Reinhard Strametz,1,* Hannah Roesner,1,2,* Thomas Neusius,1,* Isabell Wiesenhuetter,1 Stefan Bushuven,3,4 José Joaquín Mira,2,5 Dominik Hinzmann,6 Susanne Heininger7 1Wiesbaden Institute for Healthcare Economics and Patient Safety, RheinMain UAS, Wiesbaden, Germany; 2Miguel Hernández University of Elche, Elche, Spain; 3Training Center for Emergency Medicine (NOTIS e.V), Engen, 78234, Germany; 4Department of Anesthesiology and Critical Care, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, 79106, Germany; 5Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana (FISABIO), Alicante, Spain; 6Department Clinical Medicine, Department of Anesthesiology and Intensive Care, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany; 7Faculty for Applied Healthcare Sciences, Deggendorf Institute of Technology, Deggendorf, Germany*These authors contributed equally to this workCorrespondence: Reinhard Strametz, Wiesbaden Institute for Healthcare Economics and Patient Safety, RheinMain UAS, Bleichstr. 3, Wiesbaden, 65183, Germany, Email reinhard.strametz@hs-rm.dePurpose: The objective of this study is to evaluate whether the nationwide establishment and institutionalization of a peer-support program, is economically justified given the potential positive effects on the Second Victim Phenomenon (SVP) among healthcare professionals in Germany.Methods: A comprehensive methodological approach was employed, using data from the SeViD studies to assess the prevalence and duration of SVP among physicians and nurses in Germany. Economic impact assessments were conducted to estimate the potential cost savings associated with implementing a peer-support program.Results: The economic analysis reveals significant annual costs associated with SVP-induced absenteeism: approximately 1.56 billion euros for physicians and 1.87 billion euros for nurses. Implementing comprehensive peer-support programs could reduce these costs to approximately 0.85 billion (physicians) and 1.02 billion euros (nurses), respectively, demonstrating substantial potential economic benefits.Conclusion: Investing in a structured peer-support program could yield annual savings exceeding 1.55 billion euros while enhancing workforce resilience and improving patient care. This underscores the economic rationale for scaling up peer support initiatives in healthcare settings.Keywords: peer support program, economic impact, second victim phenomenon, healthcare professionals
High-resolution water depth data are of great significance in island research and coastal ecosystem monitoring. However, the acquisition of high-resolution imagery has been a challenge due to the difficulties and high costs associated with obtaining such data. To address this issue, this study proposes a water depth inversion method based on Gaofen-1 (GF-1) satellite data, which integrates multi-source satellite data to obtain high-resolution bathymetric data. Specifically, the research utilizes bathymetric data derived from Sentinel-2 and Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) as prior information, combined with high-resolution imagery obtained from the GF-1 satellite constellation (GF-1B/C/D). Then, it employs a scale-invariant effect to map bathymetry with a spatial resolution of 2 m, applied to four study areas in the Pacific Islands. The results are further evaluated using ICESat-2 data, which demonstrate that the water depth inversion results from this study possess high accuracy, with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> values exceeding 0.85, root mean square error (RMSE) ranging from 0.56 to 0.90 m, with an average of 0.7125 m, and mean absolute error (MAE) ranging from 0.43 to 0.76 m, with an average of 0.55 m. Additionally, this paper discusses the applicability of the scale-invariant assumption in this research and the improvements of the quadratic polynomial ratio model (QPRM) method compared to the classical linear ratio model (CLRM) method. The findings indicate that the integration of multi-source satellite remote sensing data based on the scale-invariant effect can effectively obtain high-precision, high-resolution bathymetric data, providing significant reference value for the application of GF-1 satellites in high-resolution bathymetry mapping.
There is a general concern that the increasing penetration of electric vehicles (EVs) will result in higher aging failure probability of equipment and reduced network reliability. The electricity costs may also increase, due to the exacerbation of peak load led by uncontrolled EV charging. This paper proposes a linear optimization model for the assessment of the benefits of EV smart charging on both network reliability improvement and electricity cost reduction. The objective of the proposed model is the cost minimization, including the loss of load, repair costs due to aging failures, and EV charging expenses. The proposed model incorporates a piecewise linear model representation for the failure probability distributions and utilizes a machine learning approach to represent the EV charging load. Considering two different test systems (a 5-bus network and the IEEE 33-bus network), this paper compares aging failure probabilities, service unavailability, expected energy not supplied, and total costs in various scenarios with and without the implementation of EV smart charging.
Production of electric energy or power. Powerplants. Central stations, Renewable energy sources
Toke Emil Panduro, Doan Nainggolan, Marianne Zandersen
Despite increasing interest in Nature-based Solutions (NbS), there is still a lack of comprehensive understanding regarding the costs of different types of NbS and their variations. Cost information is crucial for economic assessments of the viability of NbS compared to other alternatives. In this paper, we provide an overview of NbS costs in urban settings, drawing primarily from a literature review of both gray and scientific literature. We present cost estimates for establishing green and blue spaces in urban and peri-urban areas, street trees, green roofs, and green walls. Our findings show that median costs are 56 EUR/m² for green spaces, 73 EUR/m³ for blue spaces, and 201 EUR/m² for green roofs.Furthermore, we demonstrate how an economic assessment can be carried out utilizing the calculated cost estimates while providing a systematic procedure to handle the multiple potential benefits provided by NbS. We argue that a step-wise ranked cost-effectiveness analysis, based on ranking local policy objectives, is a feasible approach for making informed choices on competing solutions relative to conventional cost-effectiveness analysis and cost-benefit analysis. The proposed approach could prove useful due to its relatively straightforward application and the fact that it does not require the aggregation of multiple benefits into one common measurement or valuation unit.
In this paper, we develop a profit-sharing-based optimal routing mechanism to incentivize horizontal collaboration among urban goods distributors. The core of this mechanism is based on exchanging goods at meet points, which is optimally planned en route. We propose a Collaborative Electric Vehicle Routing Problem with Meet Points (CoEVRPMP) considering constraints such as time windows, opportunity charging, and meet-point synchronization. The proposed CoEVRPMP is formulated as a mixed-integer nonlinear programming model. We present an exact method via branching and a matheuristic that combines adaptive large neighborhood search with linear programming. The viability and scalability of the collaborative method are demonstrated through numerical case studies, including a real-world case and a large-scale experiment with up to 500 customers. The findings underscore the significance of horizontal collaboration among delivery companies in attaining both higher individual profits and lower total costs. Moreover, collaboration helps to reduce the environmental footprint by decreasing travel distance.
Mahdi Dolatabadi, Seyedreza Seyednouri, Albin Hasselström
et al.
Abstract This study aims to develop an optimization strategy for determining the optimal type and capacity of batteries in a building‐applied photovoltaic system, taking into account battery degradation, consumption profiles, and regional solar irradiation. Key performance indicators such as peak shaving, savings, net present value, self‐consumption, return on investment, and payback period are examined. The best trade‐off among these indicators is determined using the fuzzy decision‐making method. A study was conducted using real data from Kpenergy Company, focusing on a building with a 50 kW photovoltaic system located in Stockholm. Three cases were examined in MATLAB software, each categorized based on the type of contract between the utility (Vattenfall Company) and the subscriber. The results of these case studies highlight the effectiveness of the proposed optimization approach. Using the proposed approach, optimal batteries are determined, minimizing subscriber costs while maximizing profit.
Xingyu Yang, Huanyi Zhou, Mohammad Alathamneh
et al.
Grid-connected voltage-source inverters (VSIs) have evolved over the past years for the interconnection of renewable energy sources with the grid to satisfy the increased electrical load demand and utilize clean carbon-free energy. However, VSIs suffer a significant drawback of switching harmonics caused by the inverter switches. To reduce the amount of current distortion injected into the utility grid and meet harmonic constraints as defined by power quality standards, an LCL filter is often used. Although extensive literature describes assorted design procedures for an LCL filter, all these methods cannot guarantee an optimal solution, because no direct mathematical function between the LCL filter parameters and the total harmonic distortion (THD) exists. Presented in this paper is the application of an evolutionary annealing–simplex method to select the inductance values in the LCL filter. This method is compared to other methods previously presented in the literature: a systematic method and an inductance grid-search method based on a calculated distribution of total harmonic distortion for various inductance values. The goal is to select the smallest overall inductance values to ensure lower manufacturing costs while maintaining the filter performance. Simulation and experimental results verify that at least a 40% reduction in overall inductance can be achieved with the proposed method while simultaneously guaranteeing the filtering performance.
This paper takes a new look at the economic potential of investment decisions to optimize the transport system by minimizing the organizational and operational costs of cargo routing. We present a methodology of scenario forecasting based on statistical methods. Its application makes it possible to achieve a result using the optimal trend function. At the same time, correcting additive and multiplicative parameters allows gaining a given accuracy. The precision and the accuracy of the forecasting model were checked by both additional resulting indicators and the distribution of the model residuals. The simulated scenario forecast of freight turnover was used to formulate the conditions of the transport problem within a certain transport system, limited infrastructure, and the capabilities of a particular carrier. The solution to the transport problem with dynamic adjustment of restrictions determined the possibility of altering the number of routes for the transport work plan. Thus, the dynamics of transportation costs with a certain cargo turnover in various scenarios and an adjustable number of routes with changing restrictions on the throughput and carrying capacity of the transport system and a specific transport market entity will have a significant impact on the economic efficiency of the transportation. The findings of the study demonstrated a need for investment analysis of the possible removing some restrictions on throughput and carrying capacity in a certain economic situation, taking into account the development potential of both an entity and the transport system in general.
Ali Imani, Roghayeh Alibabayee, Mina Golestani
et al.
BackgroundMeasuring hospital efficiency is a systematic process to optimizing performance and resource allocation. The current review study has investigated the key input, process, and output indicators that are commonly used in measuring the technical efficiency of the hospital to promote the accuracy of the results.MethodsTo conduct this systematic review, the electronic resources and databases MEDLINE (via PubMed), Scopus, Ovid, Proquest, Google Scholar, and reference lists of the selected articles were used for searching articles between 2010 and 2019. After in-depth reviews based on the inclusion and exclusion criteria, among 1,537 studies, 144 articles were selected for the final assessment. Critical Appraisal Skills Programme (CASP) Checklist was used for evaluating the quality of the articles. The main findings of studies have been extracted using content analysis.ResultsAfter the final analysis, the Context/Input indicators that were commonly considered by studies in analyzing hospital technical efficiency include different variables related to Hospital Capacity, Structure, Characteristics, Market concentration, and Costs. The Process/Throughput indicators include different variables related to Hospital Activity or services-oriented process Indicators, Hospital Quality-oriented process indicators, and Hospital Educational processes. Finally, the Output/Outcome indicators include different variables related to Hospital Activity-related output variables and Quality-related output/outcomes variables.ConclusionThis study has identified that it is necessary to mix and assess a set of input, process, and output indicators of the hospital with both quantitative and qualitative indicators for measuring the technical efficiency of hospitals comprehensively.