Superparamagnetic particles in ZSM-5-type ferrisilicates

As-synthesized, low iron content, ferrisilicates of ZSM-5-type contain well-separated Fe(III) ions in a tetrahedral environment and display paramagnetic behavior. After hydrothermal treatment, the iron ions are partially extracted from the framework, generating nanosize iron oxide or oxyhydroxide ferrimagnetic particles. This process has been studied by transmission electron microscopy (TEM), Mossbauer spectroscopy, magnetic ac susceptibility (chi(ac)), and field dependent magnetization, on samples containing up to 6.7 wt. % Fe. The experiments evidence the growth of nonaggregated particles, with a typical size around 3 nm, presumably located at the surface of the ferrisilicate crystallites, From a thorough granulometric analysis involving TEM and chi(ac) data, it is concluded that, in the range from 1.5 to 4.6 wt. % Fe, the particle size distributions are significantly independent of the iron content

A Neural Approach to Ordinal Regression for the Preventive Assessment of Developmental Dyslexia

Developmental Dyslexia (DD) is a learning disability related to the acquisition of reading skills that affects about 5% of the population. DD can have an enormous impact on the intellectual and personal development of affected children, so early detection is key to implementing preventive strategies for teaching language. Research has shown that there may be biological underpinnings to DD that affect phoneme processing, and hence these symptoms may be identifiable before reading ability is acquired, allowing for early intervention. In this paper we propose a new methodology to assess the risk of DD before students learn to read. For this purpose, we propose a mixed neural model that calculates risk levels of dyslexia from tests that can be completed at the age of 5 years. Our method first trains an auto-encoder, and then combines the trained encoder with an optimized ordinal regression neural network devised to ensure consistency of predictions. Our experiments show that the system is able to detect unaffected subjects two years before it can assess the risk of DD based mainly on phonological processing, giving a specificity of 0.969 and a correct rate of more than 0.92. In addition, the trained encoder can be used to transform test results into an interpretable subject spatial distribution that facilitates risk assessment and validates methodology.Comment: 12 pages, 4 figure

Analyzing the Impact of Roadmap and Vehicle Features on Electric Vehicles Energy Consumption

Electric Vehicles (EVs) market penetration rate is continuously increasing due to several aspects such as pollution reduction initiatives, government incentives, cost reduction, and fuel cost increase, among others. In the vehicular field, researchers frequently use simulators to validate their proposals before implementing them in real world, while reducing costs and time. In this work, we use our ns-3 network simulator enhanced version to demonstrate the influence of the map layout and the vehicle features on the EVs consumption. In particular, we analyze the estimated consumption of EVs simulating two different scenarios: (i) a segment of the E313 highway, located in the north of Antwerp, Belgium and (ii) the downtown of the city of Antwerp with real vehicle models. According to the results obtained, we demonstrate that the mass of the vehicle is a key factor for energy consumption in urban scenarios, while in contrast, the Air Drag Coefficient (C-d) and the Front Surface Area (FSA) play a critical role in highway environments. The most popular and powerful simulations tools do no present combined features for mobility, realistic map-layouts and electric vehicles consumption. As ns-3 is one of the most used open source based simulators in research, we have enhanced it with a realistic energy consumption feature for electric vehicles, while maintaining its original design and structure, as well as its coding style guides. Our approach allows researchers to perform comprehensive studies including EVs mobility, energy consumption, and communications, while adding a negligible overhead

Mitigating Electromagnetic Noise When Using Low-Cost Devices in Industry 4.0

Transitioning toward Industry 4.0 requires major investment in devices and mechanisms enabling interconnectivity between people, machines, and processes. In this article, we present a low-cost system based on the Raspberry Pi platform to measure the overall equipment effectiveness (OEE) in real time, and we propose two filtering mechanisms for electromagnetic interferences (EMIs) to measure OEE accurately. The first EMI filtering mechanism is the database filter (DBF), which has been designed to record sealing signals accurately. The DBF works on the database by filtering erroneous signals that have been inserted in it. The second mechanism is the smart coded filter (SCF), which is used to filter erroneous signals associated with machine availability measurements. We have validated our proposal in several production lines in a food industry. The results show that our system works properly, and that it considerably reduces implementation costs compared with proprietary systems offering similar functions. After implementing the proposed system in actual industrial settings, the results show a mean error (ME) of -0.43% and a root mean square error (RMSE) of 4.85 in the sealing signals, and an error of 0% in the availability signal, thus enabling an accurate estimate of OEE

Economic analysis of flood risk applied to the rehabilitation of drainage networks

Over time, cities have grown, developing various activities and accumulating important economic assets. Floods are a problem that worry city administrators who seek to make cities more resilient and safer. This increase in flood events is due to different causes: poor planning, population increase, aging of networks, etc. However, the two main causes for the increase in urban flooding are the increment in frequency of extreme rainfall, generated mainly by climate change, and the increase in urbanized areas in cities, which reduce green areas, decreasing the percentage of water that seeps naturally into the soil. As a contribution to solve these problems, the work presented shows a method to rehabilitate drainage networks that contemplates implementing different actions in the network: renovation of pipes, construction of storm tanks and installation of hydraulic controls. This work focuses on evaluating the flood risk in economic terms. To achieve this, the expected annual damage from floods and the annual investments in infrastructure to control floods are estimated. These two terms are used to form an objective function to be minimized. To evaluate this objective function, an optimization model is presented that incorporates a genetic algorithm to find the best solutions to the problem; the hydraulic analysis of the network is performed with the SWMM model. This work also presents a strategy to reduce computation times by reducing the search space focused mainly on large networks. This is intended to show a complete and robust methodology that can be used by managers and administrators of drainage networks in cities

Microparticles from patients with metabolic syndrome induce vascular hypo-reactivity via Fas/Fas-ligand pathway in mice

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