Rich Samples Research Articles

Train-induced vibration attenuation measurements and prediction from ground soil to building column.

The investigation of the influence of soil-structure coupling on the vibration propagation pattern is the key to ensuring the reliability of the prediction of train-induced building vibration. This study selects different metro depots with over-track buildings as the research objects based on the existing engineering examples. It analyzes the general laws of vibration propagation from the ground soil to the building columns. The study provides valuable references for predicting vibration and designing vibration isolation measures for over-track buildings. Additionally, this study proposes a vibration prediction method for over-track buildings above metro depots based on the back propagation (BP) neural network model in machine learning. This model considers the soil-structure coupling loss in a data-driven manner based on the rich data samples, which avoids the problems of complex numerical simulation calculations and parameter uncertainties to some extent. This prediction method has a good balance of efficiency and convenience and also meets the accuracy requirements. It was judged to be a promising prediction method for the preliminary design stage of the over-track buildings above metro depots.

Evaluation of the sensory properties, volatile aroma compounds and functional food potentials of cold-press produced mahaleb (Prunus mahaleb L.) seed oil

The aims of this study were to cold-press mahaleb seed, and then fully characterize the oil to extent its food and functional food applications. The novelty of this study relies upon the first data provided by the thermal analysis, sensory analysis, and volatile aroma compounds composition. The seeds were pressed with a screw-type single-head press with a maximum oil exit temperature of 40 °C. Most common physico-chemical properties, composition analyses, volatile aroma compounds profile, sensory descriptive analysis and consumer tests were completed. The main properties were appropriate and the composition (fatty acids, phytosterols and tocopherols) data concurred with the available literature. Thermal data were provided, and the oil peak crystallization and melting temperatures were −44.45 °C and −8.41 °C, respectively. There were 38 volatile aroma compounds quantified mostly with almond, green, vanillin, woody, and fermented aroma definitions. The panel described the oil with 5 sensory descriptive (almond, vanillin, dough, green, cooling) terms. Consumers liked appearance the most (4.49) with a general acceptance score of 3.70 on a 5-point hedonic scale. Overall, the mahaleb seed oil is a conjugated linolenic, oleic and linoleic fatty acids, β-sitosterol and γ-tocopherol rich, very aromatic, and consumer-liked sample. Further studies with various food applications are foreseen.

Contrastive 3D Human Skeleton Action Representation Learning via CrossMoCo With Spatiotemporal Occlusion Mask Data Augmentation

Self-supervised learning methods for 3D skeleton-based action recognition via contrastive learning have obtained competitive achievements compared to classical supervised methods. Current researches show that adding a Multilayer Perceptron (MLP) to the top of the base encoder can extract high-level and global positive representations. Using a negative memory bank to store negative samples dynamically can balance the ample storage and feature consistency. However, these methods need to consider that the MLP lacks accurate encoding of fine-grained local features, and a memory bank needs rich and diverse negative sample pairs to match positive representations from different encoders. This paper proposes a new method called Cross Momentum Contrast (CrossMoCo), composed of three parts: ST-GCN encoder, ST-GCN encoder with MLP encoder (ST-MLP encoder), and two independent negative memory banks. The two encoders encode the input data into two positive feature pairs. Learning the cross representations of the two positive pairs is helpful for the model to extract both the global and the local information. Two independent negative memory banks update the negative samples according to different positive representations from two encoders, diversifying the negative samples' distribution and making negative representations close to the positive features. The increasing classification difficulty will improve the model's ability of contrastive learning. In addition, the spatiotemporal occlusion mask data augmentation method is used to enhance positive samples' information diversity. This method takes the adjacent skeleton joints that can form a skeleton bone as a mask unit, which can reduce the information redundancy after data augmentation since adjacent joints may carry similar spatiotemporal information. Experiments on the PKU-MMD Part II dataset, the NTU RGB+D 60 dataset, and the NW-UCLA dataset show that the CrossMoCo framework with spatiotemporal occlusion mask data augmentation has achieved a comparable performance.

