Forward Model Research Articles

Examining the influence of 2.5-D ultra-low velocity zone morphology on ScP waveforms and estimated elastic parameters

Summary Ultra-low velocity zones (ULVZs) have been identified as regions of extremely low velocity anomalies in the Earth's lowermost mantle using seismic observations from reflected, refracted, and diffracted arrivals along the mantle side of the core-mantle boundary (CMB). Estimation of ULVZ geometrical (i.e., shape and size) and elastic (i.e., velocity and density) parameters with uncertainties is crucial in understanding the role of ULVZs in the ongoing dynamic processes within the Earth's mantle; however, these parameters are still poorly known due to uncertainties and tradeoffs of the seismically resolved ULVZ geometries and elastic parameters. Computation of synthetic waveforms for 2-D and 3-D ULVZs shapes is currently computationally feasible, but past studies utilize higher dimensional waveform modeling of mostly only low-frequency diffracted waves. Most studies focusing on high-frequency core-reflected waveforms (e.g., ScP) still use 1-D modeling approaches to determine ULVZ properties. This approach might lead to wrong results if the imaged structures have inherently 3-D geometries. This study investigates high-frequency synthetic ScP waveforms for various 2.5-D ULVZ geometries showing that additional seismic arrivals are generated even when the ScP geometrical ray path does not directly strike the location of the ULVZ. The largest amplitude additional phases in the 2.5-D models are post-cursor arrivals that are generated at the edges of the finite-length ULVZs. These newly identified ScP post-cursors can arrive within the ScsP post-cursor time window traditionally analyzed in 1-D ULVZ studies. These post-cursors might then be misidentified or constructively/destructively interfere with the ScsP postcursor, leading to incorrect estimation of ULVZ parameters. In this study we investigate the bias introduced by the 2.5-D morphologies on the 1D estimated ULVZ elastic properties in a Bayesian waveform inversion scheme. We further expand the Bayesian method by including the data noise covariance matrix in the inversion and compare it to an autoregressive noise model that was utilized in previous studies. From the application to the observed ScP data, we find that the new approach converges faster, particularly for the inversion of data from multiple events, and the new algorithm retrieves ULVZ parameters with more realistic uncertainties. The inversion of 2.5-D synthetic ScP waveforms suggests that the retrieved ULVZ parameters can be misleading with unrealistically high confidence if we do not consider the data noise covariance matrix in the inversion. Our new approach can also retrieve the shape of a multi-dimensional Gaussian ULVZ if its length is 12o or longer in the great circle arc direction. However, 2.5-D synthetic waveforms show additional waveform complexity which can constructively interfere with the ScP wavefield. Hence, in many cases the estimation of ULVZ properties using 1-D forward modeling can provide incorrect ULVZ parameters. Hence previous ULVZ modeling efforts using 1-D parameter estimation methods may be incorrect.

Natural gas leakage: Forward and inverse method of leakage field based on multipath concentration monitoring

Natural gas leakage can easily lead to safety accidents and environmental pollution, affecting normal safe production and sustainable energy development. Among them, the leakage accident caused by the integrity failure of the process equipment is particularly noteworthy. To improve the efficiency of methane leakage monitoring and the accuracy of leakage source location, the forward prediction model of leakage concentration field, the optimization model of monitoring equipment layout, and the inversion model of leakage source location were established. To evaluate the model’s applicability, an open-air oil–gas joint station was selected for experimental simulation. The monitoring data of the open circuit laser methane sensor and the forward simulation results of the leakage concentration field were compared. It was found that at 30 % methane concentration, the monitoring distance was 0–6 m, with high monitoring accuracy. Then, by eliminating redundant monitoring sensors, three leakage field concentration monitoring paths are formed. A representative leakage source scenario set is selected for inversion localization. In the simulation of the single-point leakage experiment, the leakage location error is 1.824 m, and the average relative error is 3.6 %. Through error analysis, the results show that the inversion positioning model can also have a good adaptability to the parameter estimation of the leakage source when there are only 3 sets of observation data.

