Search Results (225)

Functional near-infrared spectroscopy (fNIRS) is a portable, non-invasive tool for studying cortical function in children with neurodevelopmental and neurological disorders. Although fNIRS use is increasing, heterogeneous study paradigms and cortical targets have limited cross-condition comparisons and the identification of shared research priorities. This systematic review maps cortical regions and reporting patterns in five key conditions—autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), cerebral palsy (CP), hypoxic–ischemic encephalopathy (HIE), and epilepsy (Ep)—from January 2015 to December 2025. A systematic search across five databases identified 72 relevant studies meeting PRISMA 2020 criteria, revealing both similarities and differences across disorders. The prefrontal cortex (PFC) was studied in all five conditions (5/5: 100%), making it the most consistently investigated cortical region. The parietal and temporal cortices were studied in 4 of 5 conditions (80%). The frontal cortex had the most regions investigated, while the temporal cortex showed the most consistent coverage across conditions (2.25 conditions per region). Beyond regional preferences, condition-specific patterns were aligned with their disorder phenotypes: social brain networks in ASD, prefrontal executive systems in ADHD, sensorimotor changes in CP, cerebrovascular monitoring in HIE, and state-dependent changes in Ep. Across conditions, researchers found altered prefrontal activity, disrupted connectivity, and compensatory brain responses, supporting broader frameworks. Collectively, these findings identify the PFC as a shared target for transdiagnostic fNIRS investigations while highlighting important gaps in regional coverage and methodological consistency. Despite methodological differences, fNIRS shows promise for identifying both shared and unique brain patterns in pediatric neurodevelopmental and neurological disorders. This review provides a framework for prioritizing cortical targets and guiding future standardized fNIRS research in pediatric populations. Full article

Robot person following (RPF) in dense crowds requires a mobile robot to maintain an appropriate relative position with respect to a moving target while avoiding surrounding pedestrians and satisfying rear-following and social constraints. This paper proposes PPO-GAT-Follow, an interaction-aware reinforcement learning framework for dense-crowd RPF under geometric visibility loss with available target-relative pose estimates. The follower, target pedestrian, and surrounding pedestrians are represented as graph nodes, and a graph attention encoder models their local interactions. A task-oriented reward mechanism jointly accounts for target maintenance, visibility preservation, collision avoidance, proximity-aware social compliance, rear position maintenance, post-arrival stabilization, and action stability. Experiments are conducted in IR-SIM under fixed-route and random-route settings, with comparisons against MPC, DWA, SFM, and an adapted SARL baseline. In the fixed-route setting with 12 background pedestrians, PPO-GAT-Follow achieves a task success rate of $98.8\%$ and a collision rate of $1.1\%$, improving task success by 10.9 percentage points over MPC. In the random-route setting at the training density, it achieves $83.1\%$ task success and an SPL of $0.815$, outperforming MPC by 18.3 percentage points in task success; at this density, it also surpasses SARL in the main task-level metrics. Zero-shot evaluations across crowd densities, together with structural and reward ablations, reward weight sensitivity analysis, tolerance shift tests, multi-seed training, and stress testing under target pose noise and heterogeneous pedestrian dynamics, further demonstrate the effectiveness and reliability of the proposed framework. Gazebo-based validation also demonstrates system integration feasibility with localization, point cloud-based surrounding pedestrian perception, tracking, and UWB-like target-relative pose input. Nevertheless, visual target identification, re-identification, and perception-level occlusion recovery remain outside the scope of the present validation. Full article

Social and behavioral research often needs to examine policy shocks, information interventions, platform-mediated attention, and governance feedback, but direct experiments on real populations are constrained by ethical risks, intervention costs, and limited repeatability. This study proposes Multi-Agent Social Simulation (MASS), a protocolized form of large language model-driven agent-based modeling (LLM-driven ABM) designed as a low-risk, repeatable, and auditable pre-experimental simulation method for quantitative research. MASS embeds LLMs in an agent-based modeling (ABM) framework and uses role settings, round-based scheduling, information control, background-rule control, structured outputs, harness checks, reason-action logs, and replication manifests to transform open-ended language generation into recordable, checkable, and statistically analyzable agent-round observations. The method is evaluated through the New Jersey–Pennsylvania minimum wage natural experiment, the 2016 UK Brexit digital campaigning context, and the 2023 Zibo barbecue tourism public-opinion event. Results show that protocolized LLM-driven ABM can generate analyzable and empirically assessable outputs across policy-shock, information-intervention, and governance-feedback scenarios. The strongest evidence concerns rule-shock identification, declining undecided share under targeting, and mechanism-chain consistency among governance response, public sentiment, and behavioral intention. MASS is not a substitute for real-world experiments or causal inference; it is a pre-experimental simulation method for mechanism rehearsal, risk identification, counterfactual comparison, and research design preparation. Full article

