Search Results (8,141)

Feature selection in high-dimensional datasets presents significant computational challenges, particularly in domains with large feature spaces and limited sample sizes. This paper introduces FL-SBA, a novel metaheuristic algorithm integrating fractional calculus enhancements with Laguerre operators into the Secretary Bird Optimization Algorithm framework for binary feature selection. The methodology incorporates fractional opposition-based learning utilizing Laguerre operators for enhanced population initialization with non-local memory characteristics, and a Laguerre-based binary transformation function replacing conventional sigmoid mechanisms through orthogonal polynomial approximation. Fractional calculus integration introduces memory effects that enable historical search information retention, while Laguerre polynomials provide superior approximation properties and computational stability. Comprehensive experimental validation across ten high-dimensional gene expression datasets compared FL-SBA against standard SBA and five contemporary methods including BinCOA, BAOA, BJSO, BGWO, and BMVO. Results demonstrate FL-SBA’s superior performance, achieving 96.06% average classification accuracy compared to 94.41% for standard SBA and 82.91% for BinCOA. The algorithm simultaneously maintained exceptional dimensionality reduction efficiency, selecting 29 features compared to 40 for competing methods, representing 27% improvement while achieving higher accuracy. Statistical analysis reveals consistently lower fitness values (0.04924 averages) and stable performance with minimal standard deviation. The integration addresses fundamental limitations in integer-based computations while enhancing convergence behavior. These findings suggest FL-SBA represents significant advancement in metaheuristic-based feature selection, offering theoretical innovation and practical performance improvements for high-dimensional optimization challenges. Full article

High-precision sensor co-registration is a critical prerequisite for achieving high-resolution imaging in Optically Pumped Magnetometer–Magnetoencephalography (OPM-MEG) systems. The conventional magnetic dipole fitting method, essentially a multipole expansion approximation of a finite-size coil, exhibits accuracy that strongly depends on spatial geometric factors such as coil–sensor distance, dipole orientation, and the projection angle of the sensor’s sensitive axis. Moreover, the approximation error increases significantly when sensors are placed either too close to the coils or at an unfavorable angular coupling. To address this issue, we propose a sensor localization and orientation method that combines magnetic dipole-equivalent modeling with a rigid coil structure (RCS). The RCS provides stable geometric constraints and eliminates uncertainties introduced by scalp-attached coils. In addition, three objective functions (the standard Frobenius norm, a weighted Frobenius norm and the structural similarity index (SSIM)) are formulated to mitigate the imbalance caused by near-field strong signals and to improve stability under noise and error propagation. Simulation results demonstrate that both under ideal conditions and with assembly perturbations, the weighted Frobenius norm and SSIM methods consistently achieve position errors below 1 mm and orientation errors below 1°, which effectively suppress large outlier deviations and achieve better performance than the standard Frobenius norm. The results confirm the effectiveness of the proposed method in achieving both high accuracy and robustness. Beyond clarifying the primary factors influencing magnetic dipole approximation errors, this study provides a geometry-constrained and optimization-based framework, offering a feasible pathway toward the practical implementation of high-precision, multi-channel OPM-MEG systems. Full article

Objectives: This study proposes a systematic strategy for optimizing sustained-release formulations using mixture experiments. Methods: Model variables were identified and screened via LASSO regression, Smoothly Clipped Absolute Deviation (SCAD), and Minimax Concave Penalty (MCP), leading to the construction of a quadratic inference function-based objective model. Using this model, three multi-objective optimization algorithms—NSGA-III, MOGWO, and NSWOA—were employed to generate a Pareto-optimal solution set. Solutions were further evaluated through the entropy weight method combined with TOPSIS to reduce subjective bias. Results: The MCP-screened model demonstrated strong fit (AIC = 19.8028, BIC = 45.2951) and suitability for optimization. Among the Pareto-optimal formulations, formulation 45, comprising HPMC K4M (38.42%), HPMC K100LV (13.51%), MgO (6.28%), lactose (17.07%), and anhydrous CaHPO₄ (7.52%), exhibited superior performance, achieving cumulative release rates of 22.75%, 64.98%, and 100.23% at 2, 8, and 24 h, respectively. Compared with the original formulation, drug release was significantly improved across all time points. Conclusions: This integrated workflow effectively accounted for component interactions and repeated measurements, providing a robust and scientifically grounded approach for optimizing multi-component sustained-release formulations. Full article

