Abstract: The present essay introduces and develops the concept of Homo constellatus as a new anthropological and metaphysical archetype, emerging from the visionary corpus of Theodor-Nicolae Carp – specifically in The Conquest from Within and the Incoming Platonic Revolution, Birthing Homo constellatus: From the Humans Who Know Everything to the Humans Who Connect Everything and Andromeda as Archetype: The Neurodiverse as the First-Called in a Post-Neurotypical Cosmology. Situated at the intersection of neurodiversity, symbolic anthropology, cosmopoetics and Platonic theology, Homo constellatus represents not a technocratic leap in cognitive performance, but a metaphysical transfiguration of the human being. It signals an evolutionary milestone defined not by biology or machinery, but by communion, emotional depth and the recovery of sacred symbolic consciousness. This emerging figure is metaphorically birthed through intellectual exile and metamorphic suffering. It is not a successor by gene but by soul: the one who integrates fragmentation into communion, rationality into sacred symbol, and loneliness into ontological design. Moreover, the present manuscript proposes the emergence of a new literary current called Axiological Cosmopoetics following two major “waves” in the history of European literary discourse (Classicism and Modern Romanticism), and it has as its core theme a poetic restoration of order and harmony in the Universal realm, and Homo constellatus appears to be the central archetype of such a new current. Axiological Cosmopoetics is transdisciplinary in nature and integrates axiology (value-theory) with cosmopoetic symbolism, drawing on literary theory, philosophy of art, religious and secular philosophy, as well as cultural myth, to articulate ethically ordered imaginaries of human reintegration, particularly amid times of post-traumatic restoration. The emergence of Homo constellatus signals a shift in consciousness marked by an integrative tendency: a gravitational impulse toward reconstellation. Rather than dissolving difference or imposing uniformity, this archetype seeks to reposition disparate elements within a wider field of meaning, drawing fragmentation toward coherence without erasing plurality. Its movement is not centrifugal but centripetal – not toward collapse into sameness, but toward relational alignment. In this sense, reconstellation describes a reordering of perception: domains once held in tension – reason and reverence, structure and fluidity, individuality and communion – are gradually perceived as dynamically interrelated. The archetype does not force convergence; it inclines toward integration. Like a system approaching a higher-order equilibrium, Homo constellatus orients consciousness toward patterns of deeper resonance, where complexity is neither denied nor absolutized, but harmonized within an ever-expanding constellation of meaning. Through references to sacred geometries – such as Gabriel’s Horn and Brâncuși’s Column of Infinity – Carp envisions Homo constellatus as a being who lives in harmony with the poetic architecture of the cosmos. Drawing on Eastern Orthodox theology, Platonic intimacy, and neurodivergent phenomenology, the essay reframes suffering as sacred gestation and neurodivergence as prophetic sensitivity. The new human archetype of Homo constellatus challenges existing anthropocentric and ableist paradigms by revealing that emotional resonance, symbolic intelligence, and spiritual wholeness are not byproducts of evolution, but its very telos. In dialogue with these literary and philosophical works, Elegy of Mine Exile serves as a lyrical-theological meditation on sacred alienation. This elegy does not mourn exile as punishment – it reclaims exile as consecration. The speaker, likened to a prophetic voice or even to the Ambassador of the Morning Star himself, is rejected by the world not because he is broken – but because he burns too brightly. By distinguishing between the fall of Christ as the true Morning Star – through humility – and the fall of Lucifer through pride, the study describes the speaker’s descent is both sacrificial and revelatory: he suffers not to disappear, but to transmute. Through metaphors of collapse and rising, the poem places spiritual alienation in direct dialogue with divine gestation – turning mourning into Morning. The expanded version of Elegy of Mine Exile amplifies this vision by incorporating ecological, theological, and anthropological dimensions. The soul’s descent is reimagined as the fermentation of the New Eden; cosmic orphanhood becomes an archetypal human condition; and the emergence of Homo constellatus is framed as both elemental fusion and divine inheritance. The eschatological arc of the poem culminates in a nuptial invocation – where divine breath, moral resuscitation, and relational transfiguration give birth to a new co-creative covenant. Suffering becomes not merely transformative, but luminous: the seedbed for Edenic restoration and planetary rebirth. Further expanding this vision, the literary commentary Luceafărul: The Morning Star, Neurodivergence, and the Birth of Homo constellatus interprets Mihai Eminescu’s Hyperion not merely as a tragic figure of cosmic distance, but as a neurodivergent archetype whose refusal of worldly assimilation prefigures Homo constellatus. Hyperion’s vertical longing, divine remoteness, and emotional clarity are re-read as prophetic attributes – illuminating how divine exile is inseparable from metaphysical fidelity. Crucially, the symbolism of the Morning Star – also known as the Evening Star – reveals a prophetic paradox: those who were unseen will become luminous. In eschatological terms, these hidden figures will not only come to light, but