Although Syrian high-yielding wheat cultivars grown under Mediterranean conditions include acceptable levels of resistance to biotic constraints, little is known about their susceptibility to Fusarium head blight (FHB), a harmful disease of wheat cultivation worldwide. The capacity of 16 fungal isolates of four FHB species to confer the disease on spikes and spikelets of six widely grown old and modern Syrian durum and bread wheat cultivars with known in vitro quantitative resistance to FHB was evaluated. Quantitative traits were visually assessed using spray and point inoculations for determining disease development rates, disease incidence (DI) and disease severity (DS) under controlled conditions. Differences in pathogenicity and susceptibility among wheat cultivars were observed, emphasizing the need for breeders to include aggressive isolates or a mixture of isolates representative of the FHB diversity in their screenings for selection of disease resistant cultivars. Bread wheat cultivars showed lower levels of spike and spikelet damage than durum cultivars regardless of the date of cultivar release. Overall, the six wheat cultivars expressed acceptable resistance levels to initial fungal infection and fungal spread. Quantitative traits showed significant correlation with previous standardized area under disease progress curve (AUDPCstandard) data generated in vitro. Thus, the predictive ability of AUDPCstandard appears to be crucial in assessing pathogenicity and resistance in adult wheat plants under controlled conditions. While in the Mediterranean countries the risk of disease is progressively increasing, the preliminary data in this report adds to our knowledge about four FHB species pathogenicity on a Syrian scale, where the environment is quite similar to some Mediterranean wheat growing areas, and show that Syrian cultivars could be new resistant donors with favorable agronomical characteristics in FHB-wheat breeding programs.

Achieving a reliable fault diagnosis for gears under variable operating conditions is a pressing need of industries to ensure productivity by averting unwanted breakdowns. In the present work, a hybrid approach is proposed by integrating Hu invariant moments and an artificial neural network for explicit extraction and classification of gear faults using time-frequency transforms. The Zhao-Atlas-Marks transform is used to convert the raw vibrations signals from the gears into time-frequency distributions. The proposed method is applied to a single-stage spur gearbox with faults created using electric discharge machining in laboratory conditions. The results show the effectiveness of the proposed methodology in classifying the faults in gears with high accuracy.