In industrial Internet, many intelligent applications are implemented based on data collection and distribution. Data collection and data distribution in the wireless sensor networks are very important, where the node topology can be described by the spanning tree for obtaining an efficient transmission. Classical algorithms in graph theory such as the Kruskal algorithm or Prim algorithm can only find the minimum spanning tree (MST) in industrial wireless sensor networks. Swarm intelligence algorithm can obtain multiple solutions in one calculation. Multiple solutions are very helpful for improving the reliability of industrial wireless sensor networks. In this paper, we combine quantum computing with artificial bee colony and design a spanning tree construction algorithm for industrial wireless sensor networks. Quantum computations are introduced into the onlooker bees search. Food source replacement strategy is improved. Finally, the algorithm is simulated and evaluated. The results show that the new proposed algorithm can obtain more alternative solutions and has a better performance in search efficiency.
B-box (BBX) proteins have been recognized as vital determinants in plant development, morphogenesis, and adaptive responses to a myriad of environmental stresses. These zinc-finger proteins play a pivotal role in various biological processes. Their influence spans photomorphogenesis, the regulation of flowering, and imparting resilience to a wide array of challenges, encompassing both biotic and abiotic factors. Chromosome localization, gene structure and conserved motifs, phylogenetic analysis, collinearity analysis, expression profiling, fluorescence quantitative analysis, and tobacco transient transformation methods were used for functional localization and expression pattern analysis of the DhBBX gene. A total of 23 DhBBX members were identified from Dendrobium huoshanense. Subsequent phylogenetic evaluations effectively segregated these genes into five discrete evolutionary subsets. The predictions of subcellular localizations revealed that all these proteins were localized in the nucleus. The genetic composition and patterns showed that the majority of these genes consisted of several exons, with a few variations that could be attributed to transposon insertion. A comprehensive analysis using qRT-PCR was conducted to unravel the expression patterns of these genes in D. huoshanense, with a specific concentration on their responses to various hormone treatments and cold stress. Subcellular localization reveals that DhBBX21 and DhBBX9 are located in the nucleus. Our results provide a deep comprehension of the complex regulatory mechanisms of BBXs in response to various environmental and hormonal stimuli. These discoveries encourage further detailed and focused investigations into the operational dynamics of the BBX gene family in a wider range of plant species.
Abstract Objectives Pb stress has a negative impact on plant growth by interfering with photosynthesis and releasing reactive oxygen species, causing major risks such as heavy metal ion accumulation in the soil matrix. A proteomics experiment was conducted to determine whether protein levels of Dendrobium huoshanense changed in response to Pb stress seven to fifteen days after being sprayed with a 200 mg/L Pb (NO 3 ) 2 solution. The proteomic data we gathered provides a model for investigations into the mechanisms underlying Dendrobium plant resistance to heavy metal stress. Data description A label-free quantitative proteomics approach was employed to examine the variations in protein expression levels of D. huoshanense at different times of Pb(NO 3 ) 2 treatment. We submitted the raw data obtained from these proteomics sequencing experiments to the ProteomeXchange database with the accession number PXD047050. 63,194 mass spectra in total were compared after being imported into the Proteome Discoverer software for database search. A total of 12,402 spectral peptides were identified with a confidence level exceeding 99%, which resulted in the identification of 2,449 significantly differential proteins. These proteins can be utilized for screening, functional annotation, and enrichment analysis of differentially expressed proteins before and after heavy metal treatment experiments.
In visual communication design (VCD), multi-source data integration plays an important role in innovation and growth. This paper studies and discusses the means of information dissemination in the new media era by analyzing typical digital interaction methods and combining current social hotspots. With the guiding ideology of "Multiple Sources", this paper proposes a brand-new, efficient method that meets the needs of the public, aiming to achieve a visual communication art form with multiple expressive effects and a design language system with rich connotations. In the process of conception, this paper covers the technical level of image digital conversion and fusion and interactive data integration, designs a VCD model, and conducts testing and research on multi-source data integration algorithms. The test results show that the data processing time of the multi-source data integration algorithm is within 6 seconds, and the fastest time is 3 seconds. The application of these technologies makes it possible to continuously change and enrich the content and form of VCD. The research on the integration of multi-source data in visual communication design covers multiple important findings. The research needs to explore data processing and integration methods in order to effectively process and integrate data from different data sources. It also considers users' feedback and evaluation of multi-source data integration visual communication design to understand their perception and attitude towards design effectiveness, usability, and information presentation.
This study aimed to provide a greater understanding of the systemic factors involved in coal mine accidents and to examine the relationships between the contributing factors across all levels of the system. Ninety-four extraordinarily major coal mine accidents that occurred in China from 1997 to 2011 were analyzed using the human factors analysis and classification system (HFACS). The empirical results showed that the frequencies of unsafe behaviors, inadequate regulation and failure to correct hidden dangers were the highest among five levels, 14 categories and 48 indicators, respectively. The odds ratio technique was applied to quantitatively examine the relationships between contributing factors. Various statistically significant associations were discovered and should receive greater attention in future attempts to develop accident measures. In addition, several strategies concerning the main contributing factors and routes to failure are proposed to prevent accidents from reoccurring in an organization.
This paper proposes a Bayesian method for underdetermined blind source separation based on the Gaussian mixture model. The proposed algorithm follows a hierarchical learning and alternative estimations for sources and mixing matrix. The independent sources are estimated from their a posteriori means and the mixing matrix is estimated by maximum likelihood (ML). Both estimations require the a posteriori correlations of sources which exist in the underdetermined model with full row rank in general. Under this framework, each source prior is modeled as a mixture of Gaussians. This mixture model provides us an advantage that it can deal with the hybrid mixtures of both sparse and non-sparse sources, the iterative learning for Gaussians leads to parametric density estimation for each hidden source as well as their recovery in the end. Simulations by using synthetic data validate the effectiveness of the learning algorithm
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