Local Fluctuations Research Articles

Multi-scale model for evaluating stress fluctuation in shale gas reservoirs considering geomechanical heterogeneity

In this article, a numerical model considering multi-scale flow and heterogeneity of shale reservoirs is developed to analyze the spatiotemporal evolution of the stress field in shale gas production. After considering the geomechanical heterogeneity, the variation in the local rock mechanical parameters of the reservoir, and the increase in the effective stress of the reservoir will cause local stress fluctuations. In the hydraulic fracture control area, the average stress fluctuation coefficient is higher, indicating a stronger “stress heterogeneity.” When the homogeneity increases from 5 to 100, the fluctuation range of the minimum horizontal principal stress increases by 28.89 MPa, and the fluctuation range of the principal stress difference increases by 7.35 MPa. The presence of natural fractures can significantly impact the coupled hydraulic-mechanical processes of the reservoir, increasing the number of high-speed permeation channels penetrating the system. The greater the density and permeability, the smaller the minimum horizontal principal stress and the larger the principal stress difference. The angle of natural fractures directly influences the direction of stress drop, with an average minimum horizontal principal stress occurring when the angle of natural fractures is at 60°/120°.

Data fusion for enhancing urban air quality modeling using large-scale citizen science data

Rapid urbanization has led to many environmental issues, including poor air quality. With urbanization set to continue, there is an urgent need to mitigate air pollution and minimize its adverse health impacts. This study aims to advance urban air quality modelling by integrating a dispersion model output with large-scale citizen science data, collected over a 4-week period by 642 participants in Cork City, Ireland. The dispersion model enabled the identification of major sources of NO2 air pollution while also addressing gaps in regulatory monitoring efforts. Integrating the diffusion tube data with the dispersion model output, we developed a data fusion model that captured localized fluctuations in air quality, with increases of up to 22μg/m3 observed at major road intersections. The data fusion model provided a more accurate representation of NO2 concentrations, with estimates within 1.3μg/m3 of the regulatory monitoring measurement at an urban traffic location, an improvement of 11.7μg/m3 from the baseline dispersion model. This enhanced accuracy enabled a more precise assessment of the population exposure to air pollution. The data fusion model showed a higher population exposure to NO2 compared to the dispersion model, providing valuable insights that can inform environmental health policies aimed at safeguarding public health.

Unleashed Remarkable Energy Storage Performance in Bi0.5K0.5TiO3-based Relaxor Ferroelectrics by Local Structural Fluctuation.

Dielectric capacitors harvest energy through an electrostaticprocess, which enables an ultrafast charging-discharging rate and ultrahigh power density. However, achieving high energy density (Wrec) and efficiency (η) simultaneously, especially when preserving them across a wide frequency/temperature range or cycling numbers, remains challenging. In this work, by especially introducing NaTaO3 into the representative ferroelectric relaxor of Bi0.5K0.5TiO3-Bi0.5Na0.5TiO3 and leveraging the mismatch between B-site atoms, we proposed a method of enhancing local structural fluctuation to refine the polar configuration and to effectively improve its overall energy-storage performances. As a consequence, the ceramic exhibits an ultrahigh Wrec of 15.0 J/cm3 and high η up to 80%, along with a very wide frequency stability of 10 - 200 Hz and extensive cycling number up to 108. In-depth local structure and chemical environment investigations, consisting of atom-scale electron microscopy, neutron total scattering, and solid-state nuclear magnetic resonance, reveal that the randomly distributed A/B-site atom pairs emerge in the system, leading to the evident local structural fluctuations and concomitant polymorphic polar nanodomains. These key ingredients contribute to the large polarization, minimal hysteresis, and high breakdown strength, thereby promoting energy-storage performances. This work opens a new path for designing high-performance dielectric capacitors via manipulating local structural fluctuations.

Unleashed Remarkable Energy Storage Performance in Bi0.5K0.5TiO3‐based Relaxor Ferroelectrics by Local Structural Fluctuation

Unleashed Remarkable Energy Storage Performance in Bi0.5K0.5TiO3‐based Relaxor Ferroelectrics by Local Structural Fluctuation

ANTIPASTI: Interpretable prediction of antibody binding affinity exploiting normal modes and deep learning.

