Dynamic Patterns Research Articles

Unveiling the role of forests in landslide occurrence, recurrence and recovery

Abstract Rain‐triggered landslides cause significant socioeconomic loss and long‐term ecological impact, disrupting topsoil and seed banks and creating unfavourable conditions for vegetation establishment. Although hazard management effectively reduces landslide risk, most management plans primarily focus on the societal impacts (loss of property and human lives), while environmental impacts related to landslide occurrence and recurrence are often overlooked. The role of different vegetation types in mitigation strategies remains uncertain due to the time and spatial constraints of traditional field study methods. Therefore, the role of vegetation in landslide restoration should be reconsidered. In this study, we investigated post‐landslide vegetation recovery patterns, analysed the duration of vegetation recovery, identified factors influencing these patterns, examined the role of forests in occurrence and recurrence probability and explored the implications of these findings for each element within an integrated management framework for landslide restoration. We utilised long‐term landslide inventory data (1924–2018) covering the entire city of Hong Kong (~1100 km2) and combined it with structural data derived from airborne LiDAR scanning in 2020 to capture landslide recovery. Our findings revealed significant differences in recovery trajectories between the main bodies and foot areas of scars, with the former taking approximately 46 years to recover and the latter recovering in about 38 years. Scar age, average wind speed, elevation and proximity to the forest were identified as key factors influencing structural recovery on scars, while the proximity to forest also regulated recovery rate of scars. We observed decreased landslide occurrence in forested areas, with recurrent landslides primarily appearing on older, unrecovered scars on barren hillsides. Synthesis and applications: Our research highlights the dynamic patterns, recovery time and drivers of vegetation recovery on landslide scars; and the higher probability of landslide occurrence in non‐forest areas and increased recurrence on bare, low vegetated scars. This information provides valuable insights for informing restoration efforts and managing landslide‐prone areas. Such knowledge is crucial for developing effective mitigation strategies and enhancing the resilience of vulnerable ecosystems facing climate change and increased frequency of extreme weather events.

Dynamic soil hydraulic resistance regulates stomata.

The onset of stomatal closure reduces transpiration during drought. In seed plants, drought causes declines in plant water status which increases leaf endogenous abscisic acid (ABA) levels required for stomatal closure. There are multiple possible points of increased belowground resistance in the soil-plant atmospheric continuum that could decrease leaf water potential enough to trigger ABA production and the subsequent decreases in transpiration. We investigate the dynamic patterns of leaf ABA levels, plant hydraulic conductance and the point of failure in the soil-plant conductance in the highly embolism-resistant species Callitris tuberculata using continuous dendrometer measurements of leaf water potential during drought. We show that decreases in transpiration and ABA biosynthesis begin before any permanent decreases in predawn water potential, collapse in soil-plant hydraulic pathway and xylem embolism spread. We find that a dynamic but recoverable increases in hydraulic resistance in the soil in close proximity to the roots is the most likely driver of declines in midday leaf water potential needed for ABA biosynthesis and the onset of decreases in transpiration.

Abnormal large-scale brain functional network dynamics in social anxiety disorder.

Although static abnormalities of functional brain networks have been observed in patients with social anxiety disorder (SAD), the brain connectome dynamics at the macroscale network level remain obscure. We therefore used a multivariate data-driven method to search for dynamic functional network connectivity (dFNC) alterations in SAD. We conducted spatial independent component analysis, and used a sliding-window approach with a k-means clustering algorithm, to characterize the recurring states of brain resting-state networks; then state transition metrics and FNC strength in the different states were compared between SAD patients and healthy controls (HC), and the relationship to SAD clinical characteristics was explored. Four distinct recurring states were identified. Compared with HC, SAD patients demonstrated higher fractional windows and mean dwelling time in the highest-frequency State 3, representing "widely weaker" FNC, but lower in States 2 and 4, representing "locally stronger" and "widely stronger" FNC, respectively. In State 1, representing "widely moderate" FNC, SAD patients showed decreased FNC mainly between the default mode network and the attention and perceptual networks. Some aberrant dFNC signatures correlated with illness duration. These aberrant patterns of brain functional synchronization dynamics among large-scale resting-state networks may provide new insights into the neuro-functional underpinnings of SAD.

Decoding pulsatile patterns of cerebrospinal fluid dynamics through enhancing interpretability in machine learning

Analyses of complex behaviors of Cerebrospinal Fluid (CSF) have become increasingly important in diseases diagnosis. The changes of the phase-contrast magnetic resonance imaging (PC-MRI) signal formed by the velocity of flowing CSF are represented as a set of velocity-encoded images or maps, which can be thought of as signal data in the context of medical imaging, enabling the evaluation of pulsatile patterns throughout a cardiac cycle. However, automatic segmentation of the CSF region in a PC-MRI image is challenging, and implementing an explained ML method using pulsatile data as a feature remains unexplored. This paper presents lightweight machine learning (ML) algorithms to perform CSF lumen segmentation in spinal, utilizing sets of velocity-encoded images or maps as a feature. The Dataset contains 57 PC-MRI slabs by 3T MRI scanner from control and idiopathic scoliosis participants are involved to collect data. The ML models are trained with 2176 time series images. Different cardiac periods image (frame) numbers of PC-MRIs are interpolated in the preprocessing step to align to features of equal size. The fivefold cross-validation procedure is used to estimate the success of the ML models. Additionally, the study focusses on enhancing the interpretability of the highest-accuracy eXtreme gradient boosting (XGB) model by applying the shapley additive explanations (SHAP) technique. The XGB algorithm presented its highest accuracy, with an average fivefold accuracy of 0.99% precision, 0.95% recall, and 0.97% F1 score. We evaluated the significance of each pulsatile feature's contribution to predictions, offering a more profound understanding of the model's behavior in distinguishing CSF lumen pixels with SHAP. Introducing a novel approach in the field, develop ML models offer comprehension into feature extraction and selection from PC-MRI pulsatile data. Moreover, the explained ML model offers novel and valuable insights to domain experts, contributing to an enhanced scholarly understanding of CSF dynamics.

Abnormal Dynamic Reconstruction of Overlapping Communities in Schizophrenia Patients.

This study aims to explore the changes in dynamic overlapping communities in the brains of schizophrenia (SZ) patients and further investigate the dynamic restructuring patterns of overlapping communities in SZ patients. A total of 43 SZ patients and 49 normal controls (NC) were selected for resting-state functional MRI (rs-fMRI) scans. Dynamic functional connectivity analysis was conducted separately on SZ patients and NC using rs-fMRI and Jackknife Correlation techniques to construct dynamic brain network models. Based on these models, a dynamic overlapping community detection method was utilized to explore the abnormal overlapping community structure in SZ patients using evaluation metrics such as the structural stability of overlapping communities, nodes' functional diversity, and activity level of overlapping communities. The stability of communities in SZ patients showed a decreasing trend. The changes in the overlapping community structure of SZ patients may be related to a decrease in the diversity of overlapping node functions. Additionally, compared to the NC group, the activity level of overlapping communities of SZ patients was significantly reduced. The structure or organization of the brain functional network in SZ patients is abnormal or disrupted, and the activity of the brain network in information processing and transmission is weakened in SZ patients.

Integrated approach for seismic wave prediction and structural evaluation of Oya tuff quarry underground spaces: Field observations and numerical modeling

This study evaluates the structural stability of large underground spaces in seismic conditions, represented by the Oya underground stone mining plant. By directly monitoring the seismic response of the underground mining site, significant earthquake activities at the plate boundaries of the Tokyo region and Ibaraki Prefecture offshore area were observed. Additionally, through an in-depth analysis of seismic records from different locations within the underground structure, the dynamic characteristics and motion patterns of the Oya underground stone mining plant were revealed, revealing its movement trajectory during earthquakes. Additionally, this study innovatively applied seismic waves measured at the original site as input parameters and artificially generated seismic waves based on their response spectra. A numerical analysis was performed after ensuring the model's high correlation with the original site was met. The findings demonstrate that the results of both parameter input methods are confirmable and valuable. Under severe seismic conditions, instability was observed in some regions of the underground mining site. The study also discusses the location and damage mechanisms of the mining site's structure under seismic effects, providing valuable insights for the safety assessment of similar large underground spaces and proposing new approaches for selecting input parameters in seismic analysis.

Multi-View disentanglement-based bidirectional generalized distillation for diagnosis of liver cancers with ultrasound images

B-mode ultrasound (BUS) mainly reflects the tissue structural, morphological, and echo characteristics of liver tumors, and contrast-enhanced ultrasound (CEUS) offers supplementary information on the dynamic blood perfusion pattern to promote diagnostic accuracy. Transfer learning (TL) is capable of improving the performance of BUS-based computer-aided diagnosis (CAD) for liver cancer by transferring information from CEUS. However, most multi-view TL algorithms cannot fully capture the view-common together with the view-unique information of three CEUS phase images in the source domain to further promote knowledge transfer. To this end, a multi-view disentanglement-based bidirectional generalized distillation (MD-BGD) algorithm is proposed to explore and learn more potential knowledge from three typical CEUS phase images for multi-view transfer. MD-BGD consists of the multi-view feature disentanglement module and the bidirectional distillation module. The former explores more potential and transferable privileged information by disentangling three CEUS phase image features in the source domain into view-common and view-unique components. The latter develops a bidirectional generalized distillation algorithm to enhance the multi-view knowledge transfer between the source and the target domains, guided by shared labels. Therefore, the BUS-based CAD model is significantly improved by our proposed MD-BGD. MD-BGD is evaluated on the bi-modal ultrasound imaging dataset. It gains the best results of 90.75±2.20 %, 89.50±3.49 %, and 91.89±3.78 %, on accuracy, sensitivity, and specificity, respectively. These results indicate the effectiveness of MD-BGD in the diagnosis of liver cancer.

One hundred years of influenza A evolution

Leveraging the simplicity of nucleotide mismatch distributions, we provide an intuitive window into the evolution of the human influenza A ‘nonstructural’ (NS) gene segment. In an analysis suggested by the eminent Danish biologist Freddy B. Christiansen, we illustrate the existence of a continuous genetic “backbone” of influenza A NS sequences, steadily increasing in nucleotide distance to the 1918 root over more than a century. The 2009 influenza A/H1N1 pandemic represents a clear departure from this enduring genetic backbone. Utilizing nucleotide distance maps and phylogenetic analyses, we illustrate remaining uncertainties regarding the origin of the 2009 pandemic, highlighting the complexity of influenza evolution. The NS segment is interesting precisely because it experiences less pervasive positive selection, and departs less strongly from neutral evolution than e.g. the HA antigen. Consequently, sudden deviations from neutral diversification can indicate changes in other genes via the hitchhiking effect. Our approach employs two measures based on nucleotide mismatch counts to analyze the evolutionary dynamics of the NS gene segment. The rooted Hamming map of distances between a reference sequence and all other sequences over time, and the unrooted temporal Hamming distribution which captures the distribution of genotypic distances between simultaneously circulating viruses, thereby revealing patterns of nucleotide diversity and epi-evolutionary dynamics.

Range dynamics of Anopheles mosquitoes in Africa suggest a significant increase in the malaria transmission risk.

Despite a more than 100-year effort to combat malaria, it remains one of the most malignant infectious diseases globally, especially in Africa. Malaria is transmitted by several Anopheles mosquitoes. However, until now few studies have investigated future range dynamics of major An. mosquitoes in Africa through a unified scheme. Through a unified scheme, we developed 21 species distribution models to predict the range dynamics of 21 major An. species in Africa under future scenarios and also examined their overall range dynamic patterns mainly through suitability overlap index and range overlap index. Although future range dynamics varied substantially among the 21 An. species, we predicted large future range expansions for all 21 An. species, and increases in suitability overlap index were detected in more than 90% of the African continent for all future scenarios. Additionally, we predicted high range overlap index in West Africa, East Africa, South Sudan, Angola, and the Democratic Republic of the Congo under future scenarios. Although the relative impacts of land use, topography and climate variables on the range dynamics depended on species and spatial scale, climate played the strongest roles in the range dynamics of most species. Africa might face an increasing risk of malaria transmissions in the future, and better strategies are required to address this problem. Mitigating climate change and human disturbance of natural ecosystems might be essential to reduce the proliferation of An. species and the risk of malaria transmissions in Africa in the future. Our strategies against their impacts should be species-specific.

