Search Results (1,562)

We consider a model problem of polarization-dependent light bending and time delays in the vicinity of neutron stars endowed with magnetar-strength magnetic fields (B∼${10}^{15}\mathrm{G}$), combining an effective-metric formulation of Heisenberg–Euler nonlinear electrodynamics with general-relativistic ray tracing. The spacetime geometry is analyzed using both the Kerr metric and a quadrupole-deformed q-metric, characterized by a quadrupole parameter varying in the range $q\in [{10}^{-3},0.5]$. In addition, the impact of complex magnetic-field topologies is examined by introducing a magnetic quadrupole component alongside the dipole configuration. The simulations performed in this study demonstrate that the inclusion of the quadrupole deformation parameter significantly modifies photon trajectory deflections compared to the standard Kerr solution. We further quantify the geometric dilution of the photon beam, finding a cross-section expansion ratio of approximately $4.7\times {10}^{13}$ for rays reaching Earth. This strong dilution imposes stringent constraints on the detectability of polarization-dependent signatures and time-delay echoes. Finally, characteristic illustrations are presented for trajectory distortions, bending-angle distributions, and intensity valleys produced by the combined gravitational and magnetic lensing effects. Full article

Background and aims: Reward-based eating reflects hedonic drivers of intake, including loss of control, diminished satiety, and preoccupation with food. We translated, adapted and studied the psychometric properties of the 13- and 5-item Reward-Based Eating Drive Scale (RED), for Portugal, Poland and Italy. Methods: A cross-cultural study was conducted with higher education students and general population samples (n = 1999). After translation and cultural adaptation, the RED was administered with food craving items, and collection of sociodemographic and anthropometric data. Factorial structure and measurement invariance were tested using confirmatory factor analysis (CFA), internal consistency with Cronbach’s alpha, and convergent validity via correlations with BMI and cravings. Results: CFA supported the expected structures of the RED-13 (three factors) and RED-X5 (unifactorial), with configural and metric invariance across countries and groups. Only partial scalar invariance was achieved for both versions. The RED-13 showed good to excellent internal consistency for total scores (0.868 ≤ α ≤ 0.906), with acceptable to good reliability for Loss of control (0.769 ≤ α ≤ 0.821), lower values for Lack of satiety (0.655 ≤ α ≤ 0.723), and good to excellent consistency for Preoccupation with food (0.881 ≤ α ≤ 0.918). The RED-X5 showed acceptable internal consistency (0.737 ≤ α ≤ 0.811) and correlated strongly with RED-13 (r = 0.949, p < 0.001). Both correlated positively with BMI and food cravings. Age, sex, and country had small to medium multivariate effects on RED scores. Conclusions: The RED-13 and RED-X5 showed good psychometric properties in Portugal, Poland, and Italy, with the RED-13 providing a multifactorial assessment and the RED-X5 offering a brief alternative. Full article

All-medium metamaterial absorbers (MMAs) have attracted considerable attention for ultra-broadband electromagnetic wave (EMW) absorption. Herein, a lightweight graphene aerogel (GA) was synthesized through a low-temperature, atmospheric-pressure reduction route. Benefiting from its 3D porous network, enriched oxygen-containing functional groups, and improved graphitization, the GA offers diverse intrinsic attenuation pathways and a limited effective absorption bandwidth (EAB) of only 6.46 GHz (11.54–18.00 GHz at 1.95 mm). To clarify its attenuation mechanism, nonlinear least-squares fitting was used to quantitatively separate electrical loss contributions. Compared with graphene, the GA shows markedly superior attenuation capability, making it a more suitable medium for MMA design. Guided by equivalent circuit modeling, a stacked frustum-configured GA-based MMA (GA-MMA) was developed, where structure-induced resonances compensate for the intrinsic absence of magnetic components in the GA, thereby substantially broadening its absorption range. The GA-MMA achieves an EAB of 40.7 GHz (9.1–49.8 GHz, reflection loss < −10 dB) and maintains stable absorption under incident angles up to ± 70°. Radar cross-section simulations further indicate its potential in electromagnetic interference mitigation, human health protection, and defense information security. This work provides a feasible route for constructing ultralight and broadband MMAs by coupling electrical loss with structural effects. Full article

