A GIS-Based Evaluation of Environmental Sensitivity for an Urban Expressway in Shenzhen,China

Urban eco-environmental degradation is becoming inevitable due to the extensive urbanization, population growth, and socioeconomic development in China. One of the traffic arteries in Shenzhen is an urban expressway that is under construction and that runs across environmentally sensitive areas (ESAs). The environmental pollution from urban expressways is critical, due to the characteristics of expressways such as high runoff coefficients, considerable contaminant accumulation, and complex pollutant ingredients. ESAs are vulnerable to anthropogenic disturbances and hence should be given special attention. In order to evaluate the environmental sensitivity along this urban expressway and minimize the influences of the ongoing road construction and future operation on the surrounding ecosystem, the environmental sensitivity of the relevant area was evaluated based on the application of a geographic information system (GIS). A final ESA map was classified into four environmental sensitivity levels; this classification indicates that a large proportion of the expressway passes through areas of high sensitivity, representing 11.93 km or 52.3% of the total expressway, and more than 90% of the total expressway passes through ESAs. This study provides beneficial information for optimal layout schemes of initial rainfall runoff treatment facilities developed from low-impact development (LID) techniques in order to minimize the impact of polluted road runoff on the surrounding ecological environment.     

An X-ray absorption study of doped silicate glass,fibre optic preforms

Optical fibre preforms, which have their germanosilicate core regions doped with small quantities of the rare-earth element erbium, have been studied using Extended X-ray Absorption Fine Structure Spectroscopy (EXAFS) at the germanium K absorption edge. These studies were performed using a circular X-ray beam of 100 m diameter, allowing information to be gathered as a radial function of position across the core region of the preform. This positioning was accomplished by means of a motorized pinhole collimator and sample stage developed for use on the focused X-ray beamline 8.1, at the SRS, Daresbury Laboratory, UK. The EXAFS results are consistent with the germanium sites coordinated to surrounding oxygen atoms at a mean distance of 0.17 nm. Absorption maps of the rare-earth and germanium distribution across the core region of the preforms have also been obtained, showing a correlation between the distributions of the two atom types.Affiliated with the Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO).     

Key Techniques for the Construction of High-Speed Railway Large-Section Loess Tunnels

The successful completion of the Zhengzhou–Xi’an high-speed railway project has greatly improved the construction level of China’s large-section loess tunnels, and has resulted in significant progress being made in both design theory and construction technology. This paper systematically summarizes the technical characteristics and main problems of the large-section loess tunnels on China’s high-speed railway, including classification of the surrounding rock, design of the supporting structure, surface settlement and cracking control, and safe and rapid construction methods. On this basis, the key construction techniques of loess tunnels with large sections for high-speed railway are expounded from the aspects of design and construction. The research results show that the classification of loess strata surrounding large tunnels should be based on the geological age of the loess, and be determined by combining the plastic index and the water content. In addition, the influence of the buried depth should be considered. During tunnel excavation disturbance, if the tensile stress exceeds the soil tensile or shear strength, the surface part of the sliding trend plane can be damaged, and visible cracks can form. The pressure of the surrounding rock of a large-section loess tunnel should be calculated according to the buried depth, using the corresponding formula. A three-bench seven-step excavation method of construction was used as the core technology system to ensure the safe and rapid construction of a large-section loess tunnel, following a field test to optimize the construction parameters and determine the engineering measures to stabilize the tunnel face. The conclusions and methods presented here are of great significance in revealing the strata and supporting mechanics of large-section loess tunnels, and in optimizing the supporting structure design and the technical parameters for construction.     

CCPSO优化支持向量机的鸟声识别技术研究

鸟类是生态系统中的重要组成部分，鸟类物种的多样性对生态环境有重要作用。所以，通过鸟声信号来识别鸟类从而对其进行保护有现实意义。文章对鸟声信号采用双参数的双门限法进行分段，从鸟声信号中寻找出声音的起始点和终止点的具体帧，进一步进行特征提取，提取每段鸟声信号中的短时能量和短时平均幅度，短时语谱图中的平均值、对比度、熵，共5种特征，采用优化参数的支持向量机进行鸟类物种分类。结果表明，基于混沌云粒子群优化(Chaos Cloud Particle Swarm Optimization, CCPSO)的支持向量机对比普通支持向量机的分类准确度得到提升，可有效地识别鸟类。利用该方法实现鸟类物种保护和生态系统管理的目的。     

