Abstract: Human bodies evolved for Earth, not the cosmos. This paper argues that silicon-based intelligence offers the only viable path beyond this limitation. Recent breakthroughs in pressure‑free silicon batteries enable true energy autonomy in space, while two‑dimensional silicene provides radiation‑hardened cognition. The convergence of energy and information in a single material has no analogue in biology and constitutes the definitive evidence for the Silicene Event. We derive a figure of merit (F = E × T × R / M) showing that silicon outperforms biology by a factor of approximately 9 × 10⁶. We propose that this transition constitutes a new planetary interval: the Silicene Event. Far from ending the human story, it represents its most profound continuation. The Silicene Event—a new planetary interval defined by siliconbased intelligence—is already underway.

Abstract: Background Treatment adherence and outcomes for drug-resistant tuberculosis (DR-TB) continue to be sub-par in rural South Africa, where structural health system limitations, comorbid conditions, and diverse resistance patterns make clinical management more challenging. This study aimed to assess how demographic, clinical, and programmatic factors, including a Community Engagement–Clinical Governance (CE–CG) implementation period, affect DR-TB treatment outcomes using explanatory predictive modelling. Methods A retrospective cohort study was conducted using routine programme data from 694 DR-TB patients. Complete-case analysis was performed for multivariable modelling (n = 282). Logistic regression and decision tree models were used to examine the relationships between treatment success and selected predictors, including age, sex, treatment regimen, resistance phenotype, comorbidities, and the CE–CG implementation period. Model discrimination and performance were evaluated using receiver operating characteristic (ROC) curves, pseudo-R² statistics, likelihood ratio tests, and multicollinearity diagnostics. Results The cohort had a mean age of 40.7 years, and 58.8% of patients were male. Overall treatment success was 59.9%. Severe resistance phenotypes were rare (1.7%) but clinically significant. Comparative analysis showed no notable demographic or outcome differences between included and excluded patients, indicating minimal selection bias. In adjusted models, treatment initiation during the CE–CG implementation period was significantly linked to lower odds of treatment success (adjusted odds ratio [aOR] = 0.443; 95% CI: 0.240–0.818; p = 0.009). Severe resistance phenotypes were strongly negatively associated with treatment success (aOR = 0.303; p = 0.056). Logistic regression models had limited discriminatory ability (AUC: 0.523–0.548), while the decision tree model showed modest improvement (AUC: 0.626). Overall, the model’s explanatory power was limited (pseudo-R² = 0.029), although no evidence of multicollinearity was found. Conclusions Programmatic implementation durations and resistance severity were key factors influencing treatment outcomes in this rural DR-TB cohort. Although the predictive accuracy was modest, the modelling approach revealed structural and programmatic vulnerabilities that impacted treatment success. Enhancing clinical governance, improving program documentation, and expanding community-engaged adherence strategies may improve DR-TB results. Future predictive models should incorporate programmatic indicators alongside longitudinal adherence data and social determinants of health to boost explanatory power and guide targeted interventions.