also sound the alarm of a nearing apocalyptic threshold, becoming the sensitive instruments of revelation before the advent of the Adversary of the Icons of the Universe on Earth (deemed as anti-Universal Messiah in religious discourse). The poem Behold, the human communing with the Stars continues this metaphysical arc, giving lyrical voice to the full manifestation of Homo constellatus. In this cosmic hymn, suffering culminates in stellar transformation; exile gives way to supernova; and the fallen Morning Star becomes the harbinger of the Eternal Morning. The New Eden is not a return, but a convergence – symbolized by the reassembled Pangaea and the fusion of past and future into infinity. Through mythopoetic eschatology, the poem celebrates a spiritual anthropology rooted not in control, but in communion – marking the fulfillment of a cosmic gestation first conceived in exile. It stands as the poetic benediction of this archetype's emergence. The model proposed here extends into geology and astronomy, as it displays a planetary cartography: the Alpine-Himalayan mountain system as observed in geography, is interpreted as the spinal cord of the “Old, Neurotypical World,” while the Rocky-Andean chain represents the backbone of a “New, Neurodiverse World.” These two continental bodies – much like the approaching collision of the Milky Way and Andromeda as hinted in astronomy – are destined not for destruction, but for synthesis. Their eventual convergence is envisioned as a tectonic, civilizational, and spiritual transformation – an emergence of a post-neurotypical world, one capable of holding both structure and fluidity, reason and reverence. Finally, the invocation of the Morning Star – held in tension between Christ’s descent and Lucifer’s fall in Christianity – serves as a theological fulcrum for this cosmopoetic vision. By distinguishing between the one who chose humility and the one who chose pride, the poem and its accompanying commentary avoid conflating rebellion with brilliance. Christ’s descent becomes the archetype of divine communion, while Lucifer’s fall reveals the tragic consequence of light divorced from love. This distinction safeguards the eschatological hope at the heart of Homo constellatus: that the radiant ones misunderstood by the world are not deviant, but divine harbingers of a healed cosmology – symbols not of rebellion, but of redemptive luminosity. This essay articulates the philosophical, theological, and societal implications of Homo constellatus across multiple domains: from education to sacred urbanism, from intimacy to symbolic linguistics, from planetary ethics to liturgical cosmology. It proposes that the future of humanity lies not in transcending our nature through technology, but in transfiguring it through love, meaning, and communion. Through its interdisciplinary method and poetic form, this work positions Homo constellatus as a necessary archetype for healing a fragmented world, initiating a planetary renaissance grounded in reverent complexity, emotional literacy, and the sacred rhythm of becoming. In its expanded formulation, the Homo constellatus framework now extends beyond symbolic anthropology into trauma-informed civic imagination. Concepts such as Urban Wombs, graduated relational housing, Touch Plazas, lullaby infrastructures, and platonic intimacy literacy are rearticulated not as utopian communal fantasies, but as phased, ethically scaffolded prototypes. These trauma-informed urban prototypes may incorporate calibrated biophilic design within dense metropolitan contexts, integrating natural light and ecological elements as regulatory supports for psychological stability rather than as aesthetic idealism. These models prioritise sovereignty, consent, and psychological pacing, especially in contexts involving survivors of violence and crime, including domestic abuse, coercive control, assault and trafficking. Platonic intimacy is therefore repositioned not as universal remedy, but as a regulated and optional dimension within broader recovery ecosystems where autonomy precedes affection and safeguarding precedes proximity. By embedding strict ethical guardrails – continuous consent, trauma-informed facilitation, independent oversight, and tiered participation structures – the vision of Homo constellatus matures from prophetic archetype into disciplined compassion. The new human is no longer defined solely by sacred exile, but by the capacity to design environments where relational safety becomes infrastructural. In this development, communion ceases to be abstract aspiration and becomes civic architecture. The eschatological horizon remains luminous, yet it is tempered by legal, psychological, and cultural accountability. Thus, Homo constellatus evolves from metaphysical figure into socially responsible archetype: radiance integrated with restraint, transcendence integrated with trauma-awareness, and love integrated with law. Ultimately, the literary and philosophical vision of Homo constellatus does not remain a theoretical construct, but emerges as a liturgical anthropology – a life-form shaped by presence, patience, and symbolic resonance. Its birth reframes neurodivergence as divine invitation, demanding structural repentance in education, theology, and care. It invites a post-neurotypical civilization to reorient itself not around efficiency, but reverence. Though rooted in Orthodox theology and European literary myth, its archetypal signature is transcultural: it echoes the bodhisattva, the qalandar, the wounded healer – universal figures of radiant exile and sacred return. Thus, this vision does not end in abstraction, but in enactment: the return of the human soul to the cosmic choir – not as soloist, but as constellation.

