Time Scale Research Articles

Periodic Boundary Value problem for the Dynamical system with neutral integro-differential equation on time scales

Examining the results of existence and Ulam stability in a fractional dynamic system, including a neutral integro-differential equation that incorporates periodic boundary conditions on time scales and the Caputo fractional nabla derivative, is the main objective of the study. Standard fixed-point techniques are applied to obtain the results. Additionally, controllability and Ulam stability results are included. The application of the theoretical results is illustrated with a suitable example.

Molecular dynamics simulations of nanoparticle sintering in additive nanomanufacturing: The role of particle size, misorientation angle, material type, and temperature

Additive nanomanufacturing, where nanoparticles (NPs) are generated via laser material ablation and sintered via laser, permits the fabrication of novel electronic devices with adequate flexibility, such as tunable material composition, adjustable sintered state, and a wide selection of substrates—including biodegradable ones—to name a few. The laser energy input often needs to be carefully balanced between achieving adequate sintering and avoiding substrate damage. The sintered state of NPs, a key influencing factor of the resistivity of electronic circuits, depends not only on temperature (the result of laser energy input), but also on NPs size, size ratio, and crystallographic misorientation. While the minuteness of NPs and the transient nature of their sintering (typically within a nanosecond) make direct experimental evaluation of such parameters challenging, the length and time scales are uniquely suitable for molecular dynamic (MD) studies. This study utilizes MD to investigate the sintering characteristics of silver and copper NP doublets at different temperatures and examines the effects of NP size, size ratios, misorientation angle (including both tilt and twist), and material type. A mathematical framework to describe the characteristic sintering time, i.e., time needed for a NP doublet to achieve a given normalized neck size, was proposed based on MD results.

Runoff Variation in Midstream Hei River, Northwest China: Characteristics and Driving Factors Analysis

Study regionThe midstream of the Hei River Basin (HRB). Study focusThe study investigated the changes in hydrological elements at different time scales in the basin from 1982 to 2020. We used the Budyko coupled balance equation to quantitatively assess the contributions of climate change and human activity-induced land surface changes to runoff during different periods. In addition, the driving factors of flow consumption within the basin were analyzed using wavelet coherence and generalized additive model (GAM). New hydrological insights for the regionPrior to 2001, there was a significant decrease in annual-scale runoff in the midstream. After that, a partial recovery was observed due to the influence of the Ecological Water Transfer Project (EWTP), while the NDVI trend exhibited an opposite pattern. The study indicates that human-induced modification of the underlying surface is the primary driving factor for the decrease in runoff, with land surface factors contributing 84.69% and 65.27% to the runoff reduction during two periods of change, respectively. Further research on the driving forces of runoff consumption reveals that vegetation cover and climate factors jointly regulate the variations in runoff consumption in the midstream, with multifactor effects surpassing those of individual factors, and NDVI emerges as the predominant controlling factor for runoff consumption. Moreover, all anthropogenic factors exhibit a high explanatory power for runoff consumption.

Heat-induced structural and chemical changes to a computationally designed miniprotein.

The de novo design of miniprotein inhibitors has recently emerged as a new technology to create proteins that bind with high affinity to specific therapeutic targets. Their size, ease of expression, and apparent high stability makes them excellent candidates for a new class of protein drugs. However, beyond circular dichroism melts and hydrogen/deuterium exchange experiments, little is known about their dynamics, especially at the elevated temperatures they seemingly tolerate quite well. To address that and gain insight for future designs, we have focused on identifying unintended and previously overlooked heat-induced structural and chemical changes in a particularly stable model miniprotein, EHEE_rd2_0005. Nuclear magnetic resonance (NMR) studies suggest the presence of dynamics on multiple time and temperature scales. Transiently elevating the temperature results in spontaneous chemical deamidation visible in the NMR spectra, which we validate using both capillary electrophoresis and mass spectrometry (MS) experiments. High temperatures also result in greatly accelerated intrinsic rates of hydrogen exchange and signal loss in NMR heteronuclear single quantum coherence spectra from local unfolding. These losses are in excellent agreement with both room temperature hydrogen exchange experiments and hydrogen bond disruption in replica exchange molecular dynamics simulations. Our analysis reveals important principles for future miniprotein designs and the potential for high stability to result in long-lived alternate conformational states.

