Finite Element Analyses of Bolted Joints Using Different Thread Modelling Techniques

Intensive finite element analyses (FEA) were carried out to investigate the effects of thread modelling techniques on torque-force relation in threaded joints. The bolt thread was modelled by solid revolving the ISO thread profile and it was meshed using hexahedral elements. The nut thread was modelled either by solid/cut revolving the ISO thread profile, with/without root fillet or by using hexahedral/tetrahedral elements. Excellent agreement was found between torque-force relationship obtained using the FEA, the experimental result and the theoretical analyses. The FEA results showed the torque-force relationship is independent of both the modelling techniques and the mesh size but highly dependent on coefficient of friction between surfaces in contact. The study outputs support the simplified approach to use the tetrahedral elements to model threaded joints. The value of the equivalent coefficient of friction to be used is approximately the sum of the coefficients of friction at the threads and that at the bearing area

Dynamics of three phytophagous mites Tetranychus urticae, Panonychus citri and Eutetranychus orientalis (Acari, Tetranychidae) on citrus in Tunisia

Phytophagous mites of the family Tetranychidae are important pests of citrus in Tunisia, reducing the quality of the product in marketing value every year. In this study, the population dynamics of Tetranychus urticae, Panonychus citri and Eutetranychus orientalis were studied in the nursery and in the field from the beginning of February until the end of May. The results showed that the population dynamics of these three mites varied with temperature. The evolution of the T. urticae population showed three peaks in February, mid-March and April. In addition, the population of E. orientalis showed four peaks in February, March, April and May. For P. citri, this mite is active in winter and spring when climatic conditions are favourable for its development. Field monitoring has shown that the fauna of predatory mites linked to these three plant pests is diverse. Three species (Neoseiulus californicus, Phytoseiulus persimilis, Euseius stipulatus) of the phytoseiid family have been identified. Given the diversification of predatory mites and their importance in the regulation of phytophagous mites, it is necessary to favor biological control by conserving these species, for which it is important to reason chemical control

Effectiveness of Flonicamid 50 wg against Bemisia tabaci (Genn.) under greenhouse conditions in Tunisia.

Flonicamid is a systemic insecticide that acts as a feed blocker with potential use against whiteflies in an IPM program. The efficacy of flonicamid against populations of Bemisia tabaci in geothermal greenhouses in southern Tunisia. Flonicamid was tested in irrigation and foliar spraying on a greenhouse tomato crop. The use of the insecticide in irrigation or foliar spraying showed significant insecticidal activity on eggs, larvae and adults of B. tabaci. They are comparable to those caused by the reference insecticide confidor and different from those of the control on the different stages of development of B. tabaci. It is advisable to use flonicamid in an integrated pest management program in a heated greenhouse

Efficacy of Emamectin benzoate, Spinosad and Tolfenpyrad on Tuta absoluta Meyrick (Lepidoptera: Gelechiidae) infesting a tomato crop under geothermal greenhouses in Southern Tunisia

The tomato leaf miner, Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae), is considered one of the most devastating pests of tomato crops in geothermal greenhouses. It leads to crop losses reaching 100% among farmers and major tomato exporters. In this research, three insecticides, Emamectin benzoate, Spinosad and Tolfenpyrad were evaluated for their efficacy in the control of T. absoluta larvae in heated greenhouses. These three treatments resulted in a significant reduction in the number of T. absoluta larvae compared to an untreated control. The Emamectin Benzoate, Spinosad and Tolfenpyrad used were effective in controlling this leafminer with efficacy percentages above 60% against young larvae and above 50% against old larvae

BrainOS: A Novel Artificial Brain-Alike Automatic Machine Learning Framework

Human intelligence is constituted by a multitude of cognitive functions activated either directly or indirectly by external stimuli of various kinds. Computational approaches to the cognitive sciences and to neuroscience are partly premised on the idea that computational simulations of such cognitive functions and brain operations suspected to correspond to them can help to further uncover knowledge about those functions and operations, specifically, how they might work together. These approaches are also partly premised on the idea that empirical neuroscience research, whether following on from such a simulation (as indeed simulation and empirical research are complementary) or otherwise, could help us build better artificially intelligent systems. This is based on the assumption that principles by which the brain seemingly operate, to the extent that it can be understood as computational, should at least be tested as principles for the operation of artificial systems. This paper explores some of the principles of the brain that seem to be responsible for its autonomous, problem-adaptive nature. The brain operating system (BrainOS) explicated here is an introduction to ongoing work aiming to create a robust, integrated model, combining the connectionist paradigm underlying neural networks and the symbolic paradigm underlying much else of AI. BrainOS is an automatic approach that selects the most appropriate model based on the (a) input at hand, (b) prior experience (a history of results of prior problem solving attempts), and (c) world knowledge (represented in the symbolic way and used as a means to explain its approach). It is able to accept diverse and mixed input data types, process histories and objectives, extract knowledge and infer a situational context. BrainOS is designed to be efficient through its ability to not only choose the most suitable learning model but to effectively calibrate it based on the task at hand