Search Results (20,317)

The fractal characteristics of coal pore–fracture networks and their evolution under compression are essential for predicting rock mass failure and fluid transport. This study combines micro-CT scanning with fractal theory and seepage mechanics to investigate the structural evolution of coal under uniaxial compression and its impact on fluid transport. CT scans were performed at four characteristic stages (initial, elastic, plastic, and failure) to reconstruct three-dimensional fracture networks. Quantitative analysis reveals that fracture porosity increases sequentially from 0.44% to 5.01%, with the failure stage reaching 11.4 times the initial value. Fracture length and aperture distributions follow power-law scaling, and their fractal dimensions exhibit distinct evolution patterns: length dimension increases from 2.43 to a peak of 2.56 in the plastic stage and then drops to 2.47 at failure, while aperture dimension decreases from 2.29 to a trough of 2.12 before rebounding to 2.26. These patterns reflect a dynamic adjustment of network complexity, transitioning from primary fractures to micro-fracture dominance and finally to main fracture coalescence. Based on the Knudsen number, three diffusion regimes of Fick, transition and Knudsen are identified. A fractal permeability model is developed by idealizing the pore space as tortuous capillaries, showing that permeability scales with the fourth power of the maximum pore diameter and is positively influenced by the fractal dimension and the number of large pores. Furthermore, a coupled seepage–stress model is derived, incorporating pressure transmission, shear transmission, and crack opening coefficients. The damage variable is expressed as a function of stress level and fractal dimension. These findings provide theoretical support for predicting gas transport and failure behavior in coal under coupled hydro-mechanical conditions. Full article

The green transition in agriculture is a key issue for achieving sustainable development. Based on panel data from 30 Chinese provinces covering the period from 2011 to 2022, this paper examines the relationship between environmental enforcement and agricultural green total factor productivity (AGTFP), with a focus on analyzing the moderating effects of land transfer and agricultural insurance, as well as their synergistic threshold characteristics. The study employs two-way fixed-effects models, moderating effect models, and Hansen threshold regression methods for empirical analysis. The baseline regression results show a significant positive association between environmental enforcement and AGTFP. This conclusion remains robust after various tests, including truncation, replacement of core explanatory variables, difference GMM, and instrumental variables. The decomposition test shows that this positive correlation is mainly reflected through the channel of technological progress, rather than the improvement in technical efficiency. Heterogeneity analysis indicates that the positive association is more pronounced in regions with high GDP, strong law enforcement capacity, and in northern regions. Moderation analysis reveals that both the land transfer rate and insurance depth positively moderate the relationship between environmental enforcement and AGTFP, and the two exhibit a synergistic effect. However, this synergistic effect exhibits nonlinear characteristics and may weaken or even reverse at extreme value intervals. A threshold model further reveals an asymmetric complementary relationship between the two institutional conditions. The moderating effect of land transfer is activated only after insurance depth crosses a threshold value, while the moderating effect of insurance depth is most effective during the small-scale farming stage. These findings suggest that environmental regulation policies should be advanced in coordination with land transfer and agricultural insurance systems, with a focus on institutional alignment and coordination. Full article

As climate change intensifies, transitioning the construction sector away from fossil fuels is vital to reducing global greenhouse gas emissions and localized urban pollution. This paper assesses the economic feasibility of electrifying construction machinery by developing an Annualized Cost of Ownership framework that incorporates mobile charging solutions, internalizes environmental and public health operational externalities (CO₂, PM_2.5, NO_x, and SO₂), and relies on Monte Carlo simulation with Cholesky decomposition to capture the interdependencies among cost drivers. We analyze twenty distinct models of excavators and wheel loaders—the two largest contributors to construction-machinery emissions—comprising functionally equivalent diesel and battery-electric variants. Our results show that several compact electric models are already cost-competitive even without internalizing environmental and public health operational externalities. When these are accounted for, the economic advantage of electric machinery increases, particularly in denser urban areas where local air pollution damages are severe. While projected battery cost reductions further lower electric ownership costs, the magnitude of this effect is modest. However, the weak penetration of electric construction equipment in the US underscores that targeted policy interventions—such as point-of-sale rebates, green procurement mandates, tax credits, charging infrastructure subsidies, or the creation of low-emission zones and noise ordinances that advantage electric construction machinery—are needed to accelerate market adoption. These measures are particularly critical in densely populated urban areas, where internalizing local air pollution and public health externalities significantly amplifies the economic value of zero-emission machinery. Full article

