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Microwave reflectometry is a key diagnostic for plasma density measurements in future fusion devices and has been selected by ITER to measure plasma density and shape. One of the main challenges for its deployment is ensuring accurate real-time density profile reconstruction, which is essential for plasma position control, feedback regulation, and overall stability. In this work, we present a multi-scale convolutional neural network for real-time reconstruction of density profiles directly from raw microwave reflectometer signals. The model leverages multi-scale feature extraction to reconstruct the full density profile in a single forward pass, achieving an inference time of ∼4 ms, which is sufficient for real-time deployment on EAST. Trained and validated on 42 000 density profiles from the 2025 EAST campaign, the model achieved an average accuracy of 98% and demonstrated strong generalization across diverse operational conditions, including ramp-up phases, L–H transitions, gas fueling, and edge-localized modes. Looking ahead, we plan to integrate the model into EAST’s microwave reflectometer system to provide real-time density profiles, supporting density control and edge monitoring in EAST and future devices.

Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information, with its application to neuroscience termed neuroinformatics. Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms, which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases. Importantly, integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile. In this review, we first summarize data mining studies utilizing datasets from the individual type of omics analysis, including epigenetics/epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and spatial omics, pertaining to Alzheimer's disease, Parkinson's disease, and multiple sclerosis. We then discuss multi-omics integration approaches, including independent biological integration and unsupervised integration methods, for more intuitive and informative interpretation of the biological data obtained across different omics layers. We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks. Finally, we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery, therapeutic development, and elucidation of disease mechanisms. We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine. [Display omitted] •Significance of data mining of multi-omics datasets in translational neuroscience.•Multi-omics data integration approaches: independent biological integration and unsupervised integration.•Systems bioinformatics approach: towards the reconstruction of a comprehensive human brain profile.•Applications in biomarker discovery, therapeutic development, and elucidation of pathogenic mechanisms of neurodegeneration.

Combining the analysis of multiple diagnostics and well-chosen prior information in the framework of Bayesian probability theory, the Integrated Data Analysis code (IDA Fischer et al 2010 Fusion Sci. Technol. 58 675–84) can provide density and temperature radial profiles of fusion plasmas. These IDA-fitted measurements are then used for further analysis, such as discharge simulations and other experimental data analysis. Since IDA considers measurement data, which is frequently fragmentary, with statistical and systematic uncertainties, which are often difficult to quantify, from a heterogeneous set of diagnostics, the fitted profiles and their gradients may be in contradiction to well-established expectations from transport theory. Using the modeling suite ASTRA coupled with the quasi-linear transport solver TGLF, we have created a loop in which simulated profiles and their uncertainties are fed back into IDA as an additional prior, thus providing constraints about the physically reasonable parameter space. We apply this physics-motivated prior to several different plasma scenarios and find improved heat flux match, while still matching the experimental data. This work feeds into a broader effort to make IDA more robust against measurement uncertainties or lack of measurements by combining multiple transport solvers with different levels of complexity and computing costs in a multi-fidelity approach.

Precision modeling of the hydraulic gear pump pressure dynamics depends on the accurate prediction of volumetric displacement in the inter-tooth spaces of the gear. By accurate reconstruction of the gear profile, detailed transient volumetric information can be determined. Therefore, this paper reports a non-contact gear measurement device using two opposing laser triangulation sensors, and the key geometrical models to reconstruct the profile with geometrical error compensation. An optimization-based key parameter calculation method is also proposed to find the unknown orientation of the sensor. Finally, an experimental setup is established, the performance of the device is tested and the geometric model is validated. Initial results showed that the method is able to reconstruct the target tooth profile and compensated results can reduce the geometrical error by up to 98% compared to the uncalibrated ones.

In nondestructive testing, magnetic flux leakage (MFL) inspection is extensively employed for the inversion of pipeline defects. Exact reconstruction of the defect plane with measurements is a pressing issue in the field of MFL detection. This article proposes a method that incorporates a layer-by-layer genetic algorithm with tabu search (GA-TS). During the iterative process, our objective is to utilize a tabu search based on evolutionary algorithms to avoid local optimal solutions, obtain global optimal solutions, and reconstruct defects with enhanced accuracy. In addition to enhancing the accuracy of pipeline defect categorization, our approach has two significant limitations: its limited global search capability and slow convergence rate. Finally, the method is evaluated via experiments in which MFL signals obtained from an experimental platform and simulation signals are used. Practical results and a thorough comparative study of alternative approaches confirm the model’s superiority.

