A general survey was made on the zoosaprophagous insects and their associates in a natural ecosystem in the Egyptian western desert (80 km west of Alexandria, 12 km from the Mediterranean Sea shore). Two types of traps were used, one for flying insects and the other for soil-burrowing insects. Two types of decaying media were used as baits: the common freshwater fish (Tilapia zilii Gerv.) and the desert snail (Eremina desertorum). More than 30 insect species were trapped. The following orders and families were represented: Diptera (Calliphoridae, Sarcophagidae, Muscidae); Coleoptera (Histeridae, Scarabaeidae, Dermestidae, Tenebrionidae); Hymenoptera (Chalcididae, Pteromalidae, Eulophidae, Formicidae). Monthly totals of numbers trapped in each of these groups are presented.
The population of supernumerary parasite progeny of Microplitis rufiventris inside Spodoptera littoralis larvae was studied under different ratios of parasite‐host density. At constant population of host larvae and various levels of parasite density, the regression coefficient revealed that an increase of 0.78% in parasite eggs was found with the increase of every unit population of M. rufiventris. An inverse density‐dependent action of the latter was obtained under various density of S. littoralis larvae at constant population of the parasite. The regression coefficient indicates that for a unit increase in host density 0.06% fall in the number of parasite eggs/host larva was achived. A fraction of supernumerary parasite progeny was found to be eliminated during the egg‐stage. Elimination magnitude of the latter increased with the increase of supernumerary parasite population and vice versa
Zusammenfassung
Beziehungen zwischen Superparasitismus, Parasitendichte und Schicksal der Eier von Microplitis rufiventris Kok. in Spodoptera littoralis Boisd.‐Larven
Es wurde die Population überzähliger Nachkommen von M. rufiventris in S. littoralis‐Larven bei verschiedenen Parasit/Wirt‐Dichterelationen untersucht. Bei konstanter Population von Wirtslarven und verschiedener Parasitendichte zeigt der Regressions‐Koeffizient, daß die Zunahme des Parasiten um eine Populations‐Einheit mit der Zunahme der Parasiteneier um 0,78% verbunden war. Bei verschiedener Wirtslarvendichte und konstanter Parasiten‐Population wurde eine umgekehrt dichteabhängige Aktion des Parasiten beobachtet. Der Regressions‐Koeffizient zeigte, daß mit der Zunahme der Wirtsdichte um eine Einheit die Zahl der Parasiteneier pro Wirtslarve um 0,06% abnahm. Ein Teil der überzähligen Parasitennachkommen wurde während des Eistadiums eliminiert. Der Umfang dieser Eliminierung stieg mit der Zunahme der überzähligen Parasiten und umgekehrt.
The increase in people’s use of mobile messaging services has led to the spread of social engineering attacks like phishing, considering that spam text is one of the main factors in the dissemination of phishing attacks to steal sensitive data such as credit cards and passwords. In addition, rumors and incorrect medical information regarding the COVID-19 pandemic are widely shared on social media leading to people’s fear and confusion. Thus, filtering spam content is vital to reduce risks and threats. Previous studies relied on machine learning and deep learning approaches for spam classification, but these approaches have two limitations. Machine learning models require manual feature engineering, whereas deep neural networks require a high computational cost. This paper introduces a dynamic deep ensemble model for spam detection that adjusts its complexity and extracts features automatically. The proposed model utilizes convolutional and pooling layers for feature extraction along with base classifiers such as random forests and extremely randomized trees for classifying texts into spam or legitimate ones. Moreover, the model employs ensemble learning procedures like boosting and bagging. As a result, the model achieved high precision, recall, f1-score and accuracy of 98.38%.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.