Search Results (1,062)

This paper develops probabilistic mean-square extensions of fractional Hermite–Hadamard–Mercer (HHM) type inequalities for convex stochastic processes. By employing generalized mean-square stochastic fractional integral operators, we establish new fractional inequalities that extend deterministic HHM estimates to a stochastic framework. The stochastic inequalities are interpreted in the almost sure sense, while the associated fractional operators are considered within the mean-square setting for second-order stochastic processes. Several special cases are also discussed, showing that the obtained results reduce to known fractional and stochastic inequalities for suitable choices of the parameters. As analytical consequences, the proposed results are applied to two-variable means, modified Bessel functions and the k-Digamma function. To strengthen the applied interpretation, we also present a stochastic nonlinear conductivity distortion model in which the effective conductivity is represented by a positive convex stochastic process. The corresponding heat-conduction setting, heat-flux interpretation and Monte Carlo illustrations show how the derived bounds can be used to estimate fractional stochastic averaging errors under nonlinear conductivity distortion. The numerical plots are presented as illustrative demonstrations of the behaviour of the theoretical bounds under admissible parameters. Full article

Background/Objectives: Homologous recombination deficiency (HRD) is a predictive biomarker in high-grade serous ovarian carcinoma for platinum-based chemotherapy and PARP inhibitors. The implementation of HRD testing in routine diagnostics has generated multiple commercial assays that differ in genomic targets, bioinformatic analysis, and HRD scoring strategies. We aimed to assess the analytical performance and feasibility of a locally semi-automated workflow based on the Agilent SureSelect CD HRR17 panel with SeqOne/SomaHRD analysis, and to compare it with established commercial HRD assays currently used in routine clinical practice: Myriad MyChoice CDx and SOPHiA DDM Dx HRD Solution. Methods: Thirty high-grade serous ovarian carcinoma cases diagnosed between 2019 and 2023 were retrospectively analyzed. HRD status was assessed with the Agilent-SeqOne workflow and compared with Myriad (n = 12) and SOPHiA (n = 18). Concordance and correlation between genomic instability metrics were evaluated. Results: The Agilent/SeqOne workflow showed high concordance with both comparison workflows. Genomic instability metrics strongly correlated across assays (R² up to 0.96). A lower proportion of inconclusive classifications was observed with the Agilent/SeqOne workflow. Discordances were mainly observed in borderline cases near classification thresholds. Variant detection was highly concordant within shared genomic regions. Conclusions: The locally semi-automated HRD workflow demonstrated high analytical concordance with established commercial assays in evaluable cases. Operational advantages related to workflow flexibility and local reanalysis support its potential implementation in routine molecular diagnostics. Full article

The sign of the maximal almost sure Lyapunov exponent determines the stability of stochastic systems, while its numerical computation for three-dimensional linear Stratonovich stochastic differential equations remains challenging due to the failure of classical two-dimensional strategies. The spherical angular motion of 3D systems produces a Fokker–Planck equation with intractable mixed partial derivatives, preventing conventional analytical solutions. This paper develops a unified computational framework for three-dimensional linear Stratonovich stochastic systems using analytical derivation for degenerate cases and physics-informed neural network (PINN) approximation for general non-degenerate scenarios. For degenerate systems, we reduce the coefficient matrix to a lower triangular form via orthogonal transformation and establish tight upper bounds based on the logarithmic growth property of the Wiener process, yielding closed-form expressions for the maximal almost sure Lyapunov exponent under all parameter sign configurations. For non-degenerate systems, we reformulate the Fokker–Planck equation in spherical coordinates and construct a customized PINN with trigonometric encoding to enforce periodic boundary conditions. The network is trained by joint loss functions of equation residuals, boundary constraints and normalization consistency, and the converged stationary density is substituted into the Furstenberg–Khasminskii formula to calculate the exponent via Gauss–Legendre quadrature. Monte Carlo simulations confirm the accuracy and robustness of the proposed method, which reliably identifies the sign of the maximal almost sure Lyapunov exponent even in near-critical regimes. Numerical experiments on a 3D stochastic Hopf bifurcation model show that noise negatively shifts the bifurcation point, with the offset linearly proportional to the squared noise intensity. This work extends Lyapunov stability analysis from two-dimensional to three-dimensional linear Stratonovich stochastic systems, offering an effective tool for stability evaluation of general three-dimensional stochastic dynamical models. Full article

