In emergencies, the quick response capability of gas pipeline network dispatch is critical for maintaining flow equilibrium. For moderate disruptions, operators can sustain system balance through flow redistribution or reserve utilisation. However, in catastrophic events—marked by high uncertainty in supply restoration timelines and magnitudes—demand prioritisation and curtailment based on user heterogeneity become imperative, posing new challenges to conventional time-window constrained hierarchical dispatch. This paper proposes a user classification mechanism based on demand elasticity. Under tight resource limits, we study hierarchical scheduling to unlock demand flexibility, fill emergency flow allocation gaps, and boost system resilience. Thus, the emergency scheduling process is modelled as a priority-driven Markov Decision Process. Departing from proximity-based allocation, the model prioritises differential urgency in user demand profiles instead. A case study of China’s gas pipelines validates the model, showing interruptible users buffer shocks via demand elasticity. Moreover, gas reserve effectiveness is limited by pipeline capacity—releasing stored gas in severe disruptions may create bottlenecks, hindering efficient delivery. This approach provides an integrated emergency allocation system and theoretical foundations for priority-based flow management.

This study comprehensively and systematically investigates the universality of the normalized, steady, laminar heat flux distribution over a hypersonic sphere, an important topic of both fundamental and practical relevance. Utilizing an extensive dataset of 70 Navier–Stokes simulations, the analysis encompasses a wide range of freestream conditions, gas models, boundary-layer thicknesses, sphere radii, and wall catalyticities. For perfect gas, equilibrium, and nonequilibrium flows with an equilibrium-catalytic wall, the normalized heat flux distribution is found to be universal under a uniform wall temperature. Conversely, for nonequilibrium flows with a noncatalytic wall, universality breaks down. These deviations are fundamentally driven by chemical nonequilibrium, which induces a nonuniform wall enthalpy distribution. Cold walls (300 K) promote near-wall recombination near the stagnation point, causing the distribution to fall below the universal curve. Sufficiently hot walls (2000–2500 K) suppress this recombination due to the inverse temperature dependence of the recombination rate coefficient, resulting in a frozen boundary layer and a distribution that lies above the universal curve. This complex behavior is characterized using a stagnation-point Damköhler number, revealing a non-monotonic trend. This work establishes, for the first time, a generalized framework for understanding the normalized heat flux distribution over a hypersonic sphere.

ABSTRACT Addressing the core imperative of integrating knowledge and practice in journalism education within the context of media convergence, alongside the prevailing contradiction of imbalanced feedback mechanisms within existing classroom ecosystems, this study employs ecosystem theory as its overarching framework. Through an exploratory case study utilizing mixed methods, it proposes redefining multi-dimensional feedback as the core driving mechanism underpinning classroom ecosystem operations. This multi-dimensional feedback originates from three information streams at different levels of the ecosystem: verbal feedback, data feedback, and social evaluation feedback. Through modal integration and hierarchical embedding, it forms a three-dimensional feedback mechanism of “absorption-linkage-influence”. Functioning as a continuously regulating variable throughout the learning process, it effectively promotes energy flow and dynamic equilibrium within the system. Theoretically, this expands the operational paradigm of “human-environment interaction” within ecosystem theory. Practically, it provides actionable pathways and model references for constructing journalism pedagogy adapted to the digital age.

The truck ban policy on freeways in central business districts (CBD) is extensively used in China nowadays to improve traffic safety and reduce traffic congestion. However, this policy will drastically impact freight transportation, especially when the freeway truck volume is high. To mitigate the negative effects and encourage truck drivers to use alternative freeways, this study proposes a freeway tolling problem for different types of trucks to reduce travel costs following the truck ban policy in CBD. It is formulated as a bi-level optimization problem. The upper-level problem optimizes the freeway toll rates for different types of trucks to reduce the total travel cost (TTC) of the network. The lower-level problem is a multiclass traffic assignment model to characterize the equilibrium flow mixed with passenger vehicles and different types of trucks following the toll strategy. The bi-level problem is solved using a line search algorithm developed based on a feasible direction method. Application of the proposed method in Ningbo, China, finds that the proposed solution algorithm can efficiently solve the bi-level problem and converges only after 11 iterations. Compared to the initial state where the truck ban is not implemented, the optimal tolling strategy can effectively reduce the TTC of the network by 8.5%, with an increase of only 1.15% for all trucks. This indicates that the proposed method can effectively nudge truck drivers to use alternative routes with a minor rise in travel costs. Therefore, it can help traffic managers design better strategies to avoid the resistance of truck users following the truck ban policy in CBD.

