Deviation Of Rate Research Articles

Application of Bayesian network and genetic algorithm enhanced Monte Carlo method in urban transportation infrastructure investment

Traditional urban transportation infrastructure investment evaluation methods cannot fully consider future uncertainties and complex environmental factors, which affect resource allocation and decision-making accuracy. In order to optimize investment decision-making, this research aims to integrate Bayesian networks, genetic algorithms, and Monte Carlo methods to propose a more flexible and accurate investment evaluation method. By combining conditional probability modeling, optimization function, and randomness simulation, we strive to realize the scientific and rational decision-making of urban transportation infrastructure investment. In this paper, a Bayesian network of transportation infrastructure investment risk is constructed. Variable nodes and logical relationships are accurately defined, and a conditional probability table is formed to model uncertainty. The investment portfolio is optimized by genetic algorithm, and the fitness function and iterative selection are set to screen out the optimal solution. Monte Carlo simulation technology is used to verify the robustness of the optimization solution and conduct multiple random experiments. Combining these three, an optimization model is formed that can efficiently evaluate investment decisions and improve the accuracy and adaptability of results. The experimental results show that at a 95% confidence level, the VaR (value at risk), and CVaR (conditional value at risk) values of the vehicle-kilometer service fee method are 32.4512 million yuan and 89.1234 million yuan, respectively, which are significantly better than the 410.2392 million yuan and 691.5735 million yuan of the agreement ticket price law. At the same time, the standard deviation rate of the IRR (internal rate of return) of the vehicle-kilometer service fee method is 0.1548, and the probability that the IRR is not lower than the social average return is 95.49%. The potential return is higher; the risk management and stability are better; it is more suitable for decision makers who pursue stable returns. The study shows that the enhanced Monte Carlo method in this paper not only provides effective theoretical support and technical means for the investment evaluation of urban transportation infrastructure.

Shadows and accretion disk images of charged rotating black hole in modified gravity theory

In this paper, we study the shadows and images of the accretion disk of Kerr–Newman (KN) black hole (BH) in modified gravity (MOG) theory by using the backward ray-tracing method. And, the influence of spin parameter (a), charge (Q), and MOG parameter (α) on the observed features of BHs are carefully addressed. Interestingly, as α increases, the flat edge of the BH’s shadow gradually becomes more rounded, the size of shadow enlarges, and the deviation rate (δs) correspondingly decreases. By tracing the photon around BH, we observe that the trajectory of photon exhibits distortion behavior, i.e., the formation of two “tails” near the Einstein ring, which elongate as a increases. For the accretion disk, it shows that the inner shadow expands with α, while decreases with Q. The increase of α exhibits an increasing effect on redshift. At the same parameter level, α has a more obvious effect on inner shadow and image of BH by comparing with that of Q. Our study implies that both α and Q have relatively significant effects on the image of the KN-MOG BH with the thin disk accretion, but the influence of α is much greater. So, this indicates that α plays a dominant role in this spacetime.

A comparative study on different machine learning approaches with periodic items for the forecasting of GPS satellites clock bias

Accurately predicting satellite clock deviation is crucial for improving real-time location accuracy in a GPS navigation system. Therefore, to ensure high levels of real-time positioning accuracy, it is essential to address the challenge of enhancing satellite clock deviation prediction when high-precision clock data is unavailable. Given the high frequency, sensitivity, and variability of space-borne GPS satellite atomic clocks, it is important to consider the periodic variations of satellite clock bias (SCB) in addition to the inherent properties of GPS satellite clocks such as frequency deviation, frequency drift, and frequency drift rate to improve SCB prediction accuracy and gain a better understanding of its characteristics. In recent applications, deep learning models have significantly improved handling time-series data. This paper presents four machine learning prediction models that take into consideration periodic variations. Specifically, we utilize precision satellite clock bias data from the International GNSS Service forecast experiments and assess the predictive effects of various models including backpropagation neural network (BPNN), wavelet neural network (WNN), long short-term memory (LSTM), and gated recurrent units (GRUs). The predicted sequences of the four machine learning models are compared with the quadratic polynomial(QP) model. The average prediction accuracy of forecasting has improved by approximately (39.45, 57.57, 27.28, 29.14)% during 1-day forecasting. The results indicate that the machine learning models incorporating periodic variations outperform the standard quadratic polynomial model in terms of predictive accuracy, and the WNN model is better than that of these three machine learning models. This highlights the promising potential of deep learning models in forecasting satellite clock bias.

