With the continued rapid growth of urban areas, problems such as traffic congestion and environmental pollution have become increasingly common. Alleviating these problems involves addressing signal ...timing optimization and control, which are critical components of urban traffic management. In this paper, a VISSIM simulation-based traffic signal timing optimization model is proposed with the aim of addressing these urban traffic congestion issues. The proposed model uses the YOLO-X model to obtain road information from video surveillance data and predicts future traffic flow using the long short-term memory (LSTM) model. The model was optimized using the snake optimization (SO) algorithm. The effectiveness of the model was verified by applying this method through an empirical example, which shows that the model can provide an improved signal timing scheme compared to the fixed timing scheme, with a decrease of 23.34% in the current period. This study provides a feasible approach for the research of signal timing optimization processes.
SUMMARY
We derive two sets of new wave equations involving the fractional time derivatives or fractional Laplacians for simulating seismic wave propagation in viscoelastic anisotropic (VA) media ...based on the Kjartansson's constant-Q model. The approximate fractional Laplacian wave equation is developed under the assumptions of the small velocity anisotropy parameter $\delta$ and attenuation anisotropy parameter ${\delta _Q}$ (or weak velocity and attenuation anisotropy). Both the formulas have the advantages of simple form and can accurately describe the constant-Q (i.e. frequency-independent quality factor) attenuation and arbitrary attenuation anisotropy behaviours compared to the widely used VA theory based on the generalized standard linear solids (GSLS) model and memory variables. Under the assumption of homogeneous plane wave and the constant-Q attenuation mechanism, we further derive exact analytical expressions for the phase velocities, attenuation coefficients, quality factors of the P- and SV-waves, and analyse their direction dependence in different attenuation anisotropy cases. For numerical modelling, we implement the fractional finite-difference (FD) method with the Grünwald–Letnikov (GL) approximation to solve the fractional time wave equation, and the generalized Fourier pseudospectral (PS) method to solve the fractional Laplacian wave equation, respectively. The PS method is highly efficient compared with the fractional FD method since it avoids additional memory to store the past wavefields. Numerical results of the homogeneous VTI (transversely isotropic with a vertical symmetry axis) model validate the accuracy of the two proposed schemes and illustrate the influence of attenuation and attenuation anisotropy on seismic wavefields, which are consistent with theoretical analysis. Finally, the modelling of the Hess VTI model shows the applicability of our formulations and algorithms in heterogeneous media.
The absorption (anelastic attenuation) and anisotropy properties of subsurface media jointly affect the seismic wave propagation and the quality of migration imaging. Anisotropic viscoelastic model ...can effectively describe seismic velocity and attenuation anisotropy effects. To reduce the computational cost and complexity of elastic wave modes decoupling for seismic imaging in anisotropic attenuating media, we have developed a pure-viscoacoustic transversely isotropic (TI) wave equation starting from the complex-valued velocity dispersion relation of quasi-compressional (qP) wave. The wave equation involving fractional Laplacians has advantages of being able to describe the constant-
Q
(frequency-independent quality factor) attenuation, arbitrary TI velocity and attenuation, decoupled amplitude loss and velocity dispersion effects. Numerical analyses showed that the simplified equation can accurately hold the velocity and attenuation anisotropy of qP-wave in viscoelastic anisotropic media in the range of moderate anisotropy. Compared to previous pseudo-viscoacoustic equations, the pure-viscoacoustic equation can be completely free from undesirable S-wave artifacts and behaves good numerical stability in tilted transversely isotropic (TTI) attenuating media. There are obvious wavefield differences between isotropic attenuation and anisotropic attenuation cases especially in the direction perpendicular to the axis of symmetry. Furthermore, to mitigate the influences of velocity and attenuation anisotropy on migrated seismic images, we have developed an anisotropic attenuation (
Q
) compensated reverse time migration (AQ-RTM) approach based on the new propagator. The compensation can be implemented by reversing the sign of the dissipation terms and keeping the dispersion terms unchanged during wavefields extrapolation. Synthetic example from a Graben model illustrated that the anisotropic
Q
-compensated RTM scheme can produce images with more balanced amplitude and accurate position of reflecters compared with conventional RTM methods under assumptions of acoustic anisotropic (uncompensated) and isotropic attenuating media. Results from a Marmousi-II model demonstrated that the new methodology is applicable for complicated geological model to significantly improve imaging resolution of the target area and deep layers.
