Longitudinal data is becoming increasingly common, especially in the medical and economic fields, and various methods have been analyzed and developed to analyze this type of data.

In this research, the focus was on compiling and analyzing this data, as cluster analysis plays an important role in identifying and grouping co-expressed subfiles over time and employing them on the nonparametric smoothing cubic B-spline model, which is characterized by providing continuous first and second derivatives, resulting in a smoother curve with fewer abrupt changes in slope. It is also more flexible and can pick up on more complex patterns and fluctuations in the data.

The longitudinal balanced data profile was compiled into subgroup

ABSTRACT Background: One of the major problems of all ceramic restorations is their probable fracture against the occlusal forces. The objective of this in vitro study was to evaluate the effect of two gingival finishing lines (90°shoulder and deep chamfer) on the fracture resistance of full contour CAD/CAM and heat press all-ceramic crowns. Materials and Methods: Thirty two maxillary first premolars were prepared to receive full contour CAD/CAM (zolid) and heat press (Cergo Kiss) ceramic crowns using a special paralleling device (Parallel-A-Prep). The teeth were divided into four groups according to the type of finishing line prepared. Each crown was cemented to its corresponding tooth using self-etch, self-adhesive dual cure resin ceme

Copper oxide nanoparticles (CuO NPs) were synthesized by two methods. The first was chemical method by using copper nitrate Cu (NO3)2 and NaOH, while the second was green method by using Eucalyptus camaldulensis leaves extract and Cu (NO3)2. These methods easily give a large scale production of CuO nanoparticles. X-ray diffraction pattern (XRD) reveals single phase monoclinic structure. The average crystalline size of CuO NPs was measured and used by Scherrer equation which found 44.06nm from chemical method, while the average crystalline size was found from green method was 27.2nm. The morphology analysis using atomic force microscopy showed that the grain size for CuO NPs was synthesized by chemical and green methods were 77.70 and 89.24

The most significant function in oil exploration is determining the reservoir facies, which are based mostly on the primary features of rocks. Porosity, water saturation, and shale volume as well as sonic log and Bulk density are the types of input data utilized in Interactive Petrophysics software to compute rock facies. These data are used to create 15 clusters and four groups of rock facies. Furthermore, the accurate matching between core and well-log data is established by the neural network technique. In the current study, to evaluate the applicability of the cluster analysis approach, the result of rock facies from 29 wells derived from cluster analysis were utilized to redistribute the petrophysical properties for six units of Mishri

Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, F

In this paper, the memorization capability of a multilayer interpolative neural network is exploited to estimate a mobile position based on three angles of arrival. The neural network is trained with ideal angles-position patterns distributed uniformly throughout the region. This approach is compared with two other analytical methods, the average-position method which relies on finding the average position of the vertices of the uncertainty triangular region and the optimal position method which relies on finding the nearest ideal angles-position pattern to the measured angles. Simulation results based on estimations of the mobile position of particles moving along a nonlinear path show that the interpolative neural network approach outperf

Channel estimation and synchronization are considered the most challenging issues in Orthogonal Frequency Division Multiplexing (OFDM) system. OFDM is highly affected by synchronization errors that cause reduction in subcarriers orthogonality, leading to significant performance degradation. The synchronization errors cause two issues: Symbol Time Offset (STO), which produces inter symbol interference (ISI) and Carrier Frequency Offset (CFO), which results in inter carrier interference (ICI). The aim of the research is to simulate Comb type pilot based channel estimation for OFDM system showing the effect of pilot numbers on the channel estimation performance and propose a modified estimation method for STO with less numb

Wireless channels are typically much more noisy than wired links and subjected to fading due to multipath  propagation which result in ISI and hence high error rate. Adaptive modulation is a powerful technique to improve the tradeoff between spectral efficiency and Bit Error Rate (BER). In order to adjust the transmission rate, channel state information (CSI) is required at the transmitter side.

In this paper the performance enhancement of using linear prediction along with channel estimation to track the channel variations and adaptive modulation were examined. The simulation results shows that the channel estimation is sufficient for low Doppler frequency shifts (<30 Hz), while channel prediction is much more suited at

Rock type identification is very important task in Reservoir characterization in order to constrict robust reservoir models. There are several approaches have been introduced to define the rock type in reservoirs and each approach should relate the geological and petrophysical properties, such that each rock type is proportional to a unique hydraulic flow unit. A hydraulic flow unit is a reservoir zone that is laterally and vertically has similar flow and bedding characteristics. According to effect of rock type in reservoir performance, many empirical and statistical approaches introduced.  In this paper Cluster Analysis technique is used to identify the rock groups in tertiary reservoir for Khabaz oil field by analyses variation o