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With the popularization of small batch production, the main cutting method for sheet metal parts has changed. Laser cutting has become the main production method for coil material cutting. Developing an irregular part nesting method for the continuous cutting of coil material is urgent. Based on the coil material cutting process, this paper proposes an intelligent approach for the unit nesting problem of coil material. Firstly, a unit nesting model of coil material was constructed. Secondly, an intelligent approach using an improved marine predator algorithm was used to solve this model. In solving the model, the minimum nesting unit was continuously updated by changing the position, angle, and quantity of the nesting parts. Thirdly, the geometric characteristics of this minimum nesting unit were extracted. Finally, the nesting units for production were obtained using a single row or opposite row of the minimum nesting unit. The computational results and comparison prove that the presented approach is feasible and effective in improving material utilization, reducing production costs, and meeting the requirements of the production site.

As the use of digital currencies, such as cryptocurrencies, increases in popularity, phishing scams and other cybercriminal activities on blockchain platforms (e.g., Ethereum) have also risen. Current methods of detecting phishing in Ethereum focus mainly on the transaction features and local network structure. However, these methods fail to account for the complexity of interactions between edges and the handling of large graphs. Additionally, these methods face significant issues due to the limited number of positive labels available. Given this, we propose a scheme that we refer to as the Bagging Multiedge Graph Convolutional Network to detect phishing scams on Ethereum. First, we extract the features from transactions and transform the complex Ethereum transaction network into three simple inter-node graphs. Then, we use graph convolution to generate node embeddings that leverage the global structural information of the inter-node graphs. Further, we apply the bagging strategy to overcome the issues of data imbalance and the Positive Unlabeled (PU) problem in transaction data. Finally, to evaluate our approach’s effectiveness, we conduct experiments using actual transaction data. The results demonstrate that our Bagging Multiedge Graph Convolutional Network (0.877 AUC) outperforms all of the baseline classification methods in detecting phishing scams on Ethereum.

To address the problem of identification of authenticity and integrity of video content and the location of video tampering area,a deep learning detection algorithm based on video noise flow was proposed.Firstly,based on SRM (spatial rich model) and C3D (3D convolution) neural network,a feature extractor,a frame discriminator and a RPN (region proposal network) based spatial locator were constructed.Secondly,the feature extractor was combined with the frame discriminator and the spatial locator respectively,and then two neural networks were built.Finally,two kinds of deep learning models were trained by the enhanced data,which were used to locate the tampered area in temporal domain and spatial domain respectively.The test results show that the accuracy of temporal-domain location is increased to 98.5%,and the average intersection over union of spatial localization and tamper area labeling is 49%,which can effectively locate the tamper area in temporal domain and spatial domain.

Text information hiding is an important technology to protect the security and integrity of text content.The research progress of Chinese text information hiding was reviewed.According to the clue of Chinese text information hiding,the existing algorithms were divided into three categories,algorithms based on text images,algorithms based on text format and algorithms based on text content.The implementation process of each type of algorithm was elaborated and the advantages and disadvantages of the algorithm were analyzed.At the same time,their principles,embedded capacity and anti-attack capability was compared and analyzed.In addition,the problems existing in Chinese text information hiding technology was summarized,and the research trends were discussed in the future.It is expected to provide reference for research in this field.

Accurate streamflow prediction plays a pivotal role in hydraulic project design, nonpoint source pollution estimation, and water resources planning and management. However, the highly non-linear relationship between rainfall and runoff makes prediction difficult with desirable accuracy. To improve the accuracy of monthly streamflow prediction, a seasonal Support Vector Regression (SVR) model coupled to the Soil and Water Assessment Tool (SWAT) model was developed for 13 subwatersheds in the Illinois River watershed (IRW), U.S. Terrain, precipitation, soil, land use and land cover, and monthly streamflow data were used to build the SWAT model. SWAT Streamflow output and the upstream drainage area were used as two input variables into SVR to build the hybrid SWAT-SVR model. The Calibration Uncertainty Procedure (SWAT-CUP) and Sequential Uncertainty Fitting-2 (SUFI-2) algorithms were applied to compare the model performance against SWAT-SVR. The spatial calibration and leave-one-out sampling methods were used to calibrate and validate the hybrid SWAT-SVR model. The results showed that the SWAT-SVR model had less deviation and better performance than SWAT-CUP simulations. SWAT-SVR predicted streamflow more accurately during the wet season than the dry season. The model worked well when it was applied to simulate medium flows with discharge between 5 m3 s-1 and 30 m3 s-1, and its applicable spatial scale fell between 500 to 3000 km2. The overall performance of the model on yearly time series is "Satisfactory". This new SWAT-SVR model has not only the ability to capture intrinsic non-linear behaviors between rainfall and runoff while considering the mechanism of runoff generation but also can serve as a reliable regional tool for an ungauged or limited data watershed that has similar hydrologic characteristics with the IRW.

