Temporal Delay Research Articles

Delay discounting validity and e-cigarette use: A comparison in e-cigarette users, combustible cigarette users, dual users, and nonusers.

Delay discounting refers to the devaluation of an outcome as temporal delay increases. Steep discounting is characterized by preferring a smaller, immediate outcome over a larger, delayed outcome and is associated with maladaptive behaviors such as tobacco use. Previous studies have compared delay discounting outcomes between combustible cigarette (CC) smokers and nonusers using various discounting tasks. With the growing use of electronic cigarettes (e-cigarettes [EC]) and various delay discounting tasks available to researchers, we extended previous work in delay discounting and EC use in two ways. The present study assessed delay discounting in a web-based sample of 259 participants to (a) establish convergent validity across four different delay discounting tasks and (b) compare the outcomes between four subgroups: dual users, exclusive EC users, exclusive CC users, and nonusers. The four delay discounting tasks (Monetary Choice Questionnaire, 5-Trial Adjusting Delay Discounting Task [ADT-5], Temporal Discounting Questionnaire, and Brief Intertemporal Choice Task [BRIC Task]) showed moderate to strong convergent validity (p < .001). Further, findings indicated significant differences between all four subgroups across the four different delay discounting tasks (p < .048) with small effect sizes. Pairwise comparisons showed that exclusive EC users exhibited significantly steeper discounting than nonusers in ADT-5 (p = .043) and BRIC Task (p = .029) and dual users exhibited significantly steeper discounting than nonusers on ADT-5 (p = .043) and BRIC Task (p = .030). Our findings replicate previous findings and suggest the potential role of delay discounting in explaining the behavioral mechanism underlying e-cigarette use. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Intelligent Predictive Networks for Cattaneo-Christov Heat and Mass Transfer Dissipated Williamson Fluid Through Double Stratification

This study aims to develop an efficient predictive model for Cattaneo-Christov heat and mass transformation of dissipative Williamson fluid with the effects of double stratification (CCHMT-DWF-DS) using the Levenberg-Marquardt Backpropagation (LMA-BP) algorithm. The under-consideration Williamson fluid flow is magneto-hydro-dynamic, incompressible and two-dimensional through a stretching sheet. The mathematical model of nonlinear partial differential equations for physical phenomena is transformed into ordinary differential equations by means of renowned similarity transformations. The solutions of physical problem are computed by bvp4c technique through MATLAB. The LMA-BP is employed to train a backward neural network capable of accurately predicting velocity, temperature, and concentration profiles under various physical conditions such as changes in the Hartmann number , Prandtl number , Schmidt number , Williamson parameter , the relaxation time of temperature , the relaxation time of concentration , temperature stratification , and concentration stratification for generating a variety of graphical outcomes and statistics. This research is significant for its innovative use of the LMA-BP in analyzing the complex dynamics of non-Newtonian fluids specifically the Williamson fluid, alongside the Cattaneo-Christov heat and mass flux model. The obtaining graphs have been discussed in detail. The thermal and solutal relaxation factors reduce heat and mass flow while fluid motion is delayed by the time-dependent parameter and further reduced by the Hartman number. The Cattaneo-Christov heat flux model enhances simulation accuracy by integrating temporal delays in heat transfer, proving beneficial for sophisticated industrial and scientific endeavors related to non-Newtonian fluids. This analysis offers a powerful predictive tool for applications in thermal management, industrial cooling systems, and biomedical fluid dynamics, advancing machine learning in fluid mechanics.

