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The Pairwise Comparison technique is a general purpose estimation approach for capturing expert judgment. This approach can be generalized to a probabilistic version using Monte Carlo methods to produce estimates of size distributions.
ABSTRACT The incidental take of golden eagles ( Aquila chrysaetos ) as a result of wind energy development requires some form of compensatory mitigation. Although several options have been proposed, only one has been formerly accepted and implemented, and the lack of options can limit the permit process for wind facilities. We developed a model to estimate numbers of golden eagles that die when struck by vehicles when eagles scavenge road kill to evaluate removal of road‐killed carcasses as an additional mitigation option. Our model estimates vehicle collision rates as a function of eagle densities, road traffic volume, and animal carcass abundance at the scale of a Wyoming, USA, county during fall‐winter, and quantifies the effects of different mitigation strategies, including estimates of uncertainty. We evaluated the plausibility of our model estimates by predicting mortality rates for each county in Wyoming and comparing overall state mortality to current estimates of mortality using derived estimates from expert judgment. We also developed a context‐dependent analysis of potential mitigation credits controlling for carcass number, traffic volume, and background carcass removals. We found that mitigation credit should be highest in areas with greatest number of carcasses. Collision mitigation is a potentially useful addition to the mitigation toolbox for wind energy development or other activities that need to offset predicted golden eagle mortality and satisfy incidental take permit requirements. © 2018 The Authors. Journal of Wildlife Management Published by Wiley Periodicals, Inc.
Rationale: Currently objective/quantitative descriptions of the quality and spatial resolution of mass spectrometry derived chemical images are not standardized. Development of these standardized metrics is required to objectively describe chemical imaging capabilities of existing and/or new mass spectrometry imaging technologies. Such metrics would allow unbiased judgment of intra-laboratory advancement and/or inter-laboratory comparison for these technologies if used together with standardized surfaces. Methods: We developed two image metrics, viz., chemical image contrast (ChemIC) based on signal-to-noise related statistical measures on chemical image pixels and corrected resolving power factor (cRPF) constructed from statistical analysis of mass-to-charge chronograms across features of interest in an image. These metrics, quantifying chemical image quality and spatial resolution, respectively, were used to evaluate chemical images of a model photoresist patterned surface collected using a laser ablation/liquid vortex capture mass spectrometry imaging system under different instrument operational parameters. Results: The calculated ChemIC and cRPF metrics determined in an unbiased fashion the relative ranking of chemical image quality obtained with the laser ablation/liquid vortex capture mass spectrometry imaging system. These rankings were used to show that both chemical image contrast and spatial resolution deteriorated with increasing surface scan speed, increased lane spacing and decreasing size of surface features. Conclusions: ChemIC and cRPF, respectively, were developed and successfully applied for the objective description of chemical image quality and spatial resolution of chemical images collected from model surfaces using a laser ablation/liquid vortex capture mass spectrometry imaging system.
Here, we introduce a novel chance-constrained stochastic unit commitment model to address uncertainty in renewables' production uncertainty in power systems operation. For most thermal generators,underlying technical constraints that are universally treated as "hard" by deterministic unit commitment models are in fact based on engineering judgments, such that system operators can periodically request operation outside these limits in non-nominal situations, e.g., to ensure reliability. We incorporate this practical consideration into a chance-constrained stochastic unit commitment model, specifically by in-frequently allowing minor deviations from the minimum and maximum thermal generator power output levels. We demonstrate that an extensive form of our model is computationally tractable for medium-sized power systems given modest numbers of scenarios for renewables' production. We show that the model is able to potentially save significant annual production costs by allowing infrequent and controlled violation of the traditionally hard bounds imposed on thermal generator production limits. Finally, we conduct a sensitivity analysis of optimal solutions to our model under two restricted regimes and observe similar qualitative results.
Mold breakout is a catastrophic accident that has serious impacts on smooth production, slab quality, and caster equipment. Accurate identification and prediction of an impending breakout are always top priorities in continuous casting operations. In view of crucial common features of mold copper plate temperatures during a breakout, such as time lag and space inversion, the concepts of density-based spatial clustering of applications with noise and dynamic time warping are introduced, and an integrated novel method for breakout prediction is developed. Through extracting and fusing the representative singularity and approximation of temperature variation, the typical temporal and spatial temperature characteristics during breakout can be distinguished and predicted accurately. Compared with traditional methods of logical judgment and artificial neural network, the method based on clustering does not need to modify forecast thresholds or parameters artificially, which overcomes the limitation of model dependence on human beings, and demonstrates excellent adaptability and robustness for online abnormality prevention.
