Incorporating the neutrosophic framework into kernel regression for predictive mean estimation.

In traditional statistics, all research endeavors revolve around utilizing precise, crisp data for the predictive estimation of population mean in survey sampling, when the supplementary information is accessible. However, these types of estimates often suffer from bias. The major aim is to uncover the most accurate estimates for the unknown value of the population mean while minimizing the mean square error (MSE). We have employed the neutrosophic approach, which is the extension of classical statistics that deals with the uncertain, vague, and indeterminate information, and proposed a neutrosophic predictive estimator of finite population mean using the kernel regression. The proposed estimator does not yield a single numerical value but instead provides an interval range within which the population parameter is likely to exist. This approach enhances the efficiency of the estimators by offering an estimated interval that encompasses the unknown value of the population mean with the least possible mean squared error (MSE). The simulation-based efficiency of the proposed estimator is discussed using the Sine, Bump and real-time temperature data set of Islamabad by using symmetric (Gaussian) kernel. The proposed non-parametric neutrosophic estimator has shown more effective results under the various bandwidth selectors than the adapted neutrosophic estimators.

Estimation of regional and at-site quantiles of extreme winds under flood index procedure.

Extreme winds are becoming more common among environmental events with the most catastrophic societal consequences. A regional frequency analysis of Daily Annual Maximum Wind Speed (DAMWS) is necessary not only for a comprehensive understanding of wind hazards but also for infrastructure design and safety, wind energy potential, disaster risk reduction, insurance and risk assessment in a particular region of study. This study investigated regional frequency analysis of DAMWS of Baluchistan and Sindh provinces of Pakistan. L-moments regionalization techniques along with flood index procedure were applied to DAMWS records of 21 stations from 1990 to 2019 across the study area. We intended to find the regional frequency distribution for maximum winds and predict the returns for extreme winds events in the future. Only one station namely Lasbella was found to be discordant. With the help of cluster analysis, the remaining 20 stations were further divided into two homogeneous. Heterogeneity measures validate that both regions are homogenous with allotted stations. Regional quantiles for both regions are estimated through best-fit probability distribution among Generalized Normal (GNO), Generalized Logistic (GLO), Pearson Type 3 (P3), Generalized Pareto (GPA), and Generalized Extreme Value (GEV). Robustness of GLO distribution compared to GEV distribution is assessed through Monte Carlo simulations of relative bias and relative root mean square error. Findings clearly show that GLO distribution is the best for regional modeling. Furthermore, with the help of index flood procedure we determined at-site quantiles of all stations for various return periods. These estimated quantiles are of valuable information for various sectors, including infrastructure, energy, disaster management, and climate resilience, leading to improved planning, development, and risk reduction in the face of wind-related hazards in Sindh and Balochistan provinces of Pakistan.

Three-fold utilization of supplementary information for mean estimation under median ranked set sampling scheme.

Ranked set sampling (RSS) has created a broad interest among researchers and it is still a unique research topic. It has at long last begun to find its way into practical applications beyond its initial horticultural based birth in the fundamental paper by McIntyre in the nineteenth century. One of the extensions of RSS is median ranked set sampling (MRSS). MRSS is a sampling procedure normally utilized when measuring the variable of interest is troublesome or expensive, whereas it might be easy to rank the units using an inexpensive sorting criterion. Several researchers introduced ratio, regression, exponential, and difference type estimators for mean estimation under the MRSS design. In this paper, we propose three new mean estimators under the MRSS scheme. Our idea is based on three-fold utilization of supplementary information. Specifically, we utilize the ranks and second raw moments of the supplementary information and the original values of the supplementary variable. The appropriateness of the proposed group of estimators is demonstrated in light of both real and artificial data sets based on the Monte-Carlo simulation. Additionally, the performance comparison is also conducted regarding the reviewed families of estimators. The results are empowered and the predominant execution of the proposed group of estimators is seen throughout the paper.

Effect of Ultrasonic Surface Mechanical Attrition Treatment-Induced Nanograins on the Mechanical Properties and Biocompatibility of Pure Titanium.

Commercially pure titanium (Ti) is widely used in bio-implants due to its high corrosion resistance. However, Ti exhibits marginally low mechanical and tribological properties, which limit its applications in some orthopedic implants. In this work, the Ti samples were subjected to ultrasonic surface mechanical attrition treatment (SMAT) for various durations to improve their surface properties such as hardness, strength and surface energy. SMAT-induced grain refinement was analyzed using optical, scanning electron and atomic force microscopy techniques. A Vickers hardness test was performed to determine the through-thickness hardness. Mechanical testing was carried out to measure the yield strength, ultimate tensile strength and ductility of the specimens. Corrosion tests were performed on a Gamry Potentiostat. The surface energy of SMAT-modified samples was calculated using the Owens-Wendt method. It was observed that SMAT reduced the average grain size from 50 µm to as low as 100 nm. The grain refinement and the corresponding grain boundary density led to a significant improvement in mechanical properties and biocompatibility in terms of increased hardness, yield and tensile strengths, surface energy, corrosion rate and hydrophilicity.

Association of Depression with Subclinical Coronary Atherosclerosis: a Systematic Review.

To assess causal association of depression with subclinical coronary atherosclerosis, we performed computer-based and manual search of literature for studies which had assessed relationship of depression disorder with coronary atherosclerosis. All studies had diagnosed depression with validated tools in patients without diagnosed coronary artery disease. The Bradford Hill criteria of cause-effect association was consistently fulfilled by those studies which achieved statistical significance and further showed incremental strength of association with one or more of the following attributes: (1) prospective cohort study, met cause-effect criteria of "temporality"; (2) relatively severe and/or longer period of depression, met cause-effect criteria of "dose-response"; (3) depression with predominantly somatic symptoms cluster, met cause-effect criteria of "scientific plausibility"; (4) multiethnic larger sample, met cause-effect criteria of "population equivalence"; and (5) multicenter study, met criteria of "environmental equivalence." Our results show that there is a significant association of depression with coronary atherosclerosis at its subclinical stages.