The Non-Perturbative Approach in Examining the Motion of a Simple Pendulum Associated with a Rolling Wheel with a Time-Delay

The present study aims to examine the movement of a simple pendulum (SP) that is connected by a lightweight spring and connected with a rotating wheel. The motivation behind this topic is to gain a comprehensive understanding of intricate dynamic systems that involve interconnected mechanical components and feedback with temporal delays. This system is not only theoretically attractive but also practically applicable in domains such as robotics, engineering, and control systems. As well known, all classical perturbation methods exploit Taylor expansion to simplify the reality of restoring forces. In contrast, the non-perturbative approach (NPA), as a novel methodology, transforms any nonlinear ordinary differential equation (ODE) into a linear one. It scrutinizes the restoring forces, away from employing Taylor expansion; hence it eliminates the previous weakness. The concept of the NPA is based mainly on the He’s frequency formula (HFF). The confidence of the NPA comes from the numerical compatibility between the nonlinear and linear ODEs via the Mathematica Software (MS). Therefore, instead of handling the nonlinear ODE, we investigate the linear one. The achieved response is plotted over time to show the impact of the acted parameters during a specified time interval. Moreover, the phase plane curves that correspond to the plotted solution are presented and examined. The stability criteria of the analogous linear ODE are provided and drawn to explore the stability/instability zones. The performance is applicable in engineering and other fields due to its ease of adaptation to different nonlinear systems. Therefore, the NPA can be regarded as substantial, successful, and interesting and can extended to be applied in further categories within the field of couples dynamical systems.

The Body Knows Better: Sensorimotor signals reveal the interplay between implicit and explicit Sense of Agency in the human mind

Sense of Agency (SoA) is the feeling of control over our actions. SoA has been suggested to arise from both implicit sensorimotor integration as well as higher-level decision processes. SoA is typically measured by collecting participants' subjective judgments, conflating both implicit and explicit processing. Consequently, the interplay between implicit sensorimotor processing and explicit agency judgments is not well understood. Here, we evaluated in one exploratory and one preregistered experiment (N = 60), using a machine learning approach, the relation between a well-known mechanism of implicit sensorimotor adaptation and explicit SoA judgments. Specifically, we examined whether subjective judgments of SoA and sensorimotor conflicts could be inferred from hand kinematics in a sensorimotor task using a virtual hand (VH). In both experiments participants performed a hand movement and viewed a virtual hand making a movement that could either be synchronous with their action or include a parametric temporal delay. After each movement, participants judged whether their actual movement was congruent with the movement they observed. Our results demonstrated that sensorimotor conflicts could be inferred from implicit motor kinematics on a trial by trial basis. Moreover, detection of sensorimotor conflicts from machine learning models of kinematic data provided more accurate classification of sensorimotor congruence than participants' explicit judgments. These results were replicated in a second, preregistered, experiment. These findings show evidence of diverging implicit and explicit processing for SoA and suggest that the brain holds high-quality information on sensorimotor conflicts that is not fully utilized in the inference of conscious agency.

Leverage Effect of New-Built Green Spaces on Housing Prices in a Rapidly Urbanizing Chinese City: Regional Disparities, Impact Periodicity, and Park Size

While newly built urban green spaces aim to address environmental concerns, the resulting green gentrification and social inequality caused by escalating property values have become critical topics of urban socio-spatial research. To prevent green initiatives from becoming unaffordable for their intended beneficiaries in rapidly urbanizing cities, it is essential to examine the spatial and temporal relationships between the construction of new green spaces and rising housing prices. This study employs a difference-in-differences methodology to analyze regional disparities, impact periodicity, and the influence of park size on housing prices, using Nanjing, China as a case study. This result reveals that the introduction of new-built parks in Nanjing significantly impacts housing prices within an 800 m radius. The premium effect of these parks is substantially higher in urban core areas compared to suburban locales, demonstrating spatial differentials. Suburban parks temporally exhibit a prolonged lag and a shorter premium impact duration. Moreover, among various park areas, medium-sized parks demonstrate the most pronounced leverage effect, approximately double that of large parks, while small parks do not significantly affect housing prices. To mitigate the exacerbation of premium effects and enhance social justice in green strategies, we advocate prioritizing the development of small parks, particularly in urban core areas, and leveraging the temporal delay in new-built park impacts for urban policy interventions.

