Temporal Dynamics Research Articles

Temporal dissociation between local and global functional adaptations of the maternal brain to childbirth: a longitudinal assessment.

The maternal brain undergoes significant reorganization during birth and the postpartum period. However, the temporal dynamics of these changes remain unclear. Using resting-state functional magnetic resonance imaging, we report on local and global brain function alterations in 75 mothers in their first postpartum week, compared to 23 nulliparous women. In a subsample followed longitudinally for the next six months, we observed a temporal and spatial dissociation between changes observed at baseline (cluster mass permutation: pFWE < 0.05). Local activity and connectivity changes in widespread neocortical regions persisted throughout the studied time period (ANCOVAs vs. controls: pFDR < 0.05), with preliminary evidence linking these alterations to behavioral and psychological adaptations (interaction effect with postpartum time: uncorrected p < 0.05). In contrast, the initially reduced whole-brain connectivity of putamen-centered subcortical areas returned to control levels within six to nine weeks postpartum (linear and quadratic mixed linear models: pFDR < 0.05). The whole-brain spatial colocalization with hormone receptor distributions (Spearman correlations: pFDR < 0.05) and preliminary blood hormone associations (interaction effect with postpartum time: uncorrected p < 0.05) suggested that the postpartum restoration of progesterone levels may underlie this rapid normalization. These observations enhance our understanding of healthy maternal brain function, contributing to the identification of potential markers for pathological postpartum adaptation processes, which in turn could underlie postpartum psychiatric disorders.

Mechanism of Vegetation Greenness Change and Its Correlation with Terrestrial Water Storage in the Tarim River Basin

The response of dryland vegetation to climate change is particularly sensitive in the context of global climate change. This paper analyzes the characteristics of spatial and temporal dynamics of vegetation cover in the Tarim River Basin, China, and its driving factors in order to investigate the response of vegetation growth to water storage changes in the basin. The Enhanced Vegetation Index (EVI), the GRACE gravity satellite, and meteorological data from 2002 to 2022 are used to decipher the characteristics of the response of water storage changes to vegetation changes, which is of great significance to the realization of regional ecological development and sustainable development. The results of the study show the following: (1) The vegetation in the Tarim River Basin has an overall increasing trend, which is mainly distributed in the Aksu Basin and the Weigangkuche River Basin and is spatially distributed in the form of a ring. (2) Vegetation distribution greatly improved during the 20-year study period, dominated by high-cover vegetation, with a change rate of 200.36%. Additionally, vegetation changes are centered on the watersheds and expand to the surrounding area, with a clear increase in vegetation in the Kumukuri Basin. Areas with a vegetation Hurst index of &lt;0.5 account for 63.27% of the study area, and the areas with a continuous decrease were mainly located in the outer contour area of the Tarim River and Kumu Kuri Basins. (3) There are obvious spatial differences in the correlation between EVI and temperature and precipitation elements. The proportion of areas with positive correlation with temperature within the study area is 64.67%. EVI tends to be consistent with the direction of migration of the center of gravity of the population and GDP, and the areas with positive correlation between vegetation and terrestrial water reserves are mainly distributed in the northern slopes of the Kunlun Mountains, with an area proportion of about 50.513%. The Kumukuli Basin also shows significantly positive correlation.

Static and temporal dynamic in functional connectivity of large-scale brain networks during acute stress regulate stress resilience differently: the promotion role of trait resilience.

Stress resilience has been largely regarded as a process in which individuals actively cope with and recover from stress. Over the past decade, the emergence of large-scale brain networks has provided a new perspective for the study of the neural mechanisms of stress. However, the role of inter-network functional-connectivity (FC) and its temporal fluctuations in stress resilience is still unclear. To bridge this knowledge gap, seventy-seven participants (age, 17-22 years, 37 women) were recruited for a ScanSTRESS brain imaging study. A static perspective was initially adopted, using changes in FC that obtained from stress vs. control condition during the entire stress induction phase as a static indicator. Further, changes in FC between different stress runs were analyzed as an index of temporal dynamics. Stress resilience was gauged using salivary cortisol levels, while trait resilience was measured via behavioral-activation-system (BAS) sensitivity. Results found that, for the static index, enhanced FC between the salience-network (SN), default-mode-network (DMN) and limbic-network (LBN) during acute stress could negatively signal stress resilience. For the temporal dynamics index, FC among the dorsal-attention-network (DAN), central-executive-network (CEN) and visual-network (VN) decreased significantly during repeated stress induction. Moreover, the decline of FC positively signaled stress resilience, and this relationship only exist in people with high BAS. The current research elucidates the intricate neural underpinnings of stress resilience, offering insights into the adaptive mechanisms underlying effective stress responses.

