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The types of marine oil spill pollution are closely related to source tracing and pollution disposal, which is an important basis for oil spill pollution punishment. The types of marine oil spill pollution generally include different types of oil products as well as crude oil and its emulsions in different states. This paper designed and implemented two outdoor oil spill simulation experiments, obtained the hyperspectral and thermal infrared remote sensing data of different oil spill pollution types, constructed a hyperspectral recognition algorithm of oil spill pollution type based on classical machine learning, ensemble learning and deep learning models, and explored to improve the identification ability of hyperspectral oil spill pollution type by adding thermal infrared features. The research shows that hyperspectral combined with thermal infrared remote sensing can effectively improve the recognition accuracy of different oils, but thermal infrared remote sensing cannot be used to distinguish crude oil and high concentration water-in-oil emulsion. On this basis, the recognition ability of hyperspectral combined with thermal infrared for different oil film thicknesses is also discussed. The combination of hyperspectral and thermal infrared remote sensing can provide important technical support for emergency response to maritime emergencies and oil spill monitoring business of relevant departments.

To separate the aging oil emulsions on offshore platforms, a series of polyether demulsifiers based on proanthocyanidins were synthesized, and a dehydration ratio of 94.4% can be obtained at a dosa...

Convolutional Neural Network (CNN) is widely used in Hyperspectral Images (HSIs) classification. However, the fine-grained spatial (FGS) details are discarded during a sequence of convolution and pooling operations for most of CNN-based HSIs classification methods. To address this issue, a unified encoder-decoder framework is proposed to integrate high-level semantics and FGS details for HSIs classification, denoted by FGSCNN. The encoder, including a series of convolution and pooling layers, captures the high-level semantic information with low resolution feature maps. The decoder fuses the high-level low-resolution semantic and the fine-grained high-resolution spatial information, namely, to get the FGS features with high-level semantics. The deconvolution layers and skip connection are used in the decoder to retain the FGS details, while, convolution layers are also used to combine the FGS features with high-level semantics. Based on the encoder-decoder framework, a unified loss function is exploited to integrate the high-level semantic information and FGS details with an end-to-end manner for HSIs classification. Experiments conducted on the three public datasets, i.e. the Indian Pines, Pavia University and Salinas, demonstrate the effectiveness of the proposed method on HSIs classification.

Additional file 1: Fig. S1. The DNMT1 promoter WT and mutant DNA sequences

Additional file 1: Table S1. The clinical characteristics of GSE130756. Table S2. The polarization and anti-inflammatory/pro-inflammatory gene sets of monocytes/neutrophils. Table S3. The relation between signaling pattern and metabolism in the TME. Fig. S1. Verification of the landscape of cell definition. Fig. S2. Heatmaps of gene expression level associated with key metabolic enzymes. Fig. S3. Polarization of myeloid immune cells in microenvironment. Fig. S4. GSEA analysis of insulin pathway and HIF pathway in AML progenitor cells and tumor-infiltrating immune cells. Fig. S5. The metabolic and oxygen preference of different monocyte subtypes in the TME. Fig. S6. The metabolic and oxygen preference of different neutrophil subtypes in the TME. Fig. S7. Cell‒cell communications in the TME. Fig. S8. Heatmaps of cytokines communication network in TME. Fig. S9 Exploration of metabolic expression differential gene in AML progenitor cells.

The water-in-aging crude oil (WACO) emulsions treatment is an urgent problem in the offshore platform because of the low demulsification efficiency of conventional demulsifier. To resolve this problem, a new kind of tannic acid phenol-amine (TAPA) polyether with multiple branches, and aromatic rings is prepared in this article. This synthetic route is expected to avoid the side reactions during the synthesis of commercial phenol-amine resin. Benefit from this structure, the demulsification efficiency reaches 91.7% within 90 min in laboratory test and 97.9% within 45 min on offshore platform measurement toward WACO emulsions. Demulsification mechanism is systematically investigated by the measurements of hydrophile-lipophile balance (HLB) value, relative solubility number (RSN) value, dosage, interfacial activity and stability analysis. To have a direct insight into demulsification, the variation of water droplets in demulsification is observed. The effect of concentration and organic salts on cloud point are discussed. The demulsification effectiveness of TAPA demulsifier is mainly ascribed to the high interfacial activity, hyperbranched structure, multiple aromatic rings and suitable HLB value. These results indicate that TAPA could be an effective demulsifier in WACO emulsions breaking.

