Applying a text mining framework to the extraction of numerical parameters from scientific literature in the biotechnology domain

Scientific publications are the main vehicle to disseminate information in the field of biotechnology for wastewater treatment. Indeed, the new research paradigms and the application of high-throughput technologies have increased the rate of publication considerably. The problem is that manual curation becomes harder, prone-to-errors and time-consuming, leading to a probable loss of information and inefficient knowledge acquisition. As a result, research outputs are hardly reaching engineers, hampering the calibration of mathematical models used to optimize the stability and performance of biotechnological systems. In this context, we have developed a data curation workflow, based on text mining techniques, to extract numerical parameters from scientific literature, and applied it to the biotechnology domain. A workflow was built to process wastewater-related articles with the main goal of identifying physico-chemical parameters mentioned in the text. This work describes the implementation of the workflow, identifies achievements and current limitations in the overall process, and presents the results obtained for a corpus of 50 full-text documents

Monitoring, Modelling and Management of Water Quality

Different types of pressures, such as nutrients, micropollutants, microbes, nanoparticles, microplastics, or antibiotic-resistant genes, endanger the quality of water bodies. Evidence-based pollution control needs to be built on the three basic elements of water governance: Monitoring, modeling, and management. Monitoring sets the empirical basis by providing space- and time-dependent information on substance concentrations and loads, as well as driving boundary conditions for assessing water quality trends, water quality statuses, and providing necessary information for the calibration and validation of models. Modeling needs proper system understanding and helps to derive information for times and locations where no monitoring is done or possible. Possible applications are risk assessments for exceedance of quality standards, assessment of regionalized relevance of sources and pathways of pollution, effectiveness of measures, bundles of measures or policies, and assessment of future developments as scenarios or forecasts. Management relies on this information and translates it in a socioeconomic context into specific plans for implementation. Evaluation of success of management plans again includes well-defined monitoring strategies. This book provides an important overview in this context

Safety Risk Management of LEED Building Construction : A BIM based Approach

Green buildings have been gaining popularity in the construction industry due to their low impact on the environment. Green buildings are aimed at creating energy-efficient, healthy, and environment-friendly buildings. However, OSHA records show that about 48% more accidents occur in green building construction as compared to traditional construction methods. Compromising the workers\u27 health and safety questions the true sustainability of the building. Green buildings have been a popular strategy in institutional sustainability agendas. Globally, LEED is the most popular green buildings rating system. Statistics show that an increasing number of construction projects intend to obtain the LEED certification in the next decade. However, elevated worker health and safety risks have been gradually becoming a concern while pursuing LEED credits. However, there exists a limited study comparing the safety hazards occurring in conventional construction practices and green construction practices.This research explores the major safety risks associated with LEED-certified building construction. Failure Mode Effect, Analysis (FMEA) is used to determine the safety risk associated with each LEED credit. LEED credits were ranked based on safety performance. Safety score and incremental cost of LEED credits were used to identify the optimal credit combination for LEED gold certification that reduces the safety risk and minimizes the cost. Bayesian Belief Networks (BBN) was used to analyze the impact of project factors on safety risk. This analysis identified how the risk level of LEED credits changes based on project parameters. Safety risks identified from FMEA and BBN were used to develop Building Information Modelling (BIM)-based solutions to improve worker safety. The outcomes of this research will address the challenges of LEED construction and inform the construction industry in enhancing the health and safety of construction workers with state-of-the-art technolog

