Search Results (1,858)

This paper develops and rigorously analyzes a fully discrete upwind interior penalty discontinuous Galerkin (IPDG) scheme for simulating solute transport in two-dimensional variable-order fractional mobile–immobile media. The temporal variable-order Caputo derivative is discretized via a Grünwald–Letnikov approximation in conjunction with a first-order backward difference, while the spatial discretization employs an IPDG method featuring an upwind numerical flux for the convection term and a penalty formulation for the diffusion operator. Under the physically relevant assumption of a divergence-free velocity field, we establish the unconditional stability of the proposed scheme. A comprehensive error analysis in the $L^2$ norm yields a convergence rate of $O(\Delta t+h^{min(k+1,s)-\chi -1/2})$, explicitly linking the polynomial degree k, solution regularity s, and the penalty variant $\chi$. Numerical experiments in two dimensions are conducted to verify the accuracy and robustness of the proposed scheme in simulating anomalous transport phenomena in subsurface environments. Full article

Satellite video provides repeated observations of the same ground scene within short acquisition intervals, but blur, detector sampling, radiometric differences, noise, and registration errors complicate joint reconstruction. This study presents mixed sparse representation-based collaborative quality improvement (MSR-CQI), a model-based method for joint radiometric and spatial reconstruction of short optical satellite video sequences. The method combines multiframe fidelity, effective PSF modeling, an intensity prior, overlapping group sparsity, high-order nonconvex regularization, intensity bounds, and optional static observation weighting. In controlled ×2 experiments with known HR references, MSR-CQI achieved 42.8714 dB PSNR and 0.9756 SSIM. With the same seven input frames, it achieved 43.1049 dB/0.9714, compared with 42.1369 dB/0.9698 for PnP-NLM and 38.1675 dB/0.9404 for DUF-16L. Retaining measured sampling shifts in the observation operators yielded 45.7461 dB/0.98046, versus 45.0380 dB/0.97904 after LR registration and resampling. The proxy derived from the reserved real frames instead favored the common-grid reconstruction, showing that agreement with this proxy does not establish recovery beyond the native sensor resolution. Static observation weighting was also scene-dependent. These results support retaining sampling phases explicitly in controlled spatial SR, while the real-data results after common-grid resampling are interpreted as multiframe restoration and proxy agreement. Full article

Background: An optimal 25OHD level can prevent osteopenia of prematurity (bone health) and premature delivery (non-skeletal activity). Although maternal and fetal vitamin D (25OHD) levels correlate, data on preterm infants’ 25OHD status at birth are limited, with a well-recognized deficiency among term infants. Objectives: To evaluate maternal and neonatal 25OHD status at preterm birth and its determinants in a northern-latitude country, Poland. Design/Methods: Cross-sectional study between August 2015 and September 2016 investigating cord and maternal blood 25OHD levels at delivery in 69 pairs of mothers and newborns ≤ 32 6/7 weeks of gestation, and analyzing correlations between maternal and neonatal 25OHD levels and their influencing factors, with univariable and multivariable linear regression. Results: Median (IQR) 25OHD levels were 25 (16.6–34 ng/mL) in cord blood and 21.2 (15–29 ng/mL) in mothers and correlated positively (r = 0.69, p < 0.0001). 25OHD insufficiency was detected in 84.06% of mothers and 62.32% of newborns. Maternal 25OHD levels were higher in summertime delivery (p = 0.0008). The rate of vitamin D supplementation during pregnancy was 54.4%, increasing maternal (27 ng/mL vs. 16.7 ng/mL, p = 0.0006), cord blood (32.4 ng/mL vs. 17.4 ng/mL, p = 0.0005), and summer seasonal cord blood 25OHD levels (38.89 ng/mL vs. 25.5 ng/mL, p = 0.023), and the percentage of maternal (72.7% vs. 27.3%, p = 0.005) and neonatal 25OHD sufficiency (80% vs. 20%, p = 0.0021). Conclusions: Vitamin D insufficiency is common in mothers and their prematurely born infants at birth; vitamin D supplementation reverses the trend. To avoid inadequate supplementation in preterm newborns and in mothers at risk of preterm delivery, regular assessment of 25OHD levels should be considered. Full article