As the number of epilepsy patients in our country is increasing year by year, and more and more families are suffering from epilepsy diseases, so there is an increasingly urgent need for fast and effective epileptic seizure detection and recognition technology. However, the traditional recognition and diagnosis method based on EEG signals cannot be extended to daily life due to the limitation of space. In this context, a system design scheme for epileptic seizure recognition based on wearable device using extremity motion signals is proposed, and its effectiveness is verified through the display of experimental results.

Additional file 4: Table S2. Univariate Cox regression analysis of LINC00460 expression and clinicopathologic variables predicting the survival of CRC patients

Additional file 5: Table S3. Multvariate Cox regression analysis of LINC00460 expression and clinicopathologic variables predicting the survival of CRC patients

Additional file 1: Table S1. Primers used for qRT-PCR.

With the continuous development of the Internet of Things technology, its application industry is also expanding. This paper takes the power of the Internet of Things as the core and conducts research and design of a general-purpose power IoT system platform architecture. Through an in-depth analysis of the system platform architecture, its implementation path and application range are clarified, which provides technical support for the rapid development of the electric power Internet of Things.

With the emergence of DevOps movement, software engineers are starting to rely on cloud platform APIs for implementing many fault tolerance, self-adaptation and continuous delivery features such as deployment changes, scaling out/in, exception handling, backup/recovery and migration. In this paper we present an empirical study of API issues in commercial cloud platforms. We classified the API failures and their causes, and discuss possible remedies for each category to improve reliability of cloud applications and introduce our solutions to deal with resource-characteristics faults and late timing failures.

Food recognition is one of the core functions for a smart refrigerator. But there are many challenges for accurate food recognition due to reasons of too many kinds of food inside the refrigerator which tends to obscure each other, and they may look very similar. This paper proposes a fruit recognition approach that fuses weight information and multi deep learning models. The proposed approach can remarkably improve recognition accuracy. We have extensively evaluated the proposed approach for its performance and accuracy, which demonstrate the effectiveness of the proposed approach.

Currently, vehicle incidence remains high, but the related research is mainly focused on single-vehicle collision warning, which is unable to notice the following multiple endangered vehicles in emergency. The main technical challenge of chain collision warning is accurate and seamless ranging or positioning of the neighboring multiple vehicles. Because of the non-line of sight (NLOS) environments, the traditional ultrasonic or laser method does not work. At the same time, the accuracy of global navigation satellite system (GNSS) is over 10 m, such as global positioning system (GPS) or BeiDou satellite positioning system (BDS), which is not efficient for the cooperative collision avoidance (CCA) system. Moreover, GNSS fails to operate in NLOS tunnels or downtown areas where blockage of satellite signals is frequent. Thus, in this paper, a seamless and high accuracy positioning method based-on three kind of multi-source information fusion is proposed, i.e., the high accuracy local positioning information is provided by IEEE 802.11p protocol, the dual-mode wide area differential positioning information provided by the fusion of BDS and GPS, the positioning information of dead reckoning provided by the fusion of on-board diagnostic (OBD) and micro-electro-mechanical system-inertial navigation system. In vehicle positioning system, the accurate time of arrival (TOA) estimation is very important, so in this paper, a two steps method for TOA estimation using the IEEE 802.11p short preamble is proposed. Simulations show that in both the international telecommunication union vehicular multipath channel and the additive white Gaussian noise channel, the proposed method provides better accuracy and is less time complex than some well-known methods. The estimated TOA fused with the GNSS and OBD information can be used for seamless and high accuracy positioning in the CCA system to avoid traffic accident.

