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So far in the literature, numerous probabilistic models and families of probabilistic models have been suggested and put into practice. A large portion of these probabilistic models are developed and updated by introducing new parameters ranging up to five. There are only a few methods that are introduced without adding additional parameters. This paper also contributed to the development of a probabilistic model without adding additional parameters. The proposed model is introduced by incorporating the weighted distributional strategy and the weighted Ramos–Louzada distribution. Therefore, the generalized weighted Ramos–Louzada distribution is a suitable name for the newly proposed model. The derivation of the estimators based on the maximum likelihood for this novel model is presented. Certain distributional properties of the generalized weighted Ramos–Louzada distribution are derived. In order to validate the effectiveness and superiority of the generalized weighted Ramos–Louzada distribution, three applications are chosen as examples. The first two applications are considered from the physical sciences, while, the third application is taken from the musical area. We consider four statistical tests to show the validatity of the proposed model over some well-known established models. The findings from the statistical tests consistently indicate that the proposed model performs better than its rivals.

Abstract Background Endogenous substances of krill oil (KO) are lipophilic in nature and have clinical significance viz. DHA/EPA, phospholipids and astaxanthin. To improve the nanodispersibility of endogenous substances of KO, a self-nanoemulsifying system (SNE) was developed. Results Ternary phase behaviour of KO was explored in ethanol (ET); propylene glycol, (PG); and PEG600 using Tween80 and Tween20 as surfactants. PEG600 induced the self-nanoemulsification of KO and yielded one phase region (OPR); dilution lines (KO/Smix fraction containing PEG600) traversed across OPR, produced a fully dilutable nanoemulsion system. PEG600-based nanoformulations (NFs) of KO underwent phase transformation via percolation behaviour in nanostructure domains (86–207 nm). PEG600 induced ternary phase behaviour of KO as revealed from rheological data (higher eta values), refractive index (nonlinear) and conductivity (bimodal) patterns. Induced phase transformation could be an interaction between aqueous phase and KO/Tween20 in PEG600 environment; generating highly viscous domains of low electrical conductivity. NFs offered antioxidant activity over corresponding coarse systems (p

Asymmetrical probability models are helpful for analyzing skewed data sets since they allow you to describe the form of the distribution and anticipate the chance of extreme events. This article defines a novel approach to continuous moment exponential distribution called the exponentiated generalized moment exponential model. The extension has two additional parameters accounting for the distribution’s shape. We extend this distribution probability density, cumulative distribution, hazard rate, and survival functions and establish different key statistical properties. Parameter estimation is obtained using different procedures, notably maximum likelihood estimation, least square, and Bayesian methods. A Monte Carlo simulation experiment is conducted to assess parameter performance and indicator risk measures. This article examines two distinct actual data sets in order to highlight the significance of the proposed model as well as its application in a variety of settings. The new model is compared to a large number of well-known extensions that were developed by other businesses.

Probability-based methodologies have gained widespread recognition for their pivotal role in steering decision-making in contexts marked by uncertainty or vagueness. In order to guarantee that decisions made in these circumstances are both significant and impactful, various methodologies focused on probability have been devised and utilized. This study seeks to significantly enrich the existing literature by proposing a new probability model termed the weighted sine exponentiated Weibull distribution. Certain characteristics are obtained for the suggested model. Additionally, the parameter estimation method and simulation studies related to the suggested model are also provided. Two distinct data sets, acquired from the disciplines of physical education and reliability, were effectively implemented using the proposed model, resulting in a successful outcome. Through the utilization of the p-value and various tests, it becomes apparent that the weighted sine exponentiated Weibull model consistently surpasses its competitor distributions in terms of statistical significance. The proposed model’s practical illustration highlights its effectiveness and potential for extensive use in both physical education and reliability research and practice.

Abstract Time-based surveys often experience missing data due to several reasons, like non-response or data collection limitations. Imputation methods play an essential role in incorporating these missing values to secure the accuracy and reliability of the survey outcomes. This manuscript proposes some optimal class of memory type imputation methods for imputing missing data in time-based surveys by utilizing exponentially weighted moving average (EWMA) statistics. The insights into the optimal conditions for incorporating our proposed methods are provided. A comprehensive examination of the proposed method utilizing simulated and real-life datasets is conducted. Comparative analyses against the existing imputation methods exhibit the superior performance of our methods, particularly in the scenarios characterized by developing trends and dynamic response patterns. The outcomes highlight the effectiveness of utilizing EWMA statistics into memory type imputation methods, displaying their flexibility to changing survey dynamics.

Abstract Drug resistance poses a significant obstacle to the success of anti-cancer therapy in head and neck cancers (HNCs). We aim to develop a platform for visualizing and analyzing molecular expression alterations associated with HNC drug resistance. Through data mining, we convened differentially expressed molecules and context-specific signaling events involved in drug resistance. The driver genes, interaction networks and transcriptional regulations were explored using bioinformatics approaches. A total of 2364 differentially expressed molecules were identified in 78 distinct drug-resistant cells against 14 anti-cancer drugs, comprising 1131 mRNAs, 746 proteins, 62 lncRNAs, 257 miRNAs, 1 circRNA, and 166 post-translational modifications. Among these, 255 molecules were considerably, the signature driver genes of HNC drug resistance. Further, we also developed a landscape of signaling pathways and their cross-talk with diverse signaling modules involved in drug resistance. Additionally, a publicly-accessible database named “HNCDrugResDb” was designed with browse, query, and pathway explorer options to fetch and enrich molecular alterations and signaling pathways altered in drug resistance. HNCDrugResDb is also enabled with a Drug Resistance Analysis tool as an initial platform to infer the likelihood of resistance based on the expression pattern of driver genes. HNCDrugResDb is anticipated to have substantial implications for future advancements in drug discovery and optimization of personalized medicine approaches.

