Abstract: Machine learning capabilities are expanding into scientific domains at an accelerating pace. When applied to high energy physics pattern discovery, they will generate candidates faster than traditional evaluation can absorb. ML finds patterns in past data. It is inherently post hoc. Whether those patterns reflect structure or coincidence is unknowable at discovery time. This limitation applies equally to human and computational pattern finding. What differs is scale. ML candidate generation is effectively unbounded, while human evaluation capacity remains fixed. When generation rate exceeds evaluation bandwidth, binary accept or reject degenerates to random sampling. Information theoretically, the only response that preserves ranking under a finite evaluation budget is stratification. By focusing on stratification rather than binary filtering, rule adjustments can be made retroactively, thresholds tuned as results accumulate, and evaluation bandwidth focused on top ranked candidates. This paper attempts to codify those criteria, proposing seven computationally evaluable standards for stratifying ML generated patterns. The goal is not to deliver verdicts but to prioritize which candidates merit preregistration and longitudinal tracking. The framework preserves the essential paradigm: pattern plus theory equals potentially real physics. Patterns alone, however striking, remain candidates until theoretical understanding arrives. Making these criteria explicit enables prefiltering at scale while creating a collaborative resource rather than a competitive one. ML capabilities extend what physicists can search while preserving how physicists evaluate. We offer this provisional framework for community calibration, with the goal of developing validation infrastructure before the capability fully arrives.

Abstract: This research answers the knowledge gap regarding the explanation of the quantum jump of the electron. This scientific paper aims to complete Einstein’s research regarding general relativity and attempt to link general relativity to quantum laws.

Abstract: To explore the responses of soil aggregate composition and stability to different grazing intensities in alpine meadow of the Qilian Mountains, no grazing (CK) was set as the control, with four treatments including light grazing (LG), moderate grazing (MG), heavy grazing (HG) and extreme grazing (EG) established. The characteristics of soil aggregates in the 0–10 cm and 10–20 cm soil layers were determined by the dry sieving method and wet sieving method, and three stability parameters including the mean weight diameter (MWD), geometric mean diameter (GMD) and fractal dimension (D) were analyzed. Combined with environmental and biological factors, the mechanisms underlying the effects of grazing on soil aggregates structure and stability were elucidated. The results showed that: (1) Soil aggregates with the particle size of 5–10 mm were the dominant fraction in the soil structure of the alpine meadow, and this fraction changed drastically with grazing intensity. CK maintained relatively high aggregate mechanical stability but exhibited weaker resistance to water erosion compared to grazed plots. Under the CK condition, the content of water-stable aggregates with the 5–10 mm particle size decreased significantly compared with mechanical-stable aggregates (by 60.07% in the topsoil and 70.66% in the subsoil). Light and moderate grazing maintained a dynamic balance and high stability of soil structure. Heavy and extreme grazing intensified soil structure fragmentation and overall stability declined. (2) Soil aggregate stability was correlated with environmental factors. Altitude and soil bulk density were significantly positively correlated with aggregate stability (P<0.001).Root biomass exerted a significant effect on the stability indices of mechanically stable aggregates in the topsoil (P<0.05); high root biomass destroyed soil macroaggregates but enhanced the resistance to water erosion. Soil microbial biomass carbon (SMBC), nitrogen (SMBN), phosphorus (SMBP) were significantly positively correlated with GMD of water-stable aggregates, but negatively correlated with GMD, MWD and D of mechanical-stable aggregates, also MWD and D of water-stable aggregates. Nitrate nitrogen had a positive effect on aggregate stability, while ammonium nitrogen had a negative effect. (3) The stability of aggregate in different soil layer varied under different grazing intensity. Under LG and MG conditions, the subsoil exhibited higher aggregate stability than the topsoil, whereas the opposite pattern was observed under HG, EG and CK conditions. Therefore, from the perspective of soil structural stability and sustainable utilization, light and moderate grazing are the optimal utilization patterns for alpine meadow in the Qilian Mountains. It not only maintains the structural stability of subsoil aggregates but also balances biological cementation and physical disturbance, avoiding aggregate water stability insufficiency under no grazing and the risk of structural fragmentation under heavy or extreme grazing. The findings provide a scientific basis for rational grazing management and soil conservation in alpine meadow of the Qilian Mountains.

