Realistic Assumptions Research Articles

Salmon lice biology, environmental factors, and smolt behaviour with implications for the Norwegian salmon farming management system: A critical review

AbstractIn 2017, a new regulatory management system, the traffic light system (TLS), was implemented to estimate the effects of salmon lice from Norwegian salmon aquaculture on marine survival of wild Atlantic salmon and forms the basis of aquaculture capacity regulation. The TLS relies on observational data and a set of models to estimate the risk for negative impact on wild salmon populations. This review of the literature that forms the basis for the TLS as well as other relevant studies is presented in the context of the currently practiced TLS and suggestions are made for immediate and long‐term improvements. The main findings of this review are that: (1) assumed timing and duration of smolt migration contribute to unreliable observational and modelled data and overestimates of infection pressure; (2) production of lice larvae from farmed salmon is overestimated; (3) TLS model systems rely on or are calibrated by the same potentially flawed data; (4) lice‐associated mortality in wild salmon smolts may be overestimated; and (5) lice infection levels on farms are not associated with measurable effects on wild salmon. Recommendations to improve the accuracy and reliability of the TLS, and hence its environmental efficiency include the more complete use of available biological and physical environmental variables, adjusting the time period that observational data are registered and modelled data are integrated, adjusting the interpretation of data including recognition of uncertainty in model outcomes, and use of more realistic assumptions concerning lice‐induced mortality thresholds.

Analysis of image formation in optical palpation.

Optical palpation is an emerging elastography technique that generates two-dimensional images of mechanical stress at the tissue surface, with clinical applications such asintraoperative cancer detection and scar assessment. It has been implemented using various imaging systems, however, an analysis of how deformation of the sample and layer influences image formation has not been performed. Here, an analysis framework is presented, which assesses performance independently of the imaging system used. Optical palpation of varying samples and layers is simulated using finite element analysis and validated with experiments on silicone phantoms, providing a characterization of detectability, feature resolution, and contrast ratio. Using our framework, we demonstrate that computational optical palpation, which incorporates realistic assumptions of layer deformation, improves the feature resolution up to a factor of four. This framework can guide the development of optical palpation and aid in the selection of appropriate imaging system and layer properties for a given application.

On the performance of seizure prediction machine learning methods across different databases: the sample and alarm-based perspectives.

Epilepsy affects 1% of the global population, with approximately one-third of patients resistant to anti-seizure medications (ASMs), posing risks of physical injuries and psychological issues. Seizure prediction algorithms aim to enhance the quality of life for these individuals by providing timely alerts. This study presents a patient-specific seizure prediction algorithm applied to diverse databases (EPILEPSIAE, CHB-MIT, AES, and Epilepsy Ecosystem). The proposed algorithm undergoes a standardized framework, including data preprocessing, feature extraction, training, testing, and postprocessing. Various databases necessitate adaptations in the algorithm, considering differences in data availability and characteristics. The algorithm exhibited variable performance across databases, taking into account sensitivity, FPR/h, specificity, and AUC score. This study distinguishes between sample-based approaches, which often yield better results by disregarding the temporal aspect of seizures, and alarm-based approaches, which aim to simulate real-life conditions but produce less favorable outcomes. Statistical assessment reveals challenges in surpassing chance levels, emphasizing the rarity of seizure events. Comparative analyses with existing studies highlight the complexity of standardized assessments, given diverse methodologies and dataset variations. Rigorous methodologies aiming to simulate real-life conditions produce less favorable outcomes, emphasizing the importance of realistic assumptions and comprehensive, long-term, and systematically structured datasets for future research.

Correlation inference attacks against machine learning models.

Despite machine learning models being widely used today, the relationship between a model and its training dataset is not well understood. We explore correlation inference attacks, whether and when a model leaks information about the correlations between the input variables of its training dataset. We first propose a model-less attack, where an adversary exploits the spherical parameterization of correlation matrices alone to make an informed guess. Second, we propose a model-based attack, where an adversary exploits black-box model access to infer the correlations using minimal and realistic assumptions. Third, we evaluate our attacks against logistic regression and multilayer perceptron models on three tabular datasets and show the models to leak correlations. We lastly show how extracted correlations can be used as building blocks for attribute inference attacks and enable weaker adversaries. Our results raise fundamental questions on what a model does and should remember from its training set.