Abstract 5-Fluorouracil (5-FU) is the leading chemotherapeutic drug used to treat hepatocellular carcinoma, one of the major cancer diseases after atherosclerosis. Because of chemo-resistance, the success rate of treatment declines with time due to continuous drug exposure. Though autophagy induction is majorly responsible for acquired resistance, the exact role of this evolutionary conserved mechanism is unknown in cancer cell survival and suppression. The usual practice involves the combinatorial use of chemotherapeutic drugs with autophagy inhibitors like Chloroquine and Bafilomycin A, while neglecting the side effects caused by autophagy impairment in healthy cells. Starvation is a well-known physiological inducer of autophagy. In this study, by caloric modulation, we tried to circumvent the resistance imposed by prolonged drug exposure and investigated the effect of 5-FU in nutrient-sufficient and deficient conditions. Our findings show a substantial correlation between autophagy and increased cancer cell death in the presence of 5-FU, with negligible effects on normal cells. Experimental data revealed that nutritional deprivation augmented cell death in the presence of 5-FU through mitochondrial membrane damage and excessive reactive oxygen species (ROS) production, initiating apoptosis. Lipidation study also unveiled that under such combinatorial treatment cellular metabolism shifts from glucose to lipid biosynthesis. Overall, our experimental findings suggest that nutritional deprivation in combination with chemotherapeutic medication can be a new effective strategy to control hepatocellular carcinoma.

Missing data occurs frequently in sample surveys. Numerous imputation techniques have been proposed to tackle the problem of missing data, and, in fact, limited works are available in the literature to deal with the issue of missingness while the data are ridden with measurement errors (ME). In addition, no work has been done on the robustness to handle the issue of missing data when it is contaminated with correlated measurement errors (CME). This article proposes some robust imputation methods (RIM) to impute the missing data in the presence of CME. The mean square error (MSE) of the proposed RIM is derived from the first-order approximation and examined with the MSE of the conventional imputation methods. The results are theoretically established and explained using a vast simulation study. Two applications of real data sets are presented to illustrate the efficiency and superiority of the suggested estimators relative to some estimators considered in this study.

Abstract Case studies from global cities contribute to more focused analyses of global warming challenges and demonstrate the performance and effectiveness of mitigation and adaptation strategies to identify lessons about success at the city scale. The case studies were chosen to demonstrate aspects of the critical messages for action priorities in global warming mitigation and adaptation. This work focuses on best practices and initiatives for mitigation and adaptation approaches from developed and developing economies, including North American cities, European cities, Asian cities, and other global cities worldwide. The case studies were grouped to examine, identify, and emphasize important factors in various areas (e.g., local programs and alliances, governance, stakeholder engagement, community actions, and scientific research) that determined the success of adaptation strategies in various global cities. Many recent studies showcase mitigation approaches, particularly those relating to blue-green infrastructure and nature-based strategies. The case studies selected reflect vulnerable regions and demonstrate how increasing global warming significantly concerns individuals, societies, and their infrastructure. The selected studies include Amsterdam in Netherlands; Singapore, as a city in a garden; Boston in USA; Ahmedabad Heat Action Plan in India, aimed at implementing strategies with the objectives of climate adaptation planning; Copenhagen, as a coastal town, is more susceptible to flooding; Portland, the most progressive city in USA; Hamburg in Germany, one of the biggest harbours in Europe; and the 'Rain City Strategy', in Vancouver, Canada. Not all global cities respond the same way, but undertaking joint complex efforts helps mitigate the impacts.

Abstract Accurate reservoir characterization is necessary to effectively monitor, manage, and increase production. A seismic inversion methodology using a genetic algorithm (GA) and particle swarm optimization (PSO) technique is proposed in this study to characterize the reservoir both qualitatively and quantitatively. It is usually difficult and expensive to map deeper reservoirs in exploratory operations when using conventional approaches for reservoir characterization hence inversion based on advanced technique (GA and PSO) is proposed in this study. The main goal is to use GA and PSO to significantly lower the fitness (error) function between real seismic data and modeled synthetic data, which will allow us to estimate subsurface properties and accurately characterize the reservoir. Both techniques estimate subsurface properties in a comparable manner. Consequently, a qualitative and quantitative comparison is conducted between these two algorithms. Using two synthetic data and one real data from the Blackfoot field in Canada, the study examined subsurface acoustic impedance and porosity in the inter-well zone. Porosity and acoustic impedance are layer features, but seismic data is an interface property, hence these characteristics provide more useful and applicable reservoir information. The inverted results aid in the understanding of seismic data by providing incredibly high-resolution images of the subsurface. Both the GA and the PSO algorithms deliver outstanding results for both simulated and real data. The inverted section accurately delineated a high porosity zone ( $$>20\%$$ > 20 % ) that supported the high seismic amplitude anomaly by having a low acoustic impedance (6000–8500 m/s $$*$$ ∗ g/cc). This unusual zone is categorized as a reservoir (sand channel) and is located in the 1040–1065 ms time range. In this inversion process, after 400 iterations, the fitness error falls from 1 to 0.88 using GA optimization, compared to 1 to 0.25 using PSO. The convergence time for GA is 670,680 s, but the convergence time for PSO optimization is 356,400 s, showing that the former requires $$88\%$$ 88 % more time than the latter.

Recent studies have discussed the application and relevance of trigonometry in probability distributions. In this paper, a new, trigonometric family of distributions derived from the alliance of the families known as Sine-G and Generalized G family, inspiring the name Sine Generalized-G Family is proposed and studied. The characteristics of the distribution are explained through the mode, stochastic ordering, quantile function, expansion of the distributional function, and moment. A sub-model of the family named Sine generalized Inverse Gompertz (SG-IG) distribution was studied, its parameters estimated using non-Bayesian and Bayesian procedures. Rigorous simulation studies involving the utilization of the Metropolis–Hasting (MH) algorithm in the Monte Carlo Markov Chain (MCMC) algorithm was carried out to determine the efficiency of the methods and the usefulness was illustrated using two real datasets. The method of Weighted Least squares (WLS) was found to be more efficient in the estimation of parameters of the SG-IG distribution using the lifetime data compared to its competitors.

