Search Results (265)

Three-dimensional printing technologies are becoming increasingly attractive for their versatility; the geometrical customizability and manageability of the final product properties are the key points. This work aims to assess the feasibility of producing radiopaque filaments for fused deposition modeling (FDM), a 3D printing technology, starting with zinc oxide (ZnO) and polylactic acid (PLA) as the raw materials. Indeed, ZnO and PLA are promising materials due to their non-toxic and biocompatible nature. Pellets of PLA and ZnO in the form of nanoparticles were mixed together using ethanol; this homogenous mixture was processed by a commercial extruder, optimizing the process parameters for obtaining mechanically stable samples. Scanning electron microscopy analyses were used to assess, in the extruded samples, the homogenous distribution of the ZnO in the PLA matrix. Moreover, X-ray microtomography revealed a certain homogenous radiopacity; this imaging technique also confirmed the correct distribution of the ZnO in the PLA matrix. Thus, our tests showed that mechanically stable radiopaque filaments, ready for FDM systems, were obtained by homogenously loading the PLA with a maximum ZnO content of 6.5% wt. (nominal). This study produced multiple outcomes. We demonstrated the feasibility of producing radiopaque filaments for additive manufacturing using safe materials. Moreover, each phase of the process is cost-effective and green-oriented; in fact, the homogenous mixture of PLA and ZnO requires only a small amount of ethanol, which evaporates in minutes without any temperature adjustment. Finally, both the extruding and the FDM technologies are the most accessible systems for the additive manufacturing commercial apparatuses. Full article

Hospital-acquired infections are considered a priority for public health systems since they pose a significant burden for society. High-touch surfaces of healthcare centers, including textiles, provide a suitable environment for pathogenic bacteria to grow, necessitating incorporating effective antibacterial agents into textiles. This paper introduces a highly durable antibacterial gel-like solution, Silver Shell™ finish, which contains chitosan-bound silver chloride microparticles. The study investigates the coating’s environmental impact, health risks, and durability during repeated washing. The structure of the Silver Shell™ finish was studied using transmission electron microscopy (TEM) and energy-dispersive X-ray spectroscopy (EDX). The TEM images showed a core–shell structure, with chitosan forming a protective shell around groupings of silver microparticles. The field-emission scanning electron microscopy (FESEM) demonstrated the uniform deposition of Silver Shell™ on the surfaces of the fabrics. AATCC Test Method 100 was employed to quantitatively analyze the antibacterial properties of the fabrics coated with silver microparticles. Two types of bacteria, Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli), were used in this study. The antibacterial results showed that after 75 wash cycles, a 100% reduction for both S. aureus and E. coli in the coated samples using crosslinking agents was observed. The coated samples without a crosslinking agent exhibited 99.88% and 99.81% reductions for S. aureus and E. coli after 50 washing cycles. To compare the antibacterial properties toward non-pathogenic and pathogenic strains of the same species, MG1655 model E. coli strain (ATCC 29213) and a multidrug-resistant clinical isolate were used. The results showed the antibacterial efficiency of the Silver Shell^TM solution (up to 99.99% reduction) coated on cotton fabric. AATCC-147 was performed to investigate the coated samples’ leaching properties and the crosslinking agent’s effects against S. aureus and E. coli. All coated samples demonstrated remarkable antibacterial efficacy, even after 75 wash cycles. The crosslinking agent facilitated durable attachment between the silver microparticles and cotton substrate, minimizing the release of particles from the fabrics. Color measurements were conducted to assess the color differences resulting from the coating process. The results indicated fixation values of 44%, 32%, and 28% following 25, 50, and 75 washing cycles, respectively. Full article

