With the high prevalence of cardiovascular diseases in modern society, there is a growing demand for effective diagnostic methods. In addition to the traditional electrocardiogram (ECG), magnetocardiography (MCG), as an emerging non-invasive diagnostic technique, has attracted increasing attention. Although mature ECG simulators for medical research are already available on the market, there is still a lack of dedicated simulation equipment for MCG, which hampers experimental validation of MCG-related algorithm research. Therefore, this paper proposes a time-sequence-controlled cardiac magnetic field simulator (CMFS), which utilizes a multi-channel programmable current source to flexibly generate source currents corresponding to a cardiac equivalent current dipole with known parameters, reflecting the heart’s electrophysiological activity. The current dipole simulators are embedded at different positions within a physical heart model to simulate the spatial distribution of the cardiac magnetic field. This simulator has the potential to support research on MCG inverse algorithms, and the design concept presented in this paper is expected to serve as a reference for the development of cardiac magnetic field simulators.

Glioma is the most common malignant brain tumor, and terahertz (THz) spectroscopy has shown potential for pathological diagnosis. Previous anisotropy studies using THz time-domain spectroscopy (THz-TDS) were limited to animal tissues such as porcine fascia and muscle. Here, we report the first verification of isotropy in human glioma pathological tissues. Tissue slices from patients were analyzed at multiple rotational angles, and parameters including absorption coefficient, refractive index, and dielectric constant were evaluated. The results showed no angular dependence, and polar plots confirmed isotropic behavior. Importantly, MGMT promoter methylation status was found to influence refractive index and dielectric constant, suggesting potential molecular-level biomarkers. Optical microscopy revealed random microstructural organization as the basis of isotropy. These findings highlight the unique contribution of human pathological validation with molecular stratification, providing a foundation for the clinical application of THz technology in rapid intraoperative diagnosis.

Ground subsidence is a typical underground disease that severely affects the operational safety of cities and the safety of people’s lives. Ground subsidence often develops from underground cavities. Therefore, to prevent the occurrence of ground subsidence, a method that can effectively detect underground cavities is needed. This paper conducts detection of underground cavities based on the Superconducting Quantum Interference Device (SQUID). Firstly, an underground cavity model is established in COMSOL, and the magnetic anomalies of the cavity are simulated. Secondly, an underground cavity detection experiment is conducted. The experiment is divided into two parts: Experiment 1 is an anomaly-free ground experiment, and Experiment 2 is a cavity detection experiment. The result of Experiment 1, the result of Experiment 2, and the simulation are then compared. The experimental results are generally consistent with the simulation results in terms of trends. At the same time, the feasibility of detecting magnetic anomalies in underground cavities is demonstrated. This provides a foundation for the inversion and positioning of underground cavities based on magnetic anomalies.

In glioma, accurate prediction of recurrence and survival is essential for clinical decision-making and individualized management. However, current prognostic tools do not fully capture outcome heterogeneity, and convenient approaches for early postoperative prognostic evaluation are lacking. We investigated whether terahertz (THz) spectroscopy could provide label-free prognostic information. In this single-center retrospective study, 56 patients were assigned to training (n = 33) and held-out internal testing (n = 23) cohorts. A total of 443 frozen sections were measured using THz time-domain spectroscopy, and patient-level features were derived from six spectral parameters across 0.2-1.4 THz. Separate progression-free survival (PFS) and overall survival (OS) signatures were developed using univariable Cox screening, LASSO-Cox selection, and multivariable Cox modeling and validated in the testing cohort. The THz-based risk score (THz-RS) significantly stratified PFS and OS in both cohorts, showed good time-dependent discrimination, and retained prognostic value after multivariable adjustment for clinicomolecular variables. Model interpretation identified the 1.2-1.3 THz band (B11) as a major contributor, while targeted LC-MS/MS revealed associations between tissue glutamate abundance and B11-derived THz features, providing preliminary biochemical support for this spectral region. These findings support THz spectroscopy as a label-free complementary tool for perioperative prognostic assessment in glioma, pending validation in larger multicenter cohorts.

