Tracing Model Research Articles

Optimization Design of Heliostat Field Based on Biomathematical Models

The optimal design of heliostat field in solar power generation device is studied Heliostat field generates high temperature by focusing the sunlight reflected from the mirror surface, thus pushing the turbine to generate electricity. In order to improve the efficiency of power generation, this paper optimizes the parameters of heliostat field, establishes a single-objective optimization model, and uses mixed strategy whale optimization algorithm to solve it. Through geometric optical analysis and ray tracing model, the optical efficiency of heliostat field and the annual average output thermal power per mirror area are calculated. Finally, through the discussion and analysis of the model, the optimization design method is evaluated

Reflectarray Design Using a Discrete Dipole Framework

We propose and numerically validate a patch reflectarray modeling approach suitable for small patches that describes each patch as a pair of polarizable magnetic dipoles. We introduce an extraction technique to obtain the effective polarizability of the patch dipoles via full-wave simulations on individual patches, followed by a beamforming design routine valid under weakly scattering configurations. This dipole framework serves as an alternative to the ray tracing model often used in reflectarray designs, in which rays are drawn from the feed point and scattered off of the patch elements. Whereas the ray tracing method solves the design problem in terms of phase delays, the dipole framework presented here has the potential to accurately design and predict beam patterns using a fully dipolar treatment of empirically characterized patches. We illustrate this technique by applying it to two modulation strategies: a variable patch size reflectarray in which the phase can be continuously tuned (grayscale patch response), and a fixed patch size (binary patch response) in which on/off modulation is achieved through selective patch electrical shorting. Methods for incorporating these cases into the dipole design framework are discussed and the results compared to those from full wave simulation.

Analysis and Modeling of Spectral Line Shapes of Collision-Induced Light Scattering of Gaseous Xenon

Collision-induced light scattering spectra of gaseous xenon at room temperature are analyzed in terms of different literature and new interatomic potentials and interaction-induced pair polarizability anisotropy and trace models. At low frequencies the spectral intensities and the associated moments are determined by both bound and free transitions.

Correspondence analysis between vegetation cover and sheet erosion rate on an abandoned farmland slope based on 7Be measurement

Scale effects, the phenomena of runoff and sediment yield differ from different spatial and temporal scales, exist in the whole process of soil erosion. Vegetation cover can reduce the degree of soil erosion at the plot and slope scales, but there is little research on the reduction effect of vegetation on soil erosion at a specific point on the plot, due to the limitations of traditional methods. Based on in situ sampling on a representatively abandoned farmland slope, the corresponding relationship between vegetation and sheet erosion was analyzed quantitatively using the modified 7Be tracing model with vegetation factor. The results showed that 7Be activity per unit mass and per unit area in soil ranged from 2.45 to 27.05 Bq kg−1 and 50.09 to 528.11 Bq m−2, and in plants ranged from 378.20 to 691.82 Bq kg−1 and 72.61 to 307.33 Bq m−2, respectively. Plants were responsible for 22.6 %±7.3 % of total 7Be interception on the slope, affected by vegetation coverage, leaf area index, dry biomass and flesh weight. Plants were mainly distributed on the backslope and toeslope, with vegetation coverage, leaf area index, dry biomass and flesh weight ranging from 0.26 to 0.75, 0.48 to 2.45, 0.17 to 0.45 kg m−2 and 0.38 to 0.85 kg m−2, respectively, while the peak area of soil erosion was concentrated on the backslope, with a rate ranging from 0.06 to 16.76 kg m−2. At the specific point scale, the corresponding relationships between soil erosion and vegetation coverage, leaf area index, dry biomass, and flesh weight of plants are not as significant as in the previous study at the plot or slope scale. This indicates that the reduction of erosion for plants varies with scale dependency, which is more complicated at the specific point scale, rather than simply decreasing with the increase of vegetation. Our results highlight that there is a scale effect of plants on soil erosion, and the 7Be measurement is of great potential to explore this effect at the specific point scale.

