Hasil untuk "Costs"

E. Bullmore, O. Sporns

J. Rehm, C. Mathers, S. Popova et al.

DW MacKenzie Ramapo

E. Helpman, Marc J. Melitz, S. Yeaple et al.

Ross Levine, Ross Levine

Lawrence J. Christiano, M. Eichenbaum, C. Evans

O. Williamson

R. Thaler

Douglas W. Diamond

Ted O’Donoghue, M. Rabin

B. Kogut

B. Kernighan, Shou-De Lin

T. Malone, J. Yates, R. Benjamin

B. Klein, R. Crawford, A. Alchian

D. Nowlan

Marie Arnold, Jonathan Brandt, Geert Tjarks et al.

A key factor in reducing the cost of green hydrogen production projects using water electrolysis systems is to minimize the degradation of the electrolyzer stacks, as this impacts the lifetime of the stacks and therefore the frequency of their replacement. To create a better understanding of the economics of stack degradation, we present a linear optimization approach minimizing the costs of a green hydrogen supply chain including an electrolyzer with degradation modeling. By calculating the levelized cost of hydrogen depending on a variable degradation threshold, the cost optimal time for stack replacement can be identified. We further study how this optimal time of replacement is affected by uncertainties such as the degradation scale, the load-dependency of both degradation and energy demand, and the costs of the electrolyzer. The variation of the identified major uncertainty degradation scale results in a difference of up to 9 years regarding the cost optimal time for stack replacement, respectively lifetime of the stacks. Therefore, a better understanding of the degradation impact is imperative for project cost reductions, which in turn would support a proceeding hydrogen market ramp-up.

Kai Chen, Yuqian Zhang

Dynamic treatment regimes (DTRs) are personalized, adaptive strategies designed to guide the sequential allocation of treatments based on individual characteristics over time. Before each treatment assignment, covariate information is collected to refine treatment decisions and enhance their effectiveness. The more information we gather, the more precise our decisions can be. However, this also leads to higher costs during the data collection phase. In this work, we propose a balanced Q-learning method that strikes a balance between the utility of the DTR and the costs associated with both treatment assignment and covariate assessment. The performance of the proposed method is demonstrated through extensive numerical studies, including simulations and a real-data application to the MIMIC-III database.

Afiba Manza-A Agovi, Caitlin T. Thompson, Kevin J. Craten et al.

Abstract Background Long-acting injectable cabotegravir plus rilpivirine (LAI CAB/RPV) has several potential benefits over daily oral formulations for HIV treatment, including the potential to facilitate long-term adherence and reduce pill fatigue. We aimed to assess facilitators of and barriers to LAI CAB/RPV implementation and delivery through the perspectives of physicians and clinical staff, and the experiences of LAI CAB/RPV use among people living with HIV (PLWH) at a Ryan-White supported safety-net clinic in North Texas. Methods We conducted semi-structured interviews with recruited clinic staff (physicians, nurses, and support staff) involved with LAI CAB/RPV implementation and PLWH who switched to LAI CAB/RPV and consented to participate in individual interviews. Data were collected from July to October 2023. Our interview guide was informed by the Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM), and Proctor Implementation Outcomes frameworks. Qualitative data were analyzed using a rapid qualitative analysis approach to summarize key themes. Results We recruited and interviewed 15 PLWH who transitioned to LAI CAB/RPV and 11 clinic staff serving these patients. PLWH conveyed that emotional and informational support from family or a trusted clinician influenced their decision to switch to LAI CAB/RPV. PLWH also reported that injectable treatment was more effective, convenient, and acceptable than oral antiretroviral therapy. Clinic staff and physicians reported that staff training, pharmacist-led medication switches, flexible appointments, refrigeration space and designated room for injection delivery facilitated implementation. Clinic staff cited medication costs, understaffing, insurance prior authorization requirements, and lack of medication access through state drug assistance programs as critical barriers. Conclusions Our study offers insights into real-world experiences with LAI usage from the patient perspective and identifies potential strategies to promote LAI CAB/RPV uptake. The barriers to and facilitators of LAI CAB/RPV program implementation reported by clinic staff in our study may be useful for informing strategies to optimize LAI CAB/RPV programs.

Janani Venkatraman Jagatha, Christoph Schneider, Tobias Sauter

Machine learning (ML) methods are widely used in particulate matter prediction modelling, especially through use of air quality sensor data. Despite their advantages, these methods’ black-box nature obscures the understanding of how a prediction has been made. Major issues with these types of models include the data quality and computational intensity. In this study, we employed feature selection methods using recursive feature elimination and global sensitivity analysis for a random-forest (RF)-based land-use regression model developed for the city of Berlin, Germany. Land-use-based predictors, including local climate zones, leaf area index, daily traffic volume, population density, building types, building heights, and street types were used to create a baseline RF model. Five additional models, three using recursive feature elimination method and two using a Sobol-based global sensitivity analysis (GSA), were implemented, and their performance was compared against that of the baseline RF model. The predictors that had a large effect on the prediction as determined using both the methods are discussed. Through feature elimination, the number of predictors were reduced from 220 in the baseline model to eight in the parsimonious models without sacrificing model performance. The model metrics were compared, which showed that the parsimonious_GSA-based model performs better than does the baseline model and reduces the mean absolute error (MAE) from 8.69 µg/m³ to 3.6 µg/m³ and the root mean squared error (RMSE) from 9.86 µg/m³ to 4.23 µg/m³ when applying the trained model to reference station data. The better performance of the GSA_parsimonious model is made possible by the curtailment of the uncertainties propagated through the model via the reduction of multicollinear and redundant predictors. The parsimonious model validated against reference stations was able to predict the PM_2.5 concentrations with an MAE of less than 5 µg/m³ for 10 out of 12 locations. The GSA_parsimonious performed best in all model metrics and improved the R² from 3% in the baseline model to 17%. However, the predictions exhibited a degree of uncertainty, making it unreliable for regional scale modelling. The GSA_parsimonious model can nevertheless be adapted to local scales to highlight the land-use parameters that are indicative of PM_2.5 concentrations in Berlin. Overall, population density, leaf area index, and traffic volume are the major predictors of PM_2.5, while building type and local climate zones are the less significant predictors. Feature selection based on sensitivity analysis has a large impact on the model performance. Optimising models through sensitivity analysis can enhance the interpretability of the model dynamics and potentially reduce computational costs and time when modelling is performed for larger areas.

Shadi Khalilolahi, Nasrin Kazemi, Saeid Besharati et al.

Background and purpose: The globalization of medical tourism has intensified competition among destinations, making it crucial to identify key success factors. While research emphasizes the role of host communities in tourism development, non-medical aspects of medical tourism remain underexplored. This study examines healthcare staff perspectives to identify the drivers and barriers affecting medical tourism in public hospitals. Methods: This qualitative study employed structured interviews with 16 healthcare staff at Masih Daneshvari Hospital, selected through purposive sampling. Interviews were conducted in person during the summer of 2024 until data saturation was reached. Data analysis followed an inductive content analysis approach using MAXQDA software for coding, grouping, and categorization of themes into drivers and barriers. Results: Key drivers of medical tourism development include skilled human resources, strong medical potential, affordable healthcare and associated costs, and the presence of complementary attractions. Conversely, major barriers include inadequate welfare services for medical tourists, managerial and institutional inefficiencies, hospital infrastructure deficiencies, and political and cultural challenges. Conclusion: Developing a successful medical tourism sector requires a holistic approach. Identifying, prioritizing, and implementing strategic plans to strengthen facilitators and address obstacles are essential steps toward sustainable growth in this field.