Analysis Of Temporal Dynamics Research Articles

Modeling and analysis of temporal dynamics in groundwater aquifers of New Valley Oases, Egypt

Water scarcity poses a significant challenge in arid and semi-arid regions, necessitating a focused exploration of groundwater resources. Egypt, confronted with various water challenges, particularly in its Western Desert, relies heavily on groundwater as the exclusive water source due to the presence of the Nubian Sandstone aquifer. Effective groundwater management in this region is imperative. This study delves into the hydrogeological characteristics of the Nubian Sandstone aquifer system (NSAS) in the prominent New Valley Oases—Kharga, Dakhla, and Farafra—where agricultural activities heavily depend on groundwater. The primary objective entails a meticulous temporal assessment of the impact of groundwater development on aquifer behavior, groundwater levels, and drawdown. Employing a remote sensing approach, agricultural expansion from 1995 to 2020 was scrutinized. The Visual MODFLOW package served as a robust tool for simulating groundwater flow in the study areas. Noteworthy findings reveal an upward trajectory in agricultural crop areas, escalating by approximately 6% from 1740 km² in 1995 to 1850 km² in 2020. Concurrently, drawdown, influenced by current groundwater extraction, is anticipated to range from 0.5 to 5 meters per year. To ensure the sustainable development of these areas, stringent regulations must be implemented, underscoring the imperative for judicious groundwater management practices. This research underscores the critical need for informed decision-making and proactive measures to address the evolving dynamics of groundwater resources in the New Valley Oases.

Analysis of temporal and spatial dynamics and driving factors in the aquaculture industry of Fuding City, China

BackgroundThe sustainable development of aquaculture is a crucial determinant of food security, the well-being of aquaculture practitioners, and economic growth within coastal regions. Considering the existing gap in research regarding spatial and temporal development of aquaculture, this study investigates the progression of aquaculture practices over time and across various locations in Fuding City, China. MethodsWe retrospectively collected temporal and spatial data on aquaculture, as well as demographic, social, and economic data for Fuding City from 2010 to 2020. By employing 3D kernel density analysis, we illustrated the temporal and spatial changes in aquaculture. Furthermore, we utilized Ordinary Least Squares regression to investigate the driving factors behind the spatial changes in the aquaculture industry. ResultsOver the past decades, we observed that in Fuding City, both the number of fishing rafts and aquaculture households initially decreased and then increased. The spatial distribution of aquaculture experienced a shift from the west (inner bay area) to the east (coastal area). Additionally, the type of fishing rafts also varied by region, with traditional rafts dominating the western inner bay and plastic rafts prevalent in the eastern offshore areas. Analysis of driving factors revealed that at least six factors have a significant positive correlation with the eastward shift of the aquaculture industry's center, including the proportion of migrant population, proportion of aquaculture to total fishery output, average temperature, investment in aquaculture technology, total fish sales, and GDP of Fuding City. ConclusionThis study examines the spatial and temporal dynamics of aquaculture in Fuding City from 2010 to 2020, proposing an innovative approach to spatial optimization that integrates both horizontal and vertical strategies. These insights aim to guide the development of coastal aquaculture policies and support sustainable regional development, fostering a balanced coexistence between human activities and marine environments.

Sex differences in age-varying trends of depressive symptoms, substance use, and their associations among South Korean adults: A Time-Varying Effect Modeling (TVEM) analysis of a nationwide sample

BackgroundThis study investigated sex differences in the age-varying trends of depressive symptoms, substance use, and their relationships throughout the adult lifespan. Using a nationwide sample from South Korea, this study aimed to confirm existing patterns and identify unique characteristics specific to the South Korean context. MethodTime-Varying Effect Modeling (TVEM) was applied to data from 17,484 participants (9987 women and 7497 men) in the Korea National Health and Nutrition Examination Survey. ResultsConsistent with global trends, the results revealed a higher prevalence of depressive symptoms among women and a greater prevalence of substance use among men. However, the findings also illuminated unique patterns within the South Korean context. Substance use among South Korean men peaked during their 40s, whereas South Korean women consistently exhibited lower rates of substance use. Additionally, a stronger association between depressive symptoms and substance use was identified in women compared to men. LimitationsThe study used cross-sectional data, limiting the analysis of temporal dynamics between depressive symptoms and substance use. Additionally, TVEM cannot distinguish between aging and cohort effects. Furthermore, the assessments of depressive symptoms and substance use were based on self-report. Finally, the study did not include adolescents or sex and gender minorities in its sample. ConclusionsThese results emphasize the need for targeted interventions, particularly among women in their 20s and older adulthood, where there is a heightened co-occurrence of depressive symptoms and substance use. These findings also highlight the importance of sex- and culture-sensitive approaches tailored to the South Korean context.

