Linear Decomposition Research Articles

Polynomial algorithms for some scheduling problems with one nonrenewable resource

This paper deals with the Extended Resource Constrained Project Scheduling Problem (ERCPSP) which is defined by events, nonrenewable resources and precedence constraints between pairs of events. The availability of a resource is depleted and replenished at the occurrence times of a set of events. The decision problem of ERCPSP consists of determining whether an instance has a feasible schedule or not. When there is only one nonrenewable resource, this problem is equivalent to find a feasible schedule that minimizes the number of resource units initially required. It generalizes the maximum cumulative cost problem and the two-machine maximum completion time flow-shop problem. In this paper, we consider this problem with some specific precedence constraints: parallel chains, series-parallel and interval order precedence constraints. For the first two cases, polynomial algorithms based on a linear decomposition of chains are proposed. For the third case, a polynomial algorithm is introduced to solve it. The priority between events is defined using the properties of interval orders.

Spatial—Temporal Traffic Flow Data Restoration and Prediction Method Based on the Tensor Decomposition

As an important part of urban big data, traffic flow data play a critical role in traffic management and emergency response. Traffic flow data contain multi-mode characteristics, which need to be deeply mined. To make full use of multi-mode characteristics, we use a 3-order tensor to represent the traffic flow data, considering “temporal-spatial-periodic” characteristics. To recover the missing data of traffic flow, we propose the Missing Data Completion Algorithm Based on Residual Value Tensor Decomposition (MDCA-RVTD), which combines linear regression, univariate spline, and CP decomposition. Then, we predict the future traffic flow data by using the proposed Traffic Flow Prediction Algorithm Based on Data Completion Strategy (TFPA-DCS). The experimental results show that recovering the missing data is helpful in improving the prediction accuracy. Additionally, the prediction accuracy of the proposed Algorithm is better than gray model and traditional tensor CP decomposition method.

Disturbance frequency, intensity and forest structure modulate cyclone‐induced changes in mangrove forest canopy cover

AbstractAimTropical cyclones are large‐scale disturbances that can shape the structure and dynamics of mangrove forests. Although tropical cyclone activity overlaps extensively with the latitudinal distribution of mangrove forests, the relationships between cyclone intensity and frequency and mangrove forest canopy damage and recovery are not understood at the global scale. Using remote sensing data, we examined how mangrove forest structure, climate and cyclone characteristics influence canopy cover loss and recovery dynamics.LocationGlobal tropics.Time period2000–2020.Major taxa studiedMangrove trees.MethodsUsing two satellite‐derived vegetation indices (the enhanced vegetation index and the normalized difference infrared index) from 86 cyclones affecting 56 mangrove sites across the globe, we quantified mangrove canopy loss in relationship to cyclones. Using linear regression and variance decomposition, we identified and ranked significant predictors of cyclone‐induced canopy loss and recovery.ResultsThree‐quarters of the studied cyclone disturbances resulted in canopy damage. Stands exposed to high wind speeds and those close to the cyclone paths were more severely damaged, whereas lower damage magnitudes were found in sites with greater past cyclone frequency. Canopy damage was greater in tall mangrove stands but decreased with higher aboveground biomass. The distance from the cyclone path and maximum wind speed were the most important factors, representing > 50% of the explained variation in cyclone damage. There was considerable variation in canopy damage among cyclones, but rates of recovery were similar across all mangrove sites, with the main predictor of recovery time being the degree of canopy loss.Main conclusionsOur results suggest that the resistance of mangrove canopy cover to cyclone disturbance is variably tuned to the cyclone regime and vegetation characteristics, but resilience is inherent to the magnitude of canopy damage because the rate of forest canopy recovery appears to be consistent globally.

Housing Poverty and Healthy Aging in China: Evidence from the China Health and Retirement Longitudinal Study.