Abstract: Turnout locomotor activity is a potentially informative indicator of health and welfare in older horses, yet objective field data in seniors remain limited. We examined whether a brief turnout recording could detect associations between chronological age and locomotor activity in senior horses under routine conditions. In this single-site observational study, 28 senior Selle Français horses (17–35 years) contributed 122 paddock sessions (2 h each), with total distance and mean speed quantified using a Polar Team Pro sensor. Associations with age were assessed using linear mixed-effects models adjusted for temperature and precipitation. Age was decomposed into between-horse and within-horse components. Log-transformed total distance was negatively associated with age (β = −0.062 per year, 95% CI −0.094 to −0.032; P < 0.001), driven by the between-horse component (β = −0.063; q = 0.003), with no within-horse association (P = 0.75). Mean speed showed a similar pattern, with a significant between-horse association (β = −0.060; q = 0.003) and no within-horse effect (P = 0.87). These findings suggest that brief paddock actimetry may help characterize between-horse heterogeneity and support group-level welfare monitoring. Larger multi-site cohorts with denser follow-up and external validation are needed before individual trajectories or clinical interpretation can be established.

Abstract: Refactoring is essential for developing maintainable software. Using Large Language Models in software engineering is widespread, but compared to well-established domains such as code generation, reliable refactoring is still relatively underexplored. In this paper, we perform a broad analysis on the refactoring capabilities of small open-weight language models (SLMs) by evaluating 12 models on 3,453 Python programs. Our study focuses on the two defining aspects of refactoring: behavior preservation and code quality improvement. We evaluate these properties using unit tests and various code metrics. Across models ranging from 0.5B to 8B parameters, most models improve code quality. Larger models are more reliable, as they preserve behavior more consistently. Reasoning models often make more significant changes while refactoring. Allowing models to generate reasoning traces improves performance, but only for models larger than 4B. For smaller models, reasoning in fact reduces refactoring reliability. The difficulty of the underlying task affects refactoring performance, with more complex tasks associated with higher failure rates. Our results indicate that current open SLMs can support refactoring tasks, especially larger ones with reasoning capabilities, but they are best used with human oversight.

Abstract: This paper presents a practical implementation of relative primary radiation thermometry (RPRT) together with MultiFixRadSoft, an open-source software package developed in accordance with the Mise-en-Pratique for the kelvin (MeP-K) for realization of the thermodynamic temperature scale and uncertainty evaluation under the new definition of the kelvin. The software enables realization of temperature scales using ITS-90 metal fixed points as well as metal–carbon and metal–carbide–carbon eutectic high-temperature fixed points (HTFPs) for both radiation thermometers and radiometers. It incorporates automated routines for melting-plateau analysis, including determination of the point of inflection, liquidus point, and melting range, together with correction modules for size-of-source effect, detector nonlinearity, emissivity, and temperature-drop. Validation is demonstrated through experimental realization using six fixed points (Cu, Fe–C, Co–C, Pd–C, Ru–C, and WC–C) and a linear radiation thermometer. The software also supports ITS-90 extrapolation procedures and flexible calibration schemes (n = 1 to n ≥ 3), with automated Sakuma–Hattori fitting and full uncertainty propagation compliant with MeP-K requirements. Results show excellent agreement with manual analyses and published data, confirming the correctness of the implemented algorithms. By integrating data processing, scale realization, and uncertainty analysis within a unified and transparent framework, MultiFixRadSoft provides a robust and accessible tool for traceable radiometric thermometry, supporting emerging NMIs and industrial laboratories while promoting wider adoption of primary thermodynamic temperature realization methods.

