Streamflow simulation in data-scarce regions using remote sensing data in combination with ground-based measurements

Global water resources are currently under unprecedented stress, which is projected to increase due to the influence of multiple factors. Therefore, changes in governance are urgently required to improve water management and water use efficiency while maintaining the health of river systems and their water quantity and quality. Data is crucial in this process; however, most rivers in the world remain ungauged, and in data-scarce regions, the hydrometric and hydrometeorological networks of stations have been decreasing during the last decades. This hinders the implementation of proactive water management approaches that strive towards informed-based decision-making. This cumulative thesis shows how open access global precipitation products can be evaluated, corrected, and used to predict streamflow at the daily temporal scale in data-scarce regions in combination with ground-based measurements by following a three-step approach: i) performance evaluation of different precipitation products over regions with different climates and at multiple temporal scales; ii) development of a novel merging method to improve the representation of precipitation at the daily scale; and iii) assessment of the ability of the novel merged product altogether with state-of-the-art precipitation products to predict daily streamflow over ungauged catchments through the implementation of regionalisation approaches. This thesis showed that the precipitation products perform differently depending on the temporal scale, elevation, and climate; and that these products still have errors in detecting particular precipitation events. These insights served as a basis to develop a novel merging procedure named RF-MEP, which combines data from precipitation products, ground-based measurements, and topographical features to improve the characterisation of precipitation. RF-MEP proved to be a powerful method as the precipitation errors at different temporal scales were substantially reduced, outperforming state-of-the-art precipitation products and merging procedures. The precipitation product derived with RF-MEP has been included in a Chilean precipitation monitor platform from the Center for Climate and Resilience Research (Mawün) and users can apply this method in a friendly manner using the R package RFmerge. This merged product altogether with three state-of-the-art precipitation products was used to implement three regionalisation approaches by calibrating an HBV-like hydrological model over 100 near-natural catchments in Chile. The results showed that although these methods yielded relatively good performances, the precipitation products corrected with daily gauge observations did not necessarily yield the best hydrological and regionalisation performance. Additionally, the hydrological regime of the catchments influenced the performance of the evaluated regionalisation techniques, with the pluvio-nival and raindominated catchments yielding the best and worst performance, respectively. This cumulative dissertation shows that precipitation datasets can help to strive towards informed-based decision-making in data-scarce regions. However, these regions often lack the infrastructure and human capacity to use this type of information efficiently. Therefore, an informed-based decision-making process requires institutional transitions and changes that help address water resources management’s present and future challenges. In this sense, there is a need to move towards data-driven water resources management by implementing strategic approaches that systematically build the capacities and infrastructure of such regions

Multiobjective scheduling for semiconductor manufacturing plants

Scheduling of semiconductor wafer manufacturing system is identified as a complex problem, involving multiple and conflicting objectives (minimization of facility average utilization, minimization of waiting time and storage, for instance) to simultaneously satisfy. In this study, we propose an efficient approach based on an artificial neural network technique embedded into a multiobjective genetic algorithm for multi-decision scheduling problems in a semiconductor wafer fabrication environment

Méthodologie d'aide à la décision multicritère pour l'ordonnancement d'ateliers discontinus