Aberrant patterns of spontaneous brain activity in schizophrenia: A resting-state fMRI study and classification analysis

BackgroundSchizophrenia is a prevalent mental disorder, leading to severe disability. Currently, the absence of objective biomarkers hinders effective diagnosis. This study was conducted to explore the aberrant spontaneous brain activity and investigate the potential of abnormal brain indices as diagnostic biomarkers employing machine learning methods. MethodsA total of sixty-one schizophrenia patients and seventy demographically matched healthy controls were enrolled in this study. The static indices of resting-state functional magnetic resonance imaging (rs-fMRI) including amplitude of low frequency fluctuations (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), and degree centrality (DC) were calculated to evaluate spontaneous brain activity. Subsequently, a sliding-window method was then used to conduct temporal dynamic analysis. The comparison of static and dynamic rs-fMRI indices between the patient and control groups was conducted using a two-sample t-test. Finally, the machine learning analysis was applied to estimate the diagnostic value of abnormal indices of brain activity. ResultsSchizophrenia patients exhibited a significant increase ALFF value in inferior frontal gyrus, alongside significant decreases in fALFF values observed in left postcentral gyrus and right cerebellum posterior lobe. Pervasive aberrations in ReHo indices were observed among schizophrenia patients, particularly in frontal lobe and cerebellum. A noteworthy reduction in voxel-wise concordance of dynamic indices was observed across gray matter regions encompassing the bilateral frontal, parietal, occipital, temporal, and insular cortices. The classification analysis achieved the highest values for area under curve at 0.87 and accuracy at 81.28% when applying linear support vector machine and leveraging a combination of abnormal static and dynamic indices in the specified brain regions as features. ConclusionsThe static and dynamic indices of brain activity exhibited as potential neuroimaging biomarkers for the diagnosis of schizophrenia.

Analysis of Spatial and Temporal Dynamics of Finland’s Boreal Forests and Types over the Past Four Decades

In the context of global warming, the study of the long-term spatial change characteristics of boreal forest cover is not only important for global climate change and sustainable development research but can also provide support for further research on the response of boreal forest changes to climate change. Using Landsat TM/OLI images from 1980 to 2020 as the data source and Google Earth Engine (GEE) as the platform, Finland was selected as the study area of boreal forests, and typical sample points of different features were chosen to classify forested and non-forested land using the random forest algorithm combined with spectral indices and classified feature sets of tasseled cap transform to obtain the four-phase forest cover change maps of the region. GEE test sample points and random selection points of images from the GF-2 and GF-7 satellites were used for verification. The classification accuracy was 97.17% and 88.9%. The five-phase forest cover images were segmented by a 2° latitude zone, and the spatial and temporal dynamic changes in forest cover in the whole area and each latitude zone were quantified by pixel superposition analysis. The results showed that, in the past 40 years, the boreal forest cover in Finland changed significantly, and the forest cover decreased from 75.79% to 65.36%, by 10.43%. Forest change mainly occurs in coniferous forests, whereas broadleaf forests are more stable. The forest coverage in each latitude zone decreased to varying degrees, with higher changes occurring in high-latitude areas above 64° N between 1980 and 2000, and higher and more severe changes occurring in low-latitude areas below 64° N between 2000 and 2020. Coniferous forests are the dominant type of forest in Finland, and the degradation of coniferous forests in the south is likely to become more severe, whereas the north and above is likely to become more favorable for coniferous forests. More monitoring and research are needed to follow up on the very different changes in the north and south regions.