Abstract: Chronic kidney disease (CKD) represents an escalating global health burden, fundamentally altering morbidity and mortality trajectories across the world, particularly as it advances into end-stage renal disease (ESRD). Beyond the primary decline in renal filtration and excretion, a wide spectrum of endocrine and metabolic derangements frequently accompanies kidney failure, with thyroid dysfunction emerging as a critical complication. The current study was designed to rigorously evaluate the nuanced association between thyroid hormone dynamics—specifically thyrotropin (TSH), triiodothyronine (T3), and thyroxine (T4)—and renal status in three distinct cohorts: individuals with suspected thyroid issues but normal renal function (NP), non-dialysis kidney patients (NDKP), and patients undergoing maintenance hemodialysis (DP). Data were collected from a clinical setting in Bangladesh, involving 161 subjects. The results demonstrated that patients in the DP cohort exhibited slightly elevated thyroid hormone levels relative to those in the NDKP cohort. Specifically, within the subgroups of patients exhibiting normal or sub-reference hormonal levels, dialysis patients maintained higher concentrations than their non-dialysis counterparts. Demographic stratification further revealed that males, females, and individuals younger than 45 years were more likely to demonstrate restorative hormonal profiles in the DP group than in the NDKP group. These collective outcomes suggest that renal replacement therapy, specifically hemodialysis, may serve to stabilize or improve thyroid function in ESRD patients by potentially mitigating the suppressive effects of uremic toxins and normalizing homeostatic feedback loops.

Abstract: The alignment between neural dynamics and environmental structures constitutes a fundamental challenge in neuroscience. While Georg Northoff's Temporo-Spatial Theory of Consciousness (TTC) posits a "common currency" of temporo-spatial dynamics, the mechanistic operationalization of this alignment remains unspecified. This report integrates the TTC with the Affective Criticality Hypo proposed by Tucker, Luu, and Friston (2025). We propose that consciousness and optimal brain-world alignment emerge when the neural system operates in a regime of Excitatory-Inhibitory (E/I) precision balance. Specifically, we identify the affective qualities of elation and anxiety not as epiphenomenal accompaniments, but as constitutive control parameters regulating precision weighting in active inference. Elation corresponds to excitatory precision (E), enhancing prior confidence, while anxiety corresponds to inhibitory precision (I), enhancing sensory vigilance. This balance is homeostatically regulated through sleep-wake cycles, where NREM and REM sleep serve as subcritical and supercritical excursions, respectively. We provide a formalization of this process within the variational free energy framework and compare its explanatory power against alternative theories (e.g., Binding by Synchrony, Population Clocks). We conclude that affective criticality offers a neurobiologically grounded mechanism for the brain-world alignment, transforming the "hard problem" of consciousness into a problem of precision-regulated inference.

Abstract: Over the last two decades, there has been a paradigm shift in geotechnical engineering driven by advances in sensing, communication, and data-driven techniques. These advancements enhanced the safety and reliability of geotechnical infrastructure through real-time monitoring and automated decision-making. In recent times, Large Language Models (LLMs) have emerged as advanced data-driven techniques contributing to automated risk assessment of geotechnical infrastructure. LLMs are advanced deep learning models widely used to solve complex numerical problems, analyze large volumes of data, and generate human language. This paper presents a comprehensive review of the application of LLM in geotechnical engineering. The integration of LLMs into geotechnical engineering has demonstrated significant advances in slope stability analysis, bearing capacity computation, numerical analysis, soil-structure interaction, and underground infrastructure. By summarizing the latest research findings and practical applications, this research paper underscores the potential of LLMs to advance and automate various processes in geotechnical engineering. The findings presented in this paper not only provide insights into the current LLM-based geotechnical practices but also emphasize the instrumental role LLM can play in advancing geotechnical engineering, ultimately ensuring a safer and more sustainable future.

Abstract: Machine learning (ML) models for predicting food recall severity could accelerate regulatory triage, yet no systematic benchmark exists on the U.S.\ Food and Drug Administration (FDA) open-access database. We construct the first comprehensive ML benchmark for FDA food recall severity classification (Class I / II / III) using 28,448 enforcement records spanning 2012--2025. A 1,437-dimensional feature space is engineered from TF-IDF and Sentence-BERT embeddings of recall narratives, structured categorical attributes, and temporal indicators. Five classifiers (Logistic Regression, Random Forest, XGBoost, LightGBM, CatBoost) are trained with Optuna-tuned hyperparameters. Under standard random splitting, XGBoost achieves Macro-F1 = 0.89; however, a multi-layer leakage audit reveals that this figure is inflated by entity-level autocorrelation. When firm-aware group splitting, temporal splitting, or their combination is applied, Macro-F1 drops to approximately 0.57. A firm-mode baseline---assigning each company's historically most frequent severity class---reaches 0.82 under random splitting, demonstrating that 92% of the apparent performance stems from firm-level memorisation. Identity-masking experiments confirm that the leakage is structural rather than attributable to explicit company-name tokens. A \( 2 \times 2 \) factorial decomposition shows that firm overlap and temporal continuity are highly collinear; removing either suffices to expose the true generalisation floor. A hazard-type decomposition reveals that pathogen--severity associations transfer across firms, whereas labelling and GMP violations are highly firm-specific, explaining the disproportionate collapse of Class~III prediction under group splitting. SHAP analysis, feature ablation, and a nine-year continuous-learning simulation provide additional insights into model behaviour and retraining strategies. We recommend that food-safety ML studies adopt group-aware or temporal evaluation protocols, report entity-overlap statistics, and include entity-prior baselines to prevent overstated conclusions.

