Search Results (182)

Finely dispersed Al–Ca–Ti composite alloys with a set of remarkable properties can be considered as new promising structural materials. For wider use of these alloys, data on their fatigue behavior are needed. In this work the comparative study of the low-cycle fatigue strength of hot-rolled Al–xCa–0.2Ti alloys with different eutectic fractions determined by different calcium contents was conducted. The fatigue tests were carried out according to a single-plane bending scheme using a dynamic mechanical analyzer. A symmetrical loading cycle (asymmetry coefficient R = −1) with a constant stress amplitude was used. The maximum number of cycles was 20,000. It was found that increasing the eutectic fraction from 40% to 80% led to a 75% increase in the fatigue limit—from 80 to 140 MPa—which directly correlated with the alloy’s yield strength. The fatigue crack propagation occurred with the formation of a scaly fracture surface, whereas final static rupture was associated with a ductile dimple fracture. The microstructural mechanisms of alloy fatigue failure were discussed. It was found that increasing the total length of the eutectic particles/aluminum matrix interphase boundaries changed the failure mechanism to a more brittle one. Full article

Whereas existing approaches to financial regime detection calibrate thresholds to historical price-and-return statistics, we propose a framework built on immunisation: a particle filter whose observation model is calibrated to the distributional shape of market stress rather than specific historical episodes. The particle filter—with theoretical foundations in probabilistic robotics and autonomous driving, and adapted to financial markets—forms the inferential backbone. Its observation model stacks distributional shape descriptors—skewness, tail asymmetry, kurtosis, and the share of leading sector-eigenvalue energy in cross-asset return covariance—computed across a hierarchy of temporal windows and injected as structured distributional archetypes, replacing the random initialisation of Thrun and Burgard, and of Reisinger. Applied to S&P 500 across four distinct crises (Dotcom 2002, Lehman 2009, COVID-19 2020, and the 2022 inflation-driven bear market), the descriptors show pre-crisis discrimination, with effect sizes (Cohen’s d) of 2.9 or more for realised volatility, Bowley downside skewness, and tail-quantile features, and 1.3 for sector concentration (${\lambda }_1$ ratio). Out of sample (2015–2026), the pipeline confirms endogenous regime transitions with lead-time before the market trough. With the flexibility of particle filters, the richness of the observation model is the primary enabler of real-time regime detection. We frame the system as a distributional-shape monitor that detects regime transitions in real time, not a pre-peak forecaster: highly sensitive, it registers deformation as stress becomes measurable, with the attendant sensitivity–specificity trade-off. On the abrupt COVID-19 shock, it confirms the transition sixteen trading days before the trough, without claiming pre-peak detection; confirmation timing scales with each crisis’s own duration, from roughly two to three weeks for the fastest episodes to several months for the slowest. Full article

Reliable electric load forecasting is an important engineering problem for power-system planning, grid stability, and mission-critical energy management. This paper presents a symmetry-aware simulation and modeling framework for medium-range electric load forecasting under noisy and uncertain operating conditions. The proposed approach combines a Multi-Model Partitioning Filter (MMPF) with Nonlinear Autoregressive Exogenous (NARX) submodels, while two adaptive optimization strategies, genetic algorithm-based resource allocation (GARA) and Particle Swarm Optimization (PSO), are used to optimize the contribution weights of the parallel predictors. The modeling process uses real commercial power-system data and evaluates the forecasting framework over April–September 2025. To simulate realistic engineering disturbances, correlated symmetric Gaussian noise is injected into the testing phase under moderate and heavy noise scenarios. The cyclic symmetry of temporal variables, such as hours and months, is preserved through unit-circle encoding, while the symmetry and asymmetry of residual error symmetric distributions are examined through scatter plot analysis. As for the context of forecasting residuals as diagnostic signals, it is important to transfer symmetry properties that can be used to evaluate the behavior of optimized predictors, along with the cyclic encoding of inputs. This means that by implementing residual-symmetry analysis, the conclusion that GARA and PSO produce concentrated, balanced, and biased errors under moderate noise and heavily correlated noise conditions can be achieved. Finally, our results show that both GARA and PSO improve the robustness of the hybrid MMPF-NARX model, but PSO consistently achieves lower MAPE values, smoother convergence, and lower computational burden. The optimal configuration is obtained with nine NARX submodels, beyond which additional model complexity offers no meaningful performance gain. Overall, the study shows that symmetry-aware modeling, adaptive optimization, and noise-based simulation can support more reliable forecasting in modern power-system engineering applications. Full article

