Optimal Combination Of Measures Research Articles

Selection of CO2 Emission Reduction Measures Affecting the Maximum Annual Income of a Container Ship

China’s carbon peaking and carbon neutrality targets have created huge challenges for all the economic sectors in China, including the shipping industry. Various emission reduction measures, such as Waste Heat Recovery Systems (WHRSs), Drag Reduction Coatings (DRCs), and Slow Steaming (SS), are the main options for container ship companies to select in advance. This paper aims to find the optimal combination of measures for container ship companies to reach the carbon reduction targets, which are mainly set according to the carbon reduction requirements of the International Maritime Organization (IMO). A 0–1 integer programming model is proposed under the Maritime Emissions Trading Scheme (METS) to help container ship companies select the optimal combination of measures in the context of carbon peaking and carbon neutrality. Our results show that combination 6 (using a WHRS alone and a 5.0% reduction in the original speed) is the most suitable solution with the selected values of parameters. Sensitivity analyses of the parameters are performed, such as bunker price, the auction and purchase prices of carbon and incentive levels. From the sensitivity analysis, it is found that using a WHRS is the optimal combination of abatement measures within the fluctuation range of the parameters. At the same time, according to the results, container ship companies could choose the most appropriate and profitable strategy in the dual-carbon context. Therefore, container ship companies and policymakers have access to relevant carbon reduction suggestions to encourage the implementation of carbon reduction initiatives.

Toward Accurate Modeling of Galaxy Clustering on Small Scales: Constraining the Galaxy-halo Connection with Optimal Statistics

Abstract Applying halo models to analyze the small-scale clustering of galaxies is a proven method for characterizing the connection between galaxies and their host halos. Such works are often plagued by systematic errors or limited to clustering statistics that can be predicted analytically. In this work, we employ a numerical mock-based modeling procedure to examine the clustering of Sloan Digital Sky Survey DR7 galaxies. We apply a standard halo occupation distribution (HOD) model to dark matter only simulations with a ΛCDM cosmology. To constrain the theoreStical models, we utilize a combination of galaxy number density and selected scales of the projected correlation function, redshift-space correlation function, group multiplicity function, average group velocity dispersion, mark correlation function, and counts-in-cells statistics. We design an algorithm to choose an optimal combination of measurements that yields tight and accurate constraints on our model parameters. Compared to previous work using fewer clustering statistics, we find a significant improvement in the constraints on all parameters of our halo model for two different luminosity-threshold galaxy samples. Most interestingly, we obtain unprecedented high-precision constraints on the scatter in the relationship between galaxy luminosity and halo mass. However, our best-fit model results in significant tension (>4σ) for both samples, indicating the need to add second-order features to the standard HOD model. To guarantee the robustness of these results, we perform an extensive analysis of the systematic and statistical errors in our modeling procedure, including a first of its kind study of the sensitivity of our constraints to changes in the halo mass function due to baryonic physics.

Sexual Dimorphism in Morphology and Plumage of Endangered Yuma Ridgway's Rails: A Model for Documenting Sex

Abstract Many applications in wildlife management require knowledge of the sex of individual animals. The Yuma Ridgway's rail Rallus obsoletus yumanensis is an endangered marsh bird with monomorphic plumage and secretive behaviors, thereby complicating sex determination in field studies. We collected morphometric measurements from 270 adult Yuma Ridgway's rails and quantified the plumage and mandible color of 91 of those individuals throughout their geographic range to evaluate intersexual differences in morphology and coloration. We genetically sexed a subset of adult Yuma Ridgway's rails (n = 101) and used these individuals to determine the optimal combination of measurements (based on discriminant function analyses) to distinguish between sexes. Males averaged significantly larger than females in all measurements, and the optimal discriminant function contained whole leg, culmen, and tail measurements and classified correctly 97.8% (95% CI: 92.5–100.0%) of genetically sexed individuals. We used two additional functions that classified correctly ≥ 95.6% of genetically sexed Yuma Ridgway's rails to assign sex to individuals with missing measurements. These simple models provide managers and researchers with a practical tool to determine the sex of Yuma Ridgway's rails based on morphometric measurements. Although color measurements were not in the most accurate discriminant functions, we quantified subtle intersexual differences in the color of mandibles and greater coverts of Yuma Ridgway's rails. These results document sex-specific patterns in coloration that allow future researchers to test hypotheses to determine the mechanisms underlying sex-based differences in plumage coloration.

