Transient Data Research Articles

Femtosecond Infrared Spectroscopy Resolving the Multiplicity of High-Spin Crossover States in Transition Metal Iron Complexes.

Tuning the photophysical properties of iron-based transition-metal complexes is crucial for their employment as photosensitizers in solar energy conversion. For the optimization of these new complexes, a detailed understanding of the excited-state deactivation paths is necessary. Here, we report femtosecond transient mid-IR spectroscopy data on a recently developed octahedral ligand-field enhancing [Fe(dqp)2]2+ (C1) complex with dqp = 2,6-diquinolylpyridine and prototypical [Fe(bpy)3]2+ (C0). By combining mid-IR spectroscopy with quantum chemical DFT calculations, we propose a method for disentangling the 5Q1 and 3T1 multiplicities of the long-lived metal-centered (MC) states, applicable to a variety of metal-organic iron complexes. Our results for C0 align well with the established assignment toward the 5Q1, validating our approach. For C1, we find that deactivation of the initially excited metal-to-ligand charge-transfer state leads to a population of a long-lived MC 5Q1 state. Analysis of transient changes in the mid-IR shows an ultrafast sub 200 fs rearrangement of ligand geometry for both complexes, accompanying the MLCT → MC deactivation. This confirms that the flexibility in the ligand sphere supports the stabilization of high spin states and plays a crucial role in the MLCT lifetime of metal-organic iron complexes.

Data-driven model order reduction for sensor positioning and indirect reconstruction with noisy data: Application to a Circulating Fuel Reactor

Sensor positioning and real-time estimation of non-observable fields is an open question in the nuclear sector, especially for advanced nuclear reactors. In Circulating Fuel Reactors (CFR), liquid fuel and coolant are homogeneously mixed, and thus these reactors will not have internal structures, making sensor positioning in the primary circuit, including the core, an unresolved problem, making most of the core blind to sensors. Thus, the possibility of estimating the system state in the whole domain using a few local measurements has important implications for safety, monitoring, and control both in nominal and accidental conditions. In this context, the integrated Model Order Reduction and Data Assimilation framework offers intriguing opportunities to reliably combine experimental data and background knowledge from a reduced mathematical model. This work discusses and applies innovative methods within this framework, based on the Generalized Empirical Interpolation and the Indirect Reconstruction algorithms, to a proposed concept of CFR. This work aims to identify the optimal sensor positioning within the core and assess the feasibility of reconstructing the quantities of interest starting only from transient sparse data on fuel temperature, possibly noisy, and testing the predictive capabilities of the discussed methods.

A Pan-STARRS1 Search for Planet Nine

We present a search for Planet Nine using the second data release of the Pan-STARRS1 survey. We rule out the existence of a Planet Nine with the characteristics of that predicted in Brown & Batygin to a 50% completion depth of V = 21.5. This survey, along with previous analyses of the Zwicky Transient Facility and Dark Energy Survey data, rules out 78% of the Brown & Batygin parameter space. Much of the remaining parameter space is at V > 21 in regions near and in the area where the northern galactic plane crosses the ecliptic.

Integrated algorithm evaluation of a scalable digital power management innovation model

Maintaining contemporary society’s steady progress depends on the reliable functioning of the electrical grid. The complexity and nonlinearity of today’s power networks make it difficult to manage them using conventional mathematical modeling approaches. To get around these problems, researchers have started using machine learning methods, such transient stability evaluation that is based on machine learning. This research suggests a way to evaluate the transient stability of power systems by making use of the complex feature extraction capabilities of multi-layer perceptron (MLP) networks. Another approach is to suggest an ensemble learning model that uses the easy-ensemble undersampling technique to deal with power system transient stability data that is unbalanced. This method preserves unstable power system samples, performs multiple undersampling on stable samples and combines unstable and undersampling samples into multiple new balanced training sets for MLP model training. The final ensemble model is obtained through voting integration strategy. Through conducting a comparative evaluation of individual models and ensemble models, this paper discovers that the ensemble learning models exhibit superior sensitivity and accuracy in effectively addressing system instability.