Visual cortical area contributions to the transient, multifocal and steady-state VEP: A forward model-informed analysis

Abstract Central to our understanding of how visual-evoked potentials (VEPs) contribute to visual processing is the question of where their anatomical sources are. Three well-established measures of low-level visual cortical activity are widely used: the first component (“C1”) of the transient and multifocal VEP, and the steady-state VEP (SSVEP). Although primary visual cortex (V1) activity has often been implicated in the generation of all three signals, their dominant sources remain uncertain due to the limited resolution and methodological heterogeneity of source modelling. Here, we provide the first characterisation of all three signals in one analytic framework centred on the “cruciform model”, which describes how scalp topographies of V1 activity vary with stimulus location due to the retinotopy and unique folding pattern of V1. We measured the transient C1, multifocal C1, and SSVEPs driven by an 18.75 Hz and 7.5 Hz flicker, and regressed them against forward models of areas V1, V2, and V3 generated from the Benson-2014 retinotopy atlas. The topographic variations of all four VEP signals across the visual field were better captured by V1 models, explaining between 2 and 6 times more variance than V2/V3. Models with all three visual areas improved fit further, but complementary analyses of temporal dynamics across all three signals indicated that the bulk of extrastriate contributions occur considerably later than V1. Overall, our data support the use of peak C1 amplitude and SSVEPs to probe V1 activity, although the SSVEP contains stronger extrastriate contributions. Moreover, we provide elaborated heuristics to distinguish visual areas in VEP data based on signal lateralisation as well as polarity inversion.

GIS and Machine Learning Models Target Dynamic Settlement Patterns and Their Driving Mechanisms from the Neolithic to Bronze Age in the Northeastern Tibetan Plateau

Traditional GIS-based statistical models are intended to extrapolate patterns of settlements and their interactions with the environment. They contribute significantly to our knowledge of past human–land relationships. Yet, these models are often criticized for their empiricism, lopsided specific factors, and for overlooking the synergy between variables. Though largely untested, machine learning and artificial intelligence methods have the potential to overcome these shortcomings comprehensively and objectively. The northeastern Tibetan Plateau (NETP) is characterized by diverse environments and significant changes to the social system from the Neolithic to Bronze Age. In this study, this area serves as a representative case for assessing the complex relationships between settlement locations and geographic environments, taking full advantages of these new models. We have explored a novel modeling case by employing GIS and random forests to consider multiple factors, including terrain, vegetation, soil, climate, hydrology, and land suitability, to construct classification models identifying environmental variation across different cultural periods. The model exhibited strong performance and a high archaeological prediction value. Potential living maps were generated for each cultural stage, revealing distinct environmental selection strategies from the Neolithic to Bronze Age. The key environmental parameters of elevation, climate, soil erosion, and cultivated land suitability were calculated with high weights, influencing human environmental decisions synergistically. Furthermore, we conducted a quantitative analysis of temporal dynamics in climate and subsistence to understand driving mechanisms behind environmental strategies. These findings suggest that past human environmental strategies were based on the comprehensive consideration of various factors, coupled with their social economic scenario. Such subsistence-oriented activities supported human beings in overcoming elevation limitation, and thus allowed them to inhabit wider pastoral areas. This study showcases the potential of machine learning in predicting archaeological probabilities and in interpreting the environmental influence on settlement patterns.

Temporal Dynamics and Trend Analysis of Areal Rainfall in Muger Subwatershed, Upper Blue Nile, Ethiopia

This study was employed to investigate the temporal variability and trend analysis of areal rainfall in the Muger subwatershed, Upper Blue Nile, Ethiopia. The study was run over the following procedures to handle the main objective: (1) determining the areal rainfall from gauged point rainfall using the Thiessen polygon method, (2) grouping the months in the season according to the study area, (3) evaluating the temporal dynamics of annual and seasonal areal rainfall using the coefficient of variation (CV), standard anomaly index (SAI), and precipitation concentration index (PCI), and (4) analyzing the trend of annual and seasonal areal rainfall using modified Mann–Kendall’s (modifiedmk) test in RStudio. Based on the temporal variability analysis, CV results depict that annual and summer areal rainfall had low variability with values of 13.43% and 13.7%, respectively. Winter and spring areal rainfall shows high variation with a CV value of 50.5% and 36%, respectively. According to the SAI output, around 70% of the considered year was in the normal condition of wetness. On the other hand, the seasonal (winter, spring, and summer) rainfall distribution of the study area shows strong irregularity distribution throughout the considered years as a result of PCI with a value of 57.5%. The trend of the areal rainfall was shown to be both increasing and decreasing. However, the trend was insignificant with a 10% confidence level.

