Search Results (1,072)

This study investigates the ${\mathbb{B}}_t$-transformation of probability measures within the framework of free probability. A primary focus is the invariance under this transformation of two fundamental families: the free Meixner family and the free analog of the Letac–Mora class. In addition, we introduce novel characteristics associated with the ${\mathbb{B}}_t$-transformation, offering refined analytical tools to probe its structural and functional properties. These tools allow us to uncover new and significant properties of several distributions in free probability, including the semicircle, the Marchenko–Pastur, and the free Gamma laws, yielding explicit invariance results and stability conditions. Our findings extend the theoretical understanding of the ${\mathbb{B}}_t$-transformation and provide practical methods for analyzing the dynamics and stability of classical free distributions under this operator. Full article

In this article, function-theoretic and probabilistic approaches to the recovery of functions from Korobov classes in Lebesgue metrics are considered. Exact order estimates are obtained for the recovery errors of functions reconstructed from both accurate and inaccurate information given by the trigonometric Fourier–Lebesgue coefficients of the recovered function in the uniform metric. Within these settings, optimal computational aggregates (optimal recovery methods) are constructed. The boundary of inaccurate information (the limiting error ${\tilde{\epsilon }}_N$) that preserves the order of recovery corresponding to accurate information is identified. Furthermore, a set of computational aggregates is constructed whose limiting errors do not exceed ${\tilde{\epsilon }}_N$. A procedure for constructing a probability measure on functional classes is presented, and upper bounds for the mean-square recovery error with respect to these measures on Korobov classes are established. Numerical experiments were conducted to validate the theoretical results. These experiments showed that for the function corresponding to the lower bound in Theorem 1 (cases C(N)D-2 and C(N)D-3), the ratio between the function value and the approximation error remains constant in the case of uniform weighting and increases indefinitely when logarithmic weighting is used as the number of terms N grows. Full article

Background: Prefabricated functional appliances (PFAs) are widely used for interceptive orthodontic treatment in children, delivering intermittent forces with potential advantages for oral function. This study evaluated the effects of PFA treatment on the root development of maxillary central incisors in children during the mixed dentition stage using panoramic radiographs. Methods: A retrospective review was conducted on 77 children in the mixed dentition phase (2020–2025). These were divided into three groups: untreated controls (n = 33); skeletal Class II malocclusion group, treated with the Pre-Ortho^® Type 1 appliance (Group 1, n = 25); and functional anterior crossbite group treated with a Type 3 appliance (Group 2, n = 19). All participants underwent at least two panoramic radiographs; treatment was initiated when the maxillary central incisors were at Nolla stages 8 or 9. Following root completion, panoramic images were analyzed using the modified Lind method to measure crown length, root length, and root-to-crown ratio. Results: Mean final root lengths of the maxillary central incisors were 14.11 ± 1.40 mm (controls), 14.46 ± 1.42 mm (Group 1), and 13.89 ± 1.04 mm (Group 2) (p > 0.05). The mean root–crown ratios showed no significant variation (p > 0.05). Root development was unaffected by wear duration, with limited sex differences. Conclusions: PFA treatment may not adversely affect the root development of maxillary central incisors and indicates a safe intervention in children. Full article

Background/Objectives: Nanocrystals (NCs) are a relatively underexplored yet adaptable platform with broad potential for various applications. Currently, the surface modification of NCs leads to the development of versatile platforms capable of enhancing targeted delivery potential and supporting the advancement of precision medicine. With this in mind, this study focused on the design and surface functionalization of a resveratrol (RSV) NC selected for its antioxidant and neuroprotective effects. Methods: The design of the RSV NC was assessed by the Quality by Design approach. With the aim of intranasal administration, we assessed the RSV NC functionalization with a cationic poly (amino acid) belonging to the class of cell-penetrating peptides. Both naked and surface-modified RSV nanosuspensions were characterized in terms of mucoadhesion, behavior in artificial cerebrospinal fluid, crystallinity, solubility, and storage stability. The scavenging activity (%) of neat RSV and its nanosized forms was measured using the DPPH assay. Results: RSV NCs were successfully designed, producing truncated cubic crystals (~240 nm) with an ~80% drug content. Functionalization was efficiently achieved with poly-l-arginine hydrochloride as revealed by DSC and FTIR and resulted in a positively charged nanosuspension. Nanonization technology improved drug solubility in water and did not affect RSV scavenging activity. Technological characterization demonstrated that both nanosuspensions present suitable properties for intranasal administration in terms of particle size, mucoadhesive tendency, and stability in artificial cerebrospinal fluid. An MTT assay revealed the safety of all treatments in human microglia (HMC3) cells. Conclusions: RSV NCs’ functionalization enhanced their brain delivery potential, establishing a promising platform to improve therapeutic outcomes in neurodegenerative diseases. Full article

