Scale Gap Research Articles

Monitoring Soil Water Content and Measurement Depth of Cosmic-Ray Neutron Sensing in the Tibetan Plateau

Abstract This work aims at evaluating the potential application of two cosmic-ray neutron sensing (CRNS) soil water content (SWC) retrieval methods and discusses the effective measurement depth of CRNS retrieval and vertical weights of different layers in a semihumid alpine meadow on the Tibetan Plateau. In this study, the widely used N0 method and the latest published method derived from physical principles named the universal transport solution (UTS) were used for SWC estimation during the whole nonfrozen season on the Tibetan Plateau. The N0 and UTS methods successfully retrieved the SWC on the Tibetan Plateau, and their variations were generally consistent. The effective measurement depths calculated by the N0 and UTS methods showed similar fluctuation patterns, ranging from 9.7 to 14.5 cm and 17.8 to 20 cm, with mean values of 12.0 and 19.2 cm, respectively. This work verified that the weighting methods of the N0 and UTS models provided credible performance for the effective measurement depths. The N0 method provided measurement depths more close to the surface, and the UTS method yielded slightly deeper averages. The UTS equation had a more sensitive response to periods of low precipitation. Including detailed Ultra Rapid Neutron-Only Simulation (URANOS) Monte Carlo simulations of the full time series, we find specifically an excellent agreement in the summer period. Our analysis suggests that CRNS is a promising innovation for SWC measurement and can be extended in its application at hectometer scales to monitor SWC in the Tibetan meadow ecosystem. Significance Statement The purpose of this study is to better understand if cosmic-ray neutron sensing (CRNS) has the potential to sense soil water content (SWC) for the Tibetan meadow ecosystem with harsh climate conditions. Techniques for measuring SWC have evolved over many decades, from point-scale measurements of a few meters to satellite-based remote sensing methods of several kilometers, each with advantages and disadvantages. This is important because CRNS has emerged to fill the scale gap between point measurements and microwave remote sensing methods. The results are helpful in assessing the applicability of CRNS in alpine meadow ecosystems on the Tibetan Plateau.

A hybrid cohesive phase-field numerical method for the stability analysis of rock slopes with discontinuities

The stability of rock slope is predominantly controlled by the fracture behavior of structural discontinuities, such as joints, faults, and bedding planes. Landslides of rock slopes usually involve concurrent tensile and shear fracture evolution within a continuous-discontinuous medium, occurring along the discontinuities and within rock mass, which poses challenges for accurately and efficiently predicting landslides and determining the factor of safety (FS). To address this issue, a hybrid cohesive phase-field numerical method based on the gravity increase method (GIM) is developed. This approach integrates the cohesive joint model and the unified fracture phase-field method, effectively bridging the scale gap between discontinuity and rock mass. Numerical simulations indicate that the developed hybrid method is validated through physical tests on both discontinuities and rock mass, and achieves computational efficiency comparable to continuum methods. It is worth noting that the critical energy release rate has a more significant effect on the stability of rock slopes than the shear strength. Furthermore, the developed hybrid method accurately captures sliding surfaces and highlights the role of rock bridges in resisting landslides, enabling reliable predictions of FS of rock slopes with persistent discontinuities and non-persistent discontinuities.

Deuterium Isotopic Effect on Nanometer-Sized Narrow Spaces Formed by Silicon Walls