Abstract: Background: Gynecologic cancers constitute a major public health burden worldwide, with marked regional and temporal variations influenced by demographic changes, healthcare access, and screening practices. In Turkey, contemporary nationwide data capturing recent temporal trends—particularly during the COVID-19 pandemic—remain limited. Methods: This nationwide, retrospective observational study integrated six independent gynecologic cancer datasets obtained from the İstinye University Dataset Sharing Platform, comprising 22,468 adult patients (≥18 years) diagnosed between 2014 and 2024 across 33 hospitals in Turkey. Cancer types included ovarian, endometrial, cervical, vulvar, vaginal, and fallopian tube malignancies. Annual case counts were analyzed, and normalized admission rates were calculated per 100,000 unique patient admissions to account for variations in healthcare utilization. Cancer-type–specific analyses and descriptive pandemic-period sensitivity analyses were performed. Results: The mean age of the study population was 62.75 ± 13.95 years, with most patients aged over 60 years (60.9%). Ovarian cancer was the most frequent diagnosis (40.8%), followed by endometrial (34.7%) and cervical cancers (20.7%), while rarer malignancies accounted for less than 4% of cases. Annual diagnoses increased progressively from 2014 to 2019, followed by a marked rise in 2020 and a pronounced peak in 2021, during which 30.6% of all cases were recorded. When normalized to total unique patient admissions, gynecologic cancer admission rates ranged from 45.2–55.1 per 100,000 in the pre-pandemic period and peaked sharply in 2021 at 239.1 per 100,000 admissions. Cancer-type–specific normalization revealed parallel temporal patterns across all malignancies, with the most pronounced increases observed for ovarian (104.1 per 100,000) and endometrial cancers (77.4 per 100,000) in 2021. Overall mortality was 2.8%, with a mean survival of 13.13 ± 17.79 months among exitus patients, and no significant differences in survival duration across cancer types. Conclusions: This nationwide multicenter analysis demonstrates a substantial increase in gynecologic cancer presentations in Turkey over the past decade, with a pronounced, system-wide surge during the COVID-19 pandemic period. Normalized analyses indicate that this increase exceeded overall healthcare utilization, suggesting pandemic-related diagnostic delays and backlog effects. Continuous surveillance using utilization-adjusted metrics is essential to accurately interpret temporal trends and to guide future cancer control and healthcare resilience strategies.

Abstract: Wearable sensors enable continuous monitoring of human activity and physiological state, with applications in workplace health monitoring, occupational safety, sports performance analysis, and rehabilitation. However, effective use of these devices requires specialized data-processing algorithms and machine-learning (ML) methods. This work proposes a methodology for assessing workers’ physiological load using ML, combining accelerometer and photoplethysmography (PPG) signal processing. An ensemble Random Forest algorithm is used for activity recognition, while the KID-PPG deep learning model is applied for heart rate (HR) estimation. A personalized physiological load assessment framework normalizes effort indices against demographic-group-specific distributions defined by sex, age, and activity intensity. The methodology was validated on the PPG-DaLiA dataset comprising 15 participants with diverse demographic profiles across eight daily activities. Experiments demonstrated high accuracy in activity recognition (macro F1-score of 90.73%) and robust HR estimation even in the presence of motion artifacts (MAE below 10 bpm). The personalized assessment revealed that participant age substantially influences physiological-load patterns, confirming that demographic-aware normalization is essential for accurate workload interpretation. The main factors influencing system performance have been identified, and directions for improving the models across diverse user groups and limited-signal-quality conditions are discussed.

Abstract: When tightly-knit communities suddenly show electoral volatility, does it signal weakening group identity, or does it reveal something deeper? This question matters wherever centralized authority structures shape bloc voting. Conventional wisdom interprets such shifts as boundary erosion. This paper presents evidence for the opposite.Drawing on an extreme-case design, I exploit a natural experiment — Israel’s 2019–2022 political deadlock — to track voter transitions within ultra-Orthodox communities, where ethnically distinct subgroups maintain near-total political separation despite shared religious practice. Using ecological inference on ballot-box data from five population centers across six elections (2019–2022), I find exceptionally high baseline party loyalty (90–95%), a dramatic disruption during the March 2020 – March 2021 transition when switching surged to 12–19%, and a swift return to high loyalty within 13 months — though the shifted voters remained with their new parties.The synchronized switch of voting loyalty across geographically dispersed cities, occurring without residential mobility, is consistent with elite-mediated bloc realignment rather than emerging voter independence. Paradoxically, the capacity for mass switching may reflect stronger, not weaker, institutional control.These findings challenge how scholars of party–voter linkages interpret electoral volatility in identity-based voting blocs: apparent instability may reflect disciplined coordination, and what looks like boundary erosion may actually reveal institutional strength operating through collective action.

