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This study is completed for the estimation of unknown population variance for the variable of mean and variance of interest. To accomplish this task, a new generalized class of robust kind of variance estimators proposed utilizing known descriptives of auxiliary variable, for example, Mid-range, Hodges-Lehmann Mean, Tri-mean, deciles mean, coefficient of skewness, interquartile range, first quartile, coefficient of kurtosis, semi-interquartile average, inter decile range and Mean, etc. These conventional measures of auxiliary variable improve the accuracy of the suggested class under simple random sampling without replacement (SRSWOR) scheme. The properties such as the bias, mean square errors (MSE), and least MSE of the suggested class are derived up to first order of approximation. The superiority conditions of the developed class of estimators over existing estimators are also made out theoretically. Finally, numerical representation is also completed for the motivations behind the article. The usual variance estimator is considered as a benchmark for comparing all considered estimators in numerical illustration. The results have been indicated that the suggested class is performing better than the usual variance estimator and all other thoughts about existing estimators.

Estimation of population mean is a determined subject issue in sampling surveys and many efforts have been paid by various researchers to enhance the precision of the estimates by utilizing the correlated auxiliary information. In connection with this, we suggest an improved exponential ratio-cum-ratio estimator using transformed auxiliary variables under ranked set sampling scheme. Theoretical comparison between estimators is made in terms of mean square errors (MSEs), percentage relative efficiencies (PREs), and percentage relative root mean squared error (PRRMSE). The numerical expression for the bias and MSE of the suggested estimator is derived up to first order of approximation. Based on the results of actual data sets and a simulation study, it is found that the suggested estimator perform well as compared to its existing counterparts.

Sarcoid Like Reaction (SLR) is a localized non-caseating epithelioid granulomatous reaction seen secondary to certain immunotherapies and malignancies. Invasive melanoma, while being associated with onset of sarcoidosis, has not shown to directly induce SLR in the literature. We present the case of a 68-year-old male with malignant melanoma, who was found to have SLR prior to starting immunotherapy, which worsened while on pembrolizumab. This case highlights the challenge of distinguishing between drug-induced SLR and melanoma-induced SLR, and the implications in terms of management.

The energy and exergy efficiency of a photovoltaic thermal (PV/T) system at various volume fractions is investigated with mono TiO2 nanofluid and new hybrid TiO2–Fe2O3 nanofluid. Serpentine tubes soldered on an absorbing plate attached to the rear of the PV module have been proposed to evaluate the effect of nanofluids on the PV/T temperature reduction, energy produced, and exergy losses. The study compared energy and exergy with previous studies and delivered an economic analysis to confirm the feasibility of applying nanofluids. The results indicated that using TiO2–Fe2O3 nanofluid reduced the PV cell's temperature by 42.19% compared to water, TiO2 nanofluid, which increased the electrical power by 74.5% and 46.22% when cooling by mono and hybrid nanofluid at 0.3 vol%. The PV/T system's maximum thermal and electrical efficiency recorded with mono and hybrid nanofluids was 34.6%, 8.44%, 47.2%, and 12.62%, respectively. Dispersion of hybrid nanocomposite in DI water has enhanced the Nu number and HTC by 42.72% and 23% higher than mono nanofluid, which improved the exergy efficiency of the PV/T system by 14.89%. A better payback period was achieved with a hybrid nanofluid by 54 days with reduced exergy losses by 45.5% and entropy generation by 86.29%.

So far in the literature, a number of probability distributions have been successfully implemented for analyzing the wind speed and energy data sets. However, there is no published work on modeling and analyzing the wind speed and energy data sets with probability distributions that are introduced using trigonometric functions. In the existing literature, there is also a lack of studies on implementing the bivariate trigonometric-based probability distributions for modeling the wind speed and energy data sets. In this paper, we take up a meaningful effort to cover these interesting research gaps. Thus, we first incorporate a cosine function and introduce a new univariate probability distributional method, namely, a univariate modified cosine-G (UMC-G) family. Using the UMC-G method, a new probability distribution called a univariate modified cosine-Weibull (UMC-Weibull) distribution is studied. We apply the UMC-Weibull distribution for analyzing the wind energy data set taken from the weather station at Sotavento Galicia, Spain. Furthermore, we also introduce a bivariate version of the UMC-G method using the Farlie–Gumble–Morgenstern copula approach. The proposed bivariate distributional method is called a bivariate modified cosine-G (BMC-G) family. A special member of the BMC-G distributions called a bivariate modified cosine-Weibull (BMC-Weibull) distribution is introduced. We apply the BMC-Weibull distribution for analyzing the bivariate data set representing the wind speed and energy taken from the weather station at Sotavento Galicia. Using different statistical tools, we observe that the UMC-Weibull and BMC-Weibull are the best-suited models for analyzing the wind speed and energy data sets.

