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Datos de caracteres para el análisis morfométrico de especies de interés taxonómico de los géneros Lactuca y Sonchus en la Península Ibérica.

This paper investigates the influence of a constant carrier phase shift on the DC-link capacitor harmonic current of cascaded converters used in fuel-cell and mild-hybrid electric vehicles. In these applications, a DC-DC converter can be adopted between the battery and the motor drive inverter in a cascaded structure, where the two converters share the same DC-link. Since the DC-link capacitor of such a system represents a critical component, the optimization of the converter operation to limit the current stress and extend the lifetime of the capacitor is an primary objective. This paper proposes the use of a carrier phase shift between the modulations of the two converters in order to minimize the harmonic current of the DC-link capacitor. By harmonic analysis, an optimal carrier phase shift can be derived depending on the converter configuration. Analytical results are presented and validated by hardware-in-the-loop experiments. The findings show that the pulse width modulation carrier phase shift between the interleaved boost converter and the voltage source motor drive inverter has a significant influence on the DC-link capacitor current and thus on its lifetime. A case study with two-cell and three-cell interleaved boost converters shows a possible DC-link capacitor lifetime extension of up to 390%.

Organ biobanking is the pending subject of cryopreservation. Although the problem is multifaceted, advances in recent decades have largely related it to achieving rapid and uniform rewarming of cryopreserved samples. This is a physical challenge largely investigated in past in addition to cryoprotectant toxicity studies, which have also shown a great amount of advancement. This paper presents a proof-of-principle, based on the nematode Caenorhabditis elegans, of a technology capable of performing such a function: high intensity focused ultrasound. Thus, avoiding the problem of recrystallization, this worm, in its adult state, preserved at − 80∘C , has been systematically brought back to life after being heated with High Intensity Focused Ultrasound (HIFU) waves. The great advantage of this technology is that it is scalable; in addition, rewarming can be monitored in real time by MRI thermography and can be controlled by acoustic interferometry. We anticipate that our findings are the starting point for a possible approach to rewarming that can be used for cryopreservation of millimeter scale systems: either alone or in combination with other promising ways of heating, like nanowarming or dielectric heating, the present technology provides new ways of solving the physical aspects of the problem of recrystallization in cryopreservation, opening the door for the long-term storage of larger samples.

This paper deals with the determination of optimal locations for pipelines and cable trays in naval design. The problem consists of finding the number and types of cable tray routes to be created between various devices in order to minimize a user defined cost function. We reduce the problem to an ad hoc min-cost multicommodity flow problem with additional constraints imposed by technical requirements. This problem is solved for small-sized instances by using off-the-shelf optimization solvers. We also develop an exact relax-and-cut strategy that allows to handle medium-sized instances. For larger instances, we propose a family of heuristic algorithms consisting on the combination of two phases: (I) Construction of initial cable trays paths; and (II) Transformation to feasible cable trays verifying the technical requirements. For each of them, we also propose different strategies which give rise to several algorithms. These algorithms are compared on a computational experience using two types of instances: the first one based on random instances of different sizes and the second one based on instances with well-defined corridors to asses the availability of our methodology to enforce the creation of cable trays. Finally, we also analyze a real size case study provided by our industrial partner, Ghenova, a leading Naval Engineering company, validating our proposal to find solutions for this problem.

This paper introduces the hub location problem under interhub link failures, a hub location problem in which activated interhub links may fail with a given probability. Two different optimization models are studied, which construct hub backbone networks protected under interhub link disruptions by imposing that, for each commodity, an addi-tional routing path exists besides its original routing path. Both models consider the minimi-zation of the fixed costs of the activated hubs and interhub links plus the expected value of the routing costs of the original and alternative paths. The first model builds explicitly the alternative routing paths, whereas the second model guarantees that, for each commodity, at least one alternative path exists using a large set of connectivity constraints although the alternative paths are not built explicitly. The results of extensive computational testing allow us to analyze the performance of the two proposed models and to evaluate the extra cost required to design a robust backbone network under interhub link failures. The obtained results support the validity of the proposal.

In this paper we propose a general methodology for the optimal automatic routing of spatial pipelines motivated by a recent collaboration with Ghenova, a leading Naval Engineering company. We provide a minimum cost multicommodity network flow based model for the problem incorporating all the tech- nical requirements for a feasible pipeline routing. A branch-and-cut approach is designed and different matheuristic algorithms are derived for solving efficiently the problem. We report the results of a battery of computational experiments to assess the problem performance as well as a case study of a real-world naval instance provided by our partner company.

Blockchain is a disruptive technology that has attracted the attention of the scientific community and compa nies, as proven by the exponential growth of publications on this topic in recent years. This growing interest is mainly due to the promise that the use of blockchain enables it to be verified, without including any trusted intermediaries, that the information received from the network is authentic and up-to-date. In this respect, blockchain is a distributed database that can be seen as a ledger that records all transactions that have ever been executed. In this context, smart contracts are pieces of software used to facilitate, verify, and enforce the negotiation of a transaction on a blockchain platform. These pieces of software are implemented by using programming languages, which are sometimes provided by the blockchain platforms themselves. This study aims to (1) identify and categorise the state-of-the-art related to smart contract languages, in terms of the existing languages and their main features, and (2) identify new research opportunities. The review has been conducted as a multivocal mapping study that followsthe guidelines proposed by Garousi et al. for conducting multivocal literature reviews, as well as the guidelines proposed by Kitchenham and Charters for conducting mapping studies. As a result of the implementation of the review protocol, 4,119 papers were gathered, and 109 of them were selected for extraction. The contributions of this article are twofold: (1) 101 different smart contract languages have been identified and classified according to a variety of criteria; (2) a discussion on the findings and their implications for future research have been outlined. As a conclusion, it could be stated that a rigorous and replicable overview of the state-of-the-art of smart contract languages has been provided that can benefit not only researchers but also practitioners in the field, thanks to its multivocal nature.

Variability models are used to build configurators, for guiding users through the configuration process to reach the desired setting that fulfils user requirements. The same variability model can be used to design different configurators employing different techniques. One of the design options that can change in a configurator is the configuration workflow, i.e., the order and sequence in which the different configuration elements are presented to the configuration stakeholders. When developing a configurator, a challenge is to decide the configuration workflow that better suits stakeholders according to previous configurations. For example, when configuring a Linux distribution the configuration process starts by choosing the network or the graphic card and then, other packages concerning a given sequence. In this paper, we present COnfiguration workfLOw proceSS mIning (COLOSSI), a framework that can automatically assist determining the configuration workflow that better fits the configuration logs generated by user activities given a set of logs of previous configurations and a variability model. COLOSSI is based on process discovery, commonly used in the process mining area, with an adaptation to configuration contexts. Derived from the possible complexity of both logs and the discovered processes, often, it is necessary to divide the traces into small ones. This provides an easier configuration workflow to be understood and followed by the user during the configuration process. In this paper, we apply and compare four different techniques for the traces clustering: greedy, backtracking, genetic and hierarchical algorithms. Our proposal is validated in three different scenarios, to show its feasibility, an ERP configuration, a Smart Farming, and a Computer Configuration. Furthermore, we open the door to new applications of process mining techniques in different areas of software product line engineering along with the necessity to apply clustering techniques for

Wireless sensor networks (WSNs), as an integral part of most Internet of Things (IoT) devices, are currently proliferating providing a new paradigm of emerging technologies. It is estimated that the number of globally connected products will increase exponentially in the next decade. Therefore, it is not surprising finding applications in different areas such as smart homes, smart cities, industry, e-health, defense, security, vehicle networks, agriculture, and logistics among others. WSNs transmit the information gathered by the existing sensors in the IoT devices to header nodes acting as gateways to reach cloud computing nodes that will process this information. The processing of the data gathered through the sensors has made it possible to provide intelligence in strategical environments such as in the agrifood sector. Securing how this information is transmitted and assuring the integrity of the information is preserved. The use of blockchain technology has proven to be effective for securing the integrity of data transactions among entities. In this paper, we seize the advantage of this circumstance to design a robust mechanism based on smart contracts and blockchain technology that allow the reliable processing of data.

Dado que la actividad ejercida por los cuidadores no profesionales de personas dependientes no se considera una labor profesional, con la consiguiente carencia de tutela a nivel socio-laboral, el presente estudio establece que el Pilar Europeo de Derechos Sociales puede servir como base para avanzar en la configuración de un marco jurídico que regularice su situación y la sitúe en el contexto del trabajo “digno”. Precisamente aquellos que no realizan otra actividad profesional son los que se encuentran en mayor nivel de desprotección, y los que demandan que se efectúen medidas legislativas concretas a nivel comunitario y estatal, actualmente reducida a aisladas actuaciones de las Comunidades Autónomas o la exigua cobertura de un Convenio Especial con la Seguridad Social. Por otra parte, se precisa adaptar nuestro ordenamiento jurídico a las resoluciones de los Tribunales que están detectando normas con impacto discriminatorio para las mujeres que ejercen las labores de cuidado, o irregularidades que sólo redundan en la desprotección de los cuidadores como el pago de la prestación de atención a la dependencia a la persona dependiente., Given that the activity carried out by non-professional caregivers of dependent people is not considered a professional work, with the consequent lack of protection at the socio-labor level, the studyaims to establishthat the European Pillar of Social Rights can serve as a basis to advance in the configuration of a legal frame work that regularizes their situation and places it in the context of “decent” work. Precisely those who do not carry out any other professional activity are thos ewho are in the highest level of vulnerability, and those who demand that specific legislative measures be carried out at the community and state level, currently reduced to isolated actions of the Autonomous Communities or the meager coverage of a Special Agreementwith Social Security. On the other hand, it is necessary to adapt our legal system to the decisions of the Courts that are detecting norms with a discriminatory impact on women who perform carework, or irregularities that only result in the lack of protection of caregivers such as payment of the benefit Dependency care to the dependent person.

Business process analytics and verification have become a major challenge for companies, especially when process data is stored across different systems. It is important to ensure Business Process Compliance in both data-flow perspectives and business rules that govern the organisation. In the verification of data-flow accuracy, the conformance of data to business rules is a key element, since essential to fulfil policies and statements that govern corporate behaviour. The inclusion of business rules in an existing and already deployed process, which therefore already counts on stored data, requires the checking of business rules against data to guarantee compliance. If inconsistency is detected then the source of the problem should be determined, by discerning whether it is due to an erroneous rule or to erroneous data. To automate this, a diagnosis methodology following the incorporation of business rules is proposed, which simultaneously combines business rules and data produced during the execution of the company processes. Due to the high number of possible explanations of faults (data and/or business rules), the likelihood of faults has been included to propose an ordered list. In order to reduce these possibilities, we rely on the ranking calculated by means of an AHP (Analytic Hierarchy Process) and incorporate the experience described by users and/or experts. The methodology proposed is based on the Constraint Programming paradigm which is evaluated using a real example. .

Business Processes facilitate the execution of a set of activities to achieve the strategic plans of a company. During the execution of a business process model, several decisions can be made that frequently involve the values of the input data of certain activities. The decision regarding the value of these input data concerns not only the correct execution of the business process in terms of consistency, but also the compliance with the strategic plans of the company. Smart decision-support systems provide information by analyzing the process model and the business rules to be satisfied, but other elements, such as the previous temporal variation of the data during the former executed instances of similar processes, can also be employed to guide the input data decisions at instantiation time. Our proposal consists of learning the evolution patterns of the temporal variation of the data values in a process model extracted from previous process instances by applying Constraint Programming techniques. The knowledge obtained is applied in a Decision Support System (DSS) which helps in the maintenance of the alignment of the process execution with the organizational strategic plans, through a framework and a methodology. Finally, to present a proof of concept, the proposal has been applied to a complete case study.

Conformance checking is one of the disciplines that best exposes the power of process mining, since it allows detecting anomalies and deviations in business processes, helping to assess and improve the quality of these. This is an indispensable task, especially in Big Data environments where large amounts of data are generated, and where the complexity of the processes is increasing. CC4Spark enables com panies to face this challenging scenario in twofold. First, it supports distributing conformance checking alignment problems by means of a Big Data infrastructure based on Apache Spark, allowing users to im port, transform and prepare event logs stored in distributed data sources, and solve them in a distributed environment. Secondly, this tool supports decomposed Petri nets. This helps to noticeably reduce the complexity of the models. Both characteristics help companies in facing increasingly frequent scenar ios with large amounts of logs with highly complex business processes. CC4Spark is not tied to any particular conformance checking algorithm, so that users can employ customised algorithms.

La aparición del nuevo título de Formación Profesional, Curso de Especialización en “Ciberseguridad en entornos de tecnologías de la información”, establece el punto de partida sobre los conocimientos del profesorado en cuestiones de Ciberseguridad. Para ello hemos realizado un estudio sobre los conocimientos del profesorado que imparte docencia en la formación profesional, basado en la propuesta curricular en ciberseguridad de la guías ACM/IEEE/AIS SIGSEC/IFIP Cybersecurity, con el objetivo de proponer un itinerario formativo en Ciberseguridad para el profesorado, de forma que estén en disposición de ofrecer una mejor respuesta y mayor calidad ante el proceso de formación de los profesionales del futuro en dicha materia. En primer lugar hemos desarrollado un estudio basado cuestionarios, a través del cual se han presentado las unidades de conocimiento en materia de Ciberseguridad al profesorado de Andalucía y sobre las que han realizado la valoración de su conocimiento en dicha materia. Se presenta un análisis cuantitativo de los resultados obtenidos priorizando las necesidades formativas. Como conclusión de nuestro estudio hemos propuesto la elaboración de un itinerario formativo para el profesorado basado en las diez unidades de conocimiento.

Current Internet of Things (IoT) scenarios have to deal with many challenges especially when a large amount of heterogeneous data sources are integrated, that is, data curation. In this respect, the use of poor-quality data (i.e., data with problems) can produce terrible consequence from incorrect decision-making to damaging the performance in the operations. Therefore, using data with an acceptable level of usability has become essential to achieve success. In this article, we propose an IoT-big data pipeline architecture that enables data acqui sition and data curation in any IoT context. We have customized the pipeline by including the DMN4DQ approach to enable us the measuring and evaluat ing data quality in the data produced by IoT sensors. Further, we have chosen a real dataset from sensors in an agricultural IoT context and we have defined a decision model to enable us the automatic measuring and assessing of the data quality with regard to the usability of the data in the context

Conformance checking unleashes the full power of process mining: techniques from this discipline enable the analysis of the quality of a process model through the discovery of event data, the identification of potential deviations, and the projection of real traces onto process models. In this way, the insights gained from the available event data can be transferred to a richer conceptual level, amenable for human interpretation. Unfortunately, most of the aforementioned functionalities are grounded in an extremely difficult fundamental problem: given an observed trace and a process model, find the model trace that most closely resembles to the trace observed. This paper presents an architecture that supports the creation and distribution of alignment subproblems based on an innovative horizontal acyclic model decomposition, disengaged from the conformance checking algorithm applied for their solution. This is supported by a Big Data infrastructure that facilitates the customised distribution of a gross amount of data. Experiments are provided that testify to the enormous potential of the architecture proposed, thereby opening the door to further research in several directions.

To succeed in their business processes, organizations need data that not only attains suitable levels of quality for the task at hand, but that can also be considered as usable for the business. However, many researchers ground the potential usability of the data on its quality. Organizations would benefit from receiving recommendations on the usability of the data before its use. We propose that the recommendation on the usability of the data be supported by a decision process, which includes a context-dependent data-quality assessment based on business rules. Ideally, this recommendation would be generated automatically. Decision Model and Notation (DMN) enables the assessment of data quality based on the evaluation of business rules, and also, provides stakeholders (e.g., data stewards) with sound support for the automation of the whole process of generation of a recommendation regarding usability based on data quality. The main contribution of the proposal involves designing and enabling both DMN-driven mechanisms and a guiding methodology (DMN4DQ) to support the automatic generation of a decision-based recommendation on the potential usability of a data record in terms of its level of data quality. Furthermore, the validation of the proposal is performed through the application of a real dataset.

Data centres have become the backbone of large Cloud services and applica-tions, providing virtually unlimited elastic and scalable computational and storage resources. The search for the efficiency and optimisation of resources is one of the current key aspects for large Cloud Service Providers and is becoming more and more challenging, since new computing paradigms such as Internet of Things, Cyber-Physical Systems and Edge Computing are spreading. One of the key aspects to achieve efficiency in data centres consists of the discovery and proper analysis of the data-centre behaviour. In this paper, we present a model to automatically retrieve execution workflows of existing data-centre logs by employing process mining tech-niques. The discovered processes are characterised and analysed according to the understandability and complexity in terms of execution efficiency of data-centre jobs. We finally validate and demonstrate the usability of the proposal by applying the model in a real scenario, that is, the Google Cluster traces

Business process discovery provides mechanisms to extract the general process behaviour from event observations. However, not always the logs are available and must be extracted from repositories, such as relational databases. Derived from the references that exist between the relational tables, several are the possible combinations of traces of events that can be extracted from a relational database. Dif ferent traces can be extracted depending on which attribute represents the case−id, what are the attributes that represent the execution of an activity, or how to obtain the timestamp to define the order of the events. This paper proposes a method to analyse a wide range of possible traces that could be extracted from a relational database, based on measuring the level of interest of extracting a trace log, later used for a discov ery process. The analysis is done by means of a set of proposed metrics before the traces are generated and the process is discovered. This anal ysis helps to reduce the computational cost of process discovery. For a possible case−id every possible traces are analysed and measured. To validate our proposal, we have used a real relational database, where the detection of processes (most and least promising) are compared to rely on our proposal.

The application of the optimisation problems in the daily decisions of companies is able to be used for finding the best management according to the necessities of the organisations. However, optimisation problems imply a high computational complexity, increased by the current necessity to include a mas sive quantity of data (Big Data), for the creation of optimisation problems to customise products and services for their clients. The irruption of Big Data technologies can be a challenge but also an impor tant mechanism to tackle the computational difficulties of optimisation problems, and the possibility to distribute the problem performance. In this paper, we propose a solution that lets the query of a data set supported by Big Data technologies that imply the resolution of Constraint Optimisation Problem (COP). This proposal enables to: (1) model COPs whose input data are obtained from distributed and heterogeneous data; (2) facilitate the integration of different data sources to create the COPs; and, (3) solve the optimisation problems in a distributed way, to improve the performance. It is done by means of a framework and supported by a tool capable of modelling, solving and querying the results of opti misation problems. The tool integrates the Big Data technologies and commercial solvers of constraint programming. The suitability of the proposal and the development have been evaluated with real data sets whose computational study and results are included and discussed

In this article, we report on our experience regarding devising, implementing, and deploying a scheduler for multi- source fusion in the context of SCADA systems (Supervisory Control and Data Acquisition). They are challenging because they commonly rely on low-end boards with very limited computing, memory, and storage capabilities, but have to run hundreds if not thousands of agents that co-ordinate by means of complex multi-way rendez- vouses. Our work was carried out in the context of a solar plant in which we could easily confirm that not scheduling the rendez-vouses fairly may easily drive the system into as many as 3 779.10 critical-failure states per hour, whereas a straightforward solution to the problem can reduce the figure to 1 094.76 critical-failure states per hour. Unfortunately, that is far from zero, which is the ideal number. In the literature, there are several proposals to deal with this problem, but most of them could not be adapted to our context, namely: some of them can deal with two-way rendez-vouses only, whereas ours involve an average of 12.89 agents; others require to instrument the agents, but many of them are hardware devices that cannot be modified; a few others cannot work with rendez-vouses that can get intermittently enabled and disabled along an execution, which makes them of little interest in our context; and some require to use shared memory, which is an advanced hardware feature that is not supported by our low-end computing boards. The two proposals that we managed to adapt were not efficient enough in our context since they led to an average of 1 102.77 and 1 458.65 critical failure states per hour, respectively. That motivated us to work on a new proposal that does not have any of the previous problems. It relies on a incremental approach that was implemented very efficiently using bounded counters and queues. Furthermore, the experimental results and the corresponding statistical analysis confirm that it works very well in

The proliferation of Cyber-Physical Systems (CPSs) is rais ing serious security challenges. These are complex systems, integrating physical elements into automated networked systems, often containing a variety of devices, such as sensors and actuators, and requiring complex management and data storage. This makes the construction of secure CPSs a challenge, requiring not only an adequate specification of secu rity requirements and needs related to the business domain but also an adaptation and concretion of these requirements to define a security configuration of the CPS where all its components are related. Derived from the complexity of the CPS, their configurations can be incorrect according to the requirements, and must be verified. In this paper, we propose a grammar for specifying business domain security requirements based on the CPS components. This will allow the definition of security requirements that, through a defined security feature model, will result in a configuration of services and security properties of the CPS, whose correctness can be verified. For this last stage, we have created a cata logue of feature models supported by a tool that allows the automatic verification of security configurations. To illustrate the results, the pro posal has been applied to automated verification of requirements in a hydroponic system scenario.

Business processes read and write data objects, usually stored in databases. Although data models and activity-oriented business process models originate from different paradigms, they need to work together properly. The data object states are transformed during each process instance by the activities of the process model. It is therefore necessary to verify whether the states of the data objects are correct according to the process model, and to discoverthe states ofthe stored data objects.Thisimplies determining the relation between the data objects stored in the database, the data objects involved in the process, and the activities that within the business processthat create the data objects and modify theirstates. In order to verify the business process annotated with data states and to reduce the existing gap between data model and business process model, we propose a methodology that includes enlarging the capability to describe data states in business processes; verifying the completeness and consistency of the data states described in accordance with their relation to the business process model; and discovering the states of the data objects stored in the database according to the business process model where they are managed. The methodology is supported by a framework that enables a natural-like language to be employed to describe the states, to apply the necessary algorithms to verify the consistency and completeness of the model, and to determine the states of the stored data objects according to the model described. To validate our proposal, an extension of ActivitiT M has been implemented and applied to a real example as an illustration of its applicability

En el presente artículo se abordan las estructuras que integran el conjunto de acceso oriental del yacimiento ibero-romano de Torreparedones (Baena, Córdoba), una puerta urbana fortificada construida hacia la segunda mitad del siglo I a.C. El estudio arquitectónico de los restos conservados y su interpretación, partiendo de una serie de paralelos peninsulares y mediterráneos, permite comprender el funcionamiento de la puerta y su sistema de control de paso. Se identifica así un acceso de tipo “patio” en el que las torres y, probablemente, las máquinas de artillería jugaron un importante papel en la defensa de la ciudad., This paper analyses the eastern gate structures of the Ibero-Roman site of Torreparedones (Baena, Córdoba), a fortified urban gate built around the second half of the first century b.C. The architectural study of the remains and their interpretation - considering Mediterranean and Peninsular parallels - allows us to understand how this gate system worked. Thus, it can identified as a “courtyard gate”, where towers and, possibly, artillery played an important role in the city’s defence.

Data Quality assessment is aimed at evaluating the suitability of a dataset for an intended task. The extensive literature on data quality describes the various methodologies for assessing data quality by means of data profiling techniques of the whole datasets. Our investigations are aimed to provide solutions to the need of automatically assessing the level of quality of the records of a dataset, where data profiling tools do not provide an adequate level of information. As most of the times, it is easier to describe when a record has quality enough than calculating a qualitative indicator, we propose a semi-automatically business rule-guided data quality assessment methodology for every record. This involves first listing the business rules that describe the data (data requirements), then those describing how to produce measures (business rules for data quality measurements), and finally, those defining how to assess the level of data quality of a data set (business rules for data quality assessment). The main contribution of this paper is the adoption of the OMG standard DMN (Decision Model and Notation) to support the data quality requirement description and their automatic assessment by using the existing DMN engines.

Cybersecurity attacks affect the compliance of cybersecurity policies of the organisations. Such disadvantages may be due to the absence of security configurations or the use of default configuration values of software products and systems. The complexity in the configuration of products and systems is a known challenge in the software industry since it includes a wide range of parameters to be taken into account. In other contexts, the configuration problems are solved using Software Product Lines. This is the reason why in this article the framework Cybersecurity Software Product Line (CyberSPL) is proposed. CyberSPL is based on a methodology to design product lines to verify cybersecurity policies according to the possible configurations. The patterns to configure the systems related to the cybersecurity aspects are grouped by defining various feature models. The automated analysis of these models allows us to diagnose possible problems in the security configurations, reducing or avoiding them. As support for this proposal, a multi-user and multi-platform solution has been implemented, enabling setting a catalogue of public or private feature models. Moreover, analysis and reasoning mechanisms have been integrated to obtain all the configurations of a model, to detect if a configuration is valid or not, including the root cause of problems for a given configuration. For validating the proposal, a real scenario is proposed where a catalogue of four different feature models is presented. In this scenario, the models have been analysed, different configurations have been validated, and several configurations with problems have been diagnosed.

Organizations execute daily activities to meet their objectives. The performance of these activities can be fundamental for achieving a business objective, but they also imply the assumption of certain security risks that might go against a company's security policies. A risk may be de ned as the effects of uncertainty on the achievement of the goals of a company, some of which can be associated with security aspects (e.g., data corruption or data leakage). The execution of the activities can be choreographed using business processes models, in which the risk of the entire business process model derives from a combination of the single activity risks (executed in an isolated manner). In this paper, a risk assessment method is proposed to enable the analysis and evaluation of a set of activities combined in a business process model to ascertain whether the model conforms to the security-risk objectives. To achieve this objective, we use a business process extension with security-risk information to: 1) de ne an algorithm to verify the level of risk of process models; 2) design an algorithm to diagnose the risk of the activities that fail to conform to the level of risk established in security-risk objectives; and 3) the implementation of a tool that supports the described proposal. In addition, a real case study is presented, and a set of scalability benchmarks of performance analysis is carried out in order to check the usefulness and suitability of automation of the algorithms.

El objetivo de este trabajo es estudiar el tráfico de productos turdetanos en la costa atlántica de la península ibérica a partir de la distribución de sus contenedores anfóricos y el papel de la Gadir púnica como interlocutor comercial. Aunque ya se conocían la mayor parte de los conjuntos de forma aislada, tratamos de sistematizar toda la evidencia arqueológica disponible para analizarla tanto en sentido geográfico como cronológico, poniéndolas en relación con otras producciones coetáneas, tanto importadas como locales. Para ello se ha recurrido a una exhaustiva pesquisa bibliográfica pero, sobre todo, se ha llevado a cabo un examen directo de buena parte de los conjuntos analizados en los depósitos de distintos centros de investigación, gabinetes municipales y museos. Las conclusiones tratan de valorar el alcance de las mercancías turdetanas en los mercados atlánticos, desde el estuario del Guadiana hasta la costa noroccidental de la península, y los mecanismos de distribución, en relación con las distintas coyunturas históricas., The aim of this work is to analyse the traffic of Turdetanian products in the Atlantic coast of the Iberian peninsula, based on the distribution of amphorae, and the role played by Punic Gadir as a commercial hub. Although most of the material assemblages used as evidence were already known, no attempt had previously been made to systematise and analyse the evidence from a geographical and a chronological perspective, linking these assemblages with other contemporary products, both local and imported. This has involved an in-depth revision of the bibliography and the detailed examination of the material in various museums. In the conclusions, I assess the presence of Turdetanian goods in the Atlantic markets, from the estuary of the Guadiana to the north-western coast of the Iberian peninsula, as well as the distribution mechanisms in place in different periods.

Los ataques de ciberseguridad se han convertido en un factor muy relevante que pueden contravenir el cumplimiento de las políticas de ciberseguridad de las empresas y organizaciones. Dichos ataques pueden estar provocados en gran medida por una ausencia de configuraciones de seguridad o de valores por defecto en la configuración de productos y sistemas. La complejidad en la configuración de productos y sistemas es un reto en la industria del software. En este artículo proponemos una plataforma, Cybersecurity Software Product Line (CyberSPL), basado en la metodología de diseño de líneas de productos de tal manera que a través de la definición de modelos de características podamos agrupar patrones de configuraciones de aplicaciones y sistemas relacionados con la ciberseguridad. Mediante el análisis automatizado de estos modelos permitiríamos la diagnosis de los posibles problemas en las configuraciones de seguridad y por tanto evitarlos. Como soporte para dicha plataforma se ha implementado una solución multiusario y multiplataforma que permite definir un catálogo de modelos de características público o privado. Además se han integrado mecanismos para determinar todas las configuraciones de un modelo, detectar si una configuración es correcta o no, además de diagnosticar las causas de fallos dada una configuración determinada. Para validar la propuesta se usará un escenario real donde se plantea la configuración de un canal seguro de transmisión mediante el protocolo SSL/TLS, aplicado a un servidor de aplicaciones. En dicho escenario se analizaran dos modelos de características, se validarán diferentes configuraciones, y se diagnosticarán varias configuraciones con problemas.

Process mining is employed by organizations to completely understand and improve their processes and to detect possible deviations from expected behavior. Process discovery uses event logs as input data, which describe the times of the actions that occur the traces. Currently, Internet-of-Things environments generate massive distributed and not always structured data, which brings about new complex scenarios since data must first be transformed in order to be handled by process min ing tools. This paper shows the success case of application of a solution that permits the transformation of complex semi-structured data of an assembly-aircraft process in order to create event logs that can be man aged by the process mining paradigm. A Domain-Specific Language and a prototype have been implemented to facilitate the extraction of data into the unified traces of an event log. The implementation performed has been applied within a project in the aeronautic industry, and promis ing results have been obtained of the log extraction for the discovery of processes and the resulting improvement of the assembly-aircraft process.

Variability models are used to build configurators. Configurators are programs that guide users through the configuration process to reach a desired configuration that fulfils user requirements. The same variability model can be used to design different configura tors employing different techniques. One of the elements that can change in a configurator is the configuration workflow, i.e., the order and sequence in which the different configuration elements are presented to the configuration stakeholders. When developing a configurator, a challenge is to decide the configuration workflow that better suites stakeholders according to previous configurations. For example, when configuring a Linux distribution, the configura tion process start by choosing the network or the graphic card, and then other packages with respect to a given sequence. In this paper, we present COnfiguration workfLOw proceSS mIning (COLOSSI), an automated technique that given a set of logs of previous configu rations and a variability model can automatically assist to determine the configuration workflow that better fits the configuration logs generated by user activities. The technique is based on process discovery, commonly used in the process mining area, with an adaptation to configuration contexts. Our proposal is validated us ing existing data from an ERP configuration environment showing its feasibility. Furthermore, we open the door to new applications of process mining techniques in different areas of software product line engineering.

Data transformation and schema conciliation are relevant topics in Industry due to the incorporation of data-intensive business processes in organizations. As the amount of data sources increases, the complexity of such data increases as well, leading to complex and nested data schemata. Nowadays, novel approaches are being employed in academia and Industry to assist non-expert users in transforming, integrating, and improving the quality of datasets (i.e., data wrangling). However, there is a lack of support for transforming semi-structured complex data. This article makes a state-of-the-art by identifying and analyzing the most relevant solutions that can be found in academia and Industry to transform this type of data. In addition, we propose a Domain-Specific Language (DSL) to support the transformation of complex data as a first approach to enhance data wrangling processes. We also develop a framework to implement the DSL and evaluate it in a real-world case study.

This is an interdisciplinary study that combines morphoseismics, sedimentology and numerical modelling to elucidate at different scales of resolution the influence of alongslope processes on the turbidite systems (TSs) and canyons in the Alboran Sea (southwestern Mediterranean). Nine TSs are mapped in the Spanish margin (La Linea, Guadiaro, Baños, Torrenueva, Fuengirola, Salobreña, Sacratif, Calahonda and Almeria) and two in the Alboran Ridge (Piedra Escuela and Al-Borani). In the Moroccan margin, there are only two canyons (Ceuta and Nekor). Distinctive morphoseismic and sedimentological signatures from TSs and canyons have enabled three regional models of alongslope influence to be distinguished: a) Alongslope processes are dominant. This scenario characterizes the canyons of the Moroccan margin. The diagnostic signature is the lack of leveed channels and lobes at the Ceuta and Nekor Canyon mouths. b) Different degrees of interplay exist between alongslope and downslope processes. This scenario occurs in the TSs of the western Spanish margin. Here, the alongslope influ-ence on TSs (La Linea, Guadiaro, Baños, Torrenueva and Fuengirola) is evidenced by the lack of overbank deposits in the La Linea and Guadiaro Canyons and an alongslope trend in the morpho-architecture of the channelized lobes and in the textural distribution of canyon/channel deposits (mass-flow deposits and turbi-dites). Both signatures indicate sandier TSs as well as Bouma turbidite sequences lacking the finest levels towards the Strait of Gibraltar. Local intercalations of contourites are also present in the Guadiaro lobe deposits. c) Downslope processes are dominant. This scenario characterizes the TSs of the eastern and central Spanish margin and Alboran Ridge. There, TSs seem to be controlled solely by the characteristics of the downslope gravity flows that transport sediment. The hydrodynamic and sediment dispersion models confirm that the main oceano-graphic factors governing the variable al

Constraint-Driven Fault Diagnosis (CDD) is based on the concept of constraint suspension [6], which was proposed as an approach to fault detection and diagnosis. In this chapter, its capabilities are demonstrated by describing how it might be applied to hardware systems. With this idea, a model-based fault diagnosis problem may be considered as a Constraint Satisfaction Problem (CSP) in order to detect any unexpected behavior and Constraint Satisfaction Optimization Problem (COP) constraint optimization problem in order to identify the reason for any unexpected behavior because the parsimony principle is taken into account

The complexity and size of software systems have rapidly increased in recent years, with software engineers facing ever-growing challenges in building and maintaining such systems. In particular, testing and debugging, that is, finding, isolating, and eliminating defects in software systems still constitute a major challenge in practice

Fungi possess diverse photosensory proteins that allow them to perceive different light wavelengths and to adapt to changing light conditions in their environment. The biological and physiological roles of the green light-sensing rhodopsins in fungi are not yet resolved. The rice plant pathogen Fusarium fujikuroi exhibits two different rhodopsins, CarO and OpsA. CarO was previously characterized as a light-driven proton pump. We further analyzed the pumping behavior of CarO by patch-clamp experiments. Our data show that CarO pumping activity is strongly augmented in the presence of the plant hormone indole-3-acetic acid and in sodium acetate, in a dose-dependent manner under slightly acidic conditions. By contrast, under these and other tested conditions, the Neurospora rhodopsin (NR)-like rhodopsin OpsA did not exhibit any pump activity. Basic local alignment search tool (BLAST) searches in the genomes of ascomycetes revealed the occurrence of rhodopsin-encoding genes mainly in phyto-associated or phytopathogenic fungi, suggesting a possible correlation of the presence of rhodopsins with fungal ecology. In accordance, rice plants infected with a CarO-deficient F. fujikuroi strain showed more severe bakanae symptoms than the reference strain, indicating a potential role of the CarO rhodopsin in the regulation of plant infection by this fungus.

As animals develop, tissue bending contributes to shape the organs into complex three-dimensional structures. However, the architecture and packing of curved epithelia remains largely unknown. Here we show by means of mathematical modelling that cells in bent epithelia can undergo intercalations along the apico-basal axis. This phenomenon forces cells to have different neighbours in their basal and apical surfaces. As a consequence, epithelial cells adopt a novel shape that we term “scutoid”. The detailed analysis of diverse tissues confirms that generation of apico-basal intercalations between cells is a common feature during morphogenesis. Using biophysical arguments, we propose that scutoids make possible the minimization of the tissue energy and stabilize three-dimensional packing. Hence, we conclude that scutoids are one of nature's solutions to achieve epithelial bending. Our findings pave the way to understand the three-dimensional organization of epithelial organs.

Configuration techniques have been used in several fields, such as the design of business process models. Sometimes these models depend on the data dependencies, being easier to describe what has to be done instead of how. Configuration models enable to use a declarative representation of business processes, deciding the most appropriate work-flow in each case. Unfortunately, data dependencies among the activities and how they can affect the correct execution of the process, has been overlooked in the declarative specifications and configurable systems found in the literature. In order to find the best process configuration for optimizing the execution time of processes according to data dependencies, we propose the use of Constraint Programming paradigm with the aim of obtaining an adaptable imperative model in function of the data dependencies of the activities described declarative.

Background: Triclustering has shown to be a valuable tool for the analysis of microarray data since its appearance as an improvement of classical clustering and biclustering techniques. The standard for validation of triclustering is based on three different measures: correlation, graphic similarity of the patterns and functional annotations for the genes extracted from the Gene Ontology project (GO). Results: We propose TRIQ, a single evaluation measure that combines the three measures previously described: correlation, graphic validation and functional annotation, providing a single value as result of the validation of a tricluster solution and therefore simplifying the steps inherent to research of comparison and selection of solutions. TRIQ has been applied to three datasets already studied and evaluated with single measures based on correlation, graphic similarity and GO terms. Triclusters have been extracted from this three datasets using two different algorithms: TriGen and OPTricluster. Conclusions: TRIQ has successfully provided the same results as a the three single evaluation measures. Furthermore, we have applied TRIQ to results from another algorithm, OPTRicluster, and we have shown how TRIQ has been a valid tool to compare results from different algorithms in a quantitative straightforward manner. Therefore, it appears as a valid measure to represent and summarize the quality of tricluster solutions. It is also feasible for evaluation of non biological triclusters, due to the parametrization of each component of TRIQ.

Los procesos de negocio permiten la descripción de modelos colaborativos donde varios procesos y sus instancias se puedan coreografiar. Un ejemplo de la dificultad que implican dichos procesos se encuentra en los entornos logísticos, donde instancias de diferentes procesos y con diferentes cardinalidades deben de trabajar para alcanzar un objetivo común. En este trabajo se identifican el conjunto de retos a resolver para facilitar la incorporación de la ingeniería de los procesos de negocio a entornos logísticos. En el artículo se analizan además los trabajos previos, y se esboza una solución basada en el análisis de los artefactos de datos involucrados y la capacidad del modelado orientado a actividades de los procesos de negocios.

Key Performance Indicators (KPIs) can be used to evaluate the success of an organization, facilitating the detection of the deviations and unexpected evolution of the behaviour of a company. The difficulty for enterprises is to ascertain what to do when a deviation is detected. In this paper, we propose a modelling approach to improve the operational business-level and to ascertain the possible actions that can be executed to maintain the right direction in a company. For business process-oriented companies, it entails knowing how KPIs can be affected by the business processes. It implies not only pointing outthat a system malfunction exists, but also to know what to do when a deviation is detected. Our proposal presents a methodology that covers: (1) an extension of the existing models in order to combine KPIs, goals of the companies, and the decision variables together with business processes; (2) a methodology based on data mining analysis to verify the correctness of the enriched proposed model according to the data stored during business evolution, and; (3) a framework to simulate the evolution of the business according to the decisions taken in the governance process, thereby supporting governance activities to achieve the defined objectives by exploiting goals and KPIs from the proposed model.

The analysis of data produced by the enterprises during pro cesses execution is key to know how these processes are working and how they can be improved. These data may be consumed to make different types of analysis, for example, data could be used as input for process dis covery, decision-making and even process querying tools. However, each type of analysis needs data in different formats because they use different techniques and tackle the problem from a different point of view. Fortu nately, if we look at the data exploitation problem from a higher level of abstraction perspective, we can realize that all the points of view share a common ground: the business process model and its instantiation are in the kernel of all of them. In this paper, we propose the use of a Busi ness Process Instance Metamodel, which serves as a common interface to make independent the applications producing business process data from those applications that consume and exploit it. A tool has been implemented as a proof of concept to facilitate the matching between data from different data sources and the metamodel.

A novel methodology is introduced in this research study to detect seismic precursors. Based on an existing approach, the new methodology searches for patterns in the historical data. Such patterns may contain statistical or soil dynamics information. It improves the original version in several aspects. First, new seismicity indicators have been used to characterize earthquakes. Second, a machine learning clustering algorithm has been applied in a very flexible way, thus allowing the discovery of new data groupings. Third, a novel search strategy is proposed in order to obtain non-overlapped patterns. And, fourth, arbitrary lengths of patterns are searched for, thus discovering long and short-term behaviors that may influence in the occurrence of medium-large earthquakes. The methodology has been applied to seven different datasets, from three different regions, namely the Iberian Peninsula, Chile and Japan. Reported results show a remarkable improvement with respect to the former version, in terms of all evaluated quality measures. In particular, the number of false positives has decreased and the positive predictive values increased, both of them in a very remarkable manner.