This paper systematically reviews the technological evolution and fundamental principles of flatness detection for hot-rolled steel strips. It provides detailed analyses of principal technical approaches including multi-point laser triangulation, laser Moiré, laser light-section, projected fringe pattern, and 3D reconstruction methods, covering their measurement principles, typical instrumentation, and industrial implementations. Comparative studies demonstrate that traditional contact-based techniques are being progressively superseded by non-contact optical measurement methods, with 3D reconstruction technology emerging as a research focus due to its capability of acquiring comprehensive 3D topographic data of strip surfaces. The article emphasizes cutting-edge applications of artificial intelligence in flatness detection, particularly innovative integrations of neural networks, machine vision and deep learning algorithms. Finally, it summarizes existing technological challenges and proposes future development trends, highlighting multi-physics coupled measurement, intelligent compensation algorithms, and digital twin technologies as crucial research directions.

Structural instability in water diversion canals is frequently precipitated by concealed leakage voids beneath concrete linings. Traditional electromagnetic inspection methods, such as ground-penetrating radar (GPR), are often severely constrained in these environments by the rapid attenuation of signals within conductive, water-saturated media. To address this limitation, this study presents a novel non-destructive testing approach utilizing a low-temperature superconducting quantum interference device (SQUID) second-order axial gradiometer. However, detecting non-magnetic voids is challenging because their responses arise from the weak magnetic-susceptibility contrast between the soil and air. Under the reference soil and geomagnetic-field conditions, hemispheroidal voids with opening diameters of 0.10–0.20m and depths of 0.02–0.12m produced predicted peak amplitudes of the second-order finite-difference anomalies ranging from 3.94×10−11 to 5.86×10−10T, all below 10−9T. Consequently, we establish a physics-based forward model for the second-order finite-difference response of the axial magnetic flux density to characterize the gradiometer signals from a hemispheroidal void beneath a lining. Subsequently, we propose a quantitative inversion algorithm combining modified Akima cubic Hermite (Makima) interpolation and an exhaustive global grid search to estimate void geometry using the peak anomaly amplitude and full width at half maximum (FWHM) of the second-order finite-difference response. Results from field experiment inversions demonstrate that, for each of the five controlled void configurations, the estimated opening diameter and depth reproduced the corresponding mean experimental feature pair obtained from 18 independent scans with a combined response misfit not exceeding 5%. This approach demonstrates the feasibility of SQUID-based quantitative detection and characterization of concealed voids beneath water-saturated canal linings in high-conductivity environments.

A magnetocardiograph (MCG) is a high-sensitivity magnetic field detection sensor based on a low-temperature superconducting quantum interference device. It measures the distribution of magnetic field parameters of the cardiac magnetic field (target magnetic field) in the plane directly above the human chest. Through complex magnetic field inversion algorithms, it obtains the isomagnetic map and current density distribution of the most active plane of the target source (heart). By analyzing and extracting electrophysiological-related indicators and parameters, it completes the classification of abnormal cardiac electrophysiology, as well as the diagnosis of myocardial ischemia and myocardial injury based on cardiac electrophysiological models and statistical methods. The quality of the signals from the magnetocardiograph is crucial to ensuring the accuracy of diagnosis and classification results. This paper will introduce the research on how to evaluate the signals collected by the magnetocardiograph and the evaluation criteria, and their application in practical clinical settings.

