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We analize the Mexican peso-US dollar exchange parity the volatility using three models in order to evaluate the exchange rate risk performance and the these models capability of modeling such risk. The period June 21st, 2017 to June 21st, 2022 is marked by singular events that pressed this exchange rate parity, making it of great interest to study the tools that can be used to measure this uncertainty besides the study of the exchange rate uncertainty itself. The arrival of a leftwing politician to the presidency was an unprecedented event. At the beginning of 2020 the price war in the oil market and the emergence of the COVID-19 pandemic had important effects in the world economy. Finally, the war in Ukraine added pressures to the international trade of raw materials, inducing an inflationary episode with bad expectations about the economic performance around the world. The exchange rate scenarios posed by the aforesaid events provide us with a natural context to test which of the posited volatility models offers a better fit. Unanimously, the results under the three models suggest that during the first year of the President López Obrador period the exchange rate uncertainty was reduced compared to previous levels, returning again to those minor levels after the forced closure of economic activities due to the emergence of COVID-19, even though the exchange fluctuation band has been slightly higher than before the pandemic and the war on East Europe. [ABSTRACT FROM AUTHOR]

Through a quantile regression, it is shown that from 1990 to 2018 the impacts of business investments, dollar debt and the exchange rate are differentiated for the various levels of exchange earnings of a group of Mexican companies. The value of the dollar and foreign currency debt negatively impact earnings, and investments in property, plant and equipment, and net working capital have a greater influence on such earnings. The relationship between foreign exchange earnings and investments in recent, intangible assets and net working capital has not been previously studied. These results can be useful for investment planning as a means of managing the risk of foreign exchange losses. [ABSTRACT FROM AUTHOR]

Building on reinvigorated scholarly debates on the concept of value, this paper examines forms of valuation that coexist with and challenge those typically associated with capitalist natures. Based on archival, ethnographic, and interview data, we analyze a historical case of the Venezuelan Society of Speleology (SVE) that since 1967 has been exploring and mapping the country's caves. Its results are published in a national cave survey that the group manages. Not only is each cave entry the representation and materialization of the group's volunteer efforts. Each is also part of a relational regime of valuation by which all caves are worth mapping. The survey's classificatory logic not only transforms underground value, it also acts as boundary object bringing together diverse actors and promoting the SVE's collective ethos, at least in theory, of valuing all members. The survey is the fulcrum of distinct regimes of valuation, wherein value and values are two sides of the same coin. The spaces where these value/s are enacted matters, suggesting the critical contributions that geographers can make in understanding the spatial qualities that contribute to—or hinder—the materialization of certain regimes of valuation over others. [ABSTRACT FROM AUTHOR]

Given integers g,n ≥ 0 satisfying 2−2g−n , let Mg,n be the moduli space of connected, oriented, complete, finite area hyperbolic surfaces of genus g with n cusps. We study the global behavior of the Mirzakhani function B : Mg,n → R≥0 which assigns to X ∈ Mg,n the Thurston measure of the set of measured geodesic laminations on X of hyperbolic length ≤ 1. We improve bounds of Mirzakhani describing the behavior of this function near the cusp of Mg,n and deduce that B is square-integrable with respect to the Weil–Petersson volume form. We relate this knowledge of B to statistics of counting problems for simple closed hyperbolic geodesics. [ABSTRACT FROM AUTHOR]

Background: The literature on fiscal theory and policy has shown evidence regarding the procyclical stance of public spending in relation to the economic cycle in developing countries, and particularly in the case of Latin American economies. This paper studies the relationship of budget spending and the economic cycle in Mexico for the 1980-2016 period, it seeks to clarify whether the shift in fiscal policy can be characterized as a tendency towards an anticyclical stance. Methodology: To analyze the quarterly seasonally adjusted series of budget expenditure and gdp (at constant 2008 prices), their cyclical components were estimated using the Hodrick-Prescott filter. To explain the cycle of expenditure through the product cycle, a regression was estimated with Markov regime changes in which the state probabilities vary according to the volatility of the product. Subsequently, the time-varying volatilities of both series were estimated as well as their dynamic conditional correlations and the latter were analyzed based on the economic cycle. Results: The evidence is consistent with an expenditure policy that has been both procyclical and counter-cyclical at different times of the period under study, as the previous literature has shown, but in recent years there has been a notable decrease in the emphasis of the procyclicality of the budget public expenditure, which even suggests that fiscal policy has become acyclical. Conclusions: Unless the policy makers act towards other direction, fiscal policy can be expected to remain acyclical and, therefore, budgetary spending will continue to have non significant impact on the management of the Mexican economy cycle. [ABSTRACT FROM AUTHOR]

We propose that gravity, when formulated in the quantum level, can make quantum effects more pronounced. This is demonstrated by considering the effect of the generalized uncertainty principle or GUP (a consequence of quantum gravity theories) on entanglement (a quantum effect). We apply the GUP to continuous-variable systems. In particular, we study the following cases: the modified uncertainty relation of two identical entangled particles [G. Rigolin, Found. Phys. Lett. 15, 293 (2002)], and the inseparability conditions for entangled particles in the bipartite [Duan et al., Phys. Rev. Lett. 84, 2722 (2000)] and tripartite [van Loock and Furusawa, Phys Rev A 67, 052315 (2003)] cases. Rigolin showed a decrease in the lower bound of the product of the uncertainties of the position and momentum for two identical entangled particles while Duan and van Loock derived inseparability conditions for Einstein-Podolsky-Rosen-like (EPR-like) operators. In all three cases, the GUP correction resulted in a higher value of the bounds: a higher lower bound for the Rigolin's result and a higher upper bound for the inseparability condition in Duan and van Loock's relations. In Rigolin's case, the GUP correction decreased the disagreement with the Heisenberg uncertainty relation while in Duan's and Loock's case, the inseparability and entanglement conditions are enhanced. Interestingly, the GUP corrections tend to boost quantum mechanical effects. [ABSTRACT FROM AUTHOR]

