Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming.

We present a general approach to forming structure-activity relationships (SARs). This approach is based on representing chemical structure by atoms and their bond connectivities in combination with the inductive logic programming (ILP) algorithm PROGOL. Existing SAR methods describe chemical structure by using attributes which are general properties of an object. It is not possible to map chemical structure directly to attribute-based descriptions, as such descriptions have no internal organization. A more natural and general way to describe chemical structure is to use a relational description, where the internal construction of the description maps that of the object described. Our atom and bond connectivities representation is a relational description. ILP algorithms can form SARs with relational descriptions. We have tested the relational approach by investigating the SARs of 230 aromatic and heteroaromatic nitro compounds. These compounds had been split previously into two subsets, 188 compounds that were amenable to regression and 42 that were not. For the 188 compounds, a SAR was found that was as accurate as the best statistical or neural network-generated SARs. The PROGOL SAR has the advantages that it did not need the use of any indicator variables handcrafted by an expert, and the generated rules were easily comprehensible. For the 42 compounds, PROGOL formed a SAR that was significantly (P < 0.025) more accurate than linear regression, quadratic regression, and back-propagation. This SAR is based on an automatically generated structural alert for mutagenicity.

Role of the monkey ventrolateral prefrontal cortex in the integration of contextual information for the selection and understanding of goal-directed actions

Action selection and organization are very complex processes that need to exploit contextual information and the retrieval of previously memorized information, as well as the integration of these different types of data. On the basis of anatomical connection with premotor and parietal areas involved in action goal coding, and on the data about the literature it seems appropriate to suppose that one of the most candidate involved in the selection of neuronal pools for the selection and organization of intentional actions is the prefrontal cortex. We recorded single ventrolateral prefrontal (VLPF) neurons activity while monkeys performed simple and complex manipulative actions aimed at distinct final goals, by employing a modified and more strictly controlled version of the grasp-to-eat(a food pellet)/grasp-to-place(an object) paradigm used in previous studies on parietal (Fogassi et al., 2005) and premotor neurons (Bonini et al., 2010). With this task we have been able both to evaluate the processing and integration of distinct (visual and auditory) contextual sequentially presented information in order to select the forthcoming action to perform and to examine the possible presence of goal-related activity in this portion of cortex. Moreover, we performed an observation task to clarify the possible contribution of VLPF neurons to the understanding of others’ goal-directed actions. Simple Visuo Motor Task (sVMT). We found four main types of neurons: unimodal sensory-driven, motor-related, unimodal sensory-and-motor, and multisensory neurons. We found a substantial number of VLPF neurons showing both a motor-related discharge and a visual presentation response (sensory-and-motor neurons), with remarkable visuo-motor congruence for the preferred target. Interestingly the discharge of multisensory neurons reflected a behavioural decision independently from the sensory modality of the stimulus allowing the monkey to make it: some encoded a decision to act/refraining from acting (the majority), while others specified one among the four behavioural alternatives. Complex Visuo Motor Task (cVMT). The cVMT was similar to the sVMT, but included a further grasping motor act (grasping a lid in order to remove it, before grasping the target) and was run in two modalities: randomized and in blocks. Substantially, motor-related and sensory-and-motor neurons tested in the cVMTrandomized were activated already during the first grasping motor act, but the selectivity for one of the two graspable targets emerged only during the execution of the second grasping. In contrast, when the cVMT was run in block, almost all these neurons not only discharged during the first grasping motor act, but also displayed the same target selectivity showed in correspondence of the hand contact with the target. Observation Task (OT). A great part of the neurons active during the OT showed a firing rate modulation in correspondence with the action performed by the experimenter. Among them, we found neurons significantly activated during the observation of the experimenter’s action (action observation-related neurons) and neurons responding not only to the action observation, but also to the presented cue stimuli (sensory-and-action observation-related neurons. Among the neurons of the first set, almost the half displayed a target selectivity, with a not clear difference between the two presented targets; Concerning to the second neuronal set, sensory-and-action related neurons, we found a low target selectivity and a not strictly congruence between the selectivity exhibited in the visual response and in the action observation.

