Physical Sciences

Abstract: Magnetic reconnection describes the rapid conversion of stored magnetic energy during solar flares, yet the physical mechanism initiating the earliest release remains incompletely resolved. This article introduces magnetic breakdown as a proposed threshold-driven phenomenon in which a pre-existing, current-supported magnetic structure loses local stability and releases energy at the onset of magnetic reassignment and reconnection. The interpretation is developed through Photony theory, in which internal free electrons associated with current transduce dynamic elemental charge photons into linked magnetic chains that provide a proposed physical organization underlying magnetic fields and magnetically confined plasma structures. These chains assemble into magnetic fibrils whose magnetic structure embodies the substantial energy required for their formation, organization, confinement, and continued maintenance within the solar environment. Additional loading develops through chain density, curvature, compression, interaction, twist, and confinement. When this loading exceeds the capacity for stable accommodation or continuous reassignment, localized chain fragmentation is proposed to initiate radiation, particle acceleration, plasma heating and motion, current redistribution, and subsequent magnetic reconfiguration. Solar-flare observations, including precursor electromagnetic emission, rapid nonthermal electron acceleration, magnetic shear, fibril interaction, and the timing of energy release relative to macroscopic reconnection, are examined as evidence relevant to this proposed sequence. Electric-field and voltage-breakdown phenomena are considered only as needed to distinguish them from magnetic breakdown and will be developed separately in a companion article.

Abstract: Portable, noninvasive analysis of biofluids demands methods that couple chemical specificity with low cost and minimal instrumentation. We report a self-powered triboelectric spectrometry platform based on an aqueous-analyte triboelectric nanogenerator (LS-TENG) that converts the motion of a single droplet into an electrode-ordered sequence of transferred charge, forming a “triboelectric spectrum.” A 16-electrode copper array on a PMMA substrate, overlaid with FEP, encodes the droplet’s passage and enables spatially resolved signal acquisition. Across eight metabolite-relevant solutions (NaCl, KCl, CaCl₂, NH₄Cl, glucose, urea, lactic acid, uric acid), three-dimensional Q–position–concentration maps (5–30 mM) reveal concentration-dependent modulation of the response: ionic solutions show attenuation at higher concentration consistent with interfacial site saturation and electrostatic screening, whereas polar non-ionic solutes exhibit divergent trends (growth for glucose/urea; suppression for lactic/uric acid) attributable to interfacial orientation, double-layer formation, and hydronium-mediated screening. To decouple classification from amplitude–concentration collinearity, we train exclusively on 5 mM data. After standardized preprocessing and pulse segmentation, a three-convolution CNN operating on time-by-electrode tensors achieves 95.3% five-fold cross-validated accuracy with diagonal-dominant confusion matrices and stable learning curves. The LS-TENG platform is self-powered, structurally simple, and supports sub-second, single-droplet measurements, yielding generalizable composition fingerprints suitable for rapid home- or field-based identification and offering a practical route toward portable, point-of-care chemical sensing.

Abstract: Background: Infrared thermography (IRT) has become a valuable non-invasive tool for monitoring exercise-induced physiological responses in soccer players. However, evidence regarding the influence of sex on skin temperature responses following soccer-specific exercise remains limited. This study compared changes in skin temperature (ΔTsk) and thermal asymmetry between women and men soccer players following a standardised small-sided game (SSG). Methods: Forty-six university soccer players (22 women and 24 men) completed a standardised 4 vs. 4 SSG. Thermographic images were acquired before (Pre), immediately after (Post), and 10 min after exercise (Post_10). Skin temperature changes (ΔPost–Pre, ΔPost_10–Post, and ΔPost_10–Pre) and bilateral thermal asymmetries were calculated for the anterior and posterior thigh and lower leg. Between-sex comparisons were performed using independent-samples Student's t-tests, and effect sizes were estimated using Hedges' g. Results: Women players exhibited significantly greater increases in ΔTsk than men players across most anatomical regions. The largest differences were observed during recovery (ΔPost_10–Pre), when all regions demonstrated greater thermal responses in women (p < 0.001), with large to very large effect sizes (g = 1.42–1.89). In contrast, thermal asymmetry did not differ significantly between sexes at any anatomical region or assessment time point (p > 0.05). Conclusions: Women soccer players demonstrated a greater acute thermoregulatory response than men players following an SSG, particularly during the early recovery period, whereas thermal asymmetry remained stable regardless of sex. These findings indicate that sex should be considered when interpreting exercise-induced skin temperature responses and suggest that assessment performed 10 min after exercise represents the most sensitive time point for detecting physiological differences using infrared thermography.

Abstract: The Gassmann–Nur critical-porosity model, as used for amplitude and pore-pressure analysis, assigns a single critical porosity to a mineral — equivalently, it holds the dry-frame Poisson’s ratio constant with porosity. This paper generalizes the model to distinct bulk and shear critical porosities, ϕs ≤ ϕc. Three results follow. First, the Vp–Vs hyperbola which follows when Gassmann is combined with Nur is exact for any constant pair of critical porosities; the equal-porosity condition fixes only the anchor velocity (Vp as Vs approaches zero), not the shape of the curve. Second, a granular frame is physically expected to lose its shear-supporting backbone before its bulk-supporting one, placing ϕs at or below ϕc. Third, the dry sandstone frames of Han (1986) give ϕc = 0.402 and ϕs = 0.350, with the dry-frame Poisson’s ratio rising across the measured porosity range at better than four sigma, rejecting the constant-Poisson idealization within the data. The generalization preserves the hyperbolic form while changing its details — the anchor and the dry-frame Poisson’s ratio — and leaves the single-porosity numerics a close approximation, the separation in critical porosities being small and constant and its effects pronounced only as porosity approaches critical; it identifies where single-porosity pore-pressure prediction carries a systematic bias. It also resolves a standing feature of the published model — the dry- rock residual of its fluid parameter d, which the single-porosity form must carry as an unexplained correction — as the same shear–bulk separation, corroborated independently in dry shale.

Abstract: Most AI-hardware research focuses on reducing the cost of inference: moving memory closer to compute, reducing data movement, and performing operations inside or near memory. Physical Spatial AI raises an additional question: once spatial or physical evidence exists, must a machine always build a richer model before acting, or can it sometimes produce a safe first action through a simpler local rule? The Pallottoliere/Abacus Paradox states that, for some embodied problems, a low-bit local rule may outperform an advanced AI accelerator in the time and energy needed to deliver the first useful physical action. The reason is not that simple logic is more intelligent than neural inference, but that the immediate physical answer is often lower-dimensional than the perceptual model. This Short Note frames the paradox as a complementarity principle, not as a replacement of inference.

Abstract: Spatial AI is moving from representation to physical action. Single-camera streaming 3D reconstruction illustrates the shift: it can provide pose and geometry on small machines, but a reconstructed model is not yet a safe actuator command. A robot, vehicle, drone, wearable or industrial machine must still decide which spatial events require immediate local action, which can be summarized for a higher layer, and which require richer world-model reasoning. LingBot-Map is used only as a representative case of this class of pipelines; no experimental benchmark claim is made. This paper proposes a Reflex-Policy approach for Spatial AI. Local reflex layers convert urgent evidence from cameras, event sensors, MEMS, tactile, acoustic or other physical sources into low-bit events; execute bounded first actions such as stop, slow, inhibit, freeze, keyframe request, fallback or quarantine; and report compact event-action traces. Policy and world-model layers remain outside the urgent path, updating thresholds, permissions, context and rules. The contribution is an event-contract level of analysis: a spatial reflex event dictionary, structured physical observability with cardinality control, and an EROIE measurement discipline for the energy and value of event-level control. The framework does not claim that layered control is new. It makes the sensor-to-actuator contract, permission envelope, measured feedback, containment state and audit trace explicit, so that Spatial AI can remain energy-proportional, actuation-aware and diagnosable.

Abstract: The inconsistency between the classical formula and the conventional relativistic formula for the Doppler effect in electromagnetic waves is revisited. The classical formula can be derived based on the hypothesis of phase invariance of a plane wave under the Galilean transformations, while the conventional relativistic formula can be derived based on the same hypothesis under the Lorentz transformations. In this paper, we propose to derive a Lorentz type relativistic formula for the Doppler effect by strictly solving the radiation fields of a moving Hertzian dipole with the Lorentz transformations and inverse Lorentz transformations. The resultant formula is exactly of the same form as the classical one instead of the conventional relativistic formula. Our analysis shows that the inconsistency is due to the fact that the angular frequencies defined in different frames have different bases because of time dilation. It may be more natural to evaluate the Doppler effect between the angular frequency of the source and that of the fields received by the observer in the same time base. Moreover, we have derived a general expression for the Doppler effect from the far field of a moving Hertzian dipole. The result clearly shows that the classical formula and the Lorentz type relativistic formula are approximate forms of the general formula by representing the far field with a plane wave.

Abstract: Fracture and crack propagation in flexible shells under extreme loading represent a fundamental challenge in continuum mechanics. Traditional shell fracture theories rely heavily on local coordinate systems and asymptotic expansions, often entangled in the contradiction between three-dimensional solid fracture and two-dimensional shell theory. Taking the geometrically exact Kirchhoff-Love shell theory based on fiber bundles and differential forms previously established by the author as a starting point, this paper strictly generalizes it to a two-dimensional mid-surface manifold topology containing evolving cracks. We model through-cracks as evolving internal boundaries and one-dimensional submanifolds on the two-dimensional mid-surface manifold, introducing a rigorous kinematic mapping for crack propagation. In terms of dynamics, based on the elastic strain energy on the two-dimensional mid-surface, we derive a geometrically exact two-dimensional Eshelby configuration stress tensor and express it as a vector-valued configuration stress 1-form. Through the generalized virtual work principle applied to the variation of the crack front, a coordinate-independent J-integral (energy release rate) is naturally defined. Based on the Griffith criterion and the maximum energy release rate principle, this paper strictly derives the control equations for the crack propagation direction vector and propagation velocity on the tangent space of the manifold. This theory implicitly contains the complex curvature-fracture coupling within the structures of exterior differentiation and pullback metrics. Furthermore, we present a complete Discrete Exterior Calculus (DEC) discretization framework for the theory.

Abstract: Remote swell generated far from land can deliver hazardous wave energy to island coasts many tens of hours after the responsible wind event has ceased locally. Forecasting this hazard typically relies on full spectral wave models forced by global winds, which require specialized input data, computational infrastructure and expert interpretation. Here we present a reduced physical framework that constrains the amplitude envelope of long-range swell along a single dominant ray, using deep-water dispersion, steepness-limited saturation and post-source attenuation. The reduced model describes three successive regimes. First, remote wind input increases the significant wave height H_s from approximately 3 m to a physically derived saturation level of about 5.8 m. Second, H_s reaches a quasi-steady plateau when the bulk steepness ε=kH_s/2 approaches a threshold ε_th≈0.06, so that whitecapping dissipation balances further wind-driven growth. Third, after leaving the forcing region, the swell decays slowly during propagation and can still arrive offshore with H_s of order 4 m after approximately 72 h. For a representative 14 s swell packet, this corresponds to a deep-water group velocity of about 10.9 m s^-1, a travel distance of about 2830 km, and an effective attenuation length of about 7500 km. The analytical envelope is tested against quality-controlled measurements from the Graciosa buoy, Azores, in the North Atlantic. In the analysed record, 117 observations satisfied H_s≥4 m and 12≤T_p≤16 s. The strongest selected long-period candidate reached H_s=5.98 m with T_p=15.4 s, remaining below the corresponding steepness-limited ceiling for ε_th=0.06. The buoy data support the proposed envelope as a physically grounded reference for energetic long-period swell near the Azores. The framework provides an interpretable first-alert diagnostic for assessing whether a remote swell packet is capable of producing hazardous offshore conditions at Azorean island coasts, while remaining complementary to full spectral wave forecasting.