Remote supervision of agricultural robots depends on continuous interpretation of robot status and wireless link quality. In smart greenhouses, crop canopies, metallic frames, cultivation rows, and non-line-of-sight propagation can cause intermittent packet loss and RSSI attenuation. Treating such transient degradation as immediate communication failure can interrupt robot operation unnecessarily, whereas delayed recognition of persistent loss can compromise safety. This study proposes a probabilistic communication-state inference method for remotely supervised agricultural robots. The robot-to-gateway wireless link is represented by three states: normal, degraded, and failure. The degraded state acts as an uncertainty buffer that preserves recoverable degradation before failure escalation. Packet reception ratio, received signal strength, and trajectory-derived context are used to update state probabilities through a bounded transition mechanism. Field experiments with a mobile agricultural robot in a smart greenhouse showed an accuracy of $0.915\pm 0.007$ and a macro F1-score of $0.907\pm 0.008$, while reducing the premature failure rate to $18.0\pm 1.4\%$. Comparisons with threshold-based, moving-average, and adapted WSN fault-detection baselines, including a FedLSTM-inspired baseline, showed that binary fault-detection logic cannot explicitly preserve recoverable degraded communication intervals. The results indicate that probabilistic degradation modeling supports communication-aware remote supervision by distinguishing transient degradation from failure-level communication loss. Full article

To systematically examine the impact of climate risks on China’s financial system, this study employs the EGARCH-SGED model to precisely fit financial market volatility based on China’s Climate Change News Index. It then combines the LASSO-CoVaR method to measure tail risk spillover effects within China’s financial system under climate risk shocks, constructs a risk contagion network, and innovatively utilizes the RF-AdaBoost model to establish the risk early warning system. Findings reveal that climate risk is a key driver of dynamic correlation evolution within the financial system, with heterogeneous impacts across different markets. Physical climate risk events intensify short-term risk contagion while generating long-term effects; transition risks undergo a dynamic process, initially amplifying uncertainty before enhancing systemic stability over the long term. The RF-AdaBoost model outperforms traditional machine learning models in risk warning, demonstrating outstanding predictive accuracy and generalization capabilities, thereby providing effective intellectual support for climate risk prevention and financial stability management. Full article

The mechanical response of cellular structures is governed not only by relative density and average cell geometry but also by the spatial arrangement of cells. However, the manner in which arrangement-dependent effects evolve with increasing cell number has not been systematically clarified. In this study, the compressive behavior of closed-cell structures with varying cell numbers was investigated using finite element analysis under dynamically equilibrated compression conditions while maintaining constant relative density and identical material parameters. Cellular models were generated using hierarchical Poisson disk sampling combined with Voronoi tessellation. The number of cells was increased through three distinct approaches: mirror replication of a reference structure, enlargement of the overall specimen size, and refinement of cell size under fixed external dimensions. To characterize arrangement-dependent effects, two distinct features of the compressive response were introduced: averaging, defined as a reduction in variability across responses from different initial cell arrangements, and smoothing, defined as the suppression of abrupt stress fluctuations within an individual response. Quantitative metrics were employed to evaluate both effects. Averaging was observed in plate-type models compressed in the z-direction and in fixed-size models, whereas mirror-connected models retained strong arrangement dependence despite large cell numbers. Smoothing occurred predominantly in plate-type models compressed in the z-direction and was strongly correlated with the number of cell layers aligned along the compression direction rather than with total cell number alone. The simulations were conducted in a dynamically equilibrated regime in which internal stress equilibrium was achieved during deformation. These results demonstrate that compressive behavior is governed not only by cell number but also by structural arrangement and directional cell-layer alignment, providing mechanistic insight into the transition from arrangement-dependent variability to stable macroscopic response under dynamic compression. Full article