Deep Robust Low Rank Correlation With Unifying Clustering Structure for Cross Domain Adaptation

Cross domain adaptation aims to improve the performance of the target domain model by making full use of information rich source domain samples. However, as information becomes richer, the noise also increases. In order to improve the reliability of cross domain adaptation, we propose a novel method based on deep robust low rank correlation. Borrowed from the traditional idea of Canonical Correlation Analysis (CCA), we developed a robust correlation model to maximize the correlation between source and target domains. Also, the low-rank characteristics of cross domain data can effectively reduce the negative influence of noisy data. Furthermore, in order that the cross-domain data can share a unifying clustering structure, we introduced a common Laplacian affinity structure. Then the learned features can be smoothed and aligned to the unifying structure. In this way, we obtain a deep robust low rank correlation model with the help of the unifying clustering structure, which can effectively reduce the influence of noise and improve the performance of cross domain adaptation. Experimental results on three data sets including Office-31, ImageCLEF-DA and Office-Home show that our model significantly outperforms state-of-the-art cross domain adaptation methods.

Self-Supervised Spectral–Spatial Transformer Network for Hyperspectral Oil Spill Mapping

Hyperspectral oil spill mapping aims to distinguish the type of oil spill. Recently, most hyperspectral oil spill detection methods are based on supervised methods that work well with rich training samples. However, in the marine oil spill detection scenario, pixel annotations are difficult and costly. Moreover, the labels obtained by domain experts within a hyperspectral image (HSI) are often scarce. To address these issues, a self-supervised spectral-spatial transformer network is proposed for hyperspectral oil spill mapping. First, we propose a transformer-based contrastive learning network to extract the deep discriminative features. Then, the learned features are transferred to the downstream classification network that is fine-tuned with very few labeled samples. Experiments on hyperspectral oil spill database (HOSD) constructed by ourselves indicate that the proposed method can obtain more promising performance than several state-of-the-art oil spill classification techniques in discriminating different types of oil spills, i.e., thick oil, thin oil, sheen, and seawater.

Semi-Supervised SAR ATR via Epoch- and Uncertainty-Aware Pseudo-Label Exploitation

Automatic target recognition (ATR) is a significant application scenario for Synthetic aperture radar (SAR) image interpretation. In recent years, deep-learning based SAR ATR approaches have made great progress, with the require of large amounts of labeled data for network training. However, the labeled SAR images are scarce, and the annotation of SAR images is expensive and time-consuming. In this paper, a novel semi-supervised learning (SSL) framework is proposed for SAR ATR, which effectively alleviates the need of labeled samples for network training, and allows for excavating the intrinsic semantic relationship information between samples belonging to different categories, greatly improving recognition performance. This method develops an epoch- and uncertainty-aware pseudo-label selection (EUAPS) mechanism, which takes advantage of the underutilized consistency between training epochs, and introduces the uncertainty estimates. EUAPS can select pseudo-label samples with high-confidence, allowing the network to perform well even when labels are extremely scarce. Furthermore, we propose a novel loss function, group relationship consistency loss (GRCL) that explicitly enforces the consistency of relations between different samples under perturbations. GRCL facilitates the network to learn rich sample relationship information and more discriminative features in sample group level, effectively reducing the recognition error rate. Extensive experiments were carried out on three public benchmark SAR datasets. And the experimental results illustrate that the proposed approach achieves the accuracy of 98.58% when 10 labeled samples per class and 88.65% when only 5 labeled samples per class respectively, outperforming the state-of-the-art SSL methods and demonstrating the superiority.

POWDER X-RAY DIFFRACTION STUDY OF THE Cu3SbS3-CuI SYSTEM

The nature of phase equilibria in the Cu3SbS3-CuI binary system over the entire concentration range were studied by means of the powder X-ray diffraction analysis (PXRD) for the first time at room temperature. It was found that the sample containing 66.7 mol.% CuI composed of a single phase and has a powder diffraction pattern completely different from the constituent phases of the system under study. The crystal lattice type and parameters, that were determined on the basis of the X-ray diffraction pattern of this sample using the TOPAS 4.2 and EVA computer programs are fully consistent with the literature data of the Cu5SbS3I2 four-component compound. The copper (I) iodide rich samples of the system consist of a two-phase mixture of Cu5SbS3I2 and CuI phases. However, the system is unstable in the Cu5SbS3I2-Cu3SbS3 composition range. In this concentration interval, the system is characterized by complex physico-chemical interaction of the initial components.

