Search Results (3,228)

Asymmetric quantum codes are useful for quantum channels in which phase and bit errors occur with different probabilities, since the two distances, $d_z$ and $d_x$, can be controlled separately. We develop a permutation-paired matrix-product construction for such codes over ${\mathbb{F}}_q$. The main task is to build classical code pairs $\mathcal{C},\mathcal{D}\subseteq {\mathbb{F}}_{q^2}^{kn}$ satisfying the Hermitian inclusion ${\mathcal{D}}^{{\perp }_{\mathrm{H}}}\subseteq \mathcal{C}$, while keeping explicit dimension and distance bounds. Let $A\in {\mathbb{F}}_{q^2}^{k\times k}$ be a non-singular-by-columns (NSC) matrix with $A{A}^{†}=D{P}_{\tau }$, where D is an invertible diagonal and $P_{\tau }$ corresponds to an involution $\tau$. For $\mathcal{C}=[C_1,\dots ,C_k]A$ and $\mathcal{D}=[D_1,\dots ,D_k]A$, we prove ${\mathcal{D}}^{{\perp }_{\mathrm{H}}}=[D_{\tau \left(1\right)}^{{\perp }_{\mathrm{H}}},\dots ,D_{\tau \left(k\right)}^{{\perp }_{\mathrm{H}}}]A$. Thus, the global inclusion ${\mathcal{D}}^{{\perp }_{\mathrm{H}}}\subseteq \mathcal{C}$ is equivalent to the shorter paired inclusions $D_{\tau \left(i\right)}^{{\perp }_{\mathrm{H}}}\subseteq C_i$. This yields asymmetric quantum codes with parameters ${\left[[kn,{\sum }_{i=1}^k(r_i+s_i)-kn,d_z/d_x]\right]}_q$, where the bounds for $d_z$ and $d_x$ follow from NSC matrix-product distance estimates. For nested maximum distance separable (MDS) constituents, the paired conditions reduce to $r_i+s_{\tau \left(i\right)}\ge n$, giving explicit infinite families. Concrete $\tau$-OD matrices and numerical examples show that nontrivial permutations can increase the quantum dimension while preserving prescribed lower bounds for $d_z$ and $d_x$. Full article

The European legislation sets the maximum residue levels for glyphosate in sesame seeds at 0.1 mg/kg (EU Regulation n. 293/2013) and for glufosinate and N-Acetyl-glufosinate expressed as glufosinate at 0.03 mg/kg (EU Regulation n. 2016/1002). The present work describes a rapid methodology to determine glyphosate, glufosinate and its metabolite and N-Acetyl-glufosinate in sesame seeds by LC/MS/MS. The method was studied in the framework of EU proficiency tests on sesame seeds. The analytical method was developed using methanol acidified with formic acid (1%, v/v) extraction with an isotope internal standard, followed by LC/MS/MS detection. The recoveries were performed in the range of 0.05–0.5 mg/kg for glyphosate and 0.02–0.2 mg/kg for glufosinate and N-Acetyl-glufosinate. All the recovery values were between 70 and 114%, which is the acceptable interval according to SANTE/11312/2021; the relative standard deviation (%RSD) values met the requirement of <20%. Linearity for each substance in solvent and matrix was studied, and the response was linear with R2 > 0.999. We considered precision, matrix effect, LOD and LOQ in the validation. All the parameters were in agreement with the acceptability criteria of the document SANTE/11312/2021. The method was considered suitable for the determination of the studied substances on sesame seeds. Full article

