Search Results (14,031)

We identify a fundamental tension between general relativity and spectral geometry arising from the global, nonlocal character of spectral data versus the local causal dynamics of spacetime. To resolve this, we postulate spectral invariance, $\delta {\Lambda }_n=0$, requiring the eigenvalues of the Laplace–Beltrami operator to remain fixed under physical evolution. This condition yields a compensatory relation between metric deformations and eigenfunction amplitudes, suggesting a kinematic coupling linking energy distribution to spacetime curvature. From the second variation of the associated energy functional, we derive a rank-4 tensor proportional to the inverse DeWitt supermetric on superspace and a contracted rank-2 tensor proportional to the spacetime metric, and we recover a invariance law of the energy functional in configuration space. Spectral invariance may suggest a framework in which geometry and energy are co-defined through fixed spectral data. Full article

Background/Objectives: Amelogenesis imperfecta (AI) is a heterogeneous group of rare hereditary conditions mainly affecting the quantity and/or quality of tooth enamel. Its phenotypic expression is diverse, as is the mutational spectrum of the AI-causing genes and mutations. Integrins are cell-surface receptors that mediate adhesion between cells and between cells and the extracellular matrix. Among these, mutations in integrin αvβ6 have been shown to cause AI; however, phenotypic variation exists between the knockout mouse model and human cases, as well as among different human AI families. Methods: We recruited AI families and performed mutational analysis using whole exome sequencing. Results: We identified compound heterozygous ITGB6 mutations in two families. In Family 1, a paternally transmitted nonsense mutation (NM_000888.5: c.1060C>T, p.(Gln354*)) and a maternally transmitted missense mutation (NM_000888.5: c.2312A>G, p.(Asn771Ser)) were identified; in Family 2, a paternal missense mutation (NM_000888.5: c.1693T>C, p.(Cys565Arg)) and a maternal frameshift mutation (NM_000888.5: c.2091delC, p.(Asn698Metfs*13)) were identified, each causing AI in the respective proband. Both probands exhibited generalized hypoplastic and hypomineralized AI, but no other extraoral symptoms. Conclusions: This report will not only expand the known mutational spectrum of the ITGB6 gene but also provide evidence for the genotype–phenotype correlations, thereby improving our understanding of the functional role of ITGB6 during amelogenesis. Full article

Meat quality serves as a pivotal determinant of consumer purchasing behavior and of the economic viability of the livestock industry; as such, research into its regulatory mechanisms is of critical significance for the development of modern agriculture. Traditional investigations into meat quality have predominantly centered on sensory and physicochemical assessments of ultimate phenotypic traits, thereby facing inherent limitations in systematically deciphering the intricate molecular regulatory networks underlying meat quality formation. By contrast, an integrated analysis of the transcriptome and metabolome effectively connects the cascade of “gene transcription—metabolic regulation—phenotypic determination,” which has emerged as a core methodological paradigm in contemporary research on the molecular mechanisms governing meat quality. This review systematically delineates the evolutionary trajectory and principal technological frameworks of meat quality evaluation systems, with a focused synthesis of recent advances achieved through combined transcriptomic and metabolomic analyses in the field of meat quality regulation. The scope of this review encompasses core transcriptional regulatory networks associated with meat quality attributes, pivotal metabolic pathways, signal transduction mechanisms, and protein degradation dynamics. Furthermore, the regulatory impacts exerted by genetic variation among breeds, nutritional modulation, rearing environments, and stress responses on meat quality characteristics are comprehensively elucidated. Integrative analysis reveals that combined transcriptome–metabolome approaches transcend the inherent limitations of single-omics investigations, systematically unraveling the hierarchical regulatory mechanisms governing fundamental meat quality traits, such as muscle fiber type differentiation, postmortem glycolytic progression, intramuscular fat deposition, and flavor compound accumulation. Such integrative strategies have facilitated the identification of functional genes and metabolic biomarkers with potential utility for the early prediction of meat quality outcomes. Concurrently, this review acknowledges persistent challenges confronting the field, including the absence of standardized protocols for multi-omics data integration, insufficient functional causal validation, and a discernible disconnect between research discoveries and practical industrial implementation. Building upon this comprehensive assessment, prospective directions for future multi-omics research in meat quality are proposed, accompanied by the formulation of an integrated end-to-end improvement framework spanning fundamental research, technological innovation, and industrial application. Collectively, this review provides a systematic theoretical foundation for the in-depth elucidation of mechanisms that determine meat quality and the precision-oriented regulation of quality-determining traits in livestock production practices, thereby offering substantial scientific guidance for quality improvement initiatives within the animal husbandry sector. Full article

