Local Model Research Articles

MemberShield: A framework for federated learning with membership privacy

Federated Learning (FL) allows multiple data owners to build high-quality deep learning models collaboratively, by sharing only model updates and keeping data on their premises. Even though FL offers privacy-by-design, it is vulnerable to membership inference attacks (MIA), where an adversary tries to determine whether a sample was included in the training data. Existing defenses against MIA cannot offer meaningful privacy protection without significantly hampering the model’s utility and causing a non-negligible training overhead. In this paper we analyze the underlying causes of the differences in the model behavior for member and non-member samples, which arise from model overfitting and facilitate MIAs. Accordingly, we propose MemberShield, a generalization-based defense method for MIAs that consists of: (i) one-time preprocessing of each client’s training data labels that transforms one-hot encoded labels to soft labels and eventually exploits them in local training, and (ii) early stopping the training when the local model’s validation accuracy does not improve on that of the global model for a number of epochs. Extensive empirical evaluations on three widely used datasets and four model architectures demonstrate that MemberShield outperforms state-of-the-art defense methods by delivering substantially better practical privacy protection against all forms of MIAs, while better preserving the target model utility. On top of that, our proposal significantly reduces training time and is straightforward to implement, by just tuning a single hyperparameter.

DT-Block: Adaptive vertical federated reinforcement learning scheme for secure and efficient communication in 6G

The necessities of security and data sharing have focused on federated learning because of using decentralized data sources. The existing works used federated learning for security, however, it still faces many challenges such as poor security and privacy, computational complexity, etc. In this research, we propose adaptive vertical federated learning using a reinforcement learning approach and blockchain. The proposed work includes three phases: user registration and authentication, machine learning-based client selection, and adaptive secure federated learning. Initially, all the users register their credentials to the cognitive agent, which generates a private key, public key, and random number using a Chaotic Isogenic Post Quantum Cryptography (CIPQC) algorithm. Second, optimal clients are selected for participating in federated learning which improves learning rate and reduces complexity. Here, optimal clients are selected by the Enhanced Multilayer Feed Forward Neural Network (EMFFN) algorithm by considering CSI, RSSI, bandwidth, energy, communication efficiency, and statistical efficiency. Finally, adaptive secure federated learning is performed by the Distributed Distributional Deep Deterministic Policy Gradient (D4PG) algorithm, where the local models are adaptively used by the private strategy based on its sensitivity. The aggregated global models are stored in DT-block (dendrimer tree-based blockchain) which stores the data in a dendrimer tree structure for increasing scalability and reducing search time during data retrieval. The simulation of this research is conducted by NS-3.26 network simulator and the performance of the proposed DT-Block model is estimated based on various performance metrics such as accuracy, delay, loss, f1-score, and security strength this demonstrated that the suggested effort produced better results both in terms of quantitative and qualitative aspects.

A generalization of the Exner law for sediment nonlocal transport at bedform scale

Recent researches have highlighted the significance of nonlocal processes in understanding the morphodynamics and sediment transport across landscapes. Nonlocal processes in sediment transport refer to the influence of landscape properties beyond the immediate vicinity of a given point on the sediment flux. Existing nonlocal models, employing fractional operators, aim to capture global correlated nonlocality using a global convolution operator. Nevertheless, such models tend to disregard nonlocal phenomena occurring at regional scales, potentially resulting in substantial inaccuracies due to an inadequate representation of sediment transport processes at these scales. This study presents a novel and more comprehensive mathematical formulation of the nonlocal Exner law, leveraging the peridynamic differential operator (PDDO). The proposed regional nonlocal model incorporates nonlocal sediment transport processes by utilizing a pre-defined weight function and interaction domain within the framework of the PDDO. The novel regional nonlocal model effectively bridges the gap between local and global models by integrating both short-range and long-range interactions in sediment transport. Application reveals that that the regional nonlocal model provides a significantly enhanced accuracy in depicting profiles at the bedform scale compared to the local and the global models.

Heat transfer characteristics of a turbulent swirl jet issuing from a circular nozzle

Experimental and numerical studies on the flow field and cooling performance of a swirling jet, impinging on a flat surface is presented. A new nozzle that resembles the swirl injector of a liquid propellant rocket engine is used. This study considers different parameters including swirl number(S), Reynolds number(Re), normalised orifice to target spacing (Z/D) and confinement. The performance of various RANS and LES turbulence models is assessed using the data obtained from present experimental studies and that reported in literature. RNG k-ϵ turbulent model is successful in predicting heat transfer in non swirling flow. In the case of swirling flow, LES WALEM(Wall Adapting Local Eddy viscosity Model) predicts the heat transfer better than the other models. The performance of Swirling Impinging Jets(SIJ) and Cylindrical Impinging Jets (CIJ) is compared. At higher Z/D, introduction of swirl results in reduction in heat transfer and this is because of the increased spreading of the jet and reduction in velocity of the jet impinging on the target. Better performance is achieved at lower Z/D when a cylindrical jet is introduced with a small amount of swirl. For Z/D = 2 and S = 0.2, the peak Nu increases by 10 % and average Nu in the stagnation region increases by 6 % in comparison to CIJ. The position of the peak Nu moves away from the axis of the jet and there is better uniformity in Nu in the stagnation region. For Z/D = 2 swirling flow creates secondary peak(which usually occurs at high Re)even at low Re. At very low Z/D, top wall confinement causes increase in both peak heat transfer and average heat transfer in the stagnation region.

Modified local Granger causality analysis based on Peter‐Clark algorithm for multivariate time series prediction on IoT data

AbstractClimate data collected through Internet of Things (IoT) devices often contain high‐dimensional, nonlinear, and auto‐correlated characteristics, and general causality analysis methods obtain quantitative causality analysis results between variables based on conditional independence tests or Granger causality, and so forth. However, it is difficult to capture dynamic properties between variables of temporal distribution, which can obtain information that cannot be obtained by the mean detection method. Therefore, this paper proposed a new causality analysis method based on Peter‐Clark (PC) algorithm and modified local Granger causality (MLGC) analysis method, called PC‐MLGC, to reveal the causal relationships between variables and explore the dynamic properties on temporal distribution. First, the PC algorithm is applied to compute the relevant variables of each variable. Then, the results obtained in the previous stage are fed into the modified local Granger causality analysis model to explore causalities between variables. Finally, combined with the quantitative causality analysis results, the dynamic characteristic curves between variables can be obtained, and the accuracy of the causal relationship between variables can be further verified. The effectiveness of the proposed method is further demonstrated by comparing it with standard Granger causality analysis and a two‐stage causal network learning method on one benchmark dataset and two real‐world datasets.

Improving a shell-tube latent heat thermal energy storage unit for building hot water demand using metal foam inserts at a constant pumping power

The phase transition heat transfer during the melting and solidification processes of phase change materials (PCMs) was modeled in a shell-tube thermal energy storage unit. For the first time, special attention was dedicated to the heat transfer enhancement on the heat transfer fluid (HTF) side by placing metal foam (MF) inserts in the HTF tube. The MF inserts on the HTF side were placed next to the fin bases to produce the maximum heat transfer enhancement. A two-temperature local thermal non-equilibrium model was applied to accurately model the transient heat transfer in the MF domains. To perform a fair heat transfer analysis and consider the hydrodynamic aspects of heat transfer, the pumping power for driving the HTF fluid was kept fixed. The finite element method with automatic time-step control was used to integrate the model equations. Results show that the insertion of the MF in the HTF tube enhances conduction heat transfer but reduces flow for a constant pump power. Using a small amount of MF provides a good flow rate and a convective heat transfer while using a large amount of MF provides good conduction heat transfer but a poor flow rate and convection. A moderate amount of MF inserts results in poor conduction and a poor flow rate, notably increasing the phase transition time. A 5 % MF insert could provide about a 13 % shorter solidification time compared to a 25 % MF insert. Thus, a well-designed and engineered MF insert configuration on the HTF side can reduce the phase transition time and improve the energy storage power.

