Quarterly literature review of the remote sensing of natural resources

The Technology Application Center reviewed abstracted literature sources, and selected document data and data gathering techniques which were performed or obtained remotely from space, aircraft or groundbased stations. All of the documentation was related to remote sensing sensors or the remote sensing of the natural resources. Sensors were primarily those operating within the 10 to the minus 8 power to 1 meter wavelength band. Included are NASA Tech Briefs, ARAC Industrial Applications Reports, U.S. Navy Technical Reports, U.S. Patent reports, and other technical articles and reports

Automated Detection of Electric Energy Consumption Load Profile Patterns

[EN] Load profiles of energy consumption from smart meters are becoming more and more available, and the amount of data to analyse is huge. In order to automate this analysis, the application of state-of-the-art data mining techniques for time series analysis is reviewed. In particular, the use of dynamic clustering techniques to obtain and visualise temporal patterns characterising the users of electrical energy is deeply studied. The performed review can be used as a guide for those interested in the automatic analysis and groups of behaviour detection within load profile databases. Additionally, a selection of dynamic clustering algorithms have been implemented and the performances compared using an available electric energy consumption load profile database. The results allow experts to easily evaluate how users consume energy, to assess trends and to predict future scenarios.The data analysed has been facilitated by the Spanish Distributor Iberdrola Electrical Distribution S.A. as part of the research project GAD (Active Management of the Demand), national project by DEVISE 2010 funded by the INGENIIO 2010 program and the CDTI (Centre for Industrial Technology Development), Business Public Entity dependent of the Ministry of Economy and Competitiveness of the Government of Spain.Benítez, I.; Diez, J. (2022). Automated Detection of Electric Energy Consumption Load Profile Patterns. Energies. 15(6):1-26. https://doi.org/10.3390/en1506217612615

Solutions for detection of non-technical losses in the electricity grid: a review

This paper is a review of literature with an analysis on a selection of scienti c studies for detection of non-technical losses. Non-technical losses occurring in the electric grid at level of transmission or of distribution have negative impact on economies, affecting utilities, paying consumers and states. The paper is concerned with the lines of research pursued, the main techniques used and the limitations on current solutions. Also, a typology for the categorization of solutions for detection of non-technical losses is proposed and the sources and possible attack/vulnerability points are identifi ed. The selected literature covers a wide range of solutions associated with non-technical losses. Of the 103 selected studies, 6 are theoretical, 25 propose hardware solutions and 72 propose non-hardware solutions. Data based classi cation models and data from consumption with high resolution are respectively required in about 47% and 35% of the reported solutions. Available solutions cover a wide range of cases, with the main limitation found being the lack of an uni ed solution, which enables the detection of all kinds of non-technical losses

Machine Learning and Deep Learning applications for the protection of nuclear fusion devices

This Thesis addresses the use of artificial intelligence methods for the protection of nuclear fusion devices with reference to the Joint European Torus (JET) Tokamak and the Wendenstein 7-X (W7-X) Stellarator. JET is currently the world's largest operational Tokamak and the only one operated with the Deuterium-Tritium fuel, while W7-X is the world's largest and most advanced Stellarator. For the work on JET, research focused on the prediction of “disruptions”, and sudden terminations of plasma confinement. For the development and testing of machine learning classifiers, a total of 198 disrupted discharges and 219 regularly terminated discharges from JET. Convolutional Neural Networks (CNNs) were proposed to extract the spatiotemporal characteristics from plasma temperature, density and radiation profiles. Since the CNN is a supervised algorithm, it is necessary to explicitly assign a label to the time windows of the dataset during training. All segments belonging to regularly terminated discharges were labelled as 'stable'. For each disrupted discharge, the labelling of 'unstable' was performed by automatically identifying the pre-disruption phase using an algorithm developed during the PhD. The CNN performance has been evaluated using disrupted and regularly terminated discharges from a decade of JET experimental campaigns, from 2011 to 2020, showing the robustness of the algorithm. Concerning W7-X, the research involved the real-time measurement of heat fluxes on plasma-facing components. THEODOR is a code currently used at W7-X for computing heat fluxes offline. However, for heat load control, fast heat flux estimation in real-time is required. Part of the PhD work was dedicated to refactoring and optimizing the THEODOR code, with the aim of speeding up calculation times and making it compatible with real-time use. In addition, a Physics Informed Neural Network (PINN) model was proposed to bring thermal flow computation to GPUs for real-time implementation

