Search Results (2,515)

We study the conditions under which momentum-space equal-time correlators of scalar fields are finite in flat space. We identify cases where these correlators can be divergent even after renormalization, and we provide sufficient criteria for their existence. Concrete examples are discussed, including the well-known $\lambda {\mathit{\varphi }}^4$ model, composite operators, and effective field theories. Full article

Materials that store a significant amount of heat in a narrow temperature range by phase change solid–liquid or solid–solid are called Phase Change Materials (PCMs). Many PCMs are members of homologous groups of materials with similar composition and properties. Often, similarities are due to a common molecular composition with a repeating unit, e.g., for n-alkanes H-(CH₂)_n-H. An n related trend is typical in the melting temperature. Based on observations on solvents, the question arises whether such a trend also exists in eutectic binary mixtures with one component fixed while the other, from a homologous series, is varied. For verification, data from the literature were collected, specifically experimental data, each set having at least three variations from a single source. Eight data sets were collected, covering eutectic binary mixtures of n-alkanes, n-alkanols, and n-alkanoic acids. With one exception, all data sets show a systematic trend in the melting temperature and the composition. It is shown that the trends can be understood from thermodynamic theories of mixtures (Schröder–van Laar equation) combined with typical trends within homologous series. The findings offer new options in PCM development as well as the selection of PCMs for specific application temperatures. Full article

We introduce Recognition Geometry (RG), an axiomatic framework in which geometric structure is not assumed a priori but derived. The starting point of the theory is a configuration space together with recognizers that map configurations to observable events. Observational indistinguishability induces an equivalence relation, and the observable space is obtained as a recognition quotient. Locality is introduced through a neighborhood system, without assuming any metric or topological structure. A finite local resolution axiom formalizes the fact that any observer can distinguish only finitely many outcomes within a local region. We prove that the induced observable map $\overline{R}:{\mathcal{C}}_R\to \mathcal{E}$ is injective, establishing that observable states are uniquely determined by measurement outcomes with no hidden structure. The framework connects deeply with existing approaches: C^*-algebraic quantum theory, information geometry, categorical physics, causal set theory, noncommutative geometry, and topos-theoretic foundations all share the measurement-first philosophy, yet RG provides a unified axiomatic foundation synthesizing these perspectives. Comparative recognizers allow us to define order-type relations based on operational comparison. Under additional assumptions, quantitative notions of distinguishability can be introduced in the form of recognition distances, defined as pseudometrics. Several examples are provided, including threshold recognizers on ${\mathbb{R}}^n$, discrete lattice models, quantum spin measurements, and an example motivated by Recognition Science. In the last part, we develop the composition of recognizers, proving that composite recognizers refine quotient structures and increase distinguishing power. We introduce symmetries and gauge equivalence, showing that gauge-equivalent configurations are necessarily observationally indistinguishable, though the converse does not hold in general. A significant part of the axiomatic framework and the main constructions are formalized in the Lean 4 proof assistant, providing an independent verification of logical consistency. Full article

We present a theoretical and numerical investigation of a far-field super-resolution dark-field microscopy technique based on longitudinal nano-optical field excitation and detection. This method is implemented by integrating vector optical field modulation into a back-scattering confocal laser scanning microscope. A complete forward theoretical imaging framework that rigorously accounts for light–matter interactions is adopted and validated. The weak interaction model and general model are both considered. For the weak interaction model, e.g., multiple discrete dipole sources with a uniform or modulated responding intensity are utilized to fundamentally demonstrate the relationship between the sample and the imaging information. For continuous nanostructures, the finite-difference time-domain simulation results of the interaction-induced optical fields in the imaging model show that the captured image information is not determined solely by system resolution and sample geometry, but also arises from a combination of sample-dependent factors, including material composition, the local density of optical states, and intrinsic physical properties such as the complex refractive index. Unlike existing studies, which predominantly focus on system design or rely on simplified assumptions of weak interactions, this paper achieves quantitative characterization and precise regulation of nanoscale vector optical fields and samples under strong interactions through a comprehensive analytical–numerical imaging model based on rigorous vector diffraction theory and strong near-field coupling interactions, thereby overcoming the limitations of traditional methods. Full article

Traditional polysaccharide extraction suffers from low efficiency and high energy consumption, while deep eutectic solvents (DESs) are promising sustainable solvents. This study used DES ChCl-LA (1:2) with ultrasonic assistance to extract polysaccharides from red alga A.taxiformis. Optimized via single-factor experiments and response surface methodology (350 W, 1:30 g/mL, 75 °C), the yield reached 11.28% ± 0.50% (1.5 times higher than that obtained by water extraction). Structural characterization revealed that the DES extract was an acidic polysaccharide, mainly composed of galactose (89.2%), glucose (4.9%), xylose (4.9%), and glucuronic acid (1.0%), with a weight-average molecular weight of 99.88 kDa. Density functional theory and molecular dynamics simulations showed that ChCl-LA enhanced galactose solubility via stronger hydrogen bonding (−25.33 vs. −5.06 kcal/mol for water). Notably, the immunological activity of the DES-extracted polysaccharide was significantly compromised compared to the water-extracted counterpart (p < 0.05). At a concentration of 0.25 mg/mL, the water-extracted polysaccharide-treated group exhibited a 33.98% higher neutral red phagocytosis rate in macrophages, a nitric oxide (NO) secretion level of 34.14 μmol/L (94.98% higher) compared with the DES-extracted polysaccharide group, as well as significantly higher secretion levels of tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6). The observed disparity in bioactivity is likely due to the distinct chemical profiles resulting from the two extraction methods, including the significantly reduced molecular weight and potential alterations of sulfation degree, monosaccharide composition, and protein content in the DES-extracted polysaccharide. This mechanistic perspective is supported by the relevant literature on the structure–activity relationships of polysaccharides. This study demonstrates the potential of ChCl-LA and elucidates the complex effects of extraction methods on polysaccharide’s structure and function, thereby informing the high-value utilization of A. taxiformis in functional foods. Full article

China’s roof-cutting and pressure relief gob-side entry retaining (RCPR-GER) technology provides an efficient non-pillar mining solution that significantly enhances coal recovery. This paper presents a systematic review of the technological progress in Chinese coal mines from 2011 to 2023, based on an analysis of 1038 publications from CNKI, EI, and Web of Science using VOS viewer and Origin software. Four main technical approaches are examined: gob-side entry retaining without roadside filling, with roadside filling, with roof-cutting and pressure relief, and hybrid methods. Five key roof-cutting techniques are evaluated: dense drilling, high-pressure water-jet slotting, hydraulic fracturing, blasting, presplitting, and roof water injection softening. Successful applications have been documented in coal seams with thicknesses of 1.6–6.15 m and burial depths of 92–1037 m, demonstrating wide adaptability. The roof-cutting short-beam theory underpins the mechanism, which reduces roadway deformation, shortens the cantilever beam length, and alters stress transfer paths. Compared to previous reviews on general gob-side entry retaining, this study offers a dedicated synthesis and comparative analysis of RCPR-GER technologies, establishing a selection framework grounded in geological compatibility and engineering practice. Future research should focus on adaptive parameter design for deep hard composite roofs, quantitative modeling of passive roof-cutting effects, optimization of cutting timing and orientation, and floor-heave control technologies to extend applications under complex geological conditions. Full article

Evaluating artificial intelligence (AI) models in clinical medicine requires more than conventional metrics such as accuracy, Area Under the Receiver Operating Characteristic (AUROC), or F1-score, which often overlook key considerations such as fairness, reliability, and real-world utility. We introduce CUES as a multiplicative composite score for clinical prediction models; it is defined as $\mathrm{CUES}=(C\cdot U\cdot E\cdot S{)}^{1/4}$, where C represents calibration, U integrated clinical utility, E equity across patient subpopulations, and S sampling stability. We formally establish boundedness, monotonicity, and differentiability on the domain ${\left(\mathrm{0,1}\right]}^4$, derive first-order sensitivity relations, and provide asymptotic approximations for its sampling distribution via the delta method. To facilitate inference, we propose bootstrap procedures for constructing confidence intervals and for comparative model evaluation. Analytic examples illustrate how CUES can diverge from traditional metrics, capturing dimensions of predictive performance that are essential for clinical reliability but often missed by AUROC or F1-score alone. By integrating multiple facets of clinical utility and robustness, CUES provides a comprehensive tool for model evaluation, comparison, and selection in real-world medical applications. Full article

The performance of convolutional neural networks (CNNs) depends not only on model architecture but also on the structure and quality of the training data. While most artificial network interpretability methods focus on explaining trained models, less attention has been given to understanding how dataset composition itself shapes learning outcomes. This work introduces a novel framework that uses programmatically generated synthetic datasets to isolate and control visual features, enabling systematic evaluation of their contribution to CNN performance. Guided by principles from set theory, Shapley values, and the Apriori algorithm, we formalize an equivalence between CNN kernel weights and pattern frequency counts, showing that feature overlap across datasets predicts model generalization. Methods include the construction of four synthetic digit datasets with controlled object and background features, training lightweight CNNs under K-fold validation, and statistical evaluation of cross-dataset performance. The results show that internal object patterns significantly improve accuracy and F1 scores compared to non-object background features, and that a dataset similarity prediction algorithm achieves near-perfect correlation ($\rho =0.97$) between the predicted and observed performance. The conclusions highlight that dataset feature composition can be treated as a measurable proxy for model behavior, offering a new path for dataset evaluation, pruning, and design optimization. This approach provides a principled framework for predicting CNN performance without requiring full-scale model training. Full article

Background/Objectives: Patient-level factors strongly influence antimicrobial resistance (AMR) through the pressure applied to healthcare professionals to prescribe antibiotics even for self-limiting viral infections, enhanced by knowledge and attitude concerns. This includes Africa, with high levels of AMR. However, validated measurement tools for African primary healthcare (PHC) are scarce. This study evaluated the reliability, structural validity, and interpretability of the Community Antimicrobial Use Scale (CAMUS) in South Africa. Methods: A cross-sectional survey was conducted with 1283 adults across 25 diverse public PHC facilities across two provinces. The 30-item theory-based tool underwent exploratory and confirmatory factor analysis (EFA/CFA), reliability, and validity testing. Results: EFA identified a coherent five-factor structure: (F1) Understanding antibiotics; (F2) Social and behavioural norms; (F3) Non-prescribed use; (F4) Understanding of AMR; and (F5) Attitudes. Internal consistency was strongest for knowledge and misuse domains (alpha approximation 0.80). Test–retest reliability was good-to-excellent (ICC: 0.72–0.89). CFA confirmed acceptable composite reliability (CR ≥ 0.63). Although average variance extracted (AVE) was low for broader behavioural constructs, indicating conceptual breadth, it was high for AMR knowledge (0.737). Construct validity was supported by positive correlations with health literacy (r = 0.48) and appropriate use intentions (r = 0.42). Measurement error metrics (SEM = 1.59; SDC = 4.40) indicated good precision for group-level comparisons. Conclusions: CAMUS demonstrated a theoretically grounded structure with robust performance in knowledge and misuse domains. While social and attitudinal domains require refinement, we believe the tool is psychometrically suitable for group-level antimicrobial use surveillance and programme evaluation in South African PHC settings and wider to help with targeting future educational programmes among patients. Full article

The gut microbiome is a modifiable factor in cancer survivorship. Diet represents the most practical intervention for modulating the gut microbiome. However, diet–microbiome relationships in prostate-cancer survivors remain poorly characterized. We conducted a comprehensive analysis of diet–microbiome associations in 79 prostate-cancer survivors (ages 62–81) enrolled in a randomized exercise intervention trial, 59.5% of whom still have active metastatic disease. Dietary intake was assessed using the Diet History Questionnaire (201 variables) and analyzed using three validated dietary pattern scores: Mediterranean Diet Adherence Score (MEDAS), Healthy Eating Index-2015 (HEI-2015), and the Mediterranean-Dash Intervention for Neurodegenerative Delay (MIND) diet score. Gut microbiome composition was characterized via 16S rRNA sequencing. Dimensionality reduction strategies, including theory-driven diet scores and data-driven machine learning (Random Forest, and Least Absolute Shrinkage and Selection Operator (LASSO)), were used. Statistical analyses included beta regression for alpha diversity, Permutational Multivariate Analysis of Variance (PERMANOVA) for beta diversity (both Bray–Curtis and Sørensen metrics), and Microbiome Multivariable Associations with Linear Models (MaAsLin2) with negative binomial regression for taxa-level associations. All models tested interactions with exercise intervention, APOLIPOPROTEIN E (APOE) genotype, and testosterone levels. There was an interaction between MEDAS and exercise type on gut alpha diversity (Shannon: p = 0.0022), with stronger diet–diversity associations in strength training and Tai Chi groups than flexibility controls. All three diet-quality scores predicted beta diversity (HEI p = 0.002; MIND p = 0.025; MEDAS p = 0.034) but not Bray–Curtis (abundance-weighted) distance, suggesting diet shapes community membership rather than relative abundances. Taxa-level analysis revealed 129 genera with diet associations or diet × host factor interactions. Among 297 dietary variables tested for cognitive outcomes, only caffeine significantly predicted Montreal Cognitive Assessment (MoCA) scores after False Discovery Rate (FDR) correction (p = 0.0009, q = 0.014) through direct pathways beneficial to cognitive performance without notable gut microbiome modulation. In cancer survivors, dietary recommendations should be tailored to exercise habits, genetic background, and hormonal status. Full article

Composite laminates possess complex anisotropic behavior, motivating the development of simplified yet accurate modeling approaches. In this paper, we present a study that introduces a stiffness-invariants-based constitutive model for symmetric, balanced composite laminates, highlighting a novel “quasi-Poisson’s ratio” parameter as a key innovation. The proposed method reconstructs the laminate stiffness matrices using invariant theory (trace of stiffness tensor) and a Master Ply concept, thereby reducing the number of independent material constants. The methods and assumptions (e.g., neglecting minor bending-twisting couplings) are outlined, and the model’s predictions of critical buckling loads are compared to classical laminate theory (CLT) results. Good agreement is observed in most cases, with a consistent conservative bias of CLT. The results confirm that the invariant-based model captures the dominant stiffness characteristics of the laminates and can slightly overestimate stability margins due to its idealizations. In conclusion, this work provides an efficient constitutive modeling framework that can be integrated with finite element analysis and extended to more general laminates in future studies. Full article

Nature-based health interventions (NBHIs) are increasingly used in the healthcare system to support people with anxiety, depression and/or stress, highlighting the need for systematic development and evaluation. This study aims to identify target group, professionals, mechanisms, and outcomes of NBHIs for people with mild to moderate anxiety, depression, and/or stress. A Delphi-based study was conducted to explore core components of NBHIs in healthcare settings. Thirteen vs. eleven researchers with expertise related to the target group responded in two rounds. Respondents rated statements on a 7-point Likert scale and prioritised core components regarding target group, professionals, mechanisms, and outcomes. A thematic analysis was applied to synthesise qualitative responses. Consensus was achieved on 12 of 21 items across the four domains. Highest agreement concerned core mechanisms (nature interaction, social community, and physical activity), outcome priorities (mental wellbeing and quality of life), and professional competencies. Greater variation was observed regarding group composition and team delivery. Analysis of qualitative expert responses highlighted four key themes: (1) Balancing Group Composition, (2) Adapting Competencies to Context, (3) Core Mechanisms for Change, and (4) Weighing Perspectives in Outcome Selection. By setting out guiding principles for a programme theory, the study lays the foundation for the design and implementation of context-adapted NBHIs. The study underscores the need to approach NBHIs as complex interventions, thus contributing to a paradigm shift towards a new era of a bio-psycho-social health perspective. Full article