Including poly(ADP-ribose) polymerase (PARP) inhibitors in managing patients with inoperable tumors has significantly improved outcomes. The PARP inhibitors hamper single-strand deoxyribonucleic acid (DNA) repair by trapping poly(ADP-ribose)polymerase (PARP) at sites of DNA damage, forming a non-functional “PARP enzyme–inhibitor complex” leading to cell cytotoxicity. The effect is more pronounced in the presence of PARP upregulation and homologous recombination (HR) deficiencies such as breast cancer-associated gene (BRCA1/2). Hence, identifying HR-deficiencies by genomic analysis—for instance, BRCA1/2 used in triple-negative breast cancer—should be a part of the selection process for PARP inhibitor therapy. Published data suggest BRCA1/2 germline mutations do not consistently predict favorable responses to PARP inhibitors, suggesting that other factors beyond tumor mutation status may be at play. A variety of factors, including tumor heterogeneity in PARP expression and intrinsic and/or acquired resistance to PARP inhibitors, may be contributing factors. This justifies the use of an additional tool for appropriate patient selection, which is noninvasive, and capable of assessing whole-body in vivo PARP expression and evaluating PARP inhibitor pharmacokinetics as complementary to the currently available BRCA1/2 analysis. In this review, we discuss [¹⁸F]Fluorine PARP inhibitor radiotracers and their potential in the imaging of PARP expression and PARP inhibitor pharmacokinetics. To provide context we also briefly discuss possible causes of PARP inhibitor resistance or ineffectiveness. The discussion focuses on TNBC, which is a tumor type where PARP inhibitors are used as part of the standard-of-care treatment strategy. Full article

With the development of computer technology, landslide recognition based on machine learning methods has been widely applied in geological disaster management and research. However, in landslide identification, the problems of an insufficient number of samples and an imbalance of samples are often ignored; that is, landslide samples are much smaller than non-landslide samples. In order to solve this problem, taking the main urban area of Lanzhou City as an example, this paper proposes to construct a semi-supervised generated countermeasure network (SSGAN) model, which aims to achieve high performance with a limited number of labeled samples for precise landslide identification, and to help prevent and reduce the harm caused by disasters. In order to express the environmental characteristics of landslide development and the optical texture features of landslide occurrence, the study constructs three sets of samples to represent landslide features, including a landslide influencing factor sample set, a Sentinel-2A optical remote sensing sample set, a joint influencing factor and Sentinel-2A sample set. The three kinds of sample sets are transferred to SSGAN for training to form a comparative study. The results show that the joint sample set has excellent feature results in discriminator and generator. Through the experimental comparison, the model proposed in this paper is compared with the model without semi-supervised generated confrontation training. The experimental results show that the proposed method is better than the unsupervised adversarial learning model in terms of accuracy, F1 score, Kappa coefficient, and MIoU. A total of 160 landslides have been identified in the study area, with a total area of 10.328 km², with an accuracy rate of 83%. Therefore, the generated results are accurate and reliable, and show that SSGAN can better distinguish landslides from non-landslides in an image, under the condition of obtaining a large number of unmarked environmental features; enhance the effect of landslide classification in complex geographical environment; and then put forward effective suggestions for the prevention and control of landslides and geological disasters in the main urban area of Lanzhou. Full article

Vibration measurement is crucial in fields like aviation, aerospace, and automotive engineering, which are trending towards larger, lighter, and more complex structures with increasingly complicated dynamics. Consequently, measuring a structure’s dynamic characteristics has gained heightened importance. Among non-contact approaches, those based on high-speed cameras combined with laser interferometry or computational imaging have gained widespread attention. These techniques yield sequences of images that form a three-dimensional space-time data set. Effectively processing these data is a prerequisite for accurately extracting dynamic deformation information. This paper presents two examples to illustrate the significant advantages of signal processing along the time axis in dynamic interferometric and digital speckle-image-based dynamic measurements. The results show that the temporal process effectively minimizes speckle and electronic noise in the spatial domain and dramatically increases measurement resolutions. Full article

In medical imaging, detecting tissue anomalies is vital for accurate diagnosis and effective treatment. Electrical impedance tomography (EIT) is a non-invasive technique that monitors the changes in electrical conductivity within tissues in real time. However, the current challenge lies in simply and accurately reconstructing multi-conductivity distributions. This paper introduces a layered fusion framework for EIT to enhance imaging in multi-conductivity scenarios. The method begins with pre-imaging and extracts the main object from the fuzzy image to form one layer. Then, the voltage difference in the other layer, where the local anomaly is located, is estimated. Finally, the corresponding conductivity distribution is established, and multiple layers are fused to reconstruct the multi-conductivity distribution. The simulation and experimental results demonstrate that compared to traditional methods, the proposed method significantly improves multi-conductivity separation, precise anomaly localization, and robustness without adding uncertain parameters. Notably, the proposed method has demonstrated exceptional accuracy in local anomaly detection, with positional errors as low as 1% and size errors as low as 33%, which significantly outperforms the traditional method with respective minimum errors of 9% and 228%. This method ensures a balance between the simplicity and accuracy of the algorithm. At the same time, it breaks the constraints of traditional linear methods, struggling to identify multi-conductivity distributions, thereby providing new perspectives for clinical EIT. Full article

