Understanding multi-asset factor models: Factor exposure interpretation

Field lab: Business project

Coupling organic substrate removal to methane production in a fully biological microbial electrolysis cell

Microbial electrolysis cells (MECs) are an innovative and emerging technique based on the use of solid-state electrodes to stimulate microbial metabolism for wastewater treatment and simultaneous production of value-added compounds (such as methane). This research studied the performance of a two-chamber MEC in terms of organic matter oxidation (at the anode) and methane production (at the cathode). MEC‟s anode had been previously inoculated with an activated sludge, whereas the cathode chamber inoculum was an anaerobic sludge (containing methanogenic microorganisms). During the experimentation, the bioanode was continuously fed with synthetic solutions in anaerobic basal medium, at an organic load rate (OLR) of around 1 g L-1 d-1, referred to the chemical oxygen demand (COD). At the beginning (Run I), the feeding solution contained acetate and subsequently (Run II) it was replaced with a more complex solution containing soluble organic compounds other than acetate. For both conditions, the anode potential was controlled at -0.1 V vs. standard hydrogen electrode, by means of a potentiostat. During Run I, over 80% of the influent acetate was anaerobically oxidized at the anode, and the resulting electric current was recovered as methane at the cathode (with a cathode capture efficiency, CCE, accounting around 115 %). The average energy efficiency of the system (i.e., the energy captured into methane relative to the electrical energy input) under these conditions was over 170%. However, reactor‟s performance decreased over time during this run. Throughout Run II, a substrate oxidation over 60% (on COD basis) was observed. The electric current produced (57% of coulombic efficiency) was also recovered as methane, with a CCE of 90%. For this run the MEC‟s average energy efficiency accounted for almost 170 %. During all the experimentation, a very low biomass growth was observed at the anode whereas ammonium was transferred through the cationic membrane and concentrated at the cathode. Tracer experiments and scanning electron microscopy analyses were also carried out to gain a deeper insight into the reactor performance and also to investigate the possible reasons for partial loss of performance. In conclusion, this research suggests the great potential of MEC to successfully treat low-strength wastewaters, with high energy efficiency and very low sludge production.

Fatigue life assessment of endodontic instruments subjected to multi planar curvature

This present study aimed to investigate the fatigue life of unused (new) endodontic instruments made of NiTi with control memory by Coltene™ and subjected to the multi curvature of a mandibular first molar root canal. Additionally, the instrument‟s structural behaviour was analysed through non-linear finite element analysis (FEA). The fatigue life of twelve Hyflex™ CM files was assessed while were forced to adopt a stance with multiple radius of curvature, similar to the ones usually found in a mandibular first molar root canal; nine of them were subjected to Pecking motion, a relative movement of axial type. To achieve this, it was designed an experimental setup with the aim of timing the instruments until fracture while worked inside a stainless steel mandibular first molar model with relative axial motion to simulate the pecking motion. Additionally, the model‟s root canal multi-curvature was confirmed by radiography. The non-linear finite element analysis was conducted using the computer aided design software package SolidWorks™ Simulation, in order to define the imposed displacement required by the FEA, it was necessary to model an endodontic instrument with simplified geometry using SolidWorks™ and subsequently analyse the geometry of the root canal CAD model. The experimental results shown that the instruments subjected to pecking motion displayed higher fatigue life values and higher lengths of fractured tips than those with only rotational relative movement. The finite element non-linear analyses shown, for identical conditions, maximum values for the first principal stress lower than the yield strength of the material and those were located in similar positions to the instrument‟s fracture location determined by the experimental testing results.

A multi-criteria decision support system for a routing problem in waste collection

Autor proof

Comparison of single and multi-objective evolutionary algorithms for robust link-state routing

Traffic Engineering (TE) approaches are increasingly impor- tant in network management to allow an optimized configuration and resource allocation. In link-state routing, the task of setting appropriate weights to the links is both an important and a challenging optimization task. A number of different approaches has been put forward towards this aim, including the successful use of Evolutionary Algorithms (EAs). In this context, this work addresses the evaluation of three distinct EAs, a single and two multi-objective EAs, in two tasks related to weight setting optimization towards optimal intra-domain routing, knowing the network topology and aggregated traffic demands and seeking to mini- mize network congestion. In both tasks, the optimization considers sce- narios where there is a dynamic alteration in the state of the system, in the first considering changes in the traffic demand matrices and in the latter considering the possibility of link failures. The methods will, thus, need to simultaneously optimize for both conditions, the normal and the altered one, following a preventive TE approach towards robust configurations. Since this can be formulated as a bi-objective function, the use of multi-objective EAs, such as SPEA2 and NSGA-II, came nat- urally, being those compared to a single-objective EA. The results show a remarkable behavior of NSGA-II in all proposed tasks scaling well for harder instances, and thus presenting itself as the most promising option for TE in these scenarios.

