Time Dependence Research Articles

An Analytical Model for Describing Transient Photocurrents in Bias‐Assisted Charge Extraction for Low‐Mobility Organic Solar Cells

Bias‐assisted charge extraction (BACE) is a powerful technique for measuring the carrier density in organic solar cells under operational conditions and to deduce information on the charge recombination properties. Hereby, the carrier density in the active layer device is determined by integrating the transient extraction current, while nongeminate recombination during the charge extraction processes is generally neglected. This assumption becomes questionable for low mobilities, for example, at low temperatures and in case of high energetic disorder. Herein, the extraction process in BACE measurement is investigated by incorporating both drift and bimolecular recombination of charges during charge extraction into a unified framework. An explicit analytical model is proposed to describe the time dependence of the transient photocurrent in BACE measurement and excellent agreement is found with drift‐diffusion simulations. It is shown that global fitting of transient photocurrents measured at various collection biases with this analytical model allows to determine the carrier density in the device with high accuracy. Furthermore, the analytical model is demonstrated to provide accurate mobilities for both the fast and slow carriers within the device, rendering it a valuable supplement to other mobility measurement techniques such as resistance‐dependent photovoltage and space–charge‐limited current.

Predicting the Color Polymorphism of ROY from a Time-Dependent Optimally Tuned Screened Range-Separated Hybrid Functional.

Polymorphism is a well-known property of molecular crystals, which allows the same molecule to form solids with several crystalline structures that can differ significantly in physical properties. Polymorphs that possess different optical absorption properties in the visible range may exhibit different perceived colors, a phenomenon known as color polymorphism. One striking example of color polymorphism is given by 5-methyl-2-[(2-nitrophenyl)amino]-3-thiophenecarbonitrile, known as ROY for its red-orange-yellow colors. First-principles prediction of color polymorphism may help in polymorph assignment and design but has proven to be challenging. Here, we predict the absorption spectra and simulate the colors of 12 ROY polymorphs using the general, nonempirical method of time-dependent (TD) optimally tuned screened range-separated hybrid (OT-SRSH) functional. For 5 ROY polymorphs with known experimental absorption spectra, we show that the TD-OT-SRSH approach predicts absorption spectra in quantitative agreement with experiment. For all polymorphs, we show that an accurate simulation of the colors is obtained, paving the way to a fully predictive, low-cost calculation of color polymorphism.

Predicting radiation-induced immune suppression in lung cancer patients treated with stereotactic body radiation therapy.

Stereotactic body radiation therapy (SBRT) is known to modulate the immune system and contribute to the generation of anti-tumor T cells and stimulate T cell infiltration into tumors. Radiation-induced immune suppression (RIIS) is a side effect of radiation therapy that can decrease immunological function by killing naive T cells as well as SBRT-induced newly created effector T cells, suppressing the immune response to tumors and increasing susceptibility to infections. RIIS varies substantially among patients and it is currently unclear what drives this variability. Models that can accurately predict RIIS in near real time based on treatment plan characteristics would allow treatment planners to maintain current protocol specific dosimetric criteria while minimizing immune suppression. In this paper, we present an algorithm to predict RIIS based on a model of circulating blood using early stage lung cancer patients treated with SBRT. This Python-based algorithm uses DICOM data for radiation therapy treatment plans, dose maps, patient CT data sets, and organ delineations to stochastically simulate blood flow and predict the doses absorbed by circulating lymphocytes. These absorbed doses are used to predict the fraction of lymphocytes killed by a given treatment plan. Finally, the time dependence of absolute lymphocyte count (ALC) following SBRT is modeled using longitudinal blood data up to a year after treatment. This model was developed and evaluated on a cohort of 64 patients with 10-fold cross validation. Our algorithm predicted post-treatment ALC with an average error of cells/L with 89% of the patients having a prediction error below 0.5×109 cells/L. The accuracy was consistent across a wide range of clinical and treatment variables. Our model is able to predict post-treatment ALC<0.8 (grade 2 lymphopenia), with a sensitivity of 81% and a specificity of 98%. This model has a ∼38-s end-to-end prediction time of post treatment ALC. Our model performed well in predicting RIIS in patients treated using lung SBRT. With near-real time model prediction time, it has the capability to be interfaced with treatment planning systems to prospectively reduce immune cell toxicity while maintaining national SBRT conformity and plan quality criteria.