Magnesium-based materials are promising candidates for solid-state hydrogen storage due to their high gravimetric capacity; however, their practical application is limited by slow sorption kinetics and high thermal stability. This study investigates the influence of hydride additives on the microstructure and hydrogen desorption performance of ball-milled Mg–Co composites. Flake-like magnesium particles modified with 7 wt.% cobalt were processed by high-energy ball milling and subsequently doped with sodium hydride, potassium hydride, and calcium hydride at concentrations of 0.5 and 5 wt.%. Microstructural, phase, and surface chemical characterization revealed that additive type strongly affects dispersion, interfacial distribution, and the formation of additive-derived surface species within the Mg–Co matrix. Hydrogen sorption measurements conducted at 300–350 °C under different pressure conditions show that alkali hydrides significantly enhance low-temperature hydrogen desorption. In particular, the composite containing 5 wt.% potassium hydride exhibits a marked improvement, releasing approximately 4 wt.% hydrogen at 300 °C, while the unmodified material shows negligible desorption under the same conditions. Thermal analysis confirms that the additives modify the dehydrogenation response, although improved performance is not solely correlated with lower onset temperatures. The results demonstrate a clear asymmetry between hydrogen absorption and desorption, indicating that the primary effect of hydride additives is an enhancement in dehydrogenation kinetics. This behavior is associated with microstructural features, including additive dispersion and interfacial effects induced during processing. These findings provide insight into the design of magnesium-based hydrogen storage materials through microstructure–property relationships. Full article

The sustained growth of cloud computing and AI training workloads drives data center expansion. Optimizing their control is therefore critical for reducing operational costs. Edge real-time control is indispensable for guaranteeing thermal safety, data sovereignty, and offline availability. Yet deploying Deep Reinforcement Learning (DRL) in production Heating, Ventilation, and Air Conditioning (HVAC) environments confronts cold-start risks, edge–cloud computational asymmetry, and multi-objective conflicts spanning energy efficiency, electricity cost, and thermal safety. To address these challenges, this paper proposes an edge-cloud collaborative physics-informed reinforcement learning framework for production data center HVAC control. The framework integrates a physics-informed cold-start solution using Adaptive Particle Swarm Optimization (APSO) to generate physically constrained initial policies on a gray-box digital twin without expert demonstration data, a three-time-scale edge–cloud architecture coordinating minute-level edge Soft Actor-Critic (SAC) real-time inference, weekly edge APSO online model identification, daily cloud Non-dominated Sorting Genetic Algorithm III (NSGA-III) thermal storage scheduling, and a constraint-aware safe projection layer that embeds thermal safety hard constraints directly into the neural network policy. The framework is validated through a seven-month production deployment spanning the complete summer-to-winter transition, comprising approximately $3.2$ million sensor records and evaluated with rigorous statistical methods. Full article

We propose a lattice structure and space group symmetry, Pmmm (No. 47), for the QCD condensate ground state, whose Wyckoff positions are occupied by the four light quarks and antiquarks $u$, $d$, $\tilde{u}$, $\tilde{d}$. These serve as the fundamental building blocks of both the condensate lattice ground state and the baryonic and leptonic particle excitations embedded within it as topological defects of the nominal Wyckoff occupation, offering a more structured alternative to the QCD instanton liquid picture. Building on Bloch quark wave solutions of a tight-binding Hamiltonian defined on this lattice, we propose a generalization of Einstein’s Equivalence Principle: composite particles embedded in the lattice and propagating by tunnelling cannot distinguish acceleration by gravity, the strong, weak, or electromagnetic forces, or curvature of the lattice itself, arising from local variation in unit cell shape. We derive an eight-by-eight tight-binding Hamiltonian that decouples into two four-by-four blocks separating the quark and antiquark sectors. Electrons, positrons, protons, neutrons, deuterons, and α-particles are embedded in the lattice as defect-induced deviations from the nominal Wyckoff occupation, with their spin and helicity emerging structurally from this picture. We further propose that the lattice’s unit cells carry a small nonzero rest mass, whose collective gravitational effect across a galactic halo may account for the discrepancy between visible mass and rotation curves, identifying the Pmmm condensate as a dark matter candidate. Finally, we outline a mechanism near black hole horizons by which local melting of the condensate lattice followed by quark reactions that conserve the number and flavor of the quarks could yield a new route to baryon asymmetry. We propose a framework that goes several steps beyond the Standard Model by introducing a Pmmm space group unit cell for the QCD condensate ground state, built from the four light quarks and antiquarks $u$, $d$, $\tilde{u}$, $\tilde{d}$. We further propose that topological defects of the Pmmm condensate lattice are the geometric origin of particle excitations. Full article

