Robust Alternatives Research Articles

LBMON234 Ultrasonographic Criteria In The Diagnosis Of Polycystic Ovary Syndrome: A Systematic Review And Meta Analysis

Abstract Ultrasonographic evaluation of polycystic ovarian morphology (PCOM) is part of the diagnostic evaluation of polycystic ovarian syndrome (PCOS). The relevance of PCOM as a diagnostic criterion for PCOS was reaffirmed in the most recent International Evidence-Based Guideline for the Assessment and Management of PCOS. However, there remains a lack of clarity regarding the best practices and specific ultrasonographic markers to define PCOM. We synthesized evidence on diagnostic test accuracy of ovarian ultrasound features to comprehensively determine the most suitable markers to diagnose PCOS. The primary outcome was diagnostic accuracy measures (e. g., thresholds, sensitivity, specificity) for PCOS using the following ovarian markers: follicle number per ovary (FNPO) or per single cross-section (FNPS), ovarian volume (OV), and stromal features. Databases of PubMed, Web of Science, Scopus, CINAHL, and CENTRAL were searched until 7 November 2021 to identify studies that compared ultrasound markers between women with PCOS to those without PCOS. Risk of bias and applicability assessment for diagnostic test accuracy studies were determined using the QUADAS-2 and QUADAS-C tool for a single index test or between multiple index tests, respectively. From a total of 1869 records initially identified, 22 studies were included and 18 (N=6,337; [n=2991 PCOS; n= 3346 Control]) were pooled for meta-analysis. FNPO was the most accurate diagnostic marker (sensitivity: 84%, CI: 81% to 87%; specificity: 91%, CI: 87% to 94%). OV and FNPS had similar but inferior pooled sensitivity (OV: 81%, CI: 75% to 86%; FNPS: 81%, CI: 70% to 89%) and specificity (OV: 79%, CI: 73% to 84%; FNPS: 83%, CI: 75% to 88%) compared to FNPO. Majority of studies had high risk of bias for patient selection and index test methodology across all markers. Subgroup analysis indicated that stratification based on age, body mass index (BMI), and previously proposed thresholds did not account for the heterogeneity in diagnostic accuracy observed across studies. For FNPO, studies that used a transducer frequency <8MHz or the Rotterdam criteria had improved positive likelihood ratio (+LR) and sensitivity, respectively. European studies had improved diagnostic accuracy (specificity, diagnostic odds ratio, +LR) compared to North American studies for FNPO, potentially due to BMI differences between PCOS populations (North America: 30.29 ± 0.61kg/m2, Europe: 27.62 ± 0.73kg/m2). Our findings support the use of FNPO as the gold standard in the ultrasonographic diagnosis of PCOS, with OV and FNPS as robust alternatives if total antral follicle counts cannot be obtained. These findings also identify avenues for future research to refine the ultrasonographic definition of PCOM for timely diagnosis of PCOS and investigations into phenotypic variations in pathogenesis and response to treatment in this condition (PROSPERO ID: CRD42021259118). Presentation: Monday, June 13, 2022 12:30 p.m. - 2:30 p.m.

Binding-driven reactivity attenuation enables NMR identification of selective drug candidates for nucleic acid targets

NMR methods, and in particular ligand-based approaches, are among the most robust and reliable alternatives for binding detection and consequently, they have become highly popular in the context of hit identification and drug discovery. However, when dealing with DNA/RNA targets, these techniques face limitations that have precluded widespread application in medicinal chemistry. In order to expand the arsenal of spectroscopic tools for binding detection and to overcome the existing difficulties, herein we explore the scope and limitations of a strategy that makes use of a binding indicator previously unexploited by NMR: the perturbation of the ligand reactivity caused by complex formation. The obtained results indicate that ligand reactivity can be utilised to reveal association processes and identify the best binders within mixtures of significant complexity, providing a conceptually different reactivity-based alternative within NMR screening methods.