The rhythmic propagation of intestinal electrophysiological activity is crucial for maintaining normal peristalsis, and its disruption is often linked to functional disorders. This study proposes an electro-magnetic coupling modeling and inversion framework that integrates the FitzHugh-Nagumo (FHN) electrophysiological model with frequency-domain magnetic-field inversion, establishing a complete simulation pipeline from electrophysiological activation to the resulting magnetic-field distribution and the reconstruction of current density from measured magnetic signals. A 3D local intestinal segment was constructed using six connected curved ellipsoids, and the simulation reproduces the spatiotemporal propagation of slow waves and typical dipolar magnetic patterns, demonstrating strong physiological-electromagnetic consistency. The reconstructed current density distribution, obtained via frequency-domain Fourier inversion, aligns well with membrane potential dynamics. Simulations under three typical functional conditions - normal conduction, local blockage, and multi-source pacing - confirm that the proposed method effectively identifies closed-loop current paths, detects conduction disruptions, and resolves multiple activation sources. This modeling and inversion framework offers a new approach for noninvasive intestinal function imaging and abnormality detection, showing promising application potential.

Magnetic positioning systems, as a key technology in minimally invasive surgical navigation, are highly susceptible to intraoperative electromagnetic interference, particularly from the spatial magnetic fields generated by current-carrying wires near surgical instruments. To suppress such magnetic interference and enhance instrument tracking stability, this study systematically proposes a magnetic interference suppression strategy based on the spatial structure optimization of wires, supported by numerical simulations and experimental validation. Using COMSOL Multiphysics® software, we established finite element models of two typical wiring structures—parallel wires and twisted pairs—and conducted a comparative analysis of their spatial magnetic field distributions under identical excitation currents. To further validate the simulation results, we constructed a high-precision experimental platform and employed a tri-axial fluxgate sensor to measure the magnetic fields of the two wire models under controlled conditions; the experimental data showed strong agreement with the simulation results. This work provides an optimized method for mitigating magnetic interference in surgical navigation systems, significantly improving intraoperative instrument positioning accuracy.

Gliomas are aggressive primary brain tumors in adults, and accurate molecular characterization plays an important role in diagnosis, tumor classification, and personalized treatment strategies. Cerebrospinal fluid (CSF)-based liquid biopsy has emerged as a minimally invasive approach for detecting tumor-associated molecular biomarkers; however, the development of sensitive and quantitative detection technologies remains challenging due to the low abundance of target molecules. In this study, we propose a quantitative biosensing platform based on terahertz spectroscopy combined with a polarization-insensitive terahertz metamaterial sensor for DNA concentration analysis. DNA solutions with different concentrations were prepared to evaluate concentration-dependent sensing responses. The prepared samples were deposited onto the metamaterial biosensor surface and dried to form thin films, enabling enhanced interaction between target molecules and the localized terahertz electromagnetic field while minimizing the influence of aqueous environments on terahertz responses. Experimental results demonstrate that the proposed sensor exhibits a stable linear response over a DNA concentration range of 3215 ng/mL to 50,000 ng/mL. The concentration sensitivity is determined to be 114.74 GHz·mL/nmol, and the limit of detection (LOD) is estimated to be 1.99 pmol/mL based on quantitative analysis and error evaluation. These results demonstrate the feasibility of terahertz metamaterial sensing for quantitative analysis of low-concentration DNA molecules.

Terahertz (THz) metasurfaces, leveraging their tailored optical responses at subwavelength scales, provide a novel platform for enhanced sensing of trace substances, demonstrating significant potential in analytical chemistry applications. However, the near-field electromagnetic coupling mechanism between metasurfaces and substances remains unclear. Moreover, the boundary between the narrowband transparency windows induced by substances against a broad absorption background and the broadband absorption enhancement enveloped by narrow resonance peaks remains equally ambiguous. This study establishes a coupled harmonic oscillator model to investigate near-field electromagnetic dynamics between terahertz metasurface and molecular fingerprint analyte. We use lactose monohydrate, a commonly studied analyte in the terahertz field, to systematically analyze the evolution of parameters and their impact on near-field coupling state transitions as the sample thickness increases. Furthermore, we identify the physical mechanism through which the relative relationship between metasurface resonance and intrinsic absorption bandwidth drives the mode switching of the coupled system, from absorption-induced transparency (AIT) to absorption-enhanced resonance (AER). Our findings deepen the understanding of terahertz metasurface-analyte interactions and provide insights for optimizing metasurface strategies aimed at surface-enhanced sensing.

