Retrospección y reflexión — la traducción de la literatura española en China en los últimos cien años

The introduction of Spanish literature is an essential part of the research on foreign literature in China during the modern and contemporary times. We specify its trajectory in four stages, including: emancipation in the New Culture Movement, ups and downs in the period from the founding of the People’s Republic of China to the end of 1970s, vindication from the Reform and Opening Up in 1978 to the new millennium, and prosperity from then to the present. This publication process reflects the manipulation of hypertextual factors such as political ideology, social psychology and cultural environment at different times in translation activities. The Spanish literature translation system in China has gradually matured in the multicultural environment, at the same time, it is also facing new problems that require novel solutions.La introducción de la literatura española es una parte imprescindible de la investigación relativa a la literatura extranjera en China durante las épocas moderna y contemporánea. Desglosamos su trayectoria en cuatro etapas, a saber: la de emancipación en el Movimiento de la Nueva Cultura, de altibajos tras el período de la fundación de la República Popular China a finales de los años 70, de reivindicación desde la Reforma y Apertura en 1978 hasta el nuevo milenario, y de prosperidad desde entonces hasta la actualidad. Este proceso de publicación refleja la manipulación de los factores hipertextuales como la ideología política, la psicología social y el ambiente cultural en diferentes épocas en las actividades traductológicas. El sistema de traducción de la literatura española en China ha madurado gradualmente en el ambiente multicultural, al mismo tiempo, también se enfrenta a nuevos problemas que requieren novedosas soluciones

Analyzing the Hardware-Software Implications of Multi-modal DNN Workloads using MMBench

The explosive growth of various types of big data and advances in AI technologies have catalyzed a new type of applications called multi-modal DNNs. Multi-modal DNNs are capable of interpreting and reasoning about information from multiple modalities, making them more applicable to real-world AI scenarios. In recent research, multi-modal DNNs have outperformed the best uni-modal DNN in a wide range of applications from traditional multimedia to emerging autonomous systems. However, despite their importance and superiority, very limited research attention has been devoted to understand the characteristics of multi-modal DNNs and their implications on current computing software/hardware platforms. To facilitate research and advance the understanding of these multi-modal DNN workloads, we first present MMbench, an open-source benchmark suite consisting of a set of real-world multi-modal DNN workloads with relevant performance metrics for evaluation. Then we use MMbench to conduct an in-depth analysis on the characteristics of multi-modal DNNs. We study their implications on application and programming framework, operating and scheduling system, as well as execution hardware. Finally, we conduct a case study and extend our benchmark to edge devices. We hope that our work can provide guidance for future software/hardware design and optimization to underpin multi-modal DNNs on both cloud and edge computing platforms

Near-theoretical strength and deformation stabilization achieved via grain boundary segregation and nano-clustering of solutes

Grain boundary hardening and precipitation hardening are important mechanisms for enhancing the strength of metals. Here, we show that these two effects can be amplified simultaneously in nanocrystalline compositionally complex alloys (CCAs), leading to near-theoretical strength and large deformability. We develop a model nanograined (TiZrNbHf)98Ni2 alloy via thermodynamic design. The Ni solutes, which has a large negative mixing enthalpy and different electronegativity to Ti, Zr, Nb and Hf, not only produce Ni-enriched local chemical inhomogeneities in the nanograins, but also segregate to grain boundaries. The resultant alloy achieves a 2.5 GPa yield strength, together with work hardening capability and large homogeneous deformability to 65% compressive strain. The local chemical inhomogeneities impede dislocation propagation and encourage dislocation multiplication to promote strain hardening. Meanwhile, Ni segregates to grain boundaries and enhances cohesion, suppressing the grain growth and grain boundary cracking found while deforming the reference TiZrNbHf alloy. Our alloy design strategy thus opens an avenue, via solute decoration at grain boundaries combined with local chemical inhomogeneities inside the grains, towards ultrahigh strength and large plasticity in nanostructured alloys

