To find out a simple and efficient equation to estimate maize ear grain weight on farm (in situ), twenty three maize crosses along with two synthetics were grown in the field. On the experimental farm of the Dept. of Field Crop Sci., College of Agric., Univ. of Baghdad, seeds of twenty five maize genotypes were grown in the fall season of 2013 with three replicates. At dough stage of the kernels, five naked ears of each experimental units were measured for length and maximum diameter. This will sum up 125 ears of the trial. The volumes of ears were calculated as cylinder (length× r2× 3.1416). Grain weight of all ears were determined after harvesting and drying to 15% grain moisture. A constant was calculated by dividing ear grain weight by each ear volume. Estimated ear grain weights were tested against observed by applying correlation coefficient and it was found to be positive and highly significant (r= 0.998**). The observed and estimated values of ear grain weights were tested by t-test. The two means of observed and estimated ear grain weights were fit to 0.89 probability of t-value. The final equation to estimate ear grain weight in situ is= r2× L× 0.94, where r is radius of ear and L is ear length. However, in case of super hybrids of high ear fertility and kernel filling, estimated ear grain weight will be= r2× L.
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Over the past few years, ear biometrics has attracted a lot of attention. It is a trusted biometric for the identification and recognition of humans due to its consistent shape and rich texture variation. The ear presents an attractive solution since it is visible, ear images are easily captured, and the ear structure remains relatively stable over time. In this paper, a comprehensive review of prior research was conducted to establish the efficacy of utilizing ear features for individual identification through the employment of both manually-crafted features and deep-learning approaches. The objective of this model is to present the accuracy rate of person identification systems based on either manually-crafted features such as D
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Biometrics represent the most practical method for swiftly and reliably verifying and identifying individuals based on their unique biological traits. This study addresses the increasing demand for dependable biometric identification systems by introducing an efficient approach to automatically recognize ear patterns using Convolutional Neural Networks (CNNs). Despite the widespread adoption of facial recognition technologies, the distinct features and consistency inherent in ear patterns provide a compelling alternative for biometric applications. Employing CNNs in our research automates the identification process, enhancing accuracy and adaptability across various ear shapes and orientations. The ear, being visible and easily captured in
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The primary objective of this paper is to improve a biometric authentication and classification model using the ear as a distinct part of the face since it is unchanged with time and unaffected by facial expressions. The proposed model is a new scenario for enhancing ear recognition accuracy via modifying the AdaBoost algorithm to optimize adaptive learning. To overcome the limitation of image illumination, occlusion, and problems of image registration, the Scale-invariant feature transform technique was used to extract features. Various consecutive phases were used to improve classification accuracy. These phases are image acquisition, preprocessing, filtering, smoothing, and feature extraction. To assess the proposed
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Background: Difficulties arise when attempting to imagine the interpupillary line and comparing it with the Fox plane guide and not more difficult than holding any instrument over the movable pupils just to demonstrate the interpupillary line. The aim of this study was to introduce ear lobes as alternative landmarks for the interpupillary line during orientation of the occlusal plane. Also, the other aim was to compare the ear lobes with the pupils of the eyes to verify that they were indifferent as anatomical landmarks. Materials & methods: The alternative landmarks, ear lobes, were presented and the method for orienting the occlusal plane with these landmarks was introduced. Digital pictures of 30 subjects, who participated in the study,
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Background: Ear infections can manifest in many forms depending on site of infection whether external, middle or internal ear and the culprit pathogen whether viral, bacterial or fungal. Acute middle ear infections are usually accompanied by aural discharge. Objective: 1. To get an overview on the bacterial pathogens involved in ear infections. 2. To assess the antibiotic resistance of bacterial pathogens. Methods: A cross sectional study conducted in Al-Kindy Teaching Hospital / Baghdad /Iraq. Swabs taken from 225 patients suffering from aural discharge were tested for culture and sensitivity for the duration of two years 2018-2019. Aural discharge is cultured by inoculating it into blood, MacConkey agar, chocolate agars and Sabou
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Background: Kocuria kristinae (earlier known as Rothia kristinae) is a relatively new opportunistic pathogen responsible for ear infections. The ability of this organism to form biofilms increases its resistance to therapy and promotes the development of antimicrobial resistance, necessitating new treatment methods. Objective: To assess the effect of 3-caffeoylquinic acid on K. kristinae isolated from patients with ear infections. Methods: Isolation of five clinical strains was done by conventional microbiological procedures. The ability of K. kristinae to form biofilms was determined by the crystal violet method (absorbance at OD₅₇₀) before and after 3-caffeoylquinic acid exposure; statistical data analysis was done using a
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Understanding of in-situ stress profiles and orientations plays a vital role in designing a successful hydraulic fracturing treatment. This paper is an attempet to examine the effect of lithology and in situ stress on geometery of hydraulic fractures. A hydraulic fracturing design simulator software called FracproPT with various capabilities for designing most of hydraulic fracture was used for simulate and optimize the hydraulic fracturing. For studying purpose, three different cases of stress gradient contrast between different formations are considered in this study (0.4, 0.5 and 0.75 psi/ft). The results obtained from the simulator showed that lithologies surrounding the pay zone have an effect on the fracture
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