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The absorption of photons by atoms encompasses fundamental quantum mechanical aspects, particularly the emergence of randomness to account for the inherent unpredictability in absorption outcomes. We demonstrate that vacuum fluctuations can be the origin of this randomness. An illustrative example of this is the absorption of a single photon by two symmetrically arranged atoms. In the absence of a mechanism to introduce randomness, the Schrödinger equation alone can govern the time evolution of the process initially. Then, it becomes stuck, and an entangled state of the two atoms emerges. This entangled state consists of two components: in one, the first atom is excited by the photon while the second is in the ground state, and in the other, the second atom is excited while the first remains in the ground state. These components form a superposition state characterized by an unbreakable symmetry in the absence of external influences. Consequently, the absorption process remains incomplete. When vacuum fluctuations come into play, they can induce fluctuations in the weights of these components, akin to Brownian motion. Over time, one component diminishes, thereby breaking the entanglement between the two atoms and allowing the photon absorption process to conclude. The remaining component shows which atom completes the photon absorption. Vacuum fluctuations not only introduce randomness but also have the potential to give rise to the Born rule in this context. Furthermore, the Casimir effect, which is closely tied to vacuum fluctuations, presents a promising experimental avenue for validating this mechanism. Similar studies can also be conducted with varying numbers of atoms.

Natural gels and biomimetic hydrogel materials have been able to achieve outstanding integrated mechanical properties due to the gain of natural biological structures. However, nearly every natural biological structure relies on water as solvents or carriers, which limits the possibility in extreme conditions, such as sub-zero temperatures and long-term application. Here, peptide-enhanced eutectic gels were synthesized by introducing α-helical “molecular spring” structure into deep eutectic solvent. The gel takes full advantage of the α-helical structure, achieving high tensile/compression, good resilience, superior fracture toughness, excellent fatigue resistance and strong adhesion, while it also inherits the benefits of the deep eutectic solvent and solves the problems of solvent volatilization and freezing. This enables unprecedentedly long and stable sensing of human motion or mechanical movement. The electrical signal shows almost no drift even after 10,000 deformations for 29 hours or in the −20 °C to 80 °C temperature range. Biomimetic hydrogel materials show outstanding mechanical properties but water as solvent or carrier limits the possibility to apply these materials under extreme conditions. Here the authors report a peptide-enhanced eutectogel with excellent mechanical, anti-freezing and anti-drying properties and its application as sensor for monitoring human motion.

The author uses cohabitation data from the 2010 Chinese Family Panel Studies to analyze the association of premarital cohabitation with subsequent divorce of first marriage. After balancing selection factors that influence premarital cohabitation through propensity score matching, the author uses Cox proportional hazards models to examine the selection, causation, and diffusion perspectives on the relationship between premarital cohabitation and marital dissolution. The results show that premarital cohabitation is positively associated with divorce for those married in the early-reform period (1980–1994) when cohabitation was uncommon. However, this relationship disappears for those married in the late-reform period (1995–2010) when cohabitation became more prevalent. The findings suggest variation in the link between premarital cohabitation and divorce across different marriage cohorts and provide strong evidence for the diffusion perspective in postreform China. Supplemental sensitivity analyses support the robustness of the conclusion.

Change detection (CD) is essential to the accurate understanding of land surface changes using available Earth observation data. Due to the great advantages in deep feature representation and nonlinear problem modeling, deep learning is becoming increasingly popular to solve CD tasks in remote-sensing community. However, most existing deep learning-based CD methods are implemented by either generating difference images using deep features or learning change relations between pixel patches, which leads to error accumulation problems since many intermediate processing steps are needed to obtain final change maps. To address the above-mentioned issues, a novel end-to-end CD method is proposed based on an effective encoder-decoder architecture for semantic segmentation named UNet++, where change maps could be learned from scratch using available annotated datasets. Firstly, co-registered image pairs are concatenated as an input for the improved UNet++ network, where both global and fine-grained information can be utilized to generate feature maps with high spatial accuracy. Then, the fusion strategy of multiple side outputs is adopted to combine change maps from different semantic levels, thereby generating a final change map with high accuracy. The effectiveness and reliability of our proposed CD method are verified on very-high-resolution (VHR) satellite image datasets. Extensive experimental results have shown that our proposed approach outperforms the other state-of-the-art CD methods.