A Bayesian-based knowledge tracing model for improving safety training outcomes in construction: An adaptive learning framework

Safety training plays a pivotal role in effectively reducing unsafe behaviors in the construction industry. Despite the numerous effort to improve safety training, existing studies lacked consideration of trainees' different learning characters, nor did they adapt the suitable training materials to trainees' mastery of safety knowledge. Against this contextual backdrop, this research proposes a Bayesian-based Knowledge Tracing (BKT) model to recommend personalized training materials according to trainees's learning progress. The proposed BKT model tracks and predicts trainees' performance with their cognitive characters, abilities, and historical training records. It also adjusts trainee's probability of mastering safety knowledge concepts for determining future training sessions. In this way, the safety training is adaptive and personalized, thus more effective. An exploration study is conducted in the laboratory environment to validate the effectiveness and feasibility of the proposed BKT model. The research results showed that 83.33% of the respondents felt gained safety knowledge after the training, and 66.67% of the respondents affirmed the effectiveness of the BKT model-based training system in improving the effectiveness of training. The results demonstrated that the proposed model performed well at educating safety knowledge and reducing unsafe behavior.

Application of Exercise-Correlated Knowledge Proficiency Tracing Model based on Multiple Learning Objectives in English Teaching Curriculum Resources

The advanced invention of information technology overcomes the limitations of traditional teaching methods. The English-oriented online teaching system not only has rich course resources, but also provides technical support for students’ personalized learning. Therefore, using big data mining techniques may help to analyze students deeply and develop personalized study plans and content. Aiming at the deficiencies of online English teaching platform in the task of knowledge level diagnosis, an Exercise-correlated Knowledge Proficiency Tracing (EKPT) model that integrates multi-objective learning factors is proposed. The learning model maps students and practice information into meaningful knowledge space through practice and knowledge-related information, and quantifies students’ knowledge learning process through forgetting curve theory and learning curve theory. The experimental test results show that the EKPT model has the best performance in knowledge ability prediction and learning performance prediction. The mean absolute error (MAE) values of the EKPT model in the English1 dataset, English2 dataset, written auxiliary dataset and adaptive dataset are 0.32, 0.25, 0.39 and 0.34, respectively, with the smallest error. Therefore, the proposed EKPT model meets the requirements of English online learning. The research content has important reference value for the construction of online English teaching platform.

ScRNA-seq profiling of neonatal and adult thymus-derived CD4+ T cells by a T cell origin-time tracing model.

It is well documented that the neonatal thymus-derived (neonatal-TD) regulatory T cells (Treg) are essential to prevent lethal autoimmune diseases and allergies, and neonatal and adult thymus possesses distinct output potentials for naïve T cells, including Treg. However, the molecular features and detailed functional differences between neonatal-TD and adult thymus-derived (adult-TD) T cells in terms of their ability to maintain immune homeostasis during long-term environmental influences are still largely unknown, partially due to the lack of appropriate animal models to precisely trace these cells at specific time points. In this study, neonatal-TD and adult-TD CD4+ T cells from the spleen and Peyer's patches were traced for 9 weeks by a T cell origin-time tracing mouse model and analysed by single-cell RNA sequencing. More Treg but fewer naïve T cells were found in neonatal-TD CD4+ T cells from both tissues than those from adult-TD counterparts. Interestingly, the neonatal-TD Treg in both the spleen and Peyer's patches exhibited augmented expression of Foxp3, Gata3, Ctla4, Icos, Il2ra, Tgfb1, and Nrp1, as well as enriched Gene Ontology terms like T cell activation and tolerance induction, indicating an enhanced immunosuppressive function. These results were further confirmed by flow cytometry analysis and in vitro immune suppression assays. Flow cytometry also revealed a significantly higher proportion of neonatal-TD Treg in total Treg than that of adult-TD counterparts, suggesting the longer lifespan of neonatal-TD Treg. To investigate the intrinsic features of neonatal-TD and adult-TD CD4+ T cells, a shortened tracing time was performed. Surprisingly, the neonatal-TD and adult-TD CD4+ T cells had similar proportions of Treg and did not exhibit significant differences in Foxp3, Gata3, Ctla4, Icos, Il2ra, and Tgfb1 expression levels after tracing for 12 days. On the other hand, neonatal-TD Treg present an increased Nrp1 expression level compared with adult-TD counterparts, indicating the enhanced stability. Together, our work reveals that the neonatal-TD Treg are more immunosuppressive, which is likely shaped primarily by environmental factors.

Knowledge Tracing via Attention Enhanced Encoder-Decoder.