Openings are often introduced in continuous reinforced concrete (RC) deep beams to accommodate utility services, which can compromise their structural capacity. This paper presents a numerical investigation—via nonlinear finite element (FE) modeling—into the effects of post-construction rectangular openings in continuous high-strength concrete (HSC) deep beams. A previously tested two-span continuous HSC deep beam with rectangular openings was used for model validation and subsequently adopted in a parametric study, maintaining consistent beam and opening dimensions. The study focuses on the influence of opening location, both symmetric and asymmetric, at mid-depth within critical shear and flexural zones of the two-span continuous deep beam. Key parameters analyzed include load-carrying capacity, support reactions, initial and post-cracking stiffness, reinforcement stresses, and concrete stress distribution. Results indicate that mid-depth openings located in flexure-critical regions have minimal impact, causing only a 3–5% reduction in load-carrying capacity and negligible changes in stress behavior. However, when openings intersect the primary strut paths, reductions in capacity ranged from 17% to 53%, depending on the number and location of the openings (i.e., crossing external or internal struts). Furthermore, symmetric placement of openings was found to significantly mitigate performance degradation compared to asymmetric configurations. These findings provide design insights that enable safe incorporation of service openings without excessive material use, thereby promoting more sustainable and resource-efficient concrete construction. Full article

This study examines Lean 4.0, defined as the integration of Lean soft practices (LSPs) and Industry 4.0 technologies (I4Ts), from a socio-technical systems perspective. While prior research has mainly linked Lean and I4Ts to operational and cost-based performance indicators, far less is known about how their human and technological elements interact as one socio-technical system during digital transformation. We investigate how LSPs and I4Ts combine to form social and technical subsystems, how their interaction reshapes work systems, and how these configurations relate to organisational performance. An inductive qualitative design was used. Fifteen managers and professionals with direct experience in continuous improvement and digital transformation completed an open-ended online questionnaire. Data were analysed using Braun and Clarke thematic analysis, guided by socio-technical systems theory and complemented by a cross-case synthesis. The findings identify four interrelated subsystems, social, technical, work, and outcomes, that co-evolve in Lean 4.0 initiatives. LSPs such as training, empowerment, and stakeholder involvement constitute a social system that enables the adoption and effective use of I4Ts in the technical system. When both subsystems are strong, their combined operation drives more extensive digital transformation of operational processes and customer facing activities, and in some cases business models, and is associated with broader improvements in efficiency, quality, customer satisfaction, employee engagement, and financial performance than medium or unbalanced configurations. The analysis also highlights recurrent integration challenges, including skill gaps, legacy system constraints, resistance to change, and data security concerns. Overall, the study conceptualises Lean 4.0 as an integrated socio-technical configuration and extends socio-technical systems theory by showing how LSPs mediate and amplify the value created by I4Ts, providing an empirically grounded framework and configuration-based insights for future testing. Full article

