When the guard honey bees, Apis mellifera L., form a clump at the hive entrance or on the flight board, the oriental hornet, Vespa orientails L., either creeps toward the clump or hovers over it in order to take a bee. Once the hornet creeps, only few bees facing the hornet become alert, rock their heads and antennae, open their wings, and take a posture of defense. The rest of the clump stays listless without any signal of concern. However, the clump stays dense and the defending bees do not detach themselves neither from the rest of the clump nor from each other. For this reason, it is very difficult for the hornet to grab a bee unless the latter makes a “mistake” by detaching herself from other adjacent bees. If the hornet grabs such a bee, the other defending bees will not attack the hornet to free that bee even when the latter is one centimeter from the others. The defending bees can capture the hornet only when the latter grabs one of them which stands very close to the others. The hornet seems very “aware” of such a situation; hence she seldom becomes a captive. On the other hand, hovering over the clump makes it easier for the hornet to grab a bee. The hovering puts all the clump, rather than part of it, on alert. If the hornet is persistent, which is not often the case, then the clump will no longer be dense. The bees will panicly disperse all over the flight board; hence the hornet can find a detached bee and grabs it easily. The hornet also waits on wing in front of the hive to capture an outgoing or incoming flying bee but the latter usually maneuver to escape. Also, foraging bees reduce their activity during main hours of hornet presence. Among the main factors reducing the hornet impact are the continuous chasing of hornets to each other and the non-persistent attempts of the hornets when they attack bee clumps.
Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod
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Phoenix dactylifera l. pinnae (the green leaves of dates palm) were used as natural reinforcing (strengthening) fibers to improve the mechanical properties of polyester as a matrix material, the fibers of the green leaves of dates palm were used in two lengths, 10 and 20mm with five rates of 0, 2.5, 5, 10, and 20% , where the reinforcing with the leaves fibers increases the hardness strength from 76.5 to be about 86.55 , the Impact value raised from about 0.313 to 0.461 , in addition to that the flexural strength from 2.66 to be about 55 , and the thermal conductivity increases from 2.54 𝑤∕𝑚.℃ to 5.41 𝑤∕𝑚.℃. The results of the present search explains that the composite samples reinforced at rate 20% and 10mm fiber length
... Show MoreLeishmaniasis is a widespread parasitic disease caused by Leishmania parasite, this disease considers a major health problem among worldwide. Treatments available are expensive or with cytotoxic side effect. This study was aimed to investigate the effect of an herbal new compound, called artemisinin, derived from a Chinese plant called Artemisia annua. Various concentrations were studied in vitro against L. tropica amastigotes by chamber counting to investigate its effect on the proliferation of promastigotes. Three incubation periods were adopted (24, 48, 72) hours. The results showed a significant decrease in surviving promastigotes, in parallel with the normal parasite count of untreated promastigotes, along the periods studied. This stu
... Show MoreLeishmaniasis is a widespread parasitic disease caused by Leishmania parasite, this disease considers a major health problem among worldwide. Treatments available are expensive or with cytotoxic side effect. This study was aimed to investigate the effect of an herbal new compound, called artemisinin, derived from a Chinese plant called Artemisia annua. Various concentrations were studied in vitro against L. tropica amastigotes by chamber counting to investigate its effect on the proliferation of promastigotes. Three incubation periods were adopted (24, 48, 72) hours. The results showed a significant decrease in surviving promastigotes, in parallel with the normal parasite count of untreated promastigotes, along the periods studied. Th
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This research aims to introduce a new technique-off-site and self-form segmental concrete masonry arches fabrication, without the need of construction formwork or centering. The innovative construction method in the current study encompasses two construction materials forms the self-form masonry arches, wedge-shape plain concrete voussoirs, and carbon fiber-reinforced polymer (CFRP) composites. The employment of CFRP fabrics was for two main reasons: bonding the voussoirs and forming the masonry arches. In addition, CFRP proved to be efficient for strengthening the extrados of the arch rings under service loadings. An experimental test was conducted on four sophisticated masonry arch specimens. The research parameters were the Keystone thic
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This paper deals with the nonlinear large-angle bending dynamic analysis of curved beams which investigated by modeling wave’s transmission along curved members. The approach depends on the wave propagation in one-dimensional structural element using the method of characteristics. The method of characteristics (MOC) is found to be a suitable method for idealizing the wave propagation inside structural systems. Timoshenko’s beam theory, which includes transverse shear deformation and rotary inertia effects, is adopted in the analysis. Only geometrical non-linearity is considered in this study and the material is assumed to be linearly elastic. Different boundary conditions and loading cases are examined.
From the results obtai
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In the petroleum industry, multiphase flow dynamics within the tubing string have gained significant attention due to associated challenges. Accurately predicting pressure drops and wellbore pressures is crucial for the effective modeling of vertical lift performance (VLP). This study focuses on predicting the multiphase flow behavior in four wells located in the Faihaa oil field in southern Iraq, utilizing PIPESIM software. The process of selecting the most appropriate multiphase correlation was performed by utilizing production test data to construct a comprehensive survey data catalog. Subsequently, the results were compared with the correlations available within the PIPESIM software. The outcomes reveal that the Hagedorn and Brown (H
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Myriophyllum spicatum distribution in Al-Burgga marsh, Hor Al-Hammar was described in relation to some of the physical-chemical properties for its habitat (water depth, light penetration, water temperature, water salinity, pH, dissolved oxygen, Ca+2, Mg+2, reactive NO2=, reactive NO3-1, and reactive PO4-3) during 2011, seasonally. CANOCO ordination program (CCA) was used to analyse the data. Its vegetation cover percentage was with its peak at summer, its value was 90 %, while the lowest value was 20 % in winter. Statistically, Positive relationships for WT, sal., Ca+2, Mg+2, reactive NO2=, reactive NO3-1, and reactive PO4-3 with the vegetation cover percentage were observed. While, negative relationships for WD, pH, and DO with the ve
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