Drain fluid biomarkers for prediction and diagnosis of clinically relevant postoperative pancreatic fistula: A narrative review.

Clinically relevant postoperative pancreatic fistula (CR-POPF) has continued to compromise patient recovery post-pancreatectomy despite decades of research seeking to improve risk prediction and diagnosis. The current diagnostic criteria for CR-POPF requires elevated drain fluid amylase to present alongside POPF-related complications including infection, haemorrhage and organ failure. These worrying sequelae necessitate earlier and easily obtainable biomarkers capable of reflecting evolving CR-POPF. Drain fluid has recently emerged as a promising source of biomarkers as it is derived from the pancreas and hence, capable of reflecting its postoperative condition. The present review aims to summarise the current knowledge of CR-POPF drain fluid biomarkers and identify gaps in the field to invigorate future research in this critical area of clinical need. These findings may provide robust diagnostic alternatives for CR-POPF and hence, to clarify their clinical utility require further reports detailing their diagnostic and/or predictive accuracy.

Comparison of multi-objective evolutionary algorithms applied to watershed management problem

Simulation-based optimization (S–O) frameworks are effective in developing cost-effective watershed management strategies, where optimization algorithms have substantial effect on the quality of strategies. Despite the development and improvement of multi-objective evolutionary algorithms (MOEAs) provide more robust alternatives for optimization, they typically have limited applications in real-world decision contexts. In this study, three advanced MOEAs, including NSGA-II, MOEA/D and NSGA-III, were introduced into the S–O framework and applied to a real-world watershed management problem, and their performance and characteristics were quantified through performance metrics. Results show that a higher crossover or mutation probability do not necessarily promote convergence and diversity of solutions, while a larger generation and population size is helpful for MOEAs to find high-quality solutions. Compared to the other two MOEAs, NSGA-II consistently exhibits robust performance in finding solutions with good convergence and high diversity, and provides more options at the same computational cost, while the degenerate Pareto front of the proposed watershed management problem may account for the poor performance of MOEA/D and NSGA-III in terms of diversity. For a 10% TN or TP reduction target, the average cost of the NSGA-II optimized strategies is 32.22% or 47.83% of the commonly used strategies. In addition, this study also discussed the development of resilient watershed management to buffer the impacts of climate change on aquatic system, the incorporation of fuzzy programming into the S–O framework to develop robust watershed management strategies under uncertainty, and the application of machine learning-based surrogate models to reduce computational cost of the S–O framework. These results can contribute to the understanding of MOEAs and provide useful guidance to decision makers.

Designing a Multi-Stage Expert System for daily ocean wave energy forecasting: A multivariate data decomposition-based approach

Accurate forecasting of the wave energy is crucial and has significant potential because every wave meter possesses an energy amount ranging from 30 to 40 kW along the shore. By harnessing, it does not produce toxic gases, which is a better alternative to the energies that use fossil fuels. In this research, a multi-stage Multivariate Variational Mode Decomposition (MVMD) integrated with Boruta-Extreme Gradient Boosting (BXGB) feature selection and Cascaded Forward Neural Network (CFNN) (i.e., MVMD-BXGB-CFNN) is proposed to forecast daily ocean wave energy in the regions of Queensland State, Australia. The modelling outcomes were benchmarked via three other robust intelligence-based alternatives comprised of Multigene Genetic Programming (MGGP), Least Square Support Machine (LSSVM), and Gradient Boosted Decision Tree (GBDT) models hybridized with MVMD and BXGB (i.e., MVMD-BXGB-MGGP, MVMD-BXGB-LSSVM, and MVMD-BXGB-GBDT), and their counterpart standalone CFNN, GBDT, LSSVM, and MGGP models. To develop the multi-step hybrid intelligent systems, first, the primary input signals were simultaneously decomposed into intrinsic mode functions (IMFs) and residual components using the MVMD pre-processing technique. Next, the significant lags at the t-1 and t-2 timescales computed using the cross-correlation function were imposed on the decomposed components and further filtered by the BXGB feature selection to identify the best IMFs and reduce the computational cost and enhance the accuracy. Finally, the filtered IMFs were incorporated into the machine learning (ML) models to forecast the wave energy. Forecasting performance of all the provided models (hybrid and counterpart standalone ones) was evaluated during the testing phase by several well-known metrics, infographic tools, and diagnostic analysis. The results showed that the MVMD-BXGB-CFNN technique, as a capable expert system, outperformed the other hybrid and counterpart standalone methods and has an adequate degree of reliability to forecast the daily wave energy in coastal regions.

