Search Results (785)

The vertical profile of PM_2.5 is important for understanding its secondary formation, transport, and deposition at high altitudes; it also provides important data support for studying the causes and sources of PM_2.5 near the ground. Based on machine learning methods, this study fully utilized simultaneous Multi-Axis Differential Optical Absorption Spectroscopy measurements of multiple air pollutants in the atmosphere and employed the measured vertical profiles of aerosol extinction—as well as the vertical profiles of precursors such as NO₂ and SO₂—to evaluate the vertical distribution of PM_2.5 concentration. Three machine learning models (eXtreme Gradient Boosting, Random Forest, and back-propagation neural network) were evaluated using Multi-Axis Differential Optical Absorption Spectroscopy instruments in four typical cities in China: Beijing, Lanzhou, Guangzhou, and Hefei. According to the comparison between estimated PM_2.5 and in situ measurements on the ground surface in the four cities, the eXtreme Gradient Boosting model has the best estimation performance, with the Pearson correlation coefficient reaching 0.91. In addition, the in situ instrument mounted on the meteorological observation tower in Beijing was used to validate the estimated PM_2.5 profile, and the Pearson correlation coefficient at each height was greater than 0.7. The average PM_2.5 vertical profiles in the four typical cities all show an exponential pattern. In Beijing and Guangzhou, PM_2.5 can diffuse to high altitudes between 500 and 1000 m; in Lanzhou, it can diffuse to around 1500 m, while it is primarily distributed between the near surface and 500 m in Hefei. Based on the vertical distribution of PM_2.5 mass concentration in Beijing, a high-altitude PM_2.5 pollutant transport event was identified from January 19th to 21st, 2021, which was not detected by ground-based in situ instruments. During this process, PM_2.5 was transported from the 200 to 1500 m altitude level and then sank to the near surface, causing the concentration on the ground surface to continuously increase. The sinking process contributes to approximately 7% of the ground surface PM_2.5 every hour. Full article

When complex computations cannot be performed on board a mobile robot, sensory data must be transmitted to a remote station to be processed, and the resulting actions must be sent back to the robot to execute, forming a repeating cycle. This involves stochastic round-trip times in the case of non-deterministic network communications and/or non-hard real-time software. Since robots need to react within strict time constraints, modeling these round-trip times becomes essential for many tasks. Modern approaches for modeling sequences of data are mostly based on time-series forecasting techniques, which impose a computational cost that may be prohibitive for real-time operation, do not consider all the delay sources existing in the sw/hw system, or do not work fully online, i.e., within the time of the current round-trip. Marginal probabilistic models, on the other hand, often have a lower cost, since they discard temporal dependencies between successive measurements of round-trip times, a suitable approximation when regime changes are properly handled given the typically stationary nature of these round-trip times. In this paper we focus on the hypothesis tests needed for marginal modeling of the round-trip times in remotely operated robotic systems with the presence of abrupt changes in regimes. We analyze in depth three common models, namely Log-logistic, Log-normal, and Exponential, and propose some modifications of parameter estimators for them and new thresholds for well-known goodness-of-fit tests, which are aimed at the particularities of our setting. We then evaluate our proposal on a dataset gathered from a variety of networked robot scenarios, both real and simulated; through >2100 h of high-performance computer processing, we assess the statistical robustness and practical suitability of these methods for these kinds of robotic applications. Full article

Evapotranspiration (ET) variation is typically influenced by climatic factors, which are considered the primary drivers of agricultural water requirements. Any changes in ET rates directly affect crop water demands. In this study, temporal trends and magnitudes of key climatic variables, and their impacts on reference evapotranspiration (ETo) during 1981–2020, were evaluated across 36 districts of Punjab, Pakistan. Positive serial correlations, ranging from 0.29 to 0.48, were identified and removed using the pre-whitening technique. Increasing trends in maximum temperature (Tmax) and wind speed (WS) across Punjab and its subregions were observed, while relative humidity (RH) exhibited both increasing and decreasing trends. No significant trends were detected for the minimum temperature (Tmin). On a monthly scale, in the Southern Punjab (SP) region, Sen’s slope estimated an increase in ETo, ranging from 0.239 mm/year in November to 0.636 mm/year in May, at a significance level of α = 0.05 (5%). At the provincial scale, significant upward trends in ETo were observed for the annual, Kharif, and autumn seasons, with Z-values of 2.04, 2.16, and 3.13, respectively, at α = 0.05 and 0.01. It was determined that, on an annual scale in Punjab, ETo sensitivity to climatic parameters followed the following order: Tmax > wind speed (WS) > Tmin > RH. The best-fitted models for Tmax, Tmin, WS, and RH were Gaussian, exponential, and spherical. ETo was found to increase spatially from North to South Punjab, with an approximate rise of 70–80 mm/decade. The results provide a scientific basis for understanding hydroclimatic drivers of ETo in semi-arid regions and contribute to improving climate impact assessments on agricultural water use. The observed ETo increases, particularly in South Punjab and lower Central Punjab, highlight the need for region-specific irrigation scheduling and water allocation. These findings can guide cropping calendars, improve irrigation efficiency, and increase canal water supplies to high-ETo areas, supporting adaptive strategies against climate variability in Punjab. Full article