Introduction Inspired by Okuyama et al. [1], fundamental studies on the wet-etching behavior of SiO2 films in single-nanometer scale gaps between silicon walls play an extremely significant role in optimizing the manufacturing of advanced semiconductors with complex 3D nanostructures. A well-known explanation for SiO2 etching behavior in narrow spaces is the change in the reactant concentration. Various attempts have been made to supply reactants to narrow spaces [2-4]. However, the wet-etching phenomenon in narrow spaces remains unclear, and further elucidation of the underlying mechanism is required. In this study, the etching behavior of SiO2 films confined between two silicon walls was investigated using a reactant consumption model. We attempted to suppress the reactions on the wall surface using the deuterium isotope effect, which is often used to control reactions [5]. Experimental Methods and Discussion First, we investigated the differences between hydrogen fluoride and deuterium fluoride in reactions on silicon surfaces. Silicon prisms machined from silicon wafers were prepared. The proportion of Si–H bonds on the prism was measured by infrared spectroscopy in multiple internal reflection geometry (MIR-IRAS) after immersing the silicon prisms in a mixture of dilute hydrogen fluoride, D2O, and deionized water for 2 min. The Si–H bond ratio on the prism surface increased with the increase in the proportion of hydrogen in the mixture, observed as the upward concave profile shown in Fig. 1. This suggests that deuterium fluoride reacts more slowly with the silicon surface than hydrogen fluoride, i.e., dilute deuterium fluoride reduces the reactant consumption on the silicon surface compared to dilute hydrogen fluoride. In dilute deuterium fluoride, the hydrogen fluoride is diluted with sufficient D2O such that hydrogen becomes negligible.Second, to examine the influence of the reactant consumption by the silicon walls, we measured the etching rates of SiO2 films with different film thicknesses sandwiched between two silicon layers. We performed etching tests on coupon samples in beakers with dilute hydrogen fluoride or dilute deuterium fluoride (w/o additive) at room temperature. The etching amounts of the confined SiO2 film and blanket SiO2 film as a control were measured by cross-sectional scanning electron microscopy (X-SEM), transmission electron microscopy with a focused ion beam (FIB-TEM), and spectroscopic film measurements. The etching rate was calculated by dividing the etched amount by the etching time. The etching rate in the narrow space was normalized to the reference etching rate. It was found that the etching rate in dilute deuterium fluoride is higher than that in hydrogen fluoride without deuterium fluoride (Fig 2(a)). Moreover, we performed an etching test on coupons with dilute hydrogen fluoride or dilute deuterium fluoride with an acidic additive (w/acid) and discovered that the etching rate in the narrow space was increased upon acid addition. Summary In this study, we verified the effect of deuterium fluoride on the etching behavior in nanometer-sized narrow spaces. We discovered that deuterium fluoride increased the etching rate in narrow spaces compared with hydrogen fluoride. We attribute these advantages of deuterium fluoride in the SiO2 etching rate in the narrow space to the consumption control of the reactant. At the PRiME 2024 conference, we would like to discuss our study and the effects of additives on the etching effectiveness of deuterium fluoride.REFERENCE:[1] A. Okuyama, S. Saito, Y. Hagimoto, K. Nishi, A. Suzuki, T. Toshima and H. Iwamoto, Solid State Phenomena, 2019 (2015) 115-118.[2] D. Ueda, Y. Hanawa H. Kitagawa, N. Fujiwara, M. Otsuji, H. Takahashi and K. Fukami, Solid State Phenomena, 314 (2021) 155-160.[3] S. Kumari, Shan. Hu and P. D’elia, Solid State Phenomena, 346 (2023) 149-154.[4] G. Vereecke, H. Debruyn, Q. D. Keyser, R. Vos, A. Dutta and F. Holsteys, Solid State Phenomena, 282 (2018) 182-189.[5] Kenneth B. Wiberg, Chemical Reviews, 55 (4) (1955) 713-743. Figure 1

Linking Electricity and Air Quality Models by Downscaling: Weather-Informed Hourly Dispatch of Generation Accounting for Renewable and Load Temporal Variability Scenarios.

National models of the electric sector typically consider a handful of generator operating periods per year, while pollutant fate and transport models have an hourly resolution. We bridge that scale gap by introducing a novel fundamental-based temporal downscaling method (TDM) for translating national or regional energy scenarios to hourly emissions. Optimization-based generator dispatch is used to account for variations in emissions stemming from weather-sensitive power demands and wind and solar generation. The TDM is demonstrated by downscaling emissions from the electricity market module in the National Energy Model System. As a case study, we implement the TDM in the Virginia-Carolinas region and compare its results with traditional statistical downscaling used in the Sparse Matrix Operator Kernel Emissions (SMOKE) processing model. We find that the TDM emission profiles respond to weather and that nitrogen oxide emissions are positively correlated with conditions conducive to ozone formation. In contrast, SMOKE emission time series, which are rooted in historical operating patterns, exhibit insensitivity to weather conditions and potential biases, particularly with high renewable penetration and climate change. Relying on SMOKE profiles can also obscure variations in emission patterns across different policy scenarios, potentially downplaying their impacts on power system operations and emissions.