Research on the load transfer performance of large-span steel truss cable-stayed bridge structures

Since the wave is accompanied by the transfer of energy in the process of transmission, and the energy density at the node directly affects the bearing capacity and service performance of the steel truss cable-stayed bridge members and joints, this paper quantitatively studies the energy density at the key nodes of the long-span steel truss cable-stayed bridge from the perspective of the wave response of the structure. Firstly, according to the drawings of Xiangshan Bridge, a local finite element model of the steel truss cable-stayed bridge is established, and by changing the angle between the cable and the steel truss girder and the bending stiffness of the cable, the variation law of energy density at the connection node between the cable and the steel truss girder is obtained by changing the angle between the cable and the steel truss girder and the bending stiffness of the cableway. Due to the limitations of finite element wave analysis, from the perspective of theoretical analysis, this paper establishes the local scattering matrix at the node, proves the obtained law to a certain extent, and uses the return wave-light matrix method to analyze and solve the energy density at the connection node between the cable bar and the steel truss girder, and the final analytical solution is in good agreement with the numerical solution, which proves that the law obtained by numerical analysis is correct.

Spatially Local Surrogate Modeling of Subgrid-Scale Effects in Idealized Atmospheric Flows: A Deep Learned Approach Using High-Resolution Simulation Data

Abstract We introduce a machine learned surrogate model from high-resolution simulation data to capture the subgrid-scale effects in dry, stratified atmospheric flows. We use deep neural networks (NNs) to model the spatially local state differences between a coarse-resolution simulation and a high-resolution simulation. The setup enables the capture of both dissipative and antidissipative effects in the state differences. The NN model is able to accurately capture the state differences in offline tests outside the training regime. In online tests intended for production use, the NN-coupled coarse simulation has higher accuracy over a significant period of time compared to the coarse-resolution simulation without any correction. We provide evidence of the capability of the NN model to accurately capture high-gradient regions in the flow field. With the accumulation of the errors, the NN-coupled simulation becomes computationally unstable after approximately 90 coarse simulation time steps. Insights gained from these surrogate models further pave the way for formulating stable, complex, physics-based spatially local NN models which are driven by traditional subgrid-scale turbulence closure models. Significance Statement Flows in the atmosphere are highly chaotic and turbulent, comprising flow structures of broad scales. For effective computational modeling of atmospheric flows, the effects of the small- and large-scale structures need to be captured by the simulations. Capturing the small-scale structures requires fine-resolution simulations. Even with the current state-of-the-art supercomputers, it can be prohibitively expensive to simulate these flows when computed for the entire earth over climate time scales. Thus, it is necessary to focus on the larger-scale structures using a coarse-resolution simulation while capturing the effects of the smaller-scale structures using some parameterization (approximation) scheme and incorporating it into the coarse-resolution simulation. We use machine learning to model the effects of the small-scale structures (subgrid-scale effects) in atmospheric flows. Data from a fine-resolution simulation is used to compute the missing subgrid-scale effects in coarse-resolution simulations. We then use machine learning models to approximate these differences between the coarse- and fine-resolution simulations. We see improved accuracy for the coarse-resolution simulations when corrected using these machine learned models.

The effect of different subgrid-scale models on the flow field in cyclone separator using large-eddy simulations: A benchmark study

Abstract The cyclone separator is extensively utilized across industries to extract solid particles from gas streams. This study is focused on the steady and unsteady simulations of the Stairmand cyclone using large-eddy simulations aiming to assess the performance using different subgrid-scale models viz. standard Smagorinsky (Cs =1), dynamic Smagorinsky, Wall-Adapting Local Eddy-Viscosity (WALE), Wall-Modeled LES (WMLES), and dynamic kinetic energy models. The velocity profiles within the cyclone separator were analyzed under both steady and unsteady conditions, with reference to the Hoekstra experiment for validation and comparison. Velocity profiles inside the cyclone separator were inadequately predicted by the steady-state simulation, whereas the unsteady-state simulation yielded results more aligned with experimental values. The present study suggests that adopting subgrid-scale LES models such as standard Smagorinsky and WMLES offers a better option for analyzing flow patterns.