Background: Although prior research on the housing–health linkage suggested that those with poor housing conditions are more likely to report poor health, it is dominated by Western studies and offers little evidence on the housing–health relation in China. Scarce is empirical evidence on the potentially detrimental impact of either qualitative or quantitative housing poverty on health outcomes, especially for seniors in China. This paper aims to fill this void by using data from the 2011–2015 China Health and Retirement Longitudinal Study (CHARLS) to provide a comprehensive analysis of the demographic, socioeconomic, and behavioral factors that contribute to changes in healthy aging among Chinese adults aged 60 and over. Methods: Data collected from 8839 adults aged 60 and over in the 2011 and 2015 CHARLS (3732 in 2011 and 5107 in 2015) were used. We first used six blood-based biomarkers to construct a composite measure of the Chinese Healthy Aging Index (CHAI, ranging from 0 (healthiest) to 12 (unhealthiest)) and then assessed the psychometric properties of the CHAI score, including acceptability, internal consistency, convergent validity, discriminative validity and precision. In addition, we employed both mean-based Blinder–Oaxaca and unconditional quantile regression decomposition to decompose the change in healthy aging within the 2011–2015 period. Results: We overall identified a decrease in CHAI score from 5.69 in 2011 to 5.20 in 2015, which implies an improvement in healthy aging during this period. Our linear decomposition revealed that dependent on the type of measure used (whether quality, quantity, or combined quality–quantity), housing poverty explained 4–8% of the differences in CHAI score. Our distributional decompositions also highlighted an important role for housing poverty in the change in healthy aging, accounting for approximately 7–23% of the explained portion. Within this latter, the relative contribution of housing quantity and quality poverty was more pronounced at the median and upper end of the CHAI distribution. We also found household expenditure to be significantly associated with healthy aging among older Chinese adults and made the largest contribution to the improvement in healthy aging over time. Conclusions: The association between housing poverty and CHAI is independent of household expenditure. Regardless of type, housing poverty is positively associated with a decrease in healthy aging. Thus, improved housing conditions boost healthy aging, and housing amelioration initiatives may offer the most effective solution for augmenting healthy aging in China. Improvement of flush toilets and the access to potable water and a separate kitchen require particular attention. Since high-density congested housing has a negative impact on healthy aging, more attention can also be paid to improvements in the available space for older people. Especially at an institutional level, the government may extend the housing policy from a homeownership scheme to a housing upgrading scheme by improving housing conditions.

A Perspective on Muscle Synergies and Different Theories Related to Their Adaptation

The human motor system is a complex neuro-musculo sensory system that needs further investigations of neuro-muscular commands and sensory-motor coupling to decode movement execution. Some researchers suggest that the central nervous system (CNS) activates a small set of modules termed muscle synergies to simplify motor control. Further, these modules form functional building blocks of movement as they can explain the neurophysiological characteristics of movements. We can identify and extract these muscle synergies from electromyographic signals (EMG) recorded in the laboratory by using linear decomposition algorithms, such as principal component analysis (PCA) and non-Negative Matrix Factorization Algorithm (NNMF). For the past three decades, the hypothesis of muscle synergies has received considerable attention as we attempt to understand and apply the concept of muscle synergies in clinical settings and rehabilitation. In this article, we first explore the concept of muscle synergies. We then present different strategies of adaptation in these synergies that the CNS employs to accomplish a movement goal.

Pore Structure and Movable Fluid Characteristics of Typical Sedimentary Lithofacies in a Tight Conglomerate Reservoir, Mahu Depression, Northwest China

