Some inconvenient truths about decarbonization, the hydrogen economy, and power-to-X technologies.

The decarbonization of the energy sector has been a subject of research and of political discussions for several decades, gaining significant attention in the last years. It is commonly acknowledged that the most obvious way to achieve decarbonization is the use of renewable energy sources. Within the context of the energy sector decarbonization, many mainly industrialized countries recently started developing national plans to establish a hydrogen-based economy in the very near future. The plans for green hydrogen initially try to (a) target sectors that are difficult to decarbonize and (b) address issues related to the storage and transportation of CO2-free energy. To achieve almost complete decarbonization, electric power must be generated exclusively from renewable sources. In so-called Power-to-X (PtX) technologies, green hydrogen is generated from electricity and subsequently converted to another energy carrier which can be further stored, transported and used. In PtX, X stands, for example, for liquid hydrogen, methanol or ammonia. The challenges associated with decarbonization include those associated with (a) the expansion of renewable energies (e.g., high capital demand, political and social issues), (b) the production, transportation, and storage of hydrogen and the energy carriers denoted by X in PtX (e.g., high cost and low overall efficiency), and (c) the expected significant increase in the demand for electrical energy. The paper discusses whether and under which conditions the current national and international hydrogen plans of many industrialized countries could lead to a maximization of decarbonization in the world. It concludes that, in general, as long as the conditions for generating large excess amounts of green electricity are not met, the quick establishment of a hydrogen economy could not only be very expensive, but also counterproductive to the worldwide decarbonization efforts.

Exergy-Based Analysis and Optimization of an Integrated Solar Combined-Cycle Power Plant.

The transition towards higher shares of electricity generation from renewable energy sources is shown to be significantly slower in developing countries with low-cost fossil fuel resources. Integrating conventional power plants with concentrated solar power may facilitate the transition towards a more sustainable power production. In this paper, a novel natural gas-fired integrated solar combined-cycle power plant was proposed, evaluated, and optimized with exergy-based methods. The proposed system utilizes the advantages of combined-cycle power plants, direct steam generation, and linear Fresnel collectors to provide 475 MW baseload power in Aswan, Egypt. The proposed system is found to reach exergetic efficiencies of 50.7% and 58.1% for day and night operations, respectively. In economic analysis, a weighted average levelized cost of electricity of 40.0 $/MWh based on the number of day and night operation hours is identified. In exergoeconomic analysis, the costs of thermodynamic inefficiencies were identified and compared to the component cost rates. Different measures for component cost reduction and performance enhancement were identified and applied. Using iterative exergoeconomic optimization, the levelized cost of electricity is reduced to a weighted average of 39.2 $/MWh and a specific investment cost of 1088 $/kW. Finally, the proposed system is found to be competitive with existing integrated solar combined-cycle plants, while allowing a significantly higher solar share of 17% of the installed capacity.

Advanced Exergy Analysis in the Dynamic Framework for Assessing Building Thermal Systems.

This work applies the Dynamic Advanced Exergy Analysis (DAEA) to a heating and domestic hot water (DHW) facility supplied by a Stirling engine and a condensing boiler. For the first time, an advanced exergy analysis using dynamic conditions is applied to a building energy system. DAEA provides insights on the components' exergy destruction (ED) by distinguishing the inefficiencies that can be prevented by improving the quality (avoidable ED) and the ones constrained because of technical limitations (unavoidable ED). ED is related to the inherent inefficiencies of the considered element (endogenous ED) and those coming from the interconnections (exogenous ED). That information cannot be obtained by any other approach. A dynamic calculation within the experimental facility has been performed after a component characterization driven by a new grey-box modelling technique, through TRNSYS and MATLAB. Novel solutions and terms of ED are assessed for the rational implementation of the DAEA in building energy installations. The influence of each component and their interconnections are valuated in terms of exergy destruction for further diagnosis and optimization purposes.

Permissible emission limit estimation via iterative back-calculation: Case of Assaluyeh energy zone, southern Iran.