The increasing volume and complexity of digital evidence pose significant challenges to its lawful collection and admissibility, particularly in on-site investigative contexts. Selective seizure has emerged as a critical approach for minimizing unnecessary data acquisition while ensuring procedural legality, privacy protection, and investigative efficiency. However, despite its growing importance, systematic evaluation criteria for selective seizure capabilities in digital forensic tools remain underdeveloped. This study proposes a structured evaluation framework for assessing selective seizure functions in Windows-based forensic tools, with a focus on live-response environments. Essential selective seizure functions were identified and organized into three investigative phases—search, selection, and seizure—reflecting practical field procedures. Based on this framework, a dedicated evaluation dataset was constructed, and six representative portable forensic tools were empirically evaluated under a controlled Windows 10 (NTFS) environment simulating active system conditions. The experimental results demonstrate notable differences in tool capabilities across investigative phases. In the search phase, variations were observed in NTFS parsing and Windows artifact analysis, while the selection phase revealed disparities in file filtering, keyword search, encrypted file handling, and preview functions. In the seizure phase, only a subset of tools sufficiently supported evidence collection, integrity verification, and reporting requirements necessary for selective seizure. These findings highlight that no single tool uniformly satisfies all functional requirements, underscoring the need for context-dependent tool selection. The proposed framework and evaluation results provide practical guidance for digital forensic practitioners in selecting appropriate tools for selective seizure in field investigations. Moreover, this study contributes a reproducible methodological foundation for future research on selective seizure evaluation, supporting the development of more precise, proportionate, and legally robust digital evidence collection practices in Windows-based forensic investigations. Full article

Non-convex stochastic optimization presents fundamental mathematical challenges across machine learning, wireless networks, data center resource allocation, and optical wireless communication systems, where complex loss landscapes with multiple local minima and saddle points impede classical variational inference methods. This paper introduces the Quantum-Inspired Variational Inference (QIVI) framework, which systematically integrates quantum mechanical principles (superposition, entanglement, and measurement operators) into classical variational inference through rigorous mathematical formulations grounded in Hilbert space theory and operator algebras. We develop a unified optimization framework that encodes classical parameters as quantum-inspired states within finite-dimensional complex Hilbert spaces, employing unitary evolution operators and adaptive basis selection governed by gradient covariance eigendecomposition. The core mathematical contribution establishes that QIVI achieves a convergence rate of $\mathit{O}({log}^{2}T/T^{1/2})$ for $\sigma$-strongly non-convex functions, provably improving upon the classical $\mathit{O}\left(T^{-1/4}\right)$ rate, yielding a theoretical speedup factor of $1.85$–$1.96\times$. Comprehensive experiments across synthetic benchmarks, Bayesian neural networks, and real-world applications in network optimization and financial portfolio management demonstrate 23–$47\%$ faster convergence, 15–$35\%$ superior objective values, and 28–$46\%$ improved uncertainty calibration. The principal contributions include: (i) a rigorous Hilbert space-based mathematical framework for quantum-inspired variational inference grounded in operator algebras, (ii) a novel hybrid quantum–classical algorithm (QIVI) with adaptive basis selection via gradient covariance eigendecomposition, (iii) formal convergence proofs establishing provable improvement over classical methods, (iv) comprehensive empirical validation across diverse problem domains relevant to machine learning and network optimization, and (v) demonstration of the framework’s applicability to optimization problems arising in wireless networks, data center resource allocation, and network system design. Statistical validation using the Friedman test (${\chi }^2=847.3$, $p<0.001$) and post hoc Wilcoxon signed-rank tests with Holm–Bonferroni correction confirm that QIVI’s improvements over all baseline methods are statistically significant at the $\alpha =0.05$ level across all benchmark categories. The framework discovers $18.1$ out of 20 true modes in multimodal distributions versus $9.1$ for classical methods, demonstrating the potential of quantum-inspired optimization approaches for challenging stochastic problems arising in machine learning, wireless communication, and network optimization. Full article