Abstract Seeds, considered as the foundation of agriculture, are invaded by a broad spectrum of seed-borne pathogens. The current study aimed to control seed-borne fungal pathogens of wheat, Aspergillus flavus and A. niger, by using Ag+ nanochitosan (Ag-NC) for nano-priming of seeds and enhancing seed quality. Nanochitosan (NC) and Ag-NC were synthesized using the gelation method and characterized by UV–vis spectrophotometry, FESEM, EDXS, and HRTEM. NC and Ag-NC showed irregular surface topography with an average particle size of 275 and 325 nm, respectively. Antifungal activity of both the nanoparticles at 0.1, 0.2, 0.3, 0.4, and 0.5 mg/mL revealed that Ag-NC at 0.5 mg/mL has completely terminated the mycelial growth of both pathogens. Malonaldehyde content increased to 77.77% in A. flavus and 82.66% in A. niger when exposed to 0.5 mg/mL Ag-NC. High-intensity fluorescence due to oxidative stress was observed in Ag-NC-treated pathogens. Ultra-structural changes in Ag-NC treated pathogenic spores under SEM displayed pronounced membrane damages. Wheat seeds were nano-primed with NC and Ag-NC at 0.5 mg/mL, and fungal load was examined to evaluate the mitigation of pathogenic stress and its effect on seedling growth promotion activity. Ag-NC priming reduced the fungal load and allowed successful seed germination. Ag-NC priming increased the albumin, gliadin, gluten, and glutenin content along with total phenol, reducing sugar and starch levels. Ag-NC priming increased the overall protein levels traced through SDS-PAGE. Seed priming with Ag-NC promotes seed germination, mean germination time, stress tolerance index, vigour, etc. NC and Ag-NC at 0.5 mg/mL showed no cytotoxic effect on the Human Embryonic Kidney (HEK293) cell line that ensures the nanoparticles are non-toxic. Thus, the synthesized nanoparticles exhibit a dual role in antifungal activity and plant growth promotion.

Abstract Ranked set sampling (RSS) is known to increase the efficiency of the estimators while comparing it with simple random sampling. The problem of missingness creates a gap in the information that needs to be addressed before proceeding for estimation. Negligible amount of work has been carried out to deal with missingness utilizing RSS. This paper proposes some logarithmic type methods of imputation for the estimation of population mean under RSS using auxiliary information. The properties of the suggested imputation procedures are examined. A simulation study is accomplished to show that the proposed imputation procedures exhibit better results in comparison to some of the existing imputation procedures. Few real applications of the proposed imputation procedures is also provided to generalize the simulation study.

Probability-arisen models play a considerable role in preparing a crucial stage for decision-making concerning reliability, engineering, and more closely related scenarios. Bearing in mind the consequential roles of probability-arisen models, we introduce and implement a new probabilistic model that has arisen by using the sine function, namely, the sine very flexible Weibull (SVF-Weibull) distribution. The proposed SVF-Weibull distribution is a result of a combination of the very flexible Weibull distribution with the sine-based strategy. For the SVF-Weibull distribution, point estimates are obtained. The assessment of the point estimates of the SVF-Weibull distribution is done via a simulation study. Finally, the consequential role of the SVF-Weibull distribution, illustrated by considering reliability and music engineering data sets. Furthermore, we implement some machine learning tools for predicting the reliability and music engineering data sets. The performances of the machine learning tools are assessed across many hidden variables. Our findings suggest that the artificial neural network method is more optimal than other methods for predicting the reliability and music engineering data sets.

The study attempts the comparatives of embodied energy, water, and carbon parameters in the cradle-to-gate phase of building construction for conventional houses in Jammu, India. Besides relying on hybrid material coefficients and field investigations-based materials database, the study adopts a general life cycle assessment framework. Descriptive statistics and regression techniques aid in meeting the findings. A strong interrelation between embodied energy and carbon, but a considerably weak embodied water-to-energy or embodied water-to-carbon relationship emerges. The top embodied energy and carbon-impacting materials (like brick) retard significantly in embodied water impacts, while steel deserves less attention than sand, aggregates, and cement considering all three parameters together. Therefore, a new dimension to appropriately using building materials and construction technologies looms by outlining a broad materials palette. The results call for a differing embodied water optimisation approach to embodied energy or carbon-pertaining ones. The construction players receive a new insight towards a sustainable building approach.

Abstract The dimension and size of data is growing rapidly with the extensive applications of computer science and lab based engineering in daily life. Due to availability of vagueness, later uncertainty, redundancy, irrelevancy, and noise, which imposes concerns in building effective learning models. Fuzzy rough set and its extensions have been applied to deal with these issues by various data reduction approaches. However, construction of a model that can cope with all these issues simultaneously is always a challenging task. None of the studies till date has addressed all these issues simultaneously. This paper investigates a method based on the notions of intuitionistic fuzzy (IF) and rough sets to avoid these obstacles simultaneously by putting forward an interesting data reduction technique. To accomplish this task, firstly, a novel IF similarity relation is addressed. Secondly, we establish an IF rough set model on the basis of this similarity relation. Thirdly, an IF granular structure is presented by using the established similarity relation and the lower approximation. Next, the mathematical theorems are used to validate the proposed notions. Then, the importance-degree of the IF granules is employed for redundant size elimination. Further, significance-degree-preserved dimensionality reduction is discussed. Hence, simultaneous instance and feature selection for large volume of high-dimensional datasets can be performed to eliminate redundancy and irrelevancy in both dimension and size, where vagueness and later uncertainty are handled with rough and IF sets respectively, whilst noise is tackled with IF granular structure. Thereafter, a comprehensive experiment is carried out over the benchmark datasets to demonstrate the effectiveness of simultaneous feature and data point selection methods. Finally, our proposed methodology aided framework is discussed to enhance the regression performance for IC50 of Antiviral Peptides.

This paper introduces the power new XLindley (PNXL) distribution, a novel two-parameter distribution derived using the power transformation method applied to the XLindley distribution. We thoroughly explore the structural properties of the PNXL distribution, including the rth moment about the origin, moment generating function, survival rate function, distribution function, hazard rate function, skewness, kurtosis, and coefficient of variation. Additionally, we derive the quantile function, fuzzy reliability, reliability measures, stochastic ordering, and actuarial measures for this new distribution. To estimate the parameters of the PNXL distribution, we propose several estimators and evaluate their performance through extensive simulation studies. To demonstrate the applicability and superiority of the PNXL distribution over existing distributions, we fit it to two real datasets and compare its performance with potential competing models. The results highlight the PNXL distribution's effectiveness and potential as a robust tool for modeling and analyzing real-world data.

Background: Alpha-1 antitrypsin (AAT) deficiency is a genetic risk factor for chronic obstructive pulmonary disease (COPD) but prevalence data in acutely exacerbated Indian patients is limited. This study determined AAT deficiency rates and correlations with inflammation and lung function among hospitalized patients with COPD. Methods: A total of 106 patients hospitalized for acute COPD exacerbations were prospectively enrolled from June 2016 to February 2018 in Kerala, India, excluding any with known AAT deficiency. Serum AAT levels were quantified and correlated with C-reactive protein (CRP) levels as well as postbronchodilator spirometry. Results: Mean serum AAT level was 1.48 ± 0.27 g/L. No AAT deficiency cases were identified, although AAT and CRP both significantly increased during flares. AAT levels positively correlated with FEV1, FVC, and FEV1/FVC ratios. Patients with lower AAT had worse pulmonary status. Conclusion: Despite finding no AAT deficiency in this regional Indian cohort, further studies across expanded, more diverse populations are warranted to definitively establish prevalence nationwide. Temporal monitoring of AAT kinetics could help gauge exacerbation trajectories.

