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Ranked set sampling (RSS) has been proven an efficient alternative to simple random sampling (SRS). The use of auxiliary information also helps to improve the efficiency of the estimation procedures. Therefore, to accomplish higher efficiency and discuss the optimality issues, we proffer some optimal classes of estimators under RSS by employing multi-auxiliary information. It is seen that the ordinary mean estimator, traditional regression, and ratio estimators are the subsets of the proffered estimators. The expressions of the bias and mean square error are reported. An analytical comparison under some optimality conditions points out the ascendancy of the proffered classes of estimators over all reviewed works. The theoretical results have been furnished with computational study by employing some artificial and natural populations. The computational results show that the proffered estimators outperform the conventional estimators reviewed in this study. Furthermore, apposite advices are suggested to the survey persons. [ABSTRACT FROM AUTHOR]

The hybrid wireless sensor network is made up of Wireless Body Area Network (WBAN). Generally, many hospitals use cellular networks to support telemedicine. To provide the treatment to the patient on time, for this, an early diagnosis is required, for treatment. With the help of WBANs, collections and transmissions of essential biomedical data to monitor human health becomes easy. Compressor Sensing (CS) is an emerging signal compression/acquisition methodology that offers a protruding alternative to traditional signal acquisition. The proposed mechanism reduces message exchange overhead and enhances trust value estimation via response time and computational resources. It reduces cost and makes the system affordable to the patient. According to the results, the proposed scheme in terms of Compression Ratio (CR) is 18.18% to 88.11% better as compared to existing schemes. Also in terms of Percentage Root-Mean-Squared Difference (PRD) value, the proposed scheme is 18.18% to 34.21% better than with respect to existing schemes. The consensus for any new block is achieved in 24% less time than the Proof-of-Work (PoW) approach. The shallow CPU usage is required for the leader election mechanism. CPU utilization while the experiment lies in the range of 0.9% and 14%. While simulating a one-hour duration, the peak CPU utilization is 21%. [ABSTRACT FROM AUTHOR]

This article examines the issue of population mean estimation utilizing past and present data in the form of an exponentially weighted moving average (EWMA) statistic under simple random sampling (SRS). We suggest memory-type logarithmic estimators and derive their properties, such as mean-square error (MSE) and bias up to a first-order approximation. Using the EWMA statistic, the conventional and novel memory-type estimators are compared. Real and artificial populations are used as examples to illustrate the theoretical findings. According to the empirical findings, memory-type logarithmic estimators are superior to the conventional mean estimator, ratio estimator, product estimator, logarithmic-type estimator, and memory-type ratio and product estimators. [ABSTRACT FROM AUTHOR]

The mullet species under the family Mugilidae, considered to be a commercially important teleost, are mostly found in coastal waters throughout the world, distributed in tropical, subtropical and temperate areas. In terms of taxonomic study, biogeography and distribution pattern of mullets remain unclear due to difficulty in separating the species based on morphological characters. Thus, there is a need to study the taxonomy of Mugilidae. Further, phylogeny of family Mugilidae also exceptionally obscure at inter and intraspecific levels challenges exist in species under the family. The present study, has brought a new observation in form of temporary black dots (patrial pigmentation abnormality), especially in three species of Mugilidae were observed. Sometimes these pigmentation pattern can lead to misidentification or identification as different species. Further, DNA Barcoding (COI gene) and morpho-meristic analysis performed to resolve the ambiguity in the species identification, confirmed these species as Mugil cephalus, Planiliza sp., Osteomugil perusii. Present study will help to avoid the misidentification of species, which will assist biologists and managers for acquiring more information their distribution and life history pattern. [ABSTRACT FROM AUTHOR]

