Managing Information to Support the Decision Making Process

Decision-making is a crucial activity during the planning, design and operation of artefacts. To make a decision several alternatives must be evaluated and compared, which are tasks that require information, knowledge and expertise. A system that organises and manages the knowledge associated with every alternative and links ideas, arguments and issues can greatly improve and facilitate the decision making process. This paper presents how an Issue Based Information System (IBIS) implemented in Compendium (http://compendium.open.ac.uk) has been extended with new functionalities such as access to a toolkit of Multi-Criteria Decision Methods (MCDM), the ability to propagate values throughout the decision records and to perform sensitivity analysis of the recommended decisions with respect to a parameter. These additional functionalities enable the applicability of the system in the support of decisions that require not only argumentation, but also numerical evaluation of the properties of the alternatives such as those proposed during the design, planning and operation of engineering artefacts. </jats:p

Towards semantic knowledge mapping: an extension of compendium with semantic knowledge representation

Compendium is a knowledge mapping application equipped with graphical representations of ideas and arguments. Extension of the processes in Compendium with Semantic Web technologies can be beneficial for the intelligent searching of concepts or ideas, and supporting decision making process. This paper presents the extended Compendium which exploits the Semantic Web for knowledge representation and user interaction. The result evaluated by the experts and users showed that the extension eases and streamlines the decision making process

An Accurate Data Preparation Approach for the Prediction of Mortality in ACLF Patients using the CANONIC Dataset

The incidence of chronic liver disease has increased in Europe and can lead to Acute on Chronic Liver Failure (ACLF) which is associated with high levels of mortality due to multisystem organ failure. The characteristics of the ACLF patients can change very rapidly within a short period of time. Continuous assessment of their recovery status is critical for clinicians to adjust and deliver effective treatment. The aim of this paper is to validate the usefulness of a data preparation approach by combining different criteria to replace missing values, balance target-class variables, select useful patient characteristics and optimise hyperparameters of machine learning models for the prediction of ACLF associated mortality rates. A key step in the data preparation is a feature selection Mutual Information (MI) based multivariate approach to build smaller, and yet equally and in some cases more informative, subsets of patient characteristics than those frequently proposed for the prediction of mortality, from patients with ACLF in the CANONIC dataset. The usefulness of the data preparation approach proposed to predict mortality was evaluated by training the XGBoost and Logistic Regression models with the prepared data. Evaluations of the models trained using a test set provided evidence of an overall high accuracy in the prediction of the mortality rates of patients for days after their diagnosis, and in some cases even higher when reduced and more informative subsets of patient characteristics were found

Conical intersection and coherent vibrational dynamics in alkyl iodides captured by attosecond transient absorption spectroscopy

The photodissociation dynamics of alkyl iodides along the C–I bond are captured by attosecond extreme-ultraviolet (XUV) transient absorption spectroscopy employing resonant ∼20 fs UV pump pulses. The methodology of previous experiments on CH3I [Chang et al., J. Chem. Phys. 154, 234301 (2021)] is extended to the investigation of a C–I bond-breaking reaction in the dissociative A-band of C2H5I, i-C3H7I, and t-C4H9I. Probing iodine 4d core-to-valence transitions in the XUV enables one to map wave packet bifurcation at a conical intersection in the A-band as well as coherent vibrations in the ground state of the parent molecules. Analysis of spectroscopic bifurcation signatures yields conical intersection crossing times of 15 ± 4 fs for CH3I, 14 ± 5 fs for C2H5I, and 24 ± 4 fs for i-C3H7I and t-C4H9I, respectively. Observations of coherent vibrations, resulting from a projection of A-band structural dynamics onto the ground state by resonant impulsive stimulated Ramanscattering, indirectly reveal multimode C–I stretch and CCI bend vibrations in the A-bands of C2H5I, i-C3H7I, and t-C4H9

Predictive Value of Carcinoembryonic Antigen in Symptomatic Patients without Colorectal Cancer: A Post-Hoc Analysis within the COLONPREDICT Cohort

We aimed to assess the risk of cancer in patients with abdominal symptoms after a complete colonoscopy without colorectal cancer (CRC), according to the carcinoembryonic antigen (CEA) concentration, as well as its diagnostic accuracy. For this purpose, we performed a post-hoc analysis within a cohort of 1431 patients from the COLONPREDICT study, prospectively designed to assess the fecal immunochemical test accuracy in detecting CRC. Over 36.5 +/- 8.4 months, cancer was detected in 115 (8%) patients. Patients with CEA values higher than 3 ng/mL revealed an increased risk of cancer (HR 2.0, 95% CI 1.3-3.1), CRC (HR 4.4, 95% CI 1.1-17.7) and non-gastrointestinal cancer (HR 1.7, 95% CI 1.0-2.8). A new malignancy was detected in 51 (3.6%) patients during the first year and three variables were independently associated: anemia (OR 2.8, 95% CI 1.3-5.8), rectal bleeding (OR 0.3, 95% CI 0.1-0.7) and CEA level >3 ng/mL (OR 3.4, 95% CI 1.7-7.1). However, CEA was increased only in 31.8% (95% CI, 16.4-52.7%) and 50% (95% CI, 25.4-74.6%) of patients with and without anemia, respectively, who would be diagnosed with cancer during the first year of follow-up. On the basis of this information, CEA should not be used to assist in the triage of patients presenting with lower bowel symptoms who have recently been ruled out a CRC

Materials characterisation and software tools as key enablers in Industry 5.0 and wider acceptance of new methods and products

Recently, the NMBP-35 Horizon 2020 projects -NanoMECommons, CHARISMA, and Easi-stress -organised a collaborative workshop to increase awareness of their contributions to the industry "commons" in terms of characterisation and digital transformation. They have established interoperability standards for knowledge management in characterisation and introduced new solutions for materials testing, aided by the standardisation of faster and more accurate assessment methods. The lessons learned from these projects and the discussions during the joint workshop emphasised the impact of recent developments and emerging needs in the field of characterisation. Specifically, the focus was on enhancing data quality through harmonisation and stand-ardisation, as well as making advanced technologies and instruments accessible to a broader community with the goal of fostering increased trust in new products and a more skilled society. Experts also highlighted how characterisation and the corresponding experimental data can drive future innovation agendas towards tech-nological breakthroughs. The focus of the discussion revolved around the characterisation and standardisation processes, along with the collection of modelling and characterisation tools, as well as protocols for data ex-change. The broader context of materials characterisation and modelling within the materials community was explored, drawing insights from the Materials 2030 Roadmap and the experiences gained from NMBP-35 pro-jects. This whitepaper has the objective of addressing common challenges encountered by the materials com-munity, illuminating emerging trends and evolving techniques, and presenting the industry's perspective on emerging requirements and past success stories. It accomplishes this by providing specific examples and high-lighting how these experiences can create fresh opportunities and strategies for newcomers entering the market. These advancements are anticipated to facilitate a more efficient transition from Industry 4.0 to 5.0 during the industrial revolution

A computational view on nanomaterial intrinsic and extrinsic features for nanosafety and sustainability

In recent years, an increasing number of diverse Engineered Nano-Materials (ENMs), such as nanoparticles and nanotubes, have been included in many technological applications and consumer products. The desirable and unique properties of ENMs are accompanied by potential hazards whose impacts are difficult to predict either qualitatively or in a quantitative and predictive manner. Alongside established methods for experimental and computational characterisation, physics-based modelling tools like molecular dynamics are increasingly considered in Safe and Sustainability-by-design (SSbD) strategies that put user health and environmental impact at the centre of the design and development of new products. Hence, the further development of such tools can support safe and sustainable innovation and its regulation. This paper stems from a community effort and presents the outcome of a four-year-long discussion on the benefits, capabilities and limitations of adopting physics-based modelling for computing suitable features of nanomaterials that can be used for toxicity assessment of nanomaterials in combination with data-based models and experimental assessment of toxicity endpoints. We review modern multiscale physics-based models that generate advanced system-dependent (intrinsic) or timeand environment-dependent (extrinsic) descriptors/features of ENMs (primarily, but not limited to nanoparticles, NPs), with the former being related to the bare NPs and the latter to their dynamic fingerprinting upon entering biological media. The focus is on (i) effectively representing all nanoparticle attributes for multicomponent nanomaterials, (ii) generation and inclusion of intrinsic nanoform properties, (iii) inclusion of selected extrinsic properties, (iv) the necessity of considering distributions of structural advanced features rather than only averages. This review enables us to identify and highlight a number of key challenges associated with ENMs’ data generation, curation, representation and use within machine learning or other advanced data-driven models to ultimately enhance toxicity assessment. Finally, the set up of dedicated databases as well as the development of grouping and read-across strategies based on the mode of action of ENMs using omics methods are identified as emerging methodologies for safety assessment and reduction of animal testing