Accurate segmentation of structural cracks is a core prerequisite for quantifying crack parameters, assessing damage severity, and providing early warning of structural safety. However, different types of structures exhibit significant individual variations in features such as color, texture, and brightness. Consequently, commonly used image segmentation algorithms struggle to establish a universal mathematical model, making it challenging to robustly identify and precisely segment crack targets amidst multi-feature disparities. To address the issue, this paper proposes a crack-segmentation algorithm based on bimodal image fusion and brightness piecewise linear enhancement (CSA-BB), and further enables parameter extraction and crack monitoring. The algorithm utilizes the complementary properties of visible-light and pseudo-color images for bimodal image fusion, thereby enhancing the detailed features of cracks. Furthermore, a brightness piecewise linear function has been devised that automatically selects appropriate parameters for image enhancement of structural cracks across varying background brightness. Subsequently, the crack region is effectively segmented using the bottom-hat transform and the OTSU algorithm. Ultimately, the crack’s safety level is determined from the acquired crack parameters, thereby enabling effective monitoring and assessment of the crack development process. In this paper, the proposed method achieves the best segmentation performance with a Dice coefficient of 0.4511 and a Jaccard index of 0.2981. Compared to the second-best algorithm, it yields significant improvements of 26.9% and 34.5%, respectively, demonstrating higher consistency with the ground truth. Moreover, superior computational efficiency and robustness are achieved, fulfilling the operational demands of real-world engineering environments. Full article

The human microbiome influences health and disease through diverse biochemical and functional outputs (e.g., enzymes, structural proteins, metabolites, and other cellular components) that affect nearly every aspect of human physiology. Metatranscriptomics (MT), an unbiased RNA sequencing approach, is a high-throughput and high-content method that quantifies both gut microbial taxonomy and active biochemical functions. Because microbial community composition and gene expression are dynamic, understanding temporal variation in the gut metatranscriptome across multiple time scales is essential. Here, we report the temporal dynamics of gut microbiome species and functions using a large cohort (n = 6157) with a clinically validated stool MT test. We quantified microbiome stability from hours to years and assessed taxonomic and functional resilience to major luminal perturbations, such as colonoscopy bowel preparation. Longitudinal analyses of samples collected within the same day, and across days, weeks, months, and years, revealed consistently high stability in both composition and gene expression within a single day and, importantly, across an approximate six-month period. Among individuals reporting stable diets and no antibiotic exposure, taxonomic and functional profiles remained stable for up to three years. Following colonoscopy preparation, our preliminary study of the microbiome demonstrated strong resilience, returning to its pre-procedure state within one week. Overall, these findings demonstrate that the gut microbiome is generally stable over a six-month time frame, with longer-term changes occurring gradually. These findings support the robustness of stool-based MT profiling for species-level and pathway-resolved functional analysis in longitudinal research and health applications. Full article