Abstract In winter, the paddy residues become wet during morning and late evening due to dew, which restricts the operation of sowing machines (Happy Seeder and Super Seeder) into paddy residues, as wet residues do not slide on furrow openers/tines. A PAU Smart Seeder (PSS) was developed and evaluated for a four-wheel tractor that can sow wheat with optimum crop establishment in combined harvested rice fields. The PSS were evaluated for its performance under varying straw load, forward speed, and rotor speed in terms of fuel consumption, field capacity, seed emergence, and grain yield. The crop establishment and wheat yield of PSS was also compared with the existing straw management machines Happy Seeder (HS) and Super Seeder (SS) under heavy paddy residue conditions. The effect of the straw load was more pronounced on dependent variables than the effect of the speed index. PSS performance was best at a forward speed of 2.6 km h−1, rotor speed of 127.5 rpm, and a straw load of 6 t ha−1. Average fuel consumption using PSS was lower than SS but higher than HS. Wheat emergence was higher by 15.6 and 25.7% on the PSS plots compared to HS and SS, respectively. Average wheat grain yield in PSS plots was significantly higher by 12.7 and 18.9% than SS and HS, respectively in one experiment, while the grain yield was similar for both PSS and HS in other experiments. PSS has a novel mechanism to manage paddy straw and simultaneously sow wheat into a heavy straw load (> 8 t ha−1) mixture of anchored and loose straw. In conclusion, PSS showed promise for in-situ management of rice straw as it eliminates most of the operational problems encountered by the existing seeders (HS and SS).

This paper addresses some classes of combined and separate imputation methods (CSIMs) of the population mean under stratified simple random sampling (SSRS) along with their characteristics. To the best of our knowledge, these imputation methods (IMs) have yet not been studied by any author under SSRS, hence these IMs are called ‘novel’. In addition, the existing CSIMs are distinguished as the members of the suggested CSIMs, respectively. The theoretical conditions under which the proposed IMs perform better are obtained by comparing the proposed IMs with the existing IMs. To validate the theoretical findings, the numerical and simulation studies are conducted on real and artificial populations, respectively.

The aim of this paper was to provide systematic approaches to study the existence of results for the system fractional relaxation integro-differential equations. Applied problems require definitions of fractional derivatives, allowing the utilization of physically interpretable boundary conditions. Impulsive conditions serve as basic conditions to study the dynamic processes that are subject to sudden changes in their state. In the process, we converted the given fractional differential equations into an equivalent integral equation. We constructed appropriate mappings and employed the Schaefer's fixed-point theorem and the Banach fixed-point theorem to show the existence of a unique solution. We presented an example to show the applicability of our results.

Abstract This research commences a unit statistical model named power new power function distribution, exhibiting a thorough analysis of its complementary properties. We investigate the advantages of the new model, and some fundamental distributional properties are derived. The study aims to improve insight and application by presenting quantitative and qualitative perceptions. To estimate the three unknown parameters of the model, we carefully examine various methods: the maximum likelihood, least squares, weighted least squares, Anderson–Darling, and Cramér-von Mises. Through a Monte Carlo simulation experiment, we quantitatively evaluate the effectiveness of these estimation methods, extending a robust evaluation framework. A unique part of this research lies in developing a novel regressive analysis based on the proposed distribution. The application of this analysis reveals new viewpoints and improves the benefit of the model in practical situations. As the emphasis of the study is primarily on practical applications, the viability of the proposed model is assessed through the analysis of real datasets sourced from diverse fields.

Abstract The Himalayan region, characterized by its substantial topographical scale and elevation, exhibits vulnerability to flash floods and landslides induced by natural and anthropogenic influences. The study focuses on the Himalayan region, emphasizing the pivotal role of geographical and atmospheric parameters in flash flood occurrences. Specifically, the investigation delves into the intricate interactions between atmospheric and surface parameters to elucidate their collective contribution to flash flooding within the Nainital region of Uttarakhand in the Himalayan terrain. Pre-flood parameters, including total aerosol optical depth, cloud cover thickness, and total precipitable water vapor, were systematically analyzed, revealing a noteworthy correlation with flash flooding event transpiring on October 17th, 18th, and 19th, 2021. Which resulted in a huge loss of life and property in the study area. Contrasting the October 2021 heavy rainfall with the time series data (2000–2021), the historical pattern indicates flash flooding predominantly during June to September. The rare occurrence of October flash flooding suggests a potential shift in the area's precipitation pattern, possibly influenced by climate change. Robust statistical analyses, specifically employing non-parametric tests including the Autocorrelation function (ACF), Mann–Kendall (MK) test, Modified Mann–Kendall, and Sen's slope (q) estimator, were applied to discern extreme precipitation characteristics from 2000 to 201. The findings revealed a general non-significant increasing trend, except for July, which exhibited a non-significant decreasing trend. Moreover, the results elucidate the application of Meteosat-8 data and remote sensing applications to analyze flash flood dynamics. Furthermore, the research extensively explores the substantial roles played by pre and post-atmospheric parameters with geographic parameters in heavy rainfall events that resulted flash flooding, presenting a comprehensive discussion. The findings describe the role of real time remote sensing and satellite and underscore the need for comprehensive approaches to tackle flash flooding, including mitigation. The study also highlights the significance of monitoring weather patterns and rainfall trends to improve disaster preparedness and minimize the impact of flash floods in the Himalayan region.

