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There is strong upswing in conservation and restoration efforts in Latin America ( LA), particularly in the recent decades after several countries have committed to international agreements such as the Aichi targets of the Convention on Biological Diversity, Initiative 20×20, and the Bonn Challenge. To fulfill these agreements, the practice of ecological restoration has to be defined based on ecological knowledge, but also on the specific social, economic, and legal aspects of each country in the region. Here, we give some examples about the national understanding of ecological restoration in 10 countries of LA. We identify difficulties and opportunities to define restoration priorities and needs, and discuss some approaches to cope with economic constraints and agreements, including the potential role of restoration networks in this process. On the basis of the socioecological complexity of these countries and the expectations they have in relation to restoration, we proposed four approaches to guide restoration practice and policy in the region: (1) including biodiversity and ecosystem services approach into ecosystem restoration initiatives; (2) promoting restoration in their frequently human-modified landscapes; (3) accounting for cost-benefit trade-offs; and (4) assembling 'horizontal' communication frameworks. These approaches should be based at national levels, but adapted to local-regional levels, in a bottom-up perspective. We consider that national and international restoration networks in the region can help to overcome difficulties, fostering a solid scientific community, helping to develop national approaches that better match the specific conditions of each country and enhancing communication among different groups of stakeholders. [ABSTRACT FROM AUTHOR]

Thin films of copper(I) oxide (Cu2O) were electrodeposited on fluorine-doped tin oxide predeposited glass substrates, by reduction of Cu2+ from Cu(II) acetate acid aqueous solutions. The Cu2O was potentiostatically grown at a potential value of −0.450 V (vs. SMSE) at 70 °C. The Cu2O thin films were characterized by means of scanning electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy (XPS), optical transmission, electrochemical impedance spectroscopy, and photoelectrochemical experiments. Through these techniques, it was possible to establish the cubic Cu2O phase with a high crystallinity and a strong preferential growth along the [200] and [220] directions. Cu2O thin films show oxygen vacancies with formation of a nonstoichiometric compound with the presence of Cu(0) in the crystal lattice as determined by XPS analysis. In addition, Cu2O was used as the photoanode for the I− oxidation reaction when the system was illuminated ( Φ0 = 50.0 mW cm−2). The films exhibited a clear n-type semiconductor behavior, which was in agreement with the Mott-Schottky results. This behavior was explained by considering the nonstoichiometry of the oxide. [ABSTRACT FROM AUTHOR]

SUMMARY The great concern about climate change and fossil fuel dependency is strongly encouraging the development and use of electric vehicles. However, the wide adoption of such vehicles is not possible without the deployment of charging infrastructures capable of integrating them into current and future electricity grids. There are many on-going research efforts and even products that propose different solutions to this problem. Standing out among them is the Spanish research project DOMOCELL, which aims at designing and developing a smart charging infrastructure for private parking lots. In this paper, we present its communications architecture, designed under the guidelines of efficiency, reliability, and scalability. In addition, we describe, supported by some practical use cases and emulations, how user authentication and authorization, as well as accounting and billing of power consumption, are performed even in roaming scenarios. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Discretization of numerical data is one of the most influential data preprocessing tasks in knowledge discovery and data mining. The purpose of attribute discretization is to find concise data representations as categories which are adequate for the learning task retaining as much information in the original continuous attribute as possible. In this article, we present an updated overview of discretization techniques in conjunction with a complete taxonomy of the leading discretizers. Despite the great impact of discretization as data preprocessing technique, few elementary approaches have been developed in the literature for Big Data. The purpose of this article is twofold: a comprehensive taxonomy of discretization techniques to help the practitioners in the use of the algorithms is presented; the article aims is to demonstrate that standard discretization methods can be parallelized in Big Data platforms such as Apache Spark, boosting both performance and accuracy. We thus propose a distributed implementation of one of the most well-known discretizers based on Information Theory, obtaining better results than the one produced by: the entropy minimization discretizer proposed by Fayyad and Irani. Our scheme goes beyond a simple parallelization and it is intended to be the first to face the Big Data challenge. WIREs Data Mining Knowl Discov 2016, 6:5-21. doi: 10.1002/widm.1173 For further resources related to this article, please visit the . [ABSTRACT FROM AUTHOR]