JOYS+: The link between the ice and gas of complex organic molecules

Context. A rich inventory of complex organic molecules (COMs) has been observed in high abundances in the gas phase toward Class 0 protostars. It has been suggested that these molecules are formed in ices and sublimate in the warm inner envelope close to the protostar. However, only the most abundant COM, methanol (CH3OH), had been firmly detected in ices before the era of the James Webb Space Telescope (JWST). Now, it is possible to detect the interstellar ices of other COMs and constrain their ice column densities quantitatively. Aims. We aim to determine the column densities of several oxygen-bearing COMs (O-COMs) in both gas and ice for two low-mass protostellar sources, NGC 1333 IRAS 2A (hereafter IRAS 2A) and B1-c, as case studies in our JWST Observations of Young proto-Stars (JOYS+) program. By comparing the column density ratios with respect to CH3OH between both phases measured in the same sources, we can probe the evolution of COMs from ice to gas in the early stages of star formation. Methods. The column densities of COMs in gas and ice were derived by fitting the spectra observed by the Atacama Large Millimeter/submillimeter Array (ALMA) and the JWST/Mid-InfraRed Instrument-Medium Resolution Spectroscopy (MIRI-MRS), respectively. The gas-phase emission lines were fit using local thermal equilibrium models, and the ice absorption bands were fit by matching the infrared spectra measured in laboratories. The column density ratios of four O-COMs (CH3CHO, C2H5OH, CH3OCH3, and CH3OCHO) with respect to CH3OH were compared between ice and gas in IRAS 2A and B1-c. Results. We were able to fit the fingerprint range of COM ices between 6.8 and 8.8 μm in the JWST/MIRI-MRS spectra of B1-c using similar components to the ones recently used for NGC 1333 IRAS 2A. We claim detection of CH4, OCN−, HCOO−, HCOOH, CH3CHO, C2H5OH, CH3OCH3, CH3OCHO, and CH3COCH3 in B1-c, and upper limits have been estimated for SO2, CH3COOH, and CH3CN. The total abundance of O-COM ices is constrained to be 15% with respect to H2O ice, 80% of which is dominated by CH3OH. The comparison of O-COM ratios with respect to CH3OH between ice and gas shows two different cases. On the one hand, the column density ratios of CH3OCHO and CH3OCH3 match well between the two phases, which may be attributed to a direct inheritance from ice to gas or strong chemical links with CH3OH. On the other hand, the ice ratios of CH3CHO and C2H5OH with respect to CH3OH are higher than the gas ratios by 1–2 orders of magnitude. This difference can be explained by gas-phase reprocessing following sublimation, or different spatial distributions of COMs in the envelope, which is an observational effect resulting from ALMA and JWST tracing different components in a protostellar system. Conclusions. The firm detection of COM ices other than CH3OH is reported in another well-studied low-mass protostar, B1-c, following the recent detection in NGC 1333 IRAS 2A. The column density ratios of four O-COMs with respect to CH3OH show both similarities and differences between gas and ice. Although the straightforward explanations would be the direct inheritance from ice to gas and the gas-phase reprocessing, respectively, other possibilities such as different spatial distributions of molecules cannot be excluded.

