Search Results (2,387)

Background and Objectives: Although adjacent vertebral fractures (AVF) frequently occur after balloon kyphoplasty (BKP), their risk factors remain unclear. This retrospective study aimed to identify risk factors for AVF and evaluate the utility of Hounsfield unit (HU) values on preoperative vertebral computed tomography (CT) scans as predictors of its occurrence. Materials and Methods: We retrospectively evaluated 180 patients (46 male and 134 female individuals; mean age: 80.3 years; range: 60–94 years) who underwent BKP for osteoporotic vertebral fractures (OVFs) between 2021 and 2023 with at least 6 months of follow-up. The patients were categorized into the AVF (n = 31) and non-AVF (n = 149) groups. Analyzed variables included patient characteristics, fracture level, prior fractures, posterior wall injury, intravertebral cleft, vacuum phenomenon in adjacent intervertebral discs, injury-to-surgery interval, cement volume, kyphosis angles, wedge ratios, and HU values. HU values were measured at three levels on preoperative CT scans in the vertebrae above and below the treated segment. Cutoff HU values predictive of AVF were determined using receiver operating characteristic (ROC) curve analysis. Results: AVF incidence was 17.2% (31/180), with 71.0% occurring in the vertebrae above the treated level. HU values in all measured slices were significantly lower in the AVF group. The mean HU values in the upper vertebra were 61.1 ± 6.03 (AVF) and 84.7 ± 2.75 (non-AVF), and in the lower vertebra, 51.5 ± 8.44 and 81.0 ± 3.85, respectively. ROC analysis showed cutoff HU values of 79.3 and 61.0 for the upper and lower vertebrae, respectively. HU values were identified as independent AVF risk factors. Conclusions: Preoperative vertebral HU values are independent AVF predictors. Values below 79.3 in the upper or 61.0 in the lower vertebrae were linked to higher AVF risk, suggesting HU measurement is a simple, useful tool for preoperative risk assessment. Full article

Background/Objectives: Current bone-based landmark approaches have shown variable accuracy and poor reproducibility. We validated a two-point “tuberculum sellae–anterior clinoid process” (TS–ACP) line traced on routine 3D-computed tomography angiography (CTA) for predicting distal dural ring (DDR) position and quantified the interobserver agreement. Methods: We retrospectively reviewed data from 85 patients (87 aneurysms) who were treated via clipping between June 2012 and December 2024. Two blinded neurosurgeons classified each aneurysm as extradural, intradural, or straddling the TS–ACP line. The intraoperative DDR inspection served as the reference standard. Diagnostic accuracy, χ² statistics, and Cohen’s κ were calculated. Results: The TS–ACP line landmarks were identifiable in all cases. The TS–ACP line classification correlated strongly with operative findings (χ² = 138.3, p = 6.4 × 10⁻²⁹). The overall accuracy was 89.7% (78/87), and sensitivity and specificity for identifying intradural aneurysms were 94% and 82%, respectively. The interobserver agreement was substantial (κ = 0.78). Nine aneurysms were misclassified, including four cavernous-sinus lesions that partially crossed the DDR. Retrospective fusion using constructive interference in steady-state magnetic resonance imaging corrected these errors. Conclusions: The TS–ACP line represents a rapid, reproducible tool that reliably localizes the DDR on standard 3D-CTA, showing higher accuracy than previously reported single-landmark techniques. Its high accuracy and substantial inter-observer concordance support incorporation into routine preoperative assessments. Because the method depends on only two easily detectable bony points, it is well-suited for automated implementation, offering a practical pathway toward artificial intelligence-assisted stratification of paraclinoid aneurysms. Full article

Prolonged operation of biomedical devices may compromise electronic component integrity due to cyclic thermal stress, thereby impacting both functionality and safety. Regulatory standards require regular inspections, particularly for surgical applications, highlighting the need for efficient and non-invasive diagnostic tools. This study introduces an integrated system that combines finite element models, infrared thermographic analysis, and artificial intelligence to monitor thermal stress in printed circuit boards (PCBs) within biomedical devices. A dynamic thermal model, implemented in COMSOL Multiphysics^® (version 6.2), identifies regions at high risk of thermal overload. The infrared measurements acquired through a FLIR P660 thermal camera provided experimental validation and a dataset for training a hybrid artificial intelligence system. This model integrates deep learning-based U-Net architecture for thermal anomaly segmentation with machine learning classification of heat diffusion patterns. By combining simulation, the proposed system achieved an F1-score of 0.970 for hotspot segmentation using a U-Net architecture and an F1-score of 0.933 for the classification of heat propagation modes via a Multi-Layer Perceptron. This study contributes to the development of intelligent diagnostic tools for biomedical electronics by integrating physics-based simulation and AI-driven thermographic analysis, supporting automatic classification and localisation of thermal anomalies, real-time fault detection and predictive maintenance strategies. Full article

Moderate electric field (MEF) technology is an electro-heating technology that involves the application of electric fields less than 1000 V cm⁻¹, with or without the effect of heat, to induce heating and enhance mass transfer in food processing operations. The rapid heating capabilities and higher energy efficiency make MEF a viable alternative to traditional processing methods in the food industry. Recent advancements in MEF processing of foods have focused on optimizing equipment design and process parameters and integrating digital tools to broaden their application across a wide range of food processes. This review provides a comprehensive overview of recent developments related to the design of MEF systems for various operations, including single and multicomponent food systems. The thermal efficiency and energy saving of MEF treatment in various food processing operations largely depend on the type and arrangement of the electrodes, and operating frequency and composition of the food matrix. A thorough understanding of the electrical properties of single and multicomponent food systems is crucial for analyzing their behavior and interactions with applied electric fields, and for designing an efficient MEF system. In addition, integrating digital tools and physics-based models could play a significant role in real-time monitoring, predictive process control, and process optimization to enhance productivity, reduce energy consumption, and ensure improved product quality and safety. This makes the MEF technology economically viable and sustainable, which also improves the scalability and integration into existing processing lines. Full article

The lubrication state of the feed system of a CNC machine tool will affect its positioning accuracy, repetition accuracy, and minimum movement amount. Insufficient or excessive lubrication will affect the accuracy. The primary objective of this study is to resolve issues related to the lubrication condition of the feed system, aiming to enhance its operational stability and accuracy. In this study, a measurement system based on images of Newton’s rings was developed. The relationship between the pattern of Newton’s rings and the oil film thickness was established based on the theoretical principle of Newton’s rings. Furthermore, fuzzy logic theory was applied to predict the oil film thickness. In the oil film thickness prediction model based on the radius of Newton’s rings, the average error is 6.5%. When the average feed rate increases by 2 m/min, the oil film thickness value decreases by 43%. Finally, the prediction model is compared with the results of an actual verification experiment. The trends in oil supply timing are consistent between the predicted and experimental results, and the relative error values are less than 10%. Therefore, this study solves the problem of insufficient or excessive oil supply in the feed system guideway, increasing the accuracy of CNC machine tools and contributing to green energy technology. Full article

Soil void ratio is a key parameter in geotechnical engineering design and geological hazard prevention. However, existing methods for determining void ratio are plagued by issues such as difficulty in sampling, susceptibility of samples to disturbance, and heavy experimental workload. The cone penetration test, with its advantages of simple operation, high survey efficiency, and high accuracy, has gradually become a commonly used in situ testing method in engineering investigations. Based on data from the Yellow River Delta, this paper evaluates the applicability of several models related to void ratio. Combined with the Robertson density prediction model, a semi-empirical model for predicting void ratio based on the piezocone penetration test (CPTu), in situ testing is proposed, which enables efficient evaluation by establishing a conversion mechanism between soil density and void ratio. Verification using a database built from six types of nearly saturated sedimentary soil data shows that underestimation of predicted density will amplify the error of soil void ratio. The prediction accuracy is significantly improved after coefficient correction. Finally, a simple model for predicting void ratio that only requires CPTu data is developed, providing a sampling-free evaluation tool for estuarine and marine sedimentary areas. Full article

Background: Spinal metastasis is the third most common site for metastatic localization, following the lung and liver. Manual detection through imaging modalities such as CT, MRI, PET, and bone scintigraphy can be costly and inefficient. Preliminary artificial intelligence (AI) techniques and computer-aided detection (CAD) systems have attempted to improve lesion detection, segmentation, and treatment response in oncological imaging. The objective of this review is to evaluate the current applications of AI across multimodal imaging techniques in the diagnosis of spinal metastasis. Methods: Databases like PubMed, Scopus, Web of Science Advance, Cochrane, and Embase (Ovid) were searched using specific keywords like ‘spine metastases’, ‘artificial intelligence’, ‘machine learning’, ‘deep learning’, and ‘diagnosis’. The screening of studies adhered to the PRISMA guidelines. Relevant variables were extracted from each of the included articles such as the primary tumor type, cohort size, and prediction model performance metrics: area under the receiver operating curve (AUC), accuracy, sensitivity, specificity, internal validation and external validation. A random-effects meta-analysis model was used to account for variability between the studies. Quality assessment was performed using the PROBAST tool. Results: This review included 39 studies published between 2007 and 2024, encompassing a total of 6267 patients. The three most common primary tumors were lung cancer (56.4%), breast cancer (51.3%), and prostate cancer (41.0%). Four studies reported AUC values for model training, 16 for internal validation, and five for external validation. The weighted average AUCs were 0.971 (training), 0.947 (internal validation), and 0.819 (external validation). The risk of bias was the highest in the analysis domain, with 22 studies (56%) rated high risk, primarily due to inadequate external validation and overfitting. Conclusions: AI-based approaches show promise for enhancing the detection, segmentation, and characterization of spinal metastatic lesions across multiple imaging modalities. Future research should focus on developing more generalizable models through larger and more diverse training datasets, integrating clinical and imaging data, and conducting prospective validation studies to demonstrate meaningful clinical impact. Full article

The structural health monitoring (SHM) of existing infrastructure and heritage buildings is essential for their preservation and safety. This is a review paper which focuses on modern three-dimensional (3D) measurement techniques, particularly those that enable the assessment of the structural response to environmental actions and operational conditions. The emphasis is on the detection of fractures and the identification of the crack geometry. While traditional monitoring systems—such as pendula, callipers, and strain gauges—have been widely used in massive, quasi-brittle structures like dams and masonry buildings, advancements in non-contact and computer-vision-based methods are increasingly offering flexible and efficient alternatives. The integration of drone-mounted systems facilitates access to challenging inspection zones, enabling the acquisition of quantitative data from full-field surface measurements. Among the reviewed techniques, digital image correlation (DIC) stands out for its superior displacement accuracy, while photogrammetry and time-of-flight (ToF) technologies offer greater operational flexibility but require additional processing to extract displacement data. The collected information contributes to the calibration of digital twins, supporting predictive simulations and real-time anomaly detection. Emerging tools based on machine learning and digital technologies further enhance damage detection capabilities and inform retrofitting strategies. Overall, vision-based methods show strong potential for outdoor SHM applications, though practical constraints such as drone payload and calibration requirements must be carefully managed. Full article

The Model for Urban Stormwater Improvement Conceptualization (MUSIC) serves as a key hydrological tool for simulating urban stormwater runoff pollution and evaluating the treatment performance in Water-Sensitive Urban Designs like constructed wetlands (CWs). However, a significant limitation exists in MUSIC’s current inability to model heavy metal contaminants, even though they are commonly found in urban stormwater and pose significant environmental risks. This eventually affects the model’s utility during critical planning phases for urban developments. Thus, there is a need to address this limitation. Field investigations were conducted across established CWs in residential and industrial catchments throughout Greater Melbourne, Australia. Through systematic monitoring and calibration, an approach was developed to extend MUSIC’s predictive capabilities to include several prevalent heavy metals. The results indicate that the enhanced model can generate plausible estimates for targeted metals while differentiating catchment-specific pollutant generation and treatment patterns. This advancement enhances MUSIC’s functionality as a planning support tool, enabling the preliminary assessment of heavy metal dynamics alongside conventional pollutants during both design and operational stages. The findings underscore the value of incorporating metal-specific parameters into stormwater models, offering improved support for urban water management decisions and long-term water quality protection. Full article