This work provides the first systematic survey of the two–photon properties of 97 natural 9,10–anthraquinones from plants and fungi. A comprehensive computational dataset of two–photon absorption properties calculated using RI–CC2/aug–cc–pVDZ is presented. Single degenerate photon energies are required for two–photon excitation span 575.8–1007.9 nm across the five lowest singlet states, with all S₀→S₁ transitions falling within the biological therapeutic window. Remarkably, S₃ state exhibits systematically enhanced TPA efficiency, with 60% of compounds surpassing 1 GM and achieving a mean cross–section of 29.9 GM–substantially higher than S₁ (mean: 7.5 GM) or S₅ (mean: 12.2 GM). Three compounds demonstrate exceptional performance: cynodontin (73.6 GM, S₂), dermocybin (68.7 GM, S₄), and morindone (50.7 GM, S₃). Natural transition orbital analysis reveals that these excitations possess high configurational purity (82.5–94.2% single–excitation character) and diagnostics validating the single–reference treatment. The observed spatial separation between hole and particle NTOs, combined with extreme transition dipole anisotropy along the molecular long axis, indicates dipolar charge–transfer enhancement. Comprehensive structure–property analysis establishes that strategic peri–hydroxylation (1,5 or 1,8), alkoxylation, and multi–site donor substitution maximise TPA cross–sections through enhanced charge–transfer character and longitudinal polarisability. Comparison with aqueous–phase calculations for three compounds reveals non–systematic solvent–induced redistributions of TPA activity across excited states, indicating that gas–phase outcomes serve primarily as internal benchmarks and intrinsic descriptors of structure–property relationships rather than quantitative predictors of photoactivity. Full article

This study presents a cost-efficient Hardware-in-the-Loop platform for liquid-level process control education, designed to bridge the gap between theoretical learning and real-world industrial practice. The proposed system integrates NI myRIO and NI myDAQ hardware with LabVIEW-based real-time simulation and controller implementation, enabling flexible experimentation across a range of linear and nonlinear tank models. Through real-time controllers, students can design, tune, and validate classical digital controllers while gaining hands-on experience with real-time process dynamics. Experimental results from Model-in-the-Loop and Hardware-in-the-Loop configurations confirm the high accuracy between simulated and hardware responses, with low normalized root mean square error (NRMSE < 0.07) and high normalized cross-correlation (NCC > 0.99) between MIL and HIL responses. Additionally, learning outcomes were assessed using rubrics and student perception surveys aligned with ABET criteria. The platform successfully satisfies ABET student outcomes (SO1, SO2, SO7) by promoting modeling, system identification, and real-time implementation skills. Student surveys reveal high satisfaction mean = 5.44 and a Cronbach’s α of 0.91367, highlighting enhanced engagement, flexibility, and confidence in control system design. This work demonstrates an adaptable, scalable educational solution that strengthens engineering competencies while keeping implementation costs low. Full article

Early prediction of casing-running sticking is essential, as the mitigation of stuck-pipe incidents often incurs significant time and economic costs. Previous studies have largely relied on purely theoretical torque and drag models that are constrained by simplified assumptions, preventing them from fully leveraging available field data and often leading to insufficient prediction accuracy. To address this challenge, we developed a hybrid mechanistic-data-driven intelligent model for hook-load prediction and casing-sticking risk assessment. The model combines mechanical models with ensemble learning algorithms, incorporating both mechanically derived parameters (theoretical hook load, casing–borehole compatibility, casing-bottom deflection and tilt angle) as well as operational and casing structural features. To evaluate its cross-field generalizability, the proposed model was trained on 13,449 samples from 14 wells across three oilfields and tested on 3961 samples from an independent well in a separate Oilfield. Three ensemble algorithms (XGBoost, Random Forest, and LightGBM) were evaluated, among which XGBoost achieved the highest predictive accuracy (RMSE = 3.50, MAE = 2.51, R² = 0.97) and was selected for subsequent friction-factor-based casing sticking risk assessment. A genetic-algorithm-based optimization framework was further developed to minimize sticking risk by optimizing the centralizer configuration under a friction constraint. The proposed sticking-risk assessment and optimization strategy was validated through field implementation. This mechanistic-data-driven intelligent model outperforms traditional theoretical approaches in predictive accuracy, interpretability, and engineering applicability, providing a practical and explainable tool for casing-running risk mitigation and design optimization in complex three-dimensional wells. Full article