Dynamic process of ecosystem water use efficiency and response to drought in the Yellow River Basin, China.

Ecosystem water use efficiency (WUE) is a crucial indicator of the impact of climate change on terrestrial ecosystems, reflecting the balance between biological processes (photosynthesis and transpiration) and physical processes (evapotranspiration). However, the response mechanisms and driving processes of WUE to drought remain to be further understood. In this study, we analyzed the spatial and temporal dynamics and response mechanisms of WUE in the Yellow River Basin (YRB) using data on Gross Primary Productivity (GPP), Evapotranspiration (ET) and Standardized Precipitation Evapotranspiration Index (SPEI), which revealed the cumulative effect of drought on WUE and assessed the ecosystem's resilience. The study results showed that (1) GPP, ET and WUE in the YRB exhibited a significant increasing trend, with 63.04 % of the area showing a marked increase in WUE. (2) GPP was the dominant factor influencing WUE in 65.36 % of the area, particularly in cropland and grassland, while ET was more influential in forested areas. Vapor pressure deficit (VPD) was identified as the principal driver affecting vegetation GPP in semi-arid and semi-humid regions of the YRB. In contrast, soil moisture (SM) was the limiting factor in arid areas. (3) 71.00 % of the WUE in the basin was affected by drought cumulative effects, with an average cumulative duration of 4.5 months. Arid regions experienced the most extended duration of 7.29 months, compared to 3.05 months in semi-humid regions. (4) 74.85 % of the regional ecosystems exhibited ecological resilience to drought, particularly in the source areas of the western basin of the YRB. Shrublands have the highest drought resilience among vegetation types, while grasslands have the lowest. The resilience of each climatic zone was in the order of semi-humid, semi-arid, and arid order. This study comprehensively analyzed of the spatial and temporal dynamics and response mechanisms of WUE in the YRB, offering a new perspective and scientific basis for understanding and predicting the ecosystem response to climate change.

Carbon Emissions and Vegetation Dynamics: Assessing the Spatiotemporal Environmental Impacts of Hydropower Dams in the Lancang River Basin

Recent decades in the Lancang River Basin have witnessed extensive construction of hydropower dams, profoundly impacting the local environment. Utilizing high-precision satellite data, we conducted a comprehensive analysis of vegetation cover and carbon emissions, integrating data-driven time series and spatial analysis models to capture both temporal and spatial dynamics. Our findings reveal that hydropower dam construction in the Lancang River Basin has significantly promoted vegetation restoration and growth, concurrently facilitating a reduction in regional carbon emissions. Employing deep learning models for time-series prediction, we observed a substantial increase in the sum of the local normalized difference vegetation index (NDVI) post-construction, with an average rise of from 16.15% to a maximum of 20.12% during the pivotal hydropower dams’ operational phase. Between 2001 and 2020, the construction of hydropower dams in the basin corresponded to notable changes in ecological and carbon metrics. Specifically, vegetation cover expansion intensity (VCEI) reversed from a negative mean of −0.009 to a positive mean of 0.008. Additionally, the carbon emission intensity (CEI) around these dams drastically reduced, shifting from an average of 0.877 to 0.052. Importantly, the Global Moran’s I for VCEI significantly increased from 0.288 pre-2016 to 0.679 post-2015, reflecting a stronger spatial autocorrelation in vegetation patterns. Accordingly, these findings illustrate the complex interplay between hydropower dams and environmental outcomes, underscoring the critical role of pivotal hydropower dam construction in ecological improvement. The research results have improved and complemented those of previous studies on the environmental impact of hydraulic engineering, providing valuable insights for the construction management and policy formulation of hydropower dams in other similar river basins around the world.

