Coverage and Spectral Efficiency of Network Assisted Full Duplex in a Millimeter Wave System

To cope with the growing trend of asymmetric data traffic, we introduce a novel network assisted full duplex (NAFD) for a millimeter wave system. NAFD can dynamically allocate the number of remote radio heads in the uplink mode or in the downlink mode, which can facilitate simultaneous uplink and downlink communications. In this manuscript, we use stochastic geometry to analyze the distribution of the signal-to-interference-plus-noise ratio and the data rate in a NAFD system. The numerical results verify the analysis and show that the NAFD outperforms the dynamic time division duplex system and the traditional flexible duplex system in terms of spectral efficiency.

Asymmetric Density for Risk Claim-Size Data: Prediction and Bimodal Data Applications

A new, flexible claim-size Chen density is derived for modeling asymmetric data (negative and positive) with different types of kurtosis (leptokurtic, mesokurtic and platykurtic). The new function is used for modeling bimodal asymmetric medical data, water resource bimodal asymmetric data and asymmetric negatively skewed insurance-claims payment triangle data. The new density accommodates the “symmetric”, “unimodal right skewed”, “unimodal left skewed”, “bimodal right skewed” and “bimodal left skewed” densities. The new hazard function can be “decreasing–constant–increasing (bathtub)”, “monotonically increasing”, “upside down constant–increasing”, “monotonically decreasing”, “J shape” and “upside down”. Four risk indicators are analyzed under insurance-claims payment triangle data using the proposed distribution. Since the insurance-claims data are a quarterly time series, we analyzed them using the autoregressive regression model AR(1). Future insurance-claims forecasting is very important for insurance companies to avoid uncertainty about big losses that may be produced from future claims.

Exponentiated Generalized Inverted Gompertz Distribution: Properties and Estimation Methods with Applications to Symmetric and Asymmetric Data

In this article, a new four-parameter lifetime model called the exponentiated generalized inverted Gompertz distribution is studied and proposed. The newly proposed distribution is able to model the lifetimes with upside-down bathtub-shaped hazard rates and is suitable for describing the negative and positive skewness. A detailed description of some various properties of this model, including the reliability function, hazard rate function, quantile function, and median, mode, moments, moment generating function, entropies, kurtosis, and skewness, mean waiting lifetime, and others are presented. The parameters of the studied model are appreciated using four various estimation methods, the maximum likelihood, least squares, weighted least squares, and Cramér-von Mises methods. A simulation study is carried out to examine the performance of the new model estimators based on the four estimation methods using the mean squared errors (MSEs) and the bias estimates. The flexibility of the proposed model is clarified by studying four different engineering applications to symmetric and asymmetric data, and it is found that this model is more flexible and works quite well for modeling these data.

Reliability of Electric Submersible Pumps ESP & Determination of Weibull Parameters for Failure Characterization, Ecuador Case Study

The current research focuses on data modeling of ESP (Electric Submersible Pumps) reliability by obtaining, through mathematical calculations, the parameters that define the Weibull shape (β), scale (ɳ) and location (ϒ) parameters. The scale parameter ɳ is the characteristic life at which 63.2% of the population has failed. These parameters can be helpful in characterizing failure behavior and the ESP system run life. This research stemed from the need to better understand failure behavior to improve maintenance program design and enhance equipment reliability.160 wells from four fields (7-21, 56, 57) of the Ecuadorian Oriente basin were analyzed. Well selection considered only the mechanic failures and excluded other type of failures leaving aside the ESP failures caused by operative issues (reservoir, completions, workover, redesign, zone change). Three mathematical tools were applied to determine the Weibull parameters more accurately. Another type of analysis could have limited this research since the normal distribution shows limitations with asymmetric data, and the exponential distribution assumes a fixed failure rate, but the ESP failure behavior is asymmetric and the failure rate is variable through time due to factors such as wear and also infant failures (e.g. installation errors). For these reasons, Weibull distribution is the best option because it fits asymmetric data better and it has a variable failure rate. Determining the value of the Weibull parameters can assist in answering questions such as: what percentage of failures is expected to occur in time? How many failures can be expected before the warranty period? When should regular maintenance be scheduled? Ultimately, Weibull parameters are the basis of any future reliability analysis. The Weibull parameters obtained in this study can be applied for future ESP reliability analyzes that are being operated in any field in the Oriente Basin of Ecuador. From the research, two relevant findings were foundThere are a significant number of failures in the initial stage of operation of the pumps,, which could be associated with the installation of the equipment, and the failure risk is drastically reduced in the equipment that reaches life time similar to the characteristic life (ɳ)and pumps that operate without failure to a time similar to characteristic life (ɳ) continue to follow this trend throughout their operating life. Initial stage failures are presumed to be associated with unanticipated conditions: solid binding, design errors, defective equipment, or assembly of equipment with reused and new parts. The performance of the ESP affects the productivity of the wells and therefore will influence decision-making to develop a field. The reliability of the ESP systems can favor the productivity of a field when the equipment works within its efficiency range without showing recurrent failures. This significantly improves field production costs and profitability.

