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A continuing trend in precision apiculture is to use computer vision methods to quantify characteristics of bee traffic in managed colonies at the hive's entrance. Since traffic at the hive's entrance is a contributing factor to the hive's productivity and health, we assessed the potential of three open-source convolutional network models, YOLOv3, YOLOv4-tiny, and YOLOv7-tiny, to quantify omnidirectional traffic in videos from on-hive video loggers on regular, unmodified one- and two-super Langstroth hives and compared their accuracies, energy efficacies, and operational energy footprints. We trained and tested the models with a 70/30 split on a dataset of 23,173 flying bees manually labeled in 5819 images from 10 randomly selected videos and manually evaluated the trained models on 3600 images from 120 randomly selected videos from different apiaries, years, and queen races. We designed a new energy efficacy metric as a ratio of performance units per energy unit required to make a model operational in a continuous hive monitoring data pipeline. In terms of accuracy, YOLOv3 was first, YOLOv7-tiny-second, and YOLOv4-tiny-third. All models underestimated the true amount of traffic due to false negatives. YOLOv3 was the only model with no false positives, but had the lowest energy efficacy and highest operational energy footprint in a deployed hive monitoring data pipeline. YOLOv7-tiny had the highest energy efficacy and the lowest operational energy footprint in the same pipeline. Consequently, YOLOv7-tiny is a model worth considering for training on larger bee datasets if a primary objective is the discovery of non-invasive computer vision models of traffic quantification with higher energy efficacies and lower operational energy footprints.

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Since bee traffic is a contributing factor to hive health and electromagnetic radiation has a growing presence in the urban milieu, we investigate ambient electromagnetic radiation as a predictor of bee traffic in the hive's vicinity in an urban environment. To that end, we built two multi-sensor stations and deployed them for four and a half months at a private apiary in Logan, UT, USA. to record ambient weather and electromagnetic radiation. We placed two non-invasive video loggers on two hives at the apiary to extract omnidirectional bee motion counts from videos. The time-aligned datasets were used to evaluate 200 linear and 3,703,200 non-linear (random forest and support vector machine) regressors to predict bee motion counts from time, weather, and electromagnetic radiation. In all regressors, electromagnetic radiation was as good a predictor of traffic as weather. Both weather and electromagnetic radiation were better predictors than time. On the 13,412 time-aligned weather, electromagnetic radiation, and bee traffic records, random forest regressors had higher maximum R2 scores and resulted in more energy efficient parameterized grid searches. Both types of regressors were numerically stable.

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The relationship between beehive weight and traffic is a fundamental open research problem for electronic beehive monitoring and digital apiculture, because weight and traffic affect many aspects of honeybee (Apis mellifera) colony dynamics. An investigation of this relationship was conducted with a nondisruptive two-sensor (scale and camera) system on the weight and video data collected on six Apis mellifera colonies in Langstroth hives at the USDA-ARS Carl Hayden Bee Research Center in Tucson, Arizona, USA, from 15 May to 15 August 2021. Three hives had positive and two hives had negative correlations between weight and traffic. In one hive, weight and traffic were uncorrelated. The strength of the correlation between weight and traffic was stronger for longer time intervals. The traffic spread and mean, when taken separately, did not affect the correlation between weight and traffic more significantly than the exact traffic counts from videos. Lateral traffic did not have a significant impact on weight.

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Investigation of the process of the NO3- anion solvation is central to understanding the chemical and physical properties of its aqueous solutions. The importance of this topic can be seen in atmospheric chemistry, as well as in nuclear waste processing research. In this work, we used a particle swarm optimization technique driven by density functional theory to sample the potential energy surface of various microsolvated [NO3·(H2O)n]- (n = 1-12) clusters. We found that the charge transfer plays a crucial role in the stabilization of the investigated species. Moreover, by conducting ab initio molecular dynamics simulations, we showed that at low concentrations (∼0.2 M) the NO3- species tend to be located on the surface of water solution. We also observed that the contact ion pair K+-NO3- undergoes a fast dissociation and each of the ions is solvated separately. As a result, from our calculations, we expect that at low concentration there could be oppositely signed concentration gradients for NO3- and K+ ions in a thin water film.

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This article presents RoboCart, a proof-of-concept prototype of a robotic shopping cart for the visually impaired in supermarkets. RoboCart autonomously leads shoppers to required locations and cues them through synthetic speech and a portable barcode reader to the salient features of the environment sufficient for product retrieval. In a longitudinal pilot feasibility study, visually impaired shoppers (n = 10) used the device to retrieve products in Lee's MarketPlace, a supermarket in Logan, Utah. The main finding is that RoboCart enables visually impaired shoppers to reliably and independently navigate to and retrieve products in a real supermarket.

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