The knowledge tracing model takes students' learning behaviours data as input to determine their current knowledge status and predict their future answers. The learning behaviours data describes three main types of learning behaviours: learning process, learning end, and learning interval. The classical knowledge tracing models only use the data of the learning end, which contains limited information and the models cannot accurately describe constraint in the same learning behaviour in the time series. Subsequent models add other types of learning behaviours data but do not integrate different types of learning behaviours, and the models cannot accurately describe collaboration in different learning behaviours. To address these issues, knowledge tracing via attention-enhanced encoder-decoder is proposed to synthesize and analyse the three types of learning behaviours mentioned above and firstly adopts the multiheaded attention mechanism to describe constraint in the same learning behaviours; secondly adopts the channel attention mechanism modelling collaboration in the three types of learning behaviours. In the experiments, various comparisons are made with related models on several real data sets, and the results show that our model achieves certain advantages in terms of performance and knowledge state representation. In terms of practical application, an intelligent learning platform based on the model has been implemented, which predicts the future answer of students in the teaching process of two offline courses: computer and English and has achieved better performance than other knowledge tracing models.

Characteristics of Aerosol Extinction Hygroscopic Growth in the Typical Coastal City of Qingdao, China

The aerosol hygroscopic growth (HG) characteristics in coastal areas are very complex, which is one of the main influences on the simulation accuracy of radiation transfer modeling for coastal environments. Previous studies have shown that aerosol HG characteristics are very different in open oceans and inland regions. However, the aerosol HG features in coastal areas are strongly affected by its type. In this work, an aerosol backward trajectory tracing model was used to classify the local aerosol type. Using long-term field campaign data in Qingdao (25 September 2019 to 25 October 2020), the HG characteristics of different types of aerosols (i.e., land source, sea source, and mixed aerosol) under different seasons and different atmospheric environments (i.e., pollution background and clean background) were studied. Quantitative models of aerosol HG factor were established for aerosols from different sources in different seasons and under different pollution background conditions. The major type of local aerosol is terrestrial aerosol, as the marine source only accounts for 10–20%. Seasonal HG characteristics (deliquescence point, DP) of mixed and land source aerosol vary significantly, from around RH = 60% to RH = 85%, while that of the marine aerosol is rather consistent (RH = 80%). When the atmospheric background is relatively clean, the DPs of aerosols from different sources are almost the same (about RH = 80%), but when the pollution is heavy, the DPs of terrestrial aerosols are almost 20% lower than those of marine sources. These models can be directly used to characterize the hygroscopic characteristics of atmospheric aerosols in Qingdao at specific seasons or pollution levels for radiative transfer modeling, remote sensing, and so forth.

Enhanced pinch flow fractionation using inertial streamline crossing

In this work, we study the influence of inertia on the dynamics of neutrally buoyant spherical microbeads of varying diameter in a pinch flow fractionation device. To that aim, we monitor their trajectory over an unprecedented wide range of flow rates and flow rate ratios. Our experimental results are supplemented by a depth-averaged 2D-model where the flow is described using the Navier-Stokes equation coupled with the shallow channel approximation and where particles trajectories are computed from Newton’s second law of motion with a particle tracing model. Above a certain flow rate, we show that particles inertia enables them to cross streamlines in response to an abrupt change of direction. These streamline crossing events combined with the increasing effect of the inertial lift forces drive particles to deviate from the inertialess trajectory. The amplitude of the resulting inertial deviation increases both with the particles diameter and the total flow rate before reaching a plateau. Consequently, based on our numerical and experimental results, we determine the optimal flow conditions to shift the particles distribution in order to significantly enhance their size-based separation.

DCKT: A Novel Dual-Centric Learning Model for Knowledge Tracing

Knowledge tracing (KT), aiming to model learners’ mastery of a concept based on their historical learning records, has received extensive attention due to its great potential in realizing personalized learning in intelligent tutoring systems. However, most existing KT methods focus on a single aspect of knowledge or learner, not paying careful attention to the coupling influence of knowledge and learner characteristics. To fill this gap, in this paper, we explore a new paradigm for the KT task by exploiting the coupling influence of knowledge and learner. A novel model called Dual-Centric Knowledge Tracing (DCKT) is proposed to model knowledge states through two joint tasks of knowledge modeling and learner modeling. In particular, we first generate concept embeddings in abundant knowledge structure information via a pretext task (knowledge-centric): unsupervised graph representation learning. Then, we deeply measure learners’ prior knowledge the knowledge-enhanced representations and three predefined educational priors for discriminative feature enhancement. Furthermore, we design a forgetting-fusion transformer (learner-centric) to simulate the declining trend of learners’ knowledge proficiency over time, representing the common forgetting phenomenon. Extensive experiments were conducted on four public datasets, and the results demonstrate that DCKT could achieve better knowledge tracing results over all datasets via a dual-centric modeling process. Additionally, DCKT can learn meaningful question embeddings automatically without manual annotations. Our work indicates a potential future research direction for personalized learner modeling, which is of both accuracy and high interpretability.