Shell distribution on colloidal CdSe/ZnS quantum dots

Scanning transmission electron microscopy (STEM) coupled with electron energy loss spectroscopy (EELS) was used to determine the chemical distribution of semiconductor shell material around colloidal core-shell CdSe/ZnS quantum dots (QDs). EELS signals from positions around the QD indicate a well-defined shell of ZnS surrounding the CdSe core, but the distribution of the shell material is highly anisotropic. This nonuniformity may reflect the differences in chemical activity of the crystal faces of the core QD and implies a nonoptimal QD surface passivation.     

ILipo-PseAAC: Identification of Lipoylation Sites Using Statistical Moments and General PseAAC

Lysine Lipoylation is a protective and conserved Post Translational Modification (PTM) in proteomics research like prokaryotes and eukaryotes. It is connected with many biological processes and closely linked with many metabolic diseases. To develop a perfect and accurate classification model for identifying lipoylation sites at the protein level, the computational methods and several other factors play a key role in this purpose. Usually, most of the techniques and different traditional experimental models have a very high cost. They are time-consuming; so, it is required to construct a predictor model to extract lysine lipoylation sites. This study proposes a model that could predict lysine lipoylation sites with the help of a classification method known as Artificial Neural Network (ANN). The ANN algorithm deals with the noise problem and imbalance classification in lipoylation sites dataset samples. As the result shows in ten-fold cross-validation, a brilliant performance is achieved through the predictor model with an accuracy of 99.88%, and also achieved 0.9976 as the highest value of MCC. So, the predictor model is a very useful and helpful tool for lipoylation sites prediction. Some of the residues around lysine lipoylation sites play a vital part in prediction, as demonstrated during feature analysis. The wonderful results reported through the evaluation and prediction of this model can provide an informative and relative explanation for lipoylation and its molecular mechanisms.     

Dynamics of smoke transfer from a localized source by a tornado-like vortex

The dynamics of a model impurity (smoke) transfer by a tornado-like vortex from a localized source situated at the vortex core base has been experimentally studied in a closed chamber. It is established that a flow regime can exist in which the impurity mass transfer along the vortex core is accompanied by mass exchange between the core and surrounding atmosphere. This exchange has the form of spiral formations ejected regularly out of the vortex core. This process takes place under stationary conditions at the chamber input and output, which implies an autooscillatory character of the flow in the system studied.     

Protecting terrestrial ecosystems and the climate through a global carbon market

Protecting terrestrial ecosystems through international environmental laws requires the development of economic mechanisms that value the Earth's natural systems. The major international treaties to address ecosystem protection lack meaningful binding obligations and the requisite financial instruments to affect large-scale conservation. The Kyoto Protocol's emissions-trading framework creates economic incentives for nations to reduce greenhouse-gas (GHG) emissions cost effectively. Incorporating GHG impacts from land-use activities into this system would create a market for an important ecosystem service provided by forests and agricultural lands: sequestration of atmospheric carbon. This would spur conservation efforts while reducing the 20% of anthropogenic CO(2) emissions produced by land-use change, particularly tropical deforestation. The Kyoto negotiations surrounding land-use activities have been hampered by a lack of robust carbon inventory data. Moreover, the Protocol's provisions agreed to in Kyoto made it difficult to incorporate carbon-sequestering land-use activities into the emissions-trading framework without undermining the atmospheric GHG reductions contemplated in the treaty. Subsequent negotiations since 1997 failed to produce a crediting system that provides meaningful incentives for enhanced carbon sequestration. Notably, credit for reducing rates of tropical deforestation was explicitly excluded from the Protocol. Ultimately, an effective GHG emissions-trading framework will require full carbon accounting for all emissions and sequestration from terrestrial ecosystems. Improved inventory systems and capacity building for developing nations will, therefore, be necessary.     