The pore structure and movable fluid characteristics of tight conglomerate reservoirs are complex, which are greatly different from conventional reservoirs. The depositional mechanism is the fundamental factor controlling the physical properties of conglomerate reservoirs. However, there is a lack of systematic research on the pore structure and movable fluid characteristics of conglomerate reservoirs with typical sedimentary facies. This paper investigates the pore structure and movable fluid characteristics of conglomerate of different sedimentary facies based on various experiments. Casting thin sections, X-ray diffraction, scanning electron microscopy, high-pressure mercury injection, and nuclear magnetic resonance experiments were conducted on 32 conglomerates samples from the Mahu Sag, Junggar Basin, China. The quality classification method of tight conglomerate reservoirs is established. The results show that the conglomerate can be divided into three sedimentary facies; traction flow conglomerate (TFC) and pebbled sandstone (PSS) mainly develop intergranular pores and dissolved pores; and the pore diameter curves are mainly a double peak, single peak, and flat peak. Gravity flow conglomerate (GFC) mainly develops dissolved pores and interstitial micropores, and the pore diameter curve is mainly a single peak. PSS includes pebbled gritty sandstone (P(G)SS) and pebbled fine sandstone (P(F)SS). TFC and P(G)SS are favorable class I reservoirs, while GFC and P(F)SS are nonfavorable class II reservoirs. A new parameter, the ratio of the major axis to the minor axis of the pore outer ellipse (axial ratio), is proposed to quantitatively describe the compaction effect. The average axial ratios of the three lithofacies are 3.04, 3.98, and 8.78, respectively, indicating that the compaction is intensified and the pore structure becomes worse. By analyzing the correlation between pore structure parameters and permeability, it is found that the main controlling factors of permeability of GFC and TFC are sorting and connectivity, respectively, and the main flow radius is the most suitable parameter to describe permeability. A linear spectral decomposition method was used to establish a new quantitative calculation method of movable fluid saturation for different types of pores, and the results show that the movable fluid saturation of intergranular pores is the highest (average: 65.43%), and the movable fluid saturation of TFC and P(G)SS with more intergranular pores is the highest. Movable fluid saturation is inversely proportional to the content of I/S and the compaction rate and positively proportional to the content of quartz and feldspar and the cementation rate. The fluid mobility of water-wet samples is weaker. The research results provide theoretical support for the identification of favorable reservoirs and the cognition of a development mechanism.

Precarious employment, psychosocial risk factors and poor mental health: A cross-sectional mediation analysis

ObjectivesThis study analyzes the mediating effect of psychosocial risk factors at work (i.e. psychological demands, control and social support) (PRFs) in the relationship between precarious employment and mental health among formal salaried men and women from 22 European countries. MethodsCross-sectional data were derived from IV European Working Conditions Survey (n = 15,932–7,650 men, 8,282 women). Linear regression models and KHB decomposition analyses were applied to estimate the mediating effect and confounding percentages of PRFs. ResultsAmong men, the association between PE and poor mental health decreased after controlling for PRFs in both PE’s terciles, leaving a significant indirect effect (βtercile2 = 1.77, 95 %CI: 0.33–3.22; βEPREStercil3 = 3.06, 1.04–5.08) (partial mediation effect). Among women, a partial mediation effect was also found in tercile 3 (βEPREStercil3 = 3.31, 1.62–5.00), but a full mediation effect was found in tercile 2, since the direct effect was not significant. ConclusionsGender differences on the PRFs mediating effect were found, which suggest that women’s employment and working conditions are more heterogeneous than among men. These differences may be related to the difficulties of women to improve their working and employment conditions within labor markets and workplaces segregated by gender. Future studies should further delve into these gender inequalities in the relationship between precarious employment, PRFs and mental health, to improve OHS policies, especially from a macro-social perspective.

Unraveling the interplay of image formation, data representation and learning in CT-based COPD phenotyping automation: The need for a meta-strategy.

