Search Results (2,867)

We introduce a new identification strategy for uncertainty shocks to explain macroeconomic volatility in financial markets. The Chicago Board Options Exchange Volatility Index (VIX) measures the market expectations of future volatility, but traditional methods based on second-moment shocks and the time-varying volatility of the VIX often do not effectively to capture the non-Gaussian, heavy-tailed nature of asset returns. To address this, we constructed a revised VIX by fitting a double-subordinated Normal Inverse Gaussian Lévy process to S&P 500 log returns, to provide a more comprehensive measure of volatility that captures the extreme movements and heavy tails observed in financial data. Using an axiomatic framework, we developed a family of risk–reward ratios that, when computed with our revised VIX and fitted to a long-memory time series model, provide a more precise identification of uncertainty shocks in financial markets. Full article

Background/Objectives: This study aimed to establish a predictive model for critical quality attributes (CQAs) related to tablet integrity, including tablet breaking force (TBF), friability, and capping occurrence, using machine learning-based models and nondestructive experimental data. Methods: The machine learning-based models were trained on data to predict the CQAs of metformin HCl (MF)-containing tablets using a commercial-scale wet granulation process, and five models were each compared for regression and classification. We identified eight input variables associated with the process and material parameters that control the tableting outcome using feature importance analysis. Results: Among the models, the Gaussian Process regression model provided the most successful results, with R² values of 0.959 and 0.949 for TBF and friability, respectively. Capping occurrence was accurately predicted by all models, with the Boosted Trees model achieving a 97.80% accuracy. Feature importance analysis revealed that the compression force and magnesium stearate fraction were the most influential parameters in CQA prediction and are input variables that could be used in CQA prediction. Conclusions: These findings indicate that TBF, friability, and capping occurrence were successfully modeled using machine learning with a large dataset by constructing regression and classification models. Applying these models before tablet manufacturing can enhance product quality during wet granulation scale-up, particularly by preventing capping during the manufacturing process without damaging the tablets. Full article

This study employed different spot pattern lasers to clean the oxide film on the surface of a TC4 titanium alloy. The variation in temperature field and ablation depth during the laser cleaning process was simulated by establishing a finite element model. The effects of various laser processing parameters on the micromorphology, elemental composition, and surface roughness of the TC4 titanium alloy were analyzed. The results show that as the laser energy density increases, both the temperature field and ablation depth increase as well. Under optimal laser processing parameters, the laser energy density is 5.27 J/cm², with a repetition frequency of 300 kHz and a scanning speed of 6000 mm/s. A comparison of the cleaning effects of Gaussian pulse lasers and Flat-top pulse lasers reveals that the Gaussian pulse laser causes less damage to the TC4 titanium alloy, resulting in lower oxygen content and roughness values after cleaning compared to Flat-top pulse laser cleaning. Full article

The objective of this study was to conduct a comparative analysis of the performance of hydrogels prepared from two distinct raw materials and to identify the hydrogels with the optimal overall capacity for dry farming applications. Ten grafted polymer hydrogels were prepared from melon peel (MP) and orange peel (OP). A comparative analysis of the degree of swelling, water absorption time, pH range, reusability, and soil water retention and water-holding capacity of the two hydrogels revealed that the MP-based hydrogels exhibited superior performance in all evaluated parameters when compared to their OP-based counterparts. The treatment group of hydrogels prepared from MPs exhibited the highest degree of swelling, with an absorptive capacity of up to 765.6 g/g in ultrapure water. The optimum absorption ratio at pH = 8.1 was 606.8 g/g, as determined by Gaussian distribution modeling. The treatment group with the best reusability demonstrated an average absorption ratio of 445.0 g/g. The degree of swelling was 84.0 g/g when the process was repeated seven times. After the MP-gels were applied to the soil, it was observed that the gels enhanced the water retention and holding capacity of the sandy soil. The water retention ratio of the sandy soil was increased by 271.0% by the addition of MP-gel, and the growth of wheat was found to be normal when 1.5% to 2.0% of MP-gel was added under drought-stress conditions. In light of the necessity to reuse agricultural waste, the preparation of MP-gel can facilitate the improvement of dry farming and address the issue of water scarcity in agriculture. This offers a viable solution for the growth and management of crops under conditions of drought stress. Full article

China has vast amounts of deep coalbed methane resources but is still in the early stage of deep coalbed methane development; thus, it lacks mature gas exploitation and development theories and technologies, particularly effective methods for evaluating final recoverable reserves. This paper intends to develop a method that can rapidly and accurately predict deep coalbed methane EUR before well spacing to guide the formulation of rational exploitation schemes and full exploitation of geological resources, thus lowering costs and enhancing efficiency. Taking deep coalbed methane in the Daning–Jixian block of the Ordos Basin as the research object, this paper first uses the production decline method to evaluate the EUR of brought-in wells and analyzes the influence of geological conditions and engineering parameters on the EUR. Second, the ADASYN method is used to process the unevenly distributed samples to solve the small number and poor representativeness of the machine learning model samples. After this, the BP neural network, support vector machine, and Gaussian process regression are used to build EUR evaluation models, and the models are compared and the best is selected. Lastly, the selected EUR evaluation model is applied to analyze the influence weights of geological conditions and engineering parameters on EUR. According to the research results, the MAPEs of the BP neural network, support vector machine, and Gaussian process regression models reach 7.03%, 7.23%, and 1.28%, respectively, after ADASYNA oversampling. However, the Gaussian process regression model may bear the risk of overfitting. The model comparison results show that the support vector machine model is superior to the BP neural network model and the Gaussian process regression model. Therefore, the support vector machine is favorably selected to predict EUR in this paper. Feature importance analysis results indicate that engineering parameters (including clusters, horizontal length, fracturing liquid, and proppant) are the major factors influencing the EUR prediction results. This paper establishes a model for predicting the EUR of deep coalbed methane, which provides a reference for the future formulation of well spacing schemes in the surveyed region. Full article

The conditional Gaussian nonlinear system (CGNS) is a broad class of nonlinear stochastic dynamical systems. Given the trajectories for a subset of state variables, the remaining follow a Gaussian distribution. Despite the conditionally linear structure, the CGNS exhibits strong nonlinearity, thus capturing many non-Gaussian characteristics observed in nature through its joint and marginal distributions. Desirably, it enjoys closed analytic formulae for the time evolution of its conditional Gaussian statistics, which facilitate the study of data assimilation and other related topics. In this paper, we develop a martingale-free approach to improve the understanding of CGNSs. This methodology provides a tractable approach to proving the time evolution of the conditional statistics by deriving results through time discretization schemes, with the continuous-time regime obtained via a formal limiting process as the discretization time-step vanishes. This discretized approach further allows for developing analytic formulae for optimal posterior sampling of unobserved state variables with correlated noise. These tools are particularly valuable for studying extreme events and intermittency and apply to high-dimensional systems. Moreover, the approach improves the understanding of different sampling methods in characterizing uncertainty. The effectiveness of the framework is demonstrated through a physics-constrained, triad-interaction climate model with cubic nonlinearity and state-dependent cross-interacting noise. Full article

In this paper, the voltage regulation in power systems is addressed from the perspective of the modern paradigm of control logic supported by phasor measurement units. The information available from measurements is used to better adapt the regulation actions to the actual operation point of the system. The use of the online measurement data allows for identifying the sensitivity matrix and for improving the regulation performances with respect to the fast load variations that increasingly affect modern power systems. With the aim of estimating the sensitivity matrices, a preliminary action is necessary to reconstruct the phases of the network voltages, which are assumed not to be provided by the phasor measurement units. This allows for obtaining a model-free adaptive control method. It is then shown how the regulation problem can be formulated in terms of a linear quadratic Gaussian problem, properly considering the load modeling in terms of the stochastic Ornstein–Uhlenbeck process. This control strategy has the advantage of avoiding dangerous oscillations of power flows, as demonstrated through the results of some simulations on a classical test network. Particularly, the advantage of the proposed approach is shown in the presence of different levels of load disturbances. Full article

