Search Results (493)

Dietary polyphenols are studied as potential adjuncts for type 2 diabetes (T2D), although many reviews still rely on an antioxidant framework that does not adequately reflect human exposure. This review organises the evidence around two metabolite waves, namely early postprandial phase-II conjugates produced by host enzymes within 0 to 4 h of a polyphenol-rich meal, and microbiome-derived catabolites including urolithins and γ-valerolactones that peak at 6 to 24 h. This biphasic interpretation is advanced as a hypothesis-generating framework rather than as an established explanation, and it offers a testable account of why polyphenol effects may be meal-contingent, metabotype-dependent and heterogeneous across clinical trials. Using randomised controlled trials and meta-analyses published between 2015 and 2025, we review the human evidence for anthocyanins, catechins, stilbenes, cocoa flavanols and olive phenolics, with briefer coverage of isoflavones, curcuminoids and ellagitannins. Anthocyanins show the most consistent glycaemic signal, with a best available pooled estimate of about 0.3% for HbA1c (−0.31%, median exposure eight weeks) derived from trials that did not verify metabolite exposure, an effect that is modest and lies at the lower bound of what is generally regarded as clinically meaningful. Green tea catechins and high-phenolic extra-virgin olive oil are associated with comparable postprandial gains, whereas cocoa flavanols and resveratrol more reliably improve vascular and inflammatory intermediates than glycaemia itself. Most interventions were well tolerated at the nutritional doses tested. Polyphenols are better regarded as meal-timed dietary adjuncts to standard T2D care than as primary glycaemic agents. Full article

Exceptional points are parameter values at which two eigenvalues and their corresponding eigenfunctions coalesce, rendering the wave operator defective. They arise widely in non-Hermitian wave physics and disrupt the modal expansions on which semi-analytic scattering methods rely. For lined acoustic waveguides, an augmented mode-matching ansatz that restores completeness at such a degeneracy—by adjoining the generalised eigenfunction obtained from the derivative of the parametrised duct mode with respect to its transverse spectral parameter—has been established for junctions between duct sections with discrete modal sets. This article extends that ansatz to an open, radiating configuration: a rigid feed duct communicates through an impedance-lined throat, tuned to an order-two exceptional point, with a half-space bounded by a rigid flange. The radiating mouth replaces the discrete modal closure by a continuous spectrum, so the augmented basis must be matched against an improper spectral integral. The half-space field is generated by the aperture velocity, which builds the rigid-flange condition into the representation exactly, and the resulting improper integrals are rendered analytic by branch-aware substitutions whose cutoff is tied to the retained modal content. The formulation is validated on the matching and boundary conditions themselves: pointwise continuity of pressure and of normal velocity at the internal junction, pointwise pressure continuity at the radiating mouth, the vanishing of the normal velocity on the rigid flange, and the recovery of the classical flanged-duct radiation problem in the rigid limit, cross-checked against an independent implementation. The full lined problem, including the defective case, has been further verified against an independent finite-volume solution of the same boundary-value problem, whose grid-converged fractions agree with the mode-matching values to within $8\times {10}^{-5}$. A conserved-power identity is monitored as a necessary but not sufficient check. Numerical experiments confirm the known breakdown of the standard expansion at the exceptional point and the well-conditioned convergence of the augmented one in this radiating setting, and a scan of the complex admittance plane, refined by local optimisation and repeated across throat lengths and frequencies, shows that flange radiation detunes the absorption optimum away from the exceptional point, by an amount that grows with the radiated share of the power budget and vanishes as the throat lengthens. Full article

This paper presents an analytical and numerical benchmark verification framework for explicit time integration procedures applied to the one-dimensional wave equation. The study combines an exact analytical solution, a closed-form discrete central-difference solution, and explicit finite element models implemented in LS-DYNA and in the in-house academic FEM code PAK-Multiphysics. Two benchmark problems with the same first sinusoidal spatial mode and homogeneous Dirichlet boundary conditions are considered. The first problem, defined by sinusoidal initial displacement and zero initial velocity, is used to analyse the central-difference discretization and its convergence behaviour. The closed-form discrete response enables direct comparison with the analytical solution and confirms the expected second-order accuracy under coupled mesh and time-step refinement at a fixed Courant number. The second problem, defined by zero initial displacement and sinusoidal initial velocity, introduces a phase-shifted temporal response suitable for explicit finite element verification. Four meshes are analysed over five periods of the first longitudinal mode. The numerical responses are assessed using displacement histories, maximum absolute errors, RMS errors, relative RMS errors, amplitude errors, and an energy check. The PAK-Multiphysics results show very close agreement with the analytical solution and a systematic reduction of the error measures, consistent with the lumped-mass central-difference formulation. The LS-DYNA results provide an independent commercial-code comparison, showing decreasing displacement errors under refinement and bounded total-energy variation. The proposed framework provides a transparent and reproducible benchmark for verifying one-dimensional explicit wave propagation models. Full article