The subcellular localization of long noncoding RNAs (lncRNAs) is a central determinant of their function, yet its molecular determinants remain incompletely defined, and most existing predictors rely on the primary sequence. Because RNA-binding proteins (RBPs) are the proximal effectors of RNA compartmentalization, this study tested whether the composition of RBPs bound to a lncRNA is predictive of its nuclear or cytoplasmic localization. Enhanced crosslinking and immunoprecipitation (eCLIP) occupancy for 139 RBPs in K562 cells was integrated with the cytoplasmic–nuclear relative concentration indices (CN-RCIs) derived from matched subcellular fractionation, and localization was modeled under chromosome-grouped cross-validation with nested regularization. RBP-occupancy composition predicted localization beyond the transcript size and total binding amount (incremental cross-validated coefficient of determination, delta-R-squared = 0.17; receiver-operating-characteristic area under the curve, AUC = 0.73, a moderate-strength association; Freedman–Lane permutation, p = 0.005). This increment persisted (delta-R-squared = 0.12; p = 0.005) against an expanded baseline that additionally absorbed the transcript abundance, intron content and exon number, indicating predictive information that is not reducible to these transcript features, and the classifier was well calibrated (Brier score = 0.10; expected calibration error = 0.02). The signed coefficient profile separated RBP function systematically: factors acting in nuclear processes (splicing, 3′-end processing, and nuclear-matrix association) carried negative, nuclear-direction weights, whereas factors acting in cytoplasmic processes (translation and messenger RNA stability) carried positive, cytoplasmic-direction weights (Mann–Whitney p = 0.013). The profile generalized across cell lines: a K562-trained model predicted HepG2 localization (transfer AUC = 0.71 using 76 shared RBPs), and HepG2 reproduced the association independently (AUC = 0.77). The association is correlational and of moderate strength; it is presented as an interpretable, RBP-occupancy-based complement to sequence-based predictors of lncRNA localization. Full article

Randomized reward mechanisms are often described as repeated trials with a fixed success probability. This constant-hazard reference case is symmetric in the limited finite-state sense that, conditional on non-absorption, the next-draw success probability is invariant with respect to the current draw count. Pity and guarantee rules break this draw-count homogeneity by making the hazard depend on the current state. This paper studies that state-dependent asymmetry for a finite soft-pity waiting-time model. The waiting time for one rare item is represented as an absorption time of a Markov chain whose transient state is the pity counter. We write the corresponding absorbing transition matrix explicitly and then derive the equivalent first-step recurrences for the expectation, variance, and full probability mass function. A simple stochastic-ordering proposition shows how increasing the statewise success probabilities decreases the waiting-time distribution in the usual tail order. Repeated convolution then yields the distribution for multiple independent stages. The numerical section reports quantiles, tail probabilities, VaR/CVaR-type summaries, expected excess values, sensitivity analyses, normal-approximation diagnostics, and distributional asymmetry indicators. A featured-target variant with a binary guarantee state is also included. Throughout, the reported quantities are consequences of the stated transition rule; Monte Carlo simulation is used only as a numerical check. Full article

Hotelling’s T-squared statistic provides an interpretable framework for multivariate fault detection; however, its rolling implementation is highly sensitive to transient casewise outliers in the reference window. Such abnormal observations may inflate the sample covariance matrix, enlarge the monitoring boundary, and consequently mask subsequent moderate fault signals. This study proposes a robust rolling Hotelling fault detection method, denoted as RRH-FD, to reduce this masking effect. The proposed method estimates the rolling reference center and scatter matrix using reweighted minimum covariance determinant (RMCD) estimators, while each newly arriving observation is evaluated directly as a potential fault signal. The monitoring threshold is obtained using a robust Hotelling approximation rather than the classical Hotelling distribution. A simulation study was conducted under both clean and contaminated rolling reference scenarios. Under clean reference windows, the proposed robust procedures remained competitive with the classical rolling Hotelling detector, showing only a modest efficiency loss. Under contaminated reference windows, RRH-FD substantially improved detection performance. The adaptive RRH-FD method reduced the average detection delay by approximately 37.6% relative to the classical rolling detector, while the fixed MCD fraction 0.85 version achieved an approximate reduction of 42.4%. The proposed methods also improved early detection rates within the first 25 and 50 post-fault monitoring points. Boundary inflation was quantified using the log-determinant ratio between the classical sample covariance matrix and the RMCD scatter estimate. This analysis further confirmed that the advantage of RRH-FD becomes more pronounced as the classical covariance boundary is more strongly inflated by transient outliers. An R package, RRHFD, was developed to facilitate implementation and reproducibility. Full article