Application of biomechanics in graphic design and ergonomic optimization

For patients recovering from spinal diseases, ordinary seats often lack support and correction functions, which can easily lead to problems such as spinal curvature and lumbar disc herniation. The study uses MediaPipe to recognize postures and perform 3D graphics modeling, combined with finite element analysis to simulate the coordinated work of the patient’s muscles, bones, and joints, and optimize the design of ergonomic orthosis. First, MediaPipe Pose is used for posture recognition to capture the key point coordinates of the spine and joint positions and obtain dynamic data of the patient’s sitting posture. Next, a 3D model of the patient’s skeleton and spine is built, and the bone structure is generated based on the posture data and refined with ZBrush. Then, a finite element analysis is performed using ANSYS Workbench, and the stress distribution of the spine under the patient’s weight and different sitting pressures is simulated. The orthopedic seat’s geometry and support area distribution are optimized, and adjustment functions are added to improve adaptability. The data results show that the MSE (Mean Square Error) of the optimized spinal curve deviation is only 0.0009; the maximum stress of the intervertebral disc is reduced from 56.1 MPa to 42.4 MPa; the area of the high-stress area is reduced to 12.4cm²; the stress uniformity is improved by 28.1%; the local pressure is reduced by 24.6%. The designed method can significantly reduce the patient’s spinal deviation rate, relieve seat pressure, and have a good rehabilitation auxiliary effect.

Research on the Parameter Configuration Law of Transfer of Ultramicro Liquid by Pipetting Needle Based on the Quasi-static State.

Microscale device surface encapsulation needs to use ultrafine liquid transfer technology. This technology can transfer a liquid from a donor surface to a receptor surface in a controlled manner. However, the requirement of microscale encapsulation for liquid transfer amounts is generally at the pL level. Controlling liquid transfer volume, ensuring consistency, and ensuring droplet position accuracy became more difficult at this level. In light of the existing pipetting needle transfer ultramicro liquid transfer technology and considering a slow stretching liquid bridge can maintain a constant liquid transfer ratio, we conducted in-depth research on the parameter configuration law of liquid transfer at the pL level. This study reveals the parameter configuration law under the condition of constant transfer ratio through the Young-Laplace equation and the Navier-Stokes equation. Research has shown that the initial distance and capillary number (Ca) affect the transfer ratio at a slow stretching speed. Through ultramicro liquid transfer experiments, with stretching speeds between 0.01 and 0.5 mm/s, the transfer ratio remained constant, the accuracy of the rheology-Cross model in predicting liquid transfer ratios was verified, and the absolute deviation rate between the actual and the predictive transfer ratio is 6.26%. This research plays a crucial role in enhancing the precision and success rate of microscale liquid transfer.

A study on the optimal allocation of photovoltaic storage capacity for rural new energy microgrids based on double-layer multi-objective collaborative decision-making

Aiming at the problems of low energy efficiency and unstable operation in the optimal allocation of optical storage capacity in rural new energy microgrids, this paper proposes an optimization method based on two-layer multi-objective collaborative decision-making. First, an outer optimization objective function containing constraints on capacity allocation, line transmission security, charging and discharging power of the energy storage system, microgrid security, and power supply reliability was constructed, and an inner optimization objective function containing constraints on energy storage self-discharge correction and power balance was constructed. The quantum-behaved particle swarm optimization algorithm is used to solve the optimal solution set of the objective function, and the interactive multi-criteria decision-making method is used to select the compromise solution to realize efficient optimal allocation of optical storage capacity. The test results show that this method can obtain the Pareto optimal solution set of multi-objective functions in the model. Comprehensive prospect value calculation results are obtained according to each configuration scheme, and a compromise scheme is obtained. The total energy consumption, power deviation rate, light rejection rate, and load loss cost were significantly reduced, with maximum values of 90.5%, 2.03% and 114,700 yuan, respectively, and the load loss probability was lower than 2.2%. The results show that the proposed method can effectively improve the total energy consumption utilization of the microgrid, reduce the power deviation rate and light abandonment rate, and provide significant advantages in different lighting and load demand scenarios.

