This evaluation addresses multi-stage shear creep loading, the immediate creep damage from shear loading, the development of creep damage over time, and the factors affecting the initial damage of rock masses. Results from the multi-stage shear creep test are correlated with calculated values from the proposed model, validating the reasonableness, reliability, and applicability of the model in question. In contrast to the established creep damage model, the shear creep model presented here accounts for the initial damage in rock masses, offering a more comprehensive description of the multi-stage shear creep damage mechanisms observed in rock masses.
Virtual Reality technology is employed in multiple sectors, and investigation into VR's creative use has seen considerable interest. This study explored how VR environments affect divergent thinking, a key feature of the creative process. Two trials were carried out to explore the supposition that immersion in visually expansive virtual reality (VR) environments using head-mounted displays (HMDs) alters the capacity for divergent thinking. Scores from the Alternative Uses Test (AUT) measured divergent thinking, with the stimuli being presented to the participants during the test. NT157 Experiment 1 explored the impact of VR viewing method. Participants in one group watched a 360-degree video through a head-mounted display, and a separate group viewed the same video on a computer monitor. Additionally, to act as a control group, participants viewed a real-world laboratory, rather than the video footage. Compared to the computer screen group, the HMD group demonstrated superior AUT scores. Experiment 2 tested variations in spatial openness within a VR environment by using 360-degree video: one group viewed a video of an open coast, while a second group experienced a video of a closed-off laboratory. A greater AUT score was recorded for the coast group than for the laboratory group. Finally, exposure to a vast VR vista via an HMD cultivates the capacity for divergent thought patterns. The study's limitations are detailed, followed by recommendations for future research.
Queensland, a state in Australia, sees the majority of peanut production, benefiting from its tropical and subtropical environment. A serious threat to peanut quality, late leaf spot (LLS) is a commonly observed foliar disease. NT157 Diverse plant traits have been the focus of research employing unmanned aerial vehicles (UAVs). Studies utilizing UAV-based remote sensing for crop disease estimation have shown promising results by using a mean or a threshold value to characterize plot-level image data, but these methods might be insufficient to accurately reflect the distribution of pixels. This investigation proposes two innovative methods, namely the measurement index (MI) and the coefficient of variation (CV), to ascertain peanut LLS disease levels. Our initial research effort targeted the relationship between LLS disease scores and multispectral vegetation indices (VIs), collected from UAVs, during the peanuts' late growth stages. For LLS disease estimation, we then compared the efficacy of the proposed MI and CV-based methods against their threshold and mean-based counterparts. Analysis of the results indicated that the MI-method yielded the highest coefficient of determination and the lowest error for five out of six selected vegetation indices, contrasting with the CV-based method, which proved superior for the simple ratio index among the four evaluated techniques. After careful evaluation of the advantages and disadvantages of each method, we developed a cooperative system for automatic disease prediction, incorporating MI, CV, and mean-based methods, which we validated by applying it to determine LLS in peanut plants.
While power outages associated with and succeeding a natural disaster drastically hinder recovery and relief initiatives, corresponding modeling and data collection protocols remain constrained. Unfortunately, no methodology exists for the analysis of long-term energy disruptions, exemplified by the situation during the Great East Japan Earthquake. To better anticipate and manage the risks of supply shortages during disasters, this study develops an integrated damage and recovery estimation framework, specifically including power generators, the high-voltage transmission network (above 154 kV), and the power demand system to facilitate a streamlined recovery process. The distinctive feature of this framework is its in-depth analysis of the vulnerability and resilience characteristics of power systems and businesses, primarily as key power consumers, observed in past disasters in Japan. These characteristics are modeled by using statistical functions, which in turn enable the implementation of a simple power supply-demand matching algorithm. Following this, the framework demonstrably reproduces the pre-existing power supply and demand equilibrium from the 2011 Great East Japan Earthquake with a degree of consistency. The average supply margin, calculated from the stochastic components of the statistical functions, is estimated to be 41%, yet a worst-case scenario entails a 56% shortfall in comparison to peak demand. NT157 Applying this framework, the study delves deeper into potential risks, examining a specific past earthquake and tsunami disaster; it is anticipated that the findings will bolster risk perception and refine preparedness for future large-scale events, particularly supply and demand management.