Les ateliers de fabrication de composants électroniques sont caractérisés par un mode opératoire discontinu et flexible, par un flux de produits cyclique et par un fort besoin en équipements qui rend complexe leur gestion. L'objectif des travaux de ce mémoire est l'optimisation multicritère de ces activités de production, donnant lieu à un problème d'ordonnancement à court terme. Le modèle de Simulation à Événements Discrets (SED) habituellement employé est cependant lourdement pénalisé par le temps de calcul nécessaire au traitement de problèmes de taille industrielle. Le SED est ainsi remplacé par une technique de modélisation reposant sur des réseaux de neurones, au sein desquels un algorithme de rétropropagation est mis en oeuvre. Le temps de calcul se trouve alors considérablement réduit. Enfin, lors de la phase d'optimisation, l'utilisation d'un Algorithme Génétique Multicritère (AGM) offre la possibilité de considérer de plusieurs critères d'évaluation. La démarche est validée sur un exemple didactique, représentatif des industries de fabrication de semi-conducteurs. ABSTRACT : Scheduling of electronic components manufacturing systems is identified as a complex task, mainly because of the typical features of the process scheme, such as cyclic flows and the high number of equipment items. Actually, production managers have to cope with various objectives, which contribute also to scheduling complexity. Discrete-event simulation (DES) is one of the most widely used methods to study, analyze, design, and improve manufacturing systems, however their applications in industrial processes takes an enormous computing time. In this study, we propose the DES substitution by an approach based on a neural network technique coupled with a multiobjective genetic algorithm for multi-decision scheduling problems in semiconductor wafer fabrication. The training phase of the neural network was performed by use of the previously developed discrete-event simulator, by using a backpropagation algorithm. The neural networks are then embedded in a multiobjective genetic algorithm (MOGA) to optimize the decision variables and to deal with the set of compromise solutions for the studied criteria, thus giving the optimal Pareto zone solutions. The computing time is then considerably reduced. The program efficiency is validate by means of a simplified industrial examples based on semiconductor manufacturing

Assessment of mono and multi-objective optimization to design a hydrogen supply chain

This work considers the potential future use of hydrogen in fuel cell electrical vehicles to face problems such as global warming, air pollution, energy security and competitiveness. The lack of current infrastructure has been identified as one of the main barriers to develop the hydrogen economy. This work is focused on the design of a hydrogen supply chain through mixed integer linear programming used to find the best solutions for a multiobjective optimization problem in which three objectives are involved, i.e., cost, global warming potential and safety risk. This problem is solved by implementing an 3-constraint method. The solution consists of a Pareto front, corresponding to different design strategies in the associated variable space. Multiple choice decision making is then recommended to find the best solution through an M-TOPSIS analysis. The model is applied to the Great Britain case study previously treated in the dedicated literature. Mono and multicriteria optimizations exhibit some differences concerning the degree of centralization of the network and the selection of the production technology type

Harmonization of Landsat and Sentinel 2 for Crop Monitoring in Drought Prone Areas: Case Studies of Ninh Thuan (Vietnam) and Bekaa (Lebanon)

Proper satellite-based crop monitoring applications at the farm-level often require near-daily imagery at medium to high spatial resolution. The combination of data from different ongoing satellite missions Sentinel 2 (ESA) and Landsat 7/8 (NASA) provides this unprecedented opportunity at a global scale; however, this is rarely implemented because these procedures are data demanding and computationally intensive. This study developed a robust stream processing for the harmonization of Landsat 7, Landsat 8 and Sentinel 2 in the Google Earth Engine cloud platform, connecting the benefit of coherent data structure, built-in functions and computational power in the Google Cloud. The harmonized surface reflectance images were generated for two agricultural schemes in Bekaa (Lebanon) and Ninh Thuan (Vietnam) during 2018–2019. We evaluated the performance of several pre-processing steps needed for the harmonization including the image co-registration, Bidirectional Reflectance Distribution Functions correction, topographic correction, and band adjustment. We found that the misregistration between Landsat 8 and Sentinel 2 images varied from 10 m in Ninh Thuan (Vietnam) to 32 m in Bekaa (Lebanon), and posed a great impact on the quality of the final harmonized data set if not treated. Analysis of a pair of overlapped L8-S2 images over the Bekaa region showed that, after the harmonization, all band-to-band spatial correlations were greatly improved. Finally, we demonstrated an application of the dense harmonized data set for crop mapping and monitoring. An harmonic (Fourier) analysis was applied to fit the detected unimodal, bimodal and trimodal shapes in the temporal NDVI patterns during one crop year in Ninh Thuan province. The derived phase and amplitude values of the crop cycles were combined with max-NDVI as an R-G-B false composite image. The final image was able to highlight croplands in bright colors (high phase and amplitude), while the non-crop areas were shown with grey/dark (low phase and amplitude). The harmonized data sets (with 30 m spatial resolution) along with the Google Earth Engine scripts used are provided for public use

