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2022 ◽  
Vol 42 (1) ◽  
pp. 105-112
Author(s):  
Guilherme Silva Cardoso

ABSTRACT A semantic change has occurred in the scope of structural reforms’ term. This article reviews Celso Furtado’s work, in particular, the ones related to this specific topic, and compares it with the current literature. It appears that structural reforms in the Furtadian conception connoted base transformations and were guided by the developmentalism school of thought. Nowadays, it is of general knowledge that, under the new-institutionalist influence, “structural reforms” are associated with liberal policies for monitoring fiscal consolidations, without consensus as to the power of effectiveness. The effort to rescue and understand the original conceptions of certain keywords in the economic development literature, as well as the way in which their interpretations and practices modify over time, is shown to be of paramount importance as the capitalist system struggle to find ways of adapting itself to the current situation of developing economies.


2022 ◽  
Vol 16 (2) ◽  
pp. 1-21
Author(s):  
Michael Nelson ◽  
Sridhar Radhakrishnan ◽  
Chandra Sekharan ◽  
Amlan Chatterjee ◽  
Sudhindra Gopal Krishna

Time-evolving web and social network graphs are modeled as a set of pages/individuals (nodes) and their arcs (links/relationships) that change over time. Due to their popularity, they have become increasingly massive in terms of their number of nodes, arcs, and lifetimes. However, these graphs are extremely sparse throughout their lifetimes. For example, it is estimated that Facebook has over a billion vertices, yet at any point in time, it has far less than 0.001% of all possible relationships. The space required to store these large sparse graphs may not fit in most main memories using underlying representations such as a series of adjacency matrices or adjacency lists. We propose building a compressed data structure that has a compressed binary tree corresponding to each row of each adjacency matrix of the time-evolving graph. We do not explicitly construct the adjacency matrix, and our algorithms take the time-evolving arc list representation as input for its construction. Our compressed structure allows for directed and undirected graphs, faster arc and neighborhood queries, as well as the ability for arcs and frames to be added and removed directly from the compressed structure (streaming operations). We use publicly available network data sets such as Flickr, Yahoo!, and Wikipedia in our experiments and show that our new technique performs as well or better than our benchmarks on all datasets in terms of compression size and other vital metrics.


2022 ◽  
Vol 14 (2) ◽  
pp. 1-24
Author(s):  
Bin Wang ◽  
Pengfei Guo ◽  
Xing Wang ◽  
Yongzhong He ◽  
Wei Wang

Aspect-level sentiment analysis identifies fine-grained emotion for target words. There are three major issues in current models of aspect-level sentiment analysis. First, few models consider the natural language semantic characteristics of the texts. Second, many models consider the location characteristics of the target words, but ignore the relationships among the target words and among the overall sentences. Third, many models lack transparency in data collection, data processing, and results generating in sentiment analysis. In order to resolve these issues, we propose an aspect-level sentiment analysis model that combines a bidirectional Long Short-Term Memory (LSTM) network and a Graph Convolutional Network (GCN) based on Dependency syntax analysis (Bi-LSTM-DGCN). Our model integrates the dependency syntax analysis of the texts, and explicitly considers the natural language semantic characteristics of the texts. It further fuses the target words and overall sentences. Extensive experiments are conducted on four benchmark datasets, i.e., Restaurant14, Laptop, Restaurant16, and Twitter. The experimental results demonstrate that our model outperforms other models like Target-Dependent LSTM (TD-LSTM), Attention-based LSTM with Aspect Embedding (ATAE-LSTM), LSTM+SynATT+TarRep and Convolution over a Dependency Tree (CDT). Our model is further applied to aspect-level sentiment analysis on “government” and “lockdown” of 1,658,250 tweets about “#COVID-19” that we collected from March 1, 2020 to July 1, 2020. The experimental results show that Twitter users’ positive and negative sentiments fluctuated over time. Through the transparency analysis in data collection, data processing, and results generating, we discuss the reasons for the evolution of users’ emotions over time based on the tweets and on our models.


2022 ◽  
Vol 128 ◽  
pp. 326-335
Author(s):  
Torbjørn Selseng ◽  
Kristin Linnerud ◽  
Erling Holden
Keyword(s):  

Author(s):  
Ana Maria Mesa-Vanegas ◽  
◽  
Esther Julia Naranjo-Gomez ◽  
Felipe Cardona ◽  
Lucia Atehortua-Garces ◽  
...  

Solanum nudum Dunal (Solanaceae) is most commonly known and used by the population of the colombian Pacific coast as an antimalarial treatment. This article study into optimization and quantitative analysis of compounds steroidal over time of development of this species when grown in vitro and wild. A new steroidal compound named SN6 was elucidated by NMR and a new method of quantification of seven steroidal compounds (Diosgenone DONA and six steroids SNs) using HPLC-DAD-MS in extracts of cultures in vitro and wild was investigated. Biology activity of extracts was found to a range of antiplasmodial activity in FCB2 and NF-54 with inhibitory concentration (IC50) between (17.04 -100 μg/mL) and cytotoxicity in U-937 of CC50 (7.18 -104.7 μg/mL). This method creates the basis for the detection of seven sterols antiplasmodial present in extracts from S. nudum plant as a quality parameter in the control and expression of phytochemicals.


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