Based on observations of real-world transport systems such as bus-subway systems, street-motorway networks, and rail-air transport frameworks, in which high-speed layers are typically constructed above pre-existing low-speed networks to alleviate congestion and improve efficiency, this study proposes a method for constructing multilayer transport networks by strategically deploying the high-speed layer according to node betweenness centrality in the underlying low-speed network. The concept of speed ratio is introduced to quantify the speed difference within the multilayer network. The multilayer network is integrated into the following model: the user equilibrium flow assignment strategy model based on the Bureau of Public Roads function. Utilizing network efficiency, high-speed layer utilization ratio, and proportion of congested edges as metrics, we analyze the impact of: (1) inter-tier speed ratio, (2) low-speed layer topology, and (3) interlayer transfer costs on system performance. Key findings indicate: Under a given traffic demand, increasing the inter-layer speed ratio elevates network efficiency while shifting congestion from lower to upper layers; incorporation of long-range connections improves efficiency, alleviating traffic congestion; introducing interlayer travel speed may enhance efficiency in specific parameter regimes.

Sharp leading edges offer aerodynamic and observability advantages in hypersonic flight. The reduction in standoff distance resulting from the adoption of sharp edges, however, results in increased convective heating that needs to be managed. Transpiration cooling is an attractive option in terms of aerothermal performance, reusability, and complexity. In this study, a method is presented for the numerical simulation of transpiration cooling at the stagnation point of sharp leading edges or tips. The method is suited to both equilibrium and nonequilibrium flow and is based on Cheng’s stagnation line theory. The method explicitly models coolant flow through the porous medium. Detailed temperature profiles and mass fractions in the shock layer and within the porous medium are computed. The sensitivity of these profiles to coolant flow rate and nature, wall thermal boundary conditions, and catalytic activity is studied. The ability of different coolant mixtures to limit the transport of reactive species to the surface is assessed. It is found that low-molecular-weight injectants provide better shielding of the surface at moderate to high blowing. At low blowing, low-molecular-weight injectants are less effective because of the increased diffusivity of shock layer species through the injectant. The effect of wall catalycity is found to be localized to the wall and of secondary importance to thermal boundary condition and blowing rate.

ABSTRACT This paper proposes a new design for a monetary system grounded in the laws of physics and in which the rate of interest would converge to zero as envisioned by Silvio Gesell. Drawing on the first and second laws of thermodynamics, the concept of exergy is introduced as a biophysical numeraire that directly links monetary circulation to real productive activity. The proposed framework replaces the exponential growth logic inherent in interest‐bearing money with a logistic growth model reflecting the finite carrying capacity of ecological systems. Money creation is strictly limited by the annually harvested exergy used in economic production and implemented through a transparent institutional procedure. Since exergy cannot reproduce itself, interest‐bearing money creation becomes physically infeasible, leading endogenously to a natural interest rate of zero. The paper discusses implications for economic stability, banking, investment behavior, and sustainability, arguing that an exergy‐based monetary architecture would enable a stationary flow equilibrium without a systemic growth imperative.

Recent laboratory experimental work has shown that heating and cooling processes exhibit intrinsic asymmetry, with heating occurring more efficiently than cooling. Two decades earlier, nonequilibrium molecular dynamics simulations addressed the topic of heating one side of a computational cell while cooling the other side by applying two different thermostats, producing a sinusoidal temperature profile. We revisit the theory underlying those computer calculations and show how it accurately predicts the laboratory results. Recent realizations of a simple two-dimensional one-particle cell model give surprisingly relevant results, where two Nosé-Hoover thermostats are applied to the two directions (x and y) at two temperatures, Tx ≥ Ty. At equilibrium, the thermostatting rate variables ξx and ξy are identical, while under nonequilibrium temperature differences, the heating variable (ξx) is weaker than the cooling one (ξy), demonstrating the ratio of thermostat effort to thermal bias, just as predicted by theory: ⟨ξx⟩/⟨ξy⟩ = -Ty/Tx. We relate this to the negative rate of change in entropy of the nonequilibrium system that accompanies the contraction of phase space onto a fractal strange attractor of lower dimension. Histograms of the thermostat variables reveal the stark differences between equilibrium and nonequilibrium heat flow. At equilibrium, the cell-model ξ-distributions are both Gaussians centered at zero. We redid much earlier many-body simulations of a Nosé-Hoover thermostatted fluid at equilibrium, which reported that the distribution was biased toward heating; we discovered that the prior work suffered from systematic integration error. In fact, we find that the distribution at equilibrium is a totally symmetric Gaussian for many-body systems, in agreement with the cell model. Under nonequilibrium conditions, when Tx > Ty, the simple 2D cell model clearly demonstrates the microscopic origin of heating-cooling asymmetry, thereby strongly confirming the results of real-world laboratory experiments.