Abstract: Based on classical concepts of the chemistry and structure of polyimide compounds, a microscopic model of intramolecular mobility in technical Kapton has been proposed. This model provides a comprehensive explanation for the full set of observed low‑temperature physico‑mechanical characteristics of the material. The comprehensive analysis of experimental data and the derived geometric, energetic, and kinetic parameters confirm the robustness of the proposed model.

Abstract: We study how learned Kähler-potential fidelity changes across a controlled hard-regime sweep in the Cefalú quartic Calabi--Yau family. Building on the globally invariant architecture introduced in earlier work, we ask whether its hard-point advantage persists uniformly as the quartic regime becomes more difficult, or instead separates across different geometry-sensitive diagnostics. Across the sweep, we find that learned geometric fidelity is not one-dimensional but splits into a structured tradeoff: the globally invariant model is consistently better on projective-invariance drift, while the local-input baseline is stronger on positivity-oriented and lower-tail stability diagnostics, especially \( \texttt{spectral_tail_mean} \). Geometry-aware regularization modifies this tradeoff selectively rather than producing a uniform gain. We further show that a modest degeneration-aware extension is already possible within the same benchmark framework: fragile geometry can be localized by a computable family-side proxy, equation-facing residual concentration can be compared on that support, and the resulting support-conditioned response exhibits low-dimensional collective structure. These findings show that hard-regime learned Calabi--Yau metric fidelity is multi-axis, regime-dependent, and partially support-localized.

Abstract: This article proposed to amend the Chu formulation Maxwell’s equations for moving media. The Fresnel’s coefficient is correctly derived from the classical electromagnetic theory. The Maxwell’s equations for moving media are theoretical bases for analyzing the electromagnetic scattering properties of moving media. However, the magnetization effect due to moving electric dipoles has not been taken into account in the formulations, and the Frensel’s dragging coefficient cannot be correctly derived from them. These inconsistencies have cast a shadow on their applications in the analysis of scattering problems involving fast-moving media. The method in modeling a moving Hertzian dipole and that in modelling the polarization in a moving medium are compared. The plane wave solutions for the Maxwell’s equations in uniformly moving media are used for deriving the Fresnel’s coefficient. The results show that the two inconsistencies are closely related and can be removed together if the Chu formulation Maxwell’s equations for moving media are amended by taking into account the magnetization effect.

Abstract: Modern ion-therapy facilities are usually equipped with rotating gantries to achieve higher dose-conformity to the tumor. The gantry is the terminating part of a beam transfer-line (shortly a beamline) from the accelerator to a gantry treatment room. The gantry is mechanically rotated around the patient. In synchrotron-based facilities, the slowly extracted beams have different emittance patterns in the two transverse planes (shortly the asymmetric beams). Several matching techniques were developed in the past to remove the angular dependence of the beam parameters at the irradiation place on the gantry rotation angle. Recently, a novel so-called rotatorlike gantry optics has been introduced. In this concept, all existing matching techniques are integrated into the gantry optics and the gantry nozzle. The underlying theoretical description of its working principle is presented in this paper. It is based on the first-order matrix analysis of the transport of asymmetric beams in rotating ion-optical systems, in general. The results are formulated in terms of ion-optical constraints imposed on the fixed incoming beamline, and the gantry transfer matrix. Theoretical matrix analysis is followed by a design study applied to the MedAustron proton-gantry layout as a typical representative of an isocentric gantry equipped with a pencil-beam scanning system.

