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Background: More and more attention has been paid to the research of muscle mass and muscle quality of quadriceps femoris (QF) in knee osteoarthritis (KOA). This study aimed to explore the asymmetric changes of muscle mass, biomechanical property and muscle activation in the inter-limbs QF of KOA patients, and tried to provide a novel insight for the evaluation, prevention and treatment of KOA.Methods: A total of 56 Participants with unilateral or bilateral KOA were included in this study: 30 patients with unilateral pain and 26 patients with bilateral pain were assigned to the bilateral group (BG) and unilateral group (UG), respectively. The symptom severity of bilateral lower limbs was evaluated by visual analogue scale, and the relatively serious leg (RSL) and relatively moderate leg (RML) were classified. The thickness of rectus femoris (RF), vastus intermedius (VI), vastus medialis (VM) and vastus lateralis (VL) were measured by ultrasound. The Shear wave elastography (SWE) techniqie was used to measure the shear modulus of RF, VM and VL. Surface electromyography (sEMG) was used to assess the root mean square (RMS) of the RF, VM, and VL during straight leg raising in a sitting position and squatting task. We calculated the asymmetry indexes of inter-limbs for the corresponding indices of the measured muscles.Result: Thickness of RF, VI and VL of RSL was lower than those on RML (p < 0.05), and thickness of VM was lower more significant (p < 0.01). Thickness of RF, VI and VL of RSL was also lower than those of RML in BG (p < 0.05), however, there was no significant difference in VM thickness (p > 0.05). There were no significant difference in Asymmetry indexes of all measured muscle thickness between the two groups (p > 0.05). The Shear modulus of RF, VM, and VL in the RML of UG and BG was higher than those in the RSL (p < 0.05). In sitting and straight leg raising task, the RMS of RF, VM and VL in RML were higher than those in RSL, UG and BG both showed this trend (p < 0.05). About squatting task, in UG, the RMS of the three muscles in RML of patients were also higher than those in the RSL (p < 0.05). However, the difference was not significant in BG (p > 0.05). In the straight leg raising task, the asymmetry indexes of RMS in RF, VM, and VL of both the two groups were positively correlated with VAS scores (p < 0.05).Conclusion: The muscle thickness, shear modulus and muscle activation electromyography of QF in RML were higher than those of RSL in unilateral KOA patients. The VM of RML in bilateral KOA patients may show muscle thickness degeneration earlier, which is closer to the VM of RSL. The shear modulus of RF, VM, and VL were higher on the RML side during the single-leg task, but there may be passive compensation for muscle activation in both lower limbs during the bipedal task. In conclusion, there is a general asymmetry of QF muscle mass, biomechanics Characteristic and performance in patients with KOA, which may provide new ideas for the assessment, treatment and rehabilitation of the disease.

Faced with complex engineering geological problems, the application of comprehensive geophysical prospecting technologies allows for various technical achievements to complement each other, verify each other, reduce the ambiguity in geophysical prospecting results, and improve the accuracy of geophysical prospecting interpretations. At present, comprehensive geophysical prospecting is predominantly a simple synthesis of quantification, lacks effective intelligent decision-making methods, and cannot maintain pace with the development direction of future intelligent geophysical prospecting technologies. Therefore, this study proposes a geophysical prospecting selection evaluation method based on comprehensive weights to realize the transformation from empirical to scientific decision-making. In the proposed method, four criteria—specifically, detection accuracy, technical reliability, economic rationality, and data richness level—are first selected to establish an evaluation index system. Then, analytical hierarchy process and entropy weight method are used to combine subjective experience with objective data to form a comprehensive weight for the evaluation of geophysical prospecting selection through the fuzzy evaluation method. The efficacy of the proposed method was evaluated through application to karst exploration in the limestone mining area of the Hepu in Guangxi, for which an optimal geophysical prospecting scheme, composed of electrical resistivity tomography, microtremor survey method, and cross-hole resistivity computed tomography, was obtained. The scheme realizes the asymptotic detection of karst development from coarse to fine and improves the timeliness and economics of disaster management. These results verify the effectiveness and scientific soundness of the proposed method.