Metode za preradu bakro-niklovih sirovina na Arktiku

The different genesis of copper-nickel objects requires the use of different technologies for their enrichment. A scheme has been proposed for processing copper-nickel ore tailings, first by granulating the tailings with sulfuric acid, storing them at below-zero temperatures, and then followed by heap leaching of granules was proposed. The effect of different ratios of tailings to binder during granulation on the subsequent recovery of the metals was considered. At a ratio of S:L = 6:1, 44.4% nickel and 8.9% copper were recovered during the 40-day experiment. For ore processing, the efficiency of heap leaching with a 2% H2SO4 solution using technogenic ores from the Nud Terrasa was considered and Allarechensk deposits ores was studied, as no additional ore preparation was required. At the end of the experiment, 12.8% nickel and 10.2% copper were recovered from the Nud Terrasa ore, and 10.2% nickel and 4.4% copper were recovered from the ore of the Allarechensk technogenic deposit. The ore from the Nud II deposit and a rich sample of the Allarechensk ore were processed by low-temperature roasting with ammonium sulfate. From the ore of the Allarechensk technogenic deposit, 91.5% of nickel and 94.8% of copper were recovered. The results obtained may be of significant practical interest, especially in the Arctic zone.

PATTERNS AND INTERPRETATION: BEYLAGAN'S LUSTERWARE SAMPLES

The medieval city of Beylagan is one of the best-studied urban areas of Azerbaijan. As a result of this long-term research work, done by A. Alakbarov, A. Gasharli, I. Meschaninov, and others during the 1930s-1960s, rich samples of material culture were discovered from this city. This paper is devoted to a comparative analysis of the lusterware findings from Beylagan. Back in the last century, B.A. Shelkovnikov conducted research and published an article about samples of Beylagan lusterwares, but this article mostly focused on the problem of their origin. The aim of this research is to explain the main pattern motifs of Beylagan's lusterware in detail. In addition, these pattern motifs were compared with the pattern motifs of Iranian cities, and the common aspects were brought to the fore. The material basis of the research includes artefacts from the Hermitage Museum of Russia and National History Museum of Azerbaijan.

Research on intelligent recognition method for self‐blast state of glass insulator based on mixed data augmentation

AbstractAutomatically and accurately detecting the self‐blast state of glass insulators is of great significance to operation and maintenance of transmission lines. To solve the shortcomings of the existing open‐loop cognitive models to detect the self‐blast state of glass insulators, this study explores a mixed data augmentation‐based intelligent recognition method to detect the self‐blast state of the glass insulator, by imitating the human cognitive mode. Firstly, generative adversarial network is utilised to obtain the high‐quality generative self‐blast samples of the glass insulator, and the non‐generative data augmentation techniques is used to obtain rich sample features. Secondly, considering the characteristics of aerial images such as large scale variations, variable shooting angles and complex backgrounds, feature maps with strong semantics and adaptive multi‐scale fusion are extracted using the feature pyramid network with adaptive hierarchy and the multi‐deformable convolutional network. Then, the extracted feature maps are transmitted to a two‐dimensional stochastic configuration network that can adaptively generate hidden nodes and basis functions so as to develop the self‐blast state classification criteria with universal approximation capability. Thirdly, based on the generalised error and entropy theory, the semantic error entropy evaluation indices of recognition results are defined to evaluate in real time, the credibility of the uncertain recognition results for the self‐blast state of the glass insulator. Then, based on transfer learning and the established self‐optimising feedback mechanism for feature pyramid network, the self‐optimising adjustment and reconstruction of the feature map space with strong semantics and multi‐scale fusion and its classification criteria are realised. Finally, the stacking method is applied to integrate the recognition results of the feature pyramid network with adaptive hierarchy and multi‐channel deformable convolutional networks to improve the robustness of the recognition model. Results of experimental comparison with other machine learning and deep learning methods verify the feasibility and effectiveness of the proposed method.