The appropriate selection of renovation plaster properties is essential for ensuring the durability and effectiveness of conservation works. This study focused on the design and characterization of cement-based renovation mortars modified with synthetic zeolites with different Si/Al ratios. It was assumed that high-silica zeolites would provide more favorable mechanical and hygric performance than low-silica types. Owing to their porous structure and pozzolanic reactivity, zeolites proved to be effective additives, enhancing both the microstructure and functionality of the mortars. The modified mixtures exhibited increased total porosity, higher capillary absorption, and improved moisture transport compared with the reference mortar based on CEM I 52.5R. Dynamic vapor sorption tests confirmed that the zeolite-containing mortars achieved Moisture Buffer Values (MBV) above 2.0 g/m², which corresponds to the “excellent” moisture buffering class. Electrical resistivity measurements further demonstrated the relationship between denser microstructure and enhanced durability. At the frequency of 10 kHz, the electrical resistivity of the reference mortar reached 43,858 Ω·m, while mortars with 15% ZSM-5 and 15% Na-A achieved 62,110 Ω·m and 21,737 Ω·m. These results show that the addition of high-silica zeolite promotes the formation of a denser and more insulating matrix, highlighting the potential of this method for non-destructive quality assessment. The best overall performance was observed in mortars containing the high-silica zeolite ZSM-5. A 35% replacement of cement with ZSM-5 increased compressive strength by 10.5% compared with the reference mortar R (4.3 MPa). Frost resistance tests showed minimal mass loss (0.03% at 15% and 1.79% at 35% replacement), and ZSM-5 mortars also maintained integrity under salt crystallization. These improvements were attributed to the reaction of reactive SiO₂ and Al₂O₃ from the zeolites with Ca(OH)₂, leading to the formation of additional C-S-H. A higher Si/Al ratio promoted a denser, fibrous C-S-H morphology, as confirmed by SEM, which explains the improved strength and durability of mortars modified with ZSM-5. Full article

As network automation and self-organizing networks (SONs) rapidly evolve, edge devices increasingly demand lightweight, real-time, and high-precision classification algorithms to support critical tasks such as traffic identification, intrusion detection, and fault diagnosis. In recent years, cross-entropy (CE) loss has been widely adopted in deep learning classification tasks due to its computational efficiency and ease of optimization. However, traditional CE methods primarily focus on class separability without explicitly constraining intra-class compactness and inter-class boundaries in the feature space, thereby limiting their generalization performance on complex classification tasks. To address this issue, we propose a novel classification loss framework—Contrastive Geometric Cross-Entropy (CGCE). Without incurring additional computational or memory overhead, CGCE explicitly introduces learnable class representation vectors and constructs the loss function based on the dot-product similarity between features and these class representations, thus explicitly reinforcing geometric constraints in the feature space. This mechanism effectively enhances intra-class compactness and inter-class separability. Theoretical analysis further demonstrates that minimizing the CGCE loss naturally induces clear and measurable geometric class boundaries in the feature space, a desirable property absent from traditional CE methods. Furthermore, CGCE can seamlessly incorporate the prior knowledge of pretrained models, converging rapidly within only a few training epochs (for example, on the CIFAR-10 dataset using the ViT model, a single training epoch is sufficient to reach 99% of the final training accuracy.) Experimental results on both text and image classification tasks show that CGCE achieves accuracy improvements of up to 2% over traditional CE methods, exhibiting stronger generalization capabilities under challenging scenarios such as class imbalance, few-shot learning, and noisy labels. These findings indicate that CGCE has significant potential as a superior alternative to traditional CE methods. Full article

Background: Colorectal cancer (CRC) is significantly associated with multiple metabolic diseases, with plasma metabolites potentially mediating this relationship. This large-scale metabolomics study aims to (1) quantify the genetic correlations and causal effects between 10 metabolic disease-related phenotypes and CRC risk; (2) identify the plasma metabolites mediating these effects; and (3) explore downstream regulatory genes and druggable targets. Methods: Using linkage disequilibrium score regression and two-sample Mendelian randomization, we assessed the causal relationships between each metabolic trait and CRC. A total of 1091 plasma metabolites and 309 metabolite ratios were identified and analyzed for mediating effects by a two-step MR approach. Colocalization analyses evaluated shared genetic loci. The findings were validated in the UK Biobank for metabolite-trait associations. The expression of candidate genes was explored using data from TCGA, GTEx, and GEO. A FADS1-centered protein–protein interaction (PPI) network was constructed via STRING. Results: BMI, waist circumference, basal metabolic rate, insulin resistance and metabolic syndrome exhibited both genetic correlation and causal effects on CRC. Five plasma metabolites—mannonate, the glucose/mannose ratio, plasma free asparagine, 1-linolenoyl-2-linolenoyl-GPC (18:2/18:3), and the mannose/trans-4-hydroxyproline ratio—were identified as shared central mediators. A colocalization analysis showed rs174546 linked CRC and 1-linolenoyl-2-linoleoyl-GPC. Validation in the UK Biobank confirmed the associations between phosphatidylcholine (the lipid class of this metabolite), adiposity measures, and CRC risk. An integrative analysis of TCGA, GTEx, and GEO revealed consistent upregulation of FADS1/2/3 and FEN1 in CRC, with high FADS1 expression predicting a poorer prognosis and showing the distinct cell-type expression in adipose and colon tissue. The PPI network mapping uncovered nine FADS1 interacting proteins targeted by supplements such as α-linolenic acid and eicosapentaenoic acid. Conclusions: This study systematically reveals, for the first time, the shared intermediary plasma metabolites and their regulatory genes in the causal pathway from metabolic diseases to CRC. These findings provide candidate targets for subsequent functional validation and biomarker development. Full article

In this paper, we prove the theorems on the simultaneous approximation of a pair of analytic functions by discrete shifts $(\zeta (s+ik{h}_1),\zeta (s+ik{h}_2,\alpha ))$, $h_1>0$, $h_2>0$ of the Riemann zeta function $\zeta \left(s\right)$ and Hurwitz zeta function $\zeta (s,\alpha )$. The lower density and density of the above approximating shifts are considered in short intervals $[N,N+M]$ as $N\to \infty$ with $M=o\left(N\right)$. If the set $\{(h_{1}logp:p\in \mathbb{P}),(h_{2}log(m+\alpha ):m\in {\mathbb{N}}_0),2\pi \}$ is linearly independent over $\mathbb{Q}$, the class of approximated pairs is explicitly given. If $\alpha$ and $h_1$, $h_2$ are arbitrary, then it is known that the set of approximated pairs is a certain non-empty closed subset of ${\mathbb{H}}^2(\Delta )$, where $\mathbb{H}(\Delta )$ is the space of analytic functions on the strip $\Delta =\{s\in \mathbb{C}:1/2<\mathrm{Re}s<1\}$. For the proof, limit theorems on weakly convergent probability measures in the space $H^2(\Delta )$ are applied. Full article

This paper addresses the problem of observer-based control (OBC) for nonlinear systems with time delay (TD). A novel hybrid modeling framework for nonlinear TD systems is first introduced by synergistically combining TD Takagi–Sugeno (TDTS) fuzzy and Lipschitz approaches. The proposed methodology broadens the range of representable systems by enabling Lipschitz nonlinearities to fulfill dual functions: they may describe essential dynamic behaviors of the system or represent aggregated uncertainties, depending on the specific application. The proposed TDTS–Lipschitz (TDTSL) model class features measurable premise variables while accommodating Lipschitz nonlinearities that may depend on unmeasurable system states. Then, through the construction of an appropriate Lyapunov–Krasovskii (L-K) functional, we derive sufficient conditions to ensure exponential stability of the augmented closed-loop model. Subsequently, through a decoupling methodology, these stability conditions are reformulated as a set of linear matrix inequalities (LMIs). Finally, the proposed OBC design is validated through application to a continuous stirred tank reactor (CSTR) with lumped uncertainties. Full article

