Robustness on distributed coupling networks with multiple dependent links from finite functional components

The rapid advancement of technology underscores the critical importance of robustness in complex network systems. This paper presents a framework for investigating the structural robustness of interconnected network models. This paper presents a framework for investigating the structural robustness of interconnected network models. In this context, we define functional nodes within interconnected networks as those belonging to clusters of size greater than or equal to $s$ in the local network, while maintaining at least $M$ significant dependency links. This model presents precise analytical expressions for the cascading failure process, the proportion of functional nodes in the stable state, and a methodology for calculating the critical threshold. The findings reveal an abrupt phase transition behavior in the system following the initial failure. Additionally, we observe that the system necessitates higher internal connection densities to avert collapse, especially when more effective support links are required. These results are validated through simulations using both Poisson and power-law network models, which align closely with the theoretical outcomes. The method proposed in this study can assist decision-makers in designing more resilient reality-dependent systems and formulating optimal protection strategies

Short video marketing : what, when and how short-branded videos facilitate consumer engagement

Purpose This study explores whether and how four main factors of short-branded video content (content matching, information relevance, storytelling and emotionality) facilitate consumer engagement (likes, comments and shares), as well as the moderating effect of the release time (morning, afternoon and evening) in such relationships. Design/methodology/approach This study uses Python to write programs to crawl relevant data information, such as consumer engagement and short video release time. It combines coding methods to empirically analyze the impact of short-branded video content characteristics on consumer engagement. A total of 10,240 Weibo short videos (total duration: 238.645 h) from 122 well-known brands are utilized as research objects. Findings Empirical results show that the content characteristics of short videos significantly affected consumer engagement. Furthermore, the release time of videos significantly moderated the relationship between the emotionality of short videos and consumer engagement. Content released in the morning enhanced the positive impact of warmth, excitement and joy on consumer engagement, compared to that released in the afternoon. Practical implications The findings provide new insights for the dissemination of products and brand culture through short videos. The authors suggest that enterprises that use brand videos consider content matching, information relevance, storytelling and emotionality in their design. Originality/value From a broader perspective, this study constructs a new method for comprehensively evaluating short-branded video content, based on four dimensions (content matching, information relevance, storytelling and emotionality) and explores the value of these dimensions for creating social media marketing success, such as via consumer engagement.© 2023 Emerald Publishing Limited. This manuscript version is made available under the Creative Commons Attribution–NonCommercial 4.0 International (CC BY–NC 4.0) license, https://creativecommons.org/licenses/by-nc/4.0/fi=vertaisarvioitu|en=peerReviewed

Robustness of coupled networks with multiple support from functional components at different scales

Robustness is an essential component of modern network science. Here, we investigate the robustness of coupled networks where the functionality of a node depends not only on its connectivity, here measured by the size of its connected component in its own network, but also the support provided by at least M links from another network. We here develop a theoretical framework and investigate analytically and numerically the cascading failure process when the system is under attack, deriving expressions for the proportion of functional nodes in the stable state, and the critical threshold when the system collapses. Significantly, our results show an abrupt phase transition and we derive the minimum inner and inter-connectivity density necessary for the system to remain active. We also observe that the system necessitates an increased density of links inside and across networks to prevent collapse, especially when conditions on the coupling between the networks are more stringent. Finally, we discuss the importance of our results in real-world settings and their potential use to aid decision-makers design more resilient infrastructure systems

CatVersion: Concatenating Embeddings for Diffusion-Based Text-to-Image Personalization

We propose CatVersion, an inversion-based method that learns the personalized concept through a handful of examples. Subsequently, users can utilize text prompts to generate images that embody the personalized concept, thereby achieving text-to-image personalization. In contrast to existing approaches that emphasize word embedding learning or parameter fine-tuning for the diffusion model, which potentially causes concept dilution or overfitting, our method concatenates embeddings on the feature-dense space of the text encoder in the diffusion model to learn the gap between the personalized concept and its base class, aiming to maximize the preservation of prior knowledge in diffusion models while restoring the personalized concepts. To this end, we first dissect the text encoder's integration in the image generation process to identify the feature-dense space of the encoder. Afterward, we concatenate embeddings on the Keys and Values in this space to learn the gap between the personalized concept and its base class. In this way, the concatenated embeddings ultimately manifest as a residual on the original attention output. To more accurately and unbiasedly quantify the results of personalized image generation, we improve the CLIP image alignment score based on masks. Qualitatively and quantitatively, CatVersion helps to restore personalization concepts more faithfully and enables more robust editing.Comment: For the project page, please visit https://royzhao926.github.io/CatVersion-page