Established geophysical methods for detecting subsurface cavities, such as Ground Penetrating Radar (GPR) and Electrical Resistivity Tomography (ERT), are often compromised by interference from near-surface soil conditions. This paper introduces the application of a Superconducting Quantum Interference Device (SQUID) second-order gradiometer as a robust alternative that overcomes these limitations. By measuring the spatial gradient of the magnetic field, this technique inherently suppresses uniform background noise while enhancing signals from local anomalies. Experimental trials were conducted over normal ground and a pre-defined void, both uncovered and concealed by a soil layer. The results demonstrate that the SQUID gradiometer produces a distinct and repeatable anomaly signature at the edges of the cavity. Crucially, this detection signature remains consistent whether the soil layer is present or absent, proving the method’s immunity to interference from the overburden. This study validates SQUID second-order gradiometry as a highly reliable and specific method for detecting subsurface cavities, unaffected by the geological and environmental factors that limit conventional techniques.

This study presents a rapid identification method for isocitrate dehydrogenase (IDH) mutation status in glioma by integrating terahertz time-domain spectroscopy (THz-TDS) with deep learning. Absorption coefficient data of glioma tissues were acquired in the 0.2–1.4 THz frequency range, followed by Savitzky–Golay smoothing and Z-score normalization to construct an 82-dimensional feature dataset. To improve classification performance, the synthetic minority oversampling technique (SMOTE) was employed to augment the training data. The spectral features were then transformed into four types of two-dimensional representations: Gramian Angular Summation Field (GASF), Gramian Angular Difference Field (GADF), Markov Transition Field (MTF), and Recurrence Plot (RP). Three convolutional neural network (CNN) architectures were developed for comparative analysis: single-input CNN, front-end fusion CNN (FFCNN), and mid-level fusion CNN (MFCNN). The results demonstrated that MFCNN achieved the highest performance when fusing GASF and GADF images, yielding an AUC of 0.907, outperforming all other configurations. However, the inclusion of MTF images led to a performance decline, potentially due to feature conflicts introduced by modality differences.

Terahertz spectroscopy, with its intrinsic molecular fingerprint characteristics, has become a powerful tool for qualitative and quantitative analysis of organic materials. Metasurfaces can significantly enhance terahertz signals through strong localized electromagnetic fields; however, the enhancement critically depends on the spatial overlap between the organic crystal and the metasurface resonance mode. To address the variation in coupling efficiency caused by different sample preparation conditions, this study employs absorption-induced transparency (AIT) as the characteristic enhancement mechanism and designs a terahertz metasurface for coupling-enhanced characterization of lactose thin films. Two AIT features including resonance redshift and resonance-depth reduction are used as quantitative indicators, together with uncertainty analysis, to evaluate the spatial coupling quality. Four preparation methods for forming lactose films were systematically compared: static convection heating, static conduction heating, static radiation heating, and spin-coating followed by radiation heating. Experimental analyses of film uniformity, crystal morphology, and vibration modes overlapping reveal that the spin-coating & radiation-heating method achieves the highest coupling efficiency. This method yields the strongest AIT response, with a resonance redshift of 133.9 ± 0.2 GHz and a transmission increase of 10.5 ± 0.3%, while also exhibiting the smallest uncertainties. Moreover, it requires only one-quarter of the solution amount used by the other three methods, providing the best balance between enhancement performance, reproducibility, and material utilization. This work clarifies the fundamental relationship between sample preparation and spatial coupling between metasurface and analyte, offering a reproducible experimental strategy for optimizing metasurface design and enabling highly sensitive terahertz detection of trace organic materials.