This study investigates whether female representation on corporate boards and carbon emissions disclosure (CED) are interrelated in an emerging market. Using critical mass theory (CMT), which posits that female directors can surely impact the decisions of boards once they reach critical mass, we examine whether the presence of three women on the board or approximately 30% board membership is necessary in Indonesia. This context is important since (i) boards are still a long way from representing the demographics of Indonesians due to low female representation on boards; (ii) in many cases board sizes are too small for meaningful communication between two directors; and (iii) regulations surrounding environmental disclosure barely exist relative to more developed markets. Based on panel data from Indonesian manufacturing firms, the study demonstrates that the effect of board gender diversity on CED is nonlinear and contextually dependent. The results demonstrate that the core idea of CMT is not fully supported in this setting. The presence of even a single female director is linked to higher levels of carbon emissions disclosure, signaling that female directors likely play a substantive role and serve more than just symbolic purposes. That said, improvements associated with having women on the board do not increase progressively with more females taking a seat around the table. However, the positive effect is diminished and becomes statistically insignificant at higher levels of female representation. The results also imply that firms whose board of directors contain moderate levels of gender diversity (with 20–40% women on the board) engage in Type I CED to the highest extent. However, boards nearing a gender balance do not seem to garner any further benefits from disclosure. Full article

Background: Aligner workflows range from outsourcing the design and manufacturing process to in-office scanning, planning, and fabrication. This exploratory retrospective study aimed to evaluate whether discrepancies between planned and achieved anterior tooth movements with SureSmile aligners remain within predefined clinical thresholds. Methods: A total of 21 dental arches from 14 patients who underwent SureSmile aligner treatment with in-office fabricated aligners were retrospectively analyzed. Digital models of the planned setup were superimposed on models of the clinically achieved outcomes to assess the accuracy of anterior tooth movements by comparing planned and clinically achieved outcomes. Results: Rotational movements showed larger discrepancies than translational movements. Mean discrepancies for angulation and axial rotation were 3.42 ± 1.50° and 4.50 ± 2.12°, respectively, both exceeding the predefined clinical threshold of 2°. In contrast, translational discrepancies remained below the clinical threshold of 0.5 mm, with mean values of 0.17 ± 0.11 mm for mesiodistal, 0.27 ± 0.16 mm for labiolingual, and 0.38 ± 0.17 mm for vertical movements. Translational discrepancies were significantly lower than the predefined clinical threshold in patient-level analyses, whereas rotational discrepancies were not. Conclusions: In-office SureSmile aligner workflows showed acceptable accuracy for translational anterior tooth movements, whereas angular movements frequently exceeded predefined clinical thresholds. Full article

Glioblastoma (GBM) is an extremely invasive and incurable tumor. We previously reported predominant ALDH1A3 expression at the invasive front of GBM tumors, which was associated with shorter patient survival, and further showed that ALDH1A3 promoted tumor angiogenesis involving plasminogen activator inhibitor-1 (PAI-1). Here, we investigated whether ALDH1A3 drives cell invasion through retinoic acid (RA) and PAI-1 signaling. Analysis of the TCGA-GBM dataset revealed a positive association between ALDH1A3 and PAI-1 (SERPINE1) expression. Overexpression of ALDH1A3 in GBM cells markedly increased PAI-1 mRNA and protein levels, with cellular colocalization of both proteins, accompanied by robust migration and invasion. These effects were reversed by treatment with a pan-RA receptor (RAR) antagonist AGN193109 (AGN), with a specific PAI-1 inhibitor tiplaxtinin (Tip) or by CRISPR/Cas9-mediated knockout of PAI-1. In a chick chorioallantoic membrane (CAM) model, ALDH1A3-overexpressing cells showed increased invasion, which was reduced by tiplaxtinin (Tip) treatment or PAI-1 knockout. Mechanistically, ChIP-qPCR demonstrated that RA treatment or ALDH1A3 overexpression increased RARα occupancy at the PAI-1 regulatory region, accompanied by increased PAI-1 expression, both of which were diminished by AGN. Collectively, the present study defines an ALDH1A3-RA-PAI-1 signaling axis that contributes to GBM cell motility and invasion. Full article