Abstract: Spatial light field metrics such as mean cylindrical illuminance provide essential information for qualitative lighting evaluation, yet they remain far less common in practice than horizontal illuminance. To address this gap, we present a multi-sensor prototype that simultaneously measures horizontal illuminance Eh and approximates mean cylindrical illuminance Ez from a set of vertical illuminances uniformly spaced around a cylindrical surface. The device uses a flexible PCB wrapped around a support barrel and an inertial and magnetic measurement unit for orientation tracking. The measurements enable direct calculation of the modelling factor defined in the technical standard EN 12 464 and visualization of directional light distribution using polar plots and illuminance solid. Results show that the prototype approximates mean cylindrical illuminance with high accuracy while preserving directional information, allowing the illuminance solid to be decomposed into vector and symmetric components. Compared with conventional approximation methods, the proposed multi-sensor approach reduces spatial error and yields richer data for lighting analysis. These findings indicate that multi-sensor systems can bridge the gap between theoretical spatial metrics and practical photometry and support the improved modelling evaluation and integration of qualitative lighting parameters into routine workflows.

Abstract: Gaze analysis often relies on hypothesized, subjectively defined ROIs or heatmaps: ROIs enable condition comparisons but reduce objectivity and exploration, while heatmaps avoid this, they require many pixel-wise comparisons, making differences hard to detect. Here, we propose an advanced data driven approach for analysing gaze behaviour. We use DNNs to classify conditions from gaze patterns, paired with reverse correlation to show where and how gaze differs between conditions. We test our approach on data from an experiment investigating the effects of object specific sound (e.g. church bell ringing) on gaze allocation. ROI-based analysis shows a significant difference between conditions (congruent sound, no sound, phase scrambled sound and pink noise) with more gaze allocation on sound associated objects in the congruent sound condition, however, as expected significance depends on the definition of the ROIs. Heatmaps show some not very clear qualitative differences, but none are significant after correcting for pixelwise comparisons. Our approach shows that sound alters gaze allocation in some scenes, revealing task-specific, non-trivial strategies: fixations are not always drawn to the sound source but shift away from salient features, sometime falling between salient features and the sound source. Overall, the method is objective, data-driven, and enables clear condition comparisons.

Abstract: To investigate the long-term effects of climate change on biological communities, our primary aim was to identify the most reliable indicators among available biodiversity, dominance, and evenness indices. We examined three distinct response types to climate change, represented by three taxonomic groups: Aculeata (Hymenoptera), Syrphidae (Diptera), and nocturnal macrolepidoptera (Lepidoptera). Using faunistic datasets derived from our own 3–5 decades of field surveys, we calculated 12 key indices with the vegan package in R 4.2.1. The robustness of these indices was assessed through 1000-fold bootstrap simulations and pairwise correlation analyses. Our results revealed that the Gini–Simpson, Simpson diversity, McIntosh diversity, and McIntosh evenness indices consistently demonstrated high temporal stability and strong correlations across all three climate response types. Therefore, we recommend these indices as primary climate indicators. In contrast, Chao1 estimates, Margalef Index, Menhinick Index, and the Shannon–Wiener diversity index are suitable only for analyzing specific response patterns. Meanwhile, the Berger–Parker, Buzas–Gibson indices, and Hill numbers showed high variability or limited ecological responsiveness, making them unreliable for tracking climate change impacts. Our findings underscore that selecting biodiversity indices must be tailored to the research question and the characteristics of the ecosystem in order to ensure valid and informative ecological analysis.

