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This paper uses district level data to estimate the general relationship between climate, income and life expectancy in Peru. The analysis finds that both incomes and life expectancy show hump-shaped relationships, with optimal average annual temperatures around 18-20 Degree Celsius. These estimated relationships were used to simulate the likely effects of both past (1958-2008) and future (2008-2058) climate change. At the aggregate level, future climate change in Peru is estimated to cause a small reduction in average life expectancy of about 0.2 years. This average, however, hides much larger losses in the already hot areas as well as substantial gains in currently cold areas. Similarly, the average impact on incomes is a modest reduction of 2.3 percent, but with some districts experiencing losses of up to 20 percent and others gains of up to 13 percent. Future climate change is estimated to cause an increase in poverty (all other things equal), but to have no significant effect on the distribution of incomes.

Climate --- Climate Change Economics --- Climate Change Mitigation and Green House Gases --- Climates --- Daily temperature --- Effect of temperature --- Environment --- Excessive rainfall --- Extreme events --- Future Climate Change --- Global temperatures --- Health, Nutrition and Population --- Impacts of Climate Change --- Macroeconomics and Economic Growth --- Meteorological stations --- Ocean currents --- Ocean temperatures --- Population Policies --- Science and Technology Development --- Science of Climate Change --- Scientific evidence --- Temperature --- Temperature anomalies --- Temperature anomaly --- Temperature changes --- Temperature increases --- Temperature variations --- Temperatures

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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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Expansion of water resources is a key factor in the socio-economic development of all countries. Dams play a critical role in water storage, especially for areas with unequal rainfall and limited water availability. While the safety of existing dams, periodic re-evaluations and life extensions are the primary objectives in developed countries, the design and construction of new dams are the main concerns in developing countries. The role of dam engineers has greatly changed over recent decades. Thanks to new technologies, the surveillance, monitoring, design and analysis tasks involved in this process have significantly improved. The current edited book is a collection of dam-related papers. The overall aim of this edited book is to improve modeling, simulation and field measurements for different dam types (i.e. concrete gravity dams, concrete arch dams, and embankments). The articles cover a wide range of topics on the subject of dams, and reflect the scientific efforts and engineering approaches in this challenging and exciting research field.

arch dams --- probabilistic --- nonlinear --- seismic --- response correlation --- stochastic --- excavation --- movement --- field --- groundwater --- soil nail --- spatial --- variability --- alkali-silica reaction --- damage --- existing concrete dam --- finite element analysis --- temperature --- saturation degree --- dams --- endurance time analysis --- dynamic capacity --- failure --- seismic effects --- dam safety --- concrete dams --- structural safety and reliability --- finite elements --- earthfill dam --- central clay core --- downstream shoulder --- settlements --- long-term behaviour --- reservoir level fluctuations --- rainfall --- seepage --- geodetic monitoring --- concrete arch dams --- seasonal temperature variations --- crack prediction --- non-linear finite element analyses --- concrete gravity dams --- seismic fragility analysis --- uncertainty quantification --- performance based earthquake engineering --- stability assessment --- instrumentation --- earth dam --- health monitoring --- passive rock bolt --- concrete dam --- progressive failure --- n/a

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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.]

Medicine --- 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

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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.]

Medicine --- 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 --- 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