In the literature on automated phenotyping of chronic obstructive pulmonary disease (COPD), there is a multitude of isolated classical machine learning and deep learning techniques, mostly investigating individual phenotypes, with small study cohorts and heterogeneous meta-parameters, e.g., different scan protocols or segmented regions. The objective is to compare the impact of different experimental setups, i.e., varying meta-parameters related to image formation and data representation, with the impact of the learning technique for subtyping automation for a variety of phenotypes. The identified associations of these parameters with automation performance and their interactions might be a first step towards a determination of optimal meta-parameters, i.e., a meta-strategy. A clinical cohort of 981 patients (53.8±15.1years, 554male) was examined. The inspiratory CT images were analyzed to automate the diagnosis of 13 COPD phenotypes given by two radiologists. A benchmark feature set that integrates many quantitative criteria was extracted from the lung and trained a variety of learning algorithms on the first 654 patients (two thirds) and the respective algorithm retrospectively assessed the remaining 327 patients (one third). The automation performance was evaluated by the area under the receiver operating characteristic curve (AUC). 1717 experiments were conducted with varying meta-parameters such as reconstruction kernel, segmented regions and input dimensionality, i.e., number of extracted features. The association of the meta-parameters with the automation performance was analyzed by multivariable general linear model decomposition of the automation performance in the contributions of meta-parameters and the learning technique. The automation performance varied strongly for varying meta-parameters. For emphysema-predominant phenotypes, an AUC of 93%-95% could be achieved for the best meta-configuration. The airways-predominant phenotypes led to a lower performance of 65%-85%, while smooth kernel configurations on average were unexpectedly superior to those with sharp kernels. The performance impact of meta-parameters, even that of often neglected ones like the missing-data imputation, was in general larger than that of the learning technique. Advanced learning techniques like 3D deep learning or automated machine learning yielded inferior automation performance for non-optimal meta-configurations in comparison to simple techniques with suitable meta-configurations. The best automation performance was achieved by a combination of modern learning techniques and a suitable meta-configuration. Our results indicate that for COPD phenotype automation, study design parameters such as reconstruction kernel and the model input dimensionality should be adapted to the learning technique and may be more important than the technique itself. To achieve optimal automation and prediction results, the interaction between input those meta-parameters and the learning technique should be considered. This might be particularly relevant for the development of specific scan protocols for novel learning algorithms, and towards an understanding of good study design for automated phenotyping.

Constraining PERMANOVA and LDM to within-set comparisons by projection improves the efficiency of analyses of matched sets of microbiome data

BackgroundMatched-set data arise frequently in microbiome studies. For example, we may collect pre- and post-treatment samples from a set of individuals, or use important confounding variables to match data from case participants to one or more control participants. Thus, there is a need for statistical methods for data comprised of matched sets, to test hypotheses against traits of interest (e.g., clinical outcomes or environmental factors) at the community level and/or the operational taxonomic unit (OTU) level. Optimally, these methods should accommodate complex data such as those with unequal sample sizes across sets, confounders varying within sets, and continuous traits of interest.MethodsPERMANOVA is a commonly used distance-based method for testing hypotheses at the community level. We have also developed the linear decomposition model (LDM) that unifies the community-level and OTU-level tests into one framework. Here we present a new strategy that can be used with both PERMANOVA and the LDM for analyzing matched-set data. We propose to include an indicator variable for each set as covariates, so as to constrain comparisons between samples within a set, and also permute traits within each set, which can account for exchangeable sample correlations. The flexible nature of PERMANOVA and the LDM allows discrete or continuous traits or interactions to be tested, within-set confounders to be adjusted, and unbalanced data to be fully exploited.ResultsOur simulations indicate that our proposed strategy outperformed alternative strategies, including the commonly used one that utilizes restricted permutation only, in a wide range of scenarios. Using simulation, we also explored optimal designs for matched-set studies. The flexibility of PERMANOVA and the LDM for a variety of matched-set microbiome data is illustrated by the analysis of data from two real studies.ConclusionsIncluding set indicator variables and permuting within sets when analyzing matched-set data with PERMANOVA or the LDM is a strategy that performs well and is capable of handling the complex data structures that frequently occur in microbiome studies.3i1UxvTTxBo8cNUR6xzMG1Video

Multi-scale feature vector reconstruction for aircraft classification using high range resolution radar signatures

High-Resolution Range Profile (HRRP) is effective in various Radar Automatic Target Recognition problems. Multi-scale techniques have been verified in aircraft target HRRP feature enrichment to achieve aircraft recognition performance optimization. The involvement of multiple training-classification procedures in existing multi-scale methods results in tremendous resource consumption which challenges their real-time classification performance and application significance. This paper introduces a novel method that enables multi-scale HRRP features to be manipulated under a single scale for efficiency enhancement. Numerical analysis indicates that multi-scale intensity variations for particular HRRP scatterers could be modeled as Gaussian noises. Linear Discriminant Analysis and Singular Value Decomposition techniques are applied on reconstructed feature vectors for better separability and noise tolerance capability. Experimental results from dynamic aircraft recognition experiments verify the expected efficiency enhancement of the proposed method while maintaining comparable classification accuracy and better noise tolerance performance.