Identifying transition states (TSs), the high-energy configurations that molecules pass through during chemical reactions, is essential for understanding and designing chemical processes. However, accurately and efficiently identifying these states remains one of the most challenging problems in computational chemistry. In this work, we introduce a new generative AI approach that improves the quality of initial guesses for TS structures. Our method can be combined with a variety of existing techniques, including both machine-learning models and fast, approximate quantum methods, to refine their predictions and bring them closer to chemically accurate results. Applied to TS guesses from a state-of-the-art machine-learning model, our approach reduces the median structural error to 0.077 Å and lowers the median absolute error in reaction barrier heights to 0.40 kcal mol-1. When starting from a widely used tight-binding approximation, it increases the success rate of locating valid TSs by 41% and speeds up high-level quantum optimization by a factor of 3. By making TS searches more accurate, robust, and efficient, this method could accelerate reaction mechanism discovery and support the development of new materials, catalysts, and pharmaceuticals.

The rapid proliferation of electric vehicles (EVs) has intensified the coupling between power distribution networks (PDNs) and transportation networks (TNs). This paper proposes an integrated modeling framework to analyze their interactions from a behavioral economics perspective, focusing on how drivers’ behavior influences traffic and power flows. Specifically, a truncated path choice model is introduced, where a path is excluded if its travel cost exceeds the driver's threshold. For other feasible paths, choices follow a utility-maximizing principle, forming a truncated stochastic user equilibrium (TSUE). The resulting TSUE is reformulated as a convex problem, enabling efficient and convergent algorithm for equilibrium flow computation. Equilibrium traffic flows determine the spatial distribution of EV charging demands in PDN. Additionally, an optimal power flow model is employed to derive locational marginal prices (LMPs) for EV charging, which in turn influence drivers’ decisions in the TN. To solve the coupled power- traffic equilibrium problem, an alternating direction method of multipliers (ADMM) framework with an adaptive step-size scheme is employed to improve convergence efficiency and reduce redundant interactions caused by suboptimal initial step-size settings. The proposed model is evaluated on both a coupled system with a 20-link TN and a modified IEEE-33 bus PDN and a larger coupled system. Numerical results validate the effectiveness of the TSUE model and demonstrate the efficiency of the proposed solution approach for computing equilibrium flows of PDN and TN.

As expressway networks expand and road system complexity increases, the demand for accurate traffic assignment results continues to grow. However, traditional models struggle to capture the behavioral inertia of travelers, limiting assignment accuracy. This study proposes a novel traffic assignment model integrating graph convolutional networks (GCNs) with the multicriteria decision‐making method TODIM. Vehicle trajectory data from expressway toll gantries enable GCN to extract route choice inertia, whereas TODIM incorporates travel time, distance, and cost to simulate dynamic path selection. An iterative optimization process achieves traffic flow equilibrium. The model was validated on expressway networks in Jinan, Taian, and Weifang, Shandong Provinces. Results show that during peak hours, the GCN‐TODIM (G‐T) model outperforms stochastic user equilibrium (SUE), bounded rational user equilibrium (BR‐UE), and mean excess travel time (METT), reducing mean absolute error by 36.1%–52.4% and root‐mean‐square error by 27.3%–43.5% and improving R 2 by 13.9%–16.6%. Similar improvements were seen off‐peak, confirming strong accuracy and robustness, especially in networks with many segments and ramps. Overall, the G‐T model offers an efficient, practical tool for traffic assignment, supporting more scientific highway planning and management strategies.

