Search Results (23)

This research utilizes Laser Doppler Vibrometer (LDV) technology to measure pavement deflection velocity under heavy moving loads at the Virginia DOT Accelerated Pavement Testing (VDOT APT) facility. While LDVs are typically integrated into Traffic Speed Deflectometers (TSDs) for measuring deflection velocities, this research employs a standalone, tripod-mounted LDV to capture highly repeatable data under controlled Heavy Vehicle Simulator (HVS) loading. A three-dimensional viscoelastic finite element (3D-FE) model was developed in Abaqus (version 2016) and calibrated using the LDV-measured deflection velocities and site-specific material properties. The model incorporates asphalt viscoelasticity, three-dimensional nonlinear contact stresses, and continuous loading conditions. Results demonstrate very good agreement between the calibrated 3D-FE model and observed responses, with calculated percentage differences of 0.6% and 3.4% for the maximum and minimum deflection velocity peaks, respectively. These findings, along with a 10% ratio between the standard deviation of the error and the measured signal, validate the model’s accuracy and the effectiveness of LDV instrumentation. This stand-alone application of a TSD-type LDV at an APT facility, to directly measure pavement deflection velocity under a moving load to calibrate a 3D-FE model, represents a key innovative aspect and addresses an identified gap in the literature on LDV-based pavement evaluation techniques. It should be noted that the proposed framework is calibrated for a single pavement structure under controlled loading and environmental conditions, and is applicable to the initial, undamaged state of the pavement. Further validation across different material configurations, environmental gradients, and damage stages is required to generalize the approach. Full article

This study presents a comparative evaluation of three modern surveying techniques—UAV photogrammetry, static tripod-based LiDAR scanning, and handheld mobile LiDAR—applied in the context of historic monument restoration. The focus is on analysing workflow efficiency, data accuracy, and adaptability to complex architectural features, including interior wall paintings, which are integral to the monument’s heritage value. Particular attention is given to how each technique captures surface texture, color fidelity, and material deterioration. The study also examines performance around intricate architectural elements such as vaulted ceilings, apses, cornices, columns, and carved stone portals, where occlusions, tight clearances, and fine ornamentation challenge coverage and resolution. By evaluating the strengths and limitations of each approach, the research highlights methodological considerations relevant for conservation professionals. The results indicate that the Static TLS is the most demanding workflow, requiring complex total station integration for control and station points. It produced the highest data density, with acquisition rates of one million points per second, making it the most hardware-intensive and difficult to manipulate. UAV photogrammetry provided a balanced middle-ground; it required minimal physical effort during acquisition and produced datasets that were significantly easier to manage. Handheld SLAM LiDAR emerged as the most productive solution for rapid coverage. While the handheld scanner’s image quality was lower than the photogrammetry, it still provided enough detail for the structural assessment and documentation needed. Although the point cloud lacked the extreme geometric detail provided by the TLS, the FARO Connect software made georeferencing and data manipulation significantly more efficient. Full article

Local scour around support structures has remained a critical barrier to tidal stream turbine deployment in energetic marine channels since loss of embedment and bearing capacity has undermined stability and delayed commercialization. This review identifies key mechanisms, practical implications, and forward-looking strategies related to local scour. It highlights that rotor operation, small tip clearance, and helical wakes can significantly intensify near-bed shear stress and erosion relative to monopile foundations without turbine rotation. Scour behavior is compared across monopile, tripod, jacket, and gravity-based foundations under steady flow, reversing tides, and combined wave and current conditions, revealing their influence on depth and morphology. The review further assesses coupled interactions among waves, oscillatory currents, turbine-induced flow, and seabed response, including sediment transport, transient pore pressure, and liquefaction risk. Advances in prediction methods spanning laboratory experiments, high-fidelity simulations, semi-empirical models, and data-driven techniques are synthesized, and mitigation strategies are evaluated across passive, active, and eco-integrated approaches. Remaining challenges and specific research needs are outlined, including array-scale effects, monitoring standards, and integration of design frameworks. The review concludes with future directions to support safe, efficient, and sustainable turbine deployment. Full article