Purpose of Review: Aging is associated with an increased prevalence of both cancer and heart disease. The progression of aortic valve calcification to aortic stenosis may be accelerated by both cardiovascular risk factors and cancer treatments, such as radiotherapy with mediastinal involvement. Symptomatic aortic stenosis is occasionally diagnosed in cancer patients undergoing cardiovascular evaluation; likewise, cancer is often recognized during assessment preceding aortic valve interventions. In these complex cases, physicians face difficult treatment decisions. Due to a myriad of clinical presentations of cancer and valve disease, specific guidelines for this patient population are not currently in place. Management is currently based on clinical judgment, on an individual basis. Recent Findings: Patients with cancer in remission or with a favorable prognosis should be treated according to current cardiovascular guidelines. In these patients, aortic valve replacement can be performed either by surgery or transcatheter. Significant challenges arise in patients with active cancer, especially those receiving anti-cancer treatment. Recent data suggests that these patients can be offered aortic valve replacement, with a trend of favoring the transcatheter route in order to minimize perioperative risk and complications associated with major surgery. Patients with advanced cancer and severe aortic stenosis should be offered palliative care and can benefit from aortic balloon valvuloplasty if indicated. Modern cancer treatments associated with improved long-term prognosis may allow the appropriate cure of aortic stenosis. Summary: We discuss the protocol, outcomes, and evolving recommendations of aortic valve replacement in cancer patients with aortic stenosis.
Cardiovascular diseases (CVDs) constitute the main cause of death in Latin America (LA), with ischemic heart disease (IHD) as the principal cause in most countries. Women are particularly at risk of premature death by CVDs. Nevertheless, in LA, as in other parts of the world, there is a misconception at public level of the importance of CVDs in women; thus, CVDs do not get as much coverage as breast cancer campaigns. Women frequently have atypical symptoms and nonobstructive coronary artery disease as well as inequity in medical and interventional treatments; therefore, care should be built upon these differences. We show the epidemiological situation of IHD in LA women and present a strategy to face the problem, considering the control of risk factors, the optimized utilization of available resources, and the role of research. The role of the International Atomic Energy Agency is highlighted. To adequately face the problem of CVD in LA women, a coordinated approach is necessary, involving medical professionals, governments, scientific societies, and international organizations, as well as the population concerned. In low- and middle-income countries, where the best use of available financial and technological resources is mandatory, a clinical management combining prevention, guidelines, and clinical judgment is required.
Energy efficiency technologies (EETs) are crucial for saving energy and reducing carbon dioxide emissions. However, the diffusion of EETs in small and medium-sized enterprises is rather slow. Literature shows the interactions between innovation adopters and potential adopters have significant impacts on innovation diffusion. Enterprises lack the motivation to share information, and EETs usually lack observability, which suppress the inter-firm influence. Thus, an information platform, together with proper policies encouraging or forcing enterprises to disclose EET-related information, should help harness inter-firm influence to accelerate EETs' diffusion. To explore whether and how such an information platform affects EETs' diffusion in small and medium-sized enterprises, this report builds an agent-based model to mimic EET diffusion processes. Based on a series of controlled numerical experiments, some counter-intuitive phenomena are discovered and explained. The results show that the information platform is a double-edged sword that notably accelerates EETs' diffusion by approximately 47% but may also boost negative information to diffuse even faster and delay massive adoption of EETs. Increasing network density and the intensity of inter-firm influence are effective to speed EET diffusion, but their impacts diminish drastically after reaching some critical values (0.05 and 0.15 respectively) and eventually harm the stability of the system. Ultimately, the findings implicate that EET suppliers should carefully launch their promising but immature products; policies that can reduce the perceived risk by enterprises and the effort to maintain an informative rather than judgmental information platform can prominently mitigate the negative side effects brought by high fluidity of information.