Going beyond Western, Educated, Industrialized, Rich, and Democratic (WEIRD) samples and problems in organizational research

Going beyond Western, Educated, Industrialized, Rich, and Democratic (WEIRD) samples and problems in organizational research

Cerebrospinal fluid concentrations of fluoroquinolones and carbapenems in tuberculosis meningitis.

Background: Tuberculosis meningitis (TBM) is the most lethal form of TB. It is difficult to treat in part due to poor or uncertain drug penetration into the central nervous system (CNS). To help fill this knowledge gap, we evaluated the cerebrospinal fluid (CSF) concentrations of fluoroquinolones and carbapenems in patients being treated for TBM. Methods: Serial serum and CSF samples were collected from hospitalized patients being treated for TBM. CSF was collected from routine lumbar punctures between alternating timepoints of 2 and 6h after drug administration to capture early and late CSF penetration. Rich serum sampling was collected after drug administration on day 28 for non-compartmental analysis. Results: Among 22 patients treated for TBM (8 with confirmed disease), there was high use of fluoroquinolones (levofloxacin, 21; moxifloxacin, 10; ofloxacin, 6) and carbapenems (imipenem, 11; meropenem, 6). Median CSF total concentrations of levofloxacin at 2 and 6h were 1.34mg/L and 3.36mg/L with adjusted CSF/serum ratios of 0.41 and 0.63, respectively. For moxifloxacin, the median CSF total concentrations at 2 and 6h were 0.78mg/L and 1.02mg/L with adjusted CSF/serum ratios of 0.44 and 0.62. Serum and CSF concentrations of moxifloxacin were not affected by rifampin use. Among the 76 CSF samples measured for carbapenem concentrations, 79% were undetectable or below the limit of detection. Conclusion: Fluoroquinolones demonstrated high CSF penetration indicating their potential usefulness for the treatment of TBM. Carbapenems had lower than expected CSF concentrations.

Estimation of Mycophenolic Acid Exposure in Chinese Renal Transplant Patients by a Joint Deep Learning Model.

To predict mycophenolic acid (MPA) exposure in renal transplant recipients using a deep learning model based on a convolutional neural network with bilateral long short-term memory and attention methods. A total of 172 Chinese renal transplant patients were enrolled in this study. The patients were divided into a training group (n = 138, Ruijin Hospital) and a validation group (n = 34, Zhongshan Hospital). Fourteen days after renal transplantation, rich blood samples were collected 0-12 hours after MPA administration. The plasma concentration of total MPA was measured using an enzyme-multiplied immunoassay technique. A limited sampling strategy based on a convolutional neural network-long short-term memory with attention (CALS) model for the prediction of the area under the concentration curve (AUC) of MPA was established. The established model was verified using the data from the validation group. The model performance was compared with that obtained from multiple linear regression (MLR) and maximum a posteriori (MAP) methods. The MPA AUC 0-12 of the training and validation groups was 54.28 ± 18.42 and 41.25 ± 14.53 µg·ml -1 ·h, respectively. MPA plasma concentration after 2 (C 2 ), 6 (C 6 ), and 8 (C 8 ) hours of administration was the most significant factor for MPA AUC 0-12 . The predictive performance of AUC 0-12 estimated using the CALS model of the validation group was better than the MLR and MAP methods in previous studies (r 2 = 0.71, mean prediction error = 4.79, and mean absolute prediction error = 14.60). The CALS model established in this study was reliable for predicting MPA AUC 0-12 in Chinese renal transplant patients administered mycophenolate mofetil and enteric-coated mycophenolic acid sodium and may have good generalization ability for application in other data sets.

