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Existing tracking algorithms mostly rely on model-driven approaches, which can be prone to inaccuracies due to unpredictable human behaviours. This article aims to address the issue of transient errors in tracking port container trucks (PCTrucks) when encountering obstructions. A data-driven algorithm for predicting vehicle trajectories is proposed in this study. The approach involves preprocessing an extensive dataset of GPS information, training a DeepLSTM-Attention model, and integrating the proposed model with the population-based training (PBT) algorithm to optimise network hyperparameters. The objective is to enhance the accuracy of predicting trajectories for vehicles moving horizontally. The trajectory data used are collected from real-world port operations. This research is conducted across nine trajectory segments and benchmarked against traditional approaches like Kalman filtering, machine learning techniques such as support vector regression (SVR) and standard long short-term memory (LSTM) networks. The results demonstrate that the proposed prediction method, that is, DeepPBM-Attention, outperforms other techniques in several evaluation metrics, including root mean square error (RMSE), mean absolute error (MAE), F1 score and trajectory reconstruction error (TRE). Compared to LSTM networks, the performance of DeepPBM-Attention is improved by approximately 40%. The proposed data-driven trajectory prediction algorithm exhibits high accuracy and practicality, which can effectively be applied to the positioning prediction of horizontally moving vehicles in port environments.

Abstract Background Postoperative hypoparathyroidism caused by parathyroid injury is a problem faced by thyroid surgeons. The current technologies for parathyroid imaging all have some defects. Methods Patients with differentiated thyroid carcinoma (DTC) who underwent unilateral thyroidectomy plus ipsilateral central lymph node dissection were recruited. We dissected the main trunk of the superior thyroid artery entering the thyroid gland and placed the venous indwelling tube into the artery. The sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) were calculated. Results A total of 132 patients enrolled in this single-arm clinical trial, 105 of them completed retrograde catheterization via the superior artery. The sensitivity was 69.23 and 83.33% respectively. The specificity was 72.91 and 64.89%. The accuracy was 72.91 and 64.89%. The PPV was 85.71 and 81.08%. The NPV was 22.58 and 45.45%. There were no patients with allergic reactions to the methylene blue, or methylene blue toxicity. Conclusions Retrograde injection of methylene blue via the superior thyroid artery is an effective and safe method to visualize parathyroid glands. This method can accurately locate the target organ by ultraselecting the blood vessel and injecting the contrast agent while avoiding background contamination and reducing the amount of contrast agent. Trial registration Clinical trial registration numbers and date of registration: ChiCTR2300077263、02/11/2023.

The authors' original concept of indirect solar flux mapping of a heliostat field measures CRD using a compact stationary array of moonlight illuminometers on the receiver aperture and a reference moonlight illuminometer on the dual-axis moon tracker. Two sets of moonlight concentration experiments (CCD camera + white target; concentrated lunar beam passing across the linear array of illuminators) were carried out on 63 heliostats at Badaling Solar Tower Power Plant in Beijing on a full moon night in 2018. How the sun and moon shapes differ for CRD as light sources on a solar dish is investigated. Solar/lunar CRDs are similar. Another lunar flux mapping model is also presented, allowing different moon shapes to estimate the solar CRD and contribute improvements in extrapolating measured lunar CRDs to a solar tower working with the real Sun by repeated smooth-filtering. In this paper, some unknown factors, effects, and improvements are carefully considered to enhance the CRD measurement accuracy of a solar tower power plant by using moonlight concentration.

Recent computer vision research has mainly focused on designing efficient network architectures, with limited exploration of high- and low-frequency information in the high-order frequency domain. This study introduces a novel approach utilizing spatial and frequency domain information to design a high-order global feature fusion module (HGM) and develop a specialized remote sensing detection network, HGNet. HGM leverages cyclic convolution to achieve arbitrary high-order features, overcoming the second-order limitation of transformers. Furthermore, HGM integrates cyclic convolution and fast Fourier transform, utilizing the former to capture interaction information between high-order spatial and channel domains and the latter to transform high-order features from spatial to frequency domain for global information extraction. This combination fundamentally addresses the issue of long-distance dependency in convolutions and avoids quadratic growth in computational complexity. Moreover, we have constructed an information truncation gate to minimize high-order redundant features, achieving a “win–win” scenario for network accuracy and parameter efficiency. In addition, HGM acts as a plug-and-play module, boosting performance when integrated into various networks. Experimental findings reveal that HGNet achieves a 93.0% $\text{mAP}_{\text{0.5}}$ with just 12.1M parameters on the HRSID remote sensing ship detection dataset. In addition, applying HGM enhances a performance in CIFAR100 classification and WHDLD remote sensing segmentation tasks.