Background: Subclinical eating-disorder tendencies may reflect early psychological vulnerability before the development of clinically diagnosed eating disorders. Body image concerns, perceived social support, and control-related beliefs are established correlates of eating pathology. However, their joint pattern across several eating-disorder tendency profiles has received less attention in Romanian nonclinical adult samples. Objective: We examined differences in perceived social support, locus of control, and body image concerns among participants with and without subclinical tendencies associated with anorexia, bulimia, and binge eating. We investigated the association between locus of control and body image concerns. Methods: A cross-sectional quantitative design was used. The sample included 108 participants who completed online self-report measures assessing perceived social support, eating-disorder tendencies, body image concerns, and locus of control. Independent-samples t-tests and Welch tests were used for group comparisons. Bonferroni correction was applied within families of comparisons. Effect sizes were reported using Hedges’ g. Pearson correlation and simple linear regression were used to examine the association between locus of control and body image concerns. Results: Body image concerns were significantly higher among participants with anorexic, binge-eating, and bulimic tendencies. Perceived social support was lower among participants with binge-eating and bulimic tendencies, but not among those with anorexic tendencies. Locus of control differed significantly only in the exploratory bulimia comparison after correction. External locus of control was positively associated with body image concerns and explained a small but significant proportion of variance. Conclusions: Body image concerns emerged as the most consistent correlate of subclinical eating-disorder tendencies. The findings support the relevance of body image, interpersonal resources, and control-related beliefs in early eating-disorder vulnerability. Full article

Background: Children growing up in alcohol-affected families are exposed to chronic stress, adverse childhood experiences (ACEs), emotional insecurity, and environmental instability, all of which may influence neurodevelopmental processes. Numerous EEG markers have been proposed as indicators of attentional regulation, emotional functioning, and stress responsivity; however, their relative diagnostic and practical value remains unclear. The aim of the present study was to verify whether commonly reported EEG markers remain valid indicators of neurofunctional difficulties in children from alcohol-affected families, to establish their hierarchy of importance, and to determine how identified neurofunctional profiles may inform the sequencing of educational interventions and the development of individualized support plans used by educators and social workers. Methods: The study included children aged 6–10 years from alcohol-affected families (n = 20) and a control group from non-dysfunctional family environments (n = 25). Resting-state EEG recordings were conducted under eyes-open and eyes-closed conditions, with analyses focused on the eyes-open condition. Quantitative EEG (qEEG) indices included global, frontal, prefrontal, and midline Theta–Beta Ratio (TBR), frontal alpha asymmetry (FAA), temporal beta stress and parietal beta2 tension. EEG preprocessing was performed using EEGLAB and included artifact rejection, filtering, epoch segmentation, and spectral power analysis. Group differences were analyzed using Welch’s t-tests with Benjamini–Hochberg correction for multiple comparisons. Results: The analyzed EEG markers differed in their ability to distinguish children from alcohol-affected families and controls. The strongest effects were observed for Theta–Beta Ratio (TBR) measures, particularly in frontal and prefrontal regions, indicating impairments in attention regulation, executive functioning, and self-control. Elevated temporal beta stress and parietal beta2 tension reflected increased physiological arousal and chronic stress. In contrast, frontal alpha asymmetry (FAA), commonly associated with depressive emotional processing, was not significant after correction for multiple comparisons. The obtained findings enabled the establishment of a hierarchy of neurofunctional markers, with attentional and executive-function indicators demonstrating greater importance than markers related to depressive symptomatology. Conclusions: The EEG profile of children from alcohol-affected families is characterized primarily by chronic stress, heightened physiological activation, and impaired attention regulation rather than by neurophysiological patterns associated with depression. The results suggest that educational difficulties in this group may stem mainly from deficits in attention control, inhibitory processes, and cognitive flexibility. Consequently, educational interventions should prioritize learning strategies, attentional training, and self-regulated learning skills. The identified hierarchy of EEG markers may also support the development of individualized educational plans and social-support programs, including participation in structured extracurricular activities and interventions aimed at strengthening executive and learning-related competencies. However, given the pilot nature of the present study and the relatively small sample size, these findings should be considered preliminary. Replication in larger, more diverse, and independent cohorts is necessary to confirm the stability, reliability, and generalizability of the identified neurofunctional profile and the proposed hierarchy of qEEG markers before they can be recommended for broader educational and social applications. Full article