The refrigerant charge level in heat pump systems significantly impacts their energy efficiency. Virtual refrigerant charge (VRC) sensing technology has been comprehensively investigated and well-established due to its lower cost compared to physical sensors. However, the previous VRC research often relied on expert judgment and physical reasoning for their variable selection, which can potentially select redundant (or highly correlated) or insignificant features, and it is also primarily focused on single-speed systems. To address these challenges, this study proposes a VRC algorithm for variable-speed heat pumps that selects features through a rigorous feature selection method in combination with physical insights. We also propose a piecewise linear model structure segmented by subcooling temperature to accurately predict charge levels, particularly when subcooling temperatures are substantially low. The proposed algorithm was evaluated using experimental data of a residential R410A heat pump, and the performance was compared with two baseline VRC algorithms. The results are: (1) The proposed algorithm outperforms for the case with subcooling temperature less than 1 °C. (2) The proposed algorithm achieves a tested mean absolute percentage error (MAPE) of 4.23%, and improves the overall accuracy for cooling conditions by approximately 60%, compared with the two baseline algorithms. (3) The proposed algorithm uses two fewer features and improves the accuracy for undercharge cooling conditions by 68.0%, compared with baseline algorithm 2. These improvements enhance prediction accuracy and prevent overfitting, providing a more reliable refrigerant charge level prediction and helping improve the heat pump energy efficiency.
Environmental sensitivity analysis provides a framework for systematically and objectively determining the potential for significant environmental impacts. The higher the natural or acquired sensitivity of the receiving environment, the less capable it is to cope with human-induced change. Given that sensitivity is context- and spatially-specific, Geographic Information Systems have been applied to develop an operational Webtool to analyse it. The Webtool enables a rapid and replicable spatial examination of environmental sensitivities and potential for land-use conflicts that supports Strategic Environmental Assessment and, ultimately, informed planning and decision-making. The novelty is on the provision of an online geoprocessing Widget that enables creation of context-specific maps. Pilot testing the Webtool in land-use and renewable energy planning through stakeholder engagement has validated its applicability. Stakeholders confirmed that it enables replicating and, in some cases, improving in-house SEA mapping processes while saving time and effort. However, its full reliance on publicly available spatial datasets renders completeness and resolution issues. The Webtool provides a critical starting-point for sectoral planning discussions and for developing plan/programme alternatives that avoid or minimise potentially incompatible or unsustainable zonings, while promoting consistency and transparency in impact assessment. - Highlights: •Sensitivity mapping can enhance baseline information for environmental assessments. •A novel online geoprocessing tool supports systematic sensitivity assessment. •It enables combining context-specific environmental criteria and value judgments. •Pilot testing it through stakeholder engagement has validated its applicability. •The tool promotes assessment consistency and transparency but data issues remain.
Discretion is an essential and unavoidable element of most decision-making and is thus often closely related to the judgment exercised by politicians and practitioners alike. It is evident that discretionary power can be executed in different ways, leading to different results. Therefore, it also has a significant influence on the effectiveness of the environmental assessment (EA) as examined in recent environment impact assessment (EIA) and strategic environment assessment (SEA) literature. However, limited attention has been given to the practitioners' role and how they exercise their discretion, while the effectiveness and implementation of decisions has been a recurrent theme in EA literature. This article explores the connections between discretion and some of the fundamental ideas behind how EIA and SEA function in our societies. Firstly, the article develops and presents a theoretical model of discretion, allowing us to explore the phenomena of discretion from four dimensions: source, form, value and dynamics of discretion. Secondly, a review of EA literature is performed with the purpose of mapping how discretion is studied and what kind of discretion is found in the context of EA – focusing on one of the dimensions – ‘source of discretion’. The results imply that it is prevalent for practitioners to exercise rule, value and task discretion in every choice they make at each stage of the EA implementation process, which influences EA effectiveness, either positively or negatively, depending on how discretionary power is exercised and reflected in EA practitioners' practice. It draws both the management's attention to how to regulate EA policies and the practitioners' attention to how to make a difference.
Long time horizon normative models are frequently used for policy analysis, strategic planning, and system analysis. Choosing the granularity of the temporal or spatial resolution of such models is an important modeling decision, often having a first order impact on model results. This type of decision is frequently made by modeler judgment, specifically when the predictive power of alternative choices cannot be tested. In this paper, we show how the implicit tradeoffs modelers make in these formulation decisions, in particular in the tradeoff between the accuracy of representation enabled by the available data and model parsimony, may be addressed with established information theoretic ideas. The paper provides guidance for modelers making these tradeoffs or, in certain cases, enables explicit tests for assessing appropriate levels of resolution. Here, we focus on optimization based normative models in the discussion, and draw our examples from the energy and climate domain.
