resolution Radiometer) IR image with Kuroshio. Also, practical experiments in detecting the eddy of Kuroshio with these methods are carried out for comparison. Results show that the ICSED algorithm has more advantages than other methods in detecting mesoscale features of ocean. Finally, the effectiveness of window size of ICSED method to oceanic features detection is quantitatively discussed.

Adaptive Particle Swarm Optimization Data Hiding for High Security Secret Image Sharing

The main aim of this work is to improve the security of data hiding forsecret image sharing. The privacy and security of digital information have becomea primary concern nowadays due to the enormous usage of digital technology.The security and the privacy of users’ images are ensured through reversible datahiding techniques. The efficiency of the existing data hiding techniques did notprovide optimum performance with multiple end nodes. These issues are solvedby using Separable Data Hiding and Adaptive Particle Swarm Optimization(SDHAPSO) algorithm to attain optimal performance. Image encryption, dataembedding, data extraction/image recovery are the main phases of the proposedapproach. DFT is generally used to extract the transform coefficient matrix fromthe original image. DFT coefficients are in float format, which assists in transforming the image to integral format using the round function. After obtainingthe encrypted image by data-hider, additional data embedding is formulated intohigh-frequency coefficients. The proposed SDHAPSO is mainly utilized for performance improvement through optimal pixel location selection within the imagefor secret bits concealment. In addition, the secret data embedding capacityenhancement is focused on image visual quality maintenance. Hence, it isobserved from the simulation results that the proposed SDHAPSO techniqueoffers high-level security outcomes with respect to higher PSNR, security level,lesser MSE and higher correlation than existing techniques. Hence, enhancedsensitive information protection is attained, which improves the overall systemperformance.

Detecting Climate Change in Using Extreme Data from Two Surface Weather Stations: Case Study Valle of Comitan and La Esperanza, Chiapas, Mexico

The study area is located between the cities of Comitan (16°10'43"N and 92°04'20''W) a city with 150,000 inhabitants and La Esperanza (16°9'15''N and 91°52'5''W) a town with 3000 inhabitants. Both weather stations are 30 km from each other in the Chiapas State, México. 54 years of daily records of the series of maximum (tmax) and minimum temperatures (tmin) of the weather station 07205 Comitan that is on top of a house and 30 years of daily records of the weather station 07374 La Esperanza were analyzed. The objective is to analyze the evidence of climate change in the Comitan valley. 2.07% and 19.04% of missing data were filled, respectively, with the WS method. In order to verify homogeneity three methods were used: Standard Normal Homogeneity Test (SNHT), the Von Neumann method and the Buishand method. The heterogeneous series were homogenized using climatol. The trends of tmax and tmin for both weather stations were analyzed by simple linear regression, Sperman’s rho and Mann-Kendall tests. The Mann-Kendal test method confirmed the warming trend at the Comitan station for both variables with ZMK statistic values equal to 1.57 (statistically not significant) and 4.64 (statistically significant). However, for the Esperanza station, it determined a cooling trend for tmin and a slight non-significant warming for tmax with a ZMK statistic of -2.27 (statistically significant) and 1.16 (statistically not significant), for a significance level α = 0.05.

THE RESEARCH ON MAXIMUM TURBIDITY OF YANGTZE RIVER ESTUARY AND URBAN GROWTH OF SHANGHAI USING TM CCT DATA

Based on the research on the diffusion of suspended sediments discharged outside of Yangtze River estuary and the landuse of Shanghai using Landsat MSS images in several years, the authors analysed the characteristics of TM CCT data of Shanghai scene, pointed out concrete range of maximum turbidity and growth of urban boundary of Shanghai through the information extraction.The feature vector combination method is used in the research process. The result is getting nice.