Dynamic pattern of postprandial bile acids in paediatric non-alcoholic fatty liver disease.

Dysregulation of bile acids (BAs), as important signalling molecules in regulating lipid and glucose metabolism, contributes to the development of non-alcoholic fatty liver disease (NAFLD). However, static BA profiles during fasting may obscure certain pathogenetic aspects. In this study, we investigate the dynamic alterations of BAs in response to an oral glucose tolerance test (OGTT) among children with NAFLD. We recruited 230 subjects, including children with overweight/obesity, or complicated with NAFLD, and healthy controls. Serum BAs, 7-hydroxy-4-cholesten-3-one (C4) and fibroblast growth factor 19 (FGF19) were quantified during OGTT. Clinical markers related to liver function, lipid metabolism and glucose metabolism were assessed at baseline or during OGTT. Conjugated BAs increased while unconjugated ones decreased after glucose uptake. Most BAs were blunted in response to glucose in NAFLD (p > .05); only glycine and taurine-conjugated chenodeoxycholic acid (CDCA) and cholic acid (CA) were responsive (p < .05). Primary BAs were significantly increased while secondary BAs were decreased in NAFLD. C4 and FGF19 were significantly increased while their ratio FGF19/C4 ratio was decreased in NAFLD. The dynamic pattern of CDCA and taurine-conjugated hyocholic acid (THCA) species was closely correlated with glucose (correlation coefficient r = .175 and -.233, p < .05), insulin (r = .327 and -.236, p < .05) and c-peptide (r = .318 and -.238, p < .05). Among which, CDCA was positively associated with liver fat content in NAFLD (r = .438, p < .05). Additionally, glycochenodeoxycholic acid (GCDCA), CDCA and THCA were potential biomarkers to discriminate paediatric NAFLD from healthy controls and children with obesity. This study provides novel insights into the dynamics of BAs during OGTT in paediatric NAFLD. The observed variations in CDCA and HCA species were associated with liver dysfunction, dyslipidaemia and dysglycaemia, highlighting their potential roles as promising diagnostic and therapeutic targets in NAFLD.

The boosted Hodrick‐Prescott filter is more general than you might think

SummaryThe global financial crisis and Covid‐19 recession have renewed discussion concerning trend‐cycle discovery in macroeconomic data, and boosting has recently upgraded the popular Hodrick‐Prescott filter to a modern machine learning device suited to data‐rich and rapid computational environments. This paper extends boosting's trend determination capability to higher order integrated processes and time series with roots that are local to unity. The theory is established by understanding the asymptotic effect of boosting on a simple exponential function. Given a universe of time series in FRED databases that exhibit various dynamic patterns, boosting timely captures downturns at crises and recoveries that follow.

ConvFormer-KDE: A Long-Term Point-Interval Prediction Framework for PM2.5 Based on Multi-Source Spatial and Temporal Data.

Accurate long-term PM2.5 prediction is crucial for environmental management and public health. However, previous studies have mainly focused on short-term air quality point predictions, neglecting the importance of accurately predicting the long-term trends of PM2.5 and studying the uncertainty of PM2.5 concentration changes. The traditional approaches have limitations in capturing nonlinear relationships and complex dynamic patterns in time series, and they often overlook the credibility of prediction results in practical applications. Therefore, there is still much room for improvement in long-term prediction of PM2.5. This study proposes a novel long-term point and interval prediction framework for urban air quality based on multi-source spatial and temporal data, which further quantifies the uncertainty and volatility of the prediction based on the accurate PM2.5 point prediction. In this model, firstly, multi-source datasets from multiple monitoring stations are preprocessed. Subsequently, spatial clustering of stations based on POI data is performed to filter out strongly correlated stations, and feature selection is performed to eliminate redundant features. In this paper, the ConvFormer-KDE model is presented, whereby local patterns and short-term dependencies among multivariate variables are mined through a convolutional neural network (CNN), long-term dependencies among time-series data are extracted using the Transformer model, and a direct multi-output strategy is employed to realize the long-term point prediction of PM2.5 concentration. KDE is utilized to derive prediction intervals for PM2.5 concentration at confidence levels of 85%, 90%, and 95%, respectively, reflecting the uncertainty inherent in long-term trends of PM2.5. The performance of ConvFormer-KDE was compared with a list of advanced models. Experimental results showed that ConvFormer-KDE outperformed baseline models in long-term point- and interval-prediction tasks for PM2.5. The ConvFormer-KDE can provide a valuable early warning basis for future PM2.5 changes from the aspects of point and interval prediction.