Background/Objectives: This study aimed to investigate the association between right ventricular (RV) structure and function and established markers of alcohol septal ablation (ASA) efficacy in patients with hypertrophic cardiomyopathy (HCM). We hypothesized that RV characteristics may serve as predictors of left ventricular outflow tract gradient (LVOT_G) in the early period following ASA. Methods: A retrospective analysis was performed in 50 HCM patients who underwent ASA. Correlations between echocardiographic RV parameters and standard indicators of ASA success were assessed at 3 months, 1 year, 3 years, and 5 years post-procedure. Results: Echocardiographic measurements of RV wall thickness (RVWT) at 3 months and 1 year after ASA showed significant correlations with maximum LVOT_G (p < 0.001), NYHA functional class, and left ventricular end-diastolic dimension (LV_D) (both p < 0.01). At 3 and 5 years, these correlations were no longer statistically significant (p = ns). No associations were observed for other parameters. Conclusions: Echocardiographic assessment of RVWT may serve as an early predictor of subsequent LVOT_G development as soon as 3 months after ASA. RVWT could therefore provide an estimate of long-term treatment effects. Further studies are needed to confirm these findings. Full article

Seasonally dry tropical forests (SDTFs) are shaped by strong climatic and edaphic constraints, including pronounced rainfall seasonality, extended dry periods, and shallow karst soils with limited water retention. Understanding how tree species respond to these pressures is crucial for predicting ecosystem resilience under climate change. In the Yucatán Peninsula, we characterized sixteen tree species along a spatial and seasonal precipitation gradient, quantifying wood density, predawn and midday water potential, saturated and relative water content, and specific leaf area. Across sites, diameter classes, and seasons, we measured ≈4 individuals per species (n = 319), ensuring replication despite natural heterogeneity. Using a principal component analysis (PCA) based on individual-level data collected during the dry season, we identified five functional groups spanning a continuum from conservative hard-wood species, with high hydraulic safety and access to deep water sources, to acquisitive light-wood species that rely on stem water storage and drought avoidance. Intermediate-density species diverged into subgroups that employed contrasting strategies such as anisohydric tolerance, high leaf area efficiency, or strict stomatal regulation to maintain performance during the dry season. Functional traits were strongly associated with precipitation regimes, with wood density emerging as a key predictor of water storage capacity and specific leaf area responding plastically to spatial and seasonal variability. These findings refine functional group classifications in heterogeneous karst landscapes and highlight the value of trait-based approaches for predicting drought resilience and informing restoration strategies under climate change. Full article

Class 1 phytoglobins (Pgbs) are known for their multifunctional roles in plant stress responses, with recent studies suggesting broader interactions involving metabolic and transcriptional regulation. Interestingly, glyceraldehyde-3-phosphate dehydrogenase (GAPDH) moonlights in many roles in colocalized spaces during cellular stress that are strikingly suitable for supporting Pgb function. This study investigates the molecular interactions of class 1 Pgb from sugar beet (Beta vulgaris), BvPgb 1.2, and an alanine-substituted mutant (C86A), focusing on their ability to bind GAPDH and DNA. Using dual-emission isothermal spectral shift (SpS) analysis, we report strong binding interactions with GAPDH, with dissociation constants (K_D) of 260 ± 50 nM for the recombinant wild-type protein (rWT) and a significantly stronger affinity for C86A (120 ± 40 nM), suggesting that the cysteine residue limits the interaction. Remarkably strong DNA-binding affinities were also observed for both variants, displaying biphasic binding. This behavior is characteristic of hexacoordinated globins and reflects the presence of two distinct species: a fast-reacting open pentacoordinated form and a slow-reacting closed hexacoordinated form with high apparent affinity. Here, the K_D in the open configuration was 120 ± 50 nm and 50 ± 20 nM for rWT and C86A, respectively. In the closed configuration, however, the cysteine appears to support the interaction, as the K_D was measured at 100 ± 10 pM and 230 ± 60 pM for rWT and C86A, respectively. Protein–protein docking studies reinforced these findings, revealing electrostatically driven interactions between BvPgb 1.2 and GAPDH, characterized by a substantial buried surface area indicative of a stable, biologically relevant complex. Protein–DNA docking similarly confirmed energetically favorable binding near the heme pocket without obstructing ligand accessibility. Together, these findings indicate a potential regulatory role for BvPgb 1.2 through its interaction with GAPDH and DNA. Full article