Acute Aortic Syndromes (AAS) and Thoracic Aortic Aneurysm (TAA) remain among the most fatal cardiovascular emergencies, with mortality rising by the hour if diagnosis and treatment are delayed. Despite advances in imaging and surgical techniques, current clinical decision-making still relies heavily on population-based parameters such as maximum aortic diameter, which fail to capture the biological and biomechanical complexity underlying these conditions. In today’s data-rich era, where vast clinical, imaging, and biomarker datasets are available, artificial intelligence (AI) has emerged as a powerful tool to process this complexity and enable precision risk prediction. To date, AI has been applied across multiple aspects of aortic disease management, with mortality prediction being the most widely investigated. Machine learning (ML) and deep learning (DL) models—particularly ensemble algorithms and biomarker-integrated approaches—have frequently outperformed traditional clinical tools such as EuroSCORE II and GERAADA. These models provide superior discrimination and interpretability, identifying key drivers of adverse outcomes. However, many studies remain limited by small sample sizes, single-center design, and lack of external validation, all of which constrain their generalizability. Despite these challenges, the consistently strong results highlight AI’s growing potential to complement and enhance existing prognostic frameworks. Beyond mortality, AI has expanded the scope of analysis to the structural and biomechanical behavior of the aorta itself. Through integration of imaging, radiomic, and computational modeling data, AI now allows virtual representation of aortic mechanics—enabling prediction of aneurysm growth rate, remodeling after repair, and even rupture risk and location. Such models bridge data-driven learning with mechanistic understanding, creating an opportunity to simulate disease progression in a virtual environment. In addition to mortality and growth-related outcomes, morbidity prediction has become another area of rapid development. AI models have been used to assess a wide range of postoperative complications, including stroke, gastrointestinal bleeding, prolonged hospitalization, reintubation, and paraplegia—showing that predictive applications are limited only by clinical imagination. Among these, acute kidney injury (AKI) has received particular attention, with several robust studies demonstrating high accuracy in early identification of patients at risk for severe renal complications. To translate these promising results into real-world clinical use, future work must focus on large multicenter collaborations, external validation, and adherence to transparent reporting standards such as TRIPOD-AI. Integration of explainable AI frameworks and dynamic, patient-specific modeling—potentially through the development of digital twins—will be essential for achieving real-time clinical applicability. Ultimately, AI holds the potential not only to refine risk prediction but to fundamentally transform how we understand, monitor, and manage patients with AAS and TAA. Full article

Background: The Fontan procedure is a palliative surgery for patients with single-ventricle congenital heart disease (CHD), but it is associated with postoperative and long-term mortality and morbidity. Accurate, individualized risk stratification remains a challenge with traditional models. This study aimed to develop and validate a deep learning (DL) model to predict postoperative mortality after the Fontan procedure and to identify key predictive factors. Methods: We retrospectively analysed data from 230 patients who underwent the Fontan procedure between 2010 and 2024. A Deep Neural Network (DNN) model was developed using comprehensive preoperative, intraoperative, and postoperative clinical, biochemical, and hemodynamic variables. The dataset was split using five-fold cross-validation, with 80% for training and 20% for testing in each fold. The Synthetic Minority Over-sampling Technique (SMOTE) was used to fix class imbalance. Model performance was evaluated using five-fold stratified cross-validation. We assessed accuracy, precision, recall, F1-score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). SHapley Additive exPlanations (SHAP) analysis was employed to enhance model interpretability and identify the importance of features. A user-friendly clinical application interface was developed using Streamlit. This study was reported in accordance with the TRIPOD + AI reporting guidelines. Results: The DNN model demonstrated superior performance in predicting postoperative mortality, achieving an overall accuracy of 91.5% (95% CI: 87.2–94.8%), precision of 83.3% (95% CI: 76.5–89.1%), recall (sensitivity) of 90.9% (95% CI: 85.2–95.1%), specificity of 92.5% (95% CI: 88.3–95.7%), F1-score of 87.0% (95% CI: 82.1–91.3%), and an AUC-ROC of 0.94 (95% CI: 0.88–0.99). SHAP analysis identified key predictors of mortality, such as pulmonary artery pressure, ventricular end-diastolic pressure, preoperative BNP levels, and severity of AV valve regurgitation. The Streamlit application offered a user-friendly interface for personalized risk evaluation. Conclusions: A deep learning model that incorporates detailed clinical data can precisely forecast postoperative mortality in patients undergoing Fontan surgeries. This AI-based method, combined with interpretability techniques, provides a valuable tool for personalized risk assessment. It has the potential to improve preoperative counseling, optimize perioperative care, and enhance patient outcomes. However, additional external validation is needed to verify its broader applicability and clinical usefulness. Full article

