Efficient Hill Climber for Constrained Pseudo-Boolean Optimization Problems

Efficient hill climbers have been recently proposed for single- and multi-objective pseudo-Boolean optimization problems. For $k$-bounded pseudo-Boolean functions where each variable appears in at most a constant number of subfunctions, it has been theoretically proven that the neighborhood of a solution can be explored in constant time. These hill climbers, combined with a high-level exploration strategy, have shown to improve state of the art methods in experimental studies and open the door to the so-called Gray Box Optimization, where part, but not all, of the details of the objective functions are used to better explore the search space. One important limitation of all the previous proposals is that they can only be applied to unconstrained pseudo-Boolean optimization problems. In this work, we address the constrained case for multi-objective $k$-bounded pseudo-Boolean optimization problems. We find that adding constraints to the pseudo-Boolean problem has a linear computational cost in the hill climber.

Modeling, Component Selection and Optimization of Servo-controlled Automatic Machinery

A servo-controlled automatic machine can perform tasks that involve synchronized actuation of a significant number of servo-axes, namely one degree-of-freedom (DoF) electromechanical actuators. Each servo-axis comprises a servo-motor, a mechanical transmission and an end-effector, and is responsible for generating the desired motion profile and providing the power required to achieve the overall task. The design of a such a machine must involve a detailed study from a mechatronic viewpoint, due to its electric and mechanical nature. The first objective of this thesis is the development of an overarching electromechanical model for a servo-axis. Every loss source is taken into account, be it mechanical or electrical. The mechanical transmission is modeled by means of a sequence of lumped-parameter blocks. The electric model of the motor and the inverter takes into account winding losses, iron losses and controller switching losses. No experimental characterizations are needed to implement the electric model, since the parameters are inferred from the data available in commercial catalogs. With the global model at disposal, a second objective of this work is to perform the optimization analysis, in particular, the selection of the motor-reducer unit. The optimal transmission ratios that minimize several objective functions are found. An optimization process is carried out and repeated for each candidate motor. Then, we present a novel method where the discrete set of available motor is extended to a continuous domain, by fitting manufacturer data. The problem becomes a two-dimensional nonlinear optimization subject to nonlinear constraints, and the solution gives the optimal choice for the motor-reducer system. The presented electromechanical model, along with the implementation of optimization algorithms, forms a complete and powerful simulation tool for servo-controlled automatic machines. The tool allows for determining a wide range of electric and mechanical parameters and the behavior of the system in different operating conditions.

The three-dimensional single-bin-size bin packing problem: combining metaheuristic and machine learning approaches

The Three-Dimensional Single-Bin-Size Bin Packing Problem is one of the most studied problem in the Cutting & Packing category. From a strictly mathematical point of view, it consists of packing a finite set of strongly heterogeneous “small” boxes, called items, into a finite set of identical “large” rectangles, called bins, minimizing the unused volume and requiring that the items are packed without overlapping. The great interest is mainly due to the number of real-world applications in which it arises, such as pallet and container loading, cutting objects out of a piece of material and packaging design. Depending on these real-world applications, more objective functions and more practical constraints could be needed. After a brief discussion about the real-world applications of the problem and a exhaustive literature review, the design of a two-stage algorithm to solve the aforementioned problem is presented. The algorithm must be able to provide the spatial coordinates of the placed boxes vertices and also the optimal boxes input sequence, while guaranteeing geometric, stability, fragility constraints and a reduced computational time. Due to NP-hard complexity of this type of combinatorial problems, a fusion of metaheuristic and machine learning techniques is adopted. In particular, a hybrid genetic algorithm coupled with a feedforward neural network is used. In the first stage, a rich dataset is created starting from a set of real input instances provided by an industrial company and the feedforward neural network is trained on it. After its training, given a new input instance, the hybrid genetic algorithm is able to run using the neural network output as input parameter vector, providing as output the optimal solution. The effectiveness of the proposed works is confirmed via several experimental tests.