A general rate equation theory for non– and catalytic coupling gas–solid reactions and its application to sorption-enhanced water gas shift

Sorption-enhanced water gas shift (SEWGS) involves the catalytic WGS reaction of CO with H2O and the non-catalytic gas–solid carbonation reaction of CO2 with CaO. This work proposes a first-principal rate equation theory for non– and catalytic coupling gas–solid reactions. The theory starts from quantum chemistry calculation and extends to larger scales to predict SEWGS experiments conducted in the reactor result without any empirical hypothesis. The coupling mechanism of non– and catalytic reactions is illustrated in a mesoscopic way, based on which the kinetic transition behavior and morphology change on the catalyst surface for WGS can be obtained. The hierarchical framework bridges the scale gap between elementary reactions and pilot plants and the prediction results of and gas profile at the exit of the bubbling bed and carbonation conversion are validated by experiments. The multiscale kinetics is promising to apply to non– and catalytic coupling gas–solid reaction kinetics studies.

Constitutive Modeling of Anisotropic Plasticity with ML Algorithms in Metals

Nuclear reactors hold significant promise for mitigating the environmental crisis and achieving a carbon-free world. The advancement of nuclear technology, however, is intricately tied to the materials used in reactors, with metals and alloys forming their core components. Understanding the plasticity and degradation of these materials is crucial to prevent critical reactor failures caused by factors such as radiation-induced embrittlement, creep, and fuel-cladding interactions. While traditional plasticity modeling has relied on constitutive laws and yield functions, the advent of machine learning (ML) and artificial intelligence has opened new avenues for this field. This paper explores the potential of data-driven approaches to enhance plasticity modeling for metals used in nuclear reactors. Recent studies have leveraged ML algorithms, including support vector machines and artificial neural networks, to model yield surfaces and correct theoretical yield functions with experimental data. Despite their accuracy, these models often lack interpretability and generality. To address these challenges, we investigate the applicability of various ML algorithms, i.e. neural networks, logistic regressions, decision trees, K-nearest neighbors, and Gaussian processes (GPs), in developing data-oriented flow rules for plasticity modeling. The paper demonstrates the superiority of GPs in terms of interpretability and generality. Using stress data generated from PyLabFEA, we compared the performance of these algorithms, highlighting the intrinsic uncertainties associated with GP predictions. Our findings indicate that Gaussian process classifiers (GPCs) offer a promising approach for modeling plasticity in metals, providing a balance between precision and physical insight. Additionally, this work presents a deep neural network (DNN) to model anisotropic Hill-type plasticity with perfect test data accuracy (accuracy score: 1.0), which is often hard to achieve with a classification problem. This shows the superiority of DNNs in terms of accuracy in modeling yield functions. The implications of our results for bridging the scale gaps in macrolevel simulations are discussed, and future directions for incorporating microstructural features into ML-based plasticity models are proposed. Overall, the ML framework presented in this paper can be employed for modeling constitutive relations that can be incorporated within traditional Finite Element Method (FEM)–based codes. Specifically, the GPC or DNN developed in this work can be readily swapped with the yield function within the so-called return mapping algorithm of nonlinear FEM-based plasticity subroutines to indicate yielding. Consequently, this merging of ML and FEM will allow us to leverage the geometric representational ability of FEM alongside the superior modeling capabilities of the ML algorithms.

Genome-wide analysis of anterior-posterior mRNA regionalization in Stentor coeruleus reveals a role for the microtubule cytoskeleton.