Abstract Rabi oscillation has been proven to be one of the cornerstones of quantum mechanics, triggering substantial investigations in different disciplines and various important applications both in the classical and quantum regimes. So far, two independent classes of wave states in the Rabi oscillations have been revealed as spin waves and orbital waves, while a Rabi wave state simultaneously merging the spin and orbital angular momentum has remained elusive. Here we report on the experimental and theoretical observation and control of spin–orbit-coupled Rabi oscillations in the higher-order regime of light. We constitute a pseudo spin-1/2 formalism and optically synthesize a magnetization vector through light-crystal interaction. We observe simultaneous oscillations of these ingredients in weak and strong coupling regimes, which are effectively controlled by a beam-dependent synthetic magnetic field. We introduce an electrically tunable platform, allowing fine control of transition between different oscillatory modes, resulting in an emission of orbital-angular-momentum beams with tunable topological structures. Our results constitute a general framework to explore spin–orbit couplings in the higher-order regime, offering routes to manipulating the spin and orbital angular momentum in three and four dimensions. The close analogy with the Pauli equation in quantum mechanics, nonlinear optics, etc., implies that the demonstrated concept can be readily generalized to different disciplines.

Abstract Question classification is an essential task in question answering (QA) systems. An effective and efficient question classification model can not only restrict the search space for answers, but also guide the QA system in selecting the optimal knowledge base and search strategy. In recent years, self-attention mechanism has been widely used in question classification for its strength of capturing global dependencies. However, it models all signals with weighted averaging, which is prone to overlooking the relation of neighboring signals. Furthermore, recent research has revealed that part-of-speech (POS) information can be used to determine and reinforce the semantics in sentence representation. In this paper, we propose a POS-aware adjacent relation attention network (POS-ARAN) for question classification, which enhance context representations with POS information and neighboring signals. To consider the local context, we propose an adjacent relation attention mechanism, which incorporates a Gaussian bias via a dynamic window to revise the vanilla self-attention mechanism. Thus, it can capture both the long-term dependency and local representation of semantic relations among words in different sentences. In addition, a POS-aware embedding layer is proposed, which helps to locate the appropriate headwords by syntactic information. Extensive experiments are conducted on Experimental Data for Question Classification (EDQC) dataset and Yahoo! Answers Comprehensive Questions and Answers 1.0, the results demonstrate that our model significantly outperforms the existing methods, achieving 95.59% in coarse-grained level accuracy and 92.91% in fine-grained level accuracy, respectively.

In this paper, we introduce PortLaneNet, an optimized lane detection model specifically designed for the unique challenges of enclosed container terminal environments. Unlike conventional lane detection scenarios, this model addresses complexities such as intricate ground markings, tire crane lane lines, and various types of regional lines that significantly complicate detection tasks. Our approach includes the novel Scene Prior Perception Module, which leverages pre-training to provide essential prior information for more accurate lane detection. This module capitalizes on the enclosed nature of container terminals, where images from similar area scenes offer effective prior knowledge to enhance detection accuracy. Additionally, our model significantly improves understanding by integrating both high- and low-level image features through attention mechanisms, focusing on the critical components of lane detection. Through rigorous experimentation, PortLaneNet has demonstrated superior performance in port environments, outperforming traditional lane detection methods. The results confirm the effectiveness and superiority of our model in addressing the complex challenges of lane detection in such specific settings. Our work provides a valuable reference for solving lane detection issues in specialized environments and proposes new ideas and directions for future research.

