On-going development of five geochemical monitoring technologies for onshore CCS

A critical aspect of Carbon Capture and Storage (CCS) will be the ability to adequately monitor the injection site, both to ensure public and environmental safety and for “carbon credit auditing”. In the unlikely event of a leakage in the near-surface environment, the study of natural CO2 emanations in volcanic and geothermal environments have shown that the gas will tend to migrate along the path of least resistance and create spatially restricted “hotspot” leaks at the ground surface that can be challenging to find and quantify. For this reason, innovative technologies are required to improve our ability to detect, locate and characterize such features. To address this need our group is developing geochemical monitoring tools that confront the significant challenges associated with spatial, analytical and temporal resolution and sensitivity. Here we describe on-going work focused on increasing the Technology Readiness Level (TRL) of five prototypes and concepts developed by the Tectonics and Fluid Chemistry Lab (TFCL) at Sapienza University of Rome: the GasPro, Mapper, Multipla, Well-Star, and SWiM systems

Considerations on Approaches and Metrics in Automated Theorem Generation/Finding in Geometry

The pursue of what are properties that can be identified to permit an automated reasoning program to generate and find new and interesting theorems is an interesting research goal (pun intended). The automatic discovery of new theorems is a goal in itself, and it has been addressed in specific areas, with different methods. The separation of the "weeds", uninteresting, trivial facts, from the "wheat", new and interesting facts, is much harder, but is also being addressed by different authors using different approaches. In this paper we will focus on geometry. We present and discuss different approaches for the automatic discovery of geometric theorems (and properties), and different metrics to find the interesting theorems among all those that were generated. After this description we will introduce the first result of this article: An undecidability result proving that having an algorithmic procedure that decides for every possible Turing Machine that produces theorems, whether it is able to produce also interesting theorems, is an undecidable problem. Consequently, we will argue that judging whether a theorem prover is able to produce interesting theorems remains a non deterministic task, at best a task to be addressed by program based in an algorithm guided by heuristics criteria. Therefore, as a human, to satisfy this task two things are necessary: An expert survey that sheds light on what a theorem prover/finder of interesting geometric theorems is, and-to enable this analysis- other surveys that clarify metrics and approaches related to the interestingness of geometric theorems. In the conclusion of this article we will introduce the structure of two of these surveys -the second result of this article- and we will discuss some future work.</p

Mapping and quantifying CO2 leakage using the Ground CO2 Mapper

The standard method for mapping and quantifying CO2 leakage flux from the ground surface to the atmosphere involves performing numerous point flux measurements using the accumulation chamber technique and then applying geostatistical interpolation to infer spatial distribution and estimate total mass transfer. Monte Carlo simulations using the program MCFlux have recently demonstrated, however, that uncertainty in the resultant estimate can be large if the chosen sample spacing is insufficient to capture the spatial complexity and size distribution of the leakage anomalies. In an effort to reduce this uncertainty we have developed a new tool, called the Ground CO2 Mapper, that rapidly measures the concentration of CO2 at the ground surface as a proxy for flux. Recently published results have illustrated the capabilities of the Mapper in terms of sensitivity and spatial resolution, as well as possible influencing parameters such as wind strength. The present work examines the potential of combining Mapper results with point flux measurements (using multivariate geostatistics) to improve data interpretation, with the MCFlux program being used once again to assess uncertainty in the final estimates

On-going and future research at the Sulcis site in Sardinia, Italy. Characterization and experimentation at a possible future CCS pilot

National Italian funding has recently been allocated for the construction of a 350 MWe coal-fired power plant / CCS demonstration plant in the Sulcis area of SW Sardinia, Italy. In addition, the recently approved EC-funded ENOS project (ENabling Onshore CO2 Storage in Europe) will use the Sulcis site as one of its main field research laboratories. Site characterization is already ongoing, and work has begun to design gas injection experiments at 100-200 m depth in a fault. This article gives an overview of results to date and plans for the future from the Sapienza University of Rome research group