Multi-objective optimization and inverse design of complementary field-effect transistor using combined approach of machine learning and non-dominated sorting genetic algorithms for next-generation semiconductor devices

Complementary field-effect transistors (CFETs), which are structures in which different types of transistors are vertically stacked with a shared control gate, are being focused on for continuing to satisfy Moore's law by overcoming the limitations in pitch scaling. The structures of semiconductor devices become more complex as technology node shrinks, and interrelated multivariate parameters increase. In addition, predicting problems and proposing solutions by identifying complex patterns within extensive data collected for emerging semiconductor designs pose significant computational challenges and are inherently difficult. As a breakthrough in design technology co-optimization for advanced devices, this study developed a novel optimization framework integrating technology computer-aided design simulations, machine learning, and non-dominated sorting genetic algorithms. The developed framework provides unbiased optimal solutions, even in a high-dimensional objective space, while considering the tradeoff relationships between multiple variables. In addition, it enables inverse design to identify the design parameters of devices that satisfy specific electrical performance criteria using only a forward model, while achieving an error rate of less than 2%. Using this framework, we analyzed the operational mechanism of CFETs by comparing the inverse designs of various devices. This novel approach is particularly important when the design space is complex and extensive and is well suited for developing devices that emerge with technological advancements in the semiconductor industry.

A fresh look at the Gulf of St. Lawrence, Eastern Canada: Geophysical imaging of salt bodies and volcanic rocks in the upper Paleozoic Magdalen Basin

In the Gulf of St. Lawrence, the late Middle Devonian to early Permian Magdalen Basin hosts numerous salt bodies, but their number and geometry remained poorly constrained due to the poor quality of industry-seismic data. Salt originates from the Middle Mississippian (Visean) Windsor Group, which includes marine carbonates and evaporites. In this study, we use multibeam bathymetry as well as unpublished aerogravity and aeromagnetic data to better characterize the geometry of salt bodies. These datasets show a high degree of coherency and illustrate deformation on the seafloor, gravity lows and magnetic highs, all associated with salt bodies and overlying volcanic rocks. In map view, the geometry of individual salt bodies varies from nearly circular to ovoid and strongly elongated. Some salt bodies coalesce in complex-shaped salt walls elongated NNE, parallel to some of the faults that controlled the early stages of the Magdalen Basin development. Forward modeling indicates that low density salt bodies overlain by a 100–300 m thick volcanic pile may account for both gravity and magnetic signals. The ubiquity of mafic volcanic rocks indicates that the late Visean to Serpukhovian volcanic episode is more voluminous than previously thought. This episode is possibly associated with a series of dykes continuous for up to 225 km and with a period of underplating now recorded by a lower crustal layer with high seismic velocity.

Efficient magnetic forward modeling for strongly magnetized bodies: An iterative integral equation approach

Magnetic surveying encounters challenges in high-precision detection and inversion on strongly magnetized bodies. Nonuniform magnetization caused by self-demagnetization and mutual magnetization makes magnetic field calculation using analytical formulas impractical. The previous discrete numerical methods have suffered from computational inefficiency or low accuracy. To resolve this issue, we develop an algorithm to solve the magnetic field integral equation for calculating the magnetization state of high-susceptibility bodies, combining the improved spatial convolution forward approach with the adaptive relaxation iteration method. To address the excessive computational burden in the magnetization field between 3D voxel arrays of the discrete model, we construct the circular convolution kernel matrix directly when the subsurface space is divided with an equidistant grid, significantly reducing redundant computations. Then, the fast Fourier transform is used to accelerate the forward discrete circular convolution operation. In addition, we derive an iterative computation scheme that adaptively adjusts the relaxation factor based on magnetic field changes to correct the effective magnetization for algorithm convergence. From a pragmatic perspective, equating the cuboid to equal-volume sphere subdivisions is developed to enhance computational efficiency further by approximately 50% despite sacrificing slight accuracy. The sphere and spherical shell models validate the algorithm accuracy, efficiency, and convergence. Furthermore, we analyze the characteristics of the mutual magnetization effect among magnetic bodies under different magnetization conditions. Finally, the applicability of the algorithm is demonstrated with a realistic example. Our algorithm shows promise for precisely exploring highly magnetized minerals, underground pipelines, and unexploded ordnance.

On the ensemble Kalman inversion under inequality constraints

Abstract The ensemble Kalman inversion (EKI), a recently introduced optimisation method for solving inverse problems, is widely employed for the efficient and derivative-free estimation of unknown parameters. Specifically in cases involving ill-posed inverse problems and high-dimensional parameter spaces, the scheme has shown promising success. However, in its general form, the EKI does not take constraints into account, which are essential and often stem from physical limitations or specific requirements. Based on a log-barrier approach, we suggest adapting the continuous-time formulation of EKI to incorporate convex inequality constraints. We underpin this adaptation with a theoretical analysis that provides lower and upper bounds on the ensemble collapse, as well as convergence to the constraint optimum for general nonlinear forward models. Finally, we showcase our results through two examples involving partial differential equations.