The steel-plate–concrete composite reinforcement method is derived from the bonded steel plate and increased-section techniques. It is employed to enhance the strength of concrete structures that require a substantial increase in load-bearing capacity. To develop a flexural deformation calculation theory that accounts for slip effects in general reinforced cross-sections with bilateral symmetry, interfacial slip and deflection equations are formulated based on the relationship between interlayer slip and the rotational angle of beams in the plane, as well as the principle of force equilibrium. A numerical method, established based on this theoretical framework, is proposed to facilitate the analytical solution and is verified to be consistent with analytical results. Furthermore, the accuracy of the calculation theory is validated through bending experiments. Finally, the influence of key parameters affecting slip on the flexural stiffness of the reinforced beam is evaluated by determining the stiffness reduction coefficient according to the theory. The results indicate that the flexural stiffness of reinforced beams is governed by three non-dimensional parameters: the boundary condition parameter (μ), composite action parameter (shear connector stiffness (βl)), and relative bending stiffness parameter (G_∞/G₀). The loading mode does not affect the flexural stiffness of the reinforced beams. As βl approaches 100 and G_∞/G₀ approaches 1, η approaches 100%. In cases where high stiffness is required, reducing interfacial slip can minimize the loss of flexural stiffness in composite structures. Conservative calculations indicate that satisfying the conditions βl ≥ 8 and G_∞/G₀ ≤ 1.6 during design can ensure that the reduction in flexural stiffness of the reinforced beam remains above 90%. Full article

This study developed a design framework for porous mixtures using a 100% sustainable non-bituminous epoxy–polyurethane binder system. Conventional design protocols for porous asphalt mixtures exhibit limitations in accurately controlling void content and mixture composition. This study proposed a novel design framework for porous mixtures containing 100% sustainable binder based on statistical analysis and theoretical calculations. The relationships among target air voids, binder content, and aggregate gradation were systematically analyzed, and calculation formulas for coarse aggregate, fine aggregate, and mineral filler contents were derived. A mix design framework was further established by applying the void-filling theory, where the combined volume of binder, fine aggregate, and filler equals the void volume of the coarse aggregate skeleton, thereby ensuring precise control of the target void ratio. Additionally, mixing procedures were investigated with emphasis on feeding sequence, compaction method, and mixing temperature. Results indicated that the optimized feeding sequence significantly improved binder distribution; specimens compacted using the Marshall double-sided compaction method achieved a density of 89.60%. Rheological analysis revealed that at 30 °C, the viscosities of sustainable binder and polyurethane filler were 1280 mPa·s and 6825 mPa·s, respectively, suggesting optimal mixture uniformity. The proposed methodology and process parameters provide essential technical guidance for engineering applications of porous mixtures containing 100% sustainable binder. Full article

Electric-field-sensitive dielectrics play a crucial role in electric field induction sensing and related capacitive conversion, with interfacial polarization and charge accumulation largely determining the signal output. This paper introduces graphene/transition metal dichalcogenide (TMD) (MoSe₂, MoS₂, and WS₂) heterojunctions as functional fillers to enhance the dielectric response and electric-field-induced voltage output of flexible polydimethylsiloxane (PDMS) composites. Density functional theory (DFT) calculations were used to evaluate the stability of the heterojunctions and interfacial electronic modulation, including binding behavior, charge redistribution, and Fermi level-referenced band structure/total density of states (TDOS) characteristics. The calculations show that the graphene/TMD interface is primarily controlled by van der Waals forces, exhibiting negative binding energy and significant interfacial charge rearrangement. Based on these theoretical results, graphene/TMD heterojunction powders were synthesized and incorporated into polydimethylsiloxane (PDMS). Structural characterization confirmed the presence of face-to-face interfacial contacts and consistent elemental co-localization within the heterojunction filler. Dielectric spectroscopy analysis revealed an overall improvement in the dielectric constant of the composite materials while maintaining a stable loss trend within the studied frequency range. More importantly, calibrated electric field induction tests (based on pure PDMS) showed a significant enhancement in the voltage response of all heterojunction composite materials, with the WS₂-G/PDMS system exhibiting the best performance, exhibiting an electric-field-induced voltage amplitude 7.607% higher than that of pure PDMS. This work establishes a microscopic-to-macroscopic correlation between interfacial electronic modulation and electric-field-sensitive dielectric properties, providing a feasible interface engineering strategy for high-performance flexible dielectric sensing materials. Full article