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This paper analyzes the advantages and disadvantages of the use of quantitative and qualitative methods in studies focused on foreign languages. We take two research cases, which are based on these two methods respectively, as examples: the first case adopts the program SPSS (https://www.ibm.com/spss) as the analysis instrument and the second one, Atlas.ti (https://atlasti.com/). Presentations of these two cases are followed by, in each subsection, reflections on the use of these research methods and the instruments in these two studies, for example, whether the research problem could be well explained by using this method or whether some key factors would be omitted in the process of data collection and analysis. As we know, with the development of digital technology and computer technology in recent decades, a wealth of online software and platforms have emerged, which makes remote data acquisition and automatic data processing possible. Therefore, this work also involves the application of new technologies to data collection and analysis. In addition, in order to correct, as much as possible, the deficiencies found in researches based on quantitative and qualitative methods, in this work, we discuss the possible solution, that is, the design of a mixed method that includes both the quantitative and qualitative methods. The last part of this paper is a brief introduction to the process of Q-Method, a research method that combined quantitative concurrently with qualitative data, in order to shed light on the future studies focused on the field of foreign languages., En el presente trabajo analizamos las ventajas y las deficiencias del uso de los métodos cuantitativo y cualitativo en estudios enfocados en lenguas extranjeras. A modo de ejemplo, observamos dos casos cuyos estudios están basados respectivamente en ambos métodos: en el primer caso se utiliza el programa SPSS (https://www.ibm.com/spss) como herramienta de análisis y en el segundo se emplea el programa Atlas.ti (https://atlasti.com/). En cada uno de estos casos se reflexiona sobre su aplicación en la investigación, analizando, por ejemplo, si la explicación es correcta con el método seguido y las herramientas adoptadas o si se han omitido algunos factores en la recogida y análisis de datos. Con el desarrollo de la tecnología digital e informática en las últimas décadas, han aparecido abundantes programas y plataformas en línea, lo cual hace factible de forma eficaz la recogida de datos a distancia y su procesamiento automático. Por consiguiente, en este trabajo también se aborda la aplicación de las nuevas tecnologías en la recogida y análisis de datos. Asimismo, para corregir, en la medida de lo posible, las deficiencias reveladas en estudios basados en métodos cuantitativo y cualitativo, se expone la posible solución, basada en el diseño de un método mixto de los dos modelos. La última parte del trabajo es una breve presentación de un procedimiento que utiliza esta estrategia mixta: la Metodología-Q. Nos proponemos, mediante su exposición y análisis, arrojar luz sobre futuros estudios en el terreno de la lengua extranjera.

As a strong climate element on interannual scales, the El Nino-Southern Oscillation (ENSO) is a major component of global weather and climate change, and it is also closely related to the interannual atmospheric angular momentum (AAM) and length-of-day changes (ΔLOD). Here, we reprocess and compare the interannual variations of AAM, ΔLOD with ENSO indices, with AAM mass and motion terms calculated over land separately from those over the ocean. Three oscillatory components (at ~ 6, ~ 7, ~ 8 years), due to angular momentum changes in Earth's interior, are removed to obtain the interannual ΔLOD solely related to climatic variations. Our results show that the AAM motion term over the ocean contributes the most to interannual ΔLOD, and that the oceanic AAM has larger variability than that over land, especially during the periods of strong ENSO events. After subtracting contributions associated with interior processes, the interannual ΔLOD anomalies corresponding to extreme ENSO events (1982–1983 ~ 0.43, 1997–1998 ~ 0.36, 2015–2016 ~ 0.42 ms) are about half as strong as those found in previous studies (~ 0.91, ~ 0.76, ~ 0.81 ms). Furthermore, we detect an intermediate La Nina event that occurred from August 2020 to May 2021, forcing the interannual ΔLOD to a minimum value of approximately -0.21 ms.

System-level diagnosis has become an important diagnosis method for multiprocessor systems. Among all system-level diagnosis measures, diagnosability is relatively small. The conditional diagnosability constraint that each vertex has at least one good neighbor is relatively conservative when the dimension is far greater than 1, and g-good-neighbor conditional diagnosability does not consider this restriction on faulty vertices. Therefore, a thorough study of diagnosability under the condition that each vertex has at least g good neighbors is an appealing subject. Motivated by R g vertex connectivity, in this paper, we introduce a novel generalized system-level diagnosis measure named R g conditional diagnosability, which assumes that every processor has at least g good neighbors. The popular conditional diagnosability is a special case of R g conditional diagnosability when g = 1 . Then, we determine that the R g conditional diagnosability of n-dimensional hypercube Q n under the Preparata Metze Chien (PMC) model is 2 2 g ( n − 2 g ) + 2 2 g − 1 − 1 .

