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The article proposes a novel approach to assess rotor angle stability in microgrids by enhancing the Modified Galerkin Method (MGM), which is based on the Polynomial Approximation, using real-time RFID data acquisition. Due to their reliance on assumptions, traditional rotor angle stability methodologies frequently fail in online transient stability testing. MGM successfully captures the dynamic behavior of microgrids by approximating state variables using a sequence of polynomials and coefficients. Redundant data, like as vibrations or noise signals, can cause delays in defect diagnosis and decrease diagnostic accuracy. This problem is addressed by integrating RFID technology. RFID technology could potentially be used with a hybrid CNN-LSTM model to develop a sophisticated fault diagnostic system. This entails identifying fault characteristics through the use of signal processing techniques and feature extraction methods, such as the Fourier transform and time-domain statistical features. In addition, we use Total Harmonic Distortion (THD) to reduce superfluous data. The suggested techniques significantly increase fault detection efficiency and precision, outperforming existing techniques with a 0.94 classification accuracy. An extensive case study on an IEEE 3-machine 9-bus system is used to illustrate its efficacy, showing observable improvements in fault detection speed and accuracy that make microgrid operations safer and more dependable. [ABSTRACT FROM AUTHOR]

Autoencoders are dimension reduction models in the field of machine learning which can be thought of as a neural network counterpart of principal components analysis (PCA). Due to their flexibility and good performance, autoencoders have been recently used for estimating nonlinear factor models in finance. The main weakness of autoencoders is that the results are less explainable than those obtained with the PCA. In this paper, we propose the adoption of the Shapley value to improve the explainability of autoencoders in the context of nonlinear factor models. In particular, we measure the relevance of nonlinear latent factors using a forecast-based Shapley value approach that measures each latent factor's contributions in determining the out-of-sample accuracy in factor-augmented models. Considering the interesting empirical instance of the commodity market, we identify the most relevant latent factors for each commodity based on their out-of-sample forecasting ability. [ABSTRACT FROM AUTHOR]

To maximize knowledge transfer and improve the data requirement for data-driven machine learning (ML) modeling, a progressive transfer learning for reduced-order modeling (p-ROM) framework is proposed. A key concept of p-ROM is to selectively transfer knowledge from previously trained ML models and effectively develop a new ML model(s) for unseen tasks by optimizing information gates in hidden layers. The p-ROM framework is designed to work with any type of data-driven ROMs. For demonstration purposes, we evaluate the p-ROM with specific Barlow Twins ROMs (p-BT-ROMs) to highlight how progress learning can apply to multiple topological and physical problems with an emphasis on a small training set regime. The proposed p-BT-ROM framework has been tested using multiple examples, including transport, flow, and solid mechanics, to illustrate the importance of progressive knowledge transfer and its impact on model accuracy with reduced training samples. In both similar and different topologies, p-BT-ROM achieves improved model accuracy with much less training data. For instance, p-BT-ROM with four-parent (i.e., pre-trained models) outperforms the no-parent counterpart trained on data nine times larger. The p-ROM framework is poised to significantly enhance the capabilities of ML-based ROM approaches for scientific and engineering applications by mitigating data scarcity through progressively transferring knowledge. [ABSTRACT FROM AUTHOR]

This research introduces a novel global sensitivity analysis (GSA) framework for agent-based models (ABMs) that explicitly handles their distinctive features, such as multi-level structure and temporal dynamics. The framework uses Grassmannian diffusion maps to reduce output data dimensionality and sparse polynomial chaos expansion (PCE) to compute sensitivity indices for stochastic input parameters. To demonstrate the versatility of the proposed GSA method, we applied it to a non-linear system dynamics model and epidemiological and economic ABMs, depicting different dynamics. Unlike traditional GSA approaches, the proposed method enables a more general estimation of parametric sensitivities spanning from the micro level (individual agents) to the macro level (entire population). The new framework encourages the use of manifold-based techniques in uncertainty quantification, enhances understanding of complex spatio-temporal processes, and equips ABM practitioners with robust tools for detailed model analysis. This empowers them to make more informed decisions when developing, fine-tuning, and verifying models, thereby advancing the field and improving routine practice for GSA in ABMs. [ABSTRACT FROM AUTHOR]

