Temporal Model-Based Federated Active Medical Image Classification

Traditional federated learning relies on fully labeled datasets in each medical institution, which is impractical in real-world clinical scenarios. Federated Active Learning (FAL) addresses this by selecting a few informative samples for labeling, but it faces challenges such as domain shift across institutions. Besides, existing FAL methods rely on single-round model knowledge to estimate prediction-level uncertainty, ignoring uncertainty from features and model evolution during training. In this work, we propose TM-FAL, a novel framework for federated active medical image classification under domain shift. TM-FAL proposes a new uncertainty by integrating feature differences and prediction confidence from temporal local and global models to capture both local-global differences and the inherent complexity of images. Additionally, we use the prediction of the global model as pseudo labels to group images to mitigate class imbalance caused by uncertainty-based selection. Experiments on two medical image classification datasets demonstrate that TM-FAL outperforms various state-of-the-art methods.This work is supported by the Guangdong Science and Technology Department (No. 2024ZDZX2004), and Guangdong Provincial Key Lab of Integrated Communication, Sensing and Computation for Ubiquitous Internet of Things(No. 2023B1212010007)

Modeling and state estimation of liquid metal batteries

Liquid metal batteries (LMBs) represent a promising solution for grid-scale energy storage compared to other battery technologies, due to their low cost, high power density, excellent cyclability, long cycle life, self-healing characteristics, high Coulombic efficiency, and ease of scalability. The operation of an LMB involves multiple physical processes, including electrochemical reactions, mass transfer, heat transfer, fluid flow, etc. These processes are highly coupled and influence each other significantly, resulting in complex and nonlinear system behavior. As a result, accurately modeling and predicting the performance of LMBs under various operating conditions remains a significant challenge. This review highlights recent advances in LMB modeling and state estimation, providing a critical evaluation of existing frameworks in terms of modeling accuracy, computational efficiency, and practical limitations. It concludes by identifying key challenges and recommending future research directions to improve the reliability and practical deployment of LMBs in large-scale renewable energy systems.This paper is an outcome of a larger program (Stor Cortex) to develop intelligent solutions for storage technologies for the ENOWA.NEOM energy systems and was funded by ENOWA.NEOM through a technical consulting services agreement with KAUST. AMBRI INC (USA) provided the LMB cells and the system on rent to ENOWA.NEOM as part of its LDES Pilot Program Evaluation and under a consulting agreement ‘Ambri's Liquid Metal Battery Demo System’, contract number 1110000033

A Causal-Holistic Adaptive Intervention Network for Tailoring Automated Coronary Artery Disease Diagnosis to Individual Patients

Given the global prevalence and high mortality of coronary artery disease (CAD), automated CAD diagnosis should evolve toward personalized methods to maximize its clinical value. However, existing techniques have been limited to artery-level prediction, lacking patient-level causality and failing to effectively account for individual patient confounders. In this work, for the first time, we introduce a Causal-Holistic Adaptive Intervention Network (CAIN) that tailors personalized CAD diagnosis for individual patients. CAIN generates semantic representations at both the patient and artery dual-levels for each case, constructing a holistic causal graph that captures individual-specific characteristics. It then implements adaptive causal intervention based on the patient’s specific condition, using dynamically updated and differentiated intervention variables. Experimental results on CCTA scans from 602 patients and 6,830 coronary branches across three clinical centers show that CAIN outperforms state-of-the-art methods, offering personalized clinical guidance. The source code is available at (https://github.com/PerceptionComputingLab/CAIN).This work was supported by the National Natural Science Foundation of China (Nos. 62372135 and 62272135), the King Abdullah University of Science and Technology (KAUST) Office of Research Administration (ORA) under Award No. REI/1/5234-01-01, REI/1/5414-01-01, REI/1/5289-01-01, REI/1/5404-0101, REI/1/5992-01-01, URF/1/4663-01-01, Center of Excellence for Smart Health (KCSH), under award number 5932, Center of Excellence on Generative AI, under award number 5940, Heilongjiang Provincial Key Research and Development Plan 2024ZX12C23, 2023ZX02C10, 2022ZX01A30, and GA23C007, Hunan Provincial Key Research and Development Plan 2023SK2060, Jiangsu Provincial Key Research and Development Plan BE2023081, and the Natural Science Foundation of Heilongjiang Province under Grant LH2024F019

Unlocking mid-to-long-term flexibility: why seasonal pumped storage outperforms conventional pumped storage in wind-solar dominated grids