Federated learning, as a distributed machine learning framework, aims to protect data privacy while addressing the issue of data silos by collaboratively training models across multiple clients. However, a significant challenge to federated learning arises from the non-independent and identically distributed (non-iid) nature of data across different clients. non-iid data can lead to inconsistencies between the minimal loss experienced by individual clients and the global loss observed after the central server aggregates the local models, affecting the model’s convergence speed and generalization capability. To address this challenge, we propose a novel federated learning algorithm based on update bias (FedUB). Unlike traditional federated learning approaches such as FedAvg and FedProx, which independently update model parameters on each client before direct aggregation to form a global model, the FedUB algorithm incorporates an update bias in the loss function of local models—specifically, the difference between each round’s local model updates and the global model updates. This design aims to reduce discrepancies between local and global updates, thus aligning the parameters of locally updated models more closely with those of the globally aggregated model, thereby mitigating the fundamental conflict between local and global optima. Additionally, during the aggregation phase at the server side, we introduce a metric called the bias metric, which assesses the similarity between each client’s local model and the global model. This metric adaptively sets the weight of each client during aggregation after each training round to achieve a better global model. Extensive experiments conducted on multiple datasets have confirmed the effectiveness of the FedUB algorithm. The results indicate that FedUB generally outperforms methods such as FedDC, FedDyn, and Scaffold, especially in scenarios involving partial client participation and non-iid data distributions. It demonstrates superior performance and faster convergence in tasks such as image classification. Full article

Background/Objectives: The newly emergent COVID-19 pandemic involved primarily the respiratory system and had also major cardiovascular system (CVS) implications, revealed by acute myocardial infarction (AMI), arrhythmias, myocardial injury, and thromboembolism. CVS involvement is done through main mechanisms—direct and indirect heart muscle injury, with high mortality rates, worse short-term outcomes, and severe complications. AMI is the echo of myocardial injury (revealed by increases in CK, CK-MB, and troponin serum markers—which are taken into consideration as possible COVID-19 risk stratification markers). When studying myocardial injury, physicians can make use of imaging studies, such as cardiac MRI, transthoracic (or transesophageal) echocardiography, coronary angiography, cardiac computed tomography, and nuclear imaging (which have been used in cases where angiography was not possible), or even endomyocardial biopsy (which is not always available or feasible). Two-case-series presentations: We present the cases of two COVID-19 positive male patients who were admitted into the Clinical Department of Cardiology in “Sfântul Apostol Andrei” Emergency Clinical Hospital of Galați (Romania), who presented with acute cardiac distress symptoms and have been diagnosed with ST elevation AMI. The patients were 82 and 57 years old, respectively, with moderate and severe forms of COVID-19, and were diagnosed with anteroseptal left ventricular AMI and extensive anterior transmural left ventricular AMI (with ventricular fibrillation at presentation), respectively. The first patient was a non-smoker and non-drinker with no associated comorbidities, and was later discharged, while the second one died due to AMI complications. Conclusions: From this two-case series, we extract the following: old age alone is not a significant risk factor for adverse outcomes in COVID-19-related CVS events, and that the cumulative effects of several patient-associated risk factors (be it either for severe forms of COVID-19 and/or acute cardiac injury) will most probably lead to poor patient prognosis (death). At the same time, serum cardiac enzymes, dynamic ECG changes, along with newly developed echocardiographic modifications are indicators for poor prognosis in acute cardiac injury in COVID-19 patients with acute myocardial injury, regardless of the presence of right ventricular dysfunction (due to pulmonary hypertension). Full article