Thermal effects critically influence the design and construction of steel-concrete composite cable-stayed bridges, where material thermal mismatch complicates structural responses. Current code-specified temperature gradient models inadequately address long-span bridges. This study employs in-situ monitoring of the Chibi Yangtze River Bridge to propose a refined vertical temperature gradient model, utilizing an exponential function for the concrete deck and a linear function for the steel web. Finite element analysis across six construction stages reveals: (1) Under negative temperature gradients, the concrete deck develops tensile stresses (2.439–2.591 MPa), approximately 30% lower than code-predicted values (3.613–3.715 MPa), highlighting risks of longitudinal cracking. (2) At the maximum double-cantilever stage, transverse stress distributions show pronounced shear lag effects, positive shear lag in deck sections connected to crossbeams and negative shear lag in non-connected sections. The proposed model reduces tensile stress conservatism in codes by 30–33%, enhancing prediction accuracy for composite girders. This work provides critical insights for thermal effect management in long-span bridge construction. Full article

Wind-blown sand concentrations decay rapidly and in an orderly manner with height above the surface. The saltation flux profiles are of interest to understand wind and sand interactions and for fundamental measurement and modeling of associated transport rates. This study compares methods to measure and represent aeolian sand flux profiles. We measured vertical flux profiles and used quality-controlled data to test power, logarithmic, and exponential functions to reproduce the profiles. These results are used in a pragmatic assessment of the efficiency of reproducing flux profiles from vertically discontinuous arrays of traps or sensors compared to profiles obtained from continuous vertical arrays of segmented traps. Our analysis corroborates previous findings demonstrating that exponential decay functions are statistically the best method to approximate flux profiles. The results are used in a novel application to compare flux profiles reproduced from vertically discontinuous arrays of devices with those obtained from continuous vertical arrays comprising nine mesh-style traps. The results indicate that discontinuous arrays of 3, 4, 5, or 6 devices deployed less than 200 mm from the surface will effectively reproduce results from the continuous array, with average errors less than 3%. Errors increase when devices are at greater heights or as the number of devices decreases. Discontinuous arrays typically do not capture creep transport which would contribute to error in our comparisons. Therefore, creep must comprise less than 3% of total aeolian sand flux, contradicting typical assumptions of 25%. Full article

We present a formal reconstruction of the conventional number systems, including integers, rationals, reals, and complex numbers, based on the principle of relational finitude over a finite field ${\mathbb{F}}_{\mathsf{p}}$. Rather than assuming actual infinity, we define arithmetic and algebra as observer-dependent constructs grounded in finite field symmetries. Consequently, we formulate relational analogues of the conventional number classes, expressed relationally with respect to a chosen reference frame. We define explicit mappings for each number class, preserving their algebraic and computational properties while eliminating ontological dependence on infinite structures. For example, relationally framed rational numbers emerge from dense grids generated by primitive roots of a finite field, enabling proportional reasoning without infinity, while scale-periodicity ensures invariance under zoom operations, approximating continuity in a bounded structure. The resultant framework—that we denote as Finite Ring Continuum—aims to establish a coherent foundation for mathematics, physics and formal logic in an ontologically finite paradox-free informational universe. Full article

This study presents a comprehensive analysis of the M/Hypo_K/1/m queuing model to evaluate the performance of marshaling yards in freight rail classification systems. The model effectively captures the complex, multi-phase nature of service and repair processes by incorporating hypo-exponential probability distributions. The marshaling yard is modeled as a finite-capacity, single-server queue subject to potential server failures, reflecting real-world disruptions. Two complementary methodological frameworks are employed: a mathematical model based on continuous-time Markov chains (CTMCs) and a simulation model constructed using Colored Petri Nets (CPNs). In the analytical approach, both service time and repair time follow hypo-exponential distributions, which are used to approximate the gamma distribution. The simulation model built in CPN Tools allows for dynamic visualization and performance evaluation. In the CPN model, we applied a gamma distribution, which allowed us to evaluate the accuracy of the approximation implemented in the analytical model. The result indicated that utilization of the marshaling yard in primary shunting was approximately 23.81%, and with secondary shunting, 22.53%. The study output proves that the hypo-exponential distribution is able to approximate the gamma distribution. This dual-framework approach, combining analytics with simulation, provides a deeper understanding of system behavior, supporting data-driven decisions for capacity planning, failure mitigation, and operational optimization in freight rail networks. Full article

