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Agriculture is certainly the most important food supplier while it globally accounts for more than 70% of water used and contributes significantly to water pollution. Irrigated agriculture is facing rising competition worldwide for access to reliable, low cost, and high-quality water resources. However, irrigation as the major tool and determinant of affecting agricultural productivity and environmental resources plays a critical role in food security and environment sustainability. Innovative irrigation technologies and practices may enhance agricultural water efficiency and production, in the meantime decrease the water demand and quality issues. I am very pleased to invite you to submit manuscripts in agricultural irrigation which assess current challenges and offer improvement approaches and opportunities for future irrigation.

semi-arid regions --- greenhouse gas emission --- model simulation --- spinach --- benchmarking --- leaf mineral composition --- available water capacity --- irrigated crops --- organic production --- site-specific irrigation --- infiltration depth --- pumping plants --- performance indicator --- treated wastewater irrigation --- precision agriculture --- evaluation of performance --- total yield --- row cover --- irrigation --- slope gradient --- farming data --- optimal irrigation time --- lettuce production --- life cycle assessment --- mulch --- monthly changes --- irrigation water use efficiency --- energy audit --- crop evapotranspiration --- irrigation management --- downy mildew --- biomass production --- water application rate --- tomato fruit yield --- temperature variations --- irrigation water regimes --- salinization --- net irrigation requirements --- center-pivot irrigation --- cover crop --- climate change adaptation --- deficit irrigation --- drip irrigation --- Mediterranean region --- principal component analysis --- global sensitivity analysis

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[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.]

melanoma detection --- deep learning --- transfer learning --- ensemble classification --- 3D-CNN --- immunotherapy --- radiomics --- self-attention --- breast imaging --- microwave imaging --- image reconstruction --- segmentation --- unsupervised machine learning --- k-means clustering --- Kolmogorov-Smirnov hypothesis test --- statistical inference --- performance metrics --- contrast source inversion --- brain tumor segmentation --- magnetic resonance imaging --- survey --- brain MRI image --- tumor region --- skull stripping --- region growing --- U-Net --- BRATS dataset --- incoherent imaging --- clutter rejection --- breast cancer detection --- MRgFUS --- proton resonance frequency shift --- temperature variations --- referenceless thermometry --- RBF neural networks --- interferometric optical fibers --- breast cancer --- risk assessment --- machine learning --- texture --- mammography --- medical imaging --- imaging biomarkers --- bone scintigraphy --- prostate cancer --- semisupervised classification --- false positives reduction --- computer-aided detection --- breast mass --- mass detection --- mass segmentation --- Mask R-CNN --- dataset partition --- brain tumor --- classification --- shallow machine learning --- breast cancer diagnosis --- Wisconsin Breast Cancer Dataset --- feature selection --- dimensionality reduction --- principal component analysis --- ensemble method --- n/a