The presence of genetically modified organisms (GMOs) in food and feed products is regulated in many countries. Various countries have issued labeling threshold levels to supervise GMO commercialization, and efficient detection methods to quantify GMO contents are critical to meet labeling regulations. The aim of this study was to develop event-specific methods to measure the event-specific/lectin gene (Le1) copy number ratio in soybean using droplet digital PCR (ddPCR), which is a recently developed technology that offers high accuracy. Duplex ddPCR methods were developed and optimized using a central composite design for the five most widely authorized GM soybean events, in which the target gene was labeled with one fluorescent reporter and the endogenous gene with another. The methods produced specific results, and the performance complied with international GMO labeling regulations. Further, the duplex ddPCR methods are compatible with chip-based digital PCR platforms and original real-time PCR, but allow higher throughput and are more sensitive in identification and quantification of these five widely authorized genetically modified soybean events.

Landslides are common occurrences along Chinese rail routes. Accordingly, the accurate determination of sliding-surface locations and the underlying causes become critical from the landslide-disaster-management and loss-mitigation viewpoints. In this study, the landslide in the K66 section of the Jiheng Railway (installed in Jinggangshan City, Jiangxi, China) was investigated. The causative factors and characteristics of the landslide were analyzed using geophysical methods as well as drilling and monitoring techniques. The results reveal that a combination of the transient electromagnetic method and electrical resistivity tomography can be used to identify the location of water-rich landslide-prone regions. When combined with the drilling information, these results reveal the tendency of landslide occurrence in regions comprising strongly weathered carbonaceous shale. Moreover, an uneven slope deformation is observed along the railway line. The seepage of surface water into the slope is revealed as the primary cause of landslides. This is because the reduced strength of the strongly weathered carbonaceous shale facilitates the formation of sliding surfaces. This paper presents a comprehensive survey plan for landslides occurring along railway routes. Furthermore, an assessment of the risk of future landslide occurrences in the investigation area is presented, thereby providing an appropriate reference for landslide prevention and loss mitigation.

Large volumes of water originating from limestone pit rock walls can cause construction hazards. To ensure safe construction and avoid inaccurate grouting, localized detection work is necessary. Borehole-to-surface electrical resistivity tomography (ERT) has high resolution, is sensitive to low resistivity anomalies such as water, can obtain deeper geoelectric information than surface ERT, and is effective in high resolution detection of water-inrush channels. In this study, a total of eight geoelectric models were established for numerical simulation analysis, using single anomaly imaging with pole-pole, pole-dipole, dipole–dipole, and mixed arrays. Complex geological anomalies were imaged under a mixed array. At the Guangxi Pingnan limestone mine water project, this method successfully located and produced images of the water channel behind the water-inrush point of the pit wall. This effectively guided the grouting and blockage of the water channel.

The riparian zone is an important ecological corridor connecting the upstream and downstream rivers. Its highly complex biological and physical environments significantly affect the biogeographical pattern of species and various ecosystem functions. However, in alpine riparian ecosystems, the distribution patterns and drivers of arbuscular mycorrhizal (AM) fungi, a group of functionally important root-associated microorganisms, remain poorly understood. In this study, we investigated the AM fungal diversity and community composition in near-bank (wetland) and far-bank (alpine meadows) soils along the Niaqu River in the Nam Co watershed, and assessed the relative importance of abiotic and biotic filtering in shaping these distributions. Overall, 184 OTUs were identified in the riparian ecosystem, predominantly belonging to the genus Glomus, especially in the downstream soils, and Claroideoglomus in near-bank soils. AM fungal colonization, spore density, and α diversity showed an overall increasing trend along the river, while the extraradical hyphae declined dramatically from the middle of the river. AM fungal communities significantly varied between the wetland and alpine meadows in the riparian zone, mainly driven by the geographic distance, soil water content, soil pH, and plant communities. Specifically, soil pH was the principal predictor of AM fungal community in near-bank wetland soils, while soil water content had a most substantial direct effect in alpine meadows. These findings indicate that abiotic factors are the most important divers in shaping AM fungal communities at the watershed scale, which could be helpful in alpine riparian biodiversity conservation and management.

