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This work reports a multiscale physics-based approach aimed at investigating the benefits of introducing a single tunnel junction (TJ) within conventional AlGaAs Vertical-Cavity Surface-Emitting Lasers (VCSELs). Our comprehensive VCSEL solver VENUS is augmented with a non-equilibrium Green's function (NEGF) approach to extract the band-to-band tunneling rate across the TJ. To showcase the NEGF-VENUS features, we apply it to the commercial pin oxide confined AlGaAs VCSEL previously investigated by VENUS, by inserting a TJ with minimal variations to the optical resonator. Besides finding the optimal position of TJ and oxide aperture, we can also compare the different hole injection schemes in the active region. Our results show the potential of doubling the maximum output power with the same threshold current, with perspectives of further enhancement by stacking more tunnel junctions.

To study the impact of complex climatic conditions on runoff and sediment, physics-based distributed hydrological models are frequently employed for representation and analysis. In this work, two physics-based hydro-sediment models, namely CASC2D-SED (CASCade 2 Dimensional SEDiment) and GSSHA (Gridded Surface Subsurface Hydrologic Analysis), are used to simulate runoff and sediment in a semi-arid watershed on the Loess Plateau. Examining the degree to which the two models provide comparable or dissimilar outcomes with regard to runoff and sediment casting is an aspect of this investigation that is of utmost importance. Both the models are derived from the principle of mass and energy balance, and arrived at very similar conclusions regarding the infiltration of precipitation and the transport of silt in surface runoff. For typical flood events that occurred between 1981 and 2017, a comparison of modeled data with measured runoff and sediment is carried out. Both models achieved satisfactory results when runoff and sediment simulations were performed over three separate time periods, with average NSE(Nash–Sutcliffe coefficient) values of runoff and sediment are 0.81 and 0.70 respectively. The GSSHA model performs better in terms of the maximum and total amount of runoff and sediment, with an 6.8%, 8.6%, and 6.7% increase, respectively, in the average Rq(relative error of peak flow) Rd(relative error of runoff depth), and Rs(relative error of peak sediment values). In contrast, the CASC2D-SED model simulates better in terms of the Tq(time difference of peak discharge), and Ts(time difference of peak sediment), with the mean time difference of peak flow and sediment values being 0.27 and 1.02 higher, respectively. This shows that the CASC2D-SED model can simulate the timing of runoff and sediment more accurate.

In this study, a stochastic tropical cyclone (TC) intensity model based on a deterministic fast physics-based TC intensity model is developed for potential applications in the TC hazard assessment. To account for the effect of randomness, an error term is introduced to the ordinary differential equation for the TC intensity. The error term is assumed to be the summation of a mean part and a white noise. The geographically weighted summary statistics method is adopted to estimate the geographically varying mean and white noise intensity of the error term using the 1,010 historical tropical cyclone records from the western North Pacific. The effects of the mean and random parts of the error term on the model's performance are investigated in terms of the individual samples and the probabilistic characteristics of TC intensity evolution. The results show that, by introducing the error term, the difference of the 25-, 50-, and 100-year recurrence interval values between the model and observation can be largely reduced from the level of 10% to less than 4%, validating the adequacy of the proposed model in the TC hazard assessment. [ABSTRACT FROM AUTHOR]

This paper provides an evaluation of a 4-kW grid-connected full-bridge PV inverter under three different scenarios to assess its reliability with a fixed PV degradation rate, with a climate-based degradation rate, and without considering PV degradation. The climate-based degradation rates are estimated using a physics-based model that considers the different parameters influencing the PV reliability. Three different locations representing three different climate zones (hot and dry, hot and humid, and moderate climates) have been chosen in this study. The estimated lifetime of the IGBT, the switching device in the PV inverter, varies depending on the location, with the inclusion of fixed and climate-based degradation rates extending the lifespan of the PV inverter in the examined locations. The results demonstrate the significant impact of PV climate-based degradation rates on power electronics' reliability assessment and the importance of considering various factors in predicting device failures. To ensure the PV inverter's lifespan over the desired period in areas with high solar irradiation rates and extremely hot climates, the design parameters should be slightly elevated above the standard value.

The intersection of physics and machine learning has given rise to the physics-enhanced machine learning (PEML) paradigm, aiming to improve the capabilities and reduce the individual shortcomings of data- or physics-only methods. In this paper, the spectrum of PEML methods, expressed across the defining axes of physics and data, is discussed by engaging in a comprehensive exploration of its characteristics, usage, and motivations. In doing so, we present a survey of recent applications and developments of PEML techniques, revealing the potency of PEML in addressing complex challenges. We further demonstrate the application of select such schemes on the simple working example of a single degree-of-freedom Duffing oscillator, which allows to highlight the individual characteristics and motivations of different “genres” of PEML approaches. To promote collaboration and transparency, and to provide practical examples for the reader, the code generating these working examples is provided alongside this paper. As a foundational contribution, this paper underscores the significance of PEML in pushing the boundaries of scientific and engineering research, underpinned by the synergy of physical insights and machine learning capabilities.

Seismic hazard estimation relies on Ground Motion Prediction Equations (GMPEs) giving the expected motion level as a function of several parameters characterizing the source and the sites of interest. However, in regions like Greater Tehran metropolitan area located in the Alborz seismotectonic province, northern Iran records of moderate to large earthquakes at short distances from the faults are still few and most local and regional GMPEs are poorly constrained at short ranges. In other words, data may only partially account for the rupture process, seismic wave propagation, and three-dimensional complex configurations (i.e., Tehran Basin in this case). Here, to investigate the capabilities of physics-based methods, two sets of 3-D numerical simulations of possible earthquakes scenarios in Tehran region along two predominate sources are carried out through the finite difference approach for low frequency motions. Then the stochastic finite fault method is used for quantifying ground motion values at higher frequencies. At last, the combined broadband simulation results are used in a probabilistic seismic hazard analysis. The seismic hazard results show the combined effects of the site and basin, and give a high-resolution representation of the hazard in the near field of active earthquake faults in the Tehran Basin, particularly over long periods. This representation is anticipated to be more accurate than those based simply on empirical GMPEs. It should be noted that effects of other less important surrounding faults are not included in the hazard analysis due to lack of sufficient data and computational limitations. [ABSTRACT FROM AUTHOR]

Tunnelling-induced ground deformations inevitably affect the safety of adjacent infrastructures. Accurate prediction of tunnelling-induced deformations is of great importance to engineering construction, which has historically been dependent on numerical simulations or field measurements. Recently, some surrogate models originating from machine learning methods have been developed, showing satisfactory prediction performance with high computational efficiency. However, these purely data-driven models show weak robustness in the absence of sufficient training data. This study proposed a hybrid deep learning model integrating both data-driven and physics-based strategies to decrease calculation costs and eliminate the dependence on large numbers of training data. The underlying physical mechanism of ground deformation due to tunnel excavation is coupled into the deep learning framework to form a physics-informed neural network (PINN) model. The performance of the hybrid model is first assessed by comparing it with the classical Verruijt-Booker solution and a conventional purely data-driven model. The potential of the proposed PINN model for engineering applications is then illustrated. It is found that the proposed PINN model can reasonably reproduce ground deformation fields obtained numerically with only a small amount of training data. This paper provides a new paradigm for incorporating hybrid deep learning frameworks and field monitoring systems to predict ground deformation fields in real time. [ABSTRACT FROM AUTHOR]

In scientific disciplines, benchmarks play a vital role in driving progress forward. For a benchmark to be effective, it must closely resemble real-world tasks. If the level of difficulty or relevance is inadequate, it can impede progress in the field. Moreover, benchmarks should have low computational overhead to ensure accessibility and repeatability. The objective is to achieve a kind of ``Turing test'' by creating a surrogate model that is practically indistinguishable from the ground truth observation, at least within the dataset's explored boundaries. This objective necessitates a large quantity of data. This data encompasses numerous features that are characteristic of chemistry and materials science optimization tasks that are relevant to industry. These features include high levels of noise, multiple fidelities, multiple objectives, linear constraints, non-linear correlations, and failure regions. We performed 494498 random hard-sphere packing simulations representing 206 CPU days’ worth of computational overhead. Simulations required nine input parameters with linear constraints and two discrete fidelities each with continuous fidelity parameters. The data was logged in a free-tier shared MongoDB Atlas database, producing two core tabular datasets: a failure probability dataset and a regression dataset. The failure probability dataset maps unique input parameter sets to the estimated probabilities that the simulation will fail. The regression dataset maps input parameter sets (including repeats) to particle packing fractions and computational runtimes for each of the two steps. These two datasets were used to create a surrogate model as close as possible to running the actual simulations by incorporating simulation failure and heteroskedastic noise. In the regression dataset, percentile ranks were calculated for each group of identical parameter sets to account for heteroskedastic noise, thereby ensuring reliable and accurate data. This differs from the conventional approach that imposes a-priori assumptions, such as Gaussian noise, by specifying mean and standard deviation. This technique can be extended to other benchmark datasets to bridge the gap between optimization benchmarks with low computational overhead and the complex optimization scenarios encountered in the real world.

The marine engine is a complex-structured multidisciplinary system that operates in a harsh environment involving high temperatures and pressures and gas/fluid/solid interactions. Many malfunctions and faults can occur to the marine engine and efficient condition monitoring is critical to ensure the expected performance. In this paper, a marine engine test rig is established and its process data are recorded, including various temperatures and pressures. Two data-driven models, i.e., principal component analysis and the sparse autoencoder, and a physics-based model are applied to the marine engine for two classic faults, i.e., lubrication oil filter blocking and cylinder leakage. Comparative studies and discussions are conducted regarding their performance in terms of anomaly detection and fault isolation. The data points collected for the filter blocking fault are generally two times higher than the fault thresholds set by the data-driven models. In the physics-based model, it is observed that the lubrication oil pressure falls from the predicted 3.2–3.8 bar to around 2.3 bar. For the cylinder leakage fault, the fault test data are nearly four times higher than the thresholds in the data-driven models. The exhaust gas temperature of the leaked cylinder falls from an estimated 150–200 °C to about 100 °C. The transferability and interpretability of these models are finally discussed. The findings of the present study offer insights into the two types of models and can provide guidance for the effective condition monitoring of marine engines. [ABSTRACT FROM AUTHOR]

The present research proposes a two-step physics- and machine-learning(ML)-based electromechanical impedance (EMI) measurement data evaluation approach for sandwich face layer debonding detection and size estimation in structural health monitoring (SHM) applications. As a case example, a circular aluminum sandwich panel with idealized face layer debonding was used. Both the sensor and debonding were located at the center of the sandwich. Synthetic EMI spectra were generated by a finite-element(FE)-based parameter study, and were used for feature engineering and ML model training and development. Calibration of the real-world EMI measurement data was shown to overcome the FE model simplifications, enabling their evaluation by the found synthetic data-based features and models. The data preprocessing and ML models were validated by unseen real-world EMI measurement data collected in a laboratory environment. The best detection and size estimation performances were found for a One-Class Support Vector Machine and a K-Nearest Neighbor model, respectively, which clearly showed reliable identification of relevant debonding sizes. Furthermore, the approach was shown to be robust against unknown artificial disturbances, and outperformed a previous method for debonding size estimation. The data and code used in this study are provided in their entirety, to enhance comprehensibility, and to encourage future research. [ABSTRACT FROM AUTHOR]

As the level of complexity of modern rotating machinery grows, the need for an effective and efficient maintenance process increases as well. In the last decade, researchers all over the world have shown strong aspiration to optimize the diagnostics phase in rotating machinery. This paper highlights some of the latest research on the detection of typical faults in rotating machinery such as mass rotor imbalance, misalignment, rub and looseness, bearing and gear faults. Various techniques for condition monitoring have been researched, and in this paper, they have been classified into three groups: physics-based, data-driven, and physics-based data-driven methods. Although most of the research falls into the first two prior mentioned groups, an intent to introduce a novel method, their symbiosis, has emerged in the last few years. The great potential for future work on physics-based data-driven methods in the field of rotating machinery has been briefly discussed. [ABSTRACT FROM AUTHOR]

Urban building energy modeling (UBEM) is a practical approach in large-scale building energy modeling for stakeholders in the energy industry to predict energy use in the building sector under different design and retrofit scenarios. UBEM is a relatively new large-scale building energy modeling (BEM) approach which raises different challenges and requires more in-depth study to facilitate its application. This paper performs a systematic literature review on physics-based modeling techniques, focusing on assessing energy conservation measures. Different UBEM case studies are examined based on the number and type of buildings, building systems, occupancy schedule modeling, archetype development, weather data type, and model calibration methods. Outcomes show that the existing tools and techniques can successfully simulate and assess different energy conservation measures for a large number of buildings. It is also concluded that standard UBEM data acquisition and model development, high-resolution energy use data for calibration, and open-access data, especially in heating and cooling systems and occupancy schedules, are among the biggest challenges in UBEM adoption. UBEM research studies focused on developing auto-calibration routines, adding feedback loops for real-time updates, future climate data, and sensitivity analysis on the most impactful modeling inputs should be prioritized for future research. [ABSTRACT FROM AUTHOR]

