Cell migration and cytoskeletal remodeling are energetically demanding processes. Reorganizing the cytoskeleton requires ATP to fuel the actomyosin complex, enabling cells to adhere to and migrate through a matrix. While it is known that energy is required for cell migration, the mechanism by which cell-extracellular matrix adhesion influences cell energetics is unclear. Here, we investigated the relationship between cell-extracellular matrix adhesion and cellular metabolic state with a focus on vinculin given its role in connecting the cytoskeleton to focal adhesions and extracellular space. Knocking out vinculin increases the metabolic activity in cells and results in fast, frequent Rho kinase activity-dependent changes in cell shape and protrusions. The cellular protrusion dynamics and bioenergetics are interrelated processes, as stimulating RhoA/Rho kinase activity increases dynamic blebbing protrusions and energy production, and inhibiting metabolism decreases the frequency of blebbing cell protrusions. This link between cell-extracellular matrix adhesion and bioenergetics provides a novel basis by which cellular metabolism and cell migration could be controlled.

Accurately quantifying energy savings in retrofitted and operational buildings remains challenging due to dynamic occupancy, weather variability, and changing operational conditions. This study proposes an AI-enabled Energy Conservation Calculation (ECC) framework to establish dynamic energy baselines and verify energy and carbon savings under real-world operation in Singapore hotel. High-resolution operational data are integrated with a hybrid LSTM-XGBoost architecture to capture temporal patterns and nonlinear interactions in building energy use, while ECC logic translates model outputs into auditable and policy-compliant savings metrics aligned with the Green Mark certification system. The framework was deployed across multiple commercial, residential, and mixed-use buildings over a three-year period. Results show consistently strong predictive performance, with root mean square error generally below 5% across heterogeneous building types. Verified outcomes indicate cumulative emissions reductions of 3221 tCO₂e and energy use intensity improvements exceeding 60% in selected retrofitted cases. Beyond performance evaluation, the framework supports closed-loop operational optimization and produces audit-ready outputs suitable for certification and sustainability-linked finance. These results show that combining dynamic AI-based baselining with standardized energy accounting enables reliable verification of decarbonization outcomes in operational buildings.

The disorderly charging of a large number of electric vehicles (EVs) intensifies the operational pressure on the distribution network and negatively impacts the users’ charging experience. This paper proposes an orderly-charging optimization strategy based on the Deep Deterministic Policy Gradient (DDPG) algorithm. First, a comprehensive EV charging behavior model is developed, incorporating regional functional characteristics, vehicle categories, and user behavioral diversity to more accurately reflect real-world charging patterns. Second, a closed-loop control architecture is designed, integrating charging load forecasting, dynamic energy storage regulation, and real-time power allocation. Finally, the DDPG algorithm is applied to enable intelligent dynamic power allocation, which effectively flattens peak–valley load disparities and minimizes user charging costs. The simulation results demonstrate that the proposed strategy significantly enhances distribution network performance and user satisfaction. Specifically, the strategy reduces peak load by 17.08% and achieves a total cost saving of USD 511.49 (17.08%). By considering real-world zones and diverse EV types, this strategy provides substantial engineering value for practical implementation in multi-zone charging systems.

Abstract The digital economy's explosive growth as a new driver of global economic expansion has highlighted its effects on human health and carbon emissions. The study uses a digital economy module to construct a computable general equilibrium model of China's dynamic energy environment. Through scenario simulation, the study quantifies the impact of developing the digital economy industry on carbon emissions and human health. The study found that in terms of energy consumption, total energy consumption fell to 250 million tce in 2030 under the green digital economy transformation scenario, a reduction of 19.4% from the base scenario. In terms of environmental and health impacts, the PM2.5 concentration under the green digital economy transformation scenario decreased from 25.14 µg/m 3 in 2015 to about 22.36 µg/m 3 in 2030, which was 11.5% lower than the base scenario. In terms of economic performance, the GDP growth rate of the green digital economy transformation scenario was significantly higher, increasing by 0.0579 in 2030 compared to the base scenario. This study demonstrates that developing the digital economy and green energy together can reduce carbon intensity, improve air quality, and minimize health losses. This provides an important policy rationale for balancing economic growth and environmental sustainability. Graphical Abstract

We present a formally exact adiabatic connection (AC) framework for the correlation energy of multireference wave functions, derived using the fermionic operator algebra. This framework can be formulated in both the particle-hole (ph) and particle-particle (pp) representations by exploiting the corresponding decompositions of the two-electron reduced density matrix. While the phAC formalism is well established, here we derive the ppAC formula and provide explicit working expressions based on the extended random-phase approximation. The second-order multireference pp correlation energy introduced by Tucholska et al. [J. Phys. Chem. Lett. 2024, 15, 12001] emerges naturally as the lowest-order approximation in this context. Exploiting the equivalence of ph and pp correlation amplitudes, we propose a combined ffAC method that incorporates both contributions and avoids double counting of correlation. We test all the considered AC methods, i.e., ph- and ppAC, their linearized variants ph- and ppAC0, and the ph-pp combined methods ffAC and ffAC0, on a variety of challenging cases, including multiple bond dissociations, atomic excitations, singlet-triplet gaps of organic biradicals, and singlet and triplet excitation energies of organic chromophores. Across all systems, the linearized ffAC0 method consistently provides the most accurate results. Its accuracy matches or exceeds that of NEVPT2, yet it is significantly more efficient, requiring only one- and two-electron reduced density matrices.