Abstract: We derive the inverse fine-structure constant \( \alpha^{-1} = 137.035999143 \) from first principles using information-theoretic channel capacity between an 8-dimensional octonionic computational substrate and 4-dimensional spacetime. The derivation requires zero free parameters. Beginning from seven axioms (Peano’s five plus triadic closure and computability), Hurwitz’s theorem forces the octonions as the unique normed division algebra larger than 4D. The projection from 8D to 4D operates through an eigenvector channel whose base capacity is \( 2\binom{8}{4} - 3 = 137 \), counting coordinate 4-planes with bidirectionality and triadic gauge redundancy. Four correction terms—each a named mathematical constant with a specific geometric role—refine this to \( \alpha^{-1} = 137.035999143 \), agreeing with the CODATA 2022 value 137.035999177(21) to 1.62σ. The correction hierarchy is: 1/(8π) (spherical projection through 8D geometry), γ (discrete-to-continuous impedance mismatch), ζ(3)/(137 × 20) (cubic lattice memory), and a logarithmic channel memory term x from Shannon’s theory of channels with memory. Statistical analysis shows the zero-parameter prediction achieves a Bayes factor (decisive on the Jeffreys scale) against the null hypothesis of coincidental agreement, computed under a KT- constrained prior conditioned on the empirically known neighborhood \( \alpha{-1} \) ≈ 137. The framework makes a Tier 1 falsifiable prediction: for fixed apparatus and fixed atomic species, α exhibits an altitude dependence of \( (4.60 \pm 0.15) \times 10^{-16}\,\mathrm{km}^{-1} \) , testable with current optical clock technology. Tier 2 differential measurements across species are proposed to probe the suppressed non-scalar components of the projection residual ε(Φ) without altering the Tier 1 prediction. The same algebraic engine generates all fundamental mathematical constants (e, π, ϕ, √2 , ln 2, γ, ζ(3)) as eigenvalues of discrete walk operators on the Fano plane.

Abstract:

A challenge of studying mammalian cardiac embryogenesis is the limited ability to perform experimental manipulations in animal models. The avian embryo is widely accepted as a model for mammalian heart developmental studies. In this study, we establish the methodology and protocols for studying the Japanese quail (Coturnix japonica) heart at embryonic day 10 (HH38) using the FUJIFILM VisualSonics Vevo 3100 ultrasound system equipped with a MX550D small animal cardiology transducer. These protocols were designed to measure right ventricular wall thickness, pulmonary artery diameter, and the outflow velocities of the right ventricular outflow tract (RVOT) and the pulmonary artery (PA), thereby establishing baseline parameters of the normally developing quail morphology. Quail embryos are an ideal model for cardiovascular research due to their short incubation period (16-17 days), experimental accessibility, and strong similarities to mammalian heart development. These developmental similarities include, but are not limited to, looping, chamber septation, and the development of a true four-chamber heart. High-resolution imaging modalities, including ultrasound and optical coherence tomography, enable noninvasive, real-time visualization of cardiac morphology and function throughout development. Echocardiography allows for quantitative and qualitative assessments of myocardial structure and cardiac hemodynamics. The similarity to the mammalian heart, combined with rapid embryogenesis, makes quail embryos a valuable model for investigating congenital heart defects, genetic modifications, and fundamental cardiac developmental processes. In this study, we describe reproducible incubation protocols and baseline echocardiographic parameters used to evaluate morphological and physiological changes in the developing embryonic quail heart on embryonic day 10.

