Abstract: Background: Since the relationship between coagulation factors and serum blood cell factors has not been extensively studied in patients with angioedema (AE) and urticaria, we wanted to examine them. Methods: This cross-sectional study investigated the relationship between coagulation factors, other serum cells and factors, and clinical disease indicators in patients suffering from chronic AE and urticaria. It involved 102 participants, i.e. three groups: 33 patients with isolated AE, 33 patients AE with urticaria (AE/Urt), and 35 healthy controls. Blood samples were collected to analyze the levels of coagulation factors (D-dimer, fibrinogen, factor VII), as well as other serum parameters such as C-reactive protein (CRP), Erythrocyte Sedimentation Rate (ESR) and complete blood count (CBC). Clinical data regarding symptoms and disease severity were also collected using the validated AECT questionnaire. Results: Only D-dimer values differed significantly between groups and were higher in patients with AE/Urt than in controls. At the same time, D-dimers were significantly more often elevated in both AE groups than in healthy individuals. Additionally, CRP values in both AE groups were significantly more often elevated than in controls, with significantly higher values in both AE groups (in both groups 85%) than in controls (57%). Conclusion: The results of this study suggest that abnormalities in coagulation factors and other serum parameters may play an important role in the pathophysiology of AE and urticaria. These findings could help improve the understanding of the mechanisms behind these diseases and provide insights into enhancing diagnostic and therapeutic approaches for patients. Further research is needed.

Abstract: Background: Criminal behaviour has been modelled through sociological, psychological, and biological lenses, yet mechanistic integration across subjective experience, neural processes, and social niche remains limited. Existing frameworks identify risk factors and correlates but rarely specify the computational mechanism through which adverse environments produce criminal inference. Framework: Active Inference (Free Energy Principle) provides a unified computational architecture in which perception, action, and learning are driven by the minimisation of variational free energy under precision-weighted prediction errors. We apply this framework to criminal behaviour, proposing that crime arises from systematic dysregulation of precision weighting within hierarchical generative models, shaped by the interaction between neural architecture and social niche. Model: We specify three distinct pathomechanisms: (1) niche-induced prior rigidity, mapping onto life-course-persistent delinquency; (2) vertical hierarchy collapse, mapping onto crimes of passion; and (3) failed inference repair, explaining recidivism as a systemic prediction of the model. Differential pathways: The model is transdiagnostic: the same upstream precision-weighting dysregulation can produce criminal behaviour, depression, anxiety, or somatic conditions depending on specific niche configuration, policy-precision parameters, and available action models. We formally specify the conditions under which the externalising (criminal) pathway dominates and argue, from a medical perspective, that this pathway warrants particular attention because it is therapeutically underserved, generates additional self-harm through moral injury, and is addressed by a justice system that systematically reinforces the underlying pathology. Predictions: Three falsifiable predictions are derived, each targeting a different pathway: (1) shifted dopaminergic value-learning in juvenile offenders, (2) PFC–amygdala decoupling in crimes of passion, and (3) superiority of inference-based rehabilitation over standard reintegration programmes. The proposed Inference Repair Programme (IRP) targets the generative model itself—not merely behavioural policies—and therefore aims at restoration of inferential flexibility rather than re-normalisation. Epistemic status: The Free Energy Principle as a mathematical principle is unfalsifiable, analogous to Hamilton’s principle of least action. Active Inference as a process theory is falsifiable. Empirical validation to date is in a stage of productive exploration: models explain behavioural data reasonably well but have been applied predominantly post hoc (Hodson et al., 2024). The present predictions offer an opportunity to test Active Inference in a novel domain with genuinely novel predictions.

Abstract: This paper investigates knowledge in the field of digitalization risk management through bibliometric analysis, in order to provide a critical overview of scientific knowledge and highlight future research directions. The main goal involved bibliometric analysis of publications from 2009-2025 using VosViewer and Biblioshiny - Bibliometrix. The research was conducted following a specific methodology and protocol for the design, planning and data collection for the review process; carrying out the review and bibliometric analysis; and evaluating and presenting research findings. The inclusion of studies in the analysis was carried out in accordance with PRISMA 2020 flow diagram template for systematic reviews. The most important results of the research seem to indicate that the analyzed period was marked by an upward trend in scientific interest, with an increase of publications after 2018-2019 and as a result of the Covid-19. The most productive countries are Germany, Italy, Russia, Ukraine and China. The most prolific institutions are Seoul National University and State University of Trade and Economics. Citations tend to follow the annual publication rate. According to thematic map, risk management is one of the motor themes and, as an element of originality, future research trends in this area include themes such as transformation, systems, resilience and digital risks.