3D human pose estimation (HPE) in autonomous vehicles (AV) differs from other use cases in many factors, including the 3D resolution and range of data, absence of dense depth maps, failure modes for LiDAR, relative location between the camera and LiDAR, and a high bar for estimation accuracy. Data collected for other use cases (such as virtual reality, gaming, and animation) may therefore not be usable for AV applications. This necessitates the collection and annotation of a large amount of 3D data for HPE in AV, which is time-consuming and expensive. In this paper, we propose one of the first approaches to alleviate this problem in the AV setting. Specifically, we propose a multi-modal approach which uses 2D labels on RGB images as weak supervision to perform 3D HPE. The proposed multi-modal architecture incorporates LiDAR and camera inputs with an auxiliary segmentation branch. On the Waymo Open Dataset, our approach achieves a 22% relative improvement over camera-only 2D HPE baseline, and 6% improvement over LiDAR-only model. Finally, careful ablation studies and parts based analysis illustrate the advantages of each of our contributions.

Generic event boundary detection aims to localize the generic, taxonomy-free event boundaries that segment videos into chunks. Existing methods typically require video frames to be decoded before feeding into the network, which demands considerable computational power and storage space. To that end, we propose a new end-to-end compressed video representation learning for event boundary detection that leverages the rich information in the compressed domain, i.e., RGB, motion vectors, residuals, and the internal group of pictures (GOP) structure, without fully decoding the video. Specifically, we first use the ConvNets to extract features of the I-frames in the GOPs. After that, a light-weight spatial-channel compressed encoder is designed to compute the feature representations of the P-frames based on the motion vectors, residuals and representations of their dependent I-frames. A temporal contrastive module is proposed to determine the event boundaries of video sequences. To remedy the ambiguities of annotations and speed up the training process, we use the Gaussian kernel to preprocess the ground-truth event boundaries. Extensive experiments conducted on the Kinetics-GEBD dataset demonstrate that the proposed method achieves comparable results to the state-of-the-art methods with $4.5\times$ faster running speed.

MON 87419 was one of the new transgenic corn events developed in US with the trait of herbicide resistance to both dicamba and glyphosate. To monitor unintended release of genetically modified organism in the future, as well as to meet GM-labeling requirements, it is requisite to develop a reliable method for the detection and quantification of MON 87419, an event-specific primer pair was designed to amplify the 3′-junction site between the endogenous genome sequence and the transferred DNA of GM event MON 87419, amplicons of desired size were produced by qualitative polymerase chain reaction (PCR) assay. For the validation of this quantitative method, the mixed samples containing 10%, 1%, and 0.1% MON 87419 ingredient were quantified. The precisions were expressed as relative standard deviations, deviated by 7.87%, 12.94%, and 19.98%, respectively. These results clearly demonstrate that the PCR methods we developed herein can be used for event-specific quantitative testing of the double-herbicide-resistant corn MON 87419.

Various geological disasters can easily occur in tunnels that pass through a limestone stratum. The fine detection of the karst system is significant for safe construction. In this paper, a progressive integrated geophysical method is proposed for fine detection of the karst area of an urban subway. The method combines ground-penetrating radar (GPR), transient electromagnetics (TEM), cross-hole electrical resistivity tomography (ERT), and 3D laser scanning to provide quantitative and integrated detection imaging in a shallow karst system. First, 3D images of reflected waves and the apparent resistivity in the study area are determined using GPR and TEM methods, and the key development of abnormal karst bodies along the mileage direction is delineated. Meanwhile, the TEM slice combination results intuitively show the low-resistance anomaly's distribution and development direction. The main water channel is inferred within the detection range. Second, cross-hole ERT exploration is performed to realize the type of judgment and accurate positioning of karst caves. Finally, 3D laser scanning is conducted to obtain the 3D spatial orientation and size of the large-scale caves identified or those that could have a large influence on metro tunnel construction. Geophysical survey results provide a reference for the design and parameter selection of the cave grouting treatment scheme. This method has been effectively applied in a shield section of the R1 line of the Jinan Metro.

