Abstract: This article introduces a tutorial-style implementation of Quantum Link Prediction (QLP) for citation network analysis in historiographical research, with a specific focus on the transnational historiography of Mongol military campaigns. Using a manually curated citation network of Russian and American military treatises from 1875 to 2012, the study applies simulated quantum random walks to identify previously unknown citation pathways. The article is structured to guide researchers through each phase of the QLP workflow, from network preprocessing and quantum circuit construction to result interpretation, making it accessible to scholars in the humanities new to quantum methods. Through this approach, we discover a previously unknown transmission link connecting the Russian and American corpora. This finding not only reshapes the existing citation network but also demonstrates the potential of QLP as an introductory use case for teaching quantum computing to learners in the humanities. To support reproducibility and future adoption the open-source QuantumRandomWalks package was published in conjunction with this paper.

Abstract: Background Generative AI (genAI) has the capacity to create realistic and convincing animal videos, however, it must simplify and reduce behavioural variation to do so, possibly leading to misinformation. Methods We categorised 29 videos in the press release for a specific video genAI engine. Twelve featured animals. We mapped each video to the Five Domains and categorised behaviour and welfare within. Results Negative welfare was rarely seen, ranging from 8% (n = 1) for Nutrition, to 42% (n =5) for Behavioural Interactions. By contrast, Mental State, Environment, and Behavioural Interactions appeared positive in >42% (n = 5) of the videos featured. However, videos were often misleading or did not represent accurate animal behaviour. Limitations This work was limited to a press-release of data and does not explore user experience. Conclusions GenAI videos pose a new route for client confusion and veterinarians need to incorporate genAI misinformation combatting in their practice.

Abstract: Humor and media effects research have a long history together, but there have been few broad-based reviews of that research. A review of 34 experimental research studies was undertaken. Though two-thirds of the studies were guided by theory, only around 21% included a humor theory. Humor was often operationalized using traditional media content, with the humor itself being disparaging humor or satire. Using some measure of humor as a manipulation check was common. However, few studies assessed positive emotion as a response to the humor content at post-exposure. A majority of studies assessed perceived funniness of humor content. Attitudinal and affective outcomes were used frequently, and moderation was included more frequently than mediation. The discussion highlights avenues for greater integration of these research areas.

Abstract: This study investigates the corrosion behavior of a WC-6Co cemented carbide (94 wt% WC, 6 wt% Co) in acidic (pH 2) and alkaline (pH 13) aqueous environments, with em-phasis on implications for reconditioning processes. Both electrolytes, characterized by their high electrical conductivity, are used in industrial electrochemical stripping of PVD coatings. While acidic electrolytes are already established for stripping coatings from hard metal substrates, the influence of the alkaline electrolytes on substrate integrity remains insufficiently explored, especially considering the implication of reconditioning. Elec-trochemical characterization was performed using potentiodynamic polarization method, followed by surface analysis via SEM, EDX, and laser confocal microscopy. Two distinct corrosion mechanisms were identified, corresponding to the respective pH conditions and consistent with predictions from Pourbaix diagrams. In acidic media, cobalt dissolution occurred alongside strong passivation of tungsten through the formation of WO₃. In contrast, under alkaline conditions, tungsten formed soluble tungstate ions (WO₄²⁻), leading to progressive leaching of WC grains, while cobalt exhibited passivation via a Co(OH)₂ layer, mitigating binder degradation. Within the scope of this work, electrolytes used for electrochemical stripping were examined. The investigation focused on their corrosive impact on uncoated hard-metal substrates under electrochemical stripping conditions, as these become exposed to both the electrolyte and applied potential once the coating is removed. Coating removal itself was not addressed. A key finding is that oxide or hydroxide passivation on cemented carbides does not inherently guarantee protection. Its effectiveness depends strongly on the nature of the formed layer. In the acidic elec-trolyte, pseudo-passivation by formation of WO₃ layer initially inhibits corrosion but leads to significant material loss upon its breakdown. These findings provide valuable guidance for the application of cemented carbides in electrochemical stripping processes used for PVD coating removal.