NtHash2: recursive spaced seed hashing for nucleotide sequences.

MotivationSpaced seeds are robust alternatives to k-mers in analyzing nucleotide sequences with high base mismatch rates. Hashing is also crucial for efficiently storing abundant sequence data. Here, we introduce ntHash2, a fast algorithm for spaced seed hashing that can be integrated into various bioinformatics tools for efficient sequence analysis with applications in genome research.ResultsntHash2 is up to 2.1× faster at hashing various spaced seeds than the previous version and 3.8× faster than conventional hashing algorithms with naïve adaptation. Additionally, we reduced the collision rate of ntHash for longer k-mer lengths and improved the uniformity of the hash distribution by modifying the canonical hashing mechanism.Availability and implementationntHash2 is freely available online at github.com/bcgsc/ntHash under an MIT license.Supplementary information Supplementary data are available at Bioinformatics online.

Rudorffites and Beyond: Perovskite‐Inspired Silver/Copper Pnictohalides for Next‐Generation Environmentally Friendly Photovoltaics and Optoelectronics

AbstractIn the wake of lead‐halide perovskite research, bismuth‐ and antimony‐based perovskite‐inspired semiconducting materials are attracting increasing attention as safer and potentially more robust alternatives to lead‐based archetypes. Of particular interest are the group IB–group VA halide compositions with a generic formula AxByXx+3y (A+ = Cu+/Ag+; B3+ = Bi3+/Sb3+; X– = I–/Br–), i.e., silver/copper pnictohalides and derivatives thereof. This family of materials forms 3D structures with much higher solar cell efficiencies and greater potential for indoor photovoltaics than the lower‐dimensional bismuth/antimony‐based perovskite‐inspired semiconductors. Furthermore, silver/copper pnictohalides are being investigated for applications beyond photovoltaics, e.g., for photodetection, ionization radiation detection, memristors, and chemical sensors. Such versatility parallels the wide range of possible compositions and synthetic routes, which enable various structural, morphological, and optoelectronic properties. This manuscript surveys the growing research on silver/copper pnictohalides, highlighting their composition–structure–property relationships and the status and prospects of the photovoltaic and optoelectronic devices based thereon. The authors hope that the insights provided herein might accelerate the development of eco‐friendly and stable perovskite‐inspired materials for next‐generation photovoltaics and optoelectronics.

Reasoning over higher-order qualitative spatial relations via spatially explicit neural networks

Qualitative spatial reasoning has been a core research topic in GIScience and AI for decades. It has been adopted in a wide range of applications such as wayfinding, question answering, and robotics. Most developed spatial inference engines use symbolic representation and reasoning, which focuses on small and densely connected data sets, and struggles to deal with noise and vagueness. However, with more sensors becoming available, reasoning over spatial relations on large-scale and noisy geospatial data sets requires more robust alternatives. This paper, therefore, proposes a subsymbolic approach using neural networks to facilitate qualitative spatial reasoning. More specifically, we focus on higher-order spatial relations as those have been largely ignored due to the binary nature of the underlying representations, e.g. knowledge graphs. We specifically explore the use of neural networks to reason over ternary projective relations such as between. We consider multiple types of spatial constraint, including higher-order relatedness and the conceptual neighborhood of ternary projective relations to make the proposed model spatially explicit. We introduce evaluating results demonstrating that the proposed spatially explicit method substantially outperforms the existing baseline by about 20%.

