Spatial and temporal variation in the diet of the California sea lion (Zalophus californianus) in the Gulf of California, Mexico

Between June 1995 and May 1996 seven rookeries in the Gulf of California were visited four times in order to collect scat samples for studying spatial and seasonal variability California sea lion prey. The rookeries studied were San Pedro Mártir, San Esteban, El Rasito, Los Machos, Los Cantiles, Isla Granito, and Isla Lobos. The 1273 scat samples collected yielded 4995 otoliths (95.3%) and 247 (4.7%) cephalopod beaks. Fish were found in 97.4% of scat samples collected, cephalopods in 11.2%, and crustaceans in 12.7%. We identified 92 prey taxa to the species level, 11 to genus level, and 10 to family level, of which the most important were Pacific cutlassfish (Trichiurus lepturus), Pacific sardine (Sardinops caeruleus), plainfin midshipman (Porichthys spp.), myctophid no. 1, northern anchovy (Engraulis mordax), Pacific mackerel (Scomber japonicus), anchoveta (Cetengraulis mysticetus), and jack mackerel (Trachurus symmetricus). Significant differences were found among rookeries in the occurrence of all main prey (P≤0.04), except for myctophid no. 1 (P>0.05). Temporally, significant differences were found in the occurrence of Pacific cutlassfish, Pacific sardine, plainfin midshipman, northern anchovy, and Pacific mackerel (P<0.05), but not in jack mackerel (χ 2=2.94, df=3, P=0.40), myctophid no. 1 (χ 2=1.67, df= 3, P=0.64), or lanternfishes (χ 2=2.08, df=3, P=0.56). Differences were observed in the diet and in trophic diversity among seasons and rookeries. More evident was the variation in diet in relation to availability of Pacific sardine

Ultra-thin 3D silicon sensors for neutron detection

We present a novel neutron detector based on an ultra-thin 3D silicon sensor with a sensitive volume only 10 µm thick. This ultra-thin active volume allows a high gamma-ray rejection, a key requirement in order to discriminate the signal coming from the neutrons in a mixed neutron-gamma ray environment. The device upper-side is covered with a novel boron-based compound that detects neutrons by means of the 10B(n,α)7Li nuclear reaction. The performance of test devices has been investigated first with a gamma-ray source to evaluate the gamma-ray rejection factor, and then with an 241AmBe neutron source to assess the neutron-gamma ray discrimination properties.Peer reviewe

Multiagent Systems in Automotive Applications

The multiagent systems have proved to be a useful tool in the design of solutions to problems of distributed nature. In a distributed system, it is possible that the data, the control actions or even both, be distributed. The concept of agent is a suitable notion for capturing situations where the global knowledge about the status of a system is complex or even impossible to acquire in a single entity. In automotive applications, there exist a great number of scenarios of distributed nature, such as the traffic coordination, routes load balancing problems, traffic negotiation among the infrastructure and cars, to mention a few. Even more, the autonomous driving features of the new generation of cars will require the new methods of car to car communication, car to infrastructure negotiation, and even infrastructure to infrastructure communication. This chapter proposes the application of multiagent system techniques to some problems in the automotive field

Innovative concepts of Integrated Solar Combined Cycles (ISCC) using a Solid Oxide Fuel Cell (SOFC)

Concentrating Solar Power (CSP) is one of the most promising ways for electricity production of the upcoming years with high penetration of intermittent renewable energy sources such as wind and solar-photovoltaics. This is due to the fact that CSP when coupled to Thermal Energy Storage (TES) system enables large, inexpensive and flexible energy dispatch, which contributes to energy grid stabilization. At the same time, TES allows for steady operation of the power block by reducing undesirable fluctuations due to weather transient conditions and increasing the number of hours that the power block operates at design conditions 1. Despite the abovementioned advantages of CSP systems, a step further is needed for increase overall system efficiency and decrease CO2 emissions. Several studies have been performed considering high efficiency plant layouts such as combined cycle. For the latter, several works have been investigated about solar integration of combined cycle using parabolic trough and solar tower technologies. In both cases, solar energy was used for water/steam preheating and evaporation steps of the Rankine cycle in combination with the exhaust gases of fossil-fuel gas turbine engine. However, no research has been performed considering ISCC coupled with a Solid Oxide Fuel Cell (SOFC). In this research, two innovative layouts of ISCC power plants will be analyzed. First considers a ISCC based on solar tower and second a ISCC with a parabolic trough collector field coupled to the Heat Recovery Steam Generator (HRSG). The objective of this research is analyze the energy behavior of both layouts, selecting the best ISCC scheme to be coupled with a SOFC. The simulations will be performed using Thermoflex software. In both layouts, a SOFC is introduced before the combustion chamber at the topping cycle, and a Rankine cycle (bottoming cycle) with 2 pressures is considered.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Portable Multi-Hypothesis Monte Carlo Localization for Mobile Robots

Self-localization is a fundamental capability that mobile robot navigation systems integrate to move from one point to another using a map. Thus, any enhancement in localization accuracy is crucial to perform delicate dexterity tasks. This paper describes a new location that maintains several populations of particles using the Monte Carlo Localization (MCL) algorithm, always choosing the best one as the sytems's output. As novelties, our work includes a multi-scale match matching algorithm to create new MCL populations and a metric to determine the most reliable. It also contributes the state-of-the-art implementations, enhancing recovery times from erroneous estimates or unknown initial positions. The proposed method is evaluated in ROS2 in a module fully integrated with Nav2 and compared with the current state-of-the-art Adaptive ACML solution, obtaining good accuracy and recovery times.Comment: Submission for ICRA 202