NoMoDEI : A framework for Norm Monitoring on Dynamic Electronic Institutions

With the growth of the Internet, computational systems have become more and more complex, often including complicate interconnected networks of autonomous components. The need to bring some organisational structure into autonomous systems becomes urgent, as this allows regulating the behaviour of the different autonomous components to ensure their objectives are aligned with the holistic objectives of the system. Normative Systems are one of the mechanisms that can be applied to define and enforce acceptable behaviour within distributed electronic systems which should comply with some (human) regulations. One of the requirements to effectively implement Normative Systems is to be able to assess, at runtime, the state of the normative environment. Existing lines of research have already tried to tackle this issue on some simple scenarios. However, more complex scenarios may appear, for instance, scenarios where the normative context is not static, but it expands and contracts as new norms are added to the institution and removed from it respectively. As in human legal systems, it is easy to foresee that some of these electronic normative environments will not be static. They should be able to evolve through time as regulations change, effectively adapting to new situations and behaviours. Under these conditions, a monitoring system must be able to continue computing the state of the normative environment at runtime, as often we can not afford to perform the changes on the normative context off-line. Furthermore, it must be guaranteed the monitoring system can keep producing states of the normative environment that are consistent with the changes performed on the normative context. For instance, if a norm has been removed from the normative context, it does not make sense anymore to compute normative states where the norm has been violated. In this thesis we present NoMoDEI, a normative monitoring framework for dynamic Electronic Institutions. We formalize and develop an extended normative framework and architecture to cope with scenarios where the normative context is dynamic, therefore norms can be added, removed and updated. The operations are to be performed at run-time, without having to stop computing the normative state. The normative states computed are consistent with the expansion and contraction operations. NoMoDEI is introduced in three steps. First, we formally define the operations to be supported in order to allow for expanding and contracting the normative context. Then, we instantiate the formal operations, providing implementation details. Finally, we demonstrate our framework by applying it to two use cases: E-health systems and waste-water management on a river basin.Amb l'expansió d'Internet els sistemes computacionals han esdevingut més complexos, sovint incorporant complicades xarxes interconnectades de components autònoms. Es per això que la necessitat d'incorporar estructures organitzacionals en el sistemes autònoms s 'accentua, donat que aquestes estructures permeten regular el comportament dels diferents components autònoms, tot assegurant que els seus objectius es troben alineats amb els objectius generals del sistema. Els Sistemes Normatius (i.e. Normative Systems) són un dels mecanismes que podem aplicar per definir i imposar patrons acceptables de comportament dintre de sistemes electrònics distribuïts. Això esdevé especialment important quan el sistema es troba regimentat per regulacions (normalment humanes). Un dels requeriments per implementar Sistemes Normatius és ser capaços de determinar, en temps d'execució, l'estat de l'entorn normatiu. Existeixen línies de recerca que ja han tractat aquest problema en alguns escenaris simples. El món real però ens ofereix escenaris més complexes, com per exemple, escenaris on el context normatiu no és estàtic, si no que s'expandeix i contrau a mesura que noves normes són afegides o eliminades de la institució. Tal com passa als sistemes legals humans, és fàcil preveure que alguns contextos normatius electrònics no seran estàtics. Aquests contextos haurien de ser capaços d'evolucionar a través del temps a mesura que les regulacions canvien, adaptant-se a noves situacions i comportaments. Sota aquestes condicions, un sistema de monitorització ha de ser capaç de continuar calculant l'estat de l'entorn normatiu en temps d'execució, ja que sovint no ens podem permetre realitzar els canvis a l'entorn normatiu aturant el procés de monitorització. És més s'ha de garantir que el sistema de monitorització sigui capaç de continuar produint es tats de l’entorn normatiu de forma consistent amb els canvis realitzats. Per exemple, el fet d'eliminar una norma fa que no tingui gaire sentit continuar calculant es tats normatius on aquesta norma ha es tat violada. A aquesta Tesi presentem NoMoDEI, una infraestructura de monitorització normativa per institucions electròniques dinàmiques. Formalitzem i desenvolupem una infraestructura de monitorització normativa estesa capaç d'operar en escenaris on el context normatiu es dinàmic. Es a dir, diverses normes poden ser introduïdes, eliminades o actualitzades del context normatiu en qualsevol moment. Aquestes operacions s'han de poder realitzar en temps d'execució, es a dir, sense deixar de calcular l'estat normatiu. Es més, els estats normatius calculats han de ser consistents amb les respectives operacions d'extensió o contracció del context. Durant la Tesi presentem NoMoDEI en tres passos. Primer proporcionem una definició formal de les operacions que la infraestructura ha de suportar per permetre expandir i contraure el context normatiu. A continuació instanciem aquestes operacions proporcionant detalls d'implementació. Finalment demostrem que la nostra infraestructura pot ser aplicada a casos d'ús del món real introduint dos casos: sistemes de salut electrònics (i.e. E-health) i sistemes de tractament d’aigües residuals a la conca d’un riuPostprint (published version

Transcriptome signatures of wastewater effluent exposure in larval zebrafish vary with seasonal mixture composition in an effluent-dominated stream