Novel single and multi-layer echo-state recurrent autoencoders for representation learning

Representation learning impacts the performance of Machine Learning (ML) models. Feature extraction-based methods such as Auto-Encoders (AEs) are used to find new, more accurate data representations from original ones. They perform efficiently on a specific task, in terms of: (1) high accuracy, (2) large short-term memory and (3) low execution time. The Echo-State Network (ESN) is a recent specific kind of a Recurrent Neural Networks (RNN), that presents very rich dynamics on account of its reservoir-based hidden layer. It is widely used in dealing with complex non-linear problems and has been shown to outperform classical approaches in a number of benchmark tasks. In this paper, the powerful dynamism and large memory provided by the ESN and complementary strengths of AEs in feature extraction are integrated, to develop a novel Echo-State Recurrent Autoencoder (ES-RA). In order to devise more robust alternatives to conventional reservoir-based networks, both single- (SL-ES-RA) and multi-layer (ML-ES-RA) models are formulated. The new features, once extracted from ESN’s hidden layer, are applied to various benchmark ML tasks including classification, time series prediction and regression. A range of evaluation metrics are shown to improve considerably compared to those obtained when applying original data features. An accuracy-based comparison is performed between our proposed recurrent AEs and two variants of ELM feed-forward AEs (Single and ML), for both noise free and noisy data. In summary, a comparative empirical study reveals the key contribution of exploiting recurrent connections in improving benchmark performance results.

The Robustness Requirement on Alternative Possibilities

In a series of recent papers, Justin Capes and Philip Swenson (together) and Michael Robinson (independently) have proposed new versions of the flickers of freedom reply to Frankfurt-style cases (FSCs). Both proposals claim, first, that what agents in FSCs are morally responsible for is performing a certain action on their own, and, second, that agents in FSCs retain robust alternative possibilities—alternatives in which the agent freely omits to perform the pertinent action on their own. In this paper, I argue that, by attending to the details of the omissions in question, it becomes clear that agents in FSCs lack robust alternatives of this sort, for in the alternative sequences such agents do not freely omit. Since the problem for these recent proposals arises from their attempt to show that agents in FSCs retain robust alternatives, I go on to consider whether the flicker theorist might be better off either revising or rejecting the robustness requirement on alternative possibilities. I argue that neither alternative is available to the flicker theorist, and yet I also point out that the reasons why these alternatives are unavailable serve to highlight what exactly is at issue in the debate between leeway and sourcehood theorists, namely the grounds of our freedom.

Visualizing meaningful change in small sample sizes.

e18656 Background: Patient Reported Outcomes (PROs) are included in trials to incorporate patient voice into the drug development process. In oncology, PROs typically evaluate patient physical function and/or symptom burden. Clinicians need to be able to describe expected patient outcomes in terms of meaningful change in conversations with patients. FDA guidances outline analysis methods for measuring meaningful change, and the results are increasingly used to inform regulatory decisions and in drug packaging. Improving best practices in measuring meaningful change is crucial for regulatory review and to help clinicians communicate effectively with patients. Current best practice under FDA guidance is to compute meaningful within-patient change (MWPC) using an anchor-based approach. The relationship between change scores and anchor groups is visualized via empirical cumulative distribution functions (eCDFs). However, when sample sizes within anchor groups are small, it may be difficult to compute the distribution function. In contrast, a scatterplot approach does not require any computation. The objective was to evaluate under what study conditions (i.e., sample sizes and magnitude of meaningful change) would it be advantageous to interpret meaningful change with a scatterplot approach. Methods: Data simulated to emulate real PRO data from a trial, including attrition due to death, were used to highlight shortcomings in current practice and propose a robust alternative. A simulation study permitted a variety sample sizes and magnitudes of meaningful change to be evaluated. The eCDF was implemented in accordance with current guidance and compared to scatterplots. The scatterplot approach was operationalized as follows: the subject scores were plotted with the baseline scores on the x-axis and the follow-up scores on the y-axis. The scatterplot was stratified by color to represent anchor group-specific scores. Subjects who did not respond at follow-up due to progression or death were plotted in a separate color, using the baseline response and an assigned follow-up score reflecting maximum severity. A 45-degree line represented No Change. The vertical distance between the subject score and the 45-degree line represented the observed change. Results: When sample size was below n = 10 subjects per anchor group, the eCDF yielded uninterpretable visualizations. At sample sizes n = 1-20 per anchor group, the scatterplot approach consistently yielded interpretable visualizations of meaningful change. Conclusions: The scatterplot approach avoids methodological complications and thus ensures transparency in the evaluation of meaningful change. Recommendations to supplement current best practice with the scatterplot approach described above should be considered.

