We propose a two-level information-theoretic framework for characterizing the informational organization of Agent-Based Model (ABM) dynamics within the broader paradigm of Complex Adaptive Systems (CAS). At the macro level, a pooled $\epsilon$-machine is reconstructed as a reference model that summarizes the system-wide informational regime. At the micro level, $\epsilon$-machines are reconstructed for each caregiver-elder dyad and variable, and are complemented with algorithm-agnostic Kolmogorov-style measures, including normalized LZ78 complexity and bits per symbol from lossless compression. The resulting feature set $\{h_{\mu}, C_{\mu}, E, \mathrm{LZ78}, \mathrm{bps}\}$ enables distributional analysis, stratified comparisons, and unsupervised clustering across agents and scenarios. This dual-scale design preserves agent heterogeneity while providing an interpretable macro-level baseline, aligning ABM practice with CAS principles of emergence, feedback, and adaptation. A case study on caregiver-elder interactions illustrates the framework's implementation; the results and discussion will be completed following final simulation runs.

In systemic lupus erythematosus (SLE), treatment decisions are guided by clinical judgment based on disease manifestations and safety profiles rather than standardized protocols, particularly for extra-renal involvement. In this study of 356 SLE patients, 190 receiving non-biologic immunosuppressants and 166 receiving biologics, we investigated the association of clinical phenotypes with treatment initiation and the impact of damage (SLICC-DI), comorbidities, and hospitalization history on these choices. Dominant clinical phenotypes were defined qualitatively using BILAG and SLEDAI domains. Logistic regression models with Simes-Hochberg correction revealed that renal phenotypes were strongly associated with mycophenolate initiation (OR = 4.09, p < 0.001), musculoskeletal phenotypes with methotrexate (OR = 4.86, p < 0.001). Belimumab was preferentially initiated in patients with musculoskeletal involvement and high SLEDAI scores (OR = 1.84, p = 0.03; OR = 2.03, p = 0.03, respectively). Notably, the association between mycophenolate and the renal phenotype persisted in the presence of comorbidities but was not observed in patients with SLICC-DI > 0 or more than one hospitalization in the previous year. Similarly, methotrexate and belimumab associations were diminished in patients with a Charlson comorbidity index > 1 or damage (SLICC-DI > 0). This study offers novel insights into the clinical determinants of immunosuppressive therapy selection in SLE and underscore the potential for tailoring treatment strategies to individual patient profiles.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-19654-8.

ABSTRACT The temporal need-threat model of ostracism, developed by Kipling Williams, provides a comprehensive framework for understanding how ostracism – being ignored and excluded – impacts fundamental human needs over time. This manuscript critically examines the temporal dimension of this model by addressing three key questions. First, does chronic ostracism inevitably lead to resignation? This section explores the inevitability of resignation outcomes and the potential role of moderating factors. Second, do individual differences influence reactions across the model’s three stages? This section investigates how personal traits may shape responses to ostracism, from reflexive reactions to long-term resignation. Third, how can the model be adapted to capture the specific features of digital ostracism? This section discusses the challenges and opportunities involved in applying the model to technology-mediated interactions. Ultimately, the manuscript advances theoretical and empirical understanding by refining the temporal structure of the model to account for both in-person and digital forms of ostracism.

In most patients with atrial fibrillation (AF), effective stroke prevention necessitates long-term (often lifelong) oral anticoagulant therapy (OAC). However, the effectiveness of OAC therapy in a clinical setting (i.e. outside the controlled environment of randomized clinical trials) is strongly influenced by patients’ adherence and persistence with prescribed therapy. However, suboptimal adherence to OAC remains a substantial problem in routine practice—available evidence suggests that patients do not take their OAC one out of every four days, and approximately one in three to four patients is poorly adherent to OAC. In addition, around 15% of high-risk OAC-eligible patients with AF refuse to take OAC for a variety of patient-specific reasons. Poor adherence to OAC therapy is associated with adverse clinical outcomes [such as stroke or systemic embolism, hospitalization, mortality, bleeding (particularly with vitamin K antagonist therapy)] and increased economic costs. In this overview, we summarize important aspects of the adherence to medication concept, including the definition and measurement of adherence, the determinants and prevalence of OAC non-adherence, the clinical importance of achieving and maintaining good adherence, strategies to improve adherence to OAC, and alternative treatment options for effective thromboprophylaxis in patients with AF who are non-adherent to OAC therapy.