Application of ARIMA model and deep learning in forecasting stock price in Vietnam

Introduction: Time series forecasting is essential in production, business, and policy-making. In Vietnam, statistical models have been used to forecast time series, such as foreign investment and consumer price index. However, only some studies have used deep learning models in predicting economic variables. The study aims to application of ARIMA model and deep learning in forecasting stock price in VietnamMethods: This article aims to review and evaluate the predictive ability of the ARIMA and deep learning models when forecasting the prices of the five stocks with the largest capitalization from January 2018 to April 2023. Results: The deep learning model's prediction results show that the deviation rate between the actual and predicted values is 3.3%. Conclusions: Businesses and stakeholders should increase the use of technology and artificial intelligence for forecasting to support decision-making

Time to glaucoma progression detection by optical coherence tomography and visual field in glaucoma individuals of African descent

To examine the time to glaucoma progression detection by retinal nerve fiber layer thickness (RNFLT) and visual field (VF) among individuals of African descent (AD). Retrospective cohort study. This multicenter study included eyes with glaucoma from individuals of AD from the Diagnostic Innovations in Glaucoma Study and the African Descent and Glaucoma Evaluation Study with ≥2 years/5 visits of optic nerve head RNFLT and 24-2 VF examinations. Rates of VF mean deviation (MD) and RNFLT worsening were analyzed using linear mixed-effects models, and longitudinal data were simulated using the variability estimates. The simulated time to detect trend-based glaucoma progression was assessed with assumed rates of VF MD and RNFLT change derived from the cohort (25th, 50th, and 75th percentile [as p25, median, and p75] slopes and mean slopes). Severity-stratified analyses were also performed. We included 184 eyes from 128 subjects of AD (mean baseline age 63.4 years; VF MD -4.2 dB; RNFLT 80.2 µm). The p25, median, mean, and p75 rates of change were -0.43, -1.01, -1.15, and -1.64 µm per year for RNFLT, and 0.00, -0.21, -0.30, and -0.51 dB per year for VF MD, respectively. Compared with VF MD, RNFLT showed an overall shorter mean time to progression detection (time difference 0.4-1.7 years), with the mean rates showing the largest difference (RNFLT 5.2 years vs VF MD 6.9 years). Similarly, we found an overall shorter time to detect RNFLT progression, compared with that of VF MD progression, in eyes with mild glaucoma (≥1 year earlier) and in eyes with moderate to advanced glaucoma (∼0.5 year earlier). Computer simulation showed a potentially shorter time to detect RNFLT progression than VF MD progression in eyes from individuals of AD. Our findings support the importance of using RNFLT to detect progressive glaucoma in individuals of AD.

NIR spectroscopy: Developing predictive models for chemical attributes and in vitro dry matter digestibility of Megathyrsus maximus cv. Tanzania

AbstractPastures in animal production are widely used, but production is conditioned to several factors. The nutritional composition of forage can be altered by soil conditions, season, plant maturity and morphology, so it is important to monitor its quality through chemical analysis. To optimize this type of analysis and speed up decision‐making by farmers and technicians, the use of near‐infrared spectroscopy (NIRS) is a tool that has been successfully applied. This research aimed to develop predictive models for chemical components of Megathyrsus maximus cv. Tanzania using NIR spectroscopy. Laboratory determinations of ash, crude protein (CP), in vitro dry matter digestibility (IVDMD), neutral detergent fiber (NDF) and acid detergent fiber (ADF) of 345 forage samples were used as reference data and correlated with their NIRS spectra. To calibrate the models, principal component analysis and partial least squares regression were applied. The results indicated that the prediction models of the studied parameters presented a coefficient of determination (R2) equal to or greater than 0.90; residual predictive deviation rate (RPD) greater than 3.0; error interval ratio (RER) greater than 12; close mean square error values between calibration and validation; and optimal number of latent variables (LV) between seven and eight for model calibration. For CP and IVDMD prediction, the regions with the highest simultaneous contribution were 1,414, 1996 and 2,384 nm; while for NDF and ADF, 1714, 1784, 1786, 2,160, 2,320 and 2,450 nm. The success in the development of predictive models by NIR spectroscopy to evaluate dry matter digestibility and other main chemical attributes of M. maximus cv. Tanzania shows that the quality of the models developed in this study enables them to be used alternatively in routine laboratory analysis in a quick, reliable and accurate way.