Abstract: Mental disorders are among the leading causes of disability worldwide and impose substantial economic costs on individuals, healthcare systems, and national economies. While the clinical rationale for early identification of mental disorders is well established, the economic implications of systematic early screening and detection remain underemphasized in policy discourse. This paper examines the economic advantages of early screening and early detection of common and severe mental disorders, integrating findings from epidemiology, cost-of-illness studies, cost-effectiveness analyses, and health systems research. Evidence consistently demonstrates that delayed diagnosis is associated with increased healthcare utilization, reduced labor force participation, lower lifetime earnings, and higher social welfare expenditures. Conversely, early detection—particularly when integrated into primary care and early intervention services—has been shown to improve functional outcomes and, in many contexts, to be cost-effective or cost-saving from a societal perspective. The analysis supports the conclusion that early mental health screening constitutes not only a clinical priority but also a fiscally responsible strategy for health system sustainability and economic productivity.

Abstract: In the present study, an investigation was carried out on the molecular, morphological, and anatomical mechanisms underpinning the differential cold tolerance of two cotton cultivars, Xinluzhong 61 (C61) and Tahe 2 (C2). The seedlings of these cultivars were exposed to 0 °C treatments for 12 and 24 hours. Comparative transcriptomic analysis (RNA-Seq) was conducted to identify differentially expressed genes (DEGs). Simultaneously, comprehensive anatomical examinations of cotyledons, true leaves, and stems were carried out to evaluate morphological alterations.Transcriptomic analysis at the 24-hour time point, the transcriptional profile had changed, with trichome differentiation and phloem/xylem histogenesis were the most significantly enriched biological process in C61. This result was verified by phenotypic observations, as C61 developed dense glandular trichomes on its stems, a characteristic not observed in C2. Anatomical investigations demonstrated that although cold stress led to a reduction in tissue thickness in both cultivars, C61 maintained significantly greater leaf thickness, palisade tissue thickness, and a higher palisade-to-spongy tissue ratio in true leaves after stress. Moreover, C61 exhibited greater xylem thickness in the stem under cold conditions, implying superior structural integrity and water transport capacity. These findings highlight key adaptive traits and offer valuable targets for the genetic improvement of cold tolerance in cotton.

Abstract: Large language models (LLMs) face significant challenges in sustaining long-term memory for agentic applications due to limited context windows. To address this limitation, many work has proposed diverse memory mechanisms to support long-term, multi-turn interactions, leveraging different approaches tailored to distinct memory storage objects, such as KV caches. In this survey, we present a unified taxonomy that organizes memory systems for long-context scenarios by decoupling memory abstractions from model-specific inference and training methods. We categorize LLM memory into three primary paradigms: natural language tokens, intermediate representations and parameters. For each paradigm, we organize existing methods by three management stages, including memory construction, update, and query, so that long-context memory mechanisms can be described in a consistent way across system designs, with their implementation choices and constraints made explicit. Finally, we outline key research directions for long-context memory system design.

