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Context: Hydroelectricity is a mature and long-lasting technology that has presented environmental and social problems, to face them, hydroelectric sustainability initiatives have been created for more than two decades. In Mexico, hydroelectric projects present environmental and social problems that cause delays during their construction and, sometimes, when they are put into operation. The objective of this research is to report how the combination of multi-criteria decision-making methods, such as the Hierarchical Analytical Process (AHP) and the Goal Programming (GP) could be applied to weight the hydroelectric sustainability criteria, using multi-criteria decision-making methods for a group of experts.

Mission-Oriented Policy (MOP) leads to action within a bold framework of wide societal relevance, clear direction, targeted at ambitious innovation activities and goes beyond the simple formulation and design of policies. It also supports their implementation, execution, and evaluation. This form of public policy design has a wide presence in developed countries and is already showing signs of adoption in Latin American countries, including Colombia. In consideration of this tendency, this study bases on the a priori approaches defined in the theoretical focus of the MOP in order to validate, a posteriori, two Colombian cases of public policy, guided by a mission and mediated by innovation processes in a broad sense. The cases analyzed are the National Optical Fiber Plan (NOFP) and the regional program of Medellin The Most Educated (MTME). Product of these cases, we derive lessons of interest to public policymakers and implementers.

Piston pumps play a key role in factory automation and their availability is very critical for the smooth running of production processes. Modern installations, such as production plants and machines, are becoming increasingly complex. Therefore, the probability of a complete system failure due to a single critical component also increases. Maintenance processes with intelligent devices are therefore very important to achieve maximum economic efficiency and safety. Periodic or continuous monitoring of system components provides key information about the current physical state of the system, enabling early detection of emerging failures. Knowledge of future failures makes it possible to move from the concept of preventive maintenance to intelligent predictive maintenance. In this way, consequential damage and complete system failure can be avoided, maximizing system availability and safety. This paper reflects the development and implementation of a neural network system for abnormal state prediction of piston pumps. After a short introduction into piston pumps and their potential abnormal states, statistical and periodical analysis are presented. Then the design and implementation of suitable neural networks are discussed. Finally, a conclusion is drawn and the observed accuracies as well as potential next steps are discussed.

Motivated by modern regression applications, in this paper, we study the convexification of quadratic optimization problems with indicator variables and combinatorial constraints on the indicators. Unlike most of the previous work on convexification of sparse regression problems, we simultaneously consider the nonlinear objective, indicator variables, and combinatorial constraints. We prove that for a separable quadratic objective function, the perspective reformulation is ideal independent from the constraints of the problem. In contrast, while rank-one relaxations cannot be strengthened by exploiting information from k-sparsity constraint for \(k\ge 2\), they can be improved for other constraints arising in inference problems with hierarchical structure or multi-collinearity.

Tetris is a popular videogame developed by Alexey Pajinov, where the player will never win. This special feature makes it a popular reason to be used in Artificial Intelligence and Computational Intelligence techniques research to study the improvement in a game’s performance. In this work, a genetic algorithms based approach will be presented, applying it to a game engine with some of the modern Tetris’ gameplay features, where the objective is to observe the game agent performance in various test scenarios. The obtained results show the feasibility of using AI as a player in a game of Tetris.

Using the minimum list of indicators for measuring the social development proposed by the United Nations, this work identifies cross-national indicators of homicide by analyzing socio-economic profiles of 202 countries. Both a correlation analysis and a decision-tree analysis indicate that countries with a relatively low homicide rate are characterized by a high life expectancy of women at birth and a very low adolescent fertility rate, while countries with a relatively high homicide rate are characterized by a low to medium life expectancy of women at birth, a high women-to-men ratio, and a high women’s share of adults with HIV/AIDS. The significance of this work stems from identifying cross-national indicators of homicide that can be used to assist policymakers in designing public policies aimed at reducing homicide rates by improving social indicators.

Detecting and identifying emotions expressed in speech signals is a very complex task that generally requires processing a large sample size to extract intricate details and match the diversity of human expression in speech. There is not an emotional dataset commonly accepted as a standard test bench to evaluate the performance of the supervised machine learning algorithms when presented with extracted speech characteristics. This work proposes a generic platform to capture and validate emotional speech. The aim of the platform is collaborative-crowdsourcing and it can be used for any language (currently, it is available in four languages such as Spanish, English, German and French). As an example, a module for elicitation of stress in speech through a set of online interviews and other module for labeling recorded speech have been developed. This study is envisaged as the beginning of an effort to establish a large, cost-free standard speech corpus to assess emotions across multiple languages.

Speech is controlled by axial neuromotor systems, highly sensible to certain neurodegenerative illnesses as Parkinson’s Disease (PD). Patients suffering PD present important alterations in speech, which manifest in phonation, articulation, prosody and fluency. Usually phonation and articulation alterations are estimated using different statistical frameworks and methods. The present study introduces a new paradigm based on Information Theory fundamentals to use common statistical tools to differentiate and score PD speech on phonation and articulation estimates. A study describing the performance of a methodology based on this common framework on a database including 16 PD patients, 16 age-paired healthy controls (HC) and 16 mid-age normative subjects (NS) is presented. The results point out to the clear separation between PD patients and HC subjects with respect to NS, but an unclear differentiation between PD and HC. The most important conclusion is that special effort is needed to establish differentiating features between PD, and organic laryngeal, from aging speech.

Nowadays, video games are not considered only mere hobbies. The use of these tools is increased in last years. On the other hand, gamification and usability techniques have empowered the improvement of communication between player/user and Aided Communication Devices (ACD). New ACDs provide a novel approach to capture biomechanical features or indicators. This work consists of a novel methodology development to capture biomechanical indicators throughout multiplatform video games. The present work has an exploratory nature to measure, hand-wrist articulation features estimated from the smartphone’s accelerometer. The intention of the study is to answer some hypothesis for instance, if the device is crucial to evaluate player’s movement capabilities or if the age of the person as a key biomarker. Once these indicators have been tested, it will be able to use them in studies of neurodegenerative diseases, where involuntary tremor is one of the most important observable correlates.

Atomic Force Microscopy (AFM) arises as an all-in-one characterization technique, capable of measuring several physical quantities by slight equipment’s modifications. In particular, for piezo and ferroelectricity properties, the AFM overcame the limitations of macroscopic techniques. This chapter covers all the aspects of piezo and ferroelectricity measurements performed with an AFM. The chapter is divided in three main parts, one for each available technique: Piezoresponse Force Microscopy (PFM), Nano-PUND method and Direct Piezoelectric Force Microscopy (DPFM). While PFM method is based in the converse piezoelectric effect, nanoPUND measures polarization charges and DPFM measures the direct piezoelectric effect. The working principle and characteristics for each AFM mode is fully exploited and explained from entry level to more advanced users. The chapter also focuses in useful guidelines and practical hands-on explanation for maximizing the image quality and data acquisition. Finally, a set of different application based in the use of piezo and ferroelectric materials is depicted, in which the AFM characterization took an important role as the primary characterization technique.

This article presents a genetic algorithm (GA) to solve the picker routing problem in multiple-block warehouses in order to minimize the traveled distance. The GA uses survival, crossover, immigration, and mutation operators, and is complemented by a local search heuristic. The genetic algorithm provides average distance savings of 13.9% when compared with s-shape strategy, and distance savings of 23.3% when compared with the GA with the aisle-by-aisle policy. We concluded that the GA performs better as the number of blocks increases, and as the percentage of picking locations to visit decreases.

It is a well-known fact that Parkinson’s Disease (PD) patients present important alterations in speech and phonation. Recent studies have shown that neuroestimulation using binaural beats have an effect on the neuromotor and cognitive conditions of patients suffering from PD, at least temporarily after stimulation. The present study aims to test if this phenomenon has any observable effect on phonation as a manifestation of the patient’s neuromotor conditions. With this aim in mind, an experimental framework has been set up, consisting in the stimulation of PD patients with two types of signals, the first consists in a supposedly active signal (binaural beats and pink noise) and the second an inert signal (consisting only of pink noise), recording specific sustained vowels and reading text before and after each stimulation process. The sustained vowels were analyzed in further depth to estimate phonation features associated with instability (jitter, shimmer, biomechanical unbalances, and tremor in different bands). A specific study case is presented, for which the analysis shows statistically significant changes in phonation before and after active neurostimulation, whereas these changes were not detected when inert neurostimulation was used. This effect could open the possibility for the development of neuroacoustic rehabilitative therapies, based on low-cost portable acoustical stimulation devices.

This paper presents some details on how to use concepts from computational creativity to inform the machine learning task of clustering. Specifically, clustering involves structuring exemplar-based knowledge. The novelty and usefulness of the way the knowledge ends up being structured can be measured. These are characteristics that traditionally computational creativity focuses on whereas machine learning doesn’t, but they can aid in selecting the best value for the parameters of the learning task. Doing so also provides us with a way to find an adequate balance between novelty and usefulness, something that still hasn’t been fully formalized in computational creativity. Thus both fields, machine learning and computational creativity, can benefit from this type of hybrid research.

Neurodegenerative pathologies produce important distortions in speech. Parkinson’s Disease (PD) leaves marks in fluency, prosody, articulation and phonation. Certain measurements based in configurations of the articulation organs inferred from formant positions, as the Vocal Space Area (VSA) or the Formant Centralization Ratio (FCR) have been classically used in this sense, but these markers represent mainly the static positions of sustained vowels on the vowel triangle. The present study proposes a measurement based on the mutual information contents of kinematic correlates derived from formant dynamics. An absolute kinematic velocity associated to the position of the articulation organs, involving the jaw and tongue is estimated and modelled statistically. The distribution of this feature is rather different in PD patients than in normative speakers when sustained vowels are considered. Therefore, articulation failures may be detected even in single sustained vowels. The study has processed a limited database of 40 female and 54 male PD patients, contrasted to a very selected and stable set of normative speakers. Distances based on Kullback-Leibler’s Divergence have shown to be sensitive to PD articulation instability. Correlation measurements show that the distance proposed shows statistically relevant relationship with certain motor and non-motor behavioral observations, as freezing of gait, or sleep disorders. These results point out to the need of defining scoring scales specifically designed for speech-based diagnose and monitoring methodologies in degenerative diseases of neuromotor origin.

Speech articulation is conditioned by the movements produced by well determined groups of muscles in the larynx, pharynx, mouth and face. The resulting speech shows acoustic features which are directly related with muscle neuromotor actions. Formants are some of the observable correlates most related to certain muscle actions, such as the ones activating jaw and tongue. As the recording of speech is simple and ubiquitous, the use of speech as a vehicular tool for neuromotor action monitoring would open a wide set of applications in the study of functional grading of neurodegenerative diseases. A relevant question is how far speech correlates and neuromotor action are related. This question is answered by the present study using electromyographic recordings on the masseter and the acoustic kinematics related with the first formant. Correlation measurements help in establishing a clear relation between the time derivative of the first formant and the masseter myoelectric activity. Monitoring disease progress by acoustic kinematics in one case of Amyotrophic Lateral Sclerosis ALS is described.

Evolutionary algorithms (EAs) have been used in varying ways for design and other creative tasks. One of the main elements of these algorithms is the fitness function used by the algorithm to evaluate the quality of the potential solutions it proposes. The fitness function ultimately represents domain knowledge that serves to bias, constrain, and guide the algorithm’s search for an acceptable solution. In this paper, we explore the degree to which the fitness function’s implementation affects the search process in an evolutionary algorithm. To perform this, the reliability and speed of the algorithm, as well as the quality of the designs produced by it, are measured for different fitness function implementations. These measurements are then compared and contrasted.