In this paper, the dynamic modeling of the anisotropic non-ideal weakly gyroscopic rotor system is considered. Equations of nonstationary transitions are derived from motion differential equations; then, the control equation, stationary frequency dependencies, and force and energy relations are obtained. When the rigidity of the elastic support is anisotropic in orthogonal directions, two critical velocities and, accordingly, two resonance regions are found. Because of the strong interaction of the rotor system with a non-ideal DC motor, slopes of the resonance curves are observed in the regions of critical speeds even in the absence of a nonlinear component of the reference stiffness, and loops are also recorded. It is proven that, compared with linear damping, the cubic nonlinearity of damping strongly suppresses the resonant amplitudes of the rotor, reduces the size of the loops even more, and strongly attenuates the Sommerfeld effects until they are completely eliminated. It is shown that an increase in the magnitude of the cubic nonlinearity of damping greatly facilitates the passage of the resonance region and expands the range of operating speeds. This proves that the amplification of linear damping with cubic nonlinearity is an effective method for controlling resonant passages and an effective damping model. Full article

This study evaluates the ecological impacts of seawater desalination discharge on coastal marine ecosystems through a sequential analytical framework linking systematic literature synthesis, field-monitoring evidence, spatial analysis, and predictive ecological modeling. The novelty of the study lies in combining multi-regional evidence from Mediterranean coastal zones, Persian Gulf waters, and Pacific coastal environments with threshold-based ecological risk assessment, thereby linking discharge-related environmental stressors with biological responses and ecosystem-function alterations. The systematic review first retained 750 studies published between 2004 and 2024 for qualitative synthesis. On this basis, 59 high-quality references with sufficient numerical information were selected for the main quantitative meta-analysis, while field-monitoring data were used to support the interpretation of distance-based discharge gradients. Spatial interpolation and hierarchical modeling were then applied to evaluate exposure–response patterns and ecological threshold behavior. The results showed that desalination facilities generated measurable ecological impacts mainly within 50–200 m of discharge points, with a critical transition distance of approximately 127 m where hypersaline conditions, typically 1.5–2.0 times ambient seawater levels, were associated with marked changes in marine community structure. Benthic assemblages showed taxon-specific responses, with mollusks and echinoderms exhibiting greater sensitivity than polychaetes and small crustaceans. Marine vegetation declined strongly under combined salinity, thermal, and chemical stress, while phosphonate-based antiscalants accumulated in filter-feeding organisms and produced bioaccumulation factors up to 42.1 times ambient levels. Ecosystem-function indicators, including microbial community composition and sediment organic matter processing, remained altered up to 300 m from discharge points, indicating that functional impacts may extend beyond the primary hypersaline plume. The predictive modeling framework further demonstrated that ecological risk decreased nonlinearly with distance and varied according to discharge intensity, local hydrodynamics, and biological sensitivity. These findings indicate that conventional uniform buffer-based assessment may underestimate the ecological footprint of desalination discharge. Sustainable desalination management should therefore adopt site-specific monitoring, species-sensitive protection thresholds, improved brine-management technologies, and adaptive mitigation strategies based on real-time environmental feedback. Full article

This study presents an intelligent framework for the analysis of multidimensional geomechanical states in underground mining systems based on numerical simulation and machine learning methods. A three-dimensional geomechanical model of the Zholymbet deposit was developed in the RS3 environment using the generalized Hoek–Brown failure criterion. Numerical simulations were performed for representative mining scenarios characterized by complex excavation interaction and stress redistribution. The modelling results were transformed into a multidimensional geomechanical dataset containing stress, deformation, displacement, and yielding parameters. Principal component analysis (PCA) was applied to investigate the internal structure of the geomechanical state space and identify dominant patterns controlling the rock mass behavior. Clustering analysis revealed several geomechanical regimes corresponding to stable, transitional, and instability-prone conditions. Isolation Forest anomaly detection demonstrated that atypical geomechanical states are not randomly distributed but spatially localized near excavation systems and mining horizons. The obtained results indicate that hazardous geomechanical conditions are governed by complex interactions between stress concentration, deformation intensity, yielding processes, and excavation geometry. The proposed approach provides a basis for intelligent interpretation of large-scale numerical modelling results and may support geomechanical risk assessment in underground mining operations. Full article