Abstract: Maintaining the integrity of waterproof strata (WPS) between mine workings and overlying aquifers is critical, because water-conducting cracks (WCC) may cause mine flooding and surface subsidence. In the Upper-Kama potash deposit, the WPS is a 50-140 m thick stratified sequence of evaporites and clays overlying mined-out cham-bers. Under long-term loading, salt rocks tend to creep, soften, and localize damage, which can cause WPS failure. In this paper the Concrete damage-plasticity model, supplemented by the N2PC-MCT viscoplastic creep model, is applied to simulate WCC initiation and evolution in the Upper-Kama WPS. Model parameters are obtained from published laboratory tests, in-cluding uniaxial and triaxial compression and tension, and then validated using ob-served ground-surface subsidence. A plane-strain finite-element model incorporates stratified lithology, interface elements between layers, and stepwise excavation. Long-term simulations up to 50 years investigate two operational scenarios: with and without backfilling. The calibrated model reproduces the main stages of surface subsidence and chamber closure. Without backfilling, simulations indicate that tensile damage localizes mainly in a stiff central salt layer of the WPS. Most cracks appear approximately between 33 and 37 years after the beginning of mining. With backfill, tensile crack propagation stops and damage remains stable. A hypothetical homogeneous WPS case confirms that the observed central-layer cracking is associated with stiffness contrasts and composite bending in the stratified system. An approximate analytical multilayer beam solution, based on energy minimization, predicts bending stress concentration in stiff intermediate layers and is consistent with the numerical stress distribution. The combined numerical and analytical results clarify the mechanisms of long-term WCC initiation in stratified WPS and may be used for hazard assessment and planning of mitigation measures, including backfilling and focused monitoring of stiff central layers.

Abstract: This study integrated Item Response Theory (IRT) models with ordinal survey instruments to assess academic performance trajectories and identify multidimensional factors associated with academic achievement among first-semester leveling students (N=1,558 pre-test; N=1,676 post-test) at the Escuela Politécnica Nacional, Ecuador. A dual-component methodology was employed: (1) an 80-item ordinal survey measuring eight latent constructs (socioeconomic, academic, motivational, vocational, social integration, psychological/emotional, institutional, and biological/health factors), validated through Confirmatory Factor Analysis (CFI > 0.95, RMSEA < 0.06); and (2) structured diagnostic assessments in mathematics, physics, chemistry, geometry, and language, calibrated using three-parameter logistic (3PL) IRT models via Expected A Posteriori (EAP) estimation. Results demonstrated high internal consistency (r = 0.93 between IRT and raw scores), with mean IRT-scaled ability θ ̅ = 10.45 (SD = 3.51) on a 1–20 scale. Item parameters indicated adequate discrimination a ̅ = 1.92) and centered difficulty (b ̅ = 0.05), though 13.75% of items exhibited poor model fit (S-X² p < 0.01), concentrated in physics and chemistry domains. Factorial scores and performance outcomes were statistically contrasted against 24 categorical demographic variables, revealing differential performance patterns across student subgroups. This research provides validated psychometric instruments, reproducible IRT-LMS integration protocols, and empirical evidence supporting targeted interventions to strengthen university transition in resource-constrained contexts.

Abstract: Northeast China’s rust-belt cities have faced persistent concerns about stagnating labor productivity amid structural change. This paper studies how urban agglomeration benefits depend on local economic structure and ownership composition using an annual city-level panel. We estimate two-way fixed-effects models with city and year effects and city-clustered standard errors, complemented by dynamic specifications that account for productivity persistence. Results show a robust positive within-city association between population density and labor productivity. This density premium is structure-conditioned: the productivity payoff to density is significantly larger in city-years that are more industry-oriented. In contrast, an information-theoretic measure of sectoral imbalance (KL divergence from an industry–services balance benchmark) adds limited explanatory power once fixed effects, structural orientation, and controls are included, suggesting that directional orientation matters more than balance per se in this two-sector setting. Ownership composition is also informative. While SOE and private employment shares correlate with labor productivity in the fixed-effects models, the strongest and most stable finding emerges from ownership-mixing entropy: binary SOE–private employment entropy is positively associated with labor productivity in dynamic specifications, with meaningful heterogeneity across provinces. Overall, the evidence supports a conditional agglomeration view in which productivity dynamics in Northeast China reflect the interaction of density, structural orientation, and ownership complexity. The results highlight the importance of aligning urbanization with higher-value structural transformation and improving the institutional environment that enables productive SOE–private coexistence.

Abstract: Monitoring the progression of large wildfires in near-real-time is essential for active-fire situational awareness and emergency response management. Current satellite-based wildfire monitoring systems face a trade-off between temporal and spatial resolution: geostationary satellites such as GOES offer frequent (~5 minutes) but coarse observations (~2 km), while low earth orbit (LEO) instruments such as VIIRS provide fine spatial detail (∼375 m) with limited temporal coverage (twice per day). To bridge this gap, this study introduces a deep learning (DL) approach that enables near real-time, high-resolution wildfire monitoring using GOES data. The proposed approach consists of two main steps: a segmentation step to distinguish active fire regions from background areas and a regression step to estimate the active fire pixels brightness temperature (BT) across a region of interest. The output of these steps is combined to generate a high-resolution fire location and BT maps. To train the DL model, multi-spectral GOES inputs are paired with VIIRS-derived fire observations from several wildfires across the United States. Spatial consistency between GOES and VIIRS data is achieved through parallax correction, reprojection, resampling, and per-image normalization. Ablation studies are performed to demonstrate the impact of different assumptions (e.g., background values in the VIIRS ground truth) and strategies (e.g., loss functions) throughout the development process. The results show that the proposed DL approach effectively enhances GOES imagery, improving both BT estimation and fire boundary localization. Overall, the proposed method offers a practical and scalable solution for wildfire boundary detection and thermal mapping using existing satellite systems.

