Source Camera Identification Algorithm Based on Multi-Scale Feature Fusion

The widespread availability of digital multimedia data has led to a new challenge in digital forensics.Traditional source camera identification algorithms usually rely on various traces in the capturing process.However,these traces have become increasingly difficult to extract due to wide availability of various image processing algorithms.Convolutional Neural Networks(CNN)-based algorithms have demonstrated good discriminative capabilities for different brands and even different models of camera devices.However,their performances is not ideal in case of distinguishing between individual devices of the same model,because cameras of the same model typically use the same optical lens,image sensor,and image processing algorithms,that result in minimal overall differences.In this paper,we propose a camera forensics algorithm based on multi-scale feature fusion to address these issues.The proposed algorithm extracts different local features from feature maps of different scales and then fuses them to obtain a comprehensive feature representation.This representation is then fed into a subsequent camera fingerprint classification network.Building upon the Swin-T network,we utilize Transformer Blocks and Graph Convolutional Network(GCN)modules to fuse multi-scale features from different stages of the backbone network.Furthermore,we conduct experiments on established datasets to demonstrate the feasibility and effectiveness of the proposed approach.

Soxllb regulates the migration and fate determination of Müller glia-derived progenitors during retina regeneration in zebrafish

The transcription factor Sox11 plays important roles in retinal neurogenesis during vertebrate eye development.However,its function in retina regeneration remains elusive.Here we report that Sox11 b,a zebrafish Sox11 homolog,regulates the migration and fate determination of Müller glia-derived progenitors(MGPCs)in an adult zebrafish model of mechanical retinal injury.Following a stab injury,the expression of Sox11 b was induced in proliferating MGPCs in the retina.Sox11 b knockdown did not affect MGPC formation at 4 days post-injury,although the nuclear morphology and subsequent radial migration of MGPCs were alte red.At 7 days post-injury,Sox11 b knockdown res ulted in an increased proportion of MGPCs in the inner retina and a decreased propo rtion of MGPCs in the outer nuclear layer,compared with controls.Furthermore,Sox11 b knockdown led to reduced photoreceptor regeneration,while it increased the numbe rs of newborn amacrines and retinal ganglion cells.Finally,quantitative polymerase chain reaction analysis revealed that Sox11 b regulated the expression of Notch signaling components in the retina,and Notch inhibition partially recapitulated the Sox11 b knockdown phenotype,indicating that Notch signaling functions downstream of Sox11 b.Our findings imply that Sox11 b plays key roles in MGPC migration and fate determination during retina regeneration in zebrafish,which may have critical im plications for future explorations of retinal repair in mammals.

Impact of three different processing methods on the digestibility and allergenicity of Chinese mitten crab(Eriocheir sinensis)tropomyosin

The tropomyosin(TM)fractions of crab protems may cause allergic reactions in mdividuals susceptible to allergies;however,efficient and safe methods by which to reduce such allergenicity are not currently available.Therefore,in this study,the effects of three different processing methods,i.e.,microwave,ultrasound,and high temperature-pressure(HTP)treatments,on the digestion stability of TM from Chinese mitten crab muscle and the allergenicity of TM digestion products were explored.sodium dodecyl sulfate-polyacrylamide gel electrophoresis analysis showed that microwaving had little effect on the digestion stability of TM.In contrast,ultrasound and HTP treatments facilitated the degradation of TM.Similarly,Western blotting and inhibition ELISA indicated that the IgE-binding activity of TM was significantly reduced after treatment with ultrasound or HTP.Among the three different proces sing methods,HTP was the most effective method for improving digestibility of TM and reducing immunoreactivity.This finding provides new insights into treatments for crab allergies.

A Sketch-Based Generation Model for Diverse Ceramic Tile Images Using Generative Adversarial Network

Ceramic tiles are one of the most indispensable materials for interior decoration.The ceramic patterns can’t match the design requirements in terms of diversity and interactivity due to their natural textures.In this paper,we propose a sketch-based generation method for generating diverse ceramic tile images based on a hand-drawn sketches using Generative Adversarial Network(GAN).The generated tile images can be tailored to meet the specific needs of the user for the tile textures.The proposed method consists of four steps.Firstly,a dataset of ceramic tile images with diverse distributions is created and then pre-trained based on GAN.Secondly,for each ceramic tile image in the dataset,the corresponding sketch image is generated and then the mapping relationship between the images is trained based on a sketch extraction network using ResNet Block and jump connection to improve the quality of the generated sketches.Thirdly,the sketch style is redefined according to the characteristics of the ceramic tile images and then double cross-domain adversarial loss functions are employed to guide the ceramic tile generation network for fitting in the direction of the sketch style and to improve the training speed.Finally,we apply hidden space perturbation and interpolation for further enriching the output textures style and satisfying the concept of“one style with multiple faces”.We conduct the training process of the proposed generation network on 2583 ceramic tile images dataset.To measure the generative diversity and quality,we use Frechet Inception Distance(FID)and Blind/Referenceless Image Spatial Quality Evaluator(BRISQUE)metrics.The experimental results prove that the proposed model greatly enhances the generation results of the ceramic tile images,with FID of 32.47 and BRISQUE of 28.44.