Background: Cardiometabolic risk in adolescents represents a growing public health concern that is closely linked to modifiable factors such as physical fitness. Traditional statistical approaches often fail to capture complex, nonlinear relationships among anthropometric and fitness-related variables. Objective: To develop and evaluate supervised machine learning algorithms, including artificial neural networks and ensemble methods, for classifying cardiometabolic risk levels among Chilean adolescents based on standardized physical fitness assessments. Methods: A cross-sectional analysis was conducted using a large representative sample of school-aged adolescents. Field-based physical fitness tests, such as cardiorespiratory fitness (in terms of estimated maximal oxygen consumption [VO_2max]), muscular strength (push-ups), and explosive power (horizontal jump) testing, were used as input variables. A cardiometabolic risk index was derived using international criteria. Various supervised machine learning models were trained and compared regarding accuracy, F1 score, recall, and area under the receiver operating characteristic curve (AUC-ROC). Results: Among all the models tested, the gradient boosting classifier achieved the best overall performance, with an accuracy of 77.0%, an F1 score of 67.3%, and the highest AUC-ROC (0.601). These results indicate a strong balance between sensitivity and specificity in classifying adolescents at cardiometabolic risk. Horizontal jumps and push-ups emerged as the most influential predictive variables. Conclusions: Gradient boosting proved to be the most effective model for predicting cardiometabolic risk based on physical fitness data. This approach offers a practical, data-driven tool for early risk detection in adolescent populations and may support scalable screening efforts in educational and clinical settings. Full article

The plastics manufacturing sector has experienced remarkable growth, requiring more optimized operations through reduced repair times and product defects. In this context, the theoretical aim of this research is to prove that the integration of classic continuous improvement tools (TPM and SMED) with advanced data science techniques (machine learning) forms a synergistic approach capable of significantly increasing operational efficiency in manufacturing environments. The study was conducted at a Peruvian plastic container manufacturing company with a first overall equipment efficiency (OEE) of 61.87%, affected by low availability of injection and blow molding machines and a high rework rate. Total Productive Maintenance (TPM) strategies were implemented to improve equipment maintenance, the SMED method to reduce setup times, and a machine learning model to predict defects and burs in products. The effectiveness of the approach was confirmed through simulations in Arena and analysis of historical data. As a result, OEE increased to 80.86%, reducing downtime and rework. In conclusion, this study shows that the combination of TPM, SMED, and machine learning not only improves operational performance but also offers a replicable and robust methodological framework for process optimization in the manufacturing industry. Full article

Borehole trajectory control is a fundamental task for directional well engineers. Now that there are inevitable errors about single-stand control in the field situation, it is difficult to deal with the complex underground problems in real time. In order to improve the efficiency of directional operation and the accuracy of wellbore trajectory control, this paper presents an improved Sparrow Search algorithm by integrating the multi-strategy model and Constant-Toolface models to calculate the single-stand control scheme for single-bent-housing motors in directional drilling. To evaluate the performance of the algorithm, the Particle Swarm algorithm, the Sparrow Search algorithm, and the improved Sparrow Search algorithm (LCSSA) are used to optimize the process parameters for each drilling, respectively. Numerical tests based on drilling data show that all three algorithms can predict the drilling parameters. In contrast, the LCSSA exhibits the fastest convergence and the smallest error after optimizing single-stand control, attaining an average convergence time of 0.08 s. It accurately back-calculated theoretical model parameters with high accuracy and met engineering requirements when applied to actual drilling data. In field applications, the LCSSA reduces the deviation from the planned trajectory by over 25%, restricting the deviation to within 0.005 m per stand; additionally the total drilling time was reduced by at least 18% compared to previous methods. The integration of the LCSSA with the drilling system significantly enhances drilling operations by optimizing trajectory accuracy and boosting efficiency and serves as an advanced tool for designing process parameters. Full article