Multi-view brain functional connectivity and hierarchical fusion for EEG-based emotion recognition

Emotional states evolve gradually from their onset to full manifestation, reflected in changes in brain functional connectivity. Existing research often focuses only on the final emotional state, missing the rich, dynamic information of emotional evolution. This paper proposes a novel network framework leveraging global and local brain functional connectivity through a multi-view approach. We introduce a global and local connectivity modeling method using electroencephalogram signals to simulate brain connectivity changes across time and specific moments. A residual-based local detail feature extractor combined with a coordinate attention module captures long-range dependencies and maintains positional accuracy. A global feature extractor is also designed for the effective extraction of emotional information. Finally, a hierarchical multi-scale cross-attention feature fusion module integrates features across different levels to enhance emotion recognition performance and robustness. Extensive experiments on three publicly available datasets demonstrate the framework’s superiority and generalization capability.

Role of two isothermal cylinders towards three-dimensional flow and melting of phase-change materials

Recently, investigators focused on examining the melting of phase change materials (PCM) in regular two-dimensional or three-dimensional flow domains. At the same time, this topic still needs more study to get a better understanding of it. Also, such problems should be reported using the local thermal non-equilibrium (LTNE) model because the latent heat substances and the included porous elements have different temperatures. Therefore, this study examines the three-dimensional flow and melting process of phase change materials (PCM) within cubic enclosures filled with copper foam, using a local thermal non-equilibrium model (LTNE). The system includes two isothermal cylinders with different temperature conditions, varying distances, and radii, placed within the flow domain. The enthalpy-porosity approach is applied to model the PCM behavior, while the Brinkman-extended non-Darcy model accounts for high-velocity flow situations. A magnetic field is introduced to control the flow, with the Lorentz force acting opposite to gravity. The governing equations are solved using a home-developed code based on the finite volume method with the SIMPLE algorithm. Key parameters investigated include cylinder radii, Fourier number, Hartmann number, horizontal and vertical distances between cylinders, and Darcy number. The results are presented through streamlines, temperature distributions for fluid and solid phases, and profiles of the Nusselt number and average liquid fraction.

A blueprint for coupling a hydrological model with fine- and coarse-scale atmospheric regional climate change models for probabilistic streamflow projections

In cold regions, climate change, including warming and changes in snowmelt dynamics, have profound impacts on streamflow patterns, often leading to flooding events. Understanding the projected changes in hydrometeorological factors contributing to streamflow, such as snowmelt runoff and rain-on-snowmelt, is crucial for effective adaptation and mitigation strategies. It is also essential to consider uncertainty in streamflow projections and incorporate this uncertainty into flood predictions. This study presents a blueprint for calculating probabilistic future streamflow and flow duration curves in a mountainous cold region. The methodology integrated forcings from an atmospheric model (WRF) with a hydrological model (MESH) at fine spatial (4 km) and temporal (3-hourly) resolutions. To account for uncertainty, an ensemble of 15 CanRCM4 regional climate simulations with varying boundary conditions for the RCP 8.5 scenario was utilized. A novel method was developed to perturb the CanRCM4 simulations’ precipitation using a WRF Pseudo Global Warming run to incorporate uncertainty. This comprehensive approach revealed significant uncertainty in streamflow driven by internal variability. WRF-driven simulations without uncertainty showed a decrease in future high streamflows, while the perturbed simulations demonstrated the potential for substantially higher streamflows under climate change. Moreover, the study derived probabilistic flow duration curves from the streamflow projections, aiding in estimating flood frequency for floodplain mapping when driving local hydraulic models. The methodology developed in this work can be extended to other river basins in Canada and elsewhere where gridded downscaled climate model outputs are available.

Bayesian State-space modelling of stock Markets in G7 Countries

This work examines the impact of Coronavirus disease (COVID-19) on the stock market of Group of Seven (G7) countries during the first wave of COVID-19 using daily data from March, 1st of 2020 to December, 31st of 2020. Focussing on such period, a Bayesian Structural Time Series Model (BSTSM) was used to capture the effects of first wave of COVID-19 on the stock market performance of these G7 countries by employing a Markov Chain Monte Carlo (MCMC) method. We considered an Autoregressive (AR) model with time-varying parameters and a local linear trend model to know if the stock price of these countries during the period of the first wave of COVID-19 is changing over time. There was a stochastic trend in stock prices of G7 countries during the period of the first wave of COVID-19 while the AR process itself was also changing over time. The stock market of the USA followed by Japan performed better than other G7 countries during the first phase of the COVID-19 pandemic while the stock market of France was affected during the COVID-19 pandemic.