A Short-Term Electricity Price Forecasting Scheme for Power Market

Electricity price forecasting has become an important aspect of promoting competition and safeguarding the interests of participants in electricity market. As market participants, both producers and consumers intent to contribute more efforts on developing appropriate price forecasting scheme to maximize their profits. This paper introduces a time series method developed by Box-Jenkins that applies autoregressive integrated moving average (ARIMA) model to address a best-fitted time-domain model based on a time series of historical price data. Using the model’s parameters determined from the stationarized time series of prices, the price forecasts in UK electricity market for 1 step ahead are estimated in the next day and the next week. The most suitable models are selected for them separately after comparing their prediction outcomes. The data of historical prices are obtained from UK three-month Reference Price Data from April 1st to July7th 2010.

Review of Methods to Calculate Congestion Cost Allocation in Deregulated Electricity Market

With maturing deregulated environment for electricity market, cost of transmission congestion becomes a major issue for power system operation. Uniform Marginal Price and Locational Marginal Price (LMP) are the two practical pricing schemes on energy pricing and congestion cost allocation, which are based on different mechanisms. In this paper, these two pricing schemes are introduced in detail respectively. Also, the modified IEEE-14-bus system is used as a test system to calculate the allocated congestion cost by using these two pricing schemes.

Steganography Using Reversible Texture Synthesis Based on Seeded Region Growing and LSB

Steganography technology has been widely used in data transmission with secret information.However,the existing steganography has the disadvantages of low hidden information capacity,poor visual effect of cover images,and is hard to guarantee security.To solve these problems,steganography using reversible texture synthesis based on seeded region growing and LSB is proposed.Secret information is embedded in the process of synthesizing texture image from the existing natural texture.Firstly,we refine the visual effect.Abnormality of synthetic texture cannot be fully prevented if no approach of controlling visual effect is applied in the process of generating synthetic texture.We use seeded region growing algorithm to ensure texture’s similar local appearance.Secondly,the size and capacity of image can be decreased by introducing the information segmentation,because the capacity of the secret information is proportional to the size of the synthetic texture.Thirdly,enhanced security is also a contribution in this research,because our method does not need to transmit parameters for secret information extraction.LSB is used to embed these parameters in the synthetic texture.

A Holographic Diffraction Label Recognition Algorithm Based on Fusion Double Tensor Features

As an efficient technique for anti-counterfeiting,holographic diffraction labels has been widely applied to various fields.Due to their unique feature,traditional image recognition algorithms are not ideal for the holographic diffraction label recognition.Since a tensor preserves the spatiotemporal features of an original sample in the process of feature extraction,in this paper we propose a new holographic diffraction label recognition algorithm that combines two tensor features.The HSV(Hue Saturation Value)tensor and the HOG(Histogram of Oriented Gradient)tensor are used to represent the color information and gradient information of holographic diffraction label,respectively.Meanwhile,the tensor decomposition is performed by high order singular value decomposition,and tensor decomposition matrices are obtained.Taking into consideration of the different recognition capabilities of decomposition matrices,we design a decomposition matrix similarity fusion strategy using a typical correlation analysis algorithm and projection from similarity vectors of different decomposition matrices to the PCA(Principal Component Analysis)sub-space,then,the sub-space performs KNN(K-Nearest Neighbors)classification is performed.The effectiveness of our fusion strategy is verified by experiments.Our double tensor recognition algorithm complements the recognition capability of different tensors to produce better recognition performance for the holographic diffraction label system.

Non-Linear Localization Algorithm Based on Newton Iterations

In order to improve the performance of time difference of arrival(TDOA)localization,a nonlinear least squares algorithm is proposed in this paper.Firstly,based on the criterion of the minimized sum of square error of time difference of arrival,the location estimation is expressed as an optimal problem of a non-linear programming.Then,an initial point is obtained using the semi-definite programming.And finally,the location is extracted from the local optimal solution acquired by Newton iterations.Simulation results show that when the number of anchor nodes is large,the performance of the proposed algorithm will be significantly better than that of semi-definite programming approach with the increase of measurement noise.