Economic Evaluation: Advantages of Implementing Drug Track and Trace beyond Health Benefits

Traceability is a valuable strategy to prevent the administration of counterfeit and substandard medicines, but despite being an efficient risk management strategy, it has high implementation and operational costs. An economic evaluation, taken with the identification of risks to be mitigated, potential benefits, related costs, and ways to fund the project implementation and operationalization, can determine the feasibility of a traceability model. This evaluation admits different perspectives depending on the stakeholder. Several countries are working on different traceability and financing models according to identified risks, expected gains, and economic impacts. This opinion paper describes the problem, track and trace models mostly adopted, most used economic evaluation methods in healthcare, and defines the hypothesis that investment return can enable the decision-making for a drug track system implementation.

Forecast of the Energetic Electron Environment of the Radiation Belts

AbstractDifferent modeling methodologies possess different strengths and weakness. For instance, data based models may provide superior accuracy but have a limited spatial coverage while physics based models may provide lower accuracy but provide greater spatial coverage. This study investigates the coupling of a data based model of the electron fluxes at geostationary orbit (GEO) with a numerical model of the radiation belt region to improve the resulting forecasts/pastcasts of electron fluxes over the whole radiation belt region. In particular, two coupling methods are investigated. The first assumes an average value for L* for GEO, namely = 6.2. The second uses a value of L* that varies with geomagnetic activity, quantified using the Kp index. As the terrestrial magnetic field responds to variations in geomagnetic activity, the value of L* will vary for a specific location. In this coupling method, the value of L* is calculated using the Kp driven Tsyganenko 89c magnetic field model for field line tracing. It is shown that this addition can result in changes in the initialization of the parameters at the Versatile Electron Radiation Belt model outer boundary. Model outputs are compared to Van Allen Probes MagEIS measurements of the electron fluxes in the inner magnetosphere for the March 2015 geomagnetic storm. It is found that the fixed coupling method produces a more realistic forecast.

Crosstalk Analysis of Printed Circuit Board Traces With Right-Angled Bent Corners via Time Domain Hybrid Method

Discontinuous impedance exists when bent corners are introduced into the traces of printed circuit board (PCB). By combining transmission line (TL) equations, higher order finite-difference time-domain (FDTD) method, state variable method, and Norton's theorem, an efficient time domain hybrid method is proposed to realize the fast crosstalk simulation of the traces with right-angled bent corners (RABCs) in this work. First, the whole structure of the traces is decomposed into a cascade network, which consists of multiple equal length parallel microstrip lines (EL-PMSLs) and the RABC structures. Then, the crosstalk responses on these EL-PMSLs can be quickly solved by applying TL equations combined with the higher order FDTD method. With the application of Norton's theorem, the equivalent circuit model of whole PCB traces is constructed. Finally, the equivalent circuits of the RABC structures are extracted, and the port voltages of these RABCs are calculated via state variable method and fed back to these EL-PMSLs as boundaries, thus the round-trip transmission of interference signals on the PCB traces is realized. Numerous crosstalk simulations of PCB traces with typical RABC structures, as well as extracted from a practical PCB, are presented and compared with the results of commercial software CST to verify the correctness and efficiency of the proposed method.

Deciphering the heterogeneity of the Lyve1+ perivascular macrophages in the mouse brain

Perivascular macrophages (pvMs) are associated with cerebral vasculature and mediate brain drainage and immune regulation. Here, using reporter mouse models, whole brain and section immunofluorescence, flow cytometry, and single cell RNA sequencing, besides the Lyve1+F4/80+CD206+CX3CR1+ pvMs, we identify a CX3CR1– pvM population that shares phagocytic functions and location. Furthermore, the brain parenchyma vasculature mostly hosts Lyve1+MHCII– pvMs with low to intermediate CD45 expression. Using the double Cx3cr1GFP x Cx3cr1-Cre;RosatdT reporter mice for finer mapping of the lineages, we establish that CD45lowCX3CR1– pvMs are derived from CX3CR1+ precursors and require PU.1 during their ontogeny. In parallel, results from the Cxcr4-CreErt2;Rosa26tdT lineage tracing model support a bone marrow-independent replenishment of all Lyve1+ pvMs in the adult mouse brain. Lastly, flow cytometry and 3D immunofluorescence analysis uncover increased percentage of pvMs following photothrombotic induced stroke. Our results thus show that the parenchymal pvM population is more heterogenous than previously described, and includes a CD45low and CX3CR1– pvM population.

