Facing the climate change challenges in the mining and mineral industry as providers of a sustainable manufacturing process

A sustainable manufacturing process must rely on an also sustainable raw materials and energy supply. This paper is intended to show the results of the studies developed on sustainable business models for the minerals industry as a fundamental previous part of a sustainable manufacturing process. As it has happened in other economic activities, the mining and minerals industry has come under tremendous pressure to improve its social, developmental, and environmental performance. Mining, refining, and the use and disposal of minerals have in some instances led to significant local environmental and social damage. Nowadays, like in other parts of the corporate world, companies are more routinely expected to perform to ever higher standards of behavior, going well beyond achieving the best rate of return for shareholders. They are also increasingly being asked to be more transparent and subject to third-party audit or review, especially in environmental aspects. In terms of environment, there are three inter-related areas where innovation and new business models can make the biggest difference: carbon, water and biodiversity. The focus in these three areas is for two reasons. First, the industrial and energetic minerals industry has significant footprints in each of these areas. Second, these three areas are where the potential environmental impacts go beyond local stakeholders and communities, and can even have global impacts, like in the case of carbon. So prioritizing efforts in these areas will ultimately be a strategic differentiator as the industry businesses continues to grow. Over the next forty years, world?s population is predicted to rise from 6.300 million to 9.500 million people. This will mean a huge demand of natural resources. Indeed, consumption rates are such that current demand for raw materials will probably soon exceed the planet?s capacity. As awareness of the actual situation grows, the public is demanding goods and services that are even more environmentally sustainable. This means that massive efforts are required to reduce the amount of materials we use, including freshwater, minerals and oil, biodiversity, and marine resources. It?s clear that business as usual is no longer possible. Today, companies face not only the economic fallout of the financial crisis; they face the substantial challenge of transitioning to a low-carbon economy that is constrained by dwindling natural resources easily accessible. Innovative business models offer pioneering companies an early start toward the future. They can signal to consumers how to make sustainable choices and provide reward for both the consumer and the shareholder. Climate change and carbon remain major risk discontinuities that we need to better understand and deal with. In the absence of a global carbon solution, the principal objective of any individual country should be to reduce its global carbon emissions by encouraging conservation. The mineral industry internal response is to continue to focus on reducing the energy intensity of our existing operations through energy efficiency and the progressive introduction of new technology. Planning of the new projects must ensure that their energy footprint is minimal from the start. These actions will increase the long term resilience of the business to uncertain energy and carbon markets. This focus, combined with a strong demand for skills in this strategic area for the future requires an appropriate change in initial and continuing training of engineers and technicians and their awareness of the issue of eco-design. It will also need the development of measurement tools for consistent comparisons between companies and the assessments integration of the carbon footprint of mining equipments and services in a comprehensive impact study on the sustainable development of the Economy.

Efficient management of available technologies. Aplication to mining operations for industry supplies

Nowadays, processing Industry Sector is going through a series of changes, including right management and reduction of environmental affections. Any productive process which looks for sustainable management is incomplete if Cycle of Life of mineral resources sustainability is not taken into account. Raw materials for manufacturing are provided by mineral resources extraction processes, such as copper, aluminum, iron, gold, silver, silicon, titanium? Those elements are necessary for Mankind development and are obtained from the Earth through mineral extractive processes. Mineral extraction processes are operations which must take care about the environmental consequences. Extraction of huge volumes of rock for their transformation into raw materials for industry must be optimized to reduce ecological cost of the final product as l was possible. Reducing the ecological balance on a global scale has no sense to design an efficient manufacturing in secondary industry (transformation), if in first steps of the supply chain (extraction) impact exceeds the savings of resources in successive phases. Mining operations size suggests that it is an environmental aggressive activity, but precisely because of its great impact must be the first element to be considered. That idea implies that a new concept born: Reduce economical and environmental cost This work aims to make a reflection on the parameters that can be modified to reduce the energy cost of the process without an increasing in operational costs and always ensuring the same production capacity. That means minimize economic and environmental cost at same time. An efficient design of mining operation which has taken into account that idea does not implies an increasing of the operating cost. To get this objective is necessary to think in global operation view to make that all departments involved have common guidelines which make you think in the optimization of global energy costs. Sometimes a single operational cost must be increased to reduce global cost. This work makes a review through different design parameters of surface mining setting some key performance indicators (KPIs) which are estimated from an efficient point of view. Those KPIs can be included by HQE Policies as global indicators. The new concept developed is that a new criteria has to be applied in company policies: improve management, improving OPERATIONAL efficiency. That means, that is better to use current resources properly (machinery, equipment,?) than to replace them with new things but not used correctly. As a conclusion, through an efficient management of current technologies in each extractive operation an important reduction of the energy can be achieved looking at downstream in the process. That implies a lower energetic cost in the whole cycle of life in manufactured product.