An Approach to Calculate the Capacity of Pump-Hydro Combined Energy Storage with Wind Power Integration

In this paper, an approach is presented to calculate the reserve capacity of Pump- Hydro Combined Energy Storage (PHCES) integrated with wind generation. The proposed approach utilizes Monte Carlo methods to obtain all the reasonable capacity of PHCES based on the power system reliability requirement. The pumping and hydro period of PHCES will be taken into consideration to estimate the reliability of wind power generation with PHCES. Finally this approach is applied in a RBTS system to calculate the minimum capacity of PHCES.

Heat Transfer and Energy Utilization of Waste Heat Recovery Device with Different Internal Component

Steel industry is high energy-consuming industry, and its waste?heat recovery is critically?important for energy utilization. In this study, pipeline bundle is used to enhance heat transfer in?waste?heat recovery device,?and?associated gas-solid heat transfer and energy utilization performance with different pipeline arrangement, pipe diameter and shape of internal component are further analyzed. The temperatures of gas and particle in device with pipeline bundle periodically fluctuate in horizontal direction, and those in staggered system distribute more uniformly than those in paralleled system. Compared with paralleled device, exergy and waste heat utilization efficiency of staggered device have been improved, and they are both higher than?those without pipeline. As pipe diameter increases, exergy and waste heat utilization efficiency first increases and then decreases, and they reach the maxima with optimal pipe diameter.?As the width of internal component keeps constant, influence of its shape on heat transfer is very little.

Numerical Study of Methane Dry Reforming Reaction in a Disk Reactor with Focused Solar Simulator

Heat transfer and thermochemical energy storage process of methane dry reforming in a disk reactor with focused solar simulator was modeled and analyzed. The results showed that thermochemical energy storage efficiency of disk reactor can reach 28.4%, and that is higher than that of tubular reactor.?The maximum reaction rate occurs at catalyst bed corner near the baffle, because the corner has high temperature and high reactant molar fraction. As reactant flow increases, methane conversion and thermochemical energy storage efficiency decrease as catalyst bed temperature and heat loss decrease.?The?thermochemical energy storage efficiency increased first and then decreased with methane molar ratio increasing, while?methane conversion?and the?thermochemical energy storage efficiency increased with reactant temperature increasing.?As catalyst bed porosity rises,?methane conversion?and?thermochemical energy storage efficiency increased first and then decreased, and optimum porosity is 0.31.

A Summary Study of Wind Turbine with Related Control

One specific issue associated with the wind turbine is how to manage and adjust the rotor speed and pitch angle in the turbine with the wind increasing to achieve the maximum power extraction from the wind. The aim of this paper is to provide a summary study of the impact of related controls and operating strategies on the wind turbine which mean how parameters affect the wind turbine operation. The software of “GH bladed” produced by GL Garrad Hassan will be used to model wind turbine and to perform the analysis. Following two strategies, control of rotor speed and control of blade pitch angle, are applied to the model of the wind turbine to see how output power are adjusted and optimized. The final part proposes the operating strategy of the wind turbine to understand the running procedure of wind turbine inside.

Forecasting the Impact of CCGT-CCS on the UK’s Electricity Market by LCOE

To achieve the target for building a low-carbon economy, the UK will have to build more low-carbon power plants to reduce carbon dioxide emissions from electricity generation. However, renewable energy is difficult to meet the increasing energy demand and keep lights on. This limitation of renewable could be solved by coal and gas-fired power station fitted with carbon capture storage (CCS) technology. CCS technology could capture up to 90% of carbon dioxide from emissions and allow fossil fuel power station to provide continuous low-carbon electricity power. This paper presents the levelised cost of electricity of CCGT with CCS and compared with renewable technology to forecast the impact of CCGT with CCS on the UK’s electricity market.

Optimal Performance for Solar Thermal Power System

Solar thermal power is currently one of the important trends and research hotspots of solar energy. In present paper, basic physical model is proposed to investigate the solar thermal power, and the operating temperature is optimized to maximize the electricity generating efficiency. When the concentrated energy flux rises, the absorption efficiency of heat receiver will first increase and then decrease, while the increasing of flow velocity can improve the absorption performance. As the working temperature rising, the heat loss of infrared radiation and natural convection increases quickly, so the absorption efficiency obviously decreases, while the Carnot efficiency of the steam turbine cycle will rise. Because of the coupling effects of the heat absorption cycle and turbine cycle, the electricity generating efficiency will reach maximum with the optimal working temperature.