Photogrammetric 3D city models contain detailed planimetric and elevation information that supports urban visualization and low-altitude applications. However, the direct dissemination of high-accuracy models may expose sensitive geometric measurements. Existing protection methods mainly focus on conventional encryption, coordinate scrambling, or two-dimensional data perturbation and do not adequately balance geometric accuracy degradation with the visual usability of textured 3D meshes. This study proposes a controlled geometric deformation method that processes the planimetric and elevation components independently. In the horizontal domain, a normalized Sigmoid function generates smooth, bounded, and spatially varying coordinate displacements. In the vertical domain, a normalized deformation function combines global elevation stretching with amplitude-constrained sine-wave superposition. The sine-wave parameters are generated using a seed-sensitive hybrid cascaded chaotic system, producing reproducible but model-dependent nonlinear deformation patterns. During processing, the mesh connectivity, face indices, texture coordinates, texture images, and material relationships remain unchanged. The method was evaluated using low-rise and high-rise photogrammetric 3D scenes with different horizontal extents and elevation characteristics. Under the selected 10 m planimetric and 5% elevation settings, the mean planimetric displacements were 10.474 and 10.045 m, while the relative elevation deformations were 5.01% and 5.30%, respectively. Both datasets maintained monotonic elevation relationships and achieved 100% direction consistency. Their spatial-shape coefficients deviated from the corresponding reference values by only 0.02% and 1.33%. The results demonstrate that the proposed method provides controllable and spatially continuous geometric deformation while maintaining mesh connectivity, overall morphology, and visual interpretability. It can therefore serve as a practical pre-processing approach for the risk-reduced dissemination and non-measurement-oriented visualization of photogrammetric 3D city models. Full article

Hybrid marine energy systems that integrate wave and current technologies can improve resource complementarity and spatial utilization. However, the ranking stability of selected hybrid concepts under changes in criterion weights, score assumptions, and multi-criteria decision analysis (MCDA) methods requires further examination. This study extends an existing two-stage concept-selection procedure by evaluating four shortlisted wave energy converter–hydrokinetic turbine configurations using stochastic weight-space sampling, criterion-wise weight sensitivity, cross-method consistency, and bounded score-perturbation analyses. A fixed normalized decision matrix is first evaluated using the Simple Additive Weighting (SAW) method across three sets of 10,000 criterion-weight scenarios generated using normalized-uniform, Dirichlet α = 1, and Dirichlet α = 0.5 distributions. The same scenarios are then evaluated using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), with ranking consistency quantified using Spearman’s rank correlation and complete-ranking agreement. Score sensitivity is subsequently examined through bounded one-point perturbations of the Stage 2 criterion scores, with SAW and TOPSIS recalculated under equal criterion weights to identify dominance-breaking and rank-reversal conditions. The oscillating water column–Savonius configuration, W1H3, remains first-ranked under all three sampled weight distributions because its normalized criterion scores are equal to or higher than those of every competing configuration across all five criteria. Criterion-wise sensitivity analysis shows that W1H3 is not outranked over the investigated weight range, although it ties with the point absorber–Savonius configuration, W2H3, when the full weight is assigned to mooring synergy or control compatibility. A crossover between W2H3 and the oscillating water column–hybrid Savonius–Darrieus configuration, W1H4, occurs at a co-location-feasibility weight of 0.384615. Across the three weight-sampling distributions, SAW and TOPSIS achieve complete-ranking agreement of 65.91–87.08%, with mean Spearman rank correlations of 0.9318–0.9742; the remaining differences are confined to the ordering of W2H3 and W1H4. Bounded score perturbations show that single one-point score change is sufficient to break the dominance of W1H3 over W2H3, whereas four changes are required for W2H3 to attain a unique first rank under both methods. The results demonstrate that W1H3 is rank-stable under the investigated weight and method variations for the adopted decision matrix, while the score-perturbation analysis identifies the bounded score changes under which the preferred ranking may change. Full article