Introduction. TLR´s play a role in host defense in HIV infection recognizing the viral DNA or RNA. Their activation induces a signaling pathway that includes the proteins MyD88, IRAK4, TRAF6 and the transcription factor NF-kBp65. Objective. To determine the expression of TLR7, TLR8 and TLR9, and activation of its signaling pathway in monocytes from patients infected with HIV. Methods. Expression of TLR7, TLR8 and TLR9 was determined in monocytes from HIV-infected patients (n = 13) and control subjects (n = 13), which were activated with specific ligands. The expression of MyD88 and NF-kBp65 were determined by flow cytometry; IRAK4 and TRAF6 were studied by immunoblotting. Results. No statistical difference was found in the expression of TLR7, 8 and 9 in monocytes from patients compared to controls, but we observed the non-significant increased expression of TLR9 in patients. The activation showed no significant difference in the expression of MyD88 and NF-kBp65 in patients when compared to controls, but were decreased in stimulated cells over non-stimulated cells. IRAK4 and TRAF6 were not detected. Conclusions. No statistical difference was observed in the expression of intracellular TLRs, MyD88 and NFkBp65 in monocytes from patients compared to controls. This was probably due to effective antiretroviral therapy being received at the time of study entry. Additional studies are needed (ARTV) under controlled conditions that include infected patients with and without ARVT, responders and non- responders, and work with different cell populations. [ABSTRACT FROM AUTHOR]

Nowadays society is demanding more and more smart healthcare services that allow monitoring patient status in a non-invasive way, anywhere and anytime. Thus, healthcare applications are currently facing important challenges guided by the u-health (ubiquitous health) and p-health (pervasive health) paradigms. New emerging technologies can be combined with other widely deployed ones to develop such next-generation healthcare systems. The main objective of this paper is to review and provide more details on the work presented in "LOBIN: E-Textile and Wireless-Sensor-Network-Based Platform for Healthcare Monitoring in Future Hospital Environments", published in the IEEE Transactions on Information Technology in Biomedicine, as well as to extend and update the comparison with other similar systems. As a result, the paper discusses the main advantages and disadvantages of using different architectures and communications technologies to develop wearable systems for pervasive healthcare applications. [ABSTRACT FROM AUTHOR]

Available data in machine learning applications is becoming increasingly complex, due to higher dimensionality and difficult classes. There exists a wide variety of approaches to measuring complexity of labeled data, according to class overlap, separability or boundary shapes, as well as group morphology. Many techniques can transform the data in order to find better features, but few focus on specifically reducing data complexity. Most data transformation methods mainly treat the dimensionality aspect, leaving aside the available information within class labels which can be useful when classes are somehow complex. This paper proposes an autoencoder-based approach to complexity reduction, using class labels in order to inform the loss function about the adequacy of the generated variables. This leads to three different new feature learners, Scorer, Skaler and Slicer. They are based on Fisher’s discriminant ratio, the Kullback-Leibler divergence and least-squares support vector machines, respectively. They can be applied as a preprocessing stage for a binary classification problem. A thorough experimentation across a collection of 27 datasets and a range of complexity and classification metrics shows that class-informed autoencoders perform better than 4 other popular unsupervised feature extraction techniques, especially when the final objective is using the data for a classification task. [ABSTRACT FROM AUTHOR]

Available data in machine learning applications is becoming increasingly complex, due to higher dimensionality and difficult classes. There exists a wide variety of approaches to measuring complexity of labeled data, according to class overlap, separability or boundary shapes, as well as group morphology. Many techniques can transform the data in order to find better features, but few focus on specifically reducing data complexity. Most data transformation methods mainly treat the dimensionality aspect, leaving aside the available information within class labels which can be useful when classes are somehow complex. This paper proposes an autoencoder-based approach to complexity reduction, using class labels in order to inform the loss function about the adequacy of the generated variables. This leads to three different new feature learners, Scorer, Skaler and Slicer. They are based on Fisher’s discriminant ratio, the Kullback-Leibler divergence and least-squares support vector machines, respectively. They can be applied as a preprocessing stage for a binary classification problem. A thorough experimentation across a collection of 27 datasets and a range of complexity and classification metrics shows that class-informed autoencoders perform better than 4 other popular unsupervised feature extraction techniques, especially when the final objective is using the data for a classification task. [ABSTRACT FROM AUTHOR]

This article presents a Seri narrative text that contains the description of a route by boat to a bay that is on the way to Baja California from Haxöl Iihom (El Desemboque del Río San Ignacio), one of two Seri villages located along the coast of the Gulf of California. The narrator of the text, Francisco Morales Herrera, also provides his audience with a description of the origin of the place name of the bay that is the destination of the trip, An Iquitj Quih An Him Ipácatx 'Where The Pants Were Thrown'. [ABSTRACT FROM AUTHOR]

The relationship between manufacturing exports and manufacturing sector productivity in Mexico is analyzed. A number of studies suggest that the most productive companies tend to compete more successfully in foreign markets and at the same time these same companies are the ones that decide to export and to channel the necessary resources to raise their productivity rates. In the present document we find that productivity in the manufacturing sector does indeed determine the level of exports in this sector, if investment occurs, or instead, if both are determined simultaneously. Contrary to the findings of Cuevas (2008), in the sense that labor productivity constitutes the principal determinant in manufacturing exports in the case of Mexico in recent years, we present evidence here that the causality between the two variables moves in the opposite direction. [ABSTRACT FROM AUTHOR]

In a recently published paper in JMLR, Demšar (2006) recommends a set of non-parametric statistical tests and procedures which can be safely used for comparing the performance of classifiers over multiple data sets. After studying the paper, we realize that the paper correctly introduces the basic procedures and some of the most advanced ones when comparing a control method. However, it does not deal with some advanced topics in depth. Regarding these topics, we focus on more powerful proposals of statistical procedures for comparing n x n classifiers. Moreover, we illustrate an easy way of obtaining adjusted and comparable p-values in multiple comparison procedures. [ABSTRACT FROM AUTHOR]