Background and Objectives: Temporomandibular disorders (TMDs) encompass a broad spectrum of functional and structural abnormalities of the temporomandibular joint (TMJ). Conventional diagnostic tools, although essential, often fail to capture the underlying biochemical mechanisms driving disease progression. Synovial fluid (SF), by virtue of its direct proximity to intra-articular tissues, represents an accessible biological matrix for identifying molecular signatures of inflammation, cartilage degradation, lubrication failure, oxidative stress, and angiogenic activation. The objective of this review is to synthesize current evidence on SF proteomics in TMD and evaluate its potential translational value in precision medicine. Materials and Methods: A narrative review of the literature was conducted on PubMed to identify human studies focused on SF proteomic and biochemical biomarkers in TMD. Eligible studies included original research articles assessing SF composition in relation to specific TMJ pathologies, diagnostic categories, or clinical phenotypes. Extracted data included study design, sample characteristics, analytic methodology, biomarkers investigated, and key findings. Google Gemini (Google LLC, Mountain View, CA, USA) was used as an AI-assisted tool to support language editing and manuscript writing during the preparation of this article. The use of this tool was limited to linguistic refinement; all scientific content, data interpretation, and conclusions were formulated and verified by the authors. Results: Across the analyzed studies, TMD phenotypes—particularly disc displacement with or without reduction (DDwR, DDwoR) and osteoarthritis (OA)—were characterized by consistent alterations in cytokines (IL-1β, IL-6, IL-8, TNF-α), extracellular matrix (ECM) components (aggrecan, glycosaminoglycans (GAGs), decorin, MMP-2, MMP-9), lubrication molecules (lubricin/PRG4), oxidative stress mediators (myeloperoxidase (MPO), nitric oxide (NO), glutathione peroxidase (GPX)), adipokines (chemerin, resistin, adiponectin), and angiogenic factors (vascular endothelial growth factor (VEGF), fibroblast growth factor-2 (FGF-2)). Recent liquid chromatography–tandem mass spectrometry (LC–MS/MS) analyses further revealed phenotype-specific protein clusters and pathways related to inflammation, ferroptosis, hypoxia signaling, and proteoglycan metabolism. Conclusions: Current evidence suggests that SF proteomics and multi-analyte biomarker profiling offer a promising, hypothesis-generating approach for understanding the biological mechanisms underlying TMD. The integration of proteomic, metabolic, and inflammatory markers holds future potential for diagnostic panel development; however, prospective clinical validation is still required before SF-based molecular profiling can be implemented as a precision medicine tool in TMJ disorders. Full article

Econometric estimation of panel data models by feasible generalized least squares (FGLS) provides an example of how conceptually simple problems may run into computational bottlenecks. I address the main computational tasks of FGLS within the R system for statistical computing, comparing different tools from the point of view of computational efficiency. I concentrate on estimating two models: the popular “random effects” with two error components and the less restrictive “general GLS” specification, which does not fit into the standard computational framework usually employed for the former. I compare the standard solution (partial time demeaning) with two alternative strategies, based respectively on algebraic properties and on object-oriented programming. I show how, while naive implementations become infeasible with large datasets, both list operators and object-oriented matrix routines available in the R environment make the problem tractable for most practically relevant sample sizes on any machine. I conclude by briefly discussing the parallelization of critical tasks. Full article

