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COVID-19 is an infectious disease with its first recorded cases identified in late 2019, while in March of 2020 it was declared as a pandemic. The outbreak of the disease has led to a sharp increase in posts and comments from social media users, with a plethora of sentiments being found therein. This paper addresses the subject of sentiment analysis, focusing on the classification of users’ sentiment from posts related to COVID-19 that originate from Twitter. The period examined is from March until mid-April of 2020, when the pandemic had thus far affected the whole world. The data is processed and linguistically analyzed with the use of several natural language processing techniques. Sentiment analysis is implemented by utilizing seven different deep learning models based on LSTM neural networks, and a comparison with traditional machine learning classifiers is made. The models are trained in order to distinguish the tweets between three classes, namely negative, neutral and positive.

Conflict analysis focuses on discovering the relationship between agents involved in a dispute and the strategy to resolve the conflicts. Formal concept analysis is an effective data analysis method which can be used for modeling conflict situations in the presence of uncertainty. In this paper, we propose the object-induced (attribute-induced) positive lower and upper approximations operators and the object-induced (attribute-induced) negative lower and upper approximations operators with respect to conflict information system. Meanwhile, we construct the object-induced (attribute-induced) similarity and object-induced (attribute-induced) conflict degree to discover the relationship between agents by combining L −fuzzy formal context and conflict situation. Three basic binary relations on the agents are presented. From the view of graph, maximal clique and alliance region are equivalent. We convert alliance agents to undirected graph in order to compute maximal clique. Then, we put forward an algorithm to compute optimal feasible consensus strategies for resolving conflict problem, which takes three indicators into consideration, that is coverage, comprehensive strength and comprehensive loss. Finally, we provide an illustrative example to verify the validity of the proposed algorithm.

The processing of complex data is admittedly among the major concerns of knowledge discovery from data ( kdd ). Indeed, a major part of the data worth analyzing is stored in relational databases and, since recently, on the Web of Data. This clearly underscores the need for Entity-Relationship and rdf compliant data mining ( dm ) tools. We are studying an approach to the underlying multi-relational data mining ( mrdm ) problem, which relies on formal concept analysis ( fca ) as a framework for clustering and classification. Our relational concept analysis ( rca ) extends fca to the processing of multi-relational datasets, i.e., with multiple sorts of individuals, each provided with its own set of attributes, and relationships among those. Given such a dataset, rca constructs a set of concept lattices, one per object sort, through an iterative analysis process that is bound towards a fixed-point. In doing that, it abstracts the links between objects into attributes akin to role restrictions from description logics ( dls ). We address here key aspects of the iterative calculation such as evolution in data description along the iterations and process termination. We describe implementations of rca and list applications to problems from software and knowledge engineering.

The identification of the source in groundwater pollution is the only way to drastically deal with resulting environmental problems. This can only be achieved by an appropriate monitoring network, the optimization of which is prerequisite for the solution of the inverse modeling problem, i.e., identifying the source of the pollutant on the basis of measurements taken within the pollution field. For this reason, a theoretical confined aquifer with two pumping wells and six suspected sources is studied. Simulations of combinations of possible source locations, and hydraulic parameters, produce sets of measurement features for a 29 × 29 grid representing potential monitoring wells. Three sets of simulations are conducted to produce synthetic datasets, representing different groundwater pollution modeling methods. Features (input- X variables) coupled with respective sources (output- Y variables) are formulated in two different dataset formats (Types A, B) in order to train classification (random forests, multilayer perceptron) and computer vision (convolutional neural networks) algorithms, respectively, to solve the inverse modeling problem. In addition, appropriate feature selection and trial-and-error tests are employed for supporting the optimization of monitoring wells’ number, locations and sampling frequency. The methodology can successfully produce various sub-optimal monitoring strategies for various budgets.

Professional socialization is defined as a process through which a person becomes a legitimate member of a professional society. This will have a great impact on an individual’s professional conduct and morality. The aim of this study was to clarify this concept and reduce the ambiguities around it. This was a qualitative research through which the concept of professional socialization was analyzed using Walker and Avant’s eight-step approach. The review of literature for this concept was done using electronic database without any time limitation. The overall search produced about 780 articles, and after reviewing these articles, 21 were selected purposefully. Based on concept analysis, we propose the following analytical definition: Professional socialization is a nonlinear, continuous, interactive, transformative, personal, psychosocial and selfreinforcing process that is formed through internalization of the specific culture of a professional community, and can be affected by individual, organizational and interactional factors. This definition is in accordance with the interactionism perspective. Existence of a particular profession and getting involved in a community of practice are the antecedents of this process, and formation of professional identity and professional development are its consequences. A case model, as well as borderline and related cases, has been introduced for this concept. The results of this study can be used to design useful educational interventions to conduct and facilitate the process.

The study aimed to provide a detailed description of a process to conduct a phased principle-based concept analysis and to introduce quality criteria assessment for a phased principle-based concept analysis. Concept analysis explores how a concept is described, used and measured in the literature. This conceptual understanding is important to guide translational research to direct the development of evidence-based practice. The principle-based concept analysis is one approach of concept analysis used in published work, but the literature is lacking in articles clearly describing how to conduct it in practice. This article provides a methodology utilising a phased approach and by advancing on previous work; this approach includes a combination of a systematic search, quality criteria and qualitative analysis with principle-based concept analysis. Quality criteria for a phased principle-based concept analysis is introduced to critically assess articles against the four principles: epistemology, pragmatic, linguistic and logical. These improvements to the methodology promote transparency, rigour and replicability. This comprehensive systematic approach will aid future phased principle-based concept analyses and enable future comparisons of concept development, advancement and related concepts to improve the evidence base.

Due to the high complexity of real problems, a considerable amount of research that deals with high volumes of information has emerged. The literature has considered new applications of data analysis for high dimensional environments in order to manage the difficulty in extracting knowledge from a database, especially with the increase in social and professional networks. Tri- adic Concept Analysis (TCA) is a technique used in the applied mathematical area of data analysis. Its main purpose is to enable knowledge extraction from a context that contains objects, attributes, and conditions in a hierarchical and systematized representation. There are several algorithms that can extract concepts, but they are inefficient when applied to large datasets because the compu- tational costs are exponential. The objective of this paper is to add a new data structure, binary decision diagrams (BDD), in the TRIAS algorithm and retrieve triadic concepts for high dimen- sional contexts. BDD was used to characterize formal contexts, objects, attributes, and conditions. Moreover, to reduce the computational resources needed to manipulate a high-volume of data, the usage of BDD was implemented to simplify and represent data. The results show that this method has a considerably better speedup when compared to the original algorithm. Also, our approach discovered concepts that were previously unachievable when addressing high dimensional contexts.

Aim This article explores and defines the concept of autonomy in nursing practice. Design Principle‐based concept analysis method. Methods The literature published between February 2013 and February 2023 was searched in CINAHL, APA PsycINFO, Academic Search Complete and Medline databases. The findings of selected articles were analysed and synthesised using a deductive approach in line with the four principles of the principle‐based concept analysis framework, as outlined by Penrod and Hupcey (2005). Preconditions, attributes and outcomes of autonomy in nursing were derived, and a theoretical definition of autonomy was formulated and discussed. Results Ninety‐nine full‐text articles were retrieved, and 37 were included for analysis. The concept of autonomy has been studied and used in nursing. Although the definition of autonomy in nursing is generally defined, some variations and inconsistencies remain in the current nursing literature. Multiple measurement scales have been developed to measure the level of nurses’ autonomy. Universal attributes were identified and developed based on the literature. Conclusion Autonomy in nursing has been a desired notion in the profession. The level of autonomy is subject to internal and external influencers and varies from individual characteristics, culture, organisational and professional arrangements. A knowledge gap remains, and further research is needed to clarify the impact of autonomy in nursing and its relationship with other concepts, such as collaboration between physicians and nurses, as well as job stress and burnout.

Formal Concept Analysis (FCA) is a field of applied mathematics with its roots in order theory. Over the past 20 years, FCA has been widely studied. Knowledge Graphs (KGs) model factual information in the form of entities and relations between them to semantically represent the world’s truth. Since existing semantic FCA (or ontology-based FCA) only relies on ontological knowledge, ontology-based FCA is limited in scope and scalability. This paper theoretically and empirically investigates a new semantic FCA by exploiting KGs to solve the scalability of ontology-based FCA. Concretely, we propose a kind of novel semantic FCA (i.e., KGs-based Formal Concept Analysis, KGs-based FCA) by semantifing FCA based on KGs. We further expand KGs-based FCA and propose a KGs-based FFCA (KGs-based Fuzzy Formal Concept Analysis) by extending KGs-based FCA. We also investigate the properties of KGs-based FCA and KGs-based FFCA in theory. The experimental results show that our proposals (KGs-based FCA and KGs-based FFCA) significantly outperform other traditional FCA and semantic FCA in information retrieval, and is 15.96–16.30 higher in Retrieval Results Ratio (R3) than other traditional FCA and semantic FCA.

Managing and extracting information from databases is one of the main goals in several fields, as in Formal Concept Analysis (FCA). One-sided concept lattices and multi-adjoint concept lattices are two frameworks in FCA that have been developed in parallel. This paper shows that one-sided concept lattices are particular cases of multi-adjoint concept lattices. As a first consequence of this characterization, a new attribute reduction mechanism has been introduced in the one-side framework.