The small tumor antigen of Merkel cell polyomavirus accomplishes cellular transformation by uniquely localizing to the nucleus despite the absence of a known nuclear localization signal

BackgroundMerkel Cell Carcinoma (MCC) is an aggressive skin cancer that is three times deadlier than melanoma. In 2008, it was found that 80% of MCC cases are caused by the genomic integration of a novel polyomavirus, Merkel Cell Polyomavirus (MCPyV), and the expression of its small and truncated large tumor antigens (ST and LT-t, respectively). MCPyV belongs to a family of human polyomaviruses; however, it is the only one with a clear association to cancer.MethodsTo investigate the role and mechanisms of various polyomavirus tumor antigens in cellular transformation, Rat-2 and 293A cells were transduced with pLENTI MCPyV LT-t, MCPyV ST, TSPyV ST, HPyV7 ST, or empty pLENTI and assessed through multiple transformation assays, and subcellular fractionations. One-way ANOVA tests were used to assess statistical significance.ResultsSoft agar, proliferation, doubling time, glucose uptake, and serum dependence assays confirmed ST to be the dominant transforming protein of MCPyV. Furthermore, it was found that MCPyV ST is uniquely transforming, as the ST antigens of other non-oncogenic human polyomaviruses such as Trichodysplasia Spinulosa-Associated Polyomavirus (TSPyV) and Human Polyomavirus 7 (HPyV7) were not transforming when similarly assessed. Identification of structural dissimilarities between transforming and non-transforming tumor antigens revealed that the uniquely transforming domain(s) of MCPyV ST are likely located within the structurally dissimilar loops of the MCPyV ST unique region. Of all known MCPyV ST cellular interactors, 62% are exclusively or transiently nuclear, suggesting that MCPyV ST localizes to the nucleus despite the absence of a canonical nuclear localization signal. Indeed, subcellular fractionations confirmed that MCPyV ST could achieve nuclear localization through a currently unknown, regulated mechanism independent of its small size, as HPyV7 and TSPyV ST proteins were incapable of nuclear translocation. Although nuclear localization was found to be important for several transforming properties of MCPyV ST, some properties were also performed by a cytoplasmic sequestered MCPyV ST, suggesting that MCPyV ST may perform different transforming functions in individual subcellular compartments.ConclusionsTogether, these data further elucidate the unique differences between MCPyV ST and other polyomavirus ST proteins necessary to understand MCPyV as the only known human oncogenic polyomavirus.

Nomogram for Predicting Survival in Locally Advanced Cervical Cancer with Concurrent Chemoradiotherapy plus or Not Adjuvant Chemotherapy: A Retrospective Analysis Based on 2018 FIGO Staging.

Background: The comprehensive treatment mode of combining concurrent chemoradiotherapy (CCRT) with adjuvant chemotherapy (AC) is a commonly used mainstream model in the clinical practice of locally advanced cervical cancer (LACC). However, the necessity for AC after CCRT lacks sufficient evidence-based medical support. This study constructs a predictive model for the survival time dependence of CCRT ± AC for LACC based on the 2018 International Federation of Gynecology and Obstetrics (FIGO) staging with internal validation, the prognosis was assessed with intensity-modulated radiotherapy (IMRT) and concurrent cisplatin, and provides guidance for future stratified treatment. Materials and Methods: The retrospective analysis included 482 patients with LACC who CCRT from January 2016 to January 2023. Patients who used the 2009 FIGO staging were all standardized for the 2018 FIGO staging. The 482 patients with LACC were divided into a training set (n = 290) and a validation set (n = 192) at a ratio of 6:4. COX multivariate regression model and LASSO regression were used to screen for independent prognostic factors affecting progression-free survival (PFS) and overall survival (OS), and a nomogram clinical prediction model was constructed based on these factors. Evaluate the effectiveness of the model through the receiver operating characteristic curve, calibration curve, decision curve, risk heat map, and survival curves for risk stratification. Results: The PFS and OS independent prognostic risk factors affecting the 2018 FIGO staging of LACC during CCRT were validated to be similar to the 2009 FIGO staging prediction model reported in previous literature. In the training cohort, area under the curve (AUC) values at 1, 3, and 5 years were 0.941, 0.882, and 0.885 for PFS, and 0.946, 0.946, and 0.969 for OS, respectively. When applied to a test cohort, the model also showed accurate prediction result (AUC at 1, 3, and 5 years were 0.869, 0.891, and 0.899 for PFS, and 0.891, 0.941 and 0.878 for OS, respectively). Subgroup analysis suggests that patients with LACC, adenocarcinoma, stage IVA, pelvic lymph node metastasis, pretreatment hemoglobin ≤100 g/l and residual tumor diameter >2 cm, who received CCRT in the 2018 FIGO stage, may benefit more from adjuvant chemtherapy. Conclusions: Based on the 2018 FIGO staging, a nomogram prediction model for PFS and OS in patients with LACC undergoing CCRT was developed. The model, established by combining weighted clinical and pathological factors, can provide more personalized treatment predictions in clinical practice. For patients with high-risk factors such as residual tumor diameter > 2 cm after CCRT for LACC, AC may bring benefits.