Abstract: The long-term outcomes of individuals exposed to similar traumatic events often diverge dramatically: while some succumb to chronic despair, others achieve posttraumatic growth. This “resilience paradox” highlights a limitation of current trauma therapies. Although Prolonged Exposure, Cognitive Processing Therapy, and EMDR reliably reduce symptoms such as hyperarousal and intrusive memories, many patients remain existentially fragmented, reporting a loss of purpose despite substantial symptom and functional improvement. This gap suggests that standard protocols—focused on sensorimotor stabilization, narrative coherence, and functional restoration—may systematically neglect a vital fourth meta-level: the capacity for non‑identified awareness.This paper introduces the Neuro-Existential Architecture System (NEAS), a theoretical framework that hypothesizes that meaning is not merely a psychological variable but a fundamental neurobiological organizing principle structuring resilience. NEAS proposes four complementary, hierarchically organized neurobiological mechanisms: (1) hierarchical recalibration via meaning-priors, using top-down signals to reorganize the brain’s predictive hierarchy; (2) emotional criticality via limbic meta‑regulation, permitting balanced oscillation between hope (Papez system) and caution (Yakovlev system); (3) spatiotemporal coherence, extending the autorelational window to restore identity continuity; and (4) Witnessing-Space as structural meta‑stabilization, theoretically instantiated through inter‑regional gamma‑frequency binding, a candidate mechanism proposed to enable global meta‑awareness and prevent system fragmentation under stress.The NEAS clinical model operationalizes these mechanisms into a four‑level architecture (Level −1: Relational Safety; Level 0: Sensorimotor Stabilization; Level 1: Narrative Coherence; Level 2: Existential Meaning Integration). Meaning-focused work at the highest level is hypothesized to be pivotal, explicitly intended to cultivate Witnessing-Space through contemplative practice integrated with trauma‑focused processing. To begin validating this framework, we propose a multi‑site, two‑arm randomized controlled trial (N = 240) comparing NEAS‑based treatment with standard trauma‑focused cognitive‑behavioral therapy. A neuroimaging subsample (n = 80) will exploratively measure autorelational window extension, gamma synchrony, and Default Mode Network connectivity. We hypothesize that the integrated four‑level NEAS condition will yield superior functional outcomes (Sheehan Disability Scale) and greater long‑term durability compared to standard care. While the present framework is grounded in Western neuroscience and clinical contexts, its ultimate value will depend on rigorous cross‑cultural adaptation and validation. By bridging neuroscience, existential psychology, and contemplative science, NEAS aims to support a shift from trauma‑focused symptom management toward existentially grounded, neurobiologically coherent healing.

Abstract: Procyanidin C1 (PCC1), a B-type procyanidin trimer derived from natural sources, has recently garnered significant attention in preclinical models due to its potential to specifically induce apoptosis in senescent cells (senolytic activity) and extend healthspan. However, existing data reveal a pronounced pharmacological paradox: the in vitro induction of apoptosis in senescent cells typically requires high micromolar concentrations (>50 μM), whereas in vivo peak plasma concentrations following intraperitoneal (i.p.) injection in mice are distributed around the 2-15 μM range, and oral (p.o.) administration yields nanomolar exposure levels (~ 0.04 μM), often falling below this threshold. This study aims to construct an objective translational medicine evaluation framework by systematically integrating cellular pharmacodynamic thresholds, Caco-2 transmembrane transport mechanisms, and in vivo pharmacokinetic (PK) data. The analysis indicates that even with i.p. administration, systemic plasma concentrations of PCC1 struggle to sustainably reach the cytotoxicity thresholds established in vitro; its in vivo efficacy is likely derived from high accumulation and localized concentration effects within specific tissues (e.g., adipose, lymphoid tissue). Further analysis points out that high-dose application may face dual barriers of safety risks and economic costs. It is postulated that combining modern nanodelivery technologies with synthetic biology to enhance bioavailability and achieve dosage minimization represents a critical pathway for the safe, economical, and efficient clinical translation of PCC1.

Abstract: We derive an exact, practical method to update Thévenin parameters (open-circuit voltage and equivalent resistance) of a linear network under a single internal branch modification (open/short/resistance change), without recomputing the full nodal solution from scratch. The change is modeled as a rank-one perturbation of the nodal admittance matrix, and the Sherman–Morrison identity yields closed-form port updates in terms of three physically interpretable scalars: local self-coupling, port–branch coupling, and state projection across the modified branch. We discuss limiting cases (open and short), include a brief note on complex admittances (phasors/Laplace), and provide a reproducible Python check.

