Search Results (43)

Background: Artificial intelligence (AI), the overarching field that includes machine learning (ML) and its subfield deep learning (DL), is rapidly transforming clinical research by enabling the analysis of high-dimensional data and automating the output of diagnostic and prognostic tests. As clinical trials become increasingly complex and costly, ML-based approaches (especially DL for image and signal data) offer promising solutions, although they require new approaches in clinical education. Objective: Explore current and emerging AI applications in oncology and cardiology, highlight real-world use cases, and discuss the challenges and future directions for responsible AI adoption. Methods: This narrative review summarizes various aspects of AI technology in clinical research, exploring its promise, use cases, and its limitations. The review was based on a literature search in PubMed covering publications from 2019 to 2025. Search terms included “artificial intelligence”, “machine learning”, “deep learning”, “oncology”, “cardiology”, “digital twin”. and “AI-ECG”. Preference was given to studies presenting validated or clinically applicable AI tools, while non-English articles, conference abstracts, and gray literature were excluded. Results: AI demonstrates significant potential in improving diagnostic accuracy, facilitating biomarker discovery, and detecting disease at an early stage. In clinical trials, AI improves patient stratification, site selection, and virtual simulations via digital twins. However, there are still challenges in harmonizing data, validating models, cross-disciplinary training, ensuring fairness, explainability, as well as the robustness of gold standards to which AI models are built. Conclusions: The integration of AI in clinical research can enhance efficiency, reduce costs, and facilitate clinical research as well as lead the way towards personalized medicine. Realizing this potential requires robust validation frameworks, transparent model interpretability, and collaborative efforts among clinicians, data scientists, and regulators. Interoperable data systems and cross-disciplinary education will be critical to enabling the integration of scalable, ethical, and trustworthy AI into healthcare. Full article

Freezing of gait (FOG) is a disabling symptom associated with Parkinson’s disease (PD). Its understanding and effective treatment is compromised due to the difficulty in reliably triggering FOG in clinical and laboratory environments. The Cleveland Clinic-Virtual Home Environment (CC-VHE) platform was developed to address the challenges of eliciting FOG by combining an omnidirectional treadmill with immersive virtual reality (VR) environments to induce FOG under physical, emotional, and cognitive triggers. Recent developments in deep brain stimulation devices that sense neural signals from the subthalamic nucleus in real time offer the potential to understand the underlying neural mechanism(s) of FOG. This manuscript presents the coupling of the CC-VHE technology, VR paradigms, and the experimental and analytical methods for recording and analyzing synchronous cortical, subcortical, and kinematic data as an approach to begin to understand the nuanced neural pathology associated with FOG. To evaluate the utility and feasibility of coupling VR and neural sensing technology, initial data from one participant are included. Full article

Understanding microscale dynamic behavior in heterogeneous materials (e.g., polycrystalline or semiconductor systems) under impact loading requires diagnostics capable of resolving ~100 μm features. This study introduces a 19-core fiber-optic array probe with 100 μm spatial resolution, integrated with DISAR velocimetry on a light gas gun platform, enabling two-dimensional continuous measurement of free-surface velocity. The system overcomes limitations of conventional single-point methods (e.g., VISAR’s millimeter-scale resolution and reflectivity constraints) by achieving nanosecond temporal resolution and sub-nanometer displacement sensitivity. Under ~8 GPa impact loading, the probe captures spatiotemporal velocity heterogeneity in polycrystalline materials, including localized pull-back signals and periodic oscillations caused by shock wave reflections at microstructural interfaces. These observations reveal dynamic processes such as damage initiation and evolution, directly linking velocity profiles to microscale material response. The results provide experimental evidence of how grain-scale defects influence shock propagation and energy dissipation, advancing predictive models for extreme-condition material performance. This high-resolution, multi-channel approach offers a paradigm shift in diagnosing heterogeneous material behavior under high-strain-rate loading. Full article

