Drainage basin morphometry of the Encadenadas del Oeste lakes, Argentina

The Las Encadenadas basin can be defined as an endorheic fluviolacustrine system. The aim of this study is to identify hydrographic sectors and subbasins within the Encadenada’s drainage basin and analyze the former’s morphometric properties including hypsometry. The morphometric analysis allowed for quantification of variables and indices for example area, perimeter, total length of streams, etc. Hypsometric curves were also plotted for each subbasin and finally, principal components analysis was used to sort basins based on results from individually calculated parameters and indices. This study’s aim was to define for the first time the various drainage subbasins that comprise the Encadenadas del Oeste’s basin. The characterization of these units shows that the basin is morphologically diverse due to the dynamic fluvial activity that prevails within its limits. One of the above mentioned morphological units are the alluvial cones which form at the mouths of the mainstreams and delineate the bases of the different subbasins. The drainage network exhibits overall a low level of ramification and hierarchy which is likely due to the sedimentary nature and high permeability of the sub-surface soil.Fil: Geraldi, Alejandra Mabel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina. Universidad Nacional del Sur; ArgentinaFil: Piccolo, Maria Cintia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina. Universidad Nacional del Sur; ArgentinaFil: Perillo, Gerardo Miguel E.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto Argentino de Oceanografía. Universidad Nacional del Sur. Instituto Argentino de Oceanografía; Argentina. Universidad Nacional del Sur; Argentin

Spatial analysis for the distribution of cells in tissue sections

Spatial analysis, playing an essential role in data mining, is applied in a considerable number of fields. It is because of its broad applicability that dealing with the interdisciplinary issues is becoming more prevalent. It aims at exploring the underlying patterns of the data. In this project, we will employ the methodology that we utilize to tackle spatial problems to investigate how the cells distribute in the infected tissue sections and if there are clusters existing among the cells. The cells that are neighboring to the viruses are of interest. The data were provided by the Medetect Company in the form of 2-dimensional point data. We firstly adopted two common spatial analysis methods, clustering methods and proximity methods. In addition, a method for constructing a 2-dimensional hull was developed in order to delineate the compartments in tissue sections. A binomial test was conducted to evaluate the results. It is detectable that the clusters do exist among cells. The immune cells would accumulate around the viruses. We also found different patterns near and far away from viruses. This study implicates that the cells are interactive with each other and thus present the spatial patterns. However, our analyses are restricted in a planar circumstance instead of treating them in 3-dimensional space. For the further study, the spatial analysis could be carried out in three dimensions.It has been popular to utilize the heuristic methods or the existing methods to discover new findings and explain the mysterious phenomena in other subjects. And it is known that everything in nature relates to each other. In this sense, we could assume that the entire distribution of objects is relative to the locations of individuals. The idea of my work is attempting to explore this spatial relationship existing among cells. In my project, the relationships between individual cells or groups of cells are interesting. Our data is presented like the point cloud. It is doubted that if there are any groups existing among these points and if the viruses have neighbors. The methods are mainly categorized into three parts. The first method is to integrate the similar objects into groups. Here the similar objects could be the ones that are close to each other. The second method analyzes the degree of closeness between objects and looks for the neighbors of viruses. The last method can be used to draw the border of a point cloud, which seems like constructing the boundary of districts. Within each method, we carried out the corresponding case studies. Since similar objects can be grouped together, it is interesting to look into the details of each group. Thus we can know which two objects are similar in the same group. Basically, different types of cells in the same group can be checked and studied. In the closeness analysis, we found that some cells are indeed closer to each other. The constructed border could help us know the shape of point clouds. It can be concluded that the spatial relationship does exist among the cells. Groups of cells can be identified at a large extent. And one certain type of cells could be more attracted by some cells from a local level. However, this study is carried out in a 2D space. Actually, we neglect the real shape of cells which have heights. This could be a more interesting topic in the future

Contextual impacts on industrial processes brought by the digital transformation of manufacturing: a systematic review

The digital transformation of manufacturing (a phenomenon also known as "Industry 4.0" or "Smart Manufacturing") is finding a growing interest both at practitioner and academic levels, but is still in its infancy and needs deeper investigation. Even though current and potential advantages of digital manufacturing are remarkable, in terms of improved efficiency, sustainability, customization, and flexibility, only a limited number of companies has already developed ad hoc strategies necessary to achieve a superior performance. Through a systematic review, this study aims at assessing the current state of the art of the academic literature regarding the paradigm shift occurring in the manufacturing settings, in order to provide definitions as well as point out recurring patterns and gaps to be addressed by future research. For the literature search, the most representative keywords, strict criteria, and classification schemes based on authoritative reference studies were used. The final sample of 156 primary publications was analyzed through a systematic coding process to identify theoretical and methodological approaches, together with other significant elements. This analysis allowed a mapping of the literature based on clusters of critical themes to synthesize the developments of different research streams and provide the most representative picture of its current state. Research areas, insights, and gaps resulting from this analysis contributed to create a schematic research agenda, which clearly indicates the space for future evolutions of the state of knowledge in this field