Abstract: Flexible transparent conductive electrodes (TCEs) based on copper (Cu) meshes on polyethylene terephthalate (PET) substrates are constrained by critical interfacial weakness and inadequate mechanical durability, which hinder their widespread practical application. This study proposes a robust alloyed interface engineering strategy to address this fundamental challenge. Magnetron sputtering is employed to deposit Cu thin films on PET substrates with intermediate aluminum oxide (Al₂O₃) and nickel-chromium (NiCr) interfacial layers. Systematic comparative analyses reveal that the direct Cu/PET interface exhibits poor adhesion and mechanical fragility, while the incorporation of NiCr interlayers significantly enhances interfacial toughness. Through optimization, the NiCr layer forms a distinct alloyed interface with Cu via interdiffusion, fundamentally reinforcing the Cu/PET interface. Maskless photolithography enables precise patterning of Cu into micrometer-scale meshes, resulting in Cu Mesh/PET electrodes with excellent optoelectronic performance. The optimized electrodes achieve a sheet resistance of ~10.8 Ω/sq with an optical transmittance exceeding 87%, alongside remarkable mechanical robustness under repeated bending cycles. The synergistic toughening mechanism is clarified through interfacial microstructure analysis, which shows that the formation of a gradient alloyed zone effectively mitigates interfacial stress concentrations and suppresses crack propagation. This work provides a viable pathway for the development of next-generation durable flexible electronics.

Abstract: Background/Objectives: Tinnitus and hyperacusis can occur together or in isolation, with hyperacusis being asso-ciated with tinnitus much more frequently than vice versa. This striking correlation be-tween tinnitus and hyperacusis prevalence implicates that there might be a common origin such as a (hidden) hearing loss and possibly interrelated neural mechanisms of pathological development of those two conditions. Here, we propose such interrelated pathological mechanisms. Methods: This is a theoretical work based solely on considerations and published data. Results: We propose a model localized in the dorsal cochlear nucleus (DCN) of the brain-stem, that is based on classical mechanisms of Hebbian and associative plasticity known from classical conditioning. Specifically, our model proposes that hyperacusis results from synaptic enhancement of cochlear input to the DCN, whereas chronic tinnitus re-sults from synaptic enhancement of somatosensory input to the DCN. Specific conditions leading to one or the other condition are discussed. Conclusions: Our model predicts that hearing loss leads to chronic tinnitus, while noise exposure (which may also cause hearing loss) leads to hyperacusis. We would like to emphasize that our aim with the proposed model is not to provide a self-contained theo-retical construct, but to stimulate thought regarding possible pathological causes of tinni-tus and hyperacusis that have not yet been investigated. Individual assumptions that cannot yet be substantiated by existing literature are intended to provide impetus for fu-ture experimental studies.

Abstract: Building law allows the use of a building that is non-compliant with fire safety regulations, provided that enhanced fire exit strategies are implemented to mitigate the negative impact of this non-compliance on fire safety. This article demonstrates the potential of using a probabilistic fire risk analysis method—multisimulation—to increase the efficiency of selecting fire exit strategies. Multisimulation is a quantitative risk analysis method that utilizes, among other things, computer models of fire development and evacuation, as well as modern mathematics and computer science. The main aim of multisimulation is to perform multiple computer simulations (hence the name) for as many fire scenarios as possible in a given building. This article demonstrates the potential of using this method in a practical approach to ensuring fire safety. For this purpose, an existing auditorium building was analyzed, in which numerous non-compliances with applicable regulations were identified. The analysis included 1000 fire and evacuation simulations in a theater auditorium equipped with two emergency exits and 1000 fire and evacuation simulations in a theater auditorium equipped with three emergency exits. In the simulations of both scenarios, the duration of a performance conducted with a full audience and people performing on stage was modelled. The results clearly demonstrated a significant improvement in safety when three emergency exits were available. In terms of both the required safe egress time (RSET) and risk analyses, when three emergency exits were available (instead of the required two), the possibility of having only one functioning exit, which may occur due to a human error, was eliminated. Therefore, it was undoubtedly confirmed that the use of a third emergency exit is justified as an optimal fire exit strategy or a future legislative requirement.

