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The present study investigated the adsorption of eosin B on the surface of powdered and chemically treated orange peels, along with this process’s condition optimizations, kinetics, and thermodynamics. The adsorbent dose (0.1–0.5 g), temperature (298, 303, 308, and 313 K), contact time (15–120 min), initial pH value (2–11) of the solution, and interfering salts (0.01 and 0.1 mol L−1) were all taken into consideration during the experiments. At all investigated temperatures and pH of 2, adsorption was more favourable. Although the Langmuir model might also represent the adsorption data, the Freundlich and Dubinin–Radushkevich (D–R) models provided a good description. The pseudo-second order kinetic model was followed during the adsorption process. The mechanics of the rate-controlling step were examined by applying the intra-particle diffusion-based mass transfer model to the experimental data. It was discovered that the only process that controlled the rate was intra-particle diffusion. The adsorption process appears to be spontaneous, and endothermic and involves van der Waals forces based on thermodynamic data.

Brain-computer interface (BCI) applications implement a direct communication path between the brain and the computer. This paper deals with the fundamentals of BCI systems and the experience of the neuroinformatics team with the design and implementation of various BCI applications. Their advantages, drawbacks and suitability are discussed in multiple contexts.

The attention of drivers is a serious issue and one of the critical factors of road safety. The question is whether the electrical activity of the human brain can be correctly measured/collected and utilized to monitor and interpret the driver's attention during simulated driving. This article summarizes four experiments that have been designed, performed, and evaluated in the neuroinformatics laboratory at the University of West Bohemia. Simulated driving under various conditions in a car simulator was organized, and electrophysiology, mainly electroencephalography, data were collected from participants/drivers. The results include experience with the design of such experiments and the suitability of methods based on the collection and interpretation of electroencephalography data for driver attention detection.

Poor lifestyle leads potentially to chronic diseases and low-grade physical and mental fitness. However, ahead of time, we can measure and analyze multiple aspects of physical and mental health, such as body parameters, health risk factors, degrees of motivation, and the overall willingness to change the current lifestyle. In conjunction with data representing human brain activity, we can obtain and identify human health problems resulting from a long-term lifestyle more precisely and, where appropriate, improve the quality and length of human life. Currently, brain and physical health-related data are not commonly collected and evaluated together. However, doing that is supposed to be an interesting and viable concept, especially when followed by a more detailed definition and description of their whole processing lifecycle. Moreover, when best practices are used to store, annotate, analyze, and evaluate such data collections, the necessary infrastructure development and more intense cooperation among scientific teams and laboratories are facilitated. This approach also improves the reproducibility of experimental work. As a result, large collections of physical and brain health-related data could provide a robust basis for better interpretation of a person’s overall health. This work aims to overview and reflect some best practices used within global communities to ensure the reproducibility of experiments, collected datasets and related workflows. These best practices concern, e.g., data lifecycle models, FAIR principles, and definitions and implementations of terminologies and ontologies. Then, an example of how an automated workflow system could be created to support the collection, annotation, storage, analysis, and publication of findings is shown. The Body in Numbers pilot system, also utilizing software engineering best practices, was developed to implement the concept of such an automated workflow system. It is unique just due to the combination of the processing and evaluation of physical and brain (electrophysiological) data. Its implementation is explored in greater detail, and opportunities to use the gained findings and results throughout various application domains are discussed.

Smoking, excessive drinking, overeating and physical inactivity are well-established risk factors decreasing human physical performance. Moreover, epidemiological work has identified modifiable lifestyle factors, such as poor diet and physical and cognitive inactivity that are associated with the risk of reduced cognitive performance. Definition, collection and annotation of human reaction times and suitable health related data and metadata provides researchers with a necessary source for further analysis of human physical and cognitive performance. The collection of human reaction times and supporting health related data was obtained from two groups comprising together 349 people of all ages - the visitors of the Days of Science and Technology 2016 held on the Pilsen central square and members of the Mensa Czech Republic visiting the neuroinformatics lab at the University of West Bohemia. Each provided dataset contains a complete or partial set of data obtained from the following measurements: hands and legs reaction times, color vision, spirometry, electrocardiography, blood pressure, blood glucose, body proportions and flexibility. It also provides a sufficient set of metadata (age, gender and summary of the participant's current life style and health) to allow researchers to perform further analysis. This article has two main aims. The first aim is to provide a well annotated collection of human reaction times and health related data that is suitable for further analysis of lifestyle and human cognitive and physical performance. This data collection is complemented with a preliminarily statistical evaluation. The second aim is to present a procedure of efficient acquisition of human reaction times and supporting health related data in non-lab and lab conditions. Keywords: Reaction time, Health related data, Cognitive and physical performance, Chronic disease, Data acquisition, Data collection, Software for data collection