Fitting Error Research Articles

Dynamic inversion and correction of ammonia pressure within the range 6611–6614 cm−1

Laser absorption spectroscopy is widely applied in gas concentration detection while the measurement accuracy would be affected by fluctuations in the environmental pressure (pressure fluctuations inside the gas cell under vacuum conditions). In this paper, a dynamic inversion and correction method of gas pressure without pressure sensors was proposed. Ammonia within the range of 6611–6614 cm−1 was selected as the research object. In contrast to previous reports, this method does not investigate the effect of pressure on gas concentration detection from a single point of view, or obtain pressure values from pressure sensors. Instead, the real-time temperature was measured to determine the broadening coefficient, which was then utilized along with full width at half-maximum (FWHM) of the spectral lines to calculate the real-time pressure value before correction. The directly acquired pressure has an error, and based on the error analysis, the pressure is corrected to get the corrected real-time pressure value. Finally, the effect of systematic error was eliminated by correcting the concentration using the corrected real-time pressure value and the measured real-time temperature. Experimental results show that the average residual of the corrected real-time pressure compared to the measured true values was 0.1993 torr with a standard deviation of 0.7387 torr. The correlation coefficient R2 between the inverted pressure value and the measured true value was 0.999, with a maximum fitting error of 1.19 %, error fluctuations within 2 %, and pressure uncertainty ranging from 0.545 % to 0.913 %. Furthermore, the ammonia concentration was corrected, and the maximum error compared to the standard ammonia concentration injected in the experiment was 1.025 % (with an effective optical path length of the detection gas cell of 15 cm). This work demonstrated the effectiveness of the proposed method in accurately inverting dynamic real-time pressure values and improving the accuracy of gas concentration measurement.

A CGLS-based method for solving magnetic moments of hybrid-model magnetic targets

In order to solve the problem of magnetic moment estimation of magnetic targets, the inverse problem of magnetic moment estimation was constructed based on the hybrid model of ellipsoid and magnetic dipole array. In order to solve the ill-posed problem of the magnetic moment estimation equations, the method was designed to estimate the magnetic moment parameters of the ellipsoid and magnetic dipole array, and the improved discrepancy principle and the maximum chi-square distribution stop criterion were introduced to improve the semi-convergence behavior of the conjugate gradient least squares (CGLS) method. Through simulation examples of magnetic target magnetic moment estimation and ship model measurement data, the performance of two CGLS methods, Tikhonov algorithm and stepwise regression method, were compared and analyzed from four aspects: relative error of magnetic moment estimation, relative error of magnetic field fitting, relative error of magnetic field extrapolation, and computational time complexity. The CGLS method has the advantages of high accuracy of magnetic moment estimation, high immunity of magnetic moment estimation to interference, high accuracy of magnetic field fitting and extrapolation, and low computational time complexity.

Accelerated multi-b-value multi-shot diffusion-weighted imaging based on EPI with keyhole and a low-rank tensor constraint

PurposeMulti-Shot (MS) Echo-Planar Imaging (EPI) may improve the in-plane resolution of multi-b-value DWI, yet it also considerably increases the scan time. Here we explored the combination of EPI with Keyhole (EPIK) and a calibrationless reconstruction algorithm for acceleration of multi-b-value MS-DWI. MethodsWe firstly analyzed the impact of nonuniform phase accrual in EPIK on the reconstructed image. Based on insights gained from the analysis, we developed a calibrationless reconstruction algorithm based on a Space-Contrast-Coil Locally Low-Rank Tensor (SCC-LLRT) constraint for reconstruction of EPIK-acquired data. We compared the algorithm with a modified SPatial-Angular Locally Low-Rank (SPA-LLR) algorithm through simulations, phantoms, and in vivo study. We then compared EPIK with uniformly undersampled EPI for accelerating multi-b-value DWI in 6 healthy subjects. ResultsThrough theoretical derivations, we found that the reconstruction of EPIK with a SENSE-encoding-based algorithm, such as SPA-LLR, may cause additional aliasing artifacts due to the frequency-dependent distortion of the coil sensitivity. Results from simulations, phantoms, and in vivo study verified the theoretical finding by showing that the calibrationless SCC-LLRT algorithm reduced aliasing artifacts compared with SPA-LLR. Finally, EPIK with SCC-LLRT substantially reduced the ghosting artifacts compared with uniform undersampled multi-b-value DWI, decreasing the fitting errors in ADC (0.05 ± 0.01 vs 0.10 ± 0.01, P < 0.001) and IVIM mapping (0.026 ± 0.004 vs 0.06 ± 0.006, P < 0.001). ConclusionThe SCC-LLRT algorithm reduced the aliasing artifacts of EPIK by using a calibrationless modeling of the multi-coil data. The dense sampling of k-space center offers EPIK a potential to improve image quality for acceleration of multi-b-value MS-DWI.

