Modelo de simulación de la operación de un embalse en avenida y su integración al sistema FEWS

En este trabajo se presentan dos modelos de gestión de embalses en avenidas, el primero, basado en reglas de operación de los órganos de desagüe definidas por el usuario y, el segundo, correspondiente al método de gestión programada de embalses de Girón. Se incluye además un módulo que integra ambos métodos al sistema hidrometeorológico de alerta temprana FEWS

Approaching the error of network models applied to the forecast of time serie

Artificial neural networks are an important technique in nonlinear time series forecasting. However, training ofneural networks is a difficult task, because of the presence of many local optimal points and the irregularity ofthe error surface. In this context, it is very easy to obtain under-fitted or over-fitted forecasting models withoutforecasting power. Thus, researchers and practitioner need to have criteria for detecting this class of problems. Inthis paper, we demonstrate that the use of well known methodologies in linear time series forecasting, such as theBox-Jenkins methodology or exponential smoothing models, are valuable tools for detecting bad specified neuralnetwork models.Las redes neuronales artificiales son una importante técnica en el pronóstico de series de tiempo no lineales. Sin embargo,el entrenamiento de las redes neuronales es una tarea difícil, a causa de la presencia de muchos puntos óptimos localesy a la irregularidad de la superficie de error. En este contexto, es muy fácil obtener modelos sub-entrenados o sobreentrenadossin poder de pronóstico. Así, los investigadores y los profesionales necesitan contar con criterios para detectaresta clase de problemas. En este artículo, se demuestra que el uso de metodologías bien conocidas en el pronóstico deseries de tiempo lineales, tales como la metodología de Box-Jenkins o los modelos de suavizado exponencial, son valiosasherramientas para detectar modelos de redes neuronales mal especificados.&nbsp

Neurosymbolic Reinforcement Learning and Planning: A Survey

The area of Neurosymbolic Artificial Intelligence (Neurosymbolic AI) is rapidly developing and has become a popular research topic, encompassing sub-fields such as Neurosymbolic Deep Learning (Neurosymbolic DL) and Neurosymbolic Reinforcement Learning (Neurosymbolic RL). Compared to traditional learning methods, Neurosymbolic AI offers significant advantages by simplifying complexity and providing transparency and explainability. Reinforcement Learning(RL), a long-standing Artificial Intelligence(AI) concept that mimics human behavior using rewards and punishment, is a fundamental component of Neurosymbolic RL, a recent integration of the two fields that has yielded promising results. The aim of this paper is to contribute to the emerging field of Neurosymbolic RL by conducting a literature survey. Our evaluation focuses on the three components that constitute Neurosymbolic RL: neural, symbolic, and RL. We categorize works based on the role played by the neural and symbolic parts in RL, into three taxonomies:Learning for Reasoning, Reasoning for Learning and Learning-Reasoning. These categories are further divided into sub-categories based on their applications. Furthermore, we analyze the RL components of each research work, including the state space, action space, policy module, and RL algorithm. Additionally, we identify research opportunities and challenges in various applications within this dynamic field.Comment: 16 pages, 9 figures, IEEE Transactions on Artificial Intelligenc

Advanced Oxygen Generation Assembly for Exploration Missions

Future Exploration missions will require an Oxygen Generation Assembly (OGA) to electrolyze water to supply oxygen for crew metabolic consumption. The system design will be based on the International Space Station (ISS) OGA but with added improvements based on lessons learned during ISS operations and technological advances since the original OGA was designed and built. These improvements will reduce system weight, crew maintenance time and spares mass while increasing reliability. Currently, the design team is investigating the feasibility of the upgrades by performing ground tests and analyses. Upgrades being considered include: redesign of the electrolysis cell stack, deletion of the hydrogen dome, replacement of the hydrogen sensors, deletion of the wastewater interface, redesign of the recirculation loop deionizing bed and redesign of the cell stack Power Supply Module. The upgrades will be first demonstrated on the ISS OGA

Circuit-specific dendritic development in the piriform cortex

Dendritic geometry is largely determined during postnatal development and has a substantial impact on neural function. In sensory processing, postnatal development of the dendritic tree is affected by two dominant circuit motifs, ascending sensory feedforward inputs and descending and local recurrent connections. Two subtypes of layer 2 neurons in the three-layered anterior piriform cortex, layer 2a and layer 2b neurons, display a clear vertical segregation of these two circuit motifs. Here, we combined electrophysiology, detailed morphometry and Ca(2+) imaging-both of neuronal networks as well as of subcellular structures-in acute mouse brain slices and modeling. This allowed us to compare the functional implications of distinct circuit-specific postnatal dendritic growth patterns in these two neuronal subtypes. We observed that determination of branching complexity, dendritic length increases and pruning occurred in distinct growth phases. Layer 2a and layer 2b neurons displayed growth phase specific developmental differences between their apical and basal dendritic trees. This was reflected by compartment-specific differences in Ca(2+) signaling. The morphological and functional developmental pattern differences between layer 2a and layer 2b neurons dendrites provide further evidence that they constitute two functionally distinct streams of olfactory information processing

Microbial fuel cells: a green and alternative source for bioenergy production

Microbial fuel cell (MFC) represents one of the green technologies for the production of bioenergy. MFCs using microalgae produce bioenergy by converting solar energy into electrical energy as a function of metabolic and anabolic pathways of the cells. In the MFCs with bacteria, bioenergy is generated as a result of the organic substrate oxidation. MFCs have received high attention from researchers in the last years due to the simplicity of the process, the absence in toxic by-products, and low requirements for the algae growth. Many studies have been conducted on MFC and investigated the factors affecting the MFC performance. In the current chapter, the performance of MFC in producing bioenergy as well as the factors which influence the efficacy of MFCs is discussed. It appears that the main factors affecting MFC’s performance include bacterial and algae species, pH, temperature, salinity, substrate, mechanism of electron transfer in an anodic chamber, electrodes materials, surface area, and electron acceptor in a cathodic chamber. These factors are becoming more influential and might lead to overproduction of bioenergy when they are optimized using response surface methodology (RSM)

Crecimiento y desarrollo del fruto de naranja Valencia en condiciones de trópico bajo

sumarios (En, Es)En el presente estudio se determinan las características básicas del crecimiento y desarrollo del fruto de naranja Valencia. En el se relacionan las condiciones climáticas en el periodo de estudio evaluando la acumulación de materia seca del fruto, su crecimiento, contenido de jugo, además del contenido de grados brix, de ácido cítrico e índice de madure

Association between parental perceptions of residential neighbourhood environments and childhood obesity in Porto, Portugal

Portugal has one of the highest rates of childhood obesity in Europe. Few studies have explored the relationship between parents’ perceptions of their residential neighbourhood (safety concerns and amenities of the built environment) and their children’s weight status. This study aims to examine the associations between parents’ perceptions of their residential neighbourhood and overweight/obesity among their children. Methods: Anthropometric measures of height and weight were taken for 2690 children in preschools and elementary schools in Porto. Body mass index (kg/m2 ) was calculated, and the International Obesity Taskforce (IOTF) cut-offs were used. Parents completed the ‘Environmental Module’ standard questionnaire of the International Physical Activity Prevalence Study. Chi-square tests and the logistic regression model adjusted for age, gender, maternal education and school cluster were used to examine the associations between parents’ perceptions of their residential neighbourhood and overweight/obesity among their children. Results: In this sample, 31.8% of the children were overweight (including obese). Significant associations were found between child obesity and the following residential environmental characteristics: the odds of children being obese were lower if their parents believed that it was safe (low/no crime rate) to walk/cycle at night (OR = 0.65, 95% CI = 0.54–0.79) and during the day (OR = 0.70, 95% CI = 0.55–0.86) and that it was easy and pleasant (pedestrian safety) to walk in their neighbourhoods (OR = 0.73, 95% CI = 0.58–0.90) and when local sidewalks were well maintained and unobstructed (OR = 1.18, 95% CI = 1.01–1.40). Conclusion: Parental perceptions of neighbourhood safety and the quality of local sidewalks are significantly associated with obesity values