Impact of a mindfulness-based intervention on neurobehavioral functioning and its association with large-scale brain networks in preterm young adolescents.

Adolescents born very preterm (VPT; <32 weeks of gestation) face an elevated risk of executive, behavioral, and socioemotional difficulties. Evidence suggests beneficial effects of mindfulness-based intervention (MBI) on these abilities. This study seeks to investigate the association between the effects of MBI on executive, behavioral, and socioemotional functioning and reliable changes in large-scale brain networks dynamics during rest in VPT young adolescents who completed an 8-week MBI program. Neurobehavioral assessments and resting-state functional magnetic resonance imaging were performed before and after MBI in 32 VPT young adolescents. Neurobehavioral abilities in VPT participants were compared with full-term controls. In the VPT group, dynamic functional connectivity was extracted by using the innovation-driven coactivation patterns framework. The reliable change index was used to quantify change after MBI. A multivariate data-driven approach was used to explore associations between MBI-related changes on neurobehavioral measures and temporal brain dynamics. Compared with term-born controls, VPT adolescents showed reduced executive and socioemotional functioning before MBI. After MBI, a significant improvement was observed for all measures that were previously reduced in the VPT group. The increase in executive functioning, only, was associated with reliable changes in the duration of activation of large-scale brain networks, including frontolimbic, amygdala-hippocampus, dorsolateral prefrontal, and visual networks. The improvement in executive functioning after an MBI was associated with reliable changes in large-scale brain network dynamics during rest. These changes encompassed frontolimbic, amygdala-hippocampus, dorsolateral prefrontal, and visual networks that are related to different executive processes including self-regulation, attentional control, and attentional awareness of relevant sensory stimuli.

Post-Takeover Proficiency in Conditionally Automated Driving: Understanding Stabilization Time with Driving and Physiological Signals

In the realm of conditionally automated driving, understanding the crucial transition phase after a takeover is paramount. This study delves into the concept of post-takeover stabilization by analyzing data recorded in two driving simulator experiments. By analyzing both driving and physiological signals, we investigate the time required for the driver to regain full control and adapt to the dynamic driving task following automation. Our findings show that the stabilization time varies between measured parameters. While the drivers achieved driving-related stabilization (winding, speed) in eight to ten seconds, physiological parameters (heart rate, phasic skin conductance) exhibited a prolonged response. By elucidating the temporal and cognitive dynamics underlying the stabilization process, our results pave the way for the development of more effective and user-friendly automated driving systems, ultimately enhancing safety and driving experience on the roads.

Determinants of Yearly CO2 Emission Fluctuations: A Machine Learning Perspective to Unveil Dynamics

In order to understand the dynamics in climate change, inform policy decisions and prompt timely action to mitigate its impact, this study provides a comprehensive analysis of the short-term trend of the year-on-year CO2 emission changes across ten countries, considering a broad range of factors including socioeconomic factors, CO2-related industry, and education. This study uniquely goes beyond the common country-based analysis, offering a broader understanding of the interconnected impact of CO2 emissions across countries. Our preliminary regression analysis, using the ten most significant features, could only explain 66% of the variations in the target. To capture the emissions trend variation, we categorized countries by the change in CO2 emission volatility (high, moderate, low with upward or downward trends), assessed using standard deviation. We employed machine learning techniques, including feature importance analysis, Partial Dependence Plots (PDPs), sensitivity analysis, and Pearson and Canonical correlation analyses, to identify influential factors driving these short-term changes. The Decision Tree Classifier was the most accurate model, with an accuracy of 96%. It revealed population size, CO2 emissions from coal, the three-year average change in CO2 emissions, GDP, CO2 emissions from oil, education level (incomplete primary), and contribution to temperature rise as the most significant predictors, in order of importance. Furthermore, this study estimates the likelihood of a country transitioning to a higher emission category. Our findings provide valuable insights into the temporal dynamics of factors influencing CO2 emissions changes, contributing to the global efforts to address climate change.