In this article, we present a tutorial overview of state-of-the-art radio frequency (RF) clutter modeling and simulation techniques. Traditional statistical approximation-based methods will be reviewed followed by more accurate physics-based stochastic transfer function clutter models that facilitate site-specific simulations anywhere on earth. The various factors that go into the computation of these transfer functions will be presented, followed by several examples across multiple RF applications. Finally, we introduce a radar challenge dataset generated using these tools that can enable testing and benchmarking of all cognitive radar algorithms and techniques. [ABSTRACT FROM AUTHOR]

Animating human face presents interesting challenges because of its familiarity as the face is the part utilized to recognize individuals. This paper reviewed the approaches used in facial modeling and animation and described their strengths and weaknesses. Realistic face animation of computer graphic models of human faces can be hard to achieve as a result of the many details that should be approximated in producing realistic facial expressions. Many methods have been researched to create more and more accurate animations that can efficiently represent human faces. We described the techniques that have been utilized to produce realistic facial animation. In this survey, we roughly categorized the facial modeling and animation approach into the following classes: blendshape or shape interpolation, parameterizations, facial action coding system-based approaches, moving pictures experts group-4 facial animation, physics-based muscle modeling, performance driven facial animation, visual speech animation. [ABSTRACT FROM AUTHOR]

The marine engine is a complex-structured multidisciplinary system that operates in a harsh environment involving high temperatures and pressures and gas/fluid/solid interactions. Many malfunctions and faults can occur to the marine engine and efficient condition monitoring is critical to ensure the expected performance. In this paper, a marine engine test rig is established and its process data are recorded, including various temperatures and pressures. Two data-driven models, i.e., principal component analysis and the sparse autoencoder, and a physics-based model are applied to the marine engine for two classic faults, i.e., lubrication oil filter blocking and cylinder leakage. Comparative studies and discussions are conducted regarding their performance in terms of anomaly detection and fault isolation. The data points collected for the filter blocking fault are generally two times higher than the fault thresholds set by the data-driven models. In the physics-based model, it is observed that the lubrication oil pressure falls from the predicted 3.2–3.8 bar to around 2.3 bar. For the cylinder leakage fault, the fault test data are nearly four times higher than the thresholds in the data-driven models. The exhaust gas temperature of the leaked cylinder falls from an estimated 150–200 °C to about 100 °C. The transferability and interpretability of these models are finally discussed. The findings of the present study offer insights into the two types of models and can provide guidance for the effective condition monitoring of marine engines.

The present research proposes a two-step physics- and machine-learning(ML)-based electromechanical impedance (EMI) measurement data evaluation approach for sandwich face layer debonding detection and size estimation in structural health monitoring (SHM) applications. As a case example, a circular aluminum sandwich panel with idealized face layer debonding was used. Both the sensor and debonding were located at the center of the sandwich. Synthetic EMI spectra were generated by a finite-element(FE)-based parameter study, and were used for feature engineering and ML model training and development. Calibration of the real-world EMI measurement data was shown to overcome the FE model simplifications, enabling their evaluation by the found synthetic data-based features and models. The data preprocessing and ML models were validated by unseen real-world EMI measurement data collected in a laboratory environment. The best detection and size estimation performances were found for a One-Class Support Vector Machine and a K-Nearest Neighbor model, respectively, which clearly showed reliable identification of relevant debonding sizes. Furthermore, the approach was shown to be robust against unknown artificial disturbances, and outperformed a previous method for debonding size estimation. The data and code used in this study are provided in their entirety, to enhance comprehensibility, and to encourage future research.

