Abstract: Finite-size suppression of the Curie temperature (T_c) in ferroelectric perovskite nanostructures remains an important yet insufficiently resolved problem, with reported scaling exponents varying considerably across experimental and theoretical studies. Although density functional theory provides atomistic insight into size-dependent behaviour, its high computational cost limits systematic exploration across broad size ranges. Conversely, purely empirical fitting approaches often lack physical interpretability and formal uncertainty quantification. In this work, a physics-informed surrogate modelling framework is developed to investigate finite-size scaling in BaTiO₃ and KNbO₃ nanostructures using a structured dataset compiled from the literature. The model is based on thermodynamically motivated scaling behaviour, enabling extraction of physically meaningful size-dependent parameters. Bootstrap resampling is employed to quantify statistical robustness, yielding scaling exponents of 1.59 (95% confidence interval: 1.43–1.72) for BaTiO₃ and 1.40 (95% confidence interval: 1.31–1.52) for KNbO₃. Gaussian Process regression is further integrated to provide uncertainty-aware predictions across the nanoscale domain. In addition to forward prediction, the framework enables inverse estimation of the minimum particle size required to preserve ferroelectric stability at a specified operating temperature. For a threshold of 300 K, the predicted critical sizes are approximately 4.96 nm for BaTiO₃ and 2.89 nm for KNbO₃. Extension to a coupled size–strain formulation produces a two-dimensional stability map, demonstrating tunable interactions between confinement and strain. Overall, the proposed methodology provides a transparent, statistically rigorous, and computationally efficient framework for predictive analysis and rational design of nanoscale ferroelectric materials.

Abstract: While enterprises amass vast quantities of data, much of it remains chaotic and effectively dormant, preventing decision-making based on comprehensive information. Existing neuro-symbolic approaches rely on disjoint pipelines and struggle with error propagation. We introduce the large ontology model (LOM), a unified framework that seamlessly integrates ontology construction, semantic alignment, and logical reasoning into a single end-to-end architecture. LOM employs a construct-align-reason (CAR) pipeline, leveraging its unified architecture across all three stages: it first autonomously constructs a domain-specific ontological universe from raw data, then aligns neural generation with this structural reality using a graph-aware encoder and reinforcement learning, and finally executes deterministic reasoning over the constructed topology, node attributes and relation types. We evaluate LOM on a comprehensive benchmark constructed from diverse real-world enterprise datasets. Experimental results demonstrate that LOM-4B achieves 88.8% accuracy in ontology completion and 94% in complex graph reasoning tasks, significantly outperforming state-of-the-art LLMs. These findings validate that autonomous logical construction is essential for achieving deterministic, enterprise-grade intelligence.

Abstract: We extend the master-integral-transform theory from entire kernels to finite-principal-part Laurent kernels and show that the resulting transform is a weighted dilation operator acting on the Fourier transform of a weighted signal. This yields a unified operator framework for several exact inversion mechanisms, including Mellin diagonalization, two-sided Mellin-symbol inversion, Dirichlet–Wiener inversion, log-scale Fourier inversion, recursive inversion, and Neumann-series recovery. The main structural result is that finite negative Laurent tails do not destroy the spectral architecture; they enlarge the one-sided dilation orbit to a two-sided one. We establish exact factorization formulas on weighted function spaces, prove branchwise Mellin inversion under explicit integrability assumptions, derive a contour-free Dirichlet–Wiener inverse, obtain a log-scale Fourier multiplier representation suitable for FFT-based recovery, and prove a practical stability bound away from multiplier zeros. A worked symbolic example and a numerical blueprint are also included.

Abstract: Detecting small objects in drone imagery remains challenging because of extreme object scale variations, dense scenes, and limited pixel information. Although recent YOLOv8 variants provide multiple model scales and architectural options, systematic guidance on their practical use in UAV-based detection remains limited. Accordingly, this study conducted a comprehensive empirical evaluation of the complete YOLOv8 family on the VisDrone dataset to assess the effects of the model capacity, input resolution, and architectural modifications on the small-object detection performance. The results showed that increasing the model capacity exhibited diminishing returns: YOLOv8l achieved the best overall accuracy (15.9% mAP50), while the larger YOLOv8x model exhibited a substantial performance degradation (7.32% mAP50) owing to training instability under data-constrained conditions. Scaling the input resolution from 640 to 1280 yielded a 25% improvement in the detection performance, substantially exceeding the gains obtained through architectural modifications, such as adding a P2 detection layer (+6%). The optimal configuration (YOLOv8l @ 1280) achieved a 488% improvement compared to the YOLOv5 baseline. These findings demonstrate that, for UAV-based small-object detection, prioritizing an appropriate model capacity and input resolution is more effective than increasing the architectural complexity.

