Circular data analysis is a particular branch of statistics that sits somewhere between the analysis of linear
data and the analysis of spherical data. Circular data are used in many scientific fields. The efficiency
of the statistical methods that are applied depends on the accuracy of the data in the study. However,
circular data may have outliers that cannot be deleted. If this is the case, we have two ways to avoid the
effect of outliers. First, we can apply robust methods for statistical estimations. Second, we can adjust
the outliers using the other clean data points in the dataset. In this paper, we focus on adjusting outliers in
circular data using the circular distance between the circular data points and the circular mean direction.
The proposed procedure is tested by applying it to a simulation study and to real data sets. The results
show that the proposed procedure can adjust outliers according to the measures used in the paper.
A study was conducted to detect and quantify lard stearin (LS) content in canola oil (CaO) using differential scanning calorimetry (DSC). Authentic samples of CaO were obtained from a reliable supplier and the adulterant LS were obtained through a fractional crystallization procedure as reported previously. Pure CaO samples spiked with LS in levels ranging from 5 to 15% (w/w) were analyzed using DSC to obtain their cooling and heating profiles. The results showed that samples contaminated with LS at 5% (w/w) level can be detected using characteristic contaminant peaks appearing in the higher temperature regions (0 to 70°C) of the cooling and heating curves. Pearson correlation analysis of LS content against individual DSC parameters of the adulterant peak namely peak temperature, peak area, peak onset temperature indicated that there were strong correlations between these with the LS content of the CaO admixtures. When these three parameters were engaged as variables in the execution of the stepwise regression procedure, predictive models for determination of LS content in CaO were obtained. The predictive models obtained with single DSC parameter had relatively lower coefficient of determination (R(2) value) and higher standard error than the models obtained using two DSC parameters in combination. This study concluded that the predictive models obtained with peak area and peak onset temperature of the adulteration peak would be more accurate for prediction of LS content in CaO based on the highest coefficient of determination (R(2) value) and smallest standard error.
Non-Fourier heat conduction model with dual phase lag wave-diffusion model was analyzed by using well-conditioned asymptotic wave evaluation (WCAWE) and finite element method (FEM). The non-Fourier heat conduction has been investigated where the maximum likelihood (ML) and Tikhonov regularization technique were used successfully to predict the accurate and stable temperature responses without the loss of initial nonlinear/high frequency response. To reduce the increased computational time by Tikhonov WCAWE using ML (TWCAWE-ML), another well-conditioned scheme, called mass effect (ME) T-WCAWE, is introduced. TWCAWE with ME (TWCAWE-ME) showed more stable and accurate temperature spectrum in comparison to asymptotic wave evaluation (AWE) and also partial Pade AWE without sacrificing the computational time. However, the TWCAWE-ML remains as the most stable and hence accurate model to analyze the fast transient thermal analysis of non-Fourier heat conduction model.
The rising trend of gastrointestinal (GI) cancer has become a global burden due to its aggressive nature and poor prognosis. Long noncoding RNAs (lncRNAs) have recently been reported to be overexpressed in different GI cancers and may contribute to cancer progression and chemoresistance. They are featured with more than 200 nucleotides, commonly polyadenylated, and lacking an open reading frame. LncRNAs, particularly urothelial carcinoma-associated 1 (UCA1), are oncogenes involved in regulating cancer progression, such as cell proliferation, invasion, migration, and chemoresistance, particularly in GI cancer. This review was aimed to present an updated focus on the molecular regulatory roles and patterns of lncRNA UCA1 in progression and chemoresistance of different GI cancers, as well as deciphering the underlying mechanisms and its interactions with key molecules involved, together with a brief presentation on its diagnostic and prognostic values. The regulatory roles of lncRNA UCA1 are implicated in esophageal cancer, gastric cancer, pancreatic cancer, hepatobiliary cancer, and colorectal cancer, where they shared similar molecular mechanisms in regulating cancer phenotypes and chemoresistance. Comparatively, gastric cancer is the most intensively studied type in GI cancer. LncRNA UCA1 is implicated in biological roles of different GI cancers via interactions with various molecules, particularly microRNAs, and signaling pathways. In conclusion, lncRNA UCA1 is a potential molecular target for GI cancer, which may lead to the development of a novel chemotherapeutic agent. Hence, it also acts as a potential diagnostic and prognostic marker for GI cancer patients.