Search Results (228)

Vacuum degassing (VD) is a critical refining step in electric arc furnace (EAF) steelmaking for producing clean steel with reduced nitrogen and hydrogen content. This study develops an Effective Equilibrium Reaction Zone (EERZ) model focused on denitrogenation (de-N) by simulating interfacial reactions at the bubble–steel interface (Z1). The model incorporates key process parameters such as argon flow rate, vacuum pressure, and initial nitrogen and sulfur concentrations. A robust empirical correlation was established between de-N efficiency and the mass of Z1, reducing prediction time from a day to under a minute. Additionally, the model was further improved by incorporating a dynamic surface exposure zone (Z_eye) to account for transient ladle eye effects on nitrogen removal under deep vacuum (<10 torr), validated using synchronized plant trials and Python-based video analysis. The integrated approach—combining thermodynamic-kinetic modeling, plant validation, and image-based diagnostics—provides a robust framework for optimizing VD control and enhancing nitrogen removal control in EAF-based steelmaking. Full article

The automation in image acquisition and processing using UAV drones has the potential to acquire terrain data that can be utilized for the accurate production of 2D and 3D digital data. In this study, the DJI Phantom 4 drone was employed for large-scale topographical mapping, and based on the photogrammetric Structure-from-Motion (SfM) algorithm, drone-derived point clouds were used to generate the terrain DSM, DEM, contours, and the orthomosaic from which the topographical map features were digitized. An evaluation of the horizontal (X, Y) and vertical (Z) coordinates of the UAV drone points and the RTK-GNSS survey data showed that the Z-coordinates had the highest MAE(_X,Y,Z), RMSE_(X,Y,Z) and Accuracy_(X,Y,Z) errors. An integrated georeferencing of the UAV drone imagery using the mobile RTK-GNSS base station improved the 2D and 3D positional accuracies with an average 2D (X, Y) accuracy of <2 mm and height accuracy of −2.324 mm, with an overall 3D accuracy of −4.022 mm. Geometrically, the average difference in the perimeter and areas of the features from the RTK-GNSS and UAV drone topographical maps were −0.26% and −0.23%, respectively. The results achieved the recommended positional accuracy standards for the production of digital geospatial data, demonstrating the cost-effectiveness of low-cost UAV drones for large-scale topographical mapping. Full article

Background/Objectives: Assessing hippocampal pathology in epilepsy is challenging, and improving diagnostic accuracy can benefit from deep learning image reconstruction, standardized imaging protocols, and advanced post-processing methods. This study compares T2 TSE DRB (Deep Resolve Boost) sequences with standard T2 TSE sequences for hippocampal segmentation and volumetry using FreeSurfer, focusing on how DRB affects image acquisition time without compromising diagnostic accuracy. Methods: FreeSurfer (version 7.4.1) was used to segment hippocampal subregions in 36 subjects (mean age of 39 ± 14 years; 21 males, 15 females) using both T2 TSE DRB and T2 TSE sequences. The segmented volumes were compared with a two-tailed t-test, and pathological volume differences were assessed using z-values based on a 95% confidence interval (−2 < z < 2). Results: Overall hippocampal segment volumes were identical between sequences. However, significant volume differences were noted in the CA1-Body (p = 0.003), CA4-Body (p = 0.002), and whole hippocampal body (p = 0.012) in the right hippocampus. Despite these differences, the low effect sizes suggest DRB sequences are comparable to conventional sequences. Additionally, DRB reduced image acquisition time by 61%. Z-scores identified pathological volume changes between the left and right hippocampus in individual subjects. Conclusions: T2 TSE DRB sequences are non-inferior to conventional T2 TSE sequences for hippocampal segmentation. The DRB method improves efficiency while providing clinically reliable results, and the proposed 95% confidence interval can aid in more objective assessments of hippocampal pathology. Full article