Abstract: Traditional investment decision-making methods struggle to reconcile multiple policy objectives with systemic risk during economic downturns. Taking enterprises in Leshan, Sichuan Province as its research subject, this paper constructs an integrated framework encompassing six dimensions: decision objectives, risk assessment, financing structure, policy instrument utilization, Evaluation Completeness and digital technology application. It further establishes a three-tier linkage mechanism of “Strategy–Execution–Support.” Drawing on literature review, case analysis, and policy text analysis, the study translates abstract strategic goals into quantifiable indicators, thereby addressing the challenge of quantifying non-financial metrics. The findings demonstrate that this framework enables a shift from a single financial objective to “optimizing strategic adaptability,” and from passive policy compliance to proactive use of policy instruments—markedly improving the precision of corporate investment decisions under uncertainty. The paper offers local enterprises in Sichuan Province an actionable theoretical basis and implementation pathway. It also provides a reference for local governments and financial institutions seeking to refine their support policies, carrying practical significance for strengthening regional economic resilience, advancing green and low-carbon transformation, and easing the financing constraints faced by small and medium-sized enterprises (SMEs).

Abstract: As low-temperature plasmas (LTPs) have gained significant attention in materials processing for the microelectronics industry, challenges in spatiotemporal analysis of plasma parameters in an RF capacitively coupled plasma (CCP) system necessitate multidimensional numerical simulations. This study investigated the conditions under which a kinetic simulation or a fluid model is effective for low-pressure CCPs, focusing on the critical role of energy-dependent electron kinetics in LTPs by comparing symmetric and asymmetric electrode structures. We provide a comprehensive investigation of particle energy distributions, elucidating the kinetic effects of non-Maxwellian distributions. The validity of standard fluid approximations, such as the drift-diffusion approximation and isotropic pressure assumptions, is assessed by comparing results from a two-dimensional fluid model with those from a particle-in-cell simulation. The dominance of the ion pressure tensor over isotropic approximations in the sheath has been observed, especially in an asymmetric electrode structure, which is more representative of realistic process chambers.

Abstract: The ultimate goal of theoretical physics is unifying the microscopic quantum realm with macroscopic general relativity. This paper proposes the 4G Model of Final Unification (having 3+1 interaction dependent gravitational constants) to bridge this gap. Central to this framework is resolving the historical mystery of the gram mole. Rather than an arbitrary chemical convention, the mole is redefined as a fundamental, intrinsic gravitational charge. Consequently, the Avogadro number is physically derived as a structural limit dictated by strong force saturation and nuclear binding energy. By treating the atom as an electromagnetic particle, the model establishes a direct equivalence between microscopic and macroscopic gravitational scales. To formalize this, honouring Einstein, Perrin, Loschmidt, Avogadro and Newton, we introduce the dimensionless EPLAN ratio. This universal scaling factor authenticates the SI-defined magnitude of the Avogadro constant and extends directly into astrophysics. Integrating this ratio with nuclear magic numbers yields a quantized mass spectrum for celestial bodies. This introduces a bottom-up geometric construction of stars, successfully deriving boundaries like the Chandrasekhar mass limit from first principles and suggesting compact objects settle into discrete gravitational orbitals. Finally, the framework reveals that the four fundamental constants corresponding to the strong, weak, electromagnetic, and Newtonian forces are not isolated. Instead, they operate as synchronized gears in a cosmic clockwork. The precise interaction of these quantum gears drives the macroscopic rotation of Newtonian gravity, proving that the subatomic stability of the atom is perfectly interwoven with the grand scale stability of the entire universe. By grounding the pursuit of unification in testable, multi-disciplinary outcomes, this framework offers a practical alternative to highly abstract theoretical models, and we respectfully present it for the physics community’s serious consideration.

Abstract: (Background/Objectives): This study aims to evaluate the efficacy of an ultrasound-guided infiltration method with hyaluronic acid and corticosteroids in the treatment of Temporomandibular Disorders (TMD). (Methods): Twenty-eight patients (16 females and 12 males), aged between 25 and 55 years, with TMD and evidence of retrodiscal tissue hyperemia on Magnetic Resonance Imaging (MRI) of the Temporomandibular Joint (TMJ) were enrolled. Before treatment, the intensity of preauricular pain was assessed using the Visual Analogue Scale (VAS), and the presence of associated symptoms such as tinnitus, vertigo, headache, and joint clicking was recorded. After the creation of individualized interocclusal devices, a bilateral ultrasound-guided infiltration of low molecular weight hyaluronic acid and corticosteroid was performed. (Results): Immediately after treatment, joint clicking disappeared in 80% of patients. Follow-up assessments at 30, 60, and 90 days, supported by control ultrasound, showed a substantial and statistically significant (p < 0.001) improvement in pain symptoms. (Conclusions): The ultrasound-guided infiltration technique proved to be a valid short-term therapeutic option for patients with TMD and inflammation of the retrodiscal tissue. A larger sample size and long-term follow-up are necessary to confirm these preliminary results.

Abstract: While falling costs have increased access to genomic sequencing, the impact of clinical sequencing is often hindered by the challenge of interpreting complex genetic data. The high prevalence of variants of unknown significance (VUSs) can lead to false reassurance or psychological distress, as patients and non-expert clinicians may misinterpret inconclusive results. We propose that artificial intelligence (AI) may serve as a critical clinical decision-support tool to improve the efficiency of genetic testing, especially in variant analysis. We advocate integrating AI throughout the genetic diagnostic workflow and outline current approaches to AI-assisted variant analysis to enable efficient personalized treatment. We also discuss anticipated challenges in this pursuit and offer recommendations to ensure precision, accuracy, reproducibility, and transparency.

Abstract: Background/Objectives: Pes planovalgus is one of the most common misalignments in children. In this study the established biomechanical foot orthoses (BMFO) are being compared with a more recent treatment: sensorimotor foot orthoses (SMFO). SMFO are a more recent treatment and aim to correct malalignment by specifically modulating muscle activity rather than relying solely on passive mechanical support, as is the case with BMFO. Methods: Thirty-two children and adolescents aged six to six-teen participated in this study. After randomized group allocation, the rearfoot angle was analyzed by two-dimensional gait analysis in the SMFO-group (n=18) and the BMFO group (n=14) under three conditions: without foot orthoses (baseline), with foot orthoses (immediate) and after four weeks of use. Results: (1) SMFO and BMFO significantly improved the rearfoot angle immediately after application, (2) the achieved correction was maintained over four weeks in both groups, and (3) despite baseline differences, the superior rate of improvement in the SMFO-group resulted in comparable rearfoot alignment between SMFO and BMFO at the four-week follow-up. Conclusions: Based on the results, it can be concluded that SMFO and BMFO are comparable methods for treating pes planovalgus in children and adolescents.