The change of stope migration caused by the failure and instability of the overlying thick key strata has a control effect on the occurrence of rock burst in front of the working face. The main disaster-causing factors of rock burst in front of the working face are explored to predict and prevent rock burst. Through the distribution characteristics of microseismic energy events, it is determined that the instability of hard roof has a great influence on the mining of working face. The influence mechanism of the instability and fracture of the overlying hard rock strata on the rock burst of the working face is clarified, and the distribution of the advance abutment pressure of the working face and the energy accumulation of the coal and rock mass under the static load condition are calculated by establishing the mechanical model of the advance abutment pressure of the working face under the condition of full mining. The superposition of dynamic and static load energy on the working face by the initial and periodic instability fracture and synergistic fracture energy release of the overlying hard rock strata is calculated theoretically. It is determined that the superposition energy transmitted to the working face by the synergistic fracture of the middle and low hard rock strata is 1.23 × 104 J, which is higher than the critical energy of Gengcun rock burst. The low hard rock layer is determined as the monitoring target layer, and the on-line monitoring scheme of hard rock layer activity is established. The dynamic change of anchor cable data of stress evolution law of low hard rock layer in the process of working face mining is quantitatively determined, which reflects the stress evolution law of coal and rock mass in front of working face, and quantitatively determines the three-stage division of the influence of working face mining on coal and rock mass in front of working face.

Addressing uncertainties on the demand side caused by electricity price fluctuations during integrated energy system (IES) dispatch, modeling biases resulting from static assumptions about equipment energy efficiency, and cost redundancy issues stemming from unreasonable seasonal allocation of carbon quotas, this study constructs an electricity PDR economic dispatch optimization model incorporating dynamic energy efficiency and dynamic carbon trading. It proposes a “distributed robust optimization (DRO)-model predictive control (MPC)” collaborative framework and a tiered dynamic carbon quota allocation strategy accounting for seasonal output and efficiency variations of equipment, tailored to match carbon emission characteristics across different seasons. At the demand response level, an electricity price elasticity coefficient matrix is introduced to quantify the impact of real-time price fluctuations on load, integrating it into the MPC model to resolve the time-scale mismatch between day-ahead and intraday scheduling. Simulation results demonstrate: The coupled dynamic energy efficiency and carbon trading model reduces total system costs by 13.07% and carbon trading costs by 11.57% compared to the conventional approach. Regarding tracking error, the combination of rolling optimization and feedback correction improves tracking accuracy by 14.66% and 6.13% compared to cases without feedback correction and rolling optimization, respectively, while reducing total costs by 4.36% compared to the case without rolling optimization. This study provides a scientifically feasible optimization solution for low-carbon economic dispatch of IES under uncertainty.

Rapid deployment of rooftop photovoltaics (PV), electric heating, and electric vehicles (EVs) is stressing low-voltage feeders in cold climates, where winter peaks push aging transformers to their limits. This paper quantifies how much stationary and mobile storage is required to keep feeder power nearly flat over a full year in such conditions. A mixed-integer linear programming (MILP) model co-optimizes stationary battery energy storage systems (BESSs) and EV flexibility, including lithium-ion degradation, under a flatness constraint on transformer loading, i.e., the magnitude of feeder power exchange (import or export) around a seasonal target. The framework is applied to a 48-dwelling neighborhood in Ardahan, northeastern Turkey (mean January ≈ −8 °C) with rooftop PV and an emerging EV fleet. Three configurations are compared: unmanaged EV charging, optimized smart charging, and bidirectional vehicle-to-grid (V2G). Relative to the unmanaged case, smart charging reduces optimal stationary BESS capacity from 4.10 to 2.95 MWh, while V2G further cuts it to 1.23 MWh (≈70% reduction) and increases flat-compliant hours within ±0.5 kW of the target transformer loading level from 92.4% to 96.1%. The levelized cost of demand equalization falls from 0.52 to 0.22 EUR/kWh, indicating that combining modest stationary BESSs with V2G can make feeder-level demand flattening technically and economically viable in cold-climate residential districts.