Decadal Thermal Variability of the Upper Southern Ocean: Zonal Asymmetry

Abstract As the major sink of anthropogenic heat, the Southern Ocean has shown quasi-symmetric, deep-reaching warming since the mid-twentieth century. In comparison, the shorter-term heat storage pattern of the Southern Ocean is more complex and has notable impacts on regional climate and marine ecosystems. By analyzing observational datasets and climate model simulations, this study reveals that the Southern Ocean exhibits prominent decadal (>8 years) variability extending to ∼700-m depth and is characterized by out-of-phase changes in the Pacific and Atlantic–Indian Ocean sectors. Changes in the Pacific sector are larger in magnitude than those in the Atlantic–Indian Ocean sectors and dominate the total heat storage of the Southern Ocean on decadal time scales. Instead of heat uptake through surface heat fluxes, these asymmetric variations arise primarily from wind-driven heat redistribution. Pacemaker and preindustrial simulations of the Community Earth System Model version 1 (CESM1) suggest that these variations in Southern Ocean winds arise primarily from natural variability of the tropical Pacific, as represented by the interdecadal Pacific oscillation (IPO). Through atmospheric teleconnection, the positive phase of the IPO gives rise to higher-than-normal sea level pressure and anticyclonic wind anomalies in the 50°–70°S band of the Pacific sector. These winds lead to warming of 0–700 m by driving the convergence of warm water. The opposite processes, involving cyclonic winds and upper-layer divergence, occur in the Atlantic–Indian Ocean sector. These findings aid our understanding of the time-varying heat storage of the Southern Ocean and provide useful implications on initialized decadal climate prediction.

The viscoelasticity of high concentration monoclonal antibodies using particle tracking microrheology.

The viscoelasticity of monoclonal antibodies (mAbs) is important during their production, formulation, and drug delivery. High concentration mAbs can provide higher efficacy therapeutics (e.g., during immunotherapy) and improved efficiency during their production (economy of scale during processing). Two humanized mAbs were studied (mAb-1 and mAb-2) with differing isoelectric points. Using high speed particle tracking microrheology, we demonstrated that the mAb solutions have significant viscoelasticities above concentrations of 40 mg/ml. Power law viscoelasticity was observed over the range of time scales (-1 s) probed for the high concentration mAb suspensions. The terminal viscosity demonstrated an exponential dependence on mAb concentration (a modified Mooney relationship) as expected for charged stabilized Brownian colloids. Gelation of the mAbs was explored by lowering the pH of the buffer and a power law scaling of the gelation transition was observed, i.e., the exponent of the anomalous diffusion of the probe particles scaled inversely with the gelation time.

A framework for multivariate analysis of compound extremes based on correlated hydrologic time series

While hydrologic design has primarily relied on use of annual maxima values, many events of hydrologic interest such as active and break spells in monsoonal rains, heat waves, flash flooding from snowmelt, etc. manifest at time scales of days to weeks, and require daily (or finer resolution) data for proper characterization. Often combinations of several hydrologic variables herald highly impactful events and are labelled as compound extremes. Using a time-varying interval-censored estimation method of copula models, a novel multivariate approach for dealing with compound extremes under temporal dependence amongst variables and when data contain significant ties is developed here to enable the determination of design magnitudes and associated risk. The efficacy of this method is demonstrated over the Godavari River Basin, India, using daily precipitation and temperature data from a recent period (1977 to 2020) during the monsoon season. A conservative approach is proposed for estimating the design magnitudes of hydrologic variables in multivariate settings. The significance of ties and temporal dependence amongst precipitation and temperature data in estimation of design magnitudes of cold-wet compound extremes at specified probabilities of exceedance is explored at various spatial scales. Ties and temporal dependence are both shown to have a profound influence on design estimates. Since ties and temporal variation in dependence amongst hydrologic variables are ubiquitous features of most hydrologic data, this framework would be applicable for characterization of other compound extremes in hydrology.

Hour-Long, Kilohertz Sampling Rate Three-Dimensional Single-Virus Tracking in Live Cells Enabled by StayGold Fluorescent Protein Fusions.

A viral infection process covers a large range of spatiotemporal scales. Tracking the viral infection process with fluorescent labels over long durations while maintaining a fast sampling rate requires bright and highly photostable labels. StayGold is a recently identified green fluorescent protein that has a greater photostability and higher signal intensity under identical illumination conditions compared to existing fluorescence protein variants. Here, StayGold protein fusions were used to generate virus-like particles (StayGold-VLPs) to achieve hour-long 3D single-virus tracking (SVT) with 1000 localizations per second (kHz sampling rate) in live cells. The expanded photon budget from StayGold protein fusions prolonged the tracking duration, facilitating a comprehensive study of viral trafficking dynamics with high temporal resolution over long time scales. The development of StayGold-VLPs presents a simple and general VLP labeling strategy for better performance in SVT, enabling exponentially more information to be collected from single trajectories and allowing for the future possibility of observing the entire life cycle of a single virus.

