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An ancient and counterintuitive phenomenon known as the Mpemba effect (water can cool faster when initially heated up) showcases the critical role of initial conditions in relaxation processes. How to realize and utilize this effect for speeding up relaxation is an important but challenging task in purely quantum system till now. Here, we experimentally study the strong Mpemba effect in a single trapped ion system in which an exponentially accelerated relaxation in time is observed by preparing an optimal quantum initial state with no excitation of the slowest decaying mode. Also, we demonstrate that the condition of realizing such effect coincides with the Liouvillian exceptional point, featuring the coalescence of both the eigenvalues and the eigenmodes of the systems. Our work provides an efficient strategy to engineer the dynamics of open quantum system, and suggests a link unexplored yet between the Mpemba effect and the non-Hermitian physics. The Mpemba effect is an archetype for various anomalous relaxation phenomena. Here, the authors experimentally study a quantum version of the Mpemba effect in a single trapped ion system, where relaxation is exponentially accelerated by removing the excitation of the slowest decaying mode. This phenomenon, seen in Markovian open quantum systems containing Liouvillian exceptional points, indicates a link between the Mpemba effect and non-Hermitian physics.

Simple development of an electrochemiluminescence (ECL) immunosensor for convenient detection of tumor biomarker is of great significance for early cancer diagnosis, treatment evaluation, and improving patient survival rates and quality of life. In this work, an immunosensor is demonstrated based on an enhanced ECL signal boosted by nanochannel-confined Au nanomaterial, which enables sensitive detection of the tumor biomarker—carcinoembryonic antigen (CEA). Vertically-ordered mesoporous silica film (VMSF) with a nanochannel array and amine groups was rapidly grown on a simple and low-cost indium tin oxide (ITO) electrode using the electrochemically assisted self-assembly (EASA) method. Au nanomaterials were confined in situ on the VMSF through electrodeposition, which catalyzed both the conversion of dissolved oxygen (O2) to reactive oxygen species (ROS) and the oxidation of a luminol emitter and improved the electrode active surface. The ECL signal was enhanced fivefold after Au nanomaterial deposition. The recognitive interface was fabricated by covalent immobilization of the CEA antibody on the outer surface of the VMSF, followed with the blocking of non-specific binding sites. In the presence of CEA, the formed immunocomplex reduced the diffusion of the luminol emitter, resulting in the reduction of the ECL signal. Based on this mechanism, the constructed immunosensor was able to provide sensitive detection of CEA ranging from 1 pg·mL−1 to 100 ng·mL−1 with a low limit of detection (LOD, 0.37 pg·mL−1, S/N = 3). The developed immunosensor exhibited high selectivity and good stability. ECL determination of CEA in fetal bovine serum was achieved.

Self-centering structures have emerged as a promising seismic design solution, offering advantages in structural safety, rapid post-earthquake functionality recovery, and life-cycle economy. This paper introduces a self-centering beam–column joint that integrates superelastic shape memory alloys (SMAs) and prestressed steel tendons as restoring components. A numerical model was developed in OpenSees and validated against experimental results, with discrepancies in residual deformation within 10%. The validated model was used for parametric studies on strand area, prestress, and SMA configuration. The results show that the proposed joint sustains a maximum drift of 6% while maintaining nearly zero residual drift (less than 0.2%), and its hysteresis curve exhibits a stable flag-shaped pattern. The equivalent viscous damping ratio exceeds 0.1, confirming excellent deformation and energy dissipation capacities. These findings highlight the joint’s potential for application in seismic-resilient steel frames.

The study on preparation technology of recycled concrete with economical and technical feasibility has gained more serious attention in each country due to its involvement and effect on the environment protection and the sustainable development of human society. In this study, we conducted a control variable test to investigate and assess the influence of the aggregate size on the strength characteristics of concrete with different diameters of recycled aggregates. Concrete with recycled aggregates of 5∼15 mm (A), 15∼20 mm (B), 20∼30 mm (C), and their combinations were subjected to a series of unconfined pressure tests after curing for 28 days. Based on the results obtained from the tests, an effort was made to study the relationship between the mechanical characteristics of recycled aggregate concrete and aggregate particle size. Also, a regression model of recycled concrete was proposed to predict the elasticity modulus and to adjust the design of mixture proportion. It is believed that these experiment results would contribute to adjust the remediation mixture for recycling plants by considering the influence of recycled aggregate size.

Metals which are widely used in many types of industries are usually subjected to fatigue and surface corrosion. There is a demand to detect the surface damage caused by fatigue and corrosion at an early stage to ensure the structural integrity. In this paper, a novel nonlinear ultrasonic technique based on the measure of third-order combined harmonic generation, is proposed to detect and locate the surface damage in 6061 aluminum alloy. Third-order combined harmonic generation caused by non-collinear mixing of one longitudinal wave and one transverse wave at different frequencies, is firstly analyzed and experimentally observed. An experimental procedure of nonlinear scanning is proposed for the damage detection and location by checking the variation of frequency nonlinear response. The correlations of nonlinear frequency mixing responses and surface damage in the specimens are obtained. Results show that the nonlinear response caused by fatigue damage and surface corrosion can be identified and located by this method. In addition, this approach can exclude the nonlinearity induced by the instruments and simplify the signal processing.