Time dependency for HPV ctDNA detection as a prognostic biomarker for anal cancer.

3513 Background: Prior HPV infection is associated with &gt; 90% of anal cancers, a malignancy with rising incidence in the United States. Standard treatment for localized anal cancer is concurrent chemoradiation (cRT). While detection of circulating tumor DNA (ctDNA) within weeks after surgery is linked to recurrence in other solid tumors, the optimal time point for ctDNA detection as a prognostic biomarker following cRT for HPV-associated anogenital cancers is not well characterized. We utilized a novel, highly sensitive HPV ctDNA assay to evaluate clinical outcomes according to HPV ctDNA status among patients with localized anal cancer treated with cRT. Methods: ctDNA was isolated from patients with stages I-III anal cancer treated at our institution with cRT prior to treatment, at the end of treatment (week 5), and at months 3, 6, 9, and 12 after treatment under an IRB-approved protocol. A droplet digital HPV ctDNA PCR assay evaluating HPV E6 and E7 oncogenes for 13 oncogenic HPV types was utilized for quantification of HPV ctDNA. A limit of quantification at 16 HPV copies/mL plasma was set for “HPV ctDNA detection.” Median recurrence-free survival (RFS) according to HPV ctDNA status was estimated via Kaplan-Meier and compared using a log-rank test. Associations between selected clinical factors and recurrence were evaluated with a Chi-square test, with a one-sided p &lt; .05 considered significant. Results: Following cRT, HPV ctDNA was detected in 9/41 (22%), 4/30 (13%), 2/20 (10%), and 0/12 (0%) patients at week 5 and months 3, 6, and 12, respectively. Detection of HPV ctDNA 3 months after cRT was associated with clinical recurrence (100% versus 8%; odds ratio 88, 95% CI 4-2000; p = .006) and inferior RFS (5.9 months vs not reached (NR); hazard ratio (HR) 24, 95% CI 1.2-475; p &lt; .001) relative to HPV ctDNA-negative status. Differences in RFS according to HPV ctDNA status were not observed at week 5/end of treatment (median RFS NR for both; HR 2.4, 95% CI .5-10; p = .15). At month 3, sensitivity and specificity for recurrence according to HPV ctDNA detection were 57% and 100%, respectively, with a PPV and NPV of 100% and 89%, respectively. Baseline clinical stage, T stage, N stage, age, and gender were not associated with clinical recurrence after cRT (p &gt; .25 for all). Conclusions: With a novel, highly sensitive assay, detection of HPV ctDNA at 3 months after cRT was associated with RFS. Further, an HPV ctDNA-positive status outperformed baseline clinical features for prognosticating anal cancer recurrence after cRT in this retrospective series. Future clinical trials should incorporate the 3-month post-treatment time point for identification of patients with HPV-positive anal cancer at elevated risk for recurrence according to HPV ctDNA status. A clinical trial evaluating anti-PD-L1 + anti-TIGIT immunotherapy for patients with HPV-associated cancers with detected HPV ctDNA after cRT is forthcoming at our institution.

Quantum versus classical quenches and the broadening of wave packets

The time dependence of one-dimensional quantum mechanical probability densities is presented when the potential in which a particle moves is suddenly changed, called a quench. Quantum quenches are mainly addressed, but a comparison with results for the dynamics in the framework of classical statistical mechanics is useful. Analytical results are presented when the initial and final potentials are harmonic oscillators. When the final potential vanishes, the problem reduces to the broadening of wave packets. A simple introduction to the concept of the Wigner function is presented, which allows a better understanding of the dynamics of general wave packets. It is pointed out how special the broadening of Gaussian wave packets is, the only example usually presented in quantum mechanics textbooks.