A Transmission Congestion Cost Allocation Method in Bilateral Trading Electricity Market

Bilateral trading in electricity is one of the typical deregulated power market trading methods and it has its own way to calculate and allocate congestion cost. In this paper, using as a practical bilateral structure example, the British electricity market is stated in details especially the operation mechanism and the methodology of imbalance settlement. A corresponding congestion cost allocation method for bilateral market is introduced briefly by equations and is simulated in a modified IEEE-14 bus model to investigate its pros and cons.

Calculating Size of Pump-Hydro Combined Energy Storage System in Wind-Diesel Systems Based on PHCES Dynamic Model

This paper will propose an approach to calculate and evaluate the reserve capacity and energy size of Pumping-Hydro Combined Energy Storage (PHCES) when wind power is integrated to power grid while considering the scheme of generation capacity allocation and operation of PHCES. This approach will use Monte Carlo Method to simulate large amount of samples to obtain the minimum value of capacity and energy size that could satisfy the requirement of system reliability. Finally this approach will apply in a RBTS system to assess the project feasibility.

Julolidine functionalized benzimidazoline-doped fullerene derivatives for efficient and stable perovskite solar cells

Fullerene derivatives are highly attractive materials in solar cells,organic thermoelectrics,and other devices.However,the intrinsic low electron mobility and electrical conductivity restrict their potential device performance,such as perovskite solar cells(PSCs).Herein,we successfully enhanced the electric properties and morphology of phenyl-C61-butyric acid methyl ester(PCBM)by n-doping it with a benzimidazoline derivative,9-(1,3-dimethyl-2,3-dihydro-1H-benzoimidazol-2-yl)-julolidine(JLBI-H)via a solution process.We found the n-doping can not only improve the conductivity and optimize the band alignment but also enable the PCBM to have a constantly strong charge extraction ability in a wide temperature from 173 to 373 K,which guarantees a stable photovoltaic performance of the corresponding PSCs under a wide range of operating temperatures.With the JLBI-H-doped PCBM,we improved the efficiency from 17.9%to 19.8%,along with enhanced stability of the nonencapsulated devices following the aging protocol of ISOS-D-1.

Neural Network-Based Variational Methods for Solving Quadratic Porous Medium Equations in High Dimensions

In this paper,we propose and study neural network-based methods for solutions of high-dimensional quadratic porous medium equation(QPME).Three variational formulations of this nonlinear PDE are presented:a strong formulation and two weak formulations.For the strong formulation,the solution is directly parameterized with a neural network and optimized by minimizing the PDEresidual.It can be proved that the convergence of the optimization problem guarantees the convergence of the approximate solution in the L^(1)sense.Theweak formulations are derived following(Brenier in Examples of hidden convexity in nonlinear PDEs,2020)which characterizes the very weak solutions of QPME.Specifically speaking,the solutions are represented with intermediate functions who are parameterized with neural networks and are trained to optimize the weak formulations.Extensive numerical tests are further carried out to investigate the pros and cons of each formulation in low and high dimensions.This is an initial exploration made along the line of solving high-dimensional nonlinear PDEs with neural network-based methods,which we hope can provide some useful experience for future investigations.

Devonian integrative stratigraphy and timescale of China

The Global Boundary Stratotype Sections and Points(GSSPs) for the bases of all seven international Devonian stages have been formally defined and ratified by IUGS till 1996, and nowadays, the main tasks for Devonian stratigraphers include further subdivision of these standard stages, strictly constrained absolute ages for the boundaries, and precise neritic-pelagic and marine-terrestrial correlations using multidisciplinary stratigraphy methods. Establishment of high-resolution Devonian integrative stratigraphy framework and timescale of China would play an important role in improving regional and international correlation, facilitating the recognition of important stratigraphic levels in different paleogeographic settings, and understanding the evolution pattern of biota, paleoclimate and paleoenvironment during this critical interval. Based on well-studied bio-and chronostratigraphy of Devonian in South China and adjacent areas, in combination with recent achievements in carbon isotope stratigraphy, event stratigraphy and radioactive isotope ages, this paper briefly summarize the research history and current status of Devonian chronostratigraphy of China, and for the first time introduce Devonian integrative stratigraphy framework of China.Up to date, few studies have been conducted on the astronomical cyclostratigraphy and high-resolution radioactive isotope dating in Devonian of China, which should be our main focuses in the near future.