Grading gliomas is crucial for prognosis and survival prediction. Clinically, gliomas are categorized into low-grade gliomas (LGG, WHO II) and high-grade gliomas (HGG, WHO III and IV). Compared to traditional diagnostic methods, terahertz spectroscopy offers advantages such as time efficiency and label-free detection. In this study, we propose a machine learning model for discriminating glioma grades based on terahertz spectral data, incorporating, for the first time, the class imbalance of terahertz spectral data from different glioma grades. A total of 420 sample data from 21 cases were included. Three data processing methods—ROS, RUS, and SMOTE—were utilized, and three classifiers were employed to construct the model. We compared the effects of the three data processing methods and validated the proposed method on a test set. The best performance of the proposed algorithm, evaluated by the area under the curve (AUC), achieved a maximum value of 88.8%. This represents a significant advancement in the development of a rapid glioma grade diagnostic tool and provides more effective guidance for developing surgical protocols for gliomas.

Superconducting quantum interference device (SQUID) gradiometers are sensitive instruments for detecting magnetic fields and are widely used in magnetocardiography (MCG). However, their performance is compromised by vibrations from foundations or refrigeration equipment. Vibration-induced effects on SQUID gradiometers occur through two main mechanisms. First, an initial angular deviation between the pickup coils prevents cancellation of the reversed magnetic flux from external fields. Vibration converts this inherent interference into a time-varying flux disturbance at the vibration frequency. Second, bubbles from boiling liquid helium passing through the gradiometer loops increase the gradiometer's noise floor in the frequency domain. Vibration alters the dynamic behavior of these bubbles. Finite element simulations are conducted to examine the influence of these mechanisms across various vibration frequencies, and the SQUID gradiometer's noise power spectral density is measured at six vibration frequencies. The noise contains both narrowband and broadband components: The narrowband noise primarily occurs at the vibration frequencies and their second harmonics when the pickup coil's angular displacement aligns with or is perpendicular to the vibration direction. Meanwhile, the introduction of vibrations elevates the broadband noise level relative to the non-vibration state. Moreover, the broadband noise exhibits distinct peaks at the gradiometer's intrinsic resonance frequency of 15 Hz.

To address the detection of current density distribution and magnetic source localization in biological electrophysiological activities, this study proposes an algorithm that establishes forward and inverse relationships between biological current density and gradients on parallel detection planes. A mathematical model is developed to link the measured gradients with magnetic field distributions. The multiscale algorithm with the Fourier frequency-domain transformation enables dynamic reconstruction of the current density distribution. An error evaluation system based on the Pearson correlation coefficient optimizes magnetic source localization parameters and accurately predicts the optimal observation distance within a typical detection range of 0.1-0.5 m. The validation using representative cardiac gradient data demonstrates that the reconstructed gradient fields align closely with the measured data in spatial structure, extreme value locations, and amplitude distribution, confirming the method's effectiveness. While the heart is used as a case study, the method is highly generalizable and applicable to a wide range of biological magnetic source research.