A recent author’s fractal fluid-dynamic dispersion theory in porous media has focused on the derivation of the associated nonergodic (or effective) macrodispersion coefficients by a 3-D stochastic Lagrangian approach. As shown by the present study, the Fickian (i.e., the asymptotic constant) component of a properly normalized version of these coefficients exhibits a clearly detectable minimum in correspondence with the same fractal dimension (d ≅ 1.7) that seems to characterize the diffusion-limited aggregation state of cells in advanced stages of cancerous lesion progression. That circumstance suggests that such a critical fractal dimension, which is also reminiscent of the colloidal state of solutions (and may therefore identify the microscale architecture of both living and non-living two-phase systems in state transition conditions) may actually represent a sort of universal nature imprint. Additionally, it suggests that the closed-form analytical solution that was provided for the effective macrodispersion coefficients in fractal porous media may be a reliable candidate as a physically-based descriptor of blood perfusion dynamics in healthy as well as cancerous tissues. In order to evaluate the biological meaningfulness of this specific fluid-dynamic parameter, a preliminary validation is performed by comparison with the results of imaging-based clinical surveys. Moreover, a multifractal extension of the theory is proposed and discussed in view of a perspective interpretative diagnostic utilization. Full article

Single-phase center multiple azimuth beam (SPC MAB) mode is an effective method for high-resolution wide-swath (HRWS) SAR imaging. The traditional azimuth spectrum reconstruction method for SPC MAB mode is based on the combination scheme from which fake targets along the azimuth direction arise because the inter-beam interference is not considered. When the real antenna mode is inconsistent with the ideal one, the disadvantages of the combination scheme become more serious. In this paper, based on the basic theory of the low-pass, band-limited, multiple-channel under-sampling and reconstruction, a novel digital beam-forming method is proposed for the SPC MAB imaging mode with ideal antenna mode first. The method analyzes the system functions of the sub-beams, based on which digital beam-forming filters are designed for all the sub-beams. The designed filters can reconstruct the correct wide-bandwidth azimuth spectrum and suppress the inter-beam interference simultaneously. Furthermore, the proposed method is extended to SPC MAB mode with the non-ideal antenna mode. The simulation experiments prove the validities of the proposed method both for azimuth spectral reconstruction and the inter-beams interfering suppressing, no matter that the SPC MAB’s antenna mode is ideal or non-ideal. Full article

The aluminum strength-to-weight ratio has become a highly significant factor in industrial applications. Placing stiffening ribs along the surface can significantly improve the panel’s resistance to bending and compression in aluminum alloys. This study used single-point incremental forming (SPIF) to fabricate stiffening ribs for 1 mm and 3 mm thick aluminum alloy EN AW-2024-T3 sheets. A universal compression machine was used to investigate sheet deformation. The resulting deformation was examined using non-contact digital image correlation (DIC) based on several high-resolution cameras. The results showed that deformation progressively escalated from the edges toward the center, and the highest buckling values were confined within the non-strengthened area. Specimens with a larger thickness (3 mm) showed better effectiveness against buckling and bending for each applied load: 8 kN or 10 kN. Additionally, the displacement from the sheet surface decreased by 60% for sheets 3 mm thick and by half for sheets 1 mm thick, which indicated that thicker sheets could resist deformation better. Full article

Predicting urban-scale carbon emissions (CEs) is crucial in drawing implications for various urgent environmental issues, including global warming. However, prior studies have overlooked the impact of the micro-level street environment, which might lead to biased prediction. To fill this gap, we developed an effective machine learning (ML) framework to predict neighborhood-level residential CEs based on a single data source, street view images (SVIs), which are publicly available worldwide. Specifically, more than 30 streetscape elements were classified from SVIs using semantic segmentation to describe the micro-level street environment, whose visual features can indicate major socioeconomic activities that significantly affect residential CEs. A ten-fold cross-validation was deployed to train ML models to predict the residential CEs at the 1 km grid level. We found, first, that random forest (R² = 0.8) outperforms many traditional models, confirming that visual features are non-negligible in explaining CEs. Second, more building, wall, and fence views indicate higher CEs. Third, the presence of trees and grass is inversely related to CEs. Our findings justify the feasibility of using SVIs as a single data source to effectively predict neighborhood-level residential CEs. The framework is applicable to large regions across diverse urban forms, informing urban planners of sustainable urban form strategies to achieve carbon-neutral goals, especially for the development of new towns. Full article