Achieving Identity-based cryptography in a personal digital assistant

Continuous technological advances have allowed that mobile devices, such as Personal Digital Assistants (PDAs), can execute sophisticated applications that more often than not must be equipped with a layer of security that should include the confidentiality and the authentication services within its repertory. Nevertheless, when compared against front-end computing devices, most PDAs are still seen as constrained devices with limited processing and storage capabilities. In order to achieve Identity-Based Cryptography (IBC), which was an open problem proposed by Adi Shamir in 1984, Boneh and Franklin presented in Crypto 2001, a solution that uses bilinear pairings as its main building block. Since then, IBC has become an active area of investigation where many efficient IBC security protocols are proposed year after year. In this paper, we present a cryptographic application that allows the secure exchange of documents from a Personal Digital Assistant (PDA) that is wirelessly connected to other nodes. The architecture of our application is inspired by the traditional PGP (Pretty Good Privacy) email security protocol. Our application achieves identity-based authentication and confidentiality functionalities at the 80-bit security level through the usage of a cryptographic library that was coded in C++. Our library can perform basic primitives such as bilinear pairings defined over the binary field and the ternary field , as well as other required primitives known as map-to-point hash functions. We report the timings achieved by our application and we show that they compare well against other similar works published in the open literature

Intrinsic and environmental factors modulating autonomous robotic search under high uncertainty

Autonomous robotic search problems deal with different levels of uncertainty. When uncertainty is low, deterministic strategies employing available knowledge result in most effective searches. However, there are domains where uncertainty is always high since information about robot location, environment boundaries or precise reference points is unattainable, e.g., in cave, deep ocean, planetary exploration, or upon sensor or communications impairment. Furthermore, latency regarding when search targets move, appear or disappear add to uncertainty sources. Here we study intrinsic and environmental factors that affect low-informed robotic search based on diffusive Brownian, naive ballistic, and superdiffusive strategies (Lévy walks), and in particular, the effectiveness of their random exploration. Representative strategies were evaluated considering both intrinsic (motion drift, energy or memory limitations) and extrinsic factors (obstacles and search boundaries). Our results point towards minimum-knowledge based modulation approaches that can adjust distinct spatial and temporal aspects of random exploration to lead to effective autonomous search under uncertaintyThis work was supported in part by Agencia Estatal de Investigación (AEI) and Fondo Europeo de Desarrollo Regional (FEDER), under Grants PGC2018-095895-B-I00, TIN2017-84452-R, and PID2020-114867RB-I0

Stand types discrimination comparing machine-learning algorithms in Monteverde, Canary Islands.

Aim of study: The main objective is to determine the best machine-learning algorithm to classify the stand types of Monteverde forests combining LiDAR, orthophotography, and Sentinel-2 data, thus providing an easy and cheap method to classify Monteverde stand types.Area of study: 1500 ha forest in Monteverde, North Tenerife, Canary Islands.Material and methods: RF, SVML, SVMR and ANN algorithms are used to classify the three Monteverde stand types.  Before training the model, feature selection of LiDAR, orthophotography, and Sentinel-2 data through VSURF was carried out.  Comparison of its accuracy was performed.Main results: Five LiDAR variables were found to be the most efficient for classifying each object, while only one Sentinel-2 index and one Sentinel-2 band was valuable.  Additionally, standard deviation and mean of the Red orthophotography colour band, and ratio between Red and Green bands were also found to be suitable.  SVML is confirmed as the most accurate algorithm (0.904, 0.041 SD) while ANN showed the lowest value of 0.891 (0.073 SD).  SVMR and RF obtain 0.902 (0.060 SD) and 0.904 (0.056 SD) respectively.  SVML was found to be the best method given its low standard deviation.Research highlights: The similar high accuracy values among models confirm the importance of taking into account diverse machine-learning methods for stand types classification purposes and different explanatory variables.  Although differences between errors may not seem relevant at a first glance, due to the limited size of the study area with only three plus two categories, such differences could be highly important when working at large scales with more stand types.ADDITIONAL KEY WORDSRF algorithm, SVML algorithm, SVMR algorithm, ANN algorithm, LiDAR, orthophotography, Sentinel-2ABBREVIATIONS USEDANN, artificial neural networks algorithm; Band04, Sentinel-2 band 04 image data; BR, brezal; DTHM, digital tree height model; DTHM-2016, digital tree height model based on 2016 LiDAR data; DTM, digital terrain model; DTM-2016, digital terrain model based on 2016 LiDAR data; FBA, fayal-brezal-acebiñal; FCC, canopy cover; HEIGHT-2009, maximum height based on 2009 LiDAR data; HGR, height growth based on 2009 and 2016 LiDAR data; LA, laurisilva; NDVI705, Sentinel-2 index image data; NMF, non-Monteverde forest; NMG, non-Monteverde ground; P95-2016, height percentile 95 based on 2016 LiDAR data; RATIO R/G, ratio between Red and Green bands orthophotograph data; RED, Red band orthophotograph data; Red-SD, standard deviation of the Red band orthophotograph data; RF, random forest algorithm; SVM, support vector machine algorithm; SVML, linear support vector machine algorithm; SVMR, radial support vector machine algorithm; VSURF, variable selection using random forest.