Smart Learning Environment (SLE) in the Fourth Industrial Revolution (IR 4.0)

This paper is attempted to examine the explanatory approach in dealing with SLE by advancing online learning sources. The systematic approach of searching for the relevant articles on SLE in IR 4.0 has been widely identified through two electronic databases, Scopus and Web of Sciences. Through adopting such digitally systematic search program, identification was made on the various elements in terms of online learning resources (OLR). This attempts to propose the SLE framework model with an innovative approach in enhancing the learning through incorporating IR 4.0 platform to utilize the variety of information sources together with knowledge attribution in the higher education (HE). The contribution provides theoretical framework with the guideline of well-adapted performance in the educational activities as the new normal trend. In achieving this attainment, the readiness of both instruction facilities and accessibility procedure is significantly the main basis in ensuring the process flow in enlarging the digital learning.

Pore Pressure Prediction in Sandstone Using a Lateral Transfer Approach

Accurate pore pressure prediction is required to determine reliable static mud weights and circulating pressures, necessary to mitigate the risk of influx, blowouts and borehole instability. To accurately estimate the pore pressure, the over-pressure mechanism has to be identified with respect to the geological environment. One of the most widely used methods for pore pressure prediction is based on Normal Compaction Trend Analysis, where the difference between a ‘normal trend' and log value of a porosity indicator log such as sonic or resistivity is used to estimate the pore pressure. This method is biased towards shales, which typically exhibit a strong relationship between porosity and depth. Overpressure in non-shale formations has to be estimated using a different method to avoid errors while predicting the pore pressure. In this study, a different method for pore pressure prediction has been performed by using the lateral transfer approach. Many offset wells were used to predict the pore pressure. Lateral transfer in the sand body was identified as the mechanism for overpressure. This form of overpressure cannot be identified by well logs, which makes the pore pressure prediction more complex. Building a 2D geomechanical model, using seismic data as an input and following an analysis methodology that considered three type of formation fluids - gas, oil and water in the sand body, all pore pressure gradients related to lateral transfer for the respective fluids were evaluated. This methodology was applied to a conventional reservoir in a field in Colombia and was helpful to select the appropriate mud weight and circulating pressure to mitigate drilling risks associated to this mechanism of overpressure. Seismic data was critical to identifying this type of overpressure mechanism and was one of the main inputs for building the geomechanical earth model. This methodology enables drilling engineers and geoscientists to confidently predict, assess and mitigate the risks posed by overpressure in non-shale formations where lateral transfer is the driving mechanism of overpressure. This will ensure a robust well plan and minimize drilling/well control hazards associated with this mode of overpressure.

Early Anomaly Detection Model Using Random Forest while Drilling Horizontal Wells with a Real Case Study

There has been discrepancy between the pre-calculated and actual T&D values, because of the dependence of the model’s predictability on assumed inputs. Therefore, to have a reliable model, the users must adjust the model inputs; mainly friction coefficient in order to match the actual T&D. This, however, can mask downhole conditions such as cutting beds, tight holes and sticking tendencies. This paper aims to introduce a machine learning model to predict the continuous profile of the surface drilling torque to detect the operational issues in advance. Actual data of Well-1, starting from the time of drilling a 5-7/8-inch horizontal section until one day prior to the stuck pipe event, was used to train and test a random forest (RF) model with an 80/20 split ratio, to predict the surface drilling torque. The input variables for the model are the drilling surface parameters, namely: flow rate, hook load, rate of penetration, rotary speed, standpipe pressure, and weight-on-bit. The developed model was used to predict the surface drilling torque, which represents the normal trend for the last day leading up to the stuck pipe incident in Well-1. Then the model was integrated with a multivariate metric distance, Mahalanobis, to be used as a classifier to measure how close an actual observation is from the predictive normal trend. Based on a pre-determined threshold, each actual observation was labeled as "NORMAL" or "ANOMAL".

