SolarStat: Modeling Photovoltaic Sources through Stochastic Markov Processes

In this paper, we present a methodology and a tool to derive simple but yet accurate stochastic Markov processes for the description of the energy scavenged by outdoor solar sources. In particular, we target photovoltaic panels with small form factors, as those exploited by embedded communication devices such as wireless sensor nodes or, concerning modern cellular system technology, by small-cells. Our models are especially useful for the theoretical investigation and the simulation of energetically self-sufficient communication systems including these devices. The Markov models that we derive in this paper are obtained from extensive solar radiation databases, that are widely available online. Basically, from hourly radiance patterns, we derive the corresponding amount of energy (current and voltage) that is accumulated over time, and we finally use it to represent the scavenged energy in terms of its relevant statistics. Toward this end, two clustering approaches for the raw radiance data are described and the resulting Markov models are compared against the empirical distributions. Our results indicate that Markov models with just two states provide a rough characterization of the real data traces. While these could be sufficiently accurate for certain applications, slightly increasing the number of states to, e.g., eight, allows the representation of the real energy inflow process with an excellent level of accuracy in terms of first and second order statistics. Our tool has been developed using Matlab(TM) and is available under the GPL license at[1].Comment: Submitted to IEEE EnergyCon 201

Forjando aglomeraciones en Chile y Centroamérica: Enseñanzas de la experiencia

En esta presentación se describen los programas de generación de redes empresariales más exitosos en América Latina y el Caribe. Además, se sintetizan principales enseñanzas metodológicas aprendidas en la práctica, a considerar para el diseño, la puesta en marcha y la gestión de programas que promueven estos modelos de integración empresarial.Integración y comercio, Desarrollo y crecimiento económicos, Desarrollo empresarial, competitividad empresarial, redes empresariales, integración empresarial, cooperación integración, aglomeración, clusters

Sodium hydroxide pretreatment as an effective approach to reduce the dye/holes recombination reaction in P-Type DSCs

We report the synthesis of a novel squaraine dye (VG21-C12) and investigate its behavior as p-type sensitizer for p-type Dye-Sensitized Solar Cells. The results are compared with O4-C12, a well-known sensitizer for p-DSC, and sodium hydroxide pretreatment is described as an effective approach to reduce the dye/holes recombination. Various variable investigation such as dipping time, dye loading, photocurrent, and resulting cell efficiency are also reported. Electrochemical impedance spectroscopy (EIS) was utilized for investigating charge transport properties of the different photoelectrodes and the recombination phenomena that occur at the (un)modified electrode/electrolyte interface

Efficacy of adalimumab for the treatment of refractory paediatric acrodermatitis continua of hallopeau

Acrodermatitis continua of Hallopeau (ACH) is a rare, chronic disease characterized by acropustular eruptions predominantly involving the distal phalanges of the hands and feet with marked involvement of the nail bed. The sterile pustules may coalesce to form groups of lesions, which, over time, may spread proximally to involve the dorsal side of the hands, forearms and feet. Pustulation of the nail bed and nail matrix are often associated with onychodystrophy and even anonychia of the involved digits. Atrophic skin changes, onychodystrophy and osteolysis are frequently present, causing painful and disabling lesions

ABE-Cities: An attribute-based encryption system for smart cities

In the near future, a technological revolution will involve our cities, where a variety of smart services based on the Internet of Things will be developed to facilitate the needs of the citizens. Sensing devices are already being deployed in urban environments, and they will generate huge amounts of data. Such data are typically outsourced to some cloud storage because this lowers capital and operating expenses and guarantees high availability. However, cloud storage may have incentives to release stored data to unauthorized entities. In this work we present ABE-Cities, an encryption scheme for urban sensing which solves the above problems while ensuring fine-grained access control on data by means of Attribute-Based Encryption (ABE). Basically, ABE-Cities encrypts data before storing it in the cloud and provides users with keys able to decrypt only those portions of data the user is authorized to access. In ABE-Cities, the sensing devices perform only lightweight symmetric cryptography operations, thus they can also be resource-constrained. ABE-Cities provides planned expiration of keys, as well as their unplanned revocation. We propose methods to make the key revocation efficient, and we show by simulations the overall efficiency of ABE-Cities

Mobile Traffic Prediction at the Edge through Distributed and Transfer Learning

Traffic prediction represents one of the crucial tasks for smartly optimizing the mobile network. The research in this topic concentrated in making predictions in a centralized fashion, i.e., by collecting data from the different network elements. This translates to a considerable amount of energy for data transmission and processing. In this work, we propose a novel prediction framework based on edge computing which uses datasets obtained on the edge through a large measurement campaign. Two main Deep Learning architectures are designed, based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), and tested under different training conditions. In addition, Knowledge Transfer Learning (KTL) techniques are employed to improve the performance of the models while reducing the required computational resources. Simulation results show that the CNN architectures outperform the RNNs. An estimation for the needed training energy is provided, highlighting KTL ability to reduce the energy footprint of the models of 60% and 90% for CNNs and RNNs, respectively. Finally, two cutting-edge explainable Artificial Intelligence techniques are employed to interpret the derived learning models.Comment: 12 pages, 9 figure

A new finite element paradigm to solve contact problems with roughness

This article's main scope is the presentation of a computational method for the simulation of contact problems within the finite element method involving complex and rough surfaces. The approach relies on the MPJR (eMbedded Profile for Joint Roughness) interface finite element proposed in [arXiv:1805.07207], which is nominally flat but can embed at the nodal level any arbitrary height to reconstruct the displacement field due to contact in the presence of roughness. Here, the formulation is generalized to handle 3D surface height fields and any arbitrary nonlinear interface constitutive relation, including friction and adhesion. The methodology is herein validated with BEM solutions for linear elastic contact problems. Then, a selection of nonlinear contact problems prohibitive to be simulated by BEM and by standard contact algorithms in FEM are detailed, to highlight the promising aspects of the proposed method for tribology

Micro and Nanoplastics Identification: Classic Methods and Innovative Detection Techniques

Micro and nanoplastics are fragments with dimensions less than a millimeter invading all terrestrial and marine environments. They have become a major global environmental issue in recent decades and, indeed, recent scientific studies have highlighted the presence of these fragments all over the world even in environments that were thought to be unspoiled. Analysis of micro/nanoplastics in isolated samples from abiotic and biotic environmental matrices has become increasingly common. Hence, the need to find valid techniques to identify these micro and nano-sized particles. In this review, we discuss the current and potential identification methods used in microplastic analyses along with their advantages and limitations. We discuss the most suitable techniques currently available, from physical to chemical ones, as well as the challenges to enhance the existing methods and develop new ones. Microscopical techniques (i.e., dissect, polarized, fluorescence, scanning electron, and atomic force microscopy) are one of the most used identification methods for micro/nanoplastics, but they have the limitation to produce incomplete results in analyses of small particles. At present, the combination with chemical analysis (i.e., spectroscopy) overcome this limit together with recently introduced alternative approaches. For example, holographic imaging in microscope configuration images microplastics directly in unfiltered water, thus discriminating microplastics from diatoms and differentiates different sizes, shapes, and plastic types. The development of new analytical instruments coupled with each other or with conventional and innovative microscopy could solve the current problems in the identification of micro/nanoplastics