Complex optimization tasks in data-driven prediction and engineering applications often involve nonlinear, multimodal, and ill-conditioned objective functions. This study proposes an Enhanced Moss Growth Optimization algorithm (EMGO), an improved variant of the baseline MGO framework, to enhance exploratory step-size control and local covariance exploitation. EMGO incorporates two key algorithmic augmentations: a budget-adaptive jump regulation mechanism that balances global dispersal and fine-grained refinement, and a shrinkage-regularized covariance-guided sampling operator with relative eigenvalue flooring to exploit correlation structures among elite individuals without rank deficiency. The proposed algorithm is evaluated on the CEC2017 benchmark suite across 50 and 100 dimensions with 29 test functions, 30 independent runs, and a budget of $3\times {10}^5$ function evaluations per run, compared against ten state-of-the-art optimizers including CMA-ES, L-SHADE, SBO, and baseline MGO. Nonparametric Friedman ranking, Holm-adjusted Wilcoxon signed-rank tests, and runtime-matched analyses demonstrate that EMGO achieves highly competitive performance across high-dimensional landscapes. Furthermore, EMGO is applied to tune support vector regression (SVR) hyperparameters for effluent suspended solid (SS) prediction using the UCI Water Treatment Plant dataset under an expanding-window rolling-origin cross-validation scheme. EMGO-SVR achieves superior predictive accuracy ($\mathrm{RMSE}=5.58\pm 0.64$, $\mathrm{MAE}=3.97\pm 0.46$, $R^2=0.889\pm 0.028$), outperforming standard SVR, tree-based ensembles, and Bayesian optimization baselines. SHAP-based feature importance analysis confirms the physical and process consistency of the model predictions. Full article

Forest fires remain a major threat to biodiversity, human settlements, and the climate. This study presents Nuthur, an intelligent wildfire prediction and early-warning system for Al-Soudah, Saudi Arabia, integrating near-real-time environmental data with artificial intelligence models. The system used the Algerian Forest Fire dataset and a newly created local Saudi Arabian dataset. L1 regularization and Recursive Feature Elimination with Cross-Validation (RFECV) were used to examine relevant environmental variables, while oversampling, undersampling, and (Conditional Tabular Generative Adversarial Network) CTGAN-based synthetic augmentation were evaluated to address class imbalance. Multiple ML and DL models were evaluated, including Random Forest (RF), SVM, XGBoost, CatBoost, ensemble models, MLP, TabNet, and exploratory LSTM and CNN models, which were not interpreted as temporal or spatial models. Under random five-fold cross-validation, ML models achieved accuracy values from 0.89 to 0.97, with XGBoost with oversampling achieving the highest accuracy of 0.97. Deep-learning models achieved accuracy values from 0.75 to 0.94, with TabNet using RFECV achieving the best deep-learning result. A separate spatial cross-validation analysis of the Saudi dataset showed lower geographic generalization performance. CatBoost without oversampling achieved the highest mean spatial accuracy (0.8317) and weighted F1-score (0.7782), while logistic regression with oversampling achieved the highest fire-class recall (0.5616). In contrast, XGBoost with oversampling had a fire-class recall of 0.1096. These results highlight the need for further geographically and temporally diverse Saudi data before operational deployment. Full article

Physical exercise is considered an effective and relatively safe practice to reduce or control levels of pro-inflammatory mediators during aging. However, findings are often interpreted primarily through statistical significance, while the magnitude and potential clinical relevance of observed effects receive less attention. Therefore, the primary objective of this cross-sectional study was to investigate whether regular physical exercise was associated with differences in circulating levels of systemic inflammatory mediators (high-sensitivity C-reactive protein [hsCRP], TNF-α, IL-6, IL-8, IL-10, and IL-12) among community-dwelling older adults, emphasizing the magnitude and potential clinical relevance of these differences rather than an interpretation based exclusively on statistical significance. The results suggest that exercisers exhibited hsCRP levels 0.68 mg/L lower than those observed among non-exercisers (95% CI: −0.98 to −0.34 mg/L; p = 0.001). Although serum IL-6 levels did not differ statistically between groups (p = 0.072), the interval estimates remained compatible with potentially relevant reductions among exercisers. The estimated between-group difference indicated that exercisers presented median IL-6 concentrations 4.34 pg/mL lower than non-exercisers (95% CI: −9.41 to 0.24 pg/mL). No meaningful differences were observed for TNF-α, IL-8, IL-10, or IL-12. Taken together, these findings suggest that regular physical exercise is associated with a more favorable inflammatory profile in older adults, particularly through lower hsCRP concentrations. The findings for IL-6 remain inconclusive but support the need for future studies with more precise estimates of the magnitude and potential clinical relevance of this association. Full article

