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No Permanent Wavesboldly enters the ongoing debates over the utility of the \"wave\" metaphor for capturing the complex history of women's rights by offering fresh perspectives on the diverse movements that comprise U.S. feminism, past and present. Seventeen essays--both original and reprinted--address continuities, conflicts, and transformations among women's movements in the United States from the early nineteenth century through today.A respected group of contributors from diverse generations and backgrounds argue for new chronologies, more inclusive conceptualizations of feminist agendas and participants, and fuller engagements with contestations around particular issues and practices. Race, class, and sexuality are explored within histories of women's rights and feminism as well as the cultural and intellectual currents and social and political priorities that marked movements for women's advancement and liberation. These essays question whether the concept of waves surging and receding can fully capture the complexities of U.S. feminisms and suggest models for reimagining these histories from radio waves to hip-hop.

The variation of ultrasonic parameters is closely linked to the mechanical properties and damage evolution of rock and soil mass. In this paper, uniaxial compression tests and real-time ultrasonic monitoring technology were used to explore the strength, deformation and damage characteristics of frozen soil-rock mixture (FSRM) with different block sizes and gradations, as well as the law of ultrasonic wave propagation. The results indicate that: (1) A wider gradation of rock blocks corresponds to a higher specimen strength and a lower breakage degree of rock blocks. Within the same gradation, specimens with smaller block sizes have higher strength. Wider gradation and smaller particle size of rock blocks exerts a delayed effect on damage and failure of FSRM. (2) The particle size of rock blocks plays a key role in the variation of ultrasonic parameters. Within the same gradation, the specimens with larger block sizes possess higher wave velocity and lower first wave amplitude. The effect of gradation on ultrasonic parameters is attributed to the variation of block size. (3) With the first wave amplitude as damage state variable, a damage prediction model based on the improved Duncan–Chang model is established, demonstrating superior prediction potential on the stress–strain curves of FSRM. The research confirms and promotes the quantitative correlation between ultrasonic parameters and mechanical properties of geotechnical materials, which may provide theoretical support for testing and evaluating the mechanical properties of roadbed filling in cold mountainous areas.

The dynamic fracture toughness of rock materials under blast load is the basis for studying the stability of rock blasting engineering. To study the dynamic fracture characteristics of sandstone with an explosive load, the initiation and propagation times of pre-crack samples with different lengths were obtained through physical experiments. The experimental–numerical method was used to obtain the stress intensity factor (SIF) curve and the crack initiation toughness of the specimens with different pre-crack lengths. By comparing and analyzing the dynamic SIF and explosion load curves at different pre-crack lengths, the following results and conclusions were obtained: (1) a polyvinylidene fluoride pressure gauge effectively measured the blast pressure curves at the blast hole wall and the blast hole with a diameter of 40 mm in the presence of a coupling water medium that could effectively avoid the formation of crushed zones and reduce the attenuation of the blast energy. (2) The crack-tip SIF curves were well correlated to the blast pressure curves with a minimal SIF lagging of 0.3–2.9 μs after the peak of the blast pressure curve and a maximum SIF lagging of 0.1–22.9 μs after the time when the peak blast pressure decayed to zero, and the lagging time increased with the pre-crack length. (3) The crack was affected by the reflected wave during the propagation process, and the reflected wave did not affect the crack propagation within 0–109.8 μs. The longer the pre-crack length was, the longer the reflected wave influence time was. (4) The SIF at the pre-crack tip was controlled to a degree such that the first wave was superposed on the surface wave of the crack surface. Both the peak value of the stress intensity factor curve and the crack initiation toughness value decreased with the increase of the pre-crack length, and an obvious separation inflection point appeared on the stress intensity factor curve with the increase of the pre-crack length. (5) The implications of these findings particularly regarding the test method for dynamic fracture of materials tested by explosion method are discussed.HighlightsThe water coupling technology for loading hole effectively avoids the formation of crushing circle in loading hole.The pressure time range of the loading hole is accurately measured by a PVDF pressure sensor.The stress intensity factor curves of internal single-cracked square plate specimens under blast loading are significantly influenced by the superposition of the first wave and the surface wave on the crack surface.The degree of superposition between the first wave and the surface wave on the crack surface is significantly affected by the length of the prefabricated crack.The average loading rate of the stress intensity factor curve varies from 1036 GPa·m1/2·s−1 to 2198GPa·m1/2·s−1 with the change of pre-crack length.

P-wave first-motion polarity is the most useful information in determining the focal mechanisms of earthquakes, particularly for smaller earthquakes. Algorithms have been developed to automatically determine P-wave first-motion polarity, but the performance level of the conventional algorithms remains lower than that of human experts. In this study, we develop a model of the convolutional neural networks (CNNs) to determine the P-wave first-motion polarity of observed seismic waveforms under the condition that P-wave arrival times determined by human experts are known in advance. In training and testing the CNN model, we use about 130 thousand 250 Hz and about 40 thousand 100 Hz waveform data observed in the San-in and the northern Kinki regions, western Japan, where three to four times larger number of waveform data were obtained in the former region than in the latter. First, we train the CNN models using 250 Hz and 100 Hz waveform data, respectively, from both regions. The accuracies of the CNN models are 97.9% for the 250 Hz data and 95.4% for the 100 Hz data. Next, to examine the regional dependence, we divide the waveform data sets according to the observation region, and then we train new CNN models with the data from one region and test them using the data from the other region. We find that the accuracy is generally high (\\[ \\] 95%) and the regional dependence is within about 2%. This suggests that there is almost no need to retrain the CNN model by regions. We also find that the accuracy is significantly lower when the number of training data is less than 10 thousand, and that the performance of the CNN models is a few percentage points higher when using 250 Hz data compared to 100 Hz data. Distribution maps, on which polarities determined by human experts and the CNN models are plotted, suggest that the performance of the CNN models is better than that of human experts.