Climate change signals of extreme precipitation return levels for Germany in a transient convection‐permitting simulation ensemble

AbstractThe increase in extreme precipitation with global warming (GW) and associated uncertainties are major challenges for climate adaptation. To project future extreme precipitation on different time and intensity scales (return periods [RPs] from 1 to 100 a and durations from 1 h to 3 days), we use a novel convection‐permitting (CP), multi‐global climate model ensemble of COSMO‐CLM regional simulations with a transient projection time (1971–2100) over Germany. We find an added value of the CP scale (2.8 km) with respect to the representation of hourly extreme precipitation intensities compared to the coarser scale with parametrized deep convection (7 km). In general, the return levels (RLs) calculated from the CP simulations are in better agreement with those of the conventional observation‐based risk products for the region for short event durations than for longer durations, where an overestimation by the simulation‐based results was found. A maximum climate change signal of 6–8.5% increase per degree of GW is projected within the CP ensemble, with the largest changes expected for short durations and long RPs. Analysis of the uncertainty in the climate change signal shows a substantial residual standard deviation of a linear approximation, highlighting the need for transient data sets instead of time‐slice experiments to increase confidence in the estimates. Furthermore, the ensemble spread is found to be smallest for intensities of short duration, where changes are expected to be based mainly on thermodynamic contributions. The ensemble spread is larger for long, multi‐day durations, where a stronger dependence on the dynamical component is ascribed. In addition, an increase in spatial variance of the RLs with GW implies a more variable future climate and points to an increasing importance of accounting for uncertainties.

Quasi-2D inversion of surface large fixed-loop transient electromagnetic sounding data

Abstract In many cases, 1D inversion is still an important step in transient electromagnetic data processing. Potential issues may arise in the calculation of apparent resistivity using induced electromotive force (EMF) due to overshoot and the presence of multi-valued functions. Obtaining reliable and consistent inversion results using a uniform half-space as the initial model is challenging, especially when aiming for efficient inversion. Focusing on these problems, we use the land-based transient electromagnetic (TEM) sounding data, which was acquired by using a large fixed-loop transmitter, and adopt a quasi-2D inversion scheme to generate improved images of the subsurface resistivity structure. First, we have considered directly using magnetic field data or converting induced EMF into magnetic field, and then calculating the apparent resistivity over the whole zone. Next, a resistivity profile that varies with depth is obtained through fast smoke ring imaging. This profile serves as the initial model for the subsequent optimal inversion. The inversion scheme uses a nonlinear least-squares method, incorporating lateral and vertical constraints, to produce a quasi-2D subsurface image. The potentiality of the proposed methodology has been exemplified through the interpretation of synthetic data derived from a 3D intricate resistivity model, as well as field data obtained from a TEM survey conducted in a coalmine field. In both cases, the inversion process yields quasi-2D subsurface images that exhibit a reasonable level of accuracy. These images appear to be less moulded by 3D effects and demonstrate a satisfactory level of agreement with the known target area.

A293 PREVALENCE OF METABOLIC ASSOCIATED STEATOTIC LIVER DISEASE AND ITS IMPACT ON ADVERSE OUTCOMES IN PRIMARY BILIARY CHOLANGITIS PATIENTS