Accurate finite element models are essential for structural condition assessment, yet nominal material properties and idealized boundary conditions can produce systematic discrepancies between numerical and measured dynamics. This study proposes a physics-constrained sparse response-surface framework that combines Elastic Net basis selection, mechanically prescribed monotonicity, adaptive sample enrichment, and identifiability-aware uncertainty assessment within a transparent finite element model-updating procedure. A scaled steel truss was tested using millimeter-wave radar, and the first three vertical natural frequencies were identified by stochastic subspace identification. The resulting sparse polynomial surrogate was independently validated before bounded inversion and ANSYS back-substitution. The mean frequency error decreased from 5.55% to 0.82%. Jacobian and bootstrap analyses further showed that several combinations of material and boundary parameters can reproduce similar modal responses, so the updated parameters are best interpreted as a coupled equivalent calibration state rather than unique direct measurements. The proposed framework therefore improves physical consistency and computational efficiency while explicitly retaining the uncertainty associated with weakly identifiable parameter directions. Full article

The dipole antenna is one of the oldest and most widely used devices in electromagnetic engineering, yet the behavior of its near-field during emission remains only partially captured by classical models. In the source-free region surrounding the arms, the vacuum Maxwell–Heaviside equations provide an insufficient number of configurations to describe the transient through which a bound signal becomes a freely propagating wave. We revisit the model equations, introducing an extended formulation in which an auxiliary velocity field complements the electromagnetic fields. Similarly to plasma physics, the outgoing signal is treated as an electromagnetic fluid carrying a charge density. As the far field is concerned, the resulting system admits an exact family of spherical free-wave solutions that follow the rules of geometrical optics. The near-to-far field transition also acquires a concrete dynamical description, thanks to the introduction of the pseudocharge, which is a charge-like density identified with the divergence of the electric field. In addition, a pressure-like potential, vanishing in the far field, tracks the conversion between bound and radiating energy. The approach is illustrated on a standard dipole antenna through direct numerical simulation of the full coupled system. The results suggest a unified analytical and computational pathway for antenna modeling, with natural extensions to more complex geometries and other radiating devices. Full article

Reservoir landslides pose a persistent threat to the safe operation of hydropower projects, particularly where altered rock masses within water-level fluctuation zones undergo repeated wetting–drying and seepage-induced deterioration. This study investigates the mechanical behavior and long-term strength evolution of altered granite from the Guobu Slope near the Laxiwa Arch Dam in Qinghai, China. Rock masses with four alteration degrees, ranging from complete to slight alteration, were examined through an integrated experimental program involving torsional shear tests, hydro-mechanical coupled triaxial tests, large-scale direct shear and ring shear tests, Brazilian splitting tests, and long-term P-wave velocity monitoring. The results demonstrate that increasing water content progressively weakens the shear strength of altered granite, while elevated seepage pressure further reduces its strength and deformation resistance under hydro-mechanical coupling. Residual shear behavior also shows a clear dependence on water content, indicating that post-peak strength deterioration should be considered in slope stability assessment. Long-term P-wave monitoring further reveals that mechanical degradation is more pronounced during the early stage and gradually approaches a relatively stable state, suggesting a site-specific decelerating deterioration process rather than unlimited strength loss. Based on the experimental results, empirical relationships between shear-strength parameters and water content are established, and long-term lower-bound strength parameters are proposed for altered granite with different degrees of alteration. These findings provide experimental support for understanding the hydro-mechanical deterioration and long-term deformation behavior of reservoir-bank altered rock masses and offer a basis for parameter selection and stability assessment when combined with rock-mass reduction, field calibration, and sensitivity analysis. Full article