Abstract: Cloud-based large language models (LLMs) have demonstrated near-human performance in medical applications; however, their clinical deployment is constrained by concerns regarding patient privacy, data security, and network dependence. Locally deployable, open-weight LLMs may provide a privacy-preserving alternative for resource-limited or security-sensitive environments. We evaluated two families of locally deployed models, Google Gemma3 (1B, 4B, 12B, and 27B parameters; vision enabled in models since 4B) and GPT-OSS-20B, using 1,200 multiple-choice questions from the Taiwan Pulmonary Specialist Board Examinations (2013–2024), including 1,156 text-only and 44 text-and-image items across 26 categories. A cloud-based GPT-4 Turbo model served as a reference. Models were queried locally via Ollama. Accuracy was analyzed by year and category using repeated-measures ANOVA with Tukey-adjusted pairwise comparisons. GPT-OSS-20B achieved the highest overall accuracy (58–78 correct answers per 100 questions) and significantly outperformed all Gemma-3 variants (p &lt; 0.001), while Gemma3-27B ranked second. No statistically significant difference was observed between GPT-OSS-20B and GPT-4 Turbo after Tukey adjustment. Larger models showed improved accuracy but longer inference time. These findings suggest that selected open-weight LLMs deployed on-device can approach the performance of cloud-based models in structured medical examinations, with trade-offs between accuracy, modality support, and computational efficiency.

Abstract: Background: Rising kidney discard rates and uncertainty around accepting higher-risk donor kidneys highlight the need for decision-support tools that integrate donor and recipient factors and communicate risk in ways that are understandable and usable at the time of offer. Conventional indices (e.g., KDPI/KDRI) provide population-level signals but do not deliver individualized, cognitively accessible information aligned with real-time clinical workflows. Objective: To describe how key transplant stakeholders—patients, coordinators, and providers—interpret and evaluate a prototype Kidney Risk Calculator app that generates donor–recipient–specific survival projections, and to identify the content, format and features, and functionality needed for clinically meaningful, patient-centered decision support. Design: Qualitative study using focus groups and individual interviews. Setting: University of New Mexico Hospital (UNMH) Kidney Transplant Center. Participants: Five patients (four transplant candidates and one patient advocate), three transplant coordinators, and five transplant providers (3 attending physicians and 2 advanced practice practitioners). Methods: Semi-structured sessions (45–60 minutes) with 13 stakeholders (patients, coordinators, and providers) included a live app demonstration and explored usability, interpretability, contextual information needs, perceived clinical utility, and anticipated barriers/facilitators. Data were collected via one coordinator focus group, one patient focus group, and five provider interviews; sessions were recorded, transcribed, de-identified, and analyzed using inductive reflexive thematic analysis. Results: Stakeholders affirmed the value of personalized projections as an adjunct to clinical judgment, particularly for higher-risk offers. Participants prioritized: 1) Content—clear education on hepatitis C virus (HCV)-positive donors and Public Health Service (PHS) risk criteria; plain explanations of Calculated Panel Reactive Antibody (CPRA); and framing that makes time on dialysis and trade-offs salient; 2) Format & Features—plain-language narratives, percentages rather than decimals, simple visuals, minimized acronyms, U.S. customary units, and a stepwise (“TurboTax‑like”) input flow preferred by patients; and 3) Functionality—attention to cognitive load and workflow alignment, given phone-based time pressure and digital-access constraints. Stakeholders emphasized that the tool’s value hinges on clarity, context, and workflow fit—not predictive accuracy alone. Limitations: Single‑center, formative prototype study with a modest sample; findings are illustrative and may have limited transferability. Participants reacted to a demonstration rather than using the app during real‑time offer calls; convenience/email recruitment and Zoom‑only English sessions may introduce selection bias; team involvement in app development may contribute residual confirmation bias despite mitigation. Conclusions: Early stakeholder input suggests that a kidney offer decision support tool should integrate individualized predictions with plain language explanations, contextual information that addresses common misconceptions, workflow aligned functionality, and accessible outputs. Tools designed and implemented with these features may support acceptance of medically complex kidneys and may help reduce offer bypass and organ discard. These inferences reflect stakeholder perceptions in a formative qualitative study and warrant prospective evaluation.

