Search Results (1,363)

Accurate estimation of the internal spatial-temporal temperature distribution is crucial for the safety and performance management of lithium-ion batteries. However, traditional lumped parameter models overlook spatial gradients, while numerical methods for partial differential equations (PDEs) incur high computational costs. This paper proposes a hard constraint physics-informed neural network (HCPINN) framework for the real-time reconstruction of the axial temperature field in 18,650 cylindrical batteries. By restructuring the neural network’s solution space through distance functions, the Robin boundary conditions are strictly embedded as hard constraints, ensuring exact satisfaction of the prescribed Robin boundary conditions within the mathematical model and eliminating boundary loss terms. An electro-thermal coupled model considering the Arrhenius effect and state-of-charge (SOC) dependent internal resistance is integrated into the loss function to capture the nonlinear heat generation dynamics. Experimental validation across discharge rates from 1C to 4C demonstrates that the HCPINN achieves high estimation accuracy with a mean absolute error (MAE) below 0.34 °C. Furthermore, by leveraging the continuous differentiability of the model, this study quantifies the evolution of spatial temperature gradients and reveals the ideal heat transfer coefficients required for thermal equilibrium are inverted, providing a quantitative basis for the design of advanced battery thermal management systems (BTMS). Full article

We investigate the blow-up behavior of solutions to the semilinear heat equation $u_t=u_{xx}+{\left|u\right|}^{p-1}u,x\in \mathbb{R},u\in \mathbb{R},$ for exponents $1<p<1+{\displaystyle \frac{2}{m}}$, where $m\in \mathbb{N}$ denotes the number of positive eigenvalues of the linearized operator in similarity variables, equivalently the dimension of the associated unstable manifold, which determines both the admissible exponent range and the structure of the blow-up profiles. We construct solutions that exist on the interval $-1<t<T$ and become unbounded both as $t\to -1$ (backward blow-up) and as $t\to T$ (forward blow-up). At blow-up time, the solution profile exhibits a finite number of critical values, which can be prescribed in advance, and possesses a structure with $m+1$ monotonicity intervals. By introducing similarity variables, we reduce the problem to an evolution equation in weighted spaces and identify the role of unstable manifolds. Our results establish a classification of blow-up dynamics in terms of spectral properties and provide a systematic framework for constructing solutions with prescribed spatial patterns of singularity. Full article

As a form of living heritage, Historic Urban Landscapes (HULs) have long been limited by the static perspectives and reductionist tendencies of conventional conservation and research approaches. Although the geological and archaeological concept of “stratification” offers a methodological basis for understanding the diachronic evolution of heritage, its unidimensional temporal lens fails to capture the inherent complexity and systemic nature of historic urban landscapes. To address this gap, this study proposes a “multidimensional stratification” theoretical framework through theoretical critique and paradigm reconstruction. The framework introduces innovations at the ontological, epistemological, and methodological levels, positing that the evolution of historic urban landscapes emerges from the nonlinear interaction and dynamic interweaving of four core dimensions: time, space, society, and value. It further systematizes five intrinsic attributes of such landscapes: authenticity, integrity, continuity, adaptability, and dynamism. Building on this foundation, the paper constructs a systematic analytical pathway—elements–processes–patterns–modes–drivers–characteristics—that enables dynamic analysis from micro-level identification to macro-level generalization, offering a scalable tool for HUL conservation and regeneration. To demonstrate the framework’s applicability, the historic urban area of Shenyang—a nationally designated historical and cultural city—is selected as a case study. Its urban landscape comprises four core districts: the Shengjing City District, the South Manchuria Railway Concession District, the Commercial Port District, and the Tiexi Industrial District, representing historical strata from the Qing dynasty capital, modern colonial planning, commercial opening, to industrial heritage. Using the multidimensional stratification approach, this study elucidates the spatial complexity, temporal nonlinearity, social dynamism, and value pluralism embedded in Shenyang’s historic urban area. Corresponding conservation strategies grounded in holism, dynamism, and differentiation are proposed. The research not only advances the theoretical understanding of HUL but also provides a novel paradigm—integrating holistic, dynamic, and operational perspectives—for the conservation, renewal, and regenerative practice of historic urban landscapes worldwide. Full article