High-speed turbulent boundary layers govern the transport of momentum, mass, and energy in compressible flows and play a central role in determining aerodynamic performance, skin-friction drag, aerodynamic heating, flow stability, and thermal protection requirements of advanced aerospace vehicles. Over the past three decades, direct numerical simulation (DNS) has revolutionized the study of compressible wall-bounded turbulence by resolving all dynamically relevant turbulent scales without turbulence-model assumptions, providing benchmark-quality databases and unprecedented physical insight into flow phenomena that remain difficult or impossible to measure experimentally. Together with complementary high-fidelity approaches, DNS has substantially advanced the understanding of turbulence dynamics across a broad range of supersonic and hypersonic flow conditions. This review presents a critical assessment of advances in the high-fidelity simulation of compressible turbulent boundary layers under non-reacting conditions. Particular emphasis is placed on the flow physics of canonical zero-pressure-gradient boundary layers, shock-wave/turbulent-boundary-layer interactions (SWTBLIs), pressure-gradient-driven flows, streamline-curvature effects, and thermochemical non-equilibrium phenomena. Recent developments in numerical methodologies are also briefly examined, including high-order discretization techniques, turbulence inflow generation methods, hybrid continuum-kinetic formulations, and advances in high-performance computing that have enabled DNS at increasingly high Reynolds and Mach numbers. The review highlights the major physical insights emerging from DNS studies, demonstrating that many fundamental characteristics of compressible wall turbulence remain closely related to their incompressible counterparts when appropriate compressibility transformations are employed. At the same time, DNS has revealed the critical influence of wall temperature, pressure gradients, streamline curvature, shock interactions, and finite-rate thermochemistry on turbulence structure, coherent motions, interscale energy transfer, boundary-layer separation, and aerodynamic heating. Full article

Objectives: Sleep staging from polysomnographic (PSG) recordings is clinically critical for diagnosing sleep-related disorders, yet manual scoring by certified technologists remains time-consuming, costly, and subject to inter-rater variability. Methods: This study presents an automated, explainable, and multimodal framework for five-class sleep stage classification using simultaneously acquired electroencephalography (EEG), electrooculography (EOG), and electromyography (EMG) signals. A total of 1946 annotated 30 s epochs from 30 healthy adult recording sessions (Sleep-EDF Expanded and Sleep Cassette subset) were processed through a 37-dimensional multimodal feature extraction pipeline encompassing temporal amplitude statistics, frequency-domain spectral band powers, nonlinear entropy and complexity measures, and Daubechies-4 discrete wavelet transform (DWT) energy coefficients. Four classical machine learning classifiers -Random Forest (RF), Support Vector Machine with radial basis function kernel (SVM-RBF), Gradient Boosting (GB), and K-Nearest Neighbours (KNN, k = 7) were benchmarked under stratified five-fold cross-validation. Results: SVM-RBF achieved the highest macro-averaged F1-score of 0.7322 (Cohen’s kappa 0.6784, overall accuracy 75.18%). N3 deep slow-wave sleep achieved the highest per-class F1 of 0.879, while N1 light sleep was the most challenging (F1 = 0.668). SHapley Additive exPlanations (SHAP) and RF mean decrease in Gini impurity (MDGI) analysis jointly identified EMG root mean square amplitude (MDGI = 0.0805), gamma band power (0.0784), and permutation entropy (0.0434) as the three most discriminative features. As a novel methodological contribution, sixteen categories of signal sculpting visualisations were developed, translating abstract multivariate features into clinically interpretable graphical representations. Conclusions: The proposed framework achieves substantial kappa agreement approaching the lower bound of expert inter-rater reliability (0.76–0.82) while providing full model transparency, with direct implications for wearable sleep monitoring device design. Full article

Nexus-NRS is an open, reproducible software tool for nuclear reaction calculations, implemented in a functional-programming style (Clojure); the underlying reaction theory is standard and pre-established, and the contribution here is the software: stable radial solvers for weakly bound (halo) states, scattering observables, and transfer reactions, packaged with optical-model potentials, Riccati–Numerov integration with regular near-origin initialization options, discrete Wronskian diagnostics for numerical stability, and a Distorted Wave Born Approximation (DWBA) implementation for single-nucleon transfer in the post form, extended with Austern-style multipole/angular ingredients. We report concrete validation results, with figures: near-origin Numerov starts (hybrid vs. finite) agree to better than ${10}^{-9}$ on a ¹¹Be halo bound-state observable, discrete Wronskian drift is reduced by a factor of ∼6–7 by the hybrid start, and a single-threaded partial-wave scan runs within a factor of ∼1.4 of an equivalent NumPy implementation. We compare directly against DWUCK4 for ¹⁶O${(d,p)}^{17}$O radial integrals (same order of magnitude, residual disagreement diagnosed but not yet resolved), and against experimental data for ¹¹Li${(p,d)}^{10}$Li populating an unbound $p_{1/2}$ resonance, where an unfitted ($S=1$) quasi-bound-resonance DWBA calculation is tested against seven measured points with error bars. With the normalization absorbed into a single scale factor, it reproduces the shape over the forward angular range (${\chi }_{\nu }^2=2.1$) but not over the full range (${\chi }_{\nu }^2=7.4$). Nexus-NRS supports scripting/REPL-driven studies and an interactive web dashboard, of which a publicly accessible instance is deployed. Full article