Complex systems derive sophisticated behavioral dynamics by connecting individual component dynamics via a complex network. The resilience of complex systems is a critical ability to regain desirable behavior after perturbations. In the past years, our understanding of large-scale networked resilience is largely confined to proprietary agent-based simulations or topological analysis of graphs. However, we know the dynamics and topology both matter and the impact of model uncertainty of the system remains unsolved, especially on individual nodes. In order to quantify the effect of uncertainty on resilience across the network resolutions (from macro-scale network statistics to individual node dynamics), we employ an arbitrary polynomial chaos (aPC) expansion method to identify the probability of a node in losing its resilience and how the different model parameters contribute to this risk on a single node. We test this using both a generic networked bi-stable system and also established ecological and work force commuter network dynamics to demonstrate applicability. This framework will aid practitioners to both understand macro-scale behavior and make micro-scale interventions., (© 2023. The Author(s).)

The long-term erosion of rock by solution can induce a series of karst problems. Therefore, this study focused on limestone and conducted dynamic dissolution experiments under deionized water and CO2 solution conditions to study the deterioration mechanism of limestone under nonequilibrium conditions. The results showed that the degree of degradation of the mechanical properties of the samples in a CO2 solution was obviously greater. In a deionized water environment, the degradation of the mechanical properties of the sample is mainly controlled by the physical softening action of the solution. In the CO2 solution environment, the degradation process can be divided into two stages. In the early stage of the experiment (10 days to 20 days), the degradation of mechanical properties of the sample is also controlled by the physical softening action of the solution. With increasing soaking time, the main rock-forming minerals of limestone gradually react with the CO2 solution, the degradation of the sample is controlled mainly by the chemical corrosion of the CO2 solution, and its degradation rate is much greater than that of physical softening. The results can be used as a reference for assessing the long-term stability of underground engineering in limestone karst development areas. [ABSTRACT FROM AUTHOR]

Electrical stimulation of biological samples such as tissues and cell cultures attracts growing attention due to its capability of enhancing cell activity, proliferation, and differentiation. Eventually, a profound knowledge of the underlying mechanisms paves the way for innovative therapeutic devices. Capacitive coupling is one option of delivering electric fields to biological samples that has advantages regarding biocompatibility. However, its biological mechanism of interaction is not well understood. Experimental findings could be related to voltage-gated channels, which are triggered by changes of the transmembrane potential. Numerical simulations by the finite element method provide a possibility to estimate the transmembrane potential. Since a full resolution of the cell membrane within a macroscopic model would lead to prohibitively expensive models, we suggest the adaptation of an approximate finite element method. Starting from a basic 2.5D model, the chosen method is validated and applied to realistic experimental situations. To understand the influence of the dielectric properties on the modelling outcome, uncertainty quantification techniques are employed. A frequency-dependent influence of the uncertain dielectric properties of the cell membrane on the modelling outcome is revealed. This may have practical implications for future experimental studies. Our methodology can be easily adapted for computational studies relying on experimental data., (© 2022. The Author(s).)

Mathematical models of complex systems rely on parameter values to produce a desired behavior. As mathematical and computational models increase in complexity, it becomes correspondingly difficult to find parameter values that satisfy system constraints. We propose a Markov Chain Monte Carlo (MCMC) approach for the problem of constrained model parameter generation by designing a Markov chain that efficiently explores a model's parameter space. We demonstrate the use of our proposed methodology to analyze responses of a newly constructed bistability-constrained model of protein phosphorylation to perturbations in the underlying protein network. Our results suggest that parameter generation for constrained models using MCMC provides powerful tools for modeling-aided analysis of complex natural processes. [ABSTRACT FROM AUTHOR]

The large-scale simulation of dynamical systems is critical in numerous scientific and engineering disciplines. However, traditional numerical solvers are limited by the choice of step sizes when estimating integration, resulting in a trade-off between accuracy and computational efficiency. To address this challenge, we introduce a deep learning-based corrector called Neural Vector (NeurVec), which can compensate for integration errors and enable larger time step sizes in simulations. Our extensive experiments on a variety of complex dynamical system benchmarks demonstrate that NeurVec exhibits remarkable generalization capability on a continuous phase space, even when trained using limited and discrete data. NeurVec significantly accelerates traditional solvers, achieving speeds tens to hundreds of times faster while maintaining high levels of accuracy and stability. Moreover, NeurVec's simple-yet-effective design, combined with its ease of implementation, has the potential to establish a new paradigm for fast-solving differential equations based on deep learning. [ABSTRACT FROM AUTHOR]

Low-fidelity data is typically inexpensive to generate but inaccurate, whereas high-fidelity data is accurate but expensive. To address this, multi-fidelity methods use a small set of high-fidelity data to enhance the accuracy of a large set of low-fidelity data. In the approach described in this paper, this is accomplished by constructing a graph Laplacian from the low-fidelity data and computing its low-lying spectrum. This is used to cluster the data and identify points closest to the cluster centroids, where high-fidelity data is acquired. Thereafter, a transformation that maps every low-fidelity data point to a multi-fidelity counterpart is determined by minimizing the discrepancy between the multi- and high-fidelity data while preserving the underlying structure of the low-fidelity data distribution. The method is tested with problems in solid and fluid mechanics. By utilizing only a small fraction of high-fidelity data, the accuracy of a large set of low-fidelity data is significantly improved. [ABSTRACT FROM AUTHOR]

The consumption of solving large-scale linear equations is one of the most critical issues in numerical computation. An innovative method is introduced in this study to solve linear equations based on deep neural networks. To achieve a high accuracy, we employ the residual network architecture and the correction iteration inspired by the classic iteration methods. By solving the one-dimensional Burgers equation and the two-dimensional heat-conduction equation, the precision and effectiveness of the proposed method have been proven. Numerical results indicate that this DNN-based technique is capable of obtaining an error of less than 10–7. Moreover, its computation time is less sensitive to the problem size than that of classic iterative methods. Consequently, the proposed method possesses a significant efficiency advantage for large-scale problems. [ABSTRACT FROM AUTHOR]

We developed a physics-informed neural network based on a mixture of Cartesian grid sampling and Latin hypercube sampling to solve forward and backward modified diffusion equations. We optimized the parameters in the neural networks and the mixed data sampling by considering the squeeze boundary condition and the mixture coefficient, respectively. Then, we used a given modified diffusion equation as an example to demonstrate the efficiency of the neural network solver for forward and backward problems. The neural network results were compared with the numerical solutions, and good agreement with high accuracy was observed. This neural network solver can be generalized to other partial differential equations. [ABSTRACT FROM AUTHOR]

We combine forward stratigraphic models with a suite of uncertainty quantification and stochastic model calibration algorithms for the characterization of sedimentary successions in large scale systems. The analysis focuses on the information value provided by a probabilistic approach in the modelling of large-scale sedimentary basins. Stratigraphic forward models (SFMs) require a large number of input parameters usually affected by uncertainty. Thus, model calibration requires considerable time both in terms of human and computational resources, an issue currently limiting the applications of SFMs. Our work tackles this issue through the combination of sensitivity analysis, model reduction techniques and machine learning-based optimization algorithms. We first employ a two-step parameter screening procedure to identify relevant parameters and their assumed probability distributions. After selecting a restricted set of important parameters these are calibrated against available information, i.e., the depth of interpreted stratigraphic surfaces. Because of the large costs associated with SFM simulations, probability distributions of model parameters and outputs are obtained through a data driven reduced complexity model. Our study demonstrates the numerical approaches by considering a portion of the Porcupine Basin, Ireland. Results of the analysis are postprocessed to assess (i) the uncertainty and practical identifiability of model parameters given a set of observations, (ii) spatial distribution of lithologies. We analyse here the occurrences of sand bodies pinching against the continental slope, these systems likely resulting from gravity driven processes in deep sea environment. [ABSTRACT FROM AUTHOR]