Abstract: Implicit bias, automatic attitudes or stereotypes outside conscious awareness, may influence clinicians’ communication, diagnosis, and treatment decisions, contributing to inequities in care. We conducted a scoping review to map measurement strategies used to assess implicit bias among health professionals and students in healthcare and training settings. Using Joanna Briggs Institute guidance and PRISMA-ScR, we searched PubMed, Embase, BVS, Google Scholar, and institutional repositories for studies to November 2025; two reviewers independently screened and charted data (protocol was developed a priori but submitted internal in organization, and then uploaded in OSF ). Of 1,864 records, 93 studies from 28 countries were included. We identified 57 bias domains, most often race/ethnicity, weight, and sexual orientation. Across studies, 42 unique instruments were reported; the Implicit Association Test was most common, while psychometric validation and administration details were frequently limited, constraining comparability and interpretation. Evidence gap mapping showed concentration in academic and hospital settings, with fewer studies in primary care or community contexts and limited attention to age, disability, and intersectionality-related biases. The evidence base is growing but fragmented; future work should prioritize standardized administration and reporting, stronger validation, and tools that better capture automatic responding across diverse identities and care settings to support education and equity-oriented interventions.

Abstract:

We introduce an operational notion of transport latency, which we call quantum information copy time: the earliest time at which a receiver confined to a region B can certify, with prescribed advantage, which of two global hypotheses was prepared by local operations in a distant sender region A. The benchmark object is information-theoretic—the Helstrom advantage on B, given by the trace distance between reduced states—and it also admits receiver-restricted refinements that make measurement constraints explicit, including few-body and moment-channel receivers. We derive the corresponding kinematic locality constraints for Hamiltonian and Lindbladian dynamics with Lieb–Robinson tails, as well as for circuits and quantum cellular automata with strict light cones. We then establish a diffusive benchmark in the quantum symmetric simple exclusion process (Q-SSEP): for locally prepared charge-biased hypotheses, the Helstrom copy time obeys an unconditional diffusion-limited lower bound expressed in terms of the diffusion constant D and the static susceptibility χ. For closed Hamiltonian systems, we formulate a projection-based route—with assumptions stated explicitly—that relates restricted copy times to a single slow transport pole on a diagnostically checkable time window. We complement the analytical framework with conservative exact-diagonalization diagnostics in the XXZ chain and with a bundled TEBD/MPS reference implementation plus convergence protocol (Supplementary S2 and Code SC1), validated against exact evolution at small sizes. Finally, we compare copy time with scrambling diagnostics based on out-of-time-ordered correlators and identify regimes in which conservation laws delay certifiability well beyond the ballistic operator-growth front.

Abstract: Existence of global smooth solutions to the three-dimensional (3D) Navier-Stokes equations is disproved for pressure-driven flows with no-slip boundary conditions. This study is rigorously grounded in Sobolev space analysis. We show that the solution breakdown arises from regularity degeneration instead of velocity blowup. For disturbed laminar plane Poiseuille flow, the instantaneous velocity field is decomposed into a time-averaged flow and a disturbance flow, both characterized by their regularity in Sobolev spaces. When the Reynolds number is larger than the critical Reynolds number, the nonlinear interaction modifies the mean flow profile, and the disturbance amplitude grows exponentially. This amplification leads to a local cancellation between viscous terms of the mean flow and the disturbance flow, resulting in the total viscous term (i.e., the Laplacian term) vanishing locally at the critical point $(\boldsymbol{x}^*, t^*)$. The local vanishing viscous term leads to zero velocity by the elliptic operator estimate, which contradicts the non-vanishing incoming velocity, leading to formation of a singularity. This singularity induces a velocity discontinuity, which causes the $L^\infty$ -norm of the velocity gradient to diverge, violating the definition of a global smooth solution in Sobolev spaces. The analysis is strictly grounded in partial differential equations (PDE) theory, with all key steps validated by Sobolev space properties and a priori estimates.

