Comparison of artificial neural network and linear regression model for the leaf morphology of fenugreek (Trigonella foenum graecum) grown under different nitrogen fertilizer doses

We have numerically investigated the electrodiffusioosmotic (EDO) transport of non-Newtonian electrolytic solution, governed by an externally applied electric field and concentration difference, in a charged nanochannel connected with two reservoirs. We have examined the EDO transport characteristics by varying electrical, chemical, and rheological parameters. The relative augmentation in net throughput due to EDO transport is compared to the pure electroosmotic (EO) flow and is found to be greater than unity [~ O(103)] for the considered range of concentration difference and flow-behavior index. As shown, the EDO throughput with concentration difference follows an increasing-decreasing trend at smaller nanochannel height (< 10 nm), while exhibiting an increasing trend at the higher nanochannel height (>10 nm). Notably, the net flow for shear-thinning fluid gets fully reversed at higher concentration differences and for a higher value of zeta potential. In the second part of the work, we discuss the use of an Artificial Neural Network (ANN) essentially to predict the net EDO throughput from the nanochannel. The ANN model considered here is of a single-hidden-layer feedforward type. For activation, we used a sigmoid- purelinear transfer function between the layers. Additionally, the Levenberg-Marquardt algorithm is used to perform the back-propagation. We have established that an ANN model with eight neurons in the hidden layer accurately predicts the flow rate per unit width with a very small root mean squared error. The inferences of this analysis could be of huge practical importance in designing the state-of-the-art nanodevices/systems intended for offering finer control over the underlying transport.

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Prediction of electrodiffusio-osmotic transport of shear-thinning fluids in a nanochannel using artificial neural network

Ismayeel

Mehta

Mondal

2023

Physics of Fluids

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Simplified methods for the design of landfill double composite liners using neural network

Shi,

Xie,

Chen

et al. 2024

Geosynthetics International

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Double composite liners (DCLs) have been widely used in landfills to protect the surrounding environment. This study aims to develop simplified empirical equations for calculating breakthrough times of DCLs based on analytical equations or experimental data. The artificial intelligence neural network called Group Method of Data Handling (GMDH) type neural network was used to perform equation simplification. New empirical equations in polynomial formats are obtained by a layer-summation method and a series of numerical experiments based on analytical solutions for contaminant transport in double composite liners. The accuracy of empirical equations is demonstrated by comparing them with the existing solutions and numerical results. The performance of four types of DCLs were then investigated. The mean absolute percentage errors (MAPEs) for each type of DCLs with different leachate heads and soil liner thicknesses are all lower than 10%. Additionally, a trend for the improvement of the GMDH equation accuracy with the increase of Δh1 is observed. The presented equations can perform well in high leachate head conditions (e.g. > 5 m) where DCLs are required.

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Natural product-based pharmacological studies for neurological disorders

Puri

Kanojia²,

Sharma³

et al. 2022

Front. Pharmacol.

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Central nervous system (CNS) disorders and diseases are expected to rise sharply in the coming years, partly because of the world’s aging population. Medicines for the treatment of the CNS have not been successfully made. Inadequate knowledge about the brain, pharmacokinetic and dynamic errors in preclinical studies, challenges with clinical trial design, complexity and variety of human brain illnesses, and variations in species are some potential scenarios. Neurodegenerative diseases (NDDs) are multifaceted and lack identifiable etiological components, and the drugs developed to treat them did not meet the requirements of those who anticipated treatments. Therefore, there is a great demand for safe and effective natural therapeutic adjuvants. For the treatment of NDDs and other memory-related problems, many herbal and natural items have been used in the Ayurvedic medical system. Anxiety, depression, Parkinson’s, and Alzheimer’s diseases (AD), as well as a plethora of other neuropsychiatric disorders, may benefit from the use of plant and food-derived chemicals that have antidepressant or antiepileptic properties. We have summarized the present level of knowledge about natural products based on topological evidence, bioinformatics analysis, and translational research in this review. We have also highlighted some clinical research or investigation that will help us select natural products for the treatment of neurological conditions. In the present review, we have explored the potential efficacy of phytoconstituents against neurological diseases. Various evidence-based studies and extensive recent investigations have been included, which will help pharmacologists reduce the progression of neuronal disease.

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Comparison of artificial neural network and linear regression model for the leaf morphology of fenugreek (Trigonella foenum graecum) grown under different nitrogen fertilizer doses

Cited by 4 publications

References 29 publications

Prediction of electrodiffusio-osmotic transport of shear-thinning fluids in a nanochannel using artificial neural network

Prediction of electrodiffusio-osmotic transport of shear-thinning fluids in a nanochannel using artificial neural network

Simplified methods for the design of landfill double composite liners using neural network

Natural product-based pharmacological studies for neurological disorders

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