Against the backdrop of rising uncertainty in global energy markets, the tail directional connectedness between energy-price fluctuations and macroeconomic conditions has become an important issue for macroprudential monitoring. Drawing on the mixed-frequency information stacking idea of the MF-VAR-DY model, this paper constructs an MF-QVAR-DY model that embeds high-frequency energy prices and low-frequency macroeconomic variables within a unified conditional quantile system, and examines the directional connectedness structure among fossil energy, clean energy, and U.S. macroeconomic variables. The results show that: first, total connectedness between energy markets and macroeconomic variables exhibits a pronounced high-at-both-ends, low-in-the-middle pattern; second, from a mixed-frequency perspective, crude oil is a stable outward risk transmitter, whereas clean energy plays a stronger outward spillover role at extreme quantiles; third, during the COVID-19 pandemic, fossil and clean energy jointly transmitted risk, whereas during the Russia-Ukraine conflict, the crude oil market dominated risk spillovers. This paper provides empirical evidence for using high-frequency energy information in macroeconomic tail-risk monitoring.
This investigation involves the numerical exploration of both stochastic and deterministic SIRS epidemic models with saturated incidence rates. The implicit Runge-Kutta method, Euler-Maruyama method, and stochastic Runge-Kutta methods are employed to analyze the dynamic patterns of the epidemic. The deterministic model is formulated using an ordinary differential system and is deep-seated in the well-established classical SIRS epidemic framework. A stochastic model is formulated by incorporating the elements of white noise in order to explain the effect of random environmental fluctuation. The proposed numerical experiments are efficiently used to assess the impact of environmentally induced changes on the SIRS epidemic model transmission dynamics. The incidence of transmission of each type of population is predicted by varying the rate of recruiting new susceptible populations, the rate of immunity decay, the rate of natural mortality, mortality due to disease, and the rate of recovery of infected individuals. The deterministic modelling solutions are approximated using the state-of-the-art implicit Runge-Kutta method, while the probabilistic versions of the model are explored using the Euler-Maruyama and stochastic Runge-Kutta techniques. Both deterministic and stochastic disease models are extensively compared and analyzed in the light of sundry scenarios of environmental changes with the view of obtaining an insight into the trends in disease transmission.
According to the new wavelet random numbers (WRNs), some applications in numerical computation have been discussed in this paper. First, an approach is introduced to generate WRNs and transform the random numbers into uniform distribution random numbers and exponential random numbers. The randomness of WRN is identified by the white noise Hurst exponent and NIST SP 800-22 tests. Second, Mentor Carlo integration method based on WRNs has been applied to compute different numerical integrations compared to the results by randn, rand and PCG. Four examples are given to illustrate the improvement in computing a numerical integration, including a continuous integrand and a discontinuous oscillating integrand. According to the linear regression test of variance, it is verified that the variance of estimators decreases with the increase of sample size. Third, WRNs can be used to construct a one-time pad (OTP) briefly, which is statistically tested by the frequency (monobit) test and Shannon entropy test. Finally, WRNs can be chosen for bootstrap. An illustrative example indicates that bootstrap based on WRNs can be a case study of improving the accuracy of estimating a confidence interval for skewed exponential distribution, compared to randi.
Stress is a major contributor to mental health disorders due to the constant activation of the sympathetic nervous system, which alters the fluctuation dynamics of the ECG signal. Analyzing these fluctuations enables the capture of short-term variations in ECG amplitude, which reflect the underlying cardiac variations during stress. This study analyses the local fluctuations of ECG signals under stress and compares them with those observed during physical activity, thereby identifying differences in the complexity and adaptability of cardiac regulation. ECG signals were collected using a wireless wearable system from 24 participants and preprocessed to remove noise and powerline interference. The preprocessed signals were normalized and segmented across multiple scales to extract local fluctuations using linear, quadratic and cubic polynomial detrending methods. The results showed that cubic polynomial detrending most effectively conformed to the signal and removed slow-varying trends. The extracted local fluctuations exhibited rapid variability at fine-grained scales, reflecting ECG beat-to-beat variability, whereas at coarse-grained scales, the baseline fluctuations dominated the ECG signal. Therefore, fluctuations at finer scales were further analyzed using fuzzy entropy to quantify the signal complexity. Stress exhibited reduced fuzzy entropy values compared to physical activity, indicating constrained short-term fluctuations and reduced complexity. Additionally, statistically significant differences (p < 0.05) were observed between stress and physical activity. These findings demonstrate that local fluctuation-based fuzzy entropy effectively captures the subtle cardiac dynamics associated with stress and can be used as a potential biomarker in wearable healthcare monitoring systems.