The development of low-cost and highly sensitive electrochemical sensing platforms for pesticide monitoring has attracted significant attention in recent years. In this study, coke-derived carbon (CDC) was successfully synthesized from petroleum coke through high-temperature carbonization under a nitrogen atmosphere. Subsequently, a CDC@CuO-NP nanocomposite was fabricated by depositing copper oxide nanoparticles onto the CDC matrix. The morphology, structure, and elemental composition of the synthesized materials were characterized using scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy-dispersive X-ray spectroscopy (EDS), and elemental mapping analyses, confirming the successful formation of the composite and the uniform distribution of CuO nanostructures on the carbon surface. Electrochemical characterization demonstrated that the incorporation of CuO significantly enhanced the electrochemical performance of CDC by increasing the electroactive surface area and facilitating electron transfer. The CDC@CuO-NP-modified glassy carbon electrode was applied for the electrochemical detection of dichlorvos (DDVP) using electrochemical impedance spectroscopy (EIS). The sensor exhibited a concentration-dependent increase in charge-transfer resistance and showed a linear response in the concentration range of 247–3770 nM, with the regression equation y = 47.1458C + 111.8162 and a correlation coefficient of R² = 0.9832. The developed sensor achieved a low limit of detection (LOD) of 2.3 nM, demonstrating high sensitivity toward DDVP. These results indicate that the CDC@CuO-NP nanocomposite is a promising, low-cost, and efficient electrode material for the sensitive determination of organophosphorus pesticides and has considerable potential for environmental monitoring and food safety applications. Full article

Fractured coal in the residual-strength stage is a primary medium for gas migration and drainage in deep mining areas. To investigate the hydro–mechanical seepage response of post-peak fractured coal under constant-pressure-difference conditions, triaxial CO₂ seepage tests were conducted on coal specimens collected from the Xinyuan Coal Mine. A Weibull-based damage constitutive model was established to characterize the confining-pressure-induced hysteresis in the damage-evolution path. The flow-rate evolution and Reynolds number analysis indicated that gas flow remained within the linear Darcy regime. A controlled-variable analysis was used to examine the competing effects governing permeability evolution. Mechanical compaction induced an exponential decrease in permeability, whereas the decrease in permeability with increasing pore pressure was interpreted, within the proposed model framework, as the combined effect of possible adsorption-induced matrix swelling and weakened gas slippage. To address the limitations of conventional constant-slip-factor models, a pressure-dependent slip modulation coefficient was introduced into a composite permeability equation incorporating effective stress, adsorption-related deformation, and dynamic gas slippage. Global nonlinear fitting yielded R² = 0.97 and an RMSE of 0.1909, with the residuals generally distributed around zero, supporting the fitting reliability of the model within the investigated stress–pressure range. Response-surface analysis identified mechanical compaction as the dominant controlling mechanism, while adsorption-related deformation and gas slippage acted as secondary correction mechanisms. The proposed framework provides a quantitative basis for distinguishing the mechanical and fluid-related effects governing permeability evolution in post-peak fractured coal. Full article

Machine learning (ML)-assisted design of epsilon-negative polymer nanocomposites requires a clear connection between experimentally controllable synthesis parameters, core–shell nanoparticle geometry, and the resulting effective optical response. The targeted optical response is unusual because the polymer film is predicted to exhibit near-zero or negative real effective permittivity in selected visible spectrum regions, arising from Ag core plasmonic polarizability, SiO₂-mediated dielectric spacing, nanoparticle filling factor, and effective medium coupling rather than from the intrinsic polymer matrix. In this study, a two-stage ML-assisted synthesis-to-optics framework is developed for Ag@SiO₂ core–shell nanoparticle/polymer composite films intended for visible spectrum effective permittivity screening. In the first stage, Stöber synthesis parameters, including water volume, ethanol volume, TEOS content, catalyst volume, reaction time, Ag nanoparticle size, and Ag nanoparticle concentration, were used to predict SiO₂ shell thickness. In the second stage, Ag core size, SiO₂ shell thickness, wavelength, and nanoparticle filling factor were used to screen the real effective permittivity of Ag@SiO₂/polymer nanocomposites within an effective medium design space. Using a duplicate-aware validation workflow, Gradient Boosting provided the strongest held-out test performance for shell thickness prediction, with a test R² of 0.8997, MAE of 7.1822 nm, RMSE of 8.8344 nm, and cross-validation R² of 0.5371 ± 0.4648. The relatively large cross-validation variability indicates that the model is useful for interpolation-based synthesis screening but should not be interpreted as fully robust across heterogeneous literature-derived data. For the optical response task, the highest held-out test performance was obtained by a Decision Tree model (test R² = 0.7586), but cross-validation results were unstable, indicating that the epsilon model should be interpreted as a design space screening tool rather than a generalizable predictor. Design window analysis identified candidate negative effective permittivity regions primarily at 400 nm and high nanoparticle filling factor, with predicted $\mathrm{R}\mathrm{e}\left({\epsilon }_{\mathrm{e}\mathrm{f}\mathrm{f}}\right)$ values ranging from −5.4229 to −0.2086 across selected windows. The main contribution of this work is the treatment of SiO₂ shell thickness as a bridge variable between Stöber-derived synthesis control and effective permittivity screening. Experimental validation remains necessary to confirm the predicted design windows, particularly because shell uniformity, Ag core polydispersity, nanoparticle aggregation, polymer dispersion, high-filling-factor feasibility, and effective medium validity can strongly influence the measured optical response. Full article