Abstract Dense video captioning (DVC) aims at generating description for each scene in a video. Despite attractive progress for this task, previous works usually only concentrate on exploiting visual features while neglecting audio information in the video, resulting in inaccurate scene event location. In this article, we propose a novel DVC model named CMCR, which is mainly composed of a cross-modal processing (CM) module and a commonsense reasoning (CR) module. CM utilizes a cross-modal attention mechanism to encode data in different modalities. An event refactoring algorithm is proposed to deal with inaccurate event localization caused by overlapping events. Besides, a shared encoder is utilized to reduce model redundancy. CR optimizes the logic of generated captions with both heterogeneous prior knowledge and entities’ association reasoning achieved by building a knowledge-enhanced unbiased scene graph. Extensive experiments are conducted on ActivityNet Captions dataset, the results demonstrate that our model achieves better performance than state-of-the-art methods. To better understand the performance achieved by CMCR, we also apply ablation experiments to analyze the contributions of different modules.

In recent years, with the development of the maritime industry, computer vision tasks based on optical remote sensing images have gained increasing attention in the field of marine remote sensing. However, in complex marine meteorological environments, traditional detection methods often suffer from high rates of missed detections and false alarms for small targets. To address this issue, this article proposes a super feature pyramid network (HFPNet) for detecting marine remote sensing objects, which includes a feature enhancement module (FEM) and a multiscale feature aggregation module (MFM). The FEM can highlight features of small targets and weaken complex background features in detection, the MFM can share high-level and low-level features to better fuse multiscale feature information. Additionally, due to the lack of marine remote sensing object datasets, this article constructs a marine target detection dataset (MTDS) containing six types of marine objects. To address the issue of imbalanced positive and negative samples in the dataset, this article designs a network loss function to accelerate network convergence and improve the accuracy of small-target detection. Compared with other models, HFPNet achieved the highest $\text{mAP}_{0.5}$ of 97.48% and 95.46% on the self-built dataset MTDS and the public dataset NWPU-VHR-10, respectively, after environmental enhancement. At the same time, it also achieved the fastest frames per second (FPS) on the test set. Finally, this article discusses the influence of attention mechanisms and postprocessing methods on HFPNet and obtains the best small-target detection model.

When dealing with sparse reward problems,existing deep RL algorithms often lead to hard exploration,they often only rely on the pre-designed environment reward,so it is difficult to achieve good results.In this situation,it is necessary to design rewards more carefully,make more accurate judgments and feedback on the exploration status of agents.The asynchronous advantage actor-critic(A3C) algorithm improves the training efficiency through parallel training,and improves the training speed of the original algorithm.However,for the environment with sparse rewards,it cannot well solve the problem of difficult exploration.To solve the problem of poor exploration effect of A3C algorithm in sparse reward environment,A3C based on exploration trajectory perception(ETP-A3C) is proposed.The algorithm can perceive the exploration trajectory of the agent when it is difficult to explore in training,further judge and decide the exploration direction of the agent,and help the agent get out of the exploration dilemma as soon as possible.In order to verify the effectiveness of ETP-A3C algorithm,a comparative experiment is carried out with baseline algorithm in five different environments of Super Mario Brothers.The results show that this method has significantly improved the learning speed and model stability.

Vector-based retrieval have been widely adopted to process online users' diverse interests for recommendations. However, most of them utilize a single vector to represent user multiple interests (UMI), inevitably impairing the accuracy and diversity of item retrieval. In addition, existing work often does not take into account the scale and speed of the model, and high-dimensional user representation vectors need high computation cost, leading to inefficient item retrieval. In this paper, we propose a novel lightweight multi-interest retrieval network (MIRN) by incorporating sequence-to-interest Expectation Maximization (EM) routing to deal with users' multiple interests. By leveraging representation ability of the Capsule network, we design a multi-interest representation learning module that clusters multiple Capsule vectors from the user's behavior sequence to represent each of their interests respectively. In addition, we introduce a composite capsule clustering strategy for the Capsule network framework to reduce the scale of the network model. Furthermore, a Capsule-aware module incorporating an attention mechanism has been developed to guide model training by adaptively learning multiple Capsule vectors of user representations. The experimental results demonstrate MIRN outperforms the state-of-the-art approaches for item retrieval and gains significant improvements in terms of metric evaluations.