Power boundaries in resonant electrodynamic wireless power transfer systems

This article focuses on deriving the maximum power that can be transmitted using recent wireless power transfer technology based on an electrodynamic receiver. Analytical derivations reveal two main constraints that can limit transmitted power: the magnetic field safety limit and the maximum stress before breaking the receiver. From these limits, expressions of maximum magnetic forces and power are derived, providing physical insights on how to optimize the transmitted power. Interestingly, the optimal electrical condition and the optimal electromechanical efficiency differ when maximizing the transmitted power under each constraint. In the final section of the paper, the proposed theoretical boundaries are applied to two types of receivers, based on either electromagnetic or piezoelectric transducers. The results demonstrate that the maximum power density achievable with both receivers is inherently limited (<10 mW/cm3) under realistic assumptions regarding electromechanical coupling and electrical circuitry. This value is smaller than some previously reported results involving rotating or magnetoelectric solutions, indicating that clamped-free beams are not the optimal solution for electrodynamic wireless power transfer. This analysis can be extended to other types of receivers and will be useful for optimizing, sizing, and assessing any technological solution for wireless power transfer.

Data-driven forced oscillation localization using inferred impulse responses

Poorly damped oscillations pose threats to the stability and reliability of interconnected power systems. In this work, we propose a comprehensive data-driven framework for inferring the sources of forced oscillation (FO) using solely synchrophasor measurements. During normal grid operations, fast-rate ambient data are collected to recover the impulse responses in the small-signal regime, without requiring the system model. When FO events occur, the source is estimated based on the frequency domain analysis by fitting the least-squares (LS) error for the FO data using the impulse responses recovered previously. Although the proposed framework is purely data-driven, the result has been established theoretically via model-based analysis of linearized dynamics under a few realistic assumptions. Numerical validations demonstrate its applicability to realistic power systems including nonlinear, higher-order dynamics with control effects using the IEEE 68-bus system, and the 240-bus system from the IEEE-NASPI FO source location contest. The generalizability of the proposed methodology has been validated using different types of measurements and partial sensor coverage conditions.

Location and routing of armed Unmanned Aerial Vehicles and carrier platforms against mobile targets

In this study, we consider a real-life combinatorial optimization problem related to deploying and routing Unmanned Aerial Vehicles (UAVs) and naval carrier platforms. In particular, we seek to determine the initial locations for carrier platforms and the optimal type and number of UAVs to be stationed on each carrier platform as well as their spatial/temporal routes for engaging hostile surface targets in the region. Our modeling framework incorporates a number of realistic but challenging ingredients and assumptions such as the mobility of surface targets and carrier platforms during the mission, capacitated multiple platforms and UAVs, UAV-carrier platform compatibility, and allowance for different takeoff/land on platforms for UAVs. In the effort to represent the problem mathematically, we first formulated an Integer Linear Program (ILP) model which seeks to maximize the total time-dependent weights of the targets engaged. Next, we proposed a heuristic solution algorithm based on the ant colony optimization framework. Our computational experiments performed on instances with different sizes showed that the heuristic approach achieves high-quality solutions even for large-size problem instances in short CPU times.

Simultaneous alleviation of verification and reference standard biases in a community-based tuberculosis screening study using Bayesian latent class analysis.