A video speed measurement method for UAV targeting ground targets based on kalman filtering

Abstract Measuring the velocity information of moving targets, as an application scenario for drone target velocity measurement, is an important focus in the process of drone intelligence reconnaissance. To improve the speed and accuracy of unmanned aerial vehicles (UAVs) in locating and measuring moving targets, a reliable target localization and velocity model based on UAV reconnaissance video frames and Kalman filtering is proposed. This algorithm model can complete the localization and velocity measurement tasks of moving targets under complex conditions without the need to obtain ranging information from drones. To verify the algorithm, the Monte Carlo simulation method was used for simulation experiments. The experimental results show that the error of this velocity measurement algorithm can meet the accuracy requirements of unmanned aerial vehicles for ground-moving targets.

Void drift in narrow rectangular channels for highly advective bubbly flows: A macroscopic drift-flux model derived from two-fluid local simulations

This article addresses the issue of reduced models to describe turbulent two-phase flows in industrial applications. This work is an application based on Bois (2021) theoretical derivations to connect the spatially-averaged drift-flux model (DFM) to the local Reynolds-averaged two-fluid model (TFM). It presents new opportunities to calibrate closure laws or propose new models based on finer-scale descriptions. Highly convective steady bubbly flows with wall-peaking void fraction profiles are considered. Dispersed bubbles are weakly deformable. A new model dedicated to void fraction dispersion in flat rectangular channels is developed for application into the DFM. The model is calibrated based on reference local Euler-Euler two-fluid simulations of pressurised water and steam mono-dispersed bubbles, in adiabatic conditions. 64 conditions are considered to cover regularly the 3D parameter space in void fraction, Reynolds and Eötvös numbers. The new formulation proposed shows a very good fit with the post-processed predictions of local CFD simulations along with a substantial reduction in grid requirement. The model contains sub-filter correlations between vapour concentration and mixture or relative velocities. This model derived from local simulations is validated a posteriori in an industrial component scale code where the full space-filtered DFM is implemented and resolved. This work proves the benefits and feasibility of one-by-one model development and calibration based on two-fluid simulations.

Helium Abundance of the Sun: A Spectroscopic Analysis

Determining the He/H ratio in cool stars presents a fundamental astrophysical challenge. While this ratio is established for hot O and B stars, its extrapolation to cool stars remains uncertain due to the absence of helium lines in their observed spectra. We address this knowledge gap by focusing on the Sun as a representative cool star. We conduct spectroscopic analyses of the observed solar photospheric lines by utilizing a combination of MgH molecular lines and neutral Mg atomic lines including yet another combination of CH and C2 molecular lines with neutral C atomic lines. Our spectroscopic analyses were further exploited by adopting solar model atmospheres constructed for distinct He/H ratios to determine the solar photospheric helium abundance. The helium abundance is determined by enforcing the fact that for an adopted model atmosphere with an appropriate He/H ratio, the derived Mg abundance from the neutral Mg atomic lines and that from the MgH molecular lines must be the same. The same holds for the C abundance derived from neutral C atomic lines and that from CH lines of the CH molecular band and C2 lines from the C2 Swan band. The estimated He/H ratio for the Sun is discussed based on the one-dimensional local thermodynamic equilibrium model atmosphere. The helium abundance (He/H = 0.091−0.014+0.019 ) obtained for the Sun serves as a critical reference point to characterize the He/H ratio of cool stars across the range in their effective temperature.

