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In the strong light–matter coupling regime, the transmission properties of metasurfaces have a remarkable similarity to those of typical two-level systems. In this work, we explore the absorption spectra of a metasurface coupled to a quantum phonon bath using the time-dependent variational principle and the flexible multi-D2 Davydov trial states. In the weak light–matter coupling regime, phonon coupling has minimal impact on system dissipation. However, in the strong coupling regime, it significantly influences dissipation dynamics. Additionally, a phonon bath with a selected number of strongly coupled modes near the phonon center line substantially narrows the absorption spectrum linewidth by controlling dissipation through a few phonon channels. These findings demonstrate the critical role of the phonon bath in shaping metasurface transmission properties, offering a promising approach for the precise engineering of metasurfaces.

The inherent uncertainty and information asymmetry in financial markets create significant challenges for accurate price forecasting. Although investor sentiment analysis has gained traction in recent research, the temporal dimension of sentiment dynamics remains underexplored. This study develops a novel framework that enhances stock price prediction by integrating time-partitioned investor sentiment, while improving model interpretability via Shapley additive explanations (SHAP) analysis. Employing the ERNIE (enhanced representation through knowledge integration) 3.0 model for sentiment extraction from China’s Eastmoney Guba stock forum, we quantitatively distinguish intraday and post-market investor sentiment then integrate these temporal components with technical indicators through neural network architecture. Our results indicate that temporal sentiment partitioning effectively reduces uncertainty. Empirical evidence demonstrates that our long short-term memory (LSTM) model integrating intraday and post-market sentiment indicators achieves better prediction accuracy, and SHAP analysis reveals the importance of intraday and post-market investor sentiment to stock price prediction models. Implementing quantitative trading strategies based on these insights generates significantly more annualized returns for representative stocks with controlled risk, outperforming sentiment-agnostic and non-temporal sentiment models. This research provides methodological innovations for processing temporal unstructured data in finance, while the SHAP framework offers regulators and investors actionable insights into sentiment-driven market dynamics.

Many theoretical frameworks have been used in order to understand health behaviors such as physical activity, sufficient sleep, healthy eating habits, etc. In most research studies, determinants within these frameworks are assessed only once and thus are considered as stable over time, which leads to rather 'static' health behavior change interventions. However, in real-life, individual-level determinants probably vary over time (within days and from day to day), but currently, not much is known about these time-dependent fluctuations in determinants. In order to personalize health behavior change interventions in a more dynamic manner, such information is urgently needed. The purpose of this study was to explore the time-dependent variability of emotions, physical complaints, intention, and self-efficacy in older adults (65+) using Ecological Momentary Assessment (EMA). Observational data were collected in 64 healthy older adults (56.3% men; mean age 72.1 ± 5.6 years) using EMA. Participants answered questions regarding emotions ( , cheerfulness, relaxation, enthusiasm, satisfaction, insecurity, anxiousness, irritation, feeling down), physical complaints ( , fatigue, pain, dizziness, stiffness, shortness of breath), intention, and self-efficacy six times a day for seven consecutive days using a smartphone-based questionnaire. Generalized linear mixed models were used to assess the fluctuations of individual determinants within subjects and over days. A low variability is present for the negative emotions ( , insecurity, anxiousness, irritation, feeling down) and physical complaints of dizziness and shortness of breath. The majority of the variance for relaxation, satisfaction, insecurity, anxiousness, irritation, feeling down, fatigue, dizziness, intention, and self-efficacy is explained by the within subjects and within days variance (42.9% to 65.8%). Hence, these determinants mainly differed within the same subject and within the same day. The between subjects variance explained the majority of the variance for cheerfulness, enthusiasm, pain, stiffness, and shortness of breath (50.2% to 67.3%). Hence, these determinants mainly differed between different subjects. This study reveals that multiple individual-level determinants are time-dependent, and are better considered as 'dynamic' or unstable behavior determinants. This study provides us with important insights concerning the development of dynamic health behavior change interventions, anticipating real-time dynamics of determinants instead of considering determinants as stable within individuals.