Global efforts toward Dual Carbon Goals have spurred rapid growth in wind and solar installations worldwide. However, the inherent randomness and intermittency of wind and solar pose critical challenges to long-term flexibility requirements in power systems. While pumped storage remains a crucial regulating power source, the differential effectiveness between seasonal pumped storage (SPS) and conventional pumped storage (CPS) in mitigating medium-to-long-term renewable fluctuations has not been systematically investigated. Therefore, this study provides a comprehensive evaluation of the performance of SPS and CPS in addressing stochastic fluctuations of wind and solar. First, the output characteristics of wind and solar resources were analyzed, and multiple wind and solar scenarios were developed to simulate the regulatory differences between SPS and CPS under varying wind and solar scenarios. Subsequently, this study proposes an integrated evaluation framework combining entropy-weighted and coefficient of variation methods to objectively assess the technical, economic, and stability indicators, using Qinghai Province as a case study to analyze the advantages of both SPS and CPS. The results show that SPS outperforms CPS across many technical indicators. Notably, the carbon emission reduction effect of SPS becomes more pronounced in scenarios with higher wind power capacity. Economically, it has to be admitted that the investment cost of SPS is 1.33 times that of CPS. Although the operating cost is one fifth of CPS, the levelized cost of electricity of SPS is still higher than CPS. Finally, the comprehensive evaluation demonstrates the superiority of SPS, achieving an overall benefit score of 98.03, in stark contrast to 68.6 for CPS. These findings offer critical insights for energy planners and policymakers in optimizing storage solutions to enhance grid flexibility and accelerate decarbonization.This work was supported by the following grants: National Natural Science Foundation of China (Grant No.: 52409120), National Natural Science Foundation of China (Grant No.: 52339006), Free Exploration Basic Research (Grant No.: 2024ZY-JCYJ-02-37), China Postdoctoral Science Foundation, 75th Batch of General Projects (Grant No.: 2024M752626), Postdoctoral Fellowship Program of CPSF (Grant No.: GZC20232157), and Shaanxi Province Postdoctoral Research Project (Grant No.: 2023BSHEDZZ105)

Concentrated solar power (CSP) driven desalination systems: A techno-economic review

The rising global demand for freshwater, coupled with the urgency to transition away from fossil fuel-based energy systems, has intensified research into sustainable desalination solutions. However, conventional desalination methods reliant on fossil fuels are highly energy-intensive, presenting substantial obstacles to achieving a low-carbon energy transition. Concentrated solar power (CSP) presents a compelling alternative, particularly for arid regions with high direct normal irradiation (DNI). This review provides a comprehensive analysis of recent advancements in CSP-driven desalination technologies, with a particular focus on key methods such as multi-stage flash distillation (MSF), multi-effect distillation (MED), membrane distillation (MD), and innovative hybrid systems. It systematically categorizes solar desalination technologies based on their functional components, economic feasibility, and research progress, highlighting advancements in hybrid system designs, thermal performance optimization, and economic evaluations. Although CSP desalination has experienced significant growth over the past five years, challenges remain in developing cost-competitive solutions, particularly in addressing parasitic losses during integration with conventional power systems. This review identifies potential strategies to overcome these challenges, including optimized system configurations, the integration of thermal energy storage, the adoption of advanced power cycles, and the hybridization of MED-RO systems. Realizing the full potential of CSP for sustainable freshwater production will require advances in materials, system integration, and hybrid configurations. A multidisciplinary approach—combining thermal sciences, desalination engineering, power systems, and techno-economic analysis, alongside supportive policies—is key to establishing CSP desalination as a viable solution for high-DNI, water-scarce regions. This review provides a timely and comprehensive overview of current progress and future directions, offering practical insights for advancing sustainable desalination technologies

Importance sampling for rare event tracking within the ensemble Kalman filtering framework

In this work we employ importance sampling (IS) techniques to track a small over-threshold probability of a running maximum associated with the solution of a stochastic differential equation (SDE) within the framework of ensemble Kalman filtering (EnKF). The proposed method acts as a post-processing step applied to the EnKF output: it uses the ensemble at a given observation time to estimate the probability of a rare event occurring before the next observation, without altering the EnKF itself. Between two observation times of the EnKF, we propose to use IS with respect to the initial condition of the SDE, IS with respect to the Wiener process via a stochastic optimal control formulation, and combined IS with respect to both initial condition and Wiener process. Both IS strategies require the approximation of the solution of Kolmogorov Backward equation (KBE) with boundary conditions. In multidimensional settings, we employ a Markovian projection dimension reduction technique to obtain an approximation of the solution of the KBE by just solving a one dimensional PDE. The proposed ideas are tested on three illustrative examples: Double Well SDE, Langevin dynamics and noisy Charney-deVore model, and showcase a significant variance reduction compared to the standard Monte Carlo method and another sampling-based IS technique, namely, multilevel cross entropy.Open access publishing provided by King Abdullah University of Science and Technology (KAUST). This work was supported by the KAUST Office of Sponsored Research (OSR) under Award No. URF/1/2584-01-01 and the Alexander von Humboldt Foundation. E. von Schwerin, G. Shaimerdenova and R. Tempone are members of the KAUST SRI Center for Uncertainty Quantification in Computational Science and Engineering