The safety and longevity of engineering structures depend on precise and timely monitoring, especially during load testing inspections. Conventional displacement measurement methods—such as LVDT sensors, GNSS, RTS, and levels—each present benefits and limitations in terms of accuracy, applicability, and practicality. Photogrammetry has emerged as a promising alternative, offering non-contact measurement, cost-effectiveness, and adaptability in challenging environments. This study investigates the potential of photogrammetric methods for determining structural displacements during load testing in real-world conditions where such approaches remain underutilized. Two photogrammetric techniques were tested: (1) a single-image homography-based approach, and (2) a multi-image bundle block adjustment (BBA) approach using both UAV and tripod-mounted imaging platforms. Displacement results from both methods were compared against reference measurements obtained by traditional LVDT sensors and robotic total station. The study evaluates the influence of different camera systems, image acquisition techniques, and processing methods on the overall measurement accuracy. The findings suggest that the photogrammetric method, especially when optimized, can provide reliable displacement data with sub-millimeter accuracy, highlighting their potential as a viable alternative or complement to established geodetic and sensor-based approaches in structural testing. Full article

Background: Approximately 50% of all oncological patients undergo radiation therapy, where personalized planning of treatment relies on gross tumor volume (GTV) delineation. Manual delineation of GTV is time-consuming, operator-dependent, and prone to variability. An increasing number of studies apply artificial intelligence (AI) techniques to automate such delineation processes. Methods: To perform a systematic review comparing the performance of AI models in tumor delineations within the body (thoracic cavity, esophagus, abdomen, and pelvis, or soft tissue and bone). A retrospective search of five electronic databases was performed between January 2017 and February 2025. Original research studies developing and/or validating algorithms delineating GTV in CT, MRI, and/or PET were included. The Checklist for Artificial Intelligence in Medical Imaging (CLAIM) and Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis statement and checklist (TRIPOD) were used to assess the risk, bias, and reporting adherence. Results: After screening 2430 articles, 48 were included. The pooled diagnostic performance from the use of AI algorithms across different tumors and topological areas ranged 0.62–0.92 in dice similarity coefficient (DSC) and 1.33–47.10 mm in Hausdorff distance (HD). The algorithms with the highest DSC deployed an encoder–decoder architecture. Conclusions: AI algorithms demonstrate a high level of concordance with clinicians in GTV delineation. Translation to clinical settings requires the building of trust, improvement in performance and robustness of results, and testing in prospective studies and randomized controlled trials. Full article

This review aims to summarize the evolution of surgical techniques for periacetabular metastatic cancer, assess their strengths and limitations, and clarify the corresponding indications. We conducted a comprehensive literature review on periacetabular metastatic cancer, summarizing surgical techniques involving both open and minimally invasive approaches. Additionally, we evaluated the indications for different minimally invasive techniques and proposed potential combinations of these techniques. Our review underscores the benefits of minimally invasive surgery, including reduced surgical trauma, improved patient mobility, lower complication rates, and expedited recovery times, facilitating earlier initiation of systemic cancer therapies. These techniques show substantial potential for broader application in the future. Despite the historical reliance on open surgery as the standard treatment, minimally invasive approaches are emerging as a promising alternative, particularly for managing osteolytic metastases around the acetabulum. This review provides insights into the optimal integration of these techniques, aiming to support evidence-based clinical decision-making and improve patient outcomes. Full article