Warped Bayesian Linear Regression for Normative Modelling of Big Data

Normative modelling is becoming more popular in neuroimaging due to its ability to make predictions of deviation from a normal trajectory at the level of individual participants. It allows the user to model the distribution of several neuroimaging modalities, giving an estimation for the mean and centiles of variation. With the increase in the availability of big data in neuroimaging, there is a need to scale normative modelling to big data sets. However, the scaling of normative models has come with several challenges.So far, most normative modelling approaches used Gaussian process regression, and although suitable for smaller datasets (up to a few thousand participants) it does not scale well to the large cohorts currently available and being acquired. Furthermore, most neuroimaging modelling methods that are available assume the predictive distribution to be Gaussian in shape. However, deviations from Gaussianity can be frequently found, which may lead to incorrect inferences, particularly in the outer centiles of the distribution. In normative modelling, we use the centiles to give an estimation of the deviation of a particular participant from the ‘normal’ trend. Therefore, especially in normative modelling, the correct estimation of the outer centiles is of utmost importance, which is also where data are sparsest.Here, we present a novel framework based on Bayesian Linear Regression with likelihood warping that allows us to address these problems, that is, to scale normative modelling elegantly to big data cohorts and to correctly model non-Gaussian predictive distributions. In addition, this method provides also likelihood-based statistics, which are useful for model selection.To evaluate this framework, we use a range of neuroimaging-derived measures from the UK Biobank study, including image-derived phenotypes (IDPs) and whole-brain voxel-wise measures derived from diffusion tensor imaging. We show good computational scaling and improved accuracy of the warped BLR for certain IDPs and voxels if there was a deviation from normality of these parameters in their residuals.The present results indicate the advantage of a warped BLR in terms of; computational scalability and the flexibility to incorporate non-linearity and non-Gaussianity of the data, giving a wider range of neuroimaging datasets that can be correctly modelled.

Analysis of Impact of Aral Sea Catastrophe on Anomaly Climate Variables and Hydrological Processes

Since 1960, water level began to decline considerably due to anthropogenic impact of the Aral Sea (AS), and it is continued to this day, which has led to dramatic changes in the climate around the AS, including ambient temperatures and sharp increases in evapotranspiration. Although, it isn't possible to see normal trend in this precipitation. Time series analysis of the FTI (First Time Interval 1901-1960) and STI (Second Time Interval, 1960-2015), highlighting climate change around the AS, based on Global Climate Data, suggests that there is a significant negative difference between precipitation and evapotranspiration during the drying of the AS. It is possible to see the logical compatibility of the air temperature and difference between precipitation and evapotranspiration observed around the AS, i.e. the temperature fluctuation trend is positive and contrary to the difference between precipitation and evapotranspiration negative trend, which means that the annual hydrological budget was reduced according to the time scale. In this article, determining the AS as the central point, we analyze the changes in the thermal and hydrological processes observed on the AS, as well as the impact to the environment of anomalous climate change observed on and around the sea like the drying out of the AS.

Comparing nexus of ranking among mutual fund categories and families of performance measures at investment policy level

The aim of the study covers, at first, the rank comparison drawn among mutual funds at categorical and investment policy level and secondly, among the selected three families of performance measures against famous Sharpe ratio. The Spearman rank order correlation and mean rank order approach have been used for this purpose. The major findings of the study reveal that the most of the performance measures have shown a similar ranking order of mutual funds, at the investment policy level, against the standard measure i.e., Sharpe ratio. However, funds that have shown a non-normal trend, led to misspecification syndrome.

Exploiting Direct Current Plasma Electrolytic Oxidation to Boost Photoelectrocatalysis

In this study, we report an investigation of the photoelectrochemical activity of TiO2 films formed by DC plasma electrolytic oxidation (PEO) at a variable potential in a sulfuric acid electrolyte at 0 and 25 °C. The surface morphology was mainly determined by the oxide-forming potential. X-Ray Diffraction and Raman analyses showed that the relative amount of the anatase and rutile phases varied from 100% anatase at low potential (110–130 V) to 100% rutile at high potential (180–200 V), while mixed-phase oxide films formed at intermediate potential. Correspondingly, the band gap of the TiO2 films decreased from about 3.20 eV (pure anatase) to 2.94 eV (pure rutile) and was red-shifted about 0.1 eV by reducing the electrolyte temperature from 25 °C to 0 °C. Glow-Discharge Optical Emission Spectroscopy (GD-OES) and X-ray Photoelectron Spectroscopy (XPS) analyses evidenced S-containing species located preferentially close to the TiO2/Ti interface. The photoelectrochemical activity was assessed by measuring the incident photon-to-current efficiency (IPCE) under Ultraviolet C (UV-C) irradiation, which showed a non-gaussian normal trend as a function of the PEO cell potential, with maximum values exceeding 80%. Photoelectrocatalytic activity was assessed by decolorization of model solutions containing methylene blue. Photoanodes having higher IPCE values showed faster decolorization kinetics.