Abstract: Background/Objectives: Total neoadjuvant therapy (TNT), integrating systemic chemotherapy and radiotherapy before surgery or surveillance, has become a standard approach for locally advanced rectal cancer (LARC). However, optimal sequencing strategies and long-term outcomes of watch-and-wait (W&W) following sandwich TNT remain insufficiently characterized. We evaluated oncologic outcomes and treatment response in patients treated with an institutional sandwich TNT protocol. Methods: We conducted a retrospective cohort study of consecutive patients with LARC treated with sandwich TNT (induction chemotherapy followed by hypofrac-tionated intensity-modulated radiotherapy with simultaneous integrated boost [IMRT-SIB] chemoradiotherapy and consolidation chemotherapy) at the Institute of Oncology Ljubljana between 2016 and 2023. The primary endpoint was an overall complete response (CR; pathological [pCR] and clinical [cCR]). Secondary endpoints included tumor regression grade (TRG), major pathological response (MPR), R0 resec-tion rate, organ preservation, overall survival (OS), and disease-free survival (DFS). Results: Among 205 patients (median age 61 years), overall CR was 29.5% (pCR 19.3% and cCR 10.2%). Major pathological response (TRG 3–4) occurred in 37.6%. R0 resec-tion was achieved in 94.5%. In the W&W cohort (n=21), local regrowth occurred in 33.3% (95% CI 14.6–57.0%) over a median follow-up of 4.96 years. Surgery-free sur-vival at 5 years was 73.1% (95% CI 55.0–97.2%). Estimated five-year OS was 81.1% (95% CI 75.5–87.2%) and 5-year DFS was 75.2% (95% CI 69.0–82.0%). In multivariable analysis, non-R0 resection (HR 6.06), MRI circumferential resection margin positivity (HR 3.11), and extramural vascular invasion positivity (HR 1.97) remained independ-ent predictors of DFS. Conclusions: Sandwich TNT yields meaningful tumor response and durable survival in MRI-defined high-risk LARC. Structured W&W offers organ preservation with acceptable oncologic control under intensive surveillance.

Abstract: Abandoned mine sites pose environmental and public health hazards due to the presence of metals in them. We extend our study beyond merely assessing total elemental contents to evaluate the contamination and potential spread of metals from contaminated mining sites into adjacent and surrounding ecosystems. Rather, we employ geo-chemical fractionation methods to measure the elemental fractions and binding forms of Pb, Cd, Mn, Cu, and Zn. We go on to estimate the mobility of these metals in soils collected from abandoned mine sites. The soil pH of the sites ranges from acidic to slightly acidic (4.88–6.48), exhibits moderate electrical conductivity and has varying cation exchangeable capacities (16.97–29.57 meq/100g). The overall concentrations of Pb, Cd, Mn, Cu, and Zn surpass FAO/WHO standards, suggesting a notable human impact stemming from past mining activities. The geochemical fractionation analyses indicate a higher proportion of Pb (88%) and Cd (75%) are present in the residual fraction, suggesting low mobility and indicating a possible source to be associated with geogenic or the parent material or geological sources. The dominance of Mn (83%), Cu (73%), and Zn (66%), on the other hand, in mobile fractions and non-residual forms, suggests that pollution is possibly traced to anthropogenic activities at the mining sites. The mobility and by extension the ecotoxicology of Pb, Cd, Zn, and Cu, may be tied to changes in pH, salinity (EC), as well as bulk density and porosity of the mining sites.

Abstract: Seagrasses play a fundamental role as ecosystem engineers and habitat architects in coastal environments. In the Mediterranean Sea, Posidonia oceanica is an endemic, vulnerable, and legally protected species that is highly sensitive to environmental degradation and is widely used as an indicator of pristine ecological conditions. Ongoing global warming and increasing anthropogenic pressures highlight the need for precautionary, non-destructive methods to assess P. oceanica meadows. Traditional SCUBA-based surveys, although accurate, are time-consuming, labour-intensive, and limited by diver availability and underwater working time, particularly when estimating biometric parameters such as shoot density and leaf length. In this study, we applied a conservative acoustic-based approach to quantitatively estimate P. oceanica meadow characteristics, moving beyond purely qualitative acoustic mapping previously restricted to distributional assessments. Acoustic data collected during winter and summer 2015 along the entire Turkish Mediterranean coast were analysed to estimate seagrass biometrics and to derive indicators of ecological status. Acoustic outputs were validated through comparison with SCUBA-diving observations, allowing evaluation of the reliability and cost-effectiveness of the method. The acoustic system enabled rapid, large-scale assessment of seagrass distribution, coverage, habitat structure, and ecological condition, overcoming limitations associated with other remote sensing techniques. The results demonstrate that acoustic data can support the estimation of multiple biometric and ecological parameters and facilitate classification of ecosystem status from poor to high (pristine), in line with updated international assessment criteria. For the first time, this study provides high-resolution spatiotemporal distribution and coverage of P. oceanica meadows and associated benthic habitats along a substantial portion of the Turkish Mediterranean coast using acoustics alone. The approach offers a valuable non-destructive alternative for monitoring seagrass ecosystems and supports sustainable conservation and management of Mediterranean coastal habitats.