Abstract Background Metabolic associated steatotic liver disease (MASLD) is highly prevalent among Canadians, thus making coexistence with other liver diseases inevitable. Primary biliary cholangitis (PBC) is the most common autoimmune liver disease diagnosed in one out of 1,000 women above 40. The impact of concomitant MASLD on liver outcomes in patients with PBC is unclear. Aims We aimed to determine the prevalence of MASLD among patients with PBC and the association between concomitant liver disease and adverse liver outcomes. Methods PBC patients diagnosed between 1984 and 2023 and followed up at the University of Alberta with vibration-controlled transient elastography data available were included. MASLD was diagnosed if the Controlled Attenuation Parameter ≥288 dB/min with at least one cardio-metabolic criteria, based on elevated body mass index, blood pressure, and abnormal glycemic and lipid tests. Proportions were compared using chi-square, medians – Mann-Whitney test. We utilized a Cox hazards regression model to calculate hazard ratios and determine associations between MASLD and the development of cirrhosis or decompensation, need for liver transplant, or death. Results A total of 115 patients (87.0% females, age at diagnosis 52.3±11.5 years) followed over a median duration of 11.0 [range 0.1-39.7] years) were included. The prevalence of MASLD (33.0% [n=38]) was higher in females than males (35.0%[n=35] vs. 20.0%[n=3]; p=0.249). The prevalence of cirrhosis at diagnosis was twice as high among PBC patients with MASLD than those without (26.3%[n=10] vs. 13.0%[n=10]; p=0.076). No difference was observed in the frequency of decompensation at diagnosis (8.8%[n=3] vs. 6.3%[n=4]; p=0.638). Cirrhosis frequency (50.0% vs. 50.6%; p=0.948) and decompensation (38.2% vs.37.1%; p=0.912) over the course of the disease were similar between PBC patients with and without MASLD. There was no association between MASLD and development of cirrhosis (HR0.51, 95%CI 0.23-1.13; p=0.095), decompensation (HR0.81, 95%CI 0.37-1.76; p=0.596), liver transplantation (HR0.72, 95%CI 0.19-2.72; p=0.628), or death (HR0.69, 95%CI 0.22-2.16; p=0.519). PBC patients with MASLD have comparable event-free (median 10.7 [3.1-30.8] vs. 10.7 [0.1-23.3] years; p=0.684) and overall survival (median 11.3 [3.2-39.7 vs. 10.7 [0.1-23.3] years; p=0.456). Conclusions Every third patient with PBC had MASLD, which tended to be more prevalent among females. We did not observe any significant differences in the development of adverse liver outcomes between patients with PBC and concurrent MASLD in comparison to those without. Studies with larger sample sizes are necessary to further explore the potential impact of MASLD on liver outcomes. Funding Agencies None

Experimental investigation on temperature-dependent effective thermal conductivity of ceramic fiber felt

This paper presents a method to invert the effective thermal conductivity of ceramic fiber felt under high temperatures using experimentally measured specific heat capacity, density, and transient temperature data. The method combines the solution of the heat transfer equation with an inversion method based on real-valued genetic algorithm (GA) optimization, and 11 target parameters can be simultaneously inverted with one experimental dataset. The effective thermal conductivities of ceramic fiber felt were obtained by inversion under ambient pressures of 2 Pa and 100 kPa, compression ratios of 0.8–1.0, and temperature ranges of 295∼1273 K. The results indicates that the effective thermal conductivities of ceramic fiber felt derived from the inversion method are in concurrence with the outcomes of the steady state method. The effective thermal conductivities of the ceramic fiber felt range from 0.069 to 0.321 W m−1 K−1. It is observed that the effective thermal conductivity increases with higher temperature, and decreases with growing compression ratio. The impact of ambient pressure on effective thermal conductivity is linked to the level of compression.

Transient Data Caching Based on Maximum Entropy Actor–Critic in Internet-of-Things Networks

AbstractWith the rapid development of the Internet-of-Things (IoT), a massive amount of transient data is transmitted in edge networks. Transient data are highly time-sensitive, such as monitoring data generated by industrial devices. Due to their inefficiency, traditional caching strategies in edge networks are inadequate for handling transient data. Thus, to improve the efficiency of transient data caching, we construct a freshness model of transient data and propose a maximum entropy Actor–Critic-based caching strategy, TD-MEAC-which can improve the freshness of cached data and reduce the long-term caching cost. Simulation results show that the proposed TD-MEAC achieves a higher cache hit rate and maintains a higher average freshness of cached transient data compared with the existing DRL and baseline caching strategies.

Study on the Effect of Three-dimensional Reconstruction Technique Based on Human Gait Plantar Transient Data on Rehabilitation of Patients with Abnormal Foot Arch

This research employs non-contact plantar 3D data scanning and gait analysis methodologies to establish a rehabilitation assistance system tailored for foot arch anomalies. The system utilizes a non-contact plantar 3D data model to mitigate dysfunctions within the plantar skeletal-muscular system. Its objectives include facilitating personalized remote diagnosis of foot arch anomalies, enabling patients to monitor their rehabilitation progress, and supporting at-home rehabilitation efforts. A dataset comprising 124 cases of physiological foot arch anomalies in adults aged 18 and above was collected and analyzed. The findings demonstrate the system’s flexibility, high spatial resolution, personalization, and innovation. Notably, the system achieves real-time measurement of positive pressure and shear force distribution at the plantar interface, facilitates the construction of accurate geometric models, and yields high-quality plantar three-dimensional coordinate data. This research contributes theoretical and technical underpinnings for the application of footwork anomaly diagnosis and correction.

