Invertebrate response to impacts of water diversion and flow regulation in high‐altitude tropical streams

Water supply systems are critical infrastructure that provides food and energy security for developed societies. The operation of reservoirs (flow regulation) and water intakes (water diversion) has known negative impacts on aquatic ecosystems; however, quantification of ecological impacts and examination of these two types of flow alteration remain a developing area of research. We investigated the individual and combined impact of flow regulation and water diversion on stream ecosystem integrity, the freshwater macroinvertebrate community, and the population structure of flow‐sensitive insects. For 2 years, we monitored quarterly discharge, physical and chemical stream conditions, and benthic invertebrates of four high‐altitude tropical streams that are part of the water supply system of Quito, Ecuador. Flow regulation caused a loss of the hydrological seasonality of these streams, including a decrease in stream depth and biotic quality. Water diversion caused a decrease in dissolved oxygen and overall ecosystem integrity. Freshwater invertebrate density and richness decreased as a result of water diversion and flow regulation. The combined flow alteration in these streams decreased the density of nymphal stages of the widely distributed mayfly Andesiops peruvianus. Given the societal needs for food and energy security, water management for diversion (e.g., irrigation) and in‐line storage practices (e.g., hydroelectric dams) are anticipated to increase. This research suggests that the negative environmental impacts of flow alteration could be mitigated with discharge releases designed to approximate the natural hydrologic regime of undisturbed streams.     

Envelopes with microplastics generated from recycled plastic bags for crude oil sorption

Oil spill accidents in marine environments and the lack of disposal of post-consumer plastic are environmental problems worldwide. This study presents a sustainable alternative for both issues through envelopes filled with microplastics (MPs) from recycled bags for the sorption of spilled crude oil. Through particle size analysis by three different sieves (4, 9, and 20 mesh), different MP sizes were characterized by scanning electron microscope (SEM), Brunauer–Emmett–Teller (BET), density, contact angle (CA), and Fourier-transform infrared spectroscopy (FTIR). According to their sizes, the MPs were distributed in envelopes and submitted to crude oil sorption capacity and efficiency evaluation. Three MP particle sizes were obtained (from the largest to the smallest, according to the sieve mesh, MP4, MP9, and MP20). SEM images of samples exhibited irregular and porous surfaces, and MP4 had the smallest pore size (8.6 μm). BET showed that MP4 had the highest surface area (0.074 m²/g). The CA > 90° exposed that all samples were hydrophobic. FTIR spectrum demonstrated that the samples from the recycled bags were made of high density polyethylene (HDPE). The MP4 envelopes also had the best crude oil sorption results in capacity and efficiency (1.73 g/g and 68%, respectively), being a promising recycled sorbent in crude oil spillage applications.     

Influence of deposited amine-functionalized Si-MCM-41 in polyacrylonitrile electrospun membranes applied for separation of water in oil emulsions

Among several oil/water emulsion separation technologies, the utilization of nanoparticle-decorated membranes with diverse functionalities has received considerable attention in recent years, particularly if the antifouling capacity can be improved. In this article, we propose a new membrane based on surface-hydrolyzed polyacrylonitrile electrospun membranes and/or decorated with amine-functionalized Si-MCM-41 nanoparticles to be used as oil/water emulsion separation treatment and to determine their antifouling ability. X-Ray photoelectron spectrometry, attenuated total reflectance Fourier transform infrared spectroscopy, and toluidine blue O assay, scanning electron microscopy, contact angle measurements for oil under water and thermogravimetry were used for characterizing the membranes and an assay of permeability was developed to quantify the diffusion of oil molecules across the electrospun membrane. The electrospun and/or decorated membranes showed an underwater oleophobic wettability, which can separate oil-in-water emulsions with 87% separation efficiency, results of fouling experiments, evaluated in terms of rejection and flux recovery ratio, exhibited good antifouling ability, but the membrane decoration process did not lead to superior outcomes compared with undecorated membranes.     