Dynamic simulation approach can be used for understanding the nonlinear behavior in mineral processing circuits. In this study, the gel point, the main parameters of batch flux density function and the main parameters of effective solid stress were determined at different conditions (pH, flocculant dosage and particle size). Therefore, the main parameters of phenomenological model of sedimentation and thickening were determined as a function of particle size, pH and flocculant dosages using the result of experimental tests and Curve expert professional software. Then, the dynamic simulation was carried out for the industrial thickener of coal washing plant and the time-dependent variation of response variables was investigated by time-dependent variation of pH of input feed to thickener using the obtained equations. It was observed that it is possible to predict the thickener behavior as a function of time for time dependent variation of pH of input feed to the thickener of coal washing plant using obtained equations that it was not possible using phenomenological model of thickener alone.

Molecular evolutionary rate estimates have been shown to depend on the time period over which they are estimated. Factors such as demographic processes, calibration errors, purifying selection, and the heterogeneity of substitution rates among sites (RHAS) are known to affect the accuracy with which rates of evolution are estimated. We use mathematical modeling and Bayesian analyses of simulated sequence alignments to explore how mutational hotspots can lead to time-dependent rate estimates. Mathematical modeling shows that underestimation of molecular rates over increasing time scales is inevitable when RHAS is ignored. Although a gamma distribution is commonly used to model RHAS, we show that when the actual RHAS deviates from a gamma-like distribution, rates can either be under- or overestimated in a time-dependent manner. Simulations performed under different scenarios of RHAS confirm the mathematical modeling and demonstrate the impacts of time-dependent rates on estimates of divergence times. Most notably, erroneous rate estimates can have narrow credibility intervals, leading to false confidence in biased estimates of rates, and node ages. Surprisingly, large errors in estimates of overall molecular rate do not necessarily generate large errors in divergence time estimates. Finally, we illustrate the correlation between time-dependent rate patterns and differential saturation between quickly and slowly evolving sites. Our results suggest that data partitioning or simple nonparametric mixture models of RHAS significantly improve the accuracy with which node ages and substitution rates can be estimated.

The Mediterranean Sea releases approximately 1 Sv of water into the North Atlantic through the Gibraltar Straits, forming the saline Mediterranean Outflow Water (MOW). Its impact on large‐scale flow and specifically its northbound Lagrangian pathways are widely debated, yet a comprehensive overview of MOW pathways over recent decades is lacking. We calculate and analyze synthetic Lagrangian trajectories in 1980–2020 reanalysis velocity data. Sixteen percent of the MOW follow a direct northbound path to the sub‐polar gyre, reaching a 1,000 m depth crossing window at the southern tip of Rockall Ridge in about 10 years. Surprisingly, time‐dependent chaotic advection, not steady currents, drives over half of the northbound transport. Our results suggest a potential 15–20 years predictability in the direct northbound transport. Additionally, monthly variability appears more significant than inter‐annual variability in Lagrangian mixing and spreading the MOW. Plain Language Summary The Mediterranean Sea and the North Atlantic Ocean sustain a constant exchange of water through the shallow, narrow Straits of Gibraltar. Due to intense evaporation, the Mediterranean water is saltier and denser than its Atlantic counterpart and flows out of the Mediterranean Sea into the middepth North Atlantic Ocean to create the Mediterranean Outflow Water (MOW). This salty input reaches 1,000 m depth and spreads and mixes into the North Atlantic Ocean, and is thought to have a non‐negligible impact on its flow regime, specifically through its northbound transport and direct contribution to the salinity of the northern water of the North Atlantic. However, a comprehensive survey of the various pathways taken by the MOW is lacking. In this work, we track the 3D pathways of virtual trajectories starting at the Gibraltar Straits at various depths for 20 years. We find that 16% of the particles take a direct northbound path. Surprisingly, over half of this transport is not due to steady currents that lead the MOW to the north. Rather, it is a result of time‐dependent chaotic advection, a phenomenon that allows pathways in a time‐dependent flow to circumvent stationary barriers of transport. Key Points Synthetic Lagrangian trajectories initialized at the Gibraltar Straits, based on SODA3.4.2 reanalysis velocity data from 1980 to 2020, are used to study the pathways, Lagrangian mixing, and spreading of the Mediterranean Outflow Water (MOW) 16% of the MOW particles take a direct northbound route Over half of the direct northbound transport results from time‐dependent chaotic advection and not from a steady northbound current