Cells have complex and beautiful structures that are important for their function. However, understanding the molecular mechanisms that produce these structures is a challenging problem due to the gap in size scales between molecular interactions and cellular structures. The giant ciliate Stentor coeruleus is a unicellular model organism whose large size, reproducible structure, and ability to heal wounds and regenerate have historically allowed the formation of structure in a single cell to be addressed using methods of experimental embryology. Such studies have shown that specific cellular structures, such as the membranellar band, always form in particular regions of the cell, which raises the question: what is the source of positional information within this organism? By analogy with embryonic development, in which regionalized mRNA is often used to mark position, we asked whether specific regionalized mRNAs might mark position along the anterior-posterior axis of Stentor. By physically bisecting cells and conducting bulk RNA sequencing, we were able to identify sets of messages enriched in either the anterior or posterior half. We then conducted half-cell RNA-sequencing in paired anteriors and posteriors of cells in which the microtubule cytoskeleton was disrupted by RNAi of β-tubulin or dynein intermediate chains. We found that many messages either lost their regionalized distribution or switched to an opposite distribution, such that anterior-enriched messages in control became posterior-enriched in the RNAi cells, or vice versa. This study indicates that mRNA can be regionalized within a single giant cell and that microtubules may play a role, possibly by serving as tracks for the movement of the messages.

Experiment and Numerical Prediction on Shock Sensitivity of HMX–Based Booster Explosive with Small Scale Gap Test at Low and Elevated Temperatures

In order to analyze the effect of temperature changes on the shock initiation performance of HMX–based booster explosive, which consists of 95% HMX and 5% FPM2602 by weight, a temperature calibration test of acceptor was designed. The temperature changes in the booster at low and elevated temperatures under the constraint of steel sleeve were obtained. Based on the temperature calibration results, polymethyl methacrylate (PMMA) was selected as gap material to conduct a small scale gap test (SSGT) of HMX–based booster under different temperature conditions. The corresponding critical gap thickness was tested. Based on SSGT results at different temperatures, the shock initiation processes were simulated by adjusting parameters of ignition and growth reactive rate model. The critical gap thickness and critical initiation pressure of HMX–based booster at different temperatures were numerically predicted. By combining SSGT experimental data and simulation results, the attenuation law and fitting prediction formula of the critical initiation pressure of HMX–based booster were proposed. The mechanism of temperature effect on the shock sensitivity of HMX–based booster explosive was analyzed. The research results indicate that the critical gap thickness of HMX–based booster gradually increases with the rise in temperature, and the critical initiation pressure gradually decreases during the shock initiation process under the heating temperature conditions. In addition, the simulation results show that the heated HMX–based booster under steel constraints becomes more sensitive at high temperatures (>120 °C), while the cooled booster is more insensitive, but its critical initial pressure does not change significantly between 88 °C and 120 °C. The experimental and numerical prediction results are important for the shock initiation safety and design of an insensitive booster explosive.

Research on the Evaluation of Competitiveness of Steel Industry Enterprises

The steel industry, as a fundamental industry in the organic development framework of China's economy, has been facing the long-standing problems of production exceeding consumption, high asset liability ratio, severe product homogenization competition, and high pollution and carbon emissions in the production process in recent times. At the same time, due to the impact of the sluggish demand caused by the COVID-19, the rapid rise in the cost of raw materials such as iron ore, and the gradual disappearance of a series of policy dividends such as supply side reform, the financial situation of steel enterprises is becoming more and more pessimistic. How to cope with the increasing financial risks and gain competitive advantages through strengthening financial competitiveness in the fierce market competition has become a major issue that China's steel enterprises urgently need to solve. This article selects steel industry enterprises as the research object for analysis. By reviewing relevant literature on enterprise competitiveness, the relevant concepts of enterprise competitiveness are extracted, and the research results and evaluation system are organized. Based on factor analysis, 16 targeted indicators are selected based on scale, efficiency, growth, operational capacity, debt paying capacity, and research and development capacity. An analysis model is constructed and analysis conclusions are drawn. In the analysis results, problems are identified regarding the poor competitiveness of steel industry enterprises. The following research results were obtained: 1) The overall competitiveness score of enterprises in the industry is not high, all less than 1. Among them, operational capability has the greatest impact on the competitiveness of steel industry enterprises, followed by scale, debt paying ability, efficiency, research and development ability, and growth. 2) The worst scoring indicators among the six enterprise competitiveness indicators are the scale indicator and the growth indicator. There is a significant gap in industry scale, with 12 companies scoring positive in the scale factor, accounting for 34.29% of the industry; The scale factor score of 23 enterprises is negative, with an industry proportion of 65.71%. In addition, there are growth indicators, with 12 companies in the industry having positive growth factor scores and 23 companies having negative growth factor scores, indicating a significant head effect on overall growth capacity.