Results of the 2023 ISBI challenge to reduce GABA-edited MRS acquisition time.

Use a conference challenge format to compare machine learning-based gamma-aminobutyric acid (GABA)-edited magnetic resonance spectroscopy (MRS) reconstruction models using one-quarter of the transients typically acquired during a complete scan. There were three tracks: Track 1: simulated data, Track 2: identical acquisition parameters with in vivo data, and Track 3: different acquisition parameters with in vivo data. The mean squared error, signal-to-noise ratio, linewidth, and a proposed shape score metric were used to quantify model performance. Challenge organizers provided open access to a baseline model, simulated noise-free data, guides for adding synthetic noise, and in vivo data. Three submissions were compared. A covariance matrix convolutional neural network model was most successful for Track 1. A vision transformer model operating on a spectrogram data representation was most successful for Tracks 2 and 3. Deep learning (DL) reconstructions with 80 transients achieved equivalent or better SNR, linewidth and fit error compared to conventional 320 transient reconstructions. However, some DL models optimized linewidth and SNR without actually improving overall spectral quality, indicating a need for more robust metrics. DL-based reconstruction pipelines have the promise to reduce the number of transients required for GABA-edited MRS.

Temporal trends in disparities in COVID-19 seropositivity among Canadian blood donors.

In Canada's largest COVID-19 serological study, SARS-CoV-2 antibodies in blood donors have been monitored since 2020. No study has analysed changes in the association between anti-N seropositivity (a marker of recent infection) and geographic and sociodemographic characteristics over the pandemic. Using Bayesian multi-level models with spatial effects at the census division level, we analysed changes in correlates of SARS-CoV-2 anti-N seropositivity across three periods in which different variants predominated (pre-Delta, Delta and Omicron). We analysed disparities by geographic area, individual traits (age, sex, race) and neighbourhood factors (urbanicity, material deprivation and social deprivation). Data were from 420319 blood donations across four regions (Ontario, British Columbia [BC], the Prairies and the Atlantic region) from December 2020 to November 2022. Seropositivity was higher for racialized minorities, males and individuals in more materially deprived neighbourhoods in the pre-Delta and Delta waves. These subgroup differences dissipated in the Omicron wave as large swaths of the population became infected. Across all waves, seropositivity was higher in younger individuals and those with lower neighbourhood social deprivation. Rural residents had high seropositivity in the Prairies, but not other regions. Compared to generalized linear models, multi-level models with spatial effects had better fit and lower error when predicting SARS-CoV-2 anti-N seropositivity by geographic region. Correlates of recent COVID-19 infection have evolved over the pandemic. Many disparities lessened during the Omicron wave, but public health intervention may be warranted to address persistently higher burden among young people and those with less social deprivation.

Numerical paradigm to explore the chemically reacting Williamson nanofluid flow with the influence of bioconvection effects using neural networks

This research focuses on using a type of advanced computer program called artificial neural networks (ANNs) using the Levenberg–Marquardt backpropagation technique (LMBPT-NNs) to stimulate the behavior of bioconvective effects on heat and mass transport in non-Newtonian chemically Williamson nanofluid through stretched surface (BEHMT-NNCRWNF-SS). Williamson nanofluid flow refers to specially engineered fluids with nanoparticles dispersed within them and moving when subjected to external forces. Understanding their flow behavior is crucial to effectively use them in various applications, especially in the field of heat and mass transfer and fluid dynamics. Before being solved numerically, a couple of governing partial differential equations are transformed into ordinary differential equations through suitable similarity functions. The finite difference method (FDM) (Lobatto IIIA) is applied for the solution of a nonlinear nanofluidic system by selecting different collocation points. These collocation points play a crucial role in approximating the solution and ensuring accurate results. To address various fluidic problems, it's important to use the appropriate results of FDM to create a reference dataset for the LMBPT-NNs. Statistical analysis should then be performed on the training, testing, and validation of the reference datasets to obtain the optimal scheme for different fluidic flow issues.. The precision of the LMBPT-NNs is checked through ANN tools like mean square error (MSE), regression analysis, curve fitness, and histogram error.