Rapid and reliable detection methods are essential for routine monitoring of environmental hygiene on farms. This pilot study evaluated luminometers (LUM) and mobile flow cytometer (MFC) for assessment of surface organic contamination in farrowing units. The study was conducted on two pig farms after animal removal prior to sanitation, with sampling performed at heated pads, pen walls, and corridors. ATP measurements were carried out using three luminometers (Clean-Trace™ LM1, EnSure, and SystemSURE Plus), and residual particles were detected using a mobile flow cytometer (Cytoquant). Microbiological cultivation (TMC 36 °C) was additionally included. Significant differences in log RLU values were observed between LUM, with large effect sizes indicating a substantial influence of device type on RLU values. A high correlation was confirmed only between EnSure and SystemSURE Plus (r_s = 0.81–1.00; p < 0.05), and no relationship was confirmed between LUM and MFC (r_s = −0.49–0.77; p > 0.05). Correlations between rapid detection methods and microbiological cultivation were inconsistent. Corridors demonstrated the highest microbiological contamination, whereas MFC identified heated pads as sites with increased residual particulate contamination. The results indicate that LUM, MFC, and microbiological cultivation characterize different dimensions of environmental contamination and should therefore be interpreted as complementary rather than interchangeable methods. Full article

The parameter space in a regression model for multinomial time series data contains the regression parameters those explain the effects of the time dependent covariates, and the dynamic dependence or category transition parameters those explain the influence of the past responses on the multinomial response at a given time. The estimation of the regression parameters can be negatively affected when higher dimension of the parameter space is considered specially for the transition parameters. In this paper we propose a parameter dimension-split approach where a conditional generalized quasi-likelihood (CGQL) estimating function is first developed for the dynamic dependence parameters in terms of unknown regression parameters which is exploited in the next step to develop an observed information matrix based maximum likelihood (ML) estimating equation for the main regression parameters. More specifically, this split approach helps to write the actual joint likelihood function of regression and dynamic dependence parameters as a likelihood function of regression parameters only by replacing the dynamic dependence parameters with their CGQL estimates obtained in the first step. As the time series length is generally large in practice, we have made sure that the proposed CGQL and ML estimators are asymptotically reliable, that is consistent for the respective parameters. Full article

Background/Objectives: Ulcerative colitis (UC) is a chronic inflammatory bowel disease in which alterations in the gut microbiota and dietary lipid composition play a central role; this study aimed to evaluate the effects of synbiotics, omega-3 polyunsaturated fatty acids, and their combination on clinical, macroscopic, microbiological, and histopathological outcomes in dextran sodium sulfate (DSS)-induced colitis in Wistar rats. Methods: Seventy-two male Wistar rats were randomly allocated to four groups (n = 18/group) and received 5% DSS in drinking water for eight days to induce colitis. Following DSS withdrawal and histological confirmation of colitis in sentinel animals, groups were treated for 8 days as follows: DSS (control), DSS-S (synbiotics, Ecologic^® 825), DSS-Ω3 (omega-3 fatty acid-enriched diet, ProSure^®), or DSS-S&Ω3 (combined therapy). Eight rats per group were sacrificed on days 4 and 8 post-DSS. Body weight, Disease Activity Index (DAI), distal colon length, hematologic parameters, bacterial translocation to the liver and mesenteric lymph nodes, histological colitis score, and myeloperoxidase (MPO)-positive cell counts were assessed. Results: DSS induced severe colitis characterized by diarrhea, rectal bleeding, and extensive mucosal erosions. After 8 days of treatment, the DSS-S&Ω3 group showed the greatest body-weight recovery (206.1→222.9 g, p < 0.05 vs. other groups), significantly preserved distal colon length, and the largest reduction in DAI (p < 0.05). Both the DSS-S and DSS-S&Ω3 groups demonstrated reduced bacterial translocation compared with DSS. The DSS-Ω3 group demonstrated persistent MPO-positive neutrophil infiltration compared with the DSS-S and DSS-S&Ω3 groups, whereas combined therapy was associated with lower MPO-positive cell counts. Histological colitis scores were significantly improved only in the DSS-S&Ω3 group (p < 0.05). Conclusions: In this DSS colitis model, the DSS-S&Ω3 group demonstrated superior clinical and histological outcomes compared with DSS-S or DSS-Ω3 alone, supporting further evaluation of combined synbiotic and omega-3 therapy as an adjunctive approach in ulcerative colitis. Full article

The post-combustion carbon capture process with monoethanolamine/piperazine (MEA/PZ) blends encounters notable modeling and optimization challenges. These arise from strong thermodynamic–kinetic nonlinear coupling, as well as limited availability of high-quality experimental data. To address this, we propose a machine learning and multi-objective optimization strategy driven by physicochemical features. By extracting explicit physical features and embedding physicochemical constraints into data-driven models, this study evaluated the predictive performance of three distinct algorithms based on wet-wall column experimental data. These algorithms included natural gradient boosting (NGBoost), sure independence screening and sparsifying operator (SISSO), and gaussian process regression (GPR). Subsequently, an optimization problem aimed at minimizing $P_{{\mathrm{CO}}_2}^{*}$ and maximizing $k_{\mathrm{g}}^{\prime }$ was formulated. The multi-objective beluga whale optimization (MOBWO) algorithm was then employed for global optimization and benchmarked against the traditional non-dominated sorting genetic algorithm II (NSGA-II). Results indicate that the Gaussian process regression (GPR) model performed best when it was enhanced by physicochemical features and optimized via Bayesian hyperparameter tuning. It achieved R² values of 0.989 and 0.953 for $P_{{\mathrm{CO}}_2}^{*}$ and $k_{\mathrm{g}}^{\prime }$, with average absolute relative deviations (AARDs) kept below 15.7% and 12.2% respectively. Feature importance analysis validated the underlying physical laws. Specifically, temperature dictates thermodynamic equilibrium, while CO₂ loading limits mass transfer kinetics. In the optimization phase, MOBWO outperformed NSGA-II by generating a more uniformly distributed Pareto front. Decision-making analysis further identified three typical operating regimes encompassing kinetics-dominant, thermodynamics-dominant, and comprehensive equilibrium conditions. This framework provides a robust paradigm for small-sample modeling and optimization in complex chemical processes. Full article

Thyroid diseases are very common endocrine diseases that afflict millions of people around the world and need proper and timely diagnosis to ensure proper treatment. Although machine learning and hybrid metaheuristic methods have advanced, current models have high computation costs, low interpretability, and low probability calibration, which limit their use in clinical settings. In this research, a new DFSel-FT (Differentiable Feature Selection and an FT-Transformer) system is suggested, which combines DFSel-FT to allow one to diagnose thyroid disease effectively and interpretably. It employs Concrete (Gumbel-Softmax) gates to select the features end-to-end to make sure that only the most relevant clinical attributes are carried through the training. A Transformer-based architecture is then used to process the chosen features to learn intricate interdependencies. The model is trained with class-balanced focal loss and temperature scaling to better enhance calibration. Experimental evaluation on the UCI Thyroid Disease Dataset (22,632 samples) showed that the proposed model achieved 97.85% accuracy, 97.65% Macro-F1, and 98.10% AUC-OVR, showing competitive performance compared with traditional machine learning models, modern tabular deep learning baselines, and hybrid metaheuristic methods. Other indicators of robustness and reliability include MCC (0.955), Cohen Kappa (0.951), and small calibration error (ECE = 0.021). SHAP and LIME explainability analysis reveals clinically relevant features that include TSH, TT4, and T3. The proposed framework provides a balanced integration of predictive performance, interpretability, and probability calibration, making it a promising benchmark-level framework for interpretable and calibrated thyroid disease classification, requiring external clinical validation before real-world deployment. Full article

The non-invasive determination of live weight and body composition of ewes is an important element in ensuring precision livestock management and animal well-being. Traditional practices tend to be subjective, labor-intensive, or rely on expensive medical imaging such as Computed Tomography (CT). This paper proposes a new hybrid deep learning method to predict live weight and carcass traits in Coopworth ewes. The dataset of 1184 images taken from 156 ewes was analyzed and compared using a hybrid model (ResNet18 with Multi-Layer Perceptron through simple concatenation) and two more advanced models: Attention-Guided Feature Fusion Network (AGFF-Net) based on cross-modal attention and a Vision Transformer-based Hybrid Regressor (ViT-HR). Auxiliary tabular variables are the Body Condition Score (BCS) and size category. The Transformer architecture predicts (R² = 0.93) the live weight of ewes by dynamically ranking each visual patch and asking it to query the self-attention sequence. This technique treats the BCS as a distinct token in the self-attention sequence. Data partitioning at the animal level was stringent, thereby giving strong generalization. Findings indicate that the best advanced fusion systems are far better than baseline concatenation, with a high accuracy confirmed with gold standards obtained by CT. Grad-CAM visual explainability makes sure that models are able to localize biologically relevant anatomical locations successfully. The study closes the gap between complex deep learning models and real-world agriculture implementation to provide a correct, interpretable and scalable solution to real-time livestock measurements. Full article

Object detection in unmanned aerial vehicle (UAV) remote sensing imagery remains a formidable challenge due to the diminutive scale of targets, complex background clutter, and extreme variability in target morphology. Standard convolutional neural networks typically suffer from irreversible fine-grained information loss during downsampling, as strided operations discard critical spatial details essential for the localization of tiny objects. To address these issues, we propose RFE-YOLO, a lightweight receptive field-enhanced network specifically tailored for high-precision small object detection in UAV scenarios. First, the Cross-Scale Receptive Field Enhancement (CSRE) module is designed to mitigate intrinsic information loss by integrating space-to-depth convolution (SPD-Conv), which preserves spatial details by migrating them into the channel dimension. This module further employs an energy-based adaptive weight generation mechanism to distinguish target signals from environmental noise. Second, this paper proposes the C3k2-Dynamic Inception Mixer Block (C3k2-DIMB), which adaptively captures anisotropic features—such as slender vehicles—via dynamic kernel weighting and multi-shape inception kernels. Third, the Shuffled Upsampling for Resolution Enhancement (SURE) module is introduced to maintain spatial fidelity during resolution recovery, utilizing a channel shuffle mechanism to overcome information isolation. Finally, the Multi-feature Fusion Module (MFM) replaces conventional static concatenation with a dynamic softmax-based competition mechanism, effectively bridging the semantic gap between multi-level features while suppressing background distractors. Experimental results on the VisDrone dataset demonstrate that RFE-YOLO significantly enhances the representation capability for small objects. Specifically, the proposed model achieves a state-of-the-art $mA{P}_{50}$ of 42.70%, representing a substantial 9.3% improvement over the baseline YOLO11n. Furthermore, our architecture maintains an exceptionally lightweight profile with only 1.91 M parameters, demonstrating that high-precision detection can be achieved through structural intelligence rather than excessive parameter scaling. This makes RFE-YOLO highly suitable for real-time inference on edge-deployed UAV platforms. Full article

In quantitative logic, the $\mathrm{\Delta }$ operator plays a crucial role in defuzzification by mapping multi-valued truth values to classical two-valued ones. This paper introduces and investigates the $\mathrm{\Delta }$-randomized divergent degree and consistent degree of theories in the continuous-valued product logic system ${\mathrm{\Pi }}_{\mathrm{\Delta }}$. These notions measure, respectively, the probability that all formulas in a theory are simultaneously completely true and the degree of internal inconsistency of the theory. Basic properties including boundedness, monotonicity, additivity, and duality are established. The relationship between divergent degree and logical consequence is investigated, and the divergent degree between two theories is defined, with the triangle inequality proved, thereby constructing a pseudo-metric space on theories. As applications, a criterion for almost surely consistent theories is provided, and the existence of maximal consistent subtheories is proved with an illustrative example. This work enriches quantitative logic theory in product logic systems and provides a unified tool for measuring inconsistency in theories under uncertainty. Full article