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National handcrafts are well-known at home and Abroad for their fine ingredients, exquisite blending of colors, and excellent skilled shaping and painting. What is more, they have great value and are deeply popular with people around the world. National handcrafts, as a cultural crystallization, must be protected. In order to better realize the protection, we try to apply the technology of reverse engineering to the protection. This article mainly takes a handcraft, Yan Zi YU (meaning “swallow fish”), as an example to expound and demonstrate the process of reverse engineering technology. During this process, by using scanning technology of reverse engineering, the collection and splicing of the point cloud data, the theoretical analysis of the NURBS surface in the reconstruction phase, and other practical operations in other stages, reverse engineering can be used to regenerate 3D modal data of the swallow fish and to inspect and analyse the errors in it through Geomagic Quality to ensure that the new model meets the precision requirements. Therefore, the reverse engineering technology can be completely applied to the protection of national handcrafts so that they can fully demonstrate their own value and function. If the precision of reverse engineering detection can reach nanometric level, a breakthrough will promote the protection of cultural relics.

Fertilization regimes greatly influence how manganese (Mn) oxides are recycled. However, information on this subject is scarce in rice managed paddies. The objective of this study was to examine the distributions of Mn pools in bulk soil and aggregates under different chemical and organic fertilizer ratios at both surface (0–20 cm) and subsurface (20–40 cm) soil depths. Depth distributions of Mn pools in bulk soil and aggregates were extracted using Mehlich 3 (M3-Mn), dithionite (Mnd), oxalate (Mno), and pyrophosphate (Mnp) solutions under a long-term fertilization experiment in a double rice cropping system. Fertilizer treatments comprised unfertilized control (CK), mineral nitrogen, phosphorus, and potassium (NPK), 30% mineral NPK + 70% organic fertilizer (30F; pig manure + milk vetch), 50% mineral NPK + 50% organic fertilizer (50F), and 70% mineral NPK + 30% organic fertilizer (70F) under a randomly complete block design (RCBD) in paddy conditions. A pattern of 30F > 50F > 70F > NPK > CK was established with positive and significant correlations among total organic carbon (TOC), total nitrogen (TN), and pH. All pools of Mn were in higher concentrations at the subsurface compared to the surface layer, where all aggregate sizes contributed equally to the enrichment of the metal oxides except for Mno in the 0.25–2.0 mm sizes at the surface layer. Distribution of manganese oxides was equal among the different soil aggregates which could cause the microaggregates to transport Mn oxides and pollute both surface and underground water sources.

Supplemental material, sj-docx-1-sms-10.1177_20563051221146182 for From #BlackLivesMatter to #StopAsianHate: Examining Network Agenda-Setting Effects of Hashtag Activism on Twitter by Jing Guo and Shujun Liu in Social Media + Society

Supplemental Material, sj-pdf-1-jou-10.1177_14648849211052438 for Strategy framing in the international arena: A cross-national comparative content analysis on the China-US trade conflict coverage by Shujun Liu, Mark Boukes and Knut De Swert in Journalism

Flow cytometry analysis of splenic immune cells including splenic T lymphocytes, neutrophil and B lymphocytes. Spleens were stripped and grinded into cell suspension after mice were treated with vehicle, blank liposomes (8 mg/kg), melittin (2 mg/kg) and melittin nano-liposomes (2 mg/kg) for two weeks. (PDF 730 kb)

Supplemental material, sj-docx-1-sms-10.1177_20563051221146182 for From #BlackLivesMatter to #StopAsianHate: Examining Network Agenda-Setting Effects of Hashtag Activism on Twitter by Jing Guo and Shujun Liu in Social Media + Society

Supplemental Material, sj-pdf-1-jou-10.1177_14648849211052438 for Strategy framing in the international arena: A cross-national comparative content analysis on the China-US trade conflict coverage by Shujun Liu, Mark Boukes and Knut De Swert in Journalism

Photo of HepG2 tumors of vehicle, blank liposomes (8 mg/kg), melittin (2 mg/kg), melittin nano-liposomes (2, 4, and 8 mg/kg), and sorafenib (30 mg/kg) treated groups in the HepG2 subcutaneous transplanted tumor model. The data are presented as the mean ± SEM. Statistical significance was calculated using Student’s t test (**p ≤ 0.01; ***p ≤ 0.001). (PDF 1070 kb)

The effects of melittin nano-liposomes on the proliferation and apoptosis of tumor cells. (a) Proliferation inhibiting rates of melittin and melittin nano-liposomes on various HCC cells lines including LM-3, Bel-7402, SMMC-7721, HepG2, and L02 cells. (b) Cell nucleus staining by DAPI to observe the apoptosis of Bel-7402 and SMMC-7721 cells after treated with vehicle, blank liposomes (1 μM), melittin (1 μM), or melittin nano-liposomes (1 μM) for 24 h. (c) Fluorescence staining of Annexin V-FITC and PI to detect the apoptosis of HepG2 cells after treatment with melittin and Melittin nano-liposomes for 24 h and observed by fluorescence microscope. HepG2 cells were pretreated with Z-VAD-FMK for 6 hours, and melittin and Melittin nano-liposomes were subsequently administered at a concentration of 2 μM. (PDF 848 kb)