To investigate the effects of incorporating nanomaterials—carbon nanotubes (CNTs) and graphene oxide (GO)—on the axial compressive mechanical properties of alkali-activated recycled aggregate concrete (AARAC) after high-temperature exposure, this study designed 51 sets of specimens with recycled coarse aggregate replacement rate, nanomaterial content, and temperature as the main parameters. Compression tests were conducted to analyze the failure mode and strength variation in AARAC specimens after heating. In addition, microscopic tests, including X-ray diffraction, scanning electron microscopy, and computed tomography (CT scanning), were performed to analyze the microstructural characteristics of the post-heated AARAC specimens. The results indicate that as the replacement rate of recycled coarse aggregate increased from 0% to 100%, the residual compressive strength after exposure to 600 °C decreased from 33.6 MPa to 19 MPa. When 0.1 wt% of CNTs is added, the compressive strength of AARAC after exposure to a high temperature of 600 °C increases by approximately 30.4% compared to that of AARAC without nanomaterial addition. When 0.1 wt% of CNTs and 0.05 wt% of GO are added, the compressive strength after exposure to a high temperature of 600 °C increases by approximately 44.3%, while the size of scattered fragments upon failure increased, and the failure mode appeared more complete. Microscopic test results indicate that the high-temperature treatment did not cause significant changes in the main phase composition of AARAC. The synergistic effect of the nanomaterials CNTs and GO can fully utilize their functions as nucleation sites, pore fillers, and crack bridging agents. By strengthening the Interfacial Transition Zone between the recycled coarse aggregate and the cement paste, refining the Matrix Pore Structure, dispersing local thermal stress, and suppressing the propagation of high-temperature cracks, the mechanical properties of AARAC after high-temperature exposure can be effectively maintained. Full article

Accurate trajectory tracking is challenging for tracked agricultural vehicles in orchards. Uneven terrain, track slip, and vehicle posture variations are the main causes, often leading to model mismatch and degraded control performance. To address these issues, this paper proposes an improved nonlinear model predictive control (NMPC) strategy integrated with curvature feedforward compensation for trajectory tracking of tracked agricultural vehicles under uneven terrain conditions. An enhanced kinematic model based on the instantaneous center of rotation is developed by incorporating vehicle roll and pitch angles, and track slip parameters are estimated online using a Levenberg–Marquardt optimization method to improve prediction accuracy. Furthermore, curvature feedforward information derived from the reference trajectory is embedded into the NMPC objective function to provide anticipatory control inputs and reduce computational burden. Simulation results demonstrate that compared to conventional NMPC, the proposed method reduces the mean and standard deviation of tracking error by 30.28% and 32.46% respectively, while decreasing the mean and standard deviation of heading error by 37.27% and 35.05%. Concurrently, the maximum of optimize solution time is significantly reduced, effectively resolving tracking accuracy degradation caused by system solution timeouts. Field experiments conducted under different load conditions further validate that the proposed control strategy significantly reduces lateral, longitudinal, and heading tracking errors compared with conventional NMPC, confirming its effectiveness and robustness for tracked agricultural vehicle trajectory tracking in complex orchard environments. Full article

Mathematical knowledge is essential for adult learners’ advancement in academic and professional settings; however, instructional strategies for adult learners in higher education often emphasize memorizing procedures while neglecting their personal and professional experiences. Such approaches limit opportunities to leverage these experiences for developing meaningful mathematical understanding. Grounded in the Models and Modeling Perspective, this exploratory qualitative case study examines how a Model Development Sequence (MDS) supports the development of mathematical knowledge of adult learners returning to higher education. The participants were a group of seven first-year business adult learners enrolled in the Applied Mathematics in Business course at a higher education institution. Data were analyzed using protocol coding to describe the types of mathematical models the participants constructed. Findings indicate that participants progressed from creating models requiring redirection, grounded in proportional reasoning, to developing more sophisticated models based on linear and exponential functions. The MDS supported learners in refining, extending, and adapting their models, strengthening their conceptual understanding of variation, linear and exponential functions, and covariational reasoning. Moreover, the participants’ personal and professional experiences were central to model development. This study contributes to research on adult mathematics education by demonstrating the potential of MDS to support meaningful mathematical learning. Full article