Identifying critically ill children at high risk of acute kidney injury and renal replacement therapy.

Acute kidney injury (AKI), a common complication in paediatric intensive care units (PICU), is associated with increased morbidity and mortality. In this single centre, prospective, observational cohort study, neutrophil gelatinase-associated lipocalin in urine (uNGAL) and plasma (pNGAL) and renal angina index (RAI), and combinations of these markers, were assessed for their ability to predict severe (stage 2 or 3) AKI in children and young people admitted to PICU. In PICU children and young people had initial and serial uNGAL and pNGAL measurements, RAI calculation on day 1, and collection of clinical data, including serum creatinine measurements. Primary outcomes were severe AKI and renal replacement therapy (RRT). Secondary outcomes were length of stay, hospital acquired infection and mortality. The area under the Receiver Operating Characteristic (ROC) curves and Youden index was used to determine biomarker performance and identify optimum cut-off values. Of 657 children recruited, 104 met criteria for severe AKI (15∙8%) and 47 (7∙2%) required RRT. Severe AKI was associated with increased length of stay, hospital acquired infection, and mortality. The area under the curve (AUC) for severe AKI prediction for Day 1 uNGAL, Day 1 pNGAL and RAI were 0.75 (95% Confidence Interval [CI] 0∙69, 0∙81), 0∙64 (95% CI 0∙56, 0∙72), and 0.73 (95% CI 0∙65, 0∙80) respectively. The optimal combination of measures was RAI and day 1 uNGAL, giving an AUC of 0∙80 for severe AKI prediction (95% CI 0∙71, 0∙88). In this heterogenous PICU cohort, urine or plasma NGAL in isolation had poorer prediction accuracy for severe AKI than in previously reported homogeneous populations. However, when combined together with RAI, they produced good prediction for severe AKI.

Objectifying grade in Ta-T1 urothelial carcinomas of the bladder using proliferative and quantitative markers: A multicentre study in 310 bladder tumors

PurposeHistological grade is an important prognostic factor in patients with non-muscle–invasive bladder cancer (NMIBC). However, interobserver variability is high. Previous studies have suggested that quantification of histological features is useful to objectify grading. We evaluated whether quantification of the mean nuclear area of the 10 largest nuclei (MNA-10), degree of aneuploidy (DNA index or DI) and mitotic activity index (MAI) are of diagnostic value for NMIBC grade. Additionally, prognostic value of the 3 measures was assessed. Material and methodsA consensus grade was determined by 3 uropathologists in 310 NMIBC tissues according to the World Health Organization (WHO) 1973 and the WHO2004. Logistic regression with forward selection was used to determine the optimal combination of measures (MNA-10, DI, and MAI) to diagnose grade 3 (G3) or high-grade (HG) NMIBC (WHO1973 and WHO2004, respectively). ResultsIn 310 tumors of 215 patients at least 1 of the measures (MNA-10, DI, or MAI) had been determined. The combination of MNA-10 and MAI was selected as the most diagnostic combination and resulted in a sensitivity of 94% (95% confidence interval [CI]: 87–100) at a specificity of 72% (95% CI: 66–78) for G3 tumors. For the diagnosis of HG tumors sensitivity was 92% (95% CI: 86–97) at a specificity of 76% (95% CI: 70–93). ConclusionsDetermination of MNA-10 and MAI is promising for diagnosing G3 and HG bladder tumors. These findings warrant further studies on the diagnostic and prognostic value of proliferative and quantitative features in bladder cancer patients.