Aquaporins are tetrameric integral membrane proteins that act as water channels, and can also permeabilize membranes to other solutes. The monomer appears to be the functional form despite all aquaporins being organized as tetramers, which therefore must provide a clear functional advantage. In addition to this quaternary organization, some aquaporins can act as adhesion molecules in membrane junctions, when tetramers located in opposing membranes interact via their extracellular domains. These stacked forms have been observed in a range of aquaporins, whether using lipidic membrane environments, in electron crystallography, or using detergent micelles, in single-particle cryo-electron microscopy (cryo-EM). In the latter technique, structural studies can be performed when the aquaporin is reconstituted into nanodiscs of lipids that are surrounded by a protein scaffold. During attempts to study E. coli Aquaporin Z (AqpZ), we have found that in some conditions these nanodiscs tend to form filaments that appear to be either thicker head-to-tail or thinner side-to-side stacks of nanodiscs. Nanodisc oligomerization was observed using orthogonal analytical techniques analytical ultra-centrifugation and mass photometry, although the nature of the oligomers (head-to-tail or side-to-side) could not be determined. Using the latter technique, the AqpZ tetramer itself formed oligomers of increasing size when solubilized only in detergent, which is consistent with multiple stacking of AqpZ tetramers. We observed images consistent with both of these filaments in negative staining EM conditions, but only thicker filaments in cryo-EM conditions. We hypothesize that the apparent nanodisc side-to-side arrangement that can only be visualized in negative staining conditions is related to artifacts due to the sample preparation. Filaments of any kind were not observed in EM when nanodiscs did not contain AqpZ, or after addition of detergent into the nanodisc cryo-EM preparation, at concentrations that did not disrupt nanodisc formation. To our knowledge, these filaments have not been observed in nanodiscs preparations of other membrane proteins. AqpZ, like other aquaporins has a charge asymmetry between the cytoplasmic (more positive) and the extracellular sides, which may explain the likely head-to-tail stacking observed, both in nanodisc preparations and also in detergent micelles. [ABSTRACT FROM AUTHOR]

ABSTRACT Background: The genus Xylaria has been reported as a rich source of biologically active secondary metabolites. In the present study, an endophytic fungus Xylaria psidii has been isolated from [...]

Pathogens, including viruses, bacteria, and fungus, are the biotic agents that cause illnesses in crops and are the major cause of yield losses of up to 16 percent in certain parts of the globe. Pathogens are the primary cause of yield losses in some parts of the world. Deep learning algorithms, which are at the cutting edge of technology, are now being used to identify crop disease at an earlier stage. Supervised learning (support vector machine and K-nearest neighbor), ensemble learning (random forest and AdaBoost), and deep learning approaches were used in this study to suggest a classification of paddy leaf diseases, including bacteria leaf blight, blast, hispa, leaf spot, and leaf folder (neural networks). In order to evaluate the performance of the learning approaches, accuracy, recall, precision, F 1 score, and area under the receiver operating characteristic curve were used to evaluate the performance of the interpretation (ROC and AUC). According to the results of the investigation, when the fold value grows, the value of the evaluation metrics (AUC, CA, F 1 , precision, and recall) increases in a progressive manner, i.e., the 0.001 value increases as compared to the values obtained with the previous folds. When comparing the neural network to the baseline classifiers, the assessment metrics demonstrate that the neural network performs much better. [ABSTRACT FROM AUTHOR]

The essence of this paper is to propose some efficient combined and separate classes of estimators for estimating population mean Y ¯ under stratified simple random sampling. The bias and mean square error of the proposed classes of estimators are obtained. The proposed estimators are theoretically justified over the conventional mean estimator, classical ratio and regression estimators, Kadilar and Cingi estimators, Shabbir and Gupta estimators, Singh and Vishwakarma estimators, Koyuncu and Kadilar estimators, Singh and Solanki estimator, Yadav et al. estimator, Solanki and Singh estimator and Shahzad et al. estimator. The theoretical findings are extended with a simulation study accomplished over some artificially generated symmetric and asymmetric populations. [ABSTRACT FROM AUTHOR]

This paper considers some efficient classes of estimators for the estimation of population mean using known population proportion. The usual mean estimator, classical ratio, and regression estimators suggested by Naik and Gupta (1996) and Abd-Elfattah et al. (2010) estimators are identified as the members of the suggested class of estimators. The expressions of bias and mean square errors are derived up to first-order approximation. The proposed estimators were put to test against various other competing estimators till date. It has been found both theoretically and empirically that the suggested classes of estimators dominate the existing estimators. [ABSTRACT FROM AUTHOR]

This article suggests some novel logarithmic predictive estimators using ranked set sampling for the computation of population mean. The bias and mean square error of the proposed predictive estimators (PPEs) are determined up to the approximation of order one. The performance of the PPEs is examined regarding the contemporary existing predictive estimators (EPEs) utilizing simple random sampling (SRS) and ranked set sampling (RSS) and the efficiency conditions are obtained. Subsequently, a comprehensive computational examination is accomplished based on natural and artificially rendered symmetric and asymmetric populations to extend the theoretical results. [ABSTRACT FROM AUTHOR]

Byline: Jai. Prakash, Kalpana. Joshi, Deepak. Malik, Omkar. Mishra, Akhilesh. Sachan, Bharat. Kumar, Shashi. Bhushan, Vivekanandan. Kalaiselvan, Gyanendra. Singh BACKGROUND: To improve the public health and promote instant adverse drug [...]