High modularity, more flexible of brain networks in patients with mild to moderate motor impairments after stroke

Stroke is recognized as a network communication disorder. Advances in neuroimaging technologies have enhanced our comprehension of dynamic cerebral alterations. However, different levels of motor function impairment after stroke may have different patterns of brain reorganization. Abnormal and adaptive patterns of brain activity in mild-to-moderate motor function impairments after stroke remain still underexplored. We aim to identify dynamic patterns of network remodeling in stroke patients with mild-to-moderate impairment of motor function. fMRI data were obtained from 30 stroke patients and 31 healthy controls to establish a spatiotemporal multilayer modularity model. Then, graph-theoretic measures, including modularity, flexibility, cohesion, and disjointedness, were calculated to quantify dynamic reconfiguration. Our findings reveal that the post-stroke brain exhibited higher modular organization, as well as heightened disjointedness, compared to HCs. Moreover, analyzing from the network level, we found increased disjointedness and flexibility in the Default mode network (DMN), indicating that brain regions tend to switch more frequently and independently between communities and the dynamic changes were mainly driven by DMN. Notably, modified functional dynamics positively correlated with motor performance in patients with mild-to-moderate motor impairment. Collectively, our research uncovered patterns of dynamic community reconstruction in multilayer networks following stroke. Our findings may offer new insights into the complex reorganization of neural function in post-stroke brain.

Tacking over-smoothing: Target-guide progressive dynamic graph learning for 3D skeleton-based human motion prediction

Graph Convolution Network-based (GCN-based) approaches show promising performance on 3D skeleton-based human motion prediction due to its natural graph representation and outstanding ability for spatial–temporal dependencies modeling for human motion. However, the existing GCN-based methods inherently have difficulties in designing deep GCN-based structures for human motion prediction due to the over-smoothing issue. Although the over-smoothing issue has been studied in many scenarios, few attempts focus on this problem in 3D human motion prediction tasks. Therefore, we propose a novel deep GCN architecture termed TPDGN (Target-guide Progressive Dynamic Graph Network) to solve this issue in a motion prediction context. The core of the proposed TPDGN is the progressive dynamic graph leaning block, which is developed to model the dynamic graph pattern. We claim that the key to solving the over-smoothing issue is to reduce the loss of motion features and boost graph representations. Accordingly, we further explore the progressive learning mode by the specific target guidance for the motion prediction task, which aims at learning rich motion dependencies to tackle over-smoothing. Empirical results show that our approach could improve the over-smoothing issue, achieving state-of-the-art results on three public datasets. The codes is available at https://github.com/1111szu/TPDGN.

ℛSCZ: A Riemannian schizophrenia diagnosis framework based on the multiplexity of EEG-based dynamic functional connectivity patterns

Abnormal electrophysiological (EEG) activity has been largely reported in schizophrenia (SCZ). In the last decade, research has focused to the automatic diagnosis of SCZ via the investigation of an EEG aberrant activity and connectivity linked to this mental disorder. These studies followed various preprocessing steps of EEG activity focusing on frequency-dependent functional connectivity brain network (FCBN) construction disregarding the topological dependency among edges. FCBN belongs to a family of symmetric positive definite (SPD) matrices forming the Riemannian manifold. Due to its unique geometric properties, the whole analysis of FCBN can be performed on the Riemannian geometry of the SPD space. The advantage of the analysis of FCBN on the SPD space is that it takes into account all the pairwise interdependencies as a whole. However, only a few studies have adopted a FCBN analysis on the SPD manifold, while no study exists on the analysis of dynamic FCBN (dFCBN) tailored to SCZ. In the present study, I analyzed two open EEG-SCZ datasets under a Riemannian geometry of SPD matrices for the dFCBN analysis proposing also a multiplexity index that quantifies the associations of multi-frequency brainwave patterns. I adopted a machine learning procedure employing a leave-one-subject-out cross-validation (LOSO-CV) using snapshots of dFCBN from (N-1) subjects to train a battery of classifiers. Each classifier operated in the inter-subject dFCBN distances of sample covariance matrices (SCMs) following a rhythm-dependent decision and a multiplex-dependent one. The proposed ℛSCZ decoder supported both the Riemannian geometry of SPD and the multiplexity index DC reaching an absolute accuracy (100 %) in both datasets in the virtual default mode network (DMN) source space.