The main purpose of blasting operation is to produce desired and optimum mean size rock fragments. Smaller or fine fragments cause the loss of ore during loading and transportation, whereas large or coarser fragments need to be further processed, which enhances production cost. Therefore, accurate prediction of rock fragmentation is crucial in blasting operations. Mean fragment size (MFS) is a crucial index that measures the goodness of blasting designs. Over the past decades, various models have been proposed to evaluate and predict blasting fragmentation. Among these models, artificial intelligence (AI)-based models are becoming more popular due to their outstanding prediction results for multi-influential factors. In this study, support vector regression (SVR) techniques are adopted as the basic prediction tools, and five types of optimization algorithms, i.e. grid search (GS), grey wolf optimization (GWO), particle swarm optimization (PSO), genetic algorithm (GA) and salp swarm algorithm (SSA), are implemented to improve the prediction performance and optimize the hyper-parameters. The prediction model involves 19 influential factors that constitute a comprehensive blasting MFS evaluation system based on AI techniques. Among all the models, the GWO-v-SVR-based model shows the best comprehensive performance in predicting MFS in blasting operation. Three types of mathematical indices, i.e. mean square error (MSE), coefficient of determination (R2) and variance accounted for (VAF), are utilized for evaluating the performance of different prediction models. The R2, MSE and VAF values for the training set are 0.8355, 0.00138 and 80.98, respectively, whereas 0.8353, 0.00348 and 82.41, respectively for the testing set. Finally, sensitivity analysis is performed to understand the influence of input parameters on MFS. It shows that the most sensitive factor in blasting MFS is the uniaxial compressive strength.

Some fundamental visual features have been found to be fully extracted before reaching the cerebral cortex. We focus on direction-selective ganglion cells (DSGCs), which exist at the terminal end of the retinal pathway, at the forefront of the visual system. By utilizing a layered pathway composed of various relevant cells in the early stage of the retina, DSGCs can extract multiple motion directions occurring in the visual field. However, despite a considerable amount of comprehensive research (from cells to structures), a definitive conclusion explaining the specific details of the underlying mechanisms has not been reached. In this paper, leveraging some important conclusions from neuroscience research, we propose a complete quantified model for the retinal motion direction selection pathway and elucidate the global motion direction information acquisition mechanism from DSGCs to the cortex using a simple spiking neural mechanism. This mechanism is referred to as the artificial visual system (AVS). We conduct extensive testing, including one million sets of two-dimensional eight-directional binary object motion instances with 10 different object sizes and random object shapes. We also evaluate AVS’s noise resistance and generalization performance by introducing random static and dynamic noises. Furthermore, to thoroughly validate AVS’s efficiency, we compare its performance with two state-of-the-art deep learning algorithms (LeNet-5 and EfficientNetB0) in all tests. The experimental results demonstrate that due to its highly biomimetic design and characteristics, AVS exhibits outstanding performance in motion direction detection. Additionally, AVS possesses biomimetic computing advantages in terms of hardware implementation, learning difficulty, and parameter quantity.

Human visual system is a crucial component of the nervous system, enabling us to perceive and understand the surrounding world. Advancements in research on the visual system have profound implications for our understanding of both biological and computer vision. Orientation detection, a fundamental process in the visual cortex where neurons respond to linear stimuli in specific orientations, plays a pivotal role in both fields. In this study, we propose a novel orientation detection mechanism for local neurons based on dendrite computation, specifically designed for grayscale images. Our model comprises eight neurons capable of detecting local orientation information, with inter-neuronal interactions facilitated through nonlinear dendrites. Through the extraction of local orientation information, this mechanism effectively derives global orientation information, as confirmed by successful computer simulations. Experimental results demonstrate that our mechanism exhibits remarkable orientation detection capabilities irrespective of variations in size, shape, or position, which aligns with previous physiological research findings. These findings contribute to our understanding of the human visual system and provide valuable insights into both biological and computer vision. The proposed orientation detection mechanism, with its nonlinear dendritic computations, offers a promising approach for improving orientation detection in grayscale images.