Design of an Efficient QOS Aware Trust-Based Security Model with Bioinspired Sidechains for Healthcare Deployments

Increased adoption of blockchain technology in healthcare systems has paved the way for transparent and secure data management. However, the inherent difficulties of scalability, performance, and security in blockchain networks necessitate the development of efficient models to ensure Quality of Service (QoS) and reliability. In this paper, we propose a novel QoS-aware trust-based security model for healthcare blockchain deployments that addresses these challenges by taking temporal energy consumption, temporal delay, temporal throughput, and temporal Packet Delivery Ratio (PDR) levels into account when selecting miner nodes. For efficient miner node selection, our model employs trust-based analysis. While for the formation of sidechains an efficient Grey Wolf Optimizer (GWO) Model is used, which is a metaheuristic technique inspired by hunting behaviour of Wolves in real-time scenarios. Effectively balancing the trade-off between exploration and exploitation, the GWO algorithm enables the selection of optimal miner nodes that meet the desired QoS requirements. By incorporating temporal metrics, our model adapts dynamically to changing network conditions, ensuring optimal resource utilization and enhanced network performance levels. To assess the efficacy of our proposed model, we ran extensive simulations and compared its performance to that of existing sidechaining models. The outcomes demonstrate significant enhancements in multiple aspects. In comparison to state-of-the-art sidechaining models, our model achieves an impressive 8.5% reduction in delay, 3.9% reduction in energy consumption, 4.5% increase in throughput, and 2.5% improvement in PDR levels. These enhancements demonstrate the efficacy and efficiency of our healthcare blockchain deployment models. The proposed model has applications in a variety of real-time healthcare scenarios. It can be used in electronic health record (EHR) systems where data integrity, confidentiality, and accessibility are crucial. By ensuring QoS-aware miner node selection, our model contributes to dependable and efficient data management, allowing for streamlined access to patient records while maintaining the necessary security standards. Moreover, the trust-based approach of our model improves the overall security of healthcare blockchain deployments. Our model reduces the risks associated with malicious or compromised miner nodes by incorporating trustworthiness metrics such as reputation and behavior analysis. This is especially important in the healthcare industry, where the sensitive nature of patient data necessitates stringent security measures.

Comprehensive analysis of wave interactions and resulting spall damage in layered heterogeneous medium under impact

The effect of dynamic spall damage on the response of layered medium subjected to one-dimensional impact involving an impactor and a multi-layered target is investigated. The study examines the damage caused by elastic waves below the Hugoniot Elastic Limit (HEL) during impact events. Analytical expressions of stress and particle velocity derived from mass and momentum conservation principles are utilized to solve the wave interactions within the impactor-target system, considering reflections, transmissions, and wave interactions between layers. These analytical expressions, integrated into a computational framework, facilitate the monitoring of material responses following each wave interaction, thereby analysing the impact response of the layered medium. Wave interactions, resulting in tensile stresses, that can cause damage to the target have been identified. A strain-based damage initiation and evolution law is incorporated into the developed computer program to predict damage in each layer of a medium. Damage alters the medium’s impact behaviour by decreasing stress magnitude and inducing temporal delays in the response due to attenuated wave propagation speed. The occurrences of tensile stress are explored, analyzing spatial and temporal variations of multiple damage incidents within the layered medium. The tension at the interface between two layers can induce damage in one or both adjacent layers, leading to debonding between layers. The effect of damage on the impact response of the medium is analysed by comparing the results for the damaged and undamaged target scenarios. The impact behaviour of a single-layer and a multi-layer target obtained from the present model, in terms of stress and particle velocity, is verified through Finite Element simulations of the identical impact problems, where the damage evolution criterion is incorporated using a VUMAT subroutine. The outcomes derived from this study align closely with Finite Element (FE) results.

Development and Validation of SCACOMS, a Composite Scale for Assessing Disease Progression and Treatment Effects in Spinocerebellar Ataxia.