Gliomas are the most common primary central nervous system tumors with high invasiveness, Glioblastoma (GBM) is the most malignant type of brain glioma, with a 5-year survival rate of only 5.6%. The epidermal growth factor receptor EGFR) plays an important role in the growth, invasion, and recurrence of glioblastoma, EGFR amplification and mutation have Leen identified as driving factors in glioblastoma, Currently, the integrated diagnosis process for glioma is limited by complex Experimental procedures, often with a certain lag, and results can only be obtained approximately 2 weeks after surgery, which does not provide real-time molecular pathological information support for the operator. This article proposes predicting EGFR amplification status based on intraoperative pathological frozen sections using terahertz time-domain Apectroscopy (THz-TDS) data combined with convolutional neural networks (CNN), During the operation, spectral data of frozen sections of brain gliomas were collected using the THE TDS system, and their absorption coefficients were calculated, After smoothing using the Savitzky-Golay filter, the absorption coefficients were converted into two-dimensional image data sing the Gram Angular Field (GAF), Markov Transition Field (MTF), and Recursive Plots (RP) as inputs for subsequent ENN models. To fully utilize image data, we employ various methods, including single image input, front-end fusion, and mid-fange fusion, to construct CNN models. By comparing and analyzing the Area Under the Curve (AUC) values of Receiver Operating Characteristic (ROC) curves under different models, it was found that the Mid range Fusion Convolutional Neural Network model with Gram Angular Summation Field (GASF) and Gram Angular Difference Field (GADF) had the best Brediction performance, with a predicted AUC value of 94.74% in the test set, In addition, the commonly used prediction models based on terahertz spectral data often employ one-dimensional spectral data for dimensionality reduction and machine earning analysis, which may result in partial loss of data information during processing. Therefore, we also trained and tested The method of combining the absorption coefficient with machine learning. By comparing the results of different models for one dimensional data and two-dimensional images, it is found that training models with two-dimensional spectral images in convolutional neural networks yields better predictive performance compared to machine learning with one-dimensional terahertz time-domain spectral data. The experimental results demonstrate that the proposed method, based on terahertz spectroscopy Aata and convolutional neural network model, can achieve real-time and rapid prediction of EGFR amplification status, providing new insights for molecular pathological classification of brain gliomas using terahertz time-domain spectroscopy. It is of great significance for the timely adjustment of surgical strategies during surgery and the early development of postoperative djuvant treatment plans.

The modern method for aluminum production is electrolysis, which extracts molten aluminum from a mixture of cryolite and alumina. During the aluminum electrolysis process, due to density differences, the molten aluminum settles at the bottom while the electrolyte remains on top. The balance of the heights of the molten aluminum and the electrolyte directly affects the current efficiency during aluminum production. Given the enormous current involved in the electrolysis process, even a slight reduction in current efficiency can lead to significant energy wastage. Therefore, maintaining the balance between the molten aluminum and the electrolyte requires regular measurements to adjust feeding strategies. However, the current measurement method is manual, time-consuming, labor-intensive, and cannot provide real-time height data. This paper addresses the issue of strong interference in the measurement of key parameters in aluminum electrolysis production and presents the design and implementation of an embedded electronic system based on an ARM processor. The system is designed to interface with sensors and various peripheral devices. Through manual and automatic control, multiple parameters within the electrolytic cell could be measured and displayed in real time. Field test results indicated that this system effectively obtained precise data on the aluminum electrolysis production process, potentially becoming a fundamental component for the intelligentization of aluminum production in the future.

There is an urgent need to develop label-free, accurate detection techniques for gliomas due to the high aggressiveness and heterogeneity of glioma tissues. In this study, a polarization-insensitive terahertz metamaterial biosensor based on a quadruple rotationally symmetric superunit is proposed to address the issues of insufficient sensitivity and polarization-dependent interference associated with conventional metamaterials in terahertz spectroscopy. The structural parameters were optimized through theoretical modeling and electromagnetic simulations, leading to the design of C4-symmetric metamaterials with stable responses over a wide incidence angle range. These metamaterials effectively mitigate signal distortion caused by the random orientation of metamaterial placement during experiments. The experiments were conducted using a terahertz frequency-domain spectroscopy system (THz-FDS) to detect isolated glioma tissues. The results demonstrate that the sensor maintains polarization insensitivity across the full 0-360° range, significantly enhancing the contrast in dielectric properties between tumor and normal tissues. Furthermore, its resonance frequency shift exhibits a strong correlation with tissue thickness, increasing up to 26 µm, after which it stabilizes and no longer exhibits significant changes. This study confirms that polarization-insensitive metamaterials can overcome existing imaging limitations, reduce operator-induced human errors, and provide a novel, non-invasive detection solution for intraoperative boundary delineation and pathological diagnosis of gliomas, with strong potential for clinical translation.