Hasil untuk "Costs"

J. Mincer

M. Pagano, A. Röell

D. Tice, R. Baumeister

Robert N. Stavins

A. Drewnowski, N. Darmon

E. Milbrandt, Stephen A. Deppen, Patricia L. Harrison et al.

M. Boksem, M. Tops

Stephen J. Redding, Daniel M Sturm

K. Chapman, D. Mannino, J. Soriano et al.

Jessica L. P. Weeks

M. Kappelman, S. Rifas-Shiman, C. Porter et al.

Jens Dibbern, Jessica K. Winkler, Armin Heinzl

A. Yazdanyar, A. Newman

N. Kartik

Caner Gocmen, Thodoris Lykouris, Deeksha Sinha et al.

In content moderation for social media platforms, the cost of delaying the review of a content is proportional to its view trajectory, which fluctuates and is apriori unknown. Motivated by such uncertain holding costs, we consider a queueing model where job states evolve based on a Markov chain with state-dependent instantaneous holding costs. We demonstrate that in the presence of such uncertain holding costs, the two canonical algorithmic principles, instantaneous-cost ($cμ$-rule) and expected-remaining-cost ($cμ/θ$-rule), are suboptimal. By viewing each job as a Markovian ski-rental problem, we develop a new index-based algorithm, Opportunity-adjusted Remaining Cost (OaRC), that adjusts to the opportunity of serving jobs in the future when uncertainty partly resolves. We show that the regret of OaRC scales as $\tilde{O}(L^{1.5}\sqrt{N})$, where $L$ is the maximum length of a job's holding cost trajectory and $N$ is the system size. This regret bound shows that OaRC achieves asymptotic optimality when the system size $N$ scales to infinity. Moreover, its regret is independent of the state-space size, which is a desirable property when job states contain contextual information. We corroborate our results with an extensive simulation study based on two holding cost patterns (online ads and user-generated content) that arise in content moderation for social media platforms. Our simulations based on synthetic and real datasets demonstrate that OaRC consistently outperforms existing practice, which is based on the two canonical algorithmic principles.

Nisreen Salama, Rebhi Bsharat, Abdallah Alwawi et al.

Abstract Background AI can improve medical practice, address staff shortages, and enhance diagnostic efficiency. The ChatGPT of Open AI, launched in 2022, uses AI in medical education. However, the long-term impact is uncertain, and integration varies globally, particularly in the Middle East. Aim To explore the knowledge, practices, and attitudes of nursing students in Palestinian universities regarding AI, specifically the use of ChatGPT. Methodology A cross-sectional design was used to conduct this study. The study was performed at 8 private and governmental universities in the West Bank, Palestine, from 1st May 2024 to 30 May 2024, and 304 nursing students participated. Results The study revealed that 84.5% of nursing students at Palestinian universities were aware of AI technology, yet 69.9% lacked formal education or training related to ChatGPT. Despite this gap, 79% supported the integration of AI into nursing curricula and specialized training programs, reflecting strong optimism about its role in education and healthcare. While 58.6% had used AI in their coursework and 68.1% felt comfortable with technology, disparities in proficiency and access remain key barriers to effective AI integration. Major challenges to AI adoption in Palestine include insufficient training, the absence of AI-focused curricula, and financial constraints, underscoring the need for institutional and pedagogical reforms. Concerns about AI’s reliability, costs, and potential diagnostic errors persist, emphasizing the complexities of its integration into nursing education and practice. Conclusion This study highlights the knowledge, attitudes, and practices of Palestinian nursing students regarding AI and ChatGPT. It reveals that, despite growing awareness, the lack of formal education on AI underscores the need for comprehensive curricula. While students’ express optimism about AI’s potential in healthcare, concerns about its reliability and integration persist. The study also reveals that barriers such as inadequate training, limited curricula, and financial constraints must be addressed to effectively integrate AI into nursing education and prepare students for its expanding role in healthcare.

Jefferson Antonio Buendía, Juan Antonio Buendia Sánchez, Diana Guerrero Patino

Abstract Background Ventilator-associated pneumonia (VAP) is a significant clinical challenge due to its morbidity, mortality, and economic burden, especially in low- and middle-income countries. This study evaluates the cost-utility of tobramycin and colistin as nebulized adjunct therapies to systemic antibiotics for managing VAP in Colombia. Methods A decision tree model was constructed comparing three interventions: tobramycin + systemic antibiotics, colistin + systemic antibiotics, and systemic antibiotics alone. The model used a one-year time horizon from a third-payer perspective. Clinical probabilities, costs, and utilities were sourced from literature and local databases. Sensitivity analyses (deterministic and probabilistic with 10,000 iterations) assessed uncertainty. Costs were reported in 2023 USD, adjusted by GDP deflator. Results Tobramycin demonstrated the highest cost-effectiveness. Incremental QALYs were 0.06 for tobramycin and 0.02 for colistin; incremental costs were US$338.09 and US$130.63, respectively. The ICER was US$5625.86 for tobramycin and US$5422.31 for colistin. At a willingness-to-pay threshold of US$5180/QALY, tobramycin had a 56.5% probability of being cost-effective. Conclusion Tobramycin is more cost-effective than colistin as an adjunctive nebulized treatment for ventilator-associated pneumonia (VAP) in Colombia. These findings may help inform clinical guidelines and reimbursement decisions. Further research is needed to evaluate long-term outcomes and to incorporate utility data specific to the Colombian population.