P-wave first-motion polarity is a critical parameter for determining earthquake focal mechanisms. Extracting relative P-wave arrival times and polarity information using waveform cross-correlation techniques can enhance the accuracy of earthquake location and focal mechanism inversion. However, seismic noise severely hampers the reliable detection of P-wave onsets and their first-motion polarities. To address this issue, we propose a noise-resistant polarity detection method based on the normal time–frequency transform (NTFT), termed the time–frequency similarity coefficient (TFSC). The TFSC method computes relative delays and similarity coefficients by calculating the real part of the NTFT coefficients between two seismic signals. We validated the proposed approach using both synthetic and real earthquake data. Without any filtering or preprocessing, the TFSC method demonstrated significantly greater robustness and reliability compared to the conventional time-domain normalized cross-correlation (NCC) method. These results indicate that the TFSC method has strong potential for practical application and provides a novel perspective for accurate detection of P-wave first-motion polarity in noisy seismic environments.

Coronavirus disease 2019 (COVID-19) is considered as the most dreaded disease that has spread all over the world in the recent past. Despite its outbreak in December 2019–January 2020, a few continents and countries such as India started to experience a significant number of COVID-19-positive cases from March 2020. GISAID clade variation analysis in the period March 2020–February 2021 (period I) and March 2021–first week of April 2021 (period II) showed a rapid variation of SARS-CoV-2 in all continents and India over time. Studying the relationship of patient age or gender with viral clades in these two periods revealed that the population under 10 years of age was the least affected, whereas the 11–60-year-old population was the most affected, irrespective of patient gender and ethnicity. In the first wave, India registered quite a low number of COVID-19-positive cases/million people, but the scenario unexpectedly changed in the second wave, when even over 400,000 confirmed cases/day were reported. Lineage analysis in India showed the emergence of new SARS-CoV-2 variants, i.e., B.1.617.1 and B.1.617.2, during April–May 2021, which might be one of the key reasons for the sudden upsurge of confirmed cases/day. Furthermore, the emergence of the new variants contributed to the shift in infection spread by the G clade of SARS-CoV-2 from 46% in period II to 82.34% by the end of May 2021. Along with the management of the emergence of new variants, few factors viz., lockdown and vaccination were also accountable for controlling the upsurge of new COVID-19 cases throughout the country. Collectively, a comparative analysis of the scenario of the first wave with that of the second wave would suggest policymakers the way to prepare for better management of COVID-19 recurrence or its severity in India and other countries.

Microseismic signals induced by mining activities often have low signal-to-noise ratios, and traditional picking methods are easily affected by noise, making accurate identification of P-wave arrivals difficult. To address this problem, this study proposes an adaptive denoising algorithm based on wavelet-threshold-enhanced Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and develops an automatic P-wave arrival picking method incorporating fractal box dimension features, along with a corresponding accuracy evaluation framework. The raw microseismic signals are decomposed using the improved CEEMDAN method, with high-frequency intrinsic mode functions (IMFs) processed by wavelet-threshold denoising and low- and mid-frequency IMFs retained for reconstruction, effectively suppressing background noise and enhancing signal clarity. Fractal box dimension is applied to characterize waveform complexity over short and long-time windows, and by introducing fractal derivatives and short-long window differences, abrupt changes in local-to-global complexity at P-wave arrivals are revealed. Energy mutation features are extracted using the short-term/long-term average (STA/LTA) energy ratio, and noise segments are standardized via Z-score processing. A multi-feature weighted fusion scoring function is constructed to achieve robust identification of P-wave arrivals. Evaluation metrics, including picking error, mean absolute error, and success rate, are used to comprehensively assess the method’s performance in terms of temporal deviation, statistical consistency, and robustness. Case studies using microseismic data from a mining site show that the proposed method can accurately identify P-wave arrivals under different signal-to-noise conditions, with automatic picking results highly consistent with manual labels, mean errors within the sampling interval (2–4 ms), and a picking success rate exceeding 95%. The method provides a reliable tool for seismic source localization and dynamic hazard prediction in mining microseismic monitoring.

The most important pre-1979 women's movements in Nicaragua were first-wave feminism and the right-wing Somocista women's movement. I argue that it is useful to examine these two movements jointly in order to assess their legacy with regard to struggles over the relationship between women's organisations and political parties, age/generational differences, electoral democracy versus clientelism, and class and leadership. I conclude that while the pre-1979 women's movements brought about women's suffrage and increased opportunities for some women, their collective legacy was, in some fundamental ways, undemocratic.