Molecules in dense environments, such as biological cells, are subjected to forces that fluctuate both in time and in space. While spatial fluctuations are captured by Lifson-Jackson-Zwanzig's model of "diffusion in a rough potential," and temporal fluctuations are often viewed as leading to additional friction effects, a unified view where the environment fluctuates both in time and in space is currently lacking. Here, we introduce a discrete-state model of a landscape fluctuating both in time and in space. Importantly, the model accounts for the reciprocal interaction of the diffusing particle with the landscape, which alters the landscape dynamics. As a result we find, surprisingly, that many features of the observable dynamics do not depend on the temporal fluctuation timescales and are already captured by the model of diffusion in a rough potential, even though this assumes a static energy landscape. Using this model, we reevaluate results of several experimental studies of protein dynamics and propose more accurate bounds on the inferred energetic roughness scales, which account for landscape dynamics.

Currently, wireless sensor networks (WSNs) have been mutually applied to environmental monitoring and industrial control due to their low-cost and low-energy sensor nodes. However, WSNs are composed of a large number of energy-limited sensor nodes, which requires balancing the relationship among energy consumption, transmission delay, and network lifetime simultaneously to avoid the formation of energy holes. In nature, gregarious herbivores, such as the white-bearded wildebeest on the African savanna, employ a “fast-transit and selective-dwell” strategy when searching for water; they cross low-value regions quickly and prolong their stay in nutrient-rich pastures, thereby minimizing energy cost while maximizing nutrient gain. Ants, meanwhile, dynamically evaluate the “energy-to-reward” ratio of a path through pheromone concentration and its evaporation rate, achieving globally optimal foraging. Inspired by these two complementary biological mechanisms, our study proposes a novel ACO-conceptualized optimization model formulated via mixedinteger linear programming (MILP). By mapping the pheromone intensity and evaporation rate into the MILP energy constraints and cost functions, the model integrates discrete decision-making (path selection) and continuous variables (dwell time) by dynamic path planning and energy optimization of mobile sink, constituting multi-objective optimization. Firstly, we can achieve flexible trade-offs between multiple objectives such as data transmission delay and energy consumption balance through adjustable weight coefficients of the MILP model. Secondly, the method transforms complex path planning and scheduling problems into deterministic optimization models with theoretical global optimality guarantees. Finally, experimental results show that the model can effectively optimize network performance, significantly improve energy efficiency, while ensuring real-time performance and extended network lifetime.

Emerging research indicates that neuronal activity is maintained by an architectural system of protons in a multi-scale fashion. Proton architecture is formed when organelles (such as mitochondria, endoplasmic reticulum, lysosomes, synaptic vesicles, etc.) are coupled together to produce dynamic energy domains. Techniques have been developed to visualize protons in neurons; recent advances include near-atomic structural imaging of organelle interfaces using cryo-tomography and nanoscale resolution imaging of organelle interfaces and proton tracking using ultra-fast spectroscopy. Results of these studies indicate that protons in neurons do not diffuse randomly throughout the neuron but instead exist in organized geometric configurations. The cristae of mitochondrial cells create oscillating proton micro-domains that are influenced by the curvature of the cristae, hydrogen bonding between molecules, and localized changes in dielectric properties that result in time-patterned proton signals that can be used to determine the metabolic load of the cell and the redox state of its mitochondria. These proton patterns also communicate to the rest of the cell via hydrated aligned proton-conductive pathways at the mitochon-dria-endoplasmic reticulum junctions, through acidic lipid regions, and through nano-tethered contact sites between mitochondria and other organelles, which are typically spaced approximately 10–25 nm apart. Other proton architectures exist in lysosomes, endosomes, and synaptic vesicles. In each of these organelles, the V-ATPase generates steep concentration gradients across their membranes, controlling the rate of cargo removal from the lumen of the organelle, recycling receptors from the surface of the membrane, and loading neurotransmitters into the vesicles. Recent super-resolution pH mapping has indicated that populations of synaptic vesicles contain significant heterogeneity in the amount of protons they contain, thereby influencing the amount of neurotransmitter released per vesicle, the probability of vesicle release, and the degree of post-synaptic receptor protonation. Additionally, proton gradients in each organelle interact with the cytoskeleton: the protonation status of actin and microtubules influences filament stiffness, protein–protein interactions, and organelle movement, resulting in the formation of localized spatial structures that may possess some type of computational significance. At multiple scales, it appears that neurons integrate the proton micro-domains with mechanical tension fields, dielectric nanodomains, and phase-state transitions to form distributed computing elements whose behavior is determined by the integration of energy flow, organelle geometry, and the organization of soft materials. Alterations to the proton landscape in neurons (e.g., due to alterations in cristae structure, drift in luminal pH, disruption in the hydration-structure of the cell, or imbalance in the protonation of cytoskeletal components) could disrupt the intracellular signaling network well before the onset of measurable electrical or biochemical pathologies. This article will summarize evidence indicating that proton–organelle interaction provides a previously unknown source of energetic substrate for neural computation. Using an integrated approach combining nanoscale proton energy, organelle interface geometry, cytoskeletal mechanics, and AI-based multiscale models, this article outlines current principles and unresolved questions related to the subject area as well as possible new approaches to early detection and precise intervention of pathological conditions related to altered intracellular energy flow.