Real-time spatial modeling to detect and track resources on construction sites

For more than 10 years the U.S. construction industry has experienced over 1,000 fatalities annually. Many fatalities may have been prevented had the individuals and equipment involved been more aware of and alert to the physical state of the environment around them. Awareness may be improved by automatic 3D (three-dimensional) sensing and modeling of the job site environment in real-time. Existing 3D modeling approaches based on range scanning techniques are capable of modeling static objects only, and thus cannot model in real-time dynamic objects in an environment comprised of moving humans, equipment, and materials. Emerging prototype 3D video range cameras offer another alternative by facilitating affordable, wide field of view, automated static and dynamic object detection and tracking at frame rates better than 1Hz (real-time). This dissertation presents an imperical work and methodology to rapidly create a spatial model of construction sites and in particular to detect, model, and track the position, dimension, direction, and velocity of static and moving project resources in real-time, based on range data obtained from a three-dimensional video range camera in a static or moving position. Existing construction site 3D modeling approaches based on optical range sensing technologies (laser scanners, rangefinders, etc.) and 3D modeling approaches (dense, sparse, etc.) that offered potential solutions for this research are reviewed. The choice of an emerging sensing tool and preliminary experiments with this prototype sensing technology are discussed. These findings led to the development of a range data processing algorithm based on three-dimensional occupancy grids which is demonstrated in detail. Testing and validation of the proposed algorithms have been conducted to quantify the performance of sensor and algorithm through extensive experimentation involving static and moving objects. Experiments in indoor laboratory and outdoor construction environments have been conducted with construction resources such as humans, equipment, materials, or structures to verify the accuracy of the occupancy grid modeling approach. Results show that modeling objects and measuring their position, dimension, direction, and speed had an accuracy level compatible to the requirements of active safety features for construction. Results demonstrate that video rate 3D data acquisition and analysis of construction environments can support effective detection, tracking, and convex hull modeling of objects. Exploiting rapidly generated three-dimensional models for improved visualization, communications, and process control has inherent value, broad application, and potential impact, e.g. as-built vs. as-planned comparison, condition assessment, maintenance, operations, and construction activities control. In combination with effective management practices, this sensing approach has the potential to assist equipment operators to avoid incidents that result in reduce human injury, death, or collateral damage on construction sites.Civil, Architectural, and Environmental Engineerin