Ethical considerations regarding animal experimentation.

Animal experimentation is widely used around the world for the identification of the root causes of various diseases in humans and animals and for exploring treatment options. Among the several animal species, rats, mice and purpose-bred birds comprise almost 90% of the animals that are used for research purpose. However, growing awareness of the sentience of animals and their experience of pain and suffering has led to strong opposition to animal research among many scientists and the general public. In addition, the usefulness of extrapolating animal data to humans has been questioned. This has led to Ethical Committees' adoption of the 'four Rs' principles (Reduction, Refinement, Replacement and Responsibility) as a guide when making decisions regarding animal experimentation. Some of the essential considerations for humane animal experimentation are presented in this review along with the requirement for investigator training. Due to the ethical issues surrounding the use of animals in experimentation, their use is declining in those research areas where alternative in vitro or in silico methods are available. However, so far it has not been possible to dispense with experimental animals completely and further research is needed to provide a road map to robust alternatives before their use can be fully discontinued.

Buying Access One Trip at a Time

Problem, research strategy, and findings New transportation options like ride-hail can expand accessibility without the costs of car ownership. Ride-hail’s potential is particularly salient for lower-income and zero-car households. We used interviews and a national (U.S.) survey to examine how and why lower-income travelers in the United States use ride-hail. Survey and interview responses provided a temporal snapshot and thus reflect, in part, travel challenges specific to COVID-19. Findings suggest that lower-income travelers, particularly those without personal cars, use ride-hail in ways distinct from those typically reported in broader travel surveys. Individuals without cars are more likely to use ride-hail, and use it more often, compared with people with cars, particularly to fill spatial and temporal gaps in public transit service and to access medical care and groceries. Costs and price unpredictability remain significant barriers limiting travelers’ use of ride-hail services. Takeaway for practice This research demonstrates a latent need for car access among lower-income travelers. Substantial gaps in alternative modes pose challenges for travelers seeking reliable and timely transportation. Planners should invest in transit, biking, and walking to provide robust alternatives to car ownership. Such investments, however, take time. In the meantime, cities and agencies should consider subsidizing ride-hail trips to bridge existing gaps in the transportation network.

Characterization of kerogen in shale core plugs using T2*-based magnetic resonance methods

Kerogen assessment is essential to evaluation of shale reservoir quality. Routine analysis techniques are sample destructive and time-consuming. 1H magnetic resonance (MR) methods offer a fast, robust, and non-destructive alternative. However, kerogen MR signal detection and quantification is challenging due to its short signal lifetime and requires new MR methods. In this work, the T1-T2* relaxation correlation method was investigated for assessment of kerogen in shales. Step pyrolysis experiments were conducted coupled with MR T1-T2* relaxation correlation measurements and thermogravimetric analysis (TGA). The results show that the short relaxation lifetime signal observed in the shale samples was dominated by the kerogen species. The shale signal was resolved in the T1-T2* relaxation correlation measurement that was calibrated to report kerogen, oil, and water 1H content of the shale samples. Kerogen 1H content was then used to evaluate the hydrocarbon generation potential of kerogen. An elemental balance analysis was conducted to estimate the kerogen chemical evolution in the Van Krevelen diagram. The results proved that a simple FID measurement was adequate to resolve and quantify the kerogen signal component in shales.

