Hydrothermal complex of the Souk Ahras Basin: geological and hydrogeochemical approaches (north east of Algeria)

North- East of Algeria, in The Souk Ahras region, the Triassic evaporates are in the form of important intrusive masses. Thermal and cold water emerge from various training. These sources present are taking their pathways along the faulting system. A complex multilayered reservoir has significant potential water. The karstic aquifer consists mainly on fresh water. Thermal water characterized by high salinity is carbo-gaseous. Collection and chemical analysis of major water elements in addition to nonionic mineral compounds (SiO2) and trace elements (Sr2+, F-, Br-) have determined a deep saline fluid circulation. The tectonic effect would be responsible for the current water flow. Cartography of fracturing system has identified a NNW-SSE hot spring distribution. Similar alignment can match the faulting system direction affecting the concerned study area.Keywords: Triassic evaporate; thermal waters; tectonic; deep fluid circulatio

WALDATA : Wavelet transform based adversarial learning for the detection of anomalous trading activities

Detecting manipulative activities in stock market trading poses a significant challenge due to the complex temporal correlations inherent to the dynamically changing stock price data. This challenge is further exacerbated by the limited availability of labelled anomalous trading data instances. Stock price manipulations, which consist of infrequent anomalies in stock price trading data, are challenging to capture due to their sporadic occurrence and dynamically evolving nature. This scarcity and inherent complexity significantly complicate the creation of labelled datasets hence hinders the development of robust detection of different stock price manipulation schemes through supervised learning methods. Overcoming these challenges is crucial for enhancing our understanding of market dynamics and implementing robust market surveillance systems. To address these challenges, we introduce a novel stock price manipulation detection approach called WALDATA (Wavelet Transform based Adversarial Learning for the Detection of Anomalous Trading Activities). We leverage the Wavelet Transform (WT) to decompose non-stationary stock price time series into informative features and capture multi-scale dynamics within the data. We encode stock price data by transforming it into scalogram images through the Continuous Wavelet Transform, effectively converting stock price time series data into a 2D image representation. Subsequently, we employ a Generative Adversarial Network (GAN) architecture, originally applied to computer vision, to learn the underlying distribution of normal trading behaviour from the encoded images. We then train the discriminator as an anomaly detector for identifying manipulative trading activities in the stock market. The efficacy of WALDATA is rigorously evaluated on diverse real-world stock datasets using 1-level tick data from the LOBSTER project and the experimental results demonstrate the significant performance of our approach achieving an average AUC of 0.99 while maintaining low false alarm rates across various market conditions. These findings not only validate the effectiveness of the proposed WALDATA approach in accurately identifying stock price manipulations but also provide investors and regulators alike with valuable insights for the development of advanced market surveillance systems. This research demonstrates the promising potential of combining wavelet-based feature extraction and stock price time series to image representation with generative adversarial learning frameworks for anomaly detection in financial time series data. The successful implementation of WALDATA contributes to the development of advanced market surveillance systems and paves the way for further advancements in market surveillance, contributing towards a more efficient and robust financial system and a fair market environment

On a New Extension of the Zero-Divisor Graph

In this paper, we introduce a new graph whose vertices are the non-zero zero-divisors of a commutative ring R, and for distincts elements x and y in the set Z(R)* of the non-zero zero-divisors of R, x and y are adjacent if and only if xy = 0 or x + y ∈ Z(R). We present some properties and examples of this graph, and we study its relationship with the zero-divisor graph and with a subgraph of the total graph of a commutative ring. </jats:p

Oestrus ovis conjunctival ophthalmomyiasis

International audienc