The Computational Thematic Analysis Toolkit

As online communities have grown, Computational Social Science has rapidly developed new techniques to study them. However, these techniques require researchers to become experts in a wide variety of tools in addition to qualitative and computational research methods. Studying online communities also requires researchers to constantly navigate highly contextual ethical and transparency considerations when engaging with data, such as respecting their members' privacy when discussing sensitive or stigmatized topics. To overcome these challenges, we developed the Computational Thematic Analysis Toolkit, a modular software package that supports analysis of online communities by combining aspects of reflexive thematic analysis with computational techniques. Our toolkit demonstrates how common analysis tasks like data collection, cleaning and filtering, modelling and sampling, and coding can be implemented within a single visual interface, and how that interface can encourage researchers to manage ethical and transparency considerations throughout their research process.

Computer Simulations of Deep Eutectic Solvents: Challenges, Solutions, and Perspectives

Deep eutectic solvents (DESs) are one of the most rapidly evolving types of solvents, appearing in a broad range of applications, such as nanotechnology, electrochemistry, biomass transformation, pharmaceuticals, membrane technology, biocomposite development, modern 3D-printing, and many others. The range of their applicability continues to expand, which demands the development of new DESs with improved properties. To do so requires an understanding of the fundamental relationship between the structure and properties of DESs. Computer simulation and machine learning techniques provide a fruitful approach as they can predict and reveal physical mechanisms and readily be linked to experiments. This review is devoted to the computational research of DESs and describes technical features of DES simulations and the corresponding perspectives on various DES applications. The aim is to demonstrate the current frontiers of computational research of DESs and discuss future perspectives.

Nature of Electronic Excitations in Non-stoichiometric Quantum Dots

Colloidal quantum dots (QDs) have emerged as nanocrystalline semiconductors with tunable optoelectronic properties that have attracted attention for numerous commercial applications. While a significant amount of computational research has focused...

Technology Elaboration of the Semi-finished Dough Product “Amygdalaceous” for Cheesecakes

Topicality. In this paper common nutritional status disorders of the present are analysed. Additionally, it is determined that one of the leading places among them is given to the zinc deficiency. The probable causes of zinc deficiency are offered, and the works of scientists dealing with this problem are analysed. It is defined that the development of zinc enriched technologies in desserts production for restaurant industry establishments, as well as semi-finished products for their yielding, is of urgent importance. Aim and methods. The aim of this study is to substantiate and elaborate the technology of the semi-finished dough product “Amygdalaceous” for cheesecakes, which should be characterized by a raised zinc content. To achieve the set aim, empirical, organoleptic, mathematical, statistical and computational research methods are used. Results. Recipes of the semi-finished dough product model compositions are substantiated and created, their physico-chemical parameters and organoleptic characteristics are studied. The technology of the semi-finished dough product “Amygdalaceous” for cheesecakes is elaborated, the technological scheme of its production is offered. The chemical content and energy value of the elaborated semi-finished product are analysed, as well as its integral score is calculated. Conclusions and discussion. It is determined that the model composition is characterised by the best indicators, which provides adding in the recipe the almond flour in amount 14.5 % mass. It is revealed that the elaborated product is characterised by the raised content of proteins, fats, zinc, potassium, calcium, magnesium and phosphorus, and the decreased content of carbohydrates. It is proved that the consumption of the elaborated semi-finished product can provide the daily requirement for zinc by 31,87 %, which corresponds to the mentioned above aim of the study. The scientific novelty of the obtained results lies in the development of technologies elaboration principles for meals, culinary and semi-finished products with a raised zinc content. The practical significance of the obtained results can be seen in the expansion of semi-finished products and desserts assortment for restaurant industry establishments, and in assistance of the Ukrainian nation enhancement.

Graphical integrity issues in open access publications: Detection and patterns of proportional ink violations

Academic graphs are essential for communicating complex scientific ideas and results. To ensure that these graphs truthfully reflect underlying data and relationships, visualization researchers have proposed several principles to guide the graph creation process. However, the extent of violations of these principles in academic publications is unknown. In this work, we develop a deep learning-based method to accurately measure violations of the proportional ink principle (AUC = 0.917), which states that the size of shaded areas in graphs should be consistent with their corresponding quantities. We apply our method to analyze a large sample of bar charts contained in 300K figures from open access publications. Our results estimate that 5% of bar charts contain proportional ink violations. Further analysis reveals that these graphical integrity issues are significantly more prevalent in some research fields, such as psychology and computer science, and some regions of the globe. Additionally, we find no temporal and seniority trends in violations. Finally, apart from openly releasing our large annotated dataset and method, we discuss how computational research integrity could be part of peer-review and the publication processes.

Computer Simulations of Deep Eutectic Solvents. Challenges, Solutions, and Perspectives

Deep eutectic solvents (DESs) are one of the most rapidly evolving types of solvents, appearing in a broad range of applications such as nanotechnology, electrochemistry, biomass transformation, pharmaceuticals, membrane technology, biocomposite development, modern 3D-printing, and many others. The range of their applicability continues to expand, which demands the development of new DESs with improved properties. To do so requires an understanding of the fundamental relationship between the structure and properties of DESs. Computer simulation and machine learning techniques provide a fruitful approach as they can provide predictions, reveal physical mechanisms and readily be linked to experiments. This review is devoted to the computational research of DESs and describes technical features of DES simulations and the corresponding perspectives on various DES applications. The aim is to demonstrate the current frontiers of computational research of DESs and discuss future perspectives.

The Marginalized Democracies of the World

This introductory article to Democratic Theory’s special issue on the marginalized democracies of the world begins by presenting the lexical method for understanding democracy. It is argued that the lexical method is better than the normative and analytical methods at finding democracies in the world. The argument then turns to demonstrating, mainly through computational research conducted within the Google Books catalog, that an empirically demonstrable imbalance exists between the democracies mentioned in the literature. The remainder of the argument is given to explaining the value of working to correct this imbalance, which comes in at least three guises: (1) studying marginalized democracies can increase our options for alternative democratic actions and democratic innovations; (2) it leads to a conservation and public outreach project, which is epitomized in an “encyclopedia of the democracies”; and (3) it advocates for a decolonization of democracies’ definitions and practices and decentering academic democratic theory.

Humans monitor learning progress in curiosity-driven exploration

Curiosity-driven learning is foundational to human cognition. By enabling humans to autonomously decide when and what to learn, curiosity has been argued to be crucial for self-organizing temporally extended learning curricula. However, the mechanisms driving people to set intrinsic goals, when they are free to explore multiple learning activities, are still poorly understood. Computational theories propose different heuristics, including competence measures (e.g., percent correct) and learning progress, that could be used as intrinsic utility functions to efficiently organize exploration. Such intrinsic utilities constitute computationally cheap but smart heuristics to prevent people from laboring in vain on unlearnable activities, while still motivating them to self-challenge on difficult learnable activities. Here, we provide empirical evidence for these ideas by means of a free-choice experimental paradigm and computational modeling. We show that while humans rely on competence information to avoid easy tasks, models that include a learning-progress component provide the best fit to task selection data. These results bridge the research in artificial and biological curiosity, reveal strategies that are used by humans but have not been considered in computational research, and introduce tools for probing how humans become intrinsically motivated to learn and acquire interests and skills on extended time scales.

Curiosity-driven exploration: Diversity of mechanisms and functions

Intrinsically motivated information-seeking, also called curiosity-driven exploration, is widely believed to be a key ingredient for autonomous learning in the real world. Such forms of spontaneous exploration have been studied in multiple independent lines of computational research, producing a diverse range of algorithmic models that capture different aspects of these processes. These algorithms resolve some of the limitations of neurocognitive theories by formally describing computational functions and algorithmic implementations of intrinsically motivated learning. Moreover, they reveal a high diversity of effective forms of intrinsically motivated information-seeking that can be characterized along different mechanistic and functional dimensions. This chapter aims at reviewing different classes of algorithms and highlighting several important dimensions of variation among them. Identifying these dimensions provides means for structuring a comprehensive taxonomy of approaches. We believe this exercise to be useful in working towards a general computational account of information-seeking. Such an account should facilitate the proposition of new hypotheses about information-seeking in humans and complement the existing psychological theory of curiosity.