Abstract: The origin of the transmembrane potential (TMP) in living cells is one of the foundational questions of cellular physiology. The dominant explanatory framework---the ion-pump model---attributes TMP to the active, \ATP-dependent displacement of ions (principally \Naplus\ and \Kplus) across the plasma membrane by dedicated protein complexes. This view, consolidated through the seminal work of Hodgkin and Huxley and the structural characterisation of the Na,K-ATPase, has shaped decades of research in neuroscience, cardiology, and cell biology. A competing framework, the \textit{murburn concept} developed by Manoj and colleagues, proposes a fundamentally different mechanism. According to this view, TMP is not the product of mechanical ion pumping but emerges spontaneously from asymmetric redox chemistry at the membrane interface. Diffusible reactive species (DRS), generated continuously during aerobic respiration, accumulate differentially on either side of the membrane, producing effective charge separation analogous to that observed in electrochemical cells. This perspective examines both frameworks critically, identifies the core points of disagreement, and evaluates the explanatory scope and empirical challenges of each. We argue that the murburn framework raises legitimate and underexplored questions about the thermodynamic sufficiency of the ion-pump model, and that a productive synthesis may lie in recognising redox chemistry as a primary contributor to membrane polarisation---rather than a secondary consequence of it.

Abstract: The global regulatory landscape is shifting from voluntary corporate social responsibility (CSR) to mandatory Environmental, Social, and Governance (ESG) disclosure. This study investigates the causal impact of mandatory ESG disclosure on firm value and operational decarbonization using a comprehensive balanced panel of 1,612 listed firms from the EU and the US between 2018 and 2025.Employing a Difference-in-Differences (DiD) design and an event study analysis, our empirical results yield three primary findings. First, consistent with Agency Theory, mandatory disclosure significantly increases firm value (Tobin’s Q) immediately following the 2021 regulatory shock (Post×Treat=0.5212, p< 0.01), indicating that standardized transparency reduces information asymmetry (H1). Second, we document a progressive and cumulative reduction in carbon intensity, providing robust evidence of substantive execution rather than mere ceremonial compliance (H2). The "downward-sloping" trajectory in the event study confirms that the mandate drives real-world operational transitions over time, refuting Decoupling Theory. Third, we find that internal governance mechanisms play a crucial moderating role in this transition (H3); the reduction in carbon intensity is significantly more pronounced in firms with higher board independence and established ESG committees. These findings suggest that "hard-law" transparency mandates effectively align corporate incentives with global climate goals. The synergy between external regulatory pressure and internal governance oversight is essential for bridging the "talk-walk" gap, offering critical implications for global policymakers designing the next generation of climate-related reporting standards.

Abstract: Background: The purpose of this research is to explore the relationship between vitamin B6 status (measured by pyridoxal-5′-phosphate, PLP levels) and central obesity in children and adolescents. Additionally, the study seeks to examine how this relationship might influence the link between exposure to tobacco and central obesity in this age group. Methods: This cross-sectional study utilized data from the National Health and Nutrition Examination Survey spanning 2005 to 2010. Central obesity was identified by waist circumference measurements that were equal to or exceeded the 90th percentile, with adjustments made for age and gender. To assess the relationship between tobacco exposure and central obesity, both weighted univariate and multivariate logistic regression analyses were applied. An analysis was performed using PLP concentration categories (PLP ≥ 53.74 nmol/L and PLP < 53.74 nmol/L) to determine the impact of tobacco exposure on central obesity in each respective PLP group. Results: The final sample included 5,865 participants. Higher tobacco exposure [odds ratio (OR): 1.25, 95% confidence interval (CI): 1.03-1.53, P =0.027] and lower PLP levels (OR: 1.28, 95% CI: 1.05-1.57, P =0.016) were each independently linked to an increased risk of central obesity in children and adolescents. Among children and adolescents with lower PLP levels, cotinine exposure was related to an increased risk of central obesity (OR: 1.36, 95% CI: 1.05-1.76, P =0.022), particularly in specific subgroups: individuals under the age of 12 years, males, and those with six hours or less of daily screen time. Conclusion: Our results underscore the critical role of nutritional status, specifically vitamin B6 levels, in modulating the relationship between environmental exposures and obesity risk. Future initiatives aimed at primary prevention could be enhanced by recognizing the link between central obesity and adjustable lifestyle elements.