Statistical modeling is a crucial phase for decision-making and predicting future events. Data arising from engineering-related fields have most often complex structures whose failure rate possesses mixed state behaviors (i.e., non-monotonic shapes). For the data sets whose failure rates are in the mixed state, the utilization of the traditional probability models is not a suitable choice. Therefore, searching for more flexible probability models that are capable of adequately describing the mixed state failure data sets is an interesting research topic for researchers. In this paper, we propose and study a new statistical model to achieve the above goal. The proposed model is called a new beta power very flexible Weibull distribution and is capable of capturing five different patterns of the failure rate such as uni-modal, decreasing-increasing-decreasing, bathtub, decreasing, increasing-decreasing-increasing shapes. The estimators of the new beta power very flexible Weibull distribution are obtained using the maximum likelihood method. The evaluation of the estimators is assessed by conducting a simulation study. Finally, the usefulness and applicability of the new beta power very flexible Weibull distribution are shown by analyzing two engineering data sets. Using four information criteria, it is observed that the new beta power very flexible Weibull distribution is the best-suited model for dealing with failure times data sets.

Background: The incidence of colorectal cancer (CRC) and associated mortality are rising in low- and middle-income countries. In Ethiopia, colorectal cancer is among the leading causes of cancer morbidity and mortality in both sexes. Although some studies provided estimations on the national burden and regional distribution, the histological characteristics, survival pattern and determinants among colorectal cancer patients are not well-documented. Aim: This study aimed to describe the histological characteristics, to determine the patterns of survival, and identify factors that determine mortality rate among CRC patients in Ethiopia. Methods: A retrospective cohort study was conducted among CRC patients registered at cancer treatment center of Tikur Anbessa Specialized Hospital, from January 2012 to December 2016. Data were extracted from a total of 161 patient medical records using a pretested abstraction form and supplemented by phone calls with the patients/caregivers. To determine colorectal cancer specific survival overtime, we performed a Kaplan-Meier survival analysis and significance of variation in survival across covariates and treatment categories was tested using log-rank test. A multivariable Cox proportional-hazards model was performed to identify determinants of survival after diagnosis with colorectal cancer. Results: Overall, the median survival time was 21 months [95%CI: 16–35], with two-, three- and five-year CRC-specific survival rates of 46.8%, 39.5% and 28.7% respectively. In the multivariable Cox regression model, the rate of death due to CRC is significantly higher for patients with elevated baseline carcinoembryonic antigen (CEA) level (Adjusted Hazard Ratio (AHR) = 2.31, 95%CI: 1.27–4.19), stage IV at diagnosis (AHR = 2.66, 95%CI: 1.44–4.91), and mucinous or signet-ring cell carcinoma histology type (AHR = 4.92, 95%CI: 1.75–13.80). Moreover, patients who underwent surgery showed a better survival than those who did not (AHR = 0.35, 95%CI: 0.14–0.88). Conclusion: In Ethiopia, patients diagnosed with CRC showed a low rate of cancer-specific survival. Histology type, stage of cancer and CEA level at diagnosis, and the type of treatment a patient received significantly determine mortality rate. Hence, cancer screening programs could help to detect the disease at an earlier stage and to initiate available treatments timely so as to extend the lifespan of CRC patients.

This study compares magnetite (Fe3O4) nanoparticles synthesized using Aspergillus elegans extract versus commercially available magnetite nanoparticles, focusing on their efficacy in dye degradation. The biosynthesis of Fe3O4 nanoparticles using fungal extracts offers a sustainable and eco-friendly alternative to conventional chemical methods. The nanoparticles were characterized using various techniques, including UV-Vis spectroscopy, XRD, FTIR, SEM, TEM, DLS, zeta potential, and VSM analysis, to assess their structural, morphological, and magnetic properties. Results showed that fungus-mediated Fe3O4 nanoparticles were smaller, with an average size of 19.2 nm, and exhibited better crystallinity, surface functionalization, and colloidal stability than their commercial counterparts, which had an average size of 60 nm. Additionally, the fungal nanoparticles displayed superior magnetic properties with a saturation magnetization of 50 emu/g compared to 36 emu/g for commercial Fe3O4. The dye degradation potential of the nanoparticles was tested using methyl violet, methyl orange, and rose bengal dyes. Fungus-mediated Fe3O4 nanoparticles demonstrated higher dye removal efficiency than commercial Fe3O4, indicating enhanced catalytic activity due to their smaller size and larger surface area. This study highlights the potential of myco-synthesized Fe3O4 nanoparticles as effective agents for environmental remediation, particularly in removing of hazardous synthetic dyes from wastewater., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)