Video Synthetic Aperture Radar (ViSAR) operates by continuously monitoring regions of interest to produce sequences of SAR imagery. The detection and tracking of ground-moving targets, through the analysis of their radiation properties and temporal variations relative to the background environment, represents a significant area of focus and innovation within the SAR research community. In this study, some key challenges in ViSAR systems are addressed, including the abundance of low-confidence shadow detections, high error rates in multi-target data association, and the frequent fragmentation of tracking trajectories. A multi-target tracking algorithm for ViSAR that utilizes re-identification (ReID) features and a multi-stage data association process is proposed. The algorithm extracts high-dimensional ReID features using the Dense-Net121 network for enhanced shadow detection and calculates a cost matrix by integrating ReID feature cosine similarity with Intersection over Union similarity. A confidence-based multi-stage data association strategy is implemented to minimize missed detections and trajectory fragmentation. Kalman filtering is then employed to update trajectory states based on shadow detection. Both simulation experiments and actual data processing experiments have demonstrated that, in comparison to two traditional video multi-target tracking algorithms, DeepSORT and ByteTrack, the newly proposed algorithm exhibits superior performance in the realm of ViSAR multi-target tracking, yielding the highest MOTA and HOTA scores of 94.85% and 92.88%, respectively, on the simulated spaceborne ViSAR data, and the highest MOTA and HOTA scores of 82.94% and 69.74%, respectively, on airborne field data. Full article

This study investigates the durability of concrete structures under severe environmental conditions, focusing on the effects of thermal stress, saline exposure, and seismic activity. The research employs a dual approach, combining laboratory experiments and field case studies to analyze various environmental impacts, mix designs, and the use of crystalline admixtures. Two concrete mix designs, CMD-01-C30/37 (mass concrete) and CMD-02-C35/45 (underwater concrete), were developed and tested for strength, permeability, and self-healing properties. The results demonstrate that both mix designs met or exceeded the required strength specifications, with improved resistance to water penetration and permeability depths lower than the code requirements set by European standards from EC2. The incorporation of crystalline admixtures in the mix designs significantly enhanced durability and performance, aligning with the priority of developing zero-carbon concrete solutions. The study also observed the self-healing capabilities of concrete treated with crystalline admixtures, as evidenced by the sealing of cracks at expansion and construction joints over time. These findings contribute to the development of a robust methodology for creating resilient structures adaptable to climate change, with potential implications for enhancing seismic resistance and structural longevity. The study underscores the importance of considering environmental factors and innovative admixtures in concrete design to improve durability and resilience, particularly in areas prone to seismic activity and extreme environmental conditions. Future research directions should focus on further investigating self-healing mechanisms, exploring the integration of durable and self-healing cement-based materials in engineering practice, and evaluating applications for both new construction and retrofitting existing structures. Full article

Introduction: Augmented reality (AR) and mixed reality (MR) technologies have revolutionized cranial neurosurgery by overlaying digital information onto the surgical field, enhancing visualization, precision, and training. These technologies enable the real-time integration of preoperative imaging data, aiding in better decision-making and reducing operative risks. Despite challenges such as cost and specialized training needs, AR and MR offer significant benefits, including improved surgical outcomes and personalized surgical plans based on individual patient anatomy. Materials and Methods: This study describes three intracranial surgeries using AR and MR technologies at Hospital Ángeles Universidad, Mexico City, in 2023. Surgeries were performed with VisAR software 3 version and Microsoft HoloLens 2, transforming DICOM images into 3D models. Preoperative MRI and CT scans facilitated planning, and radiopaque tags ensured accurate image registration during surgery. Postoperative outcomes were assessed through clinical and imaging follow-up. Results: Three intracranial surgeries were performed with AR and MR assistance, resulting in successful outcomes with minimal postoperative complications. Case 1 achieved 80% tumor resection, Case 2 achieved near-total tumor resection, and Case 3 achieved complete lesion resection. All patients experienced significant symptom relief and favorable recoveries, demonstrating the precision and effectiveness of AR and MR in cranial surgery. Conclusions: This study demonstrates the successful use of AR and MR in cranial surgery, enhancing precision and clinical outcomes. Despite challenges like training and costs, these technologies offer significant benefits. Future research should focus on long-term outcomes and broader applications to validate their efficacy and cost-effectiveness in neurosurgery. Full article

The study of high-pressure sound velocity is an important part of shock wave physics, and the study of ultra-high pressure sound velocity of iron is of great significance to many research fields such as geophysics, solid state physics, and crystallography. At present, the measurement of sound velocity is usually carried out by the catch-up sparse wave method and windowed VISAR technology, which is complex in structure and not highly adaptable. In particular, for the ultra-high pressure sonic velocity measurement of metals, it is limited by the loading platform and window materials and cannot realize the high temperature and high-pressure environment of the earth’s inner core. In this paper, the sound velocity measurement of iron under high temperature and high-pressure environment (78 GPa) is realized based on the two-stage light gas cannon experimental platform. The side-side sparse wave method was used to establish a coupling model of high-spatially resolved optical group and fiber bundle. A multiplexed all-fiber laser interferometry velocity measurement system (DISAR) was built, and the spatial resolution was better than 20 μm. In this paper, we will provide a feasible route for a method for measuring the high spatiotemporal resolution velocity. Full article

