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A Multifrequency Census of 100 Pulsars below 100 MHz with LWA: A Systematic Study of Flux Density, Spectra, Timing, Dispersion, Polarization, and Its Variation from a Decade of Observations
Get PDFWe present a census of 100 pulsars, the largest below 100 MHz, including 94 normal pulsars and six millisecond pulsars, with the Long Wavelength Array (LWA). Pulse profiles are detected across a range of frequencies from 26-88 MHz, including new narrowband profiles facilitating profile evolution studies, and breaks in pulsar spectra at low frequencies. We report mean flux density, spectral index, curvature, and low-frequency turnover-frequency measurements for 97 pulsars, including new measurements for 61 sources. Multifrequency profile widths are presented for all pulsars, including component spacing for 27 pulsars with two components. Polarized emission is detected from 27 of the sources (the largest sample at these frequencies) in multiple frequency bands, with one new detection. We also provide new timing solutions for five recently discovered pulsars. Low-frequency observations with the LWA are especially sensitive to propagation effects arising in the interstellar medium. We have made the most sensitive measurements of pulsar dispersion measures (DMs) and rotation measures, with median uncertainties of 2.9 × 10−4 pc cm−3 and 0.01 rad m−2, respectively, and can track their variations over almost a decade, along with other frequency-dependent effects. This allows for stringent limits on average magnetic fields, with no variations detected above ∼20 nG. Finally, the census yields some interesting phenomena in individual sources, including the detection of frequency- and time-dependent DM variations in B2217+47, and the detection of highly circularly polarized emission from J0051+0423
Unveiling promotion effects of Ce doping in La<inf>0.6</inf>Ca<inf>0.4</inf>Co<inf>0.2</inf>Fe<inf>0.8</inf>O<inf>3-δ</inf> as an efficient cathode for solid oxide fuel cells
No full textPerovskite La0.6Sr0.4Co0.2Fe0.8O3-δ (LSCF) is to date the most intensively studied high-performance cathode material for solid oxide fuel cells (SOFCs), but strontium segregation at elevated temperatures critically impairs the activity and longevity of LSCF cathode. By substituting Sr with Ca, i.e. La0.6Ca0.4Co0.2Fe0.8O3-δ (LCCF), the stability of the perovskite can be reinforced, however, at the cost of reduced catalytic activity. Herein, we adopt an effective A-site Ce doping strategy to modify the structure and chemistry of LCCF and therefore to boost its electrochemical performance, i.e. La0.6Ca0.4-xCexCo0.2Fe0.8O3-δ (Ce-LCCFx, x = 0.05–0.2). The results reveal that replacing Ca2+ with Ce4+ notably alters the oxygen vacancies concentration, Fe4+/Fe3+ and Co4+/Co3+ proportions in the perovskite. As a result, the thermal expansion coefficient of LCCF is drastically lowered to 12.6 × 10−6 K−1 upon x = 0.15 in Ce-LCCFx. Moreover, the single cell loaded with Ce-LCCF15 cathode demonstrates a superior maximum power density of 1.26 W cm−2 at 750 °C in H2. Interestingly, microstructure analysis suggests that abundant CeOx nanoparticles are exsolved in situ from the Ce-LCCF15 surfaces due to cathodic current polarization. The present study contributes to the understanding of the role of dopants in promoting the catalytic properties of cathode materials for SOFCs
Extreme outage prediction in power systems using a new deep generative Informer model
Get PDFExtreme weather events have made growing concerns over electric power grid infrastructure as well as the residents living in disaster areas. Moreover, the potential damages due to the extreme events can make serious challenges for supply reliability and security, leading to widespread power outages in power systems. This paper proposes a deep learning-based framework for power data rebalancing and outage prediction in power systems to cope with the extreme events. To this end, we propose an Adaptive Wasserstein Conditional Generative Adversarial Network for data generation. Also, we propose a new Wasserstein Bidirectional Generative Adversarial Network with the Informer model, embedded in both the Generator and Discriminator Networks, plus an Encoder Network for the outage prediction in power systems. Two-step classification approach has been used in the proposed outage prediction model: classifying the power grid components into impacted and non-impacted categories and classifying the impacted category into in-service and out-of-service categories. In addition, a new classification-specific loss function is proposed for the minimax objective function of the Vanilla Generative Adversarial Network to improve the prediction performance in the latent space. Evaluation results of the proposed model and 15 comparative models in three groups using six evaluation metrics on a real-world test case demonstrate the superiority of the proposed model compared to all comparative models. These results confirm that the proposed outage prediction model can be effectively employed for accurately predicting extreme outages in power systems
How does foreign economic policy uncertainty affect domestic analyst earnings forecasts?