Bifurcation to complex dynamics in largely modulated voltage-controlled parametric oscillator

An experimental demonstration of a parametric oscillation of a magnetization in a ferromagnet was performed recently by applying a microwave voltage, indicating the potential to be applied in a switching method in non-volatile memories. In the previous works, the modulation of a perpendicular magnetic anisotropy field produced by the microwave voltage was small compared with an external magnetic field pointing in an in-plane direction. A recent trend is, however, opposite, where an efficiency of the voltage controlled magnetic anisotropy (VCMA) effect is increased significantly by material research and thus, the modulated magnetic anisotropy field can be larger than the external magnetic field. Here, we solved the Landau–Lifshitz–Gilbert equation numerically and investigated the magnetization dynamics driven under a wide range of the microwave VCMA effect. We evaluated bifurcation diagrams, which summarize local maxima of the magnetization dynamics. For low modulation amplitudes, the local maximum is a single point because the dynamics is the periodic parametric oscillation. The bifurcation diagrams show distributions of the local maxima when the microwave magnetic anisotropy field becomes larger than the external magnetic field. The appearance of this broadened distribution indicates complex dynamics such as chaotic and transient-chaotic behaviors, which were confirmed from an analysis of temporal dynamics.

Layer chicken microbiota: a comprehensive analysis of spatial and temporal dynamics across all major gut sections

BackgroundThe gut microbiota influences chicken health, welfare, and productivity. A diverse and balanced microbiota has been associated with improved growth, efficient feed utilisation, a well-developed immune system, disease resistance, and stress tolerance in chickens. Previous studies on chicken gut microbiota have predominantly focused on broiler chickens and have usually been limited to one or two sections of the digestive system, under controlled research environments, and often sampled at a single time point. To extend these studies, this investigation examined the microbiota of commercially raised layer chickens across all major gut sections of the digestive system and with regular sampling from rearing to the end of production at 80 weeks. The aim was to build a detailed picture of microbiota development across the entire digestive system of layer chickens and study spatial and temporal dynamics.ResultsThe taxonomic composition of gut microbiota differed significantly between birds in the rearing and production stages, indicating a shift after laying onset. Similar microbiota compositions were observed between proventriculus and gizzard, as well as between jejunum and ileum, likely due to their anatomical proximity. Lactobacillus dominated the upper gut in pullets and the lower gut in older birds. The oesophagus had a high proportion of Proteobacteria, including opportunistic pathogens such as Gallibacterium. Relative abundance of Gallibacterium increased after peak production in multiple gut sections. Aeriscardovia was enriched in the late-lay phase compared to younger birds in multiple gut sections. Age influenced microbial richness and diversity in different organs. The upper gut showed decreased diversity over time, possibly influenced by dietary changes, while the lower gut, specifically cecum and colon, displayed increased richness as birds matured. However, age-related changes were inconsistent across all organs, suggesting the influence of organ-specific factors in microbiota maturation.ConclusionAddressing a gap in previous research, this study explored the microbiota across all major gut sections and tracked their dynamics from rearing to the end of the production cycle in commercially raised layer chickens. This study provides a comprehensive understanding of microbiota structure and development which help to develop targeted strategies to optimise gut health and overall productivity in poultry production.

Bidirectional and dynamic relationships between social isolation and activities of daily living among older adults in China.

Social isolation and disability in performing activities of daily living are increasingly recognised as significant public health concerns globally. We aimed to investigate their bidirectional associations and the related temporal dynamics in Chinese older adults. We retrieved data from the six waves of the Chinese Longitudinal Healthy Longevity Survey (2002-18) and used generalised cross-lagged modelling (GCLM) to assess the bidirectional associations between social isolation and disability in performing activities of daily living. We found that higher levels of social isolation were predictive of increased scores in disabilities in performing activities of daily living. Conversely, disabilities in performing activities of daily living showed less predictive power in relation to social isolation. The temporal dynamics analysis indicated a peak in the bidirectional associations after approximately six years, followed by decreasing trends. Our results indicate that social isolation is dominant in the bidirectional relationship. Efforts focusing on reducing it can potentially minimise disabilities in performing activities of daily living among older adults. Reinstating preventive interventions beyond the six-year mark could help maintain their effectiveness.