Natural polyphenols such as tannins, procyanidins, and cardanol are widely used in the field of surface chemistry. To further explore the application of the other natural polyphenols, epigallocatechin gallate (EGCG) derived from green tea was employed. EGCG is an ideal choice to achieve a hyperbranched surfactant. A precursor (EGA) was first obtained by the reaction among the EGCG, formaldehyde, and tetraethylenepentamine (TEPA). EGA surfactants were synthesized in the high-pressure kettle by anionic polymerization with EGA, propylene oxide (PO) and ethylene oxide (EO). The good consistency between the designed PO/EO ratios of EGA surfactants and the theoretical values were proved by 1H NMR spectra. The increase of micelle size with the increase of surfactant concentration and NaCl concentration was observed both by dynamic light scattering and TEM. Result shows EGA surfactants with higher absolute value of ∆ G ° ads and high interfacial activity have potential demulsification applications. As a result, toward aging oil emulsions of the South China Sea Oilfield, the surfactants exhibited excellent demulsification performance. The dehydration ratio reached 94.3% at a low concentration of 100 mg L−1 for 100 min at 70 °C, while the commercial demulsifier SP169 only reached 86.9%. This emulsion could be dehydrated completely at even higher temperature at 80 °C as well as higher dosage in 150 mg L−1. Meanwhile, the demulsification ratios of crude oil emulsions and aging oil emulsions produced from Bohai Oilfield reached 94.0% and 95.0%, respectively. Coming from the waste oil after demulsification, aging oil emulsions have more additives than crude oil emulsions, therefore the former is more stable. In addition to the above high interfacial activity, this excellent demulsification performance may result from the assistance of polyphenol structure and multiple branches of EGA surfactants. The demulsification of three above emulsions demonstrated that although there exist great differences in properties of emulsions, EGA surfactants exhibit the universality of efficient demulsification.

By far there is lack of research on different working conditions between rigid and flexible dynamics of truck mounted concrete pump booms. First a 3D model has been established by using virtual prototyping technology of a 37 m long boom in Pro/Engineering software. Then the rigid body simulation model has been built. Next modal superimposition method is adopted to change the 4 rigid body booms into flexible ones. Kinematics law and dynamic characteristics of 4 common working conditions had been studied then. Next tip displacement and the first boom hydraulic cylinder force of the 4 working conditions between rigid and flexible models have been researched. Furthermore the first natural frequencies of the structure have been calculated. The results show that the frequency of the horizontal condition has the lowest of all and the roof condition has the largest of all. Besides the cylinder forces of the flexible model are larger than the corresponding rigid ones because of the flexible boom vibration. Finally an experiment has been done on a boom test rig which proved that the established simulation model is reasonable and the frequency results are correct. All of these provide design reference to mechanical manipulator as well as reducing product development cost of such mechanism.

Aiming at solving of missing and false alarm in anomaly detection of In-vehicle CAN communication, which only detects a single CAN message but ignores the correlation among multiple messages, an anomaly detection algorithm of vehicular CAN bus based on linear chain conditional random field (Linear-CRF) is proposed in this paper. Based on L-BFGS (Limited-memory BFGS) learning algorithm, the double-contour modes of Linear-CRF normal model and anomaly model are constructed respectively. The algorithm of message anomaly detection based on Linear-CRF model is elaborated in detail. The proposed anomaly detection algorithm is simulated and analyzed. The simulation results show that the proposed algorithm can effectively identify the relevance of the context content of the message, and effectively improve the detection accuracy of the In-vehicle CAN bus anomaly message.

In order to ensure the safe and stable operation of smart grid, the intrusion detection scheme based on node trust value is proposed for external node attacks of wireless sensor networks (WSNs) in Advanced Metering Infrastructure (AMI). The scheme applies the positive-abnormal statistics of communication messages to calculate the trust value. In this paper, the node trust value calculation method is improved and the trust value is used to judge whether the node is invaded. The simulation results show that the scheme can effectively detect external node intrusion, and has higher detection rate and lower false detection rate.

Recently, hyperspectral image (HSI) classification has attracted increasing attention in the remote sensing field. Plenty of CNN-based methods with diverse attention mechanisms (AMs) have been proposed for HSI classification due to AMs being able to improve the quality of feature representations. However, some of the previous AMs squeeze global spatial or channel information directly by pooling operations to yield feature descriptors, which inadequately utilize global contextual information. Besides, some AMs cannot exploit the interactions among channels or positions with the aid of nonlinear transformation well. In this article, a spectral-spatial network with channel and position global context (GC) attention (SSGCA) is proposed to capture discriminative spectral and spatial features. Firstly, a spectral-spatial network is designed to extract spectral and spatial features. Secondly, two novel GC attentions are proposed to optimize the spectral and spatial features respectively for feature enhancement. The channel GC attention is used to capture channel dependencies to emphasize informative features while the position GC attention focuses on position dependencies. Both GC attentions aggregate global contextual features of positions or channels adequately, following a nonlinear transformation. Experimental results on several public HSI datasets demonstrate that the spectral-spatial network with GC attentions outperforms other related methods.