Estimation of prevalence and diagnostic test accuracy in tuberculosis (TB) prevalence surveys suffer from reference standard and verification biases. The former is attributed to the imperfect reference test used to bacteriologically confirm TB disease. The latter occurs when only the participants screening positive for any TB-compatible symptom or chest X-ray abnormality are selected for bacteriological testing (verification). Bayesian latent class analysis (LCA) alleviates the reference standard bias but suffers verification bias in TB prevalence surveys. This work aims to identify best-practice approaches to simultaneously alleviate the reference standard and verification biases in the estimates of pulmonary TB prevalence and diagnostic test performance in TB prevalence surveys. We performed a secondary analysis of 9869 participants aged ≥15 years from a community-based multimorbidity screening study in a rural district of KwaZulu-Natal, South Africa (Vukuzazi study). Participants were eligible for bacteriological testing using Xpert Ultra and culture if they reported any cardinal TB symptom or had an abnormal chest X-ray finding. We conducted Bayesian LCA in five ways to handle the unverified individuals: (i) complete-case analysis, (ii) analysis assuming the unverified individuals would be negative if bacteriologically tested, (iii) analysis of multiply-imputed datasets with imputation of the missing bacteriological test results for the unverified individuals using multivariate imputation via chained equations (MICE), and simultaneous imputation of the missing bacteriological test results in the analysis model assuming the missing bacteriological test results were (iv) missing at random (MAR), and (v) missing not at random (MNAR). We compared the results of (i)-(iii) to the analysis based on a composite reference standard (CRS) of Xpert Ultra and culture. Through simulation with an overall true prevalence of 2.0%, we evaluated the ability of the models to alleviate both biases simultaneously. Based on simulation, Bayesian LCA with simultaneous imputation of the missing bacteriological test results under the assumption that the missing data are MAR and MNAR alleviate the reference standard and verification biases. CRS-based analysis and Bayesian LCA assuming the unverified are negative for TB alleviate the biases only when the true overall prevalence is <3.0%. Complete-case analysis produced biased estimates. In the Vukuzazi study, Bayesian LCA with simultaneous imputation of the missing bacteriological test results under the MAR and MNAR assumptions produced overall PTB prevalence of 0.9% (95% Credible Interval (CrI): 0.6-1.9) and 0.7% (95% CrI: 0.5-1.1) respectively alongside realistic estimates of overall diagnostic test sensitivity and specificity with substantially overlapping 95% CrI. The CRS-based analysis and Bayesian LCA assuming the unverified were negative for TB produced 0.7% (95% CrI: 0.5-0.9) and 0.7% (95% CrI: 0.5-1.2) overall PTB prevalence respectively with realistic estimates of overall diagnostic test sensitivity and specificity. Unlike CRS-based analysis, Bayesian LCA of multiply-imputed data using MICE mitigates both biases. The findings demonstrate the efficacy of these advanced techniques in alleviating the reference standard and verification biases, enhancing the robustness of community-based screening programs. Imputing missing values as negative for bacteriological tests is plausible under realistic assumptions.

Assessing Electric Vertical Take-Off and Landing for Urban Air Taxi Services: Key Parameters and Future Transportation Impact

Urban air mobility (UAM) enabled by electric vertical take-off and landing (eVTOL) aircraft presents an innovative transportation system for mega-cities. An analysis of the techno-economic feasibility of eVTOL air taxis can provide insights into the development and potential impacts of this emerging mobility solution. This study examines eVTOL configurations and proposes a conceptual model for eVTOL air taxi services. A generic cost–revenue model is developed to evaluate the economic feasibility using the rate of return-on-investment approach. Two critical parameters are identified—maximum voyage and price per kilometer. The modeling analysis shows that eVTOL air taxis can achieve a positive rate of return given realistic assumptions on the critical parameters. Reductions in operating costs and increases in maximum voyage range improve financial viability. With technological advancement, eVTOL air taxis are expected to transform urban transportation by increasing capacity and flexibility. The techno-economic analysis provides useful implications for urban planning and policy regarding this innovative mobility mode.