Collaborative task allocation for multiple unmanned ground vehicles (UGVs) is a constrained combinatorial optimization problem in which symmetric vehicle resources must be coordinated with asymmetric task requirements. In delivery and inspection scenarios, homogeneous vehicles operate under identical rules, whereas task points differ in spatial distribution, demand, service time, and time window requirements. These asymmetries make compact, temporally feasible, and workload-balanced routing difficult. SACWDO-PSO is developed as a discrete particle swarm optimization framework that integrates Clarke–Wright savings initialization, adaptive parameter control, and simulated annealing local search. The savings strategy improves initial swarm quality, adaptive control adjusts exploration and exploitation during the search, and simulated annealing refines local route structures. The method is evaluated on Solomon VRPTW benchmark data under a soft time window penalty objective and insimulation scenarios developed using Unreal Engine 4.27 integrated with Microsoft AirSim 1.8.1. SACWDO-PSO obtains lower objective values and fewer time window violations than the compared swarm-intelligence baselines on most benchmark instances, while Wilcoxon signed-rank tests indicate statistically significant improvements over PSO, DPSO, and GA. Full article

Heterogeneous hydrogels capable of complex, programmable deformation are highly desirable for soft actuators, yet general strategies that simultaneously impart structural anisotropy, rapid responsiveness, and mechanical robustness remain limited. Here, a gradient anisotropic natural rubber-poly(N-isopropylacrylamide) (NR-PNIPAM) composite hydrogel is developed through a simple one-pot polymerization strategy by coupling pH-regulated colloidal stability with gravity-directed redistribution of natural rubber latex particles. Under an optimized pH window, NR nanoparticles gradually migrate during gelation and are fixed as a continuous gradient within the PNIPAM network, generating built-in structural asymmetry for nonuniform deformation. Meanwhile, NR nanoparticles act as soft reinforcing domains to improve mechanical strength, while water-soluble graphene nanosheets provide efficient photothermal conversion for remotely-controlled near-infrared (NIR)-responsive actuation. Benefiting from this synergistic design, the hydrogel exhibits programmable bending and localized folding with high actuation rates of 129° s⁻¹ and 46° s⁻¹, respectively, along with a tensile strength of 0.32 MPa and an active lifting capability exceeding 70 times its own weight. The material further enables biomimetic gripping and lifting under NIR stimulation. This work establishes a general route to robust gradient hydrogels by integrating colloidal regulation, structural anisotropy, and photothermal actuation, offering a versatile platform for high-performance soft intelligent systems. Full article

Microplastic (MP) contamination has become a defining feature of twenty-first century environmental change, yet the toxicological and ecological consequences for amphibians and reptiles—two vertebrate classes already facing severe extinction pressures—remain fragmented across taxa, regions, and methodological traditions. Here, we synthesize field and experimental evidence from five continents to provide a taxonomically balanced, mechanistically grounded, and geographically explicit assessment of MP exposure, bioaccumulation, and toxicity in herpetofauna, drawing on a structured literature search in Web of Science, Scopus, and PubMed (January 2015—March 2026). Field detection rates of MPs in amphibian larvae range from 26% in conservatively screened Central European populations to 73–80% in anuran tadpoles from high-anthropogenic-pressure Anatolian catchments, with fibrous polyethylene terephthalate (PET), polyethylene (PE), and polypropylene (PP) particles dominating the detected burden. Mechanistic evidence converges on oxidative stress cascades, hypothalamic–pituitary–thyroid axis disruption, gut and cutaneous microbiome dysbiosis, and compromised antiviral and antifungal immunity, with the latter potentially amplifying vulnerability to Batrachochytrium dendrobatidis and to ranavirus. Among reptiles, sea turtles display near-universal MP ingestion with documented maternal transfer to eggs; freshwater turtles, terrestrial squamates, and crocodilians remain critically understudied. Three structural asymmetries constrain current ecotoxicological risk characterization: taxonomic bias toward anurans and sea turtles, geographic bias toward the Global North, and experimental bias toward acute, supra-environmental laboratory exposures using pristine, single-polymer particles that fail to capture the chemical complexity of weathered field mixtures. We argue that MP burden may warrant consideration as a candidate stressor criterion within IUCN Red List assessments and within environmental risk assessment frameworks for freshwater and terrestrial biodiversity once a robust quantitative relationship between MP burden and demographic decline or population-level fitness has been established, and propose six hypothesis-driven research priorities: methodological standardization, reptile toxicokinetics, transgenerational epigenetics, MP–pathogen microbiome interactions and their translation into population viability models, temperature × MP interaction under climate warming, and population-genetic consequences of contemporary MP-driven selection, as the most tractable avenues for ecotoxicological progress and for the development of herpetofauna-specific risk characterization frameworks. Full article

To address the issues of strong inter-well interference during multi-well fracturing in shale reservoirs, low efficiency of conventional numerical simulation, and the tendency of machine learning models to overfit and lack interpretability under small-sample conditions, this paper constructs an explainable ensemble learning framework for predicting hydraulic fracture asymmetry. A geology–engineering integrated numerical simulation is adopted to quantify the fracture asymmetry index η as an interference metric, and an initial dataset is constructed comprising natural fracture orientation, well spacing, and injection rate. Subsequently, Jensen–Shannon (JS) divergence-constrained Gaussian data augmentation and second-order interaction features are introduced, and the GBRT model parameters are optimized using particle swarm optimization (PSO). Furthermore, random forest and ridge regression are incorporated, and ensemble weights are determined via cross-validation to build a weighted ensemble prediction model. The results show that the proposed model achieves good predictive performance in repeated validation, with an average coefficient of determination R² of 0.8484 and a 95% confidence interval of 0.8179–0.8790, while also demonstrating favorable overall accuracy in multiple baseline model comparisons and regularization-controlled experiments. Through leave-one-simulation-scenario validation, prediction interval analysis, and interpretability robustness testing, the model’s generalization boundary, prediction uncertainty, and explanation reliability under small-sample conditions are further evaluated. SHAP analysis and grouped permutation importance results indicate that the natural fracture angle is the dominant factor controlling asymmetric fracture response, while the interaction between well spacing and the natural fracture angle also significantly affects the predictions, suggesting that asymmetric fracture propagation is primarily governed by the combined effects of natural fracture steering and inter-well stress interference. The proposed framework can serve as a fast surrogate model for evaluating inter-well interference and screening fracturing designs within a given simulation parameter space, providing an interpretable data-driven approach for fracturing design optimization in shale reservoirs under small-sample conditions. Full article

This study utilizes a large-scale numerical simulation model to investigate the hydrodynamic behavior and particle transport characteristics of gas–liquid–solid three-phase flow in vertical wellbores featuring multi-source confluence and curved geometries. Simulation results indicate that increasing flow velocity shifts the dominant control mechanism from surface tension to inertial forces, transitioning the flow pattern from slug flow to churn flow. In curved pipe sections, centrifugal phase separation and geometric shielding effects cause significant flow asymmetry and maintain large bubble stability at the inner wall. Additionally, the multi-inlet structure induces shear rate gradients that result in the spatial coexistence of two distinct bubble scales. Furthermore, localized gas concentrations exceeding 70% at the upper inlet can trigger severe gas-locking phenomena and intense pressure pulsations. Full article

To characterize the transport of the mining-induced sediment plume in the Clarion–Clipperton Zone (CCZ) nodule area, this study introduces a particle relative dispersion (RD) to assess material dispersion in 2D and 3D. In 2D, forward and backward RD results show clear sub-regional differences in particle aggregation and diffusion. Forward RD reaches a maximum ridge value of 40 km in regions of strong shear and strain. Backward RD effectively identifies upstream source regions and convergence pathways. High RD values align closely with strong strain-rate gradients, indicating that particle separation and mixing are primarily driven by transition regions between flow structures rather than uniform high- or low-strain areas. In the 3D, the vertical domain was limited to the 4500–4600 m depth range above the seabed. The overall RD patterns remain broadly consistent with the 2D results, while the maximum RD increases to approximately 80 km due to the inclusion of vertical displacement and local vertical shear effects. Within the 4500–4600 m depth range, horizontal transport remains dominant, whereas vertical variations are comparatively weak, and particle trajectories exhibit only minor local differences. Compared with the 2D case, the deep-layer 3D RD distribution exhibits lower skewness values, suggesting a more spatially balanced particle separation pattern with reduced directional asymmetry. Multi scale quasi-3D RD analysis provides essential insights into material dispersion and convergence patterns, offering valuable information for evaluating transport pathways, potential pollutant spread, and ecological risks associated with deep-sea mining. Full article

In this article, the different types of self-coagulation discovered in the fluid simulations of inductively coupled plasma (abbreviated as ICP) at both the electronegative and electropositive cases are presented. Among these, the electronegative plasma sources include Ar/O₂, Ar/Cl₂, and Ar/SF₆, and the electropositive plasma source is the inertial argon plasma itself. The fluid simulation versions are not the same. Concretely, the Comsol software version 5.4 is used to simulate the Ar/O₂, Ar/Cl₂, Ar/SF₆, and the pure argon ICPs, and the self-written code of the fluid model is used to simulate the pure argon ICP as well, but in a different framework of fluid design. The types of self-coagulation refined from these fluid simulations are the physically ambi-polar self-coagulation of ions, the chemically ambi-polar self-coagulation of ions, the mono-polar self-coagulation of electrons, and the non-polar self-coagulation of argon metastable atoms. These self-coagulations are based on mass and founded through the Comsol fluid simulations, and moreover, the self-coagulation of thermal energy of electrons is founded through the self-written fluid code simulation. Based on the self-coagulations of mass and energy, together with the accompanying discharge hierarchy, we hypothesize (1) the correlation of ambi-polar self-coagulation and diffusion, (2) the mean of using the Schrodinger equation to describe the quasi-particle of anions given by self-coagulation in a certain potential barrier, (3) the analogy of the $\beta$ and ${\beta }^{+}$ decay and the asymmetry given by two types of ICP source simulation, (4) the picture of spin orientations of neutrino and anti-neutrino, and (5) the model for photon sustainment. The self-coagulation behavior is seen to be general and the interdisciplinary works of plasma physics with quantum mechanics, particle physics, nuclear physics, and optics are helpful for us to better understand the mass and energy general dynamics. Full article

In slab continuous casting, the internal hydrodynamics of the submerged entry nozzle (SEN) play a determining role in mold flow stability and product quality, particularly when external electromagnetic flow-control technologies are not employed. This study analyzes a novel bifurcated SEN design intended to promote stable, highly symmetric outlet jets under asymmetric inlet flow conditions produced by typical flow-control devices. The proposed configuration combines three geometric modifications: a square-section bore, a flow-divider bottom wall derived from a rotated mountain-type geometry, and two bell-shaped protrusions that act as flow modulators positioned immediately above the outlet ports. The hydrodynamic behavior inside the nozzle was investigated using complementary experimental and numerical approaches. Physical modeling was conducted in a scaled water model using particle image velocimetry (PIV) to characterize time-averaged velocity fields and flow fluctuations. In parallel, three-dimensional large-eddy simulations (LESs) were performed to resolve transient flow structures and quantify jet characteristics at the nozzle exits. Both approaches show consistent results. The combined action of the flow modulators and the flow-divider bottom wall robustly induces the formation of two nearly identical counter-rotating vortices in the lower region of the SEN. This flow structure suppresses stagnation and recirculation zones near the outlet ports, mitigates inlet-induced asymmetries, and enhances flow evacuation efficiency. Quantitative analysis of the outlet jets indicates a significant reduction in angular dispersion and a flow-rate imbalance below 0.2%, markedly lower than that observed in conventional SEN configurations. The results demonstrate that appropriate internal geometric design can effectively stabilize SEN hydrodynamics without active control systems, offering a feasible and scalable strategy for improving mold flow stability in industrial continuous casting operations. Full article

This study presents a mathematical model for the coupled growth of two interacting wax particles in a non-isothermal laminar flow. The formulation is based on a diffusion-controlled framework, in which the particle evolution is governed by a Stefan-type moving boundary condition with temperature-dependent interfacial concentration. An asymptotic analysis is developed in the limit where the particles’ size is small compared to their separation distance. This leads to a reduced system of nonlinear ordinary differential equations that captures the combined effects of particle interaction and thermal asymmetry. The analysis reveals that both mechanisms enter at leading order and jointly determine the growth dynamics. Numerical simulations are performed to investigate symmetric and asymmetric configurations. The results demonstrate that temperature differences induce a symmetry-breaking mechanism, leading to distinct growth rates even for initially identical particles. Furthermore, the interaction between particles amplifies this asymmetry through a competitive growth process. A key finding is the monotonic increase in the asymmetry ratio, reflecting progressive divergence driven by thermal effects. The proposed model extends the classical method of reflections for interacting Stefan problems to account for thermally induced asymmetry, incorporating non-identical boundary conditions governed by a prescribed temperature field. Full article