Linear and non-linear dynamics of ecosystem services supply, demand, and mismatches across a rapidly urbanizing region

Achieving the sustainability of coupled human-nature systems requires matching the ecosystem services (ES) provided by nature to the needs of society. However, most of the current studies on ES supply–demand relationships focus on spatial mismatches and rarely consider their temporal dynamics. In this study, the year-by-year dynamics (2001–2020) of 12 ESs at multiple spatial scales were investigated by using space–time interaction (STI) analysis, fuzzy C-mean clustering, and emerging hotspot analysis. We found that (1) three forms of temporal dynamics of ES indicators can be identified, including linear dynamics, fluctuating dynamics, and regime shift dynamics. (2) Temporal dynamic patterns of the same ES were not consistent across all counties, showing great spatial heterogeneity. The temporal dynamics of each ES type at each county were spatially explicitly clustered into linear dynamics, fluctuating dynamics, and regime shift dynamics with different means. (3) The ES supply–demand relationships can shift between supply–demand balance, excess supply, and excess demand when the temporal dynamics of ES supply do not match with that of ES demand. Our results suggest that temporal dynamic analysis in ecological restoration effectiveness assessment can help to avoid erroneous results compared to considering only pre- and post-policy ES changes. The identified emerging hotspots of excess supply and excess demand can help decision-makers more accurately identify priority locations for management.

Accelerating dynamic time warping for speech recognition with SSE

This study presents a significant enhancement to the Dynamic Time Warping (DTW) algorithm for real-time applications like speech recognition. Through integration of SIMD (Single Instruction Multiple Data) instructions to distance function, the research demonstrates how SSE accelerates DTW, markedly reducing computation time. The paper not only explores the theoretical aspects of DTW and this optimization but also provides empirical evidence of its effectiveness. Diverse dataset of 18 voice command classes was assembled, recorded in controlled settings to ensure audio quality. The audio signal of each speech sample was segmented into frames for detailed analysis of temporal dynamics. DTW search was performed on features set based on Mel Frequency Cepstral Coefficients (MFCC) and Linear Predictive Coding (LPC), combined with delta features. A comprehensive set of 27 features was extracted from each frame to capture critical speech characteristics. The core of the study involved applying traditional DTW as a baseline for performance comparison with the SSE-optimized DTW. The evaluation, focusing on computational time, included measurements like minimum, maximum, average, and total computation times for both standard and SSE-optimized implementations. Experimental results, conducted on datasets ranging from 5 to 60 WAV files per class, revealed that the SSE-optimized DTW significantly outperformed the standard implementation across all dataset sizes. Particularly noteworthy was the consistent speed of the SSE-optimized Manhattan and Euclidean distance functions, which is crucial for real-time applications. The SSE-optimized DTW maintained a low average time, demonstrating remarkable stability and efficiency, especially with larger datasets. The study illustrates the potential of SSE optimizations in speech recognition, emphasizing the SSE-optimized DTW's capability to efficiently process large datasets.

A review of skin immune processes in acne.

Acne vulgaris is one of the most prevalent skin conditions, affecting almost all teenagers worldwide. Multiple factors, including the excessive production of sebum, dysbiosis of the skin microbiome, disruption of keratinization within hair follicles, and local inflammation, are believed to trigger or aggravate acne. Immune activity plays a crucial role in the pathogenesis of acne. Recent research has improved our understanding of the immunostimulatory functions of microorganisms, lipid mediators, and neuropeptides. Additionally, significant advances have been made in elucidating the intricate mechanisms through which cutaneous innate and adaptive immune cells perceive and transmit stimulatory signals and initiate immune responses. However, our understanding of precise temporal and spatial patterns of immune activity throughout various stages of acne development remains limited. This review provides a comprehensive overview of the current knowledge concerning the immune processes involved in the initiation and progression of acne. Furthermore, we highlight the significance of detailed spatiotemporal analyses, including analyses of temporal dynamics of immune cell populations as well as single-cell and spatial RNA sequencing, for the development of targeted therapeutic and prevention strategies.