Assessment of a Comparative Bayesian-Enhanced Population-Based Decision Model for COVID-19 Critical Care Prediction in the Dominican Republic Social Security Affiliates

Introduction: The novel coronavirus disease 2019 (COVID-19) has been a major health concern worldwide. This study aims to develop a Bayesian model to predict critical outcomes in patients with COVID-19. Methods: Sensitivity and specificity were obtained from previous meta-analysis studies. The complex vulnerability index (IVC-COV2 index for its abbreviation in Spanish) was used to set the pretest probability. Likelihood ratios were integrated into a Fagan nomogram for posttest probabilities, and IVC-COV2 + National Early Warning Score (NEWS) values and CURB-65 scores were generated. Absolute and relative diagnostic gains (RDGs) were calculated based on pretest and posttest differences. Results: The IVC-COV2 index was derived from a population of 1,055,746 individuals and was based on mortality in high-risk (71.97%), intermediate-risk (26.11%), and low-risk (1.91%) groups. The integration of models in which IVC-COV2 intermediate + NEWS ≥ 5 and CURB-65 \u3e 2 led to a number needed to (NNT) diagnose that was slightly improved in the CURB-65 model (2 vs. 3). A comparison of diagnostic gains revealed that neither the positive likelihood ratio (P = 0.62) nor the negative likelihood ratio (P = 0.95) differed significantly between the IVC-COV2 NEWS model and the CURB-65 model. Conclusion: According to the proposed mathematical model, the combination of the IVC-COV2 intermediate score and NEWS or CURB-65 score yields superior results and a greater predictive value for the severity of illness. To the best of our knowledge, this is the first population-based/mathematical model developed for use in COVID-19 critical care decision-making

Growth of pineapple plantlets during acclimatisation can be monitored through automated image analysis of the canopy

