Choice Of Threshold Research Articles

Fictitious cooling/heating: from free-floating thermal discomfort to energy needs, different approaches toward labelling free-running buildings

Free-running (FR) buildings and technologies, e.g. ventilative cooling, are generally not considered and valorised in energy labelling schemes. The paper analyses three approaches to transfer discomfort conditions into comparable fictitious energy needs in order to evaluate FR building performances. The first approach adds a virtual mechanical system to the FR building; the second approach includes in the previous the effect of controlled free-running potentialities, e.g. ventilative cooling in summer, while in the third one, fictitious energy needs are calculated by coupling a double simulation flow, i.e. FR vs mechanical-treated building, assuming energy needs from the latter when the first is exceeding assumed discomfort thresholds. The paper underlines a very high correlation between energy needs and parallel FR indoor temperatures. Initial results show how the first approach loses the FR potential, while the second and the third can valorise FR technologies. The choice of different discomfort thresholds can be used to orient the impact of FR technologies from very to slightly positive.

Comparative risk aversion vs. threshold choice in the Omega ratio

We study conditions under which the threshold parameter in the Omega ratio represents risk aversion in the sense of monotonicity of risk premia. To this end, we derive asymptotic expansions for risk premia associated with taking a small additional risk on top of a background risk. These risk premia have the expected monotonicity behavior if, roughly speaking, the variance of the additional risk decreases with the background risk and if the density of the background risk is log-concave. When these conditions are violated, the threshold in the Omega ratio does not represent risk aversion in general. Finally, we compare our sufficient conditions for the Omega ratio to those that are needed to guarantee monotonicity of risk premia with an expected utility criterion under background risk. We argue that the conditions that are needed for the Omega threshold to represent risk aversion are comparable to those that are needed for expected utility with exponential utility functions.

DECOMPOSING MULTIDIMENSIONAL POVERTY: A CASE STUDY OF MAMIT DISTRICT, MIZORAM

Purpose: The study intends to examine the incidence and intensity of multidimensional poverty in Mamit District of Mizoram. Methodology: This study was based on primary data which was collected through a multi-stage stratified random sampling technique. At the first stage, Mamit District was selected solely based on socio-economic indicators for conducting the survey. The second stage involved random selection of 15 villages, five villages from each block. Requisite data were then collected randomly through structured questionnaires which was designed based on the requirement for computation of Multidimensional Poverty Index. From the collected data, the incidence of poverty (headcount ratio), the intensity of poverty and MPI were computed using Alkire-Foster Methodology. The study follows the Global MPI Brief Methodological Note, 2017 in the choice of dimensions, indicators, thresholds and weights assigned to each indicator. Results: From the result of the analysis, the multidimensional poverty in the study area is moderate. The incidence of poverty across the study area is ranging between 0.32 - 0.38 while the intensity of poverty shows slight differences with negligible margin ranging between 0.39-0.411. Decompositions by indicators show that the nutrition has surpassed all other indicators with 22 percent contribution to overall MPI of Mamit district followed by year of schooling with 15 percent. Applications of this study: The findings of the study can be based for formulation of government poverty reduction policies and can be used effectively in improving the existing poverty reduction strategies in the state. KEY WORDS: Multidimensional Poverty, Decomposition, Mamit District, Mizoram.

Use of threshold parameter variation for tropical cyclone tracking

Abstract. Assessing the capacity of numerical models to produce viable tropical cyclones, as well as assessing the climatological behavior of simulated tropical cyclones, requires an objective tracking method. These make use of parameter thresholds to determine whether a detected feature, such as a vorticity maximum or a warm core, is strong enough to indicate a tropical cyclone. The choice of parameter thresholds is generally subjective. This study proposes and assesses the parallel use of many threshold parameter combinations, combining a number of weaker and stronger values. The tracking algorithm succeeds in tracking tropical cyclones within the model data, beginning at their aggregation stage or shortly thereafter and ending when they interact strongly with extratropical flow and transition into extratropical cyclones or when their warm core decays. The sensitivity of accumulated cyclone energy to tracking errors is assessed. Tracking errors include the faulty initial detection and termination of valid tropical cyclones and systems falsely identified as tropical cyclones. They are found to not significantly impact the accumulated cyclone energy. Thus, the tracking algorithm produces an adequate estimate of the accumulated cyclone energy within the underlying data.

How thresholding in segmentation affects the regression performance of the linear model.

Evaluating any model underlying the control of speech requires segmenting the continuous flow of speech effectors into sequences of movements. A virtually universal practice in this segmentation is to use a velocity-based threshold which identifies a movement onset or offset as the time at which the velocity of the relevant effector breaches some threshold percentage of the maximal velocity. Depending on the threshold choice, more or less of the movement's trajectory is left in for model regression. This paper makes explicit how the choice of this threshold modulates the regression performance of a dynamical model hypothesized to govern speech movements.

Statistical Evaluation of Responder Analysis in Stem Cell Clinical Trials.

In evaluating treatment efficacy, there is an ongoing discussion about which endpoint is more efficient to represent the treatment effect. Absolute change (AC) is the difference between before and after treatment, while relative change (RC) is the AC relative to the baseline value. Principal investigators sometimes support the credibility of relative change, but the FDA is more likely to support absolute change. Therefore, whether these two endpoints can be translated or combined is worth investigating in order to satisfy both parties. In this article, a motivating example is presented to show that the choice of endpoint will result in different conclusions. The compared relationship of AC and RC is discussed in terms of required sample size, power, and precision. A new type of responder endpoint that combines the concepts of AC and RC is proposed. The comparative relationship regarding sample size, power, and precision of the proposed responder endpoint and the original two endpoints are also investigated. As a result, the performance of AC and RC is highly dependent on the choice of threshold that is often informed based on minimum clinically important difference or other clinical experience. Therefore, an absolute translation between them is hard to achieve. Inspired by the concept of responder analysis, three types of responder endpoints are proposed and discussed. The pattern of the third type of responder endpoint is having higher power, higher precision, and less required sample size in estimating the treatment effect compared to AC and RC within a range of thresholds. This advantage becomes more obvious when applying higher AC and RC thresholds and lower [Formula: see text] threshold. The proposed endpoint incorporates the information from the AC and RC endpoints and could be another wise choice when designing clinical trials especially when there is no absolute preference between AC and RC.

Lagrangian transport simulations using the extreme convection parameterization: an assessment for the ECMWF reanalyses

Abstract. Atmospheric convection plays a key role in tracer transport from the planetary boundary layer to the free troposphere. Lagrangian transport simulations driven by meteorological fields from global models or reanalysis products, such as the European Centre for Medium-Range Weather Forecasts' (ECMWF's) ERA5 and ERA-Interim reanalysis, typically lack proper explicit representations of convective updrafts and downdrafts because of the limited spatiotemporal resolution of the meteorology. Lagrangian transport simulations for the troposphere can be improved by applying parameterizations to better represent the effects of unresolved convective transport in the global meteorological reanalyses. Here, we implemented and assessed the effects of the extreme convection parameterization (ECP) in the Massive-Parallel Trajectory Calculations (MPTRAC) model. The ECP is conceptually simple. It requires the convective available potential energy (CAPE) and the height of the equilibrium level (EL) as input parameters. Assuming that unresolved convective events yield well-mixed vertical columns of air, the ECP randomly redistributes the air parcels vertically between the surface and the EL if CAPE is present. We analyzed statistics of explicitly resolved and parameterized convective updrafts and found that the frequencies of strong updrafts due to the ECP, i.e., 20 K potential temperature increase over 6 h or more, increase by 2 to 3 orders of magnitude for ERA5 and 3 to 5 orders of magnitude for ERA-Interim compared to the explicitly resolved updrafts. To assess the effects of the ECP on tropospheric tracer transport, we conducted transport simulations for the artificial tracer e90, which is released globally near the surface and which has a constant e-folding lifetime of 90 d throughout the atmosphere. The e90 simulations were conducted for the year 2017 with both ERA5 and ERA-Interim. Next to sensitivity tests on the choice of the CAPE threshold, an important tuning parameter of the ECP, we suggest a modification of the ECP method, i.e., to take into account the convective inhibition (CIN) indicating the presence of warm, stable layers that prevent convective updrafts in the real atmosphere. While ERA5 has higher spatiotemporal resolution and explicitly resolves more convective updrafts than ERA-Interim, we found there is still a need for both reanalyses to apply a convection parameterization such as the ECP to better represent tracer transport from the planetary boundary layer into the free troposphere on the global scale.