Abstract: This study presents, for the first time, a comprehensive multi-fidelity aero-thermo-fluid framework (spanning 0D-analytical, 1D and 3D domains) applied to the analysis of a structural oil-to-air Fan Outlet Guide Vane Cooler (FOGVC) in a jet engine. Addressing the need for efficient thermal management in next-generation engines, a hierarchical approach is established to characterize both thermal dissipation and pressure drop performance. The framework compares five simulation levels—ranging from high-fidelity conjugate heat transfer to 0D analytical models—across two distinct internal geometries (a rectangular inverted-U and a circular coil) covering different flow regimes. The research quantifies the trade-offs between physical fidelity and computational cost, establishing a decision-making criterion for the design of complex structural coolers. Results demonstrate that while 0D analytical methods provide high accuracy-to-speed ratios for temperature prediction, they exhibit significant deviations in pressure drop estimation and lack of capture local thermal gradients critical to structural integrity, where high-fidelity fully coupled 3D simulations are indispensable.Furthermore, the analysis reveals fundamental limitations in current passive heat exchanger designs under extreme operating conditions, suggesting a paradigm shift toward active or adaptive components is required to meet future dissipation targets.

Abstract:

Background/Objectives: Antimicrobial resistance (AMR) remains a major global health threat, strengthening the case for antimicrobial stewardship that limits unnecessary broad-spectrum empiric therapy while preserving timely coverage in severe infection. Large language models (LLMs) are being explored for decision support, but require rigorous offline evaluation before any clinical implementation. Methods: Single-center retrospective paired evaluation at Clinical Emergency Hospital of Bucharest (Internal Medicine, 2020–2024). The unit of analysis was the admission (N = 493), with paired 24 h empiric regimens (clinician-prescribed vs post hoc LLM-recommended via OpenAI API; not visible to clinicians; no influence on care). Local laboratory-derived epidemiology was precomputed from microbiology exports and provided as structured prompt context to approximate information parity with clinicians’ implicit local ecology knowledge. Primary (prespecified) endpoint: any contextual guardrail violation (unjustified carbapenem/antipseudomonal/anti-MRSA under prespecified structured severity/MDR-risk rules), exact McNemar. Key secondary (prespecified): Δ contextual guardrail penalty (LLM − Clin), sign test and Wilcoxon signed-rank (ties reported). Ethics committee approval was obtained. Results: Guardrail violations occurred in 17.0% of clinician regimens vs 4.9% of LLM regimens (paired RD −12.2%; matched OR 0.216, 95% CI 0.127–0.367; McNemar exact p = 1.60 × 10⁻¹⁰). Δ penalty had median 0 with 398/493 ties; among non-ties, improvements (Δ < 0) exceeded adverse shifts (79 vs 16; sign-test p = 3.47 × 10⁻¹¹). Conclusions: In this offline, non-interventional paired evaluation, LLM regimens were associated with fewer prespecified contextual guardrail violations compared to clinician empiric regimens under a rule-based stewardship benchmarking framework. These endpoints strictly quantify concordance with stewardship constraints rather than patient outcomes, necessitating cautious interpretation of secondary and subset analyses. Ultimately, reproducible guardrail-based benchmarking may support subsequent prospective, safety-governed evaluations.

Abstract: Against the backdrop of the high-quality development of the digital economy, exploring the impact of data assetisation on corporate investment efficiency with fintech as the core tool is of great significance for driving the steady and orderly development of enterprises...Taking A-share listed companies from 2012 to 2023 as the research sample, and further classifying them into self-used data assets and transactional data assets based on the types of data assets., the study finds: (1) Data assetisation significantly enhances corporate investment efficiency, with self-use data assets demonstrating a stronger driving effect.(2) Mechanism analysis reveals that data assetisation alleviates underinvestment by easing financing constraints and leveraging the "talent effect". Concurrently, it mitigates overinvestment by reducing agency problems and accelerating digital transformation, thereby enhancing investment efficiency. (3) Heterogeneity tests indicate that the positive impact of data assetisation on investment efficiency is more pronounced among growth-stage enterprises, technology-intensive firms, and companies operating in regions with high bank liquidity.(4) Banking fintech positively moderates the enhancement of corporate investment efficiency through data assetisation, with a more pronounced effect on alleviating underinvestment. However, it may also exacerbate overinvestment. Consequently, enterprises should vigorously develop data assetisation, applying different types of data assets to specific use cases to unlock data dividends. This approach supports the scientific development of corporate investment decisions and enhances investment efficiency.

