Tracing Model Research Articles

A high-performance solar simulator pursuing high flux and fine uniformity: Modelling, optimization, and experiment

The high flux solar simulator is a significant experimental tool in concentrated solar power applications. For a simulator, the uniformity of its light spot is as vital as high flux, since uneven light spots may result in the thermal-stress failure of the solar receiver. However, most constructed simulators have only pursued extremely high flux but ignored uniformity, thus degrading the simulator’s overall performance. Considering that improving uniformity comes at the expense of decreasing the flux, a high-performance solar simulator must meet a reasonable compromise between high flux and fine uniformity. In this paper, a simulator whose input power reached 30 kWe was designed and optimized using an in-house Monte Carlo Ray Tracing (MCRT) model, and corresponding processes were demonstrated to provide universal guidelines. To improve the uniformity while maintaining satisfying high flux, a non-coaxial reflector with optimized parameters was implemented. Based on the design and optimization results, a realistic solar simulator was manufactured and calibrated through experimental methods. The results demonstrate that the manufactured simulator achieved an about 10% reduction in non-uniformity while reaching a peak flux of 8.6 × 106 W·m−2, meeting the design expectation for a high-performance simulator.

A System for Teaching Advanced Colonoscopic Skills and Endoscopic Submucosal Dissection Based on Nonclinical Models.

Endoscopic submucosal dissection (ESD) requires skills that the vast majority of endoscopists do not possess. ESD be broken down into component skills and at least three of the necessary skill sets can be taught separately. In the United States most trainees initially participate in half- or full-day courses that utilize ex vivo and in vivo animal models and the great majority learn these advanced skills in the clinical setting. We describe a comprehensive training over a well-defined period using ex vivo porcine or bovine large bowel models. There are five components or modules that make up the training program: (1) bowel wall injections in ex vivo tissue, (2) inanimate figure tracing model to teach scope control, (3) ESD in plastic tube with window cutout over which square of ex vivo tissue is placed, (4) ESD in ex vivo porcine or bovine large bowel, and (5) mucosal wound closure. The authors are in the midst of training a group of residents, fellows, and young attendings using this approach. This approach has not been vetted yet; however, the preliminary results are promising.

Broad-Learning-Based Localization for Underwater Sensor Networks With Stratification Compensation

Localization is an indispensable service for underwater sensor networks (USNs). Generally, the convex optimization method is adopted to solve the localization problem. However, the acoustic ray in water medium does not propagate along a straight line, which makes it difficult or impossible to transform the non-convex optimization problem into a convex optimization problem. This paper develops a broad learning (BL) based localization solution for USNs with isogradient sound speed profile. We first employ the ray tracing model to compensate the range bias caused by straight-line propagation. On the basis of collected range information from anchor nodes, the localization optimization problem is transformed into supervised, unsupervised and semisupervised learning frameworks. Correspondingly, three BL-based location estimators are developed to seek the position information of sensor nodes, where the incremental learning schemes are conducted for fast parameter tuning and remodeling. In addition, the Cramer-Rao Lower Bound (CRLB) of positioning error and the convergence to global optimality are both analysed. Finally, simulation and experiment results are presented to show the effectiveness of our approach. It is demonstrated that the proposed solution in this paper has the following nice features: 1) relax the dependence of convex relaxation over convex optimization-based location estimators; 2) reduce the training time and improve the localization efficiency over deep learning-based location estimators.

An Experimental and Computational Approach to Development of Mixing Processes for Miscible Liquids in Unbaffled Tanks.

Mixing of liquids is a critical unit operation in the biopharmaceutical drug product manufacturing. It commonly consists of mixing miscible liquids to dilute bulk drug substance (DS) or pool multiple lots of drug substance. In the past, at-scale mixing studies have been conducted to determine the mixing parameters, namely mixing speed, and mixing time. At-scale studies have historically been utilized to overcome the challenges associated with geometric dissimilarity of mixing systems found when scaling up. In addition, such studies are quite costly, as they often use actual DS to overcome a lack of representativeness associated with simple salt trace models often employed. As a result, there is a significant need for alternative cost-effective methods that can predict mixing parameters with close agreement to actual experiments and operations. At-scale mixing experiments were conducted using full-sized tanks and surrogate solutions. Several computational fluid dynamic (CFD) simulation methods were conducted and compared with the experiments to determine the most reliable computational techniques. The experiments demonstrate that surrogate solutions can be used reliably to determine mixing parameters in at-scale studies instead of the valuable drug products. Studying different CFD methods also showed that transient simulations that use a large eddy simulation (LES) viscous modeland a sliding mesh can correctly predict the mixing parameters. Results of this study establish a practical and reliable methodology to perform mixing studies for miscible liquids with different kinematic viscosities. The methods discussed herein greatly reduce the routine mixing study costs in the biopharmaceutical industry and increase efficiency and accuracy of the results, allowing proactive scale-up of mixing operations.