Geographical origins and varietal characteristics can significantly affect the quality of Citri Reticulatae Pericarpium (Chenpi), making rapid and accurate identification essential for consumer protection. To overcome the inefficiency and high cost of conventional detection methods, this study proposed a nondestructive approach that integrates hyperspectral imaging (HSI) with deep learning to classify Chenpi varieties and their geographical origins. Hyperspectral data were collected from 15 Chenpi varieties (citrus peel) across 13 major production regions in China using three dataset configurations: exocarp-facing-upward (Z), endocarp-facing-upward (F), and a fused dataset combining random orientations (ZF). Convolutional neural networks (CNNs) were developed and compared with conventional machine learning models, including partial least-squares discriminant analysis (PLS-DA), support vector machines (SVMs), and a multilayer perceptron (MLP). The CNN model achieved 96.39% accuracy for varietal classification with the ZF dataset, while the Z-PLSDA model optimized with second derivative (D2) preprocessing and competitive adaptive reweighted sampling (CARS) feature selection attained 91.67% accuracy in geographical origin discrimination. Feature wavelength selection strategies, such as CARS, simplified the model complexity while maintaining a classification performance comparable to that of the full-spectrum models. These findings demonstrated that HSI combined with deep learning could provide a rapid, nondestructive, and cost-effective solution for Chenpi quality assessment and origin traceability. Full article

Background/objectives: Brain cancer remains difficult to treat, with survival statistics stagnant for decades. The resistance of glioblastoma brain tumours can greatly challenge the effectiveness of conventional cancer radiotherapy. However, high dose rate radiotherapy has unique effects that allow for normal tissue sparing whilst maintaining tumour control. The addition of targeted radiosensitisers, such as the chemotherapeutic drug methotrexate (MTX) or the high-Z halogenated pyrimidine drug iododeoxyuridine (IUdR), can improve radiotherapy outcomes. Combining these radiosensitiser agents with ultra-high dose rate (UHDR) synchrotron X-rays can bear synergistic effects to enhance the efficacy of these multi-modal UHDR therapies, providing a means to overcome the radioresistance of brain cancer. Methods: Here, we use controlled in vitro assays following treatment, including a clonogenic assay to determine long-term cell survival and γH2AX immunofluorescent confocal microscopy to quantify double-strand DNA breaks (DSBs). Results: We find significant enhancement for highly synergistic combinations of IUdR+MTX with synchrotron X-rays. Cell survival results demonstrate 5.4 times increased 9L gliosarcoma cell killing when these agents are combined with UHDR synchrotron X-rays compared with conventional X-rays alone at the same 5 Gy dose. The underlying mechanisms are unveiled using γH2AX imaging and reveal significant increases in DSBs and dying cells following exposure to UHDR radiation. Conclusions: Our results demonstrate that highly synergistic combination treatments using UHDR synchrotron radiation can yield significantly improved brain cancer killing compared with conventional radiotherapy. We anticipate that these additive, multi-modal combination therapies will provide options for more targeted and effective use of radiotherapies for the future treatment of brain cancer. Full article

Background/Objectives: Glaucoma is the leading cause of irreversible blindness worldwide and oxidative stress is considered to play a key role in its development. While antioxidants offer a promising approach to mitigating oxidative stress, their clinical application is often hindered by bioavailability and absorption challenges. Entrapment antioxidants within liposomes may overcome these issues, enhancing their stability and delivery. The aim of this study was to develop a novel composite liposomal formulation for glaucoma treatment, designed to enhance lipoic acid bioavailability and administration through its incorporation into the lipid bilayer. Methods: Liposomes were prepared via lipid film hydration and extrusion. To characterize them, the following analyses were performed: FTIR spectroscopy, liposomal bilayer melting temperature (Tm), TEM, DLS, Z-potential, antioxidant activity, and cytotoxicity assays. Results: The efficient incorporation of lipoic acid into the liposomes’ lipid bilayer was confirmed by FTIR. This incorporation resulted in an increase in the Tm, from 37.0 °C for liposomes to 40.0 °C for liposomes with lipoic acid (L-LA). TEM images confirmed that the spherical morphology of the lipid vesicles remained unchanged following LA incorporation. Dynamic Light Scattering analysis revealed effective diameters of 423 ± 36 nm for L liposomes and 404 ± 62 nm for L-LA liposomes. Notably, the Z-potential shifted from +4.7 ± 0.4 mV (L) to −0.4 ± 0.3 mV (L-LA). Furthermore, L-LA exhibited significant antioxidant activity (31.6 ± 0.4%) compared with L (5.3 ± 0.3%) and biocompatibility, suggesting its potential for therapeutic applications. Conclusions: In summary, biocompatible composite liposomes with antioxidant capacity were successfully developed, resulting in promising candidates for neuroprotective glaucoma therapy. Full article