Tunable temporal dynamics of dipole response in graphene-wrapped core–shell nanoparticles

The investigation on the temporal dynamics of graphene-wrapped core–shell nanoparticles under the illumination of a Gaussian impulse have been carried out. By altering the graphene layers and the aspect ratio of the core–shell structure, we can adjust the resonant modes into typical cases in regime of terahertz. Accordingly, different scenarios for the temporal evolution are detected, which include two kinds of ultrafast oscillation with exponential decay tendency, pure exponential decay, and Gaussian shape, when the pulse duration of the incident pulse is much shorter than, similar to, and much longer than the localized surface plasmon lifetime. To one's interest, when the coupling between two resonant modes exists, one predicts the long-periodic oscillation, whose period is just the difference between the frequencies of the resonant modes. Hence, the intrinsic properties of the ultrafast oscillation can be hardly influenced by the input signals. Further quantitative calculation demonstrate that the periods of the ultrafast oscillations can be tuned by different physical mechanisms, which are, respectively, based on the self-interacting correction of a single resonance and the strong coupling between the resonant modes in frequency domain. Our results may be applicable in the fields of optical sensors, optical information processing, and other nanophotonic devices.

Temporal dynamics of in vitro hemostatic function in platelets cryopreserved using a novel approach for rapid issuance.

Current procedures for thawing and issuing of cryopreserved platelets (CPPs) are laborious and have remained challenging in emergency settings such as blood banks and military operations. In this prospective study, a novel processing method designed to facilitate the rapid issuance of CPPs with no postthaw handling required was developed and functionally characterized in parallel with standard CPPs manufactured. Double-dose plateletpheresis units (n = 42) were cryopreserved at -80°C in 5%-6% dimethyl sulfoxide to produce matched pairs thawed successively over a 27-month period for comparison between two processing arms. In contrast to the standard CPPs manufactured as standalone units, platelets were frozen in tandem with resuspending plasma in a distinct partition as a single unit in the novel method, herein referred to as tandem CPPs. Postthaw (PT) CPPs from both arms were assessed at PT0-, 12-, and 24-h to measure platelet recovery, R-time (time to clot initiation; min), and maximum amplitude (MA; clot strength; mm) using thromboelastography. In the overall dataset, mean platelet recovery was higher (p < .0005) for tandem CPPs (83.9%) compared with standard CPPs (73.3%) at PT0; mean R-times were faster (p < .0005) for tandem CPPs (2.5-3.6 min) compared with standard CPPs (3.0-3.8 min); mean MA was higher for tandem CPPs (57.8-59.5 mm) compared with standard CPPs (52.1-55.8 mm) at each postthaw time point (p < .05). Robust temporal dynamics of superior hemostatic functionality were established for tandem CPPs over extended cryopreservation up to 27 months and 24 h of postthaw storage.

Temporal dynamics of forest understory on former agricultural land follows similar pathways in plantations and naturally regenerated stands

Temporal dynamics of forest understory on former agricultural land follows similar pathways in plantations and naturally regenerated stands

An acoustic exploration of sibilant contrasts and sibilant merger in Mixean Basque

Abstract This exploratory study investigates sibilants in Mixean Low Navarrese, an endangered variety of Basque. This variety has been described with ten different contrastive sibilants: /s̻, s̺, ʃ, t͡s̻, t͡s̺, t͡ʃ, z̻, z̺, ʒ, d͡z̺/. The objective of the paper is to (a) provide a detailed description of the acoustics of Mixean sibilants, and (b) elucidate whether ten categories can be proposed based only on acoustical data, or whether fewer categories should be considered. The study is based on free-conversation data of ten subjects (three females, seven males) aged between 80 and 85 years. We analyze metrics reflecting the place of articulation (spectral moments, and especially the center of gravity (CoG)), including also the temporal dynamics of CoG (using the discrete cosine transform of CoG measurements of nine intervals of each phone). We also explore the acoustic correlates of the contrasts between (a) voiced and voiceless sounds and (b) fricative and affricate sounds. The results show that only seven categories can be proposed based on acoustic measurements. The lamino-alveolar series reliably contrasts with the rest, but the distinction does not hold between the apico-alveolar and the postalveolar series. We found minimal differences in the analysis of dynamic data, and none in the static analysis.