Acoustic sound-based condition monitoring (ASCM) systems, which typically utilize machine learning algorithms on established audio features, have demonstrated effectiveness under controlled conditions. However, their application in real-world industrial environments presents significant challenges due to complex and variable soundscapes with high noise and limited fault data. The presence of random interfering sounds and variability in operating conditions can lead to lower performance and high false-positive rates. To overcome these limitations, we propose a fault detection method that leverages the underlying physical characteristics of the sound signals. By investigating the components of the acoustic signal, we found that fault-related sounds can be modeled as exponentially decaying oscillations. This insight allows for the development of a physically based signal model, setting our approach apart from purely data-driven methods. Using this model, we developed a robust detection method based on a Generalized Likelihood Ratio Test (GLRT). The effectiveness of this approach was validated using both synthetic and real-world data from a steel industry facility. Our results demonstrate that the proposed model-based approach provides superior performance compared to standard audio features, particularly in high-noise conditions. On real-world data, the GLRT-based approach outperformed all audio features, as clearly shown by the Receiver Operating Characteristic (ROC) analysis. Specifically, the Partial Area Under the Curve (pAUC) of the GLRT is more than twice that of the best-performing audio feature, demonstrating good detection at significantly lower-false-positive rates compared to audio features. Furthermore, simulations showed that our method maintains robust detection down to a Signal-to-Noise Ratio (SNR) of −13 $\mathrm{dB}$, significantly outperforming audio feature-based detection, which was limited to approximately −10 $\mathrm{dB}$. The physically informed nature of our model not only provides a more reliable and robust solution but also enables the method to be generalized to other industrial scenarios with similar fault properties, offering broader applicability for reliable acoustic condition monitoring. Full article

In recent years, in situ blasting–leaching, in the stope has emerged as an economically viable and environmentally sustainable mining technique for low-grade ore deposits. While the leaching efficiency is influenced by factors such as ore type, solution composition, and spraying speed, the most significant factor is the effect of post-blasting crushed-stone particle size and gradation on the pore structure, which subsequently influences seepage and leaching performance. To investigate how particle size and gradation affect the pore structure of granular media, physical models of ore particles with varying sizes and gradations were constructed. These models were scanned and three-dimensionally reconstructed using CT scanning technology and Avizo software (Avizo, Version 2023.1; Thermo Fisher Scientific: Waltham, MA, USA, 2023) enabling quantitative analysis of pore structure parameters. The results indicate that the coefficient of uniformity (Cu) is approximately negatively correlated with porosity, while the vertical absolute permeability (kz) follows an attenuated exponential trend. When the fine-particle content (L8 > L3 > L1) increases by 1.5-fold and 9-fold, the number of pore throats increases by 8.71% and 30.91%, respectively, the average pore size decreases by 75.1% and 64.4%, the average throat size decreases by 66.3% and 60%, and the connectivity rate decreases by 92% and 77.8%. This study further evaluates permeability based on the aforementioned pore structure parameters. Multiple regression analysis reveals that the connectivity rate and throat size have the most significant influence on permeability. Accordingly, permeability analysis and prediction are conducted using the improved Purcell formula, which demonstrates a strong correlation with the experimentally measured results. Full article

In tunnel blasting, an analytical solution for dynamic stress in the surrounding rock of adjacent tunnels is critical for dynamic response analysis, mechanical evaluations, and crack propagation control. Previous studies on stress field analytical solutions primarily modeled rock as a linear elastic material, focusing mainly on the P-wave effects from instantaneous detonation. Based on Heelan’s short cylindrical cavity model, this paper derives an analytical solution for blast-induced dynamic stresses in adjacent tunnel rock, incorporating both induced SV-waves and a rock mass damage factor through rigorous theoretical analysis. Numerical case studies and field measurements were used to analyze stress propagation during tunnel blasting, and theoretical results were compared with measured data. The key findings were as follows: Radial stress > axial stress > hoop stress. All three stresses decay with increasing distance and damage factor, following an inversely proportional relationship with distance. Radial stress decays faster than axial and hoop stresses. Stress also decays exponentially over time, with the peak occurring after the transverse wave arrival. The theoretical results show approximately 10% deviation from the existing empirical formulas, while field measurements closely match the theoretical model, showing consistent stress trends and an average error of 7.02% (radial), 7.56% (axial) and 7.05% (hoop), confirming the reliability of the proposed analytical solution. Full article