Continual All-in-One Adverse Weather Removal with Knowledge Replay on a Unified Network Structure

In real-world applications, image degeneration caused by adverse weather is always complex and changes with different weather conditions from days and seasons. Systems in real-world environments constantly encounter adverse weather conditions that are not previously observed. Therefore, it practically requires adverse weather removal models to continually learn from incrementally collected data reflecting various degeneration types. Existing adverse weather removal approaches, for either single or multiple adverse weathers, are mainly designed for a static learning paradigm, which assumes that the data of all types of degenerations to handle can be finely collected at one time before a single-phase learning process. They thus cannot directly handle the incremental learning requirements. To address this issue, we made the earliest effort to investigate the continual all-in-one adverse weather removal task, in a setting closer to real-world applications. Specifically, we develop a novel continual learning framework with effective knowledge replay (KR) on a unified network structure. Equipped with a principal component projection and an effective knowledge distillation mechanism, the proposed KR techniques are tailored for the all-in-one weather removal task. It considers the characteristics of the image restoration task with multiple degenerations in continual learning, and the knowledge for different degenerations can be shared and accumulated in the unified network structure. Extensive experimental results demonstrate the effectiveness of the proposed method to deal with this challenging task, which performs competitively to existing dedicated or joint training image restoration methods. Our code is available at https://github.com/xiaojihh/CL_all-in-one

HpSlyD inducing CDX2 and VIL1 expression mediated through TCTP protein may contribute to intestinal metaplasia in the stomach

Helicobacter pylori infection is the most important risk factor for gastric intestinal metaplasia (IM). Our previous study demonstrated that infection with H. pylori HpslyD-positive strains associated with IM. To further investigate the signalling pathway involved in HpSlyD-induced IM, CDX2 and VIL1 expressions were determined before and after HpSlyD application. TCTP was knocked down by siRNA or overexpressed by plasmid transfection. An HpSlyD binding protein was used to block HpSlyD’s enzymatic activity. The expression of CDX2 and TCTP in gastric diseases was measured by immunohistochemistry. Our results showed HpSlyD induced CDX2 and VIL1 expressions. TCTP protein expression was markedly increased after application of HpSlyD and in an HpSlyD-expressing stable cell line. Downregulation of TCTP protein led to decreased HpSlyD-induced CDX2 and VIL1. Overexpression of TCTP protein improved the expression of CDX2 and VIL1. Co-application of HpSlyD and FK506 led to significant reductions in CDX2, VIL1, and TCTP expression. Immunohistochemistry demonstrated that CDX2 and TCTP expression was higher in HpslyD-positive specimens compared with HpslyD-negative ones. Expression of CDX2 was positively correlated with TCTP in HpslyD-positive cells. Our study is the first to show that HpSlyD induction of CDX2 and VIL1 expression mediated through TCTP may contribute to IM in the stomach

Purification, Characterization, and Hydrolysate Analysis of Dextranase From Arthrobacter oxydans G6-4B

Dextran has aroused increasingly more attention as the primary pollutant in sucrose production and storage. Although enzymatic hydrolysis is more efficient and environmentally friendly than physical methods, the utilization of dextranase in the sugar industry is restricted by the mismatch of reaction conditions and heterogeneity of hydrolysis products. In this research, a dextranase from Arthrobacter oxydans G6-4B was purified and characterized. Through anion exchange chromatography, dextranase was successfully purified up to 32.25-fold with a specific activity of 288.62 U/mg protein and a Mw of 71.12 kDa. The optimum reaction conditions were 55°C and pH 7.5, and it remained relatively stable in the range of pH 7.0–9.0 and below 60°C, while significantly inhibited by metal ions, such as Ni+, Cu2+, Zn2+, Fe3+, and Co2+. Noteworthily, a distinction of previous studies was that the hydrolysates of dextran were basically isomalto-triose (more than 73%) without glucose, and the type of hydrolysates tended to be relatively stable in 30 min; dextranase activity showed a great influence on hydrolysate. In conclusion, given the superior thermal stability and simplicity of hydrolysates, the dextranase in this study presented great potential in the sugar industry to remove dextran and obtain isomalto-triose

A Mutation in the Intracellular Loop III/IV of Mosquito Sodium Channel Synergizes the Effect of Mutations in Helix IIS6 on Pyrethroid Resistance s

ABSTRACT Activation and inactivation of voltage-gated sodium channels are critical for proper electrical signaling in excitable cells