Generic Event Boundary Detection (GEBD) aims to detect moments where humans naturally perceive as event boundaries. In this paper, we present Structured Context Transformer (or SC-Transformer) to solve the GEBD task, which can be trained in an end-to-end fashion. Specifically, we use the backbone convolutional neural network (CNN) to extract the features of each video frame. To capture temporal context information of each frame, we design the structure context transformer (SC-Transformer) by re-partitioning input frame sequence. Note that, the overall computation complexity of SC-Transformer is linear to the video length. After that, the group similarities are computed to capture the differences between frames. Then, a lightweight fully convolutional network is used to determine the event boundaries based on the grouped similarity maps. To remedy the ambiguities of boundary annotations, the Gaussian kernel is adopted to preprocess the ground-truth event boundaries to further boost the accuracy. Extensive experiments conducted on the challenging Kinetics-GEBD and TAPOS datasets demonstrate the effectiveness of the proposed method compared to the state-of-the-art methods.

This report presents the algorithm used in the submission of Generic Event Boundary Detection (GEBD) Challenge at CVPR 2022. In this work, we improve the existing Structured Context Transformer (SC-Transformer) method for GEBD. Specifically, a transformer decoder module is added after transformer encoders to extract high quality frame features. The final classification is performed jointly on the results of the original binary classifier and a newly introduced multi-class classifier branch. To enrich motion information, optical flow is introduced as a new modality. Finally, model ensemble is used to further boost performance. The proposed method achieves 86.49% F1 score on Kinetics-GEBD test set. which improves 2.86% F1 score compared to the previous SOTA method., Comment: winner method at LOVEU@CVPR'22 Generic Event Boundary Detection Challenge

Fruit shape is an important quality and yield trait in melon (Cucumis melo). Although some quantitative trait loci for fruit shape have been reported in in this species, the genes responsible and the underlying mechanisms remain poorly understood. Here, we identified and characterized a gene controlling fruit shape from two melon inbred lines, B8 with long-horn fruit and HP22 with flat-round fruit. Genetic analysis suggested that the shape was controlled by a single and incompletely dominant locus, which we designate as CmFSI8/CmOFP13. This gene was finely mapped to a 53.7-kb interval on chromosome 8 based on bulked-segregant analysis sequencing and map-based cloning strategies. CmFSI8/CmOFP13 encodes an OVATE family protein (OFP) and is orthologous to AtOFP1 and SlOFP20. The transcription level of CmFSI8/CmOFP13 in the ovary of HP22 was significantly higher than that in B8, and sequence analysis showed that a 12.5-kb genomic variation with a retrotransposon insertion identified in the promoter was responsible for elevating the expression, and this ultimately caused the differences in fruit shape. Ectopic overexpression of CmFSI8/CmOFP13 in Arabidopsis led to multiple phenotypic changes, including kidney-shaped leaves and shortened siliques. Taken together, our results demonstrate the involvement of an OFP in regulating fruit shape in melon, and our improved understanding of the molecular mechanisms will enable us to better manipulate fruit shape in breeding.

The AC voltage test is an important means to effectively detect the insulation defects of the equipment. The problems of low efficiency, poor safety, and poor compatibility of the AC voltage test for smart electricity meters and data acquire terminal of the special transformer, the fully automatic AC voltage test device are studied and designed by software programming, PLC control technology, and automatic crimping technology. The automatic test and result judgment of the AC voltage test for the smart electricity meters and data acquire terminal of the special transformer are realised, which makes many problems of the existing test equipment to be effectively solved. Thus, the efficiency, compatibility, safety and reliability of the test are improved.

This report presents the approach used in the submission of Generic Event Boundary Detection (GEBD) Challenge at CVPR21. In this work, we design a Cascaded Temporal Attention Network (CASTANET) for GEBD, which is formed by three parts, the backbone network, the temporal attention module, and the classification module. Specifically, the Channel-Separated Convolutional Network (CSN) is used as the backbone network to extract features, and the temporal attention module is designed to enforce the network to focus on the discriminative features. After that, the cascaded architecture is used in the classification module to generate more accurate boundaries. In addition, the ensemble strategy is used to further improve the performance of the proposed method. The proposed method achieves 83.30% F1 score on Kinetics-GEBD test set, which improves 20.5% F1 score compared to the baseline method. Code is available at https://github.com/DexiangHong/Cascade-PC.