Ongoing introgression of a secondary sexual plumage trait in a stable avian hybrid zone.

Hybrid zones are dynamic systems where natural selection, sexual selection, and other evolutionary forces can act on reshuffled combinations of distinct genomes. The movement of hybrid zones, individual traits, or both are of particular interest for understanding the interplay between selective processes. In a hybrid zone involving two lek-breeding birds, secondary sexual plumage traits of Manacus vitellinus, including bright yellow collar and olive belly color, have introgressed asymmetrically ~50 km across the genomic center of the zone into populations more genetically similar to Manacus candei. Males with yellow collars are preferred by females and are more aggressive than parental M. candei, suggesting that sexual selection was responsible for the introgression of male traits. We assessed the spatial and temporal dynamics of this hybrid zone using historical (1989 - 1994) and contemporary (2017 - 2020) transect samples to survey both morphological and genetic variation. Genome-wide SNP data and several male phenotypic traits show that the genomic center of the zone has remained spatially stable, whereas the olive belly color of male M. vitellinus has continued to introgress over this time period. Our data suggest that sexual selection can continue to shape phenotypes dynamically, independent of a stable genomic transition between species.

Short-to-Medium-Term Wind Power Forecasting through Enhanced Transformer and Improved EMD Integration

Accurate wind power forecasting (WPF) is critical in optimizing grid operations and efficiently managing wind energy resources. Challenges arise from the inherent volatility and non-stationarity of wind data, particularly in short-to-medium-term WPF, which extends to longer forecast horizons. To address these challenges, this study introduces a novel model that integrates Improved Empirical Mode Decomposition (IEMD) with an enhanced Transformer called TransIEMD. TransIEMD begins by decomposing the wind speed into Intrinsic Mode Functions (IMFs) using IEMD, transforming the scalar wind speed into a vector form that enriches the input data to reveal hidden temporal dynamics. Each IMF is then processed with channel attention, embedding, and positional encoding to prepare inputs for an enhanced Transformer. The Direct Embedding Module (DEM) provides an alternative viewpoint on the input data. The distinctive perspectives of IEMD and DEM offer interaction through cross-attention within the encoder, significantly enhancing the ability to capture dynamic wind patterns. By combining cross-attention and self-attention within the encoder–decoder structure, TransIEMD demonstrates enhanced proficiency in detecting and leveraging long-range dependencies and dynamic wind patterns, improving the forecasting precision. Extensive evaluations on a publicly available dataset from the National Renewable Energy Laboratory (NREL) demonstrate that TransIEMD significantly improves the forecasting accuracy across multiple horizons of 4, 8, 16, and 24 h. Specifically, at the 24 h forecast horizon, TransIEMD achieves reductions in the normalized mean absolute error and root mean square error of 4.24% and 4.37%, respectively, compared to the traditional Transformer. These results confirm the efficacy of integrating IEMD with attention mechanisms to enhance the accuracy of WPF.

A “trapezoidal” relationship between solar radiation and chlorophyll concentrations at the center of the South Pacific Gyre

Understanding the driving mechanism of phytoplankton dynamics is key to forecasting future changes in the ocean. Here, we report an apparent “trapezoidal” relationship between chlorophyll concentrations (Chl) and surface photosynthetically available radiation (PAR(0)) at the center of the South Pacific Gyre (cSPG) based on 18 years of MODIS Aqua measurements. A comparison of Chl with a photoacclimation model revealed that photoacclimation alone could not explain the temporal dynamics of Chl. Instead, the Chl dynamics were explained by a combination of photoacclimation, nutrient limitation, and the grazing pressure of zooplankton at different times throughout the year. An annual “trapezoidal” spiral relationship between Chl and PAR(0) suggested that the steady state of phytoplankton populations at the cSPG could be influenced by the alternation of co-regulation mechanisms during a year. Because this same pattern occurs in other subtropical gyres, this understanding of the underlying mechanisms not only facilitates simulating and forecasting phytoplankton dynamics but also provides a new perspective on how multiple stressors may impact phytoplankton communities in a warmer climate.