Abstract: A classical fluid splitter produces the same patterns of energy redistribution as a Stern-Gerlach quantum device, with rotationally invariant coefficients of correlation between molecular paths. Alternative settings obey a cosine squared rule, leading to Tsirelson-type Bell violations with outcome independence. This is a confirmation of the Correspondence Principle of quantum mechanics, where individual quanta express system-level properties according to Born’s Rule. Kochen-Specker contextuality and Bell Locality are not contradicted by this result, but their interpretation is in question. The formal definition of “Local Realism” is limited to intrinsic particle properties. In contrast, quantum-like correlations require the acknowledgement of ensemble effects on dynamically inseparable propagating entities, even when they appear to operate one at a time.

Abstract: Resistance spot welding of dissimilar steels is a key Linkage process in the manufacturing of rail passenger car bodies. However, there are problems such as core deviation caused by material physical property differences in the welding of dissimilar steels (stainless steel/low-carbon steel). This study improves the weldability of stainless steel and low-carbon steel by adding a nickel intermediate layer between them. The results show that adding a nickel intermediate layer can Valid compensate for heat Loss, suppress the deviation of the weld nucleus, optimize the size of the weld nucleus, and improve the Stability of the welding quality.

Abstract: Built heritage is increasingly affected by climate-driven processes, yet its capacity to inform broader understandings of urban environmental change remains insufficiently explored. Here, we synthesize recent literature (2020–2024) on the application of the Historic Urban Landscape (HUL) approach to the integrated management of cultural heritage under climate risk, reframing the historic built environment as a multiscale diagnostic medium for climate–urban interactions. We analyze the steps and tools employed to support decision-making across territorial planning, risk assessment, and heritage governance in the papers selected from Web of Science, Science Direct, and Scopus databases. Results show that the approach is a flexible analytical framework that allows the integration of heterogeneous data, multi-criteria evaluations, and diverse stakeholder perspectives across spatial and temporal scales. Information modelling tools are shown to play a central role in structuring territorial knowledge, identifying patterns of vulnerability, and supporting comparative analyses across urban contexts. Nonetheless, significant challenges persist, including limited quantification of climate-induced degradation mechanisms, uncertainties in linking vulnerability assessments to predictive models, structural constraints on participatory implementation, and a tendency to apply the approach as a checklist due to inadequate understanding of its holistic dimensions. Overall, the HUL approach emerges as a scalable and transferable framework for embedding cultural heritage within climate research, advancing the conceptual integration of built heritage into resilience science and sustainability-oriented urban systems.

Abstract: The increasing digitalization of photovoltaic (PV) inverters and their integration into distributed energy resource (DER) ecosystems expose these devices to a rapidly expanding cyber‑physical attack surface. Existing security requirements are fragmented across heterogeneous technical standards—including IEC 62443, IEC 62351, UL 2900‑1, UL 1741 SB, IEEE 1547, IEEE 2030.5, and SunSpec profiles—and only partially aligned with emerging regulatory obligations such as the EU Cyber Resilience Act (CRA) and NIS2 Directive. This fragmentation complicates assurance, hinders interoperability, and leaves critical security controls inconsistently implemented across vendors and deployments. This paper introduces a Unified Security Baseline (USB) that harmonizes essential technical and lifecycle security controls for PV inverters, including secure boot, firmware signing, anti‑rollback protection, strong authentication, TLS‑secured communication, SBOM governance, secure over‑the‑air updates, and coordinated vulnerability disclosure. The USB provides a device‑centric, standards‑agnostic framework designed to strengthen the security posture of inverter‑dominated DER environments while supporting regulatory compliance. By consolidating cross‑standard requirements into a coherent baseline, this work establishes a foundation for future conformity assessment, certification efforts, and secure‑by‑design engineering practices in critical IoT/OT infrastructures.

