Study of data center communication network topologies using complex network propagation model

Data center, as the core infrastructure of data storage and processing, calls for network security protection. Information security has been addressed in a number of specific ways. However, there are few studies that employ network topology features to prevent the transmission of viruses. When a virus spreads, different topologies display various properties. In this paper, we study three types of data center network topologies, i.e., Fat-tree, Leaf-spine, and Bcube, and quantify the propagation characteristics in every topology through the IC propagation model. The probability of the device being infected, the count of propagation sources, the access of propagation sources, and the topological parameters are all considered. Given that network security defenders can only change the topology and topological parameters, we propose a computational framework that combines factor analysis, which provides us with the selection of network topological parameters with a low virus propagation rate in the candidate parameter set. Through experiments, we find that Leaf-spine has a good inhibitory effect on viruses with high propagation probability. Meanwhile, each offers unique advantages. We hope that more data center network topologies will be studied to improve the security of all data centers using these network topologies

Composites, Fabrication and Application of Polyvinylidene Fluoride for Flexible Electromechanical Devices: A Review

The technological development of piezoelectric materials is crucial for developing wearable and flexible electromechanical devices. There are many inorganic materials with piezoelectric effects, such as piezoelectric ceramics, aluminum nitride and zinc oxide. They all have very high piezoelectric coefficients and large piezoelectric response ranges. The characteristics of high hardness and low tenacity make inorganic piezoelectric materials unsuitable for flexible devices that require frequent bending. Polyvinylidene fluoride (PVDF) and its derivatives are the most popular materials used in flexible electromechanical devices in recent years and have high flexibility, high sensitivity, high ductility and a certain piezoelectric coefficient. Owing to increasing the piezoelectric coefficient of PVDF, researchers are committed to optimizing PVDF materials and enhancing their polarity by a series of means to further improve their mechanical–electrical conversion efficiency. This paper reviews the latest PVDF-related optimization-based materials, related processing and polarization methods and the applications of these materials in, e.g., wearable functional devices, chemical sensors, biosensors and flexible actuator devices for flexible micro-electromechanical devices. We also discuss the challenges of wearable devices based on flexible piezoelectric polymer, considering where further practical applications could be

Chemical Reaction Spectrum: A Holographic Image for Characterizing Multi-component Chemical Mixtures

Abstract: Multi-component chemical mixtures (MCMs) and their various effects always concerned in analytical chemistry, but current analytical techniques based on test-tube experiments often involves many high-cost and laborious operations. Today’s pop machine-learning (ML) technology has exhibited their successes in dealing with the analysis task of various complex systems. Predictably, the introduction of ML will radically accelerate the exploration of many fields involving mixture analysis. But the biggest challenge ahead for this process is how to provide some intelligible and sufficient data for various algorithms. In this study, we proposed a chemical imaging strategy to visualize various mixtures as some feature images by using ink-jet printing technology based on combinatorial chemistry. Here, these feature images were as a novel data form of chemical reaction spectrum (CRS), which can comprehensively describe and record the reaction characteristics of the complex sample. Compared with common imaging methods, the CRS with high-throughput chemical reaction dots is an efficient and economic information visualization way for the MCM sample. It is expected to be an important data acquisition approach for the application of ML in the field of chemistry in future

Machine Learning for Acute Toxicity Prediction Using High-Throughput Enzyme-Reaction Chip

Machine learning (ML) has brought significant technological innovations in many fields, but it has not been widely embraced by most researchers of natural sciences to date. Traditional understanding and promotion of chemical analysis cannot meet the definition and requirement of big data for running of ML. Over the years, we focused on building a more versatile and low-cost approach to the acquisition of copious amounts of data containing in a chemical reaction. The generated data meet exclusively the thirst of ML when swimming in the vast space of chemical effect. As proof in this study, we carried out a case for acute toxicity test throughout the whole routine, from model building, chip preparation, data collection, and ML training. Such a strategy will probably play an important role in connecting ML with much research in natural science in the future.</p

Genome-Wide Identification of DOF Gene Family and the Mechanism Dissection of SbDof21 Regulating Starch Biosynthesis in Sorghum

