Abstract: In large scale image retrieval and big data analytics it is a big challenge to search similar images from high dimensional data. Mostly used algorithms are Locality Sensitive Hashing and Random Projection Based Hashing. They are widely used for approximate nearest neighbor searching. These two algorithms treat all input features uniformly while they ignore feature importance and class separability. In this research we aim to propose a lightweight hashing framework named Adaptive Feature Aware Hashing which integrates feature weighting prior to projection-based hashing. The algorithm computes data-driven feature weights using variance, between-class separability, and Fisher-style discriminative criteria to enhance discriminative power during hash code generation. We also incorporated multi table and multi probe hashing which enhances discriminative power during hash code generation. For this research we used MNISH dataset for experimental evaluation. We compared the results against a Baseline Locality-Sensitive Hashing (LSH) method using random projections. Our results indicate that The AFAH methods (v1 and v2 Fisher) significantly improved both precision and recall compared to the Baseline LSH, with AFAH v2 Fisher showing the highest precision (0.7557) and AFAH v1 having the highest recall (0.2285).

Abstract:

The ‘Tenuifoliae irises’ are a distinctive group of beardless, rhizomatous perennial irises, which are characterised by their somewhat vertical rhizomes, typically clothed at the apex with long maroon-brown, sharp fibrous remains of leaf sheaths; perianth tube long, filiform to scapiform; stigma bilobed; capsules often trigonous to six-ribbed, apically beaked; and seeds angulose to subcubic or pyriform, lacking fleshy appendages, and with testa hard, irregularly wrinkled. The representatives of the aggregate are mostly native to the dry steppes and grasslands from lowland to high mountain habitats of Central and Eastern Asia, extending westwards to the Black Sea and Caspian regions. Morphological classification of the ‘Tenuifoliae irises’ recognises about ten to eleven species, which are arranged into two genera, Sclerosiphon to Cryptobasis. Diverse molecular research recovered members of the ‘Tenuifoliae irises’ in contrasting placements within the ‘Iris-flower clade’. Sometimes, Sclerosiphon was sister to Eremiris, but Cryptobasis aligned with the ‘Spuria irises’ (Chamaeiris) and the ‘Spanish irises’ (Xiphion and related genera); in other cases, both Sclerosiphon and Cryptobasis formed a clade sister to Chamaeiris, or Cryptobasis alone was identified as the basal member of the Iris s.l. clade, positioned immediately after Siphonostylis. To examine these taxonomic discrepancies within a rigorous molecular‑systematic framework and using 12 reliably authenticated specimens, we generated 24 sequences of the matK gene (12) and the trnL (UAA)–trnF (GAA) loci (12) from members of the ‘Tenuifoliae irises’. These sequences were subsequently incorporated into a comprehensive dataset of the ‘Iris‑flower clade’, enabling a broader analytical assessment. The obtained three-taxon statement hierarchy of patterns and maximum likelihood phylogenetic trees both recover the ‘Tenuifoliae irises’ as monophyletic and sister to Chamaeiris, and in turn to the ‘Xiphion s.l. clade’. We also found Sclerosiphon and Cryptobasis as sister genera. The morphological and karyological data supporting those relationships are discussed, which allow getting back to Rodionenko’s sources and recovering Sclerosiphon in his original sense, alongside Cryptobasis. Furthermore, the molecular results allow us expanding Sclerosiphon to include the Eastern Chinese members of the aggregate. In consequence, five new combinations (one series and four species) are established in the genus, one lectotype is designated, and data on nomenclature, distribution and ecology of the accepted species are reported.

Abstract: Cabbage is considered a healthy vegetable due to its chemical composition and high nutritional value. This is due to the presence of carbohydrates and dietary fiber as the main constituents, as well as the presence of vitamin C. The end product thus obtained (sauerkraut) is a low-calorie product with a long shelf-life. The most important role in the fermentation of cabbage is played by lactic acid bacteria whose activity is influenced by physical factors such as temperature and some chemical factors such as salt concentration or the addition of spices which, in addition to their flavoring effect, may also have an inhibitory effect on undesirable microflora. The present study investigates the effect of essential oils extracted from plants on lactic acid bacteria responsible for the fermentation of cabbage. Essential oils from thyme, dill, wild thyme, bay and basil were tested. The obtained results have shown that the essential oils that were added to the fermentation mass in concentrations of 0.015% did not inhibit the activity of lactic acid bacteria responsible for lactic fermentation.

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.