[EN] Pineapple is an economically important tropical fruit crop, but the lack of adequate planting material limits its productivity. A range of micropropagation protocols has been developed over the years to address this shortfall. Still, the final stage of micropropagation, i.e. acclimatisation, remains a challenge as pineapple plantlets grow very slowly. Several studies have been conducted focusing on this phase and attempting to improve plantlet growth and establishment, which requires tools for the non-destructive evaluation of growth during acclimatisation. This report describes the use of semi-automated and automated image analysis to quantify canopy growth of pineapple plantlets, during five months of acclimatisation. The canopy area progressively increased during acclimatisation, particularly after 90 days. Regression analyses were performed to determine the relationships between the automated image analysis and morphological indicators of growth. The mathematical relationships between estimations of the canopy area and the fresh and dry weights of intact plantlets, middle-aged leaves (D leaves) and roots showed determination coefficients (R2) between 0.84 and 0.92. We propose an appropriate tool for the simple, objective and non-destructive evaluation of pineapple plantlets growth, which can be generally applied for plant phenotyping, to reduce costs and develop streamlined pipelines for the assessment of plant growthThis research was not covered by any specific grant but supported by internal funds from the Bioplant Centre (Cuba), the Agricultural Research Council-Tropical and Subtropical Crops (South Africa), and the Universitat Politecnica de Valencia (Spain). Authors are also grateful to Mrs Lelurlis Napoles for her experienced technical assistance.Soto, G.; Lorente, G.; Mendoza, J.; Báez, ED.; Lorenzo, CM.; Rodríguez, R.; Hajari, E.... (2020). Growth of pineapple plantlets during acclimatisation can be monitored through automated image analysis of the canopy. The Eurobiotech Journal. 4(4):223-229. https://doi.org/10.2478/ebtj-2020-0026S22322944Chen H, Hu B, Zhao L, Shi D, She Z, Huang X, Priyadarshani S, Niu X, Qin Y. Differential expression analysis of reference genes in pineapple (Ananas comosus l.) during reproductive development and response to abiotic stress, hormonal stimuli. Trop Plant Biol 2019; 12: 67-77.Nath V, Kumar G, Pandey S, Pandey S. Impact of climate change on tropical fruit production systems and its mitigation strategies. In: Sheraz Mahdi S (ed.) Climate Change and Agriculture in India: Impact and Adaptation. 2019. Springer, Berlin, pp. 129-146.Priyadarshani S, Cai H, Zhou Q, Liu Y, Cheng Y, Xiong J, Patson DL, Cao S, Zhao H, Qin Y. An efficient Agrobacterium mediated transformation of pineapple with GFP-tagged protein allows easy, non-destructive screening of transgenic pineapple plants. Biomolecules 2019; 9(10): 617.Wali N. Pineapple (Ananas comosus). In: Nabavi S, Sanches Silva A (eds.) Nonvitamin and nonmineral nutritional nupplements. 2019. Elsevier, pp. 367-373.Escalona M, Lorenzo JC, González B, Daquinta M, Borroto C, González JL, Desjardines Y. Pineapple micropropagation in temporary immersion systems. Plant Cell Rep 1999; 18: 743-748.Gómez D, Escalante D, Hajari E, Vicente O, Sershen, Lorenzo JC. Assessing the effects of in vitro imposed water stress on pineapple growth in relation to biochemical stress indicators using polynomial regression analysis. Not Bot Horti Agrobot Cluj 2020; 48: 162-170.Daquinta M, Benegas R. Brief review of tissue culture of pineapple. Pineap News 1997; 3: 7-9.Botella J, Fairbairn D. Present and future potential of pineapple biotechnology. Acta Hort 2005; 622: 23-28.Wang M-L, Uruu G, Xiong L, He X, Nagai C, Cheah K, Hu J, Nan G-L, Sipes B, Atkinson H. Production of transgenic pineapple (Ananas comosus (L.) Merr.) plants via adventitious bud regeneration. In Vitro Cell Dev Biol-Plant 2009; 45: 112-121.Loyola-González O, Medina-Pérez MA, Hernández-Tamayo D, Monroy R, Carrasco-Ochoa JA, García-Borroto M. A pattern-based approach for detecting pneumatic failures on Temporary Immersion Bioreactors. Sensors 2019; 19(2): 414.Parveen S, Mir H, Ranjan T, Pal AK, Kundu M. Effect of surface sterilants on in vitro establishment of pineapple (Ananas comosus (L.) Merill.) cv. Kew. Curr J Appl Sci Technol 2019; 33(2): 1-6.Venâncio JB, Araújo WF, Chagas EA. Acclimatization of micropropagated seedlings of pineapple cultivars on organic substrates. Científica 2019; 47: 52-61.Yanes-Paz E, González J, Sánchez R (2000) A technology of acclimatization of pineapple vitroplants. Pineap News 2000; 7: 5-6.González R, Laudat T, Arzola M, Méndez R, Marrero P, Pulido L, Dibut B, Lorenzo JC. Effect of Azotobacter chroococcum on in vitro pineapple plants’ growth during acclimatization. In Vitro Cell Dev Biol-Plant 2010; 47(3): 387-390.González R, Serrato R, Molina J, Aragón C, Olalde V, Pulido L, Dibut B, Lorenzo JC. Biochemical and physiological changes produced by Azotobacter chroococcum (INIFAT5 strain) on pineapple in vitro-plantlets during acclimatization. Acta Physiol Plant 2013; 35: 3483-3487.Mengesha A, Ayenew B, Tadesse T. Acclimatization of in vitro propagated pineapple (Ananas comosus (L.), var. Smooth cayenne) plantlets to ex vitro condition in Ethiopia. Am J Plant Sci 2013; 4(2): 317-323.Rodríguez-Escriba RC, Rodríguez R, López D, Lorente GY, Pino Y, Aragón CE, Garza Y, Podestá FE, González-Olmedo JL. High light intensity increases the CAM expression in “MD-2” micro-propagated pineapple plants at the end of the acclimatization stage. Am J Plant Sci 2015; 6(19): 3109-3118.Rodríguez-Escriba RC, Rodríguez-Cartaya ID, Lorente GY, López D, Izquierdo RE, Borroto LS, Garza-García Y, Aragón CE, Podestá FE, Rodríguez R. Efecto del déficit hídrico sobre cambios morfo-fisiológicos y bioquímicos en plantas micropropagadas de piña MD-2 en la etapa final de aclimatización. Cult Trop 2016; 37: 64-73.Lorente-González GY, Pino-Legrat Y, Rodríguez-Escriba RC, Pérez-Borroto LS, Nápoles-Borrero L, Mendoza-Rodríguez J, Cardoso D, Alonso A, Rodríguez-Sánchez R, González-Olmedo J. Foliar fertilization of ‘MD-2’ pineapple plants (Ananas comosus var. comosus) during the acclimatization phase. Newsletter of the Pineapple Working Group, International Society for Horticultural Science 2018; 25: 13-17.Atkinson JA, Lobet G, Noll M, Meyer PE, Griffiths M, Wells DM. Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large-scale genetic studies. GigaScience 2017; 6: gix084.Pound MP, Atkinson JA, Townsend AJ, Wilson MH, Griffiths M, Jackson AS, Bulat A, Tzimiropoulos G, Wells DM, Murchie EH. Deep machine learning provides state-of-the-art performance in image-based plant phenotyping. GigaScience 2017; 6: gix083.Gupta SD, Ibaraki Y, Pattanayak A. Development of a digital image analysis method for real-time estimation of chlorophyll content in micropropagated potato plants. Plant Biotech Rep 2013; 7: 91-97.Niazian M, Sadat-Noori SA, Abdipour M, Tohidfar M, Mortazavian SMM. Image processing and artificial neural network-based models to measure and predict physical properties of embryogenic callus and number of somatic embryos in ajowan (Trachyspermum ammi (L.) Sprague). In Vitro Cell Dev Biol-Plant 2018; 54: 54-68.Ollier M, Talle V, Brisset AL, Le Bihan Z, Duerr S, Lemmens M, Goudemand E, Robert O, Hilbert JL, Buerstmayr H. Whitened kernel surface: A fast and reliable method for assessing Fusarium severity on cereal grains by digital picture analysis. Plant Breed 2019; 138: 69-81.Wang G, Sun Y, Wang J. Automatic image-based plant disease severity estimation using deep learning. Comp Intel Neurosci 2017; 2017: 2917536.Asaari MSM, Mishra P, Mertens S, Dhondt S, Inzé D, Wuyts N, Scheunders P. Close-range hyperspectral image analysis for the early detection of stress responses in individual plants in a high-throughput phenotyping platform. ISPRS J Photogram Rem Sens 2018; 138: 121-138.Py C, Lacoeuille JJ, Teisson C. L´ananas, sa culture, ses produits. Techniques agricoles et productions tropicales vol. 33. Maisoenneuve and Larose 1984; Paris, pp. 44-45.Ivanov Z. The Agricultural Experimentation 1989. Pueblo y Educación, Havana, pp. 332.Aguilar M, Pozo J, Aguilar F, García A, Fernández I, Negreiros J, Sánchez-Hermosilla J. Application of close-range photogrammetry and digital photography analysis for the estimation of leaf area index in a greenhouse tomato culture. Int Arch Photogram Rem Sens Spat Inf Sci 2010; 38(5): 5-10.Minervini M, Abdelsamea MM, Tsaftaris SA. Image-based plant phenotyping with incremental learning and active contours. Ecol Inf 2014; 23: 35-48.Minervini M, Giuffrida MV, Perata P, Tsaftaris SA. Phenotiki: An open software and hardware platform for affordable and easy image‐based phenotyping of rosette‐shaped plants. The Plant J 2017; 90: 204-216.Ubbens J, Cieslak M, Prusinkiewicz P, Stavness I. The use of plant models in deep learning: an application to leaf counting in rosette plants. Plant Meth 2018; 14: 6.Rincón Guerrero N, Olarte Quintero MA, Pérez Naranjo JC. Leaf area measurement in photographs taken with a webcam, a cell phone or a semi professional camera. Rev Fac Nac Agron Medellín 2012; 65: 6399-6405.Guo W, Zheng B, Duan T, Fukatsu T, Chapman S, Ninomiya S (2017) EasyPCC: benchmark datasets and tools for high-throughput measurement of the plant canopy coverage ratio under field conditions. Sensors 2017; 17: 798.Chien C-L, Tseng D-C (2011) Color image enhancement with exact HSI color model. Int J Innov Comp Inf Cont 2011; 7: 6691-6710