Ianus: an adaptive FPGA computer

Dedicated machines designed for specific computational algorithms can outperform conventional computers by several orders of magnitude. In this note we describe Ianus, a new generation FPGA based machine and its basic features: hardware integration and wide reprogrammability. Our goal is to build a machine that can fully exploit the performance potential of new generation FPGA devices. We also plan a software platform which simplifies its programming, in order to extend its intended range of application to a wide class of interesting and computationally demanding problems. The decision to develop a dedicated processor is a complex one, involving careful assessment of its performance lead, during its expected lifetime, over traditional computers, taking into account their performance increase, as predicted by Moore’s law. We discuss this point in detail.

Desenvolvimento de algoritmo de controle de tração para regeneração de energia metroviária - ACTREM: melhoria da eficiência energética de sistemas de tração metroviária.

Esta tese propõe um modelo de regeneração de energia metroviária, baseado no controle de paradas e partidas do trem ao longo de sua viagem, com o aproveitamento da energia proveniente da frenagem regenerativa no sistema de tração. O objetivo é otimizar o consumo de energia, promover maior eficiência, na perspectiva de uma gestão sustentável. Aplicando o Algoritmo Genético (GA) para obter a melhor configuração de tráfego dos trens, a pesquisa desenvolve e testa o Algoritmo de Controle de Tração para Regeneração de Energia Metroviária (ACTREM), usando a Linguagem de programação C++. Para analisar o desempenho do algoritmo de controle ACTREM no aumento da eficiência energética, foram realizadas quinze simulações da aplicação do ACTREM na linha 4 - Amarela do metrô da cidade de São Paulo. Essas simulações demonstraram a eficiência do ACTREM para gerar, automaticamente, os diagramas horários otimizados para uma economia de energia nos sistemas metroviários, levando em consideração as restrições operacionais do sistema, como capacidade máxima de cada trem, tempo total de espera, tempo total de viagem e intervalo entre trens. Os resultados mostram que o algoritmo proposto pode economizar 9,5% da energia e não provocar impactos relevantes na capacidade de transporte de passageiros do sistema. Ainda sugerem possíveis continuidades de estudos.

The Identification of Best Management Practices in a Materials Recovery Facility to Increase Solid Waste Diversion in the Department of Defense (DoD) Installations along the Front Range of Colorado to Satisfy the DoD Solid Waste Diversion Goal of 40 Percent

The five installations operated by the Department of Defense (DoD) in the Front Range region of Colorado do not meet the DoD non-hazardous solid waste diversion goal of 40 percent, further impacting landfills and generating greenhouse gases. This applied capstone project identifies and evaluates best management practices of a Materials Recovery Facility (MRF), qualitatively and quantitatively, to increase solid waste diversion at a DoD MRF. An environmental benefits model quantified the externalities of increasing solid waste diversion at the installations. By implementing best management practices at a MRF, the DoD would divert an additional 1,400 tons of solid waste per year, resulting in the equivalent of 1,502,567 gallons of gasoline being saved, among many benefits presented in this capstone.

Broadening the Scope of Ethnic Identity: A Social Sustainability Solution for Pan African Communities through Ethnically Relevant Educational Programming

This conceptual study explores ethnic identity development theory in order to argue that ethnic identity development education is a means of developing broad senses of community in the African Diaspora that expand beyond a tribal, local, familial level. This study suggests that the broadening of community understanding would contribute to establishing social sustainability on regional, national and international levels within the Pan African community. Establishing such social sustainability would have direct effects on the areas of economic and environmental sustainability. One of the goals of this project is to offer suggestions for ethnically relevant education that can develop social sustainability in several places throughout the Diaspora, such as in Nigeria where ethnic conflicts are a contemporary concern.

