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Younger granite bodies form two arches, the western and the eastern (WA, EA), which extend from the south northwards from the Meatique, ophiolitic group-island arc rocks, to the large older granite outcrop to the north. This paper concerns the feasibility of exploration in the El-Erediya-Ria El-Garah-El-Gidami-El-Missikat Y Gr regions. Fieldwork and remote sensing, together with geochemical, petrochemical, and mineralogical studies, are used to show the controlling factors, routes, and the origins of the deposits. Remote sensing is used to delineate the different rock units. Normal and strike–slip NW, NNE faults, veins, fractured ENE shear zones, and alteration zones of magmatic-hydrothermal fluids are discussed. Granites are considered using petrochemical diagrams as resources. These rocks are categorized as syeno- and alkali feldspar granites. Geochemical binary relationships recognized the granites are highly fractionated calc-alkaline-altered Monzo-, syeno-, and alkali feldspar granites formed in the active continental margin. The observed positive Ga vs. Cu, Zn, and Ni correlations are used for epithermal-magmatic-hydrothermal polymetallic veins and mineralized greisen zones. Negative Cu vs. Mo correlation patterns show probable Mo-porphyry deposits in the deeper zones at the contact point between porphyritic perthite and perthite granitic El-Erediya mass. The Zr/Sr between 1.65 to 2.93 plus fluorites in El-Missikat and up to 5.48 plus fluorites in El-Erediya show both U-poor at El-Missikat and U-rich deposits at El-Erediya. The recorded U, Th, Cu, and Pb vertical zoning sequence of deposition differentiates U aureole and deposit zones. The estimated lateral zoning sequences of deposition of these elements define the centers of U deposits. Pathfinders for the deposit of the examined area include the positive Fe2O3 vs. Mg O and Fe2O3 vs. Ca O correlations, and also negative Rb/Sr vs. K/Na and Rb vs. Sr ones, can be applied to future prospecting for similar U-F-Au-W-Mo deposits in the Eastern Desert of Egypt. [ABSTRACT FROM AUTHOR]

Speech emotion recognition poses challenges due to the varied expression of emotions through intonation and speech rate. In order to reduce the loss of emotional information during the recognition process and to enhance the extraction and classification of speech emotions and thus improve the ability of speech emotion recognition, we propose a novel approach in two folds. Firstly, a feed-forward network with skip connections (SCFFN) is introduced to fine-tune wav2vec 2.0 and extract emotion embeddings. Subsequently, ConLearnNet is employed for emotion classification. ConLearnNet comprises three steps: feature learning, contrastive learning, and classification. Feature learning transforms the input, while contrastive learning encourages similar representations for samples from the same category and discriminative representations for different categories. Experimental results on the IEMOCAP and the EMO-DB datasets demonstrate the superiority of our proposed method compared to state-of-the-art systems. We achieve a WA and UAR of 72.86% and 72.85% on IEMOCAP, and 97.20% and 96.41% on the EMO-DB, respectively. [ABSTRACT FROM AUTHOR]