A survey on tidal analysis and forecasting methods for Tsunami detection

Accurate analysis and forecasting of tidal level are very important tasks for human activities in oceanic and coastal areas. They can be crucial in catastrophic situations like occurrences of Tsunamis in order to provide a rapid alerting to the human population involved and to save lives. Conventional tidal forecasting methods are based on harmonic analysis using the least squares method to determine harmonic parameters. However, a large number of parameters and long-term measured data are required for precise tidal level predictions with harmonic analysis. Furthermore, traditional harmonic methods rely on models based on the analysis of astronomical components and they can be inadequate when the contribution of non-astronomical components, such as the weather, is significant. Other alternative approaches have been developed in the literature in order to deal with these situations and provide predictions with the desired accuracy, with respect also to the length of the available tidal record. These methods include standard high or band pass filtering techniques, although the relatively deterministic character and large amplitude of tidal signals make special techniques, like artificial neural networks and wavelets transform analysis methods, more effective. This paper is intended to provide the communities of both researchers and practitioners with a broadly applicable, up to date coverage of tidal analysis and forecasting methodologies that have proven to be successful in a variety of circumstances, and that hold particular promise for success in the future. Classical and novel methods are reviewed in a systematic and consistent way, outlining their main concepts and components, similarities and differences, advantages and disadvantages

Charting the Landscape of Digital Health: Towards A Knowledge Graph Approach to News Media Analysis

In this paper, we present our currently on-going work on a method for analyzing digital health transformation in our society by constructing a Knowledge Graph from a large corpus of 7.8 million English news articles, dating from 1987 through 2023. We firstly sampled around 95k articles relevant to the Digital Health topic by training and deploying a Deep Learning binary classifier via fine-tuning BERT. Successively, by deploying NLP techniques, we extracted triples from the identified articles to form a Digital Health News Knowledge Graph, which consists of 431k distinct triples connecting 186k entities through 1866 relations. The constructed Knowledge Graph provides insights into the evolution of Digital Health in news media and serves as a resource for further research in the field. The analysis that we have carried out reveals significant trends in Digital Health as reflected in the news, with notable peaks coinciding with key events like the COVID-19 pandemic

Triplétoile: Extraction of knowledge from microblogging text

Numerous methods and pipelines have recently emerged for the automatic extraction of knowledge graphs from documents such as scientific publications and patents. However, adapting these methods to incorporate alternative text sources like micro-blogging posts and news has proven challenging as they struggle to model open-domain entities and relations, typically found in these sources. In this paper, we propose an enhanced information extraction pipeline tailored to the extraction of a knowledge graph comprising open-domain entities from micro-blogging posts on social media platforms. Our pipeline leverages dependency parsing and classifies entity relations in an unsupervised manner through hierarchical clustering over word embeddings. We provide a use case on extracting semantic triples from a corpus of 100 thousand tweets about digital transformation and publicly release the generated knowledge graph. On the same dataset, we conduct two experimental evaluations, showing that the system produces triples with precision over 95% and outperforms similar pipelines of around 5% in terms of precision, while generating a comparatively higher number of triples

Identification of Sequence Variants in the UBL5 (Ubiquitin-like 5 or BEACON) Gene in Obese Children by PCR-SSCP: No Evidence for Association with Obesity RID A-1555-2012

Background: Childhood obesity has a strong genetic background. The human UBL5 (BEACON) gene has been suggested as a candidate gene for obesity. Previous studies in populations of different ethnicities have shown a significant association between UBL5 variants and measures of body fatness. Aims: To identify mutations that may cause early-onset obesity we screened the UBL5 gene for sequence variations in a cohort of obese children who also had at least one obese parent (BMI >30 kg/m(2)) diagnosed before the age of 30 years. Methods: We screened the UBL5 gene by PCR-SSCP and sequencing in a cohort (n = 30) of obese children (mean age 6.9 +/- 3 yr), and then analysed SNPs by HRMA in a population of 160 obese and 140 lean individuals. Results: Three sequence variations were detected: -422T>C in the 5'-UTR region, and -8007>A (rs10418248) and -8606>T in the promoter region. The SNPs -422 T>C in the 5'-UTR region and -8606>T have never been described before. These two SNPs did not co-segregate with obesity in relatives of the obese carriers. However, since in silico analysis of the -8606>T SNP region predicted a loss of the consensus binding site for RXR-alpha and RXR-beta, both involved in adipose cell regulation, we screened the -8606>T variant in a cohort of 300 individuals, 160 young obese (mean age 33 years) and 140 lean individuals. No differences in genotype distribution or in -860T allele frequencies were found between the two groups (1.8% vs 1.4%, p = NS). In addition, no association was found between obesity and the previously described -800T>A SNP (rs10418248). Conclusion: Our data suggest that the UBL5 gene is unlikely to play a major role in the genetic susceptibility to early-onset obesity in our population