Time Dependence Between Tobacco Consumption and Lung Cancer Mortality in Spain

ObjectivesLung cancer is the leading cause of cancer death and the second most common cancer in both sexes worldwide, with tobacco being its main risk factor. The aim of this study is to establish the temporal relationship between smoking prevalence and lung cancer mortality in Spain. MethodsTo model the time dependence between smoking prevalence and lung cancer mortality, a distributed lag non-linear model was applied adjusting for sex, age, year of mortality and population at risk. Smoking prevalence data from 1991-2020 were used. Considering a maximum lag of 25 years, mortality data from 2016-2020 were included. The effect of prevalence on mortality for each lag is presented in terms of relative risk (RR). To identify the lag at which smoking prevalence has the greatest effect on mortality, the RR of the different lags were compared. ResultsThe optimal lag observed between smoking prevalence and lung cancer mortality in Spain was 15 years. The maximum RR was 2.9 (95%CI: 2.0-4.3) for a prevalence of 71% and a 15-year lag. The RR was 1.8 for a prevalence of 33%, an approximate median value between 1991-2020, and a 15-year lag. ConclusionsIn Spain, lung cancer mortality is affected by smoking prevalence 15 years prior. Knowing the evolution of the smoking prevalence series in a country and establishing a lag time is essential to predict how lung cancer incidence and mortality will evolve.

The Influence of N-Acetyl-selenomethionine on Two RONS-Generating Cancer Cell Lines Compared to N-Acetyl-methionine.

Jurkat and MTC-SK cells were cultured in the absence or presence of varying concentrations (0-500 µg/mL) of NASeLM and NALM solutions. After 0, 24, 48, and 72 h, mitochondrial activity, cancer cell membrane CP levels, cell growth, and caspase-3 activity were assessed in aliquots of Jurkat and MTC-SK cells. Both substances, NASeLM and NALM, were similarly able to inhibit cell growth and mitochondrial activity of Jurkat cells in a concentration-dependent and time-dependent manner up to 70%. Only the determination of caspase activity showed that only NASeLM was able to increase this to almost 40% compared to the control as well as the same lack of NALM. However, the experiments on MTC-SK cells showed a clear difference in favor of NASeLM compared to NALM. While NASeLM was able to reduce cell growth to up to 55%, the same amount of NALM was only at around 15%, which turned out to be highly significant (p < 0.001). The same could also be measured for the reduction in MTC-SK mitochondrial activity. Time dependence could also be recognized: the longer both substances, NASeLM and NALM, were incubated, the higher the effect on cell growth and mitochondrial activity, in favour of NASeLM. Only NASeLM was able to increase caspase-3 activity in MTC-SK cells: at 250 µg/mL NASeLM, caspase-3 activity increased significantly to 28% after 24 and 48 h compared to the control (14%) or the same NALM concentration (14%). After 72 h, this could still increase to 37%. A further increase in the NASeLM concentration did not result in higher caspase-3 activity. NASeLM could clearly increase caspase-3 activity in both cell types, Jurkat or MTC-SK cells, and thus induce cell death. NALM and NASeLM showed a reduction in cell growth and mitochondrial activity in both cell lines: While NALM and NASeLM showed almost identical measurements on Jurkat cells, NASeLM was much more effective on MTC-SK than the non-selenium-containing carrier, indicating that it has additional anti-chemoprotective effects.

Optimization of heat management and utilization in batch processes

Batch plants boast operational flexibility in manufacturing process and resource sharing, making batch processes suitable to fluctuating-demand or small-scale production. However, given time dependence of operational behavior, batch processes are more demanding in energy integration than continuous processes, a task which is especially important amid the global net zero-emission current. It is, therefore, imperative to develop sustainable design strategies for intermittent processes. The study introduced a targeting method and developed an innovative over-time heat integration technique to optimize energy utilization in batch processes, involving use of heat transfer media for heat recovery over various time intervals and a heat makeup strategy to heat the media over different periods, in order to enhance heat-energy utilization efficiency. The technique is applicable to both fixed and unspecified batch schedules, the latter of which involves simultaneous design of production schedule and heat recovery systems, increasing flexibility and improving heat energy utilization. Case studies are conducted to demonstrate the proposed method. With the heat makeup technique, energy efficiency can be significantly enhanced, achieving energy savings of 51% and 93.1% in hot and cold utility, respectively. Meanwhile, the simultaneous design of production schedule and heat integration can boost savings up to 53% and 99%. Results have proven the benefits of the technique, including significant reduction of energy consumption and carbon emission, contributing to clean production, environmental protection, and sustainable development.

Unsupervised Anomaly Detection for Power Batteries: A Temporal Convolution Autoencoder Framework

Abstract To prevent potential abnormalities from escalating into critical faults, a rapid and precise algorithm should be employed for detecting power battery anomalies. An unsupervised model based on a temporal convolutional autoencoder was proposed. It can quickly and accurately identify abnormal power battery data. Its encoder utilized a temporal convolutional network (TCN) structure with residuals to parallelly process data while capturing time dependencies. A novel TCN structure with an effect–cause relationship was developed for the decoder. The same-timescale connection was established between the encoder and decoder to improve the model performance. The validity of the proposed model was confirmed using a real-world car dataset. Compared with the GRU-AE model, the proposed approach reduced the parameter count and mean square error by 19.5% and 71.9%, respectively. This study provides insights into the intelligent battery pack abnormality detection technology.

Pressure propagation mechanism of open-cell aluminium foam within a pipe under hypervelocity impact

Low-density open-cell aluminium (Al) foam exhibits a different deformation mode under hypervelocity impacts compared to being subjected to medium-low speed and quasi-static compression. In this case, material flows between cells in the form of metal jets and propagates forward. This paper establishes the open-cell Al foam model based on Voronoi tessellation. Using numerical methods, one-dimensional hypervelocity constant velocity impact experiments of 1–6 km/s inside a closed pipe are conducted on Al foam with different internal structure parameters (average cell diameter: 2–3.5 mm; ligament width: 0.251–1 mm). Propagation mechanism of pressure waves in foams and the key factors affecting the propagation velocity are investigated. Three zones are divided according to the condition of jet distribution along the impact direction: unaffected zone, jet influence zone, and particle accumulation zone. The variation of the length of different zones with impact velocity is discussed: higher velocities result in longer jet influence zones but shorter particle accumulation zones. Meanwhile, length of zones is closely related to the width of Al foam ligament and cell pore diameter: the larger the diameter of cell pores and the smaller the width of ligaments, the longer the length of the jet influence zone. Time dependence is exhibited in the length of jet influence zone for the large cell pore diameter specimens(≥0.03 cm). Particle pressures are extracted using the one-dimensional averaging method, which propagates forward significantly lower than in dense materials. Dominant factors affecting the pressure wave propagation of open-cell Al foam, i.e., the internal unloading space, are obtained by combining two-dimensional hypervelocity impact simulation experiments with different boundary conditions. A strong correlation between metal jet distribution and particle pressure propagation is observed.

Succession changes of microbial community for inferring the time since deposition of saliva.

Saliva is a common biological examination material at crime scenes and has high application value in forensic case investigations. It can reflect the suspect's time of crime at the scene and provide evidence of the suspect's criminal facts. Even though many researchers have proposed their experimental protocols for estimating the time since deposition (TsD) of saliva, there is still a relative lack of research on the use of microorganisms to estimate TsD. In the current study, the succession change of microbial community in saliva with different TsD values was explored to discern the microbial markers related to TsD of saliva. We gathered saliva samples from six unrelated healthy Han individuals living in Guizhou, China and exposed these samples to indoor conditions at six time points (0, 1, 3, 7, 15, and 28 days). Temporal changes of microbial compositions in these samples were investigated by 16S rRNA sequencing (V3-V4 regions). By assessing temporal variation patterns of microbial abundance at the genus level, four bacteria (Brucella, Prevotella, Pseudomonas, and Fusobacterium) were observed to show good time dependence in these samples. In addition, the hierarchical clustering and principal co-ordinates analysis results revealed that these saliva samples could be classified into t-short (≤7 days) and t-long (>7 days) groups. In the end, the random forest model was developed to predict the TsD of these samples. For the model, the root mean square error, R2, and mean absolute error between predicted and actual TsD values were 1.5213, 0.9851, and 1.1969, respectively. To sum up, we identified TsD-related microbial markers in saliva samples, which could be viewed as valuable markers for inferring the TsD of saliva.