Abstract Fuzzy rough entropy established in the notion of fuzzy rough set theory, which has been effectively and efficiently applied for feature selection to handle the uncertainty in real-valued datasets. Further, Fuzzy rough mutual information has been presented by integrating information entropy with fuzzy rough set to measure the importance of features. However, none of the methods till date can handle noise, uncertainty and vagueness simultaneously due to both judgement and identification, which lead to degrade the overall performances of the learning algorithms with the increment in the number of mixed valued conditional features. In the current study, these issues are tackled by presenting a novel intuitionistic fuzzy (IF) assisted mutual information concept along with IF granular structure. Initially, a hybrid IF similarity relation is introduced. Based on this relation, an IF granular structure is introduced. Then, IF rough conditional and joint entropies are established. Further, mutual information based on these concepts are discussed. Next, mathematical theorems are proved to demonstrate the validity of the given notions. Thereafter, significance of the features subset is computed by using this mutual information, and corresponding feature selection is suggested to delete the irrelevant and redundant features. The current approach effectively handles noise and subsequent uncertainty in both nominal and mixed data (including both nominal and category variables). Moreover, comprehensive experimental performances are evaluated on real-valued benchmark datasets to demonstrate the practical validation and effectiveness of the addressed technique. Finally, an application of the proposed method is exhibited to improve the prediction of phospholipidosis positive molecules. RF(h2o) produces the most effective results till date based on our proposed methodology with sensitivity, accuracy, specificity, MCC, and AUC of 86.7%, 90.1%, 93.0% , 0.808, and 0.922 respectively.

Abstract Control charts are a statistical approach for monitoring cancer data that can assist discover patterns, trends, and unusual deviations in cancer-related data across time. To detect deviations from predicted patterns, control charts are extensively used in quality control and process management. Control charts may be used to track numerous parameters in cancer data, such as incidence rates, death rates, survival time, recovery time, and other related indicators. In this study, CDEC chart is proposed to monitor the cancer patients recovery time censored data. This paper presents a composite dual exponentially weighted moving average Cumulative sum (CDEC) control chart for monitoring cancer patients recovery time censored data. This approach seeks to detect changes in the mean recovery time of cancer patients which usually follows Weibull lifetimes. The results are calculated using type I censored data under known and estimated parameter conditions. We combine the conditional expected value (CEV) and conditional median (CM) approaches, which are extensively used in statistical analysis to determine the central tendency of a dataset, to create an efficient control chart. The suggested chart's performance is assessed using the average run length (ARL), which evaluates how efficiently the chart can detect a change in the process mean. The CDEC chart is compared to existing control charts. A simulation study and a real-world data set related to cancer patients recovery time censored data is used for results illustration. The proposed CDEC control chart is developed for the data monitoring when complete information about the patients are not available. So, instead of doping the patients information we can used the proposed chart to monitor the patients information even if it is censored. The authors conclude that the suggested CDEC chart is more efficient than competitor control charts for monitoring cancer patients recovery time censored data. Overall, this study introduces an efficient new approach for cancer patients recovery time censored data, which might have significant effect on quality control and process improvement across a wide range of healthcare and medical studies.

In this article, we offer simple random sampling (SRS) based efficient class of estimators of population mean Y¯ utilizing additional information. The expression of the mean square error of the proposed class of estimators is deduced up to first degree approximation. The efficiency conditions are established which are enhanced numerically utilizing a simulation study consummated over symmetrical and asymmetrical populations. Real data sets are also utilized to exemplify the suggested estimators. The numerical findings are appeared rather acceptable demonstrating better advancement over the ordinary estimators.

The main objective of the present paper is to establish the existence and uniqueness (EU) results for nonlinear fractional Langevin equation involving Liouville-Caputo generalized fractional derivative (GFD) of different order with non-local boundary conditions. The existence solution is obtained by using Krasnoselskii's fixed point theorem, and the uniqueness result is obtained by using the Banach contraction mapping principle. An example is introduced to validate the effectiveness of the results. The results are novel and provide an extension to some of the findings known in the literature.

The blood–brain barrier (BBB) poses a significant obstacle to the administration of drugs to the brain for the development of therapies of central nervous system (CNS) disorders. Blood-brain barrier penetrating peptides (BBBPps) are a group of peptides that can traverse the BBB by different processes without causing harm to the BBB. These peptides show promise as potential drugs for CNS ailments. Nevertheless, the process of identifying BBBPps using experimental approaches is both time-consuming and labour-intensive. To discover additional BBBPps as potential treatments for CNS diseases, it is critical to develop insilico methods that can distinguish BBBPps from non- BBBPps rapidly and precisely. In the current work, machine learning aided models are developed for accurate prediction of BBBPps using physicochemical and evolutionary information. The best model achieved an accuracy of 84.8 % by using 5-fold cross-validation and 79.8 % based on the holdout testing set on a reduced set of features. Further an extensive analysis is carried out for the black box models using model agnostic interpretation approaches to infer the physicochemical and sequence space of BBBPps. Basic amino acids and conservation of Arginine and Lysine are found to be more favoured in BBBPps. Further, basic amino acid property group and its interactions with other features are found to be prominent important interactions.

This paper discussed gull alpha power Weibull distribution with a three-parameter. Different statistical inference methods of Gull Alpha Power Weibull distribution parameters have been obtained, estimated, and evaluated. Then, the results are compared to find a suitable model. The unknown parameters of the published Gull Alpha Power Weibull distribution are analyzed. Seven estimation methods are maximum likelihood, Anderson–Darling, right-tail Anderson–Darling, Cramér–von Mises, ordinary least-squares, weighted least-squares, and maximum product of spacing. In addition, the performance of this distribution is computed using the Monte Carlo method, and the limited sample features of parameter estimates for the proposed distribution are analyzed. In light of the importance of heavy-tailed distributions, actuarial approaches are employed. Applying actuarial criteria such as value at risk and tail value at risk to the suggested distribution shows that the model under study has a larger tail than the Weibull distribution. Two real-world COVID-19 infection datasets are used to evaluate the distribution. We analyze the existence and uniqueness of the log-probability roots to establish that they represent the global maximum. We conclude by summarizing the outcomes reported in this study.

Abstract Introduction HIV self‐testing (HIVST) has been shown to increase the uptake of HIV testing and help achieve the UNAIDS 95‐95‐95 targets. This study assessed the acceptability, usability (ease of use and result interpretation) and the willingness to pay for HIVST kits distributed through three distribution models, namely the community‐based, PLHIV network‐led and private practitioners models, in India. Methods This cross‐sectional study was implemented across 14 states in India between September 2021 and June 2022. All participants could choose between blood‐based or oral‐fluid‐based test kits. Participants were shown a test‐kit usage demonstration video, and pre‐ and post‐test counselling was provided for all. Participants were followed‐up after testing, and if reported reactive, were further supported for linkage to confirmatory testing and antiretroviral therapy (ART) initiation. Results Among the 90,605 participants found eligible, 88,080 (97%) accepted an HIVST kit. Among the 87,976 who reported using an HIVST kit, 45,207 (51%) preferred a blood‐based kit, and 42,120 (48%) reported testing for the first time. For future testing, 77,064 (88%) reported preferring HIVST over other HIV testing methods. Among those who used the kit, 83,308 (95%) found the kit easy to use, and 83,237 (95%) reported that the test results were easy to interpret. Among those who preferred HIVST for future use, 52,136 (69%) were willing to pay for the kit, with 35,854 (69%) of those willing to pay less than US$ 1.20. Only one instance of social harm was reported, with a participant reporting suicidal tendencies due to discord with their partner. Out of 328 participants (0.4%) who tested reactive with HIVST, 291 (89%) were linked to confirmatory testing; of these, 254 were confirmed HIV positive, and 216 (85%) successfully initiated ART. Conclusions Overall, we report that nearly all participants were willing to accept HIVST, found the test kits easy to use and interpret, and about two‐thirds were willing to pay for HIVST. Given the high levels of acceptance and the ability to reach a large proportion of first‐time testers, HIVST in India could contribute to achieving the UNAIDS first 95 and ending the HIV epidemic.

With an emphasis on memory-type approaches, this study presents a class of estimators specifically designed for estimating population variation in simple random sampling (SRS). The term ‘memory-type’ pertaining to the use of exponentially weighted moving averages (EWMA) statistic for the estimation, which utilizes the current and past information in temporal surveys. The study provides expressions for the bias and mean square error (MSE) of these estimators and establishes conditions under which their efficiency represses the conventional and other memory-type estimators. The theoretical findings are reinforced through a comprehensive simulation study conducted on hypothetically sampled populations. Additionally, the effectiveness of the proposed estimators is demonstrated utilizing real-life population data. The findings of simulation and real data application show the superiority of the proposed memory type estimator over the existing usual and memory type estimators.

In this article, we propose some direct and synthetic logarithmic estimators for estimating the domain mean of small area based on a simple random sampling design. The mean square error expressions of the proposed direct and synthetic estimators are obtained to first order approximation. The efficiency conditions are obtained under which the proposed direct and synthetic estimators dominate their conventional aspirants. The performances of the suggested direct and synthetic logarithmic estimators are examined by a comprehensive simulation study carried out on some artificially drawn symmetric and asymmetric populations. Furthermore, a real data application of the suggested methods is also provided as a case study using the paddy crop acreage data for small domains, where small domains are the revenue inspector circles (RIC) in Mohanlalganj tehsil, Uttar Pradesh, India.

Integrated pest management (IPM) is a comprehensive approach to managing diseases, focusing on combining various strategies to reduce pathogen populations effectively and in an environmentally conscious way. We investigated the effects of IPM on beneficial microbial populations and its relationship with pathogen populations in both direct-seeded rice (DSR) and transplanted rice (TR) systems. This study demonstrates that IPM practices have significantly higher populations of beneficial microbes, such as Trichoderma harzianum and Pseudomonas fluorescens, and lower level of the pathogen Fusarium verticillioides compared to non-IPM (farmer practices). The average mean population of T. harzianum was 6.38 × 103 CFU/g in IPM compared to 3.22 × 103 CFU/g in non-IPM during 2019 in TR at Bambawad. P. fluorescens mean population in 2019 was significantly higher in IPM (4.67 × 103 CFU/g) than in non-IPM (3.82 × 103 CFU/g) at the Karnal location in DSR. The F. verticillioides populations were significantly lower in IPM fields (9.46 × 103 CFU/g) compared to non-IPM fields (11.48 × 103 CFU/g) during 2017 at Haridwar in TR. Over three years, a significant increase in the populations of beneficial microbes in IPM plots was observed in all three locations of both TR and DSR, highlighting the sustainable impact of IPM practices. Disease dynamics analysis revealed that IPM effectively managed key diseases in both DSR and TR systems, with significant correlations between microbial density and disease severity. A significant positive correlation was recorded between F. verticillioides population and bakanae incidence at all three locations. Sheath blight incidence was negatively correlated with P. fluorescens population in both TR and DSR. In DSR, bacterial blight and brown spot diseases are reduced with the increased population of T. harzianum. Bioagents T. harzianum and P. fluorescens reduced disease incidence, underscoring the role of beneficial microbes in disease suppression and their importance for sustainable production using IPM practices.

This article considers the issue of domain mean estimation utilizing bivariate auxiliary information based enhanced direct and synthetic logarithmic type estimators under simple random sampling (SRS). The expressions of mean square error (MSE) of the proposed estimators are provided to the 1st order approximation. The efficiency criteria are derived to exhibit the dominance of the proposed estimators. To exemplify the theoretical results, a simulation study is conducted on a hypothetically drawn trivariate normal population from R programming language. Some applications of the suggested methods are also provided by analyzing the actual data from the municipalities of Sweden and acreage of paddy crop in the Mohanlal Ganj tehsil of the Indian state of Uttar Pradesh. The findings of the simulation and real data application exhibit that the proposed direct and synthetic logarithmic estimators dominate the conventional direct and synthetic mean, ratio, and logarithmic estimators in terms of least MSE and highest percent relative efficiency.

Agriculture is the second largest contributor (20 %) to total anthropogenic greenhouse gas (GHG) emissions in the world. There is a need to identify energy and carbon efficient cropping systems that reduce GHG emission and improve environmental quality. Using life cycle assessment (LCA), we evaluated the four cropping systems namely fallow – chickpea (F–C); Sesbania – mustard (Ses–M); blackgram – chickpea (B–C); sorghum + cowpea – mustard (S + C–M) cultivated during the 2018–2022 period. The energy use pattern and the input-output relationship were analysed. Three measures were utilized to quantify carbon footprints: CFa, which denotes emissions per unit area; CFb, indicating emissions per kilogram of yield; and CFe, representing emissions per unit of economic output. The result indicates that non-renewable sources of energy (diesel and fertilizer) contributed more than ∼80 % of the total energy consumed in the different cropping systems. The total energy requirement was the highest for S + C–M (16,972 MJ ha–1), followed by Ses–M (14,365 MJ ha–1), B–C (11,132 MJ ha–1) and F–C (8679 MJ ha–1) cropping systems. The S + C–M cropping system also had the highest energy use efficiency (9.13) followed by F–C (6.03), B–C (5.41) and Ses–M (5.41). The fallow–chickpea cropping system had the lowest values of CFa, CFb, and CFe however, the highest carbon efficiency (10.7) and the carbon sustainability index (9.7) were computed in S + C–M cropping system. Our findings indicate that thoughtfully structured, varied crop systems that integrate legumes and forage crops have the potential to significantly reduce energy consumption and carbon emissions, while sustaining or potentially improving overall productivity within these systems.