Rapid urbanization and climate change have increased the extent of impervious surfaces, intensifying urban flooding and disrupting natural water circulation. To address these challenges, Low-Impact-Development techniques have been adopted, with permeable pavements serving as effective decentralized stormwater systems. This study evaluates the runoff reduction performance of permeable pavements featuring different upper block structures under identical subbase conditions. Two types of blocks were tested: Block A, an interlocking joint-type block that allows infiltration through its joints, and Block B, a monolithic permeable block that enables direct infiltration through the block surface. Artificial rainfall tests were conducted for 60 min at rainfall intensities of 42 mm/h (2-year) and 66 mm/h (10-year). Both blocks reduced runoff and delayed the equilibrium time compared with those of the impervious pavement. Block B exhibited a lower equilibrium flow rate and a longer recession period owing to uniform infiltration and gradual drainage through the joint filler. These findings indicate that the block structure and joint material properties significantly affect runoff reduction performance.

Zonal jets are a fascinating natural example of how a rapidly rotating turbulent flow self-organizes at large scale in the presence of a β effect. Understanding the long-term, nonlinear equilibration of zonal jets and the feedback with the underlying turbulence and waves is still a challenge. Following a similar approach as in the Holton-Lindzen-Plumb model for mean flow reversals in stratified fluids, this study describes a novel, quasilinear semianalytical model to discuss the emergence and coalescence of zonal winds from the radiation of Rossby waves. This model emphasizes the role of Rossby waves in exchanging momentum with the zonal flow and the feedback of the zonal flow on the waves. It employs a Wentzel-Kramers-Brillouin expansion of the wave field to obtain an explicit expression for the Reynolds stress, leading to a closed mean flow equation. Two key feedback effects control the properties of the wave-driven zonal flow: the Doppler shift, leading to the emergence of critical latitudes, and the modification of the background β effect by the zonal flow curvature. Motivated by previous experimental observations, we integrate this quasilinear model in time with an increasing number of latitudes of wave radiation. We observe a transition between locally driven jets that remain individual and globally driven jets that coalesce and equilibrate at a new scale. In the weak wave damping limit, coalescence occurs when critical latitudes of neighboring jets overlap. This is the first purely zonal closure which self-consistently leads to an equilibrium flow with zonal jets separated by a Rhines scale. These results are further supported by quantitative comparison with experiments and nonlinear direct numerical simulations.

This paper presents a composite wall function, which can be used for a wide range of the adverse pressure gradient (APG) and is derived from the composite walllaw provided in this study. The composite wall-law combines the characteristics of the log-law and the half-power law under the APG, and it degrades to the log-law under the zero pressure gradient (ZPG). A function relationship between the parameters in the composite wall-law and the dimensionless pressure gradient parameter $\mathcal{p}^+$ is provided based on a series of DNS data for the APG. Finally, a generalized wall function incorporating both the viscous sublayer and the buffer layer is presented. The new wall function can accurately predict the mean velocity of both equilibrium and nonequilibrium flows under the APG. The new composite wall function is validated using three test cases: NACA0012, 2D channel with a bump, and turbulent separation bubble. The results demonstrate the accuracy of the new composite wall function under the APG flows.

Edge-localized modes (ELMs) are burst-like transport events observed in the edge region of tokamak plasmas, typically driven by pressure-driven instabilities such as the ballooning mode. Understanding nonlinear dynamics of ELMs is essential for future devices such as ITER. In this study, we conduct nonlinear simulations of the resistive ballooning mode turbulence using the reduced two-fluid model that includes both the heating and the neoclassically induced equilibrium flow. In the absence of the equilibrium flow, the system exhibits the quasi-steady turbulent dynamics, while the inclusion of the equilibrium flow triggers the repetitive bursts. These bursts are triggered by the formation and decay of the intermediate-scale vortices, whose radial extent and lifetime are regulated by the flow shear through nonlinear interactions. The dependence of the burst dynamics on the flow strength is non-monotonic, indicating complex nonlinear interactions between the mean flow and turbulent vortices. These findings reveal the mechanism by which the equilibrium flow modulates the burst dynamics and the vortex size selection in the ELM-like phenomena.

The second stage of development of experimental methods for determination of flash and fire point covers the period from the 1920s to the 1980s. At the beginning of the last century, the most advanced devices and techniques for determining the flash point were already selected and legalized. There are the Abel, Abel-Pensky, Pensky-Martens Granier, Lushaire, Tag, Cleveland, Brenken, Marcusson and Treumann apparatuses. However, old naphtometers are used unofficially until the 30s of the 20th century. National standards on methods of determination of flash and fire temperature are emerging and being improved in phase II. By evolution, all the variety of instruments for determining the rate of flash and ignition (open, semi-closed and closed, open, semi-closed and closed steam apparatus, distillation testers) are reduced to two types of open and closed cups. But new methods (equilibrium, set-flash and continuous flow) and various designs of manual, semi-automatic and automatic instruments are emerging. The accumulated stock of experimental data for flash and fire point of liquids is reflected in the handbooks, which are used for fire safety. In the 60s–70s of the 20th century, a boundary is established between the flash point and the lower temperature flammability limit (lower flash point). Semi-closed type devices are considered.