RECONSTRUCTION OF PART OF AN ACTUAL BLAST-WAVE FLOW FIELD TO AGREE WITH EXPERIMENTAL DATA BY USING NUMERICAL METHOD WITH HIGH IDENTIFICATION

In this paper, on the basis of experimental data of two kinds of chemical explosions, the piston-pushing model of spherical blast-waves and the second-order Godunov-type scheme of finite difference methods with high identification to discontinuity are used to the numerical reconstruction of part of an actual hemispherical blast-wave flow field by properly adjusting the moving bounary conditions of a piston. This method is simple and reliable. It is suitable to the evaluation of effects of the blast-wave flow field away from the explosion center.

Studies of Earthquake Hazard Using Microseismicity Data in Modern Times

This paper selects some representative regions to obtain their G-R relation curves according to their seismicity characteristics,by using ML≥2.0 microseismicity data(1970~1993)in North China.The annual occurrence rate of events of each magnitude can be inferred from the G-R relation.At the same tune,the actual annual occurrence rate of earthquakes of higher magnitudes can be calculated from historical earthquakes(1300-1993)recorded in the same region.It seems that both results are almost the same.Therefore,the rate of events of higher magnitudes can be obtained by using microseismicity data when the proper region is selected.However,two points should be noticed:(1)The method can only give the annual occurrence rate in a seismicity system and estimate the whole situation of the system.(2)When there is a very large earthquake in and near the period in which the microseismicity data are applied,the actual occurrence rate of the system,including this larger earthquake,cannot be obtained by this method.

Personal Data Use and Morality in the E-Business Environment

This paper deals with personal data use by firms in the e-business environment from the viewpoint of business administration and information ethics. Whereas the tremendous development of information and communication technology (ICT) has made it easier for firms to acquire, store, share, and utilise personal data on their customers, firms that use personal data are exposed to risks related to privacy issues. Since individuals fear the invasion of their privacy, the failure of a firm to appear or remain trustworthy would make it difficult for it to maintain accurate, up-to-date databases and to construct desirable business processes, which would affect the bottom line. Therefore, modern firms should do what they can to ensure that their customers trust them. For them, one promising way to remain trustworthy is to behave as a moral agent. Although it is difficult for any firm to meet the conditions necessary to be a moral agent, competence in behaving as a moral agent is a hard-to-imitate capability af firms for which personal data use is vital for enjoying the benefits of business relationships in the e-business environment.

Deep Learning Applied to Computational Mechanics:A Comprehensive Review,State of the Art,and the Classics

Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.

Clock recovery from NRZ data at 10 Gb/s using SOA loop mirror and mode-locked fiber ring laser based on SOA

All optical clock recovery from non return-to-zero （NRZ） data using an semiconductor optical amplifier （SOA） loop mirror and a mode-locked SOA fibcr lascr is firstly schematically explained and experimentally demonstrated at 10 Gb/s. Furthermore, the pulse quality of tile recovered cluck is cffcctivcly improved by using a continuous-wave （CW） assist light in the gain region of SOA, through which the amplitude modulation is reduced from 57.2% to 8.47%. This scheme is a promising method for clock recovery from NRZ data in the future all-optical communication networks.

ESTIMATION OF VERTICAL DISTRIBUTION OF RELATIVE HUMIDITY USING SATELLITE DATA

In this paper, an attempt has been made to find out the vertical distribution of RH at levels of 850, 700 and 500 hPa by using satellite-derived radiation parameters (i.e., albedo, outgoing longwave fluxes, absorb- ed solar radiation and net radiation). For this purpose, multiple regression equations are derived from MONEX-79 upsonde and dropsonde data over the Arabian Sea for the period 11--20 June 1979. Satellite- estimated RH fields have been compared with ECMWF RH fields obtained from FGGE level ⅢB data. The RMS error and error variance for satellite-estimated RH fields have been found to be less than for those of ECMWF. Satellite-estimated isohygric patterns show good agreement with the cloudiness patterns of GOES satellite, whereas ECMWF isohygric patterns do not show much resemblance with the cloudiness patterns. The results of the study suggest that satellite-estimated RH fields could be more useful than ECMWF RH fields and they can be used with some confidence in NWP models.