Vaginal Microbiome Composition in Early Pregnancy and Risk of Spontaneous Preterm and Early Term Birth Among African American Women.

ObjectiveTo evaluate the association between the early pregnancy vaginal microbiome and spontaneous preterm birth (sPTB) and early term birth (sETB) among African American women.MethodsVaginal samples collected in early pregnancy (8-14 weeks’ gestation) from 436 women enrolled in the Emory University African American Vaginal, Oral, and Gut Microbiome in Pregnancy Study underwent 16S rRNA gene sequencing of the V3-V4 region, taxonomic classification, and community state type (CST) assignment. We compared vaginal CST and abundance of taxa for women whose pregnancy ended in sPTB (N = 44) or sETB (N= 84) to those who delivered full term (N = 231).ResultsNearly half of the women had a vaginal microbiome classified as CST IV (Diverse CST), while one-third had CST III (L. iners dominated) and just 16% had CST I, II, or V (non-iners Lactobacillus dominated). Compared to vaginal CST I, II, or V (non-iners Lactobacillus dominated), both CST III (L. iners dominated) and CST IV (Diverse) were associated with sPTB with an adjusted odds ratio (95% confidence interval) of 4.1 (1.1, infinity) and 7.7 (2.2, infinity), respectively, in multivariate logistic regression. In contrast, no vaginal CST was associated with sETB. The linear decomposition model (LDM) based on amplicon sequence variant (ASV) relative abundance found a significant overall effect of the vaginal microbiome on sPTB (p=0.034) but not sETB (p=0.320), whereas the LDM based on presence/absence of ASV found no overall effect on sPTB (p=0.328) but a significant effect on sETB (p=0.030). In testing for ASV-specific effects, the LDM found that no ASV was significantly associated with sPTB considering either relative abundance or presence/absence data after controlling for multiple comparisons (FDR 10%), although in marginal analysis the relative abundance of Gardnerella vaginalis (p=0.011), non-iners Lactobacillus (p=0.016), and Mobiluncus curtisii (p=0.035) and the presence of Atopobium vaginae (p=0.049), BVAB2 (p=0.024), Dialister microaerophilis (p=0.011), and Prevotella amnii (p=0.044) were associated with sPTB. The LDM identified the higher abundance of 7 ASVs and the presence of 13 ASVs, all commonly residents of the gut, as associated with sETB at FDR < 10%.ConclusionsIn this cohort of African American women, an early pregnancy vaginal CST III or IV was associated with an increased risk of sPTB but not sETB. The relative abundance and presence of distinct taxa within the early pregnancy vaginal microbiome was associated with either sPTB or sETB.

Economic Burden of Treatment-Resistant Depression among Adults with Chronic Non-Cancer Pain Conditions and Major Depressive Disorder in the US.

Major depressive disorder (MDD) and chronic non-cancer pain conditions (CNPC) often co-occur and exacerbate one another. Treatment-resistant depression (TRD) in adults with CNPC can amplify the economic burden. This study examined the impact of TRD on direct total and MDD-related healthcare resource utilization (HRU) and costs among commercially insured patients with CNPC and MDD in the US. The retrospective longitudinal cohort study employed a claims-based algorithm to identify adults with TRD from a US claims database (January 2007 to June 2017). Costs (2018 US$) and HRU were compared between patients with and without TRD over a 12-month period after TRD/non-TRD index date. Counterfactual recycled predictions from generalized linear models were used to examine associations between TRD and annual HRU and costs. Post-regression linear decomposition identified differences in patient-level factors between TRD and non-TRD groups that contributed to the excess economic burden of TRD. Of the 21,180 adults with CNPC and MDD, 10.1% were identified as having TRD. TRD patients had significantly higher HRU, translating into higher average total costs (US$21,015TRD vs US$14,712No TRD) and MDD-related costs (US$1201TRD vs US$471No TRD) compared with non-TRD patients (all p< 0.001). Prescription drug costs accounted for 37.6% and inpatient services for 30.7% of the excess total healthcare costs among TRD patients. TRD patients had a significantly higher number of inpatient (incidence rate ratio [IRR] 1.30, 95% CI 1.14-1.47) and emergency room visits (IRR 1.21, 95% CI 1.10-1.34) than non-TRD patients. Overall, 46% of the excess total costs were explained by differences in patient-level characteristics such as polypharmacy, number of CNPC, anxiety, sleep, and substance use disorders between the TRD and non-TRD groups. TRD poses a substantial direct economic burden for adults with CNPC and MDD. Excess healthcare costs may potentially be reduced by providing timely interventions for several modifiable risk factors.

Characterising Momentum Flux Events in High Reynolds Number Turbulent Boundary Layers

The momentum flux in a canonical turbulent boundary layer is known to have a time-series signature that is characterised by a highly intermittent variation, which includes very short periods of intense flux activity. Here, we study the variation in these flux signal characteristics across almost a decade of flow Reynolds number (Reτ) by analysing datasets acquired using miniature cross-wire probes with matched spatial resolution. The analysis is facilitated by conditionally sampling the signal based on the quadrant (Qi; i = 1–4) and magnitude of the flux, revealing fractional cumulative contribution from Q4 to increase at a much faster rate than from Q2 with Reτ. An episodic description of the flux signal is subsequently undertaken, which associates this rapid increase in Q4 contributions with the emergence of extreme and rare flux events with Reτ. The same dataset is also used to test Townsend’s hypothesis on the active and inactive components of the momentum flux, which are obtained for the first time by implementing a spectral linear stochastic estimation-based decomposition methodology. While the active component is found to be the dominant contributor to the mean momentum flux consistent with Townsend’s hypothesis, the inactive component is found to be small but non-zero, owing to the non-linear interactions associated with the modulation phenomenon. Finally, an episodic description of the active and inactive momentum flux signal is undertaken to highlight the starkly different time series characteristics of the two flux components. The inactive flux signal is found to comprise individual statistically significant events associated with all four quadrants, leading to a small net contribution to the total flux.

Mechanical characterization of a woven multi-layered hyperelastic composite laminate under uniaxial loading

We characterize the material properties of a woven, multi-layered, hyperelastic composite that is useful as an envelope material for high-altitude stratospheric airships and in the design of other large structures. The composite was fabricated by sandwiching a polyaramid Nomex® core, with good tensile strength, between polyimide Kapton® films with high dielectric constant, and cured with epoxy using a vacuum bagging technique. Uniaxial mechanical tests were used to stretch the individual materials and the composite to failure in the longitudinal and transverse directions respectively. The experimental data for Kapton® were fit to a five-parameter Yeoh form of nonlinear, hyperelastic and isotropic constitutive model. Image analysis of the Nomex® sheets, obtained using scanning electron microscopy, demonstrate two families of symmetrically oriented fibers at 69.3°± 7.4° and 129°± 5.3°. Stress-strain results for Nomex® were fit to a nonlinear and orthotropic Holzapfel-Gasser-Ogden (HGO) hyperelastic model with two fiber families. We used a linear decomposition of the strain energy function for the composite, based on the individual strain energy functions for Kapton® and Nomex®, obtained using experimental results. A rule of mixtures approach, using volume fractions of individual constituents present in the composite during specimen fabrication, was used to formulate the strain energy function for the composite. Model results for the composite were in good agreement with experimental stress-strain data. Constitutive properties for woven composite materials, combining nonlinear elastic properties within a composite materials framework, are required in the design of laminated pretensioned structures for civil engineering and in aerospace applications.