Digital dependence and substance use may co-occur in post-digital cohorts when platform design, peer norms, stress exposure, and individual vulnerabilities converge to reinforce dysregulated patterns of reward seeking, self-regulation failure, and affective coping. This Entry defines their co-occurrence as a multidetermined phenomenon in which digital environments, including algorithmic feeds, notifications, social comparison, and variable rewards, interact with developmental vulnerabilities such as identity formation, impulsivity, reward sensitivity, and emotional dysregulation. Psychiatric comorbidities, particularly depression, anxiety, Attention-Deficit/Hyperactivity Disorder, and personality pathology, may increase susceptibility, while socioeconomic disadvantage and unequal access to care can intensify harm. Current evidence suggests that problematic digital use and substance use are more strongly related to functional impairment, coping motives, peer norms, and contextual stressors than to screen time alone. This Entry therefore organizes the available evidence around structural determinants, individual mechanisms, mental-health mediators, and prevention strategies, with emphasis on proportionate regulation, digital literacy, culturally adapted interventions, and integrated clinical pathways. Full article

Background/Objectives: This exploratory comparative cross-sectional observational study investigated brain fog symptoms, cognitive function, sleep quality, quality of life, and selected serum biomarkers related to inflammation and neurocognitive function in newly diagnosed patients with celiac disease (ND-CeD), patients with CeD on a gluten-free diet (GFD-CeD), and controls. Methods: A total of 62 participants were included: ND-CeD patients (n = 18), GFD-CeD patients (n = 17), and healthy controls (n = 27) with no statistically significant differences in age or sex distribution across groups. Brain fog symptoms and severity, cognitive function, sleep quality, and quality of life were assessed using the Brain Fog Scale (BFS), Brain Fog Severity Score (BFSS), Montreal Cognitive Assessment (MoCA), Single-Item Sleep Quality Scale (SQS), and World Health Organization Quality of Life Questionnaire-Brief Form-TR (WHOQOL-BREF-TR), respectively. Serum BDNF, S100B, TLR4, IL-6, and nitric oxide (NO) levels were measured by ELISA. Results: ND-CeD patients had higher BFSs and BFSSs and lower MoCA, SQS, and WHOQOL-BREF-TR scores than healthy controls (p < 0.05). GFD-CeD patients showed numerically intermediate or more favorable scores than ND-CeD patients in several outcomes; however, most differences from controls were not statistically significant. Compared with ND-CeD patients, GFD-CeD patients had higher WHOQOL-BREF-TR General Health, Psychological Health, and Social Relationships scores (p < 0.05). In exploratory within-group analyses, after correction for multiple comparisons, higher BFS scores were associated with poorer psychological health and lower MoCA scores, and higher MoCA scores were associated with better psychological and physical health domains, particularly in the ND-CeD group. In the adjusted regression model, older age, income status, and newly diagnosed disease status were independently associated with MoCA scores. No statistically detectable between-group differences were observed in serum IL-6, NO, BDNF, S100B, or TLR4 levels. Conclusions: These preliminary findings suggest that brain fog symptoms, cognitive performance, sleep quality, and quality of life may deserve greater attention at diagnosis and during follow-up in celiac disease. Although GFD-CeD patients showed more favorable scores in some outcomes, these cross-sectional differences should not be interpreted as treatment-related improvement. Larger longitudinal studies with objective assessment of gluten-free diet adherence, disease activity, micronutrient status, sleep quality, and gut–brain axis-related biomarkers are needed to confirm these findings. Full article