We study the motion of a Brownian particle diffusing in a logarithmic-harmonic potential under stochastic resetting. We derive an exact analytical expression for the steady-state position distribution and contrast it with the equilibrium distribution obtained in the absence of resetting. By exploring the parameter space, we identify distinct regimes characterizing the resulting non-equilibrium steady states and investigate the relaxation dynamics toward stationarity. Furthermore, we demonstrate that the mean first-passage time to a target exhibits a non-monotonic dependence on the resetting rate, attaining a minimum at an optimal value. All analytical predictions are corroborated by numerical simulations.
There has been an ongoing hypothesis, since at least the 1950s, that light-emitting diodes (LEDs) operated with a forward bias voltage less than the bandgap-equivalent voltage should act as optical refrigerators. Since LEDs emit luminescence radiation approximately equal to the bandgap energy, application of a sub-bandgap-equivalent voltage supposedly means that the difference in the emitted photon energy and the applied bias must be made up by the net absorption of lattice phonons. As a result, it is surmised that cooling should occur. Here, we show that the voltage is simply the change in free energy, also known as the quasi-Fermi level separation, and we distinguish free energy from the electrical energy that is required to operate LEDs. Operation of LEDs with forward bias voltage less than the bandgap-equivalent voltage is normal, and this operating condition neither implies nor requires electroluminescence cooling or any other novel explanation.
In this paper, we propose multivariate multiscale weighted dispersion transfer entropy (MM-WDTE). This method introduces weight coefficients and weight probabilities in the symbolization process of dispersion patterns to retain key information to the greatest extent and effectively improve the robustness to noise. Furthermore, this method has been extended to cover multivariable systems and multiscale levels, breaking through the limitations of the traditional binary transfer entropy model on dimensions, and comprehensively grasping the dynamic characteristics of signals on multiple time scales. By applying this method, we performed an interaction analysis of multi-channel electroencephalogram (EEG) data in four classical frequency bands (Theta, Alpha, Beta, Gamma) in children with attention deficit hyperactivity disorder (ADHD). Based on the positions of the electrodes, the brain was separated into four regions. By using a cross-regional study, we identified differences in information interaction between different brain regions in children with ADHD and controls.
Classical low-dimensional stochastic resonance (SR) systems often exhibit limitations in complex engineering signal processing due to their restricted output channels and narrow parameter tunability. Building upon existing research on multi-dimensional SR, this paper constructs a four-dimensional bistable stochastic resonance system. By establishing inter-dimensional coupling relationships, the system achieves multi-channel parallel output and coordinated resonance responses. The dynamical behavior of the system is analyzed, with its equilibrium points and stability criteria derived. Moreover, the effect of input amplitude on the system’s dynamical response is investigated across different types of signals. Furthermore, to alleviate the frequency limitations imposed by the adiabatic approximation in conventional SR, a time-scale transformation method is applied. By adjusting the sampling frequency and integration step size, this technique is utilized to broaden the applicable frequency range of the multi-dimensional system. Case studies on image restoration and mechanical bearing fault diagnosis demonstrate that the proposed 4DSR system can effectively extract weak signal features under strong background noise, indicating its promising application potential in relevant weak signal detection tasks.
Low-noise amplifiers (LNAs) constitute the most vulnerable elements of RF receivers when exposed to high-power in-band signals originating from transmitter leakage, unintentional jamming, or intentional electromagnetic attacks. Conventional receiver architectures generally rely on GaAs LNAs with external protection devices, such as diode limiters, whose insertion losses significantly degrade the overall receiver noise figure. This work investigates alternative hardening strategies based on both GaAs and GaN MMIC technologies operating in X- and Ku-bands. Three protection approaches are experimentally evaluated under dedicated RF step-stress sequences performed at room temperature: (i) A cold GaAs LNA (which needs to be associated with an external protection function), (ii) A robust GaN LNA exploiting first-stage gain compression to limit power propagation toward subsequent stages and (iii) A self-reconfigurable GaN LNA capable of switching from low-noise operation to protection mode when a critical RF level is detected. Electrical, dynamic and noise performances are monitored before, during and after RF stress through measurements of S-parameters, noise figure, gain compression, drain and gate currents and recovery behavior. The results demonstrate that the GaAs solution preserves excellent low-noise performance but requires a protection device whose insertion loss directly penalizes the receiver noise figure. In contrast, GaN-based solutions withstand substantially higher RF aggression levels, reaching 35–38[Formula: see text]dBm input power. The compression-based GaN architecture exhibits survivability up to 38[Formula: see text]dBm, while the self-reconfigurable approach maintains low-noise operation under nominal conditions and tolerates RF stress levels exceeding 30[Formula: see text]dBm before failure at 35 dBm. Moreover, partial recovery of electrical and noise performances after severe stress suggests that charge trapping mechanisms contribute significantly to the observed degradation for the compression-based GaN architecture. This work provides a comparative assessment of receiver hardening strategies using noise figure, dynamic electrical signatures and RF survivability as common figures of merit. The results highlight the potential of wide-bandgap GaN technologies for the realization of intrinsically robust low-noise receivers with reduced dependence on external protection circuitry.
Complex, powerful and representative fractional SDDMs are used to model fractional-order systems of stochasticity and time delays. This study aims to construct an approximate spectral collocation scheme, numerically solving certain types of these models. Specifically, a suitable model of conformable fractional operator sense where the stochastic term is of the standard one-dimensional SBM type, and the time delay is discrete and constant, is established and solved. The proposed scheme is based on the spectral collocation method, with SLP basis functions and SLGL collocation points. To obtain the desired approximate solutions utilizing the present method, the domain under consideration is discretized into steps, and at each step, the solution is approximated using the spectral collocation technique. Simply, complicated problems are evolved into a system of algebraic equations where the unknowns are the Legendre coefficients. These equations are solved utilizing an appropriate numerical method executed by MATHEMATICA. For convenience, the convergence analysis under Lipschitz properties is presented. To validate the reality of the method, some applications of linear and nonlinear types of the suggested model are solved, and the errors are computed. Moreover, the Log-Log plots are sketched to confirm the efficiency of the presented method when more collocation points are used. The results formulated in tables, figures, and discussions illustrate the high accuracy and capability of the proposed methodology. Final remarks and future work are reviewed and debated, too.
In the field of nanoscale sensing, it is often necessary to detect very weak signals in the presence of large environmental background noise. To overcome this problem, differencing techniques are often used in an effort to cancel the background noise from distant sources. Here, we investigate a magnetic gradiometer based on pairs of nitrogen vacancy (NV) color centers in diamond. By simulating NV pairs that are not resolved optically, it is shown that sensitivity to uniform magnetic fields can be strongly suppressed, while sensitivity to magnetic gradients remains good. The proposed scheme applies even if the NV pairs are not close enough to be entangled.
Exact solutions for the probability density function and generalized n-moments are obtained from two coupled Langevin equations driven by Gaussian white noises in heterogeneous media, with time-space-dependent drift and diffusion coefficients. In particular, exact solutions for ordinary n-moments are obtained from space-dependent power-law drift and diffusion coefficients. Simulation and analytical results are also analyzed for time-dependent diffusion coefficients with exponential decays.