Overpressure Generation Mechanisms and Its Distribution in the Paleocene Shahejie Formation in the Linnan Sag, Huimin Depression, Eastern China

The Linnan Sag is one of the main oil-producing units in the Huimin Depression, Eastern China, and the pore pressure gradients obtained from drill stem tests (DSTs) range from 9.0 to 16.0 MPa/km. Uncertainty about the origin and distribution of abnormally high pressures in the Linnan Sag has led to different interpretations of hydrocarbon accumulation and resource assessments, and it interferes with safe drilling. In the Linnan Sag, mudstone compaction curves are substantially affected by several non-compaction factors, and the normal trend of the compaction curve is difficult to determine. The determination of the origin and distribution of overpressure in the Linnan Sag is a challenge. In this study, the factors that may affect mudstone compaction—such as the shale volume, higher calcareous, and organic matter content—were carefully examined and processed. The pressures in the mudstones were estimated by the corrected mudstone compaction curves, which were compiled from acoustic, density, and neutron logs, and calibrated using DST and mud weight data. The log response–vertical effective stress and acoustic velocity-density crossplots were used to identify the mechanisms that generate overpressure. The comprehensive compaction curve shows that the mudstones in the overpressured layer exhibit clear disequilibrium compaction characteristics. The logging response crossplots demonstrate that those overpressured points were consistent with the loading curve. The findings suggest that, the fundamental mechanism resulting in overpressures is the disequilibrium compaction of thick Paleocene mudstones. Hydrocarbon generation and vertical transfer of overpressure may be the main unloading mechanisms, which corresponds to the overpressure points that deviate from the loading curves. Since organic matter cracking may occur in formations at depths greater than 4000 m (Ro > 1.0%), the contribution of hydrocarbon generation to overpressuring is expected to be limited. The transfer of overpressure through opening faults is therefore considered to be the main cause of higher overpressure in local sandstones. The overpressures in the mudstones are characterized by a gradual decrease from the center to the margin in the Linnan Sag. The pressure in the isolated sand bodies are generally similar to that in the surrounding mudstones, whereas it can be lower or higher when the overpressure in the sand bodies are vertically transferred by faults to other pressure systems. The results of this analysis provide an indication of the magnitude, mechanism, and distribution of overpressure in the Linnan Sag. This insight can be used to guide further exploration of the Linnan Sag and similar geological basins.

Improving Detection Performance of Ionospheric Disturbances due to Earthquake by Optimization of Sequential Measurement Combination

Energy generated from earthquake (EQ) is transferred to the ionosphere and results in co-seismic ionospheric disturbances (CID). CID can be observed in the ionospheric combination using L1, L2 frequency carrier phase. As ionospheric trend due to normal conditions such as elevation angle of satellites is generally larger than disturbances, a proper measure is required to extract disturbance signals. Derivative, or sequential combination, is a simple and effective way to remove the normal trend in the ionospheric delay. When using derivative, however, disturbance signals can often be obscured by noise due to its small amplitude. In order to reduce the noise while preserving the time rate of data, and thus to improve signal-to-noise ratio (SNR), we designed a new derivative method using optimization under a couple of assumptions. With simulation data, it is found that N, the number of epochs used for sequential combination, turned out to be the best when N=160 with maximum SNR. Finally, the proposed algorithm’s SNR was compared to that of the previous study which also used derivative method. 120~260% improvements were observed for the proposed method compared to the conventional method.

Effect of television broadcasts of global sporting events on short-term changes in the use of water from the water supply network

The article discusses the influence of television broadcasts of global sporting events on water usage in the city of Toruń during the final match of the FIFA 2014 World Cup in Brazil. The analyses covered accurate data of water usage (recorded every 1 minute) in the city on the day of the final match. The obtained results were compared with the data for the same days of the week (Sundays) but with no such important events. A completely different trend in water usage was documented during the television broadcast, including: exponential and short-term increases and decreases in water demand immediately after the end of the successive parts of the football match. The deviations in water usage from the normal trend for the same day of the week and the same hours ranged from −318 to more than 550 m3·h−1 (calculated on an hourly basis). Therefore, water usage can be a good indicator of the interest of audiences in television programmes, particularly in those gathering millions of viewers, such as broadcasts of global sporting events.