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This paper proposes the geometry-based polylinal routing method as a solution for motion planning in object sorting exercises in manufacturing processes. This algorithm is based on geometric properties that arise from the interaction among objects within the configuration space. The method proposed in this paper, during its experimental phase, successfully generated smooth routes with a processing time ranging from 62.5-125 ms on a computer equipped with an AMD Ryzen 7 2700X Eight-Core 3.70 GHz processor and 16 GB of RAM. When compared to the RRT algorithm, it exhibits a higher efficiency of 38% to 48%, resulting in a reduction in iterative processes and a shorter response time. Therefore, the proposed method presents a viable solution for addressing motion planning scenarios in object sorting exercises. [ABSTRACT FROM AUTHOR]

this article examines the impact, potentials, challenges, and future prospects of using Machine Learning (ML) algorithms in clinical psychology, with a focus on basic research and clinical application. ML is reshaping both research and clinical practice by analyzing complex data and uncovering patterns for understanding mental disorders and predicting diagnoses, among other noteworthy aspects. despite the challenges of interpretation, privacy, and bias, the transformative potential of ML algorithms in clinical psychology is acknowledged. interdisciplinary collaboration is essential to maximize these benefits. emphasizing the importance of addressing ethical and practical considerations, this paper envisions a future where ML algorithms become integral tools in the research, diagnosis, and treatment of mental disorders, contributing to an overall enhancement in people's quality of life. [ABSTRACT FROM AUTHOR]

There is a growing interest in the field of social sciences in the study of topics related to the so-called gig economy or platform economy and, among them, in the figure of the rider-a term that refers to the home delivery workers employed through digital platforms. In these platforms, the work of the riders is organized from algorithmic management logics, a mediation in which the panoptic quality of their forms of control and governmentality has tended to be emphasized. In contrast to these frameworks, this paper presents a theoretical-methodological approach that emphasizes the co-constitution-in an open agency- of the rider and the application, to ethnographically approach the everyday labor practices of riders. In the interest of exploring how algorithmic mediation functions in the diversity of riders' experiences, we analyze the coordination tasks responsible for producing the shared space-time geography and the flexible control strategies that mediate the application. The text describes how the algorithm generates a kind of scaffolding that defines directive spatiotemporalities in the riders' operations, within which the deliverers extend the landscape of calculability to those elements that the algorithm cannot consider and to those others that the rider must incorporate in order to make his activity compatible with the algorithmic logic. This article presents the initial results of an ethnography carried out in Madrid since September 2021 with riders from several of the main digital home delivery platforms: Glovo, Uber Eats, and Getir . Along with the fieldwork with riders, we interviewed four technologists in urban logistics. The results show that salaried riders have a less intense experience of the directive chronography of the algorithm, showing how autonomy is not necessarily equivalent to the riders' capability to choose. [ABSTRACT FROM AUTHOR]

Eggs are most fundamental and important foodstuff in everyday life for all developed and developing countries. It is not sufficient only to produce and transport eggs at a reasonable cost but arrangements must be made to ensure that the eggs reach the consumers well in time. In between the two extremes of minimization of eggs shipping cost and minimization of eggs shipping time, there exist a number of situations where an eggs transportation system decision maker would like a partial trade-off on shipping cost to attain a certain degree of shipping time advantages. From laying to the final destination, more eggs are broken during shipping than in any other step; therefore, losses due to eggshell breakage are greatest during shipping. In this paper, an algorithm and its underlying theory is developed to solve eggshell breakage restricted bi-criteria eggs transportation problem. This paper discusses a more realistic and general assumption that the eggs shipping time of bi-criteria eggs transportation problem depends on the quantity of the eggs transported and is an increasing piecewise constant function. The algorithm is also supported by a real life eggs shipping problem of Tamil Nadu, India. [ABSTRACT FROM AUTHOR]

This paper deals with the reconfigurations of Noelle-Neumann's (2010) original theory and presents an analysis categorization for the contemporary spiral of silence, which involves four mechanisms: 1) accumulation; 2) consonance; 3) ubiquity; and 4) anonymity. In order to avoid excluding the term social and analyzing meanings and technologies separately, the results in this paper were presented from a sociocultural matrix reception study with eight unionized teachers and eight neo-Pentecostal evangelicals residing in the city of Curitiba (PR). Once determining that algorithms are relevant towards defining new cultural patterns of social interaction, we found the need to investigate consolidated theories in the field of communication and journalism in the light of algorithmic mediations. [ABSTRACT FROM AUTHOR]

There is an increasing interest and growing practice in Citizen Science (CS) that goes along with the usage of websites for communication as well as for capturing and processing data and materials. From an educational perspective, it is expected that by integrating information about CS in a formal educational setting, it will inspire teachers to create learning activities. This is an interesting case for using bots to automate the process of data extraction from online CS platforms to better understand its use in educational contexts. Although this information is publicly available, it has to follow GDPR rules. This paper aims to explain (1) how CS communicates and is promoted on websites, (2) how web scraping methods and anonymization techniques have been designed, developed and applied to collect information from online sources and (3) how these data could be used for educational purposes. After the analysis of 72 websites, some of the results obtained show that only 24.8% includes detailed information about the CS project and 48.61% includes information about educational purposes or materials. [ABSTRACT FROM AUTHOR]

In this paper three prime numbers are presented as high potentials to be Mersenne numbers and their application in computational primality testing is suggested. These numbers are constructed from a regression algorithm based on Support vector machines (SVM) and using a Gaussian Kernel. Data training is carried out using the Phyton programming language, In the study we address the current data of Mersenne primes and work with the Ova-angular classification group for Mersenne primes 31. [ABSTRACT FROM AUTHOR]

This document presents in detail the mathematical formulation of the second renewable energy auction in Colombia, used for the allocation of long-term energy contracts, which was carried out through a double-sided auction, defined in Resolutions 4-0590 and 4-0591 of 2019 from the UPME. The mixed integer linear programming model developed in this paper solves an optimization problem that seeks the combination of offers that maximizes the benefit of the consumer, subject to operational and economic constraints proposed by regulatory mechanisms. The effectiveness of the algorithm developed in Python was tested by validating it with real data, obtaining the same results published by UPME. [ABSTRACT FROM AUTHOR]

This paper aims to show the opposition between the Humanist cultural tradition and the main theses currently defended by Posthumanism. The structure of the paper is built upon the corresponding answers provided by both trends to the three main philosophical questions: How do we know reality? What are the world and life, and how are they explained? What is the best way to live one’s life? Gnoseology (or theory of knowledge), Ontology, and Deontology or Ethics have respectively tried to provide answers for such questions. Certain ambiguities of the Posthumanist dehumanizing project are also addressed. The paper concludes with an exposition of the arguments that justify the continuity of Humanist values, principles, and rights, updated according to the demands of our time. This paper aims to show the opposition between the Humanist cultural tradition and the main theses currently defended by Posthumanism. The structure of the paper is built upon the corresponding answers provided by both trends to the three main philosophical questions: How do we know reality? What are the world and life, and how are they explained? What is the best way to live one’s life? Gnoseology (or theory of knowledge), Ontology, and Deontology or Ethics have respectively tried to provide answers for such questions. Certain ambiguities of the Posthumanist dehumanizing project are also addressed. The paper concludes with an exposition of the arguments that justify the continuity of Humanist values, principles, and rights, updated according to the demands of our time. [ABSTRACT FROM AUTHOR]

This paper aims at proposing a Constraint-based Learning Estimation of Distribution Algorithm (EDA) to solve the Multi-Mode Resource Constrained Project Scheduling Problem (MMRCPSP). This problem is considered a combinatorial optimization problem and consists of a set of tasks to be carried out in an ordered way, limited availability of resources, and an execution mode for each task. Generally, traditional EDAs handle the constraints of the problem using strategies such as penalty, Pareto dominance, or others, without taking into account these constraints during the learning process. These traditional approaches usually cause the generation of infeasible solutions in each iteration. In this paper, an approach based on the constraints handling during the learning process of the probabilistic model is proposed. To validate the performance of the algorithms, the new approach in two scenarios is compared. First, in the solution of a set of classical optimization problems, where different EDAs are compared in terms of efficiency and efficacy. The second group of experiments is performed on a set of instances of the PSPLIB database. In this case, the proposed algorithm is compared with a traditional EDA and with other results reported in the bibliography. The experimental results demonstrate that the proposal outperforms both the results of traditional EDA and other EDA reported in the literature. [ABSTRACT FROM AUTHOR]

In this paper, a two-dimensional sliding block model --discretized in slices- is presented, in which the geometrical, mechanical and rheological variables of the landslide are considered in order to analyse the soil landslide kinematic behaviour. The movement of the sliding mass is presented as a system of adjacent blocks; the movement strength is given by the Coulombviscous rheological model, where the friction and viscous components are considered, represented by a parallel system of a slider and a damper, respectively. The movement equation of the model corresponds to a second order differential equation, which was solved by the finite difference method, obtaining an explicit equation to know the displacement, velocity and acceleration of each block. An algorithm was developed in MATLAB® software, where movement equations were programmed to obtain results of the sliding kinematics. With the algorithm developed it was possible to make a sensitivity analysis to the parameters of the model. This was implemented in the problem of the sliding block at infinity slope, where it was possible to know the influence of each of the parameters of the model. The model has also been applied to the Hiegaesi landslide occurred in Japan in 1998, where the kinematic behaviour has been analysed in the model, regarding the records reported. However, further research is needed in order to apply the published model for prediction of the kinematic behaviour of landslides due to different limitations discussed in this paper. [ABSTRACT FROM AUTHOR]

This paper sets forth an algorithm which can recognize any type of the Spanish voseo conjugations, based on a parametric analysis of its morphological typology. The algorithm is built as a sequence of a minimum of two and a maximum of five operations, or steps. Both the linear arrangement of the steps and the structure of the queries are based on an implicit theory of the relative markedness or complexity of each variant and the parametric analysis of the typology. Thus, assessing the speed and efficiency of the algorithm becomes an indirect measure of the underlying analysis. The algorithm also invites to speculate about how the variable data relates to the way it is acquired or learned. [ABSTRACT FROM AUTHOR]

In this paper we intend to substantiate the usefulness of Deep Learning, especially feedforward neuronal networks, in the prediction of business failure. This methodology provides very good results in terms of predictive performance when large sample sizes are available. Therefore, we have developed a business failure prediction model for European companies, based on this algorithm on a sample of 61,624 companies, of which 12,128 were declared bankrupt in 2016. As independent variables were considered ratios, and economic and financial data obtained from the financial statements for the year preceding the date of failure. Deep Learning achieves a predictive performance of 94%, where companies with larger size and lower solvency are more prone to failure. The obtained results have been tested on an independent test sample, different from that used to estimate and train the model. [ABSTRACT FROM AUTHOR]

In this research paper, we examine forms of work organization implemented in Mercado Libre based on interviews with its workers in Argentina. We analyze the historical course of human resources management until the incursion of the pandemic. As part of the findings, we can point out that the evolution of work management and well-being shows ruptures and continuities, where emotions become increasingly relevant in human resources policies. Inspired by positive psychology in the 90s, such policies focused on the resilient individual. This figure has become more relevant since COVID-19, when the management of intimacy and emotions is mediated by the use of technologies supported by big data. [ABSTRACT FROM AUTHOR]

In this paper, a computer vision inspection methodology is presented for the detection and segmentation of areas of wear by electric arc on the surface of electrical contacts using segmentation by instances. Cadmium is a toxic metal frequently used in the manufacture of alloys for electrical contact, so efforts have been made to replace or eliminate this element from the industry. Our work proposes an approach oriented to the automation of the inspection process, which reduces the subjectivity of the repetitive task of inspecting electrical contacts, allowing to compare two different alloys, such as one with Cadmium (Cd) and the other with Copper (Cu)., subjected to the same conditions and determine which of them presents less wear by electric arc. In this way you can know if the new part is better, equal or worse in terms of performance. Initially, an adaptation was made of the source code hosted in a github repository of mask R-CNN of the Matterport corporation, where variables, classes were modified, and directories were created according to our needs. The results obtained show that the masks generated by the algorithm adjust very well to contact wear, despite the irregular shape of the damage. However, the "charred" edge was probably not considered, because in training images it was a feature that few images presented, so segmentation may not be considering this factor. [ABSTRACT FROM AUTHOR]

This paper presents the Hierarchical Risk Parity (hrp) approach proposed by López de Prado (2016, 2018, 2020) for the construction of optimal investment portfolios using unsupervised learning, hierarchical clustering, which allow overcome some limitations of the Mean-Variance (MV) model, in particular those related to the need to invert the covariance matrix when implementing the cla algorithm. A sample of 7 assets from the American market is taken, with which an application of the hrp algorithm proposed by López de Prado is carried out, finding that under this model the distribution of assets in different clusters generates improvements in terms of the expected return, as well as of the Sharpe coefficient compared to the results of the Mean-Variance portfolio. [ABSTRACT FROM AUTHOR]

In this paper we propose to use the principle of available data derived from the NIPALS (Nonlinear estimation by Iterative Partial Least Square) algorithm to work on the Principal Components Analysis (PCA) in the presence of missing data. This proposal is important since it does not perform data imputation, nor are individuals discarded from the database; the proposed method works with the available pairs to form the quasicorrelation matrices in 𝑹𝒑 and in 𝑹𝒏; the spectral decomposition of these matrices allows through the transition relations to realize a conventional PCA. From the simulation study carried out, it was found that as the percentage of missing data increases, the inertia explained in the first factorial plane decreases. The solution algorithm was developed under the R programming environment and the code is appended for free use. [ABSTRACT FROM AUTHOR]

Traditionally, the companies have focused on the strategy development for the creation of value; while the value capture mechanisms, better known as pricing schemes, have been lagging behind these. In this paper, we insist that "smart" pricing strategies are not exactly a zero-sum game. Plenty of examples show that it is possible to formulate price policies, which benefit suppliers as much as consumers. As it is known, the policy of price discrimination has the purpose of serving several segments of consumers, delivering the same or almost the same good but at a different price, depending on the type of consumer or segment addressed. In this paper, we review one of the price discrimination variations, called second-degree discrimination. In particular, we focus on the study of simple algorithms, but the ones which allow understand and design optimal mechanisms to set prices, which benefit the two market agents. The assumptions under which algorithms mentioned are built are strict, but we believe that it is an excellent approximation to understand the behavior and nature of both the bidder and the claimant before and during economic transactions. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid heuristic that combines Variable Neighborhood Search (VNS) with Ant Colony Optimization (ACO) to solve the scheduling problem of nonrelated parallel machines with sequence dependent setup times in order to minimize the makespan. The Variable Neighborhood Search is proposed to solve the scheduling problem with a descending scheme in a first phase, with an ACO algorithm, which successively reorder the jobs in the machine with the largest makespan in a second phase. An experimental study was performed using test problems from the literature showing that the second phase of the algorithm improves the solution obtained in the first phase. The results obtained are also compared with other methods in the literature proving to be a competitive method. [ABSTRACT FROM AUTHOR]

Introduction This paper presents a proposal to apply the meta-heuristic Optimization Based on Variable Meshes (VMO) to the discreet problem of the Traveler Seller (TSP); This model explores the search space to Objective from a population of solutions called mesh that expands and contracts in order to find good quality solutions. In this context, the expansion operator is modified in such a way that it is applicable in a discrete domain, making combinations among the solutions in order to obtain new nodes. Another element that is modified is the clearing operator, which is responsible for maintaining the diversity of the mesh in each interaction. Methodology. A study of VMO model parameters is summarized in this paper, using a set of TSP instances with different characteristics; In addition, it can be observed that the proposal of this work obtains competitive results when compared with other international reference algorithms mentioned in the state of the art. The work is structured as follows: Section 1 describes the fundamental aspects of the TSP study problem. Then, in the second, the general functioning of VMO is explained, in the third one, each of the expansion and contraction operators is defined for the study problem. Later in the fourth section a study of the parameters of the proposal and an experimental comparative analysis with the results obtained with other algorithms mentioned in the state of the art is made. Conclusions Other operators of generation of new nodes were applied in the expansion process, where a combination of solutions is made in order to comply with the restrictions imposed by the problem. [ABSTRACT FROM AUTHOR]

The databases of the Ministry of Public Health of Ecuador in the 2013-2017 period contain valuable information that can be used to determine the strengths, weaknesses, potential problems, among others, that affect the public health of the country. This knowledge can serve to draw better public health policies. This paper aims to propose a methodology that allows us to extract knowledge from these databases and at the same time to obtain association rules based on the combination of algorithms such as FP-growth and k-means. In summary, the methodology consists of the following steps: first, the dataset is stored in 5 files in the SPSS (Statistical Package for the Social Sciences) format, and then the disease-related attributes are grouped and encoded, according to the code ICD-10, for this purpose it is proposed to apply the WEKA software. Finally, the FP-Growth algorithm is used to extract association rules from frequent items with the support of RAPIDMINER, which has the advantage of allowing us the use of WEKA algorithms. The methodology is illustrated with an example that shows how to use it and its usefulness to extract association rules in real-life situations from medical databases. With these representations of the information, morbidity and incidence behavior analysis of the registered groups and diseases can be made. [ABSTRACT FROM AUTHOR]

Multiple Sequence Alignment (MSA) is one of the main topics in the in bioinformatics domain, consists finding an optimal alignment for three or more biological sequences with the number maximum of conserved zones or totally aligned columns. Different scores to assess the quality of the alignments have been proposed, so the problem can be formulated and resolved as a Multi-Objective Optimization Problem (MOP). For this reason, in this paper we present a Multi- Objective approach applied to MSA. We have considered state-of-the-art optimization algorithms aimed at solving different formulations of the MSA: NSGAII, NSGA-III, SPEA2, MOCell, SMS-EMOA, MOEA/D and GWASF-GA. Furthermore we have considered some popular metrics as objectives to be optimized: The weighted Sum-Of-Pairs with a ne gap penalties (wSOP), the Totally Aligned Columns (TC), STRIKE and BaliScore. Finally we have described the main features of our software jMetalMSA, a Multi-Objective optimization software tool applied to MSA problem and illustrated a working example for experimentations purposes. [ABSTRACT FROM AUTHOR]

Developing an effective flow control algorithm to avoid congestion is a hot topic in computer network society. This paper gives a mathematical model for general network at first, and then discrete control theory is proposed as a key tool to design a new flow control algorithm for congestion avoidance in high speed network, the proposed algorithm assures the stability of network system. The simulation results show that the proposed method can adjust the sending rate and queue level in buffer rapidly and effectively. The method is easy to implement and apply to high-speed computer network. [ABSTRACT FROM AUTHOR]

This paper presents the design and simulation of a buck charge controller for lead-acid accumulators used in photovoltaic applications, based on fuzzy control techniques. The proposed design fully implements the three-stage algorithm: bulk, absorption, and flotation, which allows the battery to be accurately charged, ensuring its integrity and durability. The control system consists of two independent fuzzy controllers, the first regulates the current accurately in the bulk stage, while the second regulates the voltage in the absorption, and flotation stages; additionally, it has switching logic, which allows transitioning between the different stages of the algorithm. The system was designed and simulated in MATLAB; and the results show the correct functionality of the design by simulating the charge of 12 V accumulators with capacities from 60 to 120 Ah. Even though in the literature, there are similar works, very few use an algorithm with multiple stages completely, and that can be implemented directly in economic systems such as microcontrollers. This work can be taken as a reference in the design of charge controllers even different from those based on fuzzy logic, which represents an important didactic contribution. [ABSTRACT FROM AUTHOR]

This paper discusses the challenges encountered by counter-hegemonic social movements and activists working on digital platforms such as Facebook and Twitter, used as tools for mobilizing and disseminating information. To achieve this goal we describe how digital platforms colonize lifeworld by collecting data based on user interactions and how it affects activists who build solidarity networks. We then discuss the performance of counter-hegemonic movements and their opponents and present the concept of coded activism. To conclude our argument, we present proposals for the action of social movements in an Internet dominated by monopolistic corporations. [ABSTRACT FROM AUTHOR]

Proper management of projects has become an element of vital importance for the solution to problems of society. In this context, there are two main objectives to be solved: the construction of project plans which may comply with the constraints of the problem and the optimization in terms of cost and time of those plans. To this end, all activities of project must be organized in such way that the constraints related to the precedence among them and the availability of renewable and non-renewable resources at each instant of time are met. The aim of this paper is to present an Estimation of Distribution Algorithm (EDA), which incorporates the handling of the constraints in the probabilistic model, for the construction of optimal or quasi-optimal project plans. For the validation of the algorithm, authors used both the PSPLib database repository for the development of the scheduling research and the databases of projects of the Repository for Research in Project Management, University of Infomatic Sciences. [ABSTRACT FROM AUTHOR]

Small and Medium Enterprises (SMEs) are important sources of social and economic development in the Latin American region. This paper aims: (1) To study the state of the process of industrialization of cocoa, produced and marketed by PyMES located in the province of Los Ríos, Ecuador, (2) To test the effectiveness of some proposed strategies to overcome the deficiencies found in the process. To achieve these goals, statistical methods were applied to a group of ten SMEs in the region. It was concluded that there exist deficiencies in the process and that the proposed strategies are effective. [ABSTRACT FROM AUTHOR]

The present paper introduces an algorithm for the integrated programming of production and preventive maintenance in a productive configuration of identical parallel machines, aiming to minimize the total manufacturing time. The algorithm has been structured in two phases; in the first one, an initial solution is obtained, resulting from the application of a designed and proposed constructive heuristic that is inspired by the Largest Processing Time dispatch rule, with some modifications allowing it to consider the stochastic nature of the problem and the possibility that several jobs may have different release dates; in phase 2, an attempt is made to improve the initial solution using a simulated annealing method. As a way of evaluating its feasibility the algorithm was implemented in a company producing products derived from plastic, demonstrating through the results obtained its potential as a mechanism to improve operational capabilities. [ABSTRACT FROM AUTHOR]

FuzzyPred is an unsupervised-learning data-mining algorithm, which allows extracting fuzzy predicates in normal forms from the data. This method is used to solve a descriptive task where it is not known the kind of relationships that are to be found. The goal is to find patterns that describe the data and their relationships. Due to the large set of solutions or search space that may have, it was modeled as an optimization problem, where metaheuristics are applied to find good solutions. FuzzyPred provides as a result a set of predicates, evaluated in each of the quality measures, in spite of the fact that only one of the measures is optimized (truth value). This paper introduces a multiobjective approach for FuzzyPred by using two of the most known multi-objective optimization techniques: Pareto technique (or pure multi-objective) and weighted factors. An experimental study is presented in order to compare the efficacy of both techniques in this problem. The results in several international databases show that better results are obtained by the pure multi-objective technique. [ABSTRACT FROM AUTHOR]

In this paper, we briefly introduce three aspects of what might be considered a Latin-American school of political economy of communication. The first aspect is an institutional history that highlights the efforts to be present at universities as well as researcher organizations and other spaces of independent discussions such as journals and observatories. Second aspect, incorporates both European and North-American theoretical and epistemological traditions, which gives a way to a Latin-American re-elaboration. The third aspect, is the making of a particular history. Such history is, nevertheless, non-homogeneous as it takes into account the specificities of Brazil, Argentina, and Mexico. Finally, some categories elaborated or re-elaborated within the Latin-American camp, stand out as they contribute for a better dialog with other strands at the world level. As a conclusion remark is that, despite its internal development, the Anglo- American intellectual and academic traditions still prevail. [ABSTRACT FROM AUTHOR]

In this paper, an iterative algorithm is constructed to improve the solution of a system of linear equations, of the form Ax = b, when it is solved using the Gauss-Jordan method and by using finite arithmetic. Understanding the functioning of the algorithm, showing its scope and analyzing how it is deduced, is achieved through the concept of matrix norm, together with some of its properties. The concept of the condition number of a matrix is introduced, and are found bounds for it by using the matrix norms. Finally, it is explained the iterative algorithm of the Refinement, showing the power of this one, when it is solved a system of linear equations ill conditioned. [ABSTRACT FROM AUTHOR]

This paper presents an algorithm oriented to improve the security conditions of electric power systems. The proposed algorithm performs a semi-exhaustive search of the possible configuration of the system substation with the objective of minimizing overloads presented in single contingencies (N-1 criterion). Topological modifications are made by changing the state of switches in substations. Results are presented for the IEEE 118 bus test system showing the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper presents the procedure of tuning and simulation of an adaptive predictive control strategy to control the longitudinal and latero-directional movements of a unmanned aerial vehicle (UAV), using the Matlab ® - Simulink® platform. Uses the mathematical model of a UAV which are initially performed simulations of the system in open loop, identification is carried on online, and predictive control and PID controllers algorithms are implemented with the parameters obtained, to control the angle of pitch, roll and yaw. Control strategies used presented a good performance and manage to stabilize the aircraft properly. Finally, they are presented and analyzed the results of the performance of the system with predictive control and PID control using metrics of the integral of the error and of the work of the manipulated variable. [ABSTRACT FROM AUTHOR]

Studies of face recognition and movements of the human body have opened frontiers for different knowledge areas. The objective of this paper is to show an alternative methodology to identify faces and make a characterization of the human body movement. This methodology has three phases. The first phase is a generation of a video database in a NoSQL model using MongDB. The second phase permits to identify human faces applying an algorithm that combines Hough Transform and the Viola-Jones algorithm. And the third phase makes an identification of human movement using optical flow histogramoriented gradients. The main results show that the face identification permits to select a relevance range for an approximation of the interest or actor area. This interest area is a base to apply the optical flow, avoiding false positives, like shadows or reflections produced by the environment. Finally, we conclude that the obtainment of a clear frontal view of the face supplies an easy way for human action tracking. [ABSTRACT FROM AUTHOR]

The quadratic eigenvalue problem has attracted the interest of many researchers due to its numerous applications in areas such as fluid mechanics, acoustics and engineering, among others. In this paper we present a hybrid algorithm to solve this problem, which combines an algorithm for the calculation of solutions of the matrix quadratic equation with a traditional one for the calculation of eigenvalues. Twelve application problems were selected, in different areas, to make a comparative analysis of the performance of the proposed algorithm with the traditional Newton method. In conclusion, it was determined that the algorithmic proposal is competitive with Newton's method and more efficient. [ABSTRACT FROM AUTHOR]

Hash functions are a fundamental part of computer security applications such as authentication systems and digital signatures. Many of these applications require a high processing rate; therefore, it is important to explore alternatives that contribute to reduce the computation time of these functions. This paper presents a two-stage pipelined architecture for SHA3-256 hash function that supports single and multi-message processing. Each pipeline stage executes two rounds of the algorithm in one clock cycle. The proposed pipelined architecture has been implemented in Xilinx Virtex-5 and achieves a throughput 27,98 Gbps. This implementation allowed to obtain an improvement in the overall performance of the two-stage pipelined KECCAK function regarding implementations of iterative round and even other implementations multi-stage pipelined. [ABSTRACT FROM AUTHOR]

Risk analyses applied to radiotherapy treatments have become an undeniable necessity, considering the dangers generated by the combination of using powerful radiation fields on patients and the occurrence of human errors and equipment failures during these treatments. The technique par excellence to execute these analyses has been the risk matrix. This paper presents the development of a new algorithm to execute the task with wide graphic and analytic potentialities, thus transforming it into a very useful option for risk monitoring and the optimization of quality assurance. The system SECURE-MR, which is the basic software of this algorithm, has been successfully used in risk analysis regarding different kinds of radiotherapies. Compared to previous methods, It offers new possibilities of analysis considering risk controlling factors as the robustness of reducers of initiators frequency and its consequences. Their analytic capacities and graphs allow novel developments to classify risk contributing factors, to represent information processes as well as accidental sequences. The paper shows the application of the proposed system to a generic process of radiotherapy treatment using a lineal accelerator. [ABSTRACT FROM AUTHOR]

A geodesic dome is a mesh of bars and nodes arranged along the edges and vertices of a polyhedron on a surface that can be in the form of a sphere, parabola or ellipse. From knowing the advantages of this type of structure, they have been widely used in various constructions such as: housing, commercial offices, greenhouses, fair stand, fixed ceilings fuel storage tanks, among others. Many researchers have studied modeling and geometry of geodesic domes. The purpose of this paper is to present, from the review and compilation of information from several researches, an algorithm for generating a wireframe model of a geodesic dome for fixed roofs of spherical fuel storage tanks, for a further analysis of resistance by the method of finite elements. In this paper, we describe in detail the procedure and geometric expressions that were used. Finally, a computer program was developed in order to evaluate mathematical expressions and the procedure described in this paper and also to have a tool for generating computational model of a geodesic dome. [ABSTRACT FROM AUTHOR]

Algorithms in the strategies of political parties are becoming increasingly significant, particularly with the use of big data and the inclusion of the automation in the production and circulation of online content obtained from personal data. This paper studies the scope of algorithmic political communication, its strengths, weaknesses and implications in democracy in order to measure the real scenario of robotized information in parties. A qualitative analysis is designed based on in-depth interviews with political advisors and consultants. The results highlight the widespread implementation of algorithmic political communication in the main Spanish parties, although their level of development is irregular. It's detected that automated information systems are being used primarily in the pre-production and circulation phase of the message and are beginning to be implemented in the production phases. Robots advance in their path as infomediaries between politicians and citizens. [ABSTRACT FROM AUTHOR]

This paper presents a learning environment that uses affective recognition techniques and gamification to teach algorithmic logic and programming. This environment was evaluated and contrasted with engineering students. The method consisted in assessing student learning of algorithmic logic using traditional techniques compared to learning with automatic recognition of emotions and motivational management using gamification. Tests and surveys were conducted with 42 students, who were divided into two groups, and two different system configurations were used to assess the gamification techniques implemented. The results showed that learning is statistically better if students' affective state is taken into account, and if students are motivated through gamification. [ABSTRACT FROM AUTHOR]

This paper presents the design of a charge regulator operating in an isolated micro grid with photovoltaic generation. The converter selected for this application is a SEPIC with a MPPT algorithm for the cases in which the energy demand is greater than the energy available. The algorithm is able to modify its point of maximum power, in order to perform the recharging and supply of power to the battery bank of the system (isolated microgrid) and a group of charges set at a level of determined of voltage. Finally, the results of the tests carried out are presented and the conclusions are given. [ABSTRACT FROM AUTHOR]

Using the classification methods for mathematical problems in textbooks (Fan & Zhu, 2000) and strategies used to solve problems (Fan & Zhu, 2007), this paper reports the results of an analysis and classification of the type of mathematical problems and problem-solving strategies in a sample made up of the five most widely-used mathematics textbooks for the first year of junior high school in the 2013- 2014 school year in Mexico. It is concluded that the textbooks focus on providing a comprehensive range of problems that, for the most part, do not allow multiple problem-solving strategies, whereas others can be solved using an algorithm or direct procedure. [ABSTRACT FROM AUTHOR]

This paper presents an overview about control methods implemented on robotic convoy systems or cooperative systems on mobile platforms which can be used for path planning, orientation, environment perception, route tracking and control systems in which involve the measurement, analysis and interpretation of different variables for further implementation. A review was made of investigation articles on bibliographic indexes and databases about control methods used in convoy systems to evidence progress, trends and application methods. [ABSTRACT FROM AUTHOR]

The development of autonomous vehicles is a subject of great interest in the international scientific community. For the accomplishment of missions without human intervention it is vitally to have an algorithm or guiding law. In this paper we present the results obtained by simulation with the guidance algorithm based on distance L1, during the tracking of straight and circular paths by an autonomous aerial vehicle (UAV). The algorithm produces a lateral acceleration command that is interpreted by the balancing controller to achieve convergence to the path. The results of the simulation demonstrate the good performance of the algorithm, for both straight and circular paths. [ABSTRACT FROM AUTHOR]

The importance of Big Data and algorithms today is undeniable. The increasing use of this technology has raised new discussions regarding the adequacy of current regulations on intellectual property. This paper intends to clarify what these shortcomings are, if any, by analyzing these debates in light of the legislation in force in Spain and the European Union. It is concluded that such insufficiency is not as broad as it is usually thought, and the particular cases (unstructured Big Data and algorithms) in which there are ambiguities or voids are specified. [ABSTRACT FROM AUTHOR]

This paper shows the development and use of a hybrid algorithm of machine learning for the missing values predict task, with this task that is carried out while the data preprocessing phase. Firstly, we go through the problem to solve, which is pointed to the study and analysis of different techniques for the missing values predict in order to suggest a hybrid technique as a product of this research for the task and link it to the OLAP (On-Line Analytical Processing) technology. Then, justifying the research methodology (scientific descriptive - probing) applied in this project. The techniques review was carried out filling out the missing values; based on the techniques proof and the study cases, k-Nearest Neighbors algorithms and artificial neural networks were selected and a hybrid technique (KMediaSom) was suggested, applied to a synthetic data set and a real one, coming from a OLAP; for the algorithms implementation was used Matlab. Right away, the analysis and results are set out in order to specify its applicability about efficacy and time complexity. Results are suitable as for the synthetic data set as for the real one; according to the test signs achieved. Finally, the conclusions, where it i's proved that the technique or hybrid algorithms generate better results than the techniques used by separately. [ABSTRACT FROM AUTHOR]

This paper describes the use of model-based design flow in the development of self-configurable image processing blocks over FPGAs. For this, some generic hardware architectures has been created and modified using a novel software proceeding using the specific characteristics of the different processing algorithms. This aspect, in addition to others configuration options of the blocks, allows the adjustment the FPGA resources consumption for a specific application. Also the automatic reconfiguration process is analyzed for a generic convolution block making some architectural comparisons over a Spartan-6 LX45 FPGA. [ABSTRACT FROM AUTHOR]

The specification and formal verification of distributed systems is usually a complex task. It requires extensive knowledge of algorithm theory and modeling of distributed or concurrent systems. In this context, we present LeGESD which is a framework oriented to support the specification and formal validation of concurrent and distributed systems. LeGESD has a graphical formal language for the specification and analysis of distributed systems which is used to describe functional and of communication requirements. The semantics of LeGESD is based on the process algebra. This semantics is also described in this paper and it is called Analysis and Design of Distributed Systems (ADSD). ADSD is an algebraic specification with operational semantics defined to be used with LeGESD. ADSD provides graphical-algebraic equivalence relations which can be used to generate formal specifications of distributed systems. Lastly, this paper presents an example which shows the usefulness and potential of the language and its semantics. [ABSTRACT FROM AUTHOR]

This paper presents an alternative procedure to solve the Capacitated Vehicle Routing Problem (CVRP) with homogeneous fleet. The paper proposes a two-phase metaheuristic algorithm: routes design and fleet scheduling. The first phase is based on heuristics and metaheuristics procedures in order to build an initial solution that is then improved using tabu search to obtain non-dominated solutions in polynomial computational time. For the second phase, corresponding to fleet scheduling, the problem is approached using an analogy with the identical parallel machine scheduling problem. This procedure looks for the minimization of the fixed cost of using installed capacity as the objective function. The proposed procedure was tested using both a random-generated instance and real data, giving competitive results in comparison with other heuristics tested. [ABSTRACT FROM AUTHOR]

This paper presents an image synthesis algorithm for rendering views of a real scene taken with a virtual camera which is located between two real cameras. Specifically, this paper presents how, under certain conditions, a pair of views is enough to obtain a full set of possible views by following the line that joins their optical centers (baseline) without reconstructing explicit 3D models. Test results show the viability of the proposed algorithm for simple scenes (an object with a simple geometry, which is opaque, and does not present any occlusions). [ABSTRACT FROM AUTHOR]