Member 2 of the Xujiahe Formation in the Anyue area of the Sichuan Basin exhibits significant resource potential for tight sandstone gas. However, its characteristic of “extensive gas presence with localized enrichment” leads to substantial variations in single-well productivity, challenges in target zone optimization, and unclear enrichment mechanisms, which hinder efficient exploration and development. This study proposes a hierarchical classification scheme of “two-level, six-type” source–reservoir structures based on the developmental characteristics of fault–fracture systems and vertical source–reservoir configurations. The gas-bearing heterogeneity is quantitatively characterized using parameters such as effective gas layer thickness, charge intensity, and effective gas layer probability, thereby revealing the differential enrichment mechanisms of tight sandstone gas controlled by source–reservoir structures. Our key findings include the following: (1) Member 2 of the Xujiahe Formation develops six subtypes of source–reservoir structures grouped into two levels, with gas-bearing capacities ranked as follows: source–reservoir separation type > source–reservoir adjacent type I > source–reservoir adjacent type II. Among these, the source–reservoir separation type (Level I) and fault–fracture conduit type (Level II) represent the most favorable structures for gas enrichment. (2) Tight sandstone gas enrichment is governed by a tripartite synergistic mechanism: hydrocarbon supply from source rocks, vertical cross-layer migration dominated by fault–fracture systems, and reservoir storage capacity determined by fracture density and reservoir thickness. (3) Three enrichment models are established: (i) a strong enrichment model characterized by “multi-layer source rocks beneath the reservoir, cross-layer migration, and thick fractured reservoirs”; (ii) a moderate enrichment model defined by “single-layer source rocks, localized migration, and medium-thick fractured reservoirs”; and (iii) a weak enrichment model featuring “single-layer hydrocarbon supply, pore-throat migration, and thin tight reservoirs.” This research provides a theoretical basis for optimizing exploration targets in Member 2 of the Xujiahe Formation in the Anyue area and offers insights applicable to analogous continental tight gas reservoirs. Full article

With the in-depth development of digital twin technology in modern agriculture, smart pig farm construction is evolving from basic environmental modeling toward refined, bio-behavior-driven approaches. This study addresses the non-standard body configurations and complex behavioral patterns of pig models by proposing a binding method that combines lightweight skeletal design with automated weight allocation strategies. The method optimizes skeletal layout schemes based on pig physiological structures and behavioral patterns, replacing manual painting processes through geometry-driven weight calculation strategies to achieve a balance between efficiency and animation naturalness. The research constructs a motion template library containing common behaviors such as walking and foraging, conducting quantitative testing and comprehensive evaluation in simulation systems. Experimental results demonstrate that the proposed method achieves significant improvements: it demonstrated superior computational efficiency with 95.2% reduction in computation time, memory storage space reduced by 91.7% through weight matrix sparsification (density controlled at 8.3%), and weight smoothness was maintained at 0.955 while cross-region weight leakage reduced from 15.3% to 2.1%. The method effectively supports animation expression of eight typical pig behavioral patterns with key joint angle errors controlled within 2.3 degrees, providing a technically viable and economically feasible pathway for virtual modeling and intelligent interaction in smart agriculture. Full article

Buried pipelines represent critical lifeline infrastructure whose seismic performance is governed by complex soil–structure interaction mechanisms. In this study, a process-based numerical framework is developed to evaluate the pseudo-static seismic response of buried steel pipelines installed within a trench. A comprehensive parametric analysis is conducted using the finite-element software Rocscience RS2 (version 11.027) to examine the influence of burial depth, pipeline diameter, slope angle, groundwater level, soil type, and permanent ground deformation. The seismic loading was represented using a pseudo-static horizontal acceleration, which approximates permanent ground deformation rather than full dynamic wave propagation. Therefore, the results represent simplified lateral seismic demand and not the complete dynamic soil–structure interaction response. To verify the reliability of the 2D plane–strain formulation, a representative configuration is re-simulated using the fully three-dimensional platform Rocscience RS3. The comparison demonstrates excellent agreement in shear forces, horizontal displacements, and cross-sectional distortion patterns, confirming that RS2 accurately reproduces the dominant load-transfer and deformation mechanisms observed in three-dimensional (3D) models. Results show that deeper burial and stiffer soils increase shear demand, while higher groundwater levels and larger permanent ground deformation intensify lateral displacement and cross-sectional distortion. The combined 2D–3D evaluation establishes a validated computational process for predicting the behavior of buried pipelines under a pseudo-static lateral load and provides a robust basis for engineering design and hazard mitigation. The findings contribute to improving the seismic resilience of lifeline infrastructure and offer a validated framework for future numerical investigations of soil–pipeline interaction. Full article