Measuring the inequality using energy consumption data in China

With the support by the National Natural Science Foundation of China,the research team led by Prof.Wei Chu(魏楚)and Prof.Zheng Xinye(郑新业)at the Department of Energy Economics,School of Economics,Renmin University of China,measures the inequality using the household energy

Security enhancement for double-random phase encryption using orthogonally encoded image and electronically synthesized key data

We prot）ose a security-enhanced double-random phase encryption （DRPE） scheme using orthogonally encoded image and electronically synthesized key data to cope with the security problem of DRPE technique caused by fixed double-random phase masks for eneryption. In the proposed scheme, we adopt the electronically synthesized key to frequently update the phase mask using a spatial light modulator, and also employ the orthogonal encoding technique to encode the image and electronically synthesized key data, which can enhance the security of both data. We provide detailed procedures for eneryption and decryption of the proposed scheme, and provide the simulation results to show the eneryption effects of the proposed scheme.

Data signal processing via manchester coding-decoding method using chaotic signals generated by PANDA ring resonator

We investigate the nonlinear behaviors of light recognized as chaos during the propagation of Gaussian laser beam inside a nonlinear polarization maintaining and absorption reducing （PANDA） ring resonator system. It aims to generate the nonlinear behavior of light to obtain data in binary logic codes for transmission in fiber optics communication. Effective parameters, such as refractive indices of a silicon waveguide, coupling coefficients （~）, and ring radius ring （R）, can be properly selected to operate the nonlinear behavior. Therefore, the binary coded data generated by the PANDA ring resonator system can be decoded and converted to Manchester codes, where the decoding process of the transmitted codes occurs at the end of the transmission link. The simulation results show that the original codes can be recovered with a high security of signal transmission using the Manchester method.

An Improved Model for Evaluating Ecosystem Service Values Using Land Use/Cover and Vegetation Parameters

It is crucial to maintain the balance of economic development and ecosystem protection. The value of ecosystem services is an indicator to help people understand the importance of ecosystem protection. Traditional models estimate ecosystem service values only according to land use/cover data while ignoring vegetation status differences in the same land use/cover. This study uses the normalized difference vegetation index(NDVI), the leaf area index(LAI),and net primary productivity(NPP) as vegetation status data to describe the differences in the same land use/cover type. The principal component analysis(PCA) approach is used to reduce the correlations among the three types of vegetation status data. Then, the calculated vegetation status index after PCA is input into the estimation model. The case study in China shows that the improved model has two major advantages. First, it can clearly distinguish the differences in ecosystem service values even for the same land use/cover type. Second, it can clearly describe the transitional zones between different land use/cover types through continuous changes in ecosystem service values. This improved model can provide a more detailed description of the distribution characteristics of ecosystem service values in China and help policymakers balance economic development and ecosystem protection.

Climate-dependent malaria disease transmission model and its analysis

Malaria infection continues to be a major problem in many parts of the world including Africa.Environmental variables are known to significantly affect the population dynamics and abundance of insects,major catalysts of vector-borne diseases,but the exact extent and consequences of this sensitivity are not yet well established.To assess the impact of the variability in temperature and rainfall on the transmission dynamics of malaria in a population,we propose a model consisting of a system of non-autonomous deterministic equations that incorporate the effect of both temperature and rainfall to the dispersion and mortality rate of adult mosquitoes.The model has been validated using epidemiological data collected from the western region of Ethiopia by considering the trends for the cases of malaria and the climate variation in the region.Further,a mathematical analysis is performed to assess the impact of temperature and rainfall change on the transmission dynamics of the model.The periodic variation of seasonal variables as well as the non-periodic variation due to the long-term climate variation have been incorporated and analyzed.In both periodic and non-periodic cases,it has been shown that the disease-free solution of the model is globally asymptotically stable when the basic reproduction ratio is less than unity in the periodic system and when the threshold function is less than unity in the non-periodic system.The disease is uniformly persistent when the basic reproduction ratio is greater than unity in the periodic system and when the threshold function is greater than unity in the non-periodic system.