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Clouds and aerosols play a significant role in determining the overall atmospheric radiation budget, yet remain a key uncertainty in understanding and predicting the future climate system. In addition to their impact on the Earth’s climate system, aerosols from volcanic eruptions, wildfires, man-made pollution events and dust storms are hazardous to aviation safety and human health. Space-based lidar systems provide critical information about the vertical distributions of clouds and aerosols that greatly improve our understanding of the climate system. However, daytime data from backscatter lidars, such as the Cloud-Aerosol Transport System (CATS) on the International Space Station (ISS), must be averaged during science processing at the expense of spatial resolution to obtain sufficient signal-to-noise ratio (SNR) for accurately detecting atmospheric features. For example, 50% of all atmospheric features reported in daytime operational CATS data products require averaging to 60 km for detection. Furthermore, the single-wavelength nature of the CATS primary operation mode makes accurately typing these features challenging in complex scenes. This paper presents machine learning (ML) techniques that, when applied to CATS data, (1) increased the 1064 nm SNR by 75%, (2) increased the number of layers detected (any resolution) by 30%, and (3) enabled detection of 40% more atmospheric features during daytime operations at a horizontal resolution of 5 km compared to the 60 km horizontal resolution often required for daytime CATS operational data products. A Convolutional Neural Network (CNN) trained using CATS standard data products also demonstrated the potential for improved cloud-aerosol discrimination compared to the operational CATS algorithms for cloud edges and complex near-surface scenes during daytime.

Digitization has changed flow music radio. Competition from music streaming services like Spotify and iTunes has to a large extend outperformed traditional playlist radio, and the global dissemination of software generated playlists in public service radio stations in the 1990s has superseded the passionate music radio host. But digitization has also changed the way we can do research in radio. In Denmark digitization of almost all radio programming back to 1989, have made it possible to actually listen to the archive to investigate how radio content has changed historically. This article investigates the research question: How has the distribution of music and talk on the Danish Broadcasting Corporation’s radio channel P3 developed 1989-2019 by comparing a qualitative case study with a new large-scale study. Methodologically this shift from a close listening to a few programs to large-scale distant listening to more than 65,000 hours of radio enables us to discuss and critically compare the methods, results, strengths and shortcomings of the two analysis. Previous studies have demonstrated that Convolutional Neural Networks (CNNs) trained for image recognition of spectograms of the audio outperforms alternative approaches, such as Support Vector Machines (SVMs). The large-scale study presented shows that the CNN-based approach generalizes well, even without fine-tuning, to speech and music classification in Danish radio, with an overall accuracy of 98%.

Non-sequential double recombination (NSDR) high harmonic generation (HHG) is a strongly correlated two-electron HHG process where two electrons combine their potential and kinetic energy into emitting a single photon. We have studied this process in a molecular-like system and found that the two-electron nature of the signal results in the emitted HHG spectrum being dependent on the structure of the molecule, i.e., the nuclear configuration at ionization. A clear dependence in the NSDR HHG signal on the internuclear distance is observed in the cutoffs of the HHG spectrum for large internuclear distances where also an indication of the point of emission (the atom within the molecule from which the electrons is kicked out) can be observed in the HHG spectrum. A change in the NSDR process is observed when a change in the electron charge transfer in the molecule shifts the observed cutoffs in a process not seen in normal one-electron HHG.

El plan desarrollado, está dirigido al fortalecimiento estratégico de Exploraciones S.A.C. (EXPLOSAC) la cual da servicios de geología a empresas mineras. Su propósito general que persigue este estudio, es entregar a la alta dirección de la empresa Exploraciones SAC una herramienta de gestión que enfoque sus esfuerzos a la creación de valor y que permita ligar la estrategia de largo plazo con las acciones que se realizan en el corto plazo, en cada una de las jefaturas. Hoy en día las organizaciones modernas, están inmersas en el logro de una nueva forma de trabajo, con una mayor interacción entre su gente, trabajo en equipo, enfoque por procesos, aprendizaje constante y actitud proactiva son parámetros que se ven cultivados hasta formar parte del accionar cotidiano de estas organizaciones. Asimismo la importancia del capital intelectual, reflejado en las competencias individuales de la gente, los niveles de comunicación, las sinergias grupales, las relaciones con ofrecimientos de proyectos de otras empresas, son aspectos que van captando la máxima atención de sus directivos para lograr el éxito y resultados trascendentales. Todos estos aspectos nos llevan a la conclusión, que en estos tiempos requerimos herramientas de administración altamente dinámicas que nos permitan estar siempre al frente de los cambios del entorno; herramientas integradas, con la capacidad de obtener un entendimiento total de la estrategia y con mecanismos sencillos de comunicación y difusión a toda la organización. La metodología de trabajo comenzó con el estudio de los lineamientos estratégicos y análisis de la situación actual. A continuación, se seleccionaron los Factores Críticos de Éxitos (FCE) de cada jefatura, se definieron las metas, y se construyeron los indicadores de gestión. Finalmente, se elaboró el plan estratégico. The developed plan is directed to the strategic strengthening of the Explorationes SAC (EXPLOSAC). The general purpose of this study is to give the high management of the Exploraciones SAC a proper tool to focus their efforts in creating value and to allowing to link the long term strategy of the Area to the short term actions in each of the Area divisions. Nowadays, modern organizations are engaged in achieving a new way of working. With more interaction between their people, teamwork process approach proactive attitude, which are the parameters that are cultivated as part of the daily actions in such organizations. Also the importance of intellectual capital, reflected in the individual skills of the people, the levels of communication, group synergies, relationships with offerings of other companies, are issues that are attracting the most attention of their managers to achieve success and transcendental results. All these factors lead to the conclusion that in these times require highly dynamic management tools that allow us to be always ahead of the changing environment, integrated tools, with the ability to get a full understanding of the strategy and simple mechanisms of communication and dissemination throughout the organization. The methodology of work began with the study of strategic guidelines and current situation analysis. Then we selected the Critical Success Factors (CSF) for each head, defined goals, and management indicators were built. Finally, an explanatory example of the strategic plan was developed. Tesis