Comparative Productive Performance of two Broiler Strains in Open Housing System

Background: The present study was conducted to compare the growth performance and ultimately to calculate the profitability of the two locally available commercial strains of broiler (Ross 308 and Cobb 500).Methods: For the purpose of study, 900 number of day-old chicks (DOC) of each strain were purchased from the local market. The birds were reared in conventional broiler house with the provision of standard managemental conditions throughout the experimental period. The parameters recorded on weekly basis were feed intake, body weight gain, feed conversion ratio (FCR) and mortality.Results: Result shown that the total body weight of Cobb-500 and Ross-308 on 1st week was 207.40±14 gram and 196.00±16 gram respectively and these result represented significant difference of weight gain (P0.05) among the strains. Furthermore, significant difference of feed conversion ratio (FCR) was observed (P<0.05) among both the strains but from day 7th till the market age weekly FCR of Cob-500 was significantly higher (P<0.05) than Ross-308. Comparatively high mortality (4.8±0.4%) was noticed in Ross broiler strain than Cobb broiler strain (3.7±0.4%). Conclusion: It was concluded from the current study that the Cobb-500 is performing better in conventional open housing system at high altitude than Ros-308.Keywords: Broiler; Cobb-500; Ross-308; Conventional broiler houses; Mortalit

Myoglobin-Based Classification of Minced Meat Using Hyperspectral Imaging

Minced meat substitution is one of the most common frauds which not only affects consumer health but impacts their lifestyles and religious customs as well. A number of methods have been proposed to overcome these frauds; however, these mostly rely on laboratory measures and are often subject to human error. Therefore, this study proposes novel hyperspectral imaging (400–1000 nm) based non-destructive isos-bestic myoglobin (Mb) spectral features for minced meat classification. A total of 60 minced meat spectral cubes were pre-processed using true-color image formulation to extract regions of interest, which were further normalized using the Savitzky–Golay filtering technique. The proposed pipeline outperformed several state-of-the-art methods by achieving an average accuracy of 88.88%

ESTIMATION OF FLASHOVER VOLTAGE PROBABILITY OF OVERHEAD LINE INSULATORS UNDER INDUSTRIAL POLLUTION, BASED ON MAXIMUM LIKELIHOOD METHOD

The performance of transmission line insulator is greatly affected by dust, fumes from industrial areas and saline deposit near the coast. Such pollutants in the presence of moisture form a coating on the surface of the insulator, which in turn allows the passage of leakage current. This leakage builds up to a point where flashover develops. The flashover is often followed by permanent failure of insulation resulting in prolong outages. With the increase in system voltage owing to the greater demand of electrical energy over the past few decades, the importance of flash over due to pollution has received special attention. The objective of the present work was to study the performance of overhead line insulators in the presence of contaminants such as industrial salts. A detailed review of the literature and the mechanism of insulator flashover due to pollution are presented. Experimental investigations on the behavior of overhead line insulators under industrial salt contamination are carried out. A special fog chamber was designed in which the contamination testing of insulators was carried out. Flashover behavior under various degrees of contamination of insulators with the most common industrial fume components such as Nitrate and Sulphate compounds was studied. Substituting the normal distribution parameter ¡n the probability distribution function based on maximum likelihood develops a statistical method. The method gives a high accuracy in the estimation of the 50% flashover voltage, which is then used to evaluate the critical flashover index at various contamination levels. The critical flashover index is a valuable parameter in insulation design for numerous applications

Intelligent Machine Vision Based Modeling and Positioning System in Sand Casting Process

Advanced vision solutions enable manufacturers in the technology sector to reconcile both competitive and regulatory concerns and address the need for immaculate fault detection and quality assurance. The modern manufacturing has completely shifted from the manual inspections to the machine assisted vision inspection methodology. Furthermore, the research outcomes in industrial automation have revolutionized the whole product development strategy. The purpose of this research paper is to introduce a new scheme of automation in the sand casting process by means of machine vision based technology for mold positioning. Automation has been achieved by developing a novel system in which casting molds of different sizes, having different pouring cup location and radius, position themselves in front of the induction furnace such that the center of pouring cup comes directly beneath the pouring point of furnace. The coordinates of the center of pouring cup are found by using computer vision algorithms. The output is then transferred to a microcontroller which controls the alignment mechanism on which the mold is placed at the optimum location

Effective Voting Ensemble of Homogenous Ensembling with Multiple Attribute-Selection Approaches for Improved Identification of Thyroid Disorder

Thyroid disease is characterized by abnormal development of glandular tissue on the periphery of the thyroid gland. Thyroid disease occurs when this gland produces an abnormally high or low level of hormones, with hyperthyroidism (active thyroid gland) and hypothyroidism (inactive thyroid gland) being the two most common types. The purpose of this work was to create an efficient homogeneous ensemble of ensembles in conjunction with numerous feature-selection methodologies for the improved detection of thyroid disorder. The dataset employed is based on real-time thyroid information obtained from the District Head Quarter (DHQ) teaching hospital, Dera Ghazi (DG) Khan, Pakistan. Following the necessary preprocessing steps, three types of attribute-selection strategies; Select From Model (SFM), Select K-Best (SKB), and Recursive Feature Elimination (RFE) were used. Decision Tree (DT), Gradient Boosting (GB), Logistic Regression (LR), and Random Forest (RF) classifiers were used as promising feature estimators. The homogeneous ensembling activated the bagging- and boosting-based classifiers, which were then classified by the Voting ensemble using both soft and hard voting. Accuracy, sensitivity, mean square error, hamming loss, and other performance assessment metrics have been adopted. The experimental results indicate the optimum applicability of the proposed strategy for improved thyroid ailment identification. All of the employed approaches achieved 100% accuracy with a small feature set. In terms of accuracy and computational cost, the presented findings outperformed similar benchmark models in its domain