Structure and Smoothness Constrained Dual Networks for MR Bias Field Correction

MR imaging techniques are of great benefit to disease diagnosis. However, due to the limitation of MR devices, significant intensity inhomogeneity often exists in imaging results, which impedes both qualitative and quantitative medical analysis. Recently, several unsupervised deep learning-based models have been proposed for MR image improvement. However, these models merely concentrate on global appearance learning, and neglect constraints from image structures and smoothness of bias field, leading to distorted corrected results. In this paper, novel structure and smoothness constrained dual networks, named S2DNets, are proposed aiming to self-supervised bias field correction. S2DNets introduce piece-wise structural constraints and smoothness of bias field for network training to effectively remove non-uniform intensity and retain much more structural details. Extensive experiments executed on both clinical and simulated MR datasets show that the proposed model outperforms other conventional and deep learning-based models. In addition to comparison on visual metrics, downstream MR image segmentation tasks are also used to evaluate the impact of the proposed model. The source code is available at:https://github.com/LeongDong/S2DNets.This work was supported by the National Natural Science Foundation of China (Nos. 62372135 and 62272135)

An experimental study on combustion behavior of candle flames in hypergravity

This work explores the combustion characteristics of candle flames with various wick diameters and lengths for the gravity level of 3 − 9 g by utilizing a centrifuge. Results show that the burning rate and flame height decrease with increasing gravity, as the suppression of capillary action inside the wick reduces the fuel supply. There exists a critical gravity level Gcr to determine whether the liquid wax can reach the wick tip. When G > Gcr at hypergravity, the liquefied wax cannot reach the wick tip, resulting in reduced flame height and burning rate, whereas at G < Gcr, sufficient liquid wax is supplied to generate higher flame height. The candle flame has a transition from a stable laminar to oscillating flame, because of the enhanced buoyancy-induced velocity in hypergravity. However, a flame extinction is observed at 9 g due to minimized fuel supply rate. Considering the capillary-driven fuel supply mechanism and enhanced buoyant flow, flame oscillation frequency and amplitude are found to initially increase and then decrease with two oscillation modes (bulk flickering and tip flickering). The oscillation frequencies can well be described in terms of the Strouhal and Froude number relationship incorporating the change of burning rate. A physical model of burning rate is established considering the variation of characteristic length. The flame height in hypergravity is well predicted based on the Roper's model by adopting the "apparent port burner" concept, taking into account the effect of fuel supply rate. This paper provides comprehensive experimental data and facilitates fundamental understanding regarding the candle flames in hypergravity.This research was funded jointly by National Natural Science Foundation of China (Nos. 52225605, 52306171), Space Application System of China Manned Space Program under the funded project (KJZ-YY-NRS0602), China National Postdoctoral Program for Innovative Talents (BX20230354) and New Cornerstone Science Foundation through the XPLORER PRIZE

On free boundary problems shaped by varying singularities

We start the investigation of free boundary variational models featuring varying singularities. The theory depends strongly on the nature of the singular power γ(x) and how it changes. Under a mild continuity assumption on γ(x), we prove the optimal regularity of minimizers. Such estimates vary point-by-point, leading to a continuum of free boundary geometries. We also conduct an extensive analysis of the free boundary shaped by the singularities. Utilizing a new monotonicity formula, we show that if the singular power γ(x) varies in a W1,n+ fashion, then the free boundary is locally a C1,δ surface, up to a negligible singular set of Hausdorff co-dimension at least 3.This publication is based upon work supported by King Abdullah University of Science and Technology (KAUST) under Award No. ORFS-CRG12-2024-6430. It was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. DJA is supported by CNPq grant 427070/2016-3 and grant 2019/0014 from Paraíba State Research Foundation (FAPESQ). JMU is partially supported by UID/00324 - Centre for Mathematics of the University of Coimbra. ET is partially supported by the Grayce B. Kerr Chair funds at Oklahoma State University. We are deeply grateful to the referee for an exceptionally careful and insightful report. The comments and suggestions were of great help and substantially improved the manuscript

Burning characteristics of ethanol droplet suspended on NiCr wire with applied AC electric field

The effect of applied AC electric field on the burning behavior of ethanol droplets suspended on a NiCr wire (0.5 mm in diameter) was investigated by varying the AC frequency fAC (10–1000 Hz) and voltage VAC (1–7 kV). In the baseline case without applying electric field, internal recirculation was developed by heat transfer through the wire to the droplet due to Marangoni convection, enhancing the evaporation rate. Depending on VAC and fAC, three regimes can be identified: vertical oscillation of the droplet due to the combined effects of vertical electrostatic and dielectrophoretic forces (Regime I), combined oscillation and dripping, leading to electrospray of fine droplets from the surface by electrostatic force (Regime II); flame extinction due to substantial fuel loss via electrospray (Regime III). Ethanol droplets exhibited distinct dynamic behaviors from previously studied burning n-decane droplets, primarily due to the differences in fuel permittivity (affecting dielectrophoretic force) and electric conductivity (influencing dielectric relaxation frequency), both of which were not considered previously. Force-based scaling analysis revealed differences in oscillation amplitude and droplet response, and captured the onset conditions for dripping and extinction with fAC,cr∼VAC−p, in good agreement with experiments. The normalized droplet lifetime correlated strongly with key physical parameters, including the radial electric field gradient, AC frequency, and flame width and height.This work was supported by Basic Science Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (RS-2023–002451310 and RS-2024–00356149)