Analysis of Temporal and Spatial Dynamics of Ecosystem Services and Trade-Offs/Synergies during Urbanization in the Loess Plateau, China

As a typical ecological fragile zone and an area with a high intensity of human activities, the Loess Plateau (LP) of China has significantly altered its ecosystem and the corresponding services under the influence of urbanization processes. However, most existing studies focus on the spatial and temporal changes of ecosystem services (ESs) and their interrelationships under the influence of ecological restoration works in the LP, leaving limited research on the impacts of urbanization on ESs. Therefore, this study constructed a research framework for exploring the spatio-temporal dynamics and interactions of ESs under the influence of urbanization based on time series data from 2000 to 2020. The results showed that: (1) based on the comprehensive urbanization level (CUL), developed and developing areas accounted for 5.63% of the total area; (2) for the whole LP, all ESs except Habitat Quality (HQ) showed an increased trend. HQ showed a trade-off with the other services, while there was a clear synergy between the other three types of services; (3) in terms of processes of urbanization, Carbon Sequestration, Water Yield and HQ gradually decreased with increased levels of urbanization, and Soil Conservation increased the least in developing areas. The trade-off between HQ and the other three services decreased with increasing urbanization, while the synergy between the other three services strengthened as urbanization deepened. These findings suggest that urbanization significantly impacts ESs. It is necessary to implement appropriate measures (e.g., sponge city construction, urban green space, etc.) to address the impacts of urbanization on ESs.

The Role of Self-Esteem in Protecting Against Cyber-Victimization and Gaming Disorder Symptoms Among Adolescents: A Temporal Dynamics Analysis.

Previous literature has suggested that victimization is linked to low self-esteem and increases the symptoms of gaming disorder. However, little is known about the intra-individual processes, and the temporal dynamics of cyber-victimization, self-esteem, and gaming disorder symptoms. To address this gap, a three-year longitudinal study was performed using data collected at six different time points from 4206 Chinese adolescents (aged 12-17 years; 50.4% boys). Results of random intercept cross-lagged panel models (RI-CLPMs) indicated that at the within-person level, the fluctuation in self-esteem weakly predicted late cyber-victimization, and the fluctuation of cyber-victimization also weakly predicted late self-esteem. Additionally, the current study identified an interactive effect between self-esteem and gaming disorder symptoms at the within-person level. Fluctuations in self-esteem negatively predicted late gaming disorder symptoms, and vice versa. However, when combining the three variables (i.e., cyber-victimization, self-esteem, and gaming disorder symptoms) into one RI-CLPM, the results did not support the mediation of self-esteem in the relationship between cyber-victimization and gaming disorder symptoms at the within-person level. Moreover, fluctuations in self-esteem negatively predicted late gaming disorder symptoms and cyber-victimization at the within-person level in the RI-CLPM. These findings emphasize the protective role of self-esteem developed against cyber-victimization and gaming disorder symptoms among adolescents.

Method for analyzing sequential services using EEG: Micro-meso analysis of emotional changes in real flight service

Capturing customers’ emotional changes in sequential service should be realized using physiological measurements to assess customer delight. Questionnaire-based customer surveys may miss significant and dissipating emotional responses. This study developed a micro‑meso analysis method of capturing emotional changes for sequential service using electroencephalograph (EEG) measurement, dealing with both service encounters (micro-level) and servicescape (meso‑level) over a couple of hours. Customers’ emotion states were defined based on emotional arousal and valence. Emotional responses caused by human interactions were evaluated, and periods of high positive affect throughout the customer journey were visualized. Experiments in actual flight services demonstrated successful emotion estimation across flight phases using a single-channel EEG measurement over two hours. Analysis results on the measurement data revealed emotional peaks outside service encounters that are not captured in customers’ individual self-reports. The results also statistically revealed that two individual services (asking about a refill and conversations started by flight attendants) evoked high positive affect. Temporal dynamic analyses around high positive affect suggested patterns of interplay between joy and surprise, which are key components of customer delight. Compared with questionnaire-based evaluation, the proposed method contributes significantly to empirical studies on sequential services in marketing and design by enabling the extraction of “high positive affect,” which needs to be identified for customer delight. This study supplements existing research on the interactions among physiology (EEG), behavior (emotional changes), and customer service research.