To address the problems of the Electromagnetic Wave Propagation Algorithm (EMWPA)—insufficient initial-population coverage, an imbalance between exploration and exploitation, and a tendency to fall into local optima—in high-dimensional complex optimization problems, this paper proposes a boosted electromagnetic wave propagation optimization algorithm, BEMWPA. First, a cubic chaotic map is introduced in the population-initialization stage to enhance the uniformity of the initial-solution distribution and the search-space coverage. Second, nonlinear phase modulation is applied to the electric- and magnetic-field driving terms, and a differentiated probabilistic switching mechanism is constructed to improve the dynamic coordination between global exploration and local exploitation. Furthermore, a Beta-distribution opposition-based learning strategy is introduced to enhance the algorithm’s ability to escape local optima by generating high-quality opposite candidate solutions. To verify the effectiveness of the proposed algorithm, systematic comparative experiments are conducted on the CEC2017 benchmark function set, and BEMWPA is combined with rapidly-exploring random tree (RRT) and applied to path planning of a welding manipulator in complex multi-workpiece scenarios. For a three-dimensional welding scenario containing 12 workpieces, 12 closed weld seams, and multiple obstacle constraints, BEMWPA-RRT reduces the initial inter-seam transfer path length of RRT from 586.00 mm to 479.11 mm, representing a relative reduction of 18.24%, and the complete end-effector path length is reduced from 2974.00 mm to 2867.11 mm, representing a relative reduction of 3.59%. Meanwhile, the optimized transfer path length is only 1.59 mm longer than the obstacle-free ideal transfer length of 477.52 mm, indicating that the proposed method can approach the geometric lower bound of this scenario while satisfying the obstacle-avoidance constraints. Kinematic verification on a seven-degrees-of-freedom welding manipulator further shows that the optimized Cartesian-space path can be converted into a continuously executable joint-space trajectory, providing an effective method for offline welding path planning of complex multi-workpiece tasks. Full article

Social sustainability and inclusive development require attention not only to the size of the middle-income group but also to households’ capacity to maintain middle-income status over time. This study examines which middle-income household-years in China face a higher probability of moving to low income at the next survey wave. Using household panel data from the China Family Panel Studies, the analysis follows middle-income origins at wave t to observed destinations at wave t + 1. It distinguishes the relative position within the middle-income interval, prior status switching, and maximum observed consecutive middle-income duration. The observed next-state transition sample contains 23,931 household-years, of which 28.2% move to low income. In joint models, a higher relative position and longer observed persistence are associated with lower subsequent downward-mobility risk, whereas prior switching is positively associated with risk but is less stable across waves. A descriptive three-item score shows a monotonic inverse-probability-weighted risk gradient, from 4.04% at score 0 to 50.79% at score 3. These findings describe longitudinal risk associations rather than causal effects. The study approaches social sustainability through one bounded empirical dimension, the durability of the household economic position, rather than as a comprehensive measure of social sustainability or household resilience. Middle-income expansion alone is therefore an incomplete indicator of social sustainability: assessments of middle-income consolidation should also distinguish current members’ observed histories and destination-specific risks. Full article