This study explores a new microdestructive on-site metallographic inspection technique for analyzing metal artifacts. In the current archeometrical work, the metallographic analysis of metal artifacts requires mechanical sampling, which not only damages the integrity of the artifacts but also brings cold working effects to the metallographic structure during the sampling process, making the information inaccurate. This study designed a set of detailed on-site metallographic inspection methods for bronze artifacts, including grinding, sealing, polishing, etching, replicating, cleaning, and other steps. After verifying its safety through simulation experiments, the method was applied to several precious bronze artifacts, including two Ming Dynasty bronze lions from the Xi’an Beilin Museum and a Shang Bronze Tripod Vessel with Cicada Designs from the China Bronze Ware Museum. The metallographic findings show that the in situ metallographic technique can flexibly and accurately reveal the metallographic texture and process information of each localized part of the bronze artifacts, e.g., the heat-affected zone of the coins on the surface of the Ming Dynasty bronze lions proved the casting-inlay process, and the different heat texture of each foot of the Shang Bronze Tripod Vessel with Cicada Designs proved the chronological sequence of its two historical restorations. This study provides a novel approach to the process analysis of bronze artifacts, a method that can provide significant advantages in analyzing the processing techniques of precious and intact artifacts. Full article

Guanine-quadruplex (G4) selective photosensitizers have huge potential for photodynamic therapy against various diseases correlated with G4 DNA and G4 RNAs; however, the types of photosensitizer skeletons available are limited. Herein, we investigated the ability of our original G4 ligands, tripodal quinone-cyanine dyes (tpQCy(s)), which were developed as fluorescent probes for G4, to act as photosensitizers for cancer-selective apoptosis inducers. The results indicated that the tpQCy skeleton has great potential for developing G4-targeted cancer-selective photosensitizers for photodynamic therapy. Among the two tpQCys, only QCy(BnBT)₃, which has greater G4 selectivity, exhibited photoinduced cytotoxicity in HeLa cell growth, suggesting that the direct oxidation of G4 DNA or RNA is crucial for photoinduced cytotoxicity. RNA-seq analysis using a next-generation sequencing technique revealed that apoptosis was clearly induced by photoirradiation after QCy(BnBT)₃ treatment. Full article

As UWB technology develops and devices become smaller, miniaturization techniques for an array antenna system are required. In addition, more in-depth research is needed for UWB direction-finding techniques using channel impulse response (CIR) data. This paper proposes an ultra-wideband (UWB) antenna using a single-radiator multiple-port (SRMP) design for the direction-finding systems of smart devices. The proposed SRMP antenna was designed using a single tripod-shaped patch that can replace the array system. The tripod-shaped radiator was optimized using the edge shape design function to improve its broadband and mutual coupling characteristics. For performance verification, the proposed antenna was fabricated, and the reflection coefficient, mutual coupling, and radiation patterns were measured in a fully anechoic chamber. The proposed antenna has an operating frequency band of 6.1 GHz (from 5.8 GHz to 11.9 GHz) for port 1 and a measured mutual coupling of −14.8 dB at 8 GHz. The SRMP antenna has measured maximum gains of 3.5 dBi for port 1 and 2.9 dBi for port 2. To examine the direction-finding performance, the fabricated antenna was connected to a circuit module with a DW3000 chip, which is widely employed in commercial mobile UWB systems. The direction of arrival (DoA) results using the measured CIR data show root-mean-square (RMS) errors of 1.57° and 4.58° at distances of 30 cm and 60 cm. Full article