The surge in interval meter data availability and associated activity in energy data analytics has inspired new interest in advanced methods for building efficiency savings estimation. Statistical and machine learning approaches are being explored to improve the energy baseline models used to measure and verify savings. One outstanding challenge is the ability to identify and account for operational changes that may confound savings estimates. In the measurement and verification (M&V) context, ‘non-routine events’ (NREs) cause changes in building energy use that are not attributable to installed efficiency measures, and not accounted for in the baseline model’s independent variables. In the M&V process NREs must be accounted for as ‘adjustments’ to appropriately attribute the estimated energy savings to the specific efficiency interventions that were implemented. Currently this is a manual and custom process, conducted using professional judgment and engineering expertise. As such it remains a barrier in scaling and standardizing meter-based savings estimation. Here in this work, a data driven methodology was developed to (partially) automate, and therefore streamline the process of detecting NREs in the post-retrofit period and making associated savings adjustments. The proposed NRE detection algorithm is based on a statistical change point detection method and a dissimilarity metric. The dissimilarity metric measures the proximity between the actual time series of the post-retrofit energy consumption and the projected baseline, which is generated using a statistical baseline model. The suggested approach for NRE adjustment involves the NRE detection algorithm, the M&V practitioner, and a regression modeling algorithm. The performance of the detection and adjustment algorithm was evaluated using a simulation-generated test data set, and two benchmark algorithms. Results show a high true positive detection rate (75%-100% across the test cases), higher than ideal false positive detection rates (20%-70%), and low errors in energy adjustment (<0.7%). These results indicate that the algorithm holds for helping M&V practitioners to streamline the process of handling NREs. Moreover, the change point algorithm and underlying statistical principles could prove valuable for other building analytics applications such as anomaly detection and fault diagnostics.
Purpose: To determine whether chronic low-dose α-particle radiation from Ra-226 over multiple cell generations can lead to an adaptive response in CHSE/F fish embryonic cells or HaCaT human epithelial cells receiving subsequent acute high-dose γ-ray radiation. Methods: CHSE/F and HaCaT cells were exposed to very low doses of Ra-226 in medium for multiple generations prior to being challenged by a higher dose γ-ray radiation. The clonogenic assay was used to test the clonogenic survival of cells with or without being pretreated by radiation from Ra-226. Results: In general, pretreatment with chronic radiation has no significant influence on the reaction of cells to the subsequent challenge radiation. Compared to unprimed cells, the change in clonogenic survival of primed cells after receiving challenge radiation is mainly due to the influence of the chronic exposure, and there's little adaptive response induced. However at several dose points, pretreatment of CHSE/F fish cells with chronic radiation resulted in a radiosensitive response to a challenge dose of γ-ray radiation, and pretreatment of HaCaT cells resulted in no effect except for a slightly radioresistant response to the challenge radiation which was not significant. Conclusion: The results suggest that chronic low-dose radiation is not effective enough to induce adaptive response. There was a difference between human and fish cells and it may be important to consider results from multiple species before making conclusions about effects of chronic or low doses of radiation in the environment. The term “radiosensitive” or “adaptive” make no judgment about whether such responses are ultimately beneficial or harmful. - Highlights: • No obvious adaptive response is induced by chronic low-dose radiation from Ra-226. • Priming radiation from Ra-226 sensitized CHSE/F cells to the challenge radiation. • Linear model is inconsistent with current work using chronic low-dose radiation.
Energy systems function as both technological systems and temporal institutions that shape how societies coordinate, justify, and support collective choices over time. This paper introduces the concept of governance horizons to explain why energy transitions can remain morally supported yet become institutionally weak under increasing pressure. We argue that governability depends on institutions' capacity to synchronize across multiple timeframes - aligning short-term decisions with intermediate coordination and long-term commitments. When this synchronization fails, transitions struggle not because their goals are dismissed, but because governance lacks sufficient time to justify, coordinate, and uphold decisions. Comparative analysis of San Antonio, Texas, and Interior Alaska reveals how energy system pressures generate distinct temporal configurations: San Antonio exhibits governance horizon stretching, where institutions must simultaneously meet near-term reliability demands and long-term transformation goals, while Interior Alaska exhibits horizon compression, where extreme environmental constraints force decision-making into short stabilization cycles. In both contexts, public support for sustainability goals coexists with institutional strain because evaluative judgments are unevenly distributed over time. A temporal configuration analysis is introduced as a diagnostic analytic stance for identifying these patterns. By treating temporal alignment as an explanatory variable rather than a background condition, this approach clarifies how feasibility, sequencing, and legitimacy are shaped by constraints on institutional time. The analysis demonstrates that successful energy transitions depend not only on technological innovation or institutional support, but on governance systems’ ability to sustain credible coordination across multiple time horizons.
