Tapping the Unused Energy Potential of Solar Water Pumps in India.

India seeks to deploy millions of solar water pumps to farmers who often lack access to electricity or face an unreliable power supply. Improving the use of this technology can bolster sustainable agriculture and expand clean energy services. We investigate farm-level impacts and opportunities with primary survey data ( = 292 farmers) and a large real-time pump operational data set ( = 1106 pumps).

Optimization of alkali pretreatment and enzymatic saccharification of jute (Corchorus olitorius L.) biomass using response surface methodology.

Reduction of inherent structural recalcitrance and improved saccharification efficiency are two important facets to enhance fermentable sugar yield for bioethanol production from lignocellulosic biomass. This study optimized alkaline pretreatment and saccharification conditions employing response surface methodology to improve saccharification yield of jute (Corchorus olitorius cv. JROB-2) biomass.

Psoriasis Lesion Detection Using Hybrid Seeker Optimization-based Image Clustering.

Background: In recent years, there has been a massive increase in the number of people suffering from psoriasis. For proper psoriasis diagnosis, psoriasis lesion segmentation is a prerequisite for quantifying the severity of this disease. However, segmentation of psoriatic lesions cannot be evaluated just by visual inspection as they exhibit inter and intra variability among the severity classes.

Swarm intelligence based clustering technique for automated lesion detection and diagnosis of psoriasis.

Background: In psoriasis skin disease, psoriatic cells develop rapidly than the normal healthy cells. This speedy growth causes accumulation of dead skin cells on the skin's surface, resulting in thick patches of red, dry, and itchy skin. This patches or psoriatic skin legions may exhibit similar characteristics as healthy skin, which makes lesion detection more challenging.

An improved fault detection classification and location scheme based on wavelet transform and artificial neural network for six phase transmission line using single end data only.

Restrictions on right of way and increasing power demand has boosted development of six phase transmission. It offers a viable alternative for transmitting more power, without major modification in existing structure of three phase double circuit transmission system. Inspite of the advantages, low acceptance of six phase system is attributed to the unavailability of a proper protection scheme.

Classification of Two Class Motor Imagery Tasks Using Hybrid GA-PSO Based K-Means Clustering.

Transferring the brain computer interface (BCI) from laboratory condition to meet the real world application needs BCI to be applied asynchronously without any time constraint. High level of dynamism in the electroencephalogram (EEG) signal reasons us to look toward evolutionary algorithm (EA). Motivated by these two facts, in this work a hybrid GA-PSO based K-means clustering technique has been used to distinguish two class motor imagery (MI) tasks.

Support vector machine and fuzzy C-mean clustering-based comparative evaluation of changes in motor cortex electroencephalogram under chronic alcoholism.

In this study, the magnitude and spatial distribution of frequency spectrum in the resting electroencephalogram (EEG) were examined to address the problem of detecting alcoholism in the cerebral motor cortex. The EEG signals were recorded from chronic alcoholic conditions (n = 20) and the control group (n = 20). Data were taken from motor cortex region and divided into five sub-bands (delta, theta, alpha, beta-1 and beta-2).

Diagnostic value of magnetic resonance imaging in thoracic outlet syndrome.

PURPOSE. To evaluate the diagnostic value of magnetic resonance imaging (MRI) in thoracic outlet syndrome (TOS). METHODS.

Fixed structure compensator design using a constrained hybrid evolutionary optimization approach.

This paper presents an efficient technique for designing a fixed order compensator for compensating current mode control architecture of DC-DC converters. The compensator design is formulated as an optimization problem, which seeks to attain a set of frequency domain specifications. The highly nonlinear nature of the optimization problem demands the use of an initial parameterization independent global search technique.

A differential evolution based approach for estimating minimal model parameters from IVGTT data.

Estimation of insulin sensitivity plays a crucial role in the diagnosis and clinical investigation of glucose related diseases. The Bergman minimal model provides a non-invasive approach for estimating insulin sensitivity from the glucose insulin time series data of intravenous glucose tolerance test (IVGTT). However, quite often in the traditional gradient based techniques for deriving insulin sensitivity from the minimal model, improper initialization leads to convergence problems and results in final solution, which are either incorrect or physiologically not feasible.

A genetic algorithm tuned optimal controller for glucose regulation in type 1 diabetic subjects.

An optimal state feedback controller is designed with the objective of minimizing the elevated glucose levels caused by meal intake in Type 1 diabetic subjects, by the minimal infusion of insulin. The states for the controller based on linear quadratic regulator theory are estimated from noisy data using Kalman filter. The controller designed for a physiological relevant mathematical model is coupled with another model for simulating meal dynamics, which converts meal intake into glucose appearance rate in the plasma.