Combining data mining and evolutionary computation for multi-criteria optimization of earthworks

Earthworks tasks aim at levelling the ground surface at a target construction area and precede any kind of structural construction (e.g., road and railway construction). It is comprised of sequential tasks, such as excavation, transportation, spreading and compaction, and it is strongly based on heavy mechanical equipment and repetitive processes. Under this context, it is essential to optimize the usage of all available resources under two key criteria: the costs and duration of earthwork projects. In this paper, we present an integrated system that uses two artificial intelligence based techniques: data mining and evolutionary multi-objective optimization. The former is used to build data-driven models capable of providing realistic estimates of resource productivity, while the latter is used to optimize resource allocation considering the two main earthwork objectives (duration and cost). Experiments held using real-world data, from a construction site, have shown that the proposed system is competitive when compared with current manual earthwork design.

Search for H→γγ produced in association with top quarks and constraints on the Yukawa coupling between the top quark and the Higgs boson using data taken at 7 TeV and 8 TeV with the ATLAS detector

A search is performed for Higgs bosons produced in association with top quarks using the diphoton decay mode of the Higgs boson. Selection requirements are optimized separately for leptonic and fully hadronic final states from the top quark decays. The dataset used corresponds to an integrated luminosity of 4.5 fb−1 of proton--proton collisions at a center-of-mass energy of 7 TeV and 20.3 fb−1 at 8 TeV recorded by the ATLAS detector at the CERN Large Hadron Collider. No significant excess over the background prediction is observed and upper limits are set on the tt¯H production cross section. The observed exclusion upper limit at 95% confidence level is 6.7 times the predicted Standard Model cross section value. In addition, limits are set on the strength of the Yukawa coupling between the top quark and the Higgs boson, taking into account the dependence of the tt¯H and tH cross sections as well as the H→γγ branching fraction on the Yukawa coupling. Lower and upper limits at 95% confidence level are set at −1.3 and +8.0 times the Yukawa coupling strength in the Standard Model.

Soil mineral N dynamics beneath mixtures of leaves from legume and fruit trees in Central Amazonian multi-strata agroforests

Long term applications of leguminous green mulch could increase mineralizable nitrogen (N) beneath cupuaçu trees produced on the infertile acidic Ultisols and Oxisols of the Amazon Basin. However, low quality standing cupuaçu litter could interfere with green mulch N release and soil N mineralization. This study compared mineral N, total N, and microbial biomass N beneath cupuaçu trees grown in two different agroforestry systems, north of Manaus, Brazil, following seven years of different green mulch application rates. To test for net interactions between green mulch and cupuaçu litter, dried gliricidia and inga leaves were mixed with senescent cupuaçu leaves, surface applied to an Oxisol soil, and incubated in a greenhouse for 162 days. Leaf decomposition, N release and soil N mineralization were periodically measured in the mixed species litter treatments and compared to single species applications. The effect of legume biomass and cupuaçu litter on soil mineral N was additive implying that recommendations for green mulch applications to cupuaçu trees can be based on N dynamics of individual green mulch species. Results demonstrated that residue quality, not quantity, was the dominant factor affecting the rate of N release from leaves and soil N mineralization in a controlled environment. In the field, complex N cycling and other factors, including soil fauna, roots, and microclimatic effects, had a stronger influence on available soil N than residue quality.

Towards high performance and multi-functional structural membranes using advanced fibrous and textile materials

Scientific and technological advancements in the area of fibrous and textile materials have greatly enhanced their application potential in several high-end technical and industrial sectors including construction, transportation, medical, sports, aerospace engineering, electronics and so on. Excellent performance accompanied by light-weight, mechanical flexibility, tailor-ability, design flexibility, easy fabrication and relatively lower cost are the driving forces towards wide applications of these materials. Cost-effective fabrication of various advanced and functional materials for structural parts, medical devices, sensors, energy harvesting devices, capacitors, batteries, and many others has been possible using fibrous and textile materials. Structural membranes are one of the innovative applications of textile structures and these novel building skins are becoming very popular due to flexible design aesthetics, durability, lightweight and cost benefits. Current demand on high performance and multi-functional materials in structural applications has motivated to go beyond the basic textile structures used for structural membranes and to use innovative textile materials. Structural membranes with self-cleaning, thermoregulation and energy harvesting capability (using solar cells) are examples of such recently developed multi-functional membranes. Besides these, there exist enormous opportunities to develop wide varieties of multi-functional membranes using functional textile materials. Additionally, it is also possible to further enhance the performance and functionalities of structural membranes using advanced fibrous architectures such as 2D, 3D, hybrid, multi-layer and so on. In this context, the present paper gives an overview of various advanced and functional fibrous and textile materials which have enormous application potential in structural membranes.