Background and Objectives: Metacognition-related processes (e.g., confidence calibration, self-evaluation and the use of feedback) have been linked to cognitive insight, self-evaluation, and daily functioning in psychosis. However, clinic-based assessments only provide limited information. Digital methods may capture state-like variations and contextual factors, but it is unclear to what extent they operationalise core metacognitive monitoring constructs versus adjacent self-evaluative/insight-related constructs. We mapped digital approaches used to assess metacognition-related constructs across the psychosis spectrum, summarising the associated feasibility and validity. Materials and Methods: We conducted a scoping review (PRISMA-ScR) of psychosis-spectrum studies that used digital tools to assess metacognition-related targets. These included ecological momentary assessment/experience sampling (EMA/ESM), task-based paradigms with confidence ratings, and hybrid approaches. Searches covered MEDLINE (via PubMed), Scopus, and IEEE Xplore, with the final search run on 15 December 2025. We charted constructs, operationalisations, feasibility/engagement indices and reported links with clinical or functional measures. Results: The empirical evidence map comprised 13 studies directly assessing metacognition-related constructs; eight additional implementation/methodological sources were synthesised separately to contextualise feasibility, reporting, ethics, and governance. EMA studies more often assessed adjacent self-evaluative constructs, including context-linked self-appraisal bias, conviction, and self-report–context mismatch in daily life, whereas task-based studies more directly assessed confidence–accuracy calibration and feedback updating. Across EMA studies, greater momentary symptom severity and more restricted contexts were often associated with inflated self-evaluations and divergence from observer-rated functioning. Task-based studies indicated that confidence calibration and feedback utilisation may diverge from objective performance; in performance-controlled paradigms, some studies reported comparable metacognitive sensitivity/efficiency, but the overall evidence remains uncertain. Passive sensing was common in psychosis research but was rarely explicitly tied to metacognitive constructs. Conclusions: Current digital work spans both core metacognitive monitoring constructs and adjacent self-evaluative/insight-related constructs, rather than a single unitary construct. Clinical translation remains hypothesis-generating: interpretability may be improved by combining clinical anchors, low-burden EMA, and optional contextual streams, but thresholds, workflows, and signal-action rules require prospective validation. Full article

Actinidia arguta is a valuable plant resource known for its diverse bioactive constituents. However, organ-dependent metabolic variation remains insufficiently explored. In this study, an integrated approach combining LC–QTOF–MS-based metabolomic profiling, multivariate analysis, and phytochemical isolation was employed to investigate metabolic differences among fruits, leaves, and roots of A. arguta. Comparative LC–QTOF–MS profiling and principal component analysis (PCA) revealed clear organ-specific metabolic differentiation. The root extract formed a distinct cluster, primarily characterized by flavan-3-ol oligomers, including procyanidin dimers and a trimer. Targeted isolation and spectroscopic analysis identified these compounds as major constituents of the root. Quantitative analysis showed that the root exhibited the highest antioxidant activity (60.8 ± 6.2%) and total phenolic content (10.8 ± 0.7 mg GAE/g dried weight), followed by leaves and fruits, indicating significant organ-dependent variation. The enhanced antioxidant activity observed in the root extract was consistent with the enrichment of oligomeric procyanidins, which are known for their strong radical-scavenging capacity. These findings demonstrate pronounced organ-specific metabolic specialization in A. arguta, with the root characterized by a condensed tannin–dominant chemical profile. This study highlights the potential of root-derived procyanidins as bioactive natural products and provides a basis for their utilization in functional and phytochemical applications, as well as insights into plant defense-related metabolism. Full article

Background. The Planas Functional Masticatory Angle [PFMA] is a functional parameter describing mandibular trajectory during mastication. Its variation across dentition stages may reflect cross-sectional physiological functional adaptation during growth. Methods. A cross-sectional pilot study recruited 30 patients [10 per group: deciduous, mixed, permanent dentition] from a university dental clinic. PFMA was measured using a standardized intraoral photographic protocol, with intra-examiner reliability assessed [ICC > 0.9]. Molar relationships were classified per Angle’s classification. PFMA differences across dentition stages were analyzed using one-way ANOVA, and molar class distributions were evaluated with chi-square tests [p < 0.05]. Results. PFMA values decreased significantly from deciduous [64.7° ± 6.9] to mixed [55.5° ± 7.8] and permanent dentition [47.2° ± 9.8] [ANOVA, p < 0.001]. Post hoc analysis revealed significant differences between deciduous and permanent stages. No significant right–left PFMA differences were observed. Class I molar relationships predominated [70%], and no significant association was found between PFMA and molar class. Conclusions. This pilot study suggests PFMA decreases with dentition progression, reflecting physiological occlusal adaptation. Class I predominance supports functional symmetry, but PFMA-molar class associations require larger samples. Longitudinal studies are needed to further explore the clinical applicability of PFMA as a functional descriptor of masticatory adaptation. Full article

