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Objective: In this study, we aimed to investigate the effects of the Ankaferd Blood Stopper (ABS) on nasal bleeding, pain, eating disorders, and sleeping problems in patients undergoing septoplasty. Methods: The prospective study included 100 patients who underwent septoplasty surgery. Patients were divided into two groups based on the chronological order of hospital admission: Group I was administered the ABS, and Group II was administered a standard Invotec Merocel nasal tampon. In both groups, the intensity of pain experienced by patients postoperatively during tampon use and tampon removal was assessed using the visual analogue scale (VAS). Results: A significant difference was found between the two groups with regard to VAS scores assessed during tampon use. In the ABS group, the VAS scores assessed during tampon use were significantly lower, and the incidence of nasal obstruction, eating disorders, dripping-type bleeding before tampon removal, and sleeping problems was significantly lower in the ABS group than in the Merocel group. Conclusions: The ABS may be a viable alternative to other nasal packs, as it did not lead to a significant reduction in postoperative patient comfort during tampon use and caused a lower incidence of postoperative pain, eating disorders, sleeping problems, and nasal obstruction than standard Merocel tampons. Level of Evidence III: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266. [ABSTRACT FROM AUTHOR]

When data exhibits heavy-tailed behavior, traditional regression approaches might be inadequate or inappropriate to model the data. In such data analyses, composite models, which are built by piecing together two or more weighted distributions at specified threshold(s), are alternative models. When data contain covariate information, composite regression models can be used. In the existing literature, there is not much work done on this topic. The only study is Gan and Valdez (N Am Actuar J 22(4):554–573, 2018) paper. In this study, a novel Lognormal-Pareto type II composite regression model is proposed. Particle swarm optimization (PSO) is performed to obtain model parameters of the proposed model. The proposed model is applied to model monthly consumption expenditure and affecting factors. The data is obtained from the National Household Budget Survey, which is conducted annually by the Turkish Statistical Institute (TurkStat). Since the sampling design of the Household Budget Survey is stratified two-stage cluster sampling, the parameters are estimated under weighted data by updating the proposed model and PSO. Additionally, the proposed regression model performance is compared with Lognormal, Lomax, Gamma, and Gamma–Pareto type II regression models. The results demonstrate that the proposed model provides an improved fit to data. [ABSTRACT FROM AUTHOR]