Cycle-to-cycle variability of switching parameters inherent to memristive devices introduces significant problems in the design of neuromorphic systems and non-volatile memory. This study investigates the dynamics of a second-order memristive system incorporating capacitive effects that model parasitic charge within individual memristors, addressing both the technical need for accurate analysis of complex regimes and the demand for exploratory environments. Simulations were performed using CUDAynamics, an interactive software suite developed by the authors, which utilizes parallel computing, primarily via NVIDIA Compute Unified Device Architecture (CUDA). It integrates multiple analysis tools for dynamical systems, including bifurcation diagrams, the largest Lyapunov exponent and periodicity mapping, and interactive navigation in multidimensional parameter spaces. The memristive system was discretized applying multiple integration methods with a fixed time step and various waveforms of the input signal. Analysis tools revealed well-defined regions of chaotic dynamics in the memristor resistance parameter space as functions of input signal properties. Sinusoidal and triangular waveforms produced topologically similar distributions of dynamical regimes, whereas the square waveform, mimicking digital inputs, generated distinct dynamical patterns while still preserving chaotic trajectories under specific conditions. Interactive visualization capabilities of CUDAynamics effectively demonstrate attractor evolution and hysteresis deformation, providing immediate visual feedback that significantly enhances conceptual comprehension of nonlinear feedback mechanisms. Beyond its practical implications for the design of analog and digital memristive devices, CUDAynamics offers a scalable, open-source toolkit to aid researchers and engineers in exploring complex dynamical phenomena. Full article

Single-phase Cu₄Bi₄Se₉ was successfully synthesized through a simple and rapid process combining mechanical alloying (MA) and hot pressing (HP). The phase formation behavior, microstructural evolution, charge transport characteristics, and thermoelectric properties were systematically investigated. X-ray diffraction analysis as a function of MA time confirmed that all powders crystallized into a single orthorhombic phase with space group Pnma. No decompositions or secondary phases were observed after HP sintering, indicating high phase stability. Thermogravimetric and differential scanning calorimetric analyses revealed distinct endothermic peaks at 714–717 K for all samples, corresponding to the onset of the decomposition of Cu₄Bi₄Se₉. Microstructural observations showed that the relative density decreased with increasing HP temperature (>573 K), accompanied by grain growth and pore formation, reflecting the competition between Cu–Se interdiffusion and pore coarsening during high-temperature sintering. Hall effect measurements indicated p-type conduction for all samples, with carrier concentrations on the order of 10¹⁷ cm⁻³ and carrier mobilities of approximately 10² cm² V⁻¹ s⁻¹. With increasing temperature, the electrical conductivity increased monotonically, while the Seebeck coefficient gradually decreased, resulting in a maximum power factor of 0.12 mW m⁻¹ K⁻² at 573 K. The total thermal conductivity remained extremely low, ranging from 0.33 to 0.48 W m⁻¹ K⁻¹, with the electronic contribution accounting for less than 10%, indicating that lattice thermal transport is dominant. The suppressed lattice thermal conductivity is attributed to the combined effects of Cu atomic rattling, asymmetric bonding induced by Bi 6s² lone-pair electrons, and strong anharmonic phonon scattering arising from the complex crystal structure. Consequently, Cu₄Bi₄Se₉ achieved a peak dimensionless figure of merit ZT of 0.19 in the temperature range of 573–623 K, demonstrating that the MA–HP process enables stable phase formation and competitive thermoelectric performance without post-annealing. Full article

Programs aimed at reducing the CO₂e footprint associated with the residential building stock should be informed by several key elements, including the expected evolution of the occupied housing stock, projected population dynamics driven by socio-economic and cultural factors, available implementation budgets, and the specific costs of intervention measures. However, in regions characterized by high seismic hazard, the occurrence of a major earthquake may substantially alter the projected outcomes of emission-reduction programs, as seismically vulnerable buildings may experience severe structural damage. This paper presents the results obtained by applying an integrated methodology for assessing the CO₂e footprint associated with residential buildings. The methodology accounts for emissions related to building operation (space heating), energy-renovation interventions, and seismic retrofitting works. While the proposed approach is applicable to other seismically exposed regions, the results presented herein refer specifically to the residential building stock in Romania and its local seismic conditions. The methodology integrates information on the existing building stock, the projected evolution of population and the built environment, energy consumption associated with building operation, changes in the energy fuel mix, construction practices across different historical periods with respect to energy efficiency and seismic protection, and the CO₂e footprint associated with energy renovation and seismic retrofitting. In addition, the analysis explicitly considers the potentially negative effects of a major earthquake, particularly the disruption of greenhouse-gas emission-reduction programs. The assessment is conducted at the building stock level and is based on combining building stock evolution with average, representative CO₂e intensity values for heating, energy renovation, and seismic retrofitting. The results demonstrate that when the sole objective is to reduce the CO₂e footprint associated with space heating, renovation of the energy fuel mix represents the most effective measure. At the same time, the analysis shows that the CO₂e footprint generated by construction works for energy renovation and/or seismic retrofitting represents only a small fraction of the emissions associated with building operation. The occurrence of a major earthquake is likely to jeopardize overall environmental objectives by increasing emissions related to building operation, energy renovation, reactive seismic retrofitting, and replacement of severely damaged buildings. Conversely, systematic preventive seismic retrofitting of the building stock does not lead to an increase in cumulative CO₂e emissions over the program implementation period. Full article

Sweeping jet (SWJ) actuators are widely used in active flow control, but explicitly resolving actuator-scale unsteadiness in full-configuration computational fluid dynamics (CFD) remains prohibitively expensive because of the small geometric scales and high-frequency oscillations involved. Existing reduced-order boundary-condition models constructed from exit-plane data alone can reproduce the observed switching waveform, but they treat the actuator as an input–output black box and provide limited insight into the internal dynamics that generate the response. This work develops a causal state-space reduced-order modeling framework that links internal mixing-chamber dynamics to time-resolved exit-plane boundary conditions. Proper orthogonal decomposition (POD) is used to obtain a low-dimensional representation of the internal flow, and a data-driven linear evolution operator is identified in the reduced space by least-squares regression of successive snapshot pairs. A POD truncation rank of $r=60$ is selected from cumulative-energy and validation-error sensitivity analyses, capturing well above 99% of the fluctuation energy while lying within the converged performance regime. A corresponding reduced operator is identified for the exit plane, and spectral comparison reveals near-neutrally stable oscillatory modes in both regions. Using a $\pm 1\%$ relative frequency-matching tolerance, the dominant reduced-operator modes exhibit a 28.3% frequency overlap, providing operator-level evidence that exit-plane oscillations are dynamically linked to internal coherent structures. This correspondence is further supported by cross-spectral coherence analysis between representative internal and exit-plane probe signals, which shows strong coherence at dynamically relevant frequencies. A delayed causal output mapping is then formulated in which the internal reduced state drives the exit-plane response after an identified lag of 149 time steps, corresponding to $2.98\times {10}^{-3}$ s. This delay provides a physically interpretable convective transport timescale from the mixing chamber to the actuator exit. Over the validation interval, the model maintains a mean relative $L_2$ error below 0.02, with maximum normalized errors below 0.04 for most of the prediction horizon, and localized increases are confined to rapid jet-switching events. Field-level reconstructions of streamwise velocity and total pressure show that the model captures both phases of the jet-switching cycle, with errors concentrated primarily in high-gradient shear-layer regions. Compared with exit-only reduced-order models, the proposed internal-driven formulation improves amplitude and phase fidelity over extended prediction horizons. The resulting framework provides a compact, interpretable, operator-based representation of SWJ actuator dynamics suitable for use as a CFD-embeddable dynamic boundary condition. Full article