Ferroelectric and antiferroelectric chiral multilactate liquid crystalline materials with negative dielectric anisotropy

In this work, the mesomorphic, electro-optic and dielectric properties have been discussed in the light of molecular structure-property correlations of two chiral multilactate liquid crystalline materials possessing the orthogonal paraelectric Smectic A* phase, tilted ferroelectric Smectic C* phase and the tilted antiferroelectric Smectic CA* phase, over a substantially broad temperature range. Interestingly, a reasonably rare re-entrant Smectic C* phase (SmCre*) has also been identified in one of the investigated materials. These materials differ in their linkage groups (keto or ether) and an additional chiral unit in the terminal chain. The phase transition temperatures and transition enthalpies were determined from Polarizing Optical Microscopy and Differential Scanning Calorimetry (DSC) measurements. The compounds exhibit negative dielectric anisotropy (Δε) throughout the mesomorphic range (maximum −18 and −4 in SmA* for both the compounds) and moderately high values of spontaneous polarization (∼153 nC/cm2 in SmCre* phase). The temperature dependence of the response time (τ), bulk viscosity (η) and the activation energies (Ea) throughout the mesomorphic phase have been determined from the spontaneous polarization measurements. To emphasize the structure-property correlations in more detail, dielectric spectroscopy measurement has also been performed to measure the dielectric strength, dielectric loss, frequency dependent permittivities, relaxation time and relaxation frequencies. The clear evidence of the relatively rare SmCre* phase has also been confirmed from the temperature and frequency dependence of the dielectric permittivity. These results shed important light on the emergence of these materials as a smart alternative for their application in multicomponent mixtures targeted for advanced electro-optic and photonic devices.

Wall modes and the transition to bulk convection in rotating Rayleigh-Bénard convection

We investigate states of rapidly rotating Rayleigh-Bénard convection in a cylindrical cell over a range of Rayleigh numbers 3×105≤Ra≤5×109 and Ekman numbers 10−6≤Ek≤10−4 for Prandtl number Pr=0.8 and aspect ratios 1/5≤Γ≤5 using direct numerical simulations. We characterize, for perfectly insulating sidewall boundary conditions, the first transition to convection via wall mode instability and the nonlinear growth and instability of the resulting wall mode states, including a secondary transition to time dependence. We show how the radial structure of the vertical velocity uz and the temperature T is captured well by the linear eigenfunctions of the wall mode instability where the radial width of uz is δuz∼Ek1/3r/H whereas δT∼e−kr (k is the wave number of a laterally infinite wall mode state). The disparity in spatial scales for Ek=10−6 means that the heat transport is dominated by the radial structure of uz since T varies slowly over the radial scale δuz. We further describe how the transition to a state of bulk convection is influenced by the presence of the wall mode states. We use temporal and spatial scales as measures of the local state of convection and the Nusselt number Nu as representative of global transport. Our results elucidate the evolution of the wall state of rotating convection and confirm that wall modes are strongly linked with the boundary zonal flow being the robust remnant of nonlinear wall mode states. We also show how the heat transport (Nu) contributions of wall modes and bulk modes are related and discuss approaches to disentangling their relative contributions. Published by the American Physical Society 2024

The Arsenal of Perturbation Bounds for Finite Continuous-Time Markov Chains: A Perspective

Perturbation bounds are powerful tools for investigating the phenomenon of insensitivity to perturbations, also referred to as stability, for stochastic and deterministic systems. This perspective article presents a focused account of some of the main concepts and results in inequality-based perturbation theory for finite state-space, time-homogeneous, continuous-time Markov chains. The diversity of perturbation bounds and the logical relationships between them highlight the essential stability properties and factors for this class of stochastic processes. We discuss the linear time dependence of general perturbation bounds for Markov chains, as well as time-independent (i.e., time-uniform) perturbation bounds for chains whose stationary distribution is unique. Moreover, we prove some new results characterizing the absolute and relative tightness of time-uniform perturbation bounds. Specifically, we show that, in some of them, an equality is achieved. Furthermore, we analytically compare two types of time-uniform bounds known from the literature. Possibilities for generalizing Markov-chain stability results, as well as connections with stability analysis for other systems and processes, are also discussed.