This paper addresses the challenge of designing efficient framework for real-time object detection and image compression. The proposed Compressive Convolutional Network (CCN) is basically a compressive-sensing-enabled convolutional neural network. Instead of designing different components for compressive sensing and object detection, the CCN optimizes and reuses the convolution operation for recoverable data embedding and image compression. Technically, the incoherence condition, which is the sufficient condition for recoverable data embedding, is incorporated in the first convolutional layer of the CCN model as regularization; Therefore, the CCN convolution kernels learned by training over the VOC and COCO image set can be used for data embedding and image compression. By reusing the convolution operation, no extra computational overhead is required for image compression. As a result, the CCN is 3.1 to 5.0 fold more efficient than the conventional approaches. In our experiments, the CCN achieved 78.1 mAP for object detection and 3.0 dB to 5.2 dB higher PSNR for image compression than the examined compressive sensing approaches.

Suffering from limited singing voice corpus, existing singing voice synthesis (SVS) methods that build encoder-decoder neural networks to directly generate spectrogram could lead to out-of-tune issues during the inference phase. To attenuate these issues, this paper presents a novel acoustic model with independent pitch encoder and phoneme encoder, which disentangles the phoneme and pitch information from music score to fully utilize the corpus. Specifically, according to equal temperament theory, the pitch encoder is constrained by a pitch metric loss that maps distances between adjacent input pitches into corresponding frequency multiples between the encoder outputs. For the phoneme encoder, based on the analysis that same phonemes corresponding to varying pitches can produce similar pronunciations, this encoder is followed by an adversarially trained pitch classifier to enforce the identical phonemes with different pitches mapping into the same phoneme feature space. By these means, the sparse phonemes and pitches in original input spaces can be transformed into more compact feature spaces respectively, where the same elements cluster closely and cooperate mutually to enhance synthesis quality. Then, the outputs of the two encoders are summed together to pass through the following decoder in the acoustic model. Experimental results indicate that the proposed approaches can characterize intrinsic structure between pitch inputs to obtain better pitch synthesis accuracy and achieve superior singing synthesis performance against the advanced baseline system., Comment: 5 pages, 3 figures

This study investigated relationships between ecoenzymatic stoichiometry and microbial community structure under long-term (38-years) combined organic and inorganic fertilization. Experiment was initiated in 1982 and seven fertilization treatments were studied that included: CK (no fertilizer), NPK (inorganic N, P and potassium fertilizers), NPM (inorganic N, P fertilizers and manure), NKM (inorganic N, K fertilizers and manure), PKM (inorganic P, K fertilizers and manure), NPKM (inorganic N, P, K fertilizers and manure) and M (manure) treatments. We analyzed soil and microbial biomass of C, N and P, enzyme activities related to C, N and P, and their effects on the microbial community structure in the top layer (0–20 cm) of the paddy soil. The results showed that combined application of manure and inorganic fertilizers significantly increased soil C, N, P contents, extracellular enzyme activities and total phospholipid fatty acid (PLFA) concentration, compared to the NPK and CK treatments. Fertilization treatments significantly changed microbial biomass stoichiometry. Among all fertilization treatments, highest microbial biomass C:N, C:P ratio and N:P ratios were 10.2, 36.0 and 3.59, respectively, under the PKM treatment. Soil C, N and P-cycling enzyme activities were highest under the NPKM treatments. Soil C:N showed positive and soil C:P ratio showed negative relationships with microbial biomass C:N and C:P ratios, respectively. Soil total PLFA concentration was highest under NPKM treatment. The order of individual PLFAs concentrations in all fertilization treatments was: Gram negative bacteria > Gram positive bacteria > Actinomycetes > AM fungi > Fungi> Eukaryotes > anaerobes. Soil organic C, total N, soil N:P and microbial N:P ratios were positively correlated with total PLFA concentration. While, soil C:N and microbial biomass C:N ratios were negatively correlation with total PLFA and individual microbial community structure. Our results suggested that ecoenzymatic stoichiometry and microbial community structure are very sensitive to combined long-term organic and inorganic fertilization that can affect the nutrient acquisition and cycling in agroecosystem.