Car-following model based on spatial expectation effect in connected vehicle environment: modeling, stability analysis and identification

In driving situations, if drivers have advanced access to downstream traffic conditions ahead of time, they may modify their driving behavior accordingly. This proactive approach can help reduce frequent acceleration and deceleration of vehicles and minimize traffic congestion. This paper considers the average headway of downstream vehicles to predict the traffic conditions of downstream roads. Based on the full velocity difference (FVD) model, we propose a car-following model that incorporates spatial expectation effect (SEFVD). Firstly, a linear stability analysis is performed on the SEFVD model, and the results indicate that the critical value of the SEFVD model is relatively small, and the stabilization region increases gradually with the number of vehicles downstream. Secondly, employing perturbation amplitudes on a virtual circular road, a numerical simulation of the SEFVD model is carried out and compared with the FVD model. The results demonstrate that the SEFVD model has the ability to suppress the formation of traffic oscillations and stabilize vehicle formation more quickly. Simulation results for the 11 cars starting from a traffic signal indicate that the delay time and kinematic wave speed of the SEFVD model are within reasonable limits. To evaluate the changes in energy use, we also carried out energy consumption experiments. The experimental findings show that the SEFVD model uses less energy compared to the FVD model. Finally, the parameters of the models are calibrated and verified by using NGSIM data. The total calibration error of the SEFVD model (m = 2) decreases by 42.97% compared to the FVD model, and the total validation error decreases by 29.3%. The results show that the proposed model can fit the actual data well, and the fitting error is smaller.

Refining Short-Term Power Load Forecasting: An Optimized Model with Long Short-Term Memory Network

Short-term power load forecasting involves the stable operation and optimal scheduling of the power system. Accurate load forecasting can improve the safety and economy of the power grid. Therefore, how to predict power load quickly and accurately has become one of the urgent problems to be solved. Based on the optimization parameter selection and data preprocessing of the improved Long Short-Term Memory Network, the study first integrated particle swarm optimization algorithm to achieve parameter optimization. Then, combined with convolutional neural network, the power load data were processed to optimize the data and reduce noise, thereby enhancing model performance. Finally, simulation experiments were conducted. The PSO-CNN-LSTM model was tested on the GEFC dataset and demonstrated stability of up to 90%. This was 22% higher than the competing CNN-LSTM model and at least 30% higher than the LSTM model. The PSO-CNN-LSTM model was trained with a step size of 1.9×10^4, the relative mean square error was 0.2345×10^-4. However, when the CNN-LSTM and LSTM models were trained for more than 2.0×10^4 steps, they still did not achieve the target effect. In addition, the fitting error of the PSOCNN-LSTM model in the GEFC dataset was less than 1.0×10^-7. In power load forecasting, the PSOCNN- LSTM model's predicted results had an average absolute error of less than 1.0% when compared to actual data. This was an improvement of at least 0.8% compared to the average absolute error of the CNNLSTM prediction model. These experiments confirmed that the prediction model that combined two methods had further improved the speed and accuracy of power load prediction compared to traditional prediction models, providing more guarantees for safe and stable operation of the power system.

Thermal dynamics assessment for multi-phase flow analysis with motile cilia and electric double layer effects: Application of Levenberg–Marquardt backpropagation NNs