Laser-driven ion acceleration in dense, hydrogen-rich media can be significantly enhanced by embedding metallic nanoantennas that support localized surface plasmon (LSP) resonances. Using large-scale particle-in-cell (PIC) simulations with the EPOCH code, we investigate how nanoantenna geometry and laser pulse parameters influence proton acceleration in gold-doped polymer targets. The study covers dipole, crossed, and advanced 3D-cross antenna configurations under laser intensities of 10¹⁷–10¹⁹ W/cm² and pulse durations from 2.5 to 500 fs, corresponding to experimental conditions at the ELI laser facility. Results show that the dipole antennas exhibit resonance-limited proton energies of ~0.12 MeV, with optimal acceleration at the intensities 4 × 10¹⁷–1 × 10¹⁸ W/cm² and pulse durations around 100–150 fs. This energy is higher by roughly three orders of magnitude than the proton energy for the same field and same polymer without dopes: ~1–2 × 10⁻⁴ MeV. Crossed antennas achieve higher energies (~0.2 MeV) due to dual-mode plasmonic coupling that sustains local fields longer. Advanced 3D and Yagi-like geometries further enhance field localization, yielding proton energies up to 0.4 MeV and larger high-energy proton populations. For dipole antennas, experimental data from ELI exists and our results agree with it. We find that moderate pulses preserve plasmonic resonance for longer and improve energy transfer efficiency, while overly intense pulses disrupt the resonance early. These findings reveal that plasmonic field enhancement and its lifetime govern energy transfer efficiency in laser–matter interaction. Crossed and 3D geometries with optimized spacing enable multimode resonance and sequential proton acceleration, overcoming the saturation limitations of simple dipoles. The results establish clear design principles for tailoring nanoantenna geometry and pulse characteristics to optimize compact, high-energy proton sources for inertial confinement fusion and high-energy-density applications. Full article

Background: Cancer poses a significant health risk to humans, with prostate cancer (PCa) being the second most common and deadly form among men, following lung cancer. Each year, it affects over a million individuals and presents substantial diagnostic challenges due to variations in tissue appearance and imaging quality. In recent decades, various techniques utilizing Magnetic Resonance Imaging (MRI) have been developed for identifying and classifying PCa. Accurate classification in MRI typically requires the integration of complementary feature types, such as deep semantic representations from Convolutional Neural Networks (CNNs) and handcrafted descriptors like Histogram of Oriented Gradients (HOG). Therefore, a more robust and discriminative feature integration strategy is crucial for enhancing computer-aided diagnosis performance. Objectives: This study aims to develop a multi-stage hybrid learning model that combines deep and handcrafted features, investigates various feature reduction and classification techniques, and improves diagnostic accuracy for prostate cancer using magnetic resonance imaging. Methods: The proposed framework integrates deep features extracted from convolutional architectures with handcrafted texture descriptors to capture both semantic and structural information. Multiple dimensionality reduction methods, including singular value decomposition (SVD), were evaluated to optimize the fused feature space. Several machine learning (ML) classifiers were benchmarked to identify the most effective diagnostic configuration. The overall framework was validated using k-fold cross-validation to ensure reliability and minimize evaluation bias. Results: Experimental results on the Transverse Plane Prostate (TPP) dataset for binary classification tasks showed that the hybrid model significantly outperformed individual deep or handcrafted approaches, achieving superior accuracy of 99.74%, specificity of 99.87%, precision of 99.87%, sensitivity of 99.61%, and F1-score of 99.74%. Conclusions: By combining complementary feature extraction, dimensionality reduction, and optimized classification, the proposed model offers a reliable and generalizable solution for prostate cancer diagnosis and demonstrates strong potential for integration into intelligent clinical decision-support systems. Full article