Abstract: In this work we will illustrate a new wavelike non linear heat conduction model aimed to implement chiral thermal management and dynamic tunable chiral thermal emission on rotating conductors exposed to chopped laser beam. We will assume the existence of a rotational thermal Hall effect due to a self-induced out of equilibrium Barnett magnetic field showing that they it allows to deviate transversally the harmonic heat flux and to modulate the phase velocity of helical thermal waves propagating on the rotating metallic disks. We deduce a new dynamic chiral Thomson effect proportional to the angular velocity vector of the disk, giving an estimate of its Thomson coefficient in the case of an iron sample. We show that the laser induced chiral Thomson electric field and the time dependent Barnett magnetic field can be exploited to enhance and dynamic control magnetic phase transitions. We introduce finally a dynamic tunable chiral thermal emissivity dependent on a gauge breaking thermal Poynting vector outlining its relevance for a novel rotational approach to non-reciprocal photonics.

Abstract: This study evaluates the effects of Al2O3 and CeO2 nanoparticles as additives to standard diesel and biodiesel fuels on the combustion and emissions characteristics of a CR diesel engine with split injection (pilot and main injections). Three nanoparticle dosing levels (50 ppm, 100 ppm, and 150 ppm) were compared with undoped standard diesel and bio-diesel fuels. The results showed that the presence of both Al2O3 and CeO2 in biodiesel in-creased the ignition delay of the pilot fuel by about 8.0% at low load and about 3.5% at high load. The addition of both nanoparticles to diesel and biodiesel fuels had an insig-nificant effect on the main injection fuel's ignition delay, MBF50 position and combustion duration. The thermal efficiency was up to 1.0% lower. Al2O3 additive in diesel had no significant effect on NOx emissions. CO emissions were higher by 4.4-7.5% in most cases. The Al2O3 additive in biodiesel reduced NOx emissions by an average of 38%, 17.1%, and 9.4% at low, medium, and high engine loads, respectively. The reduction in CO emissions was on average 15%. The addition of CeO₂ nanoparticles to diesel fuel reduced NOₓ emis-sions by 22.5%, 8.5%, and 3.1% on average at low, medium, and high engine loads, re-spectively. When the engine was operated on CeO₂ doped biodiesel, NOₓ emissions were lower by an average of 25.7%, 9.6%, and 2.5% at low, medium, and high loads, respective-ly. Adding CeO₂ nanoparticles to diesel fuel increased CO emissions, whereas adding them to biodiesel significantly reduced CO emissions.

Abstract: Autophagy is a fundamental lysosome-dependent degradation process, which maintains cellular homeostasis in response to stress. VSP34 (Vacuolar Protein Sorting 34, PIK3C3), as the only Class III phosphatidylinositol 3-kinase, generates phosphatidylinositol 3-phosphate (PI3P) for au-to-phagosome nucleation and maturation, thereby providing a critical adaptive survival pathway for cells experiencing metabolic stress. The VPS34-autophagy axis displays a context-dependent dual roles in cancer: it can restrain early tumorigenesis; however, in established tumors it can promote survival under hypoxia, nutrient deprivation, and therapeutic pressure. Additionally, VPS34 shapes the tumor microenvironment (TME) by influencing both immune and cancer cells through modulating autophagy, cGAS-STING (cyclic GMP-AMP synthase Stimulator of Interfer-on Genes) and STAT1 pathways. VPS34 inhibition has been reported to induce interferon response that enhance CD8+ T and natural killer (NK) cell infiltration and convert cold tumor into hot, providing a rationale for combination of VPS34 inhibitors with cancer immunotherapies. In this review, we summarize the molecular functions and regulations of VPS34 in autophagy and dis-cuss recent advances linking VPS34 to tumor and cancer immunotherapy.