Rodents mediate the relationship between seed rain, seed bank, and plant community with increased grazing disturbance.

Seed rain and the soil seed bank represent the dispersal of seeds in space and time, respectively, and can be important sources of recruitment of new individuals during plant community regeneration. However, the temporal dynamics of seed rain and the mechanisms by which the seed rain and soil seed bank may play a role in plant community regeneration with increased grazing disturbance remain unclear. Seed rain, soil seed bank, aboveground vegetation, and rodent density were sampled along a grazing gradient in an alpine marsh on the eastern Tibetan Plateau. We described the temporal dynamics of seed dispersal using Bayesian generalized mixed models, and nonmetric multidimensional scaling and the structural equation model were used to examine the effects of grazing disturbance on the relative role of seed rain and soil seed bank on aboveground plant community regeneration. The temporal dynamics of seed rain changed from a unimodal to a bimodal pattern with increased grazing disturbance. Both species diversity and seed density of the seed rain and seed bank increased significantly with increased grazing disturbance. Increased grazing disturbance indirectly increased the similarity of composition between seed rain, seed bank, and aboveground plant community by directly increasing species diversity and abundance of aboveground plant community. However, increased grazing disturbance also indirectly decreased the similarity of seed rain, soil seed bank, and aboveground plant community by directly increasing rodent density. The similarity between seed rain and aboveground plant community was greater than that of the soil seed bank and aboveground plant community with increased grazing disturbance. Grazing disturbance spreads the risk of seed germination and seedling establishment by changing the temporal dynamics of seed dispersal. Plants (positive) and rodents (negative) mediated the role of seed rain and soil seed bank in plant community regeneration. The role of seed rain in plant community regeneration is higher than the seed bank in disturbed alpine marshes. Our findings increase our understanding of the regeneration process of the plant community, and they provide valuable information for the conservation and restoration of alpine marsh ecosystems.

Warming effects on grassland soil microbial communities are amplified in cool months.

Global warming modulates soil respiration (RS) via microbial decomposition, which is seasonally dependent. Yet, the magnitude and direction of this modulation remain unclear, partly owing to the lack of knowledge on how microorganisms respond to seasonal changes. Here, we investigated the temporal dynamics of soil microbial communities over 12 consecutive months under experimental warming in a tallgrass prairie ecosystem. The interplay between warming and time altered (p < 0.05) the taxonomic and functional compositions of microbial communities. During the cool months (January to February and October to December), warming induced a soil microbiome with a higher genomic potential for carbon decomposition, community-level ribosomal RNA operon (rrn) copy numbers, and microbial metabolic quotients, suggesting that warming stimulated fast-growing microorganisms that enhanced carbon decomposition. Modeling analyses further showed that warming reduced the temperature sensitivity of microbial carbon use efficiency (CUE) by 28.7% when monthly average temperature was low, resulting in lower microbial CUE and higher heterotrophic respiration (Rh) potentials. Structural equation modeling showed that warming modulated both Rh and RS directly by altering soil temperature and indirectly by influencing microbial community traits, soil moisture, nitrate content, soil pH, and gross primary productivity. The modulation of Rh by warming was more pronounced in cooler months compared to warmer ones. Together, our findings reveal distinct warming-induced effects on microbial functional traits in cool months, challenging the norm of soil sampling only in the peak growing season, and advancing our mechanistic understanding of the seasonal pattern of RS and Rh sensitivity to warming.

In silico model development and optimization of in vitro lung cell population growth.

Tissue engineering predominantly relies on trial and error in vitro and ex vivo experiments to develop protocols and bioreactors to generate functional tissues. As an alternative, in silico methods have the potential to significantly reduce the timelines and costs of experimental programs for tissue engineering. In this paper, we propose a methodology to formulate, select, calibrate, and test mathematical models to predict cell population growth as a function of the biochemical environment and to design optimal experimental protocols for model inference of in silico model parameters. We systematically combine methods from the experimental design, mathematical statistics, and optimization literature to develop unique and explainable mathematical models for cell population dynamics. The proposed methodology is applied to the development of this first published model for a population of the airway-relevant bronchio-alveolar epithelial (BEAS-2B) cell line as a function of the concentration of metabolic-related biochemical substrates. The resulting model is a system of ordinary differential equations that predict the temporal dynamics of BEAS-2B cell populations as a function of the initial seeded cell population and the glucose, oxygen, and lactate concentrations in the growth media, using seven parameters rigorously inferred from optimally designed in vitro experiments.