This paper proposes a novel analytical method to address the Helmholtz fractional differential equation by combining the Aboodh transform with the Adomian Decomposition Method, resulting in the Aboodh–Adomian Decomposition Method (A-ADM). Fractional differential equations offer a comprehensive framework for describing intricate physical processes, including memory effects and anomalous diffusion. This work employs the Caputo–Fabrizio fractional derivative, defined by a non-singular exponential kernel, to more precisely capture these non-local effects. The classical Helmholtz equation, pivotal in acoustics, electromagnetics, and quantum physics, is extended to the fractional domain. Following the exposition of fundamental concepts and characteristics of fractional calculus and the Aboodh transform, the suggested A-ADM is employed to derive the analytical solution of the fractional Helmholtz equation. The method’s validity and efficiency are evidenced by comparisons of analytical and approximation solutions. The findings validate that A-ADM is a proficient and methodical approach for addressing fractional differential equations that incorporate Caputo–Fabrizio derivatives. Full article

This study focuses on analyzing progressive Type-II right censoring competing risks datasets. The latent causes of failures are assumed to follow independent generalized linear exponential distributions. The maximum likelihood and maximum product of spacing methods are employed to estimate the unknown parameters and survival indices. Furthermore, approximate confidence intervals are derived using the asymptotic normality of the maximum likelihood and the maximum product of spacing estimators. Additionally, bootstrap methods are employed to construct confidence intervals. A comprehensive simulation study is carried out to evaluate the effectiveness of these estimation approaches. Finally, real-world datasets are analyzed to illustrate the practical applicability of the proposed model. Full article

To enable safe and effective human–robot collaboration (HRC) in smart manufacturing, it is critical to seamlessly integrate sensing, cognition, and prediction into the robot controller for real-time awareness, response, and communication inside a heterogeneous environment (robots, humans, and equipment). The proposed approach takes advantage of the prediction capabilities of nonlinear model predictive control (NMPC) to execute safe path planning based on feedback from a vision system. To satisfy the requirements of real-time path planning, an embedded solver based on a penalty method is applied. However, due to tight sampling times, NMPC solutions are approximate; therefore, the safety of the system cannot be guaranteed. To address this, we formulate a novel safety-critical paradigm that uses an exponential control barrier function (ECBF) as a safety filter. Several common human–robot assembly subtasks have been integrated into a real-life HRC assembly task to validate the performance of the proposed controller and to investigate whether integrating human pose prediction can help with safe and efficient collaboration. The robot uses OptiTrack cameras for perception and dynamically generates collision-free trajectories to the predicted target interactive position. Results for a number of different configurations confirm the efficiency of the proposed motion planning and execution framework, with a 23.2% reduction in execution time achieved for the HRC task compared to an implementation without human motion prediction. Full article

The paper outlines the hybrid framework of spin hydrodynamics, combining classical kinetic theory with the Israel–Stewart method of introducing dissipation. The local equilibrium expressions for the baryon current, the energy–momentum tensor, and the spin tensor of particles with spin 1/2 following the Fermi–Dirac statistics are obtained and compared with the earlier derived versions where the Boltzmann approximation was used. The expressions in the two cases are found to have the same form, but the coefficients are shown to be governed by different functions. The relative differences between the tensor coefficients in the Fermi–Dirac and Boltzmann cases are found to grow exponentially with the baryon chemical potential. In the proposed formalism, nonequilibrium processes are studied including mathematically possible dissipative corrections. Standard conservation laws are applied, and the condition of positive entropy production is shown to allow for the transfer between the spin and orbital parts of angular momentum. Full article

The Threshold Pressure Gradient (TPG) phenomenon exerts a profound influence on fluid flow dynamics in heavy oil reservoirs. However, the discrepancies between the True Threshold Pressure Gradient (TTPG) and Pseudo-Threshold Pressure Gradient (PTPG) significantly impede accurate residual oil evaluation and rational field development planning. This study proposes a dual-exponential conversion model that effectively bridges the discrepancy between TTPG and PTPG, achieving an average deviation of 12.77–17.89% between calculated and measured TTPG values. Nonlinear seepage simulations demonstrate that TTPG induces distinct flow barrier effects, driving residual oil accumulation within low-permeability interlayers and the formation of well-defined “dead oil zones.” In contrast, the linear approximation inherent in PTPG overestimates flow initiation resistance, resulting in a 47% reduction in recovery efficiency and widespread residual oil enrichment. By developing a TTPG–PTPG conversion model and incorporating genuine nonlinear seepage characteristics into simulations, this study effectively mitigates the systematic errors arising from the linear PTPG assumption, thereby providing a scientific basis for accurately predicting residual oil distribution and enhancing oil recovery efficiency. Full article