Breast cancer results from a disruption of certain cells in breast tissue that undergo uncontrolled growth and cell division. These cells most often accumulate and form a lump called a tumor, which may be benign (non-cancerous) or malignant (cancerous). Malignant tumors can spread quickly throughout the body, forming tumors in other areas, which is called metastasis. Standard screening techniques are insufficient in the case of metastasis; therefore, new and advanced techniques based on artificial intelligence (AI), machine learning, and regression models have been introduced, the primary aim of which is to automatically diagnose breast cancer through the use of advanced techniques, classifiers, and real images. Real fine-needle aspiration (FNA) images were collected from Wisconsin, and four classifiers were used, including three machine learning models and one regression model: the support vector machine (SVM), naive Bayes (NB), k-nearest neighbors (k-NN), and decision tree (DT)-C4.5. According to the accuracy, sensitivity, and specificity results, the SVM algorithm had the best performance; it was the most powerful computational classifier with a 97.13% accuracy and 97.5% specificity. It also had around a 96% sensitivity for the diagnosis of breast cancer, unlike the models used for comparison, thereby providing an exact diagnosis on the one hand and a clear classification between benign and malignant tumors on the other hand. As a future research prospect, more algorithms and combinations of features can be considered for the precise, rapid, and effective classification and diagnosis of breast cancer images for imperative decisions. Full article

The short-form video platform TikTok has become highly popular. This study explores how professional sports teams can effectively leverage short-form videos to promote green values such as environmental conservation and sustainable development, thereby capturing user attention and enhancing user engagement. This study primarily aimed to investigate the effects of a green brand image on green word of mouth (WOM), customer stickiness, and consumer purchase intention, with further examination regarding the moderating effect of green concerns on these relationships. Few studies have explored the presence of professional sports teams on TikTok, particularly in the context of green issues. Accordingly, this study adopts a novel method to develop specific and actionable recommendations for professional sports teams who have a presence on social media. Additionally, via the application of the Stimulus–Organism–Response theory, this study explains how the green brand image presented by professional sports teams on TikTok influences the interactive relationships among green WOM, customer stickiness, and consumer purchase intention. This study recruited 600 individuals who were either fans of the Taipei Fubon Braves, which is a team in Taiwan’s professional basketball league P.LEAGUE+, or fans of other teams. After a confirmatory factor analysis, structural equation modeling was used to test the hypotheses. The results indicate positive correlations in all tested paths. The green concern of the Taipei Fubon Braves’ fans moderated the relationship between green WOM and purchase intention; however, this moderating effect was not identified among the non-Taipei Fubon Braves fans. These findings introduce innovative concepts to the field of marketing, contributing substantially to both practical applications and academic research. Full article

In this contribution we evaluate the 3D geometry reconstructed by Neural Radiance Fields (NeRFs) of an object’s occluded parts behind obstacles through a point cloud comparison in 3D space against traditional Multi-View Stereo (MVS), addressing the accuracy and completeness. The key challenge lies in recovering the underlying geometry, completing the occluded parts of the object and investigating if NeRFs can compete against traditional MVS for scenarios where the latter falls short. In addition, we introduce a new “obSTaclE, occLusion and visibiLity constrAints” dataset named STELLA concerning transparent and non-transparent obstacles in real-world scenarios since there is no existing dataset dedicated to this problem setting to date. Considering that the density field represents the 3D geometry of NeRFs and is solely position-dependent, we propose an effective approach for extracting the geometry in the form of a point cloud. We voxelize the whole density field and apply a 3D density-gradient based Canny edge detection filter to better represent the object’s geometric features. The qualitative and quantitative results demonstrate NeRFs’ ability to capture geometric details of the occluded parts in all scenarios, thus outperforming in completeness, as our voxel-based point cloud extraction approach achieves point coverage up to 93%. However, MVS remains a more accurate image-based 3D reconstruction method, deviating from the ground truth 2.26 mm and 3.36 mm for each obstacle scenario respectively. Full article