A multi-agent curiosity reward model for task-oriented dialogue systems

In practical decision-making dialogues, reinforcement learning methods face hurdles due to delays and sparse reward feedback for agents, and in some cases, lack of rewards altogether. These issues can impede efficient learning of dialogue strategies and compromise the performance of the model. To address this challenge, this paper introduces the Multi-Agent Curiosity Reward Model (MACRM) for task-oriented dialog systems. Firstly, in terms of dialog reward mechanisms, a forward dynamics model generates curiosity rewards, which are integrated with extrinsic rewards from the dialog environment feedback to mitigate the problem of sparse rewards resulting from inadequate agent exploration. Secondly, regarding the dialogue strategy training mechanism, an exploration-exploitation approach inspired by organismic exploration is adopted. This approach involves fully exploring the dialogue environment in the early stages and optimally exploiting learned knowledge later, thereby balancing exploration and exploitation and enhancing dialogue strategy learning efficiency. To assess the proposed model's effectiveness, experiments are conducted using the MultiWOZ corpus across three reward environments: (1) extrinsic rewards only, (2) curiosity rewards only, and (3) a combination of both. The experimental results demonstrate that agents employing MACRM exhibit faster learning of dialogue strategies compared to those relying on a single exploratory reward method, effectively addressing reward sparsity and delay issues in practical decision-making scenarios.

Shape Optimization for Lithium-Ion Battery with Porous Electrodes

In this presentation, we introduce a gradient-based shape optimization framework to design porous electrodes for maximum energy storage. Lithium-ion batteries are becoming an increasingly important energy source and storage device. Large surface area that advocates high reaction rates often leads to small porosities that deteriorate electrochemical transport, which significantly restricts ion penetration depth, limiting the material utilization in planar electrodes. Consequently, architected electrodes are required to optimize performance.We consider a model with two materials, namely porous electrode and pure electrolyte. Density-based topology optimization with continuous material volume fraction has previously been implemented on lithium-ion battery system for both half-cell and full-cell model to maximizes its energy storage. Despite the capability of producing complex interdigitated structures, an appropriate penalization scheme for intermediate materials is required for the optimizer to provide near binary designs. Setting up such a penalization is often time-consuming due to the large number of design-dependent variables that affect the forward model differently. Alternatively, the shape optimization approach used in this work produces binary designs naturally by morphing conformal meshes, circumventing inaccuracies associated with fuzzy interfaces. Shape sensitivities are computed via the adjoint method and leveraging automatic differentiation. The optimization problem is solved using a nonlinear programming technique.In this presentation, we perform shape optimization on a half-cell model to maximize its energy storage when a fixed charging current is applied. We study multiple arrangements of physical parameters and compare their performance against a monolithic electrode. Both 2D and 3D results are presented.

Source characteristics of the 2006 Pingtung earthquake doublet off southern Taiwan and the possible contribution of submarine landslides to the Tsunami

On 26th December 2006, two successive large earthquakes of Mw ∼7.0, occurred offshore southwest Taiwan, being 8 min apart, known as the 2006 Pingtung earthquake doublet. The earthquakes triggered a hazard chain consisting of regional-scale tsunamis, submarine landslides, and turbidity currents. The tsunami waves with moderate height (10–60 cm) were recorded by 12 tide gauges along southern Taiwan, Guangdong, and Fujian provinces. Submarine landslide-induced turbidity currents inflicted widespread damage to telecommunication cables across the Kaoping Canyon and Manila Trench. Although the mechanisms of the individual hazard in the chain have been investigated previously, the interaction mechanisms and the causal relationship among them remain unclear due to the complexity of the dynamic process. Here, with the best available instrumental records of these events, we first reexamined the earthquake doublet's source characteristics and obtained the earthquake source models using regional seismic waves, high-rate GPS displacements, and strong motion waveforms. We then conducted forward tsunami modeling, along with spectral analysis of the tide gauge records and backward tsunami ray tracing technique, to gain insight of the hydrodynamic features of tsunamis and the contribution of landslides to tsunami generation. The main findings can be summarized as follows. The centroid depth of the first earthquake was found to be ∼28 km, which is shallower than the local CWB catalog suggested. The slip distribution was characterized by two close slip patches, ranging over depths of 15–35 km and occupying 40 km along the strike. The shallower focal depth and normal-faulting mechanism can well explain the recorded tsunami waves. Notably, wave analysis suggests that tsunami waves induced by landslides may have occurred in two episodes. The first round was likely produced by the landslides triggered simultaneously by the initial earthquake doublet. The largest aftershock, which occurred 14 h after the doublet with a strike-slip mechanism, may have caused the landslides near the head of Kaoping Canyon and possibly the second-round tsunami.

Spectral prediction method based on the transformer neural network for high-fidelity color reproduction

Color distortion often occurs during transmission and reproduction processes, and existing spectral prediction methods have the disadvantage of low prediction accuracy in halftone reproduction. Addressing this issue, this paper establishes a halftone dataset composed of four-color inks (CMYK) mixtures. Based on this, the transformer network is introduced to model and characterize the spectral features of mixed inks, and a forward color formulation prediction model and a reverse spectral prediction model combining halftone reproduction with spectral sequences are proposed, namely the spectrum-color transformer (SC-Former). Color reproduction quality assessment experiments are conducted using the dataset established in this paper and the international standard Ugra/Fogra Media Wedge V3.0 test set. The experimental results show that the SC-Former model outperforms traditional physical models and data-driven prediction models in terms of color reproduction effects and spectral prediction accuracy. This research contributes to the development of high-fidelity color reproduction techniques.

Untrained physics-driven aberration retrieval network.

In the field of coherent diffraction imaging, phase retrieval is essential for correcting the aberration of an optic system. For estimating aberration from intensity, conventional methods rely on neural networks whose performance is limited by training datasets. In this Letter, we propose an untrained physics-driven aberration retrieval network (uPD-ARNet). It only uses one intensity image and iterates in a self-supervised way. This model consists of two parts: an untrained neural network and a forward physical model for the diffraction of the light field. This physical model can adjust the output of the untrained neural network, which can characterize the inverse process from the intensity to the aberration. The experiments support that our method is superior to other conventional methods for aberration retrieval.

Machine learning based aerosol and ocean color joint retrieval algorithm for multiangle polarimeters over coastal waters

NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission, recently launched in February 2024, carries two multiangle polarimeters (MAPs): the UMBC Hyper-Angular Rainbow Polarimeter (HARP2) and SRON Spectropolarimeter for Planetary Exploration One (SPEXone). Measurements from these MAPs will greatly advance ocean ecosystem and aerosol studies as their measurements contain rich information on the microphysical properties of aerosols and hydrosols. The Multi-Angular Polarimetric Ocean coLor (MAPOL) joint retrieval algorithm has been developed to retrieve aerosol and ocean color information, which uses a vector radiative transfer (RT) model as the forward model. The RT model is computationally expensive, which makes processing a large amount of data challenging. FastMAPOL was developed to expedite retrieval using neural networks to replace the RT forward models. As a prototype study, FastMAPOL was initially limited to open ocean applications where the ocean Inherent Optical Properties (IOPs) were parameterized in terms of one parameter: chlorophyll-a concentration (Chla). In this study we further expand the FastMAPOL joint retrieval algorithm to incorporate NN based forward models for coastal waters, which use multi-parameter bio-optical models. In addition, aerosols are represented by six components, i.e., fine mode non absorbing insoluble (FNAI), brown carbon (BrC), black carbon (BC), fine mode non absorbing soluble (FNAS), sea salt (SS) and non-spherical dust (Dust). Sea salt and dust are coarse mode aerosols, while the other components are fine mode. The sizes and spectral refractive indices are fixed for each aerosol component, while their abundances are retrievable. The multi-parameter bio-optical model and aerosol components are chosen to represent the coastal marine environment. The retrieval algorithm is applied to synthetic measurements in three different configurations of MAPs in the PACE mission: HARP2 observations only, SPEXone observations only and combined HARP2 and SPEXone observations. The retrieval results from synthetic measurements show that for aerosol retrieval the SPEXone-only configuration works equally well with the HAPR2-only configuration. On the other hand, for ocean color retrieval the SPEXone instrument provides better information due to its larger spectral coverage. For the surface parameters (wind speed), HARP2 measurements provide better information due to its wide field of view. Combined measurement configuration HARP2+SPEXone performed the best to retrieve all aerosol, ocean color, and surface parameters. We also studied the impact of sun glint to aerosol and ocean color retrievals. The retrieval test revealed that wind speed and absorbing aerosol retrieval improves significantly when including measurements at glint geometries. Furthermore, the retrieval algorithm is equipped with modules for atmospheric correction and bidirectional reflectance distribution (BRDF) correction to obtain the remote sensing reflectance, which enables ocean biogeochemistry studies using the PACE polarimeter data.