Ciliary movement holds substantial importance in the realm of biomedical research. Disorders arising from impaired ciliary motion, such as primary ciliary dyskinesia and specific respiratory ailments, underscore the critical need to investigate both the normal functioning and dysfunction of cilia. Such research is fundamental to the development of effective treatments and therapies. This work explores the application of Levenberg–Marquardt backpropagation neural networks (LMBP-NNs) for simulating the flow of a non-Newtonian Jeffery fluid in peristaltic motion. Inspired by synchronous ciliary movement, microfluidic systems mimicking cilia functionality have been developed. These artificial cilia can generate controlled fluid flow patterns, enabling various applications such as drug delivery, mixing, and particle sorting in lab-on-a-chip devices. The present study focuses on measuring thermal and mass transfer in beating cilia during multiphase peristaltic pumping of Jeffery fluid in two- and three-dimensional channels using transverse magnetohydrodynamics in a porous medium. The governing equations for the non-Newtonian Jeffery fluid in both the fluid and dusty phases are formulated, and exact solutions are calculated for fluid phase velocity, particulate phase velocity, temperature, concentration, and pressure gradient. The physical behavior is investigated by graphing dimensionless parameters for velocity, temperature, and concentration profiles in both solid and liquid phases. The trapping phenomena is depicted by showing streamlines of various dimensionless characteristics such as porosity velocity and volume percentage. The LMBP-NNs approach is then used to train, test, and verify the neural network models using the reference datasets. The accuracy of the LMBP-NN is assessed using statistical data such as mean square error, curve fitting graphs, regression plots, and error histograms. The study also investigates flow model limits for momentum, temperature, and concentration profiles using visual representations. This study illustrates the computational capability of LMBP-NNs in modeling the peristaltic flow of Jeffery fluid and gives useful insights into fluid flow behavior in microfluidic devices containing artificial cilia. The findings help to better understand and optimize these systems for a variety of fluid manipulation and transportation applications.

Experimental study on flow rate and pressure drop characteristics in T-junction pipes under rolling conditions

Numerous T-junction circular channels are used in the pipeline system of marine dynamic platforms. Unlike terrestrial conditions, the fluid inside the channels experiences additional inertial forces due to rolling motion, leading to complex and variable fluid mixing characteristics within T-junction pipes. The flow and pressure drop characteristics were investigated inside the T-junction pipe under rolling motion conditions, including the average value, the fluctuation value, and the instantaneous value. The working fluid is considered as the de-ionized water. The inlet Reynolds number of the main pipe ranges from 2110 to 25 320, and the flow rate ratio is from 1 to 20. The rolling time and angle are 5–15 s and 0°–15°, respectively. The range of rolling Reynolds number is 0–3520. The results indicate that the influence of the rolling motion on the flow and pressure drop characteristics inside the T-junction pipe depends on the inertial force of the fluid itself. When the inertial force of the fluid itself is large, the influence of the rolling motion on the flow parameters will be weakened. The rolling motion has a greater impact on the branch than on the main pipe. Predictive relationships for flow rates and pressure loss coefficients are established under the stationary and rolling conditions, respectively, with a fitting error of less than 10%. In addition, the boundary that ignores the influence of rolling motion on flow fluctuations and the criteria for identifying fluid backflow are also proposed.

Circle fit optimization for resonator quality factor measurements: Point redistribution for maximal accuracy

The control of material loss mechanisms is playing an increasingly important role for improving coherence times of superconducting quantum devices. Such material losses can be characterized through the measurement of planar superconducting resonators, which reflect losses through the resonance's quality factor Ql. The resonance quality factor consists of both internal (material) losses as well as coupling losses when resonance photons escape back into the measurement circuit. The combined losses are then described as Ql−1=Re{Qc−1}+Qi−1, where Qc and Qi reflect the coupling and internal quality factors of the resonator, respectively. To separate the relative contributions of Qi and Qc to Ql, diameter-correcting circle fits use algebraic or geometric means to fit the resonance signal on the complex plane. However, such circle fits can produce varied results, so to address this issue, we use a combination of simulation and experiment to determine the reliability of a fitting algorithm across a wide range of quality factor values from Qi≪Qc to Qc≪Qi. In addition, we develop a measurement protocol that can not only reduce fitting errors by factors ≳2 but also mitigates the influence of the measurement background on the fit results. This technique can be generalized for other resonance systems beyond superconducting resonators. Published by the American Physical Society 2024

Reducing Spatial Fitting Error in Distillation of Denoising Diffusion Models

Denoising Diffusion models have exhibited remarkable capabilities in image generation. However, generating high-quality samples requires a large number of iterations. Knowledge distillation for diffusion models is an effective method to address this limitation with a shortened sampling process but causes degraded generative quality. Based on our analysis with bias-variance decomposition and experimental observations, we attribute the degradation to the spatial fitting error occurring in the training of both the teacher and student model in the distillation. Accordingly, we propose Spatial Fitting-Error Reduction Distillation model (SFERD). SFERD utilizes attention guidance from the teacher model and a designed semantic gradient predictor to reduce the student's fitting error. Empirically, our proposed model facilitates high-quality sample generation in a few function evaluations. We achieve an FID of 5.31 on CIFAR-10 and 9.39 on ImageNet 64x64 with only one step, outperforming existing diffusion methods. Our study provides a new perspective on diffusion distillation by highlighting the intrinsic denoising ability of models.