The effective disposal of daily city infrastructure cases is an important issue for urban management. To more effectively utilize a large amount of historical cases data collected and accumulated in the urban grid management system, and to analyze their spatial distribution pattern information for city managers, this study used the comparative kernel density analysis method in two types of cases (i.e. power facilities and traffic guardrail) in Xicheng District, Beijing for the year 2016 and 2017. This research analyzes them at different scales (300 m, 600 m, 1,200 m), and the experiment results show that the method of comparative kernel density analysis is able to provide an intuitively spatial visualization distribution analysis of city infrastructure related cases. The quantitative information of spatial agglomeration degree is helpful for city managers making decision.

Taladegib (LY-2940680), a small molecule Hedgehog signalling pathway inhibitor, was obtained from N-benzyl-4-piperidone via Borch reductive amination, acylation with 4-fluoro-2-(trifluoromethyl)benzoyl chloride, debenzylation, substitution with 1,4-dichlorophthalazine and Suzuki cross-coupling reaction with 1-methyl-1H-pyrazole-5-boronic acid. The advantages of this synthesis route were the elimination of Boc protection and deprotection and the inexpensive starting materials. Furthermore, the debenzylation reaction was achieved with simplified operational procedure using ammonium formate as hydrogen source that provided high reaction yield. This synthetic procedure was suitable for large-scale production of the compound for biological evaluation and further study.

ispartof: POULTRY SCIENCE status: Published online

Powered by machine learning services in the cloud, numerous learning-driven mobile applications are gaining popularity in the market. As deep learning tasks are mostly computation-intensive, it has become a trend to process raw data on devices and send the deep neural network (DNN) features to the cloud, where the features are further processed to return final results. However, there is always unexpected leakage with the release of features, with which an adversary could infer a significant amount of information about the original data. We propose a privacy-preserving reinforcement learning framework on top of the mobile cloud infrastructure from the perspective of DNN structures. The framework aims to learn a policy to modify the base DNNs to prevent information leakage while maintaining high inference accuracy. The policy can also be readily transferred to large-size DNNs to speed up learning. Extensive evaluations on a variety of DNNs have shown that our framework can successfully find privacy-preserving DNN structures to defend different privacy attacks.

Quantum spin-Hall effect, a manifestation of topological properties that govern the behavior of surface states, was studied intensively in condensed matter physics resulting in the discovery of topological insulators. The quantum spin-Hall effect of light was introduced for surface plane-waves which intrinsically carry transverse optical spin, leading to many intriguing phenomena and applications in unidirectional waveguiding, metrology and quantum technologies. In addition to spin, optical waves can exhibit complex topological properties of vectorial electromagnetic fields, associated with orbital angular momentum or nonuniform intensity variations. Here, by considering both spin and angular momentum, we demonstrate a generalized spin-momentum relationship that governs vectorial properties of guided electromagnetic waves, extending optical quantum spin-Hall effect to a two-dimensional vector field of structured guided wave. The effect results in the appearance of the out-of-plane transverse optical spins, which vary progressively from the 'up' state to the 'down' state around the energy flow, and their variation is uniquely locked to the energy propagation direction. The related spin-momentum locking in a chiral spin swirl is demonstrated with four kinds of surface structured waves and proven both theoretically and experimentally. The results provide understanding of the spin dynamics in electromagnetic guided waves and show great importance in spin optics, topological photonics and optical spin-based devices and techniques., Comment: 30 pages; 13 figures; 44 equations; 1 table; 41 references for main text and 45 references for Supplementary Materials

In order to make the operation of the line more safe and reliable, the state monitoring is needed and the remote transmission of data is realized on this basis. This paper proposes a variety of communication modes suitable for transmission line condition monitoring data transmission. The corresponding communication system design is given for different application conditions. The development and application of data transmission technology for condition monitoring of transmission lines are discussed in combination with two different schemes of typical design and distributed intelligent fault diagnosis system.