In previous work, Wazzan, together with Gilbert, established a connection between the $\lambda$-Zappa–Szép product of bands and groups and Billhardt’s $\lambda$-semidirect product of inverse semigroups using automata-theoretic methods. Motivated by the need for a structural and transparent interpretation of this correspondence, we present an alternative proof based on the Ehresmann–Schein–Nambooripad theorem. The approach reformulates the construction through the associated inductive groupoid and makes the matched-pair actions explicit, so that the interaction between the band component and the group component can be followed directly at the level of arrows, identities, and composition. Within this framework, several structural features of the resulting product are clarified, including regularity, idempotents, inverse-type behavior, orthodoxness, $\mathcal{L}$-unipotence, and the connection with semidirect products. The paper also consolidates related intermediate results into a unified proof route and illustrates the construction by examples and schematic figures. This groupoid-based viewpoint provides a conceptually motivated treatment of the $\lambda$-Zappa–Szép product and highlights its role as a bridge between matched-pair decompositions, bands and groups, and inverse semigroup theory. Full article

We construct and analyze optimal quadrature formulas for the right-sided Riemann–Liouville fractional integral in the Sobolev space $L_2^{(m)}(t,1)$, using $\phi ,{\phi }^{\prime },\dots ,{\phi }^{(m-1)}$ at $N+1$ equally spaced nodes. The analysis is carried out through the Peano kernel of the error functional, for which we give an exact closed form; this yields the norm of the error functional, existence and uniqueness of the optimal coefficients by an orthogonal-projection argument, and computable error bounds. Our main result is structural: the functions attached to the coefficients form a basis of the space of discontinuous piecewise polynomials of degree $m-1$ on the mesh, so that the optimality problem is an $L_2$ projection that decouples panel by panel. Consequently, the globally optimal (Sard) coefficients—not only the sequentially optimal ones—are available in closed form for every m, at a cost of $O(m^{3}N)$ operations with no global linear system and with a condition number independent of N, h, $\alpha$ and t. The globally optimal rule is exact on polynomials of degree $2m-1$ and converges as $O(h^{2m})$ for smooth integrands, whereas the sequential rule is exact on degree m; we quantify the gap between them. We also prove sharp asymptotics for the error norm, ${\parallel R\parallel }^2\sim {\kappa }_m{(1-t)}^{2\alpha -1}{(2\alpha -1)}^{-1}{h}^{2m}$ for $\alpha >\frac{1}{2}$ with ${\kappa }_m={(-1)}^{m-1}{B}_{2m}/(2m)!$, with a $\ln (1/h)$ factor exactly at $\alpha =\frac{1}{2}$ and a loss of half an order for $\alpha <\frac{1}{2}$. An extensive numerical study over five fractional orders, three evaluation points and eight integrands of prescribed Sobolev regularity confirms every theoretical statement, and the formulas are compared with product, spline and Gauss–Jacobi quadratures and applied to an Abel integral equation. Full article