Confederate monuments have long stood at the center of public memory controversies in the United States, and social media platforms increasingly shape how these controversies circulate and receive responses. This study examines Confederate monument controversies across five Reddit communities—r/politics, r/news, r/PoliticalDiscussion, r/AskAnAmerican, and r/AskHistorians—using 2379 posts and 117,556 comments. Combining BERTopic modeling, zero-shot classification, and event-based statistical analysis, it investigates temporal patterns, topic structures, post categories, and responses. Discussion rose most sharply around the Charlottesville rally and the George Floyd event window, with peaks in r/politics and r/news. Topic modeling identified nine stable topics across national political debate, local removal actions, legal disputes, legislative processes, and historical interpretation. Among non-news posts, Seeking Information/Opinion was most common (41.9%), followed by Removal/Implementation Update (20.5%), Resource Sharing/Mobilization (18.9%), and Complaint/Normative Condemnation (18.7%). Protest, removal, and legal-conflict topics generated higher comment volumes and scores, whereas historically oriented topics showed lower interaction intensity. Comment sections were dominated by Explanation or Interpretation, indicating that Reddit discussions extended monument controversies mainly through explanation, comparison, and historical analogy rather than interpersonal conflict. The findings show that Reddit reorganizes public memory controversy through platform rules, community norms, and interactional mechanisms. By connecting event peaks, topic structures, post categories, and comment pathways, the study also interprets Reddit-based monument controversy as a platformed media ritual of attention, classification, and public memory negotiation. Full article

Background/Objectives: Borderline personality disorder (BPD) is a severe mental disorder characterised by emotion dysregulation, impulsivity and interpersonal hypersensitivity. Its prevalence ranges from 0.5% to 6.4%. Eye tracking and pupillometry provide objective indices of social attention and inhibitory control, but the BPD literature using these techniques has not been systematically reviewed. The aim of this work was to synthesise the empirical evidence on visuo-attentional and pupillary alterations in BPD. Methods: Following the PRISMA 2020 statement, Web of Science, Scopus and PubMed were searched up to 13 March 2026, with no date or language restrictions. Search terms combined borderline personality disorder and eye-tracking constructs. Two reviewers independently screened records with complete inter-rater agreement at the title-and-abstract stage (Cohen’s κ = 1.00); two generative artificial-intelligence assistants (ChatGPT, NotebookLM) were additionally consulted as a non-systematic plausibility check and returned no eligible studies beyond the database search. Risk of bias was appraised with the framework appropriate to each study design (RoB 2 for randomised trials and Newcastle–Ottawa Scale logic for observational studies, with ROBINS-I held in reserve for non-randomised intervention designs). Results: Seventeen studies met the inclusion criteria, with sample sizes ranging from 19 to 164 participants and predominantly adult female samples. Designs included antisaccade and oculomotor tasks, free-viewing, dot-probe, affective priming and pharmacological challenge. Four findings recurred across studies. First, patients with BPD showed an early reflexive vigilance to the eye region of emotional and neutral faces, followed by reduced time on positive stimuli during longer presentations. Second, self-reported impulsivity was elevated, but laboratory inhibition was largely preserved; the deficits that did emerge were limited to preparatory control and were greater in patients with comorbid ADHD or under induced negative affect. Third, autonomic dysregulation was indexed by lower heart-rate variability and a larger baseline pupil size; in a single longitudinal study, pupillary reactivity was prospectively associated with subsequent symptom change. Finally, intranasal oxytocin reduced amygdala-driven vigilance. Conclusions: Eye-tracking and pupillometric measures appear to capture meaningful aspects of the BPD clinical picture. The two-stage profile of early vigilance followed by reduced sustained engagement is most parsimoniously described as a vigilance–avoidance pattern, which is compatible with, but not uniquely explained by, the hypersensitivity hypothesis of emotion dysregulation. Because thirteen of the seventeen studies recruited women only, these conclusions apply primarily to adult women with BPD. Methodological heterogeneity, the predominance of female samples and the scarcity of longitudinal data justify the need for standardised protocols, transdiagnostic comparisons and the inclusion of male and gender-diverse populations in future research. Full article

