In recent years, there has been a growing interest in analyzing text data from different scientific fields. The significant advancement of Artificial Intelligence in Natural Language Processing enables a systematic categorization of the wealth of scientific papers into fundamental thematic clusters. In this context, topic modeling is playing a crucial role. Unfortunately, the comparative analysis between traditional and advanced topic modeling methods, including well-established techniques like Latent Dirichlet Allocation (LDA) and newer approaches like BERTopic, remains significantly underexplored. This study addresses this gap by conducting a comprehensive analysis of extensive text data focused on sustainable energy research. To achieve this, we compile a unique dataset consisting of thousands of abstracts sourced from PubMed, Scopus, and Web of Science. Our analysis involves a comparison between LDA and the transformer model BERTopic. Importantly, we introduce a novel approach to determine the optimal number of topics, achieved through the maximization of combined semantic scores, and show that the number of topics is considerably lower than from previous approaches. Overall, our study not only contributes methodologically but also enhances our understanding of the principal topics in sustainable energy research.

Massively multiplayer online games (MMOGs) played on the Web provide a new form of social, computer-mediated interactions that allow the connection of millions of players worldwide. The rules governing team-based MMOGs are typically complex and nondeterministic giving rise to an intricate dynamical behavior. However, due to the novelty and complexity of MMOGs, their behavior is understudied. In this article, we investigate the MMOG World of Tanks Blitz by using a combined approach based on data science and complex adaptive systems. We analyze data on the population level to get insights into organizational principles of the game and its game mechanics. For this reason, we study the scaling behavior and the predictability of system variables. As a result, we find a power-law behavior on the population level revealing long-range interactions between system variables. Furthermore, we identify and quantify the predictability of summary statistics of the game and its decomposition into explanatory variables. This reveals a heterogeneous progression through the tiers and identifies only a single system variable as key driver for the win rate.

Active and passive sums of direct, powered, total, and stable state impact analyses are the different criteria for studying the influence of variables in complex social, economic, and technological systems. The three methods that are applied for studying complex systems are DEMATEL (total impacts analysis), cross impact analysis (stable state), and Page rank (stable state of left normalized stochastic matrix). These approaches are applied to select influential and dependent variables of complex systems. These methods notably differ in rank order of row- and column-sums of variables as the models are based on different assumptions. However, mathematical relationships can be drawn, and several mathematical properties can be investigated and analyzed between different methods to better understand models, their interpretations, applications, and numerical accuracy. In this paper, we analytically compare the three methods, develop analytical formulas and approximations of the steady states and their differences in active and passive rank orders, and show how sparsity and the problem dimension influence the differences in rank orders and quality of approximation by numerical simulation. The research contributes by exploring the analytical relationships and approximations between normalized and unnormalized cross-impact matrices and their steady states and comparing the outcomes of different models. Its findings can help understand the application of different models in studying complex systems in business and marketing research to identify influential and dependent variables. The results can support managerial and strategic planning decision-making for selecting an appropriate method, combining methods for robust decision-making, applying them in the proper context, and accurately evaluating and analyzing the results.

Abstract Cross‐virtuality analytics (XVA) is a novel field of research within immersive analytics and visual analytics. A broad range of heterogeneous devices across the reality–virtuality continuum, along with respective visual metaphors and analysis techniques, are currently becoming available. The goal of XVA is to enable visual analytics that use transitional and collaborative interfaces to seamlessly integrate different devices and support multiple users. In this work, we take a closer look at XVA and analyse the existing body of work for an overview of its current state. We classify the related literature regarding ways of establishing cross‐virtuality by interconnecting different stages in the reality–virtuality continuum, as well as techniques for transitioning and collaborating between the different stages. We provide insights into visualization and interaction techniques employed in current XVA systems. We report on ways of evaluating such systems, and analyse the domains where such systems are becoming available. Finally, we discuss open challenges in XVA, giving directions for future research.

In recent years, many applications are using various forms of deep learning models. Such methods are usually based on traditional learning paradigms requiring the consistency of properties among the feature spaces of the training and test data and also the availability of large amounts of training data, e.g., for performing supervised learning tasks. However, many real-world data do not adhere to such assumptions. In such situations transfer learning can provide feasible solutions, e.g., by simultaneously learning from data-rich source data and data-sparse target data to transfer information for learning a target task. In this paper, we survey deep transfer learning models with a focus on applications to text data. First, we review the terminology used in the literature and introduce a new nomenclature allowing the unequivocal description of a transfer learning model. Second, we introduce a visual taxonomy of deep learning approaches that provides a systematic structure to the many diverse models introduced until now. Furthermore, we provide comprehensive information about text data that have been used for studying such models because only by the application of methods to data, performance measures can be estimated and models assessed.

BACKGROUND: sgnesR (Stochastic Gene Network Expression Simulator in R) is an R package that provides an interface to simulate gene expression data from a given gene network using the stochastic simulation algorithm (SSA). The package allows various options for delay parameters and can easily included in reactions for promoter delay, RNA delay and Protein delay. A user can tune these parameters to model various types of reactions within a cell. As examples, we present two network models to generate expression profiles. We also demonstrated the inference of networks and the evaluation of association measure of edge and non-edge components from the generated expression profiles. RESULTS: The purpose of sgnesR is to enable an easy to use and a quick implementation for generating realistic gene expression data from biologically relevant networks that can be user selected. CONCLUSIONS: sgnesR is freely available for academic use. The R package has been tested for R 3.2.0 under Linux, Windows and Mac OS X.

MOTIVATION: Data from RNA-seq experiments provide us with many new possibilities to gain insights into biological and disease mechanisms of cellular functioning. However, the reproducibility and robustness of RNA-seq data analysis results is often unclear. This is in part attributed to the two counter acting goals of (i) a cost efficient and (ii) an optimal experimental design leading to a compromise, e.g. in the sequencing depth of experiments. RESULTS: We introduce an R package called samExploreR that allows the subsampling (m out of n bootstraping) of short-reads based on SAM files facilitating the investigation of sequencing depth related questions for the experimental design. Overall, this provides a systematic way for exploring the reproducibility and robustness of general RNA-seq studies. We exemplify the usage of samExploreR by studying the influence of the sequencing depth and the annotation on the identification of differentially expressed genes. AVAILABILITY AND IMPLEMENTATION: samExploreR is available as an R package from Bioconductor. CONTACT: [email protected] information: Supplementary data are available at Bioinformatics online.

BACKGROUND: It is generally acknowledged that a functional understanding of a biological system can only be obtained by an understanding of the collective of molecular interactions in form of biological networks. Protein networks are one particular network type of special importance, because proteins form the functional base units of every biological cell. On a mesoscopic level of protein networks, modules are of significant importance because these building blocks may be the next elementary functional level above individual proteins allowing to gain insight into fundamental organizational principles of biological cells. RESULTS: In this paper, we provide a comparative analysis of five popular and four novel module detection algorithms. We study these module prediction methods for simulated benchmark networks as well as 10 biological protein interaction networks (PINs). A particular focus of our analysis is placed on the biological meaning of the predicted modules by utilizing the Gene Ontology (GO) database as gold standard for the definition of biological processes. Furthermore, we investigate the robustness of the results by perturbing the PINs simulating in this way our incomplete knowledge of protein networks. CONCLUSIONS: Overall, our study reveals that there is a large heterogeneity among the different module prediction algorithms if one zooms-in the biological level of biological processes in the form of GO terms and all methods are severely affected by a slight perturbation of the networks. However, we also find pathways that are enriched in multiple modules, which could provide important information about the hierarchical organization of the system.

Abstract Summary: NetBioV (Network Biology Visualization) is an R package that allows the visualization of large network data in biology and medicine. The purpose of NetBioV is to enable an organized and reproducible visualization of networks by emphasizing or highlighting specific structural properties that are of biological relevance. Availability and implementation: NetBioV is freely available for academic use. The package has been tested for R 2.14.2 under Linux, Windows and Mac OS X. It is available from Bioconductor. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.