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.
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.
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.
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.
Due to the urgent demand for remote sensing big data analysis, large-scale remote sensing image retrieval (LSRSIR) attracts increasing attention from researchers. Generally, LSRSIR can be divided ...into two categories as follows: uni-source LSRSIR (US-LSRSIR) and cross-source LSRSIR (CS-LSRSIR). More specifically, US-LSRSIR means the inquiry remote sensing image and images in the searching data set come from the same remote sensing data source, whereas CS-LSRSIR is designed to retrieve remote sensing images with a similar content to the inquiry remote sensing image that are from a different remote sensing data source. In the literature, US-LSRSIR has been widely exploited, but CS-LSRSIR is rarely discussed. In practical situations, remote sensing images from different kinds of remote sensing data sources are continually increasing, so there is a great motivation to exploit CS-LSRSIR. Therefore, this paper focuses on CS-LSRSIR. To cope with CS-LSRSIR, this paper proposes source-invariant deep hashing convolutional neural networks (SIDHCNNs), which can be optimized in an end-to-end manner using a series of well-designed optimization constraints. To quantitatively evaluate the proposed SIDHCNNs, we construct a dual-source remote sensing image data set that contains eight typical land-cover categories and 10 000 dual samples in each category. Extensive experiments show that the proposed SIDHCNNs can yield substantial improvements over several baselines involving the most recent techniques.
Change detection (CD) is one of the main applications of remote sensing. With the increasing popularity of deep learning, most recent developments of CD methods have introduced the use of deep ...learning techniques to increase the accuracy and automation level over traditional methods. However, when using supervised CD methods, a large amount of labeled data is needed to train deep convolutional networks with millions of parameters. These labeled data are difficult to acquire for CD tasks. To address this limitation, a novel semisupervised convolutional network for CD (SemiCDNet) is proposed based on a generative adversarial network (GAN). First, both the labeled data and unlabeled data are input into the segmentation network to produce initial predictions and entropy maps. Then, to exploit the potential of unlabeled data, two discriminators are adopted to enforce the feature distribution consistency of segmentation maps and entropy maps between the labeled and unlabeled data. During the competitive training, the generator is continuously regularized by utilizing the unlabeled information, thus improving its generalization capability. The effectiveness and reliability of our proposed method are verified on two high-resolution remote sensing data sets. Extensive experimental results demonstrate the superiority of the proposed method against other state-of-the-art approaches.
Vitrimers make up a class of polymeric materials combining the advantages of thermosets and thermoplastics, because they can be reprocessed while being at the same time permanently cross-linked. ...However, a long heating duration or an elevated temperature is necessary for most vitrimers to relax the stress from deformation and exhibit malleability. Herein, a disulfide-containing carboxylic acid is applied as a curing agent to synthesize epoxy vitrimers with simultaneous disulfide metathesis and carboxylate transesterification. The insoluble networks exhibit rapid stress relaxation and have relaxation times ranging from 1.5 s (200 °C) to 5500 s (60 °C), while the temperature of malleability is as low as 65 °C. Moreover, this vitrimer can be efficiently reprocessed at 100 °C in 1 h with full recovery of mechanical strength for at least four cycles. Additionally, such a material is simply synthesized from commercially available chemicals and may have potential applications in the electronics industry where a high temperature is not allowed.
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.
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.
Water is an important factor influencing the long-term stability of underground construction. In the present work, a series of uniaxial compressive strength tests and multistage creep tests were ...performed on red sandstone specimens under different water soaking conditions. The results show that the peak strength and elastic modulus of the red sandstone decrease with not only water content but also immersion time, which can be better expressed by a negative exponential function. Multistage compression creep tests were carried out on specimens immersed in water by using a self-developed “environmental chamber”. Based on the analysis of the test data, the creep properties of the red sandstone under different water soaking conditions were studied. Compared with the dry and saturated specimens without immersion in water, the soaking specimens exhibited significant increases in both the creep strain and the creep strain rate, while their time-to-failure and threshold stress for creep failure are much lower due to the long-term effect of immersion. Creep is accompanied by crack formation; water seeps into the newly created crack tips during creep testing, promoting crack propagation, increasing rock damage and accelerating rock failure process. The presented experimental results can provide a reference for estimating the long-term stability and safety of rock engineering.
•A series of multistage creep tests were conducted on water-immersed specimens.•The UCS greatly decreases with the increment of the duration of immersion in water.•The creep strain rate increase in a power form with the increment of stress levels•The time-to-failure of the specimen soaking in water is the shortest•The critical creep stress of the specimen soaking in water is the smallest
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