Simplified Approximations of Direct Cumulus Entrainment and Detrainment

Abstract In recent years, direct calculations of simulated cumulus entrainment and detrainment have facilitated new physical insights into these highly elusive but critically important processes. However, these calculations require substantial computational resources that may limit their widespread usage. To facilitate such calculations, two simplified approximations of direct cumulus entrainment and detrainment are examined herein. The first approximation, termed the “semidirect” method, follows a standard bulk approach but makes more realistic assumptions about the sources of entrained and detrained air near the cloud edges. In contrast, the second approximation (the “projection” method) uses the governing equations of motion to project whether grid points near the cloud edge will entrain or detrain as the mean cloud ascends by one grid point. Verification exercises using large-eddy simulations reveal that both methods generally agree better with corresponding direct entrainment/detrainment estimates than the traditional bulk formulation, with the projection method outperforming the semidirect method. The two methods can be used in a synergistic fashion, with the semidirect method helping to optimize the projection method, to suit a wide range of applications. Because the latter incorporates the essential dynamics of entrainment and detrainment at the local scale, it can be used to gain physical insight into the causal mechanisms regulating these complex processes.

Opponent Exploitation Based on Bayesian Strategy Inference and Policy Tracking

In a multi-agent competitive environment, it is important for an agent to detect the opponent's policy and adopt a suitable policy to exploit the opponent. Conventionally, most methods, e.g., Bayesian Policy Reuse (BPR) variants, assume the opponent adopts a fixed policy or a randomly changing policy. In this paper, we make a more realistic and reasonable assumption that the opponent may select its policy based on the previous observation. Here, we define the term “strategy” as the mapping from the previous observation to the opponent's selected policy, and we propose the Bayesian Strategy Inference (BSI) framework to infer the opponent's strategy. Furthermore, to deal with opponents who may randomly select their policies, the BSI framework is combined with an intra-episode policy tracking mechanism to construct the Bayesian Strategy Inference plus Policy Tracking (BSI-PT) algorithm. In our experiments, we design an extended batter vs. pitcher game (EBvPG) for the evaluation of the proposed BSI-PT framework. The experimental results demonstrate that BSI-PT obtains higher policy prediction accuracy and winning percentage than three other BPR variants against the opponents with a specific policy selection strategy, with a random selection strategy, or with a partially random strategy.

Towards Metric DBSCAN: Exact, Approximate, and Streaming Algorithms

DBSCAN is a popular density-based clustering algorithm that has many different applications in practice. However, the running time of DBSCAN in high-dimensional space or general metric space (\em e.g., clustering a set of texts by using edit distance) can be as large as quadratic in the input size. Moreover, most of existing accelerating techniques for DBSCAN are only available for low-dimensional Euclidean space. In this paper, we study the DBSCAN problem under the assumption that the inliers (the core points and border points) have a low intrinsic dimension (which is a realistic assumption for many high-dimensional applications), where the outliers can locate anywhere in the space without any assumption. First, we propose a k-center clustering based algorithm that can reduce the time-consuming labeling and merging tasks of DBSCAN to be linear. Further, we propose a linear time approximate DBSCAN algorithm, where the key idea is building a novel small-size summary for the core points. Also, our algorithm can be efficiently implemented for streaming data and the required memory is independent of the input size. Finally, we conduct our experiments and compare our algorithms with several popular DBSCAN algorithms. The experimental results suggest that our proposed approach can significantly reduce the computational complexity in practice.

Learning Newsvendor Problems With Intertemporal Dependence and Moderate Non-stationarities

This work provides performance guarantees for solving data-driven contextual newsvendor problems when the contextual data contains intertemporal dependence and non-stationarities. While machine learning tools have observed increasing use in data-driven inventory management problems, most of the existing work assumes that the contextual data are independent and identically distributed (often referred to as i.i.d.). However, such assumptions are often violated in real operational environments where the contextual data are sequentially generated with intertemporal correlations and possible non-stationarities. By accommodating these naturally arising operational environments, our work adopts comparatively more realistic assumptions and develops out-of-sample performance bounds for learning data-driven contextual newsvendor problems.

Image classification with symbolic hints using limited resources.

Typical machine learning classification benchmark problems often ignore the full input data structures present in real-world classification problems. Here we aim to represent additional information as "hints" for classification. We show that under a specific realistic conditional independence assumption, the hint information can be included by late fusion. In two experiments involving image classification with hints taking the form of text metadata, we demonstrate the feasibility and performance of the fusion scheme. We fuse the output of pre-trained image classifiers with the output of pre-trained text models. We show that calibration of the pre-trained models is crucial for the performance of the fused model. We compare the performance of the fusion scheme with a mid-level fusion scheme based on support vector machines and find that these two methods tend to perform quite similarly, albeit the late fusion scheme has only negligible computational costs.