Distributed optimization and games over networks: a system theoretical perspective

Several decision and control tasks involve networks of cyber-physical systems that need to be coordinated and controlled according to a fully-distributed paradigm involving only local communications without any central unit. This thesis focuses on distributed optimization and games over networks from a system theoretical perspective. In the addressed frameworks, we consider agents communicating only with neighbors and running distributed algorithms with optimization-oriented goals. The distinctive feature of this thesis is to interpret these algorithms as dynamical systems and, thus, to resort to powerful system theoretical tools for both their analysis and design. We first address the so-called consensus optimization setup. In this context, we provide an original system theoretical analysis of the well-known Gradient Tracking algorithm in the general case of nonconvex objective functions. Then, inspired by this method, we provide and study a series of extensions to improve the performance and to deal with more challenging settings like, e.g., the derivative-free framework or the online one. Subsequently, we tackle the recently emerged framework named distributed aggregative optimization. For this setup, we develop and analyze novel schemes to handle (i) online instances of the problem, (ii) ``personalized'' optimization frameworks, and (iii) feedback optimization settings. Finally, we adopt a system theoretical approach to address aggregative games over networks both in the presence or absence of linear coupling constraints among the decision variables of the players. In this context, we design and inspect novel fully-distributed algorithms, based on tracking mechanisms, that outperform state-of-the-art methods in finding the Nash equilibrium of the game.

Parametric correlation functions to model the structure of permanent environmental (co)variances in milk yield random regression models

The objective of the present study was to estimate milk yield genetic parameters applying random regression models and parametric correlation functions combined with a variance function to model animal permanent environmental effects. A total of 152,145 test-day milk yields from 7,317 first lactations of Holstein cows belonging to herds located in the southeastern region of Brazil were analyzed. Test-day milk yields were divided into 44 weekly classes of days in milk. Contemporary groups were defined by herd-test-day comprising a total of 2,539 classes. The model included direct additive genetic, permanent environmental, and residual random effects. The following fixed effects were considered: contemporary group, age of cow at calving (linear and quadratic regressions), and the population average lactation curve modeled by fourth-order orthogonal Legendre polynomial. Additive genetic effects were modeled by random regression on orthogonal Legendre polynomials of days in milk, whereas permanent environmental effects were estimated using a stationary or nonstationary parametric correlation function combined with a variance function of different orders. The structure of residual variances was modeled using a step function containing 6 variance classes. The genetic parameter estimates obtained with the model using a stationary correlation function associated with a variance function to model permanent environmental effects were similar to those obtained with models employing orthogonal Legendre polynomials for the same effect. A model using a sixth-order polynomial for additive effects and a stationary parametric correlation function associated with a seventh-order variance function to model permanent environmental effects would be sufficient for data fitting.

Profiles of functional deficits in mild cognitive impairment and dementia: benefits from objective measurement

The magnitude Of functional impairment that may indicate the threshold between MCI and incipient Alzheimer`s disease (AD) has not been clearly defined. The objective was to examine the pattern of functional impairment in the continuum MCI-AD. Eighty-nine older adults (32 cognitively unimpaired, 31 MCI, and 26 AD patients) were examined with the Brazilian version of the Direct Assessment of Functional Status (DAFS-BR) at a University-based memory clinic. MCI patients were sub-divided according to the progression to AD upon follow-up, and had baseline cognitive, functional and biological variables analyzed. MCI patients displayed mild deficits in functional abilities, with intermediate scores as compared to controls and AD. The DAFS-BR items that differentiated MCI from controls involved the ability to deal with finances and shopping skills. At baseline, scores obtained by MCI patients who converted to AD were not significantly different from scores of nonconverters. The magnitude of functional deficits was associated with AD-like pathological findings in the CSF. In conclusion, MCI patients present with early functional changes in complex, instrumental abilities that require the integrity of memory and executive functions. The objective measurement of the functional state may help identify older adults with increased risk of developing dementia in the MCI-AD continuum. (JINS, 2010, 16, 297-305.)

Covariance functions for body weight from birth to maturity in Nellore cows

The objective of this study was to estimate (co)variance functions using random regression models on Legendre polynomials for the analysis of repeated measures of BW from birth to adult age. A total of 82,064 records from 8,145 females were analyzed. Different models were compared. The models included additive direct and maternal effects, and animal and maternal permanent environmental effects as random terms. Contemporary group and dam age at calving (linear and quadratic effect) were included as fixed effects, and orthogonal Legendre polynomials of animal age (cubic regression) were considered as random co-variables. Eight models with polynomials of third to sixth order were used to describe additive direct and maternal effects, and animal and maternal permanent environmental effects. Residual effects were modeled using 1 (i.e., assuming homogeneity of variances across all ages) or 5 age classes. The model with 5 classes was the best to describe the trajectory of residuals along the growth curve. The model including fourth- and sixth-order polynomials for additive direct and animal permanent environmental effects, respectively, and third-order polynomials for maternal genetic and maternal permanent environmental effects were the best. Estimates of (co) variance obtained with the multi-trait and random regression models were similar. Direct heritability estimates obtained with the random regression models followed a trend similar to that obtained with the multi-trait model. The largest estimates of maternal heritability were those of BW taken close to 240 d of age. In general, estimates of correlation between BW from birth to 8 yr of age decreased with increasing distance between ages.