Research on the tool influence function characteristics of magnetorheological precession finishing (MRPF).

Magnetorheological finishing (MRF) technology is characterized by its high convergence rate and minimal subsurface damage as advantages. However, the non-Gaussian type tool influence function (TIF) it generates may cause mid-frequency errors and oriented surface texture issues. Magnetorheological precession finishing (MRPF) technology capable of generating Gaussian-like removal functions, lacks a clearly defined removal function model. This study acquired polishing spots in tilted polishing, discrete precession, and continuous precession modes via fixed-point polishing experiments. Using Multiphysics simulation software, stress and velocity distribution in the contact area were simulated. A TIF model, incorporating the synergistic effects of pressure and shear force and multiple influence coefficients, was proposed based on velocity characteristics across the three modes. To accurately predict the TIF, surface topographies with varying coefficients were constructed using this model, analyzing the coefficients' impact on the TIF profile. Optimal coefficients were identified using a least fit error algorithm. Further analysis of the TIF's internal textures revealed that the precession mode of MRPF yields superior surface quality, thereby elucidating the material removal mechanism of MRPF and laying a theoretical groundwork for advancing processing technologies.

High precision temperature measurement for cryogenic temperature sensors based on deep learning technology

Nonlinear error compensation is a significant factor that affects the measurement accuracy of temperature sensors. Cryogenic temperature sensors require a precise calibration technique to achieve accurate temperature measurements. BP (Back Propagation) neural networks are not suitable for sensor temperature prediction due to slow convergence and poor learning ability. In this study, a Chebyshev polynomial-based BiLSTM (Bi-directional Long Short-Term Memory) algorithm (C-BiLSTM) is proposed to improve the accuracy of measurement. Firstly, we demonstrate vacuum packaged temperature sensors based on zirconium oxynitride (ZrOxNy) thin films with ultra-high sensitivity at cryogenic temperatures. Secondly, a dataset consisting of sensor resistance and temperature values was obtained from five above-mentioned sensors, which contains 29 calibration points in the temperature range of 16 K-300 K measured by the Calibration System. Then, the dataset was divided into two temperature ranges (16 K-54.358 K and 40 K-300 K). In the range 16–54.358 K, 14 set-points are selected as training set and 3 set-points as testing set. In the range 40–300 K, 12 set-points are selected as training set and 2 set-points as testing set. Thirdly, a neural network model was built and trained using the TensorFlow framework. By comparing C-BiLSTM we proposed with the BP neural network and BiLSTM, the results show that the C-BiLSTM model converges faster and greatly improves the prediction accuracy after adding Chebyshev polynomial features. The fitting error and prediction error are less than 1 mK in the temperature range of 16 K-40 K. They can also keep less than 10 mK even at the wide temperature range of 40 K-300 K, which is a significant improvement respect to improving the accuracy of temperature measurement.

Levenberg-Marquardt technique analysis of thermal and concentration storage in cone-disk apparatus with neural network-enhancement