The subgroup discovery, domain of application of CN2-SD, is defined as: “given a population of individuals and a property of those individuals, we are interested in finding a population of subgroups as large as possible and have the most unusual statistical characteristic with respect to the property of interest”. The subgroup discovery algorithm CN2-SD, based on a separate and conquer strategy, has to face the scaling problem which appears in the evaluation of large size data sets. To avoid this problem, in this paper we propose the use of instance selection algorithms for scaling down the data sets before the subgroup discovery task. The results show that CN2-SD can be executed on large data set sizes pre-processed, maintaining and improving the quality of the subgroups discovered. [Copyright &y& Elsevier]

Abstract: The generation of predictive models is a frequent task in data mining with the objective of generating highly precise and interpretable models. The data reduction is an interesting preprocessing approach that can allow us to obtain predictive models with these characteristics in large size data sets. In this paper, we analyze the rule classification model based on decision trees using a training selected set via evolutionary stratified instance selection. This method faces the scaling problem that appears in the evaluation of large size data sets, and the trade off interpretability-precision of the generated models. [Copyright &y& Elsevier]

Abstract: VP16 is a virion phosphoprotein of herpes simplex virus and a transcriptional activator of the viral immediate-early (IE) genes. We identified four novel VP16 phosphorylation sites (Ser18, Ser353, Ser411, and Ser452) at late times in infection but found no evidence of phosphorylation of Ser375, a residue reportedly phosphorylated when VP16 is expressed from a transfected plasmid. A virus carrying a Ser375Ala mutation of VP16 was viable in cell culture but with a slow growth rate. The association of the mutant VP16 protein with IE gene promoters and subsequent IE gene expression was markedly reduced during infection, consistent with prior transfection and in vitro results. Surprisingly, the association of Oct-1 with IE promoters was also diminished during infection by the mutant strain. We propose that Ser375 is important for the interaction of VP16 with Oct-1, and that the interaction is required to enable both proteins to bind to IE promoters. [Copyright &y& Elsevier]

During infection by herpes simplex virus type 1 (HSV-1), the virion protein VP16 activates the transcription of viral immediate-early (IE) genes. Genetic and biochemical assays have shown that the potent transcriptional activation domain of VP16 can associate with general transcription factors and with chromatin-modifying coactivator proteins of several types. The latter interactions are particularly intriguing because previous reports indicate that HSV-1 DNA does not become nucleosomal during lytic infection. In the present work, chemical cross-linking and immunoprecipitation assays were used to probe the presence of activators, general transcription factors, and chromatin-modifying coactivators at IE gene promoters during infection of HeLa cells by wild-type HSV-1 and by RP5, a viral strain lacking the VPI6 transcriptional activation domain. The presence of VP16 and Oct-1 at IE promoters did not depend on the activation domain. In contrast, association of RNA polymerase II, TATA-binding protein, histone acetyltransferases (p300 and CBP), and ATP-dependent remodeling proteins (BRG1 and hBRM) with IE gene promoters was observed in wild-type infections but was absent or reduced in cells infected by RP5. In contrast to the previous evidence for nonnucleosomal HSV-1 DNA, histone H3 was found associated with viral DNA at early times of infection. Interestingly, histone H3 was underrepresented on IE promoters in a manner dependent on the VP16 activation domain. Thus, the VP16 activation domain is responsible for recruiting general transcription factors and coactivators to IE promoters and also for dramatically reducing the association of histones with those promoters. [ABSTRACT FROM AUTHOR]

In this paper, we present a consensus model for multiperson decision making (MPDM) problems with different preference structures based on two consensus criteria: 1) a consensus measure which indicates the agreement between experts' opinions and 2) a measure of proximity to find out how far the individual opinions are from the group opinion. These measures are calculated by comparing the positions of the alternatives between the individual solutions and collective solution. In such a way, the consensus situation is evaluated in each moment in a more realistic way. With these measures, we design a consensus support system that is able to substitute the actions of the moderator. In this system, the consensus measure is used to guide the consensus process until the final solution is achieved while the proximity measure is used to guide the discussion phases of the consensus process. The consensus support system has a feedback mechanism to guide the discussion phases based on the proximity measure. This feedback mechanism is based on simple and easy rules to help experts change their opinions in order to obtain a degree of consensus as high as possible. The main improvement of this consensus model is that it supports consensus process automatically, without moderator, and, in such a way, the possible subjectivity that the moderator can introduce in the consensus process is avoided. [ABSTRACT FROM AUTHOR]

Presents linguistic hierarchies term sets, a type of multigranular linguistic contexts to address multi-expert decision-making (MEDM) problems to overcome the drawback of losing information in the normalization process. Fuzzy linguistic approach; 2-Tuple fuzzy linguistic representation model; Linguistic hierarchies; MEDM problem defined over a linguistic hierarchy.

Presents a study which discussed a process for designing fuzzy rule-based systems (FRBS) based on evolution strategy composed of a genetic algorithm to obtain the Knowledge Base. Guidelines on the design of the FRBS; Introduction of a coding to represent the FRBS; Structure of the FRBS; Results; Conclusions.

Seeks to model the processes of the aggregation of weighted information in a linguistic framework, while presenting three aggregation operators of weighted linguistic information. Identification of these operators; Detailed information on the axiomatics of these operators.