Revealing static and dynamic biomarkers from postprandial metabolomics data through coupled matrix and tensor factorizations.

Longitudinal metabolomics data from a meal challenge test contains both fasting and dynamic signals, that may be related to metabolic health and diseases. Recent work has explored the multiway structure of time-resolved metabolomics data by arranging it as a three-way array with modes: subjects, metabolites, and time. The analysis of such dynamic data (where the fasting data is subtracted from postprandial states) reveals dynamic markers of various phenotypes, and differences between fasting and dynamic states. However, there is still limited success in terms of extracting static and dynamic biomarkers for the same subject stratifications. Through joint analysis of fasting and dynamic metabolomics data, our goal is to capture static and dynamic biomarkers of a phenotype for the same subject stratifications providing a complete picture, that will be more effective for precision health. We jointly analyze fasting and dynamic metabolomics data collected during a meal challenge test from the COPSAC cohort using coupled matrix and tensor factorizations (CMTF), where the dynamic data (subjects by metabolites by time) is coupled with the fasting data (subjects by metabolites) in the subjects mode. The proposed data fusion approach extracts shared subject stratifications in terms of BMI (body mass index) from fasting and dynamic signals as well as the static and dynamic metabolic biomarker patterns corresponding to those stratifications. Specifically, we observe a subject stratification showing the positive association with all fasting VLDLs and higher BMI. For the same subject stratification, a subset of dynamic VLDLs (mainly the smaller sizes) correlates negatively with higher BMI. Higher correlations of the subject quantifications with the phenotype of interest are observed using such a data fusion approach compared to individual analyses of the fasting and postprandial state. The CMTF-based approach provides a complete picture of static and dynamic biomarkers for the same subject stratifications-when markers are present in both fasting and dynamic states.

Improvement of hepatic fibrosis after tenofovir disoproxil fumarate switching to tenofovir alafenamide for three years.

Both tenofovir alafenamide (TAF) and tenofovir disoproxil fumarate (TDF) are the first-line treatments for chronic hepatitis B (CHB). We have showed switching from TDF to TAF for 96 weeks resulted in further alanine aminotransferase (ALT) improvement, but data remain lacking on the long-term benefits of TDF switching to TAF on hepatic fibrosis. To assess the benefits of TDF switching to TAF for 3 years on ALT, aspartate aminotransferase (AST), and hepatic fibrosis improvement in patients with CHB. A single center retrospective study on 53 patients with CHB who were initially treated with TDF, then switched to TAF to determine dynamic patterns of ALT, AST, AST to platelet ratio index (APRI), fibrosis-4 (FIB-4) scores, and shear wave elastography (SWE) reading improvement at switching week 144, and the associated factors. The mean age was 55 (28-80); 45.3%, males; 15.1%, clinical cirrhosis; mean baseline ALT, 24.8; AST, 25.7 U/L; APRI, 0.37; and FIB-4, 1.66. After 144 weeks TDF switching to TAF, mean ALT and AST were reduced to 19.7 and 21, respectively. From baseline to switching week 144, the rates of ALT and AST < 35 (male)/25 (female) and < 30 (male)/19 (female) were persistently increased; hepatic fibrosis was also improved by APRI < 0.5, from 79.2% to 96.2%; FIB-4 < 1.45, from 52.8% to 58.5%, respectively; mean APRI was reduced to 0.27; FIB-4, to 1.38; and mean SWE reading, from 7.05 to 6.30 kPa after a mean of 109 weeks switching. The renal function was stable and the frequency of patients with glomerular filtration rate > 60 mL/min was increased from 86.5% at baseline to 88.2% at switching week 144. Our data confirmed that switching from TDF to TAF for 3 years results in not only persistent ALT/AST improvement, but also hepatic fibrosis improvement by APRI, FIB-4 scores, as well as SWE reading, the important clinical benefits of long-term hepatitis B virus antiviral treatment with TAF.