The advancement of three-dimensional, time-dependent fire behaviour models is best supported by publicly available, co-located, synchronised, quality-assured measures of pre-fire, active fire and post-fire conditions (i.e. integrated datasets). Currently, there is a lack of such datasets. Consequently, we discuss essential components to produce integrated datasets: metadata, implementation of geospatial and temporal standards, data management plans, quality assurance project plans and data quality objectives. We present example data quality objectives and a data model for grassland experiments developed based on our experience integrating data from the 2014 Camp Swift Fire and the 2012 Prescribed Fire Combustion and Atmospheric Dynamics Research experiments. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid method to assess the rail health with the focus on a specific type of rail surface defect called head check. The proposed method uses physics-based and data-driven models in order to model defect initiation and defect evolution on a rail for a given rail traffic tonnage. Ultrasonic (US) and Eddy Current (EC) defect detection measurements are used to provide Infrastructure Managers (IMs) with insight in the current rail condition. The defect initiation results obtained from the first part of the hybrid method which consists of the physics-based model is successfully validated with the EC measurements. Furthermore, the US and EC measurements are utilized to derive a data-driven model for defect evolution. Finally, a set of robust and predictive Key Performance Indicators (KPIs) are proposed to quantify the future condition of the rail based on different characteristics of rail health resulting from the defect initiation and defect evolution analysis. [ABSTRACT FROM AUTHOR]

Urban building energy modeling (UBEM) is a practical approach in large-scale building energy modeling for stakeholders in the energy industry to predict energy use in the building sector under different design and retrofit scenarios. UBEM is a relatively new large-scale building energy modeling (BEM) approach which raises different challenges and requires more in-depth study to facilitate its application. This paper performs a systematic literature review on physics-based modeling techniques, focusing on assessing energy conservation measures. Different UBEM case studies are examined based on the number and type of buildings, building systems, occupancy schedule modeling, archetype development, weather data type, and model calibration methods. Outcomes show that the existing tools and techniques can successfully simulate and assess different energy conservation measures for a large number of buildings. It is also concluded that standard UBEM data acquisition and model development, high-resolution energy use data for calibration, and open-access data, especially in heating and cooling systems and occupancy schedules, are among the biggest challenges in UBEM adoption. UBEM research studies focused on developing auto-calibration routines, adding feedback loops for real-time updates, future climate data, and sensitivity analysis on the most impactful modeling inputs should be prioritized for future research.

Models of physical phenomena can be developed using two distinct approaches: using expert knowledge of the underlying physical principles or using experimental data to train a neural network. Here, our aim was to better understand the advantages and disadvantages of these two approaches. We chose to model cycling power because the physical principles are already well understood. Nine participants followed changes in cycling cadence transmitted through a metronome via earphones and we measured their cadence and power. We then developed and trained a physics-based model and a simple neural network model, where both models had cadence, derivative of cadence, and gear ratio as input, and power as output. We found no significant differences in the prediction performance between the models. Both models had good prediction accuracy despite using less input variables than traditional models and using more challenging prediction conditions by enforcing rapid speed changes during cycling. The advantages of the neural network model were that, for similar performance, it did not require an understanding of the underlying principles of cycling nor did it require measurements of fixed parameters such as system weight or wheel size. These same features also give the physics-based model the advantage of interpretability, which can be important when scientists want to better understand the process being modelled. [ABSTRACT FROM AUTHOR]

Manufacturing evolved substantially over the years and with it the inherent complexity increased well. Currently organizations are transitioning towards smart manufacturing and implementation of industry 4.0 paradigms. Artificial intelligence and machine learning can help organizations of any size on this journey. The physics of manufacturing processes have been extensively studied and documented as relationships and equations. Machine learning algorithms rely on quality data to enable inferences and smart actions. At the intersection of these two methods lies hybrid analytics which captures the knowledge in the defined physics-based relationships and uses data-driven methods to fill the gaps that the physics-based model may have. The goal of this paper is to identify approaches and use cases of hybrid modeling wherein the term hybrid is used to indicate the combination of a physics- or first-principle-based modeling component and a data-driven modeling component. VOSViewer, Voyant Tools, and Microsoft Excel have been used to understand the relationships between various attributes associated with the articles on hybrid modeling. The papers cited the most, the organizations/regions that funded the research in this domain, the contents of recent papers, and what categories and keywords are frequently used are discussed. This work is the basis for an extended literature review to understand in detail the algorithms, use cases, and implementation issues of hybrid analytics. This bibliometric analysis uses the state of the art references and procedures mentioned in the documentation of VOSViewer and Voyant Tools. [ABSTRACT FROM AUTHOR]