Alongside with the convergence of In-vehicle network (IVN) and wireless communication technology, In-vehicle CAN communication with external networks reinforces the connectivity among systems of a vehicle. Vehicle communication is facing severe challenges. Intrusion detection technology is one of the most widely used technologies to ensure the safety of In-vehicle CAN communication, so an adaptive intrusion detection method for In-vehicle CAN bus based on message periodicity is proposed. Communication load of CAN bus in the vehicle, the unique priority mechanism and transmission waiting mechanism of CAN message cause a certain fluctuation in the message cycle. The influence of this fluctuation on the detection accuracy of intrusion detection algorithm is analysed while rules are determined by establishing and optimizing the detection threshold in order to further improve the accuracy. Experimental results show that the adaptive intrusion detection based on message periodicity can effectively detect injection and interruption attacks.

In recent years, the number of DC power system in substation has been increasing. And the technical transformation of DC power system, fault maintenance and other workload is also on the rise, therefore dc emergency power emerged. The lead-acid battery is usually adopted for traditional DC emergency power supply. The disadvantage of lead-acid battery in volume and quality makes it difficult to realize the portability and mobility of dc emergency power. Lithium iron phosphate battery technology is the frontier technology in the rapid development period. However, the characteristics are not studied clearly. This paper studies the characteristics of lithium iron phosphate battery in different ambient temperature, operating conditions, and current of charge and discharge, analyses and summarizes the characteristics of battery charge and discharge, so as to improve the maintenance of station DC power supply system and the reliability of power supply network.

A novel branched polyether is prepared with 1,1,2,2-tetrakis (4-hydroxyphenyl) ethane as a core, amino groups as backbone, and polypropylene oxide (PPO)–polyethylene oxide (PEO) chains as branches based on phenol-amine resin, propylene oxide (PO), and ethylene oxide (EO). The surface activity is investigated by surface tension measurement at different temperature. The increase in PO/EO ratio or PPO chain lengths improve the surface activity and decrease the critical micelle concentration (cmc) as well as increase the temperature. The addition of inorganic salts causes a slight increase in cmc. The demulsification of water-in-crude-oil (W/O) emulsions, whether from polymer flooding or not, shows that the branched polyether is a good demulsifier to break the W/O emulsions. The stability of W/O emulsion in the presence of polyether shows that the highest water dehydration is not necessarily the highest stability index of W/O emulsion due to the turbidity of separated water. The demulsification process is also observed by microscope. Distribution of the polyether after demulsification is evaluated by partition coefficient at various concentrations.

Summary Accurate resource requests prediction is essential to achieve optimal job scheduling and load balancing for cloud Computing. Existing prediction approaches fall short in providing satisfactory accuracy because of high variances of cloud metrics. We propose a deep belief network (DBN)-based approach to predict cloud resource requests. We design a set of experiments to find the most influential factors for prediction accuracy and the best DBN parameter set to achieve optimal performance. The innovative points of the proposed approach is that it introduces analysis of variance and orthogonal experimental design techniques into the parameter learning of DBN. The proposed approach achieves high accuracy with mean square error of [10−6,10−5], approximately 72% reduction compared with the traditional autoregressive integrated moving average predictor, and has better prediction accuracy compared with the state-of-art fractal modeling approach. Copyright © 2016 John Wiley & Sons, Ltd.

Video data has become the largest source of big data. Owing to video data's complexities, velocity, and volume, public security and other surveillance applications require efficient, intelligent runtime video processing. To address these challenges, a proposed framework combines two cloud-computing technologies: Storm stream processing and Hadoop batch processing. It uses deep learning to realize deep intelligence that can help reveal knowledge hidden in video data. An implementation of this framework combines five architecture styles: service-oriented architecture, publish-subscribe, the Shared Data pattern, MapReduce, and a layered architecture. Evaluations of performance, scalability, and fault tolerance showed the framework's effectiveness. This article is part of a special issue on Software Engineering for Big Data Systems.

With the continuous development of distribution automation technology and the rapid growth of the number of distribution equipment, this paper analyzes how to carry out the operation management of distribution network equipment and the key technology of application based on the existing distribution management system. The key technologies include the unified management, automatic control and online monitoring of equipment. Through the research of related technologies, the rapid and effective management and control of mass distribution equipment is realized, the management efficiency of equipment is improved, and the technical support means for the rapid construction of distribution network are provided.