Die forging hydraulic presses, essential for metal forming in large-scale component manufacturing, often suffer from low energy utilization. Due to system complexity and load uncertainty, accurately characterizing the dynamic energy consumption during operation remains a significant challenge. Additionally, determining the optimal system configuration to minimize energy consumption in the forging process is difficult. To address these issues, this study proposes an electromechanical-hydraulic coupling model to dynamically quantify energy dissipation in key components of the hydraulic press during operation. The influence of motor speed, pump displacement, and overflow pressure settings on system energy consumption is analyzed, and an energy efficiency optimization strategy based on system parameter matching is developed. The accuracy of the proposed energy consumption model is validated through forging experiments on scaled-down aircraft connection components, with the steady-state error of each characteristic curve remaining within 5.5%. Furthermore, the optimized operating parameters result in a 18,753.3 J and 26,710.3 J reduction in motor and pump energy consumption, respectively, while overflow and throttling losses are reduced by 138,986.8 J and 30,357.1 J, leading to an overall system energy consumption reduction of 61.9% in a single operating cycle. Considering typical factory operation, this corresponds to an estimated annual energy saving of approximately 1400 kWh, demonstrating the practical significance of the proposed optimization strategy.

Lattice materials exhibit excellent impact‐resistance properties, which are advanced multifunctional materials with great design potential. In this study, by introducing the two‐phase strengthening mechanism of composite materials, the biphasic stretching‐bending synergistic lattices (BSBSLs), which contain matrix phase cells and enhanced phase backbone cells, are constructed, and their dynamic compression response and energy absorption characteristics are investigated by numerical simulation. Additionally, the effect of the height and density distribution of cells on energy absorption (EA) is analyzed. Finally, to further improve the performance of specific energy absorption (SEA), optimization models of uniform and hierarchical BSBSLs are established. The results indicate that, at the same relative density, compared with stretching‐bending synergistic lattices (SBSLs), the specific strength and SEA of BSBSLs have increased by 49.7% and 57.59% respectively. Compared with the initial structure, for uniform BSBSLs, when the overall relative density remains unchanged and varies from 10% to 70%, the SEA of the optimal structure has increased by 288% and 373% respectively; for hierarchical BSBSLs, when the relative density varies from 10% to 70%, the SEA of the optimal structure has increased by 404%. This research provides a reference for the design of dynamic compression response and optimization design of multilayer lattice structures.

As a widely used structural material in construction, the energy dissipation characteristics of concrete under dynamic impact are crucial for evaluating a structure’s impact resistance and safety performance. However, conventional methods for evaluating energy dissipation characteristics fail to adequately account for the multi-parameter coupling effects during dynamic impact processes. Herein, the dynamic behavior of C15, C20, C30, and C40 concrete specimens was investigated using a split Hopkinson pressure bar (SHPB) apparatus. The dynamic response and energy dissipation mechanisms under impact loading were analyzed. The correlation between energy dissipation density and multiple parameters—including initial loading conditions, peak strain, dynamic compressive strength, and strain rate—was examined. Based on this analysis, a performance index Pi, grounded in energy dissipation density, was proposed for evaluating dynamic energy dissipation. The results show that under dynamic impact loading, concrete specimens of different grades basically show brittle damage mode and obvious strain-rate strengthening effect. Specifically, the dynamic compressive strength of C15-3 is 22.10 MPa, representing an increase of approximately 47.3%, while that of C40-3 is 46 MPa, showing an increase of approximately 15%. The energy transfer in concrete specimens is influenced by initial loading conditions, concrete material properties, and damage modes, among other factors. All of these parameters exhibit a strong correlation with the energy dissipation density. The comprehensive multi-parameter performance index Pi for dynamic energy dissipation yields superior evaluation results compared to using energy dissipation density alone. The research results provide an innovative reference for structural safety protection.

Dynamic pricing and energy management for shore side electricity in a port microgrid: A deep reinforcement learning approach

Research on evolutionary patterns of dynamic mechanical and energy characteristics in carbon fiber-reinforced concrete and intelligent predictive modeling

Efficient Path Planning Framework Considering Dynamic Behavior and Energy Constraint of Electric Vehicle

Dynamic simulation and energy efficiency analysis of a megawatt-scale proton exchange membrane water electrolysis system

LMP-Opt: A simulation-based hybrid model for dynamic job scheduling and energy optimization in serverless computing

Analysis of the dynamic performance and energy efficiency of a three-wheel electric vehicle under standard drive cycles