With the rapid development of Computer Graphics, the computer-generated images (CG) are almost as realistic as real photographs(PG) and it is difficult to distinguish between CG and PG accurately with the naked eye. Image is an important carrier for people to get information on a daily basis. However the spread of CG produced for malicious purposes may disrupt social order and even undermine social stability. Therefore, the accurate detection of CG and PG is of great significance. In this paper, we (1) introduce 11 approaches that apply deep learning to the implementations of CG detection, and divide them into 4 categories based on the network structure; (2) give an introduction to the available datasets; (3) design a series of experiments to test the detection performance of each approach,then analyze the experimental results; The experimental results show that most approaches can differentiate CG from PG, while the detection accuracy and efficiency of each model are different. Nevertheless none of these methods is valid when the images tampered by noise. Above all (4) summarize the problems and challenges in this field, and look forward to the trends in future research.

Resistance to anti-HER2 therapy in breast cancer remains a major obstacle in the clinic. Here the authors performed a CRISPR-selective vulnerability screen to identify transaldoloase as a target that is synthetically lethal with HER2 inhibition in breast cancer cells.

Correct identifying analog circuit incipient faults is useful to the circuit's health monitoring, and yet it is very hard. In this paper, an analog circuit incipient fault diagnosis method using deep belief network (DBN) based features extraction is presented. In the diagnosis scheme, time responses of analog circuits are measured, and then features are extracted by using the DBN method. Meanwhile, the learning rates of DBN are produced by using quantum-behaved particle swarm optimization (QPSO) algorithm, which is beneficial to optimizing the structure parameters of DBN. Afterward, a support vector machine (SVM) based incipient fault diagnosis model is constructed on basis of the extracted features to classify incipient faulty components, where the regularization parameter and width factor of SVM are yielded by using the QPSO algorithm. Sallen-Key bandpass filter and four-op-amp biquad high pass filter incipient fault diagnosis simulations are conducted to demonstrate the proposed diagnosis method, and comparisons verify that the proposed diagnosis method can produce higher diagnosis accuracy than other typical analog circuit fault diagnosis methods.

With the rapid development of cloud storage, an increasing number of users store their images in the cloud. These images contain many business secrets or personal information, such as engineering design drawings and commercial contracts. Thus, users encrypt images before they are uploaded. However, cloud servers have to hide secret data in encrypted images to enable the retrieval and verification of massive encrypted images. To ensure that both the secret data and the original images can be extracted and recovered losslessly, researchers have proposed a method that is known as reversible data hiding in encrypted images (RDHEI). In this paper, a new RDHEI method using median edge detector (MED) and two’s complement is proposed. The MED prediction method is used to generate the predicted values of the original pixels and calculate the prediction errors. The adaptive-length two’s complement is used to encode the most prediction errors. To reserve room, the two’s complement is labeled in the pixels. To record the unlabeled pixels, a label map is generated and embedded into the image. After the image has been encrypted, it can be embedded with the data. The experimental results indicate that the proposed method can reach an average embedding rate of 2.58 bpp, 3.04 bpp, and 2.94 bpp on the three datasets, i.e., UCID, BOSSbase, BOWS-2, which outperforms the previous work.

Currently, users’ location information is collected to provide better services or research. Using a central server to collect, store and share location information has inevitable defects in terms of security and efficiency. Using the point-to-point sharing method will result in high computation overhead and communication overhead for users, and the data are hard to be verified. To resolve these issues, we propose a new blockchain-based multi-level location secure sharing scheme. In our proposed scheme, the location data are set hierarchically and shared with the requester according to the user’s policy, while the received data can be verified. For this, we design a smart contract to implement attribute-based access control and establish an incentive and punishment mechanism. We evaluate the computation overhead and the experimental results show that the computation overhead of our proposed scheme is much lower than that of the existing scheme. Finally, we analyze the performances of our proposed scheme and demonstrate that our proposed scheme is, overall, better than existing schemes.