A Methodological model to assist the optimization and risk management of mining investment decisions

Identifying, quantifying, and minimizing technical risks associated with investment decisions is a key challenge for mineral industry decision makers and investors. However, risk analysis in most bankable mine feasibility studies are based on the stochastic modelling of project “Net Present Value” (NPV)which, in most cases, fails to provide decision makers with a truly comprehensive analysis of risks associated with technical and management uncertainty and, as a result, are of little use for risk management and project optimization. This paper presents a value-chain risk management approach where project risk is evaluated for each step of the project lifecycle, from exploration to mine closure, and risk management is performed as a part of a stepwise value-added optimization process.

New generation of human machine interfaces for controlling UAV through depth based gesture recognition

New forms of natural interactions between human operators and UAVs (Unmanned Aerial Vehicle) are demanded by the military industry to achieve a better balance of the UAV control and the burden of the human operator. In this work, a human machine interface (HMI) based on a novel gesture recognition system using depth imagery is proposed for the control of UAVs. Hand gesture recognition based on depth imagery is a promising approach for HMIs because it is more intuitive, natural, and non-intrusive than other alternatives using complex controllers. The proposed system is based on a Support Vector Machine (SVM) classifier that uses spatio-temporal depth descriptors as input features. The designed descriptor is based on a variation of the Local Binary Pattern (LBP) technique to efficiently work with depth video sequences. Other major consideration is the especial hand sign language used for the UAV control. A tradeoff between the use of natural hand signs and the minimization of the inter-sign interference has been established. Promising results have been achieved in a depth based database of hand gestures especially developed for the validation of the proposed system.

Nanoinformatics approaches for information extraction and text mining in nanomedical research texts

