PREDVIĐANJE BRZINE SMIČNOGA VALA I MODIFIKACIJA CASTAGNINE I CARROLLOVE JEDNADŽBE U SLUČAJU JEDNOGA IRANSKOG NAFTNOG POLJA

Shear wave velocity is one of the essential parameters for describing hydrocarbon reservoirs that have several applications in petrophysical, geophysical, and geomechanical studies. Shear wave velocity usually does not exist in all wells, especially in old oil fields. In the current study, two equations of Carroll and Castagna have been modified, and linear and nonlinear multi-regressions were used to estimate shear wave velocity in an oil reservoir in southwestern Iran. Initially, compressional wave velocity and porosity were determined as the most effective wire-line logs on shear wave velocity by comparing their correlations. Then, two equations of Carroll and Castagna were modified. In addition, new equations based on porosity and compressional wave velocity for estimating the shear wave velocity were obtained. Shear wave velocity was estimated by new exponential equations in the wells of the current oil field with excellent goodness of fit by determination coefficients of 0.80 in the whole well, 0.72 in the Ghar-Shale-1, and 0.78 in Ghar-Shale-3 in X-07 well.Brzina smičnoga vala jedan je od osnovnih parametara za opisivanje ležišta ugljikovodika te ima nekoliko primjena u petrofizičkim, geofizičkim i geomehaničkim istraživanjima. Brzina smičnoga vala obično se ne očitava u svim bušotinama, osobito ako je riječ o starim poljima. U ovome su istraživanju modificirane dvije jednadžbe, Carrollova i Castagnina, za procjenu brzine smičnoga vala u naftnome ležištu u jugozapadnome Iranu uporabom linearne i nelinearne višestruke regresije. Prvo je brzina smičnoga vala određena korelacijom brzine kompresijskoga vala i šupljikavosti iz karotažnih dijagrama. Zatim su prilagođene i Carrollova i Castagnina jednadžba te su dobivene nove za procjenu brzine takva vala, koje se temelje na podatcima o šupljikavosti i brzini kompresijskoga vala. Brzina smičnoga vala procijenjena je novim (eksponencijalnim) jednadžbama u bušotinama naftnoga polja. Dobiveni su dobri koeficijenti determinacije od 0,80 u cijeloj bušotini, 0,72 u jedinici Ghar-Shale-1 i 0,78 u jedinici Ghar-Shale-3, sve to u bušotini X-07

Intergenic Transcription in In Vivo Developed Bovine Oocytes and Pre-Implantation Embryos

Background Intergenic transcription, either failure to terminate at the transcription end site (TES), or transcription initiation at other intergenic regions, is present in cultured cells and enhanced in the presence of stressors such as viral infection. Transcription termination failure has not been characterized in natural biological samples such as pre-implantation embryos which express more than 10,000 genes and undergo drastic changes in DNA methylation. Results Using Automatic Readthrough Transcription Detection (ARTDeco) and data of in vivo developed bovine oocytes and embryos, we found abundant intergenic transcripts that we termed as read-outs (transcribed from 5 to 15 kb after TES) and read-ins (transcribed 1 kb up-stream of reference genes, extending up to 15 kb up-stream). Read-throughs (continued transcription from TES of expressed reference genes, 4–15 kb in length), however, were much fewer. For example, the numbers of read-outs and read-ins ranged from 3,084 to 6,565 or 33.36–66.67% of expressed reference genes at different stages of embryo development. The less copious read-throughs were at an average of 10% and significantly correlated with reference gene expression (P \u3c 0.05). Interestingly, intergenic transcription did not seem to be random because many intergenic transcripts (1,504 read-outs, 1,045 read-ins, and 1,021 read-throughs) were associated with common reference genes across all stages of pre-implantation development. Their expression also seemed to be regulated by developmental stages because many were differentially expressed (log2 fold change ≥ 2, P \u3c 0.05). Additionally, while gradual but un-patterned decreases in DNA methylation densities 10 kb both up- and down-stream of the intergenic transcribed regions were observed, the correlation between intergenic transcription and DNA methylation was insignificant. Finally, transcription factor binding motifs and polyadenylation signals were found in 27.2% and 12.15% of intergenic transcripts, respectively, suggesting considerable novel transcription initiation and RNA processing. Conclusion In summary, in vivo developed oocytes and pre-implantation embryos express large numbers of intergenic transcripts, which are not related to the overall DNA methylation profiles either up- or down-stream

Electricity Price Modeling Using Support Vector Machines by Considering Oil and Natural Gas Price Impacts

Accurate electricity price prediction is one of the most important parts of decision making for electricity market participants to make reasonable competing strategies. Support Vector Machine (SVM) is a novel algorithm based on a predictive modeling method and a powerful classification method in machine learning and data mining. Most of SVM-based and non-SVM-based models ignore other important factors in the electricity price dynamics and electricity price models are built regard to just historical electricity prices; However, electricity price has a strong correlation with other variables like oil and natural gas price. In this paper, single SVM model is used to combine diverse influential variables as 1-Historical Electricity Price of Germany 2-GASPOOL price as first natural gas reference price 3-Net-Connect-Germany (NCG) price as second natural gas reference price 4- West Texas Intermediate (WTI) daily price as US oil benchmark. The simulation results show that using oil and natural gas prices can improve SVM model prediction ability compared to the SVM models built on mere historical electricity price

Detecting fake accounts through Generative Adversarial Network in online social media