Background/Objectives: Open-source AI models are increasingly applied in medical imaging, yet their effectiveness in detecting and classifying spinal stabilization systems remains underexplored. This study compares ChatGPT-4o (a large language model) and BiomedCLIP (a multimodal model) in their analysis of posturographic X-ray images (AP projection) to assess their accuracy in identifying the presence, type (growing vs. non-growing), and specific system (MCGR vs. PSF). Methods: A dataset of 270 X-ray images (93 without stabilization, 80 with MCGR, and 97 with PSF) was analyzed manually by neurosurgeons and evaluated using a three-stage AI-based questioning approach. Performance was assessed via classification accuracy, Gwet’s Agreement Coefficient (AC1) for inter-rater reliability, and a two-tailed z-test for statistical significance (p < 0.05). Results: The results indicate that GPT-4o demonstrates high accuracy in detecting spinal stabilization systems, achieving near-perfect recognition (97–100%) for the presence or absence of stabilization. However, its consistency is reduced when distinguishing complex growing-rod (MCGR) configurations, with agreement scores dropping significantly (AC1 = 0.32–0.50). In contrast, BiomedCLIP displays greater response consistency (AC1 = 1.00) but struggles with detailed classification, particularly in recognizing PSF (11% accuracy) and MCGR (4.16% accuracy). Sensitivity analysis revealed GPT-4o’s superior stability in hierarchical classification tasks, while BiomedCLIP excelled in binary detection but showed performance deterioration as the classification complexity increased. Conclusions: These findings highlight GPT-4o’s robustness in clinical AI-assisted diagnostics, particularly for detailed differentiation of spinal stabilization systems, whereas BiomedCLIP’s precision may require further optimization to enhance its applicability in complex radiographic evaluations. Full article

Generation Z is emerging as a powerful force in shaping global consumer behavior, particularly in the market of digital and technological products. This study examines how specific branding dimensions, such as online brand experience, engagement, image, trust, loyalty, awareness, behavioral intention, and brand knowledge, influence Gen Z’s purchase intention, emphasizing sustainable consumption. Sustainable consumption, in this context, refers to the preference for tech products that minimize the environmental impact through eco-friendly design, energy efficiency, and responsible sourcing. Using a quantitative approach, this cross-sectional research explores the effects of online brand experience, engagement, image, trust, loyalty, awareness, behavioral intention, and knowledge on purchasing newly launched technological products that align with sustainability. Data were collected from a Gen Z sample using a combination of convenience and systematic sampling, and multiple regression analysis identified the most significant predictors of purchase intention. The results indicate that online brand experience, brand image, brand trust, and brand loyalty play the most crucial roles, having a greater effects on Gen Z’s purchase intention, while brand awareness and knowledge also contribute. However, brand engagement and behavioral intention have weaker effects. These findings suggest that brands targeting Gen Z should prioritize building a strong, trustworthy, and engaging online presence while highlighting their sustainability efforts, and when Gen Z consumers have favorable digital interactions with a brand, perceive its image positively, trust it, and feel loyal to it, they are more likely to consider purchasing its sustainable offerings. This study enhances the understanding of Gen Z’s consumer behavior and offers insights for businesses seeking to foster sustainable consumption by highlighting the importance of developing marketing strategies that strengthen online brand experience, cultivate trust and loyalty, and clearly communicate sustainability values, such as through engaging digital content, transparent messaging, and eco-conscious branding, to effectively foster sustainable consumption among this environmentally conscious and digitally driven generation. Full article

The generation of digital aerial photogrammetry products using unmanned aerial vehicle-digital aerial photogrammetry (UAV-DAP) has become an essential task due to the increasing use of UAVs in the world of geomatics, thanks to their low cost and spatial resolution. Therefore, it is relevant to explore the performance of new digital cameras equipped in UAVs using electronic rolling shutters instead of ideal mechanical or global shutter cameras to achieve accurate and reliable photogrammetric products, if possible, while minimizing workload, especially for their application in projects that require a high level of detail. In this paper, we analyse performance using oblique images along the perimeter (3D perimeter) on a flat area, i.e., with slopes of less than 3%. The area was photogrammetrically surveyed with a DJI (Dà-Jiāng Innovations) Inspire 2 multirotor UAV equipped with a Zenmuse X5S rolling shutter camera. The photogrammetric survey was accompanied by a Global Navigation Satellite System (GNSS) survey, in which dual frequency receivers were used to determine the ground control points (GCPs) and checkpoints (CPs). The study analysed different scenarios, including the combination of forward and transversal strips and oblique images. After examining the ideal scenario with the least root mean square error (RMSE), six different combinations were analysed to find the best location for the GCPs. The most significant results indicate that the optimal calibration of the camera is obtained in scenarios including oblique images, which outperform the rest of the scenarios for achieving the lowest RMSE (2.5x the GSD in Z and 3.0x the GSD in XYZ) with optimum GCPs layout; with non-ideal GCPs layout, unacceptable errors can be achieved (11.4x the GSD in XYZ), even with ideal block geometry. The UAV-DAP rolling shutter effect can only be minimised in the scenario that uses oblique images and GCPs at the edges of the overlapping zones and the perimeter. Full article

