Resort workers: the role of social media in connecting youth travellers and mediating the neo-tribe

<div><p>The detection of the singleton attractors is of great significance for the systematic study of genetic regulatory network. In this paper, we design an algorithm to compute the singleton attractors and pre-images of the strong-inhibition Boolean networks which is a biophysically plausible gene model. Our algorithm can not only identify accurately the singleton attractors, but also find easily the pre-images of the network. Based on extensive computational experiments, we show that the computational time of the algorithm is proportional to the number of the singleton attractors, which indicates the algorithm has much advantage in finding the singleton attractors for the networks with high average degree and less inhibitory interactions. Our algorithm may shed light on understanding the function and structure of the strong-inhibition Boolean networks.</p></div

DNA-Interacting Characteristics of the Archaeal Rudiviral Protein SIRV2_Gp1

Whereas the infection cycles of many bacterial and eukaryotic viruses have been characterized in detail, those of archaeal viruses remain largely unexplored. Recently, studies on a few model archaeal viruses such as SIRV2 (Sulfolobus islandicus rod-shaped virus) have revealed an unusual lysis mechanism that involves the formation of pyramidal egress structures on the host cell surface. To expand understanding of the infection cycle of SIRV2, we aimed to functionally characterize gp1, which is a SIRV2 gene with unknown function. The SIRV2_Gp1 protein is highly expressed during early stages of infection and it is the only protein that is encoded twice on the viral genome. It harbours a helix-turn-helix motif and was therefore hypothesized to bind DNA. The DNA-binding behavior of SIRV2_Gp1 was characterized with electrophoretic mobility shift assays and atomic force microscopy. We provide evidence that the protein interacts with DNA and that it forms large aggregates, thereby causing extreme condensation of the DNA. Furthermore, the N-terminal domain of the protein mediates toxicity to the viral host Sulfolobus. Our findings may lead to biotechnological applications, such as the development of a toxic peptide for the containment of pathogenic bacteria, and add to our understanding of the Rudiviral infection cycle.status: publishe

Ionic Liquids Containing Block Copolymer Based Supramolecules

Block copolymer (BCP)-based supramolecules provide a versatile strategy to generate functional materials using noncovalent bond between small molecules and BCPs. Here, we report supramolecules composed of phenol-containing ionic liquids (ILs) hydrogen bonded to BCP, polystyrene-<i>block</i>-poly­(4-vinylpyridine) (PS-<i>b</i>-P4VP). IL-containing supramolecules exhibit ordered structures in a wide range of IL loading and chemistry. Rheological behaviors and nanostructures of IL-containing supramolecules can be tuned by controlling the IL loading without losing ordered structure. The hydrogen bonds and nanostructures can be retained in a wide range of temperatures with different IL chemistry. Supramolecules provide a diverse platform toward IL materials with ordered structure and tunable properties with high tolerance of thermal treatment and processing

A Robust Parallel Object Tracking Method for Illumination Variations

Illumination variation often occurs in visual tracking, which has a severe impact on the system performance. Many trackers based on Discriminative correlation filter (DCF) have recently obtained promising performance, showing robustness to illumination variation. However, when the target objects undergo significant appearance variation due to intense illumination variation, the features extracted from the object will not have the ability to be discriminated from the background, which causes the tracking algorithm to lose the target in the scene. In this paper, in order to improve the accuracy and robustness of the Discriminative correlation filter (DCF) trackers under intense illumination variation, we propose a very effective strategy by performing multiple region detection and using alternate templates (MRAT). Based on parallel computation, we are able to perform simultaneous detection of multiple regions, equivalently enlarging the search region. Meanwhile the alternate template is saved by a template update mechanism in order to improve the accuracy of the tracker under strong illumination variation. Experimental results on large-scale public benchmark datasets show the effectiveness of the proposed method compared to state-of-the-art methods

Per capita demand for cold chain logistics of aquatic products in China from 2022 to 2026.

Per capita demand for cold chain logistics of aquatic products in China from 2022 to 2026.</p

Fitting diagram of the neural network toolbox test model.

Fitting diagram of the neural network toolbox test model.</p

A Robust Parallel Object Tracking Method for Illumination Variations

Illumination variation often occurs in visual tracking, which has a severe impact on the system performance. Many trackers based on Discriminative correlation filter (DCF) have recently obtained promising performance, showing robustness to illumination variation. However, when the target objects undergo significant appearance variation due to intense illumination variation, the features extracted from the object will not have the ability to be discriminated from the background, which causes the tracking algorithm to lose the target in the scene. In this paper, in order to improve the accuracy and robustness of the Discriminative correlation filter (DCF) trackers under intense illumination variation, we propose a very effective strategy by performing multiple region detection and using alternate templates (MRAT). Based on parallel computation, we are able to perform simultaneous detection of multiple regions, equivalently enlarging the search region. Meanwhile the alternate template is saved by a template update mechanism in order to improve the accuracy of the tracker under strong illumination variation. Experimental results on large-scale public benchmark datasets show the effectiveness of the proposed method compared to state-of-the-art methods

The value of the fishery production in China from 2022 to 2026.

The value of the fishery production in China from 2022 to 2026.</p

Grey predictive model fitting and prediction.

In the background of the post-epidemic era, the consumption demand and market scale of cold chain logistics in China are expanding, but there is still an obvious gap with developed countries. To complete the balance between the supply and demand for aquatic products and the rational allocation of logistics resources and promote the rapid development trend of aquatic product cold chain logistics, it is particularly important to forecast and analyze the demand for aquatic product cold chain logistics. This article selects six main factors that affect the demand for aquatic products in cold chain logistics, uses the traditional grey model and the grey-BP neural network model to simulate and predict the demand for aquatic products in cold chain logistics in China from 2012 to 2021, and compares and analyzes the simulation results. Generally speaking, the demand for aquatic products from Chinese residents is on the rise. In the simulation prediction process, the prediction error of the grey-BP neural network is reduced compared to the traditional grey model, and the processing ability of the nonlinear system is ideal. The results show that the grey-BP neural network model is an effective method to predict the demand for cold chain logistics of aquatic products. Finally, suggestions are made on the future development of aquatic cold chain logistics in the post-epidemic era from the economic, social, and environmental aspects, which provide valuable decision-making reference for the development of marine aquaculture enterprises and cold chain logistics industry.</div

Results of the grey relational degree.

In the background of the post-epidemic era, the consumption demand and market scale of cold chain logistics in China are expanding, but there is still an obvious gap with developed countries. To complete the balance between the supply and demand for aquatic products and the rational allocation of logistics resources and promote the rapid development trend of aquatic product cold chain logistics, it is particularly important to forecast and analyze the demand for aquatic product cold chain logistics. This article selects six main factors that affect the demand for aquatic products in cold chain logistics, uses the traditional grey model and the grey-BP neural network model to simulate and predict the demand for aquatic products in cold chain logistics in China from 2012 to 2021, and compares and analyzes the simulation results. Generally speaking, the demand for aquatic products from Chinese residents is on the rise. In the simulation prediction process, the prediction error of the grey-BP neural network is reduced compared to the traditional grey model, and the processing ability of the nonlinear system is ideal. The results show that the grey-BP neural network model is an effective method to predict the demand for cold chain logistics of aquatic products. Finally, suggestions are made on the future development of aquatic cold chain logistics in the post-epidemic era from the economic, social, and environmental aspects, which provide valuable decision-making reference for the development of marine aquaculture enterprises and cold chain logistics industry.</div