Search Results (8)

Individual fluid inclusions with dense carbon dioxide hosted in quartz from the gold-bearing interval penetrated by the SD-3 Kola Superdeep Borehole were studied using modern techniques. The composition and density of the carbon dioxide fluid were determined by Raman spectroscopy and microthermometry. The density of the fluid is 0.37–1.14 g/cm³ and contains minor admixtures of nitrogen (0.3–1.8 mol %) and water (0.1–0.4 mol %). LA-ICP-MS data indicate that the carbon dioxide fluid inclusions contain high concentrations of Au (1–2611 ppm) and Ag (1–4389 ppm), and high-precision optical data indicate that the high-density CO₂ fluid of the inclusions contains Au–Ag nanoparticles. Evidently, gold and silver were transported from the Earth’s mantle to the crust by high-density carbon dioxide fluid in the form of nanoparticles. Full article

(1) Background: Our aim is to describe a new mixed indocyanine-non-adhesive liquid embolic agent (Onyx-18) pre-operative renal embolization technique for assisted-robotic and laparoscopic partial nephrectomy with near-infra-red fluorescence imaging. (2) Methods: Thirteen patients with biopsy-proven renal tumors underwent pre-operative mixed indocyanine–ethylene vinyl alcohol (EVOH) embolization (Green-embo) between June 2021 and August 2022. All pre-operative embolizations were performed with a super selective stop-flow technique using a balloon microcatheter to deliver an indocyanine-EVOH mixture into tertiary order arterial branch feeders and the intra-lesional vascular supply. Efficacy (evaluated as complete embolization, correct tumor mapping on infra-red fluorescence imaging and clamp-off surgery) and safety (evaluated as complication rate and functional outcomes) were primary goals. Clinical and pathological data were also collected. (3) Results: Two male and eleven female patients (mean age 72 years) received pre-operative Green-embo. The median tumor size was 29 mm (range 15–50 mm). Histopathology identified renal cell carcinoma (RCC) in 9 of the 13 (69%) patients, oncocytoma in 3 of the 13 (23%) patients and sarcomatoid RCC in 1 of the 13 (8%) patients. Lesions were equally distributed between polar, meso-renal, endo- and exophytic locations. Complete embolization was achieved in all the procedures. A correct green mapping was identified during all infra-red fluorescence imaging. All patients were discharged on the second day after the surgery. The median blood loss was 145 cc (10–300 cc). No significant differences were observed in serum creatinine levels before and after the embolization procedures. (4) Conclusions: The Green-tattoo technique based on a mixed indocyanine-non-adhesive liquid embolic agent (Onyx-18) is a safe and effective pre-operative embolization technique. The main advantages are the excellent lesion mapping for fluorescence imaging, reduction in surgical time, and definitive, complete and immediate tumor devascularization based on the deep Onyx-18 penetration, leading to a very low intra-operative blood loss. Full article

The dynamic effects observed in collisions represent a specific area of high-energy interaction located at the boundary of mechanics, hydrodynamics, shock wave physics, and alternating high-pressure regions. The paper shows that in the volume of a solid metal body, as a result of dynamic alloying by a high-speed stream of powder particles in the super-deep penetration mode (SDP), fiber structures of altering material arise, forming the framework of the composite material. The stream of powder particles in the metal obstacle following the path of least resistance and the impact of shock waves on particles results in a volumetric framework from the products of interaction between the injected and matrix materials. When using SDP, defective structural elements (channeled)—germs of reinforcing fibers arise. At the subsequent heat treatment, there is an intensive diffusion. The growth process of reinforcing fibers shifts to higher temperatures (as compared to the standard mode), leading to an increase in the bending strength of the fiber material up to 13 times for W6Mo5Cr4V2 high-speed tool steel. As a result of the completion of the growth of reinforcing fibers in the volume of the W6Mo5Cr4V2 high-speed tool steel, the material’s bending strength in 1.2 times is realized. Simultaneously, it provides an increase of wear resistance 1.7–1.8 times. Full article

Lightweight cement mortars containing end-of-life tire rubber (TR) as aggregate were prepared and characterized by rheological, thermal, mechanical, microstructural, and wetting tests. The mixtures were obtained after total replacement of the conventional sand aggregate with untreated TR with different grain sizes (0–2 mm and 2–4 mm) and distributions (25%, 32%, and 40% by weight). The mortars showed lower thermal conductivities (≈90%) with respect to the sand reference due to the differences in the conductivities of the two phases associated with the low density of the aggregates and, to a minor extent, to the lack of adhesion of tire to the cement paste (evidenced by microstructural detection). In this respect, a decrease of the thermal conductivities was observed with the increase of the TR weight percentage together with a decrease of fluidity of the fresh mixture and a decrease of the mechanical strengths. The addition of expanded perlite (P, 0–1 mm grain size) to the mixture allowed us to obtain mortars with an improvement of the mechanical strengths and negligible modification of the thermal properties. Moreover, in this case, a decrease of the thermal conductivities was observed with the increase of the P/TR dosage together with a decrease of fluidity and of the mechanical strengths. TR mortars showed discrete cracks after failure without separation of the two parts of the specimens, and similar results were observed in the case of the perlite/TR samples thanks to the rubber particles bridging the crack faces. The super-elastic properties of the specimens were also observed in the impact compression tests in which the best performances of the tire and P/TR composites were evidenced by a deep groove before complete failure. Moreover, these mortars showed very low water penetration through the surface and also through the bulk of the samples thanks to the hydrophobic nature of the end-of-life aggregate, which makes these environmentally sustainable materials suitable for indoor and outdoor elements. Full article