Data bases and data base systems related to NASA's Aerospace Program: A bibliography with indexes

This bibliography lists 641 reports, articles, and other documents introduced into the NASA scientific and technical information system during the period January 1, 1981 through June 30, 1982. The directory was compiled to assist in the location of numerical and factual data bases and data base handling and management systems

ANALYSIS OF IMMUNOREGULATORY BIOMARKERS IN NON-SMALL CELL LUNG CANCER

[EN] Lung cancer is the leading cause of cancer-related death worldwide, and is the third most common cancer type; it can be classified into two subgroups based on histology: non-small-cell lung cancer (NSCLC) and small-cell lung cancer (SCLC). The 5-year survival still remains poor and despite the existence of several distinct tumour phenotypes, therapeutic decisions are mainly based on clinical features such as stage or performance status. This highlights the need for new diagnostic and prognostic biomarkers in different types of samples (such as blood, fresh-frozen tissue or formalin-fixed, paraffin-embedded samples). The field of tumour immunology has changed in the last decade, and it is now accepted that the immune system plays a pivotal role in cancer. Although the immune cells that infiltrate the tumour microenvironment are potentially capable of eliminating tumour cells, they cannot prevent tumour development and progression. Tumours acquire mechanisms to regulate their immune microenvironment such as the release of a series of factors to subvert normal reaction mechanisms, the modulation of co-stimulatory pathways, also known as immune checkpoints, and the induction and attraction of suppressor cells (myeloid-derived suppressor cells, tumour-associated macrophages, and regulatory T cells). The potential effect of the patient's immune system on clinical outcome is important for the identification of prognostic markers as well as markers that predict treatment responses. The study of immune-related markers, especially those implicated in immunoregulatory processes, could provide valuable prognostic information that could help in many applications in future clinical practice. Thus, the objective of this thesis is to characterise cancer immunoregulation biomarkers and to evaluate the possible correlation between these biomarkers and clinicopathological and prognostic variables in patients with NSCLC by the use of well-tested and accurate techniques such as quantitative PCR and immunohistochemistry. Furthermore, this study will provide information about the immunological features of the tumour microenvironment in NSCLCs.[ES] El cáncer de pulmón es una de las principales causas de muerte relacionada con cáncer en el mundo, siendo el tercer tipo de cáncer más común. El cáncer de pulmón no microcítico (CPNM) representa casi el 85% de todos los cánceres de pulmón y la supervivencia a los 5 años va desde el 50% en estadios IA hasta el 15% en estadios IIIA. Hasta el momento, no se han descubierto biomarcadores capaces de predecir la progresión de la enfermedad en pacientes tanto en estadios resecables como en estadios avanzados, por lo que existe una clara necesidad de realizar estudios centrados en la búsqueda de biomarcadores pronósticos y diagnósticos en los diferentes tipos de muestra disponibles, como por ejemplo sangre, tejido fresco y tejido parafinado. El campo de la inmunología tumoral ha cambiado en la última década y actualmente se sabe que el sistema inmune juega un papel clave en cáncer. Las células inmunes que infiltran el tumor son un componente más del microambiente tumoral. Pese a que son potencialmente capaces de eliminar los antígenos tumorales, estas células no pueden evitar la formación y progresión tumoral. Esto es debido a que el tumor adquiere diversos mecanismos de regulación del microambiente tumoral con el objetivo de escapar del ataque del sistema inmune, como por ejemplo liberación de factores que impiden el correcto funcionamiento de los mecanismos de reacción inmune, modulación de vías co-estimuladoras y reclutamiento y activación de células inmunoreguladoras como las células T reguladoras, las células mieloides supresoras y los macrófagos asociados a tumores. El estudio de marcadores relacionados con la respuesta inmune y concretamente con los procesos de inmunoregulación puede proporcionarnos información pronóstica y predictiva relevante sobre los pacientes con cáncer. Por todo ello, el principal objetivo de esta tesis doctoral es analizar la presencia de marcadores relacionados con la inmunoregulación y evaluar su posible correlación con las variables clínico-patológicas y pronósticas en pacientes con CPNM mediante el uso de técnicas fiables y aplicables en la práctica clínica como la PCR cuantitativa y la inmunohistoquímica. Así mismo, esto nos permitirá conocer en mayor profundidad las características inmunológicas del microambiente tumoral en pacientes con CPNM.[CA] El càncer de pulmó és una de les principals causes de mort relacionades amb càncer al món, sent a més a més el tercer tipus de càncer més comú. El càncer de pulmó no microcític (CPNM) representa el 85% de tots els casos de càncer de pulmó aproximadament i la supervivència als 5 anys continua sent molt baixa. Fins el moment, no s'han descobert biomarcadors capaços de predir la progressió de la malaltia tant en pacients en estadis inicials com en estadis avançats. Per aquest motiu, existeix una clara necessitat de realitzar estudis centrats en la recerca de biomarcadors pronòstics i predictius en els diferents tipus de mostres disponibles, com per exemple sang, teixit fresc i teixit parafinat. El camp de la immunologia tumoural ha canviat en l'última dècada i actualment se sap que el sistema immune exerceix un paper clau en el càncer. Les cèl¿lules immunològiques que infiltren el tumour són un component més del microambient tumoural. Malgrat que aquestes cèl¿lules són potencialment capaces d'eliminar el antígens tumourals, s'ha evidenciat que no poden previndre la formació i progressió tumoural. Una de les raons per les quals s'observa aquest fenomen és que el tumour adquireix diversos mecanismes de regulació del microambient tumoural. Aquests mecanismes es basen en l'alliberació de factors que impedeixen el correcte funcionament del sistema immune, la modulació de vies coestimuladores i el reclutament i activació de cèl¿lules immunoreguladores com poden ser les cèl¿lules T reguladores, les cèl¿lules mieloides supressores i els macròfags associats a tumour. L'estudi de marcadors relacionats amb la resposta immune i més concretament amb els processos d' immunoregulació pot proporcionar informació pronòstica i predictiva rellevant sobre els pacients amb càncer. Per tot això, el principal objectiu d'aquesta tesi doctoral és analitzar la presència de marcadors relacionats amb la immunoregulació i avaluar la seva possible correlació amb les variables clinicopatològiques i pronòstiques de pacients amb CPNM mitjançant l'ús de tècniques fiables i aplicables a la pràctica clínica com són la PCR quantitativa i la immunohistoquímica. Així mateix, aquestes anàlisis ens permetran conèixer amb major profunditat les característiques immunològiques del microambient tumoural de pacients amb CPNM.Usó Marco, M. (2015). ANALYSIS OF IMMUNOREGULATORY BIOMARKERS IN NON-SMALL CELL LUNG CANCER [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/51283TESI