Effect of ratios of dopant contents on the electrical conductivity of Bi2O3 ceramics co–doped with some rare earth oxides

In this study, Bi 2 O 3 –based ceramic powders co–doped with differing quantities of Dy 2 O 3 , Sm 2 O 3 , Ho 2 O 3 , and CeO 2 rare earth oxides have been synthesized using the solid state reactions under atmospheric conditions. The XRD patterns revealed that the Dy–rich samples were more effective in stabilizing the cubic δ–phase than the others. The DTA curve of sample S1 (5%Dy: 5%Sm: 5%Ho: 5%Ce) had an endothermic peak at approximately 730 °C, indicating a phase transition from monoclinic α–phase to cubic δ–phase. The conductivity measurements show that sample S11 (10%Dy: 5%Sm: 5%Ho: 5%Ce) had the highest electrical conductivity among cubic δ–phase stabilized samples with 0.027 S/cm at 700 °C. The dopant content ratios, as well as concentration, had a significant impact on electrical conductivity and phase stabilization. According to the SEM images, the grain sizes were not uniform across the surface and decreased as the dopant concentration increased. The EDX pattern of sample S1 verified the presence of all elements in compositions with different peak locations on the pattern and the absence of any peaks indicating impurities. Order-disorder transition (ODT), which is a structural arrangement in the anion sublattice, occurred at approximately 600 °C in Ce-rich samples, according to conductivity plots produced as a function of temperature. • The concentration and type of dopant are critical in stabilizing the high ion conductor cubic δ–phase. • The DTA curve of the sample produced with the lowest concentration of dopant reveals a phase transition from the α–phase to the δ–phase at around 730 °C due to the presence of an endothermic peak on its thermal curve. • The order-disorder transition (ODT), which signifies a structural arrangement of anions in the oxygen sublattice, is only evident in Ce-rich samples. • The FE–SEM images indicate that as the dopant concentrations increase, the porosity along on the surface also begins to rise and the grain boundary line undergoes a significant change.

The synthesis of Eu–Tb–Ho co–doped Bi2O3–based ceramic powders for use in intermediate temperature–SOFCs

In this study, the solid–state reactions in the ambient atmosphere were used to produce Ho–Tb–Eu co–doped Bi2O3 solid ceramic powder solutions. All samples were stabilized with the cubic δ–phase, according to XRD patterns generated at room temperature. The main peak (111) on the diffraction pattern shifted to larger angles as the total dopant concentration increased, and the average crystal size decreased as well. On the temperature–based DTA curves, neither an endothermic nor an exothermic peak could be seen, indicating that no phase transition took place during the uniform heating circle. According to the conductivity measurement results, sample 5Eu5Tb15Ho had the highest conductivity of 0.288 S/cm at 750 °C and the lowest activation energy of 0.19 eV. The FE–SEM images revealed that the grain size of sample 5Eu5Tb15Ho and Ho–rich samples was not uniform across the surface, and the grain boundary line changed rapidly as the dopant concentration increased.

Evaluation of deformation characteristics of brittle rocks beyond the limit of strength in the mode of uniaxial servohydraulic loading

One of the most reliable methods for assessing the physical and mechanical properties of rocks as a result of their destruction are laboratory tests using hard or servo-driven test presses. They allow to obtain reliable information about changes in these properties beyond the limit of compressive strength. The results of laboratory tests of rich sulfide ore samples are presented, which made it possible to obtain graphs of their extreme deformation. Both monolithic samples and samples with stress concentrators in the form of circular holes with a diameter of 3, 5 and 10 mm were tested. It was revealed that during the destruction of the samples, the modules of elasticity and deformation decrease by 1.5-2 times, and in the zone of residual strength – by 5-7 times.

Morphometric parameters of sulphide ores as a basis for selective ore dressing

To assess the possibility of selective disintegration and reduction of overgrinding of hard-to-reproduce ores, optical microscopic and X-ray microtomographic studies were carried out and quantitative characteristics of morphological parameters of disseminated and rich cuprous ore samples from Norilsk-type Oktyabrsky deposit were identified. Among quantitative morphological parameters the most informative are area, perimeter, edge roughness, sphericity, elongation and average grain spacing for disseminated copper-nickel ores; area, perimeter, edge roughness and elongation for rich cuprous ores. The studied parameters are characterized by increased values and dispersion in ore zones, which is especially important for fine-grained ores, which are difficult to diagnose by optical methods. Three-dimensional modelling of the internal structure of sulphide mineralisation samples was carried out using computed X-ray microtomography, which allows observation of quantitative parameters of grains, aggregates and their distribution in the total rock volume and interrelationship with each other. The evaluation of rock pore space by computer microtomography made it possible to compare the results obtained with the strength characteristics of rocks and ores, including those on different types of crushers. The obtained quantitative characteristics of structural-textural parameters and analysis of grain size distribution of ore minerals allow us to evaluate the possibility of applying selective crushing at various stages of ore preparation

Population Pharmacokinetics of Prophylactic Cefazolin in Cardiac Surgery with Standard and Minimally Invasive Extracorporeal Circulation.