The insect-resistant transgenic maize event IE034 has been proved to be one of the most commercially developed transgenic maize events in China. This study was aimed to develop a stable and reliable quantitative detection method to monitor this new transgenic maize event. Here, we developed a novel event-specific real-time PCR method for this genetically modified maize event IE034. The resulting 134 base pair (bp) amplicon was designed according to the 5′ junction of inserted sequence and flanking maize genome sequence. Standard curve of the IE034 5′ event-specific sequence showed good linear regression and high PCR efficiency when using the IE034 pure line samples as calibrator. The limit of detection (LOD) for the IE034 detection method was estimated at approximately 8 initial template copies, and the limit of quantification (LOQ) was estimated at about 40 copies. The accuracy of this quantitative real-time PCR method was verified by screening four mixed DNA samples with known levels of the IE034 event (5, 1, 0.5, and 0.23 %, respectively). The quantified biases deviated from 8.7 to −12.2 %, and the relative standard deviation (RSD) ranged from 2.7 to 12.7 %. These data indicated that this new-developed IE034 event-specific real-time PCR method is suitable and reliable for the quantification of IE034 maize and its derivates.

A new fluxgate current sensor is presented in this paper. The sensor consists of an additional magnetic toroidal core and an additional winding, aside from the elements in a traditional sensor. In addition, the exciting winding is wound along the circumference of the toroidal core to decouple the exciting magnetic field and the feedback magnetic field in order to reduce internal interference. With the additional core and the additional winding, the proposed current sensor can extend its bandwidth to medium- and high-frequency ac measurement. Another purpose of introducing the additional core is to concentrate on the exciting magnetic field and to reduce interference from external disturbances. A prototype of the proposed sensor was designed and tested. Test results show that the sensor is less affected by hysteresis effects and has a measurement error (precision) within 0.4% of the full scale, a 40-kHz small-signal bandwidth, remarkable sensitivity, and linearity in the measurement range up to 25 A.

With the wide application of electronic equipment, the electric energy metering device of site operation is often subject to various types of interference. Due to the complex operation environment, various kinds of fault problems often occur in the electric energy metering devices. However, the actual failure disposal methods were not unified and standardized, which often results in the waste of electric energy metering device assets. In view of the above problems, the faults disposal methods of the electric energy metering devices were studied, the work flow of standardized fault disposal of electric energy metering devices was sorted out and the disposal requirements of each link were identified, which provides a good foundation for standardizing the unified and efficient disposal of the failed electric energy metering devices, deepening the quality management of the whole life cycle of the electric energy metering devices, and realizing the effective reuse of the electric energy metering devices.

A nonlinear and non-stationary signal processing method is proposed for the identification of low frequency oscillation in power system which is called Hilbert-Huang transform (HHT). In this paper, we first pretreat the noise and delay compensation of the oscillation signal, and then identify the modal parameters with the In this paper, we first pretreat the noise and delay compensation of the oscillation signal, and then identify the modal parameters with the HHT. Finally, the improved Prony algorithm identification results are compared with HHT. The simulation results show that the empirical mode decomposition can filter out the noise interference in the signal and lay the foundation for the accurate analysis of modal parameters. Moreover, the HHT method can effectively decompose the oscillating modes, and then accurately identify the modal parameters and obtain the time-frequency characteristics of the oscillation modes.

This paper presents a current sensor based on fluxgate principle, which consists of a cylinder-shaped amorphous core with a U-shaped magnetic gathering shell. In the designed sensor, the exciting winding and secondary winding are arranged perpendicularly so that the magnetic fields produced are mutual orthogonally, that is, the exciting magnetic field and the feedback magnetic field is decoupled. Magnetic gathering effectiveness and magnetic coefficient of the shell are analyzed by finite element method (FEM) and some concerned parameters affecting the gathering effects are also discussed. Based on theoretical analysis and simulation results, a prototype was designed. The experiments show introducing of a magnetic gathering shell into the current sensor effectively concentrates the wanted signal and reduces the interference of external stray field, thus making the sensor have an excellent sensitivity and precision. The relative error of the measurement of the current sensor with and without magnetic gathering shell were tested and compared. The sensor we proposed with this design has an accuracy of 6‰ and a remarkable resolution of 8pT/√Hz for frequencies greater than 1 Hz.