Digital twins have advanced fast in various industries, but are just emerging in postharvest supply chains. A digital twin is a virtual representation of a certain product, such as fresh horticultural produce. This twin is linked to the real-world product by sensors supplying data of the environmental conditions near the target fruit or vegetable. Statistical and data-driven twins quantify how quality loss of fresh horticultural produce occurs by grasping patterns in the data. Physics-based twins provide an augmented insight into the underlying physical, biochemical, microbiological and physiological processes, enabling to explain also why this quality loss occurs. We identify what the key advantages are of digital twins and how the supply chain of fresh horticultural produce can benefit from them in the future. A digital twin has a huge potential to help horticultural produce to tell its history as it drifts along throughout its postharvest life. The reason is that each shipment is subject to a unique and unpredictable set of temperature and gas atmosphere conditions from farm to consumer. Digital twins help to identify the resulting, largely uncharted, postharvest evolution of food quality. The benefit of digital twins particularly comes forward for perishable species and at low airflow rates. Digital twins provide actionable data for exporters, retailers, and consumers, such as the remaining shelf life for each shipment, on which logistics decisions and marketing strategies can be based. The twins also help diagnose and predict potential problems in supply chains that will reduce food quality and induce food loss. Twins can even suggest preventive shipment-tailored measures to reduce retail and household food losses. • We highlight differences between physics-based and data-driven digital twins. • Digital twins help tailor supply chains to maximize shelf life and reduce food losses. • Digital twins convert sensor data to predict postharvest evolution of food quality. • Digital twins provide actionable data for exporters, retailers and consumers. • Validation is essential to guarantee future trust in digital twins. [ABSTRACT FROM AUTHOR]

Due to the advancements in sensing technologies and computational capabilities, system health assessment and prognostics have been extensively investigated in the literature. Industry has adopted and implemented many advanced system prognostic applications. This article reviews recent research advances and applications in prognostics modeling methods for engineering systems. The reviewed papers are classified into three major areas based on whether the physics of failure knowledge is incorporated for prognostics, i.e., the data-driven, physics-based, and hybrid prognostic methods. The technical merits and limitations of each prognostic method are discussed. This review also summarizes research and technological challenges in engineering system prognostics, and points out future research directions. [ABSTRACT FROM AUTHOR]

This work is devoted to the description and comparative study of some methods of mathematical modeling. We consider methods that can be applied for building cyber-physical systems and digital twins. These application areas add to the usual accuracy requirements for a model the need to be adaptable to new data and the small computational complexity allows it to be used in embedded systems. First, we regard the finite element method as one of the “pure” physics-based modeling methods and the general neural network approach as a variant of machine learning modeling with physics-based regularization (or physics-informed neural networks) and their combination. A physics-based network architecture model class has been developed by us on the basis of a modification of classical numerical methods for solving ordinary differential equations. The model problem has a parameter at some values for which the phenomenon of stiffness is observed. We consider a fixed parameter value problem statement and a case when a parameter is one of the input variables. Thus, we obtain a solution for a set of parameter values. The resulting model allows predicting the behavior of an object when its parameters change and identifying its parameters based on observational data.

This paper provides an evaluation of a 4-kW grid-connected full-bridge PV inverter under three different scenarios to assess its reliability with a fixed PV degradation rate, with a climate-based degradation rate, and without considering PV degradation. The climate-based degradation rates are estimated using a physics-based model that considers the different parameters influencing the PV reliability. Three different locations representing three different climate zones (hot and dry, hot and humid, and moderate climates) have been chosen in this study. The estimated lifetime of the IGBT, the switching device in the PV inverter, varies depending on the location, with the inclusion of fixed and climate-based degradation rates extending the lifespan of the PV inverter in the examined locations. The results demonstrate the significant impact of PV climate-based degradation rates on power electronics' reliability assessment and the importance of considering various factors in predicting device failures. To ensure the PV inverter's lifespan over the desired period in areas with high solar irradiation rates and extremely hot climates, the design parameters should be slightly elevated above the standard value., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Omid Alavi reports a relationship with Hasselt University that includes: employment., (© 2024 The Authors.)

