Search Results (18,931)

We investigate whether Big Bang nucleosynthesis (BBN) remains compatible with the covarying coupling constants plus tired light (CCC+TL) cosmology. In this framework, only quantities with explicit length dimensionality covary through a universal scaling function $f\left(z\right)$, while dimensionless constants and dimensionless ratios remain invariant. At the redshifts $z$ relevant to BBN, $f\left(z\right)$ approaches a constant plateau $f_{max}\simeq 3$, and the tired light contribution is negligible, so the early-time dynamics reduce to a global rescaling of dimensioned quantities. In particular, the Hubble expansion rate $H$ at fixed temperature $T$ satisfies $H_{CTL}\left(T\right)=f_{max}^{-1}{H}_{\Lambda CDM}\left(T\right)$, implying a longer cooling time $\Delta t$ between weak freeze-out and the onset of nucleosynthesis by the same factor (CCC+TL labelled as CTL). We find that BBN predictions are preserved, provided the relevant interaction rates $\Gamma$ and decay rates governing the neutron lifetime ${\tau }_n$ share the same plateau scaling as $H$, so that governing combinations such as $\Gamma /H$ and $exp\left({\displaystyle \frac{-\Delta t}{{\tau }_n}}\right)$remain invariant. Implementing these plateau rescalings in the Kawano/NUC123 network (via a single control parameter fctl $\equiv f_{max}$) yields identical light-element abundances for fctl $=1$ ($\Lambda CDM$) and fctl $=3$(CCC+TL) to within ${10}^{-3}-{10}^{-4}$ level, consistent with numerical rounding. We also illustrate that adopting the lower late-time CCC+TL baryon density from Pantheon+ data fit can reduce the ⁷Li discrepancy but simultaneously increases D/H, implying that BBN alone does not select between the late-time baryon density inferences considered here. Full article

The interaction between tunnelling and retaining walls represents a complex soil–structure interaction problem that can significantly influence wall deformation and stability. This study investigates and compares the behaviour of cantilever and soil-nailed retaining walls subjected to tunnel excavation beneath the wall in dry Toyoura sand. A series of three-dimensional finite element analyses was performed using an advanced hypoplastic constitutive model to simulate nonlinear sand behaviour and stress-path dependency. Tunnel depth was varied using cover-to-diameter (C/D) ratios of 1.83, 3.33, 4.83, and 6.33. The computed results show that both retaining systems exhibit similar settlement patterns due to tunnelling, with maximum settlement reaching approximately 22.8 mm in the case of C/D = 4.83. However, soil nailing has limited influence on reducing tunnelling-induced settlement. In contrast, the soil-nailed wall develops larger rotation and overturning response due to tensile forces mobilized in the soil nails by tunnelling-induced ground movement. For the shallowest tunnel case (C/D = 1.83), maximum tensile forces reach approximately 72 kN and 60 kN in the top and middle nails, respectively. Tunnelling also causes significant redistribution of contact pressure and shear stress beneath the wall base, including partial loss of contact for shallow tunnels. In addition, lateral earth pressure increases substantially, resulting in total lateral forces up to 3.3 times the pre-tunnelling values. The results demonstrate that tunnel depth governs the wall response, while soil nailing primarily affects rotational behaviour and internal force development rather than settlement mitigation. Full article

The human–machine interface (HMI) of the mechanical support (MS) position (MS-HMIs) of manned submersibles features multiple screens, information-rich displays, and complex operational logic, which can reduce operator efficiency, increase cognitive load, and lead to human errors. The layout determines the perception of information density, complexity, and logic, making the optimization of the HMI layout highly significant. To address this issue, a layout optimization approach is proposed based on a task-information network integrating multi-objective optimization. First, the basic MS-HMI elements are decomposed, and Hierarchical Task Analysis (HTA) is used to construct task sequences and element usage sequences. The Space-P and Space-L methods are applied to build the task–information network, based on which element grouping and importance are determined through network topology analysis. Incorporating ergonomic layout principles, a multi-objective optimization model is formulated and solved using the NSGA-II algorithm to generate feasible optimized layouts. Experimental verification results demonstrate that the optimized interfaces significantly outperform the original design in terms of operational performance, eye-tracking metrics, and subjective evaluations. Operation duration and task completion time decreased by over 6%, average saccade speed was reduced by up to 17.1%, and subjective ratings improved substantially. By integrating complex network analysis, typical submersible task sequences, and ergonomic principles, this study presents a systematic, evidence-based, effective, and task-compliant method for optimizing HMI layouts. Full article

Wireless-power mobile edge computing, SWIPT-MEC, priority-aware WPT scheduling and swarm resource allocation already solve important parts of the energy-management problem. The novelty of WPT-JCCO is not any one of those elements; it is a single swarm-supervisory feasible set that couples decisions which the three adjacent method classes normally separate. Each epoch-level action jointly selects the robot to charge and one of three physically distinct WPT modalities: far-field radio-frequency, resonant near-field and directional lightwave transfer, together with the SWIPT split, local/edge task placement, CPU frequency, bandwidth and transmit power. Relative to SWIPT-MEC, the formulation adds discrete recipient–modality selection with pose, alignment, blockage and dwell-dependent feasibility. Relative to conventional WPT scheduling, charging is not a separate priority or routing stage but is solved jointly with computation and radio allocation. Relative to swarm resource-allocation methods, energy replenishment is endogenous and an individual minimum-battery constraint protects the weakest robot. A fourth coupling makes the centrally generated resource vector admissible only when the complete sense–compute–actuate age fits the one-second supervisory epoch; otherwise a previously feasible or local-safe action is applied. Nonlinear harvesting, partial offloading, priority scoring and augmented-Lagrangian primal–dual updates are treated as established techniques. This paper derives the continuous block updates, keeps the WPT variables binary through candidate screening, and declares convergence only when stationarity, feasibility, merit-change and binary-hold tests are jointly satisfied. Normalised primal steps are safeguarded by backtracking, dual and penalty updates are bounded, and a local tracking bound plus divergence monitor delimit real-time operation without claiming global mixed-integer optimality or closed-loop motion stability. Numerical evaluation over a 20-robot swarm and 30 Monte Carlo runs shows that WPT-JCCO reduces net energy depletion by 23.8% relative to communication–computation optimisation with static WPT and by 49.7% relative to local-only execution, while increasing task success from 93.5% to 97.3%. A released common-trace comparison shows normalised-cost reductions of 11.1%, 11.3% and 5.8% relative to two-stage WPT+CCO, fixed-SWIPT dynamic offloading and an offline Q-learning scheduler. Convergence and one-factor-at-a-time sensitivity studies further examine swarm size, task load, WPT budget, bandwidth, edge capacity, mobility and channel margin. The headline values remain scoped to the nominal independent-task case; mode-specific RF, near-field and lightwave operating envelopes, robust pose/CSI, WPT-safety and task-DAG extensions are formulated but not presented as hardware-validated results. Full article

This research proposes a physics-based deep learning framework, developed as a proof-of-concept based on synthetic data, for estimating the mechanical properties of layered media—namely density (ρ), Young’s modulus (E), and top layer thickness (h₁)—using synthetic seismogram images generated via Finite Element Method (FEM) simulations. The dataset, comprising 5000 simulations, incorporates physical constraints and empirical density–modulus correlations. While a ResNet-style Convolutional Neural Network (CNN) extracts density and stiffness parameters from composite time–frequency images, the estimation of h1 utilizes a direct time-domain raw-signal approach to preserve spatial resolution. A 5-fold nested cross-validation scheme with internal Bayesian Optimization ensures rigorous model evaluation, further validated by normality assessments and bootstrap confidence intervals. Performance was tested against synthetic Gaussian noise (0% to 50%) and benchmarked against classical Full Waveform Inversion (FWI). The results demonstrate high predictive accuracy for shallow properties, with R² values reaching 0.96 for Young’s modulus and 0.83 for raw-signal thickness. The neural network model requires 0.035 s per inference compared to 180 s for the FWI approach, avoiding the local minima convergence issues typical of iterative inversion. The framework exhibits resilience under moderate noise levels (up to 30%), establishing a reliable baseline for future experimental validation. Full article