Imaging of shallow crustal structure using seismic ray trace modelling and tomographic studies along Alampur–Koniki–Ganapeswaram seismic profile in North Cuddapah basin: Regional geodynamic implications

Imaging of shallow crustal structure using seismic ray trace modelling and tomographic studies along Alampur–Koniki–Ganapeswaram seismic profile in North Cuddapah basin: Regional geodynamic implications

Artificial Intelligence in Technology-Enhanced Assessment: A Survey of Machine Learning

Intelligent assessment, the core of any AI-based educational technology, is defined as embedded, stealth and ubiquitous assessment which uses intelligent techniques to diagnose the current cognitive level, monitor dynamic progress, predict success and update students’ profiling continuously. It also uses various technologies, such as learning analytics, educational data mining, intelligent sensors, wearables and machine learning. This can be the key to Precision Education (PE): adaptive, tailored, individualized instruction and learning. This paper explores (a) the applications of Machine Learning (ML) in intelligent assessment, and (b) the use of deep learning models in ‘ knowledge tracing and student modeling’. The paper concludes by discussing barriers involved in using state-of-the-art ML methods and some suggestions to unleash the power of data and ML to improve educational decision-making.

HHSKT: A learner–question interactions based heterogeneous graph neural network model for knowledge tracing

Knowledge tracing (KT) has evolved into a crucial component of the online education system with the rapid development of online adaptive learning. A key component of the online education system, knowledge tracing (KT) assesses the state of knowledge by tracing each learner’s learning activities. The deep KT model, however, is unable to completely extract the features of the questions and skills due to the heterogeneity of the knowledge structure and the sparsity of the interaction records. The model’s capacity to handle diverse data is also restricted by over parameterization. Additionally, rather than focusing solely on a precise fit, Intelligent Tutoring System (ITS) should stress interpretable feedback to the learner. The deep KT approach’s item parameters are still unable to give students useful feedback. This paper proposes to trace learner’s short-term attentional knowledge based on heterogeneous hierarchical differentiation, named HHSKT. Hierarchical heterogeneous knowledge structures and short-term memory enhancement will be used to model the effects of different interaction sequences on learners. Specifically, knowledge structure features are extracted by constructing a heterogeneous graph-based graph information augmentation component. Question differentiation parameters are derived by transforming the TrueSkill system. Besides, learners’ history-related practices are emphasized by windowing attention. Comparing regression-based and deep-based knowledge tracing experiments shows that HHSKT significantly outperforms the state-of-the-art approach on three real-world benchmark datasets (with an average AUC improvement of up to 3%), demonstrating the superiority of the proposed model.

424 Perivascular niches dictate developmental cellular hierarchies during melanoma growth

Cellular plasticity endows cancer cells with capacity towards a phenotypic state. Except from the genetic origin of cellular plasticity, cancer cells can transit to reversible phenotypic states as a result of microenvironmental cues and are often driven by stochastic epigenetic and/or transcriptional fluctuations. Melanoma is a prime example of heterogeneous and plastic tumors where the origin and magnitude of cell state diversity remains poorly understood. Combining lineage tracing, 3D imaging and quantitative mathematical modelling in a clinically-relevant mouse model of melanoma, we demonstrated that tumors follow a hierarchical model of growth supported by a population of Melanoma Stem-like Cells (MSCs) that exhibit a transcriptomic signature of pre-migratory neural crest cells established transiently during embryonic development.

A Comparative Study of Entropy Generation Analysis Through Cartesian and Cylindrical Annulus Geometries

Abstract This research paper presents a comparative study dealing with entropy generation analysis in Cartesian and cylindrical annulus geometries. This numerical investigation involves heat and mass transfer coupled with forced convection and gas radiation in a semitransparent medium during the evaporation of a thin liquid film. The systems of equations are solved with interfacial and boundary conditions using an implicit finite volume method by the tridiagonal matrix algorithm method. The radiative transfer equation (RTE) was solved by the Ray tracing model. The statistical narrow band correlated-K model with the updated model parameters was applied to calculate the gas transmissivity. The main intent of this paper is to compare the entropy generation components in two different configurations. The result shows that the entropy generation related to heat and mass transfer in the channel is higher than that estimated for the cylindrical annulus. Regarding optimal design selection, the entropy generation analysis, detailed in this paper, suggests a preference for the cylindrical annulus geometry. Our results may guide designers to select from the studied configuration.