Is attention all you need for intraday Forex trading?

The main objective of this paper is to analyse whether the Transformer neural network, which has become one of the most influential algorithms in Artificial Intelligence over the last few years, exhibits predictive capabilities for high-frequency Forex data. The prediction task is to classify short-term Forex movements for six currency pairs and five different time intervals from 60 to 720 min. We find that the Transformer exhibits high predictive power in the context of intraday Forex trading. This performance is slightly better than for the carefully selected benchmark – ResNet-LSTM, which currently is a state-of-the-art algorithm. Since intraday Forex trading based on deep learning models is largely unexplored, we offer insight on which currency pair and time interval are amenable to devising a profitable trading strategy. We also show that high predictive accuracy can be misleading in real world trading for short time intervals, as models trained on OHLC data tend to report the highest accuracy when the spread cost is the highest. This renders assessment based on typical machine learning metrics overly optimistic. Therefore, it is critical to backtest frequent intraday Forex trading strategies with realistic cost assumptions, which is rarely the case in empirical literature. Lastly, sensitivity analysis shows that the length of the time interval used for training does not play a critical role in the Transformer's predictive capabilities, whereas features derived from technical analysis are essential.     

Synthesis,In Vitro,and Computational Studies of PTP1B Phosphatase Inhibitors Based on Oxovanadium(IV) and Dioxovanadium(V) Complexes

One of the main goals of recent bioinorganic chemistry studies has been to design and synthesize novel substances to treat human diseases. The promising compounds are metal-based and metal ion binding components such as vanadium-based compounds. The potential anticancer action of vanadium-based compounds is one of area of investigation in this field. In this study, we present five oxovanadium(IV) and dioxovanadium(V) complexes as potential PTP1B inhibitors with anticancer activity against the MCF-7 breast cancer cell line, the triple negative MDA-MB-231 breast cancer cell line, and the human keratinocyte HaCaT cell line. We observed that all tested compounds were effective inhibitors of PTP1B, which correlates with anticancer activity. [VO(dipic)(dmbipy)]·2 H₂O (Compound 4) and [VOO(dipic)](2-phepyH)·H₂O (Compound 5) possessed the greatest inhibitory effect, with IC₅₀ 185.4 ± 9.8 and 167.2 ± 8.0 nM, respectively. To obtain a better understanding of the relationship between the structure of the examined compounds and their activity, we performed a computer simulation of their binding inside the active site of PTP1B. We observed a stronger binding of complexes containing dipicolinic acid with PTP1B. Based on our simulations, we suggested that the studied complexes exert their activity by stabilizing the WPD-loop in an open position and limiting access to the P-loop.     

Anti-Inflammatory and Antiviral Effects of Cannabinoids in Inhibiting and Preventing SARS-CoV-2 Infection

The COVID-19 pandemic caused by the SARS-CoV-2 virus made it necessary to search for new options for both causal treatment and mitigation of its symptoms. Scientists and researchers around the world are constantly looking for the best therapeutic options. These difficult circumstances have also spurred the re-examination of the potential of natural substances contained in Cannabis sativa L. Cannabinoids, apart from CB1 and CB2 receptors, may act multifacetedly through a number of other receptors, such as the GPR55, TRPV1, PPARs, 5-HT1A, adenosine and glycine receptors. The complex anti-inflammatory and antiviral effects of cannabinoids have been confirmed by interactions with various signaling pathways. Considering the fact that the SARS-CoV-2 virus causes excessive immune response and triggers an inflammatory cascade, and that cannabinoids have the ability to regulate these processes, it can be assumed that they have potential to be used in the treatment of COVID-19. During the pandemic, there were many publications on the subject of COVID-19, which indicate the potential impact of cannabinoids not only on the course of the disease, but also their role in prevention. It is worth noting that the anti-inflammatory and antiviral potential are shown not only by well-known cannabinoids, such as cannabidiol (CBD), but also secondary cannabinoids, such as cannabigerolic acid (CBGA) and terpenes, emphasizing the role of all of the plant’s compounds and the entourage effect. This article presents a narrative review of the current knowledge in this area available in the PubMed, Scopus and Web of Science medical databases.     

The Effect of High and Variable Glucose on the Viability of Endothelial Cells Co-Cultured with Smooth Muscle Cells

Diabetes mellitus causes endothelial dysfunction. The aim of this study was to investigate the effect of normal (5 mmol/L), high (20 mmol/L), and fluctuating (5 and 20 mmol/L changed every day) glucose concentration in the culture medium on the viability of human umbilical vein endothelial cells (HUVECs) co-cultured with human umbilical artery smooth muscle cells (HUASMCs). The cultures were conducted on semi-permeable flat polysulfone (PSU) fibronectin-coated membranes immobilized in self-made inserts. The insert contained either HUVECs on a single membrane or HUASMCs and HUVECs on two membranes close to each other. Cultures were conducted for 7 or 14 days. Apoptosis, mitochondrial potential, and the production of reactive oxygen species and lactate by HUVECs were investigated. The results indicate that fluctuations in glucose concentration have a stronger negative effect on HUVECs viability than constant high glucose concentration. High and fluctuating glucose concentrations slow down cell proliferation compared to the culture carried out in the medium with normal glucose concentration. In conclusion, HUASMCs affect the viability of HUVECs when both types of cells are co-cultured in medium with normal or variable glucose concentration.     

The unstable thermoelectric effect in non-stoichiometric Cu2Se during the non-equilibrium phase transition

Journal of Materials Science - The superionic α???β phase transition in Cu1.96Se thermoelectric material is investigated by means of thermal analysis (DSC) and...     

Retracted papers by Iranian authors: causes,journals, time lags,affiliations, collaborations

Scientometrics - This study aims to analyze 343 retraction notices indexed in the Scopus database, published in 2001–2019, related to scientific articles (co-)written by at least one author...     

Risk assessment for a video surveillance system based on Fuzzy Cognitive Maps

For various IT systems security is considered a key quality factor. In particular, it might be crucial for video surveillance systems, as their goal is to provide continuous protection of critical infrastructure and other facilities. Risk assessment is an important activity in security management; it aims at identifying assets, threats and vulnerabilities, analysis of implemented countermeasures and their effectiveness in mitigating risks. This paper discusses an application of a new risk assessment method, in which risk calculation is based on Fuzzy Cognitive Maps (FCMs) to a complex automated video surveillance system. FCMs are used to capture dependencies between assets and FCM based reasoning is applied to aggregate risks assigned to lower-level assets (e.g. cameras, hardware, software modules, communications, people) to such high level assets as services, maintained data and processes. Lessons learned indicate, that the proposed method is an efficient and low-cost approach, giving instantaneous feedback and enabling reasoning on effectiveness of security system.