This dissertation addresses the ultimate strength analysis of nonlinear beam-columns under axial compression, the sensitivity of the ultimate strength, structural optimization and reliability analysis using ultimate strength analysis, and Reliability-Based Design Optimization (RBDO) of the nonlinear beam-columns. The ultimate strength analysis is based on nonlinear beam theory with material and geometric nonlinearities. Nonlinear constitutive law is developed for elastic-perfectly-plastic beam cross-section consisting of base plate and T-bar stiffener. The analysis method is validated using commercial nonlinear finite element analysis. A new direct solving method is developed, which combines the original governing equations with their derivatives with respect to deformation matric and solves for the ultimate strength directly. Structural optimization and reliability analysis use a gradient-based algorithm and need accurate sensitivities of the ultimate strength to design variables. Semi-analytic sensitivity of the ultimate strength is calculated from a linear set of analytical sensitivity equations which use the Jacobian matrix of the direct solving method. The derivatives of the structural residual equations in the sensitivity equation set are calculated using complex step method. The semi-analytic sensitivity is more robust and efficient as compared to finite difference sensitivity. The design variables are the cross-sectional geometric parameters. Random variables include material properties, geometric parameters, initial deflection and nondeterministic load. Failure probabilities calculated by ultimate strength reliability analysis are validated by Monte Carlo Simulation. Double-loop RBDO minimizes structural weight with reliability index constraint. The sensitivity of reliability index with respect to design variables is calculated from the gradient of limit state function at the solution of reliability analysis. By using the ultimate strength direct solving method, semi-analytic sensitivity and gradient-based optimization algorithm, the RBDO method is found to be robust and efficient for nonlinear beam-columns. The ultimate strength direct solving method, semi-analytic sensitivity, structural optimization, reliability analysis, and RBDO method can be applied to more complicated engineering structures including stiffened panels and aerospace/ocean structures. Ph. D.

Context-awareness for big data applications is different from that of traditional applications in that it is getting challenging to obtain the contexts from big data due to the complexity, velocity, variety, and other aspects of big data, especially big video data. The awareness of contexts in big data is more difficult, and should be more in-depth than that of classical applications. Therefore, in this paper, we propose an in-depth context-awareness framework for a pervasive video cloud in order to obtain underlying contexts in big video data. In this framework, we propose an approach that combines the historical view with the current view to obtain meaningful in-depth contexts, where deep learning techniques are used to obtain raw context data. We have conducted initial evaluations to show the effectiveness of the proposed approach in terms of performance and also the accuracy of obtaining the contexts. The evaluation results show that the proposed approach is effective for real-time context-awareness in a pervasive video cloud.

Machine learning libraries are integral to a big data mining platform. There are three limitations on adopting current machine learning libraries in such a platform. First, these algorithms are not implemented for handling both online and offline big data analysis. Second, libraries exist in a variety of frameworks using different programming languages, which make it difficult in integrating several algorithms. Third, most machine learning libraries provides APIs for programming only, thus not user-friendly for those do not have a sufficient understanding of algorithms and those lack of programming skills. In this paper, we implement a comprehensive machine learning library including common algorithms and deep learning algorithms. We integrate this library at a platform level that allows both online and offline data analysis using this library. We further design a user-friendly portal that enables quick and agile data analysis practices. All of these form an Online-Offline Combined Big Data Mining Platform (OOBDP). We present a demonstration of big oil data analysis using this platform. We observe the that OOBDP can easily accommodate industrial requirement for adaptable data mining process, with personalized usage scenarios, and easy to use experiences.

Social data from online social networks is expanding rapidly as the number of users and articles posted increases, making public opinion analysis a greater challenge. Real-time topic detection is a key part of public opinion analysis. The complex data processing involved in traditional clustering and text categorization can lead to time delays in topic detection. In this paper we construct similar networks and detect topics from similar communities that reduces the processing overhead in obtaining real-time topics. The similar communities consist of users with high similarity between them. We collect public topics from the microposts of delegates selected from each similar community. Selecting delegates can reduce the processing time of large amounts of redundant data during topic detection. Obtaining public opinion keywords in real time allows organizations to respond to public opinion security incidents in real time. Experiments showed that our scheme can find public topics faster and more effectively than two traditional algorithms.

Identifying protein coding regions in DNA sequences by computational methods is an active research topic. Welan gum produced by Sphingomonas sp. WG has great application potential in oil recovery and concrete construction industry. Predicting the coding regions in the Sphingomonas sp. WG genome and addressing the mechanism underlying the explanation for the synthesis of Welan gum metabolism is an important issue at present. In this study, we apply a self adaptive spectral rotation (SASR, for short) method, which is based on the investigation of the Triplet Periodicity property, to predict the coding regions of the whole-genome data of Sphingomonas sp. WG without any previous training process, and 1115 suspected gene fragments are obtained. Suspected gene fragments are subjected to a similarity search against the non-redundant protein sequences (nr) database of NCBI with blastx, and 762 suspected gene fragments have been labeled as genes in the nr database.

There arises the needs for fast processing of continuous video data using embedded devices, for example the one needed for UAV aerial photography. In this paper, we proposed a distributed embedded platform built with NVIDIA Jetson TX1 using deep learning techniques for real time video processing, mainly for object detection. We design a Storm based distributed real-time computation platform and ran object detection algorithm based on convolutional neural networks. We have evaluated the performance of our platform by conducting real-time object detection on surveillance video. Compared with the high end GPU processing of NVIDIA TITAN X, our platform achieves the same processing speed but a much lower power consumption when doing the same work. At the same time, our platform had a good scalability and fault tolerance, which is suitable for intelligent mobile devices such as unmanned aerial vehicles or self-driving cars.

This paper aims to achieve the measurement of plasma parameters in Galatea. Firstly, we research the measurement principle and the theory of electron density and electron temperature calculation. Then, we analyze the probe characteristics, select the cylindrical probe to measure plasma space potential, the spherical probe to measure the plasma electron density and electron temperature. Finally, we take a simulation analysis of confined plasma in Trymyx, combing the device structure and simulation results, and we make reasonable arrangement of the probe and make effective probe measurement program to obtain the velocity distribution of the plasma shape and plasma.

Hesmondhalgh, David (2013) Why Music Matters, Malden: Wiley-Blackwell, ISBN: 978-1-4051-9242-2 (hardcover); 978-1-4051-9241-5 (paperback); 978-1-118-53581-3 (e-book), 198 pages.

Genetic algorithm is a well known bio-inspired algorithm, which has been widely used to solve practical problems in real-life. The performance of the algorithm heavily depends on the convergence related to the values of parameters involved. It is formulated as a hard problem to select suitable values of mutation and crossover rates to achieve fast or slow convergence for unknown problems. As a new study of system framework inspired by cell model, membrane computing models is with a membrane structure having region segmentation, intrinsic discrete, non-deterministic, programmable and transparent features. In this paper, a hybrid “fast-slow” convergent framework for genetic algorithm inspired by membrane computing is proposed and applied to search optimal solution of 41 benchmark functions. It is obtained by the data experimental results that our method performs well in solving benchmark functions by achieving accuracy rate about 96%.

In this paper, we propose a food image recognition system with convolutional neural networks(CNN), which has been applied to image recognition successfully in the literature. A CNN which consists of five layers has been built and two group of controlled trials have been processed on it. Two datasets are prepared: one is UEC-FOOD100 dataset which is an open 100-class food image dataset including about 15000 images and the other is a fruit dataset that established by ourselves including over 40000 images. We have achieved the best accuracy of 80.8% on the fruit dataset and 60.9% on the multi-food dataset. In addition, we validate the method on two groups of controlled trials and discover the effect of color under various conditions that the color feature is not always helpful for improving the accuracy by comparing the results of two group of controlled trials. As future work, we will combine image segmentation with image recognition to get a better performance.

Vehicle detection and type recognition are important for intelligent transportation systems in smart cities. The real time high accuracy recognition with affordable hardware is a challenging issue due to the complexities of video data. In this paper, we propose an integrated approach that combining traditional three-frame difference and deep Convolutional Neural Networks (DCNNs) to detect vehicle and recognize vehicle type in traffic videos captured with fixed mounted cameras. This integrated approach can take advantage of the real-time motion detection ability of three-frame difference and capabilities of image recognition of DCNNs. We have evaluated the proposed approach using road traffic videos in terms of accuracy and performance, which show very promising results.