La nanotecnología es un área de investigación de reciente creación que trata con la manipulación y el control de la materia con dimensiones comprendidas entre 1 y 100 nanómetros. A escala nanométrica, los materiales exhiben fenómenos físicos, químicos y biológicos singulares, muy distintos a los que manifiestan a escala convencional. En medicina, los compuestos miniaturizados a nanoescala y los materiales nanoestructurados ofrecen una mayor eficacia con respecto a las formulaciones químicas tradicionales, así como una mejora en la focalización del medicamento hacia la diana terapéutica, revelando así nuevas propiedades diagnósticas y terapéuticas. A su vez, la complejidad de la información a nivel nano es mucho mayor que en los niveles biológicos convencionales (desde el nivel de población hasta el nivel de célula) y, por tanto, cualquier flujo de trabajo en nanomedicina requiere, de forma inherente, estrategias de gestión de información avanzadas. Desafortunadamente, la informática biomédica todavía no ha proporcionado el marco de trabajo que permita lidiar con estos retos de la información a nivel nano, ni ha adaptado sus métodos y herramientas a este nuevo campo de investigación. En este contexto, la nueva área de la nanoinformática pretende detectar y establecer los vínculos existentes entre la medicina, la nanotecnología y la informática, fomentando así la aplicación de métodos computacionales para resolver las cuestiones y problemas que surgen con la información en la amplia intersección entre la biomedicina y la nanotecnología. Las observaciones expuestas previamente determinan el contexto de esta tesis doctoral, la cual se centra en analizar el dominio de la nanomedicina en profundidad, así como en el desarrollo de estrategias y herramientas para establecer correspondencias entre las distintas disciplinas, fuentes de datos, recursos computacionales y técnicas orientadas a la extracción de información y la minería de textos, con el objetivo final de hacer uso de los datos nanomédicos disponibles. El autor analiza, a través de casos reales, alguna de las tareas de investigación en nanomedicina que requieren o que pueden beneficiarse del uso de métodos y herramientas nanoinformáticas, ilustrando de esta forma los inconvenientes y limitaciones actuales de los enfoques de informática biomédica a la hora de tratar con datos pertenecientes al dominio nanomédico. Se discuten tres escenarios diferentes como ejemplos de actividades que los investigadores realizan mientras llevan a cabo su investigación, comparando los contextos biomédico y nanomédico: i) búsqueda en la Web de fuentes de datos y recursos computacionales que den soporte a su investigación; ii) búsqueda en la literatura científica de resultados experimentales y publicaciones relacionadas con su investigación; iii) búsqueda en registros de ensayos clínicos de resultados clínicos relacionados con su investigación. El desarrollo de estas actividades requiere el uso de herramientas y servicios informáticos, como exploradores Web, bases de datos de referencias bibliográficas indexando la literatura biomédica y registros online de ensayos clínicos, respectivamente. Para cada escenario, este documento proporciona un análisis detallado de los posibles obstáculos que pueden dificultar el desarrollo y el resultado de las diferentes tareas de investigación en cada uno de los dos campos citados (biomedicina y nanomedicina), poniendo especial énfasis en los retos existentes en la investigación nanomédica, campo en el que se han detectado las mayores dificultades. El autor ilustra cómo la aplicación de metodologías provenientes de la informática biomédica a estos escenarios resulta efectiva en el dominio biomédico, mientras que dichas metodologías presentan serias limitaciones cuando son aplicadas al contexto nanomédico. Para abordar dichas limitaciones, el autor propone un enfoque nanoinformático, original, diseñado específicamente para tratar con las características especiales que la información presenta a nivel nano. El enfoque consiste en un análisis en profundidad de la literatura científica y de los registros de ensayos clínicos disponibles para extraer información relevante sobre experimentos y resultados en nanomedicina —patrones textuales, vocabulario en común, descriptores de experimentos, parámetros de caracterización, etc.—, seguido del desarrollo de mecanismos para estructurar y analizar dicha información automáticamente. Este análisis concluye con la generación de un modelo de datos de referencia (gold standard) —un conjunto de datos de entrenamiento y de test anotados manualmente—, el cual ha sido aplicado a la clasificación de registros de ensayos clínicos, permitiendo distinguir automáticamente los estudios centrados en nanodrogas y nanodispositivos de aquellos enfocados a testear productos farmacéuticos tradicionales. El presente trabajo pretende proporcionar los métodos necesarios para organizar, depurar, filtrar y validar parte de los datos nanomédicos existentes en la actualidad a una escala adecuada para la toma de decisiones. Análisis similares para otras tareas de investigación en nanomedicina ayudarían a detectar qué recursos nanoinformáticos se requieren para cumplir los objetivos actuales en el área, así como a generar conjunto de datos de referencia, estructurados y densos en información, a partir de literatura y otros fuentes no estructuradas para poder aplicar nuevos algoritmos e inferir nueva información de valor para la investigación en nanomedicina. ABSTRACT Nanotechnology is a research area of recent development that deals with the manipulation and control of matter with dimensions ranging from 1 to 100 nanometers. At the nanoscale, materials exhibit singular physical, chemical and biological phenomena, very different from those manifested at the conventional scale. In medicine, nanosized compounds and nanostructured materials offer improved drug targeting and efficacy with respect to traditional formulations, and reveal novel diagnostic and therapeutic properties. Nevertheless, the complexity of information at the nano level is much higher than the complexity at the conventional biological levels (from populations to the cell). Thus, any nanomedical research workflow inherently demands advanced information management. Unfortunately, Biomedical Informatics (BMI) has not yet provided the necessary framework to deal with such information challenges, nor adapted its methods and tools to the new research field. In this context, the novel area of nanoinformatics aims to build new bridges between medicine, nanotechnology and informatics, allowing the application of computational methods to solve informational issues at the wide intersection between biomedicine and nanotechnology. The above observations determine the context of this doctoral dissertation, which is focused on analyzing the nanomedical domain in-depth, and developing nanoinformatics strategies and tools to map across disciplines, data sources, computational resources, and information extraction and text mining techniques, for leveraging available nanomedical data. The author analyzes, through real-life case studies, some research tasks in nanomedicine that would require or could benefit from the use of nanoinformatics methods and tools, illustrating present drawbacks and limitations of BMI approaches to deal with data belonging to the nanomedical domain. Three different scenarios, comparing both the biomedical and nanomedical contexts, are discussed as examples of activities that researchers would perform while conducting their research: i) searching over the Web for data sources and computational resources supporting their research; ii) searching the literature for experimental results and publications related to their research, and iii) searching clinical trial registries for clinical results related to their research. The development of these activities will depend on the use of informatics tools and services, such as web browsers, databases of citations and abstracts indexing the biomedical literature, and web-based clinical trial registries, respectively. For each scenario, this document provides a detailed analysis of the potential information barriers that could hamper the successful development of the different research tasks in both fields (biomedicine and nanomedicine), emphasizing the existing challenges for nanomedical research —where the major barriers have been found. The author illustrates how the application of BMI methodologies to these scenarios can be proven successful in the biomedical domain, whilst these methodologies present severe limitations when applied to the nanomedical context. To address such limitations, the author proposes an original nanoinformatics approach specifically designed to deal with the special characteristics of information at the nano level. This approach consists of an in-depth analysis of the scientific literature and available clinical trial registries to extract relevant information about experiments and results in nanomedicine —textual patterns, common vocabulary, experiment descriptors, characterization parameters, etc.—, followed by the development of mechanisms to automatically structure and analyze this information. This analysis resulted in the generation of a gold standard —a manually annotated training or reference set—, which was applied to the automatic classification of clinical trial summaries, distinguishing studies focused on nanodrugs and nanodevices from those aimed at testing traditional pharmaceuticals. The present work aims to provide the necessary methods for organizing, curating and validating existing nanomedical data on a scale suitable for decision-making. Similar analysis for different nanomedical research tasks would help to detect which nanoinformatics resources are required to meet current goals in the field, as well as to generate densely populated and machine-interpretable reference datasets from the literature and other unstructured sources for further testing novel algorithms and inferring new valuable information for nanomedicine.

