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INCREASED PREFERENCE OF DARKLING BEETLES AKIS SUBTRICOSTATA REDTENBACHER, 1850 AND TRACHYDERMA PHILISTINA REICHE AND SAULCY, 1857 (COLEOPTERA, TENEBRIONIDAE) FOR VEGETATION WITH INCREASING TEMPERATURE
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   A square experimental arena with vegetation on one interior side was deployed in a Sharjah, United Arab Emirates desert. Individual darkling beetles (Coleoptera, Tenebrionidae) Akis subtricostata Redtenbacher, 1850 and Trachyderma philistina Reiche and Saulcy, 1857 were placed inside the arena at temperatures ranging between 27 - 49°C. Whether they chose the vegetated side of the arena or not was recorded, as well as how long it took for them to reach the vegetated side, if they chose it. Both species preferred the vegetated side at all temperatures, and the chance of them choosing the vegetated side increased significantly with increasing temperature (logistic regression, p = 0.0096 and p = 0.0003 for T. philistina and A. subtricostata, respectively). T.philistina and A. subtricostata always chose the vegetated side at temperatures above 31°and 44°C, respectively. Individual beetles that chose the vegetated side moved quickly and directly to it at temperatures above 30°C. Below 30°C, however, beetles tended to move slower and take more pauses within the arena. Time to reach the vegetated side declined significantly with increasing temperature (least-squares regression, p < 0.00005 for both species). A few individuals of both species died at the highest temperatures (48 - 49°C).

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Publication Date
Mon Apr 08 2013
Journal Name
Chemistry And Materials Research
Synthesis, spectral and antimicrobial activity of mixed ligand complexes of Co(II), Ni(II), Cu(II) and Zn(II) with Anthranillic Acid and Tributylphosphine
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Mixed ligand complexes of bivalent metal ions, viz; Co(II), Ni(II), Cu(II) and Zn(II) of the composition [M(A)2((PBu3)2]in(1:2:2)(M:A:(PBu3). molar ratio, (where A- Anthranilate ion ,(PBu3)= tributylphosphine. M= Co(II),Ni(II),Cu(II) and Zn(II). The prepared complexes were characterized using flame atomic absorption, by FT-IR, UV/visible spectra methods as well as magnetic susceptibility and conductivity measurements. The metal complexes were tested in vitro against three types of pathogenic bacteria microorganisms: (Staphylococcus, Klebsiella SPP .and Bacillas)to assess their antimicrobial properties. Results. The study shows that all complexes have octahedral geometry; in addition, it has high activity against tested bacteria. Based on th

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Publication Date
Thu Sep 26 2013
Journal Name
Chemistry And Materials Research
Synthesis, spectral and antimicrobial activity of mixed ligand complexes of Co(II), Ni(II), Cu(II) and Zn(II) with Anthranillic Acid and Tributylphosphine
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Mixed ligand complexes of bivalent metal ions, viz; Co(II), Ni(II), Cu(II) and Zn(II) of the composition [M(A)2((PBu3)2]in(1:2:2)(M:A:(PBu3). molar ratio, (where A- Anthranilate ion ,(PBu3)= tributylphosphine. M= Co(II),Ni(II),Cu(II) and Zn(II). The prepared complexes were characterized using flame atomic absorption, by FT-IR, UV/visible spectra methods as well as magnetic susceptibility and conductivity measurements. The metal complexes were tested in vitro against three types of pathogenic bacteria microorganisms: (Staphylococcus, Klebsiella SPP .and Bacillas)to assess their antimicrobial properties. Results. The study shows that all complexes have octahedral geometry; in addition, it has high activity against tested bacteria. Based on th

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Publication Date
Fri Nov 10 2017
Journal Name
Iosr Journal Of Agriculture And Veterinary Science (iosr-javs)
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Publication Date
Mon May 25 2015
Journal Name
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Synthesis, Spectral and Antimicrobial Activity of Mixed Ligand Complexes of Co(II),Ni(II),Cu(II) and Zn(II) with 4-aminoantipyrine and Tributylphosphine
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Publication Date
Sat Jun 01 2024
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Publication Date
Thu Jun 01 2017
Journal Name
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The Use of First Order Polynomial with Double Scalar Quantization for Image Compression
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Fri Nov 09 2018
Journal Name
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Publication Date
Thu Jan 25 2024
Journal Name
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Sat Apr 15 2023
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Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

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