Is maximizing resilience compatible with established ecological goal functions?

Cropp and Gabric [Ecosystem adaptation: do ecosystems maximise resilience? Ecology. In press] used a simple phytoplanktonzooplankton-nutrient model and a genetic algorithm to determine the parameter values that would maximize the value of certain goal functions. These goal functions were to maximize biomass, maximize flux, maximize flux to biomass ratio, and maximize resilience. It was found that maximizing goal functions maximized resilience. The objective of this study was to investigate whether the Cropp and Gabric [Ecosystem adaptation: do ecosystems maximise resilience? Ecology. In press] result was indicative of a general ecosystem principle, or peculiar to the model and parameter ranges used. This study successfully replicated the Cropp and Gabric [Ecosystem adaptation: do ecosystems maximise resilience? Ecology. In press] experiment for a number of different model types, however, a different interpretation of the results is made. A new metric, concordance, was devised to describe the agreement between goal functions. It was found that resilience has the highest concordance of all goal functions trialled. for most model types. This implies that resilience offers a compromise between the established ecological goal functions. The parameter value range used is found to affect the parameter versus goal function relationships. Local maxima and minima affected the relationship between parameters and goal functions, and between goal functions. (C) 2003 Elsevier B.V. All rights reserved.

Single-objective spreading algorithm

This paper addresses the problem of finding several different solutions with the same optimum performance in single objective real-world engineering problems. In this paper a parallel robot design is proposed. Thereby, this paper presents a genetic algorithm to optimize uni-objective problems with an infinite number of optimal solutions. The algorithm uses the maximin concept and ε-dominance to promote diversity over the admissible space. The performance of the proposed algorithm is analyzed with three well-known test functions and a function obtained from practical real-world engineering optimization problems. A spreading analysis is performed showing that the solutions drawn by the algorithm are well dispersed.

Metabolic engineering with multi-objective optimization of kinetic models

Kinetic models have a great potential for metabolic engineering applications. They can be used for testing which genetic and regulatory modifications can increase the production of metabolites of interest, while simultaneously monitoring other key functions of the host organism. This work presents a methodology for increasing productivity in biotechnological processes exploiting dynamic models. It uses multi-objective dynamic optimization to identify the combination of targets (enzymatic modifications) and the degree of up- or down-regulation that must be performed in order to optimize a set of pre-defined performance metrics subject to process constraints. The capabilities of the approach are demonstrated on a realistic and computationally challenging application: a large-scale metabolic model of Chinese Hamster Ovary cells (CHO), which are used for antibody production in a fed-batch process. The proposed methodology manages to provide a sustained and robust growth in CHO cells, increasing productivity while simultaneously increasing biomass production, product titer, and keeping the concentrations of lactate and ammonia at low values. The approach presented here can be used for optimizing metabolic models by finding the best combination of targets and their optimal level of up/down-regulation. Furthermore, it can accommodate additional trade-offs and constraints with great flexibility.

Congestive heart failure. Correlation between functional class and systolic and diastolic functions assessed by Doppler echocardiography

