Machine learning of phases and mechanical properties in complex concentrated alloys

The mechanical properties of complex concentrated alloys (CCAs) depend on their formed phases and corresponding microstructures.The data-driven prediction of the phase formation and associated mechanical properties is essential to discovering novel CCAs.The present work collects 557 samples of various chemical compositions,comprising 61 amorphous,167 single-phase crystalline,and 329 multi-phases crystalline CCAs.Three classification models are developed with high accuracies to category and understand the formed phases of CCAs.Also,two regression models are constructed to predict the hard-ness and ultimate tensile strength of CCAs,and the correlation coefficient of the random forest regression model is greater than 0.9 for both of two targeted properties.Furthermore,the Shapley additive expla-nation (SHAP) values are calculated,and accordingly four most important features are identified.A significant finding in the SHAP values is that there exists a critical value in each of the top four fea-tures,which provides an easy and fast assessment in the design of improved mechanical properties of CCAs.The present work demonstrates the great potential of machine learning in the design of advanced CCAs.     

Boccia game simulator: Serious game adapted for people with disabilities

Integration in the world of sport is one way for individuals with disabilities or motor disorders to feel more socially integrated, independent, and confident. Boccia is a Paralympic sport, which is increasingly getting more attention around the world. These facts have contributed to the objectives of this work. Including it in the serious games category enables to develop and rehabilitate the cognitive capabilities. The main focus was BC3 classification athletes (users with limited motor characteristics that require the use of an assistive device—a ramp, in this case). This paper describes a realistic Boccia game simulator adapted for people with disabilities that integrates a set of features that includes real physics and social features. These features can be used to enhance the interest of nonpractitioners of the sport and to improve the training conditions. The official Boccia regulation was added to the design of the simulator. The usability and approximation to the reality of the simulator were tested and validated based on the tests performed and data collected via a survey of users with no motor or psychological disorders. Realism and usability rating was almost excellent, and good results were achieved at the assessment of the game experience.     

Coupling in situ experiments and modeling – Opportunities for data fusion,machine learning,and discovery of emergent behavior

This paper reviews recent studies, that not only includes both experiments and modeling components, but celebrates a close coupling between these techniques, in order to provide insights into the plasticity and failure of polycrystalline metals. Examples are provided of studies across multiple-scales, including, but not limited to, density functional theory combined with atom probe tomography, molecular dynamics combined with in situ transmission electron miscopy, discrete dislocation dynamics combined with nanopillars experiments, crystal plasticity combined with digital image correlation, and crystal plasticity combined with in situ high energy X-ray diffraction. The close synergy between in situ experiments and modeling provides new opportunities for model calibration, verification, and validation, by providing direct means of comparison, thus removing aspects of epistemic uncertainty in the approach. Further, data fusion between in situ experimental and model-based data, along with data driven approaches, provides a paradigm shift for determining the emergent behavior of deformation and failure, which is the foundation that underpins the mechanical behavior of polycrystalline materials.     

Social media in conflicts and crises

ABSTRACT

The growing importance of social media in conflicts and crises is accompanied by an ever-increasing research interest in the crisis informatics field in order to identify potential benefits and develop measures against the technology’s abuse. This special issue sets out to give an overview of current research on the use of social media in conflicts and crises. In doing so, it focuses on both good and malicious aspects of social media and includes a variety of papers of conceptual, theoretical and empirical nature. In six sections, the special issue presents an overview of the field, analytical methods, technical challenges, current advancements and the accepted papers before concluding. Specific topics range from cyber deception over information trustworthiness to mining and near-real-time processing of social media data.     

Feature-based ontological framework for semantic interoperability in product development

An essential requirement in integrating tasks in product development is to have a seamless exchange of product information through the entire product lifecycle. A key challenge in the integration is the exchange of shape semantics in terms of understandable labels and representations. A unified taxonomy is proposed to represent, classify, and extract shape features. This taxonomy is built using the Domain-Independent Form Feature (DIFF) model as the representation of features. All the shape features in a product model are classified under three main classes, namely, volumetric features, deformation features and free-form surface features. Shape feature ontology is developed using the unified taxonomy, which brings the shape features under a single reasoning framework. One-to-many reasoning framework is presented for mapping semantically equivalent information (label and representation) of the feature to be exchanged to target applications, and the reconstruction of the shape model automatically in that target application. An algorithm has been developed to extract the semantics of shape features and construct the model in the target application. The algorithm developed has been tested for shape models taken from literature and test cases are selected based on variations of topology and geometry. Results of exchanging product information are presented and discussed. Finally, the limitations of the proposed method for exchanging product information are explained.     

Nanoinformatics: Emerging Databases and Available Tools

Nanotechnology has arisen as a key player in the field of nanomedicine. Although the use of engineered nanoparticles is rapidly increasing, safety assessment is also important for the beneficial use of new nanomaterials. Considering that the experimental assessment of new nanomaterials is costly and laborious, in silico approaches hold promise. Several major challenges in nanotechnology indicate a need for nanoinformatics. New database initiatives such as ISA-TAB-Nano, caNanoLab, and Nanomaterial Registry will help in data sharing and developing data standards, and, as the amount of nanomaterials data grows, will provide a way to develop methods and tools specific to the nanolevel. In this review, we describe emerging databases and tools that should aid in the progress of nanotechnology research.     

Manipulating user resistance to large-scale information systems through influence tactics

This study examines how influence tactics can be used to manipulate user resistance and subsequent usage behaviors in the context of large-scale, newly implemented information systems (IS). To do so, we integrate the theory of interpersonal influence and leadership, adaptive structuration theory, and the literature on user resistance and behavioral IS theories, and develop a comprehensive theoretical model anchored in (a) social influence tactics, (b) IT artifact perceptions, and (c) user resistance and subsequent behaviors. The resultant theoretical model explains how influence tactics modulate user perceptions, user resistance to the system, and ultimately the unfaithful use of the system. This model is tested in the context of an electronic medical record (EMR) system implementation, with a sample of 442 clinicians who were exposed to various influence tactics and with the use of SEM techniques. The results indicate that certain influence tactics are beneficial in reducing user resistance while others can unintentionally increase resistance. Resistance, in turn, drives adaptive user behaviors in the form of unfaithful use of the system. Thus, it is possible to manipulate resistance and resultant adaptive behaviors through influence tactics, without necessarily altering the IT artifact. This is particularly important, as many off-the-shelf IT artifacts cannot be substantially modified.     

Collection of daily patient reported outcomes is feasible and demonstrates differential patient experience in chronic kidney disease

Introduction: Patient reported outcomes (PROs) are a critical metric documenting the impact of disease and treatment from the patient's perspective. A variety of generic and disease specific PRO measures (PROMs) are used in chronic kidney disease (CKD) but studies are primarily cross‐sectional. None of the available PROMs are designed for frequent iterative application. Methods: An online PROM for daily use in dialysis and CKD 4/5 patients was developed. The custom website utilised visual analogue scales to capture 6 PROs (general well being (GWB), pain, sleep, breathing, energy, and appetite). Outcomes of interest were uptake, response rates, intermodality variation, and change in PRO corresponding to predefined events. Findings: Forty‐three patients submitted at least once and 34 submitted beyond 30 days. Median follow‐up was 247 days, 64% male, age 62 ± 12 years. In individuals submitting for >30 days, dialysis patients had significantly worse median scores compared to CKD for sleep (47[32–80], 97[76–99], P = 0.003), appetite (66[50–96], 97[88–100], P = 0.008), energy (47[40–89], 84[67–96], P = 0.031), and GWB (63[49–94], 93[71–98], P = 0.026). Patients demonstrated a variety of stable bandwidths of response, deviations from this were associated with specific events e.g., acute admission, vascular procedures, disturbed fluid status, and dialysis start. Discussion: We successfully introduced an online, patient acceptable, iterative PROM that discriminates symptom burden, cross‐sectionally, and longitudinally. Further work will prospectively examine the predictive power of changes in PRO and more rigorously investigate the potential use of these methods to optimise patient care.     

Iconic languages: Towards end-user programming of mobile applications

After tracing the steps that led to the current generation of iconic languages starting from the original idea of S.K. Chang, we describe an iconic language, named MicroApp, for modeling pervasive mobile applications directly on the mobile device. MicroApp exploits generalized icons for composing mobile applications: services are represented by icons and are composed of adopting colors for representing data-flow. We also qualitatively evaluate the visual environment that implements this iconic language.     

Lung Cancer Assistant: a hybrid clinical decision support application for lung cancer care

Multidisciplinary team (MDT) meetings are becoming the model of care for cancer patients worldwide. While MDTs have improved the quality of cancer care, the meetings impose substantial time pressure on the members, who generally attend several such MDTs. We describe Lung Cancer Assistant (LCA), a clinical decision support (CDS) prototype designed to assist the experts in the treatment selection decisions in the lung cancer MDTs. A novel feature of LCA is its ability to provide rule-based and probabilistic decision support within a single platform. The guideline-based CDS is based on clinical guideline rules, while the probabilistic CDS is based on a Bayesian network trained on the English Lung Cancer Audit Database (LUCADA). We assess rule-based and probabilistic recommendations based on their concordances with the treatments recorded in LUCADA. Our results reveal that the guideline rule-based recommendations perform well in simulating the recorded treatments with exact and partial concordance rates of 0.57 and 0.79, respectively. On the other hand, the exact and partial concordance rates achieved with probabilistic results are relatively poorer with 0.27 and 0.76. However, probabilistic decision support fulfils a complementary role in providing accurate survival estimations. Compared to recorded treatments, both CDS approaches promote higher resection rates and multimodality treatments.