Gait Recognition Algorithm of Coal Mine Personnel Based on LoRa

This study proposes a new approach to gait recognition using LoRa signals, taking into account the challenging conditions found in underground coal mines, such as low illumination, high temperature and humidity, high dust concentrations, and limited space. The aim is to address the limitations of existing gait recognition research, which relies on sensors or other wireless signals that are sensitive to environmental factors, costly to deploy, invasive, and require close sensing distances. The proposed method analyzes the received signal waveform and utilizes the amplitude data for gait recognition. To ensure data reliability, outlier removal and signal smoothing are performed using Hampel and S-G filters, respectively. Additionally, high-frequency noise is eliminated through the application of Butterworth filters. To enhance the discriminative power of gait features, the pre-processed data are reconstructed using an autoencoder, which effectively extracts the underlying gait behavior. The trained autoencoder generates encoder features that serve as the input matrix. The Softmax method is then employed to associate these features with individual identities, enabling LoRa-based single-target gait recognition. Experimental results demonstrate significant performance improvements. In indoor environments, the recognition accuracy for groups of 2 to 8 individuals ranges from 99.7% to 96.6%. Notably, in an underground coal mine where the target is located 20 m away from the transceiver, the recognition accuracy for eight individuals reaches 93.3%

Clinical Characteristics and Risk Factors of Patients with Pulmonary Infarction Secondary to Intermediate and High-risk Pulmonary Embolism Misdiagnosed as Pneumonia

Background Although the number of case reports on pulmonary infarction (PI) secondary to pulmonary embolism (PE) is increasing in recent years, its misdiagnosis remains common, mainly as pneumonia. In patients with intermediate and high-risk pulmonary embolism, delays in diagnosis and timely treatment would lead to poor prognosis. Objective By analyzing the pneumonia-misdiagnosed cases of patients with PI, we summarized their clinical characteristics and related risk factors, and constructed a multivariate joint model to improve the accurate diagnosis rate at early stage. Methods This retrospective study included the hospitalized patients with pulmonary embolism at the First Affiliated Hospital of USTC from January 2017 to December 2023. In the group of pneumonia-misdiagnosed patients with intermediate to high-risk PI, we analyzed the clinical characteristics and compared the differences between the misdiagnosed groups and control group. Furthermore, using a multivariate Logistic regression analysis, we explored the independent predictive factors of the delayed diagnosis, analyze the predictive value of various indicators for the misdiagnosis by ROC curves, and compared the AUC values using Delong test. Results Among 101 cases of PI patients, 70 of them were misdiagnosed as pneumonia. From 2017 to 2023, the misdiagnosis rate gradually decreased in percentages of 100.0%, 83.3%, 74.1%, 71.4%, 63.2%, 66.7%, and 50.0%, respectively (χ2trend=6.672, P=0.010). Based on the results of multivariate Logistic regression analysis, the characteristics of over sixty-years-old age (OR=18.271, 95%CI=4.373-76.339, P<0.001), fever (OR=16.073, 95%CI=3.510-73.786, P<0.001), chest pain (OR=6.660, 95%CI=1.571-28.233, P=0.010) and non-dyspnea (OR=7.783, 95%CI=2.049-30.249, P=0.003) were independent predictive factors for the misdiagnosis. Therefore, a multivariate joint model was constructed as the following equation: Y=-6.624+0.095×A (factor of age) +2.510×F (factor of fever) +2.683×N (factor of non-dyspnea chest pain). The model indicated the PI misdiagnosis parameters as AUC under the curve (OR=0.880, 95%CI=0.802-0.959, P<0.001), sensitivity (0.871) and specificity (0.806). According to Delong's tests, the predictive values were superior to single-factor indicators of age (Z=2.771, P=0.006), fever (Z=4.653, P<0.001) and non-dyspnea chest pain (Z=4.014, P<0.001) . Conclusion Although the misdiagnosis rate of pulmonary infarction has decreased in recent years, clinicians should keep alert to the differential diagnosis of pulmonary infarction and pneumonia in elderly PE patients with symptoms of fever and non-dyspnea chest pain