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In response to the shortcomings of existing image noise detection algorithms that rely on the flawed intuitionistic fuzzy entropy (IFE) theory, a method of image noise detection and removal based on intuitionistic fuzzy amount of knowledge (IFAK) is proposed by introducing the latest knowledge measure (KM) theory and model. In the noise detection stage, the optimal average intensity of the noisy image foreground and background is determined based on the maximum IFAK, and the parametric model of noise detection is constructed accordingly to mark the probability of noise pixels and suspected noise pixels, showing excellent performance of noise detection. In the noise removal stage, a denoising model based on IFAK and probability of noise pixels is proposed by using the noise probability matrix, which can not only effectively denoise, but also better protect the characteristics of image edges and non-noise extreme pixels. Comparative experiments are carried out on standard datasets and classical test images, respectively. Experimental results show that the proposed method can accurately identify the image impulse noise and effectively realize image denoising. The overall performance outperforms other similar algorithms. The key metrics PSNR and SSIM are increased by 14.81% and 11.35%, respectively. In this paper, the latest KM theory is applied to image denoising, and excellent evaluation metrics and visual effects are obtained, while innovative applications of this theory in other related fields are also achieved.

A pair of circularly polarized thermally activated delayed fluorescence (CP-TADF) materials (R)-ad-PXZ/(S)-ad-PXZ were obtained, which show orange-red emission at 602 nm and clear chiroptical properties both in solution and the solid state. The solution processable circularly polarized device exhibits the maximum external quantum efficiency of 9.0% and gEL value of 10−3. [ABSTRACT FROM AUTHOR]

Background: Ischemia and reperfusion (I/R)-induced liver injury contributes to morbidity and mortality during hepatic surgery or liver transplantation. As a pivotal regulator of cancer and inflammation, the role of Von Hippel-Lindau (VHL) in hepatic I/R injury remains undetermined. Methods: We investigated the role of VHL in hepatic I/R injury by generating VHL conditional knockout (VHL-KO) mice. The downstream mechanisms of VHL were confirmed, and the role of HIF-2α in hepatic I/R injury was further investigated. Results: In this study, we discovered that VHL upregulation was associated with hepatic I/R injury in a mouse model. VHL gene knockout (VHL-KO) and overexpression (Ad-VHL) mice demonstrated that VHL aggravated liver injury, increased inflammation, and accelerated cell death in hepatic I/R injury. The VHL protein (pVHL) regulates a crucial control mechanism by targeting HIFα subunits for ubiquitin-mediated degradation. In vitro and in vivo studies demonstrated that VHL interacted with and repressed hypoxia-inducible factor 1α (HIF-1α) and hypoxia-inducible factor 2α (HIF-2α) expression during hepatic I/R injury. Notably, the inhibition of HIF-1α or 2α, as well as the concurrent inhibition of HIF-1α and 2α, abrogated the protective effect of VHL-KO. The severe stabilization of HIF-1α or 2α, as well as the simultaneous overexpression of HIF-1α and 2α, compensated for the detrimental effect of VHL. Conclusions: Thus, we identified the VHL-HIF-1α/HIF-2α axis as an indispensable pathway that may be a novel target for mediating hepatic I/R injury. [ABSTRACT FROM AUTHOR]

The study of tick olfaction is relatively new compared to that of insects, and the molecular mechanisms involved remain poorly understood. Despite several potential chemosensory genes identified in multiple tick species, these are yet to be validated through independent functional experiments. In this research, we cloned and analyzed a microplusin-like gene, HlonML-1, and investigated its role in the chemosensory activities of H. longicornis. The results showed that this gene's amino acid sequences lack histidine residues essential for antimicrobial activity, and it is evolutionarily linked to putative chemosensory microplusins in ticks. Gene expression analyses indicated that HlonML-1 was significantly more abundant in ticks exposed to potential attractants and in the forelegs of H. longicornis than in non-exposed ticks and the hindlegs, respectively. Tick forelegs support the Haller's organ, which is a sensory structure mostly specialized for chemosensation. Furthermore, Y-tube olfactometer assays indicated that silencing HlonML-1 significantly impaired adult ticks' ability to detect selected odors, while their gustatory-related behavior remained unaffected compared to the control groups. Given its unique sequences, relative abundance in chemosensory tissues, and impact on odor detection, HlonML-1 is likely involved in the olfactory chemosensation of H. longicornis. Future research validating putative chemosensory microplusins in the genomes of various tick species may enhance our understanding of their olfactory functions in tick and lead to the identification of new molecular targets for developing tick repellents. [ABSTRACT FROM AUTHOR]

The worldwide occurrence of wind droughts challenges the balance of power systems between energy production and consumption. Expanding inter‐day energy storage serves as a strategic solution, yet optimizing its capacity depends on accurately modeling future renewable energy uncertainties to avoid over‐ or under‐investment. Existing approaches that use the historical extreme scenario set (HESS) to represent future conditions are contentious due to potential inadequacies in forecasting future extreme scenarios (ESs), including those on a decadal or centennial scale. This study addresses the issue by proposing an advanced energy storage expansion framework that leverages Extreme Value Theory (EVT) and a novel Deep Generative Model, namely the Diffusion Model. To model the extremes in a principled way, this work leverages EVT to establish a severity‐probability mapping for wind droughts, guiding the training process of the Diffusion Model. This model excels in generating ESs that accurately reflect the distribution of real‐world extremes, thereby significantly enhancing the predictive capacity of HESS. Case studies on a real‐world power system confirm the method's capacity to generate high‐quality ESs, encompassing the most severe historical wind droughts not included in the training dataset, thereby facilitating resilient energy storage expansion against unforeseen extremes. [ABSTRACT FROM AUTHOR]