Postmastectomy radiation therapy (PMRT) is an adjuvant treatment for breast cancer. Some mastectomized women undergoing PMRT can have breast reconstruction with expander implant reconstruction. However, the expander implant contains a magnetic metal port for its inflation, and in patients with a high risk of recurrence, the PMRT is performed before the expander replacement. The difficulties in radiation treatment near high-Z metals are mainly due to dose alterations around them. Therefore, this study proposes using a realistic breast phantom and gel dosimetry to investigate the effects of the metallic parts of the expandable prosthesis on the 3D delivery of the treatment. A conformal radiation treatment was planned and delivered to the gel phantom with the metal port. MAGIC-f gel was used with magnetic resonance imaging for dose assessment. The treatment plan dose distribution was compared to the measured dose distribution by gamma analysis (3%/3 mm/15% threshold). A significant gamma fail region was found near the metal port, corresponding to a dose reduction of approximately 5%. This underdose is within the tolerance threshold for dose heterogeneity established by the International Commission on Radiation Units (ICRU), but should be considered when treating these patients. Full article

This study presents an automated computer vision system for assessing the cleanliness of plastic mirror caps used in the automotive industry after a washing process. These components are highly visible and require optimal surface conditions prior to painting, making the detection of residual contaminants critical for quality assurance. The system acquires high-resolution monochrome images under various lighting configurations, including natural light and infrared (IR) at 850 nm and 940 nm, with different angles of incidence. Four blob detection algorithms—adaptive thresholding, Laplacian of Gaussian (LoG), Difference of Gaussians (DoG), and Determinant of Hessian (DoH)—were implemented and evaluated based on their ability to detect surface impurities. Performance was assessed by comparing the total detected blob area before and after the cleaning process, providing a proxy for both sensitivity and false positive rate. Among the tested methods, adaptive thresholding under 30° natural light produced the best results, with a statistically significant z-score of +2.05 in the pre-wash phase and reduced false detections in post-wash conditions. The LoG and DoG methods were more prone to spurious detections, while DoH demonstrated intermediate performance but struggled with reflective surfaces. The proposed approach offers a cost-effective and scalable solution for real-time quality control in industrial environments, with the potential to improve process reliability and reduce waste due to surface defects. Full article

Background: Pulmonary hypertension (PH) is characterized by elevated pressure in the pulmonary artery. Currently, most dual-energy CT (DECT) research focuses on the application of iodine mapping in pulmonary embolism. However, little attention is paid to the parametric mapping of the lung parenchyma of PH. Methods: In total, 156 cases undergoing thoracic DECT from 2021 August to 2023 February were surveyed. For each case, the iodine density (Iod) and effective atomic number (Zeff) of four different levels of the lung, along with the iodine density of the pulmonary artery and aorta, were measured. The measured parameters and their derivatives were compared between PH cases and normal controls and between chronic thromboembolic PH (CTEPH) and non-CTEPH cases. Results: Region of interest (ROI)-Zeff was statistically lower in the PH group as compared to the normal controls on each level. The ratio of PA-iod/ROI-iod was significantly higher in the PH group than in the normal controls. ROI-iod was statistically lower in the CTEPH cases as compared with the non-CTEPH cases on each level. The CTEPH cases demonstrated a higher PA-iod/ROI-iod value as compared with the non-CTEPH cases. Conclusions: The PA-iodine density and effective Z of spectrum CT could serve as valuable imaging parameters for the diagnosis and characterization of PH and CTEPH. Full article