Abstract: This study presents a comprehensive numerical and machine learning framework for the classification of localized surface plasmon resonance (LSPR) extinction spectra generated from gold nanospheres and nanorods. This work presents a compu- tational proof-of-concept demonstrating that physics-based synthetic LSPR spectra can be used to benchmark machine-learning classifiers for nanoparticle geometry and refractive-index discrimination. The extinction spectra were simulated using Mie theory for spherical nanoparticles and Gans theory for rod-shaped particles, accounting for particle size, aspect ratio, and variations in the surrounding refractive index across the visible to near-infrared range (400–1100 nm). Synthetic spectral datasets were systematically generated for nanospheres with varying radii (50–100 nm), nanorods with controlled lengths (100–150 nm) and widths (30–80 nm), and environmental refractive indices (1.33–1.39). These datasets were utilized to train and evaluate multiple supervised machine learning classifiers, including Support Vector Machines (SVM), Random Forests (RF), K-Nearest Neighbors (KNN), Decision Trees (DT), Naïve Bayes, and Logistic Regression. The classifiers demonstrated excellent performance in identifying geometric variations, achieving near-perfect accuracy across all models. However, classification performance declined for refractive index variations, where more subtle spectral shifts posed challenges for some models. Overall, the study demonstrates that coupling accurate physical modeling of LSPR with machine learning provides a promising route for automated nanoparticle characterization and sensing applications. The developed framework may serve as a valuable computational tool to support experimental biosensing and nanoparticle-based diagnostic platforms.

Abstract: 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 symmetry-preserving physics-informed neural network framework (SP-PINN) for the 3+1-dimensional relativistic dynamics of a charged particle in a prescribed field, including a focused Gaussian laser pulse. The method is two-stage: an unsupervised physics-informed neural network learns a surrogate relativistic Hamiltonian from the covariant equations of motion using a Lorentz-invariant loss that enforces the mass-shell constraint H=mc²γ; the surrogate is then advanced with an explicit symplectic map built on Tao’s extended phase space, valid for the non-separable relativistic Hamiltonian. We benchmark against the Boris pusher and Runge–Kutta on three test problems. 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; for the demanding laser case a vector-potential light-cone reformulation reduces its error to (3.0±0.1)×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.

Abstract: The Haber-Bosch process dominates industrial ammonia synthesis but incurs massive energy consumption and carbon emissions. Here, we demonstrate a catalyst-free approach for direct ammonia synthesis from atmospheric nitrogen and water under ambient temperature and pressure, leveraging ultra-fast laser-induced plasma at the gas-liquid interface. By optimizing irradiation parameters (irradiation time, pulse energy, number of beams) and implementing a concentric laser scanning strategy, we achieved a maximum ammonia concentration of 0.624 μmol/20mL. This method bypasses the need for high temperature/pressure or catalysts, offering a sustainable path for distributed ammonia production. Our work underscores the potential of strong optical fields in activating inert molecules like N₂ and H₂O, with implications for decarbonizing chemical synthesis.

Abstract: Early hidden cracks in circular magnetic encoder rings induce only slight magnetic perturbations at the incipient stage, yet they may evolve into missing-pole, demagnetization, or severe waveform-distortion faults that degrade angular-displacement measurement and closed-loop control. This study establishes a physical mapping between crack-induced magnetization nonuniformity, pole-pitch deviation, and the amplitude-modulated and frequency-modulated components of the measured magnetic signal, and models the encoder output as a compound frequency-modulated–amplitude-modulated waveform. A precision-controlled experimental platform equipped with a self-developed tunnel magnetoresistance read head, a precision rotary stage, multi-axis positioning stages, and laser displacement sensing was built to suppress eccentricity-related disturbances and disturbances related to sensor lift-off distance. An analysis workflow combining fast Fourier transform-based band-pass filtering, Hilbert demodulation, sixth-order Butterworth low-pass filtering, and coefficient-of-variation analysis was used to extract the instantaneous amplitude and instantaneous angular frequency. Experiments on intact, hidden-crack, and visible-crack states show that the proposed normalized indicators sensitively capture weak crack-related fluctuations, reduce sensitivity to sensor lift-off distance after normalization, and increase monotonically with damage severity. The method does not require a high-accuracy external reference and shows promise for online monitoring of circular magnetic encoder rings and related electromagnetic sensing elements.