In survey research, various types of estimators have been suggested that consider only the current sample information to compute the unknown population parameters. Therefore, we utilize the past sample information along with the current sample information in the form of hybrid exponentially weighted moving averages to suggest the memory type logarithmic estimators for time-based surveys. The expression of the mean square error of the suggested estimators is determined to the first order of approximation. A relative comparison of the suggested estimators with the existing estimators is performed and efficiency conditions are obtained. Further, a simulation study is accomplished using a hypothetically rendered population and a real data illustration to improve the theoretical results. The results of the simulation study and the real data application exhibit that the consideration of past and current sample information meliorates the efficiency of the suggested estimators. [ABSTRACT FROM AUTHOR]

Summary: This article proposes a new efficient logarithmic class of estimators for estimating the population mean in the case of multi‐auxiliary information using ranked set sampling. The bias and mean square error of the proposed estimators are reported up to the first order of approximation. It is demonstrated analytically and numerically that the proposed estimators are always better than their competitors. We accomplished an empirical study using some real populations and a simulation study using some artificially generated populations to support the analytical comparisons between the proposed estimators and their competitors. [ABSTRACT FROM AUTHOR]

This study investigates some combined and separate log type estimators for the population mean using stratified random sampling. The mean square error expressions of the proposed estimators are determined to the first order of approximation. Some real data sets are considered to assess the performance of the proposed estimators. The numerical results favor the proposition of the proposed estimators over its competitors. [ABSTRACT FROM AUTHOR]

The present article discusses the issue of population mean estimation in the ranked set sampling framework. A modified class of estimators is proffered and compared in the aspect of its efficacious performance with all salient conventional estimators existing to date. Some well-known existing estimators under R S S are recognized as the members of the proffered estimators for appropriately chosen characterizing scalars. The ascendancy of the proposed class of estimators regarding the conventional estimators has been shown through an extensive computational study utilizing some natural and artificially generated populations. [ABSTRACT FROM AUTHOR]

The present paper investigates the peristaltic flow of Newtonian fluids through the porous medium. The effects of electroosmosis mechanism on peristaltic pumping are also considered. An analytical solution is obtained under lubrication approach. Poisson Boltzmann equations are also simplified using Debye linearization. The effects of permeability parameter, electrical double layer thickness and electro-osmotic parameter on the flow characteristics, pressure distribution and shear stress distributions are computed. Numerical computations reveal that electroosmosis and permeability play vital role in peristaltic pumping. The findings of present study may be applicable in biomedical engineering and chemical engineering where peristaltic micropumps may be designed.

Anthropogenic activities have a critical influence on climate change that directly or indirectly impacts plant and microbial diversity on our planet. Due to climate change, there is an increase in the intensity and frequency of extreme environmental events such as temperature rise, drought, and precipitation. The increase in greenhouse gas emissions such as CO2, CH4, NOx, water vapor, increase in global temperature, and change in rainfall patterns have impacted soil--plant-microbe interactions, which poses a serious threat to food security. Microbes in the soil play an essential role in plants' resilience to abiotic and biotic stressors. The soil microbial communities are sensitive and responsive to these stressors. Therefore, a systemic approach to climate adaptation will be needed which acknowledges the multidimensional nature of plant-microbe-environment interactions. In the last two scores of years, there has been an enhancement in the understanding of plant's response to microbes at physiological, biochemical, and molecular levels due to the availability of techniques and tools. This review highlights some of the critical factors influencing plant-microbe interactions under stress. The association and response of microbe and plants as a result of several stresses such as temperature, salinity, metal toxicity, and greenhouse gases are also depicted. New tools to study the molecular complexity of these interactions, such as genomic and sequencing approaches, which provide researchers greater accuracy, reproducibility, and flexibility for exploring plant-microbe--environment interactions under a changing climate, are also discussed in the review, which will be helpful in the development of resistant crops/plants in present and future. [ABSTRACT FROM AUTHOR]

In this manuscript, we propose the combined and separate difference and ratio type estimators of population mean using stratified ranked set sampling. Additionally, several well-known estimators are identified as the sub-class of the suggested estimators. The characteristics of the suggested estimators have been analyzed and their effective performances are compared with the prominent estimators existing till date. Moreover, to prove the credibility of the theoretical findings, an extensive empirical study is administered over some real and hypothetically yielded symmetric and asymmetric populations. [ABSTRACT FROM AUTHOR]

This manuscript considers some improved combined and separate classes of estimators of population mean using bivariate auxiliary information under stratified simple random sampling. The expressions of bias and mean square error of the proposed classes of estimators are determined to the first order of approximation. It is exhibited that under some particular conditions, the proposed classes of estimators dominate the existing prominent estimators. The theoretical findings are supported by a simulation study performed over a hypothetically generated population. [ABSTRACT FROM AUTHOR]