3LD: towards high quality, industry-ready linguistic Linked Licensed Data

The application of Linked Data technology to the publication of linguistic data promises to facilitate interoperability of these data and has lead to the emergence of the so called Linguistic Linked Data Cloud (LLD) in which linguistic data is published following the Linked Data principles. Three essential issues need to be addressed for such data to be easily exploitable by language technologies: i) appropriate machine-readable licensing information is needed for each dataset, ii) minimum quality standards for Linguistic Linked Data need to be defined, and iii) appropriate vocabularies for publishing Linguistic Linked Data resources are needed. We propose the notion of Licensed Linguistic Linked Data (3LD) in which different licensing models might co-exist, from totally open to more restrictive licenses through to completely closed datasets.

Mining mobile apps reviews to support release planning

The mobile apps market is a tremendous success, with millions of apps downloaded and used every day by users spread all around the world. For apps’ developers, having their apps published on one of the major app stores (e.g. Google Play market) is just the beginning of the apps lifecycle. Indeed, in order to successfully compete with the other apps in the market, an app has to be updated frequently by adding new attractive features and by fixing existing bugs. Clearly, any developer interested in increasing the success of her app should try to implement features desired by the app’s users and to fix bugs affecting the user experience of many of them. A precious source of information to decide how to collect users’ opinions and wishes is represented by the reviews left by users on the store from which they downloaded the app. However, to exploit such information the app’s developer should manually read each user review and verify if it contains useful information (e.g. suggestions for new features). This is something not doable if the app receives hundreds of reviews per day, as happens for the very popular apps on the market. In this work, our aim is to provide support to mobile apps developers by proposing a novel approach exploiting data mining, natural language processing, machine learning, and clustering techniques in order to classify the user reviews on the basis of the information they contain (e.g. useless, suggestion for new features, bugs reporting). Such an approach has been empirically evaluated and made available in a web-­‐based tool publicly available to all apps’ developers. The achieved results showed that the developed tool: (i) is able to correctly categorise user reviews on the basis of their content (e.g. isolating those reporting bugs) with 78% of accuracy, (ii) produces clusters of reviews (e.g. groups together reviews indicating exactly the same bug to be fixed) that are meaningful from a developer’s point-­‐of-­‐view, and (iii) is considered useful by a software company working in the mobile apps’ development market.