Scientific outreach and efforts to increase citizen participation have brought research closer to society and inspired new careers in STEM. In virtual events, it is now easier to monitor participation and get feedback through online surveys. However, in in-person events and crowded contexts, there is no technological solution to gather attendees' opinions on a given event. This is why we are presenting a flexible online linguistic Decision Support System based on a Citizen Crowd Decision Making model, which can be used in a wide range of scenarios. It is powered by a mobile application to collect citizen feedback and a web environment that publicizes the event and generates attendee evaluation reports. DIME+ is a Decision Support System and is intended for the organizers of outreach events, especially Citizen Science events. • We present an online linguistic decision support system based on a Citizen Crowd Decision Making model. • Evaluations consider importance levels, hesitant sets, opinion sentiment and linguistic hierarchies. • DIME+ is a native app to be used to gather citizen feedback at events. • DIME+ is also a web for the organizers of outreach events, especially Citizen Science. • Citizen Science, Science Fairs, and general events have an evaluation tool highly configurable. [ABSTRACT FROM AUTHOR]

Examines the role of proteasomes in eukaryotic cells. Degradation of ubiquitinated proteins; Composition of proteasomes; Influence of the Ubiquitin tagging in the stability of transcription factors.

Given the uncertainty in this regard, this paper analyzes the most appropriate monetary policy position for Mexico, which aims to maintain the inflation objective without negatively affecting product growth. The value of the neutral interest rate in Mexico for the next five years is estimated based on Taylor's rule and a co-integrated vector autoregression (VAR) model (CVAR). The results suggest that the monetary policy rate is above the neutral interest rate, which, combined with the potential international rate cut, could grant the central bank for almost the entire six-year period a degree of freedom to maintain its rate below the current level; in the short term the current monetary policy rate could be cut by at least 25 points without risking convergence to the inflation target. [ABSTRACT FROM AUTHOR]

pyDML is an open-source python library that provides a wide range of distance metric learning algorithms. Distance metric learning can be useful to improve similarity learning algorithms, such as the nearest neighbors classifier, and also has other applications, like dimensionality reduction. The pyDML package currently provides more than 20 algorithms, which can be categorized, according to their purpose, in: dimensionality reduction algorithms, algorithms to improve nearest neighbors or nearest centroids classifiers, information theory based algorithms or kernel based algorithms, among others. In addition, the library also provides some utilities for the visualization of classifier regions, parameter tuning and a stats website with the performance of the implemented algorithms. The package relies on the scipy ecosystem, it is fully compatible with scikit-learn, and is distributed under GPLv3 license. Source code and documentation can be found at https://github.com/jlsuarezdiaz/pyDML. [ABSTRACT FROM AUTHOR]

Foreign exchange (FOREX) market is a decentralized global marketplace in which different participants, such as international banks, companies or investors, can buy, sell, exchange and speculate on currencies. This market is considered to be the largest financial market in the world in terms of trading volume. Indeed, the just-in-time price prediction for a currency pair exchange rate (e.g., EUR/USD) provides valuable information for companies and investors as they can take different actions to improve their business. The trading volume in the FOREX market is huge, disperses, in continuous operations (24 h except weekends), and the context significantly affects the exchange rates. This paper introduces a context-aware algorithm to model the behavior of the FOREX Market, called parallel elastic network model (PENM). This algorithm is inspired by natural procedures like the behavior of macromolecules in dissolution. The main results of this work include the possibility to represent the market evolution of up to 21 currency pair, being all connected, thus emulating the real-world FOREX market behavior. Moreover, because the computational needs required are highly costly as the number of currency pairs increases, a hybrid parallelization using several shared memory and message passing algorithms studied on distributed cluster is evaluated to achieve a high-throughput algorithm that answers the real-time constraints of the FOREX market. The PENM is also compared with a vector autoregressive (VAR) model using both a classical statistical measure and a profitability measure. Specifically, the results indicate that PENM outperforms VAR models in terms of quality, achieving up to 930 × speed-up factor compared to traditional R codes using in this field. [ABSTRACT FROM AUTHOR]

• We discuss the consensus method which considers overconfidence behaviors in LSGDM. • A clustering method considering expert's self-confidence is presented. • A consensus measure considering expert's self-confidence is proposed. • The detection and management of overconfidence behaviors in LSGDM is analyzed. With the development of intelligent decision-making, one kind of decision modes involves a large number of decision-makers (DMs), which is called large scale group decision making (LSGDM). In LSGDM, overconfidence is one of the common behaviors because of many DMs' participation and the bounded rationality of human decision. Overconfidence usually has a negative impact on LSGDM and can even lead to failure in the final decision(s). To achieve consensus is very important for LSGDM. Different consensus models of LSGDM have been proposed, while the DMs' overconfidence behaviors in the consensus have not been concerned. Hence, the purpose of this paper is to propose a consensus model which considers overconfidence behaviors, and the paper mainly focuses on LSGDM based on fuzzy preference relations with self-confidence (FPRs-SC). In the proposed model, a DM clustering method, which combines fuzzy preference values similarity and self-confidence similarity, is used to classify the DMs with similar opinions into a subgroup. A group consensus index which considers both the fuzzy preference values and self-confidence is presented to measure the consensus level among DMs. An overconfidence measurement is given to detect the DMs' overconfidence behaviors in the consensus. Subsequently, the detailed overconfidence behavior analysis is presented involving two aspects: fuzzy preference values consensus and self-confidence consensus. A dynamic weight punishment mechanism is implemented for overconfident DMs to improve the consensus efficiently. The effectiveness and advantages of the presented consensus model are validated by a numerical example and comparative analysis. [ABSTRACT FROM AUTHOR]