Millimeter-wave vehicular communication links are prone to blockage and suffer from severe path loss, and high mobility leads to rapidly time-varying channels. In addition, large-scale reconfigurable intelligent surface (RIS) arrays impose substantial channel-estimation overhead and phase-optimization complexity. To address these issues, a group-based and position-aided phase-optimization method is proposed for RIS-assisted millimeter-wave vehicular communications. First, an RIS-assisted uplink system is modeled with a multi-antenna base station (BS), an RIS configured as a uniform planar array (UPA) and a single-antenna vehicular terminal. Channel expressions are formulated for the direct vehicle–BS link, the vehicle–RIS link and the RIS–BS link. Rician fading, line-of-sight (LoS)-dominated millimeter-wave propagation, mobility-induced Doppler shifts and a standardized path-loss model for urban microcell street-canyon scenarios are incorporated to characterize the RIS-assisted vehicular cascaded channel. Based on this model, an optimization problem for the RIS phase-shift matrix is formulated under discrete phase-shift constraints to maximize the achievable rate per unit bandwidth. To avoid the exponential increase in complexity caused by conventional exhaustive search as the number of RIS reflecting elements increases, a successive refinement algorithm is introduced to derive an equivalent channel-gain expression. The original phase-optimization problem is then transformed into an element-wise iterative update process, thereby reducing the computational complexity of large-scale RIS phase configuration. To further reduce the reliance on full channel state information (CSI), two low-overhead phase-optimization schemes are designed. In the group-based scheme, the RIS reflecting elements are partitioned into several subgroups, with all elements in each subgroup constrained to share the same phase shift. This design reduces both the channel-estimation dimensionality and the number of optimization variables. In the position-aided scheme, the spatial coordinates of the BS, RIS and vehicle are used to derive the link distances and the associated angles of arrival and departure. Based on these geometric parameters, the vehicle–RIS–BS cascaded channel is reconstructed and a corresponding phase-alignment strategy is designed. The simulation results demonstrate that both proposed schemes achieve rates of approximately 6.5 bits ${\mathrm{s}}^{-1}{\mathrm{H}\mathrm{z}}^{-1}$ at a transmit power of 30 dBm and outperform existing phase-optimization techniques. When the successive refinement algorithm is applied, the computation time required for phase optimization with a 256-element RIS remains below 0.01 s. Under high-mobility conditions, both proposed schemes approach the performance upper bound achieved with perfect CSI, demonstrating strong robustness to channel variations. Full article

Standard pushers for the relativistic equations of motion of a charged particle in an electromagnetic field—Boris, Vay, Higuera–Cary—do not, in general, preserve the full symplectic structure of the underlying Hamiltonian system, while high-order non-symplectic schemes such as Runge–Kutta accumulate secular error over long times. We propose a two-stage, symmetry-preserving framework (SP-PINN) for the 3+1-dimensional relativistic dynamics of a charged particle in a prescribed field, including a focused Gaussian laser pulse, that pairs a physics-informed neural network with an explicit symplectic integrator: the network learns a surrogate relativistic Hamiltonian, while the integrator—which is not itself learned—advances it. In Stage 1, an unsupervised physics-informed neural network learns the surrogate from the covariant equations of motion using a Lorentz-invariant loss that enforces the mass-shell constraint $\mathcal{H}=m{c}^2\gamma$; in Stage 2, the surrogate is advanced with an explicit symplectic map built on Tao’s extended phase space, valid for the non-separable relativistic Hamiltonian. To isolate the geometric integrator from neural-network approximation error, every benchmark figure advances the analytic relativistic Hamiltonian through Stage 2, the learned Stage-1 surrogate being assessed separately. We benchmark against the Boris pusher and Runge–Kutta on three core test problems (adding the Higuera–Cary pusher in the symplecticity diagnostic), supplemented by plane-wave, ensemble, and pulse-family studies, and we measure the first Poincaré–Cartan loop invariant directly as a quantitative diagnostic of symplecticity. The magnetic-field test illustrates the contrast between bounded and secular error growth: Runge–Kutta drifts secularly, the Boris pusher conserves the invariants to machine precision as a volume-preserving gyro-integrator, and the symplectic map keeps the error bounded for all time; on a non-integrable magnetic trap, where no exact volume-preserving rotation exists, the symplectic map alone keeps the energy error bounded. The learned surrogate is the current accuracy bottleneck—not yet competitive with the conventional pushers for the static cases—but for the demanding laser case, a vector-potential light-cone reformulation reduces this surrogate error to $(3.0\pm 0.1)\times {10}^{-4}$ (three seeds) and yields learned trajectories that remain phase-coherent over essentially the whole interaction. The framework targets laser–plasma acceleration, synchrotron-radiation modeling, and particle tracking. Full article