Under the effect of long-term periodic fluctuation of reservoir water level, reservoir landslide deforms continuously and the mechanical strength of slip zone varies accordingly, resulting in the stability variation. This study proposed a new time-dependent stability evaluation method based on a temporal and spatial strength evolution model of the slip zone, with consideration of the combined effect of water level fluctuation and continuous deformation of reservoir landslide with time. Then, the method was employed to evaluate the stability of the Outang landslide in the Three Gorges Reservoir area. The results indicate that: (1) a corresponding spatio-temporal deformation model is derived for quantifying landslides with advancing deformation mode; (2) the relationship between shear strength and shear displacement of slip zone soil with different moisture content conditions and different normal stress states can be quantitatively expressed by the proposed strength evolution model; (3) the stability of Outang landslide decreases with time but at a gradually declining rate, and tends to be maintained at a critical state ultimately. Overall, this novel method establishes a direct connection between the shear strength of critical geomaterial and landslide evolution process with field monitoring data, thus enables to provide a persuasive evaluation.

Variation in habitat quality affects individual fitness through the accumulation of benefits and costs over time. Although an individual's fitness and susceptibility to mortality are consequences of these past experiences, current analytical models do not quantify the cumulative effects of resources, risks, and environmental conditions on survival. We developed the Survival and Habitat Quality model (SHQ), which redefines survival as a cumulative process and measures habitat quality by its aggregate effect on survival through time. SHQ is an autoregressive time‐series model that uses fine‐scale tracking data, remotely sensed environmental data, and computational power to quantify the cumulative effects of spatial variation in habitat quality on survival without relying on subjective, user‐defined lag effects. We tested SHQ on simulated data and on pronghorn data in South Dakota, USA. Compared to a traditional survival model, SHQ was more precise and accurate at estimating cumulative effects of habitat on survival. Using model output, we were also able to generate maps predicting areas of high and low pronghorn survival. SHQ is a conceptual and methodological advance that explicitly integrates individuals' day‐to‐day interactions with their surroundings to identify ultimate sources of mortality. The model is a novel and accurate tool for assessing habitat quality and identifying management actions that increase individual survival and population growth. More broadly, SHQ's flexible mathematical framework captures the full spatial and temporal scope of processes affecting survival, providing a powerful means for understanding the environmental basis of fitness.

We study the behavior of the trajectories of a second-order differential equation with vanishing damping, governed by the Yosida regularization of a maximally monotone operator with time-varying index, along with a new Regularized Inertial Proximal Algorithm obtained by means of a convenient finite-difference discretization. These systems are the counterpart to accelerated forward–backward algorithms in the context of maximally monotone operators. A proper tuning of the parameters allows us to prove the weak convergence of the trajectories to zeroes of the operator. Moreover, it is possible to estimate the rate at which the speed and acceleration vanish. We also study the effect of perturbations or computational errors that leave the convergence properties unchanged. We also analyze a growth condition under which strong convergence can be guaranteed. A simple example shows the criticality of the assumptions on the Yosida approximation parameter, and allows us to illustrate the behavior of these systems compared with some of their close relatives.

Support was provided from the US Department of Defense (DoD) Strategic Environmental Research and Development Program (SERDP) under Project RC‐2644. Jayantha Obeysekera had support from the Institute of Environment, FIU.