Learning to rank academic experts in the DBLP dataset

Expert finding is an information retrieval task that is concerned with the search for the most knowledgeable people with respect to a specific topic, and the search is based on documents that describe people's activities. The task involves taking a user query as input and returning a list of people who are sorted by their level of expertise with respect to the user query. Despite recent interest in the area, the current state‐of‐the‐art techniques lack in principled approaches for optimally combining different sources of evidence. This article proposes two frameworks for combining multiple estimators of expertise. These estimators are derived from textual contents, from graph‐structure of the citation patterns for the community of experts and from profile information about the experts. More specifically, this article explores the use of supervised learning to rank methods, as well as rank aggregation approaches, for combining all of the estimators of expertise. Several supervised learning algorithms, which are representative of the pointwise, pairwise and listwise approaches, were tested, and various state‐of‐the‐art data fusion techniques were also explored for the rank aggregation framework. Experiments that were performed on a dataset of academic publications from the Computer Science domain attest the adequacy of the proposed approaches.     

Variational Foundations and Generalized Unified Theory of RVE-Based Multiscale Models

A unified variational theory is proposed for a general class of multiscale models based on the concept of Representative Volume Element. The entire theory lies on three fundamental principles: (1) kinematical admissibility, whereby the macro- and micro-scale kinematics are defined and linked in a physically meaningful way; (2) duality, through which the natures of the force- and stress-like quantities are uniquely identified as the duals (power-conjugates) of the adopted kinematical variables; and (3) the Principle of Multiscale Virtual Power, a generalization of the well-known Hill-Mandel Principle of Macrohomogeneity, from which equilibrium equations and homogenization relations for the force- and stress-like quantities are unequivocally obtained by straightforward variational arguments. The proposed theory provides a clear, logically-structured framework within which existing formulations can be rationally justified and new, more general multiscale models can be rigorously derived in well-defined steps. Its generality allows the treatment of problems involving phenomena as diverse as dynamics, higher order strain effects, material failure with kinematical discontinuities, fluid mechanics and coupled multi-physics. This is illustrated in a number of examples where a range of models is systematically derived by following the same steps. Due to the variational basis of the theory, the format in which derived models are presented is naturally well suited for discretization by finite element-based or related methods of numerical approximation. Numerical examples illustrate the use of resulting models, including a non-conventional failure-oriented model with discontinuous kinematics, in practical computations.     

Matrix-free aerostructural optimization of aircraft wings

In structural optimization subject to failure constraints, computing the gradients of a large number of functions with respect to a large number of design variables may not be computationally practical. Often, the number of constraints in these optimization problems is reduced using constraint aggregation at the expense of a higher mass of the optimal structural design. This work presents results of structural and coupled aerodynamic and structural design optimization of aircraft wings using a novel matrix-free augmented Lagrangian optimizer. By using a matrix-free optimizer, the computation of the full constraint Jacobian at each iteration is replaced by the computation of a small number of Jacobian-vector products. The low cost of the Jacobian-vector products allows optimization problems with thousands of failure constraints to be solved directly, mitigating the effects of constraint aggregation. The results indicate that the matrix-free optimizer reduces the computational work of solving the optimization problem by an order of magnitude compared to a traditional sequential quadratic programming optimizer. Furthermore, the use of a matrix-free optimizer makes the solution of large multidisciplinary design problems, in which gradient information must be obtained through iterative methods, computationally tractable.     

A robot learning from demonstration framework to perform force-based manipulation tasks

This paper proposes an end-to-end learning from demonstration framework for teaching force-based manipulation tasks to robots. The strengths of this work are manyfold. First, we deal with the problem of learning through force perceptions exclusively. Second, we propose to exploit haptic feedback both as a means for improving teacher demonstrations and as a human–robot interaction tool, establishing a bidirectional communication channel between the teacher and the robot, in contrast to the works using kinesthetic teaching. Third, we address the well-known what to imitate? problem from a different point of view, based on the mutual information between perceptions and actions. Lastly, the teacher’s demonstrations are encoded using a Hidden Markov Model, and the robot execution phase is developed by implementing a modified version of Gaussian Mixture Regression that uses implicit temporal information from the probabilistic model, needed when tackling tasks with ambiguous perceptions. Experimental results show that the robot is able to learn and reproduce two different manipulation tasks, with a performance comparable to the teacher’s one.