Artificial intelligence (AI) is playing an increasingly important role in driving sustainable change across coastal and marine environments. Artificial intelligence offers strong support for environmental decision-making by helping to process complex data, anticipate outcomes, and fine-tune day-to-day operations. In busy coastal zones such as the Mediterranean where tourism and boating place significant strain on marine ecosystems, AI can be an effective means for marinas to reduce their ecological impact without sacrificing economic viability. This research examines the contribution of artificial intelligence toward the development of environmental sustainability in marina management. It investigates how AI can potentially reconcile economic imperatives with ecological conservation, especially in high-traffic coastal areas. Through a focus on the impact of social and technological context, this study emphasizes the way in which local conditions constrain the design, deployment, and reach of AI systems. The marinas of Ibiza and Monaco are used as a comparative backdrop to depict these dynamics. In Monaco, efforts like the SEA Index^® and predictive maintenance for superyachts contributed to a 28% drop in CO₂ emissions between 2020 and 2025. In contrast, Ibiza focused on circular economy practices, reaching an 85% landfill diversion rate using solar power, AI-assisted waste systems, and targeted biodiversity conservation initiatives. This research organizes AI tools into three main categories: supervised learning, anomaly detection, and rule-based systems. Their effectiveness is assessed using statistical techniques, including t-test results contextualized with Cohen’s d to convey practical effect sizes. Regression R² values are interpreted in light of real-world policy relevance, such as thresholds for energy audits or emissions certification. In addition to measuring technical outcomes, this study considers the ethical concerns, the role of local communities, and comparisons to global best practices. The findings highlight how artificial intelligence can meaningfully contribute to environmental conservation while also supporting sustainable economic development in maritime contexts. However, the analysis also reveals ongoing difficulties, particularly in areas such as ethical oversight, regulatory coherence, and the practical replication of successful initiatives across diverse regions. In response, this study outlines several practical steps forward: promoting AI-as-a-Service models to lower adoption barriers, piloting regulatory sandboxes within the EU to test innovative solutions safely, improving access to open-source platforms, and working toward common standards for the stewardship of marine environmental data. Full article

Nomex honeycomb composites (NHCs) are highly sensitive to the abnormal wear state of disc tools during cutting, leading to poor product quality. This paper proposes a real-time anomaly detection method combining a novel CNN–GRU–Attention (CGA) deep learning model with an Exponentially Weighted Moving Average (EWMA) control chart to monitor sensor data from the disc tool. The CGA model integrates an improved CNN layer to extract multidimensional local features, a GRU layer to capture long-term temporal dependencies, and a multi-head attention mechanism to highlight key information and reduce error accumulation. Trained solely on normal operation data to address the scarcity of abnormal samples, the model predicts cutting force time series with an RMSE of 0.5012, MAE of 0.3942, and R² of 0.9128, outperforming mainstream time series data prediction models. The EWMA control chart applied to the prediction residuals detects abnormal tool wear trends promptly and accurately. Experiments on real NHC cutting datasets demonstrate that the proposed method effectively identifies abnormal machining conditions, enabling timely tool replacement and significantly enhancing product quality assurance. Full article

Hydraulic erosion from suspended sediment is a major degradation mechanism in Francis turbines of sediment-laden rivers, especially in Andean hydropower plants. This study presents a Python3.9-based computational tool integrating the empirical Oka erosion model within a Lagrangian particle tracking framework, coupled to single-phase CFD in OpenFOAM 10. The novelty lies in a reduced-domain approach that omits the spiral casing and replicates its particle-induced swirl via a custom algorithm, lowering meshing complexity and computational cost while preserving erosion prediction accuracy. The method was applied to a full-scale Francis turbine at the San Francisco hydropower plant in Ecuador (nominal discharge 62.4 m³/s, rated output 115 MW, rotational speed 34.27 rad/s), operating under volcanic and erosive sediment loads. Maximum erosion rates reached ~1.2 × 10⁻⁴ mm³/kg, concentrated on runner blade trailing edges and guide vane pressure sides. Impact kinematics showed most collisions at near-normal angles (85°–98°, peak at 92°) and 6–9 m/s velocities, with rare 40 m/s impacts causing over 50× more loss than average. The workflow identifies critical wear zones, supports redesign and coating strategies, and offers a transferable, open-source framework for erosion assessment in turbines under diverse sediment-laden conditions. Full article