Cr content χ of 0.4 at% for a Cr doped Nd (1 at%): YAG laser rod (LR) gave a higher laser output (I_output) than that of 0.0, 0.7, and 1.0 at% in a specially designed compact solar-pumped laser (SPL) outdoors. I_outputs were measured as a function of an 808 nm pumping laser’s power indoors, changing the transmittance of the output coupler. From the obtained slope efficiencies, round-trip resonator losses Ls for the four χs were estimated, and the best-fit function L(χ) was derived. From the experimentally estimated Cr-to-Nd effective energy transfer efficiency η_Cr→Nd at the four χs, the best-fit function η_Cr→Nd(χ) was derived. Using L(χ), η_Cr→Nd(χ), and a wavelength λ- and χ-dependent absorption coefficient α(λ, χ), inferred from the literature, the power conversion efficiency η_power(χ) under 1 Sun was estimated. The estimated η_power(0.4) and η_power(0.7) were reproduced in experimentally deduced factors at the mode-matching efficiency η_mode = 0.19. The estimated maximum η_power(χ) appeared around χ = 0.2 at%, being 20% higher than that at χ = 0.4 at%. In addition to this, a composite LR (Cr, Nd: YAG core/Gd: YAG cladding) was found to achieve η_mode = 0.68 and η_power = 0.064, ranking among the highest-class SPL η_powers. Full article

Hyrox© is a hybrid competition that is gaining prominence in the competitive field due to its physical demanding nature and broad appeal to athletes from various backgrounds. Understanding athlete profiles and performance determinants in these events is essential to optimize training and recovery strategies. Objectives: This study aimed to examine and describe the profile of athletes participating in Hyrox© competitions, focusing on variables related to their sports background, training habits, recovery strategies, physical capacities, and motivational aspects. Methods: A descriptive analysis was conducted using an ad hoc questionnaire fulfilled by 80 active Hyrox© athletes. The questionnaire addressed aspects such as training frequency and structure, previous athletic experience, strength and endurance levels, recovery practices, and personal goals. Results: The findings revealed a predominantly male athlete profile with high physical demands and prior experience in functional or endurance-based sports. Participants generally exhibited well-developed strength and endurance capacities. However, recovery strategies were often unstructured or insufficient, potentially compromising performance and increasing injury risk. Conclusions: Athletes competing in Hyrox© share characteristics with those in other high-intensity or hybrid modalities, including strong aerobic and anaerobic capacities. Nonetheless, a gap remains in the implementation of structured recovery protocols. These results highlight the need to incorporate comprehensive recovery strategies alongside physical preparation to optimize performance and athlete longevity in hybrid events. Full article

This study reports a first-principles investigation of the structural, mechanical, electronic, and vibrational properties of In₃Sc in several crystal structures: AuCu₃ (Pm$\overline{3}$m), Al₃Ti (I4/mmm), Ni₃Sn (P6₃/mmc), and BiF₃ (Fm$\overline{3}$m), with a focus on pressure effects. Calculated equilibrium lattice constants, bulk, shear, and Young’s moduli show good agreement with experimental and theoretical data, especially for the cubic AuCu₃ phase. Elastic constants, examined with the Born stability criteria, reveal that the cubic (SG 221), tetragonal (SG 139), and hexagonal (SG 194) phases are mechanically stable at zero pressure, while the BiF₃-type cubic (SG 225) is unstable. Pressure-dependent variations in lattice parameters, bulk modulus, and elastic moduli, captured by polynomial fits, demonstrate stiffening effects and pressure-induced phase transitions. Band structures and density of states confirm metallicity in all stable phases, with In–Sc hybridization governing bonding. Phonon dispersions and Grüneisen parameters, calculated under compression, establish the dynamical stability of the mechanically stable structures and provide insight into vibrational and thermal behavior. Debye temperature and sound velocities highlight favorable thermal-transport features. Altogether, the results clarify the intrinsic mechanical and thermodynamic response of In₃Sc, supporting its potential as a promising intermetallic for structural and functional use under extreme conditions. Full article

The ongoing evolution of SARS-CoV-2 has given rise to variants with enhanced transmissibility and pathogenicity, many of which harbor mutations in the receptor-binding domain (RBD) of the viral spike protein. These mutations often confer increased viral fitness and immune evasion by modulating interactions with the human ACE2 receptor (hACE2) and escaping neutralizing antibodies. Accurate prediction of the functional consequences of such mutations—particularly their effects on receptor binding and antibody escape—is critical for assessing the public health threat posed by emerging variants. In this study, we apply a Mutation-dependent Biomacromolecular Quantitative Structure–Activity Relationship (MB-QSAR) framework to quantitatively model the biochemical phenotypes of RBD variants. Trained on comprehensive deep mutational scanning (DMS) datasets, our models exhibit strong predictive performance, achieving correlation coefficients (r²) exceeding 0.8 for hACE2 binding affinity and 0.7 for antibody neutralization escape. Importantly, the MB-QSAR approach generalizes well to multi-mutant variants and currently circulating lineages. Structural analysis based on model-derived interaction profiles offers mechanistic insights into key RBD–ACE2 and RBD–antibody interfaces, helping the rational design of broadly protective vaccines and therapeutics. This work establishes MB-QSAR as a rapid, accurate, and interpretable tool for the prediction of protein–protein interaction and forecasting viral adaptation, thereby facilitating early risk assessment of novel SARS-CoV-2 variants. Full article

This paper introduces the Lambert-Lindley distribution, a two-parameter extension of the Lindley model constructed through the Lambert-F generator. The new distribution retains the non-negative support of the Lindley distribution and provides additional flexibility by incorporating a shape parameter that controls skewness and tail behavior. Structural properties are derived, including the probability density function, cumulative distribution function, quantile function, hazard rate, and moments. Parameter estimation is addressed using the method of moments and maximum likelihood, and a Monte Carlo simulation study carried out to evaluate the performance of the proposed estimators. The practical applicability of the Lambert–Lindley distribution is demonstrated with two real datasets: stress rupture times of Kevlar/epoxy composites and hospital stay durations of breast cancer patients. Comparative analyses using goodness-of-fit tests and information criteria demonstrate that the proposed model can outperform classical alternatives such as the Gamma and Weibull distributions. Consequently, the Lambert–Lindley distribution emerges as a flexible alternative for modeling positive unimodal data in contexts such as material reliability studies and clinical duration analysis. Full article

We investigate $f(R,T)$ gravity, where R is the Ricci scalar and T the trace of the energy–momentum tensor, focusing on the subclass defined by $f(R,T)=R+2f\left(T\right)$. Instead of assuming a parametric form, we adopt a non-parametric reconstruction based on genetic algorithms (GA), a machine learning technique that does not rely on predefined models. Using Hubble parameter measurements from cosmic chronometers, baryon acoustic oscillations, and the Dark Energy Spectroscopic Instrument (DESI) data, we reconstruct $H\left(z\right)$ in a model-independent way. This reconstruction allows us to derive both numerical and analytical forms of $f\left(T\right)$ through the modified Friedmann equations. The analytic expression derived via GA provides an excellent fit to the numerical reconstruction. Furthermore, we compare the evolution of the Hubble parameter predicted by our model with that of the standard $\Lambda$CDM scenario (Planck), finding a good agreement for $z\lesssim 2$. These results highlight the robustness of GA-based reconstructions and suggest that the functional form of $f(R,T)$ obtained here may serve as a promising tool for further applications in cosmology and astrophysics. Full article

Vertical line source irrigation is a localized water-saving technique suitable for deep-rooted crops, but the geometric structure of the wetted bulb lacks a systematic analytical modeling method. This study established a simplified three-dimensional (3D) analytical model to predict the wetted volume under vertical line source irrigation conditions. First, the model determined boundary points based on an empirical wetting-front equation and fitted the wetting profile with ellipse–parabola functions to derive analytical expressions for area and volume. Then, using aeolian sandy soil as the research object, the model predicted that during 0–250 min of irrigation, the wetted pattern area increased from 80.0 cm² to 5050.6 cm², and the wetted volume increased from 251.3 cm³ to 208,014.4 cm³. At 250 min, the lower, middle, and upper volume components accounted for 67.3%, 24.2%, and 8.4%, respectively. Finally, the model was validated using loam soil, and the results showed good agreement between the calculated and measured values. The model requires only simple input and enables fast computation. It effectively characterizes the three-dimensional spatiotemporal variation of the wetted bulb and provides a theoretical reference for the design of pipe spacing and irrigation quota. Full article

Phoenix Dancong tea essential oil possesses unique aroma characteristics and bioactivities, offering broad application potential in the food, pharmaceutical, and daily chemical fields. To achieve efficient extraction and expand its use in functional textiles, supercritical CO₂ (SC-CO₂) extraction was employed to optimize the extraction process of Phoenix Dancong tea essential oil. Based on single-factor experiments, the optimal extraction conditions were determined as follows: pressure of 25 MPa, temperature of 50 °C, CO₂ flow rate of 8 L/h, and extraction time of 3 h, resulting in an essential oil yield of 1.12%. Response surface methodology (RSM) revealed that the experimental data fit the regression model well (R² = 95.49%, R²Adj = 89.69%). Furthermore, the extracted essential oil was blade-coating to cotton, nylon, polyester, and wool fabrics to evaluate its aroma retention performance. Results indicated that cotton fibers exhibited the best absorption and sustained fragrance retention, maintaining a high odor grade even after 8 weeks. This study provides a theoretical basis and practical reference for the green extraction of Phoenix Dancong tea essential oil and its application in smart aromatic textiles. Full article