Organic and inorganic fertilization management in intensive cropping system is important to achieve long-term high crop yield sustainability. We quantitatively investigated crop yield sustainability through soil fertility and nutrients balance in 34-years long-term experiment under double rice cropping system in acidic paddy soil. Seven treatments were studied: CK (no fertilization); NPK (Chemical nitrogen, phosphorus and potassium fertilizer); NPM (Chemical N, P and manure); NKM (Chemical N, K and manure); PKM (Chemical P, K and manure); NPKM (Chemical N, P, K and manure) and M (Manure). Manure was applied at the rate of 45,000 kg ha−1. Results showed that crop yield and sustainability yield index under combined application of manure and chemical fertilizers was significantly higher than chemical fertilization and highest crop yield was under NPKM treatment. Long-term combined manure and chemical fertilization improved soil fertility as compared to CK and NPK treatments. Soil organic C sequestration rates under NPM, NKM, PKM and NPKM treatments were increased, while decreased under CK and NPK over the fertilization years. The uptake of N, P and K was increased over the fertilization years in all the treatments that received manure, compared with CK and NPK. Apparent K balance was negative in all the treatments. N balance (except CK and NPKM) and P balance (except CK) was positive in all fertilization treatments. P balance was exceeded the environmental risk threshold under combined application of chemical P fertilizer and manure. Boosted regression tree indicated that soil available N (AN), orgnaic carbon (OC) and total N (TN) were the most influencing factors of crop yield, accounted 36.5 %, 17.8 %, 13.4 % of variations of relative yield, respectively. Path analysis showed that long-term fertilizer inputs increased soil nutrient contents and C input directly affected soil OC. C input and soil pH indirectly influenced the relative crop yield. This study concluded that long-term combined application of manure and inorganic fertilizers increased crop yield sustainability, organic carbon sequestration rate compared to the inorganic fertilization. But long-term combined application of manure and inorganic phosphorus fertilizer increased the P balance. Therefore, rate of P inputs should be reduced under combined application of manure and inorganic P fertilizers in acidic paddy soil.

The application of asphalt with low penetration in warm areas to improve rutting-resistance has attracted great interest in recent years. Deoiled asphalt (DOA) and slurry, by-products of solvent deasphalting and fluid catalytic cracking respectively, are utilized with low value-added. The objective of this work was to prepare paving grade asphalt by blending various proportion of FCC slurry with DOA. The effect of chemical composition on viscoelastic characteristics of the blend was investigated and special emphasis was placed on the linear viscoelasticity and viscous flow behavior as well as on empirical tests. The results revealed that SARA fractions of the blends are close to asphalt and are compromise between DOA in which the content of asphaltenes is high and slurry in which is rich in aromatics. Also, SARA fractions have a significant influence on the rheological properties of the blends. Asphaltenes have been proved to enhance the moduli of asphalt and low penetration blends have demonstrated high rutting and deformation resistance especially at high temperatures. Taking into account the balance between workability and in-service performance, application of the 40 wt% slurry in DOA is highly recommended. It is indicated that a more inferior aging resistance with the increase of slurry in DOA. As a result, it is a highly effective and meaningful way to utilize DOA and FCC slurry.

Dendritic cell (DC) vaccine and fusion protein vaccine have been put into clinical use in cancer immunotherapy. This study compared DC vaccine and fusion protein vaccine directly in their capability of inducing specific immune response. We used mouse Granulocyte Macrophage-Colony Stimulating Factor (mGM-CSF) fused with gastrin-releasing peptide (GRP) and Gonadotrophin Releasing Hormone (GnRH) respectively to obtain mGM-CSF/GRP6 (mG6) and mGM-CSF/mGGn (mGGn) fusion proteins. We prepared fusion protein vaccine and DC vaccine including mG6 protein vaccine (6P), mGGn protein vaccine (nP), mG6 DC vaccine (6D) and mGGn DC vaccine (nD), then the two proteins were mixed to prepare combination proteins vaccine (6nP) and DC vaccine (6nD). After that, C57BL/6 mice were injected with B16F10 cell line to build melanoma tumor model, and were immunized with vaccines to produce antibodies to inhibit and destruct melanoma tumor cells. The discoveries showed that anti- mGM-CSF-GRP6 and anti- mGM-CSF-mGGn antibody vaccines were successfully created as expected; this was deduced from significant inhibition of melanoma tumor in vivo and significant reduction of tumor weight and volume. The effects of DC groups were better than that of the protein groups and the combination of vaccines were more effective than vaccine given separately. Our results indicate that using combination vaccine provides a new strategy to inhibit melanoma tumor growth but a complete cure of melanoma needs further investigations.

A new approach to classify synthetic aperture radar (SAR) targets is presented based on high range resolution (HRR) proflles time-frequency matrix non-negative sparse coding (NNSC). Firstly, SAR target images have been converted into HRR proflles. And the non-negative time-frequency matrix for each of the proflles is obtained by using an adaptive Gaussian representation (AGR). Secondly, NNSC is applied to learn target time-frequency basis of the training set. Feature vectors are constructed by projecting each HRR proflle time-frequency matrix to low dimensional time-frequency basis space. Finally, the target classiflcation decision is found with support vector machine and nearest neighbor algorithm respectively. To demonstrate the performance of the proposed approach, experiments are performed with Moving and Stationary Target Acquisition and Recognition (MSTAR) public release SAR database. The experimental results support the efiectiveness of the proposed technique for SAR target classiflcation.