Trajectory Planning for Unmanned Vehicles on Airport Apron Under Aircraft-Vehicle-Airfield Collaboration.

To address the issue of safe, orderly, and efficient operation for unmanned vehicles within the apron area in the future, a hardware framework of aircraft-vehicle-airfield collaboration and a trajectory planning method for unmanned vehicles on the apron were proposed. As for the vehicle-airfield perspective, a collaboration mechanism between flight support tasks and unmanned vehicle departure movement was constructed. As for the latter, a control mechanism was established for the right-of-way control of the apron. With the goal of reducing waiting time downstream of the pre-selected path, a multi-agent reinforcement learning model with a collaborative graph was created to accomplish path selection among various origin-destination pairs. Then, we took Apron NO.2 in Ezhou Huahu Airport as an example for simulation verification. The results show that, compared with traditional methods, the proposed method improves the average vehicle speed and reduces average vehicle queue time by 11.60% and 32.34%, respectively. The right-of-way signal-switching actions are associated with the path selection behavior of the corresponding agent, fitting the created aircraft-vehicle collaboration. After 10 episodes of training, the Q-values can steadily converge, with the deviation rate decreasing from 40% to below 0.22%, making the balance between sociality and competitiveness. A single trajectory can be planned in just 0.78 s, and for each second of training, 7.54 s of future movement of vehicles can be planned in the simulation world. Future research could focus on online rolling trajectory planning for UGSVs in the apron area, and realistic verification under multi-sensor networks can further advance the application of unmanned vehicles in apron operations.

A Method for Detecting Edge Continuity in Art and Design Images Based on Visual Attention Mechanism

To address the issues of low accuracy and significant noise impact in edge continuity detection of art and design images, this paper proposes a visual attention-based edge continuity detection method for art and design images. Determine the edge position points of art and design images using the Laplace operator, and obtain the horizontal and vertical gradients of the image edges by calculating the first-order difference method to determine the amplitude of the image edge gradient; By using the maximum and minimum operations in binary morphology instead of intersection and union operations, the image grayscale is determined, and the HSI color space features are determined to complete the edge feature extraction of art and design images. Using the SUSAN operator to clarify the kernel similarity zone of the edges of art and design images, removing similar edge image pixels, using spatial domain denoising and frequency domain denoising to reduce the edge noise of art and design images, and achieving edge preprocessing of art and design images. Introducing visual attention mechanism to transform the pixel space of edge features in art and design images, performing threshold segmentation on the edges of art and design images, and continuously annotating the segmented edge pixels. Introducing loss function to improve the convergence speed of detection, constructing an art and design image edge continuity detection model based on visual attention mechanism, outputting detection results, and implementing research. The experimental results show that the proposed method has better continuity due to the successful avoidance of noise interference by introducing visual attention mechanisms and other operations. As the number of detected pixels increases, the detection deviation rate of the proposed method remains between 0.02% and 0.03%. The proposed method has a lower detection bias rate and can effectively improve the performance of edge continuity detection in art and design images.