This article proposes and discusses a novel approach for generating trigonometric G-families using hybrid generalizers of distributions. The proposed generalizer is constructed by utilizing the tangent trigonometric function and distribution function of base model G(x). The newly proposed family of uni-variate continuous distributions is named the “Lomax Tangent Generalized Family of Distributions (LT-G)” and structural-mathematical-statistical properties are derived. Some special and sub-models of the proposed family are also presented.A Weibull-based model, ‘The Lomax Tangent Weibull (LT-W) Distribution,” is discussed and the plots of density (pdf) and hazard (hrf) functions are also explained. Model parameter estimates are estimated by employing the maximum likelihood estimation (MLE) procedure. The accuracy of the MLEs is evaluated through Monte Carlo simulation. Last but not least, to demonstrate the flexibility and potential of the proposed distribution, two actual hydrological and strength data sets are analyzed. The obtained results are compared with well-known, competitive, and related existing distributions.

Paleomagnetic records from the sediments provide the foundation for studying variations in the geomagnetic field across the geological time sacle. Here, we report a new paleomagnetic data from Quaternary sediments of Ramganga Paleolake, NW Himalaya, India, to provide more insight into the global geomagnetic excursions and paleoenvironmental implications. Paleomagnetic and rock magnetic analyses were performed on a 5 m thick vertical profile of unconsolidated sediments from the Ramganga paleolake (29°46′867″N; 79°14′043″E), located in the Lesser Himalaya at a height of 798 m. One hundred fifty oriented samples from 50 sites (10 cm intervals) were obtained from the vertical section. Extrapolating the optically stimulated luminescence (OSL) dates from literature indicates that the entire section was probably deposited between 38 and 0.8 ka. The rock magnetic data shows that the remanence is carried by a low coercivity mineral and a significant amount of a soft magnetic mineral, most likely magnetite. Virtual Geomagnetic Pole (VGP) latitudes were calculated. Plot of the VGP latitudes against the sample locations from the litho-column allows us for the construction of Magnetic Polarity Stratigraphy (MPS) column. The MPS of the Ramganga paleolake has detected three geomagnetic excursions that are correlated with the global events: the Mono Lake Event (∼28.4-25.8 ka), the Gulf of Mexico Event (∼12.5–17 ka), and the Bagwalipokar Excursion Events (15.5-14.7 ka and 8-2.85 ka) respectively. The lower and middle portion of the paleolake represents the low and high energy depositional environment during the sediment deposition due to wet and arid climatic conditions.

Here we present a novel study of Calcium hydroxide and iron-nickel co-doped Calcium hydroxide nanoparticles. Calcium hydroxide (CH) and iron-nickel doped Calcium hydroxide (INCH) nanoparticles (NPs) were synthesized by a simple chemical precipitation method. XRD, FESEM-EDX, and FTIR were used to characterize the structural and morphological characteristics, while UV–Vis spectroscopy was used to study the optical alterations. The average crystallite size is estimated to be 63.75 nm and 66 nm for the CH and INCH nanoparticles, respectively. The FESEM analysis revealed that CHNPs form a hexagonal-like shape, whereas INCH NPs produce highly agglomerated dispersed particles. The UV–Vis spectroscopy revealed that the band gap changes with average crystallite size concurrently.

Neurodegenerative diseases, including Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), and epilepsy sickness, currently impact millions of people globally. Thus yet, the primary etiology of these ailments has not been determined. Regretfully, no medication can prevent the onset of these illnesses. The primary risk factor for these illnesses developing is age. Recently, there has been an increase in interest among researchers to investigate the potential therapeutic benefits of herbal remedies for neurodegenerative diseases. Flavonoids, tannins, terpenoids, alkaloids, and phenols are examples of secondary metabolites that have been used to prevent adverse effects and delay disease progression. Baicalein (5,6,7-trihydroxyflavone), a flavonoid found in the traditional Chinese herb Huangqin (Scutellaria Baicalensis George), helps block the age-associated damage pathways linked to neurological diseases. This review offers a plausible argument in favour of baicalein's beneficial effect in models of AD, PD, HD, and epilepsy. Baicalein reduces amyloid-beta and Tau oligomers, oxidative stress, and acetylcholinesterase activity in AD by binding to the enzyme's active site. Additionally, Baicalein reduces mitochondrial dysfunction against N-methyl-4-phenyl-1,2,3,6-tetrahydropyridine-induced neuro-inflammation in PD via inhibiting the NLRP3/caspase-1/gasdermin-D pathway. In HD, Baicalein was used to test its possible neuroprotective ability via N-methyl-D-aspartate receptor antagonism against quinolinic acid-induced neurotoxicity. Moreover, Baicalein reduces inflammation in epilepsy by controlling the insulin-like growth factor 1 receptor in microglia to prevent pilocarpine-induced epilepsy. The protective effect of Baicalein provides the potential future therapeutic approach against various neurological disorders.

The estimation of domain mean in presence of missing data is a significant issue in sample surveys. Literature contains no imputation method to estimate the domain mean in the presence of missing data using bivariate auxiliary information. The present study uses simple random sampling to propose several novel synthetic domain mean imputation methods and the corresponding resulting estimators based on bivariate auxiliary information in the case of missing data. To evaluate how well the suggested novel imputation methods and the resulting estimators perform, the mathematical equation of mean square errors is derived. A thorough simulation experiment is also carried out using a population drawn artificially from a normal distribution. Furthermore, an application is also offered based on real data.

This research aims to investigate the approximate controllability of multivalued impulsive stochastic fractional differential inclusions in Hilbert space with ABC fractional-order derivatives. First, we confirm the existence of mild solutions for the proposed control system using stochastic analysis, resolvent operator theory, and the fixed point technique. Secondly, we discuss a new set of sufficient conditions for the approximate controllability of the systems. The results are obtained under the assumption that the associated linear system is approximately controllable. Finally, an example is provided to illustrate the obtained results.