Modeling and observations of North Atlantic cyclones: Implications for U.S. Offshore wind energy

To meet the Biden-Harris administration's goal of deploying 30 GW of offshore wind power by 2030 and 110 GW by 2050, expansion of wind energy into U.S. territorial waters prone to tropical cyclones (TCs) and extratropical cyclones (ETCs) is essential. This requires a deeper understanding of cyclone-related risks and the development of robust, resilient offshore wind energy systems. This paper provides a comprehensive review of state-of-the-science measurement and modeling capabilities for studying TCs and ETCs, and their impacts across various spatial and temporal scales. We explore measurement capabilities for environments influenced by TCs and ETCs, including near-surface and vertical profiles of critical variables that characterize these cyclones. The capabilities and limitations of Earth system and mesoscale models are assessed for their effectiveness in capturing atmosphere–ocean–wave interactions that influence TC/ETC-induced risks under a changing climate. Additionally, we discuss microscale modeling capabilities designed to bridge scale gaps from the weather scale (a few kilometers) to the turbine scale (dozens to a few meters). We also review machine learning (ML)-based, data-driven models for simulating TC/ETC events at both weather and wind turbine scales. Special attention is given to extreme metocean conditions like extreme wind gusts, rapid wind direction changes, and high waves, which pose threats to offshore wind energy infrastructure. Finally, the paper outlines the research challenges and future directions needed to enhance the resilience and design of next-generation offshore wind turbines against extreme weather conditions.

Intrinsic functional and structural network organization in the macaque insula

Abstract In recent decades, in vivo magnetic resonance imaging (MRI) studies have provided previously inaccessible insights into the structure and function of healthy and pathological human brains in the laboratory and the clinic. However, the correlational nature of this work and relatively low resolution mean that ground truth neuroanatomical studies and causal manipulations of neural circuitry must still occur in animal models offering greater tractability and higher resolution, rendering a scale and species gap in translation. Here, we bridge this gap with a detailed, multimodal investigation of the macaque insula in vivo. Using both functional and diffusion MRI—tools available for use in humans—we demonstrate a neural architecture in the macaque insula with clear correspondence to prior in vivo MRI findings in humans and postmortem cytoarchitectural and tract-tracing studies in monkeys. Results converged across analysis methods and imaging modalities, supporting the translational potential of the macaque model.

Asymptotic Criticality of the Navier–Stokes Regularity Problem

The problem of global-in-time regularity for the 3D Navier-Stokes equations, i.e., the question of whether a smooth flow can exhibit spontaneous formation of singularities, is a fundamental open problem in mathematical physics. Due to the super-criticality of the equations, the problem has been super-critical in the sense that there has been a ‘scaling gap’ between any regularity criterion and the corresponding a priori bound (regardless of the functional setup utilized). The purpose of this work is to present a mathematical framework-based on a suitably defined ‘scale of sparseness’ of the super-level sets of the positive and negative parts of the components of the higher-order spatial derivatives of the velocity field—in which the scaling gap between the regularity class and the corresponding a priori bound vanishes as the order of the derivative goes to infinity.

Improving modular bootstrap bounds with integrality

We propose methods that efficiently impose integrality — i.e., the condition that the coefficients of characters in the partition function must be integers — into numerical modular bootstrap. We demonstrate the method with a number of examples where it can be used to strengthen modular bootstrap results. First, we show that, with a mild extra assumption, imposing integrality improves the bound on the maximal allowed gap in dimensions of operators in theories with a U(1)c symmetry at c = 3, and reduces it to the value saturated by the SU(4)1 WZW model point of c = 3 Narain lattices moduli space. Second, we show that our method can be used to eliminate all but a discrete set of points saturating the bound from previous Virasoro modular bootstrap results. Finally, when central charge is close to 1, we can slightly improve the upper bound on the scaling dimension gap.

