Source Code Verification for Embedded Systems using Prolog

System relevant embedded software needs to be reliable and, therefore, well tested, especially for aerospace systems. A common technique to verify programs is the analysis of their abstract syntax tree (AST). Tree structures can be elegantly analyzed with the logic programming language Prolog. Moreover, Prolog offers further advantages for a thorough analysis: On the one hand, it natively provides versatile options to efficiently process tree or graph data structures. On the other hand, Prolog's non-determinism and backtracking eases tests of different variations of the program flow without big effort. A rule-based approach with Prolog allows to characterize the verification goals in a concise and declarative way. In this paper, we describe our approach to verify the source code of a flash file system with the help of Prolog. The flash file system is written in C++ and has been developed particularly for the use in satellites. We transform a given abstract syntax tree of C++ source code into Prolog facts and derive the call graph and the execution sequence (tree), which then are further tested against verification goals. The different program flow branching due to control structures is derived by backtracking as subtrees of the full execution sequence. Finally, these subtrees are verified in Prolog. We illustrate our approach with a case study, where we search for incorrect applications of semaphores in embedded software using the real-time operating system RODOS. We rely on computation tree logic (CTL) and have designed an embedded domain specific language (DSL) in Prolog to express the verification goals.Comment: In Proceedings WLP'15/'16/WFLP'16, arXiv:1701.0014

Model-Based Framework for On-Board-Software

Satellites carry different payloads, but the basic design in hard- and software is generally similar for small satellites. For example, they all receive telecommands, distribute them and generate telemetry packets. Reusing existing components is desirable, especially with limited time and financial budgets. This is where the Corfu comes in, which we present in this work. Corfu is a software framework for safety-critical on-board software. It follows a model-based approach. Developers formally define the structure of the software. The software design is app-centric, i.e. on-board software in Corfu is a composition of apps. Apps define a clear communication interface using a publish/-subscribe principle. This allows on-board software to connect apps among each other. Developers can use and connect apps in different on-board software and even on different missions. This encourages reusability. Based on the information of software definition, Corfu applies two tasks: formal verification of the software structure and generation of source code. In the verification step, Corfu examines the timing properties across all apps that are included into the software. Having a formal definition that is used for both static analysis and code generation, makes in possible to identify structural problems early. The generation process creates code that it can derive from the software specification. This includes communication handling, such as subscribing to topics, distributing telecommands and collecting telemetry. In addition, the generated code also covers thread handling. The result of the generation process is a collection of classes. Most of those classes are abstract, which include abstract methods that the developer fills with mission-specific code. Developers do this by inheriting from those abstract classes and overriding all the abstract methods by carrying out the desired behavior. Developers can focus on implementing the mission relevant code. The software specification defines the communication interface between space and ground as well; therefore, it is sensible to use the same definition for the ground software. Corfu comes with a library for ground software, which parses the configuration file and makes it available to the developer. It also comes with a link interface towards the space segment. Based on the library, Corfu provides a ready-to-use generic ground software with a graphical user interface printing telemetry data and for sending telemetry — according to the software definition. Beyond formal verification of the software definition, Corfu comes with an elaborated testing framework, which provides unit and integration tests to the developers. By generating test-specific classes, Corfu gives developers access to internal software parts to allow more accurate unit testing. By automatically sending telecommands and evaluating telemetry data, developers accomplish integration tests of the full on-board software stack. Corfu already comes with applications and concepts that are common to general on-board software, such as publish/subscribe communication between applications, anomaly and event handling, telecommand distribution among applications, telemetry collection, housekeeper, etc

Riesgo sísmico en edificaciones de albañilería en el sector El Milagro-Moyobamba, 2020