To avoid the unreasonable layout and location of charging piles affecting the power flow state of a power system, an intelligent layout and location method for charging piles of electric vehicles based on time-series power flow calculation is proposed. This method mainly uses the power system power flow calculation model based on time series linear big data analysis to calculate the power system power flow state, and the demand forecast model of electric vehicle charging piles based on the M/M/S queuing theory is used to forecast the demand of charging piles in the future. Based on the mastered power system power flow state and future demand of charging piles, an objective function of the intelligent layout and location of electric vehicle charging piles is designed with the introduction of the power flow entropy index. The chaotic quantum optimization algorithm with adaptive weight adjustment is used to solve the longitude and latitude of the charging pile location that can achieve the least comprehensive cost, power flow equilibrium, and meet the demand of charging piles in the future. After testing, this method can reduce the comprehensive cost of the charging pile layout and location and improve the power flow balance of the power system.

To convert suction pressure settings into practical, size-specific operating limits for suction ureteric access sheaths (sUAS). Using an 8.7-F flexible ureteroscope with a standard 3.9-F working channel, we measured irrigation inflow at various pressures with no instrument, 270-μm laser fibre, and 1.9-F basket. Outflow was measured using 10/12-, 11/13-, and 12/14-F sUAS with suction applied via Neptune 3™ (Stryker, Kalamazoo, MI, USA) at 30-s intervals and pressures ranging from 50 to 300 mmHg. Inflow and outflow were matched by linear interpolation deriving conservative size- and instrument-specific suction pressure limits for all evaluated sUAS configurations. Ureteroscope irrigation inflow ranged from 30 to 130 mL/min as irrigation pressure irrigation increased from 50 cmH20 (gravity) to 300 mmHg. Working channel instruments reduced inflow: mean (± 2 × the standard error of the mean) change of -57.1% (1.6%) for the 1.9-F basket and -42.1% (2.1%) for the 270-μm laser fibre vs no instrument. Negative pressure via sUAS generated outflow as slow as 5 mL/min (10/12-F sUAS, 50 mmHg suction) and as fast as 700 mL/min (12/14-F sUAS, 300 mmHg suction). Outflow and negative pressure were linearly related (R2 0.94-0.99). Increased scope-to-sheath ratio (SSR) reduced irrigation outflow. Fully opening the valve significantly reduced outflow by 70.8% on average. The 40-cm sUAS modestly increased outflow by 13.3% on average compared to 50-cm sUAS of same diameter. Suction of 50-80 mmHg was ideal for maintaining flow equilibrium with 11/13- and 12/14-F sUAS. The small diameter and high SSR (0.87) of the 10/12-F sUAS required greater negative pressures (150-300 mmHg) to maintain flow equilibrium. Suction pressures as low as 50-80 mmHg are sufficient for majority of sUAS cases. Increasing irrigation inflow with pressure bag systems may counter excessive sUAS outflow; however, introducing instruments can negate this effect.

Extension and Assessment of the Rotated-RoeM Scheme for Hypersonic Equilibrium Flow Computation

A block of ice in a box heated from below and cooled from above can (partially) melt. Vice versa, a box of water with less heating from below or more cooling from above can (partially) re-solidify. This study investigates the asymmetric behaviours between such melting and freezing processes in this Rayleigh–Bénard geometry, focusing on differences in equilibrium flow structures, solid–liquid interface morphology, and equilibrium mean interface height. Our findings reveal a robust asymmetry across a range of Rayleigh numbers and top cooling temperature (i.e. hysteretic behaviour), where the evolution of freezing shows a unique ‘splitting event’ of convection cells that leads to a non-monotonic height evolution trend. To characterise the differences between melting and freezing, we introduce an effective Rayleigh number and the aspect ratio for the cellular structures, and apply the heat flux balance and the Grossmann–Lohse theory. Based on this, we develop a unifying model for the melting and freezing behaviour across various conditions, accurately predicting equilibrium states for both phase-change processes. This work provides insights into the role of convective dynamics in phase-change symmetry-breaking, offering a framework applicable to diverse systems involving melting and freezing.