In squeeze casting, the injection parameters including fit clearance, speed, temperature, and their uncertainties significantly impact the forming quality. Robust optimization can improve the design reliability and reduce the influence of uncertainty while using a suitable metamodel is beneficial for prediction accuracy and efficiency. This paper proposes a robust optimization method based on a hybrid metamodel with Simplified Pareto and Updated Training Set (SPUTS) to improve the prediction accuracy along the Pareto front and in the whole design space. After the first round of robust optimization based on a general metamodel, the training set is updated by simplifying the Pareto solution set. A finite element simulation is performed to construct a high-precision metamodel that combines the kriging and radial basis function (RBF) models to run a new robust optimization. The proposed method was validated by application to the robust optimization of an injection mechanism with a large inner diameter. The results indicated that the SPUTS hybrid metamodel greatly reduced the prediction errors in the test set. The optimized design showed better reliability and robustness and had a greater clearance ratio than the initial design. Full article

Background: The hemoglobin–albumin–lymphocyte–platelet (HALP) score integrates the immunonutritional and inflammatory status. We evaluated whether baseline HALP predicts the 3-month response after lumbar medial branch radiofrequency ablation (RFA), defined as a Visual Analogue Scale (VAS) reduction of ≥50% and an Oswestry Disability Index (ODI) reduction of ≥40%, and identified a Youden-optimal cut-off. The discrimination and calibration of multivariable models were also assessed. Methods: This single-center retrospective cohort (N = 120) included rigorously selected patients (≥50% pain relief after two comparative medial branch blocks) undergoing standardized RFA. Multivariable logistic regression was adjusted for age, sex, Body Mass Index (BMI), smoking status, paraspinal tenderness, and baseline scores. We quantified the Area Under the Receiver Operating Characteristic Curve (AUC), Hosmer–Lemeshow (HL) goodness-of-fit, Brier score, and calibration slope; optimism was corrected using a 500-bootstrap method. Results: Responses occurred in 64.2% (VAS) and 65.8% (ODI) of participants. HALP independently predicted ODI (OR = 1.06, 95% CI 1.02–1.09; p < 0.001) and VAS (OR = 1.05, 95% CI 1.02–1.08; p = 0.001). As a single predictor, HALP showed fair discrimination (AUC 0.717 [VAS], 0.731 [ODI]). The Youden cut-off of 39.8 yielded high sensitivity (~0.87) with modest specificity (~0.58–0.61). Multivariable AUCs were 0.744 (VAS) and 0.774 (ODI), optimism-corrected to 0.680 and 0.720; calibration was acceptable (HL p > 0.05; slopes ≈ 0.74–0.78; Brier 0.188/0.179). Conclusions: HALP is a simple, low-cost adjunct that independently predicts short-term pain and functional outcomes after lumbar medial branch RFA. Incorporation into post-block triage may refine selection, especially for functional improvement, pending prospective external validation and recalibration of the cut-off. Full article

This paper investigates the laws governing the evolution of nanostructures on pre-patterned fused silica surfaces by energetic ion erosion. First, regular nanostructures are fabricated with the Focused Ion Beam (FIB) operating at optimized processing parameters. Then, as a function of the different ion fluences, the surface morphology evolution is studied on a surface with newly formed nanostructures. An experimental phenomenon of inter-transformation between nano-ripples and random dot-like structures is observed. In addition, the principles of the development of the nanostructures are analyzed theoretically. The simulation results fit well with the experiments. This work deeply studies the influence of the initial surface micro-morphology on the evolution of nanostructures, and is of great significance for the control of surface nanostructures generated by energetic ion sputtering. Full article

The characterization of lithology within Quaternary aquifers holds significant geological importance for the protection, management, and utilization of groundwater resources, yet it continues to present considerable challenges. Indicator Kriging (IK) is a non-parametric, probability-based method of spatial interpolation. It considers the correlation and variability between data points, and its popularity stems from its alignment with geological experts’ principles. However, it still encounters issues in complex geological conditions. To address the limited capacity of conventional IK in reproducing geological variables within heterogeneous geological settings, this study develops an ordered IK method incorporating field variogram function parameters. This framework dynamically extends IK applications by integrating stratigraphic extension trends, requiring experts to formalize spatial variation trends into geological knowledge data, subsequently transformed into constraint parameters for interpolation. Estimation paths are determined via Euclidean distances between points-to-be-estimated and valid data, executing ordered IK following near-to-far and bottom-to-top principles. Results directly depict QLS formation spatial distributions or undergo expert modification for quantitative analysis, demonstrating superior integration of geological knowledge compared to empirical variogram fitting and partitioned IK estimation. The method reduces deviation from expert-interpreted spatial distributions while maintaining computational efficiency and multi-factor integration, with three case analyses confirming enhanced accuracy in lithology distribution reproduction and improved geostructural congruence in complex geological reconstruction. This approach revitalizes Kriging applications in complex geological research, synergizing domain cognition with computational efficacy to advance precision in geological characterization and support government decision-making. Full article