The structure and magnetic field configuration of a non-Tokamak magnetic controlled fusion device—Galathea--is introduced. The force balance of plasma confined by myxines in a toroidal topology is semiquantitively analyzed and the computing formula of vertical magnetic field used to compensate the toroidal force is given, on a Galathea device "Trimyx", which has three myxines; A magnetohydrodynamic model is built for describing plasma confined by only one straight myxine, to solve the steady distribution of plasma pressure and explain the unavoidability phenomenon that plasma energy loss caused by contacting with myxines; Stationary and time-varying model are built and simulated by software COMSOL, to solve the separation of plasma and myxines, an effective separation method is given.

The drilling fluid pulse communication is a very popular technique in the measurement while drilling (MWD) field, because it has the simpler architecture compared to the continuous pressure ware systems. A noise suppression method for signal detection method based on the over complete dictionary is proposed in this paper. Considering the characteristics of the drilling fluid pulse signals, the theory of the sparse representation of those low duty cycle and spike-like pulses is adopted. Then guidelines to build different over complete dictionaries for signals are presented. Simulation results show that the PPM signals of drilling fluid pulse systems can be detected directly, and the AWGN noise can be reduced significantly by the proposed approach.

Although partner notification (PN) has long been considered as an important component in sexually transmitted disease (STD) control programs and has been implemented in both developed and developing world little information is known about the acceptability and efficacy of the strategy in China. The goal of this study was to assess the acceptability and outcome of self-referral for male patients with urethral discharge and to identify the characteristics associated with successful outcome based on a specialized STD clinic. From November 2001 to November 2002 730 patients with confirmed urethral discharge were selected and asked by STD clinicians to bring their sexual contacts to treatment in a specialized STD clinic. The demographic and sexual characteristics of returned and unreturned index patients were analyzed. Univariate and multivariate analyses were applied to identify the characteristics associated with the outcome of self-referral. Of 723 eligible index patients 294 (40.7%) who returned for follow up identified 534 sexual partners and 429 (59.3%) who did not return for follow up identified 759 sexual partners. Of the total named 1293 sexual partners 301 (23.3%) were notified by index male patients and 265 (20.5%) presented at the clinic. Of the 265 partners tested 165 (62.3%) were infected with gonorrhea chlamydia or both; of them 78 (47.3%) were asymptomatic. When the partnerships were married and steady the informed rate was higher than that when the partnership was casual. For commercial sexual partnerships only 1.8% of the partners were informed. In multivariate analysis a prior STD history and condom use during the last sexual contact were associated with successful outcome of notification. Patient referral was accepted both by STD clinic attendees and STD clinicians. The results of the study show that use of self-referral for partners of men with urethral discharge in a Chinese STD clinic was with minimal effort moderately successful. Further efforts to improve case-finding outcomes are warranted. (authors)

The technology of in situ immobilization with amendments is an important measure that remediates the soil contaminated by heavy metal, and selecting economical and effective modifier is the key. The effects and mechanism of steel slag, the silicon-rich alkaline by-product which can remediate acidic soil contaminated by heavy metal, are mainly introduced in this paper to provide theory inferences for future research. Firstly, the paper analyzes current research situation of in situ immobilization with amendments. Then, it introduces the main physicochemical properties of steel slag, and the effect on soil pH value as well as heavy metal activity. Besides, the paper elaborates the promoting effect on silicon-requiring plant and the strengthening mechanism for its resistant capability of heavy metal. According to the analysis, the application of steel slag could be a potential valuable strategy to remediate acidic soil contaminated by heavy metal by modifying the transformation of heavy metal in both soil and plant, so that the translocation of heavy metal in food chain is reduced.

In this paper, stability studies of a multichannel active noise control system are approximated in terms of equivalent transfer function matrix realization. By considering the presence of the acoustic channel, a multichannel filtered-X LMS adaptive notch filter is used with a specified plant model. It has been shown that the system behaves as a linear time-invariant system with a slow adaption. The convergence coefficient and the estimation error of the plant phase characterize the behavior of the system.

A quantified-role based access control model for cloud computing is proposed to provide fine-grained authorization and dynamic adjustment of permissions. With quantified value assigned to permissions tuples, any part privileges of a role can be accurately described and managed. Behavior value is proposed to reflect a cloud user's operations security, and a mapping function from behavior value to quantified role is established. Theoretical analysis and application results show the presented model can significantly reduce number of roles in cloud computing systems, improve efficiency of authorization, and implement dynamic adjustment of cloud users' permissions.