Sentimetrix (United States)

With the rapid increase in drone adoption, the global agricultural drone market is forecasted to grow to $11.9 billion USD by 2028 from an approximate worth of $1.4 billion USD in 2021 (Research and Markets, 2023). Butts et al. explored the combined effects of various hydraulic nozzle types and spray volumes on coverage and deposits from remotely piloted aerial application systems (spray drones). They observed increased spray recovery in the center of the spray swath compared to ground spray equipment, and identified specific nozzles to improve deposits while mitigating off-target movement potential. Paul et al. observed similar weed control in rice from a spray drone application compared to a knapsack manual sprayer despite significant reductions in spray volume, coverage, and droplet deposits. Their findings highlighted the potential of spray drones to reduce both labor and pesticide exposure in direct-seeded rice production.Proper weed identification is a critical first step towards effective weed management efforts. Venkataraju et al. explored multiple algorithms, including machine learning, deep learning, and object detection, to correctly identify troublesome weeds such as Palmer amaranth (Amaranthus palmeri S. Wats.) and waterhemp [Amaranthus tuberculatus (Moq.) J. D. Sauer]. Their deep learning model was most successful, achieving 93% accuracy in identifying the two species.Innovative techniques also involve viable strategies for improving future weed management efforts. Somala et al. explored the capabilities of a fungal extract (Diaporthe spp.) to reduce growth characteristics of barnyardgrass (Echinochloa crus-galli P. Beauv.). Their laboratory research demonstrated greater than 80% reduction in barnyardgrass seed germination, root length, and shoot length. In other laboratory allelochemical research, Trespidi et al. identified that root exudates from Baccharis halimifolia L. reduced germination by 50-75% and reduced root length by more than 85% across all tested species. However, they noted the challenges of consistently translating laboratory allelochemical results to field applications, and thus further research is needed to refine the use of allelochemicals for effective weed management. Niklolić et al. investigated biological characteristics, including germination thresholds and seed properties, of common ragweed (Ambrosia artemisiifolia L.) across different European climatic conditions. Their research demonstrated high adaptability of common ragweed to varying weather patterns, further exemplifying the need for site-specific weed management strategies.Finally, understanding practitioners' perceived needs, current practices, and thoughts about novel technologies is vital for establishing stakeholder-driven research and providing meaningful outreach. Ugljic et al. surveyed 128 stakeholders across the U.S. Midwest to assess these values. Results revealed more than 75% of respondents were unsure of adopting innovative targeted spray technologies. Survey results also showed that nearly 50% of respondents indicated they needed additional information on targeted spray equipment, underscoring the importance of further innovative technology research in conjunction with effective outreach efforts.This collection of research works serves as a critical example of the innovative weed management research being conducted globally, and the need for continued exploration. Longterm career forecasts have predicted that there will be a need for professionals who understand weed biology, ecology, and management principles, while simultaneously comprehending sensors, automation, and engineering technologies (Westwood et al., 2018). Additional funding resources and support are also required in this critical agricultural research crossroad. Collaborations with industry partners are a necessity, but there is also a pressing requirement for state, federal, and international funding resources to evaluate innovative weed management technologies and techniques. These novel tools will be released commercially regardless of the research background; therefore, funding not tied to a specific company will be crucial to validate marketing claims, supply non-biased data, and disseminate recommendations through engagement and Extension outreach for the successful implementation of these tools.

Background Proton density fat fraction (PDFF) estimated by using chemical shift-encoded (CSE) MRI is an accepted imaging biomarker of hepatic steatosis. This work aims to promote standardized use of CSE MRI to estimate PDFF. Purpose To assess the accuracy of CSE MRI methods for estimating PDFF by determining the linearity and range of bias observed in a phantom. Materials and Methods In this prospective study, a commercial phantom with 12 vials of known PDFF values were shipped across nine U.S. centers. The phantom underwent 160 independent MRI examinations on 27 1.5-T and 3.0-T systems from three vendors. Two three-dimensional CSE MRI protocols with minimal T1 bias were included: vendor and standardized. Each vendor's confounder-corrected complex or hybrid magnitude-complex based reconstruction algorithm was used to generate PDFF maps in both protocols. The Siemens reconstruction required a configuration change to correct for water-fat swaps in the phantom. The MRI PDFF values were compared with the known PDFF values by using linear regression with mixed-effects modeling. The 95% CIs were calculated for the regression slope (ie, proportional bias) and intercept (ie, constant bias) and compared with the null hypothesis (slope = 1, intercept = 0). Results Pooled regression slope for estimated PDFF values versus phantom-derived reference PDFF values was 0.97 (95% CI: 0.96, 0.98) in the biologically relevant 0%-47.5% PDFF range. The corresponding pooled intercept was -0.27% (95% CI: -0.50%, -0.05%). Across vendors, slope ranges were 0.86-1.02 (vendor protocols) and 0.97-1.0 (standardized protocol) at 1.5 T and 0.91-1.01 (vendor protocols) and 0.87-1.01 (standardized protocol) at 3.0 T. The intercept ranges (absolute PDFF percentage) were -0.65% to 0.18% (vendor protocols) and -0.69% to -0.17% (standardized protocol) at 1.5 T and -0.48% to 0.10% (vendor protocols) and -0.78% to -0.21% (standardized protocol) at 3.0 T. Conclusion Proton density fat fraction estimation derived from three-dimensional chemical shift-encoded MRI in a commercial phantom was accurate across vendors, imaging centers, and field strengths, with use of the vendors' product acquisition and reconstruction software. © RSNA, 2021 See also the editorial by Dyke in this issue.

As cinzas leves e pesadas de carvo foram utilizadas na sntese das zelitas (ZCL e ZCP) por tratamento hidrotrmico alcalino

Abstract Any project relying on data analysis is only as good as the data sources selected. The data sources used for the Community for Education, Wellness and Support (CEWS) project were carefully selected from the ever-increasing options of forums, blogs and social networks to ensure that the set contained relevant data sources to be used for training data. Following the selection of the data sources, tools were developed to extract the maximum value from the data. The Psychological Information Library (PIL), containing references to the most up to date collection of resources and texts concerning PTSD was also used to tune the analytics engine. Relevant blogs and forums have been identified by the team’s experts, brought into common environment and cleaned to enhance the accuracy of the analytics results. Open-ended questions from the Brain Fitness questionnaires provide a unique way to collect PTSD-specific textual input from the users.KeywordsStaging AreaSocial Media DataPersonal Identify InformationComma Separate ValueAutomatic Text AnalysisThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.