Exploring the effect of Ulva prolifera components on the biochar carbon sequestration potential

Exploring the effect of Ulva prolifera components on the biochar carbon sequestration potential

Full Γ-expansion of reversible Markov chains level two large deviations rate functionals

Full Γ-expansion of reversible Markov chains level two large deviations rate functionals

Optimization of process parameters for polishing aero-engine blade with abrasive cloth wheel considering spindle vibration and polished roughness

The vibration of machine tool spindle is very important for machining. Firstly, in order to explore the relationship between spindle vibration and process parameters, the spindle vibration acceleration when polishing aero-engine blade was measured, and the quadratic polynomial models between the spindle vibration acceleration in X, Y and Z direction and the process parameters were established. Secondly, a quadratic polynomial model for the influence of process parameters on polished surface roughness was also determined. Thirdly, through the Analysis of Variance (ANOVA) and main effect analysis, it can be seen that the spindle speed has the greatest impact on the vibration. Finally, a multi-objective optimization model was established with the optimization objective of minimizing spindle vibration acceleration and polished surface roughness, and the optimal process parameters were solved using genetic algorithm. The optimal process parameters were verified and the results show that the polished surface roughness with the optimal process parameters are all less than 0.4 μm, and the deviation rates between the theoretical optimization results of spindle vibration acceleration and the experimental results are all less than 10%, indicating that the optimization results are good.

The isoperimetric problem for convex hulls and large deviations rate functionals of random walks

The isoperimetric problem for convex hulls and large deviations rate functionals of random walks

Fabrication of disk ultramicroelectrode using nanoskiving method.

The detection time of the ultramicroelectrode can be reduced to nanoseconds when compared to the macroscopic electrode, enabling real-time monitoring of the instantaneous electrochemical behavior of the microstructure. Preparing ultramicroelectrode thus has drawn great attention recently. In the present study, a novel method for the preparation of disk ultramicroelectrodes with controllable electrode end sizes based on the nanoskiving method is proposed. The feature dimensions of the ultramicroelectrode can be controlled by the nanoskiving parameters. The electrochemical performance of the prepared ultramicroelectrode is evaluated in the solution system consisting of a 1mM FcMeOH (ferrocenyl methanol) and 0.1 M KCl aqueous solution. The steady-state limit current deviation rate of the electrode is 7%, and it can work continuously for 600s. Moreover, the electrode is integrated with micrometer precision scanning and positioning devices, which conduct electrochemical characterization of the micron structure array sample. The electrochemical image of the tin-doped indium oxide sample is measured successfully. The funding in this study provides a novel method to prepare ultramicroelectrodes. Importantly, high-precision electrochemical imaging equipment is established that can be used to measure electrochemical images.

Road section traffic risk propagation model based on macro traffic flow energy consumption

Traffic risk is an important source of road traffic accidents. Effectively predicting the propagation of accident risks on the road after an accident is of great significance for preventing secondary accidents. Therefore, this paper establishes a road section accident risk propagation model based on macro traffic flow energy consumption. Firstly, the influence of lane occupation and vehicle lane changing after the accident on traffic flow is analyzed, and a macro traffic flow model under the risk after the accident is constructed. Secondly, combined with the definition of traffic flow energy dissipation, the movement of the whole vehicle is abstracted as a fluid movement process, and a traffic flow energy consumption model under the risk after the accident is proposed to quantify the accident risk. Finally, according to the spatiotemporal correlation of risk propagation, a risk impact range and duration calculation model is proposed to describe the risk propagation process. In order to verify the effectiveness of the risk propagation model proposed in this paper, MATLAB and VISSIM are used to carry out numerical simulation and simulation verification of traffic flow operation under different initial traffic flow densities. The experimental results show that the greater the initial density of the road section when the accident occurs, the wider the risk impact range and the longer the duration. The absolute value of the prediction deviation rate of the proposed model is below 8%, which has a good prediction effect.