Temporal dynamics of positive emotion regulation: insights from facial electromyography

IntroductionEmotion regulation (ER) is a complex process that manifests gradually over time. This study investigated the temporal dynamics of ER in modifying positive emotions in terms of both negative affect (NA) and positive affect (PA) dimensions.MethodsAfter participants had been exposed to pleasant pictures for 8,000 ms, they received instructions to either continue viewing the picture (no regulation) or reappraise it with a neutral meaning (neutralize goal) or negative meaning (transform goal) for another 8,000 ms. We obtained corrugator supercilii and zygomaticus major electromyography (EMG) as objective measures of NA and PA.ResultsFor the no-regulation condition, upon instruction onset, we observed maintained low levels of corrugator and high levels of zygomaticus EMG reactivity, indicating sustained PA activation. Compared to the no-regulation condition, for the neutralize goal, we observed no change in corrugator reactivity, which remained at a low level, while zygomaticus reduction started at 1,000 ms after instruction onset, indicating decreased PA and generation of a neutral emotional state. For the transform goal, we observed corrugator increase and zygomaticus decrease both starting at 1,500 ms after instruction onset and co-existing throughout the regulation period. These results indicate increased NA and decreased PA, relating to generation of a negative emotional state. The transform goal differed from the neutralize goal in terms of corrugator increase starting at 2,500 ms after instruction onset. Albeit simultaneous onset of changes on corrugator and zygomaticus reactivity under the transform goal, model-fitting analyses indicated that the best-fitting trajectory was one that first emphasized PA reduction until, at 3,000 ms, it turned into primary NA increase.DiscussionThese distinct temporal patterns highlight the possibility of effecting one-dimensional PA change with the neutralize goal and sequential two-dimensional change (first decreasing PA, then increasing NA) with the transform goal. This research sheds light on the time course of emotional change brought about by different regulatory goals.

Recognizing online video genres using ensemble deep convolutional learning for digital media service management

It's evident that streaming services increasingly seek to automate the generation of film genres, a factor profoundly shaping a film's structure and target audience. Integrating a hybrid convolutional network into service management emerges as a valuable technique for discerning various video formats. This innovative approach not only categorizes video content but also facilitates personalized recommendations, content filtering, and targeted advertising. Given the tendency of films to blend elements from multiple genres, there is a growing demand for a real-time video classification system integrated with social media networks. Leveraging deep learning, we introduce a novel architecture for identifying and categorizing video film genres. Our approach utilizes an ensemble gated recurrent unit (ensGRU) neural network, effectively analyzing motion, spatial information, and temporal relationships. Additionally,w we present a sophisticated deep neural network incorporating the recommended GRU for video genre classification. The adoption of a dual-model strategy allows the network to capture robust video representations, leading to exceptional performance in multi-class movie classification. Evaluations conducted on well-known datasets, such as the LMTD dataset, consistently demonstrate the high performance of the proposed GRU model. This integrated model effectively extracts and learns features related to motion, spatial location, and temporal dynamics. Furthermore, the effectiveness of the proposed technique is validated using an engine block assembly dataset. Following the implementation of the enhanced architecture, the movie genre categorization system exhibits substantial improvements on the LMTD dataset, outperforming advanced models while requiring less computing power. With an impressive F1 score of 0.9102 and an accuracy rate of 94.4%, the recommended model consistently delivers outstanding results. Comparative evaluations underscore the accuracy and effectiveness of our proposed model in accurately identifying and classifying video genres, effectively extracting contextual information from video descriptors. Additionally, by integrating edge processing capabilities, our system achieves optimal real-time video processing and analysis, further enhancing its performance and relevance in dynamic media environments.