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Establishing proper neighbor relations between a set of spatial units under analysis is essential when carrying out a spatial or spatio-temporal analysis. However, it is usual that researchers choose some of the most typical (and simple) neighborhood structures, such as the first-order contiguity matrix, without exploring other options. In this paper, we compare the performance of different neighborhood matrices in the context of modeling the weekly relative risk of COVID-19 over small areas located in or near Valencia, Spain. Specifically, we construct contiguity-based, distance-based, covariate-based (considering mobility flows and sociodemographic characteristics), and hybrid neighborhood matrices. We evaluate the goodness of fit, the overall predictive quality, the ability to detect high-risk spatio-temporal units, the capability to capture the spatio-temporal autocorrelation in the data, and the goodness of smoothing for a set of spatio-temporal models based on each of the neighborhood matrices. The results show that contiguity-based matrices, some of the distance-based matrices, and those based on sociodemographic characteristics perform better than the matrices based on k-nearest neighbors and those involving mobility flows. In addition, we test the linear combination of some of the constructed neighborhood matrices and the reweighting of these matrices after eliminating weak neighbor relations, without any model improvement.

Tabu search (TS) is a metaheuristic that guides a local heuristic search procedure to explore the solution space beyond local optimality. One of the main components of TS is its use of adaptive memory, which creates a highly flexible search behavior. Memory-based strategies which are the hallmark of TS approaches are founded on a quest for “integrating principles,” by which alternative forms of memory are appropriately combined with effective strategies for exploiting them. The adaptive memory feature of TS allows the implementation of procedures that are capable of searching the solution space economically and effectively. Since local choices are guided by information collected during the search, TS contrasts with memoryless designs that heavily rely on semi-random processes that implement a form of sampling.

The pozzolanic materials have the capacity to modify the performance of Portland cement (PC) mortars. When they are used as supplementary materials, the physical-chemical properties such as compressive-strength and resistance to aggressive environments (chloride, sulphate, acids) are improved. In this article, a study was carried out to evaluate the effects of the addition of two natural pozzolans: volcanic pumice and zeolite. Both used as substitutes for PC in mortars based on cement, considering mechanical strength and the aggressive effect of sulphate ions. Furthermore, the electrochemical behavior of the steel rebar embedded in these mortars was studied during 730 days of exposition to aggressive conditions. The results revealed that the development of the compressive strength of mortars with pozzolans added reached similar values to the reference mortar. It was possible to observe that the addition of the pozzolan suppresses the ettringite formation proportionally to the pozzolan content. The corrosion resistance of the steel improves with the addition of pozzolans after 730 days of exposition at sulphate environment. The results of this study revealed the feasibility to use natural pozzolan as an additive during the preparation of cement up to 25% without significant changes of the physical-chemical properties of the mortars.

In fire scenarios, the application and accuracy of traditional odontological methods are often limited. Crystalline studies and elemental profiling have been evaluated for their applicability in determining biological profiles (age and sex) from human dentition, particularly fire- and heat-affected dental remains. Thirty-seven teeth were paired according to tooth type and donor age/sex for the analysis of crown and root surfaces pre- and post-incineration using X-ray diffraction (XRD) and scanning electron microscopy (SEM/EDX). In unburned crowns, carbon (C) content showed a positive correlation with age, whereas phosphorus (P) and calcium (Ca) contents showed a negative correlation with age. In unburned roots, C, P and Ca contents also showed significant changes that were opposite of those observed in the crowns. In relation to sex, females exhibited a higher C ratio than males, whereas males showed significantly higher levels of oxygen (O), P and Ca in unburned roots. Incineration resulted in an increase in the crystallite size that correlated with increasing temperature. No differences in hydroxyapatite (HA) crystallite size were found between age groups; however, unburned teeth from females exhibited a larger crystallite size than did those from males. The challenges of using XRD with a 3D sample were overcome to allow analysis of whole teeth in a nondestructive manner. Further studies may be useful in helping predict the temperature of a fire.

Research training for undergraduate medical students provides skills to introduce them to the practice of patient-oriented research. However, mandatory research training for students is hindered by sustainability and limitations on didactic time within a curriculum. We created the Research Scholarship Course consisting of eight sequential steps which may be completed as an ongoing activity during the first 3 years or during an 8-week rotation in the fourth year. To date, students have rated the course highly. Future research is needed to determine how the research skills gained during this course translate to long-term knowledge retention and application in the clinical setting.

Real-parameter optimisation is a prolific research line with hundreds of publications per year. There exists an impressive number of alternatives in both algorithm families and enhancements over their respective original proposals. In this work, we analyse if this growth in the number of publications is correlated with a real progress in the field. We have selected five approaches from one of the most significant journals in the field and compared them with the winner of the competition celebrated within the IEEE Congress on Evolutionary Computation 2005. We observe that not only these methods are unable to get the good results of the winner of the competition, published several years before, but that they often avoid this type of comparison. Instead, they usually compare with other approaches from the same family. We conclude that the comparison with the state-of-the-art of the field should be mandatory to promote a real progress and to prevent that the area becomes obfuscated for outsiders.

The quadratic multiple knapsack problem (QMKP) consists in assigning a set of objects, which interact through paired profit values, exclusively to different capacity-constrained knapsacks with the aim of maximising total profit. Its many applications include the assignment of workmen to different tasks when their ability to cooperate may affect the results.

In this paper, we address the optimization problem arising in some practical applications in which we want to maximize the minimum difference between the labels of adjacent elements. For example, in the context of location models, the elements can represent sensitive facilities or chemicals and their labels locations, and the objective is to locate (label) them in a way that avoids placing some of them too close together (since it can be risky). This optimization problem is referred to as the antibandwidth maximization problem (AMP) and, modeled in terms of graphs, consists of labeling the vertices with different integers or labels such that the minimum difference between the labels of adjacent vertices is maximized. This optimization problem is the dual of the well-known bandwidth problem and it is also known as the separation problem or directly as the dual bandwidth problem. In this paper, we first review the previous methods for the AMP and then propose a heuristic algorithm based on the variable neighborhood search methodology to obtain high quality solutions. One of our neighborhoods implements ejection chains which have been successfully applied in the context of tabu search. Our extensive experimentation with 236 previously reported instances shows that the proposed procedure outperforms existing methods in terms of solution quality.

The spatial and temporal regulation of intracellular signaling is determined by the spatial and temporal organization of complexes assembled on scaffold proteins, which can be modulated by their interactions with additional proteins as well as subcellular localization. The scaffold KSR1 protein interacts with MAPK forming a complex that conveys a differential signaling in response to growth factors. The aim of this work is to determine the unknown mechanism by which VRK2A downregulates MAPK signaling. We have characterized the multiprotein complex formed by KSR1 and the Ser-Thr kinase VRK2A. VRK2A is a protein bound to the endoplasmic reticulum (ER) and retains a fraction of KSR1 complexes on the surface of this organelle. Both proteins, VRK2A and KSR1, directly interact by their respective C-terminal regions. In addition, MEK1 is also incorporated in the basal complex. MEK1 independently interacts with the CA5 region of KSR1 and with the N-terminus of VRK2A. Thus, VRK2A can form a high molecular size (600–1,000 kDa) stable complex with both MEK1 and KSR1. Knockdown of VRK2A resulted in disassembly of these high molecular size complexes. Overexpression of VRK2A increased the amount of KSR1 in the particulate fraction and prevented the incorporation of ERK1/2 into the complex after stimulation with EGF. Neither VRK2A nor KSR1 interact with the VHR, MKP1, MKP2, or MKP3 phosphatases. The KSR1 complex assembled and retained by VRK2A in the ER can have a modulatory effect on the signal mediated by MAPK, thus locally affecting the magnitude of its responses, and can explain differential responses depending on cell type., I.F.F. has a JAE-CSIC predoctoral fellowship. This work was funded by grants from Ministerio de Ciencia e Innovación (SAF2010-14935 and CSD2007-0017), Junta de Castilla y León (Consejería de Educación CSI-006A11-2) and Kutxa-Fundación Inbiomed to P.A.L., and grant SAF2010-20203 from Ministerio de Ciencia e Innovación to J.L.

No free lunch theorems for optimisation suggest that empirical studies on benchmarking problems are pointless, or even cast negative doubts, when algorithms are being applied to other problems not clearly related to the previous ones. Roughly speaking, reported empirical results are not just the result of algorithms' performances, but the benchmark used therein as well; and consequently, recommending one algorithm over another for solving a new problem might be always disputable. In this work, we propose an empirical framework, arbitrary function optimisation framework, that allows researchers to formulate conclusions independent of the benchmark problems that were actually addressed, as long as the context of the problem class is mentioned. Experiments on sufficiently general scenarios are reported with the aim of assessing this independence. Additionally, this article presents, to the best of our knowledge, the first thorough empirical study on the no free lunch theorems, which is possible thanks to the application of the proposed methodology, and whose main result is that no free lunch theorems unlikely hold on the set of binary real-world problems. In particular, it is shown that exploiting reasonable heuristics becomes more beneficial than random search when dealing with binary real-world applications.

In this work, we tackle the problem of scheduling a set of jobs on a set of non-identical parallel machines with the goal of minimising the total weighted completion times. GRASP is a multi-start method that consists of two phases: a solution construction phase, which randomly constructs a greedy solution, and an improvement phase, which uses that solution as an initial starting point. In the last few years, the GRASP methodology has arisen as a prospective metaheuristic approach to find high-quality solutions for several difficult problems in reasonable computational times. With the aim of providing additional results and insights along this line of research, this paper proposes a new GRASP model that combines the basic scheme with two significant elements that have been shown to be very successful in order to improve GRASP performance. These elements are path-relinking and evolutionary path-relinking. The benefits of our proposal in comparison to existing metaheuristics proposed in the literature are experimentally shown.

Thermal stability of bovine α-lactalbumin in the presence of three different calcium concentrations in aqueous solutions of several concentrations of erythritol, xylitol, sorbitol, and inositol at pH 6.5 was evaluated by UV absorbance, fluorescence spectroscopy, and circular dichroism spectroscopy. At the selected conditions, the thermal denaturation process is reversible and is well described by a two-state model. Results show a higher stability for the holo form of the protein in the presence of calcium, followed by the holo- and the apo-lactalbumin, respectively. The stabilizing effect of the polyols increases with polyol concentration and it is higher for the apo-lactalbumin than holo-lactalbumin and is very small for the protein in the presence of a calcium excess.

Nowadays, large scale optimisation problems arise as a very interesting field of research, because they appear in many real-world problems (bio-computing, data mining, etc.). Thus, scalability becomes an essential requirement for modern optimisation algorithms. In a previous work, we presented memetic algorithms based on local search chains. Local search chain concerns the idea that, at one stage, the local search operator may continue the operation of a previous invocation, starting from the final configuration reached by this one. Using this technique, it was presented a memetic algorithm, MA-CMA-Chains, using the CMA-ES algorithm as its local search component. This proposal obtained very good results for continuous optimisation problems, in particular with medium-size (with up to dimension 50). Unfortunately, CMA-ES scalability is restricted by several costly operations, thus MA-CMA-Chains could not be successfully applied to large scale problems. In this article we study the scalability of memetic algorithms based on local search chains, creating memetic algorithms with different local search methods and comparing them, considering both the error values and the processing cost. We also propose a variation of Solis Wets method, that we call Subgrouping Solis Wets algorithm. This local search method explores, at each step of the algorithm, only a random subset of the variables. This subset changes after a certain number of evaluations. Finally, we propose a new memetic algorithm based on local search chains for high dimensionality, MA-SSW-Chains, using the Subgrouping Solis Wets’ algorithm as its local search method. This algorithm is compared with MA-CMA-Chains and different reference algorithms, and it is shown that the proposal is fairly scalable and it is statistically very competitive for high-dimensional problems.

Differential Evolution is a simple yet powerful algorithm for continuous optimisation problems. Traditionally, its operators combine the information of randomly chosen vectors of the population. However, four different roles are clearly identified from their formulations: receiving, placing, leading, and correcting vectors. In this work, we propose two mechanisms that emphasise the proper selection of vectors for each role in crossover and mutation operations: (1) the role differentiation mechanism defines the attributes for which vectors are selected for each role; (2) malleable mating allows placing vectors to adapt their mating trends to ensure some similarity relations with the leading and correcting vectors. In addition, we propose a new differential evolution approach that combines these two mechanisms. We have performed experiments on a testbed composed of 19 benchmark functions and five dimensions, ranging from 50 variables to 1,000. Results show that both mechanisms allow differential evolution to statistically improve its results, and that our proposal becomes competitive with regard to representative methods for continuous optimisation.

Local genetic algorithms have been designed with the aim of providing effective intensification. One of their most outstanding features is that they may help classical local search-based metaheuristics to improve their behavior. This paper focuses on experimentally investigating the role of a recent approach, the binary-coded local genetic algorithm (BLGA), as context-independent local search operator for three local search-based metaheuristics: random multi-start local search, iterated local search, and variable neighborhood search. These general-purpose models treat the objective function as a black box, allowing the search process to be context-independent. The results show that BLGA may provide an effective and efficient intensification, not only allowing these three metaheuristics to be enhanced, but also predicting successful applications in other local search-based algorithms. In addition, the empirical results reported here reveal relevant insights on the behavior of classical local search methods when they are performed as context-independent optimizers in these three well-known metaheuristics.

Thermal stability of bovine α-lactalbumin in buffer and dilute aqueous solutions of erythritol, xylitol, sorbitol, inositol and glucose was evaluated by fluorescence spectroscopy and circular dichroism. Results show that at the selected conditions, the transition is reversible and is well described by a two-state model. At low concentration the cosolutes do not show a structure stabilizing effect, and some of them even destabilize the protein. At higher concentration, all of them stabilize the native protein conformation; however, the extent of stabilization is lower than the effect shown with other proteins, presumably due to the lactalbumin incomplete unfolding.

In this paper, a diversity generating mechanism is proposed for an Evolutionary Programming (EP) algorithm that determines the basic structure of Multilayer Perceptron classifiers and simultaneously estimates the coefficients of the models. We apply a modified version of a saw-tooth diversity enhancement mechanism recently presented for Genetic Algorithms, which uses a variable population size and periodic partial reinitializations of the population in the form of a saw-tooth function. Our improvement on this standard scheme consists of guiding saw-tooth reinitializations by considering the variance of the best individuals in the population. The population restarts are performed when the difference of variance between two consecutive generations is lower than a percentage of the previous variance. From the analysis of the results over ten benchmark datasets, it can be concluded that the computational cost of the EP algorithm with a constant population size is reduced by using the original saw-tooth scheme. Moreover, the guided saw-tooth mechanism involves a significantly lower computer time demand than the original scheme. Finally, both saw-tooth schemes do not involve an accuracy decrease and, in general, they obtain a better or similar precision.

In recent years, there has been a growing interest for the experimental analysis in the field of evolutionary algorithms. It is noticeable due to the existence of numerous papers which analyze and propose different types of problems, such as the basis for experimental comparisons of algorithms, proposals of different methodologies in comparison or proposals of use of different statistical techniques in algorithms' comparison. In this paper, we focus our study on the use of statistical techniques in the analysis of evolutionary algorithms' behaviour over optimization problems. A study about the required conditions for statistical analysis of the results is presented by using some models of evolutionary algorithms for real-coding optimization. This study is conducted in two ways: single-problem analysis and multiple-problem analysis. The results obtained state that a parametric statistical analysis could not be appropriate specially when we deal with multiple-problem results. In multiple-problem analysis, we propose the use of non-parametric statistical tests given that they are less restrictive than parametric ones and they can be used over small size samples of results. As a case study, we analyze the published results for the algorithms presented in the CEC'2005 Special Session on Real Parameter Optimization by using non-parametric test procedures.

This study was designed to identify risk factors for nosocomial infections among infants admitted into eight neonatal intensive care units in Colombia. Knowledge of modifiable risk factors could be used to guide the design of interventions to prevent the problem. Data were collected prospectively from eight neonatal units. Nosocomial infection was defined as culture-proven infection diagnosed after 72 hours of hospitalization, resulting in treatment with antibiotics for >3 days. Associations were expressed as odds ratios. Logistic regression was used to adjust for potential confounders. From a total of 1504 eligible infants, 80 were treated for 127 episodes of nosocomial infection. Logistic regression analysis identified the combined exposure to postnatal steroids and H2-blockers, and use of oral gastric tubes for enteral nutrition as risk factors significantly associated with nosocomial infection. Nosocomial infections in Colombian neonatal intensive care units were associated with modifiable risk factors including use of postnatal steroids and H2-blockers.

Most real-coded genetic algorithm research has focused on developing effective crossover operators, and as a result, many different types have been proposed. Some forms of crossover operators are more suitable to tackle certain problems than others, even at the different stages of the genetic process in the same problem. For this reason, techniques which combine multiple crossovers have been suggested as alternative schemes to the common practice of applying only one crossover model to all the elements in the population. Therefore, the study of the synergy produced by combining the different styles of the traversal of solution space associated with the different crossover operators is an important one. The aim is to investigate whether or not the combination of crossovers perform better than the best single crossover amongst them. In this paper we have undertaken an extensive study in which we have examined the synergetic effects among real-parameter crossover operators with different search biases. This has been done by means of hybrid real-parameter crossover operators, which generate two offspring for every pair of parents, each one with a different crossover operator. Experimental results show that synergy is possible among real-parameter crossover operators, and in addition, that it is responsible for improving performance with respect to the use of a single crossover operator.

The genetic algorithm behaviour is determined by the exploitation and exploration relationship kept throughout the run. Adaptive genetic algorithms, that dynamically adjust selected control parameters or genetic operators during the evolution have been built. Their objective is to offer the most appropriate exploration and exploitation behaviour to avoid the premature convergence problem and improve the final results. One of the adaptive approaches are the adaptive parameter setting techniques based on the use of fuzzy logic controllers, the fuzzy adaptive genetic algorithms (FAGAs). In this paper, we analyse the FAGAs in depth. First, we describe the steps for their design and present an instance, which is studied from an empirical point of view. Then, we propose a taxonomy for FAGAs, attending on the combination of two aspects: the level where the adaptation takes place and the way the Rule-Bases are obtained. Furthermore, FAGAs belonging to different groups of the taxonomy are reviewed. Finally, we identify some open issues, and summarise a few new promising research directions on the topic. From the results provided by the approaches presented in the literature and the experimental results achieved in this paper, an important conclusion is obtained: the use of fuzzy logic controllers to adapt genetic algorithm parameters may really improve the genetic algorithm performance.

This editorial note presents the motivations, objectives, and structure of the special issue on scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems. In addition, it provides the link to an associated Website where complementary material to the special issue is available.

Genetic algorithms are adaptive methods based on natural evolution that may be used for search and optimization problems. They process a population of search space solutions with three operations: selection, crossover, and mutation. Under their initial formulation, the search space solutions are coded using the binary alphabet, however other coding types have been taken into account for the representation issue, such as real coding. The real-coding approach seems particularly natural when tackling optimization problems of parameters with variables in continuous domains. A problem in the use of genetic algorithms is premature convergence, a premature stagnation of the search caused by the lack of population diversity. The mutation operator is the one responsible for the generation of diversity and therefore may be considered to be an important element in solving this problem. For the case of working under real coding, a solution involves the control, throughout the run, of the strength in which real genes are mutated, i.e., the step size. This paper presents TRAMSS, a Two-loop Real-coded genetic algorithm with Adaptive control of Mutation Step Sizes. It adjusts the step size of a mutation operator applied during the inner loop, for producing efficient local tuning. It also controls the step size of a mutation operator used by a restart operator performed in the outer loop, for reinitializing the population in order to ensure that different promising search zones are focused by the inner loop throughout the run. Experimental results show that the proposal consistently outperforms other mechanisms presented for controlling mutation step sizes, offering two main advantages simultaneously, better reliability and accuracy.

Genetic algorithms play a significant role, as search techniques for handling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the natural evolution principles of populations. These algorithms process a population of chromosomes, which represent search space solutions, with three operations: selection, crossover and mutation. Under its initial formulation, the search space solutions are coded using the binary alphabet. However, the good properties related with these algorithms do not stem from the use of this alphabet; other coding types have been considered for the representation issue, such as real coding, which would seem particularly natural when tackling optimization problems of parameters with variables in continuous domains. In this paper we review the features of real-coded genetic algorithms. Different models of genetic operators and some mechanisms available for studying the behaviour of this type of genetic algorithms are revised and compared.

Proliferation of both mouse and human breast cancer cells was inhibited by chlorpheniramine (CPA) in a dose-response manner. At the beginning of the exponential phase of growth (two days after seeding), 250 microM CPA was able to reduce cell proliferation by 75% (in Ehrlich cell cultures) and 30% (in MCF-7 cultures). The antiproliferative effect of CPA was also tested on a poorly-differentiated and hormone-insensitive human breast cancer cell line (MDA-MB231) and on a highly proliferative human colon cancer cell line (clone 3). CPA was cytotoxic for MDA-MB231 cells at concentrations higher than 50 microM, and it was also cytotoxic for the colon cancer cell clone 3 at 250 microM CPA. Nevertheless, colon cancer cells were slightly stimulated at CPA concentrations less than 100 microM. CPA reduced (by 50-70%) the ornithine decarboxylase induction occurring early after culture seeding of experimental mammary tumors (Ehrlich carcinoma cells) and human breast cancer cells (MCF-7). The presented data suggest that in addition to ODC inhibition, CPA presents other still unknown cytotoxic effects.