Chemical weathering and CO2 consumption rates of the Koshi River Basin: modelling and quantifying

The Koshi River is a typical river originating from the Tibetan Plateau that drains into the Indian Plain. To quantify the chemical weathering and CO2 consumption rates of the upstream alpine meltwater-supplied mountainous and downstream rainfall-impacted temperate plain regions, 46 river water samples were collected at 23 sites during monsoon and post-monsoon seasons, and major ions concentrations were measured in the laboratory. The forward model was applied to calculate carbonate weathering rate (CWR), silicate weathering rate (SWR), and their CO2 consumption rates ([CO2]car and [CO2]sil). For investigating the hydrological impacts, correlation analysis was applied between the chemical weathering rates and different runoff components based on the Spatial Process in Hydrology model at each sampling site. Results show that carbonate and silicate rock weathering control the river water chemistry. The average silicate weathering rate is 7.37 tons/km2/yr, consuming 3.40 × 105 mol/km2/yr CO2 while considering sulfide-involved carbonate weathering, the carbonate weathering rate is 26.35 tons/km2/yr, consuming 3.82 × 105 mol/km2/yr CO2. Overall, chemical weathering and CO2 consumption rates are enhanced in monsoon season in both the upstream and downstream regions owing to the intense hydrological cycle. These rates are relatively higher in the alpine upstream region during the post-monsoon season due to intense physical erosion, and higher in downstream during the monsoon season due to the greater volume of water. The significant positive correlations between snowmelt runoff with CWR and [CO2]car indicated that the rising precipitation and temperature leading to enhanced snowmelt would increase the carbonate weathering rate. Hence, this study provides a valuable understanding of the spatiotemporal variations in river hydrochemistry and the role of hydrology in shaping chemical budget within the snow and glacier-fed Himalaya River Basins.

Estimating highest capacity propulsion performance using backward-directed force during walking evaluation for individuals with acquired brain injury

There are over 5.3 million Americans who face acquired brain injury (ABI)-related disability as well as almost 800,000 who suffer from stroke each year. To improve mobility and quality of life, rehabilitation professionals often focus on walking recovery soon after hospital discharge for ABI. Reduced propulsion capacity (force output of the lower limbs to counteract ground reaction forces) negatively impacts walking ability and complicates recovery during rehabilitation for brain injured people. We describe a method, using backward-directed resistance (BDR) in a robotic-based treadmill device, to allow measurement of maximum walking propulsion force (MWPF) that is not otherwise possible during overground walking assessment. Our objective was to test the construct validity of a maximum walking propulsion force (MWPF) measure that reflects a person’s propulsive strength against applied BDR, while walking on a robotic treadmill-based device for participants with acquired brain injury (ABI). Our study enrolled 14 participants with ABI at an in inpatient rehabilitation in Galveston, TX from 8/1/21 − 4/31/22. The range of weight-adjusted MWPF was 2.6–27.1% body weight (%BW), mean 16.5 ± 8.4%BW, reflecting a wide range of propulsive force capability. The strongest correlation with overground tests was between the 6-minute walk test (6-MWT) distance and the MWPF values (r = 0.83, p < 0.001) with moderate correlations between the 10-meter walk tests at comfortable (CWS) and fast speeds (FWS). The Five Times Sit-to-Stand (used as a standard clinical measure of functional lower extremity strength) and MWPF tests were poorly correlated (r = 0.26, p = 0.4). Forward model selection included 6-MWT distance, age, and overground CWS as significant partial predictors of MWPF. We conclude that this novel MWPF measure is a valid representation of maximum propulsive force effort during walking for people post-ABI. Additional research could help determine the impact of interventions designed to increase propulsive force generation during rehabilitation training to improve overground walking performance.