As the key point of full-waveform inversion (FWI), numerical solution of wave equation is required to simulate the forward propagated and adjoint wavefield. When traditional regular grids are used ...for forward modelling, scattering artifacts may occur due to the stepped approximation of layer interfaces and rugged topography. Unstructured mesh or irregular grids method can achieve certain geometric flexibility, however, its algorithm is quite complex. Mesh-free FWI can effectively reduce the scattered artifacts under regular grids and avoid the extra computation in the process of irregular grids generation. For the implementation of mesh-free FWI method, an algorithm with fast generation of node distributions is used to discretize the velocity model, radial-basis function generated finite difference is used to realise seismic wave propagation numerical simulation, and Limited-memory BFGS (L-BFGS) algorithm is used for iteration. The mesh-free FWI method we proposed achieves flexibility of simulation region and abundant wavefield information. It reduces the storage required for FWI and illustrates the applicability of high-precision reconstruction of underground velocity in the case of rugged topography, which can provide more accurate velocity information for oil and gas exploration under complex geological conditions.
Online learning to rank (OLTR) aims to learn a ranker directly from implicit feedback derived from users’ interactions, such as clicks. Clicks however are a biased signal: specifically, top-ranked ...documents are likely to attract more clicks than documents down the ranking (position bias). In this paper, we propose a novel learning algorithm for OLTR that uses reinforcement learning to optimize rankers: Reinforcement Online Learning to Rank (ROLTR). In ROLTR, the gradients of the ranker are estimated based on the rewards assigned to clicked and unclicked documents. In order to de-bias the users’ position bias contained in the reward signals, we introduce unbiased reward shaping functions that exploit inverse propensity scoring for clicked and unclicked documents. The fact that our method can also model unclicked documents provides a further advantage in that less users interactions are required to effectively train a ranker, thus providing gains in efficiency. Empirical evaluation on standard OLTR datasets shows that ROLTR achieves state-of-the-art performance, and provides significantly better user experience than other OLTR approaches. To facilitate the reproducibility of our experiments, we make all experiment code available at
https://github.com/ielab/OLTR
.
To compare the clinical values of bronchoscopic sputum suction and general sputum suction in respiratory failure patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) ...combined with sequential invasive-noninvasive mechanical ventilation at the pulmonary infection control (PIC) window (period of lower sputum production, with thinner viscosity and lighter color, and alleviated clinical signs of infection).
Patients with AECOPD-induced respiratory failure received orotracheal intubation mechanical ventilation and were randomly divided into bronchoscopic sputum suction group or general sputum suction group, and who were then treated with sequential invasive-noninvasive mechanical ventilation at PIC window (both groups). Baseline data, postoperative blood gas conditions, and postoperative clinical parameters of the patients such as appearance of PIC window, time of invasive ventilation, total time of ventilation, hospital stay, weaning success rate, reintubation rate, ventilator-associated pneumonia (VAP) incidence, and fatality rate were measured to compare the effect of 2 different ways of sputum suction.
There was no significant difference in baseline characteristics, postoperative blood gas conditions, between 2 groups (all P > .05). Nevertheless, the bronchoscopic sputum suction group showed earlier appearance of PIC window, shorter time of invasive ventilation, total time of ventilation and hospital stay, lower reintubation rate, VAP incidence and fatality rate, and higher weaning success rate than the general sputum suction group (all P < .05).
Bronchoscopic sputum suction combined with sequential invasive-noninvasive mechanical ventilation at PIC window showed clinical effects in treating respiratory failure patients with AECOPD.