Nowadays, online social media has become an inseparable part of human life, also this phenomenon is being used by individuals to send messages and share files via videos and images. Twitter, Instagram, and Facebook are well-known samples of these networks. One of the main challenges of privacy for users in these networks is anomalies in security. Anomalies in online social networks can be attributed to illegal behavior, such deviance is done by malicious people like account forgers, online fraudsters, etc. This paper proposed a new method to identify fake user accounts by calculating the similarity measures among users, applying the Generative Adversarial Network (GAN) algorithm over the Twitter dataset. The results of the proposed method showed, accuracy was able to reach 98.1% for classifying and detecting fake user accounts

Étude et modélisation des connaissances et raisonnement de l'apprenant dans un STI

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal

A New Hybrid Filter-Wrapper Feature Selection using Equilibrium Optimizer and Simulated Annealing

Data dimensions and networks have grown exponentially with the Internet and communications. The challenge of high-dimensional data is increasing for machine learning and data science. This paper presents a hybrid filter-wrapper feature selection method based on Equilibrium Optimization (EO) and Simulated Annealing (SA). The proposed algorithm is named Filter-Wrapper Binary Equilibrium Optimizer Simulated Annealing (FWBEOSA). We used SA to solve the local optimal problem so that EO could be more accurate and better able to select the best subset of features. FWBEOSA utilizes a filtering phase that increases accuracy as well as reduces the number of selected features. The proposed method is evaluated on 17 standard UCI datasets using Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers and compared with ten state-of-the-art algorithms (i.e., Binary Equilibrium Optimizer (BEO), Binary Gray Wolf Optimization (BGWO), Binary Swarm Slap Algorithm (BSSA), Binary Genetic Algorithm (BGA), Binary Particle Swarm Optimization (BPSO), Binary Social Mimic Optimization (BSMO), Binary Atom Search Optimization (BASO), Modified Flower Pollination Algorithm (MFPA), Bar Bones Particle Swarm Optimization (BBPSO) and Two-phase Mutation Gray Wolf Optimization (TMGWO)). Based on the results of the SVM classification, the highest level of accuracy was achieved in 13 out of 17 data sets (76%), and the lowest number of selected features was achieved in 15 out of 17 data sets (88%). Furthermore, the proposed algorithm using class KNN achieved the highest accuracy rate in 14 datasets (82%) and the lowest selective feature rate in 13 datasets (76%)

Development of a GC-MS Method for the Analysis of Selected Opioids in Human Hair Samples

Background: Hair samples are recognized as alternative biological specimens in forensic and clinical toxicology for detecting drug abuse and poisoning. Forensic testing for opioids in hair has become a useful diagnostic measure for assessing chronic drug usage through segmental analysis. However, accurate, sensitive and specific analytical methods are needed. The aim of this study was to introduce a simple, sensitive and specific GC-MS method for the identification and quantitation of selected and commonly abused opioids (tramadol, methadone, morphine, and codeine) in hair samples. Methods: After external decontamination, a 50 mg portion of powdered hair sample was combined with hydrochloric acid (0.1 M) and incubated on a magnetic stirrer at 56°C for 16 hours. Then, 1 mL of sodium hydroxide (0.1 M) and 2 mL of phosphate buffer (1 M, pH=8.4) were added. Chloroform-isopropanol (ratio: 80:20 V/V) was utilized as the extracting solvent and the sample was homogenized and centrifuged for 5 minutes (at 3500 rpm). After centrifugation, the organic phase was dried using dry nitrogen gas. The sample was derivatized with N-methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA). The blank, standards, and real samples were subsequently analyzed by GC-MS.Results: The limits of detection in the linearity experiments ranged from 0.12 to 0.21 ng/mg. According to the validation results, the method exhibited linearity in the concentration range of 0.1-2.5 ng/mg for all analytes, with calibration curve slopes ranging from R2=0.98 to 0.99. Good inter and intra-day precision relative standard deviations (RSDs) were observed to be <3.5% for all compounds. Extraction efficiency varied from 91.8 to 102.4%. Conclusion: The validation and analysis of actual samples indicate that this method is straightforward, sensitive, and specific for the analysis of opioids in routine hair analysis

SIMULACIJA ZAGAĐENJA PODZEMNIH VODA S MJESTA ODLAGANJA RUDARSKOGA OTPADA

Mining wastes are a great source of pollutants. Open-pit backfill materials can be found as waste rock and as tailings. The aim of the current study was the investigation of the contaminant transportation pathways by groundwater flow from these waste materials through heterogeneous porous media. Numerical Lattice Boltzmann Method (LBM) was used for examining the effects of different factors on pollutant transportation through groundwater beneath the waste materials. Grain size, vertical and horizontal fracturing, and hydraulic pressure gradient were factors considered here. The results showed that contaminant transportation by the groundwater flow from the waste materials through porous media depends on primary and secondary matrix porosity of the open-pit material, heterogeneity in permeability of aquifer rock, and hydraulic head of groundwater.Rudarski otpad velik je izvor onečišćenja. Kao materijali za zatrpavanje otvorenih jama mogu se koristiti otpadne stijene i jalovina. Cilj ove studije bio je istražiti putove transporta onečišćenja podzemnim vodama iz ovih otpadnih materijala kroz heterogene porozne medije. Boltzmannova metoda numeričke rešetke (LBM) korištena je za određivanje učinaka različitih čimbenika na transport onečišćujućih tvari kroz podzemne vode ispod otpadnih materijala. Čimbenici koji su uzeti u obzir bili su veličina zrna, okomite i vodoravne pukotine te hidraulički gradijent tlaka. Rezultati su pokazali da prijenos onečišćenja protokom podzemnih voda iz otpadnih materijala kroz porozne medije ovisi o poroznosti primarne i sekundarne matrice otkopanih materijala, heterogenosti propusnosti stijene vodonosnika i hidrauličkom tlaku podzemne vode