Optimization and Mining Methods for Effective Real-Time Embedded Systems

L’Internet des objets (IoT) est le réseau d’objets interdépendants, comme les voitures autonomes, les appareils électroménagers, les téléphones intelligents et d’autres systèmes embarqués. Ces systèmes embarqués combinent le matériel, le logiciel et la connection réseau permettant le traitement de données à l’aide des puissants centres de données de l’informatique nuagique. Cependant, la croissance exponentielle des applications de l’IoT a remodelé notre croyance sur l’informatique nuagique, et des certitudes durables sur ses capacités ont dû être mises à jour. De nos jours, l’informatique nuagique centralisé et classique rencontre plusieurs défis, tels que la latence du trafic, le temps de réponse et la confidentialité des données. Alors, la tendance dans le traitement des données générées par les dispositifs embarqués interconnectés consiste à faire plus de calcul au niveau du dispositif au bord du réseau. Cette possibilité de faire du traitement local aide à réduire la latence pour les applications temps réel présentant des fortes contraintes temporelles. Aussi, ça permet d’améliorer le traitement des quantités massives de données générées par ces périphériques. Réussir cette transition nécessite la conception de systèmes embarqués de haute performance en explorant efficacement les alternatives de conception (i.e. Exploration efficace de l’espace des solutions), en optimisant la topologie de déploiement des applications temps réel sur des architectures multi-processeurs (i.e. la façon dont le logiciel utilise le matériel) , et des algorithme d’exploration permettant un fonctionnement plus intelligent de ces dispositifs. Des efforts de recherche récents ont conduit à diverses approches automatisées facilitant la conception et l’amélioration du fonctionnement des système embarqués. Cependant, ces techniques existantes présentent plusieurs défis majeurs. Ces défis sont fortement présents sur les systèmes embarqués temps réel. Quatre des principaux défis sont : (1) Le manque de techniques d’exploration de données en ligne permettant l’amélioration des performances des systèmes embarqués. (2) L’utilisation inefficace des ressources informatiques des systèmes multiprocesseurs lors du déploiement de logiciels là dessus ; (3) L’exploration pseudo-aléatoire de l’espace des solutions (4) La sélection de la configuration appropriée à partir de la listes des solutions optimales obtenue.----------ABSTRACT: The Internet of things (IoT) is the network of interrelated devices or objects, such as selfdriving cars, home appliances, smart-phones and other embedded computing systems. It combines hardware, software, and network connectivity enabling data processing using powerful cloud data centers. However, the exponential rise of IoT applications reshaped our belief on the cloud computing, and long-lasting certainties about its capabilities had to be updated. The classical centralized cloud computing is encountering several challenges, such as traffic latency, response time, and data privacy. Thus, the trend in the processing of the generated data of IoT inter-connected embedded devices has shifted towards doing more computation closer to the device in the edge of the network. This possibility to do on-device processing helps to reduce latency for critical real-time applications and better processing of the massive amounts of data being generated by the these devices. Succeeding this transition towards the edge computing requires the design of high-performance embedded systems by efficiently exploring design alternatives (i.e. efficient Design Space Exploration), optimizing the deployment topology of multi-processor based real-time embedded systems (i.e. the way the software utilizes the hardware), and light mining techniques enabling smarter functioning of these devices. Recent research efforts on embedded systems have led to various automated approaches facilitating the design and the improvement of their functioning. However, existing methods and techniques present several major challenges. These challenges are more relevant when it comes to real-time embedded systems. Four of the main challenges are : (1) The lack of online data mining techniques that can enhance embedded computing systems functioning on the fly ; (2) The inefficient usage of computing resources of multi-processor systems when deploying software on ; (3) The pseudo-random exploration of the design space ; (4) The selection of the suitable implementation after performing the otimization process

Carbon-Dioxide Pipeline Infrastructure Route Optimization And Network Modeling For Carbon Capture Storage And Utilization

Carbon capture, utilization, and storage (CCUS) is a technology value-chain which can help reduce CO2 emissions while ensuring sustainable development of the energy and industrial sectors. However, CCUS requires large-scale deployment of infrastructure for capturing feasible amounts of CO2 that can be capital intensive for stakeholders. In addition, CCUS deployment leads to the development of extensive pipeline corridors, which can be inconsistent with the requirements for future CCUS infrastructure expansion. With the implementation and growth of CCUS technology in the states of North Dakota, Montana, Wyoming, Colorado and Utah in mind, this dissertation has two major goals: (a) to identify feasible corridors for CO2 pipelines; and (b) to develop a CCUS infrastructure network which minimizes project cost. To address these goals, the dissertation introduces the CCSHawk methodology that develops pipeline routes and CCUS infrastructure networks using a variety of techniques such as multi-criteria decision analysis (MCDA), graph network algorithms, natural language processing and linear network optimization. The pipeline route and CCUS network model are designed using open-source data, specifically: geo-information, emission quantities and reservoir properties. The MCDA of the study area reveals that North Dakota, central Wyoming and Eastern Colorado have the highest amount of land suitable for CO2 pipeline corridors. The optimized graph network routing algorithm reduces the overall length of pipeline routes by an average of 4.23% as compared to traditional routing algorithms while maintaining low environmental impact. The linear optimization of the CCUS infrastructure shows that the cost for implementing the technology in the study area can vary between $24.05/tCO2 to$42/tCO2 for capturing 20 to 90MtCO2. The analysis also reveals that there would be a declining economic impact of existing pipeline infrastructure on the future growth of CCUS networks ranging between 0.01 to 1.62$/tCO2 with increasing CO2 capture targets. This research is significant, as it establishes a technique for pipeline route modeling and CCUS economic analysis highly adaptable to various geographic regions. To the best of the author\u27s knowledge, it is also the first economic analysis that considers the effect of pre-existing infrastructure on the growth of CCUS technology for the region. Furthermore, the pipeline route model establishes a schema for considering not only environmental factors but also ecological factors for the study area

Representation and coding of 3D video data

Livrable D4.1 du projet ANR PERSEECe rapport a été réalisé dans le cadre du projet ANR PERSEE (n° ANR-09-BLAN-0170). Exactement il correspond au livrable D4.1 du projet