Comparative Analysis of Classifiers for Developing an Adaptive Computer-Assisted EEG Analysis System for Diagnosing Epilepsy

Computer-assisted analysis of electroencephalogram (EEG) has a tremendous potential to assist clinicians during the diagnosis of epilepsy. These systems are trained to classify the EEG based on the ground truth provided by the neurologists. So, there should be a mechanism in these systems, using which a system’s incorrect markings can be mentioned and the system should improve its classification by learning from them. We have developed a simple mechanism for neurologists to improve classification rate while encountering any false classification. This system is based on taking discrete wavelet transform (DWT) of the signals epochs which are then reduced using principal component analysis, and then they are fed into a classifier. After discussing our approach, we have shown the classification performance of three types of classifiers: support vector machine (SVM), quadratic discriminant analysis, and artificial neural network. We found SVM to be the best working classifier. Our work exhibits the importance and viability of a self-improving and user adapting computer-assisted EEG analysis system for diagnosing epilepsy which processes each channel exclusive to each other, along with the performance comparison of different machine learning techniques in the suggested system

Facile fabrication of a free-standing magnesium oxide-graphene oxide functionalized membrane: a robust and efficient material for the removal of pollutants from aqueous matrices

Environmental pollution significantly challenges human health, ecosystems, and the planet’s sustainability. Widespread air, water, and soil contamination from various pollutants requires effective and sustainable solutions to reduce or eliminate pollution and its impacts. In this work, we designed novel magnesium oxide and graphene oxide (MgO@GO) composite free-standing membranes for nanofiltration. The membranes were characterized with the help of Fourier-transform infrared spectroscopy, X-ray diffraction, field-emission scanning electron microscopy, and energy-dispersive X-ray spectroscopy. Further, free-standing MgO@GO composite membranes with different thicknesses were used to measure the water permeance. 410 nm-thick membranes showed high water permeance up to 480 ± 5 Lm−2 h−1bar−1. Further, the rejection efficiency of the membrane was measured against NaCl, CaCl2, Pb(NO3)2, CdCl2, and amoxicillin. The MgO@GO membrane (410 ± 10 nm) showed 100% rejection for amoxicillin and 99% for Pb(NO3)2, respectively. Additionally, the membranes were stable under acidic and neutral conditions for approximately ∼80 days and may used on an industrial scale to ensure water is clean and free from harmful substances

Facile Fabrication of a Free-Standing Magnesium Oxide-Graphene Oxide Functionalized Membrane: A Robust and Efficient Material for the Removal of Pollutants from Aqueous Matrices

Environmental pollution significantly challenges human health, ecosystems, and the planet’s sustainability. Widespread air, water, and soil contamination from various pollutants requires effective and sustainable solutions to reduce or eliminate pollution and its impacts. In this work, we designed novel magnesium oxide and graphene oxide (MgO@GO) composite free-standing membranes for nanofiltration. The membranes were characterized with the help of Fourier-transform infrared spectroscopy, X-ray diffraction, field-emission scanning electron microscopy, and energy-dispersive X-ray spectroscopy. Further, free-standing MgO@GO composite membranes with different thicknesses were used to measure the water permeance. 410 nm-thick membranes showed high water permeance up to 480 ± 5 Lm−2 h−1 bar−1. Further, the rejection efficiency of the membrane was measured against NaCl, CaCl2, Pb(NO3)2, CdCl2, and amoxicillin. The MgO@GO membrane (410 ± 10 nm) showed 100% rejection for amoxicillin and 99% for Pb(NO3)2, respectively. Additionally, the membranes were stable under acidic and neutral conditions for approximately ∼80 days and may used on an industrial scale to ensure water is clean and free from harmful substances.</p

Assessing the potential of partial root zone drying and mulching for improving the productivity of cotton under arid climate

Water scarcity constrains global cotton production. However, partial root-zone drying (PRD) and mulching can be used as good techniques to save water and enhance crop production, especially in arid regions. This study aimed to evaluate the effects of mulching for water conservation in an arid environment under PRD and to further assess the osmotic adjustment and enzymatic activities for sustainable cotton production. The study was carried out for 2 years in field conditions using mulches (NM = no mulch, BPM = black plastic mulch at 32 kg ha⁻¹, WSM = wheat straw mulch at 3 tons ha⁻¹, CSM = cotton sticks mulch at 10 tons ha⁻¹) and two irrigation levels (FI = full irrigation and PRD (50% less water than FI). High seed cotton yield (SCY) achieved in FI+WSM (4457 and 4248 kg ha⁻¹ in 2017 and 2018, respectively) and even in PRD+WSM followed by BPM&amp;gt;CSM&amp;gt;NM under FI and PRD for both years. The higher SCY and traits observed in FI+WSM and PRD+WSM compared with the others were attributed to the improved water use efficiency and gaseous exchange traits, increased hormone production (ABA), osmolyte accumulation, and enhanced antioxidants to scavenge the excess reactive oxygen. Furthermore, better cotton quality traits were also observed under WSM either with FI or PRD irrigation regimes. Mulches applications found effective to control the weeds in the order as BPM&amp;gt;WSM&amp;gt;CSM. In general, PRD can be used as an effective stratagem to save moisture along with WSM, which ultimately can improve cotton yield in the water-scarce regions under arid climatic regions. It may prove as a good adaptation strategy under current and future water shortage scenarios of climate change