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Automated Update Tools To Augment the Wisdom of Crowds in Geopolitical Forecasting
Get PDFDespite the importance of predictive judgments, individual human forecasts are frequently less accurate than those of even simple prediction algorithms. At the same time, not all forecasts are amenable to algorithmic prediction. Here, we describe the evaluation of an automated prediction tool that enabled participants to create simple rules that monitored relevant indicators (e.g., commodity prices) to automatically update forecasts. We examined these rules in both a pool of previous participants in a geopolitical forecasting tournament (Study 1) and a naïve sample recruited from Mechanical Turk (Study 2). Across the two studies, we found that automated updates tended to improve forecast accuracy relative to initial forecasts and were comparable to manual updates. Additionally, making rules improved the accuracy of manual updates. Crowd forecasts likewise benefitted from rule-based updates. However, when presented with the choice of whether to accept, reject or adjust an automatic forecast update, participants showed little ability to discriminate between automated updates that were harmful versus beneficial to forecast accuracy. Simple prospective rule-based tools are thus able to improve forecast accuracy by offering accurate and efficient updates, but ensuring forecasters make use of tools remains a challenge
Evaluating Corn, Tall Fescue And Canola Growth On Sediments Dredged From The Lorain Harbor
No full textSoil degradation is a worldwide problem, causing the declining performance of many plant species. Recently, the application of sediments dredged from aquatic waterways has received attention for their potential as an organic amendment to revive degraded agricultural soils. In Ohio, dredged sediment research has largely focused on the success of corn (Zea mays) or soybean (Glycine max) following the application of dredged sediments from the Toledo Harbor, neglecting the potential for dredged sediments from the other eight harbors and waterways to change plant performance as well as failing to quantify benefits for other commonly grown crops in the region. In a greenhouse experiment, we applied dredged sediments from the Lorain Harbor to degraded agricultural soils across a variety of application ratios and quantified changes in germination, height over the growing season, final biomass, and yield for canola (Brassica napus), tall fescue KY 31 (Festuca arundinacea), and corn to better understand the potential for dredged sediments from this location to increase performance for a variety of regionally important plant species. Overall, plants grown on agricultural soils supplemented with dredged sediments from the Lorain Harbor consistently grew taller, faster, and were larger than the 100% dredged sediment treatments. Furthermore, both corn and tall fescue grown on agricultural soil supplemented with dredged sediments had greater yield compared to their counterparts grown on unamended agricultural soil. In whole, outcomes from this research contribute to a growing body of research that support the use of dredged sediments as a soil amendment for agricultural soils
Ontology Design Facilitating Wikibase Integration — and a Worked Example for Historical Data
No full textWikibase – which is the software underlying Wikidata – is a powerful platform for knowledge graph creation and management. However, it has been developed with a crowd-sourced knowledge graph creation scenario in mind, which in particular means that it has not been designed for use case scenarios in which a tightly controlled high-quality schema, in the form of an ontology, is to be imposed, and indeed, independently developed ontologies do not necessarily map seamlessly to the Wikibase approach. In this paper, we provide the key ingredients needed in order to combine traditional ontology modeling with use of the Wikibase platform, namely a set of axiom patterns that bridge the paradigm gap, together with usage instructions and a worked example for historical data
Deep Learning based Optical Flow Analysis of High-speed Flows
Get PDFTwo-dimensional Rayleigh scattering imaging is utilized to quantify the high-speed flow velocity by employing deep learning based optical flow analysis, along with density fields from Rayleigh scattering intensity profiles
Research to Improve Clinical Care in Family Medicine: Big Data, Telehealth, Artificial Intelligence, and More
No full textThis issue highlights changes in medical care delivery since the start of the COVID-19 pandemic and features research to advance the delivery of primary care. Several articles report on the effectiveness of telehealth, including its use for hospital follow-up, medication abortion, management of diabetes, and as a potential tool for reducing health disparities. Other articles detail innovations in clinical practice, from the use of artificial intelligence and machine learning to a validated simple risk score that can support outpatient triage decisions for patients with COVID-19. Notably one article reports the impact of a voluntary program using scribes in a large health system on physician documentation behaviors and performance. One article addresses the wage gap between early-career female and male family physicians. Several articles report on inappropriate testing for common health problems; are you following recommendations for ordering Pulmonary Function Tests, mt-sDNA for colon cancer screening, and HIV testing
Low-Acuity Pediatric Emergency Department Utilization
Get PDFObjectives: Proper emergency department (ED) utilization is a hallmark of population health. Emergency department overcrowding due to nonurgent visits causes increased stress to healthcare staff, higher costs, and longer wait times for more urgent cases. This study sought to better understand post pandemic reasons caregivers have when bringing in their children for nonurgent visits and devise effective interventions to improve caregiver choice for non-ED care for nonurgent conditions.