This manuscript will present an advancement of transformative research that has been conducted at Oak Ridge National Laboratory (ORNL) over a 25-year period (2000–2025) on a variety of environmental and biological matrices. These investigations derived a fundamental understanding of how elemental detection and analysis of these matrices led to the knowledge and discovery of natural processes in plants and the environment. Each project led to the initiation of a new research area which unearthed awesome and novel breakthroughs. Highlights are listed below: 1. The preliminary research at ORNL centered on the detection of aerosols utilizing Laser-induced Breakdown Spectroscopy (LIBS) technology. The Clean Air Act Amendment (CAAA) of 1990 highlighted the importance of identifying hazardous air pollutants (HAPs) due to their impact on environmental and human health, thereby underscoring the need to detect various toxic elements. Research in aerosol chemistry aimed to identify these harmful elements released by factories during periods of increased emissions in their manufacturing processes. LIBS emerged as the most effective method for real-time, in situ measurements of metal species in both gaseous and aerosol phases. 2. An understanding of the presence of total carbon in soils gives perspective on how to develop carbon sequestration strategies. The recognition that carbon sinks can evolve back to carbon sources to emit back to the atmosphere was an important consideration. Also, the concentration of carbon in soil indicates the health of land areas for growing crops successfully. 3. The direct detection of most of the elements in a wood sample in a single emission spectrum, without sample preparation, encouraged the research to use the LIBS technique for preservative treated wood coupled with use of multivariate statistical methodology. Additionally, it encouraged the researchers to try to differentiate natural woods from different parts of the country, and it was successfully demonstrated that LIBS coupled with MVA analysis could differentiate wood of different species from each other and of similar species grown in different environments based on their elemental spectra. This was a breakthrough since it revealed a systematic approach to connect elemental scarcity and abundance to either drought or typical rainfall conditions for the hardwood trees grown in specific areas. 4. Furthermore, the research progressed to reveal physiological and developmental processes contributing to biomass production such that the variation in leaf elemental composition increases our understanding of terrestrial nutrient cycles, as well as tracking the transfer of toxic elements from soils to living organisms. 5. Recently another breakthrough viz., ionomics initiated the correlation of elements to specific genes, uncovering the function that the element performed in the plant. More recently, this has been extended from plants to fungi as well as fungi growing in symbiotic relations with plants. Full article

Optics is the science of light, which supports disciplines like biology, medicine, engineering, materials science, chemistry, physics and more. Optics helps to improve diagnostic speed, portable and user-friendly devices, cost efficiency, and sensitivity. Through time, optical components have been made with hard and non-deformable materials. However, traditional optical elements can no longer meet the needs of the market, and new optical elements are needed, such as materials with higher degrees of freedom. A candidate that has been proposed to replace traditional optical materials is polydimethylsiloxane (PDMS or silicone) because it presents suitable characteristics like biocompatibility, nontoxicity, flexibility, non-biodegradability, high transparency in the UV–visible range, low scattering and absorption, easy fabrication, cost-effective relation and more. Many articles have reported the fabrication of optical components with silicone and the use of these components in optical devices. Unfortunately, there is no review that comprehensively covers the field of optics in relation to the application of silicone. The present work is intended as a descriptive overview to provide a clear and accessible review of the topic, rather than a comparative analysis. Articles describing the use of silicone in the fabrication of optical components during the past 20 years were reviewed. Full article

Industrial gear contacts operate under mixed-to-boundary lubrication where reliable antiwear protection is essential. This study assesses whether carbon nanomaterials can enhance the performance of zinc dialkyldithiophosphate (ZDDP) under severe conditions. A crossed-cylinder Reichert configuration (2 GPa, 75 °C, 1 m/s) with PAO6 was used to test ZDDP (1 wt%) and its blends with carbon nanotubes (CNT, 0.05 wt%), graphene nanoplatelets (GNP, 0.05 wt%), and few-layer graphene (FLG, 0.05 wt%) at 1, 10 and 60 min. The lubrication regime was boundary. Friction, specific wear rate (k), and tribofilm coverage were quantified. Oils containing only carbon nanoparticles could not sustain the test (seizure within minutes), confirming the necessity of ZDDP. After 60 min, average CoF remained similar across formulations and largely governed by ZDDP. By contrast, wear showed marked differences: relative to ZDDP alone (A), ZDDP + CNT (F) and ZDDP + GNP (G) reduced k by 52% and 48%, respectively, and exhibited higher tribofilm coverage (F = 68%, G = 72% vs. A = 57%). Time-resolved tests revealed that long-duration degradation was mitigated in F and G: from 10 to 60 min, k rose by 72% (F) and 58% (G) versus 159% for A; coverage decreased by only 8% (F) and 3% (G) versus 22% for A. SEM–EDS indicated no major differences in average elemental chemistry among formulations, suggesting an improvement on tribofilm coverage/stability rather than compositional change. Full article

Anchored bored pile walls are widely used to control deformation in deep urban excavations, but their serviceability performance depends on soil stiffness, excavation depth, wall stiffness, anchor configuration, construction staging, groundwater conditions and seismic demand. This study compares three real excavation support projects in contrasting soil groups: soft to hard clay, hard to very hard clay, and dense to very dense gravel. The calculations follow a Eurocode 7-compatible Design Approach 2 framework. Static finite-element analyses, equivalent-static seismic analyses and scaled time-history analyses were compared with in situ inclinometer measurements. The seismic input included site-specific spectral parameters, horizontal acceleration coefficients, Rayleigh damping parameters and 11 scaled PEER ground-motion records. The key design insight is that increasing the number of anchor rows alone cannot compensate for low ground stiffness or limited wall stiffness; soil-structure interaction must be interpreted together with support configuration. The finite-element and measured maximum horizontal displacements were 79.97 and 75.80 mm for the sports hall excavation, 23.22 and 22.70 mm for the residential excavation, and 27.67 and 23.20 mm for the controlling square-project section. The study demonstrates the value of integrating Eurocode-based design checks, dynamic analysis and field monitoring for deep-excavation safety. Full article

Forging is a key manufacturing route for high-performance structural components, but its process design, quality prediction, and adaptive control still rely heavily on empirical rules, offline simulations, and fragmented production data. This review examines intelligent forging from the perspective of mechanism–data–knowledge fusion, with emphasis on forging-specific process chains, real alloy systems, model validation, and industrial maturity. To improve methodological traceability, a structured literature search was conducted using Web of Science Core Collection, Scopus, ScienceDirect, SpringerLink, and Google Scholar, covering studies published from 1996 to 2026. The screened literature was organized around process perception, mechanism-based modeling, data-driven learning, hybrid modeling, knowledge representation, digital twins, online prediction, and adaptive regulation. Representative cases are discussed for closed-die forging, open-die/large forging, multistage forging, radial forging, and forging of aluminum alloys, titanium alloys, steels, and Ni-based superalloys. Particular attention is given to how specific models are validated, including independent experiments, finite-element benchmarks, industrial datasets, new geometries, sensor noise, and cross-material or cross-equipment transfer. The review further distinguishes consolidated technologies, such as FEM-based process simulation and die/preform optimization, from methods still under validation, including hybrid digital twins, sensor-updated models, and adaptive control. Large-model-assisted forging is considered a prospective direction mainly for information retrieval, case recovery, diagnostic support, and engineer-supervised recommendation rather than unsupervised real-time control. This review provides a more process-specific and critically assessed reference for developing explainable, validated, and deployable intelligent forging systems. Full article