On optimal biomarker cutoffs accounting for misclassification costs in diagnostic trilemmas with applications to pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is the most deadly cancer and currently there is strong clinical interest in novel biomarkers that contribute to its early detection. Assessing appropriately the accuracy of such biomarkers is a crucial issue and often one needs to take into account that many assays include biospecimens of individuals coming from three groups: healthy, chronic pancreatitis, and PDAC. The ROC surface is an appropriate tool for assessing the overall accuracy of a marker employed under such trichotomous settings. A decision/classification rule is often based on the so-called Youden index and its three-dimensional generalization. However, both the clinical and the statistical literature have not paid the necessary attention to the underlying false classification (FC) rates that are of equal or even greater importance. In this article we provide a framework to make inferences around all classification rates as well as comparisons. We explore the trinormal model, flexible models based on power transformations, and robust non-parametric alternatives. We provide a full framework for the construction of confidence intervals, regions, and spaces for joint inferences or for clinically meaningful points of interest. We further discuss the implications of costs related to different FCs. We evaluate our approaches through extensive simulations and illustrate them using data from a recent PDAC study conducted at the MD Anderson Cancer Center.

Robust fetoscopic mosaicking from deep learned flow fields

PurposeFetoscopic laser photocoagulation is a minimally invasive procedure to treat twin-to-twin transfusion syndrome during pregnancy by stopping irregular blood flow in the placenta. Building an image mosaic of the placenta and its network of vessels could assist surgeons to navigate in the challenging fetoscopic environment during the procedure.MethodologyWe propose a fetoscopic mosaicking approach by combining deep learning-based optical flow with robust estimation for filtering inconsistent motions that occurs due to floating particles and specularities. While the current state of the art for fetoscopic mosaicking relies on clearly visible vessels for registration, our approach overcomes this limitation by considering the motion of all consistent pixels within consecutive frames. We also overcome the challenges in applying off-the-shelf optical flow to fetoscopic mosaicking through the use of robust estimation and local refinement.ResultsWe compare our proposed method against the state-of-the-art vessel-based and optical flow-based image registration methods, and robust estimation alternatives. We also compare our proposed pipeline using different optical flow and robust estimation alternatives.ConclusionsThrough analysis of our results, we show that our method outperforms both the vessel-based state of the art and LK, noticeably when vessels are either poorly visible or too thin to be reliably identified. Our approach is thus able to build consistent placental vessel mosaics in challenging cases where currently available alternatives fail.

Patient-Related Long-Term Outcome Measures After Mastoid Surgery at A Tertiary Care Center of North India

Background: Long-term postsurgical follow-up in chronic otitis media (COM) is crucial in otologic practice for outcome assessment. Validated questionnaires for the same are robust alternatives when physical visits are not feasible such as the current COVID-19 pandemic and we present our findings of outcome assessment using COM outcome test (COMOT)-15 questionnaire through nonphysical mode. Materials and Methods: A retrospective analysis of a cohort of 112 patients of COM who underwent surgery by the single senior otology surgeon between 2009 and 2019 was done using COMOT 15 questionnaire through telephonic methods to circumvent the need of office visits. Results: Fifty out of 112 patients could be contacted and consented for the assessment and their COMOT-15 scores were obtained. The numbers of patients with any otologic symptoms and their need for consultations, except hearing loss were significantly less. One-third of the patients had mild to moderate residual hearing loss and its quality of life impacts, although the same neither was statistically significant nor required increased numbers of consultations. Conclusion: Bothering symptoms and the need for a frequent visit to the doctor is significantly less with adequate clearance of disease and mastoid obliteration in cases of canal wall down procedures. The residual mild-to-moderate hearing loss are not significant and manageable with day-to-day activities. Successful outcome of COM surgery needs long-term follow-up and evaluation by all practicing otologic departments and surgeons which can well be done through nonphysical modes in unprecedented situations like the current times.