This paper proposes a frequency–wavenumber (f–k) analysis technique through deep learning-based super resolution (SR) ground penetrating radar (GPR) image enhancement. GPR is one of the most popular underground investigation tools owing to its nondestructive and high-speed survey capabilities. However, arbitrary underground medium inhomogeneity and undesired measurement noises often disturb GPR data interpretation. Although the f–k analysis can be a promising technique for GPR data interpretation, the lack of GPR image resolution caused by the fast or coarse spatial scanning mechanism in reality often leads to analysis distortion. To address the technical issue, we propose the f–k analysis technique by a deep learning network in this study. The proposed f–k analysis technique incorporated with the SR GPR images generated by a deep learning network makes it possible to significantly reduce the arbitrary underground medium inhomogeneity and undesired measurement noises. Moreover, the GPR-induced electromagnetic wavefields can be decomposed for directivity analysis of wave propagation that is reflected from a certain underground object. The effectiveness of the proposed technique is numerically validated through 3D GPR simulation and experimentally demonstrated using in-situ 3D GPR data collected from urban roads in Seoul, Korea. Full article

This paper proposes a 3D ground penetrating radar (GPR) image-based underground cavity detection network (UcNet) for preventing sinkholes in complex urban roads. UcNet is developed based on convolutional neural network (CNN) incorporated with phase analysis of super-resolution (SR) GPR images. CNNs have been popularly used for automated GPR data classification, because expert-dependent data interpretation of massive GPR data obtained from urban roads is typically cumbersome and time consuming. However, the conventional CNNs often provide misclassification results due to similar GPR features automatically extracted from arbitrary underground objects such as cavities, manholes, gravels, subsoil backgrounds and so on. In particular, non-cavity features are often misclassified as real cavities, which degrades the CNNs’ performance and reliability. UcNet improves underground cavity detectability by generating SR GPR images of the cavities extracted from CNN and analyzing their phase information. The proposed UcNet is experimentally validated using in-situ GPR data collected from complex urban roads in Seoul, South Korea. The validation test results reveal that the underground cavity misclassification is remarkably decreased compared to the conventional CNN ones. Full article

Load forecasting is one of the major challenges of power system operation and is crucial to the effective scheduling for economic dispatch at multiple time scales. Numerous load forecasting methods have been proposed for household and commercial demand, as well as for loads at various nodes in a power grid. However, compared with conventional loads, the uncoordinated charging of the large penetration of plug-in electric vehicles is different in terms of periodicity and fluctuation, which renders current load forecasting techniques ineffective. Deep learning methods, empowered by unprecedented learning ability from extensive data, provide novel approaches for solving challenging forecasting tasks. This research proposes a comparative study of deep learning approaches to forecast the super-short-term stochastic charging load of plug-in electric vehicles. Several popular and novel deep-learning based methods have been utilized in establishing the forecasting models using minute-level real-world data of a plug-in electric vehicle charging station to compare the forecasting performance. Numerical results of twelve cases on various time steps show that deep learning methods obtain high accuracy in super-short-term plug-in electric load forecasting. Among the various deep learning approaches, the long-short-term memory method performs the best by reducing over 30% forecasting error compared with the conventional artificial neural network model. Full article

The Jiaodong gold mineral province, with an overall endowment estimated as >3000 t, located at the eastern segment of the North China Craton (NCC), ranks as the greatest source of Au in China. The structural evolution, magmatic activity and metallogenesis during the Mesozoic played important roles in the large scale regional gold, silver and polymetallic mineralization in this area; among them, the intensive activation of fault structures is the most important factor for metallogenesis. This study takes the regional deep faults as main thread to discuss the controlling role of faults in large scale metallogenesis. The Jiaojia fault and Sanshandao faults in the northwest margin of the Guojiadian mantle branch not only are dominant migration channels for hydrothermal fluid but are very important favorable spaces for ore-forming and ore-hosting during the formation of world-class super large gold deposits in this area. The deep metallogenic process can be summarized as involving intensive Earth’s core, mantle and crust activity → magmatism → uplifting of metamorphic complex → detachment of cover rocks → formation of mantle branch → penetration of hydrothermal fluid along deep faults → concentration of metallogenic materials → formation of super large deposits. Full article