Data-based methods have gained increasing importance in engineering. Success stories are prevalent in areas such as data-driven modeling, control, and automation, as well as surrogate modeling for accelerated simulation. Beyond engineering, generative and large-language models are increasingly helping with tasks that, previously, were solely associated with creative human processes. Thus, it seems timely to seek artificial-intelligence-support for engineering design tasks to automate, help with, or accelerate purpose-built designs of engineering systems for instance in mechanics and dynamics, where design so far requires a lot of specialized knowledge. Compared with established, predominantly first-principles-based methods, the datasets used for training, validation, and test become an almost inherent part of the overall methodology. Thus, data publishing becomes just as important in (data-driven) engineering science as appropriate descriptions of conventional methodology in publications in the past. However, in mechanics and dynamics, quite widely, still traditional publishing practices are prevalent that largely do not yet take into account the rising role of data as much as that may already be the case in pure data-scientific research. This article analyzes the value and challenges of data publishing in mechanics and dynamics, in particular regarding engineering design tasks, showing that the latter raise also challenges and considerations not typical in fields where data-driven methods have been booming originally. Researchers currently find barely any guidance to overcome these challenges. Thus, ways to deal with these challenges are discussed and a set of examples from across different design problems shows how data publishing can be put into practice.

Coordination problems are ubiquitous in social and economic life. Political mass demonstrations, the decision whether to join a speculative currency attack, investment in a risky venture, and capital flight from a particular country are all characterized by coordination problems. Furthermore, all these events have a dynamic nature which has been largely omitted from previous experimental studies. Here I use a two-stage variant of a dynamic global game to study experimentally how the arrival of information in a dynamic setting affects the relative aggressiveness of speculators. In the first stage, subjects exhibit excess aggressiveness, which appears to be driven by beliefs about others’ actions rather than an intrinsic taste for attacking. However, following a failed first-stage attack, subjects learn to be less aggressive in the second stage. On the other hand, the arrival of new, more precise information after a failed attack leads to an increase in subjects’ aggressiveness. Beliefs, again, play a crucial role in explaining how the arrival of information affects attacking behavior.

Binding sites are key components of biomolecular structures, such as proteins and RNAs, serving as hubs for interactions with other molecules. Identification of the binding sites in macromolecules is essential for structure-based molecular and drug design. However, experimental methods for binding site identification are resource-intensive and time-consuming. In contrast, computational methods enable large-scale binding site identification, structure flexibility analysis, as well as assessment of intermolecular interactions within the binding sites. In this review, we describe recent advances in binding site identification using machine learning methods; we classify the approaches based on the encoding of the macromolecule information about its sequence, structure, template knowledge, geometry, and energetic characteristics. Importantly, we categorize the methods based on the type of the interacting molecule, namely, small molecules, peptides, and ions. Finally, we describe perspectives, limitations, and challenges of the state-of-the-art methods with an emphasis on deep learning-based approaches. These computational approaches aim to advance drug discovery by expanding the druggable genome through the identification of novel binding sites in pharmacological targets and facilitating structure-based hit identification and lead optimization.

The effect of the polarizations of two counter-propagating relativistic laser pulses interacting with subwavelength thin solid-density foil is investigated. Three-dimensional particle-in-cell simulations and analytical modelling show that the interaction and resulting transverse instability depend strongly on the polarization directions as well as the intensity distribution of the resultant light field in the foil. The left- and right-handed circularly polarized laser pair with the same phase at the common focal spot in the ultrathin foil leads to the strongest distortion of the foil. The fastest growing mode and maximum growth rate depend mainly on the laser intensity. For all polarization and phase-difference combinations, the instability is weakest when the two laser pulses are exactly out of phase at the common focusing point in the foil.

All biochemical reactions directly involve structural changes that may occur over a very wide range of timescales from femtoseconds to seconds. Understanding the mechanism of action thus requires determination of both the static structures of the macromolecule involved and short-lived intermediates between reactant and product. This requires either freeze-trapping of intermediates, for example by cryo-electron microscopy, or direct determination of structures in active systems at near-physiological temperature by time-resolved X-ray crystallography. Storage ring X-ray sources effectively cover the time range down to around 100 ps that reveal tertiary and quaternary structural changes in proteins. The briefer pulses emitted by hard X-ray free electron laser sources extend that range to femtoseconds, which covers critical chemical reactions such as electron transfer, isomerization, breaking of covalent bonds, and ultrafast structural changes in light-sensitive protein chromophores and their protein environment. These reactions are exemplified by the time-resolved X-ray studies by two groups of the FAD-based DNA repair enzyme, DNA photolyase, over the time range from 1 ps to 100 μs.

Recently, there has been a surge in interest in exploring how common macroeconomic factors impact different economic results. We propose a semiparametric dynamic panel model to analyze the impact of common regressors on the conditional distribution of the dependent variable (global output growth distribution in our case). Our model allows conditional mean, variance, and skewness to be influenced by common regressors, whose effects can be nonlinear and time-varying driven by contextual variables. By incorporating dynamic structures and individual unobserved heterogeneity, we propose a consistent two-step estimator and showcase its attractive theoretical and numerical properties. We apply our model to investigate the impact of US financial uncertainty on the global output growth distribution. We find that an increase in US financial uncertainty significantly shifts the output growth distribution leftward during periods of market pessimism. In contrast, during periods of market optimism, the increased uncertainty in the US financial markets expands the spread of the output growth distribution without a significant location change, indicating increased future uncertainty.

The past few years were marked by increased online offensive strategies perpetrated by state and non-state actors to promote their political agenda, sow discord, and question the legitimacy of democratic institutions in the US and Western Europe. In 2016, the US congress identified a list of Russian state-sponsored Twitter accounts that were used to try to divide voters on a wide range of issues. Previous research used latent Dirichlet allocation (LDA) to estimate latent topics in data extracted from these accounts. However, LDA has characteristics that may limit the effectiveness of its use on data from social media: The number of latent topics must be specified by the user, interpretability of the topics can be difficult to achieve, and it does not model short-term temporal dynamics. In the current paper, we propose a new method to estimate latent topics in texts from social media termed Dynamic Exploratory Graph Analysis (DynEGA). In a Monte Carlo simulation, we compared the ability of DynEGA and LDA to estimate the number of simulated latent topics. The results show that DynEGA is substantially more accurate than several different LDA algorithms when estimating the number of simulated topics. In an applied example, we performed DynEGA on a large dataset with Twitter posts from state-sponsored right- and left-wing trolls during the 2016 US presidential election. DynEGA revealed topics that were pertinent to several consequential events in the election cycle, demonstrating the coordinated effort of trolls capitalizing on current events in the USA. This example demonstrates the potential power of our approach for revealing temporally relevant information from qualitative text data.

Researchers in the field of network psychometrics often focus on the estimation of Gaussian graphical models (GGMs)—an undirected network model of partial correlations—between observed variables of cross-sectional data or single-subject time-series data. This assumes that all variables are measured without measurement error, which may be implausible. In addition, cross-sectional data cannot distinguish between within-subject and between-subject effects. This paper provides a general framework that extends GGM modeling with latent variables, including relationships over time. These relationships can be estimated from time-series data or panel data featuring at least three waves of measurement. The model takes the form of a graphical vector-autoregression model between latent variables and is termed the ts-lvgvar when estimated from time-series data and the panel-lvgvar when estimated from panel data. These methods have been implemented in the software package psychonetrics, which is exemplified in two empirical examples, one using time-series data and one using panel data, and evaluated in two large-scale simulation studies. The paper concludes with a discussion on ergodicity and generalizability. Although within-subject effects may in principle be separated from between-subject effects, the interpretation of these results rests on the intensity and the time interval of measurement and on the plausibility of the assumption of stationarity.

Influenza and other acute respiratory viral infections (ARVIs) are among the most common human diseases. In recent decades, the discovery of cytokines and their significance in the pathogenesis of diseases has led to extensive research on these compounds in various pathologies including ARVIs. The aim of the research was to study the cytokine profile in patients with ARVIs. The cases of 30 patients were investigated. Etiological diagnosis was performed by polymerase chain reaction. Different classes of cytokines in the serum were defined by the enzyme-linked immunosorbent assay (ELISA). The level of cytokines depended on the number of pathogens. The highest levels of pro-inflammatory interleukins and the lowest levels of anti-inflammatory IL-4 were observed in patients with a combination of five or more viruses compared to those with a monoinfection. Analysis of the data showed that in the acute phase, the levels of all studied pro-inflammatory cytokines – IL-2, IL-6, and TNF-α – increased by 8, 39, and 9 times, respectively, compared to those in healthy individuals. In the acute phase of ARVI, the levels of pro-inflammatory cytokines were significantly higher and depended on the severity of the disease. The imbalance of cytokines in the serum has been established in cases of ARVIs, depending on the severity of the disease.

Intertidal macrobenthos at the small Chernaya Bight (the White Sea) was surveyed six times during 1993–2018 in order to study spatiotemporal variability. Distributions of sediments and macrophytes were highly variable in both space and time, as were most macrofaunal community attributes. Biomass slightly increased with time, while no long-term trends were found in total abundance, diversity, or functional structure. All community attributes were patchily distributed across the beach, and their patterns were not spatially autocorrelated and poorly associated with sediment properties, but changed considerably from year to year. Temporal changes in the community composition were considerable but less substantial compared with the spatial variations. The overall dynamics of species structure did not show any regular trend-like pattern but formed quasicyclic trajectories in ordination space, with nondirectional, spatially noncorrelated fluctuations around some relatively stable state. Comparison with two other neighbouring intertidal sites, studied annually in 1987–2017, showed that macrofauna at every site had similar average biomasses and common dominant species; however, the communities maintained their specificity in structure and exhibited distinct types of dynamics. In particular, the communities demonstrated different long-term trends in total biomass and diversity and followed their own paths in dynamics, appearing as differently oriented interannual trajectories. Nine most abundant species revealed no significant among-site correlations in abundance, and only two bivalve species showed good intersite agreement in dynamics of biomass. We suggest that local benthic communities are largely influenced by site-specific environmental conditions, resulting in independent and even opposite patterns of dynamics in neighbouring localities.

Stray light from the sun is one of the most significant factors affecting image quality for the optical system of a spacecraft. This paper proposes a method to design a deployable supporting mechanism for the sunshield based on origami. Firstly, a new type of space mechanism with single-closed loop was proposed according to thick-panel origami, and its mobility was analysed by using the screw theory. In order to design a deployable structure with high controllability, the tetrahedral constraint was introduced to reduce the degree of freedom (DOF), and a corresponding deployable unit named tetrahedral deployable unit (TDU) was obtained. Secondly, the process to constructing a large space deployable mechanism with infinite number of units was explained based on the characteristics of motion and planar mosaic array, and kinematics analysis and folding ratio of supporting mechanism were conducted. A physical prototype was constructed to demonstrate the mobility and deployment of the supporting mechanism. Finally, based on the Lagrange method, a dynamic model of supporting mechanism was established, and the influence of the torsion spring parameters on the deployment process was analysed.

The vertical motions and buoyancy variations of the two VEGA super-pressure balloons, flown in the middle cloud layer of Venus, are discussed. Using data derived from these 1985 nightside flights, estimates are made of the energy required to operate some alternative balloon platform schemes under consideration for future-proposed Venus-atmosphere in situ science missions. Despite the dissimilarity of these alternative platform schemes, the energy inputs required to operate each scheme on the Venus nightside are found to be similar. Estimates of the associated mass penalties of the associated energy sources are also made. Further investigation of a vertical propulsive assist scheme is recommended.

Host-virus interactions are critically important for various stages of the viral replication cycle. The reliance of viruses on the host factors for their entry, replication, and maturation processes can be exploited for the development of antiviral therapeutics. Thus, the identification and characterization of such viral-host dependency factors has been an attractive area of research to provide novel antiviral targets. Traditional proteomic efforts based on affinity purification of protein complexes from cell lysates are limited to detecting strong and stable interactions. In this perspective, we discuss the integration of two latest proteomic techniques, based on in situ proximity labelling and chemical crosslinking methods, to uncover host-virus protein–protein interactions in living cells.