Extension during the Liebig Orogeny: Revised tectonic setting of Paleoproterozoic central Australia

Magnetotelluric imaging of a lithospheric-scale boundary in central Australia has been used to support a model of collision between the North Australian Craton and an exotic continental ribbon referred to as the Warumpi Province during the late Paleoproterozoic Liebig Orogeny (c. 1640 Ma). This idea of collision has become an important element to reconstructions of late Paleoproterozoic Australia. In this study, we present mineral equilibria forward models from c. 1640 Ma metamorphic rocks to delineate the thermal regime associated with the Liebig Orogeny. Liebig-aged cordierite–orthopyroxene–biotite–quartz–plagioclase–K-feldspar–ilmenite–magnetite assemblages constrain peak metamorphic conditions to 4–5 kbar and 770–800 °C. These conditions are indicative of a high geothermal gradient regime and the mineral assemblage evolution shows no evidence for significant pressure changes that would generally support a crustal thickening model. Detrital zircon age spectra for the metasediments in the assumed exotic crust are broadly similar to the age record in the Aileron Province in the southern part of the North Australian Craton. Furthermore, εNd and εHf isotopic data of Liebig-aged granitic rocks make it permissible that they are derived from melting of North Australian Craton crust. This suggests the Warumpi Province was not exotic to the North Australian Craton and was instead a part of it. On the basis of the shared history between the Warumpi and Aileron Provinces and the newly constrained thermal character of c. 1640 Ma metamorphism, we suggest the Liebig Orogeny was characterised by extensional deformation of the North Australian Craton in the upper plate above a southward retreating subduction system. Tectonically, the Liebig Orogeny is interpreted to record a back-arc regime characterised by sedimentation, mafic and felsic magmatism, and predominantly contact metamorphism, and represents the continuation of a long-lived (c. 200 Ma) upper plate system that dictated the evolution of the North Australian Craton during the late Paleoproterozoic.

Boundary determination for hybrid imaging from a single measurement

Abstract We recover the conductivity σ at the boundary of a domain from a combination of Dirichlet and Neumann boundary data and generalized power/current density data at the boundary, from a single quite arbitrary set of data, in AET or CDII. The argument is elementary, algebraic and local. More generally, we consider the variable exponent p ⁢ ( ⋅ ) {p(\,\cdot\,)} -Laplacian as a forward model with the interior density data σ ⁢ | ∇ ⁡ u | q {\sigma|\nabla u|^{q}} , and find out that single measurement specifies the boundary conductivity when p - q ≥ 1 {p-q\geq 1} , and otherwise the measurement specifies two alternatives. We present heuristics for selecting between these alternatives. Both p and q may depend on the spatial variable x, but they are assumed to be a priori known. We illustrate the practical situations with numerical examples with the code available.

Epidemiology, clinical features and predictors of mortality in patients with candidemia in a tertiary care hospital in Turkiye: a single-center retrospective study

Objective: We aimed to evaluate the epidemiology of candidemia among hospitalized patients and identify risk factors associated with mortality. Materials and methods: The medical data of 81 adult patients with candidemia who were hospitalized between May 1, 2015 and May 1, 2016 constituted the study. We recorded vital signs, clinical characteristics, ward or intensive care unit (ICU) interventions, comorbidities, and biochemistry findings. To identify factors independently associated with 30-day mortality, a forward conditional inclusion model was used in multivariable logistic regression. Results: The annual incidence of candidemia was 23 cases per 10000 individuals (0.23%). The three most frequently detected species were C. albicans (46.91%), C. parapsilosis (29.63%) and C. glabrata (8.64%). The 30-day mortality rate was 59.26% (n=48). Multivariable logistic regression showed that older age (OR: 1.037, 95% CI: 1.006 - 1.070, p=0.018), ICU admission (OR: 3.325, 95% CI: 1.132 - 9.766, p=0.029) and acute renal failure (OR: 3.383, 95% CI: 1.024 - 11.173, p=0.046) were independently associated with mortality. Conclusion: We revealed an annual candidemia incidence of 0.23% and a notably high 30-day mortality rate at our center. Older patients, individuals admitted to the ICU, and those with renal dysfunction may face an elevated risk of mortality, underscoring the importance of close monitoring in these populations. Population-based studies are required to create new approaches to prevent and treat candidemia.