This study presents an investigation of X-ray dose impact on Low-Frequency Noise Generation-Recombination components for NPN Si/SiGe:C heterojunction bipolar transistors (HBTs) developed in two BiCMOS technologies. Generation-Recombination (G-R) components are examined in detail both before and after irradiation. Compact modeling for these excess noise components is established after X-ray exposure. They are studied over the irradiation process as a function of current biases and geometrical parameters to locate the noise sources. A thermal annealing process at two temperatures, 100 degrees C and 130 degrees C, is accomplished to investigate any possible healing effects on Low-Frequency Noise spectra.
Flicker noise is a phenomenon present in almost any physical system, and which has recently proven to be relevant also in quantum technologies, in particular from the point of view of its action as a source of decoherence. The origin of flicker noise is usually explained as a consequence either of the effect of fluctuations in the mobility of the charge carriers, or of fluctuations in the number of such carriers. In addition, the flicker noise power spectral density is often assumed to be inversely proportional to the number of carriers in the device, as in the well-known Hooge formula, in the case of mobility fluctuations and to the square of the carrier number in the case of number fluctuations. Here, instead, we show that for the case of a bulk semiconductor the flicker noise power spectral density resulting from carrier number fluctuations can indeed, under specific conditions, be inversely proportional to the number of carriers.
The physical interpretation of the noise generated by an electron device is more easily obtained in terms of current noise. Direct current noise measurements, however, are typically performed only on high impedance devices, employing FET input operational amplifiers for the realization of the low-noise transimpedance amplifiers coupled to the device under test. The relatively large value of the equivalent noise voltage in FET input operational amplifiers limits the sensitivity of current noise measurement in the case of low impedance devices. Employing BJT input operational amplifiers might allow to reduce the equivalent input noise voltage, but at the cost of an unacceptably high level of current noise. However, if a cross-correlation approach for current noise measurement is employed, the contribution from the equivalent input current noise of the operational amplifiers can be, in principle, eliminated thus allowing to reach very low level of background noise also in the case of low impedance devices. On the other hand, the rejection of the uncorrelated noise requires extended measurement time. In this paper, after reviewing the main factors affecting the balance between sensitivity and measurement time in the case of cross-correlation current noise measurements, we propose an effective methodology for guiding the selection of the cross-correlation front-end components so that very high sensitivity can be reached also in the case of devices under test characterized by low impedances.
This paper investigates the stochastic Davey-Stewartson equation, which represents the evolution of weakly nonlinear wave packets under the effect of randomness. This equation is developed within a stochastic framework that incorporates multiplicative Brownian motion perturbations to reflect the impact of environmental fluctuations and noise-induced phenomena present in genuine physical systems. We apply the Riccati-Bernoulli sub-ordinary differential equation method, a unified and systematic approach that converts the stochastic Davey-Stewartson equation into solvable deterministic sub-ordinary differential equation, to obtain accurate stochastic solutions. The proposed method enables the production of a wide range of innovative stochastic wave solutions, such as solitons, breather-type structures, rational solutions and periodic wave patterns, all characterized in terms of hyperbolic, trigonometric, or rational functions. The resulting solutions reveal intricate dynamical characteristics and demonstrate how random perturbations influence phase modulation and stability features. The scientific significance of the resulting stochastic solutions is thoroughly examined, with a focus on applications in nonlinear optics, plasma physics, fluid dynamics, Bose-Einstein condensates and ocean wave propagation, where random disturbances play an important role. Finally, the proposed technique is a robust and adaptable analytical tool for analysing stochastic nonlinear evolution equations, providing novel insights into noise-driven wave events in complex media.
Noise-assisted ensemble empirical mode decomposition (EEMD) alleviates mode mixing by averaging decompositions of noise-perturbed replicas. In experimental records, however, a fixed injection amplitude may either over-perturb high-SNR data or fail to stabilize envelope estimation when ambient fluctuations are strong, and the ensemble size is limited. We introduce an SNR-adaptive complementary robust ensemble EMD (SCR-EMD). SCR-EMD estimates the ambient noise level from the observation, injects only the incremental perturbation needed to reach a target assistance scale while enforcing a nonzero floor, pairs +/- perturbations to suppress injected-noise bias, and aggregates trials using correlation-based weights to down-weight outlier decompositions. Across synthetic multicomponent benchmarks, structured shared-core comparisons with CEEMDAN/ICEEMDAN, amplitude-modulated and colored-noise stress tests, reconstruction-policy ablations, and ECG-related evaluations, SCR-EMD is most advantageous in high-SNR and low-to-moderate noise conditions, where reducing unnecessary first-stage assistance helps avoid over-perturbation, while complete variants remain attractive in heavier-noise regimes. The ECG-related evidence is broadened through a single-record real ECG proof-of-concept and a morphology/artifact-diversity proxy study, whereas the external motorcycle impact-acceleration case is interpreted separately as cautious cross-domain generalizability beyond ECG. SCR-EMD should therefore be viewed as a practical ambient-noise-adaptive complement to complete-ensemble methods rather than a universal replacement, and the preferred IMF cutoff remains signal- and noise-dependent.
This study examines the phased characteristics of Economic Policy Uncertainty (EPU) and Trade Policy Uncertainty (TPU) utilizing visibility graph algorithms and complex network models. By employing a modularity optimization approach, a dynamic network structure of the time series was constructed, enabling precise delineation of the evolution patterns of uncertainty from 2001 to 2020 and the identification of distinct phases of policy fluctuations. Empirical analysis reveals that the model effectively captures the significant impacts of major events - such as the International Financial Crisis and the US-China Trade War - on policy uncertainty, as well as the structural disturbance observed around the early stage of COVID-19 in 2020. In doing so, the model uncovers the dynamic properties of different periods and their external driving forces within the 2001-2020 sample window. The findings demonstrate that this methodology can adeptly unveil the temporal dynamics of uncertainty, providing robust support for policy formulation, risk assessment, and economic environment research.
This paper presents a new measure to analyze the random behavior in multivariate time series. This study extended the range of entropy choices from Shannon, R & eacute;nyi and Tsallis to fractional case. Furthermore, it is proposed explicit expressions of the Mutual Information Matrix (MIM) based on fractional entropy to analyze the nonlinear interactions between time series. Fractional entropy depends on a fractional parameter, and it is more sensitive to temporal nonlinear dynamics than other methods related to classical entropies. Additionally, the eigenvalues of MIM based on fractional entropy are used to obtain a global information measure, which it represents the total mutual information among the entire time series can be quantified. To illustrate the obtained results, four models (Poisson, sinusoidal, coupled logistic maps and controlled vector autoregressive) are simulated and results are discussed. Finally, COVID-19 pandemic data time series from various testing centers in Baghdad (Iraq) was used to validate the behavior of proposed measures. Results demonstrate that the proposed global measure is more effective in predicting the nature of COVID-19 spread, which may assist governments in planning for its containment.
Instantaneous noise-based logic (INBL) is a novel computing approach that encodes binary information using stochastic processes. It uses 2M orthogonal stochastic reference noises for M noise-bits to construct an exponentially large Hilbert space (hyperspace) of dimension 2M. INBL offers a classical alternative to quantum-style parallelism for specific problems with exponential speedup compared with classical algorithms. Building on recent work that introduced pairwise XOR and XNOR operations defined for a symmetric INBL scheme, this paper implements these gates for a squeezed INBL scheme. Hyperspace vectors are product strings corresponding to M-bit long binary numbers. The proposed operations can apply pairwise on hyperspace vectors and their superpositions (sums), while remaining compatible with the squeezed reference system. We validate that the squeezed-scheme XOR/XNOR gate operations have correct Boolean behavior over both bitwise and targeted M-bit strings and demonstrate that the operations preserve instantaneous evaluation. The results show that the XOR/XNOR toolkit, previously developed for symmetric INBL, can be tailored for the squeezed scheme. This development is a key part of the gate set needed for more complex INBL algorithms in the squeezed INBL scheme and advances the objective of gate universality in INBL. It further strengthens the case for INBL as a flexible, classical computing framework that can emulate some structural advantages of quantum computation.