Spinocerebellar ataxias (SCA) are rare inherited neurodegenerative disorders characterized by a progressive impairment of gait, balance, limb coordination, and speech. There is currently no composite scale that includes multiple aspects of the SCA experience to assess disease progression and treatment effects. Applying the method of partial least squares (PLS) regression, we developed the Spinocerebellar Ataxia Composite Scale (SCACOMS) from two SCA natural history datasets (NCT01060371, NCT02440763). PLS regression selected items based on their ability to detect clinical decline, with optimized weights based on the item's degree of progression. Following model validation, SCACOMS was leveraged to examine disease progression and treatment effects in a 48-week SCA clinical trial cohort (NCT03701399). Items from the Clinical Global Impression-Global Improvement Scale (CGI-I), the Friedreich Ataxia Rating Scale (FARS) - functional stage, and the Modified Functional Scale for the Assessment and Rating of Ataxia (f-SARA) were objectively selected with weightings based on their sensitivity to clinical decline. The resulting SCACOMS exhibited improved sensitivity to disease progression and greater treatment effects (compared to the original scales from which they were derived) in a 48-week clinical trial of a novel therapeutic agent. The trial analyses also provided a SCACOMS-derived estimate of the temporal delay in SCA disease progression. SCACOMS is a useful composite measure, effectively capturing disease progression and highlighting treatment effects in patients with SCA. SCACOMS will be a powerful tool in future studies given its sensitivity to clinical decline and ability to detect a meaningful clinical impact of disease-modifying treatments.

Latitudinal Characteristics of the Post‐Sunset Enhancements in Ionospheric Electron Density During the Geomagnetic Quiet Period in May 2021 Over East‐Asian Region

AbstractThis study investigated the latitudinal variations of post‐sunset enhancements in the ionospheric electron density during the geomagnetic quiet period in May 2021 with a combination of high‐precision ionospheric parameters obtained from four ionosondes, Beidou geostationary satellite (BD‐GEO) receiver network and Sanya incoherent scatter radar (SYISR). We identified four categories of post‐sunset enhancement phenomena (Types 1–4), each with unique spatial and temporal evolutions, yet uniformly accompanied by a decrease in hmF2. Measurements of plasma drift vector velocities from SYISR and hmF2 gradients across various latitudes provided pivotal insights, confirming that the ionospheric post‐sunset enhancements can result from downward plasma motion due to westward electric field, downward field‐aligned drift, or a combination of both. For Type 1, dominated by field‐aligned drift, plasma density enhancements not only intensify at low latitudes but may also extend to mid‐latitudes, exhibiting a distinct temporal delay with increasing latitude. In contrast, Type 4, primarily driven by the westward electric field, is characterized by modest increases in plasma density confined to localized low‐latitude regions, with no observable latitudinal time delay in the peak of enhancements. Types 2 and 3, which are subject to the combined influence of the westward electric field and field‐aligned drift, exhibit plasma density increases at certain low‐latitude areas, with Type 2 presenting a delayed pattern and Type 3 showing none with rising latitude. Meanwhile, neutral winds can partially account for the observed post‐sunset enhancement from low to middle latitudes. These findings offer new insights into the factors influencing ionospheric behavior after sunset.

The time lag in local field potential signals for the development of its Bayesian belief network

PurposeThe objective is to suggest time as an important variable to consider in the network model, specifically when discussing causality.MethodsThere is a consideration of the context of functional connectivity because of the time importance of observing the feature inside the neuroscience context. A network model was constructed using the Bayesian network method, utilizing a dataset consisting of three rats’ local field potentials. The model took into consideration the time delay of communication among brain areas, as recorded in this study. In pursuit of this objective, the delayed mutual information method was employed to ascertain the temporal delay between local field potentials and K2 score for the purpose of model comparison.ResultsBayesian network depicted the probabilistic relationship among rat’s brain areas. Delayed mutual information captured the lag among brain areas, and after its appliance on the Bayesian network model, posed better results.ConclusionThe primary novelty of this research lies in its integration of minor delays within the Bayesian network approach, accomplished through the utilization of the delayed mutual information technique prior to its implementation. The suggested methodology incorporates an essential feature that supports the analysis of functional connectivity among brain areas, thereby providing support for the dynamics of neurophysiology.