Choquet Integral-Based Fusion of Multiple Patterns for Improving EIT Spatial Resolution

Electrical impedance tomography (EIT) is a promising visualization technology with advantages of low cost, nonradiation, and rapid response. The most used EIT pattern leads to relatively limited measurements by which the EIT spatial resolution is low. However, multiple EIT patterns can generate more measurements, thus fusing multiple EIT patterns is helpful to improve the EIT spatial resolution. But the most challenging task of fusing multiple EIT patterns is how to correctly reflect the interaction among them. In this paper, eight typical EIT patterns are employed to obtain more measurements, and Choquet integral method is used to represent the interaction among EIT patterns. Consequently, the optimal combination of measurements from eight EIT patterns is selected to reconstruct the EIT image. Simulations and experiments were carried out and the results indicated that the proposed method can effectively improve the spatial resolution of reconstruction images.

Optimal ATMS Remapping Algorithm for Climate Research

In this paper, the Backus–Gilbert (B–G) method was used for the conversion from Advanced Technology Microwave Sounder (ATMS) FOVs to AMSU-A FOVs. This method provides not only an optimal combination of measurements within a specified region but also a quantitative measure of the tradeoff between resolution and noise. Based on a subpixel microwave antenna temperature simulation technique, ATMS observations at a specified FOV size with 1.1 $^{\circ}$ sampling interval are simulated. Errors of remapping results were quantified by using simulated data sets and real AMSU observations. It is shown that the biases and/or standard deviations of brightness temperatures are significantly reduced by using the B–G generated remapping coefficients. For K/Ka bands, a resolution enhancement by the remap of ATMS observations introduces about 0.6 K increase in noise. For other bands, the channel sensitivity was improved for the remapped data.

Comparative assessment of various heat island mitigation measures

With the rapid intensification of urban heat islands worldwide, measures for its mitigation are gaining attention. Some of the most popular measures are based on employing evaporative cooling, altering the surfaces' albedo or making use of shading. In this paper, we numerically investigate the influence of each of these three methods on the comfort of a pedestrian in a typical urban street canyon. The environmental conditions in the urban street canyon are obtained from a detailed microclimatic model, and serve as input for a comfort model based on the Universal Thermal Climate Index (UTCI). Simulations are conducted for average summer conditions, taken from a typical meteorological year (TMY), and for heat wave (HW) conditions. The results show that evaporative cooling can considerably reduce the air temperature and the mean radiant temperature, but that the corresponding increase in vapor pressure limits the net gain in comfort. Shading results in significantly reduced surface temperatures, in addition to decreasing the intensity of direct solar irradiation, both leading to an increased comfort sensation. Increasing the local albedo of urban surfaces also leads to lower surface temperatures, but does not affect the amount of direct solar irradiation, explaining the lower comfort gain compared to shading. The cooling effect of all methods proved to increase during a HW in comparison to a TMY. These observations show that the effectiveness of mitigation measures highly depends on the climatic conditions and demonstrate the potential of microclimatic models to determine the optimal combination of measures for a given context.

Walkability Indicators for Pedestrian-Friendly Design

This paper presents an analysis of walking indicators related to the structure of the road network, differentiated by measures of the quality, connectivity, and proximity of the road network. These measures were computed for various zones of the city of Rome and for the historical center of Lucca and Venice in Italy. The aims of the study were (a) to understand whether some measures were more suitable than others for describing the walkability (i.e., accessibility to walking) of an area, (b) to define the best single measure or the optimal combination of measures to describe the walkability of an area, (c) to define some benchmark values for the analyzed walkability measures, and (d) to obtain valuable guidelines to define a pedestrian-oriented road network. The results showed the importance of variables such as the number of nodes and the size of the blocks. Moreover, the results demonstrated that although single measures of connectivity were not self-explanatory for describing walkability, the combination of various measures could be more effective. Finally, the study derived the benchmark values of 3 to 6 nodes per hectare for the density of nodes, 0.5 to 0.9 hectare per block for the size of blocks, and 800 m for the maximum walking distance for a pedestrian-friendly, transit-oriented development.