Chlorophyll a fluorescence as a tool to monitor physiological status in the leaves of Artemisia ordosica under root cutting conditions.

Root cutting caused by underground coal mining subsidence is among the leading causes of plant damage in western China. Detection of root cutting stress is of great importance in evaluating the degree of plant damage and changes in physiological conditions in underground coal mining disturbance conditions. The present study assessed the use of chlorophyll fluorescence OJIP transient data to evaluate the disturbance characteristics of root cutting stress on leaf photosynthetic mechanisms in the typical shrub Artemisia ordosica Krasch. Different root cutting ratios (10%, 20%, 30%, 50%, 75%, and 100%) were established on the roots of A. ordosica in the field, and the OJIP transient and JIP parameters of the leaves were measured. The overall OJIP curves and each OJIP step in leaves decreased as the root cutting ratio increased, but the impact was relatively small for root cutting ratios of less than 30%. Through the analysis of JIP parameters and the established energy pipeline model, it was found that the energy capture efficiency and electron transfer efficiency of photosystem II decreased as the root cutting ratio increased. Therefore, we also inferred that the threshold for the plant root cutting ratio at which leaf photosynthetic mechanisms begin to change is 30-50%. These results indicate that OJIP transient analysis can serve as a non-destructive, rapid technique for detecting plant root cutting stress in coal mining subsidence areas, which is of great value for non-destructive monitoring of plant root damage.

Pressurised sand bed filtration model: Set up and energy requirements for a filtration cycle

Rapid deep bed filtration is a common process of drip irrigation systems to prevent emitter clogging. The particle retention in the porous media increases the pressure difference between the filter's inlet and outlet. Commercial operational instructions preset a threshold value of this pressure difference to define the end of a filtration cycle. Accurate particle retention models may contribute in the determination of this setpoint to improve the energy efficiency of a filtration cycle. A two-step method was developed to calibrate a phenomenological particle retention model with transient data of the filter pressure drop, and retained mass in different media slices at the end of the filtration cycle. The first step used the vertical profile of accumulated mass in the media to fit the input parameters of the specific deposit rate equation. The second step applied the pressure data to find the input parameters of the pressure drop equation. The use of constant values of the input parameters provided reasonable results for all tests. When the end of the filtration cycle was set by applying a threshold pressure drop, both particle mass accumulation and removal efficiency decreased as the flow rate increased. By using the threshold pressure drop criterion, the energy consumption in a filtration cycle also decreased as the flow rate increased, but the energy consumption per unit of filtered water volume increased. For high setpoint pressure drop values, a minimum of the energy consumption per unit of filtered water volume as a function of the flow rate was found.

Simultaneous inversion of permeability, skin and boundary from pressure transient test data in three-dimensional single well reservoir model

This study presents a novel approach for simultaneous inversion of the key reservoir parameters like horizontal permeability, vertical permeability, skin, and boundary distances for spatial distribution across the grid cells in a 3D single well reservoir model (SWRM). These parameters are first estimated from the standard pressure transient analysis of well test pressure and rate data, which also act as a priori for the inverse problem. A field-worthy layer cake geological model is prepared based on the prior information obtained from pressure transient analysis, followed by a sequential flow simulation of field well test operation. The simulation results provide the model pressure versus rate data as the synthetic data for this study. A cost function is defined incorporating the well test pressure data and model pressure data, which would determine the convergence. The inversion process is to optimize the spatial distribution of reservoir parameters to minimize the difference between the measured pressure transient data and the modelled one, which is obtained from the multiphase fluid flow simulator that solves the implicit black-oil fluid-flow diffusivity equations at every step. A Gauss-Newton (GN) inversion scheme is used for the inversion. The reliability of inversion results depends on the accuracy of priori reservoir parameters fed to the solver, which can be refined if required through uncertainty parameter optimization (UPO). This approach helps to obtain a faster and reliable update of reservoir parameters in a layer cake homogeneous geomodel, hereby introducing the required heterogeneity. This increases the confidence and reliability of a geomodel, which is further used for various production prediction strategies.