An artificial intelligence-driven neural network with the Levenberg-Marquardt method (NN-LMM) has been developed to investigate the thermal and mass transfer characteristics of a liquid flowing through the conical gap in a cone-disk apparatus. The disk and cone can either remain static or rotate at changing angular speeds, with discussion for heat transfer influenced by cosmic emission. The Rosseland resemblance is employed to compute heat diffusion in this study. To monitor variations in mass removal on the exterior, thermophoresis effects are appropriated into consideration. By applying appropriate transformations, the partial differential equations (PDEs) governing this problem are converted into typical ones. A reference dataset for NN-LMM is generated, covering various influential model variations, simulating scenarios using the Lobatto III-A numerical method. The purpose of employing the Levenberg-Marquardt technique in the analysis of thermal and concentration storage in a cone-disk apparatus, enhanced by neural networks, lies in optimizing the modeling and prediction accuracy of complex fluid dynamics and heat transfer phenomena. This technique, coupled with neural network enhancement, aims to address challenges associated with traditional analytical methods by offering a robust and efficient approach to modeling intricate processes in fluid dynamics and heat transfer. The novelty of utilizing the Levenberg-Marquardt technique, along with neural network enhancement, lies in its ability to effectively capture and simulate the dynamic interactions between thermal and concentration storage in the cone-disk apparatus. By leveraging the power of neural networks to learn complex patterns and relationships from data, combined with the optimization capabilities of the Levenberg-Marquardt algorithm, this approach offers improved accuracy and predictive capabilities compared to conventional methods. It is analyzed that the mass transport field for Nt decreases as temperature ratios increase. This phenomenon occurs because the thermophoretic force becomes more pronounced at higher temperatures, causing more particles to move towards the apparatus. Further it is seen that stationary cone and a rotating disk exhibit enhanced heat transport with increasing radiation parameter values. This suggests that elevating the radiation parameter leads to more efficient heat transfer in this specific model. Highest flow intensity observed around the cone. This reference data is subjected to testing, validation, and training processes to fine-tune the approximate solution to meet the desired results. The accuracy, stability, capacity, and robustness of NN-LMM are verified through mean squared error (MSE)-based fitness curves, regression plots, error histograms and absolute error assessments. A comparative analysis demonstrates the accuracy of the proposed solver, with absolute errors in the range of 10-9 to 10-6 for all influential parameters results.

Common agronomic adaptation strategies to climate change may increase soil greenhouse gas emission in Northern Europe

Climate change poses a significant threat to agriculture, highlighting the need for adaptation strategies to reduce its impacts. Agronomic adaptation strategies, such as changes in planting dates, fertilization, and irrigation, might sustain crop yield. However, their impact on soil greenhouse gas (GHG) emission is unknown under future climate scenarios. Using the LandscapeDNDC model, we assessed the effect of agronomic adaptation strategies (early sowing, increased fertilization dose, and increased irrigation amount) on soil GHG emission, yield, and yield-scaled GHG emission. A diversified crop rotation (potato – winter wheat – spring barley – faba bean) of a long-term experiment in Denmark was used for model validation. The adaptation practices to climate change were implemented for two representative concentration pathways (RCPs; 4.5 and 8.5) and five coupled global circulation and regional climate models. The adaptation scenarios were contrasted against a baseline scenario under current management practices. Soil-related variables showed better model fit (refined index of agreement ≥ 0.38) and lower errors (mean absolute error ≤ 8.18) than crop-based outputs for model validation. A total yield of ∼29 (± 3) t DW ha−1, and soil GHG emission of ∼3.02 (± 1.39) t CO2e ha−1 (RCP8.5) were obtained for the crop rotation system under the baseline for 2071–2100. Early sowing and its combination with increased fertilization decreased the yield compared to the baseline by 6.1 and 4.8 %, respectively (RCP8.5). Conversely, early sowing with increased irrigation, and early sowing with increased fertilization and irrigation, produced higher yields by 2.3 and 4.0 %, respectively (RCP8.5). All the agronomic adaptation strategies increased soil GHG emissions (ranging from 4.1 to 17.8 %) as well as yield-scaled GHG emissions (varying from 3.0 to 12.9 %) (RCP8.5). The highest soil GHG emission was simulated for early sowing in combination with increased fertilization and irrigation. Our study indicates that soil GHG emission will increase in the coming decades and that the agronomic adaptation strategies needed to sustain food production may further exacerbate this emission.

Supercollisions of NaCl + NaCl on an Accurate Full-Dimensional Potential Energy Surface.