This paper offers a comprehensive review of traditional and advanced stockpile volume-estimation techniques employed within both outdoor and indoor confined spaces, whether that be a terrestrial- or an aerial-based technique. Traditional methods, such as manual measurement and satellite imagery, exhibit limitations in handling irregular or constantly changing stockpiles. On the other hand, more advanced techniques, such as global navigation satellite system (GNSS), terrestrial laser scanning (TLS), drone photogrammetry, and airborne light detection and ranging (LiDAR), have emerged to address these challenges, providing enhanced accuracy and efficiency. Terrestrial techniques relying on GNSS, TLS, and LiDAR offer accurate solutions; however, to minimize or eliminate occlusions, surveyors must access geometrically constrained places, representing a serious safety hazard. With the speedy rise of drone technologies, it was not unexpected that they found their way to the stockpile volume-estimation application, offering advantages such as ease of use, speed, safety, occlusion elimination, and acceptable accuracy compared to current standard methods, such as TLS and GNSS. For outdoor drone missions, image-based approaches, like drone photogrammetry, surpass airborne LiDAR in cost-effectiveness, ease of deployment, and color information, whereas airborne LiDAR becomes advantageous when mapping complex terrain with vegetation cover, mapping during low-light or dusty conditions, and/or detecting small or narrow objects. Indoor missions, on the other hand, face challenges such as low lighting, obstacles, dust, and limited space. For such applications, most studies applied LiDAR sensors mounted on tripods or integrated on rail platforms, whereas very few utilized drone solutions. In fact, the choice of the most suitable technique/approach depends on factors such as site complexity, required accuracy, project cost, and safety considerations. However, this review puts more focus on the potential of drones for stockpile volume estimation in confined spaces, and explores emerging technologies, such as solid-state LiDAR and indoor localization systems, which hold significant promise for the future. Notably, further research and real-world applications of these technologies will be essential for realizing their full potential and overcoming the challenges of operating robots in confined spaces. Full article

Chronic recipients of plasma products are at risk of infection from blood-borne pathogens as a result of their inevitable exposure to agents which will contaminate a plasma manufacturing pool made up of thousands of individual donations. The generation of such a pool is an essential part of the large-scale manufacture of these products and is required for good manufacturing practice (GMP). Early observations of the transmission of hepatitis by pooled plasma and serum led to the incorporation of heat treatment of the albumin solution produced by industrial Cohn fractionation of plasma. This led to an absence of pathogen transmission by albumin over decades, during which hepatitis continued to be transmitted by other early plasma fractions, as well as through mainstream blood transfusions. This risk was decreased greatly over the 1960s as an understanding of the epidemiology and viral aetiology of transfusion-transmitted hepatitis led to the exclusion of high-risk groups from the donor population and the development of a blood screening test for hepatitis B. Despite these measures, the first plasma concentrates to treat haemophilia transmitted hepatitis B and other, poorly understood, forms of parenterally transmitted hepatitis. These risks were considered to be acceptable given the life-saving nature of the haemophilia treatment products. The emergence of the human immunodeficiency virus (HIV) as a transfusion-transmitted infection in the early 1980s shifted the focus of attention to this virus, which proved to be vulnerable to a number of inactivation methods introduced during manufacture. Further developments in the field obviated the risk of hepatitis C virus (HCV) which had also infected chronic recipients of plasma products, including haemophilia patients and immunodeficient patients receiving immunoglobulin. The convergence of appropriate donor selection driven by knowledge of viral epidemiology, the development of blood screening now based on molecular diagnostics, and the incorporation of viral inactivation techniques in the manufacturing process are now recognised as constituting a “safety tripod” of measures contributing to safety from pathogen transmission. Of these three components, viral inactivation during manufacture is the major contributor and has proven to be the bulwark securing the safety of plasma derivatives over the past thirty years. Concurrently, the safety of banked blood and components continues to depend on donor selection and screening, in the absence of universally adopted pathogen reduction technology. This has resulted in an inversion in the relative safety of the products of blood banking compared to plasma products. Overall, the experience gained in the past decades has resulted in an absence of pathogen transmission from the current generation of plasma derivatives, but maintaining vigilance, and the surveillance of the emergence of infectious agents, is vital to ensure the continued efficacy of the measures in place and the development of further interventions aimed at obviating safety threats. Full article