A deep learning methodology based on adaptive multiscale CNN and enhanced highway LSTM for industrial process fault diagnosis

Intelligent fault diagnostic techniques are crucial for ensuring the long-term reliability of manufacturing. The process variables collected by sensors in real industrial systems typically exhibit diverse time scales and data features. To overcome the above issues, we propose a comprehensive model known as adaptive multiscale CNN and enhanced highway LSTM (ACEL). To begin with, multiscale features under variable operating conditions are automatically extracted by constructing the parallel convolutional module. Then, to selectively focus on the feature representation within the channels, efficient channel attention is introduced to downplay the channels with less important information. In addition, the bidirectional LSTM is designed to generate more fine-grained hybrid features based on contextual information and local features learned by the CNN. An enhanced highway configuration is designed to be used to bolster the global temporal dependencies. Finally, ACEL is applied to the Tennessee Eastman benchmark and CSTR simulation, and multiple statistical metrics show that the proposed model outperforms other advanced fault diagnosis methods.

PCA-ICA-LSTM: A Hybrid Deep Learning Model Based on Dimension Reduction Methods to Predict S&P 500 Index Price

AbstractIn this paper, we propose a new hybrid model based on a deep learning network to predict the prices of financial assets. The study addresses two key limitations in existing research: (1) the lack of standardized datasets, time scales, and evaluation metrics, and (2) the focus on prediction return. The proposed model employs a two-stage preprocessing approach utilizing Principal Component Analysis (PCA) for dimensionality reduction and de-noising, followed by Independent Component Analysis (ICA) for feature extraction. A Long Short-Term Memory (LSTM) network with five layers is fed with this preprocessed data to predict the price of the next day using a 5 day time horizon. To ensure comparability with existing literature, experiments employ an 18 year dataset of the Standard & Poor's 500 (S&P500) index and include over 40 technical indicators. Performance evaluation encompasses six metrics, highlighting the model's superiority in accuracy and return rates. Comparative analyses demonstrate the superiority of the proposed PCA-ICA-LSTM model over single-stage statistical methods and other deep learning architectures, achieving notable improvements in evaluation metrics. Evaluation against previous studies using similar datasets corroborates the model's superior performance. Moreover, extensions to the study include adjustments to dataset parameters to account for the COVID-19 pandemic, resulting in improved return rates surpassing traditional trading strategies. PCA-ICA-LSTM achieves a 220% higher return compared to the “hold and wait” strategy in the extended S&P500 dataset, along with a 260% higher return than its closest competitor in the comparison. Furthermore, it outperformed other models in additional case studies. Graphical Abstract

Pseudo almost periodic solutions for a class of nonlinear Duffing equations on time scales

Pseudo almost periodic solutions for a class of nonlinear Duffing equations on time scales

X-ray variability of the triplet star system LTT1445 and evaporation history of the planets around its A component

The high-energy environments of host stars could prove deleterious for their planets. It is crucial to ascertain this contextual information to characterize the atmospheres of terrestrial exoplanets. We aim to fully characterize a unique triple system, LTT1445, with three known rocky planets around LTT 1445A. We studied the X-ray irradiation and flaring of this system based on a new 50 ks Chandra observation, which is divided into 10 ks, 10 ks, and 30 ks segments conducted two days apart, and two months apart, respectively. These data were complemented by an archival Chandra observation approximately one year earlier and repeated observations with extended ROentgen Survey with an Imaging Telescope Array (eROSITA), the soft X-ray instrument on the Spectrum-Roentgen-Gamma (SRG) mission. This enabled the investigation of X-ray flux behavior across multiple time scales. With the observed X-ray flux from the exoplanet host star A, we estimated the photo-evaporation mass loss of each exoplanet. With the planet modeling package VPLanet we predicted the evolution and anticipated current atmospheric conditions. Our Chandra observations indicate that LTT 1445C is the dominant X-ray source, with additional contribution from LTT 1445B. We find that LTT 1445A, a slowly rotating star, exhibits no significant flare activity in the new Chandra dataset. Comparing the flux incident occuring on the exoplanets, we find that LTT 1445BC components do not pose a greater threat to the planets orbiting LTT 1445A than the emission from A itself. According to the results from the simulation, LTT 1445Ad could have the capacity to retain its water surface.

A Gauss-Newton method for iterative optimization of memory kernels for generalized Langevin thermostats in coarse-grained molecular dynamics simulations.