The term 'Big Data' has spread rapidly in the framework of Data Mining and Business Intelligence. This new scenario can be defined by means of those problems that cannot be effectively or efficiently addressed using the standard computing resources that we currently have. We must emphasize that Big Data does not just imply large volumes of data but also the necessity for scalability, i.e., to ensure a response in an acceptable elapsed time. When the scalability term is considered, usually traditional parallel-type solutions are contemplated, such as the Message Passing Interface or high performance and distributed Database Management Systems. Nowadays there is a new paradigm that has gained popularity over the latter due to the number of benefits it offers. This model is Cloud Computing, and among its main features we has to stress its elasticity in the use of computing resources and space, less management effort, and flexible costs. In this article, we provide an overview on the topic of Big Data, and how the current problem can be addressed from the perspective of Cloud Computing and its programming frameworks. In particular, we focus on those systems for large-scale analytics based on the MapReduce scheme and Hadoop, its open-source implementation. We identify several libraries and software projects that have been developed for aiding practitioners to address this new programming model. We also analyze the advantages and disadvantages of MapReduce, in contrast to the classical solutions in this field. Finally, we present a number of programming frameworks that have been proposed as an alternative to MapReduce, developed under the premise of solving the shortcomings of this model in certain scenarios and platforms. WIREs Data Mining Knowl Discov 2014, 4:380-409. doi: 10.1002/widm.1134 For further resources related to this article, please visit the . Conflict of interest: The authors have declared no conflicts of interest for this article. [ABSTRACT FROM AUTHOR]

Subgroup discovery (SD) is a descriptive data mining technique using supervised learning. In this article, we review the use of evolutionary algorithms (EAs) for SD. In particular, we will focus on the suitability and potential of the search performed by EAs in the development of SD algorithms. Future directions in the use of EAs for SD are also presented in order to show the advantages and benefits that this search strategy contribute to this task. Conflict of interest: The authors have declared no conflicts of interest for this article. For further resources related to this article, please visit the . [ABSTRACT FROM AUTHOR]

Bibliometric mapping and visualization techniques represent one of the main pillars in the field of scientometrics. Traditionally, the main methodologies employed for representing data are multidimensional scaling, principal component analysis, or correspondence analysis. In this paper we aim to present a visualization methodology known as biplot analysis for representing bibliometric and science and technology indicators. A biplot is a graphical representation of multivariate data, where the elements of a data matrix are represented according to dots and vectors associated with the rows and columns of the matrix. In this paper, we explore the possibilities of applying biplot analysis in the research policy area. More specifically, we first describe and introduce the reader to this methodology and secondly, we analyze its strengths and weaknesses through 3 different case studies: countries, universities, and scientific fields. For this, we use a biplot analysis known as JK-biplot. Finally, we compare the biplot representation with other multivariate analysis techniques. We conclude that biplot analysis could be a useful technique in scientometrics when studying multivariate data, as well as an easy-to-read tool for research decision makers. [ABSTRACT FROM AUTHOR]

Thomson Reuters' Web of Science (WoS) is undoubtedly a great tool for scientiometrics purposes. It allows one to retrieve and compute different measures such as the total number of papers that satisfy a particular condition; however, it also is well known that this tool imposes several different restrictions that make obtaining certain results difficult. One of those constraints is that the tool does not offer the total count of documents in a dataset if it is larger than 100,000 items. In this article, we propose and analyze different approaches that involve partitioning the search space (using the Source field) to retrieve item counts for very large datasets from the WoS. The proposed techniques improve previous approaches: They do not need any extra information about the retrieved dataset (thus allowing completely automatic procedures to retrieve the results), they are designed to avoid many of the restrictions imposed by the WoS, and they can be easily applied to almost any query. Finally, a description of WoS Query Partitioner, a freely available and online interactive tool that implements those techniques, is presented. [ABSTRACT FROM AUTHOR]