Several types of green photosynthetic microalgae can grow through the process of heterotrophic growth in the dark with the help of a carbon source instead of the usual light energy. Heterotrophic growth overcomes important limitations in the production of valuable products from microalgae, such as the reliance on light, which complicates the process, raises costs, and lowers the yield of potentially useful products. The present study was conducted to explore the potential growth of green microalga Scenedesmus obliquus under mixotrophic and heterotrophic conditions utilizing Disperse orange 2RL Azo dye as a carbon source to produce a high lipid content and the maximum dye removal percentage. After 7 days of algal growth with dye under mixotrophic and heterotrophic conditions with varying pH levels (5, 7, 9, and 11), KNO3 concentrations (1, 1.5, 2, and 3 g/L), and dye concentrations (20, 40, and 60 ppm); dye removal percentage, algal dry weight, and lipid content were determined. The results showed that the highest decolorization of Disperse orange 2RL Azo dye (98.14%) was attained by S. obliquus in heterotrophic medium supplemented with glucose at the optimal pH 11 when the nitrogen concentration was 1 g/L and the dye concentration was 20 ppm. FT-IR spectroscopy of the dye revealed differences in peaks position and intensity before and after algal treatment. S. obliquus has a high concentration of oleic acid, which is enhanced when it is grown with Disperse orange 2RL Azo dye, making it ideal for production of high-quality biodiesel. In general, and in the vast majority of instances, heterotrophic cultivation is substantially less expensive, easier to set up, and requires less maintenance than mixotrophic cultivation. Heterotrophic cultivation allows for large-scale applications such as separate or mixed wastewater treatment along with biofuel production. [ABSTRACT FROM AUTHOR]

There is a complete lack of highly sensitive and specific biomarkers for early pancreatic ductal adenocarcinoma (PDAC) diagnosis, limiting multi-modal therapeutic options. Mitochondrial DNA (mtDNA) is an excellent resource for biomarker discovery because of its high copy number and increased mutational frequency in cancer cells. We examined if mtDNA mutations can be detected in circulating extracellular vesicles (EVs) of PDAC patients and used for discerning between cancer and non-cancer subjects. A greater yield of circulating EVs (~ 1.4 fold; p = 0.002) was obtained in PDAC patients (n = 20) than non-cancer (NC) individuals (n = 10). PDAC-EVs contained a higher quantity of total DNA (~ 5.5 folds; p = 0.0001) than NC-EVs and had greater enrichment of mtDNA (~ 14.02-fold; p = 0.0001). PDAC-EVs also had higher levels of cardiolipin (a mitochondrial inner-membrane phospholipid), suggestive of their mitochondrial origin. All mtDNA mutations in PDAC-EVs were unique and frequency was remarkably higher. Most mtDNA mutations (41.5%) in PDAC-EVs were in the respiratory complex-I (RCI) (ND1-ND6), followed by the RCIII gene (CYTB; 11.2%). Among the non-coding genes, D-Loop and RNR2 exhibited the most mutations (15.2% each). Altogether, our study establishes, for the first time, that mtDNA mutations can be detected in circulating EVs and potentially serve as a tool for reliable PDAC diagnosis. [ABSTRACT FROM AUTHOR]

In the study of deformation prediction and disaster warning during karst slope construction, the influencing factors and deformation law should be comprehensively considered. The layout of the deformation monitoring points of karst slope is affected by the thickness of karst overburden soil, dissolution and fragmentation degree, karst development degree, slope cracking degree, fault or weak interlayer and other factors. In this paper, the author aimed at the problem of construction deformation prediction and disaster warning of karst slope, proposed an improved model of cusp mutation by applying and optimizing the cusp mutation model, analysed the deformation trend and sudden change type of the slope, and obtained the critical control early warning value of slope deformation. Therefore, it is feasible to analyse the deformation and mutation characteristics of karstified slope by using a virtual reality-mutation model. In addition, based on the empirical formula of the slope sliding limit deformation rate and grey prediction model, the critical control warning value of slope deformation is obtained, which provides a basis to quantify the deformation index of risk evaluation. This method provides a new idea to predict karst slope construction deformation and catastrophic deformation warning and has a reference value for similar engineering examples. [ABSTRACT FROM AUTHOR]