Abstract: The significant heat generation during refueling of hydrogen pressure tanks might exceed the permissible 85 °C temperature limit for type IV tanks consisting of a thermoplastic liner and a carbon fiber composite overwrap. Common countermeasures like hydrogen pre-cooling or long filling times are energy and time consuming, hence in this paper passive means through thermally better suited materials are examined. Therefore state of the art and alternative materials are first characterized and finally compared using a transient heat model. The different material combinations are compared for maximum temperature and weight in a typical filling scenario. As alternative liner materials thermoplastics filled with short carbon fibres, minerals and graphite and concerning the composite overwrap copper coated carbon fibres were chosen to improve thermal properties. The findings show that the liner is the bottleneck while transferring heat from the inner to the outer tank surface. Using graphite filled thermoplastic as liner material shows the highest potential regarding thermal optimization with only little weight increase. Using additionally copper coated carbon fibres reduces the maximum temperature further, but at a high weight increase. This article is a revised and expanded version of a paper, which was presented at the 15th EASN International Conference, in Madrid, Spain, in October 2025 [1].

Abstract: Recent advances in large language models (LLMs) have made it feasible to use them as automated debugging tutors, but it remains unclear how much can be gained by moving from single-model tutors to multi-agent councils with separated roles. We study this question in an offline simulation on 200 debugging cases drawn from an online judge, spanning 20 problems split into course-style and contest-style challenge tracks. We compare four single-model tutors based on current frontier models with four councils that assign models to Architect, Skeptic, Secretary, Pedagogue, and Mentor roles and operate in both Blind and Guided modes. Single-model tutors achieve near-perfect repair on course problems but perform less reliably on challenge cases and often rewrite large portions of student code, show non-negligible false positive rates, and leak full or near-full solutions in a substantial share of hints. Councils designed around measured model strengths improve both technical and pedagogical behaviour. On the challenge track, the best council raises patch success by 12.2 percentage points over the best single tutor, while reducing false positives, shrinking median patch size, improving hint localisation, and cutting solution leakage in Blind mode from about one fifth of hints to under ten percent. Councils also exhibit higher stability across reruns and produce hints that two independent instructors consistently rate as more useful and better scaffolded. Guided mode, where internal components see a reference solution, yields further technical gains but introduces leakage risks that require prompt tightening and a sanitising Secretary to control the flow of ground truth. Additional trap experiments with poisoned reference solutions show a mix of resistance and fail-safe collapse rather than systematic poisoning of hints. These results indicate that orchestration and information flow are powerful levers and that well-designed councils can provide more reliable and pedagogically aligned debugging support than strong single-model tutors alone.

Abstract: Background/Objectives: Head and neck squamous cell carcinoma (HNSCC) frequently exhibits resistance to targeted therapies, including cetuximab. Identifying key drivers of tumor progression and elucidating the mechanisms underlying therapeutic resistance are essential for improving clinical outcomes. This study aimed to investigate the role of Caveolin-2 (CAV2) in HNSCC proliferation and cetuximab resistance. Methods: Prognosis-associated genes in HNSCC were screened using the TCGA database. The functional role of CAV2 in cell proliferation and apoptosis was assessed via CCK-8, colony formation, and flow cytometry assays. Mechanistic insights were obtained through co-immunoprecipitation, ubiquitination assays, and proteomic analysis. The impact of CAV2 on cetuximab sensitivity was evaluated both in vitro and in a xenograft mouse model. Results: CAV2 emerged as a top prognostic candidate. Knockdown of CAV2 significantly suppressed HNSCC cell proliferation and induced apoptosis. Mechanistically, CAV2 interacted with and stabilized the PACT protein, thereby inhibiting PKR activation via the ubiquitin–proteasome pathway. Notably, CAV2 deficiency markedly enhanced the sensitivity of HNSCC cells and tumor xenografts to cetuximab treatment. Conclusions: These findings establish CAV2 as a critical driver of HNSCC progression and cetuximab resistance through post-translational regulation of the PACT–PKR axis. Targeting CAV2 may therefore represent a promising strategy to potentiate the efficacy of EGFR-targeted therapy in HNSCC.