Objective To determine the therapeutic efficacy and safety of a nitric oxide (NO) isosorbide dinitrate donor to induce cervical ripening of women with missed abortions before surgical evacuation of the uterus. Design A prospective, randomised, double-blind controlled trial. Setting Tertiary referral maternity teaching hospital. Population Sixty women with missed abortions and no cervical dilation. Methods Women requesting surgical evacuation of the uterus were randomly selected to receive endocervical 80 mg/1.5 mL isosorbide dinitrate gel solution ( n= 30) or 400 μg/1.5 mL misoprostol gel solution ( n= 30) every 3 hours to a maximum of four doses or until reaching cervical ripening. Vital signs and symptoms were recorded at baseline and then every 3 hours until finishing therapy. Adverse events, such as headache, abdominal pain, pelvic pain, backache, nausea and vomiting, were evaluated. Main outcome measures Probability of reaching cervical ripening >8 mm Hegar dilator; evaluated at 3, 6, 9 and 12 hours after application of isosorbide dinitrate or misoprostol. Results The probabilities of induction of cervical ripening by isosorbide dinitrate and misoprostol after four repeated doses at 3-hour intervals were significantly different ( P < 0.001). Efficacy of therapy after 12 hours was 97% for the isosorbide dinitrate group and 70% for the misoprostol group. Systolic and diastolic blood pressures were lower after administration of isosorbide dinitrate than prostaglandin analogues. The difference in the mean systolic and diastolic blood pressure between treatment groups was greatest at 3 hours, with a difference of 7.7 mmHg ( P < 0.001) and 5.9 mmHg ( P < 0.003), respectively. The most frequent side effect associated with isosorbide dinitrate administration was headache, which occurred in 18 out of 30 patients, compared with only 5 out of 30 women in the misoprostol group [relative risk (RR) 2.41, 95% confidence interval (CI) 1.45–4.03, P < 0.001). Women treated with misoprostol reported mainly pelvic pain (RR 3.24, 95% CI 1.99–5.27, P < 0.001). Conclusions Intracervical administration of 80 mg isosorbide dinitrate in women with missed abortions appears to be effective for cervical ripening prior to surgical evacuation of the uterus. Differences in the incidence of non-serious adverse events are not likely to be clinically significant. [ABSTRACT FROM AUTHOR]

The k‐nearest neighbors algorithm is characterized as a simple yet effective data mining technique. The main drawback of this technique appears when massive amounts of data—likely to contain noise and imperfections—are involved, turning this algorithm into an imprecise and especially inefficient technique. These disadvantages have been subject of research for many years, and among others approaches, data preprocessing techniques such as instance reduction or missing values imputation have targeted these weaknesses. As a result, these issues have turned out as strengths and the k‐nearest neighbors rule has become a core algorithm to identify and correct imperfect data, removing noisy and redundant samples, or imputing missing values, transforming Big Data into Smart Data—which is data of sufficient quality to expect a good outcome from any data mining algorithm. The role of this smart data gleaning algorithm in a supervised learning context are investigated. This includes a brief overview of Smart Data, current and future trends for the k‐nearest neighbor algorithm in the Big Data context, and the existing data preprocessing techniques based on this algorithm. We present the emerging big data‐ready versions of these algorithms and develop some new methods to cope with Big Data. We carry out a thorough experimental analysis in a series of big datasets that provide guidelines as to how to use the k‐nearest neighbor algorithm to obtain Smart/Quality Data for a high‐quality data mining process. Moreover, multiple Spark Packages have been developed including all the Smart Data algorithms analyzed. This article is categorized under:Technologies > Data PreprocessingFundamental Concepts of Data and Knowledge > Big Data MiningTechnologies > Classification The k‐nearest neighbors algorithm plays a key role to cope with Big Data by transforming it into Smart data that is free of redundant information, noise and/or missing values. Gleaning quality data is essential for a correct data mining process that will uncover valuable insights. [ABSTRACT FROM AUTHOR]

The article presents a case study of patients who were diagnosed with histological pattern of colonic metastases resulting from primary cervical carcinoma. Clinical cases involved patients with metastatic colonic tumors who underwent series of examinations including radiotherapy and brachytherapy. Results of histopathological examination show a squamous cell epithelial carcinoma in three patients while the remaining patient was diagnosed with adenocarcinoma.