Background The aim of this study was to analyze the effects of obesity and leptin levels on patients with migraine, and to observe the change of leptin levels in migraineurs. Material and methods We enrolled 52 migraine patients from the Headache Clinic in Shandong Provincial Hospital into a randomized controlled trial with another 52 age-, sex-, and BMI-matched healthy subjects as controls. Leptin levels in all subjects were determined by radioimmunoassay. Results Compared with the control group, the migraineurs revealed no significant change in leptin levels (P>0.05). Multivariate Logistic regression analysis showed that neither abdominal obesity nor leptin had significant impact on migraine clinical features. Total body obesity had a significant effect on the frequency (OR=4.248), duration (OR=3.167), and intensity (OR=5.225) of the headache. Conclusions Total body obesity affected headache frequency, intensity, and duration, while leptin levels did not.

In this paper using BP neural network as core algorithm, Java and MATLAB as development tools, build a fault intelligent analysis system of electric energy data acquire network which have machine self-learning ability. This diagnosis system adopts the model of cloud computing in structure, artificial intelligence calculation in the cloud, friendly humancomputer interaction at the front-end. Lab simulation results showed that the system can analyze quickly and locate the fault point accurately. It can effectively reduce labor, time cost, improve work efficiency, has high practical value.

With the wide application of electronic equipment and the popularization of electrical energy meter of state Grid, the electrical energy meter of site operation is often subject to various types of interference, the EMC of electrical energy meter become increasingly prominent. This methods and the problems of radio frequency electromagnetic field radiated immunity test are analyzed and discussed, the anti-jamming capability of electrical energy meter is improved by using the EMC technology. After rectification, the electrical energy meter passed the radio frequency electromagnetic field radiated immunity test, The measurement performance of the electrical energy meter is guaranteed, the legal rights and interests of both side of power supply and power demand are not damaged.

As the important part of new low-voltage metering cabinet, the performance of watt-hour meter plug influences directly the convenience of operation, the stability of smart meter running and the reliability of metering cabinet. For lacking of the uniform technical specification and an effective, reliably and fast testing method for the performance of plug, this paper proposes a new testing method, it includes the important index of insertion and extraction power and temperature rise. The testing results shows that the method of this paper proposed can detect the mechanical and technical parameters accurately and conveniently, it provides reference for the stability and reliability of metering cabinet.

We compared metabolic changes in transgenic maize lines (differing in genetic backgrounds and exogenous genes insertion) with non-transgenic counterparts. At the corn seedling stage, entirely expanding leaves were obtained from maize lines 1 (transgenic maize SK12-5; overexpressing cry1Ab/cry2Aj; parental maize ZD958), 2 (transgenic maize IE034; overexpressing cry1Ie; parental maize ZD958), 3 (transgenic maize Bt799; overexpressing cry1Ac-M; parental maize Z58), 4 (transgenic maize Bt799; overexpressing cry1Ac-M; parental maize ZD958), 5 (non-transgenic maize Z58), and 6 (non-transgenic maize ZD958). For each line, six biological replicates were prepared; each contained an entirely expanded leaf at the top of the plant. Metabolites were identified with high-performance liquid chromatography mass spectrometry in three comparison groups (4 transgenic maize lines vs. their corresponding controls, line 3 vs. 4, and transgenic vs. non-transgenic maize). Top 200 cationic and top 200 anionic metabolites with higher variable importance in projection value were chosen for principal component analysis, followed by pathway enrichment analysis. Annotation was possible for 227 of 400 metabolites. Twelve up- and 26 down-regulated overlapping metabolites were identified in line 1 vs. 6, line 2 vs. 6, and line 4 vs. 6 comparison groups. Altered metabolites significantly enriched in purine and glutathione metabolism pathways, but no pathways were enriched in line 3 vs. 4. Comparing transgenic and non-transgenic maize lines revealed 59 up- and 37 down-regulated metabolites, which were also significantly enriched in purine and glutathione metabolism pathways. Therefore, transgenic Bt maize differed metabolically from non-transgenic maize. Additionally, purine and glutathione metabolism pathways may be important in the transgenic maize metabolism.