This paper presents a polar-format algorithm (PFA)-based synthetic-aperture radar (SAR) processor that can be mounted on a small drone to support video SAR (ViSAR) imaging. For drone mounting, it requires miniaturization, low power consumption, and high-speed performance. Therefore, to meet these requirements, the processor design was based on a field-programmable gate array (FPGA), and the implementation results are presented. The proposed PFA-based SAR processor consists of both an interpolation unit and a fast Fourier transform (FFT) unit. The interpolation unit uses linear interpolation for high speed while occupying a small space. In addition, the memory transfer is minimized through optimized operations using SAR system parameters. The FFT unit uses a base-4 systolic array architecture, chosen from among various fast parallel structures, to maximize the processing speed. Each unit is designed as a reusable block (IP core) to support reconfigurability and is interconnected using the advanced extensible interface (AXI) bus. The proposed PFA-based SAR processor was designed using Verilog-HDL and implemented on a Xilinx UltraScale+ MPSoC FPGA platform. It generates an image 2048 × 2048 pixels in size within 0.766 s, which is 44.862 times faster than that achieved by the ARM Cortex-A53 microprocessor. The speed-to-area ratio normalized by the number of resources shows that it achieves a higher speed at lower power consumption than previous studies. Full article

The video synthetic aperture radar (ViSAR) system can utilize high-frame-rate scene motion target shadow information to achieve real-time monitoring of ground mobile targets. Modeling the characteristics of moving target shadows and analyzing shadow detection performance are of great theoretical and practical value for the optimization design and performance evaluation of ViSAR systems. Firstly, based on the formation mechanism and characteristics of video SAR moving target shadows, two types of shadow models based on critical size and shadow clutter ratio models are established. Secondly, for the analysis of moving target shadow detection performance in ViSAR systems, parameters such as the maximum detectable speed of moving targets, the minimum clutter backscatter coefficient, and the number of effective shadow pixels of moving targets are derived. Furthermore, the shadow characteristics of five typical airborne/spaceborne ViSAR systems are analyzed and compared. Finally, a set of simulation experiments on moving target shadow detection for the Hamas rocket launcher validates the correctness and effectiveness of the proposed models and methods. Full article

Seismic performance of steel moment-resisting frames is investigated through the incorporation of U-shaped metallic dampers. The primary objective is to assess the effectiveness of these dampers in mitigating seismic responses by utilizing various analysis techniques. Two representative structural configurations (5 and 10-story) are studied in both damped and undamped states to reveal the impact of dampers on seismic response reduction. The study utilizes the endurance time analysis (ETA) method, known for its efficiency in evaluating structural seismic performance. To validate the analysis results, a benchmark comparison is made through nonlinear time history analysis (NTHA). Incremental dynamic analysis (IDA) is also conducted to assess structures’ intensity measures with respect to their damage intensity index. The findings demonstrate that U-shaped metallic dampers substantially reduce inter-story drift and story shear forces. Importantly, a close alignment between the results obtained from ETA and NTHA underscores the reliability of the former in assessing seismic performance with supplemental damping devices. Full article

The unmanned aerial vehicle (UAV)-borne video synthetic aperture radar (SAR) possesses the characteristic of having high-continuous-frame-rate imaging, which is conducive to the real-time monitoring of ground-moving targets. The real-time imaging-processing system for UAV-borne video SAR (ViSAR) requires miniaturization, low power consumption, high frame rate, and high-resolution imaging. In order to achieve high-frame-rate real-time imaging on limited payload-carrying platforms, this study proposes a miniaturization design of a high-integration UAV-borne ViSAR real-time imaging-processing component (MRIPC). The proposed design integrates functions such as broadband signal generation, high-speed real-time sampling, and real-time SAR imaging processing on a single-chip FPGA. The parallel access mechanism using multiple sets of high-speed data buffers increases the data access throughput and solves the problem of data access bandwidth. The range-Doppler (RD) algorithm and map-drift (MD) algorithm are optimized using parallel multiplexing, achieving a balance between computing speed and hardware resources. The test results have verified that our proposed component is effective for the real-time processing of 2048 × 2048 single-precision floating-point data points to realize a 5 Hz imaging frame rate and 0.15 m imaging resolution, satisfying the requirements of real-time ViSAR-imaging processing. Full article