This paper presents a comprehensive study of the signed distance metric for fuzzy numbers. Due to the property of directionality, this measure has been widely used. However, it has a main drawback in handling asymmetry and irregular shapes in fuzzy numbers. To overcome this rather bad feature, we introduce two new distances, the balanced signed distance (BSGD) and the generalised signed distance (GSGD), seen as generalisations of the classical signed distance. The developed distances successfully and effectively take into account the shape, the asymmetry and the overlap of fuzzy numbers. The GSGD is additionally directional, while the BSGD satisfies the requirements for being a metric of fuzzy quantities. Analytical simplifications of both distances in the case of often-used particular types of fuzzy numbers are provided to simplify the computation process, making them as simple as the classical signed distance but more realistic and precise. We empirically analyse the sensitivity of these distances. Considering several scenarios of fuzzy numbers, we also numerically compare these distances against established metrics, highlighting the advantages of the BSGD and the GSGD in capturing the shape properties of fuzzy numbers. One main finding of this research is that the defended distances capture with great precision the distance between fuzzy numbers; additionally, they are theoretically appealing and are computationally easy for traditional fuzzy numbers such as triangular, trapezoidal, Gaussian, etc., making these metrics promising. Full article

The nucleus-independent chemical shift (NICS) criterion plays a significant role in evaluating (anti-)aromaticity. While being readily accessible even for non-computational chemists, adding ghost atoms for multi-points NICS evaluations poses a significant challenge. In this article, I introduce py.Aroma 4, a freely available and open-source Python package designed specifically for analyzing (anti-)aromaticity. Through its user-friendly graphical interface, py.Aroma simplifies and enhances aromaticity analyses by offering key features such as HOMA/HOMER index computation, Gaussian-type input file generation for diverse NICS calculations and corresponding output processing, NMR spectra plotting, and computational supporting information (SI) generation for scientific manuscripts. Additionally, NICS_⊥ is suggested for evaluating (anti-)aromaticity for non-planar or tilted rings. Pre-compiled executables for macOS and Windows are freely available online. Facilitate accessibility for users lacking programming experience or time constraints. Full article

A scientific assessment of the sensitivity of the Chinese coal market has become an important research topic. This paper combines Gaussian Process Regression (GPR) and Sobol sensitivity analysis to construct a GPR–Sobol hybrid model innovatively applied to the Chinese coal market, thus addressing a gap in the economic applications of this method. The model is used to analyze the sensitivity of factors influencing coal prices in China. The GPR–Sobol model effectively handles nonlinear relationships, enabling an in-depth exploration of key factors affecting price volatility and quantifying their impacts, thus overcoming the limitations of traditional econometric models in nonlinear data processing. The results indicate that economic growth, energy prices, interest rates, exchange rates, and uncertainty factors exhibit high sensitivity and significantly impact coal price fluctuations in China. Coal prices in northwest China are more sensitive to interest rates and geopolitical risks, while prices in east and south China are more responsive to exchange rates but less so to economic policy uncertainty. Additionally, coal prices in north, south, and east China are highly sensitive to international energy prices, indicating that coal prices are dominated by the global energy market, yet their weak response to macroeconomic indicators suggests these regions is still insufficiently mature. Full article

The current accelerated expansion of the Universe remains one of the most intriguing topics in modern cosmology, driving the search for innovative statistical techniques. Recent advancements in machine learning have significantly enhanced its application across various scientific fields, including physics, and particularly cosmology, where data analysis plays a crucial role in problem-solving. In this work, a non-parametric regression method with Gaussian processes is presented along with several applications to reconstruct some cosmological observables, such as the deceleration parameter and the dark energy equation of state, in order to contribute some information that helps to clarify the behavior of the Universe. It was found that the results are consistent with $\lambda$CDM and the predicted value of the Hubble parameter at redshift zero is $H_0=68.798\pm 6.340\left(1\sigma \right)\mathrm{km}{\mathrm{s}}^{-1}{\mathrm{Mpc}}^{-1}$. Full article