In molecular dynamics simulations, dynamically consistent coarse-grained (CG) models commonly use stochastic thermostats to model friction and fluctuations that are lost in a CG description. While Markovian, i.e., time-local, formulations of such thermostats allow for an accurate representation of diffusivities/long-time dynamics, a correct description of the dynamics on all time scales generally requires non-Markovian, i.e., non-time-local, thermostats. These thermostats typically take the form of a Generalized Langevin Equation (GLE) determined by a memory kernel. In this work, we use a Markovian embedded formulation of a position-independent GLE thermostat acting independently on each CG degree of freedom. Extracting the memory kernel of this CG model from atomistic reference data requires several approximations. Therefore, this task is best understood as an inverse problem. While our recently proposed approximate Newton scheme allows for the iterative optimization of memory kernels (IOMK), Markovian embedding remained potentially error-prone and computationally expensive. In this work, we present an IOMK-Gauss-Newton scheme (IOMK-GN) based on IOMK that allows for the direct parameterization of a Markovian embedded model.

Suppression of Segmental Chain Dynamics on a Particle's Surface in Well-Dispersed Polymer Nanocomposites.

The Rouse dynamics of polymer chains in model nanocomposite polyethylene oxide/silica nanoparticles (NPs) was investigated using quasielastic neutron scattering. The apparent Rouse rate of the polymer chains decreases as the particle loading increases. However, there is no evidence of an immobile segment population on the probed time scale of tens of ps. The slowing down of the dynamics is interpreted in terms of modified Rouse models for the chains in the NP interphase region. Thus, two chain populations, one bulk-like and the other characterized by a suppression of Rouse modes, are identified. The spatial extent of the interphase region is estimated to be about twice the adsorbed layer thickness, or ≈2 nm. These findings provide a detailed description of the suppression of the chain dynamics on the surface of NPs. These results are relevant insights on surface effects and confinement and provide a foundation for the understanding of the rheological properties of polymer nanocomposites with well-dispersed NPs.

Long-term autonomous time-keeping of navigation constellations based on sparse sampling LSTM algorithm

The system time of the four major navigation satellite systems is mainly maintained by multiple high-performance atomic clocks at ground stations. This operational mode relies heavily on the support of ground stations. To enhance the high-precision autonomous timing capability of next-generation navigation satellites, it is necessary to autonomously generate a comprehensive space-based time scale on orbit and make long-term, high-precision predictions for the clock error of this time scale. In order to solve these two problems, this paper proposed a two-level satellite timing system, and used multiple time-keeping node satellites to generate a more stable space-based time scale. Then this paper used the sparse sampling Long Short-Term Memory (LSTM) algorithm to improve the accuracy of clock error long-term prediction on space-based time scale. After simulation, at sampling times of 300 s, 8.64 × 104 s, and 1 × 106 s, the frequency stabilities of the spaceborne timescale reach 1.35 × 10–15, 3.37 × 10–16, and 2.81 × 10–16, respectively. When applying the improved clock error prediction algorithm, the ten-day prediction error is 3.16 × 10–10 s. Compared with those of the continuous sampling LSTM, Kalman filter, polynomial and quadratic polynomial models, the corresponding prediction accuracies are 1.72, 1.56, 1.83 and 1.36 times greater, respectively.

Interfacial polymerization for preparation of capsaicin microcapsules: Achieving sustained release in UV-curable coatings on plastic substrates

In this study, we incorporate capsaicin microcapsules into UV-curable coatings applied on PVC substrates to achieve sustained capsaicin release coatings. The microcapsules are synthesized using interfacial polycondensation, overcoming self-limitation nature of interfacial polycondensation through solvent evaporation and swelling effects, resulting in microcapsules with well-defined thick shells that can encapsulate capsaicin successfully. These capsaicin microcapsules exhibit enhanced thermal stability compared to pure capsaicin as revealed by thermogravimetry. When embedded into UV-curable coatings, the capsaicin microcapsules contribute to increased hardness, abrasion resistance, tensile strength with uncompromised adhesion strength. Cumulative releases in accelerated tests demonstrate that these coatings have the lowest release of 40% while pure capsaicin can reach cumulative release of 90% in the investigated time scale, indicating a coordinated regulation of capsaicin release by microcapsule shell. Importantly, these UV-curable coatings embedded with sustained release capsaicin microcapsules offer a promising solution for rodent repellency, particularly applicable to power cable surfaces in the power and telecommunication industries.