Quantitative Imaging of Magnesium Biodegradation by 3D X‐Ray Ptychography and Electron Microscopy

AbstractMagnesium‐based alloys are excellent materials for temporary medical implants, but understanding and controlling their corrosion performance is crucial. Most nanoscale corrosion studies focus on the surface, providing only 2D information. In contrast, macro‐ and microscale X‐ray tomography offers representative volume information, which is, however, comparatively low in resolution and rather qualitative. Here a new mesoscale approach overcomes these drawbacks and bridges the scale gap by combining 3D measurements using ptychographic X‐ray computed tomography (PXCT) with electron microscopy. This combination allows to observe the corrosion progress non‐destructively in 3D and provides high‐resolution chemical information on the corrosion products. A medical Mg–Zn–Ca alloy is used and compared the same sample in the pristine and corroded states. With PXCT an isotropic resolution of 85 and 123 nm is achieved for the pristine and corroded states respectively, which enables to distinguish nanoscale Mg2Ca precipitates from the matrix. The corroded state in best approximation to the in situ conditions is imaged and reveals the complexity of corrosion products. The results illustrate that the corrosion‐layer is dense and defect‐free, and the corrosion of the material is grain‐orientation sensitive. The developed workflow can advance research on bioactive materials and corrosion‐sensitive functional materials.

Dominant Sources of Uncertainty for Downscaled Climate: A Military Installation Perspective

AbstractWhile the Department of Defense (DoD) infrastructure is no stranger to extremes, recent events have been unprecedented, with climate change acting as a growing risk multiplier. To assess the level of exposure of DoD installations to extreme weather and climate events, site‐specific climate information is needed. One way to bridge the scale gap between outputs from existing global climate models (GCMs) and sites is climate downscaling. This makes the information more relevant for impact assessment at the DoD installation and facility scale. However, downscaling GCMs is beset by a myriad of challenges and sources of uncertainty, and downscaling methods were not designed with specific infrastructure planning and design needs in mind. Here, we evaluate state‐of‐the‐science dynamical downscaling and statistical downscaling and bias correction for climate variables (i.e., temperature and precipitation) at the daily scale. We also combine downscaling approaches in novel ways to optimize computational efficiency and reduce uncertainty. Furthermore, we examine the sensitivity of the downscaled outputs to the choice of reference data and quantify the relative uncertainty related to downscaling approach, reference data, and other factors across the climate variables and aggregation scales. Results show that empirical quantile mapping (EQM), a statistical downscaling, consistently performs well and has less sensitivity to the choice of reference data. Moreover, the hybrid downscaling that leverages EQM improves the performance of dynamical downscaling. Our findings highlight that the choice of reference data dominates uncertainties in temperature downscaling, while their role is more muted for precipitation but still non‐negligible.

Marine connectivity conservation: Guidance for MPA and MPA network design and management

The importance of considering ecological connectivity in the design of marine protected areas (MPAs) and ecologically coherent networks of protected areas across coasts and oceans has risen in prominence with the 2022 Kunming-Montreal Global Biodiversity Framework. This short communication highlights key messages emerging from a specialist conference session on marine connectivity by members of the IUCN-WCPA Marine Connectivity Working Group at the Fifth International Marine Protected Areas Congress (IMPAC5) in Vancouver in 2023. We consider the importance of spatial and temporal scale, knowledge and data gaps and some of the technological and scientific advances that are generating insights into species movements that can inform the ecologically meaningful design of protected areas for effective conservation of ecosystem integrity, biodiversity and the flow of ecosystem services.

The glaring gaps: environmental violence and peace research and practice

ABSTRACT Human-caused global impact on socio-ecological systems has been labelled the greatest challenge to human and environmental health. It is also a significant, and perhaps the greatest threat to global peace. Restricting peace research to direct violence alone causes a glaring gap in peace research and practice between different forms of human-caused mortality and suffering. Explicitly accounting for Environmental Violence can offer a critical component for building positive peace. This paper seeks to address what I consider to be strategic gaps that require the integration of multi-disciplinary research, intersectional peacebuilding practice, and concerted policy proposals. The four gaps are (1) failure to include environmental violence in peace research; (2) the multi- and trans-disciplinary gap; (3) the gap of scale and thinking in systems; and (4) failure in the specification of the harm and power differentials in contribution to and realisation of environmental risk. Generative pathways to reconcile these gaps are identified and explored.

