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An active pH-sensitive film based on pectin-sodium alginate-xanthan gum composite film (PAX) was prepared, containing blueberry anthocyanin extract (BAEs), to monitor the freshness of blueberries. The effects of different contents of BAEs on the microstructure and physical properties of intelligent polysaccharide films were comprehensively evaluated. It was found that 75-BAEs-PAX film had a solid response to pH value and showed different and easily distinguishable colors at different pH values. In addition, when the freshness of blueberries stored at different temperatures (−1 °C, 4 °C, 10 °C, 15 °C, 25 °C) was monitored, the color of 75-BAEs-PAX film changed from purple to light pink from neutral to acidic environment, which was consistent with the change of pH value of blueberries from fresh to spoilage. The Arrhenius equation verified that the difference between the activation energy of the indicator film and the blueberry quality was less than 25 kJ/mol. Therefore, the 75-BAEs-PAX film can be used as an indicator film for blueberries freshness monitoring. In this study, the freshness of blueberries was monitored by BAEs, and the purpose of using ontology to monitor ontology was achieved. The freshness of blueberries was visualized during storage and transportation, which could effectively reduce the waste of blueberries. In the future, the method of ontology monitoring ontology could be extended to other foods.

Abstract We propose a vector dark matter model with an exotic dark SU(2) gauge group. Two Higgs triplets are introduced to spontaneously break the symmetry. All of the dark gauge bosons become massive, and the lightest one is a viable vector DM candidate. Its stability is guaranteed by a remaining Z 2 symmetry. We study the parameter space constrained by the Higgs measurement data, the dark matter relic density, and direct and indirect detection experiments. We find numerous parameter points satisfying all the constraints, and they could be further tested in future experiments. Similar methodology can be used to construct vector dark matter models from an arbitrary SO(N) gauge group.

Amplification, overexpression, and somatic mutation of the HER2 gene have been reported to play a critical role in tumorigenesis of various cancers. The HER2 H878Y mutation was recently reported in 11% of hepatocellular carcinoma (HCC) patients. However, its functional impact on the HER2 protein and its role in tumorigenesis has not been determined. Here, we show that HER2 H878Y is a gain-of-function mutation. Y878 represents a phosphorylation site, and phospho-Y878 interacts with R898 residue to stabilize the active conformation of HER2, thereby enhancing its kinase activity. H878Y mutant is transforming and the transformed cells are sensitive to HER2 kinase inhibitors. Thus, our study reveals the following novel mechanism underlying the tumorigenic function of the HER2 H878Y mutation: the introduction of a tyrosine residue into the kinase activation loop via mutagenesis modulates the conformation of the kinase, thereby enhancing its activity.

This article proposes a multi-objective optimization method for product service configuration based on customer demand constraint mechanism, targeting the diverse customer needs of product service systems (PSS). This method first forms a utility matrix based on the response of product service instances to customer needs and establishes constraints based on product attributes and enterprise resources. The PSS configuration module is divided into product basic modules and optional modules based on customer demand constraint mechanisms, aiming to express the combination of product and service attributes. Through the utility matrix, multiple optional objectives such as safety, energy efficiency, and economy of PSS configuration are established to fully express the multi-dimensional needs of customer satisfaction. Then a new MOEA/D algorithm was developed for multi-objective optimization of PSS configuration. The improved MOEA/D algorithm adopts a weight vector adjustment strategy based on correlation sparsity, which improves the convergence and diversity of optimization results in high-dimensional objective space and solves the problem of exponential growth in solution space caused by the increase in customer demand types. This method was applied to practical cases of home appliance PSS configuration and compared with the performance of five advanced methods. The experimental results showed that the model established in this paper and the improved algorithm obtained configuration schemes had significant advantages in convergence and diversity.