Recommender systems are used to address information overload, enhance personalization, and improve user experience by providing tailored suggestions based on individual preferences, thereby increasing engagement and facilitating content discovery. This paper proposes a hybrid approach for recommender system in personalized reading recommendation and literature discovery. The proposed hybrid approach is the combined performance of both the Hierarchal Gated Recurrent Neural Network (HGRNN) and Eurasian Oystercatcher Optimizer (EOO). Commonly it is named as HGRNN-EOO technique. The major objective of the proposed approach is to provide a recommender system for personalized reading recommendation and literature discovery. HGRNN is designed to provide personalized recommendations based on their preferences, behaviour, and interactions to enhance user experience and engagement. The personalized recommendations from the HGRNN are optimized by using the EOO. By then, the MATLAB working platform has been proposed and implemented, and the present processes are used to calculate the execution. Using performance metrics like accuracy, error rate, F-score, precision, recall, computation time, ROC, sensitivity, and specificity, the proposed method's effectiveness is evaluated. From the result, the proposed approach based error is less compared to existing techniques. The result shows that the accuracy level of proposed Recommender System in Personalized Reading Recommendation using Hierarchal Gated Recurrent Neural Network and Eurasian Oystercatcher Optimizer (RSPRR-HGRNN-EOO) approach is 98% that is higher than the other existing methods. The specificity and the F-score of the proposed RSPRR-HGRNN-EOO approach is 99% and 97%. The error rate of the proposed RSPRR-HGRNN-EOO approach is 2.5%, which is very less compared to other existing techniques. The proposed method shows better results in all existing methods like Recommender System in Personalized Reading Recommendation Convolutional Neural Network (RSPRR-CNN), Recommender System in Personalized Reading Recommendation Deep Neural Network (RSPRR-DNN) and Recommender System in Personalized Reading Recommendation Feed-Forward Neural Network (RSPRR-FNN). Based on the outcome, it can be concluded that the proposed strategy has a lower error rate than existing methods.

We present a novel convolutional neural network (CNN)-based change detection framework for locating changed building instances as well as changed building pixels from very high resolution (VHR) aerial images. The distinctive advantage of the framework is the self-training ability, which is highly important in deep-learning-based change detection in practice, as high-quality samples of changes are always lacking for training a successful deep learning model. The framework consists two parts: a building extraction network to produce a binary building map and a building change detection network to produce a building change map. The building extraction network is implemented with two widely used structures: a Mask R-CNN for object-based instance segmentation, and a multi-scale full convolutional network for pixel-based semantic segmentation. The building change detection network takes bi-temporal building maps produced from the building extraction network as input and outputs a building change map at the object and pixel levels. By simulating arbitrary building changes and various building parallaxes in the binary building map, the building change detection network is well trained without real-life samples. This greatly lowers the requirements of labeled changed buildings, and guarantees the algorithm’s robustness to registration errors caused by parallaxes. To evaluate the proposed method, we chose a wide range of urban areas from an open-source dataset as training and testing areas, and both pixel-based and object-based model evaluation measures were used. Experiments demonstrated our approach was vastly superior: without using any real change samples, it reached 63% average precision (AP) at the object (building instance) level. In contrast, with adequate training samples, other methods—including the most recent CNN-based and generative adversarial network (GAN)-based ones—have only reached 25% AP in their best cases.

As one of the fundamental tasks in remote sensing (RS) image understanding, multi-label remote sensing image scene classification (MLRSSC) is attracting increasing research interest. Human beings can easily perform MLRSSC by examining the visual elements contained in the scene and the spatio-topological relationships of these visual elements. However, most of existing methods are limited by only perceiving visual elements but disregarding the spatio-topological relationships of visual elements. With this consideration, this paper proposes a novel deep learning-based MLRSSC framework by combining convolutional neural network (CNN) and graph neural network (GNN), which is termed the MLRSSC-CNN-GNN. Specifically, the CNN is employed to learn the perception ability of visual elements in the scene and generate the high-level appearance features. Based on the trained CNN, one scene graph for each scene is further constructed, where nodes of the graph are represented by superpixel regions of the scene. To fully mine the spatio-topological relationships of the scene graph, the multi-layer-integration graph attention network (GAT) model is proposed to address MLRSSC, where the GAT is one of the latest developments in GNN. Extensive experiments on two public MLRSSC datasets show that the proposed MLRSSC-CNN-GNN can obtain superior performance compared with the state-of-the-art methods.