Regional Magnetic Resonance Imaging Measures for Multivariate Analysis in Alzheimer’s Disease and Mild Cognitive Impairment

Automated structural magnetic resonance imaging (MRI) processing pipelines are gaining popularity for Alzheimer’s disease (AD) research. They generate regional volumes, cortical thickness measures and other measures, which can be used as input for multivariate analysis. It is not clear which combination of measures and normalization approach are most useful for AD classification and to predict mild cognitive impairment (MCI) conversion. The current study includes MRI scans from 699 subjects [AD, MCI and controls (CTL)] from the Alzheimer’s disease Neuroimaging Initiative (ADNI). The Freesurfer pipeline was used to generate regional volume, cortical thickness, gray matter volume, surface area, mean curvature, gaussian curvature, folding index and curvature index measures. 259 variables were used for orthogonal partial least square to latent structures (OPLS) multivariate analysis. Normalisation approaches were explored and the optimal combination of measures determined. Results indicate that cortical thickness measures should not be normalized, while volumes should probably be normalized by intracranial volume (ICV). Combining regional cortical thickness measures (not normalized) with cortical and subcortical volumes (normalized with ICV) using OPLS gave a prediction accuracy of 91.5 % when distinguishing AD versus CTL. This model prospectively predicted future decline from MCI to AD with 75.9 % of converters correctly classified. Normalization strategy did not have a significant effect on the accuracies of multivariate models containing multiple MRI measures for this large dataset. The appropriate choice of input for multivariate analysis in AD and MCI is of great importance. The results support the use of un-normalised cortical thickness measures and volumes normalised by ICV.

Automatic sleep scoring: A search for an optimal combination of measures

The objective of this study is to find the best set of characteristics of polysomnographic signals for the automatic classification of sleep stages. A selection was made from 74 measures, including linear spectral measures, interdependency measures, and nonlinear measures of complexity that were computed for the all-night polysomnographic recordings of 20 healthy subjects. The adopted multidimensional analysis involved quadratic discriminant analysis, forward selection procedure, and selection by the best subset procedure. Two situations were considered: the use of four polysomnographic signals (EEG, EMG, EOG, and ECG) and the use of the EEG alone. For the given database, the best automatic sleep classifier achieved approximately an 81% agreement with the hypnograms of experts. The classifier was based on the next 14 features of polysomnographic signals: the ratio of powers in the beta and delta frequency range (EEG, channel C3), the fractal exponent (EMG), the variance (EOG), the absolute power in the sigma 1 band (EEG, C3), the relative power in the delta 2 band (EEG, O2), theta/gamma (EEG, C3), theta/alpha (EEG, O1), sigma/gamma (EEG, C4), the coherence in the delta 1 band (EEG, O1-O2), the entropy (EMG), the absolute theta 2 (EEG, Fp1), theta/alpha (EEG, Fp1), the sigma 2 coherence (EEG, O1-C3), and the zero-crossing rate (ECG); however, even with only four features, we could perform sleep scoring with a 74% accuracy, which is comparable to the inter-rater agreement between two independent specialists. We have shown that 4-14 carefully selected polysomnographic features were sufficient for successful sleep scoring. The efficiency of the corresponding automatic classifiers was verified and conclusively demonstrated on all-night recordings from healthy adults.

Hydro-generator multi-physic modeling

Hydro-Quebec's research institute initiated a project in 2002 on generators modeling to increase output with minimal impact on aging. This effort is the joint work of several experts and considers electromagnetic, thermal, mechanical and fluid dynamic simulations. The main concerns are presented herein with some of the relationships between physical phenomena. Since simulation results are dependent on some assumptions, an integral part of modeling process is validation by actual measurements on machines during standardized tests, which will also be discussed. Because of the size of the test object, the optimal combination of measurements and simulation has to be found. The current paper presents some of the challenges related to building this global hydro-generator model and the future tasks to complete this work.

Numerical study of the evaporation process and parameter estimation analysis of an evaporation experiment

Abstract. Evaporation is an important process in soil-atmosphere interaction. The determination of hydraulic properties is one of the crucial parts in the simulation of water transport in porous media. Schneider et al. (2006) developed a new evaporation method to improve the estimation of hydraulic properties in the dry range. In this study we used numerical simulations of the experiment to study the physical dynamics in more detail, to optimise the boundary conditions and to choose the optimal combination of measurements. The physical analysis exposed, in accordance to experimental findings in the literature, two different evaporation regimes: (i) a soil-atmosphere boundary layer dominated regime (regime I) close to saturation and (ii) a hydraulically dominated regime (regime II). During this second regime a drying front (interface between unsaturated and dry zone with very steep gradients) forms which penetrates deeper into the soil as time passes. The sensitivity analysis showed that the result is especially sensitive at the transition between the two regimes. By changing the boundary conditions it is possible to force the system to switch between the two regimes, e.g. from II back to I. Based on this findings a multistep experiment was developed. The response surfaces for all parameter combinations are flat and have a unique, localised minimum. Best parameter estimates are obtained if the evaporation flux and a potential measurement in 2 cm depth are used as target variables. Parameter estimation from simulated experiments with realistic measurement errors with a two-stage Monte-Carlo Levenberg-Marquardt procedure and manual rejection of obvious misfits lead to acceptable results for three different soil textures.

Management of the Web of Water and Web of Materials

The European Water Framework Directive (EWFD) demands a detailed analysis to determine which changes and measures within the surface water system are required, which actors require detailed scrutiny, and which technology has to be developed in order to guarantee that the quality of the surface water is complying with this Directive. This paper will discuss a holistic model developed for the optimization of the surface water system for a water authority in The Netherlands, which is influenced by (i) waste water streams originating from e.g. households, industry, agricultural and transport activities among others and (ii) the end-of-pipe technology of waste water treatment plants, while interfacing with (iii) thermal treatment and minerals and metallurgical processing for the recovery of specific elements from waste water sludge and other residues created during waste water treatment. The paper develops a fundamental basis that can feed factual information such as optimal combination of measures (technology and policy) into sustainability frameworks or the implementation of the EWFD. This optimization is affected by quality constraints, costs, energy, environment and interactions between the various materials present in the different streams in the water system. By incorporating these parameters into the model a tool is provided that provides metrics to measure the ‘sustainability’ of the Web of Water (WoW), while linking to and harmonising with the Web of Materials/Metals (WoM). The WoW optimization model links material cycles from e.g. food, transport, agriculture and industry to the recovery of materials from the water cycle with the pyrometallurgical and thermal processing of minerals/materials, hence quantifying resource conservation and sustainability on the interface between aquatic and product manufacturing systems and the process industries.

Optimal Measurement Combination for Local Self-Optimizing Control

Self-optimizing control is a useful method for finding the appropriate set of controlled variables. Recently, some researchers introduced local methods for finding the locally optimal subset or linear combination of measurements, which can be used as controlled variables. [Halvorsen et al. Ind. Eng. Chem. Res. 2003, 42 (14), 3273−3284.] In this paper, we present a method for finding the optimal combination of measurements for local self-optimizing control for nearly optimal steady-state operation. The proposed method only requires the use of singular value decomposition, followed by the determination of the eigenvectors of a matrix. The proposed results are computationally more efficient and are guaranteed to find the optimal solution, in comparison to the available nonlinear optimization-based and the approximate null space methods.

Objective Assessment of the Information Content of Visible and Infrared Radiance Measurements for Cloud Microphysical Property Retrievals over the Global Oceans. Part II: Ice Clouds

Abstract Cirrus clouds play an important yet poorly determined role in the earth’s climate system and its various feedback mechanisms. As such, a significant amount of work has been accomplished both in understanding the physics of the ice clouds and in using this knowledge to estimate global distributions of ice cloud properties from satellite-based instruments. This work seeks to build on these past efforts by offering a reexamination of the ice cloud retrieval problem in context of recent advancements in the understanding of optical properties for a variety of realistic ice crystal shapes. In this work, the formal information content analysis outlined in Part I is used to objectively select the optimal combination of measurements for an ice cloud microphysical property retrieval scheme given a realistic assessment of the uncertainties that govern the ice cloud retrieval problem. Although this analysis is for a theoretical retrieval combining simulated measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS) with the CloudSat Cloud Profiling Radar (CPR) above an ocean surface, the general methodology is applicable to any instrument package. Channel selection via information content is determined through a realistic characterization of not only the sensitivity of top-of-the-atmosphere radiances to desired retrieval parameters but also to the uncertainties resulting from both the measurements themselves and from the forward model assumptions used in relating observational and retrieval space. Results suggest that the channels that maximize retrieval information are strongly dependent upon the state of the atmosphere, meaning that no combination of two or three channels will always ensure an accurate retrieval. Because of the complexities of this state-dependent nature and the need for a consistent retrieval scheme for an operational retrieval, a five-channel retrieval approach consisting of a combination of error-weighted visible, near-infrared, and infrared channels is suggested. Such an approach ensures high information content regardless of cloud and atmospheric properties through use of the inherent sensitivities in each of these spectral regions.

Outcome and progression measures in rehabilitation following anterior cruciate ligament injury

There are a variety of measurements of functional recovery used in clinical practice to determine the relative success of treatment following anterior cruciate ligament (ACL) injury. These include joint laxity measures, strength testing, patient-reported measures, functional testing and proprioception testing, depending on the underlying theory of joint stability favoured. There is conflicting evidence as to which individual or combination of tests is the most useful in assessing recovery rate or functional activity level. Adequate performance of jumping, running and cutting skills involves various components of functional knee stability such as concentric strength for propulsion, eccentric strength for force absorption and proprioceptive ability to assist neuromuscular control. Although strength and proprioception tests assess these individual components, their relationship to function when tested in isolation has been questioned. Functional tests tend to be global in that they do not highlight which component may be at fault. A reduced score in a functional rating system might reflect any or all of these parameters. It appears that a combination of measurements is important to fully assess functional ability after ACL injury. Future research will be required to determine what the optimal combination of measurements is, preferably on the basis of a better understanding of the protective mechanisms of joint stability.

A Stochastic Branch-and-Bound Approach to Activity Crashing in Project Management

Many applications such as project scheduling, workflow modeling, or business process re-engineering incorporate the common idea that a product, task, or service consisting of interdependent time-related activities should be produced or performed within given time limits. In real-life applications, certain measures like the use of additional manpower, the assignment of highly-skilled personnel to specific jobs, or the substitution of equipment are often considered as means of increasing the probability of meeting a due date and thus avoiding penalty costs. This paper investigates the problem of selecting, from a set of possible measures of this kind, the combination of measures that is the most cost-efficient. Assuming stochastic activity durations, the computation of the optimal combination of measures may be very expensive in terms of runtime. In this article, we introduce a powerful stochastic optimization approach to determine a set of efficient measures that crash selected activities in a stochastic activity network. Our approach modifies the conventional Stochastic Branch-and-Bound, using a heuristic—instead of exact methods—to solve the deterministic subproblem. This modification spares computational time and by doing so provides an appropriate method for solving various related applications of combinatorial stochastic optimization. A comparative computational study shows that our approach not only outperforms standard techniques but also definitely improves conventional Stochastic Branch-and-Bound.

Iron deficiency: definition and diagnosis.

There has been a continuous refinement over the past several decades of methods to detect iron deficiency and assess its magnitude. The optimal combination of measurements differs for clinical and epidemiological assessment. Clinically, the major problem is to distinguish true iron deficiency from other causes of iron-deficient erythropoiesis, such as the anaemia of chronic disease. Epidemiologically, techniques that provide quantified estimates of body iron are preferable. For both purposes, the serum ferritin is the focal point of the laboratory detection of iron deficiency. Serum ferritin measurements provide a reliable index of body iron stores in healthy individuals, a cost-effective method of screening for iron deficiency, and a useful alternative to bone marrow examinations in the evaluation of anaemic patients. Preliminary studies indicate that measurement of the serum transferrin receptor may be the most reliable way to assess deficits in tissue iron supply.