La realización de la presente investigación tuvo como finalidad determinar el riesgo sísmico en edificaciones de albañilería confinada en el sector “El Milagro”, Moyobamba. Para iniciar la investigación se computó una población de 137 viviendas de albañilería predominante en sus paredes, de lo cual se seleccionó una muestra delimitada de 97 viviendas mediante el muestreo aleatorio simple. El procedimiento para encontrar el riesgo sísmico se realizó utilizando la matriz de vulnerabilidad y peligro de INDECI. Para obtener la vulnerabilidad sísmica se realizó una evaluación descriptiva de los rasgos de las viviendas, mediante la determinación de once parámetros según el método de índice de vulnerabilidad, los cuales a su vez analizan aspectos geométricos, aspectos constructivos y aspectos estructurales de las viviendas, a los cuales se les colocó un nivel de valoración; posteriormente se procedió a encontrar el nivel de peligro sísmico ayudándonos del programa R-CRISIS versión 20.3.0. Como resultado se obtuvo un 72.16% viviendas con riesgo sísmico medio y un 16.50 % viviendas con riesgo sísmico alto; por lo tanto, las viviendas del sector El Milagros tienen un riesgo sísmico medio

The cellular interactions of PEGylated gold nanoparticles : effect of PEGylation on cellular uptake and cytotoxicity

Poly(ethylene glycol) (PEG) is frequently used to coat various medical nanoparticles (NPs). As PEG is known to minimize NP interactions with biological specimens, the question remains whether PEGylated NPs are intrinsically less toxic or whether this is caused by reduced NP uptake. In the present work, the effect of gold NP PEGylation on uptake by three cell types is compared and evaluated the effect on cell viability, oxidative stress, cell morphology, and functionality using a multiparametric methodology. The data reveal that PEGylation affects cellular NP uptake in a cell-type-dependent manner and influences toxicity by different mechanisms. At similar intracellular NP numbers, PEGylated NPs are found to yield higher levels of cell death, mostly by induction of oxidative stress. These findings reveal that PEGylation significantly reduces NP uptake, but that at similar functional (= cell-associated) NP levels, non-PEGylated NPs are better tolerated by the cells

Feeding tube replacement: not always that simple!

Although surgical gastrostomy is not a technically troublesome surgery, the procedure may be accompanied by unfavorable outcomes. Most complications occur early in the post-operative period and include feeding tube dislodgment, stomal infection, peritonitis, and pneumonia. The authors report the case of an 83-year-old man who underwent a surgical gastrostomy because of a swallowing disorder after an ischemic stroke. Nine months after the procedure, the feeding tube dislodged and a new tube was inserted with a certain delay and with some difficulty, causing a false path and consequently an intrabdominal abscess after diet infusion. The outcome was fatal. The authors call attention for meticulous care with the insertion of feeding tubes and advise the performance of imaging control to assure its precise positioning

Empatía por defecto: correlatos en el cerebro en reposo

Background: Empathy, defined as the ability to access and respond to the inner world of another person, is a multidimensional construct involving cognitive, emotional and self-regulatory mechanisms. Neuroimaging studies report that empathy recruits brain regions which are part of the social cognition network. Among the different resting state networks, the Default Mode Network (DMN) may be of particular interest for the study of empathy since it has been implicated in social cognition tasks. Method: The current study compared the cognitive and emotional empathy scores, as measured by the Interpersonal Reactivity Index, with the patterns of activation within the DMN, through the neuroimaging methodology of resting-state functional magnetic resonance. Results: Results suggest a significant positive correlation between cognitive empathy and activation of the bilateral superior medial frontal cortex nodes of the DMN. Contrastingly, a negative correlation was found between emotional empathy and the same brain region. Conclusions: Overall, this data highlights a critical role of the medial cortical regions of the DMN, specifically its anterior node, for both cognitive and emotional domains of the empathic process.Antecedentes: la empatía, defi nida como la capacidad de acceder y responder al mundo interior de otra persona, es un constructo multidimensional que implica mecanismos cognitivos, emocionales y autorreguladores. Los estudios de neuroimagen informan que la empatía recluta regiones cerebrales que forman parte de la red de cognición social. Entre las diferentes redes de estado de reposo, la Red Neuronal por Defecto (Default Mode Network; DMN) puede ser de particular interés para el estudio de la empatía, ya que ha sido implicada en tareas de cognición social. Método: el presente estudio comparó los valores de empatía cognitiva y emocional, medidos por medio del Índice de Reactividad Interpersonal, con los patrones de activación dentro de la DMN, a través de la metodología de neuroimagen por resonancia magnética funcional en estado de reposo. Resultados: los resultados sugieren una correlación positiva signifi cativa entre la empatía cognitiva y la activación bilateral de los nodos de la región frontomedial superior de la DMN. En contraste, se encontró una correlación negativa entre la empatía emocional y la misma región del cerebro. Conclusiones: en general, estos datos destacan un papel crítico de las regiones corticales mediales de la DMN, específi camente su nodo anterior, para los dominios cognitivo y emocional del proceso empático.This study was supported by the Bial Foundation, under the fellowship numbers 89/08 and 87/12 and by the PFST Ref. UID/CED/04872/2016, Ref. SFRH/BD/65892/2009, Ref. PTDC/PSIPCL/115316/2009, and FEDER funds. Ref. POCI-01-0145-FEDER-007653.info:eu-repo/semantics/publishedVersio

an overview of the MHONGOOSE survey: Observing nearby galaxies with MeerKAT

© Copyright owned by the author(s). MHONGOOSE is a deep survey of the neutral hydrogen distribution in a representative sample of 30 nearby disk and dwarf galaxies with H I masses from ∼ 106 to ∼ 1011 M, and luminosities from MR ∼ 12 to MR ∼ −22. The sample is selected to uniformly cover the available range in log(MHI). Our extremely deep observations, down to H I column density limits of well below 1018 cm−2 — or a few hundred times fainter than the typical H I disks in galaxies — will directly detect the effects of cold accretion from the intergalactic medium and the links with the cosmic web. These observations will be the first ever to probe the very low-column density neutral gas in galaxies at these high resolutions. Combination with data at other wavelengths, most of it already available, will enable accurate modeling of the properties and evolution of the mass components in these galaxies and link these with the effects of environment, dark matter distribution, and other fundamental properties such as halo mass and angular momentum. MHONGOOSE can already start addressing some of the SKA-1 science goals and will provide a comprehensive inventory of the processes driving the transformation and evolution of galaxies in the nearby universe at high resolution and over 5 orders of magnitude in column density. It will be a Nearby Galaxies Legacy Survey that will be unsurpassed until the advent of the SKA, and can serve as a highly visible, lasting statement of MeerKAT’s capabilities

Predictive modeling of above-ground biomass in Brachiaria pastures from satellite and UAV Imagery using machine learning approaches

Grassland pastures are crucial for the global food supply through their milk and meat production; hence, forage species monitoring is essential for cattle feed. Therefore, knowledge of pasture above-ground canopy features help understand the crop status. This paper finds how to construct machine learning models to predict above-ground canopy features in Brachiaria pasture from ground truth data (GTD) and remote sensing at larger (satellite data on the cloud) and smaller (unmanned aerial vehicles (UAV)) scales. First, we used above-ground biomass (AGB) data obtained from Brachiaria to evaluate the relationship between vegetation indices (VIs) with the dry matter (DM). Next, the performance of machine learning algorithms was used for predicting AGB based on VIs obtained from ground truth and satellite and UAV imagery. When comparing more than twenty-five machine learning models using an Auto Machine Learning Python API, the results show that the best algorithms were the Huber with R² = 0.60, Linear with R² = 0.54, and Extra Trees with R² = 0.45 to large scales using satellite. On the other hand, short-scale best regressions are K Neighbors with an R2 of 0.76, Extra Trees with an R² of 0.75, and Bayesian Ridge with an R² of 0.70, demonstrating a high potential to predict AGB and DM. This study is the first prediction model approach that assesses the rotational grazing system and pasture above-ground canopy features to predict the quality and quantity of cattle feed to support pasture management in Colombia

WALLABY Pilot Survey: HI gas kinematics of galaxy pairs in cluster environment

We examine the H I gas kinematics of galaxy pairs in two clusters and a group using Australian Square Kilometre Array Pathfinder (ASKAP) WALLABY pilot survey observations. We compare the H I properties of galaxy pair candidates in the Hydra I and Norma clusters, and the NGC 4636 group, with those of non-paired control galaxies selected in the same fields. We perform H I profile decomposition of the sample galaxies using a tool, BAYGAUD which allows us to de-blend a line-of-sight velocity profile with an optimal number of Gaussian components. We construct H I super-profiles of the sample galaxies via stacking of their line profiles after aligning the central velocities. We fit a double Gaussian model to the super-profiles and classify them as kinematically narrow and broad components with respect to their velocity dispersions. Additionally, we investigate the gravitational instability of H I gas disks of the sample galaxies using Toomre Q parameters and H I morphological disturbances. We investigate the effect of the cluster environment on the H I properties of galaxy pairs by dividing the cluster environment into three subcluster regions (i.e., outskirts, infalling and central regions). We find that the denser cluster environment (i.e., infalling and central regions) is likely to impact the H I gas properties of galaxies in a way of decreasing the amplitude of the kinematically narrow H I gas (⁠MnarrowHI role= presentation style= box-sizing: border-box; margin: 0px; padding: 0px; border: 0px; font-variant: inherit; font-stretch: inherit; line-height: normal; font-family: inherit; vertical-align: baseline; display: inline; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; position: relative; \u3eMHInarrowMnarrowHI/MtotalHI role= presentation style= box-sizing: border-box; margin: 0px; padding: 0px; border: 0px; font-variant: inherit; font-stretch: inherit; line-height: normal; font-family: inherit; vertical-align: baseline; display: inline; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; position: relative; \u3eMHItotalMtotalHI⁠), and increasing the Toomre Q values of the infalling and central galaxies. This tendency is likely to be more enhanced for galaxy pairs in the cluster environment

Detection of banana plants and their major diseases through aerial images and machine learning methods: A case study in DR Congo and Republic of Benin

Front-line remote sensing tools, coupled with machine learning (ML), have a significant role in crop monitoring and disease surveillance. Crop type classification and a disease early warning system are some of these remote sensing applications that provide precise, timely, and cost-effective information at different spatial, temporal, and spectral resolutions. To our knowledge, most disease surveillance systems focus on a single-sensor based solutions and lagging the integration of multiple information sources. Moreover, monitoring larger landscapes using unmanned aerial vehicles (UAV) are challenging, and, therefore combining high resolution satellite imagery data with advanced machine learning (ML) models through the use of mobile apps could help detect and classify banana plants and provide more information on its overall health status. In this study, we classified banana under mixed-complex African landscapes through pixel-based classifications and ML models derived from multi-level satellite images (Sentinel 2, PlanetScope and WorldView-2) and UAV (MicaSense RedEdge) platforms. Our pixel-based classification from random forest (RF) model using combined features of vegetation indices (VIs) and principal component analysis (PCA) showed up to 97% overall accuracy (OA) with less than 10% omission and commission errors (OE and CE) and Kappa coefficient of 0.96 in high resolution multispectral images. We used UAV-RGB aerial images from DR Congo and Republic of Benin fields to develop a mixed-model system combining object detection model (RetinaNet) and a custom classifier for simultaneous banana localization and disease classification. Their accuracies were tested using different performance metrics. Our UAV-RGB mixed-model revealed that the developed object detection and classification model successfully classified healthy and diseased plants with 99.4%, 92.8%, 93.3% and 90.8% accuracy for the four classes: banana bunchy top disease (BBTD), Xanthomonas Wilt of Banana (BXW), healthy banana cluster and individual banana plants, respectively. These approaches of aerial image-based ML models have high potential to provide a decision support system for major banana diseases in Afric