Business data are usually managed by means of business processes during process instances. These viewpoints (business, instances and data) are strongly related because the life-cycle of business data objects need to be aligned with the business process and process instance models. However, current approaches do not provide a mechanism to integrate these three viewpoints nor to query them all together while maintaining the information in the distributed, heterogeneous systems where they have been created. In this paper, we propose the integration of the business process, business process instance, and business data models by using their metamodels and also an architecture to support this integration. The goal of this integration is to make the most of the three models and the technologies that support them in an isolated way. In our approach, it is not necessary to change the source data formats nor transforming them into a common one. Furthermore, the proposed architecture allows us to query the three models even though they come from three different technologies.

The Pooideae are a highly diverse C3 grass subfamily that includes some of the most economically important crops, nested within the highly speciose core-pooid clade. Here, we build and explore the phylogeny of the Pooideae within a temporal framework, assessing its patterns of diversification and its chromosomal evolutionary changes in the light of past environmental transformations. We sequenced five plastid DNA loci, two coding (ndhF, matk) and three non-coding (trnH-psbA, trnT-L and trnL-F), in 163 Poaceae taxa, including representatives for all subfamilies of the grasses and all but four ingroup Pooideae tribes. Parsimony and Bayesian phylogenetic analyses were conducted and divergence times were inferred in BEAST using a relaxed molecular clock. Diversification rates were assessed using the MEDUSA approach, and chromosome evolution was analyzed using the chromEvol software. Diversification of the Pooideae started in the Late-Eocene and was especially intense during the Oligocene-Miocene. The background diversification rate increased significantly at the time of the origin of the Poodae + Triticodae clade. This shift in diversification occurred in a context of falling temperatures that potentially increased ecological opportunities for grasses adapted to open areas around the world. The base haploid chromosome number n = 7 has remained stable throughout the phylogenetic history of the core pooids and we found no link between chromosome transitions and major diversification events in the Pooideae.

n this work, we present the results of applying data mining techniques to hydrogen breath test data. Disposal of H2 gas is of utmost relevance to maintain efficient microbial fermentation processes. Objectives Analyze a set of data of hydrogen breath tests by use of data mining tools. Identify new patterns of H2 production. Methods Hydrogen breath tests data sets as well as k-means clustering as the data mining technique to a dataset of 2571 patients. Results Six different patterns have been extracted upon analysis of the hydrogen breath test data. We have also shown the relevance of each of the samples taken throughout the test. Conclusions Analysis of the hydrogen breath test data sets using data mining techniques has identified new patterns of hydrogen generation upon lactose absorption. We can see the potential of application of data mining techniques to clinical data sets. These results offer promising data for future research on the relations between gut microbiota produced hydrogen and its link to clinical symptoms.

Alzheimer’s disease is a complex progressive neurodegenerative brain disorder, being its prevalence ex pected to rise over the next decades. Unconventional strategies for elucidating the genetic mechanisms are necessary due to its polygenic nature. In this work, the input information sources are five: a public DNA microarray that measures expression levels of control and patient samples, repositories of known genes associated to Alzheimer’s disease, additional data, Gene Ontology and finally, a literature review or expert knowledge to validate the results. As methodology to identify genes highly related to this disease, we present the integration of three machine learning techniques: particularly, we have used decision trees, quantitative association rules and hierarchical cluster to analyze Alzheimer’s disease gene expres sion profiles to identify genes highly linked to this neurodegenerative disease, through changes in their expression levels between control and patient samples. We propose an ensemble of decision trees and quantitative association rules to find the most suitable configurations of the multi-objective evolutionary algorithm GarNet, in order to overcome the complex parametrization intrinsic to this type of algorithms. To fulfill this goal, GarNet has been executed using multiple configuration settings and the well-known C4.5 has been used to find the minimum accuracy to be satisfied. Then, GarNet is rerun to identify de pendencies between genes and their expression levels, so we are able to distinguish between healthy individuals and Alzheimer’s patients using the configurations that overcome the minimum threshold of accuracy defined by C4.5 algorithm. Finally, a hierarchical cluster analysis has been used to validate the obtained gene-Alzheimer’s Disease associations provided by GarNet. The results have shown that the ob tained rules were able to successfully characterize the underlying information, grouping relevant genes for Alzheimer Disease. The

Background and aims The transition from outcrossing to selfing is a repeated pattern in angiosperm diversification and according to general theory this transition should occur quickly and mixed reproductive systems should be infrequent. However, a large proportion of flowering plants have mixed reproductive systems, even showing inbreeding depression. Recently, several theoretical studies have shown that mixed mating systems can be stable, but empirical studies supporting these assumptions are still scarce. • Methods Hypochaeris salzmanniana, an annual species with populations differing in their self-incompatibility expression, was used as a study case to assess the stability of its mixed reproductive system. Here a descriptive study of the pollination environment was combined with measurements of the stability of the self-incompatibility system, outcrossing rate, reproductive assurance and inbreeding depression in four populations for two consecutive years. • Key Results The reproductive system of populations exhibited a geographical pattern: the proportion of plants decreased from west to east. Pollinator environment also varied geographically, being less favourable from west to east. The self-incompatibility expression of some populations changed markedly in only one year. After selfing, progeny was mainly self-compatible, while after outcrossing both self-incompatible and self-compatible plants were produced. In general, both reproductive assurance and high inbreeding depression were found in all populations and years. The lowest values of inbreeding depression were found in 2014 in the easternmost populations, which experienced a marked increase in self-compatibility in 2015. • Conclusions The mixed reproductive system of H. salzmanniana seems to be an evolutionarily stable strategy, with selfing conferring reproductive assurance when pollinator attendance is low, but strongly limited by inbreeding depression. The fact that the highest frequencies of self-compa