The present research investigates the optimization of ball burnishing (BB) process parameters to create regular lubricating grooves on multilayer connecting rod liners to prevent engine seizure. The study utilized a Taguchi L9 fractional orthogonal array to evaluate the impact of ball diameter, deforming force, and feed rate on the resulting groove widths. Statistical analysis (ANOVA) revealed that ball diameter is the primary driver of groove width variation, exhibiting a non-linear parabolic relationship where the diameter serves as a stabilizing threshold. While deformation force showed a steady linear progression in widening traces, higher feed rates were found to restrict localized plastic flow, resulting in narrower groove widths. For the bimetallic structure (steel back with AlSn20Cu coating), the research recommends tailoring forces to the specific layer—forces for the anti-friction layer and for the substrate to avoid structural destruction. Profilometry confirmed that the height of edge inflows directly correlates with groove depth, ranging from 6 to 30 μm. The optimized non-linear regression model developed in this study achieved an exceptionally high coefficient of determination (R² = 99.84%), ensuring precise predictive accuracy. Overall, these findings provide a robust framework for researchers to enhance the durability of heavy-duty engine components through controlled surface topography. Full article

Earthquakes exhibit well-documented statistical regularities at the catalogue level, such as the Gutenberg–Richter magnitude–frequency relation and the Omori–Utsu aftershock decay, often interpreted as signatures of seismicity as a driven, dissipative system far from thermodynamic equilibrium. However, whether comparable signatures, such as scale invariance and anomalous diffusion, can be detected directly within individual ground motion recordings remains an open question. This work investigates whether acceleration, velocity, and displacement signals recorded during the 2009 $M_w$ 6.1 L’Aquila earthquake display statistical properties consistent with non-equilibrium complex systems, and whether different seismic phases carry distinct, reproducible statistical signatures. P- and S-wave onset times are estimated using AR-AIC, with adaptive search windows centred on theoretical arrivals from the CRUST1.0 velocity model. Coda onset is determined using three complementary criteria combined into a median ensemble, enabling the segmentation of each recording into up to five temporal windows. Displacement moment scaling is analysed for each window and signal type, within the framework of strong anomalous diffusion, yielding the scaling exponents $\zeta \left(q\right)$. Robustness is systematically assessed against the choice of coda onset method, the empirical thresholds defining coda onset and end, the sub-interval of $\tau$ used in the moment scaling fit, and the filter band applied to the ground motion signals. Evidence for anomalous diffusion is nuanced: both its sign and magnitude depend on the seismic phase, with only a subset of configurations remaining stable across all segmentation schemes tested. These results indicate that anomalous scaling signatures, when present, are not universal, and that systematic robustness analyses are essential to distinguish genuine physical effects from segmentation artefacts. Full article

(1) Background: Phishing remains a pervasive and economically damaging cyberthreat. The dominant detection paradigm has moved toward deep neural and transformer-based classifiers, a literature that reports high accuracy and that does not, in general, expose a per-decision justification, whereas interpretability and auditability are increasingly required in regulated environments; no comparison against transformer-scale detectors is made in this paper. This work asks how far a fully interpretable detector can close the accuracy gap to an opaque text classifier while preserving per-decision explanations, and what such a detector returns that accuracy alone does not measure. (2) Methods: HIEF, an interpretable evidence-fusion framework, is presented. Each email is represented by eighteen human-readable signals: fourteen structural and linguistic cues and four lexical aggregates derived from a published sparse log-odds lexicon. The signals are fused by three transparent layers, namely an $L_1$-regularized logistic model, a shallow interaction-rule tree, and a calibrated Dempster–Shafer stage that reports belief, disbelief and ignorance masses together with an order-invariant global conflict coefficient derived in closed form. A logistic meta-learner fitted on out-of-fold component scores integrates the three layers. The evidential layer uses a type-aware calibration in which discrete signals are calibrated on their attainable values and continuous signals by isotonic regression. Evaluation uses 38,908 public emails, 38,512 of them after exact-duplicate removal, with near-duplicate control, group-aware partitioning, ten repeated splits, a source-held-out protocol, a two-class cross-source test set, a component ablation and a human audit of 100 messages annotated independently by two evaluators. (3) Results: Under group-aware partitioning, HIEF attains an $F_1$ of 0.855 and the strongest term frequency–inverse document frequency (TF–IDF) baseline 0.954; a compact character n-gram neural reference model, evaluated over the same ten partitions, attains 0.973. The linear layer alone attains 0.872, so the two fusion layers do not improve accuracy over it, and the paired difference of 0.017 excludes zero. Type-aware calibration raises the evidential layer from 0.771 to 0.780 and more than halves its partition-to-partition standard deviation, but does not make it competitive; the weakness, therefore, lies in the fusion formulation rather than in the binning. What the evidential layer does supply is a decomposable account of decision uncertainty: the ignorance mass separates errors from correct decisions, 0.265 against 0.175. The human audit reaches an inter-annotator Cohen’s kappa of 0.950 over the five categories before adjudication, and shows that the permissive corpus label agrees with human phishing judgment at a Cohen’s kappa between 0.18 and 0.21, against 0.70 to 0.77 for the automatic strict rule; the audited block is annotated by two of the authors and its human positives are confined to the advance-fee family, so the audit is a bounded comparison of label assignments and not an independent annotation study. (4) Conclusions: HIEF is positioned as an uncertainty and explanation framework rather than as an accuracy-improving fusion method, since the measured accuracy cost of the fusion layers is not compensated by an accuracy gain. Quantifying how much of the performance reported on these widely used corpora is attributable to template leakage and to label permissiveness is a contribution independent of the detector itself. Cross-source operation has not been demonstrated: specificity falls to 0.041 on an unseen collection, so all evaluation reported here is proof-of-concept and no operational deployment claim is made. The Spanish-language evaluation rests on a small and entirely positive subset and is reported as preliminary. Full article