Activity Discovered on Mars-Crossing Jupiter Family Comet 2018 OR by Citizen Scientists

We present the discovery of cometary activity on 2018 OR as part of our Active Asteroids project, a NASA Partner Program fueled by Zooniverse Citizen Scientists. Volunteers found 2018 OR with a long, diffuse tail in archival images from the Dark Energy Camera on the Blanco 4 m telescope at the Cerro Tololo Inter-American Observatory in Chile. Our team identified additional Canada–France–Hawaii–Telescope MegaCam and Zwicky Transient Facility archival data after classification by Citizen Scientists. Activity originating from 2018 OR and directed in the anti-solar and anti-velocity directions was visible in archival images between UT 2018 September 5–18. Our dynamical simulations indicate 2018 OR experiences close encounters with Jupiter over hundred-year timescales. The orbital period and dynamics suggest 2018 OR is a Jupiter Family Comet, and we recommend further observations from the community to reduce observational uncertainties and investigate activity patterns.

Modeling transient natural convection in heterogeneous porous media with Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are gaining significant attention in applications related to coupled flow and transfer processes in porous media, especially when dealing with image-like data. In this context, the most important applications are related to surrogate modeling, where data obtained from simulators is used to train a CNN model. CNNs are also used as optimizers for inverse modeling or parameter estimation. For natural convection in porous media, applications of CNNs are scarce and limited to steady-state data. The main goal of this paper is to extend the applications of CNNs to transient data, by developing new CNN models that allow for integrating time-variant images. Thus, we suggest using an Encoder-Decoder CNN (ED-CNN) for surrogate modeling and a 3D-CNN for inverse modeling. Besides surrogate and inverse modeling, we suggest using CNN for time prediction by coupling it with long short-term memory (LSTM). The performances of these suggested approaches are investigated by applying them to the benchmark of natural convection in porous cavity with heterogeneous property fields, and by comparing the suggested approaches to other alternatives such as standard deep neural network (DNN) and 2D-CNN trained on steady-state data. The results show that, for surrogate modeling, with the same amount of data and equivalent training times, ED-CNN is more practical than DNN because it provides spatially distributed prediction while DNN is limited to local data. The transient data allows for improving the performance of CNN in inverse modeling because it provides more information about heat transfer across the different material zones and thus heterogeneity. The 3D-CNN approach is more efficient than 2D-CNN as it allows for considering the time progress in the training. For instance, the error with 3D-CNN and transient data is about 11 %, while it is about 18 % with 2D-CNN. Coupling CNN with LSTM allows for improving the performance of CNN in time series prediction.

Functional PCA and deep neural networks-based Bayesian inverse uncertainty quantification with transient experimental data

This work focuses on developing an inverse uncertainty quantification (IUQ) process for time-dependent responses, using dimensionality reduction by functional principal component analysis (PCA) and deep neural network (DNN)-based surrogate models. The demonstration is based on the IUQ of TRACE physical model parameters using the FEBA benchmark transient experimental data on peak cladding temperatures during reflooding. The quantity-of-interest (QoI) is time-dependent peak cladding temperature (PCT) profiles. Conventional PCA can hardly represent the data precisely due to the sudden temperature drop at the time of quenching. As a result, a functional alignment method is used to separate the phase and amplitude information in the PCT profiles before conventional PCA is applied for dimensionality reduction. The resulting PC scores are then used to build DNN-based surrogate models to significantly reduce the computational cost in Markov Chain Monte Carlo sampling, while the code/interpolation uncertainty is accounted for using Bayesian neural networks. We compared four IUQ processes with different dimensionality reduction methods and surrogate models. The proposed approach has demonstrated the best performance in reducing the dimensionality of the transient PCT profiles. This approach also produces the posterior distributions of the physical model parameters with which the model predictions have the best agreement with the experimental data.