In the present research work, an environmental policy procedure for setting a cap on emissions, as a crucial step in any total emission control system, has been provided and evaluated. It is shown that general regulations on emission intensities and rates do not guarantee that ambient air quality standards are met in intense industrial zones. Local emission limits are necessary to meet ambient air quality standards in these zones. To that end, we used dispersion simulators to back-calculate pollutant concentration thresholds for a large and intense energy system in the Assaluyeh region of southern Iran. Verified modeling results indicate 218 d of pollutant concentration threshold exceedance in Assaluyeh in a simulated year. Back-calculation to assess the total permissible emission level indicates the need for 68% reduction in total emission to meet ambient air quality standards. We then used the model to help identify effective control strategies, including emission reductions and appropriate timing of specific operations. Integr Environ Assess Manag 2018;14:130-138. © 2017 SETAC.

Reference intervals for plasma concentrations of adrenal steroids measured by LC-MS/MS: Impact of gender, age, oral contraceptives, body mass index and blood pressure status.

BACKGROUND: Mass spectrometric-based measurements of the steroid metabolome have been introduced to diagnose disorders featuring abnormal steroidogenesis. Defined reference intervals are important for interpreting such data. METHODS: Liquid chromatography-tandem mass spectrometry was used to establish reference intervals for 16 steroids (pregnenolone, progesterone, 11-deoxycorticosterone, corticosterone, aldosterone, 18-oxocortisol, 18-hydroxycortisol, 17-hydroxyprogesterone, 21-deoxycortisol, 11-deoxycortisol, cortisol, cortisone, dehydroepiandrosterone, dehydroepiandrosterone-sulfate, androstenedione, testosterone) measured in plasma from 525 volunteers with (n=227) and without (n=298) hypertension, including 68 women on oral contraceptives. RESULTS: Women showed variable plasma concentrations of several steroids associated with menstrual cycle phase, menopause and oral contraceptive use. Progesterone was higher in females than males, but most other steroids were higher in males than females and almost all declined with advancing age. Using models that corrected for age and gender, body mass index showed weak negative relationships with corticosterone, 21-deoxycortisol, cortisol, cortisone, testosterone, progesterone, 17-hydroxyprogesterone and 11-deoxycorticosterone, but a positive relationship with 18-hydroxycortisol. Hypertensives and normotensives showed negligible differences in plasma concentrations of steroids. CONCLUSION: Age and gender are the most important variables for plasma steroid reference intervals, which have been established here according to those variables for a panel of 16 steroids primarily useful for diagnosis and subtyping of patients with endocrine hypertension.

Discovering relations between indirectly connected biomedical concepts.

BACKGROUND: The complexity and scale of the knowledge in the biomedical domain has motivated research work towards mining heterogeneous data from both structured and unstructured knowledge bases. Towards this direction, it is necessary to combine facts in order to formulate hypotheses or draw conclusions about the domain concepts. This work addresses this problem by using indirect knowledge connecting two concepts in a knowledge graph to discover hidden relations between them. The graph represents concepts as vertices and relations as edges, stemming from structured (ontologies) and unstructured (textual) data. In this graph, path patterns, i.e. sequences of relations, are mined using distant supervision that potentially characterize a biomedical relation. RESULTS: It is possible to identify characteristic path patterns of biomedical relations from this representation using machine learning. For experimental evaluation two frequent biomedical relations, namely "has target", and "may treat", are chosen. Results suggest that relation discovery using indirect knowledge is possible, with an AUC that can reach up to 0.8, a result which is a great improvement compared to the random classification, and which shows that good predictions can be prioritized by following the suggested approach. CONCLUSIONS: Analysis of the results indicates that the models can successfully learn expressive path patterns for the examined relations. Furthermore, this work demonstrates that the constructed graph allows for the easy integration of heterogeneous information and discovery of indirect connections between biomedical concepts.

Formalizing biomedical concepts from textual definitions.

