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The single-mode Dicke model is well known to undergo a quantum phase transition from the so-called normal phase to the superradiant phase (hereinafter called the 'superradiant quantum phase transition'). Normally, quantum phase transitions can be identified by the critical behavior of quantities such as entanglement, quantum fluctuations, and fidelity. In this paper, we study the role of the quantum Fisher information (QFI) of both the field mode and the atoms in the ground state of the Dicke Hamiltonian. For a finite but large number of atoms, our numerical results show that near the critical atom-field coupling, the QFI of the atomic and the field subsystems can surpass their classical limits, due to the appearance of nonclassical quadrature squeezing. As the coupling increases far beyond the critical point, each subsystem becomes a highly mixed state, which degrades the QFI and hence the ultimate phase sensitivity. In the thermodynamic limit, we present the analytical results of the QFI and their relationship with the reduced variances of the field mode and the atoms. For each subsystem, we find that there is a singularity in the derivative of the QFI at the critical point, a clear signature of the quantum criticality in the Dicke model.

It is difficult to track the posture of players in the course, mainly because of the changing environment and players. In this paper, the improved neural network is used to extract the trajectory characteristics of the athletes in the football player’s game video, and the network is trained on a large number of data objects containing similarity objects, which improves the ability of the algorithm to distinguish the athlete’s trajectory. A scheme of soccer attitude tracking based on twin neural network. The experimental results show that the algorithm has a good effect in the field of football and the accuracy is over 90% and the Siamese neural network is better than a traditional convolutional neural network.

As social media use continues to increase, consumers are beginning to experience social media fatigue leading to concern among marketers about the efficacy of the channel. This research examines social media fatigue through a stressor–strain–outcome model to better understand how consumers cope with this phenomenon and how it impacts adoption behaviors. Data were collected from 452 valid WeChat users through questionnaires and analyzed using SEM with PLS. The results show that information overload, social overload, and privacy concerns significantly affect social media fatigue; system function overload and social overload affect the user’s negative behavior through the mediation of fatigue, not anxiety. Social overload and private concern significantly affect anxiety and fatigue, and anxiety further significantly affect negative usage behavior.

Maillard reaction (MR) is a cascade of complex interactions between reducing sugars and amine groups in food processing and storage. It produces a variety of volatile compound, nonenzymatic intermediates, and high molecular weight melanoidin contributing improved aroma, color, flavor, and antioxidant properties to the final food products. When uncontrolled, it can produce some harmful derivatives such as acrylamide, heterocyclic amine, advanced glycation end Products (AGEs), and other substances that can be detrimental to human health leading to cancer and chronic diseases. Herein, we reviewed the MR leading to different MRPs and factors affecting the MR and MRPs, their application in food model systems, their biological activities, and the formation mechanism and effective inhibition methods of common harmful MRPs. The updated overview can be useful to explore the rational use of MR, which can ameliorate its positive biological features with reduced adverse effects. The MR leading to different MRPs and factors affecting the MR and MRPs, their application in food model systems, their biological activities, and the formation mechanism and effective inhibition methods of common harmful MRPs.

Airfoil slots, as a passive flow control technique-based wing improvement structure, may successfully delay the stall angle and increase the lift coefficient in situations with a high angle of attack. But when the angle of attack is minimal, it drastically reduces the airfoil’s initial aerodynamic performance. The NACA4412 airfoil is used as the research object in this study, which uses Computational Fluid Dynamics (CFD) techniques to examine how various slot configurations affect the airfoil’s aerodynamic properties. Based on the optimal slot configuration, a scheme for dynamically adjusting the slot angle is proposed. The research results demonstrate the following: 1) Compared to previous configurations, Slot Configuration III may greatly enhance the airfoil’s stall characteristics by postponing the stall angle to 24° and raising the maximum lift coefficient by 27.6% to 1.57; 2) Through the rotation of the leading edge, the study achieved changes in slot configuration and inlet/outlet parameters, resulting in two slot configurations capable of maintaining aerodynamic performance under small angles of attack; 3) Utilizing the geometric relationship between Slot Configurations III and IV (5°, 8°), a variable-angle slot scheme is proposed, which enhances small angle of attack lift while effectively suppressing stall phenomena.

Aircraft trajectory prediction is vital for ensuring safe and efficient air travel while addressing challenges in complex and dynamic environments. Current trajectory prediction models often struggle in noisy scenarios due to their lack of robustness. This study introduces the Noise-Robust Autoregressive Transformer, a novel model that enhances prediction reliability by integrating noise-regularized embeddings within a multi-head attention equipped with hybrid positional encoding. This model effectively captures essential temporal-spatial relationships and manages positional information more precisely across varied trajectories. Moreover, we formulate the robust trajectory prediction problem as an autoregressive approach that models the encoding of historical data and the decoding of future positions as a sequence-to-sequence learning problem. Our approach effectively captures positional encodings for the complex spatial-temporal variations in aircraft trajectory prediction, improving long-term prediction accuracy while achieving real-time responsiveness. Extensive experiments on multiple datasets demonstrate our improvement over existing aircraft trajectory prediction methods.

This study explored the effect of a moderate (90 g/d) low-carbohydrate diet (LCD) in type 2 diabetes patients over 18 months. Ninety-two poorly controlled type 2 diabetes patients aged 20-80 years with HbA1c ≥7.5% (58 mmol/mol) in the previous three months were randomly assigned to a 90 g/d LCD r traditional diabetic diet (TDD). The primary outcomes were glycaemic control status and change in medication effect score (MES). The secondary outcomes were lipid profiles, small, dense low-density lipoprotein (sdLDL), serum creatinine, microalbuminuria and carotid intima-media thickness (IMT). A total of 85 (92.4%) patients completed 18 months of the trial. At the end of the study, the LCD and TDD group consumed 88.0±29.9 g and 151.1±29.8 g of carbohydrates, respectively (p < 0.05). The 18-month mean change from baseline was statistically significant for the HbA1c (-1.6±0.3 vs. -1.0±0.3%), 2-h glucose (-94.4±20.8 vs. -18.7±25.7 mg/dl), MES (-0.42±0.32 vs. -0.05±0.24), weight (-2.8±1.8 vs. -0.7±0.7 kg), waist circumference (-5.7±2.7 vs. -1.9±1.4 cm), hip circumference (-6.1±1.8 vs. -2.9±1.7 cm) and blood pressure (-8.3±4.6/-5.0±3 vs. 1.6±0.5/2.5±1.6 mmHg) between the LCD and TDD groups (p<0.05). The 18-month mean change from baseline was not significantly different in lipid profiles, sdLDL, serum creatinine, microalbuminuria, alanine aminotransferase (ALT) and carotid IMT between the groups. A moderate (90 g/d) LCD showed better glycaemic control with decreasing MES, lowering blood pressure, decreasing weight, waist and hip circumference without adverse effects on lipid profiles, sdLDL, serum creatinine, microalbuminuria, ALT and carotid IMT than TDD for type 2 diabetic patients.

In this work, we extend the Heston stochastic volatility model by including a time-dependent correlation that is driven by isospectral flows instead of a constant correlation, being motivated by the fact that the correlation between, e.g., financial products and financial institutions is hardly a fixed constant. We apply different numerical methods, including the method for backward stochastic differential equations (BSDEs) for a fast computation of the extended Heston model. An example of calibration to market data illustrates that our extended Heston model can provide a better volatility smile than the Heston model with other considered extensions.

Probabilistic reservoir slope stability analysis usually suffers from computational inefficiency of complicated implicit performance functions. To address this problem, numerous machine learning (ML) algorithms have been successfully applied to calculate the index of factor of safety ( FS ) to facilitate slope reliability analysis. However, current ML surrogate models generally select an optimal algorithm with good performance while discarding all the others. Meanwhile, ML models mainly provide a point-value deterministic evaluation of failure probability and ignores the predictive uncertainties associated with the models. How to ensemble the strengths of different ML algorithms to improve the estimation accuracy of slope failure probability and rationally quantify the predictive uncertainties still remains an open issue. This paper proposes ML ensembled probabilistic methods for time-dependent reliability analysis of reservoir slopes under rapid water level drawdown using Bayesian model averaging (BMA). A practical slope in the Three Gorges Reservoir Area is selected to illustrate the proposed approach. Firstly, two machine-learning models, namely support vector machine (SVM) and back propagation-based neural network (BPNN), are used to evaluate the safety factor of reservoir slopes. Then, the deterministic evaluations of FS are ensembled together by the weighting of each member using BMA to make probabilistic evaluation of time-dependent failure probability. Finally, the predictive performance of individual ML models and the ensemble BMA model are systematically investigated. Results show that the ensemble surrogate model has better predictive performance on slope failure probability than individual ML model. Ensemble models can not only combine the strengths of different models to improve the prediction accuracy of time-dependent failure probability of reservoir slope, but also provide probabilistic forecasts to reasonably quantify the predictive uncertainty

Plant root exudates affect root-knot nematodes egg hatch. Chemicals in root exudates can attract nematodes to the roots or result in repellence, motility inhibition or even death. However, until recently little was known about the relationship between tomato root exudates chemicals and root-knot nematodes. In this study, root exudates were extracted from three tomato rootstocks with varying levels of nematode resistance: Baliya (highly resistant, HR), RS2 (moderately resistant, MR) and L-402 (highly susceptible, T). The effects of the root exudates on Meloidogyne incognita (M. incognita) egg hatch, survival and chemotaxis of second-stage juveniles (J2) were explored. The composition of the root exudates was analysed by gas chromatography/mass spectrometry (GC/MS) prior to and following M. incognita inoculation. Four compounds in root exudates were selected for further analysis and their allopathic effect on M. incognita were investigated. Root exudates from each tomato rootstocks (HR, MR and T strains) suppressed M. incognita egg hatch and increased J2 mortality, with the highest rate being observed in the exudates from the HR plants. Exudate from HR variety also repelled M. incognita J2 while that of the susceptible plant, T, was demonstrated to be attractive. The relative amount of esters and phenol compounds in root exudates from HR and MR tomato rootstocks increased notably after inoculation. Four compounds, 2,6-Di-tert-butyl-p-cresol, L-ascorbyl 2,6-dipalmitate, dibutyl phthalate and dimethyl phthalate increased significantly after inoculation. The egg hatch of M. incognita was suppressed by each of the compound. L-ascorbyl 2,6-dipalmitate showed the most notable effect in a concentration-dependent manner. All four compounds were associated with increased J2 mortality. The greatest effect was observed with dimethyl phthalate at 2 mmol·L-1. Dibutyl phthalate was the only compound observed to repel M. incognita J2 with no effect being detected in the other compounds. Each of the four compounds were correlated with a reduction in disease index in the susceptible cultivar, T, and tomato seedlings irrigated with L-ascorbyl 2,6-dipalmitate at 2 mmol·L-1 showed the best resistance to M. incognita. Taken together, this study provided a valuable contribution to understanding the underlying mechanism of nematode resistance in tomato cultivars.