Search Results (182)

Phosphate sorption data are often analyzed by least-squares fitting to the two- or three-parameter Freundlich model. The standard methods are flawed by (1) treating the measured pseudo-equilibrium concentration C as the independent (hence error-free) variable and (2) neglecting the weighting that should accommodate the varying precision of the data. Here, we address both of these shortfalls and use a global fit model to achieve optimal precision in fitting data for five acidic Australian soil types. Each individual dataset consists of measured C values for up to nine phosphate spiking levels C₀. For each soil type, there are three–five such datasets from varying levels of phosphate fertilizer pre-exposure (P_f) two years earlier. These datasets are fitted simultaneously by expressing the Freundlich capacity factor a and exponent b as theoretically predicted functions of the assay amounts of Fe, Al, and P measured for each P_f. The analysis allows for uncertainty in both C and C₀, with inverse-variance weighting from variance functions estimated by residuals analysis. The estimated presorbed P amounts Q depend linearly on P_f, with positive intercepts at P_f = 0, indicating residual phosphate in the soils prior to the laboratory phosphate treatments. The key takeaway points are as follows: (1) global analysis yields optimal estimates and improved precision for the fit parameters; (2) allowing for uncertainty in C is essential when the data include C values near 0; (3) varying data precision requires weighting to yield optimal parameter estimates and reliable uncertainties. Full article

Quantum illumination (QI) is an entanglement-based protocol for improving LiDAR/radar detection of unresolved targets beyond what a classical LiDAR/radar of the same average transmitted energy can do. Originally proposed by Seth Lloyd as a discrete-variable quantum LiDAR, it was soon shown that his proposal offered no quantum advantage over its best classical competitor. Continuous-variable, specifically Gaussian-state, QI has been shown to offer a true quantum advantage, both in theory and in table-top experiments. Moreover, despite its considerable drawbacks, the microwave version of Gaussian-state QI continues to attract research attention. A recent QI study by Armanpreet Pannu, Amr Helmy, and Hesham El Gamal (PHE), however, has: (i) combined the entangled state from Lloyd’s QI with the channel models from Gaussian-state QI; (ii) proposed a new positive operator-valued measurement for that composite setup; and (iii) claimed that, unlike Gaussian-state QI, PHE QI achieves the Nair–Gu lower bound on QI target-detection error probability at all noise brightnesses. PHE’s analysis was asymptotic, i.e., it presumed infinite-dimensional entanglement. The current paper works out the finite-dimensional performance of PHE QI. It shows that there is a threshold value for the entangled-state dimensionality below which there is no quantum advantage, and above which the Nair–Gu bound is approached asymptotically. Moreover, with both systems operating with error-probability exponents 1 dB lower than the Nair–Gu bound, PHE QI requires enormously higher entangled-state dimensionality than does Gaussian-state QI to achieve useful error probabilities in both high-brightness (100 photons/mode) and moderate-brightness (1 photon/mode) noise. Furthermore, neither system has an appreciable quantum advantage in low-brightness (much less than 1 photon/mode) noise. Full article

Background: This study addresses the challenge of predicting data sequences characterized by a mix of partial linearity and partial nonlinearity. Traditional forecasting models often struggle to accurately capture the complex patterns of change within the data. Methods: To this end, this study introduces a novel polynomial-driven discrete grey power model (PFDPGM(1,1)) that includes time perturbation parameters, enabling a flexible representation of complex variation patterns in the data. The model aims to determine the accumulation order, nonlinear power exponent, time perturbation parameter, and polynomial degree to minimize the fitting error under various criteria. The estimation of unknown parameters is carried out by leveraging a hybrid optimization algorithm, which integrates Particle Swarm Optimization (PSO) and the Grey Wolf Optimization (GWO) algorithm. Results: To validate the effectiveness of the proposed model, the annual total renewable energy consumption in the BRICS countries is used as a case study. The results demonstrate that the newly constructed polynomial-driven discrete grey power model can adaptively fit and accurately predict data series with diverse trend change characteristics. Conclusions: This study has achieved a significant breakthrough by successfully developing a new forecasting model. This model is capable of handling data sequences with mixed trends effectively. As a result, it provides a new tool for predicting complex data change patterns. Full article

