Open Access

Studies on nutrition have historically concentrated on food-shortages and over-nutrition. The physiological states of feeling hungry or being satiated and its dynamics in food choices, dietary patterns, and nutritional behavior, have not been the focus of many studies. Currently, visual analytic using easy-to-use tooling offers applicability in a wide-range of disciplines. In this interdisciplinary pilot-study we tested a novel visual analytic software to assess dietary patterns and food choices for greater understanding of nutritional behavior when hungry and when satiated. We developed software toolchain and tested the hypotheses that there is no difference between visual search patterns of dishes 1) when hungry and when satiated and 2) in being vegetarian and non-vegetarian. Results indicate that food choices can be deviant from dietary patterns but correlate slightly with dish-gazing. Further, scene perception probably could vary between being hungry and satiated. Understanding t he complicated relationship between scene perception and nutritional behavioral patterns and scaling up this pilot-study to a full-study using our introduced software approaches is indispensable.

Artificial intelligence (AI) promises transformative impacts on society, industry, and agriculture, while being heavily reliant on diverse, quality data. The resource-intensive "data problem" has initialized a shift to synthetic data. One downside of synthetic data is known as the "reality gap", a lack of realism. Hybrid data, combining synthetic and real data, addresses this. The paper examines terminological inconsistencies and proposes a unified taxonomy for real, synthetic, augmented, and hybrid data. It aims to enhance AI training datasets in smart agriculture, addressing the challenges in the agricultural data landscape. Utilizing hybrid data in AI models offers improved prediction performance and adaptability.

The vehicle includes a communication module 3 designed to provide a data connection 4 to another vehicle, a diagnostic module 7 designed to detect errors in vehicle systems and a control command receiving unit 10 designed to receive control commands from another vehicle and send them via others To process vehicle components. Based on the vehicle described, a method for breakdown assistance in automated vehicles is proposed, with a data connection 4 being established between a breakdown vehicle 1 and an auxiliary vehicle 2, the auxiliary vehicle 2 diagnosing the vehicle systems of the breakdown vehicle and restoring the failed vehicle systems and/or the assistance vehicle accompanies the breakdown vehicle 1 as it continues its journey and controls it remotely via control commands.

Technical devices can enhance safety by warning people of unrecognized obstacles, particularly in traffic, wilderness, and industrial settings. This study aims to identify the most effective vibrotactile stimuli for localization tasks by developing and evaluating various types of vibrotactile alerts presented through a tactile vest with visual patterns. The study design involved comparing the time and consistency of interpreting visual stimuli and subsequent tactile stimuli. The tactile stimuli included: a 'point' vibration on the left or right side of the back, a 'column' pattern of five vibrations on one side of the back, and a 'wave' pattern of vibrations running along the back from left to right or vice versa. The results indicated that reaction times to visual stimuli were significantly shorter than to vibration stimuli, suggesting that visual stimuli are suitable for alert systems with low cognitive load. The 'point' and 'column' patterns were recognized significantly faster and more clearly than the'wave' pattern. Consequently, the haptic vest was classified as a potentially effective low cognitive load device in localization performance. The findings could inform the design of early warning systems for obstacle detection in real traffic situations.