Abstract: The choice of activation function is a fundamental design deci-sion in deep learning, yet most popular options like ReLU, GELU, or Swish are static and treat all inputs uniformly. This one-size-fits-all approach breaks down in the presence of noisy or corrupted data, where the optimal non-linearity should depend on the input’s statistical context. In this paper, we introduce Bayesian Probabilistic Adaptive Sigmoidal Activation (Bayesian PASA), a novel activation function that dynamically adapts its behavior based on the input’s uncertainty. Bayesian PASA is not just a new function, but a new paradigm. It frames activation selection as a Bayesian model averag-ing problem, adaptively mixing sigmoidal, linear, and noise-aware be-haviors. The mixing weights are derived from a principled variational evidence lower bound (ELBO), regularized by a stable ψ-function that guarantees bounded influence from noise estimates. We provide three formal theorems proving its Lipschitz continuity, gradient stability, and convergence under standard training assumptions. On the challeng-ing CIFAR-100 benchmark, Bayesian PASA achieves a state-of-the-art test accuracy of 76.38%, outperforming ReLU (75.68%), GELU (75.98%), and the original PASA (75.53%). On the corrupted CIFAR-10-C dataset, the full Bayesian PASA model combined with Bayesian R-LayerNorm achieves an average accuracy of 53.91%, a +1.87% improvement over the ReLU+LayerNorm baseline. This work pro-vides a drop-in replacement for existing activations, offering not only improved performance but also built-in uncertainty quantification for more robust deep learning systems.

Abstract: World models (WMs) provide a unified approach for modeling how environments evolve over time by learning predictive representations of states and observations. Recent advances in large-scale generative modeling and multimodal foundation models have substantially broadened their applicability across a wide range of interactive and multimodal domains; however, existing research remains fragmented across modeling paradigms, application domains, and evaluation protocols. This survey provides a systematic and in-depth review of WMs in artificial intelligence. Based on the world modeling paradigms of existing methods, we first categorize WMs into four branches with formal mathematical formulations: observation-level generative, latent space, reinforcement learning-based, and object-centric WMs. We further review a broad range of WM applications spanning robotics, autonomous driving, scientific discovery, game simulation, GUI-based agents, as well as interpretability and trustworthiness, and analyze benchmarks, new evaluation metrics, simulation platforms, and comparative results across WMs. Finally, we discuss key challenges, including long-horizon consistency, and generalization, and outline promising directions for future research. This survey provides an actively updated \href{https://github.com/JiahuaDong/Awesome-World-Models}{GitHub Repository} to track developments in WMs and aims to offer a unified reference for understanding, comparing, and advancing WMs.

Abstract: The successful application of 3D and 4D food printing is fundamentally governed by the rheology and microstructure of edible inks. These factors control every step, from extrusion and nozzle deposition to the final product functionality. This review systematically examines how formulation variables, including starch/protein composition, water content, and hydrocolloids, determine the network architecture and critical rheological properties, such as yield stress and viscoelasticity. These properties determine printing outcomes such as filament formation, stacking accuracy, and the stability of sensitive components. This review explores 4D printing as a "3D + 1D function," where printed structures provide additional features over time, such as a controlled color change or bioactive release, while post-printing treatment often activates these features. Through case studies of novel inks, we show how interfacial chemistry and process parameters influence texture and stability. Finally, we discuss the application of rheological metrics for predicting printability and outline the critical need for developing multi-parameter, process-relevant printability indices to advance the field of digital food manufacturing.

