Scalable Inference of Gene Regulatory Networks with the Spark Distributed Computing Platform Cristo

Inference of Gene Regulatory Networks (GRNs) remains an important open challenge in computational biology. The goal of bio-model inference is to, based on time-series of gene expression data, obtain the sparse topological structure and the parameters that quantitatively understand and reproduce the dynamics of biological system. Nevertheless, the inference of a GRN is a complex optimization problem that involve processing S-System models, which include large amount of gene expression data from hundreds (even thousands) of genes in multiple time-series (essays). This complexity, along with the amount of data managed, make the inference of GRNs to be a computationally expensive task. Therefore, the genera- tion of parallel algorithmic proposals that operate efficiently on distributed processing platforms is a must in current reconstruction of GRNs. In this paper, a parallel multi-objective approach is proposed for the optimal inference of GRNs, since min- imizing the Mean Squared Error using S-System model and Topology Regularization value. A flexible and robust multi-objective cellular evolutionary algorithm is adapted to deploy parallel tasks, in form of Spark jobs. The proposed approach has been developed using the framework jMetal, so in order to perform parallel computation, we use Spark on a cluster of distributed nodes to evaluate candidate solutions modeling the interactions of genes in biological networks.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Intrapreneurship research: A comprehensive literature review

Organizations face continuous problems of survival and sustainability in the market, so innovation is vital for their growth. Entrepreneurship in the organization has been defined in various ways over the years, which has led to terminological confusion. Due to the innovation required by organizations for a proactive adaptation to the change and sustainability, intrapreneurship acquires special relevance for business development. Therefore, a literature review that considers intrapreneurship and the issues related to this concept is much needed. The search term ‘intrapreneur’ resulted in 312 articles published in WoS (Web of Science) between 1985 and 2021. These articles were analyzed using the VOSviewer software for the bibliometric analysis. The main authors and contributions in the area have been identified, in relation to the research objectives, enabling the generation of guidelines and proposals for future research

Invirtiendo en capital natural: un marco para integrar la sostenibilidad ambiental en las políticas de cooperación

Tomando en cuenta la magnitud de la crisis ambiental que afecta al planeta y los estrechos vínculos existentes entre la conservación de los ecosistemas y la lucha contra la pobreza, cabría preguntarse por qué los temas de protección del medio ambiente continúan teniendo un peso relativo tan bajo en las agendas y prioridades de las agencias de cooperación internacional. En este artículo, se analizan las razones de este desequilibrio y se propone un marco conceptual con base socio-ecológica para facilitar una verdadera integración de la sostenibilidad ambiental como prioridad estratégica en las políticas y herramientas de ayuda oficial al desarrollo. Varios paradigmas y principios fundamentales emanan de este nuevo marco conceptual, que considera a los ecosistemas funcionales como un capital natural que, adecuadamente gestionado, es capaz de producir un rico y variado flujo de servicios sobre los cuales es posible construir un proceso de desarrollo social, económica y ambientalmente sostenible, además de justo en términos de equidad intra e intergeneracional

An approach to a reference model for a sentient smart city

The interest about Smart City concept has in creased in recent years. In fact, Smart Cities is ex pected to improve cityzens life experience by driv ing the next digital revolution, moving from the personal area (mobile computing, smart home) to the urban area (collective computing and collective intelligence). But the development of Smart Cities is not being as fast as expected. Several problems need to be undertaken in order to achieve the ob jectives of the paradigm. This paper presents an approach to address one of these problems: to or chestrate the platform that is required for gathering information about city, store it in a model and ena ble it for exploitation. The heterogeneity of the po tential data sources available and the complexity of the information nature and structure, make it a non trivial task that have to be solved before commer cial solutions appear and provide specific and non interoperable solutions

Software reference architecture for smart environments: Perception

With the increase of intelligent devices, ubiquitous computing is spreading to all scopes of people life. Smart home (or industrial) environments include automation and control devices to save energy, perform tasks, assist and give comfort in order to satisfy specific preferences. This paper focuses on the proposal for Software Reference Architecture for the development of smart applications and their deployment in smart environments. The motivation for this Reference Architecture and its benefits are also explained. The proposal considers three main processes in the software architecture of these applications: perception, reasoning and acting. This paper centres attention on the definition of the Perception process and provides an example for its implementation and subsequent validation of the proposal. The software presented implements the Perception process of a smart environment for a standard office, by retrieving data from the real world and storing it for further reasoning and acting processes. The objectives of this solution include the provision of comfort for the users and the saving of energy in lighting. Through this verification, it is also shown that developments under this proposal produce major benefits within the software life cycle.Ministerio de Economía y Competitividad TIN2009-14378-C02-01 (ARTEMISA)Junta de Andalucía TIC-8052 (Simon

Machine learning regression to boost scheduling performance in hyper-scale cloud-computing data centres

Data centres increase their size and complexity due to the increasing amount of heterogeneous work loads and patterns to be served. Such a mix of various purpose workloads makes the optimisation of resource management systems according to temporal or application-level patterns difficult. Data centre operators have developed multiple resource-management models to improve scheduling perfor mance in controlled scenarios. However, the constant evolution of the workloads makes the utilisation of only one resource-management model sub-optimal in some scenarios. In this work, we propose: (a) a machine learning regression model based on gradient boosting to pre dict the time a resource manager needs to schedule incoming jobs for a given period; and (b) a resource management model, Boost, that takes advantage of this regression model to predict the scheduling time of a catalogue of resource managers so that the most performant can be used for a time span. The benefits of the proposed resource-management model are analysed by comparing its scheduling performance KPIs to those provided by the two most popular resource-management models: two level, used by Apache Mesos, and shared-state, employed by Google Borg. Such gains are empirically eval uated by simulating a hyper-scale data centre that executes a realistic synthetically generated workload that follows real-world trace patternsMinisterio de Ciencia e Innovación RTI2018-098062-A-I0