Filter bank temporally delayed CCA for uncalibrated SSVEP-BCI.

The uncalibrated brain-computer interface (BCI) system based on steady-state visual evoked potential (SSVEP) can omit the training process and is closer to the practical application. Filter bank canonical correlation analysis (FBCCA), as a classical approach of uncalibrated SSVEP-based BCI, extracts the fundamental and harmonic ingredients through filter bank decomposition. Nevertheless, this method fails to fully leverage the temporal feature of the signal. The paper suggested utilizing reconstructed data with temporal delay in the computation of the canonical correlation coefficient, and the different combinations of the time-delayed embedding and FBCCA were discussed. We selected the data from seven participants in the Benchmark dataset for parameter optimization and evaluated the method across all participants. The experimental results showed that only embedding the time-delayed version into the first subband (FBdCCA) was better than embedding it into all subbands (FBdCCA(all)), and the accuracy of FBdCCA surpassed that of FBCCA significantly. This suggests that the approach of time-delayed embedding can further enhance the performance of FBCCA.

Quantum aggregation with temporal delay

Quantum aggregation with temporal delay

MILP and PSO approaches for solving a hydropower reservoirs intraday economic optimization problem

AbstractShort-term hydropower generation with several water reservoirs requires deciding, for each moment in time, the volume of water (outflow) that is released from every reservoir to be turbined and generate energy. Knowing the price of energy at every time period, the objective is to maximize the income earned from the generated energy. In this paper, we present (1) a Hydropower Reservoirs Operation Optimization problem with a higher level of detail than those found in the literature, encompassing temporal delays, water hammer effects, and increased temporal discretization, among others features, and (2) two distinct approaches for addressing this problem: MILP and PSO. These methods are compared across instances of varying nature to evaluate their performance. We make our code available on GitHub: https://github.com/baobabsoluciones/flowing-basin.

Quantum Informative Analysis in Smart Power Distribution

Advancements in the Internet of Things (IoT) paradigm have greatly improved the quality of services in the electricity industry through the integration of smart energy distribution and dependable electric devices. Conspicuously, the current research introduces a method for managing electricity consumption in smart residences using IoT-Fog technology, focusing on efficient energy allocation and real-time energy needs. The study specifically examines the effectiveness of electricity grid sub-stations in distributing energy using fog computing technology. By utilizing a Quantum Computing-assisted approach, optimal energy distribution is achieved by calculating a novel Electricity Usage Measure (EUM) based on actual energy usage patterns of smart homes. Furthermore, the Quantumized Neural Network (QiM-NN) technique is developed to forecast the electricity distribution over grid substations. For performance assessment, 4-month data is collected using four smart houses. Comparative analysis with existing data assessment techniques illustrates the effectiveness in terms of Temporal Delay (6.33 ms), Optimization Performance (Specificity (93.00%), Sensitivity (90.86%), Precision (96.66%), Coverage (96.66 %)), Reliability (93.76%), and Stability (71%).

Digital-Twin virtual model real-time construction via spatio-temporal cascade reconstruction for full-field plastic deformation monitoring in metal tube bending manufacturing

Digital Twin (DT) technology, which integrates multi-source information, is extensively applied for comprehensive monitoring, predicting, and optimizing manufacturing processes. The core of this technology is the Digital Twin Virtual Model (DTVM), which acts as a virtual mirror reflecting the real-world physical processes within a digital environment. In processes like tube bending, constructing a real-time DTVM capable of capturing full-field plastic deformation is essential for monitoring and analyzing plastic behavior. However, existing DTVMs often simplify spatial resolution and suffer from temporal delays, impeding the accurate real-time depiction of the complete state of the real physical processes. To address this issue, a real-time DTVM construction method based on spatio-temporal cascade reconstruction was proposed for full-field plastic deformation monitoring in metal tube bending. Initially, a joint-section driven predefined bending tube coordinate representation method was introduced to comprehensively capture the entire plastic deformation area in bending tubes. Subsequently, through a physics-derived model integrating limited real-time data and plastic forming theory, a low-fidelity model with complete but low accuracy was obtained. This model was subsequently refined into a high-fidelity model with both completeness and high accuracy using the proposed FPDR-Net. To eliminate temporal lags, the concept of compensation for time-delay through prediction was introduced. The newly developed TSCR-Net was applied to leverage past data to predict the present state, thereby achieving real-time synchronization mapping between the physical process and the DTVM. Finally, the proposed real-time reconstruction method for monitoring was validated through a case study on the bending of a 6061-T6 tube. The accuracy of full-field plastic deformation reconstruction was compared to traditional algorithms and finite element methods. The experimental results demonstrated that the proposed approach is highly efficient for real-time and full-field plastic deformation monitoring.

Combining statistical and hydrodynamic models to assess compound flood hazards from rainfall and storm surge: a case study of Shanghai

Abstract. Coastal regions have experienced significant environmental changes and increased vulnerability to floods caused by the combined effect of multiple flood drivers such as storm surge, heavy rainfall and river discharge, i.e., compound floods. Hence, for a sustainable development of coastal cities, it is necessary to understand the spatiotemporal dynamics and future trends of compound flood hazard. While the statistical dependence between flood drivers, i.e., rainfall and storm surges, has been extensively studied, the sensitivity of the inundated areas to the relative timing of a driver's individual peaks is less understood and location dependent. To fill this gap, here we propose a framework combining a statistical dependence model for compound event definition and a hydrodynamic model to assess inundation maps of compound flooding from storm surge and rainfall during typhoon season in Shanghai. First, we determine the severity of the joint design event, i.e., peak surge and precipitation, based on the copula model. Second, we use the same frequency amplification (SFA) method to transform the design event values in hourly time series so that they represent boundary conditions to force hydrodynamic models. Third, we assess the sensitivity of inundation maps to the time lag between storm surge peak and rainfall. Finally, we define flood zones based on the primary flood driver, and we delineate flood zones under the worst compound flood scenario. The study highlights that the temporal delay between storm surge and rainfall plays a pivotal role in shaping the dynamics of flooding events. More specifically, that the peak rainfall occurs 2 h before the peak storm surge would cause the deepest average cumulative inundation depth. At the same time, the results show that in Shanghai surge is the primary flood driver. High storm surge at the eastern part of the city (Wusongkou tidal gauge) propagates upstream in the Huangpu River, resulting in fluvial flooding in Shanghai city center and several surrounding districts. This calls for a better fluvial flooding control system hinging on the backwater effect during high surge in the upper and middle Huangpu River and in the newly added urbanized areas to ensure flood resilience. The proposed framework is useful to evaluate and predict flood hazard in coastal cities, and the results can provide guidance for urban disaster prevention and mitigation.

Cortico-striatal beta oscillations as a reward-related signal

The value associated with reward is sensitive to external factors, such as the time between the choice and reward delivery as classically manipulated in temporal discounting tasks. Subjective preference for two reward options is dependent on objective variables of reward magnitude and reward delay. Single neuron correlates of reward value have been observed in regions, including ventral striatum, orbital, and medial prefrontal cortex. Brain imaging studies show cortico-striatal-limbic network activity related to subjective preferences. To explore how oscillatory dynamics represent reward processing across brain regions, we measured local field potentials of rats performing a temporal discounting task. Our goal was to use a data-driven approach to identify an electrophysiological marker that correlates with reward preference. We found that reward-locked oscillations at beta frequencies signaled the magnitude of reward and decayed with longer temporal delays. Electrodes in orbitofrontal/medial prefrontal cortex, anterior insula, ventral striatum, and amygdala individually increased power and were functionally connected at beta frequencies during reward outcome. Beta power during reward outcome correlated with subjective value as defined by a computational model fit to the discounting behavior. These data suggest that cortico-striatal beta oscillations are a reward signal correlated, which may represent subjective value and hold potential to serve as a biomarker and potential therapeutic target.

Ultrafast spallation dynamics of a thin gold film characterized by imaging pump-probe interferometry

Interferometry allows to measure microscopic displacements in the nanometer scale. This work introduces an imaging pump-probe interferometer that enables to detect the spatially resolved phase shift of the probe radiation being reflected on a sample surface after ultrashort pulsed laser excitation with a temporal resolution of 40 fs and a maximum temporal delay of 3 ns. The capability of the pump-probe interferometer is demonstrated on the spallation of a thin gold film upon femtosecond laser irradiation. The pump-probe interferometer enables to measure a minimum phase change of Δφ<π/10\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta \\varphi <\\pi /10$$\\end{document} which corresponds to a displacement of Δh<\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\Delta h<$$\\end{document} 12.5 nm for the applied probe wavelength of 500 nm. Upon irradiation, two distinct phase changes are observable: First, an abrupt minor phase shift has been measured in the femtosecond range, which is attributed to the changed optical properties of the sample surface after excitation. Second, a more pronounced continuing phase shift increase is detectable after a few tens of picoseconds resulting from the onset of spallation. Based on the measured spatially resolved phase shift, the transient surface topography during the spallation is reconstructed. The determined velocity of the ablated material reaches a maximum of a few 1000 m/s at ten picosecond after irradiation and decreases to 250 m/s afterwards. Consequently, the introduced imaging pump-probe interferometer provides important insights into the physical processes during laser excitation as well as the subsequent laser-induced ablation, and will enable to validate theoretical models quantitatively in following studies.

The delayed and combinatorial response of online public opinion to the real world: An inquiry into news texts during the COVID-19 era

In sociological research based on online public opinion, scholars often overlook the delay and combinatory nature of online responses to real-world events. This study aims to explore the delayed and combinatory responses of online public opinion to the intensity of the COVID-19 pandemic. Specifically, we seek to answer the following questions: (a) Is there a temporal delay in the response of online public opinion to the intensity of the pandemic? (b) Does this delay exhibit general characteristics of social networks, such as combinatory effects and higher-order interactions? To address these questions, we employ natural language processing techniques to extract online public opinion data and utilize statistical and machine learning-based causal inference methods for analysis. The findings indicate that online public opinion’s response to the intensity of COVID-19 is not immediate but rather exhibits a long-term lag. Identical COVID-19 intensity data can trigger multiple delayed public opinion responses, while a single delayed public opinion datum may be influenced by multiple preceding COVID-19 intensity data points. This delayed response of online public opinion and its higher-order network characteristics result in a waveform structure of real-world impacts influenced by online public opinion. We also utilized machine learning causal inference techniques to investigate the sensitivity differences in online public opinion responses to COVID-19 during various time periods.

Both partners’ negative emotion drives aggression during couples’ conflict

Researchers examining conflict between intimate partners believe that the experience and expression of emotion drives aggressive behaviour. Intra-personally, increases in negative affect make aggression more likely. Inter-personally, theoretical models suggest that each individuals’ perception of their partners’ emotion also influences aggression, potentially creating a Violence Escalation Cycle. Here, using a lab-based aggression task across a primary study (n = 104, number of trials = 3095) and a replication (n = 58, number of trials = 3167), we show that both intra- and inter-personal experiences of negative emotion predict reactive aggression within couples, revealing retaliation but not escalation. Critically, analyses of facial affect reveal that prototypic displays of negative emotions have a compounding effect, leading to dramatic changes in aggression depending on whether one, both, or neither partner expressed negative emotion. We propose a mechanism by which temporal delays (i.e., experimentally imposed forced breaks) reduce aggression by decreasing negative emotional arousal and limiting impulsive action. Our results show that both forced breaks and elective breaks (i.e., extra participant-initiated extensions of the forced break time) reduce aggression, providing exciting evidence that interventions focused on preventing impulsive action when people are in a provoked state can reduce aggression within couples.