Icariin is a major bioactive pharmaceutical constituent isolated from Chinese medicine Horny Goat Weed (Ying Yang Huo) with potent cardiovascular protective functions. Emerging evidence in the past decade has shown that Icariin possesses multiple atheroprotective functions, through multiple mechanisms, including attenuating DNA damage, correcting endothelial dysfunction, inhibiting the proliferation and migration of smooth muscle cells, repressing macrophage-derived foam cell formation and inflammatory responses, as well as preventing platelet activation. All of these protective effects, combined with its lipid-modulatory effects, contribute to the broad atheroprotective effects of Icariin. In this review, we will summarize the anti-atherosclerotic properties of Icariin and highlight future perspectives in developing Icariin as a promising anti-atherosclerotic drug.

Fungal insect pathogens have evolved diverse mechanisms to evade host immune recognition and defense responses. However, identification of fungal factors involved in host immune evasion during cuticular penetration and subsequent hemocoel colonization remains limited. Here, we report that the entomopathogenic fungus Beauveria bassiana expresses an endo-β-1,3-glucanase (BbEng1) that functions in helping cells evade insect immune recognition/ responses. BbEng1 was specifically expressed during infection, in response to host cuticle and hemolymph, and in the presence of osmotic or oxidative stress. BbEng1 was localized to the fungal cell surface/ cell wall, where it acts to remodel the cell wall pathogen associated molecular patterns (PAMPs) that can trigger host defenses, thus facilitating fungal cell evasion of host immune defenses. BbEng1 was secreted where it could bind to fungal cells. Cell wall β-1,3-glucan levels were unchanged in ΔBbEng1 cells derived from in vitro growth media, but was elevated in hyphal bodies, whereas glucan levels were reduced in most cell types derived from the BbEng1 overexpressing strain ( BbEng1 OE ). The BbEng1 OE strain proliferated more rapidly in the host hemocoel and displayed higher virulence as compared to the wild type parent. Overexpression of their respective Eng1 homologs or of BbEng1 in the insect fungal pathogens, Metarhizium robertsii and M . acridum also resulted in increased virulence. Our data support a mechanism by which BbEng1 helps the fungal pathogen to evade host immune surveillance by decreasing cell wall glucan PAMPs, promoting successful fungal mycosis.

Altermagnetism, a kind of collinear magnetism that is characterized by a momentum-dependent band and spin splitting without net magnetization, has recently attracted considerable interest. Finding altermagnetic materials with large splitting near the Fermi level necessarily requires three-dimensional k -space mapping. While this is crucial for spintronic applications and emergent phenomena, it remains challenging. Here, using synchrotron-based angle-resolved photoemission spectroscopy (ARPES), spin-resolved ARPES and model calculations, we uncover a large altermagnetic splitting, up to  ~1.0 eV, near the Fermi level in CrSb. We verify its bulk-type g -wave altermagnetism through systematic three-dimensional k -space mapping, which unambiguously reveals the altermagnetic symmetry and associated nodal planes. Spin-resolved ARPES measurements further verify the spin polarizations of the split bands near Fermi level. Tight-binding model analysis indicates that the large altermagnetic splitting arises from strong third-nearest-neighbor hopping mediated by Sb ions. The large band/spin splitting near Fermi level in metallic CrSb, together with its high T N (up to 705 K) and simple spin configuration, paves the way for exploring emergent phenomena and spintronic applications based on altermagnets. Altermagnets combine the rapid dynamics and zero magnetization of collinear antiferromagnets with the spin-splitting of ferromagnets, making them an idea platform for both fundamental research and applications. Here, Yang, Li and coauthors map the large altermagnetic spin-splitting in CrSb located near the Fermi level.