The high binding affinity of antibodies toward their cognate targets is key to eliciting effective immune responses, as well as to the use of antibodies as research and therapeutic tools. Here, we propose ANTIPASTI, a convolutional neural network model that achieves state-of-the-art performance in the prediction of antibody binding affinity using as input a representation of antibody-antigen structures in terms of normal mode correlation maps derived from elastic network models. This representation captures not only structural features but energetic patterns of local and global residue fluctuations. The learnt representations are interpretable: they reveal similarities of binding patterns among antibodies targeting the same antigen type, and can be used to quantify the importance of antibody regions contributing to binding affinity. Our results show the importance of the antigen imprint in the normal mode landscape, and the dominance of cooperative effects and long-range correlations between antibody regions to determine binding affinity.

Melting of binary mixtures

The melting process of two-dimensional binary mixtures is studied using the Langevin simulation method. The melting point of each component in a binary mixture is determined by the local relative interparticle distance fluctuation method. The results show that, compared with the monodisperse system, the components of the binary mixture exhibit a multi-step melting characteristic. Further, the melting process of binary mixtures is found to depend on the mixing ratio and charge ratio. It is shown that particle hopping motion plays a key role in understanding melting in binary mixtures.

Matrix Compression and Pore Heterogeneity in the Coal-Measure Shale Reservoirs of the Qinshui Basin: A Multifractal Analysis

The application of high-pressure fluid induces the closure of isolated pores inside the matrix and promotes the generation of new fractures, resulting in a compressive effect on the matrix. To examine the compressibility of coal-measure shale samples, the compression of the coal–shale matrix in the high-pressure stage was analyzed by a low-pressure nitrogen gas adsorption and mercury intrusion porosimetry experiment. The quantitative parameters describing the heterogeneity of the pore-size distribution of coal-measure shale are obtained using multifractal theory. The results indicate that the samples exhibit compressibility values ranging from 0.154 × 10−5 MPa−1 to 4.74 × 10−5 MPa−1 across a pressure range of 12–413 MPa. The presence of pliable clay minerals enhances the matrix compressibility, whereas inflexible brittle minerals exhibit resistance to matrix compression. There is a reduction in local fluctuations of pore volume across different pore sizes, an improvement in the autocorrelation of PSD, and a mitigation of nonuniformity after correction. Singular and dimension spectra have advantages in multifractal characterization. The left and right spectral width parameters of the singular spectrum emphasize the local differences between the high- and low-value pore volume areas, respectively, whereas the dimensional spectrum width is more suitable for reflecting the overall heterogeneity of the PSD.

Coupling stress and transmissivity to define equivalent directional hydraulic conductivity of fractured rocks

A DFN (Discrete Fracture Network) modelling approach is developed to couple stresses with fracture transmissivities and to evaluate large scale rock mass hydraulic conductivity. The transmissivity-stress coupling relies on a negative exponential correlation between normal stress acting on a fracture and fracture transmissivity, bounded by residual and maximal apertures. The remote stresses and the local stress fluctuations induced by the fractures themselves are combined in a semi-analytical approach to compute the normal stress acting on each fracture of a DFN. Directional equivalent hydraulic conductivities are numerically computed in all directions from a spherical permeameter setup. The resulting properties are first a cloud of points, where each point defines a direction and an equivalent hydraulic conductivity. The distribution of equivalent hydraulic conductivities is analyzed to define mean values, preferential directions and anisotropy ratio. The entire workflow is developed in the numerical platform DFN.lab. The capacity of the method to investigate the impact of the in-situ stresses on the rock mass hydraulic conductivity is illustrated for fracturing and in-situ stress conditions similar to the conditions at the Forsmark site in Sweden. We find that the stress fluctuations induced by the fractures have a significant impact on the resulting hydraulic conductivity field. They limit the anisotropy ratio to values close to a factor of 3 while the transmissivity distribution is correlated to orientations and spans several orders of magnitude. Sensitivity analyses, performed by changing the parameters of the transmissivity-stress law, show quantitatively how the directional hydraulic conductivities are rather controlled by the orientations of the in-situ stresses or by the underlying connectivity structure of the DFN.

Superstatistics Applied to Cucurbitaceae DNA Sequences.

The short and long statistical correlations are essential in the genomic sequence. Such correlations are long-range for introns, whereas, for exons, these are short. In this study, we employed superstatistics to investigate correlations and fluctuations in the distribution of nucleotide sequence lengths of the Cucurbitaceae family. We established a time series for exon sizes to probe these correlations and fluctuations. We used data from the National Center for Biotechnology Information (NCBI) gene database to extract the temporal evolution of exon sizes, measured in terms of the number of base pairs (bp). To assess the model's viability, we utilized a timescale extraction method to determine the statistical properties of our time series, including the local distribution and fluctuations, which provide the exon size distributions based on the q-Gamma and inverse q-Gamma distributions. From the Bayesian statistics standpoint, both distributions are excellent for capturing the correlations and fluctuations from the data.