Climate–Growth Relationships of Mongolian Pine (Pinussylvestris var. mongholica) along an Altitudinal Gradient of Northeast China

To study the radial growth of Mongolian pine (Pinus sylvestris var. mongholica, MP) trees in response to climatic factors against the global warming background in the northeast part of the Greater Khingan Mountains (GKM), 101 tree cores were collected at contrasting altitudes (1100 and 650 m) in the Mordoga area, a tree-ring width chronology of MP was established for that region at both altitudes, and the relation between climatic factors and ring width trends at different time scales was investigated. The results revealed four major findings. (1) The ring width chronology of MP in the low-altitude area has better quality. (2) The growth of MP at high (low) altitude was mainly influenced by temperature (precipitation) factors. (3) Before a sudden change in temperature, there was a decreasing trend in the annual indices of MPs at higher altitudes. The chronological coefficients of MPs at both altitudes showed a significant upward trend after the increase in temperature. (4) The sliding analysis results showed that the stability of the relationship between MP growth and its response to climatic factors at both altitudes was also mostly similar. MP growth is relatively stable and sensitive and to climatic factors as temperatures increase.

Thermal decomposition and isomerization of 1-Butyl and 1-Pentyl radical by flash pyrolysis vacuum ultraviolet photoionization time-of-flight mass spectrometry.

Thermal decomposition and isomerization of 1-butyl and 1-pentyl radical were studied in the temperature range of 500-1480 K on a short time scale of 20-100 µs using flash pyrolysis vacuum ultraviolet single-photon ionization time-of-flight mass spectrometry. 1-Bromobutane and 1-bromopentane were used as precursors for the 1-butyl and 1-pentyl radical, respectively. The reactive intermediates in the thermal dissociation reactions were directly observed. The 1-butyl radical decomposed to ethene and ethyl radical with ethyl radical rapidly losing an H atom to form a second ethene molecule. Loss of H atom from butyl radical was also a significant decomposition channel. Isomerization of 1-butyl via 1,3-H migration was observed as a minor channel at 1380 K and above with a branching ratio of less than 3% at 1430 K. The 1-pentyl radical was observed to decompose mainly by isomerization to 2-pentyl radical followed by β-scission to produce propene and ethyl radicals at temperatures approximately 900 K and below. Above 900 K, β-scission of 1-pentyl to produce ethene and 1-propyl radical became increasingly important. Isomerization to 3-pentyl was verified to be a minor channel.

The LTAR-integrated grazing land common experiment at the Texas Gulf.

Extreme weather and climate events have become more frequent and directly affect the ecological structure and function of integrated grazing lands. While the Great Plains have experienced a long history of regular disturbances from drought and floods, grazing, and fires, the increased frequency and magnitude of these disturbances can reduce ecological resilience, largely depending on management practices. Alternative strategies designed to adaptively manage grazing land resources based on the ecology of the system should increase the resistance and resilience to disturbances when compared to prevailing practices. Determining the ecologic and economic value of alternative strategies will require long-term evaluations across large spatial scales. The Long-Term Agroecosystem Research Network has been established to evaluate the differences between alternative and prevailing practices among 18 strategically located sites and across decadal time scales throughout the continental United States. A key integrated grazing land site within this network is the Texas Gulf located at the Riesel Watersheds in the Blackland Prairie of Central Texas. At this study site, the differences between alternative and prevailing grazing management strategies are now being evaluated. The alternative strategy was designed using a combination of knowledge of the site and species ecology with modern-day tools and technologies. Alternatively, the prevailing practice implements a conventional year-round continuous grazing system with heavy reliance on hay and supplemental protein during winter. Results will provide grazing land managers with economically viable adaptive management choices for increasing ecological resilience following extreme and frequent disturbance events.

An Interpolation and Prediction Algorithm for XCO2 Based on Multi-Source Time Series Data

Carbon satellites are an important observation tool for analyzing ground carbon emission. From the perspective of the Earth’s scale, the spatiotemporal sparse characteristics of raw data observed from carbon satellite requires the accurate interpolation of data, and based on only this work, people predict future carbon emission trends and formulate appropriate management and conservation strategies. The existing research work has not fully considered the close correlation between data and seasons, as well as the characteristics accumulated over a long time scale. In this paper, firstly, by employing extreme random forests and auxiliary data, we reconstruct a daily average CO2 dataset at a resolution of 0.25°, and achieve a validated determination coefficient of 0.92. Secondly, introducing technologies such as Time Convolutional Networks (TCN), Channel Attention Mechanism (CAM), and Long Short-Term Memory networks (LSTM), we conduct atmospheric CO2 concentration interpolation and predictions. When conducting predictive analysis for the Yangtze River Delta region, we train the model by using quarterly data from 2016 to 2020; the correlation coefficient in summer is 0.94, and in winter it is 0.91. These experimental data indicate that compared to other algorithms, this algorithm has a significantly better performance.