A capture-recapture modeling framework emphasizing expert opinion in disease surveillance.

In disease surveillance, capture-recapture methods are commonly used to estimate the number of diseased cases in a defined target population. Since the number of cases never identified by any surveillance system cannot be observed, estimation of the case count typically requires at least one crucial assumption about the dependency between surveillance systems. However, such assumptions are generally unverifiable based on the observed data alone. In this paper, we advocate a modeling framework hinging on the choice of a key population-level parameter that reflects dependencies among surveillance streams. With the key dependency parameter as the focus, the proposed method offers the benefits of (a) incorporating expert opinion in the spirit of prior information to guide estimation; (b) providing accessible bias corrections, and (c) leveraging an adapted credible interval approach to facilitate inference. We apply the proposed framework to two real human immunodeficiency virus surveillance datasets exhibiting three-stream and four-stream capture-recapture-based case count estimation. Our approach enables estimation of the number of human immunodeficiency virus positive cases for both examples, under realistic assumptions that are under the investigator's control and can be readily interpreted. The proposed framework also permits principled uncertainty analyses through which a user can acknowledge their level of confidence in assumptions made about the key non-identifiable dependency parameter.

Rethinking the Role of Biodosimetry to Assess Risks for Acute Radiation Syndrome in Very Large Radiation Events: Reconsidering Legacy Concepts.

The development of effective uses of biodosimetry in large-scale events has been hampered by residual, i.e., "legacy" thinking based on strategies that scale up from biodosimetry in small accidents. Consequently, there remain vestiges of unrealistic assumptions about the likely magnitude of victims in "large" radiation events and incomplete analyses of the logistics for making biodosimetry measurements/assessments in the field for primary triage. Elements remain from an unrealistic focus on developing methods to use biodosimetry in the initial stage of triage for a million or more victims. Based on recent events and concomitant increased awareness of the potential for large-scale events as well as increased sophistication in planning and experience in the development of biodosimetry, a more realistic assessment of the most effective roles of biodosimetry in large-scale events is urgently needed. We argue this leads to a conclusion that the most effective utilization of biodosimetry in very large events would occur in a second stage of triage, after initially winnowing the population by identifying those most in need of acute medical attention, based on calculations of geographic sites where significant exposures could have occurred. Understanding the potential roles and limitations of biodosimetry in large-scale events involving significant radiation exposure should lead to development of the most effective and useful biodosimetric techniques for each stage of triage for acute radiation syndrome injuries, i.e., based on more realistic assumptions about the underlying event and the logistics for carrying out biodosimetry for large populations.

Modeling the performance of early fault-tolerant quantum algorithms

Progress in fault-tolerant quantum computation (FTQC) has driven the pursuit of practical applications with (EFTQC). These devices, limited in their qubit counts and fault-tolerance capabilities, require algorithms that can accommodate some degrees of error, which are known as EFTQC algorithms. To predict the onset of early quantum advantage, a comprehensive methodology is needed to develop and analyze EFTQC algorithms, drawing insights from both the methodologies of noisy intermediate-scale quantum and traditional FTQC. To address this need, we propose such a methodology for modeling algorithm performance on EFTQC devices under varying degrees of error. As a case study, we apply our methodology to analyze the performance of randomized Fourier estimation (RFE) [Kshirsagar, Katabarwa, and Johnson, ], an EFTQC algorithm for phase estimation. We investigate the runtime performance and the fault-tolerant overhead of RFE in comparison to the traditional quantum phase estimation algorithm. Our analysis reveals that RFE achieves significant savings in physical qubit counts while having a much higher runtime upper bound. We anticipate even greater physical qubit savings when considering more realistic assumptions about the performance of EFTQC devices. By providing insights into the performance trade-offs and resource requirements of EFTQC algorithms, our work contributes to the development of practical and efficient quantum computing solutions on the path to quantum advantage. Published by the American Physical Society 2024