Induction heating is widely used in industrial processes such as forging, hardening, and additive manufacturing, but its accurate numerical simulation requires coupled electromagnetic and thermal finite element analyses with nonlinear, temperature-dependent material properties. This work proposes a deep learning surrogate model based on a convolu-tional neural network for TEAM Workshop Problem 36, a reference benchmark for nonlinear magneto-thermal induction heating. A database of more than 40,000 finite element solutions was generated by varying the supply current from 2 to 6 kA and the frequency from 2 to 6 kHz, while accounting for transient nonlinear effects, including the Curie transition. The network, composed of 24 layers with transposed convolutions, batch normalization, and dropout, maps current, frequency, and time to radial temperature distributions in the steel billet. For most operating conditions, the model achieves mean absolute percentage errors of about 6–7% for radial line in the middle of the billet and about 10% for radial line close to the billet end. Larger discrepancies occur during the early heating stage and near the Curie temperature. Prediction times are reduced by three to four orders of magnitude with respect to a single finite element analysis. The results indicate that the proposed surrogate enables fast temperature estimation for optimization, digital twins, and closed-loop control of induction heating systems. Full article

The Wadi Al-Ahsaba watershed is an arid to semi-arid catchment situated in southwestern Saudi Arabia, characterized by intermittent surface flow, high evaporation and low rainfall, and a dam reservoir built for flood control. The work aims to assess hydrological and anthropogenic controls on surface and groundwater quality, pollution status, and human health risks using an integrated approach of hydrogeochemical analysis, multivariable statistics, and water quality and contamination indices. A total of 21 water samples (15 surface water, 6 groundwater) were analyzed for general chemistry, major ions, and trace elements. Hydrogeochemical analysis and principal component analysis (PCA) were implemented to differentiate the geogenic from anthropogenic control on water quality. The pollution status and associated risk were evaluated using water quality index (WQI), contamination degree (Cd), Hazard Quotient (HQ), and Hazard Index (HI). Results suggest limited surface–groundwater interaction, with surface water dominated by Ca–Mg–HCO₃ facies, indicating recent recharge and limited water–rock interaction, whereas groundwater exhibits mixed Ca–Mg–Cl and Ca–Na–Cl–SO₄ types, revealing longer residence time and water–rock interaction. Nitrate (9.5–109 mg/L) and TDS (522–1003 mg/L) exceeded drinking water standards in 90% and 95% of tested samples, respectively, and WQI ranged from 43 to 134, reflecting excellent to poor water. High non-carcinogenic risk from nitrate was observed, especially for infants. The study concluded that the geogenic processes (water–rock interaction, evaporation, and mineral dissolution) control the general chemistry of tested water, while anthropogenic input from wastewater and agriculture input are likely contributors to nitrate contamination. The study contributes to the understanding of arid wadi-dam systems by revealing how limited recharge, hydrological connectivity, and episodic flow control contaminant transport and persistence, underscoring the critical role of integrated hydrological analysis and land use management in safeguarding freshwater resources in arid environments. Full article

The assessment of seismic performance of existing buildings has traditionally focused on structural safety and damage limitation, often neglecting the explicit quantification of the associated economic consequences. In recent years, performance-based earthquake engineering (PBEE) frameworks have enabled a direct link between structural response and probabilistic loss estimation, allowing economic metrics to be integrated into seismic risk evaluation. Among these, the Expected Annual Loss (EAL) represents a comprehensive indicator that accounts for seismic hazard, structural vulnerability, and exposure over the building’s lifetime. This study presents a performance-based seismic loss assessment of an existing reinforced concrete building, adopting EAL as a global metric for seismic performance evaluation. The case study concerns an existing hospital building designed primarily for gravity loads and representative of a large portion of the Italian building stock. A detailed nonlinear numerical model is developed using OpenSees ver. 3.8.0, incorporating shear-critical behavior through nonlinear link elements. Structural performance is evaluated through modal analysis, pushover analysis, and nonlinear time-history analyses using a set of ground motions selected and scaled according to intensity-based criteria. Seismic losses are estimated following the FEMA P-58 methodology, implemented through the PACT software ver. 3.1.2, integrating structural response demands, component fragility functions, collapse probability, and seismic hazard curves. Probabilistic loss curves are derived, and the EAL is computed as a synthetic indicator of economic seismic performance. The results highlight the effectiveness of EAL in capturing the combined effects of seismic hazard and structural vulnerability, demonstrating its potential as a robust decision-support metric for seismic risk mitigation, retrofit prioritization, and insurance-related applications for existing buildings. Full article

This study focuses on the pollution assessment, potential ecological risks, influencing factors, and migration pathways of trace elements from slag heaps of coal gangue and pyrite to farmland in the mountainous areas of southeast China. Based on the pollution index and correlation analysis of trace elements, Cd, As, Pb, and Zn were identified as characteristic pollutants. In the solid waste, surrounding soil, and farmland soil, the mean concentrations of Cd, As, Pb, and Zn of pyrite slag heaps were generally 9.7–86.7 times higher than those of coal gangue dumps. In contrast, higher levels of As were found in coal gangue surrounding soil, while higher Cd and As concentrations existed in coal gangue-affected farmland soil. Mantel test results revealed significant statistical correlations between characteristic pollutants and environmental factors (geographic location, weather, and climate), particularly for pyrite slag heaps. The potential migration pathways from solid waste to the surroundings (soil and water) and then to farmland soil were finally revealed using partial least squares path modeling. This study demonstrated that the pyrite slag heaps were more heavily polluted than the coal gangue dumps. The pyrite slag heap was more susceptible to environmental factors, which could rapidly transfer trace elements to farmland soil via the surrounding soil and water. Therefore, this study offers a statistical framework to infer plausible trace element migration trends via multi-medium monitoring data. It also delivers comparative analytical references for risk assessment of two distinct types of slag heaps. Full article

Multibody dynamics (MBD) simulations involving hundreds to thousands of bodies give rise to large-scale, sparse, and structurally indefinite linear systems. Traditional direct solvers incur prohibitive memory and computational costs, while iterative methods suffer from slow convergence due to severe ill-conditioning. This paper proposes HPI-MBD, a hybrid preconditioned iterative framework. It combines an algebraic multigrid (AMG) for global error smoothing with a block Jacobi preconditioner tailored to the kinematic constraint graph. The framework exploits graph topology to construct a block-diagonal Schur complement approximation, incorporates Tikhonov regularisation for redundant constraints, and maintains $\mathcal{O}\left(n\right)$ work per iteration, where n is the number of degrees of freedom. A rigorous spectral analysis supports the problem-size independent convergence of the Minimal Residual (MINRES) solver. Evaluated on five benchmark systems with ${10}^4$ to ${10}^6$ degrees of freedom, the HPI-MBD achieves speedups up to $12.7\times$ and memory reductions up to 68% against MA57, with comparable gains against PARDISO. All solutions maintain relative residuals below ${10}^{-6}$. Comparisons against ILU(0)-preconditioned Generalised Minimal Residual (GMRES), Finite Element Tearing and Interconnecting method (FETI-1), and a block-Jacobi-only variant confirm the essential role of AMG. The framework exhibits near-linear scalability and strong parallel efficiency on up to 32 processors, along with robust performance under redundant constraints and varying time step sizes. These results position HPI-MBD as a scalable, memory-efficient alternative for real-time simulation in virtual prototyping, robotics, and biomechanics. Full article