Although usually studied as separate processes, plant growth and plant development are strictly interrelated. The BBCH scale (“Biologische Bundesanstalt, Bundessortenamt, and CHemical industry”) has become one of the primary classification systems for documenting the growth and developmental stages of many plant species. Specifically, the BBCH scale for hops (Humulus lupulus L.) separately describes growth and development during the vegetative stage. This study aims to develop an integrated approach to better understand the interaction between vertical growth rates and vegetative development in hops. Growth rates and development patterns of the Cascade hop cultivar were assessed in semi-arid (Sicily, Italy) and subtropical (Florida, USA) climates. The Gompertz model accurately described vertical growth, while a modified Gaussian model effectively captured hop growth rates (HGRs). A strong correlation between growth and developmental stages was identified, allowing for the inference of growth dynamics from developmental observations during the vegetative phase. Growth and developmental stages showed a 71% match across both environments, with minor phase shifts influenced by growing conditions. From an applied perspective, understanding the growth characteristics associated with developmental stages is crucial for addressing challenges posed by pests and diseases in emerging hop-growing regions. This integrated approach offers valuable insights into optimizing cultivation practices for diverse environmental conditions. Full article

In light of the challenges posed by intricate algorithms, subpar recognition accuracy, and prolonged recognition duration in current machine vision for bridge structure monitoring, this paper presents an innovative method for recognizing and extracting structural edges based on the Gaussian difference method. Initially, grayscale processing enhances the image’s information content. Subsequently, a Region of Interest (ROI) is identified to streamline further processing steps. Following this, Gaussian check images at different scales are processed, capitalizing on the observation that edges show reduced correspondence to the Gaussian kernel. Then, the structure image’s edges are derived using the difference algorithm. Lastly, employing the scale factor, the algorithm translates the detected edge displacement within the image into the precise physical displacement of the structure. This method enables continuous monitoring of the structure and facilitates the assessment of its safety status. The experimental results affirm that the proposed algorithm adeptly identifies and extracts the structural edge’s geometric characteristics with precision. Furthermore, the displacement information derived from the scale factor closely aligns with the actual displacement, validating the algorithm’s effectiveness. Full article

We study a separable Hilbert space of smooth curves taking values in the Segal–Bergmann space of analytic functions in the complex plane, and two of its subspaces that are the domains of unbounded non self-adjoint linear partial differential operators of the first and second order. We show how to build a Hilbert basis for this space. We study these first- and second-order partial derivation non-self-adjoint operators defined on this space, showing that these operators are defined on dense subspaces of the initial space of smooth curves; we determine their respective adjoints, compute their respective commutators, determine their eigenvalues and, under some normalisation conditions on the eigenvectors, we present examples of a discrete set of eigenvalues. Using these derivation operators, we study a Schrödinger-type equation, building particular solutions given by their representation as smooth curves on the Segal–Bergmann space, and we show the existence of general solutions using an Fourier–Hilbert base of the space of smooth curves. We point out the existence of self-adjoint operators in the space of smooth curves that are obtained by the composition of the partial derivation operators with multiplication operators, showing that these operators admit simple sequences of eigenvalues and eigenvectors. We present two applications of the Schrödinger-type equation studied. In the first one, we consider a wave associated with an object having the mass of an electron, showing that two waves, when considered as having only a free real space variable, are entangled, in the sense that the probability densities in the real variable are almost perfectly correlated. In the second application, after postulating that a usual package of information may have a mass of the order of magnitude of the neutron’s mass attributed to it—and so well into the domain of possible quantisation—we explore some consequences of the model. Full article

In order to improve the network performance of multi-unmanned ground vehicle (UGV) systems in urban environments, this article proposes a novel online autonomous motion-control method for the relay UAV. The problem is solved by jointly considering unknown RF channel parameters, unknown multi-agent mobility, the impact of the environments on channel characteristics, and the unavailable angle-of-arrival (AoA) information of the received signal, making the solution of the problem more practical and comprehensive. The method mainly consists of two parts: wireless channel parameter estimation and optimal relay position search. Considering that in practical applications, the radio frequency (RF) channel parameters in complex urban environments are difficult to obtain in advance and are constantly changing, an estimation algorithm based on Gaussian process learning is proposed for online evaluation of the wireless channel parameters near the current position of the UAV; for the optimal relay position search problem, in order to improve the real-time performance of the method, a line search algorithm and a general gradient-based algorithm are proposed, which are used for point-to-point communication and multi-node communication scenarios, respectively, reducing the two-dimensional search to a one-dimensional search, and the stability proof and convergence conditions of the algorithm are given. Comparative experiments and simulation results under different scenarios show that the proposed motion-control method can drive the UAV to reach or track the optimal relay position and improve the network performance, while demonstrating that it is beneficial to consider the impact of the environments on the channel characteristics. Full article