The low Earth orbit (LEO) satellite internet network (LEO-SIN) has become a heated issue for the next generation of mobile communications, serving as a crucial means to achieve global wide-area broadband coverage and, especially, mobile phone directly to satellite cell (MPDTSC) communication. The ultra-high-speed movement of LEO satellites relative to the Earth results in serious Doppler effects, leading to signal de-synchronization at the user end (UE), and relative high-speed motion leading to frequent satellite handovers. Satellite ephemeris, which indicates the satellite’s position, has the potential to determine the position of the transmit (Tx) within the LEO-SIN, thereby enhancing the reliability and efficiency of satellite communication. The adoption of ephemeris in the LEO-SIN has met some new challenges: (1) how UEs can acquire ephemerides before signal synchronization is complete, (2) how to minimize the frequency of ephemeris broadcasting, and (3) how to decrease the overhead of ephemeris broadcasting. To address the above challenges, this paper proposes a method for extrapolating the LEO-SIN orbit based on multi-satellite ephemeris coordination (MSEC) and the multi-stream fractional autoregressive integrated moving average (MS-FARIMA). First, a multi-factor global error analysis model for ephemeris-extrapolation error is established, which decomposes it into three types; namely, random error (RE), trending error (TE), and periodic error (PE), with a focus on increasing the extrapolation accuracy by improving RE and TE. Second, RE is eliminated by utilizing the ephemerides from multiple satellites received at the same UE at the same time, as well as multiple ephemerides from the same satellite at different times. Subsequently, we propose a new FARIMA algorithm with the innovation of a multi-stream data time-series forecast (TSF), which effectively improves ephemeris extrapolation errors. Finally, the simulation results show that the proposed method reduces ephemeris extrapolation errors by 33.5% compared to existing methods, which also contributes to a performance enhancement in the Doppler frequency offset (DFO) estimation of MPDTSC.

Nasopharyngeal carcinoma (NPC) is a kind of malignant tumor. The accurate and automatic segmentation of computed tomography (CT) images of organs at risk (OAR) is clinically significant. In recent years, deep learning models represented by U-Net have been widely applied in medical image segmentation tasks, which can help to reduce doctors’ workload. In the OAR segmentation of NPC, the sizes of the OAR are variable, and some of their volumes are small. Traditional deep neural networks underperform in segmentation due to the insufficient use of global and multi-size information. Therefore, a new SE-Connection Pyramid Network (SECP-Net) is proposed. For extracting global and multi-size information, the SECP-Net designs an SE-connection module and a pyramid structure for improving the segmentation performance, especially that of small organs. SECP-Net also uses an auto-context cascaded structure to further refine the segmentation results. Comparative experiments are conducted between SECP-Net and other recent methods on a private dataset with CT images of the head and neck and a public liver dataset. Five-fold cross-validation is used to evaluate the performance based on two metrics; i.e., Dice and Jaccard similarity. The experimental results show that SECP-Net can achieve SOTA performance in these two challenging tasks.

Non-local effect is an important issue for a pulse-coded Brillouin optical time domain analyzer. Segmental frequency modulated probe, with a dedicated frequency step of half the natural Brillouin linewidth of the sensing fiber, is proposed to solve this issue. It can provide a wideband Brillouin gain as well as loss in the frequency band of the pump to alleviate pump distortions and thus non-local effects. This method can narrow the bit interval towards a level limited by the time of the Brillouin gain transition due to frequency switching, resulting in a bit interval as short as 900 ns for a 225 MHz frequency scanning range. A hot spot at the far fiber end is successfully detected even the probe power reaches −3 dBm under a cumulated Brillouin gain of 267.4%.

Multi-source information fusion is widely used because of its similarity to practical engineering situations. With the development of science and technology, the sources of information collected under engineering projects and scientific research are more diverse. To extract helpful information from multi-source information, in this paper, we propose a multi-source information fusion method based on the Dempster-Shafer (DS) evidence theory with the negation of reconstructed basic probability assignments (nrBPA). To determine the initial basic probability assignment (BPA), the Gaussian distribution BPA functions with padding terms are used. After that, nrBPAs are determined by two processes, reassigning the high blur degree BPA and transforming them into the form of negation. In addition, evidence of preliminary fusion is obtained using the entropy weight method based on the improved belief entropy of nrBPAs. The final fusion results are calculated from the preliminary fused evidence through the Dempster’s combination rule. In the experimental section, the UCI iris data set and the wine data set are used for validating the arithmetic processes of the proposed method. In the comparative analysis, the effectiveness of the BPA determination using a padded Gaussian function is verified by discussing the classification task with the iris data set. Subsequently, the comparison with other methods using the cross-validation method proves that the proposed method is robust. Notably, the classification accuracy of the iris data set using the proposed method can reach an accuracy of 97.04%, which is higher than many other methods.