Two-Stage Optimization Model Based on Neo4j-Dueling Deep Q Network

To alleviate the power flow congestion in active distribution networks (ADNs), this paper proposes a two-stage load transfer optimization model based on Neo4j-Dueling DQN. First, the Neo4j graph model was established as the training environment for Dueling DQN. Meanwhile, the power supply paths from the congestion point to the power source point were obtained using the Cypher language built into Neo4j, forming a load transfer space that served as the action space. Secondly, based on various constraints in the load transfer process, a reward and penalty function was formulated to establish the Dueling DQN training model. Finally, according to the ε−greedy action selection strategy, actions were selected from the action space and interacted with the Neo4j environment, resulting in the optimal load transfer operation sequence. In this paper, Python was used as the programming language, TensorFlow open-source software library was used to form a deep reinforcement network, and Py2neo toolkit was used to complete the linkage between the python platform and Neo4j. We conducted experiments on a real 79-node system, using three power flow congestion scenarios for validation. Under the three power flow congestion scenarios, the time required to obtain the results was 2.87 s, 4.37 s and 3.45 s, respectively. For scenario 1 before and after load transfer, the line loss, voltage deviation and line load rate were reduced by about 56.0%, 76.0% and 55.7%, respectively. For scenario 2 before and after load transfer, the line loss, voltage deviation and line load rate were reduced by 41.7%, 72.9% and 56.7%, respectively. For scenario 3 before and after load transfer, the line loss, voltage deviation and line load rate were reduced by 13.6%, 47.1% and 37.7%, respectively. The experimental results show that the trained model can quickly and accurately derive the optimal load transfer operation sequence under different power flow congestion conditions, thereby validating the effectiveness of the proposed model.

Sintering densification mechanism of binder jet 3D printing 316L stainless steel parts via dimensional compensation technology

Sintering densification mechanism of binder jet 3D printing 316L stainless steel parts via dimensional compensation technology

Impact of nano Fe2O3 on radiation parameters of epoxy reinforced with nano carbon

This study aims to investigate the effectiveness of iron oxide (Fe2O3) and carbon black in micro and nanoscales incorporated into an epoxy adhesive matrix for gamma-ray attenuation. The composites were prepared via a simple and cost-effective synthesis method. The grain size of powder NPs was measured using a transmission electron microscope (TEM), and the particle size was about 20 ± 5 nm and 31.46 ± 2 nm for carbon and Fe2O3, respectively. The morphological properties were characterized by a scanning electron microscope, which indicated the excellent dispersion of NPs, which blocked almost all pores of the composite and increased the capability of radiation attenuation. In addition, the chemical composition of samples using energy dispersive X-rays (EDX) and the compressive strength were measured. Furthermore, the linear and mass attenuation coefficients were determined experimentally for incident photon energies of 59.51–1408.01 keV emitted from γ-ray sources using the sodium iodide scintillation detector NaI. A comparison was conducted between the experimental data and theoretical results that obtained from XCOM software, examined the validity of the experimental results. The relation deviation rate was found to vary between 0.0008 and 2.83%. Furthermore, the measurement of the relation deviation rate between the linear attenuation coefficients of micro and nano composites revealed a range of values between 1 and 25%. Also, shielding parameters such as half-value layer (HVL), tenth-value layer (TVL), mean free path (MFP), and effective atomic number (Zeff) were measured. Moreover, the equivalent atomic number (Zeq), absorption, and exposure buildup factors for prepared samples were calculated. The results showed that the incorporation of Fe2O3 NPs enhanced the shielding capability of the composites, as evidenced by the significant reduction in gamma-ray transmission. The composite materials exhibited excellent mechanical strength, making them suitable for practical applications in radiation shielding. Furthermore, it was determined that the elevation in N-Fe2O3 concentration resulted in a direct increase in the linear attenuation coefficient, from 0.314 to 0.519 cm−1 at 0.5951 MeV and from 0.099 to 0.124 cm−1 at 0.662 MeV. Nevertheless, a slight increase was discerned in the identified mass attenuation coefficients at 0.1332 and 0.1408 MeV. The experimental data for MFP, HVL, and TVL demonstrate that the EFeC4 sample exhibits optimal performance, with values of 1.9, 1.3, and 4.4 cm at 0.5951 MeV, and at 0.661 MeV, the values are 8.04, 5.57, and 18.52 cm, while at 0.1408 MeV, the values are 12.06, 8.36, and 27.78 cm, respectively. Overall, this research highlights the potential of iron oxide-carbon/epoxy composites as efficient and reliable materials for gamma-ray protection in various fields, including nuclear power plants, medical facilities, and space exploration.