Background: Linear methods of analysis of variability are concerned with the magnitude of variability and often consider deviations from a central mean as errors. The utilization of nonlinear tools to examine variability allows for the exploration and measurement of the patterns of variability displayed by the system. This methodology explores the deterministic properties of biological signals, in this case, gait, or how previous iterations within the gait cycle influence subsequent and future iterations. The nonlinear analysis of gait variability of the joint angle time series has not been investigated in developing children. Methods: We collected 3 min of treadmill walking data for 28 children between the ages of 2 and 10 years old and analyzed their joint angle time series using nonlinear methods of analysis (sample entropy, largest Lyapunov exponent, and recurrence quantification analysis). Results: Our results indicate that the nonlinear variability of children’s gait increases as children age. Interestingly, this contrasts with the findings from our previous work that showed a decrease in linear variability as children age. The combination of a decrease in linear variability, or a refined and improved stability of gait, as well as an increase in nonlinear variability, or an increase in the sophistication and quality of movement patterns, suggest an overall maturation of the neuromuscular system. Conclusions: Our study indicate that there is a refining of gait with age and motor maturation. This refining speaks to the overall multifaceted organization of systems that defines the maturation of gait. Full article

Accurate numerical simulation of turbulent flow within the milli-channels of drip irrigation emitters has long been a significant challenge. This paper presents a comprehensive Reynolds-Averaged Navier–Stokes (RANS) modeling-based analysis of the flow dynamics within the labyrinth milli-channel of a tooth-shaped emitter, with partial experimental validation. The objective was to assess the performances of four RANS turbulence models: RNG k-ε (RNG), Realizable k-ε (RKE), SST k-ω (SST), and baseline k-ω (BSL), alongside three near-wall treatments: scalable wall function (SWF), enhanced wall treatment (EWT), and y⁺-insensitive wall treatment (YIWT) for emitter flow analysis. The results showed that the RNG and RKE, coupled with EWT, are preferred options for predicting the flow rate—pressure loss relationship of the emitter, with relative errors of 2.08% and 1.02% in the discharge exponent and 5.66% and 7.58% in the flow rate coefficient, respectively. Although both RNG and RKE using SWF are viable for hydraulic performance prediction under high-flow rate conditions, the deviation of predicted flow rate reaches up to 25.46% under low-flow rate conditions. The SST and BSL models, which employ IYPT, captured induced vortices at channel corners; however, they underestimated emitter flow rates. Furthermore, computations using SWF failed to capture the asymptotic characteristics of flow parameters in the near-wall region, resulting in an overestimation of turbulent kinetic energy and turbulence intensity. Additionally, the magnitude of wall shear stress in the channel corners fell below the threshold required for self-cleaning, underscoring the necessity for optimizing channel structures to enhance the anti-clogging performance of the emitter. Full article

This study delves into the dynamics of the Ghana Stock Exchange Composite Index (GSE-CI) over the period from 2011 to 2022, a symbolic emerging market index that presents unique challenges and opportunities for financial analysis. We characterize the GSE-CI using advanced analytical tools such as the Hurst exponent and R/S analysis to uncover its fractal properties and complex dynamics. The paper then advances to predictive modeling, employing an innovative approach with four variations of Stochastic Volatility (SV) models: SV with linear regressors, SV with Student’s t errors, SV with leverage effects, and a hybrid model combining Student’s t errors with leverage. Each model offers a unique perspective on forecasting the behavior of the GSE-CI, with the SV model incorporating Student’s t errors emerging as the most effective, as evidenced by the lowest Root Mean Square Error (RMSE) in our comparative evaluation. The integration of these models highlights their robustness in capturing the intricate volatility patterns of the GSE-CI, making a compelling case for their applicability to similar financial markets in other emerging economies. This research also paves the way for future investigations into other market indices and assets within and beyond the borders of emerging markets. Full article

In the design of structures involving quasi-brittle materials such as concrete, it is essential to consider the scale dependence of the mechanical properties of the material. Among the theories used to describe the phenomenon of size effect, the fractal theory proposed by Carpinteri and colleagues has attracted attention for its results in the last three decades of research. The present study employs the fractal perspective to examine the scale effect in three-point bending tests conducted on expanded polyethylene (EPS) beam specimens. The influence of size on flexural strength, fracture energy, and critical angle of rotation is investigated. Additionally, numerical simulations based on peridynamic (PD) theory are performed based on the experimental tests. The global behavior, brittleness, failure configuration, and fractal scale effect obtained numerically are evaluated. The numerical results show a good correlation with the experimental ones and, moreover, both the experimental and numerical results are in agreement with the fractal theory of scale effect. More precisely, the error of the sum of the fractal exponents, computed with respect to the theoretical one, is equal to −1.20% and −2.10% for the experimental and numerical results, respectively. Moreover, the classical dimensional analysis has been employed to demonstrate that the scale effect can be naturally described by the PD model parameters, allowing to extend the results for scales beyond those analyzed experimentally. Full article