Background/Objectives: Very preterm body composition at term shows potential as a biomarker of later health outcomes, but effects from in-hospital formula versus human milk (HM) (maternal milk (MM) and/or pasteurised human donor milk (DM) supplement) intake studies are confounded by the effect from the fortifier. We investigated the impact of in-hospital unfortified HM (UHM), fortified HM (FHM), and preterm formula (PTF) intake on very preterm body composition at term. Methods: Preplanned analysis of the PREterM FOrmula or Donor milk (PREMFOOD) trial: Infants born at <32 weeks were randomised to either (i) UHM, (ii) FHM, or (iii) MM and/or PTF supplement. Main outcomes were assessed by anthropometry and magnetic resonance imaging of body composition at term. Secondary comparison between groups defined by proportion of milk intake from birth to 35 weeks postmenstrual age: The groups comprised exclusive UHM (ExUHM, proportion of UHM 99–100%, n = 23), predominantly UHM (PrUHM, UHM 50–98.9%, n = 15), predominantly FHM (PrFHM, FHM > 50%, n = 17), and predominantly PTF (PrPTF, PTF > 50%, n = 7). Results: At term, compared to the ExUHM group, the PrPTF group had 274.3 g (95% CI: 30.1 to 518.5) more Non-Adipose Tissue Mass (NATM) and a 1.11 times (95% CI: 0.38 to 1.84) greater increase in weight z score from birth, while both PrPTF and PrFHM had greater increases in length z scores from birth. Conclusions: High formula intake was associated with maximal gains in NATM at term, and these gains were not matched by the early fortification of HM. The alteration of body composition at term with prolonged or delayed HM fortification and its relation to later health outcomes are important research questions. Full article

The Printed Circuit Board (PCB) manufacturing industry is a rapidly expanding sector, fueled by advanced technologies and precision-oriented production processes. The placement of Surface-Mount Device (SMD) components in PCB assembly is efficiently automated using robots and design software-generated coordinate files; however, the PCB repair process remains significantly more complex and challenging. Repairing faulty PCBs, particularly replacing defective SMD components, requires high precision and significant manual expertise, making automated solutions both rare and difficult to implement. This study introduces a novel real-time machine vision-based coordinate estimation system designed for estimating the coordinates of SMD components during soldering or desoldering tasks. The system was specifically designed for Selective Compliance Articulated Robot Arm (SCARA) robots to overcome the challenges of repairing miniature PCB components. The proposed system integrates Image-Based Visual Servoing (IBVS) for precise X and Y coordinate estimation and a simplified laser triangulation method for Z-axis depth estimation. The system demonstrated accuracy rates of 98% for X and Y axes and 99% for the Z axis, coupled with high operational speed. The developed solution highlights the potential for automating PCB repair processes by enabling SCARA robots to execute precise picking and placement tasks. When equipped with a hot-air gun as the end-effector, the system could enable automated soldering and desoldering, effectively replacing faulty SMD components without human intervention. This advancement has the potential to bridge a critical gap in the PCB repair industry, improving efficiency and reducing dependence on manual expertise. Full article

(1) Background: White spot lesions (WSLs) on enamel result from demineralization and are an early sign of dental caries. Glass ionomer cement (GIC) has been widely used for its remineralization potential, and zinc-containing GIC (zGIC) has been introduced to enhance this effect. However, its efficacy compared to conventional GIC (cGIC) remains unclear. This study aimed to evaluate and compare the remineralization effects of cGIC and zGIC on WSLs. (2) Methods: Thirty-six bovine enamel specimens were prepared, demineralized for four days, and divided into three groups: control, cGIC, and zGIC. Half of each specimen’s treated window was covered with varnish, and a two-week pH cycling protocol was conducted. Mineral density (MD) changes were assessed using microcomputed tomography (Micro-CT) at five time points: pre-demineralization, after demineralization, immediately after treatment, and 1- and 2-weeks post-treatment. Scanning electron microscopy (SEM) was also performed. (3) Results: MD values in the zGIC and cGIC groups were significantly higher than in the control group throughout every post-treatment assessment (p < 0.05). zGIC demonstrated significantly higher MD than cGIC (p < 0.05), and SEM images revealed inferior mineral deposition. (4) Conclusions: These findings suggest that zGIC is more effective in remineralizing WSLs than cGIC over two weeks. Full article