A hybrid decision making aided framework for multi-criteria decision making with R-numbers and preference models

As a risk modeling about fuzzy numbers, R-numbers have successfully extended to multi-criteria decision making (MCDM) methods for the real-life decision making problems involving the risk and uncertainties associated with fuzzy numbers. To obtain more reliable and robust multi-criteria ranking alternatives in these uncertain situations, a hybrid decision making aided framework involving stochastic multiobjective acceptability analysis (SMAA), robust ordinal regression (ROR), and multi-attributive border approximation area comparison (MABAC) is proposed for MCDM problems with risk factors and preference models. Firstly, some novel operations of the R-numbers associated with triangular fuzzy numbers are proposed to explore a broader application scope. Secondly, a novel MABAC method combined with the R-numbers is proposed for MCDM problems which focus on uncertainty and error of triangular fuzzy numbers. Thirdly, a hybrid decision making aided framework which applies SMAA and ROR into the novel MABAC method is proposed for obtaining robust multi-criteria ranking alternatives through two binary relations, and two measures complement each other. Moreover, a Monte Carlo simulation of the framework is performed. Lastly, an application of assessment of wind energy potential and comparative analysis is provided to illustrate the efficiency and superiority of the proposed framework.

Effects of Institutional Investors' Activism on Corporate Risk-Taking Activities

To examines the relationship of institutional investors' activism and corporate risk-taking in Malaysian non-financial firms. Selected Malaysian non-financial firms between 2000 and 2018 were analysed using Generalised Least Square (GLS) and Generalised Method of Moments (GMM) to solve endogeneity issues. This study demonstrates that active institutional investors could play their role by surveillance to reduce unnecessary risky investment by firms. The assessment of institutional ownership is limited to institutional investors' percentage of ownership only. This study suggests updating the governance framework for institutional investors’ diversity in Malaysian context to improve corporate governance. Institutional investors may play an active or inactive role in firms in deciding how often the participation of institutional investors does not offer robust alternatives to corporate risk-taking. Data from prior studies were inconclusive.

How can renewable hydrogen compete with diesel in public transport? Robust design optimization of a hydrogen refueling station under techno-economic and environmental uncertainty

Heavy-duty transport represents nearly 6% of the greenhouse gas emissions in Europe. Renewable hydrogen is a potential option to decarbonize heavy-duty transport, such as buses. Renewable hydrogen for buses promises excellent environmental performance, at the expense of a higher fuel cost, as opposed to a diesel-powered bus fleet. Despite the inherent uncertainty, feasibility studies in this framework generally assume deterministic techno-economic and environmental parameters, which can lead to a suboptimal performance that is sensitive to the random environment. To provide robust design alternatives, we applied robust design optimization on a wind- and solar-powered hydrogen refueling system and a hydrogen- and diesel-powered bus fleet, to optimize the Levelized Cost Of Driving (LCOD) and Carbon Intensity (CI), subject to technical, economic and environmental uncertainties. A fully diesel-powered bus fleet achieves the optimized LCOD mean of 1.24€/km, but it results in the worst LCOD standard deviation (0.11€/km), CI mean (1.33kg˙CO˙2,eq /km) and CI standard deviation (0.075kg˙CO˙2,eq /km) among the optimized designs. To reduce the LCOD standard deviation, CI mean and CI standard deviation, part of the diesel-powered bus fleet is converted into hydrogen-powered buses and the renewable-powered hydrogen refueling station is scaled accordingly. Converting 54% of the diesel-powered bus fleet into hydrogen-powered buses results in a decrease in LCOD standard deviation by 36%, a decrease in CI mean by 46% and a decrease in CI standard deviation by 51%, at the expense of an increase in LCOD mean by only 11%. Future work will focus on the integration of full-electric buses.