The influence of urban green and recreational areas on the price of housing in Zagreb

Urban green and recreational spaces are important for residents and one of the foundations for the functioningof cities. They offer numerous benefits, including the influence on the price of real estate as an economic benefit.In this paper, the influence of green and recreational areas in Zagreb on the price of apartments in residential buildings is analysed using the hedonic pricing model. The data for the study was processed and analysed using a geographic information system. A local hedonic price model was developed. The results show that some of the observed green and recreational areas increase the price of housing due to their proximity, while others decrease it. Some areas have no influence on the price of housing. The influence of the observed areas on housing prices was heterogeneous. Spatial differences were also found in the influence of the observed areas on housing prices. The most attractive factors when choosing a place to live are Jarun and the parks in the city centre. The price of apartments that near the Jarun increases by 41 EUR/m2 if the apartment is 100 m closer to the Jarun. The price of the apartment increases by 91 EUR/m2 the closer the apartment is to the parks in the city centre (per 100 m). Apartments near Jarun and the parks in the city centre are not available for residents with lower socio-economic status. The forest areas, Maksimir Park and the banks of the Sava river are not favoured when choosing a place to live. Neighbourhoods near these areas are available to the lower socioeconomic status population. Bundek raises the price of one part of the apartments and lowers the price of the other. The results of this study can contribute to the study of green gentrification in Zagreb, but further research is needed on socio-economic indicators and other changes in the region.

MedLSAM: Localize and segment anything model for 3D CT images

Recent advancements in foundation models have shown significant potential in medical image analysis. However, there is still a gap in models specifically designed for medical image localization. To address this, we introduce MedLAM, a 3D medical foundation localization model that accurately identifies any anatomical part within the body using only a few template scans. MedLAM employs two self-supervision tasks: unified anatomical mapping (UAM) and multi-scale similarity (MSS) across a comprehensive dataset of 14,012 CT scans. Furthermore, we developed MedLSAM by integrating MedLAM with the Segment Anything Model (SAM). This innovative framework requires extreme point annotations across three directions on several templates to enable MedLAM to locate the target anatomical structure in the image, with SAM performing the segmentation. It significantly reduces the amount of manual annotation required by SAM in 3D medical imaging scenarios. We conducted extensive experiments on two 3D datasets covering 38 distinct organs. Our findings are twofold: (1) MedLAM can directly localize anatomical structures using just a few template scans, achieving performance comparable to fully supervised models; (2) MedLSAM closely matches the performance of SAM and its specialized medical adaptations with manual prompts, while minimizing the need for extensive point annotations across the entire dataset. Moreover, MedLAM has the potential to be seamlessly integrated with future 3D SAM models, paving the way for enhanced segmentation performance. Our code is public at https://github.com/openmedlab/MedLSAM.

Model-based dimensional NDE from few X-ray radiographs: Application to the evaluation of wall thickness in metallic turbine blades

The extraction of 3D dimensional measurements based on a limited number of 2D X-ray radiographs of a part would offer a significant speed-up of quality control procedures in industry. However, there are challenges with respect to both measurements and uncertainties. This work addresses these challenges by creating an estimated numerical model of the imaged part on which dimensional measurements can be made. The numerical model is chosen as a parametric deformable model that encodes the expected shape variability of the parts resulting from the manufacturing process. The parameters and uncertainties of the numerical model of the imaged part are estimated by the registration of the computed projections of the model and the observed radiographs without the need of any segmentation. The registration requires the model, the initial parameters, and the observed radiographs. The proposed approach is applied to the inspection of turbine blades manufactured by investment casting, and in particular to the measurement of their wall thickness, which is a critical control. The deformable model consists in partitioning the inner ceramic core into multiple subparts, which may undergo a rigid body motion with respect to the master die. Wall thickness measurements are determined from the estimation of these rigid body motions. To assess the reliability of the proposed procedure, a repeatability study is performed. In addition, wall thickness measurements were compared to corresponding measurements from the surface of the metal boundary obtained by X-ray computed tomography. This surface was determined from a reconstructed tomogram using commercial software. Both analyses show that such measurements are reliable and efficient. Furthermore, residual differences between captured and computed projections reveal localized shape deviations from the CAD model, meaning that despite localized model errors, the approach is operable.