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This study aims to evaluate the applicability of a text mining approach for extracting UUX-related issues from a dataset of user comments and not to evaluate the Instagram (IG) app. This study analyses textual data mined from reviews in English written by IG mobile application users. The article’s authors used text mining (based on the LDA algorithm) to identify the main UUX-related topics. Next, they mapped the identified topics with known theoretical constructs to place them in their nomological network relevant to the usability (the 5Es framework by Quesenbery) and UX (the Honeycomb model by Morville). Finally, to expand the study with an emotional diagnosis, sentiment analysis was performed on two levels: (i) for each recognised topic, and (ii) for the full dataset to uncover general insights into users’ emotions within all reviews. The case study of the IG app confirms the usefulness of user feedback data for software development and points out that the review data have the potential for the early detection of frustration and negative feelings introduced during the use of the application. Conducting conventional UUX evaluations with users is problematic since they are remotely located, and the user-generated content of a social app undergoes continuous and frequent changes. Thus, the consecutive stages of the proposed methodology, based on text mining algorithms, constitute a proposed framework for examining the user-perceived quality projection of applications from user feedback, and they are the main contribution of this article. The used approach can be valuable for helping developers, designers and researchers to reveal user problems and fulfil user satisfaction regarding UUX aspects for specific software features.

Recent developments in artificial intelligence (AI) may involve significant potential threats to personal data privacy, national security, and social and economic stability. AI-based solutions are often promoted as “intelligent” or “smart” because they are autonomous in optimizing various processes. Because they can modify their behavior without human supervision by analyzing data from the environment, AI-based systems may be more prone to malfunctions and malicious activities than conventional software systems. Moreover, due to existing regulatory gaps, development and operation of AI-based products are not yet subject to adequate risk management and administrative supervision. Resonating to recent reports about potential threats resulting from AI-based systems, this paper presents an outline of a prospective risk assessment for adaptive and autonomous products. This research resulted in extensive catalogs of possible damages, initiating events, and preventive policies that can be useful for risk managers involved in conducting risk assessment procedures for AI-based systems. The paper concludes with the analysis and discussion of changes in business, legal, and institutional environments required to ensure the public that AI-based solutions can be trusted, are transparent and safe, and can improve the quality of life.

Nanoparticles, exhibiting functionally relevant structural heterogeneity, are at the forefront of cutting-edge research. Now, high-throughput single-particle imaging (SPI) with X-ray free-electron lasers (XFELs) creates opportunities for recovering the shape distributions of millions of particles that exhibit functionally relevant structural heterogeneity. To realize this potential, three challenges have to be overcome: (1) simultaneous parametrization of structural variability in real and reciprocal spaces; (2) efficiently inferring the latent parameters of each SPI measurement; (3) scaling up comparisons between 105structural models and 106XFEL-SPI measurements. Here, we describe how we overcame these three challenges to resolve the nonequilibrium shape distributions within millions of gold nanoparticles imaged at the European XFEL. These shape distributions allowed us to quantify the degree of asymmetry in these particles, discover a relatively stable “shape envelope” among nanoparticles, discern finite-size effects related to shape-controlling surfactants, and extrapolate nanoparticles’ shapes to their idealized thermodynamic limit. Ultimately, these demonstrations show that XFEL SPI can help transform nanoparticle shape characterization from anecdotally interesting to statistically meaningful.

Intense, ultrashort, and high-repetition-rate X-ray pulses, combined with a femtosecond optical laser, allow pump-probe experiments with fast data acquisition and femtosecond time resolution. However, the relative timing of the X-ray pulses and the optical laser pulses can be controlled only to a level of the intrinsic error of the instrument which, without characterization, limits the time resolution of experiments. This limitation inevitably calls for a precise determination of the relative arrival time, which can be used after measurement for sorting and tagging the experimental data to a much finer resolution than it can be controlled to. The observed root-mean-square timing jitter between the X-ray and the optical laser at the SPB/SFX instrument at European XFEL was 308 fs. This first measurement of timing jitter at the European XFEL provides an important step in realizing ultrafast experiments at this novel X-ray source. A method for determining the change in the complex refractive index of samples is also presented.

The structures of three intermediate states of photosystem II, which is crucial for photosynthesis, have been solved at room temperature, shedding new light on this process.