Earthquake prediction remains one of the central unsolved problems in geophysics, and ionospheric variability offers a promising yet debated window into the earthquake preparation process through lithosphere–atmosphere–ionosphere coupling. Progress has been hindered by methodological limitations in prior studies, including the use of inappropriate performance metrics for highly imbalanced seismic data, the reliance on geographically and temporally narrow data, and inclusion of inherent spatial or temporal features that artificially inflate model performance while preventing the discovery of genuine ionospheric precursors. To address these challenges, we introduce a global, temporally validated machine learning framework grounded in thirty-eight years of ionospheric observations from more than a hundred ionosonde stations. We eliminate lookahead bias through strict temporal partitioning, prevent overlapping precursor windows across samples to eliminate autocorrelation artifacts and apply sophisticated feature selection to exclude spatial and temporal identifiers, enabling prevention of data leakage and coincidence effects. We investigate whether spatiotemporally invariant ionospheric precursors exist across diverse seismic regions, addressing the field’s reliance on geographically isolated case studies. Cross-regional validation shows that our models yield modest classification skill above chance levels, with our best-performing model achieving a weighted F1 score of 71% though performance exhibits pronounced sensitivity to temporal validation configuration, suggesting these results represent an upper bound on operational accuracy. While multimodal fusion with complementary precursor channels could possibly improve performance, our focus remains on establishing whether ionospheric observations alone contain learnable, region-independent seismic signatures. These findings suggest that ionospheric precursors, if they exist as universal phenomena, exhibit weaker cross-regional consistency than previously reported in case studies, raising questions about their standalone utility for earthquake prediction while indicating potential value as one component within multimodal observation systems. Full article

This paper presents a compact magneto-electric dipole (MED) antenna optimized for wideband circularly polarized (CP) radiation for 5G applications. It incorporates a staircase-shaped electric dipole with trimmed corners to excite orthogonal modes for enhanced CP performance. The proposed single-layer MED antenna achieves a $20.6\%$ wide-impedance bandwidth ($|S_{11}| <-10$ dB, $22.97$–$28.12$ GHz) and $21.9\%$ CP bandwidth ($AR<3$ dB, $22.23$–$27.83$ GHz) with a compact footprint of $15\times 15\times 1.6{\mathrm{mm}}^3$. There is a symmetrical radiation pattern with a co-to-cross polarization ratio $>23$ dB and a stable gain of $8.8$ dBi. An equivalent circuit model is optimized via particle swarm optimization (PSO). The optimized MED antenna is utilized to investigate various CP-MIMO configurations and wideband sequential arrays. Next, a $1\times 2$ CP-MIMO antenna system is developed, employing polarization diversity in parallel and mirror configurations. Isolation is improved by etching a ground slot between the MED elements, yielding isolation levels of below $-20$ dB and $-23$ dB, respectively. Further, a $2\times 2$ CP-MIMO configuration is designed and evaluated. This arrangement demonstrates an envelope correlation coefficient (ECC) of $1\times {10}^{-3}$ and a diversity gain of approximately 10 dB across the operating bandwidth. Finally, a sequential array is designed that applies a ${90}^{\circ }$ sequential rotation and phase excitation to MED elements for high-gain CP 5G communications. Here, various array sizes are evaluated, with an $8\times 8$ MED array providing CP radiation ($AR\le 1$ dB) from 20 to 30 GHz with enhanced impedance and axial ratio bandwidths and stable gain with a peak value of $27.47$ dBi. Full article