OBJECTIVE: To evaluate the influence of systolic or diastolic dysfunction, or both on congestive heart failure functional class. METHODS: Thirty-six consecutive patients with a clinical diagnosis of congestive heart failure with sinus rhythm, who were seen between September and November of 1998 answered an adapted questionnaire about tolerance to physical activity for the determination of NYHA functional class. The patients were studied with transthoracic Doppler echocardiography. Two groups were compared: group 1 (19 patients in functional classes I and II) and group 2 (17 patients in functional classes III and IV). RESULTS: The average ejection fraction was significantly higher in group 1 (44.84%±8.04% vs. 32.59%±11.48% with p=0.0007). The mean ratio of the initial/final maximum diastolic filling velocity (E/A) of the left ventricle was significantly smaller in group 1 (1.07±0.72 vs. 1.98±1.49 with p=0.03). The average maximum systolic pulmonary venous velocity (S) was significantly higher in group 1 (53.53cm/s ± 12.02cm/s vs. 43.41cm/s ± 13.55cm/s with p=0.02). The mean ratio of maximum systolic/diastolic pulmonary venous velocity was significantly higher in group 1 (1.52±0.48 vs. 1.08±0.48 with p=0.01). A predominance of pseudo-normal and restrictive diastolic patterns existed in group 2 (58.83% in group 2 vs. 21.06% in group 1 with p=0.03). CONCLUSION: Both the systolic dysfunction index and the patterns of diastolic dysfunction evaluated by Doppler echocardiography worsened with the evolution of congestive heart failure.

Effects of a dual inhibitor of angiotensin converting enzyme and neutral endopeptidase, MDL 100,240, on endocrine and renal functions in healthy volunteers.

OBJECTIVE: To investigate the endocrine and renal effects of the dual inhibitor of angiotensin converting enzyme and neutral endopeptidase, MDL 100,240. DESIGN: A randomized, placebo-controlled, crossover study was performed in 12 healthy volunteers. METHODS: MDL 100,240 was administered intravenously over 20 min at single doses of 6.25 and 25 mg in subjects with a sodium intake of 280 (n = 6) or 80 (n = 6) mmol/day. Measurements were taken of supine and standing blood pressure, plasma angiotensin converting enzyme activity, angiotensin II, atrial natriuretic peptide, urinary atrial natriuretic peptide and cyclic GMP excretion, effective renal plasma flow and the glomerular filtration rate as p-aminohippurate and inulin clearances, electrolytes and segmental tubular function by endogenous lithium clearance. RESULTS: Supine systolic blood pressure was consistently decreased by MDL 100,240, particularly after the high dose and during the low-salt intake. Diastolic blood pressure and heart rate did not change. Plasma angiotensin converting enzyme activity decreased rapidly and dose-dependently. In both the high- and the low-salt treatment groups, plasma angiotensin II levels fell and renin activity rose accordingly, while plasma atrial natriuretic peptide levels remained unchanged. In contrast, urinary atrial natriuretic peptide excretion increased dose-dependently under both diets, as did urinary cyclic GMP excretion. Effective renal plasma flow and the glomerular filtration rate did not change. The urinary flow rate increased markedly during the first 2 h following administration of either dose of MDL 100,240 (P < 0.001) and, similarly, sodium excretion tended to increase from 0 to 4 h after the dose (P = 0.07). Potassium excretion remained stable. Proximal and distal fractional sodium reabsorption were not significantly altered by the treatment. Uric acid excretion was increased. The safety and clinical tolerance of MDL 100,240 were good. CONCLUSIONS: The increased fall in blood pressure in normal volunteers together with the preservation of renal hemodynamics and the increased urinary volume, atrial natriuretic peptide and cyclic GMP excretion distinguish MDL 100,240 as a double-enzyme inhibitor from inhibitors of the angiotensin converting enzyme alone. The differences appear to be due, at least in part, to increased renal exposure to atrial natriuretic peptide following neutral endopeptidase blockade.

Transnational study of roles/functions and associated ICT competencies for Higher Education teachers

Aquest estudi forma part del projecte eLene-TLC1 Virtual Campus (2007-2008) recolzat pel programa eLearning de la Comissió Europea. L'objectiu d'aquest projecte és que els professors i els estudiants facin el millor ús possible de les TIC en l'educació superior, preparant als professors per als estudiants de la generació xarxa, permetent als estudiants a la transferència de coneixements i pràctiques de la vida quotidiana per al seu aprenentatge i estimular tant la integració plena de pràctiques innovadores d'ensenyament i d'aprenentatge possibilitades per un entorn tecnològic en constant evolució. Per tal de cobrir part d'aquest objectiu general, es va concebre un estudi per examinar les competències en TIC professors d'Educació Superior en entorns d'aprenentatge en línia.

