Nanofertilizers offer a promising solution to increase crop yields amid increasing population pressure. Yet  there have been safety concerns about their use, particularly in challenging conditions. This work was designed to assess the toxicological effects of a chelated multi-micronutrient nanofertilizer on albino rats (Rattus norvegicus) as non-target organisms, focusing on the liver, which has important metabolic functions. Thirty-five animals were weighed and randomly divided into 5 groups (n=7). A negative control group was included, and four experimental groups (C1-C4) were given the test item. The dosing regimen for the experimental groups was 15 oral doses of nanofertilizer every other day for 29 days. All the groups were

Gray-Scale Image Brightness/Contrast Enhancement with Multi-Model
Histogram linear Contrast Stretching (MMHLCS) method

In this research, the problem of multi- objective modal transport was formulated with mixed constraints to find the optimal solution. The foggy approach of the Multi-objective Transfer Model (MOTP) was applied. There are three objectives to reduce costs to the minimum cost of transportation, administrative cost and cost of the goods. The linear membership function, the Exponential membership function, and the Hyperbolic membership function. Where the proposed model was used in the General Company for the manufacture of grain to reduce the cost of transport to the minimum and to find the best plan to transfer the product according to the restrictions imposed on the model.

The solidification process in a multi-tube latent heat energy system is affected by the natural convection and the arrangement of heat exchanger tubes, which changes the buoyancy effect as well. In the current work, the effect of the arrangement of the tubes in a multi-tube heat exchanger was examined during the solidification process with the focus on the natural convection effects inside the phase change material (PCM). The behavior of the system was numerically analyzed using liquid fraction and energy released, as well as temperature, velocity and streamline profiles for different studied cases. The arrangement of the tubes, considering seven pipes in the symmetrical condition, are assumed at different positions in the system, i

Abstract:
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Infrared photoconductive detectors working in the far-infrared region and room temperature were fabricated. The detectors were fabricated using three types of carbon nanotubes (CNTs); MWCNTs, COOH-MWCNTs, and short-MWCNTs. The carbon nontubes suspension is deposited by dip coating and drop–casting techniques to prepare thin films of CNTs. These films were deposited on porous silicon (PSi) substrates of n-type Si. The I-V characteristics and the figures of merit of the fabricated detectors were measured at a forward bias voltage of 3 and 5 volts as well as at dark and under illumination by IR radiation from a CO2 laser of 10.6 μm wavelengths and power of 2.2 W. The responsivity and figures of merit of the photoconductive detector

The aim of the research is to apply fibrewise multi-emisssions of the paramount separation axioms of normally topology namely fibrewise multi-T0. spaces, fibrewise multi-T1 spaces, fibrewise multi-R0 spaces, fibrewise multi-Hausdorff spaces, fibrewise multi-functionally Hausdorff spaces, fibrewise multi-regular spaces, fibrewise multi-completely regular spaces, fibrewise multi-normal spaces and fibrewise multi-functionally normal spaces. Also we give many score regarding it.

We define and study new ideas of fibrewise topological space on D namely fibrewise multi-topological space on D. We also submit the relevance of fibrewise closed and open topological space on D. Also fibrewise multi-locally sliceable and fibrewise multi-locally section able multi-topological space on D. Furthermore, we propose and prove a number of statements about these ideas.

Big data analysis is essential for modern applications in areas such as healthcare, assistive technology, intelligent transportation, environment and climate monitoring. Traditional algorithms in data mining and machine learning do not scale well with data size. Mining and learning from big data need time and memory efficient techniques, albeit the cost of possible loss in accuracy. We have developed a data aggregation structure to summarize data with large number of instances and data generated from multiple data sources. Data are aggregated at multiple resolutions and resolution provides a trade-off between efficiency and accuracy. The structure is built once, updated incrementally, and serves as a common data input for multiple mining an