Seroma incidence and risk factors in women undergoing mastectomies as surgical breast cancer treatment.

Seroma is the most common early complication following surgical breast cancer treatment. Its development is associated with pain, scar complications, adjuvant therapy delays, the need for outpatient visits, and increased care costs. Assess seroma incidence and risk factors in women undergoing mastectomies. This study comprises a prospective cohort encompassing women aged 18 or over undergoing mastectomies as a breast cancer treatment. Patients underwent physiotherapy on the 1st, 7th, and 30th postoperative days for kinetic-functional, skin, and wound healing assessments and were attended to by nurses for surgical wound care, draining liquid on the 7th, 14th, and 21st postoperative days. Seroma was defined as the presence of local fluctuations requiring puncture, regardless of the punctured volume. A total of 249 women were evaluated, with a mean age of 57.5 (SD = 11.8). A total of 77.1% were classified as overweight or obese, 60.2% were hypertensive, 21.3% were diabetic, 66.7% underwent neoadjuvant chemotherapy and 62.7% underwent axillary lymphadenectomies. Seroma incidence was 71.1%, requiring, on average, two aspiration punctures until condition resolution. Overweight or obese women and those who underwent axillary lymphadenectomies exhibited 1.92- and 2.06-fold higher risk for seroma development (OR = 1.92; 95% CI 1.02-3.61; p = 0.042; and OR = 2.06; 95% CI 1.17-3.63; p = 0.012), respectively. Seroma incidence was very high. Being overweight or obese and undergoing axillary lymphadenectomy comprise independent seroma development risk factors. This study is part of a randomized clinical trial evaluating the effectiveness of applying compressive taping to prevent post-mastectomy seroma, which was approved by the Brazilian National Cancer Institute, Research Ethics Committee (2,774,824), and it is registered in the ClinicalTrials.gov (NCT04471142, on July 15, 2020).

Dynamic Local Symmetry Fluctuations of Electron Density in Halide Perovskites

Dynamic Local Symmetry Fluctuations of Electron Density in Halide Perovskites

High-resolution observations of 12CO and 13CO J = 3 2 towards the NGC 6334 extended filament. I. Emission morphology and velocity structure

NGC 6334 is a giant molecular cloud (GMC) complex that exhibits elongated filamentary structure and harbours numerous OB-stars, H II regions, and star-forming clumps. To study the emission morphology and velocity structure of the gas in the extended NGC 6334 region using high-resolution molecular line data, we made observations of the 12CO and 13CO $J=3 2$ lines with the LAsMA instrument at the APEX telescope . The LAsMA data provided a spatial resolution of 20" (sim 0.16 pc) and sensitivity of 0.4 K at a spectral resolution of 0.25 km $. Our observations revealed that gas in the extended NGC 6334 region exhibits connected velocity coherent structure over sim 80 pc parallel to the Galactic plane. The NGC 6334 complex has its main velocity component at $ -3.9$ km $ with two connected velocity structures at velocities $ -9.2$ km $ (the `bridge' features) and $ km $ (the northern filament, NGC 6334-NF). We observed local velocity fluctuations at smaller spatial scales along the filament that are likely tracing local density enhancement and infall, while the broader V-shaped velocity fluctuations observed towards the NGC 6334 central ridge and G352.1 region located in the eastern filament EF1 indicate globally collapsing gas onto the filament. We investigated the 13CO emission and velocity structure around 42 WISE H II regions located in the extended NGC 6334 region and found that most H II regions show signs of molecular gas dispersal from the centre (36 of 42) and intensity enhancement at their outer radii (34 of 42). Furthermore most H II regions (26 of 42) are associated with least one ATLASGAL clump within or just outside of their radii, the formation of which may have been triggered by H II bubble expansion. Typically towards larger H II regions we found visually clear signatures of bubble shells emanating from the filamentary structure. Overall the NGC 6334 filamentary complex exhibits sequential star formation from west to east. Located in the west, the GM-24 region exhibits bubbles within bubbles and is at a relatively evolved stage of star formation. The NGC 6334 central ridge is undergoing global gas infall and exhibits two gas bridge features possibly connected to the cloud-cloud collision scenario of the NGC 6334-NF and the NGC 6334 main gas component. The relatively quiescent eastern filament (EF1 - G352.1) is a hub-filament in formation, which shows the kinematic signature of global gas infall onto the filament. Our observations highlight the important role of H II regions in shaping the molecular gas emission and velocity structure as well as the overall evolution of the molecular filaments in the NGC 6334 complex.

High-temperature mechanical behavior and microstructural destabilization evolution of the lightweight refractory high-entropy alloy prepared by laser additive manufacturing

Lightweight refractory high-entropy alloy (LRHEA) has lower density and higher specific strength, which makes it very promising for future applications in rocket engine components. Their high-temperature resistance to softening and high microstructure stability in a wide temperature range are also essential to sustaining their performance due to being used in harsh, high-temperature situations. In this work, the high-temperature tensile behavior of Al0.3NbTi3VZr1.5 LRHEA fabricated by laser additive manufacturing has been systematically investigated from 400 °C to 800 °C, and the relationship between the deformed microstructure and the mechanical behavior was revealed, and analyzing the reasons for the thermodynamic instability of the solid solution in LRHEA are discussed in detail. It is found that the weakening of the high entropy effect at intermediate temperatures reduces the stability of the solid solution, and the accelerated diffusion between elements further promotes the solid solution destabilization, mainly manifested as the Laves phase and the local chemical fluctuation (LCFs). The high dislocation density introduced by the additive manufacturing technology leads to the irregular connection of the Laves phases within the grains, and it can be improved by grain growth. The high negative mixing enthalpy competes with the high-entropy effect, and temperature is the decisive factor. In this situation, the atomic clusters were promoted and induced spinodal decomposition for generating LCFs. The formation of LCFs exhibits solute competition with the emergence of the C14 Laves phase, and the phase proportions fluctuate at different temperatures. This work provides new thinking and attention for developing lightweight, heat-resistant materials.

Does transport infrastructure development inhibit firm-level employment fluctuations? Evidence from national expressway construction in China

Employment Stabilization has always been a key focus of the Chinese government's work. Exploring the influencing factors of employment stability is of great significance for promoting the realization of high-quality employment. Based on the manually collected national expressway data and the China Industrial Enterprise Database from 1998 to 2015, this paper conducts an empirical study on the impact of transportation infrastructure development on enterprise employment fluctuations using the multi-timepoint difference-in-differences method. We find that the development of expressways has a significant inhibitory effect on enterprise employment fluctuations. Compared with areas where expressways have not been opened, the opening of expressways reduces local enterprise employment fluctuations by approximately 0.9 %. The development of expressways affects the demand side or supply side of the enterprise labor force through market scale effects, labor allocation effects, etc., thereby influencing enterprise employment fluctuations. The impact of expressway development on employment fluctuations of enterprises with different ownership natures, different economic regions, and different scales varies to some extent. Finally, this paper proposes relevant policy recommendations, providing practical and effective references for coordinating and promoting the goal of Employment Stabilization under the background of the strategy of building a strong transportation country.

Estimation of the water level variations in the 2022 Tonga tsunami event based on multiple machine learning models

In this study, four machine learning models are applied to estimate the water level variations recorded at three buoy stations during the 2022 Tonga tsunami. A new model performance evaluation metric, the lag degree, is introduced to compensate for the limitations of the conventional evaluation metrics (such as RMSE and R2 values), which could specify the lag extent, thus lag failure, between the estimated and original time series data. The long short-term memory (LSTM) and gated recurrent unit (GRU) models can accurately estimate the water level variations with less lag failure, superior to the multi-layer perceptron (MLP) and random forest (RF) models. Model estimation at the volcano-near area is less satisfactory (R2 < 0.5 after 3 time steps) than that at the volcano-far area (R2 > 0.5 within 8 time steps for LSTM and GRU) or the tsunami-shadowed area (R2 > 0.85 within 8 time steps). This is because, at the volcano-near area, both the low- and high-frequency components co-exist, resulting in sophisticated local water level fluctuations, whereas at the volcano-far area, only the low-frequency component prevails. For the tsunami-shadowed area, only specified frequency components could reach, thus relatively easy for model estimation.

DuCFF: A Dual-Channel Feature-Fusion Network for Workload Prediction in a Cloud Infrastructure

Cloud infrastructures are designed to provide highly scalable, pay-as-per-use services to meet the performance requirements of users. The workload prediction of the cloud plays a crucial role in proactive auto-scaling and the dynamic management of resources to move toward fine-grained load balancing and job scheduling due to its ability to estimate upcoming workloads. However, due to users’ diverse usage demands, the changing characteristics of workloads have become more and more complex, including not only short-term irregular fluctuation characteristics but also long-term dynamic variations. This prevents existing workload-prediction methods from fully capturing the above characteristics, leading to degradation of prediction accuracy. To deal with the above problems, this paper proposes a framework based on a dual-channel temporal convolutional network and transformer (referred to as DuCFF) to perform workload prediction. Firstly, DuCFF introduces data preprocessing technology to decouple different components implied by workload data and combine the original workload to form new model inputs. Then, in a parallel manner, DuCFF adopts the temporal convolution network (TCN) channel to capture local irregular fluctuations in workload time series and the transformer channel to capture long-term dynamic variations. Finally, the features extracted from the above two channels are further fused, and workload prediction is achieved. The performance of the proposed DuCFF’s was verified on various workload benchmark datasets (i.e., ClarkNet and Google) and compared to its nine competitors. Experimental results show that the proposed DuCFF can achieve average performance improvements of 65.2%, 70%, 64.37%, and 15%, respectively, in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and R-squared (R2) compared to the baseline model CNN-LSTM.

1/fα noise in the Robin Hood model

Abstract We consider the Robin Hood dynamics, a one-dimensional extremal self-organized critical model that describes the low-temperature creep phenomenon. One of the key quantities is the time evolution of the state variable (force noise). To understand the temporal correlations, we compute the power spectra of the local force fluctuations and apply finite-size scaling to get scaling functions and critical exponents. We find a signature of the 1 / f α noise for the local force with a nontrivial value of the spectral exponent 0 &lt; α &lt; 2 . We also examine temporal fluctuations in the position of the extremal site and a local activity signal. We present results for different local interaction rules of the model.

Linking excess entropy and acentric factor in spherical fluids.

Introduced by Pitzer in 1955, the acentric factor (ω) has been used to evaluate a molecule's deviation from the corresponding state principle. Pitzer devised ω based on a concept called perfect liquid (or centric fluid), a hypothetical species perfectly adhering to this principle. However, its physical significance remains unclear. This work attempts to clarify the centric fluid from an excess entropy perspective. We observe that the excess entropy per particle of centric fluids approximates -kB at their critical points, akin to the communal entropy of an ideal gas in classical cell theory. We devise an excess entropy dissection and apply it to model fluids (square-well, Lennard-Jones, Mie n-6, and the two-body abinitio models) to interpret this similarity. The dissection method identifies both centricity-independent and centricity-dependent entropic features. Regardless of the acentric factor, the attractive interaction contribution to the excess entropy peaks at the density where local density is most enhanced due to the competition between the local attraction and critical fluctuations. However, only in centric fluids does the entropic contribution from the local attractive potential become comparable to that of the hard sphere exclusion, making the centric fluid more structured than acentric ones. These findings elucidate the physical significance of the centric fluid as a system of particles where the repulsive and attractive contributions to the excess entropy become equal at its gas-liquid criticality. We expect these findings to offer a way to find suitable intermolecular potentials and assess the physical adequacy of equations of state.

Origins of strength stabilities at elevated temperatures in additively manufactured refractory high entropy alloy

While refractory high-entropy alloys (RHEAs) show promising potential for extreme applications, those directly fabricated via additive manufacturing methods have been hindered by their inferior mechanical properties, particularly at high temperatures. In this study, we successfully produced a Hf-Nb-Ti-V RHEA using directed energy deposition (DED) technique, achieving satisfactory tensile properties across a wide temperature range. This was accomplished by inducing severe lattice distortions in the fabricated RHEA, which can be traced back to the local chemical fluctuations present in the newly fabricated RHEA and the significant atomic radius mismatch. Due to strong solute pinning, these factors contribute to the superior yield strength of the DED-fabricated RHEA across a wide temperature range. Furthermore, the elastic constants in the fabricated RHEA show a negligible temperature dependence, revealed by first-principles calculations, ensuring satisfactory strengths even at high temperatures. This alloy design strategy, which involves introducing significant lattice distortion and maintaining the temperature-low sensitivity of the elastic moduli, opens up new possibilities for directly fabricating RHEAs with superior high-temperature properties.

Quantum migration and its local heat impact: Understanding local heat capacity of confined systems.

For noninteracting particles confined in a constant volume, the temperature derivative of the local energy assumes negative values in thermodynamic equilibrium at low temperatures. This peculiar behavior may entail the misleading unphysical conclusion that the local heat capacity is negative. However, we show that temperature-dependent density variations of confined particles induce an energy selective particle transport within the domain, here called temperature-induced quantum migration. This macroscopic quantum phenomenon causes a redistribution of local heat and ensures a non-negative local heat capacity. Moreover, it induces local heating and cooling effects and a massive overshoot in local heat capacity. The quantum migration also builds up the thermal part of confinement energy, manifesting in an excess global heat capacity. Analyzing the local energy fluctuations shows that the linear relationship between heat capacity and fluctuations is broken at the local scale.