Assessing uncertainty in genomic offset forecasts from landscape genomic models (and implications for restoration and assisted migration)

IntroductionEcological genomic models are increasingly used to guide climate-conscious restoration and conservation practices in the light of accelerating environmental change. Genomic offsets that quantify the disruption of existing genotype–environment associations under environmental change are a promising model-based tool to inform such measures. With recent advances, potential applications of genomic offset predictions include but are not restricted to: (1) assessing in situ climate risks, (2) mapping future habitat suitability while accounting for local adaptations, or (3) selecting donor populations and recipient areas that maximize genomic diversity and minimize maladaptation to future environments in assisted migration planning. As for any model-based approach, it is crucial to understand how arbitrary decisions made during the modeling process affect predictions and induce uncertainty.MethodsHere, we present a sensitivity analysis of how various modeling components influence forecasts of genomic offset-based metrics, using red spruce (Picea rubens), a cool-temperate tree species endemic to eastern North America, as a case study. We assess the effects of genomic marker set, climatic predictor set, climate change scenario, and “not-to-exceed” offset threshold and evaluate how uncertainty in predictions varies across space.ResultsClimate change scenario induced by far the largest uncertainty to our forecasts; however, the choice of predictor set was also important in regions of the Southern and Central Appalachians that are of high relevance for conservation and restoration efforts. While much effort is often expended in identifying candidate loci, we found that genomic marker set was of minor importance. The choice of a maximum offset threshold to limit transfers between potential donor and recipient locations in assisted migration programs has mostly affected the magnitude rather than geographic variation in our predictions.DiscussionOverall, our model forecasts suggest high climate change risks across the entire distributional range of red spruce and strongly underscore the potential for assisted migration to help ameliorate these risks. In that regard, populations in the Southern and Central Appalachians as well as along the US and Canadian east coast seem the best candidates for both in situ conservation and relocation.

A time interval metric for cumulative opportunity accessibility

Cumulative accessibility measures are the most-used accessibility metric and indicate the number of opportunities reached within a given travel time threshold. However, these measures require an ad-hoc choice of a single travel time threshold, which can influence the conclusions of transport project evaluations and equity analyses. In this paper, we introduce the time interval cumulative accessibility measure, a new metric that mitigates the impacts of arbitrary choices of trip duration on cumulative accessibility analyses while preserving computation and communicability advantages. The proposed indicator estimates the average (or median) number of opportunities that can be reached considering multiple minute-by-minute cutoffs within a given travel time interval. We demonstrate the new metric in a case study assessing how a planned subway expansion could impact employment accessibility in Fortaleza, Brazil. Using sensitivity analyses with Monte Carlo simulations, we show that the proposed metric makes the results of accessibility estimates and equity analyses significantly less sensitive to ad-hoc methodological choices while yielding results that are very similar to those obtained with traditional threshold-based measures. Future accessibility-oriented research and planning could benefit from the way in which the proposed time interval cumulative opportunity measure provides more robust accessibility estimates without compromising the communicability of results.

Prediction Strength for Clustering Activity Patterns Using Accelerometer Data

Background: Clustering, a class of unsupervised machine learning methods, has been applied to physical activity data recorded by accelerometers to discover unique patterns of physical activity and health outcomes. The prediction strength metric provides a criterion to determine the optimal number of clusters for clustering methods. The aim of this study is to provide specific guidance for applying prediction strength to time series accelerometer data. Methods: For this purpose, we designed an extensive simulation study. We created a synthetic data set of accelerometer data using data from a childhood obesity management trial. We evaluated the role of a prespecified threshold of the prediction strength metric as a key input parameter. We compared the recommended threshold (between 0.8 and 0.9) with an approach we developed (Local Maxima). Results: The choice of threshold had a large impact on performance. When the noise level increased (greater overlap between true clusters), lower thresholds outperformed the recommended threshold, which tended to underestimate the true number of clusters. In addition, we found that sorting the data by magnitude of intensity in windows within the time series of interest prior to clustering alleviated sensitivity to threshold choice. Furthermore, for accelerometer data, we recommend that the Local Maxima approach be utilized together with a graphical evaluation of the prediction strength metric function over values of k. Finally, we strongly suggest sorting of the data prior to clustering if sorting retains meaning for the research question at hand. Conclusion: Our recommendations can help future researchers discover more robust patterns from accelerometer data.

Microbial Diversity Biased Estimation Caused by Intragenomic Heterogeneity and Interspecific Conservation of 16S rRNA Genes.

The 16S rRNA gene has been extensively used as a molecular marker to explore evolutionary relationships and profile microbial composition throughout various environments. Despite its convenience and prevalence, limitations are inevitable. Variable copy numbers, intragenomic heterogeneity, and low taxonomic resolution have caused biases in estimating microbial diversity. Here, analysis of 24,248 complete prokaryotic genomes indicated that the 16S rRNA gene copy number ranged from 1 to 37 in bacteria and 1 to 5 in archaea, and intragenomic heterogeneity was observed in 60% of prokaryotic genomes, most of which were below 1%. The overestimation of microbial diversity caused by intragenomic variation and the underestimation introduced by interspecific conservation were calculated when using full-length or partial 16S rRNA genes. Results showed that, at the 100% threshold, microbial diversity could be overestimated by as much as 156.5% when using the full-length gene. The V4 to V5 region-based analyses introduced the lowest overestimation rate (4.4%) but exhibited slightly lower species resolution than other variable regions under the 97% threshold. For different variable regions, appropriate thresholds rather than the canonical value 97% were proposed for minimizing the risk of splitting a single genome into multiple clusters and lumping together different species into the same cluster. This study has not only updated the 16S rRNA gene copy number and intragenomic variation information for the currently available prokaryotic genomes, but also elucidated the biases in estimating prokaryotic diversity with quantitative data, providing references for choosing amplified regions and clustering thresholds in microbial community surveys. IMPORTANCE Microbial diversity is typically analyzed using marker gene-based methods, of which 16S rRNA gene sequencing is the most widely used approach. However, obtaining an accurate estimation of microbial diversity remains a challenge, due to the intragenomic variation and low taxonomic resolution of 16S rRNA genes. Comprehensive examination of the bias in estimating such prokaryotic diversity using 16S rRNA genes within ever-increasing prokaryotic genomes highlights the importance of the choice of sequencing regions and clustering thresholds based on the specific research objectives.

Setting thresholds for good ecosystem state in marine seabed systems and beyond

AbstractOne of the aims of environmental management is to achieve good ecosystem state. Assessing the state needs to be informed by thresholds above which state is defined as “good” for both the quality that defines good state, and the extent of the habitat that needs to be in such a quality. Operationalizing such thresholds has been carried out using a wide variety of approaches, with, often, haphazard and subjective outcomes. Here, we review approaches for setting “good-state” thresholds and evaluate their strengths and weaknesses for application to marine seabed ecosystems. Only two approaches defined a current ecologically meaningful good state and estimated thresholds quantitatively from data, while two other approaches (“avoid collapse” and “allow recovery”) would result in a state that could recover to good in the future. Other methods were subjective in the choice of threshold or based on statistically detectable thresholds rather than thresholds between good and not good or degraded state. We argue that the most objective method for setting a good-state threshold is based on maintaining the state within the range of natural variation in undisturbed systems. Preliminary time-series analyses of marine seabed community biomass suggest this threshold is located between 54 and 79% of the undisturbed state.

Uncertainties in estimating the effect of climate change on 100-year return value for significant wave height

The process of estimating the effect of a changing climate on the severity of future ocean storms is plagued by large uncertainties; for safe design and operation of offshore structures, it is nevertheless important that best possible estimates of climate effects is made given the available data. We explore the variability in estimates of 100-year return value of significant wave height (HS), and changes in estimates over a period of time, for output of WAVEWATCH-III models from 7 representative Coupled Model Intercomparison Project (CMIP) Phase 5 General Circulation Models (GCMs), and the FIO-ESM v2.0 CMIP Phase 6 GCM. Non-stationary extreme value analysis of peaks-over-threshold and block maxima using Bayesian inference provide posterior estimates of return values as a function of time; MATLAB software for the extreme value analysis is provided. Best overall estimates for return values, and changes in return value over the period 1979-2100, are calculated by averaging estimates for individual GCMs. We focus attention on neighbourhoods of locations east of Madagascar and south of Australia where a previous study of CMIP5-derived output reported significant decrease and increase in HS respectively, under Representative Concentration Pathway (RCP) scenarios RCP4.5 and RCP8.5. There is large variation between return value estimates from different GCMs, and with longitude and latitude within each neighbourhood for estimates based on samples corresponding to ≤ 165 years of model output; these sources of uncertainty tend to be larger than those due to typical modelling choices (such as choice of threshold for peaks over threshold, or block length for block maxima). However, we also find that careful threshold choice and block length are critical east of Madagascar, because of the presence of a mixed population of storms there. Nevertheless, there is general evidence supporting the trends reported by others, but these findings are conditional on the choice of 8 GCMs being representative of climate evolution. We use simple randomisation testing to identify “significant” departures from steady climate. The long 700-year pre-industrial control (piControl) output of the CMIP6 GCM offers an excellent opportunity to quantify the apparent inherent variability in return value as a function of time, estimated using a subsample of output corresponding to a continuous time interval of between 20 and 160 years in length, where no climate forcing is present. We find large variation in return value estimates of approximately ±15% made from samples corresponding to periods of time of around 50 years drawn from piControl data.

ScreeNOT: Exact MSE-optimal singular value thresholding in correlated noise

We derive a formula for optimal hard thresholding of the singular value decomposition in the presence of correlated additive noise; although it nominally involves unobservables, we show how to apply it even where the noise covariance structure is not a priori known or is not independently estimable. The proposed method, which we call ScreeNOT, is a mathematically solid alternative to Cattell’s ever-popular but vague scree plot heuristic from 1966. ScreeNOT has a surprising oracle property: it typically achieves exactly, in large finite samples, the lowest possible MSE for matrix recovery, on each given problem instance, that is, the specific threshold it selects gives exactly the smallest achievable MSE loss among all possible threshold choices for that noisy data set and that unknown underlying true low rank model. The method is computationally efficient and robust against perturbations of the underlying covariance structure. Our results depend on the assumption that the singular values of the noise have a limiting empirical distribution of compact support; this property, which is standard in random matrix theory, is satisfied by many models exhibiting either cross-row correlation structure or cross-column correlation structure, and also by many situations with more general, interelement correlation structure. Simulations demonstrate the effectiveness of the method even at moderate matrix sizes. The paper is supplemented by ready-to-use software packages implementing the proposed algorithm: package ScreeNOT in Python (via PyPI) and R (via CRAN).

Assessing the importance of the choice threshold in quantifying market risk under the POT approach (EVT)

From a theoretical point of view, the selection of thresholds is a critical issue in the framework of the Peaks Over Threshold (POT) approach, which is why in the last decade numerous methodologies have been proposed for its selection. In this paper, we address this subject from an empirical point of view by assessing to what extent the selection of the threshold is decisive in quantifying the market risk. For measuring market risk, we use the Value at Risk (VaR) and the Expected Shortfall (ES) measures. The results obtained indicate that there is a large set of thresholds that provide similar Generalized Pareto Distribution (GPD) quantiles estimators and as a consequence similar market risk measures. Just only, for large thresholds, those corresponding to the 98th and 99th percentile of the GPD some differences are found. It means that the choice of threshold in the framework of the POT method may not be relevant in quantifying market risk when we use the VaR and ES measures for this task.

Comparative Risk Aversion vs. Threshold Choice in the Omega Ratio

Comparative Risk Aversion vs. Threshold Choice in the Omega Ratio

Flood mapping based on open-source remote sensing data using an efficient band combination system

Flood mapping is an essential component of planning flood mitigation. The availability of remote sensing data makes rapid flood mapping possible. This article develops an accurate method for rapid flood mapping using satellite imagery. Sentinel-2 imagery was tested by acquiring data before and after a flood event in a lowland area. Flooding extraction was performed using the newly developed Flood Inundation Extraction Index (FIEI) and compared to the Modified Normalized Difference Water Index (MNDWI), the most commonly used index. Based on the choice of threshold, the results are divided into flooded and non-flooded areas. Evaluation of the performance accuracy based on the total and kappa coefficients showed that the FIEI approach is more accurate than the MNDWI approach.

Flood mapping based on open-source remote sensing data using an efficient band combination system

Flood mapping is an essential component of planning flood mitigation. The availability of remote sensing data makes rapid flood mapping possible. This article develops an accurate method for rapid flood mapping using satellite imagery. Sentinel-2 imagery was tested by acquiring data before and after a flood event in a lowland area. Flooding extraction was performed using the newly developed Flood Inundation Extraction Index (FIEI) and compared to the Modified Normalized Difference Water Index (MNDWI), the most commonly used index. Based on the choice of threshold, the results are divided into flooded and non-flooded areas. Evaluation of the performance accuracy based on the total and kappa coefficients showed that the FIEI approach is more accurate than the MNDWI approach.

Using Correspondence Tests to Assess Replicability of Open Science Collaboration Results: Inferences from a SMART Design-based, Meta-analytic Approach

The logic of the SMART (Sequential Multiple Assignment Randomization Trial) design was applied to assess the replicability of original-replicate study pairs for Open Science Collaboration (OSC) intervention studies. Within SMART, we utilized both subtests of the correspondence test (CT) to assess study pair comparability. First, we implemented a CT difference test to determine if an effect size difference between the original study and its replicate pair was close to zero; second, we implemented a CT equivalence test to determine if the effect size difference of that study pair was within a designated threshold. In Stage 1 of SMART, each study pair was randomly assigned to one of two alphas (.01 and .05), thereby creating two, probabilistically similar subsets of study pairs. Within each alpha subset, successful difference tests (test of significance was not significantly different than zero) and unsuccessful difference tests were then determined. In Stage 2 of SMART, study pairs in each combination of alpha level and successful or unsuccessful difference tests were randomly assigned to one of two thresholds (±.25 SD, ±.50 SD). Equivalence tests were then conducted for all study pairs in each of these four subsets. Successful equivalence occurred when the distance between an original and its replicate pair was statistically significantly less than a given threshold. Thus, initial randomization followed by a second randomization was used to gauge comparability of each OSC original study and its replicate, for two alpha levels and two thresholds. In the first set of results, to mirror the common replicability assessment case in which only difference tests are conducted, 16 of 96 difference tests (16.7%) conducted in Stage 1 were successful. In the second set of results, for initially successful difference tests, two thresholds were used to determine the percent of study pairs that also passed the equivalence test. Depending on α and threshold, 8.0%-13.8% of studies successfully passed both difference and equivalence CT subtests. In the third set of results, using SMART, after randomization to two α-values and contingent on success or lack of success of a difference test, study pairs were randomized to two thresholds and a statistical test of equivalence conducted. Using meta-analysis methods within SMART-based subsets of study pairs, original-replicate average effect size differences were compared to differences in the second set of results. We found a similarly-sized 10.3% of study pairs passed both CT subtests (nine of 87 study pairs successfully passed the difference test at either alpha and successfully passed the equivalence test at either threshold). Reflecting the importance of incorporating both CT subtests, of 16 study pairs that initially passed the difference test, nearly half (43.7%) failed the equivalence test. Thus, for CT success, we found that α choice had little impact, while threshold choice was an important determinant. In all three sets of results, the percent of successful replications was substantially smaller than the 36% of OSC replicates that were statistically significant. To confirm this study’s replicability, we found very similar patterns of CT success and lack of success for two, SMART-based tables, one for alpha = .01 and one for alpha = .05. The current research extends the utility of CT established by Steiner and Wong (2018) in which results were based on simulation data.

Predictive battery thermal management using quantile convolutional neural networks

An improvement in energy efficiency of Battery Thermal Management Systems (BTMS) can increase range and reduce well-to-wheel emissions of Battery Electric Vehicles (BEV). In this work, the potential of a predictive BTMS using Quantile Convolutional Neural Networks (QCNN) was examined. The QCNN provided quantile predictions of battery temperature based on input data from both previous and following drive segments. The predictive control was designed to choose battery cooling thresholds based on a weighted sum of battery cooling, ageing and derating costs derived by the quantile predictions. The predictive BTMS was analyzed concerning its adaptability to different routes ahead, tunability of cost weights as well as robustness to uncertainty of inputs. A setup with unchanged ageing costs reduced average cooling costs by 9% compared to a fixed threshold strategy in a set of 18 scenarios. Simplifications and limitations were discussed to provide a base for further improvements, for example concerning the limited freedom of cooling threshold choice. In conclusion, the developed framework was able to use QCNN predictions to increase the BTMS energy efficiency while taking ageing and derating effects into account.