Abstract: Lake Erhai is an important plateau freshwater lake in China. It serves not only as a crucial drinking water source for the local region but also as the core area of the Cangshan Erhai National Nature Reserve. Consequently, Lake Erhai plays an extremely significant role in the local economy, society, and ecology. However, since the 1970s, the lake has experienced a series of problems, including declining water levels and water pollution. In recent years, the water quality of Lake Erhai has continued to deteriorate, showing a eutrophic trend. To identify the primary driving forces behind these water quality changes, this study employed stepwise regression analysis. Climate conditions, socio-economic development within the basin, and implementation of environmental protection measure (IEPM) were considered as influencing factors for a comprehensive and systematic analysis of Lake Erhai's water quality. The results indicate that air temperature primarily affects total phosphorus (TP) concentration and exhibits a positive correlation. Rainfall predominantly influences TP and total nitrogen (TN) concentrations, also showing positive correlations. Wind speed affects chemical oxygen demand (CODMn), TP, and TN concentrations, exhibiting negative correlations with each. Socio-economic development mainly affects CODMn concentration. Based on these findings, this paper proposes recommendations focusing on formulating more effective non-point source pollution control measures and strengthening water quality monitoring in Lake Erhai during summer. This study systematically analyzed the anthropogenic and natural factors affecting Lake Erhai's water quality, identified the dominant influencing factors, and provides technical support for the subsequent enhancement of Lake Erhai protection measures.

Abstract: This study proposes a unified physical framework integrating conservation-based spatial foundations with discrete spatial quantum mechanics. By leveraging spatial quantum's localized splitting, adjacent capture, and density gradient effects, we develop a coherent explanation for the microscopic origins of gravity, cosmic expansion, dark matter, dark energy, and vacuum energy divergence. The theoretical mechanism posits that the total spatial volume remains strictly conserved, with space composed of indivisible fundamental units called spatial quantum. To maintain energy, momentum, and angular momentum conservation, bound matter continuously undergoes virtual particle processes—quantum information exchanges that require spatial quantum as the minimal physical degree of freedom, leading to their gradual increase over time. Gravity emerges as a geometric dynamics effect driven by spatial quantum density gradients, while cosmic expansion manifests as the continuous fragmentation of this conservation-based foundation into quantum units, observable through the light-cone causality structure. This model serves as a microscopic extension and refinement of general relativity, effectively addressing black hole singularities and Big Bang singularities. Without introducing dark matter particles, dark energy scalar fields, or additional gravitational corrections, it provides a self-consistent explanation for observed phenomena including galactic rotation curves, gravitational lensing, bullet clusters, and super-diffuse galaxies, while mitigating vacuum energy density divergence-induced "vacuum catastrophe" issues. The theory satisfies Lorentz covariance and local causality, featuring a relatively closed underlying structure with minimal assumptions, offering a potential pathway toward constructing a complete, singularity-free unified description of gravity and cosmology.

Abstract: The integration of building information modeling (BIM) and geographic information systems (GIS) is an important area of research aimed at improving interoperability between these domains. These domains often use different concepts for semantics such that non-interoperable vocabularies; schemes; metamodels for semantics; and, in general, non-interoperable IT architectures are used to publish semantic concepts. This study investigates the use of BIM data dictionaries for semantic classification of vector-based geospatial data in GIS, aiming to enable the use of common dictionaries and concepts to describe objects in both domains. The study addresses a particular problem: the fact that the domains use different metaconcepts to describe conceptual information and have different classification methods. The research focuses on identifying significant standards, comparing their metamodels to find similarities and explore the practical use of BIM data dictionaries for the semantic enrichment of GIS features. As a proof of concept, three approaches for the classification of features are developed and validated through implementation in the QGIS software. The results demonstrate that BIM data dictionaries can be used to semantically enrich geospatial data in GIS, with the buildingSMART Data Dictionary (bSDD) serving as a practical example. The conclusions drawn from the study are that although there are limitations and challenges, the integration of BIM data dictionaries into GIS is possible and beneficial for improving interoperability, particularly when cross-domain concepts are employed.