Abstract: This study reported on the multi-objective optimization of atmospheric plasma spraying parameters for zirconia coatings by incorporating the response surface method (RSM) with the desirability method, while simultaneously enhancing microhardness and reducing wear rates. Experiments were achieved using a L18 orthogonal array in Taguchi-based design. An analysis of variance was used to identify the key process parameters, including accelerating power, stand-off distance, powder feed rate and carrier gas flow rate. By constructing regression models and plotting response surface contour plots, the relationship between processing parameters and coating properties was comparatively examined. Subsequently, the optimal window of parameters that meets the requirements for high hardness and excellent wear resistance was determined using the desirability-overlapped method. The influence of these parameters on the hardness of the coatings and the wear volume was analysed graphically through modelling, both individually and in interaction. Experimental results have shown that the experimental results show that the prediction error for hardness was only 1.84%, while the prediction error for wear volume was 3.27%. The validation results demonstrate a high degree of consistency, as evidenced by the striking similarity between the results, which clearly indicates the reliability of the prediction made by the model. In addition, the microstructure of the optimal coating exhibits complete fusion and fine grain size, with very few pores or cracks, while there was only minor pitting and small spalling, and most of the original sprayed structure was preserved in the worn areas. Clearly, by adopting a desirability-overlapped based on RSM by Taguchi’s design, the multi-response properties of the plasma-sprayed coatings were significantly improved, and these results met the expected values for maximum hardness and minimum wear volume in the coatings.

Abstract: Physical AI systems must respond to real-world events under strict constraints of time, energy and safety. This Perspective clarifies the distinct roles of latency, throughput, bandwidth and world models within a Reflex–Policy architecture and argues that increasingly powerful policy and world models do not eliminate the need for well-designed local reflex layers. On the contrary, reflex layers can dramatically reduce upstream data rates, preserve hard real-time safety, and allow policy models to focus on prediction, planning and rare events. A binary spintronic reflex crossbar has been demonstrated in proof-of-concept form for fast shadow bypass in photovoltaic systems and for battery cell balancing, confirming that non-volatile magnetic rule fabrics can perform sub-millisecond, parallel switching decisions in real physical domains. Multilevel spintronic devices align better with weighted inference on the policy side. Quantitative examples from robotics, automotive and energy systems illustrate how this layered approach can reduce communication bandwidth by 1–3 orders of magnitude while maintaining safety and efficiency. A comparison with existing real-time control technologies, safety microcontrollers, FPGAs and neuromorphic processors, clarifies where spintronic reflexes offer distinct advantages in standby power, non-volatility and inspectable rule logic.

Abstract: Raman spectroscopy combined with machine learning offers a rapid, label-free approach for bacterial identification, but robust translation remains challenged by spectral variability, biological heterogeneity, and limited model interpretability. Here, we present an integrated evaluation of an optimized Spectral Transformer (ST) framework for Raman-based bacterial classification benchmarked against a systematically optimized one-dimensional convolutional neural network (1D-CNN). The comparison was performed using a curated 36-class dataset comprising 15 Gram-negative bacterial entries, 15 Gram-positive bacterial entries, one non-bacterial microorganism, and five background/reference classes, enabling evaluation of both species-level and fine-grained bacterial classification. Under 15 dB noise-augmented evaluation, the ST achieved 80.6% ± 0.3% accuracy and a Matthews correlation coefficient (MCC) of 0.801 ± 0.003, outperforming the 1D-CNN baseline with 72.9% ± 0.3% accuracy andanMCCof0.721±0.003. Integrated Gradients analysis combined with attention map visualization enabled multi-level model interpretation, revealing that the ST’s improved robustness correlates with more bounded attribution patterns during misclassification, whereas the 1D-CNN’s feature attribution becomes scattered under noise perturbation. Importantly, this interpretability-driven analysis identified model-specific failure modes in the baseline architecture, including an over-reliance on non-specific spectral regions under noise, which can inform future data collection strategies and guide refinements to experimental protocols. These results demonstrate that attention-based spectral modeling improves Raman-based bacterial classification under noise-perturbed conditions while enabling multi-level interpretability that bridges model understanding with actionable feedback on experimental design and data quality requirements.