Methods: Surveys were conducted at an urban pediatric hospital for Emergency Severity Index (ESI) level 3 to 5 visits. A total of 602 surveys were completed with 8 being excluded from analysis. Survey responses and anonymized demographic information were collected. Responses were compared between surveys grouped by respondent age category, relation to child, child\u27s race, insurance type, and ESI levels.
Results: Primary reasons given for nonurgent ED visits were perceived urgency (74.2%, n = 441), ED superiority to other locations (23.9%, n = 142), and referral to the ED by a third party (17.7%, n = 105). Of those who cited perceived urgency as a reason, 80.5% (n = 355) wanted to lessen their child\u27s pain/discomfort as soon as possible, but only 13.6% said that their child was too ill to be seen anywhere else (n = 60). Demographic differences occurred in the proportions of respondents citing some of the primary and secondary reasons for bringing their child to the ED.
Conclusions: This study highlights 3 key findings. An immediate desire for care plays a key role in caregiver decision making for low-acuity visits. There is potential socioeconomic and racial bias in where care is recommended that needs to be further explored in this region. Cross community interventions that target key reasons for seeking low-acuity care have the highest likelihood of impacting the use of the ED for low-acuity conditions
Tecumseh Land Trust: Land Trusts and Land Preservation
Get PDFPart 1 of the Protecting Glen Helen with a Conservation Easement presentation. Michele Burns introduces the concept of land trusts—nonprofit organizations that protect various types of land, particularly through conservation easements, which are legally binding agreements that restrict development and preserve land forever. She explaines how Tecumseh Land Trust, founded in 1990, operates in southwest Ohio to protect farmland and natural areas without owning land directly and their work in protecting the Glen Helen preservation
Invertebrates as Indicators of Water Quality in the Runkle Woods
Get PDFThis project investigates water quality and aquatic invertebrate habitat health in the Wright State Woods, aiming to assess current conditions and implement targeted conservation strategies. Using indicators such as dissolved oxygen, nutrient concentrations, and the presence of sensitive macroinvertebrates (e.g., mayflies, stoneflies, caddisflies), the team evaluates stream health. Data collection follows established protocols, including EPT indexing and seasonal surveys. Conservation recommendations include stream aeration maintenance, removal of non-native vegetation, soil inoculation, and the construction of rain gardens and green roofs to reduce runoff
AI-Enabled Hardware Security Approach for Aging Classification and Manufacturer Identification of SRAM PUFs
Get PDFSemiconductor microelectronics integrated circuits (ICs) are increasingly integrated into modern life-critical applications, from intelligent infrastructure and consumer electronics to the Internet of Things (IoT) and advanced military and medical systems. Unfortunately, these applications are vulnerable to new hardware security attacks, including microelectronics counterfeits and hardware modification attacks. Physical Unclonable Functions (PUFs) are state-of-the-art hardware security solutions that utilize process variations of integrated circuits for device authentication, secret key generation, and microelectronics counterfeit detection. The negative impact of aging on Static Random Access Memory Physical Unclonable Functions (SRAM PUFs) has significant consequences for microelectronics authentication, security, and reliability. This research thoroughly examines the effect of aging on the reliability of SRAM PUFs used for secure and trusted microelectronics integrated circuit applications. It initially provides an overview of SRAM PUFs, highlighting their significance and essential features while addressing encountered challenges. The study then covers mitigation techniques, including multi-modal PUFs, that already exist to boost the resilience of SRAM PUFs against aging impacts, highlighting