1,721,133 research outputs found
Deep Learning Strategies for Early Warning of Deep-seated Landslides
Get PDFWith the changing global climate, the incidence of geological disasters is on a steady rise year by year. Landslides, being among the most frequently occurring geological disasters, have become a global phenomenon. The occurrence of landslides inflicts significant damage upon human-made structures, river systems, and water channels, often resulting in tragic loss of life and property. Consequently, the real-time monitoring and early warning of landslides have emerged as imperative strategies to mitigate these losses. This paper draws upon two case studies: the Baishuihe landslide in the Three Gorges Reservoir area of China and the Sant’ Andrea landslide within Italy's Alpine region. It systematically explains the methodologies of landslide monitoring and prediction. By concentrating on the causal factors of landslides and combining deep learning techniques that encompass variables beyond mere rainfall, such as subterranean water levels and snow melting, this study aims to enhance the accuracy of predicting future trends in landslide movements.....
Serum Levels of C1q/TNF-Related Protein 9 and its Association with Insulin Resistance in Gestational Diabetes Mellitus
No full textData from a current study aimed to ascertain the relationship between serum CTRP9 and GDM
Urban villages and urban integration
No full textThis article written (( Liu, Yuting, He, Shenjing, Wu, Fulong & Webster, Chris. 2010 “Urban villages under China’s rapid urbanization: unregulated assets and transitional neighbourhoods”. Habitat International, 34 (2), pp. 135-144, retrieved from 10.1016/j.habitatint.2009.08.003, ))by Liu Yuting (South China University of Technology), He Shenjing (Sun-Yatsen University), Wu Fulong Cardiff University) and Chris Webster (Cardiff University), analyses the phenomenon of urban villages in China an..
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Image analysis and LSTM methods for forecasting surficial displacements of a landslide triggered by snowfall and rainfall
Get PDFLandslide-prone areas, predominantly located in mountainous regions with abundant rainfall, present unique challenges when subject to significant snowfall at high altitudes. Understanding the role of snow accumulation and melting, alongside rainfall and other environmental variables like temperature and humidity, is crucial for assessing landslide stability. To pursue this aim, the present study focuses first on the quantification of snow accumulated on a slope through a simple parameter obtained with image processing. Then, this parameter is included in a slope displacement prediction analysis carried out with long short-term memory (LSTM) neural network. By employing image processing algorithms and filtering out noise from white-shown rocks, the methodology evaluates the percentage of snow cover in RGB images. Subsequent LSTM forecasts of landslide displacement utilize 28-day historical data on rainfall, snow, and slope movements. The presented procedure is applied to the case of a deep-seated landslide in Italy, a site that in winter 2020–2021 experienced heavy snowfall, leading to significant snow accumulation on the slope. These episodes motivated a study aimed at forecasting the superficial displacements of this landslide, considering the presence of snow both at that time and in the following days, along with humidity and temperature. This approach indirectly incorporates snow accumulation and potential melting phenomena into the model. Although the subsequent winters were characterized by reduced snowfall, including this information in the LSTM model for the period characterized by snow on the slope demonstrated a dependency of the predictions on this parameter, thus suggesting that snow is indeed a significant factor in accelerating landslide movements. In this context, detecting snow and incorporating it into the predictive model emerges as a significant aspect for considering the effects of winter snowfall. The method aims to propose an innovative strategy that can be applied in the future to the study of the landslide analyzed in this paper during upcoming winters characterized by significant snowfall, as well as to other case studies of landslides at high altitudes that lack precise snow precipitation recording instruments
Deformation evaluation and displacement forecasting of baishuihe landslide after stabilization based on continuous wavelet transform and deep learning
No full textBaishuihe Landslide is a large active landslide that threatens shipping transportation in the Three Gorges Reservoir (China). A manual monitoring system has been implemented since 2003. However, after some intervention works in 2018-2019, new automatic instruments providing continuous data on displacements, rainfall, reservoir water level, and groundwater table were installed. The data recorded by the new system show that interventions led to an effective stabilization improvement since the present displacement rate is smaller than that before interventions. However, the relevance of the Three Gorges basin and the potential hazard of a possible collapse requires a reliable forecast of the landslide evolution in a time scale from a few hours to a few days. To this aim, a two-step procedure is proposed here. In the first step, after a preliminary preprocessing-denoising of data, carried out by means of Discrete Wavelet Transform (DWT), a Continuous Wavelet Transform (CWT) procedure is used to provide scalograms of the time series of three quantities, e.g., landslide displacement rate, rainfall and the difference of water level between one piezometer and reservoir water level (RWL). In the second step, to evaluate the relationships among the velocity trend and the other significant quantities and obtain a reliable velocity forecast, the images given by binding together two or three scalograms of the mentioned quantities were analyzed using Convolutional Neural Network (CNN) tool. Several trials with different combinations of input time series of 2 or 3 quantities were carried out in order to recognize the factors which mainly affect the current displacement evolution. The results show that, after the interventions, rainfall is an important factor inducing deformation acceleration. The hydrodynamic pressure induced by the difference between the groundwater pressure and reservoir water level also plays a dominant role in accelerating the Baishuihe landslide. Furthermore, the coupling of rainfall and hydrodynamic pressure produces displacement velocities higher than what the quantities singularly do. These results provide valuable indications for optimizing the monitoring configuration on the landslide and obtaining velocity forecasts in a few hours/days
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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