Wastewater treatment plant (WWTP) effluent-dominated streams provide critical habitat for aquatic and terrestrial organisms but also continually expose them to complex mixtures of pharmaceuticals that can potentially impair growth, behavior, and reproduction. Currently, few biomarkers are available that relate to pharmaceutical-specific mechanisms of action. In the experiment reported in this paper, zebrafish (Danio rerio) embryos at two developmental stages were exposed to water samples from three sampling sites (0.1 km upstream of the outfall, at the effluent outfall, and 0.1 km below the outfall) during base-flow conditions from two months (January and May) of a temperate-region effluent-dominated stream containing a complex mixture of pharmaceuticals and other contaminants of emerging concern. RNA-sequencing identified potential biological impacts and biomarkers of WWTP effluent exposure that extend past traditional markers of endocrine disruption. Transcriptomics revealed changes to a wide range of biological functions and pathways including cardiac, neurological, visual, metabolic, and signaling pathways. These transcriptomic changes varied by developmental stage and displayed sensitivity to variable chemical composition and concentration of effluent, thus indicating a need for stage-specific biomarkers. Some transcripts are known to be associated with genes related to pharmaceuticals that were present in the collected samples. Although traditional biomarkers of endocrine disruption were not enriched in either month, a high estrogenicity signal was detected upstream in May and implicates the presence of unidentified chemical inputs not captured by the targeted chemical analysis. This work reveals associations between bioeffects of exposure, stage of development, and the composition of chemical mixtures in effluent-dominated surface water. The work underscores the importance of measuring effects beyond the endocrine system when assessing the impact of bioactive chemicals in WWTP effluent and identifies a need for non-targeted chemical analysis when bioeffects are not explained by the targeted analysis

HAZOP Analysis in Terms of Safety Operations Processes for Oil Production Units: A Case Study

The Hazard and Operability Study (HAZOP) methodology is considered one of the most effective techniques for risk analysis, developed fundamentally to provide regular processes with reduced risks that aim to guarantee the safety of activities and the operability of the production units. The study aims to apply the HAZOP methodology in process and safety operations in the oil production industry. A crude oil production unit was divided into smaller sections that were analysed. By applying the HAZOP methodology, 71 possibilities of relevant risks were identified. The environmental, health and economic impacts were estimated to establish safeguard priorities for them. The application of this methodology and the defined safeguards generated 47 recommendations to mitigate the detected problems. The study contributions were to demonstrate the efficacies of HAZOP methodology to identify potential hazards and evaluate the potential hazards obtained for malfunctioning of equipment and property in terms of the resultant impacts either new or existing process facilities, and as a useful tool to provide essential knowledge for the companies' leaders, decision-maker, and operations managers

Projecting Pathways to Food-Energy-Water Systems Sustainability through Ontology

The FEWsOnt ontology models major structural and dynamic concepts of the food-energy-water (FEW) systems from the complex system perspective by defining the emergent, nonlinear, and scale-invariant state transitions and behaviors of the network elements that result from natural and planned processes. The model represents the semantics of concepts such as security, footprint, challenge, risk, impact, and uncertainty in relation to governance and assessment of the level of sustainability of the FEW systems in varied domains of usage. The ontology will allow stakeholders working with the FEW systems' data to draw new inferences using semantic facts and discover insights and relationships among the systems' elements to make improved assessment and decisions toward sustainable growth. The knowledge-based model will lead users to optimize the tradeoffs and identify and prevent adverse changes to the FEW systems in relation to the interacting natural and social systems. The annotated terminology and formalized interactions in the ontology will facilitate the integration of the diverse FEW data types, improve communication among researchers, and help to reduce environmental stresses

A review of the use of artificial intelligence methods in infrastructure systems

The artificial intelligence (AI) revolution offers significant opportunities to capitalise on the growth of digitalisation and has the potential to enable the ‘system of systems’ approach required in increasingly complex infrastructure systems. This paper reviews the extent to which research in economic infrastructure sectors has engaged with fields of AI, to investigate the specific AI methods chosen and the purposes to which they have been applied both within and across sectors. Machine learning is found to dominate the research in this field, with methods such as artificial neural networks, support vector machines, and random forests among the most popular. The automated reasoning technique of fuzzy logic has also seen widespread use, due to its ability to incorporate uncertainties in input variables. Across the infrastructure sectors of energy, water and wastewater, transport, and telecommunications, the main purposes to which AI has been applied are network provision, forecasting, routing, maintenance and security, and network quality management. The data-driven nature of AI offers significant flexibility, and work has been conducted across a range of network sizes and at different temporal and geographic scales. However, there remains a lack of integration of planning and policy concerns, such as stakeholder engagement and quantitative feasibility assessment, and the majority of research focuses on a specific type of infrastructure, with an absence of work beyond individual economic sectors. To enable solutions to be implemented into real-world infrastructure systems, research will need to move away from a siloed perspective and adopt a more interdisciplinary perspective that considers the increasing interconnectedness of these systems