Preliminary results of geological characterization and geochemical monitoring of Sulcis Basin (Sardinia), as a potential CCS site

The Sulcis Basin is an area situated in SW Sardinia (Italy) and is a potential site for the development of CCS in Italy. This paper illustrates the preliminary results of geological characterization of fractured carbonate reservoir (Miliolitico Fm.) and the sealing sequence, composed by clay, marl and volcanic rocks, with a total thickness of more than 900 m. To characterize the reservoircaprock system an extensive structural-geological survey at the outcrop was conducted. It was also performed a study of the geochemical monitoring, to define the baseline conditions, measuring CO2 concentrations and flux in the study site

Enhancing Embedding Representations of Biomedical Data using Logic Knowledge

Knowledge Graph Embeddings (KGE) have become a quite popular class of models specifically devised to deal with ontologies and graph structure data, as they can implicitly encode statistical dependencies between entities and relations in a latent space. KGE techniques are particularly effective for the biomedical domain, where it is quite common to deal with large knowledge graphs underlying complex interactions between biological and chemical objects. Recently in the literature, the PharmKG dataset has been proposed as one of the most challenging knowledge graph biomedical benchmark, with hundreds of thousands of relational facts between genes, diseases and chemicals. Despite KGEs can scale to very large relational domains, they generally fail at representing more complex relational dependencies between facts, like logic rules, which may be fundamental in complex experimental settings. In this paper, we exploit logic rules to enhance the embedding representations of KGEs on the PharmKG dataset. To this end, we adopt Relational Reasoning Network (R2N), a recently proposed neural-symbolic approach showing promising results on knowledge graph completion tasks. An R2N uses the available logic rules to build a neural architecture that reasons over KGE latent representations. In the experiments, we show that our approach is able to significantly improve the current state-of-the-art on the PharmKG dataset. Finally, we provide an ablation study to experimentally compare the effect of alternative sets of rules according to different selection criteria and varying the number of considered rules

Surgical Management of Medication-Related Osteonecrosis of the Jaw Patients Related to Dental Implants

The aim of the present study is to report a case series of patients with peri-implant medication-related osteonecrosis of the jaw (MRONJ), in particular describing the onset of the condition and surgical treatment outcome

A LOGICAL MODELING OF SEVERE IGNORANCE

In the logical context, ignorance is traditionally defined recurring to epistemic logic $S_4$ \cite{Hintikka1962}. In particular, an agent ignores a formula $\varphi$ when s/he does not know neither $\varphi$ nor its negation $\neg\varphi$: \neg\K\varphi\land\neg\K\neg\varphi (where \K is the epistemic operator for knowledge). In other words, ignorance is essentially interpreted as ``lack of knowledge''. \textcolor{red}{This received view has - as we point out - some problems, in particular we will highlight how it does not allow to express a type of content-theoretic ignorance, i.e. an ignorance of $\varphi$ that stems from an unfamiliarity with its meaning.} Contrarily to this trend, in this paper, we introduce and investigate a modal logic having a primitive epistemic operator \I, modeling ignorance. Our modal logic is essentially constructed on the modal logics based on weak Kleene three-valued logic introduced by Krister Segerberg \cite{Segerberg67}. Such non-classical propositional basis allows to define a Kripke-style semantics with the following, very intuitive, interpretation: a formula $\varphi$ is ignored by an agent if $\varphi$ is neither true nor false in every world accessible to the agent. As a consequence of this choice, we obtain \textcolor{red}{a type of content-theoretic} notion of ignorance, which is essentially different from the traditional approach based on $S_4$. \textcolor{red}{We dub it \emph{severe ignorance}.} We axiomatize, prove completeness and decidability for the logic of reflexive (three-valued) Kripke frames, which we find the most suitable candidate for our novel proposal and, finally, compare our approach with the most traditional one