The social force model: a behavioral modeling approach for information propagation during significant events

Information diffusion otherwise known as the propagation, spread or dissemination of information occurs when a piece of information flows from a particular individual/community to another in a social network. Studies related to information propagation involve problems regarding the factors that affect the information propagation, how the information is disseminated, the speed of propagation, etc. Researchers have proposed information propagation models to understand the phenomenon and to answer these questions. These models have been effectively used in applications such as behavior analysis, public health care, etc. Although several studies are carried out in this field, the literature demands identifying the most influential factors of propagation in real time in cases of sudden unexpected significant disasters/epidemics/pandemics, since the existing propagation models seem unfit during such circumstances. In this paper, a novel information propagation model which predicts the top propagators of information related to a particular context is proposed. This model utilizes the past few weeks' data during a sudden outbreak of a disaster and identifies the most influential attributes of a user profile to predict the top propagators of the future. The proposed Social Force Model is inspired by a model used in studying the fear propagation pattern in pedestrian dynamics in real-life situation [Cornes FE, Frank GA, Dorso CO. Fear propagation and the evacuation dynamics. Simul Model Pract Theory. 2019;95:112–133.]. We have effectively mapped the various forces which constitute the Social Force Model such as the Desired Force, the Social Force and the Granular Force with respect to the online social network context in order to discover the key spreaders of information during a specific context. Apart from identifying the propagators, the proposed model discovers the key attributes by analyzing the behavior of users based on their past activities in the online social network.

Hourly impact of urban features on the spatial distribution of land surface temperature: A study across 30 cities

Global warming and the urban heat island effect exacerbate excessive heat in urban environments, adversely impacting human health. Effective urban planning can mitigate these effects by influencing land surface temperature (LST). While previous studies have examined the influence of urban features on LST across seasons and between day and night, detailed hour-by-hour comparisons remain unexplored. This study addresses this gap by analyzing the hourly impacts of various urban features on LST in 30 U.S. cities using geostationary weather satellite data. We employed cloud-based analytics and machine learning techniques to aggregate data from thousands of images, identifying the relative importance and correlation curve of each urban feature on LST at each hour. Our findings revealed two distinct correlation patterns: dynamic daytime patterns with significant hourly variability and stable nocturnal patterns with minimal hourly differences. These results demonstrate that sunlight intensity greatly affects the correlation between urban features and LST. Urban planners should therefore consider broader patterns rather than focusing on specific hours. These insights provide valuable guidance for landscape and urban planners in developing strategies for climate adaptation and heatwave mitigation, contributing to the growing body of literature on sustainable cities.

Coordination and light modulated dynamic optical switches of terpyridine-derived spiropyran for time-resolved information encryption

The development of the light-controlled assembly of smart materials for advanced information encryption with time-dependent optical characteristics is essential to meet the increasing demand on fabricating multiple optical materials for encryption security. In this work, the integration of terpyridine-derived photochromic spiropyran (Tpy-SP) as Zn2+ ion chelating agent and photo-regulator building blocks endows the materials with dynamic assembly-induced optical functionality. Under UV light irradiation, the Tpy-Zn2+-SP will be transformed into Tpy-Zn2+-MC, which forms coordinated architecture with distinct optical properties from that of Tpy-MC form. Modulated by the coordination, Tpy-Zn2+-MC and Tpy-MC show obviously different fading rate with the achievement of time-resolved multicolor optical switches. The temporal multioptical features promote the light-controlled switches as a promising candidate for time-dependent confidential materials, including dynamic optical patterns and multilevel encryption, and the relative encrypted data that could only be identified at a specified time.