Two-year follow-op of cognitive functions in Schizophrenia spectrum disorders of adolescent patients treated with electroconvulsive therapy

Objective: The aim of the current study was to investigate the long-term cognitive effects of electroconvulsive therapy (ECT) in a sample of adolescent patients in whom schizophrenia spectrum disorders were diagnosed. Methods: The sample was composed of nine adolescent subjects in whom schizophrenia or schizoaffective disorder was diagnosed according to DSM-IV-TR criteria on whom ECT was conducted (ECT group) and nine adolescent subjects matched by age, socioeconomic status, and diagnostic and Positive and Negative Syndrome Scale (PANSS) total score at baseline on whom ECT was not conducted (NECT group). Clinical and neuropsychological assessments were carried out at baseline before ECT treatment and at 2-year follow-up. Results: Significant differences were found between groups in the number of unsuccessful medication trials. No statistically significant differences were found between the ECT group and theNECT group in either severity as assessed by the PANSS, or in any cognitive variables at baseline.At follow-up, both groups showed significant improvement in clinical variables (subscales of positive, general, and total scores of PANSS and Clinical Global Impressions-Improvement). In the cognitive assessment at follow-up, significant improvement was found in both groups in the semantic category of verbal fluency task and digits forward. However, no significant differences were found between groups in any clinical or cognitive variable at follow-up. Repeated measures analysis found no significant interaction of time · group in any clinical or neuropsychological measures. Conclusions: The current study showed no significant differences in change over time in clinical or neuropsychological variables between the ECT group and the NECT group at 2-year follow-up. Thus, ECT did not show any negative influence on long-term neuropsychological variables in our sample.

Creating possibilities for collective creativity : Brokerage Functions in Practice-Based Innovation

In the network era, creative achievements like innovations are more and more often created in interaction among different actors. The complexity of today‘s problems transcends the individual human mind, requiring not only individual but also collective creativity. In collective creativity, it is impossible to trace the source of new ideas to an individual. Instead, creative activity emerges from the collaboration and contribution of many individuals, thereby blurring the contribution of specific individuals in creating ideas. Collective creativity is often associated with diversity of knowledge, skills, experiences and perspectives. Collaboration between diverse actors thus triggers creativity and gives possibilities for collective creativity. This dissertation investigates collective creativity in the context of practice-based innovation. Practice-based innovation processes are triggered by problem setting in a practical context and conducted in non-linear processes utilising scientific and practical knowledge production and creation in cross-disciplinary innovation networks. In these networks diversity or distances between innovation actors are essential. Innovation potential may be found in exploiting different kinds of distances. This dissertation presents different kinds of distances, such as cognitive, functional and organisational which could be considered as sources of creativity and thus innovation. However, formation and functioning of these kinds of innovation networks can be problematic. Distances between innovating actors may be so great that a special interpretation function is needed – that is, brokerage. This dissertation defines factors that enhance collective creativity in practice-based innovation and especially in the fuzzy front end phase of innovation processes. The first objective of this dissertation is to study individual and collective creativity at the employee level and identify those factors that support individual and collective creativity in the organisation. The second objective is to study how organisations use external knowledge to support collective creativity in their innovation processes in open multi-actor innovation. The third objective is to define how brokerage functions create possibilities for collective creativity especially in the context of practice-based innovation. The research objectives have been studied through five substudies using a case-study strategy. Each substudy highlights various aspects of creativity and collective creativity. The empirical data consist of materials from innovation projects arranged in the Lahti region, Finland, or materials from the development of innovation methods in the Lahti region. The Lahti region has been chosen as the research context because the innovation policy of the region emphasises especially the promotion of practice-based innovations. The results of this dissertation indicate that all possibilities of collective creativity are not utilised in internal operations of organisations. The dissertation introduces several factors that could support collective creativity in organisations. However, creativity as a social construct is understood and experienced differently in different organisations, and these differences should be taken into account when supporting creativity in organisations. The increasing complexity of most potential innovations requires collaborative creative efforts that often exceed the boundaries of the organisation and call for the involvement of external expertise. In practice-based innovation different distances are considered as sources of creativity. This dissertation gives practical implications on how it is possible to exploit different kinds of distances knowingly. It underlines especially the importance of brokerage functions in open, practice-based innovation in order to create possibilities for collective creativity. As a contribution of this dissertation, a model of brokerage functions in practice-based innovation is formulated. According to the model, the results and success of brokerage functions are based on the context of brokerage as well as the roles, tasks, skills and capabilities of brokers. The brokerage functions in practice-based innovation are also possible to divide into social and cognitive brokerage.