Abstract Background As a marker of differentiation, Killer cell lectin like receptor G1 (KLRG1) plays an inhibitory role in human NK cells and T cells. However, its clinical role remains inexplicit. This work intended to investigate the predictive ability of KLRG1 on the efficacy of immune-checkpoint inhibitor in the treatment of lung adenocarcinoma (LUAD), as well as contribute to the possible molecular mechanisms of KLRG1 on LUAD development. Methods Using data from the Gene Expression Omnibus, the Cancer Genome Atlas and the Genotype-Tissue Expression, we compared the expression of KLRG1 and its related genes Bruton tyrosine kinase (BTK), C-C motif chemokine receptor 2 (CCR2), Scm polycomb group protein like 4 (SCML4) in LUAD and normal lung tissues. We also established stable LUAD cell lines with KLRG1 gene knockdown and investigated the effect of KLRG1 knockdown on tumor cell proliferation. We further studied the prognostic value of the four factors in terms of overall survival (OS) in LUAD. Using data from the Gene Expression Omnibus, we further investigated the expression of KLRG1 in the patients with different responses after immunotherapy. Results The expression of KLRG1, BTK, CCR2 and SCML4 was significantly downregulated in LUAD tissues compared to normal controls. Knockdown of KLRG1 promoted the proliferation of A549 and H1299 tumor cells. And low expression of these four factors was associated with unfavorable overall survival in patients with LUAD. Furthermore, low expression of KLRG1 also correlated with poor responses to immunotherapy in LUAD patients. Conclusion Based on these findings, we inferred that KLRG1 had significant correlation with immunotherapy response. Meanwhile, KLRG1, BTK, CCR2 and SCML4 might serve as valuable prognostic biomarkers in LUAD.

As the world's biggest carbon dioxide (CO2) emitter and the largest developing country, China faces daunting challenges to peak its emissions before 2030 and achieve carbon neutrality within 40 years. This study fully considered the carbon-neutrality goal and the temperature rise constraints required by the Paris Agreement, by developing six long-term development scenarios, and conducting a quantitative evaluation on the carbon emissions pathways, energy transformation, technology, policy and investment demand for each scenario. This study combined both bottom-up and top-down methodologies, including simulations and analyses of energy consumption of end-use and power sectors (bottom-up), as well as scenario analysis, investment demand and technology evaluation at the macro level (top-down). This study demonstrates that achieving carbon neutrality before 2060 translates to significant efforts and overwhelming challenges for China. To comply with the target, a high rate of an average annual reduction of CO2 emissions by 9.3% from 2030 to 2050 is a necessity, which requires a huge investment demand. For example, in the 1.5 °C scenario, an investment in energy infrastructure alone equivalent to 2.6% of that year's GDP will be necessary. The technological pathway towards carbon neutrality will rely highly on both conventional emission reduction technologies and breakthrough technologies. China needs to balance a long-term development strategy of lower greenhouse gas emissions that meets both the Paris Agreement and the long-term goals for domestic economic and social development, with a phased implementation for both its five-year and long-term plans.

China has committed to achieve net carbon neutrality by 2060 to combat global climate change, which will require unprecedented deployment of negative emissions technologies, renewable energies (RE), and complementary infrastructure. At terawatt-scale deployment, land use limitations interact with operational and economic features of power systems. To address this, we developed a spatially resolved resource assessment and power systems planning optimization that models a full year of power system operations, sub-provincial RE siting criteria, and transmission connections. Our modeling results show that wind and solar must be expanded to 2,000 to 3,900 GW each, with one plausible pathway leading to 300 GW/yr combined annual additions in 2046 to 2060, a three-fold increase from today. Over 80% of solar and 55% of wind is constructed within 100 km of major load centers when accounting for current policies regarding land use. Large-scale low-carbon systems must balance key trade-offs in land use, RE resource quality, grid integration, and costs. Under more restrictive RE siting policies, at least 740 GW of distributed solar would become economically feasible in regions with high demand, where utility-scale deployment is limited by competition with agricultural land. Effective planning and policy formulation are necessary to achieve China's climate goals. [ABSTRACT FROM AUTHOR]

In this paper, a combination of artificial neural network and ant colony optimization (ANN-ACO) was used for dynamic conditions of retaining wall structures. The retaining walls produce different responses to dynamic loads. The applied data of this study comprising of wall height and thickness, soil density, internal friction angle, and stone density. The walls were designed in a variety of dynamic conditions. Various conditions were considered for the design of the retaining wall structures. Then, an extended data set was created for the next step. After that, the new systems were implemented using optimized artificial intelligence techniques. The neural network provided strong relationships between various wall parameters. The design of various networks in the present research led to the best evaluation of the dynamic conditions of the retaining walls. Under these conditions, an ACO was used for optimal design. Effects of parameters varied due to different wall conditions when dynamic loads were considered. Therefore, the impact of parameters was evaluated using hybrid ANN-ACO to increase the efficiency. These designs provided more control over dangers by dynamic loads of a retaining wall structure.