Microplastics have emerged as a relatively new type of pollutant and have attracted significant global attention. This study focuses on toxicology of microplastics in ambient PM_2.5 and road dustfall in Beijing. It utilizes the Plasmid Scission Assay to toxicologically evaluate the oxidative damage capacity of microplastics as a component of PM_2.5. The Pollution Load Index (PLI) method, based on the mass concentration of microplastics in ambient air, was employed to assess the ecological risk of atmospheric dustfall microplastics in Beijing. The results showed that both standard microplastic samples and mixed samples of microplastics with ambient PM_2.5 exhibited a dose–response relationship in DNA damage rates. At the same dose, microplastic samples with smaller particle sizes have a higher DNA damage rate. Based on the PLI results, most road dustfall microplastics in Beijing exhibit significant spatial variation. Analysis of road dustfall along the east–west main road across Beijing’s urban area revealed that microplastic pollution levels are higher in the eastern zone than in the western zone. Comparisons of pollution levels across functional areas in Beijing showed that university areas > residential areas > industrial areas > commercial areas > agricultural areas. In vertically collected samples, higher elevations (PLI_13.6m = 3.54) exhibit greater pollution levels than lower (PLI_1.5m = 1), which warrants special attention. These findings highlight the complex relationship between atmospheric microplastic accumulation and their oxidative capacity, providing essential insights for the design of targeted emission reduction strategies. Full article

Urban coastal wetlands along the Peruvian Pacific coast are increasingly affected by urban expansion, pollution, and hydrological alterations, compromising their ecological integrity. In this context, the spatiotemporal variation of the aquatic macrophyte community and its relationship with limnological conditions and drivers of change were evaluated in the Santa Rosa wetland (Chancay, Lima). The objective is to evaluate the spatiotemporal variation of the aquatic macrophyte community in the Santa Rosa wetland and analyze its relationship with physicochemical limnological variables and drivers of change. Sampling was conducted during two contrasting hydrological seasons in 2022: T1 (low-water season) and T2 (high-water season), at six sampling points (P1–P6). Physicochemical variables (water depth, temperature, pH, conductivity, total dissolved solids—TDS, total suspended solids—TSS, dissolved oxygen—DO, turbidity, nitrate—NO₃⁻, ammonium—NH₄⁺, phosphate—PO₄³⁻, and dissolved organic matter—DOM) were measured, and the relative abundance of aquatic macrophytes was evaluated. Drivers of change were identified through direct observation and a structured matrix, with phosphate a PCoA performed to summarize spatiotemporal trends. Data were analyzed using Principal Component Analysis (PCA), Co-inertia analysis, and Multi-Response Permutation Procedures (MRPP). Significant spatiotemporal variation was observed in physicochemical parameters (p < 0.05), with moderate covariation between the two matrices (RV = 0.47). A total of ten aquatic macrophyte species were recorded, with higher abundance of Pontederia crassipes and Pistia stratiotes in T1, and Hydrocotyle ranunculoides and Bacopa monnieri in T2. The most relevant drivers of change were solid waste, livestock grazing, organic contamination, and urban expansion. Spatial heterogeneity was observed in the drivers of change affecting the Santa Rosa wetland, forming a mosaic of areas with different impact profiles. Despite multiple anthropogenic pressures, the Santa Rosa wetland maintains a limnological structure and a functionally coupled macrophyte community, suggesting that essential ecological processes are maintained within the temporal scope of this study. The observed covariation between physicochemical conditions and vegetation confirms the persistence of essential ecological processes, even within an altered urban context. This study demonstrates that integrating biotic components, limnological variables, and drivers of change is fundamental to understanding and monitoring the ecological dynamics of urban wetlands along the Peruvian coast. Full article