Exogenous Carbon is Unlikely to be the Source of Methane Microseepage Emissions on Mars

A complete understanding of methane in the atmosphere of Mars requires information on putative sources. Organic carbon derived from meteoritic sources, such as interplanetary dust particles (IDPs), over geologic time is the best-defined source of organic carbon on the surface of Mars. Here we examine two different mechanisms for degrading this material to produce methane (1) Ultraviolet photolysis and (2) thermal decomposition. Neither of these mechanisms can produce sufficient methane under realistic assumptions to explain observed methane at Gale Crater, suggesting that IDPs are not the primary source material producing methane on Mars.

Correlations Between XRD Peak Broadening and Elastic and Plastic Deformation for Mild Steel Sheets Under Tensile Loading

The polycrystalline nature of steel sheets plays a fundamental role in their plastic behavior during forming, especially under complex stresses, such as the deep drawing process. Heterogeneous microstructures cause variable internal stress levels, which strongly impact the material’s macroscopic mechanical properties. X-ray diffraction (XRD) technique can be applied to determine microscopic internal stresses by peaks broadening analysis, which can be related to the degree of cold work at the microscopic level. As deep drawing is a complex deformation mode, an important motivating factor to perform such a study is to find the correlation between anisotropy and XRD peak broadening through tensile deformation tests. Therefore, the initial mechanical state and different deformed states were analysed by in-situ XRD peak broadening under tensile tests in various directions in the rolling plane of mild steel sheet, for both the elastic and plastic deformation domains. The results showed that the XRD peak broadening gives accurate estimations of deformation degrees. In the elastic domain, under realistic assumptions, a relation between the relative variation of peak broadening and the applied stress σ, like Hooke’s law, was obtained by exploiting the linearity between the peak broadening and the elongation measured by the strain gauge. This formula may be used to deduce The Young’s modulus. Interesting empirical relations between peak broadenings, macro deformations and hardness have been also established for the studied sheet in the plastic deformation region. Moreover, XRD peak broadenings were able to characterize the anisotropy of deformation in the rolling plane of the sheet. They are like the anisotropy coefficients in indicating the expected behavior under complex processes such as deep drawing by using ordinary tensile tests.

Proposal for a tiered regulatory framework for the aquatic risk assessment of pesticides in Brazil.

Aquatic risk assessment is essential to guarantee the sustainable use of pesticides and the conservation of water resources near agricultural fields. This article discusses a proposal for a tiered regulatory framework for the aquatic risk assessment of pesticides in Brazil. The first step is problem formulation, which includes establishing general and specific protection goals. In the exposure assessment, the Estimated Environmental Concentrations in water should be calculated based on realistic worst-case assumptions regarding application rate and frequency, the entry into the edge-of-field water body, and fate in the water body, using scenario-dependent models suggested by the Brazilian Environmental Agency. These calculations can be refined by including Efate studies with variable exposures to reflect realistic environmental conditions accurately and include mitigation measures that impact the modeling. In the hazard assessment, ecotoxicological data for toxicity to fish, aquatic invertebrates, algae, and aquatic plants should be required for all pesticides based on standardized protocols and species. Tier 2 has several refinement options, including incorporating toxicity data from additional test species and effect modeling. In Tier 3, population- and community-level effects are evaluated using semi-field studies. Considering the case study in Brazil, Tier 1 demonstrated that, from the 12 pesticides that were assessed, seven (58%) failed based on the value of the Risk Quotient. In Tier 2, when exposure refinement options and mitigation measures such as buffer zones are considered, all seven pesticides, for which Tier 1 indicated risk, still failed the assessment. The risk for four of these seven pesticides could be refined by considering toxicity information from additional species. Refinement options and mitigation measures that could be applied to the agricultural scenario in Brazil were discussed. In conclusion, the proposed tiered risk assessment is a feasible way to evaluate whether a pesticide will pose an unacceptable risk to aquatic organisms. Integr Environ Assess Manag 2024;20:1514-1528. © 2024 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).