In this paper, friction and wear tests were carried out: 45# steel (carbon steel) was rubbed with aluminum bronze, aluminum bronze-based inlaid solid self-lubricating bearing (ISSLB) material, tin bronze, and tin bronze-based ISSLB material under different loads. The friction and wear properties (friction coefficient, wear amount and friction temperature) of the above four materials were studied. The friction and wear properties of the new self-lubricating bearing material and the traditional copper alloy under the same load were compared. The friction mechanism of each material in the friction process was discussed. The effect and mechanism of C-MoS2 composite solid lubricant on friction and wear properties were analyzed. Under the experimental loads of 100 to 500 N, the average friction coefficients of aluminum bronze-based ISSLB material were maintained in the range of 0.18~0.14, while the average friction coefficients of tin bronze-based ISSLB material were maintained between 0.26~0.20, which is much lower than those of copper alloy. The wear amounts of tin bronze-based ISSLB material were always in the range of 14.7~34.4 mg, which were much less than those of aluminum bronze-based ISSLB materials and copper alloy. The results show that the copper-based ISSLB materials possess excellent wear resistance, and the friction and wear properties of tin bronze-based ISSLB material are better than that of aluminum bronze-based ISSLB material. Therefore, if the copper-based ISSLB materials are applied to self-lubricating bearings, the friction and wear resistance of bearings will be greatly improved and the service life of bearings prolonged.

A super-hydrophilic pH-sensitive electrode with a porous TiO2 thin film is proposed in this work and fabricated using the chemical etching method. In total, 30 groups of porous TiO2 thin film were obtained by immersing a Ti sheet in NaOH, with the solution concentration ranging from 0.5–4 M and the reaction time ranging from 15–240 min. SEM, XRD, XPS, and a contact angle meter were used to investigate the influence of the chemical etching parameters on the morphology, composition, and wettability of the fabricated electrodes. The chemical etching parameters were found to have a significant influence on the specific surface area and the component of the films, which strongly affected the wettability and pH sensing characteristics of the electrodes. The electrode obtained with a solution concentration of 1 M and reaction time of 120 min is the ideal product because of its excellent wettability, with a contact angle of 5.46°, and good pH sensing characteristics in pH buffer solutions. The electrode also showed good stability regarding its wettability and pH sensing properties during storage and utilization.

Anodization is performed on the Ti-30Zr-5Al-3V (TZ30) alloy to improve its surface performance. X-ray diffractometer (XRD), scanning electron microscopy (SEM), and Olympus microscope are used to determine the phase constitution, morphology, and thickness of the anodization film (AOF). Tribological tests and electrochemical corrosion experiments are carried out to measure, respectively, the wear behavior and corrosion resistance of AOFs in simulated body fluid (SBF) solution. The microstructure characteristic of the AOF anodized at low voltage (20 V) is composed of compact and loose regions. As the applied voltage increases to 60 V, the compact regions transform progressively into loose regions, and then grow into nanotube regions. Besides, an increase in thickness of the AOF from 8.6 ± 4.61 μm to 20.7 ± 2.18 μm, and a gradual increase in surface microhardness from 364.6 ± 14.4 HV to 818.4 ± 19.3 HV, are also exhibited as the applied voltage increases from 20 V to 60 V. Specimens anodized at 40 V and 60 V have a low friction coefficient (~0.15) and wear rate (~2.2 mg/N/m) in the SBF solution. The enhanced wearability originates from the high hardness and various wear mechanisms. Potentiodynamic polarization curves suggest that the corrosion resistance in the SBF solution of all anodized specimens is greatly improved, thanks to the protection from the anodized TiO2 film.

The purpose of this study is to improve the performance of indoor air purifiers. To this end, the performance and structure of indoor air purifiers were analysed in this paper. Simple background reduction method and the natural attenuation deduction method were used to analyse the performance test and structure of indoor air purifier. The study found that the purifier A has the best ability to remove formaldehyde, which can remove 80% of formaldehyde within half an hour; purifier B can remove 70% of formaldehyde within half an hour. Therefore, the structure of purifiers affects the purification efficiency, and in the design of the purifier structure, more attention should be paid to the reasonable ratio of radiation field, concentration field and flow field.