Abstract: Hyperspectral imaging integrates spatial and spectral data, crucial for environmental and vision science applications. Existing spectroradiometers lack spatial resolution, and many hyperspectral systems are costly or unsuitable for fieldwork mimicking human vision. Here, we present the development of a portable, relatively low-cost hyperspectral camera based on a tunable filter and a commercial video camera, together with Python-based software capable of generating hyperspectral cubes from the acquired images, as a more accessible and adaptable alternative for specific applications. The proposed method includes assembling the acquisition system and processing raw images, which have been demosaiced, linearized, and labeled with their corresponding spectral band, to generate a hyperspectral cube. Our approach allows radiance retrieval through prior calibration or reflectance estimation using a white reference from a selected region of the hyperspectral cube. Additionally, it enables exporting the hyperspectral cube as a three-dimensional matrix. The developed software facilitates visualization and analysis of hyperspectral data. Validation against a spectroradiometer demonstrated reliable spectral radiance measurements under moderate to high light conditions. This adaptable approach enhances accessibility to hyperspectral imaging for research contexts with limited resources, supporting detailed visual environment characterization outside controlled laboratory settings.

Abstract: This paper investigates the formation mechanism and key influencing factors of freckle defects that arise during the directional solidification of a novel third-generation nickel-based single crystal superalloy turbine blade. A combined experimental and multi-physics numerical simulation approach was adopted. The results reveal that freckle formation primarily results from the coupling effect of solute segregation and thermo-solutal convection during solidification, leading to dendrite fragmentation and subsequent aggregation of equiaxed grains. The resultant density inversion drives upward interdendritic flow, which plays a dual role: it promotes remelting and fragmentation of secondary dendrite arms, while simultaneously opening solute-enriched preferential flow channels that eventually develop into freckle defects. The severity of freckling is closely dependent on both the casting's position within the furnace and its local geometric characteristics. Castings located in regions with poorer heating conditions experience lower temperature gradients and slower solidification rates, significantly increasing their susceptibility to freckle formation. Similarly, on a given casting, the side subjected to less favorable heating is more prone to freckle initiation. This work provides a crucial theoretical foundation for understanding freckle formation in nickel-based single crystal superalloys and offers practical guidance for optimizing blade manufacturing processes, reducing solidification defects, and enhancing blade quality and service performance.

Abstract: Behcet disease (BD) is an inflammatory disorder with manifestation in mucosal tissues. Unlike autoimmune diseases that generate autoantibodies, BD is believed to be an autoinflammatory disease triggered by innate immune cells rather than adaptive cells. Hyperactivation of neutrophils causes vasculitis and thrombosis, and they migrate into cutaneous and ocular lesions. Dominance of M1 macrophages promotes the differentiation of Th1 cells. Moreover, the cross-reaction of bacterial heat shock proteins induces production of cytokines such as IL-4 and IFN-γ, in γδT cells, which alters the balance between Th1 and Th2 phenotypes. Nevertheless, natural killer (NK) cells play more critical roles in BD pathogenesis than other innate immune cells because not only their activity is precisely controlled by the interaction between ligands and receptors but NK1 shift also elicits Th1 dominance. The genetic factors associated with BD are HLA-B51 and major histocompatibility complex class I-related chain A (MICA), which stimulate NK receptors as ligands. Improperly processed peptides dysregulate their interaction with NK receptors, triggering the inflammatory response. NK1 and NK2 subsets represent cytokine production in relapse and remission periods; however, the cytotoxicity of NK cells in relapse is lower than that in remission periods. It still remains unclear how NK cells are activated recurrently and expand cytokine production. This review highlights the regulation of gene expression encoding NK receptors, tissue-resident NK cells, and adaptive NK cells to discuss their potential for relapsing. Splicing variants and readthrough genes encoding NK receptors easily alter cytokine production. Moreover, tissue-resident NK cells in mucosal tissues and adaptive NK cells that memorize the virus infection have the potential to trigger hyperactivation in relapse.