A methodology for selecting a performance-convenient ABMS development framework on HPC platforms

Agent-based modeling and simulation (ABMS) is an approach for simulating the actions and interactions of autonomous agents. Such interactions occur within a defined environment to assess their effects on a system as a whole. Depending on the complexity of the model and the number of simulated agents, an ABMS application may require a significant amount of computational resources. It makes them good candidates to be parallelized on HPC systems. However, most developers of ABMS simulators are experts in the specific simulation domain, but they lack the expertise to develop parallel applications. Consequently, several frameworks for generating HPC ABMS applications have been developed, and it may now be challenging for these non-expert users to choose which of these frameworks would provide the best performing simulator for a particular model. This paper presents a methodology that uses a benchmark to help non-expert users to select the most suitable framework to generate the best performing parallel implementation for a given ABMS model. Such a benchmark considers the common characteristics of parallel ABMS applications and includes parameters for influencing their relevant performance aspects. The methodology is based on defining a set of problem classes that represent the majority of known ABMS models and systematically conducting a series of experiments to determine which framework offers the best performance for each class. Then, users only need to identify the class that closely aligns with their model to make an informed decision regarding the appropriate development framework. The methodology is used to assess well-known ABMS parallel development frameworks (FLAME, RepastHPC, and DMASON) on real HPC platforms. The obtained results are validated using a real application for infection and contact tracing modeling.

Beam shaping for two-dimensional laser array by a computational model

The application of two-dimensional array laser source is more and more popular. Currently, many methods have been proposed for the collimation of laser beams; however, there is still a lack of research for the collimation of array laser sources so far. This paper presents a collimating method for array laser source. Through the calculation of the lens surfaces, the shape of the illumination spot on the target can be directly controlled, and the diameter of the lens can be directly constrained by setting the initial point coordinates. In this method, the edge-ray principle is used to simplify the extended light source model. The size and intensity distribution of the light spot irradiated by the light source to the target position were simulated by establishing a ray tracing model. The whole process was calculated using MATLAB software. The corresponding lens model can be obtained by fitting discrete coordinate points using Solid Works. Finally, the optical design software ZEMAX is used for simulation test. After shaping the laser light source with a luminous diameter of 1 mm, a circular and uniform spot is obtained at 100 m. For the point light source model, the divergence angle of 0.28° is achieved. Furthermore, an annular spot is also realized by setting the laser irradiation range, which provide a new solution for customized laser lighting.

Methodology for obtaining instantaneous thermal performance curves for a parabolic trough solar by experimental data and projections based on a ray tracing model

Parabolic Trough Concentration (PTC) systems represent the best alternative for implementing solar energy in different sectors due to their reachable temperatures and maturity levels. However, there is a notably lack of normativity for evaluating the experimental thermal performance curves of these systems. For this reason, a new methodology is proposed to solve problems arising in the thermal characterization of them. This methodology allows to evaluate the system at any time of year without a two-axis tracking system, using experimental data and projections based on a ray tracing model. To validate the methodology, a PTC system was evaluated during solstices and equinoxes. Variations in experimental thermal performance response along the year caused by solar declination; were observed, reaching a maximum loss of 45% of received power and 22% in efficiency for the lowest solar altitude conditions. The ray tracing model methodology was applied to the experimental thermal performance curves obtained along the year to get the best efficiency curve. The good approximation of the projections made with this methodology helped us to consider it validated. With this method, it is also possible to easily obtain a family of curves describing the power and performance of the system throughout the year and wherever it is installed.

Large-scale crops are sources of polyphagous pest populations for small farmers at the landscape level: an isotope tracing model

Large-scale crops are sources of polyphagous pest populations for small farmers at the landscape level: an isotope tracing model

Evolutionary Ensemble Learning Pathloss Prediction for 4G and 5G Flying Base Stations With UAVs

The usage of unmanned aerial vehicles (UAVs) as flying base stations (FBSs) for expanding coverage and assisting the terrestrial cellular networks constitutes a promising technology for 5G and beyond. A crucial parameter affecting cellular network design is path loss prediction. An alternative to the accurate, though time-consuming, propagation prediction with deterministic Ray Tracing models could be Machine Learning (ML) based predictions. Ensemble Learning techniques are used in order to optimally combine the predictions of standalone models. That is, they combine the best-performing individual models into a better-performing meta-model. Our proposed method of the evolutionary tuned stacked ensemble optimizes the ensemble as a whole, instead of optimizing its individual base learners. To the best of our knowledge, this is the first time that an evolutionary technique is applied in order to mutually tune an ensemble’s base learners for a path loss modeling problem in electromagnetics. Moreover, we present a model that works in more than one frequency. As opposed to the standard implementation of ensemble learning, our method offers a significant performance boost with low complexity.

SFBKT: A Synthetically Forgetting Behavior Method for Knowledge Tracing

Knowledge tracing (KT) aims to model students’ knowledge levels based on their historical learning records and predict their future learning performance, which constitutes an essential component of intelligent education. Learning and forgetting are closely related, and forgetting can often interfere with the learning process. Prior research has employed diverse techniques to address the issue of interference caused by forgetting factors in predictions, yet many of these methods fail to fully leverage the forgetting information contained within learning records. This paper proposes a synthetically forgetting behavior knowledge tracing (SFBKT) model that comprehensively models a student’s knowledge level by considering both individual forgetting factors and group status. Specifically, the model initially extracts forgetting information from exercise records in the input module, then updates the student’s knowledge state through an improved continuous-time long short-term memory network (CTLSTM), and finally combines the individual state with the group state using collaborative filtering to predict the student’s ability to correctly answer the next exercise. Our predictive model has been evaluated using four public education datasets. The experimental results indicate that our model’s predictions are effective and outperform other existing methods.

Deep Attentive Model for Knowledge Tracing

Knowledge Tracing (KT) is a crucial task in the field of online education, since it aims to predict students' performance on exercises based on their learning history. One typical solution for knowledge tracing is to combine the classic models in educational psychology, such as Item Response Theory (IRT) and Cognitive Diagnosis (CD), with Deep Neural Networks (DNN) technologies. In this solution, a student and related exercises are mapped into feature vectors based on the student's performance at the current time step, however, it does not consider the impact of historical behavior sequences, and the relationships between historical sequences and students. In this paper, we develop DAKTN, a novel model which assimilates the historical sequences to tackle this challenge for better knowledge tracing. To be specific, we apply a pooling layer to incorporate the student behavior sequence in the embedding layer. After that, we further design a local activation unit, which can adaptively calculate the representation vectors by taking the relevance of historical sequences into consideration with respect to candidate student and exercises. Through experimental results on three real-world datasets, DAKTN significantly outperforms state-of-the-art baseline models. We also present the reasonableness of DAKTN by ablation testing.

Improving Interpretability of Deep Sequential Knowledge Tracing Models with Question-centric Cognitive Representations

Knowledge tracing (KT) is a crucial technique to predict students’ future performance by observing their historical learning processes. Due to the powerful representation ability of deep neural networks, remarkable progress has been made by using deep learning techniques to solve the KT problem. The majority of existing approaches rely on the homogeneous question assumption that questions have equivalent contributions if they share the same set of knowledge components. Unfortunately, this assumption is inaccurate in real-world educational scenarios. Furthermore, it is very challenging to interpret the prediction results from the existing deep learning based KT models. Therefore, in this paper, we present QIKT, a question-centric interpretable KT model to address the above challenges. The proposed QIKT approach explicitly models students’ knowledge state variations at a fine-grained level with question-sensitive cognitive representations that are jointly learned from a question-centric knowledge acquisition module and a question-centric problem solving module. Meanwhile, the QIKT utilizes an item response theory based prediction layer to generate interpretable prediction results. The proposed QIKT model is evaluated on three public real-world educational datasets. The results demonstrate that our approach is superior on the KT prediction task, and it outperforms a wide range of deep learning based KT models in terms of prediction accuracy with better model interpretability. To encourage reproducible results, we have provided all the datasets and code at https://pykt.org/.