We consider a basic joint communication and sensing setup comprising a transmitter, a receiver and a sensor. The transmitter sends a codeword to the receiver through a discrete memoryless channel, and the receiver is interested in decoding the transmitted codeword. At the same time, the sensor picks up a noisy version of the transmitted codeword through one of two possible discrete memoryless channels. The sensor knows the codeword and wishes to discriminate between the two possible channels, i.e., to identify the channel that has generated the output given the input. We study the trade-off between communication and sensing in the asymptotic regime, captured in terms of the channel coding rate against the two types of discrimination error exponents. We characterize the optimal trade-off between the rate and the exponents for general discrete memoryless channels with an input cost constraint. Full article

The isotropic Cauchy distribution is a member of the central $\alpha$-stable family that plays a role in the set of heavy-tailed distributions similar to that of the Gaussian density among finite second-moment laws. Given a sequence of n observations, we are interested in characterizing the performance of Likelihood Ratio Tests, where two hypotheses are plausible for the observed quantities: either isotropic Cauchy or isotropic Gaussian. Under various setups, we show that the probability of error of such detectors is not always exponentially decaying with n, with the leading term in the exponent shown to be logarithmic instead, and we determine the constants in that leading term. Perhaps surprisingly, the optimal Bayesian probabilities of error are found to exhibit different asymptotic behaviors. Full article

This study presents the analysis and comparison of Wi-Fi coverage modeling for a hotspot using deterministic and empirical propagation models developed by researchers from the Universidad de Las Américas in Quito, Ecuador. Signal intensity measurements were taken from both the hotspot and the repeater at various locations within the Checa parish using a Raspberry Pi and a Global Positioning System (GPS). To assess the accuracy of the models, heat maps were generated using Matlab (R2023A). The results showed that the adjusted model, comparing the received signal levels of the hotspot with the Stanford University Interim Propagation Model (SUI), exhibited a significant error margin, especially at distances below 60 m. However, starting at −70 dBm and beyond 60 m, the sampled data aligned better with the adjusted model. The discrepancy in the heatmaps was explained by the hotspot’s higher transmission power compared to the Wi-Fi repeater. Furthermore, the reception levels of the hotspot were low near the transmitter, which led to new measurements being taken with the Wi-Fi repeater (Raspberry Pi 3). With the new measurements, the adjusted model using logarithmic regression showed a better fit, particularly in the range from −40 dBm to −98 dBm, with a path loss exponent of 8.96. This demonstrated a significant improvement in prediction accuracy, particularly at short distances. The results emphasize the importance of using tools such as Matlab and reference models to optimize network planning, providing the Universidad de Las Américas with a valuable tool to generate heat maps in areas with characteristics similar to those of Checa in the context of their community outreach programs. This approach could be crucial for future research and optimization of community Wi-Fi networks in similar environments. Full article

This study uses Artificial Neural Networks (ANNs) and multiple linear regression (MLR) models to explore the relationship between gait dynamics and the metabolic cost. Six nonlinear metrics—Lyapunov Exponents based on Rosenstein’s algorithm (LyER), Detrended Fluctuation Analysis (DFA), the Approximate Entropy (ApEn), the correlation dimension (CD), the Sample Entropy (SpEn), and Lyapunov Exponents based on Wolf’s algorithm (LyEW)—were utilized to predict the metabolic cost during walking. Time series data from 10 subjects walking under 13 conditions, with and without hip exoskeletons, were analyzed. Six ANN models, each corresponding to a nonlinear metric, were trained using the Levenberg–Marquardt backpropagation algorithm and compared with MLR models. Performance was assessed based on the mean squared error (MSE) and correlation coefficients. ANN models outperformed MLR, with DFA and Lyapunov Exponent models showing higher R² values, indicating stronger predictive accuracy. The results suggest that gait’s nonlinear characteristics significantly impact the metabolic cost, and ANNs are more effective for analyzing these dynamics than MLR models. The study emphasizes the potential of focusing on specific nonlinear gait variables to enhance assistive device optimization, particularly for hip exoskeletons. These findings support the development of personalized interventions that improve walking efficiency and reduce metabolic demands, offering insights into the design of advanced assistive technologies. Full article

