Usefulness of Vaccine-Adverse Event-Reporting System (VAERS) data and protocols required for statistical analyses were pinpointed with a set of recommendations for the application of machine learning modeling or exploratory analyses on VAERS data with a case study of COVID-19 vaccines (Pfizer-BioNTech, Moderna, Janssen). A total of 262,454 duplicate reports (29%) from 905,976 reports were identified, which were merged into a total of 643,522 distinct reports. A customized online survey was also conducted providing 211 reports. A total of 20 highest reported adverse events were first identified. Differences in results after applying various machine learning algorithms (association rule mining, self-organizing maps, hierarchical clustering, bipartite graphs) on VAERS data were noticed. Moderna reports showed injection-site-related AEs of higher frequencies by 15.2%, consistent with the online survey (12% higher reporting rate for pain in the muscle for Moderna compared to Pfizer-BioNTech). AEs {headache, pyrexia, fatigue, chills, pain, dizziness} constituted >50% of the total reports. Chest pain in male children reports was 295% higher than in female children reports. Penicillin and sulfa were of the highest frequencies (22%, and 19%, respectively). Analysis of uncleaned VAERS data demonstrated major differences from the above (7% variations). Spelling/grammatical mistakes in allergies were discovered (e.g., ~14% reports with incorrect spellings for penicillin).

A primary dataset capturing five distinct types of sheep activities in realistic settings was constructed at various resolutions and viewing angles, targeting the expansion of the domain knowledge for non-contact virtual fencing approaches. The present dataset can be used to develop non-invasive approaches for sheep activity detection, which can be proven useful for farming activities including, but not limited to, sheep counting, virtual fencing, behavior detection for health status, and effective sheep breeding. Sheep activity classes include grazing, running, sitting, standing, and walking. The activities of individuals, as well as herds of sheep, were recorded at different resolutions and angles to provide a dataset of diverse characteristics, as summarized in Table 1. Overall, a total of 149,327 frames from 417 videos (the equivalent of 59 minutes of footage) are presented with a balanced set for each activity class, which can be utilized for robust non-invasive detection models based on computer vision techniques. Despite a decent existence of noise within the original data (e.g., segments with no sheep present, multiple sheep in single frames, multiple activities by one or more sheep in single as well as multiple frames, segments with sheep alongside other non-sheep objects), we provide original videos and the original videos’ frames (with videos and frames containing humans omitted for privacy reasons). The present dataset includes diverse sheep activity characteristics and can be useful for robust detection and recognition models, as well as advanced activity detection models as a function of time for the applications.

Fencing in livestock management is essential for location and movement control yet with conventional methods to require close labour supervision, leading to increased costs and reduced flexibility. Consequently, virtual fencing systems (VF) have recently gained noticeable attention as an effective method for the maintenance and control of restricted areas for animals. Existing systems to control animal movement use audio followed by controversial electric shocks which are prohibited in various countries. Accordingly, the present work has investigated the sole application of audio signals in training and managing animal behaviour. Audio cues in the range of 125–17 kHz were used to prohibit the entrance of seven Hebridean ewes from a restricted area with a feed bowl. Two trials were performed over the period of a year which were video recorded. Sound signals were activated when the animal approached a feed bowl and a restricted area with no feed bowl present. Results from both trials demonstrated that white noise and sounds in the frequency ranges of 125–440 Hz to 10–17 kHz successfully discouraged animals from entering a specific area with an overall success rate of 89.88% (white noise: 92.28%, 10–14 kHz: 89.13%, 15–17 kHz: 88.48%, 125–440 Hz: 88.44%). The study demonstrated that unaided audio stimuli were effective at managing virtual fencing for sheep.

An approach is presented describing the development of a Bayesian network (BN) based tool for rapid assessment of the distribution of engineered nanomaterials (ENMs). The methodology was demonstrated via the construction of a BN model for estimating the exposure concentrations of nanomaterials (BN-nanoExpo) based on simulation data derived from a mechanistic multimedia compartmental fate and transport model. The results of simulations of the distribution of six ENMs in eight different regions were generated for a broad range of geographical and meteorological parameters as well as ENM release rates to the air, water and soil major compartments. Test cases with the constructed BN-nanoExpo demonstrated the capability of the BN based model to portray a wide range of simulation results that can be obtained with the mechanistic fate and transport model. Moreover, BN-nanoExpo is shown to be a suitable tool for estimating both ENM concentrations and release rates given partial information, while also enabling assessment of the impact of uncertainties in input data on the predicted outcomes. The results of the current study suggest that there is merit in exploring the utility of the approach to more complex models, which would provide decision makers with powerful tools for rapid assessment of the behavior of nanomaterials in the environment.

Pretreatment of reverse osmosis (RO) feed water of high mineral scaling propensity was evaluated with respect to downstream RO membrane scaling, for two different feed pretreatment configurations. The pretreatment schemes included (i) media sand filtration, followed by a hydrocyclone (HC) and (ii) a hydrocyclone, microfilter, and a UF module, where both configurations included mesh screens for added protection. The first pretreatment configuration reduced the source water turbidity to ~0.5 NTU, while treatment that included UF feed yielded turbidity of $\underset{˜}{<}0.1\mathrm{NTU}$; both pretreatment strategies provided feed water turbidity within the range recommended for RO desalination. Membrane scaling tests, with the pretreated water without antiscalant dosing, using a plate-and-frame RO unit and a membrane monitoring system, provided real-time membrane surface images that were quantified with respect to the progression of mineral scaling. RO desalting of source water pretreated with the first configuration revealed flux decline that was 75% greater and scale coverage (primarily gypsum) a factor of approximately eight higher relative to desalting of UF-treated source water. The results suggest that RO desalting of high mineral scaling propensity water can significantly benefit from added UF treatment to achieve feedwater turbidity to well below the typically recommended 0.5 NTU upper limit.