Highway merging and platoon formation are critical scenarios in heavy-duty vehicle aerodynamics. This study presents a transient computational fluid dynamics (CFD) analysis of two trucks undergoing a merging maneuver and subsequent platoon formation. A three-dimensional unsteady Reynolds-Averaged Navier–Stokes (uRANS) approach with the SST k–ω turbulence model is employed under zero-crosswind and yawed inflow conditions. The present work provides a time-resolved characterization of truck–truck aerodynamic interactions during dynamic spacing evolution, enabling the capture of unsteady wake effects that are not accessible in steady-state formulations commonly used in cooperative driving studies. Unlike previous steady analyses, the approach resolves transient wake development, vortex shedding, and their direct impact on instantaneous aerodynamic loads. Results identify three interaction regimes: weak interaction, strong wake interaction during wake impingement, and wake recovery at larger spacing. Under zero-crosswind conditions, significant drag reduction is observed, confirming platooning benefits. However, crosswind conditions substantially reduce this benefit and increase lateral loads due to asymmetric pressure distribution and wake deflection. A non-linear spacing–drag relationship is observed, governed by wake evolution and shear-layer interaction. These findings provide quantitative insight into transient aerodynamic interactions and highlight the importance of accounting for unsteady and crosswind effects in platoon performance assessment. Full article

In this work, we examine viable models of $f(Q,T)$ gravity theories against observational data with the aim to constrain the parameter space of these models. We have analyzed four different models of $f(Q,T)$ gravity and tested them against against late-time background probes: Cosmic Chronometer (CC), Baryon Acoustic Oscillations (DESI BAO), $Pantheo{n}^{+}$ and Gravitational wave(GWTC-3) data. We put stringent constraints on the $f(Q,T)$ gravity models, $f(Q,T)=\alpha Q+\beta T$, $f(Q,T)=\alpha Q^n+\beta T$, $f(Q,T)=-\alpha Q-\beta T^2$ and $f(Q,T)=\alpha Q^{-2}{T}^2$ along with other late-time cosmological parameters such as deceleration parameter ($q_0$), equation of state parameter ($w_0$), sound horizon distance ($r_d$) and demonstrate their alignment with the $\mathrm{\Lambda }CDM$ model and the observational data. We show that these models have the capability to alleviate the Hubble tension in late time universe, by predicting the present value of the Hubble parameter close to 74 km/s/Mpc. $f(Q,T)$ gravity theory introduces alterations in the background evolution and imposes a friction term in the propagation of gravitational waves, this phenomenon has also been examined. We have shown their agreement with the Gravitational Wave (GW) luminosity distance with the Electromagnetic (EM) counter part GWTC-3 data from Advanced LIGO and Advanced VIRGO across different observing runs capturing coalescence of Binary Neutron Stars (BNS), mergers of Binary Black Holes (BBHs), and Neutron Star-Black Hole (NSBH) binaries with EM counterparts. Full article

The rapid digitalization of district heating systems (DHSs) has driven the large-scale deployment of thermal Internet of Things (TIoT) sensors, which generate massive real-time operational data. Traditional centralized computing architectures struggle to process massive concurrent data. Furthermore, they fail to balance the stringent low-latency demands of real-time control tasks with the low-energy constraints of battery-powered terminal devices. To solve the complex problem of minimizing the weighted sum of system latency and energy consumption, we propose an Improved Mayfly Algorithm (IMA). The algorithm integrates five targeted structural enhancements: random position update masking, differential evolution (DE)-based crossover, targeted subset mutation with boundary scaling, adaptive population reset mechanism, and simulated annealing (SA)-driven local search, to efficiently navigate the high-dimensional rugged decision space and mitigate premature convergence. Extensive simulation results show that the proposed collaborative architecture achieves the lowest total system cost compared with traditional isolated computing paradigms (local-only, edge-only, and cloud-only). Notably, the proposed IMA reduces the total baseline weighted cost by 17.2% compared with the standard MA. Furthermore, under maximum practical industrial workloads (750 concurrent tasks, representing a highly complex 2250-dimensional MINLP space), the IMA maintains strong scalability and dominance, outperforming the second-best algorithm (BWO) by 15.8%. This research provides a low-latency, energy-efficient scheduling solution for TIoT-enabled DHS, and offers technical support for the intelligent and low-carbon transformation of urban energy infrastructure. Full article