Semantic array programming in data-poor environments: assessing the interactions of shallow landslides and soil erosion

This research was conducted with the main objective to better integrate and quantify the role of water-induced shallow landslides within soil erosion processes, with a particular focus on data-poor conditions. To fulfil the objectives, catchment-scale studies on soil erosion by water and shallow landslides were conducted. A semi-quantitative method that combines heuristic, deterministic and probabilistic approaches is here proposed for a robust catchment-scale assessment of landslide susceptibility when available data are scarce. A set of different susceptibility-zonation maps was aggregated exploiting a modelling ensemble. Each susceptibility zonation has been obtained by applying heterogeneous statistical techniques such as logistic regression (LR), relative distance similarity (RDS), artificial neural network (ANN), and two different landslide-susceptibility techniques based on the infinite slope stability model. The good performance of the ensemble model, when compared with the single techniques, make this method suitable to be applied in data-poor areas where the lack of proper calibration and validation data can affect the application of physically based or conceptual models. A new modelling architecture to support the integrated assessment of soil erosion, by incorporating rainfall induced shallow landslides processes in data-poor conditions, was developed and tested in the study area. This proposed methodology is based on the geospatial semantic array programming paradigm. The integrated data-transformation model relies on a modular architecture, where the information flow among modules is constrained by semantic checks. By analysing modelling results within the study catchment, each year, on average, mass movements are responsible for a mean increase in the total soil erosion rate between 22 and 26% over the pre-failure estimate. The post-failure soil erosion rate in areas where landslides occurred is, on average, around 3.5 times the pre-failure value. These results confirm the importance to integrate landslide contribution into soil erosion modelling. Because the estimation of the changes in soil erosion from landslide activity is largely dependent on the quality of available datasets, this methodology broadens the possibility of a quantitative assessment of these effects in data-poor regions

Earth Resources: A continuing bibliography with indexes, issue 17

This bibliography lists 775 reports, articles, and other documents introduced into the NASA scientific and technical information system between January 1 and March 31, 1978. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis