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In recent years, there has been growing interest in the use of computer-assisted technology for early detection of skin cancer through the analysis of dermatoscopic images. However, the accuracy illustrated behind the state-of-the-art approaches depends on several factors, such as the quality of the images and the interpretation of the results by medical experts. This systematic review aims to critically assess the efficacy and challenges of this research field in order to explain the usability and limitations and highlight potential future lines of work for the scientific and clinical community. In this study, the analysis was carried out over 45 contemporary studies extracted from databases such as Web of Science and Scopus. Several computer vision techniques related to image and video processing for early skin cancer diagnosis were identified. In this context, the focus behind the process included the algorithms employed, result accuracy, and validation metrics. Thus, the results yielded significant advancements in cancer detection using deep learning and machine learning algorithms. Lastly, this review establishes a foundation for future research, highlighting potential contributions and opportunities to improve the effectiveness of skin cancer detection through machine learning. [ABSTRACT FROM AUTHOR]

In the optimization field, the ability to efficiently tackle complex and high-dimensional problems remains a persistent challenge. Metaheuristic algorithms, with a particular emphasis on their autonomous variants, are emerging as promising tools to overcome this challenge. The term "autonomous" refers to these variants' ability to dynamically adjust certain parameters based on their own outcomes, without external intervention. The objective is to leverage the advantages and characteristics of an unsupervised machine learning clustering technique to configure the population parameter with autonomous behavior, and emphasize how we incorporate the characteristics of search space clustering to enhance the intensification and diversification of the metaheuristic. This allows dynamic adjustments based on its own outcomes, whether by increasing or decreasing the population in response to the need for diversification or intensification of solutions. In this manner, it aims to imbue the metaheuristic with features for a broader search of solutions that can yield superior results. This study provides an in-depth examination of autonomous metaheuristic algorithms, including Autonomous Particle Swarm Optimization, Autonomous Cuckoo Search Algorithm, and Autonomous Bat Algorithm. We submit these algorithms to a thorough evaluation against their original counterparts using high-density functions from the well-known CEC LSGO benchmark suite. Quantitative results revealed performance enhancements in the autonomous versions, with Autonomous Particle Swarm Optimization consistently outperforming its peers in achieving optimal minimum values. Autonomous Cuckoo Search Algorithm and Autonomous Bat Algorithm also demonstrated noteworthy advancements over their traditional counterparts. A salient feature of these algorithms is the continuous nature of their population, which significantly bolsters their capability to navigate complex and high-dimensional search spaces. However, like all methodologies, there were challenges in ensuring consistent performance across all test scenarios. The intrinsic adaptability and autonomous decision making embedded within these algorithms herald a new era of optimization tools suited for complex real-world challenges. In sum, this research accentuates the potential of autonomous metaheuristics in the optimization arena, laying the groundwork for their expanded application across diverse challenges and domains. We recommend further explorations and adaptations of these autonomous algorithms to fully harness their potential. [ABSTRACT FROM AUTHOR]

Simple Summary: Within the genus Liolaemus, the zapallarensis group is restricted to semi-arid and arid coastal habitats of the Atacama Desert in north-central Chile. While lizards of the zapallarensis group inhabit various islands of the Humboldt Archipelago, knowledge regarding the specific identification of their species is limited. To address this gap, we conducted phylogenetic analyses and examined morphological characteristics to shed light on the lizard species inhabiting these islands. Our findings reveal that lizards from the Damas, Choros, and Gaviota islands belong to Liolaemus silvai. In contrast, the lizards on Chañaral Island formed a distinct and previously unknown species and were clearly distinctive from Liolaemus silvai. In light of our phylogenetic and morphological results, we postulate that the lizards inhabiting Chañaral Island constitute a novel species which we have described as Liolaemus carezzae sp. nov. This study offers valuable insights into the unique endemic biodiversity found in Chile. The Humboldt Archipelago, situated on Chile's north-central coast, is renowned for its exceptional biodiversity. However, lizards of the Liolaemus genus are a particularly understudied group in this archipelago. Liolaemus genus is divided into two clades: chiliensis and nigromaculatus. Within the nigromaculatus clade the zapallarensis group is restricted to the semi-arid and arid coastal habitats of the Atacama Desert in north-central Chile. While it has been reported that lizards from the zapallarensis group inhabit various islands within the Humboldt Archipelago, there has been limited knowledge regarding their specific species identification. To identify the lizard species inhabiting these islands, we conducted phylogenetic analyses using a mitochondrial gene and examined morphological characteristics. Our findings reveal that lizards from the Damas, Choros, and Gaviota islands belong to Liolaemus silvai. In contrast, the lizards on Chañaral Island form a distinct and previously unrecognised group, clearly distinguishable from Liolaemus silvai. In conclusion, our study not only confirms the presence of L. silvai on the Damas, Choros, and Gaviota islands but also describes a new lizard species on Chañaral Island named Liolaemus carezzae sp. nov. These findings contribute valuable insights into the biodiversity of these islands and introduce a newly discovered endemic taxon to the region, enriching our understanding of Chile's unique island ecosystems. [ABSTRACT FROM AUTHOR]

A list Λ = {λ1, λ2, ..., λn} of complex numbers is said to be realizable if it is the spectrum of a nonnegative matrix. Λ is said to be universally realizable (UR) if it is realizable for each possible Jordan canonical form allowed by Λ. In this paper, using companion matrices and applying a procedure by Šmigoc, we provide sufficient conditions for the universal realizability of left half-plane spectra, that is, spectra Λ = {λ1, ..., λn} with λ1 > 0, Re λi ≤ 0, i = 2, ..., n. It is also shown how the effect of adding a negative real number to a not UR left half-plane list of complex numbers, makes the new list UR, and a family of left half-plane lists that are UR is characterized. [ABSTRACT FROM AUTHOR]

This paper presents a set of adaptive parameter control methods through reinforcement learning for the particle swarm algorithm. The aim is to adjust the algorithm's parameters during the run, to provide the metaheuristics with the ability to learn and adapt dynamically to the problem and its context. The proposal integrates Q–Learning into the optimization algorithm for parameter control. The applied strategies include a shared Q–table, separate tables per parameter, and flexible state representation. The study was evaluated through various instances of the multidimensional knapsack problem belonging to the NP -hard class. It can be formulated as a mathematical combinatorial problem involving a set of items with multiple attributes or dimensions, aiming to maximize the total value or utility while respecting constraints on the total capacity or available resources. Experimental and statistical tests were carried out to compare the results obtained by each of these hybridizations, concluding that they can significantly improve the quality of the solutions found compared to the native version of the algorithm. [ABSTRACT FROM AUTHOR]

Nature–inspired computing is a promising field of artificial intelligence. This area is mainly devoted to designing computational models based on natural phenomena to address complex problems. Nature provides a rich source of inspiration for designing smart procedures capable of becoming powerful algorithms. Many of these procedures have been successfully developed to treat optimization problems, with impressive results. Nonetheless, for these algorithms to reach their maximum performance, a proper balance between the intensification and the diversification phases is required. The intensification generates a local solution around the best solution by exploiting a promising region. Diversification is responsible for finding new solutions when the main procedure is trapped in a local region. This procedure is usually carryout by non-deterministic fundamentals that do not necessarily provide the expected results. Here, we encounter the stagnation problem, which describes a scenario where the search for the optimum solution stalls before discovering a globally optimal solution. In this work, we propose an efficient technique for detecting and leaving local optimum regions based on Shannon entropy. This component can measure the uncertainty level of the observations taken from random variables. We employ this principle on three well–known population–based bio–inspired optimization algorithms: particle swarm optimization, bat optimization, and black hole algorithm. The proposal's performance is evidenced by solving twenty of the most challenging instances of the multidimensional knapsack problem. Computational results show that the proposed exploration approach is a legitimate alternative to manage the diversification of solutions since the improved techniques can generate a better distribution of the optimal values found. The best results are with the bat method, where in all instances, the enhanced solver with the Shannon exploration strategy works better than its native version. For the other two bio-inspired algorithms, the proposal operates significantly better in over 70% of instances. [ABSTRACT FROM AUTHOR]

Population-based approaches have given us new search strategies and ideas in order to solve optimization problems. Usually, these methods are based on the performance carried out by a finite number of agents, which by the interaction between them they evolve and work all over the search space. Also, it is well-known that the correct employment of parameter values in this kind of method can positively impact their performance and behavior. In this context, the present work focuses on the design of a hybrid architecture which smartly balances the population size on run-time. In order to smartly balance and control the population size, a modular approach, named Linear Modular Population Balancer (LMPB), is proposed. The main ideas behind the designed architecture include the solving strategy behind a population-based metaheuristic, the influence of learning components based on multiple statistical modeling methods which transform the dynamic data generated into knowledge, and the possibilities to tackle both discrete and continuous optimization problems. In this regard, three modules are proposed for LMPB, which concern tasks such as the management of the population-based algorithm, parameter setting, probabilities, learning methods, and selection mechanism for the population size to employ. In order to test the viability and effectiveness of our proposed approach, we solve a set of well-known benchmark functions and the multidimensional knapsack problem (MKP). Additionally, we illustrate promising solving results, compare them against state-of-the-art methods which have proved to be good options for solving optimization problems, and give solid arguments for future work in the necessity to keep evolving this type of proposed architecture. [ABSTRACT FROM AUTHOR]

The main component of a cryptographic system that allows us to ensure its strength against attacks, is the substitution box. The strength of this component can be validated by various metrics, one of them being the nonlinearity. To this end, it is essential to develop a design for substitution boxes that allows us to guarantee compliance with this metric. In this work, we implemented a hybrid between the stochastic fractal search algorithm in conjunction with opposition-based learning. This design is supported by sequential model algorithm configuration for the proper parameters configuration. We obtained substitution boxes of high nonlinearity in comparison with other works based on metaheuristics and chaotic schemes. The proposed substitution box is evaluated using bijectivity, the strict avalanche criterion, nonlinearity, linear probability, differential probability and bit-independence criterion, which demonstrate the excellent performance of the proposed approach. [ABSTRACT FROM AUTHOR]

Simple Summary: Mepraia is a blood-sucking bug endemic to Chile and a vector of the parasite that causes Chagas disease. Different colonization routes have been suggested for this bug; therefore, we tested different colonization routes using DNA sequences and bioinformatics approaches to select the most probable route. Our results suggest that, after the split of Triatoma, Mepraia divided into two main groups ~2.1 Mya. The northern group would have speciated between 1.7–1.4 Mya, giving rise to M. parapatrica, M. gajardoi and to a new, still undescribed lineage (Mepraia sp.). The southern group formed M. spinolai ~1.68 Mya. We suggest that Mepraia originated from the north-central Andes due to the last Andes uplift and hyperaridity. The hyperarid cycle would have separated the southern and northern groups. Then, within the northern group, colonization would have occurred from the centre to the north and south through corridors influenced by Pleistocene climatic changes. The habitat colonized by the southern clade led to only one species (M. spinolai). Fluctuations in climatic changes probably influenced speciation strongly in this kissing bug in the Atacama Desert. Mepraia is a genus (Triatominae) endemic to Chile and a vector of Trypanosoma cruzi. Alternative phylogeographic hypotheses have been suggested for Mepraia. We tested different colonization routes hypothesized using mitochondrial sequences and phylogeographic approaches to select the best-supported hypothesis. Our results suggest that, after the split from the sister genus Triatoma at ~4.3 Mya, Mepraia formed two main clades at ~2.1 Mya. The northern clade diverged from Mepraia sp. ~1.7 Mya, giving rise to M. parapatrica and M. gajardoi about ~1.4 Mya. The southern clade originated M. spinolai ~1.68 Mya. We suggest that Mepraia had an origin in the north-central Andes along with orogenic processes, reinforced by hyperaridity during the Pliocene. The hyperarid cycle would have separated the southern and northern clades. Then, in the northern clade, dispersal occurred north and south from the centre through corridors during the Pleistocene Climatic Oscillations. Climate changes may have induced a major speciation process in the Atacama Desert, while the more homogeneous habitat colonized by the southern clade led to only one, but structured, species. [ABSTRACT FROM AUTHOR]

This paper contains a detailed description of the main insurance modalities that address the concerns of the elderly, which are fundamentally: the economic situation at that age, health and funeral expenses that death entails. Accordingly, this paper purports to analyse: (a) life insurance for both death and survival at a certain age to resolve the economic situation of the insured or their relatives or heirs; (b) health insurance to solve problems caused by an accident or illness; (c) dependency insurance to guarantee the help needed by the insured person who loses, due to age reasons, their physical, mental or intellectual capacity to fend for themselves; and (d) funeral insurance to meet burial expenses in the event of the death of the insured. [ABSTRACT FROM AUTHOR]

In complexity theory, there is a widely studied grouping of optimization problems that belongs to the non-deterministic polynomial-time hard set. One of them is the set covering problem, known as one of Karp's 21 NP -complete problems, and it consists of finding a subset of decision variables for satisfying a set of constraints at the minimum feasible cost. However, due to the nature of the problem, this cannot be solved using traditional complete algorithms for hard instances. In this work, we present an improved binary version of the monkey search algorithm for solving the set covering problem. Originally, this approximate method was naturally inspired by the cognitive behavior of monkeys for climbing mountains. We propose a new climbing process with a better exploratory capability and a new cooperation procedure to reduce the number of unfeasible solutions. For testing this approach, we present a detailed computational results section, where we illustrate how this variation of the monkey search algorithm is capable of reaching various global optimums for a well-known instance set from the Beasley's OR-Library and how it outperforms many other heuristics and meta-heuristics addressed in the literature. Moreover, we add a complete statistical analysis to show the effectiveness of the proposed approach with respect to the original version. [ABSTRACT FROM AUTHOR]

We say that a list Λ = { λ 1 , λ 2 , ... , λ n } of complex numbers is realizable if it is the spectrum of an entrywise nonnegative matrix A. We say that Λ is universally realizable (UR) if Λ is realizable for each possible Jordan canonical form allowed by Λ. The universal realizability problem of spectra (URP) has been solved for certain kind of real and complex spectra, mainly spectra in the left half-plane. In this paper we look for extending the kind of spectra that are UR by given new sufficient conditions for the URP to have a solution. In particular, we show that some realizability criteria for the real and symmetric nonnegative inverse eigenvalue problem are realizability criteria for the URP as well. [ABSTRACT FROM AUTHOR]

Chagas disease is one of the main zoonoses mediated by vectors in America. The etiological agent is the protozoan Trypanosoma cruzi, transmitted mainly by hematophagous insects of the subfamily Triatominae. Mepraia species are triatomines endemic to Chile that play an important role in T. cruzi transmission in the wild cycle and are potential vectors for humans. In addition to the continental distribution, populations of Mepraia genus have been reported inhabiting islands of northern Chile. The presence of individuals of Mepraia in insular areas might be explained through passive dispersion by marine birds or by vicariance of an ancestral widespread population. To clarify the biogeographic origin and phylogenetic relationships of island individuals of Mepraia, mitochondrial COI and cyt b genes were sequenced in individuals from island and continental areas. Gene sequences were used to estimate phylogenetic relationships, divergence dates and migration rates between insular and continental populations. The dates of divergence estimates are congruent with sea level and tectonic changes that originated the islands during Pleistocene. Migration rates suggest symmetric historical island-continent gene flow. We suggest that the origin of island triatomines can be explained by both vicariance and dispersion. Phylogenetic relationships show that individuals from Santa María Island and the continent clustered in a clade different from those previously reported, indicating a new lineage of Mepraia genus. This study will contribute to understand the origin of the T. cruzi infection in coastal islands of northern Chile. [ABSTRACT FROM AUTHOR]

Kaposi's sarcoma-associated herpesvirus (KSHV) is the causative agent of two B-cell lymphoproliferative diseases and Kaposi's sarcoma, an endothelial-cell driven cancer. KSHV viral interleukin-6 (vIL-6) is a viral homolog of human IL-6 that is expressed in KSHV-associated malignancies. Previous studies have shown that the expression of the integrin ß3 (ITGB3) subunit is induced upon KSHV infection. Here, we report that KSHV vIL-6 is able to induce the expression of ITGB3 and increase surface expression of the αVβ3 integrin heterodimer. We demonstrate using siRNA depletion and inhibitor studies that KSHV vIL-6 can increase ITGB3 by inducing STAT3 signaling. Furthermore, we found that secreted vIL-6, is capable of inducing ITGB3 in endothelial cells in a paracrine manner. Importantly, the ability to induce ITGB3 in endothelial cells seems to be specific to vIL-6 as over-expression of hIL-6 alone did not affect levels of this integrin. Our lab and others have previously shown that vIL-6 can induce angiogenesis, and we investigated whether ITGB3 was involved in this process. We found that siRNA depletion of ITGB3 in vIL-6-expressing endothelial cells resulted in a decrease in adhesion to extracellular matrix proteins. Moreover, depletion of ITGB3 hindered the ability of vIL-6 to promote angiogenesis. In conclusion, we found that vIL-6 can singularly induce ITGB3 and that this induction is dependent on vIL-6 activation of the STAT3 signaling pathway. [ABSTRACT FROM AUTHOR]

Stagnation at local optima represents a significant challenge in bio-inspired optimization algorithms, often leading to suboptimal solutions. This paper addresses this issue by proposing a hybrid model that combines the Orca predator algorithm with deep Q-learning. The Orca predator algorithm is an optimization technique that mimics the hunting behavior of orcas. It solves complex optimization problems by exploring and exploiting search spaces efficiently. Deep Q-learning is a reinforcement learning technique that combines Q-learning with deep neural networks. This integration aims to turn the stagnation problem into an opportunity for more focused and effective exploitation, enhancing the optimization technique's performance and accuracy. The proposed hybrid model leverages the biomimetic strengths of the Orca predator algorithm to identify promising regions nearby in the search space, complemented by the fine-tuning capabilities of deep Q-learning to navigate these areas precisely. The practical application of this approach is evaluated using the high-dimensional Heartbeat Categorization Dataset, focusing on the feature selection problem. This dataset, comprising complex electrocardiogram signals, provided a robust platform for testing the feature selection capabilities of our hybrid model. Our experimental results are encouraging, showcasing the hybrid strategy's capability to identify relevant features without significantly compromising the performance metrics of machine learning models. This analysis was performed by comparing the improved method of the Orca predator algorithm against its native version and a set of state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In this study, we introduce an innovative policy in the field of reinforcement learning, specifically designed as an action selection mechanism, and applied herein as a selector for binarization schemes. These schemes enable continuous metaheuristics to be applied to binary problems, thereby paving new paths in combinatorial optimization. To evaluate its efficacy, we implemented this policy within our BSS framework, which integrates a variety of reinforcement learning and metaheuristic techniques. Upon resolving 45 instances of the Set Covering Problem, our results demonstrate that reinforcement learning can play a crucial role in enhancing the binarization techniques employed. This policy not only significantly outperformed traditional methods in terms of precision and efficiency, but also proved to be extensible and adaptable to other techniques and similar problems. The approach proposed in this article is capable of significantly surpassing traditional methods in precision and efficiency, which could have important implications for a wide range of real-world applications. This study underscores the philosophy behind our approach: utilizing reinforcement learning not as an end in itself, but as a powerful tool for solving binary combinatorial problems, emphasizing its practical applicability and potential to transform the way we address complex challenges across various fields. [ABSTRACT FROM AUTHOR]

Population-based metaheuristics can be seen as a set of agents that smartly explore the space of solutions of a given optimization problem. These agents are commonly governed by movement operators that decide how the exploration is driven. Although metaheuristics have successfully been used for more than 20 years, performing rapid and high-quality parameter control is still a main concern. For instance, deciding the proper population size yielding a good balance between quality of results and computing time is constantly a hard task, even more so in the presence of an unexplored optimization problem. In this paper, we propose a self-adaptive strategy based on the on-line population balance, which aims for improvements in the performance and search process on population-based algorithms. The design behind the proposed approach relies on three different components. Firstly, an optimization-based component which defines all metaheuristic tasks related to carry out the resolution of the optimization problems. Secondly, a learning-based component focused on transforming dynamic data into knowledge in order to influence the search in the solution space. Thirdly, a probabilistic-based selector component is designed to dynamically adjust the population. We illustrate an extensive experimental process on large instance sets from three well-known discrete optimization problems: Manufacturing Cell Design Problem, Set covering Problem, and Multidimensional Knapsack Problem. The proposed approach is able to compete against classic, autonomous, as well as IRace-tuned metaheuristics, yielding interesting results and potential future work regarding dynamically adjusting the number of solutions interacting on different times within the search process. [ABSTRACT FROM AUTHOR]

1. Blood‐feeding bugs in the Triatominae are the vectors of Trypanosoma cruzi, the etiological agent of Chagas disease. Mepraia is a sylvatic genus endemic to Chile that transmits T. cruzi in the wild cycle. 2. Bloodmeal‐stealing (or 'cleptohaematophagy') is the stealing of a bloodmeal from one bug's gut by another, usually conspecific bug. Bloodmeal‐stealing can result in horizontal transmission of T. cruzi between triatomines; so far, it has been reported only in laboratory‐reared bugs. 3. We performed short laboratory experiments to test whether bloodmeal‐stealing occurs between wild‐caught Mepraia spinolai nymphs. Successful bloodmeal‐stealing was observed in one out of 17 trials (2/102 bugs). Even though bloodmeal‐stealing was not frequent in wild‐caught M. spinolai, this behaviour might contribute to explaining the maintenance of wild T. cruzi cycles. [ABSTRACT FROM AUTHOR]

This paper proposes a novel population based optimization algorithm called Andean Condor Algorithm (ACA) for solving cell formation problems. The ACA metaheuristic is inspired by the movement pattern of the Andean Condor when it searches for food. This pattern of movement corresponds to the flight distance traveled by the Andean Condor from its nest to the place where food is found. This distance varies depending on the seasons of the year. The ACA metaheuristic presents a balance of its population through a performance indicator based on the average quality of the population's fitness. This balance determines the number of Andean Condors that will perform an exploration or intensification movements. ACA metaheuristics have a flexible design. It allows to easily integrate specific heuristics according to the optimization problem to be solved. Two types of computational experiments have been performed. According to the results obtained it has been possible to determine that ACA is an algorithm with an outstanding RPD% in relation to the algorithms BAT, MBO and PSO, robust and with a convergence which tends not to be trapped in the local optimums. Besides, according to the non-parametric multiple comparison, results have been obtained in which the ACA metaheuristic has significant differences in relation to the BAT, MBO and PSO algorithms. [ABSTRACT FROM AUTHOR]

In this research, we present a Binary Cat Swarm Optimization for solving the Manufacturing Cell Design Problem (MCDP). This problem divides an industrial production plant into a certain number of cells. Each cell contains machines with similar types of processes or part families. The goal is to identify a cell organization in such a way that the transportation of the different parts between cells is minimized. The organization of these cells is performed through Cat Swarm Optimization, which is a recent swarm metaheuristic technique based on the behavior of cats. In that technique, cats have two modes of behavior: seeking mode and tracing mode, selected from a mixture ratio. For experimental purposes, a version of the Autonomous Search algorithm was developed with dynamic mixture ratios. The experimental results for both normal Binary Cat Swarm Optimization (BCSO) and Autonomous Search BCSO reach all global optimums, both for a set of 90 instances with known optima, and for a set of 35 new instances with 13 known optima. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms have been used to solve several optimization problems, showing an efficient performance. Nevertheless, when these algorithms are applied they present the difficulty to decide on the appropriate values of their parameters. Typically, parameters are specified before the algorithm is run and include population size, selection rate, and operator probabilities. This process is known as offline control and is even considered as an optimization problem in itself. On the other hand, parameter settings or control online is a variation of the algorithm original version. The main idea is to vary the parameters so that the algorithm of interest can provide the best convergence rate and thus may achieve the best performance. In this paper, we propose an adaptive black hole algorithm able to dynamically adapt its population according to solving performance. For that, we use autonomous search which appeared as a new technique that enables the problem solver to control and adapt its own parameters and heuristics during solving in order to be more efficient without the knowledge of an expert user. In order to test this approach, we resolve the set covering problem which is a classical optimization benchmark with many industrial applications such as line balancing production, crew scheduling, service installation, and databases, among several others. We illustrate encouraging experimental results, where the proposed approach is able to reach various global optimums for a well-known instance set from Beasley’s OR-Library, while improving various modern metaheuristics. [ABSTRACT FROM AUTHOR]

This research seeks to design, implement, and test a fully automatic high-frequency trading system that operates on the Chilean stock market, so that it is able to generate positive net returns over time. A system that implements high-frequency trading (HFT) is presented through advanced computer tools as an NP-Complete type problem in which it is necessary to optimize the profitability of stock purchase and sale operations. The research performs individual tests of the algorithms implemented, reviewing the theoretical net return (profitability) that can be applied on the last day, month, and semester of real market data. Finally, the research determines which of the variants of the implemented system performs best, using the net returns as a basis for comparison. The use of particle swarm optimization as an optimization algorithm is shown to be an effective solution since it is able to optimize a set of disparate variables but is bounded to a specific domain, resulting in substantial improvement in the final solution. [ABSTRACT FROM AUTHOR]

Chaotic maps are sources of randomness formed by a set of rules and chaotic variables. They have been incorporated into metaheuristics because they improve the balance of exploration and exploitation, and with this, they allow one to obtain better results. In the present work, chaotic maps are used to modify the behavior of the binarization rules that allow continuous metaheuristics to solve binary combinatorial optimization problems. In particular, seven different chaotic maps, three different binarization rules, and three continuous metaheuristics are used, which are the Sine Cosine Algorithm, Grey Wolf Optimizer, and Whale Optimization Algorithm. A classic combinatorial optimization problem is solved: the 0-1 Knapsack Problem. Experimental results indicate that chaotic maps have an impact on the binarization rule, leading to better results. Specifically, experiments incorporating the standard binarization rule and the complement binarization rule performed better than experiments incorporating the elitist binarization rule. The experiment with the best results was STD_TENT, which uses the standard binarization rule and the tent chaotic map. [ABSTRACT FROM AUTHOR]

Feature extraction and selection are very relevant processes in the design of automatic classifiers. In the context of volcanic seismic signal classification, most of the features presented in the literature have been extracted separately from a single domain, such as time or frequency. However, spectrograms, which combine time and frequency information, are widely used by experts during the classification of manual seismic events. This paper proposes to evaluate the performance of classifiers trained with features extracted from the spectro-temporal domain, individually or combined with other conventional features. The parameters were extracted from the spectrogram, based on a curve which combines the high energy components and the frequency bandwidth information through the duration of the event. The tests were performed at the Llaima volcano and seismic events were classified into four classes: long-period, tremor, volcano-tectonic and tectonic, using a database of signals recorded between the years 2009 and 2017. The main achievements of this study were the reduction of more than 70% of the error and false positive rates and also a reduction of approximately 30% of the number of features, compared with a baseline established in previous studies. Thus, the inclusion of spectro-temporal information was considered relevant to complement the conventional features and to support classification. [ABSTRACT FROM AUTHOR]

The fast-changing digital media environment has provoked new communication processes in contemporary society. In particular, younger generations engage in new ways of interacting with each other thanks to the constant emergence of digital technologies and mobile communication outlets. New habits arise every year, changing not only our interpersonal communication, but also our overall consumption of information and entertainment content, with video and instant messaging apps being particularly popular among younger populations. This study employs a mixed method approach to measure young Hispanic's motivations to use smartphones in Chile, Mexico and the U.S. The subjects, 18 to 25 years old, participated in focus groups and surveys conducted in 2015:55 Hispanic smartphone users joined focus groups whilst 1,403 respondents completed surveys in Chile, Mexico and Texas (U.S.). Results show similarities and differences in young Hispanic's motivations and uses of smartphones, including the personal and commercial value they ascribe to information, time spent on social networks and their preferences for mobile devices compared to other media. The findings are used to develop three archetypes of young users, based on smartphone's motivations and uses. Utilizing factor analysis scores, a linear regression analysis was conducted to identify the main traits of these three archetypes: "always connected," "entertained" and "secure." [ABSTRACT FROM AUTHOR]

The paper analyzes the information obligations to policyholders imposed by the sectoral legislation and the jurisprudence for the commercialization of financial products in the form of insurance. The commercialization of this type of product raises two fundamental questions: The first, concerning its legal nature, is to determine, on the one hand, whether they are real insurances or are investment products and, on the other, what elements are necessary to consider these products as insurance contracts. The second one refers to the requirements required for its commercialization, once its insurance nature has been determined. In this respect, European Union legislation has established similar information obligations for the distribution of insurance products as required for the marketing of retail investment products, but considers that the regulation of such obligations should be reflected in the specific insurance mediation legislation. [ABSTRACT FROM AUTHOR]

Constraint programming is an efficient and powerful paradigm for solving constraint satisfaction and optimization problems. Under this paradigm, problems are modeled as a sequence of variables and a set of constraints. The variables have a non-empty domain of candidate values and constraints restrict the values that variables can adopt. The solving process operates by assigning values to variables in order to produce potential solutions which are then evaluated. A main component in this process is the enumeration strategy, which decides the order in which variables and values are chosen to produce such potential solutions. There exist different ways to perform this selection, and depending on the quality of this decision, the efficiency of the solving process may dramatically vary. Unfortunately, selecting the proper strategy is known to be a hard task, as its behavior during search is generally unpredictable and certainly depends on the problem at hand. A recent trend to handle this concern, is to interleave a set of different strategies instead of using a single one during the whole process. The strategies are evaluated according to process indicators in order to use the most promising one on each part of the search process. This process is known as online control of enumeration strategies and its correct configuration can be seen itself as an optimization problem. In this paper, we present two new systems for online control of enumeration strategies based on recent nature-inspired metaheuristics: bat algorithm and black hole optimization. The bat algorithm mimics the location capabilities of bats that employ echoes to identify the objects in their surrounding areas, while black hole optimization inspires its behavior on the gravitational pull of black holes in space. We perform different experimental results by using different enumeration strategies and well-known benchmarks, where the proposed approaches are able to noticeably outperform previous work on online control. [ABSTRACT FROM AUTHOR]

The set covering problem is a classical optimization benchmark that finds application in several real-world domains, particularly in line balancing production, crew scheduling, and service installation. The problem consists in finding a subset of columns in a zero-one matrix such that they cover all the rows of the matrix at a minimum cost. In this paper, we present two new approaches for efficiently solving this problem, the first one based on cuckoo search and the second one on black hole optimization. Both are relatively modern bio-inspired metaheuristics that have attracted much attention due to their rapid convergence, easy implementation, and encouraging obtained results. We integrate to the core of both metaheuristics an effective pre-processing phase as well as multiple transfer functions and discretization methods. Pre-processing is employed for filtering the values from domains leading to infeasible solutions, while transfers function and discretization methods are used for efficiently handling the binary nature of the problem. We illustrate interesting experimental results where the two proposed approaches are able to obtain various global optimums for a set of well-known set covering problem instances, outperforming also several recently reported techniques. [ABSTRACT FROM AUTHOR]

Hematophagous insects of the subfamily Triatominae include several species with a large variety of shapes, behavior and distribution. They have great epidemiological importance since most of them transmit the flagellated protozoan Trypanosoma cruzi , the etiologic agent of Chagas disease. In this subfamily several cases of species hybridization have been reported under experimental and natural conditions. Mepraia is a genus of Triatominae endemic in Chile, responsible for transmitting T. cruzi in the sylvatic cycle. This genus includes three species, M. gajardoi , M. spinolai and M. parapatrica ; however, the differentiation of M. parapatrica as a separate species remains controversial considering the possible occurrence of introgression/hybridization processes in some populations of this putative species. Mepraia species show conspicuous wing polymorphism, and it has been proposed that the genes related to wings are linked to the Y chromosome, thus wingless males could not engender winged progeny. In order to determine the degree of reproductive isolation and to assess the wing phenotype in the offspring, we performed experimental crosses between the two most divergent Mepraia species ( M. gajardoi and M. spinolai ) together with chromosome analyses of hybrid progenies. Although fertile F 1 hybrids were obtained in only one direction of crossing, we verified the existence of different isolation mechanisms between parental species, including hybrid breakdown. The occurrence of winged males in the offspring of wingless parental males suggests that the wing character is not linked to the Y chromosome. [ABSTRACT FROM AUTHOR]

The Machine-Part Cell Formation Problem (MPCFP) is a NP-Hard optimization problem that consists in grouping machines and parts in a set of cells, so that each cell can operate independently and the intercell movements are minimized. This problem has largely been tackled in the literature by using different techniques ranging from classic methods such as linear programming to more modern nature-inspired metaheuristics. In this paper, we present an efficient parallel version of the Migrating Birds Optimization metaheuristic for solving the MPCFP. Migrating Birds Optimization is a population metaheuristic based on the V-Flight formation of the migrating birds, which is proven to be an effective formation in energy saving. This approach is enhanced by the smart incorporation of parallel procedures that notably improve performance of the several sorting processes performed by the metaheuristic. We perform computational experiments on 1080 benchmarks resulting from the combination of 90 well-known MPCFP instances with 12 sorting configurations with and without threads. We illustrate promising results where the proposal is able to reach the global optimum in all instances, while the solving time with respect to a nonparallel approach is notably reduced. [ABSTRACT FROM AUTHOR]

Chagas disease, which ranks among the world's most neglected diseases, is a chronic, systemic, parasitic infection caused by the protozoan Trypanosoma cruzi. Mepraia species are the wild vectors of this parasite in Chile. Host-parasite interactions can occur at several levels, such as co-speciation and ecological host fitting, among others. Thus, we are exploring the interactions between T. cruzi circulating in naturally infected Mepraia species in all areas endemic of Chile. We evaluated T. cruzi infection rates of 27 different haplotypes of the wild Mepraia species and identified their parasite genotypes using minicircle PCR amplification and hybridization tests with genotype-specific DNA probes. Infection rates were lower in northern Chile where Mepraia gajardoi circulates (10-35%); in central Chile, Mepraia spinolai is most abundant, and infection rates varied in space and time (0-55%). T. cruzi discrete typing units (DTUs) TcI, TcII, TcV, and Tc VI were detected. Mixed infections with two or more DTUs are frequently found in highly infected insects. T. cruzi DTUs have distinct, but not exclusive, ecological and epidemiological associations with their hosts. T. cruzi infection rates of M. spinolai were higher than in M. gajardoi, but the presence of mixed infection with more than one T. cruzi DTU was the same. The same T. cruzi DTUs (TcI, TcII, TcV, and TcVI) were found circulating in both vector species, even though TcI was not equally distributed. These results suggest that T. cruzi DTUs are not associated with any of the two genetically related vector species nor with the geographic area. The T. cruzi vectors interactions are discussed in terms of old and recent events. By exploring T. cruzi DTUs present in Mepraia haplotypes and species from northern to central Chile, we open the analysis on these invertebrate host-parasite interactions. [ABSTRACT FROM AUTHOR]

We give a necessary and sufficient condition for the nonnegative inverse elementary divisors problem to have a solution, in the case of listssatisfyingFor the casewe give new sufficient conditions. Our results improve previous results and generate an algorithmic procedure to compute a solution matrix. [ABSTRACT FROM AUTHOR]

The reliability and increasing performance of search-tree-based interval solvers for solving numerical systems of constraints make them applicable to various expert system domains. Filtering methods are applied in each node of the search tree to reduce the variable domains without the loss of solutions. Current interval-based solvers generally leave it up to the solver designer to decide which set of filtering methods to apply to solve a particular problem. In this work, we propose an adaptive strategy to dynamically determine the set of filtering methods that will be applied in each node of the search tree. Our goal is twofold: first, we want to simplify the task of the solver designer, and second, we believe that an adaptive strategy may improve the average performance of the current state-of-the-art strategies. The proposed adaptive mechanism attempts to avoid calling costly filtering methods when their probability of filtering domains is low. We assume that fruitful filtering occurs in nearby revisions or clusters . Thus, the decision about whether or not to apply a filtering method is based on a cluster detection mechanism. When a cluster is detected, the associated methods are consecutively applied in order to exploit the cluster. Alternately, in zones without clusters, only a cheap method is applied, thus reducing the filtering effort in large portions of the search. We compare our approach with state-of-the-art strategies, demonstrating its effectiveness. [ABSTRACT FROM AUTHOR]

The nonnegative inverse elementary divisors problem ( NIEDP ) is the problem of finding conditions for the existence of an n × n entrywise nonnegative matrix A with prescribed elementary divisors. We consider the case in which the solution matrix A is required to be persymmetric. Persymmetric matrices are common in physical sciences and engineering. They arise, for instance, in the control of mechanical and electric vibrations. In this paper, we solve the NIEDP for n × n matrices assuming that ( i ) there exists a partition of the given list Λ = { λ 1 , … , λ n } in sublists Λ k , along with suitably chosen Perron eigenvalues, which are realizable by nonnegative matrices A k with certain of the prescribed elementary divisors, and ( ii ) a nonnegative persymmetric matrix exists with diagonal entries being the Perron eigenvalues of the matrices A k , with certain of the prescribed elementary divisors. Our results generate an algorithmic procedure to compute the structured solution matrix. [ABSTRACT FROM AUTHOR]

The Set Covering Problem consists in finding a subset of columns in a zero-one matrix such that they cover all the rows of the matrix at a minimum cost. To solve the Set Covering Problem we use a metaheuristic called Binary Cat Swarm Optimization. This metaheuristic is a recent swarm metaheuristic technique based on the cat behavior. Domestic cats show the ability to hunt and are curious about moving objects. Based on this, the cats have two modes of behavior: seeking mode and tracing mode. We are the first ones to use this metaheuristic to solve this problem; our algorithm solves a set of 65 Set Covering Problem instances from OR-Library. [ABSTRACT FROM AUTHOR]

The Sudoku problem is a well-known logic-based puzzle of combinatorial number-placement. It consists in filling a n2 × n2 grid, composed of n columns, n rows, and n subgrids, each one containing distinct integers from 1 to n2. Such a puzzle belongs to the NP-complete collection of problems, to which there exist diverse exact and approximate methods able to solve it. In this paper, we propose a new hybrid algorithm that smartly combines a classic tabu search procedure with the alldifferent global constraint from the constraint programming world. The alldifferent constraint is known to be efficient for domain filtering in the presence of constraints that must be pairwise different, which are exactly the kind of constraints that Sudokus own. This ability clearly alleviates the work of the tabu search, resulting in a faster and more robust approach for solving Sudokus. We illustrate interesting experimental results where our proposed algorithm outperforms the best results previously reported by hybrids and approximate methods. [ABSTRACT FROM AUTHOR]

The nonnegative inverse eigenvalue problem ( NIEP ) is the problem of finding conditions for the existence of an n × n entrywise nonnegative matrix A with prescribed spectrum. This problem remains open for n ≥ 5 . If the matrix A is required to be persymmetric (bisymmetric), the problem will be called persymmetric ( bisymmetric ) nonnegative inverse eigenvalue problem ( PNIEP ) ( BNIEP ). Persymmetric and bisymmetric matrices are common in physical sciences and engineering. They arise, for instance, in the control of mechanical and electric vibrations. A persymmetric version of a perturbation result, due to Rado and presented by H. Perfect in [5] , is developed and used to give sufficient conditions for the PNIEP to have a solution. Our results generate an algorithmic procedure to compute the solution matrix. [ABSTRACT FROM AUTHOR]

Constraint programming effectively solves constraint satisfaction and optimization problems by basically building, pruning, and exploring a search tree of potential solutions. In this context, a main component is the enumeration strategy, which is responsible for selecting the order in which variables and values are selected to build a possible solution. This process is known to be quite important; indeed a correct selection can reach a solution without failed explorations. However, it is well known that selecting the right strategy is quite challenging as their performance is notably hard to predict. During the last years, adaptive enumeration appeared as a proper solution to this problem. Adaptive enumeration allows the solving algorithm being able to autonomously modifying its strategies in solving time depending on performance information. In this way, the most suitable order for variables and values is employed along the search. In this paper, we present a new and more lightweight approach for performing adaptive enumeration. We incorporate a powerful classification technique named Top-k in order to adaptively select strategies along the resolution. We report results on a set of well-known benchmarks where the proposed approach noticeably competes with classical and modern adaptive enumeration methods for constraint satisfaction. [ABSTRACT FROM AUTHOR]

The Software Project Scheduling Problem is a specific Project Scheduling Problem present in many industrial and academic areas. This problem consists in making the appropriate worker-task assignment in a software project so the cost and duration of the project are minimized. We present the design of a Max–Min Ant System algorithm using the Hyper-Cube framework to solve it. This framework improves the performance of the algorithm. We illustrate experimental results and compare with other techniques demonstrating the feasibility and robustness of the approach, while reaching competitive solutions. [ABSTRACT FROM AUTHOR]

Accurate estimation of in-situ stresses is of great importance in the oil and gas industry from the exploration to the field development and production phases. The collected logs and mini-frac data in the last 15 years in the Hassi Messaoud Field (HMD), Algeria, shows that the reported state of stresses in this field is not consistent. This called for further studies to estimate more accurately the state of stresses, which more specifically will be used for the design of hydraulic fracturing at a later stage. This paper presents the results of the Mechanical Earth Model (MEM) constructed for the HMD field in Algeria. The results of the MEM, which is continuous logs of formations' elastic and strength properties, as well as the state of the in-situ stresses will be the direct input to the hydraulic fracture (HF) design. Hydraulic fracturing is the prime technique used in the field in order to enhance recovery from this tight sandstone reservoir. MEM is built beyond the conventional correlation with both a Generalized Linear Model (GLM), which serves as a simplified "ground truth" and three Artificial Neural Networks (ANN) layers: the first one is training mechanical properties from lab data, the second one trains shear and compressional sonic waves from acquired log data and the last one uses mini-frac data to calculate and train stress regimes from a poro-elastic model. The model is calibrated with the observed breakouts and fracturing data and a close agreement is observed. The work presented in this paper elaborates a detailed roadmap to accurately approximate and synthesize missing data using ANN's and extend it across the rest of the field to build, a 3D MEM. It is hoped that the results of this study can improve the HF operations in the field, which is currently reported to be not efficient. • The work presented elaborates a roadmap to approximate and synthesize missing data using ANN's and extend it to build, a MEaM. • 440 Diagnostic Fracture Integrity Test are mined, mapped and used to fine-tune the horizontal stresses estimation. • Map of the closure pressure distribution in HMD field is presented, and the wide range observed is carefully addressed. [ABSTRACT FROM AUTHOR]

The overall objective of this article is to measure what economic indicators and trends can shape or change the evaluation of a country by influential foreign media, during turbulent times. The case analyzed is that of Spain during the last economic crisis. Before the crisis, the Spanish economy boomed and was considered a kind of miraculous paradigm, but it abruptly collapsed in 2008. Then, the coverage and the tone of the articles published by some global business newspapers and magazines, as The Wall Street Journal, The Financial Times, and The Economist, mutated considerably, as negative news outscored the positive ones. Two regression analyzes conducted demonstrate that the trends followed by the unemployment rate, the benchmark stock market index of a country, and the exchange rate can change dramatically the tone of news published by influential foreign media about a country. Other results are discussed in the paper. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we give new sufficient conditions for the existence and construction of nonnegative matrices with prescribed elementary divisors, which drastically improve and contain some of the previous known conditions. We also show how to perturb complex eigenvalues of a nonnegative matrix while keeping its nonnegativity. These results allow us, under certain conditions, to easily decide if a given list is realizable with prescribed elementary divisors. [Copyright &y& Elsevier]

Highlights: [•] We propose a new hybrid AC3-tabu search algorithm for Sudoku problems. [•] We perform experiments considering easy, medium, and hard Sudokus. [•] The approach generally outperforms the best results reached by incomplete search. [ABSTRACT FROM AUTHOR]

In the world of optimization, especially concerning metaheuristics, solving complex problems represented by applying big data and constraint instances can be difficult. This is mainly due to the difficulty of implementing efficient solutions that can solve complex optimization problems in adequate time, which do exist in different industries. Big data has demonstrated its efficiency in solving different concerns in information management. In this paper, an approach based on multiprocessing is proposed wherein clusterization and parallelism are used together to improve the search process of metaheuristics when solving large instances of complex optimization problems, incorporating collaborative elements that enhance the quality of the solution. The proposal deals with machine learning algorithms to improve the segmentation of the search space. Particularly, two different clustering methods belonging to automatic learning techniques, are implemented on bio-inspired algorithms to smartly initialize their solution population, and then organize the resolution from the beginning of the search. The results show that this approach is competitive with other techniques in solving a large set of cases of a well-known NP-hard problem without incorporating too much additional complexity into the metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

The protozoan Trypanosoma cruzi , the causative agent of Chagas disease, is transmitted by infected feces or consumption of blood-sucking triatomine insects to several mammalian orders including Chiroptera. In Chile, the distribution of several insectivorous and one hematophagous bat species overlaps with those of triatomine vectors, but the T. cruzi infection status of local chiropterans is unknown. In 2018, we live-captured bats from two protected areas in Chile to collect plagiopatagium tissue, feces and perianal swab samples, in search for T. cruzi- DNA by real time PCR assays using species-specific primers. In Pan de Azúcar island (∼26°S), we examined a roost of Desmodus rotundus (common vampire bat) and sampled tissue from 17 individuals, detecting T. cruzi -DNA in five of them. In Las Chinchillas National Reserve (∼31°S), we examined two roosts of Histiotus montanus (small big-eared brown bat), collecting feces or perianal swab samples from eight individuals, detecting T. cruzi- DNA in four of them. This is the first report of T. cruzi -DNA evidence in bat species from Chile. Both vector-borne and oral transmission are potential infection routes that can explain our results. Further investigation is needed for a better understanding of the role of bats in the T. cruzi transmission cycle. [ABSTRACT FROM AUTHOR]