Exploring Temporal Information Dynamics in Spiking Neural Networks

Most existing Spiking Neural Network (SNN) works state that SNNs may utilize temporal information dynamics of spikes. However, an explicit analysis of temporal information dynamics is still missing. In this paper, we ask several important questions for providing a fundamental understanding of SNNs: What are temporal information dynamics inside SNNs? How can we measure the temporal information dynamics? How do the temporal information dynamics affect the overall learning performance? To answer these questions, we estimate the Fisher Information of the weights to measure the distribution of temporal information during training in an empirical manner. Surprisingly, as training goes on, Fisher information starts to concentrate in the early timesteps. After training, we observe that information becomes highly concentrated in earlier few timesteps, a phenomenon we refer to as temporal information concentration. We observe that the temporal information concentration phenomenon is a common learning feature of SNNs by conducting extensive experiments on various configurations such as architecture, dataset, optimization strategy, time constant, and timesteps. Furthermore, to reveal how temporal information concentration affects the performance of SNNs, we design a loss function to change the trend of temporal information. We find that temporal information concentration is crucial to building a robust SNN but has little effect on classification accuracy. Finally, we propose an efficient iterative pruning method based on our observation on temporal information concentration. Code is available at https://github.com/Intelligent-Computing-Lab-Yale/Exploring-Temporal-Information-Dynamics-in-Spiking-Neural-Networks.

Structural and temporal dynamics analysis on drug-eluting stents: History, research hotspots and emerging trends.

This review aims to explore the history, research hotspots, and emerging trends of drug-eluting stents(DES)in the last two decades from the perspective of structural and temporal dynamics. Publications on DES were retrieved from WoSCC. The bibliometric tools including CiteSpace and HistCite were used to identify the historical features, the evolution of active topics, and emerging trends on the DES field. In the last 20 years, the field of DES is still in the hot phase and there is a wide range of extensive scientific collaborations. In addition, active topics emerge in different periods, as evidenced by a total of 41 disciplines, 511 keywords, and 1377 papers with citation bursts. Keyword clustering anchored five emerging research subfields, namely #0 dual antiplatelet therapy, #3 drug-coated balloon, #4 bifurcation, 5# rotational atherectomy, and 6# quantitative flow ratio. The keyword alluvial map shows that the most persistent research concepts in this field are thrombosis, restenosis, etc., and the emerging keywords are paclitaxel eluting balloon, coated balloon, drug-eluting balloon, etc. There are 7 recent research subfields anchored by reference clustering, namely #2 dual antiplatelet therapy, #4 drug-coated balloon, #5 peripheral artery disease, #8 fractional flow reserve, #10 bioresorbable vascular scaffold, # 13 intravascular ultrasound, #14 biodegradable polymer. The findings based on the bibliometric studies provide the current status and trends in DES research and may help researchers to identify hot topics and explore new research directions in this field.

Investigating the correlation between early vascular alterations and cognitive impairment in Alzheimer's disease in mice with SD-OCT.

Vascular alterations have recently gained some attention with their strong association with Alzheimer's disease (AD). We conducted a label-free in vivo optical coherence tomography (OCT) longitudinal imaging using an AD mouse model. We achieved the tracking of the same individual vessels over time and conducted an in-depth analysis of temporal dynamics in vasculature and vasodynamics using OCT angiography and Doppler-OCT. The AD group showed an exponential decay in both vessel diameter and blood flow change with the critical timepoint before 20 weeks of age, which precedes cognitive decline observed at 40 weeks of age. Interestingly, for the AD group, the diameter change showed the dominance in arterioles over venules, but no such influence was found in blood flow change. Conversely, three mice groups with early vasodilatory intervention did not show any significant change in both vascular integrity and cognitive function compared to the wild-type group. We found early vascular alterations and confirmed their correlation with cognitive impairment in AD.