Wearable biosensors are playing an increasingly important role in society. Compared with traditional wearable biosensors for detecting blood pressure, blood oxygen, or pulse conditions, which can only access information from the physical level, biosensors for testing body fluids can provide more details on health conditions through the analysis of biochemical criteria. Sweat secreted from glands distributed throughout the body contains abundant biochemical information and is an indicator of the physical conditions. Because of the noninvasive and safe sampling method, wearable sweat monitoring systems have the potential to realize long‐term and wearable detection. In this review, the current situation of wearable sweat monitoring systems is summarized from three critical parts: the sweat collection method, the sweat analysis method, and the energy supply. Finally, based on the existing drawbacks of wearable sweat monitoring systems in previous studies, the authors creatively propose droplet‐based detection, triboelectric nanogenerator‐based detection, and self‐powered systems as new directions for future research. The proposed approaches are expected to promote the commercialization process of wearable sweat monitoring systems.
The normal development and maturation of oocytes and sperm, the formation of fertilized ova, the implantation of early embryos, and the growth and development of foetuses are the biological basis of mammalian reproduction. Therefore, research on oocytes has always occupied a very important position in the life sciences and reproductive medicine fields. Various embryo engineering technologies for oocytes, early embryo formation and subsequent developmental stages and different target sites, such as gene editing, intracytoplasmic sperm injection (ICSI), preimplantation genetic diagnosis (PGD), and somatic cell nuclear transfer (SCNT) technologies, have all been established and widely used in industrialization. However, as research continues to deepen and target species become more advanced, embryo engineering technology has also been developing in a more complex and sophisticated direction. At the same time, the success rate also shows a declining trend, resulting in an extension of the research and development cycle and rising costs. By studying the existing embryo engineering technology process, we discovered three critical nodes that have the greatest impact on the development of oocytes and early embryos, namely, oocyte micromanipulation, oocyte electrical activation/reconstructed embryo electrofusion, and the in vitro culture of early embryos. This article mainly demonstrates the efforts made by researchers in the relevant technologies of these three critical nodes from an engineering perspective, analyses the shortcomings of the current technology, and proposes a plan and prospects for the development of embryo engineering technology in the future.
In filming, the collected video may be blurred due to camera shake and object movement, causing the target edge to be unclear or deforming the targets. In order to solve these problems and deeply optimize the quality of movie videos, this work proposes a video deblurring (VD) algorithm based on neural network (NN) model and attention mechanism (AM). Based on the scale recurrent network, Haar planar wavelet transform (WT) is introduced to preprocess the video image and to deblur the video image in the wavelet domain. Additionally, the spatial and channel AMs are fused into the overall network framework to improve the feature expression ability. Further, the residual inception spatial-channel attention (RISCA) mechanism is introduced to extract the multiscale feature information from video images. Meanwhile, skip spatial-channel attention (SSCA) accelerates the network training time to achieve a better VD effect. Finally, relevant experiments are designed, factoring in peak signal-to-noise ratio (PSNR) and structural similarity (SSI). The experimental findings corroborate that the proposed Haar and attention video deblurring (HAVD) outperforms multisize network Haar (MSNH) in PSNR and structural similarity (SSIM), improved by 0.10 dB and 0.005, respectively. Therefore, embedding the dual AMs can improve the model performance and optimize the video quality. This work provides technical support for solving the video distortion problems.
Branding is a magic weapon for enterprises to participate in international competition, and empowering enterprises through branding has become a national strategy in the new era. Economic and social development has won wide acclaim from the international community, but enterprises generally have the problem of being “big but not strong”, which is not matching with long history and great power influence. The brand bottleneck of Chinese enterprises has been highlighted. Recent brand theory research has been fruitful on the whole, but there are also some weak links, among which “the mechanism of enterprise brand value formation” is a research theme to be strengthened. This paper presents a number of suggestions for the formation of corporate brand value. The empirical analysis was conducted using valid data. The results found that: customer involvement behavior has a significant positive influence on customer citizenship behavior and customer experience value. Customer experience value has a significant positive influence on customer satisfaction and customer commitment. It plays a mediating effect in the relationship between the influence of customer involvement behavior on customer satisfaction and customer commitment, respectively. Customer satisfaction has a significant positive influence on customer commitment, and plays a mediating customer commitment has a significant positive effect on customer citizenship behavior and mediates the effect of customer experience value on customer citizenship behavior. The experimental results show that: the accuracy of crop color recognition by this method is high, and it has the advantages of faster computational efficiency and higher computational accuracy compared with other algorithms, thus verifying the reliability of the algorithm. Based on the fuzzy sentiment of online reviews, this paper improves the continuous use model ECM-ISC and formulates the inference rules of fuzzy affiliation function, and verifies the brand conversion intention and brand conversion type of cell phones by example calculation, which has good accuracy and generality and has important practical significance for brand marketing and early warning management. In addition, the use of brand economics in the study of corporate brand positioning is a development and innovation of brand economics.
Based on cloud computing and statistics theory, this paper proposes a reasonable analysis method for big data of film and television. The method selects Hadoop open source cloud platform as the basis, combines the MapReduce distributed programming model and HDFS distributed file storage system and other key cloud computing technologies. In order to cope with different data processing needs of film and television industry, association analysis, cluster analysis, factor analysis, and K-mean + association analysis algorithm training model were applied to model, process, and analyze the full data of film and TV series. According to the film type, producer, production region, investment, box office, audience rating, network score, audience group, and other factors, the film and television data in recent years are analyzed and studied. Based on the study of the impact of each attribute of film and television drama on film box office and TV audience rating, it is committed to the prediction of film and television industry and constantly verifies and improves the algorithm model.
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