With the advent of Industry 5.0, fault diagnosis is playing an increasingly important role in routine equipment maintenance and condition monitoring. From the perspective of industrial big data, this paper systematically reviews the current mainstream industrial fault diagnosis methods. The content covers the main sources of industrial big data, commonly used datasets, and the construction of related platforms. In conjunction with the development of multi-source heterogeneous data, the paper explores the evolutionary path of fault diagnosis methods. Subsequently, it provides an in-depth analysis of data-driven fault diagnosis techniques in industrial applications, with particular emphasis on the pivotal role of deep learning algorithms in fault diagnosis. Next, it discusses the applications and development of large models in the field of fault diagnosis, focusing on their potential to enhance diagnostic intelligence and generalization under big data environments. Finally, the paper looks ahead to the future development of data-driven fault diagnosis methods, pointing out that data quality, interpretability of deep learning, and edge-based large models are important research directions that urgently require breakthroughs.

Atmospheric nitrogen (N) deposition has been found to significantly affect plant growth and physiological performance in terrestrial ecosystems. Many individual studies have investigated how N addition influences plant functional traits, however these investigations have usually been limited to a single species, and thereby do not allow derivation of general patterns or underlying mechanisms. We synthesized data from 56 papers and conducted a meta-analysis to assess the general responses of 15 variables related to leaf economics, gas exchange, and hydraulic traits to N addition among 61 woody plant species, primarily from temperate and subtropical regions. Results showed that under N addition, leaf area index (+10.3%), foliar N content (+7.3%), intrinsic water-use efficiency (+3.1%) and net photosynthetic rate (+16.1%) significantly increased, while specific leaf area, stomatal conductance, and transpiration rate did not change. For plant hydraulics, N addition significantly increased vessel diameter (+7.0%), hydraulic conductance in stems/shoots (+6.7%), and water potential corresponding to 50% loss of hydraulic conductivity ( , +21.5%; i.e., became less negative), while water potential in leaves (-6.7%) decreased (became more negative). N addition had little effect on vessel density, hydraulic conductance in leaves and roots, or water potential in stems/shoots. N addition had greater effects on gymnosperms than angiosperms and ammonium nitrate fertilization had larger effects than fertilization with urea, and high levels of N addition affected more traits than low levels. Our results demonstrate that N addition has coupled effects on both carbon and water dynamics of woody plants. Increased leaf N, likely fixed in photosynthetic enzymes and pigments leads to higher photosynthesis and water use efficiency, which may increase leaf growth, as reflected in LAI results. These changes appear to have downstream effects on hydraulic function through increases in vessel diameter, which leads to higher hydraulic conductance, but lower water potential and increased vulnerability to embolism. Overall, our results suggest that N addition will shift plant function along a tradeoff between C and hydraulic economies by enhancing C uptake while simultaneously increasing the risk of hydraulic dysfunction.

In this study, silicon‐based hybrid material modifiers were synthesized using organic–inorganic hybridization methods from polyvinyl alcohol and nano‐SiO2 as principal raw materials. The effects of the hybridization ratio and various factors on the workability and mechanical properties of cement mortar were systematically studied. Subsequently, the hybrid material modifiers were optimized to improve cement performance to enhance recycled aggregate. The experimental results show that the hybrid enhancement with the developed silicon‐based modifiers improved mortar workability, increased its water retention, and reduced the water absorption rate of recycled aggregate by 35%, in addition to a 30% decrease in the crushing value. A silicon‐based hybrid material modifier for strengthening the properties of recycled aggregates was designed and developed. The impact of organic monomers, inorganic monomers, and silicon‐based hybrid modifiers on the workability and mechanical properties of cement mortar was investigated through their use as additives. The recycled aggregate was enhanced with a silicon‐based hybrid modifier, and the alteration pattern of the crushing value and water absorption of the modified recycled aggregate was investigated.

Water area segmentation in remote sensing is of great importance for flood monitoring. To overcome some challenges in this task, we construct the Water Index and Polarization Information (WIPI) multi-modality dataset and propose a multi-Modality Fusion and Gated multi-Filter U-Net (MFGF-UNet) convolutional neural network. The WIPI dataset can enhance the water information while reducing the data dimensionality: specifically, the Cloud-Free Label provided in the dataset can effectively alleviate the problem of labeled sample scarcity. Since a single form or uniform kernel size cannot handle the variety of sizes and shapes of water bodies, we propose the Gated Multi-Filter Inception (GMF-Inception) module in our MFGF-UNet. Moreover, we utilize an attention mechanism by introducing a Gated Channel Transform (GCT) skip connection and integrating GCT into GMF-Inception to further improve model performance. Extensive experiments on three benchmarks, including the WIPI, Chengdu and GF2020 datasets, demonstrate that our method achieves favorable performance with lower complexity and better robustness against six competing approaches. For example, on the WIPI, Chengdu and GF2020 datasets, the proposed MFGF-UNet model achieves F1 scores of 0.9191, 0.7410 and 0.8421, respectively, with the average F1 score on the three datasets 0.0045 higher than that of the U-Net model; likewise, GFLOPS were reduced by 62% on average. The new WIPI dataset, the code and the trained models have been released on GitHub.

This study aimed to screen DNA aptamers against the signal molecule C4-HSL of the rhl system for the inhibition of biofilm formation of Pseudomonas aeruginosa using an improved systematic evolution of ligand by exponential enrichment (SELEX) method based on a structure-switching fluorescent activating bead. The aptamers against the C4-HSL with a high affinity and specifity were successfully obtained and evaluated in real-time by this method. Results of biofilm inhibition experiments in vitro showed that the biofilm formation of P. aeruginosa was efficiently reduced to about 1/3 by the aptamers compared with that of the groups without the aptamers. Independent secondary structure simulation and computer-aided tertiary structure prediction (3dRNA) showed that the aptamers contained a highly conserved Y-shaped structural unit. Therefore, this study benefits the search for new methods for the detection and treatment of P. aeruginosa biofilm formation.