Search for low-scale gravity signatures in multi-jet final states with the ATLAS detector at s√ = 8 TeV

We search for evidence of physics beyond the Standard Model in the production of final states with multiple high transverse momentum jets, using 20.3 fb−1 of proton-proton collision data recorded by the ATLAS detector at s√ = 8 TeV. No excess of events beyond Standard Model expectations is observed, and upper limits on the visible cross-section for non-Standard Model production of multi-jet final states are set. Using a wide variety of models for black hole and string ball production and decay, the limit on the cross-section times acceptance is as low as 0.16 fb at the 95% CL for a minimum scalar sum of jet transverse momentum in the event of about 4.3 TeV. Using models for black hole and string ball production and decay, exclusion contours are determined as a function of the production mass threshold and the gravity scale. These limits can be interpreted in terms of lower-mass limits on black hole and string ball production that range from 4.6 to 6.2 TeV.

Search for heavy long-lived multi-charged particles in pp collisions at √s = 8 TeV using the ATLAS detector

A search for heavy long-lived multi-charged particles is performed using the ATLAS detector at the LHC. Data collected in 2012 at s√=8 TeV from pp collisions corresponding to an integrated luminosity of 20.3 fb−1 are examined. Particles producing anomalously high ionisation, consistent with long-lived massive particles with electric charges from |q|=2e to |q|=6e are searched for. No signal candidate events are observed, and 95% confidence level cross-section upper limits are interpreted as lower mass limits for a Drell--Yan production model. The mass limits range between 660 and 785 GeV.

Evidence for the Higgs-boson Yukawa coupling to tau leptons with the ATLAS detector

Results of a search for H→ττ decays are presented, based on the full set of proton--proton collision data recorded by the ATLAS experiment at the LHC during 2011 and 2012. The data correspond to integrated luminosities of 4.5 fb−1 and 20.3 fb−1 at centre-of-mass energies of s√ = 7 TeV and s√ = 8 TeV respectively. All combinations of leptonic (τ→ℓνν¯ with ℓ=e,μ) and hadronic (τ→hadrons ν) tau decays are considered. An excess of events over the expected background from other Standard Model processes is found with an observed (expected) significance of 4.5 (3.4) standard deviations. This excess provides evidence for the direct coupling of the recently discovered Higgs boson to fermions. The measured signal strength, normalised to the Standard Model expectation, of μ=1.43+0.43−0.37 is consistent with the predicted Yukawa coupling strength in the Standard Model.

Learning joint representations for order and timing of perceptual-motor sequences: a dynamic neural field approach

Many of our everyday tasks require the control of the serial order and the timing of component actions. Using the dynamic neural field (DNF) framework, we address the learning of representations that support the performance of precisely time action sequences. In continuation of previous modeling work and robotics implementations, we ask specifically the question how feedback about executed actions might be used by the learning system to fine tune a joint memory representation of the ordinal and the temporal structure which has been initially acquired by observation. The perceptual memory is represented by a self-stabilized, multi-bump activity pattern of neurons encoding instances of a sensory event (e.g., color, position or pitch) which guides sequence learning. The strength of the population representation of each event is a function of elapsed time since sequence onset. We propose and test in simulations a simple learning rule that detects a mismatch between the expected and realized timing of events and adapts the activation strengths in order to compensate for the movement time needed to achieve the desired effect. The simulation results show that the effector-specific memory representation can be robustly recalled. We discuss the impact of the fast, activation-based learning that the DNF framework provides for robotics applications.

Combining non-dominance, objective-order and spread metric to extend firefly algorithm to multi-objective optimization

In this paper, we propose an extension of the firefly algorithm (FA) to multi-objective optimization. FA is a swarm intelligence optimization algorithm inspired by the flashing behavior of fireflies at night that is capable of computing global solutions to continuous optimization problems. Our proposal relies on a fitness assignment scheme that gives lower fitness values to the positions of fireflies that correspond to non-dominated points with smaller aggregation of objective function distances to the minimum values. Furthermore, FA randomness is based on the spread metric to reduce the gaps between consecutive non-dominated solutions. The obtained results from the preliminary computational experiments show that our proposal gives a dense and well distributed approximated Pareto front with a large number of points.

Multi-stacks of epitaxial GeSn self-assembled dots in Si: Structural analysis

We report on the growth and structural and morphologic characterization of stacked layers of self-assembled GeSn dots grown on Si (100) substrates by molecular beam epitaxy at low substrate temperature T = 350 °C. Samples consist of layers (from 1 up to 10) of Ge0.96Sn0.04 self-assembled dots separated by Si spacer layers, 10 nm thick. Their structural analysis was performed based on transmission electron microscopy, atomic force microscopy and Raman scattering. We found that up to 4 stacks of dots could be grown with good dot layer homogeneity, making the GeSn dots interesting candidates for optoelectronic device applications.