Stefano Bonnini, Michela Borghesi, Gianfranco Piscopo et al.

The methodology carried out in this work is based on non-parametric inference. The problem is framed as a regression analysis, and the solution is derived using the permutation approach. The proposed test does not rely on the assumption that the distribution of the response follows a specific family of probability laws, unlike other parametric approaches. This makes the test powerful, particularly when the typical assumptions of parametric approaches, such as the normality of data, are not satisfied and parametric tests are not reliable. Furthermore, this method is more flexible and robust with respect to parametric tests. A permutation test on the goodness-of-fit of a multiple regression model is applied. Hence, proposed solution consists of the application of permutation tests on the significance of the single coefficients and then a combined permutation test (CPT) to solve the overall goodness-of-fit testing problem. Furthermore, a Monte Carlo simulation study was performed to evaluate the power of the previously mentioned permutation approach, comparing it with the conventional parametric <i>F</i>-test for ANOVA and the bootstrap combined test, both commonly discussed in the literature on this statistical problem. Finally, the proposed non-parametric test was applied to real-world data to investigate the impact of age and smoking habits on medical insurance costs in the USA. The findings suggest that smoking and being at least 50 years old significantly contribute to increased medical insurance costs.

Mahdi Taheri, Sepehr Shahgholian, Mehdi Jahangiri et al.

In this study, the feasibility of providing part of the hot water using 1000 flat-plate solar collectors for the Hawana Water Park in Salalah, Oman, is studied. Dynamic analyses of energy, economic, and environmental parameters are conducted over a year using TSOL V2021 software. In this solar energy–supported hot water production system, a gas-fired boiler is also used as a backup for emergencies. The results indicate that approximately 1181 MWh of solar heat is supplied annually to the water park, leading to a savings of 133,298 m3 of natural gas and preventing the emission of approximately 282 tons of CO₂ pollutants. Another significant finding is that the solar water heating system, with an efficiency of 56.6%, was able to supply 30.4% of the heat required by the water park. The cost of each kilowatt-hour of solar heat produced is calculated to be $0.066, with a payback period of 15.2 years. Furthermore, eight sensitivity scenarios were evaluated to investigate the impact of carbon credit trading and energy price increases on the economic performance of the assessed system. The results indicate that by integrating carbon pricing mechanisms, such as the Emissions Trading System (ETS), the cost of energy (COE) can be reduced by up to $0.026 per kWh, and the payback period can be shortened to 8.4 years. Moreover, the return on assets (ROA) significantly improves under scenarios involving higher carbon credit prices and rising energy costs, highlighting the economic and environmental benefits of integrating renewable energy systems with emerging carbon markets.

Mohamed Atouife, Jesse Jenkins

Hydrogen production from water electrolysis, direct air capture (DAC), and synthetic kerosene derived from hydrogen and CO2 (`e-kerosene') are expected to play an important role in global decarbonization efforts. So far, the economics of these nascent technologies hamper their market diffusion. However, a wave of recent policy support in the United States, Europe, China, and elsewhere is anticipated to drive their commercial liftoff and bring their costs down. To this end, we evaluate the potential cost reductions driven by policy-induced scale-up of these emerging technologies through 2030 using an experience curves approach accounting for both local and global learning effects. We then analyze the consequences of projected cost declines on the competitiveness of these nascent technologies compared to conventional fossil alternatives, where applicable, and highlight some of the tradeoffs associated with their expansion. Our findings indicate that enacted policies could lead to substantial capital cost reductions for electrolyzers. Nevertheless, electrolytic hydrogen production at $1-2/kg would still require some form of policy support. Given expected costs and experience curves, it is unlikely that liquid solvent DAC (L-DAC) scale-up will bring removal costs to stated targets of $100/tCO2, though a $200/tCO2 may eventually be within reach. We also underscore the importance of tackling methane leakage for natural gas-powered L-DAC: unmitigated leaks amplify net removal costs, exacerbate the investment requirements to reach targeted costs, and cast doubt on L-DAC's role in the clean energy transition. Lastly, despite reductions in electrolysis and L-DAC costs, e-kerosene remains considerably more expensive than fossil jet fuel. The economics of e-kerosene and the resources required for production raise questions about the fuel's ultimate viability as a decarbonization tool for aviation.