Abstract: Background: The late-time fate of black holes and the operational limits of General Relativity (GR) in the far future remain open problems in thermodynamic cosmology, and are central to the causal gap discussed in Penrose’s conformal framework. Objective: We determine, within Gibbs Energy Redistribution Theory (GERT), the lower density boundary of GR validity and the thermodynamic fate of supermassive black holes in the Hyperdilute Regime. Methods: Using the asymptotic gas-dominated GERT term, we derive the critical crossing λ_CMB(a) = H⁻¹(a), compute a_crit and ρ_GR,min analytically, and evaluate black-hole thermodynamic states (in cluding ∆G and inversion scales) across mass ranges, with no additional premises beyond the base framework. Results: We obtain a_crit = 10^12.88±0.12 and log₁₀(ρ_GR,min) = −65.2 ± 0.4 kg/m³, closing the Layer 3 validity domain from Planck density to a symmetric lower operational threshold (161.9 density decades). At acrit, all black holes with M > M^∗ ≈ 1.7×10⁵ M⊙ are in thermodynamic absorption, with strongly non-spontaneous redistribution (e.g., ∆G ≈ +5800 Mc² for 10⁹ M⊙). Thermal inversion occurs later in the Quasi-Vacuum, where cosmological cooling outpaces Hawking thermal change by ∼10¹⁰⁶; at a_inv(M), supermassive-black-hole Schwarzschild radii exceed the Hubble radius by factors of 4 to 10¹⁰. Conclusions: In this regime, Hawking evaporation is not the operative end-channel for high-mass black holes. GERT instead identifies a Gibbs-driven macroscopic phase transition (∆G < 0 in the Quasi-Vacuum) and establishes a symmetric but dynamically inverted boundary structure for Layer 3: Inward-dominated at emergence (dH/da < 0) and Outward-dominated at dissolution (dH/da > 0). This provides a quantitative thermodynamic completion scenario and a causal contribution to the CCC end-state problem.

Abstract: There is a worldwide hazard with microplastics (MPs), plastic production units less than 5 mm, as it increases with the increase in output (>380 million tons/year), which is even division into micro- and nanoplastics. This is a review of over 200 peer-reviewed articles through systematic database searches that combines both experimental, modeling and observational data to help fill knowledge gaps in MP migration, transformations, bioavailability, and health risks in aquatic, terrestrial, and atmospheric environments. Results indicate heterogenous distributions: coastal clumps (e.g., 0.011 +- 0.017 items/m3 oceans), bioturbation-induced soil penetration (600 particles/kg), and peaks in wet season. Prevalent (less than 100 mm) fragments/fibers of polyethylene/polypropylene support wind/river transport and sorption of contaminant. Losses Phthalates are lost through transformations by photo-oxidation, abrasion, and biofilms leading to enhanced ecotoxicity via trophic magnification. Bioaccumulation, endocrine disruption, oxidative stress, and chronic illnesses phenotypes are all potential risks of human exposure, which primarily occurs through the ingestion of fish (millions of particles per week through the ocean) pathways. This framework gives greater emphasis on ecological back-human associations whereby there is routine of such ways, prognostic models and ameliorations such as waste curbs and biodegradables to conserve the surroundings and health.

Abstract: Soil moisture (SM) governs land–atmosphere exchanges and strongly influences agricultural management and hydrological assessment, yet high-resolution mapping remains challenging due to sensor-specific confounding effects and limited field observations. This study develops a practical workflow for point-scale SM estimation and wall-to-wall mapping by integrating multi-sensor remote sensing predictors with ensemble learning. A compact predictor set was constructed from Sentinel-2 optical indices (MSI and NDWI), Sentinel-1 SAR descriptors (σVV and the polarization ratio σVH/σVV), and topographic information (DEM), collocated with in situ SM measurements along a transect in the study area. Three tree-based regressors—Random Forest, XGBoost, and CatBoost—were trained under an identical feature configuration and evaluated using R², RMSE, and MAE together with predicted–observed diagnostics. A stacking ensemble was then implemented using leakage-controlled K-fold out-of-fold predictions to generate meta-features, with a Decision Tree as the meta-learner tuned via a grid search. Results show that base learners achieve comparable skill (R² ≈ 0.60–0.62; RMSE ≈ 0.038–0.039), while stacking improves test accuracy (RMSE = 0.0346) and provides a stable mapping-ready model. The trained framework was transferred to stacked raster predictors to produce spatially continuous SM maps, revealing coherent moisture heterogeneity across the region.