Both humans and robots experience the undesirability of falls, leading to the development of predictive models for falls. Proposed metrics for predicting falls, which rely on mechanical principles, have been validated to varying degrees. These include the extrapolated center of mass, foot rotation index, Lyapunov exponents, joint and spatiotemporal variability, and average spatiotemporal characteristics. This study utilized a planar six-link hip-knee-ankle bipedal model, with curved feet, to determine the effectiveness of various metrics in predicting falls, individually and collectively, during walking at speeds ranging from 0.8 m/s to 1.2 m/s. A Markov chain's mean first passage times, applied to gait descriptions, determined the accurate count of steps that resulted in a fall. Each metric's estimation was derived from the gait's Markov chain. Fall risk metrics, never before derived from the Markov chain, were validated by employing brute-force simulations of the system. Despite the short-term Lyapunov exponents, the Markov chains were capable of accurately calculating the metrics. Employing Markov chain data, quadratic fall prediction models were formulated and subsequently evaluated. Different-length brute force simulations were then used to provide further assessment of the models. Despite evaluation of 49 fall risk metrics, none proved sufficiently accurate in anticipating the number of steps before a fall occurred. In contrast, when a model encompassing all fall risk metrics, excluding Lyapunov exponents, was constructed, accuracy saw a notable increase. A comprehensive understanding of stability requires a combined evaluation of several fall risk metrics. In line with predictions, the escalating steps involved in calculating fall risk metrics directly contributed to improved accuracy and precision. Subsequently, the precision and accuracy of the overarching fall risk model saw a proportionate increase. When considering the optimal balance between accuracy and minimizing the number of steps, 300 simulations, each with 300 steps, emerged as the most suitable approach.
Sustainable investment in computerized decision support systems (CDSS) necessitates a thorough assessment of their economic effect against the backdrop of current clinical processes. Current methods of evaluating the economic burden and implications of CDSS within hospital environments were assessed, followed by suggested improvements to the generalizability of future studies.
A scoping review was performed on peer-reviewed research papers published subsequent to 2010. The final searches of the PubMed, Ovid Medline, Embase, and Scopus databases were executed on February 14, 2023. Each study included in the report assessed the financial burdens and implications of a CDSS-centric intervention in comparison to the prevailing hospital operations. Employing narrative synthesis, the findings were comprehensively summarized. Each individual study was subsequently assessed in light of the Consolidated Health Economic Evaluation and Reporting (CHEERS) 2022 checklist.
The investigation included twenty-nine publications, appearing after 2010, to enhance the research. A comprehensive evaluation of CDSS systems was undertaken across five areas: adverse event surveillance (5 studies), antimicrobial stewardship (4 studies), blood product management (8 studies), laboratory testing (7 studies), and medication safety (5 studies). From a hospital perspective, all the studies evaluated costs, but their resource valuations and consequence measurements for CDSS implementation varied. Future research should follow the recommendations of the CHEERS checklist, employ methodologies that account for confounding variables, and examine both the financial burden of CDSS implementation and the level of patient adherence; it should further analyze the ramifications, both immediate and delayed, of behavior modifications instigated by the CDSS, and assess the impact of variability in outcomes across patient subgroups.
Implementing consistent evaluation and reporting procedures will permit a more detailed comparison of promising initiatives and their subsequent utilization by decision-makers.
Streamlined evaluation and reporting practices ensure consistent comparisons of promising programs and their subsequent uptake by decision-makers.
This study investigated the practical application of a curricular unit. This unit aimed at immersing rising ninth-grade students in socioscientific issues, with a focus on data collection and analysis of health, wealth, educational attainment, and the effect of the COVID-19 pandemic within their communities. A state university in the Northeast hosted an early college high school program. 26 rising ninth graders (14-15 years old; 16 female, 10 male) from this program were overseen by the College Planning Center.