In this paper, we discuss extensions of the canonical quantization procedure in quantum field theories. We focus specifically on S-matrix representation as a T-exponent. This extension involves flat bundles on certain infinite dimensional functional manifolds of local time. The motivating problem is first principles treatment of bound states in quantum chromodynamics as well as precision physics of the hydrogen atom and the muonium. Our main results include systematic treatment of flat bundles in an infinite dimensional setting, generalization of Hamiltonian evolution and functional renormalization group evolution equations in quantum field theories. We discuss several results from finite dimensional theory that have analogies in the functional setting. This includes construction of moduli space of flat connections and isomonodromic deformations. One of the outcomes of our analysis is a construction of a rich family of functional flat bundles with rational connections. This class of connections exhibits a rich set of mathematical properties. In particular, we construct examples of the fundamental groups of spaces which have a definable continuum of generators. Physical states correspond to points in the moduli space of bundles on these spaces. On the physics side of things, we conclude that spacetime notions, such as spaces of particle configurations, emerge effectively as spectral sets of functional differential operators. Full article

Coal mines constitute a vital component of the national security system, where the extraction and utilisation of coal resources directly impact mine stability and engineering safety. Therefore, addressing the surface deformation issues caused by repeated mining activities across multiple coal seams at the Daliuta Mine, this study proposes a multi-source remote sensing monitoring technology system, which aims to improve the accuracy of surface deformation in the mining area. At the mining area scale, optimised Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology utilised 168 Sentinel-1A image scenes to generate 789 interferometric image pairs. This extracted the long-term surface deformation field of the Daliuta mining area, revealing the spatiotemporal evolution patterns of surface subsidence under repeated mining activities. To further enhance monitoring accuracy and reliability, this study proposed a Satellite Aerial-Prior Weighting (SA-PW) method. This approach integrated satellite-based time-series InSAR, aerial Pixel Offset Tracking (POT), and unmanned aerial vehicle light detection and ranging (UAV LiDAR) data through a dynamic priority weighting model. This enabled the synergistic inversion of high-precision surface deformation parameters for repeatedly mined areas. Results demonstrated that compared to SBAS-InSAR alone, the SA-PW method achieved a 10% improvement in surface deformation parameter accuracy. By constructing a dynamic priority-weighted model, this approach systematically integrated multi-source data to achieve collaborative inversion of high-precision surface deformation parameters in repeatedly mined areas. Results demonstrated that compared to SBAS-InSAR and UAV LiDAR methods, SA-PW data fusion yielded higher accuracy, with MAE and RMSE values of 60 mm and 90 mm on the A line, and 57 mm and 83 mm on the H line, respectively. This method facilitates the establishment of integrated air–space–ground real-time monitoring networks for mining areas, enables subsidence hazard early warning and management, identifies key zones for ecological restoration, explores synergistic mechanisms between repeated mining and ecological rehabilitation, and promotes safe and green mining operations and development. Full article