Abstract: The mobile industry has experienced long-run changes in its knowledge structure, including identifiable transition points observable through meaning-based analysis. Using abstracts from 86,674 mobile-industry publications published between 2005 and 2024, we embed documents with SPECTER2, build year-specific embedding distributions, and derive knowledge regimes by combining change-point detection with inter-year distribution distances. We then extract regime-specific topics via clustering and reconstruct topic lineages by aligning topic similarities to classify inheritance, differentiation, convergence, and disappearance. The analysis delineates three regimes spanning 2005 to 2012, 2013 to 2019, and 2020 to 2024, with pronounced transitions around 2012 to 2013 and 2019 to 2020. Regime 1 centers on foundational technologies such as wireless communication, power, sensors, and reliability. Regime 2 expands toward platforms, apps, and data analytics alongside cross-domain convergence. Regime 3 is characterized by strengthened 5G operations and data-driven services, together with the independent rise of policy, governance, and regulation topics. Transitions reflect recombination built on inherited knowledge rather than abrupt replacement, and post-transition topics display distinct growth typologies by network position and growth pattern. By integrating embedding-based change-point detection with topic-lineage reconstruction, we provide a reproducible account of regime transitions and quantitative evidence to inform the timing of corporate R&D, standard and platform strategies, and policy and regulatory design.

Abstract: Molecular interactions underpin the functioning of the living cell. Molecules exist in distinct quaternary structural forms, associate with molecular partners in signaling cascades, form transient quinary interactions, localize in membrane domains, and cluster in membrane-less condensates. Measuring the concentration, size, and dynamics of these molecular assemblies remains an enduring biophysical challenge, particularly in cells, where heterogeneity is the rule rather than the exception. Orthogonal signals derived from fluorescence lifetime, fluorescence fluctuations, and fluorescence polarization provide valuable metrics for probing interactions and environments, concentration and size, as well as rotational dynamics, respectively. This paper combines fluorescence lifetime imaging microscopy with image correlation analysis and polarization to determine the concentrations, brightness, lifetime, and rotational correlation time of different fluorescent states. A two-population model is examined as a prototypical example of a heterogenous system. The analysis is illustrated on a simple fluorescence model system, where cluster densities, relative brightnesses, lifetimes and rotational correlation times are extracted.

Abstract: Quantum gravity is often probed by thought experiments that assume unlimited classical control over quantum systems. The firewall paradox is one such thought experiment. It asks if one observer can decode early Hawking radiation, and then later compare with degrees of freedom behind the horizon. Here I show that this protocol is constrained by an instruction budget. If the correct decoding depends on the black hole microstate, a hidden internal configuration, then the observer must specify which decoder to use. The number of decoders needed to cover all microstates grows exponentially with the black hole entropy, so the decoder specification requires a number of bits that scales with that entropy. A covariant entropy bound limits how much information can be stored within a bounded causal patch. This enforces a horizon-scale lower bound on the region required for single-observer verification, even when computational complexity is ignored. More broadly, the result highlights a principle for quantum gravity experiments. Control information is physical, and bounds on storage can decide what is testable.

Abstract: This paper presents a comprehensive diagnostic framework for electrical machines, based on the application of artificial neural networks (ANNs) for the analysis of electrical and vibration signals. The proposed method leverages deep learning architectures to automatically extract informative features and achieve high fault classification accuracy. The framework integrates signal pre-processing, neural network training, and a condition evaluation module, enabling the implementation of a predictive maintenance system suitable for industrial applications. A multi-sensor diagnostic system is proposed, combining CNN-LSTM architectures with a graph neural network (GNN) for correlational analysis of currents, vibrations, and thermal parameters. This approach allows early detection of inter-turn short circuits and bearing faults, improving diagnostic accuracy by 7–12% compared to existing state-of-the-art methods. The framework demonstrates robustness under varying operating conditions, including transient and self-excitation regimes, and provides physically interpretable results, bridging the gap between data-driven and physics-informed diagnostics.

Abstract: Background: Accelerating global population aging underscores the need to identify multidimensional determinants of successful aging. This review synthesizes evidence on social, spiritual, and religious factors that shape well-being, and quality of life in later adulthood. Successful aging is conceptualized as adaptation to age-related challenges through internal and external resources. Emerging research suggests that pet ownership and companion animals promote meaning, purpose, and social connectedness, while spiritual, religious, and contemplative practices support coping, psychological stability, and foster life satisfaction in older adults. Methods: This review of the literature examines the intersection of spirituality, religious practice, meditation, life purpose, and pet ownership as mediating and reinforcing influences on successful aging. This review focused on evidence linking prayer, meditation, chanting, and spiritual and religious participation to psychological, cognitive, and physiological outcomes, as well as literature exploring human-animal relationships in later life. Results: Findings indicate that spiritual and religious practices, companion animal relationships, and contemplative practices support core aspects of successful aging, including emotional well-being, reduced loneliness, enhanced coping, and greater life meaning. Yet, it remains unclear whether these influences act synergistically or independently, and how they shape older adults’ experiences of aging and adaptation to decline. Conclusions: Incorporating spiritual, religious, and contemplative practices alongside companion animals may enhance holistic models of successful aging by supporting emotional, social, and spiritual well-being. Future research should explore multidimensional mechanisms to inform interventions that improve quality of life in later adulthood.

Abstract: Extracellular vesicles (EVs) are lipid bilayer–bound structures capable of transporting molecular markers from their cell of origin and are secreted by multiple cell types, including malignant cells. EVs have emerged as promising tools for developing less invasive diagnostic approaches. In B-cell acute lymphoblastic leukemia (B-ALL), immunophenotypic characterization of extracellular vesicle–enriched populations (EVEPs) in peripheral blood (PB) may provide complementary information for disease detection and monitoring. This exploratory study aimed to characterize EVEPs obtained from peripheral blood (PB) and bone marrow (BM) of adult patients with B-ALL and to compare them with the clinical immunophenotype (CIP). EVEPs were isolated by differential centrifugation and analyzed by flow cytometry and confocal microscopy, primarily evaluating CD3 and CD19 expression. EVEPs derived from PB samples of patients with B-ALL showed increased expression of B-lineage markers (CD45, CD34, CD19, CD20, and CD10), consistent with the leukemic phenotype identified in the CIP. Additionally, CD3⁺CD19⁺ EVEPs were occasionally detected. These findings suggest that EVEPs partially reflect the leukemic immunophenotype and may serve as a complementary source of biological information. The detection of CD3⁺CD19⁺ events highlights complex cellular interactions within the leukemic niche and warrants further investigation.

Abstract: Streaming Transformer Networks: Unified Hearing-to-Speech Recognition and Intelligent Text Generation Systems introduce a groundbreaking architecture that processes real-time audio streams to produce both synthesized speech outputs and contextually intelligent text, overcoming traditional limitations in multimodal AI systems. Traditional speech recognition models often operate offline, requiring full audio sequences before generating results, which hinders interactive applications. This work proposes a transformer-based framework that unifies hearing-to-speech translation directly converting input audio into natural-sounding speech with advanced text generation capabilities, enabling seamless dual-mode responses in conversational agents. By adapting transformers for streaming via causal attention and triggered mechanisms, the system achieves low-latency performance while maintaining high fidelity in prosody preservation and semantic coherence. Key innovations include shared encoder layers for efficiency, hybrid decoding paths for modality-specific outputs, and joint optimization across diverse objectives like word error rate minimization and perceptual quality enhancement. Evaluations on standard benchmarks demonstrate superior results, with latency under 200ms and error rates rivalling non-streaming baselines, paving the way for deployment in voice assistants, live captioning, and real-time dialogue systems. This unified approach not only reduces model complexity but also advances end-to-end learning for dynamic audio-to-multimodal generation tasks.

Abstract: This paper formulates and analyzes a novel compartmental model to study the spatial dynamics of corruption, framed as a pathogenic social strategy within a biological resource-competition framework. The model incorporates a renewable resource, whose scarcity drives the transmission of a corrupt strategy among a population of cooperators. The population is stratified into Cooperators (S), Corruptors (C), and Immunes/Enforcers (I), interacting within and between two connected patches via migration. The model exhibits a resource-dependent transmission rate and predator-prey dynamics between Corruptors and Enforcers. We establish the well-posedness of the coupled two-patch system by proving the positivity and boundedness of solutions. The system exhibits a corruption-free equilibrium, whose local and global stability is determined by patch-specific basic reproduction numbers R₀⁽¹⁾ and R₀⁽²⁾ , as well as a system-level reproduction number R0 that incorporates migration. We derive critical migration thresholds where the stability of the corruption-free state changes. Bifurcation analysis reveals the existence of a forward transcritical bifurcation at R₀ = 1, implying that reducing the system-level reproduction number below unity is sufficient to eliminate the corrupt strategy even in connected populations. Sensitivity analysis via Partial Rank Correlation Coefficients (PRCC) identifies the most critical parameters influencing R₀⁽ⁱ⁾ , providing evidence-based policy insights. Numerical simulations corroborate our analytical findings and explore the impact of asymmetric migration on the persistence of corruption. This work provides a theoretical foundation for understanding corruption through an ecological and spatial lens, highlighting the paramount importance of resource availability, enforcement mechanisms, and cross-border connectivity.

Abstract: Rapid urbanization has aggravated the challenges in sustaining the peri-urban rice farming sector. The challenges arising from rapid urbanization are threatening rice farmers in peri-urban areas due to increasing economic and land pressures. This has caused a significant marginalization among rice farmers. In Indonesia, despite contributing 13.28% of the national GDP in 2021, the agricultural sector is dominated by marginal farmers who struggle with poverty and lack of land ownership. This study aims to identify different pathways for marginalization of rice farmers by integrating spatiotemporal land use and land cover (LULC) change analysis, landscape fragmentation metrics, and system dynamics through causal loop diagrams (CLD). Furthermore, the redefinition of the term marginal rice farmers is done by considering the total cultivated rice field and broader factors that contribute to the self-reinforcing loop of marginalization. This study shows that rice farmer marginalization in peri-urban areas is caused by small land size or poverty, and reinforcing feedback between ecosystem service degradation, productivity decline, economic pressure, and land conversion that interact differently across landscape configuration. Moreover, this study enhances the understanding of peri-urban agricultural transformation and provides landscape-sensitive policy insights to support inclusive and resilient agricultural systems by reconceptualizing marginalization of rice farmers as a dynamic socio-spatial process.

Abstract: The appearance of supermassive black holes (SMBHs) within approximately the first 800 million years after the Big Bang remains subjects of intense scrutiny and debate. In this work, we investigate the growth of black holes in a decaying vacuum. This scenario may offer novel insights into the formation and growth of SMBHs.

Abstract: Tropical forests are predicted to become carbon sources by mid-century under climate change. However, this trajectory may not be inevitable for forests under long-term protection. Using 12 years of eddy covariance flux data from a strictly protected tropical forest in Xishuangbanna, China, we develop an explainable machine learning framework (SHAP + Structural Equation Modeling) to disentangle the environmental drivers of net ecosystem exchange (NEE) and evapotranspiration (ET), and project their future trajectories under four CMIP6 climate scenarios. We find a fundamental divergence: while conventional climate models predict a sink-to-source transition by 2050–2066, our data-driven model—trained on conservation-era observations—projects a persistent carbon sink through 2100 across all scenarios. This divergence suggests that long-term protection may buffer tropical forests against climate-driven decline, challenging the prevailing narrative of inevitable carbon loss. We further identify critical environmental thresholds—solar radiation (~200 W m⁻²) and air temperature (~25°C)—beyond which carbon uptake efficiency declines. Our findings provide empirical support for nature-based climate solutions and highlight the need to integrate conservation legacies into Earth system models.

Abstract: As service robots increasingly enter public buildings such as hospitals and offices, human-robot sharing space has emerged as a pivotal topic in architectural design field, yet its relevant theoretical framework remains underdeveloped and incomplete. Existing frameworks—including Human-Robot Interaction (HRI), Human-Robot Collaboration (HRC), and Human-Robot Coexistence—have advanced research on interaction, coordination, and safety, but most regard the built environment as a passive backdrop, overlooking its active design value. This review retrieved literatures from 2000 to 2026 across four databases (Web of Science, Scopus, IEEE Xplore, and ScienceDirect) and analyzed 183 core publications using CiteSpace, systematically synthesizing the interdisciplinary knowledge in this field. The study introduces "Human-Robot Sharing Space (HRSS)" as an independent conceptual framework, repositioning the built environment from an interactive background to a core design variable while clarifying its boundaries with other traditional frameworks. Through bibliometric analysis, it reveals the field’s evolutionary trajectory from basic technical exploration to scenario-specific refinement. Finally, five systematic gaps in current research are identified: interdisciplinary theoretical integration, transferability to real-world scenarios, multidimensional evaluation indicators, coverage of architectural typology, and longitudinal empirical studies. This review bridges the gap between robotic technology and architectural design needs, providing a theoretical foundation for constructing an environment-centric, scale-inclusive, and practical design framework for HRSS.

Abstract: Consciousness presents a structural puzzle: a unified, context-sensitive, globally integrated mode of experience emerging from distributed neural dynamics. While classical neuroscience has mapped synaptic, oscillatory, and network-level mechanisms with increasing precision, debate persists as to whether classical formalisms fully capture the integrative and contextual features of conscious processing. This review examines whether quantum principles offer explanatory leverage in two distinct senses: as formal mathematical frameworks for modeling contextual cognition, and as mechanistic hypotheses proposing biologically instantiated non-classical states. We survey empirical and theoretical developments spanning zero-quantum-coherence in MRI signals, entanglement-structured learning paradigms, quantum-inspired computational models, and proposed neural substrates, including microtubules, nuclear spins, and photonic architectures. Although certain findings have been interpreted as consistent with a non-classical structure, no study to date has demonstrated entanglement, long-lived coherence, or collapse dynamics in neural tissue under operational criteria comparable to those used in controlled quantum systems. Replication remains limited, biological entanglement witnesses are not yet established, and nonlinear classical dynamics can reproduce many putative quantum signatures. Accordingly, the decisive question is not whether the brain is quantum, but whether its dynamics exceed the explanatory reach of rigorously defined classical models. Progress hinges on replication, adversarial scrutiny, and operational criteria precise enough to discriminate genuine non-classical correlations from classical complexity. Whether quantum mechanisms ultimately prove necessary or refined classical models remain sufficient, this inquiry compels a deeper understanding of integration, contextuality, and the physical constraints shaping conscious experience.

Abstract: Motivated by Matsas et al. (2024), who demonstrated that time can serve as the fundamental unit for physical quantities, thereby obviating the need for traditional length–mass–time (LMT) dimensions, this study expands on some of the presented results. Using a Lorentz transformation (LT) matrix approach, we first validate the three-clock protocol, confirming distance derivation as a function of three proper clock times in a round-trip-like arrangement in Minkowski spacetime and additionally identifying moving clocks velocities without a distance measurement, which is already implicitly identified. The investigation was then extended to Tangherlini’s 4D spacetime framework (1958) to test the hypothesis that absolute velocity can be resolved through subluminal motion experiments. While initial three-clock scenarios resulted in systematic absolute velocity cancellation, a breakthrough was achieved by applying relativistic transverse Doppler effect logic. This approach successfully circumvents cancellation effects by identifying those electromagnetic waves in transit as becoming ‚anonymous‛ and owned by the Absolute Rest Frame (ARF), independent of source origin. We demonstrate that the ratio of transverse to longitudinal wave-vector components ky/kx provides a direct measure of the peculiar velocity relative to the Cosmic Microwave Background (CMB), fully reconciling with aberration angle methodologies utilised in Planck 2013 mission measurements. The findings reveal that both frameworks are mathematically equivalent representations of the same underlying reality, inevitably predicting absolute velocity despite historical objections. Consequently, a plausible absolute velocity methodology without instantaneous signals is proven possible, closing the "cancellation gap" via wave-vector geometry, and confirming the Tangherlini and special relativity theory (STR) frameworks.