Browsing by Author "Khan, M"

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    Enhanced direct and synthetic estimators for domain mean with simulation and applications
    (2024-07) Kumar, A; Bhushan, S; Pokhrel, R; Emam, W; Tashkandy, Y; Khan, M
    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.
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    Novel logarithmic imputation procedures using multi auxiliary information under ranked set sampling
    (2024) Kumar, A; Bhushan, S; Emam, W; Tashkandy, Y; Khan, M
    Ranked set sampling (RSS) is known to increase the efciency 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.
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    Sea urchins like Zinc oxide nanometric mitigating Meloidogyne incognita infection in eggplant
    (2024-06) Khan, A; Mfarrej, M; Khan, M; Khan, S; Malan, P; Saxena, S; Muddassir, M; Ahmad, F
    This study focused on the nematicidal potential of sea urchin-like zinc oxide nanometrics (SUZN) against the root-knot nematode Meloidogyne incognita. XRD, SEM, UV-vis spectroscopy, TEM, and EDX have been utilized to characterize the SUZN. Various SUZN concentrations (150–750ppm) were examined for their implications for M. incognita second-stage juveniles (J2s) and egg masses, including mortality as well as hatching inhibition. The largest J2s hatching inhibition (83.07%) was seen at 750ppm SUZN after 48hours of incubation, whereas the lowest hatching occurred at 150ppm when compared to the control. SUZN illustrated increased nematicidal efficiency with increasing concentration and incubation duration. LC50 values for J2s were calculated at various time points. In root-dip trials, all SUZN doses substantially reduced J2s penetration into eggplant roots relative to the untreated control. The study emphasizes SUZN’s strong nematicidal activity against M. incognita, suggesting its promise as a long-term alternative to chemical nematicides for root-knot nematode management while reducing the environmental concerns associated with traditional pesticides.