Historically, pharmaceutical formulation development relied heavily on trial-and-error experimentation, which was useful for empirical progress but often provided limited mechanistic understanding and insufficient efficiency for increasingly complex drug products. The introduction of Design of Experiments (DoE) and Quality by Design (QbD) established a more systematic framework for studying formulation variables, manufacturing parameters, and Critical Quality Attributes (CQAs). Approaches such as factorial designs, response-surface methodology, and mixture designs have therefore become central to modern pharmaceutical development because they improve experimental efficiency and support the definition of design space. However, as formulations become more nonlinear, high-dimensional, and multi-objective, these classical approaches may no longer be sufficient on their own. This review examines the evolution of experimental design in pharmaceutical research, from one-factor-at-a-time experimentation to structured DoE/QbD strategies, and then to emerging intelligent optimization methods. Its central objective is to clarify when conventional DoE/QbD remains appropriate and when it should be complemented by machine learning, Bayesian optimization, digital twins, and closed-loop experimental systems. The review first summarizes the foundations and strengths of classical experimental design; then, it discusses its practical limitations in complex formulation settings, and finally evaluates how data-driven and hybrid approaches can extend pharmaceutical development. Evidence from tablets, capsules, nanocarriers, transdermal patches, and biotherapeutic systems suggests that intelligent optimization can improve predictive performance and experimental efficiency when used alongside, rather than instead of, established pharmaceutical development principles. Full article

Hydrogen-blended natural gas (HBNG) is widely regarded as a transitional pathway for decarbonizing urban gas systems. However, the coupled evolution from buried pipeline leakage to pre-ignition flammable cloud formation has not yet been systematically integrated across research stages. This review synthesizes experimental, numerical, and data-driven studies on leak source-term dynamics, subsurface migration through porous media, surface breakthrough and escape, accumulation in semi-enclosed spaces, and pre-ignition flammable cloud development. Hydrogen blending modifies the density, diffusivity, flammability limits, and ignition sensitivity of the gas mixture, thereby influencing breakthrough time, stratification behavior, and the available early-warning window before ignition. The hazard evolution is jointly governed by pipeline pressure, leak orifice size, burial depth, soil heterogeneity, soil moisture content, spatial confinement, and ventilation conditions. Six major research gaps are identified, including fragmented stage-specific investigations, limited full-scale multiphysics experimental data, insufficient characterization of heterogeneous soils, inadequate high-resolution gas-cloud measurements, weak integration with quantitative risk assessment, and delayed full-lifecycle integrity management. To address these gaps, this review proposes a coherent, mechanism-informed analytical framework for urban HBNG pipeline safety and further provides a numerical parameter-transfer example showing how surface breakthrough outputs can be converted into aboveground velocity, mass flux, and species-concentration boundary conditions. This framework integrates dynamic mechanistic parameters into high-consequence area zoning, sensor placement, ventilation interlocking, and full-lifecycle integrity management, thereby supporting safer engineering deployment of HBNG systems. Full article

The mechanical sensitivity of energetic materials is closely linked to the stability of their microstructures; however, in situ observation of their dynamic response under external mechanical stimuli at the atomic scale remains challenging. Here, we propose a deep-learning-based intelligent analysis method for scanning tunneling microscopy (STM) images of a next-generation insensitive energetic material 3-nitro-1,2,4-triazol-5-one (NTO). We design SpecMol, a lightweight segmentation network with frequency-domain awareness, which achieves high-precision segmentation and orientation recognition of individual NTO molecules in adsorption images. Building upon this, we apply localized external forces to one-dimensional NTO nanochains via in situ STM tip manipulation and quantitatively analyze the geometric evolution of their fundamental building blocks—dimers. Experimental results reveal that, following mechanical perturbation, the relative orientation angle within the dimer (averaging approximately 14.55°) remains highly stable (CCC = 0.834), confirming the remarkable structural rigidity of NTO dimers. This study provides, for the first time, direct microscopic evidence at real-space atomic resolution for the low mechanical sensitivity of NTO, elucidating that its exceptional local structural stability originates from rigid dimeric units stabilized by an extensive hydrogen-bonding network. Our findings not only deepen the fundamental understanding of the safety performance of energetic materials but also demonstrate the powerful potential of integrating artificial intelligence with advanced characterization techniques for molecular-scale functional materials research. Full article