The Habitat Types of Freshwater Prawns (Palaemonidae: <em>Macrobrachium</em>) with Abbreviated Larval Development in Mesoamerica (Mexico, Guatemala and Belize)

The freshwater prawns of genus Macrobrachium with abbreviated larval development have been reported from a diversity of freshwater habitats (caves, springs and primary streams from so-long basins). Here we analysed 360 sites around the Mesoamerican region (Mexico, Guatemala and Belize). At each site, we measured temperature, salinity oxygen dissolved, pH, altitude and water flow velocity values. We documented the riparian vegetation and occurrence and abundance of Macrobrachium populations. All these values were analysed by multi-dimensional scaling and principal components analysis in order to identify key features of the environmental data that determine the habitat types and habitat diversity. The results show that there are Macrobrachium populations in 70 sites inhabiting two main habitats: Lotic and Lentic; and each one have fours subhabitat types. All are defined by altitude range and water velocity that involve the temperature and oxygen variables. In some specific areas, the karstic values on salinity and pH defined some groups. Within the lentic habitats, we identified the following subhabitats: (1) temperate streams, (2) neutral streams, (3) high dissolved oxygen, (4) multifactorial; and for lotic habitats, we identified: (5) water high carbonate, (6) moderate dissolved oxygen, (7) low dissolved oxygen, and (8) high altitude streams. All these subhabitats are located on the drainage basin to the Atlantic Sea, including places from 50 to 850 meters above sea levels and have specifically ranges from temperature, water velocity, pH and salinity for some cases. Also, the geological analysis from the basins where the Macrobrachium inhabit is located showed that the geological faults align with these habitat subdivisions. In this chapter, we discuss the environmental heterogeneity, morphological plasticity and their relationship to physiographic regions across the species ranges

Análisis de la función y ultraestructura mitocondrial en ratones albinos sanos tratados con medicamentos para insuficiencia cardíaca.

 ResumenIntroducción: la actividad mitocondrial es esencial para el músculo cardíaco y esquelético. La relación entre la disfunción mitocondrial y diferentes condiciones cardiovasculares ha sido bien descrita. El tratamiento farmacológico de la insuficiencia cardíaca implica diferentes medicamentos como: inhibidores de la enzima convertidora de la angiotensina, bloqueadores B-adrenérgicos, glucósidos digitálicos y diuréticos. Los beneficios clínicos del tratamiento son claros, sin embargo, el papel de estos fármacos en el metabolismo mitocondrial no esta bien establecido.Objetivo del estudio: El objetivo de nuestro estudio fue analizar las características estructurales y funcionales de las mitocondrias del músculo cardíaco y esquelético en ratones tratados con fármacos habitualmente utilizados para la insuficiencia cardíaca y compararlo con un grupo control.Métodos: Veinticinco ratones albinos divididos en cinco grupos fueron tratados con la medicación para insuficiencia cardíaca durante 30 días (grupo I a IV). 30 días después del tratamiento se sacrificaron, el corazón y el músculo esquelético se analizaron y se compararon con un grupo control (V).Resultados: La actividad enzimática se incrementó ligeramente en los grupos tratados con medicamentos insuficiencia cardiaca en comparación con el grupo control (p&gt; 0,05). morfología mitocondrial se modificó significativamente en los grupos tratados en comparación con el grupo control, además, el área mitocondrial fue significativamente mayor en los grupos tratados, tanto en el músculo cardíaco y estriado.Conclusiones: Concluimos que la medicación insuficiencia cardíaca podría producir modificaciones en la función mitocondrial; creemos que las mitocondrias pueden mantener la actividad enzimática mediante el aumento de tamaño y modificación de la morfología.</p