Since the last century, animal viruses have posed great threats to the health of humans and the farming industry. Therefore, virus control is of great urgency, and regular, timely, and accurate ...detection is essential to it. Here, we designed a rapid on-site visual data-sharing detection method for the Newcastle disease virus with smartphone recognition-based immune microparticles. The detection method we developed includes three major modules: preparation of virus detection vectors, sample detection, and smartphone image analysis with data upload. First, the hydrogel microparticles containing active carboxyl were manufactured, which coated nucleocapsid protein of NDV. Then, HRP enzyme-labeled anti-NP nanobody was used to compete with the NDV antibody in the serum for color reaction. Then the rough detection results were visible to the human eyes according to the different shades of color of the hydrogel microparticles. Next, the smartphone application was used to analyze the image to determine the accurate detection results according to the gray value of the hydrogel microparticles. Meanwhile, the result was automatically uploaded to the homemade cloud system. The total detection time was less than 50 min, even without trained personnel, which is shorter than conventional detection methods. According to experimental results, this detection method has high sensitivity and accuracy. And especially, it uploads the detection information via a cloud platform to realize data sharing, which plays an early warning function. We anticipate that this rapid on-site visual data-sharing detection method can promote the development of virus self-checking at home.
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•A rapid virus detection method with high specificity, sensitivity, and accuracy.•Users get accurate visual results via a portable detection kit and a smartphone app.•A cloud management system monitors the users' information and their detection date.
We consider the problem of parametric sensitivity of a particular characterization of risk, with respect to a threshold parameter
Such threshold risk is modeled as the probability of a
perturbed ...function of a random variable falling below 0. We demonstrate that for polynomial and rational functions of that random variable there exist at most finitely many risk critical points. The latter are those special values of the threshold parameter for which rate of change of risk is unbounded as δ approaches them. Under weak conditions, we characterize candidates for risk critical points as zeroes of either the discriminant of a relevant
perturbed polynomial, or of its leading coefficient, or both. Thus the equations that need to be solved are themselves polynomial equations in δ that exploit the algebraic properties of the underlying polynomial or rational functions. We name these important equations as" hidden equations of risk critical thresholds".
We present a steady‐state threshold risk analysis framework for exploited populations following the Beverton–Holt recurrence. The Beverton–Holt model is widely applied in the assessment of species ...biomass and fitted to experimental data to obtain a suitable range of parameter values. To account for the uncertainty in these parameter values, such as the growth rate, we analyze the probability of the steady‐state harvested population falling below a critical threshold. More precisely, the Beverton–Holt equation with constant multiplicative survival, constant carrying capacity, and constant growth rate is considered. Under the assumption of a stochastic distributed proliferation rate, we analyze the risk of the steady‐state population falling below a specified threshold, under constant harvest. We demonstrate a characteristic sensitivity property of that risk, that we refer to as “instability wedge,” in the parameter space. We continue the study by assuming a 2‐periodic carrying capacity, representing seasonal changes in the population's environment. Again, we demonstrate the risk sensitivity phenomenon. Although the growth rate was chosen to have a uniform distribution in presented examples, the framework extends to other probability distributions.
Online learning to rank (OLTR) aims to learn a ranker directly from implicit feedback derived from users' interactions, such as clicks. Clicks however are a biased signal: specifically, top-ranked ...documents are likely to attract more clicks than documents down the ranking (position bias). In this paper, we propose a novel learning algorithm for OLTR that uses reinforcement learning to optimize rankers: Reinforcement Online Learning to Rank (ROLTR). In ROLTR, the gradients of the ranker are estimated based on the rewards assigned to clicked and unclicked documents. In order to de-bias the users' position bias contained in the reward signals, we introduce unbiased reward shaping functions that exploit inverse propensity scoring for clicked and unclicked documents. The fact that our method can also model unclicked documents provides a further advantage in that less users interactions are required to effectively train a ranker, thus providing gains in efficiency. Empirical evaluation on standard OLTR datasets shows that ROLTR achieves state-of-the-art performance, and provides significantly better user experience than other OLTR approaches. To facilitate the reproducibility of our experiments, we make all experiment code available at https://github.com/ielab/OLTR.
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