Patient capital is widely regarded as a key source of funding for corporate green technological innovation. However, existing research lacks systematic comparisons of its mechanisms, transmission pathways, and contingency characteristics across internal and external contexts. This study therefore examines how patient capital influences green technological innovation and how this influence varies across capital types, ESG channels, and internal versus external environments. The results reveal a robust positive correlation between patient capital and green innovation. Mechanism tests indicate that patient capital indirectly affects green innovation through three pathways: enhancing overall ESG (Environmental, Social, and Governance) performance and synergistically strengthening the environmental, social, and governance sub-dimensions. Stable equity exerts a stronger influence on the governance dimension than relationship-based debt. Contingency analysis further shows that managerial green cognition generally amplifies the effect of patient capital, whereas media attention primarily affects equity capital, reflecting a pattern where “managerial green cognition universally amplifies the effect, while media attention specifically targets equity capital.” This study provides empirical evidence on how patient capital drives green innovation. Future policies should promote precise alignment between capital attributes and firms’ internal and external contexts, thereby shifting green innovation from isolated efforts toward systemic synergy. Full article

Background: Alcohol use disorder (AUD) is a grave mental health condition that can result in significant health and social consequences. The medications Naltrexone and Nalmefene are indicated for the treatment of AUD, with Naltrexone having received the most extensive research attention. Methods: The majority of papers assessing universal measures of alcohol consumption employed two primary metrics: total alcohol consumption (TAC) and the number of days per month where individuals engaged in heavy drinking (HDD). Indicators pertaining to the maintenance of complete abstinence were excluded due to the absence of sufficient data. The safety of both substances was also assessed, as were the frequency of side effects and independent patient dropout. The study also incorporated practical factors of the therapy, such as the route of administration, dosage regimen, and the drug’s patient convenience, which can have a significant impact on adherence to therapy. Results: Nalmefene, administered in an “as needed” regimen, demonstrated statistically significant activity in reducing HDD and total alcohol consumption (TAC) among patients with AUD, particularly those with elevated World Health Organization (WHO) DRL risk. Preliminary findings from the ESENSE1 (Efficacy of Nalmefene in Alcohol Dependence; the first phase III study), ESENSE 2 (Efficacy of Nalmefene in Alcohol Dependence, the second phase III study), and SENSE (the final phase III long term-safety and cost-effectiveness study) studies indicate a substantial decrease in HDD and TAC following the initial month of treatment. These effects persist throughout the subsequent follow-up period. Several Japanese studies have corroborated the effectiveness of Nalmefene, demonstrating its efficacy across both short-term and long-term applications. Furthermore, these studies have substantiated its safety profile, indicating that there is no inherent risk of addiction or the emergence of withdrawal symptoms. The mild nature of adverse events (most commonly nausea and dizziness) led to a relatively low discontinuation rate of Nalmefene treatment. A subsequent study, employing a recognized methodology, corroborated the efficacy of psychosocial support in enhancing treatment outcomes. Meta-analyses demonstrate that Naltrexone exhibits comparable efficacy in reducing the frequency and severity of alcohol consumption. In select populations, the injectable form (LAI) of this pharmaceutical agent facilitates less frequent dosing, which is advantageous for the treatment process. A comparison of Nalmefene and Naltrexone reveals that the latter does not demonstrate a significant impact on the likelihood of individuals returning to heavy alcohol consumption. Conclusions: In the treatment of AUD, both naltrexone and nalmefene have been shown to yield positive outcomes, particularly in terms of reducing the HDD and TAC. According to the World Health Organization (WHO) classification, Nalmefene is indicated for individuals with a high risk of developing serious conditions. It has been demonstrated to produce rapid and sustained results while exhibiting a favorable safety profile, characterized by the absence of significant adverse effects. Naltrexone is a medication that has proven to be effective. LAI may have a positive impact on the efficacy of treatment. Full article

Longitudinal data, characterized by repeated measurements on the same subjects over time, are ubiquitous in biomedical sciences, economics, social sciences, and engineering. Analyzing such data presents unique statistical and computational challenges, including within-subject correlation, time-varying covariates, irregular observation times, informative dropout, and high dimensionality. While traditional statistical methods, such as linear mixed-effects models and generalized estimating equations, remain foundational, they often struggle with complex nonlinear dynamics, ultra-high-dimensional feature spaces, and very large sample sizes. Over the past two decades, machine learning (ML) and artificial intelligence (AI) methods have emerged as powerful complementary approaches to address these limitations. This review provides a comprehensive survey of mathematical and computational methods for longitudinal data analysis. We cover classical statistical models, penalized regression techniques, tree-based ensemble methods, kernel machines, Bayesian hierarchical models, and modern deep learning architectures, including recurrent neural networks, temporal convolutional networks, attention-based Transformers, neural ordinary differential equations, and generative models. We propose a unified taxonomy that organizes existing methods along two primary axes: the underlying mathematical framework and the analytical objective. For each category, we present detailed mathematical formulations, discuss key theoretical properties, examine computational considerations, and summarize representative reported applications drawn from the published literature. To increase the practical value of this review, we provide a cross-cutting comparison of method families against five key challenges (within-subject correlation, irregular sampling, missing data, high dimensionality, and scalability) and offer concrete guidance on method selection according to sample size, dimensionality, and analytical objective. Finally, we critically evaluate the strengths and limitations of these approaches, with particular emphasis on interpretability, scalability, handling of missing data, robustness to covariance misspecification, and uncertainty quantification. Full article