Forest ecosystem,which is the largest terrestrial ecosystem,consists of forest coenosis and environment with complex functions of energy transformation and storage.The traditional field-based investigation has always failed to solve the forest issues in complex spatial and temporal scale.Remote sensing technology,which can collect and process huge amount of diverse data in a large scale,is an efficient tool to understand forest ecosystem.Accordingly,the interdiscipline and combination with remote sensing technology has made great progress on the development of forest health issues over the past decades.At present,forest health studies based on remote sensing are in the process of combination with relevant subjects in a key transformation period from static to dynamic state,single to composite system,and fragmented to framework thought.However,these studies are lack in a synthetic and logic consideration,or a top design on forest health issues.Therefore,in this paper,based on the core theory of forest health involving vigor,organization and resilience,we give a summery of the forest health studies based on remote sensing both at home and abroad in order to make a better understanding on technical know-how of its achievements,progresses and disadvantages in theories,technologies and applications to four categories: forest resources inventory;ecological functions assessment;forest health risks control;vegetation parameter retrieval.In conclusions,the studies indicated that:(1) the basic studies should be strengthened on forest ecology and remote sensing theory and technology in order to know the relationships between forest structure,process,function and remote sensing data;(2) new remote sensing technology,the remote sensing data algorithm and software tools should be developed and perfected to increase the accuracy,utilization and efficiency of remote sensing data;(3) the transformation from forest health scientific studies to their achievements should be enhanced to speed up the analysis,assessment and auxiliary decision;to develop the effects of forest health and scientific studies,as well as to formulate the forest policies.

This paper presents a current sensor for measurement of AC and DC. The sensor consists of two toroidal magnetic cores. One core works in fluxgate principle for the measurement of DC and low frequency AC. Another additional toroidal core is used as a current transformer for higher frequency application. Bidirectionally saturated fluxgate principle is used in this sensor to achieve better temperature characteristic for high current measurement. Moreover, an improved bidirectionally saturated fluxgate principle is proposed to measure small current, which improves the resolution of the sensor. A prototype is designed and the tested results show that the sensor can measure currents up to 150 A, with an accuracy of 5%o, and a bandwidth of 60 kHz.

This paper presents a current sensor for current measurement in high frequency high voltage pulse power supplies, the output of which is DC current superposed with impulse current waveform. The current sensor is made of two magnetic cores. One works in fluxgate principle to measure DC current. While the other is based on transformer effects for impulse current measurement. The impulse current sensor has a wide bandwidth up to 340kHz. It can measure DC with an accuracy of about 3%o, and the measurement errors for impulse current peak is limited to 6%.

There is a trend to acquire high accuracy land-cover maps using multi-source classification methods, most of which are based on data fusion, especially pixel- or feature-level fusions. A probabilistic graphical model (PGM) approach is proposed in this research for 30 m resolution land-cover mapping with multi-temporal Landsat and MODerate Resolution Imaging Spectroradiometer (MODIS) data. Independent classifiers were applied to two single-date Landsat 8 scenes and the MODIS time-series data, respectively, for probability estimation. A PGM was created for each pixel in Landsat 8 data. Conditional probability distributions were computed based on data quality and reliability by using information selectively. Using the administrative territory of Beijing City (Area-1) and a coastal region of Shandong province, China (Area-2) as study areas, multiple land-cover maps were generated for comparison. Quantitative results show the effectiveness of the proposed method. Overall accuracies promoted from 74.0% (maps acquired from single-temporal Landsat images) to 81.8% (output of the PGM) for Area-1. Improvements can also be seen when using MODIS data and only a single-temporal Landsat image as input (overall accuracy: 78.4% versus 74.0% for Area-1, and 86.8% versus 83.0% for Area-2). Information from MODIS data did not help much when the PGM was applied to cloud free regions of. One of the advantages of the proposed method is that it can be applied where multi-temporal data cannot be simply stacked as a multi-layered image., Comment: 38 pages, 5 figures

Based on the analysis of Norton Model and its parameter estimation, this paper takes advantage of statistical data for the empirical analysis about diffusion trend of update and substitution for CATV in China. The findings show that compared with Bass Model, Norton Model which describes two generation products-analog TV and digital TV well with substitution curve has higher fitting degree and prediction accuracy. Now, in China, the digital television industry is rapidly developing that the number of users will reach 160 million in the year 2012, and then will exceed 200 million around the year 2015. Those conclusions have high reference value for our government and enterprises in industry chain of digital television to make decisions.