Precise modeling of learning process based on multiple behavioral features for knowledge tracing

With the increase in needs for personalized learning of online students, knowledge tracing (KT), a technique aimed at tracing the state of a student’s knowledge mastery and predicting performance in future exercises, has become a hot topic in personalized learning research. The behavioral features exhibited during students’ learning process bear information that impacts the state of a student’s knowledge mastery. To study the influence of learning behaviors on students’ knowledge mastery state in the learning process, we propose a Precise Modeling of Learning Process based on Multiple Behavioral Features for Knowledge Tracing model (MBFKT), which models a student’s learning process by making use of these behavioral features. MBFKT initially processes these features through multi-head attention networks, memory networks, and recurrent neural networks to model students’ learning process into three memory links: memory decline link, memory enhancement link, and memory update link. Various update strategies are designed for each memory link, and the performance of numerous possible combinations of behavioral features in the memory links is compared, for the rules of learning and forgetting to be explained. Furthermore, we also study the contribution and degree of influence of different behavioral features on a student’s knowledge mastery state, by which MBFKT is improved, thus enhancing the accuracy of prediction. Through experiments on real online education datasets and comparison with existing benchmark methods, it is observed that MBFKT has evident advantages in predicting performance with good interpretability.

Development of the Ames Global Hyperspectral Synthetic Data Set: Surface Bidirectional Reflectance Distribution Function

AbstractThis study introduces the Ames Global Hyperspectral Synthetic Data set (AGHSD), in particular the surface bidirectional reflectance distribution function (BRDF) product, to support the NASA Surface Biology and Geology (SBG) mission development. The data set is generated based on the corresponding multispectral BRDF products from NASA's MODIS satellite sensor. Based on theories of radiative transfer in vegetation canopies, we derive a simple but robust relationship that indicates that the hyperspectral surface BRDF can be accurately approximated as a weighted sum of the soil surface reflectance, the leaf single albedo, and the canopy scattering coefficient, where the weights or coefficients are spectrally invariant and thus readily estimated from the multispectral MODIS products. We validate the algorithm with simulations by a Monte Carlo Ray Tracing model and find the results highly consistent with the theoretic derivation. Using reflectance spectra of soil and vegetation derived from existing spectral libraries, we apply the algorithm to generate the AGHSD BRDF product at 1 km and 8‐day resolutions for the year of 2019. The data set is biogeochemically and biogeophysically coherent and consistent, and serves the goal to support the SBG community in developing sciences and applications for the future global imaging spectroscopy mission.

Improving Knowledge Tracing via Considering Two Types of Actual Differences From Exercises and Prior Knowledge

For offering adaptive learning to learners in intelligent tutoring systems, one of the fundamental tasks is knowledge tracing (KT), which aims to assess learners' learning states and make prediction for future performance. However, there are two crucial issues in deep learning-based KT models. First, the knowledge concepts are used to predict learners' performance instead of exercises. This choice ignores the actual difference among exercises, which leads to the KT model's inability to explore the rich information at the exercise level, thus weakening the KT model's performance. Second, the actual difference in prior knowledge is neglected. Therefore, in-depth research of these two types of the actual differences offers the possibility to improve the prediction performance of KT further. To this end, we propose a Fine-Grained Knowledge Tracing model (FGKT) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${}^{1}$</tex-math></inline-formula> to capture these two types of actual differences. We first obtain the exercise representations by considering the corresponding knowledge concepts and actual difference among exercises. Then, an effective attention mechanism is designed to capture the relevance between assessment exercises and historical interactions to acquire individual prior knowledge. Finally, we evaluate the proposed FGKT model on several available benchmark datasets. The experiment results show that our model surpasses benchmark KT models by up to 7% in prediction performance on the latest ASSISTments dataset.

Research on optimization and simulation of coordination of fuze and warhead for attack UAV based on damage trace model

Facing the needs UAV of achieving intelligent and efficient damage for attack UAV in combat operations, there is a lack of research on the coordination of fuze and warhead for attack UAV considering the damage model and terminal trajectory correction. This paper proposes a novel method of the coordination of fuze and warhead to optimize the damage efficiency. A pilot-fight coordination simulation experiment is conducted under a certain actual combat background in the end. The method firstly gets the optimal detonation position and attitude results based on the simulated annealing algorithm and damage trace model. Then based on the trajectory planning results using the three-dimensional Dubins algorithm, the autonomous maneuver to the optimal detonation position and attitude considering the maximization of the damage effect are realized. The process of initiating battle and coordination is verified, and the feasibility and optimization effect of the developed algorithm are demonstrated. The research of this paper will also provide a new solution for the attack UAV pilot-fight operation control technology in practical application.