Adaptive reuse of post-industrial heritage is often studied through technical performance, formal intervention strategies, or decision-support models. While these approaches clarify important aspects of reuse, they give limited attention to how projects evolve through the combined effects of architectural decisions, governance arrangements, financing mechanisms, policy instruments, social programs, and inherited fabric. This paper examines adaptive reuse as a time-structured project trajectory. It applies a hybrid methodology combining within-case reconstruction and comparative cross-case analysis to seven European projects in Brussels, Essen, Rotterdam, San Sebastián, Florence, Vienna, and Barcelona. The cases are analyzed across six dimensions: Asset & Context, Governance & Finance, Circularity, Social & Cultural, Policy & Design, and Outcomes & Transfer. The comparison shows that adaptive capacity depends on the alignment of governance, project time, and intervention strategy. Governance determines who can revise decisions and under what conditions; adaptation time is produced through funding horizons, approval procedures, institutional continuity, and civic or public stewardship; and strategies of retention, replacement, reversible insertion, and incremental occupation distribute future risk differently across project phases. From this synthesis, the paper extracts ten conditional lessons that frame adaptive reuse as configuration knowledge: transferable insights whose relevance depends on the interaction among governance capacity, temporal sequencing, inherited fabric, financing, policy support, and social objectives. The paper argues that knowledge transfer in adaptive reuse should be understood as disciplined translation across comparable constraints, not as the replication of models, rankings, or best-practice templates. Full article

Credit card fraud constitutes a core component of the contemporary cybercrime economy, in which dark web carding forums play a pivotal role in coordinating, commoditising, and disseminating illicit activities. While prior research has primarily focused on transaction-level fraud detection, comparatively limited attention has been devoted to the systematic analysis of the social and organisational ecosystems within which these practices are enacted. This study addresses this gap by proposing and validating a domain-specific taxonomy for the automated classification of content in P2P carding forums. To this end, we adopt an iterative, data-driven methodology that integrates large language models (LLMs), lexical co-occurrence analysis, and semantic network analysis. Using a corpus of 3260 posts, we define and operationalise a taxonomy structured around four predicates: activity context, actor role, products and services, and technical tools, supported by a locally deployed LLM (Llama 4 Scout). A human-annotated subset was additionally used to evaluate inter-annotator agreement and standard classification metrics, complementing the coverage-based assessment and enabling comparison against a keyword-based baseline. Evaluation was further strengthened through manual benchmarking, confidence intervals, sensitivity analysis of key pipeline components, and comparison with alternative open-weight models. The results indicate that the proposed taxonomy achieves broad corpus-level representational coverage, with at least one semantic dimension identified in 98.71% of posts. However, coverage is uneven across predicates: activity-context is highly explicit, whereas actor-role and product-service show only moderate coverage and technique-tool remains substantially underrepresented and ambiguous. Overall, the findings show that combining domain-specific taxonomies with LLM-assisted classification and network analysis offers a robust framework for understanding and monitoring carding ecosystems in the dark web. Full article

Information propagation in social media has attracted the wide attention of scholars, with great progress made in empirical and modeling studies. Branching processes, extensively utilized in theoretical biology, are increasingly applied to model information diffusion dynamics. However, detailed and data-driven studies that implement this methodology remain rare. This study, utilizing empirical data, characterizes and models information diffusion in social media with branching processes. The reliability of the branching model is verified through the comparison of theoretical predictions, numerical simulations, and empirical results, and the model can replicate the key statistical characteristics observed in realistic cascades. The research results validate the applicability of branching processes in information diffusion, and contribute to the development of more elaborate, data-driven models of information spreading in complex real-world scenarios. Full article