The exponential growth of advertising in enhancing consumer impulsivity has drawn the interest of many researchers to explore the various dimensions of advertising and its effective modeling. The branded apparel product market has grown significantly, a large number of competitors have entered into the market with variant quality, and there are a number of attractive advertising practices. The main aim of this study was to analyze the advertising practices and their relationship with consumer product involvement and the impulsive buying behavior of consumers in branded apparel in India. In a survey of 445 Indian customers, the study indicated that advertising practices significantly affect consumer involvement in branded apparel products and enhance their impulsivity toward products. The study also indicated that the level of consumer involvement in the branded apparel product significantly mediates the relationship between advertising practices and consumer impulsive buying behavior. Effective advertising practices will help companies to enhance consumer involvement that will enable branded apparel companies in enhancing consumer impulsiveness toward products. Some of the managerial implications, limitations, and scope of future research are also presented in the study.

This study introduces a new and flexible class of probability distributions known as the novel alpha-power X (NAP-X) family. A key development within this framework is the novel alpha-power half-logistic (NAP-HL) distribution, which extends the classical half-logistic model through an alpha-power transformation, allowing for greater adaptability to various data shapes. The paper explores several theoretical aspects of the proposed model, including its moments, quantile function and hazard rate. To assess the effectiveness of parameter estimation, a detailed simulation study is conducted using seven estimation techniques: Maximum likelihood estimation (MLE), Cramér–von Mises estimation (CVME), maximum product of spacings estimation (MPSE), least squares estimation (LSE), weighted least squares estimation (WLSE), Anderson–Darling estimation (ADE) and a right-tailed version of Anderson–Darling estimation (RTADE). The results offer comparative insights into the performance of each method across different sample sizes. The practical value of the NAP-HL distribution is demonstrated using two real datasets from the metrology and engineering domains. In both cases, the proposed model provides a better fit than the traditional half-logistic and related distributions, as shown by lower values of standard model selection criteria. Graphical tools such as fitted density curves, Q–Q and P–P plots, survival functions and box plots further support the suitability of the model for real-world data analysis.

The present investigation offers a systematic assessment of CuO + MWCNTs, MgO + MWCNTs, hybrid nanofluids and MWCNT nano liquid motion in direction of porous stretching and shrinking sheet. Water is utilized as the base fluid, and nanomaterials are comprised of CuO, MgO, and MWCNTs. The leading model nonlinear coupled PDEs are first transformed into nonlinear coupled ODEs employing similarity transformation, and afterward, the resultant highly nonlinear coupled ODEs are solved analytically. Also, the shear stress of the wall passed a sheet that was shrinking increase by approximately 6.3% and 12.6% when the nanoparticle concentration increases from 0% to 2% and 4%, respectively. The figures show that the nanoparticle volume fraction and thermal radiation serve as helping parameters in determining the temperature. It is concluded from the obtained results that (3%Wt) MgO + MWCNTs is more effective than the (4%wt) of the CuO + MWCNTs for the enhancement of the heat transfer rate.

We obtain explicit expressions for single and product moments of the order statistics of an omega distribution. We also discuss seven methods to estimate the omega parameters. Various simulation results are performed to compare the performance of the proposed estimators. Furthermore, the maximum likelihood method is adopted to estimate the omega parameters under the type II censoring scheme. The usefulness of the omega distribution is proven using a real data set.

Probability models are instrumental in a wide range of applications by being able to accurately model real-world data. Over time, numerous probability models have been developed and applied in practical scenarios. This study introduces the AAP-X family of distributions—a novel, flexible framework for continuous data analysis named after authors Aadil Ajaz and Parvaiz. The proposed family effectively accommodates both symmetric and asymmetric characteristics through its shape-controlling parameter, an essential feature for capturing diverse data patterns. A specific subclass of this family, termed the “AAP Exponential” (AAPEx) model is designed to address the inflexibility of classical exponential distributions by accommodating versatile hazard rate patterns, including increasing, decreasing and bathtub-shaped patterns. Several fundamental mathematical characteristics of the introduced family are derived. The model parameters are estimated using six frequentist estimation approaches, including maximum likelihood, Cramer–von Mises, maximum product of spacing, ordinary least squares, weighted least squares and Anderson–Darling estimation. Monte Carlo simulations demonstrate the finite-sample performance of these estimators, revealing that maximum likelihood estimation and maximum product of spacing estimation exhibit superior accuracy, with bias and mean squared error decreasing systematically as the sample sizes increases. The practical utility and symmetric–asymmetric adaptability of the AAPEx model are validated through five real-world applications, with special emphasis on cancer survival times, COVID-19 mortality rates and reliability data. The findings indicate that the AAPEx model outperforms established competitors based on goodness-of-fit metrics such as the Akaike Information Criteria (AIC), Schwartz Information Criteria (SIC), Akaike Information Criteria Corrected (AICC), Hannan–Quinn Information Criteria (HQIC), Anderson–Darling (A*) test statistic, Cramer–von Mises (W*) test statistic and the Kolmogorov–Smirnov (KS) test statistic and its associated p-value. These results highlight the relevance of symmetry in real-life data modeling and establish the AAPEx family as a powerful tool for analyzing complex data structures in public health, engineering and epidemiology.