The UAV-borne video SAR (ViSAR) imaging system requires miniaturization, low power consumption, high frame rates, and high-resolution real-time imaging. In order to satisfy the requirements of real-time imaging processing for the UAV-borne ViSAR under limited memory and parallel computing resources, this paper proposes a method of embedded GPU-based real-time imaging processing for the UAV-borne ViSAR. Based on a parallel programming model of the compute unified device architecture (CUDA), this paper designed a parallel computing method for range-Doppler (RD) and map drift (MD) algorithms. By utilizing the advantages of the embedded GPU characterized with parallel computing, we improved the processing speed of real-time ViSAR imaging. This paper also adopted a unified memory management method, which greatly reduces data replication and communication latency between the CPU and the GPU. The data processing of 2048 × 2048 points took only 1.215 s on the Jetson AGX Orin platform to form a nine-consecutive-frame image with a resolution of 0.15 m, with each frame taking only 0.135 s, enabling real-time imaging at a high frame rate of 5 Hz. In actual testing, continuous mapping can be achieved without losing the scenes, intuitively obtaining the dynamic observation effects of the area. The processing results of the measured data have verified the reliability and effectiveness of the proposed scheme, satisfying the processing requirements for real-time ViSAR imaging. Full article

The production of portland cement is among the major contributors to greenhouse gas emissions that adversely affect the environment. Identifying sustainable materials to partially replace portland cement in concrete, such as pozzolanic materials, is crucial in addressing this issue. These materials mainly consist of silica and alumina that react with the available calcium hydroxide to form strength-bearing phases such as calcium silicate hydrates. Understanding the degree of pozzolanic reactivity of materials using efficient reactivity test methods is an important consideration. The paper thoroughly reviews the available literature related to direct and indirect pozzolanic reactivity test methods that have been utilized over the years. Direct methods quantify the amount of consumed calcium hydroxide, whereas indirect methods assess changes in the physical properties of the specimen due to pozzolanic reactions. The aim of this paper is to identify affordable, time-saving, and effective direct and indirect methods. Based on this study, the Frattini, electrical conductivity, and pH tests are considered the most time-efficient methods to assess pozzolanic materials. Electrical conductivity and pH tests are also easy to perform. In contrast, other methods are more time-consuming. Full article

The construction of integral bridges is one of the most effective methods to reduce bridges’ construction and in-service costs. However, there are associated geotechnical problems with their abutments backfill due to the integrated abutments. The main goal of this study is to evaluate and quantify the benefits of geogrid reinforcement for reducing the backfill’s geotechnical problems. For this purpose, using small-scale physical modeling, the benefits of geogrid reinforcing of the backfill of an integral abutment bridge subjected to cyclic movements are evaluated. The results are then compared with a previous study performed on unreinforced backfill and two types of geocells. In this study, 120 loading cycles are applied to geogrid-reinforced soil to simulate the cyclic loadings on integral abutment backfill due to seasonal abutment displacement. The horizontal reaction load at the top of the wall, changes in pressure behind the wall, and deformation in backfill soil are measured during the test. Then the results are discussed in terms of equivalent peak lateral soil coefficient (K_peak), lateral earth pressure coefficient (K*), and normalized settlement behind the wall (S_g/H). The derived lateral soil coefficients and settlement behind the abutment show that geogrid substantially reduces pressure and settlements after 120 cyclic loads. Based on the results, K_peak and K* of the geogrid-reinforced backfill decrease by up to 36%, and S_g/H behind the wall decreases by 62%. In addition, the comparison of the results for geogrid with two geocell types shows that geogrid is more efficient in terms of lateral soil coefficients. Full article

Shadow detection is a new method for video synthetic aperture radar moving target indication (ViSAR-GMTI). The shadow formed by the target occlusion will reflect its real position, preventing the defocusing or offset of the moving target from making it difficult to identify the target during imaging. To achieve high-precision shadow detection, this paper proposes a video SAR moving target shadow-detection algorithm based on low-rank sparse decomposition combined with trajectory area extraction. Based on the low-rank sparse decomposition (LRSD) model, the algorithm creates a new decomposition framework combined with total variation (TV) regularization and coherence suppression items to improve the decomposition effect, and a global constraint is constructed to suppress interference using feature operators. In addition, it cooperates with the double threshold trajectory segmentation and error trajectory elimination method to further improve the detection performance. Finally, an experiment was carried out based on the video SAR data released by Sandia National Laboratory (SNL); the results prove the effectiveness of the proposed method, and the detection performance of the method is proved by comparative experiments. Full article