In this paper, fractional calculus is used to develop a generalized fractional dynamic model of an electrohydraulic system composed of a servo valve and a hydraulic cylinder, where a fractional position controller $P{I}^{\gamma }{D}^{\mu }$ is proposed for minimizing the performance index according to the integral of the time-weighted absolute error (ITAE). First, the general mathematical equations of the cylinder and servo valve are used to obtain the transfer functions in fractional order by applying Caputo’s definition and a Laplace transform. Then, through a block diagram of the closed-loop system without a controller, the fractional model is validated by comparing its performance concerning the integer-order electrohydraulic system model reported in the literature. Subsequently, a fractional PID controller is designed to control the cylinder position. This controller is included in the closed-loop system to determine the fractional exponents of the transfer functions of the servo valve, cylinder, and control, as well as to tune the controller gains, by using the ITAE objective function, with a comparison of the following: (1) the electrohydraulic system model in integer order and the controller in fractional order; (2) the electrohydraulic system model in fractional order and the controller in integer order; and (3) both the system model and the controller in fractional order. For each of the above alternatives, numerical simulations were carried out using MATLAB^®/Simulink^® R2023b and adding white noise as a perturbation. The results show that strategy (3), where electrohydraulic system and controller model are given in fractional order, develops the best performance because it generates the minimum value of ITAE. Full article

The climate impact of Arctic aerosols, like the Arctic Haze, and their origin are not fully understood. Therefore, long-term aerosol observations in the Arctic are performed. In this study, we present a homogenised data set from a sun and star photometer operated in the European Arctic, in Ny-Ålesund, Svalbard, of the 20 years from 2004–2023. Due to polar day and polar night, it is crucial to use observations of both instruments. Their data is evaluated in the same way and follows the cloud-screening procedure of AERONET. Additionally, an improved method for the calibration of the star photometer is presented. We found out, that autumn and winter are generally more polluted and have larger particles than summer. While the monthly median Aerosol Optical Depth (AOD) decreases in spring, the AOD increases significantly in autumn. A clear signal of large particles during the Arctic Haze can not be distinguished from large aerosols in winter. With autocorrelation analysis, we found that AOD events usually occur with a duration of several hours. We also compared AOD events with large-scale processes, like large-scale oscillation patterns, sea ice, weather conditions, or wildfires in the Northern Hemisphere but did not find one single cause that clearly determines the Arctic AOD. Therefore the observed optical depth is a superposition of different aerosol sources. Full article

The micromechanical properties (i.e., hardness, elastic modulus, and stress–strain curve) of AlCu films were determined by an instrumented indentation test in this work. For three AlCu films with different thicknesses (i.e., 1 µm, 1.5 µm, and 2 µm), the same critical ratio (h_max/t) of 0.15 and relative indentation depth range of 0.15–0.5 existed, within which the elastic modulus (i.e., 59 GPa) and nanoindentation hardness (i.e., 0.75 GPa, 0.64 GPa and 0.63 GPa for 1 µm, 1.5 µm and 2 µm films) without pile-up and substrate influence can be determined. The yield strength (i.e., 0.754 GPa, 0.549 GPa and 0.471 GPa for 1 µm, 1.5 µm and 2 µm films) and hardening exponent (i.e., 0.073, 0.131 and 0.150 for 1 µm, 1.5 µm and 2 µm films) of Al-(4 wt.%)Cu films for MEMS were successfully reported for the first time using a nanoindentation reverse method. In dimensional analysis, the ideal representative strain ε_r was determined to be 0.038. The errors of residual depth h_r between the simulations and the nanoindentation experiments was less than 5% when the stress–strain curve obtained by the nanoindentation reverse method was used for simulation. Full article

Short time series are fundamental in the foreign exchange market due to their ability to provide real-time information, allowing traders to react quickly to market movements, thus optimizing profits and mitigating risks. Economic transactions show a strong connection to foreign currencies, making exchange rate prediction challenging. In this study, the exchange rate estimation between the US dollar (USD) and the Chilean peso (CLP) for a short period, from 2 August 2021 to 31 August 2022, is modeled using the nonlinear Schrödinger equation (NLSE) and calculated with the fourth-order Runge–Kutta method, respectively. Additionally, the daily fluctuations of the current exchange rate are characterized using the Hurst exponent, H, and later used to generate short synthetic fluctuations to predict the USD–CLP exchange rate. The results show that the USD–CLP exchange rate can be estimated with an error of less than $5\%$, while when using short synthetic fluctuations, the exchange rate shows an error of less than $10\%$. Full article