Retinal disorders, such as diabetic retinopathy, cataract, and glaucoma, are among the leading causes of vision loss and blindness worldwide. The use of normal data in diagnostic studies provides a basis for distinguishing between pathological and healthy conditions. Complete and accurate diagnosis of these conditions is essential for effective treatment and prevention of recurrence. This study focuses on the VGG19 model and transfer learning to classify retinal conditions such as normal, diabetic, cataract, and glaucoma. A publicly available dataset from Kaggle consisting of labeled retinal images is used for training and evaluation. The data used in this study consists of 400 retinal images, each consisting of 100 images per class, where there are four classes consisting of normal eyes, cataract, diabetic retinopathy and glaucoma. In 50 epochs of training, Adam optimization and softmax function activation, the modeling performance measured using the confusion matrix, including the accuracy, precision, recall and F1 score, achieves accuracy results of 0.91 for 320 training data and 0.88 for 80 validation data. The loss value is 0.18 for the training data and 0.31 for the validation data. Using the test data, the values of the cataract class are 0.94 for precision, 0.8 for recall, and 0.86 for the F1 score. The values are 0.91 for precision, 1.00 for recall and 0.95 for the F1 score in the diabetic retinopathy class. For glaucoma, the scores are 0.74 for precision, 0.85 for recall, and 0.79 for the F1 score. The normal class has scores of 1.00 for precision, 0.9 for recall and 0.95 for the F1 score. Given the performance test results shown above, VGG19 modeling for diagnosing retinal disease provides quite good results. Future research can expand this research by combining additional datasets and exploring other neural network architectures to improve the diagnostic performance. Full article

Background/Objectives: The hyoid bone plays a central role in functions such as swallowing, speech, and airway maintenance, and its morphology may vary with anatomical and functional parameters. This study aimed to evaluate the influence of skeletal class, respiratory mode, age, and sex on the morphometric features of the hyoid bone using cone-beam computed tomography (CBCT). Methods: A total of 560 CBCT scans (295 females, 265 males; aged 8–73 years) were retrospectively analyzed. Hyoid angle, horizontal length, and vertical height were measured using Dolphin 3D software. Participants were categorized by skeletal class (I, II, III), breathing pattern (nasal vs. oral), and age group. Data were analyzed using robust three-way ANOVA and Bonferroni post hoc tests. Results: In females, nasal breathers exhibited significantly larger hyoid angles and vertical heights than oral breathers (p < 0.001), independent of age and skeletal class. In males, both age and breathing mode significantly influenced hyoid angle and vertical length (p < 0.001). Vertical height was also significantly greater in skeletal Class I compared to Class III (p = 0.008). Notably, significant respiration–skeletal class interaction was found in females (p = 0.029) but not in males. Conclusions: Hyoid bone morphology is affected by age, breathing pattern, and skeletal class, with sex-specific differences. Nasal breathing and younger age were associated with more inferior and angularly favorable hyoid positions, which may have implications for airway stability and craniofacial development. Full article

Single Event Transients (SETs) in clock-distribution networks are a major source of soft errors in synchronous systems. We present a practical framework that assesses SET risk early in the design cycle, before layout and parasitics, using a Vulnerability Function (VF) derived from Verilog fault injection. This framework guides targeted Engineering Change Orders (ECOs), such as clock-net remapping, re-routing, and the selective insertion of SET filters, within a reproducible open-source flow (Yosys, OpenROAD, OpenSTA). A new analytical Soft Error Rate (SER) model for clock trees is also proposed, which decomposes contributions from the root, intermediate levels, and leaves, and is calibrated by SPICE-measured propagation probabilities, area, and particle flux. When coupled with throughput, this model yields a frequency-aware system-level Bit Error Rate (${BER}_{sys}$). The methodology was validated on a First-In First-Out (FIFO) memory, demonstrating a significant vulnerability reduction of approximately 3.35× in READ mode and 2.67× in WRITE mode. Frequency sweeps show monotonic decreases in both clock-tree vulnerability and ${BER}_{sys}$ at higher clock frequencies, a trend attributed to temporal masking and throughput effects. Cross-node SPICE characterization between 65 nm and 28 nm reveals a technology-dependent effect: for the same injected charge, the 28 nm process produces a shorter root-level pulse, which lowers the propagation probability relative to 65 nm and shifts the optimal clock-tree partition. These findings underscore the framework’s key innovations: a technology-independent, early-stage VF for ranking critical clock nets; a clock-tree SER model calibrated by measured propagation probabilities; an ECO loop that converts VF insights into concrete hardening actions; and a fully reproducible open-source implementation. The paper’s scope is architectural and pre-layout, with extensions to broader circuit classes and a full electrical analysis outlined for future work. Full article