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The purpose of this paper is to understand how members of beekeeping associations, with long-standing sustainable traditions and products with registered geographical origins, perceive the investments in research and development (R&D) and new technological adoptions. By means of a binary logistic regression, the socio-demographic factors of the members of beekeeping associations predicting the investments in R&D and new technological adoptions were analyzed. Our findings point out that higher level of education and professional beekeeping experience predicts the willingness of investing in research and development. The higher level of education positively influences the willingness to hire professional consultants or bodies for the research and development of beekeeping practices. Serbian female beekeepers, beekeepers aged more than 41 years and professionally engaged beekeepers are more likely to admit that they need support of scientific and research institutions in the further development of beekeeping practices. A higher education has been shown to significantly predict the value added hive products due to new technology adoption. There is also a positive influence of the education level on new technology adoption.

ObjectiveA brain computer interface (BCI) allows users to control external devices using non-invasive brain recordings, such as electroencephalography (EEG). We developed and tested a novel electrotactile BCI prototype based on somatosensory event-related potentials (sERP) as control signals, paired with a tactile attention task as a control paradigm.ApproachA novel electrotactile BCI comprises commercial EEG device, an electrical stimulator and custom software for EEG recordings, electrical stimulation control, synchronization between devices, signal processing, feature extraction, selection, and classification. We tested a novel BCI control paradigm based on tactile attention on a sensation at a target stimulation location on the forearm. Tactile stimuli were electrical pulses delivered at two proximal locations on the user’s forearm for stimulating branches of radial and median nerves, with equal probability of the target and distractor stimuli occurrence, unlike in any other ERP-based BCI design. We proposed a compact electrical stimulation electrodes configuration for delivering electrotactile stimuli (target and distractor) using 2 stimulation channels and 3 stimulation electrodes. We tested the feasibility of a single EEG channel BCI control, to determine pseudo-online BCI performance, in ten healthy subjects. For optimizing the BCI performance we compared the results for two classifiers, sERP averaging approaches, and novel dedicated feature extraction/selection methods via cross-validation procedures.Main resultsWe achieved a single EEG channel BCI classification accuracy in the range of 75.1 to 88.1% for all subjects. We have established an optimal combination of: single trial averaging to obtain sERP, feature extraction/selection methods and classification approach.SignificanceThe obtained results demonstrate that a novel electrotactile BCI paradigm with equal probability of attended (target) and unattended (distractor) stimuli and proximal stimulation sites is feasible. This method may be used to drive restorative BCIs for sensory retraining in stroke or brain injury, or assistive BCIs for communication in severely disabled users.

Writing is a complex skill and it can be affected by many factors. One of the most obvious is handedness. The actual influence of handedness (especially left-handedness, since almost 10% of the population is left-handed) onto writing performance has not been fully studied in previous research. Digitalized kinematic analyses and assessments of writing strategies (i.e., graphic rules and principles) are two approaches to investigating writing characteristics poorly addressed in previous research. The aim of this study was to analyze the effects of handedness onto writing kinematics using the aforementioned approach. The study included 34 young healthy adults (of whom 11 were left-handed) performing three writing tasks on a digital board. The tasks included semicircle and figure tracing and cursive letter writing. Regarding kinematics, left-handers performed tracing movements with higher mean horizontal acceleration and lower mean horizontal jerk compared to right-handed subjects. In addition, the left-handed wrote less accurately (i.e., undershooting more writing borders) and made more pauses during the letter writing task. The obtained results suggest that handedness slightly affects writing performance, and since left-and right-handers use the same cognitive strategies to writing and tracing, the observed differences could be mainly due to biomechanical constraints, what needs further studies in more representative samples.

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