Forecasting Stock Price using Wavelet Neural Network Optimized by Directed Arti ficial Bee Colony Algorithm, Journal of Telecommunications and Information Technology, 2016, nr 2

Stock prediction with data mining techniques is one of the most important issues in finance. This field has attracted great scientific interest and has become a crucial research area to provide a more precise prediction process. This study proposes an integrated approach where Haar wavelet transform and Artificial Neural Network optimized by Directed Artificial Bee Colony algorithm are combined for the stock price prediction. The proposed approach was tested on the historical price data collected from Yahoo Finance with different companies. Furthermore, the prediction result was found satisfactorily enough as a guide for traders and investors in making qualitative decisions

SL-COMP: Competition of Solvers for Separation Logic

International audienceSL-COMP aims at bringing together researchers interested on improving the state of the art of the automated deduction methods for Separation Logic (SL). The event took place twice until now and collected more than 1K problems for different fragments of SL. The input format of problems is based on the SMT-LIB format and therefore fully typed; only one new command is added to SMT-LIB's list, the command for the declaration of the heap's type. The SMT-LIB theory of SL comes with ten logics, some of them being combinations of SL with linear arithmetics. The competition's divisions are defined by the logic fragment, the kind of decision problem (satisfiability or entailment) and the presence of quantifiers. Until now, SL-COMP has been run on the StarExec platform, where the benchmark set and the binaries of participant solvers are freely available. The benchmark set is also available with the competition's documentation on a public repository in GitHub

APPLYING ARTIFICIAL NEURAL NETWORK OPTIMIZED BY FIREWORKS ALGORITHM FOR STOCK PRICE ESTIMATION

Stock prediction is to determine the future value of a company stock dealt on an exchange. It plays a crucial role to raise the profit gained by firms and investors. Over the past few years, many methods have been developed in which plenty of efforts focus on the machine learning framework achieving the promising results. In this paper, an approach based on Artificial Neural Network (ANN) optimized by Fireworks algorithm and data preprocessing by Haar Wavelet is applied to estimate the stock prices. The system was trained and tested with real data of various companies collected from Yahoo Finance. The obtained results are encouraging

The natural history and transmission potential of asymptomatic severe acute respiratory syndrome coronavirus 2 infection

Background Little is known about the natural history of asymptomatic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Methods We conducted a prospective study at a quarantine center for coronavirus disease 2019 in Ho Chi Minh City, Vietnam. We enrolled quarantined people with reverse-transcription polymerase chain reaction (RT-PCR)–confirmed SARS-CoV-2 infection, collecting clinical data, travel and contact history, and saliva at enrollment and daily nasopharyngeal/throat swabs (NTSs) for RT-PCR testing. We compared the natural history and transmission potential of asymptomatic and symptomatic individuals. Results Between 10 March and 4 April 2020, 14 000 quarantined people were tested for SARS-CoV-2; 49 were positive. Of these, 30 participated in the study: 13 (43%) never had symptoms and 17 (57%) were symptomatic. Seventeen (57%) participants imported cases. Compared with symptomatic individuals, asymptomatic people were less likely to have detectable SARS-CoV-2 in NTS collected at enrollment (8/13 [62%] vs 17/17 [100%]; P = .02). SARS-CoV-2 RNA was detected in 20 of 27 (74%) available saliva samples (7 of 11 [64%] in the asymptomatic group and 13 of 16 [81%] in the symptomatic group; P = .56). Analysis of RT-PCR positivity probability showed that asymptomatic participants had faster viral clearance than symptomatic participants (P  Conclusions Asymptomatic SARS-CoV-2 infection is common and can be detected by analysis of saliva or NTSs. The NTS viral loads fall faster in asymptomatic individuals, but these individuals appear able to transmit the virus to others

Metformin as adjunctive therapy for dengue in overweight and obese patients : a protocol for an open-label clinical trial (MeDO)

Background: Dengue is a disease of major global importance. While most symptomatic infections are mild, a small proportion of patients progress to severe disease with risk of hypovolaemic shock, organ dysfunction and death. In the absence of effective antiviral or disease modifying drugs, clinical management is solely reliant on supportive measures. Obesity is a growing problem among young people in Vietnam and is increasingly recognised as an important risk factor for severe dengue, likely due to alterations in host immune and inflammatory pathways. Metformin, a widely used anti-hyperglycaemic agent with excellent safety profile, has demonstrated potential as a dengue therapeutic in vitro and in a retrospective observational study of adult dengue patients with type 2 diabetes. This study aims to assess the safety and tolerability of metformin treatment in overweight and obese dengue patients, and investigate its effects on several clinical, immunological and virological markers of disease severity. Methods: This open label trial of 120 obese/overweight dengue patients will be performed in two phases, with a metformin dose escalation if no safety concerns arise in phase one. The primary endpoint is identification of clinical and laboratory adverse events. Sixty overweight and obese dengue patients aged 10-30 years will be enrolled at the Hospital for Tropical Diseases in Ho Chi Minh City, Vietnam. Participants will complete a 5-day course of metformin therapy and be compared to a non-treated group of 60 age-matched overweight and obese dengue patients. Discussion: Previously observed antiviral and immunomodulatory effects of metformin make it a promising dengue therapeutic candidate in appropriately selected patients. This study will assess the safety and tolerability of adjunctive metformin in the management of overweight and obese young dengue patients, as well as its effects on markers of viral replication, endothelial dysfunction and host immune responses. Trial registration: ClinicalTrials.gov: NCT04377451 (May 6th 2020)