Recording an Electromyogram (EMG) signal is essential for diagnostic procedures like muscle health assessment and motor neurons control. The EMG signals have been used as a source of control for powered prosthetics to support people to accomplish their activities of daily living (ADLs). This work deals with studying different types of hand grips and finding their relationship with EMG activity. Five subjects carried out four functional movements (fine pinch, tripod grip and grip with the middle and thumb finger, as well as the power grip). Hand dynamometer has been used to record the EMG activity from three muscles namely; Flexor Carpi Radialis (FCR), Flexor Digitorum Superficialis (FDS), and Abductor Pollicis Brevis (ABP) with different

A genetic algorithm model coupled with artificial neural network model was developed to find the optimal values of upstream, downstream cutoff lengths, length of floor and length of downstream protection required for a hydraulic structure. These were obtained for a given maximum difference head, depth of impervious layer and degree of anisotropy. The objective function to be minimized was the cost function with relative cost coefficients for the different dimensions obtained. Constraints used were those that satisfy a factor of safety of 2 against uplift pressure failure and 3 against piping failure.
Different cases reaching 1200 were modeled and analyzed using geo-studio modeling, with different values of input variables. The soil wa

A simple, precise, and sensitive spectrophotometric method has been established for the analysis of doxycycline. The method includes direct charge transfer complexation of doxycycline withp-Bromanil in acetonitrileto form a colored complex. The intensely colored product formed was quantified based on the absorption band at 377 nm under optimum condition. Beer’s law is obeyed in the concentration range of 1–50 μg.mL-1 with molar absorptivity of 1.5725x104 L.mol-1.cm-1, Sandell's sensitivity index (0.0283) μg.cm-2, detection limit of 0.1064 μg.mL-1, quantification limit 0.3224 μg.mL-1 and association constant of the formed complex (0.75x103). The developed method could find application in routine quality control of doxycycline and has

   Reservoir characterization plays a crucial role in comprehending the distribution of formation properties and fluids within heterogeneous reservoirs. This knowledge is instrumental in constructing an accurate three-dimensional model of the reservoir, facilitating predictions regarding porosity, permeability, and fluid flow distribution. Among the various methods employed for reservoir characterization, the hydraulic flow unit stands out as a widely adopted approach. By effectively subdividing the reservoir into distinct zones, each characterized by unique petrophysical and geological properties, hydraulic flow units enable comprehensive reservoir analysis. The concept of the flow unit is closely tied to the flow zone indicator, a cr

KE Sharquie, HM Al-Hamamy, AA Noaimi, IA Al-Shawi, Journal of the Saudi Society of Dermatology & Dermatologic Surgery, 2011 - Cited by 9

The aim of this work was to estimate the concentrations of natural and artificial nuclides in some fertilized and unfertilized plant samples. These samples were collected and prepared in a petri dish for the measurements using gamma spectroscopy. The average values of 238U, 232Th, 40K, and 137Cs for the unfertilized plant samples were (11.964 ± 3.226, 8.273 ± 2.639, 402.436 ± 18.099, and 2.761 ± 1.613) respectively, and for the fertilized plant samples were (30.434 ± 5.282, 22.584 ± 4.620, 711.332 ± 25.806, and 6.986 ± 2.542) respectively. The average values of radiological hazard indices, Raeq, D, D for 137Cs, (AEDE)in, (AEDE)out, Iγ, Hin, and Hout for the unfertilized plant samples were (54.782 ± 7.216, 27.306, 0.469, 0.

The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi