Daniel C. Coster

From June to October, 2022, we recorded the weight, the internal temperature, and the hive entrance video traffic of ten managed honey bee (Apis mellifera) colonies at a research apiary of the Carl Hayden Bee Research Center in Tucson, AZ, USA. The weight and temperature were recorded every five minutes around the clock. The 30 s videos were recorded every five minutes daily from 7:00 to 20:55. We curated the collected data into a dataset of 758,703 records (280,760–weight; 322,570–temperature; 155,373–video). A principal objective of Part I of our investigation was to use the curated dataset to investigate the discrete univariate time series forecasting of hive weight, in-hive temperature, and hive entrance traffic with shallow artificial, convolutional, and long short-term memory networks and to compare their predictive performance with traditional autoregressive integrated moving average models. We trained and tested all models with a 70/30 train/test split. We varied the intake and the predicted horizon of each model from 6 to 24 hourly means. Each artificial, convolutional, and long short-term memory network was trained for 500 epochs. We evaluated 24,840 trained models on the test data with the mean squared error. The autoregressive integrated moving average models performed on par with their machine learning counterparts, and all model types were able to predict falling, rising, and unchanging trends over all predicted horizons. We made the curated dataset public for replication.

The National Agricultural Literacy Outcomes (NALOs) are knowledge benchmarks for school-aged youth and are used to improve agricultural literacy (NAITC, 2014; NCAL, 2017). Despite educational efforts, prior research indicated that high school populations remained at low or deficient literacy levels. Additionally, no agricultural literacy assessment instruments using the NALOs as a standardization tool have been developed for the 9-12th grades. The purpose of the study was to validate a summative NALO-centered assessment that could provide baseline data on agricultural literacy following the completion of secondary education (12th grade). The study followed the framework established by Longhurst et al. (2020) for similar assessments in elementary grades. A Delphi team produced 45 items for validation that were reviewed using a convenience sample of [University] undergraduate students. Those items were evaluated using factor, item, and discriminant analysis. Results finalized two 15-item assessments and determined both had acceptable reliability, were adequate for model fit, and were valid for the NALOs and the three proficiency levels. The instruments are critical tools for providing a standardized approach to evaluation efforts. Researchers and educators should use these instruments to provide comparable agricultural literacy data across populations to better identify trends, program needs, and meaningful inferences.

Particle swarm optimization (PSO) is an attractive, easily implemented method which is successfully used across a wide range of applications. In this paper, utilizing the core ideology of genetic algorithm and dynamic parameters, an improved particle swarm optimization algorithm is proposed. Then, based on the improved algorithm, combining the PSO algorithm with decision making, nested PSO algorithms with two useful decision making criteria (optimistic coefficient criterion and minimax regret criterion) are proposed . The improved PSO algorithm is implemented on two unimodal functions and two multimodal functions, and the results are much better than that of the traditional PSO algorithm. The nested algorithms are applied on the Michaelis–Menten model and two parameter logistic regression model as examples. For the Michaelis–Menten model, the particles converge to the best solution after 50 iterations. For the two parameter logistic regression model, the optimality of algorithms are verified by the equivalence theorem. More results for other models applying our algorithms are available upon request.

Plain language techniques are health literacy universal precautions intended to enhance health care system navigation and health outcomes. Physical activity (PA) is a popular topic on the Internet, yet it is unknown if information is communicated in plain language. This study examined how plain language techniques are included in PA websites, and if the use of plain language techniques varies according to search procedures (keyword, search engine) and website host source (government, commercial, educational/organizational). Three keywords (“physical activity,” “fitness,” and “exercise”) were independently entered into three search engines (Google, Bing, and Yahoo) to locate a nonprobability sample of websites (N = 61). Fourteen plain language techniques were coded within each website to examine content formatting, clarity and conciseness, and multimedia use. Approximately half (M = 6.59; SD = 1.68) of the plain language techniques were included in each website. Keyword physical activity resulted in websites with fewer clear and concise plain language techniques (p < .05), whereas fitness resulted in websites with more clear and concise techniques (p < .01). Plain language techniques did not vary by search engine or the website host source. Accessing PA information that is easy to understand and behaviorally oriented may remain a challenge for users. Transdisciplinary collaborations are needed to optimize plain language techniques while communicating online PA information.