Investigation of the Strain–Stress Field in Nanoscale Multilayer Systems by the Phase Plane Method

This paper presents the results of the study of stress relaxation fields, deformation, and temperature of the system of nanostructured multilayer coatings. In the work, a nonlinear relationship between strain and stress was used to take into account nonlinear effects in the mechanism of nanostructure formation. The paper assumes that a friction surface is provided by the self-organization of shear components: both stress and strain on the one hand, and temperature on the other. The studied objects are described in the adiabatic approximation, taking into account the fact of the evolution of stresses and strains. With the help of phase portraits of the system, the dependence of the deformation processes on the stresses arising in the system without coating and with coating is shown. It is shown that the rate of change of deformation depends on the characteristics of the mechanical impact on the coating and on the amount of stress and deformation. A conclusion is drawn regarding the transition process in the presence of two regions (Hooke and plastic deformation) in the corresponding phase portrait of the strain–stress field of the system. The results of the work can be used to determine the effective parameters of a coating in the analysis of experimental time dependences of stresses.

Can Digital Finance Enable China’s Industrial Carbon Unlocking under Environmental Regulatory Constraints? Joint Tests of Regression Analysis and Qualitative Comparative Analysis

Sustainable development goals challenge the carbon lock-in dilemma of the industrial economy, and identifying the motivation and mechanism behind carbon unlocking has become an urgent priority. With its inclusive and precise advantages, digital finance (DF) provides a new impetus for the economy’s low-carbon transformation, while reasonable environmental regulation (ER) acts as an important guiding constraint. We focus on the carbon unlocking performance of DF under ER constraints. After constructing and calculating the industrial carbon unlocking efficiency (ICUE), we observe the trends of ICUE fluctuating positively, clustering towards the eastern region, and polarization. Subsequently, based on theoretical analyses, we explore the marginal and configuration effects of DF and ER in improving ICUE using panel data from 30 Chinese provinces between 2011 and 2021 and adopt a mixed research method with regression analysis (Tobit hierarchical regression and quantile regression for panel data (QRPD)) and dynamic fuzzy-set qualitative comparative analysis (fsQCA). The regression analysis results show that DF can notably enhance China’s provincial ICUE, with ER generally serving as a positive moderator; however, the unlocking potential of informal environmental regulations needs further exploration. As ICUE improves in a specific location or time, the positive contribution of DF to ICUE also increases, whereas the moderating effect of ER exhibits an optimal range and follows an inverted U-shape. The dynamic fsQCA results support the findings of the regression analysis and further emphasize that effective cooperation between DF and ER is crucial for high ICUE, while inadequate DF support and the absence of formal environmental regulations remain bottlenecks in industrial carbon lock-in. Moreover, configuration paths demonstrate clear path dependency in both time and space, indicating a prolonged unlocking endeavor.

Engineering complete delocalization of single particle states in a class of one dimensional aperiodic lattices: A quantum dynamical study

We study quantum dynamics of a wave packet on a class of one dimensional decorated aperiodic lattices, described within a tight binding formalism. We look for the possibility of finding extended single particle states even in the absence of any translational periodicity. The chosen lattices are stubbed with one or more atoms, tunnel coupled to the backbone, thereby introducing a minimal quasi-one dimensionality. It is seen that, for a group of such lattices a certain correlation between the numerical values of the hopping amplitudes leads to a complete delocalization of single particle states. In some other cases, a special value of a magnetic flux trapped in the loops present in the geometries delocalize the states, leading to a flux driven insulator to metal transition. The mean square displacement, temporal autocorrelation function, the time dependence of the inverse participation ratio, or the information entropy – the so-called hallmarks of studying localization based on dynamics – all of them indicate such a complete turnover in the nature of the single particle states and the character of the energy spectrum under suitable conditions. The results shown in this work using quasiperiodic lattices of the Fibonacci family are much more general and hold good even for a randomly disordered arrangement of the building blocks of the systems considered, and indicate a subtle universality class under which these lattices can be grouped.

Discrete element modelling of the elastic-plastic and viscoelastic properties of a lithium-ion battery electrode layer

Mechanical degradation mechanisms are one of the leading causes of charge capacity loss in lithium-ion batteries. This study further develops a discrete element method (DEM) simulation framework, which investigates how the local contact behaviour affect the global mechanical properties of the active layer. The local microstructure consists of active particles held together by a binder domain, making up a granular medium. This study investigates the impact of the layer's global properties from the type of particle contact model. Experiments were also performed to measure size distribution and the material properties of the active material. The time dependency of the active layer, stemming from the viscoelastic binder domain, was studied in relaxation simulations, which were based on experimental measurements.