their advantages and the gap in the research addressed in this research. This work proposes a novel AI-enabled security for reliable SRAM PUFs. The proposed approach aims to study and countermeasure the impact of aging on SRAM PUF by analyzing data samples, including Bias Temperature Instability (BTI), Bit Flips, Accelerated aging, and Hot Carrier Injection (HCI) and to study their effects on SRAM PUF cell properties and output. Accelerated aging is a direct result of a change in the environmental temperature and voltage for a few hours. We aim to mitigate the impact of accelerated aging on the reliability authentication and encryption keys of SRAM PUFs. Further, AI-assisted approach is used to analyze SRAM PUF stability under accelerated aging operating conditions. Illegal memory chips from shady companies around the world have compromised the safety and reliability of electronic devices. Detecting these manufacturer details is crucial to detecting illegal and substandard manufacturing prior to their integration into key systems. This paper presents a novel approach designed specifically to analyze the SRAM PUF manufacturers. The suggested technique assigns a distinct process variation to every manufacturer, facilitating the classification of the manufacturer, without the need for elaborate registration or verification protocols. The experimental results show the reliability and stability factors of SRAM PUF, including its influence on environmental conditions and the associated effects of aging. Our findings stress the importance of reliable and trustworthy PUF-based hardware security to reliably classify older microelectronic devices from new ones. Using 345 FPGA tested chips, the results show that using different ML models, we can efficiently classify these chips to correctly distinguish the manufacturer, aging impacts, and component, with F1 scores of 96% and 98%
What a Waste: Nitrogen Runoff and Rates in the Maumee River (Ohio, USA)
Get PDFExcess anthropogenic nitrogen (N), primarily from agricultural field fertilization, causes nutrient runoff that stimulates harmful algal blooms (HABs) in western Lake Erie. As a critical tributary to Lake Erie, nutrient loading from the Maumee River drives the intensity of the annual summer HABs in the western basin. Knowledge gaps around rates of N transformations in the Maumee River currently hinder the calibration of in-river parameters in Soil and Water Assessment Tool (SWAT) models for the Maumee watershed. To address these gaps, this research quantified rates of ammonium uptake, ammonium remineralization, nitrification, and bacterial respiration alongside physicochemical parameters of the river. Monthly sampling was conducted along the Maumee River at International Park (river mile 4.53), Mary Jane Thurston (river mile 31.88), and Independence Dam (river mile 59.31) over the course of a year. Ammonium uptake rates ranged from 1.2 to 8.7 µmol N L-1 hr-1 for water samples incubated under light conditions and from 0.2 to 1.9 µmol N L-1 hr-1 under dark conditions, while ammonium regeneration ranged from \u3c0.01 to 12.0 µmol O2 L-1 hr-1. Bacterial respiration rates averaged 525.0 ± 28.5 µM O2. Respiration and both NH₄⁺ uptake & regeneration rates correlated overall with seasonal temperatures and biomass. Respiration rates closely followed temperature, with warmer months having the highest rates. November 2022 samples exhibited higher rates of respiration and both NH₄⁺ uptake & regeneration at all sites as chlorophyll was \u3e200 µg/L during the fall river bloom. Despite not being at peak temperature in the study, the highest rates of microbial activity in April and May., with the lowest observed during the coldest months, January and March. The timing of peak rates at the three sites along the river-to-lake continuum shifted with biomass, indicating the importance of parameterizing the SWAT model with models with spatially and temporally dynamic values. These findings will help refine the SWAT model to account for seasonal variations within the Maumee River, thereby informing more effective nutrient management strategies to protect the ecological health of Lake Erie