Abstract: Background: This study aims to psychometrically validate the abbreviated version of the Connor-Davidson Resilience Scale (CD-RISC-10) in Peruvian nurses, evaluating its convergent validity through its association with perceived stress and empathy. Methods: A cross-sectional psychometric study was conducted in 374 Peruvian nurses to evaluate the psychometric properties of CD-RISC-10 through confirmatory factor analysis (CFA). In addition, convergent validity was examined by correlational analysis with Spearman's ρ coefficient with empathy and resilience. Results: The CFA confirmed that the one-dimensional model has a good fit (CFI = 0.978, TLI = 0.971, RMSEA = 0.080, and SRMR = 0.044). Cronbach's alpha of 0.89 and McDonald's omega of 0.81 were obtained. Convergent validity showed significant correlations with perceived stress (ρ = -0.23, p < 0.001) and empathy (ρ = 0.31, p < 0.001). Conclusion: The CD-RISC-10 has excellent psychometric properties in Peruvian nurses. Future studies are needed to evaluate their factorial invariance between clinical specialties and determine cut-off points.

Abstract: In this article we analyze a dynamical system known as the FitzHugh-Nagumo model, which offers many characteristics of nonlinear systems, such as bifurcation, excitability or limit cycle. The dynamics associated with sets of values of the parameters associated with this model, called excitable, oscillatory and bistable, are analyzed. Then adding a perturbing or diffusive term to the system through the Laplacian, it is studied how these dynamics propagate in a one-dimensional or two-dimensional extended medium, through the definition of a cellular automaton with periodic initial conditions.

Abstract: Open-pit mining relies heavily on visual inspection to identify indicators of slope instability such as surface cracks. Early identification of these geotechnical hazards allows for the implementation of safety interventions to protect both workers and assets in the event of slope failures or landslides. While computer vision (CV) approaches offer a promising avenue for autonomous crack detection, their effectiveness remains constrained by the scarcity of labelled geotechnical datasets. Deep learning (DL) models require large amounts of representative training data to generalize to unseen conditions; however, collecting such data from operational mine sites is limited by safety, cost, and data confidentiality constraints. To address this challenge, we propose a hybrid game engine—generative artificial intelligence (AI) framework for large-scale dataset synthesis. Leveraging a parameterized virtual environment developed in Unreal Engine 5 (UE5), the framework captures realistic images of open-pit surface cracks and enriches their visual diversity using StyleGAN2-ADA. The resulting datasets were used to train the YOLOv11 real-time object detection model and evaluated on a real-world dataset of open-pit slope imagery to assess the effectiveness of the proposed framework in improving CV model generalizability under extreme data scarcity. Experimental results demonstrated that models trained on the proposed framework substantially outperformed the UE5 baseline, with average precision (AP) at intersection over union (IoU) thresholds of 0.5 and [0.5:0.95] increasing from 0.403 to 0.922 and 0.223 to 0.722 respectively, accompanied by a reduction in missed detections from 95 to eight for the best-performing configurations. These findings demonstrate the potential of hybrid generative AI frameworks to mitigate data scarcity in CV applications and support the development of scalable automated slope monitoring systems for improved worker safety and operational efficiency in open-pit mining.

Abstract: This study investigates the key determinants of firm profitability in the global automotive sector, examining whether superior returns on assets (ROA) stem from operational efficiency, strategic leverage, or innovation intensity, and highlighting the potential trade-off between efficiency and investment in capital-intensive industries. Analysing a global panel dataset of 192 automotive firms from 38 countries over 2010-2024, a fixed-effects regression model with Driscoll-Kraay standard errors was applied to control for unobserved heterogeneity, heteroskedasticity, and cross-sectional dependence across 11 financial and strategic variables. The findings reveal that firm size and inventory turnover are significant positive drivers of profitability, while research and development (R&amp;D) intensity exerts a strong negative impact. The positive association with the effective tax rate reflects reverse causality, where more profitable firms incur higher tax burdens, rather than a causal effect of taxation on performance. Notably, working capital management, leverage, sales growth, and capital expenditure showed no statistically significant effects after controlling for firm and time effects. Temporal fluctuations, including a marked profitability decline in 2024, underscore the sector’s sensitivity to macroeconomic shocks. This study contributes robust, large-scale empirical evidence on the short-term profitability trade-off associated with R&amp;D intensity in a globally integrated industry, addressing cross-sectional dependence through its methodological approach.

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.