BACKGROUND: Ontologies play a major role in life sciences, enabling a number of applications, from new data integration to knowledge verification. SNOMED CT is a large medical ontology that is formally defined so that it ensures global consistency and support of complex reasoning tasks. Most biomedical ontologies and taxonomies on the other hand define concepts only textually, without the use of logic. Here, we investigate how to automatically generate formal concept definitions from textual ones. We develop a method that uses machine learning in combination with several types of lexical and semantic features and outputs formal definitions that follow the structure of SNOMED CT concept definitions. RESULTS: We evaluate our method on three benchmarks and test both the underlying relation extraction component as well as the overall quality of output concept definitions. In addition, we provide an analysis on the following aspects: (1) How do definitions mined from the Web and literature differ from the ones mined from manually created definitions, e.g., MeSH? (2) How do different feature representations, e.g., the restrictions of relations' domain and range, impact on the generated definition quality?, (3) How do different machine learning algorithms compare to each other for the task of formal definition generation?, and, (4) What is the influence of the learning data size to the task? We discuss all of these settings in detail and show that the suggested approach can achieve success rates of over 90%. In addition, the results show that the choice of corpora, lexical features, learning algorithm and data size do not impact the performance as strongly as semantic types do. Semantic types limit the domain and range of a predicted relation, and as long as relations' domain and range pairs do not overlap, this information is most valuable in formalizing textual definitions. CONCLUSIONS: The analysis presented in this manuscript implies that automated methods can provide a valuable contribution to the formalization of biomedical knowledge, thus paving the way for future applications that go beyond retrieval and into complex reasoning. The method is implemented and accessible to the public from: https://github.com/alifahsyamsiyah/learningDL.

An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition.

BACKGROUND: This article provides an overview of the first BIOASQ challenge, a competition on large-scale biomedical semantic indexing and question answering (QA), which took place between March and September 2013. BIOASQ assesses the ability of systems to semantically index very large numbers of biomedical scientific articles, and to return concise and user-understandable answers to given natural language questions by combining information from biomedical articles and ontologies. RESULTS: The 2013 BIOASQ competition comprised two tasks, Task 1a and Task 1b. In Task 1a participants were asked to automatically annotate new PUBMED documents with MESH headings. Twelve teams participated in Task 1a, with a total of 46 system runs submitted, and one of the teams performing consistently better than the MTI indexer used by NLM to suggest MESH headings to curators. Task 1b used benchmark datasets containing 29 development and 282 test English questions, along with gold standard (reference) answers, prepared by a team of biomedical experts from around Europe and participants had to automatically produce answers. Three teams participated in Task 1b, with 11 system runs. The BIOASQ infrastructure, including benchmark datasets, evaluation mechanisms, and the results of the participants and baseline methods, is publicly available. CONCLUSIONS: A publicly available evaluation infrastructure for biomedical semantic indexing and QA has been developed, which includes benchmark datasets, and can be used to evaluate systems that: assign MESH headings to published articles or to English questions; retrieve relevant RDF triples from ontologies, relevant articles and snippets from PUBMED Central; produce "exact" and paragraph-sized "ideal" answers (summaries). The results of the systems that participated in the 2013 BIOASQ competition are promising. In Task 1a one of the systems performed consistently better from the NLM's MTI indexer. In Task 1b the systems received high scores in the manual evaluation of the "ideal" answers; hence, they produced high quality summaries as answers. Overall, BIOASQ helped obtain a unified view of how techniques from text classification, semantic indexing, document and passage retrieval, question answering, and text summarization can be combined to allow biomedical experts to obtain concise, user-understandable answers to questions reflecting their real information needs.

Prediction of drug gene associations via ontological profile similarity with application to drug repositioning.

The amount of biomedical literature has been increasing rapidly during the last decade. Text mining techniques can harness this large-scale data, shed light onto complex drug mechanisms, and extract relation information that can support computational polypharmacology. In this work, we introduce a fully corpus-based and unsupervised method which utilizes the MEDLINE indexed titles and abstracts to infer drug gene associations and assist drug repositioning. The method measures the Pointwise Mutual Information (PMI) between biomedical terms derived from the Gene Ontology and the Medical Subject Headings. Based on the PMI scores, drug and gene profiles are generated and candidate drug gene associations are inferred when computing the relatedness of their profiles. Results show that an Area Under the Curve (AUC) of up to 0.88 can be achieved. The method can successfully identify direct drug gene associations with high precision and prioritize them. Validation shows that the statistically derived profiles from literature perform as good as manually curated profiles. In addition, we examine the potential application of our approach towards drug repositioning. For all FDA approved drugs repositioned over the last 5 years, we generate profiles from publications before 2009 and show that new indications rank high in the profiles. In summary, literature mined profiles can accurately predict drug gene associations and provide insights onto potential repositioning cases.