Non-destructive in situ tests, such as the plate bearing (plate load) test, are widely used to estimate the equivalent deformation modulus (e.g., $E_{v2}$) of existing road embankments. However, the depth of influence sampled by such a test is governed by the loading plate diameter, so a single test yields only an average, diameter-dependent modulus rather than the actual variation in stiffness with depth. This study investigates whether systematically varying the plate diameter and inverting the resulting settlement–diameter (dispersion) curves can recover the full depth-dependent stiffness profile, $E\left(z\right)$. Synthetic settlement–diameter curves were generated using a Boussinesq-based forward model for four families of reference stiffness profiles, representing normal (stiffness increasing with depth) and reverse (stiffness decreasing with depth) linear and exponential trends, combined with six Poisson’s ratios and five profile slopes/exponents (30 cases per profile family, 120 cases in total). Two inversion strategies were applied to back-calculate $E\left(z\right)$ from each dispersion curve: a classical Occam-type, smoothness-constrained (Tikhonov-regularized) nonlinear inversion, and a direct, closed-form simplified inversion method (SIM) based on differencing the apparent-modulus-versus-diameter curve. The results were benchmarked against the known reference profiles. Once calibrated so that its governing parameters depend only on Poisson’s ratio and the shape of the measured dispersion curve, SIM could be applied blindly—without knowledge of the reference profile or a starting model, requiring only an assumed Poisson’s ratio and the established calibration—and recovered $E\left(z\right)$ with markedly lower error than Occam’s inversion (WAD = 2.2–4.7% and RMSPE = 2.6–5.8%, versus 7.4–19.1% and 9.4–28.3%, respectively, across the four profile families). For the Poisson’s ratio most typical of earth materials, $\nu =0.3$, the calibration further collapses to a single parameter set common to all four families investigated ($I=0.66$; $c=1.3$ for stiffness increasing with depth, $c=2.5$ for stiffness decreasing with depth), which attains WAD ≤ 4.2% across all four families with no calibration equation at all. Notably, Occam’s inversion reproduced the settlement–diameter curve itself with good accuracy in most cases, yet this close data fit did not guarantee an accurate stiffness profile—a direct manifestation of the intrinsic non-uniqueness of the settlement-based inverse problem. These findings are bounded by their evidence base: noise-free data from the same forward operator used in the inversion, smooth profiles, a calibration evaluated on the cases that produced it, and an Occam comparison specific to L-curve-selected regularization. Within these limits, SIM is a promising alternative to regularized inversion; measurement noise, layered profiles and field validation are the next steps. Full article