Chemical stability of anion exchange membranes for alkaline fuel cells and water electrolysers: Experiment, multi-dimensional modeling and hybrid simulations

The unstable nature of anion exchange membranes (AEMs) has hindered the progress of alkaline fuel cells and water electrolyzers. Density functional theory (DFT) can explore the correlation between the structure and properties of model compounds. However, theoretical explorations of AEMs remain immensely insufficient owing to the complexity of modeling the realistic solution system which caused by multi-dimensional parameters such as polymer chain entanglement, steric hindrance, and migration of ions. In this study, molecular dynamic simulation was utilized to obtain the multi-dimensional parameters of polyelectrolytes, which were considered when calculating the degradation energy barrier by DFT, thus leading to more accurate results and optimizing the conventional DFT calculations. Combined with the computational works, pyridinium functionalized polyolefin and polyether sulfone were synthesized to verify the feasibility of the simulation scheme. This optimized scheme fills the gap in the DFT calculations scale and is easy to extend to other types of AEMs, which help design the chemically stable AEMs and promote the development of alkaline fuel cells and water electrolysers.

Study of factors affecting customer patronage: engagement model of health insurance

PurposeImproving health outcomes requires a robust health-care service model that delivers cost-efficient services and increase customer patronage. The purpose of this study is to examine how service quality and convenience influence perceived value, satisfaction and customer patronage of health insurance policyholders. Based on contemporary research, this study further investigates the moderating role of trust, inertia, insurer type and word-of-mouth (WOM) on relationship between satisfaction and customer patronage.Design/methodology/approachThis study conceptualized the dimensions of SERVQUAL and SERVCON as drivers of perceived value leading to satisfaction and finally customer patronage in presence of four moderators. To test the hypotheses, data from 500 consumers who had a running health insurance policy was collected and analyzed using partial least square path modeling.FindingsThe results of this study showed service quality and convenience dimensions significantly affected perceived value. Perceived value strongly influenced satisfaction and customer patronage intentions. Satisfaction had a significant positive effect on patronage. WOM and trust moderated the satisfaction–patronage relationship for recommendation intention but not repurchase intention. The moderators had an indirect bearing on customer patronage.Social implicationsSuch an engagement ecosystem can be considered to be a revolution, as it will change the way businesses are conducted and how stakeholders interact with one another.Originality/valueThis study adapts and integrates the SERVQUAL and SERVCON models to health insurance domain. Second, this study conceptualizes a modified view of post-benefit convenience relevant for health insurance as policy renewal intention rather than returns/exchanges. This addresses a gap in the SERVCON scale's applicability to insurance services. This study also makes a novel attempt of examining implication of WOM and trust in health insurance domain.

Development of a multi-layer network model for characterizing energy cascade behavior on turbulent mixing

Eddies of various sizes are visible to the naked eye in turbulent flow. Each eddy scale corresponds to a fraction of the total energy released by the turbulence cascade. Understanding the dynamic mechanism of the energy cascade is crucial to the study of turbulent mixing. In this paper, an energy cascade multi-layer network (ECMN) based on the complex network algorithm is proposed to investigate the spatio-temporal evolution of the energy cascade, covering both the inertial and dispersive ranges. The dynamic process of energy cascade is transformed into a topological structure based on the node definition and edge determination. The topological structure allows for the exploration of eddies interaction and chaotic energy transfer across scales. The model results show the intermittent and non-uniform nature of the energy cascade. Meanwhile, the scale gap found in the model verifies the fractal property of the energy evolution. We also found that scales of the generated eddies in energy cascade process are stochastic, and a synchronous energy cascade pattern is demonstrated according to the constructed framework. Furthermore, it provides a topological way to evaluate the contribution of large and small scale eddies. In addition, a network structure coefficient κ is proposed to evaluate the energy transfer strength. It agrees very well with the fluctuation of dissipation rates. All of this shows that the network model can effectively reveal the inhomogeneous properties of the energy cascade and quantify the turbulent mixing intensity based on the intermittent scale interaction. This also provides new insights into the study of fractal scales of nonlinear complex systems and the bridging of chaotic dynamics with topological frameworks.