An explanatory model of academic achievement based on aptitudes, goal orientations, self-concept and learning strategies

As a result of studies examining factors involved in the learning process, various structural models have been developed to explain the direct and indirect effects that occur between the variables in these models. The objective was to evaluate a structural model of cognitive and motivational variables predicting academic achievement, including general intelligence, academic self-concept, goal orientations, effort and learning strategies. The sample comprised of 341 Spanish students in the first year of compulsory secondary education. Different tests and questionnaires were used to evaluate each variable, and Structural Equation Modelling (SEM) was applied to contrast the relationships of the initial model. The model proposed had a satisfactory fit, and all the hypothesised relationships were significant. General intelligence was the variable most able to explain academic achievement. Also important was the direct influence of academic self-concept on achievement, goal orientations and effort, as well as the mediating ability of effort and learning strategies between academic goals and final achievement.

Robust linear semi-infinite programming duality under uncertainty

In this paper, we propose a duality theory for semi-infinite linear programming problems under uncertainty in the constraint functions, the objective function, or both, within the framework of robust optimization. We present robust duality by establishing strong duality between the robust counterpart of an uncertain semi-infinite linear program and the optimistic counterpart of its uncertain Lagrangian dual. We show that robust duality holds whenever a robust moment cone is closed and convex. We then establish that the closed-convex robust moment cone condition in the case of constraint-wise uncertainty is in fact necessary and sufficient for robust duality. In other words, the robust moment cone is closed and convex if and only if robust duality holds for every linear objective function of the program. In the case of uncertain problems with affinely parameterized data uncertainty, we establish that robust duality is easily satisfied under a Slater type constraint qualification. Consequently, we derive robust forms of the Farkas lemma for systems of uncertain semi-infinite linear inequalities.

Calmness modulus of linear semi-infinite programs

Our main goal is to compute or estimate the calmness modulus of the argmin mapping of linear semi-infinite optimization problems under canonical perturbations, i.e., perturbations of the objective function together with continuous perturbations of the right-hand side of the constraint system (with respect to an index ranging in a compact Hausdorff space). Specifically, we provide a lower bound on the calmness modulus for semi-infinite programs with unique optimal solution which turns out to be the exact modulus when the problem is finitely constrained. The relationship between the calmness of the argmin mapping and the same property for the (sub)level set mapping (with respect to the objective function), for semi-infinite programs and without requiring the uniqueness of the nominal solution, is explored, too, providing an upper bound on the calmness modulus of the argmin mapping. When confined to finitely constrained problems, we also provide a computable upper bound as it only relies on the nominal data and parameters, not involving elements in a neighborhood. Illustrative examples are provided.

Simultaneous synthesis of work exchange networks with heat integration

The optimal integration of work and its interaction with heat can represent large energy savings in industrial plants. This paper introduces a new optimization model for the simultaneous synthesis of work exchange networks (WENs), with heat integration for the optimal pressure recovery of process gaseous streams. The proposed approach for the WEN synthesis is analogous to the well-known problem of synthesis of heat exchanger networks (HENs). Thus, there is work exchange between high-pressure (HP) and low-pressure (LP) streams, achieved by pressure manipulation equipment running on common axes. The model allows the use of several units of single-shaft-turbine-compressor (SSTC), as well as stand-alone compressors, turbines and valves. Helper motors and generators are used to respond to any demand and excess of energy. Moreover, between the WEN stages the streams are sent to the HEN to promote thermal recovery, aiming to enhance the work integration. A multi-stage superstructure is proposed to represent the process. The WEN superstructure is optimized in a mixed-integer nonlinear programming (MINLP) formulation and solved with the GAMS software, with the goal of minimizing the total annualized cost. Three examples are conducted to verify the accuracy of the proposed method. In all case studies, the heat integration between WEN stages is essential to improve the pressure recovery, and to reduce the total costs involved in the process.