No full textThis study examines the impact of foreign economic policy uncertainty (EPU) on the performance of domestic analyst earnings forecasts. We separately analyze how U.S. EPU affects the accuracy of analyst earnings forecasts in other markets and the reverse relationship. Our findings indicate that the U.S. EPU (non-U.S. Global EPU) negatively (positively) affects the accuracy of analyst earnings forecasts in other economies (the U.S.). We find that the economic dependency of a given economy on the U.S. (capital flow to the U.S.) is a channel for this negative (positive) impact. Our results remain robust after controlling for a comprehensive set of variables
Unlocking Subsurface Geology: A Case Study with Measure-While-Drilling Data and Machine Learning
Get PDFBench-scale geological modeling is often uncertain due to limited exploration drilling and geophysical wireline measurements, reducing production efficiency. Measure-While-Drilling (MWD) systems collect drilling data to analyze mining blast hole drill rig performance. Early MWD studies focused on penetration rates to identify rock types. This paper investigates Artificial Intelligence (AI)-based regression models to predict geophysical signatures like density, gamma, magnetic susceptibility, resistivity, and hole diameter using MWD data. The machine learning (ML) models evaluated include Linear Regression (LR), Decision Trees (DTs), Support Vector Machines (SVMs), Random Forests (RFs), Gaussian Processes (GP), and Neural Networks (NNs). An analytical method was validated for accuracy, and a three-tier experimental method assessed the importance of MWD features, revealing no performance loss when excluding features with less than 2% importance. RF, DTs, and GPs outperformed other models, achieving R2 values up to 0.98 with a low RMSE, while LR and SVMs showed lower accuracy. The NN’s performance improved with larger datasets. This study concludes that the DT, RF, and GP models excel in predicting geophysical signatures. While ML-based methods effectively model relationships in the data, their predictive performance remains inherently constrained by the underlying geological and physical mechanisms. Model selection depends on computational resources and application needs, offering valuable insights for real-time orebody analysis using AI. These findings could be invaluable to geologists who wish to utilize AI techniques for real-time orebody analysis and prediction
Improved characterization of the pore size distribution in full and across scale by a fractal strategy
No full textIn this study, the normalized fractal dimension (DN) model of full-scale pore size was established based on the classical fractal scaling relationship of porous materials. The methodology of the established model was described in detail, and the rationality was examined by the classical fractal relationship between the pore volume and specific surface area (SSA). The results indicate that the established model is a continuous function of the fractal dimension and pore size in the full scale, which can more comprehensively symbolize the fractal characteristic of pore size distribution in full scale. In addition, the established model can quantitatively characterize the absolute continuous pore size distribution in full scale, compared with the traditional segmented relatively continuous characterization methods that include the method based on connecting the data on pore volume and SSA, and the method based on the segmented fractal dimensions. The established model can also be employed to quantitatively characterize the pore size distribution across scales. Therefore, the proposed fractal strategy achieves a breakthrough for improving the characterization of the pore size distribution in porous materials, which provides a scientific basis for understanding the fluid transport behavior in porous materials and designing fractal coal-based materials
From Tsushima to Berlin and the Kurile Islands. Russian and Soviet Naval Power, 1905 - 1945
No full textThis handbook brings together historical and contemporary essays about Soviet and Russian military studies, to offer a comprehensive volume on the topic
Scottish Diaspora Writing in Australia: Contemporary Hybridity and Literary Representations of the Scottish Migrant Experience and Over the Sea to Skye
Get PDFUtilising both historical and contemporary texts, the project explores how the Scottish immigrant narrative has evolved in the two centuries since the Scots first arrived in Australia. Via an exegesis examining diasporic theories associated with cultural hybridity in relation to Scottish migrant identities in Australia and other former British colonies, it also consists of a work of literary fiction, depicting a working-class Scottish family who have migrated to Australia in the late-twentieth century
Heating and Cooling the Home for Thermal Comfort: Homeowner Preferences for Energy Efficiency in Perth, Western Australia
No full textThis study investigates the influencing factors for homeowner-occupiers that led to the space air-conditioning features in the design of their new homes. This research uses a bottom-up approach and considers the homeowner as a consumer who plays a significant role in the outcome of the housing product. The study makes recommendations to increase awareness of energy efficiency features for homeowners, and to improve policy guidelines