We show using numerical simulations that data driven discovery using sparse regression can be used to extract the governing differential equation model of ideal four-wave mixing in a nonlinear Schrödinger equation optical fibre system. Specifically, we consider the evolution of a strong single frequency pump interacting with two frequency detuned sidebands where the dynamics are governed by a reduced Hamiltonian system describing pump-sideband coupling. Based only on generated dynamical data from this system, sparse regression successfully recovers the underlying physical model, fully capturing the dynamical landscape on both sides of the system separatrix. We also discuss how analysing an ensemble over different initial conditions allows us to reliably identify the governing model in the presence of noise. These results extend the use of data driven discovery to ideal four-wave mixing in nonlinear Schrödinger equation systems. [ABSTRACT FROM AUTHOR]

Recent developments on the grading of cardiac pathologies suggest flow-related metrics for a deeper evaluation of cardiac function. Blood flow evaluation employs space-time resolved cardiovascular imaging tools, possibly integrated with direct numerical simulation (DNS) of intraventricular fluid dynamics in individual patients. If a patient-specific analysis is a promising method to reproduce flow details or to assist virtual therapeutic solutions, it becomes impracticable in nearly-real-time during a routine clinical activity. At the same time, the need to determine the existence of relationships between advanced flow-related quantities of interest (QoIs) and the diagnostic metrics used in the standard clinical practice requires the adoption of techniques able to generalize evidences emerging from a finite number of single cases. In this study, we focus on the left ventricular function and use a class of reduced-order models, relying on the Polynomial Chaos Expansion (PCE) technique to learn the dynamics of selected QoIs based on a set of synthetic cases analyzed with a high-fidelity model (DNS). The selected QoIs describe the left ventricle blood transit and the kinetic energy and vorticity at the peak of diastolic filling. The PCE-based surrogate models provide straightforward approximations of these QoIs in the space of widely used diagnostic metrics embedding relevant information on left ventricle geometry and function. These surrogates are directly employable in the clinical analysis as we demonstrate by assessing their robustness against independent patient-specific cases ranging from healthy to diseased conditions. The surrogate models are used to perform global sensitivity analysis at a negligible computational cost and provide insights on the impact of each diagnostic metric on the QoIs. Results also suggest how common flow transit parameters are principally dictated by ejection fraction. [ABSTRACT FROM AUTHOR]

The long noncoding RNASBF2-AS1 can promote the occurrence and development of many kinds of tumours, but its role in oesophageal squamous cell carcinoma (ESCC) is unknown. We found that SBF2-AS1 was up-regulated in ESCC, and its expression was positively correlated with tumor size (P = 0.0001), but was not related to gender, age, TNM stage, histological grade, and lymphnode metastasis (P > 0.05). It was further found that the higher the expression of SBF2-AS1, the lower the survival rate. COX multivariate analysis showed that the expression of SBF2-AS1 was an independent prognostic factor. Functional experiments show that inhibition of SBF2-AS1 can inhibit the proliferation of ESCC through in vivo and in vitro, and overexpression of SBF2-AS1 can promote the proliferation of ESCC and inhibit its apoptosis. In mechanism, SBF2-AS1/miR-338-3P, miR-362-3P/E2F1 axis are involved in the regulation of ESCC growth. In general, SBF2-AS1 may be used as ceRNA to combine with miR-338-3P and miR-362-3P to up-regulate the expression ofE2F1, and ultimately play a role in promoting cancer. It may be used as a therapeutic target and a biomarker for prognosis. [ABSTRACT FROM AUTHOR]

The propagation of poroelastic waves in a soil specimen is dependent on the physical and mechanical properties of the soil. In the geotechnical practice, such properties are obtained through in-situ geotechnical testings or element soil testings in the laboratory. These methods require almost advanced equipment and both testing and sample preparation may be expensive and time-consuming. This paper aims to present an algorithm for a laboratory-scale ultrasonic non-destructive testing to determine the physical and mechanical properties of saturated soil samples based on the distribution of stress waves. The ultrasonic setup, in comparison to most conventional soil lab testing equipment, is low-cost and non-invasive such that it reduces the soil disturbance. For this purpose, a poro-elastodynamic forward solver and differential evolution global optimization algorithm were applied to characterize the porosity, density, and other mechanical properties for a soil column. The forward solver was developed based on a semi-analytical solution which does not require intensive computational efforts encountered in standard numerical techniques such as the finite element method. It was concluded that the proposed high-frequency ultrasonic technique characterizes effectively the saturated soil samples based on the output stress wave measured by the receiver. This development makes geotechnical investigations time-efficient and cost-effective, and as such more suited to applications in remote areas. [ABSTRACT FROM AUTHOR]

A method for machine learning and serving of discrete field theories in physics is developed. The learning algorithm trains a discrete field theory from a set of observational data on a spacetime lattice, and the serving algorithm uses the learned discrete field theory to predict new observations of the field for new boundary and initial conditions. The approach of learning discrete field theories overcomes the difficulties associated with learning continuous theories by artificial intelligence. The serving algorithm of discrete field theories belongs to the family of structure-preserving geometric algorithms, which have been proven to be superior to the conventional algorithms based on discretization of differential equations. The effectiveness of the method and algorithms developed is demonstrated using the examples of nonlinear oscillations and the Kepler problem. In particular, the learning algorithm learns a discrete field theory from a set of data of planetary orbits similar to what Kepler inherited from Tycho Brahe in 1601, and the serving algorithm correctly predicts other planetary orbits, including parabolic and hyperbolic escaping orbits, of the solar system without learning or knowing Newton's laws of motion and universal gravitation. The proposed algorithms are expected to be applicable when the effects of special relativity and general relativity are important.

Nine in ten major outages in the US have been caused by hurricanes. Long-term outage risk is a function of climate change-triggered shifts in hurricane frequency and intensity; yet projections of both remain highly uncertain. However, outage risk models do not account for the epistemic uncertainties in physics-based hurricane projections under climate change, largely due to the extreme computational complexity. Instead they use simple probabilistic assumptions to model such uncertainties. Here, we propose a transparent and efficient framework to, for the first time, bridge the physics-based hurricane projections and intricate outage risk models. We find that uncertainty in projections of the frequency of weaker storms explains over 95% of the uncertainty in outage projections; thus, reducing this uncertainty will greatly improve outage risk management. We also show that the expected annual fraction of affected customers exhibits large variances, warranting the adoption of robust resilience investment strategies and climate-informed regulatory frameworks.

Chromium pollution has been historically widespread throughout the world. Most available remediation technologies often require energy consumption. This study is aimed to develop electrochemical remediation for Cr(VI) in chromium-slag leakage with self-generated electricity. Dynamic leaching experiments of chromium-slag samples were conducted to survey the release and leaching behavior of Cr(VI). Based on previous work, a unique urea-Cr(VI) was designed, in which urea was employed as the fuel and Cr(VI) from the leakage of the dichromate slag served as the oxidant. Furthermore, the electrochemical results showed that the removal percent of Cr(VI) was more than 96% after 18 h with the leakage Cr(VI) concentration of 2.69 mM. The open circuit potential (OCP) varied in the range of 1.56 ~ 1.59 V under different initial Cr(VI) leakage concentrations. The approach explores the feasibility of the promising technique without the need of energy input for simultaneous chromium-slag remediation and generation of electricity. [ABSTRACT FROM AUTHOR]

In neuroscience, computational modeling is an effective way to gain insight into cortical mechanisms, yet the construction and analysis of large-scale network models—not to mention the extraction of underlying principles—are themselves challenging tasks, due to the absence of suitable analytical tools and the prohibitive costs of systematic numerical exploration of high-dimensional parameter spaces. In this paper, we propose a data-driven approach assisted by deep neural networks (DNN). The idea is to first discover certain input-output relations, and then to leverage this information and the superior computation speeds of the well-trained DNN to guide parameter searches and to deduce theoretical understanding. To illustrate this novel approach, we used as a test case a medium-size network of integrate-and-fire neurons intended to model local cortical circuits. With the help of an accurate yet extremely efficient DNN surrogate, we revealed the statistics of model responses, providing a detailed picture of model behavior. The information obtained is both general and of a fundamental nature, with direct application to neuroscience. Our results suggest that the methodology proposed can be scaled up to larger and more complex biological networks when used in conjunction with other techniques of biological modeling. [ABSTRACT FROM AUTHOR]