The intradecadal variations in length-of-day (LOD) and their time-varying characteristics still need to be further studied. Given that the corresponding signal periods on the intradecadal scales are quite close to each other and the span of currently observed ΔLOD data (i.e., 1962–2019, only 57 years) is not long enough, accurate detection of these signals depends on using effective mathematical method. On the basis of the traditional harmonic retrieval model, this work proposes a frequency domain stepwise regression method, which can well identify the periodic components with close periods and recognize the weaker signals to estimate the relevant harmonic parameters (i.e., amplitude, frequency and phase). Furthermore, we apply this method to detection of the actual LOD intradecadal variations, the result of which shows that there are three components (i.e., the ~ 5.9 years, ~ 8.3 years and ~ 7.3 years) existing in LOD intradecadal variations. Here, we firstly give the time-domain harmonic expression of the ~ 7.3 years signal, i.e., $$y\left(t\right)=A\mathrm{cos}\left(\frac{2\pi }{T}\left(t-1962+\psi \right)\right)$$ y t = A cos 2 π T t - 1962 + ψ , where the period $$T=7.33$$ T = 7.33 years, amplitude $$A=0.0413(\pm 0.0099)$$ A = 0.0413 ( ± 0.0099 ) ms, phase $$\psi =-1.58(\pm 0.30)$$ ψ = - 1.58 ( ± 0.30 ) years. The mechanism of this ~ 7.3 years signal needs to be further studied.

The Jiangmen Underground Neutrino Observatory (JUNO) in China aiming to determine the neutrino mass hierarchy is under construction and the 15,000 MCP-PMTs produced in NNVT, which will be installed as the central liquid scintillator detector of JUNO, have been completed and delivered by August of 2020. During the mass production process, breakthrough of the photocathode technology greatly improved the quantum efficiency (QE) of the MCP-PMT from 30% to 35% and so the detection efficiency (DE) has been improved. The performance of other parameters keeps steady during the mass production process of 15,000 MCP-PMTs. The average QE at 400 nm for the 15,000 pieces of MCP-PMTs is 32%. With a layer of high-SEY material deposited on the MCP, the collection efficiency can reach nearly 100% and the average DE at 400 nm for the 15k pieces of MCP-PMTs is 31.5%.

Energy metabolic reprogramming (EMR) allows for the rearrangement of a series of metabolic genes and proteins when tumor cells adapt to their microenvironment. EMR is characterized by changes in the metabolic pattern and metabolic intermediates to meet the needs of tumor cells for their malignant proliferation and infiltrative growth. The present study investigated the role of low-dose paclitaxel (PTX) in changing the expression levels of key genes and proteins during glycolysis in CD133+ U251 glioma cells and explored the relevant regulatory mechanisms of action at the molecular level. CD133 immunomagnetic beads were applied to malignant CD133+ U251 glioma cells, which were then divided into a negative control and an experimental group treated with 1, 2, 4 or 8 µM PTX for 72 h. Cell Counting Kit-8 (CCK-8) was used to measure U251 cell proliferation. RNA and protein were extracted from the malignant glioma cells in all groups to observe changes in the expression levels of key glycolytic enzymes, such as glucose transporter 1 (GLUT1), pyruvate kinase M (PKM) and lactate dehydrogenase A (LDHA), using reverse transcription-quantitative PCR and western blot assays. Transwell migration assays were performed to quantify the effects of PTX solution on U251 cells. CD133+ U251 glioma cells were isolated successfully. CD1133+ cells had a higher rate of proliferation compared with CD1133- cells. In CD1133+ cells treated with PTX, a dose-dependent reduction in the expression levels of the key glycolytic enzymes GLUT1, PKM and LDHA was observed at both the mRNA and protein levels. PTX solution also inhibited cell migration. Differences between the control and experimental groups were statistically significant (P

Exchanged hypercube $EH(s,t)$ is a typical hypercube variant, which is built up by systematically removing a range of edges from hypercube $Q_{s+t+1}$ . $EH(s,t)$ preserves several advantages of hypercube and has better properties in terms of diameter and cost. Diagnosability and conditional diagnosability have been two important measures of self-diagnostic capability. Normally, conditional diagnosability is several times higher than its diagnosability because conditional diagnosability neglects the occurrence of low probability. In this paper, we study the properties of $EH(s,t)$ and present some elementary topological and connectivity theorems. Then, based on the above results, we determine its conditional diagnosability under the MM* model.

The typical representative of the hard clustering algorithm, K-means, is one of the fastest processing algorithms with good scalability. However, it cannot deal with categorical attributes, which is one of the important indicators to measure the pros and cons. Due to the lack of processing capabilities on categorical attributes, k-means has a large limit on data processing capabilities. This paper proposes a clustering algorithm extends from K-means. This algorithm introduces the concept of a Pseudo-mean distance calculation formula and a counting-table so that categorical attributes can be processed while reducing the time cost as much as possible. Experimental results illustrate the proposed Pseudo-means can extend the processing range of k-means to category-type data, and the counting table also effectively reduces the time cost.