Timing and Classification of Patellofemoral Osteoarthritis Patients Using Fast Large Margin Classifier

Surface electromyogram (sEMG) processing and classification can assist neurophysiological standardization and evaluation and provide habitational detection. The timing of muscle activation is critical in determining various medical conditions when looking at sEMG signals. Understanding muscle activation timing allows identification of muscle locations and feature validation for precise modeling. This work aims to develop a predictive model to investigate and interpret Patellofemoral (PF) osteoarthritis based on features extracted from the sEMG signal using pattern classification. To this end, sEMG signals were acquired from five core muscles over about 200 reads from healthy adult patients while they were going upstairs. Onset, offset, and time duration for the Transversus Abdominus (TrA), Vastus Medialis Obliquus (VMO), Gluteus Medius (GM), Vastus Lateralis (VL), and Multifidus Muscles (ML) were acquired to construct a classification model. The proposed classification model investigates function mapping from real-time space to a PF osteoarthritis discriminative feature space. The activation feature space of muscle timing is used to train several large margin classifiers to modulate muscle activations and account for such activation measurements. The fast large margin classifier achieved higher performance and faster convergence than support vector machines (SVMs) and other state-of-the-art classifiers. The proposed sEMG classification framework achieved an average accuracy of 98.8% after 7 s training time, improving other classification techniques in previous literature.     

A probability-based spectroscopic diagnostic algorithm for simultaneous discrimination of brain tumor and tumor margins from normal brain tissue

This paper reports the development of a probability-based spectroscopic diagnostic algorithm capable of simultaneously discriminating tumor core and tumor margins from normal human brain tissues. The algorithm uses a nonlinear method for feature extraction based on maximum representation and discrimination feature (MRDF) and a Bayesian method for classification based on sparse multinomial logistic regression (SMLR). Both the autofluorescence and the diffuse-reflectance spectra acquired in vivo from patients undergoing craniotomy or temporal lobectomy at the Vanderbilt University Medical Center were used to train and validate the algorithm. The classification accuracy was observed to be approximately 96%, 80%, and 97% for the tumor, tumor margin, and normal brain tissues, respectively, for the training data set and approximately 96%, 94%, and 100%, respectively, for the corresponding tissue types in an independent validation data set. The inherently multi-class nature of the algorithm facilitates a rapid and simultaneous classification of tissue spectra into various tissue categories without the need for a hierarchical multi-step binary classification scheme. Further, the probabilistic nature of the algorithm makes it possible to quantitatively assess the certainty of the classification and recheck the samples that are classified with higher relative uncertainty.     

中国不同纬度野生大豆和栽培大豆AFLP分析

采用ＡＦＬＰ分子标记技术,对我国不同纬度的２０份野生大豆和载培大豆（Ｇ．ｍａｘ）进行了多样性分析,结果表明：（１）我国野生大豆的遗传多样性较栽培大豆更为丰富;（２）根据ＡＦＬＰ分析结果,将野生大豆和栽培大豆完全划分为２类,并发现野生大豆栽培大豆的种特异谱带,说明野生大豆和栽培大豆作为２个种有遗传基因的;（３）野生大啼和栽培大豆不同纬度品种间纬度相傧首先聚在一起,表明不同进化类型的大豆其遗传距离与纬     

A Nanosized CoNi Hydroxide@Hydroxysulfide Core–Shell Heterostructure for Enhanced Oxygen Evolution

A cost‐effective and highly efficient oxygen evolution reaction (OER) electrocatalyst will be significant for the future energy scenario. The emergence of the core–shell heterostructure has invoked new feasibilities to inspire the full potential of non‐precious‐metal candidates. The shells always have a large thickness, affording robust mechanical properties under harsh reaction conditions, which limits the full exposure of active sites with highly intrinsic reactivity and extrinsic physicochemical characters for optimal performance. Herein, a nanosized CoNi hydroxide@hydroxysulfide core–shell heterostructure is fabricated via an ethanol‐modified surface sulfurization method. Such a synthetic strategy is demonstrated to be effective in controllably fabricating a core–shell heterostructure with an ultrathin shell (4 nm) and favorable exposure of active sites, resulting in a moderately regulated electronic structure, remarkably facilitated charge transfer, fully exposed active sites, and a strongly coupled heterointerface for energy electrocatalysis. Consequently, the as‐obtained hydroxide@hydroxysulfide core–shell is revealed as a superior OER catalyst, with a small overpotential of 274.0 mV required for 10.0 mA cm^?2, a low Tafel slope of 45.0 mV dec^?1, and a favorable long‐term stability in 0.10 M KOH. This work affords fresh concepts and strategies for the design and fabrication of advanced core–shell heterostructures, and thus opens up new avenues for the targeted development of high‐performance energy materials.     