An AI-assisted terahertz reconfigurable metamaterial in standard 180-nm CMOS

For large-scale terahertz (THz) reconfigurable metamaterials (RM), each unit element needs to be controlled by an independent voltage source. This leads to a huge solution space for control words to achieve the required RM performance specification. In this paper, we propose a fast AI-assisted control-word generation scheme to reduce the computation cost of electromagnetic (EM) simulations (Forward Model) and to speed up iterations for control word searches (Inverse Model). We demonstrate the effectiveness of our models by designing and fabricating a 12 × 9 RM array with a Bi-layer Bi-Split-Ring Resonator structure (B-BSRR) in standard 180 nm CMOS technology. The terahertz RM can be electrically reconfigured, providing active metamaterials with an amplitude modulation depth of around 0–1.7 dB at 0.49 THz. The results show that our Forward Model can predict the S-parameters between 100 GHz and 800 GHz with a mean absolute error of 0.61 dB. Our method is more than 200 times faster than traditional full-wave simulation methods. The Inverse Model can generate required control words within a 1.3 dB error margin in less than 200 iterations.

Rehabilitation of Frail Older Adults after Hip Fracture Surgery: Predictors for the Length of Geriatric Rehabilitation Stay at a Skilled Nursing Home.

Background: Approximately 50% of older patients hospitalized for hip fractures are admitted to a geriatric rehabilitation department at a skilled nursing home. Given the wide variation in rehabilitation stay lengths, predicting the length of stay upon hospital discharge would help manage patients' recovery expectations and create appropriate therapy schedules. Existing literature on length of stay predictors included both acute hospital and in-hospital rehabilitation phases or involved small sample sizes. The present study aims to identify predictors for the length of geriatric rehabilitation stay in skilled nursing homes for older patients after hip fracture surgery upon hospital discharge. Methods: This retrospective cohort study was conducted from 1 October 2017 to 1 July 2023, including 561 patients. Potential predictors of the length of geriatric rehabilitation stay were first tested univariately, with variables showing p < 0.15 entered into a multivariate forward linear regression model. Results: This model identified the following independent predictors of a longer length of geriatric rehabilitation stay: Functional Ambulation Categories (FACs) 0 (B = 29.9, 95% CI 24.1-35.7), 1 (B = 18.0, 95% CI 11.8-24.2), 2 (B = 12.0, 95% CI 7.1-17.0), or 3 (B = 3.6, 95% CI -1.2-9.4) at hospital discharge vs. FAC 4, living independently with home care services (B = 5.9, 95% CI 2.5-9.3) or in a residential home prior to the hip fracture (B = 0.2, 95% CI -7.4-7.8) vs. living independently without home care services, non- or partial weight-bearing mobilization vs. full weight-bearing mobilization (B = 15.4, 95% CI 8.5-22.2), internal fixation vs. hemiarthroplasty (B = 4.7, 95% CI 1.4-7.9), in-hospital delirium (B = 7.0, 95% CI 2.2-11.7), and in-hospital heart failure (B = 7.9, 95% CI 0.5-15.3). The explained variance was 32.0%. Conclusions: This study identified FAC at hospital discharge, premorbid living situation, postoperative weight-bearing protocol, surgery type, in-hospital delirium, and in-hospital heart failure as independent predictors of the length of geriatric rehabilitation stay. Future investigations are needed to identify additional predictors, such as cognitive functioning, to better predict the length of geriatric rehabilitation stay upon hospital discharge.

Validation of conductivity tensor imaging against diffusion tensor magnetic resonance electrical impedance tomography

Diffusion tensor magnetic resonance electrical impedance tomography (DT-MREIT) and electrodeless conductivity tensor imaging (CTI) are two emerging modalities that can quantify low-frequency tissue anisotropic conductivity properties by assuming similar properties underlie ionic mobility and water diffusion. While both methods have potential applications to estimating neuro-modulation fields or formulating forward models used for electrical source imaging, a direct comparison of the two modalities has not yet been performed in-vitro or in-vivo. Therefore, the aim of this study was to test the equivalence of these two modalities. We scanned a tissue phantom and the head of human subject using DT-MREIT and CTI protocols and reconstructed conductivity tensor and effective low frequency conductivities. We found both gray and white matter conductivities recovered by each technique were equivalent within 0.05 S/m. Both DT-MREIT and CTI require multiple processing steps, and we further assess the effects of each factor on reconstructions and evaluate the extent to which different measurement mechanisms potentially cause discrepancies between the two methods. Finally, we discuss the implications for spectral models of measuring conductivity using these techniques. The study further establishes the credibility of CTI as an electrodeless non-invasive method of measuring low frequency conductivity properties.