Orthodontic tooth movement (OTM) is a biologically driven process resulting from the mechanically induced remodeling of the periodontal ligament (PDL) and alveolar bone. A marked inter-individual variability exists in the rate of tooth movement, susceptibility to adverse outcomes such as external apical root resorption (EARR), and overall treatment response. This narrative review synthesizes current evidence on molecular, genetic, and epigenetic biomarkers that underline these differences. We summarize established local biomarkers derived from gingival crevicular fluid and saliva, including inflammatory cytokines, matrix metalloproteinases, and bone remodeling mediators reflecting OTM compression- and tension-side biology. Beyond fluid biomarkers, growing attention is given to genetic and epigenetic determinants of OTM. Specific gene mutations are associated with impaired or absent tooth movement, while multiple single-nucleotide polymorphisms have been linked to increased risk of EARR. Recent studies further demonstrate that orthodontic forces induce epigenetic remodeling in PDL cells, including DNA methylation changes in the gene promoters, histone modifications, and force-responsive non-coding RNAs such as miR-21 and miR-146a, which collectively regulate osteoclastogenesis, inflammation, and tissue adaptation. These findings indicate that OTM is governed by an integrated network combining mechanical stimuli with genetic predisposition and dynamic epigenetic regulation. Understanding these mechanisms provides a foundation for the development of biomarker-guided, patient-specific therapeutic strategies. Full article

As online social networks (OSNs) evolve into multilingual ecosystems, rumors can cross language boundaries through translation and bilingual re-expression, increasing governance difficulty. To characterize cross-lingual coupling and response delay, this study proposes a time-delay S2LCHR dynamical model for bilingual OSNs, in which the coupled spreader state C describes cross-lingual coupled rumor transmission and a fixed response delay represents cross-lingual comprehension, judgment, and re-expression. The basic reproduction number $R_0$ is derived using the next-generation matrix method. Lyapunov analysis, the Routh–Hurwitz criterion, characteristic-equation analysis, and numerical simulations are combined to examine equilibrium stability, delay-induced Hopf bifurcation, parameter sensitivity, and social-impact indicators. A real-world aggregate trend-fitting case study using English–Spanish COVID-19-related tweet data is further conducted to assess empirical plausibility. The results show that $R_0$ determines the threshold between rumor extinction and persistence in the delay-free system, while an excessive response delay can destabilize the rumor-prevailing equilibrium and induce bounded oscillatory behavior. Sensitivity and social-impact analyses indicate that $\alpha$ and $\beta$ promote rumor persistence, whereas $\sigma$ and $\phi$ associated with state C are key inhibitory factors. These findings suggest that coupled spreaders should be prioritized in cross-lingual rumor governance. Full article