An accurate, global, full-dimensional potential energy surface (PES) of NaCl + NaCl has been constructed by the fundamental invariant-neural network (FI-NN) fitting based on roughly 13,000 ab initio energies at the level of CCSD(T)-F12a/aug-cc-pVTZ, with the small fitting error of 0.16 meV. Extensive quasiclassical trajectory (QCT) calculations were performed on this PES to investigate the energy transfer process of the NaCl + NaCl collision at four different collision energies. Various quantities were obtained, including the cross-sections, energy transfer probability, average energy transfer, and collision lifetime. The probabilities of energy transfer (P(ΔE)) for prompt trajectories, nonreactive trajectories, and reactive trajectories deviate from a simple exponential decay pattern. Instead, a noteworthy probability is observed in the high-energy transfer region, indicative of supercollisions. The formation of the (NaCl)2 complex, coupled with a comparatively extended collision lifetime, promotes vibrational excitation in NaCl molecules. The reactive trajectories exhibit enhanced energy transfer, attributed to the longer lifetime of the NaCl dimer. This study not only provides an accurate and extensive understanding of the NaCl + NaCl collision dynamics but also reveals intriguing phenomena, such as supercollisions and enhanced energy transfer in reactive trajectories, shedding light on the complex intricacies of molecular interactions.

First detection and absolute transition frequencies in the (3-0) band of D2.

Three electric quadrupole transitions in the second overtone band of D2 are newly measured by comb-referenced cavity ring down spectroscopy around 1.18 µm. These extremely weak transitions (line intensities smaller than 10-29 cm/molecule) are the first to be detected in the (3-0) band of D2. The spectra of the O(3), O(2), and Q(2) lines near 8321, 8446, and 8607cm-1, respectively, are recorded at room temperature for pressure values ranging between 100 and 600 Torr. Accurate transition frequencies and line intensities of the three D2 transitions are determined from a line fitting procedure using beyond-Voigt profiles, including strong Dicke narrowing. Considering statistical fit errors and possible biases due to the interference with water lines (which are six orders of magnitude stronger than the studied D2 lines), total uncertainties on the frequencies extrapolated at zero pressure are estimated below 14MHz (∼4.7 × 10-4cm-1). The derived experimental frequencies and intensities are compared to abinitio values. An overall agreement is achieved, confirming the positional accuracy of the most advanced theoretical calculations.

Construction of multi-features comprehensive indicator for machinery health state assessment

Health state assessment is critical for mechanical equipment’s smooth and healthy operation. This paper proposes a novel approach for health state assessment based on acoustic signals during the process of machinery running. It consists of multi-domain feature (MF) extraction and comprehensive health indicator (CHI) construction. MF is extracted from various acoustic features, including time and frequency (TF) features, mel-frequency cepstral coefficients, and gammatone frequency cepstral coefficients. The stacked long short-term memory (LSTM) is used to extract the high-level features of the MF, which are then input to the downstream PCA to obtain the LSTM-PCA health indicator (LP-HI). Parallelly, the MF is fed into the self-organizing mapping (SOM) model to calculate the minimum quantization error (MQE) as SOM-MQE health indicator (SM-HI). These two indicators are fused using weighted fusion and nonlinear mapping to calculate CHI. The experimental results on air compressor dataset show a 25.8% reduction in evaluation error compared with SOTA results in this paper. The proposed nonlinear mapping function furthermore reduces fitting error on HI by 38.9%. These demonstrate the effectiveness and superiority of the proposed method in machinery health state assessment.

Study of the stochastic dynamics of particles in the coal screening based on the Markov process

The model of the stochastic dynamics for the screening and screen-penetration processes was developed based on the Markov process to reveal the mechanism of the coal screening effect. As a partial differential equation, the model of the stochastic dynamics describes the relationship between particle position in the y direction, screening time, and particle screen-penetration probability. The obtained model for stochastic dynamics can calculate the screen-penetration probability of a specific particle size at any given time. Moreover, two coefficients, a1 and a2, are defined where a1 represents the average velocity of particles, and a2 represents the combined effect of orderly segregation and mixing of particles in chaotic motion. These coefficients of the stochastic dynamics model were obtained by regression analysis. Additionally, the statistical analysis showed that the fitting errors were all <1. The proposed statistical dynamics model works for all particle sizes. A necessary coefficient, a1, was calculated based on screen-penetration coefficients k, a, and b. A large value of a1 means a high average velocity of particles and a greater screen-penetration probability. The value of a1 for particles smaller than 6 mm declines as the feed mass increases. Moreover, it initially decreases, followed by an increase, in response to a rising proportion of particles <6 mm. In contrast, the a1 value for particles ranging between 6 mm and 13 mm undergoes minimal changes when subjected to variations in feed mass and the proportion of particles <6 mm.