Background: The metabolic activity of endogenous nitric oxide (NO) and the medical use of nitrovasodilatory drugs like isosorbide dinitrate have been shown to be potential inducers inducers of cervical ripening prior to surgical evacuation of the uterus. Objective: To assess the therapeutic efficacy and safety of combined isosorbide dinitrate-oxytocin in the management of intrauterine foetal death (IUFD). Methods: Sixty women with IUFD after 20 weeks of gestation requesting uterine evacuation were randomly selected to receive isosorbide dinitrate gel solution (80 mg/1.5 mL; n = 30) or misoprostol gel solution (100 mcg/1.5 mL; n = 30) every 3 h with a maximum of four doses or until a Bishop score >7 was reached. Subsequently, patients received a high dose of intravenous oxytocin until complete uterus evacuation was achieved. Therapeutic efficacy was evaluated by mean the relative risk of the foetal expulsion based on comparison of event rates, and the proportion of women induced to labor at 7, 10 and 15 h after the administration of isosorbide dinitrate or misoprostol. Safety was assessed on the basis of woman´s vital signs and evaluation of adverse effects, including headache, abdominal pain, pelvic pain, lower back pain, nausea, dizziness and vomiting. Results: The foetal expulsion rate using the isosorbide dinitrate-oxytocin combination was approximately 4.4 times, and at least 2.1 times, the foetal expulsion rate with the misoprostol-oxytocin regimen at any given point in time. The proportion of women achieved vaginal delivery at 15 hours was 100% for the isosorbide dinitrate-oxytocin group and 86.7% for the misoprostol-oxytocin group. The average delivery induction interval was significantly lower when isosorbide dinitrate-oxytocin was used (8.7 ± 3.1 h) than when misoprostol-oxytocin (11.9 ± 3.1 h) was used. A total of 20% of patients in the isosorbide dinitrate-oxytocin group recorded headache, and no cases of uterine tachysystole, haemorrhage or coagulopathy were recorded. Conclusion: This study indicates that intravaginal isosorbide dinitrate followed by intravenous oxytocin was more effective than the conventional method used to induce labour in the medical management of foetal death in pregnancies after 20 weeks of gestation. Trial registration: Clinicaltrials.gov NCT02488642. [ABSTRACT FROM AUTHOR]

No abstract received. [ABSTRACT FROM AUTHOR]

Discusses developments in the field of computational linguistics. Different aspects of the foundation of computing with words; Mechanisms of computing with words, propositions and linguistic evaluations; Role of computing with words in fuzzy logic; Extension of choquet integrals to ordinal scales.

Despite technological advances in the world, antimicrobial resistance against Mycobacterium tuberculosis represents a great threat to public health. Worldwide, 20% of tuberculosis cases are multidrug-resistant, and the number of patients continues to increase day after day. The successful treatment of tuberculosis has become a great challenge for global health according to the World Health Organization. This review aims to give a general overview of tuberculosis, including its history, generalities, epidemiology, and treatment, and to give a broader review of new treatment proposals, reviewing their sensitivity, specificity, and reduced numbers of side effects. PubMed, Google Scholar, Elsevier, and EBSCOhost were used to obtain the main articles describing the research conducted on new drugs to treat multidrug-resistant tuberculosis and extensively drug-resistant tuberculosis. The clinical trials for the new drugs used to treat extensively drug-resistant tuberculosis demonstrated good therapeutic regimens and compared the new drugs to identify which of them had more efficacy and less toxicity. The new drugs developed to treat extensively drugresistant tuberculosis that were effective in 85%-90% of patients were bedaquiline, pretomanid, and linezolid. [ABSTRACT FROM AUTHOR]

The majority of components used in aeronautic, automotive or transport industries are subjected to fatigue loads. Those elements should be designed considering the fatigue life. In this paper the geometrical design of a right angle bracket has been simulated by FEM, considering the type of material, the applied load and the geometry of the pieces. The geometry of the right angle bracket has been optimized using the Taguchi's robust optimization method. As far as the authors know, there are no publications dealing with the use of the Taguchi's robust optimization method applied to the geometric design of industrial pieces having as output variable the Findley fatigue coefficient. This paper presents a method for determining the critical design parameters to extend the product life of components subject to fatigue loading. This pioneer idea is applicable to any other design or physical or mechanical application so it can be widely used as new methodology to fatigue robust design of mechanical parts and components. [ABSTRACT FROM AUTHOR]

Background: The development of new ortholog detection algorithms and the improvement of existing ones are of major importance in functional genomics. We have previously introduced a successful supervised pairwise ortholog classification approach implemented in a big data platform that considered several pairwise protein features and the low ortholog pair ratios found between two annotated proteomes (Galpert, D et al., BioMed Research International, 2015). The supervised models were built and tested using a Saccharomycete yeast benchmark dataset proposed by Salichos and Rokas (2011). Despite several pairwise protein features being combined in a supervised big data approach; they all, to some extent were alignment-based features and the proposed algorithms were evaluated on a unique test set. Here, we aim to evaluate the impact of alignment-free features on the performance of supervised models implemented in the Spark big data platform for pairwise ortholog detection in several related yeast proteomes. Results: The Spark Random Forest and Decision Trees with oversampling and undersampling techniques, and built with only alignment-based similarity measures or combined with several alignment-free pairwise protein features showed the highest classification performance for ortholog detection in three yeast proteome pairs. Although such supervised approaches outperformed traditional methods, there were no significant differences between the exclusive use of alignment-based similarity measures and their combination with alignment-free features, even within the twilight zone of the studied proteomes. Just when alignment-based and alignment-free features were combined in Spark Decision Trees with imbalance management, a higher success rate (98.71%) within the twilight zone could be achieved for a yeast proteome pair that underwent a whole genome duplication. The feature selection study showed that alignment-based features were top-ranked for the best classifiers while the runners-up were alignment-free features related to amino acid composition. Conclusions: The incorporation of alignment-free features in supervised big data models did not significantly improve ortholog detection in yeast proteomes regarding the classification qualities achieved with just alignment-based similarity measures. However, the similarity of their classification performance to that of traditional ortholog detection methods encourages the evaluation of other alignment-free protein pair descriptors in future research. [ABSTRACT FROM AUTHOR]

To estimate the performance, efficacy, modes of operation, hydrodynamic instabilities, and methodologies for harnessing energy from the pump as turbine (PAT), it is necessary to characterize these hydraulic turbomachines in all their modes of operation using an experimental test rig that simulates actual operating conditions. In this process, the characteristic curve of four quadrants of a turbomachine is constructed, especially its five modes: pump mode, brake pump mode, turbine mode, brake turbine mode, and reverse pump mode. In addition, with dynamic sensors installed in the pump volute, it is possible to know the hydrodynamic phenomena within PAT, finding its average behavior and frequency response, which is a tool for diagnosing the machine's condition. [ABSTRACT FROM AUTHOR]

Federated learning is a machine learning paradigm that emerges as a solution to the privacy-preservation demands in artificial intelligence. As machine learning, federated learning is threatened by adversarial attacks against the integrity of the learning model and the privacy of data via a distributed approach to tackle local and global learning. This weak point is exacerbated by the inaccessibility of data in federated learning, which makes the protection against adversarial attacks harder and evidences the need to furtherance the research on defence methods to make federated learning a real solution for safeguarding data privacy. In this paper, we present an extensive review of the threats of federated learning, as well as as their corresponding countermeasures, attacks versus defences. This survey provides a taxonomy of adversarial attacks and a taxonomy of defence methods that depict a general picture of this vulnerability of federated learning and how to overcome it. Likewise, we expound guidelines for selecting the most adequate defence method according to the category of the adversarial attack. Besides, we carry out an extensive experimental study from which we draw further conclusions about the behaviour of attacks and defences and the guidelines for selecting the most adequate defence method according to the category of the adversarial attack. Finally, we present our learned lessons and challenges. • We claim that adversarial attacks are a significant challenge in federated learning. • We propose taxonomies of adversarial attacks and defences in federated learning. • We conduct a wide experimental study the results of which support our claim. • We define guidelines for selecting a proper defence method according to the attack. • We stand out a set of lessons learnt, open challenges and final conclusions. [ABSTRACT FROM AUTHOR]

In group decision making (GDM) dealing with Computing with Words (CW) has been highlighted the importance of the statement, words mean different things for different people , because of its influence in the final decision. Different proposals that either grouping such different meanings (uncertainty) to provide one representation for all people or use multi-granular linguistic term sets with the semantics of each granularity, have been developed and applied in the specialized literature. Despite these models are quite useful they do not model individually yet the different meanings of each person when he/she elicits linguistic information. Hence, in this paper a personalized individual semantics (PIS) model is proposed to personalize individual semantics by means of an interval numerical scale and the 2-tuple linguistic model. Specifically, a consistency-driven optimization-based model to obtain and represent the PIS is introduced. A new CW framework based on the 2-tuple linguistic model is then defined, such a CW framework allows us to deal with PIS to facilitate CW keeping the idea that words mean different things to different people. In order to justify the feasibility and validity of the PIS model, it is applied to solve linguistic GDM problems with a consensus reaching process. [ABSTRACT FROM AUTHOR]

This paper is aimed at developing a stochastic macroeconomic model useful to explain the effects of unexpected jumps in government spending per capita and in demographic variables on per capita economic growth. To do this, it is assumed that the stochastic dynamics of output and population are driven by mixed diffusion- jumps processes modulated by a Markov Chain. The most important results are that extreme sudden jumps in government spending per capita and in the population growth rate impact negatively the rate of per capita output growth. In particular, if there is a sudden sharp jump in the rate of population growth, then capital per capita suddenly decreases impacting negatively per capita output. Finally, the theoretical findings are supplemented with empirical evidence about the Mexican case obtained through a model that takes in account the volatility of the growth rates of the population and the per capita public expense as well as the surprises in the jump intensities of both variables. [ABSTRACT FROM AUTHOR]

Ubiquitin, the peptide ‘tag’ that targets eukaryotic proteins for degradation by the proteasome, has also been implicated in transcriptional activation. The mechanism of gene activation might include recruitment of a transcriptional elongation factor by ubiquitinated activators. [Copyright &y& Elsevier]

In large-scale group decision making (LSGDM), appropriate clustering analysis is important to consensus reaching since it can reduce the interactive complexity among individuals. According to the traditional clustering method, a conflict may arise between the consensus reaching levels and total adjustment costs within clusters when individuals have different unit adjustment cost, which reflects their willingness to make concessions. Since this conflict may aggravate the consensus complexity, we propose a new $K$ -means clustering method that considers both preferences and the preference adjustment cost. The preference adjustment cost is attached to preferences with a parameter that can be determined by balancing this conflict. Because of such conflict, the proposed clustering algorithm can improve the similarity of intracluster individuals on the preference adjustment cost by offsetting some acceptable consensus reaching levels within clusters. According to the proposed clustering algorithm, individuals who have both similar preferences and adjustment willingness are classified into the same clusters. In this way, the moderator can provide similar compensation strategies for intracluster individuals, which will decrease the adjustment complexity. A practical case study of team construction examines the application of the proposed algorithm, and the related comparative analysis shows that it is convenient for managers to persuade individuals to reach a consensus under the improved clustering results. [ABSTRACT FROM AUTHOR]

Orthology detection requires more effective scaling algorithms. In this paper, a set of gene pair features based on similarity measures (alignment scores, sequence length, gene membership to conserved regions, and physicochemical profiles) are combined in a supervised pairwise ortholog detection approach to improve effectiveness considering low ortholog ratios in relation to the possible pairwise comparison between two genomes. In this scenario, big data supervised classifiers managing imbalance between ortholog and nonortholog pair classes allow for an effective scaling solution built from two genomes and extended to other genome pairs. The supervised approach was compared with RBH, RSD, and OMA algorithms by using the following yeast genome pairs: Saccharomyces cerevisiae-Kluyveromyces lactis, Saccharomyces cerevisiae-Candida glabrata, and Saccharomyces cerevisiae-Schizosaccharomyces pombe as benchmark datasets. Because of the large amount of imbalanced data, the building and testing of the supervised model were only possible by using big data supervised classifiers managing imbalance. Evaluation metrics taking low ortholog ratios into account were applied. From the effectiveness perspective, MapReduce Random Oversampling combined with Spark SVM outperformed RBH, RSD, and OMA, probably because of the consideration of gene pair features beyond alignment similarities combined with the advances in big data supervised classification. [ABSTRACT FROM AUTHOR]

Understanding black box models has become paramount as systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper highlights the advancements in XAI and its application in real-world scenarios and addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts. We bring together experts from diverse fields to identify open problems, striving to synchronize research agendas and accelerate XAI in practical applications. By fostering collaborative discussion and interdisciplinary cooperation, we aim to propel XAI forward, contributing to its continued success. We aim to develop a comprehensive proposal for advancing XAI. To achieve this goal, we present a manifesto of 28 open problems categorized into nine categories. These challenges encapsulate the complexities and nuances of XAI and offer a road map for future research. For each problem, we provide promising research directions in the hope of harnessing the collective intelligence of interested stakeholders. [ABSTRACT FROM AUTHOR]

The latest Deep Learning (DL) models for detection and classification have achieved an unprecedented performance over classical machine learning algorithms. However, DL models are black-box methods hard to debug, interpret, and certify. DL alone cannot provide explanations that can be validated by a non technical audience such as end-users or domain experts. In contrast, symbolic AI systems that convert concepts into rules or symbols – such as knowledge graphs – are easier to explain. However, they present lower generalization and scaling capabilities. A very important challenge is to fuse DL representations with expert knowledge. One way to address this challenge, as well as the performance-explainability trade-off is by leveraging the best of both streams without obviating domain expert knowledge. In this paper, we tackle such problem by considering the symbolic knowledge is expressed in form of a domain expert knowledge graph. We present the eXplainable Neural-symbolic learning (X-NeSyL) methodology, designed to learn both symbolic and deep representations, together with an explainability metric to assess the level of alignment of machine and human expert explanations. The ultimate objective is to fuse DL representations with expert domain knowledge during the learning process so it serves as a sound basis for explainability. In particular, X-NeSyL methodology involves the concrete use of two notions of explanation, both at inference and training time respectively: (1) EXPLANet : Expert-aligned eXplainable Part-based cLAssifier NETwork Architecture, a compositional convolutional neural network that makes use of symbolic representations, and (2) SHAP-Backprop , an explainable AI-informed training procedure that corrects and guides the DL process to align with such symbolic representations in form of knowledge graphs. We showcase X-NeSyL methodology using MonuMAI dataset for monument facade image classification, and demonstrate that with our approach, it is possible to improve explainability at the same time as performance. • EXplainable Neural-symbolic Learning methodology fuses deep learning and symbolic representations. • EXPLANet's compositional part-based object detection and classification outperforms regular classification. • SHAP-Backprop aligns model output with expert knowledge in a knowledge graph. • SHAP Graph Edit Distance quantifies the alignment between a knowledge graph and neural representations. • X-NeSyL shows it is possible to improve over both explainability and performance. [ABSTRACT FROM AUTHOR]

In computing with words, it has been stressed that words mean different things for different people, which entails that decision makers (DMs) have personalized individual semantics (PISs) attached to linguistic expressions in linguistic group decision making (GDM). In particular, the PISs of DMs are not fixed, and they will be changing during the consensus building process, which indicates the necessary of continual PIS learning. Therefore, in this article, we propose a continual PIS-learning-based consensus approach in linguistic GDM. Specifically, a continual PIS learning model with the consistency-driven methodology is proposed to update the PISs taking into account all the linguistic preference data given by DMs during the consensus process. Then, the consensus measurement and feedback recommendation based on PIS are developed to detect the consensus process. Finally, numerical examples and simulation analysis are presented to illustrate and justify the use of the continual PIS-learning-based consensus approach. [ABSTRACT FROM AUTHOR]

Land use and land cover (LULC) mapping are of paramount importance to monitor and understand the structure and dynamics of the Earth system. One of the most promising ways to create accurate global LULC maps is by building good quality state-of-the-art machine learning models. Building such models requires large and global datasets of annotated time series of satellite images, which are not available yet. This paper presents TimeSpec4LULC 10.5281/zenodo.5913554; , a smart open-source global dataset of multispectral time series for 29 LULC classes ready to train machine learning models. TimeSpec4LULC was built based on the seven spectral bands of the MODIS sensors at 500 m resolution, from 2000 to 2021, and was annotated using spatial–temporal agreement across the 15 global LULC products available in Google Earth Engine (GEE). The 22-year monthly time series of the seven bands were created globally by (1) applying different spatial–temporal quality assessment filters on MODIS Terra and Aqua satellites; (2) aggregating their original 8 d temporal granularity into monthly composites; (3) merging Terra + Aqua data into a combined time series; and (4) extracting, at the pixel level, 6 076 531 time series of size 262 for the seven bands along with a set of metadata: geographic coordinates, country and departmental divisions, spatial–temporal consistency across LULC products, temporal data availability, and the global human modification index. A balanced subset of the original dataset was also provided by selecting 1000 evenly distributed samples from each class such that they are representative of the entire globe. To assess the annotation quality of the dataset, a sample of pixels, evenly distributed around the world from each LULC class, was selected and validated by experts using very high resolution images from both Google Earth and Bing Maps imagery. This smartly, pre-processed, and annotated dataset is targeted towards scientific users interested in developing various machine learning models, including deep learning networks, to perform global LULC mapping. [ABSTRACT FROM AUTHOR]

Image segmentation is an important issue in many industrial processes, with high potential to enhance the manufacturing process derived from raw material imaging. For example, metal phases contained in microstructures yield information on the physical properties of the steel. Existing prior literature has been devoted to develop specific computer vision techniques able to tackle a single problem involving a particular type of metallographic image. However, the field lacks a comprehensive tutorial on the different types of techniques, methodologies, their generalizations and the algorithms that can be applied in each scenario. This paper aims to fill this gap. First, the typologies of computer vision techniques to perform the segmentation of metallographic images are reviewed and categorized in a taxonomy. Second, the potential utilization of pixel similarity is discussed by introducing novel deep learning-based ensemble techniques that exploit this information. Third, a thorough comparison of the reviewed techniques is carried out in two openly available real-world datasets, one of them being a newly published dataset directly provided by ArcelorMittal, which opens up the discussion on the strengths and weaknesses of each technique and the appropriate application framework for each one. Finally, the open challenges in the topic are discussed, aiming to provide guidance in future research to cover the existing gaps. [Display omitted] • We create a metallography dataset from additive manufacturing of steels (MetalDAM). • We provide an updated taxonomy of segmentation methods. • We propose a new DL-based ensemble specialized in the semantic segmentation task. • We compare state-of-the-art models and the new ensembles with UHCS and MetalDAM. • We present a thorough analysis of the current difficulties and challenges. [ABSTRACT FROM AUTHOR]

Most applications of Artificial Intelligence (AI) are designed for a confined and specific task. However, there are many scenarios that call for a more general AI, capable of solving a wide array of tasks without being specifically designed for them. The term General Purpose Artificial Intelligence Systems (GPAIS) has been defined to refer to these AI systems. To date, the possibility of an Artificial General Intelligence, powerful enough to perform any intellectual task as if it were human, or even improve it, has remained an aspiration, fiction, and considered a risk for our society. Whilst we might still be far from achieving that, GPAIS is a reality and sitting at the forefront of AI research. This work discusses existing definitions for GPAIS and proposes a new definition that allows for a gradual differentiation among types of GPAIS according to their properties and limitations. We distinguish between closed-world and open-world GPAIS, characterising their degree of autonomy and ability based on several factors such as adaptation to new tasks, competence in domains not intentionally trained for, ability to learn from few data, or proactive acknowledgement of their own limitations. We then propose a taxonomy of approaches to realise GPAIS, describing research trends such as the use of AI techniques to improve another AI (commonly referred to as AI-powered AI) or (single) foundation models. As a prime example, we delve into generative AI (GenAI), aligning them with the terms and concepts presented in the taxonomy. Similarly, we explore the challenges and prospects of multi-modality, which involves fusing various types of data sources to expand the capabilities of GPAIS. Through the proposed definition and taxonomy, our aim is to facilitate research collaboration across different areas that are tackling general purpose tasks, as they share many common aspects. Finally, with the goal of providing a holistic view of GPAIS, we discuss the current state of GPAIS, its prospects, implications for our society, and the need for regulation and governance of GPAIS to ensure their responsible and trustworthy development. • Presents a holistic view of General Purpose Artificial Intelligence Systems. • Consolidates a definition for general-purpose AI based on their degree of autonomy. • Breaking it down onto a taxonomy of approaches to build general-purpose AI systems. • A close look into Generative AI as a prime example of general-purpose AI systems. • Discusses open challenges, implications for our society and need for regulation. [ABSTRACT FROM AUTHOR]

Experts from different domains have resorted to machine learning techniques to produce explainable models that support decision-making. Among existing techniques, decision trees have been useful in many application domains for classification. Decision trees can make decisions in a language that is closer to that of the experts. Many researchers have attempted to create better decision tree models by improving the components of the induction algorithm. One of the main components that have been studied and improved is the evaluation measure for candidate splits. In this article, we introduce a tutorial that explains decision tree induction. Then, we present an experimental framework to assess the performance of 21 evaluation measures that produce different C4.5 variants considering 110 databases, two performance measures, and 10× 10-fold cross-validation. Furthermore, we compare and rank the evaluation measures by using a Bayesian statistical analysis. From our experimental results, we present the first two performance rankings in the literature of C4.5 variants. Moreover, we organize the evaluation measures into two groups according to their performance. Finally, we introduce meta-models that automatically determine the group of evaluation measures to produce a C4.5 variant for a new database and some further opportunities for decision tree models. [ABSTRACT FROM AUTHOR]

• A democratic consensus reaching process (DCRP) for MpMcLSDM is proposed. • A clustering method that considers both the opinion similarity and self-concern similarity of DM is presented. • A consensus contribution-based dynamic interactive weight updating method is given. • A compromise degree-based consensus feedback strategy is developed. Consensus reaching is a key issue in group decision-making, because conflicts of interest among groups are common. Democratic consensus refers to achieve a soft consensus among collective as well as ensure the effective participation and satisfaction of individuals. Multi-person multi-criteria large scale decision making (MpMcLSDM) usually involves a huge number of decision makers (DMs/participants), and different DMs usually have different interests. Thus, how to effectively manage individuals to promote democratic consensus is a current research challenge. To do that, this research develops a democratic consensus reaching process (DCRP) for MpMcLSDM problems. In the proposed approach, a clustering method that considers both the opinion similarity and individual concern similarity of DM is firstly given to decrease the complexity of MpMcLSDM issues. Subsequently, we propose to assign equal initial weight to each cluster to protect the interests of minorities. Meanwhile, a consensus contribution-based dynamic interactive weight updating method is implemented in the DCRPs to promote a high level of democratic consensus. Besides, a compromise degree-based consensus feedback strategy is developed to improve the efficiency of the DCRPs. The proposed feedback mechanism effectively considers the individual concern and adjustment willingness of DMs in the DCRPs. Finally, a case study and some comparisons are given to show the effectiveness and innovation of this research. [ABSTRACT FROM AUTHOR]

Nearest prototype methods are a successful trend of many pattern classification tasks. However, they present several shortcomings such as time response, noise sensitivity, and storage requirements. Data reduction techniques are suitable to alleviate these drawbacks. Prototype generation is an appropriate process for data reduction, which allows the fitting of a dataset for nearest neighbor (NN) classification. This brief presents a methodology to learn iteratively the positioning of prototypes using real parameter optimization procedures. Concretely, we propose an iterative prototype adjustment technique based on differential evolution. The results obtained are contrasted with nonparametric statistical tests and show that our proposal consistently outperforms previously proposed methods, thus becoming a suitable tool in the task of enhancing the performance of the NN classifier. [ABSTRACT FROM AUTHOR]