The evolution of 193 nm deep-ultraviolet (DUV) lithography toward high numerical aperture (NA > 1.35) presents challenges approaching physical limits for antireflective (AR) coatings on strongly curved lens elements. In this study, a full-stack multi-objective optimization framework is developed by coupling the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with the Transfer Matrix Method (TMM) to optimize a 7-layer LaF₃/MgF₂ system on strongly curved substrates ($R=150$ mm). The model integrates material dispersion, thermo-optic effects, deposition flux deviations, and manufacturing thickness constraints. Following 1500 generations of optimization and TOPSIS-based decision-making, the selected Pareto optimal solution achieves a full-aperture average reflectance of 1.3633% and a radial uniformity of 9.5037%. The design further exhibits high environmental robustness with a thermal drift of 0.0019% and a residual stress of 39.23 MPa. These results demonstrate that the proposed method overcomes the critical process bottleneck of achieving full-aperture uniformity below 10% on strongly curved optics. This framework provides a general paradigm for the robust design of next-generation ultra-precision DUV optical systems, effectively balancing theoretical depth with engineering feasibility. Full article

A controlled composting biodegradation system was implemented to evaluate a wood–plastic composite (WPC) composed of wood fibers and recycled HDPE (rHDPE), in accordance with ASTM D5338, by measuring CO₂ capture over 45 days. This evaluation was complemented with mechanical and physicochemical characterization, including stereomicroscopy/SEM, mass loss, water absorption, contact angle, tensile strength, FTIR, TGA, and DSC. The results showed 6.12% biodegradation, classifying the material as neither biodegradable nor compostable. SEM analysis revealed increased surface roughness, cracks, and microbial-like structures, together with a 10% decrease in contact angle. The mechanical properties declined by 33% (tensile strength), despite only 1.26% mass loss, which was attributed to weakening of the matrix–fiber interfacial adhesion due to water absorption. TGA, DSC, and FTIR supported the interpretation that degradation occurred preferentially in the wood fibers. Full article

Umbilical cord mesenchymal stromal cells (UC-MSCs) are a key element of regenerative medicine due to their ability to secrete growth factors that stimulate proliferation and angiogenesis, and modulate the inflammatory response. Despite their widespread use, the influence of the perinatal microenvironment on their biological properties remains poorly understood. The aim of this study was to assess the influence of pH and blood gas parameters in umbilical cord blood on the global transcriptomic profile of UC-MSCs and to analyze the correlation between the metabolic status of the newborn and the expression of key trophic factors: EGF, FGF2, FGFR1, FGFR3, GDNF, HGF, IGF1, NES, NGF, and PGF. Methods: The study was conducted in two stages. In the first phase, transcriptomic screening was performed using Affymetrix HuGene 2.0 ST microarray on cells isolated from three environmental groups defined by cord blood pH: acidic (pH < 7.35), physiological (7.35–7.39), and alkaline (pH ≥ 7.4). In the second phase, the results were validated using qPCR on an expanded study group (N = 50). Gene expression levels (RQ) were related to blood gas parameters (pH, pCO₂, pO₂, cHCO₃) and the presence of clinical features of threatened neonatal asphyxia. Results: Microarray analysis revealed that environmental pH acts as a molecular phenotypic switch. Under low pH conditions (<7.35), a shift in cell profile from proliferative to structural–migratory was observed. Significant overexpression of genes responsible for extracellular matrix (ECM) organization and adhesion (e.g., COMP, DCN, LUM, FMOD) was observed, while pathways related to cell cycle and cell division (↓CDK1, AURKA, TOP2A) were downregulated. qPCR validation confirmed these observations, demonstrating a strong positive correlation between blood pH and the expression of regenerative mediators: FGFR1 (r = 0.28), EGF (r = 0.30), NGF (r = 0.39), and IGF1 (r = 0.30). A negative correlation was also found between carbon dioxide pressure (pCO₂) and the expression of NGF, FGFR1, and EGF. A significant clinical finding was that in newborns diagnosed with threatened asphyxia, EGF, FGFR1, and NGF gene expression was significantly reduced, indicating impaired trophic potential of the cells in response to metabolic stress. Conclusions: These results indicate that cord blood gas parameters are critical regulators of the genetic activity of UC-MSCs. Metabolic and respiratory acidosis not only inhibit the cells’ proliferative potential but also force them into a matrix remodeling mode, permanently modifying their transcriptomic profile. This suggests that the neonatal acid–base status may serve as an objective indicator of the “biological quality” of isolated stromal cells, which has significant implications for their future applications in cell therapies. Full article