Deep learning MRI reconstruction methods are often based on Convolutional neural network (CNN) models; however, they are limited in capturing global correlations among image features due to the intrinsic locality of the convolution operation. Conversely, the recent vision transformer models (ViT) are capable of capturing global correlations by applying self-attention operations on image patches. Nevertheless, the existing transformer models for MRI reconstruction rarely leverage the physics of MRI. In this paper, we propose a novel physics-based transformer model titled, the Multi-branch Cascaded Swin Transformers (McSTRA) for robust MRI reconstruction. McSTRA combines several interconnected MRI physics-related concepts with the Swin transformers: it exploits global MRI features via the shifted window self-attention mechanism; it extracts MRI features belonging to different spectral components via a multi-branch setup; it iterates between intermediate de-aliasing and data consistency via a cascaded network with intermediate loss computations; furthermore, we propose a point spread function-guided positional embedding generation mechanism for the Swin transformers which exploit the spread of the aliasing artifacts for effective reconstruction. With the combination of all these components, McSTRA outperforms the state-of-the-art methods while demonstrating robustness in adversarial conditions such as higher accelerations, noisy data, different undersampling protocols, out-of-distribution data, and abnormalities in anatomy., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

In this paper, a physics-based compact model for the longitudinal and transverse stress profile in the channel of an uniaxially strained bulk MOS transistor is presented. The stress in the channel of a MOS transistor is not uniform and this nonuniform stress distribution results in higher average channel stress with reduction in the gate length. The developed model accurately predicts the average channel stress for different stress liners and transistor dimensions like gate length, gate height, and spacer width. The modeled average stress is then used to calculate the strain induced threshold voltage shift in HKMG nMOS transistors for different stress liners (fixed transistor dimensions) and for different transistor dimensions (fixed stress liner). The accuracy of the model is verified by comparing the threshold voltage shift with the experimental data obtained from the transistors fabricated in the 28 nm HKMG CMOS technology.

In order to reduce the data size and simplify the process of creating characters’ 3D models, a new and interactive ordinary differential equation (ODE)-based C2 continuous surface creation algorithm is introduced in this paper. With this approach, the creation of a three-dimensional surface is transformed into generating two boundary curves plus four control curves and solving a vector-valued sixth order ordinary differential equation subjected to boundary constraints consisting of boundary curves, and first and second partial derivatives at the boundary curves. Unlike the existing patch modeling approaches which require tedious and time-consuming manual operations to stitch two separate patches together to achieve continuity between two stitched patches, the proposed technique maintains the C2 continuity between adjacent surface patches naturally, which avoids manual stitching operations. Besides, compared with polygon surface modeling, our ODE C2 surface creation method can significantly reduce and compress the data size, deform the surface easily by simply changing the first and second partial derivatives, and shape control parameters instead of manipulating loads of polygon points.

Dynamic virtual plant simulation is an attractive research issue in both botany and computer graphics. Data-driven method is an efficient way for motion analysis and animation synthesis. As a widely used tool, motion capture has been used in plant motion data acquisition and analysis. The most prominent and important problem in motion capture for plants is primary data processing such as missing markers reconstruction. This paper presents a novel physics-based approach to motion capture data processing of plants. Firstly, a physics-based mechanics model is found by Lagrangian mechanics for a motion captured plant organ such as a leaf, and then its dynamic mechanical properties are analyzed and relevant model parameters are evaluated. Further, by using the physical model with evaluated parameters, we can calculate the next positions of a maker to reconstruct the missing makers in motion capture sequence. We take an example of a maize leaf and pachira leaf to examine the proposed approach, and the results show that the physics-based method is feasible and effective for plant motion data processing. [ABSTRACT FROM AUTHOR]

Context Thinning is considered useful in altering fire behavior, reducing fire severity, and restoring resilient ecosystems. Yet, few tools currently exist that enable detailed analysis of such efforts. Aims The study aims to describe and demonstrate a new modeling system. A second goal is to put its capabilities in context of previous work through comparisons with established models. Methods The modeling system, built in Python and Java, uses data from a widely used forest model to develop spatially explicit fuel inputs to two 3D physics-based fire models. Using forest data from three sites in Montana, USA, we explore effects of thinning on fire behavior and fire effects and compare model outputs. Results The study demonstrates new capabilities in assessing fire behavior and fire effects changes from thinning. While both models showed some increases in fire behavior relating to higher winds within the stand following thinning, results were quite different in terms of tree mortality. These different outcomes illustrate the need for continuing refinement of decision support tools for forest management. Conclusion This system enables researchers and managers to use measured forest fuel data in dynamic, 3D fire simulations, improving capabilities for quantitative assessment of fuel treatments, and facilitating further refinement in physics-based fire modeling. [ABSTRACT FROM AUTHOR]