Osteoarthritis (OA) is the most common joint disorder,characterizedby a vicious cycle of synovial inflammation and cartilage degradation.Intra-articular injection of hyaluronic acid (HA)-based products,one of the currently available treatments, provides only temporarysymptomatic relief without addressing the underlying inflammation.Here, we engineered several configurations of 20 × 5 μmsquare-shaped HA-based hydrogel microparticles (μHA) by photopolymerizingHA–methacrylate chains within a sacrificial template. The μHAmechano-pharmacological properties were tuned by adjusting the HAconcentration, molecular weight, and degree of methacrylation, resultingin microparticles with a Young’s modulus ranging from a fewtens (30 kPa) to a few hundred (200 kPa) kilopascals; a structurestable for over a month under oxidative stress conditions; and reducedfriction in simulated synovial fluids. Under H2O2-induced oxidative conditions, μHA decreased the productionof proinflammatory cytokines (IL-6, IL-1β, and TNF-α)in human chondrocytes to basal levels. In a three-dimensional OA cartilagemodel, μHA reduced glycosaminoglycan release and matrix metalloproteinase-13activity, demonstrating chondroprotective effects. In a rigorous murinemodel of early-stage post-traumatic OA, a single intra-articular injectionof μHA lowered proinflammatory gene expression in the synoviumto basal levels. In summary, μHA offers a drug-free approachto managing OA by enhancing lubrication and reducing inflammation,providing a sustained therapeutic activity over several weeks.

Abstract Scintillation detectors are essential tools in high‐energy physics, medical imaging, and security, due their efficiency in converting ionizing radiation into visible light. Lead‐based inorganic perovskites, particularly 3D CsPbBr3, have emerged as promising next‐generation scintillators due to their high photon attenuation and fast emission properties. In contrast, the 0D phase, Cs4PbBr6, exhibits unique emission characteristics and defect‐mediated behavior, offering additional opportunities to tune scintillation performance in hybrid systems. However, the role of the 0D Cs4PbBr6 phase in scintillation has remained largely unexplored, and the mechanism of the emission is not well understood. Herein, a simple and reproducible synthesis of polycrystalline perovskite powders is developed with the specific scope of modulating the 3D/0D CsPbBr3/Cs4PbBr6 phases in the samples, aiming to clarify the role of the 0D phase in the emission properties of the materials. The method relies on a solvent‐antisolvent approach, in which incremental water additions selectively promote the formation of the 3D phase over the 0D one. The scintillation properties of the resulting powders are evaluated, revealing an increased scintillation yield for low water volumes used in the synthesis and an ultrafast decay time under X‐ray radiation. Cathodoluminescence and temperature‐dependent radioluminescence highlight defect‐driven scintillation mechanisms, providing insights for future material optimization.

Abstract Quasiperiodic eruptions (QPEs) are luminous, recurring X-ray outbursts from galactic nuclei, with timescales of hours to days. While their origin remains uncertain, leading models invoke accretion disk instabilities or the interaction of a massive black hole (MBH) with a lower-mass secondary in an extreme mass ratio inspiral (EMRI). EMRI scenarios offer a robust framework for interpreting QPEs by characterizing observational signatures associated with the secondary’s orbital dynamics. This, in turn, enables extraction of the MBH/EMRI physical properties and provides a means to test the EMRI scenario, distinguishing models and addressing the question: what can QPE timings teach us about MBHs and EMRIs? In this study, we employ analytic expressions for Kerr geodesics to efficiently resolve the trajectory of the secondary object and perform GPU-accelerated Bayesian inference to assess the information content of QPE timings. Using our inference framework, referred to as QPE-FIT (Fast Inference with Timing; https://github.com/joheenc/QPE-FIT/tree/main), we explore QPE timing constraints on astrophysical parameters, such as EMRI orbital parameters and MBH mass/spin. We find that mild-eccentricity EMRIs (e ∼ 0.1–0.3) can constrain MBH mass and EMRI semimajor axis/eccentricity to the 10% level within tens of orbital periods, while MBH spin is unconstrained for the explored semimajor axes ≥100R g and monitoring baselines O (10–100) orbits. Introducing a misaligned precessing disk generally degrades inference of EMRI orbital parameters, but can constrain disk precession properties within 10%–50%. This work both highlights the prospect of QPE observations as dynamical probes of galactic nuclei and outlines the challenge of doing so in the multimodal parameter space of EMRI–disk collisions.

Purpose This study addresses the persistent issue of gender diversity in the high-tech sector, where women remain particularly underrepresented. We investigated the relationship between the presence of women managers and the percentage of female employees within high-tech firms. Furthermore, the moderating role of work–life balance (WLB) policies was examined. Drawing upon social role theory (SRT), our research highlighted the twofold relevance of leadership diversity and structural support in promoting gender equity at all organisational levels. Design/methodology/approach Using a sample of 512 firm-year observations from European listed companies operating in the high-tech sector, a longitudinal analysis spanning the period 2014–2020 was carried out. Findings Our findings proved that women in top management team (TMT) positions positively influence the percentage of female employees, with WLB policies significantly and positively moderating this relationship. Originality/value While much of the existing research has focused on women’s representation within boards of directors (BoD), scant attention has been devoted to the influence of women in leadership positions. This study enriches the SRT framework by emphasising the theoretical link between female leadership representation and organisational mechanisms encouraging gender diversity. To this end, this research further feeds diversity management and organisational change literature. The focus on the high-tech sector aims to provide intriguing food for gender disparities in one of the most male-dominated industries. By demonstrating that women managers can help close the gender gap through both role-modelling and structural support mechanisms, this study offers a pathway to mitigate cascading inequities and nurture a more inclusive labour market.