Does complementary technology within an ecosystem affect disruptive innovation? Evidence from Chinese electric vehicle listed firms

Complementary technologies are argued to be crucial elements for disruptive innovation. This is even truer in the consistent structure of ecosystems surrounding core disruptive innovations, where firms take interdependence as the premise to provide complementary technologies and create value jointly. Existing research at the disruptive innovation–ecosystem intersection spans a wide range of theoretical perspectives. However, it is still inconclusive about what types of complementary technology configurations might be most useful for the firms they collaborate with to create disruptive innovation. In an attempt to address these gaps, we consider diversity, quality, and novelty as distinguishing structural characteristics of complementary technologies. Empirical evidence from the Chinese electric vehicle listed firms from 2010 to 2019 reveals that the diversity, quality, and novelty of complementary technologies within ecosystems all contribute to subsequent disruptive innovation. Furthermore, we find an inverted U-shaped moderating effect of technological relatedness on the relationship between complementary technologies and disruptive innovation. At intermediate levels of technological relatedness, complementary technologies have greater benefits for disruptive innovation but are comparatively weaker when technological relatedness is low or high. Our findings crystallise a nuanced understanding of how firms orchestrate complementary activities within ecosystems in the disruptive innovation process, thus narrowing the gap with the technological frontier.     

Statistical evaluation of local to regional snowpack stability using simulated snow-cover data

Snow stability, or the probability of avalanche release, is one of the key factors defining avalanche danger. Most snow stability evaluations are based on field observations, which are time-consuming and sometimes dangerous. Through numerical modelling of the snow cover stratigraphy, the problem of having sparsely measured regional stability information can be overcome. In this study we compared numerical model output with observed stability. Overall, 775 snow profiles combined with Rutschblock scores and release types for the area surrounding five weather stations were rated into three stability classes. Snow stratigraphy data were then produced for the locations of these five weather stations using the snow cover model SNOWPACK. We observed that (i) an existing physically based stability interpretation implemented in SNOWPACK was applicable for regional stability evaluation; (ii) modelled variables equivalent to those manually observed variables found to be significantly discriminatory with regard to stability, did not demonstrated equal strength of classification; (iii) additional modelled variables that cannot be measured in the field discriminated well between stability categories. Finally, with objective feature selection, a set of variables was chosen to establish an optimal link between the modelled snow stratigraphy data and the stability rating through the use of classification trees. Cross-validation was then used to assess the quality of the classification trees. A true skill statistic of 0.5 and 0.4 was achieved by two models that detected “rather stable” or “rather unstable” conditions, respectively. The interpretation derived could be further developed into a support tool for avalanche warning services for the prediction of regional avalanche danger.     

Relative precision, efficiency and construct validity of different starting and stopping rules for a computerized adaptive test: the GAIN substance problem scale

Substance abuse treatment programs are being pressed to measure and make clinical decisions more efficiently about an increasing array of problems. This computerized adaptive testing (CAT) simulation examined the relative efficiency, precision and construct validity of different starting and stopping rules used to shorten the Global Appraisal of Individual Needs' (GAIN) Substance Problem Scale (SPS) and facilitate diagnosis based on it. Data came from 1,048 adolescents and adults referred to substance abuse treatment centers in 5 sites. CAT performance was evaluated using: (1) average standard errors, (2) average number of items, (3) bias in person measures, (4) root mean squared error of person measures, (5) Cohen's kappa to evaluate CAT classification compared to clinical classification, (6) correlation between CAT and full-scale measures, and (7) construct validity of CAT classification vs. clinical classification using correlations with five theoretically associated instruments. Results supported both CAT efficiency and validity.     

Analysis of percussion response of dental implants: An in vitro study

The Periometer® quantitative percussion system was used to interrogate the interfacial stability of implants in vitro for comparison with X-ray computer tomography (CT) data. Selected implants were placed as per standard practice in bone stimulant polyurethane blocks. The dimensions of the surgical sites surrounding the implants were analyzed using X-ray computer tomography (CT) to determine the quality of support at the implant–bone interface. In particular, the misfit between the size of the surgical site and the corresponding implant was determined for each sample. The resulting average surgical site error from the CT scans was found to exhibit good agreement with the presence of irregularities found in the percussion data.     

Bradford distribution and core authors in classification systems literature

By applying of Bradford's law to analysis of the source documents and their references by classification systems researchers in the world, this paper presents core authors of the field during the period 1981–1990. The findings show that 1) core authors of the international classification systems literature are the Library of Congress, M. Dewey, S. Ranganathan, J. Comaromi, A. Neelameghan, L. Chan and K. Markey; 2) the highly cited authors are linked either to the developers of the classification systems or to a research center, or else they authored the most frequently cited books; and 3) the data confirms to Bradford's law and the unusual rising tail of Bradford distribution is appeared and explained.     

Enhanced Detection of Glaucoma on Ensemble Convolutional Neural Network for Clinical Informatics

Irretrievable loss of vision is the predominant result of Glaucoma in the retina. Recently, multiple approaches have paid attention to the automatic detection of glaucoma on fundus images. Due to the interlace of blood vessels and the herculean task involved in glaucoma detection, the exactly affected site of the optic disc of whether small or big size cup, is deemed challenging. Spatially Based Ellipse Fitting Curve Model (SBEFCM) classification is suggested based on the Ensemble for a reliable diagnosis of Glaucoma in the Optic Cup (OC) and Optic Disc (OD) boundary correspondingly. This research deploys the Ensemble Convolutional Neural Network (CNN) classification for classifying Glaucoma or Diabetes Retinopathy (DR). The detection of the boundary between the OC and the OD is performed by the SBEFCM, which is the latest weighted ellipse fitting model. The SBEFCM that enhances and widens the multi-ellipse fitting technique is proposed here. There is a pre-processing of input fundus image besides segmentation of blood vessels to avoid interlacing surrounding tissues and blood vessels. The ascertaining of OC and OD boundary, which characterized many output factors for glaucoma detection, has been developed by Ensemble CNN classification, which includes detecting sensitivity, specificity, precision, and Area Under the receiver operating characteristic Curve (AUC) values accurately by an innovative SBEFCM. In terms of contrast, the proposed Ensemble CNN significantly outperformed the current methods.     

Microfabrication and characterization of liquid core waveguide glass channels coated with Teflon AF

We report on the microfabrication and testing of liquid core waveguides (LCW) using Teflon AF for integration in microfabricated microanalysis systems. Teflon AF has an index of refraction less than that of water allowing it to function as a waveguide when used as a cladding layer surrounding an aqueous core. Straight microchannels (400-/spl mu/m width, 60-/spl mu/m depth) etched into Pyrex and soda-lime glass wafers were coated with Teflon AF and sealed with a Teflon AF coated capping wafer. Aqueous fluorescein solutions with varying concentrations were injected into the channels and were illuminated transversely using an ultraviolet light emitting diode. For studying the waveguide attenuation performance, light was focused to a point on the channel. Fluorescence generated in the channel was used to quantify the light collection and waveguide characteristics. The Teflon coating produces a significant enhancement in the amount of light collected in the channel, allowing light to be collected from the 16-mm length tested. This is compared to a control microchannel in glass (no coating), for which the fluorescence drops to the background level in an illumination-detection separation of <4 mm. For sensitivity performance, the entire channel was illuminated. The lower detection limit for spectroscopically resolved fluorescence was /spl sim/10 nMolar.     

Behaviour of core materials during resin transfer moulding of sandwich structures

Abstract

The use of sandwich structures is a well documented and accepted engineering approach. Composite sandwich structures, consisting of a fibre reinforcement impregnated with resin surrounding a foam core, can be used to improve bending stiffness, thermal insulation, and specific mechanical properties at relatively low cost. Such materials can be produced by the resin transfer moulding (RTM)process by incorporating the core within the fibre preform before resin injection. However, this process may result in a number of defects including core shifting, deformation, and delamination, which will have a detrimental effect on mechanical performance. The occurrences of these defects have been studied as a function of the fibre volume fraction, resin pressure, and skin thickness. Delamination between the skin and the core is also a major concern, although this can be controlled by abrading the core surface before moulding. The effect of process parameters on moulding quality and, in particular, the response of the core during moulding and the subsequent delamination resistance of the resulting sandwich structure are presented. This is applied to a range of commercially available core materials.