Sensitivity of discrete symmetry metrics: implications for metric choice

Gait asymmetry is present in several pathological populations, including those with Parkinson's disease, Huntington's disease, and stroke survivors. Previous studies suggest that commonly used discrete symmetry metrics, which compare single bilateral variables, may not be equally sensitive to underlying effects of asymmetry, and the use of a metric with low sensitivity could result in unnecessarily low statistical power. The purpose of this study was to provide a comprehensive assessment of the sensitivity of commonly used discrete symmetry metrics to better inform design of future studies. Monte Carlo simulations were used to estimate the statistical power of each symmetry metric at a range of asymmetry magnitudes, group/condition variabilities, and sample sizes. Power was estimated by repeated comparison of simulated symmetric and asymmetric data with a paired t-test, where the proportion of significant results is equivalent to the power. Simulation results confirmed that not all common discrete symmetry metrics are equally sensitive to reference effects of asymmetry. Multiple symmetry metrics exhibit equivalent sensitivities, but the most sensitive discrete symmetry metric in all cases is a bilateral difference (e.g. left - right). A ratio (e.g. left/right) has poor sensitivity when group/condition variability is not small, but a log-transformation produces increased sensitivity. Additionally, two metrics which included an absolute value in their definitions showed increased sensitivity when the absolute value was removed. Future studies should consider metric sensitivity when designing analyses to reduce the possibility of underpowered research.

USING ASYMMETRIC DISTRIBUTIONS FOR MODELING GENE EXPRESSION DATA

We present a short review of the asymmetric distributions alpha-stable, skew normal, skew Student’s t and skew Laplace. We compare the performance for these distributions, in general, are used to model asymmetric data, using AIC and BIC. These criterias were able to selecting the best model for each data set. We also apply these models to gene expression data and we verify these distributions are qualified to model these observations.

Symmetric and Asymmetric Data in Solution Models

This Special Issue covers symmetric and asymmetric data that occur in real-life problems. We invited authors to submit their theoretical or experimental research to present engineering and economic problem solution models that deal with symmetry or asymmetry of different data types. The Special Issue gained interest in the research community and received many submissions. After rigorous scientific evaluation by editors and reviewers, seventeen papers were accepted and published. The authors proposed different solution models, mainly covering uncertain data in multi-criteria decision-making problems as complex tools to balance the symmetry between goals, risks, and constraints to cope with the complicated problems in engineering or management. Therefore, we invite researchers interested in the topics to read the papers provided in the Special Issue.

A Modulo Function-Based Robust Asymmetric Variable Data Hiding Using DCT

This work presents a new asymmetric data hiding technique that hides a variable number of secret message bits in the discrete cosine transform (DCT) coefficients of a cover image using a modular distance technique. Prior to data hiding, the proposed framework transforms a cover image from a spatial domain to various frequency coefficients using DCT. The DCT coefficients are arranged in two groups: one with low-frequency coefficient, and the other with the medium and high-frequency coefficients. The medium and higher frequency coefficients are processed for variable data hiding asymmetrically. The proposed technique hides variable sets of secret information bits in different coefficients. The variation in hidden secret information is maintained using a key developed based on the modulo of distance of a coefficient from the reference point. The same key is also used to retrieve the confidential information at the receiver ends. The results reveal that the presented framework does not create any visually significant distortion, and thus the hidden information does not attract the human visual system (HVS). The technique also results in high data hiding efficiency.