Automated Patent Categorization and Guided Patent Search using IPC as Inspired by MeSH and PubMed.

Document search on PubMed, the pre-eminent database for biomedical literature, relies on the annotation of its documents with relevant terms from the Medical Subject Headings ontology (MeSH) for improving recall through query expansion. Patent documents are another important information source, though they are considerably less accessible. One option to expand patent search beyond pure keywords is the inclusion of classification information: Since every patent is assigned at least one class code, it should be possible for these assignments to be automatically used in a similar way as the MeSH annotations in PubMed. In order to develop a system for this task, it is necessary to have a good understanding of the properties of both classification systems. This report describes our comparative analysis of MeSH and the main patent classification system, the International Patent Classification (IPC). We investigate the hierarchical structures as well as the properties of the terms/classes respectively, and we compare the assignment of IPC codes to patents with the annotation of PubMed documents with MeSH terms.Our analysis shows a strong structural similarity of the hierarchies, but significant differences of terms and annotations. The low number of IPC class assignments and the lack of occurrences of class labels in patent texts imply that current patent search is severely limited. To overcome these limits, we evaluate a method for the automated assignment of additional classes to patent documents, and we propose a system for guided patent search based on the use of class co-occurrence information and external resources.

A Maximum-Entropy approach for accurate document annotation in the biomedical domain.

The increasing number of scientific literature on the Web and the absence of efficient tools used for classifying and searching the documents are the two most important factors that influence the speed of the search and the quality of the results. Previous studies have shown that the usage of ontologies makes it possible to process document and query information at the semantic level, which greatly improves the search for the relevant information and makes one step further towards the Semantic Web. A fundamental step in these approaches is the annotation of documents with ontology concepts, which can also be seen as a classification task. In this paper we address this issue for the biomedical domain and present a new automated and robust method, based on a Maximum Entropy approach, for annotating biomedical literature documents with terms from the Medical Subject Headings (MeSH).The experimental evaluation shows that the suggested Maximum Entropy approach for annotating biomedical documents with MeSH terms is highly accurate, robust to the ambiguity of terms, and can provide very good performance even when a very small number of training documents is used. More precisely, we show that the proposed algorithm obtained an average F-measure of 92.4% (precision 99.41%, recall 86.77%) for the full range of the explored terms (4,078 MeSH terms), and that the algorithm's performance is resilient to terms' ambiguity, achieving an average F-measure of 92.42% (precision 99.32%, recall 86.87%) in the explored MeSH terms which were found to be ambiguous according to the Unified Medical Language System (UMLS) thesaurus. Finally, we compared the results of the suggested methodology with a Naive Bayes and a Decision Trees classification approach, and we show that the Maximum Entropy based approach performed with higher F-Measure in both ambiguous and monosemous MeSH terms.

Advanced exergoenvironmental analysis of a near-zero emission power plant with chemical looping combustion.

Carbon capture and storage (CCS) from power plants can be used to mitigate CO(2) emissions from the combustion of fossil fuels. However, CCS technologies are energy intensive, decreasing the operating efficiency of a plant and increasing its costs. Recently developed advanced exergy-based analyses can uncover the potential for improvement of complex energy conversion systems, as well as qualify and quantify plant component interactions. In this paper, an advanced exergoenvironmental analysis is used for the first time as means to evaluate an oxy-fuel power plant with CO(2) capture. The environmental impacts of each component are split into avoidable/unavoidable and endogenous/exogenous parts. In an effort to minimize the environmental impact of the plant operation, we focus on the avoidable part of the impact (which is also split into endogenous and exogenous parts) and we seek ways to decrease it. The results of the advanced exergoenvironmental analysis show that the majority of the environmental impact related to the exergy destruction of individual components is unavoidable and endogenous. Thus, the improvement potential is rather limited, and the interactions of the components are of lower importance. The environmental impact of construction of the components is found to be significantly lower than that associated with their operation; therefore, our suggestions for improvement focus on measures concerning the reduction of exergy destruction and pollutant formation.