α-Synuclein (α-Syn) is a key presynaptic protein, primarily known for its role in the pathogenesis of Parkinson’s disease (PD) and other synucleinopathies, including dementia with Lewy bodies (DLB). Although much of the research has focused on the nigrostriatal dopamine (DA) pathway, there is growing recognition that the accumulation of misfolded α-Syn in the prefrontal cortex (PFC) is a critical driver of non-motor symptoms and cognitive deficits in PD and DLB. This review examines the dual role of α-Syn in the PFC circuitry, initially exploring its regulation of synaptic vesicle (SV) dynamics and recycling to maintain stable neurotransmission. We highlight its contribution to the modulation of glutamatergic (Glu) and GABAergic (γ-aminobutyric acid, GABA) synapses, which ensures the functional excitatory/inhibitory (E/I) balance of prefrontal circuits. Conversely, in PD and DLB, the transition of functional α-Syn monomers to pathological oligomers triggers a cascade of synaptic failures. We analyze how α-Syn aggregation causes pathology in dendritic spines, leads to a progressive reduction in the density of synaptic markers, and impairs cortical plasticity. Synthesizing evidence from neuroimaging studies, post-mortem human cortical samples, and animal models, this review emphasizes the PFC as a vulnerable brain region where α-Syn-mediated synaptic dysfunction translates into cognitive and emotional deficits. Deciphering these early synaptic alterations is essential for developing neuroprotective strategies that preserve cortical function in PD and DLB. Full article

Accurate representation of the pressure drop–flow rate (Δp–q) relationship of nozzle-type inflow control devices (ICDs) is critical for reliable reservoir–wellbore coupled simulation. Conventional ICD models in reservoir simulators rely primarily on empirical correlations or tabulated data, but commonly used formulations cannot consistently capture the linear behavior in the low-flow regime or the transition between flow regimes, which may reduce physical fidelity and numerical robustness. To overcome this limitation, this study proposes a unified characteristic-curve representation that integrates linear, transitional, and quadratic flow regimes into a single continuous and differentiable function through a physically constrained least-squares formulation, and further develops a physics-informed neural network (PINN) to learn the ICD pressure–flow relationship while enforcing physical consistency. The trained PINN model is embedded into a multi-segment well model within a reservoir–wellbore coupled simulation framework and evaluated using a mechanistic reservoir model containing permeability streaks with varying permeabilities. The results show that the proposed method improves numerical convergence and accurately reproduces ICD pressure–flow behavior across multiple flow regimes, providing a more physically consistent and robust representation of ICD performance for inflow control analysis and reservoir simulation. Full article

Superheavy and hyperheavy nuclei are one of the frontiers of nuclear structure nowadays, while for many actinides rather limited experimental information exists. Therefore, theoretical methods providing parameter-independent predictions for these nuclei are of particular interest. Such a method is the proxy-SU(3) approximation to the shell model, which has been adequately tested against experimental data in medium-mass and heavy nuclei up to the rare-earth region, and it has been found to provide reliable, parameter-independent predictions for the collective deformation variables $\beta$ and $\gamma$. Within the proxy-SU(3) approach, the SU(3) symmetry of the three-dimensional harmonic oscillator, which is destroyed beyond the $sd$ shell by the strong spin–orbit interaction, is restored through a unitary transformation. For each nucleus, the most symmetric irreducible representation (irrep) allowed by the Pauli principle and the short-range nature of the nucleon–nucleon interaction, called the highest-weight (hw) irrep in mathematical language, is found to suffice, except in cases in which the hw irrep turns out to be completely symmetric, so that the next highest weight (nhw) irrep has also to be included. In this article we provide a full collection of the hw and nhw irreps, as well as of the corresponding parameter-free predictions for the deformation variables $\beta$ and $\gamma$, for all atomic nuclei ranging from $Z=82$, $N=82$ to $Z=126$, $N=258$. Several cases exemplifying the use of the collected results for studying the prolate-to-oblate shape transition, mirror symmetries, and the evolution of the collective variables along the valley of stability are also considered. Full article