The objectives of this study were to develop a population pharmacokinetic model of prophylactically administered cefazolin in patients undergoing cardiac surgery with and without the use of the cardiopulmonary bypass of both existing types-standard (ECC) and minimallyu invasive extracorporeal circulation (MiECC)-and to propose cefazoline dosing optimization based on this model. A total of 65 adult patients undergoing cardiac surgery were recruited to this clinical trial. A prophylactic cefazolin dose of 2 g was intravenously administered before surgery. Blood samples were collected using a rich sampling design and cefazolin serum concentrations were measured using the HPLC/UV method. The pharmacokinetic population model was calculated using a nonlinear mixed-effects modeling approach, and the Monte Carlo simulation was used to evaluate the PK/PD target attainment. The population cefazolin central volume of distribution (Vd) of 4.91 L increased by 0.51 L with each 1 m2 of BSA, peripheral Vd of 22.07 L was reduced by 0.77 L or 0.79 L when using ECC or MiECC support, respectively, while clearance started at 0.045 L/h and increased by 0.49 L/h with each 1 mL/min/1.73 m2 of eGFR. ECC/MiECC was shown to be covariate of cefazolin Vd, but without relevance to clinical practice, while eGFR was most influential for the PK/PD target attainment. The standard dose of 2 g was sufficient for PK/PD target attainment throughout surgery in patients with normal renal status or with renal impairment. In patients with augmented renal clearance, an additive cefazolin dose should be administered 215, 245, 288 and 318 min after the first dose at MIC of 4, 3, 2 and 1.5 mg/L, respectively.

Social dominance and anti‐immigrant prejudice: A cross‐national and prospective test of the mediating role of assimilation, multiculturalism, colour blindness, and interculturalism

AbstractDecades of research have shown that social dominance orientation (SDO) is one of the most important predictors of anti‐immigrant attitudes. However, the mechanisms through which SDO can explain prejudice have been studied insufficiently. Using rich and diverse samples from France and from the province of Québec in Canada, the present research provides a cross‐national (N = 1,852) and prospective (N = 534) analysis of a theoretical model in which the dimensions of SDO are indirectly predicting anti‐immigrant prejudice via the intergroup ideologies of assimilation, multiculturalism, colour blindness and interculturalism. Results showed that interculturalism, a hierarchy‐attenuating ideology was found to be a robust mechanism to explain the subtle effects of SDO‐E on anti‐immigrant prejudice whereas assimilation, a hierarchy‐enhancing ideology was playing a more important role to explain direct and blatant effects of SDO‐D on prejudice. The two most studied diversity approaches, multiculturalism and colour blindness, were largely redundant once interculturalism was considered. This pattern occurred in both France and Québec hence, favouring the context‐independent pathway.

Prediction model of interaction anxiousness based on Weibo data.

Adolescents who face social distress in real life are often accompanied by interaction anxiousness. To avoid direct social activities, they prefer to indulge in social networks to satisfy their psychological needs for interpersonal communication. Sina Weibo, China's leading social media platform, has a markedly young user base. It provides a rich sample of adolescents with interaction anxiousness and conditions for real-time monitoring. In this study, various word categories, such as perception of spatial distance and positional relationships, morality, and emotion, showed a significant relationship with interaction anxiousness. Furthermore, prediction models were established based on the original Weibo data of 839 active Sina Weibo users through a variety of machine learning algorithms to predict the scores of users' interaction anxiousness. The results showed that the performance of the prediction model established by the fully connected neural network was the best, and both criterion validity and split-half reliability were good (r criterionvalidity = 0.30, r split - halfreliability = 0.76). This study confirms the validity of the prediction model of interaction anxiousness based on social media behavior data, provides a feasible solution to examine adolescents' interaction anxiousness, and provides a scientific basis for more targeted mental health interventions.