Abstract In real life, situations may arise when the available data are insufficient to provide accurate estimates for the domain, the small area estimation (SAE) technique has been used to get accurate estimates for the variable under study. The problem of missing data is a serious problem that has an impact on sample surveys, but small area estimates are especially prone to it. This paper is a basic effort that suggests design based synthetic imputation methods for the domain mean estimation using simple random sampling in order to address the issue of missing data under SAE. The expression of the mean square error for the proposed imputation methods are obtained up to first order approximation. The efficiency conditions are determined and a thorough simulation study is carried out using artificially generated data sets. An application is included with real data that further supports this study.

Abstract Accurate and complete replication of genetic information is a fundamental process of every cell division. The replication licensing is the first essential step that lays the foundation for error-free genome duplication. During licensing, minichromosome maintenance protein complexes, the molecular motors of DNA replication, are loaded to genomic sites called replication origins. The correct quantity and functioning of licensed origins are necessary to prevent genome instability associated with severe diseases, including cancer. Here, we delve into recent discoveries that shed light on the novel functions of licensed origins, the pathways necessary for their proper maintenance, and their implications for cancer therapies.

The well-being of cotton crops is of utmost importance for maintaining agricultural productivity, and the early detection of diseases plays a critical role in achieving this objective. This study introduces a comprehensive approach for creating a machine learning-based system capable of identifying diseases in cotton plants through the analysis of leaf images. The research encompasses stages such as acquiring the dataset, pre-processing the data, training the model, developing an ensemble model, evaluating the models, and analyzing the results. Several machine-learning models are trained and evaluated to determine how well they can classify cotton leaves as “Healthy” or “Diseased.” These models include Random Forest, Support Vector Machine (SVM), Multi-Class SVM, and an Ensemble model. This investigation yields a practical and visually informative system for disease detection, which can contribute to disease prevention, thereby enhancing both crop yield and quality. This work underscores the significance of continuous improvement by periodically updating the models and explores the potential of advanced techniques such as deep learning.

Recently, mobile edge computing (MEC) has been proposed to improve wireless devices’ computational capabilities by offloading computation-intensive tasks to a network-edge server. Analytical research indicates that the application of non-orthogonal multiple access (NOMA) can significantly reduce the latency and energy consumption of MEC offloading. In order to capture the potential gains of NOMA in the context of MEC, this paper proposes an edge computing-aware coordinated direct and relay transmission (CDRT) based NOMA technique which can enjoy the benefits of uplink NOMA in reducing MEC users’ uplink energy consumption and computational delay. By lowering the likelihood of uplink outages for MEC users, the NOMA-assisted MEC system can benefit from uplink NOMA’s advantages. In addition, CDRT-based NOMA has the sum capacity scaling of $\log \mathop \rho \nolimits _{b}$ as signal-to-noise-ratio $\mathop \rho \nolimits _{b} $ increases, but $\frac {1}{2}\log \mathop \rho \nolimits _{b} $ for non-CDRT-based NOMA. Hence, applying CDRT-based NOMA in MEC increases the computation capability and decreases communication outage probability by extending the cell coverage of the MEC users. Based on this idea and the superior performance requirements in future wireless networks, we derive new closed-form expressions for the exact ergodic sum capacity and offloading outage probability under both residual hardware impairment and perfect/imperfect successive interference cancellation. Furthermore, to set a benchmark, accurate analytical expressions and numerical results are provided to demonstrate the effectiveness of the NOMA-assisted MEC system over the orthogonal multiple access-based scheme. At the end, we provide the basic guidelines for choosing transceiver hardware that will meet the practical requirements of a CDRT-based uplink-NOMA transmission system for efficient offloading, where the performance of the MEC server is evaluated in terms of latency and energy consumption. The Monte Carlo simulations validate the accuracy of the derived analytical expressions.

This paper considers the interference of imperfect successive interference cancellation operation and hardware noise in the joint transmission of coordinated multipoint cooperative non-orthogonal multiple access (NOMA) networks. Presumably, the statement is that distant user (cell-edge user) and base stations have no direct contacts. According to NOMA, each base station gives the information about the distant user and the near user to the cell center user (Near user), who then decodes the information of both users using the energy acquired by simultaneous wireless information and power transfer and passes it on to the cell edge user. To evaluate the performance of the suggested system, each user’s closed-form formula is represented in terms of outage probability. The findings indicate that the involvement of residual hardware impairments (RHIs) and imperfect successive interference cancellation (ipSIC) has led to an erroneous floor in the outage probability. It has been observed that ipSIC, RHIs, and both have a considerable detrimental effect on system performance. The analytical findings are verified using Monte Carlo simulations.

The steady growth of cities alters the urban environmental pattern and functions, posing significant challenges to urban ecological as well as environmental governance. Under this setting, analyzing the relationship between urban growth and ecological services is critical for management and policy-related sustainable urban development. The primary goal of this research is to analyse the dynamic urbanization and its influence on urban ecosystem services via changes in LULC of Mangaluru city agglomeration, India. For five decades (1980,1990, 2000, 2010, and 2022), the ecosystem service values (ESVs) are analysed using remote sensing data and Geographic Information System (GIS) techniques corresponding with the global value coefficient (VC) to estimate total ecosystem service values (ESVt) and individual ecosystem service function change. The study shows that ecosystem service values (ESVs) declined by US$ 116.89 million to US$ 85.14 million due to 9.54 and 63.44% decreases in agricultural land and wasteland/shrubland between 1980 and 2022. In terms of individual ecosystem service functions, regulating services increased from 1980 to 2022, with nutrient cycling (45.64%), raw material (15.59%), and erosion control (12.13%) contributing the most. The loss in total and certain individual ESV in the study landscape necessitates immediate action to improve urban ecosystem sustainability via proper planning and policy. HIGHLIGHTS The quantification of ecosystem services (ES) plays an important role in properly understanding and efficiently managing social-ecological systems.; The decrease in agricultural land and shrubland/wasteland is the main reason for the degradation of ecosystem services.; The findings point out that regional or local-level ecosystem services-related work is immensely important and a hotspot of current research.;

In the sample survey theory, the crux of survey practitioners is to provide “accurate” estimators of the parameter of choice. The conventional theory depends on the regression/difference estimators as they correspond to the best linear unbiased (BLU) estimators. This paper suggests some optimal classes of estimators by modifying the conventional estimators under stratified ranked set sampling (SRSS). The characteristics of the suggested estimators are established to the first-order approximation. The performance of the suggested class of estimators under SRSS has been theoretically and experimentally shown to be superior to traditional estimators, particularly regression (BLU) estimators.