The rapid development of artificial intelligence (AI) and deep learning has revolutionized the field of data analysis in recent years, including signal data acquired by remote sensors. Light Detection and Ranging (LiDAR) technology is widely used in atmospheric research for measuring various atmospheric parameters. Wind measurement using LiDAR data has traditionally relied on the spectral centroid (SC) algorithm. However, this approach has limitations in handling LiDAR data, particularly in low signal-to-noise ratio (SNR) regions. To overcome these limitations, this study leverages the capabilities of customized deep-learning techniques to achieve accurate wind profile reconstruction. The study uses datasets obtained from the European Centre for Medium Weather Forecasting (ECMWF) Reanalysis v5 (ERA5) and the mobile Incoherent Doppler LiDAR (ICDL) system constructed by the University of Science and Technology of China. We present a simulation-based approach for generating wind profiles from the statistical data and the associated theoretical calculations. Whereafter, our team constructed a convolutional neural network (CNN) model based on the U-Net architecture to replace the SC algorithm for LiDAR data post-processing. The CNN-generated results are evaluated and compared with the SC results and the ERA5 data. This study highlights the potential of deep learning-based techniques in atmospheric research and their ability to provide more accurate and reliable results.

The precise depth measurement of common structural defects, such as bulging, delamination, and spalling, is paramount in building condition assessment. This paper presents an efficient and portable parallel laser line‐camera system designed for accurately reconstructing defect depth profiles from projected laser stripes. The system features a telescopic design to enhance the measurement range and operational flexibility. Central to its efficacy is a machine learning–aided image processing algorithm that facilitates both robust and highly accurate depth measurements. Specifically, advanced deep learning techniques are applied to detect and segment laser stripes from background interference. A novel hypothesis optimization (HO) algorithm, grounded in a three‐layer backpropagation (BP) neural network, is proposed to reduce errors in laser baseline recovery caused by image distortion further. Comprehensive laboratory and field experiments validate the measurement accuracy and superior noise suppression capabilities of the system. Additionally, the paper studies potential errors that could emerge during field operations, thereby confirming the practical utility of the device. The proposed system quickly generates surface profiles in a single shot, making it a valuable tool for monitoring uneven objects.

The estimation of the disturbance input acting on a vehicle from its given responses is an inverse problem. To overcome some of the issues related to ill-posed inverse problems, this work proposes a method of reconstructing the road roughness based on the Kalman filter method. A half-car model that considers both the vehicle and equipment is established, and the joint input-state estimation method is used to identify the road profile. The capabilities of this methodology in the presence of noise are numerically demonstrated. Moreover, to reduce the influence of the driving speed on the estimation results, a method of choosing the calculation frequency is proposed. A road vibration test is conducted to benchmark the proposed method.

Underground coal mining leads to land subsidence, and the situation is particularly serious in the Coal-Grain Complex in eastern China, causing the crop production to be reduced or to be taken out. Backfilling with Yellow River sediment is one of the effective methods to solve the land subsidence in this area, but a key issue is how to select the optimal soil reconstruction profile so that the crop yield after backfilling and reclamation is unaffected. The main purpose of this study is to verify the feasibility of selecting the optimal soil reconstruction profile by rapid monitoring of crop growth and judging soil quality with the aid of unmanned aerial vehicle systems (UAVs). A control treatment and 13 experimental treatments were established for the study area. The control treatment consisted of using 30 cm topsoil and 90 cm subsoil and the topsoil is a proxy for native (undisturbed) soil from the study sites. All other treatments consisted of using varying combinations of subsoil and sediment overlain by 30 cm of topsoil. The vegetation indices from the UAV multispectral images, and the plant height and vegetation coverage from the UAV RGB images were used for estimation of the winter wheat biomass in a random forest regression. The results showed that the random forest regression model yielded accurate estimation of the aboveground biomass. Furthermore, knowledge of plant height and vegetation coverage improved the accuracy of prediction such that crop growth was well characterized. The optimal soil profile consisted of 0.3 m topsoil + 0.2 m subsoil + 0.2 m sediment + 0.2 m subsoil + 0.3 m sediment. A fast and effective airborne monitoring method for soil quality was established, thus providing greatly improved monitoring efficiency.

It is challenging to identify metrics that best capture hurricane destructive potential and costs. Although it has been found that the sea surface temperature and vertical wind shear can both make considerable changes to the hurricane destructive potential metrics, it is still unknown which plays a more important role. Here we present a new method to reconstruct the historical wind structure of hurricanes that allows us, for the first time, to calculate the correlation of damage with integrated power dissipation and integrated kinetic energy of all hurricanes at landfall since 1988. We find that those metrics, which include the horizontal wind structure, rather than just maximum intensity, are much better correlated with the hurricane cost. The vertical wind shear over the main development region of hurricanes plays a more dominant role than the sea surface temperature in controlling these metrics and therefore also ultimately the cost of hurricanes.