When unattended substations are popular, the knob is a vital monitoring object for unattended substations. However, in the actual scene of the substation, the recognition method of a knob gear has low accuracy. The main reasons are as follows. Firstly, the SNR of knob images is low due to the influence of lighting conditions, which are challenging to extract image features. Secondly, the image deviates from the front view affected by the shooting angle; that knob has a certain deformation, which causes the feature judgment to be disturbed. Finally, the feature distribution of each kind of knob is inconsistent, which interferes with image extraction features and leads to weak spatial generalization ability. For the above problems, we propose a three-stage knob gear recognition method based on YOLOv4 and Darknet53-DUC-DSNT models for the first time and apply key point detection of deep learning to knob gear recognition for the first time. Firstly, YOLOv4 is used as the knob area detector to find knobs from a picture of a cabinet panel. Then, Darknet53, which can extract features, is used as the backbone network for keypoint detection of knobs, combined with DUC structure to recover detailed information and DSNT structure to enhance feature extraction and improve spatial generalization ability. Finally, we obtained the knob gear by calculating the angle between the line of the rotating center point and the pointing point and horizontal direction. The experimental results show that this method effectively solves the above problems and improves the performance of knob gear detection.

The main influencing factors of the clustering effect of the k-means algorithm are the selection of the initial clustering center and the distance measurement between the sample points. The traditional k-mean algorithm uses Euclidean distance to measure the distance between sample points, thus it suffers from low differentiation of attributes between sample points and is prone to local optimal solutions. For this feature, this paper proposes an improved k-means algorithm based on evidence distance. Firstly, the attribute values of sample points are modelled as the basic probability assignment (BPA) of sample points. Then, the traditional Euclidean distance is replaced by the evidence distance for measuring the distance between sample points, and finally k-means clustering is carried out using UCI data. Experimental comparisons are made with the traditional k-means algorithm, the k-means algorithm based on the aggregation distance parameter, and the Gaussian mixture model. The experimental results show that the improved k-means algorithm based on evidence distance proposed in this paper has a better clustering effect and the convergence of the algorithm is also better.

Aiming at the problem that the number and decimal point of digital instruments in substations are prone to misdetection and missed detection, a method of digital meter readings in a substation based on connected domain analysis algorithm is proposed. This method uses Faster R-CNN (Faster Region Convolutional Neural Network) as a positioning network to localize the dial area, and after acquiring the partial image, it enhances the useful information of the digital area. YOLOv4 (You Only Look Once) convolutional neural network is used as the detector to detect the digital area. The purpose is to distinguish the numbers and obtain the digital area that may contain a decimal point or no decimal point at the tail. Combined with the connected domain analysis algorithm, the difference between the number of connected domain categories and the area ratio of the digital area is analyzed, and the judgment of the decimal point is realized. The method reduces the problem of mutual interference among categories when detecting YOLOv4. The experimental results show that the method improves the detection accuracy of the algorithm.

In this work, we propose an interfacial scheme accompanying the lattice Boltzmann method for convection-diffusion equations with general interfacial conditions, including conjugate conditions with or without jumps in heat and mass transfer, continuity of macroscopic variables and normal fluxes in ion diffusion in porous media with different porosity, and the Kapitza resistance in heat transfer. The construction of this scheme is based on our boundary schemes [Huang and Yong, J. Comput. Phys. 300, 70 (2015)] for Robin boundary conditions on straight or curved boundaries. It gives second-order accuracy for straight interfaces and first-order accuracy for curved ones. In addition, the new scheme inherits the advantage of the boundary schemes in which only the current lattice nodes are involved. Such an interfacial scheme is highly desirable for problems with complex geometries or in porous media. The interfacial scheme is numerically validated with several examples. The results show the utility of the constructed scheme and very well support our theoretical predications. [ABSTRACT FROM AUTHOR]