Starch is one of the main utilization products of sorghum (Sorghum bicolor L.), the fifth largest cereal crop in the world. Up to now, the regulation mechanism of starch biosynthesis is rarely documented in sorghum. In the present study, we identified 30 genes encoding the C2-C2 zinc finger domain (DOF), with one to three exons in the sorghum genome. The DOF proteins of sorghum were divided into two types according to the results of sequence alignment and evolutionary analysis. Based on gene expressions and co-expression analysis, we identified a regulatory factor, SbDof21, that was located on chromosome 5. SbDof21 contained two exons, encoding a 36.122 kD protein composed of 340 amino acids. SbDof21 co-expressed with 15 genes involved in the sorghum starch biosynthesis pathway, and the Pearson correlation coefficients (PCCs) with 11 genes were greater than 0.9. The results of qRT-PCR assays indicated that SbDof21 is highly expressed in sorghum grains, exhibiting low relative expression levels in the tissues of roots, stems and leaves. SbDOF21 presented as a typical DOF transcription factor (TF) that was localized to the nucleus and possessed transcriptional activation activity. Amino acids at positions 182&ndash;231 of SbDOF21 formed an important structure in its activation domain. The results of EMSA showed that SbDOF21 could bind to four tandem repeats of P-Box (TGTAAAG) motifs in vitro, such as its homologous proteins of ZmDOF36, OsPBF and TaPBF. Meanwhile, we also discovered that SbDOF21 could bind and transactivate SbGBSSI, a key gene in sorghum amylose biosynthesis. Collectively, the results of the present study suggest that SbDOF21 acts as an important regulator in sorghum starch biosynthesis, exhibiting potential values for the improvement of starch contents in sorghum

Chemical Reaction Spectrum

We proposed a new method of chemical reaction spectrum (CRS) in terms of chemical characterization, and established a method to fulfill it by combining with 3D chemical printing technology and 2D sampling. The CRS can provide a graphical data set for pure or mixed substances, which can comprehensively describe the reaction characteristics of the research object. Compared with common characterization methods (NMR, UV/vis, IR, Raman, GC or LC), it is more capable of revealing chemical behaviors enough, and is much lower in cost. It is expected to be an important data acquisition approach for the application of artificial intelligence in the field of chemistry in the future

Image_1_Profiling of transcriptional regulators associated with starch biosynthesis in sorghum (Sorghum bicolor L.).TIF

Starch presents as the major component of grain endosperm of sorghum (Sorghum bicolor L.) and other cereals, serving as the main energy supplier for both plants and animals, as well as important industrial raw materials of human beings, and was intensively concerned world widely. However, few documents focused on the pathway and transcriptional regulations of starch biosynthesis in sorghum. Here we presented the RNA-sequencing profiles of 20 sorghum tissues at different developmental stages to dissect key genes associated with sorghum starch biosynthesis and potential transcriptional regulations. A total of 1,708 highly expressed genes were detected, namely, 416 in grains, 736 in inflorescence, 73 in the stalk, 215 in the root, and 268 genes in the leaf. Besides, 27 genes encoded key enzymes associated with starch biosynthesis in sorghum were identified, namely, six for ADP-glucose pyrophosphorylase (AGPase), 10 for starch synthases (SSs), four for both starch-branching enzymes (SBE) and starch-debranching enzymes (DBEs), two for starch phosphorylases (SPs), and one for Brittle-1 (BT1). In addition, 65 transcription factors (TFs) that are highly expressed in endosperm were detected to co-express with 16 out of 27 genes, and 90 cis-elements were possessed by all 27 identified genes. Four NAC TFs were cloned, and the further assay results showed that three of them could in vitro bind to the CACGCAA motif within the promoters of SbBt1 and SbGBSSI, two key genes associated with starch biosynthesis in sorghum, functioning in similar ways that reported in other cereals. These results confirmed that sorghum starch biosynthesis might share the same or similar transcriptional regulations documented in other cereals, and provided informative references for further regulatory mechanism dissection of TFs involved in starch biosynthesis in sorghum.</p