Background: The Braden Scale is commonly used to determine Hospital-Acquired Pressure Injuries (HAPI). However, the volume of patients who are identified as being at risk stretches already limited resources, and caregivers are limited by the number of factors that can reasonably assess during patient care. In the last decade, machine learning techniques have been used to predict HAPI by utilizing related risk factors. Nevertheless, none of these studies consider the change in patient status from admission until discharge. Objectives: To develop an integrated system of Braden and machine learning to predict HAPI and assist with resource allocation for early interventions. The proposed approach captures the change in patients’ risk by assessing factors three times across hospitalization. Design: Retrospective observational cohort study. Setting(s): This research was conducted at ChristianaCare hospital in Delaware, United States. Participants: Patients discharged between May 2020 and February 2022. Patients with HAPI were identified from Nursing documents (N = 15,889). Methods: Support Vector Machine (SVM) was adopted to predict patients’ risk for developing HAPI using multiple risk factors in addition to Braden. Multiple performance metrics were used to compare the results of the integrated system versus Braden alone. Results: The HAPI rate is 3%. The integrated system achieved better sensitivity (74.29 ± 1.23) and detection prevalence (24.27 ± 0.16) than the Braden scale alone (sensitivity (66.90 ± 4.66) and detection prevalence (41.96 ± 1.35)). The most important risk factors to predict HAPI were Braden sub-factors, overall Braden, visiting ICU during hospitalization, and Glasgow coma score. Conclusions: The integrated system which combines SVM with Braden offers better performance than Braden and reduces the number of patients identified as at-risk. Furthermore, it allows for better allocation of resources to high-risk patients. It will result in cost savings and better utilization of resources. Relevance to clinical practice: The developed model provides an automated system to predict HAPI patients in real time and allows for ongoing intervention for patients identified as at-risk. Moreover, the integrated system is used to determine the number of nurses needed for early interventions. Reporting Method: EQUATOR guidelines (TRIPOD) were adopted in this research to develop the prediction model. Patient or Public Contribution: This research was based on a secondary analysis of patients’ Electronic Health Records. The dataset was de-identified and patient identifiers were removed before processing and modeling. Full article

UAV-DAP (unmanned aerial vehicle-digital aerial photogrammetry) has become one of the most widely used geomatics techniques in the last decade due to its low cost and capacity to generate high-density point clouds, thus demonstrating its great potential for delivering high-precision products with a spatial resolution of centimetres. The questions is, how should it be applied to obtain the best results? This research explores different flat scenarios to analyse the accuracy of this type of survey based on photogrammetric SfM (structure from motion) technology, flight planning with ground control points (GCPs), and the combination of forward and cross strips, up to the point of processing. The RMSE (root mean square error) is analysed for each scenario to verify the quality of the results. An equation is adjusted to estimate the a priori accuracy of the photogrammetric survey with digital sensors, identifying the best option for μ_xyz (weight coefficients depending on the layout of both the GCP and the image network) for the four scenarios studied. The UAV flights were made in Lorca (Murcia, Spain). The study area has an extension of 80 ha, which was divided into four blocks. The GCPs and checkpoints (ChPs) were measured using dual-frequency GNSS (global navigation satellite system), with a tripod and centring system on the mark at the indicated point. The photographs were post-processed using the Agisoft Metashape Professional software (64 bits). The flights were made with two multirotor UAVs, a Phantom 3 Professional and an Inspire 2, with a Zenmuse X5S camera. We verify the influence by including additional forward and/or cross strips combined with four GCPs in the corners, plus one additional GCP in the centre, in order to obtain better photogrammetric adjustments based on the preliminary flight planning. Full article