Abstract: Tumor hypoxia is a defining hallmark of solid cancers that profoundly influences tumor progression, genomic instability, and therapeutic response. Beyond its classical roles in angiogenesis and metabolic reprogramming, hypoxia has emerged as a central determinant of the tumor immune microenvironment (TME), promoting immune exclusion and resistance to immunotherapy. Our work has uncovered tumor cell–intrinsic mechanisms by which hypoxia drives immune escape. We identified hypoxia-induced autophagy as a key adaptive response that enables tumor cells to resist natural killer (NK) and cytotoxic T lymphocyte (CTL)–mediated killing. Under hypoxic stress, autophagy selectively degrades NK-derived granzyme B, neutralizing effector cytotoxicity, while genetic or pharmacologic inhibition of autophagy restores immune-mediated killing and enhances tumor regression in vivo. Furthermore, we demonstrated that Vps34 inhibition, a central regulator of autophagy and vesicular trafficking, converts poorly infiltrated “cold” tumors into inflamed “hot” tumors enriched in NK, CD4+, and CD8+ effector T cells, thereby potentiating the efficacy of PD‑1/PD‑L1 checkpoint blockade across multiple tumor models. Recently, we identified the atypical chemokine receptor ACKR2 as a hypoxia-inducible, HIF-1α–dependent checkpoint that restricts chemokine availability and limits immune infiltration. Targeting ACKR2 alleviates immune exclusion and synergizes with PD-1 blockades to induce tumor regression in otherwise refractory tumors. Collectively, these studies establish a coherent model in which hypoxia and its downstream stress-response pathways act as master regulators of tumor immune evasion. By rewiring autophagy and chemokine signaling, hypoxia shapes the immune landscape of solid tumors and defines responses to immunotherapy. Targeting these pathways represents a compelling strategy to overcome immune resistance and expand the clinical benefit of checkpoint inhibitor therapies.

Abstract: Counterfactual explanations are increasingly vital for understanding and trusting machine learning models. This study presents, Desirability Rating based Counterfactual (DeRaC), a generalized framework for generating valid counterfactual explanations applicable to multi-dimensional classification problems, including single and multi-output classification with binary and multi-label outputs. By expanding the definition of counterfactual validity through a novel “desirability rating,” the approach addresses limitations in existing methods for complex output spaces. This work details a novel framework, introducing concepts like partially valid counterfactuals and a quantitative measure of output desirability, which can be used with objective functions to find counterfactuals that also satisfy the various existing properties such as similarity, proximity, validity, actionability, etc. Experiments demonstrate the feasibility of systematically generating counterfactuals using existing optimization techniques, achieving varying degrees of validity and similarity. The research emphasizes the context-dependent nature of counterfactuals and lays the foundation for more transparent and trustworthy machine learning systems.

Abstract: As a core ground-based coronal observation facility in the low-latitude and high-altitude regions of China, the Lijiang Coronagraph takes advantage of the natural endowments of the Lijiang Astronomical Observation Station, such as an altitude of 3200 meters and low atmospheric turbulence. It has gone through a complete development process from introduction through China-Japanese cooperation to independent innovation and iteration. This paper systematically summarizes the core technological innovation achievements of this facility, including the upgrade of the automatic operating system, the integration of the dual-band observation system, the stray light suppression technology based on the image difference method before and after cleaning, as well as the high-precision image calibration and registration technology. These innovations have significantly improved observation efficiency and data quality, laying a solid foundation for high-quality observations. At the scientific research level, the observation data reveal that 1.1 solar radius is a highly correlated region between coronal green line brightness and magnetic field intensity. It also confirms a strong correlation between the coronal green line and the SDO/AIA 211 Å extreme ultraviolet band (correlation coefficient: 0.89 - 0.99), which can support the research on early warning of Coronal Mass Ejections (CMEs). These achievements provide key data support for the verification of coronal heating mechanisms and the exploration of the origin of the slow solar wind. The technical experience accumulated by the Lijiang Coronagraph has not only laid a solid foundation for the research and development of China's next-generation large-aperture coronagraphs, but also promoted China's leapfrog development from being a follower to a parallel runner in the international field of low coronal observation, making it an important part of the global coronal observation network.