Effect of geometry on the evolution of DLOFC transients in high temperature helium loop

Generation IV high-temperature gas-cooled reactors (HTGR) are designed to exhibit passive safety under all off-normal circumstances. One such scenario, known as depressurized loss of forced circulation (DLOFC), occurs after a break in the inlet/outlet header, allowing helium to leak from the reactor. Upon depressurization, the onset of natural circulation (ONC) can occur causing bulk air ingress leading to the oxidation and degradation of core components. The Transformational Challenge Reactor (TCR) has similar features to those of an HTGR, but the primary difference is the use of a more complex, additively manufactured (AM) fuel geometry. The more compact, additively manufactured, ceramic fuel elements can be conveniently produced with optimally configured channels that suppress the air ingress progress and improve thermofluidic performance. DLOFC and air ingress are experimentally studied in a scaled HTGR flow test setup. The experiments use an AM test element embedded with a distributed temperature sensor as well as a test geometry comprised of spherical pebble bed elements. With the test geometries aligned within a lowercase h-shaped tube flow setup, further data was obtained to improve the understanding of DLOFC. The thermal transient and air ingress data gathered for both test geometries are compared. The results show that the AM part delayed ONC as compared to the pebble bed test piece at higher temperatures. The distributed temperature sensor shows intra-leg circulation at higher temperature tests.

Interfacial Rheological Study of β-Casein/Pectin Mixtures at the Air/Water Interface.

Colloidal food products, such as emulsions, foams, gels, and dispersions, are complex systems that need the presence of stabilizing agents to enable their formation and provide stability. Proteins are often used for food foams and emulsions because of their ability to lower interfacial tension and make viscoelastic interfaces. Generally, to improve the resistance against rupture, polysaccharides are used in association with the proteins. Pectin is a complex polysaccharide that can help to stabilize foams or emulsions. This work aims at studying the mechanical resistance of the interface formed by mixtures of β-casein and pectin at high and low methoxylation degrees at the air/water interface using dilatational and shear kinematics. Frequency sweep tests, in the linear region, were performed in shear at different aging times and in dilatational mode, and the rheological data were analyzed. The transient data of the surface tension were analyzed by kinetic models to obtain the characteristic rates of the interfacial phenomena. The kinetic mechanisms of the protein/pectin mixed systems are controlled by protein and show a weak gel behavior for short aging times. The interfaces obtained with both pectins in a mixture with β-casein evolved with time, gelling and showing a solid-like behavior at concentrations of 1 and 10 g/L and after 3.5 h of aging time. The interfacial shear trend obtained suggests a good stabilizing effect of the pectins from citrus with long aging times.

Z-Splat: Z-Axis Gaussian Splatting for Camera-Sonar Fusion.

Differentiable 3D-Gaussian splatting (GS) is emerging as a prominent technique in computer vision and graphics for reconstructing 3D scenes. GS represents a scene as a set of 3D Gaussians with varying opacities and employs a computationally efficient splatting operation along with analytical derivatives to compute the 3D Gaussian parameters given scene images captured from various viewpoints. Unfortunately, capturing surround view (360° viewpoint) images is impossible or impractical in many real-world imaging scenarios, including underwater imaging, rooms inside a building, and autonomous navigation. In these restricted baseline imaging scenarios, the GS algorithm suffers from a well-known 'missing cone' problem, which results in poor reconstruction along the depth axis. In this paper, we demonstrate that using transient data (from sonars) allows us to address the missing cone problem by sampling high-frequency data along the depth axis. We extend the Gaussian splatting algorithms for two commonly used sonars and propose fusion algorithms that simultaneously utilize RGB camera data and sonar data. Through simulations, emulations, and hardware experiments across various imaging scenarios, we show that the proposed fusion algorithms lead to significantly better novel view synthesis (5dB improvement in PSNR) and 3D geometry reconstruction (60% lower Chamfer distance).

A Self-adaptive Algorithm of Inverse Wavefield Transform for the Vertically Magnetic Components of the Loop-source Transient Electromagnetic Data

A Self-adaptive Algorithm of Inverse Wavefield Transform for the Vertically Magnetic Components of the Loop-source Transient Electromagnetic Data