Geological carbon sequestration is an important technology for mitigating climate change by reducing atmospheric CO₂ emissions. The geological storage process is governed by complex multiphase flow behaviors in porous media. Classical two-phase flow models are generally formulated using integer-order derivatives, which may not adequately capture the memory effects caused by complex porous media structures. In this work, a pressure–saturation coupled two-phase flow model with a fractional saturation equation is established. The normalized quasi-static pressure problem is first analyzed, while the fractional saturation problem is studied through a conditional fixed-point argument. Under explicitly assumed compatibility, uniform pressure regularity, $L^2$-valued well-definedness and local Lipschitz continuity of the saturation operator, and truncation regularity, an invariant-region estimate ensures that the gas saturation remains in the physical interval $[0,1]$. These stronger operator properties are imposed as structural hypotheses rather than derived from the basic $L^2$ saturation and $H^1$ pressure spaces. The pressure and saturation solution operators are then composed, and a contraction argument on a sufficiently small time interval establishes conditional local existence and uniqueness within the prescribed admissible class ${\mathcal{K}}_R\left(T\right)\times {\mathfrak{D}}_T$, where ${\mathfrak{D}}_T\subset X_p\left(T\right)$. This work provides a conditional analytical framework for the fractional pressure–saturation system under explicit structural hypotheses. Full article

Continuity and reliability are critical for underwater gravity aided inertial navigation, while variations in gravity field suitability, sensor noise, and environmental disturbances can degrade gravity matching and navigation performance. To address this issue, a data-driven matching error compensation framework is proposed for gravity aided inertial navigation. Within this framework, a Hampel filter identifies unreliable gravity matching outputs based on local temporal consistency, and a CNN–BiLSTM–Attention model predicts compensated position increments for the flagged updates. The model maps INS position increments and measured gravity anomaly sequences to reliable gravity matching increments through local feature extraction, temporal modeling, and attention-based weighting, with offline training and online deployment. Reliable training samples were selected offline using reference trajectories, with synchronized GNSS positions serving only as the reference for sample screening in the marine experiments. Experiments were conducted across five simulated gravity field regions with five gravity matching algorithms and along three measured trajectories acquired using two types of marine gravimeters. In the marine experiments, the proposed method achieved mean APE-O values of 1.60, 1.06, and 1.06 n miles on L6, L7, and L8, respectively. The improvement was most evident on L6 with relatively extended error intervals, while the conventional RBIM method achieved comparable performance on the more regular and localized L8 error interval. Across the evaluated datasets, the proposed framework provided effective compensation for degraded gravity matching updates and improved the continuity of position corrections. Full article

Background: Migraine diagnosis and severity staging rest on clinical interviews according to the ICHD-3 criteria, with no validated objective biomarker. Functional near-infrared spectroscopy (fNIRS) is a portable and non-invasive method, but existing fNIRS migraine classification studies remain relatively small and rely on cognitive tasks. We assessed whether prefrontal hemodynamic responses with a head-down-to-knees maneuver separate migraine patients from controls, and high- from low-severity migraine. Methods: Prefrontal fNIRS, including short separation channels, was recorded during the maneuver in 50 interictal migraine patients and 52 controls screened for the absence of migraine, serious chronic conditions and hypertension. Nine hemodynamic parameters per chromophore (HbO, Hb and HbT) across thirty channels entered a hypothesis-neutral pipeline of 270 candidate pipelines (3 chromophores × 3 feature selection strategies × 30 classifiers) with no predefined region of interest. Results: Deoxyhemoglobin features selected by embedded L1 regularization with shrinkage-regularized linear discriminant analysis separated the groups with 92% balanced accuracy on the internal hold-out (92% sensitivity, 92% specificity, ROC-AUC 0.99; permutation p = 3 × 10⁻⁴), against 87% in development-set cross-validation and 88% under nested selection cross-validation, with a selection bias of +0.03. High- vs. low-severity classification (19 high, 31 low) did not exceed chance under nested validation (45%; permutation p = 0.45). Conclusions: A wide-scale pipeline achieved a robust, validated separation of migraine from screened controls, carried by a distributed venous-weighted deoxyhemoglobin signature; specificity against other headache disorders remains to be established. Attack frequency severity was not separable above chance, indicating that scalar hemodynamic descriptors are sufficient for a categorical but not a graded contrast. Full article