Integration of modular process simulators under the Generalized Disjunctive Programming framework for the structural flowsheet optimization

The optimization of chemical processes where the flowsheet topology is not kept fixed is a challenging discrete-continuous optimization problem. Usually, this task has been performed through equation based models. This approach presents several problems, as tedious and complicated component properties estimation or the handling of huge problems (with thousands of equations and variables). We propose a GDP approach as an alternative to the MINLP models coupled with a flowsheet program. The novelty of this approach relies on using a commercial modular process simulator where the superstructure is drawn directly on the graphical use interface of the simulator. This methodology takes advantage of modular process simulators (specially tailored numerical methods, reliability, and robustness) and the flexibility of the GDP formulation for the modeling and solution. The optimization tool proposed is successfully applied to the synthesis of a methanol plant where different alternatives are available for the streams, equipment and process conditions.

Mathematical programming model for heat exchanger design through optimization of partial objectives

Mathematical programming can be used for the optimal design of shell-and-tube heat exchangers (STHEs). This paper proposes a mixed integer non-linear programming (MINLP) model for the design of STHEs, following rigorously the standards of the Tubular Exchanger Manufacturers Association (TEMA). Bell–Delaware Method is used for the shell-side calculations. This approach produces a large and non-convex model that cannot be solved to global optimality with the current state of the art solvers. Notwithstanding, it is proposed to perform a sequential optimization approach of partial objective targets through the division of the problem into sets of related equations that are easier to solve. For each one of these problems a heuristic objective function is selected based on the physical behavior of the problem. The global optimal solution of the original problem cannot be ensured even in the case in which each of the sub-problems is solved to global optimality, but at least a very good solution is always guaranteed. Three cases extracted from the literature were studied. The results showed that in all cases the values obtained using the proposed MINLP model containing multiple objective functions improved the values presented in the literature.

Goal Orientation Profiles and Self-Concept of Secondary School Students

The objective of this study is to identify possible combinations of multiple goals that lead to different goal orientation profiles and to determine whether there are significant group differences in self-concept dimensions. The Achievement Goals Tendencies Questionnaire (AGTQ) and the Self-Description Questionnaire-II (SDQ-II) were administered to a sample of 2,022 students of Compulsory Secondary education, ranging in age from 12 to 16 years (M = 13.81, SD = 1.35). Cluster analysis identified four profiles of motivational goals: a group of students with a generalized high motivation profile, a group of students with generalized low motivation profile, a group of students with a predominance of learning goals and achievement goals, and a last group of students with a predominance of achievement goals and social reinforcement goals. Results reveal statistically significant differences among the profiles obtained regarding self-concept dimensions.

Comparative study of RPSALG algorithm for convex semi-infinite programming

The Remez penalty and smoothing algorithm (RPSALG) is a unified framework for penalty and smoothing methods for solving min-max convex semi-infinite programing problems, whose convergence was analyzed in a previous paper of three of the authors. In this paper we consider a partial implementation of RPSALG for solving ordinary convex semi-infinite programming problems. Each iteration of RPSALG involves two types of auxiliary optimization problems: the first one consists of obtaining an approximate solution of some discretized convex problem, while the second one requires to solve a non-convex optimization problem involving the parametric constraints as objective function with the parameter as variable. In this paper we tackle the latter problem with a variant of the cutting angle method called ECAM, a global optimization procedure for solving Lipschitz programming problems. We implement different variants of RPSALG which are compared with the unique publicly available SIP solver, NSIPS, on a battery of test problems.

Development and programming of Geophonino: A low cost Arduino-based seismic recorder for vertical geophones

The commercial data acquisition systems used for seismic exploration are usually expensive equipment. In this work, a low cost data acquisition system (Geophonino) has been developed for recording seismic signals from a vertical geophone. The signal goes first through an instrumentation amplifier, INA155, which is suitable for low amplitude signals like the seismic noise, and an anti-aliasing filter based on the MAX7404 switched-capacitor filter. After that, the amplified and filtered signal is digitized and processed by Arduino Due and registered in an SD memory card. Geophonino is configured for continuous registering, where the sampling frequency, the amplitude gain and the registering time are user-defined. The complete prototype is an open source and open hardware system. It has been tested by comparing the registered signals with the ones obtained through different commercial data recording systems and different kind of geophones. The obtained results show good correlation between the tested measurements, presenting Geophonino as a low-cost alternative system for seismic data recording.