Based on the fact that the motion (i.e. azimuth, elevation and their derivative velocities), amplitude and size of infrared target could be acquired simultaneously, a multi-feature association based approach is presented to track infrared dim moving target. The characters of the infrared imaging tracking system are first analyzed, and the motion, amplitude and size of target of interest are modeled as second order stationary random signals. The probability of motion, amplitude and size of measurement originated as target of interest is then estimated by Gaussian distribution. Subsequently, the combined probability of motion, amplitude and size is derived by probabilistic data association (PDA), and their weight coefficients are estimated adaptively according to their fluctuations. Finally

The method of moving target detection based on subimage cancellation for single-antenna airborne SAR is presented. First the subimage is obtained through frequency processing is pointed out. The imaging difference of a stationary objects and moving object in the subimage based on the frequency division is analyzed from the fundamental principle. Then the developed method combines the shear averaging algorithm to focus on the moving target in the subimage, after the clutter suppression and the focusing position in each subimage is obtained. Next the observation model and the relative movement of the moving targets between the subimages estimate the moving targets. The theoretical analysis and simulation results demonstrate that the method is effective and can not only detect the moving targets, but also estimate their motion parameters precisely.

With the development of science and technology, the researches and application of water resources including the gas water have been constantly developed. Through an analysis on the flaws of the water right theory, and by executing reconstruction and renewal of the theory and system of water right in modern society, the water right position of the gas water will be established, leading to the maturity of the whole law effectiveness and substantial results of water right.

In order to launch a soccer game between human and robots, one of the crucial problems for robots is to effectively identify human and their actions. In this paper, based on human skeleton tracking information from Kinect, GR (Gesture Recognition), FVGR (Front View Gait Recognition) and NNVGR (Non-Normative View Gait Recognition) are carried out to develop the HRI (Human Robot Interaction) systems for a middle size league soccer robot. This method is used to identify the human body language and obtain information of body movements, so that the HRI system can generate different command to control the movements of the middle size league soccer robot. The performances of the proposed HRI systems are verified by different experiments. The experimental results show that this method can quickly and safely identify human and their movements. This research can provide a practical solution for Mid-Sized League competition between human and robots in the future.

Manual calibration is generally adopted in middle size league soccer robot competition and the method is not only complex but also poor adaptability. A manually re-calibration is often needed when the environment changed. In this paper, based on threshold file, an automatic classification and color calibration method is proposed and realized in VC++ programming environment Experimental results show that the threshold file based color adaptive calibration has good effect and can meet the real-time requirements.

Reasonably selecting the capacity of different types of DGs is the key element to guarantee that microgrid system can operate economically and reliably. Firstly a grid-connected hybrid wind/solar/storage micro-grid system is constructed and the output models of various distributed generations are built. On the basis of correction of wind speed and atmospheric density, the power of wind turbine is predicted by linear interpolation. The output of the photovoltaic generation system is calculated by using the Hay, Davies, Klucher and Reindl model(HDKR model). In order to determine the amount of energy that can be absorbed by or withdrawn from the battery bank each time step, this paper uses the Kinetic battery model. Then considering tax collection of greenhouse gas emission and reliability of the system, an optimal configuration design method is proposed, taking minimizing the whole life cycle cost of the system as the optimization objective. Finally, based on the weather conditions in Yantai city, a simulation analysis is performed, the optimal configuration scheme meeting the load is given. According to Chinese meteorological conditions, a sensitivity analysis of the wind speed and solar resource is given and optimal results are obtained on different conditions. The results are conducive to Chinese planning and design work of the micro-grid.

It is well known that applying nonlinear programming method to solve the problem of security economic optimal dispatching of power system maybe deficiency because it depends tightly on initial condition. The position of initial point, such as inside or outside of the feasible area, effects the searching direction of optimal solution greatly. The mixed point penalty model proposed in this paper can solve the problem. With adopting optimal gradient algorithm, the case study presents available.

Simulating characteristics of human intelligent, a new intelligent controller was produced: human-simulation logical predictive controller (HLPC). This controller can utilize the advantage both in logical controller based on pan-boolean algebra and model-based predictive controller. It is a multi-logic hybrid dynamic system. Simulated results based on Matlab prove that HLPC shows good performance when predictive model matches and satisfactory results when model mismatches, represents better robustness and small overshoot . Compared with other artificial intelligent controller, HLPC has simple structure, clearly physics background and mathematical concepts, thus would make it easy for industrial application.