System operators and reliability coordinators currently rely on extensive baselining studies to set thresholds for their oscillation detectors. The resulting thresholds are based largely on engineering judgment and may vary significantly between organizations. In this paper, statistical distribution theory is used to derive a detection threshold for the widely deployed root-mean-square (RMS)-energy detector. This expression provides a theoretical basis for the detector's configuration, simplifies the process of selecting the threshold, and enables improved consistency among organizations that need to coordinate during system-wide events. Three methods for calculating the threshold using synchrophasor measurements are also proposed. These methods ensure that the threshold can be calculated reliably for various applications. Tests with simulated and field-measured data demonstrate that the statistics-based threshold provides consistent detection of grid disturbances while maintaining a low probability of false alarm.
Switching from fossil fuels to renewable energy is key to international energy transition efforts and the move toward net zero. For many nations, this requires decommissioning of hundreds of oil and gas infrastructure in the marine environment. Current international, regional and national legislation largely dictates that structures must be completely removed at end-of-life although, increasingly, alternative decommissioning options are being promoted and implemented. Yet, a paucity of real-world case studies describing the impacts of decommissioning on the environment make decision-making with respect to which option(s) might be optimal for meeting international and regional strategic environmental targets challenging. To address this gap, we draw together international expertise and judgment from marine environmental scientists on marine artificial structures as an alternative source of evidence that explores how different decommissioning options might ameliorate pressures that drive environmental status toward (or away) from environmental objectives. Synthesis reveals that for 37 United Nations and Oslo-Paris Commissions (OSPAR) global and regional environmental targets, experts consider repurposing or abandoning individual structures, or abandoning multiple structures across a region, as the options that would most strongly contribute toward targets. This collective view suggests complete removal may not be best for the environment or society. However, different decommissioning options act in different ways and make variable contributions toward environmental targets, such that policy makers and managers would likely need to prioritise some targets over others considering political, social, economic, and ecological contexts. Current policy may not result in optimal outcomes for the environment or society.
In this work, we demonstrate how machine learning (ML) techniques can be used as an auxiliary tool for nuclear data validation analysis. The output of the ML analysis can inform evaluators and validators on the quality, or lack thereof, of specific nuclear data and benchmark experiments with respect to simulating these experimental benchmark values. To this end, measured and simulated effective neutron multiplication factors, k eff , of 875 selected ICSBEP critical assemblies and the latter's sensitivities with respect to nuclear data as well as benchmarks' features (e.g., material nuclide, core geometry) are used as input for a random forest (RF) regression model. The RF is used to encode the complex inter-dependencies between thousands of nuclear data features (i.e., sensitivity profiles and aspects describing the measurements) and 875 simulated and experimental k eff values in order to understand what nuclear data features are most informative for predicting bias. The complexity of relationships and high-dimensional space is difficult-to-impossible to search fully using simply expert judgment. As a first proof-of-concept—step, it is demonstrated that this technique is able to correctly trace large discrepancies between simulated and experimental k eff back to fabricated shortcomings in nuclear data that were perturbed to simulated k eff values. In a second, real-case scenario, step, the RF algorithm is used to validate the ENDF/B-VIII.0 library in comparison to ENDF/B-VII.1 nuclear data. One case is showcased where the chosen ML algorithms highlighted nuclear data (the 19 F(n,inl) cross section from 0.4–0.9 MeV) that are shown to be problematic by comparing them to associated differential experimental data and nuclear data from other libraries. In addition to that, it is shown that the RF results point towards poor benchmark experiments and associated underestimated uncertainties (e.g., the PU-SOL-THERM-028 series). However, using the RF algorithm for validating nuclear data with respect to k eff is currently limited to pinpointing groups of questionable nuclear data due to the inherent correlations between features introduced by the nuclear data themselves and how k eff is simulated. Due to this, we recommend that the ML methods presented be used to augment—rather than replace—the expert knowledge of evaluators and validators.