Nonlinear Coupling Between Longwave Radiative Climate Feedbacks

AbstractThe radiative feedbacks of such climate variables as air temperature, water vapor, and clouds influence the energy budget of the climate system. Measuring the strength of each feedback is of critical importance for understanding the climate sensitivity and its spread in climate models. Most feedback analyses to‐date, such as those using the kernel method, have been based on a linear decomposition of the radiation budget and neglect the nonlinear effects between the feedbacks. In this work, we quantify the coupling effects between different longwave radiative feedbacks based on partial radiative perturbations using a radiative transfer model. Two climate change scenarios, the El niño‐Southern Oscillation (ENSO) and the quadrupling CO2 (4xCO2), are examined. We find that the coupling effect between water vapor and cloud is the strongest among all the coupling effects and can amount to 50% or greater of the univariate cloud feedback. This significant coupling effect results from the masking effect of the two feedbacks on each other and can be well explained by a simple analytic model.

A domain decomposition technique for small amplitude wave interactions with shock waves

In this paper, a domain decomposition technique in the finite volume framework is presented to propagate small amplitude acoustic and entropy waves in a linearized Euler region and simulate the interaction of these waves with an initially steady normal shock in a nonlinear region. An overset method is used to two-way couple the linear and nonlinear regions that overlap each other. Linearized solvers alone cannot capture this interaction due to the discontinuity encountered at shocks. On the other hand, nonlinear solvers based on second order shock-capturing schemes will result in excessive dissipation and dispersion for the small disturbances. The domain decomposition technique provides a good balance between minimizing dissipation and dispersion errors while enabling nonlinear shock-acoustic interactions. To preserve low dispersion and dissipation, a DRP scheme is used to simulate the incoming and outgoing waves in the linear region. To capture the shock wave interaction and motion, a hybrid central-upwind flux scheme is used in the nonlinear region that contains the shock. Grid sensitivity studies for an acoustic wave propagating in stationary flow were performed to compare the linear, nonlinear, and domain decomposition solvers. The nonlinear solver required ten times the mesh resolution to achieve similar accuracy as the linear solver, resulting in a forty-fold increase in computational time. For modest cell size ratios, the domain decomposition solver reduced the computational time by a factor of three compared to the nonlinear solver while achieving similar accuracy. Interaction of standing shocks with acoustic and entropy waves of amplitudes ϵ=±10−2 and ±10−5 was investigated using the domain decomposition technique. The numerical results for ϵ=±10−2 compared well with the linearized interaction analysis (LIA) with less than 3% discrepancy in terms of the amplification factors. The domain decomposition technique acts as a low pass filter that averages the post-shock oscillations generated by the slow-moving shocks in the nonlinear region, resulting in the correct amplification factors in the linear region. For the smaller amplitudes of ϵ=±10−5, the amplification factors deviated from LIA predictions by up to 70%. Numerical results suggest that the large discrepancy for the small amplitude cases is due to insufficient mesh resolution for capturing extremely slow-moving shocks.

Insight into Polyethylene and Polypropylene Pyrolysis: Global and Mechanistic Models

Pyrolysis of polyolefins has been proposed as a potential resource recovery strategy by converting macromolecules into valuable fuels and chemicals. Due to variations in possible backbone structures, chain-length distributions, and arrangements of pendant groups, their decomposition behavior via pyrolysis can be complex. In the present work, a review of historical data and empirical models for two distinct polyolefins, polyethylene (PE) and polypropylene (PP), is provided followed by a comparison to recent mechanistic models. The characteristic sigmoidal behavior of linear polymer decomposition is captured with global, lumped-species, and mechanistic models of high-density polyethylene. The PE model was extended to simulate PP using the same reaction families and reaction family parameters, but with distinct rate coefficients that accounted for the difference in the structure of PP with its pendant methyl groups compared to PE as manifested through heats of reaction embedded in the Evans–Polanyi relationship, Eₐ= E₀ + γ×ΔHᵣₑₐcₙ. The change in structure and its associated kinetic parameters resulted in no sigmoidal conversion, consistent with experimental reports for atactic PP. This suggests that mechanistic modeling could be an important complement to global model studies to understand when other effects are at play in the pyrolytic decomposition of polymers such as PP.

A Numerical Study on Hydrodynamic Energy Conversions of OWC-WEC with the Linear Decomposition Method under Irregular Waves

A numerical study was performed to investigate the applicability of the linear decomposition method for the hydrodynamic energy conversion of an oscillating-water-column type wave energy converter (OWC-WEC). Hydrodynamic problems of the OWC chamber were decomposed into the excitation and radiation problems with the time-domain numerical method based on the linear potential theory. A finite element method was applied to solve the potential flow in the entire fluid domain including OWC chamber structure. The validity of the linear decomposition method was examined by comparing with the direct interaction method for the turbine–chamber interaction based on the linear pressure drop characteristics. In order to estimate the hydrodynamic energy conversion performance under the irregular waves, the response spectrum method was applied with the transfer function based on the linear decomposition method. Under the various irregular wave conditions, the pneumatic power of OWC-WEC calculated by the response spectrum based on the linear decomposition method agreed well with the direct irregular wave simulation results.

Photon-Counting Multienergy Computed Tomography With Spectrally Optimized Contrast Media for Plaque Removal and Stenosis Assessment.

The aim of this study was to systematically evaluate the potential to combine investigational contrast media with spectrally optimized energy-thresholding of photon-counting detector computed tomography (PCCT) for subtraction of calcified plaques in a coronary artery stenosis phantom. A small vessel phantom containing 3 fillable tubes (diameter, 3 mm each) with calcified plaques was placed into an anthropomorphic chest phantom. The plaques had incremental thicknesses ranging from 0.3 to 2.7 mm, simulating vessel stenoses ranging from 10% to 90% of the lumen diameter. The phantom was filled with 5 different investigational contrast media (iodine, bismuth, hafnium, holmium, and tungsten) at equal mass concentrations (15 mg/mL) and was imaged on a prototype PCCT at 140 kVp using optimized, contrast media-dependent energy thresholds. Contrast maps (CMs) were reconstructed for each contrast medium by applying a linear 2-material decomposition algorithm. Image noise magnitude and noise texture of CM were compared among the contrast media using the noise power spectrum. Two blinded readers independently rated the vessel lumen visualization on short-axis and the overall subjective image quality on long-axis CM relative to iodine as the reference standard. Four readers determined the highest degree of stenosis that could be assessed with high diagnostic confidence on long-axis CM. Average image noise on CM was lower for tungsten (49 HU) and hafnium (62 HU) and higher for bismuth (81 HU) and holmium (165 HU) compared with iodine (78 HU). Noise texture of CM was similar among the contrast media. Interreader agreement for vessel lumen visualization on short-axis CM ranged from moderate to excellent (k = 0.567-0.814). Compared with iodine, lumen visualization of each reader was improved using tungsten (P < 0.001 for both readers), similar to improved using hafnium (P = 0.008, P = 0.29), similar using bismuth (P = 0.38, P = 0.69), and decreased using holmium (both, P < 0.001). Overall subjective image quality was similar for holmium and superior for tungsten, hafnium, and bismuth as compared with iodine. Higher-degree stenoses were evaluable with high confidence using tungsten (mean, 70%; interquartile range, 70%-70%), bismuth (70%; 60%-70%), and hafnium (75%; 70%-80%) compared with iodine (50%; 50%-60%) and holmium (50%; 50%-60%). Spectral optimization in PCCT combined with investigational contrast media can improve calcium subtraction and stenosis assessment in small vessels. Contrast maps of tungsten and, to a lesser extent, hafnium as contrast media yielded superior image noise properties and improved vessel lumen visualization, along with a higher subjective image quality compared with the reference standard iodine.

Efficient q-integer linear decomposition of multivariate polynomials

We present two new algorithms for the computation of the q-integer linear decomposition of a multivariate polynomial. Such a decomposition is essential for the treatment of q-hypergeometric symbolic summation via creative telescoping and for describing the q-counterpart of Ore-Sato theory. Both of our algorithms require only basic integer and polynomial arithmetic and work for any unique factorization domain containing the ring of integers. Complete complexity analyses are conducted for both our algorithms and two previous algorithms in the case of multivariate integer polynomials, showing that our algorithms have better theoretical performances. A Maple implementation is also included which suggests that our algorithms are much faster in practice than previous algorithms.