In secret image sharing, the image is divided into several stego images, which are managed by corresponding participants. The secret image can be recovered only when the number of authorized participants is no less than the threshold. Thus, it is widely used to protect essential images, such as engineering drawings and product design drawings. In the traditional secret image sharing scheme, the threshold is fixed and unique. However, in practice, the security policy and the adversarial structure may change; therefore, the threshold must be adjusted dynamically. In this paper, we propose a novel secret image sharing scheme with a changeable threshold capability. Our scheme eliminates the limit of the changeable threshold and reduces the computation required. Also, our scheme is the first threshold changeable secret image sharing scheme that can recover an undistorted cover image. The theoretical analysis shows that our scheme is safe even if the threshold is changed. The experiments demonstrated that the stego image generated by our algorithm has better quality than other changeable-threshold, secret image sharing algorithms.

Lithium-ion batteries are crucial to many types of electric equipments. Hence, lithium-ion battery capacity prognostic is significantly important, and it is yet very hard for the measured battery data that are regularly polluted by miscellaneous noises. In this paper, a battery capacity prognostic approach using the empirical mode decomposition (EMD) denoising method and multiple kernel relevance vector machine (MKRVM) approach is presented. The EMD denoising method is employed to process the measured capacity data to produce noise-free capacity data. The battery capacity prediction model using MKRVM is constructed based on the denoised capacity data. The MKRVM's kernel keeps diversity by using multiple heterogeneous kernel learning method. Meanwhile, sparse weights of basic kernel functions are yielded by using particle swarm optimization (PSO) algorithm. The measured battery capacity data are used to demonstrate the effect of EMD denoising method, and battery capacity prediction experiments reveal that the proposed MKRVM approach can predict the battery's future capacity precisely.

Watershed-scale nonpoint source (NPS) pollution models have become important tools to understand, evaluate, and predict the negative impacts of NPS pollution on water quality. Today, there are many NPS models available for users. However, different types of models possess different form and structure as well as complexity of computation. It is difficult for users to select an appropriate model for a specific application without a clear understanding of the limitations or strengths for each model or tool. This review evaluates 14 more commonly used watershed-scale NPS pollution models to explain how and when the application of these different models are appropriate for a given effort. The models that are assessed have a wide range of capacities that include simple models used as rapid screening tools (e.g., Long-Term Hydrologic Impact Assessment (L-THIA) and Nonpoint Source Pollution and Erosion Comparison Tool (N-SPECT/OpenNSPECT)), medium-complexity models that require detail data input and limited calibration (e.g., Generalized Watershed Loading Function (GWLF), Loading Simulation Program C (LSPC), Source Loading and Management Model (SLAMM), and Watershed Analysis Risk Management Frame (WARMF)), complex models that provide sophisticated simulation for NPS pollution processes with intensive data and rigorous calibration (e.g., Agricultural Nonpoint Source pollution model (AGNPS/AnnAGNPS), Soil and Water Assessment Tool (SWAT), Stormwater Management Model (SWMM), and Hydrologic Simulation Program Fortran (HSPF)), and modeling systems that integrate various sub-models and tools, and contain the highest complexity to solve all phases of hydrologic, hydraulic, and chemical dynamic processes (e.g., Automated Geospatial Watershed Assessment Tool (AGWA), Better Assessment Science Integrating Point and Nonpoint Sources (BASINS) and Watershed Modeling System (WMS)). This assessment includes model intended use, components or capabilities, suitable land-use type, input parameter type, spatial and temporal scale, simulated pollutants, strengths and limitations, and software availability. Understanding the strengths and weaknesses of each watershed-scale NPS model will lead to better model selection for suitability and help to avoid misinterpretation or misapplication in practice. The article further explains the crucial criteria for model selection, including spatial and temporal considerations, calibration and validation, uncertainty analysis, and future research direction of NPS pollution models. The goal of this work is to provide accurate and concise insight for watershed managers and planners to select the best-suited model to reduce the harm of NPS pollution to watershed ecosystems.

After any distribution of secret sharing shadows in a threshold changeable secret sharing scheme, the threshold may need to be adjusted to deal with changes in the security policy and adversary structure. For example, when employees leave the organization, it is not realistic to expect departing employees to ensure the security of their secret shadows. Therefore, in 2012, Zhang et al. proposed (t → t', n) and ({t1, t2,⋯, tN}, n) threshold changeable secret sharing schemes. However, their schemes suffer from a number of limitations such as strict limit on the threshold values, large storage space requirement for secret shadows, and significant computation for constructing and recovering polynomials. To address these limitations, we propose two improved dealer-free threshold changeable secret sharing schemes. In our schemes, we construct polynomials to update secret shadows, and use two-variable one-way function to resist collusion attacks and secure the information stored by the combiner. We then demonstrate our schemes can adjust the threshold safely.