Energy communities (ECs) are becoming a key institutional instrument for decentralizing the European energy transition, yet their implementation remains constrained by fragmented legal interpretation, uneven national transposition, and unresolved socio-technical coordination problems. This review synthesizes the peer-reviewed literature, EU primary legal texts, and national legislation to clarify the distinction between Renewable Energy Communities (RECs) and Citizen Energy Communities (CECs), alongside the amendment relationship between the RED II and RED III directives. The analysis demonstrates that the scalability of these initiatives depends less on theoretical legal recognition and more on aligning operational frameworks, including metering, settlement, cybersecurity, and equitable allocation rules. The Romanian case illustrates this challenge clearly: rapid prosumer growth creates valuable distributed generation but also exposes physical grid constraints, asymmetric socio-economic participation capacity, and weak experience with cooperative energy governance. To address these vulnerabilities, this paper contributes a focused analytical framework linking energy justice, peer-to-peer game-theoretic modeling, and the strategic integration of “anchor-prosumers.” The study argues that larger renewable self-consumers can act as stabilizing community anchors when internal energy prices are designed between wholesale export values and retail import prices, thereby improving both producer incentives and consumer affordability. Future research developments, including targeted surveys and longitudinal empirical validations, will sustain this claim and optimize the socio-economic resilience of decentralized energy markets. Full article

To address the widespread issue of abnormally high pump pressure during hydraulic fracturing of deep-to-ultra-deep reservoirs (burial depth > 4500 m) in the Junggar Basin, this study systematically reveals the mechanical mechanism underlying this phenomenon by integrating well logging curve analysis and elastoplastic mechanics theory. Statistical results demonstrate that the actual fracture initiation pressure of 60% of wells in the target block is significantly higher than the values predicted by traditional elastic theory, primarily attributed to plastic yielding and stress concentration effects around perforations induced by high in situ stress. An elastoplastic rock fracture initiation pressure model is established based on the Mohr–Coulomb criterion and the plastic zone radius criterion, which is applied to predict the fracture initiation pressure of selected wells in the target block. The relative error between the model predictions and field measurements is less than 2%, significantly improving the prediction accuracy of fracture initiation pressure in deep-to-ultra-deep formations. This provides precise guidance for subsequent optimization of operational parameters and selection of pressure ratings for wellhead equipment. The study further clarifies that in situ stress difference, rock yield stress, and the power-law hardening exponent are the key factors controlling the transition of fracture initiation modes. To mitigate the high pump pressure challenge in deep-to-ultra-deep reservoir fracturing, the field application of weighted fracturing fluid effectively increases the wellbore hydrostatic column pressure, reduces wellhead operational pressure, and ensures construction safety. The findings of this study provide critical theoretical and technical support for achieving the goal of “successful fracture initiation and effective fracture control” in deep-to-ultra-deep reservoir fracturing. Full article

High-fidelity computational fluid dynamics (CFD) remains computationally expensive for steady aerodynamic prediction under multi-condition and variable-geometry configurations, which limits rapid design iteration. To address this issue, this study proposes a data-driven surrogate framework for aircraft surface flow-field prediction on irregular meshes. The framework combines a geometry-unification strategy for variable rudder-deflection configurations with KM-GAT, a hybrid neural architecture that integrates graph attention and KAN-based nonlinear feature transformation. Geometry unification maps the surface flow fields associated with different rudder-deflection states onto a common zero-deflection reference template, thereby establishing consistent mesh correspondence and fixed prediction locations across samples while retaining the rudder angle as an operating-condition variable. The KM-GAT model further combines topology-aware message passing with localized nonlinear refinement, while the Huber loss is adopted to improve training robustness for CFD-derived data. Experiments on the F-22 research model show that the proposed framework achieves lower prediction errors and more concentrated error distributions than baseline MLP and GNN-based models. Qualitative comparisons further indicate that KM-GAT better preserves localized high-gradient structures, including pressure transitions and vortex-dominated regions. These results suggest that the proposed framework provides an effective surrogate modeling strategy for variable-geometry aerodynamic flow field prediction. Full article