An Improved 3D Indoor Positioning Study with Ray Tracing Modeling for 6G Systems

An Improved 3D Indoor Positioning Study with Ray Tracing Modeling for 6G Systems

Numerical study on the particle distribution of coal for recovery of critical metals

ABSTRACT Coal is considered to be a significant resource of critical metals and minerals. Recovery of these critical metals is considered to be highly important in terms of advancement and technology. This paper presents a numerical study of the particle size distribution of coal and its impact on the recovery of critical metals. This study uses the computational fluid dynamics (CFD) model to simulate the transport and deposition of coal particles inside a helical domain, which can be used as a device for separating particles. The numerical study , based on the particle tracing model for fluid flow and particle tracing, is used to simulate the behavior of coal particles in a slurry under different flow conditions. The transient study for a six-turn helical coil domain is analyzed for different flow velocities ranging from . The simulation results show that the particle size distribution of coal plays a crucial role in the recovery of critical metals. Additionally, the distribution of critical metals inside the domain varies with the size and density of the particles. It is observed that increasing the fluid velocity can significantly increase the recovery of critical metals. Finally, the transmission probability of particles remaining trapped inside the domain is also calculated, which could help in optimizing the helical geometry so that all the particles could come out of the flowing conduit. The number of particles coming out of the helical domain increases with the increase in flow rate. The results of the study show that the particle size distribution of coal plays a significant role in the recovery of critical metals. The findings can help guide the development of more efficient and cost-effective methods for extracting critical metals from coal.

River blockage and impulse wave evolution of the Baige landslide in October 2018: Insights from coupled DEM-CFD analyses

On 11 October 2018, the Baige landslide in Southwest China blocked the Jinsha River and induced waves amplifying the landslide-affected area significantly. Devastating flood damage was caused by the consequent dam breach. Such a complex sequence can lead to catastrophic consequences but has rarely been fully reproduced. This paper investigates the landslide-river interaction of the first Baige landslide based on coupled discrete element method (DEM) and computational fluid dynamics (CFD) analyses. To this end, the volume of fluid (VOF) and virtual sphere model are adopted to realise impacted river tracing and accurate terrain modelling. The damming process and impulse wave evolution of the first event are well represented. In addition, the simulated cumulative landslide spreading path, deposit geometry, maximum wave elevation and cumulative wave erosion area satisfactorily match the survey results. Our findings further indicate that the movement path and deposit morphology of the Baige landslide are mainly affected by local terrain, while the propagation of impulse waves is driven by the sliding mass and modulated by the riverbank and hydrodynamic conditions. In particular, we discussed the evolution patterns of impulse waves caused by river damming landslides, encompassing run-up on the opposite bank and quasi-3D propagation along the river direction. This research provides a valuable guide for the practical simulation of river blockage and impulse wave evolution and supports the mitigation of landslide disasters in mountainous areas.

Genetic models for lineage tracing in musculoskeletal development, injury, and healing

Musculoskeletal development and later post-natal homeostasis are highly dynamic processes, marked by rapid structural and functional changes across very short periods of time. Adult anatomy and physiology are derived from pre-existing cellular and biochemical states. Consequently, these early developmental states guide and predict the future of the system as a whole. Tools have been developed to mark, trace, and follow specific cells and their progeny either from one developmental state to the next or between circumstances of health and disease. There are now many such technologies alongside a library of molecular markers which may be utilized in conjunction to allow for precise development of unique cell ‘lineages’. In this review, we first describe the development of the musculoskeletal system beginning as an embryonic germ layer and at each of the key developmental stages that follow. We then discuss these structures in the context of adult tissues during homeostasis, injury, and repair. Special focus is given in each of these sections to the key genes involved which may serve as markers of lineage or later in post-natal tissues. We then finish with a technical assessment of lineage tracing and the techniques and technologies currently used to mark cells, tissues, and structures within the musculoskeletal system.